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ML_to_Predicting_ICU_Mortality (2).ipynb
22608 lines (22608 with data), 4.1 MB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Bz1rbJ_Y6Mom"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "otpI4cID6UVL"
},
"source": [
"#Predicting ICU Mortality and Identifying Key Clinical Risk Factors Using Machine Learning\n",
"#Background:\n",
"In intensive care units (ICUs), timely and accurate identification of patients at high risk of mortality is crucial for guiding treatment decisions and optimizing care. While ICU clinicians rely on their experience and standard clinical scoring systems (like APACHE) to predict outcomes, there is potential for machine learning (ML) to enhance predictive accuracy by analyzing patterns in large datasets with multiple clinical variables.\n",
"\n",
"#Objective:\n",
"The goal of this project is to develop a machine learning model that predicts ICU mortality based on clinical parameters recorded during a patient’s stay. Additionally, the project aims to uncover the key clinical features most strongly associated with patient outcomes, which can provide valuable insights into ICU patient management."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "u9VAj4nR59ky"
},
"outputs": [],
"source": [
"######Importing some Library\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 556
},
"id": "pyusZdyF6hSK",
"outputId": "04632471-59df-4575-e724-1fb70096d016"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi elective_surgery \\\n",
"0 66154 25312 118 68.0 22.73 0 \n",
"1 114252 59342 81 77.0 27.42 0 \n",
"2 119783 50777 118 25.0 31.95 0 \n",
"3 79267 46918 118 81.0 22.64 1 \n",
"4 92056 34377 33 19.0 NaN 0 \n",
"5 33181 74489 83 67.0 27.56 0 \n",
"6 82208 49526 83 59.0 57.45 0 \n",
"7 120995 50129 33 70.0 NaN 0 \n",
"8 80471 10577 118 45.0 NaN 0 \n",
"9 42871 90749 118 50.0 25.71 0 \n",
"\n",
" ethnicity gender height icu_admit_source icu_id icu_stay_type \\\n",
"0 Caucasian M 180.3 Floor 92 admit \n",
"1 Caucasian F 160.0 Floor 90 admit \n",
"2 Caucasian F 172.7 Accident & Emergency 93 admit \n",
"3 Caucasian F 165.1 Operating Room / Recovery 92 admit \n",
"4 Caucasian M 188.0 Accident & Emergency 91 admit \n",
"5 Caucasian M 190.5 Accident & Emergency 95 admit \n",
"6 Caucasian F 165.1 Accident & Emergency 95 admit \n",
"7 Caucasian M 165.0 Accident & Emergency 91 admit \n",
"8 Caucasian M 170.2 Other Hospital 114 admit \n",
"9 NaN M 175.3 Accident & Emergency 114 admit \n",
"\n",
" icu_type pre_icu_los_days weight apache_2_diagnosis \\\n",
"0 CTICU 0.541667 73.9 113.0 \n",
"1 Med-Surg ICU 0.927778 70.2 108.0 \n",
"2 Med-Surg ICU 0.000694 95.3 122.0 \n",
"3 CTICU 0.000694 61.7 203.0 \n",
"4 Med-Surg ICU 0.073611 NaN 119.0 \n",
"5 Med-Surg ICU 0.000694 100.0 301.0 \n",
"6 Med-Surg ICU 0.000694 156.6 108.0 \n",
"7 Med-Surg ICU 0.002083 NaN 113.0 \n",
"8 CCU-CTICU 0.009028 NaN 116.0 \n",
"9 CCU-CTICU 0.060417 79.0 112.0 \n",
"\n",
" apache_3j_diagnosis apache_post_operative arf_apache gcs_eyes_apache \\\n",
"0 502.01 0 0.0 3.0 \n",
"1 203.01 0 0.0 1.0 \n",
"2 703.03 0 0.0 3.0 \n",
"3 1206.03 1 0.0 4.0 \n",
"4 601.01 0 0.0 NaN \n",
"5 403.01 0 0.0 4.0 \n",
"6 203.01 0 0.0 4.0 \n",
"7 501.05 0 0.0 4.0 \n",
"8 103.01 0 0.0 4.0 \n",
"9 107.01 0 0.0 4.0 \n",
"\n",
" gcs_motor_apache gcs_unable_apache gcs_verbal_apache heart_rate_apache \\\n",
"0 6.0 0.0 4.0 118.0 \n",
"1 3.0 0.0 1.0 120.0 \n",
"2 6.0 0.0 5.0 102.0 \n",
"3 6.0 0.0 5.0 114.0 \n",
"4 NaN NaN NaN 60.0 \n",
"5 6.0 0.0 5.0 113.0 \n",
"6 6.0 0.0 5.0 133.0 \n",
"7 6.0 0.0 5.0 120.0 \n",
"8 6.0 0.0 5.0 82.0 \n",
"9 6.0 0.0 5.0 94.0 \n",
"\n",
" intubated_apache map_apache resprate_apache temp_apache \\\n",
"0 0.0 40.0 36.0 39.3 \n",
"1 0.0 46.0 33.0 35.1 \n",
"2 0.0 68.0 37.0 36.7 \n",
"3 1.0 60.0 4.0 34.8 \n",
"4 0.0 103.0 16.0 36.7 \n",
"5 0.0 130.0 35.0 36.6 \n",
"6 1.0 138.0 53.0 35.0 \n",
"7 0.0 60.0 28.0 36.6 \n",
"8 0.0 66.0 14.0 36.9 \n",
"9 0.0 58.0 46.0 36.3 \n",
"\n",
" ventilated_apache d1_diasbp_max d1_diasbp_min d1_diasbp_noninvasive_max \\\n",
"0 0.0 68.0 37.0 68.0 \n",
"1 1.0 95.0 31.0 95.0 \n",
"2 0.0 88.0 48.0 88.0 \n",
"3 1.0 48.0 42.0 48.0 \n",
"4 0.0 99.0 57.0 99.0 \n",
"5 0.0 100.0 61.0 100.0 \n",
"6 1.0 76.0 68.0 76.0 \n",
"7 1.0 84.0 46.0 84.0 \n",
"8 1.0 65.0 59.0 65.0 \n",
"9 0.0 83.0 48.0 83.0 \n",
"\n",
" d1_diasbp_noninvasive_min d1_heartrate_max d1_heartrate_min d1_mbp_max \\\n",
"0 37.0 119.0 72.0 89.0 \n",
"1 31.0 118.0 72.0 120.0 \n",
"2 48.0 96.0 68.0 102.0 \n",
"3 42.0 116.0 92.0 84.0 \n",
"4 57.0 89.0 60.0 104.0 \n",
"5 61.0 113.0 83.0 127.0 \n",
"6 68.0 112.0 70.0 117.0 \n",
"7 46.0 118.0 86.0 114.0 \n",
"8 59.0 82.0 82.0 93.0 \n",
"9 48.0 96.0 57.0 101.0 \n",
"\n",
" d1_mbp_min d1_mbp_noninvasive_max d1_mbp_noninvasive_min \\\n",
"0 46.0 89.0 46.0 \n",
"1 38.0 120.0 38.0 \n",
"2 68.0 102.0 68.0 \n",
"3 84.0 84.0 84.0 \n",
"4 90.0 104.0 90.0 \n",
"5 80.0 127.0 80.0 \n",
"6 97.0 117.0 97.0 \n",
"7 60.0 114.0 60.0 \n",
"8 71.0 93.0 71.0 \n",
"9 59.0 101.0 59.0 \n",
"\n",
" d1_resprate_max d1_resprate_min d1_spo2_max d1_spo2_min d1_sysbp_max \\\n",
"0 34.0 10.0 100.0 74.0 131.0 \n",
"1 32.0 12.0 100.0 70.0 159.0 \n",
"2 21.0 8.0 98.0 91.0 148.0 \n",
"3 23.0 7.0 100.0 95.0 158.0 \n",
"4 18.0 16.0 100.0 96.0 147.0 \n",
"5 32.0 10.0 97.0 91.0 173.0 \n",
"6 38.0 16.0 100.0 87.0 151.0 \n",
"7 28.0 12.0 100.0 92.0 147.0 \n",
"8 24.0 19.0 97.0 97.0 104.0 \n",
"9 44.0 14.0 100.0 96.0 135.0 \n",
"\n",
" d1_sysbp_min d1_sysbp_noninvasive_max d1_sysbp_noninvasive_min \\\n",
"0 73.0 131.0 73.0 \n",
"1 67.0 159.0 67.0 \n",
"2 105.0 148.0 105.0 \n",
"3 84.0 158.0 84.0 \n",
"4 120.0 147.0 120.0 \n",
"5 107.0 173.0 107.0 \n",
"6 133.0 151.0 133.0 \n",
"7 71.0 147.0 71.0 \n",
"8 98.0 104.0 98.0 \n",
"9 78.0 135.0 78.0 \n",
"\n",
" d1_temp_max d1_temp_min h1_diasbp_max h1_diasbp_min \\\n",
"0 39.9 37.2 68.0 63.0 \n",
"1 36.3 35.1 61.0 48.0 \n",
"2 37.0 36.7 88.0 58.0 \n",
"3 38.0 34.8 62.0 44.0 \n",
"4 37.2 36.7 99.0 68.0 \n",
"5 36.8 36.6 89.0 89.0 \n",
"6 37.2 35.0 107.0 79.0 \n",
"7 38.5 36.6 74.0 55.0 \n",
"8 36.9 36.9 65.0 59.0 \n",
"9 37.1 36.4 83.0 61.0 \n",
"\n",
" h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min h1_heartrate_max \\\n",
"0 68.0 63.0 119.0 \n",
"1 61.0 48.0 114.0 \n",
"2 88.0 58.0 96.0 \n",
"3 NaN NaN 100.0 \n",
"4 99.0 68.0 89.0 \n",
"5 89.0 89.0 83.0 \n",
"6 NaN NaN 79.0 \n",
"7 74.0 55.0 118.0 \n",
"8 65.0 59.0 82.0 \n",
"9 83.0 61.0 96.0 \n",
"\n",
" h1_heartrate_min h1_mbp_max h1_mbp_min h1_mbp_noninvasive_max \\\n",
"0 108.0 86.0 85.0 86.0 \n",
"1 100.0 85.0 57.0 85.0 \n",
"2 78.0 91.0 83.0 91.0 \n",
"3 96.0 92.0 71.0 NaN \n",
"4 76.0 104.0 92.0 104.0 \n",
"5 83.0 111.0 111.0 111.0 \n",
"6 72.0 117.0 117.0 117.0 \n",
"7 114.0 88.0 60.0 88.0 \n",
"8 82.0 93.0 71.0 93.0 \n",
"9 60.0 101.0 77.0 101.0 \n",
"\n",
" h1_mbp_noninvasive_min h1_resprate_max h1_resprate_min h1_spo2_max \\\n",
"0 85.0 26.0 18.0 100.0 \n",
"1 57.0 31.0 28.0 95.0 \n",
"2 83.0 20.0 16.0 98.0 \n",
"3 NaN 12.0 11.0 100.0 \n",
"4 92.0 NaN NaN 100.0 \n",
"5 111.0 12.0 12.0 97.0 \n",
"6 117.0 18.0 18.0 100.0 \n",
"7 60.0 28.0 26.0 96.0 \n",
"8 71.0 24.0 19.0 97.0 \n",
"9 77.0 29.0 17.0 100.0 \n",
"\n",
" h1_spo2_min h1_sysbp_max h1_sysbp_min h1_sysbp_noninvasive_max \\\n",
"0 74.0 131.0 115.0 131.0 \n",
"1 70.0 95.0 71.0 95.0 \n",
"2 91.0 148.0 124.0 148.0 \n",
"3 99.0 136.0 106.0 NaN \n",
"4 100.0 130.0 120.0 130.0 \n",
"5 97.0 143.0 143.0 143.0 \n",
"6 100.0 191.0 163.0 NaN \n",
"7 92.0 119.0 106.0 119.0 \n",
"8 97.0 104.0 98.0 104.0 \n",
"9 96.0 135.0 103.0 135.0 \n",
"\n",
" h1_sysbp_noninvasive_min d1_glucose_max d1_glucose_min d1_potassium_max \\\n",
"0 115.0 168.0 109.0 4.0 \n",
"1 71.0 145.0 128.0 4.2 \n",
"2 124.0 NaN NaN NaN \n",
"3 NaN 185.0 88.0 5.0 \n",
"4 120.0 NaN NaN NaN \n",
"5 143.0 156.0 125.0 3.9 \n",
"6 NaN 197.0 129.0 5.0 \n",
"7 106.0 129.0 129.0 5.8 \n",
"8 98.0 365.0 288.0 5.2 \n",
"9 103.0 134.0 134.0 4.1 \n",
"\n",
" d1_potassium_min apache_4a_hospital_death_prob apache_4a_icu_death_prob \\\n",
"0 3.4 0.10 0.05 \n",
"1 3.8 0.47 0.29 \n",
"2 NaN 0.00 0.00 \n",
"3 3.5 0.04 0.03 \n",
"4 NaN NaN NaN \n",
"5 3.7 0.05 0.02 \n",
"6 4.2 0.10 0.05 \n",
"7 2.4 0.11 0.06 \n",
"8 5.2 NaN NaN \n",
"9 3.3 0.02 0.01 \n",
"\n",
" aids cirrhosis diabetes_mellitus hepatic_failure immunosuppression \\\n",
"0 0.0 0.0 1.0 0.0 0.0 \n",
"1 0.0 0.0 1.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"5 0.0 0.0 1.0 0.0 0.0 \n",
"6 0.0 0.0 1.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" leukemia lymphoma solid_tumor_with_metastasis apache_3j_bodysystem \\\n",
"0 0.0 0.0 0.0 Sepsis \n",
"1 0.0 0.0 0.0 Respiratory \n",
"2 0.0 0.0 0.0 Metabolic \n",
"3 0.0 0.0 0.0 Cardiovascular \n",
"4 0.0 0.0 0.0 Trauma \n",
"5 0.0 0.0 0.0 Neurological \n",
"6 0.0 0.0 0.0 Respiratory \n",
"7 0.0 0.0 0.0 Sepsis \n",
"8 0.0 0.0 0.0 Cardiovascular \n",
"9 0.0 0.0 0.0 Cardiovascular \n",
"\n",
" apache_2_bodysystem Unnamed: 83 hospital_death \n",
"0 Cardiovascular NaN 0 \n",
"1 Respiratory NaN 0 \n",
"2 Metabolic NaN 0 \n",
"3 Cardiovascular NaN 0 \n",
"4 Trauma NaN 0 \n",
"5 Neurologic NaN 0 \n",
"6 Respiratory NaN 0 \n",
"7 Cardiovascular NaN 0 \n",
"8 Cardiovascular NaN 1 \n",
"9 Cardiovascular NaN 0 "
],
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" <th></th>\n",
" <th>encounter_id</th>\n",
" <th>patient_id</th>\n",
" <th>hospital_id</th>\n",
" <th>age</th>\n",
" <th>bmi</th>\n",
" <th>elective_surgery</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>height</th>\n",
" <th>icu_admit_source</th>\n",
" <th>icu_id</th>\n",
" <th>icu_stay_type</th>\n",
" <th>icu_type</th>\n",
" <th>pre_icu_los_days</th>\n",
" <th>weight</th>\n",
" <th>apache_2_diagnosis</th>\n",
" <th>apache_3j_diagnosis</th>\n",
" <th>apache_post_operative</th>\n",
" <th>arf_apache</th>\n",
" <th>gcs_eyes_apache</th>\n",
" <th>gcs_motor_apache</th>\n",
" <th>gcs_unable_apache</th>\n",
" <th>gcs_verbal_apache</th>\n",
" <th>heart_rate_apache</th>\n",
" <th>intubated_apache</th>\n",
" <th>map_apache</th>\n",
" <th>resprate_apache</th>\n",
" <th>temp_apache</th>\n",
" <th>ventilated_apache</th>\n",
" <th>d1_diasbp_max</th>\n",
" <th>d1_diasbp_min</th>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <th>d1_heartrate_max</th>\n",
" <th>d1_heartrate_min</th>\n",
" <th>d1_mbp_max</th>\n",
" <th>d1_mbp_min</th>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <th>d1_resprate_max</th>\n",
" <th>d1_resprate_min</th>\n",
" <th>d1_spo2_max</th>\n",
" <th>d1_spo2_min</th>\n",
" <th>d1_sysbp_max</th>\n",
" <th>d1_sysbp_min</th>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <th>d1_temp_max</th>\n",
" <th>d1_temp_min</th>\n",
" <th>h1_diasbp_max</th>\n",
" <th>h1_diasbp_min</th>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <th>h1_heartrate_max</th>\n",
" <th>h1_heartrate_min</th>\n",
" <th>h1_mbp_max</th>\n",
" <th>h1_mbp_min</th>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <th>h1_resprate_max</th>\n",
" <th>h1_resprate_min</th>\n",
" <th>h1_spo2_max</th>\n",
" <th>h1_spo2_min</th>\n",
" <th>h1_sysbp_max</th>\n",
" <th>h1_sysbp_min</th>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <th>d1_glucose_max</th>\n",
" <th>d1_glucose_min</th>\n",
" <th>d1_potassium_max</th>\n",
" <th>d1_potassium_min</th>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <th>aids</th>\n",
" <th>cirrhosis</th>\n",
" <th>diabetes_mellitus</th>\n",
" <th>hepatic_failure</th>\n",
" <th>immunosuppression</th>\n",
" <th>leukemia</th>\n",
" <th>lymphoma</th>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" <th>apache_3j_bodysystem</th>\n",
" <th>apache_2_bodysystem</th>\n",
" <th>Unnamed: 83</th>\n",
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" <td>Caucasian</td>\n",
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" <td>180.3</td>\n",
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" <td>89.0</td>\n",
" <td>46.0</td>\n",
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" <td>74.0</td>\n",
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" <td>115.0</td>\n",
" <td>131.0</td>\n",
" <td>115.0</td>\n",
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" <td>Sepsis</td>\n",
" <td>Cardiovascular</td>\n",
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" <td>81</td>\n",
" <td>77.0</td>\n",
" <td>27.42</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>160.0</td>\n",
" <td>Floor</td>\n",
" <td>90</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.927778</td>\n",
" <td>70.2</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>120.0</td>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>33.0</td>\n",
" <td>35.1</td>\n",
" <td>1.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>118.0</td>\n",
" <td>72.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>32.0</td>\n",
" <td>12.0</td>\n",
" <td>100.0</td>\n",
" <td>70.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>36.3</td>\n",
" <td>35.1</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>114.0</td>\n",
" <td>100.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>31.0</td>\n",
" <td>28.0</td>\n",
" <td>95.0</td>\n",
" <td>70.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>145.0</td>\n",
" <td>128.0</td>\n",
" <td>4.2</td>\n",
" <td>3.8</td>\n",
" <td>0.47</td>\n",
" <td>0.29</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>119783</td>\n",
" <td>50777</td>\n",
" <td>118</td>\n",
" <td>25.0</td>\n",
" <td>31.95</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>172.7</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>93</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.000694</td>\n",
" <td>95.3</td>\n",
" <td>122.0</td>\n",
" <td>703.03</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>102.0</td>\n",
" <td>0.0</td>\n",
" <td>68.0</td>\n",
" <td>37.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>88.0</td>\n",
" <td>48.0</td>\n",
" <td>88.0</td>\n",
" <td>48.0</td>\n",
" <td>96.0</td>\n",
" <td>68.0</td>\n",
" <td>102.0</td>\n",
" <td>68.0</td>\n",
" <td>102.0</td>\n",
" <td>68.0</td>\n",
" <td>21.0</td>\n",
" <td>8.0</td>\n",
" <td>98.0</td>\n",
" <td>91.0</td>\n",
" <td>148.0</td>\n",
" <td>105.0</td>\n",
" <td>148.0</td>\n",
" <td>105.0</td>\n",
" <td>37.0</td>\n",
" <td>36.7</td>\n",
" <td>88.0</td>\n",
" <td>58.0</td>\n",
" <td>88.0</td>\n",
" <td>58.0</td>\n",
" <td>96.0</td>\n",
" <td>78.0</td>\n",
" <td>91.0</td>\n",
" <td>83.0</td>\n",
" <td>91.0</td>\n",
" <td>83.0</td>\n",
" <td>20.0</td>\n",
" <td>16.0</td>\n",
" <td>98.0</td>\n",
" <td>91.0</td>\n",
" <td>148.0</td>\n",
" <td>124.0</td>\n",
" <td>148.0</td>\n",
" <td>124.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Metabolic</td>\n",
" <td>Metabolic</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>79267</td>\n",
" <td>46918</td>\n",
" <td>118</td>\n",
" <td>81.0</td>\n",
" <td>22.64</td>\n",
" <td>1</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>165.1</td>\n",
" <td>Operating Room / Recovery</td>\n",
" <td>92</td>\n",
" <td>admit</td>\n",
" <td>CTICU</td>\n",
" <td>0.000694</td>\n",
" <td>61.7</td>\n",
" <td>203.0</td>\n",
" <td>1206.03</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>114.0</td>\n",
" <td>1.0</td>\n",
" <td>60.0</td>\n",
" <td>4.0</td>\n",
" <td>34.8</td>\n",
" <td>1.0</td>\n",
" <td>48.0</td>\n",
" <td>42.0</td>\n",
" <td>48.0</td>\n",
" <td>42.0</td>\n",
" <td>116.0</td>\n",
" <td>92.0</td>\n",
" <td>84.0</td>\n",
" <td>84.0</td>\n",
" <td>84.0</td>\n",
" <td>84.0</td>\n",
" <td>23.0</td>\n",
" <td>7.0</td>\n",
" <td>100.0</td>\n",
" <td>95.0</td>\n",
" <td>158.0</td>\n",
" <td>84.0</td>\n",
" <td>158.0</td>\n",
" <td>84.0</td>\n",
" <td>38.0</td>\n",
" <td>34.8</td>\n",
" <td>62.0</td>\n",
" <td>44.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>92.0</td>\n",
" <td>71.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>11.0</td>\n",
" <td>100.0</td>\n",
" <td>99.0</td>\n",
" <td>136.0</td>\n",
" <td>106.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>185.0</td>\n",
" <td>88.0</td>\n",
" <td>5.0</td>\n",
" <td>3.5</td>\n",
" <td>0.04</td>\n",
" <td>0.03</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>92056</td>\n",
" <td>34377</td>\n",
" <td>33</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>188.0</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>91</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.073611</td>\n",
" <td>NaN</td>\n",
" <td>119.0</td>\n",
" <td>601.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>103.0</td>\n",
" <td>16.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>99.0</td>\n",
" <td>57.0</td>\n",
" <td>99.0</td>\n",
" <td>57.0</td>\n",
" <td>89.0</td>\n",
" <td>60.0</td>\n",
" <td>104.0</td>\n",
" <td>90.0</td>\n",
" <td>104.0</td>\n",
" <td>90.0</td>\n",
" <td>18.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>147.0</td>\n",
" <td>120.0</td>\n",
" <td>147.0</td>\n",
" <td>120.0</td>\n",
" <td>37.2</td>\n",
" <td>36.7</td>\n",
" <td>99.0</td>\n",
" <td>68.0</td>\n",
" <td>99.0</td>\n",
" <td>68.0</td>\n",
" <td>89.0</td>\n",
" <td>76.0</td>\n",
" <td>104.0</td>\n",
" <td>92.0</td>\n",
" <td>104.0</td>\n",
" <td>92.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>130.0</td>\n",
" <td>120.0</td>\n",
" <td>130.0</td>\n",
" <td>120.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Trauma</td>\n",
" <td>Trauma</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>33181</td>\n",
" <td>74489</td>\n",
" <td>83</td>\n",
" <td>67.0</td>\n",
" <td>27.56</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>190.5</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>95</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.000694</td>\n",
" <td>100.0</td>\n",
" <td>301.0</td>\n",
" <td>403.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>113.0</td>\n",
" <td>0.0</td>\n",
" <td>130.0</td>\n",
" <td>35.0</td>\n",
" <td>36.6</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>113.0</td>\n",
" <td>83.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>32.0</td>\n",
" <td>10.0</td>\n",
" <td>97.0</td>\n",
" <td>91.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>36.8</td>\n",
" <td>36.6</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>83.0</td>\n",
" <td>83.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>97.0</td>\n",
" <td>97.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>156.0</td>\n",
" <td>125.0</td>\n",
" <td>3.9</td>\n",
" <td>3.7</td>\n",
" <td>0.05</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Neurological</td>\n",
" <td>Neurologic</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>82208</td>\n",
" <td>49526</td>\n",
" <td>83</td>\n",
" <td>59.0</td>\n",
" <td>57.45</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>165.1</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>95</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.000694</td>\n",
" <td>156.6</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>133.0</td>\n",
" <td>1.0</td>\n",
" <td>138.0</td>\n",
" <td>53.0</td>\n",
" <td>35.0</td>\n",
" <td>1.0</td>\n",
" <td>76.0</td>\n",
" <td>68.0</td>\n",
" <td>76.0</td>\n",
" <td>68.0</td>\n",
" <td>112.0</td>\n",
" <td>70.0</td>\n",
" <td>117.0</td>\n",
" <td>97.0</td>\n",
" <td>117.0</td>\n",
" <td>97.0</td>\n",
" <td>38.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>87.0</td>\n",
" <td>151.0</td>\n",
" <td>133.0</td>\n",
" <td>151.0</td>\n",
" <td>133.0</td>\n",
" <td>37.2</td>\n",
" <td>35.0</td>\n",
" <td>107.0</td>\n",
" <td>79.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>79.0</td>\n",
" <td>72.0</td>\n",
" <td>117.0</td>\n",
" <td>117.0</td>\n",
" <td>117.0</td>\n",
" <td>117.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>191.0</td>\n",
" <td>163.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>197.0</td>\n",
" <td>129.0</td>\n",
" <td>5.0</td>\n",
" <td>4.2</td>\n",
" <td>0.10</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>120995</td>\n",
" <td>50129</td>\n",
" <td>33</td>\n",
" <td>70.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>165.0</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>91</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.002083</td>\n",
" <td>NaN</td>\n",
" <td>113.0</td>\n",
" <td>501.05</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>120.0</td>\n",
" <td>0.0</td>\n",
" <td>60.0</td>\n",
" <td>28.0</td>\n",
" <td>36.6</td>\n",
" <td>1.0</td>\n",
" <td>84.0</td>\n",
" <td>46.0</td>\n",
" <td>84.0</td>\n",
" <td>46.0</td>\n",
" <td>118.0</td>\n",
" <td>86.0</td>\n",
" <td>114.0</td>\n",
" <td>60.0</td>\n",
" <td>114.0</td>\n",
" <td>60.0</td>\n",
" <td>28.0</td>\n",
" <td>12.0</td>\n",
" <td>100.0</td>\n",
" <td>92.0</td>\n",
" <td>147.0</td>\n",
" <td>71.0</td>\n",
" <td>147.0</td>\n",
" <td>71.0</td>\n",
" <td>38.5</td>\n",
" <td>36.6</td>\n",
" <td>74.0</td>\n",
" <td>55.0</td>\n",
" <td>74.0</td>\n",
" <td>55.0</td>\n",
" <td>118.0</td>\n",
" <td>114.0</td>\n",
" <td>88.0</td>\n",
" <td>60.0</td>\n",
" <td>88.0</td>\n",
" <td>60.0</td>\n",
" <td>28.0</td>\n",
" <td>26.0</td>\n",
" <td>96.0</td>\n",
" <td>92.0</td>\n",
" <td>119.0</td>\n",
" <td>106.0</td>\n",
" <td>119.0</td>\n",
" <td>106.0</td>\n",
" <td>129.0</td>\n",
" <td>129.0</td>\n",
" <td>5.8</td>\n",
" <td>2.4</td>\n",
" <td>0.11</td>\n",
" <td>0.06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Sepsis</td>\n",
" <td>Cardiovascular</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>80471</td>\n",
" <td>10577</td>\n",
" <td>118</td>\n",
" <td>45.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>170.2</td>\n",
" <td>Other Hospital</td>\n",
" <td>114</td>\n",
" <td>admit</td>\n",
" <td>CCU-CTICU</td>\n",
" <td>0.009028</td>\n",
" <td>NaN</td>\n",
" <td>116.0</td>\n",
" <td>103.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>82.0</td>\n",
" <td>0.0</td>\n",
" <td>66.0</td>\n",
" <td>14.0</td>\n",
" <td>36.9</td>\n",
" <td>1.0</td>\n",
" <td>65.0</td>\n",
" <td>59.0</td>\n",
" <td>65.0</td>\n",
" <td>59.0</td>\n",
" <td>82.0</td>\n",
" <td>82.0</td>\n",
" <td>93.0</td>\n",
" <td>71.0</td>\n",
" <td>93.0</td>\n",
" <td>71.0</td>\n",
" <td>24.0</td>\n",
" <td>19.0</td>\n",
" <td>97.0</td>\n",
" <td>97.0</td>\n",
" <td>104.0</td>\n",
" <td>98.0</td>\n",
" <td>104.0</td>\n",
" <td>98.0</td>\n",
" <td>36.9</td>\n",
" <td>36.9</td>\n",
" <td>65.0</td>\n",
" <td>59.0</td>\n",
" <td>65.0</td>\n",
" <td>59.0</td>\n",
" <td>82.0</td>\n",
" <td>82.0</td>\n",
" <td>93.0</td>\n",
" <td>71.0</td>\n",
" <td>93.0</td>\n",
" <td>71.0</td>\n",
" <td>24.0</td>\n",
" <td>19.0</td>\n",
" <td>97.0</td>\n",
" <td>97.0</td>\n",
" <td>104.0</td>\n",
" <td>98.0</td>\n",
" <td>104.0</td>\n",
" <td>98.0</td>\n",
" <td>365.0</td>\n",
" <td>288.0</td>\n",
" <td>5.2</td>\n",
" <td>5.2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>42871</td>\n",
" <td>90749</td>\n",
" <td>118</td>\n",
" <td>50.0</td>\n",
" <td>25.71</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>M</td>\n",
" <td>175.3</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>114</td>\n",
" <td>admit</td>\n",
" <td>CCU-CTICU</td>\n",
" <td>0.060417</td>\n",
" <td>79.0</td>\n",
" <td>112.0</td>\n",
" <td>107.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>94.0</td>\n",
" <td>0.0</td>\n",
" <td>58.0</td>\n",
" <td>46.0</td>\n",
" <td>36.3</td>\n",
" <td>0.0</td>\n",
" <td>83.0</td>\n",
" <td>48.0</td>\n",
" <td>83.0</td>\n",
" <td>48.0</td>\n",
" <td>96.0</td>\n",
" <td>57.0</td>\n",
" <td>101.0</td>\n",
" <td>59.0</td>\n",
" <td>101.0</td>\n",
" <td>59.0</td>\n",
" <td>44.0</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>135.0</td>\n",
" <td>78.0</td>\n",
" <td>135.0</td>\n",
" <td>78.0</td>\n",
" <td>37.1</td>\n",
" <td>36.4</td>\n",
" <td>83.0</td>\n",
" <td>61.0</td>\n",
" <td>83.0</td>\n",
" <td>61.0</td>\n",
" <td>96.0</td>\n",
" <td>60.0</td>\n",
" <td>101.0</td>\n",
" <td>77.0</td>\n",
" <td>101.0</td>\n",
" <td>77.0</td>\n",
" <td>29.0</td>\n",
" <td>17.0</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>135.0</td>\n",
" <td>103.0</td>\n",
" <td>135.0</td>\n",
" <td>103.0</td>\n",
" <td>134.0</td>\n",
" <td>134.0</td>\n",
" <td>4.1</td>\n",
" <td>3.3</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "health_data"
}
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"######importing Data and overviewing the Data\n",
"health_data= pd.read_csv(\"/content/dataset.csv\")\n",
"pd.set_option('display.max_columns', None)\n",
"health_data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MOuoJ7rb7qpi",
"outputId": "b59d2e97-f84c-4af9-e813-a4e31c1aec15"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['encounter_id', 'patient_id', 'hospital_id', 'age', 'bmi',\n",
" 'elective_surgery', 'ethnicity', 'gender', 'height', 'icu_admit_source',\n",
" 'icu_id', 'icu_stay_type', 'icu_type', 'pre_icu_los_days', 'weight',\n",
" 'apache_2_diagnosis', 'apache_3j_diagnosis', 'apache_post_operative',\n",
" 'arf_apache', 'gcs_eyes_apache', 'gcs_motor_apache',\n",
" 'gcs_unable_apache', 'gcs_verbal_apache', 'heart_rate_apache',\n",
" 'intubated_apache', 'map_apache', 'resprate_apache', 'temp_apache',\n",
" 'ventilated_apache', 'd1_diasbp_max', 'd1_diasbp_min',\n",
" 'd1_diasbp_noninvasive_max', 'd1_diasbp_noninvasive_min',\n",
" 'd1_heartrate_max', 'd1_heartrate_min', 'd1_mbp_max', 'd1_mbp_min',\n",
" 'd1_mbp_noninvasive_max', 'd1_mbp_noninvasive_min', 'd1_resprate_max',\n",
" 'd1_resprate_min', 'd1_spo2_max', 'd1_spo2_min', 'd1_sysbp_max',\n",
" 'd1_sysbp_min', 'd1_sysbp_noninvasive_max', 'd1_sysbp_noninvasive_min',\n",
" 'd1_temp_max', 'd1_temp_min', 'h1_diasbp_max', 'h1_diasbp_min',\n",
" 'h1_diasbp_noninvasive_max', 'h1_diasbp_noninvasive_min',\n",
" 'h1_heartrate_max', 'h1_heartrate_min', 'h1_mbp_max', 'h1_mbp_min',\n",
" 'h1_mbp_noninvasive_max', 'h1_mbp_noninvasive_min', 'h1_resprate_max',\n",
" 'h1_resprate_min', 'h1_spo2_max', 'h1_spo2_min', 'h1_sysbp_max',\n",
" 'h1_sysbp_min', 'h1_sysbp_noninvasive_max', 'h1_sysbp_noninvasive_min',\n",
" 'd1_glucose_max', 'd1_glucose_min', 'd1_potassium_max',\n",
" 'd1_potassium_min', 'apache_4a_hospital_death_prob',\n",
" 'apache_4a_icu_death_prob', 'aids', 'cirrhosis', 'diabetes_mellitus',\n",
" 'hepatic_failure', 'immunosuppression', 'leukemia', 'lymphoma',\n",
" 'solid_tumor_with_metastasis', 'apache_3j_bodysystem',\n",
" 'apache_2_bodysystem', 'Unnamed: 83', 'hospital_death'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"health_data.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
},
"id": "9uL83Aaz-UsY",
"outputId": "f3b8b662-c76e-4483-e22e-67aab47ab759"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi \\\n",
"count 91713.000000 91713.000000 91713.000000 87485.000000 88284.000000 \n",
"mean 65606.079280 65537.131464 105.669262 62.309516 29.185818 \n",
"std 37795.088538 37811.252183 62.854406 16.775119 8.275142 \n",
"min 1.000000 1.000000 2.000000 16.000000 14.844926 \n",
"25% 32852.000000 32830.000000 47.000000 52.000000 23.641975 \n",
"50% 65665.000000 65413.000000 109.000000 65.000000 27.654655 \n",
"75% 98342.000000 98298.000000 161.000000 75.000000 32.930206 \n",
"max 131051.000000 131051.000000 204.000000 89.000000 67.814990 \n",
"\n",
" elective_surgery height icu_id pre_icu_los_days \\\n",
"count 91713.000000 90379.000000 91713.000000 91713.000000 \n",
"mean 0.183736 169.641588 508.357692 0.835766 \n",
"std 0.387271 10.795378 228.989661 2.487756 \n",
"min 0.000000 137.200000 82.000000 -24.947222 \n",
"25% 0.000000 162.500000 369.000000 0.035417 \n",
"50% 0.000000 170.100000 504.000000 0.138889 \n",
"75% 0.000000 177.800000 679.000000 0.409028 \n",
"max 1.000000 195.590000 927.000000 159.090972 \n",
"\n",
" weight apache_2_diagnosis apache_3j_diagnosis \\\n",
"count 88993.000000 90051.000000 90612.000000 \n",
"mean 84.028340 185.401739 558.216377 \n",
"std 25.011497 86.050882 463.266985 \n",
"min 38.600000 101.000000 0.010000 \n",
"25% 66.800000 113.000000 203.010000 \n",
"50% 80.300000 122.000000 409.020000 \n",
"75% 97.100000 301.000000 703.030000 \n",
"max 186.000000 308.000000 2201.050000 \n",
"\n",
" apache_post_operative arf_apache gcs_eyes_apache gcs_motor_apache \\\n",
"count 91713.000000 90998.000000 89812.000000 89812.000000 \n",
"mean 0.201106 0.027979 3.465049 5.471195 \n",
"std 0.400829 0.164912 0.951715 1.288376 \n",
"min 0.000000 0.000000 1.000000 1.000000 \n",
"25% 0.000000 0.000000 3.000000 6.000000 \n",
"50% 0.000000 0.000000 4.000000 6.000000 \n",
"75% 0.000000 0.000000 4.000000 6.000000 \n",
"max 1.000000 1.000000 4.000000 6.000000 \n",
"\n",
" gcs_unable_apache gcs_verbal_apache heart_rate_apache \\\n",
"count 90676.000000 89812.000000 90835.000000 \n",
"mean 0.009528 3.994778 99.707932 \n",
"std 0.097148 1.560166 30.870502 \n",
"min 0.000000 1.000000 30.000000 \n",
"25% 0.000000 4.000000 86.000000 \n",
"50% 0.000000 5.000000 104.000000 \n",
"75% 0.000000 5.000000 120.000000 \n",
"max 1.000000 5.000000 178.000000 \n",
"\n",
" intubated_apache map_apache resprate_apache temp_apache \\\n",
"count 90998.000000 90719.000000 90479.000000 87605.000000 \n",
"mean 0.151223 88.015873 25.811007 36.414472 \n",
"std 0.358268 42.032412 15.106312 0.833496 \n",
"min 0.000000 40.000000 4.000000 32.100000 \n",
"25% 0.000000 54.000000 11.000000 36.200000 \n",
"50% 0.000000 67.000000 28.000000 36.500000 \n",
"75% 0.000000 125.000000 36.000000 36.700000 \n",
"max 1.000000 200.000000 60.000000 39.700000 \n",
"\n",
" ventilated_apache d1_diasbp_max d1_diasbp_min \\\n",
"count 90998.000000 91548.000000 91548.000000 \n",
"mean 0.325721 88.491873 50.161314 \n",
"std 0.468646 19.798379 13.317586 \n",
"min 0.000000 46.000000 13.000000 \n",
"25% 0.000000 75.000000 42.000000 \n",
"50% 0.000000 86.000000 50.000000 \n",
"75% 1.000000 99.000000 58.000000 \n",
"max 1.000000 165.000000 90.000000 \n",
"\n",
" d1_diasbp_noninvasive_max d1_diasbp_noninvasive_min d1_heartrate_max \\\n",
"count 90673.000000 90673.000000 91568.000000 \n",
"mean 88.610513 50.242597 103.000568 \n",
"std 19.793743 13.341521 22.017346 \n",
"min 46.000000 13.000000 58.000000 \n",
"25% 75.000000 42.000000 87.000000 \n",
"50% 87.000000 50.000000 101.000000 \n",
"75% 99.000000 58.000000 116.000000 \n",
"max 165.000000 90.000000 177.000000 \n",
"\n",
" d1_heartrate_min d1_mbp_max d1_mbp_min d1_mbp_noninvasive_max \\\n",
"count 91568.000000 91493.000000 91493.000000 90234.000000 \n",
"mean 70.321848 104.651339 64.871859 104.590454 \n",
"std 17.115903 20.808358 15.679680 20.701171 \n",
"min 0.000000 60.000000 22.000000 60.000000 \n",
"25% 60.000000 90.000000 55.000000 90.000000 \n",
"50% 69.000000 102.000000 64.000000 102.000000 \n",
"75% 81.000000 116.000000 75.000000 116.000000 \n",
"max 175.000000 184.000000 112.000000 181.000000 \n",
"\n",
" d1_mbp_noninvasive_min d1_resprate_max d1_resprate_min d1_spo2_max \\\n",
"count 90234.000000 91328.000000 91328.000000 91380.000000 \n",
"mean 64.941541 28.882774 12.846279 99.241836 \n",
"std 15.701305 10.701973 5.064943 1.794181 \n",
"min 22.000000 14.000000 0.000000 0.000000 \n",
"25% 55.000000 22.000000 10.000000 99.000000 \n",
"50% 64.000000 26.000000 13.000000 100.000000 \n",
"75% 75.000000 32.000000 16.000000 100.000000 \n",
"max 112.000000 92.000000 100.000000 100.000000 \n",
"\n",
" d1_spo2_min d1_sysbp_max d1_sysbp_min d1_sysbp_noninvasive_max \\\n",
"count 91380.000000 91554.000000 91554.00000 90686.000000 \n",
"mean 90.454826 148.339745 96.92387 148.235549 \n",
"std 10.030069 25.733259 20.67793 25.792453 \n",
"min 0.000000 90.000000 41.00000 90.000000 \n",
"25% 89.000000 130.000000 83.00000 130.000000 \n",
"50% 92.000000 146.000000 96.00000 146.000000 \n",
"75% 95.000000 164.000000 110.00000 164.000000 \n",
"max 100.000000 232.000000 160.00000 232.000000 \n",
"\n",
" d1_sysbp_noninvasive_min d1_temp_max d1_temp_min h1_diasbp_max \\\n",
"count 90686.000000 89389.000000 89389.000000 88094.000000 \n",
"mean 96.993313 37.284201 36.268391 75.354508 \n",
"std 20.705016 0.693287 0.745147 18.409190 \n",
"min 41.030000 35.100000 31.889000 37.000000 \n",
"25% 84.000000 36.900000 36.100000 62.000000 \n",
"50% 96.000000 37.110000 36.400000 74.000000 \n",
"75% 110.000000 37.600000 36.660000 86.000000 \n",
"max 160.000000 39.900000 37.800000 143.000000 \n",
"\n",
" h1_diasbp_min h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min \\\n",
"count 88094.000000 84363.000000 84363.000000 \n",
"mean 62.838150 75.805934 63.270616 \n",
"std 16.363229 18.481826 16.422063 \n",
"min 22.000000 37.000000 22.000000 \n",
"25% 52.000000 63.000000 52.000000 \n",
"50% 62.000000 74.000000 62.000000 \n",
"75% 73.000000 87.000000 74.000000 \n",
"max 113.000000 144.000000 114.000000 \n",
"\n",
" h1_heartrate_max h1_heartrate_min h1_mbp_max h1_mbp_min \\\n",
"count 88923.000000 88923.000000 87074.000000 87074.000000 \n",
"mean 92.229198 83.663720 91.612950 79.400028 \n",
"std 21.823704 20.279869 20.533174 19.130590 \n",
"min 46.000000 36.000000 49.000000 32.000000 \n",
"25% 77.000000 69.000000 77.000000 66.000000 \n",
"50% 90.000000 82.000000 90.000000 78.000000 \n",
"75% 106.000000 97.000000 104.000000 92.000000 \n",
"max 164.000000 144.000000 165.000000 138.000000 \n",
"\n",
" h1_mbp_noninvasive_max h1_mbp_noninvasive_min h1_resprate_max \\\n",
"count 82629.000000 82629.000000 87356.000000 \n",
"mean 91.594126 79.709315 22.633614 \n",
"std 20.552018 19.236507 7.515043 \n",
"min 49.000000 32.000000 10.000000 \n",
"25% 77.000000 66.000000 18.000000 \n",
"50% 90.000000 79.000000 21.000000 \n",
"75% 104.000000 92.000000 26.000000 \n",
"max 163.000000 138.000000 59.000000 \n",
"\n",
" h1_resprate_min h1_spo2_max h1_spo2_min h1_sysbp_max \\\n",
"count 87356.000000 87528.000000 87528.000000 88102.000000 \n",
"mean 17.211525 98.044637 95.174310 133.247395 \n",
"std 6.072588 3.212934 6.625227 27.556986 \n",
"min 0.000000 0.000000 0.000000 75.000000 \n",
"25% 14.000000 97.000000 94.000000 113.000000 \n",
"50% 16.000000 99.000000 96.000000 131.000000 \n",
"75% 20.000000 100.000000 99.000000 150.000000 \n",
"max 189.000000 100.000000 100.000000 223.000000 \n",
"\n",
" h1_sysbp_min h1_sysbp_noninvasive_max h1_sysbp_noninvasive_min \\\n",
"count 88102.000000 84372.000000 84372.000000 \n",
"mean 116.362296 133.054686 116.549625 \n",
"std 26.510637 27.679751 26.623528 \n",
"min 53.000000 75.000000 53.000000 \n",
"25% 98.000000 113.000000 98.000000 \n",
"50% 115.000000 130.000000 115.000000 \n",
"75% 134.000000 150.000000 134.000000 \n",
"max 194.000000 223.000000 195.000000 \n",
"\n",
" d1_glucose_max d1_glucose_min d1_potassium_max d1_potassium_min \\\n",
"count 85906.000000 85906.000000 82128.000000 82128.000000 \n",
"mean 174.638023 114.380940 4.251594 3.934658 \n",
"std 86.687955 38.273013 0.667355 0.579610 \n",
"min 73.000000 33.000000 2.800000 2.400000 \n",
"25% 117.000000 91.000000 3.800000 3.600000 \n",
"50% 150.000000 107.000000 4.200000 3.900000 \n",
"75% 201.000000 131.000000 4.600000 4.300000 \n",
"max 611.000000 288.000000 7.000000 5.800000 \n",
"\n",
" apache_4a_hospital_death_prob apache_4a_icu_death_prob aids \\\n",
"count 83766.000000 83766.000000 90998.000000 \n",
"mean 0.086787 0.043955 0.000857 \n",
"std 0.247569 0.217341 0.029265 \n",
"min -1.000000 -1.000000 0.000000 \n",
"25% 0.020000 0.010000 0.000000 \n",
"50% 0.050000 0.020000 0.000000 \n",
"75% 0.130000 0.060000 0.000000 \n",
"max 0.990000 0.970000 1.000000 \n",
"\n",
" cirrhosis diabetes_mellitus hepatic_failure immunosuppression \\\n",
"count 90998.000000 90998.000000 90998.000000 90998.000000 \n",
"mean 0.015693 0.225192 0.012989 0.026165 \n",
"std 0.124284 0.417711 0.113229 0.159628 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" leukemia lymphoma solid_tumor_with_metastasis Unnamed: 83 \\\n",
"count 90998.000000 90998.000000 90998.000000 0.0 \n",
"mean 0.007066 0.004132 0.020638 NaN \n",
"std 0.083763 0.064148 0.142169 NaN \n",
"min 0.000000 0.000000 0.000000 NaN \n",
"25% 0.000000 0.000000 0.000000 NaN \n",
"50% 0.000000 0.000000 0.000000 NaN \n",
"75% 0.000000 0.000000 0.000000 NaN \n",
"max 1.000000 1.000000 1.000000 NaN \n",
"\n",
" hospital_death \n",
"count 91713.000000 \n",
"mean 0.086302 \n",
"std 0.280811 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
],
"text/html": [
"\n",
" <div id=\"df-375aa3f3-37c9-4f12-b599-8e1ec49e532a\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>encounter_id</th>\n",
" <th>patient_id</th>\n",
" <th>hospital_id</th>\n",
" <th>age</th>\n",
" <th>bmi</th>\n",
" <th>elective_surgery</th>\n",
" <th>height</th>\n",
" <th>icu_id</th>\n",
" <th>pre_icu_los_days</th>\n",
" <th>weight</th>\n",
" <th>apache_2_diagnosis</th>\n",
" <th>apache_3j_diagnosis</th>\n",
" <th>apache_post_operative</th>\n",
" <th>arf_apache</th>\n",
" <th>gcs_eyes_apache</th>\n",
" <th>gcs_motor_apache</th>\n",
" <th>gcs_unable_apache</th>\n",
" <th>gcs_verbal_apache</th>\n",
" <th>heart_rate_apache</th>\n",
" <th>intubated_apache</th>\n",
" <th>map_apache</th>\n",
" <th>resprate_apache</th>\n",
" <th>temp_apache</th>\n",
" <th>ventilated_apache</th>\n",
" <th>d1_diasbp_max</th>\n",
" <th>d1_diasbp_min</th>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <th>d1_heartrate_max</th>\n",
" <th>d1_heartrate_min</th>\n",
" <th>d1_mbp_max</th>\n",
" <th>d1_mbp_min</th>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <th>d1_resprate_max</th>\n",
" <th>d1_resprate_min</th>\n",
" <th>d1_spo2_max</th>\n",
" <th>d1_spo2_min</th>\n",
" <th>d1_sysbp_max</th>\n",
" <th>d1_sysbp_min</th>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <th>d1_temp_max</th>\n",
" <th>d1_temp_min</th>\n",
" <th>h1_diasbp_max</th>\n",
" <th>h1_diasbp_min</th>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <th>h1_heartrate_max</th>\n",
" <th>h1_heartrate_min</th>\n",
" <th>h1_mbp_max</th>\n",
" <th>h1_mbp_min</th>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <th>h1_resprate_max</th>\n",
" <th>h1_resprate_min</th>\n",
" <th>h1_spo2_max</th>\n",
" <th>h1_spo2_min</th>\n",
" <th>h1_sysbp_max</th>\n",
" <th>h1_sysbp_min</th>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <th>d1_glucose_max</th>\n",
" <th>d1_glucose_min</th>\n",
" <th>d1_potassium_max</th>\n",
" <th>d1_potassium_min</th>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <th>aids</th>\n",
" <th>cirrhosis</th>\n",
" <th>diabetes_mellitus</th>\n",
" <th>hepatic_failure</th>\n",
" <th>immunosuppression</th>\n",
" <th>leukemia</th>\n",
" <th>lymphoma</th>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" <th>Unnamed: 83</th>\n",
" <th>hospital_death</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>91713.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>87485.000000</td>\n",
" <td>88284.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>90379.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>88993.000000</td>\n",
" <td>90051.000000</td>\n",
" <td>90612.000000</td>\n",
" <td>91713.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>89812.000000</td>\n",
" <td>89812.000000</td>\n",
" <td>90676.000000</td>\n",
" <td>89812.000000</td>\n",
" <td>90835.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90719.000000</td>\n",
" <td>90479.000000</td>\n",
" <td>87605.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>91548.000000</td>\n",
" <td>91548.000000</td>\n",
" <td>90673.000000</td>\n",
" <td>90673.000000</td>\n",
" <td>91568.000000</td>\n",
" <td>91568.000000</td>\n",
" <td>91493.000000</td>\n",
" <td>91493.000000</td>\n",
" <td>90234.000000</td>\n",
" <td>90234.000000</td>\n",
" <td>91328.000000</td>\n",
" <td>91328.000000</td>\n",
" <td>91380.000000</td>\n",
" <td>91380.000000</td>\n",
" <td>91554.000000</td>\n",
" <td>91554.00000</td>\n",
" <td>90686.000000</td>\n",
" <td>90686.000000</td>\n",
" <td>89389.000000</td>\n",
" <td>89389.000000</td>\n",
" <td>88094.000000</td>\n",
" <td>88094.000000</td>\n",
" <td>84363.000000</td>\n",
" <td>84363.000000</td>\n",
" <td>88923.000000</td>\n",
" <td>88923.000000</td>\n",
" <td>87074.000000</td>\n",
" <td>87074.000000</td>\n",
" <td>82629.000000</td>\n",
" <td>82629.000000</td>\n",
" <td>87356.000000</td>\n",
" <td>87356.000000</td>\n",
" <td>87528.000000</td>\n",
" <td>87528.000000</td>\n",
" <td>88102.000000</td>\n",
" <td>88102.000000</td>\n",
" <td>84372.000000</td>\n",
" <td>84372.000000</td>\n",
" <td>85906.000000</td>\n",
" <td>85906.000000</td>\n",
" <td>82128.000000</td>\n",
" <td>82128.000000</td>\n",
" <td>83766.000000</td>\n",
" <td>83766.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>90998.000000</td>\n",
" <td>0.0</td>\n",
" <td>91713.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>65606.079280</td>\n",
" <td>65537.131464</td>\n",
" <td>105.669262</td>\n",
" <td>62.309516</td>\n",
" <td>29.185818</td>\n",
" <td>0.183736</td>\n",
" <td>169.641588</td>\n",
" <td>508.357692</td>\n",
" <td>0.835766</td>\n",
" <td>84.028340</td>\n",
" <td>185.401739</td>\n",
" <td>558.216377</td>\n",
" <td>0.201106</td>\n",
" <td>0.027979</td>\n",
" <td>3.465049</td>\n",
" <td>5.471195</td>\n",
" <td>0.009528</td>\n",
" <td>3.994778</td>\n",
" <td>99.707932</td>\n",
" <td>0.151223</td>\n",
" <td>88.015873</td>\n",
" <td>25.811007</td>\n",
" <td>36.414472</td>\n",
" <td>0.325721</td>\n",
" <td>88.491873</td>\n",
" <td>50.161314</td>\n",
" <td>88.610513</td>\n",
" <td>50.242597</td>\n",
" <td>103.000568</td>\n",
" <td>70.321848</td>\n",
" <td>104.651339</td>\n",
" <td>64.871859</td>\n",
" <td>104.590454</td>\n",
" <td>64.941541</td>\n",
" <td>28.882774</td>\n",
" <td>12.846279</td>\n",
" <td>99.241836</td>\n",
" <td>90.454826</td>\n",
" <td>148.339745</td>\n",
" <td>96.92387</td>\n",
" <td>148.235549</td>\n",
" <td>96.993313</td>\n",
" <td>37.284201</td>\n",
" <td>36.268391</td>\n",
" <td>75.354508</td>\n",
" <td>62.838150</td>\n",
" <td>75.805934</td>\n",
" <td>63.270616</td>\n",
" <td>92.229198</td>\n",
" <td>83.663720</td>\n",
" <td>91.612950</td>\n",
" <td>79.400028</td>\n",
" <td>91.594126</td>\n",
" <td>79.709315</td>\n",
" <td>22.633614</td>\n",
" <td>17.211525</td>\n",
" <td>98.044637</td>\n",
" <td>95.174310</td>\n",
" <td>133.247395</td>\n",
" <td>116.362296</td>\n",
" <td>133.054686</td>\n",
" <td>116.549625</td>\n",
" <td>174.638023</td>\n",
" <td>114.380940</td>\n",
" <td>4.251594</td>\n",
" <td>3.934658</td>\n",
" <td>0.086787</td>\n",
" <td>0.043955</td>\n",
" <td>0.000857</td>\n",
" <td>0.015693</td>\n",
" <td>0.225192</td>\n",
" <td>0.012989</td>\n",
" <td>0.026165</td>\n",
" <td>0.007066</td>\n",
" <td>0.004132</td>\n",
" <td>0.020638</td>\n",
" <td>NaN</td>\n",
" <td>0.086302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>37795.088538</td>\n",
" <td>37811.252183</td>\n",
" <td>62.854406</td>\n",
" <td>16.775119</td>\n",
" <td>8.275142</td>\n",
" <td>0.387271</td>\n",
" <td>10.795378</td>\n",
" <td>228.989661</td>\n",
" <td>2.487756</td>\n",
" <td>25.011497</td>\n",
" <td>86.050882</td>\n",
" <td>463.266985</td>\n",
" <td>0.400829</td>\n",
" <td>0.164912</td>\n",
" <td>0.951715</td>\n",
" <td>1.288376</td>\n",
" <td>0.097148</td>\n",
" <td>1.560166</td>\n",
" <td>30.870502</td>\n",
" <td>0.358268</td>\n",
" <td>42.032412</td>\n",
" <td>15.106312</td>\n",
" <td>0.833496</td>\n",
" <td>0.468646</td>\n",
" <td>19.798379</td>\n",
" <td>13.317586</td>\n",
" <td>19.793743</td>\n",
" <td>13.341521</td>\n",
" <td>22.017346</td>\n",
" <td>17.115903</td>\n",
" <td>20.808358</td>\n",
" <td>15.679680</td>\n",
" <td>20.701171</td>\n",
" <td>15.701305</td>\n",
" <td>10.701973</td>\n",
" <td>5.064943</td>\n",
" <td>1.794181</td>\n",
" <td>10.030069</td>\n",
" <td>25.733259</td>\n",
" <td>20.67793</td>\n",
" <td>25.792453</td>\n",
" <td>20.705016</td>\n",
" <td>0.693287</td>\n",
" <td>0.745147</td>\n",
" <td>18.409190</td>\n",
" <td>16.363229</td>\n",
" <td>18.481826</td>\n",
" <td>16.422063</td>\n",
" <td>21.823704</td>\n",
" <td>20.279869</td>\n",
" <td>20.533174</td>\n",
" <td>19.130590</td>\n",
" <td>20.552018</td>\n",
" <td>19.236507</td>\n",
" <td>7.515043</td>\n",
" <td>6.072588</td>\n",
" <td>3.212934</td>\n",
" <td>6.625227</td>\n",
" <td>27.556986</td>\n",
" <td>26.510637</td>\n",
" <td>27.679751</td>\n",
" <td>26.623528</td>\n",
" <td>86.687955</td>\n",
" <td>38.273013</td>\n",
" <td>0.667355</td>\n",
" <td>0.579610</td>\n",
" <td>0.247569</td>\n",
" <td>0.217341</td>\n",
" <td>0.029265</td>\n",
" <td>0.124284</td>\n",
" <td>0.417711</td>\n",
" <td>0.113229</td>\n",
" <td>0.159628</td>\n",
" <td>0.083763</td>\n",
" <td>0.064148</td>\n",
" <td>0.142169</td>\n",
" <td>NaN</td>\n",
" <td>0.280811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>16.000000</td>\n",
" <td>14.844926</td>\n",
" <td>0.000000</td>\n",
" <td>137.200000</td>\n",
" <td>82.000000</td>\n",
" <td>-24.947222</td>\n",
" <td>38.600000</td>\n",
" <td>101.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>30.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" <td>4.000000</td>\n",
" <td>32.100000</td>\n",
" <td>0.000000</td>\n",
" <td>46.000000</td>\n",
" <td>13.000000</td>\n",
" <td>46.000000</td>\n",
" <td>13.000000</td>\n",
" <td>58.000000</td>\n",
" <td>0.000000</td>\n",
" <td>60.000000</td>\n",
" <td>22.000000</td>\n",
" <td>60.000000</td>\n",
" <td>22.000000</td>\n",
" <td>14.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>90.000000</td>\n",
" <td>41.00000</td>\n",
" <td>90.000000</td>\n",
" <td>41.030000</td>\n",
" <td>35.100000</td>\n",
" <td>31.889000</td>\n",
" <td>37.000000</td>\n",
" <td>22.000000</td>\n",
" <td>37.000000</td>\n",
" <td>22.000000</td>\n",
" <td>46.000000</td>\n",
" <td>36.000000</td>\n",
" <td>49.000000</td>\n",
" <td>32.000000</td>\n",
" <td>49.000000</td>\n",
" <td>32.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>75.000000</td>\n",
" <td>53.000000</td>\n",
" <td>75.000000</td>\n",
" <td>53.000000</td>\n",
" <td>73.000000</td>\n",
" <td>33.000000</td>\n",
" <td>2.800000</td>\n",
" <td>2.400000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>32852.000000</td>\n",
" <td>32830.000000</td>\n",
" <td>47.000000</td>\n",
" <td>52.000000</td>\n",
" <td>23.641975</td>\n",
" <td>0.000000</td>\n",
" <td>162.500000</td>\n",
" <td>369.000000</td>\n",
" <td>0.035417</td>\n",
" <td>66.800000</td>\n",
" <td>113.000000</td>\n",
" <td>203.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>86.000000</td>\n",
" <td>0.000000</td>\n",
" <td>54.000000</td>\n",
" <td>11.000000</td>\n",
" <td>36.200000</td>\n",
" <td>0.000000</td>\n",
" <td>75.000000</td>\n",
" <td>42.000000</td>\n",
" <td>75.000000</td>\n",
" <td>42.000000</td>\n",
" <td>87.000000</td>\n",
" <td>60.000000</td>\n",
" <td>90.000000</td>\n",
" <td>55.000000</td>\n",
" <td>90.000000</td>\n",
" <td>55.000000</td>\n",
" <td>22.000000</td>\n",
" <td>10.000000</td>\n",
" <td>99.000000</td>\n",
" <td>89.000000</td>\n",
" <td>130.000000</td>\n",
" <td>83.00000</td>\n",
" <td>130.000000</td>\n",
" <td>84.000000</td>\n",
" <td>36.900000</td>\n",
" <td>36.100000</td>\n",
" <td>62.000000</td>\n",
" <td>52.000000</td>\n",
" <td>63.000000</td>\n",
" <td>52.000000</td>\n",
" <td>77.000000</td>\n",
" <td>69.000000</td>\n",
" <td>77.000000</td>\n",
" <td>66.000000</td>\n",
" <td>77.000000</td>\n",
" <td>66.000000</td>\n",
" <td>18.000000</td>\n",
" <td>14.000000</td>\n",
" <td>97.000000</td>\n",
" <td>94.000000</td>\n",
" <td>113.000000</td>\n",
" <td>98.000000</td>\n",
" <td>113.000000</td>\n",
" <td>98.000000</td>\n",
" <td>117.000000</td>\n",
" <td>91.000000</td>\n",
" <td>3.800000</td>\n",
" <td>3.600000</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>65665.000000</td>\n",
" <td>65413.000000</td>\n",
" <td>109.000000</td>\n",
" <td>65.000000</td>\n",
" <td>27.654655</td>\n",
" <td>0.000000</td>\n",
" <td>170.100000</td>\n",
" <td>504.000000</td>\n",
" <td>0.138889</td>\n",
" <td>80.300000</td>\n",
" <td>122.000000</td>\n",
" <td>409.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>104.000000</td>\n",
" <td>0.000000</td>\n",
" <td>67.000000</td>\n",
" <td>28.000000</td>\n",
" <td>36.500000</td>\n",
" <td>0.000000</td>\n",
" <td>86.000000</td>\n",
" <td>50.000000</td>\n",
" <td>87.000000</td>\n",
" <td>50.000000</td>\n",
" <td>101.000000</td>\n",
" <td>69.000000</td>\n",
" <td>102.000000</td>\n",
" <td>64.000000</td>\n",
" <td>102.000000</td>\n",
" <td>64.000000</td>\n",
" <td>26.000000</td>\n",
" <td>13.000000</td>\n",
" <td>100.000000</td>\n",
" <td>92.000000</td>\n",
" <td>146.000000</td>\n",
" <td>96.00000</td>\n",
" <td>146.000000</td>\n",
" <td>96.000000</td>\n",
" <td>37.110000</td>\n",
" <td>36.400000</td>\n",
" <td>74.000000</td>\n",
" <td>62.000000</td>\n",
" <td>74.000000</td>\n",
" <td>62.000000</td>\n",
" <td>90.000000</td>\n",
" <td>82.000000</td>\n",
" <td>90.000000</td>\n",
" <td>78.000000</td>\n",
" <td>90.000000</td>\n",
" <td>79.000000</td>\n",
" <td>21.000000</td>\n",
" <td>16.000000</td>\n",
" <td>99.000000</td>\n",
" <td>96.000000</td>\n",
" <td>131.000000</td>\n",
" <td>115.000000</td>\n",
" <td>130.000000</td>\n",
" <td>115.000000</td>\n",
" <td>150.000000</td>\n",
" <td>107.000000</td>\n",
" <td>4.200000</td>\n",
" <td>3.900000</td>\n",
" <td>0.050000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>98342.000000</td>\n",
" <td>98298.000000</td>\n",
" <td>161.000000</td>\n",
" <td>75.000000</td>\n",
" <td>32.930206</td>\n",
" <td>0.000000</td>\n",
" <td>177.800000</td>\n",
" <td>679.000000</td>\n",
" <td>0.409028</td>\n",
" <td>97.100000</td>\n",
" <td>301.000000</td>\n",
" <td>703.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>120.000000</td>\n",
" <td>0.000000</td>\n",
" <td>125.000000</td>\n",
" <td>36.000000</td>\n",
" <td>36.700000</td>\n",
" <td>1.000000</td>\n",
" <td>99.000000</td>\n",
" <td>58.000000</td>\n",
" <td>99.000000</td>\n",
" <td>58.000000</td>\n",
" <td>116.000000</td>\n",
" <td>81.000000</td>\n",
" <td>116.000000</td>\n",
" <td>75.000000</td>\n",
" <td>116.000000</td>\n",
" <td>75.000000</td>\n",
" <td>32.000000</td>\n",
" <td>16.000000</td>\n",
" <td>100.000000</td>\n",
" <td>95.000000</td>\n",
" <td>164.000000</td>\n",
" <td>110.00000</td>\n",
" <td>164.000000</td>\n",
" <td>110.000000</td>\n",
" <td>37.600000</td>\n",
" <td>36.660000</td>\n",
" <td>86.000000</td>\n",
" <td>73.000000</td>\n",
" <td>87.000000</td>\n",
" <td>74.000000</td>\n",
" <td>106.000000</td>\n",
" <td>97.000000</td>\n",
" <td>104.000000</td>\n",
" <td>92.000000</td>\n",
" <td>104.000000</td>\n",
" <td>92.000000</td>\n",
" <td>26.000000</td>\n",
" <td>20.000000</td>\n",
" <td>100.000000</td>\n",
" <td>99.000000</td>\n",
" <td>150.000000</td>\n",
" <td>134.000000</td>\n",
" <td>150.000000</td>\n",
" <td>134.000000</td>\n",
" <td>201.000000</td>\n",
" <td>131.000000</td>\n",
" <td>4.600000</td>\n",
" <td>4.300000</td>\n",
" <td>0.130000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>131051.000000</td>\n",
" <td>131051.000000</td>\n",
" <td>204.000000</td>\n",
" <td>89.000000</td>\n",
" <td>67.814990</td>\n",
" <td>1.000000</td>\n",
" <td>195.590000</td>\n",
" <td>927.000000</td>\n",
" <td>159.090972</td>\n",
" <td>186.000000</td>\n",
" <td>308.000000</td>\n",
" <td>2201.050000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000</td>\n",
" <td>178.000000</td>\n",
" <td>1.000000</td>\n",
" <td>200.000000</td>\n",
" <td>60.000000</td>\n",
" <td>39.700000</td>\n",
" <td>1.000000</td>\n",
" <td>165.000000</td>\n",
" <td>90.000000</td>\n",
" <td>165.000000</td>\n",
" <td>90.000000</td>\n",
" <td>177.000000</td>\n",
" <td>175.000000</td>\n",
" <td>184.000000</td>\n",
" <td>112.000000</td>\n",
" <td>181.000000</td>\n",
" <td>112.000000</td>\n",
" <td>92.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>232.000000</td>\n",
" <td>160.00000</td>\n",
" <td>232.000000</td>\n",
" <td>160.000000</td>\n",
" <td>39.900000</td>\n",
" <td>37.800000</td>\n",
" <td>143.000000</td>\n",
" <td>113.000000</td>\n",
" <td>144.000000</td>\n",
" <td>114.000000</td>\n",
" <td>164.000000</td>\n",
" <td>144.000000</td>\n",
" <td>165.000000</td>\n",
" <td>138.000000</td>\n",
" <td>163.000000</td>\n",
" <td>138.000000</td>\n",
" <td>59.000000</td>\n",
" <td>189.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>223.000000</td>\n",
" <td>194.000000</td>\n",
" <td>223.000000</td>\n",
" <td>195.000000</td>\n",
" <td>611.000000</td>\n",
" <td>288.000000</td>\n",
" <td>7.000000</td>\n",
" <td>5.800000</td>\n",
" <td>0.990000</td>\n",
" <td>0.970000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
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" <div class=\"colab-df-container\">\n",
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" .colab-df-convert:hover {\n",
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" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-bed5667f-7752-4d68-8174-56ea0643e8b3 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
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" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe"
}
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"health_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fv3UpZEj-dhf",
"outputId": "48579e62-b37f-4169-f292-fb7e75fcf148"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 91713 entries, 0 to 91712\n",
"Data columns (total 85 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 encounter_id 91713 non-null int64 \n",
" 1 patient_id 91713 non-null int64 \n",
" 2 hospital_id 91713 non-null int64 \n",
" 3 age 87485 non-null float64\n",
" 4 bmi 88284 non-null float64\n",
" 5 elective_surgery 91713 non-null int64 \n",
" 6 ethnicity 90318 non-null object \n",
" 7 gender 91688 non-null object \n",
" 8 height 90379 non-null float64\n",
" 9 icu_admit_source 91601 non-null object \n",
" 10 icu_id 91713 non-null int64 \n",
" 11 icu_stay_type 91713 non-null object \n",
" 12 icu_type 91713 non-null object \n",
" 13 pre_icu_los_days 91713 non-null float64\n",
" 14 weight 88993 non-null float64\n",
" 15 apache_2_diagnosis 90051 non-null float64\n",
" 16 apache_3j_diagnosis 90612 non-null float64\n",
" 17 apache_post_operative 91713 non-null int64 \n",
" 18 arf_apache 90998 non-null float64\n",
" 19 gcs_eyes_apache 89812 non-null float64\n",
" 20 gcs_motor_apache 89812 non-null float64\n",
" 21 gcs_unable_apache 90676 non-null float64\n",
" 22 gcs_verbal_apache 89812 non-null float64\n",
" 23 heart_rate_apache 90835 non-null float64\n",
" 24 intubated_apache 90998 non-null float64\n",
" 25 map_apache 90719 non-null float64\n",
" 26 resprate_apache 90479 non-null float64\n",
" 27 temp_apache 87605 non-null float64\n",
" 28 ventilated_apache 90998 non-null float64\n",
" 29 d1_diasbp_max 91548 non-null float64\n",
" 30 d1_diasbp_min 91548 non-null float64\n",
" 31 d1_diasbp_noninvasive_max 90673 non-null float64\n",
" 32 d1_diasbp_noninvasive_min 90673 non-null float64\n",
" 33 d1_heartrate_max 91568 non-null float64\n",
" 34 d1_heartrate_min 91568 non-null float64\n",
" 35 d1_mbp_max 91493 non-null float64\n",
" 36 d1_mbp_min 91493 non-null float64\n",
" 37 d1_mbp_noninvasive_max 90234 non-null float64\n",
" 38 d1_mbp_noninvasive_min 90234 non-null float64\n",
" 39 d1_resprate_max 91328 non-null float64\n",
" 40 d1_resprate_min 91328 non-null float64\n",
" 41 d1_spo2_max 91380 non-null float64\n",
" 42 d1_spo2_min 91380 non-null float64\n",
" 43 d1_sysbp_max 91554 non-null float64\n",
" 44 d1_sysbp_min 91554 non-null float64\n",
" 45 d1_sysbp_noninvasive_max 90686 non-null float64\n",
" 46 d1_sysbp_noninvasive_min 90686 non-null float64\n",
" 47 d1_temp_max 89389 non-null float64\n",
" 48 d1_temp_min 89389 non-null float64\n",
" 49 h1_diasbp_max 88094 non-null float64\n",
" 50 h1_diasbp_min 88094 non-null float64\n",
" 51 h1_diasbp_noninvasive_max 84363 non-null float64\n",
" 52 h1_diasbp_noninvasive_min 84363 non-null float64\n",
" 53 h1_heartrate_max 88923 non-null float64\n",
" 54 h1_heartrate_min 88923 non-null float64\n",
" 55 h1_mbp_max 87074 non-null float64\n",
" 56 h1_mbp_min 87074 non-null float64\n",
" 57 h1_mbp_noninvasive_max 82629 non-null float64\n",
" 58 h1_mbp_noninvasive_min 82629 non-null float64\n",
" 59 h1_resprate_max 87356 non-null float64\n",
" 60 h1_resprate_min 87356 non-null float64\n",
" 61 h1_spo2_max 87528 non-null float64\n",
" 62 h1_spo2_min 87528 non-null float64\n",
" 63 h1_sysbp_max 88102 non-null float64\n",
" 64 h1_sysbp_min 88102 non-null float64\n",
" 65 h1_sysbp_noninvasive_max 84372 non-null float64\n",
" 66 h1_sysbp_noninvasive_min 84372 non-null float64\n",
" 67 d1_glucose_max 85906 non-null float64\n",
" 68 d1_glucose_min 85906 non-null float64\n",
" 69 d1_potassium_max 82128 non-null float64\n",
" 70 d1_potassium_min 82128 non-null float64\n",
" 71 apache_4a_hospital_death_prob 83766 non-null float64\n",
" 72 apache_4a_icu_death_prob 83766 non-null float64\n",
" 73 aids 90998 non-null float64\n",
" 74 cirrhosis 90998 non-null float64\n",
" 75 diabetes_mellitus 90998 non-null float64\n",
" 76 hepatic_failure 90998 non-null float64\n",
" 77 immunosuppression 90998 non-null float64\n",
" 78 leukemia 90998 non-null float64\n",
" 79 lymphoma 90998 non-null float64\n",
" 80 solid_tumor_with_metastasis 90998 non-null float64\n",
" 81 apache_3j_bodysystem 90051 non-null object \n",
" 82 apache_2_bodysystem 90051 non-null object \n",
" 83 Unnamed: 83 0 non-null float64\n",
" 84 hospital_death 91713 non-null int64 \n",
"dtypes: float64(71), int64(7), object(7)\n",
"memory usage: 59.5+ MB\n"
]
}
],
"source": [
"health_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "gG_Kukwr-i_d"
},
"outputs": [],
"source": [
"#### Data Cleanining\n",
"new_data = health_data.drop(\"Unnamed: 83\", axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sVgAUo8hvu8Q",
"outputId": "52d8ce90-003a-4b74-ff8b-397c8813b0a7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 91713 entries, 0 to 91712\n",
"Data columns (total 84 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 encounter_id 91713 non-null int64 \n",
" 1 patient_id 91713 non-null int64 \n",
" 2 hospital_id 91713 non-null int64 \n",
" 3 age 87485 non-null float64\n",
" 4 bmi 88284 non-null float64\n",
" 5 elective_surgery 91713 non-null int64 \n",
" 6 ethnicity 90318 non-null object \n",
" 7 gender 91688 non-null object \n",
" 8 height 90379 non-null float64\n",
" 9 icu_admit_source 91601 non-null object \n",
" 10 icu_id 91713 non-null int64 \n",
" 11 icu_stay_type 91713 non-null object \n",
" 12 icu_type 91713 non-null object \n",
" 13 pre_icu_los_days 91713 non-null float64\n",
" 14 weight 88993 non-null float64\n",
" 15 apache_2_diagnosis 90051 non-null float64\n",
" 16 apache_3j_diagnosis 90612 non-null float64\n",
" 17 apache_post_operative 91713 non-null int64 \n",
" 18 arf_apache 90998 non-null float64\n",
" 19 gcs_eyes_apache 89812 non-null float64\n",
" 20 gcs_motor_apache 89812 non-null float64\n",
" 21 gcs_unable_apache 90676 non-null float64\n",
" 22 gcs_verbal_apache 89812 non-null float64\n",
" 23 heart_rate_apache 90835 non-null float64\n",
" 24 intubated_apache 90998 non-null float64\n",
" 25 map_apache 90719 non-null float64\n",
" 26 resprate_apache 90479 non-null float64\n",
" 27 temp_apache 87605 non-null float64\n",
" 28 ventilated_apache 90998 non-null float64\n",
" 29 d1_diasbp_max 91548 non-null float64\n",
" 30 d1_diasbp_min 91548 non-null float64\n",
" 31 d1_diasbp_noninvasive_max 90673 non-null float64\n",
" 32 d1_diasbp_noninvasive_min 90673 non-null float64\n",
" 33 d1_heartrate_max 91568 non-null float64\n",
" 34 d1_heartrate_min 91568 non-null float64\n",
" 35 d1_mbp_max 91493 non-null float64\n",
" 36 d1_mbp_min 91493 non-null float64\n",
" 37 d1_mbp_noninvasive_max 90234 non-null float64\n",
" 38 d1_mbp_noninvasive_min 90234 non-null float64\n",
" 39 d1_resprate_max 91328 non-null float64\n",
" 40 d1_resprate_min 91328 non-null float64\n",
" 41 d1_spo2_max 91380 non-null float64\n",
" 42 d1_spo2_min 91380 non-null float64\n",
" 43 d1_sysbp_max 91554 non-null float64\n",
" 44 d1_sysbp_min 91554 non-null float64\n",
" 45 d1_sysbp_noninvasive_max 90686 non-null float64\n",
" 46 d1_sysbp_noninvasive_min 90686 non-null float64\n",
" 47 d1_temp_max 89389 non-null float64\n",
" 48 d1_temp_min 89389 non-null float64\n",
" 49 h1_diasbp_max 88094 non-null float64\n",
" 50 h1_diasbp_min 88094 non-null float64\n",
" 51 h1_diasbp_noninvasive_max 84363 non-null float64\n",
" 52 h1_diasbp_noninvasive_min 84363 non-null float64\n",
" 53 h1_heartrate_max 88923 non-null float64\n",
" 54 h1_heartrate_min 88923 non-null float64\n",
" 55 h1_mbp_max 87074 non-null float64\n",
" 56 h1_mbp_min 87074 non-null float64\n",
" 57 h1_mbp_noninvasive_max 82629 non-null float64\n",
" 58 h1_mbp_noninvasive_min 82629 non-null float64\n",
" 59 h1_resprate_max 87356 non-null float64\n",
" 60 h1_resprate_min 87356 non-null float64\n",
" 61 h1_spo2_max 87528 non-null float64\n",
" 62 h1_spo2_min 87528 non-null float64\n",
" 63 h1_sysbp_max 88102 non-null float64\n",
" 64 h1_sysbp_min 88102 non-null float64\n",
" 65 h1_sysbp_noninvasive_max 84372 non-null float64\n",
" 66 h1_sysbp_noninvasive_min 84372 non-null float64\n",
" 67 d1_glucose_max 85906 non-null float64\n",
" 68 d1_glucose_min 85906 non-null float64\n",
" 69 d1_potassium_max 82128 non-null float64\n",
" 70 d1_potassium_min 82128 non-null float64\n",
" 71 apache_4a_hospital_death_prob 83766 non-null float64\n",
" 72 apache_4a_icu_death_prob 83766 non-null float64\n",
" 73 aids 90998 non-null float64\n",
" 74 cirrhosis 90998 non-null float64\n",
" 75 diabetes_mellitus 90998 non-null float64\n",
" 76 hepatic_failure 90998 non-null float64\n",
" 77 immunosuppression 90998 non-null float64\n",
" 78 leukemia 90998 non-null float64\n",
" 79 lymphoma 90998 non-null float64\n",
" 80 solid_tumor_with_metastasis 90998 non-null float64\n",
" 81 apache_3j_bodysystem 90051 non-null object \n",
" 82 apache_2_bodysystem 90051 non-null object \n",
" 83 hospital_death 91713 non-null int64 \n",
"dtypes: float64(70), int64(7), object(7)\n",
"memory usage: 58.8+ MB\n"
]
}
],
"source": [
"new_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "pbPsiF3IXku6"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "DN1pFp4mwAvj",
"outputId": "5ba7ee9b-ac24-4911-8015-8b0c41a5bde4"
},
"outputs": [
{
"output_type": "execute_result",
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"encounter_id 0.000000\n",
"patient_id 0.000000\n",
"hospital_id 0.000000\n",
"age 4.610033\n",
"bmi 3.738837\n",
"elective_surgery 0.000000\n",
"ethnicity 1.521049\n",
"gender 0.027259\n",
"height 1.454538\n",
"icu_admit_source 0.122120\n",
"icu_id 0.000000\n",
"icu_stay_type 0.000000\n",
"icu_type 0.000000\n",
"pre_icu_los_days 0.000000\n",
"weight 2.965774\n",
"apache_2_diagnosis 1.812175\n",
"apache_3j_diagnosis 1.200484\n",
"apache_post_operative 0.000000\n",
"arf_apache 0.779606\n",
"gcs_eyes_apache 2.072770\n",
"gcs_motor_apache 2.072770\n",
"gcs_unable_apache 1.130701\n",
"gcs_verbal_apache 2.072770\n",
"heart_rate_apache 0.957334\n",
"intubated_apache 0.779606\n",
"map_apache 1.083816\n",
"resprate_apache 1.345502\n",
"temp_apache 4.479191\n",
"ventilated_apache 0.779606\n",
"d1_diasbp_max 0.179909\n",
"d1_diasbp_min 0.179909\n",
"d1_diasbp_noninvasive_max 1.133972\n",
"d1_diasbp_noninvasive_min 1.133972\n",
"d1_heartrate_max 0.158102\n",
"d1_heartrate_min 0.158102\n",
"d1_mbp_max 0.239879\n",
"d1_mbp_min 0.239879\n",
"d1_mbp_noninvasive_max 1.612639\n",
"d1_mbp_noninvasive_min 1.612639\n",
"d1_resprate_max 0.419788\n",
"d1_resprate_min 0.419788\n",
"d1_spo2_max 0.363089\n",
"d1_spo2_min 0.363089\n",
"d1_sysbp_max 0.173367\n",
"d1_sysbp_min 0.173367\n",
"d1_sysbp_noninvasive_max 1.119798\n",
"d1_sysbp_noninvasive_min 1.119798\n",
"d1_temp_max 2.533992\n",
"d1_temp_min 2.533992\n",
"h1_diasbp_max 3.946005\n",
"h1_diasbp_min 3.946005\n",
"h1_diasbp_noninvasive_max 8.014131\n",
"h1_diasbp_noninvasive_min 8.014131\n",
"h1_heartrate_max 3.042099\n",
"h1_heartrate_min 3.042099\n",
"h1_mbp_max 5.058171\n",
"h1_mbp_min 5.058171\n",
"h1_mbp_noninvasive_max 9.904812\n",
"h1_mbp_noninvasive_min 9.904812\n",
"h1_resprate_max 4.750690\n",
"h1_resprate_min 4.750690\n",
"h1_spo2_max 4.563148\n",
"h1_spo2_min 4.563148\n",
"h1_sysbp_max 3.937283\n",
"h1_sysbp_min 3.937283\n",
"h1_sysbp_noninvasive_max 8.004318\n",
"h1_sysbp_noninvasive_min 8.004318\n",
"d1_glucose_max 6.331709\n",
"d1_glucose_min 6.331709\n",
"d1_potassium_max 10.451081\n",
"d1_potassium_min 10.451081\n",
"apache_4a_hospital_death_prob 8.665075\n",
"apache_4a_icu_death_prob 8.665075\n",
"aids 0.779606\n",
"cirrhosis 0.779606\n",
"diabetes_mellitus 0.779606\n",
"hepatic_failure 0.779606\n",
"immunosuppression 0.779606\n",
"leukemia 0.779606\n",
"lymphoma 0.779606\n",
"solid_tumor_with_metastasis 0.779606\n",
"apache_3j_bodysystem 1.812175\n",
"apache_2_bodysystem 1.812175\n",
"hospital_death 0.000000\n",
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" <th>resprate_apache</th>\n",
" <td>1.345502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temp_apache</th>\n",
" <td>4.479191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ventilated_apache</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_max</th>\n",
" <td>0.179909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_min</th>\n",
" <td>0.179909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <td>1.133972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <td>1.133972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_heartrate_max</th>\n",
" <td>0.158102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_heartrate_min</th>\n",
" <td>0.158102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_max</th>\n",
" <td>0.239879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_min</th>\n",
" <td>0.239879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <td>1.612639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <td>1.612639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_resprate_max</th>\n",
" <td>0.419788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_resprate_min</th>\n",
" <td>0.419788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_spo2_max</th>\n",
" <td>0.363089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_spo2_min</th>\n",
" <td>0.363089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_max</th>\n",
" <td>0.173367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_min</th>\n",
" <td>0.173367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <td>1.119798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <td>1.119798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_temp_max</th>\n",
" <td>2.533992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_temp_min</th>\n",
" <td>2.533992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_max</th>\n",
" <td>3.946005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_min</th>\n",
" <td>3.946005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <td>8.014131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <td>8.014131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_heartrate_max</th>\n",
" <td>3.042099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_heartrate_min</th>\n",
" <td>3.042099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_max</th>\n",
" <td>5.058171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_min</th>\n",
" <td>5.058171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <td>9.904812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <td>9.904812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_resprate_max</th>\n",
" <td>4.750690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_resprate_min</th>\n",
" <td>4.750690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_spo2_max</th>\n",
" <td>4.563148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_spo2_min</th>\n",
" <td>4.563148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_max</th>\n",
" <td>3.937283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_min</th>\n",
" <td>3.937283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <td>8.004318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <td>8.004318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_glucose_max</th>\n",
" <td>6.331709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_glucose_min</th>\n",
" <td>6.331709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_potassium_max</th>\n",
" <td>10.451081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_potassium_min</th>\n",
" <td>10.451081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <td>8.665075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <td>8.665075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aids</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cirrhosis</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diabetes_mellitus</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hepatic_failure</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>immunosuppression</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leukemia</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lymphoma</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" <td>0.779606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_3j_bodysystem</th>\n",
" <td>1.812175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_2_bodysystem</th>\n",
" <td>1.812175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hospital_death</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> float64</label>"
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"###### Checking the percentage of missing Data\n",
"pd.set_option(\"display.max_rows\", None)\n",
"missing_data_percentage = new_data.isna().mean() * 100\n",
"missing_data_percentage"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 637
},
"id": "ZrZB4cUR1qSp",
"outputId": "6c82968a-60d2-4d26-8df7-42cbc1b7c209"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"sns.heatmap(new_data.isna(), cbar=False, cmap='viridis', yticklabels=False)\n",
"plt.title('Missing Data Heatmap')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T-exPwqhwkNG",
"outputId": "f3a3af12-38b5-41b3-a4f0-ee045f8832c9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 56935 entries, 0 to 91712\n",
"Data columns (total 84 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 encounter_id 56935 non-null int64 \n",
" 1 patient_id 56935 non-null int64 \n",
" 2 hospital_id 56935 non-null int64 \n",
" 3 age 56935 non-null float64\n",
" 4 bmi 56935 non-null float64\n",
" 5 elective_surgery 56935 non-null int64 \n",
" 6 ethnicity 56935 non-null object \n",
" 7 gender 56935 non-null object \n",
" 8 height 56935 non-null float64\n",
" 9 icu_admit_source 56935 non-null object \n",
" 10 icu_id 56935 non-null int64 \n",
" 11 icu_stay_type 56935 non-null object \n",
" 12 icu_type 56935 non-null object \n",
" 13 pre_icu_los_days 56935 non-null float64\n",
" 14 weight 56935 non-null float64\n",
" 15 apache_2_diagnosis 56935 non-null float64\n",
" 16 apache_3j_diagnosis 56935 non-null float64\n",
" 17 apache_post_operative 56935 non-null int64 \n",
" 18 arf_apache 56935 non-null float64\n",
" 19 gcs_eyes_apache 56935 non-null float64\n",
" 20 gcs_motor_apache 56935 non-null float64\n",
" 21 gcs_unable_apache 56935 non-null float64\n",
" 22 gcs_verbal_apache 56935 non-null float64\n",
" 23 heart_rate_apache 56935 non-null float64\n",
" 24 intubated_apache 56935 non-null float64\n",
" 25 map_apache 56935 non-null float64\n",
" 26 resprate_apache 56935 non-null float64\n",
" 27 temp_apache 56935 non-null float64\n",
" 28 ventilated_apache 56935 non-null float64\n",
" 29 d1_diasbp_max 56935 non-null float64\n",
" 30 d1_diasbp_min 56935 non-null float64\n",
" 31 d1_diasbp_noninvasive_max 56935 non-null float64\n",
" 32 d1_diasbp_noninvasive_min 56935 non-null float64\n",
" 33 d1_heartrate_max 56935 non-null float64\n",
" 34 d1_heartrate_min 56935 non-null float64\n",
" 35 d1_mbp_max 56935 non-null float64\n",
" 36 d1_mbp_min 56935 non-null float64\n",
" 37 d1_mbp_noninvasive_max 56935 non-null float64\n",
" 38 d1_mbp_noninvasive_min 56935 non-null float64\n",
" 39 d1_resprate_max 56935 non-null float64\n",
" 40 d1_resprate_min 56935 non-null float64\n",
" 41 d1_spo2_max 56935 non-null float64\n",
" 42 d1_spo2_min 56935 non-null float64\n",
" 43 d1_sysbp_max 56935 non-null float64\n",
" 44 d1_sysbp_min 56935 non-null float64\n",
" 45 d1_sysbp_noninvasive_max 56935 non-null float64\n",
" 46 d1_sysbp_noninvasive_min 56935 non-null float64\n",
" 47 d1_temp_max 56935 non-null float64\n",
" 48 d1_temp_min 56935 non-null float64\n",
" 49 h1_diasbp_max 56935 non-null float64\n",
" 50 h1_diasbp_min 56935 non-null float64\n",
" 51 h1_diasbp_noninvasive_max 56935 non-null float64\n",
" 52 h1_diasbp_noninvasive_min 56935 non-null float64\n",
" 53 h1_heartrate_max 56935 non-null float64\n",
" 54 h1_heartrate_min 56935 non-null float64\n",
" 55 h1_mbp_max 56935 non-null float64\n",
" 56 h1_mbp_min 56935 non-null float64\n",
" 57 h1_mbp_noninvasive_max 56935 non-null float64\n",
" 58 h1_mbp_noninvasive_min 56935 non-null float64\n",
" 59 h1_resprate_max 56935 non-null float64\n",
" 60 h1_resprate_min 56935 non-null float64\n",
" 61 h1_spo2_max 56935 non-null float64\n",
" 62 h1_spo2_min 56935 non-null float64\n",
" 63 h1_sysbp_max 56935 non-null float64\n",
" 64 h1_sysbp_min 56935 non-null float64\n",
" 65 h1_sysbp_noninvasive_max 56935 non-null float64\n",
" 66 h1_sysbp_noninvasive_min 56935 non-null float64\n",
" 67 d1_glucose_max 56935 non-null float64\n",
" 68 d1_glucose_min 56935 non-null float64\n",
" 69 d1_potassium_max 56935 non-null float64\n",
" 70 d1_potassium_min 56935 non-null float64\n",
" 71 apache_4a_hospital_death_prob 56935 non-null float64\n",
" 72 apache_4a_icu_death_prob 56935 non-null float64\n",
" 73 aids 56935 non-null float64\n",
" 74 cirrhosis 56935 non-null float64\n",
" 75 diabetes_mellitus 56935 non-null float64\n",
" 76 hepatic_failure 56935 non-null float64\n",
" 77 immunosuppression 56935 non-null float64\n",
" 78 leukemia 56935 non-null float64\n",
" 79 lymphoma 56935 non-null float64\n",
" 80 solid_tumor_with_metastasis 56935 non-null float64\n",
" 81 apache_3j_bodysystem 56935 non-null object \n",
" 82 apache_2_bodysystem 56935 non-null object \n",
" 83 hospital_death 56935 non-null int64 \n",
"dtypes: float64(70), int64(7), object(7)\n",
"memory usage: 36.9+ MB\n"
]
}
],
"source": [
"clean_data= new_data.dropna()\n",
"clean_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aXx-GwsX0yvl",
"outputId": "d6427797-4af7-4044-b6b0-8f9e7a674b15"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Percentage of Data lost after cleaning is 37.920469290067935%\n"
]
}
],
"source": [
"#####calculationg percentage of data lost during cleaning\n",
"data_diferrence= new_data[\"patient_id\"].count()-clean_data[\"patient_id\"].count()\n",
"percentage_loss= data_diferrence/new_data[\"patient_id\"].count()*100\n",
"print(f\"Percentage of Data lost after cleaning is {percentage_loss}%\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "9gIJ80jU3DN1"
},
"outputs": [],
"source": [
"###### Need to export the clean data and upload to SQL database\n",
"clean_data.to_csv(\"C:\\\\Users\\\\iyand\\\\Downloads\\\\CleanICUdata.csv\", index= False )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 468
},
"id": "wr4tpy289sEu",
"outputId": "3abf677e-cf38-4db4-c617-a0b88fbc4870"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: xlabel='hospital_death', ylabel='count'>"
]
},
"metadata": {},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"#### pulling the data back from SQL and Doing exploratory Data Analysis\n",
"sns.countplot(x=\"hospital_death\", data=clean_data)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 468
},
"id": "FlwxCXR1EglC",
"outputId": "64649f9f-793b-43fd-ac28-525890b7280e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: xlabel='hospital_death', ylabel='count'>"
]
},
"metadata": {},
"execution_count": 14
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"sns.countplot(x=\"hospital_death\", data=health_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XKHo5KwSJAML"
},
"source": [
"Before and after data cleaning, there is an imbalance destribution in target variable, i.e the hospital death distribution"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "svsliIFgYDXd",
"outputId": "a0ac0979-eff4-47a8-96b4-9efcda848cc5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index(['ethnicity', 'gender', 'icu_admit_source', 'icu_stay_type', 'icu_type',\n",
" 'apache_3j_bodysystem', 'apache_2_bodysystem'],\n",
" dtype='object')\n"
]
}
],
"source": [
"####### Let check for distribution of columns with object value and classification\n",
"object_columns = clean_data.select_dtypes(include='object').columns\n",
"print(object_columns)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "gxAs7vtnYmv7",
"outputId": "92028e45-2228-45ed-cb33-7950f2d4217c"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"sns.countplot(x=\"ethnicity\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "YRWq3m0XZZF_",
"outputId": "134a709d-9d50-4200-fbf6-15a7307073e5"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"sns.countplot(x=\"gender\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "T0dh2KfTZY21",
"outputId": "2c436dee-48ee-4775-af61-2aa9dd1b0464"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(10, 5))\n",
"sns.countplot(x=\"icu_stay_type\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "XV1x4r5spSkW",
"outputId": "9a0d4427-cbb3-4e90-ac71-bf667e86237f"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(14, 5))\n",
"sns.countplot(x=\"icu_admit_source\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "-CduKzuopSdd",
"outputId": "7cef931e-38f4-4317-c6ce-e1dbf91d56a8"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1800x500 with 1 Axes>"
],
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},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(18, 5))\n",
"sns.countplot(x=\"apache_3j_bodysystem\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 585
},
"id": "M5IPlLkSzHpt",
"outputId": "5937295c-a2a1-42f0-abdf-6799e35479c5"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1800x500 with 1 Axes>"
],
"image/png": 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evXsnqKmpCcuXL1fol/3cmTt3rvD69WshOjpaOHXqlGBnZycAEHbt2iX2tbW1Ffr37y/e/+mnn4TKlSsrvF4nTpwoABCuX79ekEtS6NeNIPzfz6TcbosWLSrQcRctWqSwX5s2bYTIyEiFPh4eHoJMJst3rOzX1Oc3HR0d4fDhwwp9fX19BQDCli1bxLb09HShadOmgoaGhvi82Lt3rwBAWLJkidgvMzNTaNGiRY7nb9++fQVDQ0OF5/+1a9fyfJ5/6siRI4JUKhWkUqnQtGlTYdq0aUJwcLCQnp6u0C8hIUFQU1MTpk+frtA+fvx4QSaTCUlJSYIgCMKPP/4oaGlpCZmZmXkeM6/fFe/fvxd0dHSEYcOGKbRHR0cL2traCu0eHh4CAGHhwoVi29u3bwV1dXVBIpEIf/75p9j+4MEDAYAwZ86cL14LIiIi+jLO+CYiIqJ8OTk5oUqVKqhWrRrc3NygoaGBPXv2wMjICLt370ZWVhZcXV0VZjHq6+vD3NwcJ0+eVBhLVVUVgwcPVmjbtWsXKleunOuiZNklLnbs2AFtbW20bdtW4TgNGjSAhoZGjuNYW1ujRYsW4v0qVarAwsICT58+LdA5fzqL8v3794iLi0OLFi2QkpKCBw8eAPhYziM+Ph7Dhg1TKA3Qv39/VKhQQWG8HTt2wMrKCpaWlgrxZ5dn+Dz+T125cgWxsbEYPXq0wqzZTp06wdLSEn///XeBzikvv/32G44ePZrj9vmCjf/88w8AYNKkSQrt2Qv2ZceRXYLg4MGDec5+zdatWzeF2Z2NGzdGkyZNxGMV1qePW0ZGBuLj42FmZgYdHR1cu3atSGPmR0NDA+/fvwfwcQbtiRMn4OrqKj5v4uLiEB8fD2dnZzx69Egs46Cqqopy5T6+HZfL5YiPj4eGhgYsLCwKFauGhoZC7WgVFRU0btxY4bmuo6ODqKgohIaGFukc3d3dFRaZ7NWrFwwMDBQeJ3d3d4SEhCiUJwoKCkK1atXQsmXLfI8RFBSEhg0bwszMDADEkhF5lTuZM2cOqlSpAn19fTg6OuLJkydYvHgxevToAQC4desWbt++rbAOQd++fREXF4fg4GCx7d27d+LxCqOgr5tsTZo0ybV/QddJ6Nu3L44ePYqtW7eiX79+AFDg2eJ52bVrF44ePYojR44gICAAtWrVQs+ePRXKwfzzzz/Q19dXiFNZWRnjx49HUlISTp8+LfZTUlLCqFGjxH5SqTTXn+vu7u54+fKlws+9oKAgqKuro2fPnl+MuW3btrh48SK6dOmCmzdvYsmSJXB2doaRkZFCOSFtbW107doV27ZtgyAIAD6+zv766y9069YNMpkMwMfXRnJyMo4ePVqYSwfg42zxhIQE8XmVfZNKpWjSpEmuP9eHDh0q/l9HRwcWFhaQyWQKs+wtLCygo6NT4N9XRERElDsubklERET5+u2331CrVi0oKSlBT08PFhYWYsLu0aNHEAQB5ubmue6rrKyscN/IyAgqKioKbU+ePIGFhcUXFzB89OgREhMTFWq4furzRc2qV6+eo0+FChVy1APPy927dzFr1iycOHFCTIxlS0xMBPCxnjQAMVGXTUlJCSYmJjniv3//PqpUqVKg+D+VfRwLC4sc2ywtLXHu3Lkvn0w+GjdurLBwY7YKFSogLi5OIY5y5crlOF99fX3o6OiIcbZs2RI9e/bE3LlzsXz5cjg6OqJbt27o168fVFVVFfbN7XlTq1YtbN++vUjnkpqaikWLFiEgIAAvXrwQE17A/z1uxS0pKUl8Xj5+/BiCIMDT0xOenp659o+NjYWRkRGysrKwYsUKrF69GuHh4Qr14z8t15GfqlWrKtRABz4+drdu3RLvT58+HceOHUPjxo1hZmaGdu3aoV+/frC3ty/QMT5/nCQSCczMzBRKOfTp0wcTJkxAUFAQZs+ejcTERBw8eBATJ07MEd/nEhIS8M8//2Ds2LF4/Pix2G5vb49du3YhLCwMtWrVUthn+PDh6N27N8qVKwcdHR3Y2NgoPL+2bNkCmUyGGjVqiGOqqanBxMQEQUFB6NSpEwBAS0sLAMQPLwqqoK+bbJUrV4aTk1OhjvEpY2NjGBsbA/iYBB8+fDicnJzw8OHDIpc7cXBwUFjcslevXjA3N8e4ceNw9epVAB9f9+bm5uLP/GzZpT6yX/fPnj2DgYEBNDQ0FPrl9nOrbdu2MDAwQFBQENq0aYOsrCxs27YNXbt2LdAHEI0aNcLu3buRnp6OmzdvYs+ePVi+fDl69eqFGzduwNraGsDHBPtff/2Fs2fPwsHBAceOHUNMTIxCWZvRo0dj+/bt6NChA4yMjNCuXTu4urqiffv2+cbx6NEjAP9XX/5z2c+tbGpqajl+B2hra+f6GtbW1i7w7ysiIiLKHRPfRERElK+8EjzAx9rVEokEhw4dglQqzbH98yRIURM0WVlZ0NXVzXP25+fJhNxiAaCQCM1LQkICWrZsCS0tLcybNw81a9aEmpoarl27hunTpyMrK6tI8dva2sLHxyfX7dWqVSv0mKUlvySmRCLBzp07cenSJRw4cADBwcEYMmQIli1bhkuXLuV4ThSncePGISAgABMmTEDTpk2hra0NiUQCNze3Ij1u+YmKikJiYqL4YUD2MaZMmaKweN6nsvsuXLgQnp6eGDJkCObPn4+KFSuiXLlymDBhQqFiLchz3crKCg8fPsTBgwdx+PBh7Nq1C6tXr8bs2bMxd+7cAh/rSypUqAAXFxcx8b1z506kpaUpzEbPy44dO5CWloZly5Zh2bJlObYHBQXliNPc3DzPRLIgCNi2bRuSk5PFJOinYmNjkZSUBA0NDVhaWgL4uLhgvXr1CnCm34ZevXph/fr1OHPmTJ7PtcLS0NBAkyZNsG/fPiQnJ4uzooubVCpFv379sH79eqxevRrnz5/Hy5cvC/Rc+ZSKigoaNWqERo0aoVatWhg8eDB27NiBOXPmAACcnZ2hp6eHLVu2wMHBAVu2bIG+vr7C80ZXVxc3btxAcHAwDh06hEOHDiEgIADu7u65LuD5qezX6ebNm8WFQT/1+Ye5eb1Wv+b3FREREeWNiW8iIiL6KjVr1oQgCDA1Nc0xI7MwY4SEhCAjIyPHDPFP+xw7dgz29vZftZjbp/JK4J46dQrx8fHYvXu3wiKCny80lj378vHjx2jVqpXYnpmZiYiICIWSBzVr1sTNmzfRpk2bfBPHn8s+zsOHD3PMLHz48KG4vaQZGxsjKysLjx49Emd7AkBMTAwSEhJyxPHDDz/ghx9+gLe3N7Zu3Yr+/fvjzz//VPiqf/aMyU+FhYXlmDH/ubyu4c6dO+Hh4aGQPP3w4UOORVaLy+bNmwFATDzWqFEDwMdvOuQ3u3fnzp1o1aoVNmzYoNCekJCgMAu3sM+XvMhkMvTp0wd9+vRBeno6evToAW9vb8ycOTPfhSc/f5wEQcDjx49zlPVwd3dH165dERoaiqCgINjZ2cHGxibf2IKCglC7dm0xYfmptWvXYuvWrYVK0J8+fRpRUVGYN2+ewnMV+LgY6PDhw7F3714MGDAAHTp0gFQqxZYtWwq9wGVpyi5zUtzfZMjMzATw8ZsMMpkMxsbGuHXrFrKyshRmfWeXfMp+3RsbG+P48ePiBwrZHj58mOtx3N3dsWzZMhw4cACHDh1ClSpVviqBn/3h7KtXr8S27AT7xo0bsXjxYuzduxfDhg3LkWhWUVFB586d0blzZ2RlZWH06NFYu3YtPD09YWZmludrsGbNmgA+Js+/ZjY/ERERlQzW+CYiIqKv0qNHD0ilUsydOzfH7DRBEBAfH5/vGD179kRcXBxWrVqVY1v2mK6urpDL5Zg/f36OPpmZmUVKbGbPZvx83+ykyKfnk56ejtWrVyv0a9iwISpVqoT169eLySLgYxLv86+ou7q64sWLF1i/fn2OOFJTU5GcnJxnnA0bNoSuri7WrFmDtLQ0sf3QoUO4f/++WLKhpHXs2BEA4Ovrq9CePYs9O463b9/meC5kz6T9NH4A2Lt3r1jzGgAuX76MkJAQdOjQ4YuxyGSyXB9zqVSa49h+fn4KZUSKy4kTJzB//nyYmpqif//+AD4mwBwdHbF27VqFBFy2169ffzHWHTt2KFwPIO/naWF8/jpUUVGBtbU1BEHItw47AGzatEmhFMjOnTvx6tWrHI9Thw4dULlyZSxevBinT58u0Aze58+f48yZM3B1dUWvXr1y3AYPHozHjx8jJCSkgGf7f2VOpk6dmmO8YcOGwdzcXPz2SLVq1TBs2DAcOXIEfn5+OcbKysrCsmXLEBUVVeDjF6dPnzOf2rBhAyQSCerXr19sx3rz5g0uXLgAfX19sXxPx44dER0djb/++kvsl5mZCT8/P2hoaIj12zt27IjMzEz4+/uL/eRyea7XFADq1KmDOnXq4Pfff8euXbvg5ub2xXJX2U6ePJnrTOjsevOfl1YZOHAg3r59ixEjRiApKSnHc/Lz10a5cuXED3Syf17l9Rp0dnaGlpYWFi5cmOvrKK/HjoiIiP4dnPFNREREX6VmzZpYsGABZs6ciYiICHTr1g2ampoIDw/Hnj17MHz4cEyZMuWLY7i7u2PTpk2YNGkSLl++jBYtWiA5ORnHjh3D6NGj0bVrV7Rs2RIjRozAokWLcOPGDbRr1w7Kysp49OgRduzYgRUrVqBXr16Fir1evXqQSqVYvHgxEhMToaqqitatW6NZs2aoUKECPDw8MH78eEgkEmzevDlHskVFRQVeXl4YN24cWrduDVdXV0RERGDjxo2oWbOmwizBgQMHYvv27Rg5ciROnjwJe3t7yOVyPHjwANu3b0dwcHCe5WSUlZWxePFiDB48GC1btkTfvn0RExODFStWwMTEBBMnTizUeRdV3bp14eHhgXXr1onlYC5fvozAwEB069ZNnPUeGBiI1atXo3v37qhZsybev3+P9evXQ0tLS0yeZzMzM0Pz5s0xatQopKWlwdfXF5UqVcK0adO+GEuDBg1w7Ngx+Pj4wNDQEKampmjSpAlcXFywefNmaGtrw9raGhcvXsSxY8cKVTM7N4cOHcKDBw+QmZmJmJgYnDhxAkePHoWxsTH279+vMGP6t99+Q/PmzWFra4thw4ahRo0aiImJwcWLFxEVFYWbN28CAFxcXDBv3jwMHjwYzZo1w+3btxEUFCTOGs9Ws2ZN6OjoYM2aNdDU1IRMJkOTJk1gampa4PjbtWsHfX192NvbQ09PD/fv38eqVavQqVOnAtVUrlixIpo3b47BgwcjJiYGvr6+MDMzw7BhwxT6KSsrw83NDatWrYJUKi3Qwo1bt26FIAjo0qVLrts7duwIJSUlBAUFoUmTJvmOl5aWhl27dqFt27Z5zmTv0qULVqxYgdjYWOjq6mLZsmV48uQJxo8fj927d8PFxQUVKlRAZGQkduzYgQcPHsDNzS3fY3/JixcvsGXLlhztGhoa6NatW577eXt74/z582jfvj2qV6+ON2/eYNeuXQgNDcW4ceNy1NwvjJ07d0JDQwOCIODly5fYsGED3r59izVr1og/v4YPH461a9di0KBBuHr1KkxMTLBz506cP38evr6+4vOnc+fOsLe3x4wZMxAREQFra2vs3r37izPS3d3dxd8PBS1zMm7cOKSkpKB79+6wtLREeno6Lly4gL/++gsmJiY5Fk+2s7ND7dq1xQWGP/+gYOjQoXjz5g1at26NqlWr4tmzZ/Dz80O9evXEbwvk9btCV1cX/v7+GDhwIOrXrw83NzdUqVIFkZGR+Pvvv2Fvb5/rB7pERET0LxGIiIiI8hAQECAAEEJDQ/Ptu2vXLqF58+aCTCYTZDKZYGlpKYwZM0Z4+PCh2Kdly5aCjY1NrvunpKQIP//8s2BqaiooKysL+vr6Qq9evYQnT54o9Fu3bp3QoEEDQV1dXdDU1BRsbW2FadOmCS9fvhT7GBsbC506dcpxjJYtWwotW7ZUaFu/fr1Qo0YNQSqVCgCEkydPCoIgCOfPnxd++OEHQV1dXTA0NBSmTZsmBAcHK/TJtnLlSsHY2FhQVVUVGjduLJw/f15o0KCB0L59e4V+6enpwuLFiwUbGxtBVVVVqFChgtCgQQNh7ty5QmJiYn6XWPjrr78EOzs7QVVVVahYsaLQv39/ISoqSqFPYR6z/Prm9nhlZGQIc+fOFR+natWqCTNnzhQ+fPgg9rl27ZrQt29foXr16oKqqqqgq6sruLi4CFeuXBH7hIeHCwCEX3/9VVi2bJlQrVo1QVVVVWjRooVw8+ZNhWPOmTNH+Pxt64MHDwQHBwdBXV1dACB4eHgIgiAIb9++FQYPHixUrlxZ0NDQEJydnYUHDx4IxsbGYh9BEISTJ0/m+ljmdY2ybyoqKoK+vr7Qtm1bYcWKFcK7d+9y3e/JkyeCu7u7oK+vLygrKwtGRkaCi4uLsHPnTrHPhw8fhMmTJwsGBgaCurq6YG9vL1y8eDHX5+m+ffsEa2trQUlJSQAgBAQECIKQ92vKw8NDMDY2Fu+vXbtWcHBwECpVqiSoqqoKNWvWFKZOnZrv8y77Om3btk2YOXOmoKurK6irqwudOnUSnj17lus+ly9fFgAI7dq1++LY2WxtbYXq1at/sY+jo6Ogq6srZGRkKDx3crNr1y4BgLBhw4Y8xzt16pQAQFixYoXYlpmZKfz+++9CixYtBG1tbUFZWVkwNjYWBg8eLFy/fl3sV5TXjbGxscLz6NPbp49Tbo4cOSK4uLgIhoaGgrKysqCpqSnY29sLAQEBQlZWlkJfd3d3QUtL64vjCcL/vaY+vclkMqFp06bC9u3bc/SPiYkRX1cqKiqCra2t+Bz8VHx8vDBw4EBBS0tL0NbWFgYOHChcv35d4Tn7qVevXglSqVSoVatWvjFnO3TokDBkyBDB0tJS0NDQEFRUVAQzMzNh3LhxQkxMTK77LFmyRAAgLFy4MMe2nTt3Cu3atRN0dXUFFRUVoXr16sKIESOEV69eKfTL63eFIHx8nTg7Owva2tqCmpqaULNmTWHQoEEKP/M8PDwEmUyW4/h5vYbz+j1GREREBScRBK6YQURERFScsrKyUKVKFfTo0SPX0iYEREREwNTUFL/++mu+3wig/5abN2+iXr162LRp03+qZnZZ0KNHD4SGhuL58+elHUqBxMXFwcDAALNnz4anp2eJHWfFihWYOHEiIiIiUL169RI7DhEREX1bWOObiIiI6Ct8+PAhRwmUTZs24c2bN3B0dCydoIhK0fr166GhoYEePXqUdijflaysLFy7dg3W1talHUqBbdy4EXK5vEQ/IBEEARs2bEDLli2Z9CYiIvrOsMY3ERER0Ve4dOkSJk6ciN69e6NSpUq4du0aNmzYgNq1a6N3796lHR7Rv+bAgQO4d+8e1q1bh7Fjx4oLAlLJSk5OxrZt27B37148e/YMCxcuLO2Q8nXixAncu3cP3t7e6NatG0xMTIr9GMnJydi/fz9OnjyJ27dvY9++fcV+DCIiIvq2MfFNRERE9BVMTExQrVo1rFy5Em/evEHFihXh7u6OX375BSoqKqUdHtG/Zty4cYiJiUHHjh0xd+7c0g7nu/H69WuMGDEC1apVw6+//op+/fqVdkj5mjdvHi5cuAB7e3v4+fmVyDFev36Nfv36QUdHBz/99FOei6cSERFR2cUa30RERERERERERERUprDGNxERERERERERERGVKUx8ExEREREREREREVGZwhrf+LgC+suXL6GpqQmJRFLa4RARERERERERERFRLgRBwPv372FoaIhy5fKe183EN4CXL1+iWrVqpR0GERERERERERERERXA8+fPUbVq1Ty3M/ENQFNTE8DHi6WlpVXK0RARERERERERERFRbt69e4dq1aqJOd28MPENiOVNtLS0mPgmIiIiIiIiIiIi+sblV7Kai1sSERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcIa30REREXw/v17eHp6Ys+ePYiNjYWdnR1WrFiBRo0aAQBiYmIwffp0HDlyBAkJCXBwcICfnx/Mzc0LNP6ff/6Jvn37omvXrti7d6/YvnTpUixZsgQAMH36dEyePFncFhISgtGjRyMkJARKSvwVT0RERERE3wZBEJCZmQm5XF7aodB/gFQqhZKSUr41vPPDv4qJiIiKYOjQobhz5w42b94MQ0NDbNmyBU5OTrh37x4MDQ3RrVs3KCsrY9++fdDS0oKPj4+4XSaTfXHsiIgITJkyBS1atFBov3XrFmbPno2DBw9CEAS4uLigXbt2sLW1RWZmJkaOHIl169Yx6U1ERERERN+M9PR0vHr1CikpKaUdCv2HlC9fHgYGBlBRUSnyGPzLmIiIqJBSU1Oxa9cu7Nu3Dw4ODgAALy8vHDhwAP7+/nB3d8elS5dw584d2NjYAAD8/f2hr6+Pbdu2YejQoXmOLZfL0b9/f8ydOxdnz55FQkKCuO3BgweoU6cOWrduDQCoU6cOHjx4AFtbW/z6669wcHAQZ5wTERERERGVtqysLISHh0MqlcLQ0BAqKipfPYuXyjZBEJCeno7Xr18jPDwc5ubmKFeuaNW6mfgmIiIqpOyv6KmpqSm0q6ur49y5c+jTpw8AKGwvV64cVFVVce7cuS8mvufNmwddXV3873//w9mzZxW22draIiwsDJGRkRAEAWFhYahduzaePHmCgIAAXL16tRjPkog+l1+Jo6SkJMyYMQN79+5FfHw8TE1NMX78eIwcOTLPMTdu3IjBgwcrtKmqquLDhw/ifZY4IiIiov+q9PR0ZGVloVq1aihfvnxph0P/Eerq6lBWVsazZ8+Qnp6e42/vguK7YyIiokLS1NRE06ZNMX/+fFhZWUFPTw/btm3DxYsXYWZmBktLS1SvXh0zZ87E2rVrIZPJsHz5ckRFReHVq1d5jnvu3Dls2LABN27cyHW7lZUVFi5ciLZt2wIAFi1aBCsrKzg5OWHJkiUIDg6Gl5cXlJWVsWLFCnE2OhEVjy+VODIyMsKkSZNw4sQJbNmyBSYmJjhy5AhGjx4NQ0NDdOnSJc9xtbS08PDhQ/H+p7OgWOKIiIiIyoKiztil71dxPGf4rCMiIiqCzZs3QxAEGBkZQVVVFStXrkTfvn1Rrlw5KCsrY/fu3QgLC0PFihVRvnx5nDx5Eh06dMjzl/f79+8xcOBArF+/HpUrV87zuCNHjsTDhw/x8OFDjBw5EoGBgWIifujQodizZw98fHzg5uaGtLS0kjp9ou9OdomjJUuWwMHBAWZmZvDy8oKZmRn8/f0BABcuXICHhwccHR1hYmKC4cOHo27durh8+fIXx5ZIJNDX1xdvenp64rZPSxy1adNGLHEEgCWOiIiIiIi+oFQT32fOnEHnzp1haGgIiUSCvXv3KmwXBAGzZ8+GgYEB1NXV4eTkhEePHin0efPmDfr37w8tLS3o6Ojgf//7H5KSkv7FsyAiou9RzZo1cfr0aSQlJeH58+e4fPkyMjIyUKNGDQBAgwYNcOPGDSQkJODVq1c4fPgw4uPjxe2fe/LkCSIiItC5c2coKSlBSUkJmzZtwv79+6GkpIQnT57k2CcuLg5z586Fn58fQkJCUKtWLZibm6NVq1bIyMhAWFhYiV4Dou9JfiWOAKBZs2bYv38/Xrx4AUEQcPLkSYSFhaFdu3ZfHDspKQnGxsaoVq0aunbtirt374rbPi1x9OzZsxwljhYsWFD8J0tEREREVAaUauI7OTkZdevWxW+//Zbr9iVLlmDlypVYs2YNQkJCIJPJ4OzsrFDzsH///rh79y6OHj2KgwcP4syZMxg+fPi/dQpERPSdk8lkMDAwwNu3bxEcHIyuXbsqbNfW1kaVKlXw6NEjXLlyJcf2bJaWlrh9+zZu3Lgh3rp06YJWrVrhxo0bqFatWo59Jk6ciIkTJ6Jq1aqQy+XIyMgQt2Un6YioeHxa4ujly5eQy+XYsmULLl68KJYw8vPzg7W1NapWrQoVFRW0b98ev/322xfLDllYWOCPP/7Avn37sGXLFmRlZaFZs2aIiooCoFjiqF27dmKJoxEjRogljmrXrg07OzucOXPmX7kWRERERPR/TExM4Ovr+82MU5JOnToFiUSChISE0g6lQEq1GGCHDh3QoUOHXLcJggBfX1/MmjVLTBJs2rQJenp62Lt3L9zc3HD//n0cPnwYoaGhaNiwIYCPf3B07NgRS5cuhaGh4b92LkRE9H0JDg6GIAiwsLDA48ePMXXqVFhaWoqL1O3YsQNVqlRB9erVcfv2bfz444/o1q2bwsxPd3d3GBkZYdGiRVBTU0Pt2rUVjqGjowMAOdoB4OjRowgLC0NgYCAAoFGjRnjw4AEOHTqE58+fQyqVwsLCooTOnuj7tHnzZgwZMgRGRkaQSqWoX78++vbtKy4s6+fnh0uXLmH//v0wNjbGmTNnMGbMGBgaGsLJySnXMZs2bYqmTZuK95s1awYrKyusXbsW8+fPB/CxxNGnC2R+WuLIwsICoaGhiIqKgpubG8LDw6GqqlqCV4GIiIio9A0aNAgJCQk5qkecOnUKrVq1wtu3b8W/p0paaGgoZDKZeF8ikWDPnj3o1q1bsR7Hy8sLc+fOBQBIpVLo6OjA2toaPXr0wKhRo4r9PaCjoyPq1av3zSfjv+SbXQUnPDwc0dHRCn8kaGtro0mTJrh48SLc3Nxw8eJF6OjoiElvAHByckK5cuUQEhKC7t275zp2WlqaQt3Td+/eldyJEBFRmZSYmIiZM2ciKioKFStWRM+ePeHt7Q1lZWUAwKtXrzBp0iTExMTAwMAA7u7u8PT0VBgjMjKySAt2pKamYuzYsfjrr7/E/atWrQo/Pz8MHjwYqqqqCAwMhLq6+tefKBGJskscJScn4927dzAwMECfPn1Qo0YNpKam4qeffsKePXvQqVMnAECdOnVw48YNLF26NM/E9+eUlZVhZ2eHx48f57o9u8TRmTNnFEocmZubiyWObG1ti+2ciYiIiOjLqlSp8q8dy8bGBseOHUNWVhbi4+Nx6tQpLFiwAJs3b8apU6egqan5r8XyX/DNLm4ZHR0NAAqL+2Tfz94WHR0NXV1dhe1KSkqoWLGi2Cc3ixYtgra2tnjL7evjREREX+Lq6oonT54gLS0Nr169wqpVq6CtrS1uHz9+PJ4/f4709HQ8e/YM8+fPh4qKisIYp06dwsaNG/M8xsaNG3PMYAA+1hR++PAh6tWrp9A+dOhQREdH49mzZ2LijYiKX24ljjIyMpCRkZHjwyypVIqsrKwCjy2Xy3H79m0YGBjkup0ljoiIiIgKZ9euXbCxsYGqqipMTEywbNkyhe2rV6+Gubk51NTUoKenh169eonbHB0dMXbsWIwdOxba2tqoXLkyPD09IQiC2OfTEiUmJiYAgO7du0MikYj3nzx5gq5du0JPTw8aGhpo1KgRjh07VuhzUVJSgr6+PgwNDWFra4tx48bh9OnTuHPnDhYvXiz2S0tLw5QpU2BkZASZTIYmTZrg1KlT4vb4+Hj07dsXRkZGKF++PGxtbbFt2zZx+6BBg3D69GmsWLECEokEEokEERER4varV6+iYcOGKF++PJo1a4aHDx8W+lz+Dd9s4rskzZw5E4mJieLt+fPnpR0SEREREX3jgoODcfjwYYSHh+Po0aNo1aqVWOJIS0sLLVu2xNSpU3Hq1CmEh4dj48aN2LRpk8K3EN3d3TFz5kzx/rx583DkyBE8ffoU165dw4ABA/Ds2TMMHTo0x/GzSxyNGTMGgGKJo3Xr1rHEEREREdFnrl69CldXV7i5ueH27dvw8vKCp6enOAHpypUrGD9+PObNm4eHDx/i8OHDOdZnCQwMhJKSEi5fvowVK1bAx8cHv//+e67HCw0NBQAEBATg1atX4v2kpCR07NgRx48fx/Xr19G+fXt07twZkZGRX32OlpaW6NChA3bv3i22jR07FhcvXsSff/6JW7duoXfv3mjfvj0ePXoEAPjw4QMaNGiAv//+G3fu3MHw4cMxcOBAXL58GQCwYsUKNG3aFMOGDcOrV6/w6tUrhYnDP//8M5YtW4YrV65ASUkJQ4YM+erzKAnfbKkTfX19ABC/Ip4tJiZGnOGmr6+P2NhYhf0yMzPx5s0bcf/cqKqqsvYhEREVWIsR80s7hGJzdq1n/p2IKFf5lTj6888/MXPmTPTv3x9v3ryBsbExvL29Fepzf17i6O3btxg2bBiio6NRoUIFNGjQABcuXIC1tbXCsVniiIiIiEjRwYMHoaGhodD2+bfffHx80KZNG7HsZK1atXDv3j38+uuvGDRoECIjIyGTyeDi4gJNTU0YGxvDzs5OYYxq1aph+fLlkEgksLCwwO3bt7F8+XIMGzYsR0zZZU90dHQUcpN169ZF3bp1xfvz58/Hnj17sH//fowdO/brLgQ+Jr+PHDkC4OP7zYCAAERGRorrH06ZMgWHDx9GQEAAFi5cCCMjI0yZMkXcf9y4cQgODsb27dvRuHFjaGtrQ0VFBeXLl881x+rt7Y2WLVsCAGbMmIFOnTrhw4cPUFNT++pzKU7fbOLb1NQU+vr6OH78uJjofvfuHUJCQjBq1CgAHxcDSkhIwNWrV9GgQQMAwIkTJ5CVlYUmTZqUVuhEREREVAa5urrC1dU1z+36+voICAj44hiffsUUAJYvX47ly5fne+zsEkefGzp0aK6zw4mIiIjKulatWsHf31+hLSQkBAMGDBDv379/H127dlXoY29vD19fX8jlcrRt2xbGxsaoUaMG2rdvj/bt26N79+4oX7682P+HH36ARCIR7zdt2hTLli2DXC6HVCotUKxJSUnw8vLC33//jVevXiEzMxOpqanFMuMbAARBEGO8ffs25HI5atWqpdAnLS0NlSpVAvDxA4KFCxdi+/btePHiBdLT05GWlqZw3l9Sp04d8f/ZE5ZjY2NRvXr14jidYlOqie+kpCSFhXvCw8Nx48YNVKxYEdWrV8eECROwYMECmJubw9TUFJ6enjA0NBRXRbWyskL79u0xbNgwrFmzBhkZGRg7dizc3NzETzSIiIiIiIiIiIiobJHJZDAzM1Noi4qKKtQYmpqauHbtGk6dOoUjR45g9uzZ8PLyQmhoKHR0dIot1ilTpuDo0aNYunQpzMzMoK6ujl69eiE9Pb1Yxr9//z5MTU0BfMy3SqVSXL16NUdiPnuG/K+//ooVK1bA19cXtra2kMlkmDBhQoHjyf7GIwAx4V6YdW3+LaWa+L5y5QpatWol3p80aRIAwMPDAxs3bsS0adOQnJyM4cOHIyEhAc2bN8fhw4cVps0HBQVh7NixaNOmDcqVK4eePXti5cqV//q5EBEREVHZwPJG9Ln379/D09MTe/bsQWxsLOzs7LBixQo0atQIwMcFoAIDAxX2cXZ2xuHDh/Mc09/fH/7+/uJCUTY2Npg9ezY6dOgg9pk0aRI2btwImUyGX375Bf379xe37dixA5s2bcKBAweK8UyJiIjKFisrK5w/f16h7fz586hVq5aYFFZSUoKTkxOcnJwwZ84c6Ojo4MSJE+jRoweAj7PIP3Xp0iWYm5vnOdtbWVk5R8mV8+fPY9CgQeLaL0lJSQqLRX6NBw8e4PDhw+I6MnZ2dpDL5YiNjUWLFi1y3ef8+fPo2rWrODs+KysLYWFhCuX2VFRU/vMLp5dq4tvR0VFhFdTPSSQSzJs3D/PmzcuzT8WKFbF169aSCI+IiIiIiAhDhw7FnTt3sHnzZhgaGmLLli1wcnLCvXv3YGRkBABo3769Qqmb/NYUqlq1Kn755ReYm5tDEAQEBgaia9euuH79OmxsbHDgwAFs3boVR44cwaNHjzBkyBA4OzujcuXKSExMxM8//4xjx46V6HkTERH9102ePBmNGjXC/Pnz0adPH1y8eBGrVq3C6tWrAXysE/706VM4ODigQoUK+Oeff5CVlaWwYHhkZCQmTZqEESNG4Nq1a/Dz88OyZcvyPKaJiQmOHz8Oe3t7qKqqokKFCjA3N8fu3bvRuXNnSCQSeHp6FmmGdGZmJqKjo5GVlYX4+HicOnUKCxYsQL169TB16lQAH+uY9+/fH+7u7li2bBns7Ozw+vVrHD9+HHXq1EGnTp1gbm6OnTt34sKFC6hQoQJ8fHwQExOjkPg2MTFBSEgIIiIioKGhgYoVKxY63tJWLv8uRERERERE36fU1FTs2rULS5YsgYODA8zMzODl5QUzMzOFuqKqqqrQ19cXbxUqVPjiuJ07d0bHjh1hbm6OWrVqwdvbGxoaGrh06RKAj19ZdnR0RMOGDdG3b19oaWkhPDwcADBt2jSMGjXqm6ujSURE9K2pX78+tm/fjj///BO1a9fG7NmzMW/ePAwaNAjAx0Uod+/ejdatW8PKygpr1qzBtm3bYGNjI47h7u6O1NRUNG7cGGPGjMGPP/6I4cOH53nMZcuW4ejRo6hWrZq4UKaPjw8qVKiAZs2aoXPnznB2dkb9+vULfT53796FgYEBqlevDkdHR2zfvh0zZ87E2bNnFRb6DAgIgLu7OyZPngwLCwt069YNoaGh4nuHWbNmoX79+nB2doajoyP09fXF0tLZpkyZAqlUCmtra1SpUqXY6pH/myTCl6ZcfyfevXsHbW1tJCYmQktLq7TDISKibwzLHhB9X/iap0+9f/8eWlpaOHbsGNq0aSO2N2/eHEpKSjh16hQGDRqEvXv3QkVFBRUqVEDr1q2xYMECcQGp/MjlcuzYsQMeHh64fv06rK2tERwcjDFjxiA0NBRPnz5Fq1at8OzZM9y9excTJ07EpUuXCrygFhERUWn58OEDwsPDYWpqqlC6+L/C0dER9erVg6+vb2mH8t350nOnoLlczvgmIiIiIiLKg6amJpo2bYr58+fj5cuXkMvl2LJlCy5evIhXr14B+FjmZNOmTTh+/DgWL16M06dPo0OHDvnWxbx9+zY0NDSgqqqKkSNHYs+ePeJXjJ2dnTFgwAA0atRIrCEuk8kwatQorFmzBv7+/rCwsIC9vT3u3r1b4teBiIiI6L+GiW8ioq/w/v17TJgwAcbGxlBXV0ezZs0QGhoqbvfy8oKlpSVkMhkqVKgAJyenHAtjfE4ul8PT0xOmpqZQV1dHzZo1MX/+fIU1EZYuXQpdXV3o6urmqC0WEhKCBg0aIDMzs3hPloiI6Du1efNmCIIAIyMjqKqqYuXKlejbty/Klfv455Sbmxu6dOkCW1tbdOvWDQcPHkRoaChOnTr1xXEtLCxw48YNhISEYNSoUfDw8MC9e/fE7V5eXnj8+DFu376N7t27Y9GiRXBycoKysjIWLFiAc+fOYejQoXB3dy/J0yciIiL6TyrVxS2JiP7r8lvsqlatWli1ahVq1KiB1NRULF++HO3atcPjx49RpUqVXMdcvHgx/P39ERgYCBsbG1y5cgWDBw+GtrY2xo8fj1u3bmH27Nk4ePAgBEGAi4sL2rVrB1tbW2RmZmLkyJFYt24dlJT4I56IiKg41KxZE6dPn0ZycjLevXsHAwMD9OnTBzVq1Mi1f40aNVC5cmU8fvxYoTzK51RUVGBmZgYAaNCgAUJDQ7FixQqsXbs2R98HDx5gy5YtuH79Ov744w84ODigSpUqcHV1xZAhQ/D+/XtoamoWzwkTERERAOT7ITZ925gVISIqouzFrvbt2wcHBwcAH2dmHThwAP7+/liwYAH69eunsI+Pjw82bNiAW7du5fmH8IULF9C1a1d06tQJwMeVlLdt24bLly8D+PiHb506ddC6dWsAQJ06dfDgwQPY2tri119/hYODAxo1alRSp01ERPTdkslkkMlkePv2LYKDg7FkyZJc+0VFRSE+Ph4GBgaFGj8rKwtpaWk52gVBwIgRI+Dj4wMNDQ3I5XJkZGQAgPhvfmVViIiIKG9yuRwvXrxAQkICMjIyUL58eVSvXh0ymQwA8PLlS7x58wbp6emQSCQoX748jIyMFBaU/FxsbCxev34t/m5XV1eHoaEhtLW1xT7Pnz9HXFwcpFIpjIyMFNYHefPmDeLj42Fubl5CZ132MfFNRFREmZmZkMvlORZZUFdXx7lz53L0T09Px7p166CtrY26devmOW6zZs2wbt06hIWFoVatWrh58ybOnTsHHx8fAICtrS3CwsIQGRkJQRAQFhaG2rVr48mTJwgICMDVq1eL90SJiIi+c8HBwRAEARYWFnj8+DGmTp0KS0tLDB48GElJSZg7dy569uwJfX19PHnyBNOmTYOZmRmcnZ3FMdq0aYPu3btj7NixAICZM2eiQ4cOqF69Ot6/f4+tW7fi1KlTCA4OznH833//HVWqVEHnzp0BAPb29vDy8sKlS5dw6NAhWFtbQ0dH51+5FkRERGVRREQEUlNTYWpqCmVlZbx58wZhYWGwsbGBiooKVFVVUb16daiqqiIrKwsxMTF49OgRateuDWVl5VzHVFFRQdWqVaGqqgoAiI+Px+PHj2FtbQ11dXUkJCTgzZs3qFWrFj58+ICIiAhoaWlBWVkZmZmZePHiBWrVqvVvXoYyh4lvIqIi+nSxKysrK+jp6WHbtm24ePGi+LVlADh48CDc3NyQkpICAwMDHD16FJUrV85z3BkzZuDdu3ewtLSEVCqFXC6Ht7c3+vfvDwCwsrLCwoUL0bZtWwDAokWLYGVlBScnJyxZsgTBwcHw8vKCsrIyVqxYIc5GJyIioqJJTEzEzJkzERUVhYoVK6Jnz57w9vYW/zC9desWAgMDkZCQAENDQ7Rr1w7z588X/9AFgCdPniAuLk68HxsbC3d3d7x69Qra2tqoU6cOgoODxd/v2WJiYuDt7Y0LFy6IbY0bN8bkyZPRqVMn6OrqIjAwsOQvAhERURmVlZWFt2/fwszMTCwbZmhoiISEBLx+/TrHTGwAqFatGuLi4pCamppn4vvzD6WNjIwQGxuLpKQkqKur48OHD9DQ0BC/Ufb8+XOkp6dDWVkZL168gK6ursJ7CSo8Lm5JVAy+tMBhRkYGpk+fDltbW8hkMhgaGsLd3R0vX77Md9wXL15gwIABqFSpEtTV1WFra4srV66I27nAYenLb7ErAGjVqhVu3LiBCxcuoH379nB1dUVsbGyeY27fvh1BQUHYunUrrl27hsDAQCxdulThj9qRI0fi4cOHePjwIUaOHInAwEAxET906FDs2bMHPj4+cHNzy/Ur00RERFRwrq6uePLkCdLS0vDq1SusWrVK/Jqyuro6goODERsbi/T0dERERGDdunXQ09NTGCMiIgJeXl7i/Q0bNiAiIgJpaWmIjY3FsWPHciS9AUBPTw8REREwNDRUaJ89ezbi4+Nx//59NG7cuPhPmoiI6DshCAIAKPwdn30/KSkpR/+srCy8fv0aUqkU6urqBT7GmzdvkJWVJZZHUVdXR0pKCjIzM5GcnIysrCyoqqri/fv3SE5Ohq6u7leeGTHxTVQMhg4diqNHj2Lz5s24ffs22rVrBycnJ7x48QIpKSm4du0aPD09ce3aNezevRsPHz5Ely5dvjjm27dvYW9vD2VlZRw6dAj37t3DsmXLUKFCBQAQFzj8888/sW3bNsyaNQu3b98GAHGBwzVr1nCBwxKWvdhVUlISnj9/jsuXLyMjI0NhsSuZTAYzMzP88MMP2LBhA5SUlLBhw4Y8x5w6dSpmzJgBNzc32NraYuDAgZg4cSIWLVqUa/+4uDjMnTsXfn5+CAkJQa1atWBubo5WrVohIyMDYWFhxX7eRERERGVdSUxuOXPmDDp37gxDQ0NIJBLs3bs3Rx9ObiEi+ndJpVLIZDK8evUK6enpEAQB8fHxSEpKEtfSAICEhARcu3YN165dQ0xMDGrVqpXnbO9s2Tmhq1ev4tmzZzAzMxOT5dra2qhUqRLu37+PiIgImJqaoly5coiMjISxsTFev36NO3fu4MGDB0hNTS3Ra1BWMSNWzN6/fw9PT0/s2bMHsbGxsLOzw4oVK9CoUSNkZGRg1qxZ+Oeff/D06VNoa2vDyckJv/zyS44ZHJ/77bff8OuvvyI6Ohp169aFn5+fwsyOSZMmYePGjZDJZPjll1/EkggAsGPHDmzatAkHDhwosfP+nhVkgcOjR48q7LNq1So0btwYkZGRqF69eq7jLl68GNWqVUNAQIDYZmpqKv6fCxx+Wwq62BWQ98JV2VJSUnJ80iyVSpGVlZVr/4kTJ2LixImoWrUqQkNDFX4xZ9chJyIiovy1GDG/tEMoNmfXepZ2CP95Q4cOxZ07d7B582YYGhpiy5YtcHJywr1796ChoSFObqlbty7evn2LH3/8EV26dFH4hubnkpOTUbduXQwZMgQ9evTIsT17csvBgwchCAJcXFzQrl072NraipNb1q1bx8ktRETFzNTUFBEREbh16xaAj3/jV6xYESkpKWIfTU1NWFtbIzMzE3FxcXjy5AmsrKy+mPxWU1ODtbU15HI53r59i/DwcFhYWIjJb0NDQ4Wc4MuXL6GlpQWJRIJXr17B2toaiYmJCA8Ph7W1dQmdfdnFGd/FrCRm/v7111+YNGkS5syZg2vXrqFu3bpwdnYWSyUcOHAAW7duxZEjR7BkyRIMHTpUrB+YmJiIn3/+Gb/99luJn/v3qrALHAIfHxeJRPLFRYj279+Phg0bonfv3tDV1YWdnR3Wr18vbv90gcNnz57lWOBwwYIFxXJ+9GXBwcE4fPgwwsPDcfToUbRq1Upc7Co5ORk//fQTLl26hGfPnuHq1asYMmQIXrx4gd69e4tjtGnTBqtWrRLvd+7cGd7e3vj7778REREhli3p3r17juMfPXoUYWFhGDNmDACgUaNGePDgAQ4dOoR169ZBKpXCwsKi5C8EERERURmSPbllyZIlcHBwgJmZGby8vGBmZgZ/f39oa2vj6NGjcHV1hYWFBX744QesWrUKV69eRWRkZJ7jdujQAQsWLMj1fR2gOLmlTZs24uQWAJzcQkRUgtTU1GBpaQk7OzvUqVMHVlZWEARBoca2VCqFmpoaNDQ0YGJiAolEgv3790MikSAhIQEAsHHjRoVcT7ly5aCmpgaZTIaqVatCXV09z9KnqampiI+Ph6GhId6/fw8NDQ0oKyujQoUKSElJyTGpbeDAgVi4cGGBzzEuLg66urqIiooq+IX5j2PiuxiV1JsjHx8fDBs2DIMHD4a1tTXWrFmD8uXL448//gAA3L9/H46OjmjYsCH69u0LLS0thIeHAwCmTZuGUaNG5TmrmL7epwscvnz5EnK5HFu2bMHFixfx6tWrHP0/fPiA6dOni49VXp4+fQp/f3+Ym5sjODgYo0aNwvjx48U6z58ucNiuXTtxgcMRI0aICxzWrl0bdnZ2OHPmTImd//cuMTERY8aMgaWlJdzd3dG8eXMEBwdDWVkZUqkUDx48QM+ePVGrVi107twZ8fHxOHv2LGxsbMQxPl/sys/PD7169cLo0aNhZWWFKVOmYMSIEZg/X3EWWmpqKsaOHYu1a9eKM8SrVq0KPz8/DB48GN7e3ggMDCxwzTEiIiIi+qikJrfkh5NbiIhKl1QqhYqKCjIzM/Hu3Tvo6Ojg4sWLkEql6NSpU47+2fXBs/Xp0yffcqO5fZtbEAQ8e/YM1apVg1QqVRj7838B4ObNm/jnn38wfvx4hTFmz54NAwMDqKurw8nJCY8ePRK3V65cGe7u7pgzZ05+l6HM4PejilFJvDlKT0/H1atXMXPmTLGtXLlycHJywsWLFwEAdevWxbp16/D27Vs8ffoUqampMDMzw7lz53Dt2jWsXr26eE6Q8rR582YMGTIERkZGkEqlqF+/Pvr27YurV68q9MvIyICrqysEQYC/v/8Xx8zKykLDhg3FT+/s7Oxw584drFmzBh4eHgA+LnA4cuRIcZ9PFzi0sLBAaGgooqKi4ObmhvDwcK4GXAJcXV3h6uqa6zY1NTXs3r073zEiIiIU7mtqasLX1xe+vr5f3E9dXR0PHz7M0T506FAMHTo03+MSERERUe4+ndxiZWUFPT09bNu2DRcvXoSZmVmO/gWd3JKfTye3ABAntzg5OYmTW7y8vKCsrIwVK1aIpRaJiOjrJCYmAvj4d3xaWhqeP38ONTU1VKpUCTNmzMDgwYPx119/ITw8HLq6unj9+jXS09OhqakpjvHw4UNUqFBBXJQyKioK2traUFFRgVwux5s3b/D+/XvUqlUrx/Hj4uKgrKws5gc1NDTw8uVLJCUlITExEWpqagplrvz8/NC7d29xoUwAWLJkCVauXInAwECYmprC09MTzs7OuHfvnpirHDx4MBo0aIBff/0VFStWLPbr+K3hjO9iVBIzf+Pi4iCXy3OsCq+np4fo6GgAgLOzMwYMGIBGjRph0KBBCAwMhEwmw6hRo7BmzRr4+/vDwsIC9vb2uHv3bvGfOBVogcPspPezZ89w9OjRfN8QGxgY5KjfZGVllee3A7jAIRHRv+NLi50B+c+0KMqYABc7IyL6t23evBmCIMDIyAiqqqpYuXIl+vbtm2MtlsJMbimIkSNH4uHDh3j48CFGjhypMLll6NChYhk8Nze3L64bQ0T0rRIEAUlJSaVy+3yGdja5XI7IyEjcuXMH4eHh0NTUhLm5OVJSUrB9+3a4urrC3t4eS5cuxePHj5GZmQlLS0uFCYZpaWkICgoSk9dhYWHQ0tLCwYMHERYWhuTkZNSqVQsbNmxAzZo1xf2uX7+O7t27o27dutDT08PAgQORmpoKPT09PH78GG/fvlVY800ul2Pnzp3o3LmzwjX19fXFrFmz0LVrV9SpUwebNm3Cy5cvFRZStrGxgaGhIfbs2VNMj+a3jTO+i1lJzPwtCC8vL3h5eYn3586dCycnJygrK2PBggW4ffs2Dh48CHd39xyxUPHJa4HD7Mf70aNHOHnyJCpVqpTvWPb29jlm84aFhcHY2DjX/lzgsORxwSsiAr682JmRkVGBZloUdkwudkZE9O/LntySnJyMd+/ewcDAAH369MlzcsuJEye+arZ3brInt5w5c0Zhcou5ubk4ucXW1rZYj0lEVNKSk5PRtWvXUjn2vn37FGZJZ6tYsWKuM6C3b98OS0tLtGvXDunp6ZgwYQJWrVoFiUSSo2+dOnVw7do18X7r1q3RsGFD3LhxAz179hTbg4KC0K9fPwBAQkICnJ2dMXToUPz+++9ITU3F9OnT4erqihMnTigsfJnt1q1bSExMRMOGDcW28PBwREdHw8nJSWzT1tZGkyZNcPHiRbi5uYntjRs3xtmzZ/G///0vv8v1n8cZ38WsuGf+Vq5cGVKpFDExMQrtMTEx0NfXz3WfBw8eYMuWLZg/fz5OnToFBwcHVKlSBa6urrh27Rrev39fPCdLoi8tcJiRkYFevXrhypUrCAoKglwuR3R0NKKjo5Geni6O8fkChxMnTsSlS5ewcOFCPH78GFu3bsW6devERQw/xQUOiYj+Hfmt51HQmRaFGRPgYmdERKVJJpPBwMBAnNySnaz5dHLLsWPHCjS5pbA+ndwil8s5uYWI6F+2YcMGDBgwAADQvn17JCYm4vTp0wXev3///ti2bZt4PywsDFevXkX//v0BAKtWrYKdnR0WLlwoLq75xx9/4OTJk3l+c//Zs2eQSqViSRUAYlWIL1WMyGZoaIhnz54V+Bz+y5j4LiHF9eZIRUUFDRo0wPHjx8W2rKwsHD9+HE2bNs3RXxAEjBgxAj4+PtDQ0FB4c5T9L98cFb8vLXD44sUL7N+/H1FRUahXrx4MDAzE24ULF8QxPl/gsFGjRtizZw+2bduG2rVrY/78+fD19RV/OGbjAodERP+e/NbzyG+mRVHGBLjYGRFRaSiJyS1JSUm4ceMGbty4AeDjDL0bN27kWs6Qk1uIiErXw4cPcfnyZfTt2xcAoKSkhD59+mDDhg0FHsPNzQ0RERG4dOkSgI+zvevXrw9LS0sAHxepPHnyJDQ0NMRb9rYnT57kOmZqaipUVVVznXVeEOrq6khJSSnSvv81/D5sMQsODoYgCLCwsMDjx48xderUHG+Orl27hoMHD4pvjoCPX6lQUVEB8PHNUffu3TF27FgAwKRJk+Dh4YGGDRuicePG8PX1RXJyMgYPHpzj+L///juqVKki1vmxt7eHl5cXLl26hEOHDsHa2vqrVhmn3H1pgUMTE5M8a0h96vMFDgHAxcUFLi4uX9yPCxwSEf178lvsrDAzLQo6JsDFzoiISkNiYiJmzpyJqKgoVKxYET179oS3tzeUlZURERGB/fv3AwDq1aunsN/Jkyfh6OgIIOfklitXrqBVq1bi/UmTJgEAPDw8sHHjRrE9e3LLX3/9levkFlVVVU5uIaL/LJlMhn379pXasR88e1mgvr8uX4HMzEyFciOCIEBFRQXjp/0MTS0tREZ//BkfFvkKWokpeBX3FllZgsIxmjSzx29r10PHoDo2btqEvgPcxe0xr+Ph2KYtJs/4Kcfxq+jq5RprilyClJQUpKeni7nE7KoQMTExMDAwEPvGxMTk+D315s0bVKlSpUDX4L+Oie9iVhJvjvr06YPXr19j9uzZiI6ORr169XD48OEcf1THxMTA29tbYRZx48aNMXnyZHTq1Am6uroIDAwsmRMnIiL6ThR0PY/iHnPkyJEYOXKkeP/Txc4sLCwQGhqKqKgouLm5ITw8XGGhHSIiKrySmNzi6OhYoP04uYWIyjKJRJJrne1vSWZmJvbv2onps2ajWYuWCtvGDf8f/t6/F24D3As0Vueu3bH0F2907NwVUZGR6Nj5/+qbW9eujSOH/oFR1WoFXq/HytoGAHDv3j0xv2hqagp9fX0cP35cbHv37h1CQkIwatQohf3v3Lkj5iDLOia+i1lJzfwdO3asOAM8L3p6ernuO3v2bMyePTvf41LBcIFDIqLv25cWOyvMTIuCjpkbLnZGRERERFRyTh0/hsR3iejp2hean63N17Z9R+za/meBE99t23fE3FkzMW/WTDRu2gy6ev+3Zl8/90HY8edWTBk/Gv8bMRraOjqIjIjAPwf2Yf7ipZBKpTnGq1ipEqxr2+LcuXPi3xgSiQQTJkzAggULYG5uDlNTU3h6esLQ0BDdunUT901JScHVq1excOHCwl+U/yDW+CYiIiIqgtzW8/h0pkW27JkWua3NUZAxc8PFzoiIiIiISs6u7dvQ1L55jqQ3ALTr0BF3bt3Ew/v3CjSWTEMDjm3a4sH9e+jctYfCNl09fQTt3Au5PAtDB/ZDV+c2WDRvDjS1tMRSV7np1acvgoKCFNqmTZuGcePGYfjw4WjUqBGSkpJw+PBhhbWE9u3bh+rVq6NFixYFiv2/jjO+i4izfomIiL5PX1rPo6AzLT5fz+NLY34ue7Gz7PJlny529vz5cy52RkRUBPz7jrLJ5XJ4eXlhy5YtiI6OhqGhIQYNGoRZs2aJC8klJSVhxowZ2Lt3L+Lj42Fqaorx48crlCTLja+vL/z9/REZGYnKlSujV69eWLRokZiUCgoKwowZM5CUlITBgwfDx8dH3DciIgLt2rXDlStXoJVLIo6Iipf/hrxLBdepZ4f7ES/E+5/+v3vvPujeu0+OfZb/tgbLf1uT63gmpjXgt/b3QsXXvbcrAtb54+LFi+IEG4lEgnnz5mHevHl57rdixYrvqioEE99EREREhfCl9TyAjzMtkpOTMXz4cCQkJKB58+Y5Zlp8vp5HfmNm42JnREREJWvx4sXw9/dHYGAgbGxscOXKFQwePBja2toYP348gI+Lkp44cQJbtmyBiYkJjhw5gtGjR8PQ0BBdunTJddytW7dixowZ+OOPP9CsWTOEhYVh0KBBkEgk8PHxQVxcHIYOHYqNGzeiRo0a6NSpE1q3bg0XFxcAwOjRo/HLL78w6U1EAAA1NXVs2rRJ4W+K/MTFxaFHjx7o27dvCUb2bWHim4iIiKgQvrSeB1CwmRafr8mR35jZuNgZERFRybpw4QK6du2KTp06Afi4Vte2bdtw+fJlhT4eHh7i4nDDhw/H2rVrcfny5TwT3xcuXIC9vT369esnjtu3b1+EhIQAAJ4+fQptbW306fNxpmirVq1w//59uLi4YNu2bVBWVkaPHj1yHZuIvk+FXaCycuXKmDZtWskE841ijW8iIiIiIiIiIgDNmjXD8ePHERYWBgC4efMmzp07hw4dOij02b9/P168eAFBEHDy5EmEhYWhXbt2Xxz36tWrYgL96dOn+Oeff9CxY0cAgLm5OVJSUnD9+nW8efMGoaGhqFOnDt6+fQtPT0+sWrWqBM+aiKhs4oxvIiIiojyUlZqvrPdKRERUMDNmzMC7d+9gaWkJqVQKuVwOb29v9O/fX+zj5+eH4cOHo2rVqlBSUkK5cuWwfv16ODg45Dluv379EBcXh+bNm0MQBGRmZmLkyJH46aefAAAVKlRAYGAg3N3dkZqaCnd3dzg7O+N///sfxo4di/DwcHTp0gUZGRnw8vJCr169SvxaEBH91zHxTUREREREREQEYPv27QgKCsLWrVthY2ODGzduYMKECTA0NISHhweAj4nvS5cuYf/+/TA2NsaZM2cwZswYGBoawsnJKddxT506hYULF2L16tVo0qQJHj9+jB9//BHz58+Hp+fHD6i7d++O7t27i/ucPn0at27dgp+fH8zMzLBt2zbo6+ujcePGcHBwgK6ubslfECKi/zAmvomIiIiIiIiIAEydOhUzZsyAm5sbAMDW1hbPnj3DokWL4OHhgdTUVPz000/Ys2ePWAe8Tp06uHHjBpYuXZpn4tvT0xMDBw4U1+SwtbUVF8P++eefxUWrs6WlpWH06NHYvHkzHj9+jMzMTLRs2RIAUKtWLYSEhKBz584ldRmIiMoE1vgmIiIiIiIiIgKQkpKSIwktlUqRlZUFAMjIyEBGRsYX+xRmXAAQBCFH/wULFqB9+/aoX78+5HI5MjMzxW0ZGRmQy+WFOzEiou8QZ3wTEREREREREQHo3LkzvL29Ub16ddjY2OD69evw8fHBkCFDAABaWlpo2bIlpk6dCnV1dRgbG+P06dPYtGkTfHx8xHHc3d1hZGSERYsWieP6+PjAzs5OLHXi6emJzp07iwnwbPfu3cNff/2F69evAwAsLS1Rrlw5bNiwAfr6+njw4AEaNWr0L10RIqL/Lia+iYiIiIiIiIjwsX63p6cnRo8ejdjYWBgaGmLEiBGYPXu22OfPP//EzJkz0b9/f7x58wbGxsbw9vbGyJEjxT6RkZEKM7xnzZoFiUSCWbNm4cWLF6hSpYqYZP+UIAgYPnw4fHx8IJPJAADq6urYuHEjxowZg7S0NKxatQpGRkYlfCWIiP77mPgmIiIiIiIiIgKgqakJX19f+Pr65tlHX18fAQEBXxzn1KlTCveVlJQwZ84czJkz54v7SSQSnDt3Lke7i4sLXFxcvrgvEVFxioiIgKmpKa5fv4569erl2e/hw4do2bIlHj16BE1NzQKNvWbNGvz99984cOBAMUWbO9b4JiIiIiIiIiIiIvrEzMkTYGViBCsTI9iaGaN5w7oYMsANu7b/+cWa/t+bmTNnYty4cWLS+8OHDxg0aBBsbW2hpKSEbt265dhnyJAhuHbtGs6ePVuisXHGNxERERERERF9l1qMmF/aIRSbs2s9SzsEojKnRctW8P7VB1lZcsTFxeHc6ZNYOHc2gv/5G6t/D4CS0vedWo2MjMTBgwfh5+cntsnlcqirq2P8+PHYtWtXrvupqKigX79+WLlyJVq0aFFi8XHGNxEREREREREREf0rBEFAUlJSqdwEQShUrCoqKqiiqws9fQPY1LbFiDHj8dv6P3D21Ans2bld7PcuMRGzpk9Bs/q2aFjbAoP69saDe3cVxjp57Ah6d+mIurVqoKldbYwd/j9xW3paGpZ4z0PLJg1Q38oMfbq64PLFC+L2t2/fYPK40WjZpAHsLGuii3MbbNu2TWH8nTt3wtbWFurq6qhUqRKcnJyQnJwsbv/9999hZWUFNTU1WFpaYvXq1Qr7X758GXZ2dlBTU0PDhg3FBXa/ZPv27ahbt67CugMymQz+/v4YNmwY9PX189y3c+fO2L9/P1JTU/M9TlF93x9LEBERERERERER0b8mOTkZXbt2LZVj79u376vH+KFZc1haWePo4UPo7dYPADBhzAioqalh3cYt0NDUxPatWzC4fx8cOnkWOjoVcOrEMYwbMRQjxozHLz4rkJGejjMnT4hjzp8zC08ehWGZ32ro6unhWPBhDPMYgH3Bx2BiWgPpaWmwsa2DoSNHQ0NTE6dPHMfAgQNRs2ZNNG7cGK9evULfvn2xZMkSdO/eHe/fv8fZs2fFRH9QUBBmz56NVatWwc7ODtevX8ewYcMgk8ng4eGBpKQkuLi4oG3bttiyZQvCw8Px448/5nstzp49i4YNGxbpOjZs2BCZmZkICQmBo6NjkcbIDxPfRERERERERERERAVkWtMMYQ/uAwCuhl7G7Zs3cP7KTaioqgIApv08G8ePBOPIP3/Dtd8ArF21Eh07d8W4SVPEMSytbQAAL1+8wJ4df+HEhcvQ1fs4Q3rI8JE4e/ok9uz4CxOnzYSevgGGDB8p7jtg0BDcCL2E7du3i4nvzMxM9OjRA8bGxgAAW1tbsf+cOXOwbNky9OjR42P8pqa4d+8e1q5dCw8PD2zduhVZWVnYsGED1NTUYGNjg6ioKIwaNeqL1+HZs2dFTnyXL18e2traePbsWZH2LwgmvomIiIiIiIiIiIgKSBAEQCIBADy4fw8pycloaldboc+HDx8Q+f+Tug/u3UVvt/65jhX28D7kcjk6tFKsdZ2eng4dnQoAPtbNXvvbShz++yBio6ORkZGO9PR0lC9fHgBQt25dtGnTBra2tnB2dka7du3Qq1cvVKhQAcnJyXjy5An+97//YdiwYeL4mZmZ0NbWBgDcv38fderUgZqamri9adOm+V6H1NRUhX0KS11dHSkpKUXePz9MfBMRERERERER0XdNLpfDy8sLW7ZsQXR0NAwNDTFo0CDMmjULkv+f4PzUyJEjsXbtWixfvhwTJkzIc9xFixZh9+7dePDgAdTV1dGsWTMsXrwYFhYWYp9JkyZh48aNkMlk+OWXX9C///8lSHfs2IFNmzbhwIEDxXq+pUkmkxVLyZGiHhvx7756nKdPHqNqtWoAgJTkZFTR1UXgnztz9NPS+phYVv1CcjglORlSqRQ7DxxCOalUYVv58jIAwB9r/bE5YANmzp6LWhaWUC9fHn6/LkJ6ejoAQCqV4ujRo7hw4QKOHDkCPz8//PzzzwgJCRGT4+vXr0eTJk0Uxpd+drzCqly5Mt6+fVvk/d+8eYMqVap8VQxfwsQ3ERERERERERF91xYvXgx/f38EBgbCxsYGV65cweDBg6GtrY3x48cr9N2zZw8uXboEQ0PDfMc9ffo0xowZg0aNGiEzMxM//fQT2rVrh3v37kEmk+HAgQPYunUrjhw5gkePHmHIkCFwdnZG5cqVkZiYiJ9//hnHjh0rqdMuFRKJBBoaGqUdRpFdunAOYQ/uw2PIx9nT1rVtEff6NZSkSjD6/8nwz1lYWuHShXPo4donxzYrm9qQy+WIj49Hw8ZNctkbuHY1FK3bOqNL954AgKysLISFhcHa2lrsI5FIYG9vD3t7e8yePRvGxsbYs2cPJk2aBENDQzx9+lThQxWFGKyssHnzZnz48EGcwX3p0qV8r4WdnR3u3buXb7/cPHnyBB8+fICdnV2R9i8IJr6JiIiIiIiIiOi7duHCBXTt2hWdOnUCAJiYmGDbtm24fPmyQr8XL15g3LhxCA4OFvt+yeHDhxXub9y4Ebq6urh69SocHBxw//59ODo6omHDhmjYsCEmTJiA8PBwVK5cGdOmTcOoUaNQvXr14jtRKpT09HS8jo1FVpYccXFxOHf6JNatXgXHNk7o2rMXAKBZ8xaoV78Bxg4fgikzZ8HEtAZiY6Nx+sRxODl3QO06dTHmx0kY3L8PqlU3RscuXSHPzMTpkycwbNQYmNaoic7demDGpB8xbdZsWNvUxpv4eFw6fw61rKzg2NoJxiamOHLob1y/GgotbR1s/H0dYmJixMR3SEgIjh8/jnbt2kFXVxchISF4/fo1rKysAABz587F+PHjoa2tjfbt2yMtLQ1XrlzB27dvMWnSJPTr1w8///wzhg0bhpkzZyIiIgJLly7N9/o4Oztj6NChkMvlCrPH7927h/T0dLx58wbv37/HjRs3AAD16tUT+5w9exY1atRAzZo1i+nRyomJbyIiIiIiIiIi+q41a9YM69atQ1hYGGrVqoWbN2/i3Llz8PHxEftkZWVh4MCBmDp1KmxsbIp0nMTERABAxYoVAXyszbxu3Tq8ffsWT58+RWpqKszMzHDu3Dlcu3YNq1ev/vqToyI7e/okHBrbQUlJCVra2rCwssbPc+ajW6/eKFeuHICPM63XBmyG79LF+GnqJLx9E4/KVaqgYeMfUKlyZQBA46bNsHz1WqxZ6Yv1a36DhoYGGjb+QTyO968+WOO3AksWzENsTDR0KlREXbv6cGzjBAAYOe5HREVGYqh7f6irq6N33/7o1q2b+HzS0tLCmTNn4Ovri3fv3sHY2BjLli1Dhw4dAABDhw5F+fLl8euvv2Lq1KmQyWSwtbUVy/RoaGjgwIEDGDlyJOzs7GBtbY3FixejZ8+eX7w+HTp0gJKSEo4dOwZnZ2exvWPHjgqLVmbP6hYEQWzbtm2bQs3xksDENxERERERERERfddmzJiBd+/ewdLSElKpFHK5HN7e3gqlIRYvXgwlJaUcpU8KKisrCxMmTIC9vT1q1/64EKKzszMGDBiARo0aQV1dHYGBgZDJZBg1ahQ2btwIf39/+Pn5oXLlyli3bl2RE+5UeIuW+WLRMt8C9ZVpaOBnr/n42Wt+nn3ate+Idu075rpNWVkZ4yZNwbhJU3LdrqNTAavW/6HQZmn8f6V2rKyscny74HP9+vVDv3798tz+ww8/iDOzs32aqM6NkpISfvrpJ/j4+CgkviMiIr643927d3Hjxg1s3779i/2+FhPfRERERERERET0Xdu+fTuCgoKwdetW2NjY4MaNG5gwYQIMDQ3h4eGBq1evYsWKFbh27Vqui10WxJgxY3Dnzh2cO3dOod3LywteXl7i/blz58LJyQnKyspYsGABbt++jYMHD8Ld3R1Xr179mtMkKnYjRoxAQkIC3r9/D01NzQLt8+rVK2zatAna2tolGhsT30RERERERERE9F2bOnUqZsyYATc3NwCAra0tnj17hkWLFsHDwwNnz55FbGysQr1tuVyOyZMnw9fXN98ZrmPHjsXBgwdx5swZVK1aNc9+Dx48wJYtW3D9+nX88ccfcHBwQJUqVeDq6oohQ4YUKrlI9G9QUlLCzz//XKh9nJycSigaRUx8ExERERERERHRdy0lJUWs2ZxNKpUiKysLADBw4MAcyTpnZ2cMHDgQgwcPznNcQRAwbtw47NmzB6dOnYKpqekX+44YMQI+Pj7Q0NCAXC5HRkYGAIj/yuXyIp0f0feIiW8iIiIiIiIiIvqude7cGd7e3qhevTpsbGxw/fp1+Pj4YMiQIQCASpUqoVKlSgr7KCsrQ19fHxYWFmJbmzZt0L17d4wdOxbAx/ImW7duxb59+6CpqYno6GgAgLa2NtTV1RXG+/3331GlShV07twZAGBvbw8vLy9cunQJhw4dgrW1NXR0dErqEhCVOUx8ExERERERERHRd83Pzw+enp4YPXo0YmNjYWhoiBEjRmD27NmFGufJkyeIi4sT7/v7+wMAHB0dFfoFBARg0KBB4v2YmBh4e3vjwoULYlvjxo0xefJkdOrUCbq6uggMDCz8iX0j8lskkehzxfGcYeKbiIiIiIiIiIi+a5qamvD19YWvr2+B98mtrvfnbQVN3unp6eU63uzZswudfP+WKCsrA/hYSubzGe5EX5KSkgLg/55DRcHENxERERERERERERU7qVQKHR0dxMbGAgDKly8PiURSqjFlZWaW6vGLy4cPH0o7hBIhCAJSUlIQGxsLHR0dSKXSIo/FxDcREREREREREX1XWoyYX9ohFIuzaz1LO4R86evrA4CY/C5tMfEJpR1CsZCkJ5d2CCVKR0dHfO4UFRPfREREREREREREVCIkEgkMDAygq6uLjIyM0g4HswJWl3YIxSJo3ujSDqHEKCsrf9VM72xMfBMREREREREREVGJkkqlxZLM/FqxiWVjprSamlpph/DNK1faARARERERERERERERFScmvomIiIiIiIiIiIioTGHim4iIiIiIiIiIiIjKFCa+iYiIiIiIiIiIiKhMYeKbiIiIiIiIiIiIiMoUJr6JiIiIiIiIiIiIqExh4puIiIiIiIiIiIiIyhQmvomIiIiIiIiIiIioTGHim4iIiIiIiIiIiIjKlG868S2Xy+Hp6QlTU1Ooq6ujZs2amD9/PgRBEPsIgoDZs2fDwMAA6urqcHJywqNHj0oxaiIiIiIiIiIiIiIqTd904nvx4sXw9/fHqlWrcP/+fSxevBhLliyBn5+f2GfJkiVYuXIl1qxZg5CQEMhkMjg7O+PDhw+lGDkRERERERERERERlRal0g7gSy5cuICuXbuiU6dOAAATExNs27YNly9fBvBxtrevry9mzZqFrl27AgA2bdoEPT097N27F25ubqUWOxERERERERERERGVjm96xnezZs1w/PhxhIWFAQBu3ryJc+fOoUOHDgCA8PBwREdHw8nJSdxHW1sbTZo0wcWLF/McNy0tDe/evVO4EREREREREREREVHZ8E3P+J4xYwbevXsHS0tLSKVSyOVyeHt7o3///gCA6OhoAICenp7Cfnp6euK23CxatAhz584tucCJiIiIiIiIiIiIqNR80zO+t2/fjqCgIGzduhXXrl1DYGAgli5disDAwK8ad+bMmUhMTBRvz58/L6aIiYiIiIiIiIiIiKi0fdMzvqdOnYoZM2aItbptbW3x7NkzLFq0CB4eHtDX1wcAxMTEwMDAQNwvJiYG9erVy3NcVVVVqKqqlmjsRERERERERERERFQ6vukZ3ykpKShXTjFEqVSKrKwsAICpqSn09fVx/Phxcfu7d+8QEhKCpk2b/quxEhEREREREREREdG34Zue8d25c2d4e3ujevXqsLGxwfXr1+Hj44MhQ4YAACQSCSZMmIAFCxbA3Nwcpqam8PT0hKGhIbp161a6wRMRERERERERERFRqfimE99+fn7w9PTE6NGjERsbC0NDQ4wYMQKzZ88W+0ybNg3JyckYPnw4EhIS0Lx5cxw+fBhqamqlGDkRERERERERERERlZZvOvGtqakJX19f+Pr65tlHIpFg3rx5mDdv3r8XGBERERERERERERF9s77pGt9ERERERERERERERIXFxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpTHwTERERERERERERUZnCxDcRERERERERERERlSlMfBMRERERERERERFRmcLENxERERERERERERGVKUx8ExEREREREREREVGZwsQ3EREREREREREREZUpRU58JyQk4Pfff8fMmTPx5s0bAMC1a9fw4sWLYguOiIiIiIiIiIiIiKiwlIqy061bt+Dk5ARtbW1ERERg2LBhqFixInbv3o3IyEhs2rSpuOMkIiIiIiIiIiIiIiqQIs34njRpEgYNGoRHjx5BTU1NbO/YsSPOnDlTbMEBwIsXLzBgwABUqlQJ6urqsLW1xZUrV8TtgiBg9uzZMDAwgLq6OpycnPDo0aNijYGIiIiIiIiIiIiI/juKlPgODQ3FiBEjcrQbGRkhOjr6q4PK9vbtW9jb20NZWRmHDh3CvXv3sGzZMlSoUEHss2TJEqxcuRJr1qxBSEgIZDIZnJ2d8eHDh2KLg4iIiIiIiIiIiIj+O4pU6kRVVRXv3r3L0R4WFoYqVap8dVDZFi9ejGrVqiEgIEBsMzU1Ff8vCAJ8fX0xa9YsdO3aFQCwadMm6OnpYe/evXBzcyu2WIiIiIiIiIiIiIjov6FIM767dOmCefPmISMjAwAgkUgQGRmJ6dOno2fPnsUW3P79+9GwYUP07t0burq6sLOzw/r168Xt4eHhiI6OhpOTk9imra2NJk2a4OLFi3mOm5aWhnfv3inciIiIiIiIiIiIiKhsKFLie9myZUhKSoKuri5SU1PRsmVLmJmZQVNTE97e3sUW3NOnT+Hv7w9zc3MEBwdj1KhRGD9+PAIDAwFALKuip6ensJ+ent4XS64sWrQI2tra4q1atWrFFjMRERERERERERERla4ilTrR1tbG0aNHce7cOdy6dQtJSUmoX7++wszr4pCVlYWGDRti4cKFAAA7OzvcuXMHa9asgYeHR5HHnTlzJiZNmiTef/fuHZPfRERERERERERERGVEkRLf2Zo3b47mzZsXVyw5GBgYwNraWqHNysoKu3btAgDo6+sDAGJiYmBgYCD2iYmJQb169fIcV1VVFaqqqsUfMBERERERERERERGVuiIlvleuXJlru0QigZqaGszMzODg4ACpVPpVwdnb2+Phw4cKbWFhYTA2NgbwcaFLfX19HD9+XEx0v3v3DiEhIRg1atRXHZuIiIiIiIiIiIiI/puKlPhevnw5Xr9+jZSUFFSoUAEA8PbtW5QvXx4aGhqIjY1FjRo1cPLkya8qITJx4kQ0a9YMCxcuhKurKy5fvox169Zh3bp1AD4m2idMmIAFCxbA3Nwcpqam8PT0hKGhIbp161bk4xIRERERERERERHRf1eRFrdcuHAhGjVqhEePHiE+Ph7x8fEICwtDkyZNsGLFCkRGRkJfXx8TJ078quAaNWqEPXv2YNu2bahduzbmz58PX19f9O/fX+wzbdo0jBs3DsOHD0ejRo2QlJSEw4cPQ01N7auOTURERERERERERET/TUWa8T1r1izs2rULNWvWFNvMzMywdOlS9OzZE0+fPsWSJUvQs2fPrw7QxcUFLi4ueW6XSCSYN28e5s2b99XHIiIiIiIiIiIiIqL/viLN+H716hUyMzNztGdmZiI6OhoAYGhoiPfv339ddEREREREREREREREhVSkxHerVq0wYsQIXL9+XWy7fv06Ro0ahdatWwMAbt++DVNT0+KJkoiIiIiIiIiIiIiogIqU+N6wYQMqVqyIBg0aQFVVFaqqqmjYsCEqVqyIDRs2AAA0NDSwbNmyYg2WiIiIiIiIiIiIiCg/Rarxra+vj6NHj+LBgwcICwsDAFhYWMDCwkLs06pVq+KJkIiIiIiIiIiIiIioEIqU+M5maWkJS0vL4oqFiIiIiIiIiIiIiOirFTnxHRUVhf379yMyMhLp6ekK23x8fL46MCIiIiIiIiIiIiKioihS4vv48ePo0qULatSogQcPHqB27dqIiIiAIAioX79+ccdIRERERERERERERFRgRVrccubMmZgyZQpu374NNTU17Nq1C8+fP0fLli3Ru3fv4o6RiIiIiIiIiIiIiKjAipT4vn//Ptzd3QEASkpKSE1NhYaGBubNm4fFixcXa4BERERERERERERERIVRpMS3TCYT63obGBjgyZMn4ra4uLjiiYyIiIiIiIiIiIiIqAiKVOP7hx9+wLlz52BlZYWOHTti8uTJuH37Nnbv3o0ffvihuGMkIiIiIiIiIiIiIiqwIiW+fXx8kJSUBACYO3cukpKS8Ndff8Hc3Bw+Pj7FGiARERERERERERERUWEUKfFdo0YN8f8ymQxr1qwptoCIiIiIiIiIiIiIiL5GkWp816hRA/Hx8TnaExISFJLiRERERERERERERET/tiIlviMiIiCXy3O0p6Wl4cWLF18dFBERERERERERERFRURWq1Mn+/fvF/wcHB0NbW1u8L5fLcfz4cZiYmBRbcEREREREREREREREhVWoxHe3bt0AABKJBB4eHgrblJWVYWJigmXLlhVbcEREREREREREREREhVWoxHdWVhYAwNTUFKGhoahcuXKJBEVEREREREREREREVFSFSnxnCw8PL+44iIiIiIiIiIiIiIiKRZES3wBw/PhxHD9+HLGxseJM8Gx//PHHVwdGRERERERERERERFQURUp8z507F/PmzUPDhg1hYGAAiURS3HERERERERERERERERVJkRLfa9aswcaNGzFw4MDijoeIiIiIiIiIiIiI6KuUK8pO6enpaNasWXHHQkRERERERERERET01YqU+B46dCi2bt1a3LEQEREREREREREREX21IpU6+fDhA9atW4djx46hTp06UFZWVtju4+NTLMERERERERERERERERVWkRLft27dQr169QAAd+7cUdjGhS6JiIiIiIiIiIiIqDQVKfF98uTJ4o6DiIiIiIiIiIiIiKhYFKnGd7bHjx8jODgYqampAABBEIolKCIiIiIiIiIiIiKioipS4js+Ph5t2rRBrVq10LFjR7x69QoA8L///Q+TJ08u1gCJiIiIiIiIiIiIiAqjSInviRMnQllZGZGRkShfvrzY3qdPHxw+fLjYgiMiIiIiIiIiIiIiKqwi1fg+cuQIgoODUbVqVYV2c3NzPHv2rFgCIyIiIiIiIiIiIiIqiiLN+E5OTlaY6Z3tzZs3UFVV/eqgiIiIiIiIiIiIiIiKqkiJ7xYtWmDTpk3ifYlEgqysLCxZsgStWrUqtuCIiIiIiIiIiIiIiAqrSKVOlixZgjZt2uDKlStIT0/HtGnTcPfuXbx58wbnz58v7hiJiIiIiIiIiIiIiAqsSDO+a9eujbCwMDRv3hxdu3ZFcnIyevTogevXr6NmzZrFHSMRERERERERERERUYEVacY3AGhra+Pnn38uzliIiIiIiIiIiIiIiL5akWZ8BwQEYMeOHTnad+zYgcDAwK8OioiIiIiIiIiIiIioqIqU+F60aBEqV66co11XVxcLFy786qCIiIiIiIiIiIiIiIqqSInvyMhImJqa5mg3NjZGZGTkVwdFRERERERERERERFRURUp86+rq4tatWznab968iUqVKn11UERERERERERERERERVWkxHffvn0xfvx4nDx5EnK5HHK5HCdOnMCPP/4INze34o6RiIiIiIiIiIiIiKjAlIqy0/z58xEREYE2bdpASenjEFlZWXB3d2eNbyIiIiIiIiIiIiIqVYVOfAuCgOjoaGzcuBELFizAjRs3oK6uDltbWxgbG5dEjEREREREREREREREBVakxLeZmRnu3r0Lc3NzmJubl0RcRERERERERERERERFUuga3+XKlYO5uTni4+NLIh4iIiIiIiIiIiIioq9SpMUtf/nlF0ydOhV37twp7niIiIiIiIiIiIiIiL5KkRa3dHd3R0pKCurWrQsVFRWoq6srbH/z5k2xBEdEREREREREREREVFhFSnz7+voWcxhERERERERERERERMWjSIlvDw+P4o6DiIiIiIiIiIiIiKhYFKnGNwA8efIEs2bNQt++fREbGwsAOHToEO7evVtswRERERERERERERERFVaREt+nT5+Gra0tQkJCsHv3biQlJQEAbt68iTlz5hRrgEREREREREREREREhVGkxPeMGTOwYMECHD16FCoqKmJ769atcenSpWILjoiIiIiIiIiIiIiosIqU+L59+za6d++eo11XVxdxcXFfHRQRERERERERERERUVEVKfGto6ODV69e5Wi/fv06jIyMvjooIiIiIiIiIiIiIqKiKlLi283NDdOnT0d0dDQkEgmysrJw/vx5TJkyBe7u7sUdIxERERERERERERFRgRUp8b1w4UJYWVmhevXqSEpKgrW1NRwcHNCsWTPMmjWruGMkIiIiIiIiIiIiIiowpcJ0zsrKwq+//or9+/cjPT0dAwcORM+ePZGUlAQ7OzuYm5uXVJxERERERERERERERAVSqMS3t7c3vLy84OTkBHV1dWzduhWCIOCPP/4oqfiIiIiIiIiIiIiIiAqlUKVONm3ahNWrVyM4OBh79+7FgQMHEBQUhKysrJKKj4iIiIiIiIiIiIioUAqV+I6MjETHjh3F+05OTpBIJHj58mWxB0ZEREREREREREREVBSFSnxnZmZCTU1NoU1ZWRkZGRnFGhQRERERERERERERUVEVqsa3IAgYNGgQVFVVxbYPHz5g5MiRkMlkYtvu3buLL0IiIiIiIiIiIiIiokIoVOLbw8MjR9uAAQOKLRgiIiIiIiIiIiIioq9VqMR3QEBAScVRIL/88gtmzpyJH3/8Eb6+vgA+zjifPHky/vzzT6SlpcHZ2RmrV6+Gnp5eqcZKRERERERERERERKWjUDW+S1NoaCjWrl2LOnXqKLRPnDgRBw4cwI4dO3D69Gm8fPkSPXr0KKUoiYiIiIiIiIiIiKi0/ScS30lJSejfvz/Wr1+PChUqiO2JiYnYsGEDfHx80Lp1azRo0AABAQG4cOECLl26VIoRExEREREREREREVFp+U8kvseMGYNOnTrByclJof3q1avIyMhQaLe0tET16tVx8eLFPMdLS0vDu3fvFG5EREREREREREREVDYUqsZ3afjzzz9x7do1hIaG5tgWHR0NFRUV6OjoKLTr6ekhOjo6zzEXLVqEuXPnFneoRERERERERERERPQN+KZnfD9//hw//vgjgoKCoKamVmzjzpw5E4mJieLt+fPnxTY2EREREREREREREZWubzrxffXqVcTGxqJ+/fpQUlKCkpISTp8+jZUrV0JJSQl6enpIT09HQkKCwn4xMTHQ19fPc1xVVVVoaWkp3IiIiIiIiIiIiIiobPimS520adMGt2/fVmgbPHgwLC0tMX36dFSrVg3Kyso4fvw4evbsCQB4+PAhIiMj0bRp09IImYiIiIiI6P+xd9dhUaXv/8DfQyjoCgKKiqIYqKyAomILWGB3B4qJ3Qquuraugd2J3e3a3YJdgN2YqKCSc//+8Dfny4ju6n6UgeH9ui6v3XlOeB+fmRP3eYKIiIiIdCxFJ74zZcoER0dHrbKMGTPCyspKKe/QoQP69esHS0tLmJmZoWfPnihbtizKlCmji5CJiIiIiIiIiIiISMdSdOL7e0ydOhUGBgZo1KgRYmJi4OXlhTlz5ug6LCIiIiIiIiIiIiLSkVSX+D5y5IjWZxMTE8yePRuzZ8/WTUBERERERERERERElKKk6MktiYiIiIiIiIiIiIh+FBPfRERERERERERERKRXmPgmIiIiIiIiIiIiIr3CxDcRERERERERERER6RUmvomIiIiIiIiIiIhIrzDxTURERERERERERER6hYlvIiIiIiIiIiIiItIrTHwTERERERERERERkV5h4puIiIiIiIiIiIiI9AoT30RERERERERERESkV5j4JiIiIiIiIiIiIiK9wsQ3EREREREREREREekVJr6JiIiIiIiIiIiISK8w8U1EREREREREREREeoWJbyIiIiIiIiIiIiLSK0x8ExEREREREREREZFeYeKbiIiIiIiIiIiIiPQKE99EREREREREREREpFeY+CYiIiIiIiIiIiIivcLENxERERERERERERHpFSa+iYiIiIiIiIiIiEivMPFNRERERERERERERHqFiW8iIiIiIiIiIiIi0itMfBMRERERERERERGRXmHim4iIiIiIiIiIiIj0ChPfRERERERERERERKRXmPgmIiIiIiIiIiIiIr3CxDcRERERERERERER6RUmvomIiIiIiIiIiIhIrzDxTURERERERERERER6hYlvIiIiIiIiIiIiItIrTHwTERERERERERERkV5h4puIiIiIiIiIiIiI9AoT30RERERERERERESkV5j4JiIiIiIiIiIiIiK9wsQ3EREREREREREREekVJr6JiIiIiIiIiIiISK8w8U1EREREREREREREeoWJbyIiIiIiIiIiIiLSK0x8ExEREREREREREZFeYeKbiIiIiIiIiIiIiPQKE99EREREREREREREpFeY+CYiIiIiIiIiIiIivcLENxERERERERERERHpFSa+iYiIiIiIiIiIiEivMPFNRERERERERERERHqFiW8iIiIiIiIiIiIi0itMfBMRERERERERERGRXmHim4iIiIiIiIiIiIj0ChPfRERERERERERERKRXmPgmIiIiIiIiIiIiIr3CxDcRERERERERERER6RUmvomIiIiIiIiIiIhIrzDxTURERERERERERER6hYlvIiIiIiIiIiIiItIrTHwTERERERERERERkV5h4puIiIiIiIiIiIiI9AoT30RERERERERERESkV5j4JiIiIiIiIiIiIiK9wsQ3EREREREREREREekVJr6JiIiIiIiIiIiISK8w8U1EREREREREREREeoWJbyIiIiIiIiIiIiLSKyk68T1+/Hi4uroiU6ZMsLa2Rv369REaGqq1TnR0NLp37w4rKyv89ttvaNSoEZ4/f66jiImIiIiIiIiIiIhI11J04vvo0aPo3r07zpw5g/379yMuLg6enp748OGDsk7fvn2xY8cObNiwAUePHsXTp0/RsGFDHUZNRERERERERERERLpkpOsA/smePXu0Pi9btgzW1tY4f/483Nzc8O7dOyxevBirV69G5cqVAQBLly6Fg4MDzpw5gzJlyugibCIiIiIiIiIiIiLSoRTd4vtL7969AwBYWloCAM6fP4+4uDhUrVpVWadw4cLInTs3Tp8+/c39xMTE4P3791p/iIiIiIiIiIiIiEg/pJrEt1qtRp8+fVC+fHk4OjoCAMLDw5EuXTpkzpxZa91s2bIhPDz8m/saP348zM3NlT+2tra/MnQiIiIiIiIiIiIiSkapJvHdvXt3XLt2DWvXrv2f9+Xv7493794pfx49evQTIiQiIiIiIiIiIiKilCBFj/Gt0aNHD+zcuRPHjh1Drly5lPLs2bMjNjYWb9++1Wr1/fz5c2TPnv2b+0ufPj3Sp0//K0MmIiIiIiIiIiIiIh1J0S2+RQQ9evTAli1bcOjQIeTNm1dreYkSJWBsbIyDBw8qZaGhoXj48CHKli2b3OESERERERERERHp1LFjx1CnTh3Y2NhApVJh69atWsvbtWsHlUql9ad69er/uM+EhAQMGzYMefPmhampKfLnz4/Ro0dDRJR1Jk+eDGtra1hbW2PKlCla2589exYlSpRAfHz8TztOon+Tolt8d+/eHatXr8a2bduQKVMmZdxuc3NzmJqawtzcHB06dEC/fv1gaWkJMzMz9OzZE2XLlkWZMmV0HD0REREREREREVHy+vDhA4oWLYr27dujYcOGX12nevXqWLp0qfL530ZG+OuvvzB37lwEBgaiSJEiCA4Oho+PD8zNzdGrVy9cuXIFw4cPx86dOyEiqF27Njw9PeHk5IT4+Hj4+vpiwYIFMDJK0alI0jMp+ts2d+5cAICHh4dW+dKlS9GuXTsAwNSpU2FgYIBGjRohJiYGXl5emDNnTjJHSkREREREREREpHs1atRAjRo1/nGd9OnT/+MwwV86deoU6tWrh1q1agEA7OzssGbNGpw7dw4AEBISAmdnZ1SuXBkA4OzsjJCQEDg5OWHSpElwc3ODq6vrfzwiov8mRSe+E3eX+BYTExPMnj0bs2fPToaIiIiIiIiIiIiIUrcjR47A2toaFhYWqFy5MsaMGQMrK6tvrl+uXDksWLAAYWFhKFiwIC5fvowTJ04gICAAAODk5ISwsDA8fPgQIoKwsDA4Ojrizp07WLp0Kc6fP59ch0akSNGJbyIiIiIiIiIiIvp5qlevjoYNGyJv3ry4c+cOhgwZgho1auD06dMwNDT86jZ+fn54//49ChcuDENDQyQkJGDs2LFo1aoVAMDBwQHjxo1DtWrVAADjx4+Hg4MDqlatiokTJ2Lv3r0YMWIEjI2NMX36dLi5uSXb8VLaxcQ3ERERERERERFRGtG8eXPl/52cnODs7Iz8+fPjyJEjqFKlyle3Wb9+PVatWoXVq1ejSJEiuHTpEvr06QMbGxu0bdsWAODr6wtfX19lm8DAQGTKlAlly5ZFoUKFEBQUhMePH6N58+a4d+/ev44rTvS/YuKbiIiIiIiIiIgojcqXLx+yZMmC27dvfzPxPXDgQPj5+SlJcycnJzx48ADjx49XEt+JvXr1CiNHjsSxY8dw9uxZFCxYEPb29rC3t0dcXBzCwsLg5OT0S4+LyEDXARAREREREREREZFuPH78GK9fv0aOHDm+uc7Hjx9hYKCdRjQ0NIRarf7q+n379kXfvn2RK1cuJCQkIC4uTlkWHx+PhISEnxM80T9gi28iIiIiIiIiIiI9ERUVhdu3byuf7927h0uXLsHS0hKWlpYYOXIkGjVqhOzZs+POnTsYNGgQChQoAC8vL2WbKlWqoEGDBujRowcAoE6dOhg7dixy586NIkWK4OLFiwgICED79u2T/P379+9HWFgYAgMDAQCurq4ICQnB7t278ejRIxgaGqJQoUK/+F+BiIlvIiIiIiIiIiIivREcHIxKlSopn/v16wcAaNu2LebOnYsrV64gMDAQb9++hY2NDTw9PTF69GitMbfv3LmDV69eKZ9nzpyJYcOGoVu3bnjx4gVsbGzQpUsXDB8+XOvv/vTpE3r06IF169YpLcRz5cqFmTNnwsfHB+nTp0dgYCBMTU1/5T8BEQAmvomIiIiIiIiIiPSGh4cHROSby/fu3fuv+7h//77W50yZMmHatGmYNm3aP25namqK0NDQJOUdO3ZEx44d//XvJfqZOMY3EREREREREREREekVtvgmIiIiIiIiIiJKhTzX+us6hJ9mX/Pxug6B9AxbfBMRERERERERERGRXmHim4iIiIiIiIiIiIj0ChPfRERERERERERERKRXmPgmIiIiIiIiIiIiIr3CxDcRERERERERkR46duwY6tSpAxsbG6hUKmzdulVr+ebNm+Hp6QkrKyuoVCpcunTpX/e5cOFCVKxYERYWFrCwsEDVqlVx7tw5rXUmT54Ma2trWFtbY8qUKVrLzp49ixIlSiA+Pv5/PTwion/ExDcRERERERERkR768OEDihYtitmzZ39zeYUKFfDXX3999z6PHDmCFi1a4PDhwzh9+jRsbW3h6emJJ0+eAACuXLmC4cOHY+3atVizZg2GDh2Kq1evAgDi4+Ph6+uLefPmwcjI6H8/QCKif8CzDBERERERERGRHqpRowZq1KjxzeVt2rQBANy/f/+797lq1Sqtz4sWLcKmTZtw8OBBeHt7IyQkBM7OzqhcuTIAwNnZGSEhIXBycsKkSZPg5uYGV1fXHz8YIqIfxMQ3ERERERERERH9Jx8/fkRcXBwsLS0BAE5OTggLC8PDhw8hIggLC4OjoyPu3LmDpUuX4vz58zqOmIjSCg51QkRERERERERE/8ngwYNhY2ODqlWrAgAcHBwwbtw4VKtWDZ6enhg/fjwcHBzQpUsXTJw4EXv37oWjoyNcXFxw7NgxHUdPRPqMLb6JiIiIiIiIiOiHTZgwAWvXrsWRI0dgYmKilPv6+sLX11f5HBgYiEyZMqFs2bIoVKgQgoKC8PjxYzRv3hz37t1D+vTpdRE+Eek5Jr6JiIiIiIiIiOiHTJ48GRMmTMCBAwfg7Oz8zfVevXqFkSNH4tixYzh79iwKFiwIe3t72NvbIy4uDmFhYXByckrGyIkoreBQJ0RERERERERE9N0mTpyI0aNHY8+ePShZsuQ/rtu3b1/07dsXuXLlQkJCAuLi4pRl8fHxSEhI+NXhElEaxcQ3EREREQEAIiMj0adPH+TJkwempqYoV64cgoKCvrn+s2fP0LJlSxQsWBAGBgbo06dPknX279+PggULwszMDG3atEFsbKyy7N27dyhYsCAePHjwKw6HiIgozYuKisKlS5dw6dIlAMC9e/dw6dIlPHz4EADw5s0bXLp0CTdu3AAAhIaG4tKlSwgPD1f24e3tDX9/f+XzX3/9hWHDhmHJkiWws7NDeHg4wsPDERUVleTv379/P8LCwtC9e3cAgKurK0JCQrB7924sWLAAhoaGKFSo0K86fCJK45j4JiIiIiIAQMeOHbF//36sWLECV69ehaenJ6pWrYonT558df2YmBhkzZoVQ4cORdGiRZMsV6vVaNmyJXx9fXH69GkEBwdjwYIFynI/Pz/4+voiT548v+yYiIiI0rLg4GC4uLjAxcUFANCvXz+4uLhg+PDhAIDt27fDxcUFtWrVAgA0b94cLi4umDdvnrKPhw8f4tmzZ8rnuXPnIjY2Fo0bN0aOHDmUP5MnT9b6uz99+oQePXpg/vz5MDD4nH7KlSsXZs6cCR8fH4wdOxaBgYEwNTX9pf8GRJR2cYxvIiIiIsKnT5+wadMmbNu2DW5ubgCAESNGYMeOHZg7dy7GjBmTZBs7OztMnz4dALBkyZIky1+9eoVXr16hW7duMDExQd26dXHz5k0AwKlTpxAUFIRZs2b9wqMiIiJK2zw8PCAi31zerl07tGvX7h/3ceTIEa3P9+/f/66/29TUFKGhoUnKO3bsiI4dO37XPoiI/hds8U1EREREyhibJiYmWuWmpqY4ceLEf9pn1qxZkSNHDuzbtw8fP37E8ePH4ezsjLi4OHTt2hXz58+HoaHhzwifiIiIiIhIC1t803dJSEjAiBEjsHLlSoSHh8PGxgbt2rXD0KFDoVKpvrndkSNH0K9fP1y/fh22trYYOnSo1tvkVatWwc/PD1FRUfDx8UFAQICy7P79+/D09ERwcDDMzMx+5eERERGleZkyZULZsmUxevRoODg4IFu2bFizZg1Onz6NAgUK/Kd9qlQqrF+/Hn379kXv3r1Rs2ZNtG/fHhMmTEClSpVgYmKC8uXL49WrV+jZsyd69Ojxk4+KiIi+xs7O7qvzK3Tr1g2zZ89OUh4XF4fx48cjMDAQT548QaFChfDXX3+hevXqyjp8ttM9z7X+/75SKrCv+Xhdh0BEeoKJb/ouf/31F+bOnYvAwEAUKVIEwcHB8PHxgbm5OXr16vXVbe7du4datWrB19cXq1atwsGDB9GxY0fkyJEDXl5eePXqFTp27Ihly5YhX758qFWrFipXrozatWsD+HzTNWHCBN4YERERJZMVK1agffv2yJkzJwwNDVG8eHG0aNEC58+f/8/7rFChgtYEmWFhYVi+fDkuXrwINzc39O7dGzVq1ICjoyPc3Nzg7Oz8Mw6FiIj+QVBQEBISEpTP165dQ7Vq1dCkSZOvrj906FCsXLkSCxcuROHChbF37140aNAAp06dgouLC5/tiIgoReJQJ/RdTp06hXr16qFWrVqws7ND48aN4enpiXPnzn1zm3nz5iFv3ryYMmUKHBwc0KNHDzRu3BhTp04FANy9exfm5uZo1qwZXF1dUalSJWXczzVr1sDY2BgNGzZMluOjb7Ozs4NKpUryRzMr9z9Zu3YtVCoV6tevr1U+efJkWFtbw9raGlOmTNFadvbsWZQoUQLx8fE/8zCIiOg75M+fH0ePHkVUVBQePXqEc+fOIS4uDvny5ftpf0eXLl0wZcoUqNVqXLx4EU2aNIG1tTXc3d1x9OjRn/b3EBHRt2XNmhXZs2dX/uzcuRP58+eHu7v7V9dfsWIFhgwZgpo1ayJfvnzo2rUratasqdzL89mOiIhSIia+6buUK1cOBw8eRFhYGADg8uXLOHHiBGrUqPHNbU6fPo2qVatqlXl5eeH06dMAAHt7e3z8+BEXL17EmzdvEBQUBGdnZ0RERGDYsGGc7CqFCAoKwrNnz5Q/+/fvB4BvtgbRuH//PgYMGICKFStqlV+5cgXDhw/H2rVrsWbNGgwdOhRXr14F8Hl8WV9fX8ybNw9GRuyQQkSkKxkzZkSOHDkQERGBvXv3ol69ej9lv4sXL4alpSXq1q2rtDSMi4tT/pu49SERESWP2NhYrFy5Eu3bt//mMJYxMTH/OAcEn+2IiCglYuKbvoufnx+aN2+OwoULw9jYGC4uLujTpw9atWr1zW3Cw8ORLVs2rbJs2bLh/fv3+PTpEywsLBAYGAhvb2+UKlUK3t7e8PLywoABA9CjRw/cu3cPLi4ucHR0xMaNG3/1IdI3/GhrEODzmPCtWrXCyJEjk7QSDAkJgbOzMypXrowqVarA2dkZISEhAIBJkybBzc0Nrq6uv/SYiIjo6/bu3Ys9e/bg3r172L9/PypVqoTChQvDx8cHAODv7w9vb2+tbS5duoRLly4hKioKL1++xKVLl3Djxo0k+37x4gXGjBmDmTNnAgAsLCzg4OCAadOm4fTp0zh48CDKly//6w+SiIi0bN26FW/fvtWai+lLXl5eCAgIwK1bt6BWq7F//35s3rwZz549AwA+2xERpWBPnjxB69atYWVlBVNTUzg5OSE4OPib67dr1+6rPf+LFCmirLNq1SrY2trCwsIC/fr109r+/v37KFiwIN6/f//Ljul7sUklfZf169dj1apVWL16NYoUKYJLly6hT58+sLGxQdu2bf/zfhs0aIAGDRoon48ePYorV65g5syZKFCgANasWYPs2bOjVKlScHNzg7W19c84HPqPNK1B+vXr94+Tmo4aNQrW1tbo0KEDjh8/rrXMyckJYWFhePjwIUQEYWFhcHR0xJ07d7B06dL/aRxZIiL637x79w7+/v54/PgxLC0t0ahRI4wdOxbGxsYAgGfPnuHhw4da27i4uCj/f/78eaxevRp58uTB/fv3tdbr3bs3+vfvDxsbG6Vs2bJlaNu2LWbMmIGBAwfyxScRkQ4sXrwYNWrU0Do/f2n69Ono1KkTChcuDJVKhfz588PHxwdLlixR1uGzHRFRyhMREYHy5cujUqVK2L17N7JmzYpbt27BwsLim9tMnz4dEyZMUD7Hx8ejaNGiSs//1DSvAxPf9F0GDhyotPoGPicvHzx4gPHjx38z8Z09e3Y8f/5cq+z58+cwMzODqalpkvVjYmLQrVs3rFixArdv30Z8fLzSqrhgwYI4e/Ys6tSp85OPjH7E97QGOXHiBBYvXoxLly59dbmDgwPGjRuHatWqAQDGjx8PBwcHVK1aFRMnTsTevXsxYsQIGBsbY/r06XBzc/sFR0JERF/TtGlTNG3a9JvLly1blqRMRL5r32vWrElSVqpUKWUMWCIiSn4PHjzAgQMHsHnz5n9cL2vWrNi6dSuio6Px+vVr2NjYwM/P75tzQPDZjogoZfjrr79ga2uLpUuXKmV58+b9x23Mzc1hbm6ufN66dSsiIiKUXqCJ53UAoMzrULt27RQ3rwOHOqHv8vHjRxgYaH9dDA0NoVarv7lN2bJlcfDgQa2y/fv3o2zZsl9df8yYMahevTqKFy+OhIQErckNOe5nyvBvrUEiIyPRpk0bLFy4EFmyZPnmfnx9fREaGorQ0FD4+voiMDAQmTJlQtmyZdGxY0ds2bIFAQEBaN68OWJiYn7V4dC/+NHuUJs3b0a1atWQNWtWmJmZoWzZsti7d6/WOqmlOxQRERFRWrB06VJYW1ujVq1a37W+iYkJcubMifj4eGzatOmbc0Dw2Y6IKGXYvn07SpYsqUwo7+LigoULF/7QPhYvXoyqVasiT548AFLXvA5s8U3fpU6dOhg7dixy586NIkWK4OLFiwgICED79u2Vdfz9/fHkyRMsX74cwOfk5qxZszBo0CC0b98ehw4dwvr167Fr164k+79x4wbWrVuHixcvAgAKFy4MAwMDLF68GNmzZ0dISAi7P+vY97QGuXPnDu7fv6/VekPzcsTIyAihoaHInz+/1javXr3CyJEjcezYMZw9exYFCxaEvb097O3tERcXh7CwMDg5Of2ag6Jv+i/doY4dO4Zq1aph3LhxyJw5M5YuXYo6derg7NmzcHFxSVXdoYj0jedaf12H8NPsaz5e1yEQEekFtVqNpUuXom3btkkmlvf29kbOnDkxfvznc+7Zs2fx5MkTFCtWDE+ePMGIESOgVqsxaNCgJPvlsx0RUcpx9+5dzJ07F/369cOQIUMQFBSEXr16IV26dN81dPHTp0+xe/durF69WilLPK/Dp0+flHkdOnTooMzrULduXcTFxWHEiBFo3LjxrzzEf8TEN32XmTNnYtiwYejWrRtevHgBGxsbdOnSBcOHD1fW+XLcz7x582LXrl3o27cvpk+fjly5cmHRokXw8vLS2reIoHPnzggICEDGjBkBfJ4hfNmyZejevTtiYmIwa9Ys5MyZM3kOlr7qe1qDFC5cGFevXtUqGzp0KCIjIzF9+nTY2tom2aZv377o27cvcuXKhaCgIMTFxSnL4uPj2RpER/5Ld6hp06ZpfR43bhy2bduGHTt2wMXFJVV1hyIiIiLSdwcOHMDDhw+1GjNpPHz4UKvHb3R0NIYOHYq7d+/it99+Q82aNbFixQpkzpxZazs+2xERpSxqtRolS5bEuHHjAHyen+fatWuYN2/edyW+AwMDkTlzZtSvX1+rPLXM68DEN32XTJkyYdq0aUkSW4l9bdxPDw8P5U3/t6hUKpw4cSJJee3atZWWoKRb39saxMTEBI6OjlrLNTfDX5YDn4e+CQsLQ2BgIADA1dUVISEh2L17Nx49egRDQ0MUKlTo1xwU/aPt27fDy8sLTZo0wdGjR5EzZ05069YNnTp1+u59qNVqREZGwtLSEoB2d6g8efIgKCgI7du3V7pDHT58+FcdDhERERF9wdPT85vzNBw5ckTrs7u7O27cuPGv++SzHRFRypIjRw78/vvvWmUODg7YtGnTv24rIliyZAnatGmDdOnSfXO9lDyvA8f4JqJ/9W+tQZ49e/bD+/z06RN69OiB+fPnK61JcuXKhZkzZ8LHxwdjx45FYGDgVydCpV9P0x3K3t4ee/fuRdeuXdGrVy/lJcX3mDx5MqKiopSJ8hJ3hypVqpTSHWrAgAFKdygXFxc4Ojpi48aNv+rQiIiIiIiIiNKE8uXLIzQ0VKssLCxMGa/7nxw9ehS3b99Ghw4d/nG9lDyvA1t8E8f9pH/1I61BvvS1ngDA5y6PX558AaBjx47o2LHjj4ZIP9n/2h1q9erVGDlyJLZt26bVpSm1dIciIiJKiyZMmAB/f3/07t37mz09PTw8cPTo0STlNWvWVObymTx5MiZOnAgAGDx4MPr376+sd/bsWXTr1g1nz55N0pOQiIiIfq6+ffuiXLlyGDduHJo2bYpz585hwYIFWLBggbLOl3P2aSxevBilS5f+ag9+jZQ+rwPvNIiIKIn/pTvU2rVr0bFjR2zYsAFVq1b95nopuTsUERFRWhMUFIT58+fD2dn5H9fbvHkzYmNjlc+vX79G0aJF0aRJEwDAlStXMHz4cOzcuRMigtq1a8PT0xNOTk6Ij4+Hr68vFixYwKT3L8SGTUREpOHq6ootW7bA398fo0aNQt68eTFt2jS0atVKWefLOfsA4N27d9i0aROmT5/+zX2nhnkdeLdBlIbxppi+5b92h1qzZg3at2+PtWvX/uNEqIB2d6iLFy+mqO5QREREaUlUVBRatWqFhQsXYsyYMf+4rmbuDo21a9ciQ4YMSuI7JCQEzs7OqFy5MgDA2dkZISEhcHJywqRJk+Dm5qbTll9ERERpzb/Ns/C1nvrm5ub4+PHjP+43NczrwMQ3EREl8V+6Q61evRpt27bF9OnTUbp0aYSHhwP4/MbX3Nxca/8pvTsUERFRWtK9e3fUqlULVatW/dfE95cWL16M5s2bKy29nJycEBYWhocPH0JEEBYWBkdHR9y5cwdLly7F+fPnf8UhEBERESXByS2JiCgJTXeoNWvWwNHREaNHj/7X7lALFixAfHw8unfvjhw5cih/evfurbXvf+oONWrUKHTo0EHn3aGIiIjSirVr1+LChQsYP/7He8+dO3cO165d05qfxcHBAePGjUO1atXg6emJ8ePHw8HBAV26dMHEiROxd+9eODo6wsXFBceOHfuZh0JERESkhS2+iYjoq360O9S/TXSqkRq6QxEREaUFjx49Qu/evbF//36YmJj88PaLFy+Gk5MTSpUqpVXu6+sLX19f5XNgYCAyZcqEsmXLolChQggKCsLjx4/RvHlz3Lt3D+nTp/+fj4WIiCit4fC1/46JbyIiIiIiojTo/PnzePHiBYoXL66UJSQk4NixY5g1axZiYmJgaGj41W0/fPiAtWvXYtSoUf/4d7x69QojR47EsWPHcPbsWRQsWBD29vawt7dHXFwcwsLC4OTk9FOPi4iIiAhg4puIKE3SlzfDnNSUiIjov6tSpQquXr2qVebj44PChQtj8ODB30x6A8CGDRsQExOD1q1b/+Pf0bdvX/Tt2xe5cuVCUFAQ4uLilGXx8fGczJqIiIh+GSa+iYiIiIiI0qBMmTLB0dFRqyxjxoywsrJSyr29vZEzZ84kY4AvXrwY9evXh5WV1Tf3v3//foSFhSEwMBDA5zlEQkJCsHv3bjx69AiGhoYoVKjQTz4qIiIios+Y+CYiIiIiIqKvevjwIQwMDLTKQkNDceLECezbt++b23369Ak9evTAunXrlO1z5cqFmTNnwsfHB+nTp0dgYCBMTU1/afxERESUdjHxTURERERERACSTlb9tcmrCxUqBBH5x/2YmpoiNDQ0SXnHjh3RsWPH/yVEIiIiou9i8O+rEBERERERERERERGlHmzxTURERESUxh07dgyTJk3C+fPn8ezZM2zZsgX169f/5vpHjhxBpUqVkpQ/e/YM2bNnBwCsWrUKfn5+iIqKgo+PDwICApT17t+/D09PTwQHB8PMzOynHw/pz0TWACezJiIiov+GLb6JiIiIiNK4Dx8+oGjRopg9e/YPbRcaGopnz54pf6ytrQEAr169QseOHTF58mTs27cPK1euxM6dO5XtunXrhgkTJjDpTURERES/DFt8ExERERGlcTVq1ECNGjV+eDtra2tkzpw5Sfndu3dhbm6OZs2aAQAqVaqEmzdvonbt2lizZg2MjY3RsGHD/zVsIiIiIqJvYotvIiIiSmL27Nmws7ODiYkJSpcujXPnzv3j+m/fvkX37t2RI0cOpE+fHgULFsTff/+tLF+1ahVsbW1hYWGBfv36aW17//59FCxYEO/fv/8lx0JEv06xYsWQI0cOVKtWDSdPnlTK7e3t8fHjR1y8eBFv3rxBUFAQnJ2dERERgWHDhmHWrFk6jJqIiIiI0gImvomIiEjLunXr0K9fP/z555+4cOECihYtCi8vL7x48eKr68fGxqJatWq4f/8+Nm7ciNDQUCxcuBA5c+YEwCEPiPRRjhw5MG/ePGzatAmbNm2Cra0tPDw8cOHCBQCAhYUFAgMD4e3tjVKlSsHb2xteXl4YMGAAevTogXv37sHFxQWOjo7YuHGjjo+GiIiIiPQRhzrREzcPrMPrezcAERgYGSN/+VrIVqj4N9e/e3o3nl0/B1EnwGCpIezqlkX+xhWU5RcnbsCrS3cAAFlc8sNlYBNl2ZPDlxGybD/cF/aBUTp+hYiI9E1AQAA6deoEHx8fAMC8efOwa9cuLFmyBH5+fknWX7JkCd68eYNTp07B2NgYAGBnZ6cs55AHRPqnUKFCKFSokPK5XLlyuHPnDqZOnYoVK1YAABo0aIAGDRoo6xw9ehRXrlzBzJkzUaBAAaxZswbZs2dHqVKl4ObmpowPTkRERET0M7DFtx64c+pvvL57HdYFi8HBqyXSZzTHraNb8eHN86+u/+xGEJ5ePY3MOfPBwasVzPLlwN1NJ/AiKBQAEH4mBK8u3oF9y0oo0MIDry7cwfOzIQCA+Nh4hCzdj8LtvZj0JiLSQ7GxsTh//jyqVq2qlBkYGKBq1ao4ffr0V7fZvn07ypYti+7duyNbtmxwdHTEuHHjkJCQAIBDHhClFaVKlcLt27e/uiwmJgbdunXD/Pnzcfv2bcTHx8Pd3R2FChVCwYIFcfbs2WSOloiIiIj0HRPfeuB5yHlksLBGQfcGsMpTGC5NugEqFe6d2fvV9R9dPApjk4woUqMNrPIUguuI1jBMb4xb644BAN6GPoZhemPY1S6NvHXKwCC9Md6GPQEAXJ68CRlsLJHT3SnZjo+IiJLPq1evkJCQgGzZsmmVZ8uWDeHh4V/d5u7du9i4cSMSEhLw999/Y9iwYZgyZQrGjBkDgEMeEKUVly5dQo4cOb66bMyYMahevTqKFy+OhIQExMfHK8vi4uKUF2VERERERD8Lm+ymcvGxMVDHx8Eyz/91NTUwMILJb5nx4fXXExSxHyO11geATHmz4/2dpwAAS6c8eLQnGBFhjwEB1DFxsCiSG6+v30fEjQeoMKPrrzsgIiJKddRqNaytrbFgwQIYGhqiRIkSePLkCSZNmoQ///wTAIc8IErpoqKitFpr37t3D5cuXYKlpSVy584Nf39/PHnyBMuXLwcATJs2DXnz5kWRIkUQHR2NRYsW4dChQ9i3b1+Sfd+4cQPr1q3DxYsXAQCFCxeGgYEBFi9ejOzZsyMkJASurq7Jc6BERERElGYw8Z3KfXr3GgCQPlNmrXIjkwyIjfj6JGQQQfoM2hOIpc+cEer4zy1trIvbI1u533F+1GoAQLZyv8O6uD0Od5yGPLVK4cHf5/Bo7wWoVCrYt/RA7uolf+5BERGRzmTJkgWGhoZ4/lx7uKznz58je/bsX90mR44cMDY2hqGhoVLm4OCA8PBwxMbGIl26dFrra4Y8WLFihdaQBwCUIQ/q1Knzk4+MiP5JcHAwKlWqpHzu168fAKBt27ZYtmwZnj17hocPHyrLY2Nj0b9/fzx58gQZMmSAs7MzDhw4oLUPABARdO7cGQEBAciYMSMAwNTUFMuWLUP37t0RExODWbNmKZPhEhERERH9LEx801c596wL9KyrfL6xYDcMjA2RvXwRnBm8GC7+zfDh0UuELj8IGzdHGGUw0WG0RET0s6RLlw4lSpTAwYMHUb9+fQCfW3QfPHgQPXr0+Oo25cuXx+rVq6FWq2Fg8HkUtbCwMOTIkSNJ0hvQHvLg4sWLHPKAKAXw8PCAiHxz+bJly7Q+Dxo0CIMGDfrX/apUKpw4cSJJee3atVG7du0fjpOIiIiI6HtxjO9UztTcCgAQE/lWqzw++iMMjdN/fSOVCjEf32sVxbz9AAMjw6+u/uHpazw9egXF+jdC+KkbMEhvjCzOeZGnVikAgtdX7/+PR0FERClJv379sHDhQgQGBuLmzZvo2rUrPnz4AB8fHwCAt7c3/P39lfW7du2KN2/eoHfv3ggLC8OuXbswbtw4dO/ePcm+NUMejBo1CoD2kAe7du3ikAdERERERET0U7DFdypnlC49DIyM8eZhGOxKVQMAqNXxiI56i8w58391m3QZMiEy/JFWWeT95zCxzvzV9S9OWI+sJe1hXsAGL86FAupErYEEkAT1TzkWIiJKGZo1a4aXL19i+PDhCA8PR7FixbBnzx5lwsuHDx8qLbsBwNbWFnv37kXfvn3h7OyMnDlzonfv3hg8eLDWfjnkAVHy81zr/+8rpRL7mo/XdQhERERElIow8a0HshUugWfXzuDWsa2wzOOA+2f3AiLIW9oLABC0ZirSmWZE0fqdAQC2Lu64c2IHru9ZhRy/l8TTUceQEB0L+2ZuSfZ9b9tpxLz/iHI96wMArEsVwv0dZ3Fn0wl8DI8AAFg62yXLcRIRUfLp0aPHN4c2OXLkSJKysmXL4syZM/+4Tw55QERERERERMlFb4Y6mT17Nuzs7GBiYoLSpUvj3Llzug4p2eQvVxNWeYvgeehF3Ny7CjFR71DArR4yWn1umRcf8xFx0R+U9XP87gobp7J4+/gWbuxZhXe3niJfowqwdi2ktd/YqI+4s+E4inSuAQOjz18V8wI2yF7REXc3nUT4qRvI27A80v2WIfkOloiIiIiIiIiIiOhf6EWL73Xr1qFfv36YN28eSpcujWnTpsHLywuhoaGwtrbWdXjJwqFas28uK9vujyRl+crWQL6yNQAAppU+fnW7dL9lQNWVSSctcupWG07d2DKPiCg10pdhDzjkAREREREREf0TvWjxHRAQgE6dOsHHxwe///475s2bhwwZMmDJkiW6Do2IiIiIiIiIiIiIklmqb/EdGxuL8+fPw9///1qwGRgYoGrVqjh9+vRXt4mJiUFMTIzy+d27dwCA9+/ff/ffGx8b/R8jTnniP8b8+0qpxI/U4X/Fuk+ZWPc/Rl/qPjnqHWDdp0Ss+x+jL/UO8Hz/o1j3P4Z1nzKx7n8M6/7HsO5THt7n/Rh9qXeAdf+j0nLda9YXkX9cTyX/tkYK9/TpU+TMmROnTp1C2bJllfJBgwbh6NGjOHv2bJJtRowYgZEjRyZnmERERERERERERET0kzx69Ai5cuX65vJU3+L7v/D390e/fv2Uz2q1Gm/evIGVlRVUKpUOI0t+79+/h62tLR49egQzMzNdh0PJiHWfdrHu0y7WfdrEek+7WPdpF+s+7WLdp12s+7SJ9Z52pfW6FxFERkbCxsbmH9dL9YnvLFmywNDQEM+fP9cqf/78ObJnz/7VbdKnT4/06dNrlWXOnPlXhZgqmJmZpckfCrHu0zLWfdrFuk+bWO9pF+s+7WLdp12s+7SLdZ82sd7TrrRc9+bm5v+6Tqqf3DJdunQoUaIEDh48qJSp1WocPHhQa+gTIiIiIiIiIiIiIkobUn2LbwDo168f2rZti5IlS6JUqVKYNm0aPnz4AB8fH12HRkRERERERERERETJTC8S382aNcPLly8xfPhwhIeHo1ixYtizZw+yZcum69BSvPTp0+PPP/9MMvQL6T/WfdrFuk+7WPdpE+s97WLdp12s+7SLdZ92se7TJtZ72sW6/z4qERFdB0FERERERERERERE9LOk+jG+iYiIiIiIiIiIiIgSY+KbiIiIiIiIiIiIiPQKE99EREREREREREREpFeY+CYiIiIiIiIiIiIivcLENxEREf0y8fHxug6BdIhzqBMRERERka4w8U06pXkgjoyM1HEklNzGjx+PBQsW6DoMIvqFWrVqhVWrViEmJkbXoZCOqFQqqNVqXYdBREQ/yfz583H9+nVdh0HJjC+y9QPrkdIiJr5Jp1QqFTZu3IgmTZrg9evXug6Hkkl8fDxev34NX19frFixQtfhENEv8vHjR/Tq1Qvbt29n8juN6dy5M2rUqAEAMDAwYPJbD2gelhMSEnQcCRHpyr59+zB69GjMnj0boaGhug6HkpFKpQLAxGlqplarlXp88+aNjqMhXdD8fkUkTfXKZeKbdELzg7tz5w4mTJiAxo0bw8LCQsdRUXIxMjLCyJEj8eeff6Jt27ZYvny5rkOiZDBhwgT88ccfug6DkoEmybllyxY0adIE7du3x7Zt25j8TiNiY2NRpkwZ3LhxA61btwbA5HdqJyJQqVQ4fvw4VqxYgQcPHug6JNJDXybUmGBLeTw9PTF8+HCcO3cO06ZNY/I7jRk/fjx69Oih6zDoP1Cr1TAw+Jz+GzNmDLp3745bt27xPJuGaO7l9u/fj86dO8PLywuzZs3CkydPdB3aL8fEN+mESqXCxYsXsWDBAtjb26NVq1Z8IE5jMmbMiAEDBmDo0KFo164dAgMDdR0S/UIigtjYWJw8eRLPnz/XdTj0ixkYGCitQhctWoRmzZqhffv22Lp1K5PfaUC6dOnQokULTJw4EUePHkXLli0BMPmdmqlUKmzatAm1atXCgwcP8PHjR12HRHomISFBaYkYHx+v1TKRUgbN+btz587o1KkTzp49y+R3GpMlSxYcPnyYdZ6KaH63mqT34MGDMWfOHHh6eiJjxow8z6YhKpUKW7duRePGjZGQkIAKFSrgjz/+wMiRI3HhwgVdh/dLGek6AEqb1Go1AgICsG3bNuTKlQsmJibKOKCakzLpL009Z8yYEQMHDgQA+Pj4QKVSwdvbW8fR0a+gUqng7u6O2bNn4+LFi6hevTp/73pK05rA0NAQ8fHxMDIywqJFiyAi6NChAwCgfv36SJ8+vY4jpV/J1NQUdevWhYhg4MCBaNmyJVavXq0kv/nbT13Onz+Pbt26Ydq0aWjbti0MDQ11HRLpERFRvlM9e/bEo0eP8OHDB0ydOhWOjo46jo40NC+1DQ0N0aVLF4gIFixYgGnTpqFPnz4oVKiQrkOkX8zV1RUiggsXLqBQoUK8nqdwERERWr3qd+/ejRUrVmDbtm1wdXUFALx79w5Pnz5F1qxZkSVLFl2FSsngypUrGDBgACZNmoTOnTsDAGbMmIH169fj7du3GDp0KJydnXUc5a/BsxTphIGBAebNmwdvb2+8efMGY8eOxadPn2BgYMDuNnos8ZhSGpkyZWLLbz2TuH4TjwXr7u6O+vXrY8SIEYiMjOSNsh5K3EIvLi5Oq3X34sWL0bRpU3To0IEtv/XUl+d4U1NT1K5dG5MmTcKJEyfQokULAGz5ndKdPXsWHz580Cq7cuUK7Ozs0LhxYyVByTqknyFxS28fHx/s27cPhQoVQnR0NCpWrIjt27fzu6Zjif/9E7/08vX1RceOHdnyWw8lvpdP3MOnWLFiqFGjBoYNG4Z3797xXj4F69atG/z9/QH8X32+fv0aefPmhaurKy5duoRRo0ahZMmSqFq1KgYOHJgmhrxIy6KiotCqVSt06tQJjx49Qt68edGuXTts3rwZmzZtwtSpU3HmzBldh/lLsMU3JQtNC0BN6z/g81AXf/31Fz58+ICdO3fCzMwMvr6+SJcuHd8e6yHNd+DgwYNYtmwZPn78iPz582PixIkwMzODn58fgM8PPYaGhsq4sJS6JP7tTpkyRXlwdXNzAwA0b94cp0+fRkhICFxdXflb1yOJ61KT6AwNDUWHDh3QoEEDFChQAEuWLIGIoFOnTjAwMECdOnVgYmKi48jpZ0hc/0+fPoWpqSmMjIxgZmaGmjVrAgAGDhyIFi1aYM2aNWz5nUKtXLkSAQEBOHDgADJmzKiU3759G58+fYKZmRkAKK0+AeDChQvIkiULcufOrZOYKXXTfI/u3r0LExMT7N27F3Z2dgCADh06oHXr1li5ciVq167N84UOJD5Pb9iwASEhITA3N0fZsmXh6uqKbt26Afg8rNn06dPRu3dvtvxO5RLX+ezZs3H9+nXUrVsX1atXBwB07doVx48fx4EDB9CoUSNey1Oo+vXro1KlSgA+v7zImDEjbG1tcfr0aTRq1AgnT56El5cXhgwZAhGBn58fevXqhZw5c+o4cvpVChUqBCsrK6jVavTr1w8eHh4YO3YsMmTIgJIlS2L58uUwMTGBi4uL3vXM5RmKfjlNwnPfvn3o1asXqlSpgsWLF+P69evImDEjZs6cicKFC2P16tWYP38+YmJiePHUQ5oxpRo1agQTExOULFkSCxcuRMuWLfH8+XNkyJABfn5+GD58OLy9vbF27Vpdh0w/SESU3+6+fftw9uxZHD16FI0aNUL37t1x4MABVKpUCVmzZsXkyZMBgL91PaKpyz/++AMBAQEoXbo0+vbti1GjRmHSpEkIDg4GACxduhRNmzZFs2bNcPLkSV2GTD9J4ofecePGoUGDBnB3d0ft2rVx69YtmJmZKS2/T506pTXhJaUsrVu3xqZNm2BpaYnHjx8jMjISAFClShXcvHkTK1euBPB/ycqYmBisWLECp06dYo89+i7v37/HqFGj8P79e6Vs8uTJKFCgAA4dOoS4uDilfPHixWjSpAm8vb2xc+dOtvxOZonv6wYPHoyePXvi/PnzWLJkCfz8/LB06VIAn1uWdurUCUFBQRgxYgQePnyoy7Dpf5C4zrdu3Ypjx44hIiICDRo0QNu2bREYGAh7e3vkypULK1asAMBreUqjuRZ7enrC2NgYy5cvR6VKlfD06VO4u7tj69atsLa2xtSpUzFx4kT4+PigUaNGsLW1TdLbi1IvzfcgIiIC8fHxSEhIgJWVFQoVKoSYmBg8efIEpUqVQoYMGRAfH4/ixYtj4cKFGDBggN4lvQEAQpQMtmzZIhkzZpRevXpJ586dpVy5ctKwYUMJCgoSEZHIyEhp3769FCpUSObOnavjaOlXuHr1qtjb28vs2bNFRCQ8PFxy5MghhoaGUrlyZXn+/LmIiERFRcnYsWPlxo0bugyXflBCQoLy/8OGDZOcOXPKs2fPJDw8XPbs2SPu7u5StGhRqVatmgwdOlRsbW3l9OnTOoyYfoUtW7ZIgQIF5OzZsyIiEhwcLCqVSiwtLaVp06Zy4cIFZd2xY8dKXFycrkKlX+CPP/4Qa2trWbNmjezfv19KlSolOXLkkPPnz4vI52v92rVrxdjYWP7880/dBktatmzZIgcPHlQ+X7x4UQoUKCBz5syR9+/fS3R0tPj6+oqdnZ0sW7ZMRD5fx4cNGyZZs2aVW7du6Sp0SmV27twp9erV0yr7+PGjtG7dWgwNDWXfvn0iIqJWq5XlHTp0EJVKpXUNoeQza9YsyZMnj3Jtnz9/vhgbG0uJEiW0ntsmTpwoPj4+WveElHokrrdx48ZJnjx55M6dOxIXFycnT56U1q1bS+HChcXDw0MGDx4sxsbG8vfff+swYvqaL39/y5cvl7Jly0r16tXl6dOnIiISHR0tIiJxcXESGRkp1atXl/Lly/O3q2e2bt0qxYoVE3d3d/H19ZWoqCgREbl//74ULFhQevfuLUeOHJGhQ4eKnZ2dRERE6DbgX4iJb/plNDesmoenRYsWiYjI+/fvJXPmzJI/f36pVauW8kD8/v176datm9y7d09XIdMvtH//fvnjjz9EROTRo0eSN29e6dq1qwQHB4u5ubk0b95cnjx5ouMo6X/16NEj6dmzp+zatUur/N27d3Lt2jVp0aKFODg4iEqlkjFjxoiI9sMtpS6J606tVsv+/ftl5syZIvI5uZE5c2ZZvXq1HD58WFQqlXTo0EGOHz+utQ8mv/XD4cOHpWTJknLs2DEREdm+fbuYm5vL77//Lubm5sq1/t27d3LgwAGJj4/XZbiUyK1bt8Te3l6aNGkiZ86cUcobNWokv//+uyxevFji4uLk7t27MmDAADE2NpYCBQqIo6Oj5MqVi8lI+m5fXu9Xr14tz549E5HPye+aNWuKjY2NBAcHJ9lW8xxByevjx4/Sr18/mTx5soh8fkmWOXNmGT58uNSsWVPs7e1l8eLFyvqaOmYCLfW6deuWdO/eXTZv3iwi/1eXHz9+lPDwcOnSpYtUrlxZVCqV9OnTR0R4L59SJP7dXblyRfn/DRs2SMWKFaVatWoSHh4uIiIfPnyQgIAAqVixopQsWVJiY2OT7INSr8uXL4uVlZWMGjVKevToIWXKlJFSpUrJ+/fvRURk1apVYmFhIQUKFBBbW1vlPl1fMfFNP9XkyZOlQYMGWmUnT54UX19fSUhIkHv37km+fPnE19dXVq5cKVZWVlK3bl05efKkiPCiqc9iYmLk4sWLolarpWHDhtK6dWuJjY2VDx8+SKlSpUSlUkmdOnV4sU3FNmzYICqVSmxtbbWSm1/W6Y0bN2TEiBGSJUsWCQ0NTe4w6SdJXK+vX78WEZG3b9/K06dP5fXr11KuXDmZMGGCiIh8+vRJ8ufPr/XCg1K3L6/XZ86ckVGjRomIyJ49e8Ta2lpmz56tXPe/1suDye+UY9u2bVK2bFlp3ry58vJCRKR169ZKYismJkZERIKCgmT27NmyYcMGefDgga5CplRI86JTrVZLUFCQkjjT9PqLjo6WGjVqiI2NjdIr9MtzDe8Tf63E/76a3/z9+/fl8ePHEhoaKgUKFJCAgAAREfn7778lU6ZMki9fPlm3bp2yHZ/nUq9t27aJSqWSbNmyyd69e5XyL+v0xYsXMnPmTEmfPr1WgpV058vet87OzkodqtVqWbdunVSsWFE8PT2Vc+727dtlyJAhyrmZjVFSt8S/06CgIBk6dKiIfK7XQ4cOiaurq7i4uMi7d+9E5PPLkWvXrikvoPUZE9/0U23atElMTU2lffv2SllsbKzcv39fEhISpHHjxuLt7a2cVMuXLy/ZsmWT5s2by8ePH3mjpCc09fjy5Uv59OmTfPjwQVn27t07KVWqlKxYsUJZt2vXrnLw4EG5ffu2TuKlnyM6Olrat28vKpVKli9fnmR54huyu3fviouLi+zYsSM5Q6SfJHFdjh49Wlq0aKH14PPgwQMpUqSIbN26VUQ+nwu6d+8uu3btYrJTDySu/+vXryv//+LFC1Gr1VKrVi0ZNGiQiHxOnFSvXl0sLCykatWqyR4r/bPE913bt2+XUqVKfTP5vWjRIuVhiehHJf6u3blzR0RENm7cKIaGhkmS37Vq1ZKcOXMqDWMoeSQ+ty9ZskQCAwOV1qEin4dMKFGihLx580ZEPp8z6tSpI1OmTOELCT3SvXt3UalUMm7cOK1nOBHt70hUVJRUqFBBFi5cmNwh0j8YMmSIZM+eXXbs2KH1+1Wr1bJ582YpX7681KhRQxn2RIP356mb5hp79OhRmT17tvj4+Gjl5OLj45XemSVLlkxz93OciYB+qvr162Pjxo3YuHEj2rZtCwAwNjZGnjx5EBkZidDQUFSoUAFGRkb48OED8ubNi379+mHKlCkwNTWFSqXS8RHQz6CZyLJmzZooUaIEhg4digsXLgAAjIyM8OzZM+zZsweXL1/GoEGD8Pfff8PR0RH58+fXceT0vb42wVT69OkxZ84cNGnSBH369MGJEye0liee/CZv3ryIiYnBjRs3fnms9PNp6tLf3x+zZs1C9erVYWVlpSyPjo7GmzdvcOTIEaxfvx5t27bF1atXUaNGDRgaGiIhIUFXodP/KPFEliNGjICvr68yGXHWrFnx8uVL3LhxA8WKFQPwefLD3377Dbt27cK+fft0FTZ9g0qlQnx8PACgTp06GDJkCO7evYs5c+bg+PHjAIAVK1agdOnSmDp1KlasWMHJr+i7SaIJTzX3+H379sXYsWMBAI0aNcL69esxffp0jB8/Hi9evED69OmxadMm5MyZkxOdJzPNuX3gwIHw8/NDfHy81v2egYEBIiMjcfz4cXz48AELFy6Es7Mz+vbtCwMDA17bU5lvTRY7a9YstG3bFmPGjMGuXbsQGxurLEt8L58xY0ZERUXh0aNHvzxW+j43btzAxo0bERgYiNq1ayNDhgy4d+8eli1bhmvXrqFBgwYYOHAg7t27h4kTJ2ptq5m0mlInlUqF7du3w9PTE7Nnz8aRI0dw8OBBhIeHA/hcv25ubpgyZQrevn2LunXrpq1JyXWdeSf98OU4rzt27BAzMzPx9vZWysPDw8XDw0M6duwox48fl2HDhkmRIkXkxYsXugiZfqErV66Iubm5TJgwQfr27StVq1YVDw8POXr0qIiI7Nu3T8zNzcXOzk5sbW05Pmgqk7i1x6lTp+TIkSNa43HGxcVJgwYNJEuWLHLixImv7mPXrl1iYWHBSUxTsTNnzoidnZ0cPnxYq1xzPVi9erXkzJlTHBwcxM3NTRk7kD17Uqcv623o0KFiZWUl+/btk4cPH2otq1OnjuTOnVvmzp0rFStWlDJlyigtidgqMOXQ1OmXrbw2b9781Zbf9erVE1dXV3n79m2yxkmpU3R0tFSuXFm599No2LChMlSG5ru3ceNGUalU0q9fP6XlN7vc68aKFSu+Oc769evXxcvLS3LlyiW5c+cWZ2dnXttTqcTX4vXr18vo0aNlxowZWsObtGrVSjJlyiQbNmxQhr1J7NixY5I5c2YOdZKCnD17VnLkyCE3btyQEydOSM+ePaVIkSJiYWEhrq6ucuLECVGr1ZxrRY9ozr2RkZHSpUsXWbZsmURFRcnZs2elZMmS4uDgIK9evVLWT0hIkBMnTsjdu3d1FbJOMPFNP1XiJLYm+d22bVulbPr06VK8eHHJkSOH2NnZ6f0g+mnRtWvXZPz48TJixAilbO/evVK3bl2pWLGinDp1SkQ+f1eCg4OVBxxKHRI/2AwZMkRy584thQsXFiMjIxk5cqQyQWl8fLw0bNhQsmfPLocOHUqynytXrsj9+/eTLW763/Tq1Uvp2qyxc+dOsbe3l4iICOV7ofmv5kH48ePH8ujRI+UBi4kM/RAWFiZFixaV7du3a5Vr6vnKlStSv359KVGihNSrV48TJqVAmt/qgQMHpFOnTtK2bVvx8/NTfqPbtm1Tkt+J52zgJNT0vWJjY6VixYpiY2Mjp0+fVn7/1apVU4ZGUKvVyndx06ZNolKpxMfHR5l8S7MO/RoHDhxQzs8aAwcOlIYNG0pCQsJXJ6q8e/eu7Ny5UwIDA5XEGa/tqdeAAQMkS5Ys4uXlJQUKFJBChQpJ//79leVt2rSRzJkzy/Lly5N8Vx48eMBrgg59657KwcFB7OzsxNTUVLp37y7btm2T8PBwsbW1lXnz5mmty+S3fjh9+rTY2dlJxYoVtYYIu3nzppQsWVIKFy6szMeUVjHxTT/NlStXJGPGjLJlyxalTJP8bt26tVJ27do1uXjxIi+UemDs2LEyc+ZM5fPjx4+latWqkiVLFhkwYIDWunv37pU6deqIh4eHHDx4MLlDpZ9s7NixkiNHDqU1oJ+fn6hUKundu7cyZlx8fLy4u7tLzZo1dRkq/Y8ePHggnp6eSR54du3aJenTp1fGeNY8JKvVatm9e3eSnhxMeqZOLVq0kN27d2uVBQUFiZmZmZw9ezbJ+okfosLDw5XECRMjKc+WLVskffr00r59e6lXr54UKFBA7O3tlfGXN23aJOXKlZOaNWsqL62JfkRsbKzUq1dPsmXLpjyMly9fXhYsWPDV9VetWiVt2rRJzhDTrIEDB0qTJk20XiwkJCRI3bp1pWHDhiLy+aWD5todGxsrhw4dSpI8YeIs9dq1a5dkz55d+W0+ffpUAgICxM7OTpkUT0Skbt26nKMjhUl8T338+HE5ffq00ss2JiZGAgMD5fDhw1r37uXLl5clS5aICF8o6pu3b9+Km5ubqFSqJI1Sbt68KWXKlJFs2bIlacSUljDxTT9V48aNxcrKSnbu3KmUfa3lN6V+b968kWHDhiUZqmLx4sVKt5ovl+3fv1/c3d2lRo0anMw0Fbt79640aNBANm/eLCKfu8VbWFhI586dxcDAQHr37i2PHj0Skc83Zkx46o+VK1cqM3/fv39fypUrJ23btpWQkBBlnZiYGPHw8NDq9UGp0507d2TkyJFJXnqcP39e8ubNq3Vzrfmdb9q0KUlSi+f6lOfly5dStGhRGT9+vFL28OFDcXd3l4IFCyp1tmHDBqlSpYo8fvxYV6FSKpH4d3737l1l4qzY2FipX7++ZM2aVa5evSp169aV4cOHy5MnT+TKlSty9epVefTokRw5cuSb+6OfLyEhQTm3X79+XT59+iQiIgsXLhQTExOtIS9EPjduadGiRZLhzSj10FynNf+dOXOmODk5ab2YfvnypQwfPlzKly+v3Msn3oZ0L/G5sW/fvpIlSxaxsbGRzJkzi7e3t9bLqQ8fPsjjx4+lZs2aUqxYMb6o0jOhoaHy4MEDERGJiIgQNzc3yZcvX5IczLVr16RSpUpy+/ZtXYSZIjDxTf/Zl+N6a3h7e4uZmZlW8nvnzp2iUqmka9euyRoj/VqaG+YjR47IX3/9pZSvWrVKKlasKI0aNVJag2ocOnQoyXiwlLq8efNGVq1aJVFRUXLq1CmxtbWVGTNmiMjnGzAjIyPx8fFJMp4YpW6vXr0SU1NT8fDwUOp2zpw5UqZMGalevbqsW7dO1qxZI9WqVZOiRYuyha+emTNnjixdulT5XL58eXF2dpbQ0FClLDo6WurUqSPdunXTQYT0Ix4+fCi2trZKDyzNfdy9e/ckf/78MnbsWGXdyMhIncRIqUfi54BJkyZJlSpVZNWqVfLx40cR+fxCtFatWpIxY0bJmjWrZMmSRZycnMTKykoyZ84suXPnZkvvZJQ4+bV27VqxtbWVVatWSXR0tLx48UKaNWsm+fPnl23btsn79+/lzp07UqtWLSlVqhQTZ6lUdHS08v83b94Ukc8NV+zt7eXq1ata6548eVIMDQ3lzJkzWuW8l9e9xOfaoKAgKVCggJw+fVouX74s+/btEysrK6lVq5Zy3V60aJGUKlVKKlasqDy38zec+qnVann69KlkyJBBevToobykevv2rZQtW1bs7e2TJL+/bMSS1jDxTf+TI0eOfHViQm9vbzE3N5edO3cqJ+g9e/ZotQqk1CfxOL6JL7z9+/eXHDlyyJQpU5SyZcuWiYeHhzRs2JATGKZi37rJ1Yy/OXDgQGnatKnycPvnn3+Kl5eXVKhQgTfIqVzi37gmiX3jxg2xs7OTSpUqKRPcrV27Vlq0aCEmJibi6uoqdevW5c21HtDUnVqtlmfPnknz5s2lQIECsnLlShH5/CLk999/l4IFC8qQIUNk4sSJ4uHhIUWKFOFLj1QgISFBChcunGRYstjYWHF3d5c+ffroKDJKzQYOHCi5cuWSefPmJWn48OnTJ2nXrp0YGRnJ2rVr5eXLl/L48WN5+PAhG0Qkoy/vzeLj48XLy0uKFy8u69evF5HP13pfX18xMjKS3LlzS6FChaR06dKcryGVWrdundK7p3fv3lKwYEH5+PGjXL58WfLmzSsDBgzQGoL01q1b4uzs/NUJTillWLx4sbRs2VJ8fX21ykNDQ+W3334TPz8/Efk8L8fy5cs5Hr+eWrRokVhaWsqAAQOU66gm+f37778neamVljHxTf+ZWq0WDw8PyZw5s1y6dCnJ8sqVK4u9vb3WmN+UemmSYK9evZKIiAgR+Tz51cmTJ+Xp06fi7+8vhQoVkokTJyrbLFu2TKpWrSpVq1blS49UKHHic+3atTJjxgxZt26dkuSOjo6WevXqSePGjSU2NlYZGzJxN1g+HKVOiettwoQJMm/ePOVlx82bNyVXrlzi4eGhnAtERB49eiRv377lmM56Kjg4WHx9faVw4cJK8js+Pl7at28vlStXlooVK0qHDh340iMF0vye1Wq1Vr0MGzZMypQpI8uWLdNav379+jJ48OAkL7mJ/sncuXMld+7c/5gsS0hIkNq1a4utra2cPn36q8speezatUtp0RsfHy+1atUSJycn2bBhg1IPZ86ckQ0bNsjevXuZOEvFJk2aJCqVStzd3cXCwkIrGbZ69WrJlCmTdOnSRdauXSvBwcHi6ekppUuX5u8xhQoPD5cWLVqIhYWFNGjQQCnXtOqfNGmSFC1alOPx6xnN/diX9bh8+XLJlCmT9O/fX2n5/e7dO3FwcJASJUpITExMsseaEjHxTT8kcfJT5HPrjerVq4utra1cvHhRa90ePXpI+vTpJU+ePBIVFZXcodJPplar5c2bN2JlZSUBAQGybNkyUalUsnbtWhH53D160KBBSZLf8+bNkzp16miNE0cp35ct+rNmzSoFCxYUBwcHqVu3rpIE1XwPqlSpIkWKFNFq7cmESeqU+EHnxYsXUqFCBcmaNausWLFCOZdrkt9VqlSRFy9e/OM+KHVJXHeLFy8WNzc35fP58+elU6dOUrhwYVm+fLlS/unTJ/nw4YPymYkR3QsLC9P6/Pfff4uPj49Uq1ZNFixYIHfu3JGIiAhp2bKluLq6Srdu3WTDhg3SrVs3MTMz48tq+leaa7zmnNGqVSvx9/fXKj99+rQMGzZM+vXrJwsXLlS2rVu3rqhUqjQ93mhyS3xuP3XqlNjb20vbtm2VJGji5HfiRg6JMXGWelWsWFEMDAykb9++SZZt2LBBPDw8xMLCQhwdHcXd3Z2t+1OQr9VBcHCwtGvXToyNjZXGCBpz5swRZ2dnDlOmh/bs2SOTJk1S5tDQWL58uRgbG0u/fv2UMb/fvXsn9+7d00GUKRMT3/TdNDeyu3btktq1aystuaOioqRatWqSO3duuXjxovLAO3DgQDl16pSEh4frKmT6BebPny/p0qUTAwMDmTt3rtayxMnvxMOeaIZEoJTvyxZ+9+7dk/r168uVK1fk7du3snbtWildurS4u7srF91Vq1aJr6+v+Pn5Kb9/Phylfv369ZMKFSpI48aNJX/+/JIxY0ZZtmyZkuC8efOm2NnZSbFixbRaflPqlfjhau/evTJw4EBRqVTSunVrpVyT/HZwcJDVq1cn2QdfeOnenj17RKVSKfdpu3fvFhMTE2nZsqU0btxYcufOLTVq1JAzZ87I27dvZdy4ceLs7CxFihSRihUrfrUXH9G3aMb5d3R0lD/++EMp79Kli5QvX17y5MkjJUuWlFy5cklAQICIfG6ZOG3aNJ3EmxYlPi+PHTtW+vTpI7a2tpIuXTpp06aNMmxlQkKC1KlTR1xcXGTx4sVpfkxYfaC5L/f19ZV+/fqJSqWSCRMmKPfwmuv+69ev5e7du3L9+nWljC+xdS/xfdnDhw/l2rVrStm9e/fE29tb7OzsJDAwUD58+CDh4eFStWpVqVGjBu/H9NCECRNEpVLJ1KlTlUZoGqNGjZJMmTJJ79692eDwK5j4ph+yZcsWMTExkcmTJ8u5c+eU8qioKPHy8hIrKyvp0KGDtGzZUszNzeXOnTs6jJZ+Js1F9u7du6JSqUSlUsm0adOSJLzu3bsn/v7+kiVLFmXCQ0odvuy1ERgYKCVKlJDatWsryc64uDjZunWrlCpVSipVqqTcOCd+OOKNcuq3Zs0aMTMzkwsXLkhkZKRER0dLly5dxMTERJYtW6a0/L569arUr1+fLYL0TP/+/cXR0VH69Okj7u7uYmZmJnXr1lWWX7hwQXx9fcXS0lL279+vw0jpW7p06SKZMmWSnTt3yogRI2Tq1KnKsiNHjki9evWkVq1aSssgkc+JD/bQox9x8uRJcXFxkTdv3sjkyZNFpVJJixYtJFu2bFKyZEmZN2+exMfHS0xMjDRu3Fhq166dJJnK60fymThxomTKlEn2798vly9floCAAClYsKD4+PgoL7wSEhKkbNmy4u3treNo6b/6p9/UxIkTleR34sTZl3N28Xepe4kT18OGDRNnZ2fJkSOHFC9eXCZPniwfPnyQGzduiLe3t6hUKsmdO7e0a9dO3Nzc5NOnTyLCetRHmt/w5MmTtVp+z58/XwoVKiR58uSR58+f6zDClMkIRN/p2bNnGDlyJEaPHo3+/fsr5Wq1GhkzZsSePXvQr18/3L59G/Hx8Th27Bjy5cunw4jpZxERGBgY4PXr18ibNy9u376N/fv3o2vXrvj06RN8fX2ROXNmAICdnR169+4NY2Nj1KhRQ7eB03cbPXo0Dhw4gKNHjyIhIQEJCQl4+fIlYmNjcePGDWTIkAEAYGRkhNq1a0OlUmHChAkoX748zp07B1NTU2VfRka8tKR2r169goODAxwcHJAuXToYGBhg3rx5iI2NRZ8+fWBoaIgGDRrA0dERW7ZsAfD5WmBgYKDjyOl/deTIESxbtgybN2+Gm5sboqOjsX79egwfPhwNGjTAli1b4OLiAh8fH+TNmxeVKlXSdcj0FfPmzYOBgQGaNGkCGxsbDBkyRFnm7u4OAGjZsiVOnDiBli1bAgAsLS11EiulXmZmZsiRIwdOnTqF/v37w8DAAFeuXEH79u0xcOBA/PbbbzA0NIShoSFsbGzw9OlTGBsba+2D141fT0QQHx+PAwcOoFOnTqhatSoAwNnZGWZmZhgyZAji4+PRv39/FC1aFCdPnoRardZx1PRfJL4X27FjB169egUDAwPUr18f5ubmGDhwIFQqFQYPHoy4uDjUrl0bw4cPx6tXr3Dq1CmICFQqFX+XKYBKpQIAjB8/HgsWLMCSJUvg5eUFLy8vzJw5E9WqVYOzszMGDx4MIyMjHDt2DM7Ozli6dCkAICYmBunTp9flIdD/QPNb/PjxI2JjY5Vcy8CBA6FWqzFw4ECICJo1awZbW1s8ePAAY8eORbVq1WBmZqbb4FMinabdKVW5efOm5MyZU44fP66Uad5EJn4jGRsby0H09Yimbnfu3Cl16tSRbdu2KcumTZsmKpVKJk6cqLT8njx5spw6dUoXodL/4P79+0pLbc14YO/fv5eFCxdK7ty5pXnz5lotuePj42Xt2rXSsWNHDmuih6ZNmyYWFhZKyzxNy5HTp0+LSqWSrFmzKsMosP71y7p16yR79uxavXkiIyNlxowZolKppE2bNko5hzZK+fz9/UWlUskff/whcXFxWq2/PD09pXnz5uwOTT8scavtoUOHSt68eZXeAl97Bnj8+LG4uLjI+PHjky1G+j+aYezq1q0rXbp0ERHt83bPnj3F3NxcOnfuLNeuXVPK2Vo0dUl8Lh88eLBky5ZNKlasKGZmZlK/fn05dOiQsjwgIEAsLS3FwcFBihcvzmFtUhDNJJUin4cL9fDwUOZV2bdvn2TKlEnmz58vIv/3O758+bIyDJ3m/pxSL81veceOHVK9enXJnz+/dOjQQf7++29lnb/++kssLS2lZMmS4u7uLr/99pvW+Zu08VUefZOIJPlsbGyMiIiIJOsePXoUmzdvBgAYGxsjXbp0yRIj/XoqlQqbN29G48aN4ebmhkKFCinLevfujUmTJmHIkCEYNGgQ2rVrBz8/P6V1MKUeefLkgZGREXbt2oV8+fLh4MGDyJQpE5o3bw5/f3+EhYWhXbt2SEhIAAAYGhqiSZMmWLhwIQwNDZVySl2+1aKrXbt2sLGxQePGjREfHw8TExMAgKmpKQYOHIhatWqhS5cuePXqFQwNDZMzZPpFNN+FIkWKwMTEBAcOHFCW/fbbb6hVqxZy5cqFHTt2oFmzZgA+9+4QEX4HUrAxY8agV69emDx5MrZs2aL1mxcR5MiRQ2lVRvRPPn36hI4dO0KtVsPY2BixsbEAPvcYc3R0xIQJE5CQkKD1DBAREYFLly6hRo0ayJMnD/z8/AAkfcagn+vLa7tKpYJKpYKjoyPWrVuH0NBQGBoaKvVgY2ODYsWK4cyZM9ixYweA/+vtSamH5lweEBCAVatWYefOnTh27BimT5+Obdu24a+//sKhQ4cAAH379sW+ffuwaNEinDt3DsbGxoiPj9dl+ARg3759mDFjBs6dO6eUvXv3DtWrV8f+/fvRsGFDTJw4EZ07d8anT5+waNEihIWFwdnZGd27d0fFihXh6+uLbdu26fAo6H+lUqmwY8cOtGjRAsWLF8eUKVNw7do1jBs3DsuWLQMADBo0CIsWLUL16tVRrFgxnDt3DkWKFNFt4CmZDpPulIJp3jKdOnVKaeEdExMjxYoVk8qVK8uzZ8+01h8wYIC0aNFCGQeY9EdYWJjY29vLwoULReTzdyMhIUFOnTqltA6YM2eO1KhRQ7y8vDgpViqTuHXI0aNH5fDhw9KmTRuxtrZWWoa8f/9e5syZIyVKlBBvb2+27tQTiVtyrV+/XkaPHi0zZsyQgwcPiojI1q1bpWjRolK5cmW5cuWKnDlzRmrUqCHe3t7y9OlTsbS0lBUrVugqfPoffdmST3MuePnypVSvXl3q1q0rR48eVZY/evRImjdvLvPmzZMiRYpo9f4h3dPU361bt+Ty5cta87CIiHTt2lXSpUsnffv2lRkzZsiAAQPEzMyMrYPou508eVKsra2lcuXKyvctNjZW1Gq1zJkzRxo2bCivX79W1g8PD5dGjRqJq6urVk8RtiL+tRL/+wYFBcmZM2e0zgfVqlWTPHnyyIULF+TVq1cSGxsr9evXl61bt8qwYcMkc+bMnJQ+lUlc52/evJFu3brJ4sWLRURk48aNkjlzZhk5cqTY2dlJhQoVvjo3B+/tdW/JkiWSM2dO6dq1qwQFBSnl5cqVU1ruL1q0SCl/8OCBuLu7y9q1a5Wy4OBg6dmzp9y+fTtZY6ef686dO+Ls7CwzZ84Ukc+9ALJnzy558uQRV1dXPn/9B0x8UxKam9lNmzaJtbW1dO/eXR4/fiwiIiEhIWJtbS1ubm6yatUq2bVrl/Tq1UvMzMzk6tWrugybfpErV65IgQIF5ObNm5KQkCDTpk2TChUqiJWVlfz+++/KEAjv37+Xjx8/6jha+hGJb5QHDx4shQsXlvv378vNmzelTZs2YmlpqZX8njdvnuTKlUtGjhypq5DpFxgwYIBkyZJFvLy8pECBAlKwYEEZMWKEiIgcOHBASpcuLRkyZJA8efJIqVKlJC4uTl69eiX29vayb98+HUdP/0Xi3/7cuXOlR48eUrt2bdm4caNER0dLSEiIFC9eXKpVqyYjR46U3bt3S+XKlaV+/foSHh4u2bNnl4kTJ+rwCCgxzX3bli1bJF++fFK4cGExMTGRnj17ak1e2adPH1GpVOLk5CR//fUX79voh8TGxsrevXvF0dFR3N3dtV6cR0ZGSuHChWXw4MFKWUREhGzatEnWrFmjlDHpnXwGDRokBQoUkCxZskiuXLmkXr168unTJ3n58qXUrFlTMmfOLEWKFJFChQpJgQIFRK1Wy86dO6Vw4cJMfKdSW7dulaioKDly5Ii8evVKLl++LPnz55dp06aJiMjatWslXbp0Uq5cOa3EKunemjVrJEOGDLJu3TplwkLNOXbXrl1SuHBhqVixorJ+VFSU1KxZUzw8PJK8tEg8VAqlDpqGhSKfr6cPHz6UiRMnyqtXr+TJkyeSL18+6dGjhzx58kTy5s0rrq6uMnfuXB1Hnbow8U1ftW/fPsmQIYMsXbpUGbNP4/Hjx1KpUiUpVKiQ5MuXT8qVKycXL17UTaD0y92+fVvKli0rHh4ekjdvXqlbt6788ccfEhwcLFmzZpUJEyboOkT6Hz179kxat26t1QIkNDRUvL29xdLSUmkB/O7dO9myZQtbhaRyicdq37Vrl2TPnl1OnjwpIiJPnz6VgIAAyZ07t9ZYrEFBQRIWFqbclPn7+4uDg4PyUpRSp4EDB0qWLFnE29tbateuLZkzZ5YuXbrIixcvJCwsTLp16yZ58+YVBwcHcXd3V150VqxYUWlNxvGhU4Y9e/aIubm5zJs3Tz58+CAbN24UlUolPj4+yrwNIp9bftvY2LCHHv0ncXFxsnv3bilSpEiS5PehQ4ekVKlScuzYsa9uy3NF8pk+fbpYWlrKiRMn5Pz58/L3339L7ty5pUqVKso6q1atktmzZ8usWbOU+4KuXbtKxYoVJTIyUleh0w9I/CJp5MiRolKp5P79+8o4+7NmzRJ3d3elJ8bSpUulSZMm0q5dO76ESkFevHghHh4eMmvWLK3yyMhIuXDhguzatUvGjx8vv//+u5QsWVIaNGggFSpUEGdnZ6X3NZ/NUq/E4+uvWbNGfHx85OnTp/LkyRMREenWrZu0bNlS3r9/LyIibdq0ESsrK2nYsCFfUv4AJr4pifj4eOnVq5f06NFDRD639AwKCpKePXvKyJEj5e7du5KQkCDh4eFy//59/uD0iOah5NGjR3Lt2jW5f/++iIgcP35c/P39ZezYsVqtx6pUqSIrV67USaz0cyxYsEDMzMzE2dlZrl+/rrUsNDRU2rZtK1mzZtWaTEOEN1ip0eDBg5VeGZqH3NmzZ4uTk5NWMvzly5cyfPhwKVu2rJLY1pwbgoODpWvXrpI5c2a+8EylNA+7x48fF1tbW60u8CtWrBBnZ2fp06ePiHxuNRQZGak1vNngwYPFxsZGK5lKuvX27Vvx9vaWUaNGiYjI3bt3JX/+/FK3bl3JkCGDtGjRQqvb89OnT3UVKqViiYc3+Vry++HDh9KqVSulNwjvE5LHjRs3tD6r1Wrx8fGR/v37a5XfvHlTrK2tpWvXrkn2cevWLenSpYtYWlrKlStXfmm89HMkfpF0/fp1GT9+vOzdu1dr2fDhw6V48eJy8+ZN+fDhg9StW1erlSiT3ynDixcv5Pfff9ealHLOnDnSuHFjUalUYm9vL87OznL69Gnp2bOn9O7dWyZPnqzcuye+h6fU5erVqzJixAhJSEiQly9fSr58+WTGjBla69SqVUu6d++ufO7evbssXbqUjY9+EBPf9FWNGzeWUqVKyd27d6V169ZSpUoVcXV1lTx58kjTpk15gtVDmpukzZs3S9GiRSVPnjxSvnx5adiwYZJW/58+fZLhw4eLjY2N3LlzRxfh0n/05U1ueHi4uLu7i0ql+uqYf6GhoVKnTh2pXr26iLDFVmp15coVyZkzp5QpU0ZptSvy+fdub2+fZMiDkydPiqGhoZw5c0arPDg4WMaNGyc3b95Mlrjp5xgxYoQsX75cq+zQoUOSO3durZb8Ip/HmDQ1NU2STDl79qw0bNhQbGxs5MKFC8kSN2lLXE+apOKrV69E5HMrobt378qrV6+kWLFi0qFDBxERWbRokRgYGEjjxo2Vl9lE3+vL5LXm86dPn+Tvv/+WIkWKiJubm3JvsGLFCjE3N09y/qBfY/LkyaJSqbRa2SckJEjFihWlQYMGSpmm3kaNGqW06NbUWUREhKxfv14qV67MeXpSgaFDh2p93r17t6hUKrG2tpZTp06JyP/dq1+7dk2srKzE3t5e7OzsxMnJSWldyvv5lOPFixeSK1cu6dixoxw8eFAaNWokTk5O0rVrV9m7d6+sX79eChQoIFOnTk2yLV8wpl6XLl0SlUols2fPlkOHDsno0aPF19dX6ZGnVqvl48eP0qxZM6lZs6ZMnz5d+vXrJ1ZWVkprcPp+THzTV12+fFny5MkjZmZm0qRJE9m8ebOIfO4W5+zsLBEREboNkH6JgwcPSoYMGWT27Nny5s0bmTt3rqhUKlmwYIGyzqZNm6RDhw6SPXt2Jj9SsaNHj8qjR49E5PMNV8mSJcXBweGrk6E8fPiQrUJSufj4eDl48KCUKFFCSpcurSS/z58/L3nz5pUBAwZo3UTdunVLnJ2dJTg4OMm+EnfJo5TvyZMnUqJECfH09JQNGzYo5Xv37pVMmTIpiQ7NdyIhIUFsbW1l2bJlSfY1Z84cCQsLS57A6atCQ0OVe7L169eLl5eXvH//XnlBvXjxYilfvrzye162bJmUK1dObGxslHM+0fdInFCZNGmS+Pr6Ss+ePZUXotHR0Ury293dXblPaNu2rfj4+HA4nWQQFxcnzZo1k6xZs2olvxcuXChFihSRrVu3aq0/c+ZMcXV1/WqDFk03ekq5zp07JxUrVtRqgHb9+nXp2bOnpEuXTnnBnZCQoKxz48YNmT17tsyZM4cthFOwAwcOiLm5ueTLl0+KFi0qBw8eVF5sv3nzRooVK5bkpQelXtevXxdTU1P5888/RURk2LBholKppGDBgspQU5pr6rVr18TDw0OKFSsmTk5O7HH7HzHxncZp3vaGhobKgQMH5OLFi8pJNjIyMsnEF/369ZMaNWokuWGi1E2tVkt8fLwMHjxY+vXrJyKfx322tbVVhrzR2Lt3r/zxxx8SGhqqi1Dpf6RWq+X8+fOiUqlkyJAhSpf3Fy9eKBfUb7XiZ/I7ddKc5zXJbxcXFylVqpQy7ElgYKBkypRJunTpImvXrpXg4GDx9PSU0qVLs85TOU3dh4SESPXq1aVq1aqyfv16ZXmtWrXEzs5Oue6LfD73FyxYULZv366U8XuQMiQkJMiff/4pKpVK+vfvLyqVKskLiqFDh0rx4sWVybEGDx4sixYt4mRX9EMStwZt0KCBODo6SsuWLaVNmzaSIUMGpYdYdHS0MuyJg4ODiIjs3LlTpk+frtW7iH6d+Ph4adq0qVhZWcnRo0dF5HOys3bt2lKnTh1Zu3atiHy+z/Py8pLmzZuztW8qpVarlbrbuHGjUh4WFiYdOnSQdOnSyc6dO0VEO/mdGFsIp1wvXryQu3fvJil/8+aNuLm5yfz583UQFf1sV69elSxZsijXTJHPdT9x4kQxMDBQhiPS5GdEPvfMiYiIkDdv3ugkZn3AxHcaprlwbtq0SXLlyiWFCxeWXLlyibe3tzLRmcaZM2dk0KBBYm5uzm5wqZym3hPfPGm0atVKRowYIY8fP5acOXNK586dtb4nq1evFhFRJk2h1GvWrFliYWEhQ4cOVVoGvnjxQlxcXKRYsWJ8saEnEv/eRT638tm/f78UK1ZMSpYsqSS/V69eLR4eHmJhYSGOjo7i7u6utOxm0jN109RfSEiIeHl5SdWqVWXdunUi8rllf/ny5cXa2loWLVokS5YskZo1a4qLiwsfjlOwmjVrioGBgTLmY+Lf6IkTJ8TIyEiqVKkinp6eYmZmxjF76T/7448/xNnZWcLDw0VEZOzYsaJSqcTIyEgZjzY6Olq2bNkigwcPVrbTvHih5BEXFydNmjQRS0tLOXLkiIh8Hp6qcePGkiNHDsmdO7c4OTlJ0aJFOdSFHrh7966oVCqpX7++Unbr1i3p3LmzZM6cWXbt2iUivH/TBy9evJBatWpJ6dKleV+mBy5duiQZMmQQDw8PsbGxkZ49eyrLIiIilJbfmt4barWav+OfhInvNG7//v1iYWGhzCK8cOFCMTc3l2rVqild5kJCQqRLly7i4uIily9f1mW49JM8f/5cSXjt3LlTebM4bNgwqV69uuTJk0c6duwoIv83vlTHjh1l6NChHOYglUn8YPNly485c+ZIpkyZZOjQoVotv21sbMTb2ztZ46Sf78sbpcRDWRw+fFiKFi2qlfwODw+Xe/fuyc2bN5Vt2R029dL89hN/D27cuCGenp5SpUoVZbiM58+fS4cOHcTe3l6KFy8u9erVU87zfMhKeeLj46Vx48ZSqVIlMTAwkDVr1oiIduu+ffv2SdOmTaVDhw5Jxu8n+ieJ7/Hev38vffr0UXp/TJ06VSwtLWXDhg3Stm1bMTExkT179ogIrxXJ6VtJELVaLY0aNRILCwsl+f306VO5cOGCBAQEyPr165VzOusrdfnyJUV8fLzs3btXrK2tpXHjxkp5WFiYdOnSRaysrLRahFPq8/LlSxk/frzUqlVLXF1deV+mB4KCgsTY2FhGjBgh8fHxMn/+fMmSJYtW8vvt27cydOhQUalUsnLlSh1Gq3+Y+E6j1Gq1REZGire3t/j5+YmIyKNHjyRv3rzi6ekpZcqUEQ8PD2Wok9DQUHn27JkuQ6afJDIyUnnDuGLFCjEwMFC6vj98+FBy584t2bNnVybBio2NlSFDhkiuXLnYCjgVGzdunEyZMiXJMEWzZ88WQ0NDGTZsmDI7dEREBG+sUrnED8ZTp06VZs2aScmSJWX8+PHKGM1Hjx6VokWLSqlSpb7aJZ0tDFKvxHX38uVL+fjxozJm4LVr18TLy0sqV64smzZtUtZ78uSJvH//Xqt3AKUMX0t6iIj0799fDAwMlN5Ymnr/cnxIou+ReP6e3bt3i1qtlhs3bsjLly/l+PHjki9fPtm2bZuIiCxfvlxUKpWoVKokwyLSr5P4N71161aZOXOmLFu2TKs3bsOGDcXCwkIZ9uRLvL9LXRLXeUJCglb97du3TywsLLSS37du3ZKmTZuKp6dnssZJP9fFixeldu3a0rt3b47NrieOHj0qvXr1Uj6/ffv2m8lvzbB2mqGq6H/HxHcao3l40iQ5jh8/LhcuXJCIiAhxdnaWDh06iIjI/PnzxcTEREqWLCnHjx/XWbz0a5w4cUJMTU0lffr0ysSVmovp+fPnxcLCQkqVKiVly5aV+vXrS5YsWTiRZSrXt29fUalUMnfu3CTJ786dO4ulpaX0799fXr58qZTz4Sj18/PzEysrK+nfv7/06tVLrK2tpWHDhnLixAkRETl06JCULFlS8ubNyyGM9ETiJOmYMWOkfPny4uzsLJUrV1YSVDdv3hRPT0+pVq2aMuxJYkyYphya+jx27JhMnDhRevToIdu3b1eS2wMHDhRDQ0Ml+T1mzBipVauWspzoeyxfvlxcXV3l/fv3Uq1aNXFzc9MaF37+/Pni4eGh3Cvu2LFDBgwYoAx3QslrwIABkiVLFqlQoYJYWlqKq6urjB8/XlneuHFjsba2VsZip9Qp8fX8r7/+kmbNmkn58uVl2bJlSiOGvXv3ioWFhTRp0kRZ99GjR7yO64GIiAiteXpIf2jq9d27d19Nfr9580bGjh0rN27c0FWIescAlKaoVCoEBwejVatWiIyMRLly5eDi4oK///4bmTJlwpgxYwAAOXLkgJOTExwdHZEnTx4dR00/U0JCArJnz47o6GjEx8cjNDQU0dHRMDIygoigePHiuHLlCho0aIDixYujatWqOH36NFxcXHQdOn0ntVqdpCwgIABDhgxBjx49sHz5ckRFRSnLrK2tkT9/fly9ehVWVlZKuaGhYbLES7/G5cuXsX79emzatAmTJ0/G9OnTsW3bNjx79gwzZsxAZGQkKlSogJEjR8Ld3Z31rSdUKhUA4M8//0RAQAB8fHzQsmVLmJmZwc3NDTt37kThwoUxZcoUGBkZYcKECTh8+LDWPgwMeHuYUqhUKmzevBm1atVCWFgY7ty5g9GjR6NNmzaIjY3FsGHD4O/vj1atWqF8+fIYO3YsRo0ahd9++03XoVMqISLIkCEDfvvtNxQqVAgPHjzAkSNHkD59emUdlUqFo0ePIjg4GNevX8fw4cORPn161K9fH8DX7zvo19i0aRNWrlyJHTt24Pjx47h06RI8PDywceNGTJ8+HQCwdu1aODs7IyAgQMfR0n+lVquV6/nIkSMxYcIE5MyZE9mzZ8fo0aPxxx9/IDg4GJ6enli3bh2OHj2KKlWqAABy5coFAwMD/i5TucyZM0OlUkFEeI+uZzS/bTMzMzRv3hxjx47FmjVr0LdvXwCAhYUF/P394eDgoMsw9YuOE++kA4sWLZL8+fMrQ1mIfB7bO0+ePMoESP7+/uLn56fV7ZH0y82bN+XQoUOSPn166d69uzLOL98op26JW3hcuXJFgoKCJCQkRCnz8/MTQ0NDmT17tjJzeKNGjeTEiRNJJkKk1OXLerty5YrkzJlTzp49KyL/9904deqUpEuXTnbs2KFVLsLfv74IDw+X4sWLa40PGB0dLT169BBTU1O5deuWiHyeWb5v375sGZZCfK0ebt26JQUKFJB58+aJiMj9+/flt99+k4EDB2qt9/fff8v06dPl9u3byRIr6Z9atWqJSqUSNzc3efPmjYj8X2/Aly9fSpMmTUSlUkmePHmkTp06ugw1TfnyvDB58mQpXbq0VvnDhw+lffv24uXlpbTUj4+P57ldDzx+/Fg6dOggBw8eVMo2btwo1apVkzZt2sjLly8lISFBduzYITVq1GCdE6VC7969k4ULF4pKpdKaKJp+HpWIiK6T75T8nJ2d4ezsjJUrVwIADh48iGHDhiE2NhaZM2fGmTNncObMGTg6Ouo4UvoZRAQqlQp3797Fs2fPULRoUZiamsLQ0BDbt29H06ZN0aVLF0yYMAGmpqaYMWMGTExM0LlzZ2VbSvkS15W/vz+2bt2KJ0+ewM7ODgULFsTGjRsBAMOGDcP8+fORNWtWaC4BV65cUVr9s75TH7VarbTS1fz/1atXUaFCBSxcuBBNmzZFbGwsjIyMYGBgABcXF7Ru3Rr9+/fXceT0K9y9exdFixbF+vXrUaNGDaXl2Lt371C9enV4eXlh+PDhWi2IEn+HKPlp/v3v37+PK1euoG7dugCAkydPonPnzrh+/Tru3bsHDw8PeHl5YcGCBQCA06dPo3jx4lqtc4m+h+Y7l5CQgNjYWOzevRuRkZEIDAyESqXC8uXLkTNnTsTHx8PIyAifPn3ClStX8OnTJ3h4eGjtg3695cuXo3jx4jh16hTmzp2LXbt2wcbGRrlvO3LkCCpXroxLly7B2dlZ2Y51lHqtW7cOLVq0QM6cObFmzRpUqFBBWbZmzRr07NkT+/fvh4uLi9b9O+ucKPV59+4dtm7dirJly6JgwYK6Dkfv8Iyo5758r6H5PHLkSISGhuLkyZMAgCpVqmDAgAHw8vJC/vz5ce7cOSa99YhKpcKmTZtQoUIFNG7cGMWKFcPy5csRERGBunXrYv369ViwYAEaNWqE1q1bw8/PD2XLllW2pdRBU1cBAQFYsGAB5syZg/3792PAgAG4cOEC3N3dAQCjR4/GggUL0KVLF3h7eytJ74SEBNZ3KpT4AScgIAB9+vRBTEwMnJyc0KVLF/j4+OD06dNIly4dDAwMEBUVhdjYWFhaWuo4cvoZwsLClGv7nDlz8PLlS+TLlw+lS5fGqlWrEBUVpXw/zMzMkDFjRrx9+zZJt1k+JOuWgYEBnj59CldXV/j5+SkNE0xNTZElSxaEhITA3d0dXl5emDt3LgAgKCgI69evx4MHD3QZOqVCCQkJym/+/fv3iIiIQMOGDdG2bVv07dsXcXFx8Pb2xrNnz2BkZISoqCjs2rULJUqUYNI7mSQepmLixIno1asXTE1N4ezsjNDQUCxZsgQxMTHKfZu5uTmcnZ1hYmKitR/WUerx5dAkDRo0QKtWrfDkyRPcvn0b8nl+NgBAixYtYGZmhgMHDgDQfl5jnROlPubm5vD29mbS+xfhWVHPqVQqHDx4EG3btsX169cRHR0N4HOL7+joaOzbt09Zt2HDhhg7dizmzZuH33//XVch00+iVquVm6OwsDCMHj0a/v7+OHDgAMqXL4/Jkydj8eLFePPmDerWrYtDhw5BrVYjPj4eZ86cgZOTk46PgL5X4hdcMTExCA4ORq9evVCpUiWULl0arVq1wooVK/Dw4UMMHDgQAFC/fn306tULfn5+StKb48elTpoHnEGDBiEgIAC2trZ48uQJAKB3795o3LgxKlasiKFDh2LcuHFo1KgRDA0N0aZNG12GTT/B2bNn0aJFCyxduhS9e/dGjx498PbtWwBArVq1cPfuXUydOlV5qRUfH4+4uDhkyZJFt4HTV4WFheHNmzf47bffsHHjRqxevRpOTk548OABfv/9d9SrVw8LFixQztVr1qzBxYsX+RKLfoharVa+Qx07dkSdOnVQuHBhtGnTBrt27UKdOnXQt29fqFQq1KtXD7t374aLiwu2bNkCIyMjZT9Mrv1amn/fmzdv4uPHj1i6dCny58+PMmXKYNasWRg+fDj+/PNP7N+/HyEhIfD398dvv/2GAgUK6Dhy+i8Sv0g6ePAgLly4gHTp0mHhwoVo2LAhBg4ciGPHjinrv379GoaGhryeE+kRNkD7dTjUiR4TESQkJGDXrl3o2bMnsmfPDhsbG4wZMwaOjo7YunUr2rVrhyNHjqBYsWK6Dpd+kidPniBnzpzK5+DgYBw5cgR3797FrFmzlJuqXr164dChQ2jXrh3atWuHLFmy4NOnTzAwMGCX6VSuXLlysLW1xbp165QyEUGfPn1w69YtbNu2DcbGxjqMkH62Xbt2oXPnzti4caPSW0Pj3bt3WLx4MdauXYuMGTMiZ86cWLp0KYyNjfnCI5X78OEDunXrhgMHDiAyMhKHDx9GiRIlAADx8fEYMmQIDh48CJVKhXLlyiE4OBjv3r3D5cuXtRJYlHJ06NABFy5cQP78+fHy5Uv4+/sje/bsqFevHsqWLYtBgwYhOjoamzdvxqJFi3D8+HG+qKb/pGXLlrh69SqmTZuGnDlzomzZsihbtizWrFkDc3Nz7NmzBzNnzsTNmzdRoUIFLF++HAA4JFoyOnr0KCpVqgRTU1MsX74cjRo1UpatXr0af/75J96/fw9LS0tYWVnh8OHDMDY2Zmv8VCbxb8rPzw/btm2Dn58f6tSpA0tLS8TExKBp06Y4duwY2rZti3z58mH//v24f/8+Ll68yOs5EdG/4BVRD2neZahUKhgZGaFevXp4+PAhevXqBRFB+fLl0b59ezx48ABVq1bFvn37OOuznpgyZQpGjx6N6OhopU5HjhyJQYMG4fz584iJiVHWnTFjBipXroxVq1Zh7ty5iIiIgKmpKZPeqcjZs2dx7do1AICvr68y5muDBg3w5MkTHD58WFlXpVLBzs4Ob9++RWxsrE7ipV/nzp07sLe3R5kyZZRrQEJCAoDPXef69euHffv24dChQ1i5ciWMjY0RHx/PpHcqlpCQgIwZM6Js2bL48OED7OzscPHiReU8b2RkhHHjxmH48OEoWbIkwsPDUaZMGSXprfl+kG58ed+lqbdGjRqhWLFi6Ny5M7JkyYJx48bh8uXLWLlyJc6fP486derAx8cHJ06cwJEjR5j0pn+VkJCgfL8014eLFy/i6tWr2LBhA6pUqYJTp04hISEB/fr1g7m5OQCgevXq2LFjBw4dOqQkvTXzBdCv8eV5wd3dHRMmTFDGV4+Pj1eWtWzZEocPH8bhw4exYsUKHDt2TLm2M+mdumh+U6NHj8bSpUsxb948tGjRQunNkz59emzbtg3VqlXDjBkzcO7cOVSrVk1Jeif+XhARUVJs8a1nNG+MT506hcOHDyMhIQGOjo5o2LChss6qVatw9OhRbNmyBa9fv0b58uVx8OBBpEuXToeR08+wceNGODk5oVChQnj//j3MzMwAAD4+PtizZw/Gjh2L5s2bI0OGDMo27du3R2hoKHbs2MHu0qnI48ePUaJECdSuXRtqtRrr1q3DqVOnUKxYMVy/fh3t2rVDvnz50LZtW9SsWRMRERFo2rQpsmfPjhUrVug6fPrJJk2ahIULF+LMmTNav+O4uDhs3rwZ9erV0xr3ky32Uq8v6+7ixYswNjZGQEAAbt68iVatWqFLly5JenUkbgGomayOdENTF48ePUJwcDAaNGigLHv58iXc3NzQo0cPNGnSBF27dsWbN28wcOBAeHp64tatWzAwMIC1tTUsLCx0eBSUGsTExKBVq1bw8vJC69atYWpqCuDzi/Nu3brh/PnzmDBhAiZOnIi1a9fC09MTz549w4EDB1CnTh1kzpxZ2RevG8ln1apVyJMnjzKZ4ciRIzF69GgsXLgQPj4+39yOLb1Tr6dPn6JevXro378/mjdvjmfPnuHOnTvYtm0bsmfPjv79+0NE0KJFC5w4cQKbNm1C6dKl2XOPiOh7COmdjRs3ym+//SaVKlWSMmXKiEqlkm7dukl4eLiyzqdPn+TSpUvi7e0tV69e1WG09CucPHlS2rRpI6dPn1bKGjRoII6OjrJy5Ur59OmT1vqJvxuUsq1fv15iYmJEROTUqVNiaWkpxsbGsnHjRhERUavVIiJy7tw5cXNzk8KFC0uePHmkePHi4uTkJLGxsVrrUeqSkJDw1fK///5brK2tZdGiRfLu3Tul/MOHD1KhQgWZO3ducoVIv1Di+v/w4YPyexYRiYiIkNatW0uZMmVkzpw5yrr+/v7y6tWrZI+V/tnDhw/FyspKVCqV1KxZU9atWyehoaEiIrJ9+3apWLGivHjxQm7cuCENGzaUSpUqybJly3QcNaVGVapUkaJFi8qKFSvk48ePIiISGhoqWbNmldatW0uWLFlk9+7dyvr79++XypUry5UrV3QVcpoWGRkpWbJkkQoVKsiZM2eU8mHDhomhoaEsXbpUd8HRLxMRESHlypWTYcOGya5du6R58+ZSqlQpKVeunJibm8uQIUNERCQ6Olrq1q0rOXPmlGPHjuk4aiKi1IGvhPXMvXv30K9fP0yaNAmHDh3CyZMn8ffff2PZsmUYN26csp6xsTGKFi2KZcuWwdHRUYcR069w//59nDt3DgsXLkRQUBAAYPPmzShQoAAmTJiALVu24NOnT8r62bJl01Wo9APGjRuHbdu2wcDAACICU1NTZMqUCVmyZMHevXtx+fJlpTWWq6srVqxYgQULFqBTp07o06cPLly4oHSDZaut1CdxS641a9ZgypQpGDRoEG7duoUaNWqgRYsW8Pf3x7x583D69GkEBwejYcOGiI6ORseOHXUcPf2vEtf/5MmT0bp1a5QsWRLTp0/HjRs3kDlzZsyaNQv29vYIDAxEp06dULNmTcyfP1+r1SalDGq1Gnnz5kWZMmUQHh6O/fv3w9PTEwsWLMCnT59gbm6O4OBgODg4YPTo0TA0NMSmTZvw7t07XYdOqYRm2IwDBw6gYMGCmDBhAjZt2oSoqCgULFgQnTp1wpYtW9C9e3dUr14darUaz549w8CBA2Fvb89hdJKJfNH5+rfffsOlS5fw+vVr+Pn54cyZMwCAUaNGYciQIfD19cXs2bN1ESr9JF8bYtTMzAxlypTB3r17UbduXdja2mLChAk4fvw46tatqwxXlD59emzYsAH29vbo2LEjoqOjkzt8IqJUh0OdpGJ//fUXqlWrhuLFiytl165dQ/369bFjxw44ODgoD8q7du1C3bp1sWfPHlSrVk1ZX9htUW+tWrUKU6dOhaOjI7p37w5XV1cAQJMmTXD69GlMnToVTZo00XGU9COio6NhZGQEIyMjXLhwQfntHz58GO3atUPlypXRp08fFC1a9Jv7YJfI1G/QoEFYuXIlPD09ERISghcvXsDf3x+dOnXCoEGDcPjwYZw/fx5FixaFmZkZDhw4wIks9ciQIUOwcOFCDB8+HC9evMDff/+NvHnz4o8//oCLiwvev3+PCRMmICwsDCqVCqtXr+ZkZynUrVu34OfnB7VaDW9vb6hUKkyfPh2ZM2fGtm3bUKpUKRw7dgzp0qVDaGgoMmbMiFy5cuk6bEpFEg9r1LhxY4SEhMDPzw/NmjXDw4cPMXLkSGzZsgXNmzeHWq1GUFAQcufOjZ07dwLgc0JyevfuHczNzZV/86dPn6JSpUrIli0bJk2ahNKlSwMA+vbti/Pnz+Po0aOsm1Qo8bV49erVuHXrFuLi4lCzZk2UK1cOjx49wvv371GkSBFlG3d3d1SsWBFjxoxRvh+xsbF48eIFrwlERN9Dd43N6b9Sq9WSkJAgrq6ucvPmTa1l165dE5VKJXv37hURkfj4eFGr1RIVFSWOjo4ya9YsXYRMv5BmyIp79+7JtWvXtIauWbVqlZQoUULatm0r586dU8pbt24td+7cSfZY6b9LPMTB9u3bJX/+/BIQEKB0W961a5fkzp1bOnbsKOfPnxcRkUqVKsmKFSt0Ei/9GuvXr5dcuXLJpUuXRERkz549olKpZMOGDco6z58/l3Pnzsn169eV701cXJxO4qWfa8OGDWJvby9BQUEiInL48GExNDQUR0dHqVu3rly+fFlEPtd3bGyscn1g/adcISEhUqNGDfH09JTQ0FCJioqS06dPS+3atZXzN4emoh/1rWGxGjRoIA4ODrJq1SpRq9USHh4u8+fPl8qVK0uXLl1k8uTJ/7oP+vkmT54s7u7uSe7Nnz59Kra2tuLm5ianTp1SyjXnBJ4bUq+BAwdK9uzZpVOnTlKrVi2xs7OTUaNGKcsjIyPl6tWr4uXlJc7Ozsp1nHVORPTjmPhOhd6+fav1+dixY3L27FnlgtiyZUspV66cnD17VlknISFBSpUqJXPmzEnWWOnX0tz8bNy4UQoWLCg5cuSQfPnySeXKlZVxfleuXCklSpSQ9u3by8mTJ3UZLv0kUVFR0rp1aylfvnyS5HeBAgWkXLlyUrRoUcmfP78yHjjphxkzZkiTJk1ERGT16tViZmamnNffvXsnd+/eTfJQxOSF/jhw4IAMGzZMRES2bdsmFhYWsnDhQlmxYoVkypRJ6tevr/WSU4QPyalBWFiYeHp6iqenp5w4cULX4VAqFx8fr/z/kSNHlD8ajRs3VpLfmjlfvnw5xutG8rpy5YqYmppKgwYNlOS3pg42bNggBgYGUqpUKa3GLTy3p17bt2+X3LlzK2O4r1y5UkxMTGTVqlXKOqtWrZLq1auLp6enMp9H4t82ERF9Pw51ksrMnDkTjx49Qq9evZArVy6ICH7//Xd8+vQJGzduRMmSJXH48GFMmTIFL168wB9//AFra2ts27YNixYtwrlz55AvXz5dHwb9RMeOHUP16tUxdepUODk54c2bN/Dz84OhoSFOnTqFjBkzYvXq1Rg+fDg8PT0REBCA9OnTs3tkKvHl8ATy/7s4GGSdLwAANsRJREFURkVFoUePHggJCUGzZs3g6+sLU1NTnDhxAidPnsTHjx8x7P+1d9/xNZ7/H8dfmUZip60QYkaIiqhVYsesGUITexMao/aeX7PVRNWMKLFKrUopVcQqsdLaQakoagZNyLp/f/jm/KS0X23VkeT9fDzywH1f5zqfk9s557o/93V/rtGjsba2TnWrs6RNKWVKhg0bxi+//EJAQAC1a9dm2rRp9O7dG4CFCxdy/fp1Bg0aRJYsWcwcsfxTzytN8ujRIx4+fIi1tTWNGzemSZMmDB06lOTkZNzd3Xnw4AHvv/8+U6dONVPU8ndFRUUREBCAYRiMGjUKT09Pc4ckaZDxVGkSX19fTpw4QXJyMjdv3qRNmzbMnj0beFL27uzZswwZMoQWLVpgZ2dnzrAzlN9/tqd8v584cYKqVatSs2ZNZs2aZTpfW7NmDVu3biU2NpbQ0FCVLEsHAgMD2bp1K1u2bGHt2rV06dKF6dOn06tXL3777TcuXryIm5sb4eHhVK9eHUtLS43lRUT+ASW+05hx48Yxb948+vTpQ+fOnXFyciI+Pp6KFSuSkJBAaGgoHh4e7N69myVLlhAaGkqxYsWwtLQ07ZP0ZdKkSURERLBx40bTtqtXr+Ll5UWRIkUICwsDnixu6eHhQeHChc0VqvwDc+fO5ejRoxQoUIB69epRuXLlVMnv999/n169epE5c+ZUJ1Wq65w2/VE95uPHj1OnTh3u3r3L0qVLadeuHQBxcXG0atWKwoUL8+mnn77qcOUle/r4X79+HcMwcHR0NO0/f/48tWvXZu7cubz33ntcuXKFoUOH0qhRI/z8/FTLO42Kiopi4MCB3Lp1i1mzZlG5cmVzhyRpVM+ePdm1axfbt2+nYMGC+Pn58dVXX3HkyBFcXFyAJzW/w8PD2bBhA1WqVDFzxBnD05/tq1evJioqiri4OJo1a0bFihU5deoU7777LrVr16Z37964u7vTo0cP6tevj7+/P6BxXVqzc+dO9uzZQ3JyMlWqVKFevXrMmzePo0eP0rJlS1q1asWMGTPo1asXAGvXruXYsWMMHz4ce3t74I/HhCIi8oLMNtdc/raZM2caBQoUMMaPH2+6HS4+Pt5wc3MzSpYsaRw9etTU9sKFC8bly5eNW7dumStceclSbm1MOc4BAQGGm5ubaX/K7aorV640XF1djYsXL776IOUfe/o245EjRxp58uQxmjRpYrzzzjuGm5ub8dVXXxmG8aQGYKdOnYwqVaoYEydONN0OKWnX07cvr1mzxpg6daqxY8cO4/r164ZhGMZ//vMfI3/+/MaECROMa9euGQcOHDAaNmxouLu7qwZkOjNixAjDxcXFKFq0qNG3b1/Tbc5RUVFG5cqVjZ49exobN240GjVqZDRs2NB03FWmIO06ffq00apVK+Py5cvmDkXSqNjYWKNhw4bGd999ZxiGYUyZMsVwcHAwtm7dahiGkeqcICgoyCwxZnSDBg0ynJ2djebNmxtt27Y1LCwsjNDQUMMwnnwGuLm5GU5OTkb+/PmNcuXKaWyXRi1cuNB44403DC8vL6NgwYJGgQIFjG+++cb44YcfDAsLC8PCwsJYsmSJqf1vv/1m1KtXz+jVq5fGcSIiL5ES32lIyqAnPj7e6NGjh+Hs7GyMHz/e+Pnnn03b3dzcjFKlShkRERGqA5aOhYWFGbly5TL27dtn7NixwyhWrFiqgZNhPFn0ztnZ2bh06ZKZopSX4dSpU8aAAQNMNfuPHDlidO7c2ShYsKCxadMmwzCeJL+bNm1qdO/eXQPlNO7p4zdkyBAjd+7chpubm5E3b16jZ8+exvnz542YmBhjypQpRu7cuQ0HBwejTJkyRv369VUDMp1ZtWqV4ezsbCxevNj4+OOPjdy5cxvNmjUzrfMxa9Yso0KFCoazs7NRs2ZN0/HXZ0Dap7UZ5K/4/YWumzdvGrlz5zb27dtnfPbZZ0auXLlMSe/79+8bffv2NXbv3v2nfcjLl3Jh+ssvvzTy5ctnWo9h8+bNhoWFRar6zr/88ouxfft2Y8OGDabvdC1SnLYsXLjQsLW1NS0+/t133xk5cuQwOnToYBiGYcybN8+wtrY2pk6danz//ffGgQMHjHr16mkSg4jIv0CJ7zRm5cqVxttvv234+PgYhQsXNqysrIyxY8eakpvx8fGGu7u7kT9//lQzvyXtSxn8XLlyxejatasxZ84cwzAM4+rVq0abNm2Mhg0bGosXLzYM48lJ8/Dhw41y5coZt2/fNlvM8s+sW7fOyJ8/v/H2228b0dHRpu0//vij0aVLF8PZ2dmU/I6LizOduGqgnPZFREQYjRs3Ni18tGjRIuPdd9812rZta5w/f94wjCfJjfDwcOPcuXOmY68T47Tr94mnrVu3GgsWLDD9++jRo8abb75pvPfee6YFbX/55RfjwoULOv4iYnTu3NnYvn27YRiG0atXL8PT09PInj27sXPnTlObU6dOGZ6ensa6devMFGXGs23btlTjssDAQKNz586GYTy5q8ve3t6YP3++YRiGce/evedOWNEF7bRl586dhoWFhTF+/PhU2/Ply2dUrVrViImJMW7dumWsXr3acHBwMJycnIwyZcoYDRo00CQGEZF/gYpFpSEnT56kT58+9O/fn+DgYC5evMioUaMICgpi8eLFXLlyBRsbGw4dOkTBggXJkSOHuUOWl8jCwoJDhw4REBBAZGQkFSpUACBfvnyMGTOGXLlyMWnSJAoXLmyqH7do0SJy585t5sjl78qSJQvly5fnwoULXLlyxbS9dOnSDBgwgLp169KqVSv27t1L5syZsbS0JDk5WQuXpnHLli1j6tSpZM6cmXfeeQeArl270qtXLy5evMjYsWOJjIzEwcGBatWqUbx4cdOx18JHaZNhGKb6nQsXLmTMmDEMHTqUGzdumNp4eHiwdetWDh8+TJs2bbh79y6Ojo4UKVJEx18kA/ntt99Yvnw59+/fN22Ljo7mwIEDprV8qlatyoULF2jYsCHFixcH4OzZs7Rp04YCBQrQokULs8Se0dy5c4cePXpQqlQpjP8uq/Xw4UNu376dalHDHj16ALBx40amTJnCgwcPUvWjmt5pS/78+fH09OTIkSMcPnwYAG9vb27evEmOHDlo1KgRHTp0IDY2lkWLFrFu3Tq2bNlCWFgYNjY2JCYm6piLiLxESny/xownM/JN/75//z729vZUrlyZbNmyAU8Wu+zbty+TJ09m6dKlnD9/HltbW/bv329aDVzSnuTkZNPfExMTAYiNjSVr1qz89NNPREZGcvz4cVObUqVK8dFHH7Fq1Sratm2Ln58fhw4d0mKmacjTxzxFgwYNGDZsGFWqVKFHjx4cPHjQtK906dL4+/szbtw43n33XdN2LX6T9vz+2P/888/s3buXY8eOce3aNdP2Dh060Lt3b37++WdGjBjBxYsXUz1Oxz5tMgzDdLFq4sSJ9OnTh6NHj3LhwgXWrVvHiRMnTG09PDz45ptv2Lx5M9OmTUvVj46/SMawevVq2rdvz5IlS3j48CHw5Hvk/v37poRpu3btGDJkCD/++CPVq1enfPnyeHt7U6xYMVasWAGQ6hxD/h25cuVi+fLlWFtbU65cOQzDoHHjxly6dIn27dszfvx4evfuDTxJiH/xxRfY2tqaFjWUtKl48eIEBwcTHx/PuHHjqFatGpcvX+bw4cOsX7+eCRMm0LhxY8aNG0e3bt1YunQp+fLl00VsEZF/iYWhUc9rb+PGjeTKlQuAFi1asHv3bkqXLm1KhMbHx1OkSBEePHjAyJEjGThwIFZWVpr1mcadO3eOa9euUaNGDdasWcPKlStZt24dx44dIyAgAEtLS4YNG0bDhg3NHar8Q0+v1r569WquX7/OzZs36dKlC0WKFOHQoUNMmTKFy5cvM2/ePCpWrPhMH0lJSZodksYtWLCAsmXLUrFiRYKCgvj0009p0KABH374Ic7OzqZ28+bNIzIykjlz5ijZmY6cPHmSCRMmMHDgQCpWrMjly5epXLkypUuX5tNPP8XV1dXU9vz58xQuXFjveZEMatq0aYwYMYKPPvoIf39/bt26ReXKldm/fz9vvvkmtra2ABw6dIizZ89y//59ChYsSJMmTYDU4w75dyUnJ3Pw4EE6d+5Mzpw5+f7775k8eTKzZ88mICCAZs2acf/+fSZMmMD169eJiIjA2to61UVRSZuioqLw9/cnIiKCBQsW0Lp161T7Y2JiOH78OJ6envo+FxH5Fynx/ZpKGexERkbi4eFBSEgIHTt2pG7duly7do3jx4+brgbfunWLAQMGkC9fPrp3706xYsXMHL38U0lJSQwcOJDZs2czduxYxo8fb/o/AHDw4EEGDx5Mzpw56dOnD/Xr1wd0IpPWDRkyhNDQUGrXrs3p06d5+PAhgwYNonv37uzatYugoCCuXLnCrFmz8PT0NHe48pKkfN4XLVqUypUrs3z5cgCmT5/OqlWrqFmzJv3796dgwYLPPFbv+fRh4cKFzJkzB1tbW7788ksKFCgAPClfUL58eUqXLs2cOXMoUaJEqsfpgpdIxpKYmGga/0+ePJnRo0fz0UcfUb58eYYOHcr27duxs7MztTcMg/j4eDJlymTapu+Nf9ehQ4e4ffs2DRs2NB2vxMREjh49yvvvv0/+/PnZs2cPo0ePJiwsjOPHj1OpUiWyZctmKnWhz/b048KFC/Tp0wdLS0tGjBhhGr8//V4GfZ+LiPyblPh+jR05coTo6GhOnDjByJEjAfjhhx/o2LEjcXFxLFmyBEtLSzZt2sSWLVvYvXu3bo1LZ6pXr86BAwcYNGgQU6ZMMdV8s7Cw4MCBAwwdOhQHBwc6d+5smsUjadPq1asZNGgQmzdvxt3dnbCwMJo0acKXX35pqsW5d+9eRowYQbFixVi8eLGZI5aXJSUJsW7dOsaNG8f8+fNN5WumT5/O6tWrqV27Nv7+/hQuXNj0OM0GSz/OnTvH+++/z7lz5wgJCcHHx8e0Lzo6mkqVKuHg4MCmTZtSzf4XkYzjeYmxyZMnM2HCBFq3bs369et58803yZUrF9mzZycmJoaYmBgmTZqEr6+vmaLOWHbu3EmdOnUAqFSpEq6urjRr1oxy5cpRsGBBIiIi6NGjB3Z2duzdu5eEhAT2799P4cKFcXJywtLS8pmEqKR9UVFRBAQEADBq1CiqVq1q5ohERDIWJb5fU7du3aJGjRqcPn2avn37EhQUBDxJkJw+fZrBgwcTERGBnZ0dycnJrF+/3rQImqRtKcmsx48f07RpU+7cucOJEyfYvHkzderUITExEUtLSywtLfn+++9Ni+YEBwenmuUjr7ffJy1nzZrFoUOHWLlyJStXrqRXr15MnTqV3r178+DBA2JiYnBycuLYsWO4u7trtlYa9kez7c6dO0fLli3p0qULAwYMMG2fOXMmn3zyCUOHDuWDDz54laHKv+D3xz8lmfXTTz/h7e1N7ty5GT16NDVr1jS1uXz5Mn379mXjxo1674tkQE8nvQMDA8mbNy9t2rQBYMaMGQwdOpT69evToEEDHBwcyJw5M3fu3MHGxoZOnTqZMfKM5cKFC7Rv356EhAQcHBxwcXFh6dKl5MmTh9KlS1OrVi1y5szJqFGjcHV1Zdu2banGgpqNn35FRUUxYMAAbty4QXBwMGXKlDF3SCIiGYYS36+p+Ph4Nm/ezNSpU3n8+DGRkZHPtDl69Ci2trbkyZMHR0dHM0QpL1tKMvTkyZNYW1tToEABbGxs6N27N8uXLzclv1NOgAzD4Ny5c2TOnFmzANOQ8PBwIiIisLCwwNfXF0dHRz788EPu3buHv78/NWvWZPr06aYFj+bPn8/NmzcZNmyYaRaQTo7Svi+++IKHDx/SpUsX0/GcPn06s2bN4vvvv0/1nl6xYgVt2rTRbbBp3O/r+UdFRREXF0fz5s2pUKECFy9epEWLFuTNm5fhw4enSn6n0O3QIhnL058bLVq04KeffmL48OF4eXmRJ08e4P9rfs+ZM4fOnTunKm3y+z7k3xUVFcXQoUOJj49n8uTJFCxYkKNHjzJ79mzu3r3LoUOHeOONN4iOjiYgIIBPPvnE3CHLK3L69GkWLVrEjBkz9H4UEXmFlPh+jcXFxbFjxw4++OADihQpwo4dO4AnSfGURWsk/UhJeq9fv55+/foxaNAgfHx8cHR05Pbt2wwZMoSVK1eyadMmvLy8mDJlCseOHWP58uXY2NiYO3x5QUuXLmXy5Mk0atSIkiVL0qNHDwCOHTtG/fr1uXXrFsuWLaNt27bAk8+Bli1bUqxYMdOdH5I2pVzwMAyD6tWrM3XqVLZt20b16tWpU6cOvXv35u7du3Ts2JH27dvTsWNHHj9+nCqBoaRn+jB48GDWrFmDh4cHdnZ2rFixgtDQUPz8/Lh48SItW7bE0dGRfv36mdZwEJGM7YMPPmDnzp1s2bLFtA7A098RkyZNYuzYsYwdO5ZBgwaRNWtWc4aboZ07d46AgACSk5MZP368qXxZUlISX3/9NRcvXuTAgQMsW7ZMY/gMShejREReHSW+XwMpCc8jR45w7NgxLCwsqFq1Kq6urjx69Ihvv/2WwYMHU6BAAbZt2wYo+ZFebdmyBR8fH6ZOnYqfnx+5c+c27YuLiyMgIIDg4GBq1arFvn37OHDgAB4eHmaMWP6KZcuW0bNnT5YtW0bjxo1NJ6uzZs3CwcGB69evM3fuXHr27EmXLl24dOkSY8eO5ZdffuHw4cNYW1urrnMa9bwLHklJSVy7do3Jkydz9OhRrl+/zpgxY/j4448pVKgQYWFh5g5bXqKna7l/8MEHbNiwgQoVKpjq+ackvgHOnz9PtWrV8PX15eOPPzZz5CJibvfu3aNp06Z069aNDh06cPToUQ4ePMiyZcvInTs3X3zxBVmzZmX48OH88ssvfP755+YOOcOLiooylScbMWIE1atXf267hIQEJb9FRET+RUp8m1lKEivlRNjR0RE7OztOnTrF+vXr8fT0NCW/hw0bRtasWTl06JC5w5Z/QVxcHK1atcLNzY3p06cTGxvLjRs3WL9+PTlz5qRDhw5YW1uzYsUKrly5gre3N8WLFzd32PKCTp8+TZs2bejTpw89e/Y0bffx8eHLL7+kWbNmVK9encTERGbMmEFSUhJOTk689dZbhIWFYWNjowteadQfXfCYOXMmFSpUoEaNGiQkJDB69GguX77Md999x82bN1PN/Je065tvvqFixYrkypULgKCgII4dO0ZISAhr166lc+fOfPTRR/To0YOYmBju3r1LoUKFiI6OxtHRUe95EeHRo0f4+PhgGAbvvPMOu3btInPmzJQtW5a1a9dSunRpNm7cmOoxulBuflrUUERExPy0ZPQr9vtBqIWFBbt376Znz5785z//oXv37hw+fJiKFStSr1491q5dS6NGjfDy8mLChAlMnTqVn3/+mYIFC5rxVci/wdraGhsbG5KTk4mMjGTx4sWcOnWKkydP4uDgwL59+1i4cCF+fn46mUmDrly5woMHD6hRo4Zp5mefPn04fvw4X331FZ988gn79u3Dz8+Pc+fO8eOPP+Lg4ECJEiWwtLQkMTHRVN9b0o7Tp08zY8YMZs2aRcuWLU3bW7duzdq1a6lbty4xMTE0bdrU9Pl+/vx5AgIC2LZtmxLfadyDBw8YOHAgsbGxHDt2jJw5cxIbG8udO3dYu3YtXbp0Yfr06aaSRxs3bmT//v1MnToVJycnQHd4iWQ0z3vPZ86cmbZt27J27VqWLl3KyJEj8fT0xNXVFUtLS+7cuZOqvcaJr4fixYsTFBTEgAED6N+/vxY1FBERMQPN+H6FUpJdN2/e5PLlywCUL1+ecePGYRgG48eP5+rVq1SpUsW0gOHq1avZunUrNWvW5PHjxyQkJGBvb2/mVyL/liFDhvD1119z/vx5mjZtSrNmzWjWrBljxowhOjqaL774wtwhyt80efJkZs2axa1bt0zbrl27ZprZferUKXr06EFCQgJhYWE4ODiY2qkOYNq1bds2evbsyZYtW3BxcTFd8Ni2bRuBgYHMmjWLTJky0aVLF7y9vU2P27NnD/Xq1WP//v0qZ5TGnTp1ik6dOvHgwQP279/P+fPn6dGjB2fOnGHy5MkMHDgQgIcPH/L+++9TpEgRAgMDlbQSyYCeTnpPmTKFS5cuYWVlRdOmTalXrx6Wlpbcu3ePnDlzAnD37l2qV6+Ot7c348ePN2Pk8me0qKGIiIj5KPH9iqQkrlKSW9myZSNLliysW7eOI0eOEB8fT+nSpalbty7u7u7Mnz+fffv2Ua1aNQC2bt1KvXr1zPwq5GVJmYlz6dIl4uPjefTokWkGyN69e0lISKBWrVqm/ze9evUiJiaGzz//HBsbGyVE0qDVq1fTpUsXNmzYQN26dVPtSznO06dPZ/fu3axZs0aLUqUT/+uCx+nTp+nevTuGYbB69Wry58+PYRjcv3+fWrVqERgY+Id1QeX1lvK+TkxM5OrVq7Ru3RpbW1s2b97MrFmzWLBgAX379qVZs2bcv3+fCRMmcP36dSIiIlTPXySDa968OT/++CO1a9fm8OHDJCcnU7lyZWbPno2trS3R0dEcOXKEkSNHUrhwYb766itzhywvSJMZREREXi3dN/8KGIaBpaUlJ0+exNPTE39/f3r27En+/PkBeOeddwA4dOgQSUlJDBgwAICcOXPi4+ODs7OzafV2SftSkhkbN25k9OjRPHjwgKxZs+Ll5UVgYCCenp6mtlevXmXOnDmsWrWKffv2YWtra8bI5Z+oUKEC1tbWzJ8/HxcXF5ydnU37LC0tefDgAXv27KFEiRJKeqcjxYoVIy4uju3bt5sueDg6OgJPTn5LlixJ06ZN2b17N7ly5cLCwgILCwtWrlxJZGRkqv8nkjbcvn2bPHnyYGlpSXx8PLa2tjg7O5M7d26++eYbGjRowNatW3n06BHr1q1j1KhRVKpUiWzZsnHo0CGsra1V3kQkg0gZEz6dDA0NDeXMmTPs3r0bJycnDMMgKCiI1atXM2bMGKZMmcKxY8dYv349derUITAwEFBCNa3QMRIREXm19M37ClhYWHDnzh169epFhw4dmDx5MgULFsTKyork5GRTu9u3b3PkyBESExMBWLVqFQ8fPmTcuHGULFnSXOHLS2ZhYcGWLVto164dvXr1Ytu2bfTp04fZs2fj7+9vavftt9/SvXt3wsLC2LVrF25ubmaMWv6pIkWKMG/ePDZv3syIESM4fvy4ad/ly5dp2bIlV65cYfr06cCTk2FJ+56+4JFS4irF7y942NnZmfZVrlxZie80aM+ePbRq1Yrw8HAA08VKHx8frl69yvbt24mLi6N+/foMGzaMvXv3snv3blNZMxsbGxITE5X0FskAHj16RIcOHTh27BiWlpamc4IbN25gZWVFnjx5gCfjxq5du+Lp6cmuXbswDIMmTZowbtw4Jb1FRERE/gfN+H5Frl+/zrVr12jZsmWqwWnKn4Zh4OXlRfPmzSlTpgzly5fn9OnT7N27V7M/07DnnYjcunWL4OBgxo8fj7+/P9euXWPGjBnUrVuXpUuXkpiYyIIFC/Dy8iIuLo6yZctqxn860bp1a3777Tf8/f0JDw+ndOnSJCYm8uDBAwBTiQPN9kw/Ui54dO7cmUyZMjF48GDKli0LPLng0b17d3799VfWr18P/P/sP9X1TpvefPNNDMNg2rRp2NnZ8c4779CqVSvOnDnDli1bKFCgACtXrsTPz4+aNWuyY8cOU0kzePKdoUVsRTKGY8eOcf78efr27cv8+fMpXbo0AA4ODlhaWnL16lWKFSuGYRjY29vTrl07Zs6cycGDB3n33XcpVKgQ8P93loqIiIjIs1Tj+xVZsWIFHTt2JD4+/plbGlPExsayc+dOEhIS+Omnn2jcuDHFixc3U8TyT6Uc4+joaHbv3k1cXBwNGzbkjTfeYO7cuTRq1IgcOXJQp04dqlatSmBgIJMmTWLy5Mn4+vqyfPlyc78E+ZccP36cRYsWce7cOQoWLEi5cuXo2bMnVlZWJCYmKvGVziQlJRESEoK/vz9vvfXWMxc89uzZg42NjS54pBNRUVEEBARgZWVFTEwMv/32G+vWrTMlqQDOnDlD3bp1qVGjBqGhoeYLVkTMavv27QQGBnLz5k0WLVrE22+/zZUrVyhXrhwNGjQgMDCQ3LlzA3Dw4EG6du3K2rVrcXV1NXPkIiIiImmDEt+vyP79+6lTpw6hoaG0bNnyuW0+++wzNmzYwLZt215xdPKypSS9T548Sdu2bXn77bfJly8f06ZNS7U/KCiITZs2sXz5ct566y3mzZtHcHAwd+7cITw83FQHXjIGJT7TN13wyDiioqLw9/cnIiKChQsX4uPjA6S+C+jy5cs4OTnpPS+SAT39mb9lyxY+/fRTbt26xfz58ylbtiz79++nXr161K5dmzp16uDs7MzIkSMpWbIka9euNXP0IiIiImmHEt+vyNWrVylXrhyVK1cmKCjIVLc15bZ2gA8//BAbGxumTJli2iZpT8oxPXnyJNWqVaNPnz4MHjyY7NmzA7Bp0yYAmjZtSr9+/di3bx+HDx8GYMiQIeTOnZt+/fqRJUsWs70G+fc9/d6XjE0XPNKnCxcu0KdPHywtLRkxYoRp4eLf3/Gl4y8iX3/9NXPmzEmV/I6MjGTQoEFER0djZ2eHu7s7wcHBgMYQIiIiIi9Kie9XaN26dfj5+dG6dWuGDRtGqVKlgCclTiZNmsSKFSvYtm0bLi4uZo5U/qk7d+7QokULypQpw+zZs03bp02bxvDhw6lZsyYjRozAxsaGJk2aULNmTbJkycI333zDgQMHtJipSDqlZEXGklL2BGDUqFFUrVrVzBGJyOuiW7duWFlZMX/+fCB18nvevHl4eHjw8OFDkpKSiI2NxdHREdBCliIiIiJ/hUZNr1Dz5s0JDAxk5cqVtGzZki5duuDv74+vry/BwcGsX79eSe904saNG1y9ehVvb2+Sk5MBmDdvHqNHj+bTTz/F2tqaTz75hOjoaJYuXUpCQgKWlpaEh4cr6S2SjinpnbEUL16coKAgrKys6N+/Pz/88IO5QxKR10B8fDxvv/02ISEhDBkyBIBGjRrRp08fHBwc8Pf3JzIyEnt7e3LkyGFKemshSxEREZG/RjO+zeDQoUPMmDGD8+fPky1bNqpUqULXrl21kGU6EhoaSqdOnUhISDAluqKjo/npp5+oVq0aJ06coH///vz2228sXbqUIkWKkJiYSKZMmcwcuYiIvGynT59m0aJFzJgxQ0krkQzoeSWNYmNjWbFiBf7+/vTr148ZM2YAT2p+z5kzh1OnThEeHo6Tk5M5QhYRERFJF7SSlhlUrFiRVatWqaZnOlaoUCGsra1Zv3493t7eGIaBk5MTTk5OJCcnU7p0adq0acPChQuxs7PDyspK/x9ERNKpkiVL8tFHHwEqUyCSEaWM8bZv307dunUByJo1K35+fhiGgb+/P5aWlkybNo2GDRsSFxfHTz/9pKS3iIiIyD+kxLeZPH3Sq5qv6U+hQoXIkSMHn3/+Oe+8845pMVP4/2N/9uxZChUqRLZs2cwVpoiIvGJKeotkTBs3bqRFixbMmjWLfv36AU+S376+vty6dYuRI0eSPXt2Ro4cibe3t+lxOk8QERER+ft09mUmTw9gNZhNf5ycnPjss8/YunUro0eP5tSpU6Z99+/fZ8iQISxevJixY8cq8S0iIiKSziQlJZn+npiYSI0aNRg7dizjxo0jMDDQtM/e3h5PT0/s7e0ZPXo0y5cvT9WPzhNERERE/j7N+Bb5lzRv3pygoCD69u1LREQEVapUwcbGhqtXr3L48GF27NiBm5ubucMUERERkZfo6ZreEyZMwMHBgQ4dOtCnTx8sLCwYM2YMhmHQv39/ALJly0b79u1p3749lStXNmPkIiIiIumLZnyL/EusrKzo2bMne/fupVSpUhw5coSTJ09SunRp9uzZg4eHh7lDFBEREZGXyDAMU9K7efPmrFq1inz58pGQkICDgwM9e/Zk0KBBDB8+nN69e/P555/Tvn17LC0tTUnv5ORkc74EERERkXTDwjAMw9xBiKR3T8/8EREREZH0bfr06Xz++ed88803zyxSeefOHcLCwhgxYgT58uWjePHihIaGAqrpLSIiIvIyKfEt8go8fRKjExoRERGR9K1Tp05YWlqyePFi07aUMWDKnzExMTx+/Jg333wTeDLTWwvgioiIiLw8GlmJvAJazFREREQkffp9aZLHjx9z48YN091+iYmJwJMxYGxsLBs2bODmzZvkyJHDlPQ2DENJbxEREZGXTKMrERERERGRvyEpKcmUsN65cydJSUlkypSJ+vXrs3jxYo4ePYq1tbWp/c8//0xISAjnz59P1Y8mRoiIiIi8fCp1IiIiIiIi8hc9XZqkbdu2XL16lYCAALy9vbl9+zbdu3fnu+++Y/ny5Tg7OxMfH0/Hjh0pUaIEa9euNXP0IiIiIumfEt8iIiIiIiJ/k4+PDydOnGDdunXkz5+f7NmzA/Drr78ybtw4li1bhp2dHbly5aJMmTKsXr0a0LovIiIiIv82Jb5FRERERET+hh07dtC/f3+2bt1K/vz5uXHjBmfPniU8PJxWrVrh6urK4cOHSUpKwsLCgooVKwJayFJERETkVbD+301ERERERETk9wlrKysrrly5wo0bNzh+/Dhr1qxh165d2NraMmPGDE6ePEn58uVT9aGFLEVEREReDY24REREREREXkBKwjo8PJxHjx7h5uZGzZo1adCgAR06dMDZ2ZklS5Zw+vRpcubMSXh4+DN9qLyJiIiIyKuhGd8iIiIiIiIvaO7cuUybNo1x48bRsWNHFi1axJEjRyhatCjFihUD4NKlS2TLlo033njDzNGKiIiIZFyq8S0iIiIiIvKC4uLi6NixI5cuXcLf35/333+fzJkzA3Dv3j2ioqLo3r07RYsW5csvvzRztCIiIiIZlxLfIiIiIiIi/2UYxv8sR/L48WPat2/PpUuX6NWrF23btsXa2pq5c+eycuVKnJ2dWbFiBaCFLEVERETMRYlvERERERGR3zl79iwlSpQAYOLEibi5udG8eXNTEvvRo0e0a9eOH374gdGjR9OuXTvOnTvHhQsXaNSoEaCkt4iIiIg5KfEtIiIiIiLylMmTJ7N48WLGjx+Pr68vZcuWxd7enjFjxlC/fv1UyexixYqRPXt2unbtSu/evU37XmTmuIiIiIj8ezT9QERERERE5L+io6MJCQnhwYMHfPHFF2zbto2DBw9iZ2fHhAkT2Lp1K8nJycCTGd2urq7cu3ePu3fvpkqIK+ktIiIiYl5KfIuIiIiIiPyXk5MT3t7eJCYmkjlzZmbOnEl4eDibNm0iS5YsTJw4kc2bN/P48WNiYmLIli0b69evZ9SoUeYOXURERESeolInIiIiIiKSIf2+HEl8fDy2trZcunSJ4cOHU6lSJfbs2cO1a9cYO3Ys1apVw8fHh8uXL5M1a1ZiYmIoVqwYYWFhz+1PRERERMxHM75FRERERCRDSklSnz9/HgBbW1sA8uTJQ1xcHDExMSxYsABHR0cmTpxIeHg4GzZsoH///tSsWZPOnTsr6S0iIiLymtKMbxERERERybAmTZpEYGAg9evXZ+jQobzxxhvkzZuXQ4cO0bJlS8LCwrCzs2PIkCHcvn2bfv360aJFi1R9JCcnp6rvLSIiIiLmp8S3iIiIiIhkSPfu3aNWrVrcuHGDhIQEqlevzoMHD+jduzeVK1dm4sSJlC5dGn9/f3788UfGjx/PmTNnmDdvHp6enuYOX0RERET+hBLfIiIiIiKSYUVFRTFixAgAypcvT65cuZgwYQINGzYkLCyMTJkycfz4cXLkyMEPP/zArl27CAgIMHPUIiIiIvK/KPEtIiIiIiIZ2tmzZxk0aBAJCQnMmTMHW1tbdu7cyezZs4mNjWXfvn3kyJEjVQ1v1fQWEREReb0p8S0iIiIiIhneuXPn+OCDD4Andb8rVKiAYRjcv3+fHDlyqI63iIiISBqjxLeIiIiIiAhPyp6kJL+HDx9OjRo1AC1eKSIiIpIWKfEtIiIiIiLyX1FRUfTv35/k5GQGDx5M7dq1zR2SiIiIiPwNmrYgIiIiIiLyX8WLF+eTTz7h9u3bHDlyxNzhiIiIiMjfpBnfIiIiIiIiv3P9+nXy5s1r7jBERERE5G9S4ltEREREROQPGIaBhYWFucMQERERkb9IpU5ERERERET+gJLeIiIiImmTEt8iIiIiIiIiIiIikq4o8S0iIiIiIiIiIiIi6YoS3yIiIiIiIiIiIiKSrijxLSIiIiIiIiIiIiLpihLfIiIiIiIiIiIiIpKuKPEtIiIiIiJ/yMLCgg0bNpg7DBERERGRv0SJbxERERF5LR04cAArKyvee++9Z/ZdunQJCwsL00+ePHmoV68ex44de+E+AOLj45k+fTru7u5kzZoVBwcHqlatSkhICAkJCQB06tSJ5s2bP/PYXbt2YWFhwb1791L9+3k/169ff+7z3759mwYNGpAvXz4yZcpEgQIF6Nu3L/fv3ze1WbJkCTlz5vzT39XTz2VtbU3BggUZOHAgjx8//tPHvQpJSUlMnToVV1dXsmTJQu7cualUqRKLFi16Kf2/yO9HRERERDIea3MHICIiIiLyPMHBwXzwwQcEBwfzyy+/kC9fvmfafPvtt7i5uREdHU1AQAANGzbkzJkzpkTon/URHx9P/fr1iYyMZOLEiVStWpXs2bPz/fffM3PmTDw8PChbtuxfjvvs2bNkz5491bY333zzuW0tLS1p1qwZkyZN4o033uD8+fP06dOHO3fusGLFir/0vCEhITRo0ICEhAQiIyPp3LkzdnZ2TJw48S+/hpdp/PjxzJ8/n08//ZTy5ctz//59Dh8+zN27d80al4iIiIikb5rxLSIiIiKvnYcPH7J69Wp69+7Ne++9x5IlS57bLk+ePOTNm5fy5cszc+ZMbty4wcGDB1+oj08++YTw8HB27NhBnz59KFu2LEWKFMHPz4+DBw9SvHjxvxX7m2++Sd68eVP9WFo+f9idK1cuevfuTfny5XF2dqZOnTr4+/uzZ8+ev/y8OXPmJG/evBQoUIDGjRvTrFkzjh49mqrN3LlzKVq0KLa2tpQoUYJly5al2h8VFUX16tXJnDkzpUqVYvv27an2165dm759+6badvPmTWxtbdmxY8dz49q0aRP+/v74+PhQuHBh3N3d6dq1K4MGDQJg6dKl5MmT55nZ6c2bN6d9+/YAREZGUqtWLbJly0b27Nl55513OHz4MLt27aJz587ExMSYZryPGzcOgMePHzNo0CDy58+PnZ0dlSpVYteuXab+U2aKb968mRIlSpA1a1ZatWpFbGwsn3/+OYUKFSJXrlwEBASQlJT0YgdBRERERF4bSnyLiIiIyGvniy++wNXVlRIlStCuXTsWL16MYRh/+pgsWbIAT2Zyv0gfy5cvx8vLCw8Pj2f6srGxwc7O7iW+ohfzyy+/sG7dOmrUqPGP+jl37hzfffcdlSpVMm1bv349/fr148MPP+TEiRP07NmTzp07s3PnTgCSk5Px9vbG1taWgwcPMm/ePIYOHZqq327durFixYpUSerQ0FDy589P7dq1nxtL3rx5+e6777h58+Zz9/v4+JCUlMSmTZtM23799VfCwsLo0qULAG3btsXJyYmIiAiOHDnCsGHDsLGxoUqVKnzyySdkz56da9euce3aNVNCvW/fvhw4cIBVq1bxww8/4OPjQ4MGDYiKijI9T2xsLEFBQaxatYqtW7eya9cuWrRowddff83XX3/NsmXLmD9/PmvXrv0rv34REREReQ0o8S0iIiIir53g4GDatWsHQIMGDYiJiWH37t1/2P7evXtMnDgRe3t7Klas+EJ9REVF4erq+kLxbN68GXt7+1Q/DRs2fG5bJyenVO3c3Nz+Z/++vr5kzZqV/Pnzkz179r9V/9rX1xd7e3syZ85MiRIlcHNzY/jw4ab9M2fOpFOnTvj7++Pi4sLAgQPx9vZm5syZwJOyMWfOnGHp0qW4u7tTvXp1/vOf/6R6Dm9vbwA2btxo2rZkyRI6deqEhYXFc+P6+OOPuXnzJnnz5qVMmTL06tWLLVu2mPZnyZIFPz8/QkJCTNtCQ0MpWLAgNWvWBODnn3/Gy8sLV1dXihcvjo+PD+7u7tja2pIjRw4sLCxMs+vt7e35+eefCQkJYc2aNVSrVo2iRYsyaNAgPD09Uz1PQkICc+fOxcPDg+rVq9OqVSv27t1LcHAwpUqVonHjxtSqVct0cUBERERE0g4lvkVERETktXL27FkOHTqEr68vANbW1rRp04bg4OBn2lapUgV7e3ty5cpFZGQkq1ev5q233nqhPv7XDPKn1apVi+PHj6f6+aPk9J49e1K1+/rrr/9n/7NmzeLo0aNs3LiRCxcuMHDgwBeO7ek+jh8/TmRkJJs3b+bcuXOmUiEAp0+fpmrVqqkeU7VqVU6fPm3aX6BAgVR10N99991U7TNnzkz79u1ZvHgxAEePHuXEiRN06tTpD+MqVaoUJ06c4Pvvv6dLly78+uuvNGnShG7dupnadO/enW3btnH16lXg2WT6wIED6datG15eXkydOpULFy786e/ixx9/JCkpCRcXl1QXIXbv3p3qsVmzZqVo0aKmf7/11lsUKlQIe3v7VNt+/fXXP30+EREREXn9aHFLEREREXmtBAcHk5iYmCoBaxgGmTJl4tNPPyVHjhym7atXr6ZUqVLkyZPHtKDli/bh4uLCmTNnXigmOzs7ihUrlmpbdHT0c9sWLlw4VSwvImW2squrK7lz56ZatWqMHj0aR0fHv9RHSowlSpTgwYMH+Pr6MmnSpGdi/ye6detG2bJliY6OJiQkhNq1a+Ps7Pynj7G0tKRChQpUqFCB/v37ExoaSvv27Rk5ciSFCxfGw8MDd3d3li5dSr169Th58iRhYWGmx48bNw4/Pz/CwsLYsmULY8eOZdWqVbRo0eK5z/fw4UOsrKw4cuQIVlZWqfY9ndS2sbFJtc/CwuK525KTk1/odyMiIiIirw/N+BYRERGR10ZiYiJLly7lo48+SjVrOjIyknz58rFy5cpU7QsUKEDRokVTJZpftA8/Pz++/fZbjh079kwcCQkJ/Pbbb//qa/0jKUnW3y/2+FelJHzj4uIAKFmyJPv27UvVZt++fZQqVcq0/8qVK1y7ds20//vvv3+m37fffpvy5cuzcOFCVqxYYarD/VekPOfTv+Nu3bqxZMkSQkJC8PLyokCBAqke4+LiwoABA9i2bRve3t6mkiW2trbPLD7p4eFBUlISv/76K8WKFUv1kzdv3r8cr4iIiIikPZrxLSIiIiKvjc2bN3P37l26du2aamY3QMuWLQkODqZXr14vpY/+/fsTFhZGnTp1mDhxIp6enmTLlo3Dhw8zbdo0goODKVu27F9+Db/++iuPHj1KtS1PnjzPzCQG+Prrr7lx4wYVKlTA3t6ekydPMnjwYKpWrUqhQoX+0vPeu3eP69evk5ycTFRUFBMmTMDFxYWSJUsCMHjwYFq3bo2HhwdeXl589dVXrFu3jm+//RYALy8vXFxc6NixIzNmzOD+/fuMHDnyuc/VrVs3+vbti52d3R/Ouk7RqlUrqlatSpUqVcibNy8//fQTw4cPx8XFJVWNdT8/PwYNGsTChQtZunSpaXtcXByDBw+mVatWFC5cmOjoaCIiImjZsiUAhQoV4uHDh+zYsQN3d3eyZs2Ki4sLbdu2pUOHDnz00Ud4eHhw8+ZNduzYQZkyZXjvvff+0u9WRERERNIezfgWERERkddGcHAwXl5ezySs4UnS+vDhw/zwww8vpY9MmTKxfft2hgwZwvz586lcuTIVKlQgKCiIgIAASpcu/bdeQ4kSJXB0dEz1c+TIkee2zZIlCwsXLsTT05OSJUsyYMAAmjZtyubNm01tkpOTsbb+3/NVOnfujKOjI05OTvj6+uLm5saWLVtMj23evDmBgYHMnDkTNzc35s+fT0hIiGkBSUtLS9avX09cXBwVK1akW7duTJ48+bnP5evri7W1Nb6+vmTOnPlP46pfvz5fffUVTZo0MSXWXV1d2bZtW6rXlSNHDlq2bIm9vT3Nmzc3bbeysuL27dt06NABFxcXWrduTcOGDRk/fjzwpM57r169aNOmDW+88QbTp08HICQkhA4dOvDhhx9SokQJmjdvTkREBAULFvyfv0sRERERSfssjL+yqo+IiIiIiLxSU6dOJTQ0lBMnTpg7FJNLly5RtGhRIiIiKFeu3Evrt06dOri5uREUFPTS+hQRERGRjEmlTkREREREXkOxsbGcOXOGkJAQGjZsaO5wgCe1z2/fvs2oUaOoXLnyS0t63717l127drFr1y4+++yzl9KniIiIiGRsKnUiIiIiIvIaWrBgAV5eXri7uzNmzBhzhwM8WQzT0dGRiIgI5s2b99L69fDwoFOnTkybNo0SJUq8tH5FREREJONSqRMRERERERERERERSVc041tERERERERERERE0hUlvkVEREREREREREQkXVHiW0RERERERERERETSFSW+RURERERERERERCRdUeJbRERERERERERERNIVJb5FREREREREREREJF1R4ltERERERERERERE0hUlvkVEREREREREREQkXfk/HCw+qAicLT4AAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"death_counts = health_data.groupby(['apache_3j_bodysystem', 'hospital_death']).size().unstack()\n",
"death_percentage = death_counts.div(death_counts.sum(axis=1), axis=0) * 100\n",
"death_percentage = death_percentage.reset_index()\n",
"death_percentage_melted = death_percentage.melt(id_vars='apache_3j_bodysystem', var_name='hospital_death', value_name='percentage')\n",
"\n",
"plt.figure(figsize=(18, 5))\n",
"barplot = sns.barplot(data=death_percentage_melted, x='apache_3j_bodysystem', y='percentage', hue='hospital_death', palette='viridis')\n",
"\n",
"plt.title('Percentage of Hospital Deaths by APACHE 3J Body System')\n",
"plt.xlabel('APACHE 3J Body System')\n",
"plt.ylabel('Percentage')\n",
"plt.legend(title='Hospital Death', labels=['Alive (0)', 'Deceased (1)'])\n",
"plt.xticks(rotation=45)\n",
"\n",
"for p in barplot.patches:\n",
" percentage = f\"{p.get_height():.1f}%\"\n",
" barplot.annotate(percentage, (p.get_x() + p.get_width() / 2., p.get_height()),\n",
" ha='center', va='bottom')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "BlWlryR_pSVa",
"outputId": "8e65587b-19ab-4c1c-a758-d0f0e7e6fe85"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1800x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(18, 5))\n",
"sns.countplot(x=\"apache_2_bodysystem\", data=health_data , hue=\"hospital_death\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "ZyTTM7HxpSLy"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "w21kbvSHZYFH",
"outputId": "ced6ffae-2502-42ad-9478-bb3e44eaa298"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 5200x5000 with 81 Axes>"
],
"image/png": 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},
"metadata": {}
}
],
"source": [
"#######\n",
"clean_data.hist(figsize=(52,50))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2-PUJ_GbIl4V",
"outputId": "b2019692-0874-44f3-c000-d065f178dbfb"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x1200 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"#### Feature importance of each column determination using SKlearn\n",
"plt.figure(figsize=(10, 12))\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"Feature_model = RandomForestClassifier()\n",
"X1 = clean_data.drop(columns=[\"hospital_death\", \"ethnicity\", \"gender\", \"icu_admit_source\",\n",
" \"icu_stay_type\", \"icu_type\", \"apache_3j_bodysystem\",\n",
" \"apache_2_bodysystem\"])\n",
"y1 = clean_data[\"hospital_death\"]\n",
"\n",
"Feature_model.fit(X1, y1)\n",
"Feature_importance = pd.Series(Feature_model.feature_importances_, index=X1.columns)\n",
"Feature_importance.nlargest(50).plot(kind='barh')\n",
"plt.title(\"Top 50 Feature Importances\")\n",
"plt.xlabel(\"Importance Score\")\n",
"plt.ylabel(\"Features\")\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wOUAu-I1g1Qz"
},
"source": [
"The Acute Physiology and Chronic Health Evaluation (APACHE) IV model can predict the intensive care unit (ICU) length of stay (LOS) in critically ill patients. That this happen to be the most important features in the data set"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 490
},
"id": "jxUPmvTAW2EL",
"outputId": "2ae9d475-6096-4899-a0fa-3cb64665d566"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'KDE distribution of apache_4a_icu_death_prob ')"
]
},
"metadata": {},
"execution_count": 25
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"sns.kdeplot(data=clean_data, x='apache_4a_icu_death_prob', hue='hospital_death')\n",
"plt.title(\"KDE distribution of apache_4a_icu_death_prob \")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 490
},
"id": "73BgKyr3j9cC",
"outputId": "47a7243d-129c-4c11-d7df-aee64d4cccee"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'KDE distribution of apache_4a_hospital_death_prob ')"
]
},
"metadata": {},
"execution_count": 26
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"sns.kdeplot(data=clean_data, x='apache_4a_hospital_death_prob', hue='hospital_death')\n",
"plt.title(\"KDE distribution of apache_4a_hospital_death_prob \")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "LSotWKtOkI8X",
"outputId": "40ca5b21-18d7-447b-aa21-ef5893a4f32a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id \\\n",
"encounter_id 1.000000 -0.001036 -0.005555 \n",
"patient_id -0.001036 1.000000 -0.006371 \n",
"hospital_id -0.005555 -0.006371 1.000000 \n",
"age -0.004015 0.005469 -0.009069 \n",
"bmi 0.001026 0.000692 0.009028 \n",
"elective_surgery -0.002694 0.005650 0.041702 \n",
"height -0.006739 0.004081 0.033112 \n",
"icu_id -0.000704 -0.002483 0.035638 \n",
"pre_icu_los_days -0.000998 -0.005094 -0.001453 \n",
"weight -0.002298 0.002387 0.025224 \n",
"apache_2_diagnosis -0.000374 -0.001801 -0.002859 \n",
"apache_3j_diagnosis -0.001103 0.004916 0.020115 \n",
"apache_post_operative -0.002450 0.005205 0.041987 \n",
"arf_apache 0.012406 -0.000592 0.000071 \n",
"gcs_eyes_apache 0.005690 0.002008 -0.007684 \n",
"gcs_motor_apache 0.010362 0.001737 -0.017686 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache 0.008068 0.002699 0.003370 \n",
"heart_rate_apache -0.002901 0.004945 -0.006215 \n",
"intubated_apache -0.007877 0.003930 0.019899 \n",
"map_apache -0.001580 -0.003315 -0.002743 \n",
"resprate_apache 0.008714 0.002281 -0.020449 \n",
"temp_apache 0.006060 -0.000320 -0.032016 \n",
"ventilated_apache -0.013043 -0.001335 0.025514 \n",
"d1_diasbp_max -0.002887 -0.002492 -0.019117 \n",
"d1_diasbp_min -0.000322 -0.001735 0.013821 \n",
"d1_diasbp_noninvasive_max -0.002818 -0.002276 -0.018961 \n",
"d1_diasbp_noninvasive_min -0.000037 -0.001946 0.013578 \n",
"d1_heartrate_max -0.006774 -0.000357 -0.006119 \n",
"d1_heartrate_min 0.001381 0.000861 -0.008510 \n",
"d1_mbp_max -0.000186 -0.004624 -0.019417 \n",
"d1_mbp_min -0.001552 -0.002503 0.008801 \n",
"d1_mbp_noninvasive_max 0.001062 -0.004520 -0.018866 \n",
"d1_mbp_noninvasive_min -0.001624 -0.002932 0.009003 \n",
"d1_resprate_max 0.007901 0.003176 -0.032569 \n",
"d1_resprate_min 0.001453 0.003608 -0.019908 \n",
"d1_spo2_max -0.002737 -0.003005 -0.009052 \n",
"d1_spo2_min 0.002650 -0.005226 -0.001524 \n",
"d1_sysbp_max -0.003727 -0.001849 -0.028943 \n",
"d1_sysbp_min -0.002810 -0.007961 0.005046 \n",
"d1_sysbp_noninvasive_max -0.003711 -0.001642 -0.028474 \n",
"d1_sysbp_noninvasive_min -0.002786 -0.008424 0.004905 \n",
"d1_temp_max -0.006661 -0.003322 0.016621 \n",
"d1_temp_min 0.004776 -0.001115 -0.044566 \n",
"h1_diasbp_max -0.003139 -0.004722 0.001180 \n",
"h1_diasbp_min 0.002802 -0.008662 -0.014265 \n",
"h1_diasbp_noninvasive_max -0.002042 -0.003283 0.002316 \n",
"h1_diasbp_noninvasive_min 0.002508 -0.009840 -0.012543 \n",
"h1_heartrate_max -0.002534 0.001650 -0.010177 \n",
"h1_heartrate_min -0.002998 0.002568 -0.022248 \n",
"h1_mbp_max 0.000930 -0.003626 0.000636 \n",
"h1_mbp_min 0.004229 -0.007897 -0.011939 \n",
"h1_mbp_noninvasive_max 0.001418 -0.002664 -0.000345 \n",
"h1_mbp_noninvasive_min 0.004255 -0.008033 -0.011090 \n",
"h1_resprate_max 0.007569 0.003544 -0.023934 \n",
"h1_resprate_min 0.002913 -0.000946 -0.036231 \n",
"h1_spo2_max -0.000712 0.000079 0.017704 \n",
"h1_spo2_min -0.003846 -0.003214 0.001696 \n",
"h1_sysbp_max 0.003081 -0.003712 -0.007294 \n",
"h1_sysbp_min 0.000381 -0.009131 -0.020903 \n",
"h1_sysbp_noninvasive_max 0.002926 -0.003964 -0.009218 \n",
"h1_sysbp_noninvasive_min 0.000954 -0.010023 -0.021643 \n",
"d1_glucose_max 0.003829 -0.007430 -0.001785 \n",
"d1_glucose_min 0.002371 0.000074 0.022893 \n",
"d1_potassium_max -0.004673 0.002094 0.002906 \n",
"d1_potassium_min -0.003733 0.003155 -0.012871 \n",
"apache_4a_hospital_death_prob -0.005155 0.003026 -0.003032 \n",
"apache_4a_icu_death_prob -0.005044 0.004431 0.004436 \n",
"aids 0.001709 -0.000660 -0.006257 \n",
"cirrhosis 0.011650 0.004198 0.006344 \n",
"diabetes_mellitus 0.006541 -0.001333 0.011368 \n",
"hepatic_failure 0.001078 -0.002630 0.003995 \n",
"immunosuppression -0.002094 0.001413 0.000381 \n",
"leukemia -0.003623 0.000975 -0.005046 \n",
"lymphoma -0.001809 -0.002327 0.006326 \n",
"solid_tumor_with_metastasis -0.003849 -0.006374 -0.005487 \n",
"\n",
" age bmi elective_surgery height \\\n",
"encounter_id -0.004015 0.001026 -0.002694 -0.006739 \n",
"patient_id 0.005469 0.000692 0.005650 0.004081 \n",
"hospital_id -0.009069 0.009028 0.041702 0.033112 \n",
"age 1.000000 -0.080486 0.057514 -0.119639 \n",
"bmi -0.080486 1.000000 0.006735 -0.063923 \n",
"elective_surgery 0.057514 0.006735 1.000000 0.013584 \n",
"height -0.119639 -0.063923 0.013584 1.000000 \n",
"icu_id -0.022573 0.004809 -0.010871 0.021729 \n",
"pre_icu_los_days 0.054951 -0.001774 0.122012 -0.015351 \n",
"weight -0.125288 0.880132 0.014730 0.382199 \n",
"apache_2_diagnosis 0.026553 0.019565 0.361392 -0.006182 \n",
"apache_3j_diagnosis -0.063351 -0.015095 0.795891 0.011063 \n",
"apache_post_operative 0.046583 0.009118 0.923298 0.015781 \n",
"arf_apache -0.002780 -0.007792 -0.027586 -0.009353 \n",
"gcs_eyes_apache 0.043832 0.012599 0.019979 -0.005365 \n",
"gcs_motor_apache 0.044063 0.020875 0.027753 -0.014861 \n",
"gcs_unable_apache NaN NaN NaN NaN \n",
"gcs_verbal_apache -0.002946 0.027426 -0.006857 0.011777 \n",
"heart_rate_apache -0.155835 -0.028840 -0.068348 -0.020577 \n",
"intubated_apache -0.003185 0.032551 0.120489 0.012339 \n",
"map_apache -0.017040 0.056656 0.007780 0.036710 \n",
"resprate_apache 0.034650 0.005877 -0.133643 -0.051661 \n",
"temp_apache -0.081164 0.035419 -0.033948 0.014273 \n",
"ventilated_apache 0.023629 0.076165 0.116024 -0.004478 \n",
"d1_diasbp_max -0.056655 0.053021 -0.158602 0.040730 \n",
"d1_diasbp_min -0.208602 -0.028818 0.003968 0.133386 \n",
"d1_diasbp_noninvasive_max -0.056564 0.053025 -0.158423 0.040656 \n",
"d1_diasbp_noninvasive_min -0.207904 -0.029103 0.005656 0.133630 \n",
"d1_heartrate_max -0.145065 -0.036482 -0.072214 -0.015156 \n",
"d1_heartrate_min -0.147906 0.006305 -0.028268 -0.028183 \n",
"d1_mbp_max 0.006689 0.061985 -0.130329 0.028673 \n",
"d1_mbp_min -0.129142 0.000166 0.010672 0.094093 \n",
"d1_mbp_noninvasive_max 0.006209 0.062245 -0.131006 0.029074 \n",
"d1_mbp_noninvasive_min -0.129187 -0.000184 0.011451 0.094313 \n",
"d1_resprate_max 0.027702 0.012221 -0.064207 -0.021115 \n",
"d1_resprate_min 0.048954 -0.005307 -0.159557 -0.054963 \n",
"d1_spo2_max -0.043002 -0.082259 0.042150 -0.030168 \n",
"d1_spo2_min -0.081491 -0.031478 0.038151 0.007965 \n",
"d1_sysbp_max 0.105590 0.085527 -0.083295 0.004081 \n",
"d1_sysbp_min -0.060245 0.044222 0.013901 0.051203 \n",
"d1_sysbp_noninvasive_max 0.105191 0.085545 -0.085121 0.003608 \n",
"d1_sysbp_noninvasive_min -0.060213 0.044172 0.014866 0.051262 \n",
"d1_temp_max -0.100176 0.018651 0.053148 0.016985 \n",
"d1_temp_min -0.066129 0.034165 -0.028402 0.004376 \n",
"h1_diasbp_max -0.138641 0.023306 -0.106479 0.079122 \n",
"h1_diasbp_min -0.186812 -0.009829 -0.064613 0.109389 \n",
"h1_diasbp_noninvasive_max -0.136606 0.022982 -0.103227 0.077343 \n",
"h1_diasbp_noninvasive_min -0.187165 -0.011490 -0.058989 0.111256 \n",
"h1_heartrate_max -0.172870 -0.019961 -0.107990 -0.006082 \n",
"h1_heartrate_min -0.172761 -0.010091 -0.124757 -0.012351 \n",
"h1_mbp_max -0.059667 0.033733 -0.074414 0.052886 \n",
"h1_mbp_min -0.100795 0.010981 -0.050104 0.071101 \n",
"h1_mbp_noninvasive_max -0.060153 0.033016 -0.075593 0.052867 \n",
"h1_mbp_noninvasive_min -0.100612 0.010546 -0.049100 0.071595 \n",
"h1_resprate_max 0.026192 0.014155 -0.177796 -0.029496 \n",
"h1_resprate_min 0.035833 0.001556 -0.240363 -0.044985 \n",
"h1_spo2_max -0.067605 -0.063018 0.091235 -0.019162 \n",
"h1_spo2_min -0.079734 -0.035852 0.053323 0.000253 \n",
"h1_sysbp_max 0.043745 0.059963 -0.042952 0.012182 \n",
"h1_sysbp_min -0.001140 0.042186 -0.032432 0.020399 \n",
"h1_sysbp_noninvasive_max 0.043261 0.060096 -0.048673 0.011332 \n",
"h1_sysbp_noninvasive_min -0.001372 0.041629 -0.034775 0.020537 \n",
"d1_glucose_max 0.005115 0.097297 -0.020991 -0.016805 \n",
"d1_glucose_min 0.066914 0.136203 0.022987 0.018818 \n",
"d1_potassium_max 0.055840 0.089247 0.078894 0.055173 \n",
"d1_potassium_min 0.114173 0.099579 0.047976 0.068632 \n",
"apache_4a_hospital_death_prob 0.171258 -0.028964 -0.118039 -0.026078 \n",
"apache_4a_icu_death_prob 0.089739 -0.012521 -0.086606 -0.008080 \n",
"aids -0.032535 -0.022728 -0.004932 0.011321 \n",
"cirrhosis -0.031029 -0.002425 -0.033169 0.012351 \n",
"diabetes_mellitus 0.065859 0.170040 -0.011640 -0.004218 \n",
"hepatic_failure -0.024565 -0.000597 -0.035008 0.008594 \n",
"immunosuppression 0.023724 -0.033248 -0.011333 0.001106 \n",
"leukemia 0.030634 -0.015873 -0.016247 0.001257 \n",
"lymphoma 0.020841 -0.010283 -0.010116 0.009898 \n",
"solid_tumor_with_metastasis 0.026358 -0.047853 0.019365 0.007713 \n",
"\n",
" icu_id pre_icu_los_days weight \\\n",
"encounter_id -0.000704 -0.000998 -0.002298 \n",
"patient_id -0.002483 -0.005094 0.002387 \n",
"hospital_id 0.035638 -0.001453 0.025224 \n",
"age -0.022573 0.054951 -0.125288 \n",
"bmi 0.004809 -0.001774 0.880132 \n",
"elective_surgery -0.010871 0.122012 0.014730 \n",
"height 0.021729 -0.015351 0.382199 \n",
"icu_id 1.000000 -0.016119 0.013536 \n",
"pre_icu_los_days -0.016119 1.000000 -0.008648 \n",
"weight 0.013536 -0.008648 1.000000 \n",
"apache_2_diagnosis -0.016546 0.078499 0.017658 \n",
"apache_3j_diagnosis -0.004083 0.078661 -0.008548 \n",
"apache_post_operative -0.011575 0.119609 0.017741 \n",
"arf_apache -0.009765 0.052277 -0.012238 \n",
"gcs_eyes_apache -0.010539 -0.015469 0.010306 \n",
"gcs_motor_apache -0.012416 -0.005204 0.013349 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.020057 -0.038746 0.030702 \n",
"heart_rate_apache 0.007270 0.054741 -0.036486 \n",
"intubated_apache -0.072495 0.047833 0.036267 \n",
"map_apache -0.005271 -0.032297 0.070071 \n",
"resprate_apache -0.003775 0.021648 -0.016454 \n",
"temp_apache 0.001765 0.008130 0.041245 \n",
"ventilated_apache 0.016636 0.076306 0.069353 \n",
"d1_diasbp_max 0.005453 -0.050342 0.069792 \n",
"d1_diasbp_min -0.023755 -0.046183 0.032016 \n",
"d1_diasbp_noninvasive_max 0.005764 -0.049986 0.069772 \n",
"d1_diasbp_noninvasive_min -0.023987 -0.045732 0.031905 \n",
"d1_heartrate_max -0.011112 0.061604 -0.040901 \n",
"d1_heartrate_min 0.019052 0.054006 -0.006231 \n",
"d1_mbp_max -0.006270 -0.050178 0.072860 \n",
"d1_mbp_min -0.022464 -0.050439 0.042409 \n",
"d1_mbp_noninvasive_max -0.009059 -0.051078 0.073350 \n",
"d1_mbp_noninvasive_min -0.019549 -0.049819 0.042221 \n",
"d1_resprate_max -0.012600 0.032238 0.003680 \n",
"d1_resprate_min -0.033504 0.013877 -0.029463 \n",
"d1_spo2_max -0.027147 0.023592 -0.092802 \n",
"d1_spo2_min 0.007943 -0.030216 -0.027094 \n",
"d1_sysbp_max -0.057790 -0.037123 0.085038 \n",
"d1_sysbp_min -0.002549 -0.052632 0.065099 \n",
"d1_sysbp_noninvasive_max -0.057536 -0.037590 0.084811 \n",
"d1_sysbp_noninvasive_min -0.002797 -0.052169 0.065108 \n",
"d1_temp_max -0.021521 0.027014 0.026071 \n",
"d1_temp_min -0.005269 -0.004411 0.034824 \n",
"h1_diasbp_max -0.014473 -0.058319 0.057222 \n",
"h1_diasbp_min -0.000882 -0.054409 0.040001 \n",
"h1_diasbp_noninvasive_max -0.014354 -0.057275 0.056167 \n",
"h1_diasbp_noninvasive_min -0.000800 -0.054913 0.039112 \n",
"h1_heartrate_max -0.010873 0.061045 -0.022502 \n",
"h1_heartrate_min 0.004188 0.056917 -0.015731 \n",
"h1_mbp_max -0.011939 -0.056631 0.055863 \n",
"h1_mbp_min -0.018469 -0.057832 0.042884 \n",
"h1_mbp_noninvasive_max -0.013593 -0.056725 0.055136 \n",
"h1_mbp_noninvasive_min -0.017409 -0.058227 0.042662 \n",
"h1_resprate_max -0.021284 0.040266 0.000007 \n",
"h1_resprate_min -0.019165 0.035592 -0.017901 \n",
"h1_spo2_max -0.007165 -0.001046 -0.069060 \n",
"h1_spo2_min 0.014544 -0.016554 -0.034535 \n",
"h1_sysbp_max -0.047580 -0.046267 0.063750 \n",
"h1_sysbp_min -0.007691 -0.051324 0.050606 \n",
"h1_sysbp_noninvasive_max -0.049443 -0.046436 0.063471 \n",
"h1_sysbp_noninvasive_min -0.009021 -0.051621 0.050175 \n",
"d1_glucose_max 0.008273 -0.009819 0.085200 \n",
"d1_glucose_min 0.004540 -0.002267 0.138689 \n",
"d1_potassium_max -0.002938 0.016778 0.107234 \n",
"d1_potassium_min -0.011041 0.014645 0.122040 \n",
"apache_4a_hospital_death_prob -0.004768 0.095801 -0.038453 \n",
"apache_4a_icu_death_prob -0.009903 0.068496 -0.015454 \n",
"aids -0.002201 0.012552 -0.018215 \n",
"cirrhosis -0.016893 0.014046 0.002399 \n",
"diabetes_mellitus 0.024944 0.015722 0.157113 \n",
"hepatic_failure -0.014062 0.014342 0.003829 \n",
"immunosuppression -0.037019 0.037962 -0.031774 \n",
"leukemia -0.000094 0.050971 -0.015303 \n",
"lymphoma -0.002586 0.017935 -0.004868 \n",
"solid_tumor_with_metastasis -0.012947 0.039016 -0.042026 \n",
"\n",
" apache_2_diagnosis apache_3j_diagnosis \\\n",
"encounter_id -0.000374 -0.001103 \n",
"patient_id -0.001801 0.004916 \n",
"hospital_id -0.002859 0.020115 \n",
"age 0.026553 -0.063351 \n",
"bmi 0.019565 -0.015095 \n",
"elective_surgery 0.361392 0.795891 \n",
"height -0.006182 0.011063 \n",
"icu_id -0.016546 -0.004083 \n",
"pre_icu_los_days 0.078499 0.078661 \n",
"weight 0.017658 -0.008548 \n",
"apache_2_diagnosis 1.000000 0.386372 \n",
"apache_3j_diagnosis 0.386372 1.000000 \n",
"apache_post_operative 0.388472 0.870505 \n",
"arf_apache -0.007688 -0.028409 \n",
"gcs_eyes_apache 0.048408 -0.021989 \n",
"gcs_motor_apache 0.064847 0.009909 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.021887 -0.045118 \n",
"heart_rate_apache -0.098076 0.008754 \n",
"intubated_apache 0.005791 0.097536 \n",
"map_apache 0.039976 -0.001793 \n",
"resprate_apache -0.089370 -0.127062 \n",
"temp_apache 0.009267 0.010111 \n",
"ventilated_apache -0.034906 0.079998 \n",
"d1_diasbp_max -0.048332 -0.145480 \n",
"d1_diasbp_min 0.060281 -0.000491 \n",
"d1_diasbp_noninvasive_max -0.048097 -0.145177 \n",
"d1_diasbp_noninvasive_min 0.061099 0.000730 \n",
"d1_heartrate_max -0.118243 -0.007217 \n",
"d1_heartrate_min -0.046336 0.046211 \n",
"d1_mbp_max -0.019555 -0.127620 \n",
"d1_mbp_min 0.072619 0.001836 \n",
"d1_mbp_noninvasive_max -0.021060 -0.127689 \n",
"d1_mbp_noninvasive_min 0.073237 0.002437 \n",
"d1_resprate_max -0.074171 -0.066415 \n",
"d1_resprate_min -0.090492 -0.142348 \n",
"d1_spo2_max 0.009111 0.061274 \n",
"d1_spo2_min 0.059437 0.053945 \n",
"d1_sysbp_max 0.019146 -0.088787 \n",
"d1_sysbp_min 0.090089 0.010185 \n",
"d1_sysbp_noninvasive_max 0.018353 -0.089752 \n",
"d1_sysbp_noninvasive_min 0.090912 0.010741 \n",
"d1_temp_max -0.015975 0.111763 \n",
"d1_temp_min 0.029711 0.007027 \n",
"h1_diasbp_max -0.013812 -0.107680 \n",
"h1_diasbp_min 0.032898 -0.074403 \n",
"h1_diasbp_noninvasive_max -0.013681 -0.104877 \n",
"h1_diasbp_noninvasive_min 0.036588 -0.069530 \n",
"h1_heartrate_max -0.147792 -0.040191 \n",
"h1_heartrate_min -0.136036 -0.047355 \n",
"h1_mbp_max 0.018900 -0.085596 \n",
"h1_mbp_min 0.055932 -0.062483 \n",
"h1_mbp_noninvasive_max 0.018299 -0.086540 \n",
"h1_mbp_noninvasive_min 0.056155 -0.061586 \n",
"h1_resprate_max -0.138576 -0.163706 \n",
"h1_resprate_min -0.149565 -0.208767 \n",
"h1_spo2_max 0.043086 0.097911 \n",
"h1_spo2_min 0.055448 0.065570 \n",
"h1_sysbp_max 0.051567 -0.055298 \n",
"h1_sysbp_min 0.078730 -0.043522 \n",
"h1_sysbp_noninvasive_max 0.049374 -0.060177 \n",
"h1_sysbp_noninvasive_min 0.075952 -0.045033 \n",
"d1_glucose_max -0.047425 0.016651 \n",
"d1_glucose_min -0.028468 -0.007121 \n",
"d1_potassium_max 0.051021 0.066359 \n",
"d1_potassium_min 0.050009 0.002477 \n",
"apache_4a_hospital_death_prob -0.097084 -0.118604 \n",
"apache_4a_icu_death_prob -0.104199 -0.085070 \n",
"aids -0.006816 0.000255 \n",
"cirrhosis -0.004227 -0.019918 \n",
"diabetes_mellitus -0.004628 -0.008994 \n",
"hepatic_failure -0.003222 -0.027882 \n",
"immunosuppression -0.011232 -0.000904 \n",
"leukemia -0.004233 -0.006425 \n",
"lymphoma -0.004681 -0.002530 \n",
"solid_tumor_with_metastasis 0.008605 0.021846 \n",
"\n",
" apache_post_operative arf_apache \\\n",
"encounter_id -0.002450 0.012406 \n",
"patient_id 0.005205 -0.000592 \n",
"hospital_id 0.041987 0.000071 \n",
"age 0.046583 -0.002780 \n",
"bmi 0.009118 -0.007792 \n",
"elective_surgery 0.923298 -0.027586 \n",
"height 0.015781 -0.009353 \n",
"icu_id -0.011575 -0.009765 \n",
"pre_icu_los_days 0.119609 0.052277 \n",
"weight 0.017741 -0.012238 \n",
"apache_2_diagnosis 0.388472 -0.007688 \n",
"apache_3j_diagnosis 0.870505 -0.028409 \n",
"apache_post_operative 1.000000 -0.029429 \n",
"arf_apache -0.029429 1.000000 \n",
"gcs_eyes_apache 0.001300 -0.003291 \n",
"gcs_motor_apache 0.014019 -0.002774 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache -0.028723 -0.000044 \n",
"heart_rate_apache -0.053109 -0.015231 \n",
"intubated_apache 0.142304 -0.002325 \n",
"map_apache 0.012210 0.008417 \n",
"resprate_apache -0.141530 0.008824 \n",
"temp_apache -0.036426 -0.019997 \n",
"ventilated_apache 0.141123 -0.002529 \n",
"d1_diasbp_max -0.161697 0.000905 \n",
"d1_diasbp_min 0.001930 -0.046441 \n",
"d1_diasbp_noninvasive_max -0.161432 0.000787 \n",
"d1_diasbp_noninvasive_min 0.003695 -0.046509 \n",
"d1_heartrate_max -0.056786 -0.018662 \n",
"d1_heartrate_min -0.019946 -0.010110 \n",
"d1_mbp_max -0.132677 0.021157 \n",
"d1_mbp_min 0.007748 -0.024207 \n",
"d1_mbp_noninvasive_max -0.133263 0.022015 \n",
"d1_mbp_noninvasive_min 0.008560 -0.024457 \n",
"d1_resprate_max -0.069187 0.014016 \n",
"d1_resprate_min -0.168874 -0.021308 \n",
"d1_spo2_max 0.051252 0.037592 \n",
"d1_spo2_min 0.037978 -0.035431 \n",
"d1_sysbp_max -0.082411 0.054935 \n",
"d1_sysbp_min 0.012446 -0.016247 \n",
"d1_sysbp_noninvasive_max -0.084154 0.054852 \n",
"d1_sysbp_noninvasive_min 0.013384 -0.016477 \n",
"d1_temp_max 0.069778 -0.022309 \n",
"d1_temp_min -0.034584 -0.030998 \n",
"h1_diasbp_max -0.103299 -0.011401 \n",
"h1_diasbp_min -0.065023 -0.032482 \n",
"h1_diasbp_noninvasive_max -0.099954 -0.010648 \n",
"h1_diasbp_noninvasive_min -0.059248 -0.032678 \n",
"h1_heartrate_max -0.098490 -0.018119 \n",
"h1_heartrate_min -0.119469 -0.017100 \n",
"h1_mbp_max -0.071144 0.005185 \n",
"h1_mbp_min -0.049850 -0.010165 \n",
"h1_mbp_noninvasive_max -0.072910 0.007339 \n",
"h1_mbp_noninvasive_min -0.048824 -0.010562 \n",
"h1_resprate_max -0.185293 0.017168 \n",
"h1_resprate_min -0.248028 0.003499 \n",
"h1_spo2_max 0.098814 0.018901 \n",
"h1_spo2_min 0.055965 -0.018911 \n",
"h1_sysbp_max -0.038583 0.030642 \n",
"h1_sysbp_min -0.032774 0.007560 \n",
"h1_sysbp_noninvasive_max -0.044924 0.031027 \n",
"h1_sysbp_noninvasive_min -0.034978 0.008561 \n",
"d1_glucose_max -0.019896 0.033822 \n",
"d1_glucose_min 0.030704 -0.062408 \n",
"d1_potassium_max 0.081878 0.108981 \n",
"d1_potassium_min 0.042118 0.086886 \n",
"apache_4a_hospital_death_prob -0.107271 0.037495 \n",
"apache_4a_icu_death_prob -0.074592 0.031092 \n",
"aids -0.006550 0.007424 \n",
"cirrhosis -0.035495 0.024045 \n",
"diabetes_mellitus -0.018465 0.107260 \n",
"hepatic_failure -0.036657 0.018832 \n",
"immunosuppression -0.012610 0.001526 \n",
"leukemia -0.013973 0.016028 \n",
"lymphoma -0.009933 -0.006626 \n",
"solid_tumor_with_metastasis 0.015989 -0.009522 \n",
"\n",
" gcs_eyes_apache gcs_motor_apache \\\n",
"encounter_id 0.005690 0.010362 \n",
"patient_id 0.002008 0.001737 \n",
"hospital_id -0.007684 -0.017686 \n",
"age 0.043832 0.044063 \n",
"bmi 0.012599 0.020875 \n",
"elective_surgery 0.019979 0.027753 \n",
"height -0.005365 -0.014861 \n",
"icu_id -0.010539 -0.012416 \n",
"pre_icu_los_days -0.015469 -0.005204 \n",
"weight 0.010306 0.013349 \n",
"apache_2_diagnosis 0.048408 0.064847 \n",
"apache_3j_diagnosis -0.021989 0.009909 \n",
"apache_post_operative 0.001300 0.014019 \n",
"arf_apache -0.003291 -0.002774 \n",
"gcs_eyes_apache 1.000000 0.794596 \n",
"gcs_motor_apache 0.794596 1.000000 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.776900 0.696600 \n",
"heart_rate_apache -0.096108 -0.086743 \n",
"intubated_apache -0.417651 -0.382408 \n",
"map_apache -0.007712 -0.012069 \n",
"resprate_apache 0.003187 -0.003500 \n",
"temp_apache 0.145228 0.192582 \n",
"ventilated_apache -0.500394 -0.443245 \n",
"d1_diasbp_max -0.044874 -0.041852 \n",
"d1_diasbp_min 0.107759 0.096103 \n",
"d1_diasbp_noninvasive_max -0.044373 -0.041570 \n",
"d1_diasbp_noninvasive_min 0.107441 0.096157 \n",
"d1_heartrate_max -0.140660 -0.133273 \n",
"d1_heartrate_min 0.015849 0.028772 \n",
"d1_mbp_max -0.030762 -0.034748 \n",
"d1_mbp_min 0.133706 0.122254 \n",
"d1_mbp_noninvasive_max -0.029471 -0.033965 \n",
"d1_mbp_noninvasive_min 0.133569 0.122598 \n",
"d1_resprate_max -0.047472 -0.044868 \n",
"d1_resprate_min 0.073119 0.058125 \n",
"d1_spo2_max -0.123692 -0.101453 \n",
"d1_spo2_min 0.084127 0.092508 \n",
"d1_sysbp_max -0.036611 -0.033340 \n",
"d1_sysbp_min 0.162278 0.154878 \n",
"d1_sysbp_noninvasive_max -0.035408 -0.032642 \n",
"d1_sysbp_noninvasive_min 0.162169 0.155134 \n",
"d1_temp_max -0.153986 -0.113055 \n",
"d1_temp_min 0.211348 0.263712 \n",
"h1_diasbp_max -0.034360 -0.033919 \n",
"h1_diasbp_min 0.072886 0.061247 \n",
"h1_diasbp_noninvasive_max -0.033682 -0.033285 \n",
"h1_diasbp_noninvasive_min 0.072800 0.061085 \n",
"h1_heartrate_max -0.087839 -0.083696 \n",
"h1_heartrate_min -0.041512 -0.039050 \n",
"h1_mbp_max -0.008476 -0.012495 \n",
"h1_mbp_min 0.097894 0.083482 \n",
"h1_mbp_noninvasive_max -0.007695 -0.011679 \n",
"h1_mbp_noninvasive_min 0.097492 0.083178 \n",
"h1_resprate_max -0.009141 -0.016466 \n",
"h1_resprate_min 0.022648 0.010979 \n",
"h1_spo2_max -0.070952 -0.058519 \n",
"h1_spo2_min 0.025469 0.028647 \n",
"h1_sysbp_max 0.006956 0.009633 \n",
"h1_sysbp_min 0.116574 0.108234 \n",
"h1_sysbp_noninvasive_max 0.007303 0.009781 \n",
"h1_sysbp_noninvasive_min 0.115242 0.106860 \n",
"d1_glucose_max -0.074507 -0.084388 \n",
"d1_glucose_min 0.038209 0.028602 \n",
"d1_potassium_max -0.064467 -0.064191 \n",
"d1_potassium_min 0.082590 0.086013 \n",
"apache_4a_hospital_death_prob -0.465077 -0.507284 \n",
"apache_4a_icu_death_prob -0.449872 -0.504793 \n",
"aids -0.002167 -0.002878 \n",
"cirrhosis -0.013570 -0.007157 \n",
"diabetes_mellitus 0.034854 0.031341 \n",
"hepatic_failure -0.010770 -0.004973 \n",
"immunosuppression 0.022177 0.022238 \n",
"leukemia 0.006807 0.009805 \n",
"lymphoma 0.012052 0.008518 \n",
"solid_tumor_with_metastasis 0.017411 0.016147 \n",
"\n",
" gcs_unable_apache gcs_verbal_apache \\\n",
"encounter_id NaN 0.008068 \n",
"patient_id NaN 0.002699 \n",
"hospital_id NaN 0.003370 \n",
"age NaN -0.002946 \n",
"bmi NaN 0.027426 \n",
"elective_surgery NaN -0.006857 \n",
"height NaN 0.011777 \n",
"icu_id NaN -0.020057 \n",
"pre_icu_los_days NaN -0.038746 \n",
"weight NaN 0.030702 \n",
"apache_2_diagnosis NaN 0.021887 \n",
"apache_3j_diagnosis NaN -0.045118 \n",
"apache_post_operative NaN -0.028723 \n",
"arf_apache NaN -0.000044 \n",
"gcs_eyes_apache NaN 0.776900 \n",
"gcs_motor_apache NaN 0.696600 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache NaN 1.000000 \n",
"heart_rate_apache NaN -0.116841 \n",
"intubated_apache NaN -0.476666 \n",
"map_apache NaN -0.006972 \n",
"resprate_apache NaN -0.002626 \n",
"temp_apache NaN 0.114977 \n",
"ventilated_apache NaN -0.581067 \n",
"d1_diasbp_max NaN -0.039461 \n",
"d1_diasbp_min NaN 0.118994 \n",
"d1_diasbp_noninvasive_max NaN -0.039126 \n",
"d1_diasbp_noninvasive_min NaN 0.118219 \n",
"d1_heartrate_max NaN -0.157384 \n",
"d1_heartrate_min NaN -0.007727 \n",
"d1_mbp_max NaN -0.025780 \n",
"d1_mbp_min NaN 0.148542 \n",
"d1_mbp_noninvasive_max NaN -0.023749 \n",
"d1_mbp_noninvasive_min NaN 0.148113 \n",
"d1_resprate_max NaN -0.049435 \n",
"d1_resprate_min NaN 0.056118 \n",
"d1_spo2_max NaN -0.147973 \n",
"d1_spo2_min NaN 0.080855 \n",
"d1_sysbp_max NaN -0.034973 \n",
"d1_sysbp_min NaN 0.176170 \n",
"d1_sysbp_noninvasive_max NaN -0.033439 \n",
"d1_sysbp_noninvasive_min NaN 0.175646 \n",
"d1_temp_max NaN -0.188147 \n",
"d1_temp_min NaN 0.182067 \n",
"h1_diasbp_max NaN -0.027725 \n",
"h1_diasbp_min NaN 0.084225 \n",
"h1_diasbp_noninvasive_max NaN -0.027240 \n",
"h1_diasbp_noninvasive_min NaN 0.082832 \n",
"h1_heartrate_max NaN -0.108800 \n",
"h1_heartrate_min NaN -0.061233 \n",
"h1_mbp_max NaN -0.000979 \n",
"h1_mbp_min NaN 0.110696 \n",
"h1_mbp_noninvasive_max NaN -0.001454 \n",
"h1_mbp_noninvasive_min NaN 0.110544 \n",
"h1_resprate_max NaN -0.018440 \n",
"h1_resprate_min NaN 0.006286 \n",
"h1_spo2_max NaN -0.085101 \n",
"h1_spo2_min NaN 0.018238 \n",
"h1_sysbp_max NaN 0.010672 \n",
"h1_sysbp_min NaN 0.126867 \n",
"h1_sysbp_noninvasive_max NaN 0.011126 \n",
"h1_sysbp_noninvasive_min NaN 0.126260 \n",
"d1_glucose_max NaN -0.068488 \n",
"d1_glucose_min NaN 0.050847 \n",
"d1_potassium_max NaN -0.066874 \n",
"d1_potassium_min NaN 0.090999 \n",
"apache_4a_hospital_death_prob NaN -0.451790 \n",
"apache_4a_icu_death_prob NaN -0.419053 \n",
"aids NaN -0.001815 \n",
"cirrhosis NaN -0.010962 \n",
"diabetes_mellitus NaN 0.031691 \n",
"hepatic_failure NaN -0.008965 \n",
"immunosuppression NaN 0.026622 \n",
"leukemia NaN 0.012140 \n",
"lymphoma NaN 0.011510 \n",
"solid_tumor_with_metastasis NaN 0.017257 \n",
"\n",
" heart_rate_apache intubated_apache \\\n",
"encounter_id -0.002901 -0.007877 \n",
"patient_id 0.004945 0.003930 \n",
"hospital_id -0.006215 0.019899 \n",
"age -0.155835 -0.003185 \n",
"bmi -0.028840 0.032551 \n",
"elective_surgery -0.068348 0.120489 \n",
"height -0.020577 0.012339 \n",
"icu_id 0.007270 -0.072495 \n",
"pre_icu_los_days 0.054741 0.047833 \n",
"weight -0.036486 0.036267 \n",
"apache_2_diagnosis -0.098076 0.005791 \n",
"apache_3j_diagnosis 0.008754 0.097536 \n",
"apache_post_operative -0.053109 0.142304 \n",
"arf_apache -0.015231 -0.002325 \n",
"gcs_eyes_apache -0.096108 -0.417651 \n",
"gcs_motor_apache -0.086743 -0.382408 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache -0.116841 -0.476666 \n",
"heart_rate_apache 1.000000 0.082599 \n",
"intubated_apache 0.082599 1.000000 \n",
"map_apache 0.027456 -0.013506 \n",
"resprate_apache 0.196669 -0.014614 \n",
"temp_apache 0.108176 -0.125002 \n",
"ventilated_apache 0.118081 0.601956 \n",
"d1_diasbp_max 0.101246 -0.010973 \n",
"d1_diasbp_min -0.008455 -0.111654 \n",
"d1_diasbp_noninvasive_max 0.101551 -0.011267 \n",
"d1_diasbp_noninvasive_min -0.008987 -0.110250 \n",
"d1_heartrate_max 0.808829 0.118692 \n",
"d1_heartrate_min 0.572694 0.003739 \n",
"d1_mbp_max 0.048618 -0.029078 \n",
"d1_mbp_min -0.070482 -0.149357 \n",
"d1_mbp_noninvasive_max 0.047616 -0.031091 \n",
"d1_mbp_noninvasive_min -0.070210 -0.148440 \n",
"d1_resprate_max 0.172729 0.031298 \n",
"d1_resprate_min 0.115617 -0.071848 \n",
"d1_spo2_max 0.020593 0.120012 \n",
"d1_spo2_min -0.107299 -0.057270 \n",
"d1_sysbp_max -0.037824 -0.011818 \n",
"d1_sysbp_min -0.143812 -0.183552 \n",
"d1_sysbp_noninvasive_max -0.037444 -0.013041 \n",
"d1_sysbp_noninvasive_min -0.144073 -0.182124 \n",
"d1_temp_max 0.246108 0.153535 \n",
"d1_temp_min 0.064424 -0.169867 \n",
"h1_diasbp_max 0.081382 -0.002164 \n",
"h1_diasbp_min 0.035233 -0.087451 \n",
"h1_diasbp_noninvasive_max 0.081874 -0.000954 \n",
"h1_diasbp_noninvasive_min 0.036045 -0.087045 \n",
"h1_heartrate_max 0.721986 0.081267 \n",
"h1_heartrate_min 0.691027 0.046190 \n",
"h1_mbp_max 0.010655 -0.032933 \n",
"h1_mbp_min -0.030935 -0.118845 \n",
"h1_mbp_noninvasive_max 0.011240 -0.032970 \n",
"h1_mbp_noninvasive_min -0.031043 -0.118491 \n",
"h1_resprate_max 0.210330 -0.003491 \n",
"h1_resprate_min 0.200072 -0.038077 \n",
"h1_spo2_max -0.044663 0.077693 \n",
"h1_spo2_min -0.071950 -0.019951 \n",
"h1_sysbp_max -0.069510 -0.039953 \n",
"h1_sysbp_min -0.104163 -0.140067 \n",
"h1_sysbp_noninvasive_max -0.068836 -0.041261 \n",
"h1_sysbp_noninvasive_min -0.104698 -0.139769 \n",
"d1_glucose_max 0.103486 0.083622 \n",
"d1_glucose_min 0.060540 -0.026432 \n",
"d1_potassium_max 0.011124 0.107169 \n",
"d1_potassium_min -0.072557 -0.064286 \n",
"apache_4a_hospital_death_prob 0.124840 0.332905 \n",
"apache_4a_icu_death_prob 0.115316 0.339431 \n",
"aids 0.007786 0.006411 \n",
"cirrhosis 0.014019 0.005999 \n",
"diabetes_mellitus -0.018177 -0.013904 \n",
"hepatic_failure 0.012780 0.002573 \n",
"immunosuppression 0.058280 -0.009710 \n",
"leukemia 0.021585 -0.001891 \n",
"lymphoma 0.019672 -0.005110 \n",
"solid_tumor_with_metastasis 0.044459 -0.010965 \n",
"\n",
" map_apache resprate_apache temp_apache \\\n",
"encounter_id -0.001580 0.008714 0.006060 \n",
"patient_id -0.003315 0.002281 -0.000320 \n",
"hospital_id -0.002743 -0.020449 -0.032016 \n",
"age -0.017040 0.034650 -0.081164 \n",
"bmi 0.056656 0.005877 0.035419 \n",
"elective_surgery 0.007780 -0.133643 -0.033948 \n",
"height 0.036710 -0.051661 0.014273 \n",
"icu_id -0.005271 -0.003775 0.001765 \n",
"pre_icu_los_days -0.032297 0.021648 0.008130 \n",
"weight 0.070071 -0.016454 0.041245 \n",
"apache_2_diagnosis 0.039976 -0.089370 0.009267 \n",
"apache_3j_diagnosis -0.001793 -0.127062 0.010111 \n",
"apache_post_operative 0.012210 -0.141530 -0.036426 \n",
"arf_apache 0.008417 0.008824 -0.019997 \n",
"gcs_eyes_apache -0.007712 0.003187 0.145228 \n",
"gcs_motor_apache -0.012069 -0.003500 0.192582 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.006972 -0.002626 0.114977 \n",
"heart_rate_apache 0.027456 0.196669 0.108176 \n",
"intubated_apache -0.013506 -0.014614 -0.125002 \n",
"map_apache 1.000000 0.111839 -0.012181 \n",
"resprate_apache 0.111839 1.000000 0.050784 \n",
"temp_apache -0.012181 0.050784 1.000000 \n",
"ventilated_apache -0.016349 0.024450 -0.125458 \n",
"d1_diasbp_max 0.432408 0.006292 -0.006421 \n",
"d1_diasbp_min 0.356524 0.067508 0.069681 \n",
"d1_diasbp_noninvasive_max 0.432948 0.006381 -0.006451 \n",
"d1_diasbp_noninvasive_min 0.355927 0.066720 0.069535 \n",
"d1_heartrate_max 0.003571 0.169273 0.078107 \n",
"d1_heartrate_min -0.016752 0.145635 0.175441 \n",
"d1_mbp_max 0.553784 0.125476 -0.003541 \n",
"d1_mbp_min 0.408331 0.120239 0.073489 \n",
"d1_mbp_noninvasive_max 0.553319 0.126928 -0.002554 \n",
"d1_mbp_noninvasive_min 0.408167 0.119610 0.073666 \n",
"d1_resprate_max 0.057543 0.561446 0.020480 \n",
"d1_resprate_min 0.017138 0.331596 0.101112 \n",
"d1_spo2_max -0.039301 -0.078245 -0.046599 \n",
"d1_spo2_min 0.023344 -0.071114 0.057146 \n",
"d1_sysbp_max 0.477629 0.041616 0.007933 \n",
"d1_sysbp_min 0.368801 0.022501 0.096424 \n",
"d1_sysbp_noninvasive_max 0.478301 0.042496 0.008841 \n",
"d1_sysbp_noninvasive_min 0.368645 0.021807 0.096650 \n",
"d1_temp_max -0.035858 0.061428 0.414180 \n",
"d1_temp_min -0.003270 0.030762 0.787391 \n",
"h1_diasbp_max 0.390322 0.023518 0.002494 \n",
"h1_diasbp_min 0.373429 0.043064 0.048547 \n",
"h1_diasbp_noninvasive_max 0.391338 0.023909 0.004235 \n",
"h1_diasbp_noninvasive_min 0.374294 0.043308 0.046943 \n",
"h1_heartrate_max -0.008144 0.167701 0.122384 \n",
"h1_heartrate_min -0.023693 0.174211 0.150136 \n",
"h1_mbp_max 0.478144 0.111467 0.013527 \n",
"h1_mbp_min 0.437433 0.111741 0.052644 \n",
"h1_mbp_noninvasive_max 0.477121 0.113088 0.014439 \n",
"h1_mbp_noninvasive_min 0.437557 0.112462 0.052593 \n",
"h1_resprate_max 0.047885 0.479384 0.059421 \n",
"h1_resprate_min 0.016095 0.389324 0.084145 \n",
"h1_spo2_max -0.007026 -0.087868 -0.033760 \n",
"h1_spo2_min 0.014667 -0.074162 0.017170 \n",
"h1_sysbp_max 0.423854 0.024309 0.033627 \n",
"h1_sysbp_min 0.392038 0.023338 0.079091 \n",
"h1_sysbp_noninvasive_max 0.424494 0.025022 0.034800 \n",
"h1_sysbp_noninvasive_min 0.392033 0.022385 0.079162 \n",
"d1_glucose_max 0.014743 0.029153 -0.079647 \n",
"d1_glucose_min 0.049499 0.027490 0.030358 \n",
"d1_potassium_max -0.038048 -0.012051 -0.104586 \n",
"d1_potassium_min -0.030348 -0.013941 0.003755 \n",
"apache_4a_hospital_death_prob -0.030694 0.098826 -0.197472 \n",
"apache_4a_icu_death_prob -0.023802 0.082287 -0.206398 \n",
"aids 0.002195 0.012499 0.001945 \n",
"cirrhosis -0.036326 -0.003093 -0.018439 \n",
"diabetes_mellitus -0.000444 -0.013947 -0.000627 \n",
"hepatic_failure -0.044452 -0.006893 -0.023581 \n",
"immunosuppression -0.021612 0.036010 0.005193 \n",
"leukemia -0.018537 0.019099 0.003955 \n",
"lymphoma -0.009331 0.013559 -0.001379 \n",
"solid_tumor_with_metastasis -0.010763 0.016087 -0.002067 \n",
"\n",
" ventilated_apache d1_diasbp_max \\\n",
"encounter_id -0.013043 -0.002887 \n",
"patient_id -0.001335 -0.002492 \n",
"hospital_id 0.025514 -0.019117 \n",
"age 0.023629 -0.056655 \n",
"bmi 0.076165 0.053021 \n",
"elective_surgery 0.116024 -0.158602 \n",
"height -0.004478 0.040730 \n",
"icu_id 0.016636 0.005453 \n",
"pre_icu_los_days 0.076306 -0.050342 \n",
"weight 0.069353 0.069792 \n",
"apache_2_diagnosis -0.034906 -0.048332 \n",
"apache_3j_diagnosis 0.079998 -0.145480 \n",
"apache_post_operative 0.141123 -0.161697 \n",
"arf_apache -0.002529 0.000905 \n",
"gcs_eyes_apache -0.500394 -0.044874 \n",
"gcs_motor_apache -0.443245 -0.041852 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache -0.581067 -0.039461 \n",
"heart_rate_apache 0.118081 0.101246 \n",
"intubated_apache 0.601956 -0.010973 \n",
"map_apache -0.016349 0.432408 \n",
"resprate_apache 0.024450 0.006292 \n",
"temp_apache -0.125458 -0.006421 \n",
"ventilated_apache 1.000000 -0.000551 \n",
"d1_diasbp_max -0.000551 1.000000 \n",
"d1_diasbp_min -0.147475 0.133011 \n",
"d1_diasbp_noninvasive_max -0.000708 0.998410 \n",
"d1_diasbp_noninvasive_min -0.146697 0.131743 \n",
"d1_heartrate_max 0.168589 0.171201 \n",
"d1_heartrate_min 0.017658 -0.040394 \n",
"d1_mbp_max -0.010859 0.837572 \n",
"d1_mbp_min -0.183136 0.107034 \n",
"d1_mbp_noninvasive_max -0.012669 0.834341 \n",
"d1_mbp_noninvasive_min -0.182826 0.106048 \n",
"d1_resprate_max 0.094779 0.097553 \n",
"d1_resprate_min -0.068154 -0.081754 \n",
"d1_spo2_max 0.152683 0.061128 \n",
"d1_spo2_min -0.121671 -0.097912 \n",
"d1_sysbp_max 0.007514 0.599638 \n",
"d1_sysbp_min -0.215911 0.093446 \n",
"d1_sysbp_noninvasive_max 0.006469 0.599525 \n",
"d1_sysbp_noninvasive_min -0.215455 0.092971 \n",
"d1_temp_max 0.207720 0.013822 \n",
"d1_temp_min -0.184548 -0.020902 \n",
"h1_diasbp_max -0.000577 0.604668 \n",
"h1_diasbp_min -0.108953 0.337590 \n",
"h1_diasbp_noninvasive_max 0.000128 0.603192 \n",
"h1_diasbp_noninvasive_min -0.107023 0.336034 \n",
"h1_heartrate_max 0.119361 0.118285 \n",
"h1_heartrate_min 0.068821 0.050352 \n",
"h1_mbp_max -0.024937 0.516366 \n",
"h1_mbp_min -0.140207 0.305863 \n",
"h1_mbp_noninvasive_max -0.026200 0.515910 \n",
"h1_mbp_noninvasive_min -0.139786 0.305352 \n",
"h1_resprate_max 0.056546 0.084637 \n",
"h1_resprate_min 0.006394 -0.001960 \n",
"h1_spo2_max 0.083931 -0.001744 \n",
"h1_spo2_min -0.046864 -0.054609 \n",
"h1_sysbp_max -0.031710 0.432473 \n",
"h1_sysbp_min -0.157782 0.263487 \n",
"h1_sysbp_noninvasive_max -0.034463 0.435978 \n",
"h1_sysbp_noninvasive_min -0.157497 0.265481 \n",
"d1_glucose_max 0.090256 0.004768 \n",
"d1_glucose_min -0.004030 0.017044 \n",
"d1_potassium_max 0.141289 -0.026528 \n",
"d1_potassium_min -0.032025 -0.028403 \n",
"apache_4a_hospital_death_prob 0.351655 0.013195 \n",
"apache_4a_icu_death_prob 0.337154 0.012630 \n",
"aids 0.006785 0.010762 \n",
"cirrhosis -0.006741 -0.017529 \n",
"diabetes_mellitus -0.002992 -0.019824 \n",
"hepatic_failure -0.006189 -0.023154 \n",
"immunosuppression -0.004677 -0.019109 \n",
"leukemia -0.004202 -0.009462 \n",
"lymphoma -0.004654 -0.007399 \n",
"solid_tumor_with_metastasis -0.016788 -0.023977 \n",
"\n",
" d1_diasbp_min d1_diasbp_noninvasive_max \\\n",
"encounter_id -0.000322 -0.002818 \n",
"patient_id -0.001735 -0.002276 \n",
"hospital_id 0.013821 -0.018961 \n",
"age -0.208602 -0.056564 \n",
"bmi -0.028818 0.053025 \n",
"elective_surgery 0.003968 -0.158423 \n",
"height 0.133386 0.040656 \n",
"icu_id -0.023755 0.005764 \n",
"pre_icu_los_days -0.046183 -0.049986 \n",
"weight 0.032016 0.069772 \n",
"apache_2_diagnosis 0.060281 -0.048097 \n",
"apache_3j_diagnosis -0.000491 -0.145177 \n",
"apache_post_operative 0.001930 -0.161432 \n",
"arf_apache -0.046441 0.000787 \n",
"gcs_eyes_apache 0.107759 -0.044373 \n",
"gcs_motor_apache 0.096103 -0.041570 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.118994 -0.039126 \n",
"heart_rate_apache -0.008455 0.101551 \n",
"intubated_apache -0.111654 -0.011267 \n",
"map_apache 0.356524 0.432948 \n",
"resprate_apache 0.067508 0.006381 \n",
"temp_apache 0.069681 -0.006451 \n",
"ventilated_apache -0.147475 -0.000708 \n",
"d1_diasbp_max 0.133011 0.998410 \n",
"d1_diasbp_min 1.000000 0.133066 \n",
"d1_diasbp_noninvasive_max 0.133066 1.000000 \n",
"d1_diasbp_noninvasive_min 0.998499 0.132062 \n",
"d1_heartrate_max -0.107851 0.171342 \n",
"d1_heartrate_min 0.127837 -0.039583 \n",
"d1_mbp_max 0.223327 0.836061 \n",
"d1_mbp_min 0.862357 0.107176 \n",
"d1_mbp_noninvasive_max 0.223679 0.835963 \n",
"d1_mbp_noninvasive_min 0.862618 0.106437 \n",
"d1_resprate_max -0.089984 0.097024 \n",
"d1_resprate_min 0.136697 -0.081261 \n",
"d1_spo2_max -0.149467 0.060630 \n",
"d1_spo2_min 0.213147 -0.097955 \n",
"d1_sysbp_max 0.154440 0.599013 \n",
"d1_sysbp_min 0.666660 0.093600 \n",
"d1_sysbp_noninvasive_max 0.155061 0.600818 \n",
"d1_sysbp_noninvasive_min 0.665898 0.093238 \n",
"d1_temp_max -0.106248 0.013531 \n",
"d1_temp_min 0.110313 -0.020802 \n",
"h1_diasbp_max 0.348050 0.604847 \n",
"h1_diasbp_min 0.628693 0.337591 \n",
"h1_diasbp_noninvasive_max 0.348236 0.604102 \n",
"h1_diasbp_noninvasive_min 0.629021 0.336400 \n",
"h1_heartrate_max -0.028420 0.118575 \n",
"h1_heartrate_min 0.038660 0.050873 \n",
"h1_mbp_max 0.389268 0.516849 \n",
"h1_mbp_min 0.591288 0.305999 \n",
"h1_mbp_noninvasive_max 0.392297 0.516466 \n",
"h1_mbp_noninvasive_min 0.592603 0.305559 \n",
"h1_resprate_max -0.052964 0.084279 \n",
"h1_resprate_min 0.034346 -0.001902 \n",
"h1_spo2_max -0.021251 -0.001848 \n",
"h1_spo2_min 0.117988 -0.054659 \n",
"h1_sysbp_max 0.271229 0.432758 \n",
"h1_sysbp_min 0.438911 0.263474 \n",
"h1_sysbp_noninvasive_max 0.271816 0.436690 \n",
"h1_sysbp_noninvasive_min 0.437978 0.265585 \n",
"d1_glucose_max -0.058470 0.004872 \n",
"d1_glucose_min 0.029621 0.017193 \n",
"d1_potassium_max -0.147363 -0.026837 \n",
"d1_potassium_min -0.064450 -0.028376 \n",
"apache_4a_hospital_death_prob -0.193258 0.012716 \n",
"apache_4a_icu_death_prob -0.168312 0.012448 \n",
"aids 0.012464 0.010759 \n",
"cirrhosis -0.046882 -0.017306 \n",
"diabetes_mellitus -0.054714 -0.019583 \n",
"hepatic_failure -0.050688 -0.023155 \n",
"immunosuppression -0.015537 -0.019113 \n",
"leukemia -0.028716 -0.009464 \n",
"lymphoma -0.007431 -0.007400 \n",
"solid_tumor_with_metastasis -0.002172 -0.023979 \n",
"\n",
" d1_diasbp_noninvasive_min d1_heartrate_max \\\n",
"encounter_id -0.000037 -0.006774 \n",
"patient_id -0.001946 -0.000357 \n",
"hospital_id 0.013578 -0.006119 \n",
"age -0.207904 -0.145065 \n",
"bmi -0.029103 -0.036482 \n",
"elective_surgery 0.005656 -0.072214 \n",
"height 0.133630 -0.015156 \n",
"icu_id -0.023987 -0.011112 \n",
"pre_icu_los_days -0.045732 0.061604 \n",
"weight 0.031905 -0.040901 \n",
"apache_2_diagnosis 0.061099 -0.118243 \n",
"apache_3j_diagnosis 0.000730 -0.007217 \n",
"apache_post_operative 0.003695 -0.056786 \n",
"arf_apache -0.046509 -0.018662 \n",
"gcs_eyes_apache 0.107441 -0.140660 \n",
"gcs_motor_apache 0.096157 -0.133273 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.118219 -0.157384 \n",
"heart_rate_apache -0.008987 0.808829 \n",
"intubated_apache -0.110250 0.118692 \n",
"map_apache 0.355927 0.003571 \n",
"resprate_apache 0.066720 0.169273 \n",
"temp_apache 0.069535 0.078107 \n",
"ventilated_apache -0.146697 0.168589 \n",
"d1_diasbp_max 0.131743 0.171201 \n",
"d1_diasbp_min 0.998499 -0.107851 \n",
"d1_diasbp_noninvasive_max 0.132062 0.171342 \n",
"d1_diasbp_noninvasive_min 1.000000 -0.108221 \n",
"d1_heartrate_max -0.108221 1.000000 \n",
"d1_heartrate_min 0.127608 0.475471 \n",
"d1_mbp_max 0.222847 0.101443 \n",
"d1_mbp_min 0.861639 -0.177674 \n",
"d1_mbp_noninvasive_max 0.222657 0.099977 \n",
"d1_mbp_noninvasive_min 0.863828 -0.177521 \n",
"d1_resprate_max -0.090210 0.249481 \n",
"d1_resprate_min 0.136028 0.037477 \n",
"d1_spo2_max -0.148878 0.076288 \n",
"d1_spo2_min 0.213191 -0.186051 \n",
"d1_sysbp_max 0.154595 0.018402 \n",
"d1_sysbp_min 0.665910 -0.248651 \n",
"d1_sysbp_noninvasive_max 0.154209 0.018522 \n",
"d1_sysbp_noninvasive_min 0.667129 -0.248627 \n",
"d1_temp_max -0.105454 0.281152 \n",
"d1_temp_min 0.110311 0.012378 \n",
"h1_diasbp_max 0.347372 0.105612 \n",
"h1_diasbp_min 0.628457 -0.023891 \n",
"h1_diasbp_noninvasive_max 0.347740 0.104771 \n",
"h1_diasbp_noninvasive_min 0.629334 -0.022509 \n",
"h1_heartrate_max -0.028917 0.787313 \n",
"h1_heartrate_min 0.038356 0.677401 \n",
"h1_mbp_max 0.388923 0.018777 \n",
"h1_mbp_min 0.591434 -0.093579 \n",
"h1_mbp_noninvasive_max 0.391856 0.017876 \n",
"h1_mbp_noninvasive_min 0.592823 -0.093994 \n",
"h1_resprate_max -0.053816 0.254646 \n",
"h1_resprate_min 0.033463 0.175305 \n",
"h1_spo2_max -0.021066 -0.032473 \n",
"h1_spo2_min 0.118203 -0.115732 \n",
"h1_sysbp_max 0.270993 -0.050685 \n",
"h1_sysbp_min 0.439004 -0.160615 \n",
"h1_sysbp_noninvasive_max 0.271499 -0.050559 \n",
"h1_sysbp_noninvasive_min 0.438266 -0.160745 \n",
"d1_glucose_max -0.058270 0.109506 \n",
"d1_glucose_min 0.029728 0.053443 \n",
"d1_potassium_max -0.146484 0.039406 \n",
"d1_potassium_min -0.064412 -0.076311 \n",
"apache_4a_hospital_death_prob -0.193442 0.171262 \n",
"apache_4a_icu_death_prob -0.168482 0.158557 \n",
"aids 0.012426 0.009853 \n",
"cirrhosis -0.046496 0.015702 \n",
"diabetes_mellitus -0.054751 -0.026411 \n",
"hepatic_failure -0.050795 0.015661 \n",
"immunosuppression -0.015711 0.066606 \n",
"leukemia -0.028798 0.023870 \n",
"lymphoma -0.007495 0.019838 \n",
"solid_tumor_with_metastasis -0.002327 0.048240 \n",
"\n",
" d1_heartrate_min d1_mbp_max d1_mbp_min \\\n",
"encounter_id 0.001381 -0.000186 -0.001552 \n",
"patient_id 0.000861 -0.004624 -0.002503 \n",
"hospital_id -0.008510 -0.019417 0.008801 \n",
"age -0.147906 0.006689 -0.129142 \n",
"bmi 0.006305 0.061985 0.000166 \n",
"elective_surgery -0.028268 -0.130329 0.010672 \n",
"height -0.028183 0.028673 0.094093 \n",
"icu_id 0.019052 -0.006270 -0.022464 \n",
"pre_icu_los_days 0.054006 -0.050178 -0.050439 \n",
"weight -0.006231 0.072860 0.042409 \n",
"apache_2_diagnosis -0.046336 -0.019555 0.072619 \n",
"apache_3j_diagnosis 0.046211 -0.127620 0.001836 \n",
"apache_post_operative -0.019946 -0.132677 0.007748 \n",
"arf_apache -0.010110 0.021157 -0.024207 \n",
"gcs_eyes_apache 0.015849 -0.030762 0.133706 \n",
"gcs_motor_apache 0.028772 -0.034748 0.122254 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.007727 -0.025780 0.148542 \n",
"heart_rate_apache 0.572694 0.048618 -0.070482 \n",
"intubated_apache 0.003739 -0.029078 -0.149357 \n",
"map_apache -0.016752 0.553784 0.408331 \n",
"resprate_apache 0.145635 0.125476 0.120239 \n",
"temp_apache 0.175441 -0.003541 0.073489 \n",
"ventilated_apache 0.017658 -0.010859 -0.183136 \n",
"d1_diasbp_max -0.040394 0.837572 0.107034 \n",
"d1_diasbp_min 0.127837 0.223327 0.862357 \n",
"d1_diasbp_noninvasive_max -0.039583 0.836061 0.107176 \n",
"d1_diasbp_noninvasive_min 0.127608 0.222847 0.861639 \n",
"d1_heartrate_max 0.475471 0.101443 -0.177674 \n",
"d1_heartrate_min 1.000000 -0.075752 0.076523 \n",
"d1_mbp_max -0.075752 1.000000 0.260372 \n",
"d1_mbp_min 0.076523 0.260372 1.000000 \n",
"d1_mbp_noninvasive_max -0.075587 0.985035 0.263331 \n",
"d1_mbp_noninvasive_min 0.077167 0.261208 0.996876 \n",
"d1_resprate_max 0.054984 0.163470 -0.063132 \n",
"d1_resprate_min 0.250033 -0.066112 0.150740 \n",
"d1_spo2_max -0.065965 0.022811 -0.167315 \n",
"d1_spo2_min 0.058831 -0.064223 0.227411 \n",
"d1_sysbp_max -0.140109 0.748749 0.252108 \n",
"d1_sysbp_min 0.021449 0.236612 0.803036 \n",
"d1_sysbp_noninvasive_max -0.139506 0.747446 0.252553 \n",
"d1_sysbp_noninvasive_min 0.021410 0.236445 0.802242 \n",
"d1_temp_max 0.182858 -0.006464 -0.127599 \n",
"d1_temp_min 0.158806 -0.014090 0.123000 \n",
"h1_diasbp_max -0.006163 0.575372 0.307212 \n",
"h1_diasbp_min 0.065950 0.381715 0.572007 \n",
"h1_diasbp_noninvasive_max -0.005030 0.573603 0.307468 \n",
"h1_diasbp_noninvasive_min 0.066044 0.380865 0.573238 \n",
"h1_heartrate_max 0.553763 0.052258 -0.096207 \n",
"h1_heartrate_min 0.680537 -0.012835 -0.027182 \n",
"h1_mbp_max -0.049830 0.656704 0.431133 \n",
"h1_mbp_min 0.009224 0.449462 0.649424 \n",
"h1_mbp_noninvasive_max -0.048665 0.655768 0.434475 \n",
"h1_mbp_noninvasive_min 0.009344 0.449958 0.650209 \n",
"h1_resprate_max 0.129373 0.122220 -0.038282 \n",
"h1_resprate_min 0.210351 0.012532 0.039833 \n",
"h1_spo2_max -0.059037 -0.002551 -0.022553 \n",
"h1_spo2_min 0.002237 -0.042100 0.121927 \n",
"h1_sysbp_max -0.114373 0.561486 0.364491 \n",
"h1_sysbp_min -0.049718 0.397023 0.537579 \n",
"h1_sysbp_noninvasive_max -0.113736 0.563790 0.364968 \n",
"h1_sysbp_noninvasive_min -0.050214 0.398017 0.536927 \n",
"d1_glucose_max 0.099929 0.025616 -0.046836 \n",
"d1_glucose_min 0.051655 0.031988 0.049530 \n",
"d1_potassium_max -0.005189 -0.037166 -0.146353 \n",
"d1_potassium_min -0.044381 -0.029030 -0.046372 \n",
"apache_4a_hospital_death_prob -0.004565 0.011926 -0.203707 \n",
"apache_4a_icu_death_prob -0.007087 0.009259 -0.182120 \n",
"aids 0.011322 0.009370 0.007909 \n",
"cirrhosis 0.022203 -0.029211 -0.050296 \n",
"diabetes_mellitus 0.018222 0.015416 -0.020560 \n",
"hepatic_failure 0.024140 -0.034595 -0.054639 \n",
"immunosuppression 0.054844 -0.021104 -0.026259 \n",
"leukemia 0.013753 -0.011892 -0.026944 \n",
"lymphoma 0.017229 -0.010193 -0.009239 \n",
"solid_tumor_with_metastasis 0.044348 -0.024688 -0.010799 \n",
"\n",
" d1_mbp_noninvasive_max d1_mbp_noninvasive_min \\\n",
"encounter_id 0.001062 -0.001624 \n",
"patient_id -0.004520 -0.002932 \n",
"hospital_id -0.018866 0.009003 \n",
"age 0.006209 -0.129187 \n",
"bmi 0.062245 -0.000184 \n",
"elective_surgery -0.131006 0.011451 \n",
"height 0.029074 0.094313 \n",
"icu_id -0.009059 -0.019549 \n",
"pre_icu_los_days -0.051078 -0.049819 \n",
"weight 0.073350 0.042221 \n",
"apache_2_diagnosis -0.021060 0.073237 \n",
"apache_3j_diagnosis -0.127689 0.002437 \n",
"apache_post_operative -0.133263 0.008560 \n",
"arf_apache 0.022015 -0.024457 \n",
"gcs_eyes_apache -0.029471 0.133569 \n",
"gcs_motor_apache -0.033965 0.122598 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache -0.023749 0.148113 \n",
"heart_rate_apache 0.047616 -0.070210 \n",
"intubated_apache -0.031091 -0.148440 \n",
"map_apache 0.553319 0.408167 \n",
"resprate_apache 0.126928 0.119610 \n",
"temp_apache -0.002554 0.073666 \n",
"ventilated_apache -0.012669 -0.182826 \n",
"d1_diasbp_max 0.834341 0.106048 \n",
"d1_diasbp_min 0.223679 0.862618 \n",
"d1_diasbp_noninvasive_max 0.835963 0.106437 \n",
"d1_diasbp_noninvasive_min 0.222657 0.863828 \n",
"d1_heartrate_max 0.099977 -0.177521 \n",
"d1_heartrate_min -0.075587 0.077167 \n",
"d1_mbp_max 0.985035 0.261208 \n",
"d1_mbp_min 0.263331 0.996876 \n",
"d1_mbp_noninvasive_max 1.000000 0.261688 \n",
"d1_mbp_noninvasive_min 0.261688 1.000000 \n",
"d1_resprate_max 0.163748 -0.063771 \n",
"d1_resprate_min -0.064142 0.150175 \n",
"d1_spo2_max 0.021926 -0.167117 \n",
"d1_spo2_min -0.063036 0.228135 \n",
"d1_sysbp_max 0.746551 0.251774 \n",
"d1_sysbp_min 0.238019 0.803383 \n",
"d1_sysbp_noninvasive_max 0.748714 0.251632 \n",
"d1_sysbp_noninvasive_min 0.237239 0.804875 \n",
"d1_temp_max -0.006817 -0.126997 \n",
"d1_temp_min -0.013644 0.123449 \n",
"h1_diasbp_max 0.573164 0.307228 \n",
"h1_diasbp_min 0.381789 0.573139 \n",
"h1_diasbp_noninvasive_max 0.572296 0.307626 \n",
"h1_diasbp_noninvasive_min 0.380650 0.574665 \n",
"h1_heartrate_max 0.050773 -0.096533 \n",
"h1_heartrate_min -0.012912 -0.026903 \n",
"h1_mbp_max 0.655184 0.431227 \n",
"h1_mbp_min 0.449962 0.649377 \n",
"h1_mbp_noninvasive_max 0.655304 0.434106 \n",
"h1_mbp_noninvasive_min 0.450112 0.651138 \n",
"h1_resprate_max 0.122971 -0.039792 \n",
"h1_resprate_min 0.013815 0.039593 \n",
"h1_spo2_max -0.002958 -0.022383 \n",
"h1_spo2_min -0.042061 0.122594 \n",
"h1_sysbp_max 0.560690 0.364109 \n",
"h1_sysbp_min 0.396912 0.538366 \n",
"h1_sysbp_noninvasive_max 0.563820 0.364611 \n",
"h1_sysbp_noninvasive_min 0.398145 0.537856 \n",
"d1_glucose_max 0.025646 -0.046577 \n",
"d1_glucose_min 0.031901 0.049670 \n",
"d1_potassium_max -0.037627 -0.146024 \n",
"d1_potassium_min -0.027664 -0.046391 \n",
"apache_4a_hospital_death_prob 0.010603 -0.204198 \n",
"apache_4a_icu_death_prob 0.008541 -0.182673 \n",
"aids 0.009354 0.007944 \n",
"cirrhosis -0.028820 -0.049825 \n",
"diabetes_mellitus 0.015328 -0.020812 \n",
"hepatic_failure -0.033779 -0.054604 \n",
"immunosuppression -0.020487 -0.026956 \n",
"leukemia -0.013337 -0.026687 \n",
"lymphoma -0.010189 -0.009197 \n",
"solid_tumor_with_metastasis -0.025317 -0.010691 \n",
"\n",
" d1_resprate_max d1_resprate_min d1_spo2_max \\\n",
"encounter_id 0.007901 0.001453 -0.002737 \n",
"patient_id 0.003176 0.003608 -0.003005 \n",
"hospital_id -0.032569 -0.019908 -0.009052 \n",
"age 0.027702 0.048954 -0.043002 \n",
"bmi 0.012221 -0.005307 -0.082259 \n",
"elective_surgery -0.064207 -0.159557 0.042150 \n",
"height -0.021115 -0.054963 -0.030168 \n",
"icu_id -0.012600 -0.033504 -0.027147 \n",
"pre_icu_los_days 0.032238 0.013877 0.023592 \n",
"weight 0.003680 -0.029463 -0.092802 \n",
"apache_2_diagnosis -0.074171 -0.090492 0.009111 \n",
"apache_3j_diagnosis -0.066415 -0.142348 0.061274 \n",
"apache_post_operative -0.069187 -0.168874 0.051252 \n",
"arf_apache 0.014016 -0.021308 0.037592 \n",
"gcs_eyes_apache -0.047472 0.073119 -0.123692 \n",
"gcs_motor_apache -0.044868 0.058125 -0.101453 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.049435 0.056118 -0.147973 \n",
"heart_rate_apache 0.172729 0.115617 0.020593 \n",
"intubated_apache 0.031298 -0.071848 0.120012 \n",
"map_apache 0.057543 0.017138 -0.039301 \n",
"resprate_apache 0.561446 0.331596 -0.078245 \n",
"temp_apache 0.020480 0.101112 -0.046599 \n",
"ventilated_apache 0.094779 -0.068154 0.152683 \n",
"d1_diasbp_max 0.097553 -0.081754 0.061128 \n",
"d1_diasbp_min -0.089984 0.136697 -0.149467 \n",
"d1_diasbp_noninvasive_max 0.097024 -0.081261 0.060630 \n",
"d1_diasbp_noninvasive_min -0.090210 0.136028 -0.148878 \n",
"d1_heartrate_max 0.249481 0.037477 0.076288 \n",
"d1_heartrate_min 0.054984 0.250033 -0.065965 \n",
"d1_mbp_max 0.163470 -0.066112 0.022811 \n",
"d1_mbp_min -0.063132 0.150740 -0.167315 \n",
"d1_mbp_noninvasive_max 0.163748 -0.064142 0.021926 \n",
"d1_mbp_noninvasive_min -0.063771 0.150175 -0.167117 \n",
"d1_resprate_max 1.000000 0.036134 0.037225 \n",
"d1_resprate_min 0.036134 1.000000 -0.185536 \n",
"d1_spo2_max 0.037225 -0.185536 1.000000 \n",
"d1_spo2_min -0.179488 0.076185 0.051419 \n",
"d1_sysbp_max 0.102212 -0.063872 0.035251 \n",
"d1_sysbp_min -0.134621 0.154223 -0.154899 \n",
"d1_sysbp_noninvasive_max 0.101917 -0.062863 0.034190 \n",
"d1_sysbp_noninvasive_min -0.134492 0.153707 -0.154006 \n",
"d1_temp_max 0.120650 0.014322 0.085534 \n",
"d1_temp_min -0.022532 0.105672 -0.072233 \n",
"h1_diasbp_max 0.045533 -0.034335 -0.003621 \n",
"h1_diasbp_min -0.049506 0.075952 -0.092584 \n",
"h1_diasbp_noninvasive_max 0.044027 -0.032759 -0.004764 \n",
"h1_diasbp_noninvasive_min -0.048420 0.073918 -0.091553 \n",
"h1_heartrate_max 0.190578 0.103607 0.018531 \n",
"h1_heartrate_min 0.131655 0.184471 -0.027774 \n",
"h1_mbp_max 0.086647 -0.016149 -0.037766 \n",
"h1_mbp_min -0.008806 0.083973 -0.108473 \n",
"h1_mbp_noninvasive_max 0.086132 -0.014409 -0.039170 \n",
"h1_mbp_noninvasive_min -0.008186 0.083542 -0.109327 \n",
"h1_resprate_max 0.566479 0.244139 -0.034198 \n",
"h1_resprate_min 0.266823 0.532418 -0.109716 \n",
"h1_spo2_max -0.033790 -0.129555 0.482151 \n",
"h1_spo2_min -0.122824 -0.004842 0.193868 \n",
"h1_sysbp_max 0.030429 -0.018405 -0.019100 \n",
"h1_sysbp_min -0.069583 0.084817 -0.096505 \n",
"h1_sysbp_noninvasive_max 0.029964 -0.016364 -0.020528 \n",
"h1_sysbp_noninvasive_min -0.069059 0.084742 -0.096337 \n",
"d1_glucose_max 0.035801 0.011728 0.013158 \n",
"d1_glucose_min 0.014376 0.048707 -0.081946 \n",
"d1_potassium_max 0.029741 -0.055836 0.039711 \n",
"d1_potassium_min -0.011542 -0.019968 -0.042741 \n",
"apache_4a_hospital_death_prob 0.117654 0.001213 0.062270 \n",
"apache_4a_icu_death_prob 0.102399 -0.010671 0.053668 \n",
"aids 0.009845 0.010695 0.005565 \n",
"cirrhosis 0.003894 -0.013251 0.015644 \n",
"diabetes_mellitus 0.002041 -0.019699 0.007692 \n",
"hepatic_failure 0.002154 -0.006476 0.018583 \n",
"immunosuppression 0.038590 0.015082 0.010419 \n",
"leukemia 0.023426 0.015824 0.003315 \n",
"lymphoma 0.015426 0.012541 -0.002064 \n",
"solid_tumor_with_metastasis 0.023527 0.008194 0.000517 \n",
"\n",
" d1_spo2_min d1_sysbp_max d1_sysbp_min \\\n",
"encounter_id 0.002650 -0.003727 -0.002810 \n",
"patient_id -0.005226 -0.001849 -0.007961 \n",
"hospital_id -0.001524 -0.028943 0.005046 \n",
"age -0.081491 0.105590 -0.060245 \n",
"bmi -0.031478 0.085527 0.044222 \n",
"elective_surgery 0.038151 -0.083295 0.013901 \n",
"height 0.007965 0.004081 0.051203 \n",
"icu_id 0.007943 -0.057790 -0.002549 \n",
"pre_icu_los_days -0.030216 -0.037123 -0.052632 \n",
"weight -0.027094 0.085038 0.065099 \n",
"apache_2_diagnosis 0.059437 0.019146 0.090089 \n",
"apache_3j_diagnosis 0.053945 -0.088787 0.010185 \n",
"apache_post_operative 0.037978 -0.082411 0.012446 \n",
"arf_apache -0.035431 0.054935 -0.016247 \n",
"gcs_eyes_apache 0.084127 -0.036611 0.162278 \n",
"gcs_motor_apache 0.092508 -0.033340 0.154878 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache 0.080855 -0.034973 0.176170 \n",
"heart_rate_apache -0.107299 -0.037824 -0.143812 \n",
"intubated_apache -0.057270 -0.011818 -0.183552 \n",
"map_apache 0.023344 0.477629 0.368801 \n",
"resprate_apache -0.071114 0.041616 0.022501 \n",
"temp_apache 0.057146 0.007933 0.096424 \n",
"ventilated_apache -0.121671 0.007514 -0.215911 \n",
"d1_diasbp_max -0.097912 0.599638 0.093446 \n",
"d1_diasbp_min 0.213147 0.154440 0.666660 \n",
"d1_diasbp_noninvasive_max -0.097955 0.599013 0.093600 \n",
"d1_diasbp_noninvasive_min 0.213191 0.154595 0.665910 \n",
"d1_heartrate_max -0.186051 0.018402 -0.248651 \n",
"d1_heartrate_min 0.058831 -0.140109 0.021449 \n",
"d1_mbp_max -0.064223 0.748749 0.236612 \n",
"d1_mbp_min 0.227411 0.252108 0.803036 \n",
"d1_mbp_noninvasive_max -0.063036 0.746551 0.238019 \n",
"d1_mbp_noninvasive_min 0.228135 0.251774 0.803383 \n",
"d1_resprate_max -0.179488 0.102212 -0.134621 \n",
"d1_resprate_min 0.076185 -0.063872 0.154223 \n",
"d1_spo2_max 0.051419 0.035251 -0.154899 \n",
"d1_spo2_min 1.000000 -0.038928 0.235267 \n",
"d1_sysbp_max -0.038928 1.000000 0.352306 \n",
"d1_sysbp_min 0.235267 0.352306 1.000000 \n",
"d1_sysbp_noninvasive_max -0.039044 0.997947 0.352594 \n",
"d1_sysbp_noninvasive_min 0.235052 0.352295 0.998132 \n",
"d1_temp_max -0.061721 0.015645 -0.119892 \n",
"d1_temp_min 0.095773 -0.000781 0.150254 \n",
"h1_diasbp_max -0.001225 0.460062 0.242244 \n",
"h1_diasbp_min 0.139330 0.305443 0.476147 \n",
