Datasets
Models
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{
"cells": [
{
"cell_type": "raw",
"id": "d376da16-8f91-47c7-ad46-3e1566652552",
"metadata": {},
"source": [
"Author: Aya Balbaa\n",
"\n",
"email: ab72@sanger.ac.uk"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8b1713ad-f8ed-49ba-aa47-2f5fd7ebb0b2",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-05-17 03:44:28.534464: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-05-17 03:44:28.534572: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-05-17 03:44:29.166427: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-05-17 03:44:30.411304: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-05-17 03:44:50.806491: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import scanpy as sc\n",
"import scrublet as scr\n",
"import sys\n",
"import bbknn\n",
"from statsmodels import robust\n",
"import matplotlib.pyplot as plt\n",
"import os.path\n",
"import anndata\n",
"import harmonypy as hm\n",
"\n",
"sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)\n",
"#sc.settings.set_figure_params(dpi=80, color_map='viridis')\n",
"#sc.logging.print_versions()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ae809523-b59f-4d74-b772-cc5e8e405dec",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#import scanpy as sc\n",
"#adata=sc.read('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_by_pasha_15_5.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "14bf2d18-7d99-4848-a21d-b200a491183b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sanger_ID</th>\n",
" <th>batch</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AAACCTGAGAAACCAT-0</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL1</td>\n",
" <td>WSSS_SKN8090612</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AAACCTGAGAAACCGC-0</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL1</td>\n",
" <td>WSSS_SKN8090612</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AAACCTGAGAAACCTA-0</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL1</td>\n",
" <td>WSSS_SKN8090612</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AAACCTGAGAAACGAG-0</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL1</td>\n",
" <td>WSSS_SKN8090612</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AAACCTGAGAAACGCC-0</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL1</td>\n",
" <td>WSSS_SKN8090612</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TTTGTCATCTTTACAC-38</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TTTGTCATCTTTACGT-38</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TTTGTCATCTTTAGGG-38</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TTTGTCATCTTTAGTC-38</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TTTGTCATCTTTCCTC-38</th>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>28753920 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_type Donor Sanger_ID batch\n",
"AAACCTGAGAAACCAT-0 CTCL CTCL1 WSSS_SKN8090612 0\n",
"AAACCTGAGAAACCGC-0 CTCL CTCL1 WSSS_SKN8090612 0\n",
"AAACCTGAGAAACCTA-0 CTCL CTCL1 WSSS_SKN8090612 0\n",
"AAACCTGAGAAACGAG-0 CTCL CTCL1 WSSS_SKN8090612 0\n",
"AAACCTGAGAAACGCC-0 CTCL CTCL1 WSSS_SKN8090612 0\n",
"... ... ... ... ...\n",
"TTTGTCATCTTTACAC-38 CTCL CTCL4 CTCL4_GEX_4 38\n",
"TTTGTCATCTTTACGT-38 CTCL CTCL4 CTCL4_GEX_4 38\n",
"TTTGTCATCTTTAGGG-38 CTCL CTCL4 CTCL4_GEX_4 38\n",
"TTTGTCATCTTTAGTC-38 CTCL CTCL4 CTCL4_GEX_4 38\n",
"TTTGTCATCTTTCCTC-38 CTCL CTCL4 CTCL4_GEX_4 38\n",
"\n",
"[28753920 rows x 4 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d45dfb04-163c-4de5-8a2b-e6041c4012e9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"path1= '/nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c2a36afc-9fa3-4f18-820b-8f7b3d94212d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"file_path=('/lustre/scratch126/cellgen/team298/ab72/CTCL/skin_info.csv')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1d8429af-c033-447e-98c2-105d30fa910e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"samples = pd.read_csv(file_path)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d9035a61-c116-4d90-851e-6b9c8943ea01",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" 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>irods/farm</th>\n",
" <th>Sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sample_id</th>\n",
" <th>Tissue</th>\n",
" <th>Site</th>\n",
" <th>Sex</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4820STDY7388991</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4820STDY7388992</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388992</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4820STDY7388993</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388993</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4820STDY7388994</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388994</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4820STDY7388995</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388995</td>\n",
" <td>Epidermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>/lustre/scratch126/cellgen/team205/rl20/CTCL/r...</td>\n",
" <td>CTCL</td>\n",
" <td>CTCL3</td>\n",
" <td>CTCL3_GEX_4</td>\n",
" <td>Epidermis</td>\n",
" <td>lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>/lustre/scratch126/cellgen/team205/rl20/CTCL/r...</td>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_1</td>\n",
" <td>Dermis</td>\n",
" <td>lesion</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>/lustre/scratch126/cellgen/team205/rl20/CTCL/r...</td>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_2</td>\n",
" <td>Dermis</td>\n",
" <td>lesion</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>/lustre/scratch126/cellgen/team205/rl20/CTCL/r...</td>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_3</td>\n",
" <td>Epidermis</td>\n",
" <td>lesion</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>/lustre/scratch126/cellgen/team205/rl20/CTCL/r...</td>\n",
" <td>CTCL</td>\n",
" <td>CTCL4</td>\n",
" <td>CTCL4_GEX_4</td>\n",
" <td>Epidermis</td>\n",
" <td>lesion</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>136 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" irods/farm Sample_type Donor \\\n",
"0 4820STDY7388991 healthy_skin S1 \n",
"1 4820STDY7388992 healthy_skin S1 \n",
"2 4820STDY7388993 healthy_skin S1 \n",
"3 4820STDY7388994 healthy_skin S1 \n",
"4 4820STDY7388995 healthy_skin S1 \n",
".. ... ... ... \n",
"131 /lustre/scratch126/cellgen/team205/rl20/CTCL/r... CTCL CTCL3 \n",
"132 /lustre/scratch126/cellgen/team205/rl20/CTCL/r... CTCL CTCL4 \n",
"133 /lustre/scratch126/cellgen/team205/rl20/CTCL/r... CTCL CTCL4 \n",
"134 /lustre/scratch126/cellgen/team205/rl20/CTCL/r... CTCL CTCL4 \n",
"135 /lustre/scratch126/cellgen/team205/rl20/CTCL/r... CTCL CTCL4 \n",
"\n",
" Sample_id Tissue Site Sex Unnamed: 7 Unnamed: 8 \n",
"0 4820STDY7388991 Dermis non_lesion Female NaN NaN \n",
"1 4820STDY7388992 Dermis non_lesion Female NaN NaN \n",
"2 4820STDY7388993 Dermis non_lesion Female NaN NaN \n",
"3 4820STDY7388994 Dermis non_lesion Female NaN NaN \n",
"4 4820STDY7388995 Epidermis non_lesion Female NaN NaN \n",
".. ... ... ... ... ... ... \n",
"131 CTCL3_GEX_4 Epidermis lesion Female NaN NaN \n",
"132 CTCL4_GEX_1 Dermis lesion Male NaN NaN \n",
"133 CTCL4_GEX_2 Dermis lesion Male NaN NaN \n",
"134 CTCL4_GEX_3 Epidermis lesion Male NaN NaN \n",
"135 CTCL4_GEX_4 Epidermis lesion Male NaN NaN \n",
"\n",
"[136 rows x 9 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "87cabbb3-8e22-438c-a05a-7cfbf087c051",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Directory path\n",
"directory_path = path1\n",
"\n",
"# Get list of all subdirectories\n",
"sub = [name for name in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, name))]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4ef0e705-a33a-499e-8b90-5554fce82a48",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"sample_filt = samples.loc[samples['irods/farm'].isin(sub)]\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "38541e41-24cf-49b3-b1ee-fa52fca7532b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"column_list=sample_filt['irods/farm'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d9460f0b-bb58-4871-91f3-402214b8e44f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"path2= '/cellbender_out_filtered.h5'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "094b267d-a403-4b52-a15a-10f7332fe73a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"path= [path1+name+path2 for name in column_list]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "1f805d6a-b7ba-4f67-8605-fcb56bc47ef9",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "c38af71e-7577-493c-af37-9ba799df7c37",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_411959/2675726948.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" sample_filt['path']= path\n"
]
},
{
"data": {
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"<div>\n",
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" .dataframe tbody tr th:only-of-type {\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>irods/farm</th>\n",
" <th>Sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sample_id</th>\n",
" <th>Tissue</th>\n",
" <th>Site</th>\n",
" <th>Sex</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>path</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>4820STDY7388991</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4820STDY7388992</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388992</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4820STDY7388993</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388993</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4820STDY7388994</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388994</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4820STDY7388995</td>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388995</td>\n",
" <td>Epidermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>SKN8105195</td>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105195</td>\n",
" <td>Epidermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>SKN8105197</td>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105197</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>SKN8105198</td>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105198</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>SKN8105199</td>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105199</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>SKN8105200</td>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>/nfs/cellgeni/pasham/projects/2211.adult.skin/...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>93 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" irods/farm Sample_type Donor Sample_id Tissue \\\n",
"0 4820STDY7388991 healthy_skin S1 4820STDY7388991 Dermis \n",
"1 4820STDY7388992 healthy_skin S1 4820STDY7388992 Dermis \n",
"2 4820STDY7388993 healthy_skin S1 4820STDY7388993 Dermis \n",
"3 4820STDY7388994 healthy_skin S1 4820STDY7388994 Dermis \n",
"4 4820STDY7388995 healthy_skin S1 4820STDY7388995 Epidermis \n",
".. ... ... ... ... ... \n",
"91 SKN8105195 healthy_skin S5 SKN8105195 Epidermis \n",
"92 SKN8105197 healthy_skin S5 SKN8105197 Dermis \n",
"93 SKN8105198 healthy_skin S5 SKN8105198 Dermis \n",
"94 SKN8105199 healthy_skin S5 SKN8105199 Dermis \n",
"95 SKN8105200 healthy_skin S5 SKN8105200 Dermis \n",
"\n",
" Site Sex Unnamed: 7 Unnamed: 8 \\\n",
"0 non_lesion Female NaN NaN \n",
"1 non_lesion Female NaN NaN \n",
"2 non_lesion Female NaN NaN \n",
"3 non_lesion Female NaN NaN \n",
"4 non_lesion Female NaN NaN \n",
".. ... ... ... ... \n",
"91 non_lesion Female NaN NaN \n",
"92 non_lesion Female NaN NaN \n",
"93 non_lesion Female NaN NaN \n",
"94 non_lesion Female NaN NaN \n",
"95 non_lesion Female NaN NaN \n",
"\n",
" path \n",
"0 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"1 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"2 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"3 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"4 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
".. ... \n",
"91 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"92 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"93 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"94 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"95 /nfs/cellgeni/pasham/projects/2211.adult.skin/... \n",
"\n",
"[93 rows x 10 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_filt['path']= path\n",
"sample_filt"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "170e958a-5f27-4a49-bad6-7252540395a1",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388991/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388992/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388993/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388994/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388995/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388996/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388997/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388998/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7388999/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389000/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389001/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389002/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389003/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389004/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389005/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389006/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389007/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389008/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389009/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389010/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389011/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389012/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389013/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/4820STDY7389014/cellbender_out_filtered.h5\n",
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090524/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090525/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090526/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090528/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090529/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090530/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090531/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090536/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090537/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:04)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090538/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:03)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090539/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090540/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090541/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090542/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090543/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090548/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090549/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090550/cellbender_out_filtered.h5\n",
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090551/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:03)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090552/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090553/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:03)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090554/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090555/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:03)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090560/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090561/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090562/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090563/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090564/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090565/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090567/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090576/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090577/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090578/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090579/cellbender_out_filtered.h5\n",
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090580/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090581/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090582/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090583/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090588/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090589/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090590/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090591/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090592/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090594/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090595/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090600/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090601/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090602/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090603/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090604/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:02)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090605/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090606/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8090607/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:01)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104894/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104895/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104896/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104897/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104899/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104900/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104901/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8104902/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105192/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105193/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105194/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105195/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105197/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105198/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105199/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n",
"reading /nfs/cellgeni/pasham/projects/2211.adult.skin/data.nfs/singlecell/reynolds.reprocessing/starsolo/cellbender/SKN8105200/cellbender_out_filtered.h5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0:00:00)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/anndata/_core/anndata.py:1899: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
" utils.warn_names_duplicates(\"var\")\n",
"/tmp/ipykernel_411959/2305451509.py:24: FutureWarning: Use anndata.concat instead of AnnData.concatenate, AnnData.concatenate is deprecated and will be removed in the future. See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html\n",
" concatenated_adata = adata_list[0].concatenate(adata_list[1:])\n"
]
}
],
"source": [
"# Initialize a list to store Adata objects\n",
"adata_list = []\n",
"\n",
"# Iterate over each row in the DataFrame\n",
"for index, row in sample_filt.iterrows():\n",
" # Read Adata file\n",
" adata = sc.read_10x_h5(row['path'])\n",
"\n",
" # add sample_type and donor\n",
" adata.obs['sample_type'] = row['Sample_type']\n",
" adata.obs['Donor'] = row['Donor']\n",
" adata.obs['Sanger_ID'] = row['irods/farm']\n",
" adata.obs['tissue'] = row['Tissue']\n",
" adata.obs['site'] = row['Site']\n",
" adata.obs['Sex'] = row['Sex']\n",
" \n",
" adata.obs_names_make_unique()\n",
" adata.var_names_make_unique()\n",
" \n",
" # Append the Adata object to the list\n",
" adata_list.append(adata)\n",
"\n",
"# Concatenate all Adata objects\n",
"concatenated_adata = adata_list[0].concatenate(adata_list[1:])\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "1bc7243c-a763-4686-a6e1-274c85c691d5",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 647026 × 36601\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex', 'batch'\n",
" var: 'gene_ids'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concatenated_adata"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "85f0b8ae-9083-4f3a-add8-f6551df7962f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"concatenated_adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/Skin_atlas_cellbender_raw_by_pasha_17_5.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8c6feff4-977d-484b-999b-bf1354b311a6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata=sc.read('/lustre/scratch126/cellgen/team298/ab72/CTCL/Skin_atlas_cellbender_raw_by_pasha_17_5.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "c9973f29-e763-4f1d-833b-23751750a2c4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata=concatenated_adata.copy()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "179b8738-f991-44d4-885d-094b3c7d1495",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 647026 × 36601\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex', 'batch'\n",
" var: 'gene_ids'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c683050f-f658-455a-93b8-5f65cd34ce21",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata.obs['n_counts'] = adata.X.sum(axis=1).A1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8cb46d15-5099-49b7-9098-06f6e74f68d3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata = adata[adata.obs['n_counts'] > 1, :]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "0edc4660-495a-4467-90a0-9bfd93084bdb",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"View of AnnData object with n_obs × n_vars = 646559 × 36601\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex', 'batch', 'n_counts'\n",
" var: 'gene_ids'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "raw",
"id": "bb937f93-8396-4266-9997-4dcc75fe534b",
"metadata": {},
"source": [
"Doublet detection"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "85bf5d7b-680a-47f4-9b9c-18c242550a2c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<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>sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sanger_ID</th>\n",
" <th>batch</th>\n",
" <th>n_counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CACCACTAGCCCTAAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>s1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>0</td>\n",
" <td>46398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TGCTACCAGCGCCTCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>s1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>0</td>\n",
" <td>45420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AATCCAGAGGTGATAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>s1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>0</td>\n",
" <td>44099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAACCAAGTGCAGACA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>s1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>0</td>\n",
" <td>39982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GGGCATCCAGATGGCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>s1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>0</td>\n",
" <td>39492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CTGATCCTCCGATATG-209</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>209</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TGCTACCCATCTATGG-209</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>209</td>\n",
" <td>104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGCCTAAGTTTGTTTC-209</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>209</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TAAGAGAGTATAATGG-209</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>209</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGCTCCTTCCCTCTTT-209</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>209</td>\n",
" <td>108</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1448643 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_type Donor Sanger_ID batch n_counts\n",
"CACCACTAGCCCTAAT-0 healthy_skin s1 4820STDY7388991 0 46398\n",
"TGCTACCAGCGCCTCA-0 healthy_skin s1 4820STDY7388991 0 45420\n",
"AATCCAGAGGTGATAT-0 healthy_skin s1 4820STDY7388991 0 44099\n",
"CAACCAAGTGCAGACA-0 healthy_skin s1 4820STDY7388991 0 39982\n",
"GGGCATCCAGATGGCA-0 healthy_skin s1 4820STDY7388991 0 39492\n",
"... ... ... ... ... ...\n",
"CTGATCCTCCGATATG-209 healthy_skin S5 SKN8105200 209 114\n",
"TGCTACCCATCTATGG-209 healthy_skin S5 SKN8105200 209 104\n",
"AGCCTAAGTTTGTTTC-209 healthy_skin S5 SKN8105200 209 37\n",
"TAAGAGAGTATAATGG-209 healthy_skin S5 SKN8105200 209 106\n",
"AGCTCCTTCCCTCTTT-209 healthy_skin S5 SKN8105200 209 108\n",
"\n",
"[1448643 rows x 5 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "75ced8e8-6e7b-4e14-90fa-f02b8546d22f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2512789/2340965974.py:1: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
" adata.obs['Donor']=adata.obs['Donor'].astype(str)\n"
]
}
],
"source": [
"adata.obs['Donor']=adata.obs['Donor'].astype(str)\n",
"adata.obs['Sanger_ID']=adata.obs['Sanger_ID'].astype(str)\n",
"adata.obs['donor_lane']= adata.obs['Donor'] + \"_\" + adata.obs['Sanger_ID']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "b22d377b-e9f6-413c-b21c-9758e52ab8d2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 1448643 × 36601\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'batch', 'n_counts', 'donor_lane'\n",
" var: 'gene_ids'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6fb2add9-9c29-4de3-878a-7acce7cbbe16",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4820STDY7388991\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.69\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 3.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 4.7%\n",
"Elapsed time: 9.3 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 2.5%\n",
"Estimated detectable doublet fraction = 22.9%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 10.8%\n",
"\n",
"\n",
"4820STDY7388992\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.78\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 2.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 1.7%\n",
"Elapsed time: 29.2 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.8%\n",
"Estimated detectable doublet fraction = 15.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 5.4%\n",
"\n",
"\n",
"4820STDY7388993\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.77\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 1.6%\n",
"Elapsed time: 24.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 2.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.4%\n",
"\n",
"\n",
"4820STDY7388994\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.68\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.5%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 0.0%\n",
"Elapsed time: 18.3 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.5%\n",
"Estimated detectable doublet fraction = 4.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 12.1%\n",
"\n",
"\n",
"4820STDY7388995\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.02\n",
"Detected doublet rate = 96.8%\n",
"Estimated detectable doublet fraction = 99.8%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 97.0%\n",
"Elapsed time: 83.9 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.3%\n",
"Estimated detectable doublet fraction = 1.6%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 19.7%\n",
"\n",
"\n",
"4820STDY7388996\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.66\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 0.0%\n",
"Elapsed time: 49.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.4%\n",
"Estimated detectable doublet fraction = 10.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 13.4%\n",
"\n",
"\n",
"4820STDY7388997\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.79\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 0.0%\n",
"Elapsed time: 28.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.3%\n",
"Estimated detectable doublet fraction = 2.8%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 11.2%\n",
"\n",
"\n",
"4820STDY7388998\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.68\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 4.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 3.2%\n",
"Elapsed time: 24.3 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.7%\n",
"Estimated detectable doublet fraction = 12.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.0%\n",
"\n",
"\n",
"4820STDY7388999\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.47\n",
"Detected doublet rate = 0.6%\n",
"Estimated detectable doublet fraction = 14.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 4.1%\n",
"Elapsed time: 12.0 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.6%\n",
"Estimated detectable doublet fraction = 20.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 8.1%\n",
"\n",
"\n",
"4820STDY7389000\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.71\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 1.8%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.8%\n",
"Elapsed time: 11.5 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.4%\n",
"Estimated detectable doublet fraction = 6.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 7.2%\n",
"\n",
"\n",
"4820STDY7389001\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.71\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 4.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 3.5%\n",
"Elapsed time: 12.6 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.9%\n",
"Estimated detectable doublet fraction = 14.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.6%\n",
"\n",
"\n",
"4820STDY7389002\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.72\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 1.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 3.8%\n",
"Elapsed time: 15.0 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.0%\n",
"Estimated detectable doublet fraction = 13.8%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 7.5%\n",
"\n",
"\n",
"4820STDY7389003\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.51\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 1.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 8.3%\n",
"Elapsed time: 0.9 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.9%\n",
"Estimated detectable doublet fraction = 4.6%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 20.3%\n",
"\n",
"\n",
"4820STDY7389004\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.67\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 0.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 57.1%\n",
"Elapsed time: 17.2 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.2%\n",
"Estimated detectable doublet fraction = 9.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 12.9%\n",
"\n",
"\n",
"4820STDY7389005\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.70\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 0.0%\n",
"Elapsed time: 7.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 1.8%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 12.9%\n",
"\n",
"\n",
"4820STDY7389006\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.72\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 1.6%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 4.8%\n",
"Elapsed time: 11.8 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.4%\n",
"Estimated detectable doublet fraction = 5.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.9%\n",
"\n",
"\n",
"4820STDY7389007\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.65\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 5.5%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 0.6%\n",
"Elapsed time: 5.0 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 3.3%\n",
"Estimated detectable doublet fraction = 26.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 12.7%\n",
"\n",
"\n",
"4820STDY7389008\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.67\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 1.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.4%\n",
"Elapsed time: 6.5 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.7%\n",
"Estimated detectable doublet fraction = 6.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 11.4%\n",
"\n",
"\n",
"4820STDY7389009\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.68\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 8.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.9%\n",
"Elapsed time: 6.4 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.2%\n",
"Estimated detectable doublet fraction = 17.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.9%\n",
"\n",
"\n",
"4820STDY7389010\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.63\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 1.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 9.4%\n",
"Elapsed time: 4.5 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.7%\n",
"Estimated detectable doublet fraction = 5.9%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 11.6%\n",
"\n",
"\n",
"4820STDY7389011\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.67\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 7.4%\n",
"Elapsed time: 5.3 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.6%\n",
"Estimated detectable doublet fraction = 4.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 14.3%\n",
"\n",
"\n",
"4820STDY7389012\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.03\n",
"Detected doublet rate = 90.5%\n",
"Estimated detectable doublet fraction = 98.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 91.8%\n",
"Elapsed time: 7.0 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 0.9%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 20.9%\n",
"\n",
"\n",
"4820STDY7389013\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.06\n",
"Detected doublet rate = 75.2%\n",
"Estimated detectable doublet fraction = 93.6%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 80.3%\n",
"Elapsed time: 9.2 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.0%\n",
"Estimated detectable doublet fraction = 0.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 8.3%\n",
"\n",
"\n",
"4820STDY7389014\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.64\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 6.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.8%\n",
"Elapsed time: 4.4 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.4%\n",
"Estimated detectable doublet fraction = 9.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 4.8%\n",
"\n",
"\n",
"SKN8090524\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.47\n",
"Detected doublet rate = 0.5%\n",
"Estimated detectable doublet fraction = 17.9%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.9%\n",
"Elapsed time: 10.0 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.3%\n",
"Estimated detectable doublet fraction = 23.5%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 5.5%\n",
"\n",
"\n",
"SKN8090525\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.57\n",
"Detected doublet rate = 0.1%\n",
"Estimated detectable doublet fraction = 9.2%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 1.6%\n",
"Elapsed time: 3.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 2.1%\n",
"Estimated detectable doublet fraction = 18.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 11.4%\n",
"\n",
"\n",
"SKN8090526\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.36\n",
"Detected doublet rate = 0.9%\n",
"Estimated detectable doublet fraction = 1.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 66.7%\n",
"Elapsed time: 0.2 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 0.9%\n",
"Estimated detectable doublet fraction = 1.3%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 66.7%\n",
"\n",
"\n",
"SKN8090528\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.54\n",
"Detected doublet rate = 0.3%\n",
"Estimated detectable doublet fraction = 15.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.0%\n",
"Elapsed time: 1.1 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.3%\n",
"Estimated detectable doublet fraction = 29.7%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 4.4%\n",
"\n",
"\n",
"SKN8090529\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.46\n",
"Detected doublet rate = 0.3%\n",
"Estimated detectable doublet fraction = 17.5%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 1.8%\n",
"Elapsed time: 2.3 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.8%\n",
"Estimated detectable doublet fraction = 33.5%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 5.4%\n",
"\n",
"\n",
"SKN8090530\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.61\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 12.0%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 1.5%\n",
"Elapsed time: 8.9 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 2.2%\n",
"Estimated detectable doublet fraction = 30.4%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 7.4%\n",
"\n",
"\n",
"SKN8090531\n",
"Preprocessing...\n",
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n",
"Calculating doublet scores...\n",
"Automatically set threshold at doublet score = 0.52\n",
"Detected doublet rate = 0.2%\n",
"Estimated detectable doublet fraction = 9.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 2.3%\n",
"Elapsed time: 5.7 seconds\n",
"Threshold found by scrublet\n",
"Detected doublet rate = 1.4%\n",
"Estimated detectable doublet fraction = 23.1%\n",
"Overall doublet rate:\n",
"\tExpected = 10.0%\n",
"\tEstimated = 6.0%\n",
"\n",
"\n",
"SKN8090536\n",
"Preprocessing...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/scrublet/helper_functions.py:252: RuntimeWarning: invalid value encountered in sqrt\n",
" CV_input = np.sqrt(b);\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulating doublets...\n",
"Embedding transcriptomes using PCA...\n"
]
},
{
"ename": "ValueError",
"evalue": "n_components=30 must be between 1 and min(n_samples, n_features)=29 with svd_solver='arpack'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m x \u001b[38;5;241m=\u001b[39m ad\u001b[38;5;241m.\u001b[39mX\n\u001b[1;32m 7\u001b[0m scrub \u001b[38;5;241m=\u001b[39m scr\u001b[38;5;241m.\u001b[39mScrublet(x)\n\u001b[0;32m----> 8\u001b[0m ds, prd \u001b[38;5;241m=\u001b[39m \u001b[43mscrub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscrub_doublets\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m RUNs\u001b[38;5;241m.\u001b[39mappend(run)\n\u001b[1;32m 10\u001b[0m DSs\u001b[38;5;241m.\u001b[39mappend(ds)\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/scrublet/scrublet.py:237\u001b[0m, in \u001b[0;36mScrublet.scrub_doublets\u001b[0;34m(self, synthetic_doublet_umi_subsampling, use_approx_neighbors, distance_metric, get_doublet_neighbor_parents, min_counts, min_cells, min_gene_variability_pctl, log_transform, mean_center, normalize_variance, n_prin_comps, svd_solver, verbose)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mean_center:\n\u001b[1;32m 236\u001b[0m print_optional(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmbedding transcriptomes using PCA...\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose)\n\u001b[0;32m--> 237\u001b[0m \u001b[43mpipeline_pca\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_prin_comps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_prin_comps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msvd_solver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msvd_solver\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 239\u001b[0m print_optional(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmbedding transcriptomes using Truncated SVD...\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose)\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/scrublet/helper_functions.py:89\u001b[0m, in \u001b[0;36mpipeline_pca\u001b[0;34m(self, n_prin_comps, random_state, svd_solver)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 87\u001b[0m X_sim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_E_sim_norm\n\u001b[0;32m---> 89\u001b[0m pca \u001b[38;5;241m=\u001b[39m \u001b[43mPCA\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_components\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_prin_comps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msvd_solver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msvd_solver\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_obs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_manifold(pca\u001b[38;5;241m.\u001b[39mtransform(X_obs), pca\u001b[38;5;241m.\u001b[39mtransform(X_sim)) \n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1145\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1148\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1149\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/sklearn/decomposition/_pca.py:434\u001b[0m, in \u001b[0;36mPCA.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 418\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model with X.\u001b[39;00m\n\u001b[1;32m 419\u001b[0m \n\u001b[1;32m 420\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;124;03m Returns the instance itself.\u001b[39;00m\n\u001b[1;32m 433\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/sklearn/decomposition/_pca.py:512\u001b[0m, in \u001b[0;36mPCA._fit\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_full(X, n_components)\n\u001b[1;32m 511\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_svd_solver \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandomized\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_truncated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_svd_solver\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/sklearn/decomposition/_pca.py:585\u001b[0m, in \u001b[0;36mPCA._fit_truncated\u001b[0;34m(self, X, n_components, svd_solver)\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 581\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_components=\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m cannot be a string with svd_solver=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;241m%\u001b[39m (n_components, svd_solver)\n\u001b[1;32m 583\u001b[0m )\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m n_components \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(n_samples, n_features):\n\u001b[0;32m--> 585\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_components=\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m must be between 1 and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin(n_samples, n_features)=\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msvd_solver=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 589\u001b[0m \u001b[38;5;241m%\u001b[39m (n_components, \u001b[38;5;28mmin\u001b[39m(n_samples, n_features), svd_solver)\n\u001b[1;32m 590\u001b[0m )\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m svd_solver \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m n_components \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mmin\u001b[39m(n_samples, n_features):\n\u001b[1;32m 592\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 593\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_components=\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m must be strictly less than \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 594\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin(n_samples, n_features)=\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 595\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msvd_solver=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;241m%\u001b[39m (n_components, \u001b[38;5;28mmin\u001b[39m(n_samples, n_features), svd_solver)\n\u001b[1;32m 597\u001b[0m )\n",
"\u001b[0;31mValueError\u001b[0m: n_components=30 must be between 1 and min(n_samples, n_features)=29 with svd_solver='arpack'"
]
}
],
"source": [
"RUNs, DSs, CELLs, THRs, MEDs, MADs, CUTs, no_thr = [], [], [], [], [], [], [], []\n",
"\n",
"for run in adata.obs['Sanger_ID'].unique():\n",
" print(run)\n",
" ad = adata[adata.obs['Sanger_ID'] == run, :]\n",
" x = ad.X\n",
" scrub = scr.Scrublet(x)\n",
" ds, prd = scrub.scrub_doublets()\n",
" RUNs.append(run)\n",
" DSs.append(ds)\n",
" CELLs.append(ad.obs_names)\n",
" # MAD calculation of threshold:\n",
" MED = np.median(ds)\n",
" MAD = robust.mad(ds)\n",
" CUT = (MED + (4 * MAD))\n",
" MEDs.append(MED)\n",
" MADs.append(MAD)\n",
" CUTs.append(CUT)\n",
"\n",
" try: # not always can calculate automatic threshold\n",
" THRs.append(scrub.threshold_)\n",
" print('Threshold found by scrublet')\n",
" except:\n",
" THRs.append(0.4)\n",
" no_thr.append(run)\n",
" print('No threshold found, assigning 0.4 to', run)\n",
" scrub.call_doublets(threshold=0.4) # so that it can make the plot\n",
" #fig = scrub.plot_histogram()\n",
" #fig[0].savefig(run + '.png')\n",
" \n",
" \n",
" scrub.call_doublets(threshold=CUT)\n",
" #fig = scrub.plot_histogram()\n",
" #fig[0].savefig(run + '_mad_' + '.png')\n",
" #plt.close('all')\n",
" print()\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "797ec1d0-3ff4-4c7f-a37f-5baf14f5edfa",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ns = np.array(list(map(len, DSs)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "65f4b272-adb1-47c3-8d6f-f092ffc09f92",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tbl = pd.DataFrame({\n",
" 'run': np.repeat(RUNs, ns),\n",
" 'ds': np.concatenate(DSs),\n",
" 'thr': np.repeat(THRs, ns),\n",
" 'mad_MED': np.repeat(MEDs, ns),\n",
" 'mad_MAD': np.repeat(MADs, ns),\n",
" 'mad_thr': np.repeat(CUTs, ns),\n",
" }, index=np.concatenate(CELLs))\n",
"\n",
"tbl['auto_prd'] = tbl['ds'] > tbl['thr']\n",
"tbl['mad_prd'] = tbl['ds'] > tbl['mad_thr']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5c35b8e6-803e-4b9a-ac7c-b1d577facbbf",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_642202/4188707388.py:1: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
" adata.obs['mad_prd']=tbl['mad_prd'].copy()\n"
]
}
],
"source": [
"adata.obs['mad_prd']=tbl['mad_prd'].copy()\n",
"adata.obs['ds']=tbl['ds'].copy()\n",
"adata.obs['mad_MED']=tbl['mad_MED'].copy()\n",
"adata.obs['mad_MAD']=tbl['mad_MAD'].copy()\n",
"adata.obs['mad_thr']=tbl['mad_thr'].copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ef45bb88-c170-4ffb-838d-054f5f83383e",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<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>sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sanger_ID</th>\n",
" <th>tissue</th>\n",
" <th>site</th>\n",
" <th>Sex</th>\n",
" <th>batch</th>\n",
" <th>n_counts</th>\n",
" <th>mad_prd</th>\n",
" <th>ds</th>\n",
" <th>mad_MED</th>\n",
" <th>mad_MAD</th>\n",
" <th>mad_thr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CACCACTAGCCCTAAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>46398</td>\n",
" <td>False</td>\n",
" <td>0.284768</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TGCTACCAGCGCCTCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>45420</td>\n",
" <td>False</td>\n",
" <td>0.094675</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AATCCAGAGGTGATAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>44099</td>\n",
" <td>False</td>\n",
" <td>0.080645</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAACCAAGTGCAGACA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39982</td>\n",
" <td>False</td>\n",
" <td>0.094675</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GGGCATCCAGATGGCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39492</td>\n",
" <td>False</td>\n",
" <td>0.153374</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CTGATCCTCCGATATG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>114</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>TGCTACCCATCTATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>104</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>AGCCTAAGTTTGTTTC-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>37</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>TAAGAGAGTATAATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>106</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>AGCTCCTTCCCTCTTT-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>108</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>646559 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_type Donor Sanger_ID tissue site \\\n",
"CACCACTAGCCCTAAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"TGCTACCAGCGCCTCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"AATCCAGAGGTGATAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"CAACCAAGTGCAGACA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"GGGCATCCAGATGGCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TGCTACCCATCTATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCCTAAGTTTGTTTC-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TAAGAGAGTATAATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCTCCTTCCCTCTTT-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"\n",
" Sex batch n_counts mad_prd ds mad_MED \\\n",
"CACCACTAGCCCTAAT-0 Female 0 46398 False 0.284768 0.098634 \n",
"TGCTACCAGCGCCTCA-0 Female 0 45420 False 0.094675 0.098634 \n",
"AATCCAGAGGTGATAT-0 Female 0 44099 False 0.080645 0.098634 \n",
"CAACCAAGTGCAGACA-0 Female 0 39982 False 0.094675 0.098634 \n",
"GGGCATCCAGATGGCA-0 Female 0 39492 False 0.153374 0.098634 \n",
"... ... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 Female 92 114 NaN NaN NaN \n",
"TGCTACCCATCTATGG-92 Female 92 104 NaN NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 Female 92 37 NaN NaN NaN \n",
"TAAGAGAGTATAATGG-92 Female 92 106 NaN NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 Female 92 108 NaN NaN NaN \n",
"\n",
" mad_MAD mad_thr \n",
"CACCACTAGCCCTAAT-0 0.060705 0.341455 \n",
"TGCTACCAGCGCCTCA-0 0.060705 0.341455 \n",
"AATCCAGAGGTGATAT-0 0.060705 0.341455 \n",
"CAACCAAGTGCAGACA-0 0.060705 0.341455 \n",
"GGGCATCCAGATGGCA-0 0.060705 0.341455 \n",
"... ... ... \n",
"CTGATCCTCCGATATG-92 NaN NaN \n",
"TGCTACCCATCTATGG-92 NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 NaN NaN \n",
"TAAGAGAGTATAATGG-92 NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 NaN NaN \n",
"\n",
"[646559 rows x 13 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "9f605c63-b607-48ba-b426-a210767302cf",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for column in adata.obs.select_dtypes(include=['object']).columns:\n",
" adata.obs[column] = adata.obs[column].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "3b9485da-c157-44f4-84a9-850f39260935",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/skinatlas_cellbender_raw_by_pasha_17_5_db.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cc44005b-0d42-4bb9-9e0f-14e2a56e610a",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"mad_prd\n",
"False 172098\n",
"True 1481\n",
"Name: count, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs['mad_prd'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "a210fbdc-d029-476c-85a8-76e77bd18a4b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <tr>\n",
" <th>CACCACTAGCCCTAAT-0</th>\n",
" <td>healthy_skin</td>\n",
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" <td>Dermis</td>\n",
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" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TGCTACCAGCGCCTCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
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" <td>Dermis</td>\n",
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" <td>False</td>\n",
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" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AATCCAGAGGTGATAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
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" <td>0</td>\n",
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" <td>False</td>\n",
" <td>0.087324</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAACCAAGTGCAGACA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39982</td>\n",
" <td>False</td>\n",
" <td>0.098634</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GGGCATCCAGATGGCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39492</td>\n",
" <td>False</td>\n",
" <td>0.153374</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CTGATCCTCCGATATG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>114</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>TGCTACCCATCTATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>104</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>AGCCTAAGTTTGTTTC-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>37</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>TAAGAGAGTATAATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>106</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>AGCTCCTTCCCTCTTT-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>108</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>646559 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_type Donor Sanger_ID tissue site \\\n",
"CACCACTAGCCCTAAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"TGCTACCAGCGCCTCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"AATCCAGAGGTGATAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"CAACCAAGTGCAGACA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"GGGCATCCAGATGGCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TGCTACCCATCTATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCCTAAGTTTGTTTC-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TAAGAGAGTATAATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCTCCTTCCCTCTTT-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"\n",
" Sex batch n_counts mad_prd ds mad_MED \\\n",
"CACCACTAGCCCTAAT-0 Female 0 46398 False 0.284768 0.098634 \n",
"TGCTACCAGCGCCTCA-0 Female 0 45420 False 0.094675 0.098634 \n",
"AATCCAGAGGTGATAT-0 Female 0 44099 False 0.087324 0.098634 \n",
"CAACCAAGTGCAGACA-0 Female 0 39982 False 0.098634 0.098634 \n",
"GGGCATCCAGATGGCA-0 Female 0 39492 False 0.153374 0.098634 \n",
"... ... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 Female 92 114 NaN NaN NaN \n",
"TGCTACCCATCTATGG-92 Female 92 104 NaN NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 Female 92 37 NaN NaN NaN \n",
"TAAGAGAGTATAATGG-92 Female 92 106 NaN NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 Female 92 108 NaN NaN NaN \n",
"\n",
" mad_MAD mad_thr \n",
"CACCACTAGCCCTAAT-0 0.060705 0.341455 \n",
"TGCTACCAGCGCCTCA-0 0.060705 0.341455 \n",
"AATCCAGAGGTGATAT-0 0.060705 0.341455 \n",
"CAACCAAGTGCAGACA-0 0.060705 0.341455 \n",
"GGGCATCCAGATGGCA-0 0.060705 0.341455 \n",
"... ... ... \n",
"CTGATCCTCCGATATG-92 NaN NaN \n",
"TGCTACCCATCTATGG-92 NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 NaN NaN \n",
"TAAGAGAGTATAATGG-92 NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 NaN NaN \n",
"\n",
"[646559 rows x 13 columns]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "5a658145-7c54-42ba-9cb5-8c2291a8aa63",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata = adata[adata.obs['mad_prd'] != True].copy()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "7bd1f36f-fde1-45c7-b175-3bacd009cda0",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 645064 × 36601\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex', 'batch', 'n_counts', 'mad_prd', 'ds', 'mad_MED', 'mad_MAD', 'mad_thr'\n",
" var: 'gene_ids'"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "31b6fcb1-71d3-4efd-a1fe-18dfec107554",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<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>sample_type</th>\n",
" <th>Donor</th>\n",
" <th>Sanger_ID</th>\n",
" <th>tissue</th>\n",
" <th>site</th>\n",
" <th>Sex</th>\n",
" <th>batch</th>\n",
" <th>n_counts</th>\n",
" <th>mad_prd</th>\n",
" <th>ds</th>\n",
" <th>mad_MED</th>\n",
" <th>mad_MAD</th>\n",
" <th>mad_thr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CACCACTAGCCCTAAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>46398</td>\n",
" <td>False</td>\n",
" <td>0.284768</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TGCTACCAGCGCCTCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>45420</td>\n",
" <td>False</td>\n",
" <td>0.094675</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AATCCAGAGGTGATAT-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>44099</td>\n",
" <td>False</td>\n",
" <td>0.087324</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAACCAAGTGCAGACA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39982</td>\n",
" <td>False</td>\n",
" <td>0.098634</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GGGCATCCAGATGGCA-0</th>\n",
" <td>healthy_skin</td>\n",
" <td>S1</td>\n",
" <td>4820STDY7388991</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>39492</td>\n",
" <td>False</td>\n",
" <td>0.153374</td>\n",
" <td>0.098634</td>\n",
" <td>0.060705</td>\n",
" <td>0.341455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CTGATCCTCCGATATG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>114</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>TGCTACCCATCTATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>104</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>AGCCTAAGTTTGTTTC-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>37</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>TAAGAGAGTATAATGG-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>106</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>AGCTCCTTCCCTCTTT-92</th>\n",
" <td>healthy_skin</td>\n",
" <td>S5</td>\n",
" <td>SKN8105200</td>\n",
" <td>Dermis</td>\n",
" <td>non_lesion</td>\n",
" <td>Female</td>\n",
" <td>92</td>\n",
" <td>108</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>645064 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_type Donor Sanger_ID tissue site \\\n",
"CACCACTAGCCCTAAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"TGCTACCAGCGCCTCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"AATCCAGAGGTGATAT-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"CAACCAAGTGCAGACA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"GGGCATCCAGATGGCA-0 healthy_skin S1 4820STDY7388991 Dermis non_lesion \n",
"... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TGCTACCCATCTATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCCTAAGTTTGTTTC-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"TAAGAGAGTATAATGG-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"AGCTCCTTCCCTCTTT-92 healthy_skin S5 SKN8105200 Dermis non_lesion \n",