"h1_diasbp_noninvasive_max -0.002144 0.459162 0.241827 \n",
"h1_diasbp_noninvasive_min 0.138820 0.304616 0.476876 \n",
"h1_heartrate_max -0.118527 -0.033329 -0.171506 \n",
"h1_heartrate_min -0.050125 -0.093220 -0.094678 \n",
"h1_mbp_max 0.030277 0.573858 0.382264 \n",
"h1_mbp_min 0.146458 0.426337 0.581176 \n",
"h1_mbp_noninvasive_max 0.031380 0.574136 0.383675 \n",
"h1_mbp_noninvasive_min 0.146860 0.425817 0.582080 \n",
"h1_resprate_max -0.144794 0.076431 -0.086536 \n",
"h1_resprate_min -0.038978 -0.008355 0.022207 \n",
"h1_spo2_max 0.218068 0.013028 -0.017220 \n",
"h1_spo2_min 0.539191 -0.019954 0.135052 \n",
"h1_sysbp_max 0.036028 0.730176 0.465559 \n",
"h1_sysbp_min 0.153235 0.533874 0.661781 \n",
"h1_sysbp_noninvasive_max 0.036011 0.732608 0.465509 \n",
"h1_sysbp_noninvasive_min 0.152361 0.534950 0.660638 \n",
"d1_glucose_max -0.029429 0.067252 -0.024403 \n",
"d1_glucose_min -0.005771 0.066461 0.065053 \n",
"d1_potassium_max -0.099509 -0.024258 -0.136593 \n",
"d1_potassium_min -0.046367 -0.015672 -0.028504 \n",
"apache_4a_hospital_death_prob -0.154740 0.016077 -0.217784 \n",
"apache_4a_icu_death_prob -0.151416 0.006298 -0.201911 \n",
"aids 0.002393 -0.000834 0.000649 \n",
"cirrhosis -0.003627 -0.033478 -0.046788 \n",
"diabetes_mellitus 0.004558 0.069493 0.025228 \n",
"hepatic_failure -0.007684 -0.034691 -0.048606 \n",
"immunosuppression -0.024263 -0.031275 -0.037169 \n",
"leukemia -0.023573 -0.018230 -0.023957 \n",
"lymphoma -0.009974 -0.015322 -0.012975 \n",
"solid_tumor_with_metastasis -0.015686 -0.031206 -0.025428 \n",
"\n",
" d1_sysbp_noninvasive_max \\\n",
"encounter_id -0.003711 \n",
"patient_id -0.001642 \n",
"hospital_id -0.028474 \n",
"age 0.105191 \n",
"bmi 0.085545 \n",
"elective_surgery -0.085121 \n",
"height 0.003608 \n",
"icu_id -0.057536 \n",
"pre_icu_los_days -0.037590 \n",
"weight 0.084811 \n",
"apache_2_diagnosis 0.018353 \n",
"apache_3j_diagnosis -0.089752 \n",
"apache_post_operative -0.084154 \n",
"arf_apache 0.054852 \n",
"gcs_eyes_apache -0.035408 \n",
"gcs_motor_apache -0.032642 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache -0.033439 \n",
"heart_rate_apache -0.037444 \n",
"intubated_apache -0.013041 \n",
"map_apache 0.478301 \n",
"resprate_apache 0.042496 \n",
"temp_apache 0.008841 \n",
"ventilated_apache 0.006469 \n",
"d1_diasbp_max 0.599525 \n",
"d1_diasbp_min 0.155061 \n",
"d1_diasbp_noninvasive_max 0.600818 \n",
"d1_diasbp_noninvasive_min 0.154209 \n",
"d1_heartrate_max 0.018522 \n",
"d1_heartrate_min -0.139506 \n",
"d1_mbp_max 0.747446 \n",
"d1_mbp_min 0.252553 \n",
"d1_mbp_noninvasive_max 0.748714 \n",
"d1_mbp_noninvasive_min 0.251632 \n",
"d1_resprate_max 0.101917 \n",
"d1_resprate_min -0.062863 \n",
"d1_spo2_max 0.034190 \n",
"d1_spo2_min -0.039044 \n",
"d1_sysbp_max 0.997947 \n",
"d1_sysbp_min 0.352594 \n",
"d1_sysbp_noninvasive_max 1.000000 \n",
"d1_sysbp_noninvasive_min 0.352182 \n",
"d1_temp_max 0.014964 \n",
"d1_temp_min -0.000029 \n",
"h1_diasbp_max 0.460567 \n",
"h1_diasbp_min 0.305603 \n",
"h1_diasbp_noninvasive_max 0.460121 \n",
"h1_diasbp_noninvasive_min 0.304871 \n",
"h1_heartrate_max -0.032927 \n",
"h1_heartrate_min -0.092647 \n",
"h1_mbp_max 0.574656 \n",
"h1_mbp_min 0.426743 \n",
"h1_mbp_noninvasive_max 0.575231 \n",
"h1_mbp_noninvasive_min 0.426225 \n",
"h1_resprate_max 0.076923 \n",
"h1_resprate_min -0.007178 \n",
"h1_spo2_max 0.012553 \n",
"h1_spo2_min -0.020202 \n",
"h1_sysbp_max 0.730792 \n",
"h1_sysbp_min 0.534169 \n",
"h1_sysbp_noninvasive_max 0.733799 \n",
"h1_sysbp_noninvasive_min 0.535452 \n",
"d1_glucose_max 0.067300 \n",
"d1_glucose_min 0.066677 \n",
"d1_potassium_max -0.025633 \n",
"d1_potassium_min -0.015752 \n",
"apache_4a_hospital_death_prob 0.015920 \n",
"apache_4a_icu_death_prob 0.006253 \n",
"aids -0.000794 \n",
"cirrhosis -0.033091 \n",
"diabetes_mellitus 0.069483 \n",
"hepatic_failure -0.034506 \n",
"immunosuppression -0.031031 \n",
"leukemia -0.018103 \n",
"lymphoma -0.015227 \n",
"solid_tumor_with_metastasis -0.030990 \n",
"\n",
" d1_sysbp_noninvasive_min d1_temp_max \\\n",
"encounter_id -0.002786 -0.006661 \n",
"patient_id -0.008424 -0.003322 \n",
"hospital_id 0.004905 0.016621 \n",
"age -0.060213 -0.100176 \n",
"bmi 0.044172 0.018651 \n",
"elective_surgery 0.014866 0.053148 \n",
"height 0.051262 0.016985 \n",
"icu_id -0.002797 -0.021521 \n",
"pre_icu_los_days -0.052169 0.027014 \n",
"weight 0.065108 0.026071 \n",
"apache_2_diagnosis 0.090912 -0.015975 \n",
"apache_3j_diagnosis 0.010741 0.111763 \n",
"apache_post_operative 0.013384 0.069778 \n",
"arf_apache -0.016477 -0.022309 \n",
"gcs_eyes_apache 0.162169 -0.153986 \n",
"gcs_motor_apache 0.155134 -0.113055 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.175646 -0.188147 \n",
"heart_rate_apache -0.144073 0.246108 \n",
"intubated_apache -0.182124 0.153535 \n",
"map_apache 0.368645 -0.035858 \n",
"resprate_apache 0.021807 0.061428 \n",
"temp_apache 0.096650 0.414180 \n",
"ventilated_apache -0.215455 0.207720 \n",
"d1_diasbp_max 0.092971 0.013822 \n",
"d1_diasbp_min 0.665898 -0.106248 \n",
"d1_diasbp_noninvasive_max 0.093238 0.013531 \n",
"d1_diasbp_noninvasive_min 0.667129 -0.105454 \n",
"d1_heartrate_max -0.248627 0.281152 \n",
"d1_heartrate_min 0.021410 0.182858 \n",
"d1_mbp_max 0.236445 -0.006464 \n",
"d1_mbp_min 0.802242 -0.127599 \n",
"d1_mbp_noninvasive_max 0.237239 -0.006817 \n",
"d1_mbp_noninvasive_min 0.804875 -0.126997 \n",
"d1_resprate_max -0.134492 0.120650 \n",
"d1_resprate_min 0.153707 0.014322 \n",
"d1_spo2_max -0.154006 0.085534 \n",
"d1_spo2_min 0.235052 -0.061721 \n",
"d1_sysbp_max 0.352295 0.015645 \n",
"d1_sysbp_min 0.998132 -0.119892 \n",
"d1_sysbp_noninvasive_max 0.352182 0.014964 \n",
"d1_sysbp_noninvasive_min 1.000000 -0.118846 \n",
"d1_temp_max -0.118846 1.000000 \n",
"d1_temp_min 0.150539 0.262450 \n",
"h1_diasbp_max 0.242187 -0.021517 \n",
"h1_diasbp_min 0.476351 -0.088714 \n",
"h1_diasbp_noninvasive_max 0.241794 -0.022187 \n",
"h1_diasbp_noninvasive_min 0.477498 -0.088131 \n",
"h1_heartrate_max -0.171516 0.232984 \n",
"h1_heartrate_min -0.094509 0.208689 \n",
"h1_mbp_max 0.382100 -0.047581 \n",
"h1_mbp_min 0.581479 -0.103215 \n",
"h1_mbp_noninvasive_max 0.383293 -0.049599 \n",
"h1_mbp_noninvasive_min 0.582561 -0.103002 \n",
"h1_resprate_max -0.087145 0.103586 \n",
"h1_resprate_min 0.021591 0.064994 \n",
"h1_spo2_max -0.016914 0.027792 \n",
"h1_spo2_min 0.135297 -0.022957 \n",
"h1_sysbp_max 0.465495 -0.023551 \n",
"h1_sysbp_min 0.662012 -0.084481 \n",
"h1_sysbp_noninvasive_max 0.465623 -0.024818 \n",
"h1_sysbp_noninvasive_min 0.661442 -0.083563 \n",
"d1_glucose_max -0.024169 0.002251 \n",
"d1_glucose_min 0.064926 -0.011571 \n",
"d1_potassium_max -0.135992 -0.022483 \n",
"d1_potassium_min -0.028606 -0.088875 \n",
"apache_4a_hospital_death_prob -0.217980 0.048238 \n",
"apache_4a_icu_death_prob -0.202009 0.050052 \n",
"aids 0.000632 0.007963 \n",
"cirrhosis -0.046513 -0.012707 \n",
"diabetes_mellitus 0.025260 -0.015323 \n",
"hepatic_failure -0.048668 -0.010524 \n",
"immunosuppression -0.037259 0.015804 \n",
"leukemia -0.024003 0.018855 \n",
"lymphoma -0.013010 0.000974 \n",
"solid_tumor_with_metastasis -0.025508 -0.010663 \n",
"\n",
" d1_temp_min h1_diasbp_max h1_diasbp_min \\\n",
"encounter_id 0.004776 -0.003139 0.002802 \n",
"patient_id -0.001115 -0.004722 -0.008662 \n",
"hospital_id -0.044566 0.001180 -0.014265 \n",
"age -0.066129 -0.138641 -0.186812 \n",
"bmi 0.034165 0.023306 -0.009829 \n",
"elective_surgery -0.028402 -0.106479 -0.064613 \n",
"height 0.004376 0.079122 0.109389 \n",
"icu_id -0.005269 -0.014473 -0.000882 \n",
"pre_icu_los_days -0.004411 -0.058319 -0.054409 \n",
"weight 0.034824 0.057222 0.040001 \n",
"apache_2_diagnosis 0.029711 -0.013812 0.032898 \n",
"apache_3j_diagnosis 0.007027 -0.107680 -0.074403 \n",
"apache_post_operative -0.034584 -0.103299 -0.065023 \n",
"arf_apache -0.030998 -0.011401 -0.032482 \n",
"gcs_eyes_apache 0.211348 -0.034360 0.072886 \n",
"gcs_motor_apache 0.263712 -0.033919 0.061247 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache 0.182067 -0.027725 0.084225 \n",
"heart_rate_apache 0.064424 0.081382 0.035233 \n",
"intubated_apache -0.169867 -0.002164 -0.087451 \n",
"map_apache -0.003270 0.390322 0.373429 \n",
"resprate_apache 0.030762 0.023518 0.043064 \n",
"temp_apache 0.787391 0.002494 0.048547 \n",
"ventilated_apache -0.184548 -0.000577 -0.108953 \n",
"d1_diasbp_max -0.020902 0.604668 0.337590 \n",
"d1_diasbp_min 0.110313 0.348050 0.628693 \n",
"d1_diasbp_noninvasive_max -0.020802 0.604847 0.337591 \n",
"d1_diasbp_noninvasive_min 0.110311 0.347372 0.628457 \n",
"d1_heartrate_max 0.012378 0.105612 -0.023891 \n",
"d1_heartrate_min 0.158806 -0.006163 0.065950 \n",
"d1_mbp_max -0.014090 0.575372 0.381715 \n",
"d1_mbp_min 0.123000 0.307212 0.572007 \n",
"d1_mbp_noninvasive_max -0.013644 0.573164 0.381789 \n",
"d1_mbp_noninvasive_min 0.123449 0.307228 0.573139 \n",
"d1_resprate_max -0.022532 0.045533 -0.049506 \n",
"d1_resprate_min 0.105672 -0.034335 0.075952 \n",
"d1_spo2_max -0.072233 -0.003621 -0.092584 \n",
"d1_spo2_min 0.095773 -0.001225 0.139330 \n",
"d1_sysbp_max -0.000781 0.460062 0.305443 \n",
"d1_sysbp_min 0.150254 0.242244 0.476147 \n",
"d1_sysbp_noninvasive_max -0.000029 0.460567 0.305603 \n",
"d1_sysbp_noninvasive_min 0.150539 0.242187 0.476351 \n",
"d1_temp_max 0.262450 -0.021517 -0.088714 \n",
"d1_temp_min 1.000000 -0.002624 0.072596 \n",
"h1_diasbp_max -0.002624 1.000000 0.620328 \n",
"h1_diasbp_min 0.072596 0.620328 1.000000 \n",
"h1_diasbp_noninvasive_max -0.001287 0.984080 0.617953 \n",
"h1_diasbp_noninvasive_min 0.071550 0.617860 0.980861 \n",
"h1_heartrate_max 0.066399 0.157816 0.024337 \n",
"h1_heartrate_min 0.104108 0.070153 0.102719 \n",
"h1_mbp_max 0.016054 0.861305 0.620635 \n",
"h1_mbp_min 0.081151 0.572630 0.876153 \n",
"h1_mbp_noninvasive_max 0.017484 0.858602 0.621360 \n",
"h1_mbp_noninvasive_min 0.080985 0.571663 0.874145 \n",
"h1_resprate_max 0.014114 0.107731 -0.035700 \n",
"h1_resprate_min 0.060637 -0.014656 0.087366 \n",
"h1_spo2_max -0.034333 0.032579 -0.036388 \n",
"h1_spo2_min 0.039702 -0.028811 0.116132 \n",
"h1_sysbp_max 0.035546 0.651842 0.468802 \n",
"h1_sysbp_min 0.108863 0.431099 0.687683 \n",
"h1_sysbp_noninvasive_max 0.036354 0.652747 0.470402 \n",
"h1_sysbp_noninvasive_min 0.108976 0.430986 0.684967 \n",
"d1_glucose_max -0.101493 -0.015901 -0.043229 \n",
"d1_glucose_min 0.037687 0.024736 0.024580 \n",
"d1_potassium_max -0.118506 -0.074244 -0.123494 \n",
"d1_potassium_min 0.028393 -0.054918 -0.064156 \n",
"apache_4a_hospital_death_prob -0.256898 -0.020015 -0.141526 \n",
"apache_4a_icu_death_prob -0.270149 -0.012538 -0.120126 \n",
"aids -0.000950 0.013123 0.013015 \n",
"cirrhosis -0.018276 -0.025047 -0.048752 \n",
"diabetes_mellitus -0.003782 -0.047827 -0.066676 \n",
"hepatic_failure -0.024858 -0.036471 -0.052422 \n",
"immunosuppression 0.003599 -0.024897 -0.025463 \n",
"leukemia -0.002237 -0.023125 -0.029693 \n",
"lymphoma -0.001466 -0.014152 -0.013254 \n",
"solid_tumor_with_metastasis -0.001434 -0.015965 -0.010569 \n",
"\n",
" h1_diasbp_noninvasive_max \\\n",
"encounter_id -0.002042 \n",
"patient_id -0.003283 \n",
"hospital_id 0.002316 \n",
"age -0.136606 \n",
"bmi 0.022982 \n",
"elective_surgery -0.103227 \n",
"height 0.077343 \n",
"icu_id -0.014354 \n",
"pre_icu_los_days -0.057275 \n",
"weight 0.056167 \n",
"apache_2_diagnosis -0.013681 \n",
"apache_3j_diagnosis -0.104877 \n",
"apache_post_operative -0.099954 \n",
"arf_apache -0.010648 \n",
"gcs_eyes_apache -0.033682 \n",
"gcs_motor_apache -0.033285 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache -0.027240 \n",
"heart_rate_apache 0.081874 \n",
"intubated_apache -0.000954 \n",
"map_apache 0.391338 \n",
"resprate_apache 0.023909 \n",
"temp_apache 0.004235 \n",
"ventilated_apache 0.000128 \n",
"d1_diasbp_max 0.603192 \n",
"d1_diasbp_min 0.348236 \n",
"d1_diasbp_noninvasive_max 0.604102 \n",
"d1_diasbp_noninvasive_min 0.347740 \n",
"d1_heartrate_max 0.104771 \n",
"d1_heartrate_min -0.005030 \n",
"d1_mbp_max 0.573603 \n",
"d1_mbp_min 0.307468 \n",
"d1_mbp_noninvasive_max 0.572296 \n",
"d1_mbp_noninvasive_min 0.307626 \n",
"d1_resprate_max 0.044027 \n",
"d1_resprate_min -0.032759 \n",
"d1_spo2_max -0.004764 \n",
"d1_spo2_min -0.002144 \n",
"d1_sysbp_max 0.459162 \n",
"d1_sysbp_min 0.241827 \n",
"d1_sysbp_noninvasive_max 0.460121 \n",
"d1_sysbp_noninvasive_min 0.241794 \n",
"d1_temp_max -0.022187 \n",
"d1_temp_min -0.001287 \n",
"h1_diasbp_max 0.984080 \n",
"h1_diasbp_min 0.617953 \n",
"h1_diasbp_noninvasive_max 1.000000 \n",
"h1_diasbp_noninvasive_min 0.618727 \n",
"h1_heartrate_max 0.158213 \n",
"h1_heartrate_min 0.070898 \n",
"h1_mbp_max 0.859137 \n",
"h1_mbp_min 0.571843 \n",
"h1_mbp_noninvasive_max 0.861525 \n",
"h1_mbp_noninvasive_min 0.572937 \n",
"h1_resprate_max 0.106255 \n",
"h1_resprate_min -0.013824 \n",
"h1_spo2_max 0.032494 \n",
"h1_spo2_min -0.029284 \n",
"h1_sysbp_max 0.650027 \n",
"h1_sysbp_min 0.431317 \n",
"h1_sysbp_noninvasive_max 0.653810 \n",
"h1_sysbp_noninvasive_min 0.431564 \n",
"d1_glucose_max -0.015465 \n",
"d1_glucose_min 0.023985 \n",
"d1_potassium_max -0.075539 \n",
"d1_potassium_min -0.055765 \n",
"apache_4a_hospital_death_prob -0.021459 \n",
"apache_4a_icu_death_prob -0.013593 \n",
"aids 0.013092 \n",
"cirrhosis -0.025062 \n",
"diabetes_mellitus -0.047512 \n",
"hepatic_failure -0.035785 \n",
"immunosuppression -0.024784 \n",
"leukemia -0.022945 \n",
"lymphoma -0.015404 \n",
"solid_tumor_with_metastasis -0.015739 \n",
"\n",
" h1_diasbp_noninvasive_min h1_heartrate_max \\\n",
"encounter_id 0.002508 -0.002534 \n",
"patient_id -0.009840 0.001650 \n",
"hospital_id -0.012543 -0.010177 \n",
"age -0.187165 -0.172870 \n",
"bmi -0.011490 -0.019961 \n",
"elective_surgery -0.058989 -0.107990 \n",
"height 0.111256 -0.006082 \n",
"icu_id -0.000800 -0.010873 \n",
"pre_icu_los_days -0.054913 0.061045 \n",
"weight 0.039112 -0.022502 \n",
"apache_2_diagnosis 0.036588 -0.147792 \n",
"apache_3j_diagnosis -0.069530 -0.040191 \n",
"apache_post_operative -0.059248 -0.098490 \n",
"arf_apache -0.032678 -0.018119 \n",
"gcs_eyes_apache 0.072800 -0.087839 \n",
"gcs_motor_apache 0.061085 -0.083696 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.082832 -0.108800 \n",
"heart_rate_apache 0.036045 0.721986 \n",
"intubated_apache -0.087045 0.081267 \n",
"map_apache 0.374294 -0.008144 \n",
"resprate_apache 0.043308 0.167701 \n",
"temp_apache 0.046943 0.122384 \n",
"ventilated_apache -0.107023 0.119361 \n",
"d1_diasbp_max 0.336034 0.118285 \n",
"d1_diasbp_min 0.629021 -0.028420 \n",
"d1_diasbp_noninvasive_max 0.336400 0.118575 \n",
"d1_diasbp_noninvasive_min 0.629334 -0.028917 \n",
"d1_heartrate_max -0.022509 0.787313 \n",
"d1_heartrate_min 0.066044 0.553763 \n",
"d1_mbp_max 0.380865 0.052258 \n",
"d1_mbp_min 0.573238 -0.096207 \n",
"d1_mbp_noninvasive_max 0.380650 0.050773 \n",
"d1_mbp_noninvasive_min 0.574665 -0.096533 \n",
"d1_resprate_max -0.048420 0.190578 \n",
"d1_resprate_min 0.073918 0.103607 \n",
"d1_spo2_max -0.091553 0.018531 \n",
"d1_spo2_min 0.138820 -0.118527 \n",
"d1_sysbp_max 0.304616 -0.033329 \n",
"d1_sysbp_min 0.476876 -0.171506 \n",
"d1_sysbp_noninvasive_max 0.304871 -0.032927 \n",
"d1_sysbp_noninvasive_min 0.477498 -0.171516 \n",
"d1_temp_max -0.088131 0.232984 \n",
"d1_temp_min 0.071550 0.066399 \n",
"h1_diasbp_max 0.617860 0.157816 \n",
"h1_diasbp_min 0.980861 0.024337 \n",
"h1_diasbp_noninvasive_max 0.618727 0.158213 \n",
"h1_diasbp_noninvasive_min 1.000000 0.024987 \n",
"h1_heartrate_max 0.024987 1.000000 \n",
"h1_heartrate_min 0.103236 0.855270 \n",
"h1_mbp_max 0.620699 0.067196 \n",
"h1_mbp_min 0.874465 -0.047734 \n",
"h1_mbp_noninvasive_max 0.622858 0.067486 \n",
"h1_mbp_noninvasive_min 0.877306 -0.047593 \n",
"h1_resprate_max -0.037863 0.317010 \n",
"h1_resprate_min 0.084763 0.227996 \n",
"h1_spo2_max -0.035914 -0.055897 \n",
"h1_spo2_min 0.116508 -0.132102 \n",
"h1_sysbp_max 0.469236 -0.010766 \n",
"h1_sysbp_min 0.687388 -0.127068 \n",
"h1_sysbp_noninvasive_max 0.470721 -0.009731 \n",
"h1_sysbp_noninvasive_min 0.687473 -0.126437 \n",
"d1_glucose_max -0.042439 0.120078 \n",
"d1_glucose_min 0.025106 0.062685 \n",
"d1_potassium_max -0.124229 0.008160 \n",
"d1_potassium_min -0.065128 -0.080941 \n",
"apache_4a_hospital_death_prob -0.141760 0.126071 \n",
"apache_4a_icu_death_prob -0.120348 0.120051 \n",
"aids 0.012944 0.010233 \n",
"cirrhosis -0.049390 0.018175 \n",
"diabetes_mellitus -0.066902 -0.012669 \n",
"hepatic_failure -0.052745 0.020534 \n",
"immunosuppression -0.024206 0.066942 \n",
"leukemia -0.029332 0.023266 \n",
"lymphoma -0.013997 0.019361 \n",
"solid_tumor_with_metastasis -0.010381 0.045671 \n",
"\n",
" h1_heartrate_min h1_mbp_max h1_mbp_min \\\n",
"encounter_id -0.002998 0.000930 0.004229 \n",
"patient_id 0.002568 -0.003626 -0.007897 \n",
"hospital_id -0.022248 0.000636 -0.011939 \n",
"age -0.172761 -0.059667 -0.100795 \n",
"bmi -0.010091 0.033733 0.010981 \n",
"elective_surgery -0.124757 -0.074414 -0.050104 \n",
"height -0.012351 0.052886 0.071101 \n",
"icu_id 0.004188 -0.011939 -0.018469 \n",
"pre_icu_los_days 0.056917 -0.056631 -0.057832 \n",
"weight -0.015731 0.055863 0.042884 \n",
"apache_2_diagnosis -0.136036 0.018900 0.055932 \n",
"apache_3j_diagnosis -0.047355 -0.085596 -0.062483 \n",
"apache_post_operative -0.119469 -0.071144 -0.049850 \n",
"arf_apache -0.017100 0.005185 -0.010165 \n",
"gcs_eyes_apache -0.041512 -0.008476 0.097894 \n",
"gcs_motor_apache -0.039050 -0.012495 0.083482 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.061233 -0.000979 0.110696 \n",
"heart_rate_apache 0.691027 0.010655 -0.030935 \n",
"intubated_apache 0.046190 -0.032933 -0.118845 \n",
"map_apache -0.023693 0.478144 0.437433 \n",
"resprate_apache 0.174211 0.111467 0.111741 \n",
"temp_apache 0.150136 0.013527 0.052644 \n",
"ventilated_apache 0.068821 -0.024937 -0.140207 \n",
"d1_diasbp_max 0.050352 0.516366 0.305863 \n",
"d1_diasbp_min 0.038660 0.389268 0.591288 \n",
"d1_diasbp_noninvasive_max 0.050873 0.516849 0.305999 \n",
"d1_diasbp_noninvasive_min 0.038356 0.388923 0.591434 \n",
"d1_heartrate_max 0.677401 0.018777 -0.093579 \n",
"d1_heartrate_min 0.680537 -0.049830 0.009224 \n",
"d1_mbp_max -0.012835 0.656704 0.449462 \n",
"d1_mbp_min -0.027182 0.431133 0.649424 \n",
"d1_mbp_noninvasive_max -0.012912 0.655184 0.449962 \n",
"d1_mbp_noninvasive_min -0.026903 0.431227 0.649377 \n",
"d1_resprate_max 0.131655 0.086647 -0.008806 \n",
"d1_resprate_min 0.184471 -0.016149 0.083973 \n",
"d1_spo2_max -0.027774 -0.037766 -0.108473 \n",
"d1_spo2_min -0.050125 0.030277 0.146458 \n",
"d1_sysbp_max -0.093220 0.573858 0.426337 \n",
"d1_sysbp_min -0.094678 0.382264 0.581176 \n",
"d1_sysbp_noninvasive_max -0.092647 0.574656 0.426743 \n",
"d1_sysbp_noninvasive_min -0.094509 0.382100 0.581479 \n",
"d1_temp_max 0.208689 -0.047581 -0.103215 \n",
"d1_temp_min 0.104108 0.016054 0.081151 \n",
"h1_diasbp_max 0.070153 0.861305 0.572630 \n",
"h1_diasbp_min 0.102719 0.620635 0.876153 \n",
"h1_diasbp_noninvasive_max 0.070898 0.859137 0.571843 \n",
"h1_diasbp_noninvasive_min 0.103236 0.620699 0.874465 \n",
"h1_heartrate_max 0.855270 0.067196 -0.047734 \n",
"h1_heartrate_min 1.000000 -0.001983 0.024450 \n",
"h1_mbp_max -0.001983 1.000000 0.702511 \n",
"h1_mbp_min 0.024450 0.702511 1.000000 \n",
"h1_mbp_noninvasive_max -0.001110 0.986971 0.701429 \n",
"h1_mbp_noninvasive_min 0.024419 0.700775 0.996283 \n",
"h1_resprate_max 0.236913 0.123443 -0.008337 \n",
"h1_resprate_min 0.314475 0.001486 0.089050 \n",
"h1_spo2_max -0.104773 0.024761 -0.033778 \n",
"h1_spo2_min -0.046809 -0.006907 0.116581 \n",
"h1_sysbp_max -0.081952 0.794590 0.612757 \n",
"h1_sysbp_min -0.045043 0.602304 0.822004 \n",
"h1_sysbp_noninvasive_max -0.080701 0.792552 0.613190 \n",
"h1_sysbp_noninvasive_min -0.045213 0.601161 0.819863 \n",
"d1_glucose_max 0.131464 0.002567 -0.026086 \n",
"d1_glucose_min 0.061894 0.036399 0.039970 \n",
"d1_potassium_max 0.000349 -0.081778 -0.123529 \n",
"d1_potassium_min -0.073151 -0.050733 -0.052324 \n",
"apache_4a_hospital_death_prob 0.080865 -0.033841 -0.143307 \n",
"apache_4a_icu_death_prob 0.078412 -0.029171 -0.126340 \n",
"aids 0.011083 0.008723 0.008622 \n",
"cirrhosis 0.020529 -0.035903 -0.050043 \n",
"diabetes_mellitus -0.001135 -0.010005 -0.026996 \n",
"hepatic_failure 0.023841 -0.044933 -0.054471 \n",
"immunosuppression 0.068321 -0.032257 -0.027287 \n",
"leukemia 0.019161 -0.023421 -0.029801 \n",
"lymphoma 0.018820 -0.015548 -0.012395 \n",
"solid_tumor_with_metastasis 0.048555 -0.021113 -0.015536 \n",
"\n",
" h1_mbp_noninvasive_max h1_mbp_noninvasive_min \\\n",
"encounter_id 0.001418 0.004255 \n",
"patient_id -0.002664 -0.008033 \n",
"hospital_id -0.000345 -0.011090 \n",
"age -0.060153 -0.100612 \n",
"bmi 0.033016 0.010546 \n",
"elective_surgery -0.075593 -0.049100 \n",
"height 0.052867 0.071595 \n",
"icu_id -0.013593 -0.017409 \n",
"pre_icu_los_days -0.056725 -0.058227 \n",
"weight 0.055136 0.042662 \n",
"apache_2_diagnosis 0.018299 0.056155 \n",
"apache_3j_diagnosis -0.086540 -0.061586 \n",
"apache_post_operative -0.072910 -0.048824 \n",
"arf_apache 0.007339 -0.010562 \n",
"gcs_eyes_apache -0.007695 0.097492 \n",
"gcs_motor_apache -0.011679 0.083178 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache -0.001454 0.110544 \n",
"heart_rate_apache 0.011240 -0.031043 \n",
"intubated_apache -0.032970 -0.118491 \n",
"map_apache 0.477121 0.437557 \n",
"resprate_apache 0.113088 0.112462 \n",
"temp_apache 0.014439 0.052593 \n",
"ventilated_apache -0.026200 -0.139786 \n",
"d1_diasbp_max 0.515910 0.305352 \n",
"d1_diasbp_min 0.392297 0.592603 \n",
"d1_diasbp_noninvasive_max 0.516466 0.305559 \n",
"d1_diasbp_noninvasive_min 0.391856 0.592823 \n",
"d1_heartrate_max 0.017876 -0.093994 \n",
"d1_heartrate_min -0.048665 0.009344 \n",
"d1_mbp_max 0.655768 0.449958 \n",
"d1_mbp_min 0.434475 0.650209 \n",
"d1_mbp_noninvasive_max 0.655304 0.450112 \n",
"d1_mbp_noninvasive_min 0.434106 0.651138 \n",
"d1_resprate_max 0.086132 -0.008186 \n",
"d1_resprate_min -0.014409 0.083542 \n",
"d1_spo2_max -0.039170 -0.109327 \n",
"d1_spo2_min 0.031380 0.146860 \n",
"d1_sysbp_max 0.574136 0.425817 \n",
"d1_sysbp_min 0.383675 0.582080 \n",
"d1_sysbp_noninvasive_max 0.575231 0.426225 \n",
"d1_sysbp_noninvasive_min 0.383293 0.582561 \n",
"d1_temp_max -0.049599 -0.103002 \n",
"d1_temp_min 0.017484 0.080985 \n",
"h1_diasbp_max 0.858602 0.571663 \n",
"h1_diasbp_min 0.621360 0.874145 \n",
"h1_diasbp_noninvasive_max 0.861525 0.572937 \n",
"h1_diasbp_noninvasive_min 0.622858 0.877306 \n",
"h1_heartrate_max 0.067486 -0.047593 \n",
"h1_heartrate_min -0.001110 0.024419 \n",
"h1_mbp_max 0.986971 0.700775 \n",
"h1_mbp_min 0.701429 0.996283 \n",
"h1_mbp_noninvasive_max 1.000000 0.703233 \n",
"h1_mbp_noninvasive_min 0.703233 1.000000 \n",
"h1_resprate_max 0.124302 -0.008330 \n",
"h1_resprate_min 0.002567 0.088542 \n",
"h1_spo2_max 0.024791 -0.034226 \n",
"h1_spo2_min -0.006462 0.116505 \n",
"h1_sysbp_max 0.791118 0.611515 \n",
"h1_sysbp_min 0.602092 0.820007 \n",
"h1_sysbp_noninvasive_max 0.795027 0.613387 \n",
"h1_sysbp_noninvasive_min 0.602368 0.821673 \n",
"d1_glucose_max 0.002428 -0.026171 \n",
"d1_glucose_min 0.036576 0.039749 \n",
"d1_potassium_max -0.082839 -0.124130 \n",
"d1_potassium_min -0.050068 -0.052542 \n",
"apache_4a_hospital_death_prob -0.034958 -0.143961 \n",
"apache_4a_icu_death_prob -0.030081 -0.126812 \n",
"aids 0.008777 0.008645 \n",
"cirrhosis -0.035794 -0.050119 \n",
"diabetes_mellitus -0.010453 -0.027522 \n",
"hepatic_failure -0.045180 -0.054756 \n",
"immunosuppression -0.031041 -0.026931 \n",
"leukemia -0.023258 -0.029596 \n",
"lymphoma -0.015457 -0.013360 \n",
"solid_tumor_with_metastasis -0.020619 -0.015739 \n",
"\n",
" h1_resprate_max h1_resprate_min h1_spo2_max \\\n",
"encounter_id 0.007569 0.002913 -0.000712 \n",
"patient_id 0.003544 -0.000946 0.000079 \n",
"hospital_id -0.023934 -0.036231 0.017704 \n",
"age 0.026192 0.035833 -0.067605 \n",
"bmi 0.014155 0.001556 -0.063018 \n",
"elective_surgery -0.177796 -0.240363 0.091235 \n",
"height -0.029496 -0.044985 -0.019162 \n",
"icu_id -0.021284 -0.019165 -0.007165 \n",
"pre_icu_los_days 0.040266 0.035592 -0.001046 \n",
"weight 0.000007 -0.017901 -0.069060 \n",
"apache_2_diagnosis -0.138576 -0.149565 0.043086 \n",
"apache_3j_diagnosis -0.163706 -0.208767 0.097911 \n",
"apache_post_operative -0.185293 -0.248028 0.098814 \n",
"arf_apache 0.017168 0.003499 0.018901 \n",
"gcs_eyes_apache -0.009141 0.022648 -0.070952 \n",
"gcs_motor_apache -0.016466 0.010979 -0.058519 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.018440 0.006286 -0.085101 \n",
"heart_rate_apache 0.210330 0.200072 -0.044663 \n",
"intubated_apache -0.003491 -0.038077 0.077693 \n",
"map_apache 0.047885 0.016095 -0.007026 \n",
"resprate_apache 0.479384 0.389324 -0.087868 \n",
"temp_apache 0.059421 0.084145 -0.033760 \n",
"ventilated_apache 0.056546 0.006394 0.083931 \n",
"d1_diasbp_max 0.084637 -0.001960 -0.001744 \n",
"d1_diasbp_min -0.052964 0.034346 -0.021251 \n",
"d1_diasbp_noninvasive_max 0.084279 -0.001902 -0.001848 \n",
"d1_diasbp_noninvasive_min -0.053816 0.033463 -0.021066 \n",
"d1_heartrate_max 0.254646 0.175305 -0.032473 \n",
"d1_heartrate_min 0.129373 0.210351 -0.059037 \n",
"d1_mbp_max 0.122220 0.012532 -0.002551 \n",
"d1_mbp_min -0.038282 0.039833 -0.022553 \n",
"d1_mbp_noninvasive_max 0.122971 0.013815 -0.002958 \n",
"d1_mbp_noninvasive_min -0.039792 0.039593 -0.022383 \n",
"d1_resprate_max 0.566479 0.266823 -0.033790 \n",
"d1_resprate_min 0.244139 0.532418 -0.129555 \n",
"d1_spo2_max -0.034198 -0.109716 0.482151 \n",
"d1_spo2_min -0.144794 -0.038978 0.218068 \n",
"d1_sysbp_max 0.076431 -0.008355 0.013028 \n",
"d1_sysbp_min -0.086536 0.022207 -0.017220 \n",
"d1_sysbp_noninvasive_max 0.076923 -0.007178 0.012553 \n",
"d1_sysbp_noninvasive_min -0.087145 0.021591 -0.016914 \n",
"d1_temp_max 0.103586 0.064994 0.027792 \n",
"d1_temp_min 0.014114 0.060637 -0.034333 \n",
"h1_diasbp_max 0.107731 -0.014656 0.032579 \n",
"h1_diasbp_min -0.035700 0.087366 -0.036388 \n",
"h1_diasbp_noninvasive_max 0.106255 -0.013824 0.032494 \n",
"h1_diasbp_noninvasive_min -0.037863 0.084763 -0.035914 \n",
"h1_heartrate_max 0.317010 0.227996 -0.055897 \n",
"h1_heartrate_min 0.236913 0.314475 -0.104773 \n",
"h1_mbp_max 0.123443 0.001486 0.024761 \n",
"h1_mbp_min -0.008337 0.089050 -0.033778 \n",
"h1_mbp_noninvasive_max 0.124302 0.002567 0.024791 \n",
"h1_mbp_noninvasive_min -0.008330 0.088542 -0.034226 \n",
"h1_resprate_max 1.000000 0.548624 -0.077623 \n",
"h1_resprate_min 0.548624 1.000000 -0.181717 \n",
"h1_spo2_max -0.077623 -0.181717 1.000000 \n",
"h1_spo2_min -0.185844 -0.046627 0.450923 \n",
"h1_sysbp_max 0.083688 -0.015259 0.032449 \n",
"h1_sysbp_min -0.046970 0.082306 -0.035064 \n",
"h1_sysbp_noninvasive_max 0.085509 -0.013212 0.031098 \n",
"h1_sysbp_noninvasive_min -0.045844 0.082581 -0.035624 \n",
"d1_glucose_max 0.063251 0.071794 -0.002983 \n",
"d1_glucose_min 0.050925 0.053458 -0.062018 \n",
"d1_potassium_max 0.009906 -0.011577 -0.006664 \n",
"d1_potassium_min -0.011402 -0.018120 -0.048023 \n",
"apache_4a_hospital_death_prob 0.136787 0.096252 -0.015187 \n",
"apache_4a_icu_death_prob 0.115561 0.074214 -0.015543 \n",
"aids 0.016430 0.009873 0.005167 \n",
"cirrhosis 0.002881 -0.011457 0.010287 \n",
"diabetes_mellitus 0.003224 -0.002641 0.013671 \n",
"hepatic_failure 0.003297 -0.007012 0.015094 \n",
"immunosuppression 0.054017 0.038334 -0.012804 \n",
"leukemia 0.029426 0.024732 -0.002035 \n",
"lymphoma 0.022068 0.020294 -0.009216 \n",
"solid_tumor_with_metastasis 0.022905 0.015658 -0.003942 \n",
"\n",
" h1_spo2_min h1_sysbp_max h1_sysbp_min \\\n",
"encounter_id -0.003846 0.003081 0.000381 \n",
"patient_id -0.003214 -0.003712 -0.009131 \n",
"hospital_id 0.001696 -0.007294 -0.020903 \n",
"age -0.079734 0.043745 -0.001140 \n",
"bmi -0.035852 0.059963 0.042186 \n",
"elective_surgery 0.053323 -0.042952 -0.032432 \n",
"height 0.000253 0.012182 0.020399 \n",
"icu_id 0.014544 -0.047580 -0.007691 \n",
"pre_icu_los_days -0.016554 -0.046267 -0.051324 \n",
"weight -0.034535 0.063750 0.050606 \n",
"apache_2_diagnosis 0.055448 0.051567 0.078730 \n",
"apache_3j_diagnosis 0.065570 -0.055298 -0.043522 \n",
"apache_post_operative 0.055965 -0.038583 -0.032774 \n",
"arf_apache -0.018911 0.030642 0.007560 \n",
"gcs_eyes_apache 0.025469 0.006956 0.116574 \n",
"gcs_motor_apache 0.028647 0.009633 0.108234 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache 0.018238 0.010672 0.126867 \n",
"heart_rate_apache -0.071950 -0.069510 -0.104163 \n",
"intubated_apache -0.019951 -0.039953 -0.140067 \n",
"map_apache 0.014667 0.423854 0.392038 \n",
"resprate_apache -0.074162 0.024309 0.023338 \n",
"temp_apache 0.017170 0.033627 0.079091 \n",
"ventilated_apache -0.046864 -0.031710 -0.157782 \n",
"d1_diasbp_max -0.054609 0.432473 0.263487 \n",
"d1_diasbp_min 0.117988 0.271229 0.438911 \n",
"d1_diasbp_noninvasive_max -0.054659 0.432758 0.263474 \n",
"d1_diasbp_noninvasive_min 0.118203 0.270993 0.439004 \n",
"d1_heartrate_max -0.115732 -0.050685 -0.160615 \n",
"d1_heartrate_min 0.002237 -0.114373 -0.049718 \n",
"d1_mbp_max -0.042100 0.561486 0.397023 \n",
"d1_mbp_min 0.121927 0.364491 0.537579 \n",
"d1_mbp_noninvasive_max -0.042061 0.560690 0.396912 \n",
"d1_mbp_noninvasive_min 0.122594 0.364109 0.538366 \n",
"d1_resprate_max -0.122824 0.030429 -0.069583 \n",
"d1_resprate_min -0.004842 -0.018405 0.084817 \n",
"d1_spo2_max 0.193868 -0.019100 -0.096505 \n",
"d1_spo2_min 0.539191 0.036028 0.153235 \n",
"d1_sysbp_max -0.019954 0.730176 0.533874 \n",
"d1_sysbp_min 0.135052 0.465559 0.661781 \n",
"d1_sysbp_noninvasive_max -0.020202 0.730792 0.534169 \n",
"d1_sysbp_noninvasive_min 0.135297 0.465495 0.662012 \n",
"d1_temp_max -0.022957 -0.023551 -0.084481 \n",
"d1_temp_min 0.039702 0.035546 0.108863 \n",
"h1_diasbp_max -0.028811 0.651842 0.431099 \n",
"h1_diasbp_min 0.116132 0.468802 0.687683 \n",
"h1_diasbp_noninvasive_max -0.029284 0.650027 0.431317 \n",
"h1_diasbp_noninvasive_min 0.116508 0.469236 0.687388 \n",
"h1_heartrate_max -0.132102 -0.010766 -0.127068 \n",
"h1_heartrate_min -0.046809 -0.081952 -0.045043 \n",
"h1_mbp_max -0.006907 0.794590 0.602304 \n",
"h1_mbp_min 0.116581 0.612757 0.822004 \n",
"h1_mbp_noninvasive_max -0.006462 0.791118 0.602092 \n",
"h1_mbp_noninvasive_min 0.116505 0.611515 0.820007 \n",
"h1_resprate_max -0.185844 0.083688 -0.046970 \n",
"h1_resprate_min -0.046627 -0.015259 0.082306 \n",
"h1_spo2_max 0.450923 0.032449 -0.035064 \n",
"h1_spo2_min 1.000000 0.001391 0.128929 \n",
"h1_sysbp_max 0.001391 1.000000 0.736723 \n",
"h1_sysbp_min 0.128929 0.736723 1.000000 \n",
"h1_sysbp_noninvasive_max 0.000310 0.995454 0.735436 \n",
"h1_sysbp_noninvasive_min 0.127970 0.734443 0.987171 \n",
"d1_glucose_max -0.021912 0.035373 0.003493 \n",
"d1_glucose_min -0.036017 0.064003 0.056359 \n",
"d1_potassium_max -0.059276 -0.069402 -0.109430 \n",
"d1_potassium_min -0.045481 -0.033458 -0.033254 \n",
"apache_4a_hospital_death_prob -0.106906 -0.034774 -0.142781 \n",
"apache_4a_icu_death_prob -0.102579 -0.037108 -0.135120 \n",
"aids -0.000169 -0.000523 0.000184 \n",
"cirrhosis 0.005423 -0.036144 -0.049068 \n",
"diabetes_mellitus -0.001203 0.040022 0.020799 \n",
"hepatic_failure 0.004372 -0.041831 -0.051120 \n",
"immunosuppression -0.014791 -0.034089 -0.036965 \n",
"leukemia -0.002042 -0.021371 -0.025332 \n",
"lymphoma -0.009525 -0.016606 -0.017167 \n",
"solid_tumor_with_metastasis -0.011597 -0.026780 -0.026765 \n",
"\n",
" h1_sysbp_noninvasive_max \\\n",
"encounter_id 0.002926 \n",
"patient_id -0.003964 \n",
"hospital_id -0.009218 \n",
"age 0.043261 \n",
"bmi 0.060096 \n",
"elective_surgery -0.048673 \n",
"height 0.011332 \n",
"icu_id -0.049443 \n",
"pre_icu_los_days -0.046436 \n",
"weight 0.063471 \n",
"apache_2_diagnosis 0.049374 \n",
"apache_3j_diagnosis -0.060177 \n",
"apache_post_operative -0.044924 \n",
"arf_apache 0.031027 \n",
"gcs_eyes_apache 0.007303 \n",
"gcs_motor_apache 0.009781 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache 0.011126 \n",
"heart_rate_apache -0.068836 \n",
"intubated_apache -0.041261 \n",
"map_apache 0.424494 \n",
"resprate_apache 0.025022 \n",
"temp_apache 0.034800 \n",
"ventilated_apache -0.034463 \n",
"d1_diasbp_max 0.435978 \n",
"d1_diasbp_min 0.271816 \n",
"d1_diasbp_noninvasive_max 0.436690 \n",
"d1_diasbp_noninvasive_min 0.271499 \n",
"d1_heartrate_max -0.050559 \n",
"d1_heartrate_min -0.113736 \n",
"d1_mbp_max 0.563790 \n",
"d1_mbp_min 0.364968 \n",
"d1_mbp_noninvasive_max 0.563820 \n",
"d1_mbp_noninvasive_min 0.364611 \n",
"d1_resprate_max 0.029964 \n",
"d1_resprate_min -0.016364 \n",
"d1_spo2_max -0.020528 \n",
"d1_spo2_min 0.036011 \n",
"d1_sysbp_max 0.732608 \n",
"d1_sysbp_min 0.465509 \n",
"d1_sysbp_noninvasive_max 0.733799 \n",
"d1_sysbp_noninvasive_min 0.465623 \n",
"d1_temp_max -0.024818 \n",
"d1_temp_min 0.036354 \n",
"h1_diasbp_max 0.652747 \n",
"h1_diasbp_min 0.470402 \n",
"h1_diasbp_noninvasive_max 0.653810 \n",
"h1_diasbp_noninvasive_min 0.470721 \n",
"h1_heartrate_max -0.009731 \n",
"h1_heartrate_min -0.080701 \n",
"h1_mbp_max 0.792552 \n",
"h1_mbp_min 0.613190 \n",
"h1_mbp_noninvasive_max 0.795027 \n",
"h1_mbp_noninvasive_min 0.613387 \n",
"h1_resprate_max 0.085509 \n",
"h1_resprate_min -0.013212 \n",
"h1_spo2_max 0.031098 \n",
"h1_spo2_min 0.000310 \n",
"h1_sysbp_max 0.995454 \n",
"h1_sysbp_min 0.735436 \n",
"h1_sysbp_noninvasive_max 1.000000 \n",
"h1_sysbp_noninvasive_min 0.736818 \n",
"d1_glucose_max 0.035512 \n",
"d1_glucose_min 0.064903 \n",
"d1_potassium_max -0.070546 \n",
"d1_potassium_min -0.032997 \n",
"apache_4a_hospital_death_prob -0.034832 \n",
"apache_4a_icu_death_prob -0.037322 \n",
"aids -0.000393 \n",
"cirrhosis -0.035746 \n",
"diabetes_mellitus 0.040407 \n",
"hepatic_failure -0.041875 \n",
"immunosuppression -0.033463 \n",
"leukemia -0.021404 \n",
"lymphoma -0.016309 \n",
"solid_tumor_with_metastasis -0.026137 \n",
"\n",
" h1_sysbp_noninvasive_min d1_glucose_max \\\n",
"encounter_id 0.000954 0.003829 \n",
"patient_id -0.010023 -0.007430 \n",
"hospital_id -0.021643 -0.001785 \n",
"age -0.001372 0.005115 \n",
"bmi 0.041629 0.097297 \n",
"elective_surgery -0.034775 -0.020991 \n",
"height 0.020537 -0.016805 \n",
"icu_id -0.009021 0.008273 \n",
"pre_icu_los_days -0.051621 -0.009819 \n",
"weight 0.050175 0.085200 \n",
"apache_2_diagnosis 0.075952 -0.047425 \n",
"apache_3j_diagnosis -0.045033 0.016651 \n",
"apache_post_operative -0.034978 -0.019896 \n",
"arf_apache 0.008561 0.033822 \n",
"gcs_eyes_apache 0.115242 -0.074507 \n",
"gcs_motor_apache 0.106860 -0.084388 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.126260 -0.068488 \n",
"heart_rate_apache -0.104698 0.103486 \n",
"intubated_apache -0.139769 0.083622 \n",
"map_apache 0.392033 0.014743 \n",
"resprate_apache 0.022385 0.029153 \n",
"temp_apache 0.079162 -0.079647 \n",
"ventilated_apache -0.157497 0.090256 \n",
"d1_diasbp_max 0.265481 0.004768 \n",
"d1_diasbp_min 0.437978 -0.058470 \n",
"d1_diasbp_noninvasive_max 0.265585 0.004872 \n",
"d1_diasbp_noninvasive_min 0.438266 -0.058270 \n",
"d1_heartrate_max -0.160745 0.109506 \n",
"d1_heartrate_min -0.050214 0.099929 \n",
"d1_mbp_max 0.398017 0.025616 \n",
"d1_mbp_min 0.536927 -0.046836 \n",
"d1_mbp_noninvasive_max 0.398145 0.025646 \n",
"d1_mbp_noninvasive_min 0.537856 -0.046577 \n",
"d1_resprate_max -0.069059 0.035801 \n",
"d1_resprate_min 0.084742 0.011728 \n",
"d1_spo2_max -0.096337 0.013158 \n",
"d1_spo2_min 0.152361 -0.029429 \n",
"d1_sysbp_max 0.534950 0.067252 \n",
"d1_sysbp_min 0.660638 -0.024403 \n",
"d1_sysbp_noninvasive_max 0.535452 0.067300 \n",
"d1_sysbp_noninvasive_min 0.661442 -0.024169 \n",
"d1_temp_max -0.083563 0.002251 \n",
"d1_temp_min 0.108976 -0.101493 \n",
"h1_diasbp_max 0.430986 -0.015901 \n",
"h1_diasbp_min 0.684967 -0.043229 \n",
"h1_diasbp_noninvasive_max 0.431564 -0.015465 \n",
"h1_diasbp_noninvasive_min 0.687473 -0.042439 \n",
"h1_heartrate_max -0.126437 0.120078 \n",
"h1_heartrate_min -0.045213 0.131464 \n",
"h1_mbp_max 0.601161 0.002567 \n",
"h1_mbp_min 0.819863 -0.026086 \n",
"h1_mbp_noninvasive_max 0.602368 0.002428 \n",
"h1_mbp_noninvasive_min 0.821673 -0.026171 \n",
"h1_resprate_max -0.045844 0.063251 \n",
"h1_resprate_min 0.082581 0.071794 \n",
"h1_spo2_max -0.035624 -0.002983 \n",
"h1_spo2_min 0.127970 -0.021912 \n",
"h1_sysbp_max 0.734443 0.035373 \n",
"h1_sysbp_min 0.987171 0.003493 \n",
"h1_sysbp_noninvasive_max 0.736818 0.035512 \n",
"h1_sysbp_noninvasive_min 1.000000 0.004344 \n",
"d1_glucose_max 0.004344 1.000000 \n",
"d1_glucose_min 0.055871 0.370571 \n",
"d1_potassium_max -0.109438 0.189215 \n",
"d1_potassium_min -0.033244 -0.040980 \n",
"apache_4a_hospital_death_prob -0.142490 0.119818 \n",
"apache_4a_icu_death_prob -0.134811 0.117201 \n",
"aids 0.000257 -0.012616 \n",
"cirrhosis -0.048662 -0.007844 \n",
"diabetes_mellitus 0.021763 0.430235 \n",
"hepatic_failure -0.051282 -0.013572 \n",
"immunosuppression -0.037091 -0.006169 \n",
"leukemia -0.025405 -0.006901 \n",
"lymphoma -0.017958 -0.000503 \n",
"solid_tumor_with_metastasis -0.026701 -0.013888 \n",
"\n",
" d1_glucose_min d1_potassium_max \\\n",
"encounter_id 0.002371 -0.004673 \n",
"patient_id 0.000074 0.002094 \n",
"hospital_id 0.022893 0.002906 \n",
"age 0.066914 0.055840 \n",
"bmi 0.136203 0.089247 \n",
"elective_surgery 0.022987 0.078894 \n",
"height 0.018818 0.055173 \n",
"icu_id 0.004540 -0.002938 \n",
"pre_icu_los_days -0.002267 0.016778 \n",
"weight 0.138689 0.107234 \n",
"apache_2_diagnosis -0.028468 0.051021 \n",
"apache_3j_diagnosis -0.007121 0.066359 \n",
"apache_post_operative 0.030704 0.081878 \n",
"arf_apache -0.062408 0.108981 \n",
"gcs_eyes_apache 0.038209 -0.064467 \n",
"gcs_motor_apache 0.028602 -0.064191 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.050847 -0.066874 \n",
"heart_rate_apache 0.060540 0.011124 \n",
"intubated_apache -0.026432 0.107169 \n",
"map_apache 0.049499 -0.038048 \n",
"resprate_apache 0.027490 -0.012051 \n",
"temp_apache 0.030358 -0.104586 \n",
"ventilated_apache -0.004030 0.141289 \n",
"d1_diasbp_max 0.017044 -0.026528 \n",
"d1_diasbp_min 0.029621 -0.147363 \n",
"d1_diasbp_noninvasive_max 0.017193 -0.026837 \n",
"d1_diasbp_noninvasive_min 0.029728 -0.146484 \n",
"d1_heartrate_max 0.053443 0.039406 \n",
"d1_heartrate_min 0.051655 -0.005189 \n",
"d1_mbp_max 0.031988 -0.037166 \n",
"d1_mbp_min 0.049530 -0.146353 \n",
"d1_mbp_noninvasive_max 0.031901 -0.037627 \n",
"d1_mbp_noninvasive_min 0.049670 -0.146024 \n",
"d1_resprate_max 0.014376 0.029741 \n",
"d1_resprate_min 0.048707 -0.055836 \n",
"d1_spo2_max -0.081946 0.039711 \n",
"d1_spo2_min -0.005771 -0.099509 \n",
"d1_sysbp_max 0.066461 -0.024258 \n",
"d1_sysbp_min 0.065053 -0.136593 \n",
"d1_sysbp_noninvasive_max 0.066677 -0.025633 \n",
"d1_sysbp_noninvasive_min 0.064926 -0.135992 \n",
"d1_temp_max -0.011571 -0.022483 \n",
"d1_temp_min 0.037687 -0.118506 \n",
"h1_diasbp_max 0.024736 -0.074244 \n",
"h1_diasbp_min 0.024580 -0.123494 \n",
"h1_diasbp_noninvasive_max 0.023985 -0.075539 \n",
"h1_diasbp_noninvasive_min 0.025106 -0.124229 \n",
"h1_heartrate_max 0.062685 0.008160 \n",
"h1_heartrate_min 0.061894 0.000349 \n",
"h1_mbp_max 0.036399 -0.081778 \n",
"h1_mbp_min 0.039970 -0.123529 \n",
"h1_mbp_noninvasive_max 0.036576 -0.082839 \n",
"h1_mbp_noninvasive_min 0.039749 -0.124130 \n",
"h1_resprate_max 0.050925 0.009906 \n",
"h1_resprate_min 0.053458 -0.011577 \n",
"h1_spo2_max -0.062018 -0.006664 \n",
"h1_spo2_min -0.036017 -0.059276 \n",
"h1_sysbp_max 0.064003 -0.069402 \n",
"h1_sysbp_min 0.056359 -0.109430 \n",
"h1_sysbp_noninvasive_max 0.064903 -0.070546 \n",
"h1_sysbp_noninvasive_min 0.055871 -0.109438 \n",
"d1_glucose_max 0.370571 0.189215 \n",
"d1_glucose_min 1.000000 0.004936 \n",
"d1_potassium_max 0.004936 1.000000 \n",
"d1_potassium_min 0.094505 0.653250 \n",
"apache_4a_hospital_death_prob 0.008919 0.111682 \n",
"apache_4a_icu_death_prob 0.004978 0.108460 \n",
"aids -0.007544 0.002606 \n",
"cirrhosis -0.021855 0.025160 \n",
"diabetes_mellitus 0.139433 0.101334 \n",
"hepatic_failure -0.023665 0.020301 \n",
"immunosuppression 0.004412 -0.002128 \n",
"leukemia -0.008404 0.003742 \n",
"lymphoma 0.004127 0.002158 \n",
"solid_tumor_with_metastasis 0.013219 -0.000153 \n",
"\n",
" d1_potassium_min \\\n",
"encounter_id -0.003733 \n",
"patient_id 0.003155 \n",
"hospital_id -0.012871 \n",
"age 0.114173 \n",
"bmi 0.099579 \n",
"elective_surgery 0.047976 \n",
"height 0.068632 \n",
"icu_id -0.011041 \n",
"pre_icu_los_days 0.014645 \n",
"weight 0.122040 \n",
"apache_2_diagnosis 0.050009 \n",
"apache_3j_diagnosis 0.002477 \n",
"apache_post_operative 0.042118 \n",
"arf_apache 0.086886 \n",
"gcs_eyes_apache 0.082590 \n",
"gcs_motor_apache 0.086013 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache 0.090999 \n",
"heart_rate_apache -0.072557 \n",
"intubated_apache -0.064286 \n",
"map_apache -0.030348 \n",
"resprate_apache -0.013941 \n",
"temp_apache 0.003755 \n",
"ventilated_apache -0.032025 \n",
"d1_diasbp_max -0.028403 \n",
"d1_diasbp_min -0.064450 \n",
"d1_diasbp_noninvasive_max -0.028376 \n",
"d1_diasbp_noninvasive_min -0.064412 \n",
"d1_heartrate_max -0.076311 \n",
"d1_heartrate_min -0.044381 \n",
"d1_mbp_max -0.029030 \n",
"d1_mbp_min -0.046372 \n",
"d1_mbp_noninvasive_max -0.027664 \n",
"d1_mbp_noninvasive_min -0.046391 \n",
"d1_resprate_max -0.011542 \n",
"d1_resprate_min -0.019968 \n",
"d1_spo2_max -0.042741 \n",
"d1_spo2_min -0.046367 \n",
"d1_sysbp_max -0.015672 \n",
"d1_sysbp_min -0.028504 \n",
"d1_sysbp_noninvasive_max -0.015752 \n",
"d1_sysbp_noninvasive_min -0.028606 \n",
"d1_temp_max -0.088875 \n",
"d1_temp_min 0.028393 \n",
"h1_diasbp_max -0.054918 \n",
"h1_diasbp_min -0.064156 \n",
"h1_diasbp_noninvasive_max -0.055765 \n",
"h1_diasbp_noninvasive_min -0.065128 \n",
"h1_heartrate_max -0.080941 \n",
"h1_heartrate_min -0.073151 \n",
"h1_mbp_max -0.050733 \n",
"h1_mbp_min -0.052324 \n",
"h1_mbp_noninvasive_max -0.050068 \n",
"h1_mbp_noninvasive_min -0.052542 \n",
"h1_resprate_max -0.011402 \n",
"h1_resprate_min -0.018120 \n",
"h1_spo2_max -0.048023 \n",
"h1_spo2_min -0.045481 \n",
"h1_sysbp_max -0.033458 \n",
"h1_sysbp_min -0.033254 \n",
"h1_sysbp_noninvasive_max -0.032997 \n",
"h1_sysbp_noninvasive_min -0.033244 \n",
"d1_glucose_max -0.040980 \n",
"d1_glucose_min 0.094505 \n",
"d1_potassium_max 0.653250 \n",
"d1_potassium_min 1.000000 \n",
"apache_4a_hospital_death_prob -0.010123 \n",
"apache_4a_icu_death_prob -0.021224 \n",
"aids 0.002778 \n",
"cirrhosis 0.017554 \n",
"diabetes_mellitus 0.037639 \n",
"hepatic_failure 0.013184 \n",
"immunosuppression -0.000445 \n",
"leukemia -0.002258 \n",
"lymphoma 0.003344 \n",
"solid_tumor_with_metastasis 0.012609 \n",
"\n",
" apache_4a_hospital_death_prob \\\n",
"encounter_id -0.005155 \n",
"patient_id 0.003026 \n",
"hospital_id -0.003032 \n",
"age 0.171258 \n",
"bmi -0.028964 \n",
"elective_surgery -0.118039 \n",
"height -0.026078 \n",
"icu_id -0.004768 \n",
"pre_icu_los_days 0.095801 \n",
"weight -0.038453 \n",
"apache_2_diagnosis -0.097084 \n",
"apache_3j_diagnosis -0.118604 \n",
"apache_post_operative -0.107271 \n",
"arf_apache 0.037495 \n",
"gcs_eyes_apache -0.465077 \n",
"gcs_motor_apache -0.507284 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache -0.451790 \n",
"heart_rate_apache 0.124840 \n",
"intubated_apache 0.332905 \n",
"map_apache -0.030694 \n",
"resprate_apache 0.098826 \n",
"temp_apache -0.197472 \n",
"ventilated_apache 0.351655 \n",
"d1_diasbp_max 0.013195 \n",
"d1_diasbp_min -0.193258 \n",
"d1_diasbp_noninvasive_max 0.012716 \n",
"d1_diasbp_noninvasive_min -0.193442 \n",
"d1_heartrate_max 0.171262 \n",
"d1_heartrate_min -0.004565 \n",
"d1_mbp_max 0.011926 \n",
"d1_mbp_min -0.203707 \n",
"d1_mbp_noninvasive_max 0.010603 \n",
"d1_mbp_noninvasive_min -0.204198 \n",
"d1_resprate_max 0.117654 \n",
"d1_resprate_min 0.001213 \n",
"d1_spo2_max 0.062270 \n",
"d1_spo2_min -0.154740 \n",
"d1_sysbp_max 0.016077 \n",
"d1_sysbp_min -0.217784 \n",
"d1_sysbp_noninvasive_max 0.015920 \n",
"d1_sysbp_noninvasive_min -0.217980 \n",
"d1_temp_max 0.048238 \n",
"d1_temp_min -0.256898 \n",
"h1_diasbp_max -0.020015 \n",
"h1_diasbp_min -0.141526 \n",
"h1_diasbp_noninvasive_max -0.021459 \n",
"h1_diasbp_noninvasive_min -0.141760 \n",
"h1_heartrate_max 0.126071 \n",
"h1_heartrate_min 0.080865 \n",
"h1_mbp_max -0.033841 \n",
"h1_mbp_min -0.143307 \n",
"h1_mbp_noninvasive_max -0.034958 \n",
"h1_mbp_noninvasive_min -0.143961 \n",
"h1_resprate_max 0.136787 \n",
"h1_resprate_min 0.096252 \n",
"h1_spo2_max -0.015187 \n",
"h1_spo2_min -0.106906 \n",
"h1_sysbp_max -0.034774 \n",
"h1_sysbp_min -0.142781 \n",
"h1_sysbp_noninvasive_max -0.034832 \n",
"h1_sysbp_noninvasive_min -0.142490 \n",
"d1_glucose_max 0.119818 \n",
"d1_glucose_min 0.008919 \n",
"d1_potassium_max 0.111682 \n",
"d1_potassium_min -0.010123 \n",
"apache_4a_hospital_death_prob 1.000000 \n",
"apache_4a_icu_death_prob 0.844291 \n",
"aids 0.010033 \n",
"cirrhosis 0.048312 \n",
"diabetes_mellitus 0.011216 \n",
"hepatic_failure 0.031628 \n",
"immunosuppression 0.047476 \n",
"leukemia 0.053369 \n",
"lymphoma 0.024351 \n",
"solid_tumor_with_metastasis 0.062040 \n",
"\n",
" apache_4a_icu_death_prob aids cirrhosis \\\n",
"encounter_id -0.005044 0.001709 0.011650 \n",
"patient_id 0.004431 -0.000660 0.004198 \n",
"hospital_id 0.004436 -0.006257 0.006344 \n",
"age 0.089739 -0.032535 -0.031029 \n",
"bmi -0.012521 -0.022728 -0.002425 \n",
"elective_surgery -0.086606 -0.004932 -0.033169 \n",
"height -0.008080 0.011321 0.012351 \n",
"icu_id -0.009903 -0.002201 -0.016893 \n",
"pre_icu_los_days 0.068496 0.012552 0.014046 \n",
"weight -0.015454 -0.018215 0.002399 \n",
"apache_2_diagnosis -0.104199 -0.006816 -0.004227 \n",
"apache_3j_diagnosis -0.085070 0.000255 -0.019918 \n",
"apache_post_operative -0.074592 -0.006550 -0.035495 \n",
"arf_apache 0.031092 0.007424 0.024045 \n",
"gcs_eyes_apache -0.449872 -0.002167 -0.013570 \n",
"gcs_motor_apache -0.504793 -0.002878 -0.007157 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache -0.419053 -0.001815 -0.010962 \n",
"heart_rate_apache 0.115316 0.007786 0.014019 \n",
"intubated_apache 0.339431 0.006411 0.005999 \n",
"map_apache -0.023802 0.002195 -0.036326 \n",
"resprate_apache 0.082287 0.012499 -0.003093 \n",
"temp_apache -0.206398 0.001945 -0.018439 \n",
"ventilated_apache 0.337154 0.006785 -0.006741 \n",
"d1_diasbp_max 0.012630 0.010762 -0.017529 \n",
"d1_diasbp_min -0.168312 0.012464 -0.046882 \n",
"d1_diasbp_noninvasive_max 0.012448 0.010759 -0.017306 \n",
"d1_diasbp_noninvasive_min -0.168482 0.012426 -0.046496 \n",
"d1_heartrate_max 0.158557 0.009853 0.015702 \n",
"d1_heartrate_min -0.007087 0.011322 0.022203 \n",
"d1_mbp_max 0.009259 0.009370 -0.029211 \n",
"d1_mbp_min -0.182120 0.007909 -0.050296 \n",
"d1_mbp_noninvasive_max 0.008541 0.009354 -0.028820 \n",
"d1_mbp_noninvasive_min -0.182673 0.007944 -0.049825 \n",
"d1_resprate_max 0.102399 0.009845 0.003894 \n",
"d1_resprate_min -0.010671 0.010695 -0.013251 \n",
"d1_spo2_max 0.053668 0.005565 0.015644 \n",
"d1_spo2_min -0.151416 0.002393 -0.003627 \n",
"d1_sysbp_max 0.006298 -0.000834 -0.033478 \n",
"d1_sysbp_min -0.201911 0.000649 -0.046788 \n",
"d1_sysbp_noninvasive_max 0.006253 -0.000794 -0.033091 \n",
"d1_sysbp_noninvasive_min -0.202009 0.000632 -0.046513 \n",
"d1_temp_max 0.050052 0.007963 -0.012707 \n",
"d1_temp_min -0.270149 -0.000950 -0.018276 \n",
"h1_diasbp_max -0.012538 0.013123 -0.025047 \n",
"h1_diasbp_min -0.120126 0.013015 -0.048752 \n",
"h1_diasbp_noninvasive_max -0.013593 0.013092 -0.025062 \n",
"h1_diasbp_noninvasive_min -0.120348 0.012944 -0.049390 \n",
"h1_heartrate_max 0.120051 0.010233 0.018175 \n",
"h1_heartrate_min 0.078412 0.011083 0.020529 \n",
"h1_mbp_max -0.029171 0.008723 -0.035903 \n",
"h1_mbp_min -0.126340 0.008622 -0.050043 \n",
"h1_mbp_noninvasive_max -0.030081 0.008777 -0.035794 \n",
"h1_mbp_noninvasive_min -0.126812 0.008645 -0.050119 \n",
"h1_resprate_max 0.115561 0.016430 0.002881 \n",
"h1_resprate_min 0.074214 0.009873 -0.011457 \n",
"h1_spo2_max -0.015543 0.005167 0.010287 \n",
"h1_spo2_min -0.102579 -0.000169 0.005423 \n",
"h1_sysbp_max -0.037108 -0.000523 -0.036144 \n",
"h1_sysbp_min -0.135120 0.000184 -0.049068 \n",
"h1_sysbp_noninvasive_max -0.037322 -0.000393 -0.035746 \n",
"h1_sysbp_noninvasive_min -0.134811 0.000257 -0.048662 \n",
"d1_glucose_max 0.117201 -0.012616 -0.007844 \n",
"d1_glucose_min 0.004978 -0.007544 -0.021855 \n",
"d1_potassium_max 0.108460 0.002606 0.025160 \n",
"d1_potassium_min -0.021224 0.002778 0.017554 \n",
"apache_4a_hospital_death_prob 0.844291 0.010033 0.048312 \n",
"apache_4a_icu_death_prob 1.000000 0.009050 0.044584 \n",
"aids 0.009050 1.000000 0.012506 \n",
"cirrhosis 0.044584 0.012506 1.000000 \n",
"diabetes_mellitus 0.004636 -0.013817 0.012295 \n",
"hepatic_failure 0.037314 0.005179 0.535318 \n",
"immunosuppression 0.030028 0.023775 -0.004254 \n",
"leukemia 0.037671 -0.002850 -0.004375 \n",
"lymphoma 0.014551 0.023303 0.003606 \n",
"solid_tumor_with_metastasis 0.033893 -0.001095 -0.006598 \n",
"\n",
" diabetes_mellitus hepatic_failure \\\n",
"encounter_id 0.006541 0.001078 \n",
"patient_id -0.001333 -0.002630 \n",
"hospital_id 0.011368 0.003995 \n",
"age 0.065859 -0.024565 \n",
"bmi 0.170040 -0.000597 \n",
"elective_surgery -0.011640 -0.035008 \n",
"height -0.004218 0.008594 \n",
"icu_id 0.024944 -0.014062 \n",
"pre_icu_los_days 0.015722 0.014342 \n",
"weight 0.157113 0.003829 \n",
"apache_2_diagnosis -0.004628 -0.003222 \n",
"apache_3j_diagnosis -0.008994 -0.027882 \n",
"apache_post_operative -0.018465 -0.036657 \n",
"arf_apache 0.107260 0.018832 \n",
"gcs_eyes_apache 0.034854 -0.010770 \n",
"gcs_motor_apache 0.031341 -0.004973 \n",
"gcs_unable_apache NaN NaN \n",
"gcs_verbal_apache 0.031691 -0.008965 \n",
"heart_rate_apache -0.018177 0.012780 \n",
"intubated_apache -0.013904 0.002573 \n",
"map_apache -0.000444 -0.044452 \n",
"resprate_apache -0.013947 -0.006893 \n",
"temp_apache -0.000627 -0.023581 \n",
"ventilated_apache -0.002992 -0.006189 \n",
"d1_diasbp_max -0.019824 -0.023154 \n",
"d1_diasbp_min -0.054714 -0.050688 \n",
"d1_diasbp_noninvasive_max -0.019583 -0.023155 \n",
"d1_diasbp_noninvasive_min -0.054751 -0.050795 \n",
"d1_heartrate_max -0.026411 0.015661 \n",
"d1_heartrate_min 0.018222 0.024140 \n",
"d1_mbp_max 0.015416 -0.034595 \n",
"d1_mbp_min -0.020560 -0.054639 \n",
"d1_mbp_noninvasive_max 0.015328 -0.033779 \n",
"d1_mbp_noninvasive_min -0.020812 -0.054604 \n",
"d1_resprate_max 0.002041 0.002154 \n",
"d1_resprate_min -0.019699 -0.006476 \n",
"d1_spo2_max 0.007692 0.018583 \n",
"d1_spo2_min 0.004558 -0.007684 \n",
"d1_sysbp_max 0.069493 -0.034691 \n",
"d1_sysbp_min 0.025228 -0.048606 \n",
"d1_sysbp_noninvasive_max 0.069483 -0.034506 \n",
"d1_sysbp_noninvasive_min 0.025260 -0.048668 \n",
"d1_temp_max -0.015323 -0.010524 \n",
"d1_temp_min -0.003782 -0.024858 \n",
"h1_diasbp_max -0.047827 -0.036471 \n",
"h1_diasbp_min -0.066676 -0.052422 \n",
"h1_diasbp_noninvasive_max -0.047512 -0.035785 \n",
"h1_diasbp_noninvasive_min -0.066902 -0.052745 \n",
"h1_heartrate_max -0.012669 0.020534 \n",
"h1_heartrate_min -0.001135 0.023841 \n",
"h1_mbp_max -0.010005 -0.044933 \n",
"h1_mbp_min -0.026996 -0.054471 \n",
"h1_mbp_noninvasive_max -0.010453 -0.045180 \n",
"h1_mbp_noninvasive_min -0.027522 -0.054756 \n",
"h1_resprate_max 0.003224 0.003297 \n",
"h1_resprate_min -0.002641 -0.007012 \n",
"h1_spo2_max 0.013671 0.015094 \n",
"h1_spo2_min -0.001203 0.004372 \n",
"h1_sysbp_max 0.040022 -0.041831 \n",
"h1_sysbp_min 0.020799 -0.051120 \n",
"h1_sysbp_noninvasive_max 0.040407 -0.041875 \n",
"h1_sysbp_noninvasive_min 0.021763 -0.051282 \n",
"d1_glucose_max 0.430235 -0.013572 \n",
"d1_glucose_min 0.139433 -0.023665 \n",
"d1_potassium_max 0.101334 0.020301 \n",
"d1_potassium_min 0.037639 0.013184 \n",
"apache_4a_hospital_death_prob 0.011216 0.031628 \n",
"apache_4a_icu_death_prob 0.004636 0.037314 \n",
"aids -0.013817 0.005179 \n",
"cirrhosis 0.012295 0.535318 \n",
"diabetes_mellitus 1.000000 0.008549 \n",
"hepatic_failure 0.008549 1.000000 \n",
"immunosuppression -0.006081 0.005264 \n",
"leukemia 0.002402 0.000514 \n",
"lymphoma -0.009503 0.000902 \n",
"solid_tumor_with_metastasis -0.012553 0.006025 \n",
"\n",
" immunosuppression leukemia lymphoma \\\n",
"encounter_id -0.002094 -0.003623 -0.001809 \n",
"patient_id 0.001413 0.000975 -0.002327 \n",
"hospital_id 0.000381 -0.005046 0.006326 \n",
"age 0.023724 0.030634 0.020841 \n",
"bmi -0.033248 -0.015873 -0.010283 \n",
"elective_surgery -0.011333 -0.016247 -0.010116 \n",
"height 0.001106 0.001257 0.009898 \n",
"icu_id -0.037019 -0.000094 -0.002586 \n",
"pre_icu_los_days 0.037962 0.050971 0.017935 \n",
"weight -0.031774 -0.015303 -0.004868 \n",
"apache_2_diagnosis -0.011232 -0.004233 -0.004681 \n",
"apache_3j_diagnosis -0.000904 -0.006425 -0.002530 \n",
"apache_post_operative -0.012610 -0.013973 -0.009933 \n",
"arf_apache 0.001526 0.016028 -0.006626 \n",
"gcs_eyes_apache 0.022177 0.006807 0.012052 \n",
"gcs_motor_apache 0.022238 0.009805 0.008518 \n",
"gcs_unable_apache NaN NaN NaN \n",
"gcs_verbal_apache 0.026622 0.012140 0.011510 \n",
"heart_rate_apache 0.058280 0.021585 0.019672 \n",
"intubated_apache -0.009710 -0.001891 -0.005110 \n",
"map_apache -0.021612 -0.018537 -0.009331 \n",
"resprate_apache 0.036010 0.019099 0.013559 \n",
"temp_apache 0.005193 0.003955 -0.001379 \n",
"ventilated_apache -0.004677 -0.004202 -0.004654 \n",
"d1_diasbp_max -0.019109 -0.009462 -0.007399 \n",
"d1_diasbp_min -0.015537 -0.028716 -0.007431 \n",
"d1_diasbp_noninvasive_max -0.019113 -0.009464 -0.007400 \n",
"d1_diasbp_noninvasive_min -0.015711 -0.028798 -0.007495 \n",
"d1_heartrate_max 0.066606 0.023870 0.019838 \n",
"d1_heartrate_min 0.054844 0.013753 0.017229 \n",
"d1_mbp_max -0.021104 -0.011892 -0.010193 \n",
"d1_mbp_min -0.026259 -0.026944 -0.009239 \n",
"d1_mbp_noninvasive_max -0.020487 -0.013337 -0.010189 \n",
"d1_mbp_noninvasive_min -0.026956 -0.026687 -0.009197 \n",
"d1_resprate_max 0.038590 0.023426 0.015426 \n",
"d1_resprate_min 0.015082 0.015824 0.012541 \n",
"d1_spo2_max 0.010419 0.003315 -0.002064 \n",
"d1_spo2_min -0.024263 -0.023573 -0.009974 \n",
"d1_sysbp_max -0.031275 -0.018230 -0.015322 \n",
"d1_sysbp_min -0.037169 -0.023957 -0.012975 \n",
"d1_sysbp_noninvasive_max -0.031031 -0.018103 -0.015227 \n",
"d1_sysbp_noninvasive_min -0.037259 -0.024003 -0.013010 \n",
"d1_temp_max 0.015804 0.018855 0.000974 \n",
"d1_temp_min 0.003599 -0.002237 -0.001466 \n",
"h1_diasbp_max -0.024897 -0.023125 -0.014152 \n",
"h1_diasbp_min -0.025463 -0.029693 -0.013254 \n",
"h1_diasbp_noninvasive_max -0.024784 -0.022945 -0.015404 \n",
"h1_diasbp_noninvasive_min -0.024206 -0.029332 -0.013997 \n",
"h1_heartrate_max 0.066942 0.023266 0.019361 \n",
"h1_heartrate_min 0.068321 0.019161 0.018820 \n",
"h1_mbp_max -0.032257 -0.023421 -0.015548 \n",
"h1_mbp_min -0.027287 -0.029801 -0.012395 \n",
"h1_mbp_noninvasive_max -0.031041 -0.023258 -0.015457 \n",
"h1_mbp_noninvasive_min -0.026931 -0.029596 -0.013360 \n",
"h1_resprate_max 0.054017 0.029426 0.022068 \n",
"h1_resprate_min 0.038334 0.024732 0.020294 \n",
"h1_spo2_max -0.012804 -0.002035 -0.009216 \n",
"h1_spo2_min -0.014791 -0.002042 -0.009525 \n",
"h1_sysbp_max -0.034089 -0.021371 -0.016606 \n",
"h1_sysbp_min -0.036965 -0.025332 -0.017167 \n",
"h1_sysbp_noninvasive_max -0.033463 -0.021404 -0.016309 \n",
"h1_sysbp_noninvasive_min -0.037091 -0.025405 -0.017958 \n",
"d1_glucose_max -0.006169 -0.006901 -0.000503 \n",
"d1_glucose_min 0.004412 -0.008404 0.004127 \n",
"d1_potassium_max -0.002128 0.003742 0.002158 \n",
"d1_potassium_min -0.000445 -0.002258 0.003344 \n",
"apache_4a_hospital_death_prob 0.047476 0.053369 0.024351 \n",
"apache_4a_icu_death_prob 0.030028 0.037671 0.014551 \n",
"aids 0.023775 -0.002850 0.023303 \n",
"cirrhosis -0.004254 -0.004375 0.003606 \n",
"diabetes_mellitus -0.006081 0.002402 -0.009503 \n",
"hepatic_failure 0.005264 0.000514 0.000902 \n",
"immunosuppression 1.000000 0.148845 0.098902 \n",
"leukemia 0.148845 1.000000 0.024657 \n",
"lymphoma 0.098902 0.024657 1.000000 \n",
"solid_tumor_with_metastasis 0.277153 0.003979 0.012069 \n",
"\n",
" solid_tumor_with_metastasis \n",
"encounter_id -0.003849 \n",
"patient_id -0.006374 \n",
"hospital_id -0.005487 \n",
"age 0.026358 \n",
"bmi -0.047853 \n",
"elective_surgery 0.019365 \n",
"height 0.007713 \n",
"icu_id -0.012947 \n",
"pre_icu_los_days 0.039016 \n",
"weight -0.042026 \n",
"apache_2_diagnosis 0.008605 \n",
"apache_3j_diagnosis 0.021846 \n",
"apache_post_operative 0.015989 \n",
"arf_apache -0.009522 \n",
"gcs_eyes_apache 0.017411 \n",
"gcs_motor_apache 0.016147 \n",
"gcs_unable_apache NaN \n",
"gcs_verbal_apache 0.017257 \n",
"heart_rate_apache 0.044459 \n",
"intubated_apache -0.010965 \n",
"map_apache -0.010763 \n",
"resprate_apache 0.016087 \n",
"temp_apache -0.002067 \n",
"ventilated_apache -0.016788 \n",
"d1_diasbp_max -0.023977 \n",
"d1_diasbp_min -0.002172 \n",
"d1_diasbp_noninvasive_max -0.023979 \n",
"d1_diasbp_noninvasive_min -0.002327 \n",
"d1_heartrate_max 0.048240 \n",
"d1_heartrate_min 0.044348 \n",
"d1_mbp_max -0.024688 \n",
"d1_mbp_min -0.010799 \n",
"d1_mbp_noninvasive_max -0.025317 \n",
"d1_mbp_noninvasive_min -0.010691 \n",
"d1_resprate_max 0.023527 \n",
"d1_resprate_min 0.008194 \n",
"d1_spo2_max 0.000517 \n",
"d1_spo2_min -0.015686 \n",
"d1_sysbp_max -0.031206 \n",
"d1_sysbp_min -0.025428 \n",
"d1_sysbp_noninvasive_max -0.030990 \n",
"d1_sysbp_noninvasive_min -0.025508 \n",
"d1_temp_max -0.010663 \n",
"d1_temp_min -0.001434 \n",
"h1_diasbp_max -0.015965 \n",
"h1_diasbp_min -0.010569 \n",
"h1_diasbp_noninvasive_max -0.015739 \n",
"h1_diasbp_noninvasive_min -0.010381 \n",
"h1_heartrate_max 0.045671 \n",
"h1_heartrate_min 0.048555 \n",
"h1_mbp_max -0.021113 \n",
"h1_mbp_min -0.015536 \n",
"h1_mbp_noninvasive_max -0.020619 \n",
"h1_mbp_noninvasive_min -0.015739 \n",
"h1_resprate_max 0.022905 \n",
"h1_resprate_min 0.015658 \n",
"h1_spo2_max -0.003942 \n",
"h1_spo2_min -0.011597 \n",
"h1_sysbp_max -0.026780 \n",
"h1_sysbp_min -0.026765 \n",
"h1_sysbp_noninvasive_max -0.026137 \n",
"h1_sysbp_noninvasive_min -0.026701 \n",
"d1_glucose_max -0.013888 \n",
"d1_glucose_min 0.013219 \n",
"d1_potassium_max -0.000153 \n",
"d1_potassium_min 0.012609 \n",
"apache_4a_hospital_death_prob 0.062040 \n",
"apache_4a_icu_death_prob 0.033893 \n",
"aids -0.001095 \n",
"cirrhosis -0.006598 \n",
"diabetes_mellitus -0.012553 \n",
"hepatic_failure 0.006025 \n",
"immunosuppression 0.277153 \n",
"leukemia 0.003979 \n",
"lymphoma 0.012069 \n",
"solid_tumor_with_metastasis 1.000000 "
],
"text/html": [
"\n",
" <div id=\"df-3c52654e-11ef-4b18-aa64-78360804d7c7\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>encounter_id</th>\n",
" <th>patient_id</th>\n",
" <th>hospital_id</th>\n",
" <th>age</th>\n",
" <th>bmi</th>\n",
" <th>elective_surgery</th>\n",
" <th>height</th>\n",
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" <th>apache_3j_diagnosis</th>\n",
" <th>apache_post_operative</th>\n",
" <th>arf_apache</th>\n",
" <th>gcs_eyes_apache</th>\n",
" <th>gcs_motor_apache</th>\n",
" <th>gcs_unable_apache</th>\n",
" <th>gcs_verbal_apache</th>\n",
" <th>heart_rate_apache</th>\n",
" <th>intubated_apache</th>\n",
" <th>map_apache</th>\n",
" <th>resprate_apache</th>\n",
" <th>temp_apache</th>\n",
" <th>ventilated_apache</th>\n",
" <th>d1_diasbp_max</th>\n",
" <th>d1_diasbp_min</th>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <th>d1_heartrate_max</th>\n",
" <th>d1_heartrate_min</th>\n",
" <th>d1_mbp_max</th>\n",
" <th>d1_mbp_min</th>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <th>d1_resprate_max</th>\n",
" <th>d1_resprate_min</th>\n",
" <th>d1_spo2_max</th>\n",
" <th>d1_spo2_min</th>\n",
" <th>d1_sysbp_max</th>\n",
" <th>d1_sysbp_min</th>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <th>d1_temp_max</th>\n",
" <th>d1_temp_min</th>\n",
" <th>h1_diasbp_max</th>\n",
" <th>h1_diasbp_min</th>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <th>h1_heartrate_max</th>\n",
" <th>h1_heartrate_min</th>\n",
" <th>h1_mbp_max</th>\n",
" <th>h1_mbp_min</th>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <th>h1_resprate_max</th>\n",
" <th>h1_resprate_min</th>\n",
" <th>h1_spo2_max</th>\n",
" <th>h1_spo2_min</th>\n",
" <th>h1_sysbp_max</th>\n",
" <th>h1_sysbp_min</th>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <th>d1_glucose_max</th>\n",
" <th>d1_glucose_min</th>\n",
" <th>d1_potassium_max</th>\n",
" <th>d1_potassium_min</th>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <th>aids</th>\n",
" <th>cirrhosis</th>\n",
" <th>diabetes_mellitus</th>\n",
" <th>hepatic_failure</th>\n",
" <th>immunosuppression</th>\n",
" <th>leukemia</th>\n",
" <th>lymphoma</th>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>encounter_id</th>\n",
" <td>1.000000</td>\n",
" <td>-0.001036</td>\n",
" <td>-0.005555</td>\n",
" <td>-0.004015</td>\n",
" <td>0.001026</td>\n",
" <td>-0.002694</td>\n",
" <td>-0.006739</td>\n",
" <td>-0.000704</td>\n",
" <td>-0.000998</td>\n",
" <td>-0.002298</td>\n",
" <td>-0.000374</td>\n",
" <td>-0.001103</td>\n",
" <td>-0.002450</td>\n",
" <td>0.012406</td>\n",
" <td>0.005690</td>\n",
" <td>0.010362</td>\n",
" <td>NaN</td>\n",
" <td>0.008068</td>\n",
" <td>-0.002901</td>\n",
" <td>-0.007877</td>\n",
" <td>-0.001580</td>\n",
" <td>0.008714</td>\n",
" <td>0.006060</td>\n",
" <td>-0.013043</td>\n",
" <td>-0.002887</td>\n",
" <td>-0.000322</td>\n",
" <td>-0.002818</td>\n",
" <td>-0.000037</td>\n",
" <td>-0.006774</td>\n",
" <td>0.001381</td>\n",
" <td>-0.000186</td>\n",
" <td>-0.001552</td>\n",
" <td>0.001062</td>\n",
" <td>-0.001624</td>\n",
" <td>0.007901</td>\n",
" <td>0.001453</td>\n",
" <td>-0.002737</td>\n",
" <td>0.002650</td>\n",
" <td>-0.003727</td>\n",
" <td>-0.002810</td>\n",
" <td>-0.003711</td>\n",
" <td>-0.002786</td>\n",
" <td>-0.006661</td>\n",
" <td>0.004776</td>\n",
" <td>-0.003139</td>\n",
" <td>0.002802</td>\n",
" <td>-0.002042</td>\n",
" <td>0.002508</td>\n",
" <td>-0.002534</td>\n",
" <td>-0.002998</td>\n",
" <td>0.000930</td>\n",
" <td>0.004229</td>\n",
" <td>0.001418</td>\n",
" <td>0.004255</td>\n",
" <td>0.007569</td>\n",
" <td>0.002913</td>\n",
" <td>-0.000712</td>\n",
" <td>-0.003846</td>\n",
" <td>0.003081</td>\n",
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" <td>0.002926</td>\n",
" <td>0.000954</td>\n",
" <td>0.003829</td>\n",
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" <td>-0.003733</td>\n",
" <td>-0.005155</td>\n",
" <td>-0.005044</td>\n",
" <td>0.001709</td>\n",
" <td>0.011650</td>\n",
" <td>0.006541</td>\n",
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" <td>-0.002094</td>\n",
" <td>-0.003623</td>\n",
" <td>-0.001809</td>\n",
" <td>-0.003849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>patient_id</th>\n",
" <td>-0.001036</td>\n",
" <td>1.000000</td>\n",
" <td>-0.006371</td>\n",
" <td>0.005469</td>\n",
" <td>0.000692</td>\n",
" <td>0.005650</td>\n",
" <td>0.004081</td>\n",
" <td>-0.002483</td>\n",
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" <td>-0.000592</td>\n",
" <td>0.002008</td>\n",
" <td>0.001737</td>\n",
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" <td>0.002699</td>\n",
" <td>0.004945</td>\n",
" <td>0.003930</td>\n",
" <td>-0.003315</td>\n",
" <td>0.002281</td>\n",
" <td>-0.000320</td>\n",
" <td>-0.001335</td>\n",
" <td>-0.002492</td>\n",
" <td>-0.001735</td>\n",
" <td>-0.002276</td>\n",
" <td>-0.001946</td>\n",
" <td>-0.000357</td>\n",
" <td>0.000861</td>\n",
" <td>-0.004624</td>\n",
" <td>-0.002503</td>\n",
" <td>-0.004520</td>\n",
" <td>-0.002932</td>\n",
" <td>0.003176</td>\n",
" <td>0.003608</td>\n",
" <td>-0.003005</td>\n",
" <td>-0.005226</td>\n",
" <td>-0.001849</td>\n",
" <td>-0.007961</td>\n",
" <td>-0.001642</td>\n",
" <td>-0.008424</td>\n",
" <td>-0.003322</td>\n",
" <td>-0.001115</td>\n",
" <td>-0.004722</td>\n",
" <td>-0.008662</td>\n",
" <td>-0.003283</td>\n",
" <td>-0.009840</td>\n",
" <td>0.001650</td>\n",
" <td>0.002568</td>\n",
" <td>-0.003626</td>\n",
" <td>-0.007897</td>\n",
" <td>-0.002664</td>\n",
" <td>-0.008033</td>\n",
" <td>0.003544</td>\n",
" <td>-0.000946</td>\n",
" <td>0.000079</td>\n",
" <td>-0.003214</td>\n",
" <td>-0.003712</td>\n",
" <td>-0.009131</td>\n",
" <td>-0.003964</td>\n",
" <td>-0.010023</td>\n",
" <td>-0.007430</td>\n",
" <td>0.000074</td>\n",
" <td>0.002094</td>\n",
" <td>0.003155</td>\n",
" <td>0.003026</td>\n",
" <td>0.004431</td>\n",
" <td>-0.000660</td>\n",
" <td>0.004198</td>\n",
" <td>-0.001333</td>\n",
" <td>-0.002630</td>\n",
" <td>0.001413</td>\n",
" <td>0.000975</td>\n",
" <td>-0.002327</td>\n",
" <td>-0.006374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hospital_id</th>\n",
" <td>-0.005555</td>\n",
" <td>-0.006371</td>\n",
" <td>1.000000</td>\n",
" <td>-0.009069</td>\n",
" <td>0.009028</td>\n",
" <td>0.041702</td>\n",
" <td>0.033112</td>\n",
" <td>0.035638</td>\n",
" <td>-0.001453</td>\n",
" <td>0.025224</td>\n",
" <td>-0.002859</td>\n",
" <td>0.020115</td>\n",
" <td>0.041987</td>\n",
" <td>0.000071</td>\n",
" <td>-0.007684</td>\n",
" <td>-0.017686</td>\n",
" <td>NaN</td>\n",
" <td>0.003370</td>\n",
" <td>-0.006215</td>\n",
" <td>0.019899</td>\n",
" <td>-0.002743</td>\n",
" <td>-0.020449</td>\n",
" <td>-0.032016</td>\n",
" <td>0.025514</td>\n",
" <td>-0.019117</td>\n",
" <td>0.013821</td>\n",
" <td>-0.018961</td>\n",
" <td>0.013578</td>\n",
" <td>-0.006119</td>\n",
" <td>-0.008510</td>\n",
" <td>-0.019417</td>\n",
" <td>0.008801</td>\n",
" <td>-0.018866</td>\n",
" <td>0.009003</td>\n",
" <td>-0.032569</td>\n",
" <td>-0.019908</td>\n",
" <td>-0.009052</td>\n",
" <td>-0.001524</td>\n",
" <td>-0.028943</td>\n",
" <td>0.005046</td>\n",
" <td>-0.028474</td>\n",
" <td>0.004905</td>\n",
" <td>0.016621</td>\n",
" <td>-0.044566</td>\n",
" <td>0.001180</td>\n",
" <td>-0.014265</td>\n",
" <td>0.002316</td>\n",
" <td>-0.012543</td>\n",
" <td>-0.010177</td>\n",
" <td>-0.022248</td>\n",
" <td>0.000636</td>\n",
" <td>-0.011939</td>\n",
" <td>-0.000345</td>\n",
" <td>-0.011090</td>\n",
" <td>-0.023934</td>\n",
" <td>-0.036231</td>\n",
" <td>0.017704</td>\n",
" <td>0.001696</td>\n",
" <td>-0.007294</td>\n",
" <td>-0.020903</td>\n",
" <td>-0.009218</td>\n",
" <td>-0.021643</td>\n",
" <td>-0.001785</td>\n",
" <td>0.022893</td>\n",
" <td>0.002906</td>\n",
" <td>-0.012871</td>\n",
" <td>-0.003032</td>\n",
" <td>0.004436</td>\n",
" <td>-0.006257</td>\n",
" <td>0.006344</td>\n",
" <td>0.011368</td>\n",
" <td>0.003995</td>\n",
" <td>0.000381</td>\n",
" <td>-0.005046</td>\n",
" <td>0.006326</td>\n",
" <td>-0.005487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>-0.004015</td>\n",
" <td>0.005469</td>\n",
" <td>-0.009069</td>\n",
" <td>1.000000</td>\n",
" <td>-0.080486</td>\n",
" <td>0.057514</td>\n",
" <td>-0.119639</td>\n",
" <td>-0.022573</td>\n",
" <td>0.054951</td>\n",
" <td>-0.125288</td>\n",
" <td>0.026553</td>\n",
" <td>-0.063351</td>\n",
" <td>0.046583</td>\n",
" <td>-0.002780</td>\n",
" <td>0.043832</td>\n",
" <td>0.044063</td>\n",
" <td>NaN</td>\n",
" <td>-0.002946</td>\n",
" <td>-0.155835</td>\n",
" <td>-0.003185</td>\n",
" <td>-0.017040</td>\n",
" <td>0.034650</td>\n",
" <td>-0.081164</td>\n",
" <td>0.023629</td>\n",
" <td>-0.056655</td>\n",
" <td>-0.208602</td>\n",
" <td>-0.056564</td>\n",
" <td>-0.207904</td>\n",
" <td>-0.145065</td>\n",
" <td>-0.147906</td>\n",
" <td>0.006689</td>\n",
" <td>-0.129142</td>\n",
" <td>0.006209</td>\n",
" <td>-0.129187</td>\n",
" <td>0.027702</td>\n",
" <td>0.048954</td>\n",
" <td>-0.043002</td>\n",
" <td>-0.081491</td>\n",
" <td>0.105590</td>\n",
" <td>-0.060245</td>\n",
" <td>0.105191</td>\n",
" <td>-0.060213</td>\n",
" <td>-0.100176</td>\n",
" <td>-0.066129</td>\n",
" <td>-0.138641</td>\n",
" <td>-0.186812</td>\n",
" <td>-0.136606</td>\n",
" <td>-0.187165</td>\n",
" <td>-0.172870</td>\n",
" <td>-0.172761</td>\n",
" <td>-0.059667</td>\n",
" <td>-0.100795</td>\n",
" <td>-0.060153</td>\n",
" <td>-0.100612</td>\n",
" <td>0.026192</td>\n",
" <td>0.035833</td>\n",
" <td>-0.067605</td>\n",
" <td>-0.079734</td>\n",
" <td>0.043745</td>\n",
" <td>-0.001140</td>\n",
" <td>0.043261</td>\n",
" <td>-0.001372</td>\n",
" <td>0.005115</td>\n",
" <td>0.066914</td>\n",
" <td>0.055840</td>\n",
" <td>0.114173</td>\n",
" <td>0.171258</td>\n",
" <td>0.089739</td>\n",
" <td>-0.032535</td>\n",
" <td>-0.031029</td>\n",
" <td>0.065859</td>\n",
" <td>-0.024565</td>\n",
" <td>0.023724</td>\n",
" <td>0.030634</td>\n",
" <td>0.020841</td>\n",
" <td>0.026358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bmi</th>\n",
" <td>0.001026</td>\n",
" <td>0.000692</td>\n",
" <td>0.009028</td>\n",
" <td>-0.080486</td>\n",
" <td>1.000000</td>\n",
" <td>0.006735</td>\n",
" <td>-0.063923</td>\n",
" <td>0.004809</td>\n",
" <td>-0.001774</td>\n",
" <td>0.880132</td>\n",
" <td>0.019565</td>\n",
" <td>-0.015095</td>\n",
" <td>0.009118</td>\n",
" <td>-0.007792</td>\n",
" <td>0.012599</td>\n",
" <td>0.020875</td>\n",
" <td>NaN</td>\n",
" <td>0.027426</td>\n",
" <td>-0.028840</td>\n",
" <td>0.032551</td>\n",
" <td>0.056656</td>\n",
" <td>0.005877</td>\n",
" <td>0.035419</td>\n",
" <td>0.076165</td>\n",
" <td>0.053021</td>\n",
" <td>-0.028818</td>\n",
" <td>0.053025</td>\n",
" <td>-0.029103</td>\n",
" <td>-0.036482</td>\n",
" <td>0.006305</td>\n",
" <td>0.061985</td>\n",
" <td>0.000166</td>\n",
" <td>0.062245</td>\n",
" <td>-0.000184</td>\n",
" <td>0.012221</td>\n",
" <td>-0.005307</td>\n",
" <td>-0.082259</td>\n",
" <td>-0.031478</td>\n",
" <td>0.085527</td>\n",
" <td>0.044222</td>\n",
" <td>0.085545</td>\n",
" <td>0.044172</td>\n",
" <td>0.018651</td>\n",
" <td>0.034165</td>\n",
" <td>0.023306</td>\n",
" <td>-0.009829</td>\n",
" <td>0.022982</td>\n",
" <td>-0.011490</td>\n",
" <td>-0.019961</td>\n",
" <td>-0.010091</td>\n",
" <td>0.033733</td>\n",
" <td>0.010981</td>\n",
" <td>0.033016</td>\n",
" <td>0.010546</td>\n",
" <td>0.014155</td>\n",
" <td>0.001556</td>\n",
" <td>-0.063018</td>\n",
" <td>-0.035852</td>\n",
" <td>0.059963</td>\n",
" <td>0.042186</td>\n",
" <td>0.060096</td>\n",
" <td>0.041629</td>\n",
" <td>0.097297</td>\n",
" <td>0.136203</td>\n",
" <td>0.089247</td>\n",
" <td>0.099579</td>\n",
" <td>-0.028964</td>\n",
" <td>-0.012521</td>\n",
" <td>-0.022728</td>\n",
" <td>-0.002425</td>\n",
" <td>0.170040</td>\n",
" <td>-0.000597</td>\n",
" <td>-0.033248</td>\n",
" <td>-0.015873</td>\n",
" <td>-0.010283</td>\n",
" <td>-0.047853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>elective_surgery</th>\n",
" <td>-0.002694</td>\n",
" <td>0.005650</td>\n",
" <td>0.041702</td>\n",
" <td>0.057514</td>\n",
" <td>0.006735</td>\n",
" <td>1.000000</td>\n",
" <td>0.013584</td>\n",
" <td>-0.010871</td>\n",
" <td>0.122012</td>\n",
" <td>0.014730</td>\n",
" <td>0.361392</td>\n",
" <td>0.795891</td>\n",
" <td>0.923298</td>\n",
" <td>-0.027586</td>\n",
" <td>0.019979</td>\n",
" <td>0.027753</td>\n",
" <td>NaN</td>\n",
" <td>-0.006857</td>\n",
" <td>-0.068348</td>\n",
" <td>0.120489</td>\n",
" <td>0.007780</td>\n",
" <td>-0.133643</td>\n",
" <td>-0.033948</td>\n",
" <td>0.116024</td>\n",
" <td>-0.158602</td>\n",
" <td>0.003968</td>\n",
" <td>-0.158423</td>\n",
" <td>0.005656</td>\n",
" <td>-0.072214</td>\n",
" <td>-0.028268</td>\n",
" <td>-0.130329</td>\n",
" <td>0.010672</td>\n",
" <td>-0.131006</td>\n",
" <td>0.011451</td>\n",
" <td>-0.064207</td>\n",
" <td>-0.159557</td>\n",
" <td>0.042150</td>\n",
" <td>0.038151</td>\n",
" <td>-0.083295</td>\n",
" <td>0.013901</td>\n",
" <td>-0.085121</td>\n",
" <td>0.014866</td>\n",
" <td>0.053148</td>\n",
" <td>-0.028402</td>\n",
" <td>-0.106479</td>\n",
" <td>-0.064613</td>\n",
" <td>-0.103227</td>\n",
" <td>-0.058989</td>\n",
" <td>-0.107990</td>\n",
" <td>-0.124757</td>\n",
" <td>-0.074414</td>\n",
" <td>-0.050104</td>\n",
" <td>-0.075593</td>\n",
" <td>-0.049100</td>\n",
" <td>-0.177796</td>\n",
" <td>-0.240363</td>\n",
" <td>0.091235</td>\n",
" <td>0.053323</td>\n",
" <td>-0.042952</td>\n",
" <td>-0.032432</td>\n",
" <td>-0.048673</td>\n",
" <td>-0.034775</td>\n",
" <td>-0.020991</td>\n",
" <td>0.022987</td>\n",
" <td>0.078894</td>\n",
" <td>0.047976</td>\n",
" <td>-0.118039</td>\n",
" <td>-0.086606</td>\n",
" <td>-0.004932</td>\n",
" <td>-0.033169</td>\n",
" <td>-0.011640</td>\n",
" <td>-0.035008</td>\n",
" <td>-0.011333</td>\n",
" <td>-0.016247</td>\n",
" <td>-0.010116</td>\n",
" <td>0.019365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>height</th>\n",
" <td>-0.006739</td>\n",
" <td>0.004081</td>\n",
" <td>0.033112</td>\n",
" <td>-0.119639</td>\n",
" <td>-0.063923</td>\n",
" <td>0.013584</td>\n",
" <td>1.000000</td>\n",
" <td>0.021729</td>\n",
" <td>-0.015351</td>\n",
" <td>0.382199</td>\n",
" <td>-0.006182</td>\n",
" <td>0.011063</td>\n",
" <td>0.015781</td>\n",
" <td>-0.009353</td>\n",
" <td>-0.005365</td>\n",
" <td>-0.014861</td>\n",
" <td>NaN</td>\n",
" <td>0.011777</td>\n",
" <td>-0.020577</td>\n",
" <td>0.012339</td>\n",
" <td>0.036710</td>\n",
" <td>-0.051661</td>\n",
" <td>0.014273</td>\n",
" <td>-0.004478</td>\n",
" <td>0.040730</td>\n",
" <td>0.133386</td>\n",
" <td>0.040656</td>\n",
" <td>0.133630</td>\n",
" <td>-0.015156</td>\n",
" <td>-0.028183</td>\n",
" <td>0.028673</td>\n",
" <td>0.094093</td>\n",
" <td>0.029074</td>\n",
" <td>0.094313</td>\n",
" <td>-0.021115</td>\n",
" <td>-0.054963</td>\n",
" <td>-0.030168</td>\n",
" <td>0.007965</td>\n",
" <td>0.004081</td>\n",
" <td>0.051203</td>\n",
" <td>0.003608</td>\n",
" <td>0.051262</td>\n",
" <td>0.016985</td>\n",
" <td>0.004376</td>\n",
" <td>0.079122</td>\n",
" <td>0.109389</td>\n",
" <td>0.077343</td>\n",
" <td>0.111256</td>\n",
" <td>-0.006082</td>\n",
" <td>-0.012351</td>\n",
" <td>0.052886</td>\n",
" <td>0.071101</td>\n",
" <td>0.052867</td>\n",
" <td>0.071595</td>\n",
" <td>-0.029496</td>\n",
" <td>-0.044985</td>\n",
" <td>-0.019162</td>\n",
" <td>0.000253</td>\n",
" <td>0.012182</td>\n",
" <td>0.020399</td>\n",
" <td>0.011332</td>\n",
" <td>0.020537</td>\n",
" <td>-0.016805</td>\n",
" <td>0.018818</td>\n",
" <td>0.055173</td>\n",
" <td>0.068632</td>\n",
" <td>-0.026078</td>\n",
" <td>-0.008080</td>\n",
" <td>0.011321</td>\n",
" <td>0.012351</td>\n",
" <td>-0.004218</td>\n",
" <td>0.008594</td>\n",
" <td>0.001106</td>\n",
" <td>0.001257</td>\n",
" <td>0.009898</td>\n",
" <td>0.007713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>icu_id</th>\n",
" <td>-0.000704</td>\n",
" <td>-0.002483</td>\n",
" <td>0.035638</td>\n",
" <td>-0.022573</td>\n",
" <td>0.004809</td>\n",
" <td>-0.010871</td>\n",
" <td>0.021729</td>\n",
" <td>1.000000</td>\n",
" <td>-0.016119</td>\n",
" <td>0.013536</td>\n",
" <td>-0.016546</td>\n",
" <td>-0.004083</td>\n",
" <td>-0.011575</td>\n",
" <td>-0.009765</td>\n",
" <td>-0.010539</td>\n",
" <td>-0.012416</td>\n",
" <td>NaN</td>\n",
" <td>-0.020057</td>\n",
" <td>0.007270</td>\n",
" <td>-0.072495</td>\n",
" <td>-0.005271</td>\n",
" <td>-0.003775</td>\n",
" <td>0.001765</td>\n",
" <td>0.016636</td>\n",
" <td>0.005453</td>\n",
" <td>-0.023755</td>\n",
" <td>0.005764</td>\n",
" <td>-0.023987</td>\n",
" <td>-0.011112</td>\n",
" <td>0.019052</td>\n",
" <td>-0.006270</td>\n",
" <td>-0.022464</td>\n",
" <td>-0.009059</td>\n",
" <td>-0.019549</td>\n",
" <td>-0.012600</td>\n",
" <td>-0.033504</td>\n",
" <td>-0.027147</td>\n",
" <td>0.007943</td>\n",
" <td>-0.057790</td>\n",
" <td>-0.002549</td>\n",
" <td>-0.057536</td>\n",
" <td>-0.002797</td>\n",
" <td>-0.021521</td>\n",
" <td>-0.005269</td>\n",
" <td>-0.014473</td>\n",
" <td>-0.000882</td>\n",
" <td>-0.014354</td>\n",
" <td>-0.000800</td>\n",
" <td>-0.010873</td>\n",
" <td>0.004188</td>\n",
" <td>-0.011939</td>\n",
" <td>-0.018469</td>\n",
" <td>-0.013593</td>\n",
" <td>-0.017409</td>\n",
" <td>-0.021284</td>\n",
" <td>-0.019165</td>\n",
" <td>-0.007165</td>\n",
" <td>0.014544</td>\n",
" <td>-0.047580</td>\n",
" <td>-0.007691</td>\n",
" <td>-0.049443</td>\n",
" <td>-0.009021</td>\n",
" <td>0.008273</td>\n",
" <td>0.004540</td>\n",
" <td>-0.002938</td>\n",
" <td>-0.011041</td>\n",
" <td>-0.004768</td>\n",
" <td>-0.009903</td>\n",
" <td>-0.002201</td>\n",
" <td>-0.016893</td>\n",
" <td>0.024944</td>\n",
" <td>-0.014062</td>\n",
" <td>-0.037019</td>\n",
" <td>-0.000094</td>\n",
" <td>-0.002586</td>\n",
" <td>-0.012947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pre_icu_los_days</th>\n",
" <td>-0.000998</td>\n",
" <td>-0.005094</td>\n",
" <td>-0.001453</td>\n",
" <td>0.054951</td>\n",
" <td>-0.001774</td>\n",
" <td>0.122012</td>\n",
" <td>-0.015351</td>\n",
" <td>-0.016119</td>\n",
" <td>1.000000</td>\n",
" <td>-0.008648</td>\n",
" <td>0.078499</td>\n",
" <td>0.078661</td>\n",
" <td>0.119609</td>\n",
" <td>0.052277</td>\n",
" <td>-0.015469</td>\n",
" <td>-0.005204</td>\n",
" <td>NaN</td>\n",
" <td>-0.038746</td>\n",
" <td>0.054741</td>\n",
" <td>0.047833</td>\n",
" <td>-0.032297</td>\n",
" <td>0.021648</td>\n",
" <td>0.008130</td>\n",
" <td>0.076306</td>\n",
" <td>-0.050342</td>\n",
" <td>-0.046183</td>\n",
" <td>-0.049986</td>\n",
" <td>-0.045732</td>\n",
" <td>0.061604</td>\n",
" <td>0.054006</td>\n",
" <td>-0.050178</td>\n",
" <td>-0.050439</td>\n",
" <td>-0.051078</td>\n",
" <td>-0.049819</td>\n",
" <td>0.032238</td>\n",
" <td>0.013877</td>\n",
" <td>0.023592</td>\n",
" <td>-0.030216</td>\n",
" <td>-0.037123</td>\n",
" <td>-0.052632</td>\n",
" <td>-0.037590</td>\n",
" <td>-0.052169</td>\n",
" <td>0.027014</td>\n",
" <td>-0.004411</td>\n",
" <td>-0.058319</td>\n",
" <td>-0.054409</td>\n",
" <td>-0.057275</td>\n",
" <td>-0.054913</td>\n",
" <td>0.061045</td>\n",
" <td>0.056917</td>\n",
" <td>-0.056631</td>\n",
" <td>-0.057832</td>\n",
" <td>-0.056725</td>\n",
" <td>-0.058227</td>\n",
" <td>0.040266</td>\n",
" <td>0.035592</td>\n",
" <td>-0.001046</td>\n",
" <td>-0.016554</td>\n",
" <td>-0.046267</td>\n",
" <td>-0.051324</td>\n",
" <td>-0.046436</td>\n",
" <td>-0.051621</td>\n",
" <td>-0.009819</td>\n",
" <td>-0.002267</td>\n",
" <td>0.016778</td>\n",
" <td>0.014645</td>\n",
" <td>0.095801</td>\n",
" <td>0.068496</td>\n",
" <td>0.012552</td>\n",
" <td>0.014046</td>\n",
" <td>0.015722</td>\n",
" <td>0.014342</td>\n",
" <td>0.037962</td>\n",
" <td>0.050971</td>\n",
" <td>0.017935</td>\n",
" <td>0.039016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>weight</th>\n",
" <td>-0.002298</td>\n",
" <td>0.002387</td>\n",
" <td>0.025224</td>\n",
" <td>-0.125288</td>\n",
" <td>0.880132</td>\n",
" <td>0.014730</td>\n",
" <td>0.382199</td>\n",
" <td>0.013536</td>\n",
" <td>-0.008648</td>\n",
" <td>1.000000</td>\n",
" <td>0.017658</td>\n",
" <td>-0.008548</td>\n",
" <td>0.017741</td>\n",
" <td>-0.012238</td>\n",
" <td>0.010306</td>\n",
" <td>0.013349</td>\n",
" <td>NaN</td>\n",
" <td>0.030702</td>\n",
" <td>-0.036486</td>\n",
" <td>0.036267</td>\n",
" <td>0.070071</td>\n",
" <td>-0.016454</td>\n",
" <td>0.041245</td>\n",
" <td>0.069353</td>\n",
" <td>0.069792</td>\n",
" <td>0.032016</td>\n",
" <td>0.069772</td>\n",
" <td>0.031905</td>\n",
" <td>-0.040901</td>\n",
" <td>-0.006231</td>\n",
" <td>0.072860</td>\n",
" <td>0.042409</td>\n",
" <td>0.073350</td>\n",
" <td>0.042221</td>\n",
" <td>0.003680</td>\n",
" <td>-0.029463</td>\n",
" <td>-0.092802</td>\n",
" <td>-0.027094</td>\n",
" <td>0.085038</td>\n",
" <td>0.065099</td>\n",
" <td>0.084811</td>\n",
" <td>0.065108</td>\n",
" <td>0.026071</td>\n",
" <td>0.034824</td>\n",
" <td>0.057222</td>\n",
" <td>0.040001</td>\n",
" <td>0.056167</td>\n",
" <td>0.039112</td>\n",
" <td>-0.022502</td>\n",
" <td>-0.015731</td>\n",
" <td>0.055863</td>\n",
" <td>0.042884</td>\n",
" <td>0.055136</td>\n",
" <td>0.042662</td>\n",
" <td>0.000007</td>\n",
" <td>-0.017901</td>\n",
" <td>-0.069060</td>\n",
" <td>-0.034535</td>\n",
" <td>0.063750</td>\n",
" <td>0.050606</td>\n",
" <td>0.063471</td>\n",
" <td>0.050175</td>\n",
" <td>0.085200</td>\n",
" <td>0.138689</td>\n",
" <td>0.107234</td>\n",
" <td>0.122040</td>\n",
" <td>-0.038453</td>\n",
" <td>-0.015454</td>\n",
" <td>-0.018215</td>\n",
" <td>0.002399</td>\n",
" <td>0.157113</td>\n",
" <td>0.003829</td>\n",
" <td>-0.031774</td>\n",
" <td>-0.015303</td>\n",
" <td>-0.004868</td>\n",
" <td>-0.042026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_2_diagnosis</th>\n",
" <td>-0.000374</td>\n",
" <td>-0.001801</td>\n",
" <td>-0.002859</td>\n",
" <td>0.026553</td>\n",
" <td>0.019565</td>\n",
" <td>0.361392</td>\n",
" <td>-0.006182</td>\n",
" <td>-0.016546</td>\n",
" <td>0.078499</td>\n",
" <td>0.017658</td>\n",
" <td>1.000000</td>\n",
" <td>0.386372</td>\n",
" <td>0.388472</td>\n",
" <td>-0.007688</td>\n",
" <td>0.048408</td>\n",
" <td>0.064847</td>\n",
" <td>NaN</td>\n",
" <td>0.021887</td>\n",
" <td>-0.098076</td>\n",
" <td>0.005791</td>\n",
" <td>0.039976</td>\n",
" <td>-0.089370</td>\n",
" <td>0.009267</td>\n",
" <td>-0.034906</td>\n",
" <td>-0.048332</td>\n",
" <td>0.060281</td>\n",
" <td>-0.048097</td>\n",
" <td>0.061099</td>\n",
" <td>-0.118243</td>\n",
" <td>-0.046336</td>\n",
" <td>-0.019555</td>\n",
" <td>0.072619</td>\n",
" <td>-0.021060</td>\n",
" <td>0.073237</td>\n",
" <td>-0.074171</td>\n",
" <td>-0.090492</td>\n",
" <td>0.009111</td>\n",
" <td>0.059437</td>\n",
" <td>0.019146</td>\n",
" <td>0.090089</td>\n",
" <td>0.018353</td>\n",
" <td>0.090912</td>\n",
" <td>-0.015975</td>\n",
" <td>0.029711</td>\n",
" <td>-0.013812</td>\n",
" <td>0.032898</td>\n",
" <td>-0.013681</td>\n",
" <td>0.036588</td>\n",
" <td>-0.147792</td>\n",
" <td>-0.136036</td>\n",
" <td>0.018900</td>\n",
" <td>0.055932</td>\n",
" <td>0.018299</td>\n",
" <td>0.056155</td>\n",
" <td>-0.138576</td>\n",
" <td>-0.149565</td>\n",
" <td>0.043086</td>\n",
" <td>0.055448</td>\n",
" <td>0.051567</td>\n",
" <td>0.078730</td>\n",
" <td>0.049374</td>\n",
" <td>0.075952</td>\n",
" <td>-0.047425</td>\n",
" <td>-0.028468</td>\n",
" <td>0.051021</td>\n",
" <td>0.050009</td>\n",
" <td>-0.097084</td>\n",
" <td>-0.104199</td>\n",
" <td>-0.006816</td>\n",
" <td>-0.004227</td>\n",
" <td>-0.004628</td>\n",
" <td>-0.003222</td>\n",
" <td>-0.011232</td>\n",
" <td>-0.004233</td>\n",
" <td>-0.004681</td>\n",
" <td>0.008605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_3j_diagnosis</th>\n",
" <td>-0.001103</td>\n",
" <td>0.004916</td>\n",
" <td>0.020115</td>\n",
" <td>-0.063351</td>\n",
" <td>-0.015095</td>\n",
" <td>0.795891</td>\n",
" <td>0.011063</td>\n",
" <td>-0.004083</td>\n",
" <td>0.078661</td>\n",
" <td>-0.008548</td>\n",
" <td>0.386372</td>\n",
" <td>1.000000</td>\n",
" <td>0.870505</td>\n",
" <td>-0.028409</td>\n",
" <td>-0.021989</td>\n",
" <td>0.009909</td>\n",
" <td>NaN</td>\n",
" <td>-0.045118</td>\n",
" <td>0.008754</td>\n",
" <td>0.097536</td>\n",
" <td>-0.001793</td>\n",
" <td>-0.127062</td>\n",
" <td>0.010111</td>\n",
" <td>0.079998</td>\n",
" <td>-0.145480</td>\n",
" <td>-0.000491</td>\n",
" <td>-0.145177</td>\n",
" <td>0.000730</td>\n",
" <td>-0.007217</td>\n",
" <td>0.046211</td>\n",
" <td>-0.127620</td>\n",
" <td>0.001836</td>\n",
" <td>-0.127689</td>\n",
" <td>0.002437</td>\n",
" <td>-0.066415</td>\n",
" <td>-0.142348</td>\n",
" <td>0.061274</td>\n",
" <td>0.053945</td>\n",
" <td>-0.088787</td>\n",
" <td>0.010185</td>\n",
" <td>-0.089752</td>\n",
" <td>0.010741</td>\n",
" <td>0.111763</td>\n",
" <td>0.007027</td>\n",
" <td>-0.107680</td>\n",
" <td>-0.074403</td>\n",
" <td>-0.104877</td>\n",
" <td>-0.069530</td>\n",
" <td>-0.040191</td>\n",
" <td>-0.047355</td>\n",
" <td>-0.085596</td>\n",
" <td>-0.062483</td>\n",
" <td>-0.086540</td>\n",
" <td>-0.061586</td>\n",
" <td>-0.163706</td>\n",
" <td>-0.208767</td>\n",
" <td>0.097911</td>\n",
" <td>0.065570</td>\n",
" <td>-0.055298</td>\n",
" <td>-0.043522</td>\n",
" <td>-0.060177</td>\n",
" <td>-0.045033</td>\n",
" <td>0.016651</td>\n",
" <td>-0.007121</td>\n",
" <td>0.066359</td>\n",
" <td>0.002477</td>\n",
" <td>-0.118604</td>\n",
" <td>-0.085070</td>\n",
" <td>0.000255</td>\n",
" <td>-0.019918</td>\n",
" <td>-0.008994</td>\n",
" <td>-0.027882</td>\n",
" <td>-0.000904</td>\n",
" <td>-0.006425</td>\n",
" <td>-0.002530</td>\n",
" <td>0.021846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_post_operative</th>\n",
" <td>-0.002450</td>\n",
" <td>0.005205</td>\n",
" <td>0.041987</td>\n",
" <td>0.046583</td>\n",
" <td>0.009118</td>\n",
" <td>0.923298</td>\n",
" <td>0.015781</td>\n",
" <td>-0.011575</td>\n",
" <td>0.119609</td>\n",
" <td>0.017741</td>\n",
" <td>0.388472</td>\n",
" <td>0.870505</td>\n",
" <td>1.000000</td>\n",
" <td>-0.029429</td>\n",
" <td>0.001300</td>\n",
" <td>0.014019</td>\n",
" <td>NaN</td>\n",
" <td>-0.028723</td>\n",
" <td>-0.053109</td>\n",
" <td>0.142304</td>\n",
" <td>0.012210</td>\n",
" <td>-0.141530</td>\n",
" <td>-0.036426</td>\n",
" <td>0.141123</td>\n",
" <td>-0.161697</td>\n",
" <td>0.001930</td>\n",
" <td>-0.161432</td>\n",
" <td>0.003695</td>\n",
" <td>-0.056786</td>\n",
" <td>-0.019946</td>\n",
" <td>-0.132677</td>\n",
" <td>0.007748</td>\n",
" <td>-0.133263</td>\n",
" <td>0.008560</td>\n",
" <td>-0.069187</td>\n",
" <td>-0.168874</td>\n",
" <td>0.051252</td>\n",
" <td>0.037978</td>\n",
" <td>-0.082411</td>\n",
" <td>0.012446</td>\n",
" <td>-0.084154</td>\n",
" <td>0.013384</td>\n",
" <td>0.069778</td>\n",
" <td>-0.034584</td>\n",
" <td>-0.103299</td>\n",
" <td>-0.065023</td>\n",
" <td>-0.099954</td>\n",
" <td>-0.059248</td>\n",
" <td>-0.098490</td>\n",
" <td>-0.119469</td>\n",
" <td>-0.071144</td>\n",
" <td>-0.049850</td>\n",
" <td>-0.072910</td>\n",
" <td>-0.048824</td>\n",
" <td>-0.185293</td>\n",
" <td>-0.248028</td>\n",
" <td>0.098814</td>\n",
" <td>0.055965</td>\n",
" <td>-0.038583</td>\n",
" <td>-0.032774</td>\n",
" <td>-0.044924</td>\n",
" <td>-0.034978</td>\n",
" <td>-0.019896</td>\n",
" <td>0.030704</td>\n",
" <td>0.081878</td>\n",
" <td>0.042118</td>\n",
" <td>-0.107271</td>\n",
" <td>-0.074592</td>\n",
" <td>-0.006550</td>\n",
" <td>-0.035495</td>\n",
" <td>-0.018465</td>\n",
" <td>-0.036657</td>\n",
" <td>-0.012610</td>\n",
" <td>-0.013973</td>\n",
" <td>-0.009933</td>\n",
" <td>0.015989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>arf_apache</th>\n",
" <td>0.012406</td>\n",
" <td>-0.000592</td>\n",
" <td>0.000071</td>\n",
" <td>-0.002780</td>\n",
" <td>-0.007792</td>\n",
" <td>-0.027586</td>\n",
" <td>-0.009353</td>\n",
" <td>-0.009765</td>\n",
" <td>0.052277</td>\n",
" <td>-0.012238</td>\n",
" <td>-0.007688</td>\n",
" <td>-0.028409</td>\n",
" <td>-0.029429</td>\n",
" <td>1.000000</td>\n",
" <td>-0.003291</td>\n",
" <td>-0.002774</td>\n",
" <td>NaN</td>\n",
" <td>-0.000044</td>\n",
" <td>-0.015231</td>\n",
" <td>-0.002325</td>\n",
" <td>0.008417</td>\n",
" <td>0.008824</td>\n",
" <td>-0.019997</td>\n",
" <td>-0.002529</td>\n",
" <td>0.000905</td>\n",
" <td>-0.046441</td>\n",
" <td>0.000787</td>\n",
" <td>-0.046509</td>\n",
" <td>-0.018662</td>\n",
" <td>-0.010110</td>\n",
" <td>0.021157</td>\n",
" <td>-0.024207</td>\n",
" <td>0.022015</td>\n",
" <td>-0.024457</td>\n",
" <td>0.014016</td>\n",
" <td>-0.021308</td>\n",
" <td>0.037592</td>\n",
" <td>-0.035431</td>\n",
" <td>0.054935</td>\n",
" <td>-0.016247</td>\n",
" <td>0.054852</td>\n",
" <td>-0.016477</td>\n",
" <td>-0.022309</td>\n",
" <td>-0.030998</td>\n",
" <td>-0.011401</td>\n",
" <td>-0.032482</td>\n",
" <td>-0.010648</td>\n",
" <td>-0.032678</td>\n",
" <td>-0.018119</td>\n",
" <td>-0.017100</td>\n",
" <td>0.005185</td>\n",
" <td>-0.010165</td>\n",
" <td>0.007339</td>\n",
" <td>-0.010562</td>\n",
" <td>0.017168</td>\n",
" <td>0.003499</td>\n",
" <td>0.018901</td>\n",
" <td>-0.018911</td>\n",
" <td>0.030642</td>\n",
" <td>0.007560</td>\n",
" <td>0.031027</td>\n",
" <td>0.008561</td>\n",
" <td>0.033822</td>\n",
" <td>-0.062408</td>\n",
" <td>0.108981</td>\n",
" <td>0.086886</td>\n",
" <td>0.037495</td>\n",
" <td>0.031092</td>\n",
" <td>0.007424</td>\n",
" <td>0.024045</td>\n",
" <td>0.107260</td>\n",
" <td>0.018832</td>\n",
" <td>0.001526</td>\n",
" <td>0.016028</td>\n",
" <td>-0.006626</td>\n",
" <td>-0.009522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gcs_eyes_apache</th>\n",
" <td>0.005690</td>\n",
" <td>0.002008</td>\n",
" <td>-0.007684</td>\n",
" <td>0.043832</td>\n",
" <td>0.012599</td>\n",
" <td>0.019979</td>\n",
" <td>-0.005365</td>\n",
" <td>-0.010539</td>\n",
" <td>-0.015469</td>\n",
" <td>0.010306</td>\n",
" <td>0.048408</td>\n",
" <td>-0.021989</td>\n",
" <td>0.001300</td>\n",
" <td>-0.003291</td>\n",
" <td>1.000000</td>\n",
" <td>0.794596</td>\n",
" <td>NaN</td>\n",
" <td>0.776900</td>\n",
" <td>-0.096108</td>\n",
" <td>-0.417651</td>\n",
" <td>-0.007712</td>\n",
" <td>0.003187</td>\n",
" <td>0.145228</td>\n",
" <td>-0.500394</td>\n",
" <td>-0.044874</td>\n",
" <td>0.107759</td>\n",
" <td>-0.044373</td>\n",
" <td>0.107441</td>\n",
" <td>-0.140660</td>\n",
" <td>0.015849</td>\n",
" <td>-0.030762</td>\n",
" <td>0.133706</td>\n",
" <td>-0.029471</td>\n",
" <td>0.133569</td>\n",
" <td>-0.047472</td>\n",
" <td>0.073119</td>\n",
" <td>-0.123692</td>\n",
" <td>0.084127</td>\n",
" <td>-0.036611</td>\n",
" <td>0.162278</td>\n",
" <td>-0.035408</td>\n",
" <td>0.162169</td>\n",
" <td>-0.153986</td>\n",
" <td>0.211348</td>\n",
" <td>-0.034360</td>\n",
" <td>0.072886</td>\n",
" <td>-0.033682</td>\n",
" <td>0.072800</td>\n",
" <td>-0.087839</td>\n",
" <td>-0.041512</td>\n",
" <td>-0.008476</td>\n",
" <td>0.097894</td>\n",
" <td>-0.007695</td>\n",
" <td>0.097492</td>\n",
" <td>-0.009141</td>\n",
" <td>0.022648</td>\n",
" <td>-0.070952</td>\n",
" <td>0.025469</td>\n",
" <td>0.006956</td>\n",
" <td>0.116574</td>\n",
" <td>0.007303</td>\n",
" <td>0.115242</td>\n",
" <td>-0.074507</td>\n",
" <td>0.038209</td>\n",
" <td>-0.064467</td>\n",
" <td>0.082590</td>\n",
" <td>-0.465077</td>\n",
" <td>-0.449872</td>\n",
" <td>-0.002167</td>\n",
" <td>-0.013570</td>\n",
" <td>0.034854</td>\n",
" <td>-0.010770</td>\n",
" <td>0.022177</td>\n",
" <td>0.006807</td>\n",
" <td>0.012052</td>\n",
" <td>0.017411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gcs_motor_apache</th>\n",
" <td>0.010362</td>\n",
" <td>0.001737</td>\n",
" <td>-0.017686</td>\n",
" <td>0.044063</td>\n",
" <td>0.020875</td>\n",
" <td>0.027753</td>\n",
" <td>-0.014861</td>\n",
" <td>-0.012416</td>\n",
" <td>-0.005204</td>\n",
" <td>0.013349</td>\n",
" <td>0.064847</td>\n",
" <td>0.009909</td>\n",
" <td>0.014019</td>\n",
" <td>-0.002774</td>\n",
" <td>0.794596</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.696600</td>\n",
" <td>-0.086743</td>\n",
" <td>-0.382408</td>\n",
" <td>-0.012069</td>\n",
" <td>-0.003500</td>\n",
" <td>0.192582</td>\n",
" <td>-0.443245</td>\n",
" <td>-0.041852</td>\n",
" <td>0.096103</td>\n",
" <td>-0.041570</td>\n",
" <td>0.096157</td>\n",
" <td>-0.133273</td>\n",
" <td>0.028772</td>\n",
" <td>-0.034748</td>\n",
" <td>0.122254</td>\n",
" <td>-0.033965</td>\n",
" <td>0.122598</td>\n",
" <td>-0.044868</td>\n",
" <td>0.058125</td>\n",
" <td>-0.101453</td>\n",
" <td>0.092508</td>\n",
" <td>-0.033340</td>\n",
" <td>0.154878</td>\n",
" <td>-0.032642</td>\n",
" <td>0.155134</td>\n",
" <td>-0.113055</td>\n",
" <td>0.263712</td>\n",
" <td>-0.033919</td>\n",
" <td>0.061247</td>\n",
" <td>-0.033285</td>\n",
" <td>0.061085</td>\n",
" <td>-0.083696</td>\n",
" <td>-0.039050</td>\n",
" <td>-0.012495</td>\n",
" <td>0.083482</td>\n",
" <td>-0.011679</td>\n",
" <td>0.083178</td>\n",
" <td>-0.016466</td>\n",
" <td>0.010979</td>\n",
" <td>-0.058519</td>\n",
" <td>0.028647</td>\n",
" <td>0.009633</td>\n",
" <td>0.108234</td>\n",
" <td>0.009781</td>\n",
" <td>0.106860</td>\n",
" <td>-0.084388</td>\n",
" <td>0.028602</td>\n",
" <td>-0.064191</td>\n",
" <td>0.086013</td>\n",
" <td>-0.507284</td>\n",
" <td>-0.504793</td>\n",
" <td>-0.002878</td>\n",
" <td>-0.007157</td>\n",
" <td>0.031341</td>\n",
" <td>-0.004973</td>\n",
" <td>0.022238</td>\n",
" <td>0.009805</td>\n",
" <td>0.008518</td>\n",
" <td>0.016147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gcs_unable_apache</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gcs_verbal_apache</th>\n",
" <td>0.008068</td>\n",
" <td>0.002699</td>\n",
" <td>0.003370</td>\n",
" <td>-0.002946</td>\n",
" <td>0.027426</td>\n",
" <td>-0.006857</td>\n",
" <td>0.011777</td>\n",
" <td>-0.020057</td>\n",
" <td>-0.038746</td>\n",
" <td>0.030702</td>\n",
" <td>0.021887</td>\n",
" <td>-0.045118</td>\n",
" <td>-0.028723</td>\n",
" <td>-0.000044</td>\n",
" <td>0.776900</td>\n",
" <td>0.696600</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>-0.116841</td>\n",
" <td>-0.476666</td>\n",
" <td>-0.006972</td>\n",
" <td>-0.002626</td>\n",
" <td>0.114977</td>\n",
" <td>-0.581067</td>\n",
" <td>-0.039461</td>\n",
" <td>0.118994</td>\n",
" <td>-0.039126</td>\n",
" <td>0.118219</td>\n",
" <td>-0.157384</td>\n",
" <td>-0.007727</td>\n",
" <td>-0.025780</td>\n",
" <td>0.148542</td>\n",
" <td>-0.023749</td>\n",
" <td>0.148113</td>\n",
" <td>-0.049435</td>\n",
" <td>0.056118</td>\n",
" <td>-0.147973</td>\n",
" <td>0.080855</td>\n",
" <td>-0.034973</td>\n",
" <td>0.176170</td>\n",
" <td>-0.033439</td>\n",
" <td>0.175646</td>\n",
" <td>-0.188147</td>\n",
" <td>0.182067</td>\n",
" <td>-0.027725</td>\n",
" <td>0.084225</td>\n",
" <td>-0.027240</td>\n",
" <td>0.082832</td>\n",
" <td>-0.108800</td>\n",
" <td>-0.061233</td>\n",
" <td>-0.000979</td>\n",
" <td>0.110696</td>\n",
" <td>-0.001454</td>\n",
" <td>0.110544</td>\n",
" <td>-0.018440</td>\n",
" <td>0.006286</td>\n",
" <td>-0.085101</td>\n",
" <td>0.018238</td>\n",
" <td>0.010672</td>\n",
" <td>0.126867</td>\n",
" <td>0.011126</td>\n",
" <td>0.126260</td>\n",
" <td>-0.068488</td>\n",
" <td>0.050847</td>\n",
" <td>-0.066874</td>\n",
" <td>0.090999</td>\n",
" <td>-0.451790</td>\n",
" <td>-0.419053</td>\n",
" <td>-0.001815</td>\n",
" <td>-0.010962</td>\n",
" <td>0.031691</td>\n",
" <td>-0.008965</td>\n",
" <td>0.026622</td>\n",
" <td>0.012140</td>\n",
" <td>0.011510</td>\n",
" <td>0.017257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>heart_rate_apache</th>\n",
" <td>-0.002901</td>\n",
" <td>0.004945</td>\n",
" <td>-0.006215</td>\n",
" <td>-0.155835</td>\n",
" <td>-0.028840</td>\n",
" <td>-0.068348</td>\n",
" <td>-0.020577</td>\n",
" <td>0.007270</td>\n",
" <td>0.054741</td>\n",
" <td>-0.036486</td>\n",
" <td>-0.098076</td>\n",
" <td>0.008754</td>\n",
" <td>-0.053109</td>\n",
" <td>-0.015231</td>\n",
" <td>-0.096108</td>\n",
" <td>-0.086743</td>\n",
" <td>NaN</td>\n",
" <td>-0.116841</td>\n",
" <td>1.000000</td>\n",
" <td>0.082599</td>\n",
" <td>0.027456</td>\n",
" <td>0.196669</td>\n",
" <td>0.108176</td>\n",
" <td>0.118081</td>\n",
" <td>0.101246</td>\n",
" <td>-0.008455</td>\n",
" <td>0.101551</td>\n",
" <td>-0.008987</td>\n",
" <td>0.808829</td>\n",
" <td>0.572694</td>\n",
" <td>0.048618</td>\n",
" <td>-0.070482</td>\n",
" <td>0.047616</td>\n",
" <td>-0.070210</td>\n",
" <td>0.172729</td>\n",
" <td>0.115617</td>\n",
" <td>0.020593</td>\n",
" <td>-0.107299</td>\n",
" <td>-0.037824</td>\n",
" <td>-0.143812</td>\n",
" <td>-0.037444</td>\n",
" <td>-0.144073</td>\n",
" <td>0.246108</td>\n",
" <td>0.064424</td>\n",
" <td>0.081382</td>\n",
" <td>0.035233</td>\n",
" <td>0.081874</td>\n",
" <td>0.036045</td>\n",
" <td>0.721986</td>\n",
" <td>0.691027</td>\n",
" <td>0.010655</td>\n",
" <td>-0.030935</td>\n",
" <td>0.011240</td>\n",
" <td>-0.031043</td>\n",
" <td>0.210330</td>\n",
" <td>0.200072</td>\n",
" <td>-0.044663</td>\n",
" <td>-0.071950</td>\n",
" <td>-0.069510</td>\n",
" <td>-0.104163</td>\n",
" <td>-0.068836</td>\n",
" <td>-0.104698</td>\n",
" <td>0.103486</td>\n",
" <td>0.060540</td>\n",
" <td>0.011124</td>\n",
" <td>-0.072557</td>\n",
" <td>0.124840</td>\n",
" <td>0.115316</td>\n",
" <td>0.007786</td>\n",
" <td>0.014019</td>\n",
" <td>-0.018177</td>\n",
" <td>0.012780</td>\n",
" <td>0.058280</td>\n",
" <td>0.021585</td>\n",
" <td>0.019672</td>\n",
" <td>0.044459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>intubated_apache</th>\n",
" <td>-0.007877</td>\n",
" <td>0.003930</td>\n",
" <td>0.019899</td>\n",
" <td>-0.003185</td>\n",
" <td>0.032551</td>\n",
" <td>0.120489</td>\n",
" <td>0.012339</td>\n",
" <td>-0.072495</td>\n",
" <td>0.047833</td>\n",
" <td>0.036267</td>\n",
" <td>0.005791</td>\n",
" <td>0.097536</td>\n",
" <td>0.142304</td>\n",
" <td>-0.002325</td>\n",
" <td>-0.417651</td>\n",
" <td>-0.382408</td>\n",
" <td>NaN</td>\n",
" <td>-0.476666</td>\n",
" <td>0.082599</td>\n",
" <td>1.000000</td>\n",
" <td>-0.013506</td>\n",
" <td>-0.014614</td>\n",
" <td>-0.125002</td>\n",
" <td>0.601956</td>\n",
" <td>-0.010973</td>\n",
" <td>-0.111654</td>\n",
" <td>-0.011267</td>\n",
" <td>-0.110250</td>\n",
" <td>0.118692</td>\n",
" <td>0.003739</td>\n",
" <td>-0.029078</td>\n",
" <td>-0.149357</td>\n",
" <td>-0.031091</td>\n",
" <td>-0.148440</td>\n",
" <td>0.031298</td>\n",
" <td>-0.071848</td>\n",
" <td>0.120012</td>\n",
" <td>-0.057270</td>\n",
" <td>-0.011818</td>\n",
" <td>-0.183552</td>\n",
" <td>-0.013041</td>\n",
" <td>-0.182124</td>\n",
" <td>0.153535</td>\n",
" <td>-0.169867</td>\n",
" <td>-0.002164</td>\n",
" <td>-0.087451</td>\n",
" <td>-0.000954</td>\n",
" <td>-0.087045</td>\n",
" <td>0.081267</td>\n",
" <td>0.046190</td>\n",
" <td>-0.032933</td>\n",
" <td>-0.118845</td>\n",
" <td>-0.032970</td>\n",
" <td>-0.118491</td>\n",
" <td>-0.003491</td>\n",
" <td>-0.038077</td>\n",
" <td>0.077693</td>\n",
" <td>-0.019951</td>\n",
" <td>-0.039953</td>\n",
" <td>-0.140067</td>\n",
" <td>-0.041261</td>\n",
" <td>-0.139769</td>\n",
" <td>0.083622</td>\n",
" <td>-0.026432</td>\n",
" <td>0.107169</td>\n",
" <td>-0.064286</td>\n",
" <td>0.332905</td>\n",
" <td>0.339431</td>\n",
" <td>0.006411</td>\n",
" <td>0.005999</td>\n",
" <td>-0.013904</td>\n",
" <td>0.002573</td>\n",
" <td>-0.009710</td>\n",
" <td>-0.001891</td>\n",
" <td>-0.005110</td>\n",
" <td>-0.010965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>map_apache</th>\n",
" <td>-0.001580</td>\n",
" <td>-0.003315</td>\n",
" <td>-0.002743</td>\n",
" <td>-0.017040</td>\n",
" <td>0.056656</td>\n",
" <td>0.007780</td>\n",
" <td>0.036710</td>\n",
" <td>-0.005271</td>\n",
" <td>-0.032297</td>\n",
" <td>0.070071</td>\n",
" <td>0.039976</td>\n",
" <td>-0.001793</td>\n",
" <td>0.012210</td>\n",
" <td>0.008417</td>\n",
" <td>-0.007712</td>\n",
" <td>-0.012069</td>\n",
" <td>NaN</td>\n",
" <td>-0.006972</td>\n",
" <td>0.027456</td>\n",
" <td>-0.013506</td>\n",
" <td>1.000000</td>\n",
" <td>0.111839</td>\n",
" <td>-0.012181</td>\n",
" <td>-0.016349</td>\n",
" <td>0.432408</td>\n",
" <td>0.356524</td>\n",
" <td>0.432948</td>\n",
" <td>0.355927</td>\n",
" <td>0.003571</td>\n",
" <td>-0.016752</td>\n",
" <td>0.553784</td>\n",
" <td>0.408331</td>\n",
" <td>0.553319</td>\n",
" <td>0.408167</td>\n",
" <td>0.057543</td>\n",
" <td>0.017138</td>\n",
" <td>-0.039301</td>\n",
" <td>0.023344</td>\n",
" <td>0.477629</td>\n",
" <td>0.368801</td>\n",
" <td>0.478301</td>\n",
" <td>0.368645</td>\n",
" <td>-0.035858</td>\n",
" <td>-0.003270</td>\n",
" <td>0.390322</td>\n",
" <td>0.373429</td>\n",
" <td>0.391338</td>\n",
" <td>0.374294</td>\n",
" <td>-0.008144</td>\n",
" <td>-0.023693</td>\n",
" <td>0.478144</td>\n",
" <td>0.437433</td>\n",
" <td>0.477121</td>\n",
" <td>0.437557</td>\n",
" <td>0.047885</td>\n",
" <td>0.016095</td>\n",
" <td>-0.007026</td>\n",
" <td>0.014667</td>\n",
" <td>0.423854</td>\n",
" <td>0.392038</td>\n",
" <td>0.424494</td>\n",
" <td>0.392033</td>\n",
" <td>0.014743</td>\n",
" <td>0.049499</td>\n",
" <td>-0.038048</td>\n",
" <td>-0.030348</td>\n",
" <td>-0.030694</td>\n",
" <td>-0.023802</td>\n",
" <td>0.002195</td>\n",
" <td>-0.036326</td>\n",
" <td>-0.000444</td>\n",
" <td>-0.044452</td>\n",
" <td>-0.021612</td>\n",
" <td>-0.018537</td>\n",
" <td>-0.009331</td>\n",
" <td>-0.010763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>resprate_apache</th>\n",
" <td>0.008714</td>\n",
" <td>0.002281</td>\n",
" <td>-0.020449</td>\n",
" <td>0.034650</td>\n",
" <td>0.005877</td>\n",
" <td>-0.133643</td>\n",
" <td>-0.051661</td>\n",
" <td>-0.003775</td>\n",
" <td>0.021648</td>\n",
" <td>-0.016454</td>\n",
" <td>-0.089370</td>\n",
" <td>-0.127062</td>\n",
" <td>-0.141530</td>\n",
" <td>0.008824</td>\n",
" <td>0.003187</td>\n",
" <td>-0.003500</td>\n",
" <td>NaN</td>\n",
" <td>-0.002626</td>\n",
" <td>0.196669</td>\n",
" <td>-0.014614</td>\n",
" <td>0.111839</td>\n",
" <td>1.000000</td>\n",
" <td>0.050784</td>\n",
" <td>0.024450</td>\n",
" <td>0.006292</td>\n",
" <td>0.067508</td>\n",
" <td>0.006381</td>\n",
" <td>0.066720</td>\n",
" <td>0.169273</td>\n",
" <td>0.145635</td>\n",
" <td>0.125476</td>\n",
" <td>0.120239</td>\n",
" <td>0.126928</td>\n",
" <td>0.119610</td>\n",
" <td>0.561446</td>\n",
" <td>0.331596</td>\n",
" <td>-0.078245</td>\n",
" <td>-0.071114</td>\n",
" <td>0.041616</td>\n",
" <td>0.022501</td>\n",
" <td>0.042496</td>\n",
" <td>0.021807</td>\n",
" <td>0.061428</td>\n",
" <td>0.030762</td>\n",
" <td>0.023518</td>\n",
" <td>0.043064</td>\n",
" <td>0.023909</td>\n",
" <td>0.043308</td>\n",
" <td>0.167701</td>\n",
" <td>0.174211</td>\n",
" <td>0.111467</td>\n",
" <td>0.111741</td>\n",
" <td>0.113088</td>\n",
" <td>0.112462</td>\n",
" <td>0.479384</td>\n",
" <td>0.389324</td>\n",
" <td>-0.087868</td>\n",
" <td>-0.074162</td>\n",
" <td>0.024309</td>\n",
" <td>0.023338</td>\n",
" <td>0.025022</td>\n",
" <td>0.022385</td>\n",
" <td>0.029153</td>\n",
" <td>0.027490</td>\n",
" <td>-0.012051</td>\n",
" <td>-0.013941</td>\n",
" <td>0.098826</td>\n",
" <td>0.082287</td>\n",
" <td>0.012499</td>\n",
" <td>-0.003093</td>\n",
" <td>-0.013947</td>\n",
" <td>-0.006893</td>\n",
" <td>0.036010</td>\n",
" <td>0.019099</td>\n",
" <td>0.013559</td>\n",
" <td>0.016087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temp_apache</th>\n",
" <td>0.006060</td>\n",
" <td>-0.000320</td>\n",
" <td>-0.032016</td>\n",
" <td>-0.081164</td>\n",
" <td>0.035419</td>\n",
" <td>-0.033948</td>\n",
" <td>0.014273</td>\n",
" <td>0.001765</td>\n",
" <td>0.008130</td>\n",
" <td>0.041245</td>\n",
" <td>0.009267</td>\n",
" <td>0.010111</td>\n",
" <td>-0.036426</td>\n",
" <td>-0.019997</td>\n",
" <td>0.145228</td>\n",
" <td>0.192582</td>\n",
" <td>NaN</td>\n",
" <td>0.114977</td>\n",
" <td>0.108176</td>\n",
" <td>-0.125002</td>\n",
" <td>-0.012181</td>\n",
" <td>0.050784</td>\n",
" <td>1.000000</td>\n",
" <td>-0.125458</td>\n",
" <td>-0.006421</td>\n",
" <td>0.069681</td>\n",
" <td>-0.006451</td>\n",
" <td>0.069535</td>\n",
" <td>0.078107</td>\n",
" <td>0.175441</td>\n",
" <td>-0.003541</td>\n",
" <td>0.073489</td>\n",
" <td>-0.002554</td>\n",
" <td>0.073666</td>\n",
" <td>0.020480</td>\n",
" <td>0.101112</td>\n",
" <td>-0.046599</td>\n",
" <td>0.057146</td>\n",
" <td>0.007933</td>\n",
" <td>0.096424</td>\n",
" <td>0.008841</td>\n",
" <td>0.096650</td>\n",
" <td>0.414180</td>\n",
" <td>0.787391</td>\n",
" <td>0.002494</td>\n",
" <td>0.048547</td>\n",
" <td>0.004235</td>\n",
" <td>0.046943</td>\n",
" <td>0.122384</td>\n",
" <td>0.150136</td>\n",
" <td>0.013527</td>\n",
" <td>0.052644</td>\n",
" <td>0.014439</td>\n",
" <td>0.052593</td>\n",
" <td>0.059421</td>\n",
" <td>0.084145</td>\n",
" <td>-0.033760</td>\n",
" <td>0.017170</td>\n",
" <td>0.033627</td>\n",
" <td>0.079091</td>\n",
" <td>0.034800</td>\n",
" <td>0.079162</td>\n",
" <td>-0.079647</td>\n",
" <td>0.030358</td>\n",
" <td>-0.104586</td>\n",
" <td>0.003755</td>\n",
" <td>-0.197472</td>\n",
" <td>-0.206398</td>\n",
" <td>0.001945</td>\n",
" <td>-0.018439</td>\n",
" <td>-0.000627</td>\n",
" <td>-0.023581</td>\n",
" <td>0.005193</td>\n",
" <td>0.003955</td>\n",
" <td>-0.001379</td>\n",
" <td>-0.002067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ventilated_apache</th>\n",
" <td>-0.013043</td>\n",
" <td>-0.001335</td>\n",
" <td>0.025514</td>\n",
" <td>0.023629</td>\n",
" <td>0.076165</td>\n",
" <td>0.116024</td>\n",
" <td>-0.004478</td>\n",
" <td>0.016636</td>\n",
" <td>0.076306</td>\n",
" <td>0.069353</td>\n",
" <td>-0.034906</td>\n",
" <td>0.079998</td>\n",
" <td>0.141123</td>\n",
" <td>-0.002529</td>\n",
" <td>-0.500394</td>\n",
" <td>-0.443245</td>\n",
" <td>NaN</td>\n",
" <td>-0.581067</td>\n",
" <td>0.118081</td>\n",
" <td>0.601956</td>\n",
" <td>-0.016349</td>\n",
" <td>0.024450</td>\n",
" <td>-0.125458</td>\n",
" <td>1.000000</td>\n",
" <td>-0.000551</td>\n",
" <td>-0.147475</td>\n",
" <td>-0.000708</td>\n",
" <td>-0.146697</td>\n",
" <td>0.168589</td>\n",
" <td>0.017658</td>\n",
" <td>-0.010859</td>\n",
" <td>-0.183136</td>\n",
" <td>-0.012669</td>\n",
" <td>-0.182826</td>\n",
" <td>0.094779</td>\n",
" <td>-0.068154</td>\n",
" <td>0.152683</td>\n",
" <td>-0.121671</td>\n",
" <td>0.007514</td>\n",
" <td>-0.215911</td>\n",
" <td>0.006469</td>\n",
" <td>-0.215455</td>\n",
" <td>0.207720</td>\n",
" <td>-0.184548</td>\n",
" <td>-0.000577</td>\n",
" <td>-0.108953</td>\n",
" <td>0.000128</td>\n",
" <td>-0.107023</td>\n",
" <td>0.119361</td>\n",
" <td>0.068821</td>\n",
" <td>-0.024937</td>\n",
" <td>-0.140207</td>\n",
" <td>-0.026200</td>\n",
" <td>-0.139786</td>\n",
" <td>0.056546</td>\n",
" <td>0.006394</td>\n",
" <td>0.083931</td>\n",
" <td>-0.046864</td>\n",
" <td>-0.031710</td>\n",
" <td>-0.157782</td>\n",
" <td>-0.034463</td>\n",
" <td>-0.157497</td>\n",
" <td>0.090256</td>\n",
" <td>-0.004030</td>\n",
" <td>0.141289</td>\n",
" <td>-0.032025</td>\n",
" <td>0.351655</td>\n",
" <td>0.337154</td>\n",
" <td>0.006785</td>\n",
" <td>-0.006741</td>\n",
" <td>-0.002992</td>\n",
" <td>-0.006189</td>\n",
" <td>-0.004677</td>\n",
" <td>-0.004202</td>\n",
" <td>-0.004654</td>\n",
" <td>-0.016788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_max</th>\n",
" <td>-0.002887</td>\n",
" <td>-0.002492</td>\n",
" <td>-0.019117</td>\n",
" <td>-0.056655</td>\n",
" <td>0.053021</td>\n",
" <td>-0.158602</td>\n",
" <td>0.040730</td>\n",
" <td>0.005453</td>\n",
" <td>-0.050342</td>\n",
" <td>0.069792</td>\n",
" <td>-0.048332</td>\n",
" <td>-0.145480</td>\n",
" <td>-0.161697</td>\n",
" <td>0.000905</td>\n",
" <td>-0.044874</td>\n",
" <td>-0.041852</td>\n",
" <td>NaN</td>\n",
" <td>-0.039461</td>\n",
" <td>0.101246</td>\n",
" <td>-0.010973</td>\n",
" <td>0.432408</td>\n",
" <td>0.006292</td>\n",
" <td>-0.006421</td>\n",
" <td>-0.000551</td>\n",
" <td>1.000000</td>\n",
" <td>0.133011</td>\n",
" <td>0.998410</td>\n",
" <td>0.131743</td>\n",
" <td>0.171201</td>\n",
" <td>-0.040394</td>\n",
" <td>0.837572</td>\n",
" <td>0.107034</td>\n",
" <td>0.834341</td>\n",
" <td>0.106048</td>\n",
" <td>0.097553</td>\n",
" <td>-0.081754</td>\n",
" <td>0.061128</td>\n",
" <td>-0.097912</td>\n",
" <td>0.599638</td>\n",
" <td>0.093446</td>\n",
" <td>0.599525</td>\n",
" <td>0.092971</td>\n",
" <td>0.013822</td>\n",
" <td>-0.020902</td>\n",
" <td>0.604668</td>\n",
" <td>0.337590</td>\n",
" <td>0.603192</td>\n",
" <td>0.336034</td>\n",
" <td>0.118285</td>\n",
" <td>0.050352</td>\n",
" <td>0.516366</td>\n",
" <td>0.305863</td>\n",
" <td>0.515910</td>\n",
" <td>0.305352</td>\n",
" <td>0.084637</td>\n",
" <td>-0.001960</td>\n",
" <td>-0.001744</td>\n",
" <td>-0.054609</td>\n",
" <td>0.432473</td>\n",
" <td>0.263487</td>\n",
" <td>0.435978</td>\n",
" <td>0.265481</td>\n",
" <td>0.004768</td>\n",
" <td>0.017044</td>\n",
" <td>-0.026528</td>\n",
" <td>-0.028403</td>\n",
" <td>0.013195</td>\n",
" <td>0.012630</td>\n",
" <td>0.010762</td>\n",
" <td>-0.017529</td>\n",
" <td>-0.019824</td>\n",
" <td>-0.023154</td>\n",
" <td>-0.019109</td>\n",
" <td>-0.009462</td>\n",
" <td>-0.007399</td>\n",
" <td>-0.023977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_min</th>\n",
" <td>-0.000322</td>\n",
" <td>-0.001735</td>\n",
" <td>0.013821</td>\n",
" <td>-0.208602</td>\n",
" <td>-0.028818</td>\n",
" <td>0.003968</td>\n",
" <td>0.133386</td>\n",
" <td>-0.023755</td>\n",
" <td>-0.046183</td>\n",
" <td>0.032016</td>\n",
" <td>0.060281</td>\n",
" <td>-0.000491</td>\n",
" <td>0.001930</td>\n",
" <td>-0.046441</td>\n",
" <td>0.107759</td>\n",
" <td>0.096103</td>\n",
" <td>NaN</td>\n",
" <td>0.118994</td>\n",
" <td>-0.008455</td>\n",
" <td>-0.111654</td>\n",
" <td>0.356524</td>\n",
" <td>0.067508</td>\n",
" <td>0.069681</td>\n",
" <td>-0.147475</td>\n",
" <td>0.133011</td>\n",
" <td>1.000000</td>\n",
" <td>0.133066</td>\n",
" <td>0.998499</td>\n",
" <td>-0.107851</td>\n",
" <td>0.127837</td>\n",
" <td>0.223327</td>\n",
" <td>0.862357</td>\n",
" <td>0.223679</td>\n",
" <td>0.862618</td>\n",
" <td>-0.089984</td>\n",
" <td>0.136697</td>\n",
" <td>-0.149467</td>\n",
" <td>0.213147</td>\n",
" <td>0.154440</td>\n",
" <td>0.666660</td>\n",
" <td>0.155061</td>\n",
" <td>0.665898</td>\n",
" <td>-0.106248</td>\n",
" <td>0.110313</td>\n",
" <td>0.348050</td>\n",
" <td>0.628693</td>\n",
" <td>0.348236</td>\n",
" <td>0.629021</td>\n",
" <td>-0.028420</td>\n",
" <td>0.038660</td>\n",
" <td>0.389268</td>\n",
" <td>0.591288</td>\n",
" <td>0.392297</td>\n",
" <td>0.592603</td>\n",
" <td>-0.052964</td>\n",
" <td>0.034346</td>\n",
" <td>-0.021251</td>\n",
" <td>0.117988</td>\n",
" <td>0.271229</td>\n",
" <td>0.438911</td>\n",
" <td>0.271816</td>\n",
" <td>0.437978</td>\n",
" <td>-0.058470</td>\n",
" <td>0.029621</td>\n",
" <td>-0.147363</td>\n",
" <td>-0.064450</td>\n",
" <td>-0.193258</td>\n",
" <td>-0.168312</td>\n",
" <td>0.012464</td>\n",
" <td>-0.046882</td>\n",
" <td>-0.054714</td>\n",
" <td>-0.050688</td>\n",
" <td>-0.015537</td>\n",
" <td>-0.028716</td>\n",
" <td>-0.007431</td>\n",
" <td>-0.002172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <td>-0.002818</td>\n",
" <td>-0.002276</td>\n",
" <td>-0.018961</td>\n",
" <td>-0.056564</td>\n",
" <td>0.053025</td>\n",
" <td>-0.158423</td>\n",
" <td>0.040656</td>\n",
" <td>0.005764</td>\n",
" <td>-0.049986</td>\n",
" <td>0.069772</td>\n",
" <td>-0.048097</td>\n",
" <td>-0.145177</td>\n",
" <td>-0.161432</td>\n",
" <td>0.000787</td>\n",
" <td>-0.044373</td>\n",
" <td>-0.041570</td>\n",
" <td>NaN</td>\n",
" <td>-0.039126</td>\n",
" <td>0.101551</td>\n",
" <td>-0.011267</td>\n",
" <td>0.432948</td>\n",
" <td>0.006381</td>\n",
" <td>-0.006451</td>\n",
" <td>-0.000708</td>\n",
" <td>0.998410</td>\n",
" <td>0.133066</td>\n",
" <td>1.000000</td>\n",
" <td>0.132062</td>\n",
" <td>0.171342</td>\n",
" <td>-0.039583</td>\n",
" <td>0.836061</td>\n",
" <td>0.107176</td>\n",
" <td>0.835963</td>\n",
" <td>0.106437</td>\n",
" <td>0.097024</td>\n",
" <td>-0.081261</td>\n",
" <td>0.060630</td>\n",
" <td>-0.097955</td>\n",
" <td>0.599013</td>\n",
" <td>0.093600</td>\n",
" <td>0.600818</td>\n",
" <td>0.093238</td>\n",
" <td>0.013531</td>\n",
" <td>-0.020802</td>\n",
" <td>0.604847</td>\n",
" <td>0.337591</td>\n",
" <td>0.604102</td>\n",
" <td>0.336400</td>\n",
" <td>0.118575</td>\n",
" <td>0.050873</td>\n",
" <td>0.516849</td>\n",
" <td>0.305999</td>\n",
" <td>0.516466</td>\n",
" <td>0.305559</td>\n",
" <td>0.084279</td>\n",
" <td>-0.001902</td>\n",
" <td>-0.001848</td>\n",
" <td>-0.054659</td>\n",
" <td>0.432758</td>\n",
" <td>0.263474</td>\n",
" <td>0.436690</td>\n",
" <td>0.265585</td>\n",
" <td>0.004872</td>\n",
" <td>0.017193</td>\n",
" <td>-0.026837</td>\n",
" <td>-0.028376</td>\n",
" <td>0.012716</td>\n",
" <td>0.012448</td>\n",
" <td>0.010759</td>\n",
" <td>-0.017306</td>\n",
" <td>-0.019583</td>\n",
" <td>-0.023155</td>\n",
" <td>-0.019113</td>\n",
" <td>-0.009464</td>\n",
" <td>-0.007400</td>\n",
" <td>-0.023979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <td>-0.000037</td>\n",
" <td>-0.001946</td>\n",
" <td>0.013578</td>\n",
" <td>-0.207904</td>\n",
" <td>-0.029103</td>\n",
" <td>0.005656</td>\n",
" <td>0.133630</td>\n",
" <td>-0.023987</td>\n",
" <td>-0.045732</td>\n",
" <td>0.031905</td>\n",
" <td>0.061099</td>\n",
" <td>0.000730</td>\n",
" <td>0.003695</td>\n",
" <td>-0.046509</td>\n",
" <td>0.107441</td>\n",
" <td>0.096157</td>\n",
" <td>NaN</td>\n",
" <td>0.118219</td>\n",
" <td>-0.008987</td>\n",
" <td>-0.110250</td>\n",
" <td>0.355927</td>\n",
" <td>0.066720</td>\n",
" <td>0.069535</td>\n",
" <td>-0.146697</td>\n",
" <td>0.131743</td>\n",
" <td>0.998499</td>\n",
" <td>0.132062</td>\n",
" <td>1.000000</td>\n",
" <td>-0.108221</td>\n",
" <td>0.127608</td>\n",
" <td>0.222847</td>\n",
" <td>0.861639</td>\n",
" <td>0.222657</td>\n",
" <td>0.863828</td>\n",
" <td>-0.090210</td>\n",
" <td>0.136028</td>\n",
" <td>-0.148878</td>\n",
" <td>0.213191</td>\n",
" <td>0.154595</td>\n",
" <td>0.665910</td>\n",
" <td>0.154209</td>\n",
" <td>0.667129</td>\n",
" <td>-0.105454</td>\n",
" <td>0.110311</td>\n",
" <td>0.347372</td>\n",
" <td>0.628457</td>\n",
" <td>0.347740</td>\n",
" <td>0.629334</td>\n",
" <td>-0.028917</td>\n",
" <td>0.038356</td>\n",
" <td>0.388923</td>\n",
" <td>0.591434</td>\n",
" <td>0.391856</td>\n",
" <td>0.592823</td>\n",
" <td>-0.053816</td>\n",
" <td>0.033463</td>\n",
" <td>-0.021066</td>\n",
" <td>0.118203</td>\n",
" <td>0.270993</td>\n",
" <td>0.439004</td>\n",
" <td>0.271499</td>\n",
" <td>0.438266</td>\n",
" <td>-0.058270</td>\n",
" <td>0.029728</td>\n",
" <td>-0.146484</td>\n",
" <td>-0.064412</td>\n",
" <td>-0.193442</td>\n",
" <td>-0.168482</td>\n",
" <td>0.012426</td>\n",
" <td>-0.046496</td>\n",
" <td>-0.054751</td>\n",
" <td>-0.050795</td>\n",
" <td>-0.015711</td>\n",
" <td>-0.028798</td>\n",
" <td>-0.007495</td>\n",
" <td>-0.002327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_heartrate_max</th>\n",
" <td>-0.006774</td>\n",
" <td>-0.000357</td>\n",
" <td>-0.006119</td>\n",
" <td>-0.145065</td>\n",
" <td>-0.036482</td>\n",
" <td>-0.072214</td>\n",
" <td>-0.015156</td>\n",
" <td>-0.011112</td>\n",
" <td>0.061604</td>\n",
" <td>-0.040901</td>\n",
" <td>-0.118243</td>\n",
" <td>-0.007217</td>\n",
" <td>-0.056786</td>\n",
" <td>-0.018662</td>\n",
" <td>-0.140660</td>\n",
" <td>-0.133273</td>\n",
" <td>NaN</td>\n",
" <td>-0.157384</td>\n",
" <td>0.808829</td>\n",
" <td>0.118692</td>\n",
" <td>0.003571</td>\n",
" <td>0.169273</td>\n",
" <td>0.078107</td>\n",
" <td>0.168589</td>\n",
" <td>0.171201</td>\n",
" <td>-0.107851</td>\n",
" <td>0.171342</td>\n",
" <td>-0.108221</td>\n",
" <td>1.000000</td>\n",
" <td>0.475471</td>\n",
" <td>0.101443</td>\n",
" <td>-0.177674</td>\n",
" <td>0.099977</td>\n",
" <td>-0.177521</td>\n",
" <td>0.249481</td>\n",
" <td>0.037477</td>\n",
" <td>0.076288</td>\n",
" <td>-0.186051</td>\n",
" <td>0.018402</td>\n",
" <td>-0.248651</td>\n",
" <td>0.018522</td>\n",
" <td>-0.248627</td>\n",
" <td>0.281152</td>\n",
" <td>0.012378</td>\n",
" <td>0.105612</td>\n",
" <td>-0.023891</td>\n",
" <td>0.104771</td>\n",
" <td>-0.022509</td>\n",
" <td>0.787313</td>\n",
" <td>0.677401</td>\n",
" <td>0.018777</td>\n",
" <td>-0.093579</td>\n",
" <td>0.017876</td>\n",
" <td>-0.093994</td>\n",
" <td>0.254646</td>\n",
" <td>0.175305</td>\n",
" <td>-0.032473</td>\n",
" <td>-0.115732</td>\n",
" <td>-0.050685</td>\n",
" <td>-0.160615</td>\n",
" <td>-0.050559</td>\n",
" <td>-0.160745</td>\n",
" <td>0.109506</td>\n",
" <td>0.053443</td>\n",
" <td>0.039406</td>\n",
" <td>-0.076311</td>\n",
" <td>0.171262</td>\n",
" <td>0.158557</td>\n",
" <td>0.009853</td>\n",
" <td>0.015702</td>\n",
" <td>-0.026411</td>\n",
" <td>0.015661</td>\n",
" <td>0.066606</td>\n",
" <td>0.023870</td>\n",
" <td>0.019838</td>\n",
" <td>0.048240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_heartrate_min</th>\n",
" <td>0.001381</td>\n",
" <td>0.000861</td>\n",
" <td>-0.008510</td>\n",
" <td>-0.147906</td>\n",
" <td>0.006305</td>\n",
" <td>-0.028268</td>\n",
" <td>-0.028183</td>\n",
" <td>0.019052</td>\n",
" <td>0.054006</td>\n",
" <td>-0.006231</td>\n",
" <td>-0.046336</td>\n",
" <td>0.046211</td>\n",
" <td>-0.019946</td>\n",
" <td>-0.010110</td>\n",
" <td>0.015849</td>\n",
" <td>0.028772</td>\n",
" <td>NaN</td>\n",
" <td>-0.007727</td>\n",
" <td>0.572694</td>\n",
" <td>0.003739</td>\n",
" <td>-0.016752</td>\n",
" <td>0.145635</td>\n",
" <td>0.175441</td>\n",
" <td>0.017658</td>\n",
" <td>-0.040394</td>\n",
" <td>0.127837</td>\n",
" <td>-0.039583</td>\n",
" <td>0.127608</td>\n",
" <td>0.475471</td>\n",
" <td>1.000000</td>\n",
" <td>-0.075752</td>\n",
" <td>0.076523</td>\n",
" <td>-0.075587</td>\n",
" <td>0.077167</td>\n",
" <td>0.054984</td>\n",
" <td>0.250033</td>\n",
" <td>-0.065965</td>\n",
" <td>0.058831</td>\n",
" <td>-0.140109</td>\n",
" <td>0.021449</td>\n",
" <td>-0.139506</td>\n",
" <td>0.021410</td>\n",
" <td>0.182858</td>\n",
" <td>0.158806</td>\n",
" <td>-0.006163</td>\n",
" <td>0.065950</td>\n",
" <td>-0.005030</td>\n",
" <td>0.066044</td>\n",
" <td>0.553763</td>\n",
" <td>0.680537</td>\n",
" <td>-0.049830</td>\n",
" <td>0.009224</td>\n",
" <td>-0.048665</td>\n",
" <td>0.009344</td>\n",
" <td>0.129373</td>\n",
" <td>0.210351</td>\n",
" <td>-0.059037</td>\n",
" <td>0.002237</td>\n",
" <td>-0.114373</td>\n",
" <td>-0.049718</td>\n",
" <td>-0.113736</td>\n",
" <td>-0.050214</td>\n",
" <td>0.099929</td>\n",
" <td>0.051655</td>\n",
" <td>-0.005189</td>\n",
" <td>-0.044381</td>\n",
" <td>-0.004565</td>\n",
" <td>-0.007087</td>\n",
" <td>0.011322</td>\n",
" <td>0.022203</td>\n",
" <td>0.018222</td>\n",
" <td>0.024140</td>\n",
" <td>0.054844</td>\n",
" <td>0.013753</td>\n",
" <td>0.017229</td>\n",
" <td>0.044348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_max</th>\n",
" <td>-0.000186</td>\n",
" <td>-0.004624</td>\n",
" <td>-0.019417</td>\n",
" <td>0.006689</td>\n",
" <td>0.061985</td>\n",
" <td>-0.130329</td>\n",
" <td>0.028673</td>\n",
" <td>-0.006270</td>\n",
" <td>-0.050178</td>\n",
" <td>0.072860</td>\n",
" <td>-0.019555</td>\n",
" <td>-0.127620</td>\n",
" <td>-0.132677</td>\n",
" <td>0.021157</td>\n",
" <td>-0.030762</td>\n",
" <td>-0.034748</td>\n",
" <td>NaN</td>\n",
" <td>-0.025780</td>\n",
" <td>0.048618</td>\n",
" <td>-0.029078</td>\n",
" <td>0.553784</td>\n",
" <td>0.125476</td>\n",
" <td>-0.003541</td>\n",
" <td>-0.010859</td>\n",
" <td>0.837572</td>\n",
" <td>0.223327</td>\n",
" <td>0.836061</td>\n",
" <td>0.222847</td>\n",
" <td>0.101443</td>\n",
" <td>-0.075752</td>\n",
" <td>1.000000</td>\n",
" <td>0.260372</td>\n",
" <td>0.985035</td>\n",
" <td>0.261208</td>\n",
" <td>0.163470</td>\n",
" <td>-0.066112</td>\n",
" <td>0.022811</td>\n",
" <td>-0.064223</td>\n",
" <td>0.748749</td>\n",
" <td>0.236612</td>\n",
" <td>0.747446</td>\n",
" <td>0.236445</td>\n",
" <td>-0.006464</td>\n",
" <td>-0.014090</td>\n",
" <td>0.575372</td>\n",
" <td>0.381715</td>\n",
" <td>0.573603</td>\n",
" <td>0.380865</td>\n",
" <td>0.052258</td>\n",
" <td>-0.012835</td>\n",
" <td>0.656704</td>\n",
" <td>0.449462</td>\n",
" <td>0.655768</td>\n",
" <td>0.449958</td>\n",
" <td>0.122220</td>\n",
" <td>0.012532</td>\n",
" <td>-0.002551</td>\n",
" <td>-0.042100</td>\n",
" <td>0.561486</td>\n",
" <td>0.397023</td>\n",
" <td>0.563790</td>\n",
" <td>0.398017</td>\n",
" <td>0.025616</td>\n",
" <td>0.031988</td>\n",
" <td>-0.037166</td>\n",
" <td>-0.029030</td>\n",
" <td>0.011926</td>\n",
" <td>0.009259</td>\n",
" <td>0.009370</td>\n",
" <td>-0.029211</td>\n",
" <td>0.015416</td>\n",
" <td>-0.034595</td>\n",
" <td>-0.021104</td>\n",
" <td>-0.011892</td>\n",
" <td>-0.010193</td>\n",
" <td>-0.024688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_min</th>\n",
" <td>-0.001552</td>\n",
" <td>-0.002503</td>\n",
" <td>0.008801</td>\n",
" <td>-0.129142</td>\n",
" <td>0.000166</td>\n",
" <td>0.010672</td>\n",