"\n",
" Sex batch n_counts mad_prd ds mad_MED \\\n",
"CACCACTAGCCCTAAT-0 Female 0 46398 False 0.284768 0.098634 \n",
"TGCTACCAGCGCCTCA-0 Female 0 45420 False 0.094675 0.098634 \n",
"AATCCAGAGGTGATAT-0 Female 0 44099 False 0.087324 0.098634 \n",
"CAACCAAGTGCAGACA-0 Female 0 39982 False 0.098634 0.098634 \n",
"GGGCATCCAGATGGCA-0 Female 0 39492 False 0.153374 0.098634 \n",
"... ... ... ... ... ... ... \n",
"CTGATCCTCCGATATG-92 Female 92 114 NaN NaN NaN \n",
"TGCTACCCATCTATGG-92 Female 92 104 NaN NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 Female 92 37 NaN NaN NaN \n",
"TAAGAGAGTATAATGG-92 Female 92 106 NaN NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 Female 92 108 NaN NaN NaN \n",
"\n",
" mad_MAD mad_thr \n",
"CACCACTAGCCCTAAT-0 0.060705 0.341455 \n",
"TGCTACCAGCGCCTCA-0 0.060705 0.341455 \n",
"AATCCAGAGGTGATAT-0 0.060705 0.341455 \n",
"CAACCAAGTGCAGACA-0 0.060705 0.341455 \n",
"GGGCATCCAGATGGCA-0 0.060705 0.341455 \n",
"... ... ... \n",
"CTGATCCTCCGATATG-92 NaN NaN \n",
"TGCTACCCATCTATGG-92 NaN NaN \n",
"AGCCTAAGTTTGTTTC-92 NaN NaN \n",
"TAAGAGAGTATAATGG-92 NaN NaN \n",
"AGCTCCTTCCCTCTTT-92 NaN NaN \n",
"\n",
"[645064 rows x 13 columns]"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obs"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "225712b0-80f8-4baa-9f88-b763c79407fd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for column in adata.obs.select_dtypes(include=['object']).columns:\n",
" adata.obs[column] = adata.obs[column].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "35ac5bf2-78d7-4312-af33-c27c41976e81",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/skinatlas_cellbender_raw_by_pasha_17_5_db_removed.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "e49a3c41-8fe3-424e-8f43-bb65684e76c3",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"filtered out 93389 cells that have less than 200 genes expressed\n",
"filtered out 5576 genes that are detected in less than 3 cells\n"
]
}
],
"source": [
"sc.pp.filter_cells(adata, min_genes=200)\n",
"sc.pp.filter_genes(adata, min_cells=3)\n",
"mito_genes = adata.var_names.str.startswith('MT-')\n",
"adata.obs['percent_mito'] = np.sum(adata[:, mito_genes].X, axis=1).A1 / np.sum(adata.X, axis=1).A1\n",
"adata.obs['n_counts'] = adata.X.sum(axis=1).A1"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "5735d697-b416-49d7-8ac4-94c0a2b83bdd",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1511.11x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sc.pl.violin(adata, ['n_genes', 'n_counts', 'percent_mito'],\n",
" jitter=0.4, multi_panel=True)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "ffefcd20-9061-40f1-908e-be9ce5c41702",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'n genes'}, xlabel='n_counts', ylabel='n_genes'>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x500 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"sc.pl.scatter(adata, x='n_counts', y='n_genes', color='n_genes', show=False, ax=ax1)\n",
"sc.pl.scatter(adata[adata.obs['n_counts'] < 10000], x='n_counts', y='n_genes', color='n_genes', show=False, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "1ff56610-3feb-41a3-a4e7-d9d2df7915f1",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'percent mito'}, xlabel='n_counts', ylabel='percent_mito'>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0XDenOzAaIiIiIqKdzTDSnR7CltD1850eAhHRjsRKtA4SBLntcck9sMUjISIiIiLa+XZLVZUk7e30EIg2Vff2RONyzm7HEI2IiIiIiIiIuka9J1r3LY3sxjFTq+6Lbrsct+gmIiIiop2I81wios1x8803QxRFuFwuKIqCz3/+88ue//M///OQJAkulwuiKOL5z38+crnc1gx2h2OItsUsFDs9BCIiIiKiDWdZuU4PgYh2CQsCzC68rWdH0TvuuAP3338/brnlFvzjP/4jhoeHcdttt+GZZ55pe/573vMefOMb38C73vUuPPLII/jDP/xDPPHEE3jVq151sW87gcs5t5wohAEAhpmF6Obum0RERES0M4hibNOubRjpXdPPjIhWtpt6on3uc5/DlVdeifvuuw8AcOONN8Lr9eL9738/HnjggUXnP/LII4hGo7jnnnsAAC95yUvwta99DSdOnLi4wRMAVqJtKcPMOl8zQCMiIiKibmNYatvd5TcbAzQiambNV3V14w0ApqenMTEx4dwKhULb16mqKkqlEt74xjc6x0RRxOjoKJ544om2j3npS1+KbDbrLPl8+OGH8dRTT+FFL3rRBv8t7E6sRNtCC4Mz3ZiBJPZ3aDRERERERGsjCkqnh7Ciin4OPmm008MgIlrStdde2/L9DTfcgIcffnjReY3qsX379rUc7+npwezsbNtr33PPPZiZmcGv/uqv4ld/9VcBAEeOHME//dM/bcDIiSFaBzFAIyIiIiLaWAzQiHY+03bBtLtvp8vGmB999FEMDAw4x0Oh0IY9x913341vfOMbuOWWW/CWt7wFjz76KD7xiU/gVa96FR588MENe57diiHaFmLlGRERERF1u4o+Dp803OlhENEu1mjU321M1HuiDQwMYGhoaMXzDx06BAA4e/Zsy/G5uTkEg8G2j7nrrrvw/Oc/H1/84hcBAL/4i7+IQqGAe++9F4ZhQBQZA12M7vuvrotJYj8sy4JhcYdOIiIiIupOFxOgGVYBlqVt4GiIiHYuRVEQCATw7W9/2zlmGAbOnTuHq6++uu1jarUaBKE16mkEZ5Zlbd5gdwmGaFtMEASIQvvEmIiIiIhoJxOFEARB7vQwiKjLWbbQtbe1uu222/DUU0/hXe96F771rW/hyiuvhG3b+PM//3MAwIEDB3Ddddc551977bV49NFH8d73vhff//738Sd/8ie49957MTw8DEmSNuzvYLdiHR8RERERERERdY1uX865Fp/61KcwNTWFz3/+8/ibv/kbBAIBfPazn8UVV1wBoL600+W60B/ugQcewBve8Ab8r//1v/DpT38abrcbl112Gb71rW9t2OvYzRiibROWZS0quSQiIiIiou5lWQYEgb9yEdHFuf/++5e8L5fLtXwvyzIeeuihTR7R7sV/0bcJC2UI2P5bhhMRERER0cosy4IFjXN8ok1gAV25Oyc7knU/hmjbhCi0/nA1LA0i+0UQEREREXUlQRC2ZYBmWGWIgr/TwyC6KBYEWF24nLMbx0ytGKJ1iGGVAQjLBGXMqImIiIiIVlI1UvCKiU4Po4usvScT0XZj2gLMdTTp77RuHDO1YojWISt9+sNPh4iIiIiIVsYAbW1EIdDpIRARdS2GaERERERERETUNSy4YKEbe6J135ip1baoJbznnnswOjoKWZbxwhe+EI8++uiy59999904fPgwfD4fhoeH8b73vQ+apm3RaImIiIhotTjPIyKijdZYztmNN+puHf8b/MpXvoI777wTd911F37605/i6quvxute9zokk8m253/5y1/G7/7u7+Kuu+7CsWPH8LnPfQ5f+cpX8Hu/93tbPPL1sSwDlmV0ehhEREREm263zfNoZfW+wERERN2p4yHaJz/5SbzrXe/CrbfeiiNHjuDee++F3+/Hfffd1/b8f/u3f8P111+Pt7/97RgdHcVrX/ta/PIv//KKn2puB7qldnoIRERERFtmN83zdhvLMhiIEVHHmBC69kbdraN/g7qu47HHHsOrX/1q55ggCHj1q1+NH/7wh20f8+IXvxiPPfaYM5k6c+YMvvOd7+DGG29se361WkWhUGi5dYJu5KBVkxAEEYLAVnRERES0s23FPA/YPnO9bqcZ6VWelwEA6FYBAqQ1Pw83zyKijWDZrq69UXfraJqTTqdhmib6+vpajvf19eH48eNtH/P2t78d6XQaL3nJS2DbNgzDwH/5L/9lyTL/j33sY/jIRz6y4WNfC93IAQBCvv0dHQcRERHRVtmKeR6wPeZ63UIz0pDFOCxLh2FpkMSQc58sxld1DVmMtfxJRES0m3RdLeHDDz+Mj370o/irv/or/PSnP8XXv/51fPvb38Yf/uEftj3/Ax/4APL5vHMbHx/f4hEDkhiBJEYAABV9dsufn4iIiKgbrHWeB2yPuV63aARlgiC1BGibRdUnN/05iGh3srbBssz13Kzui2BogY5WosXjcbjdbszOtgZLs7Oz6O/vb/uYD33oQ/iVX/kV/Nqv/RoA4Morr0SpVMLtt9+OD37wgxCE1v8ovV4vvF7v5ryANdKNPHxS38onEhEREXW5rZjnAdtrrrcTGVYFgL2uZZiKtGfjB0REBMCyBVhduNNlN46ZWnX0b1CSJBw9ehQPPvigc8yyLDz44IO47rrr2j6mXC4vmkC53W4AgG3bmzfYi1A18ijrSUhiuNNDISIiItoSu2We1y0avcwAoKivvlpPFHzsY0ZE244JV9feqLt1vMP9nXfeiXe84x14wQtegGuvvRZ33303SqUSbr31VgDALbfcgj179uBjH/sYAODNb34zPvnJT+J5z3seXvjCF+LUqVP40Ic+hDe/+c3OJGu78TaFZ2U9Cb/U6/xJREREtFPthnlet2juYRaUhjf9+VR9Coo0uInXn4HPneB/F0REtKU6HqK97W1vQyqVwh/8wR9gZmYG11xzDR544AGnCe358+dbPpH8/d//fbhcLvz+7/8+JicnkUgk8OY3vxl//Md/3KmXsCaN4IwBGhEREe10u22eRxdsZoBWv377JcFEtDtwOSd1isveZbXxhUIB4XAY+XweodDmN1Rt0Iw8ZDGMip6GT4q3vY+IiIi2r07NIWht+Pe0dpqRgySE2vacW62iPoXgJgdnnaAZeUhC8KLeGyLaOBMTExgeHsYf/PjVkBVPp4ezZppaw/944fcwPj6OoaGhTg+Hmqx2/tDxSrTdohGSLQzQmu8jIiIiItpq8vwu8hdjJwZoAOfpRETUiiHaFtGMAkRBQkXPISiz/JyIiIiIOsOwdFhWFZIY3JDr6UYRguCFKEgwDA0GdMgiqwCJaPNwOSd1CkO0LSQKMiQx0OlhEBEREdEuJgoSIEjO92UjA3/TxgNr1RzGMUAjoq1g2gLMLgykunHM1Ip/g1ukMZmwYUMzCh0eDRERERFR3cUEaAs15rxlI7Pua5SNuY0aDhER0YZiiLYFZopPI6WegVqdgSyGYFjVTg+JiIiIiHaJoj7rhFp5fcI5Zprmso9rnNuwlg+CVxPM5fXpJR7bs+rnWYuVXi8RdQ8bLlhdeLPh6vRbRxeJyzm3QMJ/GUxXFapWn7w0QrSKmYcEH9xuabmHExERERGtm98ddeabYam+G1xQ6lv2MbpVhl+MI69POI+RBP+GjKdsZOEXowhLA22Pb5aymUbQvfzrJqLuwOWc1CkM0baA2+1GzTAQ89cnIBG5/qfPzd1+iIiIiGhzreYD24UBViMwawRoACAIG/Orw1JB2WYGaMDKwSERdQ/LdsGyu6+qqxvHTK0Yg24BzShAFkNtS+BVPdWBERERERHRTlRaZy+y1QZYhqVDM9R1Pcd2sdQyUiIiopWwEm2TWZaBZPks9oaudhqtNkI1AFCkRCeHR0REREQ7QMnIICDGEGjqRVbQZ2HDWrRs8mKIglTf3XMDNca+VTby/SCizjAhwOzCmqBuHDO1Yoi2yQRBRMAdbQnOuO03EREREW2kdiFUwB1H1b6wEmKpnmNbHWIt1MnnJqLuxOWc1CmMQbdAwBuB7bKd70vVLIC17XBERERERLQStWk5p9vtbgnNllqy2S7EUvX1LQtdD7XNEtSL2UmzbGQvZjhERERLYiXaFjCsKrSaCl+gvpGAy+WCaZqsSCMiIiKiDaXMB2JZfQpRaXDJ83RDQ8nIIiq3X9oouX2bMr52lHYhnjmHsLt3Xdfb7A0KiKjzLAiwurAmqBvHTK0Yom0Bt+BFPHCh95lfinRuMERERES04y0M0EpGDgEx4nwviTIkceneYFsZorUTltYXoBHR7mDaLphduDSyG8dMrRiDboFKjcs2iYiIiGhrZPUp52vdrMA0zZYAjYiIiNaHIdomm1JPAELr21zQ0x0aDRERERHtFOfVZ1p6hzV6iy1exrn+/mKdkGkKAYmI2mlsLNCNN+puXM65yRQpBs1QUdDTCElxAHD+JCIiIiJar6DUA7fb7XzvWvD5eEafQmyJvmg5fQYRqX9Tx7deS42ZiKjBtgVYdvfVBNldOGZqxb/BLdDrH4XHLaO8CbtxsqqNiIiIaHfJVOuVWtEFIdjCJZvLhVHtAjRVzy/7vJqhwrD0VY6SiDZa4/99Aky4uvZG3Y0h2iaz5/9Ml6fg34TdOFnVRkRERLS7xLyDyFSnL/o6tVoZs+VzzveSW1r2fFlUIArLn0NEmyfmZZUmUadxOecmkwQZqfIk9gQu6fRQiIiIiGiHiHkv7KypGjkoYgTFWhZBT3TR8aV4PH70eUad75t35DQtA26BvyoQ0fZk2ejK/mKWvfI5tL3xJ+Mm84kKfKKCqllGWp3EHuVgp4dERERERDtIIyhrDtCaj69H1arALwQBABl9FjGpr+15c9Vp9DQFekREW8Hq0p5o3ThmasW/wU2mmxpmK+dh2iYDNCIiIiLaFBl91vlat3QUa9k1X0M1cs7XfjHofL1UgAaAARoREe0qDNE2mRselGtF1NiElYiIiIg2mG7W55iNoEs1crAs06lKaw7Gmr9uaA7fLqZyjYhoK1lwde2NuhuXc26yZ3I/xlU9L+70MIiIiIhohzlZeBI+IYAh5YBzbGEQ1vx9u5BsuSozIqLtyrRdMLuwJ1o3jplaMUTbZCPBw50eAhERERF1uXZ9yQ6GrurQaIiIiHYnLufcZB7Bi+Nzj3d6GERERETUxRYGaKpRaHteQa/3Qmss8wSAtDYNAMhUZ9s+hoio2zQ2FujGG3U3/g1uMsuyUEEFlmUBAMqGuuz5eT2zFcMiIiIioi4mC34U9FzLsZPFZxCS6r3QKuaFOWdcrjf/97uD0DexT+/C8dDGmKsmOz0Eom3HgguW3YU39kTrelzOuckKRg7P67kOZwrHMejfC7+oLHt+WIpt0ciIiIiIqJuouoqKXUDCOwhREBGSIi33Hwxe7nzdPKe0LAuCIEAW/c6xuWoSHpfkXEO3NFgWIIty2+cuGSoCK8xjF46HNkaPt7fTQyDaduwubdJvd+GYqRUr0TZZWIphSj2PvcolzrHm8noiIiIiouVoRhm6pUORFCS8g6t+XGMFRLI6vei+Hm9vS+glCfKiAK25sswrtA/XLlbFrGzKdYmIiDYDQ7QtYMFAUpuCLPpRMlSGaERERES0arLohyRIi463Wz6ZqsygWMsDqAdjANDv27Ps9c8WT7Q93hyyicLKC1gaYd9amJwXE9E6dHxZ5kXcqLtxOecm8woyPG4Z2vynbCuVwRMRERERrUZzyFXQ8whJYSR8/c4xURCRqsy0HAMAzdBQMgvOMsF9wUMt9xdreQQ94TWPp3m56Gop0tqfh4ioW5v0d+OYqRX/BjfZ8dwzCHhCCM83eSUiIiIiWo+0trjBfLIyi7SWRGg+jDJN07kvr+cWBWhAve9ZI0Brd831BGhERES7AUO0TVQxyvCLPsyo44hIMcxUxjs9JCIiIiLqEvn55ZoFvb48My73tnyf13Po9fU5xwEgW5tre42lND92Oaq+eIf5ZGV2VY8lItponV6SyeWcuxdDtE3kFkRolg634EFSS0KE3PLpYEPZKHVgdERERES0nbngcpZpzlWT0AwNAJyqs3DTcs5GWFY2ys6xsBRpOaehEcIB9aWduqWtOBa3e/GxXl8fgPbVbDvBSgEkEXWONb87ZzfeqLuxJ9omkgQJRb2I/sggYlIMuqXDLSyegfjFQAdGR0RERETbWSMsa1R8yV4ZxWoRqlXEgK91l86wFEFKS2KvMrrq6wJYtCPnUnxuBVVTg9e9+PzVVrN1m3YBJBER7W6sRNtEGT2D0eA+VIwyCnoetm0ho2c6PSwiIiIi2qaKtSIsywJwoRKq19fnVH0FvUEE3Erb3d6DYqjlce0U9ILzdb6pIm01bHt156W01JquS0S0Vp1eksnlnLsXQ7RNVjbKKOkllM0KvG4ZMSnW6SERERER0TalmRoEoT5Fb66Eau5JFnAHYNg15/vpyhQMy3CqyhqPa1SwNfcuC0kh52vJLS16/uUCsNVWrSXkxKrOIyJar04HYQzRdi+GaJuoZtVQMTTsDx2A5JYwUZ5Y8yd+RERERLTzNSrEAmIAhmUsur9iVgDUq8d06C3tQAJuBaKwuEtLo3qt8WdDbr5Szef2LXpMIwBr9F9rp/H42TVuLLDW84mIltLpIIwh2u7FEG0T9cl9kEUZxwvPISSGMOQfQlhqv2W4aize8YiIiIiIdodGhZhf9LcEYo1Aze/xo6AXYMNeFH41V5c1TJYmnfmlbuotLUUiq+n1tczveY3H9y0I51ay1vO7XfPSWSIi2hkYom2iTDWDifIk/KIf2vyuRwzLiIiIiKid/ILQJVVJOXNIwzIRkkKISBHk9BxSlRR0S2/ZjbNZ0BN0equVzfKiliK5FXaelNtsILBWuzFEan5f24WbRLQxOl1Nxkq03Yu7c26imDeGK6KXo6iXoFuLm782U0Rli0ZFRERERNtReEHokvDVl1bm9DwiTasZmivJJEFqe47dcr3Fv7RFpAhmK0n0+TZ+Z03DMlC19F0ZIq2qyo+ILpoNwFquZHabWuX+LLSNsRJtE2mGBsMykNEzzqd/jbCsXa8LIiIiIqKG3HwvXc2srnhuRAo7589UkosCuYVVbgA2JUADAFEQERD9F32dlT6EJiIi2moM0TaRLMqQBRkHQwcwUZ4AUF/OaVkWLFjOec09KoiIiIiIADiVZTFvZMllm40lm83n98+HY41QLSKFW0K1doFas5lKcv2DXuVzrMZ2+NA5WVl6t1Ii6pxOL8nkcs7diyHaJhovjuOx1M8A1JvEAvVKNEEQnNJ7AIt6VBARERHR7tUIsfJ6ATOVJCRBavr4tS6lZZDSMqjUKkuGTREp3LZv2sIqtYVBUf8GVKgtfI718G9ANdvF6p1fUktE20ungzCGaLsXQ7RNpMPEgG8Iz2RPtA3KMtVsB0ZFRERERNtJc+XXTCXphFhhKeR8LTd9AJvT84h5wtCMCgLeQMtuns1LOgGgbFRQqC2/sdVuCIo2orqOiIiIIdomGvUPI1nLQBED+M749wAAlVoFmqGjZhmIeaMdHiERERERdVpz5dfCKrDp+fBHFEQnCIpIYaT1LAKeC5VajWWdC5d0Bt0K3BCR1fPIzS+xbARtAJCqpKHqFyrVns4+BwCYraTX/Xo0o97LTDfX39Mst8E7e25EdR0RbR+driZjJdruxd05N1HOyEMWPIh6I/B5fACAsfIELg0fXPIxqlGGsg1K14mIiIioM3J6AREphJxewMB8+FPQVfR640641eeLtzxGNcoISUrL4wHA7XYj4buwIqJiaC27eHrcEhTJj+lKEgO+XlwRPdz2+qsxW0mjzxeHPb/4tGiU0OOWVnhUe5FduLPnWhVrKgJuPwSBdRG0+3RrINWNY6ZW/Bd3E53NjyOrFVAxdYjw4LnCWVwS3IdM06d/CzFAIyIiItqdzqtTMCwDESmEtJZBrnqhGitby+JE4RT6fPGWgKtRsdUI0ADAL8ooG5WWazcq2lxN0//ZStoJqwbaVGrl9IJzW052fm7bGJdPlAEAPetcdaGbOubY9mRFDNBoN7NtV9feqLvxX91N5HMHIAgCYlIIPtGLuDcCURARa/r0j4iIiIgoq+exVxmEOr8JgNctwefxIq/nkdHz8AsB7PENArgQiAHtK7YkQYIkeFBs6oXWCMlEQXCOt6s2aw7MIlLIuS1ltpJGdIPntpJbWncAt5swQCMi2nr8l3cTXd5zCTSriqg3DFmoTwaK+vKNXYmIiIho92kEUY3AKuhR0CcnAAg4nj8Nr1tCza7vwtmuamwhURAR9ChLHs8uUV221mWUyy373Oi+ZkREDRZcXXuj7sYQbRM9mn4SkssPVa+X059TpxCUWiczC0vtiYiIiGj3aYRaWb2A2UoaFbOCvF5EWAriqvBlCLh9iMkR57wT+XMtIdXMGjcCCIvKmj7cXXj9pUK4Zt3Q16xR+UdE3aXTmwNwY4HdiyHaJvKLARwK78XJ0mkEJQWjyuCicyTB04GREREREdF2EpVCmKmkEZVC8LllmCZgwQYAKJIPbrfbOQ8ADoVHEZl/DADICxr4Lwy9VL2CmfKc870gCIs+3F1O/4KKs2gXBGSrIQnr2/iAiIh2J4ZomygmhaAaZWhGDUntwqQlreWcr0VBhG7pzlbgRERERLQ7yW4vgHp4pki+tkGVbl6YM05VUtDNGoB61ddMeQ5lQ3Ou0cyCiZAUAABkmqrINqoSy7KsdT92pjIH0zQ3ZBxrJQniRV8jw2WrRFuu05sDcGOB3Ysh2ibyCzLOFqYQcPkhC15ktDw0Q4cg1P/HUfUKKmYFkiBBFvkpGBEREdFu1AiQIlLQ+bMRzDQHNCfyY8hWC8jpRQDAoC+BXjmKgl5CTi+iZGjwz++MOehLtDyHDTj3xZrCuYWVWGVDWzYQWyowKl5EGNfv68HcMrvXb5S8rjptVjZSbIdU5RF1k04vyeRyzt3r4j96oSWl9Rxy1RJ6e6IoGxqm1Axi3gAC858CKpKvwyMkIiIiok4rmhV4IcHnllDQSwhJAcSkEHJ6ETEphOlKCj63D4fCIwDqlVtAPdDK6yq8bg8GfYmWKrWF3HC3Pb6wEitTzWMo0LfkdWJSCLqlo1zTULMsJHwRzFTmYMFCGK3LQ3N60QkGV9Lrizmvrd/Xs6rHrFV4DctXiWh769aqrm4cM7ViJdomOpY9h5tGb4AkeeF2iXh+7yEEpAASvohzTkEvAbhQAt/4noiIiIh2h4ikoGJqyOgF5Gr1Zv9PZU45AdSAL4GIpDhVYI2QKSaFsE8ZdKrOXEsEZcDKH95OVVIAsGyA1iAJEiLekDOn7ff1LKp8m6qkVh2gNdusAG0lJUODbm1OexXDMi76Go3qQyLanW6++WaIogiXywVFUfD5z39+2fPHxsZw5ZVXwu12w+VyQZIkfOQjH9mi0e5sDNE20V5lECfy48hXS/C6228gUJ3vY1GZ/6Hd6FVBRERERDvfOXUaQD0Qi0kh7J0PsXrkMAzLaPmAtbFssLni7Lw6i3QlBwAtH9Q2y1Tr4dt0Za7t/Vm9uCgEu1iDvgRSlRwqy1THbQeN9yQgypuyyYBhGdA3IERbTyBJtJPZ22BZ5npu66lEu+OOO3D//ffjlltuwT/+4z9ieHgYt912G5555pm256uqiiNHjmB2dhZ/+Zd/iUceeQR/9md/hssuu+xi33YCl3NuqpgcwrHMafhFCfCG8VT2NBTRhwQizjmNyU5gvkcFEREREe0eo8pA2+OlWhmiPwHDbu01dix/DnsD/WjEPbJbcnbubDZVTmHQXw/GfPPzTO/8rvBZvYhoUygTXSGgyVQLqFo1JLxhlI3qqj/0Fd1u+OZ3DZ2uzGGgQ1Vmy9nsMYmCCHEDNi8golY2ANte8bRtpzHk6enpluOhUAihUPv+ip/73Odw5ZVX4r777gMA3HjjjfB6vXj/+9+PBx54YNH5t99+O3RdRyqVgt/vBwC85CUv2bgXscuxEm0TpSppKB4FGa2IgFvGldED2BccdD5RTFU2v4EqEREREW1/2QXL9Qq6BsMyILgE6KaOVCWDrF5Enxxr+fC11xdtG4I1AjSgXu2WqRbgc3uQ1dUVQ7OFYt4QBnw9EAVxTasmmp9npbCqUS23kZaqvCMi6rRrr70Ww8PDzu0tb3lL2/NUVUWpVMIb3/hG55goihgdHcUTTzzR9jHf+973MDQ0hKNHj0IQBMiyjNe+9rXQ9e1dGdwtGKJtoplyHv+ePIawFETJrGJSTQOoL9lMVfJI+MIdHiERERERbQfP5SZwPD8G0zRhmiaOJg5DFEQU9TKSlRwkUcZz+XHEvKH5UC3vBG+ZamFRCNdQqVXQ7+tBzBtCxawhusrm+s3XW+raSx1fj5h3dTtcTpbTq77mdqx8I6KNYcHVtTcAePTRRzE+Pu7cvvnNb7Z9nSdOnAAA7Nu3r+V4T08PisX2/wbn83mcO3cOpmniS1/6Et797nfje9/7Hl73utdt4N/A7sXa4k1UMXUcio7iyp79KOhl7FHizn1rDdA0Q4csbnyfBiIiIiLqjLPqNCKSgqgUxIt6671qzquzcMHGsNKPrF6ESxAw5I9jujKHS8N7AQCSW0LCt7plkj6PD40tBZYLqpZb4rlU5dpaK9o2wh5/fOWTaEcoGxr8bHlDS+j23TkHBgYwNDS0Sc9hw+124+mnn4YkSfjP//k/49y5c/jWt761Kc+327ASbRNdHh3F0Z6DOJOfQkjyo6DXe1qUa5U1X6v7/nkgIiIiouXsUwacICqr13fl3Kv0wS1I0AwdUSmIofnQaMDXA9MyF12jEaA1Ht+OZuiYKC1fwbWWQGwjK9AaVH3t82Pa2QT+qkqEQ4cOAQDOnj3bcnxubg7BYPt/t/1+P0KhECTpQhHO0aNHYVkWVHXpnxW0OvyXaRM9MXcW+VoJisePH84ew6niFAAgVy2v8MgLGsGbl1VoRERERDvSmeIsDLMekGmGDq8g4ljhfMs5WV1Fj9y6kmG2koU5/7hgU8XOwkBNFiUMBeLOfRPzSyKLehm6ZWC6kllxjFVDdx7XCNwsy8KEmlr162wo6GUYC3asNLE4IKTuMbcJPe24CoeW0+ldNi/mthaKoiAQCODb3/62c8wwDJw7dw5XX31128ccOXIEhUIBhnHh39nHH38cgiBAUVa3pJ+WxhBtE+WqKj517B9h2AaORIdxJFwv1xxUepCq5J2AbDkhyb/ZwyQiIiKiLZbRVWTmw66ENwjTZUMzdMyUsxAFAZKrvpPmVCWDf0+fhA1gopTG09lzeDp7FmfUGfT5ojitzgAAKsaFhtHNfc8yCwK1gFuCd/7aQckPSRAx4Iu1HaNlWU7Y5RUlpyquQRAE9Pqja37tIcm/aMfKcJtebRldRWkdFWq6ZaC4ink2bZyeVfa0I9oott29t7W67bbb8NRTT+Fd73oXvvWtb+HKK6+Ebdv48z//cwDAgQMHcN111znn/8//+T9hmiae//zn47vf/S4+/OEP4xvf+AZe+cpXbtTbv6uxJ9omOhAexM/1HMRjmZN4Wf/VKOs65PlPCbmpABEREdHuFZsPjSzLggkL/XIEE6U0gpIPYUlBWqtX9gz6YvC6RMQkBTFJwVAgjoyuOo8fVRI4pyYx5I/hp5lTeH7sEgD1AMq2LPTIreFGWlcxuERottCslkNUUiAu87G7uImfycdWuQnCQpIgQpL4aw7RTtbtPdHW4lOf+hSmpqbw+c9/Hn/zN3+DQCCAz372s7jiiisA1Jd2ulwXrvvCF74Qf/3Xf40777wTr3/96yGKIl796lezJ9oG4U+XTSQKIvaG+3GJMISQx4+iXW6pPmtUmSUrefQyVCMiIiLaFTRDhwELiihDEARE5sOioUAcz+bHAQAHQoNIV/KI+8JwCa1BVXO4JLkljCq9AOAEaM3nNAduAFYdoAHAgH/lcwtG2Rl/szmtsCjAa3mcXoZflBZVpK3Wwte1mWYqWfT71l5xR0S0Ue6///4l78vlcouO3X777bj99ts3cUS7F5dzbqJj2QmczM9grpzH96aewFylCEkQEZL8Lcs0lwrQkpX8Vg2ViIiIiLaAaZrwuNwo13Tkmz5cfSJzFhOlNPrlKFwuAaZpwpq/z7asttfKVFXMaQXMzVet5ZqWburzyzAbQVNuwbLOkl5BRlcxVb7QD61xHQAtxxvPtZC1xLgAoEcOQWtaYrpQuyWda7FVARoAJKTdt1RxpY0oiDqtUYnWjTfqbtsiRLvnnnswOjoKWZbxwhe+EI8++uiy5+dyObznPe/BwMAAvF4vDh06hO985ztbNNrVKekVHI4MQq2V8PTcOSS8IfgkL5KVvBOOrRSSsTqNiIiIut1OnOddjIqpQzN19PpCCEt+J6y4IrwXmmkg5lXgdgmwXTZ6fWFMlNJORdfCYCPmVVAxdZi2Dd3UERIvfEjbHGD98/RTiEgKNEOHqlcwUUojIPkAGxicrzbLVNWWyrHBBVVoMa+yaAfNrK62rUJrjNXC0iFbu/MvxmaGPm63e9OuvV01NqIg2q46vTnAVm0sQNtPx0O0r3zlK7jzzjtx11134ac//SmuvvpqvO51r0MymWx7vq7reM1rXoNz587hq1/9Kp577jl89rOfxZ49e7Z45MsLSD6cyacwoiSwJ9iHQ5E9OFdMYm8w4ZzDkIyIiIh2sp06z7sYiuRDQPIhrRUB1MMKzdDhdrshuIB0pYCT+UkUaho0Q0fMeyGkUjz13rqNxzYe3+sLQ3JLmCxnneMhye+c98qBKwEABizoMJ2ApPnazV8vJVXN43wx5bQnWW655lAgDn/TjqELLQy9Lja0YehDRERboeMh2ic/+Um8613vwq233oojR47g3nvvhd/vx3333df2/Pvuuw+ZTAbf+MY3cP3112N0dBQ33HDDktu7dophGbgk0oeQ5EfF1vFcdhI/F6/3qUhXNn4LaCIiIqLtZqfO89bLsAxnCWRcDjrHU9X63HB/sB8Vs4a9Si8UUYJm6ZAFCUB9OaYiyIse22xY6XG+fjIz5pw3rs4BABRRhmVd2Bqu3RJNoB7SNQd1DfuC/dgbTMAvSqt7wcsYCsSdcS1cOkpEtJJO77C5lbtz0vbS0RBN13U89thjePWrX+0cEwQBr371q/HDH/6w7WO++c1v4rrrrsN73vMe9PX14YorrsBHP/pRmKbZ9vxqtYpCodBy2wq6ZUCt6Tidn8GV0b0YURI4U5wFAByJDW/JGIiIiIg6ZSvmeUDn5nrrYVhWyxLHRojlhhtlQ6v3S3MLMGwTkltCRFKQ0UsoGxoikgJRFJEq5zFTybZcVzcX9x67KjbifN0I1zRDh+z2OMetJX6bi8vBJYO6vF7CVCnb9r61aoxr4dJRIqKV1AOpzvc3W/ut0+8cXayOhmjpdBqmaaKvr6/leF9fH2ZmZto+5syZM/jqV78K0zTxne98Bx/60IfwiU98An/0R3/U9vyPfexjCIfDzm14eGsCLL8oQ7BsHM9N4d/Tp3CyMIO9Sk9LA1mAmwcQERHRzrQV8zygc3O99ZAX7EbZWELZ7wtjTivB7XYjVVERl0Mo6GVkqirKRgWTaj200i0DXtGDfl/UCeAqho6cXsHT2fNQdQ15vYTifO+yhZVmsig5S0KBelj2s8y5lnPaVaA1C0uBlvYklTYB3kIrXXO7mGRFHBERraDjyznXyrIs9Pb24jOf+QyOHj2Kt73tbfjgBz+Ie++9t+35H/jAB5DP553b+Pj4lo016A1gqpJDjyeMpJaHaugIN+3KCbAvGhEREVHDWud5QGfnehslb1QwrPTgVGEGV8aGYVgGPIII2EBBr2JfqB5aSYKI8VIG4+ocSrUq0loRNiyUDA0jgQQUSUZYCqBgVJDWisv2OctoKiZLGYz4YjhXTDnHBdfaml6XdG3Fc5aqattu9rAijqhrdL6ijLtz7lbr31d6A8TjcbjdbszOzrYcn52dRX9/f9vHDAwMwOPxtOySc9lll2FmZga6rkOSWns0eL1eeL3ejR/8CvJ6GYdCA/juxOMomTpe2n8ZwpIfqXIBlm1D9ogAXItCNSIiIqKdYCvmeUDn5nrrkdFUxOTFwVZUCgAA+uUIADgN+dNaEW6XgNlKAXPVIq6KjeDy6BCO56YQEL3wCG7IgoSwJ4Cg5MN0JYcBXwR7/DGn31hDVi85zwMAMVlBxdBhw0LMd2GDgNVsMNAs7lt6c4FmFUNHxdDbvv6l3pdxda6lz9taLXVdIup+9vyt23TjmKlVRyvRJEnC0aNH8eCDDzrHLMvCgw8+iOuuu67tY66//nqcOnXKacoKACdOnMDAwEDbiVWn2ADuPf4g9gUGABtIVgo4mZ+ByyVAcrsRlgJwuwRoho5ZLukkIiKiHWYnz/M22mQpg5+kz0Az60FT2aji4eljMCwTklvEnkAMV0SGUTaqmCxlMKrEMaz0ICz58e9zZxGTFWQ0FbVazbnmgD+MwvyyzpxebgnQAMA0TeRq5ZbAbrnxNWz00szlgq6LCdAALLpuY+yasfIS1KU0vxdE1DmdriZjJdru1fHlnHfeeSc++9nP4otf/CKOHTuGX//1X0epVMKtt94KALjlllvwgQ98wDn/13/915HJZHDHHXfgxIkT+Pa3v42PfvSjeM973tOpl9BWRPLj6p69uGHoUigeL04VZqCZNRT0MqLzP9AVjwwLNvrWuKQzt6CvGhEREdF2tFPneevVLiya01QkfCH8XHw/arBwqjCDsqHj5QOXYV+oFwdC9Z5ygiBAFNyQ3RJKTSHQtYkDzrUDks85ntMrCEk+pLUilDa7aU5reQz4IvWvKznE5SCmKzkAgFrToFuGc25PU3XawqWZE/PLS4F6tVs7aa0Inyi1ff1bWSnWGLthWyucubQ9AS753K26pbcfEW2uji7nBIC3ve1tSKVS+IM/+APMzMzgmmuuwQMPPOA0oT1//jwE4ULWNzw8jO9+97t43/veh6uuugp79uzBHXfcgd/5nd/p1EtY0v5gHx449xRePnQZrus7hLKho1+ul7zn9DIikh9+sXX5wWwlv2KoFuESUCIiIuoCO3met1F6ZAU5vQxJEmFaFq6M7XXuG1fnEPcF8Vx2BkNKFBHJh56m0OnR5BkM+MNOxVbzfY3AqLGMcmFV11AghrRWRFwOoleqn9v4s3nzAaC+q2jj3IWGmkKlsOhbdH/zWDbanKa2vObVWvj6trOLXdJKG6dbevvtGlzPSR3isu3dtclqoVBAOBxGPp9HKLS6Hg7r9Y/nHsN4KQtYFq4fOIweyY+A1wfDNOEVPaiatTVXoREREVFnbOUcgtav2/6e5jQVBb2CfaEEnslOICGH0esLttwPAGWjioKu4fLYHpwpppCQAnC5XFCaqs8mShn43BJs2BAFN57KjOOl/Ydb7m8OveY0FRWziqHA4pBmqdCsXXC1lqCn8fj1BmDLjQNY/BqJaGeZmJjA8PAw9n/hgxD83ROIN1hlDWfe+ccYHx/H0NBQp4dDTVY7f+j4cs6dbLw4BwkCXtZ3KYKiDN02kdPKEF0CQqKMWlOZPBERERHtHrppYLaSh2lZUCQZc5qKsMcPa36poWbUcDwzhR5ZQY+s4MnMBC4N1zdk2B9MIOj1Q51f1jmnqTiRm8FQIIYeWUFcDkIRJby0/7ATwgEXqsYax3pkBTFvsG2rkLgcxIS6uP9Xj6ws2rRguQCt+fkn1IwTfPXICrLVUkv/u4WWu685QNOMmnPuUgFa4/0mIiK6GAzRNtGRyDCGlTj+efoY+v1h+NwSDoR7Efb6UTb0tpWcql7d8nESERER0daS3CL6fGHE5AByWhk9sgKv6EG/P4y0VoRuGbg0Nuic/5qhy51dS8fUOcxpKhTRizlNhQ0bsujBRP5C6PXY3HnnOqZpOnPMlFZsCaD8ogRFbL+76ZCyOJBKaUUMKz0wLBMlY+V5a/NzLbxe1BtoWc67UL5WWfH6SxlbEPQ13u+dorFpBNFuZdvde6PuxhBtk4yrc8jVirBcFq6JjQAActUKTheTOJ1PwYCF4Tal80Vd2+qhEhEREdEmS803JZ8q51qOq0YVB0IJAIBHcGOqXG/yb8HGD5OnnWowSbjQylitaYhKfiiS7FSe7VV6IIluTKkZTJVyeGFiP+JyEAP+CNxuN3SzvnNnos0STVFwLznehd83Hp+tlmG1+W1wYXjVrLkqDUDbyrDm5416Aygb+ophnSx6FoVxIzu8j1iBG43RLtfpHTa5O+fuxRBtkwwrPTBMF1wQEJJ9UCQZl0UHMOiP4JJQAl632LZ0vs/PhpVEREREO00jfOrz1v8sGzpSWhE1y3TOmdNKMA0Tc5qKiOTHdb0H0OsPtVQdjalzuDy6B1PlXMtyS1WvomqYUCQfBgORRc8f8ymLQqzVjBcADMtcFL4lfEEE2zToXxheNa+yWNjDLC4t7mm28Hn8ooTAEpVyDe2Wne50/f5Ip4dARLQrMUTbRD+YPYmA2+t8uvdkdhIzah5FswqfW0JE8jtBWk4vI6eXly1pJyIiIqLu5na7kdPLOJlPIiEHcTw3g7NqGnOaipDkg+QRUbNN6GY9TEuWiwg1bR5wIaRywStKznFF8sLvlZBdZpmft6mabbWezExCM2owmsK+5eT0CnTzwrmNCrh2GstTm6XWEPQ1tFt2ul5rCRqXstJr2IjnaFc9SLSr2K7uvVFXW/tPUlo12eVB1TKQr5aQ1ArwutwoaCXsCycwps5hROlBRPIDgPMnEREREe1siijj6p76rmwv7T8IANBNE8/kJlEzTKSqRRwI9uLSaH0jAcuykNM1CEL9saIgwISNhKxAN01kdBX9vnB9E4Km50lpKtRqBV5RwmAgDHEVIVpKU5GYrxhLaSquiu2p918Tlt4Fb05TYQFIyApKNR2RptAv5qtfq6BrcAsCAk3BXzuJJXbsPF1IYp8S3/QPnC9mx9CGpV5DQ5TzfqKL1q39xbpxzNSKZU+b6HQuibOFDCq1GnrlEAzY8Ms+ZCoqRpSeluWc7ZZ2EhEREdHOIwoCnkxPoGLqOFtIY6wwh3PqHDKVMqqWget6D8DrdiOlqUhpKgRBQLJSgGlZEOdDpLDkw5lCCoLLBdntaal+anydkBUIbjcGA2GkNBWapa84tuYAqPF1YxfQCTXb9jE9soKErGBCzWJPoH3zfguWE6DNrGOXzAOh3jUHaBtR8bUZz8eVJ0QbwO7iG3U1/gu+id526QtxabQfl4YH8eOZM0j4FBwM9yEs+TCpZmCZljOJiEj+dU0oiIiIiKj7XBUfglrTsS8UhyyK6PH68aqhS/HSgYOQBDcEwY2ZSh4xjw8pTUV/IORUSaU0FRHJh/2hBERBQETytw2/AGBkfqljQlYACziZSy673LARlGmGsegaKxlSokve17zqItamF9pqjC3T+6xdwLeaqrLlrrlWq61i002T834ioi617uWcp0+fxt13341jx44BAI4cOYI77rgDBw4c2LDBdTPdMvDE7ARMjOFwtB8ulxtRrwIJKopmte2OQf2+MGYqefTvoO23iYiIqPtwnre5JkpZ6IaJ/eE4MloZbkFEozyhoGv4SeosruoZhl+UcLY0h4DodYKxH8+exdU9wxhTM8hrZQwoESRkxQmDRpQYnsslcSAUx0Qpi15fEP75CrCI7EcNFhKyAsuy6hVupTwylTIisg9VWE5gZlgGdKt1V1Cf6AEAmKYJ1dQRblq2OaFmlw3RmklNvdDmNNUJn5q/bme5MG+1z72Wa24Wye2+6Pn+Wt5vop2oW3e67MYxU6t1VaJ997vfxZEjR/Doo4/iqquuwlVXXYUf//jHuPzyy/H//t//2+gxdiVJEJGsqHhR/z64BBcs08Cp7Ax8kgSvW8S/p8ZwIj+76HEM0IiIiKiTOM/bfBVDdwKQpzOTmFQzcAsCDMuCLIp4YWIfvj95CgKAsOSHbVnOY/eHE5it5DGixHBVfMgJ1xph0Ewlj4Q3gJlyAWWj5gRoDY3zc7qGCTWL3kAYl8YH4BFF+EUJJ3NJAEDJ0KEbBizLcpYpNgKuiUoeWa15x9DMugOd5tCsZ35JaDtzmrpouWS7KrLGsfVsUNBNGKARofPLMrmUc1daVyXa7/7u7+J973sf/uRP/mTR8d/5nd/Ba17zmg0ZXDd7LjuNiCwhU9Uguz3I2CpGowlMqQUMByOQ3R6nrD2nlzdkY4HlrsMKNyIiIloNzvM211Nzkwh5fRgrzuFgpBeXxwbggoCQKCOrl2EBeDx9Hi/t3+cETLPlAoB6MFQ2dCger1NJ9qPkWbyodx8sy4Jl2+j3hZ1jjSApo5URk1vniH5RQmr+umNqBn5RQkJWUDUMqLqGkqGjzx8CUO+J1ryGQhGllvCrXTXX2fwcEr4AFGnpDQkaNMOALNZ/LWkXDpUMHT5RWhQItnveluWrG2SlCjminY7/DxBdsK5KtGPHjuG2225bdPxXf/VX8eyzz170oHaCPcEehCUF/f4gwpKEQ6E+HIkOoMfnh25ZLWGXG/VPHi/WckEcAzQiIiJaDc7zNld/IIwRJYbIfKglCfUKMEEQ8O8z51GoltHnC6N3vkH/uJpFsmmjgBElBtXQneb0L+rdB6DerF6r6c6xxi6bGa2M0Hz41FydVTQ0HI7Vd/8cUWJIyApSmoqsVgIgYH8o7py7MKxazS/Te5XIogAtvUR1WM0y2h5vCLQJ0LbScq93p1e8EQEbs2vtRmss5+zGG3W3dYVoiUQCjz/++KLjjz/+OHp7ey92TDuCIkrwuUU8m5rCc7k0MtUKdNPAuUIW06UcKqaOE7n6cs6gJEOtaR0eMRERERHneZutUSFVrulIVVRktTIm55cwvnr4MA6Ee3FVzx78ZHYMqVIRql6FYVoYK2YwVrzQ9yynl1s+hB1Xs7gsNgAAsCwLea0+t4zJfkxWCnh09hw0o9YyjvEFSyd7JD+icgB5vYRnsjNrel2NgGyuUv/T3dT3rDHueNMv4s3PHWxTrbaehv/LBVqNMWy0xt/nUgEhEW2STi/L5JLOXWtdyznf9a534fbbb8eZM2fw4he/GADwr//6r/j4xz+OO++8c0MH2K2Ozc1AN4HLegYxU8mjahpIlVWcy6Vx0yVXY6ZcQMIXQqqiIuFTEPEuriLLVcttjxMRERFtFs7zNs9Ysb5s8nQhhRf17YNuGujx+vFMZgY5TUPV0uEXJai1Ki7vGYBflOD1eDBbKkCrGbgkfGFRZWMFQlpTEZP8GFaieC6bxOFoL0q1KmS3G2PFDEaCMfhFCQOBMIYXLJVsfF/UNbgFAU8lJ9GnhNDj9SPsDaz4ehrz2LSmolTTEfKYyFYq6PHVg6VxNYthJYpe3+IqloVjWWg1Df8b13cs88vpSLB+vSk1jz6f0hLybYT4NqzU2UiNv2siot1uXSHahz70IQSDQXziE5/ABz7wAQDA4OAgPvzhD+O9733vhg6wW13W0497n34EMdmLlwwcxIFwD3weL6pmvVxddtdL96tmDTPlAvrne040Y4BGREREW43zvM3TCHIaYcSpfAoBjxeiy4VMtQgTNlwABgJhpDUVJ7MpxP0BHIz2IquVMVtRMajUl3meLqSQkIOIy4oTljX6inncHgwFfU511MLwwzRNABeqxTyCCFkU8YK+vWsKlxrXjcsK4vPFZJfEEs79w0oUaU2FbQO+dS7HTGvqkgHVwiBO8az8HI33r1lB1xBaRe+2BtM0W/4udgMGaLT9uOZv3aYbx0zN1rWc0+Vy4X3vex8mJiaQz+eRz+cxMTGBO+64Ay4X/6MAgHKthv5AEKfyGYguAf/7uZ/hbHEOM5WCU2FWNqrI6xr6/SHkquUlrzUz3/SViIiIaLNxnrc58m3mekdiAxgJxpDTq6haJg6Ge5HXNfxw5izisoKrE3sgCSKeTk/jBzNnoZv15ZhpTUWPN+B8ONsI5xp/yqKIVOVC+KQZBspGvV9aqqLiJ6lxzFRUpOZvjfCtEaClKssvTTxfyELV9SXvb16qGZcVJHwKUhV1ySWVyz3fWiq8GkHdWpdWyu4LdQWrWfbpdruXDdC4tHNlqYqKSm3p/4aIVtTpJZlczrlrrStEe+UrX4lcLgcACAaDCAaDAIBCoYBXvvKVGza4bna2kIRhufCC2BAsy8KN+49guphHVPahVKtPgMbVHIaUCIDlq87aVakRERERbQbO8zZHWivP/6k6IcvxbL0/7lAw7IRFbggYDIRxtpCGZhhI+BT0+gO4Jj6I0XAcz2WTiMsKFM+FyqmyobcEN2PFDBI+BZoxvwJCFJ3G/Amfghf1j2KPEkbCpzgVRs3B13JVR7phwCu6oUgSxoqZtoFRc4WYZVnOUsCRYAz/Nn120fkbWeWUqqgIuOuvdWHPt6VITSFaI4i8GDt9aedGSPgU+FZROUi0pE4HYQzRdq11hWgPP/ww9DafPmmahkceeeSiB7UTPJdJwzAMPDh+Aj+aOgcACLglKB4ZJaMKABgORDBWzOBELtnBkRIRERFdwHnexmpUNomCgDP5ORiWhbisoKBriPsCmFRzGPDXA62z+TQEQUDAIyEoyfjXmTM4XUihaplO5dfhaC9+OHMOk6UctPnKtKphIFUuOc/Z621UoNXDtafT0ytWlzWCryk17yz3BBZXic1qRfTNf8A7Eowh3maDgmaqoaOn6cPiFw/sw0y5AH2Z57gYLheccGalvmsLPZddPCdfKYhj1RkR0e6ypp5oTz75pPP1s88+i5mZC7v2mKaJBx54AHv27Nm40XWxPn8AgmUjJPswEIriS8/+BFf3DCARCKK36dOpjfi0i4iIiOhicZ63OUaCMaQ1FQGPhIpRgyyIMCwLlm2hVNMxML/RlCgI2BeOA7iwAcF0qYBXDR3Cj6fO4nm9wwDq1WsB0YORYAymaWJKzaNmm7gs1oeKrsMnSVBNHT5IyOlVjIaiiHp8mKpcaA/ys/QE4nIAw0oUqq7jfCGNiBzAoBJ2limOq1nIbo9TJdbou9YIphrVZY3jqYqKqNcPUWj9jD4kySgbOvzChaqjhassFlaiLdcHbSXtHqcZBmqW0XYX0GaHo4t3n10piGPVGVGH2K76rdt045ipxZpCtGuuuQYulwsul6ttOb/P58OnP/3pDRtcN7syMYR0qYRxNYdn5mZwaawPrxq5FD+ZOY8BJYSI7HcamOa0MiIyNxEgIiKizuE8b/M0By1prQTDshDx+hHx+pHWSpipFHF1bABprYS4HHA+ZH39yGUAgKFQDMdzSVwVH0TcF4BHcCOrVWDYJgaVMNJavQrtuXwS/f4QYnIAaa2EqCzjXCGDoEdqaew/5I8g4a+PSZEkJPwhuOfDr7RWQkTyQbcsDCtLf/DbCL6aN0tozGnHChmMhC6cb1kXXntcbt31M1VWnbE02NZa3t06w7IWBXgNsihCXt9+ag5V1yEIcJbFElFn2Xb91m26cczUak0/Tc6ePQvbtrF//348+uijSCQu7L4jSRJ6e3s3fLvobiUYFp5ITyNbKeHGA5fjSE8/REFA1OeD7BbrEx+jBtMF9AdCmCkV0B9g7zMiIiLqDM7zNp5lWbCAlnAnIHgwWypgJBxDWiuhpFfxvPgePDJxGntDUfhFD5JlFaOhmBM4/cvkaeimUQ/RmgK5yvwST9u2YVgWhoJR5zFxd/3PmmW1/NY2V7kQWjXCroRfgTWfdDUefyDUs+bXK0BATis7AVojNFMkqeXaDQvDtoaFodpKxgoZhCV5TR9KL/XcS2m8hrVa6/MQEdH2tqYQbWRkBACcH7K0tG+deRZncyk8v3cY6XIJaljHQ+MnEJcV/GvqLI72D2O06QcqAzQiIiLqJM7zNl5SKyEkeSEKEh5PTmA03IOMVkKvL+QETP8yfhJZrYyg5EXZ0DFdLOBANN5ynVcPH4Rt2/jXybPo8fnR6w8iJvvhmw92wpIPBa2MkqG3BFUPjZ/EK4YPQtV1JwTyzjfdT5XVlnBHEATMlAqIyQFIC8LSZ+ZmcDCSgOR2I6eVUTEMDCitc9dspYKqraPff2HXysZY2lWgAcBIqB4kpsoqLov1AQB+PHMez0vsaRlDc7Vau2utJ6TaqmCLARrRJunWJv3dOGZqseoQ7Zvf/Cbe8IY3wOPx4Jvf/Oay577lLW+56IF1uxv2HYJbFPHw2Ek8X/Lix1NjeO3oYchuEV63B9r8Dp1EREREncZ53ubo9wedr6/pHcL5Qhb75/ueKaiHWdcP7nc+TLUsC0+lpzCjFhCR/UiXi4jIAXjF+iqGg7EEvG4RWa2MR6fPo18J4ZrEICS3G2mtjJjvQrik6jqujA/iVDaNvsCFyi7NrEGBhHJNx1PpaUS8Ppi2hdFQbMkPdQMeyQm1IrIfkbbneCBY7Xv9tAvQmu9rvv+F/XsB1MO0xteNAC1ZVtG7oEqt3TEi2gXYE406ZNUh2k033YSZmRn09vbipptuWvI8l8vVsqPPbvXj82fw49kzeMnQfgguF0aiMYzlsxAEAQcjay+PJyIiItosnOdtvLJRX2rZ3EMrr1fwdKqKol7GgWgv0loJw0oYE8U8DLv+vl7dOwQASFdKCMo+KJKE0/k0DoTjeDYzi0tCPQiHYlA8XsTnQ7NkWcWh2IWm+KquY1rNo2zWcHViEMmKCnF+04GY1wegvoPlSDi2ZDAluFyI+wJIllUoHu+i15eulJznb/RCmyoUEJZkjBdzGA5GVvU+LRWCvSCxeBMLwbX4l08GaEREtJVWHaI1l/azzH9l1wzuxdPZJNyCgBftGUGmUp9cVE2jpV9DUddW3CmIiIiIaDNxnrfxGuFZc9jU5w+h169A1XVMFHM40tNXD8u8EsJSPdzK6xqSpQLOF3J41cghVHQdQUlGulKCX/RAEkUn5GpYGCQpkoSDsQR0w0Be19A7vwlA8y6VPfNhWq9faTl+IpvCoWii5Xpx3+JKsrgv4Ly2xmZZble995vXvfqOMUuFYO3677Ubx0bgJl9E3cdl12/dphvHTK3ab2FDF80rCDg+l8SJ7DROptNQDR0/nBpzJkgzagHjhTk8NnW+wyMlIiIios3SCH7SlRIi88FV2dAxFIzgRzPnoNaqsEzgXCGDn8yMo2oYCHtkvGrkEB44dxw+SXLCKr8oYa5Sr9yK+wIYL+ZwKjeHZFnFz5ITAIDTuTnn+QxYCDd9WCuLovPh7VS56ByvWQamSvXvFwZoEa8Pz2ZmAQDjxRzSlZJznzW/YUGmWgEAp/qs16+0nLdRkhXV2UxhrQzLwlh+Dnm9sui+k/n6e1Y2dKjrvD4RbTG7i2/U1da91/NPfvITPPTQQ0gmk4s+sfzkJz950QPrdk+mZzCshBCSAnhk+gz+29GX4ZJwHONqFv1KEB6XGz5ZwstHe1qavRIRERF1Gud5GyOnlaFIMkRBQMWowTBNaJYBvyhBkSS8qH8UjycnoXi8UDxejPbXl1eWTAPnChn0eOvVUZdGEphWC6haBuKy4iyX9LpF1EwLvX7Fqeg6k0sjXVGxL9KDsqbDr0hIllVEJBkVy3BCtebllkFJRs8Sy3QltxtH5pv+7wmEIAgCTNPETEXFHqW+iUBjiSgAZ2zN4WG7r9ejUVE3V1HR41tbbzRREDASbt9S5ef6hgG0Lr1tltcrzgfhRLRNsCcadci6QrSPfvSj+P3f/30cPnwYfX19cDWVk7va9CrYjZKlAnTLBdsFvHz4Evxo+jzKho5XDl+C5zJJWLBwWaAfAGCCvUWIiIhoe+A8b+NEZD/O5TMYDccgugTkdA0zpSJePnwAGa2Mp5PTuH7PKMZLeWfX9qfSU3hx/yhKZg1KpN6LzEK9Wmw0FINlWfjB5AyGgxHULBMjwTCSZRUAMFsq4IahA5BEsR5YKfXAqhEuSU1T/ydSU7g6Meh8H/cFMKMWEPPVd+dshFKqruO5XBJHe4dwKjeHQ7EEymYNPU2bAQiC4Jy/sBdac2i2MEArGzpMy1rU2qRdT7WirqFmWZAE0dlhtNlm9kazOFUnIqJ56wrRPvWpT+G+++7DO9/5zg0ezs4xVSgiKHlQNQ38eHoM77j8WkyUskiXy/CLIqKygucyKRyOJWCz9QgRERFtE5znbYxGeDYajuFcPgPLtur9ybwBnMymEPb6EAv4MVNRMRqKOcHRwUgviobuLInMViqYKOYQ9HpxLDOLy2J9ePXIIWceCVwIkHr9CiYLOXhE0Tl2Lp/BTLmISyJxJMsqjvTUq8raVVb1Kxd252w8Plct4ej8ZgcRuf6Y5tCrEZ71+pUVNxRYWImm6rrTm61Z4xrN1WWr6SH8VHoaR6K9bfupXYyor3WMF1tRR0QboFuXRnbjmKnFunqiCYKA66+/fqPHsqPcMHoAmmnABzcM08JYIYNZtQTNrCFbKUFyu3E4lsDPZqcQkX3IaYv7M6zHRl2HiIiIdifO8zbGaDjW8rXgcmNPIIwD0R54BDd6/QoE24U9ShiGZeH4XBLn8lnsDUWcAO3x5BSiPh+GQxGMhmPYF+rBqWwaAgARLkyqeZim6eyY+kRqCgFJQq9fwY/n++7GfQoORnpQMWot1Vqj4WjLeMeLOaeiDagHRcmyiqHghfN6/UrLOY/NTrZscOATPQAA0zQxqeYXvSeN3T4b1494fUsGXuPF3Jqry66MD6wqQFvYr+1cPut8na2sPJdeTYDW/D4R7WSb0f9wVTrd14w90XatdYVo73vf+3DPPfds9Fh2lGSpjFSpiDOFHA6F4jiVS2M4GEGhquNgtA+S242cVsHBaBw5rYLZ0sb8oG18QkhERES0HpznbZx0uYR0uf4LpkdwIyCKSFZKGA3Xe58diddbe0wU83he7x5MFvN4dOo8/uX8GeS0Cl62Zx8eT07CmO9LdzyTxGCw3pdMNXR4BRFutxvH5lJIllVcnRiEbllIl0sw5tcgKpKEHp/S2qesXMKj0+MtYx0Ozod35RLKho5iVXfOBS6EQs3B1tG+PW2Xa2arGvYoYeexjeuYpgnLsp1zpWUCr+Uq2lYro5Xb7ja7MARrDhSFVRaxtbu2ZVnIaGUA9fdpvJBb24CJuhCrMmm3Wddyzt/6rd/CG9/4Rhw4cABHjhyBx+Npuf/rX//6hgyum+U1DZf39mM8V8SYmsHRwRGMBCN44PRJ7ItE8FhyEi8Z2gegXj12uCex7PVyWoUBGREREW06zvM2Ttx/4ZfLfn8AD0+cRUASMRyKYKKYg24YGArVw62sVsb1Q6P49+lxvHRoFBbqzfAjXj8m1Tzi/gAiXh+yWgUzegEDSggns2nE/QFc0duPZFnFuVwONkzsi/Qg7t+HZFmFABfi/gByWgWn8xkc7duDdKUEAe372zXGvC/S2ndsuaqw8UIOpm1jNBxF2dAhz1ekxf0BjBdy8DYtLxWF5T/DX2qDgPFCDsOhCB5PTuKa3j3LXqMhJvtXdV6z1W4gEJP9SJdLLX/HgiC0POdwKLLm56eVLXzfaZfq1qqubhwztVhXiPbe974XDz30EF7xilegp6eHTWbbqFY1HJtO4qWj+/CvY2cwokRxwuPFZb1x5DUdV8T78WRyGpf29CIi+zCjFtGvBJe8HgM0IiIi2gqc522MdLmEilEDUA9TjmVTeOnQPkhuN75+8mlc0dOHoVAEybKKdEXFkZ56VVrYK+O5XAo9cgDlWg0hrxej4Sgsy4LikVAxatgfizvPczo7h75AEB7bhbxWQth3IcTJVMrOEkqtZiBXKSOjqtBMA4d74tAMA7IoQjdNSG63E1QB9Q+Ew7I8X9VmwbAtDAXDbV9rc1jkFyVnOeceJdxyX1HXkCwXoRsGBkPtr7VUWNe4TiNAMywLql5dco6c0cqISLKz6UEjTFxORdfhk9rv0NnOUtdrfh9p4zFAIwDcnZM6Zl0h2he/+EV87Wtfwxvf+MaNHs+OUYIJAxb+7fxpREU/Hk/P4DWXXIacpsEjuuCCC3tDERR1DT2+wLIBGsBKNCIiItoanOdtDAu2E6Q8eO4kEr4AClUNAPDK4QOQBDe+cfJpvGLkACwLOJ1Jo08Jwe0SILs98Iv13mZnsnOIyX48lUrCJ4roC9YDhBOZFGKyH7NqHi6XC+cyaRzo6cVIOOKMoWoa8IgCaoaFveEIJLeAGU2FZdk4k07DEFw42r8HOa2CHtkHr3jhVwPf/NeNwKJ5aeZK3G43vMLiXzOqhokj8X5naWhR1xAQJQjLVKdltHLbijJREBbNjZuDs+bHrLa3WrG2thBtKQzQNtdS1YpERFthXT3RYrEYDhw4sNFj2VGO9u9Bjy+ArKbDH/DjusG9+NHkOKqmgbg3AFXTIIluBD1eFHVtxQ0BlgvQuJkAERERbRTO8zZGo79YsqziVaMHMRgMI+4PoGLU6qsQyipe0D+MqmEi7JXRp4SgSBK8HhE108RYPgMAOJaexfF0Egd7YvB5RNQMC8+mk6gZJkJeGZbLhf2RGJ4/OIzsfD8uAHhsZhJX9w7CNOFUxPUrITybTuP5/XtwZf8gjvbXq7p6Awrc7vpmB8+mkwAA3bJwfC7lXC/uDzhBmmYYOJ6Zbfu60+USzuWzbauFGscaAYhvhQBtrlJCZMGunMs17I/J/mWvt1Kz/97A6oOZ1c6/ucHAxmOA1mq3/jfmsrv3Rt1tXSHahz/8Ydx1110ol8srn7xL/XRqHLlqCUHZj6t6+iBCQI+/HoSlyyV45idBFasGwboQkp0vZJe7bFusUCMiIqKNwnnexlFruvML/7F0EpphIFct4/NP/gSCywXbttHrVzAcimBOm6/0Mi3EfQGcy88BAF6z/zBkjxtTxSLyVQ1xfwBBScK+WAyzpSIquonpYgFFreostxwv5JyALObztUz4L0/U+/A2wqbmnSl/NjOFI/FeAPUNCS5d0LM37q8vDZVFETVzccP+RnjWaNS/UvWa2GYMzbxujzPOdhsbNFtNkLCW8GWlTQEUybuq61xM4KMbBvKatuT9ZUOHquvrvj7tDLs2VOz0DpvcnXPXWtdyzr/4i7/A6dOn0dfXh9HR0UUNZ3/6059uyOC62f5wHLmajrjox1SlgKPhPRBtAU8np3DToStwKpvB4fjizQT2hqJtrkZERES0NTjP2zij4ShM08TJbAYv3bsPP5kax5W9AzgY7UXNMlA1TDw+O4keXwA9cr3SS/H6EJJlvHjPfowXcojKfigeGXF/AFOFPHTTvLBc0Ae8Zv8l+OHEGLyiG8+PDAEActUKKpkaDsUSsCwL4fmljZZlwSt4Fo1RMwxMFQt4Xv9g26bt5/JZ9PkC8EmS8wv73qZlo8mSCq/ghn/BfyvN13kqNQ3F48W+SKzt+9SO0rS0sjkoGC/kILncSAQCTsi20UHCSksyV9ogYSNIoghJXPrXNb948UtPiYhobdYVot10000bPIydp1qr4UAogb998if4lategNOZHLySgERAwalsBmFZXvSYfz53Cq8cvaQDoyUiIiKq4zxvYx2bS+GK3n6ky6V68GJZMABMFYuIyj4cjiacPlxVswb3fDgT9flwrpCB3+NB3B/ADyfGoHi8SFVKCMs+PHL+DN50yRGczEzhuqER5/nS5RJ65QBOZNPYG4pC1atOmCUIAkYXhF+9AQVPzE7hqkR9Y4PGueP5HLye+q6asltEXq864xzP5zDcdB24gIpptO3xezydQszvw5WJgUX3NXpbnctnlwzS2hkORTCezy27dHO7e2J2Cl5RxKU9vZ0eChERrYHLtu1NKyj83//7f+Mtb3kLAoHts4NKoVBAOBxGPp9HKBTatOd5ZOwsPvbQPyERCmA4FMf+aAw37N0HSxBwsKcHlmW1/OBvtzsnNxMgIiLaPrZqDtEttuM8D9i+f0/ThQKCsoyz+QwGAkFUajWEPBJ+MDmGmNcHRfYi6JUxUchhOBR1Ngj41vFjeNHevXC7BIzlMwh4vOgPKqgYBk5k0njJ0GjLskm1pteDN9mPx2Yn8XMDw8hXKxjP550wrxGUjeUyGInEMJ3PYVar4Jq+AScgK9d0+D0XKp1UXUfVrKHH1/r3rZums1HWchpjvNidFQ3L2pIqsPVYaxjYrdjYnzppYmICw8PDGPn4H0FoU5iy3VmahrHf+X2Mj49jaGio08OhJqudP2zqT6B3v/vdmJ1t33R0p/uXsdMYjkVg1CzsjUSxNxLB/z31HAQAT85O45l0EqquY6KQx3PpFOQ2pdoM0IiIiGi72s3zvJU0enypuo7T2TmYpokTuTlMFwsYCAQR9wfg9YgQRAEDgRAOxhO4PN4HxSPhYKQHx9L19/X7Y6dxzUA/5ioVCAJg2S4ILqBYrfdaG1RC+OnUBERBQLpSQkz2YTQcRa9fgSAI+LmBYRR1DUGPF6LgxsNjp1tCrJH5pZUGgGv66pViPo8H4/kcLLu+gUBRr/fkUiQJPb4AxvM5APUgxTRNPJuawWOT551rLtWbLO4PtDx34z1aqh/aQo3rns1lVnV+O40gr/Ea1qP5sQtf68LlrDsVAzTaFmxX996oq21qiLaJRW7bXlySMRqKYTQUxdWJXrjdbrz8kktwLpfBVX0DGA6FoepVDCpBHI4nYNtAsVqBbhidHjoRERHRinbzPG8ljWqkk9kURkMRPJ6awQ179yHm96FmWvjOyePo9SsISjKeP7gHcX8Atm0jp2mI+vy4dmAIP5g4h0PRBObUEnxuN8JeH6qmgZFIDIPBEHTDgG1auKZ/ELmqhqFgpGWVQ7KsQjcMFDUdgiDg0ngCV8wv2VyosTTze+dOIe4PYDgcgW4akEURwQW7Yw6HI1B1HXHZj2m1gH2RHrz2kstQ0XUkVdXZlRSoV6kB9fAqp7XOcxvv0Woqt5ornw7G4iueb1mtmx40gq9GiDccjsCwFm+MsBrNy1gXhknLhUvZSgWVVW4CYFkWMhVu7EFEtB1tz1roHcCwgZ/NTGK8ouKJmWkMKWHouu7s5FOvMnNhopCHZVnIVUowbTjNQxuTDiIiIiLqTsPBCI7NpeAT6xVK57JZRLxevGhgCNPFIk7OpZEul/BvE2P46cw0bNuGYVmo2RZ6ZD/CsoyhWBR7I1Gcy2dx3dBepFQVybKKs9kMHhw7iRPZNPweT0sTfgCIeH14Lp3CWC6LdLkE3TDgEdwt5yzcPfPVTb15c1rF+Xph5ZYkCBAEAUPhKEpGPRjySRJ6FcXZodOcX+YJABZsRGTfsk3yl7PWyqdctXVHy0bw1byTpapX2z52Nbt8LvWY5XYjDUmS01NupecSBAExn3/N41it1YZ5RNtap3fY5O6cuxZDtE1iu2zopom3XHIZwgE/Joo5HOhJ4PK++ieAx1KziPv9EOcnIfti8ZblmyX+cCMiIiLqaulyBf1KEDXTRLKk4nn9g5hVVYyreTyVmkFvIABZ9OBwTxwhWUbNsvDo5CQE24WwJMMnSajU6tVbjYotvyRhLJeDT/Lg9udfh0t7eltCpp/NTAIAJLcb+2I9iCt+yKIHkijCXvDbW9wfwMNjZ9qOvXkTrJZNBICWMGxQCeFc7sKSzMY43W630yftYpf/JVW15TlWslQA1fyLz8K2KaZpYrKYX9dYG49Zrt+b2+1edKxTyyKL/D2DdoJOB2EM0XYthmib5MxcGl7Ji3P5PCazWRxLJfH/Tp/EeDaL6UIeJb0GURAwGAo7j2n+xC/qYz80IiIiom4W9/th2zZElwtpVcVjUxM4n8+jZlrYH4khLPvws+kJZMoavG4R+8IRHOlN4LHZKQyGQrAsC0Pzc8V0uQxV1+ERBMRlP8z51YiPz0zieDrtLE8MST5MFQsAgKRaxMFYAkm1iBPplLMioqhrzvlx2Y+fTE1gPJ9vGbvX7UGypCJdLsOyLPxo4nzL/cnShSqq0cjKSzJVXcd4Loekqi5a2rmSoCQt+Rw/OH9u1dfxL6gEa+Z2u7EnGF7y/o2WLnduuWavwp5m1P1cdvfeqLsxRNskVw3swUwuj1JNR8wXwGU9CRyIxPBsOomCruMFe4ZaPlGbUYvcSICIiIhoB4nJMtwuAT0+P2SPhJjsx0BQwSU9cbhdAv728Z/hYE8vegN+VA0dP5o8j5pp4tJoHHOVEr767NNIl8tIl8uwbQuaYcAvSejx++F2uTCey+Ga/j24NB7HuWy94f6BWAxuAJlyGXsjUaRKKqI+PwZDYUwXiwAAnyg5O1z2B0P4ucEhDIfrAdKzqVmky2UokoTegALbru8of7CnB1pT8NUbqAcxqdLiJYnnCxfmuI37p4tFCIILvYrSsrRzRi1CN81lQ6WFyyCbvWTv6KJjC8e0lYHVwjByKXH/5i3XJCKizbOpIdrIyAg8u2SXmoVCHi8Ox3rxxMwUMtUKwh4PHh47g0siEcCy8GxytmXJpmVdiKSbK9KWO0ZERETUKbt5nrdagiAg5vcjW9VQqJaxJxJB1B9AsapDEgQc7R9A3O/HjFrE4XgvAh4vJLcbstuNglbFq/dfgh+dP4fvnz2FjKYhJssYy2VRNQ3sjURQrtWQKqkYz+dxSU+94f53T55EXzCEmN+PE3NJJAIKoj4fFEmCbtRwZi6N7HzT+pSqIrCgT9klsXhLwJMIKPjh+Hk8NTMLWRSRLpcx1vRBcCLQWtWUUlX0+oMA6tVnjfsP9vQgLPswWywgVVKdnT/7lSAkt3vFUGm5cOrx6emW8xaOabMDK8OynPekEUZuJ52setsM7YJb2oU6vSSTyzl3rXWFaPv378fc3Nyi47lcDvv373e+f/rppzE8PLz+0XWxbFVD1TZwRaIXgsuN/9+xZzEYDMNyCSgZBtyCgD2hEABANwzEmn64t6tIY5UaERERbQXO8zbOsVQSABD3+bEnFEGlVsPx5CxOpJNwu90YCIdxPJmELHpgWBb6FAXlWg1prYJ90Rhifj+u7B/Ai4ZHEfZIsCwL2YrmhEQxvw+nMhkMh8MYy2VxOpNBj/9CL7P90ThON/1d9gVDCPn8KNdqAICEoqBqmfj6s0/jZCqF89ks8gs+uP3R+BgO9sTx8n37cWpuDnG/HyORKMpL9NVSJAnyfDBXNWvO8bFcFookwe0WkQgokEUR2ny/t3YB2cLdM9uFU41A75qBgWXPAzY3eFH1KkZWsaS1U3Za1dvCkJR2qU4HYQzRdq11hWjnzp2D2Wb3yGq1isnJyYse1E4wWyhArVYxXS7hYDSKV+zfj8GgAtg2Lo0n0OP3YyKXA1BvzqoZFyYZ+UqFlWdERETUEZznbZzLEr1IlVRnGeW5bAYVvYrrhkfQryhQdR1hnw8DwRBOp9N4amYGU8Uigl4vxvN5jOWyEFwC0uUy3G4BU6qKgLe+tLFRceUTPfjR+BhGIlGIggsv2FMPNtPlMmRRhO2C039MkSQnBAOAE+k0FMmLG/btQ6+iQJG9iwKKA9GeC9VqrgvHWyOuCyGVT5KcUKyxsUCmXHaec2GVG9A++CpoF3bYXCoAa34tzdVWqZK66DGbGbw0PuzeDhVfc8vsENqwHcZJRNSt1rTP9De/+U3n6+9+97sIN/3AM00TDz74IEZHRzdscN1MNQy4IOCgEkayXMJIJIaBYAQPnjyOilHDi0f2Ie4PoKzryGka/NKF5RCiy4VA045IRERERJuN87yNky6XUdKrEFwChsNhTBdViIIbB+MJqLUaHpuawEtH98Pn8aBS0/FcOgmrZuLnhobws+kpxGQZB3vi+MnkBA70xOF1C7BtICLLCMkyUiUVw+EwUiUVR3p7cXIujfF8HgHJi1RJhcslIO73Yzyfx95wBOWa7vQgS5VUJAIKMuUyLonFMFcpY66i4dJ4vO1rSTQ1oY/7/FB1HYokQZnvU6YbBlLlErKVCmKyD1Pq/NhUFTGfD263G5H5ee14Pg9ZdDuB1lgu27aC63gqiUsTvRfGsIoArF04t1aWdaHv3FLKug6/JCFdLi+q8LLthdHi1vOK7ZdYG5bl9MHbaZVptDt1a5P+bhwztVpTiHbTTTcBAFwuF97xjne03OfxeDA6OopPfOITGza4bhaVZVR1HTNaFaGaibSqwuUCXrJ3H0bicVRrNag1HUHJi5jf75S9A4AtCC0/6JqZpolcVUPPMltoExEREa0V53kbx7YtjESiOJ5KIlMu44reXvxo4jxevHcUo5Eonp2dcYK2Ss3AwZ4Efjo5gZOpFF5/8DDG83k8k0piNBLF2WwGRwcGMZ7PQRQEPDkzjajPj3S5jERAwXg+j8t7+1pCnUal0XA4jHS5jEqtBsOycWouhRcNjwAAzPkNAxIBBYmA4oxnJBLFWC6LgOR1vm+I+C5UXDWeSzMM5KsaDkRjcLvdTlWZIklIVcroV4IQ5ue0CyvOlloCeel8Bd9qwzDdNJGvVFoCv/US2sy/293fLojaDssMlSUCQFWvsj3MKo3n89uytx0tYLvqt27TjWOmFmtazmlZFizLwt69e5FMJp3vLctCtVrFc889hze96U2bNdau8uj5CcCyIdgWvnvyBHoCfkzmC+hRFEzksvB6PHALAiRRbAnQgPoPv3YBGlDfftu9wg93IiIiorXiPG/jNMIUn8eDc9kMPv3jHyHuV/BsMonZfB5hn4zxXBZarQav6IbH5cKLR0bRFwohX6lgVi1iuqgi6vMh4JFwcm4OfcEQitUqgHoYVanVUNZ1DIfDeGp2Bn5RxOPT0zieSiLq9eKxyUmM5bJ48PQphCQJp9IpHOntB1APwUy7tRyiORSKyr6WpZLNPctSJdWpuHr4zGmEZBlHEn2LdtD0SRL6laDzfA2Npa0LNZ/TCAhXS3K7lw3QVtsPTRCERfPyhVa6f7tigLZ6S/XfIyIC1tkT7ezZs4gvUfJNdbcefQG8XhnPTE1jyB9AwheA4pVQ1KvQLRs5rbLkD7OV+qHxhyARERFtFs7zLl4jtBmJRHGkrx/X792L0UgER3p7cai3F15RwpX9A+gLBmGYJuYqFTw+PQVJcOOH4+PoU4J43kA/jqWSsC0LumnAMAxIgoDBUNgJtX46OYlUScXJdBr/en4MiihiJByBCSDk9SLklfHivSM4lU7jRXtHgPngLO73o1arYU5tDZcaoZnctPQTqIcK1nyj/9OZjBNwXd7Xh1RJde5rdiKddr5uDuh8HtEZf6NXW/M57ZZJLmRZFjJrCDW2Q4VYJ3AXy43DJbDbUKc3B+DGArvWuj9KefDBB/Hggw86n1Q2u++++y56YN3ubDYDt20jX64gNqLg/qeewmV9vQhKXhQqS2/RDSwOyZYL3IiIiIg2Gud5FycRqG8acDqdQtW0MBAKOT3JqoaBoWAQpzIZmKYBwQaOzc5Ct21cMzCIkWgUzyWTGA73Ajbwg7FzuCzRC0WW8aPxcWQrFRzo6UHE68VAMAjNMHF5bx96AgHnF/2UqmJPKISyYaBmGBiNRAAAz6VSiMgyBEGA4HIhWS6jp6mCK6WqSCgKKrUavn/2LF598KBz3xMz07istw/PHxhEQdOgW5YTTp2am8MlPT0t78GhBUFs49oR2efMazXDcN6X5iWZY9ksRqJRWJaFnKa17GIP1CvGFh6jxbZreLiaoJRoJeyJRp2yrhDtIx/5CP7H//gfeMELXoCBgQG4XFzXu1BFr8GEC686dAjXDA3hqalpjEZjSFcq2BuNOpOHXKXi9JdYqNEXjQEaERERbRXO8zaGIkmYUUu4NJFwloc9dPo0XnHgAGaKRZimiT4liO+dPIm3XnEFRFHEj8bGcKCnBzOlEvq0CoKShMsSvbBdLmTKZbxgeMiZF+qGgdNzcwjJMixRdEKJiq5jrlJGQlHgn9+Ns8HrdsMripA8IuZKZVyWSAAAzmWzCEqSsyRyrlxG74Llkc8b3ON8LYn1arJ0uQxFktAXWLpXb65SgeL1QpEkJxxrCDVtpJWZ758GwDmnU2EZQ56V6U0B6FrxvaUN0a1VXd04Zmqxrn/57r33XnzhC1/Ar/zKr2z0eHaM6Xwe+XIZSUnC/Y8/gcsTCeSqVRxOJNATCGCmWER/MIhKrQbNMNAfrE8amkM1zTCWbA5KREREtBk4z7s4jYqrsq5jNBqFWq1iLJuFWq0i5PNBMwxM5nLoC4UwnstBFkVMl0qYzOehmSaCsowb9u1DulzGv02cR0SW4fV4kNU0jMxXlAH13mJV06zvJump78g4WyjAsiy47HoQ9P8/dgy/fPXVODM3h73RKK4aHMT3z5zBy/bvx9lM1qnyGo1G8aPz5xH0ep3qsKvmK8uOJ5PYEwohU6m0BGCy2+2EIcv1CWvMa7O63vL4hRoB2mpdTPN3VdehGcaSYc5KIc92aDzf+O+sU7SLCNGIiLrZunqi6bqOF7/4xRs9lh0lrihQ1RIEwY1yzcBIrAcFrYqTySSemp6GLIr49/Fx+D0eqNUqMqUSALRUpcmiiKKmdeolEBER0S7Eed7FaQQbfknC4UQCh3t7YVgWzmSy6A0E8LPxCRwdHoYsihiJRvHaQ4dwPJVCTtNQNU3Iooh/Pn0ap1Jp+CQJw5EIBJcLarUKtVpFulzGc8kkqqaJawYHEfJ6IbrdGMtm0RcK4VyhANHthm1ZuPXoURxPpZAqlfC9kycBAKOxGADgur17YTYt1X3B0BDKtRoCHg+enppyjl/a24vHJiacoK75dY5lMs73zZsPNH8NAAVNQ8+CnUObjWWzztcptX0fr4XHlwqxFl5/LJtd9NhqrbamaqiFr6f5uZcaL1BfVZKrLN/reL06GaABF6oIy7oOVdc7OhbapewLSzq76cZKtO63rhDt137t1/DlL395o8eyo5ycmcXeWBRPzc7iA694OSyXC30BPw719tb7Qfh8eMHwMGwAl8TjiM2XwTf/oBUFAcGmMveZJXYzIiIiItoonOddPM0wUNQ0ZMplWLaNZ2dnEfB48FwyhYOJOP7xmWcRDwRQrFaRKpUQlbyIyz68br4H2UtGRwEAg4oC3TQhuFzQajX832PH8VwyiblyGT7Rg6dnZnBqbg5Rn8+p8rpu717E/X5MzAc/1wwM4Ib9+3HT5ZcDAHyNTQNUFeVaDelSPXSazOfh93hQqtVww4EDAADTNJEulfHySy5pG9ropukEYI1gKaWqiwIuRZIgzO8u3y68aq5QSygKxnOL+wevNjRaeP2RaHTRYy17bb/FLld1tty4REFYsm3LRmj83XWSX5K27cqZiq4vG3JSl+v05gDcWGDXWlcNrqZp+MxnPoPvfe97uOqqq+BZ8MnUJz/5yQ0ZXDf7heddg6/++0/xov4B/HjsPHyiG3ujI/h/J07icF8v/B4PbNtGzTRblnAu94O2seSTiIiIaLNwnnfx1KqOeMCP6VwOAPCaQ4cAAFP5POKKgv9w+RE8MTkFuFy4enAAEVnG2UwWPxo7jwM9MSQUBT5JwkxRhV/ywCMIeOHevZA9HqgVDS/evw+qrsPnERFv6kc2mcshX6ngyMAAnrdnD7KVCqI+H8ay9So4VdcxV6r3MWuEP2PZLOIBP0aiUYzn8vC4BdRMC8OShJPpORyM9yx6fQ0H53uqAcCTk1MYCIdaQqVGDzRBEJyvp/J59CkK3G63syQxXSojHrgQfg1HLgRyG1lxNZ7LYzgSRkJRUNS0lg+rV7LUJged1Pye0WI+SYJvmwZ8RGt188034+tf/zpM00QgEMCnP/1p3HrrrSs+7r3vfS8+/elPo7+/H9PT01sw0p1vXSHak08+iWuuuQYA8PTTT7fcx+azdc9MTeNUJou+gIIeRYFaqeC52ST8Hgk100ReqyLq9yHs8aBcqzmPaw7UGhMfIiIioq3Ced7FU/UqCpoG0zIhiiLG0nOQJA/2zldczaklXD7QjzNzGWQrFczkCziWTGIgGIILwIPHTyClVfCy/fug1WoYDIeRrVSwJxRGfGAAP5ucxFA4jNOZDGSXC5oNuAUXEsEgZtV6ixDTNCGgXo3TqPRKlyu4tK8XAJApl1EzTHjmK8TSpTKGI2E8OTWFQ729SJfKzrnN0qUybNtCQlFwPpNxggrF64Xf40FRq6Jqmijp1ZYKs8bXpg243W4AwGS+gISiLBkGNQdoBU1zlhAuDN0a8pUKwsvMnRvhHAB41tjPq3mTA90woBlGy8YIm0Wb32F1LYHfVphTy+hRGOJRB3VrVdc6xnzHHXfg/vvvx6233oqbbroJv/M7v4PbbrsN1157LS6frzJu5wc/+AHuuecehEKhixgwLbSuEO2hhx7a6HHsOGczOciWjYxh4rnZWYS9Pjw9NYsX7RuGT/SgWK0i5JUwWy5juKlJrOByYaaooj+oQJ6fYBARERFtFc7zLp7bdiEeDMAvijify0PyiMiVKzgxm8ZINIKEouDp6Rm4BRcm8gXoNR2DioIj/b3wuN040NeL8sQkDN2AadX7pP1kfByjkSgQ8MPn8cArirgskcCj45N45aFLkK9UILndeP7QHhS1KnKahuFIGA+fPoPLm8Kws3MZ7OuJoVitolwz0B9UkFZVKPMhjWXX+/LaloWUqsLjFhHxyU5VmG1bOD2XQdTvR7JcwQtiMZR1HT0BP+ZKJeyNxRBEvUpqPJdHRJYRlL3O8zcHWdfsGXQCKRdc8Ipup1n9wqCsefOCeMCPJyancPWewZb33cLqNV+v8VyZcnlVlWaSKG5ZU31ZFNtu3LDasS7cEXWjeCVuKkCd5fQY6zLrGfPnPvc5XHnllbjvvvsAADfeeCO8Xi/e//7344EHHmj7GF3XceONN+JXfuVX8IMf/ACl+R7sdPHW1ROt4dSpU/jud7+LynwfL3uN/QV2MrdtQ/RICLvdULUaAh4JPjcQkLyQ3CJifj9s24W++d2bGkKyjP5g/VO3RvlxrszNBYiIiGhrcZ63frIkoqobeOTMWUR8PuiWhZFYtB5+ud2wYEEWPdgbDiNXKuFgIoF4wI9sRYNumnhuNokj/b1IlUuomRaOzSRxaTyB4WgEqq5DFtyQRRG6aeKVhy7Bv5w8Da/Hg5SqIl0qwyO6kVZVfPPpY7iivx8JRYHLJWA4EkbYVw/LRqJRJAIBRH0+TBWKOJlMA6gHWylVharrKOv1XeSB+rx0LJNFQlEwEAxCFAQE5ueqJV1H2OfD3vlNCxqGI2F4RDd+NjHZcry5T5UkigjJMjLlckswtbDSrPm+sWx2UYAGAIZprvnvqvm5Ik3VXtu9l1ZklZVpmxGgAbjoPmjb/f0l2mzT09OYmJhwboVCoe15qqqiVCrhjW98o3NMFEWMjo7iiSeeWPL6r3nNa6AoCr7whS9s9NB3vXWFaHNzc3jVq16FQ4cO4cYbb3TW1t522214//vfv6ED7FZ7QmGE/X4E/X7YloV8VUNYCaKo6ShoGvqDCgShPiHwS9KioKz5+4h/e5VvExER0c7Fed7Fc7kE2C7ghaMjqJkGnp2eRdjrxb54DIlwEJphwrQtnEzPoTcYQrZcQW8ohEOJOLyiCLfLBckjQXK7ofhkXNbfC7dbwFQ+D0kQEJC9kEQRCUXB4xNTGIqEUDNMJBQFU/k8ZFGE4BLQHwhA9oj4p+dOOkFRc1XTZC6HM3Nz2BeLYSAccoKN03NzSCgKRmJR58Pd55JJiEJ9lYQieZFSVVzWm1jUt6zR7D5dKiOlqpBFEfEFfc0a54/n8s75I7HoqoOVpYIht3BR9QHO5gfNY9wIm7EBgHARr3U7bEjQ6d1FiTrt2muvxfDwsHN7y1ve0va8EydOAAD27dvXcrynpwfFJTYe/Ku/+iv84Ac/YGX5JlnXv77ve9/74PF4cP78efibyojf9ra3LVlOuNskSypkjweT+RxsuHA40YvXHDyIffPNYk8m0yhqGoqVCnTDWBSUNX/PSjQiIiLaKpznXbyQ7IVhWlCrOryiiDceuRQ/HBvHeC6Hfx8bR65cwkAoiEyljKpRw9lMxnms7PFgfyyG4UgYvaEQpjJ5PDtVDzJNGyhUdSQUBZZlYSqfx3ShgL5wGEHZi8cnpnDV4ACempzG1XsGIHs9EAAEJQ8sy8JYJusseRzP5SG63RgKh6EZNcQDfifYuGJgYNFrOjo0BEms/+rglUTn3IVhSCOsa1yv0WstV9agz1e1NQxHws75KVVtqW4q63rLao2GqfzinTsbodBK1Vljmeyy9zfbyEqp7bYBwHYbD8DKNFqHTu+weZG7cz766KMYHx93bt/85jc35G2ZmprCHXfcgbvuuguHDx/ekGtSq3WFaP/0T/+Ej3/84xgaGmo5fvDgQYyNjW3IwLrdz191JVLFMgaDYZxPzyFdVlHU61VoCcWPTKmEgCTDtF3IlDVnYlHW9UWhGSvRiIiIaKtwnnfxJLcb/SEFlVoNIVmGJIq4enAAg6Ewrh0ZRqlmwLQsvPbwYahaFVcNDiAsy/g/TzyNJ6dnUNSr+OG589BNE/sTPQj5/fAIbid0+tnEJHKahppp4ZqhQRhGPRoT3fWNHzxuAQ+fOourBgfgdbuR0yr45tPHUNFrTlA1HAmjLxhESlUxkctjPFcPp8Yy2SWX6jVCscb9qwmlGqFXxF9/H5aqgmrsSNrglyT4m75/cj5I7JsfQ7NGKLRcdVa6VMZILArNMFDUqiuOuzkcbLw3a3UxFV8XWy2mGwYKWvd8EM/KNFqrRk+0brwBwMDAAIaGhpzbUs3/D83v7nz27NmW43NzcwgGg4vOf+SRR2AYBu666y64XC64XC6cPn0aMzMzcLlc+Od//ueN/YvYhdYVopVKpZZPJhsymQy8Xm+bR+w+Y9kcQpKEmmXiiuFBvGz/KH42MYU5tYxnZ5Jwu92oWfUKtP6Q4kwsXGBoRkRERJ3Ded7Fa4RLLrteYTOnlhGUvRBdLlR0A4OhMH46MY05VcWR/n5M5grQazW85MAoZnN5jERjuHKwH0HJC9EF5EsVFKsaHj55CgBgGTYisoyRWBQDoRAifhljmSwCnnroFPP7cTARw4nZJPLVKl5z8CCeNzSIwUgIx2eTqMxXeMUDfoiCgOcN7XEa/o/Elu6h9VwyCdkjYjyTw5xaD6WWCph+NjGJlKriqsEBzKkXAqF2VVCWZSFTLi/qnQYAE9n69RvXcbvdsO21bCFw4XnHMlnIotiy0cFqNG+GsNbnXK/mx6ptKvJW0ug1R0TdTVEUBAIBfPvb33aOGYaBc+fO4eqrr150/ute9zp8/etfb7n19/cjGo3i61//Oq699tqtHP6OtK4Q7aUvfSm+9KUvOd+7XC5YloU//dM/xSte8YoNG1w3OzabwslUGrOZPKCb+PJPnsDecAiDoYDTkDU6P0Edy+SgalXMFFRU19kQlYiIiGgjcJ538RpBVNWswbAsWLAwp5bx0/EJzBTymM7nAduGLEn44dgYTsymMFVUkS1VcPWePUiWijiZTOJ8LoOnZpIQRQE1w8bzh4eQq1RweLAXp1IZlHUdU7k8nksmMRKLYl+8BwAwmSvifCaHQ329kEQRlssFr+jBbKGIqmG0VHzlK1U8OTGNqVwBU/OB2MlkCnNq2Vli1wgFD/f2QpEkDMci6FH80A0DYdnrnGc0LdfsCypOdZG8wk6OgiDAtCwMtQmrhqIXjvUo9blz47pzahmZchmWtbpQbbmAcCnNywzXuuRwtZVgK1X06bqx7P1Eu1anl2WucynnWt1222146qmn8K53vQvf+ta3cOWVV8K2bfz5n/85AODAgQO47rrrAACRSAQ///M/33ILBALwer34+Z//eSis+rxo6wrR/vRP/xSf+cxn8IY3vAG6ruO3f/u3ccUVV+D73/8+Pv7xj6/5evfccw9GR0chyzJe+MIX4tFHH13V4/7hH/4BLpcLN91005qfc7M9cfY8rh0ZxHAsgvFcDsM9EexLxKGZNryiiD3RMERBgG6aSCh+yJKnXpHm8130cxurnEgQERERLcR53sUxTRMnZ9M4k5pDRPYjW6rg389PwIKFqVwBsAGP4EZC8UPVNFyaSCCuBHCoNwE3BAxFwziUSOB5Q0MYiUYR9fmQCCrwe0UIcKGk6ShUKvC6BYiCAI/oxuHeXliWhVPp+g6bik/CJYkeTGTz+NGZ/4+9Pw1yHM3Pe9EfQRIkQXBfct8qa1+7eu+Z6RnNImtGu3R9ZMm2RrZ0Tpx7b1xZx7J8LTsUUoSskBzng0L28Q3H0XgsO2QfWR5blo5mrNHs07P03tW1V2VlZeW+cAcJAiBIgPcDkyxmVmZV1t7V/f4iGFUJgsALJJl88eB5/v8FvIBpN3AcSClhoCMInV9Z4+BAhpgSYjgeZXhTxIorIVzcXu21iWSCVU3D2Xazt+t26opaZcvixuYYBvou1G5X8L8rIGVU9a4jfSlVIako91Vk37yDy6t/TNvHV7/Da/ubONyOO4l7SfW9V8NMIHiQbK+XuCcetxj2CIW0f/kv/yV/82/+Tf7oj/6IH/uxH2NpaYnPfe5zHD9+HOhEO/P5/N1vWHBP3NM3zvHjx5mZmeEjH/kIP/ETP0G9Xuenf/qnOXPmDNPT03e1rT/90z/lV3/1V/mt3/ot3nnnHU6dOsUP/dAPkcvlbvu6+fl5fu3Xfo2XX375Xg7hoRMJKSg+mT89d4WnxkYoVXT+9698g4BXYjCqUtIN1qs6X798DUnqTIIeFI17+SMkEAgEAoFAgJjn3S9er5cDA2nmiiWu5fNcWS8woIQp1uv8zx95gWwsQkj2E/T78Uk+JMnDxw7so6gbTA+kmC+WKOoGq5rGzHqehVLHcbZWrbKmabyzssLFtRy247BUqfDfz1xAbzS4tJ4jrXREngOZNKosEw+HGI51auZUzQaHhzKMJmIslzUyqsqhwSx/duY82T6RZqFUJqOq+L1eLqyuc71QYrms4bbBbHbmmN14ZlE3WCiVee3GItARmdTNCKHX6+2tczsx6U4CUv/N4f5Y6Hbqtn3b57dzdmUV2BqVvJvGAwCeOzwv71FEEwg+6Fji+vWOfOELX6DVatFut9F1nV/6pV/qPVepVJidnd31tbOzs71O24L7x9Nut+/RVPhgeOGFF3juuef41//6XwOdmghjY2P88i//Mr/+67++42scx+GjH/0ov/iLv8h3vvMdKpUKf/7nf76n/VWrVWKxGJqm7Vq870Hw//mP/5WVSoVwOESrbvEzH36RWEjGsJv82Mmj/I/zV3h2YhTZ58NqtRiMdiY9C8UKE6n4QxuXQCAQCASCe+NRzSHeTzzqeR48/t+Tadt4vV7mCkXenF/kyECWsWSSgc253nKpjNVssX8gA8CNQpGK0UDyeAjJfnySh1dmb/D3XnqWpVKF5UoFVQ5wZHiA+UKJhKLg8bSRJAnF7+fLl67w0sQERdPk8loeJSDx8v5pbuSLBP0+prMZKqaJz+tFlWVM26ZYN3HaHYeZ3Wr1xJ6ibpBSFWbWCxwcTN9ybOdX1xhLxPEAsc30RF7Xew6tum1TrpuMJmLoVgOfz9sT0N5dXuWp0eG7Pp/XCwXiQaUX5Vwua4T8/t7PXQzb3tKIoH9cu9E93t3QrQZqX/00q9Wi1XK2LHuYdI9hL8ciEHxQWF5eZmxsjAP/39/FG3jy6v45DYtr//s/Y2lp6ZYGPoLHy17nD/dkf/qjP/ojvvCFL9yy/Atf+AL/4T/8hz1vx7Zt3n77bT71qU/dHJAk8alPfYpXX31119f99m//Ntlsdov6uhuNRoNqtbrl8bCxHYd1TWOtqnMwm2JqZJATQwNEAjKLpTLfvHKNTx87yFypSFwJkuxrJBBTAswXSrfZukAgEAgEAsHD40ma58HjmevthGaaFHWDuUKR+XwRTxtenJpgMB6jaNQ5v7LGclkjHY1QNExen5vn4uoaKTVM29NmMKZSNg2u5Yo8NTbMclljrlhkLJFgPBXHJ0mYzSYpVWGlrBEPhZgvlvnR40fJxCKkVIWxVJTTI6MossyxkSHqdhPDtpG9XmbWc5QMA922Cfn9PQdYv1uqKygdHEz3Cvr3c3Qgi27ZPQGtqBtkVLXn4KpbjV4Ns9lCsSegLZc10uFwpxNoWcPYIQY5s55jXdOxt0VGp9PpLULXaCK2o4Am+3wUdYOq1el4v5PotN1pdjsBDUCStnrNgj7fIxPQ4GZ89HYC2u3cc3frrBMInigedyTzEddFE7x3uCcR7fd+7/dIp2+9O5XNZvnd3/3dPW+nUCjgOA4DAwNblg8MDLC+vr7ja7773e/y+c9/ns997nN7HmssFus9xsbG9jy++2H/YJZsLEK90cIvSXxr9hqy389Pnz7OUCzOulYjsnm3rH/yEg+FmEwnH8kYBQKBQCAQCLbzJM3zuuN9HHO97bTpiDKT6RRN1yWpKhzIZqANjuOSDCkoAT+lmkHTdVGCAdRgkLJhcnJkiD95610igQA/dOwg6XCYkN/Pxw5Ms1zRuFHoRDyHYx2HVyYSxXEcEuEQK5Uqddsmo6pMxBPMFPI4jsNatUp20/22WKwwEFVJKkpPkMnrOquatkVoqZgmC5s3c0N+f2+9Ll6vl9FE7JbY5EQywYpWJRK46QTrd52NJmKMJmJkVJXRRGzLNWR3+wlVYTCmIm/GQHdjJ3HPcV2kzfMfDQZ7c+vOjW2dkmH0xnk39Dvb3qvc7pjupZGCQCAQCG7PPYloi4uLTE1N3bJ8YmKCxcXF+x7UbtRqNX7+53+ez33ucztO7nbin/7Tf4qmab3H0tLSQxtfF9nrZV2rMxAKM7dWIhYIEfIFKFVrrFZ0JMlDJBQg+5hs2SXduKUwrEAgEAgEAgE8WfM8eDxzvZ3oNocKyzLHhgcp1y3yus7MRhGj2WIkGeOt+WUabqej5aFsBqvVJKt2Cv3/xIljqLLMq3PzmE2bmmVhNJsslcsU6wbxUACz2cTn86LbFoV6xwVWtxuslKqcXVllo1bnQ1OTlEyTUt3EQ0cISkUURuLxLeNVZZmKYaLKgZ4oFvB6mdi8mdvtKNrvgirqBhvVaq9+Ub+TS/X50JvNHc/NduGr2XJ7tc6625e9Xiqmecfz3N+ts8tKZWf3oez1MhhTiQefvMiXQCC4PZ72k/t4VOx000Fw/9xTtctsNsu5c+eYnJzcsvzs2bOkUqk9byedTuP1etnY2NiyfGNjg8HBwVvWv379OvPz8/zYj/1Yb1m3pbXP5+Pq1au3FLwNBAIEAo/Odt1lpVikZNp84tgUG7UaRaNOsRrm+f0T6A1YKTsofXfr1jWdwditolrFsIgrD/aLX3T4EQgEAoFAsBtP0jwPHt9cbzuu6yJJEgulMorsJyT7Keh1plJx6rbNX1+6SqFaZzQeZywR4/J6jngwyFevzPCh6Sk8kgetYTIUi/Lf3j7HydFhokqQF8fHWK/pLGtV7JZDoVZnf+Zm3HJfJk2uqnMwkebM0gqrWg1Jgn3JODeKFRYKGs/v67jz8rpOwOcjKPvweTxYrRaaaeHBQ0pVyOl1JpKd+alhN5nZyPOJg9OUTJOMqpJSFSzb5tvX5wn6fFtEtFi48/9VTcPn8eLzSSSVzrLRRAzdtlE3nV1Nt0WzJeHrc3q1nDYpVcF1XaqW3Zv/Lpe1nnBWMgziweCWbpy243B4MLvr76Vb96ykG49kDtw/3g8idquF1WoRFcKl4GHzpEYjH+GYP8h/ix4m9+RE+7mf+zn+wT/4B3zzm9/EcRwcx+Eb3/gGv/Irv8LP/uzP7nk7sizzzDPP8PWvf723zHVdvv71r/PSSy/dsv7hw4c5f/487777bu/x4z/+43z84x/n3XfffWz2/e3YrRaT6TSD0QjvLmxgmE2Cfj8v7J9iuVij0Wzhum0sswGA3rC3CGi5qt6bND5oAU0gEAgEAoHgdoh53r1RsSwWSmUmkomOQ8yyKdTqTGWSHB8ZpO22OZjNEA0FiCsK0UCQq7k8yaBCvqZj2A2yaoSg7OdwNstT48MkFYVgUMZpO1xcXqfRahEJyvzFuxeoNzvzyC+evYwaCPDu8goBrw+f5MXv9VI0LDKRME+ND2HYNsulTkfOmfUcsxsF/H4/T4+NklYVoqEAFcNCq3eOoeW6xIIhfvDIQTY245bd2GUb+KEjB3esJ1bUDQZUlexmdHS5pKHbNnldR9PNXs2zjKrS2HxvQUd4SqkKddvGarW2zH+78dGqaZFUlC0CGkBJv+les1tbkxYLpXJvnLL8YDplandwy70XL1rtVovVyqNxpMg+nxDQBI+Gx13XTNRE+8ByT98m//yf/3Pm5+f55Cc/iW+z5oDrunz2s5+9q1oZAL/6q7/KL/zCL/Dss8/y/PPP8wd/8AfU63X+/t//+wB89rOfZWRkhN/7vd8jGAxy/PjxLa+Pb1rTty9/nMg+H0pQRlut86GpcWzX5UdOHiVf0zkxOkBcCRMNyaxX6lRNi/YOn6TtEwSBQCAQCASCR4GY590bSUXpOa/m8iVkn5eXD+wDOuKS1bQZSQ6xUChj2A2WClW8ksSL05Osa1XadG6kHhkaIB1TeXNuGb9X4rmpcWKhMImwSsjnI+Dz88nDBzDtTnTypX3jOK7LWLxT/6pkGMwXyownY6TCcdYqVbySh9HN+ljPTo5Tb9gYtk3LcZE89BxgI31F+7tC1nAs1juG5ZJGwO9Fb9hkIltjnilV2SKsWc2OGKbKMqosk6/pW17Tjb/CTeEpLMsslzSU5NZaZClVwd6MkELn/dhyXWSfb8uN6FLdxO+VeuPorwmm9rneSnWDZPjeXGntJ+ACePu5ljwS2UjkMY5IIBAI3j/ctYjWbrdZX1/n3//7f8/v/M7v8O677xIKhThx4gQTExN3PYC/9bf+Fvl8nt/8zd9kfX2dp556ii9/+cu9IrSLi4tPpKAUDYQ4mE1yo1Ll5NgQb83Nk45E+PrFa3z0yH4sO8i+7M7FPrtFYA3bxidJWxoP7IZh20geiaD/wdxlEwgEAoFA8MFDzPPun8VSCZw2dtvpiUvzhRIfO7ifRFihZtmkwwrD8TjfnZnjjflFxuIJEmqIsWQcgMFohP2ZNGfml9nQNCSPh0azxXKhzHgqwUK1huu2qeVLhAN+UqrClbUcmYiKX5J4aXqCYlWnbJi9GmcAZ5fWODU2xNmlNcaTcUaTN11TktQRnxaKZSZSCS6trhP0y+zLdF7fFdrevLHEC9Od98JySWM0eVN4m90oYDstjg4Pkq/pmI0mI8kY4YBMJqKimeaWzp7d11nNFi3HQQ0GGE3GqJoWQb9vyxy4oNcZiKh4vV7cNrg7iFk7lUbZiXjo3p1ScSV055UeM/0CGoDP+/A+Y9sFO8GTTf/n8r3Oo64v9qB4Escs2Iqn3b67+ymu6xIMBrl48SIHDhx4WON6aFSrVWKxGJqmEY1GH9p+/qf/499R1qp85vQpjk4OkivXcB2YHEjz8cOdu5LdOmgLhQoT6fhdbb9bd0MgEAgEAsGj4VHNIR4nT/o8Dx7v76moG3g8bdY1naPDHaHwS2cv8fT4CF+5NMNTY6OcGh+iUNVpe+CVK3Nk4xGeHhvizNIqkWCQU2PDXFnLMRyP8tbcMplYmOF4R6jqv8CdWS+wP5tEkiSKuoG33UZvNVEDMnFFwbBt2m0Ib9bg7a+ze3F1nXgwRCwcQg3c6hLL13Rqls14Io7Pd3O+uVLR0M0G+7MpHBdqjQYFXedQX02yaxt5Dgxkej8btt3rcnlueZWTfV07HyRd8e9Bkq/qeL3SPbvWBB8sxPXZo2F5eZmxsTEO/W+/izfw5EWHnYbF1T/4ZywtLTE6Ovq4hyPoY6/zh7v+lEuSxIEDBygWi/c1wPc7Yb+fRDTKaq3K//nl73BhcZ160+TIUJZKvU7FsHqusYl0HMtu7bqtimHdskxv2A9t7AKBQCAQCD6YiHne/ZFSFZLhMJlImDdvLHF2aZXReAIHDydGBgGHesOmanXikMlwGD8SkiQRCYYAD1XTwit5KOkGJ8YHabkuKVXBtG0U2cfltbWemPb2wgoAqxWNlgfeWVjh8mqOfE3nr8/PcD1XwG526oS1XIdSrd75v9Ombts0mi2+ffV6T0BbKVUAqFkd4atsdrp2nl1aA0CrNzg0lGWuWMZ2HbySxPS2Tqr9AhrQE9DWNZ0D2a3rtlrujuexYty5S2fVtLZEPHcT0PI1/Y7b2o1MVN2zgFYXc/O7pt6w31fnrWLees0mEAjef9yTVP4v/sW/4B//43/MhQsXHvR43heYts16pcpsXiPtD/HCgSme3z/KaqVGvlojXzOpWzZV09wikG0Xy7o/x5XgLc9F78OGLhAIBAKBQLAbYp53byyXOoXbi7rBG3OLRIMyqXCYgOxFDciogQA+r48vn7nKRrVGUTc4PTnC9WKBSys5MuEQAa+HpXIZw2ry6vUFCjWDpBLGaDTRGzYhWWYo1un26bZdnpsaI1/TOTSY4asXZnhmfISX9k+Siaj81DPHGYpHcdttbuRLePAgeb04jsOpsSHY7Mj5sUPT5Co6FcOkTZuqZbEvk2QwpiJ5OpcKnfUhFg6Qr+mcnV/lwvI6cSWIzyexUCxT1I1bzkn/sqDfR6ivLlm+pqPbOwsoPsm75eedhLBoKNiLey4Uy7c8X6obuK57V1HDnY5hr3g8d7f+/ezrcfGgxxwOyD2n5E7cjwD6OBCOxUfM424OIBoLfGC5JxHts5/9LG+88QanTp0iFAqRTCa3PD7oBHw+npqeRAK+d32etaqO1Jb40VPHuJYrcmAwTTgoE/D5e7b6oOzbtRPnSqmKJMLTAoFAIBAIHgFinndvDEYjPYfY8dEhkmqY0WSMkNyZ7+mNJmtFnU+fPsS+bAq37bJUqnBsaJiUqnBmeZWvX75OwOdnoVji5YP78EoelICPvzhzgYphsVDsNCxwHYdqvdOxslw3+N7MPB+aHqPlOj1Ba2Y9jxqQ8Xra1BsNUqpC02mxsSlMHBxM90SKgq7j8cBoMkE0GOwJgl26643EY2QiKs9Nj/Hi9HjveUX2b6mjlK/pvXNht1qU6ybLmy63LpmIuuvcVw3Kt6zbP47t7ORCS4Y7nTy3H8vtuJ9aUIq8uxj0oPf1uOiO+UGJWxXD2tGN2BVFRa01we3o1kR7Eh+CJ5t7qkL/B3/wBw94GO8v7JbL/EaJsXgYWfai+iSWSlUy0Sgep80rV+b46OF9O8Y0++lOLEaSd67n0V/nQiAQCAQCgeBeEfO8e0PfdIcBFKsGl6pVPnPqCF7Jg241SEUU5vMlVisayXCYRqtJqVZjOJVgo6bz4r5Jmk2XhKLw408f3xQqOt0qP350P/FgCMd1WSqW8Uo+ArKPuXyRRtNlNBljIpOmalislMu0nBYHBzMsFMuE/T4arRZ1y0bywHA81msI0NiMek5mk3z1/Aw/8cxx1su1XsOBbow0E1G31ByLBAO9+k/dbfVjNZuMbXbG/OqFa3zm5CES4cEdz9vrc4uMxGO3bGMn7kVU6W73SSqY/l7nQYlbu127POjadoLbIz4bAsHdcU8i2i/8wi886HG8r/BJEs1Wk6VSnY8dnuDgwBAXV9fIRMOsVTQ+fvwA37o0y1OTnUKCG5pOwOcl4PdusbnfDUJAEwgEAoFA8CAQ87x7JxNRqVs2umnx0UPTvH59nlgoRDKk8M6NJYbjcUJ+GdNu8ubcIpOZJK2Ww1KxxAv7xnl+eoxvX77Gc9Pj/Nc3z3F6YoQX9o0T8Pn44ruX+PGnj9NouRwfSXA9V2I8maDZchmIq+iWTUk3SKgqwc2YY1eMSEcjvHZtgWenxgAIyX4WimXUQIBK3cTFZSTeEUYur+cYTETI13TUgIxu2eRqdcYSMaqGRVQJ9mJr3525wUcOTgFsEdNm14uMJRObkdVhKqZFPBRkqaxtEUjyNZ0X9t10tG3HbrWQfT4qdRNJgmgoxIXldQ5mM8iyd9fX7dQx8n5EggfVtOBhND8QCO6XJ1ZAe1KjkU/imAVbuOf2IdevX+c3fuM3+Lmf+zlyuRwAf/VXf8XFixcf2OCeVHw+Cb1uMRANs1o2+P7MDOmYSqFe53/++EuslTWeGhtE8fkp6QYDMZV4OERjl+KqsHNzAYFAIBAIBIKHgZjn3T3q5o3QL529yORgmophUjFsNio688UyHzu8n8MjWdpuG6ft8slj+0mqCuPJBOFgEMOykYADg1mur5UYjsWYSCYIyTJ/fW6GuKKwUCwzkoihWzaRoMxGVUdvdOaI3525geSFUsVAb9jM5UtcXun87t6cW+LoyECv06ZpN5lIJUipCuvVKo7TJhgI0mq5vHxgiqtreRS/TNW0yURVJlIJFksaalDmm5dnse2Og80r3SwE1hXQ6pbNx4/uBzoX5wMRtRetnEglWCiWWcxXgDs7muyWg+u6xMMhoqEQtu3cUUC703bvJt7Z5UEJX7fbzpNW/+tR8STWjhM8Gh53JFPEOT+43JOI9u1vf5sTJ07w+uuv82d/9mfoeueP/tmzZ/mt3/qtBzrAJ5XxdJyyXicW9FNruHhacGxwmJxWQ5b8zBerXF1f2/KabgMB23ZuEc0eltNMiHMCgUAgEAj6EfO8e6MrUP3MC6eRvV5Csp/DQxmOjw9xaXmdumWRq+qsaTUkj4dr60WqhkWhXmcoovKty9doNB3OLa0SlL38P549SUk3OL+0xg+dPIjjtGk2HUy7SdN1yUZVptJJAn4/AOGAn/FUkmrLwnVddKuBEvRzbnGVA9kUqxWtU8zftAnJ/t64Qz4/C4VKpwkBLhvVKgGfF6vVYiCmcml5HYBsNIwkSXz8yH5qdoOibvDS/slbzoNpN7b8vKZtFYcmUgmiSqD3c1E3du1SrwYDSJLEzFpHDCyb5u0daNWb+1osVHbcruPuftP6cbKT8LdbzbDt6OaD63D5XhOtnliXlODh8yAL/T/qx/uU99rfj4fFPYlov/7rv87v/M7v8NWvfhW5L374iU98gtdee+2BDe5JpW7ZzKwVCPi8LBTLRIIyb1ybQ/bBSqGCYTdouS32D2YJyj6qfW2840oQo2k/snimiIEKBAKBQCDoR8zz7o3uxcPXLsywUiyTVBX++txVXrs2z9OTo9guXF5e5/npMcaScW5slMhGI+S1TmfMw0NZrqwXCMsBxjJJfD6JhBrGL3lwXTg+NsBgLEpaDXNlZYO/fOciF5bXWC1rfPXcVV6anmS5pDESj1I1LZJhhYlUgtFklOWyRkiWyURUvnFphkhQ5sJS52buRCaJ3y9h2jZvzi3h9UicXVjFsm2KusFoKobdaqHIMguFMgCu6+4qbnQ1n+66o8lY7//Vbuf5cKi3fkpVcHFxN8Wt7rr5qk5Z73TYPDiUBWAg1hGalkvajhdrmehNISoZVgjKt1aumUjfdIO1Wu6uAt6WY2q5VHTzjus9aLrdT++E2XxwIpoQrQQCwb3yQfn7cU8i2vnz5/mpn/qpW5Zns1kKhcJ9D+pJJxyUGU8lUfw+BmJhhlMxPnzsIF+5cIUb+TLrtSqxkMJiXkNCYkOrbXl9PBwSDjGBQCAQCASPBTHPuze6Fw+nJ0d7wkxGDfOxw/vJVXXarsOR0UEWCmVWK1UquslSscxUNkVMUdg3mOHYyAAnR4cYiUf55oVZ7FaLfK3O5ZV1DLtJxTSRfRJHRwZ4et8YsWCQdEThQwenWCiWcByXoN/PQCxCwNdxbBm2w3g6jtN2yVd1fvyZ40hIpCJhbuSKAAzHogzHo3z44BRer8SpiSHaeCjqBtFQiLVKle9cvdEToLpz12vr+S3ur4VCmWKtI25NpBMsFMosl7Te6zTT6kVB65bde23DblHUDYq60Vs3E1VJqJ0Y6HaG4xEktkYzt4tqaqgjAFdNc1d3hCR1HndivVqj9R62j/SLh/fCB8U9Inif8bjdZMKJ9oHlnkS0eDzO2traLcvPnDnDyMjIfQ/qScdutchrNYbjSYJ+mQvz66zlKoxE47x8ZJrDQwME/D6GkiqaaRENKWxss7pvd4gJUU0gEAgEAsGjQMzz7o1+MSkVUXl9dpG22+KVi3NMZVIk1TBrpQpej8RAJMyxiSzP7htjsdBxrQGsljTSMaUjXoUDlGt1kkoIu+1SMxqMJmNIkkQkGGSpqDGUjOD1ePFIMD2QZiwV49paAZ8kUdQN1koaX3r7IlXDZl8mheu0efXaPBdXNxiKR0mpYQB0q8GV9Y5AuqHVCAcChGQ/tXpH2BqKRhmMRXrHd3xsCIC4EtpyM3gineDwcHrLz3ElyOx6HoCxVLwXx/RIN8WfimGSiap7cjHMrBeQJImEqmzp6LmrM85p3/JcV3yTJAnZd/s+a4ZtM5qMkd5h+1XDwm7t7GS7l9prj4v3unuk/7MlEHTxPMEPwZPNPYloP/uzP8s/+Sf/hPX1dTweD67r8r3vfY9f+7Vf47Of/eyDHuMTh+zzoQQCrGsaxUqNaChIxbJAajO/UWR+o8R4Oo7b9jAQU4koMgMxdU92coFAIBAIBIKHiZjn3RtdQWitrHF4JMN0NslT0+MMpVW+fWkWw7ZZKtUYTcWomA0SYYV351eIKwGuruQo6gbDyRhr5Rq62WC5WGUsk2RN0xmIRpjMJAF4Z26Z9WqVaMDPtfUCmViYjUqN/3HmEkuFCiOpOAlVwWq2CAUD/K8/+CFspwnAYqnEWCrB2fllXrk8R3Tzpq1XkhhNRKiaJsfHhtjQaqRUhVREIaUqyLKPA4NpZlZzXFndwLBtlksamahKPHxTgFkulCjVTQz7ZrxQDQbYP5jpCSGFqs65hVWUvqjwVDa15VwubkY6d+LgYHrX57r01xJL7iAQ9Ytvd6J9m5JkUSW4qwh3u330nx/B7SlU9ft22gkEAsGD5J5EtN/93d/l8OHDjI2Noes6R48e5eWXX+ZDH/oQv/Ebv/Ggx/jEYdg2bdehXDHwe3xMp5PEwiE+PD2F1wuHR7KsljTiSpC1UhXDalExLCp9tdG2C2r3U7tM001M8WUtEAgEAoFgD4h53r1R1A0KVR3darCQK2O3HPKVOm4LDg2niAWDjKWiLJc0IkGZkmbioc2fvX6eAwNpLs2vohsWh4azzK4W+NCBCeZzReLhECHZT8U0Obu4xmAiQtDn4/DwIHmtjiz5KOsmP3TyEFElyEQ6Tr6q45e8KLKPqmEh+/ycW1zjuekJVksan/3ocxweynJjvdPB02q0WCpptDtJy57TrNFythyjEgxweHgARZZ7ItFoMkbV7MxhQ4FAR/DoE54su0Ve0wn5ZZZLGumoysmJ4duey/F0Ys9Orp0Et73UEivcwd3UjTiGgzKGbWNYe59L3zEeuYswdztn24PkSRLx0kJAE+zG445kijjnB5Z7EtFkWeZzn/scc3NzfPGLX+Q//af/xMzMDH/8x3+M13v7ltMfBBRZJuD1o7nQclyubuQo6xazuQIByY/f7+ObF2bRrQZDySjpqEJcCTIYj/Rine5u3673QEwNEeq72ycQCAQCgUCwG2Ked2+kVIWkqpCNh/H7PARkPzc2Ok6xU5NjrJWrDCeiLBUqvH51kXPLK9Qsmw8f3Uc46GehVKZiWCyXNHxeuLySI6KEGIxHWa/UWMyXmEzGGU50Ip3r1RqNZotiTWcim+TqeoFLy+sYlo3ZbCJ7JXLVOnqjQUU3GIxFKFR1BmIR5taL6JbF1GASx3X45qVZjgxliak3C/7PrOUI+PxbjlENdrpqdgWu7r+FTdEoFgrx7vwKSvDmvLPlOoRkGTUk7+jO2k1w6l/3K2dnbnm+K4JJknRP4tOdxJn+iKMiy1uO6U7cKR6527Zu52zbiXuOjL43G5QKBHeFp/3kPgRPNvckogF8/vOf5zOf+Qw/9VM/xd/9u3+Xn/zJn+Tf/tt/+yDH9kTTbjV5bmQAzTAJ+Px84ugUcSVMyTCZz5X4yMEpvnd5nmurOxfoVYToJRAIBAKB4DEh5nn3TtDrx+v1MrOyzieOHyQZDvOn33+XvG4wkoxxZLjTnf1HnznOZCbFi9MT1Mwmf/ejz/HsvhGKVZ2a0WQoEWEsGcNqNDk2OsCHDk3RdB2+9M5lZlY3yEbDZKNhzi+v8fV3r3J0ZICTE0N85+oNPG3w+71ItFkvaRwfHyIbU5nbKKHIfoaSEfYNpqkaFkdHBvjYkX2sFatbYpAHh7LIPj9Vw+y5vdTN+Wm/C225qLEv04lj+nwSY33dL0u6jmU3md3I95bZtrO1FnCbXjOC3fiBI9MALBXKvXRFVwQbTcaIKkFqZmd5pU+U6+5rudgRmwzLxrBtijVjy7E+KSxtc93dTSy1n7sRBAUCgUCwlb3f6ujjN3/zN/n93/99fvmXf5mXXnoJgFdffZV/+A//IYuLi/z2b//2Ax3kk0ar5XKtoBPy6ESjAcJemXdmV/iBEwrhoMxTk8PMrObJqGHG0vEtr+227n4cbGj6Y92/QCAQCASCx4+Y590bxZrBzOoaAZ+fDa3Ovmyaa2s5hpIxhuIRfB742tlrnJ4aQpY8fPHNC/zMh09vRkAt3ry2SCQUpOm4nN43RK3R4Oz8MjWjhWaaDMSizK7mOTo8wNRgkkJVx+/18qkTh3jr+hIAmmHzzNQI+ZrBkZEsxZpBVAlzdm6Z0UyS5w+M8/0r8wwlI0xlU+SrNZpOmMOjA5yZW2JfKMDsRg41GCIaCqAE/eSrNUaSCRbyFQZiKpWqscXFNZrqCDnLRY3RVIyUqmDYNoosk1Q766Wjak/IGk3FGJBvvj4V2d21pVsNfJKXoNy5ZBlLJ9jQ9FsSFrrVIBVRWCqUCfQ5uWTZu2VfPp+EZbduu8/3Mv0CpUDwgedJjUY+iWMWbMHTbrfv+teYyWT4V//qX/FzP/dzW5b/yZ/8Cb/8y7/8nm5/Xq1WicViaJpGNBp9aPv5e//yP/L2Yp6peAg1GOLHnjuKJHkJh2UODqQoGBYv7p9gtVxF9vlotRySkXBvkiAQCAQCgeC9xaOaQzxunuR5Hjze35Nh2VQME7vlsJgrkolHiARD2K6D02ySiITRLRs1KPPu3CqmbbNcqvFDTx/kxlqJkWSEfNWk5TSR/T7mN0qcnBomsdlF0+eBfLlKIh6l3XYJyX4KNQPZ5+XL71zhf/nU8/j9fl65cJ1EJMRwMk5Jr5OKhFGDMjXTZqOscXR8CMtuYdlN4mqIK8sbHB4doFDVabYcsjEVp92m7bQp1Q30hk1cCZHpu9m6VCj3RJ1CVcdsNBnLJFguaiTDIVza+CQvsk9Ckm6GX5aLGtlYGMNu9Wr+6qaNGrrVHdW/D9d10QyLeqPZE+66WHbrrufQtu1g2vaWCGs/xZqxJ7GtKx52KVT1XaOiZd0g8R7vhCkQvNdZXl5mbGyMY//r7+KV771u+OPCsS0u/p//jKWlJUZHRx/3cAR97HX+cE9xzmazybPPPnvL8meeeYbWIyiG+SQws5gnAthteGb/MJeXctRMAxwPA4koyVBnMjQUj5CNqQynYkJAEwgEAoFA8NgR87x7Z7lYYaWgYVpNIkqIs/NrhAJ+DLNBLBzie5fneevaInbLwef3EldD/O2PPMU33r1OJOgnE4/SdFp87Ph+Tk2O8vS+ERJqmLbrYjSaDCZjlC2by4vrrBWqJMIhqoZFTAkRDwdoNB0KVZ3jk0OEZJlUROHAUIa4EqTVcklFFA6NZFnKlzFsm3qjyVK+zL7BFG/MzGM1HWR/J44q+3zMbRRx2mA1mmRiaq+BAEA0uPXidT7fiRqOpmIoQRk1GCAo+1gtVYGbxexHUzFkn68noLVaLraz8/uq33klSRIJVWE0FevFPwtVnUJVv6c5tCx7dxTuuuzVrbZd0LtdrbXYfTQKEwgEW3ncdc1ETbQPLvckov38z/88/+bf/Jtblv/hH/4hf+fv/J37HtSTjuM4ZONham1IqgEuLuaZGkzgk3y0nBZO20MionBlKUfNajK/XmC5WNlxWxuV23cOEggEAoFAIHiQiHnevaHpJuOZJM8dHGelpGHZLU5PDfP27CJDyRiVusV0NsknTu3n+kaJSqVORAkyu57n48eneHNukUrdQAnK/NlrZ7m0uMa782sAqKEA5VqntlfA5+XDR6YYSsdYLdd4/sA4BU3nB08dptFqocgy3zw3w3gqzlfOXgE6ApTVsplZyZPT6gAkVYWRVOdOu+zzcWA4S9vt1AhrtVxaLZeRTIyQ7O916Wz3Ness128Kah6PxLHxLNBxlRWqN+uQdUWlQl8dtP4u9HrDJnmX7qxQwNfbb3f7O3Wit22Hcl+NtO6YuuzUKKNbN62fqmHtOI5izaBS37222vb99Tvy3q9sP2aBQCB4v3HP1qfPf/7zfOUrX+HFF18E4PXXX2dxcZHPfvaz/Oqv/mpvvd///d+//1E+gViNBqNqgFbTQ75WpWY2qDValE2DF5v7qNRNGo5Do9liNJ3kykoONWhRNSzGM/HedgbiokaZQCAQCASCR4uY5909Hm+nE+XVpSJGs0k44Ec3GhwbHyJf1anpJs8dmuDNmQWOjw3wat3EbjpIHg/Twxn+X4Mp3pxdYl82gWE18Pjg5z76NMtFjUsLa1QbTZJqA0UOoNXrRINBKpvijtm0ycSyfP3dGU7tG+alw1N8+9IsL+yf4OyNFUZSMS4urDOYiGA2O00LunFFzbAYo+OSqjeapCIKJd1A8kjMrOZ5/sA419c7Ed7+6OPkQJILC2tMZlOcubHCU+NDANhOC49HIhLwslwoEwp0HGkt56bQtKHpTGzOd+Phu3dnLeXLHBoZ6LnF8pqO7TiMJLc6y8qGSSZ6U6Db7hrbabtjmVvrju3mdNvuVut2DO01Pdjc307RULvVwmi09nz8/du4XWT0cXOncywQPDBETTTBY+KeRLQLFy7w9NNPA3D9+nUA0uk06XSaCxcu9NbzeDwPYIhPHl6vF9tp42s38UttJJ9M1bLJxCL8jZMHeOPaIp88Oc2bM8uc3jcCwPGJQeDeJhICgUAgEAgEDwoxz7s3oqEQS/kyVsshEQqRjkXIVWo4bpNytc6JqWG+fX4WSYKFjSLpaJhMNIxP6rihvnXuGtFwiGtrBU5ODlO3Grxy8Tqu0+bE1DAhvw/NMElG/SwXyoyk/FTNjoiWUhXeurZEXA2RVBUMu0m73ebKygZWs8VoOsFoJk48rHQK8G+KRWvFKgdHMizlyzhum2LdIB0JY1g22bjK8wfGWS5qTA+mgY7LKOj3YjaahAJ+jk8MUajqfOrkgd55cFoumbiK4zg90a1YM5jMJnvrqEF5i2DVLwp1a4zdribZoZGBLT+7bRhJxnAcZ7Mzao6DI1kG4mpv23sRnnYS0KDj1NsLu21/p+OQfb49b3f7Nt6rAppA8Ch5UqORT+KYBVu5JxHtm9/85oMex/uOpu1Qc6At1UiEorx6eZ4XpsepmE2e2z/G967MkwkHKVZ1fJJ3y50923aQ5Vvt5ZW6JUQ2gUAgEAgEDxUxz7t3slEVt+2S16ocGE5zYDjNq5dukK/VmV3NUapbtJo27XiUdDzCO9eWmBpIMpaJ89T+MVaLGumYwuuXFhjOxFgtVrCbLiemhlkuVshrNU5PjjOaTtBuu8SUIK9fneeFQ5O8cvEGR8cHWC5V0OsNAj4fw8kYY5kES/kymahK1WzQarm9BgCW3WL26iKpaBijYfPs/jH+/dff4G+//BRffvMyHzs5jeTx4PN4+O7FOWLhIOFQoFfXDLYKOkv5MuFgANgalUxFlC2iWCqi7CoKBf23zoE3KjoDcZWybnQaH8QjwE13lm5ZDKCyVtEZTcU4OJK9ZdvpqLqjMGdYNkrw1tpo2xsGPMmYtn1LR9P3O3ttDCEQCAR3i6hk/xAwLBtF9lI1HYKSH6/Hw8uHRlkuVglInYimEhgnFlZYyleQ/dD/FW3aNrJ8a6egOwloi/nKliioQCAQCAQCgeDRUKjqndhjG57aN8qZuRUKFY1jEyNkEiqRUIjRdIILC+uEgjJ1q4Es+1kua2y8dQVJgpDs59J8lYNjGaYHkzSaDkbLpqjphAJ+Tu8bYyFfomY2iIeDKMEALxya5LuXbvB3fuAZLi+tk1ZVhuNRMrEIjVaL1YJGzbQ4v7iOGpCRcBlOJRhNxShUdfYP7ePcjVUqusFyUePvffJ5AI5MDhILK1SNMn/09TP8bz/xMQAuLq5xfa3AkbHBLcdfrBkMJ6OUdPOWcwMd4WwvbrDu86mI0utmmVA68+LtnS27IknXKXcn0Wu7qLKUL5OOhXdct7ut94OYZtqtD5yIJgS0B0//5/c9ESkWcU7BY0KIaA8Btw2W7ZAIePDi4ch4mrFsBiUc5sLiGoPJOLWGRSysIPt9DMTVnstsragR2ryDd7cIAU0gEAgEAoHg8WA2miiBAAeGMrx+dZ5kVEFvOAynYvzlq+eIRxQCAZln949QbzislzWysTCNZotwUGZyIE290cB1OyLc18/OUK1bPHdogtVSlXLdIBTwowZlsvEIiXCQuY0yMysb0G6zlC8jb7q45vMV0tFON83poQzDxHBpM5FO4u0rbp+Oqj0nW91sEJR96KaNbjZIqWEKVZ1YOMT/+0c+jGW38EkSsVCINu1eHHMpXybklzFbTexmE71h4/Hs7Pzay0W3bjaQPB6UoExMCbJW0sjG1FvWUUP3Nl/uZ3t8cyfB7EEIaPfqitqo6CSU0I4JFaAXX73TPu+2cYNAsBP9n9/HLqAh4pyCx8f7v0XMY0ANyTSb0DTbhNUAF+fXKFYMIv4AiYhKvWGzuF7m6tJGr3FAPBykUrfw+bzEw0FardaWTkCV+s5dgQQCgUAgEAgEj5+xTILBeIQ3rs7z9P4R4kqIRCTERrkKUpvVYgVF9nPm+hrVep2a2UDySESVIF6PRFk30GomY5kEb1xd5IWDY9h2E5/kYSiukq+azK0XiCghUhGFaysF9g0kODgyQCKm4LYh4Jf5z989w3qpjE/yMJZJsLBRwrRtFL+fiBJkIV9iZnmDXEVHNxu8cGiS5aJGOBQgp+moIZnBZATDslCDQSSPB9nnQzMMwCWbUFktVRlOdjp7mnaLZDREJBQkE41wYChDOCiT1/RO1NJsAHBjo7Sn8+jzeiltdtRsuS5+n/cWocjn9bKUL+/aCbJb4L/L/HpxT/t+WI6z2wloxZqx63MDcXVXAc20bXTz1o6ke9nnbvR3Gd1+Dt9LVA0Lu9W684oCgUDwEBAi2kNgOa8hecAGiqUK6UicUs0kKHtpu3B2fpWJwQSLuTLQiWFCR0gL+DvmwHXNIKrcjG+KWmgCgUAgEAgE7y3WShqO4wCdaGAmpjKQiBIJhRjLJEiGQ3zn0jxLGxUOjwxR0HRqdYPBZJSpbIrjk4PguEhtCckDa6UqbdehVKvz569eJB0PU9I6YoZt20xk01iWzbWVDcq1OiXdwHEcQl4/uUqNobjKp04eYDSVoGZa3Fgv4ff6KFYMQoGOsOW6bZRQkGxcRQ0FOHdjhdFUjFLVYCgZ4+L8GgDXlvM0nSZqKMBSvsxAPEq5buG4LsPJKF6vF9d18Xo8SJJEPBzE55MoVHUKWh2ft3OZ4dsUwKQdMkwrhZsimGnbGJZNUPahbUZC10vVHc97UPYxlkncInptVDrnyu+TuLHZURS455THTpSqxgMVmO41dhiSZQKyH8PaXUi7G3IlfYsw9V5wGu1GVAneVVMGwfuU9hP8EDzRCBHtIZCIBIiqMlIbqjoYpk7TdQjKISQPpMNBDKvJqakRKnVri0W9K5yNpqL3PQ7Xdbe42QQCgUAgEAgED46hZKznkgoF/KyXqrTdNv/9+2dZypU5OTXCc/vH+ImXTuGX/TRaDs8cnOTrZ2bJVWv8l1fOUNDqaKZFOCgTDsgsFyt8+tkjfPT4fj751GE8khfTbtFuO/gliapl4dJmqahRt2zO3VjlxmoBxSfz598/hxryE1UUXBc8njbD6Sij2TjLBQ2vR0INyoQDfgA03cTeTD84bZelQomuF+ng2CCRUKcWWTiw2SzAIxGSZcYyCUpVA0mSsO0mutnobadcM0lFwkSCnTltUO6IHRMDKZby5S3nLxkNMbOc65w/We4V+I9vxg9H03EkJCy7hWXf6jwqVbe6uBJKiFbLpdlymdqskwb0kh97oVK3tjiytpOMKoR3aETwOAjKvh2bItwL2aT6wLYlEDwSHrcQJkS0DyxCRHsI1OsORc2m3Yao4qMFyAEfXgnm1gpk4hGWcxU2yjrNVouKbnJtpXDH7d4tkiRtcbMJBAKBQCAQCB4OZ+fWkH0+ijWTZ6YmuDC/xrW1DZpui//63bM4TpPReAzabT797BEODGWJhkIkoiqG3eDtmSWOjA+Sq9T46ttXWCmUyFVq+L1eoM0PP3+cy0sbeCUPPsnLJ08dZKNcY2ogjdFscj2XJxlVSUYiJKMKAdlLw3ZYKWjcWC/y3MFxCjWdqmETU4Kd+GjdIhYOYbdatNttxtJJTkwOkdd01iuV3rElox1Ry8Xl+lqeQlXvLRtKJ9AtG8NqodVNDoxkWC5W8PkkZlcKW4SzrhjXdaCFZJmDo9mem69LbDOBIUkSLi6yT0L2dS5btgtxpaqBppsUqjqy7EW37FtcVNvFttsRDwfZqNRuu07bBctu9aKqcGv8cfs4BQLBg6VbE+1JfAiebISI9hCIRwK02uC0wW23iSoBfE6b2bU8JyaHWdqo8PT+MQaTKkVNZzAZIeD3oW+2Hd+JjfJ7ty6BQCAQCAQCwQedU1NDvHNtmQ8fm6Ri1knHVGSPTEGr89GT06wVa7x9fYm59RyXFtf5//3FK7x0ZIKVgsbxiWHSURXXdfmZjz5NGw/tdptcuUq5pjOciBOSZWqGyUQmxfnZVd6ZXSHol2k5DpPZFB85Os2zB0cBWC/VWC/pmA0bJehnZaMj6MQUhaFkhIpu0Wy2UEMBDgxnubyQo1g1sFstdNPGK0kcHxvuHdu5uRVWChrpqIpubhWpLsyvMJiIoAY7DrWVgsbBkSwrBY39I2kioVBvftsV3kbSsS3C1tm51d7/C5q+pWlAOqoiSRLSZkOE/mYAPp9EMqogeW/GD+PqrTeQ42pHNASYXSngOE7v550YScdu+7tWgjJB2be1ucG2C+P+cVb027vbtlPQxLx/O7btiOshgUDwnkCIaA+BxUKZkASZsEzQH2B+ucSZ+TXCcoCZpXUqjQZqSCYg+0iqEQAazRay14fPd/NXspirUNE7ccxurbR+ul8k3XUEAoFAIBAIBI+elYJGqVpncjDJwkaZ4VQM13WBNsWyjgeJU9OjuHjwef2sFSq8eHSS9UKNg2MZAn4vk4NJXLfNV9+5ylgmzvOHpvD5vAwkohi2zf94/RLPHBzn9cvz/OTLp1BDPlbzRWr1Bif2DQHwxe9fZKWgce7GCl4cnto3RkJV2D82wMxyjrdnl2g0HZJRhavLBd6cWeDc3Br7hlJkYmGuLOW4sZYn4PNQMcze8anBEI7TEYFO7RsBOsXdVwoag4kYZd2gUjcoVQ1G0jGW8iXWihUKmk5cDW6Z39ZMk7nVIlWzs/28pjOevSk4pWN7j152o6KRUIhCZXeBRZIkEpsR0fFMAq/XS2yXtMa9CljpmLqlxhvcdMBtPwfbcRxnixvvbs7Bo6CsG5vv5630n6u7cfvdy/5l2ctA4r11Xh4U298398sHRoR93JFMEef8wCJEtIeA67i0HTDrNnXDpFCziYRkIoEAB8cGKOYrGHaLr5+dIZsMU9EtDoyk0RuNLYLYeDbeu5u201217hdJ/3M7fcEJBAKBQCAQCB4eI+kYb19bYf9ImkK1ysxKgYDs4/yNNcymA66D23Y4OpohpgR57tA4CSXEq1fmWc5ppGMqs8t5ynWTQ8MZJE8bF5dy1cS0W7iuy6HRDF976wqHxrN89a0rHBsf5G88d5T/8eYloDMHPDA6gKbX2TeQYj5X5WvvXGGloKHpBtV6gwPDSb746gXsVou4GmT/SJq4Guo5w+xGi7gSIhwKMZCIUtB0SlWDfcNJKrpBXtMx7U4h+6DsIyT72T+Sxi95Scc6NbV0s8FYJsmzhya2iEGmbbOUK1OpWdQtm/amZqSGZNIxlfVilfn1vXXw7IoE/cXlvX0ilaabzCyvo5s2i9u6gna7XXadbdvZi4C1m2C03cHWdd7dCdN2MG3nzivuYQwPg4Sq7Hi++s/VXo/1Xvf/fuZOzse75XGLsHfjurwfPO32E/sQPNkIEe0hkImpmG0YyEZxnTbpaJCEEiJXrvCV169wcv8YHuDpyTFM26ZuNphdKdBquT1BTNvBXZbbg4XZsJoP+nAEAoFAIBAIBHfghcMTlKoGss/PswdHKVdNUlGFmBLAacNfv3EZTbepWw3OzKzgl738rR94mqFUJ5VQrnY6Wnp9Xg6NDbJRqhEOyhway/Ld89dxHJfJ4RRmo8lTB0Z549oC33xnhp/8yHH+/DtnMRo2hWoVjySxVKjwwuEJDo1nGUnHODQ+gGFZbJTr/MIPvcC15TwnpoYxTRtJalPWDdIxhcmhJGODSVYKGteWc8ys5ElGFRzHIZNQsRpNQnKn+LxudI6loOlslDtdNK+t5FBDgU6Nsk2ha2G9xLWlHOeurzGWTSBJEif2DREJd6KQIVmmoOkMpqI71vLdyQW1k0iQUJVet9SYGuLg6CBe770LFEu58uZxNm5panA3glFX7Opuz3Ec1kpbnUdqSEYN3V1R/wcpWj1KQe5h86BdXYK7p/6AOsYKBO9VhIj2ELi2UmAyoZIrVmk0IeL3Mp1NUqxaVAwTzWxwfSnH9fUiIVnGtJvYrRbZPotybAfnWff5nQS2Lqry4Fp4CwQCgUAgEAjuzNffvsq+4RRu20WVZd6+uowaCDA9nGFqMM3EQIK/9+kXuby8TiIc5CMn93FjrcTcWo6BRIQvvnqBtseLhMRaQcO0bRbWSxTKBu9cXmByJI3P7+XAcIa1UpV3ry0znk4yko3juqCZFumYSk1vkI2rPLV/CLftMpZNUNB0/ur1y0yPZDk0kkGSQPZJrBQ0ZNmP7PVvOn2kXiRxJB3jwGiWlKowt1JkrVjDdaFUM/jaW5eBjogzlk2QjqlMj2RYKWjEwyHO31gFHGqbRfeHkjFqDZPJgU69tJDsZ6VYoc1NR1lXFOsXhrpiyG4uqLymc30t3/vZtG3W8lU8Hk9vWUiWWb/HOlpjmxFTJejvue+2i1870T2mUtWgrBu9Y+puz+v1MpR8sM6j++VhusgeNQ/S1fV+EhcfJTtdxz4UHnckU8Q5P7AIEe0hMDWYZL6kU7Ph5acmaXk9fPv8PJNDSYaSEcpVg9WyjtFokNd0kmqIkVQMu8/GfTvX2V7+MO3FtSYQCAQCgUAguH9OTY9gNhq8M7PMzFqewXiY8YEYakimUjd4a2aRN6/Ok44qXF0q8JU3Z5A8bRbWNaxmk1P7hnnx6DhXVzYYSsf47rnrBGU/zxweI5OK8vS+ESy7STqmsm8oxTMHx3GB0XQcq9nkuYOT5Mo1Do0PsJQrcXkuz2qhyvxaCckj8czBMVzXpdV2kX0+ggE/st+L47QpVeu8O7vMSqHCl169QLPZYna50zX+0PgAowMx4qrMelnj1PQIx/eNUNB0zl1f2XIOktEQC2slTkwNE1NVljabGVTqJk/vn6ANqMEAyaiCGgiQ2RTOlnJlcmUdx3EoaHpPhOpvPtB1cXVxHIflfJnpoUzv+ZAs8/ShsZ7g1t1OudbZxt04lPr3J0kS7U0jXHYPMbl+QXB73bXtx/FeRDi5bvJ+EhefdHb67DzuDpuiO+cHFyGiPQw8HrxtaLeholkU8jq2aWBZDkcnR3CbDqOZCHW9idVo4fV6qRoNTPtmFDN7n4Uz7/f1AoFAIBAIBIK90XJc2HRAhWQ/ETXI+RtreCTYN5QmHQ1R1AyG0zHi0RBxJYDX5+Op6SHKmkGtblCo6Ciyn1q9wQ8+e4S8pnNtOddzMMUUhZph9kSaStXgK2/OdJbXG4SDQfYPpRlOJjGaDXTTplI3mF8vsVyoUKmbFLQ652ZXGEhEabUcFFnmnZlFvB4PstdLNhEjFVdotDpz0qVcGd2wsVtQ0zvOspphkYwonJwewbDsXtQyJMt85NQ0AMu5Sq87pc/budxw3PYtN4IXNkoMp6JkEypnZpYJb9ZH69IVMcLBm0mLpVwZr9fL6f1jvWVd4ez6aqG3rFwzKVUNjk91mi5EFRlNv9ksAXZ2GhU0vXfO+8dRqhp4vd5bf/m7UKoalGpbt799u7vxOAvDP+j6XALBg2Cvnx2B4FEgRLSHQEJVcDdV5o1iicnBBB86dQB188v7mSMTlKoWH3t6mlrdYnYlx1g2Tr6i8/aVJRzHQdMtXNelaojOmwKBQCAQCATvZQZTUaxGi3K1zmQ2zXfOzhHweQnJMrLPy/WlCrIkMbeYx2rY6LZNuazTbDnkKjrXNyoE/H7SMZWpwQRXFzewbBun7VCo6LxzbQkPbb57fp61oobrOqSiKj9wej+aUScVC6EbDb51bpZsUmUgFmUiEyccCPD0wVEysTB6vcGJqWH2jWSQfT6GUjEmhhJ85sVjnJgeIZOIUKnVub5UJBEOAZ0L12RUIRlVODiSIVeuEleUTrSzauDzSdxYK90i+rSBiBJgpaARlH2cn1vdIs5057fPHBzvCVPPHpkgJMtbhC3dtMmVdLx9cU7Js9VpBjeFn+nhdG/ZgdEMsu+m6NVodmql9ZOMKrxxeX7Lsu311roOmH5X0uxK7pb3wHaSUeW2Bd53c6UVNP22r1vJ33SKlarGlp/vhgcp1InYo+Cx8LgjmSLO+YFFiGgPAZ9PwguMRRWWSjarpSo1s8HCWomjk1lWChVyRQ272UI3GgzFo5SqBgG/j2P7hnqTiX77+HaEuCYQCAQCgUDw3iERUfjks4eQpI4Qk4ypXFvO8e7sCpFwAL/fx/6JAWaXCnzy6YP8zCefIROP8dyhcU5NjZIra7QcB59P4tD4AK2Wy3/52tt4PG0GElHeuDRP0OfjzLUlzEYLt+2ysFakXm/Spk1BqzExkOT6SgFJclkuaNT0BudmV7m+XKTR6hTHt2ybgtaJT/5fX32bRKQjDrltl/GBJAdHBxjOxClVDXSjgWnbFCo6gYCPpYLGekVjrVghqgQxrRbTI2marU5JEt20KVUNxrJx0nGVkXSMYrXOiX3DW85VOBi4pVnARqnKlYX1LWKVpps0HWeLgy0U8LOS13YU07aT7ytv0i9MreQ7decKms7zRyZ3FYE6x3KrA2YgEd3y870IWf3b7d9/97h2IxkN9f1fYSRzb86xnYS6gqbf8nvZC93f2QddTCvXPtjH/6h53JFMEef84CJEtIfASq6Mrw1L2uYf0na7I5iZOn/xynnWChVO7h9mdqXAoYksNauBV5IYG4jTbrtoukX3+7NaN2/Z/sxijnDAv2VZrlR72IclEAgEAoFAINiBfkdQKhIiGg4SVxUazRZqMECj2UL2S1yaXedjp/fzldcvc21xgy987W2+f/YGr16YJZuIogaCNJoOb15aZHIwwf/y4x/lD/7Ltwn4/IRDQdKJMAdG0pR1E9dtM7OYx7Rb6KbN1EgKNSQT9EtEFIWxTIILc8uMpeLIfg9SGwoVHbvZQpIklvIaf/sHnwE64klRMxhMRjl3Y4VS1SBXruLzeZldKpKOq1i2w0Q2QcN2qBk2Pp9ETA1SqhoYhs1r5+dRQzLJqMKFudXeuWm1XGaWcqyXauQrOrphkYwqaHWrJ7pcvLHGQDJKwLe1Q6XbdhnJxLYIZV3haLd6Vf0Or6mRFKWqwdxqEd1o9JaPZGKE5E50tNVy8UqenTa1Ix2haeuy7UJW1bCwW1s7et6O/mO5Ux2u9l1cgN+t2ywdU9Hq936jvn/s3d/t44ym3g97qV+3fZ1Y+BEV1L8P7tW5+J7kcbvJhBPtA4sQ0R4C4WAAu93G026TCLSJhEKkQmEGU0n8sgdNNynpJoZls5KvkIgoqCE/+UqNSs3ccrdtbKBzl6pfJDs4nr2lJkM2GXk0BycQCAQCgUAg2EJUCeLxeEjHVBZyFVY2KpiNJqPpOLppkY2HubyQYzQd47WLC3z8mYPUrRY/9KGjuJ42TgvevrJEvlLh22eu4bhtpoYyWLbN//MnX+bijVWmRzOsblRwHA+u42A0bD790hECPg9xNUQ6pjIxmMJotKiZDb515io//vJJVkoa2USM549NMb9WYD2vUdLqTA4mATh3fYV0TKXZbBFTgzy1fxjabcYzKYpanZFMjFypyuJ6CQkPk0NJjk4NAh2BJBlVSMTCHJzIAh1h4fi+4Z6IUrMsSpU62XgYu+mgKkE03UTyePj+hTmWcmWObdYtmxrpjMm2HXKlWs+tFQ51xLVS1dgiXCzlylscVVcWc4QDnUhod//JqMK+4RRW82bt4X5hx+eTaDo3m3tV+txE/aJQ17mWjql3bPIVDviRfb7brmO3WrdNluzm6lKC8p6FqZ3cZpU7uKW6zsT7pXvubhdN3QuPqxnDXmpwbV9npy6y7zXu1bkoEAhu8t7/pD+B2C2HFw4OMZ4M0bBADcjYNBlOxZFcLz/90dN85OQ0hycGencsyrpJJh6hZjTQ6xbRbd18ssnILcVQBQKBQCAQCASPn7rVoNlqcW1xg3gkhBLyE/R7iYaDDCQjHJoY4IXDk/zoR4/z0x87RcDvRTcbyH4fpw6M8rc//Syjg0mmRwdotZr4/J0pelwNkitX+fgzBwn4fXz4qf1cXczTaDpcnF1jaaOC1XTwSRLvzixz5uoizaZLXFV49sgEjabD8ekh9o+mWVgr4gKhkMz+0QxXFjaw7BapqMp33r1OOhoGIBiQialBijWdkUyMYqWKBw8vnZii0WhuEUU0vSMCdeumvXV5kXAwQKGi94rxn9w3QjoR5tKNDUKyD920iakhYmqIH/3Q8VuEiFLVQJa9W24Qh2S5t5/+9be/djQdRQkFqJuNWxxd/ePeLuz0/xyPKMwu3VrzLCTLBGWZklbf5V3QwXEcynuYs8s+3y3z/X52ikgWKvot491LhFLvE+uiT4Bbqp/7KSjfPV+C9yePO5Ip4pwfXISI9hBQgn5ev7qGYTYZyES4tlSiVjX4yquXCAS8+GWJb5+5BkCz5dKmjX/TWXZwPIscuHnnyrTtnni2vRiqQCAQCAQCgeDxE1ND+CWJuu2g1SyiIZm/eu0yX3v9Ml/8zkXW8xWK1TrffGuGtWKVyzfW0esNLlxb5vLcGpWawWAywuJaiYPjQwzEVdJxFa/kZSybYHY5T0wN8caFeY5MZklFFV44Os5AUiXo96PpJvvHsiyslfD6JEKyj8sL62i6RaGi85+/8haL6yXcVpuW0+7UOPP7kH0SzZbDS8enWCloPUGm++9fff8i06NZMskIq4UqRa3Oal5jbjnPu9eWiYQCfO/dud55mBhMUDcbpOMq52ZXsBpNSlWD/aNZBlMRWo6L3WxhWPau53K7+LW0Ub4ZDdwURXYTjlQlSFD29ZIc0ImK3o7VHeJt+8eyOwowdrNFMhbesqxbQ6y7vtfrvW/3VT/95yMdv3W7d4p/Avj6XHGP0y31qKOEO50vwfuIxx3JFHHODyxCRHsINGwHTxuK9RZmo0EsKnNjTeP4/mG8bptvvTXD2pqGGgyQK9cIB2UadrMX2ey3f4dkec/i2U5OtXtxr9m2c8e7bAKBQCAQCASCDoWKjtNuMxBTWFjN02y5JCIKrsfD+GACWZap6CapeJhYOMh61WB8MMH+iQxen4ez15ZRgzJLeY3vvDuLR4I//tIbmFaTqmFR002+8volDoxnCPp92C0HyecjX9HxeOD49DCruQqpWJhIKMC1pQ2ePjhGzbCwGk2mR9M8d2yCqBrEMBr4fV5SkSDfPnOdhm1zYW6FqBroRQyXchr5co3PfOhYT7AaTkfx+X04joMHUGSZbDLCh5/ax9JGJ3LnuG5PwNooVYmGAxQrOrppk46rxFWlJ/o4jsN6UWN2ubCjkNVpbGCh1xt4JQ+W3SIod+bI/cLR1U1H3WqhuuPvphsV3c5qXuPSjTWGd4m37VWwqm7WENtpfctu3SL4dc/Vo6R73h43Ikr4ZCOcfQJBByGiPQTatPG2wdeGZMDLYDTCcDJCOCQzmImSjKpopsnVG+scGEly5cYalZp5S2QzV6rdlQi2k9gWU0N3LaTJsveWu2wCgUAgEAgEgt1JREJoeoPRwSRXFjb40IkpTuwbxnXbLG+UiCoBanULu+nwyaf3Mz4QI5OIcmh8EK/Hx3ffneP4viGePzrJzHyeoqbzzpV5HLeFR4KnDowRCvhx2m2yiQj7R9O0Wi6nD42xmq/gtF0CcqfxVFAOMDaQIKGGWCtWGRtI4sFDNhHh2aPjxNQQ0UiYolZjMBNlfq3MQCpGs+lQ0y2ePjTKUwdHgZvi0NJGmcOTA3zv3ByxaJjxgXjPUdYVznxeb+9C+/njE1h2i4nBFNXNuWhFN5hfKaEEZbR6g2bLJa4GdxSyklEFVQmSSaqUdZNK1cSytxbrL1R0mk2HluOiBv23dJa8trCObXfqnW2fDw8kVY5ODe04T+5u5/pKvle3rN891y+MxW9TQ6zVauG67pb1+11y90OlZtxTJ8174U7C353quwneH7wXnX2PO5YpopwfTISI9hBQZD9hPzhtuLJmsl6o4LZd3r28yNxymfV8mUhIplwzub5W5vDUMFrV5NLcGvmyzmquAnTubG0XwbZ/0Tt9hVB3Yzcnm+joKRAIBAKBQHD/pOMqVqPF4nqJRFThB184iuz3kooqfPzZA2SSMV47ewNoI/u9fPn7l7i0uME7l5c4P7vKSqHM5FAcx3Gwmk0mh1J8+KlpCpU6DcthOV/j+mqBQrnGynqZKwsbAJw8MMLXXr9KoVKnUK6zbyTNakGj2Wzy3755BiUko5sWfsmH47jYTYdyrTOXfO3iHGOZJNVagx/98HGiShBNt0hFFd68eIPKZhQ0V6phWDaRUEeg+9Tzh3nl7RlKNZOLsyvops25ayus5jWiSoiW47Ca14gpCum4yka5Sjgks1qoUDUatJxWr0PnToKSblhYdoszV5d653ZyMMlgOtKLIubLNb751tXOcyNpbLuJLPuxWy6tloumm6zmNYIhmW4vLu9mA86uyFeumdi201veT9ddNj2S6dUt649B7hQ53U6hoqMqQSRJYrfmnzs58PqXbX++3wkUjyg7RjMtu7WlBlr1Prptdo9rN+GvKw7eqb7bbjxuZ9Ne6sm9F9jL9d4Hknb7yX08oTwOJ+17ESGiPQQ2ylVqmzerPEDQJ5Evm4xkYyghH0PpBEenhzl9ZIzxgQRnry5z4sAwI9k4Ab+PYEBG02860/pFsJgawrBu1knrToTuBdHRUyAQCAQCgeD+OX99lXRCJZOIoNcaLK6WKFfr/MmX3+LL37vAcCbCqf0jbOSqzK5soFVNhpIx4mqAas3gQyenKFTqBPxenj86iarIXJxb4+//xEsoIZkf/fAxisUqAW+A1ZKG1XDIV2p8+fuXkf0+qnWToCyxmq/g83iJRUKcOjBGtW4xlk2ymisiy35ajkOz1XEv1esNUlEFyevhS9+/xNuXF4mpQQy7xUg2STKqEFWDVKoWRa2OvFncPx1XOXFghOFMDI/kwbQajA4kGM7E0E2Las3qOcs03WQ4HWVhtYjjtMnEw8h+P6oSRDft3va6rOY1DKtJUPZx+tAYAOeurfSe74pXmUSEE/tHAFBDMvFIiKDsIyj78Pmk3tx5LJvsdbRXw51lXeEkHVeRZW9veT87ucvWChp63dzxIjIU8N+yrHtcyahySzH/XkR2Bwde/7Lu/3sNBTa3eTvxKSj7UJUg1bpFq+USDOwe5bydiFSqGnd0zd2pHtudRKrH7WzaSz052LsQ+bBEwbyIUe7I43aUfRDdaA/KSfukI0S0h0DL9RBsg7cNtGEgHWMorZIv68wtFomoQSq6ieO4xKMKa3mNVrsjkI0NJvBu3hKLhoM0W07v7sPSeudLW/b5eg61x/3lIxAIBAKBQPBBpXtTcygVZWmjTCwSoN12CSsyhUqdn/rEaZ49Os6bFxYoVuocmRom6A3wA88fIODzEg0reL1eLs2tEQ4HCAcDXF3IY5hNnpoe5v/+1rtcmFtlNa/xzNEJag2LlBpmZaPIlesbxMJBPvr0NEf3DWM0bCYGE+BxScUjKAE/YwMJRgcSVOomakhmfDDJpblVriysc/rwGKMDHbHswycnCcj+nuBlmjZ6vXNsUTXA7FIey25RqOgs58vMrxaZXcrx7NFJMokIruty5soSbrtNuWai6SaGZRMOBrhwfZ10PIJhNYmpIcaHEtitFnMrhVscNsOZGHH1prBRqOgMZ2KYtn2LK6t/DnxjtdT7/3pBw3GcXWudHRzP7ul3W6oaFCp6TxiZGk6hhkMMp6O8dWF+y7p28/ZOoe2Osb2KN122z/f3Mv+X/RJGw95Saxm2Cj23G8fdjvFO29jJdfcwMCwby7KpPECX2e2EyH7u9brsTmLjYGrn97JAIHg8CBHtIaCGfDTa4LYh4of55QKLaxpqKMCJg2P8+VfPEFdDWHaTuaUCz5+YwDAbfPPNGTS9M8npF8m8Xi+abhJVO3exfL7Or+1+Gg4IBAKBQCAQCO6PcDBAqWqQjqsoAT+G1SSZiHB9OU/Tdvjr1y7wpVcu4pM8vHByipGhOE7bYX1dY36tyMXZNWSfh4PjgyyvlvjS9y7yze9fpFytgQcy6STHp4cpVnRkn5e3Lszz3MlJPv7sIQ7tyzIxnOQbb85wYzlHPKxSt1o0Wg6z8xus5WuYdoOSVmd8MEmxonPh+ioB2UfbaaPVGnz5tQvkSjpBWWajoOH1ejl9eAyz2WStWKOmWwymo3z82YPMrRTIl3TmV8ucPjzO/rEs1ZrJjdUiyajCockBGnaLF05MUqtb+HwShUodSYLhbIxDEx3xajWvsbyu4aHNRkHHsGyuL+aZW8qzmteQZW/v/HZisk1Mq7WjKNYVH6ZH05y50ol/DqZjPffZ3bK86TLTaha026Q3u6TarVZvf16vl2MHh7e8bvvY9iIYzS7msPrqrHVfczcRw+IuDiXbdgjK8i0OOLg/99du+9sLu4mad+JuI5dKUCYYlHvXTd148P2wXYh80DwIwfIDSfsJfgieaISI9hAYSsc7/2lD0+7UYJjMJAjKMomownMnJ0hEwigBPxt5jVypytJqgeeOT24KZhbNZpO60ej90Y+poS2i2f02HBAIBAKBQCAQ3B8+n9S7AE7FVYbTcd48fwNF9rOYK5GJxTg4mWX/2ABen8RGTsPv9ZLXder1BrmiTjqmslbQSCUivHR8kqAiM5SJo4YVUuEQWq0BwMW5NeKREP/5r95EkjyUKgaSBw6MZTiyb5hj00Msr5dxmi4RJUA46MM0myytl4iEFRq205k/tl2O7BsiGg7w7JEJWnYLVZF5+Zn9QEdIKpbqVHWLVFzlzYvzLG+UefbIOGElwL6hFG3XpaQZlGoGU8MpXBeaLYeRbByA0YEE1xcLDKYjjA8mOXvlZiRzOBNDVWRO7B+h0bJRgjKBgI99Y5ktF5eO46AbnWjodpFhNdcRnOLqTZFoeixzy++n3+lW0jpiTKVq9F7fj2W3iEc6ZVMaDbvXZGs1p2E1NkWYzfGFNqOt28fTf4zdfXYet3a93z+eJRiUb3lN91h3GuN2UrsIYhX99sLTXhoSLO8QW91pf/cjrO2FexWYuu4/VQk+8u6k3fea4OHicZ/ch+DJRohoD4E3LywQ8kBc6TQXCCthQqpMIhzi3YtzXLqxxl9+8wznZ1fx+CXSiSixqILjOIRkL+vFKqbtMJyN7/pH/16EMeFIEwgEAoFAIHiwrOY0ljfKvHNpkWuLGzx/YpKIqnBofIDpsRSmYbOaq3Rq0Urg8Xh44ehUp27Z4WHC4SBeoGE3GR9K8VM/cJJLs2v4fR4OTGTweyWUoIxl2Pz0p05zfHqU1Y0q5arBX33vPCs5jYDfy/fOzpJJqhyczHJ5fpWCZtBug8cjkYwpDGdjHJsa4iOnDwKwuFZmdCDB9HiWutngytwa3z0zi+O2+dhzBxhIdWrnTo9m8Xq8nL+2Au02w9kYG8UayZjC5HAK6DSrWtmoMLeUZ3mtE608MJ6lpBnU6hZjQwlKmkGxovOdt2eJR0Msb5SZGukIX90Omiv5m8JNvtxxyFVqnXpUyxtlvvbaZUzTZji7WStM03v1qvojd2cud1xpmm5h2h23l2F1xMj4NlFG1zvx00q1jqoEUYIy2XS016DAbbs9R1cytrOg0x1Pl1xJR9NNkjGFZExBlm+tmdZlNxFq+za77KXIfH/d42JF74l5XSy71XPX7cboHmsf7SbkfZDZ7X0iEAjeHwgR7SEwlIlhOaDVOyJaLl8nrYao1A0++uxhWo6HUEBmbCBOIhwgEVUYzSawGjbXlwsko2HafV077qerTj/CkSYQCAQCgUDwYAkG/AxnYgymYxRLdc7NrLFW0Kg3LDLJCFWjwfWlHH/xjXeRPBJ+n8RXvn+J6fEsptUiEVZ47eINIkGFV8/N4vH6+cSLh9GqFt984yqrGxVGMjGyA1HeuriIxwPFSg1Pu42n7WX/eJr/+1sXCAVkfJJEpVrDsjwcnOiIWPmCxtdfu8xqTsNxXWzbYTWnsW8s3RNYwqEAg6kEk8NphjJRLs6uogQDPYHHbrU4cWCEqt6Zkx6dHuLS7DqrOY3VnMZwNtZxqY1lGB1KbjbBMmg5DnjaPTEpFVd5+Zn95Io1aHcEnvMzK+wbS3Pp+hoDiZvij+z34/NJWI0mCysl2o7Lp148Qs3siGHFio5tOz2Bqz9yd/rIGLphUdGMnmtsdCDRc1f1C1TqZtojk1BZzWloukmr5fYaFHTFJLvV2vOcPJtUt8y71VBnDDs5lPpFqP7nu+LedvLlnUW37dvuduhMxdXe+e+iBGVkn+896Zh62M42wfuIxx3JFHHODyxCRHsIXJld7rWvnc6GUcIS337zKlXD4q1LCwwnFJRQkGDQz9RollLF6Ni52x4qNYNkXNnyxbtTPYO94rruljbXAoFAIBAIBIIHh+yVeOfSAjeWc6jhANDmwFiGarXB1RsbjA4mef7EFOPDSaBNOh5mYjjF2xcWmZ5I8e//4vv87U8/RyAAOPDX373Al79zgeX1CtOjA6RiId64uMBgIsrJAyOcODhCy3U5ODXEp148TDqu8uKpSfaPZbmxVODSbJ7njo3yxrk5BlNRfvBDxwgFZdq4hJUgb1y8wXA2xtxSkTOXl2g1m1xbKuD1QiQs81evXMSyWyRjCvnNwvpzi3ls2+H4gWFeP3+DjUKNdDLMcDbWE6QGUlGgI4KUq3VScZVsMkLQL/P1Vy8zM7/eiyh2halUXOXEwRHevrjI0ekhYlGFpdUiAMamWDacjSFJkE53BLZ4NMTbFxdJxVWUYACA5fWbDrauCKMqQeKbkcwut3NXeb1ehrMxYmoIn0+6RcyRfT6i4SBzS4W7fYv06BeyVnMarZa76/PqtlIuXQbTnfN9fmZlS+Rzu/vJt4c6Xu9Fx5Rwtgn2yuPusPlB7M4p6CBEtIdAPLr5x78N63kdreaghP1EgjI4YFktFpZytB24fH0Nv8/Ln375TRzHZSgdx7SaQCd+mSvVtmz7biOZkiShKreKcCLaKRAIBAKBQHD/qGoQj0ciqir4vBKhgL/TKCoSxLIaDCRV/D4fxZJJKBhAq1osrxdptlp867Wr/KO/94NkklHevrDEeqHGxEgCn8fl4vVVZL9EJh3FtpsE/D5ePz/HjaU8owMJVjfKFMo6xXKNdy4totXqhEJ+fvwTJ2nYNnWzgRIK8JffOkc0GqKqGcwu5IgqCt97axYl6OfEgVFSCZWaplPWDDweiZ/4xCmG0jEuziyh1y0K5RqnjoyRK1UpaQYvnJhiIB0hm4ywmtMoVnSW18u0cSmUddbyGiPZRK/u1sJqiVOHx6jUzC0OMNkvsV7QuDi7wv6JDK7rUtMtwuGOcDQ6mODM5aVeNLLrKLs4s0bXytEVgUYHO+LYak4jFVfJlWrYtrOrSNQfb6zqFuvF6i3r7CTmdB1821leL/fqGNu2w3phaz2z+aU8l66vATdFvuFsjPpmxBR2jmn2C3nbXWMnDo7sGvkECMq+B+rqupdt7aWu2+Og6zYUCD5oVHXrjlFuwZ0RItpDwKhbHZUZMBxIR2XS0Qh61eYTLxzg9LFx2m1YyZVRAn7sZovJkRRVwyKqBvFKHhZWS4SDAVSlc4dtafMO2+0imf0dfraLb9sR0U6BQCAQCASC+8N1Xa4v5oiqATweD9l0BK1mceHqKrblEFFDVKomwZCPqmnypVcu4LYdJoczZFJRUtEIq/kKX/zGWZ49NkHVtGk0HEJhhR/5yBH+21+/zWAyzpmLS3jw8PIzB6jWTdbyFRpOi4XVEosrRWTJ24taXr2xgYOHT7xwiBvLBU4eGiEeCpJIqETVIErQSyYTYyQTJVeqUqmZhMNBBlMRDKNBvlQjm4zQcmA4k2ByKEVA9hEJB7g6tw7A7GKB60t56qZFw24xOpggJMukEyrHD4xgt1qs5qtcW9jgmWPjpBMqz5+YAm6KQS2n46o6tn+EmBpCkiRGBxO02x1xY3m9zOkjY1xfzBGPKL1lMVVmciTVS1p0t9eNlZY0g2wygix70XWzN+Z++uON+XKNwU0XXb9Qtf1CU9fNXQvyjw4maG2uL8venlusO66BgVgvWdIvzvXPx/NlfYujbvu6yZiyZyGre7N8N1fX9v1sZ6f97MUh1n0PdrmdyHc3VKrGlnN/v+Jg120ouHuMvuvNx8EWYXYz+fVEPh4TUTX40LvNfhAQfz0eAmMjKeTNvHMy4mUgoXJtMU/VsPjiN86SK9VJJiMEZB+u28bj8xBVQpiWjRqSWVgtMTGcxOeTkJBY3igxNpjYIowZlt27g9JzlUk3f539BUUFAoFAIBAIBA8eSZIYGUwyNpSm2XJIRlWeOzHBT/zgSRKRIK9fuIFeb3Dx6joDyTA/8+mnkf1+LLvJjaUNBtNhSprJ6WPjhAIyHz49xYdO76OimVgthwPjA3zhr9/kUy8dJhTys7xe5sWTU/zwR0+Qjqk8fXScUs1iIBNl/8QAh6eGKVbrHJkawu/z8cyxcZbWimyUO7XPYpEQI0MJcvkKb11aIpvsRjANcuU682sFvLKHNy/O03ScTlxzIM7iWom1Qo1MKspqTqPtugxlYtTNJnXDYmGlE8GsmzYlrY7rQkj2oesW5WqdGytFSprB996Zw242cTa3vZrrONH6RZGuWNN1l40NJnr/l7wSihIkFVfJb86LK1qd2cV8b3vJmNITiVQ1xMTorc6xfkYyN4We7mtLmoFlbRXRnL4x7STiqErwFnGqK+yFZLn32u10hbtMQu0JO7uJXHcSspbXy+iGRXgz5tplewJlt7HsdT+7EQze/uJ8N/Grbtq3TcnEo0qv2+bdjq/7nnyc1HRrTx1RHzQPo+bd4xYP+oXZxx3JFHHODy6P+3PwviSVUGlutq/Vqw6LKyWGkyqZVJyRoQRtp4VpNjh6YBjdsnHsFkemhwjJfuqmzfBAHICqbhIM+hgdSAJs6dSpBOXeF233Llb/8w/KolwVsU+BQCAQCASCXQnKPuqGxcmDw3znzVmUkMy5KysEZB/HpoZIx8NcX1ynYbSo1AxWcxofffYgY8MpZNnPUDrK1fkcS6sF/vpbF7mxUmYgFcE0m6iRTl0sv9/Hl759DqvR5GuvzlDSDEyrydhQgpbrUK2Z5Aoaa/kKTx0YQzcbrBQrvPbudRJRlVy+SsNusrpRplwxef7kPqZHk1yYWaVctTh+YIh9Y2lePDmNoTcxDJt9Y1kqVYNCWefIvkEcp8VoNoqi+MmmIyhBGXczgihJHgCshk0uX2EjV8Xv9TIxmqFu2EyNpEjGFD789D5aLRdrs7nBcDbG1GgGy27ecl6X18tcml1DCXUEodWcxnAmRqvlsrxeJroZ+9w3nukV7u9eYPeLRK07RJdKVbO3/e5rbbtJONTpqFnbdFf1u8ZScXVHoWu7OLUXJ1a39pvX23GwmbZNKLBzN887iSKjgwl8Pt8tLivvHUexO3cjxNzJ4bKb+BUOyQ8sJbNdqAuHZJLbauM9asKKvEUEfFQ8jJp3waD8wLd5O257Tdt+gh+CJxohoj0EjMamlbkNTRdaLhSqJqPpCKVKg7Vchf1jA3zvjVmOHhwim+p8wR6eHiKqhkhsWtajm18m3TsX0W1fLqa99a5Nvs+pZtkPxmq7fZ8CgUAgEAgEgq1E1CDzyyW8ksSf/uUb7J8YwGm5vHV2AdoSfp9M03W5NreBYTWZWyoQDStslHTeOH+DgM+L1Wzzgy8fY3mtxI3lIlevr1PV6thNm8FMjIDXh2FabBQ16qbFRqHG3FIRXJgcTXfqerU9vHt1kaW1MoVCjcmRbCdmmlTRqiZHpocxLHtzDtng5OFh4mGZlXWNM5eWOH9lsRNFdOH1d68T8Pu4vpDDtG2O7R/BaLSo1ixiaojl9TJPH5vgwOQgYeVmVHFupUIkEkTfjH2Fgn5KmtETN0YHEzTsJqoib4qBNiPZBLOLhZ5gs57XaLfbHN0/hBoKbJnjjg4mkCQJJSz3tplJdsSZ7uu7Nc9WcxqNzVplu4lBiXio54zrMpiJoekdcSuwi7vqTm6uvbJ9OyFZJpXYWWzaiyjSf1O9i3of8/mHIcQ8zHpk78XGBHcroL1XasndKfb7KDD66gYKBO8VhIj2EDhzfvmmwtyG8aEYIZ+fRqMF7SbT+wdYyWvEoyH+r//+OjcWC5gNm9VcBeiIYVXd7LnADMsmX7y1xllI3nrXJpOM9CYZOzUTEAgEAoFAIBA8OOxmi9UNjXNXVrHsJtlUmAPjaRZXilxbyPGTn36Kglal1Wzi90u0nDb7JzK88sYVzl1aZn5hg+dOTeL3eYlEQuBpc3FmhQMTGcZHkxhmkw+fPsi1GzmSsQhnr6zx0588xeJykUxC5fLsCivrFUYHEwwkY4yPJolFQuwbS6EEgzSaNqGAlxNHRsmmVOaWcqxvVKhUDSaGU/i9EqWqwUZRY2IkRcuFty8sMD2e4dSRcUIhmRee2odpdoSoG4sFVtYrAFvcUqsbGheudQrnf+KFAxhGg7V8RwhIxVXabbcnbrx1bp5kLExFszDMBoloR6TZP57uCTaZpMrYUJJLs2tIkodMMtKbWxcrOq7rEpJlKptimdfrpVjWScYUbNthebVEMqYwnI319ttfU6xbT211Q2NpTeP8lbXesSyvl6nWTOKRIMWyvsVdtbx2U1Qolu9cl6tu7u2mdrVm3lf07n5Fl7utMXa/McF7FUZWN94b4tLD5kHVkrtf7lUotm1ni/B9P9zO0PG4I5kizvnBRYhoD4HpsWzvAzKUDBANKeh1E9O2MUybt9+eJx4O4jjw4dNTJBIK3371Kj6vRL1ukklGiKohrM0unWsbGpnU3mqcZbbVQqttK/ApEAgEAoFAIHgwaDWD4YEY+0bTJKJhsqko4+NpLNPAD5y7tIqHNg3T4cB4ho0NjUJRRwkG+Njz+zl1fJJIKIjk8RDwSvzFV84yNBDH7/eSjkU5dmCIlQ2NQMDPMycm+JGPH6dUNXDdjjh1dP8IBybSlCoGkXAQ27LxShLlqkE44CObipBJxjDNJplkjGqtweRIirWNKlrNxLBaKMEAmmbgOg4D6SjppMr5K0u0221uLBUplnXmFnNUqgYnDg9z+sgo0Clfcn2hAEBF0xnOxCiWdVY2Kmg1i/3jmU6NsbUyqbjK6oZGqWL0CuyPj3RqnVV1i2JZ56++dQHoCE/XFvIUyzqH9w2wvinGBeROKNHn9RJXg1xfyJFI3mwQkEp09iHLXk5ujvHStTWqNbNX3ywVV3FdF8PoiFvDAzEOTmR46thobz+jgwmikRC5Yq3nCOuKN6NDN0WFndxiuWKV19653nNaNXaIqe6EEgr0jqMrzu0k0tn2rR084f5Fl93cW8YOIqBlt5D993YJadtO5z2wTRjZqzg2PPDeEJcEt0eWvbdckz4UHndzgCewsYDgwSBEtIeA7TR7HxC92uDS3BqRiEK9ZhILKYwNJmnT5ntnrqGGwygBP2NDKYKyH2PzS75aM2k2HWzbQVVvuspyJR3DtHe1QevbaphFVOFIEwgEAoFAIHgYSL2pdBurYbO0UuTqbJ7BVJJX3piholVZX69w6NAAZy+tMDKcoFCuoQRl5lcK1Cp1vvrKRQJBmWvzOUYH41iWwfX5ArOLOaxGk41cBTXgJxlXMA0by7TB42Fmfp2qbvChZ/azul7iO29cwev3omkGs3MbuO02Rt0mGVe4dmOdXKFKNhXh0uwaL56eIhT0U9bqKCE/p4+PkU5GeO2tWQKyl2dOTpIrVambNqbV5NkTk8SjCq+fmcNtw1q+im5YTE+kub6Q4+SREayGTSqhEpD9HDs4hNcDF2dWsZstimWdYNBHMq4gBzrOrq6ry2m5pBIqf+PlIxTLOuGQTDzSEVnOXVkhFOw43rqilSx7adgO0xNZkhGVklZndiHP6kaF4YEYtu3w7sV5arpFw26iGw3s5k0xS5IksulOQ4WLV1d6y7uR0J1+3i7e6IZFtXZr3eBsKsrTJyYwN6Os5crujq1+kay/hln3OHcS6bRap0vlg4hD7sVJJ3kgX6xtEe+Csu+eEy9azSAZV3pOwC6PUhzb7bhbLRe7efv6eXvd1geZx93EQSB4FAgR7SEQ8N20t+t2m7rZplFvcH25wFqhQtWwObxvkB/52El0w+KNd25QNQxW1yuUKzqr62VyhRqD2Siy7CUVD1Otmcwt5IkqMkpI3rUts9d/s3ToTl/u94P2gLcnEAgEAoFA8CSTSqoUyzoeD6jBAG08jA3FWS/U+Mwnn2J6cohkPMKJgyPYTZdaTadaqTE7n8OyWrS9EkcPDPHmO3MEAhJPn5jgh3/gNKeOjrBvNEUiFmYgE+PGcoE//cs3CYVkYtEwR/cPsrJaYW4+j9frZTVfQfZ7+dLXzxFVg8zMrnN+Zpmr11colnX2jWcZysYwLZu/8dFjnLm4SMNuYdtN1nIaptnk7fPz/MCHjqAEA1SrJoZukYyFsJstLl1bZWZug4mRNLlCBdkr0bBaLK+XcNsu5y6voOn1nqjw9vkFNoo1rEaTeFTpiWvQqd8GUNu88auEZS5cXeGtc4s94SiTVEklVNIJtVfTDKBUqRMKyKT6BK5kLMz+iUyvMVehXGN6YpCIGiSTjDA8ECcauTUStrxW5tihkd7PXm9nDl0s6xTLOl6vl5X1CnPzud46rutSqRroeqPX8KCf81dXkDxS7yb2yKY4VKrcKiyYVpPVjcrt3l63vC6TitByXFqb9ZK7z++0/f7x7sR2ka5/LF2BMxiUyaQiyPL9tCa4STdZ47tDA4J+7kao2ul8VqrGls6Yu9Wbuxce5LbeL3Q/54+Cxx3JFHHODy5CRHsI5Ipa7wPia8P0YBTHhaMHBjkyNcL4YJQ3z8xiNZvImx10jkwPMzwYR1GCRNQQ+6eyeL3eTcu0j4bdYt9E5o4dUUKBm8/vNGHQdXPX+gx3Et0e1BeoQCAQCAQCwfuFVEIlX9S5cG0F1+OghgMMDUTJ56r88X/7Ppl0hNfenkMJ+ojHooyOZBgfTtC0Xebm85imw9R4mk++dJTl5QJnLixy/UaeG0s5FpaLWI0mhtVkKBthebWI3+9lI1clFQ/x8gsH+Mb3rzA+kGR8NMMzJyY4cmCYH//0aX7ux17k6vUNAgG5F8ubWypQqtQ5fWycYMDP6eMTeOgIDVdm1ylrdb76nYvIfi+nj0/ium10o9FpUjCaZiAdYSATp9FskdoUupIxlQ89M01cDZNKqFyeWeWZExNMT2R55sQEyXiYUqVOOCSzulHh8rVVAI4cGAY6c9ehbIwXTk/1zmmz6fDmuRsUSjUG0jdjYUrQ3xNKLl7tbKcr+BTLOiWtTiyqEAj4KFXqW+KXsFVs6n+uX6hRFLlXf20wEyG42flTq5kYRoN4VMHv9+LzSbRaLivrFaxNoW9qLLXlRnd33p6M39odcnQo0RP+dhpf93WlSn3Lctnv6zUP6G63++92wUySJOLRvTUG6B/L9vN2Jyzr7hqa7dT8YCds28F19644bD+fAPGosqfC/j6fhOzffVyOs3OUVrCVcOgRdu9sP8GP+8C2nR3rpQseHUJEewhomtX7gCgBsGyHmKpw/tIKBa3G5dkNJEkiFQmzlqsQS4S5NLPKhasrjA4liKjBm4KW62JaNplUpFfItVjSbxG89uo6U9XQrn/cdhLd+ukX6B41D9pVJxAIBAKBQHC/OI7DO+cXCSo+RoYS4LQ5c3GRStXg4x8+zMdfOsTTx8aYmVvn8KEh1nIVUnGVTDKKR/Lw9PFxVtfLLK4W+c4bV/mRTz3FcycniEWCKKEQxw4OsZrTeOH0FEowwMRYBl03yBXKnD45wcqGxic+dJhK1cButGi1XN589wbBgJ+3zs5z7OAohtFACXXcIacOj/D2uXl03aTVanFpZoWA7COqhvjYi4cYHoijKgH2TWaZvbFBNhXF025jtxxk2cu3X73KwmoZq9FxURVLdTweD5WqydBAnNWNCh976TC5gsbla6tcm9tgea28RUSKbYo6qxsVFpaLveWG0eQv/vodoCM+PXdyiqeOjbNRqPVErk5kM871+RyS5AFuCj4ej4dkLEw4JCP7fSTjYVotd8scsr1DLaJWy8Xv8+K6LpZlc/nqGom4QqlSx+v19oSZWCSE7bidm9FtsO0Wlm0zMhjviULbY47La+Xeo3ecpo1lt3rC2J3caMl4eEcRbieid1HG5UFGEe9C57oj/Tf7Zdm757rQOx1PTbceWCfQfOnO5+tJinfu5l4UvHf5mZ/5GXw+H4GAj6mJIf7oj/5o13U/+9nPEovFkCQJSZJIJpO3XV9wdwgR7SGQSm1ae9vQaECtXkermnz4mYPUKhamZfE//dgLuG6boYE48XCQgM9DRA0yt5DvbaemWwSDMk7LoVoziYQ7tvFUUr1F8NpJACvu4Y/97cSp3YqXPg7uJPAJBAKBQCAQPGq8Xi8To0m0ikVA9mM7LvvG0oQC/s5NVSTWCzVGh5Mo/gB13eT1M9eRAz4i4QANu8G1+TXGhpMcPTBMSavzxrkFXnnjGgHZx2tvzTE7t861uQ2ePTVFsVjD5/MxNpLl3OVlpkZSFEs6x4+OEArKDA/EcV0wGzbxWIhoVOHKzApf/uZ53j63gNfr5cDUIDNzGywsF4lGFKq1BnbTYXQogWk1+ZFPnuKLX3mXQklHlr2cODJGOhHm0swyJ4+NMZyJEgz4ScYURocSPZGnVKkzPBCnVjfJpmN4JS91o8HoUILvvD5DuaLj90nkizUuXV3BddvEYiEuX1slHg1RrRucPj655fzqhkUqofZic/smsgBMT2Y5cmBoy7rJeJh8sdpzYxXLOj6ftGUOmUqo6Ia1GWXtuMe661R1C6/Xw9MnJ6jq1o7CVTIWptFoEosquK7bmyv3u+H6GR1K9G6Qd9dTQjKlsk4yHsZ1XZS+m9s77XN5rbxnwWMvjqsu3XO6W+TzbkQW5QG6j27XjOF2Y+oeT/86YWX3Ejh3y2DmznXbnqR4516F2fcyjzuS+SjjnL/yK7/CF77wBT772c/yF3/xF4yNjfFLv/RLXLx4ccf1X3nlFT7zmc/wJ3/yJ3zxi18klUrxi7/4i7z11lv3edZvz5MkJN8Pew+kC/aMV7r54Wi6EPd7mRxLUi7XqNR0BrMjfPf1ayghmXcuLvJjn3qKaFQlsdkCvFoze1/42/9/N6SS6pbX78TtnjMbNrIsxCuBQCAQCASCnSiWdVIJlbV1jcnRFJevrmLbTU4eHeO/f+kdsukwf/nls6yuFwn6vDxzYoqnjo/xx1/4PqFgp7bXJ18+znffmuGZ45N8+RvnGRyIMzGa4tDUEEPZKE+fmuDt8/N87buX0asW1+bX+cgLB4lHQkSjIWZv5FCdAKZlE1GD5PIalUoNyecjEVX4yIsHqeoWhmETDslouoVhtRjOxhlIq1gNm0wywiuvzvDRlw4C8EMfP8ZGvnMxlC/WGB1JEvD7yRd1omqIVELFcVwuXl1FkiCVCGOYNl7Jg9crcfnaKuu5GieODlOq1BkbTuI4bSpanVNHx1jJlQn4fFQ0i1BIxjRtouEQVSx03UJVgyyvlRkdSvT+7adQrOGRPJhWk1DQ3xMvTKtJJBxk9sYG+6cGADAMG0m6Ga3susVquoUs+1hcKdJsOfi8HrSqB7/fRzBw+7pOVd0kGQ8TDMrUdKs3vp1EFMuyWctXiUVCvfW67jZJknCcTuoktEvJlu5rui68O70X75bdIp8PQmS5lzElY7vvdy9j6gq6yXj4rkRFwROI236wNshHxT2M+fOf/zwnTpzg3/27fwfAD//wDxMIBPhH/+gf8eUvf/mW9efn57f8/KlPfYpAIMAf/uEf8uyzz97TsPfCkyQk3w/iL8tD4Nr1tS0tbPVai4pmYTWapJNxHBfevTDP7MIGp4+PMTIYx+uVMI0GC8sFLlxdYfbGBvPLBdrtjni2XQwr77Hzyf04uGLC/SUQCAQCgUCwK5LHw4XLKzRbTV55fYZgyMdgNsF/+fPXKGp1Mukozz89xcc/coQDUwNUqjp/9CffZXgwwac/foLrN3LM3VjnxKFh1gpVJsZSrOcrrK9rvHH2Om+8PY/VaKLXGpSKGj/8qZMMpKOMDSdpt+E//rdXGR9JsZGvYpgN/uTP3+DwgWFkv48PPTPF1ESac5eWWFmrMDwY540zNzD0Bum4QqvtsLCcJxlXeevcPOOjiZ6j6sZigdHhBMWyTqvlEgoECMg+9k2kqRsNVtcr3FjMk01FqddtArKMaTVZWi3jum3q9QYvv3AArySRjIexGjbpVISRwRTFss5INoHVaFEoarSaLo2mgyTBYCaKX+7UMwvIPhaWiowOJVhYLvLqm7NAx5nVcl3abQgF/Fsu2qKREJWqSbPlcv1GpyGAosjYrRbzywUAtKqJ4zhE1OCmMJVgeiLLxGiGidE08YhCZQfHU9e51nXedWOCgR3qe61uVHqOjGBQ5vC+gV3fQ6mEimF0IozbXRz9rqrhgfgtTqy5xZsJlu55uHJ9jfVc5Y4330uV+m2jpP1j6Y+j3g3dyO1u270Xyru40fprlr0XXFaiZtUj4HHXNbvPmmhra2ssLy/3HtVqdcfD1HWder3Oj/zIj/SW+Xw+JicnOXv27J5OVaHQ+fs3ODi4p/UFt0eIaA+BRDyyxa7pC0C9VmNxtcjSYo6228DnlZD9MqVinX/9777CwmIBySsRj4R48el9tF0YzsZ559w80UiIaCTEW2dv9PZhGM27dqbdbdFPgUAgEAgEAsHu1A2bgOxlfDTNyGCc5/7/7P15kCPpfd8NfnAlrsR9A3VXdVX1Ud3TPdNzD0lRFClSB2UtZUu2pV2HHbHr2HU4QmuHrY21/Ub4Dx/Bf+yQXr+7XEuW3te2bMk6TFHiNZwhZ4Yz0/dZV9dduO8jcSWAxP6RBTTq6q4ezkniE4GoApD55JNPJoB8vvn9/X7nJ8hkiljNRsbDbpZWk9TqDS6cGcfltLK+lWLuVAip3uBPvnmd0YibqfEAsWiJRCyHTqPhmYUpZqeCzEz4aHfaVKUmlxbGsZhNfP+dZZ6+MEE8mSeTl6hKDUqVGvW6Wi1yPOwmmSrw3DMzXLuzS1fpIlqMjI+6WX4QJ+Bz0FE6dLptDFotY5EAyw8SmAwGdmMFzCbVgZUvPAzvK5QkYokCyw+SAPznP/4hPreNmckAej2MRlw47GZK5Tqi1USz2UIUjej1WgqFKveWY1SqTUrlGlu7GUrlBoVilUajRSToYmbSD2ho7oVGttptms0WUrXB+KiHe8sxoMsLl2eQ5TZmk4Ggz0E2VyGVKferd65tpnHaLThsZux2477rXkGvZ2LES64gsbWT7b8eDjj7VUJ7gmG70yHodyLLbdY2030BqSm3URSF7b3148kCyw/iVKqNfnu9ZcMBdf1e6OhxwlFv+V610Z4Q1hwQ7HrcW4xiF9Ub3L1qk1NjvkNtzk+HCPqPrkg6WOnU7bTuEwATBwS1QXHyUYUGeqLWxlbm0HvVanPfMgfbfS+4jhHI0llVtDoutPbDxmFT3X2lch1JevSc7aPu65CPhmeffZbR0dH+4xd/8RePXG51dRWAycnJfa97PB4qlZOJtZ///OfR6/X85m/+5o/W6SHAMJzzAyGTLfcVZrsI5SqY9F3OnPZhNAq4nE42dzPMzXjpdrW4XRZCficGnYbvvLNKrlDj4sIYjWaLc/Mj/XZ7eRfK5TqRkLP/uqIoSFITu92MLLdZ30pzejZMJlvB57VRLtex281oTmBpflz455AhQ4YMGTJkyBAVm2jELpp568oqotXI999eIeRzYDQaqDZadNsKZqOB63c3mJsJcen8FKVyg7OnIjRqTb7zg/sUpToWqxG0cGdpG6fNyljETSJZ5NRUkFZbQbRp+cyLcyyvpxgJulhcTTAadHDxzCi7O3mUjoaZaR+vv7XExLift6+t0e4oVKtNHmym6KLBbDJhsehZWatwejbEg7UU95ZinDsdodlqMz8T4u2ra7SVDmNhD6AKHlevb/KpF+colKokUkW+8ovPYrEYUBRwOUUKpSprm2nowsSoup7DbmFpNY5oNXNuQs1jDcf24wABAABJREFU9sNrazx3cRKdbq/auwZcDivRRIFcrsL0hI9aTaZUqhMJuajVZGo1mVarzfhIhKXVOM1GC6/PTixRIBxUhSKp1kCDBqtFDYc0mwUiZjcmQX1er8vU6jImk4DHJWIxCeh0un6ooctp7YeMZnMVvHuJ7HMFCatFQBD05AoSstzGJpoYH/MCMDbiOXQ+9ETIXEEitBd6WShW9wlHgyGOx4lTrXZ7n8CVK0iYTAb0ei0PNlI47Gb8XjuJVBFB0Pfbk+U2zb1+9vocGggBPdjuYL9CjwgVPcjgPvRELbP5cAjsSNi9b5kPikSqiF6vo9Pp7AutLZXr6LRqYbX3i5OGqAqCep477I/f9o8qLLbbyolzvxWK1Q/8eBykXK5jsRgpVWrH7msiVXyicxBAw3vLL/ZR0/NnXrlyhVDoYW5Hu93+gWzvZ3/2Z1lcXOS///f/jtPp/EC28ZPGx8KJ9ju/8ztMTExgMpl47rnnuHLlyrHLfu1rX+OVV17B5XLhcrn43Oc+98jlPwrisVL//7IEdCFbbOGy2djayHHl5gOmxn1Et4tsrGeIpUoYjXqSmTJ/7ecvc2YuTKPRwm4z93/oVh8kODcX6Qtig6ztpNFq1YsEQdBzelYtGe7bKwneW77XVrl8+G5IZu/uzVBAGzJkyJAhQ4a8n/y4XecNYjYJ3F/exWwxsvQgCUoXs9VIrlhDr9OSl2rkihXevbLB6lqK5aU4VpOBZqNFvtLklRfniMcLqsuprSC32hjNJvKlBk25Rb1eJxywE0+X+M5ri+g0CrfuRqk2GuzGSygdhXduPUC06KnWm8zPhimVJOrVBqdnQuzE80yO+mjUWxSKFfQ6HTvRLLvRPBOjHnaiWUxGAwGvnT/9ixuUpQbn5kZoyh3iSbWq5MXz4+RLVSIhF6GAk1K5hlar7Tu4Go0Wep2W55+Z5ofvrpDKlLCYBU7PhtnYTvP21TXK5TqnJgK8c22TWl3myvUNjIKB775+H7PJwEjEzeJqglKlhsthpVCsYrEIWCwCFxfGSaSKnJ4NMznhZyTkwmo1QreLLLdJxAvcXdohFHAi1Rp9R5XcVF1XxVKt7/QCaMgtrt3eQqt5GGrYE17KlTqprBpS5XGJKIoae5XJVvaJYuVyvR/OOejg6gkEHpdIu62wsZk+JFgcJyLkChKpTIlsroxoMdHpdCjtXbN7XCIzeyGhp6YC+L3qZDsUcO5rTxD0/SIGHpe4T5TIFSREi+nYcMgn4ah96G3r/XJV9fpZLB0uenBwO6GAE5/H9lCg3cNhN9NsHS6U9qOMwcct51OuIFGrNY98rym3D0UiPU5Ae6+hu4/Cbjej12sfOXZPKqAB+9InfeIeQCgUYmRkpP84TkSbnVVzVW5ubu57PZfLYbM9rF571Hn98z//83zrW9/iP/2n/8RXvvKVJx/jj4gP4jx8P/nIRbT/9t/+G7/5m7/Jv/gX/4IbN25w4cIFvvCFL5BOp49c/vXXX+fXfu3XeO2113j77bcZHR3l85//PLFY7EPu+fG8+PzMvnBOXRcMGvj+D5eZOxXAaNDSabXRGXX8zE+dJeJzEosXyWQlDHo9Oq2W7Z0MpUqVzZ00mWyF2VMhsnkJu93cF7x6zIz5EUUz5mMq4wyKZpls5ZAIBw8FtyfhYD+GDBkyZMiQIUMG+XG8zhtEEPS88OwpyuUqv/bLz/HFz57HoNWi02i5fGGci2dGiKdqhENOukqHcq3GjXs75Es17t3foVaVGR/1cPdOlJLU5KdfWUDQaYknClhtZn7mMwtYLAbkeoeRsIt8vok/YOPsqQiVSp1kpsLf+PLzbG7nWVxJcGoqwKnJEGdPj3Lz7haNaovVtSQjERdPLYyzvZslHHChdDvo9DounBtjezdLo9FibMSF3JS5ez+GTqfB67YzEnKRTJW4e3ebu/d3ef2tFexWI8l0mXyxxtZOhlKpjtdtpSI1mJuNEPA52NzOsLgS59MvzjEzGVCrzStdLp0fY3MnSzjs4t7SLgtnRvC4RIrlGqemAoQCTnKFCsVyjZt3tkkki31XV6FY7bt67KKJQrGOIOhptBSevjBJIlmk2Wzj89gpl+uYzAZqdZlQ0AmoYkMuL+FyWHnmwsQhMSGRLDI14cc3ILhFQi5AQ8D3cHLrclqx280kMyVyBQm/10YqXdrX1uJKjHa7jesY0eDGne1+n3poNRqcDitej7otnU53bLGBg5xEuBp0jtVq8iMnqdFE4YmEJlluE43nkeX2IaHkqO0kksXHttk7Pk7H4aIHcFjMyhWkI7d1lHDzfjixTrIPHwYel3jk3A7AoNciHJGz71E8KnR3yIdL73wWRRGr1co3vvGN/nvtdputrS0uXLjQf+3gef2lL32Jb3zjG3zta1/jN37jNz6cTr9PfNzPQ0232/1ITZDPPfccly9f5rd/+7cBNTRxdHSUf/AP/gH/9J/+08eu3+l0cLlc/PZv//aJTo5yuYzD4aBUKn1glsk7dzf5B//0j/a9ZhTAaRM4czpMpdripz99GpPZRK3awuU0o0HL1KSPza0MZ+cjZPMSfr+NXLaKy6n+eFitAtWqjMlkoFSuYxT0NOX2exLAjqMXAjpkyJAhQ4YM2c+HcQ3x48aHfZ0HH81x2t7JUpGa5AsV3G4bq+sxZqdCvPr6MvlChV//1RdYWUsBXVJZCafdjNWoR6vXU2/IiE4rNpOeel3GYTeTSFU4NeOnWKxTrTapN2RqtSYXzo9z5do6Wi00ai0uLIzT6XYZH3XTanX44z+7itNp5flnT9FuyyyvJvG6bP0ww4kJNYdWKlPC5bBiMuq5emMdvUFAMGi5dGGSjqJw4+42olnA5RRRugpmk0Cn3SadkZibDVEq1UhnK4QCDpbXUly6MMI772xy9mwEnVZDKOgkGitQLNc4dzqijtF2BrfHRrFUo16XsViMjIRdJBJFqo0mHpdItSaTz1c4f26MG7e3uXRhvD/GubyExy2SSpexigIoXSpSsy+SJZJqaKPZLGAxCySSxf57PWp1maasFkKwmAVyhTKyrOxzwSSSRWx2Ewa9nnxe6rehKArlSgOnw9LvS+/vYP8ANreSGAShH844SC+07ajwO6nW6FcPPUhvPKLxAiPhx08wB/tzkJ47SVHUwgsnWadHRWr03W4HOSpi5qjj0OOkoYU/aghip9PZ51J71DEYRFEU2m3liYUoOHq/T7rdx9E7j12Oj5cr7sMiGo0yOjrKy5/9X9Drjz4XP8602w3e/N7/wu7uLiMjI49fAfiH//Af8u///b/n7/29v8eXv/xl/vE//sesrKxw584dzp07x/T0NH6/n7fffhuAL37xi3zzm9/kH/2jf8Sv//qv99vx+/3D4gKP4KTXDx+pE02WZa5fv87nPve5/mtarZbPfe5z/RPgcdRqNVqtFm734R8pgGazSblc3vf4oLm/nOjnRKMLoyM2Lj89SSjiYWu3iEGnYXElwcZ6EqvJyNMXJmg0mtRqMgG/o//jYxIExsc8/efFkuooUxQFo6DHbjfjclqPDM8cpFyuU6/LJ3KOefbuJA4ZMmTIkCFDhvwofBjXefDRXOv1kGoNHqynqDdabGwkUTpdvvvaHQq5BmMjXjrdNufOhPnm95fQaGBrp8D0mBefR0Rj0GEy6ygUa0yPePB7HdTqbcxmM1ptl9e+v8z6ZppiuUal0mBizIdUbhAKusjmq8idDrG0WiUzm6tSqTb4f/xff4az8xGqUh1Bb+DTL83jdFkJBJ0EAg7K5RpNuYVNNFMoVikU67z0/BwzE35efmFu7z0TC3MR/D4HtbqM1WxEqjQolBpEwi4kqcFIxM2lC+NYzEZGQi4qFZlzZ8OYTQY1h1heIl+ocO50hGi8wP/4+jUsVhM/fHeVgM+Bxy1itQhcu7mFYNSj1WjVRPxSnfPnxojGC0yOe2nKbXajed65so4omljbTBHw22nJHUTRjNVi7DuCOooadplOq8dfq9XQ6XSIxfP942UxCzTq7b7I5nHZVfdbXuq3Ewo6ES0mjIJ+nwii1Wr7rqie0CTuiUm5vNSv3hmNF/B6nf0iAAex2839UNhBovHCsQIagMmoCjknEdAG+3gU1ZqaI85iEYjGCydap8dR1Uh7HOWIOk5Ag5M5wnJ56Ud2jmVz+516druZjqIg1R4951GUh1O6R3GUa++o/e6FNZ4UeaAQxCAWs9AX0Jp7BSx+IvmoK2z+iNU5n4R/9+/+HV/5ylf4vd/7PX7hF36B3d1dvva1r3Hu3DlADe3MZB4W9/jOd74DwFe/+lUuXLjQf/zqr/7qk298yCE+UhEtm83S6XQIBPaXfQ4EAiSTyRO18U/+yT8hHA7vu0Ab5F/9q3+Fw+HoP0ZHR3/kfj+Ovu147wMS262QShQRdFqajQ7z8yM8//Q0LpuFCwujLC7HMJsFxkZcrKwnePX1RdqtNjdubRKN5ymX68STRUrlOsl0CdOAtbshy/t+sCpSQ71TNiCsqQUHOidymGm12mPvLg0ZMmTIkCFDhpyUD+M6Dz6aa70exWIdt8tKvS7z0otzNJotDHotWi189/v36CptAgEn7WaDnd08FqOBLl2askK1UqdeUxCtAo1GC7dbRKuBldU4Z2YjiKKRFy5PMzPpZ3LST73R4t7SDp9+aY5zZ0bx+xy47SJmwcD0pJ9bd7ap1WWqNRmfx8btezu88fYyzUadzc0MtXoTp9PK1lYWq1nAIBgQDFo2d3KsrMW5dlPNU9ZoyNjtFkYjbnQaDR2lS5cuF86NsvIgQSjopN1uc+f+Lg6HGVEUcDrM3Lqzg8ctEo3lqTdajI54WFlNsLmdZm4mRDSW54XLp/qCQ7Umc3Y+jMct4nJa8HoGqkHuCUXJVInRETfPPzuNUdBjNRv3iSoWi5FQUA31NJsMTE34MBh1KIqCXq9Dp9Nhtap5wEp7N6N74sagyOFxi5hM+xPjNxpqYYODJJJFcnkJRVH6IofHLeL3qdfZfq+dtfU4Fotx33oVqYEst2m3lSNFoaPEsbWNNKtrKQDOzEeOPAeXVxO02wq70Vz/tc3tw5Uyc3lVSKrVZKwWoT8eve0+KnxTqjb668vt9r4qn8ur8WPXG9zuQU4aLjoo7PUEv4OhlIMi6FEE/I5Dr21vZ9Fpjp4K96qf6vXaR4qGD5f/YAK7mseIaIMYBT1m4+GiDh8XjvscnZTjzp+fRP7oj/6IdrtNt9tFkiT+7t/9u/33isUia2tr/ee95Q4+Xn/99Y+g5z9+fOQ50X4U/vW//tf84R/+IX/6p3+KyXS08PNbv/VblEql/mN3d/cD79fGVm5fTrRuF6bHvNSrLaxmPbdvbyPLbRxukVy+jFSVWX2Q5NbtXSYjXoJ+B4JBj1ajCnK9qpuglhFvym0qlTqy3EG0mMjlpb5o1lW6aLXaQ3eCHI79zw+61waf96qAwvuf9+yDyqM2zM82ZMiQIUOG/Hhxkus8+Giu9XqMhF1ksiXu3N8llakwNeXn85+7wNnTI7RbXS4+NU0yWSIccvP85Rmmpr2USg1WHyTwuGyEww6Wl+N863u3ePfaBi6nldERN7vxAga9nrWNNMl0gU6rxdZOms99doFMtoLLZub8mRECfhuXn57mf/3/vcrF85NYzAKxRI54skTA5+DZi9ME/G5yBQmf1873Xl/Ebjej1eoZDTsxmPQ4HRYiIQ9n50doyi2WVhJ9t4zfb0en1dBuqyLBhYUxtrazXL+5jdslEo0XqFZliqU6Lpe4l8TfSi5XodFoEQk7cbusmE06fF71mlZRFHZ2c9xfUm8i31uM4XJa0el0mEwCubxEas9N5napYlMur+YzCwWd6HWqSJbLS+j12n2hfp2OgkkwoNVqsezlCu5lrjmQc35v+cNJ52s1mUKx2ndqHYXHLXLlxma/IijA+maGTqdDoShx8cIUyYE8aYlkUa2YmZdYWokeam+wUEGPXF5iZsrPxJiXYqnWF3ZgvwA1PxtCr9diGygONjnu2+cw6/U5Gi9gsQiYTMK+8TjK7VUs1frbkeU2omhCkup9l16PmangI6NijnO3ncRddlCg6wl+oaBzn7jicYuPDLk8SmCbnvIdn0+6cnJnV6H4+BDYg8fipJzU2HDQafco3o/CEk/S9qM+RyfhUWOr6XY/sY+PA58UgfKDPGffKx+piOb1etHpdKRSqX2vp1Kpx8bqfvWrX+Vf/+t/zbe//W3Onz9/7HJGoxG73b7v8UEzPeXbV3kDurz6g1WUrkK5VMdiMnHvfgzRrCZmNQo6Zk8FicZzCCY9waADg6CjVKmTzpSJxvOEgg7ogk6nxyjocbvFfulkj1tNKFku145NLHmQg8sNPq/WHv4Qvt/50T6ofGvDPG5DhgwZMmTIx4sP4zoPPpprvf3bF3hqYZxWs8V3X73Fnbu7CIIOq8XA1SsbdJUu25sprl1f597dOGubCS4/PYXPZ2NzM8UrL58mm5FIpQps72RZ38hit5kwCXrMJgNOu4jZYiLoc7KxkaFWk2l1FDZ3s4yOeDCZ9ERCTsZGPPzZ168xPuIhm6tgsejZ3M6CBhbOjrKxmeH5505RqdQplsqkMhW+9717pFJFXC4rb195AMDYqIe7izGu39xkaSVGoyn3J32JZJFiScJqEXC7rDgdZmam/DjtJk7PhgB4sJYCOrjdItVai6XlOKLVQqksE43nKZVrOBwmzp5Wq8mfO6M6rHJ5CYNey81bW1SqDVxOKxq6rG+mSafL/cm0VG1w884uDruFcrmGbS9s0uMWqdVltFoNiqL0BRK7zYTLaSWeKB46dtmcRCZbYXsn0xd1tNqHAk8vNBRUwW2wgMDzz0yj1T6cStlEEzqdjoDfQaMh03urUKzuc79NTwW5eXuLnd1sf1273Uwyvb9/vf0VBB2djtIXFuGwAFUq1Q8l4O8JTtJA6Oig260iPazoeFCoKJdrasjg3nbcLhGjoKfTUcdhZTXRF6b0ei2x9ygSHWRj62HBkWg8z/xs+NhlD/a59/wowSwUdBIdCOsFDlXyHOS4YgZHYdAf306Pk4bgvleOctodx3sNjT2JkPF+FGx4IpRP8ONjwEnCtz8OfOjn1Qn4SEU0QRB4+umnefXVV/uvKYrCq6++ygsvvHDsev/23/5b/uW//Jd885vf5JlnnvkwuvpElIuVfiinoO+i6YIgQCZdQNtVaDWb3LqzwfLSLm+/s4LXLSJaTFy6MMaDB0muXX/A4lKU03MjeN0iI2E3UqWBLLf6wtnBOyeqgHbyL/xH4XIe/YEql48uMf1+0m4rSNWf0Lj+IUOGDBky5MeIH9frvEHyeYlmo43bbSVXkLDZbJjNArvRHCMRNx63SKvVIRLxMjriRquBuekQ9VoDnU6LXm+g0WzxmZfn+eLPXODihXF2dlPIzRaZdJm52RBWq4lWs0Wn2yEQtDMScZJJlwh6RW7e3mRjM8XZ0xG+8c0bjEScbG1lGB91MDbiw2YV0KBhcyuDy2HEbDIwOuphcztDPl/hy7/wDOsbKcrlOpcuTNFVumiA23e2cDosjI96+fZ37tHqtADV4ZTJSAT8dkqlGuvrqkCazlZoNFvEEwXmToW4sDDJX37rNhaLwNnTI2xtpSkUJawWI/OzYUqlOiNhN7F4nkSySKlUx+MWcbtEwiEXmq7Cmz9cIV+sEvDZ8A7cLP3W9+7QaskUizWacptYotAXThoNmWSyiFar7U/6ey6dgN8J0BdT7i1GMeh1pNMlxsd81Ooyi0sxGo1WP8LBZFLdTYViFY1Gg9FoODbHVyjopF5vktpLvRIOqsKJy2mlVKr3xSw1n5WVcGh/nr+DRQiKpRqy3KZQrOJxi/3tptIldnYz/X7BQ5fdUUJHp6MKS9G9se5R2ruuLxSr1Osy9frDkDu73dJ3dklSo++SczjMFEt15mZD+8bh9Pxhseu9uEemJvz9/0fC7keGaOaPcdH0+nX77s4+p+FRRR7eD8Rjct99EjhuDI/CcUKjxofJR+0m+6Q70Ya8dz7ycM7f/M3f5Gtf+xq///u/z9LSEn//7/99qtUqf+fv/B0AfuM3foPf+q3f6i//b/7Nv+Gf/bN/xu/+7u8yMTFBMpkkmUwiSR8fO2I2W0GDGsrZkoEuCFotnU6XZy7PEo3mefbiDBbRwoWFCTa30rxzZZnvvHofv8/OuTPjPHV+ArvNhN1uIZMpk0qX8LhFFhejKIrC5qZ6pyaTKe8T0DKZMpnMkyfUXVl5dD4D4H0T6R6FXq9FtA5zsg0ZMmTIkCE/Dvw4XucN4naLGI1abtxcp9PuYDHp+dxnzrK5lSafq+BxizSaahXNs2dG8AdtLK3EuHtvh9feXMFiNuB2mLl6fZN3r64TTxbw+5y4XCKj4z62d7Jks2Ua9Raixcj2VoZ0poLRpAd01Osy2zs5llfi2EUTXrcTr9dONFbmf/3/fJdSuY7TYcLvtfFgI0ul0sBpN2MS9JQKNba3s3z+c+fRahVS6SIWi0CXLgtnRiiV6iwtx/jZz58h4HOxuBzl3XdXiYQdxJMlavUmdoeFfF5iasKPTVTzk5lMBmLxPD/3hQsY9FrcTisGg46XX5jrO7kmxn1cubZOsVjj3SvrpPeuXTOZMh2lQ7PZYeHsCEpHdQxt7zzM8XVxYZznL5/C6xXxee1oUYWTRLJIJOzm9HyEQlENT5Qk9SY0gNJVSCSLfTFFbrVxu0XOnlGr41nMAmdOR3A6raT3+lmRGiSSRRoNmUajfcihJMsdCsWqWhSh2iBfqBHwO0gki9RqMnfu7pBIFvdyx6kiRKFYxWgycOfuNgCxeJ7F5cMhnk6HZS+f1EPHWCJZJOB3MDaqVlntOTR6bfeeF4rVfvin0lUI+O2MhN37hK+ey8vltCIIOhqN5r6Q0p4IZrEI+5Lhn9S9clL3SOyAQ2yQ3nGFww6zTkfZl4IGVNddL9fbhYWxI91mjxKOMpkyyWTp2Pcfx3FtP2ofT0qhKO0TOp+0D0fhfgIn0qDr8qScdL/fj/EZ8uND73yo1+UnOp8/TD5yEe1v/I2/wVe/+lX++T//5zz11FPcunWLb37zm/0ktDs7OyQSif7y/+E//AdkWeYrX/kKoVCo//jqV7/6Ue3CISxWw+EKHBo1R8Pq6i4+n0MtFFCqsbQcQ683cOnCNP/nX/80jWaLUrlBo9neKxBQw2jU8/xzpwAYGXGj1WoZH/ciVRv4fPZ94pbHI2K1mo6s5rK0fFgokyS1cufc3PF26R7vRZx7v/kw3HBDhgwZMmTIkPeHH8frvB6KorC+kaJRb3H29BiNuswv/eIz/MF/foNPvXgGu93CzLQPv89GsVAlk5FoNxU+9fI8DocVn9NCLltFr9dxaibAqZkAmXQFo0HDN/7yBlot+L02drZztNotcvkaLpfIxkYKo16H02Hh8tPT/PRnztDtdBAEA5vbaSJhB5Gwg5/70kWyGYlsTs2d2+l00Grgv/73d3n28gyf+cwZypU66XQJRYHVBwlWVhNIlSZjo16ajSan5yNotQImk450ssTs7AjVqozFLDA9GUDpKMRiBeKJPLF4nnS6giQ1MJsE1tfTCIIel0sVadbWUyiKQqFQJZ+XiIRdyHKHy89MqiGCKwlGRtwsnB3DaNRTb7TQ6zVsbWeYngxQKFRJp8tcvDBBoShx+842S8sxRkc96gHZM3cUClXSqUr/GIlWc//9RqPVP36jYTeyrIYmvv6DJWS5Qz4vUShUmZ5SHVGRPeEpFHRhsQj9voPqCCuV1Hxs1WoTul1Eq4n8Xu62ZrPF+YWxQ841l9NKKOjk0sXJ/jbOzI8cOr/yefXYRQYcVL22EgOhqWpV0/1RHC6nFa1WS70uUyhKRwogg2GeOp0Ol8u271r/YXirtj+uJ+VJlh3cv6MmzEcVggDw+ez9CB1Qx0QUzYfyQB9kUDg6uD2fz04wePLQyEe1PUgk7N4TYt97gn2XUzw2h9tJ+vBh024r+47to+gtdzD332P5qCtsfojVOT+pvBeBtHc+mM3Cx+Z8Poim2/3J8hOWy2UcDgelUukDy5nxr/7tn/Ht7yyDBuiCSdDQpYsoCljMAk9dGEOnN3Dh/Dhen51UMsdrr6/wmU+fIZks8dSFMba3s8zMBDAYDIyPqydSNiuRTpdYWBgjFssTiez/YpLlNqVSDUmqMzkZONSvu3e3WVgY/0D2eciQIUOGDPlx58O4hhjyo/NhHaeeUBCN5rly7QFf+eXn+ZM/u4rVauKZpyf5+l9cQ1EUzp2bQKvRoHQV9DodG1tJnr98iu2dLLu7WX7py8+xvp5ifMLLjetr6AQDfq+DUNBBOlMmm6ug6XZ54YVTvP6DZcZG3Ny9H2VkxEO3o6A36IhGc0xOBJic9KHValhdSzE+5mZlJUnA76DVaeP3OohGs5w+Pcr162s8/cw0UqXOg/UEcluh1exQr7d45aVZNjfTWCxGGo0O4bCTlZUEkRE301M+isUa1WoD0WYim6mwG8thMQn4A06u39zkyz//DIKg4+btLSqVBvOzIaRqk5GIG0HQc/XqOja7kfm5ETY20xgMOkZHPGpobLNNKOTsj28iWcRqEQiFXNy5FyXgFxkd8VKvy5jNAo2GjMkkIEkNBEG/LwRRFE202wp6vRZFUVhejTM57qdelw9NzDqdDjqdjkSiSCjkRKo2qJQb/b7cvbvL+LgHu91CoVDF5bL21wF4881l2u0u/oAdh8OMTTQdiuBYWopzei8PXKFQpdFoEQo5SSSKGI36fX3q7V8+L+0VDDOh1WqJxfNEwm7yeQlJatBqd5ieenjNn8mUcTisfXEpn5dwu8X+OPWeg5pHzeEwE4vlsVgFugrUG/I+4WNjM83U5MMQS2DvJn8Dp/NwhEpv/A59VorSselijqK3/++V3jgpinKkgDg4DoMsLkePFDRPSq/d49r/pCDL7UcWajgJg5FSvXDh48akd7xOSjQaZXR0lE+99M/Q6z95EUztdoMfvPUvuXrtDpcunj328/RRcNxn5ieFk14//OSO0AeIxSSgUfaqcwJNuYssQ02Sabe7+Hwi0Z0sf/EXN7h2dRODQeDy01M47GaevjSBw2EmMuLGbBZoNmvIcodcvkKt1mRhYQxQ75TIcqdvWQZV8ff57EcKaACRiIdC4eEdl4N3rQZJJkuH3GxDF9iQIUOGDBkyZIiKy2UlHi+wsZniS198imvXNhmNuFla2uHV796l2ZAx6E3IjSYdpc3UhI+lxRiLiykSqRJjY170Oh23bm3y1jurlEs1QiE34aAb0Spw5+4u7VYHDWq43tXrW2rlzt08r7w0y5n5MMGgE7/fQb5QpViusrwaZ2kpSlVq0qjL6HSQzpYIBVy89vp9rFYTm1sZavUGr756H7fbTjxa4tL5SRbOjaDVaKg3ZLa2C/j9Dq7d2ODNN5f4zGdOE4/lWF1NsLObJZkqo9XoaLe7zJ0K8/zzsxiNepS2giDoWFyKcfHCBAtnRykWq1gtRjY2k/zR/3iHcNjJ/JwqVExN+jHodX23Qk+AicXzNBotms02xVINQdBjF43YRAsrK3EMBnWCryhw69Y29bqMJDW4cnWdRKLYv4a9dkN93m4rKO0uZrPA6gPV+VgoVPvXtj0xLBRyUihUWV9L9QUugIWFUapVGVlu49qrGKrT6YhG1X4/++wp5uZChEOuPddRu+9y6oXg2e0PJ/sul7W/r6GQc1+Yniy32dxUw1fdbhGn09Kf1PaEhkKxitttZXTEsy8E86A7y+0WSSSK9Mw9PRFDltvo9Jr+a/l8Fbdb3CdkFArSIQENVGfa4IT/5u2tvqB8lIAGYDI+mSB2MGzxSUO6ImE3iUSRcvnouc5xYs57EdAG3WW9dn9UAe2jDm+UW4cjmp6UQRH5cY6iJxHQfpwIBlz9z1M+/zBc96MMYTzuMzNkP0MR7QNAo9n7Z6ACh14LjQYYBR2xmARouHx5hm63Q6VSw+UyA13W1tLo9HpEqwG9XofDYWNlJU40WiAUciLL7b7NVRB0/TwP5XKNVKoIsE8oG8TtFnG5Hn6BiY8omxwMOg7dgfgwcqINGTJkyJAhQ4Z8EkgmCxhNes6cHiGZKNNut5ie9mG3WZifD/PKy3PMz3oolRqsPcjy7W/fYWoqwOVLU7z9wwc06m3iCQm/38YzF8f5y7+8ztZ2nu2tNOVyHYvZQCpdwumwsLaWwGIS0Gl1aDQ6Xnttke985w75fJWlxV2MBoFErIROq15fjo+5MRoFpIqMTqPlG9+4wS99+TKy3KarKDTqCmOjHvR6DS+/PMef/fkV0ukqAb8NvU7H5IQLt9vKF35mAX/ART4vYbebCYWclEsNNjbSLC1FOX06zLe+fZe//Mvr6HUaLl2c5OatbTxuKysrMVwuK7OzYW7d2qJUbPDFLzxFJKKGX0pSg+vXNwgGnchNVTCLxfJ7oZ5uGo0GjYbM6fkI+byERqPB6bRQqdSJxbKsPUhgsQjMzoao1Vo4nRZ8XjsulwW3WyQWy3Ppqcn+dXPPBTY6om6/2+2yvJwgn5fodDrEYvn+6xcuqJEboZCz/zrsr9YJIAjqVCoazREKObHb1ZQqFstD0WBrr+LkwQiSwXYjEXc/VFSW25zZq1jaYzDXlxpuGqDVUlhaiqLVPrzRfWQ4ZMiJxSL011X7radUVNcxmwUmxr2H1svlTjaRv3hh4rHLDIpiiUSRTKZ0bH9hvwiVSBQPCTCrq8nHbjMUcu4T+54kxPRJeJLow5NylKg0eL68nxw1Lr2wZNgfOvxBIUl1Gg35yUI52TOsfEIfB3G7H4brfpQuxo+LI+7jzlBE+wC4/MzkoQocnZb6V2l32NlOIAg69DqF9QdJMqkqHo8Dq9WERqNQrzXY2cmzuZHGajWzs5PBZDSQy0kIgr5/N0pRFIxGPZlMGbvdwuSkn0ymzFERuuVyjXpdptPpoChK34X2KDdaoyEP3WhDhgwZMmTIkCFHEAy6CAacTE/7GR11k81U2NjI4nZa0Wo1fOMvbnHrVpR8rsLoiJMvfOEcNpuAIIDfJ7L2IMbpuQC3b+2Qy0k8dWEcnRYMeh3VaoN796KcmglSKtbpdLpYrQLLSzFMJj0XLkwgyy0mxt3Mz4/y/HMzzM2FcLts/MEf/ACPRySZLGKzmXnrzUWsVhPlco2bN7fIZCpcujTOrVub/Mf/+BoWi5GXXpxlazOO0ymiKF0ajTaLizEKhSpGg47f/09v4PHYWF1NYbOZ6CpqUvdYLM8rL89iNpvQavWcOhVkeTGGJMnodHru31cT5n/hCxew20zUak3eemuVcrnGD36wTLGo5uYNBp3YbEZaLQW3W6RcrtHtarj8zDSy3CadrqDVqnepn3lmmkajQ6FQpl6XsVgE2u0W5XKDyUk14f79+1FCISf5vEQgoN6E7rnNemGSbrfIqVNBCoUyGo2GWk2mUKiyupogFstTKFT76VNisTyhkBOr1QiowsKDB0mMew6rqSk/stxGq9UiCHpE0US9LrO4GOX06cPupmSy2BfVZLlNrSbjclnVAltH3OSW5U4/15dWC/fv76DVahkb86LVarFY1HWazcMOokGhyuWy9vOeRSLufvXKbFbqL9sTa2Zmgty+vX3kuV8oVKnVVGEslSr13XnHMSgKhEJOFGW/eNM7T3ptD6Iur1AsPhQKw+GHecse5doZFIAe18f3Sk+gfBy98XpSeuNxUIQ9KY8TwY4bl94xO85deBQ/SiJ4rVb75GGE3e4n93EEg3kK308+LGebJDWeWAj9pDIU0T4ANtYzhxIH7rnOcThMRHfyeL0i3a4Gk0lAMGqIx3JqRZhEGUEQ8HlFWq020d0sp+dH8Xpt+Hx2EvEi169voNVqeeutFUC1bgNsbqYxGvV0Ol22t7L7+jToItNqH/5Am0zHf/HLcvtQAsyPixttKOYNGTJkyJAhQz5qEomSWsVyKcbzL8wS280gGAUSiRJf+ZUXAA2Tkz4a9RZv/OABdruFZKKM3++k0WxTr9cJhZ2YTQLdro6JcS/FUo27d+OEgk5uXt9AltvodXpu3Njk1Ck/breFltwhk6lQrbW4eWOdkVEXBr2W+/e3mJkJ8tabq2yuJ3nuuWk+81PncDnNxON5Xnh+BtFiIBTy8NJLc4xE3ORyZX741hqf+cwC0Wgeh8PMzEwIpaNBbraZmg4g2gxsb+eYmPDSaMi8/PIcoZCTleUEWm2X558/hSy3kOU2n/r0PFqt6tI6e3aE1167T7utsLaexmYzc/p0mESiSLPR5Pz5Ed55e4233nrQz9GVSBTZ2cljMgncvLFBq9Wh3e4giqa+IDA3F+LcwmTfuSGK5r7bqV5vEQ67KJXqeL12arVG/7o3FsvjdossLsYAtdCA1+silSpz6lSQZLLI6dMRIhE321vZvnDRE97i8QKgCgsej7hPGEkmi+TzEu22QrutYDYLnNmr/LmxobrReoJIp6P0nwuCvi/EWCymvoiVz0v9x+rKw+IbjUabU6cidDqdfoRJr3rmQcGjl9tqcBLtdj8UTXriWSDg2Bs7mUjE3b/OvnBhfJ8w0ut/t9ulp3c0m0eLQ4lE8djJeyDgwOWyotkL3zl7dmRf+wcZDCFVRUNVzOzlvjuOkwpA8b3wyccl/lervba5cWPzRO0O0huvdlstHNcrYjE4RrLcPjTH6Ylc79VJ9yQi2EEG3W8nEWLMZqF/TJ9kG6JoPjYH2/pa6idGmOl0Hr/Me+HDcraZTMJPTD61n4y9/JBpd5T9CnMXWi1wOkAUrYxP+JmeCvLmD1bw+20kEmXu34tSLFT5ws8ukM2WuX8vzvx8GLvDTDKp5qWIxfJYRQG7zUyt1uCVV073RS1JajA+7qXZbJPLVbFYD4tjZrNwqNTzYMnqgzSb7Y+NaHaQj2u/hgwZMmTIkCE/OYRCDpaX4zTqHcbHfQRDDrLpMjbRQCFfpduFBysJtFotW5sp/scfvQ0o/LW/dpmRiAuL1cj8fJguXdqdFjs7ObQaDb/w8xc5tzDCxkaK7e0sitLE57Gxvp4mFHJz5+42waCLRKLI2JiHa1c2+Z9/fhWtRo8GmDkV5tIzMwiCHqdTxOe1Y7EI7EZzFEtV3n5rhVSyjMtlJRLxcPpMhFqtuZe3S8vuTpYHD3Z5sKaGTC6cG+f0fIjNzQx6vZZWq4PPZ1fzgIW93L+3i89n5/Xv3ScScVOryYyNqa6wCxfGuX59g2eemepfizqdVqZngsjNDi+9PIfbbSGdKtNudwiFnGi1qtgwOurD5bJy7twIOq1WdZflVPFBljt9YSEQcNBpt7l3b5fbt7eQpDoul5W3316l01ErghYK1b6r7OWX5ygUqoRCTm7f3OoLNKdPR/oT9uZANEYqVYLuQ7Enn5coFtVKgolEgVg0jyiaEEUTUqXRf31xMUqhIGE06snnpL4gEom4qddl2u2Hs+b793bR67VEIm5u39rCoNej12txu0UuPT3ZXy4QcCAIusdOjAsFqS+iWSxq8YV8XurPBRKJIga9+r8k1cnnJRqN9t5N9If7PpjPqquo8xu3W+zfiB8UMDudTj+tTCjkPLKPvWOWSBT3uaBi0TzQfaxjbDBHnCiaVJHwiNDTJwlDdO4VPThKq+kMKButVhtJqjM9fThXHDxa6OqNl16vxW637KXZse4bI0HQHzvH+aCcdIMcFPAG3W8nFWJcLuu+8OMeR41NoSD1Q5mPY3LKp37+jkCjfHIfR/G4yrIfdx6lK/y48ZOzpx8iirJXPvuAbVMUrawux0ininzvu3eANrvbGUyCjsuXpwENy8tx2q02l56eYmMjjcGgZ2Qvd4O4V+mnVm9iMgqsLMcBtRJPtdpAq9Xi89kZH/f03WkAd+/sAIdDN9UkrMfbRgfbeL8ZOsmGDBkyZMiQIZ90RNFEIGDH6TTxX/6PN3E4bHi8IslkhXarzeZGgt1okd1ojtm5ES5cmKZUqvM7v/0tfvjDVfw+kQerCd76wTJOh5XNjSQb62m+8fVrZNISL79ymsX7uzz73GlGx/zcv7dNLlvB6RSZGPfgcoq4XHZKpRrnzo+qgly3i1bb5fatbf74j95lfS1FMOTC6RDR63SEI15m50OcWxghX6jxzb+6hUGnIZUsobS7vPPDVZpyC7td5CtfeY54vEil3EBv0HLmTJiREQ9ajYaV5RhouqSSJaamArz63Xu0FYVoNIsG+OM/fhuAlaU4kYgbm011DBWLFQIBB9vbGYIhB1fefYDTYcMqClitJpYWY8zOhvnhW8uUytU9cQWWlqIUizVK5RqNhrw34exy69YmUqVBuVxnZSnO3FyYcNjFrZtbTE35cLlUsaInWN24vtlP4p3PSThcZhoNmUJBoj7gLLt8eXJPsJOYGPfh9ojEonlyuSr37+8yNeWHLoRCLsxmgUS8qLppNOpkMhRycebMCC6XSCTixu0R94k9xWK1f61dKEicmg2Tz0kkEgUuPDVBLJ6j3VKIRfNHunR6YlWtJvedVIPU6y30ejW8tNFoI4qmfUKIy2XB7VGfx6KqQ2962o8g6PH7988Besfg4P+g5oLuOQKzWWlf/uWDyHKnfxweVmFV9yMy4n7kuo+itx+DKJ3Hu5d6AtlDJ+B+E0IsmieffyjwuFwiZpPxWPfbcULXSUI58zmJnZ3soVQ6J+XgcXkv6/fCgn9URNF8SBA6amx6BSceJRBqtepn6Ug+6pDM9yGc85M4Jz74XfmTyFBE+wBYWYzuD+fsgkEHckOhVGxx/vw4nY6GL3z+ImPjamWmQqHG6TMhqqU6Wq2WTKaE12vFbDb244vv393h3p1tpqeCSNUGTpeFbLaCzWbC5RTJZMpkMmXMZmHfF2lw70eq98PQy4VgNguI4ntXvKuPyKf2OIZOsiFDhgwZMmTIJ5l3335APifRaXXQ6aBcqrC8GKMtt2m3FDQaDV/4wlOMjLlYurfN/dvbfO+7t6lXq5gEDeGwh2KxiVajZWYuwp3bO1gsApWyhN1uxGjUcuvmFn6fyPe+u8idO9v8/C9cJLqb56nzYzz77CzX3l1Dp9fi81tJxcvcvLnJlbcfcO/2FiMjXjLpApsbCVKJIj94bZFTp8JEd3MUclW++537ZFMFXnp5DlG0IFqNPP/iKU6fCWOxGBkbdxPdzZOI5pmbCyBJDba3s6STJfJZicvPTpPPVfH6RGS5g9yUacldNtazpFNlQgF14vv05SksZoErb6+SThVpNNrEonlmZkJ0OqDVahgZdeH1qG6502ci7O5m+dVfewm7zYxer+Hu3R0uPztNvd7ELOhZX0tTKkl9Z55B0HHm7CgvvHCKUMiFTqfjqYsTRCIeZLnNg5U4DoeVeDTPy6/MkUwWackt3B6R0REvLpdIo9HC3E/AL6HVatFooauA2SKor+k0GI1afB676qIZcROP5tFoIRxRXXJKR9kX0pRIqCGgklTvtz+I6lATVXeZR+wLBsGgC6fLQmTEzeZmhvjetX10N4uiKAgGPXJTLWIQDrv72+n9DYddfffToKDUa0dR1P9lucPMqeCRhcl6r/XEPo1WFbsGqVcfTqZ7YaHHIQg6fD47m5vp/muDwln8PQhBvT729rv3f6+fPeFSlttIlYdzl2JBIrr76O1FRtyHTAVmy8PInuOKuR1Eko4WSgb31+0RGRvzHhvW2ENuto903h08LoNUpQbyEfnyDq5/nJPovRyXHoPH5SBHfR4GKRSkffnwDtH9BD/2eL+Eyx6PGu/3C5dLfOyx+3FnKKJ9ADz/wtn9hQWAdhsy6Ro+r4Hl+wn8fhsb6xlachfBqMNq05PPVnnxlTmKBQm/XySZklh7kOjnZdCgweOx0Wq3KOQlzGYBudGiVKpRLEn4fPb+F71toIx277XeD/qjHGY9ge0kGA/kU5PlDyiQe8iQIUOGDBky5GPGcy+cwmQW2N0t4HCI/N/+719kYsJHVZIxGjX877//fcIjLj7zyjxPPzPDZz57jp/5wgLPvzCPggbBoGN7I0lXoyGXztGsy+TzNX7qp84yOuonFi+Qy5b4zE+fZ27OT70mIRjM+P0if/kXN3nrjWW+8HMXeOP1+xTzNfx+O6V8lV/926/w5V9+nukZP6LNzOxMhFg8i9Es4HRZmJzwUS7XaDXq6HQavv1Xd6jVahTyEt/6q5usb6RJxQsUczWuvbvOpz97hlqjxeZGBqOgw+OzcfvuNs1GG6/Pxv/+e6+TyRT54pcu4XAIOO0mRkfdTE/7SSQK7O7k0Bu0zJ8e4d79KHa7mUqlgUajiiqdTlcNObQK/P7vvs6d2ztMTvrJ5ySsohGP10E47MRkEkjGiviCDtwuK3Kzi9ViQBB07GxnyWQqhEfcLC2p+c56k0lB0DM1E2B5OYrTLZJKlAiHnYRCqujQczGZTAYW70VJJIr0sjrZ7WYymTLxaJ6dnSxGwYAomhmb8PXFH7dXFeBcLhGdXket3tyXw8lkMuz1Q1Bzq+XUUNBeKKXboxZSaLeVfe6OXthioyEzPu7BvJeqJRhyo9VqsYomBKMquFSlBq1mh3xO6otwtZpMdDe7bx8BwnvCn8UiEB5xI8systw55AJbXFRTzeRzUn87RznFwiPuQ0JHdCeL3GwfKYCkUiUmJ48OhwwfIwQNijgH23Q41Bvzvf1OJAr9/wsFicZe5E293kK0mfpROE6XyPiEr9/OoCDW6XT2haYeh8slHrvMPoHMffTcy3xE+p3H0Wq1cXvERwpbB/tkFU1HFpM7qQh43HE5Cce6yE6AyyXuy4d3lHj4Sef9DoH8UcZ7yMkZimgfAFubmf0vDFg30ykZDbC1nuLalWUS8Twmo57YTp67d3b57rfvoDcYuH8vTrfToVCosL6ewusV0em1tDodort59Ht3QMIjbgIBJ1WpRSya27fZg4JYuVw7NjFjb9mThnCWy7VDH/pa9YOpKDJkyJAhQ4YMGfJxpN3u4PfbyWZK/G+/8y1SqSJKW2F3Rw2N+vY3bnLl3TX0Og3b22mCQQ9v/GCZTLJKsVBGrzcS3yng9bpQgM/9zDnSqTJ37myzvpLgN//xLxHw25mfj3Dx0inW19NYRRNf+oWnuHdvlz/4ve+zsDCO2WxAtAqEwm4yqQJ6vZbbNzf4qc+exeGwYLVYyabL1KoyhXyVtbUkn//iJU7NjfLiS7N0FJic9lEsNblwYZRwxEW5UuP0wgjpVJlv/sVNrr+7RiJRJBbNcOb0CO+8vUpsN8eZhXE6bYX/+adXqVVlUukSV959QDpToVyskc+VuXcnSkWq8/LL8+SzFZLxAoGAmk/ulU/Nk0gUiEfzhMIORkbd7G5lSSaKbG5kkco1UokyiqLgcFp4/bUlpmb8oOly+swo+XyVRDxPuVTl1o0tbKKRbKaMLLdQFIWlxRj3bu8wEvGwshSjVKwhihYEo56tjTTrD5LU6zLFQpUz50YwCQaqVZlisYZOpzqnwiNuLlyYwOE0s7ubJ5UsUChIlEr1fh6t+F5Rhlaz079xnUgU6O69X63WURQFt0fE6bSg02uR5TbxaJ5ORyGTKdFsyqwsx2i3lb4I2HPFJeMFZLnD6mr80HlYrtQZm/DuE8sEQU9z7wb3QXdKT0gDNfSuuJeT6s6trf6+nDkzwuSU6kAcdHD12NhI9f/vCR2ppJq7yh9w0mqp1Uq3tx+6zhKJAl7vwz4m4g9desVHCDrhEXe/D4OiCkC5vL9v3Y5q82m3FVwukfCIm+h2rh9eeDB5e8+h5nKJtNvqwZKbHep1uS8a9t1u8cMuH5dLpFisHRK1BoWn44SSg6Jk4QTJ+617oaSPEraOEjt758agCHmUCNgLc4aPn2jV24diUT1f95lWPmGPQU4yzj8pxRWO4sNw1z0pQxHtAyC2mzkygaDXI+BwaqhWythtJs5fmOb0XIiVezE6bYW23OLc+THsNiM+n0ghW0WjgMVsoNFsMX0qiCDoWTg/zui4Z19I5OSUj8iIpy+GNRotdDr1Plov1tput5BMFvvrDMZgP2n+s6PCMZ3vMY/BQT6JseFDhgwZMmTIkJ88vvn1G3g8IutrGebnw2yupchmCnjdIuGQi6efOUUiVsDltBHdzLO7k0GutZmd9aF0dGjpUJMk2i146qlx1ldTjI15GB31cOmZKV799k1+8Np9tjbTxHez0FWoV2Xe/uEDBIOWSxdGKeZrZNIV0OmwWE2Ui3W+9527bK4nuXl9k4WL42i0XT77uXP4A3YiI048bhsPVhK0m212NjN7OXsrvPLyLNubWapSk8vPzjA64sEfsPPMszPMnxnB57ETDqshZwG/+vrsbIhGQ+bcwigvvzLHK6+c5szCGPlclXYbTGaBC0+NMTcf4d0frlIo1NBo1DBAv8/O7RubdBXQaLSEgx7SqTK5nITJZODcQoTtrQxnzo2wshQnGS9y8eIEGw9SVPdEFZfTyrmFcaZngvj9dsIRNyazAZtooVioMTHpQ1GgUZeJhF3MzofY3VZveFtFI9OngpjNAmaLkUS8wNRMAMGop6t0WF6K7ROmVpdT5LNlJqcCbG9myWRKatL+coNg2Ek8ViAQsrO0GENRFMxm48P1u6rg0xNK6jUZudlGo9WoAo7cxmI102l10Ou1GAXVwRYIOAEIhlyUChXGRryHzkO9Xl12UOTJpEtMTweAo90p4YFwx067TalU5/xTEyTihX0Czdi4l+hOtr9sb8LvdFj3PQfw+kSKeQmdXoNGqyURLzA+7iefq/T7MVjkLBRW+9Vud1H2ihakkqV9yfxBFRB6xQsOMiio5XNS3xnXHKi0Ob9XEAKg097ftiDoEffy9TX2tmG27E950xOlev09iN1u+pHcWv3tfAhVFA+KkAcFN7NZ6J+zR+WaOwk/SvgnPF5MdDr3cqh91HnN3oecaHCyce6JxVWpgdx4b3nznoSPk4D6cXTXDUW0D4DRiPvQB8Xp0CE3ZCyCGZ3WgKIo3L21xfJijE9/9jRanRa9Ts+3v3GL1eUY7Y5Crd7E63NhENQfm0ajSTJWYu1BnGtX12k0ZDbW1btAmUyJcrmGz2enXK7h9zswG42sryaR5Q6ZjHpnKBx++AV/XF6yR4lYvXaOuiN13LJPyo9DvrT3uu9DhgwZMmTIkE8GiqLwS7/yHMVilYBPJB7LUSrX6HQVNFots/NBKhUJWe7QkmVMZi2peI6f/bnzdLtw/vwYer2AThCYnPaSS1cIhlxk0hWmpr1srScJBL1qQniPDZPJqIbetdvMzgbZXE+j1RtYXY2itDtYLQYsZgO+oINErEhVaiDoNPzgtfvcubnO3TtbJJN5/vLr19HptHi8dgyCjgtPTzBzagSX24pGq0Ew6Hnxlfn+ft66vrWXd8tJNiOxuZHG4bCQTpd5+4erhMIunn9xjkJRYu1Bmm4XZmYCnL8wxsZ6AlluYzQJbG8mqZTrvPypeVKpIm1FIZkoMDruY2UxhkYDkzN+HA4zmUyJjtImm61gd4rcvb3F6bMjvPipOcwWAZ/Xhtxs8/3v3e87mO7c2KTZbHP7xjaFrMTWZprXvnOPSrnOxWcmMZkF/CEnUqXO5J64tLWVZmczxZuvLxEMOvsiSqPWpNvVMH86AjycUHo9Fi5cnABgfMJLcE/gajRaJONFwhEXFouJQMDRFyuWF9Xw0m5XzU3mcouUy2o1TLPFgFarpZiXEIwCKN3+TWmLVeDOza3+5NnlFvEFnKBR3VPL96O88foiAKk90WJQ5HE4rdy6sQE8FCUGRbaeG8ntEdHqdAiCjky61G+jJ2Ztrqf7YYeNuvww/NUsYNl7XZbbtNsKOp0Oi2hGp9Oh1ar9cTotNOqtIz9DPbHF6bTg9tj6n6tBoQ1U4cftOZx8vl6VWbof6z8376Wa6YW7HiQRLxwpWPSOb6Xy+Kia5fvRQ23Was191ShLpfoji7c9ikcJSPFo/kRFCg6KkEcx6Dh7P/o2yEkExUe19WGIiZ80esKnVTQhmB6dN+/94L0KqD8pDEW0DwCLzfwwYeDe32KpjUGvp6106Cgt6CpcujyF0tWweC/K7nYGt9/GmYUxTCYjVovAT3/hHC2lid/nYulenAfLCRwuMzabhWajhSy3acotbt/Ywudz0Gy2SKUK6LRaNddFp830bBBQqFabj3V4yXKHUqneF7GOEoJ8PjVhaO+OzaPa7C37k8hP8r4PGTJkyJAhPwnksqoLJxB0ItotWK1mnnlmmmDAwwsvzfPuW2tUpTZzp8MsLcWxWAV0OrX4U6PeZnszQyqdIbadI5ss0my22NlKMzkVIJ+t8df/1svUanXqtRZ//F/fZu1BijNnx7BaTXzr67d48eU5DAYdl5+f5W/+Xz5NwOeErob4bp5qrYHNZsFuFxkd83D5+TlEq4mlO3GeemYSp9NCJllCq9WwvBhl+pSfzfU01WqDiiTzxutL1GsyyUQRk9GA2WTEKppxe0VqFVl1etlMjIy42VxXw/WevnwKi9VAoylTkxo06jIvf2qeSr7OvTvbJBMVvH4Ht69v8vTTU+i0Gor5KtGdLE89PUGr1WZ5KUYo7MLvt6NBQ2wnTyZVJJko9UPtMskyqWyF0TEvL33qNFMzQTQaOHU6wuS0n2DYyeiEGqHx7IvTaLQa8jmJ1F40hmAUuHV9C4BmrcXYZICz50e5eXUDjUaN4vAFHLg9IjubWdptpT+h3N7OkU4VKJdrWC0m2q0O8Wgek1lPeMTN1maG6G6uv3y9KiO3FFaWY8hyB7dHZHM9hd1uxu0S0el0uNwiRqNAIOigLNUIj7hJxAsUshLnL07wYHl/+KZoMyEIehwuK888NwPAuYtjfUGqkJco5CUsFoFAUBXEXG6R5fuxfSKbNHANH4641XxtA5UoA0EHOp2OyWk/kT3326A4YrEI1KoyaDSkEjnkRntf0THTQO7k3noHhZOjxJZQ2NUXd9ptpT/XyOelvjjUe79YqnL6bKS/rtkqkIgXqNflfmgpwNKe8NXb/4Nhmb3Kqcc5zeDhnGf+7Mi+nGyhsKtfjXJrI0W92sThMBPbOdqNNbhPRzEoICmKsq+v4RE3AzUrjnR8lUp1shnpsY6lQcfZSen17eD4HRXmepCDgt2PIpQV9sKP6QLKJ/Cx34j2sXJ9DTkZQxHtA6CYK6r/DAppCuRzLZo1GaNRQKszUi3X2drMIBiNmASB6++skk7mSacLLN2N8+b3lhB0OlZWErRaqsVbtJlQuh0WLoyRiBcQDDqCEScARqMBq9VEvdFCi6YvhlXKNSYm/NjtFjLphz8o5XKNYlEin5VIp0sIgm5fOeJBIegoQa1crv1YuMaGDBkyZMiQIUOeFJ/fTjDkJJctEtvJk4jmWLwXR6vVkM2UmJnxMnsmQKfV5dRsGIfTikbboVxqki+UEe0mLl6axeu1sbiY4PIL05SKNUrFKqVSnbe+v4xdNNNoyMyeDjEx6ef+nW3K+Rpf/MWLzMyGsYgmalKDzY0M0Z0sU5NuRsd92G0mziyMIdVk7t+J4nCKPHVpCovNiNViZXMjwcKlMRKJPOVijY3VJIrSxWI28vU/eYdspkit3mB7M43dZWZ7K0OtWsdqFXB5rWysZ7j2zjr37+ywtZFWXW+CjmZNzS12/26UB6sJHqzGCYRFXC6RQFDNd1aRmkzPhrBYjOrNY4uBVrNDKlFgZzPNu2+tcuHSJFbRTDDkxOGw8PTlafR6LfmcRKvToSY1ebAaJ5MuUavKfWHhO391G50OkokcgaCD0TEf+UwFt0fEbN6rvJmrcGZhlCtvr2K0GFi8H6VcqhIIOtneSpNKlChkJXI5CZvDRLMhIzfavPvWKucvjuP2OMilJQSTnlanTXjEzfhEgNvXN7HbzTidIvmsRL0uYzDq8fnUipvBkINb1zcxGg3Eo3nMFoH7d3colSTqe6GHpUKNzbUUobCLUqlOIS/hGcghFo/m+0JUKOyiUqr3BYzwiJt8TqJek1l/kCQRL/RFoUJeYnTcAzx0Uo2MeUknS/vyeY2NeinmJTKZEunUw9zKlSNEn3xO4v7dbdxuK6PjASyiQLPZplQ6LAg0GjK1mrxPOOntx9pq4tDyrYbqktLrtdjtFhLxAj6fo9+ng6JXIS8R3clSLNYIhV1IUp1A8OE85vReOGdvrI4Syx4nKFkspv442e1HV1ScmApgthoB8Af331DvicC9ferRa3NQiOqNTbnc6Pe1uPfaQXHyoDCp06kC6AfpWOoVy+gxOJ6DjrxBBsf3US60Rzn4eu+5XKor8aPOa/Z+5UR73Ln3o4bHvp88ieBXyEuHquIex+PE5Sfd9gfNUET7AKjV28d+YJR2B42iZXczzrWrK7TaMuWiRD4jcf6pUSanA3TaXZ57aQaTRUDparn49DiddpvYboZivobVYsZiMjE5FcBk0NPeSxq6vZ4lvlvAajUiD8T7y02F6FaW6Jaaz6D3BWS3W3A6RTpKB71Ot+/u0UGOclYNinKFQuWx4zIo4H3UlEvDvGtDhgwZMmTIkB+NdLJELqe6Ii48M8nEpBe9UcvX/+QK6USFV//qPiOjLoqFIplkiUDAjUbTJbqeY/H2FvV6g2SqjMWk48/+6xUmJrzcv72DBrh7e5dkooBcb6O0FVxuC6LNRHxHvd679s4qGw8SrK0nie5mOHUmwttvr3HjyhpPX56mWW9x5twoE1MeNteSFIoSY+NeUok8z79wmj/+Lz/k7LlxFp6aIBxxY3eYuXF9g8/97AVsNivL96OUyxJX3lpFtBqIjPoolxq4XSJKq8GzL8wwOe3H53dQ2hNixib9RHdzeH12LCYj9apMu6NBowWtTovZYqC4N7Eql2oUcurN3OXFHU7NR/j0Ty8wNunj5rUNrFYjZovA3JkIS3d3kcoNXvv2XaZnAoyMuijkJTKJIm+8do+7N3fYWk/hdJpIxIt0FIV0ssSt6xvoDbq+OymTLtFqKWTTJWZmw2i64HZaWFtJYbWZuPjMJFvrCdqKQiZRxOUW1cqG1Qazp8PUpAZX31llckatLrm6GKdUqtNptWk01eqPWxspGg0Zs1mgmFeFnq2NDLLc2cu9plbFdLlVYVGn0fXDEG02M5MzAfI5iWZDFZ2mZoL98623Xk9wsYgCguHhdG53W73WD4QchMKuvlhRzEs0m6orqZcfbGsjjcenFjnoOcLMVgGnW0Sn1eEP2PuCSK9qaM/lBurEf2Y2fCi88OCcYfleFJNJwGLZX4myJ6iNjfk4SCDixu0RkcoNVpfifZGm9/fm1Y19wkNVagI8dBL6HCTiBTqdzsPiBeUGNtFMIS+9p8m4Xq/tj5NWq31saONBp1WtevQ8q9fmoBDVG+/B3GVG49GVPA9upyUfDuU8aRjmSdaRyg2soonNtWT/tcF51YFI3CPbeZQL7VGRqCeIUv3EcpybLx7NP3G+veOEzPeDJ3EvutzivpyDj3IsHhSXBxn8zvm4MBTRPgBcovVQTjTBCJExK6026E165s+MIVqs/M2//TJajZbT5yOkExUSuyVOzYfYWk/jDzrJJAvcvLaJ12vn/FNjVEo1rr61SqlcRVEUqvUWu9s5yqUaCxfHEO1GzGaBltxC2suhMHc6gt1lwWgx4POrP9blUo1CoUJ0J6te/BSq1GrNY/fpONGpLqlf8t2OZt/rRy3v8x8f4vh+iVonbcfuGDrohgwZMmTIkCHvnXKpRj5bZuHcOBbRQKlQpZQv43WJnL8whtfnYGLKi8/nIBktUq01uH9nm4as8NTT43zmcwss3YljNmtxuKxUpAYNucP5pycol6v83Jcv8eZrSwQjDmbmQsR2cmh0WorlOh6/iF6rQdBpsZkELGYjkREPy/d3adRbvPnaXZKJPIv3dvD5XRj1eu7e3OTujU1sNhNStc6FpyeoVZvIcodGQ6ZUlFh4agKTycDIiJuZmTCnZiKMTnpxukXcHpFAyEk2XcSgFzh/aYK50yMEIw52t7MYBB3ZdAWTSc/0bIBA0MHZC6OYTALRzTzxaI4rP1xFr+uSy1bodiEQdnHp8jTj0yHu395heyuJ2WJCo4F0uohWqyUWzWEw6lTn3uVJ6nWZ8IiHhYtTuP0OJqYDnL0whmAw0GrB7HwEt9PBg6UYqXiJVlMmFHYRCDpptxWmZvz9/dHr9QgmgZ/+2fM4nBY6nS4Ll6YYHfMyf+5hMvoffOce7XYHm8PMzGyo//rkdIBcuoTZamR0TC24cO7CGOY9wchsEdjZzjAy5qFUlHC5rLTkTl/EyaYl3F4Rs1Xg6g9Xae/dBHd7RMYnfWTSJWS5gyTV1fMt99CFBpBOlWm1QW602drMMDruxe40Mzrmo5CXcLlF5EabyZngvgloIl5gYsrfzz3Wc7p0Oh3u3d7pL6vT0S80kM+p7Q0KIE67dV944Vt7OdoGGRzHQfGqNzHuuaV6fRhcRrSbmD0d3rd8Il4gGNnvJLPsCa6DAk4orBYx6M0/BJMewaSnXpOPnYwX8tKxrp+eCSERL5CIF2g0js7z1uOgYHDSuUepWDskmhTyUj8v3cF289mH49U7RoPCVSJeOHHo5ODYH7eOaFfDiScHxF2L9aEzr1xUx0mW249s5zh6UVFHHYfBiClAjfb6qAsEvKfH/t1QKxMfHUr8XgpWHBQye7wXMRXUsOKD6x50DJ4kpPdR4dI9ajWZxp4zt/f993HMkTcU0T4AHG6LGu88gNzoEtuWaDW6JLZKbKzFQWnzzpurjE8HMAlGwhEXzWaVdLxMvS5Tl+rUqjIWq0AxJ7H+IE1HUbC5LXz/2/eQyg0EQcvYlBe7w8LmWopcSj3BCzmJXEZ1h5VLNZrNFj6/g0y6RKej0Gy2sNstePaqck7OBDAa91tzs9kyGw/Uuwx2h+VIgWpsSr17pDfsP5VO8kNRLtX61uYnFbXabeXIxJpDcWzIkCFDhgwZ8mHQbLbxh1y8+doSn/rsAmfOjfH0C3OsrSSoVRrUazUeLMb4k//2Fh6vneh2nkDQTiaWxekWadZbdNot/D4nLoeJL375Ii67mY21FFaLwDe/fo0XPzWLLyCyvBjFoNcimgVEq0Ah30CSZHQGA1a7mbEJH7dvbPHLv/ISn/7sPA6HyMRUEEXpktjN4/ba2FhNsnBpktFxH2vLCbRoie/mSSeLxGIF5FqLptTk9LkxFu/vYhA0tFttvD4H7VaHcqnG7esb5PI17t7a5ht/dp1vfeMmt97dwOGw0pIVYrsZvF4nd29tcfWdB/zw9WV0WshmS2i6UK+2+PTPXKDb6SIIer71P6/z+//x+1itAlOngkTCXrbWE5TyVZbvxshny7z1+hJmsxFFUfD41LDMeDTP+nIMvV6D1WzkxrU1NLouE5M+6lUZqVrHG3AwMuklNOrjB9+7h9sjkstUqEkNottZCnmJsQkvcrNFPJonn1WvnYuFKsnE/tC6X/rV53E4razcj+HzO7h/dwcAl0dkfO9a2GwRSCXydDodunvzgGajjcloRKNRbyZbbSbV/eVSr1fnz0aI76ohmmefmuhXlFy6H8XpFmm3Feq1JlK5gcVqwu0RiUfzlPZycpmMBoIhB9l0Ga9PFQY7HXVS7nKLPFiN02o/zIu1eGdXXW8gHC8ezeP2iizd20Gn03Huwlj/vZbc2TfxzWcfhngW8hJNWe6HF26upRmf8u97H/YLM48Sr3pigdsjcvOqWhAhES/02+lNpENh1z6XXXw3j9sjkk6WEEUzm+vp/iQcHjrABEGPIOj3iRKJeIGtjXT/ucstEh5xE93JHepjKlHqb99oNOwbl53tLDvbWW5e2ei/djDk8XE0GjKFvITtiFDRdruDvOcwG9zu4t3dvrgGD8d3UHQ4iXBxcP1BCnnpsQKJXq9+lwD9gng/agXJ5oH1jxSAPnIx7Ed4DPAkQllvnB9Fr7LswTE7KEalkyeLEtNqtYfWPegKfJLz7FFYLEL/O8XndyAIxyiCHzFDEe0DoF7bs+sek0zQ7tRgt5rwh7yEg06uvLXEtXdWKeYlsuk6d29uIRgNxGJ5dney3L22RXjMw8Wnp+gqCiajgYUL42xtpBCMRkSrCancIBB24XCbWVmMUavKjE+rP+rNZgt5z8JtNBpQ9n7ZdTodZrPA5lqKcql2SICyWEx4Btxjg+/3QjN7wlqz+ei7MT2xbBC7w7Kv/PSToNdrD9nChwwZMmTIkCFDPix8fju72xlcXpFSscqDlSSLN3fRaLqcOjtKMlZm/uwIU9MBfD4bFy6M0Za7fPkrz5FJV/jG/7zK+HgQr9fO9laBRq3Ja9+6i9KWMZlMvPTKWQSjEUWGudNhDEY9927sUpUa3L21SVOWOXNuFLfXzpW3Vkns5Pn6n12lXKmzGy2w9SDJF770FFvrSfLZEmNTfjZWE7z1+goVqUm5VOfurW1KxRpnz48yOu7n+tU1Vu5tMzrmZWc7R7Ui02q0ufbOGt/55k0qxSpzc0F2tvJcfGYSr8+OVTRx7e1VlE4Hi2hm6e4u2WSVFz9zmnDESyJWIJetEBnzcnohQqMuk8mUoasWtXrq4ijZVAml26UptzAaDXz6ZxYIj7hJJ8t8+qfPoNdrqZQb3Hh7U3XlWAT0Bi12h5W20uGnP/8UTqcNudFGEPSUinVqtQZmwYjclHn+xXmiO1mcbgu+oJOZuSDtdgfRZsFsNREIOchly9RrMnqtZt8ct9NRWFmKqSKMyUC9LtNqKUhSnaU7MTbXUmytZ3C5RSyimVxGopCtcPPdDRp1Gb1enYBm02Wiuzk0Gg2xnVw/NNJkFugq0KjJTM4EKeQlAgEncqNNo9YktpMjEc2j16vTtvCIm1xPzNrrp1kUqOxFoDgcZsIjbm68u0Gt2uT+zV3iu3niu3nOnB/t71chLxHfVXOzmUwCp8+N7XM1pZOl/sQ5vpsnkyphNAn9a/quom4+n5VotxUmZ/x4vXY1dFZSQ1E7nQ6x7WxfvOuJMT2X3KDTJL6b5/a1TQAmplUxLhR2HelCSe9VDgUIj6oCxOlz6r5NTvv74ZC949fjoAARCruYmPL32+uJDvYBx1PvtelTwX7/e2JTrz2f347Pb+fis1P9ZY7q91FVUnsoirpOMlY89F5PTDi43pmF0X6uv+O2dRSNR8y/Bs8BUPt0EoHEtNcPh1Pdb9FuOrHzaVB87DE54ye+m+/3p149os8fdYGAH+VxAo4av975ftz7gxx1DvbCnOFw3r4n4ZAr8AkZ/CwePOeO46TLfRgMRbQPgNe/fe+RiQSbVYXNB0WqpSrf/94dDAYDer2Gzc005y9N8HO/9DRLt3YYHfGA0kFut7DZzQgmPT6fE4PegMagZfZ0BINBy+ZamsV7uyze2cHpEglG3Jw6HaFcqpFJl5DKDQqFKpl0iS4amnWZZLzY7+/kTOBIB5fFIhz7AelZo3vrPSpUEyCdLB75+lHlp4cMGTJkyJAhQz7uJOMFalUZudXhf/vq10lsZ+lqOlSLTVbv7+Jym6hJTaZmgswvjJDL1BgZ8/D69+5jtRo4c3aMOzc32F5PEgo7WFlK0dV2iG7m0em03L21xcZykrd/uMy1H67TarYpSzVMRhNTE14CQTc7m0msZoHd7SSRCS8Tkz6cTjvRzQyVUo0/+P++Skfp0JA7TE0HUDoKvqBIo6a6XhwuM1PTAaRCneWlXfQ6LQaDgNliZHs9g8Gs4erby8yfG6OUqTE9F6Le7PD//H//AplUBZfLSjyew+awgEaDTqshMu7hqWcm2HyQIjziQnSIxDayXH1nFX/QxcpinFJe4vbVTRYujbLw1BTJeAGnw0opV0Wuy9y9ucPYmIdGvcXirRjNmszqcpRAyMG3vn6dN763iFU0U8iUQemSShYplWqUy1XKpRozs0FOzYfZ2UrRaSvEonksFhOtpsL9WztsrSWpFGok4wUK+TK5jESl3CCTLCJVG2QzJXY20ty+sYXXZ0feC93TaXVEtzJERtVqlecujiLazEzs3biulurYnWoxiMi4E71Bh06vZWstxepyjLEJH1rtXoGERrufM0hRFIqFCj/8/hJSRQ2TEkz6PWdXl4vPTtNuK/1J5NRMkK31dL+4mMstYhRU51M+K9FoyMwvjHDh4iQjEx7Co27Co+7+TfB6VRW5ctkyqUSR7Y10P3F9D49vr8JoXaYiNVQnnWjqi3kdRRW/3F6Rm1fWAXWCX8rXkKT6nvBWZuHSBGfOj5IcKHTQE8vMJiNarZoSRqvTEAg/3J+DbK2nURRVffAHHXgP5F7rhQ8COJwPI2h6bXU6nX0CxODn2B900Go9DBtT9oS35IFQyH3VTaU6FqtAMl5Qc93tiUgHBafByJtBN12P3jHtmQOO6+NRbfc4eOzgsMtocBlFedjmYPsA7r1CFgfbXFmKPTJtTm89QdD11z1pGN7FZ6cA+gU5QD2e4VF3v92jxuWTTuoxDr/jxq8n+j5ufA8ew3xWzdGYij88N2S53U8B9WEyeDzd3pOdJydd7sNgKKJ9AJw5O36MbVO9XSQ3uoh29cvS6RLJp0uIokA+VaKQr5BMFJmdD/HG6/eZng0xNu7h5tUNvvtXd1i6s0O92iAXz3Pr6jpWi4lyWcLnt+N2W7jx9irdbodSUUJRFN74zn1kuc2582P4/A5uvfsAg6BnZi7E9mYK2J9HrFyq9Z/3/s+kS6wsxg4t+zh668DRNtWTfGDb7eMzSD6uUEF897AV+8eVYaGEIUOGDBky5MOj0ZCxO62MRNwEg3b+2l9/gVSqhN1pweW10Gy2abUUdBq4f3ubP//PV/H6THQ6HS5dmmB3K8fp8yNcfm6K809Psr6cJBXPcebcOJdfmeWt7y2RjJXIZQrMzYc4f2kc0WbC67VRyBeg22Vq2sfcuTHuXNvk6WdOsba4y9pSjP/xf7zJL/7KZS49P8OXvnyZZ549xfW3HqB0wSKa8PsdzM0H0eq6vPTyPPWGzNvfX8IfcGFzWLj+1hqpeJb502FK+To2m5Virsz5p8eJjHqxWA1kUmXCY070Wg25dInZ+SDFXIVAwEFVqqPRQqfT5tVv3kajUfjSV57BLlrIpko0pAb5nMT4bIB0rEg2U8DptvLf/+AHaHQwOu2n3qiTzVU4cz7CuafHiEx6sZiN3L25xZf/+gvotBq63S73bm4yMRPE57djNukxmQ3961tRNPOZzy/QbLTIpgvksmWMRh2RMTdur51csUqtUsPlstHpdFC6LSyigMVkwCFasDstBAJ2bl/fxBdwkE4W0Wi7+AIOapUq927tkE2XqElNCnmJYr5KU25jMgl4/HaUDrRbbWx2E4JBz6m5EGazwO1rG/j8dqx2E9lMmXa7TXw3i8ViROko2Gxm1lfVa+joTg6v10Z8N0d8J7tvEjkx7Uer1ZKM5mk0ZNxekXu3tnF7RUwmgWJOTesSHnVTqzVJxNSwx3xW6k9gFy5O4A84aTVbKEoXt1ek0ZDZ2cr0CxGYzQLVcpXtDXVc47s5qlIDh1Pk7o1NOp0Ol1881e+XwahFp9diMhtwuKzUak3y2QrBAQHo/KVxJKmOyWKgVm2ws5UhGHb1QyAHJ/+96/mJaT/J2EPR4e6Nzf7/+WyF9eV4/3kxX+3f6G/UZNaW4+QyUr+t3t/BfinKw9ede+JE771k7HAIXXwnpy43YFvMZyu0253+dbkstzGZBTSa7r4cUr39a7cV0HQpF6vsbKb3tS8P5FwLHiOelUs19bzUPZzS9+Zfp+ZD+5Z1DgguFotAMOzq9yMYdtHpdEgni32XkvOAQONyiftynw3Osw6Oj3PAwZiM5U8UggiqSFKvqjm6TxIO+lFX2PxRq3MGjjiu8d3