" <td>0.094093</td>\n",
" <td>-0.022464</td>\n",
" <td>-0.050439</td>\n",
" <td>0.042409</td>\n",
" <td>0.072619</td>\n",
" <td>0.001836</td>\n",
" <td>0.007748</td>\n",
" <td>-0.024207</td>\n",
" <td>0.133706</td>\n",
" <td>0.122254</td>\n",
" <td>NaN</td>\n",
" <td>0.148542</td>\n",
" <td>-0.070482</td>\n",
" <td>-0.149357</td>\n",
" <td>0.408331</td>\n",
" <td>0.120239</td>\n",
" <td>0.073489</td>\n",
" <td>-0.183136</td>\n",
" <td>0.107034</td>\n",
" <td>0.862357</td>\n",
" <td>0.107176</td>\n",
" <td>0.861639</td>\n",
" <td>-0.177674</td>\n",
" <td>0.076523</td>\n",
" <td>0.260372</td>\n",
" <td>1.000000</td>\n",
" <td>0.263331</td>\n",
" <td>0.996876</td>\n",
" <td>-0.063132</td>\n",
" <td>0.150740</td>\n",
" <td>-0.167315</td>\n",
" <td>0.227411</td>\n",
" <td>0.252108</td>\n",
" <td>0.803036</td>\n",
" <td>0.252553</td>\n",
" <td>0.802242</td>\n",
" <td>-0.127599</td>\n",
" <td>0.123000</td>\n",
" <td>0.307212</td>\n",
" <td>0.572007</td>\n",
" <td>0.307468</td>\n",
" <td>0.573238</td>\n",
" <td>-0.096207</td>\n",
" <td>-0.027182</td>\n",
" <td>0.431133</td>\n",
" <td>0.649424</td>\n",
" <td>0.434475</td>\n",
" <td>0.650209</td>\n",
" <td>-0.038282</td>\n",
" <td>0.039833</td>\n",
" <td>-0.022553</td>\n",
" <td>0.121927</td>\n",
" <td>0.364491</td>\n",
" <td>0.537579</td>\n",
" <td>0.364968</td>\n",
" <td>0.536927</td>\n",
" <td>-0.046836</td>\n",
" <td>0.049530</td>\n",
" <td>-0.146353</td>\n",
" <td>-0.046372</td>\n",
" <td>-0.203707</td>\n",
" <td>-0.182120</td>\n",
" <td>0.007909</td>\n",
" <td>-0.050296</td>\n",
" <td>-0.020560</td>\n",
" <td>-0.054639</td>\n",
" <td>-0.026259</td>\n",
" <td>-0.026944</td>\n",
" <td>-0.009239</td>\n",
" <td>-0.010799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <td>0.001062</td>\n",
" <td>-0.004520</td>\n",
" <td>-0.018866</td>\n",
" <td>0.006209</td>\n",
" <td>0.062245</td>\n",
" <td>-0.131006</td>\n",
" <td>0.029074</td>\n",
" <td>-0.009059</td>\n",
" <td>-0.051078</td>\n",
" <td>0.073350</td>\n",
" <td>-0.021060</td>\n",
" <td>-0.127689</td>\n",
" <td>-0.133263</td>\n",
" <td>0.022015</td>\n",
" <td>-0.029471</td>\n",
" <td>-0.033965</td>\n",
" <td>NaN</td>\n",
" <td>-0.023749</td>\n",
" <td>0.047616</td>\n",
" <td>-0.031091</td>\n",
" <td>0.553319</td>\n",
" <td>0.126928</td>\n",
" <td>-0.002554</td>\n",
" <td>-0.012669</td>\n",
" <td>0.834341</td>\n",
" <td>0.223679</td>\n",
" <td>0.835963</td>\n",
" <td>0.222657</td>\n",
" <td>0.099977</td>\n",
" <td>-0.075587</td>\n",
" <td>0.985035</td>\n",
" <td>0.263331</td>\n",
" <td>1.000000</td>\n",
" <td>0.261688</td>\n",
" <td>0.163748</td>\n",
" <td>-0.064142</td>\n",
" <td>0.021926</td>\n",
" <td>-0.063036</td>\n",
" <td>0.746551</td>\n",
" <td>0.238019</td>\n",
" <td>0.748714</td>\n",
" <td>0.237239</td>\n",
" <td>-0.006817</td>\n",
" <td>-0.013644</td>\n",
" <td>0.573164</td>\n",
" <td>0.381789</td>\n",
" <td>0.572296</td>\n",
" <td>0.380650</td>\n",
" <td>0.050773</td>\n",
" <td>-0.012912</td>\n",
" <td>0.655184</td>\n",
" <td>0.449962</td>\n",
" <td>0.655304</td>\n",
" <td>0.450112</td>\n",
" <td>0.122971</td>\n",
" <td>0.013815</td>\n",
" <td>-0.002958</td>\n",
" <td>-0.042061</td>\n",
" <td>0.560690</td>\n",
" <td>0.396912</td>\n",
" <td>0.563820</td>\n",
" <td>0.398145</td>\n",
" <td>0.025646</td>\n",
" <td>0.031901</td>\n",
" <td>-0.037627</td>\n",
" <td>-0.027664</td>\n",
" <td>0.010603</td>\n",
" <td>0.008541</td>\n",
" <td>0.009354</td>\n",
" <td>-0.028820</td>\n",
" <td>0.015328</td>\n",
" <td>-0.033779</td>\n",
" <td>-0.020487</td>\n",
" <td>-0.013337</td>\n",
" <td>-0.010189</td>\n",
" <td>-0.025317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <td>-0.001624</td>\n",
" <td>-0.002932</td>\n",
" <td>0.009003</td>\n",
" <td>-0.129187</td>\n",
" <td>-0.000184</td>\n",
" <td>0.011451</td>\n",
" <td>0.094313</td>\n",
" <td>-0.019549</td>\n",
" <td>-0.049819</td>\n",
" <td>0.042221</td>\n",
" <td>0.073237</td>\n",
" <td>0.002437</td>\n",
" <td>0.008560</td>\n",
" <td>-0.024457</td>\n",
" <td>0.133569</td>\n",
" <td>0.122598</td>\n",
" <td>NaN</td>\n",
" <td>0.148113</td>\n",
" <td>-0.070210</td>\n",
" <td>-0.148440</td>\n",
" <td>0.408167</td>\n",
" <td>0.119610</td>\n",
" <td>0.073666</td>\n",
" <td>-0.182826</td>\n",
" <td>0.106048</td>\n",
" <td>0.862618</td>\n",
" <td>0.106437</td>\n",
" <td>0.863828</td>\n",
" <td>-0.177521</td>\n",
" <td>0.077167</td>\n",
" <td>0.261208</td>\n",
" <td>0.996876</td>\n",
" <td>0.261688</td>\n",
" <td>1.000000</td>\n",
" <td>-0.063771</td>\n",
" <td>0.150175</td>\n",
" <td>-0.167117</td>\n",
" <td>0.228135</td>\n",
" <td>0.251774</td>\n",
" <td>0.803383</td>\n",
" <td>0.251632</td>\n",
" <td>0.804875</td>\n",
" <td>-0.126997</td>\n",
" <td>0.123449</td>\n",
" <td>0.307228</td>\n",
" <td>0.573139</td>\n",
" <td>0.307626</td>\n",
" <td>0.574665</td>\n",
" <td>-0.096533</td>\n",
" <td>-0.026903</td>\n",
" <td>0.431227</td>\n",
" <td>0.649377</td>\n",
" <td>0.434106</td>\n",
" <td>0.651138</td>\n",
" <td>-0.039792</td>\n",
" <td>0.039593</td>\n",
" <td>-0.022383</td>\n",
" <td>0.122594</td>\n",
" <td>0.364109</td>\n",
" <td>0.538366</td>\n",
" <td>0.364611</td>\n",
" <td>0.537856</td>\n",
" <td>-0.046577</td>\n",
" <td>0.049670</td>\n",
" <td>-0.146024</td>\n",
" <td>-0.046391</td>\n",
" <td>-0.204198</td>\n",
" <td>-0.182673</td>\n",
" <td>0.007944</td>\n",
" <td>-0.049825</td>\n",
" <td>-0.020812</td>\n",
" <td>-0.054604</td>\n",
" <td>-0.026956</td>\n",
" <td>-0.026687</td>\n",
" <td>-0.009197</td>\n",
" <td>-0.010691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_resprate_max</th>\n",
" <td>0.007901</td>\n",
" <td>0.003176</td>\n",
" <td>-0.032569</td>\n",
" <td>0.027702</td>\n",
" <td>0.012221</td>\n",
" <td>-0.064207</td>\n",
" <td>-0.021115</td>\n",
" <td>-0.012600</td>\n",
" <td>0.032238</td>\n",
" <td>0.003680</td>\n",
" <td>-0.074171</td>\n",
" <td>-0.066415</td>\n",
" <td>-0.069187</td>\n",
" <td>0.014016</td>\n",
" <td>-0.047472</td>\n",
" <td>-0.044868</td>\n",
" <td>NaN</td>\n",
" <td>-0.049435</td>\n",
" <td>0.172729</td>\n",
" <td>0.031298</td>\n",
" <td>0.057543</td>\n",
" <td>0.561446</td>\n",
" <td>0.020480</td>\n",
" <td>0.094779</td>\n",
" <td>0.097553</td>\n",
" <td>-0.089984</td>\n",
" <td>0.097024</td>\n",
" <td>-0.090210</td>\n",
" <td>0.249481</td>\n",
" <td>0.054984</td>\n",
" <td>0.163470</td>\n",
" <td>-0.063132</td>\n",
" <td>0.163748</td>\n",
" <td>-0.063771</td>\n",
" <td>1.000000</td>\n",
" <td>0.036134</td>\n",
" <td>0.037225</td>\n",
" <td>-0.179488</td>\n",
" <td>0.102212</td>\n",
" <td>-0.134621</td>\n",
" <td>0.101917</td>\n",
" <td>-0.134492</td>\n",
" <td>0.120650</td>\n",
" <td>-0.022532</td>\n",
" <td>0.045533</td>\n",
" <td>-0.049506</td>\n",
" <td>0.044027</td>\n",
" <td>-0.048420</td>\n",
" <td>0.190578</td>\n",
" <td>0.131655</td>\n",
" <td>0.086647</td>\n",
" <td>-0.008806</td>\n",
" <td>0.086132</td>\n",
" <td>-0.008186</td>\n",
" <td>0.566479</td>\n",
" <td>0.266823</td>\n",
" <td>-0.033790</td>\n",
" <td>-0.122824</td>\n",
" <td>0.030429</td>\n",
" <td>-0.069583</td>\n",
" <td>0.029964</td>\n",
" <td>-0.069059</td>\n",
" <td>0.035801</td>\n",
" <td>0.014376</td>\n",
" <td>0.029741</td>\n",
" <td>-0.011542</td>\n",
" <td>0.117654</td>\n",
" <td>0.102399</td>\n",
" <td>0.009845</td>\n",
" <td>0.003894</td>\n",
" <td>0.002041</td>\n",
" <td>0.002154</td>\n",
" <td>0.038590</td>\n",
" <td>0.023426</td>\n",
" <td>0.015426</td>\n",
" <td>0.023527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_resprate_min</th>\n",
" <td>0.001453</td>\n",
" <td>0.003608</td>\n",
" <td>-0.019908</td>\n",
" <td>0.048954</td>\n",
" <td>-0.005307</td>\n",
" <td>-0.159557</td>\n",
" <td>-0.054963</td>\n",
" <td>-0.033504</td>\n",
" <td>0.013877</td>\n",
" <td>-0.029463</td>\n",
" <td>-0.090492</td>\n",
" <td>-0.142348</td>\n",
" <td>-0.168874</td>\n",
" <td>-0.021308</td>\n",
" <td>0.073119</td>\n",
" <td>0.058125</td>\n",
" <td>NaN</td>\n",
" <td>0.056118</td>\n",
" <td>0.115617</td>\n",
" <td>-0.071848</td>\n",
" <td>0.017138</td>\n",
" <td>0.331596</td>\n",
" <td>0.101112</td>\n",
" <td>-0.068154</td>\n",
" <td>-0.081754</td>\n",
" <td>0.136697</td>\n",
" <td>-0.081261</td>\n",
" <td>0.136028</td>\n",
" <td>0.037477</td>\n",
" <td>0.250033</td>\n",
" <td>-0.066112</td>\n",
" <td>0.150740</td>\n",
" <td>-0.064142</td>\n",
" <td>0.150175</td>\n",
" <td>0.036134</td>\n",
" <td>1.000000</td>\n",
" <td>-0.185536</td>\n",
" <td>0.076185</td>\n",
" <td>-0.063872</td>\n",
" <td>0.154223</td>\n",
" <td>-0.062863</td>\n",
" <td>0.153707</td>\n",
" <td>0.014322</td>\n",
" <td>0.105672</td>\n",
" <td>-0.034335</td>\n",
" <td>0.075952</td>\n",
" <td>-0.032759</td>\n",
" <td>0.073918</td>\n",
" <td>0.103607</td>\n",
" <td>0.184471</td>\n",
" <td>-0.016149</td>\n",
" <td>0.083973</td>\n",
" <td>-0.014409</td>\n",
" <td>0.083542</td>\n",
" <td>0.244139</td>\n",
" <td>0.532418</td>\n",
" <td>-0.129555</td>\n",
" <td>-0.004842</td>\n",
" <td>-0.018405</td>\n",
" <td>0.084817</td>\n",
" <td>-0.016364</td>\n",
" <td>0.084742</td>\n",
" <td>0.011728</td>\n",
" <td>0.048707</td>\n",
" <td>-0.055836</td>\n",
" <td>-0.019968</td>\n",
" <td>0.001213</td>\n",
" <td>-0.010671</td>\n",
" <td>0.010695</td>\n",
" <td>-0.013251</td>\n",
" <td>-0.019699</td>\n",
" <td>-0.006476</td>\n",
" <td>0.015082</td>\n",
" <td>0.015824</td>\n",
" <td>0.012541</td>\n",
" <td>0.008194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_spo2_max</th>\n",
" <td>-0.002737</td>\n",
" <td>-0.003005</td>\n",
" <td>-0.009052</td>\n",
" <td>-0.043002</td>\n",
" <td>-0.082259</td>\n",
" <td>0.042150</td>\n",
" <td>-0.030168</td>\n",
" <td>-0.027147</td>\n",
" <td>0.023592</td>\n",
" <td>-0.092802</td>\n",
" <td>0.009111</td>\n",
" <td>0.061274</td>\n",
" <td>0.051252</td>\n",
" <td>0.037592</td>\n",
" <td>-0.123692</td>\n",
" <td>-0.101453</td>\n",
" <td>NaN</td>\n",
" <td>-0.147973</td>\n",
" <td>0.020593</td>\n",
" <td>0.120012</td>\n",
" <td>-0.039301</td>\n",
" <td>-0.078245</td>\n",
" <td>-0.046599</td>\n",
" <td>0.152683</td>\n",
" <td>0.061128</td>\n",
" <td>-0.149467</td>\n",
" <td>0.060630</td>\n",
" <td>-0.148878</td>\n",
" <td>0.076288</td>\n",
" <td>-0.065965</td>\n",
" <td>0.022811</td>\n",
" <td>-0.167315</td>\n",
" <td>0.021926</td>\n",
" <td>-0.167117</td>\n",
" <td>0.037225</td>\n",
" <td>-0.185536</td>\n",
" <td>1.000000</td>\n",
" <td>0.051419</td>\n",
" <td>0.035251</td>\n",
" <td>-0.154899</td>\n",
" <td>0.034190</td>\n",
" <td>-0.154006</td>\n",
" <td>0.085534</td>\n",
" <td>-0.072233</td>\n",
" <td>-0.003621</td>\n",
" <td>-0.092584</td>\n",
" <td>-0.004764</td>\n",
" <td>-0.091553</td>\n",
" <td>0.018531</td>\n",
" <td>-0.027774</td>\n",
" <td>-0.037766</td>\n",
" <td>-0.108473</td>\n",
" <td>-0.039170</td>\n",
" <td>-0.109327</td>\n",
" <td>-0.034198</td>\n",
" <td>-0.109716</td>\n",
" <td>0.482151</td>\n",
" <td>0.193868</td>\n",
" <td>-0.019100</td>\n",
" <td>-0.096505</td>\n",
" <td>-0.020528</td>\n",
" <td>-0.096337</td>\n",
" <td>0.013158</td>\n",
" <td>-0.081946</td>\n",
" <td>0.039711</td>\n",
" <td>-0.042741</td>\n",
" <td>0.062270</td>\n",
" <td>0.053668</td>\n",
" <td>0.005565</td>\n",
" <td>0.015644</td>\n",
" <td>0.007692</td>\n",
" <td>0.018583</td>\n",
" <td>0.010419</td>\n",
" <td>0.003315</td>\n",
" <td>-0.002064</td>\n",
" <td>0.000517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_spo2_min</th>\n",
" <td>0.002650</td>\n",
" <td>-0.005226</td>\n",
" <td>-0.001524</td>\n",
" <td>-0.081491</td>\n",
" <td>-0.031478</td>\n",
" <td>0.038151</td>\n",
" <td>0.007965</td>\n",
" <td>0.007943</td>\n",
" <td>-0.030216</td>\n",
" <td>-0.027094</td>\n",
" <td>0.059437</td>\n",
" <td>0.053945</td>\n",
" <td>0.037978</td>\n",
" <td>-0.035431</td>\n",
" <td>0.084127</td>\n",
" <td>0.092508</td>\n",
" <td>NaN</td>\n",
" <td>0.080855</td>\n",
" <td>-0.107299</td>\n",
" <td>-0.057270</td>\n",
" <td>0.023344</td>\n",
" <td>-0.071114</td>\n",
" <td>0.057146</td>\n",
" <td>-0.121671</td>\n",
" <td>-0.097912</td>\n",
" <td>0.213147</td>\n",
" <td>-0.097955</td>\n",
" <td>0.213191</td>\n",
" <td>-0.186051</td>\n",
" <td>0.058831</td>\n",
" <td>-0.064223</td>\n",
" <td>0.227411</td>\n",
" <td>-0.063036</td>\n",
" <td>0.228135</td>\n",
" <td>-0.179488</td>\n",
" <td>0.076185</td>\n",
" <td>0.051419</td>\n",
" <td>1.000000</td>\n",
" <td>-0.038928</td>\n",
" <td>0.235267</td>\n",
" <td>-0.039044</td>\n",
" <td>0.235052</td>\n",
" <td>-0.061721</td>\n",
" <td>0.095773</td>\n",
" <td>-0.001225</td>\n",
" <td>0.139330</td>\n",
" <td>-0.002144</td>\n",
" <td>0.138820</td>\n",
" <td>-0.118527</td>\n",
" <td>-0.050125</td>\n",
" <td>0.030277</td>\n",
" <td>0.146458</td>\n",
" <td>0.031380</td>\n",
" <td>0.146860</td>\n",
" <td>-0.144794</td>\n",
" <td>-0.038978</td>\n",
" <td>0.218068</td>\n",
" <td>0.539191</td>\n",
" <td>0.036028</td>\n",
" <td>0.153235</td>\n",
" <td>0.036011</td>\n",
" <td>0.152361</td>\n",
" <td>-0.029429</td>\n",
" <td>-0.005771</td>\n",
" <td>-0.099509</td>\n",
" <td>-0.046367</td>\n",
" <td>-0.154740</td>\n",
" <td>-0.151416</td>\n",
" <td>0.002393</td>\n",
" <td>-0.003627</td>\n",
" <td>0.004558</td>\n",
" <td>-0.007684</td>\n",
" <td>-0.024263</td>\n",
" <td>-0.023573</td>\n",
" <td>-0.009974</td>\n",
" <td>-0.015686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_max</th>\n",
" <td>-0.003727</td>\n",
" <td>-0.001849</td>\n",
" <td>-0.028943</td>\n",
" <td>0.105590</td>\n",
" <td>0.085527</td>\n",
" <td>-0.083295</td>\n",
" <td>0.004081</td>\n",
" <td>-0.057790</td>\n",
" <td>-0.037123</td>\n",
" <td>0.085038</td>\n",
" <td>0.019146</td>\n",
" <td>-0.088787</td>\n",
" <td>-0.082411</td>\n",
" <td>0.054935</td>\n",
" <td>-0.036611</td>\n",
" <td>-0.033340</td>\n",
" <td>NaN</td>\n",
" <td>-0.034973</td>\n",
" <td>-0.037824</td>\n",
" <td>-0.011818</td>\n",
" <td>0.477629</td>\n",
" <td>0.041616</td>\n",
" <td>0.007933</td>\n",
" <td>0.007514</td>\n",
" <td>0.599638</td>\n",
" <td>0.154440</td>\n",
" <td>0.599013</td>\n",
" <td>0.154595</td>\n",
" <td>0.018402</td>\n",
" <td>-0.140109</td>\n",
" <td>0.748749</td>\n",
" <td>0.252108</td>\n",
" <td>0.746551</td>\n",
" <td>0.251774</td>\n",
" <td>0.102212</td>\n",
" <td>-0.063872</td>\n",
" <td>0.035251</td>\n",
" <td>-0.038928</td>\n",
" <td>1.000000</td>\n",
" <td>0.352306</td>\n",
" <td>0.997947</td>\n",
" <td>0.352295</td>\n",
" <td>0.015645</td>\n",
" <td>-0.000781</td>\n",
" <td>0.460062</td>\n",
" <td>0.305443</td>\n",
" <td>0.459162</td>\n",
" <td>0.304616</td>\n",
" <td>-0.033329</td>\n",
" <td>-0.093220</td>\n",
" <td>0.573858</td>\n",
" <td>0.426337</td>\n",
" <td>0.574136</td>\n",
" <td>0.425817</td>\n",
" <td>0.076431</td>\n",
" <td>-0.008355</td>\n",
" <td>0.013028</td>\n",
" <td>-0.019954</td>\n",
" <td>0.730176</td>\n",
" <td>0.533874</td>\n",
" <td>0.732608</td>\n",
" <td>0.534950</td>\n",
" <td>0.067252</td>\n",
" <td>0.066461</td>\n",
" <td>-0.024258</td>\n",
" <td>-0.015672</td>\n",
" <td>0.016077</td>\n",
" <td>0.006298</td>\n",
" <td>-0.000834</td>\n",
" <td>-0.033478</td>\n",
" <td>0.069493</td>\n",
" <td>-0.034691</td>\n",
" <td>-0.031275</td>\n",
" <td>-0.018230</td>\n",
" <td>-0.015322</td>\n",
" <td>-0.031206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_min</th>\n",
" <td>-0.002810</td>\n",
" <td>-0.007961</td>\n",
" <td>0.005046</td>\n",
" <td>-0.060245</td>\n",
" <td>0.044222</td>\n",
" <td>0.013901</td>\n",
" <td>0.051203</td>\n",
" <td>-0.002549</td>\n",
" <td>-0.052632</td>\n",
" <td>0.065099</td>\n",
" <td>0.090089</td>\n",
" <td>0.010185</td>\n",
" <td>0.012446</td>\n",
" <td>-0.016247</td>\n",
" <td>0.162278</td>\n",
" <td>0.154878</td>\n",
" <td>NaN</td>\n",
" <td>0.176170</td>\n",
" <td>-0.143812</td>\n",
" <td>-0.183552</td>\n",
" <td>0.368801</td>\n",
" <td>0.022501</td>\n",
" <td>0.096424</td>\n",
" <td>-0.215911</td>\n",
" <td>0.093446</td>\n",
" <td>0.666660</td>\n",
" <td>0.093600</td>\n",
" <td>0.665910</td>\n",
" <td>-0.248651</td>\n",
" <td>0.021449</td>\n",
" <td>0.236612</td>\n",
" <td>0.803036</td>\n",
" <td>0.238019</td>\n",
" <td>0.803383</td>\n",
" <td>-0.134621</td>\n",
" <td>0.154223</td>\n",
" <td>-0.154899</td>\n",
" <td>0.235267</td>\n",
" <td>0.352306</td>\n",
" <td>1.000000</td>\n",
" <td>0.352594</td>\n",
" <td>0.998132</td>\n",
" <td>-0.119892</td>\n",
" <td>0.150254</td>\n",
" <td>0.242244</td>\n",
" <td>0.476147</td>\n",
" <td>0.241827</td>\n",
" <td>0.476876</td>\n",
" <td>-0.171506</td>\n",
" <td>-0.094678</td>\n",
" <td>0.382264</td>\n",
" <td>0.581176</td>\n",
" <td>0.383675</td>\n",
" <td>0.582080</td>\n",
" <td>-0.086536</td>\n",
" <td>0.022207</td>\n",
" <td>-0.017220</td>\n",
" <td>0.135052</td>\n",
" <td>0.465559</td>\n",
" <td>0.661781</td>\n",
" <td>0.465509</td>\n",
" <td>0.660638</td>\n",
" <td>-0.024403</td>\n",
" <td>0.065053</td>\n",
" <td>-0.136593</td>\n",
" <td>-0.028504</td>\n",
" <td>-0.217784</td>\n",
" <td>-0.201911</td>\n",
" <td>0.000649</td>\n",
" <td>-0.046788</td>\n",
" <td>0.025228</td>\n",
" <td>-0.048606</td>\n",
" <td>-0.037169</td>\n",
" <td>-0.023957</td>\n",
" <td>-0.012975</td>\n",
" <td>-0.025428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <td>-0.003711</td>\n",
" <td>-0.001642</td>\n",
" <td>-0.028474</td>\n",
" <td>0.105191</td>\n",
" <td>0.085545</td>\n",
" <td>-0.085121</td>\n",
" <td>0.003608</td>\n",
" <td>-0.057536</td>\n",
" <td>-0.037590</td>\n",
" <td>0.084811</td>\n",
" <td>0.018353</td>\n",
" <td>-0.089752</td>\n",
" <td>-0.084154</td>\n",
" <td>0.054852</td>\n",
" <td>-0.035408</td>\n",
" <td>-0.032642</td>\n",
" <td>NaN</td>\n",
" <td>-0.033439</td>\n",
" <td>-0.037444</td>\n",
" <td>-0.013041</td>\n",
" <td>0.478301</td>\n",
" <td>0.042496</td>\n",
" <td>0.008841</td>\n",
" <td>0.006469</td>\n",
" <td>0.599525</td>\n",
" <td>0.155061</td>\n",
" <td>0.600818</td>\n",
" <td>0.154209</td>\n",
" <td>0.018522</td>\n",
" <td>-0.139506</td>\n",
" <td>0.747446</td>\n",
" <td>0.252553</td>\n",
" <td>0.748714</td>\n",
" <td>0.251632</td>\n",
" <td>0.101917</td>\n",
" <td>-0.062863</td>\n",
" <td>0.034190</td>\n",
" <td>-0.039044</td>\n",
" <td>0.997947</td>\n",
" <td>0.352594</td>\n",
" <td>1.000000</td>\n",
" <td>0.352182</td>\n",
" <td>0.014964</td>\n",
" <td>-0.000029</td>\n",
" <td>0.460567</td>\n",
" <td>0.305603</td>\n",
" <td>0.460121</td>\n",
" <td>0.304871</td>\n",
" <td>-0.032927</td>\n",
" <td>-0.092647</td>\n",
" <td>0.574656</td>\n",
" <td>0.426743</td>\n",
" <td>0.575231</td>\n",
" <td>0.426225</td>\n",
" <td>0.076923</td>\n",
" <td>-0.007178</td>\n",
" <td>0.012553</td>\n",
" <td>-0.020202</td>\n",
" <td>0.730792</td>\n",
" <td>0.534169</td>\n",
" <td>0.733799</td>\n",
" <td>0.535452</td>\n",
" <td>0.067300</td>\n",
" <td>0.066677</td>\n",
" <td>-0.025633</td>\n",
" <td>-0.015752</td>\n",
" <td>0.015920</td>\n",
" <td>0.006253</td>\n",
" <td>-0.000794</td>\n",
" <td>-0.033091</td>\n",
" <td>0.069483</td>\n",
" <td>-0.034506</td>\n",
" <td>-0.031031</td>\n",
" <td>-0.018103</td>\n",
" <td>-0.015227</td>\n",
" <td>-0.030990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <td>-0.002786</td>\n",
" <td>-0.008424</td>\n",
" <td>0.004905</td>\n",
" <td>-0.060213</td>\n",
" <td>0.044172</td>\n",
" <td>0.014866</td>\n",
" <td>0.051262</td>\n",
" <td>-0.002797</td>\n",
" <td>-0.052169</td>\n",
" <td>0.065108</td>\n",
" <td>0.090912</td>\n",
" <td>0.010741</td>\n",
" <td>0.013384</td>\n",
" <td>-0.016477</td>\n",
" <td>0.162169</td>\n",
" <td>0.155134</td>\n",
" <td>NaN</td>\n",
" <td>0.175646</td>\n",
" <td>-0.144073</td>\n",
" <td>-0.182124</td>\n",
" <td>0.368645</td>\n",
" <td>0.021807</td>\n",
" <td>0.096650</td>\n",
" <td>-0.215455</td>\n",
" <td>0.092971</td>\n",
" <td>0.665898</td>\n",
" <td>0.093238</td>\n",
" <td>0.667129</td>\n",
" <td>-0.248627</td>\n",
" <td>0.021410</td>\n",
" <td>0.236445</td>\n",
" <td>0.802242</td>\n",
" <td>0.237239</td>\n",
" <td>0.804875</td>\n",
" <td>-0.134492</td>\n",
" <td>0.153707</td>\n",
" <td>-0.154006</td>\n",
" <td>0.235052</td>\n",
" <td>0.352295</td>\n",
" <td>0.998132</td>\n",
" <td>0.352182</td>\n",
" <td>1.000000</td>\n",
" <td>-0.118846</td>\n",
" <td>0.150539</td>\n",
" <td>0.242187</td>\n",
" <td>0.476351</td>\n",
" <td>0.241794</td>\n",
" <td>0.477498</td>\n",
" <td>-0.171516</td>\n",
" <td>-0.094509</td>\n",
" <td>0.382100</td>\n",
" <td>0.581479</td>\n",
" <td>0.383293</td>\n",
" <td>0.582561</td>\n",
" <td>-0.087145</td>\n",
" <td>0.021591</td>\n",
" <td>-0.016914</td>\n",
" <td>0.135297</td>\n",
" <td>0.465495</td>\n",
" <td>0.662012</td>\n",
" <td>0.465623</td>\n",
" <td>0.661442</td>\n",
" <td>-0.024169</td>\n",
" <td>0.064926</td>\n",
" <td>-0.135992</td>\n",
" <td>-0.028606</td>\n",
" <td>-0.217980</td>\n",
" <td>-0.202009</td>\n",
" <td>0.000632</td>\n",
" <td>-0.046513</td>\n",
" <td>0.025260</td>\n",
" <td>-0.048668</td>\n",
" <td>-0.037259</td>\n",
" <td>-0.024003</td>\n",
" <td>-0.013010</td>\n",
" <td>-0.025508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_temp_max</th>\n",
" <td>-0.006661</td>\n",
" <td>-0.003322</td>\n",
" <td>0.016621</td>\n",
" <td>-0.100176</td>\n",
" <td>0.018651</td>\n",
" <td>0.053148</td>\n",
" <td>0.016985</td>\n",
" <td>-0.021521</td>\n",
" <td>0.027014</td>\n",
" <td>0.026071</td>\n",
" <td>-0.015975</td>\n",
" <td>0.111763</td>\n",
" <td>0.069778</td>\n",
" <td>-0.022309</td>\n",
" <td>-0.153986</td>\n",
" <td>-0.113055</td>\n",
" <td>NaN</td>\n",
" <td>-0.188147</td>\n",
" <td>0.246108</td>\n",
" <td>0.153535</td>\n",
" <td>-0.035858</td>\n",
" <td>0.061428</td>\n",
" <td>0.414180</td>\n",
" <td>0.207720</td>\n",
" <td>0.013822</td>\n",
" <td>-0.106248</td>\n",
" <td>0.013531</td>\n",
" <td>-0.105454</td>\n",
" <td>0.281152</td>\n",
" <td>0.182858</td>\n",
" <td>-0.006464</td>\n",
" <td>-0.127599</td>\n",
" <td>-0.006817</td>\n",
" <td>-0.126997</td>\n",
" <td>0.120650</td>\n",
" <td>0.014322</td>\n",
" <td>0.085534</td>\n",
" <td>-0.061721</td>\n",
" <td>0.015645</td>\n",
" <td>-0.119892</td>\n",
" <td>0.014964</td>\n",
" <td>-0.118846</td>\n",
" <td>1.000000</td>\n",
" <td>0.262450</td>\n",
" <td>-0.021517</td>\n",
" <td>-0.088714</td>\n",
" <td>-0.022187</td>\n",
" <td>-0.088131</td>\n",
" <td>0.232984</td>\n",
" <td>0.208689</td>\n",
" <td>-0.047581</td>\n",
" <td>-0.103215</td>\n",
" <td>-0.049599</td>\n",
" <td>-0.103002</td>\n",
" <td>0.103586</td>\n",
" <td>0.064994</td>\n",
" <td>0.027792</td>\n",
" <td>-0.022957</td>\n",
" <td>-0.023551</td>\n",
" <td>-0.084481</td>\n",
" <td>-0.024818</td>\n",
" <td>-0.083563</td>\n",
" <td>0.002251</td>\n",
" <td>-0.011571</td>\n",
" <td>-0.022483</td>\n",
" <td>-0.088875</td>\n",
" <td>0.048238</td>\n",
" <td>0.050052</td>\n",
" <td>0.007963</td>\n",
" <td>-0.012707</td>\n",
" <td>-0.015323</td>\n",
" <td>-0.010524</td>\n",
" <td>0.015804</td>\n",
" <td>0.018855</td>\n",
" <td>0.000974</td>\n",
" <td>-0.010663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_temp_min</th>\n",
" <td>0.004776</td>\n",
" <td>-0.001115</td>\n",
" <td>-0.044566</td>\n",
" <td>-0.066129</td>\n",
" <td>0.034165</td>\n",
" <td>-0.028402</td>\n",
" <td>0.004376</td>\n",
" <td>-0.005269</td>\n",
" <td>-0.004411</td>\n",
" <td>0.034824</td>\n",
" <td>0.029711</td>\n",
" <td>0.007027</td>\n",
" <td>-0.034584</td>\n",
" <td>-0.030998</td>\n",
" <td>0.211348</td>\n",
" <td>0.263712</td>\n",
" <td>NaN</td>\n",
" <td>0.182067</td>\n",
" <td>0.064424</td>\n",
" <td>-0.169867</td>\n",
" <td>-0.003270</td>\n",
" <td>0.030762</td>\n",
" <td>0.787391</td>\n",
" <td>-0.184548</td>\n",
" <td>-0.020902</td>\n",
" <td>0.110313</td>\n",
" <td>-0.020802</td>\n",
" <td>0.110311</td>\n",
" <td>0.012378</td>\n",
" <td>0.158806</td>\n",
" <td>-0.014090</td>\n",
" <td>0.123000</td>\n",
" <td>-0.013644</td>\n",
" <td>0.123449</td>\n",
" <td>-0.022532</td>\n",
" <td>0.105672</td>\n",
" <td>-0.072233</td>\n",
" <td>0.095773</td>\n",
" <td>-0.000781</td>\n",
" <td>0.150254</td>\n",
" <td>-0.000029</td>\n",
" <td>0.150539</td>\n",
" <td>0.262450</td>\n",
" <td>1.000000</td>\n",
" <td>-0.002624</td>\n",
" <td>0.072596</td>\n",
" <td>-0.001287</td>\n",
" <td>0.071550</td>\n",
" <td>0.066399</td>\n",
" <td>0.104108</td>\n",
" <td>0.016054</td>\n",
" <td>0.081151</td>\n",
" <td>0.017484</td>\n",
" <td>0.080985</td>\n",
" <td>0.014114</td>\n",
" <td>0.060637</td>\n",
" <td>-0.034333</td>\n",
" <td>0.039702</td>\n",
" <td>0.035546</td>\n",
" <td>0.108863</td>\n",
" <td>0.036354</td>\n",
" <td>0.108976</td>\n",
" <td>-0.101493</td>\n",
" <td>0.037687</td>\n",
" <td>-0.118506</td>\n",
" <td>0.028393</td>\n",
" <td>-0.256898</td>\n",
" <td>-0.270149</td>\n",
" <td>-0.000950</td>\n",
" <td>-0.018276</td>\n",
" <td>-0.003782</td>\n",
" <td>-0.024858</td>\n",
" <td>0.003599</td>\n",
" <td>-0.002237</td>\n",
" <td>-0.001466</td>\n",
" <td>-0.001434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_max</th>\n",
" <td>-0.003139</td>\n",
" <td>-0.004722</td>\n",
" <td>0.001180</td>\n",
" <td>-0.138641</td>\n",
" <td>0.023306</td>\n",
" <td>-0.106479</td>\n",
" <td>0.079122</td>\n",
" <td>-0.014473</td>\n",
" <td>-0.058319</td>\n",
" <td>0.057222</td>\n",
" <td>-0.013812</td>\n",
" <td>-0.107680</td>\n",
" <td>-0.103299</td>\n",
" <td>-0.011401</td>\n",
" <td>-0.034360</td>\n",
" <td>-0.033919</td>\n",
" <td>NaN</td>\n",
" <td>-0.027725</td>\n",
" <td>0.081382</td>\n",
" <td>-0.002164</td>\n",
" <td>0.390322</td>\n",
" <td>0.023518</td>\n",
" <td>0.002494</td>\n",
" <td>-0.000577</td>\n",
" <td>0.604668</td>\n",
" <td>0.348050</td>\n",
" <td>0.604847</td>\n",
" <td>0.347372</td>\n",
" <td>0.105612</td>\n",
" <td>-0.006163</td>\n",
" <td>0.575372</td>\n",
" <td>0.307212</td>\n",
" <td>0.573164</td>\n",
" <td>0.307228</td>\n",
" <td>0.045533</td>\n",
" <td>-0.034335</td>\n",
" <td>-0.003621</td>\n",
" <td>-0.001225</td>\n",
" <td>0.460062</td>\n",
" <td>0.242244</td>\n",
" <td>0.460567</td>\n",
" <td>0.242187</td>\n",
" <td>-0.021517</td>\n",
" <td>-0.002624</td>\n",
" <td>1.000000</td>\n",
" <td>0.620328</td>\n",
" <td>0.984080</td>\n",
" <td>0.617860</td>\n",
" <td>0.157816</td>\n",
" <td>0.070153</td>\n",
" <td>0.861305</td>\n",
" <td>0.572630</td>\n",
" <td>0.858602</td>\n",
" <td>0.571663</td>\n",
" <td>0.107731</td>\n",
" <td>-0.014656</td>\n",
" <td>0.032579</td>\n",
" <td>-0.028811</td>\n",
" <td>0.651842</td>\n",
" <td>0.431099</td>\n",
" <td>0.652747</td>\n",
" <td>0.430986</td>\n",
" <td>-0.015901</td>\n",
" <td>0.024736</td>\n",
" <td>-0.074244</td>\n",
" <td>-0.054918</td>\n",
" <td>-0.020015</td>\n",
" <td>-0.012538</td>\n",
" <td>0.013123</td>\n",
" <td>-0.025047</td>\n",
" <td>-0.047827</td>\n",
" <td>-0.036471</td>\n",
" <td>-0.024897</td>\n",
" <td>-0.023125</td>\n",
" <td>-0.014152</td>\n",
" <td>-0.015965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_min</th>\n",
" <td>0.002802</td>\n",
" <td>-0.008662</td>\n",
" <td>-0.014265</td>\n",
" <td>-0.186812</td>\n",
" <td>-0.009829</td>\n",
" <td>-0.064613</td>\n",
" <td>0.109389</td>\n",
" <td>-0.000882</td>\n",
" <td>-0.054409</td>\n",
" <td>0.040001</td>\n",
" <td>0.032898</td>\n",
" <td>-0.074403</td>\n",
" <td>-0.065023</td>\n",
" <td>-0.032482</td>\n",
" <td>0.072886</td>\n",
" <td>0.061247</td>\n",
" <td>NaN</td>\n",
" <td>0.084225</td>\n",
" <td>0.035233</td>\n",
" <td>-0.087451</td>\n",
" <td>0.373429</td>\n",
" <td>0.043064</td>\n",
" <td>0.048547</td>\n",
" <td>-0.108953</td>\n",
" <td>0.337590</td>\n",
" <td>0.628693</td>\n",
" <td>0.337591</td>\n",
" <td>0.628457</td>\n",
" <td>-0.023891</td>\n",
" <td>0.065950</td>\n",
" <td>0.381715</td>\n",
" <td>0.572007</td>\n",
" <td>0.381789</td>\n",
" <td>0.573139</td>\n",
" <td>-0.049506</td>\n",
" <td>0.075952</td>\n",
" <td>-0.092584</td>\n",
" <td>0.139330</td>\n",
" <td>0.305443</td>\n",
" <td>0.476147</td>\n",
" <td>0.305603</td>\n",
" <td>0.476351</td>\n",
" <td>-0.088714</td>\n",
" <td>0.072596</td>\n",
" <td>0.620328</td>\n",
" <td>1.000000</td>\n",
" <td>0.617953</td>\n",
" <td>0.980861</td>\n",
" <td>0.024337</td>\n",
" <td>0.102719</td>\n",
" <td>0.620635</td>\n",
" <td>0.876153</td>\n",
" <td>0.621360</td>\n",
" <td>0.874145</td>\n",
" <td>-0.035700</td>\n",
" <td>0.087366</td>\n",
" <td>-0.036388</td>\n",
" <td>0.116132</td>\n",
" <td>0.468802</td>\n",
" <td>0.687683</td>\n",
" <td>0.470402</td>\n",
" <td>0.684967</td>\n",
" <td>-0.043229</td>\n",
" <td>0.024580</td>\n",
" <td>-0.123494</td>\n",
" <td>-0.064156</td>\n",
" <td>-0.141526</td>\n",
" <td>-0.120126</td>\n",
" <td>0.013015</td>\n",
" <td>-0.048752</td>\n",
" <td>-0.066676</td>\n",
" <td>-0.052422</td>\n",
" <td>-0.025463</td>\n",
" <td>-0.029693</td>\n",
" <td>-0.013254</td>\n",
" <td>-0.010569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <td>-0.002042</td>\n",
" <td>-0.003283</td>\n",
" <td>0.002316</td>\n",
" <td>-0.136606</td>\n",
" <td>0.022982</td>\n",
" <td>-0.103227</td>\n",
" <td>0.077343</td>\n",
" <td>-0.014354</td>\n",
" <td>-0.057275</td>\n",
" <td>0.056167</td>\n",
" <td>-0.013681</td>\n",
" <td>-0.104877</td>\n",
" <td>-0.099954</td>\n",
" <td>-0.010648</td>\n",
" <td>-0.033682</td>\n",
" <td>-0.033285</td>\n",
" <td>NaN</td>\n",
" <td>-0.027240</td>\n",
" <td>0.081874</td>\n",
" <td>-0.000954</td>\n",
" <td>0.391338</td>\n",
" <td>0.023909</td>\n",
" <td>0.004235</td>\n",
" <td>0.000128</td>\n",
" <td>0.603192</td>\n",
" <td>0.348236</td>\n",
" <td>0.604102</td>\n",
" <td>0.347740</td>\n",
" <td>0.104771</td>\n",
" <td>-0.005030</td>\n",
" <td>0.573603</td>\n",
" <td>0.307468</td>\n",
" <td>0.572296</td>\n",
" <td>0.307626</td>\n",
" <td>0.044027</td>\n",
" <td>-0.032759</td>\n",
" <td>-0.004764</td>\n",
" <td>-0.002144</td>\n",
" <td>0.459162</td>\n",
" <td>0.241827</td>\n",
" <td>0.460121</td>\n",
" <td>0.241794</td>\n",
" <td>-0.022187</td>\n",
" <td>-0.001287</td>\n",
" <td>0.984080</td>\n",
" <td>0.617953</td>\n",
" <td>1.000000</td>\n",
" <td>0.618727</td>\n",
" <td>0.158213</td>\n",
" <td>0.070898</td>\n",
" <td>0.859137</td>\n",
" <td>0.571843</td>\n",
" <td>0.861525</td>\n",
" <td>0.572937</td>\n",
" <td>0.106255</td>\n",
" <td>-0.013824</td>\n",
" <td>0.032494</td>\n",
" <td>-0.029284</td>\n",
" <td>0.650027</td>\n",
" <td>0.431317</td>\n",
" <td>0.653810</td>\n",
" <td>0.431564</td>\n",
" <td>-0.015465</td>\n",
" <td>0.023985</td>\n",
" <td>-0.075539</td>\n",
" <td>-0.055765</td>\n",
" <td>-0.021459</td>\n",
" <td>-0.013593</td>\n",
" <td>0.013092</td>\n",
" <td>-0.025062</td>\n",
" <td>-0.047512</td>\n",
" <td>-0.035785</td>\n",
" <td>-0.024784</td>\n",
" <td>-0.022945</td>\n",
" <td>-0.015404</td>\n",
" <td>-0.015739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <td>0.002508</td>\n",
" <td>-0.009840</td>\n",
" <td>-0.012543</td>\n",
" <td>-0.187165</td>\n",
" <td>-0.011490</td>\n",
" <td>-0.058989</td>\n",
" <td>0.111256</td>\n",
" <td>-0.000800</td>\n",
" <td>-0.054913</td>\n",
" <td>0.039112</td>\n",
" <td>0.036588</td>\n",
" <td>-0.069530</td>\n",
" <td>-0.059248</td>\n",
" <td>-0.032678</td>\n",
" <td>0.072800</td>\n",
" <td>0.061085</td>\n",
" <td>NaN</td>\n",
" <td>0.082832</td>\n",
" <td>0.036045</td>\n",
" <td>-0.087045</td>\n",
" <td>0.374294</td>\n",
" <td>0.043308</td>\n",
" <td>0.046943</td>\n",
" <td>-0.107023</td>\n",
" <td>0.336034</td>\n",
" <td>0.629021</td>\n",
" <td>0.336400</td>\n",
" <td>0.629334</td>\n",
" <td>-0.022509</td>\n",
" <td>0.066044</td>\n",
" <td>0.380865</td>\n",
" <td>0.573238</td>\n",
" <td>0.380650</td>\n",
" <td>0.574665</td>\n",
" <td>-0.048420</td>\n",
" <td>0.073918</td>\n",
" <td>-0.091553</td>\n",
" <td>0.138820</td>\n",
" <td>0.304616</td>\n",
" <td>0.476876</td>\n",
" <td>0.304871</td>\n",
" <td>0.477498</td>\n",
" <td>-0.088131</td>\n",
" <td>0.071550</td>\n",
" <td>0.617860</td>\n",
" <td>0.980861</td>\n",
" <td>0.618727</td>\n",
" <td>1.000000</td>\n",
" <td>0.024987</td>\n",
" <td>0.103236</td>\n",
" <td>0.620699</td>\n",
" <td>0.874465</td>\n",
" <td>0.622858</td>\n",
" <td>0.877306</td>\n",
" <td>-0.037863</td>\n",
" <td>0.084763</td>\n",
" <td>-0.035914</td>\n",
" <td>0.116508</td>\n",
" <td>0.469236</td>\n",
" <td>0.687388</td>\n",
" <td>0.470721</td>\n",
" <td>0.687473</td>\n",
" <td>-0.042439</td>\n",
" <td>0.025106</td>\n",
" <td>-0.124229</td>\n",
" <td>-0.065128</td>\n",
" <td>-0.141760</td>\n",
" <td>-0.120348</td>\n",
" <td>0.012944</td>\n",
" <td>-0.049390</td>\n",
" <td>-0.066902</td>\n",
" <td>-0.052745</td>\n",
" <td>-0.024206</td>\n",
" <td>-0.029332</td>\n",
" <td>-0.013997</td>\n",
" <td>-0.010381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_heartrate_max</th>\n",
" <td>-0.002534</td>\n",
" <td>0.001650</td>\n",
" <td>-0.010177</td>\n",
" <td>-0.172870</td>\n",
" <td>-0.019961</td>\n",
" <td>-0.107990</td>\n",
" <td>-0.006082</td>\n",
" <td>-0.010873</td>\n",
" <td>0.061045</td>\n",
" <td>-0.022502</td>\n",
" <td>-0.147792</td>\n",
" <td>-0.040191</td>\n",
" <td>-0.098490</td>\n",
" <td>-0.018119</td>\n",
" <td>-0.087839</td>\n",
" <td>-0.083696</td>\n",
" <td>NaN</td>\n",
" <td>-0.108800</td>\n",
" <td>0.721986</td>\n",
" <td>0.081267</td>\n",
" <td>-0.008144</td>\n",
" <td>0.167701</td>\n",
" <td>0.122384</td>\n",
" <td>0.119361</td>\n",
" <td>0.118285</td>\n",
" <td>-0.028420</td>\n",
" <td>0.118575</td>\n",
" <td>-0.028917</td>\n",
" <td>0.787313</td>\n",
" <td>0.553763</td>\n",
" <td>0.052258</td>\n",
" <td>-0.096207</td>\n",
" <td>0.050773</td>\n",
" <td>-0.096533</td>\n",
" <td>0.190578</td>\n",
" <td>0.103607</td>\n",
" <td>0.018531</td>\n",
" <td>-0.118527</td>\n",
" <td>-0.033329</td>\n",
" <td>-0.171506</td>\n",
" <td>-0.032927</td>\n",
" <td>-0.171516</td>\n",
" <td>0.232984</td>\n",
" <td>0.066399</td>\n",
" <td>0.157816</td>\n",
" <td>0.024337</td>\n",
" <td>0.158213</td>\n",
" <td>0.024987</td>\n",
" <td>1.000000</td>\n",
" <td>0.855270</td>\n",
" <td>0.067196</td>\n",
" <td>-0.047734</td>\n",
" <td>0.067486</td>\n",
" <td>-0.047593</td>\n",
" <td>0.317010</td>\n",
" <td>0.227996</td>\n",
" <td>-0.055897</td>\n",
" <td>-0.132102</td>\n",
" <td>-0.010766</td>\n",
" <td>-0.127068</td>\n",
" <td>-0.009731</td>\n",
" <td>-0.126437</td>\n",
" <td>0.120078</td>\n",
" <td>0.062685</td>\n",
" <td>0.008160</td>\n",
" <td>-0.080941</td>\n",
" <td>0.126071</td>\n",
" <td>0.120051</td>\n",
" <td>0.010233</td>\n",
" <td>0.018175</td>\n",
" <td>-0.012669</td>\n",
" <td>0.020534</td>\n",
" <td>0.066942</td>\n",
" <td>0.023266</td>\n",
" <td>0.019361</td>\n",
" <td>0.045671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_heartrate_min</th>\n",
" <td>-0.002998</td>\n",
" <td>0.002568</td>\n",
" <td>-0.022248</td>\n",
" <td>-0.172761</td>\n",
" <td>-0.010091</td>\n",
" <td>-0.124757</td>\n",
" <td>-0.012351</td>\n",
" <td>0.004188</td>\n",
" <td>0.056917</td>\n",
" <td>-0.015731</td>\n",
" <td>-0.136036</td>\n",
" <td>-0.047355</td>\n",
" <td>-0.119469</td>\n",
" <td>-0.017100</td>\n",
" <td>-0.041512</td>\n",
" <td>-0.039050</td>\n",
" <td>NaN</td>\n",
" <td>-0.061233</td>\n",
" <td>0.691027</td>\n",
" <td>0.046190</td>\n",
" <td>-0.023693</td>\n",
" <td>0.174211</td>\n",
" <td>0.150136</td>\n",
" <td>0.068821</td>\n",
" <td>0.050352</td>\n",
" <td>0.038660</td>\n",
" <td>0.050873</td>\n",
" <td>0.038356</td>\n",
" <td>0.677401</td>\n",
" <td>0.680537</td>\n",
" <td>-0.012835</td>\n",
" <td>-0.027182</td>\n",
" <td>-0.012912</td>\n",
" <td>-0.026903</td>\n",
" <td>0.131655</td>\n",
" <td>0.184471</td>\n",
" <td>-0.027774</td>\n",
" <td>-0.050125</td>\n",
" <td>-0.093220</td>\n",
" <td>-0.094678</td>\n",
" <td>-0.092647</td>\n",
" <td>-0.094509</td>\n",
" <td>0.208689</td>\n",
" <td>0.104108</td>\n",
" <td>0.070153</td>\n",
" <td>0.102719</td>\n",
" <td>0.070898</td>\n",
" <td>0.103236</td>\n",
" <td>0.855270</td>\n",
" <td>1.000000</td>\n",
" <td>-0.001983</td>\n",
" <td>0.024450</td>\n",
" <td>-0.001110</td>\n",
" <td>0.024419</td>\n",
" <td>0.236913</td>\n",
" <td>0.314475</td>\n",
" <td>-0.104773</td>\n",
" <td>-0.046809</td>\n",
" <td>-0.081952</td>\n",
" <td>-0.045043</td>\n",
" <td>-0.080701</td>\n",
" <td>-0.045213</td>\n",
" <td>0.131464</td>\n",
" <td>0.061894</td>\n",
" <td>0.000349</td>\n",
" <td>-0.073151</td>\n",
" <td>0.080865</td>\n",
" <td>0.078412</td>\n",
" <td>0.011083</td>\n",
" <td>0.020529</td>\n",
" <td>-0.001135</td>\n",
" <td>0.023841</td>\n",
" <td>0.068321</td>\n",
" <td>0.019161</td>\n",
" <td>0.018820</td>\n",
" <td>0.048555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_max</th>\n",
" <td>0.000930</td>\n",
" <td>-0.003626</td>\n",
" <td>0.000636</td>\n",
" <td>-0.059667</td>\n",
" <td>0.033733</td>\n",
" <td>-0.074414</td>\n",
" <td>0.052886</td>\n",
" <td>-0.011939</td>\n",
" <td>-0.056631</td>\n",
" <td>0.055863</td>\n",
" <td>0.018900</td>\n",
" <td>-0.085596</td>\n",
" <td>-0.071144</td>\n",
" <td>0.005185</td>\n",
" <td>-0.008476</td>\n",
" <td>-0.012495</td>\n",
" <td>NaN</td>\n",
" <td>-0.000979</td>\n",
" <td>0.010655</td>\n",
" <td>-0.032933</td>\n",
" <td>0.478144</td>\n",
" <td>0.111467</td>\n",
" <td>0.013527</td>\n",
" <td>-0.024937</td>\n",
" <td>0.516366</td>\n",
" <td>0.389268</td>\n",
" <td>0.516849</td>\n",
" <td>0.388923</td>\n",
" <td>0.018777</td>\n",
" <td>-0.049830</td>\n",
" <td>0.656704</td>\n",
" <td>0.431133</td>\n",
" <td>0.655184</td>\n",
" <td>0.431227</td>\n",
" <td>0.086647</td>\n",
" <td>-0.016149</td>\n",
" <td>-0.037766</td>\n",
" <td>0.030277</td>\n",
" <td>0.573858</td>\n",
" <td>0.382264</td>\n",
" <td>0.574656</td>\n",
" <td>0.382100</td>\n",
" <td>-0.047581</td>\n",
" <td>0.016054</td>\n",
" <td>0.861305</td>\n",
" <td>0.620635</td>\n",
" <td>0.859137</td>\n",
" <td>0.620699</td>\n",
" <td>0.067196</td>\n",
" <td>-0.001983</td>\n",
" <td>1.000000</td>\n",
" <td>0.702511</td>\n",
" <td>0.986971</td>\n",
" <td>0.700775</td>\n",
" <td>0.123443</td>\n",
" <td>0.001486</td>\n",
" <td>0.024761</td>\n",
" <td>-0.006907</td>\n",
" <td>0.794590</td>\n",
" <td>0.602304</td>\n",
" <td>0.792552</td>\n",
" <td>0.601161</td>\n",
" <td>0.002567</td>\n",
" <td>0.036399</td>\n",
" <td>-0.081778</td>\n",
" <td>-0.050733</td>\n",
" <td>-0.033841</td>\n",
" <td>-0.029171</td>\n",
" <td>0.008723</td>\n",
" <td>-0.035903</td>\n",
" <td>-0.010005</td>\n",
" <td>-0.044933</td>\n",
" <td>-0.032257</td>\n",
" <td>-0.023421</td>\n",
" <td>-0.015548</td>\n",
" <td>-0.021113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_min</th>\n",
" <td>0.004229</td>\n",
" <td>-0.007897</td>\n",
" <td>-0.011939</td>\n",
" <td>-0.100795</td>\n",
" <td>0.010981</td>\n",
" <td>-0.050104</td>\n",
" <td>0.071101</td>\n",
" <td>-0.018469</td>\n",
" <td>-0.057832</td>\n",
" <td>0.042884</td>\n",
" <td>0.055932</td>\n",
" <td>-0.062483</td>\n",
" <td>-0.049850</td>\n",
" <td>-0.010165</td>\n",
" <td>0.097894</td>\n",
" <td>0.083482</td>\n",
" <td>NaN</td>\n",
" <td>0.110696</td>\n",
" <td>-0.030935</td>\n",
" <td>-0.118845</td>\n",
" <td>0.437433</td>\n",
" <td>0.111741</td>\n",
" <td>0.052644</td>\n",
" <td>-0.140207</td>\n",
" <td>0.305863</td>\n",
" <td>0.591288</td>\n",
" <td>0.305999</td>\n",
" <td>0.591434</td>\n",
" <td>-0.093579</td>\n",
" <td>0.009224</td>\n",
" <td>0.449462</td>\n",
" <td>0.649424</td>\n",
" <td>0.449962</td>\n",
" <td>0.649377</td>\n",
" <td>-0.008806</td>\n",
" <td>0.083973</td>\n",
" <td>-0.108473</td>\n",
" <td>0.146458</td>\n",
" <td>0.426337</td>\n",
" <td>0.581176</td>\n",
" <td>0.426743</td>\n",
" <td>0.581479</td>\n",
" <td>-0.103215</td>\n",
" <td>0.081151</td>\n",
" <td>0.572630</td>\n",
" <td>0.876153</td>\n",
" <td>0.571843</td>\n",
" <td>0.874465</td>\n",
" <td>-0.047734</td>\n",
" <td>0.024450</td>\n",
" <td>0.702511</td>\n",
" <td>1.000000</td>\n",
" <td>0.701429</td>\n",
" <td>0.996283</td>\n",
" <td>-0.008337</td>\n",
" <td>0.089050</td>\n",
" <td>-0.033778</td>\n",
" <td>0.116581</td>\n",
" <td>0.612757</td>\n",
" <td>0.822004</td>\n",
" <td>0.613190</td>\n",
" <td>0.819863</td>\n",
" <td>-0.026086</td>\n",
" <td>0.039970</td>\n",
" <td>-0.123529</td>\n",
" <td>-0.052324</td>\n",
" <td>-0.143307</td>\n",
" <td>-0.126340</td>\n",
" <td>0.008622</td>\n",
" <td>-0.050043</td>\n",
" <td>-0.026996</td>\n",
" <td>-0.054471</td>\n",
" <td>-0.027287</td>\n",
" <td>-0.029801</td>\n",
" <td>-0.012395</td>\n",
" <td>-0.015536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <td>0.001418</td>\n",
" <td>-0.002664</td>\n",
" <td>-0.000345</td>\n",
" <td>-0.060153</td>\n",
" <td>0.033016</td>\n",
" <td>-0.075593</td>\n",
" <td>0.052867</td>\n",
" <td>-0.013593</td>\n",
" <td>-0.056725</td>\n",
" <td>0.055136</td>\n",
" <td>0.018299</td>\n",
" <td>-0.086540</td>\n",
" <td>-0.072910</td>\n",
" <td>0.007339</td>\n",
" <td>-0.007695</td>\n",
" <td>-0.011679</td>\n",
" <td>NaN</td>\n",
" <td>-0.001454</td>\n",
" <td>0.011240</td>\n",
" <td>-0.032970</td>\n",
" <td>0.477121</td>\n",
" <td>0.113088</td>\n",
" <td>0.014439</td>\n",
" <td>-0.026200</td>\n",
" <td>0.515910</td>\n",
" <td>0.392297</td>\n",
" <td>0.516466</td>\n",
" <td>0.391856</td>\n",
" <td>0.017876</td>\n",
" <td>-0.048665</td>\n",
" <td>0.655768</td>\n",
" <td>0.434475</td>\n",
" <td>0.655304</td>\n",
" <td>0.434106</td>\n",
" <td>0.086132</td>\n",
" <td>-0.014409</td>\n",
" <td>-0.039170</td>\n",
" <td>0.031380</td>\n",
" <td>0.574136</td>\n",
" <td>0.383675</td>\n",
" <td>0.575231</td>\n",
" <td>0.383293</td>\n",
" <td>-0.049599</td>\n",
" <td>0.017484</td>\n",
" <td>0.858602</td>\n",
" <td>0.621360</td>\n",
" <td>0.861525</td>\n",
" <td>0.622858</td>\n",
" <td>0.067486</td>\n",
" <td>-0.001110</td>\n",
" <td>0.986971</td>\n",
" <td>0.701429</td>\n",
" <td>1.000000</td>\n",
" <td>0.703233</td>\n",
" <td>0.124302</td>\n",
" <td>0.002567</td>\n",
" <td>0.024791</td>\n",
" <td>-0.006462</td>\n",
" <td>0.791118</td>\n",
" <td>0.602092</td>\n",
" <td>0.795027</td>\n",
" <td>0.602368</td>\n",
" <td>0.002428</td>\n",
" <td>0.036576</td>\n",
" <td>-0.082839</td>\n",
" <td>-0.050068</td>\n",
" <td>-0.034958</td>\n",
" <td>-0.030081</td>\n",
" <td>0.008777</td>\n",
" <td>-0.035794</td>\n",
" <td>-0.010453</td>\n",
" <td>-0.045180</td>\n",
" <td>-0.031041</td>\n",
" <td>-0.023258</td>\n",
" <td>-0.015457</td>\n",
" <td>-0.020619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <td>0.004255</td>\n",
" <td>-0.008033</td>\n",
" <td>-0.011090</td>\n",
" <td>-0.100612</td>\n",
" <td>0.010546</td>\n",
" <td>-0.049100</td>\n",
" <td>0.071595</td>\n",
" <td>-0.017409</td>\n",
" <td>-0.058227</td>\n",
" <td>0.042662</td>\n",
" <td>0.056155</td>\n",
" <td>-0.061586</td>\n",
" <td>-0.048824</td>\n",
" <td>-0.010562</td>\n",
" <td>0.097492</td>\n",
" <td>0.083178</td>\n",
" <td>NaN</td>\n",
" <td>0.110544</td>\n",
" <td>-0.031043</td>\n",
" <td>-0.118491</td>\n",
" <td>0.437557</td>\n",
" <td>0.112462</td>\n",
" <td>0.052593</td>\n",
" <td>-0.139786</td>\n",
" <td>0.305352</td>\n",
" <td>0.592603</td>\n",
" <td>0.305559</td>\n",
" <td>0.592823</td>\n",
" <td>-0.093994</td>\n",
" <td>0.009344</td>\n",
" <td>0.449958</td>\n",
" <td>0.650209</td>\n",
" <td>0.450112</td>\n",
" <td>0.651138</td>\n",
" <td>-0.008186</td>\n",
" <td>0.083542</td>\n",
" <td>-0.109327</td>\n",
" <td>0.146860</td>\n",
" <td>0.425817</td>\n",
" <td>0.582080</td>\n",
" <td>0.426225</td>\n",
" <td>0.582561</td>\n",
" <td>-0.103002</td>\n",
" <td>0.080985</td>\n",
" <td>0.571663</td>\n",
" <td>0.874145</td>\n",
" <td>0.572937</td>\n",
" <td>0.877306</td>\n",
" <td>-0.047593</td>\n",
" <td>0.024419</td>\n",
" <td>0.700775</td>\n",
" <td>0.996283</td>\n",
" <td>0.703233</td>\n",
" <td>1.000000</td>\n",
" <td>-0.008330</td>\n",
" <td>0.088542</td>\n",
" <td>-0.034226</td>\n",
" <td>0.116505</td>\n",
" <td>0.611515</td>\n",
" <td>0.820007</td>\n",
" <td>0.613387</td>\n",
" <td>0.821673</td>\n",
" <td>-0.026171</td>\n",
" <td>0.039749</td>\n",
" <td>-0.124130</td>\n",
" <td>-0.052542</td>\n",
" <td>-0.143961</td>\n",
" <td>-0.126812</td>\n",
" <td>0.008645</td>\n",
" <td>-0.050119</td>\n",
" <td>-0.027522</td>\n",
" <td>-0.054756</td>\n",
" <td>-0.026931</td>\n",
" <td>-0.029596</td>\n",
" <td>-0.013360</td>\n",
" <td>-0.015739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_resprate_max</th>\n",
" <td>0.007569</td>\n",
" <td>0.003544</td>\n",
" <td>-0.023934</td>\n",
" <td>0.026192</td>\n",
" <td>0.014155</td>\n",
" <td>-0.177796</td>\n",
" <td>-0.029496</td>\n",
" <td>-0.021284</td>\n",
" <td>0.040266</td>\n",
" <td>0.000007</td>\n",
" <td>-0.138576</td>\n",
" <td>-0.163706</td>\n",
" <td>-0.185293</td>\n",
" <td>0.017168</td>\n",
" <td>-0.009141</td>\n",
" <td>-0.016466</td>\n",
" <td>NaN</td>\n",
" <td>-0.018440</td>\n",
" <td>0.210330</td>\n",
" <td>-0.003491</td>\n",
" <td>0.047885</td>\n",
" <td>0.479384</td>\n",
" <td>0.059421</td>\n",
" <td>0.056546</td>\n",
" <td>0.084637</td>\n",
" <td>-0.052964</td>\n",
" <td>0.084279</td>\n",
" <td>-0.053816</td>\n",
" <td>0.254646</td>\n",
" <td>0.129373</td>\n",
" <td>0.122220</td>\n",
" <td>-0.038282</td>\n",
" <td>0.122971</td>\n",
" <td>-0.039792</td>\n",
" <td>0.566479</td>\n",
" <td>0.244139</td>\n",
" <td>-0.034198</td>\n",
" <td>-0.144794</td>\n",
" <td>0.076431</td>\n",
" <td>-0.086536</td>\n",
" <td>0.076923</td>\n",
" <td>-0.087145</td>\n",
" <td>0.103586</td>\n",
" <td>0.014114</td>\n",
" <td>0.107731</td>\n",
" <td>-0.035700</td>\n",
" <td>0.106255</td>\n",
" <td>-0.037863</td>\n",
" <td>0.317010</td>\n",
" <td>0.236913</td>\n",
" <td>0.123443</td>\n",
" <td>-0.008337</td>\n",
" <td>0.124302</td>\n",
" <td>-0.008330</td>\n",
" <td>1.000000</td>\n",
" <td>0.548624</td>\n",
" <td>-0.077623</td>\n",
" <td>-0.185844</td>\n",
" <td>0.083688</td>\n",
" <td>-0.046970</td>\n",
" <td>0.085509</td>\n",
" <td>-0.045844</td>\n",
" <td>0.063251</td>\n",
" <td>0.050925</td>\n",
" <td>0.009906</td>\n",
" <td>-0.011402</td>\n",
" <td>0.136787</td>\n",
" <td>0.115561</td>\n",
" <td>0.016430</td>\n",
" <td>0.002881</td>\n",
" <td>0.003224</td>\n",
" <td>0.003297</td>\n",
" <td>0.054017</td>\n",
" <td>0.029426</td>\n",
" <td>0.022068</td>\n",
" <td>0.022905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_resprate_min</th>\n",
" <td>0.002913</td>\n",
" <td>-0.000946</td>\n",
" <td>-0.036231</td>\n",
" <td>0.035833</td>\n",
" <td>0.001556</td>\n",
" <td>-0.240363</td>\n",
" <td>-0.044985</td>\n",
" <td>-0.019165</td>\n",
" <td>0.035592</td>\n",
" <td>-0.017901</td>\n",
" <td>-0.149565</td>\n",
" <td>-0.208767</td>\n",
" <td>-0.248028</td>\n",
" <td>0.003499</td>\n",
" <td>0.022648</td>\n",
" <td>0.010979</td>\n",
" <td>NaN</td>\n",
" <td>0.006286</td>\n",
" <td>0.200072</td>\n",
" <td>-0.038077</td>\n",
" <td>0.016095</td>\n",
" <td>0.389324</td>\n",
" <td>0.084145</td>\n",
" <td>0.006394</td>\n",
" <td>-0.001960</td>\n",
" <td>0.034346</td>\n",
" <td>-0.001902</td>\n",
" <td>0.033463</td>\n",
" <td>0.175305</td>\n",
" <td>0.210351</td>\n",
" <td>0.012532</td>\n",
" <td>0.039833</td>\n",
" <td>0.013815</td>\n",
" <td>0.039593</td>\n",
" <td>0.266823</td>\n",
" <td>0.532418</td>\n",
" <td>-0.109716</td>\n",
" <td>-0.038978</td>\n",
" <td>-0.008355</td>\n",
" <td>0.022207</td>\n",
" <td>-0.007178</td>\n",
" <td>0.021591</td>\n",
" <td>0.064994</td>\n",
" <td>0.060637</td>\n",
" <td>-0.014656</td>\n",
" <td>0.087366</td>\n",
" <td>-0.013824</td>\n",
" <td>0.084763</td>\n",
" <td>0.227996</td>\n",
" <td>0.314475</td>\n",
" <td>0.001486</td>\n",
" <td>0.089050</td>\n",
" <td>0.002567</td>\n",
" <td>0.088542</td>\n",
" <td>0.548624</td>\n",
" <td>1.000000</td>\n",
" <td>-0.181717</td>\n",
" <td>-0.046627</td>\n",
" <td>-0.015259</td>\n",
" <td>0.082306</td>\n",
" <td>-0.013212</td>\n",
" <td>0.082581</td>\n",
" <td>0.071794</td>\n",
" <td>0.053458</td>\n",
" <td>-0.011577</td>\n",
" <td>-0.018120</td>\n",
" <td>0.096252</td>\n",
" <td>0.074214</td>\n",
" <td>0.009873</td>\n",
" <td>-0.011457</td>\n",
" <td>-0.002641</td>\n",
" <td>-0.007012</td>\n",
" <td>0.038334</td>\n",
" <td>0.024732</td>\n",
" <td>0.020294</td>\n",
" <td>0.015658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_spo2_max</th>\n",
" <td>-0.000712</td>\n",
" <td>0.000079</td>\n",
" <td>0.017704</td>\n",
" <td>-0.067605</td>\n",
" <td>-0.063018</td>\n",
" <td>0.091235</td>\n",
" <td>-0.019162</td>\n",
" <td>-0.007165</td>\n",
" <td>-0.001046</td>\n",
" <td>-0.069060</td>\n",
" <td>0.043086</td>\n",
" <td>0.097911</td>\n",
" <td>0.098814</td>\n",
" <td>0.018901</td>\n",
" <td>-0.070952</td>\n",
" <td>-0.058519</td>\n",
" <td>NaN</td>\n",
" <td>-0.085101</td>\n",
" <td>-0.044663</td>\n",
" <td>0.077693</td>\n",
" <td>-0.007026</td>\n",
" <td>-0.087868</td>\n",
" <td>-0.033760</td>\n",
" <td>0.083931</td>\n",
" <td>-0.001744</td>\n",
" <td>-0.021251</td>\n",
" <td>-0.001848</td>\n",
" <td>-0.021066</td>\n",
" <td>-0.032473</td>\n",
" <td>-0.059037</td>\n",
" <td>-0.002551</td>\n",
" <td>-0.022553</td>\n",
" <td>-0.002958</td>\n",
" <td>-0.022383</td>\n",
" <td>-0.033790</td>\n",
" <td>-0.129555</td>\n",
" <td>0.482151</td>\n",
" <td>0.218068</td>\n",
" <td>0.013028</td>\n",
" <td>-0.017220</td>\n",
" <td>0.012553</td>\n",
" <td>-0.016914</td>\n",
" <td>0.027792</td>\n",
" <td>-0.034333</td>\n",
" <td>0.032579</td>\n",
" <td>-0.036388</td>\n",
" <td>0.032494</td>\n",
" <td>-0.035914</td>\n",
" <td>-0.055897</td>\n",
" <td>-0.104773</td>\n",
" <td>0.024761</td>\n",
" <td>-0.033778</td>\n",
" <td>0.024791</td>\n",
" <td>-0.034226</td>\n",
" <td>-0.077623</td>\n",
" <td>-0.181717</td>\n",
" <td>1.000000</td>\n",
" <td>0.450923</td>\n",
" <td>0.032449</td>\n",
" <td>-0.035064</td>\n",
" <td>0.031098</td>\n",
" <td>-0.035624</td>\n",
" <td>-0.002983</td>\n",
" <td>-0.062018</td>\n",
" <td>-0.006664</td>\n",
" <td>-0.048023</td>\n",
" <td>-0.015187</td>\n",
" <td>-0.015543</td>\n",
" <td>0.005167</td>\n",
" <td>0.010287</td>\n",
" <td>0.013671</td>\n",
" <td>0.015094</td>\n",
" <td>-0.012804</td>\n",
" <td>-0.002035</td>\n",
" <td>-0.009216</td>\n",
" <td>-0.003942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_spo2_min</th>\n",
" <td>-0.003846</td>\n",
" <td>-0.003214</td>\n",
" <td>0.001696</td>\n",
" <td>-0.079734</td>\n",
" <td>-0.035852</td>\n",
" <td>0.053323</td>\n",
" <td>0.000253</td>\n",
" <td>0.014544</td>\n",
" <td>-0.016554</td>\n",
" <td>-0.034535</td>\n",
" <td>0.055448</td>\n",
" <td>0.065570</td>\n",
" <td>0.055965</td>\n",
" <td>-0.018911</td>\n",
" <td>0.025469</td>\n",
" <td>0.028647</td>\n",
" <td>NaN</td>\n",
" <td>0.018238</td>\n",
" <td>-0.071950</td>\n",
" <td>-0.019951</td>\n",
" <td>0.014667</td>\n",
" <td>-0.074162</td>\n",
" <td>0.017170</td>\n",
" <td>-0.046864</td>\n",
" <td>-0.054609</td>\n",
" <td>0.117988</td>\n",
" <td>-0.054659</td>\n",
" <td>0.118203</td>\n",
" <td>-0.115732</td>\n",
" <td>0.002237</td>\n",
" <td>-0.042100</td>\n",
" <td>0.121927</td>\n",
" <td>-0.042061</td>\n",
" <td>0.122594</td>\n",
" <td>-0.122824</td>\n",
" <td>-0.004842</td>\n",
" <td>0.193868</td>\n",
" <td>0.539191</td>\n",
" <td>-0.019954</td>\n",
" <td>0.135052</td>\n",
" <td>-0.020202</td>\n",
" <td>0.135297</td>\n",
" <td>-0.022957</td>\n",
" <td>0.039702</td>\n",
" <td>-0.028811</td>\n",
" <td>0.116132</td>\n",
" <td>-0.029284</td>\n",
" <td>0.116508</td>\n",
" <td>-0.132102</td>\n",
" <td>-0.046809</td>\n",
" <td>-0.006907</td>\n",
" <td>0.116581</td>\n",
" <td>-0.006462</td>\n",
" <td>0.116505</td>\n",
" <td>-0.185844</td>\n",
" <td>-0.046627</td>\n",
" <td>0.450923</td>\n",
" <td>1.000000</td>\n",
" <td>0.001391</td>\n",
" <td>0.128929</td>\n",
" <td>0.000310</td>\n",
" <td>0.127970</td>\n",
" <td>-0.021912</td>\n",
" <td>-0.036017</td>\n",
" <td>-0.059276</td>\n",
" <td>-0.045481</td>\n",
" <td>-0.106906</td>\n",
" <td>-0.102579</td>\n",
" <td>-0.000169</td>\n",
" <td>0.005423</td>\n",
" <td>-0.001203</td>\n",
" <td>0.004372</td>\n",
" <td>-0.014791</td>\n",
" <td>-0.002042</td>\n",
" <td>-0.009525</td>\n",
" <td>-0.011597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_max</th>\n",
" <td>0.003081</td>\n",
" <td>-0.003712</td>\n",
" <td>-0.007294</td>\n",
" <td>0.043745</td>\n",
" <td>0.059963</td>\n",
" <td>-0.042952</td>\n",
" <td>0.012182</td>\n",
" <td>-0.047580</td>\n",
" <td>-0.046267</td>\n",
" <td>0.063750</td>\n",
" <td>0.051567</td>\n",
" <td>-0.055298</td>\n",
" <td>-0.038583</td>\n",
" <td>0.030642</td>\n",
" <td>0.006956</td>\n",
" <td>0.009633</td>\n",
" <td>NaN</td>\n",
" <td>0.010672</td>\n",
" <td>-0.069510</td>\n",
" <td>-0.039953</td>\n",
" <td>0.423854</td>\n",
" <td>0.024309</td>\n",
" <td>0.033627</td>\n",
" <td>-0.031710</td>\n",
" <td>0.432473</td>\n",
" <td>0.271229</td>\n",
" <td>0.432758</td>\n",
" <td>0.270993</td>\n",
" <td>-0.050685</td>\n",
" <td>-0.114373</td>\n",
" <td>0.561486</td>\n",
" <td>0.364491</td>\n",
" <td>0.560690</td>\n",
" <td>0.364109</td>\n",
" <td>0.030429</td>\n",
" <td>-0.018405</td>\n",
" <td>-0.019100</td>\n",
" <td>0.036028</td>\n",
" <td>0.730176</td>\n",
" <td>0.465559</td>\n",
" <td>0.730792</td>\n",
" <td>0.465495</td>\n",
" <td>-0.023551</td>\n",
" <td>0.035546</td>\n",
" <td>0.651842</td>\n",
" <td>0.468802</td>\n",
" <td>0.650027</td>\n",
" <td>0.469236</td>\n",
" <td>-0.010766</td>\n",
" <td>-0.081952</td>\n",
" <td>0.794590</td>\n",
" <td>0.612757</td>\n",
" <td>0.791118</td>\n",
" <td>0.611515</td>\n",
" <td>0.083688</td>\n",
" <td>-0.015259</td>\n",
" <td>0.032449</td>\n",
" <td>0.001391</td>\n",
" <td>1.000000</td>\n",
" <td>0.736723</td>\n",
" <td>0.995454</td>\n",
" <td>0.734443</td>\n",
" <td>0.035373</td>\n",
" <td>0.064003</td>\n",
" <td>-0.069402</td>\n",
" <td>-0.033458</td>\n",
" <td>-0.034774</td>\n",
" <td>-0.037108</td>\n",
" <td>-0.000523</td>\n",
" <td>-0.036144</td>\n",
" <td>0.040022</td>\n",
" <td>-0.041831</td>\n",
" <td>-0.034089</td>\n",
" <td>-0.021371</td>\n",
" <td>-0.016606</td>\n",
" <td>-0.026780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_min</th>\n",
" <td>0.000381</td>\n",
" <td>-0.009131</td>\n",
" <td>-0.020903</td>\n",
" <td>-0.001140</td>\n",
" <td>0.042186</td>\n",
" <td>-0.032432</td>\n",
" <td>0.020399</td>\n",
" <td>-0.007691</td>\n",
" <td>-0.051324</td>\n",
" <td>0.050606</td>\n",
" <td>0.078730</td>\n",
" <td>-0.043522</td>\n",
" <td>-0.032774</td>\n",
" <td>0.007560</td>\n",
" <td>0.116574</td>\n",
" <td>0.108234</td>\n",
" <td>NaN</td>\n",
" <td>0.126867</td>\n",
" <td>-0.104163</td>\n",
" <td>-0.140067</td>\n",
" <td>0.392038</td>\n",
" <td>0.023338</td>\n",
" <td>0.079091</td>\n",
" <td>-0.157782</td>\n",
" <td>0.263487</td>\n",
" <td>0.438911</td>\n",
" <td>0.263474</td>\n",
" <td>0.439004</td>\n",
" <td>-0.160615</td>\n",
" <td>-0.049718</td>\n",
" <td>0.397023</td>\n",
" <td>0.537579</td>\n",
" <td>0.396912</td>\n",
" <td>0.538366</td>\n",
" <td>-0.069583</td>\n",
" <td>0.084817</td>\n",
" <td>-0.096505</td>\n",
" <td>0.153235</td>\n",
" <td>0.533874</td>\n",
" <td>0.661781</td>\n",
" <td>0.534169</td>\n",
" <td>0.662012</td>\n",
" <td>-0.084481</td>\n",
" <td>0.108863</td>\n",
" <td>0.431099</td>\n",
" <td>0.687683</td>\n",
" <td>0.431317</td>\n",
" <td>0.687388</td>\n",
" <td>-0.127068</td>\n",
" <td>-0.045043</td>\n",
" <td>0.602304</td>\n",
" <td>0.822004</td>\n",
" <td>0.602092</td>\n",
" <td>0.820007</td>\n",
" <td>-0.046970</td>\n",
" <td>0.082306</td>\n",
" <td>-0.035064</td>\n",
" <td>0.128929</td>\n",
" <td>0.736723</td>\n",
" <td>1.000000</td>\n",
" <td>0.735436</td>\n",
" <td>0.987171</td>\n",
" <td>0.003493</td>\n",
" <td>0.056359</td>\n",
" <td>-0.109430</td>\n",
" <td>-0.033254</td>\n",
" <td>-0.142781</td>\n",
" <td>-0.135120</td>\n",
" <td>0.000184</td>\n",
" <td>-0.049068</td>\n",
" <td>0.020799</td>\n",
" <td>-0.051120</td>\n",
" <td>-0.036965</td>\n",
" <td>-0.025332</td>\n",
" <td>-0.017167</td>\n",
" <td>-0.026765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <td>0.002926</td>\n",
" <td>-0.003964</td>\n",
" <td>-0.009218</td>\n",
" <td>0.043261</td>\n",
" <td>0.060096</td>\n",
" <td>-0.048673</td>\n",
" <td>0.011332</td>\n",
" <td>-0.049443</td>\n",
" <td>-0.046436</td>\n",
" <td>0.063471</td>\n",
" <td>0.049374</td>\n",
" <td>-0.060177</td>\n",
" <td>-0.044924</td>\n",
" <td>0.031027</td>\n",
" <td>0.007303</td>\n",
" <td>0.009781</td>\n",
" <td>NaN</td>\n",
" <td>0.011126</td>\n",
" <td>-0.068836</td>\n",
" <td>-0.041261</td>\n",
" <td>0.424494</td>\n",
" <td>0.025022</td>\n",
" <td>0.034800</td>\n",
" <td>-0.034463</td>\n",
" <td>0.435978</td>\n",
" <td>0.271816</td>\n",
" <td>0.436690</td>\n",
" <td>0.271499</td>\n",
" <td>-0.050559</td>\n",
" <td>-0.113736</td>\n",
" <td>0.563790</td>\n",
" <td>0.364968</td>\n",
" <td>0.563820</td>\n",
" <td>0.364611</td>\n",
" <td>0.029964</td>\n",
" <td>-0.016364</td>\n",
" <td>-0.020528</td>\n",
" <td>0.036011</td>\n",
" <td>0.732608</td>\n",
" <td>0.465509</td>\n",
" <td>0.733799</td>\n",
" <td>0.465623</td>\n",
" <td>-0.024818</td>\n",
" <td>0.036354</td>\n",
" <td>0.652747</td>\n",
" <td>0.470402</td>\n",
" <td>0.653810</td>\n",
" <td>0.470721</td>\n",
" <td>-0.009731</td>\n",
" <td>-0.080701</td>\n",
" <td>0.792552</td>\n",
" <td>0.613190</td>\n",
" <td>0.795027</td>\n",
" <td>0.613387</td>\n",
" <td>0.085509</td>\n",
" <td>-0.013212</td>\n",
" <td>0.031098</td>\n",
" <td>0.000310</td>\n",
" <td>0.995454</td>\n",
" <td>0.735436</td>\n",
" <td>1.000000</td>\n",
" <td>0.736818</td>\n",
" <td>0.035512</td>\n",
" <td>0.064903</td>\n",
" <td>-0.070546</td>\n",
" <td>-0.032997</td>\n",
" <td>-0.034832</td>\n",
" <td>-0.037322</td>\n",
" <td>-0.000393</td>\n",
" <td>-0.035746</td>\n",
" <td>0.040407</td>\n",
" <td>-0.041875</td>\n",
" <td>-0.033463</td>\n",
" <td>-0.021404</td>\n",
" <td>-0.016309</td>\n",
" <td>-0.026137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <td>0.000954</td>\n",
" <td>-0.010023</td>\n",
" <td>-0.021643</td>\n",
" <td>-0.001372</td>\n",
" <td>0.041629</td>\n",
" <td>-0.034775</td>\n",
" <td>0.020537</td>\n",
" <td>-0.009021</td>\n",
" <td>-0.051621</td>\n",
" <td>0.050175</td>\n",
" <td>0.075952</td>\n",
" <td>-0.045033</td>\n",
" <td>-0.034978</td>\n",
" <td>0.008561</td>\n",
" <td>0.115242</td>\n",
" <td>0.106860</td>\n",
" <td>NaN</td>\n",
" <td>0.126260</td>\n",
" <td>-0.104698</td>\n",
" <td>-0.139769</td>\n",
" <td>0.392033</td>\n",
" <td>0.022385</td>\n",
" <td>0.079162</td>\n",
" <td>-0.157497</td>\n",
" <td>0.265481</td>\n",
" <td>0.437978</td>\n",
" <td>0.265585</td>\n",
" <td>0.438266</td>\n",
" <td>-0.160745</td>\n",
" <td>-0.050214</td>\n",
" <td>0.398017</td>\n",
" <td>0.536927</td>\n",
" <td>0.398145</td>\n",
" <td>0.537856</td>\n",
" <td>-0.069059</td>\n",
" <td>0.084742</td>\n",
" <td>-0.096337</td>\n",
" <td>0.152361</td>\n",
" <td>0.534950</td>\n",
" <td>0.660638</td>\n",
" <td>0.535452</td>\n",
" <td>0.661442</td>\n",
" <td>-0.083563</td>\n",
" <td>0.108976</td>\n",
" <td>0.430986</td>\n",
" <td>0.684967</td>\n",
" <td>0.431564</td>\n",
" <td>0.687473</td>\n",
" <td>-0.126437</td>\n",
" <td>-0.045213</td>\n",
" <td>0.601161</td>\n",
" <td>0.819863</td>\n",
" <td>0.602368</td>\n",
" <td>0.821673</td>\n",
" <td>-0.045844</td>\n",
" <td>0.082581</td>\n",
" <td>-0.035624</td>\n",
" <td>0.127970</td>\n",
" <td>0.734443</td>\n",
" <td>0.987171</td>\n",
" <td>0.736818</td>\n",
" <td>1.000000</td>\n",
" <td>0.004344</td>\n",
" <td>0.055871</td>\n",
" <td>-0.109438</td>\n",
" <td>-0.033244</td>\n",
" <td>-0.142490</td>\n",
" <td>-0.134811</td>\n",
" <td>0.000257</td>\n",
" <td>-0.048662</td>\n",
" <td>0.021763</td>\n",
" <td>-0.051282</td>\n",
" <td>-0.037091</td>\n",
" <td>-0.025405</td>\n",
" <td>-0.017958</td>\n",
" <td>-0.026701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_glucose_max</th>\n",
" <td>0.003829</td>\n",
" <td>-0.007430</td>\n",
" <td>-0.001785</td>\n",
" <td>0.005115</td>\n",
" <td>0.097297</td>\n",
" <td>-0.020991</td>\n",
" <td>-0.016805</td>\n",
" <td>0.008273</td>\n",
" <td>-0.009819</td>\n",
" <td>0.085200</td>\n",
" <td>-0.047425</td>\n",
" <td>0.016651</td>\n",
" <td>-0.019896</td>\n",
" <td>0.033822</td>\n",
" <td>-0.074507</td>\n",
" <td>-0.084388</td>\n",
" <td>NaN</td>\n",
" <td>-0.068488</td>\n",
" <td>0.103486</td>\n",
" <td>0.083622</td>\n",
" <td>0.014743</td>\n",
" <td>0.029153</td>\n",
" <td>-0.079647</td>\n",
" <td>0.090256</td>\n",
" <td>0.004768</td>\n",
" <td>-0.058470</td>\n",
" <td>0.004872</td>\n",
" <td>-0.058270</td>\n",
" <td>0.109506</td>\n",
" <td>0.099929</td>\n",
" <td>0.025616</td>\n",
" <td>-0.046836</td>\n",
" <td>0.025646</td>\n",
" <td>-0.046577</td>\n",
" <td>0.035801</td>\n",
" <td>0.011728</td>\n",
" <td>0.013158</td>\n",
" <td>-0.029429</td>\n",
" <td>0.067252</td>\n",
" <td>-0.024403</td>\n",
" <td>0.067300</td>\n",
" <td>-0.024169</td>\n",
" <td>0.002251</td>\n",
" <td>-0.101493</td>\n",
" <td>-0.015901</td>\n",
" <td>-0.043229</td>\n",
" <td>-0.015465</td>\n",
" <td>-0.042439</td>\n",
" <td>0.120078</td>\n",
" <td>0.131464</td>\n",
" <td>0.002567</td>\n",
" <td>-0.026086</td>\n",
" <td>0.002428</td>\n",
" <td>-0.026171</td>\n",
" <td>0.063251</td>\n",
" <td>0.071794</td>\n",
" <td>-0.002983</td>\n",
" <td>-0.021912</td>\n",
" <td>0.035373</td>\n",
" <td>0.003493</td>\n",
" <td>0.035512</td>\n",
" <td>0.004344</td>\n",
" <td>1.000000</td>\n",
" <td>0.370571</td>\n",
" <td>0.189215</td>\n",
" <td>-0.040980</td>\n",
" <td>0.119818</td>\n",
" <td>0.117201</td>\n",
" <td>-0.012616</td>\n",
" <td>-0.007844</td>\n",
" <td>0.430235</td>\n",
" <td>-0.013572</td>\n",
" <td>-0.006169</td>\n",
" <td>-0.006901</td>\n",
" <td>-0.000503</td>\n",
" <td>-0.013888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_glucose_min</th>\n",
" <td>0.002371</td>\n",
" <td>0.000074</td>\n",
" <td>0.022893</td>\n",
" <td>0.066914</td>\n",
" <td>0.136203</td>\n",
" <td>0.022987</td>\n",
" <td>0.018818</td>\n",
" <td>0.004540</td>\n",
" <td>-0.002267</td>\n",
" <td>0.138689</td>\n",
" <td>-0.028468</td>\n",
" <td>-0.007121</td>\n",
" <td>0.030704</td>\n",
" <td>-0.062408</td>\n",
" <td>0.038209</td>\n",
" <td>0.028602</td>\n",
" <td>NaN</td>\n",
" <td>0.050847</td>\n",
" <td>0.060540</td>\n",
" <td>-0.026432</td>\n",
" <td>0.049499</td>\n",
" <td>0.027490</td>\n",
" <td>0.030358</td>\n",
" <td>-0.004030</td>\n",
" <td>0.017044</td>\n",
" <td>0.029621</td>\n",
" <td>0.017193</td>\n",
" <td>0.029728</td>\n",
" <td>0.053443</td>\n",
" <td>0.051655</td>\n",
" <td>0.031988</td>\n",
" <td>0.049530</td>\n",
" <td>0.031901</td>\n",
" <td>0.049670</td>\n",
" <td>0.014376</td>\n",
" <td>0.048707</td>\n",
" <td>-0.081946</td>\n",
" <td>-0.005771</td>\n",
" <td>0.066461</td>\n",
" <td>0.065053</td>\n",
" <td>0.066677</td>\n",
" <td>0.064926</td>\n",
" <td>-0.011571</td>\n",
" <td>0.037687</td>\n",
" <td>0.024736</td>\n",
" <td>0.024580</td>\n",
" <td>0.023985</td>\n",
" <td>0.025106</td>\n",
" <td>0.062685</td>\n",
" <td>0.061894</td>\n",
" <td>0.036399</td>\n",
" <td>0.039970</td>\n",
" <td>0.036576</td>\n",
" <td>0.039749</td>\n",
" <td>0.050925</td>\n",
" <td>0.053458</td>\n",
" <td>-0.062018</td>\n",
" <td>-0.036017</td>\n",
" <td>0.064003</td>\n",
" <td>0.056359</td>\n",
" <td>0.064903</td>\n",
" <td>0.055871</td>\n",
" <td>0.370571</td>\n",
" <td>1.000000</td>\n",
" <td>0.004936</td>\n",
" <td>0.094505</td>\n",
" <td>0.008919</td>\n",
" <td>0.004978</td>\n",
" <td>-0.007544</td>\n",
" <td>-0.021855</td>\n",
" <td>0.139433</td>\n",
" <td>-0.023665</td>\n",
" <td>0.004412</td>\n",
" <td>-0.008404</td>\n",
" <td>0.004127</td>\n",
" <td>0.013219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_potassium_max</th>\n",
" <td>-0.004673</td>\n",
" <td>0.002094</td>\n",
" <td>0.002906</td>\n",
" <td>0.055840</td>\n",
" <td>0.089247</td>\n",
" <td>0.078894</td>\n",
" <td>0.055173</td>\n",
" <td>-0.002938</td>\n",
" <td>0.016778</td>\n",
" <td>0.107234</td>\n",
" <td>0.051021</td>\n",
" <td>0.066359</td>\n",
" <td>0.081878</td>\n",
" <td>0.108981</td>\n",
" <td>-0.064467</td>\n",
" <td>-0.064191</td>\n",
" <td>NaN</td>\n",
" <td>-0.066874</td>\n",
" <td>0.011124</td>\n",
" <td>0.107169</td>\n",
" <td>-0.038048</td>\n",
" <td>-0.012051</td>\n",
" <td>-0.104586</td>\n",
" <td>0.141289</td>\n",
" <td>-0.026528</td>\n",
" <td>-0.147363</td>\n",
" <td>-0.026837</td>\n",
" <td>-0.146484</td>\n",
" <td>0.039406</td>\n",
" <td>-0.005189</td>\n",
" <td>-0.037166</td>\n",
" <td>-0.146353</td>\n",
" <td>-0.037627</td>\n",
" <td>-0.146024</td>\n",
" <td>0.029741</td>\n",
" <td>-0.055836</td>\n",
" <td>0.039711</td>\n",
" <td>-0.099509</td>\n",
" <td>-0.024258</td>\n",
" <td>-0.136593</td>\n",
" <td>-0.025633</td>\n",
" <td>-0.135992</td>\n",
" <td>-0.022483</td>\n",
" <td>-0.118506</td>\n",
" <td>-0.074244</td>\n",
" <td>-0.123494</td>\n",
" <td>-0.075539</td>\n",
" <td>-0.124229</td>\n",
" <td>0.008160</td>\n",
" <td>0.000349</td>\n",
" <td>-0.081778</td>\n",
" <td>-0.123529</td>\n",
" <td>-0.082839</td>\n",
" <td>-0.124130</td>\n",
" <td>0.009906</td>\n",
" <td>-0.011577</td>\n",
" <td>-0.006664</td>\n",
" <td>-0.059276</td>\n",
" <td>-0.069402</td>\n",
" <td>-0.109430</td>\n",
" <td>-0.070546</td>\n",
" <td>-0.109438</td>\n",
" <td>0.189215</td>\n",
" <td>0.004936</td>\n",
" <td>1.000000</td>\n",
" <td>0.653250</td>\n",
" <td>0.111682</td>\n",
" <td>0.108460</td>\n",
" <td>0.002606</td>\n",
" <td>0.025160</td>\n",
" <td>0.101334</td>\n",
" <td>0.020301</td>\n",
" <td>-0.002128</td>\n",
" <td>0.003742</td>\n",
" <td>0.002158</td>\n",
" <td>-0.000153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d1_potassium_min</th>\n",
" <td>-0.003733</td>\n",
" <td>0.003155</td>\n",
" <td>-0.012871</td>\n",
" <td>0.114173</td>\n",
" <td>0.099579</td>\n",
" <td>0.047976</td>\n",
" <td>0.068632</td>\n",
" <td>-0.011041</td>\n",
" <td>0.014645</td>\n",
" <td>0.122040</td>\n",
" <td>0.050009</td>\n",
" <td>0.002477</td>\n",
" <td>0.042118</td>\n",
" <td>0.086886</td>\n",
" <td>0.082590</td>\n",
" <td>0.086013</td>\n",
" <td>NaN</td>\n",
" <td>0.090999</td>\n",
" <td>-0.072557</td>\n",
" <td>-0.064286</td>\n",
" <td>-0.030348</td>\n",
" <td>-0.013941</td>\n",
" <td>0.003755</td>\n",
" <td>-0.032025</td>\n",
" <td>-0.028403</td>\n",
" <td>-0.064450</td>\n",
" <td>-0.028376</td>\n",
" <td>-0.064412</td>\n",
" <td>-0.076311</td>\n",
" <td>-0.044381</td>\n",
" <td>-0.029030</td>\n",
" <td>-0.046372</td>\n",
" <td>-0.027664</td>\n",
" <td>-0.046391</td>\n",
" <td>-0.011542</td>\n",
" <td>-0.019968</td>\n",
" <td>-0.042741</td>\n",
" <td>-0.046367</td>\n",
" <td>-0.015672</td>\n",
" <td>-0.028504</td>\n",
" <td>-0.015752</td>\n",
" <td>-0.028606</td>\n",
" <td>-0.088875</td>\n",
" <td>0.028393</td>\n",
" <td>-0.054918</td>\n",
" <td>-0.064156</td>\n",
" <td>-0.055765</td>\n",
" <td>-0.065128</td>\n",
" <td>-0.080941</td>\n",
" <td>-0.073151</td>\n",
" <td>-0.050733</td>\n",
" <td>-0.052324</td>\n",
" <td>-0.050068</td>\n",
" <td>-0.052542</td>\n",
" <td>-0.011402</td>\n",
" <td>-0.018120</td>\n",
" <td>-0.048023</td>\n",
" <td>-0.045481</td>\n",
" <td>-0.033458</td>\n",
" <td>-0.033254</td>\n",
" <td>-0.032997</td>\n",
" <td>-0.033244</td>\n",
" <td>-0.040980</td>\n",
" <td>0.094505</td>\n",
" <td>0.653250</td>\n",
" <td>1.000000</td>\n",
" <td>-0.010123</td>\n",
" <td>-0.021224</td>\n",
" <td>0.002778</td>\n",
" <td>0.017554</td>\n",
" <td>0.037639</td>\n",
" <td>0.013184</td>\n",
" <td>-0.000445</td>\n",
" <td>-0.002258</td>\n",
" <td>0.003344</td>\n",
" <td>0.012609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <td>-0.005155</td>\n",
" <td>0.003026</td>\n",
" <td>-0.003032</td>\n",
" <td>0.171258</td>\n",
" <td>-0.028964</td>\n",
" <td>-0.118039</td>\n",
" <td>-0.026078</td>\n",
" <td>-0.004768</td>\n",
" <td>0.095801</td>\n",
" <td>-0.038453</td>\n",
" <td>-0.097084</td>\n",
" <td>-0.118604</td>\n",
" <td>-0.107271</td>\n",
" <td>0.037495</td>\n",
" <td>-0.465077</td>\n",
" <td>-0.507284</td>\n",
" <td>NaN</td>\n",
" <td>-0.451790</td>\n",
" <td>0.124840</td>\n",
" <td>0.332905</td>\n",
" <td>-0.030694</td>\n",
" <td>0.098826</td>\n",
" <td>-0.197472</td>\n",
" <td>0.351655</td>\n",
" <td>0.013195</td>\n",
" <td>-0.193258</td>\n",
" <td>0.012716</td>\n",
" <td>-0.193442</td>\n",
" <td>0.171262</td>\n",
" <td>-0.004565</td>\n",
" <td>0.011926</td>\n",
" <td>-0.203707</td>\n",
" <td>0.010603</td>\n",
" <td>-0.204198</td>\n",
" <td>0.117654</td>\n",
" <td>0.001213</td>\n",
" <td>0.062270</td>\n",
" <td>-0.154740</td>\n",
" <td>0.016077</td>\n",
" <td>-0.217784</td>\n",
" <td>0.015920</td>\n",
" <td>-0.217980</td>\n",
" <td>0.048238</td>\n",
" <td>-0.256898</td>\n",
" <td>-0.020015</td>\n",
" <td>-0.141526</td>\n",
" <td>-0.021459</td>\n",
" <td>-0.141760</td>\n",
" <td>0.126071</td>\n",
" <td>0.080865</td>\n",
" <td>-0.033841</td>\n",
" <td>-0.143307</td>\n",
" <td>-0.034958</td>\n",
" <td>-0.143961</td>\n",
" <td>0.136787</td>\n",
" <td>0.096252</td>\n",
" <td>-0.015187</td>\n",
" <td>-0.106906</td>\n",
" <td>-0.034774</td>\n",
" <td>-0.142781</td>\n",
" <td>-0.034832</td>\n",
" <td>-0.142490</td>\n",
" <td>0.119818</td>\n",
" <td>0.008919</td>\n",
" <td>0.111682</td>\n",
" <td>-0.010123</td>\n",
" <td>1.000000</td>\n",
" <td>0.844291</td>\n",
" <td>0.010033</td>\n",
" <td>0.048312</td>\n",
" <td>0.011216</td>\n",
" <td>0.031628</td>\n",
" <td>0.047476</td>\n",
" <td>0.053369</td>\n",
" <td>0.024351</td>\n",
" <td>0.062040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <td>-0.005044</td>\n",
" <td>0.004431</td>\n",
" <td>0.004436</td>\n",
" <td>0.089739</td>\n",
" <td>-0.012521</td>\n",
" <td>-0.086606</td>\n",
" <td>-0.008080</td>\n",
" <td>-0.009903</td>\n",
" <td>0.068496</td>\n",
" <td>-0.015454</td>\n",
" <td>-0.104199</td>\n",
" <td>-0.085070</td>\n",
" <td>-0.074592</td>\n",
" <td>0.031092</td>\n",
" <td>-0.449872</td>\n",
" <td>-0.504793</td>\n",
" <td>NaN</td>\n",
" <td>-0.419053</td>\n",
" <td>0.115316</td>\n",
" <td>0.339431</td>\n",
" <td>-0.023802</td>\n",
" <td>0.082287</td>\n",
" <td>-0.206398</td>\n",
" <td>0.337154</td>\n",
" <td>0.012630</td>\n",
" <td>-0.168312</td>\n",
" <td>0.012448</td>\n",
" <td>-0.168482</td>\n",
" <td>0.158557</td>\n",
" <td>-0.007087</td>\n",
" <td>0.009259</td>\n",
" <td>-0.182120</td>\n",
" <td>0.008541</td>\n",
" <td>-0.182673</td>\n",
" <td>0.102399</td>\n",
" <td>-0.010671</td>\n",
" <td>0.053668</td>\n",
" <td>-0.151416</td>\n",
" <td>0.006298</td>\n",
" <td>-0.201911</td>\n",
" <td>0.006253</td>\n",
" <td>-0.202009</td>\n",
" <td>0.050052</td>\n",
" <td>-0.270149</td>\n",
" <td>-0.012538</td>\n",
" <td>-0.120126</td>\n",
" <td>-0.013593</td>\n",
" <td>-0.120348</td>\n",
" <td>0.120051</td>\n",
" <td>0.078412</td>\n",
" <td>-0.029171</td>\n",
" <td>-0.126340</td>\n",
" <td>-0.030081</td>\n",
" <td>-0.126812</td>\n",
" <td>0.115561</td>\n",
" <td>0.074214</td>\n",
" <td>-0.015543</td>\n",
" <td>-0.102579</td>\n",
" <td>-0.037108</td>\n",
" <td>-0.135120</td>\n",
" <td>-0.037322</td>\n",
" <td>-0.134811</td>\n",
" <td>0.117201</td>\n",
" <td>0.004978</td>\n",
" <td>0.108460</td>\n",
" <td>-0.021224</td>\n",
" <td>0.844291</td>\n",
" <td>1.000000</td>\n",
" <td>0.009050</td>\n",
" <td>0.044584</td>\n",
" <td>0.004636</td>\n",
" <td>0.037314</td>\n",
" <td>0.030028</td>\n",
" <td>0.037671</td>\n",
" <td>0.014551</td>\n",
" <td>0.033893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aids</th>\n",
" <td>0.001709</td>\n",
" <td>-0.000660</td>\n",
" <td>-0.006257</td>\n",
" <td>-0.032535</td>\n",
" <td>-0.022728</td>\n",
" <td>-0.004932</td>\n",
" <td>0.011321</td>\n",
" <td>-0.002201</td>\n",
" <td>0.012552</td>\n",
" <td>-0.018215</td>\n",
" <td>-0.006816</td>\n",
" <td>0.000255</td>\n",
" <td>-0.006550</td>\n",
" <td>0.007424</td>\n",
" <td>-0.002167</td>\n",
" <td>-0.002878</td>\n",
" <td>NaN</td>\n",
" <td>-0.001815</td>\n",
" <td>0.007786</td>\n",
" <td>0.006411</td>\n",
" <td>0.002195</td>\n",
" <td>0.012499</td>\n",
" <td>0.001945</td>\n",
" <td>0.006785</td>\n",
" <td>0.010762</td>\n",
" <td>0.012464</td>\n",
" <td>0.010759</td>\n",
" <td>0.012426</td>\n",
" <td>0.009853</td>\n",
" <td>0.011322</td>\n",
" <td>0.009370</td>\n",
" <td>0.007909</td>\n",
" <td>0.009354</td>\n",
" <td>0.007944</td>\n",
" <td>0.009845</td>\n",
" <td>0.010695</td>\n",
" <td>0.005565</td>\n",
" <td>0.002393</td>\n",
" <td>-0.000834</td>\n",
" <td>0.000649</td>\n",
" <td>-0.000794</td>\n",
" <td>0.000632</td>\n",
" <td>0.007963</td>\n",
" <td>-0.000950</td>\n",
" <td>0.013123</td>\n",
" <td>0.013015</td>\n",
" <td>0.013092</td>\n",
" <td>0.012944</td>\n",
" <td>0.010233</td>\n",
" <td>0.011083</td>\n",
" <td>0.008723</td>\n",
" <td>0.008622</td>\n",
" <td>0.008777</td>\n",
" <td>0.008645</td>\n",
" <td>0.016430</td>\n",
" <td>0.009873</td>\n",
" <td>0.005167</td>\n",
" <td>-0.000169</td>\n",
" <td>-0.000523</td>\n",
" <td>0.000184</td>\n",
" <td>-0.000393</td>\n",
" <td>0.000257</td>\n",
" <td>-0.012616</td>\n",
" <td>-0.007544</td>\n",
" <td>0.002606</td>\n",
" <td>0.002778</td>\n",
" <td>0.010033</td>\n",
" <td>0.009050</td>\n",
" <td>1.000000</td>\n",
" <td>0.012506</td>\n",
" <td>-0.013817</td>\n",
" <td>0.005179</td>\n",
" <td>0.023775</td>\n",
" <td>-0.002850</td>\n",
" <td>0.023303</td>\n",
" <td>-0.001095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cirrhosis</th>\n",
" <td>0.011650</td>\n",
" <td>0.004198</td>\n",
" <td>0.006344</td>\n",
" <td>-0.031029</td>\n",
" <td>-0.002425</td>\n",
" <td>-0.033169</td>\n",
" <td>0.012351</td>\n",
" <td>-0.016893</td>\n",
" <td>0.014046</td>\n",
" <td>0.002399</td>\n",
" <td>-0.004227</td>\n",
" <td>-0.019918</td>\n",
" <td>-0.035495</td>\n",
" <td>0.024045</td>\n",
" <td>-0.013570</td>\n",
" <td>-0.007157</td>\n",
" <td>NaN</td>\n",
" <td>-0.010962</td>\n",
" <td>0.014019</td>\n",
" <td>0.005999</td>\n",
" <td>-0.036326</td>\n",
" <td>-0.003093</td>\n",
" <td>-0.018439</td>\n",
" <td>-0.006741</td>\n",
" <td>-0.017529</td>\n",
" <td>-0.046882</td>\n",
" <td>-0.017306</td>\n",
" <td>-0.046496</td>\n",
" <td>0.015702</td>\n",
" <td>0.022203</td>\n",
" <td>-0.029211</td>\n",
" <td>-0.050296</td>\n",
" <td>-0.028820</td>\n",
" <td>-0.049825</td>\n",
" <td>0.003894</td>\n",
" <td>-0.013251</td>\n",
" <td>0.015644</td>\n",
" <td>-0.003627</td>\n",
" <td>-0.033478</td>\n",
" <td>-0.046788</td>\n",
" <td>-0.033091</td>\n",
" <td>-0.046513</td>\n",
" <td>-0.012707</td>\n",
" <td>-0.018276</td>\n",
" <td>-0.025047</td>\n",
" <td>-0.048752</td>\n",
" <td>-0.025062</td>\n",
" <td>-0.049390</td>\n",
" <td>0.018175</td>\n",
" <td>0.020529</td>\n",
" <td>-0.035903</td>\n",
" <td>-0.050043</td>\n",
" <td>-0.035794</td>\n",
" <td>-0.050119</td>\n",
" <td>0.002881</td>\n",
" <td>-0.011457</td>\n",
" <td>0.010287</td>\n",
" <td>0.005423</td>\n",
" <td>-0.036144</td>\n",
" <td>-0.049068</td>\n",
" <td>-0.035746</td>\n",
" <td>-0.048662</td>\n",
" <td>-0.007844</td>\n",
" <td>-0.021855</td>\n",
" <td>0.025160</td>\n",
" <td>0.017554</td>\n",
" <td>0.048312</td>\n",
" <td>0.044584</td>\n",
" <td>0.012506</td>\n",
" <td>1.000000</td>\n",
" <td>0.012295</td>\n",
" <td>0.535318</td>\n",
" <td>-0.004254</td>\n",
" <td>-0.004375</td>\n",
" <td>0.003606</td>\n",
" <td>-0.006598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diabetes_mellitus</th>\n",
" <td>0.006541</td>\n",
" <td>-0.001333</td>\n",
" <td>0.011368</td>\n",
" <td>0.065859</td>\n",
" <td>0.170040</td>\n",
" <td>-0.011640</td>\n",
" <td>-0.004218</td>\n",
" <td>0.024944</td>\n",
" <td>0.015722</td>\n",
" <td>0.157113</td>\n",
" <td>-0.004628</td>\n",
" <td>-0.008994</td>\n",
" <td>-0.018465</td>\n",
" <td>0.107260</td>\n",
" <td>0.034854</td>\n",
" <td>0.031341</td>\n",
" <td>NaN</td>\n",
" <td>0.031691</td>\n",
" <td>-0.018177</td>\n",
" <td>-0.013904</td>\n",
" <td>-0.000444</td>\n",
" <td>-0.013947</td>\n",
" <td>-0.000627</td>\n",
" <td>-0.002992</td>\n",
" <td>-0.019824</td>\n",
" <td>-0.054714</td>\n",
" <td>-0.019583</td>\n",
" <td>-0.054751</td>\n",
" <td>-0.026411</td>\n",
" <td>0.018222</td>\n",
" <td>0.015416</td>\n",
" <td>-0.020560</td>\n",
" <td>0.015328</td>\n",
" <td>-0.020812</td>\n",
" <td>0.002041</td>\n",
" <td>-0.019699</td>\n",
" <td>0.007692</td>\n",
" <td>0.004558</td>\n",
" <td>0.069493</td>\n",
" <td>0.025228</td>\n",
" <td>0.069483</td>\n",
" <td>0.025260</td>\n",
" <td>-0.015323</td>\n",
" <td>-0.003782</td>\n",
" <td>-0.047827</td>\n",
" <td>-0.066676</td>\n",
" <td>-0.047512</td>\n",
" <td>-0.066902</td>\n",
" <td>-0.012669</td>\n",
" <td>-0.001135</td>\n",
" <td>-0.010005</td>\n",
" <td>-0.026996</td>\n",
" <td>-0.010453</td>\n",
" <td>-0.027522</td>\n",
" <td>0.003224</td>\n",
" <td>-0.002641</td>\n",
" <td>0.013671</td>\n",
" <td>-0.001203</td>\n",
" <td>0.040022</td>\n",
" <td>0.020799</td>\n",
" <td>0.040407</td>\n",
" <td>0.021763</td>\n",
" <td>0.430235</td>\n",
" <td>0.139433</td>\n",
" <td>0.101334</td>\n",
" <td>0.037639</td>\n",
" <td>0.011216</td>\n",
" <td>0.004636</td>\n",
" <td>-0.013817</td>\n",
" <td>0.012295</td>\n",
" <td>1.000000</td>\n",
" <td>0.008549</td>\n",
" <td>-0.006081</td>\n",
" <td>0.002402</td>\n",
" <td>-0.009503</td>\n",
" <td>-0.012553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hepatic_failure</th>\n",
" <td>0.001078</td>\n",
" <td>-0.002630</td>\n",
" <td>0.003995</td>\n",
" <td>-0.024565</td>\n",
" <td>-0.000597</td>\n",
" <td>-0.035008</td>\n",
" <td>0.008594</td>\n",
" <td>-0.014062</td>\n",
" <td>0.014342</td>\n",
" <td>0.003829</td>\n",
" <td>-0.003222</td>\n",
" <td>-0.027882</td>\n",
" <td>-0.036657</td>\n",
" <td>0.018832</td>\n",
" <td>-0.010770</td>\n",
" <td>-0.004973</td>\n",
" <td>NaN</td>\n",
" <td>-0.008965</td>\n",
" <td>0.012780</td>\n",
" <td>0.002573</td>\n",
" <td>-0.044452</td>\n",
" <td>-0.006893</td>\n",
" <td>-0.023581</td>\n",
" <td>-0.006189</td>\n",
" <td>-0.023154</td>\n",
" <td>-0.050688</td>\n",
" <td>-0.023155</td>\n",
" <td>-0.050795</td>\n",
" <td>0.015661</td>\n",
" <td>0.024140</td>\n",
" <td>-0.034595</td>\n",
" <td>-0.054639</td>\n",
" <td>-0.033779</td>\n",
" <td>-0.054604</td>\n",
" <td>0.002154</td>\n",
" <td>-0.006476</td>\n",
" <td>0.018583</td>\n",
" <td>-0.007684</td>\n",
" <td>-0.034691</td>\n",
" <td>-0.048606</td>\n",
" <td>-0.034506</td>\n",
" <td>-0.048668</td>\n",
" <td>-0.010524</td>\n",
" <td>-0.024858</td>\n",
" <td>-0.036471</td>\n",
" <td>-0.052422</td>\n",
" <td>-0.035785</td>\n",
" <td>-0.052745</td>\n",
" <td>0.020534</td>\n",
" <td>0.023841</td>\n",
" <td>-0.044933</td>\n",
" <td>-0.054471</td>\n",
" <td>-0.045180</td>\n",
" <td>-0.054756</td>\n",
" <td>0.003297</td>\n",
" <td>-0.007012</td>\n",
" <td>0.015094</td>\n",
" <td>0.004372</td>\n",
" <td>-0.041831</td>\n",
" <td>-0.051120</td>\n",
" <td>-0.041875</td>\n",
" <td>-0.051282</td>\n",
" <td>-0.013572</td>\n",
" <td>-0.023665</td>\n",
" <td>0.020301</td>\n",
" <td>0.013184</td>\n",
" <td>0.031628</td>\n",
" <td>0.037314</td>\n",
" <td>0.005179</td>\n",
" <td>0.535318</td>\n",
" <td>0.008549</td>\n",
" <td>1.000000</td>\n",
" <td>0.005264</td>\n",
" <td>0.000514</td>\n",
" <td>0.000902</td>\n",
" <td>0.006025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>immunosuppression</th>\n",
" <td>-0.002094</td>\n",
" <td>0.001413</td>\n",
" <td>0.000381</td>\n",
" <td>0.023724</td>\n",
" <td>-0.033248</td>\n",
" <td>-0.011333</td>\n",
" <td>0.001106</td>\n",
" <td>-0.037019</td>\n",
" <td>0.037962</td>\n",
" <td>-0.031774</td>\n",
" <td>-0.011232</td>\n",
" <td>-0.000904</td>\n",
" <td>-0.012610</td>\n",
" <td>0.001526</td>\n",
" <td>0.022177</td>\n",
" <td>0.022238</td>\n",
" <td>NaN</td>\n",
" <td>0.026622</td>\n",
" <td>0.058280</td>\n",
" <td>-0.009710</td>\n",
" <td>-0.021612</td>\n",
" <td>0.036010</td>\n",
" <td>0.005193</td>\n",
" <td>-0.004677</td>\n",
" <td>-0.019109</td>\n",
" <td>-0.015537</td>\n",
" <td>-0.019113</td>\n",
" <td>-0.015711</td>\n",
" <td>0.066606</td>\n",
" <td>0.054844</td>\n",
" <td>-0.021104</td>\n",
" <td>-0.026259</td>\n",
" <td>-0.020487</td>\n",
" <td>-0.026956</td>\n",
" <td>0.038590</td>\n",
" <td>0.015082</td>\n",
" <td>0.010419</td>\n",
" <td>-0.024263</td>\n",
" <td>-0.031275</td>\n",
" <td>-0.037169</td>\n",
" <td>-0.031031</td>\n",
" <td>-0.037259</td>\n",
" <td>0.015804</td>\n",
" <td>0.003599</td>\n",
" <td>-0.024897</td>\n",
" <td>-0.025463</td>\n",
" <td>-0.024784</td>\n",
" <td>-0.024206</td>\n",
" <td>0.066942</td>\n",
" <td>0.068321</td>\n",
" <td>-0.032257</td>\n",
" <td>-0.027287</td>\n",
" <td>-0.031041</td>\n",
" <td>-0.026931</td>\n",
" <td>0.054017</td>\n",
" <td>0.038334</td>\n",
" <td>-0.012804</td>\n",
" <td>-0.014791</td>\n",
" <td>-0.034089</td>\n",
" <td>-0.036965</td>\n",
" <td>-0.033463</td>\n",
" <td>-0.037091</td>\n",
" <td>-0.006169</td>\n",
" <td>0.004412</td>\n",
" <td>-0.002128</td>\n",
" <td>-0.000445</td>\n",
" <td>0.047476</td>\n",
" <td>0.030028</td>\n",
" <td>0.023775</td>\n",
" <td>-0.004254</td>\n",
" <td>-0.006081</td>\n",
" <td>0.005264</td>\n",
" <td>1.000000</td>\n",
" <td>0.148845</td>\n",
" <td>0.098902</td>\n",
" <td>0.277153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leukemia</th>\n",
" <td>-0.003623</td>\n",
" <td>0.000975</td>\n",
" <td>-0.005046</td>\n",
" <td>0.030634</td>\n",
" <td>-0.015873</td>\n",
" <td>-0.016247</td>\n",
" <td>0.001257</td>\n",
" <td>-0.000094</td>\n",
" <td>0.050971</td>\n",
" <td>-0.015303</td>\n",
" <td>-0.004233</td>\n",
" <td>-0.006425</td>\n",
" <td>-0.013973</td>\n",
" <td>0.016028</td>\n",
" <td>0.006807</td>\n",
" <td>0.009805</td>\n",
" <td>NaN</td>\n",
" <td>0.012140</td>\n",
" <td>0.021585</td>\n",
" <td>-0.001891</td>\n",
" <td>-0.018537</td>\n",
" <td>0.019099</td>\n",
" <td>0.003955</td>\n",
" <td>-0.004202</td>\n",
" <td>-0.009462</td>\n",
" <td>-0.028716</td>\n",
" <td>-0.009464</td>\n",
" <td>-0.028798</td>\n",
" <td>0.023870</td>\n",
" <td>0.013753</td>\n",
" <td>-0.011892</td>\n",
" <td>-0.026944</td>\n",
" <td>-0.013337</td>\n",
" <td>-0.026687</td>\n",
" <td>0.023426</td>\n",
" <td>0.015824</td>\n",
" <td>0.003315</td>\n",
" <td>-0.023573</td>\n",
" <td>-0.018230</td>\n",
" <td>-0.023957</td>\n",
" <td>-0.018103</td>\n",
" <td>-0.024003</td>\n",
" <td>0.018855</td>\n",
" <td>-0.002237</td>\n",
" <td>-0.023125</td>\n",
" <td>-0.029693</td>\n",
" <td>-0.022945</td>\n",
" <td>-0.029332</td>\n",
" <td>0.023266</td>\n",
" <td>0.019161</td>\n",
" <td>-0.023421</td>\n",
" <td>-0.029801</td>\n",
" <td>-0.023258</td>\n",
" <td>-0.029596</td>\n",
" <td>0.029426</td>\n",
" <td>0.024732</td>\n",
" <td>-0.002035</td>\n",
" <td>-0.002042</td>\n",
" <td>-0.021371</td>\n",
" <td>-0.025332</td>\n",
" <td>-0.021404</td>\n",
" <td>-0.025405</td>\n",
" <td>-0.006901</td>\n",
" <td>-0.008404</td>\n",
" <td>0.003742</td>\n",
" <td>-0.002258</td>\n",
" <td>0.053369</td>\n",
" <td>0.037671</td>\n",
" <td>-0.002850</td>\n",
" <td>-0.004375</td>\n",
" <td>0.002402</td>\n",
" <td>0.000514</td>\n",
" <td>0.148845</td>\n",
" <td>1.000000</td>\n",
" <td>0.024657</td>\n",
" <td>0.003979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lymphoma</th>\n",
" <td>-0.001809</td>\n",
" <td>-0.002327</td>\n",
" <td>0.006326</td>\n",
" <td>0.020841</td>\n",
" <td>-0.010283</td>\n",
" <td>-0.010116</td>\n",
" <td>0.009898</td>\n",
" <td>-0.002586</td>\n",
" <td>0.017935</td>\n",
" <td>-0.004868</td>\n",
" <td>-0.004681</td>\n",
" <td>-0.002530</td>\n",
" <td>-0.009933</td>\n",
" <td>-0.006626</td>\n",
" <td>0.012052</td>\n",
" <td>0.008518</td>\n",
" <td>NaN</td>\n",
" <td>0.011510</td>\n",
" <td>0.019672</td>\n",
" <td>-0.005110</td>\n",
" <td>-0.009331</td>\n",
" <td>0.013559</td>\n",
" <td>-0.001379</td>\n",
" <td>-0.004654</td>\n",
" <td>-0.007399</td>\n",
" <td>-0.007431</td>\n",
" <td>-0.007400</td>\n",
" <td>-0.007495</td>\n",
" <td>0.019838</td>\n",
" <td>0.017229</td>\n",
" <td>-0.010193</td>\n",
" <td>-0.009239</td>\n",
" <td>-0.010189</td>\n",
" <td>-0.009197</td>\n",
" <td>0.015426</td>\n",
" <td>0.012541</td>\n",
" <td>-0.002064</td>\n",
" <td>-0.009974</td>\n",
" <td>-0.015322</td>\n",
" <td>-0.012975</td>\n",
" <td>-0.015227</td>\n",
" <td>-0.013010</td>\n",
" <td>0.000974</td>\n",
" <td>-0.001466</td>\n",
" <td>-0.014152</td>\n",
" <td>-0.013254</td>\n",
" <td>-0.015404</td>\n",
" <td>-0.013997</td>\n",
" <td>0.019361</td>\n",
" <td>0.018820</td>\n",
" <td>-0.015548</td>\n",
" <td>-0.012395</td>\n",
" <td>-0.015457</td>\n",
" <td>-0.013360</td>\n",
" <td>0.022068</td>\n",
" <td>0.020294</td>\n",
" <td>-0.009216</td>\n",
" <td>-0.009525</td>\n",
" <td>-0.016606</td>\n",
" <td>-0.017167</td>\n",
" <td>-0.016309</td>\n",
" <td>-0.017958</td>\n",
" <td>-0.000503</td>\n",
" <td>0.004127</td>\n",
" <td>0.002158</td>\n",
" <td>0.003344</td>\n",
" <td>0.024351</td>\n",
" <td>0.014551</td>\n",
" <td>0.023303</td>\n",
" <td>0.003606</td>\n",
" <td>-0.009503</td>\n",
" <td>0.000902</td>\n",
" <td>0.098902</td>\n",
" <td>0.024657</td>\n",
" <td>1.000000</td>\n",
" <td>0.012069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" <td>-0.003849</td>\n",
" <td>-0.006374</td>\n",
" <td>-0.005487</td>\n",
" <td>0.026358</td>\n",
" <td>-0.047853</td>\n",
" <td>0.019365</td>\n",
" <td>0.007713</td>\n",
" <td>-0.012947</td>\n",
" <td>0.039016</td>\n",
" <td>-0.042026</td>\n",
" <td>0.008605</td>\n",
" <td>0.021846</td>\n",
" <td>0.015989</td>\n",
" <td>-0.009522</td>\n",
" <td>0.017411</td>\n",
" <td>0.016147</td>\n",
" <td>NaN</td>\n",
" <td>0.017257</td>\n",
" <td>0.044459</td>\n",
" <td>-0.010965</td>\n",
" <td>-0.010763</td>\n",
" <td>0.016087</td>\n",
" <td>-0.002067</td>\n",
" <td>-0.016788</td>\n",
" <td>-0.023977</td>\n",
" <td>-0.002172</td>\n",
" <td>-0.023979</td>\n",
" <td>-0.002327</td>\n",
" <td>0.048240</td>\n",
" <td>0.044348</td>\n",
" <td>-0.024688</td>\n",
" <td>-0.010799</td>\n",
" <td>-0.025317</td>\n",
" <td>-0.010691</td>\n",
" <td>0.023527</td>\n",
" <td>0.008194</td>\n",
" <td>0.000517</td>\n",
" <td>-0.015686</td>\n",
" <td>-0.031206</td>\n",
" <td>-0.025428</td>\n",
" <td>-0.030990</td>\n",
" <td>-0.025508</td>\n",
" <td>-0.010663</td>\n",
" <td>-0.001434</td>\n",
" <td>-0.015965</td>\n",
" <td>-0.010569</td>\n",
" <td>-0.015739</td>\n",
" <td>-0.010381</td>\n",
" <td>0.045671</td>\n",
" <td>0.048555</td>\n",
" <td>-0.021113</td>\n",
" <td>-0.015536</td>\n",
" <td>-0.020619</td>\n",
" <td>-0.015739</td>\n",
" <td>0.022905</td>\n",
" <td>0.015658</td>\n",
" <td>-0.003942</td>\n",
" <td>-0.011597</td>\n",
" <td>-0.026780</td>\n",
" <td>-0.026765</td>\n",
" <td>-0.026137</td>\n",
" <td>-0.026701</td>\n",
" <td>-0.013888</td>\n",
" <td>0.013219</td>\n",
" <td>-0.000153</td>\n",
" <td>0.012609</td>\n",
" <td>0.062040</td>\n",
" <td>0.033893</td>\n",
" <td>-0.001095</td>\n",
" <td>-0.006598</td>\n",
" <td>-0.012553</td>\n",
" <td>0.006025</td>\n",
" <td>0.277153</td>\n",
" <td>0.003979</td>\n",
" <td>0.012069</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-e0e58464-e522-45ba-9db0-75b93ca344a5 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" <div id=\"id_6b1210dc-19a2-420e-a1f7-2f8433b44215\">\n",
" <style>\n",
" .colab-df-generate {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-generate:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('correlation')\"\n",
" title=\"Generate code using this dataframe.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
" </svg>\n",
" </button>\n",
" <script>\n",
" (() => {\n",
" const buttonEl =\n",
" document.querySelector('#id_6b1210dc-19a2-420e-a1f7-2f8433b44215 button.colab-df-generate');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" buttonEl.onclick = () => {\n",
" google.colab.notebook.generateWithVariable('correlation');\n",
" }\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "correlation"
}
},
"metadata": {},
"execution_count": 27
}
],
"source": [
"correlation = X1.corr()\n",
"correlation"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "T6WF6c-bn9Hp",
"outputId": "9c2e93b6-cdd4-4987-cbce-3cc27dae16df"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x2000 with 2 Axes>"
],
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BsLAwfH19iYmJYcGCBU7bNGvWjMTERBITEylbtiwVKlRg2LBhFBYWOraZN28e9evXx9/fn8qVK/PEE09w9OhRp3J27dpFmzZtCAgIwN/fn8aNG3Po0CGnbSZMmEBISAhBQUH07dvXqaMsLy+PpKQkwsLC8PPzo2HDhqSlpV22fSIiIiIiIiIiIiIipU0dTdeBAQMGkJ6ezpIlS1i5ciVr165l27ZtjvWdO3dmwYIFTJ06lT179vDWW29htVoBGDZsGLt37+azzz5jz549zJgxgwoVKpS47pdeeomhQ4eybds2PDw8eOKJJxg8eDBTpkxh7dq1HDx4kOHDhzu2nzJlChMnTmTChAns2LGDli1b8sADD3DgwAEANm3aBMDnn3/OkSNHWLRoUYniLnjxxRfp168fe/bsoWXLlpfN/dy5c9SrV4+lS5fy1Vdf0bNnTzp16uTI4YK5c+fi4eHBpk2bmDJlCpMmTeLtt992rC8oKGD06NFkZmayePFisrKy6Nq1q2P9999/T5MmTfD29uaLL75g69atdO/enfPnzzu2Wb16NYcOHWL16tXMnTuXOXPmMGfOHMf6xMRE1q9fT2pqKjt27ODRRx+lVatWF7VfRERERERERERERORa4lHaCcjl/frrr8ydO5f33nuP5s2bA5CSkkJoaCgA+/fvZ+HChaxcuZIWLVoAUL16dUd8dnY28fHx1K9fH4CIiIgrqj8pKcnRodOvXz86dOjAqlWruPPOOwHo0aOHU4fJhAkTeOGFF3j88ccBeOWVV1i9ejWTJ09m+vTpBAcHAxAUFETlypVLHHdB//79efjhh0uUe1hYGElJSY7fn332WZYvX87ChQtp0KCBY3l4eDivvfYaFouFGjVqsHPnTl577TWefvppALp37+7Ytnr16kydOpXbb7+dnJwcrFYr06dPp2zZsqSmpuLp6QlAdHS0Uy7ly5fn9ddfx93dnVtvvZW///3vrFq1iqeffprs7GxSUlLIzs52vK9JSUksW7aMlJQUxo4de1Hb8vLyyMvLc1pWUGjD0+Jeon0jIiIiIiIiIiIi4hKF9tLOQEqZRjRd477++msKCgqcOkbKli1LjRo1AMjIyMDd3Z2mTZsWGf/MM8+QmprKbbfdxuDBg/nyyy+vqP7Y2FjHz5UqVQIgJibGadmFaeR++eUXfvjhB0cn1AV33nkne/bsuWQdVxJ3ocOsJGw2G6NHjyYmJobAwECsVivLly8nOzvbabs77rgDi8Xi+D0hIYEDBw5gs9kA2Lp1K23btqVKlSr4+/s79vWFcjIyMmjcuLGjk6kotWvXxt39/zqBQkJCHPtt586d2Gw2oqOjsVqtjteaNWsumn7vguTkZMqWLev0WvHLrhLvGxERERERERERERERV9CIpuucj4/PZde3bt2ab775hk8//ZSVK1fSvHlz+vbty4QJE0pU/u87Ty50xvxxmd3+1/VY+/n5lXjb8ePHM2XKFCZPnkxMTAx+fn7079+f/Pz8EpeRm5tLy5YtadmyJfPnzyc4OJjs7GxatmzpKKe49wC4qBPq9/stJycHd3d3tm7d6tQZBTimQPyjIUOGMGDAAKdlKbV6lbhdIiIiIiIiIiIiIiKuoBFN17jq1avj6enJ5s2bHctOnz7N/v37gd9GF9ntdtasWXPJMoKDg+nSpQvvvvsukydPZubMmX9KrgEBAYSGhpKenu60PD09nVq1agHg5eUF4BgtVNK4q5Gens6DDz7Ik08+SVxcHNWrV3fst9/buHGj0+8bNmwgKioKd3d39u7dy/Hjxxk3bhyNGzfm1ltvdYxEuiA2Npa1a9dSUFBwVXnGx8djs9k4evQokZGRTq/fTy/4e97e3gQEBDi9NG2eiIiIiIiIiIiIiPzV1NF0jfP396dLly4MGjSI1atXs2vXLnr06IGbmxsWi4WIiAi6dOlC9+7dWbx4MYcPHyYtLY2FCxcCMHz4cD7++GMOHjzIrl27+OSTT6hZs+aflu+gQYN45ZVXeP/999m3bx8vvvgiGRkZ9OvXD4CKFSvi4+PDsmXL+Omnnzh9+nSJ4q5GVFQUK1eu5Msvv2TPnj306tWLn3766aLtsrOzGTBgAPv27WPBggVMmzbNUW+VKlXw8vJi2rRpfP311yxZsoTRo0c7xScmJvLLL7/w+OOPs2XLFg4cOMC8efPYt29fifKMjo6mY8eOdO7cmUWLFnH48GE2bdpEcnIyS5cuver2i4iIiIiIiIiIiIj82dTRdB2YNGkSCQkJtGnThhYtWnDnnXdSs2ZNypQpA8CMGTN45JFH6NOnD7feeitPP/00ubm5wG8jiIYMGUJsbCxNmjTB3d2d1NTUPy3X5557jgEDBjBw4EBiYmJYtmwZS5YsISoqCgAPDw+mTp3KW2+9RWhoKA8++GCJ4q7G0KFDqVu3Li1btqRZs2ZUrlyZdu3aXbRd586dOXv2LA0aNKBv377069ePnj17Ar+NBpszZw4ffPABtWrVYty4cRdNOxgUFMQXX3xBTk4OTZs2pV69esyaNeuyz2z6o5SUFDp37szAgQOpUaMG7dq1Y/PmzVSpUuWq2y8iIiIiIiIiIiIi8mezFBYWFpZ2EnJlcnNzCQsLY+LEifTo0aO007muNWvWjNtuu43JkyeXdirG3gh/0rgM06dtFVjM4svbit/mcvIM6wfz3vccwwJc0fvvZnhVL3kXadH8Dd9HgNOGM0Ga7sermwjT2VPvNDKK96jbyijes0J1o3iAs3NfNIp/+aVso/il574xigfo6lXNKN70fLrddsYo/on8w2YJAD39ahvFR+eZ3R32e5tf2UxzWFPGLL7OefPHmp40vK7VPmd2cd1VxnyKXdMcdhjmEO6Ci/NOL7M2NDxn9mHjoAvOh/rnSv680aJsKeNVqvV/6mN+LPbwPG0U/9/8QKP4MBcci98bfuAKNcxhr5f5c3athWbHs4/hPTa0wPxPGT94mp3Tpjl8Z1g/gLfhbjhu+GGnss28DabfI8MKzI7n7z3NjmXT+gE2lzGLr3rerA1H3M3Pp1pmtwd65mwyinezmB+LL5VtYBRvN0zB6oJHoJ8xzMHL8FBwwSXBmOmnrd7fvuuSPG42Z2Y+X9opXHd8e75W2im4lPm3ZvnTbd++nb1799KgQQNOnz7NqFGjAByjgUREREREREREREREREqDps67TkyYMIG4uDhatGhBbm4ua9eupUKFCldd3tixY7FarUW+Wrdu7cLM/zytW7e+ZBvGjh1b2umJiIiIiIiIiIiIiNzwNKLpOhAfH8/WrVtdWmbv3r1p3759ket8fHxcWtef5e233+bs2bNFrgsMLNl0GGlpaS7MSERERERERERERETk5qKOpptUYGBgiTtjrlVhYWGlnYKIiIiIiIiIiIiIyE3NUlhYaP7EPxERERERERERERERuemcmfl8aadw3fHt+Vppp+BSGtEkIiIiIiIiIiIiIiJXx24v7QyklLmVdgIiIiIiIiIiIiIiIiJyfVJHk4iIiIiIiIiIiIiIiFwVdTTJX8ZisbB48eJLrs/KysJisZCRkfGX5fRXKK7dIiIiIiIiIiIiIiLXK3U0iYiIiIiIiIiIiIiIyFVRR5OIiIiIiIiIiIiIiIhcFXU0XUN+/fVXOnbsiJ+fHyEhIbz22ms0a9aM/v37A5CXl8cLL7xAeHg43t7eREZG8s477wBw8uRJOnbsSHBwMD4+PkRFRZGSklJsnWlpaVgsFk6dOuVYlpGRgcViISsrC4A5c+ZQrlw5li9fTs2aNbFarbRq1YojR444YjZv3sy9995LhQoVKFu2LE2bNmXbtm0X1XfkyBFat26Nj48P1atX58MPP7xsfl999RWtW7fGarVSqVIlOnXqxLFjx4ptF8CyZcu46667KFeuHEFBQbRp04ZDhw451l+Yqi81NZVGjRpRpkwZ6tSpw5o1axzb2Gw2evToQbVq1fDx8aFGjRpMmTLlorpmz55N7dq18fb2JiQkhMTERKf1x44d46GHHsLX15eoqCiWLFnisnaKiIiIiIiIiIiIlJpCu15X+rrBqKPpGjJgwADS09NZsmQJK1euZO3atU6dNZ07d2bBggVMnTqVPXv28NZbb2G1WgEYNmwYu3fv5rPPPmPPnj3MmDGDChUquCy3M2fOMGHCBObNm8f//vc/srOzSUpKcqz/9ddf6dKlC+vWrWPDhg1ERUVx//338+uvvzqVM2zYMP7xj3+QmZlJx44defzxx9mzZ0+RdZ46dYp77rmH+Ph4tmzZwrJly/jpp59o3759iXLOzc1lwIABbNmyhVWrVuHm5sZDDz2E3e58Ig8aNIiBAweyfft2EhISaNu2LcePHwfAbrdzyy238MEHH7B7926GDx/OP//5TxYuXOiInzFjBn379qVnz57s3LmTJUuWEBkZ6VTHyJEjad++PTt27OD++++nY8eOnDhxwiXtFBEREREREREREREpLZbCwsLC0k5CfuuoCQoK4r333uORRx4B4PTp04SGhvL000/Tp08fatSowcqVK2nRosVF8Q888AAVKlRg9uzZV1RvWload999NydPnqRcuXLAbyOa4uPjOXz4MBEREcyZM4du3bpx8OBB/va3vwHwxhtvMGrUKH788cciy7Xb7ZQrV4733nuPNm3aAGCxWOjduzczZsxwbHfHHXdQt25d3njjDbKysqhWrRrbt2/ntttuY8yYMaxdu5bly5c7tv/uu+8IDw9n3759REdHX1Fbjx07RnBwMDt37qROnTqO+saNG8cLL7wAwPnz56lWrRrPPvssgwcPLrKcxMREfvzxR8dorLCwMLp168aYMWOK3N5isTB06FBGjx4N/NYBZrVa+eyzz2jVqtVVtTMvL4+8vDynZd7e3nh7e1/RPhERERERERERERExcWbGs6WdwnXH95lppZ2CS2lE0zXi66+/pqCggAYNGjiWlS1blho1agC/df64u7vTtGnTIuOfeeYZUlNTue222xg8eDBffvmlS/Pz9fV1dDIBhISEcPToUcfvP/30E08//TRRUVGULVuWgIAAcnJyyM7OdionISHhot8vNaIpMzOT1atXY7VaHa9bb70VwGkKvEs5cOAAHTp0oHr16gQEBBAREQFw2Zw8PDyoX7++U07Tp0+nXr16BAcHY7VamTlzpqOMo0eP8sMPP9C8efPL5hIbG+v42c/Pj4CAAMf+u5p2JicnU7ZsWadXcnJysftERERERERERERERMSVPEo7ASkZHx+fy65v3bo133zzDZ9++ikrV66kefPm9O3blwkTJlw2zs3tt77G3w9sKygouGg7T09Pp98tFotTTJcuXTh+/DhTpkyhatWqeHt7k5CQQH5+frFtu5ScnBzatm3LK6+8ctG6kJCQYuPbtm1L1apVmTVrFqGhodjtdurUqXNFOaWmppKUlMTEiRNJSEjA39+f8ePHs3HjRqD49+WCovbfhSn8rqadQ4YMYcCAAU7LNJpJRERERERERERERP5qGtF0jahevTqenp5s3rzZsez06dPs378fgJiYGOx2O2vWrLlkGcHBwXTp0oV3332XyZMnM3PmzGLrDQ4OBuDIkSOOZRkZGVecf3p6Os899xz3338/tWvXxtvbm2PHjl203YYNGy76vWbNmkWWWbduXXbt2kVERASRkZFOLz8/v8vmc/z4cfbt28fQoUNp3rw5NWvW5OTJk0Vu+/uczp8/z9atWx05paen06hRI/r06UN8fDyRkZFOo4z8/f2JiIhg1apVl83ncq6mnd7e3gQEBDi91NEkIiIiIiIiIiIiIn81dTRdI/z9/enSpQuDBg1i9erV7Nq1ix49euDm5obFYiEiIoIuXbrQvXt3Fi9ezOHDh0lLS2PhwoUADB8+nI8//piDBw+ya9cuPvnkk0t24PxeZGQk4eHhjBgxggMHDrB06VImTpx4xflHRUUxb9489uzZw8aNG+nYsWORo30++OADZs+ezf79+3nppZfYtGkTiYmJRZbZt29fTpw4QYcOHdi8eTOHDh1i+fLldOvWDZvNdtl8ypcvT1BQEDNnzuTgwYN88cUXF40AumD69Ol89NFH7N27l759+3Ly5Em6d+/uaNeWLVtYvnw5+/fvZ9iwYU6dgQAjRoxg4sSJTJ06lQMHDrBt2zamTSv5HJsm7RQREREREREREREpVfZCva70dYNRR9M1ZNKkSSQkJNCmTRtatGjBnXfeSc2aNSlTpgwAM2bM4JFHHqFPnz7ceuutPP300+Tm5gLg5eXFkCFDiI2NpUmTJri7u5OamlpsnZ6enixYsIC9e/cSGxvLK6+8wpgxY64493feeYeTJ09St25dOnXqxHPPPUfFihUv2m7kyJGkpqYSGxvLv//9bxYsWECtWrWKLDM0NJT09HRsNhv33XcfMTEx9O/fn3Llyjmm/LsUNzc3UlNT2bp1K3Xq1OH5559n/PjxRW47btw4xo0bR1xcHOvWrWPJkiVUqFABgF69evHwww/z2GOP0bBhQ44fP06fPn2c4rt06cLkyZN54403qF27Nm3atOHAgQMl2W3G7RQRERERERERERERKU2Wwt8/aEeuKbm5uYSFhTFx4kR69OhR2unccLKysqhWrRrbt2/ntttuK+10RERERERERERERK47Z6YXPWOVXJpv39dLOwWX8ijtBOT/bN++nb1799KgQQNOnz7NqFGjAHjwwQdLOTMREREREREREREREZGLaV6ua8yECROIi4ujRYsW5ObmsnbtWsc0bldj7NixWK3WIl+tW7d2YeZ/rezs7Eu2y2q1kp2dXdopioiIiIiIiIiIiIjc8DR13g3uxIkTnDhxosh1Pj4+hIWF/cUZucb58+fJysq65PqIiAg8PDRgT0REREREREREROTPpKnzrpymzpPrSmBgIIGBgaWdhst5eHgQGRlZ2mmIiIiIiIiIiIiI3Nzs9tLOQEqZOppEbhBvhD9pXEZp3xJMh1eWccH4zDOGE4p6GuZw1mIWD1BgWEawzSz+vFk4ACfczeJN3wdXHEs93qxvVsCxH43CfbqMM6sfKDj2tVH8kPr/MoqffyrTKB7gxYC6RvFuhsdCRIHZCfXQif+ZJQCMCbnbKD7mnFkbdpYxPKFdkMMnPmbxt9q9jOIBCgzj4/PMSsjw9jTMAG4zzGFTGbMcwk13IrDBy6yQpnlmX58Oe5nf6BPyzhnFr/cuU6r1z3bBTfapc2b7cb2X2T6oWmDehm88zdpgmsNmb/NP/v6FZh+cy9nN9oHpPRYgy9PsHlWlwGw/fu1l/jQDb8PD8Qd3szaEnzdvg83w0hiRb/YNJMvL7NpuWj/AJsPPS9XPm+3ELA/z61qtfLP4gfk7jeJ93L3NEgD6etcwije8rGF1wR9lcgxPSV/DHEzPZzD/25TpVan3t+8aliByc9IzmkREREREREREREREROSqqKNJREREREREREREREREroo6mkREREREREREREREROSqqKNJ/jJdu3alXbt2RmVkZWVhsVjIyMhwSU5/hREjRnDbbbeVdhoiIiIiIiIiIiIiIi5n9sRDERERERERERERERG5edntpZ2BlDJ1NMmfzmazYbFYSjsNERERERERERERERFxMU2ddw379ddf6dixI35+foSEhPDaa6/RrFkz+vfvD0BeXh4vvPAC4eHheHt7ExkZyTvvvAPAyZMn6dixI8HBwfj4+BAVFUVKSkqxdTZq1IgXXnjBadnPP/+Mp6cn//vf/xz1JiUlERYWhp+fHw0bNiQtLc2x/Zw5cyhXrhxLliyhVq1aeHt7k52d7Vg/cuRIgoODCQgIoHfv3uTn5zvWLVu2jLvuuoty5coRFBREmzZtOHTo0FXtP5vNRo8ePahWrRo+Pj7UqFGDKVOmOG1zYTo/05y+++47OnToQGBgIH5+ftSvX5+NGzc6bTNv3jwiIiIoW7Ysjz/+OL/++qtjnd1uJzk52ZFrXFwcH3744VW1W0RERERERERERETkr6IRTdewAQMGkJ6ezpIlS6hUqRLDhw9n27Ztjuf9dO7cmfXr1zN16lTi4uI4fPgwx44dA2DYsGHs3r2bzz77jAoVKnDw4EHOnj1bbJ0dO3bk1VdfZdy4cY5RSO+//z6hoaE0btwYgMTERHbv3k1qaiqhoaF89NFHtGrVip07dxIVFQXAmTNneOWVV3j77bcJCgqiYsWKAKxatYoyZcqQlpZGVlYW3bp1IygoiJdffhmA3NxcBgwYQGxsLDk5OQwfPpyHHnqIjIwM3NyurF/Ubrdzyy238MEHHxAUFMSXX35Jz549CQkJoX379o7tTHPKycmhadOmhIWFsWTJEipXrsy2bduw/27I6KFDh1i8eDGffPIJJ0+epH379owbN85RR3JyMu+++y5vvvkmUVFR/O9//+PJJ58kODiYpk2bXlG7RURERERERERERET+Kupoukb9+uuvzJ07l/fee4/mzZsDkJKSQmhoKAD79+9n4cKFrFy5khYtWgBQvXp1R3x2djbx8fHUr18fgIiIiBLV2759e/r378+6descHUvvvfceHTp0wGKxkJ2dTUpKCtnZ2Y5ckpKSWLZsGSkpKYwdOxaAgoIC3njjDeLi4pzK9/LyYvbs2fj6+lK7dm1GjRrFoEGDGD16NG5ubvzjH/9w2n727NkEBweze/du6tSpcyW7EE9PT0aOHOn4vVq1aqxfv56FCxc6dTSZ5vTee+/x888/s3nzZgIDAwGIjIx0irHb7cyZMwd/f38AOnXqxKpVq3j55ZfJy8tj7NixfP755yQkJAC/vZfr1q3jrbfeKrKjKS8vj7y8PKdlBYU2PC3uV7SPRERERERERERERERMaOq8a9TXX39NQUEBDRo0cCwrW7YsNWrUACAjIwN3d/dLjnZ55plnSE1N5bbbbmPw4MF8+eWXJao3ODiY++67j/nz5wNw+PBh1q9fT8eOHQHYuXMnNpuN6OhorFar47VmzRqn6eS8vLyIjY29qPy4uDh8fX0dvyckJJCTk8O3334LwIEDB+jQoQPVq1cnICDA0UH2+6n3rsT06dOpV68ewcHBWK1WZs6ceVFZpjllZGQQHx/v6GQqSkREhKOTCSAkJISjR48CcPDgQc6cOcO9997rtE///e9/X3LawOTkZMqWLev0WvHLrivfQSIiIiIiIiIiIiIiBjSi6Trl4+Nz2fWtW7fmm2++4dNPP2XlypU0b96cvn37MmHChGLL7tixI8899xzTpk3jvffeIyYmhpiYGABycnJwd3dn69atuLs7j56xWq1O+V2Yeu9KtG3blqpVqzJr1ixCQ0Ox2+3UqVPH6ZlJJZWamkpSUhITJ04kISEBf39/xo8ff9Gzk0xzKu69gN9GV/2exWJxTK2Xk5MDwNKlSwkLC3Paztvbu8jyhgwZwoABA5yWpdTqVbIGiYiIiIiIiIiIiLhKYWFpZyClTCOarlHVq1fH09OTzZs3O5adPn2a/fv3AxATE4PdbmfNmjWXLCM4OJguXbrw7rvvMnnyZGbOnFmiuh988EHOnTvHsmXLeO+99xyjmQDi4+Ox2WwcPXqUyMhIp1flypWLLTszM9PpWVEbNmzAarUSHh7O8ePH2bdvH0OHDqV58+bUrFmTkydPlijnoqSnp9OoUSP69OlDfHw8kZGRRY4QMs0pNjaWjIwMTpw4cVV51qpVC29vb7Kzsy/ap+Hh4UXGeHt7ExAQ4PTStHkiIiIiIiIiIiIi8lfTiKZrlL+/P126dGHQoEEEBgZSsWJFXnrpJdzc3LBYLERERNClSxe6d+/O1KlTiYuL45tvvuHo0aO0b9+e4cOHU69ePWrXrk1eXh6ffPIJNWvWLFHdfn5+tGvXjmHDhrFnzx46dOjgWBcdHU3Hjh3p3LkzEydOJD4+np9//plVq1YRGxvL3//+98uWnZ+fT48ePRg6dChZWVm89NJLJCYm4ubmRvny5QkKCmLmzJmEhISQnZ3Niy++eNX7MCoqin//+98sX76catWqMW/ePDZv3ky1atVcmlOHDh0YO3Ys7dq1Izk5mZCQELZv305oaKjjmUuX4+/vT1JSEs8//zx2u5277rqL06dPk56eTkBAAF26dLnqfSAiIiIiIiIiIiIi8mfSiKZr2KRJk0hISKBNmza0aNGCO++8k5o1a1KmTBkAZsyYwSOPPEKfPn249dZbefrpp8nNzQV+e0bSkCFDiI2NpUmTJri7u5Oamlriujt27EhmZiaNGzemSpUqTutSUlLo3LkzAwcOpEaNGrRr147NmzdftF1RmjdvTlRUFE2aNOGxxx7jgQceYMSIEQC4ubmRmprK1q1bqVOnDs8//zzjx48vcc5/1KtXLx5++GEee+wxGjZsyPHjx+nTp4/Lc/Ly8mLFihVUrFiR+++/n5iYGMaNG3fR1IKXM3r0aIYNG0ZycjI1a9akVatWLF269KJOMRERERERERERERGRa4mlsFATKF4vcnNzCQsLY+LEifTo0aO007khdO3alVOnTrF48eLSTsXYG+FPGpdhd0EeJkwvRmVccDU7Y9j97mmYw9krf7TZRQoMywi2mcWfNwsH4IThTJCm74MrjqUeb9Y3K+DYj0bhPl3GmdUPFBz72ih+SP1/GcXPP5VpFA/wYkBdo3g3w2MhosDshHroxP/MEgDGhNxtFB9zzqwNO8uYT+1qmsMnPmbxt9q9jOIBCgzj4/PMSsjw9ix+o2LcZpjDpjJmOYSb7kRgg5dZIU3zzCaEOOxlfqNPyDtnFL/eu0yp1j/bBTfZp86Z7cf1Xmb7oGqBeRu+8TRrg2kOm73NP/n7F5p9cC5nN9sHpvdYgCxPs3tUlQKz/fi1l/n//nobHo4/uJu1Ify8eRtshpfGiHyzbyBZXmbXdtP6ATYZfl6qft5sJ2Z5mF/Xal35o7WdDMzfaRTv4170M66vRF/vGkbxhpc1rC74o0yO4Snpa5iD6fkM5n+bMr0q9f72XcMSbk5nJuvZ8VfKt/9bpZ2CS2nqvGvY9u3b2bt3Lw0aNOD06dOMGjUK+O0ZSiIiIiIiIiIiIiIiIqVNU+dd4yZMmEBcXBwtWrQgNzeXtWvXUqFChasub+zYsVit1iJfrVu3dmHmf77evXtfsi29e/cu7fREREREREREREREbnx2u15X+rrBaETTNSw+Pp6tW7e6tMzevXvTvn37Itf5+Pi4tK4/26hRo0hKSipyXUBAQInKmDNnjgszEhERERERERERERG5uaij6SYTGBhIYGBgaafhEhUrVqRixYqlnYaIiIiIiIiIiIiIyE1LU+eJiIiIiIiIiIiIiIjIVbEUFhYWlnYSImJuWviTxmVYDONNLya73fON4v9m9zLMAM5YzFrhV2i2F31ccEW2Gk7z+qPhWNeTFvN5ZsNtZv8HcdLNbEf6Gr6PAEfdzPaDu2H9Y7LeMywBkiI6GMUnb3nZKH5xzDCjeIBT7mbvZYz9jFH8T/YyRvEeLviYVmAx2wffeJmdj1Xzza8Jpjn0nNvMKN6++nOjeIAP3jG7uGZ5mB0LEefNr2umOTTPyzOKP4WnUTzADx5mV9dDHjaj+MBC06s7fGcpMIqvUmi2H7MN6+9lMbuuAuzIK2sU72V4bf3F8N4CEGAr3RyeGP83o3gAzp01Ck8Z/oNR/BnztwFfw9usaQ7N7DlmBQA2u9k98gtPX6P4PONvgebOGn7/8Ck024em9QMknDM7mHLczNrwi/ntiaOG38EanDO7x9oMP/MCfOtptiNM/5v/rAuua6Z/U7CavQ3kXgNDGrwN90HX7991TSI3mTOTni7tFK47vgNmlXYKLnUNnP4iIiIiIiIiIiIiIiJyPdIzmkRERERERERERERE5OrYS3+Eq5QujWi6xjVr1oz+/fuXdhpylUaMGMFtt91W2mmIiIiIiIiIiIiIiPwp1NEkRcrKysJisZCRkfGn15WWlobFYuHUqVN/el0iIiIiIiIiIiIiIuI66miSi+Tn519T5YiIiIiIiIiIiIiIyLVJHU3XAbvdzuDBgwkMDKRy5cqMGDHCse7UqVM89dRTBAcHExAQwD333ENmZqZj/aFDh3jwwQepVKkSVquV22+/nc8//9yp/IiICEaPHk3nzp0JCAigZ8+eVKtWDYD4+HgsFgvNmjUrNs+uXbvSrl07Xn75ZUJDQ6lRowYA8+bNo379+vj7+1O5cmWeeOIJjh49Cvw2curuu+8GoHz58lgsFrp27epod3JyMtWqVcPHx4e4uDg+/PDDEu0zm81Gjx49HLE1atRgypQpReY7cuRIx/7r3bu3UwfZsmXLuOuuuyhXrhxBQUG0adOGQ4cOOZXz3Xff0aFDBwIDA/Hz86N+/fps3LjRaZt58+YRERFB2bJlefzxx/n1118d60zaKSIiIiIiIiIiIiJSmtTRdB2YO3cufn5+bNy4kVdffZVRo0axcuVKAB599FGOHj3KZ599xtatW6lbty7NmzfnxIkTAOTk5HD//fezatUqtm/fTqtWrWjbti3Z2dlOdUyYMIG4uDi2b9/OsGHD2LRpEwCff/45R44cYdGiRSXKddWqVezbt4+VK1fyySefAFBQUMDo0aPJzMxk8eLFZGVlOTqTwsPD+c9//gPAvn37OHLkiKNDKDk5mX//+9+8+eab7Nq1i+eff54nn3ySNWvWFJuH3W7nlltu4YMPPmD37t0MHz6cf/7znyxcuPCifPfs2UNaWhoLFixg0aJFjBw50rE+NzeXAQMGsGXLFlatWoWbmxsPPfQQdrvdsX+bNm3K999/z5IlS8jMzGTw4MGO9fBbZ9/ixYv55JNP+OSTT1izZg3jxo1zrDdpp4iIiIiIiIiIiIhIafIo7QSkeLGxsbz00ksAREVF8frrr7Nq1Sp8fHzYtGkTR48exdvbG/itw2jx4sV8+OGH9OzZk7i4OOLi4hxljR49mo8++oglS5aQmJjoWH7PPfcwcOBAx+/u7u4ABAUFUbly5RLn6ufnx9tvv42Xl5djWffu3R0/V69enalTp3L77beTk5OD1WolMDAQgIoVK1KuXDkA8vLyGDt2LJ9//jkJCQmO2HXr1vHWW2/RtGnTy+bh6enp1GFUrVo11q9fz8KFC2nfvr1juZeXF7Nnz8bX15fatWszatQoBg0axOjRo3Fzc+Mf//iHU7mzZ88mODiY3bt3U6dOHd577z1+/vlnNm/e7GhHZGSkU4zdbmfOnDn4+/sD0KlTJ1atWsXLL79s3E4RERERERERERGRUlVoL34buaGpo+k6EBsb6/R7SEgIR48eJTMzk5ycHIKCgpzWnz171jG9W05ODiNGjGDp0qUcOXKE8+fPc/bs2YtGNNWvX98lucbExDh1MgFs3bqVESNGkJmZycmTJx2jfbKzs6lVq1aR5Rw8eJAzZ85w7733Oi3Pz88nPj6+RLlMnz6d2bNnk52dzdmzZ8nPz+e2225z2iYuLg5fX1/H7wkJCeTk5PDtt99StWpVDhw4wPDhw9m4cSPHjh1zyr1OnTpkZGQQHx/v6GQqSkREhKOTCf7v/TNpZ15eHnl5eU7LCgpteFrcL79TRERERERERERERERcSB1N1wFPT0+n3y0WC3a7nZycHEJCQkhLS7so5sLIoKSkJFauXMmECROIjIzEx8eHRx55xOk5RPDbSCRX+GM5ubm5tGzZkpYtWzJ//nyCg4PJzs6mZcuWF+Xwezk5OQAsXbqUsLAwp3UXRm9dTmpqKklJSUycOJGEhAT8/f0ZP378Rc9OKk7btm2pWrUqs2bNIjQ0FLvdTp06dRy5+/j4FFvGpd4/uPp2JicnO43YAmjlH8P9ZWMvESEiIiIiIiIiIiIi4nrqaLqO1a1blx9//BEPDw8iIiKK3CY9PZ2uXbvy0EMPAb91bGRlZRVb9oVRSTabzSjHvXv3cvz4ccaNG0d4eDgAW7ZsKbauWrVq4e3tTXZ29lVNH5eenk6jRo3o06ePY9mFUV6/l5mZydmzZx0dRhs2bMBqtRIeHs7x48fZt28fs2bNonHjxgCsW7fOKT42Npa3336bEydOXHZU06VcbTuHDBnCgAEDnJa9XavXFdcvIiIiIiIiIiIiImLCrbQTkKvXokULEhISaNeuHStWrCArK4svv/ySf/3rX47OnKioKBYtWkRGRgaZmZk88cQTjtE0l1OxYkV8fHxYtmwZP/30E6dPn76qHKtUqYKXlxfTpk3j66+/ZsmSJYwePdppm6pVq2KxWPjkk0/4+eefycnJwd/fn6SkJJ5//nnmzp3LoUOH2LZtG9OmTWPu3LnF1hsVFcWWLVtYvnw5+/fvZ9iwYWzevPmi7fLz8+nRowe7d+/m008/5aWXXiIxMRE3NzfKly9PUFAQM2fO5ODBg3zxxRcXde506NCBypUr065dO9LT0/n666/5z3/+w/r160u0f662nd7e3gQEBDi9NG2eiIiIiIiIiIiIiPzV1NF0HbNYLHz66ac0adKEbt26ER0dzeOPP84333xDpUqVAJg0aRLly5enUaNGtG3blpYtW1K3bt1iy/bw8GDq1Km89dZbhIaG8uCDD15VjsHBwcyZM4cPPviAWrVqMW7cOCZMmOC0TVhYGCNHjuTFF1+kUqVKJCYmAjB69GiGDRtGcnIyNWvWpFWrVixdupRq1aoVW2+vXr14+OGHeeyxx2jYsCHHjx93Gt10QfPmzYmKiqJJkyY89thjPPDAA4wYMQIANzc3UlNT2bp1K3Xq1OH5559n/PjxTvFeXl6sWLGCihUrcv/99xMTE8O4ceNwdy95p49JO0VERERERERERERESpOlsLCwsLSTECkNXbt25dSpUyxevLi0U3GJaeFPGpdhMYw3vZjsdr/0c7tK4m92L8MM4IzFrBV+hWZ70ccFV2Rr8YMWL+tHw0lVT1oMEwDCbWb/B3HSzWxH+hq+jwBH3cz2g+kYxTFZ7xmWAEkRHYzik7e8bBS/OGaYUTzAKXez9zLGfsYo/id7GaN4Dxd8TCuwmO2Db7zMzseq+ebXBNMces5tZhRvX/25UTzAB++YXVyzPMyOhYjz5tc10xya5+UZxZ/Cs/iNivGDh9nV9ZCH2bTSgYXmI9C/sxQYxVcpNNuP2Yb197KYXVcBduSVNYr3Mry2/mJ4bwEIsJVuDk+M/5tRPADnzhqFpwz/wSj+jPnbgK/hbdY0h2b2HLMCAJvd7B75haevUXye8bdAc2cNv3/4FJrtQ9P6ARLOmR1MOW5mbfjFBROkHDX8DtbgnNk91mb4mRfgW0+zHWH63/xnXXBdM/2bgtXsbSD3GhjS4G24D7p+/65rErnJnHmlW2mncN3xfSGltFNwqWvg9BcREREREREREREREZHrkTqapMSsVuslX2vXrv1Lc+ndu/clc+ndu/dfmouIiIiIiIiIiIiIyM3KcIIkuZlkZGRccl1YWNhflwgwatQokpKSilwXEBBQojLmzJnjwoxERERERERERERERG4+6miSEouMjCztFBwqVqxIxYoVSzsNEREREREREREREZGbmqbOExERERERERERERERkauiEU0iNwi7xbwM90KzeC/D+Fo2L6N4X8P6AWoW2I3iv/VyN4r/0c28Eb6GB0OQzax+q8X8fxgqG74PUYVmjdjr5WkUD/DRmQNG8T5uZufDGKPo38w/lWkU3zBmmFF8u52jjeIBXq873Ch+vZuvUXy223mj+DG9vI3iAd6eYXY+fO9meFEwvC66IocvHl9lFH/Yy/wj8znD3XDUUmAUb3U3u6YAHLOYHc/7PcoYxX9r+kEFuP2c2bF01N3sHufjgs8qZyxmbfAsNLvHmdZ/9IzZdRUgzSfPKD7WZnZt/dHd7HMKQGXDz0umOXwy4KBRPECum9lnzhzDS+uPhtckgMqFZkmY5rC+0GoUD5Br+LG1er7Zhek7T/MvogWGRZzA7Hwoj9lN2rR+gAKL4bFoeD5F5Zu34Zi32RsZU+Vno3g3D/Ob7LffhRrFm+9Fc6ZXxuOGx5IL/qRh7JyGVZSKQvu1cAZIadKpJyIiIiIiIiIiIiIico2aPn06ERERlClThoYNG7Jp06bLbn/q1Cn69u1LSEgI3t7eREdH8+mnn/5p+WlEk4iIiIiIiIiIiIiIyDXo/fffZ8CAAbz55ps0bNiQyZMn07JlS/bt20fFihUv2j4/P597772XihUr8uGHHxIWFsY333xDuXLl/rQc1dEkIiIiIiIiIiIiIiLyF8nLyyMvz3lKaG9vb7y9L57medKkSTz99NN069YNgDfffJOlS5cye/ZsXnzxxYu2nz17NidOnODLL7/E0/O3eXYjIiJc34jf0dR516lmzZrRv3//P72eiIgIJk+e/KfXU1Jz5sz5U3te/wx/1XslIiIiIiIiIiIiIte+5ORkypYt6/RKTk6+aLv8/Hy2bt1KixYtHMvc3Nxo0aIF69evL7LsJUuWkJCQQN++falUqRJ16tRh7Nix2GyGz2C+DHU0XacWLVrE6NEle0h6VlYWFouFjIyMPzepS7geO4dERERERERERERERP4MQ4YM4fTp006vIUOGXLTdsWPHsNlsVKpUyWl5pUqV+PHHH4ss++uvv+bDDz/EZrPx6aefMmzYMCZOnMiYMWP+lLaAps67bgUGBpZ2CiIiIiIiIiIiIiJys7MXlnYG151LTZPnCna7nYoVKzJz5kzc3d2pV68e33//PePHj+ell176U+rUiKbr1O+nY4uIiGDs2LF0794df39/qlSpwsyZMx3bVqtWDYD4+HgsFgvNmjW7qIwL2rVrR9euXZ2W/frrr3To0AE/Pz/CwsKYPn260/pJkyYRExODn58f4eHh9OnTh5ycHADS0tLo1q0bp0+fxmKxYLFYGDFiBPDbPJRJSUmEhYXh5+dHw4YNSUtLcyp7zpw5VKlSBV9fXx566CGOHz9e4n106NAhHnzwQSpVqoTVauX222/n888/d9omIiKC0aNHX3X7LkhPT6dZs2b4+vpSvnx5WrZsycmTJx3r7XY7gwcPJjAwkMqVKzv2wQWnTp3iqaeeIjg4mICAAO655x4yMzNL3FYRERERERERERERubFUqFABd3d3fvrpJ6flP/30E5UrVy4yJiQkhOjoaNzd3R3LatasyY8//kh+fv6fkqc6mm4QEydOpH79+mzfvp0+ffrwzDPPsG/fPgA2bdoEwOeff86RI0dYtGjRFZU9fvx44uLi2L59Oy+++CL9+vVj5cqVjvVubm5MnTqVXbt2MXfuXL744gsGDx4MQKNGjZg8eTIBAQEcOXKEI0eOkJSUBEBiYiLr168nNTWVHTt28Oijj9KqVSsOHDgAwMaNG+nRoweJiYlkZGRw9913X9HwvpycHO6//35WrVrF9u3badWqFW3btiU7O9tl7QPIyMigefPm1KpVi/Xr17Nu3Tratm3rNOfl3Llz8fPzY+PGjbz66quMGjXKqY5HH32Uo0eP8tlnn7F161bq1q1L8+bNOXHiRInbKyIiIiIiIiIiIiI3Di8vL+rVq8eqVascy+x2O6tWrSIhIaHImDvvvJODBw9it9sdy/bv309ISAheXl5/Sp6aOu8Gcf/999OnTx8AXnjhBV577TVWr15NjRo1CA4OBiAoKOiSvZyXc+edd/Liiy8CEB0dTXp6Oq+99hr33nsvgNOoqIiICMaMGUPv3r1544038PLyomzZslgsFqe6s7OzSUlJITs7m9DQUACSkpJYtmwZKSkpjB07lilTptCqVStHp050dDRffvkly5YtK1HecXFxxMXFOX4fPXo0H330EUuWLCExMdEl7QN49dVXqV+/vuN3gNq1azvlEhsb6xiWGBUVxeuvv86qVau49957WbduHZs2beLo0aOO4ZITJkxg8eLFfPjhh/Ts2fOituXl5ZGXl+e07HyhDQ+L+0XbioiIiIiIiIiIiMj1acCAAXTp0oX69evToEEDJk+eTG5uLt26dQOgc+fOhIWFkZycDMAzzzzD66+/Tr9+/Xj22Wc5cOAAY8eO5bnnnvvTctSIphtEbGys4+cLnTpHjx51Sdl/7BlNSEhgz549jt8///xzmjdvTlhYGP7+/nTq1Injx49z5syZS5a5c+dObDYb0dHRWK1Wx2vNmjUcOnQIgD179tCwYcPL5nI5OTk5JCUlUbNmTcqVK4fVamXPnj0XjWgybd+FEU2X8/v3B34bvnjh/cnMzCQnJ4egoCCnfXH48GHHvvij5ORkypYt6/Ra+cuuku0YEREREREREREREbkuPPbYY0yYMIHhw4dz2223kZGRwbJly6hUqRLw26COI0eOOLYPDw9n+fLlbN68mdjYWJ577jn69evnGGzxZ9CIphuEp6en0+8Wi8VpaFxR3NzcKCx0flBbQUHBFdWblZVFmzZteOaZZ3j55ZcJDAxk3bp19OjRg/z8fHx9fYuMy8nJwd3dna1btzrNFQlgtVqvKIdLSUpKYuXKlUyYMIHIyEh8fHx45JFHrmgeypK0z8fHp9hyLvf+5OTkEBISctHzqQDKlStXZHlDhgxhwIABTstm1e5VskaJiIiIiIiIiIiIyHUjMTHRaZau3yvq78oJCQls2LDhT87q/6ij6SZwYd7F3z8zCCA4ONipp9Nms/HVV19x9913O233xwNyw4YN1KxZE4CtW7dit9uZOHEibm6/DZBbuHDhRfX/se74+HhsNhtHjx6lcePGReZds2ZNNm7ceNlcLic9PZ2uXbvy0EMPAb916GRlZV20nWn7YmNjWbVqFSNHjixxbr9Xt25dfvzxRzw8PIiIiChRjLe3t2OavQs0bZ6IiIiIiIiIiIj85QovP+BBbnyaOu8mULFiRXx8fFi2bBk//fQTp0+fBuCee+5h6dKlLF26lL179/LMM89w6tSpi+LT09N59dVX2b9/P9OnT+eDDz6gX79+AERGRlJQUMC0adP4+uuvmTdvHm+++aZTfEREBDk5OaxatYpjx45x5swZoqOj6dixI507d2bRokUcPnyYTZs2kZyczNKlSwF47rnnWLZsGRMmTODAgQO8/vrrJX4+E/z2LKRFixaRkZFBZmYmTzzxRJGjvEzbN2TIEDZv3kyfPn3YsWMHe/fuZcaMGRw7dqxEebZo0YKEhATatWvHihUryMrK4ssvv+Rf//oXW7ZsKXF7RURERERERERERET+aupougl4eHgwdepU3nrrLUJDQ3nwwQcB6N69O126dKFz5840bdqU6tWrXzSaCWDgwIFs2bKF+Ph4xowZw6RJk2jZsiUAcXFxTJo0iVdeeYU6deowf/58x0PHLmjUqBG9e/fmscceIzg4mFdffRWAlJQUOnfuzMCBA6lRowbt2rVj8+bNVKlSBYA77riDWbNmMWXKFOLi4lixYgVDhw4tcbsnTZpE+fLladSoEW3btqVly5bUrVvX5e2Ljo5mxYoVZGZm0qBBAxISEvj444/x8CjZgEGLxcKnn35KkyZN6NatG9HR0Tz++ON88803jnk2RURERERERERERESuRZbCPz6kR+QmEhERQf/+/enfv39pp2JsSpUnjctwN7waeBrG51vM4n1dcDWrXGArfqPL+NbLbArDH93MGxFx3mxH2gzfhzzDeIDKBWZDrv0Lzd7HvV6exW9UjOl5+4zifdy8jOK3/5huFA8QUq6WUfxUn3ij+HY7RxvFA7xed7hxGSay3c4bxY/p5V38RsV4e4bZ+fC9m1l8mN18alfTHJqeNav/sOG1HeCc4bXxa7eSP2OyKNXtZtcUgCy3K3uW5x/Fnze7tn5r+kEFuP2c2f1lWxmz/9MLKDS/SWa6nTOKj7GXMYrfaVh/x7Pm51Oqj9n7GGszu7b+6G4+NUxlm9mxZJrDbS74wJbrZlbGUcOJ/H+0mN1jASoXmiVhmkM1m/nTDHIN/324Wr7ZtfU7T/NjqcCwiB8sZven0EKz+5Np/QCN88yOhW8M34eofPPrWqa3WQ7dKv5oFO/mYf454ePvQo3LMOGK79Kmf5cx/XuAC/6kYcxu2IZ+2e+6JpGbTO4Y879L3mz8ht5Yx5pGNImIiIiIiIiIiIiIiMhVUUeTXLdq166N1Wot8jV//vzSTk9ERERERERERERE5IZnPk5bpJR8+umnFBQUPUS9pM82ysrKcmFGIiIiIiIiIiIiIjcZ+zUwb6KUKnU0yXWratWqpZ2CiIiIiIiIiIiIiMhNTR1NIjcIVzxw0bSIfMMHLp43jPc2e1Y8AN8YPvDd8NnUBGH+9E/TByuHF5gdCW4W8zZ872k2s6un4cyw5o8ph2fK1HBBKaXrxYC6RvGnDM+H1+sONysASNw2yih+WZ1/GcW7eZmdkDNnmF/Yujb8zij+3BGzc7pMiPkNyjSH/3wTZhRv+pBygN4dzxrF5311zCjeu04Fo3hX5PDZ1nCj+MouuL/sLGN2ha9oeEp2aPmTWQHA2UNmD533+ZvZQ+9N6597+BajeIBY0/eh1rdG8bZ8s/oB3L3M4k1zSD1kdj4CnDeM793C7Hyw/WJ2LAK4B5idD6Y5fPyl+flg1gL40dPs2urngu+h7euZnZP2M2b1u/maxZvWD/DhbrNz0vT+tMfb/MkavobHwvs/hRjFu+DjGt6G8WcMd6Ov4fcngF8Nc/AyfB/dXPBG6DkvItcnnbsiIiIiIiIiIiIiIiJyVdTRJCIiIiIiIiIiIiIiIldFHU0iIiIiIiIiIiIiIiJyVfSMJpE/mcVi4aOPPqJdu3alnYqIiIiIiIiIiIiIa9ld8JAxua5pRJOIiIiIiIiIiIiIiIhcFXU0iYiIiIiIiIiIiIiIyFVRR5O4RLNmzXj22Wfp378/5cuXp1KlSsyaNYvc3Fy6deuGv78/kZGRfPbZZwDYbDZ69OhBtWrV8PHxoUaNGkyZMsWpzK5du9KuXTtGjhxJcHAwAQEB9O7dm/z8/BLltGzZMu666y7KlStHUFAQbdq04dChQ471WVlZWCwWUlNTadSoEWXKlKFOnTqsWbPGsU1J8gSYPXs2tWvXxtvbm5CQEBITE53WHzt2jIceeghfX1+ioqJYsmSJ0/qvvvqK1q1bY7VaqVSpEp06deLYsWMlaqeIiIiIiIiIiIiISGlRR5O4zNy5c6lQoQKbNm3i2Wef5ZlnnuHRRx+lUaNGbNu2jfvuu49OnTpx5swZ7HY7t9xyCx988AG7d+9m+PDh/POf/2ThwoVOZa5atYo9e/aQlpbGggULWLRoESNHjixRPrm5uQwYMIAtW7awatUq3NzceOihh7D/Yc7QQYMGMXDgQLZv305CQgJt27bl+PHjACXKc8aMGfTt25eePXuyc+dOlixZQmRkpFMdI0eOpH379uzYsYP777+fjh07cuLECQBOnTrFPffcQ3x8PFu2bGHZsmX89NNPtG/f/orfAxERERERERERERGRv5JHaScgN464uDiGDh0KwJAhQxg3bhwVKlTg6aefBmD48OHMmDGDHTt2cMcddzh1GFWrVo3169ezcOFCpw4WLy8vZs+eja+vL7Vr12bUqFEMGjSI0aNH4+Z2+X7Sf/zjH06/z549m+DgYHbv3k2dOnUcyxMTEx3bzpgxg2XLlvHOO+8wePBgPD09i81zzJgxDBw4kH79+jm2u/32253q7tq1Kx06dABg7NixTJ06lU2bNtGqVStef/114uPjGTt2rFOu4eHh7N+/n+jo6IvalpeXR15entOygkIbnhb3y+4TERERERERERERERFX0ogmcZnY2FjHz+7u7gQFBRETE+NYVqlSJQCOHj0KwPTp06lXrx7BwcFYrVZmzpxJdna2U5lxcXH4+vo6fk9ISCAnJ4dvv/222HwOHDhAhw4dqF69OgEBAURERABcVEdCQoLjZw8PD+rXr8+ePXscyy6X59GjR/nhhx9o3rx5ifeNn58fAQEBjv2QmZnJ6tWrsVqtjtett94K4DTV3+8lJydTtmxZp9fKX3YVu09EREREREREREREXMpeqNeVvm4wGtEkLuPp6en0u8VicVpmsViA36ajS01NJSkpiYkTJ5KQkIC/vz/jx49n48aNLsunbdu2VK1alVmzZhEaGordbqdOnTolfsYTUGyePj4+JSqnqH1zYQq/nJwc2rZtyyuvvHJRXEhISJHlDRkyhAEDBjgte7tWrxLlIiIiIiIiIiIiIiLiKupoklKRnp5Oo0aN6NOnj2NZUaN3MjMzOXv2rKNDZ8OGDVitVsLDwy9b/vHjx9m3bx+zZs2icePGAKxbt67IbTds2ECTJk0AOH/+PFu3biUxMbFEefr7+xMREcGqVau4++67S9L0i9StW5f//Oc/RERE4OFRslPS29sbb29vp2WaNk9ERERERERERERE/mqaOk9KRVRUFFu2bGH58uXs37+fYcOGsXnz5ou2y8/Pp0ePHuzevZtPP/2Ul156icTExGKfz1S+fHmCgoKYOXMmBw8e5IsvvrhoBNAF06dP56OPPmLv3r307duXkydP0r179xLnOWLECCZOnMjUqVM5cOAA27ZtY9q0aSXeF3379uXEiRN06NCBzZs3c+jQIZYvX063bt2w2WwlLkdERERERERERERE5K+mjiYpFb169eLhhx/mscceo2HDhhw/ftxp1NAFzZs3JyoqiiZNmvDYY4/xwAMPMGLEiGLLd3NzIzU1la1bt1KnTh2ef/55xo8fX+S248aNY9y4ccTFxbFu3TqWLFlChQoVSpxnly5dmDx5Mm+88Qa1a9emTZs2HDhwoMT7IjQ0lPT0dGw2G/fddx8xMTH079+fcuXKFduhJiIiIiIiIiIiIiJSmjR1nrhEWlraRcuysrIuWlZY+H8POktJSSElJcVpfXJy8kUxI0eOZOTIkVecU4sWLdi9e/cl67+gZs2al3w2lLe3d4ny7NWrF716Ff2MpKLqPHXqlNPvUVFRLFq0qMh4EREREREREREREZFrlTqaRERERERERERERETk6hTaSzsDKWWal0uuS9nZ2Vit1ku+srOzSztFEREREREREREREZEbnkY0yTVrzpw5l1wXGhpKRkbGZdcXJyIioshp7UREREREREREREREpGTU0STXJQ8PDyIjI0s7DRERERERERERERGRm5qlUEM6RG4Ib4Q/aVyG6WyqeRaz+HKGCZjWD7DP/bxRfMVCd6P4ALt5I7wMr+pnDCdVPeZmPi/vGcOj0YbZTrjV5mkUD+bnk+nN+dlv3zUsAaYZXldut50xil/v4WsUDxCZbzOKb/XVy0bxC+KGG8W74rpWYFhGeZvZ0XjS3bwRZc3eRsLteUbxeYXms00XYLYfCg3jLcZXFfMcmj1ldo89seKEUTzArJ8rG8W7Ge7GqufNz4d8wyJMPyeY1m91wfT95wxPSbMjEbxd8A3a9PpumkN1m9l1EcyvK6cw/7x1vWv692PGZeR/l28U/9q+MKN4z0Lz61qYzawM08uK6V3eFU8lKe3nWph+BwTz/VAt36yEAov5sXjcw6yMAsP6XfAxwfhYMr1Pm95jAVxwWTHS2wXfpW9GucPal3YK1x2/0QtLOwWXKu17mYiIiIiIiIiIiIiIiFynNHWeiIiIiIiIiIiIiIhcHbsmTbvZaUSTiIiIiIiIiIiIiIiIXBV1NIn8ibp27Uq7du1KOw0RERERERERERERkT+FOpqk1M2ZM4dy5cqVdhoiIiIiIiIiIiIiInKF1NF0k8jPz78p6hQRERERERERERERkb+OOppuUM2aNSMxMZH+/ftToUIFWrZsyVdffUXr1q2xWq1UqlSJTp06cezYMUfMhx9+SExMDD4+PgQFBdGiRQtyc3OB/5sCbuTIkQQHBxMQEEDv3r2dOpOKqhNg0qRJxMTE4OfnR3h4OH369CEnJweAtLQ0unXrxunTp7FYLFgsFkaMGAFAXl4eSUlJhIWF4efnR8OGDUlLSytR+48fP06HDh0ICwvD19eXmJgYFixYUOQ+SkxMpGzZslSoUIFhw4ZRWPh/D6+bN28e9evXx9/fn8qVK/PEE09w9OhRp3J27dpFmzZtCAgIwN/fn8aNG3Po0CGnbSZMmEBISAhBQUH07duXgoICxzqTdoqIiIiIiIiIiIiIlCZ1NN3A5s6di5eXF+np6YwbN4577rmH+Ph4tmzZwrJly/jpp59o3749AEeOHKFDhw50796dPXv2kJaWxsMPP+zU6bJq1SrHugULFrBo0SJGjhx5yTrffPNNANzc3Jg6dSq7du1i7ty5fPHFFwwePBiARo0aMXnyZAICAjhy5AhHjhwhKSkJgMTERNavX09qaio7duzg0UcfpVWrVhw4cKDYtp87d4569eqxdOlSvvrqK3r27EmnTp3YtGnTRfl6eHiwadMmpkyZwqRJk3j77bcd6wsKChg9ejSZmZksXryYrKwsunbt6lj//fff06RJE7y9vfniiy/YunUr3bt35/z5845tVq9ezaFDh1i9ejVz585lzpw5zJkzx7HepJ0iIiIiIiIiIiIipanQbtfrCl83Gkvh73sS5IbRrFkzfvnlF7Zt2wbAmDFjWLt2LcuXL3ds89133xEeHs6+ffvIycmhXr16ZGVlUbVq1YvK69q1K//973/59ttv8fX1BeDNN99k0KBBnD59Gjc3t4vqvJQPP/yQ3r17O0ZTzZkzh/79+3Pq1CnHNtnZ2VSvXp3s7GxCQ0Mdy1u0aEGDBg0YO3bsFe+TNm3acOuttzJhwgTHPjp69Ci7du3CYrEA8OKLL7JkyRJ2795dZBlbtmzh9ttv59dff8VqtfLPf/6T1NRU9u3bh6en50Xbd+3albS0NA4dOoS7uzsA7du3x83NjdTU1KtuZ15eHnl5eU7LUmr1wtPifsX75fdML3F5FrP4coYJmNYPsM/9fPEbXUbFQrP3IMBu3ggvw6v6GcN/QTjmZn6zPGN4NNow2wm32i4+n6+U6V4wvTk/++27hiXAtPAnjeJvt50xil/v4WsUDxCZbzOKb/XVy0bxC+KGG8W74rpWYFhGeZvZ0XjS3bwRZc3eRsLtecVvdBl5heb/m1WA2X4oNIy3GF9VzHNo9pTZPfbEihNG8QCzfq5sFO9muBurnjc/H/INizD9nGBav9UF36nPGZ6SZkcieLvgG7Tp9d00h+o2s+simF9XTmH+eet61/Tvx4rfqBj535lNWf/avjCjeM9C8+tamM2sDNPLiuld3hV/Kizt/wI3/Q4I5vuhWr5ZCQUW82PxuIdZGQXFb3JZLviYYHwsmd6nTe+xAC64rBjp7YLv0jejnCH/KO0UrjvW5P+UdgouVdr3MvkT1atXz/FzZmYmq1evxmq1Ol633norAIcOHSIuLo7mzZsTExPDo48+yqxZszh58qRTeXFxcY5OJoCEhARycnL49ttvi6zzgs8//5zmzZsTFhaGv78/nTp14vjx45w5c+k/QO7cuRObzUZ0dLRTzmvWrLloWrqi2Gw2Ro8eTUxMDIGBgVitVpYvX052drbTdnfccYejk+lCmw4cOIDN9ttftLZu3Urbtm2pUqUK/v7+NG3aFMBRTkZGBo0bNy6yk+mC2rVrOzqZAEJCQhzT711tO5OTkylbtqzTa8Uvu4rdLyIiIiIiIiIiIiIiruRR2gnIn8fPz8/xc05ODm3btuWVV165aLuQkBDc3d1ZuXIlX375JStWrGDatGn861//YuPGjVSrVu2q6gTIysqiTZs2PPPMM7z88ssEBgaybt06evToQX5+vlPH1e/l5OTg7u7O1q1bnTppAKxWa7F5jB8/nilTpjB58mTH86H69+/v9Eyp4uTm5tKyZUtatmzJ/PnzCQ4OJjs7m5YtWzrK8fHxKbacP3ZCWSwW7P9/eOTVtnPIkCEMGDDAaVlKrV4lapeIiIiIiIiIiIiIiKuoo+kmUbduXf7zn/8QERGBh0fRb7vFYuHOO+/kzjvvZPjw4VStWpWPPvrI0aGRmZnJ2bNnHZ0rGzZswGq1Eh4efsl6t27dit1uZ+LEibi5/TaAbuHChU7beHl5OUYQXRAfH4/NZuPo0aM0btz4itubnp7Ogw8+yJNP/jbtk91uZ//+/dSqVctpu40bNzr9vmHDBqKionB3d2fv3r0cP36ccePGOdq4ZcsWp+1jY2OZO3cuBQUFlx3VdClX205vb2+8vb2dlplOmyciIiIiIiIiIiIicqU0dd5Nom/fvpw4cYIOHTqwefNmDh06xPLly+nWrRs2m42NGzcyduxYtmzZQnZ2NosWLeLnn3+mZs2ajjLy8/Pp0aMHu3fv5tNPP+Wll14iMTHR0YFUlMjISAoKCpg2bRpff/018+bN480333TaJiIigpycHFatWsWxY8c4c+YM0dHRdOzYkc6dO7No0SIOHz7Mpk2bSE5OZunSpcW2NyoqyjFCa8+ePfTq1Yuffvrpou2ys7MZMGAA+/btY8GCBUybNo1+/foBUKVKFby8vBy5L1myhNGjRzvFJyYm8ssvv/D444+zZcsWDhw4wLx589i3b1+xOQLG7RQRERERERERERERKU3qaLpJhIaGkp6ejs1m47777iMmJob+/ftTrlw53NzcCAgI4H//+x/3338/0dHRDB06lIkTJ9K6dWtHGc2bNycqKoomTZrw2GOP8cADDzBixIjL1hsXF8ekSZN45ZVXqFOnDvPnzyc5Odlpm0aNGtG7d28ee+wxgoODefXVVwFISUmhc+fODBw4kBo1atCuXTs2b95MlSpVim3v0KFDqVu3Li1btqRZs2ZUrlyZdu3aXbRd586dOXv2LA0aNKBv377069ePnj17AhAcHMycOXP44IMPqFWrFuPGjWPChAlO8UFBQXzxxRfk5OTQtGlT6tWrx6xZs65odJNJO0VERERERERERERKlb1Qryt93WAshYWFN16rxOW6du3KqVOnWLx4cWmn4jLNmjXjtttuY/LkyaWdiku8Ef6kcRl2w/g8i1l8OcMETOsH2Od+3ii+YqHZFIYBdvNGeBle1c8Y/gvCMTfTIwnOGB6NNsx2wq22K58K849M94LpzfnZb981LAGmGV5XbredMYpf71H0c/yuRGS+rfiNLqPVVy8bxS+IG24U74rrWoFhGeVtZkfjSXfzRpQ1exsJt+cZxecVmv9vVgFm+6HQMN5ifFUxz6HZU2b32BMrThjFA8z6ubJRvJvhbqx63vx8yDcswvRzgmn9VvOPCZwzPCXNjkTwdsE3aNPru2kO1W1m10Uwv66cwvzz1vWu6d+PGZeR/13Jn0NclNf2hRnFexaaX9fCbGZlmF5WTO/yLrislfp/gZt+BwTz/VAt36yEAov5sXjcw6yMAsP6XfAxwfhYMr1Pm95jAVxwWTHS2wXfpW9GOS88XNopXHesrywq7RRcqrTvZSIiIiIiIiIiIiIiInKdUkeTXJdat26N1Wot8jV27NjSTk9ERERERERERERE5KbgUdoJyPVhzpw5pZ2Ck7fffpuzZ88WuS4wMLBEZaSlpbkwIxERERERERERERGRm486muS6FBZmNo+0iIiIiIiIiIiIiIiYU0eTiIiIiIiIiIiIiIhcHXthaWcgpUwdTSLiMjaLWfxhD7tRfLXz5o+dCys0uywGmDWB82bhgPn7UM5mFu9d6IrH/5mVkWOYwlnDfQgQm1dgFO/vbhbvChEFZgfDT5Qxis92Mz8j3LzMzukFccON4jtkjjKKH1z/n0bxANVtpftxz/CSAoAbZl9ajli8jOJDyTOKB9jraXY+VLCZ7YNj7uYXtornzXJ4d663UXyl85WM4gF8vM32Q7jhpfkXd7N4gMoFZh82fvQ0u0ma1t+s4fdG8QA+fR41in/nqQ1G8d4u+DtKgeEpaZrDr4afeQHKYHYsfO9ldkL4Gn7uBjhj+JnRNIcln5lf17wLzQ6Gsl5mB6Pp9x8wf3h4gOE9MsfwHmlaP8Aus1sk4efN2vCDm/kntnCb2Tn9URmzz1s2w3MB4A67r1G84duAhwvuL/mGObjiu7ApdVeIXJ9c8ddAERERERERERERERERuQmpo0lERERERERERERERESuijqaRP5kXbt2pV27dqWdhoiIiIiIiIiIiIiIy6mjSa5Ys2bN6N+/f2mnISIiIiIiIiIiIiIipax0nw4tIiIiIiIiIiIiIiLXr0J7aWcgpUwjmuSKdO3alTVr1jBlyhQsFgsWi4WsrCy++uorWrdujdVqpVKlSnTq1Iljx4454po1a8azzz5L//79KV++PJUqVWLWrFnk5ubSrVs3/P39iYyM5LPPPnPEpKWlYbFYWLp0KbGxsZQpU4Y77riDr776qkS5Hj9+nA4dOhAWFoavry8xMTEsWLDAaZtmzZqRmJhIYmIiZcuWpUKFCgwbNozCwkLHNvPmzaN+/fr4+/tTuXJlnnjiCY4ePepUzq5du2jTpg0BAQH4+/vTuHFjDh065LTNhAkTCAkJISgoiL59+1JQUOBYl5eXR1JSEmFhYfj5+dGwYUPS0tJK1E4RERERERERERERkdKijia5IlOmTCEhIYGnn36aI0eOcOTIEfz9/bnnnnuIj49ny5YtLFu2jJ9++on27ds7xc6dO5cKFSqwadMmnn32WZ555hkeffRRGjVqxLZt27jvvvvo1KkTZ86ccYobNGgQEydOZPPmzQQHB9O2bVunTppLOXfuHPXq1WPp0qV89dVX9OzZk06dOrFp06aL8vLw8GDTpk1MmTKFSZMm8fbbbzvWFxQUMHr0aDIzM1m8eDFZWVl07drVsf7777+nSZMmeHt788UXX7B161a6d+/O+fPnHdusXr2aQ4cOsXr1aubOncucOXOYM2eOY31iYiLr168nNTWVHTt28Oijj9KqVSsOHDhQkrdFRERERERERERERKRUaOo8uSJly5bFy8sLX19fKleuDMCYMWOIj49n7Nixju1mz55NeHg4+/fvJzo6GoC4uDiGDh0KwJAhQxg3bhwVKlTg6aefBmD48OHMmDGDHTt2cMcddzjKeumll7j33nuB3zqFbrnlFj766KOLOrL+KCwsjKSkJMfvzz77LMuXL2fhwoU0aNDAsTw8PJzXXnsNi8VCjRo12LlzJ6+99pojr+7duzu2rV69OlOnTuX2228nJycHq9XK9OnTKVu2LKmpqXh6egI42nxB+fLlef3113F3d+fWW2/l73//O6tWreLpp58mOzublJQUsrOzCQ0NBSApKYlly5aRkpLitF9FRERERERERERERK4l6mgSY5mZmaxevRqr1XrRukOHDjk6XWJjYx3L3d3dCQoKIiYmxrGsUqVKABdNS5eQkOD4OTAwkBo1arBnz55i87LZbIwdO5aFCxfy/fffk5+fT15eHr6+vk7b3XHHHVgsFqf6Jk6ciM1mw93dna1btzJixAgyMzM5efIkdvtvc45mZ2dTq1YtMjIyaNy4saOTqSi1a9fG3d3d8XtISAg7d+4EYOfOndhstos6p/Ly8ggKCiqyvLy8PPLy8pyWFRTa8LS4F7m9iIiIiIiIiIiIiMifQR1NYiwnJ4e2bdvyyiuvXLQuJCTE8fMfO2IsFovTsgudPRc6ckyNHz+eKVOmMHnyZGJiYvDz86N///7k5+eXuIzc3FxatmxJy5YtmT9/PsHBwWRnZ9OyZUtHOT4+PsWWU1TbL7QzJyfH0aH1+84ooMjOO4Dk5GRGjhzptOx+/xj+Xja2yO1FRERERERERERERP4M6miSK+bl5YXNZnP8XrduXf7zn/8QERGBh4frD6kNGzZQpUoVAE6ePMn+/fupWbNmsXHp6ek8+OCDPPnkk8BvHVj79++nVq1aTttt3LjxovqioqJwd3dn7969HD9+nHHjxhEeHg7Ali1bnLaPjY1l7ty5FBQUXHZU06XEx8djs9k4evQojRs3LlHMkCFDGDBggNOylFq9rrhuERERERERERERESP2wtLOQEqZW2knINefiIgINm7cSFZWFseOHaNv376cOHGCDh06sHnzZg4dOsTy5cvp1q2bU4fU1Ro1ahSrVq3iq6++omvXrlSoUIF27doVGxcVFcXKlSv58ssv2bNnD7169eKnn366aLvs7GwGDBjAvn37WLBgAdOmTaNfv34AVKlSBS8vL6ZNm8bXX3/NkiVLGD16tFN8YmIiv/zyC48//jhbtmzhwIEDzJs3j3379pWofdHR0XTs2JHOnTuzaNEiDh8+zKZNm0hOTmbp0qVFxnh7exMQEOD00rR5IiIiIiIiIiIiIvJXU0eTXLGkpCTc3d2pVasWwcHB5Ofnk56ejs1m47777iMmJob+/ftTrlw53NzMD7Fx48bRr18/6tWrx48//sh///tfvLy8io0bOnQodevWpWXLljRr1ozKlSsX2UHVuXNnzp49S4MGDejbty/9+vWjZ8+eAAQHBzNnzhw++OADatWqxbhx45gwYYJTfFBQEF988QU5OTk0bdqUevXqMWvWrCsa3ZSSkkLnzp0ZOHAgNWrUoF27dmzevNkxkktERERERERERERE5FqkqfPkikVHR7N+/fqLli9atOiSMWlpaRcty8rKumhZYeHFwyzvuusuvvrqqyvKESAwMJDFixcXu52npyeTJ09mxowZRa7v0KEDHTp0uGyesbGxLF++vMj4OXPmXLRs8uTJF+UwcuTIi567JCIiIiIiIiIiIiJyLdOIJhEREREREREREREREbkq6miS61br1q2xWq1FvsaOHVva6YmIiIiIiIiIiIiI3PA0dZ5cs5o1a1bkVHoXvP3225w9e7bIdYGBgSWqo6gp/URERERERERERESkZArtl/4brtwc1NEk162wsLDSTkFERERERERERERE5KamqfNERERERERERERERETkqmhEk8gNIs9iXoaH4SjX8jaz+LKGfd9Wm/kw3Yrn7UbxWZ7uRvGeRtG/KX/ebD/YDY+ln93MD8Zq+WZtiLSbHYz7vMxvj0+cyzCK9/f0NYr/2ij6Nw+d+J9R/MflmxjFj+nlbRQPMHOG2bFgem0dXP+fRvGvbjF/5uCb8cON4tO9zhvF17CZX9lMc2h/zuzavsuzjFE8mF9bV3rlGcXH2c3bsNzbLIc257yM4nd4m99f7jxXYBS/1Mcsh0ib+f3lQ++ip44uqbvsfqVaf/iWkk1xfTkzeqUZxcdjdj7s9jT80AtUsZl9ZjTNoanZZRWAo+5mx3Ou4Sm929PsfAaoYje7R5nm0CDf/B75s4fZjjT93H3Qy/zabHY2wBYPs/ehWqHZ/cm0foDmeWbHwhHD20vzs+bfpTMMP2qMu+WEUbytwPxYXPKD2Xewa+G/+b0M38obYfKz8y74+5iIXLlr4RooIiIiIiIiIiIiIiIi1yF1NImIiIiIiIiIiIiIiMhV0dR5IiIiIiIiIiIiIiJydew3wsSLYkIjmqRYzZo1o3///o7fIyIimDx5cqnkMmfOHMqVK1cqdV+tP+4/EREREREREREREZEbhTqaxCEtLQ2LxcKpU6ecli9atIjRo0dfdbnXY+eQiIiIiIiIiIiIiIgUT1PnSbECAwNLOwUREREREREREREREbkGaUTTdWLmzJmEhoZit9udlj/44IN0794dgI8//pi6detSpkwZqlevzsiRIzl//rxjW4vFwttvv81DDz2Er68vUVFRLFmyBICsrCzuvvtuAMqXL4/FYqFr165A8VO/TZo0iZiYGPz8/AgPD6dPnz7k5OQAv42S6tatG6dPn8ZisWCxWBgxYgQAeXl5JCUlERYWhp+fHw0bNiQtLc2p7Dlz5lClShV8fX156KGHOH78eIn32aFDh3jwwQepVKkSVquV22+/nc8//9xpm4iICEaPHk2HDh3w8/MjLCyM6dOnl7h9F6Snp9OsWTN8fX0pX748LVu25OTJk471drudwYMHExgYSOXKlR374IJTp07x1FNPERwcTEBAAPfccw+ZmZklbquIiIiIiIiIiIiISGlQR9N14tFHH+X48eOsXr3asezEiRMsW7aMjh07snbtWjp37ky/fv3YvXs3b731FnPmzOHll192KmfkyJG0b9+eHTt2cP/999OxY0dOnDhBeHg4//nPfwDYt28fR44cYcqUKSXKzc3NjalTp7Jr1y7mzp3LF198weDBgwFo1KgRkydPJiAggCNHjnDkyBGSkpIASExMZP369aSmprJjxw4effRRWrVqxYEDBwDYuHEjPXr0IDExkYyMDO6++27GjBlT4n2Wk5PD/fffz6pVq9i+fTutWrWibdu2ZGdnO203fvx44uLi2L59Oy+++CL9+vVj5cqVJWofQEZGBs2bN6dWrVqsX7+edevW0bZtW2w2m2ObuXPn4ufnx8aNG3n11VcZNWqUUx2PPvooR48e5bPPPmPr1q3UrVuX5s2bc+LEiRK3V0RERERERERERETkr6ap864T5cuXp3Xr1rz33ns0b94cgA8//JAKFSpw9913c9999/Hiiy/SpUsXAKpXr87o0aMZPHgwL730kqOcrl270qFDBwDGjh3L1KlT2bRpE61atXJMkVexYsUreqbS70c7RUREMGbMGHr37s0bb7yBl5cXZcuWxWKxULlyZcd22dnZpKSkkJ2dTWhoKABJSUksW7aMlJQUxo4dy5QpU2jVqpWjUyc6Opovv/ySZcuWlSivuLg44uLiHL+PHj2ajz76iCVLlpCYmOhYfuedd/Liiy866khPT+e1117j3nvvLbZ9AK+++ir169d3/A5Qu3Ztp1xiY2Md70NUVBSvv/46q1at4t5772XdunVs2rSJo0eP4u3tDcCECRNYvHgxH374IT179ryobXl5eeTl5TktO19ow8PiXqJ9IyIiIiIiIiIiIuISf5iFS24+GtF0HenYsSP/+c9/HB0M8+fP5/HHH8fNzY3MzExGjRqF1Wp1vJ5++mmOHDnCmTNnHGXExsY6fvbz8yMgIICjR48a5fX555/TvHlzwsLC8Pf3p1OnThw/ftyp3j/auXMnNpuN6Ohop5zXrFnDoUOHANizZw8NGzZ0iktISChxXjk5OSQlJVGzZk3KlSuH1Wplz549F41o+mOZCQkJ7Nmzp8TtuzCi6XJ+v98BQkJCHPs9MzOTnJwcgoKCnPbF4cOHHfvij5KTkylbtqzT6/NfdpVsx4iIiIiIiIiIiIiIuIhGNF1H2rZtS2FhIUuXLuX2229n7dq1vPbaa8BvnSojR47k4YcfviiuTJkyjp89PT2d1lksloue+3QlsrKyaNOmDc888wwvv/wygYGBrFu3jh49epCfn4+vr2+RcTk5Obi7u7N161bc3Z1H4Vit1qvO5/eSkpJYuXIlEyZMIDIyEh8fHx555BHy8/NLXEZJ2ufj41NsOZfb7zk5OYSEhFz0fCrgkiPLhgwZwoABA5yWvVW7V8kaJSIiIiIiIiIiIiLiIupouo6UKVOGhx9+mPnz53Pw4EFq1KhB3bp1Aahbty779u0jMjLyqsv38vICcHq2UHG2bt2K3W5n4sSJuLn9NkBu4cKFF5X7xzLj4+Ox2WwcPXqUxo0bF1l2zZo12bhxo9OyDRs2lDi39PR0unbtykMPPQT81qGTlZV10XZ/LHPDhg3UrFmzxO2LjY1l1apVjBw5ssS5/V7dunX58ccf8fDwICIiokQx3t7ejmn2LtC0eSIiIiIiIiIiIiLyV9PUedeZjh07snTpUmbPnk3Hjh0dy4cPH86///1vRo4cya5du9izZw+pqakMHTq0xGVXrVoVi8XCJ598ws8//0xOTk6xMZGRkRQUFDBt2jS+/vpr5s2bx5tvvum0TUREBDk5OaxatYpjx45x5swZoqOj6dixI507d2bRokUcPnyYTZs2kZyczNKlSwF47rnnWLZsGRMmTODAgQO8/vrrJX4+E/z2LKRFixaRkZFBZmYmTzzxRJGjt9LT03n11VfZv38/06dP54MPPqBfv34lbt+QIUPYvHkzffr0YceOHezdu5cZM2Zw7NixEuXZokULEhISaNeuHStWrCArK4svv/ySf/3rX2zZsqXE7RURERERERERERER+aupo+k6c8899xAYGMi+fft44oknHMtbtmzJJ598wooVK7j99tu54447eO2116hatWqJyw4LC2PkyJG8+OKLVKpUicTExGJj4uLimDRpEq+88gp16tRh/vz5JCcnO23TqFEjevfuzWOPPUZwcDCvvvoqACkpKXTu3JmBAwdSo0YN2rVrx+bNm6lSpQoAd9xxB7NmzWLKlCnExcWxYsWKK+o4mzRpEuXLl6dRo0a0bduWli1bOkaA/d7AgQPZsmUL8fHxjBkzhkmTJtGyZcsSty86OpoVK1aQmZlJgwYNSEhI4OOPP8bDo2QDBi0WC59++ilNmjShW7duREdH8/jjj/PNN99QqVKlErdXREREREREREREROSvZiksLCws7SRESktERAT9+/enf//+pZ2KsdeqPGlchofh1aCMYfzVPy3sN+Vs5pcz30KzLLI8zaYw9Cx+k2KVP2+2H+wWs/q/9TQsAKiWb9aGsvaSTwFalH1e5jPLjvpls1G8v2fRz7grqa+PbTeKB/DwCjOK/7h8E6P4Zv29jOIBZs4wOxashhem3R7njeJf3TLWLAHgzfjhRvH73AuM4mvYzK9spjm0P2f2Ru729C5+o2LkGV4aM93OGcXH2csUv9GfnEObc2bn9Ffe5veXO8+ZnZNLfcxyiLSZ31/WueUaxd9l9yvV+p83ewsAmGH4eSveZnY+fOtudm8BqGIza0O2YQ5NzxqFA/CLu9n/rf7gYXY+feNmdm8AqGI3u0dlG+bQIN/8HvmL4QzqwYbfHQ56mV+bTSeBP2wp+XOYi1Kt0Oz+ZFo/QPM8s2PhiOH5VCPf/OKcUcbsHtcp/HujeFuB+bG45IdQo3jTFNz0F1qXOG/4Pjyf/a5rErnJ/NqndWmncN3xf+Oz0k7BpTSiSURERERERERERERERK6KOprkulW7dm2sVmuRr/nz55d2eiIiIiIiIiIiIiIiNzzzuRtESsmnn35KQUHRUxWU9NlGWVlZLsxIREREREREREREROTmoo4muW5VrVq1tFMQEREREREREREREbmpqaNJ5AbhcQ08NPIXw8k4z1jMGuFTaP7wzzRPswcrNygwe5RtrgsmND1o9ixbqhs+V9n88diwxtvsYbQF2I3iY1zwsPYB5W43ijc/ms2NCbnbKL4gz+x9eHuG+dHUteF3RvELNtxiFF/d8Fh6M364UTxA7+2jjOJX1f6nUXzzXWb1uyIH0xPqlAuuzf3fM3s47vJ/LDWKb/kf84fzmuZgekbnG35OADjibvawdW/D+0uXPqaPvIfKU3yM4lv2M8vBtP6tnuYnVG2zt4Huhu/DycU/mCUAlG8XXqo5ZGaVbAaIyymwmF1ce7Y7ZRR/bE2eUTxAhabepZrDlqMVjeIBbIY3uR89zOLtmF+bezf43ij+xwxfo/jKt50xiv8p06x+gM9tgUbxZnc32OFt/v3HzfBQ+PibMOMcSpvh7Ynz18KXQEPXwh+aTd8HEbk618L5LyIiIiIiIiIiIiIi1yP7NfAf8FKqXPD/mSIiIiIiIiIiIiIiInIzUkeTiIiIiIiIiIiIiIiIXBV1NImTZs2a0b9//z+tfIvFwuLFiwHIysrCYrGQkZHhkrJHjBjBbbfd5pKyRERERERERERERESkeOpokktatGgR9913H0FBQS7tELogPDycI0eOUKdOHZeWKyIiIiIiIiIiIiIifw11NMkl5ebmctddd/HKK6/8KeW7u7tTuXJlPDw8/pTyRURERERERERERETkz6WOpptYbm4unTt3xmq1EhISwsSJE53Wd+rUieHDh9OiRYurKv/AgQM0adKEMmXKUKtWLVauXOm0/o9T59lsNnr06EG1atXw8fGhRo0aTJkyxSkmLS2NBg0a4OfnR7ly5bjzzjv55ptvnLZ56623CA8Px9fXl/bt23P69GnHuq5du9KuXTtGjhxJcHAwAQEB9O7dm/z8/BK1qVmzZjz77LP079+f8uXLU6lSJWbNmkVubi7dunXD39+fyMhIPvvsM0dMce06d+4ctWvXpmfPno5lhw4dwt/fn9mzZ5coLxEREREREREREZHSUFhYqNcVvm406mi6iQ0aNIg1a9bw8ccfs2LFCtLS0ti2bZtLyrbb7Tz88MN4eXmxceNG3nzzTV544YViY2655RY++OADdu/ezfDhw/nnP//JwoULATh//jzt2rWjadOm7Nixg/Xr19OzZ08sFoujjIMHD7Jw4UL++9//smzZMrZv306fPn2c6lm1ahV79uwhLS2NBQsWsGjRIkaOHFnits2dO5cKFSqwadMmnn32WZ555hkeffRRGjVqxLZt27jvvvvo1KkTZ86cKVG7ypQpw/z585k7dy4ff/wxNpuNJ598knvvvZfu3buXOC8RERERERERERERkb+a5iy7SeXk5PDOO+/w7rvv0rx5c+C3DpRbbrnFJeV//vnn7N27l+XLlxMaGgrA2LFjad269SVjPD09nTp8qlWrxvr161m4cCHt27fnl19+4fTp07Rp04a//e1vANSsWdOpjHPnzvHvf/+bsLAwAKZNm8bf//53Jk6cSOXKlQHw8vJi9uzZ+Pr6Urt2bUaNGsWgQYMYPXo0bm7F973GxcUxdOhQAIYMGcK4ceOoUKECTz/9NADDhw9nxowZ7NixgzvuuKPYdgHcdtttjBkzhqeeeorHH3+cb775hk8++eSSOeTl5ZGXl+e0rKDQhqfFvdj8RURERERERERERERcRSOablKHDh0iPz+fhg0bOpYFBgZSo0YNl5S/Z88ewsPDHZ1MAAkJCcXGTZ8+nXr16hEcHIzVamXmzJlkZ2c78uvatSstW7akbdu2TJkyhSNHjjjFV6lSxdHJdKFOu93Ovn37HMvi4uLw9fV12iYnJ4dvv/22RG2LjY11/Ozu7k5QUBAxMTGOZZUqVQLg6NGjJWrXBQMHDiQ6OprXX3+d2bNnExQUdMkckpOTKVu2rNNr5S+7SpS/iIiIiIiIiIiIiIirqKNJrhmpqakkJSXRo0cPVqxYQUZGBt26dXN6flJKSgrr16+nUaNGvP/++0RHR7Nhw4a/NE9PT0+n3y0Wi9OyC1P52e12oGTtgt86pvbv34+7uzsHDhy4bA5Dhgzh9OnTTq97A2q7onkiIiIiIiIiIiIiIiWmjqab1N/+9jc8PT3ZuHGjY9nJkyfZv3+/S8qvWbMm3377rdOIo+I6hNLT02nUqBF9+vQhPj6eyMhIDh06dNF28fHxDBkyhC+//JI6derw3nvvOdZlZ2fzww8/ONXp5ubmNFIrMzOTs2fPOm1jtVoJDw+/qrYWp6Tt6t69OzExMcydO5cXXniBPXv2XLJMb29vAgICnF6aNk9ERERERERERERE/mp6RtNNymq10qNHDwYNGkRQUBAVK1bkX//6l9Mzik6cOOHUcXNh+rnKlSs7nnd0KS1atCA6OpouXbowfvx4fvnlF/71r39dNiYqKop///vfLF++nGrVqjFv3jw2b95MtWrVADh8+DAzZ87kgQceIDQ0lH379nHgwAE6d+7sKKNMmTJ06dKFCRMm8Msvv/Dcc8/Rvn17p3zz8/Pp0aMHQ4cOEYACdgABAABJREFUJSsri5deeonExMQSPZ/pahTXLvhtar3169ezY8cOwsPDWbp0KR07dmTDhg14eXn9KXmJiIiIiIiIiIiIGLMXlnYGUso0oukmNn78eBo3bkzbtm1p0aIFd911F/Xq1XOsX7JkCfHx8fz9738H4PHHHyc+Pp4333yz2LLd3Nz46KOPOHv2LA0aNOCpp57i5ZdfvmxMr169ePjhh3nsscdo2LAhx48fp0+fPo71vr6+7N27l3/84x9ER0fTs2dP+vbtS69evRzbREZG8vDDD3P//fdz3333ERsbyxtvvOFUT/PmzYmKiqJJkyY89thjPPDAA4wYMaIku+yqFNeuvXv3MmjQIN544w3HqKo33niDY8eOMWzYsD8tLxERERERERERERERUxrRdBOzWq3MmzePefPmOZYNGjTI8XPXrl3p2rXrVZcfHR3N2rVrnZYVFv5f73ZERITT797e3qSkpJCSkuIUk5ycDEClSpX46KOPLlnfiBEjHB1GzzzzzGVzGzlyJCNHjixRO34vLS3tomVZWVkXLbuSdt16662cOXPGaV25cuXIzs6+4vxERERERERERERERP5KGtEkIiIiIiIiIiIiIiIiV0UdTXJV5s+fj9VqLfJVu3bt0k7vqmRnZ1+yTVarVSOMRERERERERERERET+QFPnyVV54IEHaNiwYZHrPD09/+JsSm7OnDmXXBcaGkpGRsZl14uIiIiIiIiIiIiIyP9RR5NcFX9/f/z9/Us7DZfy8PAgMjKytNMQERERERERERERuX7YC/8fe3ceF1XZ/4//NcCwziCKCKggKouCIKjFLSpikJqKon3FhUSQciFLK9BIRZA7d8ulzOVWcUHRjNTMNRRyIRcUSkEUErBbjBSXQFmH3x/+nE9zy6bX6Ci+no/HeTzgnPO+rvecc+bMcs11XfXvQ40aG5qIGgmJGsoQfUkwEizgvOSBULyBtqFYAgCcqnSF4i0rK4XiL+qJ35a1BK+G+4KDqlpViMUDgFxbsz0j/1bDwLKOZQqh+CrxFIQ5l4plka2nLRT/Xy3xo1BaIPZ8aFql2TfLJ3TF7ikAkOj0mVC898W5Gq1fHTnM6DZDKN7rgfh5yBy1Qyi+3w+BYvW/vUkoXh05xAz/Tii+9wOx+yoA7DIQO5fjKsuF4k8tFXufAQD9Y1oJxf8y679i9UdbCsVHzS8UigeAHqVi9/a7P4gdg2YjxH+cdmdntkZzyFwq9r4bAEwFX6bLsv4Wim8x3EosAQAPjl0Ty8G/jVD85TXi73UsBIu4I/i+17VU/N5c+JuBULxlF7Hr+c90sfrNO98XigeA26ebCsVbVIndF3O1xT/EtagW+yz7m1apULyuGmYH6VqlJxQv+PIEhfC3MuIMq8UehBq+DoDotwG6mj+MRK8kztFERERERERERERERERET4UNTURERERERERERERERPRU2NBERERERERERERERERET4UNTVQnLy8vTJ069ZmVL5FIsGvXLgBAbm4uJBIJ0tLS1FJ2VFQUXF1d1VKWOiUlJUEikeDOnTuaToWIiIiIiIiIiIiISAgbmqjBEhIS0LdvX5iamqq1QegRKysrFBQUoFOnTmot90Xj4eGBgoICNGnSRNOpEBEREREREREREQmpVlRzecKlsWFDEzVYSUkJevbsiQULFjyT8rW1tWFhYQEdHZ1nUv6LQldXFxYWFpBIJJpOhYiIiIiIiIiIiIhICBuaSKmkpASBgYGQyWSwtLTEkiVLVLaPGTMGkZGR8PHxearyr1y5Ak9PT+jr68PR0RGHDx9W2f6/Q+dVVVUhJCQEbdu2hYGBARwcHLBs2TKVmKSkJLz++uswMjKCiYkJevTogby8PJV9Vq9eDSsrKxgaGsLf3x93795VbgsKCoKfnx+io6NhZmYGY2NjTJw4EeXl5Q16TF5eXvjggw8wdepUNG3aFObm5li7di1KSkoQHBwMuVwOW1tb7N+/XyXnfw6dFxsbCxMTExw8eBAdO3aETCZD//79UVBQ0NBDS0RERERERERERESkEWxoIqXw8HAkJydj9+7dOHToEJKSknDu3Dm1lK1QKDBs2DDo6uri1KlTWLVqFaZPn15vTOvWrfHtt98iIyMDkZGR+Oyzz7Bjxw4AQGVlJfz8/NC7d2/8+uuvSElJwfjx41V6CmVnZ2PHjh344YcfcODAAZw/fx6hoaEq9SQmJiIzMxNJSUnYtm0bEhISEB0d3eDHtnHjRjRv3hynT5/GBx98gEmTJmH48OHw8PDAuXPn0LdvX4wZMwb379+vtYz79+9j8eLF2Lx5M37++Wfk5+cjLCyswTkQEREREREREREREWlC4x6jjBqsuLgY69atw5YtW+Dt7Q3gYQNK69at1VL+Tz/9hEuXLuHgwYNo2bIlAGDu3Ll46623ao2RSqUqDT5t27ZFSkoKduzYAX9/f9y7dw93797FoEGD0L59ewBAx44dVcooLS3Fpk2b0KpVKwDAihUrMHDgQCxZsgQWFhYAHg5lt379ehgaGsLJyQlz5sxBeHg4YmJioKVVf1ts586dMXPmTABAREQE5s+fj+bNm+O9994DAERGRuKbb77Br7/+in/96181llFRUYFVq1YpH8fkyZMxZ86cWussKytDWVmZahnVVZBKtOvNl4iIiIiIiIiIiIhIXdijiQAAOTk5KC8vh7u7u3Jds2bN4ODgoJbyMzMzYWVlpWxkAoDu3bvXG/f111+ja9euMDMzg0wmw5o1a5Cfn6/MLygoCP369YOvry+WLVv22HBz1tbWykamR3UqFApkZWUp13Xu3BmGhoYq+xQXF+PatWsNemwuLi7Kv7W1tWFqagpnZ2flOnNzcwBAYWFhrWUYGhoqG5kAwNLSss79582bhyZNmqgsh+9dbFC+RERERERERERERETqwoYmemHFx8cjLCwMISEhOHToENLS0hAcHKwyf9KGDRuQkpICDw8PbN++Hfb29vjll1+ea55SqVTlf4lEorLu0VB+CoXiicqorq6udf+IiAjcvXtXZXnT2Olp0iciIiIiIiIiIiJ6eopqLk+6NDJsaCIAQPv27SGVSnHq1Cnlutu3b+Py5ctqKb9jx464du2aSo+j+hqETpw4AQ8PD4SGhsLNzQ22trbIycl5bD83NzdERETg5MmT6NSpE7Zu3arclp+fj+vXr6vUqaWlpdJTKz09HQ8ePFDZRyaTwcrK6qke6/Ogp6cHY2NjlYXD5hERERERERERERHR88aGJgIAyGQyhISEIDw8HEeOHMGFCxcQFBSkMkdRUVER0tLSkJGRAQDIyspCWloabty4UW/5Pj4+sLe3x9ixY5Geno5jx45hxowZdcbY2dnh7NmzOHjwIC5fvoxZs2bhzJkzyu1Xr15FREQEUlJSkJeXh0OHDuHKlSsq8zTp6+ur1Pnhhx/C399fOT8TAJSXlyMkJAQZGRnYt28fZs+ejcmTJzdofiYiIiIiIiIiIiIioleZjqYToBfHokWLUFxcDF9fX8jlcnzyySe4e/eucvuePXsQHBys/H/kyJEAgNmzZyMqKqrOsrW0tPD9998jJCQEr7/+OmxsbLB8+XL079+/1pgJEybg/PnzGDFiBCQSCUaNGoXQ0FDs378fwMN5jS5duoSNGzfi1q1bsLS0xPvvv48JEyYoy7C1tcWwYcMwYMAAFBUVYdCgQVi5cqVKPd7e3rCzs4OnpyfKysowatSoeh8PERERERERERERERGxoYn+QSaTYfPmzdi8ebNyXXh4uPLvoKAgBAUFPXX59vb2OHbsmMq6f85DZGNjo/K/np4eNmzYgA0bNqjEzJs3DwBgbm6O77//vtb6oqKilA1GkyZNqjO36OhoREdHN+hx/FNSUtJj63Jzcx9b98/H5eXlpfJ/TcfVz8+vzjmaiIiIiIiIiIiIiIheBBwbjIiIiIiIiIiIiIiIiJ4KG5pILeLi4iCTyWpcnJycNJ3eU8nPz6/1MclkMuTn52s6RSIiIiIiIiIiIiLNUnB54qWR4dB5pBaDBw+Gu7t7jdukUulzzqbhYmNja93WsmVLpKWl1bmdiIiIiIiIiIiIiOhVxoYmUgu5XA65XK7pNNRKR0cHtra2mk6DiIiIiIiIiIiIiOiFxaHziIiIiIiIiIiIiIiI6KmwRxNRI1Gt6QQAPJCIxTvAQCi+RZVY/QCQIi0XijfS0RWKN1DDiSyViBViKDhObK5U8EIA8F9JhVB8heBgt85VekLxAJChJ/ZbDvGjKO43fW2h+A5lgheTrlj9AKBvKfZ8uJ0ndiZEb0sOVeLDz3pfnCMUn+j0mWD9c4Xi1ZHDIInYmTipL35PeGOrv1D8Qd/vhOL7fSdWvzpy6CpY/88G4r+R8ygX+/jzna5Y/Mz3xZ/TByL/FIrvN1Xs/daBqL+E4ttJxe/t2WJvt/DmEBuh+KLtOWIJAGjqZ63RHDqWmQvFA8AtHbFzqecgNiJG4bdizwUAaN5bMIcdBULx9uUthOIB4Ibgtzq6gp8/0vTVcG92fiAUX3DOUCjewvW+UPyf6WL1A0BTheB7TsEPDzYKzU950F0h9vqkDqJTpugLPp8UL8CnQNFjoPkrCSjX/GEkeiWxRxMRERERERERERERERE9FfZoIiIiIiIiIiIiIiKip1KteBHGWiJNYo8mIiIiIiIiIiIiIiIieiovTUOTl5cXpk6d+szKl0gk2LVrFwAgNzcXEokEaWlpaik7KioKrq6uailL0571eXhS6j5XRERERERERERERETUcC/l0HkJCQlYtWoVUlNTUVRUhPPnz6u1IcfKygoFBQVo3ry52spsLBISEiCVvghT+z3Ec0VEREREREREREREpDkvTY+mfyopKUHPnj2xYMGCZ1K+trY2LCwsoKPzUrbDPVPNmjWDXC7XdBpKPFdERERERERERERERJrzQjY0lZSUIDAwEDKZDJaWlliyZInK9jFjxiAyMhI+Pj5PVf6VK1fg6ekJfX19ODo64vDhwyrb/3c4tqqqKoSEhKBt27YwMDCAg4MDli1bphKTlJSE119/HUZGRjAxMUGPHj2Ql5enss/q1athZWUFQ0ND+Pv74+7du8ptQUFB8PPzQ3R0NMzMzGBsbIyJEyeivLy8QY/Jy8sLH374IaZNm4ZmzZrBwsICUVFRKvvk5+djyJAhkMlkMDY2hr+/P/7880/l9kdD/G3evBk2NjZo0qQJRo4cib///lulnn8OnWdjY4O5c+di3LhxkMvlsLa2xpo1a5TbPTw8MH36dJU8/vrrL0ilUvz8888AgM2bN6Nbt26Qy+WwsLDA6NGjUVhYqNz/9u3bCAgIgJmZGQwMDGBnZ4cNGzYAUD1XCoUCrVu3xjfffKNS3/nz56GlpaU8H3fu3MG7776rPM5vvPEG0tPTG3ScHx2j9evXw9raGjKZDKGhoaiqqsLChQthYWGBFi1a4PPPP1eJ++KLL+Ds7AwjIyNYWVkhNDQUxcXFyu3jxo2Di4sLysrKAADl5eVwc3NDYGBgg/IiIiIiIiIiIiIiItKEF7KhKTw8HMnJydi9ezcOHTqEpKQknDt3Ti1lKxQKDBs2DLq6ujh16hRWrVr1WENITTGtW7fGt99+i4yMDERGRuKzzz7Djh07AACVlZXw8/ND79698euvvyIlJQXjx4+HRCJRlpGdnY0dO3bghx9+wIEDB3D+/HmEhoaq1JOYmIjMzEwkJSVh27ZtSEhIQHR0dIMf28aNG2FkZIRTp05h4cKFmDNnjrIRTaFQYMiQISgqKkJycjIOHz6M33//HSNGjFApIycnB7t27cLevXuxd+9eJCcnY/78+XXWu2TJEnTr1k35mCZNmoSsrCwAQEBAAOLj41FdXa3cf/v27WjZsiV69eoFAKioqEBMTAzS09Oxa9cu5ObmIigoSLn/rFmzkJGRgf379yMzMxPffPNNjUPlaWlpYdSoUdi6davK+ri4OPTo0QNt2rQBAAwfPhyFhYXYv38/UlNT0aVLF3h7e6OoqKhBxzknJwf79+/HgQMHsG3bNqxbtw4DBw7EH3/8geTkZCxYsAAzZ87EqVOnVHJbvnw5Ll68iI0bN+LIkSOYNm2acvvy5ctRUlKCTz/9FAAwY8YM3LlzB1999VWDciIiIiIiIiIiIiLSCEU1lyddGpkXbryx4uJirFu3Dlu2bIG3tzeAhw0orVu3Vkv5P/30Ey5duoSDBw+iZcuWAIC5c+firbfeqjVGKpWqNPi0bdsWKSkp2LFjB/z9/XHv3j3cvXsXgwYNQvv27QEAHTt2VCmjtLQUmzZtQqtWrQAAK1aswMCBA7FkyRJYWFgAAHR1dbF+/XoYGhrCyckJc+bMQXh4OGJiYqClVX+boIuLC2bPng0AsLOzw1dffYXExES8+eabSExMxG+//YarV6/CysoKALBp0yY4OTnhzJkzeO211wA8bJCKjY1VDo83ZswYJCYmPtZD558GDBigbDSbPn06vvzySxw9ehQODg7w9/fH1KlTcfz4cWXD0tatWzFq1ChlQ9y4ceOUZbVr1w7Lly/Ha6+9huLiYshkMuTn58PNzQ3dunUD8LAXVW0CAgKwZMkS5Ofnw9raGgqFAvHx8Zg5cyYA4Pjx4zh9+jQKCwuhp6cHAFi8eDF27dqFnTt3Yvz48fUeZ4VCgfXr10Mul8PR0RF9+vRBVlYW9u3bBy0tLTg4OGDBggU4evQo3N3dAeCxXmD//ve/MXHiRKxcuRIAIJPJsGXLFvTu3RtyuRxLly7F0aNHYWxsXGMOZWVlyt5Pj1RUV0Eq0a43fyIiIiIiIiIiIiIidXnhejTl5OSgvLxc+QU98HBeIAcHB7WUn5mZCSsrK2UjEwB079693rivv/4aXbt2hZmZGWQyGdasWYP8/HxlfkFBQejXrx98fX2xbNkyFBQUqMRbW1srG5ke1alQKJQ9fwCgc+fOMDQ0VNmnuLgY165da9Bjc3FxUfnf0tJSOQTdo8f9qJEJABwdHWFiYoLMzEzlOhsbG5U5mP5ZRkPqlUgksLCwUMaYmZmhb9++iIuLAwBcvXoVKSkpCAgIUMakpqbC19cX1tbWkMvl6N27NwAoj++kSZMQHx8PV1dXTJs2DSdPnqw1F1dXV3Ts2FHZqyk5ORmFhYUYPnw4ACA9PR3FxcUwNTWFTCZTLlevXkVOTk6dj7O2Y2Rubg5HR0eVxkBzc3OV4/bTTz/B29sbrVq1glwux5gxY3Dr1i3cv39fuU/37t0RFhaGmJgYfPLJJ+jZs2etOcybNw9NmjRRWQ7fu9ig/ImIiIiIiIiIiIiI1OWFa2h6EcXHxyMsLAwhISE4dOgQ0tLSEBwcrDJ/0oYNG5CSkgIPDw9s374d9vb2+OWXX55rnlKpVOV/iUQChULxzMuoLyYgIAA7d+5ERUUFtm7dCmdnZzg7OwN4OB9Xv379YGxsjLi4OJw5cwbff/89ACiP71tvvYW8vDx89NFHuH79Ory9vREWFlZrPgEBAcqGpq1bt6J///4wNTUF8LDHnKWlJdLS0lSWrKwshIeHN+QQ1fh46zoGubm5GDRoEFxcXPDdd98hNTUVX3/9tcpjBB72lDpx4gS0tbWRnZ1dZw4RERG4e/euyvKmsVOD8iciIiIiIiIiIiIiUpcXrqGpffv2kEqlKvPb3L59G5cvX1ZL+R07dsS1a9dUehzV1yB04sQJeHh4IDQ0FG5ubrC1ta2x94ubmxsiIiJw8uRJdOrUSWWuoPz8fFy/fl2lzkfDrD2Snp6OBw8eqOwjk8lUeiE9rUeP+5+9ozIyMnDnzh04OjoKl1+XIUOGoLS0FAcOHMDWrVtVejNdunQJt27dwvz589GrVy906NChxh5UZmZmGDt2LLZs2YKlS5dizZo1tdY3evRoXLhwAampqdi5c6dKfV26dMGNGzego6MDW1tblaWmeZ/UITU1FQqFAkuWLMG//vUv2Nvbq1wLjyxatAiXLl1CcnIyDhw4gA0bNtRapp6eHoyNjVUWDptHRERERERERERERM/bC9fQJJPJEBISgvDwcBw5cgQXLlxAUFCQyrBkRUVFSEtLQ0ZGBgAgKysLaWlpuHHjRr3l+/j4wN7eHmPHjkV6ejqOHTuGGTNm1BljZ2eHs2fP4uDBg7h8+TJmzZqFM2fOKLdfvXoVERERSElJQV5eHg4dOoQrV66ozNOkr6+vUueHH34If39/5fxMwMPeLSEhIcjIyMC+ffswe/ZsTJ48uUHzMzXkcTs7OyMgIADnzp3D6dOnERgYiN69eyvnPnpWjIyM4Ofnh1mzZiEzMxOjRo1SbrO2toauri5WrFiB33//HXv27EFMTIxKfGRkJHbv3o3s7GxcvHgRe/fufWwOrH+ysbGBh4cHQkJCUFVVhcGDByu3+fj4oHv37vDz88OhQ4eQm5uLkydPYsaMGTh79qz6HzwAW1tbVFRUKB/j5s2bsWrVKpV9zp8/j8jISPznP/9Bjx498MUXX2DKlCn4/fffn0lORERERERERERERETq8MI1NAEPe3b06tULvr6+8PHxQc+ePdG1a1fl9j179sDNzQ0DBw4EAIwcORJubm6PfXlfEy0tLXz//fd48OABXn/9dbz77rv4/PPP64yZMGEChg0bhhEjRsDd3R23bt1CaGiocruhoSEuXbqEt99+G/b29hg/fjzef/99TJgwQbmPra0thg0bhgEDBqBv375wcXHBypUrVerx9vaGnZ0dPD09MWLECAwePBhRUVENOWT1kkgk2L17N5o2bQpPT0/4+PigXbt22L59u1rKr09AQADS09PRq1cvWFtbK9ebmZkhNjYW3377LRwdHTF//nwsXrxYJVZXVxcRERFwcXGBp6cntLW1ER8f36D6hg4dCgMDA+V6iUSCffv2wdPTE8HBwbC3t8fIkSORl5cHc3Nz9T7o/1/nzp3xxRdfYMGCBejUqRPi4uIwb9485fbS0lK88847CAoKgq+vLwBg/Pjx6NOnD8aMGYOqqqpnkhcRERERERERERGRMAWXJ14aGUl1dXW1ppMgICgoCHfu3MGuXbs0nQq9pFZYvaPpFFApEYsvlojdjtqIJgAgRVpe/051eL1CVyj+gRqa/29riR3HdhVi9edKxc/DfyViSVQIvmI7V+kJxQPA34LnUvQofpq3RbAEYH4bsftKhzKx85CnK/6ECHT/Qyh+2y+theJfhJ8KTDw/Ryg+0ekzoXjvi3OF4tWRg6FE7Eyc1BO/J0zd+pZQ/MG3fxSK7/fdQKF4deQg+nw4py9YAIAO5WL3lYu6Yve1mROk9e9Uj4PLxF4j+00Ry0G0/kKp+L29RPBFctL7YsegKCFPLAEATf3Ehke/veta/TvVIT1X/Ed2t3TEhu4e+vYdofibyWVC8QDQvLfY/V00h7OFLYTiAeCGjvh7bxH3BD97AEDo6/8Vir+RZigUb+F6Xyj+z3Sx+gHgpwfNhOJ1BOsv1uxlBACQvQDfTop+71smeBwbw/fOoteiOlQKxn+SL/5Z+lV0Z0QfTafw0jHZflTTKajVC9mjiYiIiIiIiIiIiIiIiF58ja6hKS4uDjKZrMbFyclJ0+k9lfz8/Fofk0wmQ35+vqZTbDScnJxqPc5xcXGaTo+IiIiIiIiIiIiI6IXyIvRoVKvBgwfD3d29xm1SqfhQFc9KbGxsrdtatmyJtLS0OreTeuzbtw8VFTUPB/Ks5nAiIiIiIiIiIiIiInpZNbqGJrlcDrlcruk01EpHRwe2traaTuOV0KZNG02nQERERERERERERET00mh0DU1Eryp1zN0pOvemgWABqZIHYvVri0/C6lSlKxRvWSk27eRFPfHbspbg1XBfcFBVK7E5wgEAcm3N9kD9Ww0DyzqWiU3lWiWegjDnUrEssvXEJgn/r5b4USgtEHs+NBFMQUvwznpCV3QqWyDR6TOheO+LczVavzpymNFthlC81wPx85A5aodQfL8fAsXqf3uTULw6cogZ/p1QfO8H4lNk7zIQO5fjKsuF4k8tFXufAQD9oy2F4n+ZXSBWf0wrofjouTeE4gHAo1Ts3n5nd65QfLMR4j8CvLMzW6M5ZC4Ve98NAKaCr5FlWX8LxbcYbiWWAIAHx66J5eAv9kPFy2vE3+tYCBZxR/B9r2up+L35z3Sxz3GWXcSu5z/TDYTiLdzEn0+3fxF7z2hRJXZfzNUW/xDXolrss+xvWqVC8bpqmB2ka5WeULzgyxMUwt/KiDOsFnsQavg6AKLfBuhq/jC+kqoVPPCvukY3RxMRERERERERERERERE9H2xoIiIiIiIiIiIiIiIioqfChiYiIiIiIiIiIiIiIiJ6Ki9tQ5OXlxemTp36zMqXSCTYtWsXACA3NxcSiQRpaWlqKTsqKgqurq5qKUvTnvV5eFLqPlfPyj+vLyIiIiIiIiIiIiKil5X4rPMvgISEBKxatQqpqakoKirC+fPn1dqQY2VlhYKCAjRv3lxtZTYWCQkJkEpFp+lTn5flXBUUFKBp06aaToOIiIiIiIiIiIiISEijaGgqKSlBz5494e/vj/fee0/t5Wtra8PCwkLt5TYGzZo103QKKl6Wc/Uy5EhERERERERERERUL4WmEyBNeymGzispKUFgYCBkMhksLS2xZMkSle1jxoxBZGQkfHx8nqr8K1euwNPTE/r6+nB0dMThw4dVtv/vcGxVVVUICQlB27ZtYWBgAAcHByxbtkwlJikpCa+//jqMjIxgYmKCHj16IC8vT2Wf1atXw8rKCoaGhvD398fdu3eV24KCguDn54fo6GiYmZnB2NgYEydORHl5eYMek5eXFz788ENMmzYNzZo1g4WFBaKiolT2yc/Px5AhQyCTyWBsbAx/f3/8+eefyu2PhvjbvHkzbGxs0KRJE4wcORJ///23Sj3/HDrPxsYGc+fOxbhx4yCXy2FtbY01a9Yot3t4eGD69Okqefz111+QSqX4+eefAQCbN29Gt27dIJfLYWFhgdGjR6OwsFC5/+3btxEQEAAzMzMYGBjAzs4OGzZsAKB6rhQKBVq3bo1vvvlGpb7z589DS0tLeT7u3LmDd999V3mc33jjDaSnpzfoOD86RuvXr4e1tTVkMhlCQ0NRVVWFhQsXwsLCAi1atMDnn3+uElfT0IwJCQno06cPDA0N0blzZ6SkpDQoByIiIiIiIiIiIiIiTXkpGprCw8ORnJyM3bt349ChQ0hKSsK5c+fUUrZCocCwYcOgq6uLU6dOYdWqVY81hNQU07p1a3z77bfIyMhAZGQkPvvsM+zYsQMAUFlZCT8/P/Tu3Ru//vorUlJSMH78eEgkEmUZ2dnZ2LFjB3744QccOHAA58+fR2hoqEo9iYmJyMzMRFJSErZt24aEhARER0c3+LFt3LgRRkZGOHXqFBYuXIg5c+YoG9EUCgWGDBmCoqIiJCcn4/Dhw/j9998xYsQIlTJycnKwa9cu7N27F3v37kVycjLmz59fZ71LlixBt27dlI9p0qRJyMrKAgAEBAQgPj4e1dXVyv23b9+Oli1bolevXgCAiooKxMTEID09Hbt27UJubi6CgoKU+8+aNQsZGRnYv38/MjMz8c0339Q4VJ6WlhZGjRqFrVu3qqyPi4tDjx490KZNGwDA8OHDUVhYiP379yM1NRVdunSBt7c3ioqKGnScc3JysH//fhw4cADbtm3DunXrMHDgQPzxxx9ITk7GggULMHPmTJw6darOcmbMmIGwsDCkpaXB3t4eo0aNQmVlZYNyICIiIiIiIiIiIiLShBd+6Lzi4mKsW7cOW7Zsgbe3N4CHDSitW7dWS/k//fQTLl26hIMHD6Jly5YAgLlz5+Ktt96qNUYqlao0+LRt2xYpKSnYsWMH/P39ce/ePdy9exeDBg1C+/btAQAdO3ZUKaO0tBSbNm1Cq1atAAArVqzAwIEDsWTJEuWwarq6uli/fj0MDQ3h5OSEOXPmIDw8HDExMdDSqr+N0MXFBbNnzwYA2NnZ4auvvkJiYiLefPNNJCYm4rfffsPVq1dhZWUFANi0aROcnJxw5swZvPbaawAeNkjFxsZCLpcDeNh7LDEx8bEeOv80YMAAZaPZ9OnT8eWXX+Lo0aNwcHCAv78/pk6diuPHjysblrZu3YpRo0YpG+LGjRunLKtdu3ZYvnw5XnvtNRQXF0MmkyE/Px9ubm7o1q0bgIe9qGoTEBCAJUuWID8/H9bW1lAoFIiPj8fMmTMBAMePH8fp06dRWFgIPT09AMDixYuxa9cu7Ny5E+PHj6/3OCsUCqxfvx5yuRyOjo7o06cPsrKysG/fPmhpacHBwQELFizA0aNH4e7uXms5YWFhGDhwIAAgOjoaTk5OyM7ORocOHR7bt6ysDGVlZSrrKqqrIJVo15svEREREREREREREZG6vPA9mnJyclBeXq7yBX2zZs3g4OCglvIzMzNhZWWlbGQCgO7du9cb9/XXX6Nr164wMzODTCbDmjVrkJ+fr8wvKCgI/fr1g6+vL5YtW4aCggKVeGtra2Uj06M6FQqFsucPAHTu3BmGhoYq+xQXF+PatWsNemwuLi4q/1taWiqHoHv0uB81MgGAo6MjTExMkJmZqVxnY2OjbGT63zIaUq9EIoGFhYUyxszMDH379kVcXBwA4OrVq0hJSUFAQIAyJjU1Fb6+vrC2toZcLkfv3r0BQHl8J02ahPj4eLi6umLatGk4efJkrbm4urqiY8eOyl5NycnJKCwsxPDhwwEA6enpKC4uhqmpKWQymXK5evUqcnJy6nyctR0jc3NzODo6qjQGmpubP9Fxs7S0BIBaY+bNm4cmTZqoLIfvXWxQvkRERERERERERERE6vLCNzS9iOLj4xEWFoaQkBAcOnQIaWlpCA4OVpk/acOGDUhJSYGHhwe2b98Oe3t7/PLLL881T6lUqvK/RCKBQvFkM7M9TRn1xQQEBGDnzp2oqKjA1q1b4ezsDGdnZwAP5+Pq168fjI2NERcXhzNnzuD7778HAOXxfeutt5CXl4ePPvoI169fh7e3N8LCwmrNJyAgQNnQtHXrVvTv3x+mpqYAHvaYs7S0RFpamsqSlZWF8PDwhhyiGh+v6HF71LurtpiIiAjcvXtXZXnT2KlB+RIRERERERERERERqcsL39DUvn17SKVSlfltbt++jcuXL6ul/I4dO+LatWsqPY7qaxA6ceIEPDw8EBoaCjc3N9ja2tbY+8XNzQ0RERE4efIkOnXqpDJXUH5+Pq5fv65S56Nh1h5JT0/HgwcPVPaRyWQqvZCe1qPH/c/eURkZGbhz5w4cHR2Fy6/LkCFDUFpaigMHDmDr1q0qvZkuXbqEW7duYf78+ejVqxc6dOhQY68eMzMzjB07Flu2bMHSpUuxZs2aWusbPXo0Lly4gNTUVOzcuVOlvi5duuDGjRvQ0dGBra2tylLTvE8vCj09PRgbG6ssHDaPiIiIiIiIiIiInrdqRTWXJ1wamxe+oUkmkyEkJATh4eE4cuQILly4gKCgIJVhyYqKipCWloaMjAwAQFZWFtLS0nDjxo16y/fx8YG9vT3Gjh2L9PR0HDt2DDNmzKgzxs7ODmfPnsXBgwdx+fJlzJo1C2fOnFFuv3r1KiIiIpCSkoK8vDwcOnQIV65cUZmnSV9fX6XODz/8EP7+/sr5mYCHPXhCQkKQkZGBffv2Yfbs2Zg8eXKD5mdqyON2dnZGQEAAzp07h9OnTyMwMBC9e/dWzn30rBgZGcHPzw+zZs1CZmYmRo0apdxmbW0NXV1drFixAr///jv27NmDmJgYlfjIyEjs3r0b2dnZuHjxIvbu3fvYHFj/ZGNjAw8PD4SEhKCqqgqDBw9WbvPx8UH37t3h5+eHQ4cOITc3FydPnsSMGTNw9uxZ9T94IiIiIiIiIiIiIqJG5IVvaAKARYsWoVevXvD19YWPjw969uyJrl27Krfv2bMHbm5uGDhwIABg5MiRcHNzw6pVq+otW0tLC99//z0ePHiA119/He+++y4+//zzOmMmTJiAYcOGYcSIEXB3d8etW7cQGhqq3G5oaIhLly7h7bffhr29PcaPH4/3338fEyZMUO5ja2uLYcOGYcCAAejbty9cXFywcuVKlXq8vb1hZ2cHT09PjBgxAoMHD0ZUVFRDDlm9JBIJdu/ejaZNm8LT0xM+Pj5o164dtm/frpby6xMQEID09HT06tUL1tbWyvVmZmaIjY3Ft99+C0dHR8yfPx+LFy9WidXV1UVERARcXFzg6ekJbW1txMfHN6i+oUOHwsDAQLleIpFg37598PT0RHBwMOzt7TFy5Ejk5eXB3NxcvQ+aiIiIiIiIiIiIiKiRkVRXVze+flqNQFBQEO7cuYNdu3ZpOhV6SXxl9Y5wGaI3A2n9u9TpuNZ9oXjXakPBDAB9wYNgW14pFH9RT0csAQDlErH4FlVi8XpPNhVcje5oeCTIv9XwMwzHMrEDIXgaMOzG1vp3qseP5qPq36kO2XpiJ/K/WqJHAfiodf29m+ty5PdWQvFagnfWE7pi9xQAGPRA7KbgfXGuUHyi02dC8erIYUa3unur18frgfjbZUu9B/XvVIeO3wUKxWe+vUkoXh05xAz/Tii+9wPxF5hdBmJljKssr3+nOtyr0BWKB4B/RVsKxf8yu6D+neqqP0bsvhg9V+y+DAAepWL3tdfaiR0Dk/9nKxQPAHd2Zms0h5VLxe5JAGAq+DLt2+la/TvVwaCX+JDyD44J5uDZRih+6Rrx9zoWVWLPhzuC73s7lIk/hvZN7grFW7iJXc9/pusLxZt3LhWKB4AVv7QUihe9DtJ1KoTiAaBFtdhn2RyJ2HHUVcNv6btW6QnF3xZMQSH8rYw4w2qxa0n8jiD+3ZKW4GH84NoWwQxeTUVDe2s6hZdOs++TNZ2CWr0UPZqIiIiIiIiIiIiIiIjoxdPoG5ri4uIgk8lqXJycnDSd3lPJz8+v9THJZDLk5+drOsVGw8nJqdbjHBcXp+n0iIiIiIiIiIiIiIg0SnyMphfc4MGD4e7uXuM2qVS0M+azExsbW+u2li1bIi0trc7tpB779u1DRUXNXcg5hxMRERERERERERG98tQwjQK93Bp9Q5NcLodcLtd0Gmqlo6MDW1vxccGpfm3aiI25TURERERERERERETUmEmqq6s1P9McEQlbZv2OcBnagncD0ZtJlrbYBKTW1eK9FO8LPoomghNnqmM80+aVYvHXBA9joUQwAQBtFGK/g7gnETuPMsHzCABXtcSuZwnEcliWGy8UDwCTbPyF4r/Y0Fco/sjIRKF4ALiqqy0U71JRJhRfINEViresLheKV4e9+mLPx0Gl4vcE0Rw+P/u5UPyD6ROE4gHgyI9mQvF79cWuhUGlYteiOnKYqiU2yfeZMhOheAAwVIi9PhzQE3sM/6o0EIoHgANad4Xi+yuaaLT+BUbiE84fKW4uFG9ZIfaT22xd8XdstuWazSF07b+E4gEA134XCt8UVSgUn6sj/tPpNlVixzFPWyyHMdpizycAuFUidl9J0dMXii+UVAnFA0DTarHz8IdE7L7SWvBzpGj9ANC/VOy9zl1tsWMofhaBq1Kx11ifUrH3GZVq+Ax3SVfs/ZLoq0Ox+EOATPCLGbngxVD8AkzSoid4DIL+u0U9ibxiiob01nQKL51mu5M1nYJavQBPfyIiIiIiIiIiIiIiInoZsaGJiIiIiIiIiIiIiIiIngobmoiIiIiIiIiIiIiIiOipsKGpEfDy8sLUqVPVUpZEIsGuXbvUUhYRERERERERERERNW7VCi5PujQ2bGhqZBISEtC3b1+YmppCIpEgLS1N0yk1WGxsLExMTNRWXlRUFFxdXdVWHhERERERERERERERqWJDUyNTUlKCnj17YsGCBZpO5YlUVFQ0eN/y8vJnmAkRERERERERERERETUUG5peMiUlJQgMDIRMJoOlpSWWLFmisn3MmDGIjIyEj4/PU9dx8+ZNDB06FIaGhrCzs8OePXtUtl+4cAFvvfUWZDIZzM3NMWbMGNy8eVO5/cCBA+jZsydMTExgamqKQYMGIScnR7k9NzcXEokE27dvR+/evaGvr4+4uDgEBwfj7t27kEgkkEgkiIqKAgDY2NggJiYGgYGBMDY2xvjx4wEA06dPh729PQwNDdGuXTvMmjVL2WAVGxuL6OhopKenK8uLjY0FANy5cwfvvvsuzMzMYGxsjDfeeAPp6ekNOjaPekmtX78e1tbWkMlkCA0NRVVVFRYuXAgLCwu0aNECn3/+uUrcF198AWdnZxgZGcHKygqhoaEoLi5Wbh83bhxcXFxQVlYG4GFjmpubGwIDAxuUFxERERERERERERGRJrCh6SUTHh6O5ORk7N69G4cOHUJSUhLOnTun1jqio6Ph7++PX3/9FQMGDEBAQACKiooAPGykeeONN+Dm5oazZ8/iwIED+PPPP+Hv76+MLykpwccff4yzZ88iMTERWlpaGDp0KBQK1cEnP/30U0yZMgWZmZno06cPli5dCmNjYxQUFKCgoABhYWHKfRcvXozOnTvj/PnzmDVrFgBALpcjNjYWGRkZWLZsGdauXYsvv/wSADBixAh88skncHJyUpY3YsQIAMDw4cNRWFiI/fv3IzU1FV26dIG3t7fyMdYnJycH+/fvx4EDB7Bt2zasW7cOAwcOxB9//IHk5GQsWLAAM2fOxKlTp5QxWlpaWL58OS5evIiNGzfiyJEjmDZtmnL78uXLUVJSgk8//RQAMGPGDNy5cwdfffVVg88bEREREREREREREdHzpqPpBKjhiouLsW7dOmzZsgXe3t4AgI0bN6J169ZqrScoKAijRo0CAMydOxfLly/H6dOn0b9/f3z11Vdwc3PD3LlzlfuvX78eVlZWuHz5Muzt7fH222+rlLd+/XqYmZkhIyMDnTp1Uq6fOnUqhg0bpvy/SZMmkEgksLCweCynN954A5988onKupkzZyr/trGxQVhYGOLj4zFt2jQYGBhAJpNBR0dHpbzjx4/j9OnTKCwshJ6eHoCHjVi7du3Czp07lb2l6qJQKLB+/XrI5XI4OjqiT58+yMrKwr59+6ClpQUHBwcsWLAAR48ehbu7u/Kx/jPXf//735g4cSJWrlwJAJDJZNiyZQt69+4NuVyOpUuX4ujRozA2Nq4xh7KyMmXvp0cqq6ugI9GuN38iIiIiIiIiIiIiInVhQ9NLJCcnB+Xl5crGCwBo1qwZHBwc1FqPi4uL8m8jIyMYGxujsLAQAJCeno6jR49CJpPVmJ+9vT2uXLmCyMhInDp1Cjdv3lT2ZMrPz1dpaOrWrVuDc6pp3+3bt2P58uXIyclBcXExKisra22YeSQ9PR3FxcUwNTVVWf/gwQOV4f3qYmNjA7lcrvzf3Nwc2tra0NLSUln36JgBwE8//YR58+bh0qVLuHfvHiorK1FaWor79+/D0NAQANC9e3eEhYUhJiYG06dPR8+ePWvNYd68eYiOjlZZ18/YGW81caklgoiIiIiIiIiIiOgZUNS/CzVubGiix0ilUpX/JRKJsrGouLgYvr6+WLBgwWNxlpaWAABfX1+0adMGa9euRcuWLaFQKNCpUyeUl5er7G9kZNTgnP5335SUFAQEBCA6Ohr9+vVDkyZNEB8f/9icVf+ruLgYlpaWSEpKemybiYlJg3Kp6fjUdcxyc3MxaNAgTJo0CZ9//jmaNWuG48ePIyQkBOXl5cqGJoVCgRMnTkBbWxvZ2dl15hAREYGPP/5YZd1apwkNyp+IiIiIiIiIiIiISF3Y0PQSad++PaRSKU6dOgVra2sAwO3bt3H58mX07t37ueTQpUsXfPfdd7CxsYGOzuOXz61bt5CVlYW1a9eiV69eAB4OV9cQurq6qKqqatC+J0+eRJs2bTBjxgzlury8vHrL69KlC27cuAEdHR3Y2Ng0qC5RqampUCgUWLJkibLX044dOx7bb9GiRbh06RKSk5PRr18/bNiwAcHBwTWWqaenpxz67xEOm0dEREREREREREREz5tW/bvQi0ImkyEkJATh4eE4cuQILly4gKCgIJUh24qKipCWloaMjAwAQFZWFtLS0nDjxg215PD++++jqKgIo0aNwpkzZ5CTk4ODBw8iODgYVVVVaNq0KUxNTbFmzRpkZ2fjyJEjj/W8qY2NjQ2Ki4uRmJiImzdv4v79+7Xua2dnh/z8fMTHxyMnJwfLly/H999//1h5V69eRVpaGm7evImysjL4+Pige/fu8PPzw6FDh5Cbm4uTJ09ixowZOHv2rNCxqY2trS0qKiqwYsUK/P7779i8eTNWrVqlss/58+cRGRmJ//znP+jRowe++OILTJkyBb///vszyYmIiIiIiIiIiIiIXg5ff/01bGxsoK+vD3d3d5w+fbpBcfHx8ZBIJPDz83um+bGh6SWzaNEi9OrVC76+vvDx8UHPnj3RtWtX5fY9e/bAzc0NAwcOBACMHDkSbm5ujzVsPK2WLVvixIkTqKqqQt++feHs7IypU6fCxMQEWlpa0NLSQnx8PFJTU9GpUyd89NFHWLRoUYPK9vDwwMSJEzFixAiYmZlh4cKFte47ePBgfPTRR5g8eTJcXV1x8uRJzJo1S2Wft99+G/3790efPn1gZmaGbdu2QSKRYN++ffD09ERwcDDs7e0xcuRI5OXlwdzcXOjY1KZz58744osvsGDBAnTq1AlxcXGYN2+ecntpaSneeecdBAUFwdfXFwAwfvx49OnTB2PGjGlwLy8iIiIiIiIiIiIialy2b9+Ojz/+GLNnz8a5c+fQuXNn9OvXD4WFhXXG5ebmIiwsTDny2LMkqa6urn7mtRDRM7fM+h3hMrQF7waiN5Ms7QqheOtqaf071eO+4KNoUi0RildH63/zSrH4a4KHsVAimACANgqxkV3vScTOo0zwPALAVS2x61kCsRyW5cYLxQPAJBt/ofgvNvQVij8yMlEoHgCu6ooNK+pSUSYUXyDRFYq3rC6vf6dnbK++2PNxUKn4PUE0h8/Pfi4U/2C6+DyIR340E4rfqy92LQwqFbsW1ZHDVK1SofgzZSZC8QBgqBB7fTigJ/YY/lVpIBQPAAe07grF91c00Wj9C4zEXh8B4Ehxc6F4ywqx2aqzdcXfsdmWazaH0LX/EooHAFwTG31hU1TdX4zUJ1dHfNbxNlVixzFPWyyHMdpizycAuFUidl9J0dMXii+UiP8wsmm12Hn4QyJ2X2kt+DlStH4A6F8q9l7nrrbYMVTHz1uvSsVeY31Kxd5nVKrhM9wlXbH3S6KvDsXiDwEywS9m5IIXQ/EL0KVBT/AYBP13i3oSecXcGvh8pnVpTEx/TG7wvu7u7njttdfw1VdfAQAUCgWsrKzwwQcf4NNPP60xpqqqCp6enhg3bhyOHTuGO3fuYNeuXepIvUaco4mIiIiIiIiIiIiIiJ5KtfjvP145ZWVlKCtT/XGrnp4e9PT0VNaVl5cjNTUVERERynVaWlrw8fFBSkpKreXPmTMHLVq0QEhICI4dO6be5GvwArQz0/MSFxcHmUxW4+Lk5KTp9F4ITk5OtR6juLg4TadHRERERERERERERC+5efPmoUmTJirLP6dbeeTmzZuoqqp6bNoXc3Nz3Lhxo8ayjx8/jnXr1mHt2rXPJPeasEfTK2Tw4MFwd3evcZtUKj7kWGOwb98+VFTU3O39Wc3hRERERERERERERESvjoiICHz88ccq6/63N9PT+PvvvzFmzBisXbsWzZuLDTn9JNjQ9AqRy+WQy+WaTuOF1qZNG02nQERERERERERERESNWE3D5NWkefPm0NbWxp9//qmy/s8//4SFhcVj++fk5CA3Nxe+vr7KdQrFw7ENdXR0kJWVhfbt2wtm/zg2NBE1EtqCkx2qg0INE1eKkKlhPFgtidiDEJ1E1VANj6FK8DwYCeZgpiX+0tJE8EBWaosdBHWMK+tYJdZT9EV4TndQiE2Gqzj6k1D8VV3xa6lC8PlQJjg5dUuU1b9THS5KxSboBoA7ghe014NKofiT+uK/yBLN4cH0CULxBgtWC8UDwLnEWULxb5aJHcdz4peSeA5aYkm8pndHKB4AEqqaCMV7lxsIxZ/WVcOE8ZVij+GijlgOovX/dbtUKB4A7KvFJoy/KDjZu4XYLQkAkC8VuzmL5lCVdFSsAADatm2F4ou0xeq3qhJ/x3ZbsAjRHPIfiP8QtLlU7DlVBrE3nTZVgicSQK622Jt/62qx5/QtiVj9ravFR4ipFjwPoorV8AHIoFrsjXe6VOx9hjq+j5AKngaJYLz8BfgMKPp9guBloB4vwHEkUiddXV107doViYmJ8PPzA/Cw4SgxMRGTJ09+bP8OHTrgt99+U1k3c+ZM/P3331i2bBmsrKyeSZ5saCIiIiIiIiIiIiIiInoBffzxxxg7diy6deuG119/HUuXLkVJSQmCg4MBAIGBgWjVqhXmzZsHfX19dOrUSSXexMQEAB5br05saCIiIiIiIiIiIiIioqejhhF6qHYjRozAX3/9hcjISNy4cQOurq44cOAAzM3NAQD5+fnQ0lLH+DxPjw1NREREREREREREREREL6jJkyfXOFQeACQlJdUZGxsbq/6E/odmm7nomfDy8sLUqVPVUpZEIsGuXbvUUhb9Hx5XIiIiIiIiIiIiImoM2NDUyCUkJKBv374wNTWFRCJBWlqaplNqsNjYWOX4keoQFRUFV1dXtZUnoqCgAG+99Zam0yAiIiIiIiIiIiIiEsKGpkaupKQEPXv2xIIFCzSdyhOpqKho8L7l5eXPMJNnw8LCAnp6eppOg4iIiIiIiIiIiIhICBuaXnIlJSUIDAyETCaDpaUllixZorJ9zJgxiIyMhI+Pz1PXcfPmTQwdOhSGhoaws7PDnj17VLZfuHABb731FmQyGczNzTFmzBjcvHlTuf3AgQPo2bMnTExMYGpqikGDBiEnJ0e5PTc3FxKJBNu3b0fv3r2hr6+PuLg4BAcH4+7du5BIJJBIJIiKigIA2NjYICYmBoGBgTA2Nsb48eMBANOnT4e9vT0MDQ3Rrl07zJo1S9lgFRsbi+joaKSnpyvLezQ25Z07d/Duu+/CzMwMxsbGeOONN5Cent6gY/Ool9T69ethbW0NmUyG0NBQVFVVYeHChbCwsECLFi3w+eefq8T9c+i8R48/ISEBffr0gaGhITp37oyUlJQGnyMiIiIiIiIiIiIiIk1gQ9NLLjw8HMnJydi9ezcOHTqEpKQknDt3Tq11REdHw9/fH7/++isGDBiAgIAAFBUVAXjYSPPGG2/Azc0NZ8+exYEDB/Dnn3/C399fGV9SUoKPP/4YZ8+eRWJiIrS0tDB06FAoFAqVej799FNMmTIFmZmZ6NOnD5YuXQpjY2MUFBSgoKAAYWFhyn0XL16Mzp074/z585g1axYAQC6XIzY2FhkZGVi2bBnWrl2LL7/8EgAwYsQIfPLJJ3ByclKWN2LECADA8OHDUVhYiP379yM1NRVdunSBt7e38jHWJycnB/v378eBAwewbds2rFu3DgMHDsQff/yB5ORkLFiwADNnzsSpU6fqLGfGjBkICwtDWloa7O3tMWrUKFRWVjYoByIiIiIiIiIiIiJNqFZwedKlsdHRdAL09IqLi7Fu3Tps2bIF3t7eAICNGzeidevWaq0nKCgIo0aNAgDMnTsXy5cvx+nTp9G/f3989dVXcHNzw9y5c5X7r1+/HlZWVrh8+TLs7e3x9ttvq5S3fv16mJmZISMjA506dVKunzp1KoYNG6b8v0mTJpBIJLCwsHgspzfeeAOffPKJyrqZM2cq/7axsUFYWBji4+Mxbdo0GBgYQCaTQUdHR6W848eP4/Tp0ygsLFQOZbd48WLs2rULO3fuVPaWqotCocD69eshl8vh6OiIPn36ICsrC/v27YOWlhYcHBywYMECHD16FO7u7rWWExYWhoEDBwJ42Ljn5OSE7OxsdOjQod4ciIiIiIiIiIiIiIg0gQ1NL7GcnByUl5erNF40a9YMDg4Oaq3HxcVF+beRkRGMjY1RWFgIAEhPT8fRo0chk8lqzM/e3h5XrlxBZGQkTp06hZs3byp7MuXn56s0NHXr1q3BOdW07/bt27F8+XLk5OSguLgYlZWVMDY2rrOc9PR0FBcXw9TUVGX9gwcPVIb3q4uNjQ3kcrnyf3Nzc2hra0NLS0tl3aNjVpt/HmdLS0sAQGFhYY0NTWVlZSgrK1NZV1FdBalEu0E5ExERERERERERERGpAxuaqF5SqVTlf4lEomwsKi4uhq+vLxYsWPBY3KPGEl9fX7Rp0wZr165Fy5YtoVAo0KlTJ5SXl6vsb2Rk1OCc/nfflJQUBAQEIDo6Gv369UOTJk0QHx//2JxV/6u4uBiWlpZISkp6bJuJiUmDcqnp+NR1zBpSjkQiAYBaY+bNm4fo6GiVdW/JnTGgiUuN+xMRERERERERERERPQtsaHqJtW/fHlKpFKdOnYK1tTUA4Pbt27h8+TJ69+79XHLo0qULvvvuO9jY2EBH5/HL6datW8jKysLatWvRq1cvAA+Hq2sIXV1dVFVVNWjfkydPok2bNpgxY4ZyXV5eXr3ldenSBTdu3ICOjg5sbGwaVNeLICIiAh9//LHKunWOEzSUDRERERERERERERG9qrTq34VeVDKZDCEhIQgPD8eRI0dw4cIFBAUFqQzZVlRUhLS0NGRkZAAAsrKykJaWhhs3bqglh/fffx9FRUUYNWoUzpw5g5ycHBw8eBDBwcGoqqpC06ZNYWpqijVr1iA7OxtHjhx5rIGkNjY2NiguLkZiYiJu3ryJ+/fv17qvnZ0d8vPzER8fj5ycHCxfvhzff//9Y+VdvXoVaWlpuHnzJsrKyuDj44Pu3bvDz88Phw4dQm5uLk6ePIkZM2bg7NmzQsfmWdLT04OxsbHKwmHziIiIiIiIiIiIiOh5Y0PTS27RokXo1asXfH194ePjg549e6Jr167K7Xv27IGbmxsGDhwIABg5ciTc3NywatUqtdTfsmVLnDhxAlVVVejbty+cnZ0xdepUmJiYQEtLC1paWoiPj0dqaio6deqEjz76CIsWLWpQ2R4eHpg4cSJGjBgBMzMzLFy4sNZ9Bw8ejI8++giTJ0+Gq6srTp48iVmzZqns8/bbb6N///7o06cPzMzMsG3bNkgkEuzbtw+enp4IDg6Gvb09Ro4ciby8PJibmwsdGyIiIiIiIiIiIqLGrlrB5UmXxkZSXV1drekkiEjcV1bvaDoFVEnE4q9oVQjFO1ZJ69+pHvcFH4MouRpeaAwE7+p3BX+CUKyGnzBYVorF3xLs4KeOcWX1BM+DtmD8e39sESsAwDJrsfvKu0FiJ3LDBvEzUSH4nO5UJnZf0pc0bAjY2lyU6gvFA8AdweekS6nYefxVX/w8iubQc9AtoXiDBauF4gEgptus+neqg1OZWP0X9cTi1ZFDmZbYE7KL3h2xBAAkVDURim8neFM5rSt2TwEAp0qx9zsXdcRyEK2/U2WpUDwAlFWL3dgu6uoKxTcTu7UDAIoE36uI5jBk7AOxAgBo27YVil86R2yUjSZqeN8s+r5XNId25YJvegE0l4o9p/ZrN3yu5Jo0VYh/gMrVFrugTavFnlC3BN+vNRW8JwGAU5nYm/972mI5iD4XAPHPgTLB55MaLkVINfwZ7kUg+n3C/RegS4O+4LUU9F/xz9KvokLv5zONS2PSIjFZ0ymo1Qvw9CciIiIiIiIiIiIiIqKXERuaXmFxcXGQyWQ1Lk5OTppO74Xg5ORU6zGKi4vTdHpERERERERERERERBqljtGB6CU1ePBguLu717hNKhUfgqwx2LdvHyoqah5ehHM4EREREREREREREdGrjg1NrzC5XA65XK7pNF5obdq00XQKREREREREREREREQvLDY0ERERERERERERERHRU6lWaDoD0jQ2NBGRUrVgfKVgvGOV2JCNTaoEEwCQpSv2KDpVit1WSyVC4QCAm9piZ7JdzaNFNli5RPxBpEvFzoPo+xtHwfMIAH8LzoKoeAFmURS8FPDtOsHng7ZgAgAmBjwQiv95vdh96ZJUXyheoYZ7wtStbwnFZ47aIRT/xlZ/oXh15HDkRzOh+HOJs4TiAWDW2Rih+Ozuk4Xihx3/UigeALJ7fiQU/+c9I6H4BJ0mQvEAYF4l9qT6XSr2GrvgSzeheAC48tEJofjAL3totP5kifh5rBS8N44fJ/amseTYdbEEABj1sBTL4USBUHzCJiuheAC4rX1DKH5KRFOh+PKfLwjFA4CuZyeN5nDiiIVQPACclciE4k0E3zgXq+E9a3SA2Hv/B+fEnpMG3cSGxX9wVvye8P1vYs9J0dPwQA3nUfAlEqITOChEv9BQA9HPgDI1PIZ7gjkUC+bwInzRXKKGz5FE9ORegK+xiIiIiIiIiIiIiIiI6GXEhiYiIiIiIiIiIiIiIiJ6KmxoIiIiIiIiIiIiIiIioqfChiZSGy8vL0ydOlVj9UdFRcHV1VVj9RMRERERERERERERvWrY0ETPREJCAvr27QtTU1NIJBKkpaVpOiUiIiIiIiIiIiIiUrdqCZcnXRoZNjTRM1FSUoKePXtiwYIFmk6FiIiIiIiIiIiIiIieETY00VMpKSlBYGAgZDIZLC0tsWTJEpXtY8aMQWRkJHx8fJ6qfIlEgtWrV2PQoEEwNDREx44dkZKSguzsbHh5ecHIyAgeHh7Iycl5LHb16tWwsrKCoaEh/P39cffuXeW2oKAg+Pn5ITo6GmZmZjA2NsbEiRNRXl7eoLy8vLzwwQcfYOrUqWjatCnMzc2xdu1alJSUIDg4GHK5HLa2tti/f78ypqqqCiEhIWjbti0MDAzg4OCAZcuWKbeXlpbCyckJ48ePV67LycmBXC7H+vXrn+bwERERERERERERERE9F2xooqcSHh6O5ORk7N69G4cOHUJSUhLOnTun1jpiYmIQGBiItLQ0dOjQAaNHj8aECRMQERGBs2fPorq6GpMnT1aJyc7Oxo4dO/DDDz/gwIEDOH/+PEJDQ1X2SUxMRGZmJpKSkrBt2zYkJCQgOjq6wXlt3LgRzZs3x+nTp/HBBx9g0qRJGD58ODw8PHDu3Dn07dsXY8aMwf379wEACoUCrVu3xrfffouMjAxERkbis88+w44dOwAA+vr6iIuLw8aNG7F7925UVVXhnXfewZtvvolx48YJHkUiIiIiIiIiIiIiomeHDU30xIqLi7Fu3TosXrwY3t7ecHZ2xsaNG1FZWanWeoKDg+Hv7w97e3tMnz4dubm5CAgIQL9+/dCxY0dMmTIFSUlJKjGlpaXYtGkTXF1d4enpiRUrViA+Ph43btxQ7qOrq4v169fDyckJAwcOxJw5c7B8+XIoFIoG5dW5c2fMnDkTdnZ2iIiIgL6+Ppo3b4733nsPdnZ2iIyMxK1bt/Drr78CAKRSKaKjo9GtWze0bdsWAQEBCA4OVjY0AYCrqyv+/e9/491338XUqVORl5eHtWvX1ppDWVkZ7t27p7JUVFc9wdElIiIiIiIiIiIiIhLHhiZ6Yjk5OSgvL4e7u7tyXbNmzeDg4KDWelxcXJR/m5ubAwCcnZ1V1pWWluLevXvKddbW1mjVqpXy/+7du0OhUCArK0u5rnPnzjA0NFTZp7i4GNeuXXvivLS1tWFqavpYXgBQWFioXPf111+ja9euMDMzg0wmw5o1a5Cfn69S7ieffAJ7e3t89dVXWL9+PUxNTWvNYd68eWjSpInKcvjexQblT0RERERERERERESkLmxooheWVCpV/i2RSGpd19CeSM8ir0d51JVXfHw8wsLCEBISgkOHDiEtLQ3BwcGPzQtVWFiIy5cvQ1tbG1euXKkzh4iICNy9e1dledPYSR0Pj4iIiIiIiIiIiKjBqhVcnnRpbNjQRE+sffv2kEqlOHXqlHLd7du3cfnyZQ1m9VB+fj6uX7+u/P+XX36BlpaWSm+r9PR0PHjwQGUfmUwGKyurZ5LTiRMn4OHhgdDQULi5ucHW1hY5OTmP7Tdu3DjlMITTp09HZmZmrWXq6enB2NhYZZFKtJ9J/kREREREREREREREtdHRdAL08pHJZAgJCUF4eDhMTU3RokULzJgxA1pa/9duWVRUpNLo82joOgsLC1hYWDyz3PT19TF27FgsXrwY9+7dw4cffgh/f3+VOsvLyxESEoKZM2ciNzcXs2fPxuTJk1XyVyc7Ozts2rQJBw8eRNu2bbF582acOXMGbdu2Ve7z9ddfIyUlBb/++iusrKzw448/IiAgAL/88gt0dXWfSV5ERERERERERERERKLYo4meyqJFi9CrVy/4+vrCx8cHPXv2RNeuXZXb9+zZAzc3NwwcOBAAMHLkSLi5uWHVqlXPNC9bW1sMGzYMAwYMQN++feHi4oKVK1eq7OPt7Q07Ozt4enpixIgRGDx4MKKiop5ZThMmTMCwYcMwYsQIuLu749atWwgNDVVuv3TpEsLDw7Fy5Uplr6qVK1fi5s2bmDVr1jPLi4iIiIiIiIiIiIhIFHs00VORyWTYvHkzNm/erFwXHh6u/DsoKAhBQUFPXX51dbXK/zY2No+t8/LyUlkXFRWlbDCaNGlSneVHR0cjOjr6ifNKSkp6bF1ubu5j6/6Zl56eHjZs2IANGzao7DNv3jwAQIcOHXD//n2VbSYmJsjPz3/i/IiIiIiIiIiIiIiInif2aCIiIiIiIiIiIiIiIqKnwh5N9NzFxcVhwoQJNW5r06YNLl68+Jwzeig/Px+Ojo61bs/IyIC1tfVzzIiIiIiIiIiIiIjoxVatkGg6BdIwNjTRczd48GC4u7vXuE0qlT7TumNjY2vd1rJlS6SlpdW5nYiIiIiIiIiIiIiI/g8bmui5k8vlkMvlmk7jMTo6OrC1tdV0GkRERERERERERERELw02NBE1EtWaTgCAgWASV7QrheLbq+GW5lAlVoZppUIo/qqu5qfOK5WIdXduWSl+Neppi50H0aNYrIbT0KlM7Hqugua7nbuVVQjFH9MXO4+FErH6AaDswk2h+GqI9WZtXiX2fDisWyYUDwAH3/5RKL7fD4Fi9ft+JxSvjhy+Hr5NKP7NMj2heADI7j5ZKN425SuN1q+OHL7tOksovkep2H0VAOINxF6nQxXlQvE5nxwXigcAu3WDxXJ4b49Y/f8ZJBS/4d0koXgA8Hkg9hpZ+L3Ya0OLIZZC8QBQuPuWRnPIzBF7LgCAqULsDVPZT78Jxeu90UkoHgDKjgjm4CWWw28n7wjFA4C54K2xRFss3rm0SqwAAMUn/hKKN3qtmVj9xwo1Wj8A3MwQi28leB3kq+F9dyuIjVBzQnJfKF4qEf8Q161KXyi+TCL23l+ihs+AFYLfDmkJ5iB+JUHwSuKX3USaovlvNImIiIiIiIiIiIiIiOilxIYmIiIiIiIiIiIiIiIieirsTUhERERERERERERERE+lWnxUXnrJsUcTPTNeXl6YOnWqxuqPioqCq6urxuqvTVJSEiQSCe7cuaPpVIiIiIiIiIiIiIiIhLChiZ6LhIQE9O3bF6amppBIJEhLS9N0Shrj4eGBgoICNGnSRNOpEBEREREREREREREJYUMTPRclJSXo2bMnFixYoOlUNE5XVxcWFhaQSCSaToWIiIiIiIiIiIiISAgbmkgtSkpKEBgYCJlMBktLSyxZskRl+5gxYxAZGQkfH5+nKl8ikWD16tUYNGgQDA0N0bFjR6SkpCA7OxteXl4wMjKCh4cHcnJyHotdvXo1rKysYGhoCH9/f9y9e1e5LSgoCH5+foiOjoaZmRmMjY0xceJElJeXNygvLy8vfPDBB5g6dSqaNm0Kc3NzrF27FiUlJQgODoZcLoetrS3279+vjPnfofNiY2NhYmKCgwcPomPHjpDJZOjfvz8KCgqe6lgRERERERERERERET0vbGgitQgPD0dycjJ2796NQ4cOISkpCefOnVNrHTExMQgMDERaWho6dOiA0aNHY8KECYiIiMDZs2dRXV2NyZMnq8RkZ2djx44d+OGHH3DgwAGcP38eoaGhKvskJiYiMzMTSUlJ2LZtGxISEhAdHd3gvDZu3IjmzZvj9OnT+OCDDzBp0iQMHz4cHh4eOHfuHPr27YsxY8bg/v37tZZx//59LF68GJs3b8bPP/+M/Px8hIWFPdkBIiIiIiIiIiIiIiJ6ztjQRMKKi4uxbt06LF68GN7e3nB2dsbGjRtRWVmp1nqCg4Ph7+8Pe3t7TJ8+Hbm5uQgICEC/fv3QsWNHTJkyBUlJSSoxpaWl2LRpE1xdXeHp6YkVK1YgPj4eN27cUO6jq6uL9evXw8nJCQMHDsScOXOwfPlyKBSKBuXVuXNnzJw5E3Z2doiIiIC+vj6aN2+O9957D3Z2doiMjMStW7fw66+/1lpGRUUFVq1ahW7duqFLly6YPHkyEhMTa92/rKwM9+7dU1kqqqsalC8RERERERERERGRulRXS7g84dLYsKGJhOXk5KC8vBzu7u7Kdc2aNYODg4Na63FxcVH+bW5uDgBwdnZWWVdaWop79+4p11lbW6NVq1bK/7t37w6FQoGsrCzlus6dO8PQ0FBln+LiYly7du2J89LW1oapqeljeQFAYWFhrWUYGhqiffv2yv8tLS3r3H/evHlo0qSJynL43sUG5UtEREREREREREREpC5saKKXhlQqVf4tkUhqXdfQnkjPIq9HeTxpXjWVUV1dXev+ERERuHv3rsryprHT06RPRERERERERERERPTU2NBEwtq3bw+pVIpTp04p192+fRuXL1/WYFYP5efn4/r168r/f/nlF2hpaan0tkpPT8eDBw9U9pHJZLCysnquuT4JPT09GBsbqyxSibam0yIiIiIiIiIiIiKiV4yOphOgl59MJkNISAjCw8NhamqKFi1aYMaMGdDS+r92zKKiIpVGn0dD11lYWMDCwuKZ5aavr4+xY8di8eLFuHfvHj788EP4+/ur1FleXo6QkBDMnDkTubm5mD17NiZPnqySPxERERERERERERERPY4NTaQWixYtQnFxMXx9fSGXy/HJJ5/g7t27yu179uxBcHCw8v+RI0cCAGbPno2oqKhnlpetrS2GDRuGAQMGoKioCIMGDcLKlStV9vH29oadnR08PT1RVlaGUaNGPdOciIiIiIiIiIiIiIgaCzY0kVrIZDJs3rwZmzdvVq4LDw9X/h0UFISgoKCnLv9/5yuysbF5bJ2Xl5fKuqioKGWD0aRJk+osPzo6GtHR0U+cV1JS0mPrcnNzH1v3z7z+N8+ajo2fn1+dczQRERERERERERERvQiqa5+anl4RHBuMiIiIiIiIiIiIiIiIngobmkjj4uLiIJPJalycnJw0lld+fn6teclkMuTn52ssNyIiIiIiIiIiIiKiFwGHziONGzx4MNzd3WvcJpVKn2ndsbGxtW5r2bIl0tLS6txORERERERERERERPQqY0MTaZxcLodcLtd0Go/R0dGBra2tptMgIiIiIiIiIiIiInphsaGJqJGQqKGMasH4MsEk7KrEbklNqsTqB4DTupVC8dJqscfwItyU9avFroTrOuJX4x9aYudBdA5Kx0rxM3FBT6wMheBhHCgWDgBI0xPrVWojdhoh09YVKwCAXqfmQvGS02LPh5vaYieys0JfKB4A+n33llB85tubBOv3F4pXRw6Dyo2E4s+JnwYMO/6lUHx298lC8bYpXwnFqyMHz1Kx8/Czvvg9oVul2HPyB8H7Uvhi8WGhr4T8IBRv92VPsfrf/VEo3kbRRCgeAC7ricV7DTEVii85USCWAIAWQyw1mkPHCiuheAC4pS0Wr+fjLBRf/vMFsQQA6L2h2RycSy2E4gHgmq7YidAV/BD4m77ghQDAq4eZUPyDc4VC8bJe5mL1n/1TKB4AmleJvUZWCH52aF0tPpqMjuC11L3aUDgHTdOrFjsRRqJfygCokojlIPpp/kWYo6VC0wkQvaJehO80iYiIiIiIiIiIiIjoJVQt+mtZeum9CA3NRERERERERERERERE9BJiQxMRERERERERERERERE9lVeiocnLywtTp07VWP1RUVFwdXXVWP3qpOlj+b9yc3MhkUiQlpam6VSIiIiIiIiIiIiIiF45r9wcTQkJCVi1ahVSU1NRVFSE8+fPN5pGoOchISEBUqn4JJHqYmVlhYKCAjRvLjbZOxERERERERERERERPblXokfTP5WUlKBnz55YsGCBplN5KTVr1gxyuVzTaShpa2vDwsICOjqvXJspEREREREREREREZHGNbqGppKSEgQGBkImk8HS0hJLlixR2T5mzBhERkbCx8fnqcqXSCRYvXo1Bg0aBENDQ3Ts2BEpKSnIzs6Gl5cXjIyM4OHhgZycnMdiV69eDSsrKxgaGsLf3x93795VbgsKCoKfnx+io6NhZmYGY2NjTJw4EeXl5Q3Ky8vLCx9++CGmTZuGZs2awcLCAlFRUSr75OfnY8iQIZDJZDA2Noa/vz/+/PNP5fZHQ/xt3rwZNjY2aNKkCUaOHIm///5bpZ5/Dp1nY2ODuXPnYty4cZDL5bC2tsaaNWuU2z08PDB9+nSVPP766y9IpVL8/PPPAIDNmzejW7dukMvlsLCwwOjRo1FYWKjc//bt2wgICICZmRkMDAxgZ2eHDRs2AFAdOk+hUKB169b45ptvVOo7f/48tLS0kJeXBwC4c+cO3n33XeVxfuONN5Cent6g4/zoGK1fvx7W1taQyWQIDQ1FVVUVFi5cCAsLC7Ro0QKff/65StwXX3wBZ2dnGBkZwcrKCqGhoSguLlZuHzduHFxcXFBWVgYAKC8vh5ubGwIDAxuUFxEREREREREREZEmVFdzedKlsWl0DU3h4eFITk7G7t27cejQISQlJeHcuXNqrSMmJgaBgYFIS0tDhw4dMHr0aEyYMAERERE4e/YsqqurMXnyZJWY7Oxs7NixAz/88AMOHDiA8+fPIzQ0VGWfxMREZGZmIikpCdu2bUNCQgKio6MbnNfGjRthZGSEU6dOYeHChZgzZw4OHz4MAFAoFBgyZAiKioqQnJyMw4cP4/fff8eIESNUysjJycGuXbuwd+9e7N27F8nJyZg/f36d9S5ZsgTdunVTPqZJkyYhKysLABAQEID4+HhU/+PZs337drRs2RK9evUCAFRUVCAmJgbp6enYtWsXcnNzERQUpNx/1qxZyMjIwP79+5GZmYlvvvmmxqHytLS0MGrUKGzdulVlfVxcHHr06IE2bdoAAIYPH47CwkLs378fqamp6NKlC7y9vVFUVNSg45yTk4P9+/fjwIED2LZtG9atW4eBAwfijz/+QHJyMhYsWICZM2fi1KlTKrktX74cFy9exMaNG3HkyBFMmzZNuX358uUoKSnBp59+CgCYMWMG7ty5g6+++qpBORERERERERERERERaUKjGm+suLgY69atw5YtW+Dt7Q3gYeNL69at1VpPcHAw/P39AQDTp09H9+7dMWvWLPTr1w8AMGXKFAQHB6vElJaWYtOmTWjVqhUAYMWKFRg4cCCWLFkCCwsLAICuri7Wr18PQ0NDODk5Yc6cOQgPD0dMTAy0tOpvE3RxccHs2bMBAHZ2dvjqq6+QmJiIN998E4mJifjtt99w9epVWFlZAQA2bdoEJycnnDlzBq+99hqAhw1SsbGxyuHxxowZg8TExMd66PzTgAEDlI1m06dPx5dffomjR4/CwcEB/v7+mDp1Ko4fP65sWNq6dStGjRoFiUQC4GFvnkfatWuH5cuX47XXXkNxcTFkMhny8/Ph5uaGbt26AXjYi6o2AQEBWLJkCfLz82FtbQ2FQoH4+HjMnDkTAHD8+HGcPn0ahYWF0NPTAwAsXrwYu3btws6dOzF+/Ph6j7NCocD69eshl8vh6OiIPn36ICsrC/v27YOWlhYcHBywYMECHD16FO7u7gDwWC+wf//735g4cSJWrlwJAJDJZNiyZQt69+4NuVyOpUuX4ujRozA2Nq4xh7KyMmXvp0cqqqsglWjXmz8RERERERERERERkbo0qh5NOTk5KC8vV365DzycU8jBwUGt9bi4uCj/Njc3BwA4OzurrCstLcW9e/eU66ytrZWNTADQvXt3KBQKZc8fAOjcuTMMDQ1V9ikuLsa1a9eeOC8AsLS0VA5Bl5mZCSsrK2UjEwA4OjrCxMQEmZmZynU2NjYqczD9s4yG1CuRSGBhYaGMMTMzQ9++fREXFwcAuHr1KlJSUhAQEKCMSU1Nha+vL6ytrSGXy9G7d28AD4f6A4BJkyYhPj4erq6umDZtGk6ePFlrLq6urujYsaOyV1NycjIKCwsxfPhwAEB6ejqKi4thamoKmUymXK5evVrjcIc1+d9jZG5uDkdHR5XGQHNzc5Xj9tNPP8Hb2xutWrWCXC7HmDFjcOvWLdy/f1+5T/fu3REWFoaYmBh88skn6NmzZ605zJs3D02aNFFZDt+72KD8iYiIiIiIiIiIiIjUpVE1ND0vUqlU+fejXjk1rVMoFBrL61EeT5rD05RRX0xAQAB27tyJiooKbN26Fc7OzsqGuZKSEvTr1w/GxsaIi4vDmTNn8P333wOAcn6qt956C3l5efjoo49w/fp1eHt7IywsrNZ8AgIClA1NW7duRf/+/WFqagrgYa83S0tLpKWlqSxZWVkIDw9vyCGq8fHWdQxyc3MxaNAguLi44LvvvkNqaiq+/vprlccIPLxeTpw4AW1tbWRnZ9eZQ0REBO7evauyvGns1KD8iYiIiIiIiIiIiIjUpVE1NLVv3x5SqVRlbpzbt2/j8uXLGszqofz8fFy/fl35/y+//KIcZu2R9PR0PHjwQGUfmUym0gvpaXXs2BHXrl1T6R2VkZGBO3fuwNHRUbj8ugwZMgSlpaU4cOAAtm7dqtKb6dKlS7h16xbmz5+PXr16oUOHDjX2oDIzM8PYsWOxZcsWLF26FGvWrKm1vtGjR+PChQtITU3Fzp07Verr0qULbty4AR0dHdja2qosNc37pA6pqalQKBRYsmQJ/vWvf8He3l7lWnhk0aJFuHTpEpKTk3HgwAFs2LCh1jL19PRgbGyssnDYPCIiIiIiIiIiIiJ63hrVHE0ymQwhISEIDw+HqakpWrRogRkzZqgMaVZUVKTS6PNo6DoLCwvlXEnPgr6+PsaOHYvFixfj3r17+PDDD+Hv769SZ3l5OUJCQjBz5kzk5uZi9uzZmDx5coPmZ6qPj48PnJ2dERAQgKVLl6KyshKhoaHo3bu3cu6jZ8XIyAh+fn6YNWsWMjMzMWrUKOU2a2tr6OrqYsWKFZg4cSIuXLiAmJgYlfjIyEh07doVTk5OKCsrw969e9GxY8da67OxsYGHhwdCQkJQVVWFwYMHK7f5+Pige/fu8PPzw8KFC5WNPj/++COGDh36TI6Fra0tKioqsGLFCvj6+uLEiRNYtWqVyj7nz59HZGQkdu7ciR49euCLL77AlClT0Lt3b7Rr107tORERERERERERERGpQ7VCoukUSMMaVY8m4GGvkF69esHX1xc+Pj7o2bMnunbtqty+Z88euLm5YeDAgQCAkSNHws3N7bEv/tXN1tYWw4YNw4ABA9C3b1+4uLhg5cqVKvt4e3vDzs4Onp6eGDFiBAYPHoyoqCi11C+RSLB79240bdoUnp6e8PHxQbt27bB9+3a1lF+fgIAApKeno1evXrC2tlauNzMzQ2xsLL799ls4Ojpi/vz5WLx4sUqsrq4uIiIi4OLiAk9PT2hrayM+Pr5B9Q0dOhQGBgbK9RKJBPv27YOnpyeCg4Nhb2+PkSNHIi8vTznflrp17twZX3zxBRYsWIBOnTohLi4O8+bNU24vLS3FO++8g6CgIPj6+gIAxo8fjz59+mDMmDGoqqp6JnkREREREREREREREYmSVFdXV2s6CQKCgoJw584d7Nq1S9Op0EvqK6t3hMsQvRlUCv54QU8wgSZqaJM7rVspFN+pUqyj6H01NP8XS8QOZLsKsfqv64j/iuUPLbHzIDpDnqPgeQSAvwXPpeiPgablbRErAMASa7H7iqngc/KmGkYEnRjwoP6d6vDzemn9O9UhR1fsWlLHj8ImbHtLKD5z1A6h+I7b/IXi1ZFDXrmRUPw5ffETMetUpFB8ds+PhOJtU74SigeA7O6TheL/vCd2Hn7W1xWKBwDzKrFz+ae22Gts+Jfic2pe+ThFKN7uyx5i9X90Qij+qKKJUDwg/jo/PkjsfUbJiQLBDACjHpYazeHAJfHh2W8Jvk5PiGgqFF/+8wWxBADoenbSaA4njoiPqnJNV+xECL71R4kaPr988E6ZUPyDc48Pv/8kDLqJ/ej0wdk/heIB4PvfxJ6ToqfhLzW879YR/Dxv2Ai+nbwv+JZRpoZjcE/wYhB8q6OWHg2iZYje18LyxT9Lv4ryuvhoOoWXTptzP2k6BbVqdD2aiIiIiIiIiIiIiIiI6PlgQ9M/xMXFQSaT1bg4OYn/+vBp5efn15qXTCZDfn6+xnJrbJycnGo9znFxcZpOj4iIiIiIiIiIiIjohSI+NlAjMnjwYLi7u9e4TSoVGzqnPrGxsbVua9myJdLS0urcTuqxb98+VFTU3Mn2Wc3hRERERERERERERET0smJD0z/I5XLI5XJNp/EYHR0d2NraajqNV0KbNm00nQIRERERERERERER0UuDDU1EjcSLMG+mnmASV7TFJmZur4ZbmkOVWBmmlWLTU9/TFR/RVAtiM5CWCk5g2rJS/GrU0xY7D6JHsVgNA8t2KhO7nqsEz6M6uJaJTaN6TF/sPN6UiB1DACi7cFMovhpivYZbCD4fDuqJTY4NAAff/lEovt8PgWL1+34nFK+OHL4evk0o/s0yPaF4AMju+ZFQvG3KV2L1d58sFK+OHL7tOksovkep+D1hh4HY6/SkarHnZE7YSaF4ALBbN1gsh/f2aLT+DSFHhOIB4M0HYvGFu28JxbcYYimWgDpyGCr2+pSZI/ZcAABThdgbprKffhOK13ujk1A8AJQdEczBSyyH307eEYoHAHPBW2OJtli8c2mVWAEAik/8JRRv9FozsfqPFWq0fgC4mSEW30rwOvhDIva+HwBaQWwkoBTJfaF4qUT8Q1y3Kn2h+ArBj3AlYuEPcxD8dkj0+wTxO4L45/lnOyYV1aZaofnvMEizOEcTERERERERERERERERPRU2NBEREREREREREREREdFTYUMTERERERERERERERERPZVXsqHJy8sLU6dO1Vj9UVFRcHV11Vj96qTpY/m/cnNzIZFIkJaWpulU6iSRSLBr1y5Np0FEREREREREREREJERslu5GICEhAatWrUJqaiqKiopw/vz5RtMI9DwkJCRAKn1xptmzsrJCQUEBmjdvrulU6lRQUICmTZtqOg0iIiIiIiIiIiIiIiGvfENTSUkJevbsCX9/f7z33nuaTuel06xZM02noEJbWxsWFhaaTqNeL0OORERERERERERERPWprtZ0BqRpjX7ovJKSEgQGBkImk8HS0hJLlixR2T5mzBhERkbCx8fnqcqXSCRYvXo1Bg0aBENDQ3Ts2BEpKSnIzs6Gl5cXjIyM4OHhgZycnMdiV69eDSsrKxgaGsLf3x93795VbgsKCoKfnx+io6NhZmYGY2NjTJw4EeXl5Q3Ky8vLCx9++CGmTZuGZs2awcLCAlFRUSr75OfnY8iQIZDJZDA2Noa/vz/+/PNP5fZHQ/xt3rwZNjY2aNKkCUaOHIm///5bpZ5/Dp1nY2ODuXPnYty4cZDL5bC2tsaaNWuU2z08PDB9+nSVPP766y9IpVL8/PPPAIDNmzejW7dukMvlsLCwwOjRo1FYWKjc//bt2wgICICZmRkMDAxgZ2eHDRs2AFAdOk+hUKB169b45ptvVOo7f/48tLS0kJeXBwC4c+cO3n33XeVxfuONN5Cent6g4/zoGK1fvx7W1taQyWQIDQ1FVVUVFi5cCAsLC7Ro0QKff/65Stw/h857lHNCQgL69OkDQ0NDdO7cGSkpKQ3KgYiIiIiIiIiIiIhIUxp9Q1N4eDiSk5Oxe/duHDp0CElJSTh37pxa64iJiUFgYCDS0tLQoUMHjB49GhMmTEBERATOnj2L6upqTJ48WSUmOzsbO3bswA8//IADBw7g/PnzCA0NVdknMTERmZmZSEpKwrZt25CQkIDo6OgG57Vx40YYGRnh1KlTWLhwIebMmYPDhw8DABQKBYYMGYKioiIkJyfj8OHD+P333zFixAiVMnJycrBr1y7s3bsXe/fuRXJyMubPn19nvUuWLEG3bt2Uj2nSpEnIysoCAAQEBCA+Ph7V/2jm3r59O1q2bIlevXoBACoqKhATE4P09HTs2rULubm5CAoKUu4/a9YsZGRkYP/+/cjMzMQ333xT41B5WlpaGDVqFLZu3aqyPi4uDj169ECbNm0AAMOHD0dhYSH279+P1NRUdOnSBd7e3igqKmrQcc7JycH+/ftx4MABbNu2DevWrcPAgQPxxx9/IDk5GQsWLMDMmTNx6tSpOsuZMWMGwsLCkJaWBnt7e4waNQqVlZUNyoGIiIiIiIiIiIiISBMadUNTcXEx1q1bh8WLF8Pb2xvOzs7YuHGj2r+8Dw4Ohr+/P+zt7TF9+nTk5uYiICAA/fr1Q8eOHTFlyhQkJSWpxJSWlmLTpk1wdXWFp6cnVqxYgfj4eNy4cUO5j66uLtavXw8nJycMHDgQc+bMwfLly6FQKBqUl4uLC2bPng07OzsEBgaiW7duSExMBPCwEeu3337D1q1b0bVrV7i7u2PTpk1ITk7GmTNnlGUoFArExsaiU6dO6NWrF8aMGaMsozYDBgxAaGgobG1tMX36dDRv3hxHjx4FAPj7++P69es4fvy4cv+tW7di1KhRkEgkAIBx48bhrbfeQrt27fCvf/0Ly5cvx/79+1FcXAzgYU8sNzc3dOvWDTY2NvDx8YGvr2+NuQQEBODEiRPIz89XPp74+HgEBAQAAI4fP47Tp0/j22+/Rbdu3WBnZ4fFixfDxMQEO3fubNBxVigUWL9+PRwdHeHr64s+ffogKysLS5cuhYODA4KDg+Hg4KA8BrUJCwvDwIEDYW9vj+joaOTl5SE7O7tBORARERERERERERERaUKjbmjKyclBeXk53N3dleuaNWsGBwcHtdbj4uKi/Nvc3BwA4OzsrLKutLQU9+7dU66ztrZGq1atlP93794dCoVC2fMHADp37gxDQ0OVfYqLi3Ht2rUnzgsALC0tlUPQZWZmwsrKClZWVsrtjo6OMDExQWZmpnKdjY0N5HJ5jWU0pF6JRAILCwtljJmZGfr27Yu4uDgAwNWrV5GSkqJs+AGA1NRU+Pr6wtraGnK5HL179wYAZWPRpEmTEB8fD1dXV0ybNg0nT56sNRdXV1d07NhR2aspOTkZhYWFGD58OAAgPT0dxcXFMDU1hUwmUy5Xr16tcbjDmvzvMTI3N4ejoyO0tLRU1j3JcbO0tASAWmPKyspw7949laWiuqpB+RIRERERERERERERqUujbmh6XqRSqfLvR71yalrX0J5IzyKvR3k8aQ5PU0Z9MQEBAdi5cycqKiqwdetWODs7KxvmSkpK0K9fPxgbGyMuLg5nzpzB999/DwDK+aneeust5OXl4aOPPsL169fh7e2NsLCwWvMJCAhQNjRt3boV/fv3h6mpKYCHvd4sLS2RlpamsmRlZSE8PLwhh6jGxyt63Oq7ZubNm4cmTZqoLIfvXWxQvkRERERERERERERE6tKoG5rat28PqVSqMjfO7du3cfnyZQ1m9VB+fj6uX7+u/P+XX36BlpaWSm+r9PR0PHjwQGUfmUym0gvpaXXs2BHXrl1T6R2VkZGBO3fuwNHRUbj8ugwZMgSlpaU4cOAAtm7dqtKb6dKlS7h16xbmz5+PXr16oUOHDjX26jEzM8PYsWOxZcsWLF26FGvWrKm1vtGjR+PChQtITU3Fzp07Verr0qULbty4AR0dHdja2qosNc379KKIiIjA3bt3VZY3jZ00nRYRERERERERERG9YqoVEi5PuDQ2OppO4FmSyWQICQlBeHg4TE1N0aJFC8yYMUNlSLOioiKVRp9HQ9dZWFjAwsLimeWmr6+PsWPHYvHixbh37x4+/PBD+Pv7q9RZXl6OkJAQzJw5E7m5uZg9ezYmT56skv/T8vHxgbOzMwICArB06VJUVlYiNDQUvXv3Rrdu3YTLr4uRkRH8/Pwwa9YsZGZmYtSoUcpt1tbW0NXVxYoVKzBx4kRcuHABMTExKvGRkZHo2rUrnJycUFZWhr1796Jjx4611mdjYwMPDw+EhISgqqoKgwcPVm7z8fFB9+7d4efnh4ULF8Le3h7Xr1/Hjz/+iKFDhz7zY/G09PT0oKenp7JOKtHWUDZERERERERERERE9Kpq1D2aAGDRokXo1asXfH194ePjg549e6Jr167K7Xv27IGbmxsGDhwIABg5ciTc3NywatWqZ5qXra0thg0bhgEDBqBv375wcXHBypUrVfbx9vaGnZ0dPD09MWLECAwePBhRUVFqqV8ikWD37t1o2rQpPD094ePjg3bt2mH79u1qKb8+AQEBSE9PR69evWBtba1cb2ZmhtjYWHz77bdwdHTE/PnzsXjxYpVYXV1dREREwMXFBZ6entDW1kZ8fHyD6hs6dCgMDAyU6yUSCfbt2wdPT08EBwfD3t4eI0eORF5ennK+LSIiIiIiIiIiIiIiqpmkurq6WtNJ0OOCgoJw584d7Nq1S9Op0EtihdU7mk4Bon2qrmhXCsW3rxLvpCna+m5RITYX21Vd8fb/csHetxZipwH6anhZuaUt9iBEj+IDNfRgdioTO5BVEEti4J/bhOIBINF8hFD8MX2x5+RNieDFCCDa+YZQ/C+nWwrFFwv2Qj6oVyYUDwBDSnWF4vv98LZQ/EHf74Ti1ZHD5OFiz4c3y/Tq36keLrLbQvG2KV8JxWd3nywUr44c5nWdJRTfo1T8nrDDQOx1elK12HNSR1t8ztb2awfXv1Mdct7bo9H6Pws5IhQPAG8+qH+funS0uCUU32KIqVgCAAp3C+YwVGyY73/Hir/ZMVWIvcaFvPaHULzeG52E4gGgLOmCWA5eYjksX3hHKB4AzAVvjUWCH+I6lFWJFQCgW8cCoXij15oKxZecEXuNFq0fAFbsMBKKbyV4HZyWVogVAKBVtbT+neqQgftC8VKJ+GfpblX6QvF3BVPQVcM3tKUSsUL0qjU/nJfYlSRuSv4WDWfwcvrdua+mU3jptPvtkKZTUKtG36OJiIiIiIiIiIiIiIiIng02NNUhLi4OMpmsxsXJyUljeeXn59eal0wmQ35+vsZya2ycnJxqPc5xcXGaTo+IiIiIiIiIiIiISKPEx5lqxAYPHgx3d/cat0mlz7YjZ2xsbK3bWrZsibS0tDq3k3rs27cPFRU1dyHnHE5ERERERERERET0qqt+AYZdJM1iQ1Md5HI55HK5ptN4jI6ODmxtbTWdxiuhTZs2mk6BiIiIiIiIiIiIiOiFxaHziIiIiIiIiIiIiIiI6KlIqqurqzWdBBGJW2n1jnAZCsH4+4JN102rxOKr1NBLt1BL7JZoJNhVWB3dTGWCJ/KB4HG8KXgMAfFfQYheyxYK8YvptoZ/yhGRt0W4jHltxO4rPctKheIv6+gLxQOAnuDFMDS4TCh+y0Y9oXizStGrWVyq4GnoKnYZqCWHUVr3hOLPlZmIJQDAulLsWvpZX1co3rO0XCheHTlEpMYIxW9yjRSKBwDRl6gTUrELelCp2DEEgKP6YveFPqViL1Ci9fcVfaMB4JKetlC8qeB7zgI1vGGzrNRsDr3LH4gVAOCeQmw4+WItsfP4X6n4tdSqQuymIJrDu2PF783XthcLxe8payYUf0MieDEDcKoUu6CvaYudx1aCHyT/K1g/AFgJ5lAh+HRQw0MQ/j7Atkzs5lwhEb8nXJeKPQjNv3MXZyT4IMrV8L2M6AhsEsHrecIf4p+lX0U5nfppOoWXTvsLBzWdglqxRxMRERERERERERERERE9FTY0ERERERERERERERER0VNRxyhNRERERERERERERET0CqpuDGNHkhD2aGrkvLy8MHXqVE2nQUREREREREREREREjRAbml4hCQkJ6Nu3L0xNTSGRSJCWlqbplJ6poKAg+Pn5aToNIiIiIiIiIiIiIqJGiw1Nr5CSkhL07NkTCxYsUEt5FRUVainnZamXiIiIiIiIiIiIiIhUsaGpESkpKUFgYCBkMhksLS2xZMkSle1jxoxBZGQkfHx8nqp8iUSCb775BoMHD4aRkRE+//xzAMDu3bvRpUsX6Ovro127doiOjkZlZSUAoLq6GlFRUbC2toaenh5atmyJDz/8UFmmjY0NYmJiMGrUKBgZGaFVq1b4+uuv6623qqoKISEhaNu2LQwMDODg4IBly5YpY6KiorBx40bs3r0bEokEEokESUlJAIBr167B398fJiYmaNasGYYMGYLc3NwGHYNHvaTmzp0Lc3NzmJiYYM6cOaisrER4eDiaNWuG1q1bY8OGDSpx06dPh729PQwNDdGuXTvMmjVL2WBWXV0NHx8f9OvXD9XV1QCAoqIitG7dGpGRkQ0/QUREREREREREREREzxkbmhqR8PBwJCcnY/fu3Th06BCSkpJw7tw5tdYRFRWFoUOH4rfffsO4ceNw7NgxBAYGYsqUKcjIyMDq1asRGxurbIT67rvv8OWXX2L16tW4cuUKdu3aBWdnZ5UyFy1ahM6dO+P8+fP49NNPMWXKFBw+fLjOehUKBVq3bo1vv/0WGRkZiIyMxGeffYYdO3YAAMLCwuDv74/+/fujoKAABQUF8PDwQEVFBfr16we5XI5jx47hxIkTkMlk6N+/P8rLyxt0DI4cOYLr16/j559/xhdffIHZs2dj0KBBaNq0KU6dOoWJEydiwoQJ+OOPP5QxcrkcsbGxyMjIwLJly7B27Vp8+eWXAB42pG3cuBFnzpzB8uXLAQATJ05Eq1at2NBERERERERERERERC80HU0nQOpRXFyMdevWYcuWLfD29gYAbNy4Ea1bt1ZrPaNHj0ZwcLDy/3HjxuHTTz/F2LFjAQDt2rVDTEwMpk2bhtmzZyM/Px8WFhbw8fGBVCqFtbU1Xn/9dZUye/TogU8//RQAYG9vjxMnTuDLL7/Em2++WWu9ABAdHa38u23btkhJScGOHTvg7+8PmUwGAwMDlJWVwcLCQrnfli1boFAo8J///AcSiQQAsGHDBpiYmCApKQl9+/at9xg0a9YMy5cvh5aWFhwcHLBw4ULcv38fn332GQAgIiIC8+fPx/HjxzFy5EgAwMyZM5XxNjY2CAsLQ3x8PKZNmwYAaNWqFVavXo3AwEDcuHED+/btw/nz56GjU/NTtKysDGVlZSrrKqqrIJVo15s/ERERERERERERkbooqiWaToE0jD2aGomcnByUl5fD3d1dua5Zs2ZwcHBQaz3dunVT+T89PR1z5syBTCZTLu+99x4KCgpw//59DB8+HA8ePEC7du3w3nvv4fvvv1cOq/dI9+7dH/s/MzOzznoB4Ouvv0bXrl1hZmYGmUyGNWvWID8/v87809PTkZ2dDblcrsy3WbNmKC0tRU5OToOOgZOTE7S0/u+pY25urtJLS1tbG6ampigsLFSu2759O3r06AELCwvIZDLMnDnzsVyHDx+OoUOHYv78+Vi8eDHs7OxqzWHevHlo0qSJynLo3sUG5U9EREREREREREREpC5saKInYmRkpPJ/cXExoqOjkZaWplx+++03XLlyBfr6+rCyskJWVhZWrlwJAwMDhIaGwtPTUzk/0dPWGx8fj7CwMISEhODQoUNIS0tDcHBwvcPfFRcXo2vXrir5pqWl4fLlyxg9enSDcpFKpSr/SySSGtcpFAoAQEpKCgICAjBgwADs3bsX58+fx4wZMx7L9f79+0hNTYW2tjauXLlSZw4RERG4e/euytLX2KlB+RMRERERERERERERqQuHzmsk2rdvD6lUilOnTsHa2hoAcPv2bVy+fBm9e/d+ZvV26dIFWVlZsLW1rXUfAwMD+Pr6wtfXF++//z46dOiA3377DV26dAEA/PLLLyr7//LLL+jYsWOd9Z44cQIeHh4IDQ1VrvvfHkm6urqoqqp6LN/t27ejRYsWMDY2btBjFHXy5Em0adMGM2bMUK7Ly8t7bL9PPvkEWlpa2L9/PwYMGICBAwfijTfeqLFMPT096OnpqazjsHlERERERERERERE9LyxoamRkMlkCAkJQXh4OExNTdGiRQvMmDFDZYi3oqIi5Ofn4/r16wCArKwsAICFhYXKPEZPIjIyEoMGDYK1tTX+3//7f9DS0kJ6ejouXLiAf//734iNjUVVVRXc3d1haGiILVu2wMDAAG3atFGWceLECSxcuBB+fn44fPgwvv32W/z444911mtnZ4dNmzbh4MGDaNu2LTZv3owzZ86gbdu2yn1sbGxw8OBBZGVlwdTUFE2aNEFAQAAWLVqEIUOGYM6cOWjdujXy8vKQkJCAadOmqX1Oq0e55ufnIz4+Hq+99hp+/PFHfP/99yr7/Pjjj1i/fj1SUlLQpUsXhIeHY+zYsfj111/RtGlTtedERERERERERERERKQOHDqvEVm0aBF69eoFX19f+Pj4oGfPnujataty+549e+Dm5oaBAwcCAEaOHAk3NzesWrXqqevs168f9u7di0OHDuG1117Dv/71L3z55ZfKhiQTExOsXbsWPXr0gIuLC3766Sf88MMPMDU1VZbxySef4OzZs3Bzc8O///1vfPHFF+jXr1+d9U6YMAHDhg3DiBEj4O7ujlu3bqn0bgKA9957Dw4ODujWrRvMzMxw4sQJGBoa4ueff4a1tTWGDRuGjh07IiQkBKWlpc+sh9PgwYPx0UcfYfLkyXB1dcXJkycxa9Ys5fa//voLISEhiIqKUvbyio6Ohrm5OSZOnPhMciIiIiIiIiIiIiIiUgdJdXV1taaToFeXjY0Npk6diqlTp2o6lZfeSqt3hMtQCMbfF2y6blpV/z51qZKIxQNAoZbYLdGoWiwJdXQzlQmeyAeCx/Gm4DEExH8FIXotWyjEL6bbGv4pR0TeFuEy5rURu6/0LCsVir+soy8UDwB6ghfD0OAyofgtG/Xq36kOZpWiV7O4VMHT0FXsMlBLDqO07gnFnyszEUsAgHWl2LX0s76uULxnad3zWD6PHCJSY4TiN7lGCsUDgOhL1Amp2AU9qFTsGALAUX2x+0KfUrEXKNH6+4q+0QBwSU9syGhTwfecBWp4w2ZZqdkcepc/ECsAwD2FtP6d6lCsJXYe/ysVv5ZaVYjdFERzeHes+L352vZiofg9Zc2E4m9IBC9mAE6VYhf0NW2x89hK8IPkfwXrBwArwRwqBJ8OangIwt8H2JaJ3ZwrJOL3hOtSsQeh+Xfu4owEH0S5Gr6XEfxaBRLB63nCH+KfpV9FWR3e0nQKLx2HS/s1nYJasUcTERERERERERERERERPRU2NBEAIC4uDjKZrMbFyclJ0+k9N7UdA5lMhmPHjmk6PSIiIiIiIiIiIiKiF4o6RmmiRmDw4MFwd3evcZtUKjYkQl1yc3OfWdlPIy0trdZtrVq1en6JEBERERERERERERG9BNjQRAAAuVwOuVyu6TQ0ztbWVtMpEBERERERERERERG9NNjQRNRIvAiTTopOIKonGH9XDZNOFkgqhOJbQ6wHoBTiD0JPsIhSwfgK0Zk3AfwNsYlgHwg+I+QSPaF4ALARnFy6Sg3XgigrsacD7gg+H0QndgYAC8FJgYsOFQnFm1eaC8X/KvqEBlAu+Jzs/UDs+fSzgfhIzaI5nNExEYp/Te+OUDwAJOg0EYrvUSo22frP+rpC8erIYZNrpFB8YNocoXgAmNBtmlB8vzKx14dfxV9eMETw+XBcX1sofvADsXvKFV3xj6C25WLHIEtP7L7Urlz89SlPV+z+LppDYbX4xWgmKReKz5SKXQutBd9rAcA1qdh5EM3h9y0PhOIBoO3bYu+3CnaK3dvbVYk/p+8JvlVwErsU8YfgIC6i9QPAX4KH0VDwC4H/6og/n/SrxZ5PV3XFXp+q1PDxSSp4GCoEc1DH9zq6go+hrBFMsqKOa4GInhwbmoiIiIiIiIiIiIiI6KlUK9jC96prBO3UREREREREREREREREpAlsaCIiIiIiIiIiIiIiIqKnwoamV4yXlxemTp2q6TReabm5uZBIJEhLS9N0KkREREREREREREREQtjQ9ApLSEhA3759YWpq+ko0fAQFBcHPz0/TacDKygoFBQXo1KmTplMhIiIiIiIiIiIiIhLChqZXWElJCXr27IkFCxaopbyKigq1lPOy1Pu0tLW1YWFhAR0dHU2nQkREREREREREREQkhA1NjVhJSQkCAwMhk8lgaWmJJUuWqGwfM2YMIiMj4ePj81TlSyQSfPPNNxg8eDCMjIzw+eefAwB2796NLl26QF9fH+3atUN0dDQqKysBANXV1YiKioK1tTX09PTQsmVLfPjhh8oybWxsEBMTg1GjRsHIyAitWrXC119/XW+9VVVVCAkJQdu2bWFgYAAHBwcsW7ZMGRMVFYWNGzdi9+7dkEgkkEgkSEpKAgBcu3YN/v7+MDExQbNmzTBkyBDk5uY26Bg86iU1d+5cmJubw8TEBHPmzEFlZSXCw8PRrFkztG7dGhs2bFDG/O/QeUlJSZBIJEhMTES3bt1gaGgIDw8PZGVlPekpISIiIiIiIiIiInquqqu5POnS2LChqRELDw9HcnIydu/ejUOHDiEpKQnnzp1Tax1RUVEYOnQofvvtN4wbNw7Hjh1DYGAgpkyZgoyMDKxevRqxsbHKRqjvvvsOX375JVavXo0rV65g165dcHZ2Vilz0aJF6Ny5M86fP49PP/0UU6ZMweHDh+usV6FQoHXr1vj222+RkZGByMhIfPbZZ9ixYwcAICwsDP7+/ujfvz8KCgpQUFAADw8PVFRUoF+/fpDL5Th27BhOnDgBmUyG/v37o7y8vEHH4MiRI7h+/Tp+/vlnfPHFF5g9ezYGDRqEpk2b4tSpU5g4cSImTJiAP/74o85yZsyYgSVLluDs2bPQ0dHBuHHjGnoaiIiIiIiIiIiIiIg0gmN3NVLFxcVYt24dtmzZAm9vbwDAxo0b0bp1a7XWM3r0aAQHByv/HzduHD799FOMHTsWANCuXTvExMRg2rRpmD17NvLz82FhYQEfHx9IpVJYW1vj9ddfVymzR48e+PTTTwEA9vb2OHHiBL788ku8+eabtdYLANHR0cq/27Zti5SUFOzYsQP+/v6QyWQwMDBAWVkZLCwslPtt2bIFCoUC//nPfyCRSAAAGzZsgImJCZKSktC3b996j0GzZs2wfPlyaGlpwcHBAQsXLsT9+/fx2WefAQAiIiIwf/58HD9+HCNHjqy1nM8//xy9e/cGAHz66acYOHAgSktLoa+v/9i+ZWVlKCsrU1lXUV0FqUS73nyJiP4/9u48vqZr///46yQyOgkSJIZESkIjTUgpNRS3gl6EXLeUpghqaKqtbytVNSX0ahU1dcI11VhtDdWWVrXRVk1F0EpjKKIX5RpvkDm/P/rruT0XEdbh0L6fj8d+PHL23p+1Pns4++xzVtbaIiIiIiIiIiIijqIeTX9QBw4cIDc3l4YNG9rm+fn5UatWLYfWU79+fbvXO3fuZPTo0VitVtvUt29fjh07xsWLF+ncuTOXLl2ievXq9O3bl+XLl9uG1ftNo0aNLnudnp5ebL0Ab7zxBvXq1aNChQpYrVZmzJhBZmZmsfnv3LmT/fv34+PjY8vXz8+P7OxsDhw4UKJ9EBERgYvLf99KAQEBdr20XF1d8ff358SJE8WWExUVZfu7UqVKAFeNefnllylTpozdtPb8DyXKV0RERERERERERETEUdSjSYyULl3a7nVWVhYpKSl06tTpsnU9PT0JCgoiIyODzz//nLVr15KYmMj48eNZv349bm5uN1zvkiVLGDx4MBMnTqRRo0b4+Pgwfvx4Nm/eXGw5WVlZ1KtXj4ULF162rEKFCiXK5X/ztlgsV5xXWFhY4nJ+6111tZihQ4fy7LPP2s2bVbt/ifIVEREREREREREREXEUNTT9QdWoUQM3Nzc2b95McHAwAGfOnGHv3r224dluhnvvvZeMjAxCQ0Ovuo6XlxexsbHExsby5JNPcvfdd7N7927uvfdeADZt2mS3/qZNmwgPDy+23g0bNtC4cWMSExNt8/63R5K7uzsFBQWX5fvuu+9SsWJFfH19S7SNtwMPDw88PDzs5mnYPBERERERERERERG51dTQ9AdltVrp06cPSUlJ+Pv7U7FiRYYNG2Y3xNvp06fJzMzk6NGjAGRkZAAQGBho9xyj6zFy5Ejat29PcHAwDz/8MC4uLuzcuZPvv/+el156iblz51JQUEDDhg3x9vZmwYIFeHl5Ua1aNVsZGzZs4NVXXyUuLo61a9fy3nvv8fHHHxdbb1hYGO+88w6ffvopd911F/Pnz2fr1q3cddddtnVCQkL49NNPycjIwN/fnzJlyhAfH8/48ePp2LEjo0ePpmrVqhw+fJhly5bx/PPPO/yZViIiIiIiIiIiIiJ/JEWFFmenIE6mZzT9gY0fP54HHniA2NhYYmJiaNq0KfXq1bMt//DDD4mOjqZdu3YAdO3alejoaN5+++0brrNNmzZ89NFHfPbZZ9x3333cf//9TJo0ydaQVLZsWWbOnEmTJk2Iiori888/Z9WqVfj7+9vKeO655/juu++Ijo7mpZde4rXXXqNNmzbF1tu/f386derEI488QsOGDTl16pRd7yaAvn37UqtWLerXr0+FChXYsGED3t7efPXVVwQHB9OpUyfCw8Pp06cP2dnZd1QPJxERERERERERERERZ7AUFRUVOTsJkd+EhIQwaNAgBg0a5OxU7jivBz3m7BTIMfznhQoF116nOOcc0HS+xzXXKL5qUcmfNXYlbpj/B4hv8Y8Du6YswxTOuBgmAPwHs5PhEmY53F3oce2VrqFyvtnHa4HhufDY0QVG8QALKptdV8oUmh3HbR7mb+pAw/+qahd4zCh+25EAo/hdHubXhFyL2bnY7JLZ++krL/PjaJrDv0qZdeK/z+OsUTzAsoIyRvFNsvON4jd4mg9kYJrDoet4HueV9EgbbRQP0L/+80bxbXLMPh8y3I3CAWianWcU/42n2XEwPQ/2uZvVD1Apz+yakGH4+VIt1/wr9GF3s+u7aQ4eDvgZoAJm981b3D2N4qvmmW/Dz25mx8E0h7s9zxvFA9z1d7PzecT7Xkbx1QvMP1/yDW93TI+Ds88DgJOlzHLwNvwK9q9S5tvgWWS2DR6GKRQ4oDOFm2EOpr+JmH+TBnfDbTD/lHY+0/2YeMT8u/Sf0Z4a7Zydwh2n9oHiR/C606hHk4iIiIiIiIiIiIiIiNwQNTTJFS1cuBCr1XrFKSIiwtnp3TJX2wdWq5Wvv/7a2emJiIiIiIiIiIiIiDiVeR9n+UPq0KEDDRs2vOIyN8NhT4pz6NChm1b2jUhLS7vqsipVqty6REREREREREREREREbkNqaJIr8vHxwcfHx9lpOF1oaKizUxARERERERERERG5bRUaPqdN7nxqaBL5gzB/dKc50wvKUcMCsizmj85skGf2lO5TrsYpGLMYngxnXM32Y5UC81FZyxiWYfog2AKzcAC2upuV4obzb9I2uZs9cD4y36wH7H3Z5kdit6fZm3LmyUCjeC8Ps+PYJNvsGAAcczU7Diu88o3iG+ea326a5tA8x+zCuKygjFE8QIDhhWmJl9m1ub7pk9aBpYY53G94Ovev/7xZAcD07141il9cZ6RRfBUHfMC872UWX8/wXPjAy+z99BfD9yPADsNrq7/hLeMu0yfWA4GG1wTTHILzze/X9rt4GsWbXpa2epjf+/sVmd0nmOZwsND88+Xf75tdWFJanTKK3/Ox1SgeYEMpb6P47Ybvh/KFZiejaf0A1QyvCfvdzM7FGnnm14RfDG/5As1u94y/AwKcN9wNhh+RmF/VwNUwB9P96IhtMH1HOf+btMifk57RJCIiIiIiIiIiIiIiIjdEDU0iIiIiIiIiIiIiIiJyQ9TQJCIiIiIiIiIiIiIiIjdEDU1irEWLFgwaNMjZaYiIiIiIiIiIiIiIyC2mhiZxqGXLltG6dWv8/f2xWCykpaU5OyU7y5Yto1WrVlSoUAFfX18aNWrEp59+6uy0RERERERERERERO5IRUUWTdc5/dGooUkc6sKFCzRt2pRx48Y5O5Ur+uqrr2jVqhWffPIJ27Zt4y9/+QuxsbHs2LHD2amJiIiIiIiIiIiIiNxx1NAk1+XChQv06NEDq9VKpUqVmDhxot3y7t27M3LkSGJiYq677KKiIpKTkwkODsbDw4PKlSvz9NNP25aHhIQwZswYunXrRunSpalSpQpvvPGGXRmZmZl07NgRq9WKr68vXbp04ZdffrEtnzx5Ms8//zz33XcfYWFhjB07lrCwMFatWlWiHFu0aMFTTz3FoEGDKFeuHAEBAcycOZMLFy7Qq1cvfHx8CA0NZfXq1baYgoIC+vTpw1133YWXlxe1atViypQptuXZ2dlERETQr18/27wDBw7g4+PD7Nmzr3s/ioiIiIiIiIiIiIjcKmpokuuSlJTE+vXrWblyJZ999hmpqals377dIWV/8MEHTJo0ienTp7Nv3z5WrFhBZGSk3Trjx4+nTp067NixgxdeeIFnnnmGtWvXAlBYWEjHjh05ffo069evZ+3atfz000888sgjV62zsLCQ//znP/j5+ZU4z3nz5lG+fHm2bNnCU089xRNPPEHnzp1p3Lgx27dvp3Xr1nTv3p2LFy/a6qhatSrvvfcee/bsYeTIkbz44ossXboUAE9PTxYuXMi8efNYuXIlBQUFPPbYY7Rq1YrevXtf724UEREREREREREREbllSjk7AblzZGVlMWvWLBYsWEDLli2BXxtdqlat6pDyMzMzCQwMJCYmBjc3N4KDg2nQoIHdOk2aNOGFF14AoGbNmmzYsIFJkybRqlUr1q1bx+7duzl48CBBQUEAvPPOO0RERLB161buu+++y+qcMGECWVlZdOnSpcR51qlTh+HDhwMwdOhQXnnlFcqXL0/fvn0BGDlyJG+99Ra7du3i/vvvx83NjZSUFFv8XXfdxcaNG1m6dKmt3rp16/LSSy/x+OOP07VrVw4fPsxHH3101RxycnLIycmxm5dXVICbxbXE2yEiIiIiIiIiIiIiYko9mqTEDhw4QG5uLg0bNrTN8/Pzo1atWg4pv3Pnzly6dInq1avTt29fli9fTn5+vt06jRo1uux1eno6AOnp6QQFBdkamQBq165N2bJlbev83qJFi0hJSWHp0qVUrFixxHlGRUXZ/nZ1dcXf39+u51VAQAAAJ06csM174403qFevHhUqVMBqtTJjxgwyMzPtyn3uueeoWbMmr7/+OrNnz8bf3/+qObz88suUKVPGblp7/ocSb4OIiIiIiIiIiIiIiCOooUluG0FBQWRkZPDmm2/i5eVFYmIizZo1Iy8vz+F1LVmyhMcff5ylS5de9/Ok3Nzc7F5bLBa7eRaLBfh1yLzf6ho8eDB9+vThs88+Iy0tjV69epGbm2tXzokTJ9i7dy+urq7s27ev2ByGDh3KuXPn7KZWvhHXtR0iIiIiIiIiIiIipoqKNF3v9EejhiYpsRo1auDm5sbmzZtt886cOcPevXsdVoeXlxexsbFMnTqV1NRUNm7cyO7du23LN23aZLf+pk2bCA8PByA8PJwjR45w5MgR2/I9e/Zw9uxZateubZu3ePFievXqxeLFi2nXrp3Dcr+aDRs20LhxYxITE4mOjiY0NJQDBw5ctl7v3r2JjIxk3rx5DBky5Iq9sH7j4eGBr6+v3aRh80RERERERERERETkVtMzmqTErFYrffr0ISkpCX9/fypWrMiwYcNwcflve+Xp06fJzMzk6NGjAGRkZAAQGBhIYGBgseXPnTuXgoICGjZsiLe3NwsWLMDLy4tq1arZ1tmwYQOvvvoqcXFxrF27lvfee4+PP/4YgJiYGCIjI4mPj2fy5Mnk5+eTmJhI8+bNqV+/PvDrcHk9e/ZkypQpNGzYkOPHjwO/NnCVKVPGcTvrd8LCwnjnnXf49NNPueuuu5g/fz5bt27lrrvusq3zxhtvsHHjRnbt2kVQUBAff/wx8fHxbNq0CXd395uSl4iIiIiIiIiIiIiIKfVokusyfvx4HnjgAWJjY4mJiaFp06bUq1fPtvzDDz8kOjra1lOoa9euREdH8/bbb1+z7LJlyzJz5kyaNGlCVFQUn3/+OatWrbJ7VtFzzz3Hd999R3R0NC+99BKvvfYabdq0AX4dsm7lypWUK1eOZs2aERMTQ/Xq1Xn33Xdt8TNmzCA/P58nn3ySSpUq2aZnnnnGUbvoMv3796dTp0488sgjNGzYkFOnTpGYmGhb/uOPP5KUlMSbb75pe77Um2++yb///W9GjBhx0/ISERERERERERERETGlHk1yXaxWK/Pnz2f+/Pm2eUlJSba/ExISSEhIuKGy4+LiiIuLK3YdX19fli5detXlwcHBrFy58qrLU1NTbyi34uIPHTp02byi3w206eHhwZw5c5gzZ47dOi+//DIAd999NxcvXrRbVrZsWTIzM41yFRERERERERERERG52dSjSURERERERERERERERG6IejTJLbNw4UL69+9/xWXVqlXjhx9+uMUZ2cvMzKR27dpXXb5nzx6Cg4NvYUYiIiIiIiIiIiIit7fCIouzUxAnU0OT3DIdOnSgYcOGV1zm5uZ2zfgrDVHnSJUrVyYtLa3Y5SIiIiIiIiIiIiIi8l9qaJJbxsfHBx8fH2encVWlSpUiNDTU2WmIiIiIiIiIiIiIiNwx9IwmERERERERERERERERuSHq0STyB+GIkVCLDONdDAvIcik0ivctcn7bubfpTnSA/xjuhnKFZgX4FpjVD1BgeEKbpnDSAZ+OjXJcjeJdi5x/MjXPMdsRWz3MjsQJV/P3dEXDkyHb7DASlGcW/7GX+dXdA7Nra+/8XKP4D9zN31CmObxheC62zPUyigf4yc3sPZ1YaLYPVrm6G8UDPFGUYxT/uuE+aJPjYRQPsLjOSKP4bjtHG8VPrGdWP8AALhnFf2J4LpjWv9L92kNmX0tUjtm59C83s2tr80tm11WADHezzzjTHNZ4mW+Df5HZh2RFwxu+ennm25DpZrYNpjmsdMBxqFpk9p7av8bsM+6eePN71h8Wm8XXzTH7nD/kZvZ+NK0fYIeHWQ4BBWbxP7qZb0NZw2vCBnezex1HqF1g9hlpeOvvkN918gy/QuU7IAdTpt8ezK+sInIjnP+rrIiIiIiIiIiIiIiIiNyR1KNJRERERERERERERERuSFGRI/rkyZ1MPZrE4Vq0aMGgQYOcncZtKzU1FYvFwtmzZ52dioiIiIiIiIiIiIiIETU0yU21bNkyWrdujb+/PxaLhbS0NGenZGfZsmW0atWKChUq4OvrS6NGjfj0009vap2NGzfm2LFjlClT5qbWIyIiIiIiIiIiIiJys6mhSW6qCxcu0LRpU8aNG+fsVK7oq6++olWrVnzyySds27aNv/zlL8TGxrJjx46bVqe7uzuBgYFYLOpSKiIiIiIiIiIiIiJ3NjU0iZELFy7Qo0cPrFYrlSpVYuLEiXbLu3fvzsiRI4mJibnusouKikhOTiY4OBgPDw8qV67M008/bVseEhLCmDFj6NatG6VLl6ZKlSq88cYbdmVkZmbSsWNHrFYrvr6+dOnShV9++cW2fPLkyTz//PPcd999hIWFMXbsWMLCwli1alWJcmzRogVPPfUUgwYNoly5cgQEBDBz5kwuXLhAr1698PHxITQ0lNWrV9ti/nfovLlz51K2bFk+/fRTwsPDsVqtPPTQQxw7duy695mIiIiIiIiIiIiIyK2khiYxkpSUxPr161m5ciWfffYZqampbN++3SFlf/DBB0yaNInp06ezb98+VqxYQWRkpN0648ePp06dOuzYsYMXXniBZ555hrVr1wJQWFhIx44dOX36NOvXr2ft2rX89NNPPPLII1ets7CwkP/85z/4+fmVOM958+ZRvnx5tmzZwlNPPcUTTzxB586dady4Mdu3b6d169Z0796dixcvXrWMixcvMmHCBObPn89XX31FZmYmgwcPLnEOIiIiIiIiIiIiIiLOUMrZCcidKysri1mzZrFgwQJatmwJ/NroUrVqVYeUn5mZSWBgIDExMbi5uREcHEyDBg3s1mnSpAkvvPACADVr1mTDhg1MmjSJVq1asW7dOnbv3s3BgwcJCgoC4J133iEiIoKtW7dy3333XVbnhAkTyMrKokuXLiXOs06dOgwfPhyAoUOH8sorr1C+fHn69u0LwMiRI3nrrbfYtWsX999//xXLyMvL4+2336ZGjRoADBw4kNGjR5c4BxERERERERERERFnKCpydgbibOrRJDfswIED5Obm0rBhQ9s8Pz8/atWq5ZDyO3fuzKVLl6hevTp9+/Zl+fLl5Ofn263TqFGjy16np6cDkJ6eTlBQkK2RCaB27dqULVvWts7vLVq0iJSUFJYuXUrFihVLnGdUVJTtb1dXV/z9/e16XgUEBABw4sSJq5bh7e1ta2QCqFSpUrHr5+TkcP78ebspr6igxDmLiIiIiIiIiIiIiDiCGprkthUUFERGRgZvvvkmXl5eJCYm0qxZM/Ly8hxe15IlS3j88cdZunTpdT9Pys3Nze61xWKxm2exWIBfh+W7njKKivlXgJdffpkyZcrYTWvP/3BdeYuIiIiIiIiIiIiImFJDk9ywGjVq4ObmxubNm23zzpw5w969ex1Wh5eXF7GxsUydOpXU1FQ2btzI7t27bcs3bdpkt/6mTZsIDw8HIDw8nCNHjnDkyBHb8j179nD27Flq165tm7d48WJ69erF4sWLadeuncNyv5mGDh3KuXPn7KZWvhHOTktERERERERERERE/mT0jCa5YVarlT59+pCUlIS/vz8VK1Zk2LBhuLj8t/3y9OnTZGZmcvToUQAyMjIACAwMJDAwsNjy586dS0FBAQ0bNsTb25sFCxbg5eVFtWrVbOts2LCBV199lbi4ONauXct7773Hxx9/DEBMTAyRkZHEx8czefJk8vPzSUxMpHnz5tSvXx/4dbi8nj17MmXKFBo2bMjx48eBXxu4ypQp47id5WAeHh54eHjYzXOzuDopGxERERERERERERH5s1KPJjEyfvx4HnjgAWJjY4mJiaFp06bUq1fPtvzDDz8kOjra1lOoa9euREdH8/bbb1+z7LJlyzJz5kyaNGlCVFQUn3/+OatWrcLf39+2znPPPcd3331HdHQ0L730Eq+99hpt2rQBfh1+buXKlZQrV45mzZoRExND9erVeffdd23xM2bMID8/nyeffJJKlSrZpmeeecZRu0hERERERERERERE5A9LPZrEiNVqZf78+cyfP982LykpyfZ3QkICCQkJN1R2XFwccXFxxa7j6+vL0qVLr7o8ODiYlStXXnV5amrqDeVWXPyhQ4cum/f75y21aNHC7vWV9lFcXFyxz2gSERERERERERERuR0UFlmcnYI4mXo0iYiIiIiIiIiIiIiIyA1RQ5M4zcKFC7FarVecIiIinJ0emZmZV83ParWSmZnp7BRFRERERERERERERJxKQ+eJ03To0IGGDRtecZmbm9s14680RJ0jVa5cmbS0tGKXi4iIiIiIiIiIiIj8mamhSZzGx8cHHx8fZ6dxVaVKlSI0NNTZaYiIiIiIiIiIiIiI3LY0dJ6IiIiIiIiIiIiIiIjcEPVoEvmDKHJ2AkCOxSz+vmyz+FyL+V7Y6WFWRmCBWfv9GRfzbfDA7EDclWuWw2lXwxMByCxVaBRvmkHNPPP/wzjobpqF+X40ZboNfkWuRvFeDriwdWvzi1H8B2sCjOLPm+0CQgvMb9V6JpolsXmyu1H88CevPRzuzc7hfszit7jnGcUDjJsUbRR/4LlvjOKTJpg///LA4G+N4ttfNDsOuzyMwgGoUmAWP7HeSKP457aNNksA+HdcH7McVkxxav0VDpcxigf4l5vZ51OPFseM4k/vMr9PaBhldq9jmkPaWbPPN4AzFrNtGPS4WXzubrPPeIDGkZWcmsPpb6sYxQMcN/yY3YLZKCNfvmtWP0DiazWN4gu2bDOKb9zA7DPStH6A44vM75dM5FoMb1ox/0/2qEKz+wSzK8qvDG8TuGS4E4LyzL8AHTH8jDT9ScL8TDI/lqbHUW5MUZHzf8MQ51KPJhEREREREREREREREbkhamgSERERERERERERERGRG6KGJhEREREREREREREREbkhamiSEmnRogWDBg1ydhrFSk5Opm7dus5OQ0RERERERERERETkT0MNTXLdli1bRuvWrfH398disZCWlubslERERERERERERERE/pDeeOMNQkJC8PT0pGHDhmzZsuWq686cOZMHHniAcuXKUa5cOWJiYopd3xHU0CTX7cKFCzRt2pRx48Y5OxURERERERERERERcaLCIoum65yux7vvvsuzzz7LqFGj2L59O3Xq1KFNmzacOHHiiuunpqbSrVs3vvzySzZu3EhQUBCtW7fmX//6lyMO9xWpoUkuc+HCBXr06IHVaqVSpUpMnDjRbnn37t0ZOXIkMTEx1112UVERycnJBAcH4+HhQeXKlXn66acBGD16NPfcc89lMXXr1mXEiBHAr2+SBg0aULp0acqWLUuTJk04fPiw3frTp08nKCgIb29vunTpwrlz52zLEhISiIuLIyUlhQoVKuDr68uAAQPIzc0tUf4tWrTgqaeeYtCgQZQrV46AgABmzpzJhQsX6NWrFz4+PoSGhrJ69WpbTEFBAX369OGuu+7Cy8uLWrVqMWXKFNvy7OxsIiIi6Nevn23egQMH8PHxYfbs2SXKS0RERERERERERET+eF577TX69u1Lr169qF27Nm+//Tbe3t5X/e144cKFJCYmUrduXe6++27++c9/UlhYyLp1625ajmpoksskJSWxfv16Vq5cyWeffUZqairbt293SNkffPABkyZNYvr06ezbt48VK1YQGRkJQO/evUlPT2fr1q229Xfs2MGuXbvo1asX+fn5xMXF0bx5c3bt2sXGjRvp168fFst/W4D379/P0qVLWbVqFWvWrGHHjh0kJiba5bBu3TrS09NJTU1l8eLFLFu2jJSUlBJvw7x58yhfvjxbtmzhqaee4oknnqBz5840btyY7du307p1a7p3787FixcBKCwspGrVqrz33nvs2bOHkSNH8uKLL7J06VIAPD09WbhwIfPmzWPlypUUFBTw2GOP0apVK3r37n3D+1pEREREREREREREbj85OTmcP3/ebsrJyblsvdzcXLZt22bX6cPFxYWYmBg2btxYorouXrxIXl4efn5+Dsv/f6mhSexkZWUxa9YsJkyYQMuWLYmMjGTevHnk5+c7pPzMzEwCAwOJiYkhODiYBg0a0LdvXwCqVq1KmzZtmDNnjm39OXPm0Lx5c6pXr8758+c5d+4c7du3p0aNGoSHh9OzZ0+Cg4Nt62dnZ/POO+9Qt25dmjVrxrRp01iyZAnHjx+3rePu7s7s2bOJiIigXbt2jB49mqlTp1JYWFiibahTpw7Dhw8nLCyMoUOH4unpSfny5enbty9hYWGMHDmSU6dOsWvXLgDc3NxISUmhfv363HXXXcTHx9OrVy9bQxP82mvrpZde4vHHH2fQoEEcPnyYmTNnXjWHK12I8ooKSnYQRERERERERERERMRpXn75ZcqUKWM3vfzyy5et9+9//5uCggICAgLs5gcEBNj95l2cIUOGULly5Rsaoayk1NAkdg4cOEBubi4NGza0zfPz86NWrVoOKb9z585cunSJ6tWr07dvX5YvX27XiNW3b18WL15MdnY2ubm5LFq0yNarx8/Pj4SEBNq0aUNsbCxTpkzh2LFjduUHBwdTpUoV2+tGjRpRWFhIRkaGbV6dOnXw9va2WycrK4sjR46UaBuioqJsf7u6uuLv72/rlQXY3vS/HyPzjTfeoF69elSoUAGr1cqMGTPIzMy0K/e5556jZs2avP7668yePRt/f/+r5nClC9Ha8z+UKH8RERERERERERERcZ6hQ4dy7tw5u2no0KEOr+eVV15hyZIlLF++HE9PT4eX/xs1NMktFRQUREZGBm+++SZeXl4kJibSrFkz8vLyAIiNjcXDw4Ply5ezatUq8vLyePjhh23xc+bMYePGjTRu3Jh3332XmjVrsmnTplu6DW5ubnavLRaL3bzfhvL7rYfUkiVLGDx4MH369OGzzz4jLS2NXr16XfZcqBMnTrB3715cXV3Zt29fsTlc6ULUyjfCEZsnIiIiIiIiIiIiIjeRh4cHvr6+dpOHh8dl65UvXx5XV1d++eUXu/m//PILgYGBxdYxYcIEXnnlFT777DO7zhM3gxqaxE6NGjVwc3Nj8+bNtnlnzpxh7969DqvDy8uL2NhYpk6dSmpqKhs3bmT37t0AlCpVip49ezJnzhzmzJlD165d8fLysouPjo5m6NChfPvtt9xzzz0sWrTItiwzM5OjR4/aXm/atAkXFxe7Hlk7d+7k0qVLdutYrVaCgoIcto2/t2HDBho3bkxiYiLR0dGEhoZy4MCBy9br3bu3bajCIUOGkJ6eftUyr3QhcrO43pT8RURERERERERERK6mSNN1TyXl7u5OvXr1WLdunW1eYWEh69ato1GjRleNe/XVVxkzZgxr1qyhfv3611HjjSl102uQO4rVaqVPnz4kJSXh7+9PxYoVGTZsGC4u/22TPH36tF2Dzm/D0gUGBl6zFXXu3LkUFBTQsGFDvL29WbBgAV5eXlSrVs22zuOPP054eDjwayPNbw4ePMiMGTPo0KEDlStXJiMjg3379tGjRw/bOp6envTs2ZMJEyZw/vx5nn76abp06WKXV25uLn369GH48OEcOnSIUaNGMXDgQLttdKSwsDDeeecdPv30U+666y7mz5/P1q1bueuuu2zrvPHGG2zcuJFdu3YRFBTExx9/THx8PJs2bcLd3f2m5CUiIiIiIiIiIiIit7dnn32Wnj17Ur9+fRo0aMDkyZO5cOECvXr1AqBHjx5UqVLF9oyncePGMXLkSBYtWkRISIjtWU5WqxWr1XpTclRDk1xm/PjxZGVlERsbi4+PD8899xznzp2zLf/www9tJzFA165dARg1ahTJycnFll22bFleeeUVnn32WQoKCoiMjGTVqlV2zyMKCwujcePGnD592u5ZUd7e3vz444/MmzePU6dOUalSJZ588kn69+9vWyc0NJROnTrRtm1bTp8+Tfv27XnzzTftcmjZsiVhYWE0a9aMnJwcunXrds28TfTv358dO3bwyCOPYLFY6NatG4mJiaxevRqAH3/8kaSkJGbNmmXrVfXmm28SFRXFiBEjGDdu3E3LTURERERERERERERuX4888ggnT55k5MiRHD9+nLp167JmzRoCAgKAX0f5+n0nirfeeovc3Fy7R9JAyX6/v1FqaJLLWK1W5s+fz/z5823zkpKSbH8nJCSQkJBwQ2XHxcURFxdX7DpFRUUcPXqUxMREu/kBAQEsX778qnHJycm2N8oTTzxRbB0pKSmkpKSUKOffS01NvWzeoUOHLptXVPTfDpAeHh62oQB/77cW5rvvvpuLFy/aLStbtiyZmZnXnZ+IiIiIiIiIiIiI/LEMHDiQgQMHXnHZ//5mfaXfq282NTTJbeXkyZMsWbKE48eP2/WaEhERERERERERERGR248amsShFi5caDeU3e9Vq1aNH374odj4ihUrUr58eWbMmEG5cuVuRopXlZmZSe3ata+6fM+ePQQHB9/CjEREREREREREREREbm9qaBKH6tChg91zlX7Pzc3tmvG/H3LuZpg7d+5Vl1WuXJm0tLRil4uIiIiIiIiIiIjIfxUWWZydgjiZGprEoXx8fPDx8XF2GjekVKlShIaGOjsNEREREREREREREZE7hhqaRMSm0PCfDzwNO6Tt8jBLICjf/L8nqhaYleFdaFZ/EebbcN7F7EDkWsxy8C8w75lYYHExijeLhlOuhgUADbJzjOILHXAumGqUk20U/66n2Y68aCkwige4dCDPKD7X8DAE5pldFN73uGSWABA4xcso/qExVYzi14z8xSge4KGUSkbx08b+ZFZ/fhmjeIB9/7fBKD5sVgez+vusMop3RA4z+3xhFN/xkuGHLPC+2duBAZi9J/8d18csAaD8illOzcG0/r31XzSKB2h3yexeI/Mbb6P4ag+afT4CHP7CuTnsW2d2nwJQnmuPWFGcC18fNYov3byqUTzAxa9/Nor3fsAsh93bzO+bTb8DXTS8cY7IyTcrAMj9eL1RvFuje+7o+gGOlzplFB9oeBgOu5jdMwMEF5pdE9a7ZBnFlzL+FggNC82uzabfXrJczL8DGn+DMkzB4oCBisyPpIg4g967IiIiIiIiIiIiIiIickPU0CQiIiIiIiIiIiIiIiI3RA1NIiIiIiIiIiIiIiIickPU0CQ3pEWLFgwaNMjZaRQrOTmZunXrOjuNy6SmpmKxWDh79qyzUxERERERERERERExUlRk0XSd0x+NGprE2LJly2jdujX+/v5YLBbS0tKcndJtrXHjxhw7dowyZcwfLC4iIiIiIiIiIiIi4kxqaBJjFy5coGnTpowbN87ZqdwR3N3dCQwMxGL547Vci4iIiIiIiIiIiMifixqa5JouXLhAjx49sFqtVKpUiYkTJ9ot7969OyNHjiQmJua6yy4qKiI5OZng4GA8PDyoXLkyTz/9NACjR4/mnnvuuSymbt26jBgxAvh1GLoGDRpQunRpypYtS5MmTTh8+LDd+tOnTycoKAhvb2+6dOnCuXPnbMsSEhKIi4sjJSWFChUq4Ovry4ABA8jNzS1R/i1atOCpp55i0KBBlCtXjoCAAGbOnMmFCxfo1asXPj4+hIaGsnr1alvM/w6dN3fuXMqWLcunn35KeHg4VquVhx56iGPHjl3XvhQRERERERERERERudXU0CTXlJSUxPr161m5ciWfffYZqampbN++3SFlf/DBB0yaNInp06ezb98+VqxYQWRkJAC9e/cmPT2drVu32tbfsWMHu3btolevXuTn5xMXF0fz5s3ZtWsXGzdupF+/fnY9hfbv38/SpUtZtWoVa9asYceOHSQmJtrlsG7dOtLT00lNTWXx4sUsW7aMlJSUEm/DvHnzKF++PFu2bOGpp57iiSeeoHPnzjRu3Jjt27fTunVrunfvzsWLF69axsWLF5kwYQLz58/nq6++IjMzk8GDB5c4BxERERERERERERERZ1BDkxQrKyuLWbNmMWHCBFq2bElkZCTz5s0jPz/fIeVnZmYSGBhITEwMwcHBNGjQgL59+wJQtWpV2rRpw5w5c2zrz5kzh+bNm1O9enXOnz/PuXPnaN++PTVq1CA8PJyePXsSHBxsWz87O5t33nmHunXr0qxZM6ZNm8aSJUs4fvy4bR13d3dmz55NREQE7dq1Y/To0UydOpXCwsISbUOdOnUYPnw4YWFhDB06FE9PT8qXL0/fvn0JCwtj5MiRnDp1il27dl21jLy8PN5++23q16/Pvffey8CBA1m3bt1V18/JyeH8+fN2U15RQYnyFRERERERERERERFxFDU0SbEOHDhAbm4uDRs2tM3z8/OjVq1aDim/c+fOXLp0ierVq9O3b1+WL19u14jVt29fFi9eTHZ2Nrm5uSxatIjevXvb8khISKBNmzbExsYyZcqUy4abCw4OpkqVKrbXjRo1orCwkIyMDNu8OnXq4O3tbbdOVlYWR44cKdE2REVF2f52dXXF39/f1isLICAgAIATJ05ctQxvb29q1Khhe12pUqVi13/55ZcpU6aM3bT2/A8lyldERERERERERETEUQo1Xff0R6OGJnGqoKAgMjIyePPNN/Hy8iIxMZFmzZqRl5cHQGxsLB4eHixfvpxVq1aRl5fHww8/bIufM2cOGzdupHHjxrz77rvUrFmTTZs23dJtcHNzs3ttsVjs5v02lF9xPaSuVEZRUdFV1x86dCjnzp2zm1r5RtxI+iIiIiIiIiIiIiIiN0wNTVKsGjVq4ObmxubNm23zzpw5w969ex1Wh5eXF7GxsUydOpXU1FQ2btzI7t27AShVqhQ9e/Zkzpw5zJkzh65du+Ll5WUXHx0dzdChQ/n222+55557WLRokW1ZZmYmR48etb3etGkTLi4udj2ydu7cyaVLl+zWsVqtBAUFOWwbHc3DwwNfX1+7yc3i6uy0RERERERERERERORPppSzE5Dbm9VqpU+fPiQlJeHv70/FihUZNmwYLi7/baM8ffq0XYPOb8PSBQYGEhgYWGz5c+fOpaCggIYNG+Lt7c2CBQvw8vKiWrVqtnUef/xxwsPDAdiwYYNt/sGDB5kxYwYdOnSgcuXKZGRksG/fPnr06GFbx9PTk549ezJhwgTOnz/P008/TZcuXezyys3NpU+fPgwfPpxDhw4xatQoBg4caLeNIiIiIiIiIiIiIiJyOTU0yTWNHz+erKwsYmNj8fHx4bnnnuPcuXO25R9++CG9evWyve7atSsAo0aNIjk5udiyy5YtyyuvvMKzzz5LQUEBkZGRrFq1Cn9/f9s6YWFhNG7cmNOnT9s9K8rb25sff/yRefPmcerUKSpVqsSTTz5J//79beuEhobSqVMn2rZty+nTp2nfvj1vvvmmXQ4tW7YkLCyMZs2akZOTQ7du3a6Zt4iIiIiIiIiIiIiIqKFJSsBqtTJ//nzmz59vm5eUlGT7OyEhgYSEhBsqOy4ujri4uGLXKSoq4ujRoyQmJtrNDwgIYPny5VeNS05OtjUYPfHEE8XWkZKSQkpKSoly/r3U1NTL5h06dOiyeb9/3lKLFi3sXl9p/8XFxRX7jCYRERERERERERERkduBGprktnby5EmWLFnC8ePH7XpNiYiIiIiIiIiIiIjzFWFxdgriZGpokptq4cKFdkPZ/V61atX44Ycfio2vWLEi5cuXZ8aMGZQrV+5mpHhVmZmZ1K5d+6rL9+zZQ3Bw8C3MSERERERERERERETk9qKGJrmpOnToYPdcpd9zc3O7ZvzNHj5u7ty5V11WuXJl0tLSil0uIiIiIiIiIiIiIvJnpoYmual8fHzw8fFxdho3pFSpUoSGhjo7DRERERERERERERGR25YamkTExsWwA1m24XCsUTlmCeQ6YDjY/W5mOQTiYhR/1vQgAG6G4+K6G/YkPOVqfiD+5VpoFG+aQc08s+MIsMXTw7gMEw86oIyNHp5G8cGGp7Nb0bV7vl6LVw2zMtz3mdV/3M3sXGpaWNosAaDNM65G8ZtG/Mus/kFeRvEAm0YdM4p/yK2MUfwPpfKM4gF6TGpiFH+g74dG8WGTmhrFOyKHv2T7GsV/42l2LgPUyzf7hPjE1d0o/rkVU4ziAf4d18covvyKWU6tv2ZBFaN4gHSzw0D9pheN4k99Z36fEOzkHMKKzO9TzlnM7tdKP2A2QkTuTrPPJwDvJs7NITLH/P1w3PB2ycvwfu0HD/OflZq3a24UX7Blm1G8u5PrBwjMd+4/6AYXmt93m35KNy+0GsWbXZF+VWAYb7oPrIXmvwecMfw+bvqThPknpIjcqfT+FxERERERERERERERkRuiHk0iIiIiIiIiIiIiInJDHNAhT+5w6tEkIiIiIiIiIiIiIiIiN+SOb2hq0aIFgwYNcnYaxUpOTqZu3brOTsMhbrf9fejQISwWC2lpac5ORURERERERERERETkT+cPNXTesmXLePvtt9m2bRunT59mx44df5gGntvFsmXLcHMzf0ikowQFBXHs2DHKly/v7FRERERERERERERERP507vgeTb934cIFmjZtyrhx45ydyh+Wn58fPj4+zk7DxtXVlcDAQEqV+kO1mYqIiIiIiIiIiIiI3BHuqIamCxcu0KNHD6xWK5UqVWLixIl2y7t3787IkSOJiYm57rKLiopITk4mODgYDw8PKleuzNNPPw3A6NGjueeeey6LqVu3LiNGjAAgNTWVBg0aULp0acqWLUuTJk04fPiw3frTp08nKCgIb29vunTpwrlz52zLEhISiIuLIyUlhQoVKuDr68uAAQPIzc0tUf4tWrTg6aef5vnnn8fPz4/AwECSk5Pt1snMzKRjx45YrVZ8fX3p0qULv/zyi235b0P8zZ8/n5CQEMqUKUPXrl35z3/+Y1fP74fOCwkJYezYsfTu3RsfHx+Cg4OZMWOGbXnjxo0ZMmSIXR4nT57Ezc2Nr776CoD58+dTv359fHx8CAwM5NFHH+XEiRO29c+cOUN8fDwVKlTAy8uLsLAw5syZA9gPnVdYWEjVqlV566237OrbsWMHLi4utuNx9uxZHn/8cdt+fvDBB9m5c2eJ9vNv+2j27NkEBwdjtVpJTEykoKCAV199lcDAQCpWrMg//vEPu7jXXnuNyMhISpcuTVBQEImJiWRlZdmW9+7dm6ioKHJycgDIzc0lOjqaHj16lCgvERERERERERERERFnuKMampKSkli/fj0rV67ks88+IzU1le3btzuk7A8++IBJkyYxffp09u3bx4oVK4iMjAR+bQRIT09n69attvV37NjBrl276NWrF/n5+cTFxdG8eXN27drFxo0b6devHxaLxbb+/v37Wbp0KatWrWLNmjXs2LGDxMREuxzWrVtHeno6qampLF68mGXLlpGSklLibZg3bx6lS5dm8+bNvPrqq4wePZq1a9cCUFhYSMeOHTl9+jTr169n7dq1/PTTTzzyyCN2ZRw4cIAVK1bw0Ucf8dFHH7F+/XpeeeWVYuudOHEi9evXt23TE088QUZGBgDx8fEsWbKEoqIi2/rvvvsulStX5oEHHgAgLy+PMWPGsHPnTlasWMGhQ4dISEiwrT9ixAj27NnD6tWrSU9P56233rriUHkuLi5069aNRYsW2c1fuHAhTZo0oVq1agB07tyZEydOsHr1arZt28a9995Ly5YtOX36dIn284EDB1i9ejVr1qxh8eLFzJo1i3bt2vHzzz+zfv16xo0bx/Dhw9m8ebNdblOnTuWHH35g3rx5fPHFFzz//PO25VOnTuXChQu88MILAAwbNoyzZ8/y+uuvlygnEREREREREREREWcoxKLpOqc/mjtmvLGsrCxmzZrFggULaNmyJfBrw0rVqlUdUn5mZiaBgYHExMTg5uZGcHAwDRo0AKBq1aq0adOGOXPmcN999wEwZ84cmjdvTvXq1Tl9+jTnzp2jffv21KhRA4Dw8HC78rOzs3nnnXeoUqUKANOmTaNdu3ZMnDiRwMBAANzd3Zk9ezbe3t5EREQwevRokpKSGDNmDC4u124TjIqKYtSoUQCEhYXx+uuvs27dOlq1asW6devYvXs3Bw8eJCgoCIB33nmHiIgItm7datuuwsJC5s6daxser3v37qxbt+6yHjq/17ZtW1uj2ZAhQ5g0aRJffvkltWrVokuXLgwaNIhvvvnG1rC0aNEiunXrZmuI6927t62s6tWrM3XqVO677z6ysrKwWq1kZmYSHR1N/fr1gV97UV1NfHw8EydOJDMzk+DgYAoLC1myZAnDhw8H4JtvvmHLli2cOHECDw8PACZMmMCKFSt4//336dev3zX3c2FhIbNnz8bHx4fatWvzl7/8hYyMDD755BNcXFyoVasW48aN48svv6Rhw4YAl/UCe+mllxgwYABvvvkmAFarlQULFtC8eXN8fHyYPHkyX375Jb6+vlfMIScnx9b76Td5RQW4WVyvmb+IiIiIiIiIiIiIiKPcMT2aDhw4QG5uru2He/j1eUG1atVySPmdO3fm0qVLVK9enb59+7J8+XLy8/Nty/v27cvixYvJzs4mNzeXRYsW2RpI/Pz8SEhIoE2bNsTGxjJlyhSOHTtmV35wcLCtkQmgUaNGFBYW2nr+ANSpUwdvb2+7dbKysjhy5EiJtiEqKsrudaVKlWxD0KWnpxMUFGRrZAKoXbs2ZcuWJT093TYvJCTE7hlMvy+jJPVaLBYCAwNtMRUqVKB169YsXLgQgIMHD7Jx40bi4+NtMdu2bSM2Npbg4GB8fHxo3rw58GvjH8ATTzzBkiVLqFu3Ls8//zzffvvtVXOpW7cu4eHhtl5N69ev58SJE3Tu3BmAnTt3kpWVhb+/P1ar1TYdPHiQAwcOFLudV9tHAQEB1K5d264xMCAgwG6/ff7557Rs2ZIqVarg4+ND9+7dOXXqFBcvXrSt06hRIwYPHsyYMWN47rnnaNq06VVzePnllylTpozdtPb8DyXKX0RERERERERERETEUe6YhqabLSgoiIyMDN588028vLxITEykWbNm5OXlARAbG4uHhwfLly9n1apV5OXl8fDDD9vi58yZw8aNG2ncuDHvvvsuNWvWZNOmTbd0G9zc3OxeWywWCgsLb3oZ14qJj4/n/fffJy8vj0WLFhEZGWkblvDChQu0adMGX19fFi5cyNatW1m+fDmA7flUf/3rXzl8+DD/93//x9GjR2nZsiWDBw++aj7x8fG2hqZFixbx0EMP4e/vD/zaM65SpUqkpaXZTRkZGSQlJZVkF11xe4vbB4cOHaJ9+/ZERUXxwQcfsG3bNt544w27bYRfe0pt2LABV1dX9u/fX2wOQ4cO5dy5c3ZTK9+IEuUvIiIiIiIiIiIiIuIod0xDU40aNXBzc7N77s2ZM2fYu3evw+rw8vIiNjaWqVOnkpqaysaNG9m9ezcApUqVomfPnsyZM4c5c+bQtWtXvLy87OKjo6MZOnQo3377Lffcc4/ds4IyMzM5evSo7fWmTZtsw6z9ZufOnVy6dMluHavVatcL6UaFh4dz5MgRu95Re/bs4ezZs9SuXdu4/OJ07NiR7Oxs1qxZw6JFi+x6M/3444+cOnWKV155hQceeIC77777ij2oKlSoQM+ePVmwYAGTJ09mxowZV63v0Ucf5fvvv2fbtm28//77dvXde++9HD9+nFKlShEaGmo3Xem5T46wbds2CgsLmThxIvfffz81a9a0Oxd+M378eH788UfWr1/PmjVrmDNnzlXL9PDwwNfX127SsHkiIiIiIiIiIiIicqvdMc9oslqt9OnTh6SkJPz9/alYsSLDhg2zG67s9OnTdg06vw1LFxgYaHsO0tXMnTuXgoICGjZsiLe3NwsWLMDLy4tq1arZ1nn88cdtz17asGGDbf7BgweZMWMGHTp0oHLlymRkZLBv3z569OhhW8fT05OePXsyYcIEzp8/z9NPP02XLl3s8srNzaVPnz4MHz6cQ4cOMWrUKAYOHFii5zNdS0xMDJGRkcTHxzN58mTy8/NJTEykefPmtmcf3SylS5cmLi6OESNGkJ6eTrdu3WzLgoODcXd3Z9q0aQwYMIDvv/+eMWPG2MWPHDmSevXqERERQU5ODh999NFlz8D6vZCQEBo3bkyfPn0oKCigQ4cOtmUxMTE0atSIuLg4Xn31VVujz8cff8zf/va3m7IvQkNDycvLY9q0acTGxrJhwwbefvttu3V27NjByJEjef/992nSpAmvvfYazzzzjO05YCIiIiIiIiIiIiIit6M7pkcT/Nrj44EHHiA2NpaYmBiaNm1KvXr1bMs//PBDoqOjadeuHQBdu3YlOjr6sh/1r6Rs2bLMnDmTJk2aEBUVxeeff86qVatsQ64BhIWF0bhxY+6++267Z0V5e3vz448/8ve//52aNWvSr18/nnzySfr3729bJzQ0lE6dOtG2bVtat25NVFQUb775pl0OLVu2JCwsjGbNmvHII4/QoUMHkpOTb3R32bFYLKxcuZJy5crRrFkzYmJiqF69Ou+++65Dyr+W+Ph4du7cyQMPPEBwcLBtfoUKFZg7dy7vvfcetWvX5pVXXmHChAl2se7u7gwdOpSoqCiaNWuGq6srS5YsKVF9f/vb3+x6nlksFj755BOaNWtGr169qFmzJl27duXw4cMEBAQ4dqP/vzp16vDaa68xbtw47rnnHhYuXMjLL79sW56dnc1jjz1GQkICsbGxAPTr14+//OUvdO/enYKCgpuSl4iIiIiIiIiIiIipIiyarnP6o7EUFRUVOTuJO0VRURFhYWEkJiby7LPPOrTshIQEzp49y4oVKxxarvx5TAt6zNkpkGN4jQzLvb5niv2vXIv5RXqnh9klMbDArP3+jIv5JdnN8MOqRq5ZDqddzY9DZimzc8E0g5p55v+HcdzJfYafP7zAuIxXqzn3uuLmgDuUPq1+MYr/YI3ZPyFcug3+padnotnQqpsnX7r2SsVoOMjr2ivd5Bz2ubkbxf9QKs8oHmDcpGij+APPfWMUX2NiU6N4R+Sw/6KvUfwuD/PPlyoFZmWcNByp+Llto80KAP4d18covvyKWU6tf+XhKkbxAPmG8d1bHDOKP73L/OLuF2V2r2Oaw3tnzf/J7pzFbBteTDD7oM/dbXYcAdwjKzk1h8++NX8/HHczu64Zfv3hggN+J0ucUNMovmDLNqN41wb1rr3STawfYOUiH+MyTBxzwHcX08H8vQzPRbMr0q9M/83X9N4/KM/8C9ARw2uC6U8St8NDHUyP4+BM8+/Sf0brAh5xdgp3nJa/3JoOILfKHTN0nrOdPHmSJUuWcPz4cXr16uXsdERERERERERERERERJzuNvg/21tj4cKFWK3WK04RERHXjK9YsSKjR49mxowZlCtX7hZk/F+ZmZlXzd1qtZKZmXlL8/kji4iIuOp+XrhwobPTExERERERERERERG5rfxpejR16NDB7rlKv+fm5nbN+Js9wuDcuXOvuqxy5cqkpaUVu1wc45NPPiEv78rD49ysZziJiIiIiIiIiIiIiNyp/jQNTT4+Pvj4OHfM2xtVqlQpQkNDnZ3Gn0K1atWcnYKIiIiIiIiIiIiIyB3jT9PQJCIiIiIiIiIiIiIijlXo7ATE6dTQJCI2hRazeE/DESZ3eZglEJRvuAFA1QKzMrwNP1mLMN+G8y5mByLXYpaDf4H5UKMFFrNHCJo+gPCUq2EBQIPsHKP4QgecC6Ya5WQbxb/rabYjL1oKjOIBLh248nCoJZVreBgC88wuCu97XDJLAAic4mUU/1BKJaP4NcknjeIBHhpTxSh+2kv7zerPL2MUD7Dv/zYYxYf9s71Z/Y9/bBQPEDarg1H8zD5fGMV3uGT++fKBl1kZAzB7T/47ro9RPED5FbOcmoNp/Xvrv2gUD9DO8FzI/MbbKL7ag2afjwCHv3BuDvvWmd2nAJTn2kPQF+fC10eN4ks3r2oUD3Dx65+N4r0fMMth1zbz61qw4Xegi4Y3zhE5+WYFALkfrzeKd2t0zx1dP8DxUqeM4gMND8MRF/PjGFRo9hPjepcso/hSDngMfcNCs2uz6beXLBfz74DG36AMU7A44Mkj5kdSRJxB710RERERERERERERERG5IWpoEhERERERERERERERkRvyh2toatGiBYMGDXJ2GsVKTk6mbt26zk7DIW63/X3o0CEsFgtpaWnOTqVYFouFFStWODsNEREREREREREREREjf+hnNC1btoy3336bbdu2cfr0aXbs2PGHaeC5XSxbtgw3N7OxuR0pKCiIY8eOUb58eWenUqxjx45Rrlw5Z6chIiIiIiIiIiIiImLkD93QdOHCBZo2bUqXLl3o27evs9P5Q/Lz83N2CnZcXV0JDAx0dhrXdCfkKCIiIiIiIiIiInItRVicnYI42R09dN6FCxfo0aMHVquVSpUqMXHiRLvl3bt3Z+TIkcTExFx32UVFRSQnJxMcHIyHhweVK1fm6aefBmD06NHcc889l8XUrVuXESNGAJCamkqDBg0oXbo0ZcuWpUmTJhw+fNhu/enTpxMUFIS3tzddunTh3LlztmUJCQnExcWRkpJChQoV8PX1ZcCAAeTm5pYo/xYtWvD000/z/PPP4+fnR2BgIMnJyXbrZGZm0rFjR6xWK76+vnTp0oVffvnFtvy3If7mz59PSEgIZcqUoWvXrvznP/+xq+f3Q+eFhIQwduxYevfujY+PD8HBwcyYMcO2vHHjxgwZMsQuj5MnT+Lm5sZXX30FwPz586lfvz4+Pj4EBgby6KOPcuLECdv6Z86cIT4+ngoVKuDl5UVYWBhz5swB7IfOKywspGrVqrz11lt29e3YsQMXFxfb8Th79iyPP/64bT8/+OCD7Ny5s0T7+bd9NHv2bIKDg7FarSQmJlJQUMCrr75KYGAgFStW5B//+Idd3O+Hzvst52XLlvGXv/wFb29v6tSpw8aNG0uUg4iIiIiIiIiIiIiIs9zRDU1JSUmsX7+elStX8tlnn5Gamsr27dsdUvYHH3zApEmTmD59Ovv27WPFihVERkYC0Lt3b9LT09m6datt/R07drBr1y569epFfn4+cXFxNG/enF27drFx40b69euHxfLflt39+/ezdOlSVq1axZo1a9ixYweJiYl2Oaxbt4709HRSU1NZvHgxy5YtIyUlpcTbMG/ePEqXLs3mzZt59dVXGT16NGvXrgWgsLCQjh07cvr0adavX8/atWv56aefeOSRR+zKOHDgACtWrOCjjz7io48+Yv369bzyyivF1jtx4kTq169v26YnnniCjIwMAOLj41myZAlFRUW29d99910qV67MAw88AEBeXh5jxoxh586drFixgkOHDpGQkGBbf8SIEezZs4fVq1eTnp7OW2+9dcWh8lxcXOjWrRuLFi2ym79w4UKaNGlCtWrVAOjcuTMnTpxg9erVbNu2jXvvvZeWLVty+vTpEu3nAwcOsHr1atasWcPixYuZNWsW7dq14+eff2b9+vWMGzeO4cOHs3nz5mLLGTZsGIMHDyYtLY2aNWvSrVs38vPzS5SDiIiIiIiIiIiIiIgz3LFD52VlZTFr1iwWLFhAy5YtgV8bVqpWreqQ8jMzMwkMDCQmJgY3NzeCg4Np0KABAFWrVqVNmzbMmTOH++67D4A5c+bQvHlzqlevzunTpzl37hzt27enRo0aAISHh9uVn52dzTvvvEOVKlUAmDZtGu3atWPixIm2YdXc3d2ZPXs23t7eREREMHr0aJKSkhgzZgwuLtduI4yKimLUqFEAhIWF8frrr7Nu3TpatWrFunXr2L17NwcPHiQoKAiAd955h4iICLZu3WrbrsLCQubOnYuPjw/way+xdevWXdZD5/fatm1razQbMmQIkyZN4ssvv6RWrVp06dKFQYMG8c0339galhYtWkS3bt1sDXG9e/e2lVW9enWmTp3KfffdR1ZWFlarlczMTKKjo6lfvz7way+qq4mPj2fixIlkZmYSHBxMYWEhS5YsYfjw4QB88803bNmyhRMnTuDh4QHAhAkTWLFiBe+//z79+vW75n4uLCxk9uzZ+Pj4ULt2bf7yl7+QkZHBJ598gouLC7Vq1WLcuHF8+eWXNGzY8KrlDB48mHbt2gGQkpJCREQE+/fv5+67775mDiIiIiIiIiIiIiIiznDH9mg6cOAAubm5dj/c+/n5UatWLYeU37lzZy5dukT16tXp27cvy5cvt+td0rdvXxYvXkx2dja5ubksWrTI1kDi5+dHQkICbdq0ITY2lilTpnDs2DG78oODg22NTACNGjWisLDQ1vMHoE6dOnh7e9utk5WVxZEjR0q0DVFRUXavK1WqZBuCLj09naCgIFsjE0Dt2rUpW7Ys6enptnkhISG2Rqb/LaMk9VosFgIDA20xFSpUoHXr1ixcuBCAgwcPsnHjRuLj420x27ZtIzY2luDgYHx8fGjevDnwa+MfwBNPPMGSJUuoW7cuzz//PN9+++1Vc6lbty7h4eG2Xk3r16/nxIkTdO7cGYCdO3eSlZWFv78/VqvVNh08eJADBw4Uu51X20cBAQHUrl3brjEwICDguvZbpUqVAK4ak5OTw/nz5+2mvKKCEuUrIiIiIiIiIiIiIuIod2xD080WFBRERkYGb775Jl5eXiQmJtKsWTPy8vIAiI2NxcPDg+XLl7Nq1Sry8vJ4+OGHbfFz5sxh48aNNG7cmHfffZeaNWuyadOmW7oNbm5udq8tFguFhYU3vYxrxcTHx/P++++Tl5fHokWLiIyMtA1LeOHCBdq0aYOvry8LFy5k69atLF++HMD2fKq//vWvHD58mP/7v//j6NGjtGzZksGDB181n/j4eFtD06JFi3jooYfw9/cHfu0ZV6lSJdLS0uymjIwMkpKSSrKLrri9pvvtt95dV4t5+eWXKVOmjN209vwPJcpXRERERERERERERMRR7tiGpho1auDm5mb33JszZ86wd+9eh9Xh5eVFbGwsU6dOJTU1lY0bN7J7924ASpUqRc+ePZkzZw5z5syha9eueHl52cVHR0czdOhQvv32W+655x67ZwVlZmZy9OhR2+tNmzbZhln7zc6dO7l06ZLdOlar1a4X0o0KDw/nyJEjdr2j9uzZw9mzZ6ldu7Zx+cXp2LEj2dnZrFmzhkWLFtn1Zvrxxx85deoUr7zyCg888AB33333FXv1VKhQgZ49e7JgwQImT57MjBkzrlrfo48+yvfff8+2bdt4//337eq79957OX78OKVKlSI0NNRuutJzn24XQ4cO5dy5c3ZTK98IZ6clIiIiIiIiIiIifzKFmq57+qO5Y5/RZLVa6dOnD0lJSfj7+1OxYkWGDRtmN1zZ6dOn7Rp0fhuWLjAw0PYcpKuZO3cuBQUFNGzYEG9vbxYsWICXlxfVqlWzrfP444/bnr20YcMG2/yDBw8yY8YMOnToQOXKlcnIyGDfvn306NHDto6npyc9e/ZkwoQJnD9/nqeffpouXbrY5ZWbm0ufPn0YPnw4hw4dYtSoUQwcOLBEz2e6lpiYGCIjI4mPj2fy5Mnk5+eTmJhI8+bNbc8+ullKly5NXFwcI0aMID09nW7dutmWBQcH4+7uzrRp0xgwYADff/89Y8aMsYsfOXIk9erVIyIigpycHD766KPLnoH1eyEhITRu3Jg+ffpQUFBAhw4dbMtiYmJo1KgRcXFxvPrqq9SsWZOjR4/y8ccf87e//e2m74sb5eHhYXum1G/cLK5OykZERERERERERERE/qzu2B5NAOPHj+eBBx4gNjaWmJgYmjZtSr169WzLP/zwQ6Kjo2nXrh0AXbt2JTo6mrfffvuaZZctW5aZM2fSpEkToqKi+Pzzz1m1apVtyDWAsLAwGjduzN133233rChvb29+/PFH/v73v1OzZk369evHk08+Sf/+/W3rhIaG0qlTJ9q2bUvr1q2JiorizTfftMuhZcuWhIWF0axZMx555BE6dOhAcnLyje4uOxaLhZUrV1KuXDmaNWtGTEwM1atX591333VI+dcSHx/Pzp07eeCBBwgODrbNr1ChAnPnzuW9996jdu3avPLKK0yYMMEu1t3dnaFDhxIVFUWzZs1wdXVlyZIlJarvb3/7m13PM4vFwieffEKzZs3o1asXNWvWpGvXrhw+fJiAgADHbrSIiIiIiIiIiIiIyB+MpaioqMjZSdypioqKCAsLIzExkWeffdahZSckJHD27FlWrFjh0HLlj2ta0GPGZRRazOJdDa8mZ1zMCgjKN9wAINuw+d3bsO/rOQc0/5833I8heWb70cMBHysnSpnlYLobL5mfSkTn5BjFF2KWxIO/LDWKB/g68OFrr1SMdz3NelpepMAoHuDlkH8bxa/YX9UovkK+2UXhfY9L117pGh7O8br2SsV4KLmCUfya5JNG8QAPjTb754/4l/ab1V9YxigeoFGpc0bxYf9sZxS/7/GPjeIBwmbFGsW/0OcLo/gOl8w/Xz7wMitjAGbvyQpVs4ziAcqvmGUU/++4Pk6tf0j9F43iAdoZngsVvS8axVd7MNsoHuDwF55OzeG5dWWN4gHK43btlYrxfzX+ZRRfurnZZzTAxa9/Nor3fsAsh7Gzza9rwflmd74XDW+cw3PyzQoAGj1kdq/g1ugeo/i8jd87tX6At8aeMooPNDwMW9zNj2NQodmgSdssZp+RpRzwv/QNC72N4v9jmEJ5868//NtwsBvTsXJMfxcC8+/zprtxcOYCwxL+nNYEdHV2Cnech34pvuPEneaOHTrP2U6ePMmSJUs4fvw4vXr1cnY6IiIiIiIiIiIiIiIit9wdPXSeiYULF2K1Wq84RUREXDO+YsWKjB49mhkzZlCuXLlbkPF/ZWZmXjV3q9VKZmbmLc3njywiIuKq+3nhwoXOTk9ERERERERERERExKn+tD2aOnToYPdcpd9zc7v2EAA3e8TBuXPnXnVZ5cqVSUtLK3a5OMYnn3xCXl7eFZfpGU4iIiIiIiIiIiLyZ2f4JAn5A/jTNjT5+Pjg4+Pj7DRuSKlSpQgNDXV2Gn8K1apVc3YKIiIiIiIiIiIiIiK3rT/t0HkiIiIiIiIiIiIiIiJixlJ0s8eAE5FbYlrQY85OgWsPOlm8g64FRvHV810NMwAvwyviRcPm+wsWs3gAXyf3V85ywL8whOaanQtZLmZJHC1lfiAOuOQaxXtjdj5POLTYKB6gV8jfjeKftZjtgxMXvY3iAbZ5ml2Zqlx59NQS++t9R4ziD3znZ5YAsM2ltFH8T6bX5gLza7Pp50MfrzNG8SfPWI3iAba7eRrF/+SabxQfUmg+kMEhF7McYi6ZXVuPuJufS/75Zh/0P7qb1V+h0PzzZa/huVCzwOxcMK1/3HdjjeIB9jcaaBT/SY7ZtfW0i/nNll+h2b2KaQ6di7KM4gHyDO+9Py9l9vl01mL22QBQtshsG0xzaJRtfk24YHjfa/r95fxt8O/LZwzfD+UM34+m9QMEFpjlUGB4Kpld2X9lel0KyzPbB444FU2/y+YZHgdHfJU33Q8ehr+JWBzwK7PpfjR9PzyTucCsgD+pTwK6OjuFO07bX5Y4OwWHug1uCUREREREREREREREROROpIYmERERERERERERERERuSHmY2iIiIiIiIiIiIiIiMifUhEOeBaE3NHUo0luSIsWLRg0aJCz0xARERERERERERERESdSQ5MYW7ZsGa1bt8bf3x+LxUJaWtp1xVssFlasWHFTchMRERERERERERERkZtHDU1i7MKFCzRt2pRx48Y5OxUREREREREREREREbmF1NAk13ThwgV69OiB1WqlUqVKTJw40W559+7dGTlyJDExMddddkhICAB/+9vfsFgsttcAK1eu5N5778XT05Pq1auTkpJCfn6+bbnFYmH69Om0b98eb29vwsPD2bhxI/v376dFixaULl2axo0bc+DAAVtMcnIydevWZfr06QQFBeHt7U2XLl04d+5cifJNSEggLi6OsWPHEhAQQNmyZRk9ejT5+fkkJSXh5+dH1apVmTNnjl3ckCFDqFmzJt7e3lSvXp0RI0aQl5cHQFFRETExMbRp04aioiIATp8+TdWqVRk5cuR171MRERERERERERERkVtFDU1yTUlJSaxfv56VK1fy2WefkZqayvbt2x1S9tatWwGYM2cOx44ds73++uuv6dGjB8888wx79uxh+vTpzJ07l3/84x928WPGjKFHjx6kpaVx99138+ijj9K/f3+GDh3Kd999R1FREQMHDrSL2b9/P0uXLmXVqlWsWbOGHTt2kJiYWOKcv/jiC44ePcpXX33Fa6+9xqhRo2jfvj3lypVj8+bNDBgwgP79+/Pzzz/bYnx8fJg7dy579uxhypQpzJw5k0mTJgG/NpjNmzePrVu3MnXqVAAGDBhAlSpV1NAkIiIiIiIiIiIiIre1Us5OQG5vWVlZzJo1iwULFtCyZUsA5s2bR9WqVR1SfoUKFQAoW7YsgYGBtvkpKSm88MIL9OzZE4Dq1aszZswYnn/+eUaNGmVbr1evXnTp0gX4tddQo0aNGDFiBG3atAHgmWeeoVevXnZ1Zmdn884771ClShUApk2bRrt27Zg4caJdDlfj5+fH1KlTcXFxoVatWrz66qtcvHiRF198EYChQ4fyyiuv8M0339C1a1cAhg8fbosPCQlh8ODBLFmyhOeffx6AKlWqMH36dHr06MHx48f55JNP2LFjB6VKXfktmpOTQ05Ojt28vKIC3Cyu18xfRERERERERERExFEKLc7OQJxNDU1SrAMHDpCbm0vDhg1t8/z8/KhVq9ZNrXfnzp1s2LDBrgdTQUEB2dnZXLx4EW9vbwCioqJsywMCAgCIjIy0m5ednc358+fx9fUFIDg42NbIBNCoUSMKCwvJyMgoUUNTREQELi7/7QwYEBDAPffcY3vt6uqKv78/J06csM179913mTp1KgcOHCArK4v8/HxbPr/p3Lkzy5cv55VXXuGtt94iLCzsqjm8/PLLpKSk2M17yCeStmWirhIhIiIiIiIiIiIiIuJ4GjpPbktZWVmkpKSQlpZmm3bv3s2+ffvw9PS0refm5mb722KxXHVeYWGhw3L7ffm/1XGleb/VuXHjRuLj42nbti0fffQRO3bsYNiwYeTm5trFXLx4kW3btuHq6sq+ffuKzWHo0KGcO3fObmrlG+GArRMRERERERERERERKTn1aJJi1ahRAzc3NzZv3kxwcDAAZ86cYe/evTRv3twhdbi5uVFQUGA379577yUjI4PQ0FCH1PF7mZmZHD16lMqVKwOwadMm2zB4N8O3335LtWrVGDZsmG3e4cOHL1vvueeew8XFhdWrV9O2bVvatWvHgw8+eMUyPTw88PDwsJunYfNERERERERERERE5FZTQ5MUy2q10qdPH5KSkvD396dixYoMGzbMbui406dP2xpvADIyMgAIDAws0VB0ISEhrFu3jiZNmuDh4UG5cuUYOXIk7du3Jzg4mIcffhgXFxd27tzJ999/z0svvWS0TZ6envTs2ZMJEyZw/vx5nn76abp06VKiXG9EWFgYmZmZLFmyhPvuu4+PP/6Y5cuX263z8ccfM3v2bDZu3Mi9995LUlISPXv2ZNeuXZQrV+6m5CUiIiIiIiIiIiIiYkpD58k1jR8/ngceeIDY2FhiYmJo2rQp9erVsy3/8MMPiY6Opl27dgB07dqV6Oho3n777RKVP3HiRNauXUtQUBDR0dEAtGnTho8++ojPPvuM++67j/vvv59JkyZRrVo14+0JDQ2lU6dOtG3bltatWxMVFcWbb75pXO7VdOjQgf/7v/9j4MCB1K1bl2+//ZYRI0bYlp88eZI+ffqQnJzMvffeC0BKSgoBAQEMGDDgpuUlIiIiIiIiIiIiImLKUlRUVOTsJERuleTkZFasWEFaWpqzU3G4aUGPOTsF3K69SrEOuhZce6ViVM83Hz7Qy/CKeNGw+f6CxSwewNdxjyS7IVkO+BeG0FyzcyHLxSyJo6XMD8QBl9xrr1QMb8zO5wmHFhvFA/QK+btR/LMWs31w4qK3UTzANk+zK1OVPLP6/3rfEaP4A9/5mSUAbHMpbRT/k+m1ucD82mz6+dDH64xR/MkzVqN4gO1untdeqRg/ueYbxYcUmg9kcMjFLIeYS2bX1iPu5ueSf77ZB/2P7mb1Vyg0/3zZa3gu1CwwOxdM6x/33VijeID9jQYaxX+SY3ZtPe1ifrPlV2h2r2KaQ+eiLKN4gDzDe+/PS5l9Pp21mH02AJQtMtsG0xwaZZtfEy4Y3veafn85fxv8+/IZw/dDOcP3o2n9AIEFZjkUGJ5KZlf2X5lel8LyzPaBI05F0++yeYbHwRFf5U33g4fhbyIWB/zKbLofTd8Pz2QuMCvgT2pl4KPOTuGO0/H4Imen4FC3wS2BiIiIiIiIiIiIiIiI3InU0CQ31cKFC7FarVecIiIinJ3eZa6Wq9Vq5euvv3Z2eiIiIiIiIiIiIiIitxXzMTREitGhQwcaNmx4xWVubqYDrV2/5ORkkpOTr7q8uCH1qlSp4viERERERERERERERETuYGpokpvKx8cHHx8fZ6dRYqGhoc5OQURERERERERERETkjqGGJpE/CPPHyILpMxvPGA7GWbrIrABHXNBOGD5n3NNwJ7o74MGZps8U9jbMweqAJ5gecTM7EKYPcXVxwHHonZ9rFF/ayyzeER43PJl2uZQxik/1yjGKB4gyfE54tuG55JXY2Sj+rf6pZgkAEYbvySaG58F+d7P6ARob5vBFQXmj+JpF5u/HfMNrc8wlswL2epjVD9Dqkll8uofZtT001/wDZoeH2X6MyjH7gPiXm/kdW7tLZjmkG74nTevf32igWQJA6MbXjeLdo0caxd+Ta34c/214z2maww/4miUAlC40e0+WM/yMDc03fxLAKcMvEKY5HHfANcFqeK9zxvBcrJBvFg9QaLgbQg0/pv9teh444Lb9W0+zA3l3ntmB3O1mfiDD88125HLXs0bxBca/aECbonJG8aZ3KqUc8D3U9CPK8JJAkSN+nDLkiO/zInL91NAkIiIiIiIiIiIiIiI3RO17Yv4vQCIiIiIiIiIiIiIiIvKnpIYmERERERERERERERERuSFqaBKHaNGiBYMGDXJ2GneEQ4cOYbFYSEtLc3YqIiIiIiIiIiIiIiJG1NAkDrds2TJat26Nv7//DTWoWCwWVqxYcVNyux0EBQVx7Ngx7rnnHmenIiIiIiIiIiIiIiJiRA1N4nAXLlygadOmjBs3ztmp3JZcXV0JDAykVKlSzk5FRERERERERERERMSIGprkul24cIEePXpgtVqpVKkSEydOtFvevXt3Ro4cSUxMzHWXHRISAsDf/vY3LBaL7TXAypUruffee/H09KR69eqkpKSQn59vW26xWJg+fTrt27fH29ub8PBwNm7cyP79+2nRogWlS5emcePGHDhwwBaTnJxM3bp1mT59OkFBQXh7e9OlSxfOnTtXonwTEhKIi4tj7NixBAQEULZsWUaPHk1+fj5JSUn4+flRtWpV5syZY4v536HzUlNTsVgsrFu3jvr16+Pt7U3jxo3JyMi47v0nIiIiIiIiIiIicisVarru6Y9GDU1y3ZKSkli/fj0rV67ks88+IzU1le3btzuk7K1btwIwZ84cjh07Znv99ddf06NHD5555hn27NnD9OnTmTt3Lv/4xz/s4seMGUOPHj1IS0vj7rvv5tFHH6V///4MHTqU7777jqKiIgYOHGgXs3//fpYuXcqqVatYs2YNO3bsIDExscQ5f/HFFxw9epSvvvqK1157jVGjRtG+fXvKlSvH5s2bGTBgAP379+fnn38utpxhw4YxceJEvvvuO0qVKkXv3r1LnIOIiIiIiIiIiIiIiDOooUmuS1ZWFrNmzWLChAm0bNmSyMhI5s2bZ9ezyESFChUAKFu2LIGBgbbXKSkpvPDCC/Ts2ZPq1avTqlUrxowZw/Tp0+3ie/XqRZcuXahZsyZDhgzh0KFDxMfH06ZNG8LDw3nmmWdITU21i8nOzuadd96hbt26NGvWjGnTprFkyRKOHz9eopz9/PyYOnUqtWrVonfv3tSqVYuLFy/y4osvEhYWxtChQ3F3d+ebb74ptpx//OMfNG/enNq1a/PCCy/w7bffkp2dfcV1c3JyOH/+vN2UV1RQonxFRERERERERERERBxFDU1yXQ4cOEBubi4NGza0zfPz86NWrVo3td6dO3cyevRorFarberbty/Hjh3j4sWLtvWioqJsfwcEBAAQGRlpNy87O5vz58/b5gUHB1OlShXb60aNGlFYWFjioesiIiJwcfnvWykgIMCuTldXV/z9/Tlx4kSx5fw+90qVKgFcNebll1+mTJkydtPa8z+UKF8REREREREREREREUcp5ewEREoiKyuLlJQUOnXqdNkyT09P299ubm62vy0Wy1XnFRY6biTM35f/Wx1XmnetOq8nz6FDh/Lss8/azZtVu3+JcxYRERERERERERERcQQ1NMl1qVGjBm5ubmzevJng4GAAzpw5w969e2nevLlD6nBzc6OgwH4YuHvvvZeMjAxCQ0MdUsfvZWZmcvToUSpXrgzApk2bcHFxuem9tEx4eHjg4eFhN8/N4uqkbERERERERERERETkz0oNTXJdrFYrffr0ISkpCX9/fypWrMiwYcPsho47ffq0rfEGsA1BFxgYSGBg4DXrCAkJYd26dTRp0gQPDw/KlSvHyJEjad++PcHBwTz88MO4uLiwc+dOvv/+e1566SWjbfL09KRnz55MmDCB8+fP8/TTT9OlS5cS5SoiIiIiIiIiIiLyZ1b4/0dnkj8vPaNJrtv48eN54IEHiI2NJSYmhqZNm1KvXj3b8g8//JDo6GjatWsHQNeuXYmOjubtt98uUfkTJ05k7dq1BAUFER0dDUCbNm346KOP+Oyzz7jvvvu4//77mTRpEtWqVTPentDQUDp16kTbtm1p3bo1UVFRvPnmm8blioiIiIiIiIiIiIj80alHk1w3q9XK/PnzmT9/vm1eUlKS7e+EhAQSEhJuuPzY2FhiY2Mvm9+mTRvatGlz1biioiK71yEhIZfNa9GixWXzAJ544gmeeOKJ68517ty5l81LTU29bN6hQ4eumteVcqpbt+4V8xQRERERERERERERuZ2oR5OIiIiIiIiIiIiIiIjcEDU0yS21cOFCrFbrFaeIiAhnp3eZq+VqtVr5+uuvnZ2eiIiIiIiIiIiIiIhTaeg8uaU6dOhAw4YNr7jMzc3tFmcDycnJJCcnX3V5WlraVZdVqVLF8QmJiIiIiIiIiIiIiNxB1NAkt5SPjw8+Pj7OTqPEQkNDnZ2CiIiIiIiIiIiIyG1LT5oXNTSJ/EHcDhd0n0Kz+L2u+UbxAZj3ijvhUmAUXy/HbETSo24Wo3iALIvZ2VAuzyyHkw74ZDlmeBxM92LlIlfDEuALV6tZAblm4eFm4QBsdPc0iq+WZ3YuRhV4GMUDdKt9xCj+gx+CjOJnPb7JKD4as2MA0DvR7Hw+t+pfRvGtOoYYxQOcXXnIKH7rT5WM4n9wdzeKB+jX2+y6dmL5v43iW3T0N4oHOLHylFn8vwOM4jM8zEf99je8V/mX4ed0jxbHzBIAMr/xNoqv3/SiU+v/JMfPKB7APXqkUfyAHaON4vPmjjWKB3DrnmSWw/zxRvGbxp83igf4xdXs3jth2d+M4gtWvWcUD+Aa29mpOXw+w/y69pO7WRn+Zh9PnDK/bSZxSFmj+JwvvjeK93jwHqfWD/DT1qpG8fmGX4B8HfD954Sr2b1/s6KyRvGO+IHT9BcFw69wZDngASemRzLHPAVjes6LyJ1J710RERERERERERERERG5IWpoEhERERERERERERERkRuihiYRERERERERERERERG5IWpo+pNq0aIFgwYNumX1HTp0CIvFQlpaGgCpqalYLBbOnj3rkPITEhKIi4tzSFkiIiIiIiIiIiIiIlIyamiSK5oxYwYtWrTA19fXoQ1Cv2ncuDHHjh2jTJkyDi1XRERERERERERERG6dQk3XPf3RqKFJrujixYs89NBDvPjiizelfHd3dwIDA7FYLDelfBERERERERERERERufnU0PQnVlhYyPPPP4+fnx+BgYEkJyfblg0aNIgXXniB+++//4bK3rJlC9HR0Xh6elK/fn127Nhht/x/h847deoU3bp1o0qVKnh7exMZGcnixYvtYt5//30iIyPx8vLC39+fmJgYLly4YLdOSkoKFSpUwNfXlwEDBpCbm2tb1qJFCwYOHMjAgQMpU6YM5cuXZ8SIERQVFZVom0JCQnjppZfo0aMHVquVatWq8eGHH3Ly5Ek6duyI1WolKiqK7777zhZzre06efIkgYGBjB071jbv22+/xd3dnXXr1pUoLxERERERERERERERZ1FD05/YvHnzKF26NJs3b+bVV19l9OjRrF271rjcrKws2rdvT+3atdm2bRvJyckMHjy42Jjs7Gzq1avHxx9/zPfff0+/fv3o3r07W7ZsAeDYsWN069aN3r17k56eTmpqKp06dbJrJFq3bp1t2eLFi1m2bBkpKSmXbXOpUqXYsmULU6ZM4bXXXuOf//xnibdt0qRJNGnShB07dtCuXTu6d+9Ojx49eOyxx9i+fTs1atSgR48etryutV0VKlRg9uzZJCcn89133/Gf//yH7t27M3DgQFq2bFnivEREREREREREREREnKGUsxMQ54mKimLUqFEAhIWF8frrr7Nu3TpatWplVO6iRYsoLCxk1qxZeHp6EhERwc8//8wTTzxx1ZgqVarYNUY99dRTfPrppyxdupQGDRpw7Ngx8vPz6dSpE9WqVQMgMjLSrgx3d3dmz56Nt7c3ERERjB49mqSkJMaMGYOLy69tqkFBQUyaNAmLxUKtWrXYvXs3kyZNom/fviXatrZt29K/f38ARo4cyVtvvcV9991H586dARgyZAiNGjXil19+ITAw8Jrb9VuZffv2JT4+nvr161O6dGlefvnlYvPIyckhJyfHbl5eUQFuFtcSbYeIiIiIiIiIiIiIiCOoR9OfWFRUlN3rSpUqceLECeNy09PTiYqKwtPT0zavUaNGxcYUFBQwZswYIiMj8fPzw2q18umnn5KZmQlAnTp1aNmyJZGRkXTu3JmZM2dy5swZuzLq1KmDt7e3XZ1ZWVkcOXLENu/++++3ey5Uo0aN2LdvHwUFBSXatt/vs4CAAMC+weu3eb/tx2tt128mTJhAfn4+7733HgsXLsTDw6PYPF5++WXKlCljN609/0OJtkFERERERERERERExFHU0PQn5ubmZvfaYrFQWFjolFzGjx/PlClTGDJkCF9++SVpaWm0adPG9owlV1dX1q5dy+rVq6lduzbTpk2jVq1aHDx48Jbm+ft99luD1ZXm/bYfr7Vdvzlw4ABHjx6lsLCQQ4cOXTOPoUOHcu7cObuplW+E6eaJiIiIiIiIiIiIXJdCi6brnf5o1NAkDhceHs6uXbvIzs62zdu0aVOxMRs2bKBjx4489thj1KlTh+rVq7N37167dSwWC02aNCElJYUdO3bg7u7O8uXLbct37tzJpUuX7Oq0Wq0EBQXZ5m3evNmuzE2bNhEWFoar680Zcq4k25Wbm8tjjz3GI488wpgxY3j88cev2bPMw8MDX19fu0nD5omIiIiIiIiIiIjIraaGJrmi48ePk5aWxv79+wHYvXs3aWlpnD59+pqxjz76KBaLhb59+7Jnzx4++eQTJkyYUGxMWFgYa9eu5dtvvyU9PZ3+/fvzyy+/2JZv3ryZsWPH8t1335GZmcmyZcs4efIk4eHhtnVyc3Pp06ePrc5Ro0YxcOBA2/OZADIzM3n22WfJyMhg8eLFTJs2jWeeeeZ6d0+JXWu7AIYNG8a5c+eYOnUqQ4YMoWbNmvTu3fum5SQiIiIiIiIiIiIi4iilnJ2A3J7efvttUlJSbK+bNWsGwJw5c0hISCg21mq1smrVKgYMGEB0dDS1a9dm3Lhx/P3vf79qzPDhw/npp59o06YN3t7e9OvXj7i4OM6dOweAr68vX331FZMnT+b8+fNUq1aNiRMn8te//tVWRsuWLQkLC6NZs2bk5OTQrVs3kpOT7erp0aMHly5dokGDBri6uvLMM8/Qr1+/69w7JXet7UpNTWXy5Ml8+eWX+Pr6AjB//nzq1KnDW2+9xRNPPHHTchMRERERERERERERMaWGpj+p1NTUy+atWLHC9ndycvJljTTX4/777yctLc1uXlFRke3vFi1a2L328/Ozq/9/hYeHs2bNmqsunzt3ru3v3zeQ/S83NzcmT57MW2+9dfXkr+JKz076/TYAhISEXNd2tWjRgry8vMvK+K0hSkRERERERERERETkdqah80REREREREREREREROSGqKFJrtvYsWOxWq1XnH4/lN2d5Ouvv77qNlmtVmenJyIiIiIiIiIiInJbKsSi6TqnPxoNnSfXbcCAAXTp0uWKy7y8vG5xNiV3peECf1O/fv3LhvoTEREREREREREREZHiqaFJrpufnx9+fn7OTsOhvLy8CA0NdXYaIiIiIiIiIiIiIiJ3FA2dJyIiIiIiIiIiIiIiIjdEPZpE/iAcMbJngWEhhYb1/4d8o/hsFzfDDMBaZNb+7llUZBRfqsj8SLoYFpFnGO9reiIA5yxmx8H0vyhyHPCGCsozOxduh/F6qxluw3lXs2047mp+MhXkmsV7mO0C4/g9bgVmBQBnVhw1ivd7xKzH7+l3DxjFOyKH/ZMvGcUHmn08AXDha7PjULFjJbP6NxwzindEDsfmmdVfPdfwDQXsMnxTNr9kdl06vcv8//yqPZhtFH/qO7McTOs/vc7HKB7gnlyzz5e8uWON4t0SXjSKd0gO8c8ZxR+Y/A+jeIAK+Wbvh8L1q43iXeMeNYoHKPziQ6fmkDFvqVE8mF8bf3I3ez+F55jfq+RtSTeK92zfwCg+96vtZvW3rW8UD5Dz3XGjeNMnS5+3mB/HskWuRvGHXfKM4ks54PtTzQKzn0kNP57Iw/xex/QblMtt8D3UlHpViDiH3nsiIiIiIiIiIiIiIiJyQ9SjSUREREREREREREREboh5fzy506lHk4iIiIiIiIiIiIiIiNwQNTSJTYsWLRg0aNAtq+/QoUNYLBbS0tIASE1NxWKxcPbsWYeUn5CQQFxcnEPKcqS5c+dStmxZZ6chIiIiIiIiIiIiImJMDU1SYjNmzKBFixb4+vo6tEHoN40bN+bYsWOUKVPGoeXebh555BH27t3r7DRERERERERERERERIypoUlK7OLFizz00EO8+OKLN6V8d3d3AgMDsVgsN6X824WXlxcVK1Z0dhoiIiIiIiIiIiIiIsbU0CR2CgsLef755/Hz8yMwMJDk5GTbskGDBvHCCy9w//3331DZW7ZsITo6Gk9PT+rXr8+OHTvslv/v0HmnTp2iW7duVKlSBW9vbyIjI1m8eLFdzPvvv09kZCReXl74+/sTExPDhQsX7NZJSUmhQoUK+Pr6MmDAAHJzc23LWrRowcCBAxk4cCBlypShfPnyjBgxgqKikj3CLiQkhJdeeokePXpgtVqpVq0aH374ISdPnqRjx45YrVaioqL47rvvbDH/O3RecnIydevWZf78+YSEhFCmTBm6du3Kf/7znxLlICIiIiIiIiIiIiLiLGpoEjvz5s2jdOnSbN68mVdffZXRo0ezdu1a43KzsrJo3749tWvXZtu2bSQnJzN48OBiY7Kzs6lXrx4ff/wx33//Pf369aN79+5s2bIFgGPHjtGtWzd69+5Neno6qampdOrUya6RaN26dbZlixcvZtmyZaSkpFy2zaVKlWLLli1MmTKF1157jX/+858l3rZJkybRpEkTduzYQbt27ejevTs9evTgscceY/v27dSoUYMePXoU23h14MABVqxYwUcffcRHH33E+vXreeWVV0qcg4iIiIiIiIiIiIgzFFo0Xe/0R1PK2QnI7SUqKopRo0YBEBYWxuuvv866deto1aqVUbmLFi2isLCQWbNm4enpSUREBD///DNPPPHEVWOqVKli1xj11FNP8emnn7J06VIaNGjAsWPHyM/Pp1OnTlSrVg2AyMhIuzLc3d2ZPXs23t7eREREMHr0aJKSkhgzZgwuLr+2swYFBTFp0iQsFgu1atVi9+7dTJo0ib59+5Zo29q2bUv//v0BGDlyJG+99Rb33XcfnTt3BmDIkCE0atSIX375hcDAwCuWUVhYyNy5c/Hx8QGge/furFu3jn/84x9XXD8nJ4ecnBy7eXlFBbhZXEuUs4iIiIiIiIiIiIiII6hHk9iJioqye12pUiVOnDhhXG56ejpRUVF4enra5jVq1KjYmIKCAsaMGUNkZCR+fn5YrVY+/fRTMjMzAahTpw4tW7YkMjKSzp07M3PmTM6cOWNXRp06dfD29rarMysriyNHjtjm3X///XbPhWrUqBH79u2joKCgRNv2+30WEBAA2Dd4/TavuP0YEhJia2SCa+/3l19+mTJlythNa8//UKJ8RUREREREREREREQcRQ1NYsfNzc3utcViobCw0Cm5jB8/nilTpjBkyBC+/PJL0tLSaNOmje0ZS66urqxdu5bVq1dTu3Ztpk2bRq1atTh48OAtzfP3++y3BqsrzStuP17vfh86dCjnzp2zm1r5RtxQ/iIiIiIiIiIiIiIiN0oNTXJLhIeHs2vXLrKzs23zNm3aVGzMhg0b6NixI4899hh16tShevXq7N27124di8VCkyZNSElJYceOHbi7u7N8+XLb8p07d3Lp0iW7Oq1WK0FBQbZ5mzdvtitz06ZNhIWF4ep6+w5D5+Hhga+vr92kYfNERERERERERERE5FZTQ5OU2PHjx0lLS2P//v0A7N69m7S0NE6fPn3N2EcffRSLxULfvn3Zs2cPn3zyCRMmTCg2JiwsjLVr1/Ltt9+Snp5O//79+eWXX2zLN2/ezNixY/nuu+/IzMxk2bJlnDx5kvDwcNs6ubm59OnTx1bnqFGjGDhwoO35TACZmZk8++yzZGRksHjxYqZNm8YzzzxzvbtHRERERERERERERORPp5SzE5A7x9tvv01KSortdbNmzQCYM2cOCQkJxcZarVZWrVrFgAEDiI6Opnbt2owbN46///3vV40ZPnw4P/30E23atMHb25t+/foRFxfHuXPnAPD19eWrr75i8uTJnD9/nmrVqjFx4kT++te/2spo2bIlYWFhNGvWjJycHLp160ZycrJdPT169ODSpUs0aNAAV1dXnnnmGfr163ede0dERERERERERETkz8c5D16R24kamsQmNTX1snkrVqyw/Z2cnHxZI831uP/++0lLS7ObV1RUZPu7RYsWdq/9/Pzs6v9f4eHhrFmz5qrL586da/v79w1k/8vNzY3Jkyfz1ltvXT35qzh06NBl836/DQAhISF28xISEuwa5q60XwcNGsSgQYOuOx8RERERERERERERkVtJQ+eJiIiIiIiIiIiIiIjIDVFDkzjE2LFjsVqtV5x+P5TdneTrr7++6jZZrVZnpyciIiIiIiIiIiIi4nQaOk8cYsCAAXTp0uWKy7y8vG5xNiV3peECf1O/fv3LhvoTEREREREREREREZH/UkOTOISfnx9+fn7OTsOhvLy8CA0NdXYaIiIiIiIiIiIiIiK3LTU0ifxBFDmgDBfDQlwN6/cxvCR5FhomABwtZVZItsVsRNJ8i1E4AKa7wc3wPDhpeiIAlywOOJgGyhSab8QRNwccTCc7bLgN1fLMTqZAw/cTgKu7WXyO4WHMM4wPLjA/F8vFBRnFn31/v2H9wUbxjsghNLeSUXymm/m5WLqJWQ4nVp4yiq/Y0ax+R+RQKT/AKP6wu/l1NbDArIwMd7NzoWGU+efb4S+8jeKDm150av1+hebvp38bXhrduicZxefNHWuWAOCW8KJTc6iRl2sUD/CLq5tRvEtzsyHWC1YsMooHcI3t7NQcauWYvx9+Mrwu+ReY1Z/hbn6v0rJBuFF89kdbjOI9HrzHrP5PvjOKB/AoqmoUb/rpYjX+Nm/+e0C1QsNrimH9juBu+sOMxfxex/Q43PnfYs3fD3JjHPG7pNzZbofrsIiIiIiIiIiIiIiIiNyB1NAkIiIiIiIiIiIiIiIiN0QNTSIiIiIiIiIiIiIiInJDbruGphYtWjBo0KBbVt+hQ4ewWCykpaUBkJqaisVi4ezZsw4pPyEhgbi4OIeU5WwhISFMnjzZ2WnYOPpYiYiIiIiIiIiIiIjI9bntGpquZcaMGbRo0QJfX9+b0sjQuHFjjh07RpkyZRxa7h/B1q1b6devn7PTsNGxEhERERERERERERFxrlLOTuB6Xbx4kYceeoiHHnqIoUOHOrx8d3d3AgMDHV7uH0GFChWcnYIdHSsRERERERERERER5yq0ODsDcbbbskdTYWEhzz//PH5+fgQGBpKcnGxbNmjQIF544QXuv//+Gyp7y5YtREdH4+npSf369dmxY4fd8v8dju3UqVN069aNKlWq4O3tTWRkJIsXL7aLef/994mMjMTLywt/f39iYmK4cOGC3TopKSlUqFABX19fBgwYQG5urm1ZixYtGDhwIAMHDqRMmTKUL1+eESNGUFRUVKJtCgkJYezYsfTu3RsfHx+Cg4OZMWOG3Tq7d+/mwQcftOXYr18/srKybMt/G+JvwoQJVKpUCX9/f5588kny8vLs6vn90HkWi4V//vOf/O1vf8Pb25uwsDA+/PBD4NdjWLVqVd566y27PHbs2IGLiwuHDx8G4LXXXiMyMpLSpUsTFBREYmKiXV6HDx8mNjaWcuXKUbp0aSIiIvjkk08uO1bnz5/Hy8uL1atX29W3fPlyfHx8uHjxIgBHjhyhS5culC1bFj8/Pzp27MihQ4dKtJ9/20djx44lICCAsmXLMnr0aPLz80lKSsLPz4+qVasyZ84cu7ghQ4ZQs2ZNvL29qV69OiNGjLDt16KiImJiYmjTpo3teJ8+fZqqVasycuTIEuUlIiIiIiIiIiIiIuIst2VD07x58yhdujSbN2/m1VdfZfTo0axdu9a43KysLNq3b0/t2rXZtm0bycnJDB48uNiY7Oxs6tWrx8cff8z3339Pv3796N69O1u2bAHg2LFjdOvWjd69e5Oenk5qaiqdOnWyayRat26dbdnixYtZtmwZKSkpl21zqVKl2LJlC1OmTOG1117jn//8Z4m3beLEibaGs8TERJ544gkyMjIAuHDhAm3atKFcuXJs3bqV9957j88//5yBAwfalfHll19y4MABvvzyS+bNm8fcuXOZO3dusfWmpKTQpUsXdu3aRdu2bYmPj+f06dO4uLjQrVs3Fi1aZLf+woULadKkCdWqVQPAxcWFqVOn8sMPPzBv3jy++OILnn/+edv6Tz75JDk5OXz11Vfs3r2bcePGYbVaL8vD19eX9u3bX7G+uLg4vL29ycvLo02bNvj4+PD111+zYcMGrFYrDz30kF3DX3G++OILjh49yldffcVrr73GqFGjaN++PeXKlWPz5s0MGDCA/v378/PPP9tifHx8mDt3Lnv27GHKlCnMnDmTSZMmAb821s2bN4+tW7cydepUAAYMGECVKlXU0CQiIiIiIiIiIiIit73bsqEpKiqKUaNGERYWRo8ePahfvz7r1q0zLnfRokUUFhYya9YsIiIiaN++PUlJScXGVKlShcGDB1O3bl2qV6/OU089xUMPPcTSpUuBXxua8vPz6dSpEyEhIURGRpKYmGjXGOLu7s7s2bOJiIigXbt2jB49mqlTp1JYWGhbJygoiEmTJlGrVi3i4+N56qmnbI0RJdG2bVsSExMJDQ1lyJAhlC9fni+//NK23dnZ2bzzzjvcc889PPjgg7z++uvMnz+fX375xVZGuXLleP3117n77rtp37497dq1u+Z+T0hIoFu3boSGhjJ27FiysrJsjXDx8fFs2LCBzMxM4NdeTkuWLCE+Pt4WP2jQIP7yl78QEhLCgw8+yEsvvWTbtwCZmZk0adKEyMhIqlevTvv27WnWrNkVc4mPj2fFihW23kvnz5/n4wExy5sAAQAASURBVI8/ttX37rvvUlhYyD//+U8iIyMJDw9nzpw5ZGZmkpqaWqL97Ofnx9SpU6lVqxa9e/emVq1aXLx4kRdffJGwsDCGDh2Ku7s733zzjS1m+PDhNG7cmJCQEGJjYxk8eLDdNlapUoXp06fzwgsvMHToUD755BMWLFhAqVJXH9kyJyeH8+fP2015RQUl2gYREREREREREREREUe5bRuafq9SpUqcOHHCuNz09HSioqLw9PS0zWvUqFGxMQUFBYwZM4bIyEj8/PywWq18+umntsaTOnXq0LJlSyIjI+ncuTMzZ87kzJkzdmXUqVMHb29vuzqzsrI4cuSIbd7999+PxWKxW2ffvn0UFJSs8eD3+8xisRAYGGjbZ+np6dSpU4fSpUvb1mnSpAmFhYW2Xk8AERERuLq62l6XZL//vt7SpUvj6+tri6lbty7h4eG2Xkbr16/nxIkTdO7c2Rbz+eef07JlS6pUqYKPjw/du3fn1KlTtsaip59+mpdeeokmTZowatQodu3addVc2rZti5ubm234vg8++ABfX19iYmIA2LlzJ/v378fHxwer1YrVasXPz4/s7GwOHDhQ7Hb+fh+5uPz3bRMQEEBkZKTttaurK/7+/nb77d1336VJkyYEBgZitVoZPny47fz5TefOnfnb3/7GK6+8woQJEwgLCys2j5dffpkyZcrYTWvP/1CibRARERERERERERERcZTbsqHJzc3N7rXFYrHr/XMrjR8/nilTpjBkyBC+/PJL0tLSaNOmjW2oNVdXV9auXcvq1aupXbs206ZNo1atWhw8ePCW5umIfXYjZVwrJj4+3tbQtGjRIh566CH8/f0BOHToEO3btycqKooPPviAbdu28cYbbwDY9u/jjz/OTz/9RPfu3dm9ezf169dn2rRpV8zF3d2dhx9+2K6+Rx55xNYzKCsri3r16pGWlmY37d27l0cfffSG91Fx+2Djxo3Ex8fTtm1bPvroI3bs2MGwYcMuG6rv4sWLbNu2DVdXV/bt23fNPIYOHcq5c+fspla+ESXaBhERERERERERERERR7ktG5pulvDwcHbt2kV2drZt3qZNm4qN2bBhAx07duSxxx6jTp06VK9enb1799qtY7FYaNKkCSkpKezYsQN3d3eWL19uW75z504uXbpkV6fVaiUoKMg2b/PmzXZlbtq0ibCwMLseRjcqPDycnTt3cuHCBbvtcnFxoVatWsblF+fRRx/l+++/Z9u2bbz//vt2w+Zt27aNwsJCJk6cyP3330/NmjU5evToZWUEBQUxYMAAli1bxnPPPcfMmTOvWl98fDxr1qzhhx9+4IsvvrCr795772Xfvn1UrFiR0NBQu6lMmTKO3fD/79tvv6VatWoMGzaM+vXrExYWxuHDhy9b77nnnsPFxYXVq1czdepUvvjii2LL9fDwwNfX125ys5ifKyIiIiIiIiIiIiLXo1DTdU9/NHdcQ9Px48dJS0tj//79AOzevZu0tDROnz59zdhHH30Ui8VC37592bNnD5988gkTJkwoNiYsLIy1a9fy7bffkp6eTv/+/e2ea7R582bGjh3Ld999R2ZmJsuWLePkyZOEh4fb1snNzaVPnz62OkeNGsXAgQPthmDLzMzk2WefJSMjg8WLFzNt2jSeeeaZ6909VxQfH4+npyc9e/bk+++/58svv+Spp56ie/fuBAQEOKSOqwkJCaFx48b06dOHgoICOnToYFsWGhpKXl4e06ZN46effmL+/Pm8/fbbdvGDBg3i008/5eDBg2zfvp0vv/zSbt/+r2bNmhEYGEh8fDx33XUXDRs2tC2Lj4+nfPnydOzYka+//pqDBw+SmprK008/zc8//+z4jefX8yczM5MlS5Zw4MABpk6datcICfDxxx8ze/ZsFi5cSKtWrUhKSqJnz56XDcEoIiIiIiIiIiIiInK7ueMamt5++22io6Pp27cv8P/Yu/P4mK7/f+CvyT7JZBsiCUJoIoKEEHtpamliaakSjZE0H7GkBPmo2oosNEGtoeWDklRjb6s+LVUlqdQSayIqXwQRSlBBmkTWmd8ffp1PRxbhTIzE6/l43Mcjc+99n/Oec5e5Myf33McdCx4eHurn8lRFJpPhv//9L9LS0uDh4YFPPvkECxcurDJm9uzZaN++Pby9veHl5QU7OzsMHjxYvdzCwgKHDh1C//790aJFC8yePRtLlixBv3791Ov07t0bzs7O6NmzJ4YPH4533nkH4eHhGvUEBATg0aNH6NSpEyZMmIDJkydj7Nix1W+YKpiammLfvn3IyclBx44dMXToUPTu3RurVq3SSvlPo1AokJqainfffRdSqVQ9v23btli6dCkWLlyINm3aID4+HtHR0RqxZWVlmDBhAlxdXeHj44MWLVrgiy++qLQuiUQCPz8/pKamatzNBDxuh0OHDqFJkyYYMmQIXF1dERQUhMLCQlhYWGj3Tf9/77zzDv79738jJCQE7dq1w5EjRzBnzhz18rt37yIoKAjh4eFo3749ACAiIgK2trYIDg6ukZyIiIiIiIiIiIiIiLRFolKpVLpO4lXn5eWFdu3aYfny5bpOhWqxlQ4jdZ0CJILxF/VLhOJblxo+faWnuGJQJhTfvkis//6moWgrAnkSsdN6sxKxHO4aCIUDALL1xLaDqIZK8aEoi8Q3pZBp174WLmNxE7HzStMSsX0x20C8Ef1aXReK/+Z3h6evVAXBw0kr/jVebH9++N8soXjLt5sIxWsjhxNX7IXiswzF/zdr1L9KheLvfH9PKL7BoHpC8drI4dc/xe6kv6OFzxepjseoCPC6JVxG1m+mQvFNXi/Qaf0/l4kPWW0k+A02+OScp69UhZJNn4klAMAwcJZYDrFRQvHHPssVigeA2/pi197vfj9EKL7svzuE4gFA/+1hOs3hl7Xiny9XjMTKsBA8L97Xwr8vj59uJRRfdPCcULxxrzY6rR8AYk80Foo3FdyOfxiI/zRoLFiEiUrswlkb/0kvFXwP+YLX/sVa+O4g+k1W/yX4lVjXd0X8O0v8u/SraF1j3f8uWduMuVG39jVdH7tERERERERERERERERUS9WpjqaoqCjIZLIKp38OZVebJCUlVfqeZDKZrtOrU6pq56SkJF2nR0RERERERERERET00tHCABQvj+DgYPj6+la47J/PBnrZJCYmVrrM09MTKSkpLyyXV1lV7dyoUaMXlwgRERERERERERFRLaHjEarpJVCnOprkcjnkcrmu09AqqVQKJycnXafxSmA7ExERERERERERERE9mzrV0UT0KtPG8+bLdPzQ+r8g9qD0Qj2xBxIDgEwlNqKoqUrsfzgMVKKP7gT0BLdjiWC8VZlYPAA8lIhtB9FWLNLCseBQIvYU1TKtHNVimgq+h1x9sfeQrS/+P1FlxWLxog81Fo0/byh+QN3fdVMoXj5c7B8xcrZdForXRg4Zyx8JxduJfTwBAPIP3xKKbzDIXqf1A0CDdxsKxd/aKFZ/82Lxp1OfFTwo33gkdl7KOSs+cnnTXoVC8fdOiuUgWn/OAXOheABoI/i09JJNnwnFGyo+EooHgJLYKLEcAmcJxV9ePlcoHgBsSsWOB+Wve4Xi9QePEIoHAOXB3WI5DBF78Hl63FaheAB4TfDceNVI7HhyLRK/Vik5+X9C8SYDOwnFFx86rdP6AaDopNj1muiDFf6C+HY0EPwWdk2vRLB+8e9PLcrEfiYV/HhCCcSvdUS/QekLtqP4OxBXp54TQ1SL8NgjIiIiIiIiIiIiIiKi58KOJiIiIiIiIiIiIiIiInou7GgiIiIiIiIiIiIiIiKi5/LSdzR5eXkhNDT0hdWXmZkJiUSClJQUAEBiYiIkEgkePHiglfIDAwMxePBgrZSla46Ojli+fLmu01DT9raqCU/uX0RERERERERERES1mUrC6VmnukbsKXcvgbVr12Lz5s04ffo0/vrrL9y/fx9WVlZaK79bt264desWLC0ttVZmXXHixAmYmZnpOg212rCtHBwccOvWLdSvX1/XqRARERERERERERERCXvp72h6moKCAvj4+GDWrFk1Ur6RkRHs7OwgkdTBbkZBNjY2MDU11XUaarVhW+nr68POzg4GBrW+j5eIiIiIiIiIiIiIqHZ0NCmVSkybNg1yuRx2dnYIDw9XLwsNDcWMGTPQpUuX5yr7+PHj8PDwgImJCTw9PXHmzBmN5U8Ox3bv3j34+fmhUaNGMDU1hZubG7Zs2aIRs3PnTri5uUEqlaJevXro06cP8vPzNdaJiIiAjY0NLCwsEBwcjOLiYvUyLy8vhISEICQkBJaWlqhfvz7mzJkDlUpVrffk6OiIqKgojBo1Cubm5mjSpAnWrl2rsU5aWhp69eqlznHs2LHIy8tTL/97iL/FixfD3t4e9erVw4QJE1BSUqJRzz+HzpNIJFi/fj3effddmJqawtnZGbt37wbweBs2btwYq1ev1sjjzJkz0NPTw7Vr1wAAS5cuhZubG8zMzODg4IDx48dr5HXt2jW8/fbbsLa2hpmZGVq3bo09e/aU21a5ubmQSqXYu3evRn3fffcdzM3NUVBQAAC4fv06fH19YWVlBblcjkGDBiEzM7Na7fx3G0VFRcHW1hZWVlaIjIxEaWkpPv74Y8jlcjRu3BgbN25Ux1Q2NOOBAwfg6ekJU1NTdOvWDRcuXKhWDkREREREREREREREulQrOpri4uJgZmaG5ORkLFq0CJGRkdi/f79wuXl5eRg4cCBatWqFU6dOITw8HFOnTq0yprCwEB06dMCPP/6Ic+fOYezYsfD398fx48cBALdu3YKfnx9GjRqF9PR0JCYmYsiQIRqdRAcOHFAv27JlC7799ltERESUe88GBgY4fvw4VqxYgaVLl2L9+vXVfm9LlixRd5yNHz8eH374obrzIj8/H97e3rC2tsaJEyewY8cO/PLLLwgJCdEoIyEhAZcvX0ZCQgLi4uIQGxuL2NjYKuuNiIiAr68vzp49i/79+0OhUCAnJwd6enrw8/PD5s2bNdaPj49H9+7d0bRpUwCAnp4eYmJi8PvvvyMuLg4HDx7EtGnT1OtPmDABRUVFOHToENLS0rBw4ULIZLJyeVhYWGDgwIEV1jd48GCYmpqipKQE3t7eMDc3R1JSEg4fPgyZTAYfHx+Njr+qHDx4EDdv3sShQ4ewdOlShIWFYeDAgbC2tkZycjKCg4Mxbtw43Lhxo8pyPvnkEyxZsgQnT56EgYEBRo0aVa36iYiIiIiIiIiIiIh0qVZ0NLm7uyMsLAzOzs4ICAiAp6cnDhw4IFzu5s2boVQq8eWXX6J169YYOHAgPv744ypjGjVqhKlTp6Jdu3Zo3rw5Jk6cCB8fH2zfvh3A446m0tJSDBkyBI6OjnBzc8P48eM1OkOMjIywYcMGtG7dGgMGDEBkZCRiYmKgVCrV6zg4OGDZsmVwcXGBQqHAxIkTsWzZsmq/t/79+2P8+PFwcnLC9OnTUb9+fSQkJKjfd2FhIb766iu0adMGvXr1wqpVq7Bp0ybcvn1bXYa1tTVWrVqFli1bYuDAgRgwYMBT2z0wMBB+fn5wcnJCVFQU8vLy1J1wCoUChw8fRlZWFoDHdzlt3boVCoVCHR8aGoo333wTjo6O6NWrF+bPn69uWwDIyspC9+7d4ebmhubNm2PgwIHo2bNnhbkoFArs2rVLffdSbm4ufvzxR3V927Ztg1KpxPr16+Hm5gZXV1ds3LgRWVlZSExMrFY7y+VyxMTEwMXFBaNGjYKLiwsKCgowa9YsODs7Y+bMmTAyMsJvv/1WZTmffvop3njjDbRq1QozZszAkSNHUFhYWOn6RUVFyM3N1ZhKVGXVypmIiIiIiIiIiIiISFtqTUfTP9nb2+POnTvC5aanp8Pd3R0mJibqeV27dq0ypqysDPPmzYObmxvkcjlkMhn27dun7jxp27YtevfuDTc3NwwbNgzr1q3D/fv3Ncpo27atxrONunbtiry8PFy/fl09r0uXLhrPGuratSsuXbqEsrLqdSb8s80kEgns7OzUbZaeno62bdvCzMxMvU737t2hVCo1hmxr3bo19PX11a+r0+7/rNfMzAwWFhbqmHbt2sHV1VV9l9Gvv/6KO3fuYNiwYeqYX375Bb1790ajRo1gbm4Of39/3Lt3T91ZNGnSJMyfPx/du3dHWFgYzp49W2ku/fv3h6GhoXr4vm+++QYWFhbo06cPACA1NRUZGRkwNzeHTCaDTCaDXC5HYWEhLl++XOX7/Gcb6en97zCytbWFm5ub+rW+vj7q1av3TO1mb28PAFXGREdHw9LSUmPan/t7tXImIiIiIiIiIiIi0hYlp2ee6ppa0dFkaGio8VoikWjc/fMiffbZZ1ixYgWmT5+OhIQEpKSkwNvbWz3Umr6+Pvbv34+9e/eiVatWWLlyJVxcXHD16tUXmqc22ux5ynhajEKhUHc0bd68GT4+PqhXrx6Ax88vGjhwINzd3fHNN9/g1KlT+PzzzwFA3b6jR4/GlStX4O/vj7S0NHh6emLlypUV5mJkZIShQ4dq1Dd8+HAYGBgAeDx0YocOHZCSkqIxXbx4ESNGjHjuNhJtt787GKuKmTlzJh4+fKgx9bVoXa2ciYiIiIiIiIiIiIi0pVZ0NNUUV1dXnD17VmOIsmPHjlUZc/jwYQwaNAgjR45E27Zt0bx5c1y8eFFjHYlEgu7duyMiIgJnzpyBkZERvvvuO/Xy1NRUPHr0SKNOmUwGBwcH9bzk5GSNMo8dOwZnZ2eNO4yel6urK1JTU5Gfn6/xvvT09ODi4iJcflVGjBiBc+fO4dSpU9i5c6fGsHmnTp2CUqnEkiVL0KVLF7Ro0QI3b94sV4aDgwOCg4Px7bff4qOPPsK6desqrU+hUOCnn37C77//joMHD2rU1759e1y6dAkNGjSAk5OTxmRpaandN65lxsbGsLCw0JgMJeL7BhERERERERERERHRs6j1HU3Z2dlISUlBRkYGACAtLQ0pKSnIycl5auyIESMgkUgwZswYnD9/Hnv27MHixYurjHF2dsb+/ftx5MgRpKenY9y4cRrPNUpOTkZUVBROnjyJrKwsfPvtt7h79y5cXV3V6xQXFyMoKEhdZ1hYGEJCQjSGYMvKysKUKVNw4cIFbNmyBStXrsTkyZOftXkqpFAoYGJigg8++ADnzp1DQkICJk6cCH9/f9ja2mqljso4OjqiW7duCAoKQllZGd555x31MicnJ5SUlGDlypW4cuUKNm3ahDVr1mjEh4aGYt++fbh69SpOnz6NhIQEjbZ9Us+ePWFnZweFQoFmzZqhc+fO6mUKhQL169fHoEGDkJSUhKtXryIxMRGTJk3CjRs3tP/miYiIiIiIiIiIiIjqmFrf0bRmzRp4eHhgzJgxAB53LHh4eKify1MVmUyG//73v0hLS4OHhwc++eQTLFy4sMqY2bNno3379vD29oaXlxfs7OwwePBg9XILCwscOnQI/fv3R4sWLTB79mwsWbIE/fr1U6/Tu3dvODs7o2fPnhg+fDjeeecdhIeHa9QTEBCAR48eoVOnTpgwYQImT56MsWPHVr9hqmBqaop9+/YhJycHHTt2xNChQ9G7d2+sWrVKK+U/jUKhQGpqKt59911IpVL1/LZt22Lp0qVYuHAh2rRpg/j4eERHR2vElpWVYcKECXB1dYWPjw9atGiBL774otK6JBIJ/Pz8kJqaqnE3E/C4HQ4dOoQmTZpgyJAhcHV1RVBQEAoLC2FhYaHdN01EREREREREREREVAdJVCqVStdJkCYvLy+0a9cOy5cv13UqVIuschgpXEaZRAuJCEjRe/T0larQTil9+kpPkSsROyW2KxJ7flymofgQiLl6Yu+hYamOdwQA1wzE3oNoKxpDvA0cSsTeQ5lgDiNvfi0UDwA77BVPX6kKufpi7+GKgfjzGCc1+0Mofu9Fh6evVIPOG5YJlzHJ9vbTV6qCfPhrQvE52y4LxWsjh9XLxT5f7EqFwgEAfV2vC8WbdbcXis8/fEsoHgDMejQUio/ZKHZ2bl4s/rXlrLFYGW88EjsvOdvdE4oHgHqeYvH3Tuq2/oUHbMQKANCmWOzzZdAUsWtGQ8VHQvEAUBK/RCyHwFlC8bHt5grFA4BNqdjx8NZUU6F4vV7vPH2lp1AefPo/qFaZQ5/BQvHLB20VigeA1wTPjVeNxI4n1yLxa5We/e4KxRv1bC8UX3zotE7rB4AV4eUfG/As6gluhvMG4hc71iqxz/k/JWI5GGjhO1yLMgOh+PuC/85fAvFrHdHvwiYqsXbUxo/Mou9B9K6KyVni36VfRV9o4XfJV83463VrXxM7gxIRERERERERERER0StL/N9Eqbar9UPnVSUqKgoymazC6Z9D2dUmSUlJlb4nmUym6/TqlKraOSkpSdfpERERERERERERERHpXJ2+oyk4OBi+vr4VLvvns4FeNomJiZUu8/T0REpKygvL5VVWVTs3atToxSVCRERERERERERERK+szz//HJ999hmys7PRtm1brFy5Ep06dap0/R07dmDOnDnIzMyEs7MzFi5ciP79+9dYfnW6o0kul0Mul+s6Da2SSqVwcnLSdRqvBLYzEREREREREREREenStm3bMGXKFKxZswadO3fG8uXL4e3tjQsXLqBBgwbl1j9y5Aj8/PwQHR2NgQMHYvPmzRg8eDBOnz6NNm3a1EiOEpVKpY3ntBGRjq3UwkP3RB+dWSZYwDm9IqH4lkpjsQQA3NYTe4qqQ5nYYyuV4s8vRT3B57jeFvwXhKt6JWIFAGisMhSKfygRGx24gVJ8ZNk/9cRyEN0V5mduFiwBmOboJxQfEd1CKP6HKRlC8QBw01CsJT2Kxc5Lf6lq///0pBuLnde08ZBw0RwmrOsiFF+WmCAUDwDffiV2N326odg5xbVE/LwmmsPbRWLH0x2VFj7nDcTa4YLgw9LttHBOuCQRa0dnwXYUrX+CSiweAH4vsRCKty8rFoq/bGgkFA8Ar5XoNofAlEiheAAou3JKKP6bwd8LxWeJXS4CABxLxH4OyRS8zhhRL1soHgDu3zMViv8R5kLxdyTin/PNlWLnxkw9sXOzo47rB4A+j8T2pQf6YtdKD8XCAQA39cWuE7o9EotXCX+DAq4ZiTWEvuAvrIVaeMCJieCDcmSC8XkvwUNajAW3Q+AfX2snkVfMKi38LvmqCble/X2tc+fO6NixI1atWgUAUCqVcHBwwMSJEzFjxoxy6w8fPhz5+fn44Ycf1PO6dOmCdu3aYc2aNeLJV+AlOPyJiIiIiIiIiIiIiIheDUVFRcjNzdWYiir457zi4mKcOnUKffr0Uc/T09NDnz59cPTo0QrLPnr0qMb6AODt7V3p+trAjiYiIiIiIiIiIiIiInouKk7PPEVHR8PS0lJjio6OLte2f/75J8rKymBra6sx39bWFtnZFd8dnZ2d/Uzra0PtH8+FiIiIiIiIiIiIiIiolpg5cyamTJmiMc/YWHy4cF3hHU21iJeXF0JDQ2uk7MzMTEgkEqSkpNRI+URERERERERERERE9LhTycLCQmOqqKOpfv360NfXx+3btzXm3759G3Z2dhWWbWdn90zrawM7muqQtWvXwsvLCxYWFpBIJHjw4IGuU6q28PBwtGvXTmvlBQYGYvDgwVorj4iIiIiIiIiIiIjoRTIyMkKHDh1w4MAB9TylUokDBw6ga9euFcZ07dpVY30A2L9/f6XrawM7muqQgoIC+Pj4YNasWbpOpdpUKhVKS0urvX5JSUkNZkNERERERERERERE9PKYMmUK1q1bh7i4OKSnp+PDDz9Efn4+/vWvfwEAAgICMHPmTPX6kydPxk8//YQlS5bg//7v/xAeHo6TJ08iJCSkxnJkR1Mto1QqMW3aNMjlctjZ2SE8PFy9LDQ0FDNmzECXLl2eu/wrV67gzTffhKmpKdq2bYujR49qLP/tt9/Qo0cPSKVSODg4YNKkScjPz1cv37RpEzw9PWFubg47OzuMGDECd+7cUS9PTEyERCLB3r170aFDBxgbG+Prr79GREQEUlNTIZFIIJFIEBsbCwCQSCRYvXo13nnnHZiZmeHTTz9FWVkZgoKC0KxZM0ilUri4uGDFihXqOsLDwxEXF4fvv/9eXV5iYiIA4Pr16/D19YWVlRXkcjkGDRqEzMzMarXN33dJRUVFwdbWFlZWVoiMjERpaSk+/vhjyOVyNG7cGBs3btSImz59Olq0aAFTU1M0b94cc+bMUXeYqVQq9OnTB97e3lCpVACAnJwcNG7cGHPnzq1WXkRERERERERERERUNw0fPhyLFy/G3Llz0a5dO6SkpOCnn36Cra0tACArKwu3bt1Sr9+tWzds3rwZa9euRdu2bbFz507s2rULbdq0qbEc2dFUy8TFxcHMzAzJyclYtGgRIiMjsX//fq2V/8knn2Dq1KlISUlBixYt4Ofnp77j6PLly/Dx8cF7772Hs2fPYtu2bfjtt980ekJLSkowb948pKamYteuXcjMzERgYGC5embMmIEFCxYgPT0dffv2xUcffYTWrVvj1q1buHXrFoYPH65eNzw8HO+++y7S0tIwatQoKJVKNG7cGDt27MD58+cxd+5czJo1C9u3bwcATJ06Fb6+vvDx8VGX161bN5SUlMDb2xvm5uZISkrC4cOHIZPJ4OPjg+Li4mq1z8GDB3Hz5k0cOnQIS5cuRVhYGAYOHAhra2skJycjODgY48aNw40bN9Qx5ubmiI2Nxfnz57FixQqsW7cOy5YtA/C4Iy0uLg4nTpxATEwMACA4OBiNGjViRxMRERERERERERG99JQSTs86PauQkBBcu3YNRUVFSE5ORufOndXLEhMT1Tdu/G3YsGG4cOECioqKcO7cOfTv319wK1fNoEZLJ61zd3dHWFgYAMDZ2RmrVq3CgQMH0LdvX62UP3XqVAwYMAAAEBERgdatWyMjIwMtW7ZEdHQ0FAoFQkND1fXHxMTgjTfewOrVq2FiYoJRo0apy2revDliYmLQsWNH5OXlQSaTqZdFRkZq5CyTyWBgYFDhA8lGjBihvg3wbxEREeq/mzVrhqNHj2L79u3w9fWFTCaDVCpFUVGRRnlff/01lEol1q9fD4nk8dG8ceNGWFlZITExEW+99dZT20culyMmJgZ6enpwcXHBokWLUFBQoB6ucObMmViwYAF+++03vP/++wCA2bNnq+MdHR0xdepUbN26FdOmTQMANGrUCP/5z38QEBCA7Oxs7NmzB2fOnIGBAQ9PIiIiIiIiIiIiInq58ZfsWsbd3V3jtb29vcbQdNos397eHgBw584dtGzZEqmpqTh79izi4+PV66hUKiiVSly9ehWurq44deoUwsPDkZqaivv370OpVAJ4fPteq1at1HGenp7VzqmidT///HNs2LABWVlZePToEYqLi9GuXbsqy0lNTUVGRgbMzc015hcWFuLy5cvVyqV169bQ0/vfjYC2trYatxzq6+ujXr16Gttk27ZtiImJweXLl5GXl4fS0lJYWFholDts2DB89913WLBgAVavXg1nZ+cq8ygqKkJRUZHGvBJVGQwl+tV6H0RERERERERERERE2sCOplrG0NBQ47VEIlF35mi7/L/v+vm7/Ly8PIwbNw6TJk0qF9ekSRPk5+fD29sb3t7eiI+Ph42NDbKysuDt7V1uaDozM7Nq5/Tkulu3bsXUqVOxZMkSdO3aFebm5vjss8+QnJxcZTl5eXno0KGDRkfZ32xsbKqVS0XtX9U2OXr0KBQKBSIiIuDt7Q1LS0ts3boVS5Ys0YgpKCjAqVOnoK+vj0uXLj01j+joaI27ugDAx9wN/S3dK4kgIiIiIiIiIiIiItI+djRRtbVv3x7nz5+Hk5NThcvT0tJw7949LFiwAA4ODgCAkydPVqtsIyMjlJWVVWvdw4cPo1u3bhg/frx63pN3JFVUXvv27bFt2zY0aNCg3B1FNeXIkSNo2rQpPvnkE/W8a9eulVvvo48+gp6eHvbu3Yv+/ftjwIAB6NWrV6Xlzpw5E1OmTNGYt77VOO0lTkRERERERERERERUDXpPX4Vqi+zsbKSkpCAjIwPA446flJQU5OTkaKX86dOn48iRIwgJCUFKSgouXbqE77//HiEhIQAe39VkZGSElStX4sqVK9i9ezfmzZtXrbIdHR1x9epVpKSk4M8//yw3LNw/OTs74+TJk9i3bx8uXryIOXPm4MSJE+XKO3v2LC5cuIA///wTJSUlUCgUqF+/PgYNGoSkpCRcvXoViYmJmDRpEm7cuPH8DVMFZ2dnZGVlYevWrbh8+TJiYmLw3Xffaazz448/YsOGDYiPj0ffvn3x8ccf44MPPsD9+/crLdfY2BgWFhYaE4fNIyIiIiIiIiIiIqIXjR1NdciaNWvg4eGBMWPGAAB69uwJDw8P7N69Wyvlu7u749dff8XFixfRo0cPeHh4YO7cuWjYsCGAx8PPxcbGYseOHWjVqhUWLFiAxYsXV6vs9957Dz4+PnjzzTdhY2ODLVu2VLruuHHjMGTIEAwfPhydO3fGvXv3NO5uAoAxY8bAxcUFnp6esLGxweHDh2FqaopDhw6hSZMmGDJkCFxdXREUFITCwsIau8PpnXfewb///W+EhISgXbt2OHLkCObMmaNefvfuXQQFBSE8PBzt27cHAERERMDW1hbBwcE1khMRERERERERERGRtig5PfNU10hUKpVK10kQkbiVDiOFy5AIxpcJFnBOr/I72aqjpdJYLAEAt/WqN4RjZRzKxO4sU4puBAD1SsXibwsOqnpVr0SsAACNVYZPX6kKDyViH9kNlOL/h/GnnlgOorvC/MzNgiUA0xz9hOIjolsIxf8wJUMoHgBuGoq1pEex2HnpL1XtH6U43VjsvOZaJHZe1UYOE9Z1EYovS0wQigeAb7+SCsWnG4qdU1xLxM9rojm8XcUd69VxR6WFz3kDsXa4YCD2IWunhXPCJYlYOzoLtqNo/RNUYvEA8HuJ2D+J2ZcVP32lKlw2NBKKB4DXSnSbQ2BKpFA8AJRdOSUU/83g74Xis8QuFwEAjiViP4dkCl5njKiXLRQPAPfvmQrF/whzofg7EvHP+eZKsXNjpp7YudlRx/UDQJ9HYvvSA32xa6WHWhgg5aa+2HVCt0di8Srhb1DANSOxhtAX/IW1UAu3A5gI/notE4zPewluaTAW3A6Bf3ytnUReMcuaiP8u+ar5d1bd2tdegsOfiIiIiIiIiIiIiIiIaiN2NL0ioqKiIJPJKpz69eun6/ReCpW1j0wmQ1JSkq7TIyIiIiIiIiIiIiJ66dT+8VyoWoKDg+Hr61vhMqlUbCiXuiIlJaXSZY0aNXpxiRARERERERERERER1RLsaHpFyOVyyOVyXafxUnNyctJ1CkREREREREREREREtQo7moiIiIiIiIiIiIiI6LkodZ0A6Rw7mohIa0p1XL82TmglUAnFKyVi9euLVa8Vog/v04dgIwCCWwEoEr7EEX+Eobmq9j8GUfg9FD4SCs/XE9+XRM9LEsG90URwX7yjL35mK5GItWO9MrH67xnoixWghRxw/YpQuL5TM8EEgPv62ULx9ZRix+M98c0gnEOu0lAo3kZSLBQPABl6JkLx9VRiDXlfIv4VvD7E2vGhYA6i9ZeUil8xminF3sNtfcF9sVR8O+o6h7Irp4TiAUC/eQeh+Bz974Xi64t+NgD4U1/sM1I0h4c54sPYW9crEIrPzTETirdTiV+r3NUT258bCOZwT0/sek+0fgDQ1/G3aW38QGygEjue8vXEPmPrwo/cgruidnIQ/jYu/h2OiGqn2v8rGBEREREREREREREREekEO5qIiIiIiIiIiIiIiIjoubCjiYiIiIiIiIiIiIiIiJ4LO5pqOS8vL4SGhtZI2ZmZmZBIJEhJSamR8l9VbFciIiIiIiIiIiIiqivY0VTHrV27Fl5eXrCwsIBEIsGDBw90nVK1hYeHo127dlorLzAwEIMHD9Zaec/LwcEBt27dQps2bXSdChEREREREREREZEQFadnnuoadjTVcQUFBfDx8cGsWbN0nUq1qVQqlJaWVnv9kpKSGsxG+/T19WFnZwcDAwNdp0JEREREREREREREJIQdTXWAUqnEtGnTIJfLYWdnh/DwcPWy0NBQzJgxA126dHnu8q9cuYI333wTpqamaNu2LY4ePaqx/LfffkOPHj0glUrh4OCASZMmIT8/X71806ZN8PT0hLm5Oezs7DBixAjcuXNHvTwxMRESiQR79+5Fhw4dYGxsjK+//hoRERFITU2FRCKBRCJBbGwsAEAikWD16tV45513YGZmhk8//RRlZWUICgpCs2bNIJVK4eLighUrVqjrCA8PR1xcHL7//nt1eYmJiQCA69evw9fXF1ZWVpDL5Rg0aBAyMzOr1TZ/3yUVFRUFW1tbWFlZITIyEqWlpfj4448hl8vRuHFjbNy4UR3z5NB5f7//AwcOwNPTE6ampujWrRsuXLjwDFuJiIiIiIiIiIiIiOjFY0dTHRAXFwczMzMkJydj0aJFiIyMxP79+7VW/ieffIKpU6ciJSUFLVq0gJ+fn/qOo8uXL8PHxwfvvfcezp49i23btuG3335DSEiIOr6kpATz5s1Damoqdu3ahczMTAQGBparZ8aMGViwYAHS09PRt29ffPTRR2jdujVu3bqFW7duYfjw4ep1w8PD8e677yItLQ2jRo2CUqlE48aNsWPHDpw/fx5z587FrFmzsH37dgDA1KlT4evrCx8fH3V53bp1Q0lJCby9vWFubo6kpCQcPnwYMpkMPj4+KC4urlb7HDx4EDdv3sShQ4ewdOlShIWFYeDAgbC2tkZycjKCg4Mxbtw43Lhx46ntvGTJEpw8eRIGBgYYNWpUteonIiIiIiIiIiIiItIVjt1VB7i7uyMsLAwA4OzsjFWrVuHAgQPo27evVsqfOnUqBgwYAACIiIhA69atkZGRgZYtWyI6OhoKhQKhoaHq+mNiYvDGG29g9erVMDEx0egwad68OWJiYtCxY0fk5eVBJpOpl0VGRmrkLJPJYGBgADs7u3I5jRgxAv/617805kVERKj/btasGY4ePYrt27fD19cXMpkMUqkURUVFGuV9/fXXUCqVWL9+PSQSCQBg48aNsLKyQmJiIt56662nto9cLkdMTAz09PTg4uKCRYsWoaCgQD1c4cyZM7FgwQL89ttveP/99yst59NPP8Ubb7wB4HGn24ABA1BYWAgTE5Ny6xYVFaGoqEhjXomqDIYS/afmS0RERERERERERESkLbyjqQ5wd3fXeG1vb68xNJ02y7e3twcAdfmpqamIjY2FTCZTT97e3lAqlbh69SoA4NSpU3j77bfRpEkTmJubqztTsrKyNOrx9PSsdk4Vrfv555+jQ4cOsLGxgUwmw9q1a8vV8aTU1FRkZGTA3Nxcnb9cLkdhYSEuX75crVxat24NPb3/HUq2trZwc3NTv9bX10e9evWeuk2qaucnRUdHw9LSUmPan/t7tfIlIiIiIiIiIiIiItIW3tFUBxgaGmq8lkgkUCqVNVL+33f9/F1+Xl4exo0bh0mTJpWLa9KkCfLz8+Ht7Q1vb2/Ex8fDxsYGWVlZ8Pb2Ljc0nZmZWbVzenLdrVu3YurUqViyZAm6du0Kc3NzfPbZZ0hOTq6ynLy8PHTo0AHx8fHlltnY2FQrl4ra/3m2SVXt/KSZM2diypQpGvPWtxpXrXyJiIiIiIiIiIiItEUp0XUGpGvsaCIh7du3x/nz5+Hk5FTh8rS0NNy7dw8LFiyAg4MDAODkyZPVKtvIyAhlZWXVWvfw4cPo1q0bxo8fr5735B1JFZXXvn17bNu2DQ0aNICFhUW16noZGBsbw9jYWGMeh80jIiIiIiIiIiIioheNQ+fVcdnZ2UhJSUFGRgaAxx0/KSkpyMnJ0Ur506dPx5EjRxASEoKUlBRcunQJ33//PUJCQgA8vqvJyMgIK1euxJUrV7B7927MmzevWmU7Ojri6tWrSElJwZ9//lnumUT/5OzsjJMnT2Lfvn24ePEi5syZgxMnTpQr7+zZs7hw4QL+/PNPlJSUQKFQoH79+hg0aBCSkpJw9epVJCYmYtKkSbhx48bzNwwRERERERERERER0SuAHU113Jo1a+Dh4YExY8YAAHr27AkPDw/s3r1bK+W7u7vj119/xcWLF9GjRw94eHhg7ty5aNiwIYDHw8/FxsZix44daNWqFRYsWIDFixdXq+z33nsPPj4+ePPNN2FjY4MtW7ZUuu64ceMwZMgQDB8+HJ07d8a9e/c07m4CgDFjxsDFxQWenp6wsbHB4cOHYWpqikOHDqFJkyYYMmQIXF1dERQUhMLCwlp1hxMRERERERERERERkS5IVCqVStdJEJG4lQ4jhcsQHU61SLCA/9Or/K616mijNH76Sk+RqVcqFO+oFBuRVF8LZ2Sr6o04Wam7goOqirYhANipxJK4KxHLwUFwOwLix4OoGde+Fi5jXlOFUPzU8EZC8dtm3xSKB4B7gqOKdiouFIovVoklcEdffF8skYjtjIKnFGhjYFfRHN6PaCBWgET8f7PWRmQLxZcI1v8ybIc2RWLnZnPBczsAHDUyEYovkoh9UOcJxmuD6N4s+iTYISWPBEsA/iiTCsU/0hM7L0qV4ttR1zn47H5XKB4A9Jt3EIpf4zFXKN5EC4dToeD1mmgOnQ0eihUAwFIudkx9kVO9ZxNXxkrwWgcA8iViZxYzldiZ7ZHguVmqEr/w9ygU+4y7J3jNeF8LFwp/6om1Y+vip69TFW08qfyegdi2FP0+X6yF75BGgjlYCH6+5Ap+vmmDsWAbBP4h/l36VbSoqfjvkq+aaVr43eZlwjuaiIiIiIiIiIiIiIiI6Lmwo+kVFhUVBZlMVuHUr18/Xaf3UqisfWQyGZKSknSdHhEREREREREREZFOKTk981TXiI/HQrVWcHAwfH19K1wmlYoNR1FXpKSkVLqsUSOxIaGIiIiIiIiIiIiIiGo7djS9wuRyOeRyua7TeKk5OTnpOgUiIiIiIiIiIiIiopcWO5qI6ghtPG5R9Hm6og9cbKIyEoqXaeG+U0uJ2FNQrQWflK6Nh7BeNRTbEM1KxPYmUz3xj5brBmIb00LwocSCTQigboxNa6UU2xc2zr0pFJ+nhauU4D63heJ//bG+UPwfRmL7Yr4WTu5jBz8Qii+68JdQvLGLuVC8NnL4Klys/hwtnJsnz7QWii/6JU0o3riPm1C8NnI4cETsbvB0Q/GTQqngMdWgTKyA0NHiFyv5SWLnVrMeDXVa/5cZDkLxAGAt+CEb+O27QvHKX/eKJQBA7w2xocpFc/hm8PdC8QCQoy9WRvCZSKH40j3rhOIBwMA7UCyHfbFC8QkflwrFA8Chu5ZC8U6C170FWrjoDV33ulC88lSyULxeh846rR8ANn4hFi8V3I7Z+uKfT1KV2GfkVSOxeD0tfIczFSzjoeA1o+hvKgCQI3hMPtQX2w76L8F36TzxFIjoOdSF38GIiIiIiIiIiIiIiIhIB9jRRERERERERERERERERM+FQ+cREREREREREREREdFz0cKoiVTL8Y4mei5eXl4IDQ3VdRoaAgMDMXjwYF2nQURERERERERERET0ymBHE9WItWvXwsvLCxYWFpBIJHjw4IGuUyIiIiIiIiIiIiIiIi1jRxPViIKCAvj4+GDWrFm6ToWIiIiIiIiIiIiIiGoIO5rouSmVSkybNg1yuRx2dnYIDw9XLwsNDcWMGTPQpUuXZy43MzMTEokE27dvR48ePSCVStGxY0dcvHgRJ06cgKenJ2QyGfr164e7d++Wi4+IiICNjQ0sLCwQHByM4uJi9TIvLy+EhIQgJCQElpaWqF+/PubMmQOVqnojiTo6OmL+/PkICAiATCZD06ZNsXv3bty9exeDBg2CTCaDu7s7Tp48qY65d+8e/Pz80KhRI5iamsLNzQ1btmxRL7979y7s7OwQFRWlnnfkyBEYGRnhwIEDz9x+REREREREREREREQvCjua6LnFxcXBzMwMycnJWLRoESIjI7F//36tlR8WFobZs2fj9OnTMDAwwIgRIzBt2jSsWLECSUlJyMjIwNy5czViDhw4gPT0dCQmJmLLli349ttvERERUS5vAwMDHD9+HCtWrMDSpUuxfv36aue1bNkydO/eHWfOnMGAAQPg7++PgIAAjBw5EqdPn8Zrr72GgIAAdedVYWEhOnTogB9//BHnzp3D2LFj4e/vj+PHjwMAbGxssGHDBoSHh+PkyZP466+/4O/vj5CQEPTu3VuwFYmIiIiIiIiIiIiIao6BrhOg2svd3R1hYWEAAGdnZ6xatQoHDhxA3759tVL+1KlT4e3tDQCYPHky/Pz8cODAAXTv3h0AEBQUhNjYWI0YIyMjbNiwAaampmjdujUiIyPx8ccfY968edDTe9yv6uDggGXLlkEikcDFxQVpaWlYtmwZxowZU628+vfvj3HjxgEA5s6di9WrV6Njx44YNmwYAGD69Ono2rUrbt++DTs7OzRq1AhTp05Vx0+cOBH79u3D9u3b0alTJ3WZY8aMgUKhgKenJ8zMzBAdHV1pDkVFRSgqKtKYV6Iqg6FEv1rvgYiIiIiIiIiIiEgblKjeaFFUd/GOJnpu7u7uGq/t7e1x586dGinf1tYWAODm5qYx78n62rZtC1NTU/Xrrl27Ii8vD9evX1fP69KlCyQSicY6ly5dQllZmdbyAqDOraysDPPmzYObmxvkcjlkMhn27duHrKwsjXIXL16M0tJS7NixA/Hx8TA2Nq40h+joaFhaWmpM+3N/r1b+RERERERERERERETawo4mem6GhoYaryUSCZRKZY2U/3fH0JPztFmfNvMCoM7ts88+w4oVKzB9+nQkJCQgJSUF3t7eGs+OAoDLly/j5s2bUCqVyMzMrDKHmTNn4uHDhxpTX4vW2nh7RERERERERERERETVxqHzqE5JTU3Fo0ePIJVKAQDHjh2DTCaDg4ODep3k5GSNmGPHjsHZ2Rn6+jUz7Nzhw4cxaNAgjBw5EsDjDqiLFy+iVatW6nWKi4sxcuRIDB8+HC4uLhg9ejTS0tLQoEGDCss0NjYud8cTh80jIiIiIiIiIiIioheNdzRRjcjOzkZKSgoyMjIAAGlpaUhJSUFOTk6N1ltcXIygoCCcP38ee/bsQVhYGEJCQtTPZwKArKwsTJkyBRcuXMCWLVuwcuVKTJ48ucZycnZ2xv79+3HkyBGkp6dj3LhxuH37tsY6n3zyCR4+fIiYmBhMnz4dLVq0wKhRo2osJyIiIiIiIiIiIiIibeAdTVQj1qxZg4iICPXrnj17AgA2btyIwMDAGqu3d+/ecHZ2Rs+ePVFUVAQ/Pz+Eh4drrBMQEIBHjx6hU6dO0NfXx+TJkzF27Ngay2n27Nm4cuUKvL29YWpqirFjx2Lw4MF4+PAhACAxMRHLly9HQkICLCwsAACbNm1C27ZtsXr1anz44Yc1lhsRERERERERERERkQh2NNFzSUxMLDdv165d6r/Dw8PLdfBUl6OjI1QqlcY8Ly+vcvMCAwM1Oq1iY2PVf/+zk+tJhoaGWL58OVavXv3MuVX07KQn83oyf7lcrtE2T/Ly8kJJSUm5Mv7uiCIiIiIiIiIiIiJ6WSl1nQDpHIfOIyIiIiIiIiIiIiIioufCjiZ64aKioiCTySqc+vXrp7O8kpKSKs1LJpPpLC8iIiIiIiIiIiIiopcVh86jFy44OBi+vr4VLpNKpTVad0VD/v3N09MTKSkpNVo/EREREREREREREVFdwo4meuHkcjnkcrmu0yhHKpXCyclJ12kQEREREREREREREdUa7GgiIjWlRLf150nEHh1YJNEXzsFQNF6lEopXQscbAUCpYApmWngCpJFKLAlDwXYs08JmcCgWa4gyie73BceSMqH4C0Zix2S2pFQoHgDKckuEyxBhKng8nDcUz//PX4uE4hsMcxCKv7PjtlC8NnLIvCC2IRzKxEebLj50TijeuFcbndavjRz+OPFAKL5xidhnLACcMBbbFzqUiMUXp4kfD2ZvNBbLIfUPndb/4LLYZwsAOJWKHZNl/90hFK8/eIRQPACU7dqs0xyyVm4XigeA+oKbsnTPOqF4g/5jxBJ4CXK4NHuuUDwANBI8N14xErvmdC0SP6ZVqSeF4vW6vylWf/Ihsfq7viEUDwCFqw8KxYt+ByuA+Jc4KcSu/bMkYte9hlr4/uRcJvYzaQnEjkc9LfweoBTMwVDwu7j41Rqf80JUW7GjiYiIiIiIiIiIiIiInos2OhmpdmMnMRERERERERERERERET0XdjQRERERERERERERERHRc2FHE2mFl5cXQkNDdZ2GhsDAQAwePFjXaZQTGxsLKysrXadBRERERERERERERCSMHU30QqxduxZeXl6wsLCARCLBgwcPdJ2SzgwfPhwXL17UdRpERERERERERERERMLY0UQvREFBAXx8fDBr1ixdp6JzUqkUDRo00HUaRERERERERERERETC2NFEWqNUKjFt2jTI5XLY2dkhPDxcvSw0NBQzZsxAly5dnrnczMxMSCQSbN++HT169IBUKkXHjh1x8eJFnDhxAp6enpDJZOjXrx/u3r1bLj4iIgI2NjawsLBAcHAwiouL1cu8vLwQEhKCkJAQWFpaon79+pgzZw5UKlW1cnN0dMT8+fMREBAAmUyGpk2bYvfu3bh79y4GDRoEmUwGd3d3nDx5Uh3z5NB54eHhaNeuHTZt2gRHR0dYWlri/fffx19//fXMbUVERERERERERET0Iik5PfNU17CjibQmLi4OZmZmSE5OxqJFixAZGYn9+/drrfywsDDMnj0bp0+fhoGBAUaMGIFp06ZhxYoVSEpKQkZGBubOnasRc+DAAaSnpyMxMRFbtmzBt99+i4iIiHJ5GxgY4Pjx41ixYgWWLl2K9evXVzuvZcuWoXv37jhz5gwGDBgAf39/BAQEYOTIkTh9+jRee+01BAQEVNl5dfnyZezatQs//PADfvjhB/z6669YsGDBszUQEREREREREREREdELxo4m0hp3d3eEhYXB2dkZAQEB8PT0xIEDB7RW/tSpU+Ht7Q1XV1dMnjwZp06dwpw5c9C9e3d4eHggKCgICQkJGjFGRkbYsGEDWrdujQEDBiAyMhIxMTFQKv/Xb+zg4IBly5bBxcUFCoUCEydOxLJly6qdV//+/TFu3Dg4Oztj7ty5yM3NRceOHTFs2DC0aNEC06dPR3p6Om7fvl1pGUqlErGxsWjTpg169OgBf3//KtuuqKgIubm5GlOJqqzaORMRERERERERERERaQM7mkhr3N3dNV7b29vjzp07NVK+ra0tAMDNzU1j3pP1tW3bFqampurXXbt2RV5eHq5fv66e16VLF0gkEo11Ll26hLKy6nXcVCcvAFW2haOjI8zNzdWvn9Z20dHRsLS01Jj25/5erXyJiIiIiIiIiIiIiLSFHU2kNYaGhhqvJRKJxp1D2iz/746hJ+dpsz5t5gWgytyete1mzpyJhw8fakx9LVo/V/5ERERERERERERERM/LQNcJENWk1NRUPHr0CFKpFABw7NgxyGQyODg4qNdJTk7WiDl27BicnZ2hr6//QnN9FsbGxjA2NtaYZyh5efMlIiIiIiIiIiIiorqJHU30QmRnZyM7OxsZGRkAgLS0NJibm6NJkyaQy+U1Vm9xcTGCgoIwe/ZsZGZmIiwsDCEhIdDT+9/NfFlZWZgyZQrGjRuH06dPY+XKlViyZEmN5URERERERERERERUVyglT1+H6jZ2NNELsWbNGkRERKhf9+zZEwCwceNGBAYG1li9vXv3hrOzM3r27ImioiL4+fkhPDxcY52AgAA8evQInTp1gr6+PiZPnoyxY8fWWE5ERERERERERERERHUFO5pIKxITE8vN27Vrl/rv8PDwch081eXo6AiVSqUxz8vLq9y8wMBAjU6r2NhY9d//7OR6kqGhIZYvX47Vq1c/c26ZmZnl5j2Z15P5P5lnRW0TGhqK0NDQZ86HiIiIiIiIiIiIiOhF0nv6KkRERERERERERERERETlsaOJdC4qKgoymazCqV+/fjrLKykpqdK8ZDKZzvIiIiIiIiIiIiIiInpZcOg80rng4GD4+vpWuEwqldZo3RUN+fc3T09PpKSk1Gj9RERERERERERERES1GTuaSOfkcjnkcrmu0yhHKpXCyclJ12kQEREREREREREREb202NFERGp6KrF4iWD9MpXYaJ7GgvkDQJHgmyiRiLaC7hkItmO+FgZlLZaIJaEUfA+WKvHteN2o9o9Om2moLxRvKrgd7FTilyn6FobCZYgoENwNmijF86//hrFQ/KOk64L1mwvFayOHpmUOQvH3tXA4G/VsIxRfdDBNKN64l5tQvDZyaFTSSCj+uqH4uVmuEjuvZQmeF7u52QvFA0BB0g2heNPuDXVav5VK7HgEgHuCHw/6bw8Tilce3C2WwEuQg2OJ+IXzn/pix6SBd6BQfOmedULxAGDQf4xOc3AuKhWKB4ArRmIHhHWZWP2i14sAIGnrKRSvPJwgFK/XobNY/Ud/FYoHABMtfJcVYfoSPFmjiUrsulf09wxtMBT8VUQb31xKhH+ZEaON2pU6jqfno8RLcBCSTun+k4SIiIiIiIiIiIiIiIhqJXY0ERERERERERERERER0XNhRxMRERERERERERERERE9lzrV0eTl5YXQ0FBdp6EhMDAQgwcP1nUaWuHo6Ijly5frOg21xMRESCQSPHjwQNepEBERERERERERERG9kupUR9PTrF27Fl5eXrCwsGAHxXM4ceIExo4dq+s01Lp164Zbt27B0tJS16kQEREREREREREREb2SXqmOpoKCAvj4+GDWrFm6TqVWsrGxgampqa7TUDMyMoKdnR0kEomuUyEiIiIiIiIiIiJ6Jak4PfNU19S5jialUolp06ZBLpfDzs4O4eHh6mWhoaGYMWMGunTp8szlZmZmQiKRYPv27ejRowekUik6duyIixcv4sSJE/D09IRMJkO/fv1w9+7dcvERERGwsbGBhYUFgoODUVxcrF7m5eWFkJAQhISEwNLSEvXr18ecOXOgUlVvl3N0dERUVBRGjRoFc3NzNGnSBGvXrtVYJy0tDb169YJUKkW9evUwduxY5OXlqZf/PcTf4sWLYW9vj3r16mHChAkoKSnRqOefQ+dJJBKsX78e7777LkxNTeHs7Izdu3cDeLwdGjdujNWrV2vkcebMGejp6eHatWsAgKVLl8LNzQ1mZmZwcHDA+PHjNfK6du0a3n77bVhbW8PMzAytW7fGnj17AGgOnZebmwupVIq9e/dq1Pfdd9/B3NwcBQUFAIDr16/D19cXVlZWkMvlGDRoEDIzM6vVzn+3UVRUFGxtbWFlZYXIyEiUlpbi448/hlwuR+PGjbFx40aNuOnTp6NFixYwNTVF8+bNMWfOHHW7qlQq9OnTB97e3urtnZOTg8aNG2Pu3LnVyouIiIiIiIiIiIiISFfqXEdTXFwczMzMkJycjEWLFiEyMhL79+/XWvlhYWGYPXs2Tp8+DQMDA4wYMQLTpk3DihUrkJSUhIyMjHIdBAcOHEB6ejoSExOxZcsWfPvtt4iIiCiXt4GBAY4fP44VK1Zg6dKlWL9+fbXzWrJkCTw9PXHmzBmMHz8eH374IS5cuAAAyM/Ph7e3N6ytrXHixAns2LEDv/zyC0JCQjTKSEhIwOXLl5GQkIC4uDjExsYiNja2ynojIiLg6+uLs2fPon///lAoFMjJyYGenh78/PywefNmjfXj4+PRvXt3NG3aFACgp6eHmJgY/P7774iLi8PBgwcxbdo09foTJkxAUVERDh06hLS0NCxcuBAymaxcHhYWFhg4cGCF9Q0ePBimpqYoKSmBt7c3zM3NkZSUhMOHD0Mmk8HHx0ej468qBw8exM2bN3Ho0CEsXboUYWFhGDhwIKytrZGcnIzg4GCMGzcON27cUMeYm5sjNjYW58+fx4oVK7Bu3TosW7YMwOPOuri4OJw4cQIxMTEAgODgYDRq1IgdTURERERERERERET00qtzHU3u7u4ICwuDs7MzAgIC4OnpiQMHDmit/KlTp8Lb2xuurq6YPHkyTp06hTlz5qB79+7w8PBAUFAQEhISNGKMjIywYcMGtG7dGgMGDEBkZCRiYmKgVCrV6zg4OGDZsmVwcXGBQqHAxIkT1Z0R1dG/f3+MHz8eTk5OmD59OurXr6/OY/PmzSgsLMRXX32FNm3aoFevXli1ahU2bdqE27dvq8uwtrbGqlWr0LJlSwwcOBADBgx4atsFBgbCz88PTk5OiIqKQl5eHo4fPw4AUCgUOHz4MLKysgA8vstp69atUCgU6vjQ0FC8+eabcHR0RK9evTB//nxs375dvTwrKwvdu3eHm5sbmjdvjoEDB6Jnz54V5qJQKLBr1y713Uu5ubn48ccf1fVt27YNSqUS69evh5ubG1xdXbFx40ZkZWUhMTGxWu0sl8sRExMDFxcXjBo1Ci4uLigoKMCsWbPg7OyMmTNnwsjICL/99ps6Zvbs2ejWrRscHR3x9ttvY+rUqRrvsVGjRvjPf/6DGTNmYObMmdizZw++/vprGBgYVCsnIiIiIiIiIiIiIiJdqZMdTf9kb2+PO3fu1Ej5tra2AAA3NzeNeU/W17ZtW41nG3Xt2hV5eXm4fv26el6XLl00njXUtWtXXLp0CWVlZc+cl0QigZ2dnTqP9PR0tG3bFmZmZup1unfvDqVSqb7rCQBat24NfX199evqtN0/6zUzM4OFhYU6pl27dnB1dVXfZfTrr7/izp07GDZsmDrml19+Qe/evdGoUSOYm5vD398f9+7dU3cWTZo0CfPnz0f37t0RFhaGs2fPVppL//79YWhoqB6+75tvvoGFhQX69OkDAEhNTUVGRgbMzc0hk8kgk8kgl8tRWFiIy5cvV/k+/9lGenr/O2xsbW01tr++vj7q1aun0W7btm1D9+7dYWdnB5lMhtmzZ6s73/42bNgwvPvuu1iwYAEWL14MZ2fnKvMoKipCbm6uxlSiqt6+QkRERERERERERESkLXWuo8nQ0FDjtUQi0bhzSJvl/90x9OQ8bdb3PHk9bx7PU8bTYhQKhbqjafPmzfDx8UG9evUAPH7u1cCBA+Hu7o5vvvkGp06dwueffw4A6qHsRo8ejStXrsDf3x9paWnw9PTEypUrK8zFyMgIQ4cO1ahv+PDh6juD8vLy0KFDB6SkpGhMFy9exIgRI567japqg6NHj0KhUKB///744YcfcObMGXzyySflhuorKCjAqVOnoK+vj0uXLj01j+joaFhaWmpM+3N/r9Z7ICIiIiIiIiIiIiLSljrX0fQySk1NxaNHj9Svjx07BplMBgcHB/W85ORkjZhjx47B2dlZ4w6j5+Xq6orU1FTk5+er5x0+fBh6enpwcXERLr8qI0aMwLlz53Dq1Cns3LlTY9i8U6dOQalUYsmSJejSpQtatGiBmzdvlivDwcEBwcHB+Pbbb/HRRx9h3bp1ldanUCjw008/4ffff8fBgwc16mvfvj0uXbqEBg0awMnJSWOytLTU7hv//44cOYKmTZvik08+gaenJ5ydnXHt2rVy63300UfQ09PD3r17ERMTg4MHD1ZZ7syZM/Hw4UONqa9F6xp5D0RERERERERERESVUXJ65qmueaU6mrKzs5GSkoKMjAwAQFpaGlJSUpCTk1Oj9RYXFyMoKAjnz5/Hnj17EBYWhpCQEI0h2LKysjBlyhRcuHABW7ZswcqVKzF58mSt1K9QKGBiYoIPPvgA586dQ0JCAiZOnAh/f3/18H81xdHREd26dUNQUBDKysrwzjvvqJc5OTmhpKQEK1euxJUrV7Bp0yasWbNGIz40NBT79u3D1atXcfr0aSQkJMDV1bXS+nr27Ak7OzsoFAo0a9YMnTt3Vi9TKBSoX78+Bg0ahKSkJFy9ehWJiYmYNGkSbty4of03D8DZ2RlZWVnYunUrLl++jJiYGHz33Xca6/z444/YsGED4uPj0bdvX3z88cf44IMPcP/+/UrLNTY2hoWFhcZkKBHvlCQiIiIiIiIiIiIiehavVEfTmjVr4OHhgTFjxgB43Cnh4eGhfqZPTenduzecnZ3Rs2dPDB8+HO+88w7Cw8M11gkICMCjR4/QqVMnTJgwAZMnT8bYsWO1Ur+pqSn27duHnJwcdOzYEUOHDkXv3r2xatUqrZT/NAqFAqmpqXj33XchlUrV89u2bYulS5di4cKFaNOmDeLj4xEdHa0RW1ZWhgkTJsDV1RU+Pj5o0aIFvvjii0rrkkgk8PPzQ2pqqsbdTMDjdjh06BCaNGmCIUOGwNXVFUFBQSgsLISFhYV23/T/98477+Df//43QkJC0K5dOxw5cgRz5sxRL7979y6CgoIQHh6O9u3bAwAiIiJga2uL4ODgGsmJiIiIiIiIiIiIiEhbJCqVSqXrJF51Xl5eaNeuHZYvX67rVKgWW+UwUrgM0ZOBRDD+vp5YBg3LRDMArhmI5eBUIpbDPS3cmJYnEXsPzQTfQ74W/oXhuoHYTcQGKrH30EApvi8JNqOwyVlfC5exUvC8Iro752lhXwruc1so/tcf6wvF/2Eo1gp5WtiPxg1+IBRfdOEvoXhjF3OheG3ksPO8w9NXqsJ9LeyLk2dYC8UXHUwTijfu5SYUr40cDvzWSCj+uqH4AVEkWIRNmVj8+6MFCwBQcLj8UNPPwrR7Q53Wv/6i2PEIANaCY40EfvuuULzy171iCQDQe6OfTnP4fumjp6/0FH/qix1QwSfnPH2lKpTuixWKBwCD/mPEcthT+bDu1ZHw8VWheAC4YmQgFG8s+CWwUAvXKqO/7CYUrzyV/PSVqqDXofPTV6rB+gFg4xdinw9mgufFy4bigzhJBb+DGQr+oiD4cwIAwFSwDNHvL6LHIwAUCB6ThoI5aONrsOilt+je/JEWvku/imY6jtB1CrVOdOZmXaegVa/UHU1ERERERERERERERESkPexo+v+ioqIgk8kqnPr1E/tvMxFJSUmV5iWTyXSWV11UVTsnJSXpOj0iIiIiIiIiIiIiopeO2D3WdUhwcDB8fX0rXPbP5wrVhMTExEqXeXp6IiUlpUbrp8eqaudGjcSGeiEiIiIiIiIiIiKqi5TCD+Sg2o4dTf+fXC6HXC7XdRrlSKVSODk56TqNVwLbmYiIiIiIiIiIiIjo2XDoPCIiIiIiIiIiIiIiInouvKOJiNSUEt3WnydRCsUXSfSFc5CqxBrBUCV6q7CONwKAEsEUzMQ2IwDAWHA7GAi2o2gbAIBDsVhDlEl0vy80KRF7D5eMxP6fJVtSKhQPAGW5JcJliDAVPB7OG4rn/+evRULxDXybCsXf2X5LKF4bOVy7ILYvOZSJ/29W8aFzQvHGXm10Wr82cvgj+YFQfOMS8eE4ThiLHZQdBM+LxWm3heIBwLRHY7EcUv/Qaf0PLpUJxQOAU6nYMVn23x1C8fqDRwjFA0DZrs1iOQwZKRSfuXKrUDwA1BfclKX7YoXiDfqPEUsAQOmedTrN4dLsuULxANBI8Nx4xUjsmtO1SPyYVqWeFIrX6/qGWP0nfhOrv/ubQvEAULj6F6F4M8H68yH+JU4Kse/jWRKx615DLXx/ci4T+5m0RHDoMD0t/B4gPnyZWA4vw+BpvKuCSDd47BEREREREREREREREdFzYUcTERERERERERERERERPRcOnUdERERERERERERERM/lZRg2kXSrzt/R5OXlhdDQUF2noSEwMBCDBw/WdRpa4ejoiOXLl+s6DbXExERIJBI8ePBA16lUKjMzExKJBCkpKbpOhYiIiIiIiIiIiIhIyCt/R9PatWuxefNmnD59Gn/99Rfu378PKysrXadVa5w4cQJmZqKPndSebt264datW7C0tNR1KpVycHDArVu3UL9+fV2nQkREREREREREREQkpM7f0fQ0BQUF8PHxwaxZs3SdSq1kY2MDU1NTXaehZmRkBDs7O0gkEl2nUil9fX3Y2dnBwOCV7+clIiIiIiIiIiIiolrulehoUiqVmDZtGuRyOezs7BAeHq5eFhoaihkzZqBLly7PXO7fQ6Bt374dPXr0gFQqRceOHXHx4kWcOHECnp6ekMlk6NevH+7evVsuPiIiAjY2NrCwsEBwcDCKi4vVy7y8vBASEoKQkBBYWlqifv36mDNnDlSq6o146ejoiKioKIwaNQrm5uZo0qQJ1q5dq7FOWloaevXqBalUinr16mHs2LHIy8tTL/97iL/FixfD3t4e9erVw4QJE1BSUqJRzz+HzpNIJFi/fj3effddmJqawtnZGbt37wbweDs0btwYq1ev1sjjzJkz0NPTw7Vr1wAAS5cuhZubG8zMzODg4IDx48dr5HXt2jW8/fbbsLa2hpmZGVq3bo09e/YA0Bw6Lzc3F1KpFHv37tWo77vvvoO5uTkKCgoAANevX4evry+srKwgl8sxaNAgZGZmVqud/26jqKgo2NrawsrKCpGRkSgtLcXHH38MuVyOxo0bY+PGjeqYJ4fO+zvnAwcOwNPTE6ampujWrRsuXLhQrRyIiIiIiIiIiIiIiHTllehoiouLg5mZGZKTk7Fo0SJERkZi//79Wis/LCwMs2fPxunTp2FgYIARI0Zg2rRpWLFiBZKSkpCRkYG5c+dqxBw4cADp6elITEzEli1b8O233yIiIqJc3gYGBjh+/DhWrFiBpUuXYv369dXOa8mSJfD09MSZM2cwfvx4fPjhh+rOi/z8fHh7e8Pa2honTpzAjh078MsvvyAkJESjjISEBFy+fBkJCQmIi4tDbGwsYmNjq6w3IiICvr6+OHv2LPr37w+FQoGcnBzo6enBz88Pmzdv1lg/Pj4e3bt3R9OmTQEAenp6iImJwe+//464uDgcPHgQ06ZNU68/YcIEFBUV4dChQ0hLS8PChQshk8nK5WFhYYGBAwdWWN/gwYNhamqKkpISeHt7w9zcHElJSTh8+DBkMhl8fHw0Ov6qcvDgQdy8eROHDh3C0qVLERYWhoEDB8La2hrJyckIDg7GuHHjcOPGjSrL+eSTT7BkyRKcPHkSBgYGGDVqVLXqJyIiIiIiIiIiIiLSlVeio8nd3R1hYWFwdnZGQEAAPD09ceDAAa2VP3XqVHh7e8PV1RWTJ0/GqVOnMGfOHHTv3h0eHh4ICgpCQkKCRoyRkRE2bNiA1q1bY8CAAYiMjERMTAyUSqV6HQcHByxbtgwuLi5QKBSYOHEili1bVu28+vfvj/Hjx8PJyQnTp09H/fr11Xls3rwZhYWF+Oqrr9CmTRv06tULq1atwqZNm3D79m11GdbW1li1ahVatmyJgQMHYsCAAU9tu8DAQPj5+cHJyQlRUVHIy8vD8ePHAQAKhQKHDx9GVlYWgMd3OW3duhUKhUIdHxoaijfffBOOjo7o1asX5s+fj+3bt6uXZ2VloXv37nBzc0Pz5s0xcOBA9OzZs8JcFAoFdu3apb57KTc3Fz/++KO6vm3btkGpVGL9+vVwc3ODq6srNm7ciKysLCQmJlarneVyOWJiYuDi4oJRo0bBxcUFBQUFmDVrFpydnTFz5kwYGRnht99+q7KcTz/9FG+88QZatWqFGTNm4MiRIygsLKxw3aKiIuTm5mpMJaqyauVLREREREREREREpC1KTs881TWvTEfTP9nb2+POnTs1Ur6trS0AwM3NTWPek/W1bdtW49lGXbt2RV5eHq5fv66e16VLF41nDXXt2hWXLl1CWVn1OhT+mZdEIoGdnZ06j/T0dLRt2xZmZmbqdbp37w6lUqkxZFvr1q2hr6+vfl2dtvtnvWZmZrCwsFDHtGvXDq6uruq7jH799VfcuXMHw4YNU8f88ssv6N27Nxo1agRzc3P4+/vj3r176s6iSZMmYf78+ejevTvCwsJw9uzZSnPp378/DA0N1cP3ffPNN7CwsECfPn0AAKmpqcjIyIC5uTlkMhlkMhnkcjkKCwtx+fLlKt/nP9tIT+9/h5Ktra3G9tfX10e9evWeqd3s7e0BoNKY6OhoWFpaakz7c3+vVr5ERERERERERERERNrySnQ0GRoaaryWSCQadw5ps/y/O4aenKfN+p4nr+fN43nKeFqMQqFQdzRt3rwZPj4+qFevHoDHzy8aOHAg3N3d8c033+DUqVP4/PPPAUA9lN3o0aNx5coV+Pv7Iy0tDZ6enli5cmWFuRgZGWHo0KEa9Q0fPhwGBgYAgLy8PHTo0AEpKSka08WLFzFixIjnbiPRdvt7P6osZubMmXj48KHG1NeidbXyJSIiIiIiIiIiIiLSlleio+lllJqaikePHqlfHzt2DDKZDA4ODup5ycnJGjHHjh2Ds7Ozxh1Gz8vV1RWpqanIz89Xzzt8+DD09PTg4uIiXH5VRowYgXPnzuHUqVPYuXOnxrB5p06dglKpxJIlS9ClSxe0aNECN2/eLFeGg4MDgoOD8e233+Kjjz7CunXrKq1PoVDgp59+wu+//46DBw9q1Ne+fXtcunQJDRo0gJOTk8ZkaWmp3TeuRcbGxrCwsNCYDCXi+wURERERERERERER0bN45TuasrOzkZKSgoyMDABAWloaUlJSkJOTU6P1FhcXIygoCOfPn8eePXsQFhaGkJAQjSHYsrKyMGXKFFy4cAFbtmzBypUrMXnyZK3Ur1AoYGJigg8++ADnzp1DQkICJk6cCH9/f/XwfzXF0dER3bp1Q1BQEMrKyvDOO++olzk5OaGkpAQrV67ElStXsGnTJqxZs0YjPjQ0FPv27cPVq1dx+vRpJCQkwNXVtdL6evbsCTs7OygUCjRr1gydO3dWL1MoFKhfvz4GDRqEpKQkXL16FYmJiZg0aRJu3Lih/TdPRERERERERERERFSHvPIdTWvWrIGHhwfGjBkD4HGnhIeHh/qZPjWld+/ecHZ2Rs+ePTF8+HC88847CA8P11gnICAAjx49QqdOnTBhwgRMnjwZY8eO1Ur9pqam2LdvH3JyctCxY0cMHToUvXv3xqpVq7RS/tMoFAqkpqbi3XffhVQqVc9v27Ytli5dioULF6JNmzaIj49HdHS0RmxZWRkmTJgAV1dX+Pj4oEWLFvjiiy8qrUsikcDPzw+pqakadzMBj9vh0KFDaNKkCYYMGQJXV1cEBQWhsLAQFhYW2n3TRERERERERERERER1jESlUql0nQSV5+XlhXbt2mH58uW6ToVqiVUOI4XLKJNoIREBN/XKhOKblokPH/hQsPvdsUTslJptIL4R8iRiOTQuFcvBUAufKlcFCzGA2Hsw1cJ7cCgWezZfmUTsPQy7FS8UDwDf21XvWXWVuWQkdkDd1CsVigeAOR2zheJ/S7IXir8vONztScMSoXgA+Lel2F3aDXzF2uDO9ltC8drIYf46sX3JoUz8f7NGvP6HULzR62LPYiz+7XeheG3ksG7hA6H4hoKfsQBwwljs3Nz3kVi85+u3heIBwKhtI6H44lTBfVGw/sgN4s+u9SwSOyb7jxG75tQfLPb5CABluzaL5TBE7Np/2aCtQvEAUF+sGTEyyuHpK1XBoP8YsQQAlO6pfAj2F5HDF+3nCsUDQCPBc+MVI7FrTtciwR0BQO+PpE9fqQqSjq8LxatO/CZWf+eeQvEA8PnIX4Ti6wluhrOG4tfd9VVi1723JGI5GAp+BwQA5zIDofj7emLHo7FK/D0UCf4eoI0cROn6wRD/zvpaxxnUTlMc39d1CrXO0kzx67GXySt/RxMRERERERERERERERE9H3Y0VSEqKgoymazCqV+/fjrLKykpqdK8ZDKZzvKqi6pq56SkJF2nR0RERERERERERESkU2L3hNZxwcHB8PX1rXDZP58rVBMSExMrXebp6YmUlJQarZ8eq6qdGzUSGzaEiIiIiIiIiIiIiKi2Y0dTFeRyOeRyua7TKEcqlcLJyUnXabwS2M5ERERERERERERERJWTqFQqLTzynIh07QsHsQcCA4Doo5mLBJ8ZaSGYQJkWnll5Ur9QKL6l0kQoXhuP3ZQLPgg2X3BQ1Yv6JWIFACgS3BtLIPbR1qlMbDsC4seD6PGojQeYLmsidl7pocwTij8qER8O1lywIYf43BaK373XViwBLTAWvNS7aCRWf4tisXht5PCuwQOh+Kw8c7EEAJQJnuHTTMQei+xWKP6wdtEcJgaI7QxXvn4kFA8Ae5WWQvFZErHPuB5F4v/nl2Ysdky7CX5AnRWsv6PYpRYAINtQ7GLFvkTsw+GCsfgI9C5FYjmkC+bgVy9bKB4AHuaIjfJx45GZUPwlY/HjybmoVKc5jD8dKRQPAEWfTRWKX7RNbDteh/hB3b9I7No7w0jsvOZULHZeE60fAJoIfoUS/S6cp4UHa5QI5uAqeDwWScS3wx+GYtc6Yu/g5SD6u4zo92BA/LuwqHE3xL9Lv4qmOL6v6xRqnaWZW3WdglbxjiYiIiIiIiIiIiIiInouvJOFtPA/C0RERERERERERERERPQqYkcTERERERERERERERERPRd2NNURXl5eCA0N1XUaRERERERERERERET0CmFH0yti7dq18PLygoWFBSQSCR48eKDrlGoUO96IiIiIiIiIiIiIiGoeO5peEQUFBfDx8cGsWbO0Ul5JSYlWyqkt9RIRERERERERERERUXnsaKpDlEolpk2bBrlcDjs7O4SHh6uXhYaGYsaMGejSpcszl5uZmQmJRIJt27bhjTfegImJCeLj4wEA69evh6urK0xMTNCyZUt88cUX6rji4mKEhITA3t4eJiYmaNq0KaKjo9XLJRIJVq9ejX79+kEqlaJ58+bYuXPnU+u9d+8e/Pz80KhRI5iamsLNzQ1btmxRxwUGBuLXX3/FihUrIJFIIJFIkJmZCQA4d+4c+vXrB5lMBltbW/j7++PPP/+sVjt4eXlh4sSJCA0NhbW1NWxtbbFu3Trk5+fjX//6F8zNzeHk5IS9e/eqY8rKyhAUFIRmzZpBKpXCxcUFK1asUC8vLCxE69atMXbsWPW8y5cvw9zcHBs2bKjmFiIiIiIiIiIiIiLSDSWnZ57qGnY01SFxcXEwMzNDcnIyFi1ahMjISOzfv19r5c+YMQOTJ09Geno6vL29ER8fj7lz5+LTTz9Feno6oqKiMGfOHMTFxQEAYmJisHv3bmzfvh0XLlxAfHw8HB0dNcqcM2cO3nvvPaSmpkKhUOD9999Henp6lfUWFhaiQ4cO+PHHH3Hu3DmMHTsW/v7+OH78OABgxYoV6Nq1K8aMGYNbt27h1q1bcHBwwIMHD9CrVy94eHjg5MmT+Omnn3D79m34+vpWuw3i4uJQv359HD9+HBMnTsSHH36IYcOGoVu3bjh9+jTeeust+Pv7o6CgAMDjzr/GjRtjx44dOH/+PObOnYtZs2Zh+/btAKDuPIuLi8P333+PsrIyjBw5En379sWoUaOed1MREREREREREREREb0QBrpOgLTH3d0dYWFhAABnZ2esWrUKBw4cQN++fbVSfmhoKIYMGaJ+HRYWhiVLlqjnNWvWDOfPn8d//vMffPDBB8jKyoKzszNef/11SCQSNG3atFyZw4YNw+jRowEA8+bNw/79+7Fy5UqNO6OerBcApk6dqv574sSJ2LdvH7Zv345OnTrB0tISRkZGMDU1hZ2dnXq9VatWwcPDA1FRUep5GzZsgIODAy5evIgWLVo8tQ3atm2L2bNnAwBmzpyJBQsWoH79+hgzZgwAYO7cuVi9ejXOnj2LLl26wNDQEBEREer4Zs2a4ejRo9i+fbu6g6tdu3aYP38+Ro8ejffffx/Xrl3DDz/8UGUeRUVFKCoq0phXoiqDoUT/qe+BiIiIiIiIiIiIiEhbeEdTHeLu7q7x2t7eHnfu3NFa+Z6enuq/8/PzcfnyZQQFBUEmk6mn+fPn4/LlywAeD2GXkpICFxcXTJo0CT///HO5Mrt27Vru9ZN3NP2zXuDxcHTz5s2Dm5sb5HI5ZDIZ9u3bh6ysrCrzT01NRUJCgka+LVu2BAB1zk/zzzbW19dHvXr14Obmpp5na2sLABrt/vnnn6NDhw6wsbGBTCbD2rVry+X60UcfoUWLFli1ahU2bNiAevXqVZlHdHQ0LC0tNaafc3+v1nsgIiIiIiIiIiIiItIW3tFUhxgaGmq8lkgkUCq1N+KjmZmZ+u+8vDwAwLp169C5c2eN9fT1H99V0759e1y9ehV79+7FL7/8Al9fX/Tp00fjOUzPWi8AfPbZZ1ixYgWWL18ONzc3mJmZITQ0FMXFxVWWk5eXh7fffhsLFy4st8ze3r5auVTUxv+cJ5FIAEDd7lu3bsXUqVOxZMkSdO3aFebm5vjss8+QnJysUc6dO3dw8eJF6Ovr49KlS/Dx8akyj5kzZ2LKlCka8za2Glet90BEREREREREREREpC3saKLnYmtri4YNG+LKlStQKBSVrmdhYYHhw4dj+PDhGDp0KHx8fJCTkwO5XA4AOHbsGAICAtTrHzt2DB4eHlXWffjwYQwaNAgjR44E8LhT5+LFi2jVqpV6HSMjI5SVlWnEtW/fHt988w0cHR1hYPBidv3Dhw+jW7duGD9+vHpeRXdPjRo1Cm5ubggKCsKYMWPQp08fuLq6VlqusbExjI2NNeZx2DwiIiIiIiIiIiIietHY0fSKyM7ORnZ2NjIyMgAAaWlpMDc3R5MmTdSdPs8qIiICkyZNgqWlJXx8fFBUVISTJ0/i/v37mDJlCpYuXQp7e3t4eHhAT08PO3bsgJ2dHaysrNRl7NixA56ennj99dcRHx+P48eP48svv6yyXmdnZ+zcuRNHjhyBtbU1li5ditu3b2t0NDk6OiI5ORmZmZmQyWSQy+WYMGEC1q1bBz8/P0ybNg1yuRwZGRnYunUr1q9fr74TS5ucnZ3x1VdfYd++fWjWrBk2bdqEEydOoFmzZup1Pv/8cxw9ehRnz56Fg4MDfvzxRygUChw7dgxGRkZaz4mIiIiIiIiIiIhIW1RQ6ToF0jE+o+kVsWbNGnh4eGDMmDEAgJ49e8LDwwO7d+9+7jJHjx6N9evXY+PGjXBzc8Mbb7yB2NhYdSeKubk5Fi1aBE9PT3Ts2BGZmZnYs2cP9PT+t9tFRERg69atcHd3x1dffYUtW7ZodBhVZPbs2Wjfvj28vb3h5eUFOzs7DB48WGOdqVOnQl9fH61atYKNjQ2ysrLQsGFDHD58GGVlZXjrrbfg5uaG0NBQWFlZaeSkTePGjcOQIUMwfPhwdO7cGffu3dO4u+n//u//8PHHH+OLL76Ag4MDAOCLL77An3/+iTlz5tRITkRERERERERERERE2sI7muqIxMTEcvN27dql/js8PBzh4eHPVbajoyNUqop7pUeMGIERI0ZUuGzMmDHqjq3KNGzYED///PMz1SuXyzXeW0VatGiBo0ePlpvv7OyMb7/9tsrYylTUxpmZmeXm/TNnY2NjbNy4ERs3btRYJzo6GgDQsmVLFBQUaCyzsrJCVlbWc+VIRERERERERERERPQi8Y4mIiIiIiIiIiIiIiIiei7saCJERUVBJpNVOPXr10/X6b0QWVlZlbaBTCbjHUZERERERERERERERBXg0HmE4OBg+Pr6VrhMKpXWWL2VDcenCw0bNkRKSkqVy4mIiIiIiIiIiIiISBM7mghyuRxyuVzXaeiUgYEBnJycdJ0GERERERERERERUa2i1HUCpHPsaCKqI7RxQhe9x6xMIhYvOpZnkWA8ADxQFQvFl0hMhOKNtHCjn1JwO5QIxpcK70lAvqpMML5EKL4UYtsRAEwED0rR40kbjAU3ZZlS7KjONxSrHwBEiyi+IXZOMBa8e/eugfiOUAaxMuzEDkdka+FqUzSHe0Vid2jXNywUSwDASYlMKN62VKz+60b6YgVoI4dteULxzd4TPyn8uVNsZ2qsEsshWwvnNYdSsWNaNIcmgvXna2HwdpngOeGKkVgSzYvFr3VEc3hNMIf790yF4gHAul6BUPyhu5ZC8Y1KtLEdxD6kRHMo+myqUDwAGH+8WCj+xrZpQvEtVOKjoNwQPC85CR4PNwzFzmui9QOA4KlV+HpP9DsgIP6bRJ5E7LxYIhF/E6LvQfg3kZfg9wDBj9iXguol+C5N9CriM5qIiIiIiIiIiIiIiIjoubCjiYiIiIiIiIiIiIiIiJ4LO5qIiIiIiIiIiIiIiIjoubCjqQ7z8vJCaGiortOgJyQmJkIikeDBgwe6ToWIiIiIiIiIiIiISAg7ml5ha9euhZeXFywsLF6Jjo+XpeOtW7duuHXrFiwtxR5+S0RERERERERERKRrSqg4PeNU17Cj6RVWUFAAHx8fzJo1SyvllZSUaKWc2lLv8zIyMoKdnR0kEomuUyEiIiIiIiIiIiIiEsKOpjpOqVRi2rRpkMvlsLOzQ3h4uHpZaGgoZsyYgS5dujxzuZmZmZBIJNi2bRveeOMNmJiYID4+HgCwfv16uLq6wsTEBC1btsQXX3yhjisuLkZISAjs7e1hYmKCpk2bIjo6Wr1cIpFg9erV6NevH6RSKZo3b46dO3c+td579+7Bz88PjRo1gqmpKdzc3LBlyxZ1XGBgIH799VesWLECEokEEokEmZmZAIBz586hX79+kMlksLW1hb+/P/78889qtYOXlxcmTpyI0NBQWFtbw9bWFuvWrUN+fj7+9a9/wdzcHE5OTti7d6865smh82JjY2FlZYV9+/bB1dUVMpkMPj4+uHXr1jNvFyIiIiIiIiIiIiKiF4kdTXVcXFwczMzMkJycjEWLFiEyMhL79+/XWvkzZszA5MmTkZ6eDm9vb8THx2Pu3Ln49NNPkZ6ejqioKMyZMwdxcXEAgJiYGOzevRvbt2/HhQsXEB8fD0dHR40y58yZg/feew+pqalQKBR4//33kZ6eXmW9hYWF6NChA3788UecO3cOY8eOhb+/P44fPw4AWLFiBbp27YoxY8bg1q1buHXrFhwcHPDgwQP06tULHh4eOHnyJH766Sfcvn0bvr6+1W6DuLg41K9fH8ePH8fEiRPx4YcfYtiwYejWrRtOnz6Nt956C/7+/igoKKi0jIKCAixevBibNm3CoUOHkJWVhalTp1Y7ByIiIiIiIiIiIiIiXTDQdQJUs9zd3REWFgYAcHZ2xqpVq3DgwAH07dtXK+WHhoZiyJAh6tdhYWFYsmSJel6zZs1w/vx5/Oc//8EHH3yArKwsODs74/XXX4dEIkHTpk3LlTls2DCMHj0aADBv3jzs378fK1eu1Lgz6sl6AWh0zEycOBH79u3D9u3b0alTJ1haWsLIyAimpqaws7NTr7dq1Sp4eHggKipKPW/Dhg1wcHDAxYsX0aJFi6e2Qdu2bTF79mwAwMyZM7FgwQLUr18fY8aMAQDMnTsXq1evxtmzZyu9e6ykpARr1qzBa6+9BgAICQlBZGRkpXUWFRWhqKhIswxVGQwl+k/Nl4iIiIiIiIiIiIhIW3hHUx3n7u6u8dre3h537tzRWvmenp7qv/Pz83H58mUEBQVBJpOpp/nz5+Py5csAHg9hl5KSAhcXF0yaNAk///xzuTK7du1a7vWTdzT9s14AKCsrw7x58+Dm5ga5XA6ZTIZ9+/YhKyuryvxTU1ORkJCgkW/Lli0BQJ3z0/yzjfX19VGvXj24ubmp59na2gJAle1uamqq7mQCnr6doqOjYWlpqTHtz/29WvkSEREREREREREREWkL72iq4wwNDTVeSyQSKJVKrZVvZmam/jsvLw8AsG7dOnTu3FljPX39x3fatG/fHlevXsXevXvxyy+/wNfXF3369NF4DtOz1gsAn332GVasWIHly5fDzc0NZmZmCA0NRXFxcZXl5OXl4e2338bChQvLLbO3t69WLhW18T/nSSQSAKiy3SsqQ6VSVbr+zJkzMWXKFI15X7YaV618iYiIiIiIiIiIiLSl8l8x6VXBjibSGltbWzRs2BBXrlyBQqGodD0LCwsMHz4cw4cPx9ChQ+Hj44OcnBzI5XIAwLFjxxAQEKBe/9ixY/Dw8Kiy7sOHD2PQoEEYOXIkgMedOhcvXkSrVq3U6xgZGaGsrEwjrn379vjmm2/g6OgIA4PaczgYGxvD2NhYYx6HzSMiIiIiIiIiIiKiF632/LJOWpednY3s7GxkZGQAANLS0mBubo4mTZqoO32eVUREBCZNmgRLS0v4+PigqKgIJ0+exP379zFlyhQsXboU9vb28PDwgJ6eHnbs2AE7OztYWVmpy9ixYwc8PT3x+uuvIz4+HsePH8eXX35ZZb3Ozs7YuXMnjhw5AmtrayxduhS3b9/W6GhydHREcnIyMjMzIZPJIJfLMWHCBKxbtw5+fn6YNm0a5HI5MjIysHXrVqxfv159JxYREREREREREREREZXHZzS9wtasWQMPDw+MGTMGANCzZ094eHhg9+7dz13m6NGjsX79emzcuBFubm544403EBsbi2bNmgEAzM3NsWjRInh6eqJjx47IzMzEnj17oKf3v10xIiICW7duhbu7O7766its2bJFo8OoIrNnz0b79u3h7e0NLy8v2NnZYfDgwRrrTJ06Ffr6+mjVqhVsbGyQlZWFhg0b4vDhwygrK8Nbb70FNzc3hIaGwsrKSiMnIiIiIiIiIiIiIiIqT6Kq6kEwRC+YRCLBd999V66TiJ5ulcNI4TJETwaPBPvm6pU9fZ0q65eIxQNAol6uUHwHWAjFG2nhjGwl+Bi2XMHteEmv6mejVSsHValQfL6qRCi+t8pSKB4ADAW3ZZng/jz++tdiBQD4QvC80qGsQCg+wdBUKB4QPx6GtrouFJ9wtrFQ/F0D8ROb4KkVpoL7coEWzs2iObQoLRSKlxqInZMA4KREJhRvLLgvF2nh/2dEc+hqliMU3/gdw6ev9BSRO8XOK3KV2N3uUi18zpsIbodCwX1BtH6ZFh4XK9qMdwXH9XAoEd+Q1w3FTo6NBXNwNvpLKB4ArOuJfc7/966dULxdqfh2yBb8nBXNwSfgkVA8ABh/vFgofpznNKF4Z5WJUDwAmAhuStHj4YaOj0cAKBW8XiqRiBVwXwuDuYie3psWi5Ug2gYAcE/wnFAimIKeFq4TlII5WAh+eRDdlwHxfUklmEOwFr5Lv4o+dPTVdQq1zurM7bpOQat4ywYRERERERERERERERE9F3Y0UYWioqIgk8kqnPr166fr9F6IrKysSttAJpMhKytL1ykSERERERERERER6ZQSKk7PONU1goMGUF0VHBwMX9+Kb3mUSqU1Vu/LNJJjw4YNkZKSUuVyIiIiIiIiIiIiIqJXGTuaqEJyuRxyuVzXaeiUgYEBnJycdJ0GEREREREREREREdFLi0PnERERERERERERERER0XPhHU1EdYQ2Bh2UCMZbKMXi/9DX/dCJ7xebC8VfMxSrXxsn5QLBDflQIrYdOpUYiSUAwKJMrCX0YCIUf1sLG+KW4P6sL56CsJv6Ygf1Qz1TofjmxeLnhGxDsQNi2YVGQvGWRmL1N9NGGxiI5fBA8N+SjLRwahfN4aix2DmhSAufslaCn5H5gicFbWwH0Rx2F4ndLX9rZ6lYAgAi+t4Tis/4SWwI6eMQu84AgALB40EquC+I1q+N/3S8L7gv1isTi78ieG7XRg5XBXO4qIV9MTfHTCjeSXBf1MZ2sNbxvrBom/iw9De2TROK/8/JRULx+ZNHC8UDwDe/ig1LL3o86Pp4BACp4HXCAz2xA8pKKf4eRD8frhnp/n/hDQXPS6LxWmkBwRxyBT9jBXdFAIDo7qiNHIjo2en+LE5ERERERERERERERES1Eu9oIiIiIiIiIiIiIiKi5yJ4YybVAbyjiZ6Jl5cXQkNDdZ0GERERERERERERERG9BNjRRFq1du1aeHl5wcLCAhKJBA8ePNB1ShrWrVuHHj16wNraGtbW1ujTpw+OHz+u67SIiIiIiIiIiIiIiGoldjSRVhUUFMDHxwezZs3SdSoVSkxMhJ+fHxISEnD06FE4ODjgrbfewh9//KHr1IiIiIiIiIiIiIiIah12NNEzUyqVmDZtGuRyOezs7BAeHq5eFhoaihkzZqBLly7PXG5xcTFCQkJgb28PExMTNG3aFNHR0erlEokEq1evRr9+/SCVStG8eXPs3LlTo4y0tDT06tULUqkU9erVw9ixY5GXl6deHh8fj/Hjx6Ndu3Zo2bIl1q9fD6VSiQMHDlQrR0dHR8yfPx8BAQGQyWRo2rQpdu/ejbt372LQoEGQyWRwd3fHyZMn1TH37t2Dn58fGjVqBFNTU7i5uWHLli3q5Xfv3oWdnR2ioqLU844cOQIjI6Nq50VEREREREREREREpAvsaKJnFhcXBzMzMyQnJ2PRokWIjIzE/v37hcuNiYnB7t27sX37dly4cAHx8fFwdHTUWGfOnDl47733kJqaCoVCgffffx/p6ekAgPz8fHh7e8Pa2honTpzAjh078MsvvyAkJKTSOgsKClBSUgK5XF7tPJctW4bu3bvjzJkzGDBgAPz9/REQEICRI0fi9OnTeO211xAQEACVSgUAKCwsRIcOHfDjjz/i3LlzGDt2LPz9/dVD9tnY2GDDhg0IDw/HyZMn8ddff8Hf3x8hISHo3bv3M7YiEREREREREREREdGLY6DrBKj2cXd3R1hYGADA2dkZq1atwoEDB9C3b1+hcrOysuDs7IzXX38dEokETZs2LbfOsGHDMHr0aADAvHnzsH//fqxcuRJffPEFNm/ejMLCQnz11VcwMzMDAKxatQpvv/02Fi5cCFtb23LlTZ8+HQ0bNkSfPn2qnWf//v0xbtw4AMDcuXOxevVqdOzYEcOGDVOX2bVrV9y+fRt2dnZo1KgRpk6dqo6fOHEi9u3bh+3bt6NTp07qMseMGQOFQgFPT0+YmZlp3M1FRERERERERERE9DJSQaXrFEjHeEcTPTN3d3eN1/b29rhz545wuYGBgUhJSYGLiwsmTZqEn3/+udw6Xbt2Lff67zua0tPT0bZtW3UnEwB0794dSqUSFy5cKFfWggULsHXrVnz33XcwMTGpdp7/fP9/d165ubmVm/d3m5SVlWHevHlwc3ODXC6HTCbDvn37kJWVpVHu4sWLUVpaih07diA+Ph7GxsaV5lBUVITc3FyNqURVVu33QERERERERERERESkDexoomdmaGio8VoikUCpVAqX2759e1y9ehXz5s3Do0eP4Ovri6FDhwqXW5HFixdjwYIF+Pnnn8t1nD3NP9+/RCKpdN7fbfLZZ59hxYoVmD59OhISEpCSkgJvb28UFxdrlHv58mXcvHkTSqUSmZmZVeYQHR0NS0tLjWl/7u/P9D6IiIiIiIiIiIiIiESxo4leKhYWFhg+fDjWrVuHbdu24ZtvvkFOTo56+bFjxzTWP3bsGFxdXQEArq6uSE1NRX5+vnr54cOHoaenBxcXF/W8RYsWYd68efjpp5/g6elZw+/ocQ6DBg3CyJEj0bZtWzRv3hwXL17UWKe4uBgjR47E8OHDMW/ePIwePbrKu8RmzpyJhw8fakx9LVrX9FshIiIiIiIiIiIiItLAZzSRVmVnZyM7OxsZGRkAgLS0NJibm6NJkyaQy+VVxi5duhT29vbw8PCAnp4eduzYATs7O1hZWanX2bFjBzw9PfH6668jPj4ex48fx5dffgkAUCgUCAsLwwcffIDw8HDcvXsXEydOhL+/v3o4u4ULF2Lu3LnYvHkzHB0dkZ2dDQCQyWSQyWQ10CKPn2O1c+dOHDlyBNbW1li6dClu376NVq1aqdf55JNP8PDhQ8TExEAmk2HPnj0YNWoUfvjhhwrLNDY2Lje0nqFEv0byJyIiIiIiIiIiIiKqDO9oIq1as2YNPDw8MGbMGABAz5494eHhgd27dz811tzcHIsWLYKnpyc6duyIzMxM7NmzB3p6/9tNIyIisHXrVri7u+Orr77Cli1b1B02pqam2LdvH3JyctCxY0cMHToUvXv3xqpVq9Txq1evRnFxMYYOHQp7e3v1tHjxYi23xP/Mnj0b7du3h7e3N7y8vGBnZ4fBgwerlycmJmL58uXYtGkTLCwsoKenh02bNiEpKQmrV6+usbyIiIiIiIiIiIiIiETxjiZ6JomJieXm7dq1S/13eHg4wsPDn6vsMWPGqDuoKtOwYUP8/PPPlS53c3PDwYMHK13+tGcfPU1F8SqVSuO1o6Ojxjy5XK7RRk/y8vJCSUlJuTIePnwolCsRERERERERERFRTVPqOgHSOd7RRERERERERERERERERM+FHU30wkRFRamfhfTk1K9fP12nh6SkpErzq6nnNxERERERERERERER1WYcOo9emODgYPj6+la4TCqVPjX+ySHqtM3T0xMpKSk1WgcRERERERERERERUV3CjiZ6YeRyOeRyua7TqJRUKoWTk5Ou0yAiIiIiIiIiIiIiqjU4dB4RERERERERERERERE9F97RRERqooMTlorGS8TiZSrBAgAUCHa/Gws2ojZOyo8Em8EUYgUYaGGUy0d6YhtCNAXR/QAAGpWJ74+65lAq1hC39MW2xA1D8TY0E9wZDAXPKxZKsfozjMTbQCl4RLQrFHsTKSbiB5RoDglPH6G3So5l+mIFAMgTbAa3wjKh+DQT8fcgmsMBqdi+2LxM/FPy/I9iz91soxB7DwnbhMIBAK2LxK64fjcWa0dd1w8ANoIXnfcEDwfXIrFjAQAuGIklIZrDLybiF2x2KrFtKXq9pY3tkGmo2+3wrUmxUDwAtFCJfcjlTx4tFG+2Yr1QPAA88pgrFN+mUOykcMVIbF8WrR8A0gXPjcaC3+Ee6ImfEwwEc7ilJ3Y8aePbl4PgNZ/gpT/Ez2ri7SD6Hl6GOxqUtf+reK2kEv4lhmq7l+H4JyIiIiIiIiIiIiIiolqIHU1ERERERERERERERET0XNjRRERERERERERERERERM+FHU0kxMvLC6GhobpOo1aJjY2FlZWVrtMgIiIiIiIiIiIiIhLGjiaqUWvXroWXlxcsLCwgkUjw4MEDXaekYd26dejRowesra1hbW2NPn364Pjx4zVa5/Dhw3Hx4sUarYOIiIiIiIiIiIiI6EVgRxPVqIKCAvj4+GDWrFm6TqVCiYmJ8PPzQ0JCAo4ePQoHBwe89dZb+OOPP2qsTqlUigYNGtRY+UREREREREREREQvipLTM091DTuaSJhSqcS0adMgl8thZ2eH8PBw9bLQ0FDMmDEDXbp0eeZyi4uLERISAnt7e5iYmKBp06aIjo5WL5dIJFi9ejX69esHqVSK5s2bY+fOnRplpKWloVevXpBKpahXrx7Gjh2LvLw89fL4+HiMHz8e7dq1Q8uWLbF+/XoolUocOHCgWjk6Ojpi/vz5CAgIgEwmQ9OmTbF7927cvXsXgwYNgkwmg7u7O06ePKmOeXLovPDwcLRr1w6bNm2Co6MjLC0t8f777+Ovv/565jYjIiIiIiIiIiIiInqR2NFEwuLi4mBmZobk5GQsWrQIkZGR2L9/v3C5MTEx2L17N7Zv344LFy4gPj4ejo6OGuvMmTMH7733HlJTU6FQKPD+++8jPT0dAJCfnw9vb29YW1vjxIkT2LFjB3755ReEhIRUWmdBQQFKSkogl8urneeyZcvQvXt3nDlzBgMGDIC/vz8CAgIwcuRInD59Gq+99hoCAgKgUqkqLePy5cvYtWsXfvjhB/zwww/49ddfsWDBgmrnQERERERERERERESkC+xoImHu7u4ICwuDs7MzAgIC4OnpWe07gqqSlZUFZ2dnvP7662jatClef/11+Pn5aawzbNgwjB49Gi1atMC8efPg6emJlStXAgA2b96MwsJCfPXVV2jTpg169eqFVatWYdOmTbh9+3aFdU6fPh0NGzZEnz59qp1n//79MW7cODg7O2Pu3LnIzc1Fx44dMWzYMLRo0QLTp09Henp6pXUCj+8Ki42NRZs2bdCjRw/4+/tX2YZFRUXIzc3VmEpUZdXOmYiIiIiIiIiIiIhIG9jRRMLc3d01Xtvb2+POnTvC5QYGBiIlJQUuLi6YNGkSfv7553LrdO3atdzrv+9oSk9PR9u2bWFmZqZe3r17dyiVSly4cKFcWQsWLMDWrVvx3XffwcTEpNp5/vP929raAgDc3NzKzauqTRwdHWFubq5+/bQ2jI6OhqWlpca0P/f3audMRERERERERERERHVLTk4OFAoFLCwsYGVlhaCgII1HyVS0/sSJE+Hi4gKpVIomTZpg0qRJePjw4TPVy44mEmZoaKjxWiKRQKkUf6RZ+/btcfXqVcybNw+PHj2Cr68vhg4dKlxuRRYvXowFCxbg559/Ltdx9jT/fP8SiaTSeVW1ybO24cyZM/Hw4UONqa9F62fKm4iIiIiIiIiIiIjqDoVCgd9//x379+/HDz/8gEOHDmHs2LGVrn/z5k3cvHkTixcvxrlz5xAbG4uffvoJQUFBz1SvgWjiRDXJwsICw4cPx/DhwzF06FD4+PggJydH/QylY8eOISAgQL3+sWPH4OHhAQBwdXVFbGws8vPz1Xc1HT58GHp6enBxcVHHLFq0CJ9++in27dsHT0/PF/junp+xsTGMjY015hlK9HWUDREREREREREREb2qlFU8m55enPT0dPz00084ceKE+nfulStXon///li8eDEaNmxYLqZNmzb45ptv1K9fe+01fPrppxg5ciRKS0thYFC9LiR2NFGNys7ORnZ2NjIyMgAAaWlpMDc3R5MmTdSdRZVZunQp7O3t4eHhAT09PezYsQN2dnawsrJSr7Njxw54enri9ddfR3x8PI4fP44vv/wSwOPe27CwMHzwwQcIDw/H3bt3MXHiRPj7+6uHs1u4cCHmzp2LzZs3w9HREdnZ2QAAmUwGmUxWAy1CRERERERERERERK+yoqIiFBUVacyr6OaCZ3H06FFYWVlp3EzRp08f6OnpITk5Ge+++261ynn48CEsLCyq3ckEcOg8qmFr1qyBh4cHxowZAwDo2bMnPDw8sHv37qfGmpubY9GiRfD09ETHjh2RmZmJPXv2QE/vf7ttREQEtm7dCnd3d3z11VfYsmULWrVqBQAwNTXFvn37kJOTg44dO2Lo0KHo3bs3Vq1apY5fvXo1iouLMXToUNjb26unxYsXa7kliIiIiIiIiIiIiIiA6OhoWFpaakzR0dFCZWZnZ6NBgwYa8wwMDCCXy9U3WDzNn3/+iXnz5lU53F5FeEcTCUlMTCw3b9euXeq/w8PDER4e/lxljxkzRt1BVZmGDRvi559/rnS5m5sbDh48WOnyzMzM58qtqnjVE7eKOjo6aswLDAxEYGCg+nVFbRQaGorQ0FCh3IiIiIiIiIiIiIjo5TNz5kxMmTJFY15ldzPNmDEDCxcurLK89PR04Zxyc3MxYMAAtGrV6pl/02dHExERERERERERERER0QvyLMPkffTRRxo3LlSkefPmsLOzw507dzTml5aWIicnB3Z2dlXG//XXX/Dx8YG5uTm+++47GBoaViu3v7GjiXQmKioKUVFRFS7r0aMH9u7d+4Iz0pSUlIR+/fpVujwvL+8FZkNERERERERERERErxobGxvY2Ng8db2uXbviwYMHOHXqFDp06AAAOHjwIJRKJTp37lxpXG5uLry9vWFsbIzdu3fDxMTkmXNkRxPpTHBwMHx9fStcJpVKnxr/5BB12ubp6YmUlJQarYOIiIiIiIiIiIioNqvZX2mpulxdXeHj44MxY8ZgzZo1KCkpQUhICN5//300bNgQAPDHH3+gd+/e+Oqrr9CpUyfk5ubirbfeQkFBAb7++mvk5uYiNzcXwOMOLn19/WrVzY4m0hm5XA65XK7rNCollUrh5OSk6zSIiIiIiIiIiIiIiJ4qPj4eISEh6N27N/T09PDee+8hJiZGvbykpAQXLlxAQUEBAOD06dNITk4GgHK/hV+9ehWOjo7VqpcdTUR1hEQLZYj+90GZYBLNS8QKMNDCv0/ceLbhR8uxUorF39cTi9eGJiVi8aL7AQBcF9yY+oJHhE2ZUDgAoEgbB6WOaWNbihA8JQAAfDtcF4r/5oSDULzoIV29/xuqWnCnP4Ti76Q9/S7jqnRzeyQUDwC3U02F4lOKrIXiM/XFTwoRilKh+LzDd4Xivbo/fZiFms4hO6OxUHyuFj4jDxuI7Uu/bxGrf/zSFmIFACj+8Veh+DcGvKHT+n9PsBeKBwCl4OfD+OlWQvElx8UftNy7k6tYDif/Tyj+yoGqx+ivjrt6Yhe+oeteF4pXpZ4UigcASVtPneZQuFT8xCb6/eWbXxsKxT/ymCuWAIBxZyKF4ksTNwvF9/UaodP6AeBWqNgxbSL4PfSS4H4EANV7ykjl6qu0ceUrRvQnhUeCh7SN2OUiAOC24C+9omclwV1RKzlo4es8Ua0ml8uxeXPln02Ojo4aI4V5eXlpZeSwl+AnTSIiIiIiIiIiIiIiIqqN2NFEREREREREREREREREz4UdTURERERERERERERERPRc+Iwm0uDl5YV27dph+fLluk7lmQQGBuLBgwfYtWuXrlMhIiIiIiIiIiIiemUohZ9yRrUd72iiZ7J27Vp4eXnBwsICEokEDx480HVKRERERERERERERESkI+xoomdSUFAAHx8fzJo1S9epEBERERERERERERGRjrGjicpRKpWYNm0a5HL5/2PvzuOqqvb/j78OkwIHRHAAlcQBCREVw0zRrjnkkJpaTpFDTnmVzHJIyzlzKi2HtJvlkDmkZeYt00wlixRHFIccSMW+Tjk04AAI/P7w57meRETXURzeTx/78ZC992etz157n33OYbHXwt/fn+HDh9u29enTh4EDB/LYY4/ddLlpaWnExMQQEBBA/vz5KVmyJGPGjAGgc+fONGnSxG7/9PR0ihQpwscffwzA559/Tnh4OO7u7vj5+VGvXj3OnTtnFzNixAgKFy6Mt7c3PXr0IC0tzbatdu3axMTEEBMTQ4ECBShUqBBDhgwhKyt3j3YGBQUxatQoOnTogNVqpWTJkixbtozff/+dp59+GqvVSsWKFdm8ebMt5vTp07Rr147ixYvj4eFBeHg4CxYssG3//fff8ff3Z/To0bZ1P//8M25ubqxevTqXLSsiIiIiIiIiIiIikjfU0STXmDNnDp6ensTHxzN+/HhGjhzJqlWrjMudPHkyy5YtY9GiRezdu5d58+YRFBQEQNeuXVmxYgXHjh2z7f/1119z/vx52rRpw7Fjx2jXrh2dO3dmz549xMbG0rJlS7tOotWrV9u2LViwgCVLljBixIhrjs3FxYWNGzcyadIkJk6cyEcffZTrY3j33XeJiopi27ZtPPXUU7Rv354OHTrw/PPPs3XrVsqUKUOHDh1seV28eJFHHnmEb775hp07d9K9e3fat2/Pxo0bAShcuDAzZ85k+PDhbN68mb///pv27dsTExND3bp1b7WpRURERERERERERETuCJe8TkDuPhUrVmTYsGEABAcHM3XqVFavXk39+vWNyk1OTiY4OJiaNWtisVgoWbKkbVuNGjUICQlh7ty5DBgwAIBZs2bRqlUrrFYr+/bt49KlS7Rs2dIWFx4eble+m5sbM2fOxMPDg7CwMEaOHEn//v158803cXK63KcaGBjIu+++i8ViISQkhMTERN599126deuWq2No3LgxL774IgBDhw5l+vTpVK1alVatWgHw2muvUb16dU6cOIG/vz/FixenX79+tviXXnqJlStXsmjRIh599FFbmd26dSM6OprIyEg8PT1tT3pdT2pqKqmpqXbr0rMycLU45+o4REREREREREREREQcQU80yTUqVqxo93NAQAAnT540LrdTp04kJCQQEhJC7969+e677+y2d+3alVmzZgFw4sQJvv32Wzp37gxApUqVqFu3LuHh4bRq1YoZM2Zw9uxZu/hKlSrh4eFh+7l69eqkpKRw5MgR27rHHnsMi8Vit8/+/fvJyMjI1TFc3TZFixYF7Du8rqy70l4ZGRm8+eabhIeH4+vri9VqZeXKlSQnJ9uV+84773Dp0iUWL17MvHnzyJcvX455jBkzhgIFCtgtq/7alatjEBEREREREREREXGULP276X/3G3U0yTVcXV3tfrZYLGRmZhqXW6VKFQ4ePMibb77JhQsXaN26Nc8++6xte4cOHfj1119Zv349n376KaVKlaJWrVoAODs7s2rVKr799lvKly/PlClTCAkJ4eDBg8Z53Yyr2+ZKh1V2666019tvv82kSZN47bXXWLt2LQkJCTRo0MBu7iiApKQkjh49SmZmJocOHbphHoMGDeLPP/+0W+p7h5kenoiIiIiIiIiIiIjITdHQeXJHeXt706ZNG9q0acOzzz5Lw4YNOXPmDL6+vvj5+dG8eXNmzZrF+vXreeGFF+xiLRYLUVFRREVFMXToUEqWLMmXX37Jq6++CsD27du5cOEC7u7uAGzYsAGr1UpgYKCtjPj4eLsyN2zYQHBwMM7Ot2fIubi4OJ5++mmef/554HIH1L59+yhfvrxtn7S0NJ5//nnatGlDSEgIXbt2JTExkSJFily33Hz58l3z1JOGzRMRERERERERERGRO00dTXJTjh8/zvHjxzlw4AAAiYmJeHl58dBDD+Hr65tj7MSJEwkICCAiIgInJycWL16Mv78/Pj4+tn26du1KkyZNyMjIoGPHjrb18fHxrF69mieffJIiRYoQHx/P77//TmhoqG2ftLQ0unTpwuDBgzl06BDDhg0jJibGNj8TXJ4n6tVXX+XFF19k69atTJkyhQkTJjioda4VHBzM559/zs8//0zBggWZOHEiJ06csOtoeuONN/jzzz+ZPHkyVquV5cuX07lzZ77++uvblpeIiIiIiIiIiIiIiCOoo0luygcffMCIESNsPz/++OMAzJo1i06dOuUY6+Xlxfjx49m/fz/Ozs5UrVqV5cuX23UE1atXj4CAAMLCwihWrJhtvbe3N+vWreO9997jr7/+omTJkkyYMIFGjRrZ9qlbty7BwcE8/vjjpKam0q5dO4YPH26XQ4cOHbhw4QKPPvoozs7OvPzyy3Tv3t2gRXI2ePBgfv31Vxo0aICHhwfdu3enefPm/PnnnwDExsby3nvvsXbtWry9vQGYO3culSpVYvr06fz73/++bbmJiIiIiIiIiIiIiJhSR5PYiY2NvWbd0qVLbf8fPnz4NZ03udWtWze6deuW4z7nzp3j7NmzdOnSxW59aGgoK1asuG7c7Nmzbf+/uiPsn1xdXXnvvfeYPn167pK+SnZzJ2Vl2U/cFhQUZLfO19fXrv3+qXbt2qSnp19TxpWOKBERERERERERERGRu5k6muSukJmZyalTp5gwYQI+Pj40a9Ysr1MSERERERERERERkRvIzOsEJM853XgXkdwZPXo0Vqs12+XqIe6yk5ycTNGiRZk/fz4zZ87ExeXO9oH++OOP183darXe0VxERERERERERERERO4VeqJJHKZHjx60bt06223u7u45xv5zyLnbIbthAa+IjIwkISHhttYvIiIiIiIiIiIiInK/UUeTOIyvry++vr55ncYtcXd3p2zZsnmdhoiIiIiIiIiIiIjIPUUdTSJik2kxi79kWP/fhoN5Wh0wIKzpeKIWwwfzPBzwYF+KYRIZFrMLIV+m+UE4Y5aD6XlMNXwtAASmZxjFZxq2gSMEpZm9qg+7mx3DGQeM8px53jDesH7vDLPXw2aXdMMM4HiCh1F8QJULRvHHtprV74gcfos3Gwb3oSw3o3iAC1uPGsV7VjX7Y54LW08axTsihyMHzV4PYWlG4QBszWeWQ+VUs3t7xsYtRvEArtUr5GkOpvWf/eF3o3iAsobXQuqanUbx+Zs8apYAcPHrjXmaw6G1yUbxAEWyzH6dkLkl3ijeKeoJo3iAzLi1ZjlU/5dR/AG3NUbxAGXTzO5rB93MPq9VuGj6LRAuxc43inep/Vze1l+rlVE8wAXLm0bx+QzrP28x/9ztkeVsFJ9sMfvca/odEiDY8NekZp8SIM0BXwEzMLsnOBm24+0dq0hE7maao0lERERERERERERERERuiZ5oEhERERERERERERGRW5Kp59keeHqiSURERERERERERERERG6JOprkhmrXrk2fPn3yOo2b1qlTJ5o3b57XaVxj9uzZ+Pj45HUaIiIiIiIiIiIiIiLG1NEkxj788ENq166Nt7c3FouFP/74I69Tuqu1adOGffv25XUaIiIiIiIiIiIiIiLG1NEkxs6fP0/Dhg15/fXX8zqVe4K7uztFihTJ6zRERERERERERERERIypo0lyJTMzkwEDBuDr64u/vz/Dhw+3bevTpw8DBw7kscceu+ly09LSiImJISAggPz581OyZEnGjBkDQOfOnWnSpInd/unp6RQpUoSPP/4YgM8//5zw8HDc3d3x8/OjXr16nDt3zi5mxIgRFC5cGG9vb3r06EFaWpptW+3atYmJiSEmJoYCBQpQqFAhhgwZQlZW7iawCwoKYtSoUXTo0AGr1UrJkiVZtmwZv//+O08//TRWq5WKFSuyefNmW8w/h84bPnw4lStXZu7cuQQFBVGgQAHatm3L33//fVNtKSIiIiIiIiIiIiJyp6mjSXJlzpw5eHp6Eh8fz/jx4xk5ciSrVq0yLnfy5MksW7aMRYsWsXfvXubNm0dQUBAAXbt2ZcWKFRw7dsy2/9dff8358+dp06YNx44do127dnTu3Jk9e/YQGxtLy5Yt7TqJVq9ebdu2YMEClixZwogRI645NhcXFzZu3MikSZOYOHEiH330Ua6P4d133yUqKopt27bx1FNP0b59ezp06MDzzz/P1q1bKVOmDB06dMix8yopKYmlS5fy9ddf8/XXX/PDDz8wduzYXOcgIiIiIiIiIiIikhey9O+m/91vXPI6Abk3VKxYkWHDhgEQHBzM1KlTWb16NfXr1zcqNzk5meDgYGrWrInFYqFkyZK2bTVq1CAkJIS5c+cyYMAAAGbNmkWrVq2wWq3s27ePS5cu0bJlS1tceHi4Xflubm7MnDkTDw8PwsLCGDlyJP379+fNN9/EyelyP2tgYCDvvvsuFouFkJAQEhMTeffdd+nWrVuujqFx48a8+OKLAAwdOpTp06dTtWpVWrVqBcBrr71G9erVOXHiBP7+/tmWkZmZyezZs/Hy8gKgffv2rF69mrfeeivb/VNTU0lNTbVbl56VgavFOVc5i4iIiIiIiIiIiIg4gp5oklypWLGi3c8BAQGcPHnSuNxOnTqRkJBASEgIvXv35rvvvrPb3rVrV2bNmgXAiRMn+Pbbb+ncuTMAlSpVom7duoSHh9OqVStmzJjB2bNn7eIrVaqEh4eH7efq1auTkpLCkSNHbOsee+wxLBaL3T779+8nIyMjV8dwddsULVoUsO/wurIup/YKCgqydTLBjdt3zJgxFChQwG5Z9deuXOUrIiIiIiIiIiIiIuIo6miSXHF1dbX72WKxkJmZaVxulSpVOHjwIG+++SYXLlygdevWPPvss7btHTp04Ndff2X9+vV8+umnlCpVilq1agHg7OzMqlWr+PbbbylfvjxTpkwhJCSEgwcPGud1M65umysdVtmty6m9brZ9Bw0axJ9//mm31PcOu6X8RURERERERERERERulTqaJM95e3vTpk0bZsyYwWeffcYXX3zBmTNnAPDz86N58+bMmjWL2bNn88ILL9jFWiwWoqKiGDFiBNu2bcPNzY0vv/zStn379u1cuHDB9vOGDRuwWq0EBgba1sXHx9uVuWHDBoKDg3F2vnuHocuXLx/e3t52i4bNExEREREREREREZE7TXM0ibHjx49z/PhxDhw4AEBiYiJeXl489NBD+Pr65hg7ceJEAgICiIiIwMnJicWLF+Pv74+Pj49tn65du9KkSRMyMjLo2LGjbX18fDyrV6/mySefpEiRIsTHx/P7778TGhpq2yctLY0uXbowePBgDh06xLBhw4iJibHNzwSX54l69dVXefHFF9m6dStTpkxhwoQJDmodEREREREREREREZH7lzqaxNgHH3zAiBEjbD8//vjjAMyaNYtOnTrlGOvl5cX48ePZv38/zs7OVK1aleXLl9t1BNWrV4+AgADCwsIoVqyYbb23tzfr1q3jvffe46+//qJkyZJMmDCBRo0a2fapW7cuwcHBPP7446SmptKuXTuGDx9ul0OHDh24cOECjz76KM7Ozrz88st0797doEVEREREREREREREHgzmE6zIvU4dTXJDsbGx16xbunSp7f/Dhw+/pvMmt7p160a3bt1y3OfcuXOcPXuWLl262K0PDQ1lxYoV142bPXu27f9Xd4T9k6urK++99x7Tp0/PXdJXOXTo0DXrsrKy7H4OCgqyW9epUye7Drjs2q9Pnz706dPnpvMREREREREREREREbmT1NEkd63MzExOnTrFhAkT8PHxoVmzZnmdkoiIiIiIiIiIiIiIXMXpxruI3LrRo0djtVqzXa4e4i47ycnJFC1alPnz5zNz5kxcXO5sv+iPP/543dytVusdzUVERERERERERERE5G6kJ5rkturRowetW7fOdpu7u3uOsf8ccu52yG5YwCsiIyNJSEi4rfWLiIiIiIiIiIiIiNzL1NEkt5Wvry++vr55ncYtcXd3p2zZsnmdhoiIiIiIiIiIiIjIXUsdTSIiIiIiIiIiIiIicktu96hUcvdTR5OI2DgZvie4WMzivTIN63fAe1qGabxhG5w3jAdwwqwQZ8N2THUyP4h0zJJwMWyDfA64lo64OpsXkscOuZl9THA3/KBZEPM2dPIwjDesP8XZ7FosleVmmAH4Vz5vFH9ie85D3d7u+h2RQ4ksV6P40xbTdwdwjyxqFJ/y40mjeGsts/odkUPxDE+j+N/MTiMAhTLNXpOHXM3uCjUeDTOKB0j75gejeLen/pWn9RfMDDCKBzhl+C02X50KRvFp67aaJXAX5BCU6W8UD3Da8MuD0yPVjOKz4tcZxTskh00/GcWXTTP/0Pmbq9l9zc/wLe5Xw8+LAPVrP2cUfyl2vlG8Sx7XD+BueCmYfgPzyMr7KdwfMvy8djcw/fbi5oDvoc7GV4OZvK1dRPJS3r+TiIiIiIiIiIiIiIiIyD1JHU0iIiIiIiIiIiIiIiJyS+6ZjqbatWvTp0+fvE7jpnXq1InmzZvndRoOERQUxHvvvZfXadjExsZisVj4448/8joVEREREREREREREZEH0j3T0XQjH374IbVr18bb21udD7fJpk2b6N69e16nYVOjRg2OHTtGgQIF8joVEREREREREREREZEHkvmsjXeJ8+fP07BhQxo2bMigQYPyOp37UuHChfM6BTtubm74+5tPYisiIiIiIiIiIiIityaTrLxOQfLYPfVEU2ZmJgMGDMDX1xd/f3+GDx9u29anTx8GDhzIY489dtPlpqWlERMTQ0BAAPnz56dkyZKMGTMGgM6dO9OkSRO7/dPT0ylSpAgff/wxAJ9//jnh4eG4u7vj5+dHvXr1OHfunF3MiBEjKFy4MN7e3vTo0YO0tDTbttq1axMTE0NMTAwFChSgUKFCDBkyhKys3L1Ag4KCGD16NJ07d8bLy4uHHnqIDz/80G6fxMRE6tSpY8uxe/fupKSk2LZfGeLvnXfeISAgAD8/P3r16kV6erpdPVcPnWexWPjoo49o0aIFHh4eBAcHs2zZMuDyuSpRogTTp0+3y2Pbtm04OTlx+PBhACZOnEh4eDienp4EBgbSs2dPu7wOHz5M06ZNKViwIJ6enoSFhbF8+XLAfui8v/76C3d3d7799lu7+r788ku8vLw4f/48AEeOHKF169b4+Pjg6+vL008/zaFDh3LVzlfaaPTo0RQtWhQfHx9GjhzJpUuX6N+/P76+vpQoUYJZs2bZxb322muUK1cODw8PSpcuzZAhQ2ztmpWVRb169WjQoIHtfJ85c4YSJUowdOjQXOUlIiIiIiIiIiIiIpJX7qmOpjlz5uDp6Ul8fDzjx49n5MiRrFq1yrjcyZMns2zZMhYtWsTevXuZN28eQUFBAHTt2pUVK1Zw7Ngx2/5ff/0158+fp02bNhw7dox27drRuXNn9uzZQ2xsLC1btrTrJFq9erVt24IFC1iyZAkjRoy45thcXFzYuHEjkyZNYuLEiXz00Ue5PoYJEyYQGRnJtm3b6NmzJ//+97/Zu3cvAOfOnaNBgwYULFiQTZs2sXjxYr7//ntiYmLsyli7di1JSUmsXbuWOXPmMHv2bGbPnp1jvSNGjKB169bs2LGDxo0bEx0dzZkzZ3BycqJdu3bMnz/fbv958+YRFRVFyZIlAXBycmLy5Mns2rWLOXPmsGbNGgYMGGDbv1evXqSmprJu3ToSExMZN24cVqv1mjy8vb1p0qRJtvU1b94cDw8P0tPTadCgAV5eXvz444/ExcVhtVpp2LChXcdfTtasWcPRo0dZt24dEydOZNiwYTRp0oSCBQsSHx9Pjx49ePHFF/ntt99sMV5eXsyePZvdu3czadIkZsyYwbvvvgtc7qybM2cOmzZtYvLkyQD06NGD4sWLq6NJRERERERERERERO5691RHU8WKFRk2bBjBwcF06NCByMhIVq9ebVxucnIywcHB1KxZk5IlS1KzZk3atWsHXJ4HKCQkhLlz59r2nzVrFq1atcJqtXLs2DEuXbpEy5YtCQoKIjw8nJ49e9p1hri5uTFz5kzCwsJ46qmnGDlyJJMnTyYzM9O2T2BgIO+++y4hISFER0fz0ksv2TojcqNx48b07NmTsmXL8tprr1GoUCHWrl0LwPz587l48SKffPIJFSpUoE6dOkydOpW5c+dy4sQJWxkFCxZk6tSpPPzwwzRp0oSnnnrqhu3bqVMn2rVrR9myZRk9ejQpKSls3LgRgOjoaOLi4khOTgYuP+W0cOFCoqOjbfF9+vThiSeeICgoiDp16jBq1CgWLVpkd26ioqIIDw+ndOnSNGnShMcffzzbXKKjo1m6dKnt6aW//vqLb775xlbfZ599RmZmJh999BHh4eGEhoYya9YskpOTiY2NzVU7+/r6MnnyZEJCQujcuTMhISGcP3+e119/neDgYAYNGoSbmxs//fSTLWbw4MHUqFGDoKAgmjZtSr9+/eyOsXjx4vznP/9h4MCBDBo0iOXLl/Ppp5/i4nLfjGwpIiIiIiIiIiIiIvepe66j6WoBAQGcPHnSuNxOnTqRkJBASEgIvXv35rvvvrPb3rVrV9twaCdOnODbb7+lc+fOAFSqVIm6desSHh5Oq1atmDFjBmfPnrWLr1SpEh4eHrafq1evTkpKCkeOHLGte+yxx7BYLHb77N+/n4yMjFwdw9VtY7FY8Pf3t7XNnj17qFSpEp6enrZ9oqKiyMzMtD31BBAWFoazs7Pt59y079X1enp64u3tbYupXLkyoaGhtqeMfvjhB06ePEmrVq1sMd9//z1169alePHieHl50b59e06fPm3rLOrduzejRo0iKiqKYcOGsWPHjuvm0rhxY1xdXW3D933xxRd4e3tTr149ALZv386BAwfw8vLCarVitVrx9fXl4sWLJCUl5XicV7eRk9P/XjZFixYlPDzc9rOzszN+fn527fbZZ58RFRWFv78/VquVwYMH2zrfrmjVqhUtWrRg7NixvPPOOwQHB+eYR2pqKn/99Zfdkp6Vu2tFRERERERERERERMRR7qmOJldXV7ufLRaL3VNBt6pKlSocPHiQN998kwsXLtC6dWueffZZ2/YOHTrw66+/sn79ej799FNKlSpFrVq1gMsdC6tWreLbb7+lfPnyTJkyhZCQEA4ePGic181wRNvcShk3iomOjrZ1NM2fP5+GDRvi5+cHwKFDh2jSpAkVK1bkiy++YMuWLbz//vsAtqHsunbtyq+//kr79u1JTEwkMjKSKVOmZJuLm5sbzz77rF19bdq0sT0ZlJKSwiOPPEJCQoLdsm/fPp577rlbbqOc2mD9+vVER0fTuHFjvv76a7Zt28Ybb7xxzVB958+fZ8uWLTg7O7N///4b5jFmzBgKFChgt6z6a1eujkFERERERERERERExFHuqY6m28nb25s2bdowY8YMPvvsM7744gvOnDkDgJ+fH82bN2fWrFnMnj2bF154wS7WYrEQFRXFiBEj2LZtG25ubnz55Ze27du3b+fChQu2nzds2IDVaiUwMNC2Lj4+3q7MDRs2EBwcbPeE0a0KDQ1l+/btnDt3zrYuLi4OJycnQkJCjMvPyXPPPcfOnTvZsmULn3/+ud2weVu2bCEzM5MJEybw2GOPUa5cOY4ePXpNGYGBgfTo0YMlS5bQt29fZsyYcd36oqOjWbFiBbt27WLNmjV29VWpUoX9+/dTpEgRypYta7cUKFDAsQf+//3888+ULFmSN954g8jISIKDgzl8+PA1+/Xt2xcnJye+/fZbJk+ezJo1a3Isd9CgQfz55592S33vsNtyDCIiIiIiIiIiIiLXk6nlppf7zX3T0XT8+HESEhI4cOAAAImJiSQkJNg6i3IyceJEFixYwC+//MK+fftYvHgx/v7++Pj42Pbp2rUrc+bMYc+ePXTs2NG2Pj4+ntGjR7N582aSk5NZsmQJv//+O6GhobZ90tLS6NKlC7t372b58uUMGzaMmJgYuyHYkpOTefXVV9m7dy8LFixgypQpvPzyyw5omcudL/nz56djx47s3LmTtWvX8tJLL9G+fXuKFi3qkDquJygoiBo1atClSxcyMjJo1qyZbVvZsmVJT09nypQp/Prrr8ydO5cPPvjALr5Pnz6sXLmSgwcPsnXrVtauXWvXtv/0+OOP4+/vT3R0NKVKlaJatWq2bdHR0RQqVIinn36aH3/8kYMHDxIbG0vv3r357bffHH/wQHBwMMnJySxcuJCkpCQmT55s1wkJ8M033zBz5kzmzZtH/fr16d+/Px07drxmCMar5cuXD29vb7vF1WLeKSkiIiIiIiIiIiIicjPum46mDz74gIiICLp16wZc7nCIiIiwzdeTEy8vL8aPH09kZCRVq1bl0KFDLF++3K4jqF69egQEBNCgQQOKFStmW+/t7c26deto3Lgx5cqVY/DgwUyYMIFGjRrZ9qlbty7BwcE8/vjjtGnThmbNmjF8+HC7HDp06MCFCxd49NFH6dWrFy+//DLdu3c3bJXLPDw8WLlyJWfOnKFq1ao8++yz1K1bl6lTpzqk/BuJjo5m+/bttGjRAnd3d9v6SpUqMXHiRMaNG0eFChWYN28eY8aMsYvNyMigV69ehIaG0rBhQ8qVK8e0adOuW5fFYqFdu3Zs377d7mkmuNwO69at46GHHqJly5aEhobSpUsXLl68iLe3t2MP+v9r1qwZr7zyCjExMVSuXJmff/6ZIUOG2Lb//vvvdOnSheHDh1OlShUARowYQdGiRenRo8dtyUlERERERERERERExFEsWVlZWXmdxL0gJSWF4sWLM2vWLFq2bOnQsmvXrk3lypV57733HFquPFimBj5vXIbpzSDTYhZfIMMs3sUBd7MjrjfeJycFDJ99/fMu6P5/KN0sPsPwOgA4bHgyXTBLoojhtQiQ6oB2MNHzyKfGZUwzvK+cdDI7j/kNzyNA9ypHjOK/2BR4451yYPqSPu2Ah1W7PWr21O7JRPcb75SDIuEXbrzTbc7h8wu+RvFnLeYDG4xob3ZjSfnxpFG8tVYRo3hH5LDsQAmz+h3wHml6Jn0MC3huYjnDDCDtmx+M4t2e+lee1v/R2gCjeDA/D+0G+RjFp2/+xSwBwDXy4TzNYfZqf6N4gNOG7/ODPowyis/avtkoHsBSKTJPc/hmYqpRPMBvrmafl7wNX08XHPCZ98VtI43iL8XON4p3qZ27OZtvV/0A8/uYvabzG57H/a7mX6Y9HPDZPa95GLbjecPPKoUvmcUDnHAxizf9uOXsgN/LmOZg+nW+X7L5d+kHUdOHmuR1Cvec/yZ/ndcpOJTh7ef+l5mZyalTp5gwYQI+Pj52Q7+JiIiIiIiIiIiIiIg8yO6Cv52//UaPHo3Vas12uXqIu+wkJydTtGhR5s+fz8yZM3FxubN9cz/++ON1c7darXc0l/tdTu38448/5nV6IiIiIiIiIiIiIiJ3nQfiiaYePXrQunXrbLddPWdQdoKCgrjdowvGxsZed1tkZCQJCQm3tX65LKd2Ll68+J1LREREREREREREROQekWU8IYfc6x6IjiZfX198fc3G5s8r7u7ulC1bNq/TeCConUVEREREREREREREbs4DMXSeiIiIiIiIiIiIiIiION4D8USTiOROpsUs/pJh/X8bdn1bMw0TAJxN4w2fFPZwwJPGKRazQjIsZhdCvkzzg3DBLAfT85hq+FoACEzPMIrPNGwDRwhKM3tVH3Y3O4YzmL+oM88bxhvW751h9nrY7JJumAGc2O5hFF+0klkjmtYP4B9xwSj+t3izdiyR5WoUD3Bh81GjeM+qZk/nX9h8wijeETn830Gz10NYmlE4AFvzmeVQOdXs3p6xcYtRPIBr9Qp5moNp/Wd/+N0oHqCs4bWQumanUXz+xpFmCQAXl282y6HJo0bxh9YmG8UDFMky+3VC5pZ4o3in6v8yigfIXP+DWQ5RTxjFH3D73igeoGya2X3toJvZ57UKF02/BcKl2PlG8S61WuVt/bWfM4oHuGAZahSfz7D+8xbzz90eWWbfwn6zmH1eszjg+1Ow4a9JzT4lQJoDvgJmGA5f5mTYjho8TeTBpSeaRERERERERERERERE5Jaoo0lERERERERERERERERuiYbOExERERERERERERGRW5KpgRMfePfsE021a9emT58+eZ3GTevUqRPNmzfP6zQcIigoiPfeey+v07CJjY3FYrHwxx9/5HUq13Xo0CEsFgsJCQl5nYqIiIiIiIiIiIiIiLH79ommDz/8kPnz57N161b+/vtvzp49i4+PT16ndV/ZtGkTnp6eeZ2GTY0aNTh27BgFChTI61SuKzAwkGPHjlGoUKG8TkVERERERERERERExNg9+0TTjZw/f56GDRvy+uuv53Uq963ChQvj4eGR12nYuLm54e/vj8ViyetUrsvZ2Rl/f39cXO7bPl4REREREREREREReYDc0x1NmZmZDBgwAF9fX/z9/Rk+fLhtW58+fRg4cCCPPfbYTZeblpZGTEwMAQEB5M+fn5IlSzJmzBgAOnfuTJMmTez2T09Pp0iRInz88ccAfP7554SHh+Pu7o6fnx/16tXj3LlzdjEjRoygcOHCeHt706NHD9LS0mzbateuTUxMDDExMRQoUIBChQoxZMgQsrJyN9ZlUFAQo0ePpnPnznh5efHQQw/x4Ycf2u2TmJhInTp1bDl2796dlJQU2/YrQ/y98847BAQE4OfnR69evUhPT7er5+qh8ywWCx999BEtWrTAw8OD4OBgli1bBlw+VyVKlGD69Ol2eWzbtg0nJycOHz4MwMSJEwkPD8fT05PAwEB69uxpl9fhw4dp2rQpBQsWxNPTk7CwMJYvXw7YD533119/4e7uzrfffmtX35dffomXlxfnz58H4MiRI7Ru3RofHx98fX15+umnOXToUK7a+UobjR49mqJFi+Lj48PIkSO5dOkS/fv3x9fXlxIlSjBr1ixbzD+HzruS8+rVq4mMjMTDw4MaNWqwd+/eXOUgIiIiIiIiIiIiIpKX7umOpjlz5uDp6Ul8fDzjx49n5MiRrFq1yrjcyZMns2zZMhYtWsTevXuZN28eQUFBAHTt2pUVK1Zw7Ngx2/5ff/0158+fp02bNhw7dox27drRuXNn9uzZQ2xsLC1btrTrJFq9erVt24IFC1iyZAkjRoy45thcXFzYuHEjkyZNYuLEiXz00Ue5PoYJEyYQGRnJtm3b6NmzJ//+979tnRfnzp2jQYMGFCxYkE2bNrF48WK+//57YmJi7MpYu3YtSUlJrF27ljlz5jB79mxmz56dY70jRoygdevW7Nixg8aNGxMdHc2ZM2dwcnKiXbt2zJ8/327/efPmERUVRcmSJQFwcnJi8uTJ7Nq1izlz5rBmzRoGDBhg279Xr16kpqaybt06EhMTGTduHFar9Zo8vL29adKkSbb1NW/eHA8PD9LT02nQoAFeXl78+OOPxMXFYbVaadiwoV3HX07WrFnD0aNHWbduHRMnTmTYsGE0adKEggULEh8fT48ePXjxxRf57bffciznjTfeYMKECWzevBkXFxc6d+6cq/pFRERERERERERERPLSPd3RVLFiRYYNG0ZwcDAdOnQgMjKS1atXG5ebnJxMcHAwNWvWpGTJktSsWZN27doBl+cBCgkJYe7cubb9Z82aRatWrbBarRw7doxLly7RsmVLgoKCCA8Pp2fPnnadIW5ubsycOZOwsDCeeuopRo4cyeTJk8nMzLTtExgYyLvvvktISAjR0dG89NJLvPvuu7k+hsaNG9OzZ0/Kli3La6+9RqFChVi7di0A8+fP5+LFi3zyySdUqFCBOnXqMHXqVObOncuJEydsZRQsWJCpU6fy8MMP06RJE5566qkbtm+nTp1o164dZcuWZfTo0aSkpLBx40YAoqOjiYuLIzk5Gbj8lNPChQuJjo62xffp04cnnniCoKAg6tSpw6hRo1i0aJHduYmKiiI8PJzSpUvTpEkTHn/88WxziY6OZunSpbanl/766y+++eYbW32fffYZmZmZfPTRR4SHhxMaGsqsWbNITk4mNjY2V+3s6+vL5MmTCQkJoXPnzoSEhHD+/Hlef/11goODGTRoEG5ubvz00085lvPWW2/xr3/9i/LlyzNw4EB+/vlnLl68eN39U1NT+euvv+yW9KyMXOUsIiIiIiIiIiIi4ihZWVlabnK539zzHU1XCwgI4OTJk8bldurUiYSEBEJCQujduzffffed3fauXbvahkM7ceIE3377re0JlEqVKlG3bl3Cw8Np1aoVM2bM4OzZs3bxlSpVspvbqHr16qSkpHDkyBHbuscee8xurqHq1auzf/9+MjJy15lwddtYLBb8/f1tbbNnzx4qVaqEp6enbZ+oqCgyMzPthmwLCwvD2dnZ9nNu2vfqej09PfH29rbFVK5cmdDQUNtTRj/88AMnT56kVatWtpjvv/+eunXrUrx4cby8vGjfvj2nT5+2dRb17t2bUaNGERUVxbBhw9ixY8d1c2ncuDGurq624fu++OILvL29qVevHgDbt2/nwIEDeHl5YbVasVqt+Pr6cvHiRZKSknI8zqvbyMnpfy+jokWLEh4ebvvZ2dkZPz+/m2q3gIAAgBxjxowZQ4ECBeyWVX/tylXOIiIiIiIiIiIiIiKOck93NLm6utr9bLFY7J4KulVVqlTh4MGDvPnmm1y4cIHWrVvz7LPP2rZ36NCBX3/9lfXr1/Ppp59SqlQpatWqBVzuWFi1ahXffvst5cuXZ8qUKYSEhHDw4EHjvG6GI9rmVsq4UUx0dLSto2n+/Pk0bNgQPz8/4PL8RU2aNKFixYp88cUXbNmyhffffx/ANpRd165d+fXXX2nfvj2JiYlERkYyZcqUbHNxc3Pj2WeftauvTZs2uLi4AJCSksIjjzxCQkKC3bJv3z6ee+65W24j03a70sGYU8ygQYP4888/7Zb63mG5yllERERERERERERExFHu6Y6m28nb25s2bdowY8YMPvvsM7744gvOnDkDgJ+fH82bN2fWrFnMnj2bF154wS7WYrEQFRXFiBEj2LZtG25ubnz55Ze27du3b+fChQu2nzds2IDVaiUwMNC2Lj4+3q7MDRs2EBwcbPeE0a0KDQ1l+/btnDt3zrYuLi4OJycnQkJCjMvPyXPPPcfOnTvZsmULn3/+ud2weVu2bCEzM5MJEybw2GOPUa5cOY4ePXpNGYGBgfTo0YMlS5bQt29fZsyYcd36oqOjWbFiBbt27WLNmjV29VWpUoX9+/dTpEgRypYta7cUKFDAsQfuYPny5cPb29tucbWYXxsiIiIiIiIiIiIiIjfjvu1oOn78OAkJCRw4cACAxMREEhISbJ1FOZk4cSILFizgl19+Yd++fSxevBh/f398fHxs+3Tt2pU5c+awZ88eOnbsaFsfHx/P6NGj2bx5M8nJySxZsoTff/+d0NBQ2z5paWl06dKF3bt3s3z5coYNG0ZMTIzdEGzJycm8+uqr7N27lwULFjBlyhRefvllB7TM5c6X/Pnz07FjR3bu3MnatWt56aWXaN++PUWLFnVIHdcTFBREjRo16NKlCxkZGTRr1sy2rWzZsqSnpzNlyhR+/fVX5s6dywcffGAX36dPH1auXMnBgwfZunUra9eutWvbf3r88cfx9/cnOjqaUqVKUa1aNdu26OhoChUqxNNPP82PP/7IwYMHiY2NpXfv3vz222+OP3gRERERERERERERkfvMfdvR9MEHHxAREUG3bt2Ayx0OERERtvl6cuLl5cX48eOJjIykatWqHDp0iOXLl9t1BNWrV4+AgAAaNGhAsWLFbOu9vb1Zt24djRs3ply5cgwePJgJEybQqFEj2z5169YlODiYxx9/nDZt2tCsWTOGDx9ul0OHDh24cOECjz76KL169eLll1+me/fuhq1ymYeHBytXruTMmTNUrVqVZ599lrp16zJ16lSHlH8j0dHRbN++nRYtWuDu7m5bX6lSJSZOnMi4ceOoUKEC8+bNY8yYMXaxGRkZ9OrVi9DQUBo2bEi5cuWYNm3adeuyWCy0a9eO7du32z3NBJfbYd26dTz00EO0bNmS0NBQunTpwsWLF/H29nbsQYuIiIiIiIiIiIiI3IcsWVlZWXmdxL0oJSWF4sWLM2vWLFq2bOnQsmvXrk3lypV57733HFqu3N+mBj5vXEaGxSz+kmH9boZ3I6v5FG387mIWXyDDLP6iA7r/UyxmDRl4yexCyJdp/rbyq5tZDqYDSTriWgpMN7sYMjFrg6ePzzeKB1hetK1R/A/uZsdwAfMTMTTkuFH8F7sCb7xTDnwyzF4PsfnSjeIBXnZLMYovWum8UfyJ7R5G8QD+ERduvFMOBsUXNoovkeV6451u4MXyZk9L56/gaxR/ceeNn+q/3Tm8s8jsWghLMwoHYGs+s9dkvQtm9/Zq7S8axQM4lQ0yis88cChP6x899nejeICqhs34eNS1w3PfjPyNI80SAC4u32yWQ5NHjeJfH5FsFA9QJMvsg3PvHmaf2Jyq/8soHiBz/Q9mOUQ9YRT/XvvvjeIByqaZ3dcOGn7urnDR9FsgPPF+BaN4l1qtjOIv/bjYrP7auZvjOSf/iRhqFO9j+D10h5thAUChLLPX9G8Ws8+9FsPvTwDBGWb3tb8Nv8/7mp8GTjmb3RNcDdvRyQG/ZTb9Pm/ajP2SPzUs4cHUILDRjXcSOyuPfJvXKTiU4a9UHzyZmZmcOnWKCRMm4OPjYzf0m4iIiIiIiIiIiIiIyIPkvh06LyejR4/GarVmu1w9xF12kpOTKVq0KPPnz2fmzJm4uNzZvroff/zxurlbrdY7msv9Lqd2/vHHH/M6PRERERERERERERGRPPdAPtHUo0cPWrdune22q+cMyk5QUBC3e7TB2NjY626LjIwkISHhttYvl+XUzsWLF79ziYiIiIiIiIiIiIiI3KUeyI4mX19ffH3NxpjPK+7u7pQtWzav03ggqJ1FRERERERERERERHJmybrdj+eIyB0xNfB54zIyDOfONJ300XTqzmLpmYYlwEE3sxFFTccjNZyjHIB0w4Yslm6WxAHDCYUBChqeynOGKfiYX0ocMZyE9ZLFLH7UoflG8QDDS0YbxVdONTuGdIv5tXTaxawMw9PIERezAiqmmtUPcNywDc4a3twLZpqfR9McKhpei474sPy7i9k7xCnDWZELOWRyabP4IoY5pJpfSsbv0ymGOQRcMkwAOG74p4L+hjmY1u+IzzpHnM0uJv9Ms4vZEdeiaTuY5hB+0fym4Gx4dzzgZnYxXXTAechveB5Mc3DEPcHJ8DyccjY7iDQHnAfTz94XDHNwN7wOTOsHeHHbSKP4M61eMIpP+qWQUTzAOrecRwi6EUfcl0wdyGf4YcdQh6q/GZcxe1MJo3jDry844KO/8Xcw09+JvJL8qVkBD6gGgTlPRyPXWnnk27xOwaEeyCeaRERERERERERERETEXJZD/jxP7mWmf9QnIiIiIiIiIiIiIiIiDyh1NImIiIiIiIiIiIiIiMgtUUfTA6h27dr06dPnjtR16NAhLBYLCQkJd6Q+ERERERERERERERG5c9TR9IBbsmQJTz75JH5+fuoQEhERERERERERERGRm6KOpgfcuXPnqFmzJuPGjcvrVERERERERERERERE5B6jjqb73Llz5+jQoQNWq5WAgAAmTJhgt719+/YMHTqUevXq3VL5v/zyCzVr1iR//vyUL1+e77//HovFwtKlS7Pdf/bs2fj4+NitW7p0KRaLxW7df//7X6pWrUr+/PkpVKgQLVq0sG07e/YsHTp0oGDBgnh4eNCoUSP2799v23748GGaNm1KwYIF8fT0JCwsjOXLl9u279y5k0aNGmG1WilatCjt27fn1KlTuTre2rVr89JLL9GnTx8KFixI0aJFmTFjBufOneOFF17Ay8uLsmXL8u2339piMjIy6NKlC6VKlcLd3Z2QkBAmTZpk237x4kXCwsLo3r27bV1SUhJeXl7MnDkzV3mJiIiIiIiIiIiI5IVMsrTc5HK/UUfTfa5///788MMPfPXVV3z33XfExsaydetWh5SdkZFB8+bN8fDwID4+ng8//JA33njDuNxvvvmGFi1a0LhxY7Zt28bq1at59NFHbds7derE5s2bWbZsGevXrycrK4vGjRuTnp4OQK9evUhNTWXdunUkJiYybtw4rFYrAH/88Qd16tQhIiKCzZs3s2LFCk6cOEHr1q1znd+cOXMoVKgQGzdu5KWXXuLf//43rVq1okaNGmzdupUnn3yS9u3bc/78eQAyMzMpUaIEixcvZvfu3QwdOpTXX3+dRYsWAZA/f37mzZvHnDlz+Oqrr8jIyOD555+nfv36dO7c2bg9RURERERERERERERuF5e8TkBun5SUFD7++GM+/fRT6tatC1zuJClRooRDyl+1ahVJSUnExsbi7+8PwFtvvUX9+vWNyn3rrbdo27YtI0aMsK2rVKkSAPv372fZsmXExcVRo0YNAObNm0dgYCBLly6lVatWJCcn88wzzxAeHg5A6dKlbeVMnTqViIgIRo8ebVs3c+ZMAgMD2bdvH+XKlbthfpUqVWLw4MEADBo0iLFjx1KoUCG6desGwNChQ5k+fTo7duzgsccew9XV1e5YSpUqxfr161m0aJGtg6ty5cqMGjWKrl270rZtWw4fPszXX3993RxSU1NJTU21W5eelYGrxfmG+YuIiIiIiIiIiIiIOIqeaLqPJSUlkZaWRrVq1WzrfH19CQkJcUj5e/fuJTAw0NbJBNg9eXSrEhISbB1j/7Rnzx5cXFzsjsnPz4+QkBD27NkDQO/evRk1ahRRUVEMGzaMHTt22Pbdvn07a9euxWq12paHH34YuNxeuVGxYkXb/52dnfHz87N1agEULVoUgJMnT9rWvf/++zzyyCMULlwYq9XKhx9+SHJysl25ffv2pVy5ckydOpWZM2fi5+d33RzGjBlDgQIF7JZVf+3KVf4iIiIiIiIiIiIiIo6ijia5o5ycnMjKsh+D8sqQd1e4u7sb1dG1a1d+/fVX2rdvT2JiIpGRkUyZMgW4/JRX06ZNSUhIsFv279/P448/nqvyXV1d7X62WCx2667MN5WZmQnAwoUL6devH126dOG7774jISGBF154gbS0NLtyTp48yb59+3B2drabcyo7gwYN4s8//7Rb6nuH5Sp/ERERERERERERERFHUUfTfaxMmTK4uroSHx9vW3f27Fn27dvnkPJDQkI4cuQIJ06csK3btGlTjjGFCxfm77//5ty5c7Z1CQkJdvtUrFiR1atXZxsfGhrKpUuX7I7p9OnT7N27l/Lly9vWBQYG0qNHD5YsWULfvn2ZMWMGAFWqVGHXrl0EBQVRtmxZu8XT0zPXx34zrgzz17NnTyIiIihbtmy2T0917tyZ8PBw5syZw2uvvWZ7Qis7+fLlw9vb227RsHkiIiIiIiIiIiIicqepo+k+ZrVa6dKlC/3792fNmjXs3LmTTp064eT0v9N+5swZEhIS2L17N3B5OLyEhASOHz9+w/Lr169PmTJl6NixIzt27CAuLs42d9GVp3r+qVq1anh4ePD666+TlJTE/PnzmT17tt0+w4YNY8GCBQwbNow9e/aQmJjIuHHjAAgODubpp5+mW7du/PTTT2zfvp3nn3+e4sWL8/TTTwPQp08fVq5cycGDB9m6dStr164lNDQUgF69enHmzBnatWvHpk2bSEpKYuXKlbzwwgtkZGTcXAPnUnBwMJs3b2blypXs27ePIUOGXNMh9/7777N+/XrmzJlDdHQ0zZs3Jzo6+pqnnkRERERERERERETuJllZWVpucrnfqKPpPvf2229Tq1YtmjZtSr169ahZsyaPPPKIbfuyZcuIiIjgqaeeAqBt27ZERETwwQcf3LBsZ2dnli5dSkpKClWrVqVr16688cYbAOTPnz/bGF9fXz799FOWL19OeHg4CxYsYPjw4Xb71K5dm8WLF7Ns2TIqV65MnTp12Lhxo237rFmzeOSRR2jSpAnVq1cnKyuL5cuX24avy8jIoFevXoSGhtKwYUPKlSvHtGnTAChWrBhxcXFkZGTw5JNPEh4eTp8+ffDx8bHrgHOkF198kZYtW9KmTRuqVavG6dOn6dmzp237L7/8Qv/+/Zk2bRqBgYEATJs2jVOnTjFkyJDbkpOIiIiIiIiIiIiIiCNYsu7H7jPJM3FxcdSsWZMDBw5QpkyZvE7ngTI18HnjMjKyfxAt15wM7yaG1VMsPdOwBDjoZtbhaNpdmc8Bd+R0w4Yslm6WxAE30zMJBQ1P5TnDFHzMLyWOOJu14yWLWfyoQ/ON4gGGl4w2iq+canYM6dd5OvZmnHYxK8PwNHLExayAiqlm9QMcN2yDs4Y394KZ5ufRNIeKhteiIz4s/+5i9g5xynCE3EIOeHDbNIcihjmkml9Kxu/TKYY5BFwyTAA47mIW72+Yg2n9jvisc8TZ7GLyzzS7mB1xLZq2g2kO4RfNbwrOhnfHA25mF9NFB5yH/IbnwTQHR9wTnAzPwylns4NIc8B5MP3sfcEwB3fD68C0foAXt400ij/T6gWj+KRfChnFA6xzM5tv2xH3JVMH8uXtlAQdqv5mXMbsTSWM4g2/vuCAj/7G38FMfyfySvKnZgU8oOqWeDKvU7jnrP7tu7xOwaEMvybIg+7LL7/EarUSHBzMgQMHePnll4mKilInk4iIiIiIiIiIiIjIA0BD58l1zZs3D6vVmu0SFhYGwN9//02vXr14+OGH6dSpE1WrVuWrr77K48xvTXJy8nWP12q1kpycnNcpioiIiIiIiIiIiIjcVfREk1xXs2bNqFatWrbbrsyH1KFDBzp06HAn07ptihUrRkJCQo7bRURERERERERERETkf9TRJNfl5eWFl5dXXqdxx7i4uFC2bNm8TkNERERERERERETknpHpkJlt5V6mjiYRsTGdcNHVMN5qOAmsNcuwAMCambcjippOWgngZHgeCmSaTcLqmmX+1mI6wbXphML/Z/piAEpeMjuISxYHXAyGShseQ4qTWbzphPMARQznFDa9FgMN2/CYA9rA1TDeP8PsGAzDHZLDn4YTnTuC6btLccMJ4x3x/mKaw3nDRvAwf5vngKtZIUUz8n7kcX/D85DX9Z9ywH3t4XSzydoNb81YzcIBML2cTXP4wzlvJ7wHcDf8uOXpgHuCKU/DeEe8R2ZgVsgfhl8e8hnWD5Df8FzmM6zf9AhM6wc40+oFo3jfxbOM4hdHDDWKByiXanYiD7uZ3ZfuglsCpm/Rm37yN84h0/CCzPtPzeZM319E5Nbk/TclERERERERERERERERuSepo0lERERERERERERERERuiTqaRERERERERERERERE5Jaoo0moXbs2ffr0uSN1HTp0CIvFQkJCwh2p724UGxuLxWLhjz/+yOtURERERERERERERESMqKNJ7CxZsoQnn3wSPz+/B75D6HapUaMGx44do0CBAnmdioiIiIiIiIiIiIiRLP276X/3G3U0iZ1z585Rs2ZNxo0bl9ep3Lfc3Nzw9/fHYrHkdSoiIiIiIiIiIiIiIkbU0fSAOXfuHB06dMBqtRIQEMCECRPstrdv356hQ4dSr169Wyr/l19+oWbNmuTPn5/y5cvz/fffY7FYWLp0abb7z549Gx8fH7t1S5cuvaYT5r///S9Vq1Ylf/78FCpUiBYtWti2nT17lg4dOlCwYEE8PDxo1KgR+/fvt20/fPgwTZs2pWDBgnh6ehIWFsby5ctt23fu3EmjRo2wWq0ULVqU9u3bc+rUqVwdb+3atXnppZfo06cPBQsWpGjRosyYMYNz587xwgsv4OXlRdmyZfn2229tMf8cOu9KG6xcuZLQ0FCsVisNGzbk2LFjucpBRERERERERERERCSvqKPpAdO/f39++OEHvvrqK7777jtiY2PZunWrQ8rOyMigefPmeHh4EB8fz4cffsgbb7xhXO4333xDixYtaNy4Mdu2bWP16tU8+uijtu2dOnVi8+bNLFu2jPXr15OVlUXjxo1JT08HoFevXqSmprJu3ToSExMZN24cVqsVgD/++IM6deoQERHB5s2bWbFiBSdOnKB169a5zm/OnDkUKlSIjRs38tJLL/Hvf/+bVq1aUaNGDbZu3cqTTz5J+/btOX/+/HXLOH/+PO+88w5z585l3bp1JCcn069fv1tsMRERERERERERERGRO8MlrxOQOyclJYWPP/6YTz/9lLp16wKXO0lKlCjhkPJXrVpFUlISsbGx+Pv7A/DWW29Rv359o3Lfeust2rZty4gRI2zrKlWqBMD+/ftZtmwZcXFx1KhRA4B58+YRGBjI0qVLadWqFcnJyTzzzDOEh4cDULp0aVs5U6dOJSIigtGjR9vWzZw5k8DAQPbt20e5cuVumF+lSpUYPHgwAIMGDWLs2LEUKlSIbt26ATB06FCmT5/Ojh07eOyxx7ItIz09nQ8++IAyZcoAEBMTw8iRI69bZ2pqKqmpqfZlZGXganG+Yb4iIiIiIiIiIiIiIo6iJ5oeIElJSaSlpVGtWjXbOl9fX0JCQhxS/t69ewkMDLR1MgF2Tx7dqoSEBFvH2D/t2bMHFxcXu2Py8/MjJCSEPXv2ANC7d29GjRpFVFQUw4YNY8eOHbZ9t2/fztq1a7Farbbl4YcfBi63V25UrFjR9n9nZ2f8/PxsnVoARYsWBeDkyZPXLcPDw8PWyQQQEBCQ4/5jxoyhQIECdsuqv3blKl8REREREREREREREUdRR5PkKScnJ7KysuzWXRny7gp3d3ejOrp27cqvv/5K+/btSUxMJDIykilTpgCXn/Jq2rQpCQkJdsv+/ft5/PHHc1W+q6ur3c8Wi8Vu3ZX5pjIzM2+qjH+2y9UGDRrEn3/+abfU9w7LVb4iIiIiIiIiIiIijpKZlaXlJpf7jTqaHiBlypTB1dWV+Ph427qzZ8+yb98+h5QfEhLCkSNHOHHihG3dpk2bcowpXLgwf//9N+fOnbOtS0hIsNunYsWKrF69Otv40NBQLl26ZHdMp0+fZu/evZQvX962LjAwkB49erBkyRL69u3LjBkzAKhSpQq7du0iKCiIsmXL2i2enp65PvY7LV++fHh7e9stGjZPRERERERERERERO40dTQ9QKxWK126dKF///6sWbOGnTt30qlTJ5yc/ncZnDlzhoSEBHbv3g1cHg4vISGB48eP37D8+vXrU6ZMGTp27MiOHTuIi4uzzV105amef6pWrRoeHh68/vrrJCUlMX/+fGbPnm23z7Bhw1iwYAHDhg1jz549JCYmMm7cOACCg4N5+umn6datGz/99BPbt2/n+eefp3jx4jz99NMA9OnTh5UrV3Lw4EG2bt3K2rVrCQ0NBaBXr16cOXOGdu3asWnTJpKSkli5ciUvvPACGRkZN9fAIiIiIiIiIiIiIiIPGHU0PWDefvttatWqRdOmTalXrx41a9bkkUcesW1ftmwZERERPPXUUwC0bduWiIgIPvjggxuW7ezszNKlS0lJSaFq1ap07dqVN954A4D8+fNnG+Pr68unn37K8uXLCQ8PZ8GCBQwfPtxun9q1a7N48WKWLVtG5cqVqVOnDhs3brRtnzVrFo888ghNmjShevXqZGVlsXz5cttwdBkZGfTq1YvQ0FAaNmxIuXLlmDZtGgDFihUjLi6OjIwMnnzyScLDw+nTpw8+Pj52HXAiIiIiIiIiIiIiInItS1ZOE8GIGIqLi6NmzZocOHCAMmXK5HU697Wpgc/ndQq4Gt5NrNefxipXCmdcMisAOOjqYlyGifTsH/67KaY39ZA0s3bckc+8DV1vvEuOTK+l/3M2f2sMvGR2Mg3D6fbbp2YFAJ8UN7uvOBk243EHvByLGD6cmuqA16SJiw6o3/T1ZPpyyHDAMZjmkO8u+LRren83fY91xPuLaQ7nDf+GJ7/hvR3ggKtZIUUzzA7CVw/Mc8oB93Zvw3Y0fY91BNPL2fRP4jwc8HoyddHwIEw/Z9wN7obBzw+7mDVkPsxfUMXTb7xPTkwvBdMjcMSlWD/kiFG87+JZRvH/iRhqFA9QLN3sxnLc1eym4IjbmunnVtPfSISnmv9OI9Hw+7jp5+a74bO/6UeNHkfMv0s/iB4vXjevU7jnrPu/7KeKuVfl7W9U5b7z5ZdfYrVaCQ4O5sCBA7z88stERUWpk0lERERERERERERE5D6kscEk1+bNm4fVas12CQsLA+Dvv/+mV69ePPzww3Tq1ImqVavy1Vdf5XHmtyY5Ofm6x2u1WklOTs7rFEVERERERERERETyVJaWm17uN3qiSXKtWbNmVKtWLdttV+ZD6tChAx06dLiTad02xYoVIyEhIcftIiIiIiIiIiIiIiIPMnU0Sa55eXnh5eWV12ncMS4uLpQtWzav0xARERERERERERERuWtp6DwRERERERERERERERG5JXqiSeQ+4YixPS2G8emGBZgew19OzoYlgE+GWRbpFrNGOGt+CLgaxl+wmP0NQpEMwwQAZ8OLwS3LrIBCl8xfUdvzGReR5w65GLZjptnrITgt0ygeYE8+s+vZ3fBSOOpk9oKoe8H8WtyRz+zj3iHndKP4oEzTu5J5Do+km+WQ4oA/zbpgWEayxawNSmSZn4ffDHOodMksh/8zvCcBlEk3OxG/uJq9ptMs5m/0h53MzsNDhq/JI06XDOs3/wqa6GqWg3eW2Xn4y2L+YceKWQ5/Y5ZDcIb5eTB9lz7ubFbCeeMMwMPwb2/PGebgiPNg+h3Mx/Dz2h9O5vfm/YZvUectZufBI8vsOjCtH6D0L4WM4hdHDDWKf3HbSKN4gPTPJhrFr37ztFF8pvFvNOCIq9m14GH4cjhv+F0cwGp4OaYZNqPpd3lHMPuUICK3Sk80iYiIiIiIiIiIiIiIyC3RE00iIiIiIiIiIiIiInJLMh0y1pLcy/REk4iIiIiIiIiIiIiIiNwSdTTdI2rXrk2fPn3yOo08Exsbi8Vi4Y8//sjrVERERERERERERERE5P9TR9M9aMmSJTz55JP4+flhsVhISEi44zl06tSJ5s2b37H6atSowbFjxyhQoMAdq1NERERERERERERERHKmjqZ70Llz56hZsybjxo3L61TuGDc3N/z9/bFYLHmdioiIiIiIiIiIiIiI/H/qaLoLnTt3jg4dOmC1WgkICGDChAl229u3b8/QoUOpV6/eLZVvsViYPn06jRo1wt3dndKlS/P555/b7ZOYmEidOnVwd3fHz8+P7t27k5KSAsDw4cOZM2cOX331FRaLBYvFQmxsLACvvfYa5cqVw8PDg9KlSzNkyBDS09Nt5W7fvp0nnngCLy8vvL29eeSRR9i8eTMAhw8fpmnTphQsWBBPT0/CwsJYvnw5cO3QecOHD6dy5cp2Ob/33nsEBQXZfr7y1NXo0aMpWrQoPj4+jBw5kkuXLtG/f398fX0pUaIEs2bNylW7HTp0CIvFwqJFi6hVqxbu7u5UrVqVffv2sWnTJiIjI7FarTRq1Ijff//dFrdp0ybq169PoUKFKFCgAP/617/YunWrbXtsbCxubm78+OOPtnXjx4+nSJEinDhxIle5iYiIiIiIiIiIiIjkBXU03YX69+/PDz/8wFdffcV3331HbGysXceEIwwZMoRnnnmG7du3Ex0dTdu2bdmzZw9wuaOrQYMGFCxYkE2bNrF48WK+//57YmJiAOjXrx+tW7emYcOGHDt2jGPHjlGjRg0AvLy8mD17Nrt372bSpEnMmDGDd99911ZvdHQ0JUqUYNOmTWzZsoWBAwfi6uoKQK9evUhNTWXdunUkJiYybtw4rFar0XGuWbOGo0ePsm7dOiZOnMiwYcNo0qQJBQsWJD4+nh49evDiiy/y22+/5brMYcOGMXjwYLZu3YqLiwvPPfccAwYMYNKkSfz4448cOHCAoUOH2vb/+++/6dixIz/99BMbNmwgODiYxo0b8/fffwP/m3+rffv2/Pnnn2zbto0hQ4bw0UcfUbRoUaPjFxEREREREREREbmdMsnScpPL/cYlrxMQeykpKXz88cd8+umn1K1bF4A5c+ZQokQJh9bTqlUrunbtCsCbb77JqlWrmDJlCtOmTWP+/PlcvHiRTz75BE9PTwCmTp1K06ZNGTduHEWLFsXd3Z3U1FT8/f3tyh08eLDt/0FBQfTr14+FCxcyYMAAAJKTk+nfvz8PP/wwAMHBwbb9k5OTeeaZZwgPDwegdOnSxsfp6+vL5MmTcXJyIiQkhPHjx3P+/Hlef/11AAYNGsTYsWP56aefaNu2ba7K7NevHw0aNADg5Zdfpl27dqxevZqoqCgAunTpwuzZs23716lTxy7+ww8/xMfHhx9++IEmTZoAMGrUKFatWkX37t3ZuXMnHTt2pFmzZtfNITU1ldTUVLt16VkZuFqcc3UMIiIiIiIiIiIiIiKOoCea7jJJSUmkpaVRrVo12zpfX19CQkIcWk/16tWv+fnKE0179uyhUqVKtk4mgKioKDIzM9m7d2+O5X722WdERUXh7++P1Wpl8ODBJCcn27a/+uqrdO3alXr16jF27FiSkpJs23r37s2oUaOIiopi2LBh7Nixw/g4w8LCcHL632VetGhRW0cWgLOzM35+fpw8eTLXZVasWNGuPMCuzKJFi9qVd+LECbp160ZwcDAFChTA29ublJQUu3Zxc3Nj3rx5fPHFF1y8eNHuKbDsjBkzhgIFCtgtq/7aletjEBERERERERERERFxBHU0icOsX7+e6OhoGjduzNdff822bdt44403SEtLs+0zfPhwdu3axVNPPcWaNWsoX748X375JQBdu3bl119/pX379iQmJhIZGcmUKVOyrcvJyYmsLPtHDK+eC+qKK8PyXWGxWLJdl5mZmevjvDreYrFku+7q8jp27EhCQgKTJk3i559/JiEhAT8/P7t2Afj5558BOHPmDGfOnMkxh0GDBvHnn3/aLfW9w3J9DCIiIiIiIiIiIiIijqCOprtMmTJlcHV1JT4+3rbu7Nmz7Nu3z6H1bNiw4ZqfQ0NDAQgNDWX79u2cO3fOtj0uLs42/BxcfgInIyPDroyff/6ZkiVL8sYbbxAZGUlwcDCHDx++pu5y5crxyiuv8N1339GyZUtmzZpl2xYYGEiPHj1YsmQJffv2ZcaMGdnmX7hwYY4fP27X2ZSQkHBzjXCHxMXF0bt3bxo3bkxYWBj58uXj1KlTdvskJSXxyiuvMGPGDKpVq0bHjh1z7PzKly8f3t7edouGzRMRERERERERERGRO00dTXcZq9VKly5d6N+/P2vWrGHnzp106tTJbvi3M2fOkJCQwO7duwHYu3cvCQkJHD9+PNf1LF68mJkzZ7Jv3z6GDRvGxo0biYmJASA6Opr8+fPTsWNHdu7cydq1a3nppZdo3769bai4oKAgduzYwd69ezl16hTp6ekEBweTnJzMwoULSUpKYvLkybanlQAuXLhATEwMsbGxHD58mLi4ODZt2mTr4OrTpw8rV67k4MGDbN26lbVr19q2/VPt2rX5/fffGT9+PElJSbz//vt8++23N9fYd0hwcDBz585lz549xMfHEx0djbu7u217RkYGzz//PA0aNOCFF15g1qxZ7NixgwkTJuRh1iIiIiIiIiIiIiIiN6aOprvQ22+/Ta1atWjatCn16tWjZs2aPPLII7bty5YtIyIigqeeegqAtm3bEhERwQcffJDrOkaMGMHChQupWLEin3zyCQsWLKB8+fIAeHh4sHLlSs6cOUPVqlV59tlnqVu3LlOnTrXFd+vWjZCQECIjIylcuDBxcXE0a9aMV155hZiYGCpXrszPP//MkCFDbDHOzs6cPn2aDh06UK5cOVq3bk2jRo0YMWIEcLnDpVevXoSGhtKwYUPKlSvHtGnTss0/NDSUadOm8f7771OpUiU2btxIv379ct/Id9DHH3/M2bNnqVKlCu3bt6d3794UKVLEtv2tt97i8OHD/Oc//wEgICCADz/8kMGDB7N9+/a8SltERERERERERETkhrKysrTc5HK/sWTdj0clObJYLHz55Zc0b948r1MRB5oS+LxxGRYH5GHCJ+PG++QkvwNuZ4YpkG4xa8WzDhgB0fXGu+QoID33c5Zl509n879hcDY8lW6G14JpPMD2fHn7ihp6eJ5xGSNLRhvFF8o0a4PihtciwJ58Zteju+GlcNTJ7K5S94L5tbgjn4tR/CGna+dAvBlBmaZ3JfMcHkk3yyHFAX+adcGwjGSLWRuUyDI/D78Z5lDpklkOJ03fHIBil8zuS7+4mr2mi2Sav9EfNnw9PGT4mjzidMmwfrN7EsD/GebgnWV2Hv6ymH5iBCtmOfxt+Kk1OMP8PJi+Sx93NivhvHEG4GH4t7fnDHNwxHlIN/zI6WR4a/3DtADMv4Odt5idB48ss+vAtH6A5qlpN94pB1td3G+8Uw5e3DbSKB4g/bOJRvGr3zxtFJ/pgN9oHHE1uxbyGb4cCl8yv5ZOuJgdQ5phM+b175UcIebIp3mdwj3psWK18zqFe86Go7F5nYJD6YkmERERERERERERERERuSXqaLrPzJs3D6vVmu0SFhaW1+nd1UaPHn3dtmvUqFFepyciIiIiIiIiIiIictcxf05b7irNmjWjWrVq2W5zdb08TIZGS8xejx49aN26dbbb3N3NHkMXEREREREREREREbkfqaPpPuPl5YWXl1dep3FP8vX1xdfXN6/TEBERERERERERERG5Z6ijSUQcJsNw1scCmWbTwGY4YNrJFGfTyT/NnhjMtDhg6kzDhxZd7oKnHs8azpXumWnWjl7mc7ASajaf710xiWp5w2PY6WZ2LZ3KZ94KHoaXs+mlEJhhdjEn5DdMAPNJvotk5f3HRdMcDrqaNYJ7lvm1aJgCxXE1indxwK3dNIfzhoN253fAeThheDn7ZJm9ph0xbvlDmWbnwfAtlsBMs0Y842T+Jht6ySyHk85mLwjT6wDMz4OLYQlHnc3Pg4vha9L03upu3IrmTHNId8AHPtMzaXpvdnHAp9Z8hvEeDnhN5nX969zMRlEpl2p2JaR/NtEoHsC1zatG8b+OG2oUf8ko+rJ8hp+XTH8nctbw9xEAph+XTF/RjvhtgmkOef/t5cGU6ZCzL/cyzdEkIiIiIiIiIiIiIiIit0QdTSIiIiIiIiIiIiIiInJL1NEkIiIiIiIiIiIiIiIit0QdTfeo2rVr06dPn7xOI8/ExsZisVj4448/8jqVm9apUyeaN2+e12mIiIiIiIiIiIiIiBhTR9N9YMmSJTz55JP4+flhsVhISEi44znc6c6TGjVqcOzYMQoUKHDH6nSUSZMmMXv27LxOQ0RERERERERERETEmDqa7gPnzp2jZs2ajBs3Lq9TuWPc3Nzw9/fHYrHkdSo3rUCBAvj4+OR1GiIiIiIiIiIiIiLGsvTvpv/db9TRdA84d+4cHTp0wGq1EhAQwIQJE+y2t2/fnqFDh1KvXr1bKt9isTB9+nQaNWqEu7s7pUuX5vPPP7fbJzExkTp16uDu7o6fnx/du3cnJSUFgOHDhzNnzhy++uorLBYLFouF2NhYAF577TXKlSuHh4cHpUuXZsiQIaSnp9vK3b59O0888QReXl54e3vzyCOPsHnzZgAOHz5M06ZNKViwIJ6enoSFhbF8+XLg2qHzhg8fTuXKle1yfu+99wgKCrL9fOWpq9GjR1O0aFF8fHwYOXIkly5don///vj6+lKiRAlmzZqVq3Y7dOgQFouFRYsWUatWLdzd3alatSr79u1j06ZNREZGYrVaadSoEb///vs1eVxRu3ZtevfuzYABA/D19cXf35/hw4fnKgcRERERERERERERkbykjqZ7QP/+/fnhhx/46quv+O6774iNjWXr1q0OrWPIkCE888wzbN++nejoaNq2bcuePXuAyx1dDRo0oGDBgmzatInFixfz/fffExMTA0C/fv1o3bo1DRs25NixYxw7dowaNWoA4OXlxezZs9m9ezeTJk1ixowZvPvuu7Z6o6OjKVGiBJs2bWLLli0MHDgQV1dXAHr16kVqairr1q0jMTGRcePGYbVajY5zzZo1HD16lHXr1jFx4kSGDRtGkyZNKFiwIPHx8fTo0YMXX3yR3377LddlDhs2jMGDB7N161ZcXFx47rnnGDBgAJMmTeLHH3/kwIEDDB06NMcy5syZg6enJ/Hx8YwfP56RI0eyatUqo2MVEREREREREREREbndXPI6AclZSkoKH3/8MZ9++il169YFLndKlChRwqH1tGrViq5duwLw5ptvsmrVKqZMmcK0adOYP38+Fy9e5JNPPsHT0xOAqVOn0rRpU8aNG0fRokVxd3cnNTUVf39/u3IHDx5s+39QUBD9+vVj4cKFDBgwAIDk5GT69+/Pww8/DEBwcLBt/+TkZJ555hnCw8MBKF26tPFx+vr6MnnyZJycnAgJCWH8+PGcP3+e119/HYBBgwYxduxYfvrpJ9q2bZurMvv160eDBg0AePnll2nXrh2rV68mKioKgC5dutxwTqaKFSsybNgw4HIbTJ06ldWrV1O/fv1s909NTSU1NdVuXXpWBq4W51zlLCIiIiIiIiIiIiLiCHqi6S6XlJREWloa1apVs63z9fUlJCTEofVUr179mp+vPNG0Z88eKlWqZOtkAoiKiiIzM5O9e/fmWO5nn31GVFQU/v7+WK1WBg8eTHJysm37q6++SteuXalXrx5jx44lKSnJtq13796MGjWKqKgohg0bxo4dO4yPMywsDCen/132RYsWtXVkATg7O+Pn58fJkydzXWbFihXtygPsyixatOgNy7u6DICAgIAcY8aMGUOBAgXsllV/7cp1ziIiIiIiIiIiIiIijqCOJrlt1q9fT3R0NI0bN+brr79m27ZtvPHGG6Slpdn2GT58OLt27eKpp55izZo1lC9fni+//BKArl278uuvv9K+fXsSExOJjIxkypQp2dbl5OREVpb9JGpXzwV1xZVh+a6wWCzZrsvMzMz1cV4db7FYsl13o/JuNodBgwbx559/2i31vcNynbOIiIiIiIiIiIiIiCOoo+kuV6ZMGVxdXYmPj7etO3v2LPv27XNoPRs2bLjm59DQUABCQ0PZvn07586ds22Pi4uzDT8H4ObmRkZGhl0ZP//8MyVLluSNN94gMjKS4OBgDh8+fE3d5cqV45VXXuG7776jZcuWzJo1y7YtMDCQHj16sGTJEvr27cuMGTOyzb9w4cIcP37crrMpISHh5hrhHpIvXz68vb3tFg2bJyIiIiIiIiIiIndaVlaWlptc7jfqaLrLWa1WunTpQv/+/VmzZg07d+6kU6dOdsO/nTlzhoSEBHbv3g3A3r17SUhI4Pjx47muZ/HixcycOZN9+/YxbNgwNm7cSExMDADR0dHkz5+fjh07snPnTtauXctLL71E+/btbUPFBQUFsWPHDvbu3cupU6dIT08nODiY5ORkFi5cSFJSEpMnT7Y9rQRw4cIFYmJiiI2N5fDhw8TFxbFp0yZbB1efPn1YuXIlBw8eZOvWraxdu9a27Z9q167N77//zvjx40lKSuL999/n22+/vbnGFhERERERERERERGRm6KOpnvA22+/Ta1atWjatCn16tWjZs2aPPLII7bty5YtIyIigqeeegqAtm3bEhERwQcffJDrOkaMGMHChQupWLEin3zyCQsWLKB8+fIAeHh4sHLlSs6cOUPVqlV59tlnqVu3LlOnTrXFd+vWjZCQECIjIylcuDBxcXE0a9aMV155hZiYGCpXrszPP//MkCFDbDHOzs6cPn2aDh06UK5cOVq3bk2jRo0YMWIEABkZGfTq1YvQ0FAaNmxIuXLlmDZtWrb5h4aGMm3aNN5//30qVarExo0b6devX+4bWUREREREREREREREbpol6358TktuisVi4csvv6R58+Z5nYoYmBL4vHEZFsP4DMMCSqdl3HinnOo3PgL4w9ms/z2f4S31pIv5MeQzvKsHGp6H0y7mwzj+ZfhnEJ65n2YtW16Z5m+NpiWYXgnPHptnWAJ8HhBtFL/Tzax+R/w1jEeWWUuaXs2uhhfCeQc0gpNpDhazAkzPgSNyuGSYgrsDjsH0Wkg3TMG0fkfk4GGYwyWzcMD8GEybMb8DzoPZu7T5fc20/rNOhm/SQNAls5vjSWezE2HaBpD35yHN8L4K4GJ4b3S98S73PasD3l9MX1EXzVMwpkHgzc9juVSzEhoO8TXMAFzbvGoUP63KUKN4R3xOMP0u7WJav/lbJKmG3x9M29ERv2Q2vS2ZnoceRz41LOHBFBlQK69TuOdsPvZjXqfgUHqiSURERERERERERERERG6JOpruc/PmzcNqtWa7hIWF5XV6d7XRo0dft+0aNWqU1+mJiIiIiIiIiIiIiOQ506cJ5S7XrFkzqlWrlu02V9fLgxVo9MTs9ejRg9atW2e7zd3d/Q5nIyIiIiIiIiIiInL3yXTIwIlyL1NH033Oy8sLLy+vvE7jnuTr64uvr/k4xSIiIiIiIiIiIiIi9yt1NImIw5hOOF/AcNrJjW75zBIAjjqZ5VAs0+y2ajr5KECRdLMZRBPzm03H+6fFfAbTVMPpcF2czaYPDU03n5J4yIUEo/jz6alG8c8aRV/WNy3RKH56Znmj+PCHfjeKB/jsRIBRfKk0s2vxy/xm53FsiTNG8QBfHS5uFJ/odNEovnqm+VPApjl0uWg2WvR2V/P3F9NJ7+Ms543iq2d5GGYA6w1zaJtq1o4H3czvzf6GM1zHuaUZxVfMdDNLAPjBKcUo/l+Z1jytv0G6Wf0AXzr/YRT/eJaPUfxhp3SjeICSmWZ3BdMcGl4wnWodzjmZvSYPupnlkGwxPw8PZZmdB9Mcml00Pw8pFrP3uMNuZvHHnDKM4gEKZZldS6bnwfQ6+M0B12KDC6bn0awNV7952ige4NdxQ43ie24daRSfsT/eKB5gZutvjOLzG34VPu+ACU5Mf6eQ3zDeEXO0pBreGj3MfyUhIrdAczSJiIiIiIiIiIiIiIjILVFHk4iIiIiIiIiIiIiIiNwSdTSJiIiIiIiIiIiIiIjILbknO5osFgtLly7N6zRu2ezZs/Hx8cnTHA4dOoTFYiEhISFPy4mNjcVisfDHH38Y5ZEbtWvXpk+fPre9npvlqHMhIiIiIiIiIiIicqdlZWVpucnlfnNPdjTdSWPHjsVisdyVHRQmAgMDOXbsGBUqVADubIfP7XY/HYuIiIiIiIiIiIiIyN1MHU052LRpE//5z3+oWLFiXqficM7Ozvj7++Pi4pLXqdzX0tPT8zoFEREREREREREREZHb5qY7mlasWEHNmjXx8fHBz8+PJk2akJSUBPxvCLCFCxdSo0YN8ufPT4UKFfjhhx9s8RkZGXTp0oVSpUrh7u5OSEgIkyZNuqaemTNnEhYWRr58+QgICCAmJsZu+6lTp2jRogUeHh4EBwezbNkyu+07d+6kUaNGWK1WihYtSvv27Tl16lSujzMlJYXo6GhmzJhBwYIFr9k+ceJEwsPD8fT0JDAwkJ49e5KSkpLr8gFWrlxJaGgoVquVhg0bcuzYMdu2zMxMRo4cSYkSJciXLx+VK1dmxYoVtu1paWnExMQQEBBA/vz5KVmyJGPGjLFtt1gsTJ8+nUaNGuHu7k7p0qX5/PPPbduvHq7t0KFDPPHEEwAULFgQi8VCp06dgJzP961Yvnw55cqVw93dnSeeeIJDhw5ds89PP/1ErVq1cHd3JzAwkN69e3Pu3Dnb9rlz5xIZGYmXlxf+/v4899xznDx50nZc1zuWK+06YMAAfH198ff3Z/jw4bnOPbdt+tlnn/Gvf/2L/PnzM2/evBueyyt++eWX675uRERERERERERERETuRjfd0XTu3DleffVVNm/ezOrVq3FycqJFixZkZmba9unfvz99+/Zl27ZtVK9enaZNm3L69Gng8i/6S5QoweLFi9m9ezdDhw7l9ddfZ9GiRbb46dOn06tXL7p3705iYiLLli2jbNmydnmMGDGC1q1bs2PHDho3bkx0dDRnzpwB4I8//qBOnTpERESwefNmVqxYwYkTJ2jdunWuj7NXr1489dRT1KtXL9vtTk5OTJ48mV27djFnzhzWrFnDgAEDcl3++fPneeedd5g7dy7r1q0jOTmZfv362bZPmjSJCRMm8M4777Bjxw4aNGhAs2bN2L9/PwCTJ09m2bJlLFq0iL179zJv3jyCgoLs6hgyZAjPPPMM27dvJzo6mrZt27Jnz55rcgkMDOSLL74AYO/evRw7dszW+Zeb851bR44coWXLljRt2pSEhAS6du3KwIED7fZJSkqiYcOGPPPMM+zYsYPPPvuMn376ya6jMT09nTfffJPt27ezdOlSDh06ZOtMyulYAObMmYOnpyfx8fGMHz+ekSNHsmrVqlwfQ27adODAgbz88svs2bOHBg0a3PBcXpHT60ZERERERERERERE5G500+OmPfPMM3Y/z5w5k8KFC7N7926sVisAMTExtv2mT5/OihUr+PjjjxkwYACurq6MGDHCFl+qVCnWr1/PokWLbB1Bo0aNom/fvrz88su2/apWrWpXb6dOnWjXrh0Ao0ePZvLkyWzcuJGGDRsydepUIiIiGD16tF2egYGB7Nu3j3LlyuV4jAsXLmTr1q1s2rTpuvtcPWdTUFAQo0aNokePHkybNi3Hsq9IT0/ngw8+oEyZMsDlNhs5cqRt+zvvvMNrr71G27ZtARg3bhxr167lvffe4/333yc5OZng4GBq1qyJxWKhZMmS19TRqlUrunbtCsCbb77JqlWrmDJlyjU5Ojs74+vrC0CRIkXw8fGxbcvpfF+Z3ym3pk+fTpkyZZgwYQIAISEhJCYmMm7cONs+Y8aMITo62ta+wcHBTJ48mX/9619Mnz6d/Pnz07lzZ9v+pUuXZvLkyVStWpWUlBSsVut1jwWgYsWKDBs2zFb21KlTWb16NfXr18/VMeSmTfv06UPLli1tP9/oXF6R0+vmn1JTU0lNTbVbl56VgavFOVfHISIiIiIiIiIiIiLiCDf9RNP+/ftp164dpUuXxtvb2/YUTXJysm2f6tWr2/7v4uJCZGSk3VMf77//Po888giFCxfGarXy4Ycf2uJPnjzJ0aNHqVu3bo55XD1vkqenJ97e3rbh07Zv387atWuxWq225eGHHwa44bBvR44c4eWXX2bevHnkz5//uvt9//331K1bl+LFi+Pl5UX79u05ffo058+fz7H8Kzw8PGydTAABAQG2/P/66y+OHj1KVFSUXUxUVJStHTt16kRCQgIhISH07t2b77777po6rj4PV37O7ommnOTmfOfWnj17qFatWo45bt++ndmzZ9uduwYNGpCZmcnBgwcB2LJlC02bNuWhhx7Cy8uLf/3rX7nO6Z/zbV3d7rmRmzaNjIy0/T835zK7srN73VxtzJgxFChQwG5Z9deuXB+HiIiIiIiIiIiIiCNkkqXlJpf7zU13NDVt2pQzZ84wY8YM4uPjiY+PBy7PGZQbCxcupF+/fnTp0oXvvvuOhIQEXnjhBVu8u7t7rspxdXW1+9lisdiGc0tJSbENz3b1sn//fh5//PEcy92yZQsnT56kSpUquLi44OLiwg8//MDkyZNxcXEhIyODQ4cO0aRJEypWrMgXX3zBli1bbE+m5LYdsss/Kyv3F1iVKlU4ePAgb775JhcuXKB169Y8++yzuY7PLdPzfbNSUlJ48cUX7c7b9u3b2b9/P2XKlOHcuXM0aNAAb29v5s2bx6ZNm/jyyy9znVNO142jeHp6OrS87AwaNIg///zTbqnvHXbb6xURERERERERERERudpNdTSdPn2avXv3MnjwYOrWrUtoaChnz569Zr8NGzbY/n/p0iW2bNlCaGgoAHFxcdSoUYOePXsSERFB2bJl7Z4y8vLyIigoiNWrV9/qMVGlShV27dpFUFAQZcuWtVtu1AlQt25dEhMT7To6IiMjiY6OJiEhAWdnZ7Zs2UJmZiYTJkzgscceo1y5chw9evSW8/0nb29vihUrRlxcnN36uLg4ypcvb7dfmzZtmDFjBp999hlffPGFbZ4qsD8PV36+ch7+yc3NDYCMjAzbutye79wKDQ1l48aN1+R0tSpVqrB79+5rzlvZsmVxc3Pjl19+4fTp04wdO5ZatWrx8MMPX/NEUnbH4ig306aQ+3P5z7L/+br5p3z58uHt7W23aNg8EREREREREREREbnTbmqOpoIFC+Ln58eHH35IQEAAycnJDBw48Jr93n//fYKDgwkNDeXdd9/l7Nmztnl1goOD+eSTT1i5ciWlSpVi7ty5bNq0iVKlStnihw8fTo8ePShSpAiNGjXi77//Ji4ujpdeeilXefbq1YsZM2bQrl07BgwYgK+vLwcOHGDhwoV89NFHODtf/xfyXl5e18w95OnpiZ+fn2192bJlSU9PZ8qUKTRt2pS4uDg++OCDXOWWW/3792fYsGGUKVOGypUrM2vWLBISEpg3bx4AEydOJCAggIiICJycnFi8eDH+/v52cxItXryYyMhIatasybx589i4cSMff/xxtvWVLFkSi8XC119/TePGjXF3d8/1+c6tHj16MGHCBPr370/Xrl3ZsmULs2fPttvntdde47HHHiMmJoauXbvi6enJ7t27WbVqFVOnTuWhhx7Czc2NKVOm0KNHD3bu3Mmbb755w2O5Mn+YqZtp0ytudC6vyOl1IyIiIiIiIiIiIiJyN7qpJ5qcnJxYuHAhW7ZsoUKFCrzyyiu8/fbb1+w3duxYxo4dS6VKlfjpp59YtmwZhQoVAuDFF1+kZcuWtGnThmrVqnH69Gl69uxpF9+xY0fee+89pk2bRlhYGE2aNGH//v25zvPKEyQZGRk8+eSThIeH06dPH3x8fHByuunRAq9RqVIlJk6cyLhx46hQoQLz5s1jzJgxxuVerXfv3rz66qv07duX8PBwVqxYwbJlywgODgYud4iNHz+eyMhIqlatyqFDh1i+fLnd8Y0YMYKFCxdSsWJFPvnkExYsWHDNUzRXFC9enBEjRjBw4ECKFi1KTExMrs93bj300EN88cUXLF26lEqVKvHBBx8wevRou30qVqzIDz/8wL59+6hVqxYREREMHTqUYsWKAVC4cGFmz57N4sWLKV++PGPHjuWdd9654bE4ys206RU3OpdX5PS6ERERERERERERERG5G1mybmZioBs4dOgQpUqVYtu2bVSuXNlRxcotsFgsfPnllzRv3jyvU7lv3O1tOiXweeMyLIbxpjeTSumpRvEb3fIZZgBHnS4ZxRfLvKkHRa+RzwF35OLpZvOO7ctn1iH/p8V83rNUzMpwMbyaQ9PNh6IcciHBKP684evhzN+5/wON6ynpV9EofrpLzh3xNxL+0O9G8QCfnQgwii+bZnYtfpnf7DyOLXHmxjvdwFeHixvFr3e+YBRfPSN382/ezhy6XDS7r213NX9/cb3xLjmKczpvFF8908MwA1hvmEPbi2bteNDN/N5c0HBk5Tg3s3lKK2a4mSUA/OCUYhT/r0yzJ/1N629wyXykgS+d/zCKfxwfo/jDTulG8QAlM83uCqY5NLxg+skfzjmZvSYPupnlkGwxPw8PZZmdB9McmjngPKRYzN7jDruZxR9zMh+yvlCW2bVkeh5Mr4PfHHAtNrhgeh7N2jAw3fw8/mqYQ8+tI43iM/bHG8UDzGz9jVG8h+FX4fPmfxtv/DsFZ8N4BxwCqYa3RtPzEH30U7MCHlCV/GvkdQr3nO3Hf87rFBzK7DeiIiIiIiIiIiIiIiLywMoy/vNzudc5oqP5npKcnIzVar3ukpycbFxHo0aNrlv+P4eKu5/06NHjusfdo0ePvE4vR/Pmzbtu7mFhYXmdnoiIiIiIiIiIiIjIXcmhTzQFBQXhwJH4botixYqRkJCQ43ZTH330ERcuZD/Ei6+vr3H5uZEX52HkyJH069cv223e3t53OJub06xZM6pVq5btNlfXy4/R3+3XtoiIiIiIiIiIiIjInfbADZ3n4uJC2bJlb2sdxYubzYVwrypSpAhFihTJ6zRuiZeXF15eXnmdhoiIiIiIiIiIiIjIPeWB62gSkdvHdCrbkxazCbJNJ1oH8DAcUdQ0h0zz+YBJdTIrxHTy0EyL+dN/pufBJcusDRxxHlwtZpPhOlkckIQhd+d8RvEZhsfg5GJ+LZm2YrrhMWQYPg2bkZ7314HbXTDSsmkOl+6Ce0Km4eXsajjZuyOY5mD8enLAeXBEGSYM56YGwMXw9WCag2n9jriSMwznADD9Euxi/O5i3g6mOWQ54BhMryUn4/uiA85DHueQ6oBjML23msr7TyrgnMdZWO6CVjB9PWY64BguGcZn7I83incOzn6UmpvzjQPKuHWO+Jxgel8z/frhgK9wxp/XLub9x2aRB5JeeiIiIiIiIiIiIiIiInJL9ESTiIiIiIiIiIiIiIjckkzNbf/A0xNNIiIiIiIiIiIiIiIi97gzZ84QHR2Nt7c3Pj4+dOnShZSUlFzFZmVl0ahRIywWC0uXLr2petXRZOhWGv1uMnv2bHx8fPKs/kOHDmGxWEhISLjtdXXq1InmzZvf9npuxb1+HYmIiIiIiIiIiIhI3oqOjmbXrl2sWrWKr7/+mnXr1tG9e/dcxb733ntYbnH+R3U03UPGjh2LxWKhT58+DiuzTZs27Nu3z2Hl3Q3uZOeViIiIiIiIiIiIiMjNSE1N5a+//rJbUlNTjcrcs2cPK1as4KOPPqJatWrUrFmTKVOmsHDhQo4ePZpjbEJCAhMmTGDmzJm3VLc6mu4RmzZt4j//+Q8VK1Z0aLnu7u4UKVLEoWXK/6Snp+d1CiIiIiIiIiIiIiJyFxkzZgwFChSwW8aMGWNU5vr16/Hx8SEyMtK2rl69ejg5OREfH3/duPPnz/Pcc8/x/vvv4+/vf0t139MdTStWrKBmzZr4+Pjg5+dHkyZNSEpKAv73VMvChQupUaMG+fPnp0KFCvzwww+2+IyMDLp06UKpUqVwd3cnJCSESZMmXVPPzJkzCQsLI1++fAQEBBATE2O3/dSpU7Ro0QIPDw+Cg4NZtmyZ3fadO3fSqFEjrFYrRYsWpX379pw6dSrXx5mSkkJ0dDQzZsygYMGC12yfOHEi4eHheHp6EhgYSM+ePXM97mJ2Q+f997//pWrVquTPn59ChQrRokUL27bshnjz8fFh9uzZuapv48aNREREkD9/fiIjI9m2bds1+9yovXI67wClSpUCICIiAovFQu3ate3Kf+eddwgICMDPz49evXrlujMoKCiIN998k3bt2uHp6Unx4sV5//337faxWCxMnz6dZs2a4enpyVtvvQXA9OnTKVOmDG5uboSEhDB37txryj927BiNGjXC3d2d0qVL8/nnn+cqLxERERERERERERG5dwwaNIg///zTbhk0aJBRmcePH7/moRIXFxd8fX05fvz4deNeeeUVatSowdNPP33Ldd/THU3nzp3j1VdfZfPmzaxevRonJydatGhBZmambZ/+/fvTt29ftm3bRvXq1WnatCmnT58GIDMzkxIlSrB48WJ2797N0KFDef3111m0aJEtfvr06fTq1Yvu3buTmJjIsmXLKFu2rF0eI0aMoHXr1uzYsYPGjRsTHR3NmTNnAPjjjz+oU6cOERERbN68mRUrVnDixAlat26d6+Ps1asXTz31FPXq1ct2u5OTE5MnT2bXrl3MmTOHNWvWMGDAgFyXf7VvvvmGFi1a0LhxY7Zt28bq1at59NFHb6msf0pJSaFJkyaUL1+eLVu2MHz4cPr162e3T27a60bnfePGjQB8//33HDt2jCVLlthi165dS1JSEmvXrmXOnDnMnj07151kAG+//TaVKlVi27ZtDBw4kJdffplVq1bZ7TN8+HBatGhBYmIinTt35ssvv+Tll1+mb9++7Ny5kxdffJEXXniBtWvX2sUNGTKEZ555hu3btxMdHU3btm3Zs2dPrnMTERERERERERERudOy9O+m/+XLlw9vb2+7JV++fNm278CBA7FYLDkuv/zyyy2du2XLlrFmzRree+89gysAXIyi89gzzzxj9/PMmTMpXLgwu3fvxmq1AhATE2Pbb/r06axYsYKPP/6YAQMG4OrqyogRI2zxpUqVYv369SxatMjWsTFq1Cj69u3Lyy+/bNuvatWqdvV26tSJdu3aATB69GgmT57Mxo0badiwIVOnTiUiIoLRo0fb5RkYGMi+ffsoV65cjse4cOFCtm7dyqZNm667z9VzNgUFBTFq1Ch69OjBtGnTciw7O2+99RZt27a1a5dKlSrddDnZmT9/PpmZmXz88cfkz5+fsLAwfvvtN/7973/b9slNe+V03itUqEDhwoUB8PPzu+ZRv4IFCzJ16lScnZ15+OGHeeqpp1i9ejXdunXL1TFERUUxcOBAAMqVK0dcXBzvvvsu9evXt+3z3HPP8cILL9h+bteuHZ06daJnz54AvPrqq2zYsIF33nmHJ554wrZfq1at6Nq1KwBvvvkmq1atYsqUKbd0HkVERERERERERETk3te3b186deqU4z6lS5fG39+fkydP2q2/dOkSZ86cue6QeGvWrCEpKemaUc+eeeYZatWqRWxsbK5yvKc7mvbv38/QoUOJj4/n1KlTtidakpOTKV++PADVq1e37e/i4kJkZKTdUyLvv/8+M2fOJDk5mQsXLpCWlkblypUBOHnyJEePHqVu3bo55nH1vEmenp54e3vbTuj27dtZu3atrePraklJSTl2NB05csT2xEz+/PmxeBFtAAEAAElEQVSvu9/333/PmDFj+OWXX/jrr7+4dOkSFy9e5Pz583h4eOSY+z8lJCTkutPlZu3Zs4eKFSvaHcvV5wdy1145nfcKFSrkmENYWBjOzs62nwMCAkhMTMz1Mfwz3+rVq1/T23v1GJhw+bi7d+9uty4qKuqaYRqzKzshISHbPFJTU6+ZHC49KwNXi3O2+4uIiIiIiIiIiIjIvadw4cK2hytyUr16df744w+2bNnCI488AlzuSMrMzKRatWrZxgwcOND28MMV4eHhvPvuuzRt2jTXOd7THU1NmzalZMmSzJgxg2LFipGZmUmFChVIS0vLVfzChQvp168fEyZMoHr16nh5efH222/bJsZyd3fPVTmurq52P1ssFlvnR0pKCk2bNmXcuHHXxAUEBORY7pYtWzh58iRVqlSxrcvIyGDdunVMnTqV1NRUjhw5QpMmTfj3v//NW2+9ha+vLz/99BNdunQhLS3tpjuabnTMFouFrKwsu3W5neMoN3LTXibnPadz5Sienp4OLS87Y8aMsXvqDKChVziNC1S8ToSIiIiIiIiIiIiI3K9CQ0Np2LAh3bp144MPPiA9PZ2YmBjatm1LsWLFAPi///s/6tatyyeffMKjjz6Kv79/tk87PfTQQ5QqVSrXdd+zczSdPn2avXv3MnjwYOrWrUtoaChnz569Zr8NGzbY/n/p0iW2bNlCaGgoAHFxcdSoUYOePXsSERFB2bJlSUpKsu3v5eVFUFAQq1evvuU8q1Spwq5duwgKCqJs2bJ2y406JOrWrUtiYiIJCQm2JTIykujoaBISEnB2dmbLli1kZmYyYcIEHnvsMcqVK8fRo0dvOd+KFSvmeLyFCxfm2LFjtp/379/P+fPnc1V2aGgoO3bs4OLFi7Z1V58fuHF75ea8u7m5AZc75Rztn/lu2LDBdj1dT2hoKHFxcXbr4uLibE/d3UrZ2U0WV987LLeHISIiIiIiIiIiIiL3mXnz5vHwww9Tt25dGjduTM2aNfnwww9t29PT09m7d2+uf6efW/fsE00FCxbEz8+PDz/8kICAAJKTk21z51zt/fffJzg4mNDQUN59913Onj1L586dAQgODuaTTz5h5cqVlCpVirlz57Jp0ya7nrrhw4fTo0cPihQpQqNGjfj777+Ji4vjpZdeylWevXr1YsaMGbRr144BAwbg6+vLgQMHWLhwIR999JHdMG7/5OXldc1QcJ6envj5+dnWly1blvT0dKZMmULTpk2Ji4vjgw8+yFVu2Rk2bBh169alTJkytG3blkuXLrF8+XJee+01AOrUqcPUqVOpXr06GRkZvPbaa9c8JXQ9zz33HG+88QbdunVj0KBBHDp0iHfeecdunxu1V27Oe5EiRXB3d2fFihWUKFGC/PnzU6BAgVtuk6vFxcUxfvx4mjdvzqpVq1i8eDHffPNNjjH9+/endevWREREUK9ePf773/+yZMkSvv/+e7v9Fi9eTGRkJDVr1mTevHls3LiRjz/+ONsy8+XLd83kcBo2T0REREREREREROTB5evry/z586+7PSgo6JoRy/7pRtuzc88+0eTk5MTChQvZsmULFSpU4JVXXuHtt9++Zr+xY8cyduxYKlWqxE8//cSyZcsoVKgQAC+++CItW7akTZs2VKtWjdOnT9OzZ0+7+I4dO/Lee+8xbdo0wsLCaNKkCfv37891nsWKFSMuLo6MjAyefPJJwsPD6dOnDz4+Pjg5mTd/pUqVmDhxIuPGjaNChQrMmzePMWPG3HJ5tWvXZvHixSxbtozKlStTp04dNm7caNs+YcIEAgMDqVWrFs899xz9+vXL9fB8VquV//73vyQmJhIREcEbb7xxzRB5N2qv3Jx3FxcXJk+ezH/+8x+KFSvG008/fcvt8U99+/Zl8+bNREREMGrUKCZOnEiDBg1yjGnevDmTJk3inXfeISwsjP/85z/MmjWL2rVr2+03YsQIFi5cSMWKFfnkk09YsGDBNU89iYiIiIiIiIiIiNxNMrOytNzkcr+xZN1K99Q94NChQ5QqVYpt27ZRuXLlvE5H7gNBQUH06dOHPn365HUq2ZoS+LxxGRYH5GHC/5LZ7ej/XM2P4Hcnszm7Cmfmff99EcN2PO5i1o7HncyHrXQ1vBpdssziAzPMr6VRFxKN4v9Ov2AUf+qvfUbxAA8XqWoU/7ZTWaP4KqVOGMUDLPy/YkbxD6WbvZ6+cjM7j2P9/zCKB/j6qFkbbHFONYp/JCPfjXe6zTm0u2D2mk7M52YUD+Bq+Il7s/PFG++Ug8iM/GYJOCCHZhfN2vGwm/l7bEHDt6gNrrmbC/Z6KmSYX0vrncyGuKieeXNzuDq6/vqXzOoHWOR87ZDpN5UDBY3iDztdMooHCMw0G1zkiGEOdQ3viwAphn84ecTws/v/OeA8FDc8D6Y51Llofh7OW8zOw1HD8+CIz/5+WWajc/yfxew8FM8yvA4M6wd40vA1edDNrA0D083nrj5g+D7d67OnjOKdg7Of5P5mzIgYahTvYdiMKQ74dYKnYQ6XDG9LLg74LXOqYTuYfu7u8tunZgU8oEKLPJrXKdxz9pzceOOd7iF5/xtRERERERERERERERERuSepoykPJScnY7Var7skJycb19GoUaPrlj969GgHHMX/jB49+rp1NWrUyKF1OdqPP/6Y47kQEREREREREREREZFrmT0ffBfLzaRWea1YsWIkJCTkuN3URx99xIUL2Q/b4+vra1z+1Xr06EHr1q2z3ebu7u7QuhwtMjIyx3MBl4djFBERERERERERERGR/7lvO5ruBS4uLpQtazZ/xY0UL178tpZ/NV9fX4d3Xt0p7u7ut/1ciIiIiIiIiIiIiIjcb9TRJCIiIiIiIiIiIiIitySLu3tkMbn9LFl3+/hyIpIrUwOfNy4jr28GFwxnjXN2wAG4GZbhbFh/hmE8gKth/AWLWbwjJv8zPZeml4LpeQTHnEsTLx351LiMKYb3FUe0o6lMw3jTv8i5ZBh/N0ymaZqD6Tm4W3KQ+0NeXwuO+Kxl+DZtnINp/Y64r5m+x5p+Vrof5PVrQS5zxOvB9FzeDddCXt+X7gYZeXwQjvgubepu+NzbbdtIo/iPI4YaxZt+FwdwNTyXpvFpDjgG09/LmObgiO/SD6KHi1TN6xTuOb+c3JTXKTjU3XAfFxERERERERERERERkXuQOppERERERERERERERETklqijSURERERERERERERERG6JOppEboNDhw5hsVhISEi47j6xsbFYLBb++OOPO5aXiIiIiIiIiIiIiIgjmc5xLSLZCAwM5NixYxQqVCivUxERERERERERERG5bTKzsvI6Bclj6mgSuQ2cnZ3x9/fP6zRERERERERERERERG4rDZ0ncotWrFhBzZo18fHxwc/PjyZNmpCUlARkP3Te8uXLKVeuHO7u7jzxxBMcOnTIrrzDhw/TtGlTChYsiKenJ2FhYSxfvvwOHpGIiIiIiIiIiIiIyM1RR5PILTp37hyvvvoqmzdvZvXq1Tg5OdGiRQsyMzOv2ffIkSO0bNmSpk2bkpCQQNeuXRk4cKDdPr169SI1NZV169aRmJjIuHHjsFqtd+pwRERERERERERERERumobOE7lFzzzzjN3PM2fOpHDhwuzevfuaDqLp06dTpkwZJkyYAEBISIitM+mK5ORknnnmGcLDwwEoXbr0detOTU0lNTXVbl16VgauFmejYxIRERERERERERERuRl6oknkFu3fv5927dpRunRpvL29CQoKAi53GP3Tnj17qFatmt266tWr2/3cu3dvRo0aRVRUFMOGDWPHjh3XrXvMmDEUKFDAbln11y7zgxIRERERERERERERuQnqaBK5RU2bNuXMmTPMmDGD+Ph44uPjAUhLS7ul8rp27cqvv/5K+/btSUxMJDIykilTpmS776BBg/jzzz/tlvreYbd8LCIiIiIiIiIiIiK3Ikv/bvrf/UYdTSK34PTp0+zdu5fBgwdTt25dQkNDOXv27HX3Dw0NZePGjXbrNmzYcM1+gYGB9OjRgyVLltC3b1/+H3v3HRXV8f8N/L303hQBFQSkiAXF2LGXr5XYe1TsRomaKJaoKJZYggVrNDGiRqPGHmOvEXsDjQURRU3EmFiDWCjz/MHD/lwpu+ys2dW8X+fsOXD3zszn3plbdmfvzLfffptnfubm5rCzs1N5cdg8IiIiIiIiIiIiIvq3saOJSAuOjo4oUqQIli1bhhs3buDgwYP44osv8l1/0KBBSExMRHh4OBISErB27VrExMSorDN8+HDs2bMHt27dwvnz53Ho0CEEBAS84y0hIiIiIiIiIiIiItIeO5qItGBkZIR169bh3LlzKF++PD7//HN8/fXX+a7v4eGBTZs2YevWrahYsSK++eYbfPXVVyrrZGZmYsiQIQgICECzZs3g5+eHxYsXv+tNISIiIiIiIiIiIiLSmom+AyB6XzVu3BhXrlxRWSaEyPNvAGjVqhVatWqlsqx3797Kv/Obj4mIiIiIiIiIiIiIyFDxiSYiIiIiIiIiIiIiIiLSCp9oIiIiIiIiIiIiIiIirWS9NbIT/ffwiSYiIiIiIiIiIiIiIiLSCjuaiIiIiIiIiIiIiIiISCscOo/oA6GLXuMsPcdgJPmUrZkOntJ9IbkRdpI78ZVCLj0AmEruB9n0unhYWnY/ZEqmt8+USw8AryTbkiH8EiRLcj/KtiXZcxIApEnuSEvJIDIk96EhnNtfSm6DhQ5OCrIxOEruBIUOtuEfycp8JRmEuZC/wKRLZmErWQ+y5QOApWRdpkuWL3ufAQCylyhjPZdvpYPjSfa89lo2vQ7aoux9q2wMTjq415H1VLIxpuvgrtMUcjtSNgZ72ZstyN/3GsL9muy58X0/LwLy58YMPZcPyLdFC100JknLgyKk0ve9MFmv5QPAJ53/kUq/Yr2tdAyydHHPR0T/PkP4HouIiIiIiIiIiIiIiIjeQ+xoIiIiIiIiIiIiIiIiIq1w6DwiIiIiIiIiIiIiItKK0MlECvQ+4xNN9MFKTk6GQqFAXFxcodMePnwYCoUCT5480XlcOTw9PTFv3rx3lj8RERERERERERER0bvGJ5rog+Xu7o6UlBQULVpU36Hk6cyZM7C2ttZ3GEREREREREREREREWmNHE32wjI2N4erqmu/7QghkZmbCxET1MHj9+vW7Dg0A4Ozs/K+UQ0RERERERERERET0rnDoPHrvZWVlYdasWfDx8YG5uTk8PDwwbdq0XEPn5QyHt2vXLnz00UcwNzdHbGws6tevj7CwMAwfPhxFixZF06ZNlXmfO3cOVapUgZWVFWrVqoWEhASVspcsWYLSpUvDzMwM/v7+WL16tfI9IQQmTZoEDw8PmJubo3jx4hg6dKjy/TeHzlO3LhERERERERERERGRIeITTfTeGzt2LL799lvMnTsXtWvXRkpKCq5du5bv+mPGjEFUVBS8vb3h6OgIAFi5ciU+/fRTHDt2DACQkpICABg3bhxmz54NZ2dnDBo0CH369FGus2XLFgwbNgzz5s1D48aNsWPHDvTu3RslS5ZEgwYNsGnTJsydOxfr1q1DuXLlcP/+fcTHx+cZU2HWJSIiIiIiIiIiIiIyFOxoovfaP//8g+joaCxcuBC9evUCAJQuXRq1a9dGcnJynmkmT56MJk2aqCzz9fXFrFmzlP/ndDRNmzYN9erVA5DdQdWyZUu8fPkSFhYWiIqKQmhoKAYPHgwA+OKLL3Dy5ElERUWhQYMGuHPnDlxdXdG4cWOYmprCw8MD1apVyzOmwqwLAK9evcKrV69UlqWLTJgqjAvYW0RERERERERERES6JUSWvkMgPePQefReu3r1Kl69eoVGjRppnKZKlSq5ln300Ud5rhsYGKj8283NDQDw4MEDZdnBwcEq6wcHB+Pq1asAgI4dO+LFixfw9vZG//79sWXLFmRkZORZTmHWBYDp06fD3t5e5bX32eUCtpqIiIiIiIiIiIiISPfY0UTvNUtLy0Knsba21mgZAJiamir/VigUALLnhNKEu7s7EhISsHjxYlhaWmLw4MGoW7cu0tPTpdYFsocLfPr0qcrrf3blNIqLiIiIiIiIiIiIiEhX2NFE7zVfX19YWlriwIED/3rZAQEByvmachw7dgxly5ZV/m9paYmQkBDMnz8fhw8fxokTJ3Dp0qU88yvMuubm5rCzs1N5cdg8IiIiIiIiIiIiIvq3cY4meq9ZWFhg9OjRGDVqFMzMzBAcHIy//voLly9fLtRwetoIDw9Hp06dEBQUhMaNG+Pnn3/G5s2bsX//fgBATEwMMjMzUb16dVhZWeGHH36ApaUlSpUqlSuvwqxLRERERERERERERGQo2NFE770JEybAxMQEERERuHfvHtzc3DBo0KB3Xm6bNm0QHR2NqKgoDBs2DF5eXlixYgXq168PAHBwcMCMGTPwxRdfIDMzExUqVMDPP/+MIkWK5MqrMOsSERERERERERERERkKhRBC6DsIIpK32P0T6Tw0m30qf7JjcaYp5NKb6+Bs9kJyI+wkd+ILyX0AAJaS+yFDsnxdXFReS+6HTMn09ply6QEgTbItyR5Pw+78IJkDEO0hd16RPSZlz0kAkCq5Iy0lg8iQbIu6GONYdj++lNwGCx2cFGRjcJTcCQodbMM/kpX5SjIIcyF/gUmXzMJWsh5kywfkr5F5z56pOdn7DACQvUTJDrYsW76VDo4n2fOabHrZ+xQAMJPcD7IxOOngXkfWU8nGmK6Du05TyO1I2Rjss+Qbk+x9r6kB3K/J3je/7+dFQP7cKPsZTvZzLCDfFi100ZgkvZRsi30vTJZKvzwoQi4AAJ90/kcq/Yr1ttIxyJI9M8peHT67K/9Z+r+oVJFAfYfw3rn98KK+Q9ApztFEREREREREREREREREWmFHExEREREREREREREREWmFHU1ERERERERERERERESkFXY0ERERERERERERERERkVZM9B0AERkOHczNLMVNcgZT96xX0jFcMDOXSi87SbiNAUzCWvb1a6n0l83M5AKA/MXJSLIxu2bIV8TfJu//bzlk2+MzA9gFVpLbkC55PJno+8QKIENyG7Ikrw5Z0tP5yseQqpCLwVYH9WgjmYdCcj9a62AbnstnIUUXc4TL5iHbmt3T5Ssi1UguCpssyeNJsvxUHVwbZM+tsjGk6+KuWfK8JBvDS8l6BOTvt8wl0xvp4PpiKpleFzHIkq0H2UMyUzI9ADhLfg58LVkNZpL7ULZ8AOhU/Xep9GdiXaXSpynkT86PjeXySJMMQRf3CbL3/suDIqTS970wWS4AAN9KxiBLF2dF2e80iEg/2NFERERERERERERERERaEcIAfmVJemUAvzUmIiIiIiIiIiIiIiKi9xE7moiIiIiIiIiIiIiIiEgr7Gj6QNWvXx/Dhw8HAHh6emLevHkap42JiYGDg8M7iet99Pb+UygU2Lp1KwAgOTkZCoUCcXFxeomNiIiIiIiIiIiIiEif2NH0H3DmzBkMGDDgXy/3zQ6ZD5W7uztSUlJQvnx5AMDhw4ehUCjw5MkT/QZGRERERERERERERPQvMNF3APTuOTs76zuED5axsTFcXV31HQYRERERERERERERkV7wiaYPwPPnz9GzZ0/Y2NjAzc0Ns2fPVnn/7aHf5syZgwoVKsDa2hru7u4YPHgwUlNTc+W7detW+Pr6wsLCAk2bNsXdu3dV3t+2bRsqV64MCwsLeHt7IzIyEhkZGcoyAaBt27ZQKBTK/9WlE0Jg0qRJ8PDwgLm5OYoXL46hQ4dqtB88PT0xdepU5b4oVaoUtm/fjr/++gutW7eGjY0NAgMDcfbsWZV0sbGxqFOnDiwtLeHu7o6hQ4fi+fPnGpX55tB5ycnJaNCgAQDA0dERCoUCoaGhytjeHr6wUqVKmDRpkvR2ExEREREREREREelLFgRfhXx9aNjR9AEIDw/HkSNHsG3bNuzduxeHDx/G+fPn813fyMgI8+fPx+XLl7Fy5UocPHgQo0aNUlknLS0N06ZNw6pVq3Ds2DE8efIEXbp0Ub5/9OhR9OzZE8OGDcOVK1ewdOlSxMTEYNq0aQCyh+sDgBUrViAlJUX5v7p0mzZtwty5c7F06VIkJiZi69atqFChgsb7Yu7cuQgODsaFCxfQsmVL9OjRAz179sQnn3yC8+fPo3Tp0ujZsyeEyD6Yk5KS0KxZM7Rv3x4XL17E+vXrERsbi7CwMI3LzOHu7o5NmzYBABISEpCSkoLo6GiN0spuNxERERERERERERGRPnDovPdcamoqli9fjh9++AGNGjUCAKxcuRIlS5bMN83w4cOVf+c8BTRo0CAsXrxYuTw9PR0LFy5E9erVlXkGBATg9OnTqFatGiIjIzFmzBj06tULAODt7Y0pU6Zg1KhRmDhxonK4PgcHB5Wh5dSlu3PnDlxdXdG4cWOYmprCw8MD1apV03h/tGjRAgMHDgQAREREYMmSJahatSo6duwIABg9ejRq1qyJP//8E66urpg+fTq6d++u3Ce+vr6YP38+6tWrhyVLlsDCwkLjso2NjeHk5AQAKFasGBwcHDROW9jtfvXqFV69eqWyLF1kwlRhrHGZRERERERERERERESy+ETTey4pKQmvX79WdggBgJOTE/z9/fNNs3//fjRq1AglSpSAra0tevTogYcPHyItLU25jomJCapWrar8v0yZMnBwcMDVq1cBAPHx8Zg8eTJsbGyUr/79+yMlJUUln7epS9exY0e8ePEC3t7e6N+/P7Zs2aIcVk8TgYGByr9dXFwAQOXJoJxlDx48UMYTExOjEk/Tpk2RlZWFW7duaVyurMJu9/Tp02Fvb6/y2vvs8r8WLxERERERERERERERwI6m/5zk5GS0atUKgYGB2LRpE86dO4dFixYBAF6/fq1xPqmpqYiMjERcXJzydenSJSQmJhb4FJC6dO7u7khISMDixYthaWmJwYMHo27dukhPT9coLlNTU+XfCoUi32VZWVnKeAYOHKgST3x8PBITE1G6dGmN94c6RkZGyuH6cry5TYXd7rFjx+Lp06cqr//ZldNZvEREREREREREREREmuDQee+50qVLw9TUFKdOnYKHhwcA4PHjx7h+/Trq1auXa/1z584hKysLs2fPhpFRdj/jhg0bcq2XkZGBs2fPKodvS0hIwJMnTxAQEAAAqFy5MhISEuDj45NvbKampsjMzFRZpkk6S0tLhISEICQkBEOGDEGZMmVw6dIlVK5cWc3eKLzKlSvjypUrBcZTGGZmZgCQa7udnZ2RkpKi/P/Zs2e5npgqzHabm5vD3NxcZRmHzSMiIiIiIiIiIiKifxs7mt5zNjY26Nu3L8LDw1GkSBEUK1YM48aNU3Yivc3Hxwfp6elYsGABQkJCcOzYMXzzzTe51jM1NcVnn32G+fPnw8TEBGFhYahRo4ay4ykiIgKtWrWCh4cHOnToACMjI8THx+O3337D1KlTAWTP/3TgwAEEBwfD3Nwcjo6OatPFxMQgMzMT1atXh5WVFX744QdYWlqiVKlS72T/jR49GjVq1EBYWBj69esHa2trXLlyBfv27cPChQsLnV+pUqWgUCiwY8cOtGjRApaWlrCxsUHDhg0RExODkJAQODg4ICIiAsbG/9cx9G9vNxEREREREREREZEuvD2SE/33cOi8D8DXX3+NOnXqICQkBI0bN0bt2rXx0Ucf5bluxYoVMWfOHMycORPly5fHmjVrMH369FzrWVlZYfTo0ejWrRuCg4NhY2OD9evXK99v2rQpduzYgb1796Jq1aqoUaMG5s6dq9IxMnv2bOzbtw/u7u4ICgrSKJ2DgwO+/fZbBAcHIzAwEPv378fPP/+MIkWK6HKXKQUGBuLIkSO4fv066tSpg6CgIERERKB48eJa5VeiRAlERkZizJgxcHFxQVhYGIDsoe7q1auHVq1aoWXLlmjTpo3K0Hz/9nYTEREREREREREREemCQrC7keiDsNj9E+k8MtWvUiDZwfvsJQNwz3olGQFwwcxc/UoFMJI8o1ro4IycqZBL71eI+drycvn/DyEpI0tyG7Iky/d+LZsD8LeJfn/L0ff3H6TzWF5S7rzyzAB+zmIqeUylS7ZFEwO4y3otuQ0vFXIbYSEkA9BBDOaSMdjKnxIguxueS6a31kFblI3BXDKGV/JNCZaSMcg2hSKZ8hWRaiS3I2yy5GKQLT9VB9cG2XOrbAzpkK9HU8jtR9kY7HVwbpa975W939NsFt+Cmapf5Z3GoIt7f9ks9L0PAMBG8uQqe69jJrkTZcsHgE7Vf5dKfybWVSp9mkL+5PzYWC6PF5Ih6OB2TfreX/Y+o++FyXIZAPg2KEIqvez3Qjo4HKS/05A17I78Z+n/ohKOnDu+sP54fFnfIeiUAXwFRERERERERERERERERO8jdjTRe+Ho0aOwsbHJ90VERERERERERERERP8+E30HQKSJKlWqIC4uTt9hEBERERERERERERHRG9jRRO8FS0tL+Pj46DsMIiIiIiIiIiIiIiJ6AzuaiD4Qupj4UpZsDPclz0iPhLlkBPITjdvoeXJqQH4C0itmZvJBSJKdVFh2cun7JvIV8VIyC9kJtnVBtj3KtsUMueQAgH8kt8FKz5NDy05OTdlkz8268EyyLaZLTveeqZCfVVk2BnMhF4MujgdjyTzSJevxrql8PchO0v3YWC4G2fJlz6uA/LnVWLJ8Xdx36zsGCwP48PBI8njKkjwnAUC65LT1sjFYSJ4XAfn7XtndKL8FwJ+SnwMzJTfCWHIrZMsHgJgzJaXSZ0l+FLbRwTlBtjmbS+5GXXx+ei55Xvqk8z9S6b8NipALAED/C5Ol0i/VQQyyZO/XOE+MfmQJA/jQRXrFY4+IiIiIiIiIiIiIiIi0wo4mIiIiIiIiIiIiIiIi0go7moiIiIiIiIiIiIiIiEgr7GgiFfXr18fw4cP1HUaeJk2ahEqVKukkr/v376NJkyawtraGg4ODRmliYmJU1tVlPERERERERERERERE7yN2NJFBUigU2Lp1q8qykSNH4sCBAzrJf+7cuUhJSUFcXByuX7+uUZrOnTtrvC4RERERERERERER0X+Bib4DINKUjY0NbGxsdJJXUlISPvroI/j6+mqcxtLSEpaWljopP8fr169hZmam0zyJiIiIiIiIiIiI/i0CQt8hkJ7xiSbKJSsrC6NGjYKTkxNcXV0xadIk5XtPnjxBv3794OzsDDs7OzRs2BDx8fHK93OGk1u6dCnc3d1hZWWFTp064enTp8p1zpw5gyZNmqBo0aKwt7dHvXr1cP78eeX7np6eAIC2bdtCoVAo/89rqLrvv/8e5cqVg7m5Odzc3BAWFqZ2+zw9PbFp0yasWrUKCoUCoaGhAIA5c+agQoUKsLa2hru7OwYPHozU1FRlureHzntbXsMOtmnTRpl/TtlTpkxBz549YWdnhwEDBgAAYmNjUadOHVhaWsLd3R1Dhw7F8+fP1W4LEREREREREREREZE+saOJclm5ciWsra1x6tQpzJo1C5MnT8a+ffsAAB07dsSDBw+wa9cunDt3DpUrV0ajRo3w6NEjZfobN25gw4YN+Pnnn7F7925cuHABgwcPVr7/zz//oFevXoiNjcXJkyfh6+uLFi1a4J9//gGQ3REFACtWrEBKSory/7ctWbIEQ4YMwYABA3Dp0iVs374dPj4+arfvzJkzaNasGTp16oSUlBRER0cDAIyMjDB//nxcvnwZK1euxMGDBzFq1CjtdmIBoqKiULFiRVy4cAETJkxAUlISmjVrhvbt2+PixYtYv349YmNjNeo0IyIiIiIiIiIiIiLSJw6dR7kEBgZi4sSJAABfX18sXLgQBw4cgKWlJU6fPo0HDx7A3NwcQHanydatW7Fx40bl0zkvX77EqlWrUKJECQDAggUL0LJlS8yePRuurq5o2LChSnnLli2Dg4MDjhw5glatWsHZ2RkA4ODgAFdX13zjnDp1KkaMGIFhw4Ypl1WtWlXt9jk7O8Pc3ByWlpYq+b/5NJKnpyemTp2KQYMGYfHixWrzLIyGDRtixIgRyv/79euH7t27K8v39fXF/PnzUa9ePSxZsgQWFhY6LZ+IiIiIiIiIiIiISFfY0US5BAYGqvzv5uaGBw8eID4+HqmpqShSpIjK+y9evEBSUpLyfw8PD2UnEwDUrFkTWVlZSEhIgKurK/7880+MHz8ehw8fxoMHD5CZmYm0tDTcuXNH4xgfPHiAe/fuoVGjRlpuZW779+/H9OnTce3aNTx79gwZGRl4+fIl0tLSYGVlpbNyqlSpovJ/fHw8Ll68iDVr1iiXCSGQlZWFW7duISAgIFcer169wqtXr1SWpYtMmCqMdRYnEREREREREREREZE67GiiXExNTVX+VygUyMrKQmpqKtzc3HD48OFcaQqau+htvXr1wsOHDxEdHY1SpUrB3NwcNWvWxOvXrzXOw9LSUuN1NZGcnIxWrVrh008/xbRp0+Dk5ITY2Fj07dsXr1+/1qijycjICEKoTnyXnp6eaz1ra2uV/1NTUzFw4EAMHTo017oeHh55ljV9+nRERkaqLGtuWwEt7APzXJ+IiIiIiIiIiIiI6F1gRxNprHLlyrh//z5MTEzg6emZ73p37tzBvXv3ULx4cQDAyZMnYWRkBH9/fwDAsWPHsHjxYrRo0QIAcPfuXfz9998qeZiamiIzMzPfMmxtbeHp6YkDBw6gQYMGklsGnDt3DllZWZg9ezaMjLKnLtuwYUOh8nB2dkZKSory/8zMTPz2229q46tcuTKuXLmi0fxSOcaOHYsvvvhCZdnysgMLFS8RERERERERERGRrLd/fE//PUb6DoDeH40bN0bNmjXRpk0b7N27F8nJyTh+/DjGjRuHs2fPKtezsLBAr169EB8fj6NHj2Lo0KHo1KmTcj4kX19frF69GlevXsWpU6fQvXv3XE8o5XQi3b9/H48fP84znkmTJmH27NmYP38+EhMTcf78eSxYsECrbfPx8UF6ejoWLFiAmzdvYvXq1fjmm28KlUfDhg3xyy+/4JdffsG1a9fw6aef4smTJ2rTjR49GsePH0dYWBji4uKQmJiIbdu2ISwsLN805ubmsLOzU3lx2DwiIiIiIiIiIiIi+rexo4k0plAosHPnTtStWxe9e/eGn58funTpgtu3b8PFxUW5no+PD9q1a4cWLVrgf//7HwIDA7F48WLl+8uXL8fjx49RuXJl9OjRA0OHDkWxYsVUypo9ezb27dsHd3d3BAUF5RlPr169MG/ePCxevBjlypVDq1atkJiYqNW2VaxYEXPmzMHMmTNRvnx5rFmzBtOnTy9UHn369EGvXr3Qs2dP1KtXD97e3ho9bRUYGIgjR47g+vXrqFOnDoKCghAREaF8IoyIiIiIiIiIiIiIyFApBJ9rIx2aNGkStm7diri4OH2H8p+z0P0T6TxkTwYKyfSvJDMw08HZ7IVk97tNllx62X0AAJaS+yFDsnxdXFRk90OWZHr7/Efu1NhLybZkJLkjP7v7g1wGAKI95M4r5npuiwDwj2Q9WOl5G3RxXnsteTy8VMgFYSHkT2yyMRSRPClIFg8AeCr54HG65NnVVPoqLR+DvWQ96OL6IntMyZ7bdXGdl71EyT4DL1u+7HkVkD+3ZkrWg+yxAMgfk7IxFJXdCTrwSLIxZumgHowk60E2BtnzIiDfng3hfi1V8tyaKVkPxpLtQLZ8ALCSvF+S/Bgq/TkWAGRv+Uwld6Ps5ycAeC7ZFkM7/yOVftV6W7kAAPS/MFkq/dKgCOkY9E32qYrBOvgs/V/kYl9G3yG8d/58ek3fIegUn2giIiIiIiIiIiIiIiIirbCjiT44a9asgY2NTZ6vcuXK6Ts8IiIiIiIiIiIiIqIPhom+A6APy6RJkzBp0iS9xvDxxx+jevXqeb5namr6L0dDRERERERERERE9OHSxZC29H5jRxN9cGxtbWFrKz+uLRERERERERERERERFYxD5xEREREREREREREREZFW+EQT0QdCFw+oKnSQhwybLLn0T43lYzCX3JGSm4B0HVSCQ6Zc+meSVwbZfaiLPAyhHixkgzAAVpLbYCmZ/qEO7lLMJNuS7HnphWRbMoTBB6yE3Ebo4lCQjcFW8ryYqYNzQqpkZRpJXqV1cY2XjcFasjG80sFP5GTrMkOyfCMDuGGTjkGyfF3cJ8je8r2STC97LADyx6SxZA6y1zcAMJK8Sj01ltsGU8lrg27IxWCng3qQvMThmeQBpYvrvOzpXRfHpL7LN5E8N8pG8FoHu1A2CwvJfaCLz3CmkjGsWK//0XWWBkVIpR94YbJU+l/LjZVKDwD/KOROTAEOj6VjIKLC4xNNREREREREREREREREpBV2NBEREREREREREREREZFWOHQeERERERERERERERFpRQhDGPid9IlPNOlR/fr1MXz4cH2HQTqmUCiwdetWfYdBRERERERERERERPTO8YkmPdq8eTNMTU31HQbpWEpKChwdHfUdBhERERERERERERHRO8eOJj1ycnLSdwj/OUIIZGZmwsTk3TV9V1fXd5Y3EREREREREREREZEh4dB5evTm0Hmenp6YOnUqevbsCRsbG5QqVQrbt2/HX3/9hdatW8PGxgaBgYE4e/asMn1MTAwcHBywY8cO+Pv7w8rKCh06dEBaWhpWrlwJT09PODo6YujQocjMzFSmy2toNwcHB8TExAAAkpOToVAosHnzZjRo0ABWVlaoWLEiTpw4oZJm06ZNKFeuHMzNzeHp6YnZs2ervL948WL4+vrCwsICLi4u6NChg/I9T09PzJs3T2X9SpUqYdKkSSpxLlmyBM2bN4elpSW8vb2xceNG5fs5ca5btw61atWChYUFypcvjyNHjijXOXz4MBQKBXbt2oWPPvoI5ubmiI2NRVZWFqZPnw4vLy9YWlqiYsWKKnk/fvwY3bt3h7OzMywtLeHr64sVK1YAAF6/fo2wsDC4ubnBwsICpUqVwvTp0/Pdv5cuXULDhg1haWmJIkWKYMCAAUhNTVW+HxoaijZt2iAqKgpubm4oUqQIhgwZgvT0dBARERERERERERERGTJ2NBmQuXPnIjg4GBcuXEDLli3Ro0cP9OzZE5988gnOnz+P0qVLo2fPniqTq6WlpWH+/PlYt24ddu/ejcOHD6Nt27bYuXMndu7cidWrV2Pp0qUqnSiaGjduHEaOHIm4uDj4+fmha9euyMjIAACcO3cOnTp1QpcuXXDp0iVMmjQJEyZMUHZWnT17FkOHDsXkyZORkJCA3bt3o27duoWOYcKECWjfvj3i4+PRvXt3dOnSBVevXlVZJzw8HCNGjMCFCxdQs2ZNhISE4OHDhyrrjBkzBjNmzMDVq1cRGBiI6dOnY9WqVfjmm29w+fJlfP755/jkk0+UnVQTJkzAlStXsGvXLly9ehVLlixB0aJFAQDz58/H9u3bsWHDBiQkJGDNmjXw9PTMM/7nz5+jadOmcHR0xJkzZ/DTTz9h//79CAsLU1nv0KFDSEpKwqFDh7By5UrExMQo9yURERERERERERERkaHi0HkGpEWLFhg4cCAAICIiAkuWLEHVqlXRsWNHAMDo0aNRs2ZN/Pnnn8rh2dLT07FkyRKULl0aANChQwesXr0af/75J2xsbFC2bFk0aNAAhw4dQufOnQsVz8iRI9GyZUsAQGRkJMqVK4cbN26gTJkymDNnDho1aoQJEyYAAPz8/HDlyhV8/fXXCA0NxZ07d2BtbY1WrVrB1tYWpUqVQlBQUKH3SceOHdGvXz8AwJQpU7Bv3z4sWLAAixcvVq4TFhaG9u3bAwCWLFmC3bt3Y/ny5Rg1apRyncmTJ6NJkyYAgFevXuGrr77C/v37UbNmTQCAt7c3YmNjsXTpUtSrVw937txBUFAQqlSpAgAqHUl37tyBr68vateuDYVCgVKlSuUb/9q1a/Hy5UusWrUK1tbWAICFCxciJCQEM2fOhIuLCwDA0dERCxcuhLGxMcqUKYOWLVviwIED6N+/f575vnr1Cq9evVJZli4yYaowVr9TiYiIiIiIiIiIiHQk640HI+i/iU80GZDAwEDl3zkdEBUqVMi17MGDB8plVlZWyk6mnHU8PT1hY2OjsuzNNNrE4+bmplL21atXERwcrLJ+cHAwEhMTkZmZiSZNmqBUqVLw9vZGjx49sGbNGqSlpRU6hpyOoDf/f/uJpjfXMTExQZUqVXKtk9NhBAA3btxAWloamjRpAhsbG+Vr1apVSEpKAgB8+umnWLduHSpVqoRRo0bh+PHjyvShoaGIi4uDv78/hg4dir179+Yb/9WrV1GxYkVlJxOQvZ+ysrKQkJCgXFauXDkYG/9fJ5Gbm1uBdTZ9+nTY29urvPY9u5zv+kRERERERERERERE7wI7mgyIqamp8m+FQpHvsqysrDzT5KyT17I30ygUCpXh9wDkOR+QurILYmtri/Pnz+PHH3+Em5sbIiIiULFiRTx58gQAYGRkpFEMuvJmR0/O/Ei//PIL4uLilK8rV64ohxhs3rw5bt++jc8//xz37t1Do0aNMHLkSABA5cqVcevWLUyZMgUvXrxAp06dVOaf0oa6Onvb2LFj8fTpU5VXE7tyUjEQERERERERERERERUWO5r+g5ydnZGSkqL8PzExsdBPGwUEBODYsWMqy44dOwY/Pz/lkzkmJiZo3LgxZs2ahYsXLyI5ORkHDx7MM4Znz57h1q1buco5efJkrv8DAgLyXScjIwPnzp3Ltc6bypYtC3Nzc9y5cwc+Pj4qL3d3d+V6zs7O6NWrF3744QfMmzcPy5YtU75nZ2eHzp0749tvv8X69euxadMmPHr0KM/9FB8fj+fPn6vsJyMjI/j7++cbozrm5uaws7NTeXHYPCIiIiIiIiIiIiL6t3GOpv+ghg0bYuHChahZsyYyMzMxevToXE/UqDNixAhUrVoVU6ZMQefOnXHixAksXLhQOXfSjh07cPPmTdStWxeOjo7YuXMnsrKylJ0rDRs2RExMDEJCQuDg4ICIiAiVoeNy/PTTT6hSpQpq166NNWvW4PTp01i+fLnKOosWLYKvry8CAgIwd+5cPH78GH369Mk3dltbW4wcORKff/45srKyULt2bTx9+hTHjh2DnZ0devXqhYiICHz00UcoV64cXr16hR07dig7r+bMmQM3NzcEBQXByMgIP/30E1xdXeHg4JCrrO7du2PixIno1asXJk2ahL/++gufffYZevTooRwKkYiIiIiIiIiIiIjofcWOpv+g2bNno3fv3qhTpw6KFy+O6OhonDt3rlB5VK5cGRs2bEBERASmTJkCNzc3TJ48GaGhoQAABwcHbN68GZMmTcLLly/h6+uLH3/8EeXKZQ/vNnbsWNy6dQutWrWCvb09pkyZkucTTZGRkVi3bh0GDx4MNzc3/PjjjyhbtqzKOjNmzMCMGTMQFxcHHx8fbN++HUWLFi0w/ilTpsDZ2RnTp0/HzZs34eDggMqVK+PLL78EAJiZmWHs2LFITk6GpaUl6tSpg3Xr1gHI7qiaNWsWEhMTYWxsjKpVq2Lnzp0wMsr9gKCVlRX27NmDYcOGoWrVqrCyskL79u0xZ86cQu1vIiIiIiIiIiIiIiJDpBBvT5RDZCAUCgW2bNmCNm3a5Pl+cnIyvLy8cOHCBVSqVOlfjc0QLXD/RDoPhQ7ikGEqeTZ6qoPRA80lYzCWTJ+mgwFNnTPk0v8l+RME2X0IAEaSeWRJNmbZegTk27Osgb//IJ3HtyXlziuWmk3rl6+HOvg5jOThAAfJbXgh2RZ10YxeS8Yge1qS3IU6iaGYZEPI1MEF8i/Ja5RsW9DFNV42hiKZculf6eAaKdseDeGYlr3G6fsaa6eDk4Lsuf2VAdSjvu+7XWV3IgAjyT1x11RuL8i2ZUNQTPK8CACyWTyTvD7p4jqfru8DwgDI3jfL7kJDOK/ZSO4DXbQj2fYse9+tC7IhDLwwWSr9r+XGSkYA/CM5NUSAw2Op9P7Xdkml/69ytPHRdwjvncepN/Qdgk5xjiYiIiIiIiIiIiIiIiLSCjuaiIiIiIiIiIiIiIiISCuco4kMlrpRHT09PdWuQ0RERERERERERERE7w6faCIiIiIiIiIiIiIiIiKt8IkmItIZ2efLZE9Ipjp4wE128k/JuXRhpYPZdGVjkN2PhjAxs+xuNNdBDJkGMBGsLNlteC75cxZdtCUjyW3QwVzpeid7bk2XTG8qmV4XMaRKtkWhg+NZ3/Wgi1+XyU44bwgTZOti0noZstdoAFDIXqf1XL5sekA3x+T7TnY3yp4Xs8lVhLHkRujillO2KcnG8MoA2rLs/ZYumpLsuVm2HvTdDgAgS89tQfZ4BOT3g2xbMtHBNujm3Kg9Azgl4NdyY6XS1708XTqGmEoRUul/f15EKr2/VGqi/y52NBERERERERERERERkVaydNLtTu8zDp1HREREREREREREREREWmFHExEREREREREREREREWmFHU30Xqlfvz6GDx+us/xCQ0PRpk0bneWnKYVCga1bt/7r5RIRERERERERERER6RLnaCLSg5SUFDg6Ouo7DCIiIiIiIiIiIiIiKexoItIDV1dXfYdARERERERERERERCSNQ+fRe+vVq1cYOXIkSpQoAWtra1SvXh2HDx9Wvj9p0iRUqlRJJc28efPg6emZb55nzpyBs7MzZs6cCQB48uQJ+vXrB2dnZ9jZ2aFhw4aIj4/PVcb3338PDw8P2NjYYPDgwcjMzMSsWbPg6uqKYsWKYdq0aSrlvD103ujRo+Hn5wcrKyt4e3tjwoQJSE9P13rfEBEREREREREREf0bhBB8FfL1oeETTfTeCgsLw5UrV7Bu3ToUL14cW7ZsQbNmzXDp0iX4+voWOr+DBw+iXbt2mDVrFgYMGAAA6NixIywtLbFr1y7Y29tj6dKlaNSoEa5fvw4nJycAQFJSEnbt2oXdu3cjKSkJHTp0wM2bN+Hn54cjR47g+PHj6NOnDxo3bozq1avnWbatrS1iYmJQvHhxXLp0Cf3794etrS1GjRql/Q4iIiIiIiIiIiIiInrH2NFE76U7d+5gxYoVuHPnDooXLw4AGDlyJHbv3o0VK1bgq6++KlR+W7ZsQc+ePfHdd9+hc+fOAIDY2FicPn0aDx48gLm5OQAgKioKW7duxcaNG5WdUVlZWfj+++9ha2uLsmXLokGDBkhISMDOnTthZGQEf39/zJw5E4cOHcq3o2n8+PHKvz09PTFy5EisW7cu346mV69e4dWrVyrL0kUmTBXGhdpuIiIiIiIiIiIiIiIZ7Gii99KlS5eQmZkJPz8/leWvXr1CkSJFCpXXqVOnsGPHDmzcuBFt2rRRLo+Pj0dqamqu/F68eIGkpCTl/56enrC1tVX+7+LiAmNjYxgZGakse/DgQb4xrF+/HvPnz0dSUhJSU1ORkZEBOzu7fNefPn06IiMjVZY1s62AFvaBareXiIiIiIiIiIiIiEhX2NFE76XU1FQYGxvj3LlzMDZWfYrHxsYGAGBkZJRrvMu85j0qXbo0ihQpgu+//x4tW7aEqampsgw3NzeVeZ9yODg4KP/OWT+HQqHIc1lWVlae23LixAl0794dkZGRaNq0Kezt7bFu3TrMnj07740HMHbsWHzxxRcqy74rOzDf9YmIiIiIiIiIiIiI3gV2NNF7KSgoCJmZmXjw4AHq1KmT5zrOzs64f/8+hBBQKBQAgLi4uFzrFS1aFJs3b0b9+vXRqVMnbNiwAaampqhcuTLu378PExMTeHp6vrNtOX78OEqVKoVx48Ypl92+fbvANObm5srh/HJw2DwiIiIiIiIiIiIi+rcZqV+FyPD4+fmhe/fu6NmzJzZv3oxbt27h9OnTmD59On755RcAQP369fHXX39h1qxZSEpKwqJFi7Br16488ytWrBgOHjyIa9euoWvXrsjIyEDjxo1Rs2ZNtGnTBnv37kVycjKOHz+OcePG4ezZszrbFl9fX9y5cwfr1q1DUlIS5s+fjy1btugsfyIiIiIiIiIiIqJ3JUsIvgr5+tCwo4neWytWrEDPnj0xYsQI+Pv7o02bNjhz5gw8PDwAAAEBAVi8eDEWLVqEihUr4vTp0xg5cmS++bm6uuLgwYO4dOkSunfvjqysLOzcuRN169ZF79694efnhy5duuD27dtwcXHR2XZ8/PHH+PzzzxEWFoZKlSrh+PHjmDBhgs7yJyIiIiIiIiIiIiJ6VxTi7UlsiOi9tMD9E+k8FJLpZU8mVpIZPDOArnNTAzij2uU9HZjGHkqOwmhiAPsgQ7IxW+pgGySrQdrguz9I5/GN5HlFIbkfX8uelCDfFqwkKzJdsnxdHE5ZkjHknt2wcEzVr/LOY7CVrEehg7b4Ss/1oIsBdjMl0xtCPWRIppetR13cqsheX2RjkC3fRgcXSNnrg2x6XZyb9X3fLXs86kKqZGP8EOrB3gDq4aUOzq2y9H1M6rsdALq5X5JhrION0PfxIHufAsifl2QZwOGIgNdyd511L0+XjiGmUoRUetn7tTAdfJb+L7Kx8tJ3CO+d1LRb+g5Bpwzga1kiIiIiIiIiIiIiIiJ6H7GjiYiIiIiIiIiIiIiIiLTCjiYiIiIiIiIiIiIiIiLSiom+AyAiIiIiIiIiIiIioveT0MmMdfQ+Y0cTESnJXhJkJxqXnaDbEB7RlI1BFxOYpkkGYfIB3BvI1oPc9KfZZCf0NYC5oaVjkD0n6IJsWxAGMGG8vul7cmrAMGLQN0PYB/o+nnRB9piU/fCki3O7vu93pK+xBtAO9L0PKZtsPeiiHvV9v6Xv8gEgi8ekQTD+AG4aZZvSK8kMMnXQls0k60H2GqeLbZBtS/8o5D7FxVSKkAsAQGjcZKn0y4PkYyCiwuP1nIiIiIiIiIiIiIiIiLTCjiYiIiIiIiIiIiIiIiLSCjuaiIiIiIiIiIiIiIiISCvsaCK9q1+/PoYPH67XGA4fPgyFQoEnT57oNQ4iIiIiIiIiIiIioveJ7Hy2RERERERERERERET0H5UlhL5DID3jE01ERERERERERERERESkFXY0kcGYPHkyypcvn2t5pUqVMGHCBABAaGgo2rRpg6+++gouLi5wcHDA5MmTkZGRgfDwcDg5OaFkyZJYsWKFMn1ycjIUCgXWrVuHWrVqwcLCAuXLl8eRI0dylXXu3DlUqVIFVlZWqFWrFhISElTeX7JkCUqXLg0zMzP4+/tj9erVKu8rFAosXboUrVq1gpWVFQICAnDixAncuHED9evXh7W1NWrVqoWkpCRlmqSkJLRu3RouLi6wsbFB1apVsX//fql9SURERERERERERET0b2BHExmMPn364OrVqzhz5oxy2YULF3Dx4kX07t1buezgwYO4d+8efv31V8yZMwcTJ05Eq1at4OjoiFOnTmHQoEEYOHAgfv/9d5X8w8PDMWLECFy4cAE1a9ZESEgIHj58qLLOuHHjMHv2bJw9exYmJibo06eP8r0tW7Zg2LBhGDFiBH777TcMHDgQvXv3xqFDh1TymDJlCnr27Im4uDiUKVMG3bp1w8CBAzF27FicPXsWQgiEhYUp109NTUWLFi1w4MABXLhwAc2aNUNISAju3Lmjk/1KRERERERERERERPSusKOJDEbJkiXRtGlTlaeRVqxYgXr16sHb21u5zMnJCfPnz4e/vz/69OkDf39/pKWl4csvv4Svry/Gjh0LMzMzxMbGquQfFhaG9u3bIyAgAEuWLIG9vT2WL1+uss60adNQr149lC1bFmPGjMHx48fx8uVLAEBUVBRCQ0MxePBg+Pn54YsvvkC7du0QFRWlkkfv3r3RqVMn+Pn5YfTo0UhOTkb37t3RtGlTBAQEYNiwYTh8+LBy/YoVK2LgwIEoX748fH19MWXKFJQuXRrbt2/Pd1+9evUKz549U3mli8xC73MiIiIiIiIiIiIiIhnsaCKD0r9/f/z44494+fIlXr9+jbVr16o8VQQA5cqVg5HR/zVdFxcXVKhQQfm/sbExihQpggcPHqikq1mzpvJvExMTVKlSBVevXlVZJzAwUPm3m5sbACjzuXr1KoKDg1XWDw4OLjAPFxcXAFCJz8XFBS9fvsSzZ88AZD/RNHLkSAQEBMDBwQE2Nja4evVqgU80TZ8+Hfb29iqvfc8u57s+EREREREREREREdG7YKLvAIjeFBISAnNzc2zZsgVmZmZIT09Hhw4dVNYxNTVV+V+hUOS5LCsrq9Dlv5mPQqEAgELnk1ceBeU7cuRI7Nu3D1FRUfDx8YGlpSU6dOiA169f51vG2LFj8cUXX6gs+67swELFSURERERERERERCRLCKHvEEjP+EQTGRQTExP06tULK1aswIoVK9ClSxdYWlrqJO+TJ08q/87IyMC5c+cQEBCgcfqAgAAcO3ZMZdmxY8dQtmxZqbiOHTuG0NBQtG3bFhUqVICrqyuSk5MLTGNubg47OzuVl6nCWCoOIiIiIiIiIiIiIqLC4hNNZHD69eun7AB6u2NHxqJFi+Dr64uAgADMnTsXjx8/zjUsX0HCw8PRqVMnBAUFoXHjxvj555+xefNm7N+/XyouX19fbN68GSEhIVAoFJgwYYJWT2MREREREREREREREf3b2NFEBsfX1xe1atXCo0ePUL16dZ3lO2PGDMyYMQNxcXHw8fHB9u3bUbRoUY3Tt2nTBtHR0YiKisKwYcPg5eWFFStWoH79+lJxzZkzB3369EGtWrVQtGhRjB49Wjl/ExERERERERERERGRIVMIDqBIBkYIAV9fXwwePDjXPETaSE5OhpeXFy5cuIBKlSrJB2igFrh/ou8QIDt4n7Hk2eilAQwGaiq5DZm6iEEyfboOYtC3LIVceiMdXBll60H2ucbBd3+QzAFYLHlekT2mX0vWIyDfFswlt8EQjifZffAhsJA8oIQO9uEr1oP08aQLssek7PHEZ9bl75UAQDaLDB6PsDKAxpgmee+ui1t/fe8GQ6gHQ/gMZQC7Qe90cW5838neJ2Tq4NyukL33l4xBF9sg+xmsZLrcEfmXifxJJTRuslT65UERUukH6eCz9H+RhYWHvkN477x8eUffIegUn2gig/LXX39h3bp1uH//Pnr37q3vcIiIiIiIiIiIiIiIqADsaCKDUqxYMRQtWhTLli2Do6OjvsMhIiIiIiIiIiIiogII6WfO6X3HjiYyKO9iJEdPT893ki8RERERERERERER0X+dAYzGS0RERERERERERERERO8jdjQRERERERERERERERGRVjh0HhHpTKZkelPJ9GkKyQwAOEluRKZkDBk62AYbyW14InllsMuSSw8AxpKjXcoOlvlKB/UgG4Qh/BJENgZzyX3w0hB2gp7p4pwge0iaSdbjax1sg2wMsm1RF8ONPzeWSy97054umR6Qv04rJPej7DUWAGSzkD2eZO+VPgRGOqhHIz2Piq2Ly5NsW9L3NVoXUvUdAPRfD7ogJI8p2eMpSwfHNM+NkL5AWUrWY4ZccgDy9ypWkgekLj476OI7BX2T3Q0BDo+l0v/+vIhkBMDyoAip9H0vTJaOgYgKzxDui4iIiIiIiIiIiIiIiOg9xCeaiIiIiIiIiIiIiIhIK0IYwOPSpFcfxBNNycnJUCgUiIuLAwAcPnwYCoUCT548yTdNTEwMHBwc/pX43jdv78/81K9fH8OHD/9XYvoQTZo0CZUqVdJ3GEREREREREREREREWvsgOpreVqtWLaSkpMDe3l4n+f3XOgTc3d2RkpKC8uXLA9Cs486QKBQKbN26Vad5votOtZEjR+LAgQM6zZOIiIiIiIiIiIiI6N/0QQ6dZ2ZmBldXV32H8a8RQiAzMxMmJrqpTmNj4//U/tMXGxsb2NjY6DsMIiIiIiIiIiIiIiKtGdQTTRs3bkSFChVgaWmJIkWKoHHjxnj+/DmysrIwefJklCxZEubm5qhUqRJ2796dbz55PYETExMDDw8PWFlZoW3btnj48KFGMcXExCAyMhLx8fFQKBRQKBSIiYnJc3i5J0+eQKFQ4PDhwypx7NmzB0FBQbC0tETDhg3x4MED7Nq1CwEBAbCzs0O3bt2QlpamzOfVq1cYOnQoihUrBgsLC9SuXRtnzpzJtX27du3CRx99BHNzc8TGxua7DU+fPoWxsTHOnj0LAMjKyoKTkxNq1KihXOeHH36Au7s7ANWh85KTk9GgQQMAgKOjIxQKBUJDQ5XpsrKyMGrUKDg5OcHV1RWTJk3SaL8C2U8eLV26FK1atYKVlRUCAgJw4sQJ3LhxA/Xr14e1tTVq1aqFpKQklXTbtm1D5cqVYWFhAW9vb0RGRiIjIwMA4OnpCQBo27YtFAqF8v+kpCS0bt0aLi4usLGxQdWqVbF//36VfBcvXgxfX19YWFjAxcUFHTp0AACEhobiyJEjiI6OVraB5ORkZGZmom/fvvDy8oKlpSX8/f0RHR2tkufhw4dRrVo1WFtbw8HBAcHBwbh9+zaA3E/KFbQuEREREREREREREZEhMpiOppSUFHTt2hV9+vTB1atXcfjwYbRr1w5CCERHR2P27NmIiorCxYsX0bRpU3z88cdITEzUKO9Tp06hb9++CAsLQ1xcHBo0aICpU6dqlLZz584YMWIEypUrh5SUFKSkpKBz586F2rZJkyZh4cKFOH78OO7evYtOnTph3rx5WLt2LX755Rfs3bsXCxYsUK4/atQobNq0CStXrsT58+fh4+ODpk2b4tGjRyr5jhkzBjNmzMDVq1cRGBiYb/n29vaoVKmSsgPs0qVLUCgUuHDhAlJTUwEAR44cQb169XKldXd3x6ZNmwAACQkJSElJUelMWblyJaytrXHq1CnMmjULkydPxr59+zTeN1OmTEHPnj0RFxeHMmXKoFu3bhg4cCDGjh2Ls2fPQgiBsLAw5fpHjx5Fz549MWzYMFy5cgVLly5FTEwMpk2bBgDKDrkVK1YgJSVF+X9qaipatGiBAwcO4MKFC2jWrBlCQkJw584dAMDZs2cxdOhQTJ48GQkJCdi9ezfq1q0LAIiOjkbNmjXRv39/ZRtwd3dHVlYWSpYsiZ9++glXrlxBREQEvvzyS2zYsAEAkJGRgTZt2qBevXq4ePEiTpw4gQEDBkChUOTaD4VZl4iIiIiIiIiIiIjIUBjM0HkpKSnIyMhAu3btUKpUKQBAhQoVAABRUVEYPXo0unTpAgCYOXMmDh06hHnz5mHRokVq846OjkazZs0watQoAICfnx+OHz9e4FNROSwtLWFjYwMTExOth5ObOnUqgoODAQB9+/bF2LFjkZSUBG9vbwBAhw4dcOjQIYwePRrPnz/HkiVLEBMTg+bNmwMAvv32W+zbtw/Lly9HeHi4Mt/JkyejSZMmGsVQv359HD58GCNHjsThw4fRpEkTXLt2DbGxsWjWrBkOHz6s3D9vMjY2hpOTEwCgWLFicHBwUHk/MDAQEydOBAD4+vpi4cKFOHDggMZx9e7dG506dQIAjB49GjVr1sSECRPQtGlTAMCwYcPQu3dv5fqRkZEYM2YMevXqBQDw9vbGlClTMGrUKEycOBHOzs4AAAcHB5X6qlixIipWrKj8f8qUKdiyZQu2b9+OsLAw3LlzB9bW1mjVqhVsbW1RqlQpBAUFAcjuqDMzM4OVlZVKnsbGxoiMjFT+7+XlhRMnTmDDhg3o1KkTnj17hqdPn6JVq1YoXbo0ACAgICDP/VCYdYmIiIiIiIiIiIgMhRBC3yGQnhnME00VK1ZEo0aNUKFCBXTs2BHffvstHj9+jGfPnuHevXvKjpocwcHBuHr1qkZ5X716FdWrV1dZVrNmTZ3Frs6bTxu5uLjAyspK2cmUs+zBgwcAsod4S09PV9leU1NTVKtWLdf2VqlSReMY6tWrh9jYWGRmZuLIkSOoX7++svPp3r17yuHqZLYNANzc3JTbUtj0Li4uAP6vgzFn2cuXL/Hs2TMAQHx8PCZPnqyc38jGxkb5pNGbww++LTU1FSNHjkRAQAAcHBxgY2ODq1evKp9oatKkCUqVKgVvb2/06NEDa9asKTC/HIsWLcJHH30EZ2dn2NjYYNmyZco8nZycEBoaiqZNmyIkJATR0dFISUnJM5/CrAtkD6/47NkzlVe6yFQbLxERERERERERERGRLhlMR5OxsTH27duHXbt2oWzZsliwYAH8/f1x69YtfYeWJyOj7F33Zm9tenp6nuuampoq/1YoFCr/5yzLysoqdAzW1tYar1u3bl38888/OH/+PH799VeVjqYjR46gePHi8PX1LXQMstvy9r7Jb1lOnqmpqYiMjERcXJzydenSJSQmJsLCwiLfckaOHIktW7bgq6++wtGjRxEXF4cKFSrg9evXAABbW1ucP38eP/74I9zc3BAREYGKFSuqzPP1tnXr1mHkyJHo27cv9u7di7i4OPTu3VuZJ5A9hN+JEydQq1YtrF+/Hn5+fjh58mSe+RVm3enTp8Pe3l7lte/Z5XxjJSIiIiIiIiIiIiJ6FwymownI7lQIDg5GZGQkLly4ADMzMxw4cADFixfHsWPHVNY9duwYypYtq1G+AQEBOHXqlMqy/L7Az4uZmRkyM1WfFskZou3Np07i4uI0zjM/pUuXhpmZmcr2pqen48yZMxpvb14cHBwQGBiIhQsXwtTUFGXKlEHdunVx4cIF7NixI8/5mXKYmZkBQK59oA+VK1dGQkICfHx8cr1yOv9MTU1zxXrs2DGEhoaibdu2qFChAlxdXZGcnKyyjomJCRo3boxZs2bh4sWLSE5OxsGDBwHk3QaOHTuGWrVqYfDgwQgKCoKPjw+SkpJyxRwUFISxY8fi+PHjKF++PNauXZvv9mm67tixY/H06VOVVxO7cmr3HxERERERERERERGRLhnMHE2nTp3CgQMH8L///Q/FihXDqVOn8NdffyEgIADh4eGYOHEiSpcujUqVKmHFihWIi4vDmjVrNMp76NChCA4ORlRUFFq3bo09e/ZoND9TDk9PT9y6dQtxcXEoWbIkbG1tYWlpiRo1amDGjBnw8vLCgwcPMH78eG03X8na2hqffvopwsPD4eTkBA8PD8yaNQtpaWno27evVN7169fHggUL0KFDBwDZw7UFBARg/fr1Bc51VapUKSgUCuzYsQMtWrRQzlulDxEREWjVqhU8PDzQoUMHGBkZIT4+Hr/99humTp0KILu+Dhw4gODgYJibm8PR0RG+vr7YvHkzQkJCoFAoMGHCBJUnr3bs2IGbN2+ibt26cHR0xM6dO5GVlQV/f39lnqdOnUJycjJsbGzg5OQEX19frFq1Cnv27IGXlxdWr16NM2fOwMvLCwBw69YtLFu2DB9//DGKFy+OhIQEJCYmomfPnrm2qzDrAoC5uTnMzc1VlpkqjHWyj4mIiIiIiIiIiIiINGUwTzTZ2dnh119/RYsWLeDn54fx48dj9uzZaN68OYYOHYovvvgCI0aMQIUKFbB7925s375d46HeatSogW+//RbR0dGoWLEi9u7dW6hOofbt26NZs2Zo0KABnJ2d8eOPPwIAvv/+e2RkZOCjjz7C8OHDlR0dsmbMmIH27dujR48eqFy5Mm7cuIE9e/bA0dFRKt969eohMzNTZS6m+vXr51r2thIlSiAyMhJjxoyBi4sLwsLCpOKQ0bRpU+zYsQN79+5F1apVUaNGDcydOxelSpVSrjN79mzs27cP7u7uCAoKAgDMmTMHjo6OqFWrFkJCQtC0aVNUrlxZmcbBwQGbN29Gw4YNERAQgG+++QY//vgjypXLfkpo5MiRMDY2RtmyZeHs7Iw7d+5g4MCBaNeuHTp37ozq1avj4cOHGDx4sDJPKysrXLt2De3bt4efnx8GDBiAIUOGYODAgbm2qzDrEhEREREREREREREZCoV4c5IhInpvLXD/RN8hSLOQPBv9rYOHupwkR4jMVMilfyWZHgAcJLfhT8lnXe0KP+VcLsaSbUEYQD2Y6/nqOujuD9J5fCN5XrGQbAtPDeBBTdl6zHv2Rs1l6KAtyh6SZpL74LUOtkE2BlsdnJdkPZRsz7LDEMi2RQAwVb9KgWTPCbLXWED+eEiXjEH/A1Hrn2w7AgAjyXOC7LlVF7/WlG2LsjHYG0BjlD0vfgj1YKWD65Psfa/s9SFLB+dmXVyj3neyt72WsudFyfIB+XsVW8nz0ksdnBTSdNCeZejieDKVbAuNrB9Kpd/3vIhcAJBvS30vTJZKb1rUWzKC/yYTsxL6DuG9k/H6D32HoFMG80QTERERERERERERERERvV/Y0QSgXLlysLGxyfOl6TxQ+mZo27BmzZp848kZjo6IiIiIiIiIiIiIiN5vsk8jfhB27tyJ9PS8H9Z2cXH5l6PRjqFtw8cff4zq1avn+Z6pqS4GzCAiIiIiIiIiIiIiIn1jRxOAUqVK6TsEaYa2Dba2trC1tdV3GERERERERERERERE9A5x6DwiIiIiIiIiIiIiIiLSjiCiD97Lly/FxIkTxcuXL/WWh77TG0IM3AbDiEHf6Q0hBm6DYcTAbTCMGLgN3AeGEgO3wTBi4DYYRgz6Tm8IMXAbDCMGboNhxMBt4D4wpBiIKG/saCL6D3j69KkAIJ4+faq3PPSd3hBi4DYYRgz6Tm8IMXAbDCMGboNhxMBt4D4wlBi4DYYRA7fBMGLQd3pDiIHbYBgxcBsMIwZuA/eBIcVARHnj0HlERERERERERERERESkFXY0ERERERERERERERERkVbY0URERERERERERERERERaYUcT0X+Aubk5Jk6cCHNzc73loe/0hhADt8EwYtB3ekOIgdtgGDFwGwwjBm4D94GhxMBtMIwYuA2GEYO+0xtCDNwGw4iB22AYMXAbuA8MKQYiyptCCCH0HQQRERERERERERERERG9f/hEExEREREREREREREREWmFHU1ERERERERERERERESkFXY0ERERERERERERERERkVbY0URERERERERERERERERaYUcTERERERERERERERERaYUdTURk8G7cuIE9e/bgxYsXAAAhhJ4j+m+ZPHky0tLSci1/8eIFJk+erIeI3m/6bs/6Lp/kPXnyBN999x3Gjh2LR48eAQDOnz+PP/74Q8+RyTOE9mgIMejb+7YPXr9+jd9//x137txReZE8tgUiov+mv/76K9/3Ll269C9G8mHJzMxEXFwcHj9+rJf0/1V3797F77//rvz/9OnTGD58OJYtW6bHqIg+POxoIvqAPHv2TOPX++Dhw4do3Lgx/Pz80KJFC6SkpAAA+vbtixEjRvwrMdSrVw+rVq1SfimvDW9vbzx8+DDX8idPnsDb21smvH9FZGQkUlNTcy1PS0tDZGSk2vTnz59X+TCybds2tGnTBl9++SVev36tUQy6yCMpKQnjx49H165d8eDBAwDArl27cPny5X8lvS7as0wMujqejh49ik8++QQ1a9ZUdmysXr0asbGxGuchSxcdLfrscJOpx4sXL8LPzw8zZ85EVFQUnjx5AgDYvHkzxo4dq1H5MTExeS7PyMjQKI8///wTPXr0QPHixWFiYgJjY2OVlzqhoaF4/vx5ruXJycmoW7eu2vQ5srKycP36dcTGxuLXX39Vef0bMciUD7AeAGDSpEnIysrKtfzp06fo2rWr2vSJiYmoU6cOLC0tUapUKXh5ecHLywuenp7w8vLSKH5914Ns+bqIQd/Hg2w7AHTTFg4dOpTve0uXLtUoD0Oh7w43bctnHRgGQ6gHQ4hBV7RtC/q+PlWoUAG//PJLruVRUVGoVq2a2vQ5Dhw4gC+//BL9+vVDnz59VF6aePz4MaKiotC3b1/07dsXUVFRyvt/TSQkJCAsLAyNGjVCo0aNEBYWhoSEBI3T7969W+VzzqJFi1CpUiV069ZNo86e4cOHY/ny5QCyO4nq1auHypUrw93dHYcPH37n6d/0+vVrJCQkICMjo1DpPgTdunVTnlfu37+PJk2a4PTp0xg3bhx/PEukS4KIPhgKhUIYGRlp9MrP06dPNX7l5fPPP9f4pU6PHj1E06ZNxd27d4WNjY1ISkoSQgixe/duUbZs2XzTOTo6ir/++ksIIYSDg4NwdHTM96XOsGHDhLOzs7CzsxP9+vUTJ06cUJvmbQqFQvz555+5lt+/f1+YmZnlmaZt27Yav9TZtWuXOHr0qPL/hQsXiooVK4quXbuKR48eaRT/gwcPci0/cOCAKFq0qNr0VapUERs3bhRCCJGUlCQsLCxE165dhY+Pjxg2bJja9LrI4/Dhw8LS0lI0btxYmJmZKdvS9OnTRfv27d95eiG0b8+6ikG2fCGE2Lhxo7C0tBT9+vUT5ubmyjwWLFggmjdvrlEev/76q+jevbuoUaOG+P3334UQQqxatUqljRYkPj5eODs7Cx8fH2FiYqKMYdy4caJHjx5q0//999+iUaNGyvNlTvrevXuLL774QqMYbty4IcaNGye6dOmiPLZ37twpfvvtN7VpZeuxUaNGIjw8XAghVOrx2LFjolSpUhrFb2trKzp06KBy/F+7dk1UrlxZozyaNWsmypYtKxYvXiy2bNkitm7dqvJSp1KlSsLb21scP35cuSwmJkbY2dmJNm3aaLQNJ06cEF5eXsLIyEgoFAqVV0HXOF3FIFu+EKwHIYQoWbKkqFmzprIdCyHEoUOHhLu7u6hatara9LVq1RJ169YVO3fuFBcuXBBxcXEqL03oux5ky9dFDPo+HmTbgRC6aQtmZmZi5MiR4vXr18plf/31l2jVqpVwcHBQmz41NVWMHz9e1KxZU5QuXVp4eXmpvNSZOHGiyMzMzLX8yZMnokuXLhptw/Xr10Xt2rVz3fdrWheyMciWL1sHQui/HmT3gRBCrFixIs/l6enpYsyYMWrT379/X3zyySfCzc1NGBsba/w5MIch1IO+j0ch9N8W9H19mjlzpjA3NxeDBg0SaWlp4vfffxcNGzYUzs7OYvPmzWrTCyHEpEmThJGRkahWrZpo3bq1aNOmjcpLnSNHjgh7e3vh7u6u/Ozr4eEh7OzsxJEjR9Sm37hxozAxMRE1atRQfgdRs2ZNYWJiovxsqU758uXFL7/8IoQQ4uLFi8Lc3FyMHTtW1KhRQ4SGhqpNX6JECXHmzBkhhBBbtmwRxYsXFwkJCWL8+PGiVq1a7zy9EEI8f/5c9OnTRxgbGwtjY2Pl9TYsLExMnz5dbfqMjAzx3Xffia5du4pGjRqJBg0aqLw0kZGRIb7++mtRtWpV4eLiUujvZYQQ4u7du2LRokVi9OjRhf5uycHBQVy7dk0IIUR0dLRy3+3Zs0fjcwIRqceOJqIPyOHDh5WvmJgY4erqKsaMGSO2bdsmtm3bJsaMGSPc3NxETExMvnnIdlbVr19f5WVnZyesrKxEUFCQCAoKEtbW1sLOzk6jGxIXFxfllwNvfqGalJQkrK2t800XExMjXr58qfy7oJcm0tPTxaZNm8THH38sTE1NRUBAgPj666/F/fv3C0yXs98VCoVYtWqV8v9t27aJzZs3iyFDhgg/P78804aGhipfvXr1EnZ2dnneYGtyc6vtzXFOJ52RkVGuDjs7OzthZGQkBg8erLZ8Ozs7cePGDSGEEDNmzBD/+9//hBBCxMbGipIlS6pNr4s8atSoIWbPni2EUG1Lp06dEiVKlHjn6YXQvj3rKgbZ8oXI/jJy5cqVufI4f/68cHFxUZteFx1Vsh0t+u7wk63HN4+FN9MnJycLc3NztemFyO4oq1GjhihRooTYu3evWLhwobCyshLdunUTT548UZvexsZGXLhwQaOy8vL69WsxcuRIYWZmJsaOHSs6duwobGxsxLJlyzTOo2LFiqJjx47iypUr4vHjx+LJkycqr3cdg2z5QrAehBDi0aNHomPHjsLW1lYsW7ZMjBw5Upiamoovv/xSpKenq01vZWUlrl69qnG8edF3PciWr4sY9H08yLYDIXTTFo4dOyZKly4tKlasKC5fvix27NghXFxcRN26dUVycrLa9F26dBFubm5i1KhRYu7cuWLevHkqL3UMocNN352/snUghP7r4UPoADeEetD38SiE/tuCvq9PQmTf45crV074+PgIJycn0bx5c5GSkqJxeldXV7Fq1Sqtyy9fvrzo37+/yMjIUC7LyMgQAwYMEOXLl1eb3tvbW0yYMCHX8oiICOHt7a1RDNbW1uLWrVtCiOzOx5z7/XPnzmn0+cfc3FzcvXtXCCFE//79lT+SvHnzprC1tX3n6YUQYujQoeKjjz4SR48eFdbW1so2vXXrVlGpUiW16YcMGSKsra1Fp06dxLBhw8Tw4cNVXpqYMGGCcHNzE1FRUcLCwkJMmTJF9O3bVxQpUkRER0erTb9//35hZWUlypcvL0xMTESlSpWEg4ODsLe31+i7pTfrMSQkRMyYMUMIIcTt27eFhYWFRttAROqxo4noA9WwYUOxdu3aXMvXrFkj6tWrl286XXRW5Zg9e7YICQlR+ZD06NEj0bp1axEVFaU2vY2Njbh+/bry75wbojNnzggnJye16d+FP//8U0yZMkVYWFgIU1NT0bp1a3HgwIE8133z17xv/8LXzMxM+Pn5iZ9//lltmaNGjRL9+vXL8wZ75MiRatNre3McExMjVqxYIRQKhYiOjlbpoFu7dq3KL58LYmtrq6zHxo0bKz/cFeamTjYPa2trcfPmTSGEalu6deuWRl/Oy6bPSSfTnmVj0MXxZGlpqWxLb3dWaRKDbEeVEPIdLfru8JOtR2dnZ3H+/Plc6ffu3atxx60QQmRmZorPPvtMGBkZCVNT0zyvF/kJCAhQxiAjIiJCKBQKYWpqqvH5JIeVlZVITEzUWwy6Kp/1kG3s2LHKGPbv369xuipVqmj8NGRB9F0PMuXrKgYh9H88aNsOhNBdW/jnn39E9+7dhbm5uTA1NRUzZswQWVlZGqW1t7cXsbGxWpdtCB1uhtD5K1MHQui/Hj6EDnAh9F8PsjHoonxDaAv6vj49e/ZMdO7cWZiYmAgTExONf6iZw8nJSXnfrg0LCwvlUyhvunbtmkafAS0tLfO8Pl2/fl1YWlpqFIOjo6O4fPmyEEKI4OBgsXTpUiFE9r27Jnl4eHiIPXv2iIyMDOHu7i527NghhBDit99+0+jpPNn0OXnkjMzy5ueHxMREjTqrihQpovzhqra8vb2VsdvY2CjbRXR0tOjatava9FWrVhURERHK9ElJSeKff/4RH3/8sVi8eLHa9NWqVROjR48Wv/76q7CwsFB+Hjxx4oTGPxwlIvXY0UT0gbK0tFR+qfymhIQEjW+qtO2sylG8ePE8h5K6dOmScHNzU5u+efPmYvz48UKI7JuJmzdviszMTNGxY0eNhyvL8eeff4pLly6J+Ph4lVdhnDp1SgwaNEg4ODgIDw8PERERIfr27SssLS3FiBEj8k3n6empHMpPG0WLFs33BluTDgLZm+PDhw+rDFtRWA0aNBA9e/YUq1atEqampsqb/cOHD2s8LJBsHiVKlBDHjh0TQqjeXG/evFmjX7PJphdCvj3LxqCL48nLy0vs27cvVwwrV64UAQEBatPLdlQJId/Rou8OP9l67Nu3r2jTpo14/fq1sh5v374tgoKCNB6KUgghtm/fLpydnUVwcLBwdnYWjRo1En/88YdGaffs2SP+97//KeuysF6/fi2++OILYW5uLr788ktRt25d4erqWqgPsA0aNBC7du3SqnxdxCBbfo7/ej0IIcT8+fOVX6D6+/uLsmXLavyr/wMHDoiaNWuKQ4cOib///lujIX7zos96kC1fFzEYwvEg0w6E0F1bOHfunPD39xelS5cWlpaWonfv3iI1NVWjtJ6enuLKlSsal5UfQ+hw02fnr0wdCKH/evhQOsANoR4M4XgUQr9tQZ/Xp9jYWOHp6SkqV64srly5Ir799ltha2srOnXqpNHw60Jk/2By8uTJWpUvRPZTYVu2bMm1fMuWLaJ69epq0zdv3lx8//33uZZ///33yhEy1AkJCRFNmzYVkydPFqampsqhv/fs2SN8fX3Vpp84caKwt7cXZcqUER4eHsqRV5YvXy5q1KjxztMLkf0ZLOczx5ufP+Li4oSdnZ3a9G5ubiIhIUGjsvJjZWUlbt++LYTIftLt3LlzQojsz4GaxPBm55SDg4Pye6a4uDiNvg84dOiQcHBwEEZGRqJ3797K5WPHjtVoOgIi0gw7mog+UH5+fsrhpd4UHh6e73Btb5PtrLKxsRGHDh3KtfzgwYPCxsZGbfpLly6JYsWKiWbNmgkzMzPRoUMHERAQIFxcXDT+ZdTZs2dFuXLltJ434M8//xRRUVGiXLlywszMTLRv317s2rVL5dd0OY+gvysODg55DnOxdetWjX7FJHtzLET2h92EhARx9OhRceTIEZWXOvHx8aJ8+fLCzs5OTJo0Sbk8LCxMo18v6SKPESNGiNq1a4uUlBRha2srEhMTRWxsrPD29lbJ712lF0K+PcvGoIvj6auvvhJly5YVJ0+eFLa2tuLo0aPihx9+EM7OzmL+/Plq08t2VAkh39Gi7w4/2Xp88uSJaNy4sXBwcBDGxsbC3d1dmJqairp162r85cuAAQOEubm5iIqKEllZWSIlJUU0b95cODk5ifXr16tN7+DgIMzMzISRkZGwsbEp9BjrgYGBwsfHR/nLyqysLDFjxgxhbm4uPv30U422YfPmzaJs2bJixYoV4uzZs4X+EYFsDLLlC8F6EEKIpk2biiJFioiffvpJCCFEWlqaGDRokLCwsBAzZ85Um/7N67m2c6Houx5ky9dFDPo+HmTbgRC6aQvTp08XZmZmIiwsTLx48UJcunQpz/mr8rN69WrRoUMH8fz5c43Ky4shdLjps/NXtg6E0H89fAgd4IZQD4ZwPAqh37ag7+uTmZmZGD16tMoPDt982k4TQ4cOFQ4ODqJu3boiLCxMo3l13rx+rFu3Tnh4eIivv/5aHD16VBw9elR8/fXXwtPTU6xbty7P9G8OV79kyRLh7OwshgwZIlavXi1Wr14thgwZIooVKyaWLFmi0Tbcvn1btGzZUgQGBorvvvtOuXz48OHis88+0yiPn376ScyZM0c5BJ4Q2aOHaDKUpS7S16lTR/lZLefzjxDZn6WbNm2qNn1UVJQYPHhwoZ5qfJufn584efKkECL7x685c0OtW7dOODs7q03v4uKi7DwOCAgQ27ZtE0JkdzRp+l1MRkZGrk7SW7du5TmfNhFphx1NRB+oX375RVhYWIjy5cuLvn37ir59+4oKFSoICwsLjX+hKttZ1aNHD+Hp6Sk2bdok7t69K+7evSs2btwovLy8RM+ePTWK4cmTJ2Lq1KmiY8eOonnz5mLcuHHi3r17GqUVIvvLk7Zt24qTJ0+KW7duieTkZJWXOqampqJMmTJi1qxZ4sGDB3mu8/TpU1G/fv0C89m/f78YO3as6Nu3r+jdu7fKS53PP/9cFClSRMyePVt5gx0VFSWKFi2q0cSXsjfHupj0Pi8vXryQelKqMHm8evVK9OvXT5iYmCh/kWhkZCQ++eQTlSEJ31X6HDLtWRcxyB5PWVlZYurUqcLa2lrZBiwsLJQdN+rIdlTlbINMR4u+O/x01ZaOHj0qFi1aJGbOnKnsvNNUuXLl8vySZOHChRp9UJOd+65Pnz551lXOPACaePtc9OYwpZqcl2RjkC1fCNaDENlDoeb1xemOHTuEq6ur2vRvDveb10sT+q4H2fJ1EYO+jwfZdiCEbtqCq6ur2Llzp8qyN+evUqdSpUrC1tZW2NjYiPLlyyvnJ815qWMIHW767vyVrQMh9F8PH0IHuCHUg76PRyH03xb0fX3K79yZmZmp8VNKb8/f/OYrv3l18ht6XtPPoerS6eJz7Pvm6NGjwsbGRtl+hw0bJpo0aSKsra3F2bNn1aZv06aNsLe3F15eXqJVq1bKeaNzXpoYPXq0mDZtmhAiu3PJxMRE+Pj4KDs01WndurVy7sgRI0YIHx8fMXXqVFG5cmXRqFEjjWIgondPIYQQIKIP0t27d7FkyRJcu3YNABAQEIBBgwbB3d1do/Q7d+5E+/bt4ePjg+rVqwMATp8+jcTERGzatAktWrQoMH1aWhpGjhyJ77//Hunp6QAAExMT9O3bF19//TWsra0ltk4ztra2uHDhAnx8fAqdVgiB2NhYVKlSBZaWllrHEBkZicmTJ6NKlSpwc3ODQqFQeX/Lli0Fps/KykJUVBSio6ORkpICAHBzc8OwYcMwYsQIGBsbax2bJipVqgQ/Pz9ERkbmGb+9vf07LV+X7ty5g99++w2pqakICgqCr6/vv5peFwwhhtevX+PGjRtITU1F2bJlYWNjo1E6IQS++uorTJ8+HWlpaQAAc3NzjBw5ElOmTClUDLGxsbh48SJSU1NRuXJlNG7cWOO0T58+xcKFCxEfH69MP2TIELi5ualN+/r1awwZMgQxMTHIzMyEiYkJMjMz0a1bN8TExGh8POqzHl+9egVzc/M830tISIC/v/+/FsvbCortTbdv3y7w/VKlSr3TGHRRPuuhYH///TeKFi2qdXpN6bse9F2+Ov/W8ZCff6sdqCvryJEjqFevXoHpIyMjC3x/4sSJBb7fpEkTrFy5EsWLF1dZ/ssvv6Bfv37Ke8CCHDlypMD31W2DbAyy5cvWAaD/epDdBwBQvnx5rFmzBhUrVlRZvmjRIowePRqpqakFpl+5cmWB7/fq1avA9w2hHvR9PAL6bwuGfn14V9RdU94kc30pyLNnzzRe187OLtey+fPnY8CAAbCwsMD8+fMLTD906NAC3588eXKB70dERKgPEsDNmzcxffp0lc8/o0ePRoUKFdSm7d27d4Hvr1ixQqMY3nTixAmcOHECvr6+CAkJUbv+zZs3kZqaisDAQDx//hwjRozA8ePH4evrizlz5uTZFipXrowDBw7A0dERQUFBub7HeNP58+cLvQ1ElBs7moioQLKdVQDw/PlzJCUlAQBKly6tcQfTxYsX81yuUChgYWEBDw8PtV9+tGnTBj169ED79u01jjdHVlYWLCwscPnyZakvgd3c3DBr1iz06NFD6zxy5Nz05nVDm5969eqhb9++6Nixo1YdZtbW1oiPjy9UZ52TkxOuX7+OokWLwtHRscCbukePHr2zPHLExsaidu3amgX/Dj158gSnT5/GgwcPkJWVpfJez549P/jyc2jbUWVI9NlRdODAARw4cCDPevz+++/fSZnPnj1TnnfUffguzPnp5cuXeP36tdbpdcEQYtDUh1wPmrp48SLKly8PIyOjfO8TcgQGBr6TGN5VPRhCDGwLhunf7HAz5Bj07b/SAU7qGerxoOtrw/Pnz3HkyBHcuXMn17VBXQfJ+8zIyKjAz55vyszMzLXMy8sLZ8+eRZEiReDl5ZVvWoVCgZs3bxaYf1BQkMr/6enpuHXrFkxMTFC6dGm1HSTp6ekYOHAgJkyYUGAsH6LIyEiEh4fDyspKJ53PRKQeO5qIPiAf2gfuN2/wck5Vb97wmZqaonPnzli6dCksLCzyzOPvv/9Gr169UK1aNZQvXx6mpqYq73/88ccFxlCuXDksX74cNWrU0Ho7ihQpgtOnT6N06dJa5yFj+PDhWLt2LV69eoVOnTqhb9++hdqehg0bYtSoUWjWrJnGaVauXIkuXbrA3NwcMTExBd6o5/erSl3kkcPMzAwlSpRA165d8cknn6Bs2bKabcj/l5mZiZiYmHy/3D948KDaPH7++Wd0794dqampsLOzU9kehUKhtrNMNgbZ8oHsD5szZszINwZ1H5R0RbajRZ8dbrL1KPuEZI6NGzdiw4YNeX5xkNcHVmNjY6SkpKBYsWL5fvgWQkChUOT5gftNz58/x+jRo7FhwwY8fPgw1/v5pd++fTuaN28OU1NTbN++vcAy1J3btYlBl+Xn+K/XQ2ZmJubOnZvvPsjrvGRkZIT79++r7IO8Ps5osg9y6LMetClf1zHo+3jQph0A76YtnD17Nt84Nm/erFEe/zZ93//runzWgXZ03cFgCPVgCDEUlq7bgj6vTxcuXECLFi2QlpaG58+fw8nJCX///TesrKxQrFixfO/727Vrh5iYGNjZ2aFdu3YFlqFJPSYlJWHevHm4evUqAKBs2bIYNmyYxp+tjxw5gqioKJX04eHhqFOnToFpciQnJ2PMmDEIDQ1FzZo1AWQ/jbNy5UpMnz5d7efQd+HZs2cIDQ1F27ZtNfoxq729PeLi4qQ7mv766y8kJCQAAPz9/eHs7Fzg+u/i3hkAUlNTc32GMtQfxRD915joOwAi0p1KlSopP3BXqlRJqw/csjfHuryx3LJlC0aPHo3w8HBUq1YNQPbQfbNnz8bEiRORkZGBMWPGYPz48YiKisozjxMnTuDYsWPYtWtXrvc0ucGeMWMGwsPDsWTJEpQvX77AdfPTr18/rF27FhMmTNA4jS4f8543bx6ioqKwfft2rFy5EnXr1oWPjw/69OmDHj16wMXFJVeaN+v+s88+w4gRI3D//n1UqFAhV2ddXu3gzRvu0NDQAuPLjy7yyHHv3j2sW7cOP/74I2bMmIHAwEB0794dXbt2RcmSJdWmHzZsGGJiYtCyZUuUL19e41+4vWnEiBHo06cPvvrqK1hZWRU6vWwMsuUD2W35yJEj6NGjR56dHOrooqNKXUeLOuo63NR1NMl2FMnW4zfffIOYmBipJyTnz5+PcePGITQ0FNu2bUPv3r2RlJSEM2fOYMiQIXmmOXjwIJycnAAAhw4d0rpsABg1ahQOHTqEJUuWoEePHli0aBH++OMPLF26FDNmzMg3XZs2bZTXuDZt2uS7nibndm1i0GX5AOsByD6ev/vuO4wYMQLjx4/HuHHjkJycjK1bt+Y7FMytW7eUX2zcunVL/Yaqoe960KZ8Xceg7+NBm3YA6L4trFu3Dj179kTTpk2xd+9e/O9//8P169fx559/om3btnmm0eXT19p2uOni/l8mBl2Wr00dAPqvB13ugxyF7WBwdHRUdjA4ODhIdTAYQj3o+3gE9N8W9H19+vzzzxESEoJvvvkG9vb2OHnyJExNTfHJJ59g2LBh+aazt7dX7nvZIdb37NmDjz/+GJUqVUJwcDAA4NixYyhXrhx+/vlnNGnSpMD0P/zwA3r37o127dopn8A6duwYGjVqhJiYGHTr1i3PdG8Oazh58mTMmTMHXbt2VS77+OOPUaFCBSxbtqzQHU2ZmZm4dOkSSpUqBUdHx0KlzWFnZ4fIyEiEhIRo9JmgTZs22Lp1Kz7//HOtynv+/Dk+++wzrFq1SvnZx9jYGD179sSCBQvy/Wypy3uFW7duISwsDIcPH8bLly+VyzU9r929excKhUL52f/06dNYu3YtypYtiwEDBhSYlog0xyeaiD4gt2/fhoeHBxQKhdbj5cv+OrR3796YP38+bG1tpcfyrVatGqZMmYKmTZuqLN+zZw8mTJiA06dPY+vWrRgxYoRyaL63eXp6olWrVpgwYUKeHSrqODo6Ii0tDRkZGTAzM8s19Fx+H1K++OIL5d9ZWVlYuXIlAgMDERgYmKujZs6cObnSv8vHvB88eIBly5Zh2rRpyMzMRIsWLTB06FA0bNhQuU5BdQ9A+Z4mN3Xnz5+Hqampcvznbdu2YcWKFShbtiwmTZoEMzMztTHrIo8ct27dwtq1a/Hjjz/i2rVrqFu3rtrOgaJFi2LVqlVq5yUriLW1NS5dugRvb2+t0svGIFs+ADg4OOCXX35RftArrK5duxbYUVXQh9YcskNR+vn5oUWLFlp3uIWFhSk7ivLahrlz5xaYXrYedfGEZJkyZTBx4kR07doVtra2iI+Ph7e3NyIiIvDo0SMsXLhQ67w14eHhgVWrVqF+/fqws7PD+fPn4ePjg9WrV+PHH3/Ezp0732n5hhID6yF7ON358+ejZcuWsLW1RVxcnHLZyZMnsXbt2nceg77rQd/lA/pvC4bQDoDsH84MHDgQQ4YMUdaFl5cXBg4cCDc3tzzvx958+lp2XpyIiIgCO9zyG6ZKF/f/MjHosnxt6gDQfz3och8Aqh0My5Yty9XBMG3atFxpjhw5guDgYJiYmEjPDWQI9aDv4xHQf1vQ9/XBwcEBp06dgr+/PxwcHHDixAkEBATg1KlT6NWrl3Jo/XcpKCgITZs2zfWjhzFjxmDv3r1qf3AZEBCAAQMG5OpgmTNnDr799lvlU04FsbKyQnx8fK5hsq9fv45KlSop553Nz/Dhw1GhQgX07dsXmZmZqFu3Lk6cOAErKyvs2LED9evXVxtDXmJjYxESEoLHjx+rXXfq1KmYPXs2GjVqhI8++ijXVAbqhkEcOHAg9u/fj4ULFyo/B8bGxmLo0KFo0qQJlixZotU2FEZwcDCEEBg2bBhcXFxyfQZTd16rU6cOBgwYgB49euD+/fvw8/ND+fLlkZiYiM8++0zjua6ISA1BRP9pLVq0EPfu3VP+n5ycLLKyspR/F/TSldjYWPHy5ctcyy0sLMTVq1dzLb969aqwsLAQQghx69YtYWlpmW/eNjY24saNG1rHFhMTU+ArP/Xr19fo1aBBA61je9vatWtFampqgeucOnVKDBo0SDg4OAgPDw8REREh+vbtKywtLcWIESOU66mr+8K0gypVqoiNGzcKIYRISkoS5ubmomvXrsLHx0cMGzZMo23TRR5vysjIED///LOoVKmSMDIyUru+m5ubSEhIKHQ5b2rbtq1Yv3691ullY5AtXwghPD09xZUrV7ROb29vL2JjY6VicHJykjqmraysRFJSktbpixQpIn755Ret08vW46hRo8TkyZO1Ti+EEJaWlspj19nZWcTFxQkhhLh+/bpwcnLSKI8XL16IU6dOiZ9//lls27ZN5aWOtbW1uH37thBCiBIlSohTp04JIYS4efOmsLa21maTCs0QYmA9ZB+POTG4urqKc+fOCSGyz/N2dnYa5fHHH3+I9evXiwULFojo6GiVlyb0XQ+6KF82Bn23BV20AyHk24KVlZW4deuWECL7WnPx4kUhhBBXrlwRrq6uhdgi7Xh7e4sdO3YIIVTvX6Ojo0XXrl3fefmGEIO+60AI/e8DIYTw9/cXa9euVcaQc98yYcIEMWTIkHdeviHUgyHEoO+2oO/rU9GiRcX169eFEEL4+vqK3bt3CyGyP4tbWVlps0mFZm5urozhTQkJCcLc3FxtejMzM5GYmJhreWJiokbphRDCz89PhIeH51oeHh4u/Pz81KYvUaKEOHPmjBBCiC1btojixYuLhIQEMX78eFGrVi216d++ns2bN0+MHj1aFC9eXON26Onpme/Ly8tLbfoiRYqIQ4cO5Vp+8OBBUbRoUY1ikGVtbS2uXbumdXoHBwdl+ujoaOW+37Nnj0b7gIg0w6HziP7jfv31V7x48UL5/5u/rNLkF3cA0LJlS3z33Xdwc3PTKobmzZsjLi4u15MWZcqUwYwZM7Bs2TLlEyvp6emYMWMGypQpAwD4448/CnxSqV27djh06JDWv/7Xdsxl2aEKtDFw4EBUr14913588OABVq9ejRUrViAxMREhISH48ccf0bRpU+UvgUJDQ9GsWTPlEISa1r0mcn7tBQA//fQT6tWrh7Vr1+LYsWPo0qUL5s2b96/kAWQPlbBmzRps3LgRL1++ROvWrTF9+nS16UaMGIHo6GgsXLiwUEOdvTkWdcuWLREeHo4rV67kOQShunGptYlBl+UDwJQpUxAREYGVK1dq9TSQo6OjcjgPbWkzFOWbmjZtirNnz2r9ZJeZmRl8fHy0SgtoV49vPyG5bNky7N+/X+MnJN/m6uqKR48eoVSpUvDw8MDJkydRsWJF3Lp1K9+nGN+0e/du9OzZE3///Xeu9zR5ytHb2xu3bt2Ch4cHypQpgw0bNqBatWr4+eef4eDgoLb8HGfOnMGhQ4fyHMJQ3X7QRQwy5QOsBwAoWbIkUlJS4OHhgdKlS2Pv3r2oXLkyzpw5A3Nzc7Vlx8TEYODAgTAzM0ORIkVyDYWpyUTl+q4H2fJ1EYO+jwfZdgDopi04Ojrin3/+AQCUKFECv/32GypUqIAnT56o/cX6mx48eJDnflA3H0vOEMUAYGNjg6dPnwKA8sl8Td27dw+xsbF5xqBuP+giBpnydVUHgH7rQWYfAMCdO3dQq1YtAIClpaVyn/To0QM1atTQ6EmWly9f4uLFi3nGoO6ezxDqQd/HI6D/tqDv61NQUBDOnDkDX19f1KtXDxEREfj777+xevVqjYeUf/jwISIiIvK9PqgbwtDZ2RlxcXG5niaKi4tDsWLF1Jbv7u6OAwcO5Lp3379/P9zd3TXahrlz56J9+/bYtWsXqlevDiB72LXExERs2rRJbfq///4brq6uAICdO3eiY8eO8PPzQ58+fRAdHa1R+W8yMjKCs7MzevXqhbFjx2q0DbLDy6alpeX5nUuxYsUKdTzK3CtUrVoVd+/ehb+/v8blvSk9PV15T7F//37lebBMmTJISUnRKk8iyo0dTUQk7e3OqsLK70Z50aJF+Pjjj1GyZEnlh4FLly4hMzMTO3bsAJA9p8vgwYPzzdvPzw9jx45FbGxsnl+ua/JhLykpCStWrEBSUhKio6NRrFgx7Nq1Cx4eHihXrpymm/nO5bcfS5YsidKlS6NPnz4IDQ3Nc9LOwMBAVK1aNc/0+U3cqVAoYGFhAR8fnwInFhVCKG8k9+/fj1atWgHIvvHP64PPu8hj7NixWLduHe7du4cmTZogOjoarVu3LrCz5O05xg4ePIhdu3ahXLlyudpRfvON5TUW9eTJk3Mty+/DnmwMsuUDyDVH2I0bN+Di4gJPT89cMagbvkLbjirZjhZ9d/jJ1uOFCxdU/s/pdP3tt99UlmsaT8OGDbF9+3YEBQWhd+/e+Pzzz7Fx40acPXtW7dx6QPa8bR07dkRERIRWQ5L27t0b8fHxqFevHsaMGYOQkBAsXLgQ6enpGnXQAMBXX32F8ePHw9/fP9fwGZrsB9kYZMsHWA8A0LZtWxw4cADVq1fHZ599hk8++QTLly/HnTt3NJpHYMKECYiIiMDYsWNhZGSkUcxv03c9yJavixj0fTzItgNAN22hbt262LdvHypUqICOHTti2LBhOHjwIPbt24dGjRqpTX/u3Dn06tULV69ezXVPpsmXuobQ4abvzl/ZOgD0Xw8fQge4IdSDvo9HQP9tQd/Xp6+++krZ2Tdt2jT07NkTn376KXx9ffH9999rlEePHj1w48YN9O3bN8/hztTp378/BgwYgJs3byo7X48dO4aZM2eqfD7Iz4gRIzB06FDExcWppI+JidGokwcAWrRogevXr2PJkiXK4QJDQkIwaNAgjTqrXFxccOXKFbi5uWH37t3KYebS0tJgbGysNr0u5iCUVbNmTUycOBGrVq2ChYUFAODFixeIjIxEzZo1NcpD9l7hu+++w6BBg/DHH3+gfPnyGs0b/aZy5crhm2++QcuWLbFv3z5MmTIFQHZncJEiRTTaBiJSj3M0Ef3HvTnes77yKCj9P//8gzVr1uD69esAAH9/f3Tr1g22trYa5V1QB4hCocDNmzcLTH/kyBE0b94cwcHB+PXXX3H16lV4e3tjxowZOHv2LDZu3Kg2hrZt2+Z58/RmR023bt20/nVOjvz249GjR1GnTh2t881vvqY352mqXbs2tm7dmueEpg0bNoS7uzsaN26Mvn374sqVK/Dx8cGRI0fQq1cvJCcnq41BNo/g4GB0794dnTp1QtGiRTXabnVzjL1J3Xxj2jKEGNTNEfamvOYLy6ujSghRqI6qBg0aaFS+QqHIc74tTb90LEyHn5OTk8YdRYZQj2/KyspCVlYWTEyyf2+0bt06HD9+HL6+vsovRApiZ2eHCxcuSM0T9abbt2/j3Llz8PHx0egXxkD2h/aZM2ciNDRULzHoonzWQ24nTpzAiRMn4Ovri5CQELXr62LOMn3Xg2z5uojhbfo4Ht5U2HYA6KYtPHr0CC9fvkTx4sWRlZWFWbNmKeti/Pjxaidtr1ixIkqXLo3Ro0fn+YWquqfFx4wZAzs7O3z55ZdYv349PvnkE3h6eio73N6eoyQv7u7uGDRokNYdbrIxyJYvWweA/utBdh8A2U9vu7u7Y+LEiVi0aBHCw8MRHBys7GBYvnx5gel9fX3xv//9T+sOBkOoB30fj4D+24K+r0+6YGtri9jYWFSsWFGr9EIIzJs3D7Nnz8a9e/cAAMWLF0d4eDiGDh2qUQfFli1bMHv2bOV8TAEBAQgPD0fr1q21iqmwJk2ahHnz5sHNzQ1paWm4fv06zM3N8f333+Pbb7/FiRMnCkyf8+TT299/PH/+HJ999plGnX59+vQp8H11efz2229o2rQpXr16pazL+Ph4WFhYYM+ePRr9+Fb2XuHkyZPo1q2byuf+wswbffjwYbRt2xbPnj1Dr169lNv85Zdf4tq1a/n+cJSICocdTUT/cYbe0QQAV65cwZ07d/D69WuV5ZoM9fWmnNNdYX5JVbNmTXTs2BFffPGFSpynT59Gu3bt8Pvvv6vNIzQ0FFu3boWDgwM++ugjANlfqD958gT/+9//EB8fj+TkZBw4cEA5uaY2dFGXeTlw4ADGjRuHadOmoVq1agCyhwuYMGECxo8fD3t7e+WwfXl98L148SK6d++OO3fu4IsvvlB2Rnz22Wd4+PChRpN86yIP0g/ZjipDYEgdRU+fPkVmZmauIQgfPXoEExMT2NnZvdPygewPq8HBwejbt+87Lys/bm5u+PXXX3MNpfJfKR9gPQDAqFGj4OTkhDFjxuilfMAw6kHfMei7HQCG0RZsbW1x4cIFqeFV36SvDjeZGHRdvjb0XQ8fQge4Lui6Hgyh/PfteND3tQHIHu5swYIFqFGjRqHTZmRkYO3atWjatClcXFyUT1dp+oPTjIwMfPXVV+jTpw9KlixZqLIvXryI8uXLw8jICBcvXixwXU1+kLFx40bcvXsXHTt2VMaycuVKODg4qO3wMjY2RkpKSq6hAnOG5MvIyFBbftu2bVX+T09Px2+//YYnT56gYcOGGnWypKWlYc2aNcqnugICAtC9e3dYWlqqTQvI3yuULVsWAQEBGDVqlNadx5mZmXj27JlKR3VycjKsrKw0GoqRiNRjRxPRf5whdzTdvHkTbdu2xaVLl1R+rZJDkyEPAGD58uWYO3cuEhMTAWT/ym/48OHo16+f2rQ2Nja4dOkSvLy8VOJMTk5GmTJl8PLlS7V5jBkzBs+ePcPChQuVv2bLysrCsGHDYGtri2nTpmHQoEG4fPkyYmNjNdqmvBRUDxs3bsSGDRvy7LBTN9xZ+fLlsWzZMuVwAzmOHTuGAQMG4PLly9i/fz/69OmDO3fuaBzvy5cvYWxsnOuJkMIoKI/t27ejefPmMDU1zXf4vxzqOi1v3bqFjIyMXDfGiYmJMDU1haenp9pYhw4dCh8fn1zDZCxcuBA3btxQO8+UbAyy5QPZ42pnZWUpxyfPcerUKRgbG6NKlSpq85BlCB0tMmTrsXnz5ggJCck1ZOg333yD7du3Y+fOnRrFITN3Q1paGjp27AhnZ2ethySVnd9o1qxZuHfvnsbzs+k6Bl2UD7AeALn5KzIzM9GqVSu8ePEiz32g6RCA+q4HmfJ1FYO+jwfZOW101RYA7ed0adOmDXr06IH27dtrXJau6bvDTVfly8yro+960HcdALrrYDCEevivH4/6vD7Jzq8EZF9bxowZg4iIiDyHO1N3325lZYWrV69qPX+wjY0NfvvtN40+q73JyMgI9+/fR7FixfId3QPQfBhGbTx79gxCCDg6OiIxMVFl+PvMzEz8/PPPGDNmjPJJr8LKysrCp59+itKlS2PUqFG6CjtfsvcK1tbWiI+P11vnNRFphh1NRP9xhtDRZGdnh7i4uFzpQ0JCYGxsjO+++w5eXl44deoUHj16hBEjRiAqKkqj4eAiIiIwZ84cfPbZZ8rxg0+cOIGFCxfi888/z3O+mjeVLFkSGzZsQK1atVS2c8uWLRg5ciSSkpLUxuDs7Ixjx47Bz89PZfn169dRq1Yt/P3337h06RLq1KmDJ0+eqM0vP/nVw/z58zFu3DiEhoZi2bJl6N27N5KSknDmzBkMGTIE06ZNKzBfS0tLnDlzJtekr5cuXUK1atXw4sUL3L59GwEBAQVOBnru3DnlkAVly5ZF5cqVC72Nhcnj7Q8I+dHkA0K9evXQp08f9OrVS2X5Dz/8gO+++w6HDx9WG3uJEiWwfft25VNtOc6fP4+PP/5Y7dNxsjHIlg8A1apVw6hRo9ChQweV5Zs3b8bMmTNx6tSpAtProqNKtqNF3x1+svXo5OSEY8eOISAgQGX5tWvXEBwcjIcPHxaYHpCfu2H58uUYNGgQLCws8pxzQN2QpOrGaM9r+MO3ZWVloWXLlrh+/TrKli2r8bxpuopBtnyA9QCon79C3TZMnToVERERUtug73qQLV8XMej7eJBtB4Bu2oLsnC5///03evXqhWrVquX5haomnYaG0OGmz85fXcyro+96+BA6wA2hHgzheAT02xb0fX1q0aJFgfMrvX0vm5fExER069Yt1w8bNR3urH79+hg+fHie885qonXr1mjXrp1Gsb7p9u3b8PDwgEKhwO3btwtcV5NOsCNHjiAqKkrlc2x4eHiB32fkdHDlR6FQIDIyEuPGjVNbfn4SEhJQv359pKSk5HpPlz/aBOTvFUJCQhAaGirVeSzz41si0pAgog/O69evRe/evcXNmzfVrvvVV1+Jx48fS5VnY2MjkpKSdJ6+SJEiIj4+XgghhJ2dnbh27ZoQQogDBw6ISpUqaZR30aJFxdq1a3MtX7t2rShSpIja9CNGjBC1a9cWKSkpwtbWViQmJorY2Fjh7e0tJk2apFEMDg4OYtu2bbmWb9u2TTg4OAghhLh+/bryb22VK1dO3LlzJ9dyf39/5T54c19PmDBBDBkyRG2+wcHBolmzZuLBgwfKZQ8ePBDNmjUTderUEUIIsW/fPuHn55dn+j///FPUr19fKBQK4ejoKBwdHYVCoRANGzZUybMgushDRk7dvy0xMVHY29trlIe5uXm+eZibm7/zGGTLF0IIa2vrPI/VmzdvChsbG7Xpq1atKn766adcyzdt2iSqVaumUQyOjo7iypUruZZfvXpVODk5qU1fvHhxcfbs2VzLz507J0qUKKE2fd26dUVMTEyu5atXrxb16tVTm162Hq2srMTFixdzLb948aKwtLRUm14IIXx8fMTgwYPF/fv3NVr/bS4uLmLatGkiMzNTq/TFihUTK1as0CptjiFDhghzc3PRrFkz0atXLxEaGqryetcxyJYvBOtBCCFKliwppk6dqvU2ODg4SG+DvutBtnxdxKDv40G2HQihm7YQGBgo2rZtK06ePClu3bolkpOTVV7qbN++Xdjb2wuFQpHrZWRkpDb9ihUrhJmZmbCxsRGlSpUSnp6eypeXl5dG2zBlyhShUChEmTJlRL169UT9+vWVrwYNGrzzGGTLl60DIfRfD7L7QAghdu3aJZydnbXehu+++06YmJhovQ2GUA/6Ph6F0H9b0Pf1ycbGRsTFxWmVNkfVqlVFzZo1xbp168ShQ4fE4cOHVV7qrF+/Xnh7e4sFCxaI48ePi/j4eJWXOkuWLBGurq5ixIgRYu3atWLbtm0qr8K4fPmy2LVrl0r67du3q023evVqYWJiIjp16iSio6NFdHS06NSpkzA1NRVr1qzJN93hw4fFoUOHhEKhEJs3b1bZb8ePHxd//PFHoeLPyy+//CKKFi2a53sKhUL8+eefyr/ze2l6PMneKyxdulS4u7uLiRMnio0bNxa6LqOjo4WNjY0ICwsTZmZmYuDAgaJx48bC3t5efPnllxptAxGpx44mog+UnZ2dRh1NBTly5IhIT0/PtTw9PV0cOXJE+b8uOqvy4uDgoNwGb29vcfDgQSGEEDdu3ND4C1V7e3tx/fr1XMsTEhI0+lL31atXol+/fsLExEQoFAphamoqjIyMxCeffCIyMjI0iuGzzz4TRYsWFXPmzBFHjx4VR48eFXPmzBFFixYVQ4cOFUII8e2334rg4GCN8issS0tL5QcyZ2dn5QeG69eva/TF/LVr14S/v78wMzMTpUuXFqVLlxZmZmaiTJkyIiEhQQghxJYtW8SqVavyTN+pUydRpUoVlc6By5cviypVqoguXbpotA2yeaxcuVK8fPky1/JXr16JlStXqk1vZ2cnzp8/n2v52bNnNepgESK7I3DBggW5ls+fP18EBAS88xhkyxdCCCcnJ3H8+PFcy48dO6ZRR6lsR5UQ8h0t+u7wk63H+vXri7CwsFzLBw8eLGrXrq02vRDZ23Djxg2N1s2Lo6OjVHpXV9c8z8uFYWNjI3bs2KG3GGTLF4L1IET2OUVmG1xcXKS3Qd/1IFu+LmLQ9/Eg2w6E0E1bsLGxyfP8rqlSpUqJIUOGaP2lsCF0uOm781e2DoTQfz18CB3ghlAP+j4ehdB/W9D39alKlSrixIkTWqcXIvtzaM6PRbWRX8eGph0cuuggSUpKEoGBgSpl5/ytSR5lypQRc+bMybV89uzZokyZMmrTJycni6ysLI1izc/nn3+u8ho+fLjo3LmzsLGx0eiHp7oge68gW5eyP74lIs2wo4noA9WzZ888b2gKw8jISPkrljf9/fff+V7MK1WqJIKCgjR6qVO7dm2xZcsWIYQQXbt2Fc2aNROxsbGiZ8+eoly5chptQ1hYmPj8889zLR8xYoQYPHiwRnkIIcTt27fFL7/8ItavX1/oLzIyMjLE1KlThaurq/JmyNXVVUybNk3ZWXX79m1x9+7dPNO/eSOb10sdLy8v5RfbH330kfjmm2+EEELs2bNHODo6arQNmZmZYteuXcpfYe3evVvjD112dnbi9OnTuZafOnVK46eBZPPQpi2/qVWrVqJjx44qnYsZGRmiffv2olmzZmrTCyHE8uXLhaWlpYiIiFD+Gm3ChAnCyspKLFu27J3HIFu+EEJ06dJF1KtXTzx58kS57PHjx6JevXqiY8eOatPLdlQJId/Rou8OP9l6jI2NFRYWFqJOnTpi0qRJYtKkSaJOnTrCwsJC/Prrr2rTCyFE7969xXfffafRunkZPny4mDZtmtbpZ86cKYYNG6Z1eiGE8PDwEFevXtVbDLLlC8F6EEKI8PBwMX36dK3Tf/XVV+Kzzz7TOr0Q+q8H2fJ1EYO+jwfZdiCEbtpC69atxcaNG7VOb2NjI/WlriF0uOm781e2DoTQfz18CB3ghlAP+j4ehdB/W9D39en06dOiYcOG4vDhw+Lvv/8WT58+VXlpok6dOmLfvn1ax/D2k2zaPF0nq1WrVqJ169bir7/+EjY2NuLy5cvi6NGjolq1ahrde5uZmRX6R27x8fHKz9pvP8VV2Ke6hBAqT9PVr19fNGzYUHTu3FksXbo0zx8Wa6KwPzTWxb2zDNkf3xKRZjhHE9EHaurUqZg9ezYaNWqEjz76CNbW1irvazLOu5GREf7880+ViSeB7LmFqlSpgmfPnuVKExkZqXGMEydOLPD9PXv24Pnz52jXrh1u3LiBVq1a4fr16yhSpAjWr1+Phg0b5pnuiy++UP6dkZGBmJgYeHh4oEaNGgCy54O5c+cOevbsiQULFmgcry7k7DN1E5++adu2bSr/p6en48KFC1i5ciUiIyPVTvTbr18/uLu7Y+LEiVi0aBHCw8MRHByMs2fPol27dli+fHnhN6QQbG1tcfToUVSqVEll+YULF1CvXr0825Gu88ivLcfHx6NBgwZqJ7O9cuUK6tatCwcHB+VY2kePHsWzZ89w8ODBXPNX5WfJkiWYNm2actJWT09PTJo0CT179lSbVhcxyJQPAH/88Qfq1q2Lhw8fIigoCAAQFxcHFxcX7Nu3D+7u7gWm79q1K1JSUrBt2zbY29sDAJ48eYI2bdqgWLFi2LBhg9oYjh07hsaNG6Nq1apo1KgRAODAgQM4c+YM9u7dq3butu+//x5hYWEIDw9XnkMOHDiA2bNnY968eejfv3+B6UNCQmBpaYkff/wRxsbGALLH4O/cuTOeP3+OXbt2FZheF/UYFxeHr7/+GnFxcbC0tERgYCDGjh2ba96o/MjO3TB06FCsWrUKFStWRGBgYKHnHNDF/EYrVqzA7t27sWLFClhZWaldX9cxyJYPsB4A+fkr2rZti4MHD6JIkSIoV66cVtug73qQLV8XMej7eNDFnDa6aAuyc7r06tULderUQb9+/dSWlZdRo0bByckJY8aM0So9AEyfPh0pKSmYP3++XmKQLV8X8+roux5k9wEA9OnTB8HBwWrv8fPz+eefw9nZGV9++aVW6Q2hHvR9PAL6bwv6vj7Jzq8EAD/99BMmTZqE8PDwPLchMDBQbR76VrRoURw8eBCBgYGwt7fH6dOn4e/vj4MHD2LEiBG4cOFCgel9fHwQHh6OgQMHqiz/5ptvMHv2bCQmJuZK8/ZcwwqFItdcZYDmc6bJmjlzJjw9PdG5c2cAQMeOHbFp0ya4ublh586dqFixoto8dHHvLMPb2xubNm1CUFAQqlSpgv79+2PgwIHYu3cvunTpovb7ACLSDDuaiD5QXl5e+b6nbvLPdu3aAcju4GjWrBnMzc2V72VmZuLixYvw9/fH7t27dRewhh49egRHR8cCJ8Zs0KCBRnlpMjn0m51Wb6e1sLCAj48PWrduDScnJ43K1KW1a9di/fr1uTqi3paVlYWsrCyYmJgAANatW4fjx4/D19dXOfn22+bPn48BAwbAwsJC7YcjdR9yWrdujSdPnuDHH39E8eLFAWR3WHTv3h2Ojo7YsmVLgell8ggKCoJCoUB8fDzKlSun3AdAdlu+desWmjVrplEHx71797Bw4ULEx8crv9wPCwvTqu7/+usvWFpawsbGplDpdBWDtuUDwPPnz7FmzRqVGLp27Zrrg2NeZDuqcsh2tOi7w0+XbUkbspNDF3SO1eS8GhYWhu+++w4NGjTIc3LpFStWqN2GoKAgJCUlQQgBT0/PXO1P3YS+sjHIlg+wHoDsH8VERETA398/VwyabEPv3r0LfF+TbdB3PciWr4sY9H08yLYDQDdt4eeff0aPHj3y/PGKJl/mTZs2DfPmzUPLli21+lLYEDrc9N35K1sHgP7r4UPoADeEetD38Qjovy3o+/pUrVo1mJiYYNiwYXleG+rVq1dgeiC7wySvsgvTWZWQkIAFCxbg6tWrAICAgAB89tln8Pf3V5sWyP5B2dy5c1XSDx8+HI0bN9YovaOjI86fPw8vLy+ULl1aeb1MSkpChQoVkJaWVmD6JUuWYPjw4ejTpw9q1aoFIPuHczExMYiOjs7VAQUAt2/fhoeHBxQKBW7fvl1g/qVKldJoO2R4eXlhzZo1qFWrFvbt24dOnTph/fr12LBhA+7cuYO9e/eqzUMX985HjhxBVFSUsi7Lli2L8PBwtT80BPT/41ui/wp2NBFRLjkf1leuXIlOnTrB0tJS+Z6ZmRk8PT3Rv39/FC1aVF8h/msaNGiA8+fPIzMzU3kze/36dRgbG6NMmTJISEiAQqFAbGwsypYtq0xXuXJlHDhwAI6OjsrOjvxoclOVl5s3byIwMBCpqalapS+Il5cXzp49iyJFikh1WgLA3bt38fHHH+Py5cvKjoS7d++ifPny2L59O0qWLKk2Hm3zyHnCLjIyEiNGjFDpWMlpy+3bt8+zs43eDZmOKl3Td4efrJcvX+L169cqyzR5WtLV1RVDhw7FmDFj8vwCQFd+//13FC9ePFcZtra2WLduHVq2bKl13uqenlX3xKxsDLLlA6wHIPvLm7lz5yI0NFTrGDRx7NgxVKlSReWHMzn0XQ//VvkFxaDv4+HfagdAwW3B09MTrVq1woQJE+Di4lLovGXvlwyhw03fnb+ydQDovx4+hA5wQ6gHfR+PgP7bgr6vT1ZWVrhw4YLGHTp5ke0k2bRpE7p06YIqVaqgZs2aAICTJ0/izJkzWLduHdq3b19g+sWLF2PYsGHo0KGDSvqNGzdi7ty5GDJkiNptqFOnDkaMGIE2bdqgW7duePz4McaPH49ly5bh3Llz+O2339TmsWXLFsyePVulsys8PBytW7dWm1Zb6r6DeJO67yMsLS1x/fp1uLu7Y9iwYXj58iWWLl2K69evo3r16nj8+LHaMmTvFX744Qf07t0b7dq1Q3BwMIDsa/qWLVsQExODbt26FZhemx/fElHhsaOJ6AP3+vVr3Lp1C6VLl1Z5mkMTkZGRGDlyZK5h9zSVmZmJuXPnKn/p8vYXou/D48nz5s3D0aNHsWLFCuUXuE+fPkW/fv1Qu3Zt9O/fH926dcOLFy+wZ88eZbrIyEiEh4fDyspKJ19Ivu3FixcYO3Ysdu3ahYSEhFzvX7x4UeO8/o0hC4QQ2L9/P65duwYg++Za01+R6SKPlStXonPnzrCwsChc4G9JS0vLsy1rug83btyY7/GgaYejTAy6KB/Ifqonrzw0GUpFl7TtaDEE2tZjWloaRo0ahQ0bNuDhw4e53tfkl6H/j73zjooi6dr4M+QcBExkUJEs6prAjAkEw5owK2ZEzGGNiGLOuuaEK6JiXrMYwLgqCioqICjmnMAI3O8PvumXYUI30wPDuvM7p49S07equqs61a26T7ly5XD16lU4OjoWr9LFxMjICDdv3oSDg4NIuq2tLY4fP47q1auXaPkAsGPHDgQGBoo9x0qrDtLKB1TtABQMoiUkJHBejSgv0s4BoPx2KK3yZdVB2ddDafUDQHZfMDQ0xM2bN0ulLSRRFhxuynb+KrsNAOWfA0D5Doay0A5loQ7K7gvKfj41atQI06ZNK/Y3mzz4+/tjw4YNqFSpkki6o6MjevTogZkzZ4qkT58+HX/99RcePHggM18rKytMnDgRw4cPF0lftWoVIiMj8fTpU9a6yRvOnw8HDx7kvK+07y9FShpUrlwZsbGxaNCgAZycnDBr1ix07twZ9+/fx2+//cYpFD5fnJ2dMWjQIIwaNUokffHixVi/fj3jxJNGVlYWrK2txZxvRITHjx/DxsZG4XVWoeI/SSlrQqlQoaKUyMnJof79+5O6ujqpq6vTgwcPiIho+PDhvAWXuTJ16lSqVKkSLVy4kHR0dCgiIoKCg4PJzMyMli1bVip14EvlypXpzp07Yum3b9+mypUrExHR9evXyczMrMTqYGJiQqampsxmYmJC6urqZGhoSAcOHJBoIxAISE1NjflX1saV79+/07179+QWDP038+rVK/L39+d1DpctW0YGBgY0fPhw0tLSosGDB5Ovry8ZGxvTH3/8UeJ14Fs+EdGDBw/Iw8NDpH8V7mNcuXPnDh09epQOHDggsnEhJyeHQkJCyMLCQu622L17N3Xu3Jnq1q1LXl5eIhtXcnJy6O7du8UW5OXbjsOGDSNnZ2eKjY0lXV1d2rRpE0VERJCVlRX99ddfnOrOVxyaKwYGBsyzpzCbNm2iLl26UE5OTonXwdDQUKl1kFY+kaodiIgiIyMpNDS0xMuXdg6IlN8OpVW+rDoo+3oorX5AJLsv9O7dm9avX1/idZB2HipUqECpqaklXn5ZqIO08kurDWTVQdnngIjI1NSU0tPTlVaHstAOyr4eiZTfF5T9fNq1axe5uLjQ5s2b6dq1a8V+51VEHXR1dSktLU0sPTU1lXR1dVnz1dfXl2qvr68vX2WJ6O3bt5Sfn18sm+/fv9Pjx4/p0aNHIpskhN9YbFtxvr/4EBISQra2tuTr60tmZmb0+fNnIiLasWNHsb6f3r9/T+vXr6eJEyfS27dviahgLOXJkyestlpaWhLbMi0tjbS1tVnt1dTU6OXLl2Lpb968KbXzqELFf4HiLW9QoULFv4ZJkyYhKSkJZ8+eRevWrZl0X19fzJgxg5Oo6cuXLzF27FjExcXh1atXYgKUbDPnt2/fjvXr18Pf3x8zZsxAUFAQHB0d4eHhgcuXL3OKja1sPn78iFevXomExQMKQm4JZ+6YmJiIrUooyocPHxAbG4sHDx5g3LhxKFeuHBITE1GhQgVYWlrKtF2yZInIzBs1NTVYWFigbt26MDU1lWiTmZnJ/P/GjRsYO3Ysxo0bx4QMuHTpEhYtWoT58+fLLBsoWEERGhqKrVu3AigIHejg4IDQ0FBYWlpy6ktxcXFMP8rPzxf5bdOmTaz2fPPgu7pu5MiR+PDhA65cuYImTZpg3759ePnyJWbNmoVFixZxqv+ff/6JdevWISgoCFu2bMH48ePh4OCAadOmcVrdx7cOfMsHgLCwMNjb2yMuLg729vb4559/8PbtW4wZMwYLFy5ktc/IyECHDh1w69YtEVFbYf/mshpn3LhxOHPmDFavXo1evXph1apVePr0KdauXYu5c+ey2i9fvhyTJ09G3759ceDAAfTr1w8PHjzA1atXOYXPeP36Nfr164ejR49K/J3tGPi246FDhxAVFYUmTZqgX79+aNiwIapUqQJbW1ts374dPXr0YM0jLy8P8+fPx/Hjx+XSbuDL8uXL8eDBA1SoUEHuGO1cKfrcKu06SCsfULUDAPzzzz84ffo0/v77b7m1TPii7HZQdvmA8q+HstAPAKBatWqYNGkSzp8/L7emCxeknYewsDCsWLGCVRvzV6iDtPJLqw1k1UHZ5wAA+vTpg507d+KPP/5QSh3KQjso+3oElN8XlP186Nq1KwCgf//+TFpx9ZX40qRJEyQkJKBKlSoi6efPn+ekyxMYGIh9+/Zh3LhxIukHDhxA27Zt5a5XccJlp6WloX///rh48aJIuqzzWPRbV9ksWbIEdnZ2ePz4MebPn8+EHX/+/DmGDRvGKY/k5GT4+vrC2NgYDx8+xMCBA1GuXDns3bsXWVlZiIqKkmlvbW2NuLg4sb5w6tQpTjq/wvNdlOzsbN5RT1SoUPE/VI4mFSp+Ufbv34+dO3eiXr16Ig9UV1dX1iXmQvr27YusrCxMnToVlSpV4hzjV8iLFy/g7u4OADAwMMDHjx8BgIm3/W+gXbt26N+/PxYtWoTffvsNAHD16lWMHTsW7du3B1AwQFKtWjWpefB9qZInXEPheNedO3fG8uXL4efnx6R5eHjA2toaU6dOZY5DGnydluHh4Zg5cyZq164tVz9SRB7h4eHYsGEDxowZgylTpmDy5Ml4+PAh9u/fj2nTprHanz59GgcOHEDt2rWhpqYGW1tbtGjRAkZGRpgzZw4nXYusrCxGAFZXVxefP38GAPTq1Qv16tXDypUrS7QOfMsHChyUp0+fhrm5OdTU1KCmpgYfHx/MmTMHI0aMwI0bN2Ta83VUAfwdLcp2+PFtx3fv3jGhTYyMjJg6+/j4YOjQoazlA8CtW7fg5eUFAGJx5eW5PosL2z2nNCgLdVC1Q8FEjY4dOyq1DspuB2WXDyi/L5SFfgAAGzZsgIGBAc6dO4dz586J/CYQCEp8glRZcLgpuw7KbgNA+ecAUL6DoSy0Q1mog7L7grKfD4UnLiqLwMBATJgwAdevX0e9evUAFGgs7d69G+Hh4SIh5iSFkHNxccHs2bNx9uxZEY2mCxcuYMyYMSJOxJLqU3379oWGhgb+/vtvub+F5aFcuXJITU2Fubk5TE1NZZbL9g2kqamJsWPHiqUXDWMni9GjR6Nv376YP38+DA0NmXQ/Pz9WfSUAGDNmDEaMGIGbN28y37QXLlzAli1bsGzZMpnlAgXXzNSpU6Gnp8f8lpeXhytXrqBGjRqcj0OFChWyUTmaVKj4RXn9+jXKly8vlp6Tk8P55eb8+fNISEiQ+8FrZWWF58+fw8bGBo6Ojjhx4gRq1qyJq1evSoxHXhZZu3YtRo0ahW7duiE3NxcAoKGhgT59+mDJkiUAgOrVq2PDhg1S8+D7UrV582YYGBigc+fOIum7d+/Gly9f0KdPH5n2t27dkiiIa29vj5SUFNby+Tot16xZgy1btqBXr16s+5ZUHnxX1+Xk5DDXk6mpKV6/fo1q1arB3d2d80zvihUr4t27d7C1tYWNjQ0uX74MT09PZGZmypxNqag68C0fKHgZF/Zhc3NzPHv2DE5OTrC1tZWoFVYUvo4qgL+jRdkOP77t6ODggMzMTNjY2KB69erYtWsX6tSpg0OHDsHExITVHgDOnDnDaT9p2g184apLJ0vf6Feog6od2IXQhcjSMuGLsttB2eUDyu8LZaEfAMofVC0LDjdl10HZbQAo/xwAKgdDWamDsvuCsp8PhScuykKavpIiEK6W+fPPP/Hnn39K/A2A1JVBGzduhKmpKVJSUkS+e01MTLBx40YR+5JyNN28eRPXr18vlg5icVbRSav3kiVLmO+2pUuXcs5PElu3boW5uTnznTN+/HisW7cOLi4u2LFjB6e+cvXqVaxdu1Ys3dLSEi9evGC1Hzp0KCpWrIhFixZh165dAAp0m3bu3Il27dpJtRN+XxIRbt26BS0tLeY3LS0teHp6SnSiqVChQk5KNVCfChUqSo2GDRvS8uXLiagg5nFGRgYRFWg0tWrVilMezs7OlJiYKHcdJkyYwMSVjomJIQ0NDapSpQppaWnRhAkT5M63tMjNzaVz587Ru3fv6PPnz0w8amFMYq4YGRkxcdYLx59++PAhp3jCVatWpdOnT4ulnz17lqpVq8Zq7+XlRb169aLv378zad+/f6devXpxiqmsq6vL1Llw/W/evElGRkas9uXKleMdZ55vHnp6ekwM7IoVK9L169eJqEBziMsx1K5dm44dO0ZERAEBAdSrVy968uQJjR8/nhwcHDjVITg4mGbMmEFERCtXriRdXV3y9fUlExMT6t+/f4nXgW/5REQ+Pj60b98+IiIKCgqi1q1b0/nz56l3797k6urKam9iYsLcixwcHJh+nZ6ezinOOhGRu7s7nT17loiImjdvTmPGjCGiAg0qS0tLVnt7e3vmvlarVi1as2YNEREdP36cTE1NWe0NDQ0pMzOTiIhsbGzo/PnzRESUkZHB6Rj4tuPixYsZjbuTJ0+Sjo4OaWtrk5qaGi1dupTVvjjI0i34N9gTydZjKY068C1fEXVQtj2R8ttBEceg7Dr8Cseg7H5QVvLgex7Onz9P3759k9u+LNShLPSFf/s5ICJ6/Pgx5eXlKa0OZaEdlH09Eim/L/zb7+2KyqOsUrt2bUpISCiWjZ2dHafN3t6+hGotSrVq1SguLo6IiC5evEh6enq0du1aCggIoA4dOnDKw8LCgvkGK9zeJ06cICsrq5KpeCH69u1LHz9+LPFyVKj4r6Na0aRCxS9KZGQk2rRpg5SUFOTm5mLZsmVISUnBxYsXxcIPSGPp0qWYOHEi1q5dCzs7u2LXobBeSteuXWFra4uLFy+iatWqCAgIKHZ+pY26ujpatmyJu3fvwt7eHh4eHnLlo62tzeg5FSY1NRUWFhas9llZWRJXJNna2iIrK4vVfs2aNQgICICVlRVzDMnJyRAIBDh06BCrfe3atXH48GGEhoYC+N9Myg0bNjAhCGQxYMAAREdH8wqXyDcPvqvrwsLC8Pz5cwAFM79bt26N7du3Q0tLC1u2bOFUh3Xr1jHxtkNCQmBmZoaLFy8iMDAQgwcPLvE68C0fAKZMmYKcnBwAwMyZM9G2bVs0bNgQZmZm2LlzJ6u9m5sbkpKSYG9vj7p162L+/PnQ0tLCunXrmFVKbPTr1w9JSUlo3LgxJk6ciICAAKxcuRI/f/7kFEqmWbNmOHjwILy8vNCvXz+MGjUKsbGxuHbtGqdZq05OTrh//z7s7Ozg6enJ3B/XrFnDaSYn33YsHKLC19cXd+/eRWJiIqpUqSL3PUoaxHGlW1m1VwS/Qh2Uba8IysIxKLsOv8Ix8KUsnANF5MF3RUqbNm1w8+ZNzs/NslgHvuUroh3/7ecAKAgL9l9vB2Vfj4Dy+8K//d6uCNzd3XHkyBFOWj2SMDIy4n1fLUzhb/958+Zh/PjxiIyMlKg3ZmRkJGZfkiv6vn37JqZXLKkOhXn8+DGjjbR//378/vvvGDRoELy9vdGkSRNO5QYGBmLmzJnMaiSBQICsrCxMmDABv//+O+f6//jxQ6Jms42NjUy7oqunP336hNOnT6N69erFWm2mQoUK2agcTSpU/KL4+Pjg5s2bmDt3Ltzd3ZmB9UuXLjG6SWx07doVX758gaOjI/T09MReithi+cbHx6NBgwbQ0Ci41dSrVw/16tVDbm4u4uPj0ahRI/kOrhRxc3NDRkaGREcPV/i+VJUvXx7Jyclizr6kpCSYmZmx2tepUwcZGRnYvn077t27B6Cgbbt3784p/A1fp+W3b9+wbt06nDp1Su4483zz6NChA+Li4lC3bl2EhoaiZ8+e2LhxI7KysjjFlu7Zsyfz/1q1auHRo0e4d+8ebGxsYG5uzmoPgAkVJ6Rbt27o1q0bJ1tF1IFv+QDQqlUr5v9VqlTBvXv38O7dO9a430L4OqoA/o4WZTv8FNGXCmNnZyfXRABF8fjxYwCQ+GGfkpKCypUrl3aVRLC1tRW7X/yK5f+X2yE3Nxdnz57FgwcP0L17dxgaGuLZs2cwMjJixKqFITKVSWm1Q1nuC6VxPbx69YoJ5erk5CQWRros9IWyMCis7DqUhYFtZR/Dr9COikDZdfgvtEN6ejoePHiARo0aQVdXF0Qk8t6u7GcDAPzxxx8oV66c3PYPHz7Ez58/5bZXdBuYmJiInGMiQvPmzcXKlBbyTxI/fvxAZmYmHB0dmfEVruTk5GDChAnYtWsX3r59K/Y7Wx0MDAzw9u1b2NjY4MSJE4zukY6ODr5+/cqpDosWLUKnTp1Qvnx5fP36FY0bN8aLFy9Qv359zJ49m9U+LS0N/fv3x8WLF0XSuZ7HLl26oFGjRhg+fDi+fv2K2rVr4+HDhyAixMTEFMvZpUKFCumoHE0qVPzCODo6Yv369XLb843l27RpUzx//lzsI//jx49o2rQp55cqZTJr1iyMHTsWERERqFWrlphjhm32DyD9papevXqcXqqCgoIwYsQIGBoaMs65c+fOISwsjLOjQF9fH4MGDZK5j7T42j4+PkhKSsKcOXPkclomJyczOl/yxpnnm4ciV9cREXR1dVGzZs1i2QHA+/fvsXHjRty9exdAwUzUfv36FfvDSt46KKp8QPZgpjT4OqokUVxHi7IdfoWRtx3j4uKwZMkSph2dnZ0xcuRI+Pr6FisfecnNzUV4eDiWL1+O7OxsAAUfoKGhoZg+fTozkCzvrFKufPjwAbGxsXjw4AHGjRuHcuXKITExERUqVIClpSUA8fvFr1S+qh2AR48eoXXr1sjKysL379/RokULGBoaYt68efj+/TvWrFlTIuUKSUpKwqFDh1CuXDl06dJF5B7w6dMnjBw5Eps2bQJQsu1QFvqCsq+Hz58/Y9iwYYiJiWHeL9XV1dG1a1esWrUKxsbGJVa2JISDlpKebUePHmXOSUmgbOersssvjLLaoaycg3+Dg6Gkrwfgv3s9vn37Fl27dsXp06chEAiQlpYGBwcHBAcHw9TUFIsWLQJQ8u8Jz549w/nz5yWuQhHqC02aNKlE61DacNXX4sKXL18QGhqKrVu3AiiIiuLg4IDQ0FBYWlpi4sSJrHmMHz8eZ86cwerVq9GrVy+sWrUKT58+xdq1a0W+k6XRokULDBgwAF5eXkhNTYWfnx8A4M6dO5y/w4yNjXHy5ElcuHABSUlJyM7ORs2aNeHr68vJ0de3b19oaGjg77//RqVKlYr97RgfH4/JkycDAPbt2wciwocPH7B161bMmjVL5WhSoUJRKDgUnwoVKsoIampq9PLlS7H0N2/ekJqaWqnUQSAQ0KtXr8TS79+/T4aGhqVSB74IBAJmU1NTYzbh38Xh/PnztGrVKpo3bx6dPHmSs93379+pS5cuJBAISFNTkzQ1NUlNTY369evHO0Z/YaTFxu7Vqxdt2rSJt86Ssvjx4wf169eP0QaSlw0bNpCrqytpaWmRlpYWubq60vr16znbnzt3joyNjcna2po6dOhAHTp0IBsbGzIyMqJz586VeB0UUf7Pnz9pypQpZGRkxFwLRkZGNHnyZPrx4wenPIRkZWVRVlZWsWyEnDp1ivz9/cnBwYEcHBzI39+/WNfUu3fvaMGCBdS/f3/q378/LVy4kN6+fVvseuTn51N+fn6x7fi046pVq0hDQ4O6detGy5Yto2XLllFQUBBpamrSypUri10XWUi7JwwZMoTKly9Pa9asYbTr1qxZQxUrVqQhQ4aUePlERElJSWRhYUFVqlQhDQ0NZr/JkydTr169SrwOpVW+rDqo2oGoXbt21LNnT/r+/bvIPmfOnKEqVaoorHxJdTh+/Dhz/drY2JCZmZmInuKLFy8U+r4lqx2U3ReU3Q+IiLp06UJVq1alY8eO0cePH+njx4907NgxcnJyoq5duyqsDmz14PuuwLf8hw8fUvXq1UlPT4/U1dWZ/UaMGEGDBw8u8Toou3whymyH0joHsurw5s0bat68OfO9ItynX79+NHr06BIv/9mzZ7Rt2zY6fPiwiEYsEVF2djaFh4eXeB2ISqcfyKqDsq+HXr16UatWrejx48ci+xw7doxcXFxKvHwios2bN5OWlhYZGBiQra1tiekL8dV4KimNqB8/flCzZs0oNTVV7jxGjBhBtWrVooSEBNLX12fquX//fqpRowanPKytrenMmTNEVKDJlZaWRkREUVFR1KZNG1b79+/fU0hICAUGBtLRo0eZ9GnTptGsWbM41WH+/PkS03Nzc6lbt26s9np6enT37l1OZUlCR0eH+fbs1asXoxn+6NEj0tfXlztfFSpUiKJyNKlQ8YsiEAgkOpqePn1KOjo6nPNJT0+nyZMnU7du3Zj8jhw5Qrdv35ZqIxzEVlNTIz8/P+bvDh06UGBgINnZ2VGrVq2Kf1BK4OzZszI3rpw6dYomTZpEwcHB1K9fP5GNK6mpqbRr1y46dOgQPXz4UJ7DkYm0F+zg4GCqWrUqqampkZWVFfXo0YPWr1/P64W5tDEyMuLlaJo6dSrp6+vTxIkT6cCBA3TgwAGaOHEiGRgY0NSpUznl4ebmRgMHDqTc3FwmLTc3lwYNGkRubm4lXge+5RPxH8xUhKOKr6NF2Q4/vu1oaWlJK1asEEtfuXIlVa5cmVMduCJNHNrIyIiOHDkiln748GEyMjJSWPmyPvqbN29O48aNE9vvwoULZGtrW+J1KK3yiVTtQCT9HJQrV47u3bsnVn5mZibp6uoqrHxJdahfvz798ccfRFTgdJ43bx4ZGBgwAzCKdjTJEmtXdl9Qdj8gKhiAkiS2Hh8fT3p6egqrg6x6KOJdgQuyrsnScr4q2/krqy8oux2U6QAXUloOBknt8M8//5CJiQkZGRmRrq4uValSReS7sbTujaXVD4iU3xeknYMKFSrQzZs3xer44MEDhQ6sy7onWVlZ0axZsygvL09h5RW3DqVhLwtzc3Ne3802NjZ06dIlIhKtZ1paGufJu/r6+vTo0SMiKviWuHLlChERZWRklJqTxcLCgjZs2CCSlpubS506daLq1auz2teuXVvic54rVatWpZ07d1J2djZZWFhQXFwcERHdvHmTzMzM5M5XhQoVoqhC56lQ8YuxfPlyAAWhATZs2MAsyQcKYu/Gx8dzFjs8d+4c2rRpA29vb8THx2P27NkoX748kpKSsHHjRsTGxkq0E4YnISIYGhpCV1eX+U1LSwv16tXDwIED5T3EUqVx48a88wgPD8fMmTNRu3Ztzsu8R48ejYiICOjr6zMxkAtz+vRp5v9cNI74sGHDBgDA06dPER8fj3PnzmHRokUYPHgwKlWqhCdPnsi079Chg8RjFggE0NHRQZUqVdC9e3c4OTmVWB7t27fH/v37OekxSWL16tVYv349goKCmLTAwEB4eHggNDQUM2fOZM0jPT0dsbGxUFdXZ9LU1dUxevRoREVFlXgd+JYPANHR0YiJiUGbNm2YNA8PD1hbWyMoKAirV6+WaR8aGoq9e/di/vz5qF+/PgDg0qVLmDFjBt6+fctqDxRohi1ZsgTDhw9n0kaMGAFvb29ERkYiJCREpn1ISAi6dOmC1atXM+ciLy8Pw4YNQ0hICG7duiXTftq0aVi8eDFCQ0NFjmHUqFHIyspibQe+7fjhwwe0bt1aLL1ly5aYMGGCTNviQlLCWGhra0sMk2Fvbw8tLS2FlS9Lz+Xq1atYu3atWLqlpSVevHhR4nUorfIBVTsA0s9Bfn6+xDC8T548gaGhocLKl1SHO3fuYNu2bQAKnkXjx4+HlZUVOnXqhJiYGPz2228lWn5hlN0XlN0PAMDMzExieDxjY2OYmpoqrA6y6qGIdwUuyHqPTEhIwMWLF8Xa3c7ODk+fPlVI+bLqUFrly+oLym6H0joHsupw4sQJHD9+HFZWViLpVatWxaNHjxRWvqR2+OOPP9ChQwds2LCB0YVp3LgxTp48CS8vL4WVLasOQOn1A0D5fUHaOcjJyYGenp5Y+rt376Ctra2w8mXpK3358gXdunUTCVtdFpE3hDcXhNrAXELUSeL169diUgRAQftyrbeDgwMyMzNhY2OD6tWrY9euXahTpw4OHToEExMTTnkkJCRg7dq1yMjIwO7du2FpaYlt27bB3t4ePj4+rPaHDx9Gy5YtYWxsjE6dOiE3NxddunTBvXv3pIYa/PTpE/P/efPmYfz48YiMjIS7u7vY+wibpMHIkSPRo0cPGBgYwNbWFk2aNAFQEFKPq4a5ChUq2FE5mlSo+MVYsmQJgIIXzjVr1ogMKmtpacHOzo6zZsHEiRMxa9YsjB49WmTAplmzZli5cqVUu82bNwMoeIkeO3asmK7Rv4n4+HiZvws1k2SxZs0abNmyBb169eJc7o0bNxhB0xs3bkjdryRfiotiamoKMzMzmJqawsTEBBoaGrCwsGC1MzY2xv79+2FiYoJatWoBABITE/Hhwwe0bNkSO3fuxLx58xAXFwdvb+8SyaNq1aqYOXMmLly4IFFrSxgfXBo/f/5E7dq1xdJr1aqF3Nxc1nMAADVr1sTdu3fFnGF3796Fp6cnqz3fOvAtH+A/mMnXUQXwd7Qo2+HHtx0DAwOxb98+jBs3TiT9wIEDaNu2Lat9UWRpbUnTbhg+fDgiIiKwefNmZqDi+/fvmD17togDUBZ89Vy0tbVFPj6FpKamcrov8a2DIsovzH+5HfjoV7Rs2RJLly7FunXrABQ8E7OzszF9+nRGP0BRFG0HbW1tfPjwQWSf7t27Q01NDV27dmW0L4qDPP0AUH5fUNT18OrVK9y/fx8A4OTkJDawJkvHZMqUKRg9ejS2bduGihUrAgBevHiBcePGYerUqZzrIITk0HRRxLtCceomidJyvpaG81eeNgCU3w7KdIALKS0Hg6R2uH79OlatWgU1NTUYGhrizz//hI2NDZo3b47jx4/Dxsam2OWU5euxcP2Koqi+IK/WVsOGDREVFYWIiAgABecvPz8f8+fPR9OmTTmVzVdfKTg4GLt37+akI6RMZN1X+ZKbm4tNmzbh1KlTEr9D2SaN1q5dG4cPH0ZoaCiA/10HGzZsYCa9sdGvXz8kJSWhcePGmDhxIgICArBy5Ur8/PmT06TVPXv2oFevXujRowcSExPx/ft3AAXa25GRkThy5AhrHr/99hv27NmD9u3bQ0tLCxs3bkR6ejrOnDmDChUqSLQxMTER6etEhObNm4vsI7we2PS/hw0bhrp16yIrKwstWrRgnJ8ODg6YNWsWa/1VqFDBkVJbO6VChYpSpUmTJvTu3Tteeejr6zPhxoqGpNHW1uZdx38DhTWaJGk1caFcuXL/Cn0jaSEDJk2aRPXr1ycdHR3y8vKikSNH0v79+zn3rwkTJtDQoUNFQibk5eXR8OHDadKkSZSfn0+DBg0ib2/vEsujcDzwohuX+ODDhw+nUaNGiaWPGTOGhg0bxmpPRBQTE0M2Nja0YMECSkhIoISEBFqwYAHZ2dlRTEwME4ouKSmpROrAt3wiovDwcAoKChLRBvv27Rv16NGDZsyYwVoHCwsLSklJEUtPSUkhc3NzVnsioqCgIIkxvhcsWMBJh6NBgwa0b98+sfR9+/ZR3bp1We2NjY0lhr+4f/8+GRsbs9rzbceIiAgyNjYmPz8/ioiIoIiICPL39ycTExOKiIhgwgkuW7ZMah58Qxi2b9+eDA0NydzcnJo3b07Nmzcnc3NzMjIyEgmV2qFDB4n2itBzCQ4Opvbt29OPHz/IwMCAMjIy6NGjR+Tl5UVhYWGs9nzrwLd8IlU7EPHXr3j8+DG5uLiQs7MzaWhoUL169cjMzIycnJwkhg8uys2bNykiIoJWrVpFr1+/Fvnt48ePMsPbtmjRghYsWCDxt+joaEZPkQ1FhBRVdl/g2w8+ffpEPXv2JA0NDeZdS0NDg3r06EEfPnzgdA5q1KhBBgYGpKmpSY6OjuTo6EiamppkYGBAXl5eIpss+IRGVcS7ws+fP+nkyZO0Zs0a+vTpExEVhL3+/PkzJ/suXbrQwIEDiYiYtvj8+TM1a9aM+vbtW+J1UET5fHV1lN0OijgHQtLS0ujYsWP05csXIiIxXcisrCyRkMhC2rRpQ1OmTBGpQ15eHnXu3Jl+//13mWXy1VcyNTWV+C65YMECMjExob1793L+hlL29Uik3L7AV2vr1q1bVL58eWrdujVpaWlRp06dyNnZmSpUqMDp21QR+kq5ubnUunVraty4MdMmhTdFsX37dsrOzpbbPiEhQaHax4Vp0qSJ1K1p06ac6mZgYEBDhgwhHR0dCgsLoxYtWpC+vj5du3ZNrjo9fPiQ9uzZI/O7rzA1atSgrVu3EpHomEFiYiJVqFChWGXv27ePNDQ0yN3dXezdqyhsMgbySBqoUKGiZBEQlaDrXoUKFf9qrKyssGvXLjRo0ACGhoZISkqCg4MD9u3bh7Fjx+LBgwdiNjVr1kRcXBxMTU3h5eUlc8VNYmJiSVZfIXz8+FHk758/f+LGjRuYOnUqZs+eLTajRhITJkyAgYGBXDNqS5PCbVwYNTU1WFhYYNSoUejYsSOqVatWrHwtLCxw4cIFMbvU1FQ0aNAAb968wa1bt9CwYUOx2eGKzIMPoaGhiIqKgrW1NerVqwcAuHLlCrKystC7d2+RpfvSZoWxhYwQCAQyZ2TxrQPf8oGCEIZxcXHQ1tZmVkElJSXhx48fYtfC3r17xexnzpyJe/fuic26Dw4ORtWqVTF9+nSZdQSAWbNmYeHChfD29mZm8V2+fBkXLlzAmDFjRMImSFqptnPnTowfPx6hoaHMebx8+TJWrVqFuXPnwtnZmdnXw8NDzD40NBSamppi53js2LH4+vUrVq1aJbP+fNvR3t5eZv5CBAIBMjIyJP42dOhQ7N27FzNnzhQLYdi+fXvWlWX9+vXjVAfgfytcC+Pr64uaNWti/vz5Ivedixcvonv37nj48CFrvh8/fkSnTp1w7do1fP78GZUrV8aLFy9Qv359HDlyhHUlLd868C0fULUDUBDW1NDQEBs3boSZmRlTh7Nnz2LgwIFIS0tjrUNubi527tyJpKQkZGdno2bNmujRo4dI2F5JnDhxAgEBAahatSo+f/6MnJwc7N69m5nl/fLlS1SuXFnq/XDfvn2Ij49nVpIXJTo6GuvXr5caDkYI334AKL8v8O0HXbt2xY0bN7BixQqRcxAWFoYaNWogJiaG9bjCw8NZ9xEi7VkjLTTqypUrMWrUKNYVq3zv748ePULr1q2RlZWF79+/IzU1FQ4ODggLC8P37985RSR48uQJWrVqBSJCWloaateujbS0NJibmyM+Pl5i+CVF1oFv+XzbAFB+O/A9BwDw9u1bdO3aFadPn4ZAIEBaWhocHBzQv39/mJqasq6YvH37Npo3b46aNWvi9OnTCAwMxJ07d/Du3TtcuHABjo6OEu2uXr2Kli1bIj8/Hz9//oSlpSX2798PV1dXAOz3RaAg2kP37t0xZMgQsd/mz5+PadOm4efPn6yrD5R9PQLK7wu9e/fGq1evsGHDBjg7OzP35uPHj2P06NG4c+eOTHug4P68cuVKkWdkSEgIKlWqxGprbW2NIUOGYNKkSXKHvps1axamTZsGJycnVKhQQWRsQCAQiISDl0ZcXBzi4uIkrqratGmTTNu8vDxs2bJFqj2X8vmQl5eHCxcuwN3dnVcY1wcPHmDu3Lki7ThhwgSZId/KlSuH1NRUmJubo3///li2bJncqyr19PSQkpICOzs7kfeEjIwMuLi44Nu3bxLtOnbsKDH98uXLqFKlCszNzZk0Sd+OiubJkyc4ePAgsrKy8OPHD5HfSlqOQIWK/woqR5MKFb8oinipGjt2LK5cuYLdu3ejWrVqSExMxMuXL9G7d2/07t1b4od6eHg4xo0bBz09PdaPfi6DymWVc+fOYfTo0bh+/brE3wvrKuXn52Pr1q3w8PCAh4eHWDzhsvJSM2fOHAwdOlQsTnNSUhLOnTuHs2fPIiEhAVpaWmjcuDGaNGmCJk2asDqeTE1NsXXrVgQGBoqkHzx4EH369MH79++RlpaGOnXq4P379yWWBwD8+PEDmZmZcHR0hIYG9+ixXMNLyPpgKk5MfFtbW4XXgW/5AP/BTL6OKoC/o0XZDj9F9CW+GBsbi4UwBIAjR44gKChIzMFeEuUnJibC0dFR5GP10aNHcHJykvqxKonz588jOTmZ+ej29fUt1TpcuHBB5KOfa/nCOvzX28HMzAwXL16Ek5OTSB0ePnwIFxcXfPnyRaZ9fHw8GjRoIHY/z83NxcWLF2WGt23QoAGaNm2K2bNng4iwYMECREREYPfu3WjdujWnAVVFoOx+IKyDIvqCvP1AX18fx48fF9N4SEhIQOvWrZGTk1PsY5IHCwsLLF++XCQ0KgDs2LEDoaGhePPmjUx7vvd3RThegYL+HxMTI9IWXJyviqqDvM5fgH8bAGWjHfi0AaA8B0OLFi1gbW0toq+0a9cuRl+Jy31xw4YNOHfuHKNhV5R58+ZhzZo1yMzMlFl/ZV+PgPL7QsWKFXH8+HF4enqKDe57eHggOzub0zHKi5mZGf755x+pjkkumJqaYsmSJejbt69c9mxax/v27ZNpP3z4cGzZsgX+/v4S7aVNFlEkOjo6uHv3LudvGEVhYGCA5ORkODg4QF1dHS9evJArvDNQEF5u3bp18PX1FemLUVFRmDt3LlJSUiTa8f12LMr79++xceNG3L17FwDg4uKCfv36SdUIK0xcXBwCAwPh4OCAe/fuwc3NDQ8fPgQRMU55FSpU8Eel0aRCxS9KWFgY81Ll5uYml5ZPZGQkQkJCYG1tjby8PLi4uCAvLw/du3fHlClTJNoUdh79mx1JbFSoUIHREZBEUV2lGjVqABDXNygNjaWtW7fC3Nwc/v7+AIDx48dj3bp1cHFxwY4dOxingrT42p6envD09GRWhyQlJWHJkiUICQmRGnu8ML169UJwcDD++OMPRhz96tWriIyMRO/evQEUOO6EsyVLIo8vX74gNDQUW7duBQBmRmJoaCgsLS1Z44azzUjngjTnDVf41oFv+QC3DwBZmJiY4PfffxdJk6RFIgu2gYmStr99+zZq1qwJAMyqTnNzc5ibm4tc39KubUX0Jb7w1doSwqanIqt8vnoujx8/hrW1NXx8fDgJECu6DlFRUejatSu8vb1FdOF+/PiBmJgY5r7EVof/ejvw1a9o2rQpnj9/LnbMHz9+RNOmTWU+n+7cucMMhAoEAowfPx5WVlbo1KkTYmJimGcNV65fvy4y8CG8T7ChqH4AKK8v8O0HZmZmMDY2Fks3NjaWawZ4dna22AQrNpFwgL+mC9/7e0JCAi5evCjW7nZ2dnj69CmnPL59+wYdHR307NlTKXUQOn979OiBHj16MOm5ubmIj49n1TZVhK6OstuBbxsABSsujx8/DisrK5H0qlWrcpo4lJWVBWtra0yePFnib9J0khShrzRgwAAMGDBA6u8TJkzgpKmp7OsRUH5f4Ku1dezYMRgYGDD35VWrVmH9+vVwcXHBqlWrWO+vitBX0tbWlqrBywV5tI4LExMTg127dilct7E4uLm5ISMjg7ej6dWrVxInEEuKwAAA9evXR/v27VGrVi0QEUaMGCHVwcm2MmzgwIEICwvDpk2bIBAI8OzZM1y6dAljx46VGbWF77djYeLj4xEQEABjY2Pm3rB8+XLMnDkThw4dYn2+TJo0CWPHjkV4eDgMDQ2xZ88elC9fHj169JCo/6tChQo5UUa8PhUqVJQ8ZmZmdPjwYYXk9ejRIzp8+DDt3LlTojYJG1evXqWoqCiKioqSO46wsiisW5OUlEQ3b96ko0ePUuPGjWVqCpUlqlWrRnFxcUREdPHiRdLT06O1a9dSQECAVL2GwuTn59P169dp0aJFFBAQQKampqSurs7oNbGRm5tLs2bNoooVKzLaCxUrVqTZs2czceUfPXpEjx8/LrE8RowYQbVq1aKEhATS19dn4krv37+fatSowXoMiuTOnTt09OhROnDggMj2XylfBX8eP35Mq1atogkTJsgV656v1tbHjx956akoQt9ITU2NGjVqROvWrZNLj5BvHdTU1CRqAL1584az9oSqHfjrVwgEAnr16pVY+v3798nQ0FCmrYWFhcR3kh07dpCenh6tXr2aU1u+fPmSmjZtSgKBgExNTcnU1JQEAgE1a9ZMYt2KwrcfECm/L/DtB2vXriVfX196/vw5k/b8+XNq2bIlrVmzhlMeGRkZ5OfnR3p6eiJamkJtEy4oStOFqEA7Jysrq1g2JiYmdOfOHSIS1cBISEig8uXLc8rD0NCQevfuTSdOnBDRtSytOvC9NyqyDYiU0w5820BYrvCbq3Adrl69SuXKlWO1l7cdFKmvJOTVq1eMNiiXe6IQZV+PRMrvC3y0toiI3NzcmPGA5ORk0tLSokmTJlG9evU4PWMVoa8UGRlJoaGhnPaVBF+t40qVKtH9+/fltlcER48epRo1atChQ4fo2bNn9PHjR5GNjWvXrpGrqyvzTCuqHS2NFy9e0IQJE6hTp06kpqZGbdq0ofbt20vc2MjPz6dZs2aRvr4+U7aOjg7TP0sDNzc3GjhwoIguXW5uLg0aNIjc3NxY7Q0MDJi+ZGJiQrdv3yaiAr1OW1vbEqmzChX/RVSh81So+EWpXLkyzp49W2w9HUXy5MkTBAUF4cKFC0w4tg8fPqBBgwaIiYkRm6VXFlFTU2NCaRWmXr162LRpE6pXr66kmnFHT08P9+7dg42NDSZMmIDnz58jKioKd+7cQZMmTfD69WuZ9qampsjOzoanpycTMq9hw4ZiIfa4IJwxzWVmsSLzsLW1xc6dO1GvXj2R5f7p6emoWbOmxJncRbl27Rp27dolMaYzl5jSGRkZ6NChA27duiXSp4QrX7iEZ+JTB0WUDwCxsbFS61BaumuKiK+dkpIi0b5oeMaSgE87KiLsA98Qhnz1VBShb3Tjxg1ER0cjJiYGr1+/RuvWrdGzZ08EBARwmuXLtw5qamp4+fKl2GqPpKQkNG3aFO/evWOtg6od5NevEMb8P3DgAFq3bi1SVl5eHpKTk+Hk5IRjx45JLbtly5Zo2bIlxo4dK/bbjh070KdPH+Tl5bHeG7t27YqMjAxERUUxGm8pKSno06cPqlSpgh07dsi0V0RIUWX3Bb79wMvLC+np6fj+/TuzYiIrKwva2tqoWrWqyL7SnjPe3t4gIoSFhYnpgABA48aNWevBNzRqbm4uwsPDsXz5ciaklYGBAUJDQzF9+nSx0MlF6dq1K4yNjbFu3ToYGhoiOTkZFhYWaNeuHWxsbDjNDN+3bx+io6Nx+PBhGBsbo2vXrujZs6fElSElUQdp98bU1FTUrl2b9X1LEbo6ym4Hvm0AAH5+fqhVqxYiIiKYOtja2qJbt27Iz89HbGysTHtp7fDo0SO4uLhIDUepKH0loGA1TmhoKLZt28bsr66ujt69e2PFihUSV+oURtnXI6D8viCv1pYQAwMD3L59G3Z2dpgxYwZu376N2NhYJCYmws/PDy9evJBprwh9pQ4dOuD06dMwMzODq6ur2Hlne+/lq3W8aNEiZGRkYOXKlaUSSUQShcN2F64DsWjjCvH09ISjoyMmTJgg8fnGJWqFvb09rl27BjMzs2LW/n86Ux4eHtDT00N6ejqys7Ph4uICAwMDzvnY29vLbANp2rJCdHV1cfPmTTg5OYmk379/HzVq1MDXr19l2lesWBFnzpyBs7MzXFxcMHfuXAQGBiIpKQne3t4lHopShYr/CipHkwoVvyjyvlSNHj0aERER0NfXF9EZkgTbgG7r1q3x4cMHbN26lXkhuH//Pvr16wcjIyOZA0BlhaLhKdTU1GBhYQEdHR0l1aj4lC9fHsePH4eXlxe8vLwwevRo9OrVCw8ePICnpyfrS9Xhw4fRsGFDXs4hZaOnp4fbt2/DwcFBxNGUlJSERo0asWpgCENhtWrVCidOnEDLli2RmpqKly9fokOHDpwGfwICAqCuro4NGzbA3t4e//zzD96+fYsxY8Zg4cKFaNiwYYnWgW/5QEF4gsmTJ6Nv375Yt24d+vXrhwcPHuDq1asICQnB7NmzWfPg66ji62hRtsOPbzvWqVMHbdq0YcI+JCUliYR9GDp0KGv9+cZLV5Seirx6LoUhIpw9exbR0dHYs2cP8vPz0bFjR9YQIEKKq7Hk5eUFgUCApKQkuLq6imgD5eXlITMzE61bt8auXbtYy1a1QwHy6LkIz93WrVvRpUsXkX21tLRgZ2eHgQMHiohMF2Xfvn2Ij4+Xqs8QHR2N9evXs4ZfMjY2xqlTp8RC7f3zzz9o2bIlPnz4INNeEfoFZaUvyNsP2DQ9CyMtLLOBgQGuX78uNgBVHPhqugwdOhR79+7FzJkzRRx+M2bMQPv27bF69WqZ+crreJXE58+fERsbix07duD06dNwcHBAz549MW3atBKpgyKcv4BidHXKSjvI2waA/A4G4bfbsmXLMHDgQBFnTl5eHq5cuQJ1dXVcuHBBor2i9JUAYPDgwTh16hRWrlzJhE47f/48RowYgRYtWrC2g7KvR6Bs9AV5tLaElCtXDufPn4eLiwt8fHzQu3dvDBo0iLMOIl99JYD9GSfpucZX61h4PxJy+vRplCtXTi5HlyI4d+6czN/ZJkIYGhrixo0bqFKliiKrJRF3d3ccOXJELLS5InSmli1bJvL3z58/cePGDRw7dgzjxo1jDdHo7e2NcePGoX379iLp+/fvx9y5c3H58mWZ9u3bt4e/vz8GDhyIsWPH4sCBA+jbty/27t0LU1NTnDp1Sq7jUqFChSgqR5MKFb8oHTp0wJkzZ4r9UtW0aVPs27cPJiYmrC/4bIMvurq6uHjxIry8vETSr1+/joYNG7K+3JZVPnz4INdqHmXRo0cP3Lt3D15eXtixYweysrJgZmaGgwcP4o8//hDTjSoJFLEKhk8ejRo1QufOnREaGsrMSLS3t0doaCjS0tJYBz48PDwwePBghISEMIP79vb2GDx4MCpVqsRpkMzc3BynT5+Gh4cHjI2N8c8//8DJyQmnT5/GmDFjxHS9FF0HvuUDQPXq1TF9+nQEBQWJOOymTZuGd+/eYeXKlTLtFeGo4utoUbbDj287Ghoa4ubNm3B0dISpqSnOnz8PV1dXJCUloV27dnj48KFMe0VgY2ODw4cPw93dXSQ9OTkZfn5+ePLkiUx7oZ6LoklMTERwcDCSk5NZHYZCjaWiqy3YNJaE7RMeHo4xY8aIzOQUOjh+//33YmvryMOv0A5CPZfCDjugwPl08eJF1nj74eHhGDt2LKfVVyWFoaEhEhISGC1GITdu3EDjxo05rZjlS1nsC8XpB4qgadOmmDx5slyOUkVhbGyMmJgYtGnTRiT9yJEjCAoKYp3UAhT0/ZiYGBGHH5vjlY2UlBT06NGDc1soy/mrKMpiOxS3DQD5HAzCb7dz586hfv36Is8iYTuMHTtWbKVgSWBubo7Y2Fg0adJEJP3MmTPo0qULa0QFviiiHwBloy/IS2BgIH78+AFvb29EREQgMzMTlpaWOHHiBIYPH47U1FSZ9hUrVkRCQkKp9JfCcHUyApLHIxQxgaMs0b59e/Tq1UtM57YkKPx9V5jatWtj3rx5YiutFcGqVatw7do11rbYuXMnxo8fj9DQUGaV4+XLl7Fq1SrMnTuXWVUOSNatysjIQHZ2Njw8PJCTk4MxY8bg4sWLqFq1KhYvXqwQPWMVKlRApdGkQsWvSt++fWVupUHVqlXpypUrYulXrlwhR0fHUqkDX+bOnUsxMTHM3507dyaBQECVK1emmzdvKrFm3Hn//j2FhIRQYGAgHT16lEmfNm0azZo1q8TLX7ZsGRkYGNDw4cNJS0uLBg8eTL6+vmRsbEx//PFHqeSRkJBABgYGNGTIENLR0aGwsDBq0aIF6evrc9IN09PTo8zMTCIqiBWenJxMREQpKSlUsWJFTsdgYmJCGRkZRETk4OBAp0+fJiKi9PR00tXVLfE68C2fiEhXV5cePnxIRAX6JsJrIDU1lZNmgJOTE0VHRxORaKz7qVOnUkhICKc68I2vbWZmxugPGBkZ0b1794iIKC4ujpNel7u7O61cuVLkGPLz82ngwIE0bdo0Vnu+7VihQgVKSUkhIiJnZ2dGX+vmzZukr6/Pal+Yly9fUnx8PMXHx0vUcpAGXz0VvnouhXn8+DHNmzePPD09SV1dnXx8fGj16tWc6sBHR2TLli309etXuepcFFU78NO6UhTXrl2jbdu20bZt2+j69euc7QIDA6lRo0b09OlTJu3JkyfUuHFjTroHQuTtB0Rlpy/I2w8K8/nz52LrVxAVPMt8fX1py5YtdO3aNTGNzeIij6aLhYUFc38uTEpKCpmbm7PaK+qeIsxr586d1K5dO9LW1iYbGxuaMGECq925c+fo58+fYuk/f/6kc+fOsdrPmDGDsrOz5apzUeTV1Skr7SBvGxAV6I7m5+dL/Y2Nvn37cr52ZCGvvhJRwTujpHa4ffs26enpFSsvZVyPRMrvC0ePHqWEhATm75UrV5KnpycFBQVxulc/evSI/P39ycPDgzZs2MCkjxw5kpNuEl99pf8ySUlJjC5X0edRcZ9Pr1+/Jj8/P5oxYwbFxsaWqM5u4e+zwvDVmZLFgwcPWHU1iUhMn0qSXlVxdBlVqFBRMqgcTSpUqJBKv3796NOnT2Lp2dnZ1K9fP1b7/fv3U506dejq1atM2tWrV6levXq0b98+RVa1xLCzs6MLFy4QEdGJEyfIxMSEjh8/TsHBwdSiRQsl1+7fgSKcC4rIIz09nQYMGEC//fYbOTs7U48ePZhBfjYsLS2Zfd3d3Zm6XLx4kYyMjDjl4ePjw/T7oKAgat26NZ0/f5569+5Nrq6uJV4HvuUTEdnb21NiYiIREdWqVYsZwDx+/DiZmpqy2vN1VBHxd7Qo2+HHtx3btWtH69atI6ICQewqVarQrFmzqGbNmtS8eXNWeyKijx8/Us+ePUlDQ4P5ONPQ0KAePXrQhw8fJNrUqFGDvLy8mM3AwIA0NTXJ0dGRHB0dSVNTkwwMDMjLy4u1/MTERBo7dixZWVmRtrY2tWvXjnbv3k3fvn3jVH8iojVr1lCjRo1IXV2dXF1dKTIykulbXBAIBBIHzW7evMmpLysCVTtIb4f79+9zGnQgItq9ezd17tyZ6tatK3JuuJwDogIHT9OmTUkgEJCpqSmZmpqSQCCgZs2acRpYzcrKoho1apCmpiY5ODiQg4MDaWpqkpeXFz1+/JjVXp5+QFS2+gLffpCRkUF+fn6kp6dHampqzFacAaNLly6Rvb09r0Gnnz9/0pQpU8jIyIipg5GREU2ePJl+/PjBah8eHk5BQUEi5+3bt2/Uo0cPmjFjBqu9oaEh9e7dm06cOMEMUBaXY8eOUe/evcnIyIjKlStHgwYN4uQgEqJs5y/fNiBSfjvwbQMi5beD8Fuv6H2pf//+lJOTwymPZs2aUefOnUWcNV++fKHOnTtzel9R9vVIpPy+4ObmRocPHyYiouTkZNLS0qJJkyZRvXr1SmXiaPv27cnIyIjs7e2pbdu21KFDB5GNC3Z2dmRvby91Y4PveETTpk3p/fv3YukfP36kpk2bcjoGeRAIBMw1XPh5JMk5wsbBgwfJ2NhYbvviIM3RVLRMeZ7T0pg3bx6nyYIPHz7kvEnC3t6e3rx5I5b+/v17Tn1RhQoV3NBgX/OkQoWK/ypbt27F3LlzYWhoKJL+9etXREVFscbc79u3L758+YK6desyYXFyc3OhoaGB/v37o3///sy+XITTlcGLFy+YcDJ///03unTpgpYtW8LOzg5169ZVcu248+HDB2zcuBF3794FALi6uqJ///4wNjYu8bKzsrLQoEEDAAXhFD9//gwA6NWrF+rVq8cabk1ReTg6OmL9+vVyHUOjRo1w8uRJuLu7o3PnzggLC8Pp06dx8uRJziEEpkyZwuhkzJw5E23btkXDhg1hZmaGnTt3lngd+JYPAM2aNcPBgwfh5eWFfv36YdSoUYiNjcW1a9fE4qFLomLFinj37h1sbW1hY2ODy5cvw9PTE5mZmYxWEhv16tXD+fPn4ezsDD8/P4wZMwa3bt3C3r17mTAKsnBzc2PC1dWtWxfz58+HlpYW1q1bJxYmQhKmpqZM/7O0tMTt27fh7u6ODx8+cAoHyrcdFy9ezOiqhYeHIzs7Gzt37mTCPnBh4MCBuHHjBv7++28R3YKwsDAMHjwYMTExYjZF46HzQagXN3/+fEbPZdCgQcXS9Zk1axaCgoKwfPlyeHp6FqtsgUAAgUCA5s2bS9VYYiMvLw9LliyRGs6TyzPtv9wOwvuFQCBA3759Jeq5CO/5sigcjvPAgQNi4Ti5EBoais+fP+POnTtM2JWUlBT06dMHI0aMwI4dO2TaW1tbIzExEadOncK9e/cAAM7OzpxDuMnTD4Cy1Rfk7QdCevbsCSLCpk2bJAqdc6F///5MiGB58wgNDcXevXsxf/58MU2Xt2/fStR0KfrsO3XqFKysrJjzkJSUhB8/fnC6v2/duhXR0dFo164djI2N0bVrV/Ts2RO1a9fmfAwdOnRA27ZtERUVBT8/P7HQ2WzQ/4vTF+Xt27ecQ1TyCXUsTxsAZasd+LYBIL0dsrOzOevE8tGTHD16NM6dO4eDBw+K6SuNGTOGk77RsmXL0KpVK7F20NHRwfHjx1ntlX09AsrvC5mZmXBxcQEA7NmzBwEBAYiMjERiYiL8/Pw45ZGXl4f9+/eLfAMGBgZCXV2d1dbExITT+70sRo4cKfJ3UV0eNviOR5w9e1as/wPAt2/fkJCQwH4AcpKZmQkLCwvm/3wIDQ1Fz549MXXqVFSoUEER1Ss2bJIJXBC+gwshIrx48QKvX7/Gn3/+yWrPN7Tdw4cPJYar/P79O54+fcorbxUqVPwPlUaTChW/KPb29jI/sjMyMqT+9unTJxARTE1NkZaWxrwkAQUvq4cOHcLEiRPx7NkzmXXYunUr5/r26dOH876lSeXKlREbG4sGDRrAyckJs2bNQufOnXH//n389ttvpaK9wJdr166hVatW0NXVRZ06dQAAV69exdevX3HixAnUrFmzRMt3cHDAnj174OXlhdq1a2PgwIEYPHgwTpw4gW7dunEakFVEHnl5edi3bx/zoeXi4oJ27dqJaYNI4t27d/j27RsqV66M/Px8zJ8/n4npPGXKFJiamrKfCCn5mpqailyrT548QeXKlaGmplbidShO+UCBIG9+fj5zzmJiYpg6DB48mFWXZsCAAbC2tsb06dOxatUqjBs3Dt7e3oyjauPGjax15htf+/jx48jJyUHHjh2Rnp6Otm3bIjU1lXG4NWvWTKZ99+7dUbt2bYwePRoRERFYsWIF2rVrh5MnT6JmzZqsgzcl1ZeKsmPHDgQGBkocGNTX18fx48fh4+Mjkp6QkIDWrVszDsnSpLh6LtIG4dhQlMbStGnTsGHDBowZMwZTpkzB5MmT8fDhQ+zfvx/Tpk3DiBEjWOvyX24HRem58NWNAwp0PE6dOoXffvtNJP2ff/5By5Yt8eHDh2IfX3Eoi/0AKF5fkLcfCDEwMMD169fh5OQkdx76+vpISkriJZYuj6ZLSWiBfP78GbGxsdixYwdOnz4NBwcH9OzZE9OmTeNkW3RAlgvCweQDBw6gdevWEp2/Tk5OrJqWfLUY5dXVKUvtIG8bAAUOHqDASTNw4EDo6ekxv+Xl5eHKlStQV1fHhQsXZObDV09SUfpKX758wfbt20Wc8Fz1jcrK9Qgopy8AQLly5XD+/Hm4uLjAx8cHvXv3xqBBg/Dw4UO4uLiwTnBKT0+Hn58fnj59ytxf79+/D2traxw+fBiOjo5y140vbLo8fMcjkpOTAQA1atTA6dOnUa5cORH7Y8eOYe3ataWibTpnzhxUqFBBZJItAGzatAmvX7/GhAkTZNoX1mctaaRpNCmCojq0ampqsLCwQJMmTVC9enVOeTx79gznz5/Hq1evkJ+fL/KbtHfvgwcPAiiYoLN161aRibZ5eXmIi4vDyZMncf/+/eIcjgoVKqShnIVUKlSoKGmWLl0qsi1YsIC6d+9O5cqVozlz5si0Lbokuuimrq5eKto+ZYGQkBCytbUlX19fMjMzo8+fPxMR0Y4dOziH5VE2Pj4+1LdvX5GY+z9//qQ+ffpQw4YNS7z84OBgJkTFypUrSVdXl3x9fcnExIT69+9fKnncvn2bHBwcSE9PjwkzpK+vT3Z2dnTr1i35D64Ic+bMkRieoTgYGhpKDFlQWnXgWz4R0dChQ+n169di6Xl5eSL9cMeOHRQaGkrLly+n79+/8yqzKNHR0Zx1It6+fSumhfD48WOJYVLevn3LaLHk5eXRnDlzKCAggEaPHs1b56YwJdmO1tbWEsNGJiUlkaWlJWveWVlZIiHBrly5QmFhYbR27dpi1bG4ei6KjHfPV2PJwcGB/v77byIS1Q1btmwZBQUFccqDbzsURl5dGyLltgNfPRdFhOM0MDCgGzduiKUnJiZyDt936tQp8vf3Z0Ln+fv708mTJznZKqIfKOOaVGQ/aNKkCefzJY22bdtSbGwsrzwUoemiaO7cuUM1atSQGZqo8PVe9D7A9b4g1HAVCATUtWtXEV3XQYMGUWRkpMTnelH4hjoui21AxN4OimgDooJroUmTJiQQCKhBgwbM302aNKGWLVvSoEGDKDU1lbW+fPUkFamvJC//9b5ARBQQEECtWrWimTNnkqamJj158oSICkJWV61aldW+TZs21Lp1a3r79i2T9ubNG2rdujX5+fmx2pckbLo8fMcjCttLCjmnp6dHGzduLIlDE8PW1pYJxV+Yy5cvk52dHat97969af369SVRNTGkhc4jInr37h0tWLCA+vfvT/3796eFCxeK9K2SZvPmzaSlpUUGBgZka2tLdnZ2zCYr9F3RcLqFNy0tLapWrRodOnSo1I5DhYpfHZWjSYWK/xgrV65kjel89uxZOnPmDAkEAtq7dy+dPXuW2S5evCgieC2L69eviwye7N+/n9q1a0eTJk1S+KBySfHjxw9asGABjRgxgtGmISJavHhxqb3w8UVHR4fu3r0rln7nzh1OmjR8UYRzgW8e9erVo4CAABFHwLt37ygwMJDq169fjKORjSKcNLJe8EujDnzLV0QdpDmqSrMO/3aHn6x2XLt2Lfn6+tLz58+ZtOfPn1PLli0Z3S1Z+Pj4UFRUFGNnaGhI9evXJ3NzcwoPD2e1l1fPhS3efWmKAOvp6TGC7BUrVqTr168TUcHACVfdNr7twFfX5ldoB766cUREgYGB1KhRI5F3mydPnlDjxo2pffv2rParVq0iDQ0N6tatGy1btoxxNmpqajIDvbLg2w+IlHNNKrIfpKenk6+vL23ZsoWuXbtWbEcVUcF5tLa2punTp8stls5X0+XLly8i+jUPHz6kJUuW0PHjxzmVL+Tr16+0c+dOateuHWlra5ONjQ1NmDBB6v6F9XykDc5ybQtlO38VoaujKIrTDopsA6ICx19xJgwUha+eJF99JSH37t2jkJAQatasGTVr1oxCQkIkfpNIoqxcj0TK6wuPHj0if39/8vDwoA0bNjDpI0eOpNDQUFZ7PT09iRMZuOqa8tVXkgWbLg/f8YiHDx9SZmYmCQQCunr1qoh+z7Nnzyg3N5dX/YuDtrY2ow9bmAcPHpC2tjar/axZs8jc3Jz69OlDCxcuZN41hJsi2b59u8RnwLlz58jIyIisra0ZjS4bGxsyMjIqlu5Yeno6TZ48mbp168ZcJ0eOHKHbt2+z2lpZWdGsWbPk1jC0s7Pj/X2pQoUKdlQaTSpU/Mdo06YNJk2aJDNcQOPGjQEUxBO2traWGEKLC4MHD8bEiRPh7u6OjIwMdO3aFR07dsTu3bvx5csXLF26VK58SxNNTU2MHTtWLH3UqFEif/v7+2PDhg2oVKlSaVWNM0ZGRsjKyhJbkv748WNe4Ry4oqamJtKHunXrhm7dupVqHjdv3sS1a9dEwpKZmppi9uzZYuGS+EBlIBrtr1CHv/76C2PHjmUNm1WSdeBrHxkZiS5dusDExEQp5RelaFz0tLQ02NjYwMbGBkCBDpq2tjZev36NwYMHy8zr9u3bTBjOXbt2wd3dHRcuXMCJEycwZMgQ1lAy8uq5KDLePV+NJSsrKzx//hw2NjZwdHRkwpBevXpVJORUURTZDnx1bcpCOwD89Fz46sYBwMqVKxEYGAg7OztGk/Hx48dwc3PDX3/9xWofGRmJJUuWYPjw4UzaiBEj4O3tjcjISIlaUYrsB4ByrklF9oPXr1/jwYMHImGvBAIBE5KPSxjHIUOGACjQICyKrDwUqenSrl07dOzYEUOGDMGHDx9Qp04daGlp4c2bN1i8eDGGDh0q0/748eOIjo7G/v37oaGhgU6dOuHEiRNo1KiRTLvCYaH46mhMnz6dl708WoyK1tV5+/Ytpk2bhjNnzkgMr8R2f5enHRTZBkDxwrpJgq+eJF99JaBAU6hbt26oXbs2o7F0+fJluLu7IyYmBr///ruYTVm6HgHl9wUbGxv8/fffYulLlizhZK+trc30g8JkZ2ezhggG+OsrAfLr8vAdjxCG0i56/SsDa2trXLhwAfb29iLpFy5cQOXKlVntN2zYAAMDA5w7dw7nzp0T+U0gEHAK1zxixAhUqVJFbN+VK1ciPT2dGZfp3r27RPuQkBB07doVq1evZvS98vLyMGzYMISEhODWrVusdTh37hzatGkDb29vxMfHY/bs2ShfvjySkpKwceNGxMbGyrT/8uULunXrJvfYVOF3lW/fvnHWu1OhQkXxUDmaVKj4jxEbGysSo1gWwhe0L1++SBwA8vDwkGmfmpqKGjVqAAB2796Nxo0bIzo6GhcuXEC3bt3+FY4mrsTHx+Pr16/KroZEunbtiuDgYCxcuJARV79w4QLGjRuHoKCgEikzOTkZbm5uUFNTY2JkS0NaP1JEHkKqVauGly9fwtXVVST91atXvPQcVJQMZcFZxpeydgzt27dXWF4/f/5knCmnTp1CYGAggAK9nOfPn7PaZ2VlyaXnUlh/i68gcHh4uEyNJTY6dOiAuLg41K1blxFp3rhxI7KyssQmIhRGke2QlJTES9emLLRDYT2XAwcOiOm5sLFu3TpmECkkJARmZma4ePEiAgMDOTlogIIBoMTERJw6dUpER8TX15eT/YcPH9C6dWux9JYtW0rVXVBkPwCUc00qsh/0798fXl5e2LFjh1xOU0D+wcTCWg0AxAa/hc5HLiQmJjIDwLGxsahYsSJu3LiBPXv2YNq0aawD2x06dEDbtm0RFRUFPz8/aGpqcipXOCBb9P/yUtrOX0W2AQD06tUL6enpCA4Olqs/ydMOim4DoEBjVVo7sOlBNmrUCCdPnoS7uzs6d+6MsLAwnD59GidPnuTkpHFzc0NaWpqIvlJQUBBnfSUAGD9+PCZNmiTm/J0+fTrGjx8v0dFUlq5HoGz0hby8POzfv5/RmHV1dUVgYCAz2C+Ltm3bYtCgQdi4cSMzGeHKlSsYMmQI85yQRVhYmMR0ob4SF4o+74qry8N3PEJISkqKRHsu54EvAwcOxMiRI/Hz509GCzYuLg7jx4/HmDFjWO0VMalnz549jFZRYRo0aIC5c+eyjsukp6cjNjZWpN+pq6tj9OjRiIqK4lSHiRMnYtasWRg9erTIZNdmzZpx0tQMDg7G7t27MXHiRE7lFSU/Px+zZ8/GmjVr8PLlS6SmpsLBwQFTp06FnZ0dgoOD5cpXhQoVRVDOQioVKlSUNDVq1GC0aLy8vKhGjRpUsWJFUldX5xyz/9WrV+Tv7y81NjIbhoaGTBxxX19fWrp0KREVhAHQ0dGR/+DKIIoIN1ZSfP/+nUaMGEFaWlpM22lra9PIkSNFwlEoEraQOoVD65RkHkIOHz5Mrq6utHv3bnr8+DE9fvyYdu/eTe7u7nT48GG5dE0koYh+wDcPZdur6vBr2MuiTp06NGHCBIqPjycdHR0mNNKlS5ekasooUs+FqEBjSaiRREQ0btw4MjY2pvr163MK/6YIjaXCXLp0iRYtWkQHDx4stq28yKNrU9bagY+ey8+fPyk8PFxEm0gZBAUF0fz588XSFyxYQF27di2VOij7muTbD/T09CgtLY3LoXKCj/4aH3R1dZmQmp07d2bCe2VlZXEKVfzp0yfedTh69CglJCQwf69cuZI8PT0pKCiIk47gsmXLyMDAgIYPH05aWlo0ePBg8vX1JWNjY/rjjz9Y7UtTi1EaBgYGzDUgD3zbgW8bEBWcN01NTWrbti1paWlR27ZtqVq1amRsbMwaAp2o9PQkZaGrqyvxuk5NTS2V0N18r0ci5feFtLQ0qlq1qojGrJ6eHjk5OTHvLbJ4//49BQYGMlo0wm/B9u3b04cPH+Q+LjZ9JUXCdzziwYMH5OHhIfYtydVeEeTn59P48eNJR0eHKVdPT49TaNviICvstra2tsTrMS0tjVP4vgYNGtC+ffvE0vft20d169blVD99fX0mhGDhd77MzExOdcjNzaXWrVtT48aNafjw4TRq1CiRjY3w8HBycHCgv/76i3R1dZnyY2JiqF69epyOQYUKFeyoHE0qVPyizJgxQ2SbOXMmrV69mnNcbCKi7t27k7e3N129epX09fXpxIkTtG3bNnJychIZUJBG06ZNqXfv3hQVFUWamprMy83Zs2dlxmT+N1KWHU1CcnJyKDk5mZKTk0VilpcEDx8+pPz8fOb/sraSzENIUceUpA8NRWiKlAV9I2VrPCmiDipHU8naZ2VliQzOX7lyhcLCwjhPQjhz5gyZmJiQmpoa9evXj0mfNGkSdejQQaKNonV9qlWrRnFxcUREdPHiRdLV1aW1a9dSQECA1DoUho/G0o8fP6hfv34S4+3Ly+fPn4stEi6Prk1Zawe+ei76+vqMDgkfTp06Rf7+/uTg4EAODg7k7+/P2YkXERFBxsbG5OfnRxERERQREUH+/v5kYmJCERERrDoKfK9HIuVfk3z7Qdu2bSk2NpbLoUolNzeXZs6cSZUrVyZ1dXXm/jdlyhQRbRNZ8NV0cXd3p2XLllFWVhYZGRnRxYsXiYjo2rVrVKFCBYk2ha/1oveA4t4TiIjc3Nzo8OHDRESUnJxMWlpaNGnSJKpXrx4nB8Wv4PytXbs2Xbp0qVg2imwHvm1AVNCXhBpvwnbIz8+ngQMH0rRp02Ta/vz5k7Zu3UovXrzgVJY0+OgrERG1adOGNm3aJJa+adMmatmyJau9Mq5HorLVF9q0aUOtW7emt2/fMmlv3ryh1q1bk5+fH6u9kLS0NDp48CAdPHhQIU59Nn0lRZ5DvuMRbdu2pXbt2tHr16/JwMCAUlJSKCEhgerUqUPx8fGcjldRfP78mf755x+6detWiUz2lPXu7+rqSitWrBBLX758OTk7O7PmHRMTQzY2NrRgwQJKSEighIQEWrBgAdnZ2VFMTAynySmWlpZ04cIFsbru3buXHBwcWOsQERFBAoGAqlevTo0bN6YmTZowW9OmTVntHR0d6dSpU2Ll3717l0xMTFjtVahQwQ2Vo0mFChVSqVixIl25coWICgaf79+/T0REBw4cIG9vb1b7pKQkcnNzIyMjIxHR1uHDh8s1Y7ws829wNP2XKSwgy7bxQeUg+XXqoGyHH197V1dXysrKkvibj48PRUVFERHR8+fPydDQkOrXr0/m5uacZ1fm5uaKzcbNzMxkBq6LokjHMZHoTOXx48dTr169iIjo9u3bZG5uzmpfrVo1unz5MhEReXt705w5c4io4EPawsKC1d7IyIi3oykjI4P8/PxIT09PLpHwS5cukb29fbGcA2WtHezt7SkxMZGIiGrVqkVr1qwhIqLjx4+Tqakpq31gYCBt2bKFU12lsWrVKtLQ0KBu3boxDqGgoCDS1NRkBnplYWdnx2mTJpyuiOuRSLnXJN9+sHbtWrK2tqbp06dTbGwsHThwQGTjgiJmKrdo0YJWr15NRAUrAcqXL09WVlako6NDf/75J6v97t27SVNTk9TU1KhFixZMemRkJLVu3VqijZqampjDr+hWHOdvYefr9OnT6ffffyciouvXr8scXBeibOfvmzdvaNiwYeTs7ExmZmZkamoqsnHhn3/+oWbNmtHZs2fpzZs3nAa2FdkOfNuAqGAyhDCPcuXKUXJyMhERpaSkUMWKFVntC7ejPMTGxpKGhgbVq1ePWS1Qv3590tDQ4OwUXr16NVlYWFBISAht27aNtm3bRiEhIVS+fHlavXo16zWujOuRqGz1BT09PabtC3Pz5k3S19dnteeLvBFSFHkO+Y5HmJmZMc4PIyMjunfvHhERxcXFUY0aNVjt/03I+nbYuHEj6erq0rRp05hv3qlTp5Kenh6tW7eONW9JUUWKOzllzJgx5OPjw7zrpKWl0fnz58nBwUFkrEgaJiYmtHnzZtb9pKGjo8PcFwufqzt37pTK9aRCxX8FlUaTChW/MHxiOgNATk4OypcvD6BAVPb169eoVq0a3N3dWWO0AwUxkyUJQy5YsIBzHVTIB1cRdIA9zrs8SIoBLQ1psbEVkYcQeWKkjx49GhEREdDX10d8fDwaNGgADQ3Zj82GDRtyjl3/6dMnnD59Gk5OTnB2dmbSU1JSxIRhc3NzER0djVatWqFChQq86pCeno4HDx6gUaNG0NXVZcTWZZVfXHr27AkjIyNeefDF1taWs76FJEjJGkvF6UuSuH37tszfhLH6d+3aBXd3d1y4cAEnTpzAkCFDOGkUqaurw9TUVCTNzs5O6v6K1HMBAAMDA7x9+xY2NjY4ceIERo8eDQDQ0dHhpJcnr8aSkPbt22P//v2c9pVGz549QUTYtGmTXDoi8ujalLV2kEfPpTBt2rTBxIkTcevWLdSqVQv6+voiv3PRXoiMjMSSJUswfPhwJm3EiBHw9vZGZGQkq1YUX+0ERVyP06dPR//+/cXatLSuSb79YMiQIQAgpuUCFAid5+XlseYRFRWFdevWoXnz5kx+AODp6clozLDBV9OlU6dO8PHxwfPnz+Hp6cmkN2/eHB06dJBoc/r0aUY79cyZM5zqKQstLS18+fIFQIFeV+/evQEA5cqVw6dPn1jtK1asiHfv3sHW1hY2Nja4fPkyPD09kZmZyem52Lx5c5w7d05m35MFX30lADAxMcGnT58YLRQhwvcdSf1Jke3Atw2Agu+uz58/AwAsLS1x+/ZtuLu748OHD0zesqhTpw5u3rwp97Utj75SUYYNGwYA+PPPP/Hnn39K/A2Qfo0r43oEylZf0NbWZvpBYbKzs6GlpcVq//vvv6NOnTpieoHz58/H1atXsXv3bpn28uorKfIc8h2PyMvLY/SAzM3N8ezZMzg5OcHW1hb379/nVbd/E/3798f3798xe/ZsREREACh4R1i9ejXTL2WhCJ0o4TuVtbU18vLy4OLigtzcXPTo0QNTpkxhtdfW1oa3t7fc5bu4uCAhIUHsvhgbGwsvLy+581WhQoUoKkeTChW/KOnp6fDz88PTp08ZofA5c+bA2toahw8fhqOjI2seTk5OuH//Puzs7ODp6Ym1a9fCzs4Oa9asQaVKlTjX5fr164yzy8XFBTVr1pTvoFRwpqiYbmlT9MNEIBCIDFAUHjiQNoCkiDwK8+HDB2zcuFHE8dq/f3+p52rFihWYMGEC9PX10bRpUzx//pz50JHGkSNHpP7WpUsXNGrUCMOHD8fXr19Ru3ZtPHz4EESEmJgY5qNdktCxhoYGhgwZwtRdnjq8ffsWXbt2xenTpyEQCJCWlgYHBwcEBwfD1NQUixYtklh+cnIya5lChIK8q1ev5mwjCUU4qmQ5WoQ8fvwYgORzztfhJstRlJeXh3379jHt6ezsjPbt24s4Mgu3o6mpKefBtnfv3rHu8/PnT2hrawMoGPgQDsZXr14dz58/51QOH7H4rVu3wtzcHP7+/gAKBrTWrVsHFxcX7Nixg9PAWIsWLTBgwAB4eXkhNTUVfn5+AIA7d+5wGuCcO3cu8/+uXbvC1tYWFy9eRNWqVREQEMBqX7VqVcycORMXLlyQ6OAYMWIEax5JSUm4fv0684wuLo8ePcLBgwdRpUoVuezLQjusW7cO+fn5AICQkBCYmZnh4sWLCAwMxODBg1nthYOVixcvFvuNq4Piw4cPaN26tVh6y5YtxQbnuJCXl4dbt27B1tZWzBkrCUVcjwcOHMDs2bPRuHFjBAcH4/fff2fy5ALfvsC3Hwj7AB+ePn0q8VrIz8/Hz58/OeXx5csXZkDyxIkT6NixI9TU1FCvXj08evSIUx4VK1ZExYoVRdKEjkRJFJ4II8+kmKL4+Phg9OjR8Pb2xj///IOdO3cCAFJTU2FlZcVqr2znb0JCAs6fPy/iGCguPXr0gKamJqKjozk7qxTZDnzbAAAaNWqEkydPwt3dHZ07d0ZYWBhOnz6NkydPonnz5qz2w4YNw+jRo/H48WOJ7SB8X5PG8+fPJQ4+9+zZEwsWLOB0DHyva77X4+bNm9GtW7diXY9A2eoLbdu2xaBBg7Bx40am3leuXMGQIUM4TaSIj4/HjBkzxNLbtGnDvPfLYvr06az7SEKR55DveISbmxuSkpJgb2+PunXrYv78+dDS0sK6devg4ODAq27/NoYOHYqhQ4fi9evX0NXVhYGBAWdbRUxO0tLSwvr16zFt2jTcunUL2dnZ8PLyQtWqVTnZh4WFYcWKFVi+fLlc5U+bNg19+vTB06dPkZ+fj7179+L+/fuIiorC33//LVeeKlSokIDyFlOpUKGiJFFETOdt27Yxy5OvXbtG5ubmJBAISFtbm2JiYljtX758SU2aNCGBQMCEvBAIBNSsWTN69eqVXMdVVomMjKT3798ruxq8OH/+fInEiz558iTVrFmTjh07xoQtOXbsGNWuXZtOnDhRKnlcvXqVypUrR5aWltShQwfq0KEDWVlZkZmZGaPNUpQqVarQH3/8QWfPniWBQED79++nc+fOSdy4UKFCBSYEzfbt26lKlSqUk5NDf/75J6fQDY0bN6b9+/dzKksSvXr1olatWtHjx49FwgUcO3aMXFxcpNpJ0u7go+kSHx9PPXr0oHr16tGTJ0+IiCgqKkpELLkoJiYmYuFzpG1s/Pz5k6ZMmUJGRkZM+A4jIyOaPHky/fjxg9MxpKen0+TJk6lbt25MaJAjR47Q7du3WW1v375NDg4OIsLO+vr6ZGdnR7du3ZJos2XLFmZbtGgRmZqaioT56tatG5mamtLixYs51b9OnTo0YcIEio+PJx0dHaZfXrp0iSwtLVnt+YrF89VzISoIoxMSEkKBgYF09OhRJn3atGk0a9YsmbaK0FiSJ0RaUZo0acJZB0gSfHVtlN0OZUXPJSgoiObPny+WvmDBAuratSurfVhYGKMBlJubSw0aNCCBQED6+vp05swZVnu+16OQxMRECg0NJXNzczIxMaEhQ4bQP//8w8mWb1/g0w+K8vXr12LtL6RmzZq0bds2IhINiRMeHk4+Pj6c8pBX06UwV69epXHjxlHXrl2Z9w3hxsbRo0dFnoUrV64kT09PCgoKEguLKI1Hjx6Rv78/eXh4iGhTjRw5kkJDQ1nt8/Ly6OfPn8zfO3bsoNDQUFq+fDl9//6d1Z4ttBIb8ugrFUVXV5cJjyUPfNuBbxsQEb19+5aePn1KRAVtMmfOHAoICKDRo0dzqgPf9zW++krSKM63Et/rsXz58mRoaEj9+/dndGGKi7L7wvv37ykwMJAEAgFpaWmRlpYWqampUfv27enDhw+s9jo6OhKvhbt375KOjo5EG0XrxvE9h5LGI9TU1EhHR4fTeMSxY8doz549RFSgVeXk5EQCgYDMzc2Z596vgiJ0dmUhSbeN7V4rDL3JZWOjffv2ZGRkRPb29tS2bdtiP2OJCr5DfX19ycLCgnR1dcnb25uz7psKFSq4oXI0qVDxi6LomM75+fmUk5ND169fp9evX3Oy6dKlC9WuXZtSUlKYtDt37lDt2rWpW7duxa6DsoiKiqIGDRpQpUqVmLi+S5Ys4TXoXxYpqZdTV1dXiU6E+Ph4ql69eqnk4ePjQ3379hUZPPn58yf16dOHGjZsKNFm3759VKFCBU6OFi7o6Ogwmjm9evWiCRMmEFHBRyiXa3Lnzp3k4OBAK1asoIsXL4qIrsoSXhVS2NFVeBDuwYMHMstn0+4ojo5HbGws6erq0oABA0hbW5upw4oVK6hNmzZS7RTpaBkyZAiVL1+e1qxZw5y7NWvWUMWKFWnIkCGs9mfPniVdXV3y9fUlLS0t5hjmzJnDxN6XRb169SggIEDk4/rdu3cUGBhI9evXZ7Xv2LGjRDHfFStWULt27VjtiYjOnDlDJiYmpKamRv369WPSJ02axOlDjY9YPBF/PRdFoAiNJb6kp6eTr68vbdmyha5du1bsa5qvrk1ZaAe+ei6KICIigoyNjcnPz48iIiIoIiKC/P39ycTEhCIiIpj7zLJlyyTaW1pa0tWrV4mo4LlRuXJlun//Pk2ZMoUaNGjAWj7f67EoP378oD179lDbtm1JU1OT3N3daenSpTIHJZXdF3Jzc2nmzJlUuXJlUldXZ+4pU6ZMERmclcX+/fvJ2NiY5s6dS3p6erRgwQIaMGAAaWlpcZ7UIq+mi5AdO3aQpqYmtW3blrS0tKht27ZUrVo1MjY2pr59+7Lau7m50eHDh4mIKDk5mbS0tGjSpElUr149TvZ8KQvOX3n0lYrSsGFDXk78stAOW7dupRcvXsidB9/3Nb76SkREc+fOFXEEdOrUiQQCAVWuXJl5H5UF3+vx58+ftHfvXgoMDCRNTU1ycnKiuXPn0vPnz1lthSi7LwhJS0ujgwcP0sGDByktLY2z3W+//SZR62/69OlUs2ZNiTaK1o1T9Dks7niEJN6+fctoFP5KFNVo8vLyYr43iuptFd3YkFe3rUmTJpy2pk2bstahb9++MjcVKlSUDVSOJhUqv3QGVQAA2lNJREFUflFMTU0lzt46f/48ZzFdIqINGzaQq6srM4vK1dWV1q9fz8nWyMhI4mzaK1eukLGxMec6KJM///yTzM3NadasWSLi0ps3b6YmTZoouXaKRZaAKB90dHQkrtRISkqSOptO0Xno6OjQ3bt3xdLv3LlDurq6Mm0/f/5MAoGA0tLS6MOHDxI3LlStWpV27txJ2dnZZGFhwcyiu3nzJpmZmbHa852damBgQKmpqcz/hW0tXO1VGtSoUYO2bt0qVofExETOs8X5OlqMjIzoyJEjYumHDx8mIyMjVvt69erRokWLiEj0GK5cucJp9YGOjo7ElU+3bt3i1Jf19fUlDjKkpaUVaxJBbm6u2EzSzMxMZnBBFnzF4i0sLCgxMZGICvpEVFQUERU4Xoo7ESInJ4fu3r1bbCdN7969Oa8Ak0R4eDjl5OSIpX/58kXioI4kLl26RPb29nJf03xXDpSFdggMDKQtW7YUq6yinDp1ivz9/cnBwYEcHBzI39+/WIPMslancVmppq2tzQzMDxw4kMLCwoiIKCMjgwwNDTnVgc/1WJTv379TTEwMtWzZkjQ0NKhRo0ZUpUoVMjQ0lDr7W1F9Qd5+EB4eTg4ODvTXX3+JvGvFxMRQvXr1OJeviJnKz58/p8TERMrLy2PSrly5IvEdoiju7u60cuVKIvrf8yE/P58GDhxI06ZNY7Uv7HidPn06M3nh+vXrnJ+Rhfn69WuxHTV8nb9bt26VuDr++/fvzPNfFqmpqVS7dm1eA9u7du0iFxcX2rx5s1xOfEW2gzxtQCT6nFUGsp4vXJ81dnZ2zLfoiRMnyMTEhI4fP07BwcEijiNZ8LkeC/PixQtauHAhubu7k6amJgUEBND+/ftF8pVEWegLfDh48CBpaGhQ7969mQlbvXr1Ig0NDdq3b59Em7NnzzIT886ePStz44KizuH379/p3r17IpMGi0NaWhodO3aMvnz5QkT0r3Y05ebm0o0bN8TeGxISEkTuvzNmzGDeVadPn04zZsyQurHh4OBAU6dOFUufNm0aOTg48Dyi0ufz58+lfj2qUPFfQeVoUqHiF6VXr17k6upKly9fpvz8fMrPz6dLly6Rm5sb9enTh1MeU6dOJX19fZo4cSIza23ixIlkYGAg8UWjKAYGBnTjxg2x9MTERM6DL8rG2dmZeREvPKh869YtTs6BfxMl5Whq2LAhtWjRQmRm5osXL6hly5bUqFGjUsmjfPnyEgebjh07RuXLl5dp+/PnT9qyZQuzGkleVq1aRRoaGmRiYkIeHh7Mx+3y5cs5OS35zk5t06YNTZkyhYgK2jojI4Py8vKoc+fOnFbiFObOnTt09OhRuVZQCD82i66q0tbW5lQ2X0eLhYWFyCpLISkpKZxm7uvr6zMrYQofQ2ZmJqdj8PDwkBiqIy4ujtzc3FjtbWxsaOHChWLpCxcuJBsbG1Z7RWBvb88MSteqVYvWrFlDRETHjx/nNJGhe/fuVLNmTQoODiY9PT168+YNEREdOHCAXF1dOdXh1atX5OfnJ3GmLZfByIiICDIxMaHff/+dIiMjRVatSFu5UpjCM34L8+bNG86Doc7OztSxY0e6fPkyZWZmFvua5ktZaIfVq1dTxYoVacyYMRQdHV3se4rwvlp4hWNQUBBpamoyA/4ljY2NDR0/fpxyc3PJ2tqa/v77byIqWA1kYmLCaj9t2jSFtPe1a9coJCSEypUrR5UqVaIJEyaI3CuXL18u9XnHty/w7QeOjo506tQpIhK9r969e5fTOSwr6OnpMc+4cuXKMZEFUlJSqGLFiqz2pqamdOfOHSIi8vb2prVr1xJRwfOFbVKMkOzsbAoJCSELCwu52oKv85fvvfG3336j+vXrU0xMDJ05c0augW2+E3P4tgPfNiDiHy6ZSL4wV4qk8Er+ESNG0KBBg4iI6P79+5yu602bNjFOAUVw+fJlGjRoEGlra5OdnR0ZGxuTnZ2dzBCnyu4LHTt2pLlz54qlz5s3jzp16sRqT0T0999/U4MGDUhPT4/MzMyoadOmnK8lRcD3HObk5FD//v1JXV1dZMXr8OHDac6cOaz2b968oWbNmjHXv9C+X79+NHr0aHkPq1QpGqLX29u7WCF6FYGurq7E76/U1FTOzye+8H1fysjIID8/P9LT05N7IoMKFSrYUTmaVKj4ReEb05mIyNzcnAmPVJjo6GhOTpbAwEBq1KgRE2OciOjJkyfUuHFjat++PfeDUSI6OjrMC03hwY/U1FTOq3H+LZSUoyktLY3c3NxIS0uLHB0dydHRkVkdxzX8A988QkNDycrKimJiYigrK4uysrJox44dZGVlxcw+l4WiZpZevXqV9u7dS58/f2bS/v77bzp//jzvvNm4desWlS9fnlq3bk1aWlrUqVMncnZ2pgoVKlB6ejqnPB48eEAeHh5i4QS5fjDb29szKw0K97etW7eSs7MzpzrwdbSEh4dTUFCQyIy/b9++UY8ePTjN6LO0tGRm6BY+hr1793Ka0Xf48GFydXWl3bt30+PHj+nx48e0e/ducnd3p8OHD7POrNu8eTOpq6tT27ZtmTBfbdu2JQ0NDSaGPRd2795NnTt3prp16xY7fEZwcDBzrlauXMmEEjQxMaH+/fuz2itCz6V79+7k7e1NV69eJX19fTpx4gRt27aNnJycmIF+WfDVWBIIBBK1BuPi4jiHGtPT0ytWCBx5cXNzk+goLwvtwHdVlqWlpcQVjitXrqTKlStzOoaiSJspLI3p06eTsbExVa9enWxsbJh7y8aNGzmtxvH09CR1dXVq1qwZbd++XS6tRDc3N9LQ0CA/Pz/at28f5ebmiu3z+vVrEggEEu359gW+/UDau9adO3c4r6iyt7dnHGRFj42rbhoRP40lS0tLxrnk7u7OvENfvHiR04rZgIAAatWqFc2cOZM0NTUZHcPjx49T1apVOdV/2LBh5OzszISq3bRpE0VERJCVlRX99ddfrPZ8nb/S7o03b97kNBGBr74SEf+JOXzbgW8bEPEPlyxvmCtZFFeLtlKlSsz7UrVq1WjXrl1EVOAA4zLhUBEaSy9evKAFCxaQi4sL6ejoULdu3Zj30OzsbBo/frzMd0dl9wVzc3OJofCTk5NZJ8opAkXoxvE9hyNGjKBatWpRQkIC6evrM8+H/fv3c9K4lVejtizBN0QvEf9nZEnpthUHvu9LDRo04D2RQYUKFeyoHE0qVPziyBvTmYjI2NiYCbVVmPv373MKfZeVlUU1atQgTU1NJqSNhoYGeXl5KV38myvOzs7MjMLCL6fLly/nNCD7b6KkHE1EBeEJjh8/zsw4P3HiRLFDFvDJ4/v37zRixAjG4aqmpkba2to0cuRITi+piphZWrgu8oZ+SE9Pp+HDh1Pz5s2pefPmFBoaytlJRET04cMHmjVrFnXu3JnatGlDkydPpmfPnnG2b9u2LbVr145ev35NBgYGlJKSQgkJCVSnTh2Kj49ntY+MjCQXFxe6fPkyGRoaUkJCAv31119kYWFBy5cv51QHvo6W9u3bk6GhIZmbmzPn0dzcnIyMjDgNKo4ZM4Z8fHzo+fPnZGhoSGlpaXT+/HlycHDg5KgqOpAuyWHHNsh++fJl6t69O+MY6t69O12+fJm1bCHLli0jAwMDGj58OGlpadHgwYPJ19eXjI2N6Y8//mC15ysWrwgqVqxIV65cIaICfbn79+8TUcEKDG9v7xIr18TEhExNTUlNTY35v3AzMjIiNTU1GjZsGKe82rZtK/eAX3EoyXu7stpBiLQVjqmpqZwdFEVnCjdo0KDYM4V3795NixcvFnm32bJlC+fnRmJiIoWGhpK5uTmZmJjQkCFDJIYelsbMmTOZwTtlwLcf1KxZk7Zt20ZEov01PDycfHx8ONVBIBBIXEnz4sUL0tLS4pQHX42loKAgJrTqzJkzycLCggYMGEC2tracHFWPHj0if39/8vDwENGmGjlyJIWGhnI6Bmtra6bfCp9RRAV6o7K0EIXI6/wV6n+oqamRu7u7yOQFDw8PMjQ0pM6dO7OWz1dfqTj4+flJfAfi2w5824CI/6osvmGu+OorERGFhISQra0t+fr6kpmZGTPJaseOHZy+ofhqLAl16lxdXWnJkiX09u1bsX1evnwp1QFPpPy+oKOjI9Hxevfu3VKZ8KgIfSW+59DGxoYuXbpERKLPh7S0NE4OS3k1assSigjRK+sZqampyWqvCN02RcDnfUlfX79UV3WqUPFfReVoUqFChVSGDx9Oo0aNEksfM2YM54G0/Px8OnnyJC1fvpyWL19eah+PimL9+vVkaWlJMTExpK+vTzt27KBZs2Yx//+VMDQ0LLHBSC5Im3WvyDxycnIoOTmZkpOTJeqrPH78WGK8dr4zS4Vl8wn9cOzYMdLS0qI6deows1Pr1KlD2tranIXO+WJmZsYcr5GREfOyHhcXx2lWYX5+PnP9CAdOdHR0mJB+XOHjaGETkmUTlf3+/TsNGDCANDQ0SCAQMELVPXv2lLiKoChs8e5LY2adk5MTM9O+8Ef31KlTKSQkpMTKLYq8ei5EBfcrYYgqGxsbZlVgRkYGpxAe8mosbdmyhTZv3kwCgYCWLVvGaB5s2bKFoqOj6eLFi5zqT0S0du1asra2punTp1NsbGyxVw5whc3RpMx24EtQUBDNnz9fLH3BggXUtWtXTnkoYqYwF7g84378+EF79uxhBkjd3d1p6dKlnFeiCynuqiwh8vYFvv1g//79ZGxsTHPnziU9PT1asGABDRgwgLS0tFifb8LrRSAQUFRUlMg1tHfvXgoJCaFq1aqxHzzx11h6+/Yts4o/Ly+P5syZQwEBATR69Ohit4W86Ovr06NHj4iooG8LHYAZGRklOqgq1PkQCAQ0duxYEe2PyMhIio6O5jQRga++UnEoKSe8ItqA76osvmGuFKGv9OPHD1qwYAGNGDGCCbdLRLR48WLOer9C5NFY6t+/P+szOT8/v0RD1fLtC7/99pvEd5Lp06dTzZo1We0LT2KSJ3SfonXj5KGwbl/ha/bmzZucVoqWBY1avvAJ0auoZ6QidNsUiTzvS02aNPnXjUWpUPFvROVoUqHiF0URMZ2HDx9ORkZG5OrqSsHBwRQcHExubm5kZGTEOKGEmzROnTpFkyZNouDgYOrXr5/I9m/hr7/+oipVqjAvUJaWliIzsn4VSnLWe2mVzzcPac42RbxQ8w39UKNGDZowYYJY+oQJE6TODC06QCNr44KJiQmjT+Tg4ECnT58mooKVVsUZVP7+/TvduXOHrly5IhJG8N9EVlYWHT58mHbu3Clx5WdJkp6eTpMnT6agoCBmduKRI0fo9u3bnOwLh4K0sLBgZnqmpqZK/ehOSkpiBnT49iW+ei5ERLVr16Zjx44RUUFYll69etGTJ09o/PjxnGZr89ERUZRuW2l9qEu7L5aFdiAqeE/w9/dnVj77+/tzHgiIiIggY2Nj8vPzY1Y4+vv7k4mJCUVERHDS3VLETGEucHk+ff/+nWJiYqhly5akoaFBjRo1oipVqpChoaHI6oKi8NVv4NsXFNEP4uPjydfXlywsLEhXV5e8vb0laisWpehqj8KblpYWVatWjQ4dOsSpDnw1lhTJ169f5RIqd3d3ZyYqNG/enMaMGUNEBStZLS0tWe23bt0qcaX39+/faevWrTJtc3NzacuWLcVaKV0Uvit5igOXa1KeduDbBoqAb5grvvpKxUHayrKiyKOxVJTihv8rjDL6wsGDB0lDQ4N69+7NTGrp1asXaWhoMBrCsti/f7/Itnv3bvrjjz84f8sqQjfu+vXrIuH/9u/fT+3ataNJkyZxcj43bNiQiXog1JglKhinaNWqFau9IjVqlQWfEL2KfEaWJeR5X0pPTydfX1/asmVLiU9kUKHiv4zK0aRCxS+KImI6N2nShNPWtGlTifYzZswgNTU1qlOnDrVr147at28vsv3byMnJkTgwqUIxlAVHkzR7vjNLifiHftDW1pYaylJbW1uijbQPC3kHtX18fJgP26CgIGrdujWdP3+eevfuzUkwvl+/fvTp0yex9Ozs7GI5n/k6Wvgg70qYwnz9+pWuXLlChw4dKvYqlrNnzzKaSFpaWkw/mjNnDucPZnt7e2Z2ca1atWjNmjVEVBAvX5qGRuGQG7L6FZe+xFfPhYho27ZtTKjEa9eukbm5OampqZGOjo7MAfnCx8NHY0lRum2lgbT7Wlloh1WrVpGGhgZ169aNcQgFBQWRpqYms7JEFrK0trjqbvGZKVwcZD2frl27RiEhIVSuXDmqVKkSTZgwQWQ1wvLly2W+u/FdlcW3L/DtB4rAzs6OXr9+zSsPeTSWCg82Fx2ELu6gdHZ2NoWEhJCFhYXczt/FixczjtWTJ0+Sjo4OaWtrk5qaGi1dupTVno8TnqjgXUU4ECwPinjf4oq0a5JvO/BtAyH37t2jkJAQatasGTVr1oxCQkI4h33iG+aKr75ScZB1b+SjsVQ0/F/nzp1JTU2tWOH/ykJf+Pvvv6lBgwakp6dHZmZm1LRpU96r3rdv306BgYGs+ylCN6527dpMmOAHDx6Qjo4OBQUFUZUqVTjp5CYkJJCBgQENGTKEdHR0KCwsjFq0aEH6+vp07do1VntFaNSWBfiG6FXEM7IswOd96dKlS2Rvb18qExlUqPgvo3I0qVDxi6LsmM5EBTH7o6KiSqWskiIiIoLXB3NZQDjIJm0rK5RlR5OQO3fu0NGjR0U+0A8ePMgpb76hH6ysrJgP/cLs3LmTrK2tJdqwDdgUd/Dm2LFjtGfPHiIqcJA5OTmRQCAgc3NziouLY7WXNoD1+vVrUldX51QHRThadu/eTZ07d6a6deuK6Ehw0QzgOwh39OhRsrCwkNtJU69ePUYDpHA/unLlCueZ0sHBwYye1MqVK5nzaWJiQv3795do8/DhQ0YTjW9fKgldn5ycHLp+/TrrR7SiNJYaN27MaTaxIuAbVlTafU2Z7SDE0tKSVqxYIZa+cuVKqly5slx1KC58ZgoXB2nt4ObmRhoaGuTn50f79u2TGILz9evXMnVE+K7KUnRfKG4/4CtSXhxkXU/yaCwVfiZIC1PF9f4+bNgwcnZ2ptjYWNLV1aVNmzZRREQEWVlZ0V9//SXX8T58+JD27NnDeba2NCf8zZs3pU5EKEytWrXo1KlTxa5nceG6CkYW0q5JRbdDcduAiCg2NpY0NDSoXr16TPSI+vXrk4aGBidtP75hrvjqKxUHae3AV2NJEeH/ykJfKAm46hMpQjfOyMiIcejMnTuXWVF3/vx5srKy4pRHeno6DRgwgH777TdydnamHj16SJxQKw2+GrVlia9fvyql3PDwcJlbaeDm5kbq6upyvy85OztTx44d6fLly5SZmVmiExlUqPgvo3I0qVDxi8I3prMiKFeu3L9qppAkPDw8SE1NjerXr0+rVq36V84EWrp0qci2YMEC6t69O5UrV46TNlBpUZYdTQ8ePCAPDw+xlRzFmWXMN/RDeHg4mZiY0Ny5cyk+Pp7i4+Npzpw5ZGJiQjNnzizGUSqWt2/fMg4IaXz8+JE+fPhAAoGA0tPTRWZ4v3v3jrZu3UqVKlXiVB5fR8uyZcvIwMCAhg8fTlpaWjR48GDy9fUlY2Nj+uOPP1jt+a6EqVKlCg0bNoxevHjBuq8k9PX1mb5T+PgzMzOlrmwrSl5eHv38+ZP5e8eOHRQaGkrLly/nFMaEL4rQ9UlISJCrbEVpLClCt40rJXVfU2Y7CNHX15eqIyKPnoy82kR8ZwpzQVo7zJw5k5klLi98V2Xx7Qt8+4EskXItLS1eeRdF1vUkj8bS2bNnmfspX909a2trJgyYoaEhc21ERUVRmzZtOB2fvE7pGjVqkJeXF6mpqZG7u7vIBAwPDw8yNDSkzp07s+Zz9OhRqlGjBh06dIiePXsmV/g/LpTkOyPfduAbVpWoIDzx1KlTxdKnTZvGORwlHxSpr8SGtHbgq7GkiPB/ZaEvKJovX75QWFgYZ+06vhgaGjIRGXx9fZmVXI8ePSq1ya//dnJzc2nmzJlUuXJlEZ3fKVOmFCucf3Z2Nh0+fJhWr14tElpYVnhhITVq1BDZXF1dSU9Pj4yMjBTufJYG3/clPT09ie+cKlSoUCwaUKFCxS/J1KlT0bFjRzx48ADNmjUDAMTFxWHHjh3YvXt3qdRhwIABiI6OxtSpU0ulvJIgKSkJd+7cwfbt27Fw4UKMHDkSLVq0QI8ePdC+fXvo6ekpu4qshIWFSUxftWoVrl27Vsq1+XcSFhYGe3t7xMXFwd7eHleuXMG7d+8wZswYLFy4kFMekZGRaNOmDVJSUpCbm4tly5YhJSUFFy9exLlz51jtp06dCkNDQyxatAiTJk0CAFSuXBkzZszAiBEjONXh/v37WLFiBe7evQsAcHZ2RmhoKJycnDjZF+bx48cAAGtra9Z9TUxMIBAIIBAIUK1aNbHfBQIBwsPDOZV769YtREdHi6WXL18eb968YbX/888/sW7dOgQFBWHLli0YP348HBwcMG3aNLx7906qnampqcgxCAQC5re8vDxkZ2djyJAhrOW/fPkSo0ePRoUKFVj3lYSJiQmeP38Oe3t7kfQbN27A0tKSUx5qampQU1Nj/u7WrRu6desm0+bgwYOc6xgYGCjzdycnJ9y/fx92dnbw9PTE2rVrYWdnhzVr1qBSpUqcymjWrBksLS0RFBSEnj17wsXFhZNdnz59kJubC4FAgGbNmnHqv5IQnq/C155AIAARQSAQIC8vT658S4K1a9dK7G/KbAchgYGB2LdvH8aNGyeSfuDAAbRt25bVfuTIkXB3d0dwcDDy8vLQqFEjXLp0CXp6evj777/RpEkTTvXo1KmTWFqfPn1E/nZ3d8eRI0fk7jPSKPqOlJeXh1u3bsHW1hampqac8ujXrx+6dOmCSpUqQSAQwNfXFwBw5coVVK9endWeb1+Qtx8Uvq8cP34cxsbGzN95eXmIi4uDnZ0dp7wUQbly5Zj/q6mpYeLEiaw2jRs3lvh/eXj37h0cHBwAAEZGRswzycfHB0OHDuWUh52dHXx8fNCzZ0906tSJcx9q3749AODmzZto1aoVDAwMmN+0tLRgZ2eH33//nTUfPz8/AAXXduHnZFm8N0qDbzvI2waFef78OXr37i2W3rNnTyxYsKDY+QHAhw8fYGJiwmlfTU1NjB07Vix91KhRIn/7+/tjw4YNnJ8ZxWHjxo1iaUWPQSAQwNbWVqK9qakpHj9+DGtraxw7dgyzZs0CUNAXufZDZfcFNTU1keuoKGzHIXx3FUJE+Pz5M/T09PDXX3+xlp+YmAhNTU24u7sDKHg2b968GS4uLpgxYwa0tLRY86hduzZmzZoFX19fnDt3DqtXrwYAZGZmSn0X/vTpE2u+QoyMjMTSkpOTOdt7eHhw3ldZzJ49G1u3bsX8+fMxcOBAJt3NzQ1Lly5FcHAwax43btyAn58fvnz5gpycHJQrVw5v3ryBnp4eypcvz/oteePGDbG0T58+oW/fvujQoUPxD4ojo0ePFvl70aJFUvddvHixzLyaNWuGpKQkVKlSRSF1U6FChWRUjiYVKn5RAgICsH//fkRGRiI2Nha6urrw8PDAqVOneH8Iy6Lwy0B+fj7WrVuHU6dOwcPDA5qamiL7sr0MlBVcXV0RGRmJyMhIXLhwAdHR0Rg5ciSGDBlSrBfhskabNm0wadIkbN68WdlVKTNI+5i7dOkSTp8+DXNzc6ipqUFdXR0+Pj6YM2cORowYIfHluyg+Pj64efMm5s6dC3d3d5w4cQI1a9bEpUuXmA84trqNGjUKo0aNwufPnwEAhoaGnI9tz5496NatG2rXro369esDAC5fvgw3NzfExMRwGkDKzc1FeHg4li9fjuzsbACAgYEBQkNDMX36dLFrXMiZM2dARGjWrBn27NkjMpinpaUFW1tbVK5cmdNx8HW0ZGVloUGDBgAAXV1d5lz26tUL9erVw8qVKyXaLV26FESE/v37Izw8XGRAVDgIJzyvsujUqRPOnj0LR0dH1n0l0a1bN0yYMAG7d++GQCBAfn4+Lly4gLFjx0oclBKSnJwMNzc3qKmpsX6AS/roFg5CChE6VQr/LYRt4CMsLAzPnz8HAEyfPh2tW7fG9u3boaWlhS1btsi0FfLs2TPExMRgx44dmDt3Ljw8PNCjRw8EBQXByspKpq2GhgaGDh3KOFzlITMzU27bkubly5dYu3Ytpk2bBgDo3r27xP2U3Q4A4OLigtmzZ+Ps2bMi96ULFy5gzJgxWL58ObOvpEGQ2NhY9OzZEwBw6NAhPHz4EPfu3cO2bdswefJkXLhwgdNxcOHhw4f4+fOnwvITUtRZ1rhxY1y8eLFYzrIZM2bAzc0Njx8/RufOnaGtrQ0AUFdX5+Qs4dsX5O0HwvuKQCAQc+xpamrCzs5O5qCSIvj06RMzUMn2TlfSA5oODg7IzMyEjY0Nqlevjl27dqFOnTo4dOgQZwfBtWvXEB0djZkzZyI0NBStW7dGz549ERAQwPQLSUyfPh15eXmws7NDy5Yt5XYcnDlzRi47ZfDHH3+IvI8I4dsO8rZBYZo0aYKEhASxAdHz58+jYcOGrPbz5s2DnZ0dunbtCgDo3Lkz9uzZg0qVKuHIkSPw9PTkVA824uPj8fXrV4XkVZSix9ClSxfs2bMHFStW5HQMHTt2RPfu3VG1alW8ffsWbdq0AVDwvsh1oFnZfWHfvn0if//8+RM3btzA1q1bOU3QWrJkicj7mZqaGiwsLFC3bl1OTq/Bgwdj4sSJcHd3R0ZGBrp164YOHTpg9+7d+PLlC5YuXcqax9KlS9GjRw/s378fkydPZs59bGws8z5eFOEENS5IeuesUaOGyOQfIcL31uK8s5YFoqKisG7dOjRv3lxkUpunpyfu3bvHKY9Ro0YhICAAa9asgbGxMS5fvgxNTU307NlT6qRUNoyMjBAeHo6AgAD06tVLrjzY4PKNDUj/hi9MQEAARo0ahVu3bsHd3V3su5VtopwKFSo4orS1VCpUqPgladKkCaetadOmyq6qXNy4cYPGjBlDlpaW//rl/vPmzSNbW1tlV4Nh+/btlJ2drdQ8pIXvMDExYcKVOTg40OnTp4moIGY41xBTfGnatCm9f/9eLP3jx4+cridFhGEZMmQIlS9fntasWcOECFuzZg1VrFiRhgwZwmr/8OFDysvL41SWNMaMGUM+Pj70/PlzJozJ+fPnycHBgdEdkoW9vT0TBqZWrVq0Zs0aIioQNuaiP3H27Fn68eOH3PXPyckhPz8/6tOnDy1cuLDYoSu+f/9OAwYMIA0NDRIIBKSpqUlqamrUs2dPibHKhRQOTVU0BGRxdaJOnjxJNWvWpGPHjjHhkI4dO0a1a9emEydOcD8Z/09x9VyKkpGRQbNmzSJXV1dSV1fndD2UpsYSX4obIurmzZtyiRorox3s7Ow4bdJ0evhqExUHvqG6pD2fLC0t6erVq0REtG/fPqpcuTLdv3+fpkyZQg0aNJC7PElw1fvi0xfk7QelFZq4aDvy1VgqKiYua2Nj8eLFzHPg5MmTpKOjQ9ra2qSmpsaEm+JKfn4+nT59mgYMGECmpqZkbGxM/fr1Y7XT1tb+V2iTynM9CkP1sqGodpC3DYiIVq9eTRYWFhQSEkLbtm2jbdu2UUhICJUvX55Wr14tohUqCUXoE3GhJEMY8j0GRYT/Kwt9QRLbt2+nwMBAue25ogh9JWl8/fpV6vt04ZCjW7ZsoYoVK9LEiROZPj9x4kSqVKkSbdmyRaJ9Yd2dffv2kaOjo9i3S9WqVf8174E6OjpMiMjC18udO3c4hxk2NjZm9LuNjY0pJSWFiIguX75MTk5OctctISGBcyhKZSOPXp0KFSqKj4Co0HRUFSpU/HL8+PEDr169Qn5+vki6jY2Nkmr07yMzMxPR0dGIjo7G/fv30bhxY3Tv3h2dOnUSWdVQVvHy8hKbzfXixQu8fv0af/75JwYNGlRiZX/9+hXXr19HuXLlxELpfPv2Dbt27ZK6CmP58uUYNGgQdHR0RGa1S8LAwACurq6oW7euxN/79++PZcuWia0AysnJQWhoKDZt2gSgIBxc5cqVoa6uLrJfw4YNMWbMGLRv3x7du3fH+/fvMWXKFKxbtw7Xr1/H7du3ZdYPKAipExwcjM6dO0NXV5d1/6KoqanhxYsXKF++vEj6q1evYGlpyTrTXk9PD8nJyWKzONPS0uDp6YkvX76w1sHY2BgxMTHMrFAhR44cQVBQED5+/MjpWL58+YKsrCz8+PFDJJ1L+IofP34gJCQEW7ZsQV5eHjQ0NJCXl4fu3btjy5YtYm1XlAEDBsDa2hrTp0/HqlWrMG7cOHh7e+PatWvo2LGjxFAt0vj27ZvYMUia8V6YjRs3YsiQIdDR0YGZmZnItSkQCJCRkcGp7MePH+PWrVvIzs6Gl5cXqlatKnP/R48ewcbGBgKBAI8ePZK5r7QwNELc3NywZs0a+Pj4iKQnJCRg0KBBrCuFzp8/L2bLl7y8PBw9ehRTp05FcnIy6wzVXbt2YdKkSRg1ahRq1aoFfX19kd+5hlJJSUmR2JcVOSvS0NAQSUlJTPgethUU9+7dQ1BQEOs5KAvtwBdbW1usX78ezZs3h729PVavXg1/f3/cuXMHPj4+eP/+vcLKKtoObBRdWSYNHR0dpKenw8rKCoMGDYKenh6WLl2KzMxMeHp6KnTltLRjUHRfKKl+oIjwhUXPwblz5+Dt7Q0NDQ3WMLaSIgIUvp/euHEDY8eOxbhx45gVepcuXcKiRYswf/58sZWhbDx69AjXr19HlSpVeIV3SkxMRHBwMKe2qF27NubNm4fmzZvLXR7A7znPheJej0BBOOyaNWsWuz8qoh2K0wYARMLbykJaOEJdXV2kpqbC2toaYWFh+PbtG9auXYvU1FTUrVtXYfdGedqhKHPmzMHQoUPFVgiV1jEUJ/yfMvqCJDIyMuDh4cFEFyiMIldZGhkZ4fr166hatSpatGiBtm3bIiwsDFlZWXByciqx1WyFad68OQYMGICgoCCR9OjoaKxbtw5nz56VaV+nTh3MmDGDCesp5MiRI5g6dSquX7+u6CornFq1amHUqFHo2bOnyDU3c+ZMnDx5EgkJCax5WFhY4OLFi6hatSqqVauGFStWoFWrVrh37x5q1aqFnJwcmfZFv8WJCM+fP8e2bdvQuHFjiWHNVahQ8R9FuX4uFSpUlBSpqank4+NTrFmZKsSpW7cuqampUY0aNWjBggW8BbuVwYwZM0S2mTNn0urVq+nu3bslWu79+/fJ1taW6XONGjWiZ8+eMb+/ePFCZl+0s7OjN2/eMP+XtVWqVInU1dVp7NixEvMqPGO5MK9fvyZ1dXXWYzl27Bjt2bOHiIjS0tLIycmJBAIBmZubU1xcHKs9EVFYWBhZWFiQkZERDRgwgC5dusTJTjj7TiAQ0JkzZ5i/k5KSKDExkSIjIzmtTGvTpg1t2rRJLH3Tpk3MDEU2LCwsmBlwhUlJSSFzc3NW+1evXpG/v7/cs70Lk5WVRYcPH6adO3cyIsNcyMvLY4TbiYhiYmIoNDSUli9fzmmlUk5ODoWEhJCFhYVcx1ChQgWaPXu23Cu7wsPDKScnRyz9y5cvFB4eLleexUVHR4du3bollp6UlMRppaempibZ2dnRpEmT6M6dO7zqcv78eRo6dChZWFiQoaEh9ezZk44ePcpqJ202I9dn5IMHD8jDw0NsdZg8fZmNorO92VakcT2GstAORcnNzaUbN27Qu3fvOO0/ffp0MjY2purVq5ONjQ19+/aNiIg2btxI9erVK3b5siiplWU2NjZ0/Phxys3NJWtra/r777+JiOj27dsKnyUs7RgU1RcU1Q+kUZKrJxTBb7/9RocPHxZLP3z4MNWsWZPVnstqM648fvyY5s2bR56enqSurk4+Pj60evVqVrujR49SjRo16NChQ/Ts2TNm1apwY0ORz3lZSGrHonUtuiUkJHCqg6LaQd42UASVKlViVgNVq1aNdu3aRURE9+7dU+hqz5JcWVYWjqEs9oUvX75QWFgYVatWTeLvst4RiruCo2nTptS7d2+KiooiTU1NSktLI6KCFUdco2Lk5ubSggUL6LfffqMKFSqQqampyMaGrq6uxPf8+/fvc4oqoaOjI/Xb5d8SnWT//v1kbGxMc+fOJT09PVqwYAENGDCAtLS0OEcSaNGiBW3fvp2IiAYMGEB16tShv/76i1q1akV16tRhtS/67e3g4EB169alSZMm0adPn3gdX1mD6+pvFSpUSEblaFKh4helQYMG1KhRIzpy5AjduHGDbt68KbKp4MYff/zBewDuv0r79u3J39+fXr9+TWlpaeTv70/29vb06NEjImJ3NBWXEydOiDk7Pn78SB8+fCCBQEDp6ekiAw7CD91KlSrJVd7bt28pPz+/WDY/f/6kPXv2UGBgIGlqapKzszMtWLCAXrx4IdWm8OC1pA9FPT092rhxo0TbwqFV2MKwcCE8PJyCgoKYwVwiom/fvlGPHj04ha3r3r07eXt709WrV0lfX59OnDhB27ZtIycnJ2ZwlUsd+Dpavn79SleuXKFDhw6JnKODBw+y2g4bNoycnZ0pNjaWdHV1adOmTRQREUFWVlb0119/sdqbmpoyYUjkQZrT9M2bNzKvp8LHybax0bBhQ2rRooVIv33x4gW1bNmSGjVqxGr/+vVrWrFiBTVo0IAEAgF5enrS/PnzmRBoXJg4cSLZ2dmRlpYW+fv7U3R0tMR+IY3CYVUkbWy0bduW2rVrR69fvyYDAwNKSUmhhIQEqlOnDsXHx3OuBxeKhlwzMzOjjRs3Sq374cOHOd1by0I7hIWF0YYNG4ioYDBKWBd9fX06c+YMpzx2795NixcvFqn3li1baP/+/ZzrwYWig5GFnf6Stp07d3Jqh7LgLOPbF/j2A64owklU9Hpia8fCGxt8BzSFk3LWrVvH2dlalDVr1lCjRo1IXV2dXF1dKTIyktM9TUjRgejiTlRTxHOeC5GRkWLhhNnCF3I9Br7twLcNpCEpfLI0QkJCyNbWlnx9fcnMzIw+f/5MREQ7duwgLy8v3nURIs81ydUJXxaOQdl9wcTERMQpY2JiQurq6mRoaCj1fY3t/aY47zpJSUnk5uZGRkZGIu/5w4cPp6CgIE7HMHXqVKpUqRItXLiQdHR0KCIigoKDg8nMzIxTyOhq1arRuHHjxNLHjRsn1dlWGC8vL+rVqxd9//6dSfv+/Tv16tVLof2opImPjydfX1+ysLAgXV1d8vb2puPHj3O2v3r1KhP6/eXLl9SqVSsyNDSkmjVrqsaGilCSE1JUqPgvoHI0qVDxi6Knp1fiK1ZU/Ds4fPgwHTt2TCz92LFjdOTIkRIrt3z58pScnMz8nZ+fT0OGDCEbGxt68OCBwh1NX758EYuXzjbooK6uTrNmzVJYHYrDy5cvKSIignR0dEhTU5PatWsncXXUw4cPKTMzkwQCAV29elXkA/HZs2esujxcNlnt0KFDB5HN0NCQzM3NqXnz5tS8eXMyNzcnIyMj6tChA+sxV6xYka5cuUJERIaGhnT//n0iKnCCeHt7s9oTye9oEXL06FEyNzeXe3antbU1MwAu1IgiIoqKiqI2bdqw2o8cOZJmz57Nup80BAIBvXr1Siw9Li5O5qoyaStfJA0ospGWlkZubm6kpaVFjo6O5OjoSFpaWuTq6sqcD67Io+dCVDCZYtWqVaWm61IUMzMzZuDZyMiIiXsfFxdHNWrU4JX3ixcvZDpNW7ZsSREREVJ/v3nzJgkEgmKVqax2UKQ+0devX+WqA1dKamUZkfKcZZKQpy+U1vUoz+AP2/WkSI0lvgOaiYmJNHbsWLKysiJtbW1q164d7d69W2RyBxtWVlY0btw4uQcOC2ujSNrYUMRzXhJcVsEYGRnRvHnzpNZ9/fr1nNqRbzvwbQOiAj2cmJgY5u9OnTqRQCCgypUrc8pXEfpEXCjJlWXKPAYhyu4Lmzdvpi1btjBbVFQUHT16VG5HtKKQpa9UFAcHB8bJbGBgwEy2WrZsGSdn1eHDh0lHR4fc3NwoODiYgoODyd3dnXR0dCSuIC3KlStXqHz58mRhYcF8u1hYWFD58uWZe5WK4vPx40fat2+fxMkV/3ZUjiYVKvih0mhSoeIX5bfffsOSJUsUrr/wX2D06NGIiIiAvr4+Ro8eLXPfxYsXl1Kt5MfDwwNz584Vi0197NgxTJgwAUlJSSVSrpGREa5cuQJnZ2eR9OHDh+PAgQOIjo5GkyZNWOOT3717F5cvX0b9+vVRvXp13Lt3D8uWLcP379/Rs2dPNGvWTKrtuXPnQERo1qwZ9uzZg3LlyjG/aWlpwdbWFpUrV+Z3oHLwzz//YPPmzYiJiYGRkRH69u2Lp0+fIjo6GsOGDcPChQtLvU7S6NevH+d9N2/eLPN3IyMjJCcnw87ODra2toiOjoa3tzcyMzPh6urKSSdKTU0NL1++hIWFhUj66dOn0bVrV7x+/VqmfdWqVdGyZUtMmzYNFSpUYD+oIhgYGCAlJQU2NjawsrLC3r17UadOHWRmZsLd3V1ivPzCjBgxAlFRUfD09ISHhwc0NTVFfpd2TzE1NYVAIMDHjx9hZGQkou2Ul5eH7OxsDBkyBKtWrWI9hlOnTmHChAmIjIwU0RGZMmUKIiMj0aJFC9Y8iAgnT57EvXv3AADOzs7w9fUVqRdXSlvXpzDyaiyZmpoiMTER9vb2cHR0xIYNG9C0aVM8ePAA7u7unPqyNNh0RPbt24ecnBz07NlT4u/v37/HwYMH0adPn2KVq4x24KtPlJeXh8jISKxZswYvX75EamoqHBwcMHXqVNjZ2SE4OFhhdY2Ojka7du0YPS9zc3PMnz9fqpbNnTt3EBAQUKb1iaShzGtSFiWhy6NIjaV//vkHAQEBICJG+yQ5ORkCgQCHDh1CnTp1ONWZiHD27FlER0djz549yM/PR8eOHRk9STZbee7DikIRz3lJcNFXatq0Kdq0aYPx48dLzcPLy0tMt1Ya8raDItrA3t4e27dvR4MGDXDy5El06dIFO3fuxK5du5CVlYUTJ07wyl9RSNJXUlNTk3n8wvOjqPtKcTSWJMHlvqLMvsCHgwcPSkwXCATQ0dFBlSpVYG9vX6J10NfXx927d2FjY4NKlSrh8OHDqFmzJjIyMuDl5cVJ3/XJkydYvXo1owHq7OyMIUOGcH4e5uTkYPv27SLvrN27dxfT5yyrODg44OrVqzAzMxNJ//DhA3MuufLq1Svcv38fAFC9enWx7ylpdOnSBY0aNcLw4cPx9etXeHp64uHDhyAixMTE4Pfff+d+QGUcRWjPqVDxX0ZD2RVQoUJFyTBv3jyMHz8ekZGRcHd3FxvMZBOs/y9z48YN/Pz5k/n/v520tDS4uLiIpVevXh3p6eklVm716tVx7do1MUfTypUrAbAP5AIFzrB27drBwMAAX758wb59+9C7d294enoiPz8fLVu2xIkTJ6Q6m4TC3ZmZmbCxsVHqx96rV6+wbds2bN68GWlpaQgICMCOHTvQqlUrpl59+/ZF69atpTqaHjx4gKVLlzIfWi4uLggLC4Ojo2OJ1VvoPCIiPH78GBYWFtD9P/buPK6m/P8D+OsWoU0pFdGmRFoUE2MXyjL2r6WyTPaGakpZBlH2TMk2yl7GOmHIVvbdkFJCFJItUhlaTKrz+6PHPb9udzvde+5SPs/Hw+PBufec+7k+n3Puvef9+bzfTZpIdCxra2s8efIEZmZmcHBwQHR0NMzMzBAVFSX2BgE30MLhcNC2bVuhgRZx3r9/j4CAAImCTEDVjz3ueGrXrh0OHz4MZ2dnxMfH8xWyFuTBgwdwdHQEAKSnp/M8Jmp8RkZGgqIoTJkyBSEhIWjatCn9mJqaGszMzOgbo+L8+uuviIqK4pmI4ObmBnV1dcyYMYMeX6JwOBy4urrC1dVV6HPE3Ri/ceMG9u3bh7i4OHz9+hXDhw/H6tWrGb0HoOradunSJXz48IHvxmFwcLDIfZ8/f46RI0fiwYMH4HA44M674vaBuBtgtra2SE1Nhbm5Obp06YKwsDCoqalh27ZtYn+YiivUzb0BIMzIkSNFPq6rq1urIJMi+8HQ0BCPHj1CixYtcPbsWWzduhUAUFJSAlVVVbGvvXLlSsTExCAsLAzTp0+nt9va2iIyMlKqQNP79+8RHR1NvwcPDw+exzt16oS3b9/C1NRU4P6fPn0Cm/P5srOz6e8msiLNWJBmHEhD2vOpev+NGTMGGzdu5JmYY29vj9atW2PJkiViA03Ozs54/vw5zw3NcePG1fqGJofDQd++fdG3b194e3tj6tSpiImJYRRo4nA4+PTpE+7cuSOwLyZNmsSoDSUlJQKD8NwAmjCSfs6LCyp/+fJFbJs9PDxQWloq9HEjIyMsXbpU7HG4JO0HNvogNzeX/uw8efIkxo4dC1dXV5iZmaFLly6M2r93715ER0fj+fPnuHXrFkxNTREZGQlzc3MMHz6c0TFqKiwsRHx8PP0eFi5cyPccLS0tLFq0SGg7MzMzMXPmTIleX5CrV6+K7Hc2yHMsiLumVSfufBwxYgTPd5zq7eIGwXr06IG///4burq6fPtXVFRg/fr1dICz5vWgoKBAbBtbtWqFd+/ewcTEBG3atEFiYiKcnJxw9+5dNGrUiMG7rDrGypUrRT7nl19+QWhoKPT19fke09DQwIwZM0TuL23AUpays7MFfi/977//8ObNG0bH+PLlC3755RccPHiQPpaqqirGjRuHLVu28PyuEOTq1atYtGgRgKoJTxRF4dOnT4iJicGKFSvqVaCJIAgpyXcBFUEQ8iJtjnWi/jA0NBSYku3cuXNU8+bNZfa6q1atEplKzNvbW2x6px9//JFatGgRRVFVOdl1dXWp3377jX58wYIF1IABA8S25cyZM9S1a9fof2/evJlycHCg3N3d5ZZ+omHDhlS7du2osLAwganPKKoqDUGfPn0EPnb27FlKTU2NcnZ2pvz9/Sl/f3/K2dmZatSoEeNCsEVFRdSpU6eorVu3Uhs2bOD5I05FRQXVsGFDgQV5mdq7dy+1e/duiqIoKikpidLX16dUVFSoxo0b86SIEWTPnj3U7t27KQ6HQ23YsIEnlcj+/fupmzdvMmqDl5cXXRNGEhEREfT/17lz56jGjRtTjRo1olRUVPhSN8rC5cuXGacrEaZx48bUgwcP+LanpqayWhhZWOoJNuq5bNu2jVJVVaUMDQ0pBwcHqmPHjvQfJimqpK2xdPbsWerIkSMURVWlErS2tqY4HA6lr68v8HpbnTQp17jnPpM/4ihDP0hbn6hNmzbU+fPnKYriHW+PHz+mdHR0avVeahJXR+To0aPU3r17hT5eUFBA7dmzR6o2VCeL+kRc0o4FaccBU4L+D9hMYSivovGDBw+m3r59K/TxV69eUWvXrqUcHBwoVVVVqkePHoxrKZ44cYLS0tKiOBwO1bRpU0pHR4f+o6urK3b/Dx8+UEOGDJE4faCkn/Ns1Vdik6T9IG0fUBRFtWjRgrpx4wZFUVU1ag4fPkxRFEVlZGRQWlpaYvf/448/KH19fWrFihVUkyZN6PNm9+7dQr9nMsGkvlKfPn2otWvXijxGbdO7iiLttZHJ/vIcC6KuabVN93z+/HmqS5cu1Pnz56nPnz9Tnz9/ps6fP0/9+OOP1KlTp6jr169THTp0oKZMmSJwf2nrK1EURc2fP59OGX3w4EGqQYMGlKWlJaWmpkbNnz+f0TGY0NLSkvk4kDdu7VQOh0PFxsby1FM9evQoNXv2bEZ1qiiKosaOHUtZWVlRZ8+epdNYnj17lrK2tqbGjRsndv/GjRtTOTk5FEVR1MSJE+m+e/nyJaWhoSH5m1RCyjgWCKIuIYEmgqinpM2xTlTx8vKiPn/+zLe9qKiI8vLyUkCLam/GjBmUnZ0dnROboqpujNrb21NTp05VYMt4vXr1iqqoqODZpq2tTdd8qaiooBo0aMCTp/3BgweUoaGh2GPb2trSebzT0tIoNTU1auHChVTXrl2pn3/+mcV3IRyTm9eidOzYUeAPsvnz5zOu/WBkZERpa2tTqqqqVPPmzSkOh0NpaGhQ5ubmjNpgY2ND3bp1q9ZtF6a4uJi6d+9erep6SBtoKS4upgYPHkxNnjyZ+v3332sdcKspOzubOnLkCKNC8dVlZmZSZ8+epUpKSiiKqqphVlulpaV8tQ+Y6NmzJzVgwAAqNzeX3pabm0u5urpSvXr1qnU7hBH2Q42Nei4mJibUmjVrJN5fFjWW8vPzGfWjnp4etXPnTqHFuU+dOiX0BlKfPn14/mhra1Pq6uqUo6Mj5ejoSGloaFDa2tpyq6sjbT9QlHT1iRo3bkwXNK8+3h4+fCj2xkdqaqrIP4cOHVKqiTmyqE/EJe1YYGMcMCEoUCbN+VSTvIrGC+vLqKgoqlevXpSqqirVoUMHatWqVfT4ZsrKyory8/OrddCYy8PDg+revTt19+5dSkNDg0pMTKT27t1LWVtb03VWaoPp5zxb9ZW4zp8/Ty1cuJCaOnUq5eXlxfNHHGn7Qdo+oCiKmj17NmVqakr179+f0tPTo758+UJRVNWkKyZjsX379tSxY8coiuIdbw8ePKD09PSE7sdGfaVt27aJ/D6Vm5tLLVu2TOx7YEram8KrVq2iCgsLBT6miLEg7Fom6I84HTp0oAOW1V2/fp2ysbGhKKpq0lTr1q0F7i9tfSVBbt26RYWHh1MnTpyQaH9h5BFwlDdhdVU5HA6lpqZGtW3bloqPj2d0LHV1dZ5Jl1xXr16l1NXVxe5vZWVFHTp0iCoqKqKaN29OT6q6f/++yGtKXaSMY4Eg6hISaCKIeuzq1auUp6cn1bVrV+r169cURVUVrBf0JYMQTEVFhXr//j3f9ry8PEpVVVUBLaq9T58+UV27dqUaNGhAmZmZUWZmZlSDBg2ovn37Cv1hpQiCZqJpa2vzBMhqfvHLzs5mNMtYQ0ODevHiBUVRVTPoR48eTVEURd27d49RoIot3759o86dO0dFRUXRAcw3b97QNxBEadSokcDVRE+ePKEaNWokdv/evXtT06dPpyoqKuj/x5ycHKpXr170ygxxTpw4QfXo0UPgahhxysrKKAsLC1aLxkoSaNmxYwfVoEEDSlNTkzI1NaXPCTMzM7EBt7KyMsrFxUWqVV0fP36kXFxc6B+O3PHs5eVFBQQEiN2/uLiYmj17NtW8eXOJZptTVFWQy9bWllJTU6PatGlDtWnThlJTU6M6dOhAB3bZIMsfatLOXNXR0aGeP39OUVTVjZSLFy9SFEVRWVlZVJMmTRgfR5KAoaurK7V8+XKhjzOd7R0eHk4NHTqUZ1VmQUEBNXz4cOr3339n0HrpSdsP1ZWWltZ6HycnJ3pVUfXxFhISQvXo0UPkvsqysowpSc4nJqsP2MDmOKiOSaCMrfOJouRXNF5YX7Zq1YoKCgqi7t+/L/Gx1dXVpeoLIyMj+r1qaWlRT548oSiqamZ99+7dGR/nv//+ozIyMqhv374xej6bq2CWLVtGqaioUM7OztTw4cOpESNG8PwRR9p+kLYPKKrq+8a6desoX19fnglWERER1Pbt28XuLywI//TpU5Hfm5VxZZk4tb02FhQUUDExMYyeqwxjQRrCVrCnpaXR4yA7O1vo9x51dXXq5cuXFEVVXRvu3btHURRFPXv2jNLW1mbUhitXrgi8Dnz79o26cuUKo2MwUR8DTVxmZmZSTQqiKIpq3bo1lZaWxrc9NTWVMjY2Frv/li1bqAYNGlA6OjqUg4MDPTl048aNUq2SVEbCVn8TBMEMqdFEEPXUkSNHMHHiRHh6eiIlJQX//fcfAODff//FqlWrcPr0aQW3ULl9/vwZVFUwHl++fEHjxo3pxyoqKnD69GkYGBgosIXMNW3aFDdv3sS5c+eQmpqKJk2awN7eHr169VJ003hQAmpZmJmZITMzk65BdOvWLZiYmNCP5+TkMMqlraamRhegPn/+PJ0TvVmzZmLrArDl5cuXGDhwIHJycvDff/9hwIAB0NLSwtq1a/Hff/8hKipK5P7NmzfH/fv3YWVlxbP9/v37jMbi/fv3ER0dDRUVFaiqquK///6DhYUFwsLCMHnyZIwaNUrsMSZNmoSSkhI4ODhATU2Nr1aTqFztDRs2xNevX8W+hjglJSWYN28eDh8+jPz8fL7HxdXWWbRoEUJCQrBgwQKoqKjU6rUbNmxYq9z5gvj7+6Nhw4bIycnhqV82btw4BAQEIDw8XOT+QUFBuHTpErZu3YqJEydiy5YtePPmDaKjo7FmzRpGbbC0tERaWhrOnTvHUxi5f//+cqtjJm09lzFjxiAxMZFRXS5BpKmxBAD5+fkYO3YsLl26BA6Hg8zMTFhYWGDq1KnQ1dUV2Y+zZs1CcXGx0MdNTEzo2miihIeHIzExkaeugq6uLlasWAFXV1fMnTtX7DEU3Q8VFRVYtWoVoqKi8P79ezx9+hQWFhZYsmQJzMzMxNZYCg4OxuTJk/HmzRtUVlbi6NGjePLkCWJjY3Hy5EmR+zZr1gxhYWHo16+fwMcfPnyIoUOHCnysZv3G5ORklJeXw9raGgDw9OlTqKqqolOnTiLbIC1p6xNVJ81YkHYcCJObm4uQkBCRr8/W+QSwV2NJUjk5OVJfg93c3JCUlCRxEfPi4mL6O4Wuri7y8vLQtm1b2NnZITk5Wez+JSUl8PHxQUxMDADQ57SPjw+MjY2xYMECgfuxWV8pKioKe/bswcSJExk9vyZp+0HaPgCqvm8EBgbybff392e0v7m5Oe7fv89XQ+7s2bN8tVOrY7u+0oULF3DhwgWB1xUmNcdkIScnB15eXoxqZSl6LJw4cULgdg6Hg8aNG8PS0hLm5uZC9+/UqROCgoIQGxuL5s2bAwDy8vIwb948/PDDDwCq+lRYLU026iv17dsX79694/ut8u+//6Jv375iv7cTVbWGub5+/cpzX4KpxYsXIyAgAHv37oWRkRGAqs/YoKAgLFmyROz+v/zyC5ydnfHq1SsMGDCA/g1lYWGBFStW1Lo9isLkmlSzJidBELVDAk0EUU+tWLECUVFRmDRpEg4ePEhv7969e536MqAoOjo64HA44HA4aNu2Ld/jHA4HISEhCmiZZDgcDlxdXeHq6qroptSKt7c3zw8QW1tbnsfPnDkDFxcXscfp0aMHAgIC0L17d9y5cweHDh0CUHUDpFWrVuw2Wgg/Pz907twZqamp0NPTo7ePHDmSp4i9MNOnT8eMGTPw/PlzdOvWDUBV4fa1a9ciICBA7P4NGzakfxQYGBjQgY6mTZvi1atXjN5DZGQko+cJM3v2bKxduxY7duxAgwaSfQWRNtBSVlaGcePG1TrIxDVhwgTs3LmTcVCnpsTERCQkJPCNOysrK7x8+VLs/vHx8YiNjUWfPn3g5eWFnj17wtLSEqampti3bx88PT0ZtYPJNcHOzg6nT58WegNCUtu3b4e3tzf09fVhZGTEcxOHw+EwCnBYWlpiyZIluH37Nuzs7NCwYUOex319fUXuv3jxYvrmdGhoKH766Sf07NkTenp69PVBFGkChiNHjhR5bF1dXUyePFlsGz5//oy8vDy+7Xl5efjy5YvY/ZWhH1auXImYmBiEhYXxXAdtbW0RGRkpNtA0fPhwxMfHIzQ0FBoaGggODoaTkxPi4+MxYMAAkft26tQJb9++5bsRy/Xp0yeBEyAA4NKlS/TfIyIioKWlhZiYGDroV1hYSJ+fstSxY0eBhd4B3mLv4kg7FiQdB2wEytg6n7gUWTSew+Hg06dPuHPnjsCbYExujA8ZMgRBQUF49OiRwL4YNmyYyP2tra3x5MkTmJmZwcHBAdHR0TAzM0NUVBSj97tw4UKkpqbi8uXLGDhwIL29f//+WLZsmdBAk7jvQYaGhowDTWVlZfT3JElI2w/S9gHX3r17ER0djefPn+PWrVswNTVFZGQkzM3NMXz4cJH7BgQEYPbs2fj69SsoisKdO3dw4MABrF69Gjt27BC6n5OTEwCgd+/eAh/X0dERel2sKSQkBKGhoejcuTNatGght4ks4iaQMfl85FL0WBgxYoTAa3z163uPHj3w999/80w64dq5cyeGDx+OVq1a0d/lXr16BQsLCxw/fhwAUFRUhMWLFwt8/ZEjR+LChQvo0qULfHx86O/AOTk5jIOewj6H8vPz5RLArw8qKyuxcuVKiSflAMDWrVuRlZUFExMTetJmTk4OGjVqhLy8PERHR9PPFTapoHPnzujcuTM9GZfD4WDIkCHsvEk5UNQ1iSC+NxyK6TcFgiDqFHV1dTx69AhmZmbQ0tJCamoqLCws8Pz5c9jY2LCysqA+u3LlCiiKgouLC44cOYJmzZrRj6mpqcHU1BQtW7ZUYAtF27hxI2bMmIHGjRtj48aNIp8r7kagvFQfp5J6/fo1WrZsyRdEyMnJwS+//IJXr17B19eX/kLu7++PiooKsf9HbNDT08PNmzdhbW3N816zs7NhY2NDr7gShqIoREZGIjw8HG/fvgUAtGzZEkFBQfD19RX7ZdnV1RU///wzPDw8MH36dKSlpcHX1xd79+5FYWEh/vnnH9beqzDcH6yampqws7Pj+4F59OhRsccwMTGhAy3a2tpITk6GpaUl9u7diwMHDohdrenv74/mzZvjt99+k+g9+Pj4IDY2FlZWVujUqRPfe4iIiBC5v5aWFpKTk2FlZcUzDpKSkuDm5iZwlVZ1mpqaePToEUxMTNCqVSscPXoUzs7OePHiBezs7FBUVCTR+xLWVmnOSWH7m5qa4pdffsH8+fMlbpuoGbwcDgfPnz+v9TELCgqgq6vL6IenkZEREhIS4ODgwPcZa29vL7QfmASFucSNpUmTJuHatWsIDw+Hs7MzAOCff/5BUFAQevbsSa8oEEYZ+sHS0hLR0dHo168fz/9jRkYGfvzxRxQWFgrdt7y8HKtWrcKUKVMkmjBw7NgxFBcXY8KECQIfLywsxIkTJ8QGKYyNjZGYmIgOHTrwbE9PT4erqyt9vZbW/v37MXz4cJ5rjr6+PqNVWeJmjEs7FiQdByoqKowCZcLaz+b5VBuyujbGx8fD09MTRUVF0NbW5gv4iVo1zCVqEoWo/0uuP//8E+Xl5fj5559x7949DBw4EAUFBVBTU8OePXswbtw4kfubmpri0KFD6Nq1K8/7zMrKgpOTE6NV5NKugpk/fz40NTUZzdIXRNp+kLYPgKqbwsHBwfj111+xcuVKpKenw8LCAnv27EFMTAxPsFuYffv2YdmyZXj27BmAqu+MISEhIm9Kb9++HaWlpUJ/G7x//x5RUVGMgn4tWrRAWFiYxCvLmFq9ejW8vb2ho6MD4P+vK8KIu65Up+ixcOHCBSxatAgrV66kP+fv3LmDJUuWYPHixWjatClmzpyJLl26YOfOnQKPUVlZicTERDx9+hRAVTC5+oqU2rh9+zZu3rwJKysroSt+ubiZEo4fP46BAwfyrICqqKhAWloarK2tcfbs2Vq3QxBZXZeVQWhoKGJiYhAaGorp06fT14NDhw4hMjISt27dEnuM2kyQFXZ+x8bGYt26dcjMzAQAtG3bFkFBQTI/x9kir2sSQXzvyIomgqinjIyMkJWVBTMzM57t169fV8ovUMqGO5PvxYsXMDExqXMzXtavXw9PT080btwY69evF/o8DoejNIEmNtjY2OD+/ft8Y9zExERgGiVR/zdsq6ysFPiD8vXr19DS0hK7P4fDgb+/P/z9/enZmEz241q1ahW938qVKzFp0iR4e3vDysqqVulLnj17ht27d+PZs2fYsGEDDAwMcObMGZiYmPDdaK1JR0cHo0ePZvxaghQUFND9q62tTf/I79GjB7y9vcXuX1FRgbCwMCQkJMDe3p5vdqm4m5Hp6en0jF/uj3YuJteJnj17IjY2FsuXL6f3qaysRFhYGPr27St2fwsLC/q61K5dOxw+fBjOzs6Ij4+nb7Ioi+joaBgaGvJtLywsxJgxY6Q6dvU0ItLIysrCs2fP0KtXLzRr1ozxTO3i4mKoq6vzbS8oKBCZTobNlGtRUVEIDAyEh4cHvn37BgBo0KABpk6dinXr1ondXxn64c2bN7C0tOTbXllZSb8nYRo0aICwsDBGqzwEUZaVZcK8f/8e0dHR9GoiQalcpFmVVZ20Y0HScSBN+kJAeVIYsmXu3LmYMmUKVq1aJfD6wkTNwExtVQ+8durUCS9fvkRGRgZMTEygr68vdv+8vDyB6XyLi4sZfUZKOuO8etCxsrIS27Ztw/nz5yX6nJe2H6TtAwDYtGkTtm/fjhEjRvCsoO7cubPAlHrVlZeXY//+/XBzc4OnpydKSkpQVFTEKM2yMq0sE6awsBDx8fH0tX/hwoU8j7OZ/k/RY8HPzw/btm3j+X/s168fGjdujBkzZuDhw4eIjIzElClThB5DRUUFAwcO5FlhWJOwFexXr15Ft27d6CwEXbt2RdeuXVFeXo6rV6+KTMPetGlTAFWBPS0tLZ5022pqaujatSujjA5MTZgwAdra2hLv/9tvv/FMLFUmsbGx2LZtG/r168eTotbBwYFO8yoO0/NWmIiICCxZsgRz5sxB9+7dAVTdV5o1axY+fvzIeIWbIsnqmkQQBC8SaCKIemr69Onw8/PDrl27wOFw8PbtW9y6dQuBgYESz/D7Hl28eBGampp8N2D++usvlJSU1CodizxVv+nD1g1ZWWMjmCfqhlpFRQX+/vtvPH78GADQoUMHDBs2DKqqqlK/LhOurq6IjIzEtm3bAFS936KiIixduhSDBw+u1bFqE2Di6ty5M/13AwMDiWYQXrlyBYMGDUL37t1x9epVrFy5EgYGBkhNTcXOnTsRFxcncn+mdTJu3LiBzp07C7xhL22g5cGDB3B0dARQFTSqjskYZDKLWBTuTdWkpCSUlZVh3rx5ePjwIQoKCnDjxg2x+3t5eSE1NRW9e/fGggULMHToUGzevBnfvn1jdca+JJjcGAdkV8+lNqSpsQRIHjBkM+Wauro6/vjjD6xbt46esd6mTRvGqWiUoR9sbGxw7do1vkBJXFwcfZ6K0q9fP1y5coVvUo04bK6EGTlyJLy8vASuLGNS+04YedYnUtRYkDZQpiwpDGtL2A3NN2/ewNfXV+IgE5vKysrw4sULtGnThp5cwUTnzp1x6tQp+Pj4APj/z9UdO3bgxx9/FLu/pPWVagYdO3bsCECyz3ll6IcXL14IvAY2atRI5DkPVAXhZ82aRX/fVVdXl+i9SLuybNq0adi/fz/rvzvF1VhiM/2fosfCs2fPBAZPtLW16ZWiVlZW+Pjxo1Svk52dLXByhzT1lbifPWZmZggMDKxVmrza1EO1t7cHULUKUJDY2FiR+wsLWCoTaSblVPfp0yfExcXh2bNnCAoKQrNmzZCcnAxDQ0MYGxuL3HfTpk3YunUrz3k3bNgwdOjQAcuWLasTgSZZXZMIguBFAk0EUU8tWLAAlZWV6NevH0pKStCrVy80atQIgYGB9I8/QrzVq1fz5CzmMjAwwIwZM5Q20CQJbW1tgauB5EWWmVyzsrIwePBgvHnzhp7pvHr1arRu3RqnTp1CmzZtZPbaXOHh4XBzc6NTV3p4eCAzMxP6+vo4cOCA2P3z8/MRHBwstFg7k5Q60lqwYAFWrFiBgIAAnmCXi4sLNm/ezNrrDBo0SOhYlDbQIm2gqDpubava1DDS1tbG48ePsXXrVmhpaaGoqAijRo3C7NmzGf1YrP5Drn///sjIyMC9e/dgaWlJ/9hWFCY3xgHJ67kEBARg+fLl0NDQEBsoEDcWpKmxBEgfMASqrgmJiYk8dRV0dXWxYsUKuLq6Yu7cuYyOo6GhIVHfK0M/BAcHY/LkyXjz5g0qKytx9OhRPHnyBLGxsQJXodY0aNAgLFiwAA8ePBCYylJY/QtlWFmmTPWJJBkLbIwDtgJlAHvnkyyIW4HB5ebmhqSkpFp/D2MzXXJJSQl8fHzo1JvcWiA+Pj4wNjYWWmOJa9WqVRg0aBAePXqE8vJybNiwAY8ePcLNmzdx5coVse9F0hnnbH62S9IPbKesNjc3x/379/mCsGfPnuX5zBLG2dkZKSkpQoO44ihyZZm0NZY8PDxQWloq9HEjIyPGqzsUPRY6deqEoKAgxMbGonnz5gCqVg3OmzcPP/zwA4CqFVps19LkYqO+Evf/Oi8vj/5cs7a2pt+PIKLqDwLMUqty+fn58fz727dvKCkpgZqaGtTV1SVeFS1P0k7KAaq+c/Tv3x9NmzZFdnY2pk+fjmbNmuHo0aPIyckRG5B79+6dwGtzt27d8O7dO+ZvRs7YXO1KEAQzpEYTQdRzZWVlyMrKQlFREWxsbKCpqanoJtUpjRs3RkZGBt9s6ezsbLRv317kD5m6Rta5qaunqGrSpAnfj5dXr16hZcuWUq0wEvYeBg8eDIqisG/fPnoWcX5+PiZMmAAVFRWcOnVK4tesjfLychw6dAipqakoKiqCk5MTPD09edJJCDN48GBkZWVh6tSpMDQ05PvhJ+5m4vv37xEYGEjPTq358c8kV72mpiYePHgAc3NzvjpT7dq1Y632W23GYnZ2Nl2nSR6BlvLycoSEhGDjxo10HR5NTU34+Phg6dKlfD9aalJVVRU4OzQ/Px8GBgaM+kFeavaDuBvjGRkZcHd3F/seJK3n0rdvXxw7dgw6OjoiVw1xOBxcvHhRZBskrbFU3b///ovNmzfznM+zZ89GixYtxO4LVP3/xsfHo0+fPjzbL126hGHDhkmVdo0JZegHALh27RpCQ0N5/h+Dg4Ph6uoqdl82aqFERETg8uXLQlfCMA1QFBcX12plmTLVJ5JkLLA9DqQlz/Optt+XUlNT4eTkJHY87ty5E6GhofDy8hIY8BMWODU3N0dSUhL09PSkrpvm5+eHGzduIDIyEgMHDkRaWhosLCxw/PhxLFu2jC9IK8izZ8+wZs0annN6/vz5sLOzE7uvtPWV2CBJP7DZB0DVCrBly5YhPDwcU6dOxY4dO/Ds2TOsXr0aO3bswPjx40Xuf/jwYSxcuBD+/v4Cg/Divi9JWsuESQpgQPR1gc0aS9JS9Fh48uQJhg8fjhcvXtDBpFevXtHnZNu2bfH333/jy5cvUtWdqXlNY7O+UklJCebMmYPY2Fh6kpyqqiomTZqETZs2CVwt9vLlS8ZtlySYmpmZCW9vbwQFBcHNza3W+8vb8ePHMXnyZCxcuBChoaEICQnhmZQzYMAAscfo378/nJycEBYWxtPfN2/ehIeHB7Kzs0Xub2trCw8PD74atytWrMChQ4fw4MEDad6izDC9JgHsTlggiO8ZCTQRBEGIYGJigs2bN/P9kDh+/Dhmz56N169fK6hl7JNVoCk/Px/jxo3DxYsXeVJUTZkyhVGKqtoQ9h40NDTomdrVpaamonv37oxuKsvLkCFDsGPHDr6b1VpaWrh+/TocHBwkOu6gQYOQk5ODOXPmCJydOnz4cLHHaNWqFQ4fPoxu3brx/F8fO3YMgYGB9E1WaSlzQV5vb28cPXoUoaGhdBqgW7duYdmyZRgxYoTQ1B1cKioqyM3N5Qs0vXz5EjY2NgJn94ubEVsdmzXXavaDtDfGlYmWlhaSk5NhZWXF8z6TkpLg5uaG/Px8mbdh0qRJuHbtmsCUaz179qRXFNRX5eXlWLVqFaZMmYJWrVoprB3GxsZITEzkqzGXnp4OV1dXvH37Viavq6+vz6g+kbDzqebNE1GrsuQR6KktNgNlgHzPp5rXRnErMNLS0tC7d2+x10Y2AqfSMjU1xaFDh9C1a1ee95mVlQUnJyex71USNWecx8TEwN7eXmEzzpWhHwBg3759WLZsGf3dqmXLlggJCcHUqVPF7ivoPdTmc1pPTw937tyRy4r/mpo2bcqoxhKTfpA2/Z8yjIXKykokJibSdUGtra0xYMAAkW2rrZrXNC8vLwBATEwMxo4dy1dfyczMDNOnT2dUt23mzJk4f/48Nm/ezFPbx9fXFwMGDBD7vVlWkpKSMGHCBMY1jhRNmkk5QNV5lZycjDZt2vD098uXL2FtbS12suCRI0cwbtw49O/fn+7HGzdu4MKFCzh8+LDYFdYEQXw/SOo8giAIEdzd3eHr6wstLS264OmVK1fg5+cndjYhUcXf3x8NGjSQOEVVbQibAdmoUSOBs5mLioqgpqbG2uuz4erVqwJXyrVr106qFXTXr1/HtWvX6LoFkhg/fjzmz5+Pv/76i65Jc+PGDQQGBso19cSFCxewfv16uv5A+/bt8euvv6J///4yf+39+/fj4MGDGDRoEL3N3t4erVu3hru7u9AfzNwbaRwOB8HBwTwzOCsqKvDPP/8I7Zv169czahuHw2E10BQdHQ1DQ0P6382aNWN0Y7wukLTGUnWfPn3CnTt3BN7AYnI+SJpyrb5o0KABwsLC5HLtEFboHKgKEuTl5fFtz8vLk+mqsu+1PhEXm+kLAfmeTzVrLOno6DBagSFOzeuIIuTl5fFNhACqVuwxTZ9WWVmJrKwsgddG7nfp6tisr8QGRfdDeXk59u/fDzc3N3h6eqKkpARFRUUC+0UYaeuzKrKWCVs1liRN/1edoscCUBXsGjhwIAYOHCj0OaI+4yQhTX2lmo4cOYK4uDie1aaDBw9GkyZNMHbsWMaBpkePHiEnJwdlZWU824Wt9BSnQYMGMptIIgs9e/bEuXPnJN6/UaNGAicKPH36VGQaQ67Ro0fjn3/+wfr16/H3338DqPr9defOHcbp+xRtypQp2LBhA1+t4+LiYvj4+DAKPhMEIR4JNBEEQYiwfPlyZGdno1+/fmjQoOqSWVlZiUmTJmHVqlUKbl3dkJiYiISEBL4Z61ZWVrVKjcCEsB+eP/30E2bMmIGdO3fyzHSeNWuWxD9Q5O2PP/7AggULEBwcDFtbW75ZvoKKBVfXunVrqetgrVq1CrNnz0br1q1RUVEBGxsbVFRUwMPDA4sXL5bq2Ez98ccf8PPzw//+9z867/rt27cxePBgrF+/HrNnz5bp6zdq1IgvlSZQlSpFVNCSeyONoig8ePCA57lqampwcHBAYGCgwH2lvWHE1Pv37xEdHU3XWPLw8OB5XJob42zW9QGqfhSuWbNG6ExlcelopK2xFB8fD09PTxQVFUFbW5vnBhaHw2EUPFFXV8cff/yBdevW1SrlmjSUrR/69euHK1euCDyn2CSs0DlQVefIy8tL4EoYbvogWVB0fSI2x4Ik44DtQJkszydxNZa0tLQYrcCQh4qKCuzZs0doX4hb3da5c2ecOnWKrufKvbbt2LGDXsUryu3bt+Hh4YGXL1/yfR4IWwFS39IVSdsHDRo0wKxZs+jJNOrq6gLTi4nCNJ1Y9VX0ylLLhK0aS1FRUdizZ49UKeWkJe1YYErUZ5w0JKmvVFNJSQnPpCUuAwMDlJSUiN3/+fPnGDlyJB48eMCzqp57bRK3quzEiRM8/6YoCu/eveNZYVVXJCUl0dcFGxsbxhMxgKqAXGhoKA4fPgyg6v8vJycH8+fPx+jRoxkdo1OnTvjzzz9r33AlERMTgzVr1vAFmkpLSxEbG0sCTQTBEhJoIgiCEEFNTQ2HDh3C8uXLkZqaiiZNmsDOzk7i4rrKTFYzRYuLiwX+QC4oKODJ+c2EuDpPjx49QsuWLfn227hxIyZPnowff/yR/rFcXl6OYcOGYcOGDbV8R4qho6ODz58/w8XFhWc70zQokZGRWLBgAaKjoyW+qaumpobt27djyZIlSE9PR1FRERwdHWFlZSXR8YQRNRZXrVqF9evXY86cOfQ2X19fdO/enQ6EydKcOXOwfPly7N69mx6///33H1auXMnTppq4N9K8vLywYcMGsYFBRcjNzUVISAgdaKpJmhvjKSkp9E0QUTU+mF6Hpk2bhitXrmDixIkSzVS2tbXF06dPsXnzZmhpaaGoqAijRo1iXGNp7ty5mDJlClatWlXrG4A1aWhoyKW+GKB8/TBo0CAsWLAADx48EFhHRB4TARS1skxcmhldXV2xtfe4JFmVxeZYkHYcSBIoE0YW51NOTg68vLyEBpClWYGxceNGzJgxA40bNxabJpXJilU/Pz/s2bMHQ4YMga2tba37YtWqVRg0aBAePXqE8vJybNiwAY8ePcLNmzdx5coVsfvPmjWLDlZJuopEEdjsB2n7AACcnZ2RkpIi898b1VfRK8vKsunTp4t83NDQkFGgqaysDN26dav16yvbWJCHmivYuSSpr1TTjz/+iKVLlyI2NhaNGzcGUHVjPyQkhFHw2s/PD+bm5rhw4QLMzc1x584d5OfnY+7cufj999/F7j9ixAief3M4HDRv3hwuLi6sZtWQpdevX8Pd3R03btyAjo4OgKrJXd26dcPBgwcZpR8ODw/H//73PxgYGKC0tBS9e/dGbm4uunbtipUrVzJqR21XqyqLz58/g6IoUBSFL1++0OMQqApUnj59ulYrRgmCEI3UaCIIgmCgrKwML168QJs2beiVTfWNrOriDB48GJ06dcLy5cuhpaWFtLQ0mJqaYvz48aisrERcXJzYY7BV5ykzMxOPHz8Gh8NB+/btYWlpKe3bY52wfnB2dkaDBg3g5+cHQ0NDvh+rwm5wcenq6qKkpATl5eVQV1fnm51aUFAgtm3Xr19Hjx49GL4TyYkai5qamrh//z5f32VmZsLR0VHm9bZGjhyJCxcuoFGjRnS9rNTUVJSVlfGllDt69Cgrr8nWyoO0tDSR+2ZkZMDd3b1O1FjS0dHBqVOnFDYbVUNDAw8ePFDKOmLyJG0/yKv+BZPPt+LiYrmuLGNK2eoTCSLtONDS0kJ8fDxPaiWgKkA/bNgwmaYwBKSvsbR9+3aUlpYKven8/v17REVFCbw5bm5ujqSkJOjp6cHc3FxoGzgcjtgVgkBV7a/Y2FgMHjxY7HOFefbsGdasWcNTC2T+/Pl8dS4F0dDQQGpqqlJ+vxKFzX5gow8OHz6MhQsXwt/fX2AQnq1gqjLXxASkq7E0f/58aGpq1jr9n7KNBSZq2481V7ALw0Z9pfT0dLi5ueG///7j+d7cuHFjJCQk8NVHrElfXx8XL16Evb09mjZtijt37sDa2hoXL17E3LlzRU6UqC8GDhyIT58+ISYmhk4v++TJE3h5eUFbWxtnz55lfKwbN27wXNuZph2XZLWqsuDWmBWGw+EgJCQEixYtkmOrCKL+qp93SwmCIFhSUlICHx8f+ibN06dPYWFhAR8fHxgbG2PBggUKbiFz4oJlZ86cgbGxMeuvK22KKoC9Ok9WVlb0zQ9lnVUoTHp6OlJSUugfGLUVGRkpdRtcXFxgbGwMd3d3TJgwATY2NrXe/+jRo/RsPK7Pnz9jxIgRdAoRUTcVhw0bhmPHjiEoKIhn+/Hjx/HTTz/Vqj2S0NHR4UsxwVZOfGGYrjwQp2PHjjxpR6qrXiRcELZvjEtLV1eXp0aKJKSpseTm5oakpCSlvTknL9L2gzLUv+CS98qy6upSfSJBpB0HikpfyCVtjSVpVmBUT43KRppUNTU1qYM8bdq0wfbt2yXat0uXLsjKyqpzgSY2+4GNPuDWga0evKz+Oa3MN3XZIkmNJTbS/ynbWJAFcSvYudior2Rra4vMzEzs27cPGRkZAKpqIHt6eqJJkyZi96+oqKBTnenr6+Pt27ewtraGqakpnc6vvrty5Qpu3rzJ8xvQ2toamzZtqlUdxpqB24yMDOzfvx+A+MBtXV2tClRNWqEoCi4uLjhy5AjP9xU1NTWYmpoKzIhCEIRkSKCJIAhChIULFyI1NRWXL1/mKcLav39/LFu2rE4EmpgGy2S1UkXaFFUAO3Wedu7cifXr1yMzM5Pe99dff8W0adNq94YUpHPnznj16pXEgSamKZhEefv2LQ4ePIgDBw5gzZo1sLe3h6enJ9zd3Rmlbbh8+TJfEV8A+Pr1K65duyZ0v+qpS2xsbLBy5UpcvnyZTrlx+/Zt3Lhxo1bplSTFtGYKm6rXr5CmlkWzZs3owK8gDx8+xNChQwU+xuaNcWnr+gBV9fOCg4MRExMjUeo6SWosVc/zP2TIEAQFBeHRo0ews7Pju4FVF2q/KUM/MMV2oXNFU7b6RNKOBWnHgaIDZWzWWJJmBQYb5s6diw0bNmDz5s0S3wisbXqk6qtlfXx8MHfuXOTm5gq8NsormKtIbPSBvGozKjNJaiwpS/o/LjbGgiTErWBnGqCRtr4Sl7q6utiAvDC2trZITU2Fubk5unTpgrCwMKipqWHbtm2MJvuMHj0azs7OmD9/Ps/2sLAw3L17F3/99ZdE7ZKn1q1bC6zBVVFRwThAIkngtrrMzEzExcUpZeBUHG7WjxcvXsDExKROBckIoi4iqfMIgiBEMDU1xaFDh9C1a1eetARZWVlwcnISm25FGfj5+eHGjRuIjIzEwIEDkZaWBgsLCxw/fhzLli2rEykHtLS0kJycDCsrK55+SEpKgpubG/Lz80XuHxwcjIiICPj4+NDBiVu3bmHz5s3w9/dHaGioPN4GI6tXr4a3tzffqp+//voLy5YtQ1BQkEQ3b3JyckQ+bmJiUqt2vnjxAvv378eBAweQkZGBXr16CS1qzP3B27FjR1y8eJFnJllFRQXOnj2L6OhoZGdnC9xfVOqS6pimFpJGaWkpKIqib6a+fPkSx44dg42NDVxdXWX62gAwZcoUbNiwga+QbXFxMXx8fETeyHRzc0PPnj2xePFigY+npqbC0dFR7CqTiIgIXL58WeiNcXEBP3d3d5H1XPz8/ETuDwCOjo549uwZKIqCmZkZ3/mQnJwscv+2bdti8ODBtaqxJCrNW3V1Zba5MvQDU9Kmd1Lm9FDGxsZITEzkSx+Unp4OV1dXvH37VuZtkHYssDUO5Jm+sLq+ffti0KBBmDdvnsDHmV4bxd3IO3bsmMj9KyoqsGfPHqGBKmGfsdWNHDkSly5dQrNmzdChQwe+vhCX0lWS9EjctETCbivUtZU40vaDtH1QG0OGDMGOHTsYT9yqSZmvjXp6erhz5w7atGmjsDbUlbFQsx9FnZO1OR/79esHPT09vvpKkydPRkFBAc6fP8+ofU+ePMGmTZvw+PFjAED79u0xZ84ctGvXTuy+CQkJKC4uxqhRo5CVlYWffvoJT58+hZ6eHg4ePCh08hRX8+bNcfHiRb7Unw8ePED//v3x/v17Ru9BkY4fP45Vq1Zhy5Yt6Ny5MwAgKSkJPj4+mD9/Pl8dKkFatGiBsLCwWgVuq3NxccG8efN4Jt7WBWlpabC1tYWKiorYAOz3MBGCIOSBrGgiCIIQIS8vT2BxyOLi4jozG+bvv/+mg2XV29yhQwf6ho6sSZOiCgB69uyJ2NhYLF++HEDVj6TKykqEhYWhb9++YvffunUrtm/fDnd3d3rbsGHDYG9vDx8fH7kEmmJiYqCvr48hQ4YAAObNm4dt27bBxsYGBw4coAs+L1y4UOD+48aNA1AVaOCqzY9FMzMzkWO2tjd/zM3NsWDBAjg4OGDJkiUii4RzU7ZxOBy4uLjwPd6kSRNs2rRJ6P7KNLN3+PDhGDVqFGbNmoVPnz7B2dkZampq+PjxIyIiIuDt7S3T14+JicGaNWv4Ak2lpaWIjY0VGWiaNWsWiouLhT5uYmLCaMVWeHg4EhMT6SATUJU6a8WKFXB1dRUbaDpz5ozU9ZWY/KgW5c2bN/D19a3V6gtlSvPGBmXoB3kRVuhcGXz+/Bl5eXl82/Py8mRem4hL2rHA1jiQZ/rC6jw8PFBaWir0cSMjI6Gp76qTZAVGdX5+ftizZw+GDBkCW1tbib5n6ujoYOTIkRK9PiBZeiRl+oxmg7T9IG0f1MbVq1dFjt26bNq0adi/f3+tayyxqa6MhZqfcdKsYK9uw4YNcHNzQ6tWrQTWV2LiyJEjGD9+PDp37syTicDOzg4HDx7kS0ddk5ubG/13S0tLZGRkoKCgALq6uoz6o6ioCGpqanzbGzZsqNQTRmu+v+LiYnTp0oVOf19eXo4GDRpgypQpjD6Dy8rK0K1bN4nbU1dXq3bs2BG5ubkwMDAQm0K8LkyEIIi6gKxoIgiCEKFXr14YM2YMfHx8oKWlhbS0NJibm8PHxweZmZm1Kr6pKOrq6khPT4eFhQXPjLfU1FT06tUL//77r0xfX1yKqoKCArHHSE9PR79+/eDk5ISLFy9i2LBhPHWexM121NHRwd27d2FlZcWz/enTp3B2dsanT58kem+1YW1tja1bt8LFxQW3bt1C//79sX79epw8eRINGjQQO6tRXIpAbqBKmNTUVJ5/f/v2DSkpKYiIiMDKlStrVQfjxo0b2LdvH+Li4vD161cMHz4cnp6eQme5cWdGW1hY4M6dO2jevDn9mJqaGgwMDKCqqsr49RVJX18fV65cQYcOHbBjxw5s2rQJKSkpOHLkCIKDg+nZmmz7/PkzKIqCrq4uMjMzef4PKyoqEB8fjwULFshl9YOWlhbi4+N5cvYDVenAhg0bJvbmuLm5OU6fPs1Tb03eRo0ahfHjx2Ps2LEyfR1lTvmmDP3AlKwKnSuDSZMm4dq1awLrE/Xs2ZNOeytLdWksKDNpV2Do6+sjNjYWgwcPZrllzGloaCA1NVXm6ZGkXYkjS8rQD0xJuyJJ2Cp6RalZYykmJgb29va1qrHEJmUdC+I+49hawQ5Upc+rXl+pffv2jOsrAVWrUz09Pfkm9S1duhR//vmn2EmP0qzkBwBnZ2f89NNPfP9Xy5YtQ3x8PO7du8fofchbbT77maRHnz9/PjQ1NSUO3Apa1V8XVqu+fPmSTpcn7W9pgiCYIYEmgiAIEa5fv45BgwZhwoQJ2LNnD2bOnIlHjx7h5s2buHLlCuNC3Yqk6GCZJCmqBPn333+xefNmpKamoqioCE5OTozrPPn4+KBhw4Z8P0oDAwNRWlqKLVu2SNwuptTV1ZGRkQETExPMnz8f7969Q2xsLB4+fIg+ffoInNEuidrevDl16hTWrVuHy5cvi33uwoULcfDgQbx9+xYDBgyAp6cnhg8fzqhfv337hhkzZiA4OJhxKjxBqq/oEkTWNTCq9+PYsWPRoUMHLF26lK6fVZuc9bXBTYMiDIfDQUhICBYtWiTw8eo3b8QRd/NG2hvjf/75J44fP85KXZ979+7Rwb0OHTrA0dFR6HOr11jKy8tDaGgovLy8ZFpjSZnTEimqHyRR2//H1NRUODk5Ke2Nj+pKSkoQGBiIXbt2CaxPJI/0cWyNBVmPA3mQpsaStDfyWrZsicuXL6Nt27YS7V9dXl4eXYfF2tqaZ3KCKPJKj6TM10a2+kHSPqiN2v4/FhYWIj4+nlE2AUVgkqUAqPrOwySVpLSUdSyI+4w7duwYiouLMWHCBIGPFxYW4sSJE6zUbxVHXV0daWlpfMHrzMxMODg4iP3erKqqinfv3vFlGPn48SOMjIxQXl4ucv/4+HiMGjUKHh4edFaFCxcu4MCBA/jrr7/qzMpsSbAZuCVBGoIgmCKBJoIgCDGePXuGNWvW8AQ45s+fz5frWVkpOlimoaGBBw8eKPRmgo+PD2JjY9G6dWt07doVQNWN8ZycHEyaNInny7asZkgaGBggISEBjo6OcHR0REBAACZOnIhnz57BwcEBRUVFrLxObW86ZGVlwcHBQWRKNa7u3bvD09MTY8eOhb6+fq3bpqOjg5SUFKkCTTVTkHz79g3p6en49OkTXFxcWK19IIi9vT2mTZuGkSNHwtbWFmfPnsWPP/6Ie/fuYciQIcjNzZXJ6165cgUURcHFxQVHjhzhqXOlpqYGU1NTkQWBa968SU5ORnl5OaytrQFUre5TVVVFp06dxN68kfbGOBv1XD58+IDx48fj8uXL9EzsT58+oW/fvjh48KDAmziKqLGkzDdTFdUPkqj5/yguz35GRgbc3d3rRKCJS1H1iQDpx4K8xoGsSVJjic0beeHh4Xj+/Dk2b94scXpm7iz/2NhYOlCmqqqKSZMmYdOmTQIDidXPp2fPnmHx4sUS14NkSpmvjdL2gyR9IKn6HIRXBooaC8r0GSdNfSUAGDx4MMaMGQMvLy+e7bt378bBgweFpuBjcyX/qVOnsGrVKty/fx9NmjSBvb09li5dit69ezN6D8rk69evKCsr49mmra0t8LnKFrhVNBMTE/Tp0we9e/dGnz59FFr/jSDqM1KjiSAIQow2bdpg+/btim6GxHr06IHU1FSsXr0adnZ2SExMhJOTE27duiWXYJmbmxuSkpKkvpkgTZ2n9PR0ODk5AQB9I09fXx/6+vpIT0+nnyfLulsDBgzAtGnT4OjoiKdPn9JpOB4+fAgzMzOZvS5XzTzkFEXh3bt3WLZsGV9KQWFu3LjB6HnCVlUNHz4cf//9N/z9/Zk1WgBBN/oqKyvh7e0tlx8MwcHB8PDwgL+/P1xcXOh884mJiTKdvc/9MfzixQu0bt2acdCE69KlS/TfIyIioKWlhZiYGLrOUmFhIby8vNCzZ0+xx1JXV8cff/yBdevWSXRjnI3Zoz4+Pvjy5QsePnxIp/t69OgRJk+eDF9fXxw4cIBvn/pWY0laiuoHNojLs89N5VKXKKo+ESD9WFDUOGCbJDWWUlJSeP7dsWNHAOD5bgEw+35x/fp1XLp0CWfOnEGHDh34gjxMJlIEBATgypUriI+Pp2tuXb9+Hb6+vpg7dy62bt3Kt4+g80nSepD1gbT9IEkfsEVczRl51X2rLxQ1FqT5jGNzBbu09ZWAqhXi8+fPx7179+jJfrdv38Zff/2FkJAQntXm1VeT6+jo0PVdBa0o467kZ2LIkCF0jdy6qLi4GPPnz8fhw4eRn5/P97iw63L17/7Sio2NFfm4sq6SrG7VqlW4evUq1q5di+nTp8PY2Bi9e/emA09Mfw8TBCEaWdFEEARRQ20KgwqbQaQsvn37hpkzZ2LJkiVSrSKpLbZTVLFR54mJ169fo2XLlrW+ic/Ep0+fsHjxYrx69Qre3t50WpqlS5dCTU1NaMqz2hI2u1VQ6jWKotC6dWscPHiQ/gEpyzasWLEC4eHh6NevHzp16sQXmPD19ZX4NZ88eYI+ffrg3bt3Eh+DqdzcXLx79w4ODg70WLlz5w60tbUZz/CUhjRBVwAwNjZGYmIiOnTowLM9PT0drq6ucqnzJK2mTZvi/Pnz+OGHH3i237lzB66urqzVXZO2xpIyz9png7z6Yf/+/Rg+fDh9zdDX12dU6Px7uCmuDOQ1DmRN2hpL0qo547+m3bt3iz2Gvr4+4uLiBNbQGzt2rMA0veJSIlXHVnokZb42StsPkvSBpGr+P4pLs/s9BQzZoKixIM1nHJsr2KWtrwRIvppc2pX89cns2bNx6dIlLF++HBMnTsSWLVvw5s0bREdHY82aNfD09JR5G7gT07i+ffuGkpISqKmpQV1dnbXf4vLy7t07XLlyBSdPnsShQ4dQWVlJrosEwRKyookgCKIG7gwqUerKD7WGDRviyJEjEtcLkJSg2dE1f6QAzFNUzZ07F1OmTJG6zpM4NjY2uH//vkxufOjo6GDz5s1825nOxpNWzVltKioqaN68OSwtLdGggXy+DuzcuRM6Ojq4d+8eX/FdDocjVaDp2bNnYvO0s8XIyAhFRUU4d+4cevXqhSZNmuCHH36QywoKcUFXJoGmz58/C7y5kZeXJ9fZztLUc6msrOQLWgNV1zw2Vy5lZ2fT6QHrK2Xsh5qFzj08PHge79SpE96+fSv0pvenT58EzgQnRJN0LMjrfJS1adOmYf/+/XL/zsTFJJAkTklJCQwNDfm2GxgYCK2FIknwqLb1IOsSaftBkj6Q1G+//cZzA15LSwuLFi1Cly5dBD4/MzMTM2fOZLUN9ZmixoI0n3FsrmB/9+6dwO+VEyZMwLp168TuD0i+mrz6Sn4TE5Nafcdu1qwZnj59Cn19fejq6orcty4ESOLj4xEbG4s+ffrQfWdpaQlTU1Ps27dPLoGmwsJCvm2ZmZnw9vZGUFCQzF+fLSUlJbh+/TouX76MS5cuISUlBba2tnzBYIIgJEcCTQRBEDWwucxcGYwYMULqdGW1xfaNpTdv3sDX11emQSYAMr0xuXv3bmhqamLMmDE82//66y+UlJTIvCAv0zzksrx59OLFC6mPUTMlCDcF4KlTp+RS1Dg/Px9jx47FpUuXwOFwkJmZCQsLC0ydOhW6uroIDw+X6euzEXQdOXIkvLy8EB4eDmdnZwBVNcuCgoIwatQoNpsrEBv1XFxcXODn54cDBw7QM1rfvHkDf39/oTOACV7K3A+5ubkICQmhA001zZo1S2RdORMTE1Zu2n8vpB0Ldfl8rFljadu2bTh//rxENZbYkpeXhydPngAArK2ta1Xj6scff8TSpUsRGxuLxo0bAwBKS0sREhLC6srlq1evorS0lLXjKSNJ+0GWfVBYWIj4+Hj65v/ChQt5HuemiRb2nU9HR4cE4SUg77HA1mdceHg4EhMTeVaj6OrqYsWKFXB1dcXcuXNF7t+nTx9cu3YNlpaWPNuvX7/OKFBVG9VXkKelpcHW1hYqKir4999/8eDBA6H7CUo5u379emhpaQEAIiMjWW2nIhQUFNCTILW1tengWI8ePeDt7a2wdllZWWHNmjWYMGECMjIyFNYOprp164aUlBS0b98effr0wYIFC9CrVy++1VoEQUiHBJoIgiBqqPnj7Nq1a4iOjsazZ88QFxcHY2Nj7N27V66p6KRhZWWF0NBQ3Lhxg/V0ZfLCVp0nRVq9ejWio6P5thsYGGDGjBlyCZIwoew3j2rWw+CuzAoPD+epKSEr/v7+aNiwIXJycuhaJAAwbtw4BAQEyDzQxEbQNSoqCoGBgfDw8KBX6zRo0ABTp05lPENVGmzUc9m8eTOGDRsGMzMzOq3dq1evYGtriz///FOm7a+N6OhogbOZlYEi+0FcoXPuDT1hRo4cKfJxXV1dpbmm1gXSjoW6cj4KwmaNJWkVFxfDx8cHsbGx9IQdVVVVTJo0CZs2bWJ03d+wYQPc3NzQqlUrODg4AABSU1PRuHFjJCQkyLT9tVFzJY4ykbYfZNkHOTk58PLyErp62cPDQ+R3OCMjIyxdulSqNnxPFDUW2PqMk3YFu6T1lSRRfQV5x44dkZubCwMDA7H1qgRlxqj+f8P0u8CaNWswa9YserKFMrGwsKBXdrVr1w6HDx+Gs7Mz4uPjFd7eBg0a1ImU2wCQkZEBDQ0NtGvXDu3atUP79u1JkIkgZIDUaCIIghDhyJEjmDhxIjw9PbF37148evQIFhYW2Lx5M06fPo3Tp08ruoliiQqIcTgcPH/+XKav7+vrC0tLS76A1ubNm5GVlSV0phnbdZ6YkGXNgMaNGyMjIwNmZmY827Ozs9G+fXvWgjurV6+Gt7e3xD882Pg/EHWM169f48SJE8jJyUFZWRnPY/KaLS4NIyMjJCQkwMHBged9Pn/+HPb29igqKpLp648aNQrjx4/H2LFjpT5WcXExnV+/TZs2fEFoWWGrngtFUTh//jw9i7J9+/bo378/q22t7flQM+WbMlNkP3DriIgrdC7oBhKbhc6JKmyMBXmcj/XdzJkzcf78eWzevBndu3cHULVywNfXFwMGDMDWrVsZHaekpAT79u3j6QtPT080adKEtbbW9tpYcyWOMmOjHyTtA3F1YtPS0tC7d2+lT91dXyhiLLD5GTdp0iRcu3ZN4Ar2nj17IiYmRuT+ktZXkkT1a8rLly/pdHniasixVTdOW1tbZunTpbV+/XqoqqrC19cX58+fx9ChQ0FRFL59+4aIiAj4+fnJvA3Vf5cD/59RYvPmzWjdujXOnDkj8zZIi6IoPHjwAJcvX8aVK1dw9epVqKmpoXfv3ujbty+mT5+u6CYSRL1AAk0EQRAiODo6wt/fH5MmTeL5ApySkoJBgwYhNzdX0U2sFe4lXx4zc7mMjY1x4sQJdOrUiWd7cnIyhg0bhtevXwvcT54/brhkGWgyMTGhZ31Xd/z4ccyePVvo/wNXTEwM9PX1MWTIEADAvHnzsG3bNtjY2ODAgQNKVaBb2DEuXLiAYcOGwcLCAhkZGbC1tUV2djYoioKTk5PYosRAVYqmo0eP8gXSPn/+jBEjRjA6hjS0tLSQnJwMKysrnveZlJQENzc35Ofny/T1d+7cKZegqyxpaWnh2rVr9MoBrpSUFPTu3VvsjTZ5qu35kJqaCicnpzpxE1CR/aAshc6JKnXpnKzP9PX1ERcXx1cr4tKlSxg7dqzAlQmKUp+vjYrsB24QXpja1Ii9cOECLly4gA8fPvCltN61a5fUbf0eKGIssPkZV1JSgsDAQOzatUvgCnZ5TTBiQpa/werC69fGy5cvce/ePVhaWgpMHSgLNX+XczgcNG/eHC4uLggPD69z9fooisK9e/ewefNm7Nu3D5WVlXXi84kg6gKSOo8gCEKEJ0+eoFevXnzbmzZtyni2tzLYuXMn1q9fj8zMTABV6fR+/fVXTJs2TeavnZ+fj6ZNm/Jt19bWxsePH4Xup4gC4rIMwLm7u8PX1xdaWlr0mLpy5Qr8/Pwwfvx4sfuvWrWKnjl569YtbNmyBevXr8fJkyfh7++Po0ePyqzttSUsJc7ChQsRGBiIkJAQaGlp4ciRIzAwMICnpycGDhzI6NiXL1/mWwkFAF+/fsW1a9ekbrs4PXv2RGxsLJYvXw6gasxUVlYiLCyM7+aALHBn24WGhvI9xmbQVZbYqueiiJto0qZ8UyaK7AdlKXROVGFjLJCb2tIrKSkRmGrTwMAAJSUljI+TmZmJS5cuCewLWa22FBeMZJKmS1mw0Q+S9oGWlhYWLVqELl26CD3uzJkzxb5+SEgIQkND0blzZ7Ro0UKuE8zqE0WMBTY/49TV1fHHH39g3bp1Ml3BXr2+kiw8efIEmzZtwuPHjwFUrQrz8fGhg2/fG1NTU9YmGDKliN/lbEtOTsbly5dx+fJlXL9+HV++fIGdnR18fHwY1zImCEI8EmgiCIIQwcjICFlZWXzpzq5fv14nZjwBVT9iIiIi4OPjQxeevXXrFvz9/ZGTkyPwhjWbLC0tcfbsWcyZM4dn+5kzZ5Tu/1CWi3yXL1+O7Oxs9OvXDw0aVH38VlRUYPLkyVi5cqXY/V+9ekUX4/37778xevRozJgxA927d+ebaSkrTFdV1SxOzfX48WO61keDBg1QWloKTU1NhIaGYvjw4SIL2la/wf/o0SOe1YQVFRU4e/YsjI2NpX6P4qxbtw4uLi5ISkpCWVkZ5s2bh4cPH6KgoAA3btyQ+evXhx96bNRzkddNtJo1lsTVCuDONq8LFNkPylLonKgi7VggN7XZ8eOPP2Lp0qWIjY1F48aNAQClpaUICQmhv7+Js337dnh7e0NfXx9GRkY8fcHhcGQWaNLR0WG0EqcukLYfpOkDJycnAPz1Yrl0dHQYfVeNiorCnj17MHHiRLHPJYRT5FgA2PuM09DQkOnKl+r1ldh25MgRjB8/Hp07d6b/z2/fvg1bW1scPHgQo0ePlsnrKtrGjRsxY8YMNG7cGBs3bhT53LpQb1kZODs7w9HREb1798b06dPRq1cvgZNhCYKQEkUQBEEItWrVKsrGxoa6ffs2paWlRV27do36888/qebNm1MbN25UdPMY0dfXp/bv38+3ff/+/ZSenp7MX3/nzp1UkyZNqODgYOry5cvU5cuXqSVLllDq6urUtm3bGB3Dx8eH2rBhA9/2TZs2UX5+fozbkpmZSZ09e5YqKSmhKIqiKisreR7PycmhysvLGR9PEk+fPqUOHz5MxcfHU9nZ2Yz3a968OZWcnExRFEV17NiRio2NpSiKorKysigNDQ3W2qepqUk9e/ZM4GNt27alLly4QFEURd28eZNSV1enoqOjqaFDh1IjR44Ue2xDQ0Pq0aNHFEVRVPv27anjx49TFEVR9+/fF/seOBwOpaKiQqmoqFAcDofvj7q6OrVz587avNVaKysro1xcXKh//vmHWrFiBTVmzBhq0KBB1KJFi6i3b9/K9LUFKS0tlftrsqWyspJKTEykNm7cSG3cuJE6d+5crfY3MjKizwE25ebmUiEhIUIf19PTo3bu3EllZ2cL/HPq1ClKRUWF9XbJirL2A1OamprUpUuX+LZfvHiR0tTUlH+D6jBpxoKix0F98eDBA6ply5aUnp4e5eLiQrm4uFB6enqUsbExlZ6ezugYJiYm1Jo1a2Tc0qrvx4WFhfS/tbW1qbVr19Lf82r+2b59e525NkrbD9L0wbZt2wR+3+XKzc2lli1bJvY4zZo1o7KysiRqA/H/FDkWKKrufMaJ+u0g7TEsLCyoJUuW8G0PDg6mLCwspHpNJq+vKGZmZtTHjx/pvwv7Y25uLpf2VFZWUocPH6a8vb2p0aNHUyNHjuT5Uxf8+++/jJ63f/9+qqioSMatIYj6i9RoIgiCEIGiKKxatQqrV6+mUyQ0atQIgYGBdOosZaejo4O7d+/CysqKZ/vTp0/h7OwslxSAW7duxcqVK/H27VsAgJmZGZYtW8a4KLSkdZ648vPzMW7cOFy8eBEcDgeZmZmwsLDAlClToKuri/DwcMneWC0IK+7L4XDQuHFjWFpaYvjw4QJTzgGAp6cnMjIy4OjoiAMHDiAnJwd6eno4ceIEfvvtN6Snp7PSztWrV8Pb25uvBhJQlYIjIyMDJiYmmD9/Pt69e4fY2Fg8fPgQffr0EZurfsSIERgyZAimT5+OwMBAHD9+HD///DOOHj0KXV1dnD9/Xui+L1++BEVRsLCwwJ07d9C8eXP6MTU1NRgYGEBVVVXi981U8+bNcfPmTb7zSV4qKiqwatUqREVF4f3793j69CksLCywZMkSmJmZYerUqQppl7zp6enhzp07aNOmDavHFVdHxM3NDT179sTixYuF7u/o6FgvVp4xIUk/KFOhc4Idsjofv0clJSXYt28fMjIyAFSliPL09ESTJk0Y7S+rgvaFhYWIj48X+r2tb9++GDRoEObNmyfw8bp2bZSmH2TVB7Uxf/58aGpqYsmSJQprQ32hyLFQVz7j2KhvtH//fgwfPpwvrZ+6ujrS0tLorA5cmZmZcHBwqFVaUVHqUo0mRfDz80N0dDT69u0LQ0NDvhWqTFah1xXKcA0niLqMBJoIgiAYKCsrQ1ZWFoqKimBjYwNNTU1FN4kxHx8fNGzYkO+GXWBgIEpLS7Flyxa5tSUvLw9NmjSp9f9f48aNkZ6ezvcjIysrC7a2tvj69avI/SdNmoQPHz5gx44daN++Pf1DIiEhAQEBAXj48GGt30tt9e3bF8nJyaioqOAr6NuuXTs8efIEHA4H169fh42NDd/+nz59wuLFi/Hq1St4e3vTNY2WLl0KNTU1LFq0SOTrM017J4qBgQESEhLg6OgIR0dHBAQEYOLEiXj27BkcHBxQVFQkcv/nz5+jqKgI9vb2KC4uxty5c+mgTUREhNzzjUvC398fjRo1wpo1axTy+qGhoYiJiUFoaCimT5+O9PR0WFhY4NChQ4iMjMStW7cU0q7akraei6Q30cTVWMrIyIC7u7vQQNOxY8dQXFyMCRMmCHy8sLAQJ06cwOTJk2vVLkVRRD98r4XOlZ00Y4Hc1FYeU6dOxQ8//IBZs2axelxxQfjt27ejtLRUaAqn9+/fIyoqCkuXLmW1XcqIrT6o7TlZPYhfWVmJmJgY2Nvbw97eHg0bNuR5rrggPsEOacdCXfmMkyRI8/79e0RHR4tNHzh48GCMGTMGXl5ePNt3796NgwcPIiEhQaI2C3qdnTt3okWLFqwcT1pMJ+VwOBy5TJhs1qwZ/vzzTwwePFjmr6VoJOhIENIhgSaCIIh6zsfHB7GxsWjdujW6du0KoGo2XE5ODiZNmsTz41NZf3ja2tpi1qxZfHWeNm3ahK1bt+LRo0ci9zcyMkJCQgIcHBx4vjw+f/4c9vb2YgMkbIiMjMS1a9ewe/duaGtrAwD+/fdfTJs2DT169MD06dPh4eGB0tJS1n40VWdtbY2tW7fCxcUFt27dQv/+/bF+/XqcPHkSDRo0wNGjR8UeQ16rqsRRRKFzLu75ZGVlhU6dOvH9yJf1OWRpaYno6Gj069ePZyxnZGTgxx9/RGFhoUxfnw3i6rkcO3ZM7DH8/PwQGxtb65toKioqjGosCbuZWp8osh+qP3758mWhhc6Z1p8oLi6WaaHz+k7asSDtOCD+n7Sfb6tXr0ZERASGDBkCOzs7vr4QFgj6/PmzyOOmpaWhd+/e38W1EZCuHyTtg+okOSdrBvGF4XA4YoP4xP9T9FgAlP8zTpIb8+KC11xRUVEIDg7G2LFj6d+xt2/fxl9//YWQkBC0bNmSfu6wYcNEHuvr168oKyvj2cb9TaZs2JyUwwZzc3OcOXMG7dq1k/lrKRoJNBGEdEigiSAIop5Thh+ecXFxOHz4MHJycvi+4CcnJ4vdf9euXZgzZw6CgoLg4uICoGqmZ3h4OCIjIzF9+nSR+2tpaSE5ORlWVlY8Xx6TkpLg5uaG/Px8yd8cQ8bGxjh37hzfaqWHDx/C1dUVb968QXJyMlxdXfHx40e+/Xfv3g1NTU2MGTOGZ/tff/2FkpISsSsopE17B0i/qop7jLi4ODx79gxBQUFo1qwZkpOTYWhoCGNjY7H7iyuszGQ8SUPU+SSPmzdNmjRBRkYGTE1Necbyo0eP4OzsLJegqbRatGiBsLAwqYqUS9oP+vr6CAsLQ79+/QQ+/vDhQwwdOlTgjQ82U74pA0X2A5exsTESExPRoUMHnu3p6elwdXWl060SsiXtWFD0dbG+YOPzzdzcXOhjHA4Hz58/F/gYNwgvTG2C8NKulFQ0aftB0j6ojo3rMyE9ZRgLdYGgG/PSriDnUlFRYdQGYdenkpISzJs3D4cPHxb4e68uBM/ZmpQjjZiYGJw9exa7du1inMq1riKBJoKQTgNFN4AgCIKQrUuXLin09Tdu3IhFixbh559/xvHjx+Hl5YVnz57h7t27mD17NqNjTJkyBf/99x9WrlxJ18YyMzPD1q1bGdV56tmzJ2JjY+l9ORwOKisrERYWxjgQJ61///0XHz584As05eXl0TOJdXR0+AJxXKtXr0Z0dDTfdgMDA8yYMUNsoElTUxP5+fkwMTFBYmIifdO8cePGKC0tZfQedHR0sHnzZr7tISEhjPZPS0tD//790bRpU2RnZ2P69Olo1qwZjh49ipycHMTGxoo9xooVK7By5UrMnz+f0WuyTdHnk42NDa5du8aXZjAuLg6Ojo4KalXtlJWVoVu3blIdg2k/vH79Gi1btqRvVHTq1Alv374Vmqbx06dPAlc7AUBKSgrPv0XNLq0LFNkPXJ8/fxYY5M7Ly8OXL1+kahvBnLRjQdpxQFRh4/PtxYsXEu2npaWFRYsWoUuXLgIfz8zMxMyZM8UeR9xKnLpA2n6QtA+qY+P6TEhPGcZCXRAdHQ1DQ0OebR07dmS0glwcaeu6BQUF4dKlS9i6dSsmTpyILVu24M2bN4iOjlZYGuzaCg8PR2JiIh1kAgBdXV2sWLECrq6ucgk0jR07FgcOHICBgQHMzMz4VufJeqIfQRB1Bwk0EQRBEDL1xx9/YNu2bXB3d8eePXswb948WFhYIDg4GAUFBYyP4+3tDW9vb4nqPHFXMCQlJaGsrAzz5s3Dw4cPUVBQgBs3bkjytmpt+PDhmDJlCsLDw/HDDz8AAO7evYvAwECMGDECAHDnzh20bdtW4P45OTkCZ0aampoiJydH7OsPGDAA06ZNg6OjI54+fUrn2H748CHMzMwYvQdpV1UFBATg559/RlhYGLS0tOjtgwcPhoeHB6M2FBYW8r3+9yQ4OBiTJ0/GmzdvUFlZiaNHj+LJkyeIjY3FyZMnFd08RqZNm4b9+/fLpZ6LjY0NT0HfWbNmobi4WOjzTUxMhBY0rn4zPSIiAlpaWkJnl9YFiuwHrpEjR8LLy0tgofNRo0bJvF1EFXmNBWHjgKgiz8+3msXOnZycAAC9e/cW+HwdHR2hQfjqoqKisGfPnjq9Ekde/SCq4Lw8r8+EcMowFpRRzfpKgr7DN2vWjNEKckl8+vQJOjo6jJ4bHx+P2NhY9OnTh/6OZmlpCVNTU+zbtw+enp4StUGelGFSzuTJk3Hv3j1MmDABhoaGdXISAUEQ8kECTQRBEIRM5eTk0LMymzRpQn8hnjhxIrp27SpwhYwozZs3r3UbbG1t8fTpU2zevBlaWlooKirCqFGjMHv2bLkVfY2Ojoa/vz/Gjx+P8vJyAFUFfSdPnoz169cDANq1a4cdO3YI3N/AwABpaWl8QaHU1FTo6emJff0tW7bQae+OHDlC73Pv3j24u7szeg/Srqq6e/euwP2NjY2Rm5vLqA1jxoxBYmIi64XO64rhw4cjPj4eoaGh0NDQQHBwMJycnBAfH48BAwYounmMfP36Fdu2bcP58+dlXs+l5o3RkSNHiny+rq6u2HEMKMfsUmkpsh+4oqKiEBgYCA8PD4GFzgn5kNdYIBnbRZPn51vNvuDWiBTGyMgIS5cuFXvc+rASR179ULMPqqdnrayslNv1mRBOUWNB2eXm5iIkJERkjSppVpBXt3btWpiZmWHcuHEAqvrkyJEjaNGiBU6fPg0HBweR+xcUFNABPG1tbXqSY48ePeDt7S329ZWBMkzKOXXqFBISEtCjRw+5vB7bKioqcOPGDdjb24sNUpqamvJdcwmCYI4EmgiCIAiZMjIyQkFBAUxNTWFiYoLbt2/DwcEBL168qNUPK2nrPDVt2pRRDSFZ0dTUxPbt27F+/Xo6J7uFhQXPyqyOHTsK3d/d3R2+vr7Q0tJCr169AABXrlyBn58fxo8fL/b1pU17B0i/qqpRo0YCC44/ffqUcQDR0tISS5Yswe3bt6UqrFxXTZs2DRMmTMC5c+cU3RSJpaWl0WM9PT2d5zFZzpBks8aSMswulZai+qE6dXV1/PHHH1i3bp1SFzqv75RhLBCK/XwTV+vS0NCQUaCpPqzEUVQ/1EzPSs5Jxftev3OKq6/05MkTsceQZgV5dVFRUdi3bx8A4Ny5czh//jzOnj2Lw4cPIygoCImJiSL3t7CwwIsXL2BiYoJ27drh8OHDcHZ2Rnx8PONVUYqmDJNyWrduDW1tbbm8liyoqqrC1dUVjx8/FtvvNa+5BEHUDoeqa9MnCIIgiDpl2rRpaN26NZYuXYotW7YgKCgI3bt3R1JSEkaNGoWdO3eKPUb1Ok/btm3jq/O0cuVKscf49OkT7ty5I7A4NZM6T4pWVlaGiRMn4q+//kKDBlXzRCoqKjB58mRs3boVjRo1Erm/tGnvgKofhZs3b8awYcN4th8/fhyzZ8/G69evRe4/bdo05Ofn4/Dhw2jWrBnS0tKgqqqKESNGoFevXoiMjBTbhu+lsLIww4cPR0JCApo3bw53d3d4enqKnc1ZV7FRz6V6Qd+a9dhE1Vi6ePGiyONOmjQJ165dEzi7tGfPnoiJiZG4zcqG7X4g6i5pxwIZB6LJ8/NNVF9cuHABFy5cEPh9adeuXXzPr7kSJyYmBvb29nV2JY68+oGcD8rvex0LKioqjOorVVRUyLwtTZo0wdOnT9G6dWv4+fnh69eviI6OxtOnT9GlSxcUFhaK3H/9+vVQVVWFr68vzp8/j6FDh4KiKHz79g0RERHw8/OT+XtgS3FxscIm5Zw6dQqbNm1CVFQU45TryqZz585Yu3at0HSOBEGwgwSaCIIgCJmqrKxEZWUlHRw5ePAgbt68CSsrK8ycORNqampij9GuXTssXboU7u7uPD/GuHWexKXfi4+Ph6enJ4qKiqCtrc0zG5TD4dSqVpSiZWZm4v79+2jSpAns7OyEpqSoqW3btoiOjua72X7lyhXMmDGD0ezE+fPn49ChQ9i9ezfPqqopU6bgf//7H37//XeR+//777/43//+h6SkJHz58gUtW7ZEbm4uunbtijNnzpBVDAwVFhbir7/+wv79+3Ht2jW0a9cOnp6e8PDwqLM//gRho2aBsJs3ERERuHz5stAaS+JS35WUlCAwMBC7du0SOLu0Po1lWfYDUbdIOxbIOFAewvoiJCQEoaGh6Ny5M1q0aMG3eubYsWN8x6r5vUIYDocjNoj/PSHnA8GlbGNBX1+fUX0lYYEmNleQt2zZEnFxcejWrRusra2xYsUKjBkzBk+ePMEPP/wgMFOCKNnZ2UhOToalpSXs7e1rte/3TFdXFyUlJSgvL4e6ujrfJIK68Fv67NmzWLhwIZYvX45OnTrxfVevyyu2CEKZkEATQRAEofTU1dXx+PFjmJqawsDAAOfOnYODgwMyMzPRtWtX5Ofni9y/bdu2GDx4MFatWgV1dXU5tZpdwn60cTgcNG7cGJaWlhg+fDiaNWsm8HmNGzdGRkYGXyAiOzsb7du3F1mbgUvaVVVcN27cQGpqKoqKiuDk5IT+/fuLfH5AQACWL18ODQ0NkT9eORwOwsPDGbWhvnj9+jUOHDiAXbt2ITMzk67/VR+wceNF2I1xY2NjJCYmokOHDjzb09PT4erqirdv3zI6viJnl8qLLPuBqFukHQtkHLCDjf9HYcdo0aIFwsLCMHHiRGmbWe9J2w/kfKg/6ttYcHNzQ8+ePbF48WKBj6empsLR0ZFvxSMXmyvI58yZg5MnT8LKygopKSnIzs6GpqYmDh48iLCwMEbp06vjrsYiakfcSn0mmTEUrfpq7OpjQJ4r9Ajie0BqNBEEQRAyV1hYiJ07d+Lx48cAABsbG3h5eQkNitQkbZ2nN2/ewNfXt84GmYCq3P3JycmoqKjg+6HWrl07/PHHH5g7dy6uX78OGxsbvv0NDAyQlpbGF2hKTU2Fnp4eozaoqanh0KFDWLFihUSrqgD+lDwZGRnYv38/AMEpebjvnbtqpGYNg+q+tx+O3759Q1JSEv755x9kZ2fD0NBQ0U1SOsKuD2zVWNLQ0CAzYhkg89oIgIwDtrDx/yjsGGVlZejWrZvUx/8eSNsP5HyoP+rbWJC2vtKlS5fov0dEREBLS0voCnJx1q9fDzMzM7x69QphYWF0bdt3797hl19+YfqWaI0aNUJqairat29f632/Z3UhkCRO9XFJEITskEATQRAEIVNXr17FsGHDoK2tjc6dOwOoqrkUGhqK+Ph4OgWbKC4uLjhx4gQcHR3h5eUFf39/xMXF0XWexHFzc0NSUpLSzBSUBHe10u7du+ml/f/++y+mTZuGHj16YPr06fDw8IC/vz8SEhL49nd3d4evry+0tLR40t75+flh/PjxjNogaDXRxYsXGa+qEpeSR5jqPwzIj4Sq/4P9+/fjyJEjqKysxKhRo3Dy5Em4uLgoumkK8erVKwBVhYprevToEVq2bMm3feTIkfDy8hJYY4nJNYXgJ0k/EPUPGQfKg3vzWtBn7ZkzZ2BsbMy3fdq0adi/fz+WLFki8/Z9DyTpA6J+qktjYeTIkSIf19XVZRx4CA8PR2JiIh1k4u6/YsUKuLq6ik1V3LBhQwQGBvJt9/f3F7mfsAwIFRUVWLNmDT3Jri7UjVMmHz58EFi/ry5Muurdu7eim0AQ3wWSOo8gCIKQKTs7O/z444/YunUrVFVVAVR9yf/ll19w8+ZNPHjwQOwxJKnzdOLECfrveXl5CA0NhZeXF+zs7PjySg8bNkyatygXxsbGOHfuHN9qpYcPH8LV1RVv3rxBcnIyXF1d8fHjR7792Uh717dvX5Grqp48eQIOhyN0VRVJySM9Y2NjFBQUYODAgfD09MTQoUMZpyysa0Sl6SovL0dISAg2btyIoqIiAICmpiZ8fHywdOlSvnO8pu+pxpK0ZNkPRN0ibCyQcSBf4lIY7ty5E+vXr0dmZiYAwMrKCr/++iumTZsm8PnVb8hWVlYiJiYG9vb2sLe35+s7clP2/4nqh9r2AVG31aexwGZ9JaDq/yY+Ph59+vTh2X7p0iUMGzaM0SryZ8+eITIykiczxq+//ipyAqGKigocHBygo6PDs/3KlSvo3LkzNDQ0SN24Wrh37x4mT56Mx48f863Aq0tp5z59+sSTZaVDhw6YMmUKmjZtquCWEUT9QVY0EQRBEDKVlZWFuLg4OsgEAKqqqggICEBsbCyjY6ioqPDkVR4/frzYVTgjRozg2xYaGsq3ra58Of7333/x4cMHvgBOXl4eXQhXR0cHZWVlAvdnI+2dtKuqSEoe6S1btgxjxozh++FcH4la8ebj44OjR48iLCwMP/74IwDg1q1bWLZsGfLz87F161aRx1ZXV8cff/yBdevW1fsaS9KSZT8QdYuwsUDGgfIIDg5GREQEfHx8ePrC398fOTk5Ar8H1UxJ27FjRwBVNeuq+97S00pKkj4g6qe6OBZqXg9E1VdiQtoV5AkJCRg2bBg6duyI7t27A6iq9WpjY4P4+HgMGDBA4H6rVq3Ctm3bEB4ezrPiv2HDhtizZ4/ACXGEcFOmTEHbtm2xc+dOGBoa1snPg6SkJLi5uaFJkyb0WIyIiMDKlSuRmJgIJycnBbeQIOoHsqKJIAiCkKnu3bsjKCiIL/Dz999/Y82aNbh9+zaj40hb56mu8/T0xK1btxAeHo4ffvgBAHD37l0EBgaiW7du2Lt3Lw4ePIjff/8dSUlJfPsLm6HINO0dIP2qqvnz50NTU5Ok5CEYETVDuGnTpjh48CAGDRrEs/306dNwd3fHv//+K69m1nukHwguYWOBjAP50tbWxv379wWek82bN8fGjRvh7u7Os/3AgQPw8fER+NlMSEZYP5A++P7U17EQERGBy5cvC62vJC7tHSD9CnJHR0e4ublhzZo1PNsXLFiAxMREJCcnC9337t27mDBhAoYOHYrVq1ejYcOGaNiwIVJTU0mgqZa0tLSQkpICS0tLRTdFYj179oSlpSW2b99OZ/coLy/HtGnT8Pz5c1y9elXBLSSI+oGsaCIIgiBkytfXF35+fsjKykLXrl0BALdv38aWLVuwZs0apKWl0c8Vlt+ZjTpPdV10dDT8/f0xfvx4lJeXA6j6oTZ58mSsX78eANCuXTvs2LFD4P4pKSki09798ccfmDt3rtC0d4Bkq6pqpuTZtm0bzp8/T1LyEAAkr+fSqFEjmJmZ8W03NzcXmEqTEI30A8ElyVgg40C+RM0T/fbtG/09qbpOnTrR3x0IdgjrB9IH35/6Ohakra8ESL+C/PHjxzh8+DDf9ilTpiAyMlLkvj/88APu3buH2bNno3Pnzti3b1+dXImjDPr164fU1NQ6HWhKSkriCTIBVb+l582bJ/A8JQhCMmRFE0EQBCFT1VPeCcLhcEBRlMgUdtLWefL19YWlpSV8fX15tm/evBlZWVlif6gok6KiIjx//hwAYGFhAU1NTUb7RUZG4tq1a2LT3pWWlgpMewdItqqqb9++jNpH8qR/P9io5xIaGoqMjAzs3r2brlH133//YerUqbCyssLSpUtl+h7qA9IPBJe0Y4GMA/Zxf6ILuil6/fp1/PDDDwLr8/n4+KBhw4Z8EzcCAwNRWlqKLVu2yKbB9ZQk/UD6oH76HscCG/WVpNW6dWtERERgzJgxPNsPHz6MwMBA5OTkMDrOwYMH8euvvyIvLw8PHjwgK5pq6ePHj5g8eTKcnZ1ha2tbJ+sdGxoaYu/evXB1deXZnpCQgEmTJuH9+/cKahlB1C8k0EQQBEHI1MuXLxk/V1i9oCZNmuD+/fv0ShyuJ0+eoGPHjigtLRV5XGNjY5w4cYIvn3hycjKGDRuG169fM25jXSVt2jugKsjl7++P2NhYgauqNDQ0cP/+fQD/X+OBIGry9vbG0aNHERoaylfPZcSIEYzquYwcORIXLlxAo0aN4ODgAABITU1FWVkZ+vXrx/Pco0ePsv8m6gHSDwSXtGOBjAP27Ny5E+vXr0dmZiYAwMrKCr/++iumTZvGaH8fHx/ExsaidevW9Cryf/75Bzk5OZg0aRLPzUGyilg4afqB9EH98j2PhUmTJuHatWsC6yv17NkTMTExMm9DaGgo1q9fjwULFtB1Xm/cuIG1a9ciICCgVum4X79+jXv37qF///58K6pev36Nli1bip0g+b2Kj4/HxIkT6QwW1dWVese+vr44duwYfv/9d56xFBQUhNGjR9epiacEocxIoIkgCIKQi0ePHiEnJ4cnrRqHw8HQoUPF7ittnafGjRsjPT2db7l/VlYWbG1t8fXrV+ZvpI7S1NTEyZMn+WYlXr58GUOHDsWXL1/w/PlzdOzYUeCPiOokXVVFEAA79Vy8vLwYv97u3btr3cbvAekHgkvasUDGATuCg4MREREBHx8fnoDf5s2b4e/vj9DQULHHIKuIpSdtP5A+qD++97EgbX0lNlAUhcjISISHh+Pt27cAgJYtWyIoKAi+vr6spcITVfuOAMzMzPDTTz9hyZIlMDQ0VHRzJFJWVoagoCBERUXREyYbNmwIb29vrFmzRuAqYYIgao8EmgiCIAiZev78OUaOHIkHDx7QafKA/089wWQG1KFDhzBv3jz4+PgIrPPUvn17+rmC6jzZ2tpi1qxZmDNnDs/2TZs2YevWrXj06JHE76+ukCTtHUHIgoGBAa5cucJz3gJVefh79eqFvLw8BbXs+0L6geAiY0E5NG/eHBs3boS7uzvP9gMHDsDHx0foamOCXaQfCC4yFqoUFxdLVF+JbdxUfVpaWqwfW0tLC6mpqSTQJISWlhbu37+PNm3aKLopUispKeEZz+rq6gpuEUHULw3EP4UgCIIgJOfn5wdzc3NcuHAB5ubm+Oeff1BQUIC5c+fi999/Z3QM7g+8efPmCXxMXJ2ngIAAzJkzB3l5eXBxcQEAXLhwAeHh4d/NMvno6Gj4+/tj/PjxAtPeAUC7du2wY8cORTaT+A7MmTMHy5cv56vnsnLlSr5gsDgfPnzAkydPAADW1tYwMDBgvb31FekHgoutsUDGgXS+ffsmsCB5p06d6M/t2nj16hWAqhonBHNs9gPpg7qNjIUqGhoaAifyyZssAkwEM6NGjcKlS5fqRaBJXV0ddnZ2im4GQdRbZEUTQRAEIVP6+vq4ePEi7O3t0bRpU9y5cwfW1ta4ePEi5s6di5SUFLHHYKPO09atW7Fy5Uo67YKZmRmWLVuGSZMmMT52fUDS3hGKxkY9l8+fP2P27Nk4ePAgHVxWVVXFuHHjsGXLFjRt2lS2b6IeIP1AcEk7Fsg4YIePjw8aNmzIV6slMDAQpaWl2LJli9hjlJeXIyQkBBs3bkRRURGAqtS5Pj4+WLp0KV8Bd4KftP1A+qD+IGNB8d6/f4/AwEBcuHABHz58QM3bl2zVBiIrmkRbuXIlIiMjMWTIENjZ2fGNXV9fXwW1jLmvX79i06ZNuHTpEj58+IDKykqex5OTkxXUMoKoX8iKJoIgCEKmKioq6Blo+vr6ePv2LaytrWFqakrPfBaHGzySps6Tt7c3vL29kZeXhyZNmny3ARZNTU2lmJVIfL90dHQwevRonm21neE7ffp0pKSk4OTJkzx1E/z8/DBz5kwcPHiQtfbWV6QfCC5pxwIZB+zZuXMnEhMT6TTB//zzD3JycjBp0iQEBATQz6t545vLx8cHR48eRVhYGE9fLFu2DPn5+di6davs30Q9IE0/kD6oX8hYUKyff/4ZOTk5WLJkCVq0aMFaTSaidnbs2AFNTU1cuXIFV65c4XmMw+HUiUDT1KlTkZiYiP/9739wdnYmY4kgZISsaCIIgiBkqmfPnpg7dy5GjBgBDw8PFBYWYvHixdi2bRvu3buH9PR0scdgo84TQRD1h4aGBhISEtCjRw+e7deuXcPAgQNRXFysoJZ9X0g/EAAZB2zp27cvo+dxOBxcvHhR4GNNmzbFwYMHMWjQIJ7tp0+fhru7O/7991+p21nfSdsPpA/qDzIWFE9LSwvXrl1Dx44dZfo62trauH//PlnRVI81bdoUp0+fRvfu3RXdFIKo18iKJoIgCEKmFi9eTN9kCg0NxU8//YSePXtCT08Phw4dYnQMNuo8xcXF4fDhw3wrogCyVJ4gFEGaei56enoC03E1bdoUurq6rLXxe0D6geCSdCyQccCOS5cuSX2MRo0awczMjG+7ubk51NTUpD7+90DafiB9UH+QsaB4rVu35kuXJwtk/j2/gIAALF++HBoaGjyr92ricDgIDw+XY8skY2xsTOp8EYQcqCi6AQRBEET95ubmhlGjRgEALC0tkZGRgY8fP+LDhw9wcXFhdIxbt24hNDQU+vr6UFFRgaqqKnr06IHVq1czWqq/ceNGeHl5wdDQECkpKXB2doaenh6eP3/ON8uQIAjZ+vz5MyZOnAhjY2P07t0bvXv3hrGxMSZMmMB4du/ixYsREBCA3Nxceltubi6CgoKwZMkSWTW9XiH9QHBJOxbIOGDfq1ev8OrVq1rvN2fOHCxfvhz//fcfve2///7DypUrMWfOHDab+F2QpB9IH9RPZCwoRmRkJBYsWIDs7GypjpOVlYWEhASUlpYC4A8sPXr0SGid3+9VSkoKvn37Rv9d1J+6IDw8HPPnz69V7WeCIGqPpM4jCIIglJ6uri6Sk5Nhbm6ONm3aYMeOHejbty+ePXsGOzs7lJSUiNy/Xbt2WLp0Kdzd3XmKvQYHB6OgoACbN2+W0zshCGLcuHFISUnBpk2b+Oq5dOzYUWg9F0dHR5586pmZmfjvv/9gYmICAMjJyUGjRo1gZWVFVikyQPqB4JJkLJBxwL7y8nKEhIRg48aNKCoqAlBVV9HHxwdLly7lK77OxZ3Mw3X+/Hk0atQIDg4OAIDU1FSUlZWhX79+OHr0qGzfRD0gST+QPqifyFhQDF1dXZ7Pl+LiYpSXl0NdXZ3v/7ygoEDksfLz8zFu3DhcvHgRHA4HmZmZsLCwwJQpU6Crq1snVuIQ7MjLy8PYsWNx9epVicYSQRDMkNR5BEEQhNKztbVFamoqzM3N0aVLF4SFhUFNTQ3btm1jlEs7JycH3bp1AwA0adIEX758AQBMnDgRXbt2JYEmgpCjkydP8tVzcXNzw/bt2zFw4ECh+40YMUIOrft+kH4guCQZC2QcsM/HxwdHjx5FWFgYT8Bv2bJlyM/Px9atWwXuVzNt4ejRo3n+3bp1a9k0uJ6SpB9IH9RPZCwoRmRkJGvH8vf3R4MGDZCTk4P27dvT28eNG4eAgAASaPqOuLu7482bN1i1ahUMDQ15gpkEQbCHrGgiCIIglF5CQgKKi4sxatQoZGVl4aeffsLTp0/pOk/iUvBZWFjgyJEjcHR0ROfOnTF9+nTMnDkTiYmJGD9+PJnBRBByZGJiglOnTsHOzo5ne1paGgYPHozXr18rqGXfF9IPBBcZC8qhadOmOHjwIF9K39OnT8Pd3Z1xSktCOqQfCC4yFuqONWvWYNasWdDR0eHZbmRkhISEBDg4OPBktXj+/Dns7e3plWpE/aeuro5bt27RqwsJgpANUqOJIAiCUHrS1nlycXHBiRMnAABeXl7w9/fHgAEDMG7cOIwcOVKmbScIghcb9VxevXrFc/P7zp07+PXXX7Ft2zbW21tfsV1Xp6ioCJ8/f+b5Q9QN0o4Fcj6yo1GjRjAzM+Pbbm5uDjU1NUbHKC0t5Ukn/PLlS0RGRiIxMZGtZtZ7bPQDUT9IOxbI+Sg/q1atEjhxsLi4GOrq6nzbCwoK0KhRI3k0jVAS7dq1o2t0EQQhO2RFE0EQBFHvVVZWorKyEg0aVGWMPXjwIG7evAkrKyvMnDmT3DggCBlju55Lz549MWPGDEycOBG5ublo27YtbG1tkZmZCR8fHwQHB8vsvdRlbPfDixcvMGfOHFy+fBlfv36lt1MUBQ6Hg4qKCvbfBMEKNscCOR/ZERoaioyMDOzevZu+Afrff/9h6tSpsLKywtKlS8Uew9XVFaNGjcKsWbPw6dMnWFtbQ01NDR8/fkRERAS8vb1l/TbqPGn7IT8/H8HBwbh06RI+fPiAyspKnsfJKvq6Q9qxQM5H+am+Wqm6wYMHo1OnTli+fDm0tLSQlpYGU1NTjB8/HpWVlYiLi1NQiwl5S0xMREhICFauXAk7Ozu+Gk3a2toKahlB1C+kRhNBEARR76moqEBF5f8X8Y4fPx7jx49XYIsI4vvCdj2X9PR0ODs7AwAOHz4MOzs73LhxA4mJiZg1axa5sS0E2/0wYcIEUBSFXbt2kXz3dQybY4Gcj5LjrtbmOn/+PFq1akWn9klNTUVZWRn69evH6HjJyclYv349ACAuLg5GRkZISUnBkSNHEBwcTG5sC8FmP0ycOBFZWVmYOnUquS7WQWyOBXI+Kl5YWBj69euHpKQklJWVYd68eXj48CEKCgpw48YNRTePkCNuzcma5y6ZHEUQ7CKBJoIgCOK7UFhYiJ07d+Lx48cAABsbG3h5eaFZs2YKbhlB1H9MZuLXxrdv3+jZxefPn8ewYcMAVKXFePfuHauvVZ+w3Q+pqam4d+8erK2tWT0uIXtsjgVyPkquadOmPP8ePXo0z79bt25dq+OVlJRAS0sLQNXs7VGjRkFFRQVdu3bFy5cvpWtsPcZmP1y7dg3Xr18ndUDqKDbHAjkfFc/W1hZPnz7F5s2boaWlhaKiIowaNQqzZ89GixYtFN08Qo4uXbqk6CYQxHeBBJoIgiCIeu/q1asYNmwYtLW10blzZwDAxo0bERoaivj4ePTq1UvBLSSI78erV6/A4XDQqlUrAFX1XPbv3w8bGxvMmDGD0TE6dOiAqKgoDBkyBOfOncPy5csBAG/fvoWenp7M2l5fFRUV8aV3YpJC5IcffsCrV69IoKmOk/acJOej5Hbv3s3q8SwtLfH3339j5MiRSEhIgL+/PwDgw4cPJC2QCGz2A6kDUrexORbI+agcmjZtikWLFim6GYSC9e7dW9FNIIjvAqnRRBAEQdR7dnZ2+PHHH7F161aoqqoCACoqKvDLL7/g5s2bePDggYJbSBDfDzbquVy+fBkjR47E58+fMXnyZOzatQsA8NtvvyEjIwNHjx6V9duo89ior/Ts2TPMmjULEyZMgK2tLV++e3t7e9bbTbBP2nOSnI/sKC0tBUVRdOH6ly9f4tixY7CxsYGrqyujY8TFxcHDwwMVFRXo168fEhMTAQCrV6/G1atXcebMGZm1n6hy9+5dLFiwAMHBwQKviyTAUHdIe06S81F+hNVoAoBPnz7hzp07AmumTZo0SV5NJBTs6tWrIh8nE08Jgh0k0EQQBEHUe02aNMH9+/f5Zt0/efIEHTt2JDNPCUKOdHV1cfv2bVhbW2Pjxo04dOgQTz2X58+fMzpORUUFPn/+DF1dXXpbdnY21NXVYWBgIKvm1xvdu3cHRVHw8/MTWEeEyczP27dvw8PDA9nZ2fQ2DodD8t3XMWyck+R8lJ6rqytGjRqFWbNm4dOnT7C2toaamho+fvyIiIgIxvVccnNz8e7dOzg4OND1Ke/cuQNtbW20a9dOlm+hXsjPz0dwcDAuXbok8MZ0QUGByP0zMzPh4eGB5ORknu3kulj3sHFOkvNRPgYPHoydO3fypcOLj4+Hp6cnioqKoK2tzfNdh8PhiD2fifqjer1mrurjgVybCYIdJHUeQRAEUe85OTnh8ePHfIGmx48fkxz6BCFnbNVzUVVV5bmpDQBmZmastbO+Y6O+0pQpU+Do6IgDBw6Qovd1mLTn5NKlSzFlyhSYmprybCfnY+0kJydj/fr1AKpWQhgZGSElJQVHjhxBcHAw40CTkZERjIyMeLY5Ozuz3t76auLEicjKysLUqVMluq55enqiYcOG2L9/P7ku1nHSnpO7d+/G+PHjyfnIkq9fv6KsrIxnG3eF4OnTpwXuM3fuXEyZMgWrVq2iV6YR36fCwkKef3/79g0pKSlYsmQJVq5cqaBWEUT9QwJNBEEQRL3n6+sLPz8/ZGVloWvXrgCqZuJv2bIFa9asQVpaGv1ckuqJIGSLrXoucXFxOHz4MHJycvhuPNScSU7wY6O+0suXL3HixAlYWlqy2DJC3qQ9J48fP46VK1eid+/emDp1KkaPHk0HrgjmSkpKoKWlBQBITEzEqFGjoKKigq5du+Lly5eMj5OUlCT02kjSGIp37do1XL9+XeKJSOnp6UhJSSG16+oBac/JBQsWwM/PD2PGjMHUqVPRrVs3WTe53ikpKcG8efNw+PBh5Ofn8z0ubhXKmzdv4OvrS4JMBJo2bcq3bcCAAVBTU0NAQADu3bungFYRRP3Dv3aQIAiCIOoZd3d3vHr1CvPmzUOvXr3Qq1cvzJs3Dy9fvoS7uzscHR3RsWNHODo6KrqpBFHvrV27FtHR0ejTpw/c3d3pm3knTpxgPMt348aN8PLygqGhIVJSUuDs7Aw9PT08f/4cgwYNkmXz640dO3Zg7dq1iImJwb1795CWlsbzhwkXFxekpqbKuKWErEl7Tt6/fx93795Fhw4d4OfnByMjI3h7e+Pu3buybnq9Ymlpib///huvXr1CQkICXQPmw4cPjOv6HDx4EN26dcPjx49x7NgxfPv2DQ8fPsTFixcF3mQj+LVr106qlMqdO3fGq1evWGwRoSjSnpNv3rxBTEwMPn78iD59+qBdu3ZYu3YtcnNzZd30eiMoKAgXL17E1q1b0ahRI+zYsQMhISFo2bIlYmNjxe7v5uaGpKQkObSUqKsMDQ3x5MkTRTeDIOoNUqOJIAiCqPdqMxO4ZuofgiDYJ209l3bt2mHp0qVwd3fnKQAdHByMgoICbN68WZbNrxfYqK+0bds2rFixAlOmTIGdnR1f0XtuCjZC+bFVY+nbt2+Ij4/H7t27kZCQgHbt2mHq1Kn4+eefSaBDjLi4OHh4eKCiogL9+vVDYmIiAGD16tW4evUqzpw5I/YY9vb2mDlzJmbPnk1fG83NzTFz5ky0aNECISEhsn4bdd7du3exYMECBAcHw9bWlu+6Ji7A8Ndff2HZsmUICgoSeF0kK+frDjbOSa7379/jzz//RExMDDIyMjBw4EBMnToVQ4cOFVg7hqhiYmKC2NhY9OnTB9ra2khOToalpSX27t2LAwcOCEyZd+LECfrveXl5CA0NhZeXF/me8p2rOYmKoii8e/cOa9asQXl5Oa5fv66glhFE/UICTQRBEMR349GjR3ypZDgcDoYOHarAVhEEUVvq6up4/PgxTE1NYWBggHPnzsHBwQGZmZno2rWrwPQqBC8bGxu0b98e8+bNE1hHhEnQXdTNMVL0vu4QVmNJEmVlZTh27Bh27dqFixcvolu3bnj79i3ev3+P7du3Y9y4cSy0uP7Kzc3Fu3fv4ODgQJ9fd+7cgba2Ntq1ayd2fw0NDTx8+BBmZmbQ09PD5cuXYWdnh8ePH8PFxaVWdfC+V5mZmfDw8OBLwco0CC+s4HxtgviE8pD2nKzun3/+wa5duxATE4MWLVqgsLAQurq62L17N/r06SOD1td9mpqaePToEUxMTNCqVSscPXoUzs7OePHiBezs7FBUVMS3D9PAHTkfvy8qKir0tbi6rl27YteuXbU+nwmCEIzUaCIIgiDqvefPn2PkyJF48OABzxdM7o1V8iODIORL2vpKRkZGKCgogKmpKUxMTHD79m04ODjgxYsXfD8gCcHYqK9UWVnJYosIRWGjxtK9e/ewe/duHDhwAI0aNcKkSZOwZcsWenxt2rQJvr6+JNAkhpGREYyMjHi2MU0pCgC6urr48uULAMDY2Bjp6emws7PDp0+fUFJSwmpb6ytPT080bNgQ+/fvFxiEF+fFixcyahkhb7t378b48eOlOiffv3+PvXv3Yvfu3Xj+/DlGjBiBkydPon///iguLkZoaCgmT55cq+wL3xMLCwu8ePECJiYmaNeuHQ4fPgxnZ2fEx8dDR0dH4D7kuwkhSM1rs4qKCpo3b47GjRsrqEUEUT+RFU0EQRBEvTd06FCoqqpix44dMDc3xz///IOCggLMnTsXv//+O3r27KnoJhLEd2Pjxo1YtGgRfv75Z2zbtg1eXl549uwZ7t69i9mzZ2PlypVijzFt2jS0bt0aS5cuxZYtWxAUFITu3bsjKSkJo0aNws6dO+XwTuq2oUOH4ueff8bo0aNl/lp2dnY4ffo0WrduLfPXIiSTkpJCB4rKy8sxfvx4TJkyBT/88IPYfe3s7JCRkQFXV1dMnz6d/syt7uPHjzAwMCA3AMVISkoSGoQ/evSo2P09PDzQuXNnBAQEYPny5di0aROGDx+Oc+fOwcnJidExvnfq6upISUmBtbW1TF9nyJAh2LFjB1q0aCHT1yEkZ2hoiNLSUowZMwZTp05Ft27darX/0KFDkZCQgLZt22LatGmYNGkSmjVrxvOcDx8+wMjIiFwbhVi/fj1UVVXh6+uL8+fPY+jQoaAoCt++fUNERAT8/PwU3USijoiNjcW4ceP4JtKUlZXh4MGDmDRpkoJaRhD1Cwk0EQRBEPWevr4+Ll68CHt7ezRt2hR37tyBtbU1Ll68iLlz5yIlJUXRTSSI7wYb9ZUqKytRWVmJBg2qFucfPHgQN2/ehJWVFWbOnAk1NTVZv406T571lar3M6HcJKmxtHz5ckyZMgXGxsZybm39wr3R5ebmhsTERLi6uuLp06d4//49Ro4cid27d4s9RkFBAb5+/YqWLVuisrISYWFh9LVx8eLFPDW4CMF69eqF4OBg9O/fX6avQ66Lyq+8vBzx8fHYs2cPzpw5AwsLC3h5eWHy5Ml8q5wEmTp1KqZNm4Yff/xR6HMoikJOTg6pEctQdnY2XaeJSb0zX19fWFpawtfXl2f75s2bkZWVhcjISBm1lFA2qqqqePfuHV/dyfz8fBgYGJAMJwTBEhJoIgiCIOo9XV1dJCcnw9zcHG3atMGOHTvQt29fPHv2DHZ2diSdDEHIEamvpBzkWV+J3FCtO9iosVRRUYEHDx7A1NSUBDZqwd7eHjNnzsTs2bPpc8bc3BwzZ85EixYtEBISougmfhf++usvLFu2DEFBQQKD8ExubjNBrot1y/v37/Hnn38iJiYGGRkZGDhwIKZOnYqhQ4cyrgkEAJ8+fRKa8o0Qj1vrjCljY2OcOHECnTp14tmenJyMYcOG4fXr12w3kVBSKioqeP/+PZo3b86zPTU1FX379kVBQYGCWkYQ9Qup0UQQBEHUe7a2tvQNmy5duiAsLAxqamrYtm0b+YFPEHImaX2ltLQ02NraQkVFBWlpaSJfg60bgfUZSdNDVCdNjaVff/0VdnZ2mDp1KioqKtC7d2/cvHkT6urqOHnyJClyz9CzZ88wZMgQAICamhqKi4vB4XDg7+8PFxcXoYGmz58/Q1tbm/67KNznEcJxx/iUKVPobdz6nmwH4Ym6w9DQED169MDTp0/x9OlTPHjwAJMnT4auri52794t8Dq3du1amJmZ0WNq7NixOHLkCIyMjHD69Gk4ODjI+V3UfY0aNUJqairat2/P6Pn5+fkCV+Nqa2vj48ePbDePUEKOjo7gcDjgcDjo168fnQ0BqJoY8+LFCwwcOFCBLSSI+oUEmgiCIIh6b/HixSguLgYAhIaG4qeffkLPnj2hp6eHQ4cOKbh1BPF9cXFxwYkTJ+Do6AgvLy/4+/sjLi6Orq8kTMeOHZGbmwsDAwN07NiRvvFXE7kRyC5SX6n+q15jaefOnQJrLLm7uwuthREXF4cJEyYAAOLj4/HixQtkZGRg7969WLRoEW7cuCHz91Af6Orq4suXLwCqZuGnp6fDzs4Onz59ErnyWldXl04HpKOjI3C2PwmSMFezYDzxfXv//j327t2L3bt34/nz5xgxYgROnjyJ/v37o7i4GKGhoZg8eTJevnzJt29UVBT27dsHADh37hzOnTuHM2fO4PDhwwgKCkJiYqK8306dERAQIHB7RUUF1qxZAz09PQBARESEyONYWlri7NmzmDNnDs92bipEov4bMWIEAOD+/ftwc3ODpqYm/ZiamhrMzMzkUq+UIL4XJNBEEARB1Htubm703y0tLZGRkYGCggLo6urWKv0CQRDS27ZtG72aZvbs2dDT08PNmzcxbNgwzJw5U+h+L168oNNdkBuB8pOdnY1v374puhmEDI0dO1ZsjSV9fX2hq+A+fvxI1ys5ffo0xowZg7Zt22LKlCnYsGGDTNpcH/Xq1Qvnzp2DnZ0dxowZAz8/P1y8eBHnzp1Dv379hO538eJFNGvWDABw6dIleTW33mJaK2fIkCHYsWMHWrRoIeMWEYoydOhQJCQkoG3btpg+fTomTZpEn2sAoKGhgblz52LdunUC98/NzaUnaZw8eRJjx46Fq6srzMzM0KVLF7m8h7oqMjISDg4OfGkGKYrC48ePoaGhweg3XEBAAObMmYO8vDy4uLgAAC5cuIDw8HBSn+k7sXTpUgCgVxc2btxY5PMPHDiAYcOGQUNDQx7NI4h6h9RoIgiCIAiCIAhCIDbqiJBaJHVLbWssmZqaYvv27ejXrx/Mzc2xdetWDBkyBA8fPkSPHj1QWFgoh1bXfQUFBfj69StatmyJyspKhIWF4ebNm7CyssLixYtJvSslI+11jVwXld/UqVMxbdo0/Pjjj0KfQ1EUcnJyBAYoW7Zsibi4OHTr1g3W1tZYsWIFxowZgydPnuCHH34Qm+rye7ZmzRps27YNO3bsoANEANCwYUOkpqbCxsaG8bG2bt2KlStX4u3btwCqAg7Lli3DpEmTWG83Ufdpa2vj/v375NpMEBIiK5oIgiAIgiAImWKjvtKJEycYv96wYcNq1T5CtqKjo2FoaKjoZhBCSFtjycvLC2PHjkWLFi3A4XDQv39/AMA///yDdu3ayeEd1A/VV0qoqKhgwYIFjPYTd02tjtSvUx6//fYbT58Tymfnzp182z59+sSzyobD4QhdBTdq1Ch4eHjAysoK+fn5GDRoEAAgJSWFrn9HCLZgwQL069cPEyZMwNChQ7F69Wo0bNhQomN5e3vD29sbeXl5aNKkCU/qNIKoiazFIAjpkBVNBEEQBEEQhEypqKjQ9ZVUVFQkqq+koqLC99zqx6ieQoXUIWGPJLPu379/j+joaAQHB8uwZQRbWrVqhb///hudO3fG33//jdmzZ+PSpUvYu3cvLl68yKjGUlxcHF69eoUxY8agVatWAICYmBjo6Ohg+PDhsn4Lddbnz5+hra1N/10U7vNqqn5NFZdKilwb2VPba2NhYSHi4+PJKoo6ZO3atXS6LaAqzeiRI0dgZGSE06dPw8HBQeT+3759w4YNG/Dq1Sv8/PPPcHR0BACsX78eWlpamDZtmszfQ11XVFSE2bNn4/79+9i3bx+cnJxw//79Wq1oIojaIKtNCUI6JNBEEARBEARByNTLly9hYmICDocjsGB2dUzqY5w/fx7z58/HqlWr6JQ2t27dwuLFi7Fq1SoMGDCAlXYTkv3gTk1NhZOTE7mpXUc0btwYWVlZaNWqFWbMmAF1dXVERkbixYsXcHBwYC29k52dHU6fPk3XLCEAVVVVvHv3jicIXxM3gCTsfKp+TU1JSUFgYCCCgoJ4ro3h4eEICwuji6IT0qvttZFcF+sec3Nz7Nu3D926dcO5c+cwduxYHDp0CIcPH0ZOTg4SExNZeR1S70u8gwcP4tdff0VeXh4ePHhQq0BTXFwc3WdlZWU8jyUnJ7PdVKKOI4EmgpAOSZ1HEARBEARByFT14BHTQuui/Prrr4iKikKPHj3obW5ublBXV8eMGTPw+PFjqV+DEE5cqq4nT57IqSUEGwwNDfHo0SO0aNECZ8+exdatWwEAJSUlUFVVZe11srOz8e3bN9aOVx9cvHiRTp926dIliY5R/Zo6ZswYbNy4EYMHD6a32dvbo3Xr1liyZAkJNMmQuIDsly9f5NQSgi25ubl0YPzkyZMYO3YsXF1dYWZmhi5durD2OlevXkVpaSlrx6uPxo8fjx49euDevXsCv0e+fv0aLVu25Fv9vnHjRixatAg///wzjh8/Di8vLzx79gx3797F7Nmz5dV8giCI7wYJNBEEQRAEQRAyxXZ9pWfPnvHUSOBq2rQpsrOza9EyQhxB9ZU6duwoMv0hkxRehPIgNZYUp3fv3gL/LqkHDx7A3Nycb7u5uTkePXok9fEJ4XR0dERe98h1se7R1dXFq1ev0Lp1a5w9exYrVqwAUNWXZGWa/LVq1YpOzVqTjY0N7t+/z7cK5Y8//sC2bdvg7u6OPXv2YN68ebCwsEBwcDAKCgrk0WyCIIjvCgk0EQRBEARBEDJVcxa9tPWVfvjhBwQEBGDv3r10EOT9+/cICgqCs7MzO43+TtWsr+Th4cH3nGbNmiEsLAz9+vUTeIyHDx9i6NChMm0nwZ5ly5bB1taWrrHUqFEjAFVp3RYsWKDg1tVv4lYHVmdvby/2Oe3bt8fq1auxY8cOqKmpAQDKysqwevVqtG/fXuJ2Evx+++03ejUaUJVuadGiRUJXumRmZmLmzJnyah7BglGjRsHDwwNWVlbIz8/HoEGDAFSlqLS0tFRw64jqhFUEycnJQbdu3QAATZo0oVcWTpw4EV27dsXmzZvl1kaibjA1NUXDhg0V3QyCqLNIoIkgCIIgCIKQqcrKSvrv4uorMbFr1y6MHDkSJiYmdFqbV69ewcrKCn///Tfr7f+e5ObmIiQkhA40CdKpUye8fftWaBrET58+Cb3pQyin//3vf3zbJk+ezPNvUmOJfdVXB4pb7cIkCB8VFYWhQ4eiVatWdGAqLS0NHA4H8fHxrLT5e1VYWIj4+HhMmjQJALBw4UKex52cnAAIX5mmo6NDrot1zPr162FmZoZXr14hLCwMmpqaAIB3797hl19+UXDrCCaMjIxQUFAAU1NTmJiY4Pbt23BwcMCLFy/I+UgIlJ6erugmEESdRgJNBEEQBEEQhNywUV/J0tISaWlpOHfuHDIyMgBUzeTv378/SU0kBhv1lWbNmoXi4mKhj5uYmGD37t21bhuh3EiNJfa9ePGC/ntKSgoCAwMRFBTEE4QPDw9HWFgYo+M5Ozvj+fPn2LdvH31tHDduHDw8PKChocH+G/iO5OTkwMvLiw401eTh4SGyzo6RkRGWLl0qq+YRMtCwYUMEBgbybff39+f595AhQ7Bjxw60aNFCXk0jGHJxccGJEyfg6OgILy8v+Pv7Iy4uDklJSRg1apSim0fImK6uLuPfBSSVIkGwg0ORMD5BEARBEAQhJ02aNMHdu3dha2vLsz0tLQ1dunRhtSA2WYHBT0VFhVF9JVJ/gqhJS0sLqampfDUw5LV/fefs7Ixly5Zh8ODBPNtPnz6NJUuW4N69e6y9Frkxzu/z588iH09LS0Pv3r3JtZHgQ66Niifs/7CyshKVlZVo0KBqjv3Bgwdx8+ZNWFlZYebMmXSKUaJ+iomJof+en5+PFStWwM3NjWcyR0JCApYsWcIXQCYIQjIk0EQQBEEQBEHITa9evdC4cWO++kqTJk3C169fceXKFdZei9y84aevr8+ovpKwm6kBAQGMXysiIkKiNhLKSdrzaf/+/Rg+fDhZWSNEkyZNkJyczFdL6fHjx3BycmI1CE+ujfy4QXhhahOEv3DhAi5cuIAPHz7wpI4FqlK/EvWLtOfT6tWr4e3tDR0dHXYb9h3R1tbG/fv3yTWNEGr06NHo27cv5syZw7N98+bNOH/+PEm9TRAsIanzCIIgCIIgCLkh9ZUUS9r6SikpKTz/Tk5ORnl5OaytrQEAT58+haqqKjp16sReo4k66f3794iOjqbrfXl4eCi4Rcqtffv2WL16NXbs2EHPsi8rK8Pq1av5gk8E+7S0tLBo0SJ06dJF4OOZmZmYOXOm2OOEhIQgNDQUnTt3RosWLUg6V4KPuHpfRO2J+t5SWFiInTt30qmZbWxs4OXlhWbNmsmreYQSSEhIwNq1a/m2Dxw4EAsWLFBAiwiifiKBJoIgCIIgCEJuSH0lxZK2vtKlS5fov0dEREBLSwsxMTHQ1dUFUHVDx8vLCz179mSv0USdlJubi5CQEDrQRIgWFRWFoUOHolWrVrC3twdQla6Nw+EgPj5ewa2r/5ycnAAAvXv3Fvi4jo6OyJvZXFFRUdizZw8mTpzIavuI+kNcvS+CX1ZWFp49e4ZevXqhSZMm9ApDrkePHqFly5Z8+129ehXDhg2DtrY2OnfuDADYuHEjQkNDER8fj169esntPRCKpaenh+PHj2Pu3Lk8248fPw49PT0FtYog6h8SaCIIgiAIgiDkisPhwNXVFa6urkKfQ+orycbIkSNFPq6rq4vJkyczOlZ4eDgSExPpIBN3/xUrVsDV1ZXvxzxRv6SlpYl8/MmTJ3JqSf3g7OyM58+fY9++fXQQfty4cfDw8CDpBuXAw8NDZHpCIyMjLF26VOxxysrK0K1bNzabRtQx4up9ffnyRU4tqfvy8/Mxbtw4XLx4ERwOB5mZmbCwsMDUqVOhq6uL8PBwABD6XXH27NkYO3Ystm7dClVVVQBARUUFfvnlF8yePRsPHjyQ23shFCskJATTpk3D5cuX6ZWr//zzD86ePYvt27cruHUEUX+QQBNBEARBEAShdLKzs/Ht2zdFN6NeYbu+0ufPn5GXl8e3PS8vj9xIq4eio6PpumoA0LFjR3A4HIGrPLjbySrF2tHQ0MCMGTNEPmfIkCHYsWMHWrRoIadWfR+mT58u8nFDQ0NGgaZp06Zh//79WLJkCVtNI+oYHR0dRvW+CPH8/f3RoEED5OTk8KQQHTduHAICAuhAkzBZWVmIi4ujg0wAoKqqioCAAMTGxsqs3YTy+fnnn9G+fXts3LgRR48eBVCVUeH69etCU6YSBFF7JNBEEARBEARBEN8BtusrjRw5El5eXggPD4ezszOAqtmhQUFBGDVqFLuNJ+ROXI2lZs2aISwsDP369RO4/8OHDzF06FCZt/N7c/XqVZErbwjpXbhwARcuXMCHDx9QWVnJ89iuXbv4nl89iF9ZWYlt27bh/PnzsLe3R8OGDXmeyySIT9Qtv/32G0+9H7bqfRFAYmIiEhIS0KpVK57tVlZWePnypdj9nZyc8PjxY/p7Dtfjx4/h4ODAalsJ5delSxfs27dP0c0giHqNBJoIgiAIgiCIeqnmCozvHdv1laKiohAYGAgPDw969VmDBg0wdepUrFu3jv03QMiVuBpLnTp1wtu3b2Fqairw8U+fPjGqaUPIX80b48T/CwkJQWhoKDp37owWLVowWnlSM4jfsWNHAEB6ejrPdrKKpX4oLCxEfHw8XWNp4cKFPI+zVe+LAIqLi6Gurs63vaCgAI0aNRK7v6+vL/z8/JCVlYWuXbsCAG7fvo0tW7ZgzZo1PClgubXxiPrj8+fP0NbWpv8uCvd5BEFIh0ORTziCIAiCIAhCyWhpaSE1NRUWFhaM96m5AoMQztjYGImJiejQoQPP9vT0dLi6uuLt27eMj1VcXIxnz54BANq0aUPqydQR4mosZWRkwN3dHRUVFQIfP3bsGIqLizFhwgSBjxcWFuLEiROMa34RzEhybax5Y5wQrkWLFggLC8PEiRMV3RRCSaWmpsLJyUnotXH79u0oLS2Fr6+vwMffv3+PqKgoRqkYv3eDBw9Gp06dsHz5cmhpaSEtLQ2mpqYYP348KisrERcXJ3J/FRUVkY9XT/MqrD+JuktVVRXv3r2DgYEBVFRUBAb7Sf8TBLtIoIkgCIIgCIJQOpLcTBV384f4f1paWoiPj0efPn14tl+6dAnDhg0jNZa+A9ybLuJqLJHzSbmQa6Ns6enp4c6dO2jTpo2im0IoiLiVD2lpaejduzc5n+QgPT0d/fr1g5OTEy5evIhhw4bh4cOHKCgowI0bN8Sep0zS63EJW51L1F1XrlxB9+7d0aBBA1y5ckXkc4WtQCQIonZI6jyCIAiCIAiiThC3AuPJkydyakndR+orEdLUWKpek0YcUpNG9sTdGCeBY+amTZuG/fv3Y8mSJYpuCqEgOjo6ItMccoPwTNS23hfBy9bWFk+fPsXmzZuhpaWFoqIijBo1CrNnz0aLFi3E7s8NHj169Ag5OTkoKyujH+NwOKSOYD1XPXhEAkkEIR8k0EQQBEEQBEEoHUH1lTp27MhoBQYhHqmvREhTY6lmTZrk5GSUl5fTBdefPn0KVVVVdOrUid1GEwKxeWP8e1Q9cFpZWYlt27bh/PnzsLe3R8OGDXmeSwKn9Z+WlhYWLVqELl26CHw8MzMTM2fOFHscSep9EfyaNm2KRYsWSbTv8+fPMXLkSDx48IDn+yO3L8iqtPpN3AS16kiNLoJgBwk0EQRBEARBEApXs76Sh4cH33OkWYFB8FJXV8cff/yBdevWkfpK36lZs2ah+P/au9+Yqsv/j+OvQ+gEAgQsYGwaZIMBHtEY/mknB02Zy5hwp0VBOypZrcESbK0wklUyCsacTUb7mqcblo5ygxsqC3I6nbBAVBazECZjZtIOTFMaDfjd8Cfx50CIBz6Hw/OxuR2v65yz17kBY9f7vK/33bsT7i9dulRff/21w72ffvpp+HFpaal8fX1ls9kUEBAg6f5MIKvVKovF4tzQ0AcffKDAwMBRa846GJ+vxhZO4+LiJN2/tmskCgXzw+rVqyVN3AGxePHiCYvwI5WXl+vw4cPM+3pEvb29amhocNgV9l9z53JychQeHq7a2lqFh4ervr5edrtdubm5+uKLL2YyNlzAZF9QG4lrggHnYUYTAAAADDeVGSLJycmyWCzKz8+f8D1WrVo17iACwMwJCwtTTU2NYmJiRq23tLRo06ZNunHjhkHJ3ENPT4+qq6snPVBNTEzU5s2b9d577znc53cjMHVfffWV+vr6lJ2d7XD/jz/+UHl5uQoKCiZ9H+Z9Pbrq6mq9+uqr+uuvv+Tn5zeq2GsymWS32yd9/ZIlS1RXVyez2Sx/f381NDQoMjJSdXV1ys3NHVdkhnthRhcw++hoAgAAwIxzxnylR+nAAPAvZ85Yun37trq7u8etd3d3MxvICTo7O2W1WictNKWnp6uvr2/C/ZCQkP88FAdwX1ZW1qT7wcHBU/p5Yt7Xo8vNzdW2bdv02Wefydvb+6FfPzAwIF9fX0n3i043btxQZGSkli1bxlzPeYDiETD76GgCAADAjPPw8JjSfCWurgBmXmJi4qj/TzZjqa6ubtL3yszM1NmzZ1VSUqKEhARJUn19vXbv3i2LxSKbzTYzH8JN3L59e9L9y5cva8OGDfxuBAxQW1ur2tpah9e2HTp0aNzzx877stlsMpvNzPuaJh8fH125ckURERHTer3FYlFubq62bt2q9PR09fT0KD8/XxUVFWpsbBx3PSbc27Vr11RWVqbW1lZJUnR0tHJycug6BJyIjiYAAADMuEedr+TMDgxgvnPmjKXy8nLl5eUpPT1d//zzjyTJ09NT27dv1+effz4zH8CNLF68eNLZPw+K8FP1sAfjABzbu3evCgsLFR8fr9DQ0Cn9HDLvy7mSk5P1888/T7vQlJ+fP9wJX1hYqC1btshisSgoKEhHjx51ZlS4uFOnTiklJUVxcXF67rnnJEnnzp1TTEyMqqurtXHjRoMTAu6BjiYAAADMuEedr+TMDgwA/3LWjKW7d+/q2rVrkqSnn35aPj4+Ts/qjvz9/fXhhx9qzZo1Dvd/++037dy5c0odTf91MH78+HGnZAbmg9DQUBUXFysjI8PoKPNKVVXV8OPu7m4VFhbKarVqxYoV47rCUlJSHvr97Xa7AgICKPbNM6tWrVJycrKKiopGrb///vuqqalRU1OTQckA90JHEwAAAGbco85XcmYHBoB/OWvGko+Pj8xmszOjzQurV6+WJG3YsMHh/uLFix1eOepIeXm5Dh8+zME44AT9/f1av3690THmna1bt45bKywsHLc23euWAwMDpxMLc1xra6uOHTs2bn3btm0qKyub/UCAm/IwOgAAAADcX2pqql577bUJ9wMCAvT6669P6b1KSkq0b9++4SLTg9d/8sknKikpeeSswHySmpoqq9WqH374QV1dXerq6tL333+v7du3Ky0tzeh4bi89PV2LFi2acD8kJEQFBQVTei8OxgHn2bFjh44cOWJ0jHlncHBwSv+YW4eH8cQTT6i5uXncenNzs5588snZDwS4KTqaAAAAMKOcPV/JWR0YAJixZLSsrKxJ94ODg6dcaHpwML5nzx5nRAPmnZF/rwwODqqiokI//vijzGbzuGvbmAcJzB1ZWVl644031N7ePvyFjHPnzqmoqEi5ubkGpwPcBzOaAAAAMKOcPV8pMzNTZ8+eVUlJiRISEiRJ9fX12r17tywWi2w2m/M/BODmmLFkvNraWtXW1urWrVvj5tUdOnTI4WvGHozbbDaZzWYOxoFpGPv3ykRMJhPzIGdBdna2li9fruzs7FHrBw4cUFtbG1eeYcqGhoZUVlamkpKS4dmTYWFhysvLU3Z2NjO7ACeh0AQAAIBZU1paqtOnT084X2kq3yq8d++e8vLydOjQIYcdGByQA5hr9u7dq8LCQsXHxys0NHTcodfx48cdvo6DcQDuKiwsTFVVVXr22WdHrTc1NSklJUVdXV0GJcNc09fXp6GhIXl7e+vOnTvq6OhQbW2toqOjlZycbHQ8wG1QaAIAAMCsCQsLU01NjWJiYkatt7S0aNOmTcPfMpwKOjAAuIvQ0FAVFxcrIyPD6CgA4BIWLVqklpYWLV++fNR6W1ubYmNj9ffffxuUDHPNpk2blJaWpjfffFO9vb2KiorSggUL9Oeff6q0tFRvvfWW0REBt+BhdAAAAADMH86cr+Tj4zN8RRRFJgBzWX9///DcCACAtHz5cp08eXLc+okTJxQREWFAIsxVTU1NslgskqTKykoFBwfr+vXr+uabb7R//36D0wHuw9PoAAAAAJg/UlNTZbVaHc5XSktLMzgdABhjx44dOnLkiPbs2WN0FABwCbt27dI777yj7u5uJSUlSbo/y66kpIT5THgo9+7dk6+vrySppqZGaWlp8vDw0Nq1a3X9+nWD0wHug6vzAAAAMGuYrwQA9+3atWv48eDgoGw223CX5oIFC0Y9t7S0dLbjAYDhDh48qE8//XT4auWnnnpKH3/8sTIzMw1OhrnEbDZrx44dSk1NVWxsrE6ePKl169apsbFRL774om7evGl0RMAtUGgCAADArGO+EoD5LjExcUrPM5lMqqurm+E0AOC6uru75eXlpccff9zoKJiDKisrlZ6eroGBAb3wwguqqamRJO3bt09nzpzRiRMnDE4IuAcKTQAAAAAAAAAAt3Tz5k39/vvvWrlypTw8PCRJDQ0N8vPzU1RUlMHpAPdAoQkAAAAAAACAS6msrNSxY8fU2dmp/v7+UXtNTU0GpQIAOOJhdAAAAAAAAAAAeGD//v2yWq0KDg7WxYsXlZCQoKCgILW3t2vz5s1GxwMAjEFHEwAAAAAAAACXERUVpYKCAr3yyivy9fXVpUuXFBERoY8++kh2u10HDhwwOiIAYAQ6mgAAAAAAAAC4jM7OTq1fv16S5OXlpTt37kiSMjIy9O233xoZDQDgAIUmAAAAAAAAAC4jJCREdrtdkrR06VJduHBBktTR0SEuZwIA10OhCQAAAAAAAIDLSEpKUlVVlSTJarXq3Xff1caNG/Xyyy8rNTXV4HQAgLGY0QQAAAAAAADAZQwODmpwcFCenp6SpO+++07nz5/XM888o507d2rhwoUGJwQAjEShCQAAAAAAAAAAANPiaXQAAAAAAAAAABipp6dH//vf/9Ta2ipJio6OltVqVWBgoMHJAABj0dEEAAAAAAAAwGWcOXNGKSkp8vPzU3x8vCSpsbFRvb29qq6u1vPPP29wQgDASBSaAAAAAAAAALiMFStWaN26dTp48KAee+wxSdLAwIDefvttnT9/XleuXDE4IQBgJApNAAAAAAAAAFyGl5eXmpubFRkZOWr96tWriouLU19fn0HJAACOeBgdAAAAAAAAAAAeWL169fBsppFaW1u1cuVKAxIBACbjaXQAAAAAAAAAAHggOztbOTk5amtr09q1ayVJFy5c0JdffqmioiJdvnx5+Llms9momACA/8fVeQAAAAAAAABchofH5JcwmUwmDQ0NyWQyaWBgYJZSAQAmQkcTAAAAAAAAAJfR0dFhdAQAwEOgowkAAAAAAACAy/nll1/U2dmp/v7+4TWTyaSXXnrJwFQAgLHoaAIAAAAAAADgMtrb25WamqorV64MX5Mn3S8ySeK6PABwMZNfeAoAAAAAAAAAsygnJ0fh4eG6deuWvL291dLSojNnzig+Pl6nT582Oh4AYAyuzgMAAAAAAADgMpYsWaK6ujqZzWb5+/uroaFBkZGRqqurU25uri5evGh0RADACHQ0AQAAAAAAAHAZAwMD8vX1lXS/6HTjxg1J0rJly3T16lUjowEAHGBGEwAAAAAAAACXERsbq0uXLik8PFxr1qxRcXGxFi5cqIqKCkVERBgdDwAwBlfnAQAAAAAAAHAZp06d0t27d5WWlqa2tjZt2bJFv/76q4KCgnT06FElJSUZHREAMAKFJgAAAAAAAAAuzW63KyAgQCaTyegoAIAxKDQBAAAAAAAAAABgWjyMDgAAAAAAAAAAAIC5iUITAAAAAAAAAAAApoVCEwAAAAAAAAAAAKaFQhMAAAAAAAAAAACmhUITAAAAAAAAAAAApoVCEwAAAAAAAAAAAKaFQhMAAAAAAAAAAACm5f8Aht6SDbi36SEAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(20, 20))\n",
"sns.heatmap(correlation)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h-jyI0RzozrN"
},
"source": [
"#Data Processing For ML training"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 765
},
"id": "u_gVGWJzoGbG",
"outputId": "1607a3ff-9613-41cd-9862-bf547ba77fcb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi elective_surgery \\\n",
"0 66154 25312 118 68.0 22.730000 0 \n",
"1 114252 59342 81 77.0 27.420000 0 \n",
"5 33181 74489 83 67.0 27.560000 0 \n",
"10 105427 125898 77 72.0 28.257052 1 \n",
"17 22471 112115 118 46.0 25.845717 0 \n",
"18 48056 114220 118 65.0 28.408929 0 \n",
"23 95460 120539 118 87.0 21.963763 0 \n",
"24 7220 92453 77 60.0 29.509959 0 \n",
"25 29208 114628 118 68.0 26.010703 0 \n",
"26 32902 17922 118 85.0 23.809770 0 \n",
"27 16847 8036 33 79.0 23.408979 0 \n",
"29 6777 83373 118 60.0 26.485715 0 \n",
"30 129675 45336 77 76.0 32.374349 0 \n",
"31 6603 17124 83 68.0 27.560503 1 \n",
"33 48566 94258 118 45.0 32.129842 0 \n",
"\n",
" ethnicity gender height icu_admit_source icu_id \\\n",
"0 Caucasian M 180.3 Floor 92 \n",
"1 Caucasian F 160.0 Floor 90 \n",
"5 Caucasian M 190.5 Accident & Emergency 95 \n",
"10 Hispanic F 154.9 Operating Room / Recovery 113 \n",
"17 Hispanic M 167.6 Accident & Emergency 92 \n",
"18 Hispanic M 167.6 Accident & Emergency 100 \n",
"23 Caucasian M 180.3 Floor 97 \n",
"24 Caucasian M 188.0 Accident & Emergency 113 \n",
"25 Caucasian F 165.1 Floor 114 \n",
"26 Caucasian F 152.4 Accident & Emergency 93 \n",
"27 Caucasian F 149.9 Operating Room / Recovery 91 \n",
"29 African American M 180.3 Accident & Emergency 114 \n",
"30 Caucasian M 182.9 Floor 113 \n",
"31 Caucasian M 172.7 Operating Room / Recovery 95 \n",
"33 Caucasian M 190.5 Accident & Emergency 97 \n",
"\n",
" icu_stay_type icu_type pre_icu_los_days weight apache_2_diagnosis \\\n",
"0 admit CTICU 0.541667 73.9 113.0 \n",
"1 admit Med-Surg ICU 0.927778 70.2 108.0 \n",
"5 admit Med-Surg ICU 0.000694 100.0 301.0 \n",
"10 admit Med-Surg ICU 0.004861 67.8 303.0 \n",
"17 admit CTICU 0.000000 72.6 108.0 \n",
"18 admit Neuro ICU 0.000000 79.8 301.0 \n",
"23 admit MICU 5.046528 71.4 113.0 \n",
"24 admit Med-Surg ICU 0.081250 104.3 112.0 \n",
"25 admit CCU-CTICU 0.758333 70.9 113.0 \n",
"26 admit Med-Surg ICU 0.000694 55.3 124.0 \n",
"27 admit Med-Surg ICU 0.009028 52.6 202.0 \n",
"29 admit CCU-CTICU 0.000000 86.1 112.0 \n",
"30 admit Med-Surg ICU 0.125694 108.3 110.0 \n",
"31 admit Med-Surg ICU 0.005556 82.2 209.0 \n",
"33 admit MICU 0.000000 116.6 124.0 \n",
"\n",
" apache_3j_diagnosis apache_post_operative arf_apache gcs_eyes_apache \\\n",
"0 502.01 0 0.0 3.0 \n",
"1 203.01 0 0.0 1.0 \n",
"5 403.01 0 0.0 4.0 \n",
"10 1304.08 1 0.0 4.0 \n",
"17 203.01 0 0.0 1.0 \n",
"18 410.01 0 0.0 4.0 \n",
"23 501.05 0 0.0 3.0 \n",
"24 107.01 0 0.0 4.0 \n",
"25 501.06 0 0.0 4.0 \n",
"26 305.01 0 0.0 4.0 \n",
"27 1204.01 1 0.0 4.0 \n",
"29 107.01 0 0.0 4.0 \n",
"30 104.01 0 0.0 4.0 \n",
"31 1302.02 1 0.0 4.0 \n",
"33 305.02 0 0.0 4.0 \n",
"\n",
" gcs_motor_apache gcs_unable_apache gcs_verbal_apache heart_rate_apache \\\n",
"0 6.0 0.0 4.0 118.0 \n",
"1 3.0 0.0 1.0 120.0 \n",
"5 6.0 0.0 5.0 113.0 \n",
"10 6.0 0.0 5.0 101.0 \n",
"17 4.0 0.0 1.0 114.0 \n",
"18 6.0 0.0 4.0 98.0 \n",
"23 6.0 0.0 1.0 99.0 \n",
"24 6.0 0.0 5.0 104.0 \n",
"25 6.0 0.0 3.0 136.0 \n",
"26 6.0 0.0 5.0 118.0 \n",
"27 6.0 0.0 5.0 94.0 \n",
"29 6.0 0.0 5.0 90.0 \n",
"30 6.0 0.0 5.0 57.0 \n",
"31 6.0 0.0 5.0 63.0 \n",
"33 6.0 0.0 5.0 132.0 \n",
"\n",
" intubated_apache map_apache resprate_apache temp_apache \\\n",
"0 0.0 40.0 36.0 39.3 \n",
"1 0.0 46.0 33.0 35.1 \n",
"5 0.0 130.0 35.0 36.6 \n",
"10 0.0 72.0 15.0 36.8 \n",
"17 1.0 113.0 34.0 36.4 \n",
"18 0.0 55.0 4.0 36.6 \n",
"23 0.0 133.0 33.0 36.3 \n",
"24 0.0 64.0 12.0 36.5 \n",
"25 1.0 47.0 42.0 32.1 \n",
"26 0.0 162.0 20.0 36.7 \n",
"27 0.0 170.0 15.0 36.6 \n",
"29 0.0 139.0 14.0 36.7 \n",
"30 0.0 72.0 22.0 35.0 \n",
"31 0.0 186.0 39.0 36.4 \n",
"33 0.0 167.0 14.0 36.7 \n",
"\n",
" ventilated_apache d1_diasbp_max d1_diasbp_min \\\n",
"0 0.0 68.0 37.0 \n",
"1 1.0 95.0 31.0 \n",
"5 0.0 100.0 61.0 \n",
"10 0.0 72.0 53.0 \n",
"17 1.0 89.0 61.0 \n",
"18 0.0 73.0 43.0 \n",
"23 1.0 88.0 65.0 \n",
"24 0.0 76.0 54.0 \n",
"25 1.0 48.0 36.0 \n",
"26 0.0 102.0 64.0 \n",
"27 1.0 104.0 68.0 \n",
"29 0.0 108.0 31.0 \n",
"30 0.0 83.0 55.0 \n",
"31 0.0 80.0 51.0 \n",
"33 0.0 116.0 75.0 \n",
"\n",
" d1_diasbp_noninvasive_max d1_diasbp_noninvasive_min d1_heartrate_max \\\n",
"0 68.0 37.0 119.0 \n",
"1 95.0 31.0 118.0 \n",
"5 100.0 61.0 113.0 \n",
"10 72.0 53.0 101.0 \n",
"17 89.0 61.0 98.0 \n",
"18 73.0 43.0 102.0 \n",
"23 88.0 65.0 116.0 \n",
"24 76.0 54.0 77.0 \n",
"25 48.0 36.0 134.0 \n",
"26 102.0 64.0 104.0 \n",
"27 104.0 68.0 92.0 \n",
"29 108.0 31.0 95.0 \n",
"30 83.0 55.0 70.0 \n",
"31 80.0 51.0 90.0 \n",
"33 116.0 75.0 132.0 \n",
"\n",
" d1_heartrate_min d1_mbp_max d1_mbp_min d1_mbp_noninvasive_max \\\n",
"0 72.0 89.0 46.0 89.0 \n",
"1 72.0 120.0 38.0 120.0 \n",
"5 83.0 127.0 80.0 127.0 \n",
"10 67.0 93.0 70.0 93.0 \n",
"17 64.0 113.0 76.0 113.0 \n",
"18 66.0 103.0 55.0 103.0 \n",
"23 74.0 123.0 90.0 123.0 \n",
"24 54.0 93.0 64.0 93.0 \n",
"25 70.0 78.0 47.0 78.0 \n",
"26 68.0 162.0 93.0 162.0 \n",
"27 72.0 134.0 101.0 134.0 \n",
"29 53.0 139.0 73.0 139.0 \n",
"30 56.0 104.0 72.0 104.0 \n",
"31 57.0 123.0 73.0 123.0 \n",
"33 84.0 167.0 90.0 167.0 \n",
"\n",
" d1_mbp_noninvasive_min d1_resprate_max d1_resprate_min d1_spo2_max \\\n",
"0 46.0 34.0 10.0 100.0 \n",
"1 38.0 32.0 12.0 100.0 \n",
"5 80.0 32.0 10.0 97.0 \n",
"10 70.0 23.0 14.0 99.0 \n",
"17 76.0 22.0 9.0 100.0 \n",
"18 55.0 22.0 8.0 100.0 \n",
"23 90.0 36.0 16.0 100.0 \n",
"24 64.0 18.0 12.0 99.0 \n",
"25 47.0 42.0 16.0 100.0 \n",
"26 93.0 28.0 19.0 99.0 \n",
"27 101.0 26.0 10.0 99.0 \n",
"29 73.0 20.0 14.0 100.0 \n",
"30 72.0 22.0 16.0 100.0 \n",
"31 73.0 39.0 9.0 100.0 \n",
"33 90.0 27.0 14.0 100.0 \n",
"\n",
" d1_spo2_min d1_sysbp_max d1_sysbp_min d1_sysbp_noninvasive_max \\\n",
"0 74.0 131.0 73.0 131.0 \n",
"1 70.0 159.0 67.0 159.0 \n",
"5 91.0 173.0 107.0 173.0 \n",
"10 92.0 145.0 95.0 145.0 \n",
"17 88.0 169.0 102.0 169.0 \n",
"18 92.0 129.0 84.0 129.0 \n",
"23 90.0 179.0 129.0 179.0 \n",
"24 96.0 132.0 83.0 132.0 \n",
"25 51.0 112.0 74.0 112.0 \n",
"26 86.0 199.0 138.0 199.0 \n",
"27 91.0 170.0 136.0 170.0 \n",
"29 90.0 171.0 85.0 171.0 \n",
"30 89.0 146.0 100.0 146.0 \n",
"31 94.0 176.0 102.0 176.0 \n",
"33 96.0 176.0 113.0 176.0 \n",
"\n",
" d1_sysbp_noninvasive_min d1_temp_max d1_temp_min h1_diasbp_max \\\n",
"0 73.0 39.9 37.2 68.0 \n",
"1 67.0 36.3 35.1 61.0 \n",
"5 107.0 36.8 36.6 89.0 \n",
"10 95.0 37.0 36.7 72.0 \n",
"17 102.0 37.1 36.4 89.0 \n",
"18 84.0 36.8 36.6 66.0 \n",
"23 129.0 36.8 35.6 71.0 \n",
"24 83.0 36.7 36.5 76.0 \n",
"25 74.0 39.0 36.2 44.0 \n",
"26 138.0 37.2 36.7 79.0 \n",
"27 136.0 37.6 36.6 104.0 \n",
"29 85.0 37.8 36.7 70.0 \n",
"30 100.0 36.6 35.0 70.0 \n",
"31 102.0 36.7 36.4 79.0 \n",
"33 113.0 37.7 36.7 108.0 \n",
"\n",
" h1_diasbp_min h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min \\\n",
"0 63.0 68.0 63.0 \n",
"1 48.0 61.0 48.0 \n",
"5 89.0 89.0 89.0 \n",
"10 56.0 72.0 56.0 \n",
"17 63.0 89.0 63.0 \n",
"18 53.0 66.0 53.0 \n",
"23 65.0 71.0 65.0 \n",
"24 61.0 76.0 61.0 \n",
"25 36.0 44.0 36.0 \n",
"26 70.0 79.0 70.0 \n",
"27 80.0 104.0 80.0 \n",
"29 52.0 70.0 52.0 \n",
"30 57.0 70.0 57.0 \n",
"31 57.0 79.0 57.0 \n",
"33 97.0 108.0 97.0 \n",
"\n",
" h1_heartrate_max h1_heartrate_min h1_mbp_max h1_mbp_min \\\n",
"0 119.0 108.0 86.0 85.0 \n",
"1 114.0 100.0 85.0 57.0 \n",
"5 83.0 83.0 111.0 111.0 \n",
"10 90.0 70.0 91.0 87.0 \n",
"17 94.0 80.0 104.0 88.0 \n",
"18 100.0 74.0 84.0 78.0 \n",
"23 116.0 92.0 102.0 90.0 \n",
"24 72.0 54.0 93.0 87.0 \n",
"25 100.0 84.0 78.0 47.0 \n",
"26 76.0 68.0 111.0 101.0 \n",
"27 76.0 72.0 124.0 108.0 \n",
"29 95.0 53.0 86.0 86.0 \n",
"30 63.0 57.0 95.0 82.0 \n",
"31 69.0 65.0 113.0 82.0 \n",
"33 126.0 122.0 124.0 124.0 \n",
"\n",
" h1_mbp_noninvasive_max h1_mbp_noninvasive_min h1_resprate_max \\\n",
"0 86.0 85.0 26.0 \n",
"1 85.0 57.0 31.0 \n",
"5 111.0 111.0 12.0 \n",
"10 91.0 87.0 23.0 \n",
"17 104.0 88.0 21.0 \n",
"18 84.0 78.0 21.0 \n",
"23 102.0 90.0 36.0 \n",
"24 93.0 87.0 18.0 \n",
"25 78.0 47.0 29.0 \n",
"26 111.0 101.0 28.0 \n",
"27 124.0 108.0 23.0 \n",
"29 86.0 86.0 18.0 \n",
"30 95.0 82.0 16.0 \n",
"31 113.0 82.0 25.0 \n",
"33 124.0 124.0 27.0 \n",
"\n",
" h1_resprate_min h1_spo2_max h1_spo2_min h1_sysbp_max h1_sysbp_min \\\n",
"0 18.0 100.0 74.0 131.0 115.0 \n",
"1 28.0 95.0 70.0 95.0 71.0 \n",
"5 12.0 97.0 97.0 143.0 143.0 \n",
"10 14.0 99.0 93.0 145.0 114.0 \n",
"17 9.0 99.0 95.0 169.0 115.0 \n",
"18 12.0 99.0 95.0 121.0 93.0 \n",
"23 28.0 99.0 90.0 159.0 129.0 \n",
"24 18.0 99.0 97.0 127.0 109.0 \n",
"25 16.0 100.0 78.0 98.0 74.0 \n",
"26 19.0 98.0 93.0 165.0 152.0 \n",
"27 14.0 99.0 95.0 170.0 146.0 \n",
"29 18.0 100.0 95.0 123.0 85.0 \n",
"30 16.0 99.0 98.0 137.0 117.0 \n",
"31 9.0 100.0 99.0 176.0 119.0 \n",
"33 16.0 99.0 97.0 156.0 155.0 \n",
"\n",
" h1_sysbp_noninvasive_max h1_sysbp_noninvasive_min d1_glucose_max \\\n",
"0 131.0 115.0 168.0 \n",
"1 95.0 71.0 145.0 \n",
"5 143.0 143.0 156.0 \n",
"10 145.0 114.0 158.0 \n",
"17 169.0 115.0 143.0 \n",
"18 121.0 93.0 114.0 \n",
"23 159.0 129.0 144.0 \n",
"24 127.0 109.0 118.0 \n",
"25 98.0 74.0 154.0 \n",
"26 165.0 152.0 100.0 \n",
"27 170.0 146.0 175.0 \n",
"29 123.0 85.0 149.0 \n",
"30 137.0 117.0 100.0 \n",
"31 176.0 119.0 216.0 \n",
"33 156.0 155.0 131.0 \n",
"\n",
" d1_glucose_min d1_potassium_max d1_potassium_min \\\n",
"0 109.0 4.0 3.4 \n",
"1 128.0 4.2 3.8 \n",
"5 125.0 3.9 3.7 \n",
"10 133.0 4.2 4.2 \n",
"17 143.0 4.9 4.9 \n",
"18 88.0 4.0 4.0 \n",
"23 87.0 3.4 3.3 \n",
"24 118.0 3.7 3.7 \n",
"25 63.0 4.0 3.3 \n",
"26 100.0 4.1 4.1 \n",
"27 172.0 3.5 3.2 \n",
"29 149.0 4.2 3.6 \n",
"30 100.0 3.9 3.9 \n",
"31 82.0 4.1 4.1 \n",
"33 131.0 4.0 4.0 \n",
"\n",
" apache_4a_hospital_death_prob apache_4a_icu_death_prob aids cirrhosis \\\n",
"0 0.10 0.05 0.0 0.0 \n",
"1 0.47 0.29 0.0 0.0 \n",
"5 0.05 0.02 0.0 0.0 \n",
"10 0.01 0.00 0.0 0.0 \n",
"17 0.11 0.06 0.0 0.0 \n",
"18 0.03 0.01 0.0 0.0 \n",
"23 0.31 0.14 0.0 0.0 \n",
"24 0.02 0.01 0.0 0.0 \n",
"25 0.60 0.51 0.0 0.0 \n",
"26 0.03 0.01 0.0 0.0 \n",
"27 0.02 0.01 0.0 0.0 \n",
"29 0.01 0.01 0.0 0.0 \n",
"30 0.03 0.01 0.0 0.0 \n",
"31 0.01 0.00 0.0 0.0 \n",
"33 0.01 0.01 0.0 0.0 \n",
"\n",
" diabetes_mellitus hepatic_failure immunosuppression leukemia lymphoma \\\n",
"0 1.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 \n",
"5 1.0 0.0 0.0 0.0 0.0 \n",
"10 0.0 0.0 1.0 0.0 0.0 \n",
"17 0.0 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 0.0 \n",
"23 0.0 0.0 0.0 0.0 0.0 \n",
"24 0.0 0.0 0.0 0.0 0.0 \n",
"25 0.0 0.0 0.0 0.0 0.0 \n",
"26 0.0 0.0 0.0 0.0 0.0 \n",
"27 0.0 0.0 0.0 0.0 0.0 \n",
"29 0.0 0.0 0.0 0.0 0.0 \n",
"30 0.0 0.0 1.0 0.0 0.0 \n",
"31 1.0 0.0 0.0 0.0 0.0 \n",
"33 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" solid_tumor_with_metastasis apache_3j_bodysystem apache_2_bodysystem \\\n",
"0 0.0 Sepsis Cardiovascular \n",
"1 0.0 Respiratory Respiratory \n",
"5 0.0 Neurological Neurologic \n",
"10 0.0 Respiratory Respiratory \n",
"17 0.0 Respiratory Respiratory \n",
"18 0.0 Neurological Neurologic \n",
"23 0.0 Sepsis Cardiovascular \n",
"24 0.0 Cardiovascular Cardiovascular \n",
"25 0.0 Sepsis Cardiovascular \n",
"26 0.0 Gastrointestinal Gastrointestinal \n",
"27 0.0 Cardiovascular Cardiovascular \n",
"29 0.0 Cardiovascular Cardiovascular \n",
"30 0.0 Cardiovascular Cardiovascular \n",
"31 1.0 Respiratory Respiratory \n",
"33 0.0 Gastrointestinal Gastrointestinal \n",
"\n",
" hospital_death \n",
"0 0 \n",
"1 0 \n",
"5 0 \n",
"10 0 \n",
"17 0 \n",
"18 0 \n",
"23 0 \n",
"24 0 \n",
"25 1 \n",
"26 0 \n",
"27 0 \n",
"29 0 \n",
"30 0 \n",
"31 0 \n",
"33 0 "
],
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>encounter_id</th>\n",
" <th>patient_id</th>\n",
" <th>hospital_id</th>\n",
" <th>age</th>\n",
" <th>bmi</th>\n",
" <th>elective_surgery</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>height</th>\n",
" <th>icu_admit_source</th>\n",
" <th>icu_id</th>\n",
" <th>icu_stay_type</th>\n",
" <th>icu_type</th>\n",
" <th>pre_icu_los_days</th>\n",
" <th>weight</th>\n",
" <th>apache_2_diagnosis</th>\n",
" <th>apache_3j_diagnosis</th>\n",
" <th>apache_post_operative</th>\n",
" <th>arf_apache</th>\n",
" <th>gcs_eyes_apache</th>\n",
" <th>gcs_motor_apache</th>\n",
" <th>gcs_unable_apache</th>\n",
" <th>gcs_verbal_apache</th>\n",
" <th>heart_rate_apache</th>\n",
" <th>intubated_apache</th>\n",
" <th>map_apache</th>\n",
" <th>resprate_apache</th>\n",
" <th>temp_apache</th>\n",
" <th>ventilated_apache</th>\n",
" <th>d1_diasbp_max</th>\n",
" <th>d1_diasbp_min</th>\n",
" <th>d1_diasbp_noninvasive_max</th>\n",
" <th>d1_diasbp_noninvasive_min</th>\n",
" <th>d1_heartrate_max</th>\n",
" <th>d1_heartrate_min</th>\n",
" <th>d1_mbp_max</th>\n",
" <th>d1_mbp_min</th>\n",
" <th>d1_mbp_noninvasive_max</th>\n",
" <th>d1_mbp_noninvasive_min</th>\n",
" <th>d1_resprate_max</th>\n",
" <th>d1_resprate_min</th>\n",
" <th>d1_spo2_max</th>\n",
" <th>d1_spo2_min</th>\n",
" <th>d1_sysbp_max</th>\n",
" <th>d1_sysbp_min</th>\n",
" <th>d1_sysbp_noninvasive_max</th>\n",
" <th>d1_sysbp_noninvasive_min</th>\n",
" <th>d1_temp_max</th>\n",
" <th>d1_temp_min</th>\n",
" <th>h1_diasbp_max</th>\n",
" <th>h1_diasbp_min</th>\n",
" <th>h1_diasbp_noninvasive_max</th>\n",
" <th>h1_diasbp_noninvasive_min</th>\n",
" <th>h1_heartrate_max</th>\n",
" <th>h1_heartrate_min</th>\n",
" <th>h1_mbp_max</th>\n",
" <th>h1_mbp_min</th>\n",
" <th>h1_mbp_noninvasive_max</th>\n",
" <th>h1_mbp_noninvasive_min</th>\n",
" <th>h1_resprate_max</th>\n",
" <th>h1_resprate_min</th>\n",
" <th>h1_spo2_max</th>\n",
" <th>h1_spo2_min</th>\n",
" <th>h1_sysbp_max</th>\n",
" <th>h1_sysbp_min</th>\n",
" <th>h1_sysbp_noninvasive_max</th>\n",
" <th>h1_sysbp_noninvasive_min</th>\n",
" <th>d1_glucose_max</th>\n",
" <th>d1_glucose_min</th>\n",
" <th>d1_potassium_max</th>\n",
" <th>d1_potassium_min</th>\n",
" <th>apache_4a_hospital_death_prob</th>\n",
" <th>apache_4a_icu_death_prob</th>\n",
" <th>aids</th>\n",
" <th>cirrhosis</th>\n",
" <th>diabetes_mellitus</th>\n",
" <th>hepatic_failure</th>\n",
" <th>immunosuppression</th>\n",
" <th>leukemia</th>\n",
" <th>lymphoma</th>\n",
" <th>solid_tumor_with_metastasis</th>\n",
" <th>apache_3j_bodysystem</th>\n",
" <th>apache_2_bodysystem</th>\n",
" <th>hospital_death</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>66154</td>\n",
" <td>25312</td>\n",
" <td>118</td>\n",
" <td>68.0</td>\n",
" <td>22.730000</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>180.3</td>\n",
" <td>Floor</td>\n",
" <td>92</td>\n",
" <td>admit</td>\n",
" <td>CTICU</td>\n",
" <td>0.541667</td>\n",
" <td>73.9</td>\n",
" <td>113.0</td>\n",
" <td>502.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>118.0</td>\n",
" <td>0.0</td>\n",
" <td>40.0</td>\n",
" <td>36.0</td>\n",
" <td>39.3</td>\n",
" <td>0.0</td>\n",
" <td>68.0</td>\n",
" <td>37.0</td>\n",
" <td>68.0</td>\n",
" <td>37.0</td>\n",
" <td>119.0</td>\n",
" <td>72.0</td>\n",
" <td>89.0</td>\n",
" <td>46.0</td>\n",
" <td>89.0</td>\n",
" <td>46.0</td>\n",
" <td>34.0</td>\n",
" <td>10.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>131.0</td>\n",
" <td>73.0</td>\n",
" <td>131.0</td>\n",
" <td>73.0</td>\n",
" <td>39.9</td>\n",
" <td>37.2</td>\n",
" <td>68.0</td>\n",
" <td>63.0</td>\n",
" <td>68.0</td>\n",
" <td>63.0</td>\n",
" <td>119.0</td>\n",
" <td>108.0</td>\n",
" <td>86.0</td>\n",
" <td>85.0</td>\n",
" <td>86.0</td>\n",
" <td>85.0</td>\n",
" <td>26.0</td>\n",
" <td>18.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>131.0</td>\n",
" <td>115.0</td>\n",
" <td>131.0</td>\n",
" <td>115.0</td>\n",
" <td>168.0</td>\n",
" <td>109.0</td>\n",
" <td>4.0</td>\n",
" <td>3.4</td>\n",
" <td>0.10</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Sepsis</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>114252</td>\n",
" <td>59342</td>\n",
" <td>81</td>\n",
" <td>77.0</td>\n",
" <td>27.420000</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>160.0</td>\n",
" <td>Floor</td>\n",
" <td>90</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.927778</td>\n",
" <td>70.2</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>120.0</td>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>33.0</td>\n",
" <td>35.1</td>\n",
" <td>1.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>118.0</td>\n",
" <td>72.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>32.0</td>\n",
" <td>12.0</td>\n",
" <td>100.0</td>\n",
" <td>70.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>36.3</td>\n",
" <td>35.1</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>114.0</td>\n",
" <td>100.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>31.0</td>\n",
" <td>28.0</td>\n",
" <td>95.0</td>\n",
" <td>70.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>145.0</td>\n",
" <td>128.0</td>\n",
" <td>4.2</td>\n",
" <td>3.8</td>\n",
" <td>0.47</td>\n",
" <td>0.29</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>33181</td>\n",
" <td>74489</td>\n",
" <td>83</td>\n",
" <td>67.0</td>\n",
" <td>27.560000</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>190.5</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>95</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.000694</td>\n",
" <td>100.0</td>\n",
" <td>301.0</td>\n",
" <td>403.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>113.0</td>\n",
" <td>0.0</td>\n",
" <td>130.0</td>\n",
" <td>35.0</td>\n",
" <td>36.6</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>113.0</td>\n",
" <td>83.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>32.0</td>\n",
" <td>10.0</td>\n",
" <td>97.0</td>\n",
" <td>91.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>36.8</td>\n",
" <td>36.6</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>83.0</td>\n",
" <td>83.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>97.0</td>\n",
" <td>97.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>156.0</td>\n",
" <td>125.0</td>\n",
" <td>3.9</td>\n",
" <td>3.7</td>\n",
" <td>0.05</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Neurological</td>\n",
" <td>Neurologic</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>105427</td>\n",
" <td>125898</td>\n",
" <td>77</td>\n",
" <td>72.0</td>\n",
" <td>28.257052</td>\n",
" <td>1</td>\n",
" <td>Hispanic</td>\n",
" <td>F</td>\n",
" <td>154.9</td>\n",
" <td>Operating Room / Recovery</td>\n",
" <td>113</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.004861</td>\n",
" <td>67.8</td>\n",
" <td>303.0</td>\n",
" <td>1304.08</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>15.0</td>\n",
" <td>36.8</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>53.0</td>\n",
" <td>72.0</td>\n",
" <td>53.0</td>\n",
" <td>101.0</td>\n",
" <td>67.0</td>\n",
" <td>93.0</td>\n",
" <td>70.0</td>\n",
" <td>93.0</td>\n",
" <td>70.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>92.0</td>\n",
" <td>145.0</td>\n",
" <td>95.0</td>\n",
" <td>145.0</td>\n",
" <td>95.0</td>\n",
" <td>37.0</td>\n",
" <td>36.7</td>\n",
" <td>72.0</td>\n",
" <td>56.0</td>\n",
" <td>72.0</td>\n",
" <td>56.0</td>\n",
" <td>90.0</td>\n",
" <td>70.0</td>\n",
" <td>91.0</td>\n",
" <td>87.0</td>\n",
" <td>91.0</td>\n",
" <td>87.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>93.0</td>\n",
" <td>145.0</td>\n",
" <td>114.0</td>\n",
" <td>145.0</td>\n",
" <td>114.0</td>\n",
" <td>158.0</td>\n",
" <td>133.0</td>\n",
" <td>4.2</td>\n",
" <td>4.2</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>22471</td>\n",
" <td>112115</td>\n",
" <td>118</td>\n",
" <td>46.0</td>\n",
" <td>25.845717</td>\n",
" <td>0</td>\n",
" <td>Hispanic</td>\n",
" <td>M</td>\n",
" <td>167.6</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>92</td>\n",
" <td>admit</td>\n",
" <td>CTICU</td>\n",
" <td>0.000000</td>\n",
" <td>72.6</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>114.0</td>\n",
" <td>1.0</td>\n",
" <td>113.0</td>\n",
" <td>34.0</td>\n",
" <td>36.4</td>\n",
" <td>1.0</td>\n",
" <td>89.0</td>\n",
" <td>61.0</td>\n",
" <td>89.0</td>\n",
" <td>61.0</td>\n",
" <td>98.0</td>\n",
" <td>64.0</td>\n",
" <td>113.0</td>\n",
" <td>76.0</td>\n",
" <td>113.0</td>\n",
" <td>76.0</td>\n",
" <td>22.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>88.0</td>\n",
" <td>169.0</td>\n",
" <td>102.0</td>\n",
" <td>169.0</td>\n",
" <td>102.0</td>\n",
" <td>37.1</td>\n",
" <td>36.4</td>\n",
" <td>89.0</td>\n",
" <td>63.0</td>\n",
" <td>89.0</td>\n",
" <td>63.0</td>\n",
" <td>94.0</td>\n",
" <td>80.0</td>\n",
" <td>104.0</td>\n",
" <td>88.0</td>\n",
" <td>104.0</td>\n",
" <td>88.0</td>\n",
" <td>21.0</td>\n",
" <td>9.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>169.0</td>\n",
" <td>115.0</td>\n",
" <td>169.0</td>\n",
" <td>115.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>4.9</td>\n",
" <td>4.9</td>\n",
" <td>0.11</td>\n",
" <td>0.06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>48056</td>\n",
" <td>114220</td>\n",
" <td>118</td>\n",
" <td>65.0</td>\n",
" <td>28.408929</td>\n",
" <td>0</td>\n",
" <td>Hispanic</td>\n",
" <td>M</td>\n",
" <td>167.6</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>100</td>\n",
" <td>admit</td>\n",
" <td>Neuro ICU</td>\n",
" <td>0.000000</td>\n",
" <td>79.8</td>\n",
" <td>301.0</td>\n",
" <td>410.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>98.0</td>\n",
" <td>0.0</td>\n",
" <td>55.0</td>\n",
" <td>4.0</td>\n",
" <td>36.6</td>\n",
" <td>0.0</td>\n",
" <td>73.0</td>\n",
" <td>43.0</td>\n",
" <td>73.0</td>\n",
" <td>43.0</td>\n",
" <td>102.0</td>\n",
" <td>66.0</td>\n",
" <td>103.0</td>\n",
" <td>55.0</td>\n",
" <td>103.0</td>\n",
" <td>55.0</td>\n",
" <td>22.0</td>\n",
" <td>8.0</td>\n",
" <td>100.0</td>\n",
" <td>92.0</td>\n",
" <td>129.0</td>\n",
" <td>84.0</td>\n",
" <td>129.0</td>\n",
" <td>84.0</td>\n",
" <td>36.8</td>\n",
" <td>36.6</td>\n",
" <td>66.0</td>\n",
" <td>53.0</td>\n",
" <td>66.0</td>\n",
" <td>53.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>21.0</td>\n",
" <td>12.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>93.0</td>\n",
" <td>121.0</td>\n",
" <td>93.0</td>\n",
" <td>114.0</td>\n",
" <td>88.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Neurological</td>\n",
" <td>Neurologic</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>95460</td>\n",
" <td>120539</td>\n",
" <td>118</td>\n",
" <td>87.0</td>\n",
" <td>21.963763</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>180.3</td>\n",
" <td>Floor</td>\n",
" <td>97</td>\n",
" <td>admit</td>\n",
" <td>MICU</td>\n",
" <td>5.046528</td>\n",
" <td>71.4</td>\n",
" <td>113.0</td>\n",
" <td>501.05</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>99.0</td>\n",
" <td>0.0</td>\n",
" <td>133.0</td>\n",
" <td>33.0</td>\n",
" <td>36.3</td>\n",
" <td>1.0</td>\n",
" <td>88.0</td>\n",
" <td>65.0</td>\n",
" <td>88.0</td>\n",
" <td>65.0</td>\n",
" <td>116.0</td>\n",
" <td>74.0</td>\n",
" <td>123.0</td>\n",
" <td>90.0</td>\n",
" <td>123.0</td>\n",
" <td>90.0</td>\n",
" <td>36.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>179.0</td>\n",
" <td>129.0</td>\n",
" <td>179.0</td>\n",
" <td>129.0</td>\n",
" <td>36.8</td>\n",
" <td>35.6</td>\n",
" <td>71.0</td>\n",
" <td>65.0</td>\n",
" <td>71.0</td>\n",
" <td>65.0</td>\n",
" <td>116.0</td>\n",
" <td>92.0</td>\n",
" <td>102.0</td>\n",
" <td>90.0</td>\n",
" <td>102.0</td>\n",
" <td>90.0</td>\n",
" <td>36.0</td>\n",
" <td>28.0</td>\n",
" <td>99.0</td>\n",
" <td>90.0</td>\n",
" <td>159.0</td>\n",
" <td>129.0</td>\n",
" <td>159.0</td>\n",
" <td>129.0</td>\n",
" <td>144.0</td>\n",
" <td>87.0</td>\n",
" <td>3.4</td>\n",
" <td>3.3</td>\n",
" <td>0.31</td>\n",
" <td>0.14</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Sepsis</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>7220</td>\n",
" <td>92453</td>\n",
" <td>77</td>\n",
" <td>60.0</td>\n",
" <td>29.509959</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>188.0</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>113</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.081250</td>\n",
" <td>104.3</td>\n",
" <td>112.0</td>\n",
" <td>107.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>104.0</td>\n",
" <td>0.0</td>\n",
" <td>64.0</td>\n",
" <td>12.0</td>\n",
" <td>36.5</td>\n",
" <td>0.0</td>\n",
" <td>76.0</td>\n",
" <td>54.0</td>\n",
" <td>76.0</td>\n",
" <td>54.0</td>\n",
" <td>77.0</td>\n",
" <td>54.0</td>\n",
" <td>93.0</td>\n",
" <td>64.0</td>\n",
" <td>93.0</td>\n",
" <td>64.0</td>\n",
" <td>18.0</td>\n",
" <td>12.0</td>\n",
" <td>99.0</td>\n",
" <td>96.0</td>\n",
" <td>132.0</td>\n",
" <td>83.0</td>\n",
" <td>132.0</td>\n",
" <td>83.0</td>\n",
" <td>36.7</td>\n",
" <td>36.5</td>\n",
" <td>76.0</td>\n",
" <td>61.0</td>\n",
" <td>76.0</td>\n",
" <td>61.0</td>\n",
" <td>72.0</td>\n",
" <td>54.0</td>\n",
" <td>93.0</td>\n",
" <td>87.0</td>\n",
" <td>93.0</td>\n",
" <td>87.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>99.0</td>\n",
" <td>97.0</td>\n",
" <td>127.0</td>\n",
" <td>109.0</td>\n",
" <td>127.0</td>\n",
" <td>109.0</td>\n",
" <td>118.0</td>\n",
" <td>118.0</td>\n",
" <td>3.7</td>\n",
" <td>3.7</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>29208</td>\n",
" <td>114628</td>\n",
" <td>118</td>\n",
" <td>68.0</td>\n",
" <td>26.010703</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>165.1</td>\n",
" <td>Floor</td>\n",
" <td>114</td>\n",
" <td>admit</td>\n",
" <td>CCU-CTICU</td>\n",
" <td>0.758333</td>\n",
" <td>70.9</td>\n",
" <td>113.0</td>\n",
" <td>501.06</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>136.0</td>\n",
" <td>1.0</td>\n",
" <td>47.0</td>\n",
" <td>42.0</td>\n",
" <td>32.1</td>\n",
" <td>1.0</td>\n",
" <td>48.0</td>\n",
" <td>36.0</td>\n",
" <td>48.0</td>\n",
" <td>36.0</td>\n",
" <td>134.0</td>\n",
" <td>70.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>42.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>51.0</td>\n",
" <td>112.0</td>\n",
" <td>74.0</td>\n",
" <td>112.0</td>\n",
" <td>74.0</td>\n",
" <td>39.0</td>\n",
" <td>36.2</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>100.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>78.0</td>\n",
" <td>98.0</td>\n",
" <td>74.0</td>\n",
" <td>98.0</td>\n",
" <td>74.0</td>\n",
" <td>154.0</td>\n",
" <td>63.0</td>\n",
" <td>4.0</td>\n",
" <td>3.3</td>\n",
" <td>0.60</td>\n",
" <td>0.51</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Sepsis</td>\n",
" <td>Cardiovascular</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>32902</td>\n",
" <td>17922</td>\n",
" <td>118</td>\n",
" <td>85.0</td>\n",
" <td>23.809770</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>152.4</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>93</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.000694</td>\n",
" <td>55.3</td>\n",
" <td>124.0</td>\n",
" <td>305.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>118.0</td>\n",
" <td>0.0</td>\n",
" <td>162.0</td>\n",
" <td>20.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>102.0</td>\n",
" <td>64.0</td>\n",
" <td>102.0</td>\n",
" <td>64.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>162.0</td>\n",
" <td>93.0</td>\n",
" <td>162.0</td>\n",
" <td>93.0</td>\n",
" <td>28.0</td>\n",
" <td>19.0</td>\n",
" <td>99.0</td>\n",
" <td>86.0</td>\n",
" <td>199.0</td>\n",
" <td>138.0</td>\n",
" <td>199.0</td>\n",
" <td>138.0</td>\n",
" <td>37.2</td>\n",
" <td>36.7</td>\n",
" <td>79.0</td>\n",
" <td>70.0</td>\n",
" <td>79.0</td>\n",
" <td>70.0</td>\n",
" <td>76.0</td>\n",
" <td>68.0</td>\n",
" <td>111.0</td>\n",
" <td>101.0</td>\n",
" <td>111.0</td>\n",
" <td>101.0</td>\n",
" <td>28.0</td>\n",
" <td>19.0</td>\n",
" <td>98.0</td>\n",
" <td>93.0</td>\n",
" <td>165.0</td>\n",
" <td>152.0</td>\n",
" <td>165.0</td>\n",
" <td>152.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>4.1</td>\n",
" <td>4.1</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Gastrointestinal</td>\n",
" <td>Gastrointestinal</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>16847</td>\n",
" <td>8036</td>\n",
" <td>33</td>\n",
" <td>79.0</td>\n",
" <td>23.408979</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>F</td>\n",
" <td>149.9</td>\n",
" <td>Operating Room / Recovery</td>\n",
" <td>91</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.009028</td>\n",
" <td>52.6</td>\n",
" <td>202.0</td>\n",
" <td>1204.01</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>94.0</td>\n",
" <td>0.0</td>\n",
" <td>170.0</td>\n",
" <td>15.0</td>\n",
" <td>36.6</td>\n",
" <td>1.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>92.0</td>\n",
" <td>72.0</td>\n",
" <td>134.0</td>\n",
" <td>101.0</td>\n",
" <td>134.0</td>\n",
" <td>101.0</td>\n",
" <td>26.0</td>\n",
" <td>10.0</td>\n",
" <td>99.0</td>\n",
" <td>91.0</td>\n",
" <td>170.0</td>\n",
" <td>136.0</td>\n",
" <td>170.0</td>\n",
" <td>136.0</td>\n",
" <td>37.6</td>\n",
" <td>36.6</td>\n",
" <td>104.0</td>\n",
" <td>80.0</td>\n",
" <td>104.0</td>\n",
" <td>80.0</td>\n",
" <td>76.0</td>\n",
" <td>72.0</td>\n",
" <td>124.0</td>\n",
" <td>108.0</td>\n",
" <td>124.0</td>\n",
" <td>108.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>170.0</td>\n",
" <td>146.0</td>\n",
" <td>170.0</td>\n",
" <td>146.0</td>\n",
" <td>175.0</td>\n",
" <td>172.0</td>\n",
" <td>3.5</td>\n",
" <td>3.2</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>6777</td>\n",
" <td>83373</td>\n",
" <td>118</td>\n",
" <td>60.0</td>\n",
" <td>26.485715</td>\n",
" <td>0</td>\n",
" <td>African American</td>\n",
" <td>M</td>\n",
" <td>180.3</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>114</td>\n",
" <td>admit</td>\n",
" <td>CCU-CTICU</td>\n",
" <td>0.000000</td>\n",
" <td>86.1</td>\n",
" <td>112.0</td>\n",
" <td>107.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>139.0</td>\n",
" <td>14.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>31.0</td>\n",
" <td>108.0</td>\n",
" <td>31.0</td>\n",
" <td>95.0</td>\n",
" <td>53.0</td>\n",
" <td>139.0</td>\n",
" <td>73.0</td>\n",
" <td>139.0</td>\n",
" <td>73.0</td>\n",
" <td>20.0</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>171.0</td>\n",
" <td>85.0</td>\n",
" <td>171.0</td>\n",
" <td>85.0</td>\n",
" <td>37.8</td>\n",
" <td>36.7</td>\n",
" <td>70.0</td>\n",
" <td>52.0</td>\n",
" <td>70.0</td>\n",
" <td>52.0</td>\n",
" <td>95.0</td>\n",
" <td>53.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>100.0</td>\n",
" <td>95.0</td>\n",
" <td>123.0</td>\n",
" <td>85.0</td>\n",
" <td>123.0</td>\n",
" <td>85.0</td>\n",
" <td>149.0</td>\n",
" <td>149.0</td>\n",
" <td>4.2</td>\n",
" <td>3.6</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>129675</td>\n",
" <td>45336</td>\n",
" <td>77</td>\n",
" <td>76.0</td>\n",
" <td>32.374349</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>182.9</td>\n",
" <td>Floor</td>\n",
" <td>113</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.125694</td>\n",
" <td>108.3</td>\n",
" <td>110.0</td>\n",
" <td>104.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>57.0</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>22.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>83.0</td>\n",
" <td>55.0</td>\n",
" <td>83.0</td>\n",
" <td>55.0</td>\n",
" <td>70.0</td>\n",
" <td>56.0</td>\n",
" <td>104.0</td>\n",
" <td>72.0</td>\n",
" <td>104.0</td>\n",
" <td>72.0</td>\n",
" <td>22.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>89.0</td>\n",
" <td>146.0</td>\n",
" <td>100.0</td>\n",
" <td>146.0</td>\n",
" <td>100.0</td>\n",
" <td>36.6</td>\n",
" <td>35.0</td>\n",
" <td>70.0</td>\n",
" <td>57.0</td>\n",
" <td>70.0</td>\n",
" <td>57.0</td>\n",
" <td>63.0</td>\n",
" <td>57.0</td>\n",
" <td>95.0</td>\n",
" <td>82.0</td>\n",
" <td>95.0</td>\n",
" <td>82.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>99.0</td>\n",
" <td>98.0</td>\n",
" <td>137.0</td>\n",
" <td>117.0</td>\n",
" <td>137.0</td>\n",
" <td>117.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>3.9</td>\n",
" <td>3.9</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Cardiovascular</td>\n",
" <td>Cardiovascular</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>6603</td>\n",
" <td>17124</td>\n",
" <td>83</td>\n",
" <td>68.0</td>\n",
" <td>27.560503</td>\n",
" <td>1</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>172.7</td>\n",
" <td>Operating Room / Recovery</td>\n",
" <td>95</td>\n",
" <td>admit</td>\n",
" <td>Med-Surg ICU</td>\n",
" <td>0.005556</td>\n",
" <td>82.2</td>\n",
" <td>209.0</td>\n",
" <td>1302.02</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>63.0</td>\n",
" <td>0.0</td>\n",
" <td>186.0</td>\n",
" <td>39.0</td>\n",
" <td>36.4</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>51.0</td>\n",
" <td>80.0</td>\n",
" <td>51.0</td>\n",
" <td>90.0</td>\n",
" <td>57.0</td>\n",
" <td>123.0</td>\n",
" <td>73.0</td>\n",
" <td>123.0</td>\n",
" <td>73.0</td>\n",
" <td>39.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>94.0</td>\n",
" <td>176.0</td>\n",
" <td>102.0</td>\n",
" <td>176.0</td>\n",
" <td>102.0</td>\n",
" <td>36.7</td>\n",
" <td>36.4</td>\n",
" <td>79.0</td>\n",
" <td>57.0</td>\n",
" <td>79.0</td>\n",
" <td>57.0</td>\n",
" <td>69.0</td>\n",
" <td>65.0</td>\n",
" <td>113.0</td>\n",
" <td>82.0</td>\n",
" <td>113.0</td>\n",
" <td>82.0</td>\n",
" <td>25.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>99.0</td>\n",
" <td>176.0</td>\n",
" <td>119.0</td>\n",
" <td>176.0</td>\n",
" <td>119.0</td>\n",
" <td>216.0</td>\n",
" <td>82.0</td>\n",
" <td>4.1</td>\n",
" <td>4.1</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>Respiratory</td>\n",
" <td>Respiratory</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>48566</td>\n",
" <td>94258</td>\n",
" <td>118</td>\n",
" <td>45.0</td>\n",
" <td>32.129842</td>\n",
" <td>0</td>\n",
" <td>Caucasian</td>\n",
" <td>M</td>\n",
" <td>190.5</td>\n",
" <td>Accident & Emergency</td>\n",
" <td>97</td>\n",
" <td>admit</td>\n",
" <td>MICU</td>\n",
" <td>0.000000</td>\n",
" <td>116.6</td>\n",
" <td>124.0</td>\n",
" <td>305.02</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>132.0</td>\n",
" <td>0.0</td>\n",
" <td>167.0</td>\n",
" <td>14.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>116.0</td>\n",
" <td>75.0</td>\n",
" <td>116.0</td>\n",
" <td>75.0</td>\n",
" <td>132.0</td>\n",
" <td>84.0</td>\n",
" <td>167.0</td>\n",
" <td>90.0</td>\n",
" <td>167.0</td>\n",
" <td>90.0</td>\n",
" <td>27.0</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>176.0</td>\n",
" <td>113.0</td>\n",
" <td>176.0</td>\n",
" <td>113.0</td>\n",
" <td>37.7</td>\n",
" <td>36.7</td>\n",
" <td>108.0</td>\n",
" <td>97.0</td>\n",
" <td>108.0</td>\n",
" <td>97.0</td>\n",
" <td>126.0</td>\n",
" <td>122.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>27.0</td>\n",
" <td>16.0</td>\n",
" <td>99.0</td>\n",
" <td>97.0</td>\n",
" <td>156.0</td>\n",
" <td>155.0</td>\n",
" <td>156.0</td>\n",
" <td>155.0</td>\n",
" <td>131.0</td>\n",
" <td>131.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Gastrointestinal</td>\n",
" <td>Gastrointestinal</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-cab9e050-6168-4fd8-9559-5c53ff5bcdbd');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
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" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "clean_data"
}
},
"metadata": {},
"execution_count": 29
}
],
"source": [
"clean_data.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 557
},
"id": "uCjnCgYmpCLz",
"outputId": "3847d121-bee3-4b56-9a6e-d7ea405041c5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi elective_surgery \\\n",
"0 66154 25312 118 68.0 22.730000 0 \n",
"1 114252 59342 81 77.0 27.420000 0 \n",
"5 33181 74489 83 67.0 27.560000 0 \n",
"10 105427 125898 77 72.0 28.257052 1 \n",
"17 22471 112115 118 46.0 25.845717 0 \n",
"18 48056 114220 118 65.0 28.408929 0 \n",
"23 95460 120539 118 87.0 21.963763 0 \n",
"24 7220 92453 77 60.0 29.509959 0 \n",
"25 29208 114628 118 68.0 26.010703 0 \n",
"26 32902 17922 118 85.0 23.809770 0 \n",
"27 16847 8036 33 79.0 23.408979 0 \n",
"29 6777 83373 118 60.0 26.485715 0 \n",
"30 129675 45336 77 76.0 32.374349 0 \n",
"31 6603 17124 83 68.0 27.560503 1 \n",
"33 48566 94258 118 45.0 32.129842 0 \n",
"\n",
" height icu_id pre_icu_los_days weight apache_2_diagnosis \\\n",
"0 180.3 92 0.541667 73.9 113.0 \n",
"1 160.0 90 0.927778 70.2 108.0 \n",
"5 190.5 95 0.000694 100.0 301.0 \n",
"10 154.9 113 0.004861 67.8 303.0 \n",
"17 167.6 92 0.000000 72.6 108.0 \n",
"18 167.6 100 0.000000 79.8 301.0 \n",
"23 180.3 97 5.046528 71.4 113.0 \n",
"24 188.0 113 0.081250 104.3 112.0 \n",
"25 165.1 114 0.758333 70.9 113.0 \n",
"26 152.4 93 0.000694 55.3 124.0 \n",
"27 149.9 91 0.009028 52.6 202.0 \n",
"29 180.3 114 0.000000 86.1 112.0 \n",
"30 182.9 113 0.125694 108.3 110.0 \n",
"31 172.7 95 0.005556 82.2 209.0 \n",
"33 190.5 97 0.000000 116.6 124.0 \n",
"\n",
" apache_3j_diagnosis apache_post_operative arf_apache gcs_eyes_apache \\\n",
"0 502.01 0 0.0 3.0 \n",
"1 203.01 0 0.0 1.0 \n",
"5 403.01 0 0.0 4.0 \n",
"10 1304.08 1 0.0 4.0 \n",
"17 203.01 0 0.0 1.0 \n",
"18 410.01 0 0.0 4.0 \n",
"23 501.05 0 0.0 3.0 \n",
"24 107.01 0 0.0 4.0 \n",
"25 501.06 0 0.0 4.0 \n",
"26 305.01 0 0.0 4.0 \n",
"27 1204.01 1 0.0 4.0 \n",
"29 107.01 0 0.0 4.0 \n",
"30 104.01 0 0.0 4.0 \n",
"31 1302.02 1 0.0 4.0 \n",
"33 305.02 0 0.0 4.0 \n",
"\n",
" gcs_motor_apache gcs_unable_apache gcs_verbal_apache heart_rate_apache \\\n",
"0 6.0 0.0 4.0 118.0 \n",
"1 3.0 0.0 1.0 120.0 \n",
"5 6.0 0.0 5.0 113.0 \n",
"10 6.0 0.0 5.0 101.0 \n",
"17 4.0 0.0 1.0 114.0 \n",
"18 6.0 0.0 4.0 98.0 \n",
"23 6.0 0.0 1.0 99.0 \n",
"24 6.0 0.0 5.0 104.0 \n",
"25 6.0 0.0 3.0 136.0 \n",
"26 6.0 0.0 5.0 118.0 \n",
"27 6.0 0.0 5.0 94.0 \n",
"29 6.0 0.0 5.0 90.0 \n",
"30 6.0 0.0 5.0 57.0 \n",
"31 6.0 0.0 5.0 63.0 \n",
"33 6.0 0.0 5.0 132.0 \n",
"\n",
" intubated_apache map_apache resprate_apache temp_apache \\\n",
"0 0.0 40.0 36.0 39.3 \n",
"1 0.0 46.0 33.0 35.1 \n",
"5 0.0 130.0 35.0 36.6 \n",
"10 0.0 72.0 15.0 36.8 \n",
"17 1.0 113.0 34.0 36.4 \n",
"18 0.0 55.0 4.0 36.6 \n",
"23 0.0 133.0 33.0 36.3 \n",
"24 0.0 64.0 12.0 36.5 \n",
"25 1.0 47.0 42.0 32.1 \n",
"26 0.0 162.0 20.0 36.7 \n",
"27 0.0 170.0 15.0 36.6 \n",
"29 0.0 139.0 14.0 36.7 \n",
"30 0.0 72.0 22.0 35.0 \n",
"31 0.0 186.0 39.0 36.4 \n",
"33 0.0 167.0 14.0 36.7 \n",
"\n",
" ventilated_apache d1_diasbp_max d1_diasbp_min \\\n",
"0 0.0 68.0 37.0 \n",
"1 1.0 95.0 31.0 \n",
"5 0.0 100.0 61.0 \n",
"10 0.0 72.0 53.0 \n",
"17 1.0 89.0 61.0 \n",
"18 0.0 73.0 43.0 \n",
"23 1.0 88.0 65.0 \n",
"24 0.0 76.0 54.0 \n",
"25 1.0 48.0 36.0 \n",
"26 0.0 102.0 64.0 \n",
"27 1.0 104.0 68.0 \n",
"29 0.0 108.0 31.0 \n",
"30 0.0 83.0 55.0 \n",
"31 0.0 80.0 51.0 \n",
"33 0.0 116.0 75.0 \n",
"\n",
" d1_diasbp_noninvasive_max d1_diasbp_noninvasive_min d1_heartrate_max \\\n",
"0 68.0 37.0 119.0 \n",
"1 95.0 31.0 118.0 \n",
"5 100.0 61.0 113.0 \n",
"10 72.0 53.0 101.0 \n",
"17 89.0 61.0 98.0 \n",
"18 73.0 43.0 102.0 \n",
"23 88.0 65.0 116.0 \n",
"24 76.0 54.0 77.0 \n",
"25 48.0 36.0 134.0 \n",
"26 102.0 64.0 104.0 \n",
"27 104.0 68.0 92.0 \n",
"29 108.0 31.0 95.0 \n",
"30 83.0 55.0 70.0 \n",
"31 80.0 51.0 90.0 \n",
"33 116.0 75.0 132.0 \n",
"\n",
" d1_heartrate_min d1_mbp_max d1_mbp_min d1_mbp_noninvasive_max \\\n",
"0 72.0 89.0 46.0 89.0 \n",
"1 72.0 120.0 38.0 120.0 \n",
"5 83.0 127.0 80.0 127.0 \n",
"10 67.0 93.0 70.0 93.0 \n",
"17 64.0 113.0 76.0 113.0 \n",
"18 66.0 103.0 55.0 103.0 \n",
"23 74.0 123.0 90.0 123.0 \n",
"24 54.0 93.0 64.0 93.0 \n",
"25 70.0 78.0 47.0 78.0 \n",
"26 68.0 162.0 93.0 162.0 \n",
"27 72.0 134.0 101.0 134.0 \n",
"29 53.0 139.0 73.0 139.0 \n",
"30 56.0 104.0 72.0 104.0 \n",
"31 57.0 123.0 73.0 123.0 \n",
"33 84.0 167.0 90.0 167.0 \n",
"\n",
" d1_mbp_noninvasive_min d1_resprate_max d1_resprate_min d1_spo2_max \\\n",
"0 46.0 34.0 10.0 100.0 \n",
"1 38.0 32.0 12.0 100.0 \n",
"5 80.0 32.0 10.0 97.0 \n",
"10 70.0 23.0 14.0 99.0 \n",
"17 76.0 22.0 9.0 100.0 \n",
"18 55.0 22.0 8.0 100.0 \n",
"23 90.0 36.0 16.0 100.0 \n",
"24 64.0 18.0 12.0 99.0 \n",
"25 47.0 42.0 16.0 100.0 \n",
"26 93.0 28.0 19.0 99.0 \n",
"27 101.0 26.0 10.0 99.0 \n",
"29 73.0 20.0 14.0 100.0 \n",
"30 72.0 22.0 16.0 100.0 \n",
"31 73.0 39.0 9.0 100.0 \n",
"33 90.0 27.0 14.0 100.0 \n",
"\n",
" d1_spo2_min d1_sysbp_max d1_sysbp_min d1_sysbp_noninvasive_max \\\n",
"0 74.0 131.0 73.0 131.0 \n",
"1 70.0 159.0 67.0 159.0 \n",
"5 91.0 173.0 107.0 173.0 \n",
"10 92.0 145.0 95.0 145.0 \n",
"17 88.0 169.0 102.0 169.0 \n",
"18 92.0 129.0 84.0 129.0 \n",
"23 90.0 179.0 129.0 179.0 \n",
"24 96.0 132.0 83.0 132.0 \n",
"25 51.0 112.0 74.0 112.0 \n",
"26 86.0 199.0 138.0 199.0 \n",
"27 91.0 170.0 136.0 170.0 \n",
"29 90.0 171.0 85.0 171.0 \n",
"30 89.0 146.0 100.0 146.0 \n",
"31 94.0 176.0 102.0 176.0 \n",
"33 96.0 176.0 113.0 176.0 \n",
"\n",
" d1_sysbp_noninvasive_min d1_temp_max d1_temp_min h1_diasbp_max \\\n",
"0 73.0 39.9 37.2 68.0 \n",
"1 67.0 36.3 35.1 61.0 \n",
"5 107.0 36.8 36.6 89.0 \n",
"10 95.0 37.0 36.7 72.0 \n",
"17 102.0 37.1 36.4 89.0 \n",
"18 84.0 36.8 36.6 66.0 \n",
"23 129.0 36.8 35.6 71.0 \n",
"24 83.0 36.7 36.5 76.0 \n",
"25 74.0 39.0 36.2 44.0 \n",
"26 138.0 37.2 36.7 79.0 \n",
"27 136.0 37.6 36.6 104.0 \n",
"29 85.0 37.8 36.7 70.0 \n",
"30 100.0 36.6 35.0 70.0 \n",
"31 102.0 36.7 36.4 79.0 \n",
"33 113.0 37.7 36.7 108.0 \n",
"\n",
" h1_diasbp_min h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min \\\n",
"0 63.0 68.0 63.0 \n",
"1 48.0 61.0 48.0 \n",
"5 89.0 89.0 89.0 \n",
"10 56.0 72.0 56.0 \n",
"17 63.0 89.0 63.0 \n",
"18 53.0 66.0 53.0 \n",
"23 65.0 71.0 65.0 \n",
"24 61.0 76.0 61.0 \n",
"25 36.0 44.0 36.0 \n",
"26 70.0 79.0 70.0 \n",
"27 80.0 104.0 80.0 \n",
"29 52.0 70.0 52.0 \n",
"30 57.0 70.0 57.0 \n",
"31 57.0 79.0 57.0 \n",
"33 97.0 108.0 97.0 \n",
"\n",
" h1_heartrate_max h1_heartrate_min h1_mbp_max h1_mbp_min \\\n",
"0 119.0 108.0 86.0 85.0 \n",
"1 114.0 100.0 85.0 57.0 \n",
"5 83.0 83.0 111.0 111.0 \n",
"10 90.0 70.0 91.0 87.0 \n",
"17 94.0 80.0 104.0 88.0 \n",
"18 100.0 74.0 84.0 78.0 \n",
"23 116.0 92.0 102.0 90.0 \n",
"24 72.0 54.0 93.0 87.0 \n",
"25 100.0 84.0 78.0 47.0 \n",
"26 76.0 68.0 111.0 101.0 \n",
"27 76.0 72.0 124.0 108.0 \n",
"29 95.0 53.0 86.0 86.0 \n",
"30 63.0 57.0 95.0 82.0 \n",
"31 69.0 65.0 113.0 82.0 \n",
"33 126.0 122.0 124.0 124.0 \n",
"\n",
" h1_mbp_noninvasive_max h1_mbp_noninvasive_min h1_resprate_max \\\n",
"0 86.0 85.0 26.0 \n",
"1 85.0 57.0 31.0 \n",
"5 111.0 111.0 12.0 \n",
"10 91.0 87.0 23.0 \n",
"17 104.0 88.0 21.0 \n",
"18 84.0 78.0 21.0 \n",
"23 102.0 90.0 36.0 \n",
"24 93.0 87.0 18.0 \n",
"25 78.0 47.0 29.0 \n",
"26 111.0 101.0 28.0 \n",
"27 124.0 108.0 23.0 \n",
"29 86.0 86.0 18.0 \n",
"30 95.0 82.0 16.0 \n",
"31 113.0 82.0 25.0 \n",
"33 124.0 124.0 27.0 \n",
"\n",
" h1_resprate_min h1_spo2_max h1_spo2_min h1_sysbp_max h1_sysbp_min \\\n",
"0 18.0 100.0 74.0 131.0 115.0 \n",
"1 28.0 95.0 70.0 95.0 71.0 \n",
"5 12.0 97.0 97.0 143.0 143.0 \n",
"10 14.0 99.0 93.0 145.0 114.0 \n",
"17 9.0 99.0 95.0 169.0 115.0 \n",
"18 12.0 99.0 95.0 121.0 93.0 \n",
"23 28.0 99.0 90.0 159.0 129.0 \n",
"24 18.0 99.0 97.0 127.0 109.0 \n",
"25 16.0 100.0 78.0 98.0 74.0 \n",
"26 19.0 98.0 93.0 165.0 152.0 \n",
"27 14.0 99.0 95.0 170.0 146.0 \n",
"29 18.0 100.0 95.0 123.0 85.0 \n",
"30 16.0 99.0 98.0 137.0 117.0 \n",
"31 9.0 100.0 99.0 176.0 119.0 \n",
"33 16.0 99.0 97.0 156.0 155.0 \n",
"\n",
" h1_sysbp_noninvasive_max h1_sysbp_noninvasive_min d1_glucose_max \\\n",
"0 131.0 115.0 168.0 \n",
"1 95.0 71.0 145.0 \n",
"5 143.0 143.0 156.0 \n",
"10 145.0 114.0 158.0 \n",
"17 169.0 115.0 143.0 \n",
"18 121.0 93.0 114.0 \n",
"23 159.0 129.0 144.0 \n",
"24 127.0 109.0 118.0 \n",
"25 98.0 74.0 154.0 \n",
"26 165.0 152.0 100.0 \n",
"27 170.0 146.0 175.0 \n",
"29 123.0 85.0 149.0 \n",
"30 137.0 117.0 100.0 \n",
"31 176.0 119.0 216.0 \n",
"33 156.0 155.0 131.0 \n",
"\n",
" d1_glucose_min d1_potassium_max d1_potassium_min \\\n",
"0 109.0 4.0 3.4 \n",
"1 128.0 4.2 3.8 \n",
"5 125.0 3.9 3.7 \n",
"10 133.0 4.2 4.2 \n",
"17 143.0 4.9 4.9 \n",
"18 88.0 4.0 4.0 \n",
"23 87.0 3.4 3.3 \n",
"24 118.0 3.7 3.7 \n",
"25 63.0 4.0 3.3 \n",
"26 100.0 4.1 4.1 \n",
"27 172.0 3.5 3.2 \n",
"29 149.0 4.2 3.6 \n",
"30 100.0 3.9 3.9 \n",
"31 82.0 4.1 4.1 \n",
"33 131.0 4.0 4.0 \n",
"\n",
" apache_4a_hospital_death_prob apache_4a_icu_death_prob aids cirrhosis \\\n",
"0 0.10 0.05 0.0 0.0 \n",
"1 0.47 0.29 0.0 0.0 \n",
"5 0.05 0.02 0.0 0.0 \n",
"10 0.01 0.00 0.0 0.0 \n",
"17 0.11 0.06 0.0 0.0 \n",
"18 0.03 0.01 0.0 0.0 \n",
"23 0.31 0.14 0.0 0.0 \n",
"24 0.02 0.01 0.0 0.0 \n",
"25 0.60 0.51 0.0 0.0 \n",
"26 0.03 0.01 0.0 0.0 \n",
"27 0.02 0.01 0.0 0.0 \n",
"29 0.01 0.01 0.0 0.0 \n",
"30 0.03 0.01 0.0 0.0 \n",
"31 0.01 0.00 0.0 0.0 \n",
"33 0.01 0.01 0.0 0.0 \n",
"\n",
" diabetes_mellitus hepatic_failure immunosuppression leukemia lymphoma \\\n",
"0 1.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 \n",
"5 1.0 0.0 0.0 0.0 0.0 \n",
"10 0.0 0.0 1.0 0.0 0.0 \n",
"17 0.0 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 0.0 \n",
"23 0.0 0.0 0.0 0.0 0.0 \n",
"24 0.0 0.0 0.0 0.0 0.0 \n",
"25 0.0 0.0 0.0 0.0 0.0 \n",
"26 0.0 0.0 0.0 0.0 0.0 \n",
"27 0.0 0.0 0.0 0.0 0.0 \n",
"29 0.0 0.0 0.0 0.0 0.0 \n",
"30 0.0 0.0 1.0 0.0 0.0 \n",
"31 1.0 0.0 0.0 0.0 0.0 \n",
"33 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" solid_tumor_with_metastasis hospital_death ethnicity_African American \\\n",
"0 0.0 0 False \n",
"1 0.0 0 False \n",
"5 0.0 0 False \n",
"10 0.0 0 False \n",
"17 0.0 0 False \n",
"18 0.0 0 False \n",
"23 0.0 0 False \n",
"24 0.0 0 False \n",
"25 0.0 1 False \n",
"26 0.0 0 False \n",
"27 0.0 0 False \n",
"29 0.0 0 True \n",
"30 0.0 0 False \n",
"31 1.0 0 False \n",
"33 0.0 0 False \n",
"\n",
" ethnicity_Asian ethnicity_Caucasian ethnicity_Hispanic \\\n",
"0 False True False \n",
"1 False True False \n",
"5 False True False \n",
"10 False False True \n",
"17 False False True \n",
"18 False False True \n",
"23 False True False \n",
"24 False True False \n",
"25 False True False \n",
"26 False True False \n",
"27 False True False \n",
"29 False False False \n",
"30 False True False \n",
"31 False True False \n",
"33 False True False \n",
"\n",
" ethnicity_Native American ethnicity_Other/Unknown gender_F gender_M \\\n",
"0 False False False True \n",
"1 False False True False \n",
"5 False False False True \n",
"10 False False True False \n",
"17 False False False True \n",
"18 False False False True \n",
"23 False False False True \n",
"24 False False False True \n",
"25 False False True False \n",
"26 False False True False \n",
"27 False False True False \n",
"29 False False False True \n",
"30 False False False True \n",
"31 False False False True \n",
"33 False False False True \n",
"\n",
" icu_admit_source_Accident & Emergency icu_admit_source_Floor \\\n",
"0 False True \n",
"1 False True \n",
"5 True False \n",
"10 False False \n",
"17 True False \n",
"18 True False \n",
"23 False True \n",
"24 True False \n",
"25 False True \n",
"26 True False \n",
"27 False False \n",
"29 True False \n",
"30 False True \n",
"31 False False \n",
"33 True False \n",
"\n",
" icu_admit_source_Operating Room / Recovery \\\n",
"0 False \n",
"1 False \n",
"5 False \n",
"10 True \n",
"17 False \n",
"18 False \n",
"23 False \n",
"24 False \n",
"25 False \n",
"26 False \n",
"27 True \n",
"29 False \n",
"30 False \n",
"31 True \n",
"33 False \n",
"\n",
" icu_admit_source_Other Hospital icu_admit_source_Other ICU \\\n",
"0 False False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" icu_stay_type_admit icu_stay_type_readmit icu_stay_type_transfer \\\n",
"0 True False False \n",
"1 True False False \n",
"5 True False False \n",
"10 True False False \n",
"17 True False False \n",
"18 True False False \n",
"23 True False False \n",
"24 True False False \n",
"25 True False False \n",
"26 True False False \n",
"27 True False False \n",
"29 True False False \n",
"30 True False False \n",
"31 True False False \n",
"33 True False False \n",
"\n",
" icu_type_CCU-CTICU icu_type_CSICU icu_type_CTICU icu_type_Cardiac ICU \\\n",
"0 False False True False \n",
"1 False False False False \n",
"5 False False False False \n",
"10 False False False False \n",
"17 False False True False \n",
"18 False False False False \n",
"23 False False False False \n",
"24 False False False False \n",
"25 True False False False \n",
"26 False False False False \n",
"27 False False False False \n",
"29 True False False False \n",
"30 False False False False \n",
"31 False False False False \n",
"33 False False False False \n",
"\n",
" icu_type_MICU icu_type_Med-Surg ICU icu_type_Neuro ICU icu_type_SICU \\\n",
"0 False False False False \n",
"1 False True False False \n",
"5 False True False False \n",
"10 False True False False \n",
"17 False False False False \n",
"18 False False True False \n",
"23 True False False False \n",
"24 False True False False \n",
"25 False False False False \n",
"26 False True False False \n",
"27 False True False False \n",
"29 False False False False \n",
"30 False True False False \n",
"31 False True False False \n",
"33 True False False False \n",
"\n",
" apache_3j_bodysystem_Cardiovascular \\\n",
"0 False \n",
"1 False \n",
"5 False \n",
"10 False \n",
"17 False \n",
"18 False \n",
"23 False \n",
"24 True \n",
"25 False \n",
"26 False \n",
"27 True \n",
"29 True \n",
"30 True \n",
"31 False \n",
"33 False \n",
"\n",
" apache_3j_bodysystem_Gastrointestinal apache_3j_bodysystem_Genitourinary \\\n",
"0 False False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 True False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 True False \n",
"\n",
" apache_3j_bodysystem_Gynecological apache_3j_bodysystem_Hematological \\\n",
"0 False False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" apache_3j_bodysystem_Metabolic apache_3j_bodysystem_Musculoskeletal/Skin \\\n",
"0 False False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" apache_3j_bodysystem_Neurological apache_3j_bodysystem_Respiratory \\\n",
"0 False False \n",
"1 False True \n",
"5 True False \n",
"10 False True \n",
"17 False True \n",
"18 True False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False True \n",
"33 False False \n",
"\n",
" apache_3j_bodysystem_Sepsis apache_3j_bodysystem_Trauma \\\n",
"0 True False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 True False \n",
"24 False False \n",
"25 True False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" apache_2_bodysystem_Cardiovascular apache_2_bodysystem_Gastrointestinal \\\n",
"0 True False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 True False \n",
"24 True False \n",
"25 True False \n",
"26 False True \n",
"27 True False \n",
"29 True False \n",
"30 True False \n",
"31 False False \n",
"33 False True \n",
"\n",
" apache_2_bodysystem_Haematologic apache_2_bodysystem_Metabolic \\\n",
"0 False False \n",
"1 False False \n",
"5 False False \n",
"10 False False \n",
"17 False False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" apache_2_bodysystem_Neurologic apache_2_bodysystem_Renal/Genitourinary \\\n",
"0 False False \n",
"1 False False \n",
"5 True False \n",
"10 False False \n",
"17 False False \n",
"18 True False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 False False \n",
"33 False False \n",
"\n",
" apache_2_bodysystem_Respiratory apache_2_bodysystem_Trauma \\\n",
"0 False False \n",
"1 True False \n",
"5 False False \n",
"10 True False \n",
"17 True False \n",
"18 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"29 False False \n",
"30 False False \n",
"31 True False \n",
"33 False False \n",
"\n",
" apache_2_bodysystem_Undefined Diagnoses \\\n",
"0 False \n",
"1 False \n",
"5 False \n",
"10 False \n",
"17 False \n",
"18 False \n",
"23 False \n",
"24 False \n",
"25 False \n",
"26 False \n",
"27 False \n",
"29 False \n",
"30 False \n",
"31 False \n",
"33 False \n",
"\n",
" apache_2_bodysystem_Undefined diagnoses \n",
"0 False \n",
"1 False \n",
"5 False \n",
"10 False \n",
"17 False \n",
"18 False \n",
"23 False \n",
"24 False \n",
"25 False \n",
"26 False \n",
"27 False \n",
"29 False \n",
"30 False \n",
"31 False \n",
"33 False "
],
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" <th>ethnicity_African American</th>\n",
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" <th>apache_3j_bodysystem_Cardiovascular</th>\n",
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" <td>37.0</td>\n",
" <td>68.0</td>\n",
" <td>37.0</td>\n",
" <td>119.0</td>\n",
" <td>72.0</td>\n",
" <td>89.0</td>\n",
" <td>46.0</td>\n",
" <td>89.0</td>\n",
" <td>46.0</td>\n",
" <td>34.0</td>\n",
" <td>10.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>131.0</td>\n",
" <td>73.0</td>\n",
" <td>131.0</td>\n",
" <td>73.0</td>\n",
" <td>39.9</td>\n",
" <td>37.2</td>\n",
" <td>68.0</td>\n",
" <td>63.0</td>\n",
" <td>68.0</td>\n",
" <td>63.0</td>\n",
" <td>119.0</td>\n",
" <td>108.0</td>\n",
" <td>86.0</td>\n",
" <td>85.0</td>\n",
" <td>86.0</td>\n",
" <td>85.0</td>\n",
" <td>26.0</td>\n",
" <td>18.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>131.0</td>\n",
" <td>115.0</td>\n",
" <td>131.0</td>\n",
" <td>115.0</td>\n",
" <td>168.0</td>\n",
" <td>109.0</td>\n",
" <td>4.0</td>\n",
" <td>3.4</td>\n",
" <td>0.10</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>114252</td>\n",
" <td>59342</td>\n",
" <td>81</td>\n",
" <td>77.0</td>\n",
" <td>27.420000</td>\n",
" <td>0</td>\n",
" <td>160.0</td>\n",
" <td>90</td>\n",
" <td>0.927778</td>\n",
" <td>70.2</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>120.0</td>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>33.0</td>\n",
" <td>35.1</td>\n",
" <td>1.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>95.0</td>\n",
" <td>31.0</td>\n",
" <td>118.0</td>\n",
" <td>72.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>120.0</td>\n",
" <td>38.0</td>\n",
" <td>32.0</td>\n",
" <td>12.0</td>\n",
" <td>100.0</td>\n",
" <td>70.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>159.0</td>\n",
" <td>67.0</td>\n",
" <td>36.3</td>\n",
" <td>35.1</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>61.0</td>\n",
" <td>48.0</td>\n",
" <td>114.0</td>\n",
" <td>100.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>85.0</td>\n",
" <td>57.0</td>\n",
" <td>31.0</td>\n",
" <td>28.0</td>\n",
" <td>95.0</td>\n",
" <td>70.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>95.0</td>\n",
" <td>71.0</td>\n",
" <td>145.0</td>\n",
" <td>128.0</td>\n",
" <td>4.2</td>\n",
" <td>3.8</td>\n",
" <td>0.47</td>\n",
" <td>0.29</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>33181</td>\n",
" <td>74489</td>\n",
" <td>83</td>\n",
" <td>67.0</td>\n",
" <td>27.560000</td>\n",
" <td>0</td>\n",
" <td>190.5</td>\n",
" <td>95</td>\n",
" <td>0.000694</td>\n",
" <td>100.0</td>\n",
" <td>301.0</td>\n",
" <td>403.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>113.0</td>\n",
" <td>0.0</td>\n",
" <td>130.0</td>\n",
" <td>35.0</td>\n",
" <td>36.6</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>100.0</td>\n",
" <td>61.0</td>\n",
" <td>113.0</td>\n",
" <td>83.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>127.0</td>\n",
" <td>80.0</td>\n",
" <td>32.0</td>\n",
" <td>10.0</td>\n",
" <td>97.0</td>\n",
" <td>91.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>173.0</td>\n",
" <td>107.0</td>\n",
" <td>36.8</td>\n",
" <td>36.6</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>89.0</td>\n",
" <td>83.0</td>\n",
" <td>83.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>111.0</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>97.0</td>\n",
" <td>97.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>156.0</td>\n",
" <td>125.0</td>\n",
" <td>3.9</td>\n",
" <td>3.7</td>\n",
" <td>0.05</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>105427</td>\n",
" <td>125898</td>\n",
" <td>77</td>\n",
" <td>72.0</td>\n",
" <td>28.257052</td>\n",
" <td>1</td>\n",
" <td>154.9</td>\n",
" <td>113</td>\n",
" <td>0.004861</td>\n",
" <td>67.8</td>\n",
" <td>303.0</td>\n",
" <td>1304.08</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>15.0</td>\n",
" <td>36.8</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>53.0</td>\n",
" <td>72.0</td>\n",
" <td>53.0</td>\n",
" <td>101.0</td>\n",
" <td>67.0</td>\n",
" <td>93.0</td>\n",
" <td>70.0</td>\n",
" <td>93.0</td>\n",
" <td>70.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>92.0</td>\n",
" <td>145.0</td>\n",
" <td>95.0</td>\n",
" <td>145.0</td>\n",
" <td>95.0</td>\n",
" <td>37.0</td>\n",
" <td>36.7</td>\n",
" <td>72.0</td>\n",
" <td>56.0</td>\n",
" <td>72.0</td>\n",
" <td>56.0</td>\n",
" <td>90.0</td>\n",
" <td>70.0</td>\n",
" <td>91.0</td>\n",
" <td>87.0</td>\n",
" <td>91.0</td>\n",
" <td>87.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>93.0</td>\n",
" <td>145.0</td>\n",
" <td>114.0</td>\n",
" <td>145.0</td>\n",
" <td>114.0</td>\n",
" <td>158.0</td>\n",
" <td>133.0</td>\n",
" <td>4.2</td>\n",
" <td>4.2</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>22471</td>\n",
" <td>112115</td>\n",
" <td>118</td>\n",
" <td>46.0</td>\n",
" <td>25.845717</td>\n",
" <td>0</td>\n",
" <td>167.6</td>\n",
" <td>92</td>\n",
" <td>0.000000</td>\n",
" <td>72.6</td>\n",
" <td>108.0</td>\n",
" <td>203.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>114.0</td>\n",
" <td>1.0</td>\n",
" <td>113.0</td>\n",
" <td>34.0</td>\n",
" <td>36.4</td>\n",
" <td>1.0</td>\n",
" <td>89.0</td>\n",
" <td>61.0</td>\n",
" <td>89.0</td>\n",
" <td>61.0</td>\n",
" <td>98.0</td>\n",
" <td>64.0</td>\n",
" <td>113.0</td>\n",
" <td>76.0</td>\n",
" <td>113.0</td>\n",
" <td>76.0</td>\n",
" <td>22.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>88.0</td>\n",
" <td>169.0</td>\n",
" <td>102.0</td>\n",
" <td>169.0</td>\n",
" <td>102.0</td>\n",
" <td>37.1</td>\n",
" <td>36.4</td>\n",
" <td>89.0</td>\n",
" <td>63.0</td>\n",
" <td>89.0</td>\n",
" <td>63.0</td>\n",
" <td>94.0</td>\n",
" <td>80.0</td>\n",
" <td>104.0</td>\n",
" <td>88.0</td>\n",
" <td>104.0</td>\n",
" <td>88.0</td>\n",
" <td>21.0</td>\n",
" <td>9.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>169.0</td>\n",
" <td>115.0</td>\n",
" <td>169.0</td>\n",
" <td>115.0</td>\n",
" <td>143.0</td>\n",
" <td>143.0</td>\n",
" <td>4.9</td>\n",
" <td>4.9</td>\n",
" <td>0.11</td>\n",
" <td>0.06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>48056</td>\n",
" <td>114220</td>\n",
" <td>118</td>\n",
" <td>65.0</td>\n",
" <td>28.408929</td>\n",
" <td>0</td>\n",
" <td>167.6</td>\n",
" <td>100</td>\n",
" <td>0.000000</td>\n",
" <td>79.8</td>\n",
" <td>301.0</td>\n",
" <td>410.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>98.0</td>\n",
" <td>0.0</td>\n",
" <td>55.0</td>\n",
" <td>4.0</td>\n",
" <td>36.6</td>\n",
" <td>0.0</td>\n",
" <td>73.0</td>\n",
" <td>43.0</td>\n",
" <td>73.0</td>\n",
" <td>43.0</td>\n",
" <td>102.0</td>\n",
" <td>66.0</td>\n",
" <td>103.0</td>\n",
" <td>55.0</td>\n",
" <td>103.0</td>\n",
" <td>55.0</td>\n",
" <td>22.0</td>\n",
" <td>8.0</td>\n",
" <td>100.0</td>\n",
" <td>92.0</td>\n",
" <td>129.0</td>\n",
" <td>84.0</td>\n",
" <td>129.0</td>\n",
" <td>84.0</td>\n",
" <td>36.8</td>\n",
" <td>36.6</td>\n",
" <td>66.0</td>\n",
" <td>53.0</td>\n",
" <td>66.0</td>\n",
" <td>53.0</td>\n",
" <td>100.0</td>\n",
" <td>74.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>21.0</td>\n",
" <td>12.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>93.0</td>\n",
" <td>121.0</td>\n",
" <td>93.0</td>\n",
" <td>114.0</td>\n",
" <td>88.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>95460</td>\n",
" <td>120539</td>\n",
" <td>118</td>\n",
" <td>87.0</td>\n",
" <td>21.963763</td>\n",
" <td>0</td>\n",
" <td>180.3</td>\n",
" <td>97</td>\n",
" <td>5.046528</td>\n",
" <td>71.4</td>\n",
" <td>113.0</td>\n",
" <td>501.05</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>99.0</td>\n",
" <td>0.0</td>\n",
" <td>133.0</td>\n",
" <td>33.0</td>\n",
" <td>36.3</td>\n",
" <td>1.0</td>\n",
" <td>88.0</td>\n",
" <td>65.0</td>\n",
" <td>88.0</td>\n",
" <td>65.0</td>\n",
" <td>116.0</td>\n",
" <td>74.0</td>\n",
" <td>123.0</td>\n",
" <td>90.0</td>\n",
" <td>123.0</td>\n",
" <td>90.0</td>\n",
" <td>36.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>179.0</td>\n",
" <td>129.0</td>\n",
" <td>179.0</td>\n",
" <td>129.0</td>\n",
" <td>36.8</td>\n",
" <td>35.6</td>\n",
" <td>71.0</td>\n",
" <td>65.0</td>\n",
" <td>71.0</td>\n",
" <td>65.0</td>\n",
" <td>116.0</td>\n",
" <td>92.0</td>\n",
" <td>102.0</td>\n",
" <td>90.0</td>\n",
" <td>102.0</td>\n",
" <td>90.0</td>\n",
" <td>36.0</td>\n",
" <td>28.0</td>\n",
" <td>99.0</td>\n",
" <td>90.0</td>\n",
" <td>159.0</td>\n",
" <td>129.0</td>\n",
" <td>159.0</td>\n",
" <td>129.0</td>\n",
" <td>144.0</td>\n",
" <td>87.0</td>\n",
" <td>3.4</td>\n",
" <td>3.3</td>\n",
" <td>0.31</td>\n",
" <td>0.14</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>7220</td>\n",
" <td>92453</td>\n",
" <td>77</td>\n",
" <td>60.0</td>\n",
" <td>29.509959</td>\n",
" <td>0</td>\n",
" <td>188.0</td>\n",
" <td>113</td>\n",
" <td>0.081250</td>\n",
" <td>104.3</td>\n",
" <td>112.0</td>\n",
" <td>107.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>104.0</td>\n",
" <td>0.0</td>\n",
" <td>64.0</td>\n",
" <td>12.0</td>\n",
" <td>36.5</td>\n",
" <td>0.0</td>\n",
" <td>76.0</td>\n",
" <td>54.0</td>\n",
" <td>76.0</td>\n",
" <td>54.0</td>\n",
" <td>77.0</td>\n",
" <td>54.0</td>\n",
" <td>93.0</td>\n",
" <td>64.0</td>\n",
" <td>93.0</td>\n",
" <td>64.0</td>\n",
" <td>18.0</td>\n",
" <td>12.0</td>\n",
" <td>99.0</td>\n",
" <td>96.0</td>\n",
" <td>132.0</td>\n",
" <td>83.0</td>\n",
" <td>132.0</td>\n",
" <td>83.0</td>\n",
" <td>36.7</td>\n",
" <td>36.5</td>\n",
" <td>76.0</td>\n",
" <td>61.0</td>\n",
" <td>76.0</td>\n",
" <td>61.0</td>\n",
" <td>72.0</td>\n",
" <td>54.0</td>\n",
" <td>93.0</td>\n",
" <td>87.0</td>\n",
" <td>93.0</td>\n",
" <td>87.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>99.0</td>\n",
" <td>97.0</td>\n",
" <td>127.0</td>\n",
" <td>109.0</td>\n",
" <td>127.0</td>\n",
" <td>109.0</td>\n",
" <td>118.0</td>\n",
" <td>118.0</td>\n",
" <td>3.7</td>\n",
" <td>3.7</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>29208</td>\n",
" <td>114628</td>\n",
" <td>118</td>\n",
" <td>68.0</td>\n",
" <td>26.010703</td>\n",
" <td>0</td>\n",
" <td>165.1</td>\n",
" <td>114</td>\n",
" <td>0.758333</td>\n",
" <td>70.9</td>\n",
" <td>113.0</td>\n",
" <td>501.06</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>136.0</td>\n",
" <td>1.0</td>\n",
" <td>47.0</td>\n",
" <td>42.0</td>\n",
" <td>32.1</td>\n",
" <td>1.0</td>\n",
" <td>48.0</td>\n",
" <td>36.0</td>\n",
" <td>48.0</td>\n",
" <td>36.0</td>\n",
" <td>134.0</td>\n",
" <td>70.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>42.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>51.0</td>\n",
" <td>112.0</td>\n",
" <td>74.0</td>\n",
" <td>112.0</td>\n",
" <td>74.0</td>\n",
" <td>39.0</td>\n",
" <td>36.2</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>100.0</td>\n",
" <td>84.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>78.0</td>\n",
" <td>47.0</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>78.0</td>\n",
" <td>98.0</td>\n",
" <td>74.0</td>\n",
" <td>98.0</td>\n",
" <td>74.0</td>\n",
" <td>154.0</td>\n",
" <td>63.0</td>\n",
" <td>4.0</td>\n",
" <td>3.3</td>\n",
" <td>0.60</td>\n",
" <td>0.51</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>32902</td>\n",
" <td>17922</td>\n",
" <td>118</td>\n",
" <td>85.0</td>\n",
" <td>23.809770</td>\n",
" <td>0</td>\n",
" <td>152.4</td>\n",
" <td>93</td>\n",
" <td>0.000694</td>\n",
" <td>55.3</td>\n",
" <td>124.0</td>\n",
" <td>305.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>118.0</td>\n",
" <td>0.0</td>\n",
" <td>162.0</td>\n",
" <td>20.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>102.0</td>\n",
" <td>64.0</td>\n",
" <td>102.0</td>\n",
" <td>64.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>162.0</td>\n",
" <td>93.0</td>\n",
" <td>162.0</td>\n",
" <td>93.0</td>\n",
" <td>28.0</td>\n",
" <td>19.0</td>\n",
" <td>99.0</td>\n",
" <td>86.0</td>\n",
" <td>199.0</td>\n",
" <td>138.0</td>\n",
" <td>199.0</td>\n",
" <td>138.0</td>\n",
" <td>37.2</td>\n",
" <td>36.7</td>\n",
" <td>79.0</td>\n",
" <td>70.0</td>\n",
" <td>79.0</td>\n",
" <td>70.0</td>\n",
" <td>76.0</td>\n",
" <td>68.0</td>\n",
" <td>111.0</td>\n",
" <td>101.0</td>\n",
" <td>111.0</td>\n",
" <td>101.0</td>\n",
" <td>28.0</td>\n",
" <td>19.0</td>\n",
" <td>98.0</td>\n",
" <td>93.0</td>\n",
" <td>165.0</td>\n",
" <td>152.0</td>\n",
" <td>165.0</td>\n",
" <td>152.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>4.1</td>\n",
" <td>4.1</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>16847</td>\n",
" <td>8036</td>\n",
" <td>33</td>\n",
" <td>79.0</td>\n",
" <td>23.408979</td>\n",
" <td>0</td>\n",
" <td>149.9</td>\n",
" <td>91</td>\n",
" <td>0.009028</td>\n",
" <td>52.6</td>\n",
" <td>202.0</td>\n",
" <td>1204.01</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>94.0</td>\n",
" <td>0.0</td>\n",
" <td>170.0</td>\n",
" <td>15.0</td>\n",
" <td>36.6</td>\n",
" <td>1.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>104.0</td>\n",
" <td>68.0</td>\n",
" <td>92.0</td>\n",
" <td>72.0</td>\n",
" <td>134.0</td>\n",
" <td>101.0</td>\n",
" <td>134.0</td>\n",
" <td>101.0</td>\n",
" <td>26.0</td>\n",
" <td>10.0</td>\n",
" <td>99.0</td>\n",
" <td>91.0</td>\n",
" <td>170.0</td>\n",
" <td>136.0</td>\n",
" <td>170.0</td>\n",
" <td>136.0</td>\n",
" <td>37.6</td>\n",
" <td>36.6</td>\n",
" <td>104.0</td>\n",
" <td>80.0</td>\n",
" <td>104.0</td>\n",
" <td>80.0</td>\n",
" <td>76.0</td>\n",
" <td>72.0</td>\n",
" <td>124.0</td>\n",
" <td>108.0</td>\n",
" <td>124.0</td>\n",
" <td>108.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>99.0</td>\n",
" <td>95.0</td>\n",
" <td>170.0</td>\n",
" <td>146.0</td>\n",
" <td>170.0</td>\n",
" <td>146.0</td>\n",
" <td>175.0</td>\n",
" <td>172.0</td>\n",
" <td>3.5</td>\n",
" <td>3.2</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>6777</td>\n",
" <td>83373</td>\n",
" <td>118</td>\n",
" <td>60.0</td>\n",
" <td>26.485715</td>\n",
" <td>0</td>\n",
" <td>180.3</td>\n",
" <td>114</td>\n",
" <td>0.000000</td>\n",
" <td>86.1</td>\n",
" <td>112.0</td>\n",
" <td>107.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>139.0</td>\n",
" <td>14.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>31.0</td>\n",
" <td>108.0</td>\n",
" <td>31.0</td>\n",
" <td>95.0</td>\n",
" <td>53.0</td>\n",
" <td>139.0</td>\n",
" <td>73.0</td>\n",
" <td>139.0</td>\n",
" <td>73.0</td>\n",
" <td>20.0</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>171.0</td>\n",
" <td>85.0</td>\n",
" <td>171.0</td>\n",
" <td>85.0</td>\n",
" <td>37.8</td>\n",
" <td>36.7</td>\n",
" <td>70.0</td>\n",
" <td>52.0</td>\n",
" <td>70.0</td>\n",
" <td>52.0</td>\n",
" <td>95.0</td>\n",
" <td>53.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>86.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>100.0</td>\n",
" <td>95.0</td>\n",
" <td>123.0</td>\n",
" <td>85.0</td>\n",
" <td>123.0</td>\n",
" <td>85.0</td>\n",
" <td>149.0</td>\n",
" <td>149.0</td>\n",
" <td>4.2</td>\n",
" <td>3.6</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>129675</td>\n",
" <td>45336</td>\n",
" <td>77</td>\n",
" <td>76.0</td>\n",
" <td>32.374349</td>\n",
" <td>0</td>\n",
" <td>182.9</td>\n",
" <td>113</td>\n",
" <td>0.125694</td>\n",
" <td>108.3</td>\n",
" <td>110.0</td>\n",
" <td>104.01</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>57.0</td>\n",
" <td>0.0</td>\n",
" <td>72.0</td>\n",
" <td>22.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>83.0</td>\n",
" <td>55.0</td>\n",
" <td>83.0</td>\n",
" <td>55.0</td>\n",
" <td>70.0</td>\n",
" <td>56.0</td>\n",
" <td>104.0</td>\n",
" <td>72.0</td>\n",
" <td>104.0</td>\n",
" <td>72.0</td>\n",
" <td>22.0</td>\n",
" <td>16.0</td>\n",
" <td>100.0</td>\n",
" <td>89.0</td>\n",
" <td>146.0</td>\n",
" <td>100.0</td>\n",
" <td>146.0</td>\n",
" <td>100.0</td>\n",
" <td>36.6</td>\n",
" <td>35.0</td>\n",
" <td>70.0</td>\n",
" <td>57.0</td>\n",
" <td>70.0</td>\n",
" <td>57.0</td>\n",
" <td>63.0</td>\n",
" <td>57.0</td>\n",
" <td>95.0</td>\n",
" <td>82.0</td>\n",
" <td>95.0</td>\n",
" <td>82.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>99.0</td>\n",
" <td>98.0</td>\n",
" <td>137.0</td>\n",
" <td>117.0</td>\n",
" <td>137.0</td>\n",
" <td>117.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>3.9</td>\n",
" <td>3.9</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>6603</td>\n",
" <td>17124</td>\n",
" <td>83</td>\n",
" <td>68.0</td>\n",
" <td>27.560503</td>\n",
" <td>1</td>\n",
" <td>172.7</td>\n",
" <td>95</td>\n",
" <td>0.005556</td>\n",
" <td>82.2</td>\n",
" <td>209.0</td>\n",
" <td>1302.02</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>63.0</td>\n",
" <td>0.0</td>\n",
" <td>186.0</td>\n",
" <td>39.0</td>\n",
" <td>36.4</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>51.0</td>\n",
" <td>80.0</td>\n",
" <td>51.0</td>\n",
" <td>90.0</td>\n",
" <td>57.0</td>\n",
" <td>123.0</td>\n",
" <td>73.0</td>\n",
" <td>123.0</td>\n",
" <td>73.0</td>\n",
" <td>39.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>94.0</td>\n",
" <td>176.0</td>\n",
" <td>102.0</td>\n",
" <td>176.0</td>\n",
" <td>102.0</td>\n",
" <td>36.7</td>\n",
" <td>36.4</td>\n",
" <td>79.0</td>\n",
" <td>57.0</td>\n",
" <td>79.0</td>\n",
" <td>57.0</td>\n",
" <td>69.0</td>\n",
" <td>65.0</td>\n",
" <td>113.0</td>\n",
" <td>82.0</td>\n",
" <td>113.0</td>\n",
" <td>82.0</td>\n",
" <td>25.0</td>\n",
" <td>9.0</td>\n",
" <td>100.0</td>\n",
" <td>99.0</td>\n",
" <td>176.0</td>\n",
" <td>119.0</td>\n",
" <td>176.0</td>\n",
" <td>119.0</td>\n",
" <td>216.0</td>\n",
" <td>82.0</td>\n",
" <td>4.1</td>\n",
" <td>4.1</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>48566</td>\n",
" <td>94258</td>\n",
" <td>118</td>\n",
" <td>45.0</td>\n",
" <td>32.129842</td>\n",
" <td>0</td>\n",
" <td>190.5</td>\n",
" <td>97</td>\n",
" <td>0.000000</td>\n",
" <td>116.6</td>\n",
" <td>124.0</td>\n",
" <td>305.02</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>132.0</td>\n",
" <td>0.0</td>\n",
" <td>167.0</td>\n",
" <td>14.0</td>\n",
" <td>36.7</td>\n",
" <td>0.0</td>\n",
" <td>116.0</td>\n",
" <td>75.0</td>\n",
" <td>116.0</td>\n",
" <td>75.0</td>\n",
" <td>132.0</td>\n",
" <td>84.0</td>\n",
" <td>167.0</td>\n",
" <td>90.0</td>\n",
" <td>167.0</td>\n",
" <td>90.0</td>\n",
" <td>27.0</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>96.0</td>\n",
" <td>176.0</td>\n",
" <td>113.0</td>\n",
" <td>176.0</td>\n",
" <td>113.0</td>\n",
" <td>37.7</td>\n",
" <td>36.7</td>\n",
" <td>108.0</td>\n",
" <td>97.0</td>\n",
" <td>108.0</td>\n",
" <td>97.0</td>\n",
" <td>126.0</td>\n",
" <td>122.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>124.0</td>\n",
" <td>27.0</td>\n",
" <td>16.0</td>\n",
" <td>99.0</td>\n",
" <td>97.0</td>\n",
" <td>156.0</td>\n",
" <td>155.0</td>\n",
" <td>156.0</td>\n",
" <td>155.0</td>\n",
" <td>131.0</td>\n",
" <td>131.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed02e77c-829e-466e-abae-b86138454fdb')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "one_hot_encode_data"
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"metadata": {},
"execution_count": 30
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],
"source": [
"#### one hot encoding for the categorical data in string\n",
"###['ethnicity', 'gender', 'icu_admit_source', 'icu_stay_type', 'icu_type','apache_3j_bodysystem', 'apache_2_bodysystem']\n",
"clean_data1=clean_data.copy()\n",
"one_hot_encode_data= pd.get_dummies(clean_data1, columns= ['ethnicity', 'gender', 'icu_admit_source', 'icu_stay_type', 'icu_type','apache_3j_bodysystem', 'apache_2_bodysystem'], drop_first=False )\n",
"one_hot_encode_data.head(15)\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
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"height": 400
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"id": "QJMObJPo6xKA",
"outputId": "1600f55e-639c-40c4-be18-ed41a5c4516c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi elective_surgery height \\\n",
"0 66154 25312 118 68 22 0 180 \n",
"1 114252 59342 81 77 27 0 160 \n",
"5 33181 74489 83 67 27 0 190 \n",
"10 105427 125898 77 72 28 1 154 \n",
"17 22471 112115 118 46 25 0 167 \n",
"18 48056 114220 118 65 28 0 167 \n",
"23 95460 120539 118 87 21 0 180 \n",
"24 7220 92453 77 60 29 0 188 \n",
"25 29208 114628 118 68 26 0 165 \n",
"26 32902 17922 118 85 23 0 152 \n",
"\n",
" icu_id pre_icu_los_days weight apache_2_diagnosis apache_3j_diagnosis \\\n",
"0 92 0 73 113 502 \n",
"1 90 0 70 108 203 \n",
"5 95 0 100 301 403 \n",
"10 113 0 67 303 1304 \n",
"17 92 0 72 108 203 \n",
"18 100 0 79 301 410 \n",
"23 97 5 71 113 501 \n",
"24 113 0 104 112 107 \n",
"25 114 0 70 113 501 \n",
"26 93 0 55 124 305 \n",
"\n",
" apache_post_operative arf_apache gcs_eyes_apache gcs_motor_apache \\\n",
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"24 0 0 4 6 \n",
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" gcs_unable_apache gcs_verbal_apache heart_rate_apache intubated_apache \\\n",
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" map_apache resprate_apache temp_apache ventilated_apache \\\n",
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"\n",
" d1_diasbp_max d1_diasbp_min d1_diasbp_noninvasive_max \\\n",
"0 68 37 68 \n",
"1 95 31 95 \n",
"5 100 61 100 \n",
"10 72 53 72 \n",
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"24 76 54 76 \n",
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"26 102 64 102 \n",
"\n",
" d1_diasbp_noninvasive_min d1_heartrate_max d1_heartrate_min d1_mbp_max \\\n",
"0 37 119 72 89 \n",
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"5 61 113 83 127 \n",
"10 53 101 67 93 \n",
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"25 36 134 70 78 \n",
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"\n",
" d1_mbp_min d1_mbp_noninvasive_max d1_mbp_noninvasive_min \\\n",
"0 46 89 46 \n",
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"10 70 93 70 \n",
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"26 93 162 93 \n",
"\n",
" d1_resprate_max d1_resprate_min d1_spo2_max d1_spo2_min d1_sysbp_max \\\n",
"0 34 10 100 74 131 \n",
"1 32 12 100 70 159 \n",
"5 32 10 97 91 173 \n",
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"26 28 19 99 86 199 \n",
"\n",
" d1_sysbp_min d1_sysbp_noninvasive_max d1_sysbp_noninvasive_min \\\n",
"0 73 131 73 \n",
"1 67 159 67 \n",
"5 107 173 107 \n",
"10 95 145 95 \n",
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"18 84 129 84 \n",
"23 129 179 129 \n",
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"25 74 112 74 \n",
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"\n",
" d1_temp_max d1_temp_min h1_diasbp_max h1_diasbp_min \\\n",
"0 39 37 68 63 \n",
"1 36 35 61 48 \n",
"5 36 36 89 89 \n",
"10 37 36 72 56 \n",
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"24 36 36 76 61 \n",
"25 39 36 44 36 \n",
"26 37 36 79 70 \n",
"\n",
" h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min h1_heartrate_max \\\n",
"0 68 63 119 \n",
"1 61 48 114 \n",
"5 89 89 83 \n",
"10 72 56 90 \n",
"17 89 63 94 \n",
"18 66 53 100 \n",
"23 71 65 116 \n",
"24 76 61 72 \n",
"25 44 36 100 \n",
"26 79 70 76 \n",
"\n",
" h1_heartrate_min h1_mbp_max h1_mbp_min h1_mbp_noninvasive_max \\\n",
"0 108 86 85 86 \n",
"1 100 85 57 85 \n",
"5 83 111 111 111 \n",
"10 70 91 87 91 \n",
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"25 84 78 47 78 \n",
"26 68 111 101 111 \n",
"\n",
" h1_mbp_noninvasive_min h1_resprate_max h1_resprate_min h1_spo2_max \\\n",
"0 85 26 18 100 \n",
"1 57 31 28 95 \n",
"5 111 12 12 97 \n",
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"25 47 29 16 100 \n",
"26 101 28 19 98 \n",
"\n",
" h1_spo2_min h1_sysbp_max h1_sysbp_min h1_sysbp_noninvasive_max \\\n",
"0 74 131 115 131 \n",
"1 70 95 71 95 \n",
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"\n",
" h1_sysbp_noninvasive_min d1_glucose_max d1_glucose_min \\\n",
"0 115 168 109 \n",
"1 71 145 128 \n",
"5 143 156 125 \n",
"10 114 158 133 \n",
"17 115 143 143 \n",
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"24 109 118 118 \n",
"25 74 154 63 \n",
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"\n",
" d1_potassium_max d1_potassium_min apache_4a_hospital_death_prob \\\n",
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" apache_4a_icu_death_prob aids cirrhosis diabetes_mellitus \\\n",
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"0 0 0 0 \n",
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"\n",
" ethnicity_Asian ethnicity_Caucasian ethnicity_Hispanic \\\n",
"0 0 1 0 \n",
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"\n",
" ethnicity_Native American ethnicity_Other/Unknown gender_F gender_M \\\n",
"0 0 0 0 1 \n",
"1 0 0 1 0 \n",
"5 0 0 0 1 \n",
"10 0 0 1 0 \n",
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"18 0 0 0 1 \n",
"23 0 0 0 1 \n",
"24 0 0 0 1 \n",
"25 0 0 1 0 \n",
"26 0 0 1 0 \n",
"\n",
" icu_admit_source_Accident & Emergency icu_admit_source_Floor \\\n",
"0 0 1 \n",
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"17 1 0 \n",
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"23 0 1 \n",
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"25 0 1 \n",
"26 1 0 \n",
"\n",
" icu_admit_source_Operating Room / Recovery \\\n",
"0 0 \n",
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"5 0 \n",
"10 1 \n",
"17 0 \n",
"18 0 \n",
"23 0 \n",
"24 0 \n",
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"\n",
" icu_admit_source_Other Hospital icu_admit_source_Other ICU \\\n",
"0 0 0 \n",
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"10 0 0 \n",
"17 0 0 \n",
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"\n",
" icu_stay_type_admit icu_stay_type_readmit icu_stay_type_transfer \\\n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"5 1 0 0 \n",
"10 1 0 0 \n",
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"18 1 0 0 \n",
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"25 1 0 0 \n",
"26 1 0 0 \n",
"\n",
" icu_type_CCU-CTICU icu_type_CSICU icu_type_CTICU icu_type_Cardiac ICU \\\n",
"0 0 0 1 0 \n",
"1 0 0 0 0 \n",
"5 0 0 0 0 \n",
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"18 0 0 0 0 \n",
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"24 0 0 0 0 \n",
"25 1 0 0 0 \n",
"26 0 0 0 0 \n",
"\n",
" icu_type_MICU icu_type_Med-Surg ICU icu_type_Neuro ICU icu_type_SICU \\\n",
"0 0 0 0 0 \n",
"1 0 1 0 0 \n",
"5 0 1 0 0 \n",
"10 0 1 0 0 \n",
"17 0 0 0 0 \n",
"18 0 0 1 0 \n",
"23 1 0 0 0 \n",
"24 0 1 0 0 \n",
"25 0 0 0 0 \n",
"26 0 1 0 0 \n",
"\n",
" apache_3j_bodysystem_Cardiovascular \\\n",
"0 0 \n",
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"5 0 \n",
"10 0 \n",
"17 0 \n",
"18 0 \n",
"23 0 \n",
"24 1 \n",
"25 0 \n",
"26 0 \n",
"\n",
" apache_3j_bodysystem_Gastrointestinal apache_3j_bodysystem_Genitourinary \\\n",
"0 0 0 \n",
"1 0 0 \n",
"5 0 0 \n",
"10 0 0 \n",
"17 0 0 \n",
"18 0 0 \n",
"23 0 0 \n",
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"\n",
" apache_3j_bodysystem_Gynecological apache_3j_bodysystem_Hematological \\\n",
"0 0 0 \n",
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" apache_3j_bodysystem_Metabolic apache_3j_bodysystem_Musculoskeletal/Skin \\\n",
"0 0 0 \n",
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"\n",
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" <td>56</td>\n",
" <td>90</td>\n",
" <td>70</td>\n",
" <td>91</td>\n",
" <td>87</td>\n",
" <td>91</td>\n",
" <td>87</td>\n",
" <td>23</td>\n",
" <td>14</td>\n",
" <td>99</td>\n",
" <td>93</td>\n",
" <td>145</td>\n",
" <td>114</td>\n",
" <td>145</td>\n",
" <td>114</td>\n",
" <td>158</td>\n",
" <td>133</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>22471</td>\n",
" <td>112115</td>\n",
" <td>118</td>\n",
" <td>46</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>167</td>\n",
" <td>92</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>108</td>\n",
" <td>203</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>114</td>\n",
" <td>1</td>\n",
" <td>113</td>\n",
" <td>34</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>61</td>\n",
" <td>89</td>\n",
" <td>61</td>\n",
" <td>98</td>\n",
" <td>64</td>\n",
" <td>113</td>\n",
" <td>76</td>\n",
" <td>113</td>\n",
" <td>76</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>100</td>\n",
" <td>88</td>\n",
" <td>169</td>\n",
" <td>102</td>\n",
" <td>169</td>\n",
" <td>102</td>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>89</td>\n",
" <td>63</td>\n",
" <td>89</td>\n",
" <td>63</td>\n",
" <td>94</td>\n",
" <td>80</td>\n",
" <td>104</td>\n",
" <td>88</td>\n",
" <td>104</td>\n",
" <td>88</td>\n",
" <td>21</td>\n",
" <td>9</td>\n",
" <td>99</td>\n",
" <td>95</td>\n",
" <td>169</td>\n",
" <td>115</td>\n",
" <td>169</td>\n",
" <td>115</td>\n",
" <td>143</td>\n",
" <td>143</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>48056</td>\n",
" <td>114220</td>\n",
" <td>118</td>\n",
" <td>65</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>167</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" <td>79</td>\n",
" <td>301</td>\n",
" <td>410</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>98</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>4</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" <td>73</td>\n",
" <td>43</td>\n",
" <td>73</td>\n",
" <td>43</td>\n",
" <td>102</td>\n",
" <td>66</td>\n",
" <td>103</td>\n",
" <td>55</td>\n",
" <td>103</td>\n",
" <td>55</td>\n",
" <td>22</td>\n",
" <td>8</td>\n",
" <td>100</td>\n",
" <td>92</td>\n",
" <td>129</td>\n",
" <td>84</td>\n",
" <td>129</td>\n",
" <td>84</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>66</td>\n",
" <td>53</td>\n",
" <td>66</td>\n",
" <td>53</td>\n",
" <td>100</td>\n",
" <td>74</td>\n",
" <td>84</td>\n",
" <td>78</td>\n",
" <td>84</td>\n",
" <td>78</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>99</td>\n",
" <td>95</td>\n",
" <td>121</td>\n",
" <td>93</td>\n",
" <td>121</td>\n",
" <td>93</td>\n",
" <td>114</td>\n",
" <td>88</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>95460</td>\n",
" <td>120539</td>\n",
" <td>118</td>\n",
" <td>87</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>180</td>\n",
" <td>97</td>\n",
" <td>5</td>\n",
" <td>71</td>\n",
" <td>113</td>\n",
" <td>501</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>99</td>\n",
" <td>0</td>\n",
" <td>133</td>\n",
" <td>33</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>88</td>\n",
" <td>65</td>\n",
" <td>88</td>\n",
" <td>65</td>\n",
" <td>116</td>\n",
" <td>74</td>\n",
" <td>123</td>\n",
" <td>90</td>\n",
" <td>123</td>\n",
" <td>90</td>\n",
" <td>36</td>\n",
" <td>16</td>\n",
" <td>100</td>\n",
" <td>90</td>\n",
" <td>179</td>\n",
" <td>129</td>\n",
" <td>179</td>\n",
" <td>129</td>\n",
" <td>36</td>\n",
" <td>35</td>\n",
" <td>71</td>\n",
" <td>65</td>\n",
" <td>71</td>\n",
" <td>65</td>\n",
" <td>116</td>\n",
" <td>92</td>\n",
" <td>102</td>\n",
" <td>90</td>\n",
" <td>102</td>\n",
" <td>90</td>\n",
" <td>36</td>\n",
" <td>28</td>\n",
" <td>99</td>\n",
" <td>90</td>\n",
" <td>159</td>\n",
" <td>129</td>\n",
" <td>159</td>\n",
" <td>129</td>\n",
" <td>144</td>\n",
" <td>87</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>7220</td>\n",
" <td>92453</td>\n",
" <td>77</td>\n",
" <td>60</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>188</td>\n",
" <td>113</td>\n",
" <td>0</td>\n",
" <td>104</td>\n",
" <td>112</td>\n",
" <td>107</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>104</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>12</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>54</td>\n",
" <td>76</td>\n",
" <td>54</td>\n",
" <td>77</td>\n",
" <td>54</td>\n",
" <td>93</td>\n",
" <td>64</td>\n",
" <td>93</td>\n",
" <td>64</td>\n",
" <td>18</td>\n",
" <td>12</td>\n",
" <td>99</td>\n",
" <td>96</td>\n",
" <td>132</td>\n",
" <td>83</td>\n",
" <td>132</td>\n",
" <td>83</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>76</td>\n",
" <td>61</td>\n",
" <td>76</td>\n",
" <td>61</td>\n",
" <td>72</td>\n",
" <td>54</td>\n",
" <td>93</td>\n",
" <td>87</td>\n",
" <td>93</td>\n",
" <td>87</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>99</td>\n",
" <td>97</td>\n",
" <td>127</td>\n",
" <td>109</td>\n",
" <td>127</td>\n",
" <td>109</td>\n",
" <td>118</td>\n",
" <td>118</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>29208</td>\n",
" <td>114628</td>\n",
" <td>118</td>\n",
" <td>68</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>165</td>\n",
" <td>114</td>\n",
" <td>0</td>\n",
" <td>70</td>\n",
" <td>113</td>\n",
" <td>501</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>136</td>\n",
" <td>1</td>\n",
" <td>47</td>\n",
" <td>42</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>48</td>\n",
" <td>36</td>\n",
" <td>48</td>\n",
" <td>36</td>\n",
" <td>134</td>\n",
" <td>70</td>\n",
" <td>78</td>\n",
" <td>47</td>\n",
" <td>78</td>\n",
" <td>47</td>\n",
" <td>42</td>\n",
" <td>16</td>\n",
" <td>100</td>\n",
" <td>51</td>\n",
" <td>112</td>\n",
" <td>74</td>\n",
" <td>112</td>\n",
" <td>74</td>\n",
" <td>39</td>\n",
" <td>36</td>\n",
" <td>44</td>\n",
" <td>36</td>\n",
" <td>44</td>\n",
" <td>36</td>\n",
" <td>100</td>\n",
" <td>84</td>\n",
" <td>78</td>\n",
" <td>47</td>\n",
" <td>78</td>\n",
" <td>47</td>\n",
" <td>29</td>\n",
" <td>16</td>\n",
" <td>100</td>\n",
" <td>78</td>\n",
" <td>98</td>\n",
" <td>74</td>\n",
" <td>98</td>\n",
" <td>74</td>\n",
" <td>154</td>\n",
" <td>63</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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"\n",
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" document.querySelector('#df-a7ae8681-56a5-4930-aa2e-232a76f2ccb2 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-a7ae8681-56a5-4930-aa2e-232a76f2ccb2');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"\n",
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" 40% {\n",
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" 60% {\n",
" border-color: transparent;\n",
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"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "encoded_data"
}
},
"metadata": {},
"execution_count": 31
}
],
"source": [
"encoded_data=one_hot_encode_data.astype(int)\n",
"encoded_data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "YjGDR5kB7czB"
},
"outputs": [],
"source": [
"from scipy import stats\n",
"z_score= stats.zscore(encoded_data.select_dtypes(include= [\"float\", \"int64\"]))\n",
"outlier = (z_score>3) | (z_score< -3)\n",
"df_ourliers = encoded_data[outlier.any(axis=1)]\n",
"df_ourliers\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "QqMoFFfV0oX6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0d7aa14b-f442-4a6b-fd82-2d617561fa47"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"apache_2_bodysystem_Metabolic 5123\n",
"apache_3j_bodysystem_Metabolic 5123\n",
"icu_type_Neuro ICU 4927\n",
"hospital_death 4891\n",
"icu_type_CCU-CTICU 4574\n",
"icu_stay_type_admit 3898\n",
"icu_type_SICU 3846\n",
"icu_stay_type_transfer 3662\n",
"gcs_motor_apache 3616\n",
"icu_type_Cardiac ICU 3206\n",
"ethnicity_Other/Unknown 2796\n",
"icu_type_CSICU 2549\n",
"apache_3j_bodysystem_Trauma 2419\n",
"apache_2_bodysystem_Trauma 2419\n",
"ethnicity_Hispanic 2390\n",
"temp_apache 2116\n",
"apache_2_bodysystem_Undefined diagnoses 1940\n",
"icu_type_CTICU 1708\n",
"icu_admit_source_Other Hospital 1700\n",
"arf_apache 1684\n",
"immunosuppression 1674\n",
"apache_2_bodysystem_Renal/Genitourinary 1549\n",
"apache_3j_bodysystem_Genitourinary 1399\n",
"solid_tumor_with_metastasis 1270\n",
"d1_glucose_max 1243\n",
"d1_temp_min 1186\n",
"d1_resprate_max 1163\n",
"pre_icu_los_days 1061\n",
"d1_spo2_min 1056\n",
"bmi 1001\n",
"cirrhosis 999\n",
"d1_glucose_min 938\n",
"h1_resprate_max 854\n",
"weight 848\n",
"hepatic_failure 846\n",
"apache_4a_hospital_death_prob 825\n",
"apache_4a_icu_death_prob 762\n",
"h1_spo2_min 755\n",
"d1_diasbp_max 701\n",
"d1_diasbp_noninvasive_max 700\n",
"apache_3j_bodysystem_Musculoskeletal/Skin 680\n",
"d1_mbp_max 661\n",
"ethnicity_Asian 659\n",
"d1_mbp_noninvasive_max 647\n",
"h1_diasbp_max 555\n",
"h1_diasbp_noninvasive_max 554\n",
"h1_resprate_min 550\n",
"apache_3j_diagnosis 539\n",
"ethnicity_Native American 528\n",
"d1_heartrate_min 518\n",
"h1_mbp_noninvasive_max 516\n",
"h1_mbp_max 498\n",
"d1_spo2_max 490\n",
"h1_spo2_max 487\n",
"d1_heartrate_max 454\n",
"leukemia 450\n",
"h1_sysbp_max 438\n",
"h1_sysbp_noninvasive_max 431\n",
"apache_3j_bodysystem_Hematological 427\n",
"apache_2_bodysystem_Haematologic 427\n",
"d1_sysbp_max 417\n",
"d1_sysbp_noninvasive_max 416\n",
"h1_heartrate_max 415\n",
"icu_admit_source_Other ICU 381\n",
"d1_resprate_min 350\n",
"h1_mbp_min 343\n",
"h1_mbp_noninvasive_min 338\n",
"h1_diasbp_min 305\n",
"h1_diasbp_noninvasive_min 302\n",
"d1_mbp_min 300\n",
"d1_mbp_noninvasive_min 292\n",
"d1_sysbp_min 287\n",
"d1_sysbp_noninvasive_min 287\n",
"height 268\n",
"d1_potassium_max 266\n",
"lymphoma 242\n",
"icu_stay_type_readmit 236\n",
"d1_diasbp_noninvasive_min 213\n",
"d1_diasbp_min 213\n",
"apache_2_bodysystem_Undefined Diagnoses 201\n",
"apache_3j_bodysystem_Gynecological 160\n",
"aids 58\n",
"apache_3j_bodysystem_Neurological 0\n",
"apache_3j_bodysystem_Sepsis 0\n",
"apache_3j_bodysystem_Respiratory 0\n",
"apache_3j_bodysystem_Cardiovascular 0\n",
"apache_2_bodysystem_Respiratory 0\n",
"icu_type_MICU 0\n",
"icu_type_Med-Surg ICU 0\n",
"apache_2_bodysystem_Neurologic 0\n",
"apache_3j_bodysystem_Gastrointestinal 0\n",
"apache_2_bodysystem_Gastrointestinal 0\n",
"apache_2_bodysystem_Cardiovascular 0\n",
"encounter_id 0\n",
"icu_admit_source_Operating Room / Recovery 0\n",
"icu_admit_source_Floor 0\n",
"hospital_id 0\n",
"age 0\n",
"elective_surgery 0\n",
"icu_id 0\n",
"apache_2_diagnosis 0\n",
"apache_post_operative 0\n",
"gcs_eyes_apache 0\n",
"gcs_unable_apache 0\n",
"gcs_verbal_apache 0\n",
"heart_rate_apache 0\n",
"intubated_apache 0\n",
"map_apache 0\n",
"resprate_apache 0\n",
"ventilated_apache 0\n",
"d1_temp_max 0\n",
"h1_heartrate_min 0\n",
"h1_sysbp_min 0\n",
"patient_id 0\n",
"d1_potassium_min 0\n",
"diabetes_mellitus 0\n",
"ethnicity_African American 0\n",
"ethnicity_Caucasian 0\n",
"gender_F 0\n",
"gender_M 0\n",
"icu_admit_source_Accident & Emergency 0\n",
"h1_sysbp_noninvasive_min 0\n",
"dtype: int64\n"
]
}
],
"source": [
"#######listing features with ouliers\n",
"outlier_counts = outlier.sum(axis=0)\n",
"sorted_outlier_counts = outlier_counts.sort_values(ascending=False)\n",
"print(sorted_outlier_counts)\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(25, 8))\n",
"sns.barplot(x=sorted_outlier_counts.index, y=sorted_outlier_counts.values)\n",
"plt.xticks(rotation=90)\n",
"plt.xlabel('Columns')\n",
"plt.ylabel('Number of Outliers')\n",
"plt.title('Number of Outliers per Column in 56k patient data')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 868
},
"id": "ylYWcUQu9H09",
"outputId": "cf979940-3d0a-462b-9a52-361519b1f2e1"
},
"execution_count": 48,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"In the context of this medical dataset, I have chosen not to remove the outliers due to their potential clinical significance. Medical data is inherently variable, reflecting the complex and diverse conditions of individual patients. Outliers in this dataset may represent critically ill patients or rare, yet important, clinical conditions. Removing these values could lead to a loss of meaningful information, particularly in a healthcare setting where extreme values can indicate vital health states or key medical emergencies that are essential for accurate diagnosis and treatment."
],
"metadata": {
"id": "Dsp1AoNFHBiu"
}
},
{
"cell_type": "code",
"source": [
"print(sorted_outlier_counts.head(82).index)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MLFSBATcDGZ4",
"outputId": "704a8402-1bbb-46dc-a91c-fe164f587d8a"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index(['apache_2_bodysystem_Metabolic', 'apache_3j_bodysystem_Metabolic',\n",
" 'icu_type_Neuro ICU', 'hospital_death', 'icu_type_CCU-CTICU',\n",
" 'icu_stay_type_admit', 'icu_type_SICU', 'icu_stay_type_transfer',\n",
" 'gcs_motor_apache', 'icu_type_Cardiac ICU', 'ethnicity_Other/Unknown',\n",
" 'icu_type_CSICU', 'apache_3j_bodysystem_Trauma',\n",
" 'apache_2_bodysystem_Trauma', 'ethnicity_Hispanic', 'temp_apache',\n",
" 'apache_2_bodysystem_Undefined diagnoses', 'icu_type_CTICU',\n",
" 'icu_admit_source_Other Hospital', 'arf_apache', 'immunosuppression',\n",
" 'apache_2_bodysystem_Renal/Genitourinary',\n",
" 'apache_3j_bodysystem_Genitourinary', 'solid_tumor_with_metastasis',\n",
" 'd1_glucose_max', 'd1_temp_min', 'd1_resprate_max', 'pre_icu_los_days',\n",
" 'd1_spo2_min', 'bmi', 'cirrhosis', 'd1_glucose_min', 'h1_resprate_max',\n",
" 'weight', 'hepatic_failure', 'apache_4a_hospital_death_prob',\n",
" 'apache_4a_icu_death_prob', 'h1_spo2_min', 'd1_diasbp_max',\n",
" 'd1_diasbp_noninvasive_max',\n",
" 'apache_3j_bodysystem_Musculoskeletal/Skin', 'd1_mbp_max',\n",
" 'ethnicity_Asian', 'd1_mbp_noninvasive_max', 'h1_diasbp_max',\n",
" 'h1_diasbp_noninvasive_max', 'h1_resprate_min', 'apache_3j_diagnosis',\n",
" 'ethnicity_Native American', 'd1_heartrate_min',\n",
" 'h1_mbp_noninvasive_max', 'h1_mbp_max', 'd1_spo2_max', 'h1_spo2_max',\n",
" 'd1_heartrate_max', 'leukemia', 'h1_sysbp_max',\n",
" 'h1_sysbp_noninvasive_max', 'apache_3j_bodysystem_Hematological',\n",
" 'apache_2_bodysystem_Haematologic', 'd1_sysbp_max',\n",
" 'd1_sysbp_noninvasive_max', 'h1_heartrate_max',\n",
" 'icu_admit_source_Other ICU', 'd1_resprate_min', 'h1_mbp_min',\n",
" 'h1_mbp_noninvasive_min', 'h1_diasbp_min', 'h1_diasbp_noninvasive_min',\n",
" 'd1_mbp_min', 'd1_mbp_noninvasive_min', 'd1_sysbp_min',\n",
" 'd1_sysbp_noninvasive_min', 'height', 'd1_potassium_max', 'lymphoma',\n",
" 'icu_stay_type_readmit', 'd1_diasbp_noninvasive_min', 'd1_diasbp_min',\n",
" 'apache_2_bodysystem_Undefined Diagnoses',\n",
" 'apache_3j_bodysystem_Gynecological', 'aids'],\n",
" dtype='object')\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"outlier_columns = encoded_data[['apache_2_bodysystem_Metabolic', 'apache_3j_bodysystem_Metabolic',\n",
" 'icu_type_Neuro ICU', 'hospital_death', 'icu_type_CCU-CTICU',\n",
" 'icu_stay_type_admit', 'icu_type_SICU', 'icu_stay_type_transfer',\n",
" 'gcs_motor_apache', 'icu_type_Cardiac ICU', 'ethnicity_Other/Unknown',\n",
" 'icu_type_CSICU', 'apache_3j_bodysystem_Trauma',\n",
" 'apache_2_bodysystem_Trauma', 'ethnicity_Hispanic', 'temp_apache',\n",
" 'apache_2_bodysystem_Undefined diagnoses', 'icu_type_CTICU',\n",
" 'icu_admit_source_Other Hospital', 'arf_apache', 'immunosuppression',\n",
" 'apache_2_bodysystem_Renal/Genitourinary',\n",
" 'apache_3j_bodysystem_Genitourinary', 'solid_tumor_with_metastasis',\n",
" 'd1_glucose_max', 'd1_temp_min', 'd1_resprate_max', 'pre_icu_los_days',\n",
" 'd1_spo2_min', 'bmi', 'cirrhosis', 'd1_glucose_min', 'h1_resprate_max',\n",
" 'weight', 'hepatic_failure', 'apache_4a_hospital_death_prob',\n",
" 'apache_4a_icu_death_prob', 'h1_spo2_min', 'd1_diasbp_max',\n",
" 'd1_diasbp_noninvasive_max', 'apache_3j_bodysystem_Musculoskeletal/Skin',\n",
" 'd1_mbp_max', 'ethnicity_Asian', 'd1_mbp_noninvasive_max', 'h1_diasbp_max',\n",
" 'h1_diasbp_noninvasive_max', 'h1_resprate_min', 'apache_3j_diagnosis',\n",
" 'ethnicity_Native American', 'd1_heartrate_min', 'h1_mbp_noninvasive_max',\n",
" 'h1_mbp_max', 'd1_spo2_max', 'h1_spo2_max', 'd1_heartrate_max', 'leukemia',\n",
" 'h1_sysbp_max', 'h1_sysbp_noninvasive_max', 'apache_3j_bodysystem_Hematological',\n",
" 'apache_2_bodysystem_Haematologic', 'd1_sysbp_max',\n",
" 'd1_sysbp_noninvasive_max', 'h1_heartrate_max',\n",
" 'icu_admit_source_Other ICU', 'd1_resprate_min', 'h1_mbp_min',\n",
" 'h1_mbp_noninvasive_min', 'h1_diasbp_min', 'h1_diasbp_noninvasive_min',\n",
" 'd1_mbp_min', 'd1_mbp_noninvasive_min', 'd1_sysbp_min',\n",
" 'd1_sysbp_noninvasive_min', 'height', 'd1_potassium_max', 'lymphoma',\n",
" 'icu_stay_type_readmit', 'd1_diasbp_noninvasive_min', 'd1_diasbp_min',\n",
" 'apache_2_bodysystem_Undefined Diagnoses',\n",
" 'apache_3j_bodysystem_Gynecological', 'aids']]\n"
],
"metadata": {
"id": "xS5NKpFoHND8"
},
"execution_count": 63,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(25, 8))\n",
"sns.violinplot(data=outlier_columns, orient=\"v\")\n",
"plt.xticks(rotation=90)\n",
"plt.xlabel('Columns')\n",
"plt.ylabel('Value Distribution')\n",
"plt.title('Violin Plot of Outlier Columns')\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 864
},
"id": "97wmVC_GILW3",
"outputId": "caab4592-66fb-4365-fa24-4f88c29941d5"
},
"execution_count": 65,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(25, 10))\n",
"sns.boxplot(data=outlier_columns, orient=\"v\")\n",
"plt.xticks(rotation=90)\n",
"plt.xlabel('Columns')\n",
"plt.ylabel('Value Distribution')\n",
"plt.title('box Plot of Outlier Columns')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 995
},
"id": "RksaEnsEIuKl",
"outputId": "dea8d152-d6d6-492e-9297-2cfe96c794db"
},
"execution_count": 68,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2500x1000 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#####Train-Test Split for ML process\n",
"encoded_data.head(5)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "TbCikRrOKFNz",
"outputId": "8ab44839-206c-4ca8-d481-e8b46b7da4c2"
},
"execution_count": 71,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" encounter_id patient_id hospital_id age bmi elective_surgery height \\\n",
"0 66154 25312 118 68 22 0 180 \n",
"1 114252 59342 81 77 27 0 160 \n",
"5 33181 74489 83 67 27 0 190 \n",
"10 105427 125898 77 72 28 1 154 \n",
"17 22471 112115 118 46 25 0 167 \n",
"\n",
" icu_id pre_icu_los_days weight apache_2_diagnosis apache_3j_diagnosis \\\n",
"0 92 0 73 113 502 \n",
"1 90 0 70 108 203 \n",
"5 95 0 100 301 403 \n",
"10 113 0 67 303 1304 \n",
"17 92 0 72 108 203 \n",
"\n",
" apache_post_operative arf_apache gcs_eyes_apache gcs_motor_apache \\\n",
"0 0 0 3 6 \n",
"1 0 0 1 3 \n",
"5 0 0 4 6 \n",
"10 1 0 4 6 \n",
"17 0 0 1 4 \n",
"\n",
" gcs_unable_apache gcs_verbal_apache heart_rate_apache intubated_apache \\\n",
"0 0 4 118 0 \n",
"1 0 1 120 0 \n",
"5 0 5 113 0 \n",
"10 0 5 101 0 \n",
"17 0 1 114 1 \n",
"\n",
" map_apache resprate_apache temp_apache ventilated_apache \\\n",
"0 40 36 39 0 \n",
"1 46 33 35 1 \n",
"5 130 35 36 0 \n",
"10 72 15 36 0 \n",
"17 113 34 36 1 \n",
"\n",
" d1_diasbp_max d1_diasbp_min d1_diasbp_noninvasive_max \\\n",
"0 68 37 68 \n",
"1 95 31 95 \n",
"5 100 61 100 \n",
"10 72 53 72 \n",
"17 89 61 89 \n",
"\n",
" d1_diasbp_noninvasive_min d1_heartrate_max d1_heartrate_min d1_mbp_max \\\n",
"0 37 119 72 89 \n",
"1 31 118 72 120 \n",
"5 61 113 83 127 \n",
"10 53 101 67 93 \n",
"17 61 98 64 113 \n",
"\n",
" d1_mbp_min d1_mbp_noninvasive_max d1_mbp_noninvasive_min \\\n",
"0 46 89 46 \n",
"1 38 120 38 \n",
"5 80 127 80 \n",
"10 70 93 70 \n",
"17 76 113 76 \n",
"\n",
" d1_resprate_max d1_resprate_min d1_spo2_max d1_spo2_min d1_sysbp_max \\\n",
"0 34 10 100 74 131 \n",
"1 32 12 100 70 159 \n",
"5 32 10 97 91 173 \n",
"10 23 14 99 92 145 \n",
"17 22 9 100 88 169 \n",
"\n",
" d1_sysbp_min d1_sysbp_noninvasive_max d1_sysbp_noninvasive_min \\\n",
"0 73 131 73 \n",
"1 67 159 67 \n",
"5 107 173 107 \n",
"10 95 145 95 \n",
"17 102 169 102 \n",
"\n",
" d1_temp_max d1_temp_min h1_diasbp_max h1_diasbp_min \\\n",
"0 39 37 68 63 \n",
"1 36 35 61 48 \n",
"5 36 36 89 89 \n",
"10 37 36 72 56 \n",
"17 37 36 89 63 \n",
"\n",
" h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min h1_heartrate_max \\\n",
"0 68 63 119 \n",
"1 61 48 114 \n",
"5 89 89 83 \n",
"10 72 56 90 \n",
"17 89 63 94 \n",
"\n",
" h1_heartrate_min h1_mbp_max h1_mbp_min h1_mbp_noninvasive_max \\\n",
"0 108 86 85 86 \n",
"1 100 85 57 85 \n",
"5 83 111 111 111 \n",
"10 70 91 87 91 \n",
"17 80 104 88 104 \n",
"\n",
" h1_mbp_noninvasive_min h1_resprate_max h1_resprate_min h1_spo2_max \\\n",
"0 85 26 18 100 \n",
"1 57 31 28 95 \n",
"5 111 12 12 97 \n",
"10 87 23 14 99 \n",
"17 88 21 9 99 \n",
"\n",
" h1_spo2_min h1_sysbp_max h1_sysbp_min h1_sysbp_noninvasive_max \\\n",
"0 74 131 115 131 \n",
"1 70 95 71 95 \n",
"5 97 143 143 143 \n",
"10 93 145 114 145 \n",
"17 95 169 115 169 \n",
"\n",
" h1_sysbp_noninvasive_min d1_glucose_max d1_glucose_min \\\n",
"0 115 168 109 \n",
"1 71 145 128 \n",
"5 143 156 125 \n",
"10 114 158 133 \n",
"17 115 143 143 \n",
"\n",
" d1_potassium_max d1_potassium_min apache_4a_hospital_death_prob \\\n",
"0 4 3 0 \n",
"1 4 3 0 \n",
"5 3 3 0 \n",
"10 4 4 0 \n",
"17 4 4 0 \n",
"\n",
" apache_4a_icu_death_prob aids cirrhosis diabetes_mellitus \\\n",
"0 0 0 0 1 \n",
"1 0 0 0 1 \n",
"5 0 0 0 1 \n",
"10 0 0 0 0 \n",
"17 0 0 0 0 \n",
"\n",
" hepatic_failure immunosuppression leukemia lymphoma \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"5 0 0 0 0 \n",
"10 0 1 0 0 \n",
"17 0 0 0 0 \n",
"\n",
" solid_tumor_with_metastasis hospital_death ethnicity_African American \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"5 0 0 0 \n",
"10 0 0 0 \n",
"17 0 0 0 \n",
"\n",
" ethnicity_Asian ethnicity_Caucasian ethnicity_Hispanic \\\n",
"0 0 1 0 \n",
"1 0 1 0 \n",
"5 0 1 0 \n",
"10 0 0 1 \n",
"17 0 0 1 \n",
"\n",
" ethnicity_Native American ethnicity_Other/Unknown gender_F gender_M \\\n",
"0 0 0 0 1 \n",
"1 0 0 1 0 \n",
"5 0 0 0 1 \n",
"10 0 0 1 0 \n",
"17 0 0 0 1 \n",
"\n",
" icu_admit_source_Accident & Emergency icu_admit_source_Floor \\\n",
"0 0 1 \n",
"1 0 1 \n",
"5 1 0 \n",
"10 0 0 \n",
"17 1 0 \n",
"\n",
" icu_admit_source_Operating Room / Recovery \\\n",
"0 0 \n",
"1 0 \n",
"5 0 \n",
"10 1 \n",
"17 0 \n",
"\n",
" icu_admit_source_Other Hospital icu_admit_source_Other ICU \\\n",
"0 0 0 \n",
"1 0 0 \n",
"5 0 0 \n",
"10 0 0 \n",
"17 0 0 \n",
"\n",
" icu_stay_type_admit icu_stay_type_readmit icu_stay_type_transfer \\\n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"5 1 0 0 \n",
"10 1 0 0 \n",
"17 1 0 0 \n",
"\n",
" icu_type_CCU-CTICU icu_type_CSICU icu_type_CTICU icu_type_Cardiac ICU \\\n",
"0 0 0 1 0 \n",
"1 0 0 0 0 \n",
"5 0 0 0 0 \n",
"10 0 0 0 0 \n",
"17 0 0 1 0 \n",
"\n",
" icu_type_MICU icu_type_Med-Surg ICU icu_type_Neuro ICU icu_type_SICU \\\n",
"0 0 0 0 0 \n",
"1 0 1 0 0 \n",
"5 0 1 0 0 \n",
"10 0 1 0 0 \n",
"17 0 0 0 0 \n",
"\n",
" apache_3j_bodysystem_Cardiovascular \\\n",
"0 0 \n",
"1 0 \n",
"5 0 \n",
"10 0 \n",
"17 0 \n",
"\n",
" apache_3j_bodysystem_Gastrointestinal apache_3j_bodysystem_Genitourinary \\\n",
"0 0 0 \n",
"1 0 0 \n",
"5 0 0 \n",
"10 0 0 \n",
"17 0 0 \n",
"\n",
" apache_3j_bodysystem_Gynecological apache_3j_bodysystem_Hematological \\\n",
"0 0 0 \n",
"1 0 0 \n",
"5 0 0 \n",
"10 0 0 \n",
"17 0 0 \n",
"\n",
" apache_3j_bodysystem_Metabolic apache_3j_bodysystem_Musculoskeletal/Skin \\\n",
"0 0 0 \n",
"1 0 0 \n",
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" <td>145</td>\n",
" <td>95</td>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>72</td>\n",
" <td>56</td>\n",
" <td>72</td>\n",
" <td>56</td>\n",
" <td>90</td>\n",
" <td>70</td>\n",
" <td>91</td>\n",
" <td>87</td>\n",
" <td>91</td>\n",
" <td>87</td>\n",
" <td>23</td>\n",
" <td>14</td>\n",
" <td>99</td>\n",
" <td>93</td>\n",
" <td>145</td>\n",
" <td>114</td>\n",
" <td>145</td>\n",
" <td>114</td>\n",
" <td>158</td>\n",
" <td>133</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>22471</td>\n",
" <td>112115</td>\n",
" <td>118</td>\n",
" <td>46</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>167</td>\n",
" <td>92</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>108</td>\n",
" <td>203</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>114</td>\n",
" <td>1</td>\n",
" <td>113</td>\n",
" <td>34</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>61</td>\n",
" <td>89</td>\n",
" <td>61</td>\n",
" <td>98</td>\n",
" <td>64</td>\n",
" <td>113</td>\n",
" <td>76</td>\n",
" <td>113</td>\n",
" <td>76</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>100</td>\n",
" <td>88</td>\n",
" <td>169</td>\n",
" <td>102</td>\n",
" <td>169</td>\n",
" <td>102</td>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>89</td>\n",
" <td>63</td>\n",
" <td>89</td>\n",
" <td>63</td>\n",
" <td>94</td>\n",
" <td>80</td>\n",
" <td>104</td>\n",
" <td>88</td>\n",
" <td>104</td>\n",
" <td>88</td>\n",
" <td>21</td>\n",
" <td>9</td>\n",
" <td>99</td>\n",
" <td>95</td>\n",
" <td>169</td>\n",
" <td>115</td>\n",
" <td>169</td>\n",
" <td>115</td>\n",
" <td>143</td>\n",
" <td>143</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c86ebd94-8717-4546-8899-c1e7b1a7cc3a')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-c86ebd94-8717-4546-8899-c1e7b1a7cc3a button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-c86ebd94-8717-4546-8899-c1e7b1a7cc3a');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-60a4f4f3-f2e6-461c-92ee-d9555ad28651\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60a4f4f3-f2e6-461c-92ee-d9555ad28651')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-60a4f4f3-f2e6-461c-92ee-d9555ad28651 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "encoded_data"
}
},
"metadata": {},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"source": [
"encoded_data1= encoded_data.copy()"
],
"metadata": {
"id": "XPdmJslrNvKW"
},
"execution_count": 73,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "eLOuoZhvMDME"
}
},
{
"cell_type": "code",
"source": [
"X= encoded_data1.drop(\"hospital_death\", axis= 1)\n",
"y= encoded_data1[\"hospital_death\"]\n",
"#############\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
],
"metadata": {
"id": "8mTIJ6JELT0S"
},
"execution_count": 76,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "huh31LspN2KB",
"outputId": "a957dd63-f7aa-493f-9551-2843912acef8"
},
"execution_count": 78,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(56935, 121)"
]
},
"metadata": {},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"source": [
"y.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WJ8eu8R7N_fq",
"outputId": "5283b881-11b9-471e-a282-0d528cb1c83e"
},
"execution_count": 79,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(56935,)"
]
},
"metadata": {},
"execution_count": 79
}
]
},
{
"cell_type": "code",
"source": [
"##### Training ML model\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
"LG_model= LogisticRegression(max_iter=1000)\n",
"LG_model.fit(X_train, y_train)\n",
"y_pred = LG_model.predict(X_test)\n",
"\n",
"#####evaluation\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion_matrix(y_test, y_pred))\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_test, y_pred))\n",
"print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w0qqwkdLOHe3",
"outputId": "c1ee1ebe-f883-483b-a67f-e93ab815ea21"
},
"execution_count": 81,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Confusion Matrix:\n",
"[[17052 78]\n",
" [ 1517 142]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.92 1.00 0.96 17130\n",
" 1 0.65 0.09 0.15 1659\n",
"\n",
" accuracy 0.92 18789\n",
" macro avg 0.78 0.54 0.55 18789\n",
"weighted avg 0.89 0.92 0.88 18789\n",
"\n",
"Accuracy Score: 0.9151099047314918\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"####Trying of Random Forest Model\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"rf_model= RandomForestClassifier(n_estimators=100, random_state=42)\n",
"rf_model.fit(X_train, y_train)\n",
"rf_pred = rf_model.predict(X_test)\n",
"#######\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion_matrix(y_test, rf_pred))\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_test, rf_pred))\n",
"print(\"Accuracy Score:\", accuracy_score(y_test, rf_pred))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ao-L7zsqStH9",
"outputId": "dd85a0d8-2c7d-4c84-f55a-e2ad5fb79ca9"
},
"execution_count": 84,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Confusion Matrix:\n",
"[[17027 103]\n",
" [ 1386 273]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.99 0.96 17130\n",
" 1 0.73 0.16 0.27 1659\n",
"\n",
" accuracy 0.92 18789\n",
" macro avg 0.83 0.58 0.61 18789\n",
"weighted avg 0.91 0.92 0.90 18789\n",
"\n",
"Accuracy Score: 0.9207515035393049\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "EN1pv7ftU2tQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"During the Analysis, we find out there was imbalance distribution in the target variable, So i will be doing SMOTE"
],
"metadata": {
"id": "5QVQOwKqWtVs"
}
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(6, 4))\n",
"sns.countplot(x=y)\n",
"plt.title('Class Distribution')\n",
"plt.xlabel('Hospital Death')\n",
"plt.ylabel('Count')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 410
},
"id": "6OWyyNIHafPc",
"outputId": "c15327b0-6887-43db-9936-2c4ff2d144a6"
},
"execution_count": 85,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
],
"image/png": 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SMjMzdfHiRUVFRZm1bdq0UZMmTZSRkSFJysjIUPv27c3QIknR0dFyOBw6cOCAWXP5GuU15WuUlJQoMzPTqcbNzU1RUVFmTWWKi4vlcDicXgAA4NZyaXCJiIhQcnKyUlNT9dprr+no0aO67777dObMGeXl5cnT01P+/v5O7wkICFBeXp4kKS8vzym0lM+Xz12rxuFw6Pz58/rhhx9UWlpaaU35GpWZPXu2/Pz8zFdwcHCVPgMAAHD9XPpHFvv372/+u0OHDoqIiFDTpk21evVq1axZ04Wd/biEhATFx8eb2w6Hg/ACAMAt5vKvii7n7++vu+++W4cPH1ZgYKBKSkpUWFjoVJOfn6/AwEBJUmBgYIW7jMq3f6zGbrerZs2aql+/vtzd3SutKV+jMl5eXrLb7U4vAABwa91WweXs2bM6cuSIGjVqpPDwcNWoUUPp6enm/KFDh5STk6PIyEhJUmRkpPbt2+d0909aWprsdrvCwsLMmsvXKK8pX8PT01Ph4eFONWVlZUpPTzdrAADA7cGlwWXy5MnaunWrvvnmG23fvl2//OUv5e7urieeeEJ+fn4aNWqU4uPjtXnzZmVmZmrkyJGKjIxU9+7dJUn9+vVTWFiYhg0bpi+++EIbNmzQ9OnTFRcXJy8vL0nSc889p6+//lovvPCCsrOztWzZMq1evVoTJ040+4iPj9df/vIXrVixQgcPHtSYMWN07tw5jRw50iWfCwAAqJxLr3H57rvv9MQTT+jkyZNq0KCBevbsqc8//1wNGjSQJC1YsEBubm4aNGiQiouLFR0drWXLlpnvd3d317p16zRmzBhFRkbKx8dHsbGxSkxMNGuaNWumlJQUTZw4UUlJSWrcuLFef/11RUdHmzVDhgzRiRMnNGPGDOXl5alTp05KTU2tcMEuAABwLZc+x+VOwnNcgJvHc1yAnydLPccFAADgehFcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZRBcAACAZdw2weUPf/iDbDabJkyYYI5duHBBcXFxqlevnnx9fTVo0CDl5+c7vS8nJ0cxMTGqVauWGjZsqClTpujSpUtONVu2bFHnzp3l5eWlli1bKjk5ucL+ly5dqpCQEHl7eysiIkI7d+68FYcJAABuwm0RXHbt2qU//elP6tChg9P4xIkT9dFHH2nNmjXaunWrcnNz9dhjj5nzpaWliomJUUlJibZv364VK1YoOTlZM2bMMGuOHj2qmJgY9enTR1lZWZowYYJGjx6tDRs2mDWrVq1SfHy8Zs6cqT179qhjx46Kjo5WQUHBrT94AABw3WyGYRiubODs2bPq3Lmzli1bpt///vfq1KmTFi5cqKKiIjVo0EArV67U4MGDJUnZ2dkKDQ1VRkaGunfvrvXr12vAgAHKzc1VQECAJGn58uWaOnWqTpw4IU9PT02dOlUpKSnav3+/uc+hQ4eqsLBQqampkqSIiAh17dpVS5YskSSVlZUpODhY48aN07Rp067rOBwOh/z8/FRUVCS73V6dH5EkKXzKm9W+JnC7yZw73NUtAHCBG/kd6vIzLnFxcYqJiVFUVJTTeGZmpi5evOg03qZNGzVp0kQZGRmSpIyMDLVv394MLZIUHR0th8OhAwcOmDVXrh0dHW2uUVJSoszMTKcaNzc3RUVFmTWVKS4ulsPhcHoBAIBby8OVO3/nnXe0Z88e7dq1q8JcXl6ePD095e/v7zQeEBCgvLw8s+by0FI+Xz53rRqHw6Hz58/r9OnTKi0trbQmOzv7qr3Pnj1br7zyyvUdKAAAqBYuO+Ny7NgxjR8/Xm+99Za8vb1d1UaVJSQkqKioyHwdO3bM1S0BAHDHc1lwyczMVEFBgTp37iwPDw95eHho69atWrRokTw8PBQQEKCSkhIVFhY6vS8/P1+BgYGSpMDAwAp3GZVv/1iN3W5XzZo1Vb9+fbm7u1daU75GZby8vGS3251eAADg1nJZcHnwwQe1b98+ZWVlma8uXbroqaeeMv9do0YNpaenm+85dOiQcnJyFBkZKUmKjIzUvn37nO7+SUtLk91uV1hYmFlz+RrlNeVreHp6Kjw83KmmrKxM6enpZg0AALg9uOwal9q1a6tdu3ZOYz4+PqpXr545PmrUKMXHx6tu3bqy2+0aN26cIiMj1b17d0lSv379FBYWpmHDhmnOnDnKy8vT9OnTFRcXJy8vL0nSc889pyVLluiFF17Q008/rU2bNmn16tVKSUkx9xsfH6/Y2Fh16dJF3bp108KFC3Xu3DmNHDnyJ/o0AADA9XDpxbk/ZsGCBXJzc9OgQYNUXFys6OhoLVu2zJx3d3fXunXrNGbMGEVGRsrHx0exsbFKTEw0a5o1a6aUlBRNnDhRSUlJaty4sV5//XVFR0ebNUOGDNGJEyc0Y8YM5eXlqVOnTkpNTa1wwS4AAHAtlz/H5U7Bc1yAm8dzXICfJ0s9xwUAAOB6EVwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBlEFwAAIBluDS4vPbaa+rQoYPsdrvsdrsiIyO1fv16c/7ChQuKi4tTvXr15Ovrq0GDBik/P99pjZycHMXExKhWrVpq2LChpkyZokuXLjnVbNmyRZ07d5aXl5datmyp5OTkCr0sXbpUISEh8vb2VkREhHbu3HlLjhkAAFRdlYJL8+bNdfLkyQrjhYWFat68+XWv07hxY/3hD39QZmamdu/erQceeECPPvqoDhw4IEmaOHGiPvroI61Zs0Zbt25Vbm6uHnvsMfP9paWliomJUUlJibZv364VK1YoOTlZM2bMMGuOHj2qmJgY9enTR1lZWZowYYJGjx6tDRs2mDWrVq1SfHy8Zs6cqT179qhjx46Kjo5WQUFBVT4eAABwi9gMwzBu9E1ubm7Ky8tTw4YNncbz8/PVpEkTFRcXV7mhunXrau7cuRo8eLAaNGiglStXavDgwZKk7OxshYaGKiMjQ927d9f69es1YMAA5ebmKiAgQJK0fPlyTZ06VSdOnJCnp6emTp2qlJQU7d+/39zH0KFDVVhYqNTUVElSRESEunbtqiVLlkiSysrKFBwcrHHjxmnatGnX1bfD4ZCfn5+Kiopkt9urfPxXEz7lzWpfE7jdZM4d7uoWALjAjfwO9biRhT/88EPz3xs2bJCfn5+5XVpaqvT0dIWEhNxYt5e9f82aNTp37pwiIyOVmZmpixcvKioqyqxp06aNmjRpYgaXjIwMtW/f3gwtkhQdHa0xY8bowIEDuueee5SRkeG0RnnNhAkTJEklJSXKzMxUQkKCOe/m5qaoqChlZGRctd/i4mKngOZwOKp03AAA4PrdUHAZOHCgJMlmsyk2NtZprkaNGgoJCdG8efNuqIF9+/YpMjJSFy5ckK+vr95//32FhYUpKytLnp6e8vf3d6oPCAhQXl6eJCkvL88ptJTPl89dq8bhcOj8+fM6ffq0SktLK63Jzs6+at+zZ8/WK6+8ckPHCgAAbs4NBZeysjJJUrNmzbRr1y7Vr1//phto3bq1srKyVFRUpHfffVexsbHaunXrTa97qyUkJCg+Pt7cdjgcCg4OdmFHAADc+W4ouJQ7evRotTXg6empli1bSpLCw8O1a9cuJSUlaciQISopKVFhYaHTWZf8/HwFBgZKkgIDAyvc/VN+19HlNVfeiZSfny+73a6aNWvK3d1d7u7uldaUr1EZLy8veXl5Ve2gAQBAlVQpuEhSenq60tPTVVBQYJ6JKfe3v/2tyg2VlZWpuLhY4eHhqlGjhtLT0zVo0CBJ0qFDh5STk6PIyEhJUmRkpF599VUVFBSYFwqnpaXJbrcrLCzMrPn444+d9pGWlmau4enpqfDwcKWnp5tfhZWVlSk9PV1jx46t8nEAAIDqV6Xg8sorrygxMVFdunRRo0aNZLPZqrTzhIQE9e/fX02aNNGZM2e0cuVKbdmyxbzwd9SoUYqPj1fdunVlt9s1btw4RUZGqnv37pKkfv36KSwsTMOGDdOcOXOUl5en6dOnKy4uzjwb8txzz2nJkiV64YUX9PTTT2vTpk1avXq1UlJSzD7i4+MVGxurLl26qFu3blq4cKHOnTunkSNHVum4AADArVGl4LJ8+XIlJydr2LBhN7XzgoICDR8+XMePH5efn586dOigDRs2qG/fvpKkBQsWyM3NTYMGDVJxcbGio6O1bNky8/3u7u5at26dxowZo8jISPn4+Cg2NlaJiYlmTbNmzZSSkqKJEycqKSlJjRs31uuvv67o6GizZsiQITpx4oRmzJihvLw8derUSampqRUu2AUAAK5Vpee41KtXTzt37lSLFi1uRU+WxHNcgJvHc1yAn6cb+R1apSfnjh49WitXrqxScwAAAFVVpa+KLly4oD//+c/auHGjOnTooBo1ajjNz58/v1qaAwAAuFyVgsvevXvVqVMnSXJ6lL6kKl+oCwAA8GOqFFw2b95c3X0AAAD8qCpd4wIAAOAKVTrj0qdPn2t+JbRp06YqNwQAAHA1VQou5de3lLt48aKysrK0f//+Cn98EQAAoLpUKbgsWLCg0vFZs2bp7NmzN9UQAADA1VTrNS6//vWvb+rvFAEAAFxLtQaXjIwMeXt7V+eSAAAApip9VfTYY485bRuGoePHj2v37t16+eWXq6UxAACAK1UpuPj5+Tltu7m5qXXr1kpMTFS/fv2qpTEAAIArVSm4vPHGG9XdBwAAwI+qUnApl5mZqYMHD0qS2rZtq3vuuadamgIAAKhMlYJLQUGBhg4dqi1btsjf31+SVFhYqD59+uidd95RgwYNqrNHAAAASVW8q2jcuHE6c+aMDhw4oFOnTunUqVPav3+/HA6Hnn/++eruEQAAQFIVz7ikpqZq48aNCg0NNcfCwsK0dOlSLs4FAAC3TJXOuJSVlalGjRoVxmvUqKGysrKbbgoAAKAyVQouDzzwgMaPH6/c3Fxz7Pvvv9fEiRP14IMPVltzAAAAl6tScFmyZIkcDodCQkLUokULtWjRQs2aNZPD4dDixYuru0cAAABJVbzGJTg4WHv27NHGjRuVnZ0tSQoNDVVUVFS1NgcAAHC5GzrjsmnTJoWFhcnhcMhms6lv374aN26cxo0bp65du6pt27b65z//eat6BQAAP3M3FFwWLlyoZ555Rna7vcKcn5+ffvOb32j+/PnV1hwAAMDlbii4fPHFF3rooYeuOt+vXz9lZmbedFMAAACVuaHgkp+fX+lt0OU8PDx04sSJm24KAACgMjcUXO666y7t37//qvN79+5Vo0aNbropAACAytxQcHn44Yf18ssv68KFCxXmzp8/r5kzZ2rAgAHV1hwAAMDlbuh26OnTp+u9997T3XffrbFjx6p169aSpOzsbC1dulSlpaV66aWXbkmjAAAANxRcAgICtH37do0ZM0YJCQkyDEOSZLPZFB0draVLlyogIOCWNAoAAHDDD6Br2rSpPv74Y50+fVqHDx+WYRhq1aqV6tSpcyv6AwAAMFXpybmSVKdOHXXt2rU6ewEAALimKv2tIgAAAFcguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMtwaXCZPXu2unbtqtq1a6thw4YaOHCgDh065FRz4cIFxcXFqV69evL19dWgQYOUn5/vVJOTk6OYmBjVqlVLDRs21JQpU3Tp0iWnmi1btqhz587y8vJSy5YtlZycXKGfpUuXKiQkRN7e3oqIiNDOnTur/ZgBAEDVuTS4bN26VXFxcfr888+Vlpamixcvql+/fjp37pxZM3HiRH300Udas2aNtm7dqtzcXD322GPmfGlpqWJiYlRSUqLt27drxYoVSk5O1owZM8yao0ePKiYmRn369FFWVpYmTJig0aNHa8OGDWbNqlWrFB8fr5kzZ2rPnj3q2LGjoqOjVVBQ8NN8GAAA4EfZDMMwXN1EuRMnTqhhw4baunWr7r//fhUVFalBgwZauXKlBg8eLEnKzs5WaGioMjIy1L17d61fv14DBgxQbm6uAgICJEnLly/X1KlTdeLECXl6emrq1KlKSUnR/v37zX0NHTpUhYWFSk1NlSRFRESoa9euWrJkiSSprKxMwcHBGjdunKZNm/ajvTscDvn5+amoqEh2u726PxqFT3mz2tcEbjeZc4e7ugUALnAjv0Nvq2tcioqKJEl169aVJGVmZurixYuKiooya9q0aaMmTZooIyNDkpSRkaH27duboUWSoqOj5XA4dODAAbPm8jXKa8rXKCkpUWZmplONm5uboqKizJorFRcXy+FwOL0AAMCtddsEl7KyMk2YMEE9evRQu3btJEl5eXny9PSUv7+/U21AQIDy8vLMmstDS/l8+dy1ahwOh86fP68ffvhBpaWlldaUr3Gl2bNny8/Pz3wFBwdX7cABAMB1u22CS1xcnPbv36933nnH1a1cl4SEBBUVFZmvY8eOubolAADueB6ubkCSxo4dq3Xr1mnbtm1q3LixOR4YGKiSkhIVFhY6nXXJz89XYGCgWXPl3T/ldx1dXnPlnUj5+fmy2+2qWbOm3N3d5e7uXmlN+RpX8vLykpeXV9UOGAAAVIlLz7gYhqGxY8fq/fff16ZNm9SsWTOn+fDwcNWoUUPp6enm2KFDh5STk6PIyEhJUmRkpPbt2+d0909aWprsdrvCwsLMmsvXKK8pX8PT01Ph4eFONWVlZUpPTzdrAACA67n0jEtcXJxWrlypDz74QLVr1zavJ/Hz81PNmjXl5+enUaNGKT4+XnXr1pXdbte4ceMUGRmp7t27S5L69eunsLAwDRs2THPmzFFeXp6mT5+uuLg484zIc889pyVLluiFF17Q008/rU2bNmn16tVKSUkxe4mPj1dsbKy6dOmibt26aeHChTp37pxGjhz5038wAACgUi4NLq+99pokqXfv3k7jb7zxhkaMGCFJWrBggdzc3DRo0CAVFxcrOjpay5YtM2vd3d21bt06jRkzRpGRkfLx8VFsbKwSExPNmmbNmiklJUUTJ05UUlKSGjdurNdff13R0dFmzZAhQ3TixAnNmDFDeXl56tSpk1JTUytcsAsAAFzntnqOi5XxHBfg5vEcF+DnybLPcQEAALgWggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMggsAALAMlwaXbdu26ZFHHlFQUJBsNpvWrl3rNG8YhmbMmKFGjRqpZs2aioqK0ldffeVUc+rUKT311FOy2+3y9/fXqFGjdPbsWaeavXv36r777pO3t7eCg4M1Z86cCr2sWbNGbdq0kbe3t9q3b6+PP/642o8XAADcHJcGl3Pnzqljx45aunRppfNz5szRokWLtHz5cu3YsUM+Pj6Kjo7WhQsXzJqnnnpKBw4cUFpamtatW6dt27bp2WefNecdDof69eunpk2bKjMzU3PnztWsWbP05z//2azZvn27nnjiCY0aNUr/+te/NHDgQA0cOFD79++/dQcPAABumM0wDMPVTUiSzWbT+++/r4EDB0r679mWoKAgTZo0SZMnT5YkFRUVKSAgQMnJyRo6dKgOHjyosLAw7dq1S126dJEkpaam6uGHH9Z3332noKAgvfbaa3rppZeUl5cnT09PSdK0adO0du1aZWdnS5KGDBmic+fOad26dWY/3bt3V6dOnbR8+fLr6t/hcMjPz09FRUWy2+3V9bGYwqe8We1rArebzLnDXd0CABe4kd+ht+01LkePHlVeXp6ioqLMMT8/P0VERCgjI0OSlJGRIX9/fzO0SFJUVJTc3Ny0Y8cOs+b+++83Q4skRUdH69ChQzp9+rRZc/l+ymvK91OZ4uJiORwOpxcAALi1btvgkpeXJ0kKCAhwGg8ICDDn8vLy1LBhQ6d5Dw8P1a1b16mmsjUu38fVasrnKzN79mz5+fmZr+Dg4Bs9RAAAcINu2+Byu0tISFBRUZH5OnbsmKtbAgDgjnfbBpfAwEBJUn5+vtN4fn6+ORcYGKiCggKn+UuXLunUqVNONZWtcfk+rlZTPl8ZLy8v2e12pxcAALi1btvg0qxZMwUGBio9Pd0cczgc2rFjhyIjIyVJkZGRKiwsVGZmplmzadMmlZWVKSIiwqzZtm2bLl68aNakpaWpdevWqlOnjllz+X7Ka8r3AwAAbg8uDS5nz55VVlaWsrKyJP33gtysrCzl5OTIZrNpwoQJ+v3vf68PP/xQ+/bt0/DhwxUUFGTeeRQaGqqHHnpIzzzzjHbu3KnPPvtMY8eO1dChQxUUFCRJevLJJ+Xp6alRo0bpwIEDWrVqlZKSkhQfH2/2MX78eKWmpmrevHnKzs7WrFmztHv3bo0dO/an/kgAAMA1eLhy57t371afPn3M7fIwERsbq+TkZL3wwgs6d+6cnn32WRUWFqpnz55KTU2Vt7e3+Z633npLY8eO1YMPPig3NzcNGjRIixYtMuf9/Pz0ySefKC4uTuHh4apfv75mzJjh9KyXe++9VytXrtT06dP14osvqlWrVlq7dq3atWv3E3wKAADget02z3GxOp7jAtw8nuMC/DzdEc9xAQAAuBLBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWAbBBQAAWIaHqxsAgDtB+JQ3Xd0CcMtlzh3u6hY44wIAAKyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4AIAACyD4HKFpUuXKiQkRN7e3oqIiNDOnTtd3RIAAPg/BJfLrFq1SvHx8Zo5c6b27Nmjjh07Kjo6WgUFBa5uDQAAiODiZP78+XrmmWc0cuRIhYWFafny5apVq5b+9re/ubo1AAAgycPVDdwuSkpKlJmZqYSEBHPMzc1NUVFRysjIqFBfXFys4uJic7uoqEiS5HA4bkl/pcXnb8m6wO3kVv38/BT4GcXPwa36GS1f1zCMH60luPyfH374QaWlpQoICHAaDwgIUHZ2doX62bNn65VXXqkwHhwcfMt6BO50foufc3ULAK7hVv+MnjlzRn5+ftesIbhUUUJCguLj483tsrIynTp1SvXq1ZPNZnNhZ6gODodDwcHBOnbsmOx2u6vbAXAFfkbvLIZh6MyZMwoKCvrRWoLL/6lfv77c3d2Vn5/vNJ6fn6/AwMAK9V5eXvLy8nIa8/f3v5UtwgXsdjv/UQRuY/yM3jl+7ExLOS7O/T+enp4KDw9Xenq6OVZWVqb09HRFRka6sDMAAFCOMy6XiY+PV2xsrLp06aJu3bpp4cKFOnfunEaOHOnq1gAAgAguToYMGaITJ05oxowZysvLU6dOnZSamlrhgl3c+by8vDRz5swKXwcCuD3wM/rzZTOu594jAACA2wDXuAAAAMsguAAAAMsguAAAAMsguAAAAMsguACVWLp0qUJCQuTt7a2IiAjt3LnT1S0BkLRt2zY98sgjCgoKks1m09q1a13dEn5iBBfgCqtWrVJ8fLxmzpypPXv2qGPHjoqOjlZBQYGrWwN+9s6dO6eOHTtq6dKlrm4FLsLt0MAVIiIi1LVrVy1ZskTSf5+gHBwcrHHjxmnatGku7g5AOZvNpvfff18DBw50dSv4CXHGBbhMSUmJMjMzFRUVZY65ubkpKipKGRkZLuwMACARXAAnP/zwg0pLSys8LTkgIEB5eXku6goAUI7gAgAALIPgAlymfv36cnd3V35+vtN4fn6+AgMDXdQVAKAcwQW4jKenp8LDw5Wenm6OlZWVKT09XZGRkS7sDAAg8dehgQri4+MVGxurLl26qFu3blq4cKHOnTunkSNHuro14Gfv7NmzOnz4sLl99OhRZWVlqW7dumrSpIkLO8NPhduhgUosWbJEc+fOVV5enjp16qRFixYpIiLC1W0BP3tbtmxRnz59KozHxsYqOTn5p28IPzmCCwAAsAyucQEAAJZBcAEAAJZBcAEAAJZBcAEAAJZBcAEAAJZBcAEAAJZBcAEAAJZBcAEAAJZBcAFgGSEhIVq4cOFts86ttGXLFtlsNhUWFrq6FeC2QnABcE0jRozQwIEDK4y74hfrrl279Oyzz5rbNptNa9eurfb9zJo1SzabTTabTR4eHqpfv77uv/9+LVy4UMXFxdW+v969e2vChAnVvi5wJyK4ALCMBg0aqFatWj/Jvtq2bavjx48rJydHmzdv1uOPP67Zs2fr3nvv1ZkzZ36SHgBURHABUG3+8Y9/qG3btvLy8lJISIjmzZvnNL9s2TK1atVK3t7eCggI0ODBg8253r17a+zYsRo7dqz8/PxUv359vfzyy7r8z6ld/hVPSEiIJOmXv/ylbDabuX3kyBE9+uijCggIkK+vr7p27aqNGzfe8LF4eHgoMDBQQUFBat++vcaNG6etW7dq//79+t///V+zrri4WJMnT9Zdd90lHx8fRUREaMuWLeb8yZMn9cQTT+iuu+5SrVq11L59e7399tvm/IgRI7R161YlJSWZZ3m++eYbcz4zM1NdunRRrVq1dO+99+rQoUM3fCzAnYTgAqBaZGZm6le/+pWGDh2qffv2adasWXr55ZfNv9i7e/duPf/880pMTNShQ4eUmpqq+++/32mNFStWyMPDQzt37lRSUpLmz5+v119/vdL97dq1S5L0xhtv6Pjx4+b22bNn9fDDDys9PV3/+te/9NBDD+mRRx5RTk7OTR9jmzZt1L9/f7333nvm2NixY5WRkaF33nlHe/fu1eOPP66HHnpIX331lSTpwoULCg8PV0pKivbv369nn31Ww4YN086dOyVJSUlJioyM1DPPPKPjx4/r+PHjCg4ONtd/6aWXNG/ePO3evVseHh56+umnb/o4AEszAOAaYmNjDXd3d8PHx8fp5e3tbUgyTp8+bRiGYTz55JNG3759nd47ZcoUIywszDAMw/jHP/5h2O12w+FwVLqfXr16GaGhoUZZWZk5NnXqVCM0NNTcbtq0qbFgwQJzW5Lx/vvv/+gxtG3b1li8ePFV17nSzJkzjY4dO1Y6N3XqVKNmzZqGYRjGt99+a7i7uxvff/+9U82DDz5oJCQkXHX9mJgYY9KkSeZ2r169jPHjxzvVbN682ZBkbNy40RxLSUkxJBnnz5+/6trAnY4zLgB+VJ8+fZSVleX0uvJMyMGDB9WjRw+nsR49euirr75SaWmp+vbtq6ZNm6p58+YaNmyY3nrrLf3nP/9xqu/evbtsNpu5HRkZab7/ep09e1aTJ09WaGio/P395evrq4MHD1bLGRdJMgzD7HHfvn0qLS3V3XffLV9fX/O1detWHTlyRJJUWlqq3/3ud2rfvr3q1q0rX19fbdiw4br76dChg/nvRo0aSZIKCgqq5VgAK/JwdQMAbn8+Pj5q2bKl09h33313Q2vUrl1be/bs0ZYtW/TJJ59oxowZmjVrlnbt2iV/f/9q63Xy5MlKS0vTH//4R7Vs2VI1a9bU4MGDVVJSUi3rHzx4UM2aNZP035Dk7u6uzMxMubu7O9X5+vpKkubOnaukpCQtXLhQ7du3l4+PjyZMmHDd/dSoUcP8d3lgKisrq45DASyJ4AKgWoSGhuqzzz5zGvvss8909913m7/UPTw8FBUVpaioKM2cOVP+/v7atGmTHnvsMUnSjh07nN7/+eefq1WrVhVCQbkaNWpUOBvz2WefacSIEfrlL38p6b/h4vKLXW9Gdna2UlNTlZCQIEm65557VFpaqoKCAt13332Vvuezzz7To48+ql//+teS/hs6vvzyS4WFhZk1np6eN3RWCfg546siANVi0qRJSk9P1+9+9zt9+eWXWrFihZYsWaLJkydLktatW6dFixYpKytL3377rd58802VlZWpdevW5ho5OTmKj4/XoUOH9Pbbb2vx4sUaP378VfcZEhKi9PR05eXl6fTp05KkVq1a6b333lNWVpa++OILPfnkk1U6Q3Hp0iXl5eUpNzdX+/bt0+LFi9WrVy916tRJU6ZMkSTdfffdeuqppzR8+HC99957Onr0qHbu3KnZs2crJSXF7CctLU3bt2/XwYMH9Zvf/Eb5+fkVjmPHjh365ptv9MMPP3BGBbgGgguAatG5c2etXr1a77zzjtq1a6cZM2YoMTFRI0aMkCT5+/vrvffe0wMPPKDQ0FAtX75cb7/9ttq2bWuuMXz4cJ0/f17dunVTXFycxo8f7/TAuSvNmzdPaWlpCg4O1j333CNJmj9/vurUqaN7771XjzzyiKKjo9W5c+cbPp4DBw6oUaNGatKkiXr37q3Vq1crISFB//znP82vgaT/3tU0fPhwTZo0Sa1bt9bAgQO1a9cuNWnSRJI0ffp0de7cWdHR0erdu7cCAwMrPNBv8uTJcnd3V1hYmBo0aFBt1+MAdyKbYVz2kAQAcJHevXurU6dOt/2j+AG4FmdcAACAZRBcAACAZfBVEQAAsAzOuAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMsguAAAAMv4/37etkqBcETcAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from imblearn.over_sampling import SMOTE"
],
"metadata": {
"id": "N5WPzPTKagQc"
},
"execution_count": 86,
"outputs": []
},
{
"cell_type": "code",
"source": [
"smote = SMOTE(random_state=42)\n",
"X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
"print(\"Original dataset shape:\", y_train.value_counts())\n",
"print(\"Resampled dataset shape:\", y_train_resampled.value_counts())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bUZ5dMBNa7xq",
"outputId": "3f819385-a265-4dda-eb27-94dbce4445fd"
},
"execution_count": 88,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Original dataset shape: hospital_death\n",
"0 34914\n",
"1 3232\n",
"Name: count, dtype: int64\n",
"Resampled dataset shape: hospital_death\n",
"0 34914\n",
"1 34914\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.ensemble import GradientBoostingClassifier\n",
"gb_model= GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
"gb_model.fit(X_train_resampled, y_train_resampled)\n",
"####\n",
"gb_pred= gb_model.predict(X_test)\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion_matrix(y_test, gb_pred))\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_test, gb_pred))\n",
"print(\"Accuracy Score:\", accuracy_score(y_test, gb_pred))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tazOpfU8bRZh",
"outputId": "d871f399-1310-45a7-db0a-2260d445098d"
},
"execution_count": 89,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Confusion Matrix:\n",
"[[16528 602]\n",
" [ 1140 519]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.96 0.95 17130\n",
" 1 0.46 0.31 0.37 1659\n",
"\n",
" accuracy 0.91 18789\n",
" macro avg 0.70 0.64 0.66 18789\n",
"weighted avg 0.89 0.91 0.90 18789\n",
"\n",
"Accuracy Score: 0.9072861780829209\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"importances = gb_model.feature_importances_\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"print(\"\\nFeature importances:\")\n",
"for f in range(X.shape[1]):\n",
" print(f\"{X.columns[indices[f]]}: {importances[indices[f]]:.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MnuwXbEucy_i",
"outputId": "766d1b5b-0e72-4489-86e1-2afe98006857"
},
"execution_count": 90,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Feature importances:\n",
"gcs_motor_apache: 0.3203\n",
"gcs_verbal_apache: 0.0845\n",
"d1_temp_min: 0.0549\n",
"gender_M: 0.0528\n",
"apache_2_diagnosis: 0.0479\n",
"gcs_eyes_apache: 0.0412\n",
"icu_admit_source_Operating Room / Recovery: 0.0365\n",
"d1_spo2_min: 0.0325\n",
"icu_admit_source_Accident & Emergency: 0.0221\n",
"gender_F: 0.0219\n",
"apache_3j_diagnosis: 0.0217\n",
"apache_3j_bodysystem_Cardiovascular: 0.0216\n",
"apache_2_bodysystem_Metabolic: 0.0187\n",
"icu_type_Med-Surg ICU: 0.0148\n",
"d1_sysbp_min: 0.0145\n",
"apache_3j_bodysystem_Metabolic: 0.0142\n",
"icu_admit_source_Floor: 0.0123\n",
"apache_3j_bodysystem_Neurological: 0.0119\n",
"apache_2_bodysystem_Respiratory: 0.0117\n",
"resprate_apache: 0.0100\n",
"apache_2_bodysystem_Neurologic: 0.0098\n",
"h1_spo2_min: 0.0096\n",
"apache_post_operative: 0.0083\n",
"h1_spo2_max: 0.0081\n",
"diabetes_mellitus: 0.0076\n",
"age: 0.0076\n",
"icu_type_MICU: 0.0072\n",
"icu_type_Neuro ICU: 0.0062\n",
"icu_id: 0.0058\n",
"d1_heartrate_max: 0.0056\n",
"d1_diasbp_min: 0.0048\n",
"apache_3j_bodysystem_Sepsis: 0.0040\n",
"d1_glucose_max: 0.0040\n",
"d1_resprate_max: 0.0037\n",
"ethnicity_Caucasian: 0.0036\n",
"elective_surgery: 0.0036\n",
"d1_mbp_min: 0.0032\n",
"apache_2_bodysystem_Gastrointestinal: 0.0027\n",
"hospital_id: 0.0025\n",
"icu_type_SICU: 0.0025\n",
"ethnicity_African American: 0.0024\n",
"temp_apache: 0.0023\n",
"apache_3j_bodysystem_Respiratory: 0.0021\n",
"icu_type_CCU-CTICU: 0.0018\n",
"icu_admit_source_Other Hospital: 0.0016\n",
"d1_temp_max: 0.0016\n",
"apache_3j_bodysystem_Gastrointestinal: 0.0016\n",
"icu_type_Cardiac ICU: 0.0016\n",
"icu_stay_type_transfer: 0.0013\n",
"apache_2_bodysystem_Trauma: 0.0011\n",
"icu_type_CSICU: 0.0011\n",
"ethnicity_Other/Unknown: 0.0011\n",
"d1_heartrate_min: 0.0006\n",
"icu_type_CTICU: 0.0005\n",
"d1_sysbp_noninvasive_min: 0.0004\n",
"apache_3j_bodysystem_Trauma: 0.0004\n",
"ethnicity_Hispanic: 0.0004\n",
"h1_resprate_min: 0.0003\n",
"heart_rate_apache: 0.0002\n",
"d1_glucose_min: 0.0002\n",
"apache_2_bodysystem_Renal/Genitourinary: 0.0002\n",
"d1_diasbp_noninvasive_min: 0.0002\n",
"intubated_apache: 0.0002\n",
"h1_diasbp_min: 0.0001\n",
"d1_mbp_noninvasive_min: 0.0001\n",
"pre_icu_los_days: 0.0001\n",
"d1_resprate_min: 0.0001\n",
"map_apache: 0.0001\n",
"h1_mbp_min: 0.0000\n",
"d1_mbp_noninvasive_max: 0.0000\n",
"apache_4a_hospital_death_prob: 0.0000\n",
"h1_resprate_max: 0.0000\n",
"d1_spo2_max: 0.0000\n",
"ventilated_apache: 0.0000\n",
"encounter_id: 0.0000\n",
"immunosuppression: 0.0000\n",
"d1_mbp_max: 0.0000\n",
"d1_diasbp_noninvasive_max: 0.0000\n",
"d1_diasbp_max: 0.0000\n",
"gcs_unable_apache: 0.0000\n",
"arf_apache: 0.0000\n",
"weight: 0.0000\n",
"height: 0.0000\n",
"bmi: 0.0000\n",
"patient_id: 0.0000\n",
"apache_2_bodysystem_Undefined diagnoses: 0.0000\n",
"d1_sysbp_max: 0.0000\n",
"icu_stay_type_admit: 0.0000\n",
"leukemia: 0.0000\n",
"lymphoma: 0.0000\n",
"solid_tumor_with_metastasis: 0.0000\n",
"ethnicity_Asian: 0.0000\n",
"ethnicity_Native American: 0.0000\n",
"icu_admit_source_Other ICU: 0.0000\n",
"icu_stay_type_readmit: 0.0000\n",
"cirrhosis: 0.0000\n",
"apache_3j_bodysystem_Genitourinary: 0.0000\n",
"apache_3j_bodysystem_Gynecological: 0.0000\n",
"apache_3j_bodysystem_Hematological: 0.0000\n",
"apache_3j_bodysystem_Musculoskeletal/Skin: 0.0000\n",
"apache_2_bodysystem_Cardiovascular: 0.0000\n",
"apache_2_bodysystem_Haematologic: 0.0000\n",
"hepatic_failure: 0.0000\n",
"aids: 0.0000\n",
"d1_sysbp_noninvasive_max: 0.0000\n",
"h1_mbp_noninvasive_max: 0.0000\n",
"h1_diasbp_max: 0.0000\n",
"h1_diasbp_noninvasive_max: 0.0000\n",
"h1_diasbp_noninvasive_min: 0.0000\n",
"h1_heartrate_max: 0.0000\n",
"h1_heartrate_min: 0.0000\n",
"h1_mbp_max: 0.0000\n",
"h1_mbp_noninvasive_min: 0.0000\n",
"apache_4a_icu_death_prob: 0.0000\n",
"h1_sysbp_max: 0.0000\n",
"h1_sysbp_min: 0.0000\n",
"apache_2_bodysystem_Undefined Diagnoses: 0.0000\n",
"h1_sysbp_noninvasive_min: 0.0000\n",
"d1_potassium_max: 0.0000\n",
"d1_potassium_min: 0.0000\n",
"h1_sysbp_noninvasive_max: 0.0000\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"r_model= RandomForestClassifier(n_estimators=100, random_state=42)\n",
"r_model.fit(X_train_resampled, y_train_resampled)\n",
"r_pred = r_model.predict(X_test)\n",
"\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion_matrix(y_test, r_pred))\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_test, r_pred))\n",
"print(\"Accuracy Score:\", accuracy_score(y_test, r_pred))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FvViddo1dYnI",
"outputId": "e0b85257-16e3-4b32-dda6-4e4643555862"
},
"execution_count": 92,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Confusion Matrix:\n",
"[[16748 382]\n",
" [ 1254 405]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.98 0.95 17130\n",
" 1 0.51 0.24 0.33 1659\n",
"\n",
" accuracy 0.91 18789\n",
" macro avg 0.72 0.61 0.64 18789\n",
"weighted avg 0.89 0.91 0.90 18789\n",
"\n",
"Accuracy Score: 0.9129277768907339\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Conclusion\n",
"\n",
"This project successfully developed machine learning models to predict mortality in Intensive Care Unit (ICU) patients using various clinical parameters. Both Logistic Regression and Random Forest classifiers demonstrated strong predictive performance, achieving accuracy scores of approximately 91.5% and 92.1%, respectively. However, the models exhibited challenges in correctly identifying patients at high risk of mortality (class 1), as indicated by lower precision and recall values for this class.\n",
"\n",
"Following the application of SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, the models maintained competitive accuracy, but the challenge of low recall for the minority class persisted, suggesting that while more balanced data can enhance overall model performance, targeted efforts are still needed to improve predictions for high-risk patients.\n",
"\n",
"Feature importance analysis revealed that the Glasgow Coma Scale (GCS) scores, particularly the motor component, were significant predictors of mortality, followed by other clinical parameters such as temperature and gender. These insights highlight the importance of specific clinical indicators in predicting ICU outcomes, providing valuable information for clinicians in patient management."
],
"metadata": {
"id": "qxFusA95L3Kg"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "7J9Xa6esigdN"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}