cvrHNpEs0Gi1KxWr/NX/Q0c9nOEhdau47zw4ew6MESUHQI+6FEMd3cxTzEnXp+Dzp75WjhN5PMkMR7QNg9mxk4At1wA/eBbpd7C4D3W6XsUkfrUabXK6E0WLi0rNTlAtVCrkiW1spFi6MUyxUcditBMMOBA24vDbu39kmkSyqlS60XZSWQqlUpdmUmTkzwsZqkt3NLLvbOcZn/MydiaAoCuVSjbGpADaHGbNZYHwyQC5TRjDq2VxL9UUpu8NCpVjHZBawOyz4/A7mzkT67/WEtUE6nQ75bKX/F+ivA/TvHA1Sbzz+A9rc+wF50kIFAG6f7bHtH8cnTZQa5oIbMmTIkCFDPjxMJoFGrcnolI96rYnL7+Dlz8yh02hIZyVsokCt0mB7M0t0K0+zLXP31g6FXIXVpTiCScOdK+t4gzaW7mzzymdPowUMgp5bV9Z4+bOnOX9plPCIh7deW+Lqm6usL8VoyTJGo4DTY8NqM7OyFEWrUx0LZy6M4nLZEAx6rFYzq4s7vPHaPWwuM898ShXZ2kqH6HaWU/NhdtfS3LqxQbkocfbSGO1Wi1gswzMvzxAIumm12jicJgIhJ5dfPEW13GB3M83da1v80X9+k/hOnmSiSCFXY3M9g1anJZcvo9NpSCVKTJ8KMnMmRHwnx4P7UTQahVPzEfxhJ/6AnTPnRigVqkS3cpgtJn75b7/A2QsT1KtN9HqBtqzgCzi59c46omhGp9HwzEtz/M//9jY2h5mO3OHTnz/P1nqKek2mVKgh2syEI25KpVr/enR8ys/MfASjSU941IOidCmX6xSzFQqFGlWpjtFkwGw0cunZU0R3cgQjLrKZCsGIm2K+wu2ra7RbHSwWI1vrKXbW9sI3LUamZoMUshWW7+1iMOr2zg8DwYib8KgHrVZLcNRFdEedoE7PhTAYDXQV0Bt01KpNTs2p18zPvTKrPp9Xn2u70Gy18YecuL22Q2JFYjevilR7eZBDESebayny2QpOj0i5WKWYl7BYjIQibjLJUn8inYypAsfmWpKZ0+r2Op0O6XgRs0VAq9Vw79Y25VKNS8+d4uKz0wCERz1UynWkco2FS5P7JvPJWJ7xST9urw3RbubezU20Wg1ur43V+9H+cjq9DkGvJ76Tw+6wEAw6gcOT/t72AK6/u4bL+/DafuHSZP9/s9VIufxQPHDvLZfPVmjKLdxeG/6go+96C496yGcryAPpaOSG3N/WIPlshWKx2o+qyaRL1OtNnG5xr9Jsqy86mK1G0okidodlb16k0OkoNOotOk11LhTfzfX3U6/X4vbYUJQuI+Oqu7GYV40QysHs80dgd1gwmQVKBakvUkzPBinmJbRaLVXp0YYFtbBIm0y6hE6nwx909nNqpxLFfct6fCLZ1MPXBscqGHEfKTSGR90EI26sdtOhRPQ95+DgebH//daRbZaK1Yfzyo86r9n7lBNt/5h59o2tz+/AZDJQrajH8lFilGDWHxveexLCox6cbhGzaHxP61elxj7xF+h/Dx/12X4cvX09LqLto2Qoon0ARLcy6j/dLkdVDSnnZDrtLl//k5vUKjXCIQcrd3fZXksSDDnptDV4A07ajTbvvL7C9759k8XbO6DRoNUpPPvyHJeem0XpaHj71ftY7AJOpxWDUaDV6iAYDIRGXLg9VtwekRtX1qjXZBKxPIV8pZ/gM5MuYTQa6La7SNU6Pr8Du8Oihn1q1C/9o8Sko3Kj6XQ63F4bOp2OzhGCWe0IRfsoEezg9nrhnkeJRAcddAfpJSV8LwxFqSFDhgwZMmTIo2jshR3pDTru3dokEStx79o24+MeBIOAw2PFLBo4//QYTz09TjEvodNocditaDoa5s+NsXIvjs/vJJ0uYTLrWV2KYnNYMei03L6+QVfTZXTCg9sr8ou/9iJf/IXLeP12rDYT3/jjd3iwtIvDI1KpNvjeX9zGaNJgthioVxtIZZn15TjtjoKg1yFVZAqxMqLDzI0r64xOBYiMerCLZsqFKnq9jl/4pedZfxDn/t1drE4rVtHC5LSfu7c2qNXbzC2MMTHpJxRxUJUaPPvyHC995jSFXJlg2IXZbKRSqJNNl1hfTeHz2KkUqkzOhmjJHV7/1h0MBrUa4x//l7c4c2GMSqHG/VubVIoN7t7YpJCtcuHSGK1Wm3y2gkkUePUb12l3ukyfCvDCZ84wOx8mlSxw/cr6nmhXJJMqEh71EBr18Lu//S3cXhu3r21Qrzd567V7XHljic21JA+W4gTDLnRaDWcvjFHMV8kmS5x9aoK331jizq0t6nUZk1lPqVRjYirAKz9zAYtVdYo9dXmaWq2JyaQnGSuQz1YwmgUCYSfX3lknnSjSanWI7+aQKnXu39xGbrY5dSqMJDVUR1dXod5o4nRaCY96KJWqBCNuVu7HKBSqbO2FV45M+hgd96HX62i3OwQj7r5TqJiXSMZzODy2vsDm8TkwGvW4vTYsFiP5nEQ2UyK2m6NRlwlG3P11K5UGbp+duTMRatUGJouATqdDtJvptDqYTAJuj4jdYVHFzu0s7ba6X8Gwqy9UXX/7AdFNtb/egINvf/0GckMVQBxOkfu31CIMs2fVKp3peAGzSeCdN5YZm/JTLtX6Obry2Qrf/85dnG5xz8H00JET8Ntp1mXi0Vx/H3qTbLNZ4LmX54D9LjOpWKVebSKKJrXfkcEQMltf/MlnKwPuqf1uNbfXht1uwb8n9Pn8DsxmI/6gE6dbZGTch1k00qg1kRutvgDi9toehnkq0NX03EfOfsjuIJVSHUVRaNRltFotxdx+saS3r51Op99nAINOx9iEf0CY0/XHryeilQoPhadBEaYXnjs4J+vtp1anCo49IUun0+0bP6AvwIEqpB0U7XpCmcNh6Se77/W79/05e3Zk33HunVe94zPYBwCH00r1A3BKfZQcFAt749E7VslYvn9ePUqMyqUrH0h/TopVNCGY1ErVPdzek5laBs8lqVxHltv9fVU6J0wi9yEyFNE+AMrF2rFJBF1u1abcbYNOCx6fm2ymQTqax2qz8u7bDxgb9xLbTpNIVvj0FxY4NT+BYNARHvNQr7aQKg3eeWOJ6bkgxVIdh93KD757D5toweURqVabjIx5Mei0uH12bDYzVtGE22PjzMIYDqeVG++u4fM7WF2KIVUbCHr1gqZelxGMBuhq6LQ7fSdYj96drt6X7VFC2FGvifaTJR98EvFqcNkTVQMtfrLcZUOGDBkyZMiQjy/hUQ91qcmVH6yg0Wg5NRchXyghlZoYrQLZRIWm1KYitTh7fgK/z8nK3RhOlwWtTkdkzMPYuJdA2InDaSUQ8FArNXE6Rba3MoxPBTh9bhTBZMDvt/Fv/19/xOZmCsEkcPWtVXxBF1aLWU0av5GhmK9wanaE0IiHrY00jUqdX/5brxDdzDA+6eOzXzyP02MDRUNsO0t8O8v6coKd3QwanZZKsca776zg9Tr48q88hyLLmIx63vn+MkpLQyhiZ/HWNsv3djl9dhybaOHaWyu0mm0aVRmzVWBrLUOt2uTcU2M88+IpZubDfOn/9Cwjox4MBj3nLo6j02iYPBWk1exQzFdpt9ucOT/Kznoa0W6mVKyyci/KxWenuXNjE5NRQGmrTpg7V9Xn6USR0KiL8SkfRqNAu91GMOrJZyvkM2W+8IuXWLyzQzpRZOnmDjOzYaZmImQSJcanfLzz2iJPXZ6mWKhy5vxYX+CJRDycWxjj5vUNqpUm2w9SGAw6KoUqyWiBYqHGvdvbBEIOzBYjwYgLudkiMupBA3h9NkqFKhurSZbv7NJRuuTyZbY30uTyFbKpEqcXxui0lf71ck/cuf7OGnqdjpFRL76Ag3j04UR2YzWB063mHsulK2ytJalVm0yeCiEVq/2k7gCtPeGiUZeZmA4wMxfBbBYwmQWW76oJ/tdXk5Tztb1QLjNarTolLBWquL02Mnv5tLY3M5RKNRwuKyPjXmpSsz+Zj+/myGVKaljzpCriZFMlfvpLFxBMBqRKA6/fzvy5kX2fG2/QgV7Q8amfWWD1fhS7w4LdaQVAq9UQGnWTy1UoF2t955gqKPpp1GXcHhvFvITTLeJ0i/vEoVKhSjCihsi5vTbGZoLUazJtRSE86jnSKROMuJGbrf7YqKJmbZ8byHSEu6fnoOqNXUfp4nBZyWcr/Rxxi3dUAbHd6fQFAZ1Oh06zfwrudIs4XFbacmefUNXrUzKW76+/uZ4mGHH3xYmm3DoyhU0w4u4LYg6XleV7UZKxfL/oQnQ7i8Nl7c+PeoJNrdakUZcx6HX9do5DEPT75n29dXocHGtQ3XqgCiO9MeyNtaIo1GqHBbKDfbD15pUftZvsfXCiHRR3B8ejH1Z84P2eyHQQf8h5ZJgnHA6RflTao0cd88e1nYzlqZT3z7lPkmJp8FwS7WYEQd9ft9efj1OqpqGI9gGwuxk/nAtt78NSyLZAC0aTFqtZRz5TwKCFC8+dIpsr4XfbePf7K0xNh+h22myuJFhZ3CCTKJNLlZmaCdKoynz+5y7x1uv3MAkattaTTM0F0Gi7FDMVxid97G6mqdVlWo0WGo2GbKqEL+Bg6c4OpUIVm81IOlVkbMKHL+Bg7uwIpT03l06nYfluDNFmxhd4+MUoleuHQiQHhan4TvbQawc56r1s6nBuM6l8+A7NSdp61HJ259BdNmTIkCFDhgx5f0jG8qSTRYxmAU0X/vD3XsNoMBHbKaDXaLE5BZ56dpzNlTivfus2M3NB9AaFUqmGVqfh+9+5x9W3V/m933mV175xnehGCp0eYtspzl0Y5871Ld789iJWs5VkoozNLVDIVygX6vzNv/sZLCaBQMjFX//1l7GKJl763AKpVJ6nX5jl3IVxpLrM1TcXefcHKxRzEqv3Y1RqNZr1Bq16m/HJAB6fDYvZRFfpksuWKSQkpuZCfPPPr7G7lSW6ncXnsyE6jOh1OlKxPJ/+wgK1apNivkK+UCU84cYqCkS3s4h2Exptl+hmlj/9L2+zthjju395g1qtRT4vUSnVSSdLNOoy/oCdSrHK2acmMJnNmCxGErt5xiZ9aNDwnb+4yeS0H4fTwsufO4s3YAcNJONZ4tE8925s4w86KRWruNw2DEaBd99YQTAaCEc8RMa9TJ4K0uko2JwWrHYzktSgWmsQHHNSr8skd3N910QxLyE3W6wuRalV6sydHUGWW1htZnwhBxaLAegSibgoFmt9gUNRulSlhuqwa3eZnA0wf26ESy/M4HBY+NRPL2A06mnJbQwGLav3Y+gNWlXwy1b6QpHHZ2N+YZTlezu4XSJyo0273SYZy9NotCjmJS4+O43eoGNiJgiARqPBF3KSjOXxh5zEtrLcubHB1noKSWqQjOUp5qW+4OIPOYlH8zz9/AxPvzjTP5dNZgGLxcitq2uUClXmz6vCl91uolmXkRstqlIDRVGoSg1qUoNEVHW+nTod7rezsZKku5db7Mz5MeRmi0q5Tnw3Rz5fYXMtSblY48a7a0iVOrNnR8imSlT3+up0ixgNeiqFKt6Aoy9eGQUDjbqMonTJZyvsbmb6E/dKqc7NK+t9IUqr1e5z0kyeClIuqGJtzymjHjelf+yDETdTp4J9h5hOq2FrLdkXgQbDQ3vr9YTXHr3oGbfX1g99PXN+DKDv0Oo75/bC5TYfJMkmi/25SqvdoZiXuHtjE1/QwfzCaL9/PQRB1+9L7/gOCn4bK4l9/ept0+1RzymH20oxL+Hfm+P1hJreX4vFiMks4HSLtNv7hZpOp0P6QJjnoIDSG1tQz4GjTBS9cEOnW8TttSHLD4U2rVaLXn+0CNRjn0vqoxbC3gcR7WAIcalQJb6bfeR+90SmJxGVjIKhf/wOhoyCehwPOiR7rsHHMeiOC0bcjIz7SCeKAxV0D4dJn5TBdX+Udt5vhiLaB0CnpUEDDx/d3t8uenMXTatLJd+hkm/TanVoyR22HsQw6wzqHQttm7dfXaSYrxMMOVHkLoqicOvqGm98/y5Kt00ykadVbxOa8FMp18mlyrz+7dusLO2ycneXxG6BZKxIu9Phu39+nWajhaIouDxWbHYzdqcVm9WMN+Doi18OhwVZbiOKZs489fCHIZ0sUC7WEO1mVu9FyefKxKNZinlpnzAVHvPuE8SOErkOClnlYg3vgFDX4yTOtZOKYkPxbMiQIUOGDBnyfpLPVjCZBdLJAh6vne2NFMGIg9FJF3aXieU7O5w7P87rf3UPm82I0pCxuazUK22y8RL3b+4QGnHicNk4f2mEWlVGb9bx6c8v8PRzc9RqTX7hV56lXqvTklu8+FOnGR/zMDrmp9Vp8Yf/8XW+91c3iW2leOvV+9y88oD1xTiCWcAqCmg1Gp57ZZZnXznNl375GW5d2eD8pQm6bTX5tC9oQ27JjE54yWfLvPnde0zOhXn6pWne+OY9vD475UKVSqmGM+RkczXJ8v04eqOejfVdunKbrtJlZjqAUWfg/OUZ3n5tkZExF4GgG6fbytikF9FuZmTER2jEweUXZhCMOvR6DToNnL0whlk08dq37lCrNhAEPdOnQ9y9ukFNalIpSdy5toXeoCMRzZNJlhEEPdlkBbNRYGIqwOr9GB6fnUyqSKNSx7N3szcdL3L97VX1utZp5s7VTe7c2ODZl2a58fYGbq9zr5oqGIy6PTFLq4ZBWk2Mjfp45/vLdOmSThRJxgpEJn1YrEaqNVl1i4Xs1KQGm6tJdjcz6PRa5s+PcufqFmargbWlOIu3tgH1ujaTKBIa8aIoCtGtHJVS7WFIYTTPxHSAd99c5rlX5inkK7Q7Cu22QlVqEh5xU8hKvPHdezTqMsW8RGI3h2g3sbWWIhhxUypU0Qo6nn7+FE6XFa/PjmA04HSLvPHtu2w8SGA06WnXW9y8ssH2eopkLM/tq+t9MWnh0hSVcp1qpUkqUVSdQdUG2UwZq2ii3VKjVErFGk+/cIparcnq/Wj/RvyLnz1DPlshPOrpO0gye5N20WLC53ewuZbk0nMzaHVarr61itLuYNDrEIwGijkJDTAxEySdKPYn57lcmVK+ujeJ7hIZ95CMFSjmJRwuKw6nheV7u1SlBlWpQTDiJp+tsLOVYXcrQy6tHt/15TjFbJnFOztotVrqA66nVrtDR1HFDalcxx92UTkgKri9NtKJIg6X6pwrFarUpeaReaoGBadBZ9HudhZFUdhYSWC2mmh3lP5cxSqasIhGJk8FKRWONguMTfgB1bVXzFfpKF0adbnv4DIZ9fuWr+0l6g9G3EiVhlrAQun2Ba/4bm6fWLK1mui/nowW9iWa1+l0+0SvfLaCYDL03YDR7Uxf2Bl0LPYYHKfNB0m8AQeCYOgLRMW81Hcg9UhG86QTRWLbmf5+/LjSqMs4XFbCe9V/S4Uq+WwZODrXd2+Mj3LvDRLfzWEWjWreu5DzyGWcbrF//vc4aSjmURzliuu5HH8cigwMRbQPgHSqcqzq3Kl2MZr3nuu7GAUD9WqdYMhBvS6TiGaYno3gCdrQaFoonS6zC2PMngvj8zsw6HQk4mVS2znisSxKp00pV0GDBp1Gj8VmwukT8frt6PQ62s0Op8+N0ZJbpBMl6rUWa0txzBYTZtFIXVLzIcgNmXKxRrXcYGczjWgzs3xvh60HSSyWh5VYZs+NYDIbsdosFPO1/oegJ5h5A47+D8FR4tWgyNbpdIYC15AhQ4YMGTLkE4fba8PpFvH4Hbzz+iLFTIVqTc0DZRR0BEZd3HjnASMTbnZ20igaDbubWU5dHCOfKjMz62d3K8vIqBdRNDM9FyGfKPFHv/sG3/2rG3zrT6/y6p/fZHI2jGg3c/vKOrsbOa69uYrf58AfdHH67BhGs8ALn57jp37uIiargVZdoVJu0Gi2KefrJHfyrC7GiIx7uf7DNe7f2kWn0eD22jl3YZyJmQB0FJ7/9DxGg46v/+G7TJ8Nkk+VuPj8LF6/Aylf4VM/fY5f/JXLdJodfvBXy7QBwWjghz9Yxh1wsPMghWDUs7YUp5Qvc/3qGgaDlo0HSdpym2K+Ri5bpSbJCGYDxWKdYqFKsybz0mfOMD0XoNVsEQi6OHtxgvhuHo/bxuRsiN21FNGtLPeub1EqVXC4rWj0Gkan/LSbbdqtFppuF6lUo1mXySZLFAoVdGhwu9VKlW6/yMKFcfKpEr/yGy9TyFQwiyY0ioZ3Xl1icyXBnXfXaTbb/PLfehl/2MH8uRGsVhPpRJFcqsz9W1vkMhUsFiPNpszOWhq7W0QwGZhfGFVFoEIVvUFHMVvFKgoER9RJbiFTYm5hBL1eR3jUw5nzo4RG///s/XeQY3li3wl+4B6890hkIn1mVVZWlu/qrvbjDWc4IjnkiFztSidpV7F7sbexG2f2Iu4u7uJiLxTauNXeSqIoWnE45LDHz7Sv7uryVVkmK71Hwnvv8eDuD2RmV3X3OJLNGVL5jXgB5MPDw8N7eEj8vu9rrFRLdW5fXaPd6XD1jSXGpzxsLIXQaFV4B0386b97h0K+SjqeQ6tXIpdKEAQ5tZqIQiFnezWKad9CKEGC3aFHo1EdWgwPBsETxwcwGDVsLIUx2PUoBSmhvTQGsxan23xICvVJPRNSqZRSscbMqWF0OjUtsU0ikkNQCahVCowWLf6tOLlMEb1Jg7DfDunfiqPR9FVWB7/xrQ4D/q04gkqBzqBm5uQQbbHNxlKY85cmcQxYqFQa/XPKqkMuyElEck+MEfQGDc59m6ZKrcRiMxyeg4lIjtFJN9MnBvs5apEciUgOi02PXCZlcNjOsblBFu/tYXeZUKgEpk94KeYrhxlv0CewjEYNYkOk2+7SqIn0ZRH9McvivkXz8Vw5vVFNIpE/JPtEscXiPT8AzWb7CcIiFs6yOL+LhB5SqZTRKTcuj4lcpkLiMeuuTCYll678VAKjVm3i8pjxr8cOlYQAequWrZX3ic0DomVjKcTxU0NPfC4OHldrlaw9CiA2RHqS9+d7h+3Uqs0nguYNRi1bqxG21yMYzRrkctnh581g1B4SqI+jWmmQihfQG9XU97PQRiZch+/7YJ9+MO8rEcnh8lpwuE0M+OxUS/UnCZgfE5/0d2L6wPsE6PZ6T6jBjGYtFpuBXKZM6ceQqqLYIpcu/URV2k9TcPVtziLGv8FM8I/K/Xtc5fhBfDAD8JcdRyTaxwCbVfMT5ZvNag+LXYlSCZVyE41aRacjJbyXYMhnY/76OmqNQCySp90VCW4lWXsUwuYw0qy20GuU7AaSmEx6ons5hodd1BsN6HaJ+zNsr0ZZWwyQiee4e3UdqSAlFs3j3wmTieex2HRo9Up2NyOsLQTZ244jqPotPK4B02HtrVQiQatXUas2SERyBLYTQP8EKOXr2Bz6w5NAoZSTSRZ/qjXzcdVZJPDL42s+whGOcIQjHOEIR/h5EQ2kuXBpHLVOiUIi5cyFCRbv+Vma3yGfLHLtzUU+/fmzNGtNUpEsxVQJg1XL4JiDVCTH9cuLFNIVcqk8EzODnH9ugsBGnIbYxGzXsL68RyKUYWnez+TJIVRqGY4BIwqFFN+kk/vXtvj6H1zm9jur5NIlnB49i/d2ufnOMkpBzvpKCKNZS7fdo0sHm1XJ7k4M/04csd3m8g8f7qudRAxGLS98+iQ2uwmtQc3t99bpdHpI5TKarQ6/+69eBRn8Z//iE5TyZXQGFa4BC/PX1tndjjM1M8jgiBO7x4pCKiGwmWRvI4Jv1MH89U0iwTTB7QSB7RQqjZxSrsLOVpy93QTdVg+luh9IffPyMlq9krkL46jUCtxDFubOjWK261AoBORyGUPDdiSAyapjay2KSiMwOO4iGctjtmkZHHZgcxlJJwvsrseYOzuGa8BCLJbn6puL5DNl9jbieEdsSAVZv4HyuUmq5TqbK2GUaiXVehOr3cDsWR8qtYBcJufh7S0sNj02u56RCReh3eThwNNi01MrNzh5bgS9QU02U6aYrxAJpFEICkBCpVynUq4R2kuzeHeXrbUIM3PDiPU2SKHVbFOtNsmmSyRiRX71Hz6Ne8CMRq9Fp1eDDORKBdJuF7vHhEavopSv0qiLpOIFwv40JquO7fUYAAt3d1i858fltRAP5Tj7zCSCIKVeFZmY9lDIVQ4tkweDcLlc3t/OfP/3u0IlZ2czhkQKuWSRpthGrLewWPUYDDqCuynqB9lXEgmNukguU6Zc6D/fM2hldNJ9+BoP7uySSBQ4sU/oJCI52q0OOxvRw+Vd+8q7Axvk4xlLB+v3b0aJhbMohP5wtpCtMDblwWrT4/Ja2N2IQhfa7TZSqZTTT41hMGkObZeCIKNcqCGVSBAbItVKg3AgRS5TweOzYbHpkUolJCI5yoU6c/sWzYP3oRTklPI13AMWYuEs9bpIaDfN3PnRfWFCnc3V8OFzPINWZs+NoFIpWLi7Q70ukkuX0WgE1HrVYcaVTCZjaNR+qO46UIkdEC2JSL9wQSqRYLLqeOFzs4evUSrUCO2mmDzhRSaVPGG9nN63lsL7WWsHx1ytFtBoVQgqgUQ0e/g6iUjuQwRMpVzHYtMzccxLuVhndyNyuE21/eP1QQueQi49VCbVHysFcHkth/lcB+Tj4+/X5X1fdRYLZ2l3upgsOpKxPNB3ef1dnQDS8fwT+0GjUdLp9lh6sEss9D6xarHpD8fQBxbJAwiCAq/P/sQ+P7CK/6xQqRUI+4V8jz+vXmn8WNXY2mLoI8f9B3hc1XawzZlkEVHsk6iPE8fQ/y7/u4QjEu1jQFcqfmQe2uPIpRo0yz30ehWiKBIPJjl7cZxCsc7UzCAdscvwsJ2N5RixUIpBrxOx3mBrNYpYa9Ks1oiEkrS6HXZ34ujUGjqdNl1Jj7NPjzF5wkO73eHs89NYLFpiuynazR6CSmDxwR5by2EkSJk86WV3I0alVEdnUFMu1ynk6jRqIsMTbix2Pd1OD5VWxfCEa/9KQh6bs38F44Ag67Q6aA3qj7RmanRKSoXah+ydvjHHITH3ceJnzU47sLUe4QhHOMIRjnCEI/wkBHeTiGKbbKaMd9SBTAoSKVx5Y4FPffEUU9Me1BoVQ2NOrry1hN6gZWjSSTlfo1ap8fZ3F7DY9ZhsJh7Nb6Izq2nUa6TjJb7wGxf49d96FrNNz+S0F0Eu46mXjrOxFEGjU3Hr8hodscPueoTP/uo5jHod48cHqBRr3LyyTq/T4av/+AU214KkQv2Q52K+gkarRK5W8vSLMwR2UuxtpygXmqi1Sj73lXM8vLmJ2Gmy/ihAIV/j9FMjIOlx+711tpYinH9uGrVaoJSvMXN6hHg0x4PbG5QLdX77n71MpVRnay1CaDfVv0isVzJ5YohMKs/IhJ2xcSfnnp1CqVSwcHsHhULGwJANm83A2mKIjeUIf/knVzn3whSSTpd0osCj+V0SsSLJeAGx2UKlkjMx42F4wsX6coh2q02lUMfmMrFwx8/TLx7rB9JLJBRyFbKpMmarjrXFEO+9ucTJ8yP4t+IcP+XjpS+eIpss8eJnTjI66SbqT1Mu1pALcjqdLolgHolUwpU3l7E69P3Cgq+c4/a7q9g9ZpLxYj+83W06JCPa7RbL9/cIh9IoZAIWmx6FWk6pWCMWzBALZjEYdRhNOsaOefAM2qhVGhgtGubOjiJXSPH6bJhtek5dGMPuNJGM5fFvxkhE83TaPdpNkUSyQKfdRqtVYnHokUok6IxqRibdFLIVpme9faudIEdv7EekzJ4bIZcpo9GoOHtpkmS8wOZSmO7+OEWKhMZ+6LxUIuH002M8uLVNNJBhbNyFwaDBYNH21V+xPCarDoNJw8Xnp4kEM9x5b41mrYnLazm8EB/xp/p5Z5EchWwFQZBx9pkxpBIJW+sxttYiVCoNYqEMgtC3FxayfWXWsblBtDoViUgOr8/O3v6YQaXu79ehMdf+Pu+fkweD8GqpTmArgVIloDWpkO+Xp+UyZdYWg6wvBGg22yRiRY7N+SgWq9TqTVYfBBgcduDy9q2gnU6n32jqtRyu+2DgL5VIKO6PL4LbCTyDVuavb2Cx68llyiiUcuwuE0aj9onvjUZDhB6cfmqcreUwarUCk1WH0agh7E+xtRJhe61PJtZrIvdubtGst+h0Otjd/TGWy2vBM2hFuq/+21mLk8sV+yUNJg1ma3+MJpNIsDr0T+zTg/1wkLVms+upVhpkkkU8PguFbIWJY4OHr5PL9os6DtYBYHPqySRL+/u+jVKpxOW1oNEoD0mvdrvN7Strh+9bUAkUi33L8AdtiZ5BKzKZhLnzo7Tb7cNMtIPjcACbU09LbNPpdHB6+qrEX3iu2V8zE83uNh++P6mkLwE0GjWcPDuGyaZ5Yr8f7It8pnI4/q5XGodEbaMmIor9RmOx0fqJSsbHVV+5TJlirnZIdD3+vGKh9oRqrN1uHz53/JjniXH/wXfHAR5XtW0shvaPoRFBEPpW7/3j+/OWBfyyWEGPSLSPAz3FY/cPbj/6BMqna7SaIqlkjchenmqpxv1bmywv+ElGS8yd9iEIUnZ3whRLNYbH3exsJVAIGkZHXKQieYbHnQS3E5TyNRxeC5FAivs3/GiUSsq5MpvrEQwOHdlUkXAohUpQ0Gi0WXkYILCT5JlPzBLci7PyMECz3kJsNAj5UwiCHJlMRrfbZWjEdhgu6XCbSUZyNOrNw+wynUH9hEf+cVRKDQwmzaEs+4DUyiSLDE+4PkRy/bzFBD8O8f1Qxp/VMuoZsv3M6z7CEY5whCMc4Qj/6cJq76uObA4Dt9/bYOLEMI16k15Pxq0ryxjsesrlOtMnBum2RQK7KQwGLW1RRCoBg0mJpCfFYlZzbG6ceq3L5mIEm92ARCbnze8+oN3osLawx+CEi7tvr/Fb/+QlKqU6n/+1p+hJe+i0WrLZMk89d5xYLMvolJsXPz1HMprnz3/vXU6eG8PqNDI26WHp7i6+ERfpdB6tQc1XfusSbbGNxanHM2Tlu392DbHTYfVekE5LpFmvsfrAj8Gg5R/+05dJxHLMzPlQqQRarTblUgOb3YjTbWH8hIff+59fRasVmJnzMXXCg3fQxrGTQ2yvx0jEi8jkcsrlBrFwlkyyQK/dI5Mq026IVEp1ut0eL3zmJCfPjKBUCAyOO9laiyCTSlBrBBRyGSMTTrKpMptLEd790QKCIEetEThzcZzoXpbF+9tksxX8m3FcXgt2p4mW2GZwzM7whBOX20RoJ8XwsINv/fE1FHIpTo+R0G6CkD+F1qhmamaQaCCNVCJh5cEO0b0Mn/nyGVLxAvV6X51VKtXJxAuoVArymRImq45Gtcn6YgCVVoVEBtcur6A3qwhsJ9CqVXiHHdy9scH4cQ+JSA6jVU230yUR7att0skS3V6PjZUwWp2ScqHGvWub3H53lZnTQ1gdffuifyNGNl1maNTB7macVKKIXCbD4jCwvR7l4a1tur0e2VQJsdHC7bMS3IyzuRzeV5WUDsmUwRE7Y1Nu0okiv/evXkWulJGI5thYDuHyWogE00iQYDRrkCChJ4FyvkohWyG7T6JAXyF08uwI07NDHJvzsbYY3LdlSvGOOmg12xjMGnLZMrWKSGA7RXGf6Gs1WoxP90lRm8vA3laCWq2JXOhbBJfv7x3+9j8gGdQqxWF2VjKex+kxPbEtTq+FfL6Kw2UktJ06nG+x6Tk+58Pts9GsiYcB91Mzg0glMqbnBgntJveVZzXe/sGDQ1LogATQ7KvYHB4TzWarT67tq9Ve+PRJxEYLg0lNLJghtJd+Qkm1txmjWm4gV8gpFWo4PWbUOhVKQU6lVGNw2MbkCS/eETuNmkgxV8Vi1VFv1Clkq2TjT6p+ms0+aeHyWlCr1YdOIuiTJE2xfZhJZbLq6PZ6LNzdQaNREA9nD+25Wp0KuVyGIAiYrDqiofdJjeNzPtQ6FYVs5fCxeCiHUi1nbTGITqfB4TbR6XSeUBLJ5XKmTnjZWokczpNJ+mUaH2XbK+Zqh88Tmy1EUTw8ZgfkUSlfJ5MsIpPJDhVcf9eRz1X2FYklXF7LEzl6od1+BtzBfpVKJfvlHj06vW5/XwhSpmb6pGcuU6KUr6DWKp8oeIAP5949fqwsNj2NhviRrZ4ur+UJxVitIrK9Hu1/dgQ5otjuTw2RLl1iwQ8TYoVshZkzI0/MO1DNWWx6PIPWjywU/CC5dlDoofxAZt4vCkck2seAXLb6s/uhO1ApdBFkUKvVkXQlGPRKSoUSjUaNwF4SiUSKQiZnfSFMeDdGtVpDrugRCcepluoszwfIZoskk0Uk7R7ZeIXRCSfNrkix1G/QsVgNmI0GvF4L1WodvUGN0aTmzrvrbC2HUQkqdEYVwe0E5WIDg1nDg1vbrC8GMdsNpBIF5HI59WqTUqGGx2djdz3OnWvrh+/baOzXJB9IOw9uDSbNE+TXAal1wF4f/C2KbUqF2ofItsfx82SoOff99T9JanqEIxzhCEc4whGO8PNCZ9BQyFZwDZhwOgx8+w+uYTBoGZt08itffZrVeyFGJ11IemAxm6gVylx/9REXXzyB3WmmnCuyuxrE5rHhG7WRT+VxuAxcfX2R9YU9vGN28ukyM2dGmX9vA6NVxd2rawS3UqwvBciEi1RKNbQGLd//izucOjfG2oMw0l6PkTEnv/lPn6eUr3HxpWPUq3Wm59zcv7HJV37rWeo1kUatQaPRQq2Q8/DGNo2CiNWiB3qsLYVwe+2MTAxw4901vv677/DUc1Nsr0WoVxoEtxLcfncVnV7JyJgLq0XPhWcneP3b81x/9REKQU40mGX5nh+5QorHayG0m+biC8fotPvE0eCoE5VCweC4A/9WkuUHfl77yzuYLXre+9ECS/e28focnH5qHKlEglQqQSqRMTzu5K3v3WNidgCx0eT2lXUK+RrQ47/877+IQi7H7rbw7g8XWF0KolYreOvb95FKJLRaLTrtLp0uXHzxGIJK4MH1bYqFOst39whuJ0hE8ni8ZkL+FJMnvejMagJbKeiB3WnB4tCTT5VRaVXUayKXf/iIWDCLfzvBzbeXcbnMOD1G/sFvX2L1wR46gwrooTOoGZ/0UCk3GJ12kU2V6PV6KJUKFu/2VXmrDwI43WYElUAP0OgEhqec3Lu2SSlXo1yq8ekvn2dk0s3Nt1apFBuIdRGL3UC71WZkwoXFoiOXLlIv9xVUd9/b4PxLx7Duq0uqZfFw8JeK5ikV6/jXowwMWlEo5EjkMtrtvo0wFSvgHbbhGrAglUEuVcLmNpFLlxg/7mZtIcitK6uo1f0Bu8mqo5Ct4PFaqddF1hdCiGKbRl0kHs4hl0gpFipMznhxD1nZWotRLvbtYiaLjt21OCOTLrR6FYl9Imf23AiTJ7zEgln0Rg25dJmN5QjdTo9cuszYZL8ZdG2hX+BQKtR549v3mJp1s7Uaw2o37L+XPulyYFE0WXUkYjk6nQ7b+wqunbUYnW4Xh8eEQiHnzMVJEpH+MnaXkaX7e5QKfUVXuVjHajdQyFaYOjGIy2uh2+32lYxyOUarFrtdj9gQScXzRIMZYpE8vW5/P2l0wiHB1hTbFDIVgv5UnyjIVrh7bQOpVMLYtAeZVIbVYUCuVBALZt8nQYcdxIJZxEaLeqXB7ka/EMAzaCW8lz5UICYiOaqVBg9ubuMesNBsdnAPWomH++vZWAw/Qaq43SZ2N2KHyqR8qozJqiO5v7/q1RateovRCff+d6H6kIB5nERR61TvW1LTZXQGNY2qiFwhOzwOB7C5TIfW3QO1klQioVZt0KiKh/OnTw5RKdVR7ue/0e393Z0As0VHMVuhUesLUWQy2WGxwoFa8GC/KgU5YkPE4e4rHNutDvFQnvp+npnNYcDmNKFWC4f77ICw9AxaP6RMy6XLh8t4Bq1PkJsHpFYuXe5bTYP9FmODScPEsYHDz242WSQSyBLaSz+RoX7wuYsFs4efrfBemmKhevh4Olk4vN/pfiAkjg/nuEmlUoxmLaG99IeW/UXgiET7GGD/KZloB/K0fgZj/2+1Ro5Wq0Yhl5JJF3E5bIjNDpIuFDMl2j0JA8M2kvEC7XqdyFYCrU5Pt9Xi1NPjHJ8boSOKvP7N27z53du8/f0HJIL9q2MarZJStkwoEGdnI0ElW+fu1VXOXprmK//oWWxOAw9ubUEXhsYcRINp5HIZo1MuJk94SYYyKFUKEtE8UnmfyCpky8gFKcdP+g7lo4lonlK+iqBUsLUcJhbKIor9eu697TilQg1R7DdyFHKVJzzdmWSRbrv7BEkmk/9sH88fR5IdhFx+lMX0F4FfFvnpEY5whCMc4QhH+OshEkjhHDRz570NJHI5T704yblLY7TENltLUWKhFMV8DbmgYGjcjVqjQSLrEgwm2FgJYnaYcY+4uPnGI1771l3ymQrOIRsDQ1a8LgtTx9zIlQJbS2EMJg0mu4Zet4tCJeHOe+u8/KU5Zs4MUcoVqObKXP7efYxmFSsPAqTTBZbmAxQKNd79wSM6rS6DIwO0O/CjP7/N/LtL1Koiu8tROhKYmPEyd2mchTvbTB33cu6FKQYGLdQrdQwGgXqlTiZZ6jczih2gh2fATLXQILSbYncrTq8Dn/ryOc69NEO5UMdo1DA560WnVvLw9i5ajcDWSpib7yxx6sIYzYaIVJBi0GuwWLWMjrmZOTOMRq+iWKwjk8tZvr9LsyGSzZRpdzqIjRYanZJ//n/8Io1qk1ymgt6gYWs5jMVupFpu0KjVCWwmUKkVDI84cQ5YmDkzgn8zitlmwGjWUq022FqN8I1/9w5zF8cpZMrMnvcxOu1CIunRbHQ4eX6UVqNLV+wS8idRahQ8vLVFpVhnYNjG+mIQm9PEl7/2DMlIFoNBzSe/coHNlRALt3ewWA14Bm3k0lWy6SKCIOfpTxyjWReRSCT4NxLodEqGxh10e/12xec+M4tCLsW/HsXm0GO26VEo5CgEBYJS3rdcddvcurLKs5+Z5ewzk/QkENtLEdpO4Bm0ILbauDwWUukcD25vUq+1EettUokiap0Kq82Aw2th4c4Oaq0SuVzK2UuTHDs9RCFfIR7KoNX2CQqtXoPRpKbX61HIVQjsJVm6u0un1cFs0+HyWpg9PUy50uT+zS0A2u2+IqlZFzl1sZ8hlk2VkEok9Ohhd5nIJgtIJVI6zTbuIQvZdInVB35mz40Q2E6QimTRGjSHg/DwXoJSoYpUKsFi1+P0mLHY9Uye8GIwaVi862dgxMajuztMz3r50teeplIUOXF2GINZjX8jhkLxvkXQ4TYTC2ZRyGSU8lWSkTx7WzE8Q1YatSZbKxHMVg1yQUKPPhmgMaix2Q1otGrWF4PIpCCoZNTrDWLhDO12m2yyRKu1P7bp9q1wW6tR2u0eMqmU889NodEK5LIl5HI5tWqDG28vYjBp8I46GBiykk1XUKsUSCVQq9aJhrPoDRo6nQ5is4XH18+LC+32FXYqTV9NZ7EbmLsw1ifhchVmz42w/ihMp9PB5bWg1an4ld+6iNGqI7xPQpx+epxCtoLeqDoM+09Ecji8FgZHbaTjRUqFGo4BIxtLIVqtDqVCFatTj1QqRaURUGkEwrt9m22lVGfjUZDFe34SkRxqtYBCULCzHqFWabB8349KI7D6wE+t0kBnUPP29+4T3Ushk0tIf0Bp5/JaKOSqOL0mNhb2uPPuGisPAgS2k3Ta+/v5F23J/GvaOavlRt8K+1hxQ63SZGl+B+iTv+lEAY1OiVqnQmt4f5zc7fTwjTtR61RUa00a+7mEhWwFjU7J3lYCpUZ4gqw8IM4SkRxSaV/Zub3atw+LzfcdZQek1oH11uOzHpaPPEG4DloZnXQyfmzgQ/8jXV4LHl+fCOt0OgyO2DGatPvbUaJee//1DuYfbNvaQpBCtkIuXaL4gfGz1WH40Gv9InBEon0MMFlVHw4QhPenXj8IsdfpIemBWgulTItEJEUh3cSk19LqdjCZNZx/ehqXz0EukyWXLKDTK9lZjaM1a/oFn4KS9UdB7lxZQalSYXdbmDnrw2TXsLUUYOnODrFglt3NGMVilU67h8tn5rf++SeoVOpsLIUI7SRxD5rJZ0t02l1Gp9yYrFpS8TyZZBGDpS9TL2Yr6LT9qxoqtZKJ44MYTBpUGoFGTUQul6JSC+TSRSZnBzl+eoh2q41cLmdgyIbBpKEjdjBZdEilUvKZ908Kg/n9kyeT6H+JKlV9Jr6Qez9Y9HEc5KwdkGQfRab9PPbPjxsanfCL3oQjHOEIRzjCEY7wNwCVWiC8k+J3/sWnsNi0eAdt/OjP7xHdTdPpdvCN2em02oh1ka0FP+VyBZPVyI1Xl3ENmcnHS3h9FrrSHiazDptRRzFZYnDcQTicYXMtSq1S4xO/chrngJFyrooglzMy7mF0ys3VN5d447sPiARyvPCFkxj0eqo1kUd3ttBoVYT9KTYXgjz1/AQ331lDbDYR6w26HZg84ePutXXOPTtJNpEjHEiTz5Rxei0ggVPnx/nhN24zf2OLSrnB1/75JzBZNazc26aULSOTShEbbcLBNApBDt0uSw93sDhNhP0J/NsxNEYl8VAWqSDHN+akUqyythhkdMKD2Gjx7KdnkUi6XH9zmZPnRzA79HTaPb7xu29hNKuolxs4nCamZofwjdjZWAojqORIJFCvNLn59hrJSB6NTsnsmSFe/Ytb5DMlXvmj65y5NM70nA+tTg3S/gDwxNkRCplK/8Jvo4XTbebcpQlWF/cI+uNIpFIW7/iJhTLEgmlCOwnMNi0Or5kzl8aJBrO89PnTbCxH8AzZUGkENh4F0BmU2N0mxqYHWLq7y+QJL4/u7/L6X97lxPlhvD4zhVydtYUA2ytR5t9bY2MxjHvQQnA3QyZWJJsq4/SYSURyOAdt1GpN0tEckVAa/3qCYrbM0KgTuULK6ryfc5cmyKeL3HpnhVMXxgkEkmQSBRbv+ZmcHUQU2zz3yZOYzQZe/vxsvzjMa+HhjS1C/jSP7u6gkMsQBClKpQKHx0y9IuIbdXLs5CDZVJm9rQTeYSuRUIZUtMDYzAAjYy48QxZ0Zg2B7RSNhkir1cFi0XLy/CgABnN/nGCy6JDJZNTKDWbODFOrN5HKpbgGLDTrLfQmNYOjDqx2Pa1GG/eQHQC5Qkq7A7VyA7kgw+OzYjDrsTiMmKw69rbjZDMlNpZCrD0KEAtm6bZb7G3GcA30M71igfQhsZNLltHpVId2suBOgsU72zTqTWbOjmC2GZALMkw2PSq1wOCwHZ1BjVqnZvX+HuVSA7lMSiyQwWBWsbEUwu42kk4WmX9vnVazh8VqQC6XozWoGD/uZmctRrfdRi7IUChk2Jw6BJWctQcB2q0ugmJ/fJOpMuBzEt0vWtPo1MikYLTpmT03Cj0JEX8StVqBbN+yu7cVJ5cuI99XK1nserrtzuGF+uBOCuN+Y2mv2z1UiG0s9e2o5XztkASJhtIUilVS0T7hlYjkCO+l2VoOk4wU+vbCdptGTaSQqaDTqRkccdCoiYyf8B5+F27tZ7jpDGpe/vIZxIZ4qF5SqxU43BZ0BjUj02663S5PvTSD22cjFsgye3YUjV5No9bAZNF+KGyeLuxtxnEN2bn48nFsTgNIeodCib/rSO6/38eJKY/PisXWH9s6PGbsLhMAC7d3iATShxl1BwQVQK8D7Vb3cF0mqw6jWcPeRvwJS/EBKfZ4zt/556cAsDrfJ6ceJ7VigY9Wfh1sRyzYJ5H7TrUn1WOxYD9a6XGCtJCtYLEbGBqxk0v37eWV/eKQA1urxd5v6rXYDRgtOsL+1OHzf1mO/S/HVvw9Qy79YV/vT2KiG7X+bVtso9L0SMYLJEIZcokiv/+//Ij1h35SoTRby0Ea5Sann55g+1GIyE6KZr1OKppj7twoxWyZAZ+FVrOHy2VDZ1QjlfbIJgoMDtk49fQxBobMSKXwnT++xo23VijkKn0Sqicllyhx58oasVCe+9c2SMf6mRX1apNivobdYyKwnTx8S4lonly6SGArjtho0et02duKYbEbWX3o7yvRmm1q1QYGs5ZMosjOWr8tyGDSYHMaySSKZBJFGjXx8GqKzdX/4pDL+5kBJovusEnng3hcufZRirOfZv88INn+Jsm2H7cuQTgi0Y5whCMc4QhH+PsAQSmg0SuJRzLYnUYS8TzdXpd2F1r1NnJBSavRYeXeDhKZhJFxBwaTgTPPTVDJ1imX6zy8tk41WURGj0Kpwp2rS/TENnJJj+BWkm63x8I9Pw+ublCvd6g3m5TzVe68s0IynOHs0+PodEpuvr3G9BkvlWKN/+K/+xxyqRyb08D5549x7Nwoe2tRJFI5jWqTgWEzi3d3efr5Y4jNFjKpDKfLgEqr4vyzkySjeb73JzcxmHT84//DZzFo1dx+d4XQbopms4PYaqEzqZmcHWDlQQCrW0vMn2JqdgRJr825S1OUUmVK+QobixEalQYWmwaZTMZLnztFOJBmYMzG6oM9dtbjnLk0zpvfvk9LbDN5wkunDc+8fALPqJ3RSSe7a2GkUilSSY9H83vceWedUraKpNvh7KUJ5DIJt99Z56UvzFHIlvkf/+evIQhyvvX7V3j9lTuEd9OsLfiJBrJUK3VEscXwhIN8oUylVKecqaBSqzFYNHz6H5xn4oSXdKKAzqhBISioFGrQkaBSKhgYsfPZXzuHWqNAIumh06v49//T97nx9hLFQoXdjShvvXKfL3zlIudfmuKtb8+zcHeHlfld0vECZqsek93I1EkvHbFNqVjF4jDw0hfmyCSLyBVSXv3mHRSCnGSijNGkodtrM31qiIc3NjFZdOgtemLBDIlQjtmzPgLbCYbH3FjdJsRmm+X7u+TSJZKxHFannqW72xRyFfa2YpQKNWbO+fBNuNDolESDOVLxPHevriGVSwjtplh5sIfVYWBrOUSv28VqN+Lx2Sikq9x6Z4Vetx9WL5NKiQZTWOwGooEMMX+KjaUQ115d4tobiyw/2CURyVGrNEjF8kgkPVrNvmtFa1Bz+8oaqWiO3c0E3mE78VCG+WsbeIcd5DIlioUysUCaRCTH/JVVLHYt8UgW94AVu1NPMppjYMiGSqNg5NgAva6EKz9aIJcuUa40GBxzsnB7G0GtIJUscv/GBsGdBEazjkufOcnotIdYMEMqXiCTKLK9EiYaSNNu96iW6mQSRTQ6DcPjNpxeK1q9Cp1Bi9trpteBkUkPp5+ZwmBSk8uUyKVLaLQqcqkK6Xge/2aScrFOLlXeJ8l6nHxqlMheih/82W263S4en5WRSRcGi4bwXoqtlTAWu4FcuoRSI6DWKTFb9cTjBTqdDoIgp93qYLHrMRhVrC0EuH99E4fXjMmio16pMzLpYm0hQL3WxO7U8/q354F+M2cuXcLhMeH12UhEcsikMlwuM6cvTbG834x5/rkpJmcHkfdFSmgNGjaWQjjcJsRWm92tGGKz/QTZde65PgnTbrf50TduceLcKJ1OB4/PikojYDBp6Ha76HTvF9AVsxWq1TquQQuhvSTQb3Xtdt639YX3ktSqDcaPew8JH5fXQq8LSuEgv+uXQFH2V1Kh9ZVoo8f6duQDsukgH81g0R5mkUOfGBuZdKHRKGm33ndyQV/llYrlsdj1TxwXi93A8dM+wrvvk2CFbIWNpdChdbOQrRxabnf3x+jQJ7N2NmJUinU0hn7G3wez7Cz7VmmPz4ZcLsdg6qtHVx/633+9fXJXrXtfEfc4YajWqXB5LQgq4ZBcA7A4dOyuv7893W7vsNXzg8UUvygckWgfA7KZys/niW71QNajXuyh1Cto1DuI1R4yhQyZXIrLbQJRhlqlpFYReXBjk1a7x+52jOBuikQ4RTyRQ6w1ySYr9HqwdH8Hu0tHMVfFZNPx3htLXP7WXSaODxDcTZKK5dBqlQhKKZVqnbFjAxw7PcyJMyOkYzmKuQZKtcDuRoxWs8PgmINYIEWxUCMVLyCVSLBYdUhlMsxOA+lkgVpN5PzzxwBweixkEyVMFh0arYp0vH/iuB8L788kithcxkP2+YN4PB8NoF55kpw8eKxeFykWKj97C2fw/RbOg3X8JLLtQBn30+Z9cJ1HOMIRjnCEIxzh7yfarTaLd/y898OHdHtdUtE8doeesWMOpDIpLpeJSqnC+RenmZobol5rkwwlqRVqrD7Yw+Ux0umByW3imc8e5/jcCBcuzZAKF2i1usw9M4VBq0Ell9Pr9TBatFSKdarlKr/xXzyPw2NBoZDTrImYbQbC/jgOh5ZkLIdKI0enV7Fwe5Pv/ckNXvjiHHevrODwmgmsJ2g02mwvhynnSwgqGQ9ubBDdS3L73VWUKiVz58cx2/S0mx0+/1sXsDtMWG1GxqcGMOi1WKx66vU6v/qPLtEstzl2fpz1h3vcv77F1//Nm9RrIvlMFZtbz6lLY9x5bwOpXMblH8xzbG6IUqaOZ9DC8TkfKw+D+CbsSKUSHtzc4NInTmB3mzEYtGxvxCkWasQiWVqNNiPjDmSClHu3tnEN2th8FKJabfLp37hAaDeF3WnivVeX+N5/vI53xM6lT0zTbncIrCcoFWtMnvCyfG+XXqfHxPQA9bqI2qCmmKnQqrd55wcPuPvuBheeP8bKgwD1Sp3le35uXV5DKpHw7d+/wtK9PbKpAtuLYRqNNl/5z5/n/AvHiAeyzJ0bZvbCMDK5lL3NBJ/7zYtceOEYz3zqOJlkCbNVy9MvHeP7X7+JTC6nXm6wsxXFvxFDbLR554ePOHZ6GO+IHZ1RzcCwDbHVRqNR0mw00ehUTM16USjkuHxW7G4LdLuU8jVqtRazZ3yMzwxSzFcJbCX6xyFb49rrS5y91Cc6Vu77kUmlrNzfo1Kq0Wl2MBg15JJFpDIJpUINo1mDy2um1ewcDnw7rTZnL01RKteI+DNYHQYEhZydtQhjxz2odCqaNZFzL0yTDOeYmh0kly2zuRSiXmviclpwDVpIRnMkY3lsDj1Or4WRCQf+rTgTswMMjToAcLrMjB0boFpqoFILvPCFMyzc2mFp3k+pWKOQqaLVqvqKq2iOeDCDe8DEp37tLKGdJKHNBEqlAr1Bg8trQa6QYTDpKOSqtFsd9jYiPLyxhVQmJbST4OLLMzg8FjrdDmJDZOrkEF3AOWCmUWuRjOW4f3ONa28sMTBio1FvEg/n2FwOUas0CGzFKe2TBSq1gFIroNGqGPBZGJl2k4kViUfy5DMlwttJXv7SGcL7dsztlTB6g3qfKO4TgQ+ub5JLFqlWmuj0ajQaJelonnQiRyqWY2s5jM6gwWI3YHMaSUUL3HxrCbHdP142lwG5DCwuMzIk5NIlFm5vP6HgcXktlPJVur0uG4shjp/xodII7G0nuPKjBdwjDh7c2KBWbqBUCowe8+BwmRgecaDeFz0s3N4GILCV7BM50QJnLk1STJeoV0QSkdwhKZKKFZ74/nR5LZx+eoJYMIPdaSERzmN3G8mmisxfX2d3PUat2kShkB2q6A7Gb0PjdhriPsH0iybD/pp2zlarhX8jhsdnY2nej1otYLEbMJg0h62yB+/dZO1bqA8UY/79gP9sssT03BAAao1At9t9gvAqFyqHqjGTVcf0ySGCO8nDdS7d26WQqzA40h+j963DbVweEzqjGpNFRzKWZ7/X40PIJIqHJKzLa8HpeV+Ndvz08OFxO1DEPU70HZR7dDqtJ+ykgiAwtk8wAuiN6kMxysF7+UXjiET7ONDh52KhAdQqUKihGBOR0L+fjmdpVWuUSlW8E3akSglSJRideoZHHdhsRgS5lHpVJBnMkwznaJYbbK+FKaSKLN7eZnspTDZR4MScjwGfje9//RbJSJ7puSGyyQIqpZJkKEMhW+Lm5WV0ehWeIRvTpwYYGLIxPG5HroBSvoJn2I5EAtsrEXRGNWKzhcmiIxXNo9EKJEJZSoUauXSB9YUQzgEzW8v92l2lICA2W5QLVarVBsVC5VBxNjzpxmDSUKs2EMX3WfcPklH5jyDJSoUaarWATCp9opDggyTX4wSbx/fztXAebOdPm/dXxS+T5fTnxd/lbT/CEY5whCMc4a8KqVSKzqxGrLdZvLODVC5h7uI47VaHbht21oKUSw22Hka48r37iBURpVogGSvh8ZpQqZQU0mX0GiXr9yNo9Upq9SZai5rRGS9LtzZYuLnG+iM/z3x6Fv9KBGmvi0qr5vrlVcZnBtBqlNSbLWSSLsVMkzPPHSceyhHcSVLM1RgZdzNxbIBKqUklW+P0xUkUgoyJYx7CoTS1WgOx1mHA5yDmz+Dx2rB7TAgqGfNX1/nG777F979+i5UHfsw2LasLe1jtGkSxjWfAwdZSmLGZAYLbcRQyCYlAHr1BQ7vRIrwbQ6UUuP76Im6vBaNdQ6XYZG89ikTWppivEdpNYjbrkMtk5AtlBIWE+1fXefOVu8hkEtRqgcB2khtvLDI5O8jeRpzp2SGqxRqzT41Sq7eQyyTcvbpOtSr2t2UnikatYHjKRSJWopwvMXrMw8rdPUrZKkqNkpbYQqUTqJbrnH56kpNPjVDIVzCYtAyOO1l7FGBg2IZckDM67ebMM+P4t+JYXAZC23GkSJk86cPrs+AZttKoNBBbIifOjfcvfnutFLIldtcjdDsSFIIStVYgkciTjOUZ8FnQm9QoVFKGhh0IKjkatZTf/KcvotMI/OBPbhALpFm5t4dcIufK64t06WEwaSgXauxtx3nvhwskIjk2lsPUyk1MZg1Ld/YIbEQZmvRQyJbZWo4wccKDb8LB+mKQyVkvI+Nu2q02k7ODWOx6nvnMLMvze2j1araXQsxdmMBiN5CI5CgVqihVAqHdFIlogdEpD/VKg16vx71rG/jG3ShVCl778zt0O10CWwn2NhN8/mtP0xE7tJstXvqVM5RyVcqVGg9ubtJutynkqowf91ItNxDrbYZGHdQqfQXOlR/cZ2jMTq8LvgkXJquOeDDD2DEPvgkXmUSB6VM+bC4juVQJQVD0L9BLpf3QekGOe8zRJ1W9JtYWAgyOOqiWGsxdGMPmMjJ1cpi5p/t5bTq9mntX1jBZtAyNunjvR4+IBTMs3tnCO2ynVm7S6/bwDrtwDZiJBTP4JhwoFVIkPbC5TQz47AxNuKgU66QTBTxeK0PjjsNBv6CScWxuiMhelvMvHkMQZAS2EiQiOUam3Ty6tc2Az8b4jBeVSsHUKR9KlcD4tAeNXkU+UyYcSBPcTXPpUycpFWvsbkRxeS2IzRbbKxEGRhysL4QwmtSs3A8QDeVJxQo8/7k5Svkap5+eOFQAFbL9cgSDWcvyvT2qpTp72wkaNRGlIGdqdpDl+35SiQK1SgO1VmDhTj/zTmdU887376NSC0zPelmZ38EzZCGbLGEwaygVa7iG+m4om8vA2kLgsNlzYynErbeXuf7mMsv7mV8AniELCkFGLl1m9vwYcpmcsWMetlej+CZcjB/3sHh3h2QsTyyYQayLJEMfsH3+HYVCoWB02oMovl+4sHh3l4W72+TSpcNj9fjY9cCi22p3qO23dR4QU0aLjs3VCK1W+5C8Gp8ZoJDtW9kPyLW5p8Yw72eLHZ/r55vL9tV92WTfUqkzaA7XIZVKDm3CB2q5A+SyFcaP9UsmCtkK7faTSjmPz4bR9P5n73F76YEN9MYbK8gVB+UUmQ/Zeg/I2A/e/0VC0uv1ej99sb8/KJVKGI1GisUiBsPHcxB+/bn/E9XEB3arREKfNPsxNO5HwORR0qyKSCVSLE4D1WId54CVeCiDQiWg1SoJ7Gaw2DUYjDqK+RIqjQqFXE6lUmNoxAZdKJVFBnw2MokCCrmMyVk3q49izF0YJZ0okojkOPfsNIHtCOefm0Fn0SI2mgQ3EwyPu+nSRW/UEg2lkUqkfPrXzrGzFqMtdshlyujMWoZ8NrLpAhKk2DwGaqUm3pH+FaVSoUa1WEVr1KJQymg1O4eNnSqNgCDIiYczOAcsh1dJEpE8epOaXrcLPQkyQXbIVn8UDsicH6cCe/zxSrGObr+x5seh0+l8ZNXvXxUfVNUd4QhHOMIR/u7hb+M3xBH++vjbOE6i2KbT6bK7EiYZKbK5GiDiT1DON3AMmLjw8gxvvnKfRDiHoJEglyk4ftLL+kqIdhcUUjntdpNyXsQ3aaWYbnD8/AjNqkh4L4XbbUWuk9HtdRkccTD/7gYDozYq2SoOrwWx1cE36iTiTyK2OqiUCprNJqV8k2w8z//+//0bfPdPryKTyhiedJKPl3F4TQT3Mjg9ZkxmLVaPmVtvPGJi1kcuV6Kcq/H0C8fYWo2wsRLC4TRx4twYcoWMnY0w3Q64Bq3EAhkq5ToKqQSrx4zdbQJ6XP7uPb7yn7/A/WsbnHt+iu3lMIlQFrPTzMmLw0T3MpTyDYr5Mg63hVK+hm/Cwe5alNOXJjCYtLzxrbuceXaSzYUQ48cGUGkU5HNlKvu/ZaVSKdFQihNnx2mLTXQGHQu3t7G5DAiCgoW7W5x7epKNxRBSmZyetMfppyaIBJJIelIGRmwsz+9isupZvOPHOWjCO+ZCrZLTbLSQyUBQqzj//BSv/cUdTBYdcrmUQq5Cvd7E7DBSTJdpNkSGpz3kkkVGpzx0JSA22v3r5J0ub3/vPi//ymmKuSpnn53itb+4Sa3SYvr0ECqVEplMwtpiCJVShnfMwfqjIFOzPk5eHObWGysoNSqW7+/wtf/qk9y/toHeokepVCCTSfoKxHoTQSXQbneo10Ruvb3EV//Zy9y6vIrJrKdSrfLi50/zw6/f4plPHufR/BadFgxPexgec1HIVei2Wzy8tc2FF2bQGdWk4nlUKoF8tsLeWoRnPj3H0ISDQrZCYDPO8JSbYq6Cb8LFd/74Pc5cnESuEmg1msgUCsx2HXsbCXq9LhargWKujHfMztZSjGq1htlmQKy3GT/uod3p9sPFy03OPj/VLzhrd1BpleQzZaZP+ogGM8jkUpRaJUaTGofb0o+GWY1gsuqZmB1g41GQWCDD8fOjyOUyzBYt3/3j60yd8qE3qvEMWwhvZzBYtIcNmqlYP+/rzjurXPzEDN/+wyucenqSRrXJzLlRYsEMjgETtYpIpVTj1b+4zZd++1l216KYHVry6TJnnpmmJ+nbEu+9t86v/M4ldlaiJOJZ6qUmn/q185QKNYq5EoV8DavDiM2pp1YS8W/FcQ70ybfleT/js17WH+7h9FqxOLT88M9u86v/6FlqJZGrrz9Cb9LgGrRgMuvY24px6VMnefM784xPe5Ap5JgsOubfWaYnk2FzGNGZ1HTaXbQ6JTaXGa2+H4lTKdZZf+RHo1PhGLAi6XbJpEvoTRpA0s+wq/Qz4OxeCws3NjHb9ZSLdUanPAgqBQ+urTP71BjVUoMuoFTKodcnSnKpEhaHgTvvrlDK17HY9aj1/TKLvn21TwStLQTotjucOD/GxmIQjVbZJ2F3UticJoqZIh1Jj6kTg+yuJ5g6OUBL7KJUySnkK7RqLZrUOHnqOJ/0/TfIpcof9zX9S4t2t8nl4P9GOBzG6/V+6PHFuztIpdJ9Ut6C2GyTz5YZnnSTT5UwOwwUsxUEpZxUosDQqINCpoLHZ6NSrKPWCchkMhLh3KFd02Tru8MArr66gEyQY3MaMRg1eHw2onspWu0u+UyZ009PUMhUMNneJ78OSNhYMEO328U74uD6m4tMzAySjORwDvTVpo/jgAxzeS1UinWuvfaIU89MsLcdxzfqxDvqILiTRG9UfyQ5lorl8W9EUWtVzD01/jd3AH4CftbfD0dKtI8BBuVHEDS9Xp9D+1klnrIe9WIDlUpJtdImlywgl0vJJHNUqw3SkRKFXBmdRobLaSQVTeHymoAeVqeeXLJKryslspcmE88SC8TQmdTozXqq5RbnX5hkfTGEUiNjYNTC2qKfY6fH+80e7S7PfXqO6VO+fuhkrkqj3mRwxE6t1uDr/+Z1br+9RDqRxTVgRqeSs7EUolKs0+v2sFiNeEccrCz42VzcQ2y0sO574FvNDlq9ksBWnHy6RFtsk0kUkUqkSKXSQ7JLo1Oi1anodvtXParFfj7BQZnABxVQMpn0Q8H99UqdSrFvATWYNIckVrX84ZKCD+LxJhCxIdKoiT/j0f9oHBFoRzjCEY5whCP8/cHGowClXHU/91XNyIQbQS4wPO0mFcvxvT++jtGiQquR0221aTZFIoEMaq2WcrJMrdKATg/PkIVKoYHFaUTagbXlEEarhmqjxpd+8yIqFAhyAceQgeh2CpNNy4lzY6TjOQqFMmq9ArHeptMDs81EtVZm+LiH2++uceLkMFOzQyzc2CKXLTF7boyLz03TEju0xA5KuQSDQ0cymuXYiWHMFg2lco10LM8//m+/wOTsEO4hK4t3d5HJILqXwj1g5nO/eZFf+dozdCU91hZDNGoimUiB0WkPPUCjVfLO9x8S2EojkUrx+MzIZAJivc2Zp8bwDFoYnnDTbDa4c2UVi11HNlnmze/cIx5MoVDLOPv8NHJBikKlwOG2YrHpGRpzENhMUC7WWXu4Rxcpy/d2aFQbtMUO0UAai03HveubeIbt/MP/+pOENlNodSp2ViPIFDIW53eQKWQ4B21c/NQ0vnEn6/O7XHvtEVaXGYlMhlol8L/9376D1qCi0+kiqJWU8jUsdgOZeJHnP3+KyeNeCukivV4PtUbJ3cvLrN7fodPqMH3ax+zZYY6fHaKUL/Hud+eRSCRI5OAashAJpIiGM3gGTYxOD6DWqPGNuhmZdLN4a4+xE4NojSq++FuXuPb6Eul4EblcRng7iUwmwWTVYbDoiIXSlHIl9Do1T714jEyywMCQGatHz4mzI+j0Kl7+8ile/Yvb2F1WJk8OMXPGRyaZ58G1dTw+J26vlaFxJy2xTWgrQTSQodVs8dnffBqJBHZWohSyFSqlOu12m4c3drjy/QVMZj0muwGZVEKt0mJzMUStUsc1YEEukyGRSZGpFEhlcqrlBqPTXrR6NcNTburVBvFQFpVWSSqVZ28jSiqSo5iv0W73OP3MFNFAGrpdTpwbZXDEyqOb22ws9t0t3hEn28shfvT1m5htBj71axeI+pMYDCp2N2P8yu9cwu4yUcrX8K8liAbStMQWW0thHlzbIrqX4sH1LcwOI/PvrfH5r15keNJFMVehXu9nNFcKDSrFGtFgmk//2gXK+Qr5XAm1WkWvC5FQlp19O6d7yMrrr9xmYMxOvdLA4TXx6M4O3/rD99CbdNgdRnLJEo9u75CK55EgoVpskAjnmDrppZitIIotet0uq/dCeIedRPb6CqAv/2fP0qw1cbjMdDptjBY9d95dwWzRUcxXKGTLiI0WOpOOT/7qWVyDVgxGLYJCgUJQoNWr+tlvsTypeJ7VB0HkgoyVeT87mwmkEhlWuwn/fgB9u9VhaMJFvdJg8uQg3XaPmXMj/eNbbTAxO0hb7FKtNjGaNHiGbDy8vs7inR0yqSLz19YwGrW8/OXTnLw4itmio5AtH5I30Lf4tVt9scnQmJPgThKQcuriOJVyDZvbzOCwg83FMG1RZPm+n51HIe5f2SC8m2Jyzke7eWDn7P7dnYA7l1dIhHOHkyi2Wbi5hdhoM3t+FI1GhVTSJ9KOzfnIp0p4fDZCWwlaYhtBKaeQqaDRqvrZhZkKsWAGmUzWD+kftCCRSPD4bJRyNRLhPqn1whdOc+L0CNMnh7C5TWwth9HqNXQ6XXzjTmLBDI39xtYH1zcwWXUs3tlhbSGAx2ej24F4KMtzn5nD5bUwd3EcjV71RI7b4p0dZDIJV3+4QCKcQ2dU8/mvPY1UKuXSJ2fRGTQkwjlaYusJAu3a64uHCriQP8H4tBelUjjc9l8WHJFoHwNq5eZf3R8t379t9WiWe6iVCmj1qOY6pEIVitkarRrQ7VcJI++y9jCK1WUiG6sSD+aJhdOYrSpCu3GazRa1qki5UGVvPYHYaLK3HeU7f3wVjUpGJV+jlKnhGbASCyRIRtLcu7rKO9+9h389itXevwKXCGbwb8Ww2rSMjHk4++w0giAQC6Yx2vSkwik0ejX1ej8boJCroFErqdfa2FxG2u0unbZIIVsisBVneNKNw92fXy5WiEdyh2qtTKJItVynlKugUvXLBWwuI3KFDJVG/gQhBpBJFmi324fe8W63SyZRRK1TH34BQJ85LxVqOL3mDx2zUqHW/0G7D7lCcThfUAmHpQd/E/ibtEDm0j8+m+0IRzjCEY5whCN8POgTDx1K+RrFYoXAdhyzU8/ucpRsokpgI0k2mqOYq5BL18jGS2wuR0nHk+jsBqx2A10glcohV8lRq6QsPdhFRpt6pf977+HdXULBFNfeWMQ36MHsMGL1WPn6//d1CskK3TbE/VkErZR6pUYymUOlUJCO5bHb9URDWZZubXPyqQkEpZJX/+wGUoWMVDTD9lKQpVubNKtNWq0u/u0wu2tRitkqg2MuXvmDy9SrTW69tUwpX6bbkTBx3MvuZozt5SCXvzvP53/9EkM+KwajhoEJCzqdisB6FJNNx5d++xnGZ9wg7bEy7yedzGFzG/nLP3gHQa4gEUmTSRZwec0kYnmsbgMj4y4GRpzkYiUyiQISmZSrry707T3hNA+ub+L2WVFIpSgUUrKxHKV8Da1ZjVxQcPLcGJlkgVS0QCyUZnl+m5EJO/evbzB9aoTJU14UCgXjJ7zcubwMbQjtJYkn8zz7uZPcu7LKsZOD5DIFfNNuqqUaY8cHWLi9hSDIUKjlSOmxuRxhdztGIpInFclw58oKFqsRi93E9kqYlXk/5VKdt165j96g5/xLx5m9ME671aacq5GMpNHpVO+TmUo5Z54Zo5SvMjTh4NHNTa69toBCkDI5O8hX/8uXCO/EOXbax+ZSkEKuQjFXRafTYLH3lUcgJZcqsfEowvbDEO98+z73r29y971NLrx4jFMXJygXa0T8afybcdqt/mC33enyvT+9xvLdLXzH3PgmXcT20uyuRbnx1iP8G2GC23G2VsNcfW2Rcy9OMff0GIJaSngnhUwhIxnNcPrSJPPvbtDtdnF7rVjsOhxOA4IgZ+bsMLlkkbbYolKo8OjuLjaXgWQky4nToyzf32PAZ2N4yo3TY2ZnrW9HW7y7Sy5dZP6dTWRyKXK5BINFS48OjYaITCFDKpXyyn94F8+wjZtvryKTyIj40yzc3KJWriOXyTj9zCT+jTilYg3noBnfhBOzQ4NU0sNi0/P6K/P86E9v4hy0srvaz99KRLJ4fDZ0Og3lfIXAdoIv/OYzbD4KcenTJ3lwbRWZQsbty8u0Wm2eemmG+1fXsXusCEol4d0kT73Yz8JrNJrojCq67X6ek91jolJu0O122VgMc+vyKp5BO4lQHp1RzfTJQfKZKmKrTSFTYfS4h0qlTrvVIZ8pMzU7hEIppVFvYnOaiUVzpJNFVu7tMX9lhcXbuyDp0RZbhP1JSoUay/NbWGwGvvg7z7K7luClL51Bp1Xh9BoJ7yUZnnBSLTfodLvsrocRG21W7vmRSmB3JYzFqcVgVAFSut0uYv39/Cqb20osmKFHl167i0It8Oiun2K+zs5amNB2irVH/n4TYzjH4p0dXIMWwjsJInspZs4OE49mCPrTDI+7mJobQq1RMXVyEIvThFhr0ep2GZxwY7L01VDZZKH/4r/oXLO/ZiaaxWnENWhBUMkpl6r86Os3cA/bGBx1kAjnMFl1yBUyOp0OO6thPD4biXBf9ZVLldAZNFx86TgA64/2+O6fXGVwwglAdC9NNJBGUCtIhHPUqg1cgxbuX93gm793GbkgZW8zxvyVVVQaJSabjmQoS6Mm4vHZcA32lZsHrZ/dbhfFvuXSYNJgtLzf4LmxGKJcqFLK98e4hUwF6NIWe7h9VgLb75cEdDqdfh6bTUc6kUNstA/VcgDjxwfw+GyIoohWp0GtV1EuVnENWn6piLQjEu1jQDHb/slFAr39Wz7itvXYiro9EskiyEBv6X9o5bIeCnWfbJP0oJLv+44juzlS8SIqjYRGuUkxX0ejFdBqVTjcZmQKAbVeQSVb4vTTUzidJupNEYlUhs6oZmc9SK1WRSrpq8Fq1SYun4V8qkoxWWby1DAqQUCuUKC3aMmlCyjVcjLxAvPvrnHq0jTlfBWFIOcHX7/B+sMAo9MDGC0acqkif/ivfsj6wzAqpQKNRr0vNVVz990VqhURel3ERmuftFKQCGfpSSAWyh5aP01WHaVcnZX7/fDCAzJKpRbQGfrqv3qlTipaQFD1SbCD7LJKqYbHZ/vI3DRRbPdfI1c9fOxnKRz4q+Jvcp0W+99cNtsRjnCEIxzhCEf42bCxEEBvVJFJlFCrFZQrDe5fW0dnUWH26EAG9UaHyVPDuIctOL06nv7Ucbw+F0NjVoKbEeQyCTZTf5C0tRmjmC/iG/GQS1UYGLdz/Y1HtDodhias3L2xhlip0yhXOPvcJBqdgo2FPSqNJm6vnamTPrQqAWQyTFY9IX+artiiWhOx2PQghaFpL/NXV+l1e+iNGkplEf9GGL1GTi5W4OSFvo3y5FNjtEVYvrvN8vwOzWqDa68/opgtk4rkqVdbtBoib/zlTarlGqHNGLff3iQZLYBEwtXXFrj9zjoGiwaFRIHVaSS2l6aSqzNxcpjpcz42F0IMDzt4/jOn0GiVbN7z02l3eOrl43Q7HTQqJburUXrtHjqDmkqxycLNbYJbYZLhDCsPdqhUm0zODLG9GMS/FuCt78zjctnRWzUMjTlYXwjTk/RoNFtotEq+/yfXGJl2s/koyMSMl0q5htVqZPbMMAvXt6iW6nzj31ymVmmS2E3xwhfOUsiUcTj1GKw61u8FCG4laNTraNQqbHYjFoeJVrODoJKRCmY5fsZHZDeBpNdj9NgAiViOfK5KPJTB6jCy9nCXyTkfrVYLl9dEMpKlUqpy5bWFvrLvfgCJVIJn0E6n2yWyneBf/vff4LlPn6TZaDE6NYjDbaFWrVPIVVi8vcN7P3qI3WngxNlRXvryWYw2DcfOjBLaSTIwZEWtVlIvi+TieXbX4iQiWZxeC5sre0S249QqdTyjTgJrcZZu73DxEzNUylVaYo/PfvUZJLIeEmmPxdtbXPnBA4L+JIs3djE79JSyZU5dnGRnKcjECS/xcIZMukguVWJzKYx/M45r0EIpVwYJVMtNJk962V3rW7T86xE0KiVIJMhkUvLZUr/JUa9i8tQQhXSZyZODzD41hs3ZV3RlEkUMJi3Tc0NsrYaQIMFkNhAJpRDUCgrZCjPnhukBtXoDhVJOOpan02qTCOfQGrWIFXFf/dbPwvvMVy9idRqQy2VE99LozRoysTyDY3aKuQoavYYrP3zI2ecnKWQqXHzuOPlMhYkTXpKRPhHgGrSiVslQqeW4B604PWYEhYxqscGDq+vY3EYuvjxDWxS5/upDBIWMUqGCWgb5TJFzL0yhVMnRGVRIJF2kEli8u832coRcosjq/QAKhYxkrIBv3INEIkMqBYNezfnnp5k6NYREKkVvVBDYSVCpNojtpWmJDcxOI8V8mU6rzTOfPMHr37xNPl/i1ptLZJIFwntpyoUacrmMalnENWjh+c+fotPtEPanyGfLvP29+5QLVbLpAu12k42lPZbndzFYNBitOqKBDAqVAu+wHe+Qlb21KBMzQ7iHzeh0KkI7Ca784B4AMqmUXLaEy2vFYjeiUikx6pX97LRHQXRmgfn31oj4U5x8aoJGQ0StkSMo5dx8a4nWYxnaf5dh3m+a3FmL0Wo0mZ7zYTT2G187nTaLd7bZXAlSr4iMzwwCkIrl0BgELA4DqVie628sAnDs1Aif++rTqNUCothm9sIY7iErEiCwHcVg0iI2RMZODOAbd6MzaBiZ8nD89AihzRhvffcuz3x6lnS8wOKdHQqZMolwjqdePk4inOP0M5No9Wpy6RJhf5LNpRCBjTjQH9sODNuBHolwFpNNh2/cQz5X4vnPn0Iul+0Ta6DVqckmi4gNkdnz44xOe0gnisSCGZbnd1FpBDLJApHdDO5BKwqFFEEhpZDptz7/pHK/v00ckWgfB1S9fZlm9/1b3mdYD/sEuh++lWseI9uAbrUHYo9ysU/M1Qo95HKg2aNV7SGX9qDTQyrbV6/VuxSTDbr1HtHdApVSjXQsS6NUpyOKRENpLn9rnt3VBLVijchWlHQyj8qgo92QcOudJbRaJe1Wv61FIodcIU+9XKNaqiGXSFm8vUW13CTsT+McMNGT9lh7sIcotmnVW0xMDzB+zMPS3R22V0M06i3+m//Hb9Dtdul0e5RLVWQyePf79/oee0GGWqugUuqTVxqdgEKQsbseodUUCfmTiI1Wv95ZpeDEuVGie2nSsSyi2FegdVodKqUaap16f17/o31wojVqLYr5J6t5Dwi2jYU9AFxD/TaRJ1RuH3Giph9rmCkVqkfh+kc4whGOcIQj/CeGi5+YpVJu0GrXuf7qEp/98nmMZj3lXIlypoRM2sNg1aBUypBLoF5qE3wUJhvJE95MMT7jJRMvohQEtAYlMkmH8WNeup0206dGKedr0JYyOeGmXqxTL5Rpd3tEggV2NmLk0kVMZi2CREpwNcHaPT+JQA6VQsKv/ePnCW/H2F6LYLaqWL3vR0GPpdtbNGotNFpVPwi9UMNoMNLpSaiWROLBDApBzo03FnF7jWj0anRmNeeeP8bnfu0p9BYd3VYXsdEkla7w0pfPUSnW+dI/eRaFREK1XKNRa+JwGbFatEzPjSBoBMr5GnqdlnQqR2Ajxp3La3jHbcw+Nc57rz6kI7aQKwTOvTBNMV2iVm2w+tAPXfjaf/1JXINmRqbcHDs1SL3S5tJnTmG3mymnq0QCCSaOD6Iz6UHaIxHL8uWvPUM1XyO2nUIqlzM64SYVzhLZTrB+b49GrY5EKkGmgKg/xelnpvjEl8+i1Qm0Wm1Gp134jntYe7DL1lKYzdUInkE76VgBjVZFs9xi9cEeYzNeauUm1UqdVqfL7MUR7ryzglIlkEmUyccLFDNlHlxdZ3jKjddn49JnTrP+wE+93KKQrWN3GwnvpskmSmwvhcini7SaPeI7SSL+DCeeGueLv/0MMkFOp92i2WySimUZGnUwd3GMZ794CqNFQ6lY59t/9C6r89tYXUZS8Rwmqwa5Qkql2mD54Q6RQJpytkSv1UOjU5GJlZi5OEE5WyObLDE+4+H006NsLoYo5Wqcf3GKXLqE1WHm01+5wK//717E5jCQSxZ57gtnyCYLrC2Eeed798ikchQzFbaXg6ze22P+vRWOnxmhUqhy550V5IKM0FYSx4CZlXt7aHVKNh8E8G/FETRyTl4YQ2y3aYsd9AY164sBpPSHSK5BC8HNBO98d55CvkyjJjJxop8jdeLsCHJBRqVU48VPn2R1X72m1ihZvb9HIVXi+390helTw6jUCsx2PddffUi53KBcrhH2p+l1Oize2eLtb91DqVYS9ifRaJTEIzl2VsIMDNvRaQVe+pUzXP3RItCjWKyikMvJJAqolHKyiQKSHnS7sHp3D6NBzf1rqxSKZVYXgjz/xdOs3N1hdy2KRqdhYNzJ2sMAUX+arqyvnlu8vc36wz2iwTzNWov1hwEMZi2f/epTmKx6nv/cHMGNBL1uh/d+uMDEzCDFXIVyqcrWoxB3Lq/iGbKiNWqJ+BNIkeIesWF3WUmE8whKBQpBRiaWQ2/SQlfC6UvTSCQSbE4zkb0kWp0KnV7F8p1d/uhfvsrCzR20Bh1arYaRcRd762GMJh3DE16kEjmzF8ZQKuU0SjV8I06OnR7pB+Ln+vbXeChDu9UhmyrRqLc5fmaUWqXBd//jexSzdbaWw3zzd98hupdAqVGgUMpYvLPF3mqMiVkfcikENqOUsiXqpTqJcJZGrYHBsm//+0milV/2Cei2u1RKNS68ME2lKDI05WLh9g6xQBq7y8SZZyeR9PrRRmF/ksBWnGKuwmt/fptypsTeZozZ82PU6yIbiwG63S6JcJZ4IM2tt5e5fXmVXreLUqUkHsqQS5W5/fYyo8cGuHd1lVQ0x8KNTeq1Bs1qP+T/2Jlhjp0ZIp8tEQllWH/oR1DJKWQrXH9zEYvdgG/ShcNjplZr0Gg02FoKUK+LVPI1ut0e2WSBZDxLu9UX+8ycH6NaqXPt1QfUGyLjs4O0293DfDWFQsrOapjx4x66nR6ddofRYx6UShmCSmDumSkS0Ryh3SQGy0/ONf/bwhGJ9nGgus+KHZBlvYPpp/uj2+Uf85j4/v16rgO9LhqzhHa9P69T7YKiS6/+2HM6XSqFJq1Kl1q1TmwrTynTJB0pMTrlpJCtkozmSARSeOwGQnsxJud8IIFirsjSrU1WH/gpZWoEt5J0e12kShm5ZIVStkglXyMRSVNMlkglslTLdeLRLEF/nHg0SzSQQqlUUMiUuPbaAgazGplcyrXXH5HP1ZDJZRitOiqFGsVMDUGQs7UU5NHNbVxeK+PHB/FNDTA06qRSrFIu1PdVaVUCWzFGpgcQBDnJWIZEPI/O0H/MYNag0akIbiWwOPoMv81lpNN6n8g8IMqyiQLTp0c+dAhLhb4qTVD1LaKdTofNpQAARsv7JJvBpP2ZlWUH6/ybWu4IRzjCEY5whCP84qDWCnRb4Bgw8Re/9xalbAm1RoPVYEAi7VIv1bn91gr5VBGZDIxuPfFggWw8SyiQQamWs7cTx78Uw2wyk02VqdTqpEMZ5FIJRqMKQa+gVGyiUsmpFEqcOO9l7uIYar2abrfDzPkxvJNW2t02WouS6XOjrD30U86XsbhMKAWBTqdLpdhE3pUyPG5DqxXIpgrozBrKhTIyiYSGKGJ368il85TyZeQaGVqNgMGgY+VhgL3NBJl4Frlayr1r6zidRtYe7CHIpfz5//IWozNuKukypUyJQq5GsVjjyvfu0mo0iOzFWVvYplyqQK+L06VHrVTwh//qdXYeBfAMOZDK4C/+7WXGTwyikCuwuU2kojl++I3r3HrzEY9ub7G9HGLi5CBrC34MFi35fAmDRcvuWoTNR0HiwSxnnp6g3emRTOUxDxiYPTdKPl8msBNnYNyJfyeMRqtldz3ExmIMrUGJfzXC3naU1Xs7zJzxsbEQxr8a5pV//w6tlkitUOPR7c1+2HauSCqZ4+yLE8y/u0I6XcTqNpKK5HjtlbuYLRpOXhyhmCvTlUhQCDK8wza2V0K8+/0HvPK7l7HadIyecLO5FCC8lyIVzfL0J0+QTxX7FkO7lp4UrHYDN99a5NZrSyzd3ebB9S22FyP0ul2+9QfX2FoM4V9PUCk1aNbryCX9C9AbD4PEAylkcjmBrSiCSk4xXcQ5YGTqzBB6k5rQVgL3sI1MNMfkyUE6zRbXXl9mcX4Pk13P4IidTgfaYptOu8fuWoy7V5YRGy16rS6paAaX14JM2qOYr5BN1ahVapx77ji/9k9fpN3qcvXVBUwWHZHdFM1mi06vy8K1NaS9LpKehM/+1kVGJpxIpXL+8t9fppwrs7kURGy2adbbJEJZYnspHl5bJ5PIc/LpSa79cIG3v3OHvfUYj25vUcxVkcklbC4FScQL1Osipy5Nks+Wee4Lp9AZNcxcGKdSqFFIl7j8ym0sDh2rCwE0OiUWh4622CIZzjEwYuPma4/YWQrT6bSxOAy0Wh1azTa1aoOtpSC/9k9fZGs5TLvbZWjMwcL1dZ7/4hke3NjAZNOxvRzG6TNhHTDiGbJBV8LFT8ww/84KriErqWiG4Fack2dHENQKTl+a5NhpH6cvTVIp1zjz7DQKQULUn2bqlI/QVpzAVoKrP3zI4p0tvvDbz+AZtlOvN7A49Czd2aYlikjlEqbnhlBrlaj1SjQqAaVSQT5R4NHNDcSqiFqrYvH2DkqtgEojp9tpk4z1yyR6nQ4mu4F8ukTEn+qrFYcsvPDFMygUMDjmJB7OYnGaUWsFGnWR4QkXN99cYmsxzImLowxPu8mniv0xqlag02yTSeWI7CbJpysUi0UmT/kw23R8+tefQmcQSMezCGopJ85NcOutJd753jxz50fJpco4PEb0Jg3pWIFqpUGr28Vg1uL0WBD2xRK/cEvmX9POGQ2m2duI8eYrdxgYtaPTqbE5TQyNOfjh12+Sz5SRy6Qsz+8wOOrk0c0NnvvcKdLhDGKnb69853v3Wbm3i1IpEAmkyKVL+CbdtMU2KpWC8G4aSa/H7IUx9GYtn/zVc6SiOSx2I+12h2NnhzFadJy8MIpCKefBtXUEQcA1ZMdm01OvNjFatCze3MRgUu8rwmr4t+JsPgrwrf9wheNnR6hX68gEGbVynWajhdGkQybrFyrWKw12VyNo9Gq2lwIkghki/iQarRKNrp/75vHZufr6I2wuI9lkv/1T3CfhcukSFpueT37l/GHr7S8aRyTax4GP6UR7/35/quU6/fsH85qPLXswv9O/bVZ6IOPwuf7VGM1qA4NZh0Iu5+GdbWpVkeR2lI31CBJZB5VGRWQnSWA7Tmg3SqfVZnc5TCqSYnMhAr0Om48iNOsiNoeZ3eUQCzc2GBixE93uV5ufemqCzUdBKoUqk7PD/Ojr15mcGUQul+IetNEURSwOIxdenqFcqKLRq5m5MMaDG2v0el3q1QbFfIVWo0UhU0IU29QrTS59Zo7FW9sUshXSiTImc98jLzZaNGpNALQGFYlQFugTUwpBQr1SPySpYoE0VpcJQZAfzhPFvi+78pi6rFZp0On0mDgxRCZRIB7O0el0PkR2Pf53Kl44vF8pHTSDvu8dT0RyhxlsmUR/2VyqSKMm9n+wdJ6sB/44cPC6v2yoVRqIf09k2kc4whGOcIS/nyhkyiRCfUX85oM9crEiBrOGVDzHxnIUmUyKTKZAqYdSuUkuU2VrI4p33MzwcRdmk4ZGpYnJqkelFjjx1AiNapPAegSNUYUgl9GVS5i/vEoynEFj1qFUqfjBH15n/uoKx08MkIrmuP3OGrHdNJG9OGargbdfuUMylsVuNfI7/+JTtHsdytkSO1tBrD494Z006VSF1fkdNFoF7XabqVM+Bn02SsU6dpeZeqXGyu0gFpcBh9vI9KlBBKWcrcUw8WAGnUrF1KlBaqUKFqeJidM+br6xhFTR/20h1ho0xSbNZgfPsIN2s41cKaCSqThxZoyFW1vsrsf48j96hqc/P8eN1xbY2gwzPOZg8eYWw9MedldCTM0N0etIkEoVXPzkDF/9rz5FR2wR3o3TlcL4cS92t5lyqclnf/MCkk6PbLrI3SurhNYStOoiCzc2yaWKDAzZcA+YmZwepNkU0em1KBRSlEL/YmkhXeLiJ06wtRpDrVMR3okzfsyD3WnC7bNRyldYvLXFy79yjlKmhEqlwmzTUMuVSe6mGZ50Y9AqyadrfPPfv4fFoWX+vRUalTpyAZSCgkufnkUul6LSafjBH1xl+uQg9CT0emCx6VEICmr5Bot3drjw8gmuv/aIXrdDNpsjGS3whX/4NCaLmmQkj9NjIBlN0yhXyMZyBDfSeMednLo0zeCEE9+EG7VOwGTREt1OMnp8iHSk/xtW0uvRo0c1X8fjs9EWO3jHbLg8RhQyCUi6pBMFwptR/uzfvEkqnKFZE3F6HHiG7WRS/VbNy6/co1yq88lfv0h0J0EkkCK4E+fmWw8BKeVshWqliaCUoVIrKedqeMfdyBRy0vE8G4+C2AfMSHpdJPTYWY6QDGW58+Yikl4Xp8+K3qQlGc8xOOagWW/zO//tZ/kn/8OXcI/YeeGLZ9laDNHrdMnEi0yc8JJL59leCpFLFvjWv7uMw2NBa1Cj0ijYWY/wma89hWvQicWiIZ8o8/Y37zB63IXGoGF5fofpM8N4xuz84E9vEA/2izHq1QauISvtVpdHd3bQm9REt5JkkgU8I3bS+0RUMV8FSZdkOEs6XsQ76iIVzmEwqFHI5LQ6/TFMsyEyOedDqZJitKopZivcfH0Rh8dEvdpEb9Qgl0m59fYSz3zmJJlkngsvH8M76uLOOyusPthFpZJz7bVHPP3JEyzf9qMzaVh5sMP2Yoj1uwEGJzxc+cF9bB4bjgErBrOGneUQuWSRwVEXnkE7xWwFqUJOKpYlny5SzpbRGVUISjkKlRylWobaINBudnh0fZNStopn2Ep4J0XUn2R3LYJ3zE7YH+e1P7/LW6/cJrARI5suce/ddRqNJltLYSbmBvCOOuiKEr7xr19jeX6XaqGO2OwwNePj2U/N0Wo2OX1pCqvTyN5OikKuithok0mUeOrlGT75lQvE9tJsLkfJJAusL/h/sf8A/oaQDGXR6tQMjjuR7auxTFYdmUSRuUvjqLUCb3/3PtlkicXb20zM+nj723c59ewUKpUCQSlj/MQA7Wab7/7he/TaXUq5KnsbUbwjDgx2DYVcBZvbyDf/3WXe/f49sqkSyb00rVYXlVqgXKiQTZXo0Y9FGp8ZYPnuDmq1wOixARqNNjurYaRyGbl4iVggTa1cR9KFz371IiOTA3Q7IJPJEAQ5OqMGj89OxJ+kXKhSKfZzx08+Nc7M+XF0eg1qrRKFICcaTPKt37+CyaZHZ1Dj9TnIpUuUCxXEhki5WCPsT1HOVVh/FCAayDyRYf6LxBGJ9nHhryzt7L3//EMpGx8mxw6Vbe/PfmLZD87vAe3+fLm6/7fYFOn1OkS2skR3MlgdJna2YshbInsrUWTdDoOTNuQyWLnrZ+n2HrvrAaQSKdVKDb1Zi1QKw5Me6sU6Y8fcHD83xqMbW7hGXMxeHGVvJ4FjwMLcM1NUSjVGpgco5SrcfmuJu+8ts3B9A4lUwqvfuIFSLbC1GGJrOYBSriQVKbK5GCC4Fcc1bKdWbXDv3VW67Q7lYpnJOR+paJYzz/Rr0Yu5Cq26iNGqPyS0DjPRCjUatQ75/dyzTKKAZ9gO9Mkvg0lLIpJFbIiEd1KHj2l0SgSVnLbYRiqVYnOZ8E24kMlkT5Bi8CRJptxXsAHoDB9Wqml0fea9v40mACwOIyqNgMVhRCaTHS77cSnTDl7348Jfdbs1OhWCIP/pC/6SoJjvtzkd4QhHOMIR/tOByabHM2Tjq//i03QkHTyjFqRSKVqtwMhxB81ih2KmhEqmZGDAjNur5+SJYXR6Nfl4GaVSweCoi0q2RMKf4+ZrDxmddvPcF85RyFQolxpoNDLMTiPlUpl2pcWpS5NY7Hp6zS5mt41eu4fNpkallPOF33yOvfUII5MDNKotlGaB/+e/+EMysSJ6k47P/c4L3Hp7BavLQDlfYurUMIHNCHKZjIUbq/i3o0R201z8xCxiE3yTDoxWIws3N3jzm3dxDpj4x//Dr+DzOTBYNcxf2SC4lWR53s/Dq+tMzA4yfW6U85+YIRHOEPcnkAKvfv09FDIZsc041VrfvgVQrzb43h9fJRsv0Gq2kLTh9rU1gptx/uJ/fYPps2Mszm9RzJbIZ0okg1luvb3EzkoMp9OC12tl836A22884umXjtPrSuh2O+RiBaJbCXQ2PRKFBJvXQmg7zsq9HdbvBekhJbgRQ2dUI5PKySQrpOI5BLWaY2fHMVu0NBpNxmYGaYldFCo5k7NDNMU2516eIRHNkUlVyIaztFs9Ot0eSCWkIjlsg2bMDh1D4w4y8SJnnpskk83jX43jXwtz++0V9CYdNpeebq9FPJQnEUyTjKT4zu+/i1IpoDSomD3vY2MpiMWuRVCpmJkbwWzU8OZf3qGQq/DeDx9w6bNzZONFUrEcyEBrUhPZSSIolSRCGRKhLFq9lmSkQKVU5exzU5SLDVRKAbPDQCaeI7qXYm8jTmQvw9bDAA6PhWZTpJyr4t+IUcrXOXlhvJ+HbNUydtxDcCPG8LSTjYUA7baI221m7d4uU2eGOfPcNDK5nPvXt5h7Zgybw0Cj3qDV7CJXSFEpZQyM2theDOIYMLP+wM/i7V38WxEqxQYOj5Hj530MHx9ibGaI3aUw3U6PVq3NzkqY0HaU228tcfl7d1m7t0OPLvWqiMlqQGcQmL+8ystfOk9kJ871N5bwTjq48r155FIZRouWsWkve6txIjsJ9AYtPXqcujTJ5e88xD1oRm/Q8uD6BplYHin9odPlb90hmyoR9ad48N4auyshXvl3b2N26pEr5IxOeunSIRcvolErCG7FOX5uhMvfuc3GIz9qrYLNxRCFUhW700gyWiQdznL5ldvcfmed5TtBVh/sEthJ8Bf/69tIpRJ++Cc3gB4Prq3RrIu0xS7JaA6zTU+t0kCjUeIZdmAya7hzZQXkPW6+uoDTZaHZEqlVagz4bEyf8rFwfZ1KsY7dY0KhVNCoi/z+//RdXv2za9g9ZlJ7aeqlOiqtknanjcmqxeLQozMoMVoM/P/+z99AZ9XQaHdQahSkowUe3dmmXmtSLtXZXgozPD3A8KQbJFJMNj3uQSsz50doiV3++f/4q3RbUgqpEgajms/8xtNotAqufP8+9WqTH/zJVW69uUhwO8H8lXUcXhuj0x5mzo2QS5UYnxmgVqnzJ//yB2RSZfQ6JcV8lamzY/0v4h6/eEXZX2nqb/6Jc8No9Cp0OjVGi57t5RCr93dpi22UgoJCrozDbWZozIHRrOu3BSvkhHbSTJ8ewea2otUqyWdKHDvlY/r0IN4RO2qting4Qy5WJhvNEtpNMXrczfEzwxRzZcROm5tvPMLiMHL/2gaVUhlBkGOxGynmqoR3Eohim+2VILG9NGqNmqEJB95RO5VSHb1FR7vRQFALXPrMSSJ7KXaWwwR3kjQbTeKRHBqdmuGpATR6FY2qyGvfuMHGAz96k4ZkvEAimuLuOxt4RvpxShanHolUQiKcoVET2duM02136XU61EpVdhaDxPeSLN/d/lv+b//ROCLRPg50ez99mR/73Meef3D/ienx+R/1+E+f2pX+bb3QIb5TAGkPjV7C1oMddGY1O+sp7l7fJrgVZ+t+mEa9jcNrRG9SUSu2ENtttHoNUX8Su8fK9TcfEo+m2VlLsHJ3C5fPRiKU4Xt/dJX1B3sMjNlYurPF3XdX0Rq0zF4YQwIMj3uYOTdGs97AYjciKBU8/ZkZHl7d4sVfPcvmoh+DQcfk3CDxUAaTzcClz86xsRiiWhGJBpJ4hqwsz++wdn+XWqWO2O3SETt0u10ElYLCPmlmsOkxWjRodEpCWwno9dhaClKvi2T3VWMur5W22MW8bwHNJAo0a/3MtY9ivWuVBoVcGfgwYWTcV8b1M9OefCyTKHyIgPtJ+HmW/euiWvrx7P7PqxD729zuXySMZh1q9S+HtPgIRzjCEY7wt4dEOMvlV+6gVasJriXp9SC0niERSDE8ZqNWqlEq1Gk1WpSSZR7e3abT6SJW29Srdar1FsPjLmwDBiqFOvffW6dZbXHx5Rk6zSalTI1CtszJi1OILZFWvcnQmAux3iK8HkahEdhbjxEJJpm/sko2kadSrJJPFkiG83jGbBiManKpEqVkFp/Xzvy1TSI7ceavrpPP1DFZ9HQ7cPL8FOVSBaNdw8CwiZ3FMP6VED2xg1wu4847S2yvhNlcDeH2WhGkUmJ7Keh1KObK9Lpt3vyzO8xfWeflL13AYOk3uwl6HdVak5mLE0ilUk5e8iGRSklFsvyj/+7z0AXf8QHUMikS2gwfH8A36aGULWA2G/CvRmlUG4S3oqSiOcrFIrFQmuBWHK1BYHMpxN56iO//0XtUaw2kgpTzn5ih02hRSpW58pd3kXelpMMF7F4D3lEbNreB8EaMcq6MWivg8FjZXvATWI0ye2Gc+5eXycZziGKbbLLIzTcWcLvNPLyxQSldxu40sra4h3PQjtVt4vzLxxgac5CJlhmedOL0WHDYTWhUCgS5jPdeXWBvM0ElV2XypJd2p8c/+b98pR+pEsnyud98jvFZH/l0CafHSKfVw2LSYrEaSIZTBHbihP0pttZCtDtdnvn0LNd/+IjJuRFAgtmiZ+nWBlqdgH81iEanwuuzEfMncbrNGK0Gbl9eZmpuEDo9fvgfb2BxmtCaNIyfGMQzbCGRKLG5EqaQreKbdBHciLD1aI+rP3jA9nKAdqPNzdcWkQtyhiddnHlpCqlchtakQSqToJIrWL61i0ql4OT5Me5dXqPVg8B6jEw8T2wvTa3e5vI37/LP/u//gFg0i86kp1lt0Ba7TJwYIJeuE97JUCtX6XV76Ew6Vu5sUqk0mJwbQakTWLy9zeCwA4Ug8Po3b3D+5eM4vBYWb+9SKdbZXAoSXE/y/OfP4XKbOfP8Me5cWSGwmaDX6+EZcdJsdMiky2RSJZRqgeNnR4ntJTh5cQTXgIUzzx1DIpOwcs/P6WcmEZstMrECKw936XS6+CacbKxEyWdLlIs1tpcinHx2gshemi987Tne+LNbCDI51XyTbrdHaDtOo9bg3pV1IjsxioUKQ9Nujs0NMzTpJBnM4R60cvziGJe/fY96rY5twILFauAP/z8/otPoZ8V989++SalQZW8jwdgxNxqDisBahGa5iSDI2VwJMzE7jM1t4vZbi7z+57dQaQUGxx3IFVJS0Rwjk24++Q/O02n3qNcaiJ0OSq2KK9+9R6vR49v/4Ro2p4VuR8Kt1x7x1IsnuHd5lec/c5Jeu4dSK9Cutcilylx44Tj5dJnQbpzwXhJJR4Kk1yUTLzBy3MPTL89w40ePEJQytjcjzL+3xvL8LmqNGq/Pht6gxjvpRiKVcflbdxkYs/H1f/0aMgkYzDo8PhuvfeM2kUCKoUkX7iETWq2SqD/DznKg/yX8CyfD/hoTcO2NR+RSBe5fXeOVf/sWZrsRtUGNya6j2WjRFiWceX6chVvbrN7fRaWUIxPkNBstirkK3//DKyhVSiI7CfY2Ilx7fZnNxSBKrRKVSuDRzU1mnhqHTpdsvEx0J4XJauCpl2dRq5XcenuJ5z9/homZEd565Q7/r//y93jjT29RqzZoVJvMX15l5JiLVCTD1R8sMPvUBE6vhfX7O9RbHf71//WbxALpfjZfsUY6kmVjYY9sPE88mGFz0c/eRozIbhLfhIfAZoJ4IM349ABmmwmnx8SFF45z5/Iyq/N7TJzwsrceRatXEtiIEtiIY/daOXZ+gs/81iUm5nxotapf2P/8x3FEon0c+EWflD/v1OpbQ1uVHhl/Fbo9LFYN7W4XrVlJqykS3U2RjmUZ8NqQSKQM+hws392llKsS202zt5FgYMTCznIEpUrBncuLTM0OU8qWeeMv7rB6d5tuu4PWoGJrPYTWqMJo07J8ewd6EqRSCTurYd74szvYHHp+8MdXsTpMOAbN3L+yTrPeQFDIWbyzxfCkm+3FMKVClXa3i1wmxWQ1oFAoyKeL7CwHiewkWLy9Ta1UJxnJkQpnaLe71ErN/z97/x1tS3bfd2KfCqeqTs435/By7ByAbmQQAAlSlETKFDkKw9GSRrJGHnv8h5fWrLGXPbYky9ZoZI9kkdJQzCRAgAAJNNA59+vXL6eb88k5VJ2qOnWq/Me59777XncDDQINsKH7Xeveqtp7185VZ9d3/0LfSxUCR85MYnUspo+P0qz1ia7V25tsLufIrpeoV1oEIxrNmk5+q0xmrQSA4zgsXdtECyjEEn3CbY8wyu2qj5ZzdWzbBq8f5zh98qlZ0/clwLLrJZo1fb/s+4/txl2V0r2wg3ivsA8S7zjO+5Jl/tC9ZNDBPD5qEmKHOMQhDnGIQ3yYGJlK87HPnyExFCWcCFEt1sD18DyPVrODXrNIDkapFhvo7R5T82naDZ1f/kefYmexTHmtSLHQoF1rIAoCrumhtw3eeu46wbCK4ImogoBnOpi6SS5X60vPpyJsrxSpF5toYR+hqIo/IhKI+qnmG8yfmuYLf+NJOk2Tdt1kYn6Amxc3ERSJgYEgv/wPPsuxkyN0Ggbrt3dYvbHDwqVVhocSPPfVt7n60hInH59lfWGH0blBTj44iWO7rN/Y4rFPnmFrLY9hdDCtLu2GQaPSIRKP8i++8t+A43DrwgqeYxEMa5w8P058MMTNt5c59cAkjaKBP6gQTUe5dXGN3HqBUCRIF4+JqSEuvbTI0ESCWxfWqVVbqKrK2ceOEAiHcCwHzxEYnkriD2qs3NmhVe1w89Imiizx2KdOc/3CKq8/exXL6LK9mic9GqNUqvDUz53F71cxmh2MhkliJEqt0kY3DC6+eAMtpOLi8u/+L19l6sQ4n/sbT+K5Pa69toSmKX3bX8Um+VyR+ECQcDTMhe/eQBI9cusluraLILi8+exNLr+2SKPdIrNZQ1GUvp03RSYQVrjx5gJr17O8/PVL1EpVPvMLj2DbXTYWd6hVWlx86Q6XXlrg4su3Mbo9NhfyzJ2colZqoioBFi+ssnJjG3/E3z8GNbbWSqSGoozNDPHGt28SCAUpFmt0uw65rQovfe1t3nn+Fs26wfZGiS/+6iNsLxVo1ppcfPkm7aaJoogkh0MIwBvPXOf0E3Mkh6PE02G6VpcLz93i6PlxYvEg115dolUzaVZaCKLAzkoRnyYRiQeQRYHLL/RVkDPrBXyKTClTRZZl6tUWsWSE1/7sCgMDMQqbJR78+HEGhiNkN2r4fAJdy6FeaDB7coyBsRiCItBpWyzf2OTIycm+ZogsMzSeIJGI82/++z/kK//2uwyNxxmfGyC7WaJeb7N4bRl/qG+WJhTW+gTbtU1e+PpFsptFJNFjMBVm404WRHjha5d59quXyWyU2VrIosoqZttiZ61IuVCjXm/z5M+co1VtI/oUcLqIooze7PDWszew7S7tpsnanW1OPXqEoelBZs+M0zEMFq9tEghonDg/SbPa4vhDM1x6cQHb6vLmty9z9IFJorEAs0eG+OQvPEg4HsAyTWKpIJ7bZer4COGon4nZEXqWxeT8INdfX+XGm2soYZXJIyOEk0EUUSAQ9NHruQSjfv7JP/sVjJbJ5lKON751g1qxiet5/MG/eZaR8TRXX1tmezHD7YurzJ+aYPXOFiOTCf7o3z2L3uyQHAxTylfR/Aqvf+caD3/6FAG/yonHphgeifHKty5z7vE5WqU2RlNHDaokh+JcfHmBP/yfn+XZr7/Nn/zm8/zm//inQI94OkwxW2Pp2gaSX2HxxiZPfPY0jXqbqWPDlLdrnH54lq//ry/y1gvXef07V4mnQgTDAQLRINtLebKbZT731x6hVdn9PnHdj+4fMDCSRAtofOnXnmT22BiVXIXcRoHtpTwd3aJeblDLt/EJAicemmHtTpaNhSxPfP4kPafHF3/lSbZW8jz62TOce/IEIb/K7IlRNm7t4A8pPPz0MVRN5dbFNfwhhXbb5Bv/4UU2V/IMj8YpZapcfX2BxWub/MLf+QSTR0ZQYjJzp8ZZvrVNfCiK3jRxXA/X6XHr4gq3LixT2q5z5qE5JNfj5tsrdBoGbz93k2DYT71sIAjw0CeOY3ZcssslblxYoVFrM3lkiEgyxO1Lq/gDKl/61Y/x7Fcu4rrgD6pkN8pMHhml14XRqUFq5Qbf+t2XeeelW1x7fRFBdBEV6f1+kn+sEDzP+yHEpj56aDabRKNRGo0GkUjkQynjC2P/+EPJ94eGQF98dPcoBQR6hndv+P4FKCEBEZAUCcED2a8QDgVQ/T5atQ6JwQiFnSqPf+YMl9+4w+OfPcPyzS26RpfJ42M88PRRrr20wOD0IKaukxiKc/vCOvHBEOmhOKnhONnNIuF4iI2FbaKxCGNzQ3R0k9HpQd5+6Qaf/cXHyaznuHl5jVMPzBJLhWnUOsRSQfx+DfBYvrZJcjhOKOpHEERkn4wakDGaFq26TjQZJjkY4cabq0RTfQOVn/j5h9heznHj7TUkCR7//Dki8SDNms6tt1eZPTUKQCjaF+tvlnVs20HAI5oMMzCawHEcsutlRqZTyLJMdr1EYjBKtdD36BkI+5F9Iq7r9d21hzQUrU9SVQsNDMNibHrgniFq1nQi8T4h57ou7UZnv1574fen+2HQbhiEogEcx8EyuvR67nvmu0em/SjKPMQhDnGIjyp+HGuIQ/zw+HGMU7XYJBTT+NbvvcGrX79MbDjMlVcWkXxg6l00VcYwHWIxFRsB17JQFD/RgRCZtSKOAYIMgZCMIPno0cMnC3RNh9GZNK2STjDmR/X5cGWZ0lYJWfOhhRUcC4IRCUd38QSBockUlVwN2+nRLDcJxQKkRqOs38gyfXyEUDxM17TR2xa1bBV/PEgkHOTMx2bZXirTKDaIDAWp5do0K216PQdP9ZFMBdHrJggSpt4hFFERZYnUSBxV87F2O8vP/NJjxIei7KyWaNYMfD64fWGVIw/P0ut5XHrpBifOzRBMBGlXDRyri96x2FnZIRAOI0gOpx46SqPUZmMhi+wTSY/HMQwLWfBRyVb5zF9/hOe+fhERCVkVOPexE6xf36GQrfDA00dZvLqBPxik1dLxCR7BhJ/8RoPBkQiS5GNoPEmlWOfYw3Nc+O4NhidijEwNER8I8zv/6tv80t//DCu3Mqzc2GHyaJp4Kka7rrO5XqTbtnj65x5k9XbfHtyDT5+kVmwQjAewzS7vvHybz/zCY5TyFW5fXsEyHE49Mo/eNHjw6RO8/cpt4skgGzd2OPLADI7To5qtotsOmqaRXynw8GdPcPLhOd589gaDEzHWb2VYu53jqZ87j9my6NgOIh6tukEo7EdSRIIxP7IgIfpk1m5t89jPnOGlr73D0bMTVDJ1WrrJ4HCcmVPjGHWd25fWaBsWn/trj3Hr4iqmbjM8mUJRfbQaHdbvbDMwlsDUbU4/NkPXcXj9m9c4+/gRXn/uJo984hidtsV3/+gtHv/cWR797GluX14lORSnWWmht0xUtW9j+ORDs1x4/gZzJye5+uYCM6fGSCQjbNzJo0Z8JJORvl2wM5MsXN0gs1FheDJBrdjik19+kCuvLeJ0HWaOjxGM+hmeSvPmd65hdmx+5peeYPHGBj7Vx9f+/fNMzo/z83/7SX7v//NdHv7ESV5/5hpPfOEMz/3RG5x4+AjxdARJgJUb2wxPJdneqHLm4RkGJ1PcfGuJuVMTLFzbYnQqxeqNHaaPDwMelmGT3SyRzzSYPz1Gs9KmlG+QSgcp5ur0HA9NlYkNxpg9McrV1+8gSj4eeOoI/qDKjQurKKpCo9zikU+dIrteYPrkBOu3d7jy1hJGS+cTX3qI6GCI5cs7IPZIpGM0ak0aZYN222L25BDBQICWbjB/coxu1wVBZvHSMrLqJz0SZmsxx/GHZmnXdC6+dAt/KMDgaJzV2zt86W8+ycvfvMT4kWFWr+8wfWKUaqGOKIiceGiSmVOTvP7MVYrbFSaOjDB9bJjnvnIBo2Fw4tF5spsl4ukQm3fyJAYiBKMBwtG+Z8RSrsaJh+exOxbpsRgLV3dYvbrBUz93jl7PpVJo8omff5j8dont1QKiT+KtZ65z/PwM86fH+dpvvkglX+PTv/go6fEEF569jqr66bldnvryeV7+5mVqxSbDE2mOnpuklKkSH4jQqhuEowFe+e47/N9+87/hMwO/jix+9LRBHNfmueJv8B//p6/gmgozx0bo2l1C0SB6QyezUaTnukzMDLO5kqNVNnjg6SPIqo9ILIhldul0TIbGEmTWKqSHosiKzHf+6E3mz0wwPjNIejTGrQur1Ms6M6fHWL66xeZSjo9/+TyT88MsXN7g1W9fYvb4OF/6mx+j3ezQqhtsrxZID8fQdZOZo8PoHZvBoQSvfOsyM0dHaDUMnK5Ls9FmYjqN0ekRT4b47h+9Tno0SSwZppKr0nNB9SvUKg3GJ4dIjyVYurlDcqBv/2xrOc8jnzyJ3rE5enqMr/3GC5x54ghXX13GdRzGT4xAt0e91uLOxXVOPzLL6PwQkWiQySMjH9rYfND1w6Ek2oeF7yvC+YOIex74e6+we/44cLynQvuio3vHnuEdSO6xr1cOSAGwWx5my0OvOLhej4DmwzRtVq7mqJTqVMt1TNvi4uu3GJoeYON2jmKmSnw4QqlQ56v/y4s0mjrri1tMzI1y+aVFXLeHKLnsbBa48cYKxUyVbsdhcHwAw7LZWMpw7Y1F1u5sMX1qlMUr6xhNi098+REWr22xsZTl9sVVmhWdWqlJrdSmkKlhm13q5TaZ9SKRZID8dhW9ZeDTJFp1nVvvrHPi0WniqSgjU2kqxQa1qs5TP3+e+TNTLFzb4NJLd3A9FyXgIxDyowU0AiEN23AYmUqhKDKqX6XnuCxd30BvdjB1i4XLGzRrOqFYgGqhgSAK2FZ311GChm12iaUiLF3f2h+NxGD0HnVHvdnBNE1k3112XRTFfdIqEg/eo0r5oyCzmrV+/Zo1HVmW8WkSgbB6j+rq1nLuXWWWDzhN+IuWe4hDHOIQhzjERxmJgQi3L66zfmcLJQgLVzdQNR/tpo3T8Wi3u7i2RzVv4hgWRtOlXtCpZspMzo3gDwlEkgG8noeiQCSoEAz66dQdbKtHvdKi0+qwupih02wTG4qhaD6Cfj/BgI/MRoUHP3kSQfC49MpNAmE/wbDGyPQgR89N02lbzJ8YQ/X76Vo2qqZQKzVIjCUYHI0QSUWIJiLo7TaLt7eoZpo89sUzlMo1fvX/+CUCPh+iAN1uj1OPTCLKApFEmKGJNHa3y9L1DfwBH6VSnW/+h1cYHEvwzgs3qBTqPPzZM2RXiixfW+fhp0+wenub5/7gTVKDEfSOSTwW5OgDs6g+mXgiRr3URm+3kX0SwYAfTVUIaCqp4RDxkSiXX73DkbPTpEbDKJLAzQvLdDoG4ZCMFg3wa//tz6H6ZeZPjFHYrhGNx5g6NoxPVTj/9AmaDZ16zeDSiwuMHR0ikohhWQ7PfuUtUoMRWg2L7Goeo9XuGy2/vEaz1mJ0YpCf+9sf5+rbCwTCfjw8Nldy/bXohVUuvniTv/J3PsnWaoZwIoSiypx9cp5qoUG7ZXL78ioyArm1IiNzg4RjfkQ8hqcHiMdDpAbDPPLZk1x44QZvfvc6gbDChe/eIpYO88lfeIjt1Tx626BZqbO+kOPxz59h8fom5VyV7eUSr37rKtVygyd/5jzP//EbjIynyG1XqNba4His3c7w1X/3LJ4gYtsOH//8OZ7/6kV6PYeZ4yNkt4v89r/+JheevYrRNlFUGbfbwzY9tpaLnH3yKC/92SUmZwZZvrHJ6Gyav/r3P4Os+Xj1mSvcurCOpRt9QimgkN0s4w/6Wbm1w+jMAAvXVwnHFERXYOnaOmpIQhQ86uUW22tFnJ7HzmoREZdaocH00SFe+Hq/fhNHRwiENV7+s4v0bIditsrK9S2e+aM32FrMc/PiGg89dZJu1yKzWSIUD3HtrUVGp1IokgyezNBoksJOBcMwefzzZyhsVpF9ItcuLPFH/+Y73LmyQTThp5qr8vZzN3F6Lm985zLNqsGRs1OcfHiGY+fGkSWRVqONLHp4QL2uMzqVYu70OANjMd5+4SaPPH2KTsfg5W9eo5hpUCu2SA3HCIQ17lxZxfU8XvvTS5hWF59P5K//vc8gSAKFrRrtloFlOri4PPDxE0yfGCWWDCHIEq7rEQr5efUbV+kaXV74ypvEEmECQYmLz9/mgY8fwXV73L60hhbQGBmPMjgU4wt/80nCyShP/+yDVHJ1zn1sltVb2ySGwiD3uPrqEhefu8HoZIp8rkzH6PDan18iNZrgr/7Dz9ExOjSKbQaH4oxOp6iXW9RLdTp6l8mjI9iWg9220II+Lr+0jOB5PPDJE9x+Z4NquUOjrvPM77/OrbdXufbqEtdfWebY6Ukss0N2vcj43CBf/rtPsXRti9Ub2zzw1EmSg2ESiQhLV7c5cX6W6flhOq0OgaDG6cePgufhDyq89KdvI0h9Sa6fuDbXD6nOub2SJRpXWbm1ya0LS9gdm1e+eYmTj8wzd2Icf1BleCzF4FSCQqbOyo0trry2yMZilp2VEsvXNsmuFbj6+gLvvHybeCpMR7e5fWmF7/zem1TLLR75zEle/eY7BGMav/JPPs/ciVF8Ppnt5RyJ4TAf++I5/t3/8Ee88cx1AmGV/HaFjmkRCPlZW86zcWuLb//+a1RyFf7st1+lY5gcPTdJtdjkz377VX77//l13nnpJhNHR5g+PkIl1+DYA7MMjsbZWtohkYrQdbpcfPkGAb9v37RS32lBl2d++xWe/8pblLM1qvkm+e0SwViQlStbrN/JYtRNTj08jxYMsLlU4Jnff+sn98N/AIck2oeBDyLc94PI/93Pkd0fds/fATLsnr8PEr977nr02vfGG1WX7Eqd0kYDPA9H9yisNgj6VVJDMSqbZeq1FoVsnRuvL6EIAqbZweraPPWFhykXamQ3clidDnrNplZqkZyIkFkrY7Q73HprmWAkSGa1xOjMIJuLOW6+vkgkEabd6vDiH1/Ap/oQXImBkRjxVIRLr/Y9eJ5/6iixVJi1W9s0qk2WrmxiGTZf/43nqOSb7KzliaUjmLrFm89eRwsq5NaL1PJ1/v3/+avsrOWo5GsIosv115bxeh6ZjSJXX1tg4coatUqLpWubTB0bwe50Wby6QSgapGs5pEbinHpkDoDCdhnP8+i0LbpdB8FzMQ2bUDSAbTtMzA/TrOlk10v7w7inzhmM+NG0PmlXr7Rw3b5NAdPoG6x3XRfHcaiXmvv3HTzuYS/9/Xiv9KGon07b2ifHjKZFvdTG6d71DBpL9Rn4g6Rdajj2vWbr98X7EYCH5NohDnGIQxzio4RgRKOSaRCPh6mXm1i2te8pXQuICHL/3Gz2EHYdRrWaDrmdEp1mj1a1zeypEeLpKIIo4XkOiB7lXJWZU2MYus3k/EhfRa1Yo1aso/kVoukgRsli8fI6PaeHz+cjs5Ins5Zl4842S9e32V4usrmSp16ss3Zrm1ZVx9ZNChsljKbFlZdvQc/DbFnMnx7D80S++3uvM3d8hK/+25eoFSu0Gh1mjg3z/Ncucez8DPFUhPJOhbVrO9hGj8RwhG/91qsEoxqvf+sKD3/mNNVcg+f++E1mTw+zeSfDs19/h3BUY3Q6zcr1LeqFOu2WgW10ESUJU7fR9Q757Rr+qIKsimyvFhmdGmRwLElxp8zOWomFK2v4VB+ST6VRaZBIhVCjUWrbVW5cWESQBCyzSygWZHOhTzRZlk0hXyG7WmJqdoila6usXNsit1lElkTUgIpjO2wsbLNwZZ1TD80zc2KSUFSh5wrgmLz2resMjaZo1du4XaiX65x/+jh6q8OX/tbHuPbmMvVym43FHRp1mzsXN2jV28ydHWX9ZpZitkajrDM2O8CdSxvohsnKrR1cyWVzOceRc1N85hcf486lVW68sYre6PDqN66SXS9w+tE5Nu7kiCZDTM8P8uZ3LqMGFFrVNoWNPGOzKTy7x823l2lWOoyMx/EcF7HnMndqFFmTGZ5IsLWUoZyvsXx7k8njQ1RLLTaX8hQ2yxw5OUHPExBEkZXr24STfvSGSaPY4uobK0TjEWJDYfwhjbefu8Hl15ewOjrRmMrP/52PI8kKQ6NJLjxzlUg8SGGnSKdpcvvCKvFkFMWnUc6WCSdjSJ6E0bJoFJqoio8XvnIBRfVh2z1sy2Hp0hpGyyQ9kuC5P3mLQqZGNBriG//riwyNxYkng/hkmeRghOWrmyzd2CCSirC5WMBpd7j6yhKF7RK3Lq/yyKeOUcrXWL2+zbXXF/lP//KbZLeLdKptzjx6hIljI0TjQTptm531EpZlk10v4FP6tv5++199iyuvLBCNhTB0k5lj44iyD6cHEzODPP3lB2nWDAYn48gy/OY//zpGy0Lyubz6zYuIgkAxUyWfKSMIAq2aTks3eevb1xgdT7O9lqde1tle2uHpn3+QwlZl3+51s9bG6TosX93kzuU1ut0upWKderWFS48bby6zs1FmaDTOy9+4zIXv3kRVfBw5M8rEkVGWbu7w/B+9ye0Li1x54w71ShNVVRkeT9AoGzg2WF2H628t8cwfvE4qGWPzZobcZoW1G5t8/d8/j15qk56MEYwFUTSVgfE4o7OjdG2Hy8/dYngizZ1raxQ2aziuQ3IgyNVXFihsl5g9NkgpWyUcD5LdLJIajSNKItmdEsWdOqcencOn+mhU2px+cp7t1SLPfuUC195apGs7FLeq3Hp7Bdu2OfXYUTYWs3x71wGd2baoFVu0m+3+S/gnTYT9kCRaz/YobdfRNIn8dhVDN5g/M8H2YpblG1ssXd6g3dQ5/8RRMpt5EDwC8RDthkk5U6HdsvH5fdy+soE/qBEbiJHbKCJJfe+qyze2sUwLx3TIZ0o8+4dvcuX1Zb7+m88TT4dJJeP87v/7WyRGY8iqyEtfv8jNN+9g6SYT0ym8Xo8Lz9+gkq8gCiJPfOEUbz5zk+2VPPOnRnjySw/y9JcfxnMFwtEQ7bpOuVBj9eY2S9e3iCaj3LqwQaPcJpGMMjyVZu7EKLgul1+8Ta3YYOrIMGany9ypSQJhDcd0KGUqPP1z5wmENY4+ME2z0UZWZLbvZIkk/nLYRDtU5/wQ8IXhf/ih5PuXErs0rBbu+y3w+zWkgIRn9RgcHSQ+EGR7pYwguPhUH6VMlTOPz9K1XI6em+X6xTtoioYW1giGfeDKKJpMcjjKys1t0gMJel4Pza8xMpNiczHH7PFxHMfDsTvUyjqG3hdP/91//S0isSDTpyYQcSnmqhx7cIZGuYks+RAEyG9WEX0eA6MpYukwhY0y8aEw7YZFOKwyeWycO5dXsTpd0kMxZFUmFA6C0H9MFNVHx7BpVloEo36CYT/NeptgOMDOaoEjZycRJBFNU9BbHTJreWZPTQB9I5nQ/3HcO+/1ejSqOrZhEYj4icRD3LmyQTCkMTE/RH67xNB4mmatjSiKNOttRiYH3j0OuzDaBoHQu72B2nbfnspeuffX4+D5QXy/NHvqoB80vx8F3F07AqJ4dw/gwyzvEIc4xCH2cKjO+dHAj2Oc2g2DV/70HTp6317Md792AZ8nsLmQ7yfYFZRAAgQBHI9QSqJd7oEoguvij8l0HRfHcAmnfTgWyLKPVtPAr0qMHR2gVjTwqTKNWoNQOMzQSIJCqcHoUIrLby4SjmsMjqfIrOeJD4QIR8JE4yHymTJet4dlu2iqD6fngevi9nqIiki12CIRj1Kr1vnkzz1CIV+mXmghCB5qLEB6KEat1KTbcWjV2iSGkyTSQXbu5PFkkfx2kaHJIY6fnyAQ8XPhxQXOPDzNW9+5gS8gMjaRJhAOsXJ7i0QqwshMiltvrKDGQ6QHY7QqNdIjSXKZJiNTKVYvrzJ+YgzLsBBFkUbNYPzoMEvXNzh5cpy1pRzTJ0bYvJlH1ET0pk6306PRaNBpdwlG/fyNf/QFvv17r9NpdOg6No7toaoSoVSEsblBdpZyDI6mKe2UOPXEMW6+s4zPJ3P07AxXXr1No6Ezc3QYSZbYXs8zMJxE8/tp1OsIosT41CBz58fZWshz641lnvprj9CstNm4tUWx0OSpn32A4maFYrZKOBGgXu7QqDeZPTbG7UtLTJ+YYWouzcZyllg8jO04SKKEKAssX9sgHAshih5uV+D4I3PceWcRD4FP/eLjXHnxNmt3dvAFFYJ+jfWFHYamU0QiQebPTXDzwiozJyeoFOuYdR0t7EcUJSzHplFs02o0SAwmSY0kcG2HcqGOosj4IxqK6qPb6TI0kWLt9g6qqhKMqmyvVQE4cmqcjt7BaHcQFQGjYjE2P0g5X8cwOswfH+edV+8QS4cYnk4ieSIT8yOsL+TwnB53Lq9y5mMnGRiN0WmZ1MtNJFminKly5IFJaiUDnyIQioawbAuz1SWzWSKS8FPcrvP4F05jd7o0yjo3L9zm6MPHqRUrOLbLyGSK/HaV9EgCQRZJp6PIqkizYjA6NcBbz16hkm9x9uljNEptPNclMRjG7tgEYgHq+RbNegu9bRIIBijlKnzqrzxKKVMjGFFpNXQs08PotImEg3g9D6Nlk8uUOfPYPJF4gI5h0ay2kU63po8AAIN3SURBVESJelWnUWvhEwUatTbxZJiu5fLX/7ef53/6P/wOPlUmmooyPpsCVyS3VcYf1AgnVSaPTXDnwjJnnphj7VYW3ejQrHTotEzOfewoxUKd9FCUar7O9mIOT5I4/uA0Ny+sEI2HmD4xxqWXbjB+dASz2WHq6BgvfPMiDz19glZJR9QglY4xMj1IJV9D9imsLWzRMxzCqRBLN7b46//w83z7t15BVGTOffwYb3yrT6ArqoKHx9ZSlpkz4xj1Dht3skxMDzBzboI3nrnOE589y+rtbWRFwudX2FjKIIkSXcMiHA8RCKqceHSO4naVWr5FtdLki7/6BH/8b7/L9JExrI7Nw58+xerNDMGohiQJvP3CdT7/Nz/Od377VVKjKRzHJBwO4x9Q+Hv/3S/zmdTf/eiqc5b/A9/8g+e5/vI6iWQIy+6yfnubiaPjuEIPn+hD88k4ONQLbU4+Ns+FFy+TGhrAJ8vEh6IYtXb/23Auwcb1PE986QE2l3IsXtrgl//bn6GaqXLt9VUGx+Is39pG9olYHQtcAccT6HVsZs6MIgkCW8sFTjw2j+t4bC1nyK6VSI9EeeDpE9x4sy9k8rN/6+O89ewNlq5tMX9qEjUkM3V8lIVLG2h+FZ/m4+qrNwlHg/gUDZ8mUs83OPHEEaLxMLLi4/VvvcNn/9oTvPbnl5k7M4neaFErt5B8Il6vx6OfPstb37nG8Mwgt95eJpKIEEuEGZsd5Ku/8SznnzjKP/i//m8+tLE5VOf8SeInzWx/SGw5B1VCASQIJ1VwwWyA3YRGwaS6rlPLmWwub7H4zhaBkIwsiWws5AjFFCSfwvZGAduyKG7WkUWRYMDH+q0SsXQQo2kiINGud+i5PQo7dTq6zs5qjvmzE2S3iyTGwrzz0hKSLBCM+nn+Ty5y+uEZzjx2lG6nQyQRQZYUzJaF6wiceHiWWqHJ+aeOc+bJY7RqHfSWQXIoRrvRYWQqTcf2yKzlKWfqqIqCEtCIJsKs3tpmYyHLxJFhStkqyZEYgV2bAIZu0rV7OFaPmZNjaAGVSrbO8o2+44F4Korb8yhnq/dIiTVr/R2UjYUMbtfBNGwi8RDNWhufJDAxP9TPv2mR2yjjui6haICRyYH9e7Prxf3z/annCvv57/25rsvG7Z13kUwHr++PW7q68Z7h99d/j0C7Hx8moeU4Lo7j3hP24ybQbNPGNt9b6u8QhzjEIQ7x049ytoo/qKK3TF762kXMqkFhq8zRB8f2CbRAXIEe+FQPRGiXe2gJAVyX6LAfeoDncuKxKVqFLkOjCRAcRqdTdFo9EGR6jktxu0I0GiOeClPYLqEXGqzeXmdmPs1TP/sQsiQwNpVGBIyWiaqpjEwM0a4ZHDk/RjjmJ572k9uoYNkGicEg9BxETWR8dhAkF73VobBTZm0xj9HU2Vku4ZMkclsl5k9PUSlUKW6VkYMirWqbU48d5cipMd5+4RZvfusSrmOTXS8hCDAwmsKyety+skog7MPq2ty6sEokGSEZD7B1ZwtJFsnulKnkity8sEy9qbN5Ywur02VsfphyvkJmOUOvZbK9U2JjaYelaxs0Wk30eofRmWHG5gZ44OMnGZxIEkmEeeZ3XyUYVhAkkV/73/8sj//sg4SHoqiKyOatTSSfxOriFgPTSd567ipHT09x7okjrNxex2jrxNJBcpslVm5lMOsd1q6v09HbDI+l8bpdul2Hqy8tsXx1k8HxFK/8ydu8/ueXMJoGqirzxrevUs430UIKhY0S9XydE6fHWby2zuTsGKoq0jYsTN1m8fI6jVKD/GaZzFqZeqmNT5bZWCuj+kUsy0KWFVTNx+VXblMoVJEVkVMPTTE6n+Yf/7O/gU9VSI/GsTpdMhtl3vjOO7Rqbc49dZJGTUcNKWh+DZ8kMTw+yOh0muGJJJqm4DoucyfG6XZsWqUO0XiY9cUcHh663ia7VWHm+CADQyHK+Qo+TaRWbtMoN4kNRKmWmuA6DAzFWbm9yfknjtKs6zTyBjfeXkNUZFKjUbZWCgiyQGG7yNL1dXbWShx/dBYlIGPaPVZu7rB6fZXNxRyrN7fIr5T69Q74SA1EUQMSty+u0jEsKqUG7ZaNXmmS26qg+XwEgip622RwJIoqCrz6jcvUyk2Wrm/ywjfeRgv07RNH4zFsU6dRrrO2WKReamPqNpVqlbMfO8bxh+cIBHyMzg1x5+IK9XqLhcvrdHsumZUMOB56y2Dp2jbBiJ9gSKNZM8AVKef7Xj4RBUJhlcGhBMMzQ7RqFptLWTLbRZ79/dc589hRRqaTIPWwTItgTKOUb2KaBvGBCMuXN9BbFqu3srSqHfAkPvHlB4gmg6hBCcd0qGTr1ItNxuYHOXZunM07O4zNDaEFfBR3Spx6ZI6NG1vgwuXXbjE5P8iph2eZe2gKTZUpFhr8+e++Sma9wMbiNtFEkEK+zOZKnqnjgzzzu69gWxb+sEo5UyGeDhNKhdnaLBKLB5k9NUk10+DKa0tMHR+m1dHZvJ1BU2U2FjNsLmbQ2ybhuIqj2wyOpKhkG4iyxNypKXaW8+jNNj3P4uFPHOPN797Ath26XQdZlviT//giUyeGuP32Iq2WwdypaXYWcnTaNmcen0eQfCxcXKNd6Wvm4Hof3T/g5T+5iD8oMzKVptft8ev/9K+SWcuzfj2DoolIfhmvJzB3ZpJitsz0/DRbt7exLQu7rdNuGfQcl54pMDCe4PY7a7imTSQW5K1vX+G5P34Lq6PTqraZmBtmam6Yx79wnkbNwAd88hcf4WNffJDXnrmGrCqMzw0SH4zSKreIp8MMjqY58dAsiWSM1GCMK68uEE6G+OLffgqn20Wvd1A0mfmT4ziex8Xv3kBVfERSMR774mkS6QhTp0Z445uXeP3PL/Ht33mRkbkUWytZTj9+lNVbGzQqbYKREMfPzWKZLutLecbnR/GH/EwcGUESwRUcaqUKP/PLH2d8bvgn8ZP/LhxKon0I+MLQf30v2fSD4q5t/w+e5r3uORi25zTA83bP3yORR3+39EeM9FQM2+jSExw8DxTJh9Fu4/Nr+HwyR85M0O0KdJotRAXa5Q6DUymi8TC6YdGoNRkaSqE3dBLDUVoVHTWo4XkeqbEYF565zpf/y0/iOT1cT6TZbKOqCjffWOTLv/5pvJ7D4vUtRibSWGYPs2MwOJmiVmizs5zjC7/6cW68tcSRczO0Kk0aNZ38Zpm/9l9/jpf+9G1GZwbZXiowf3oCu+sQjKiYHYdWtcW115d46mcfIDEUI79ZZu7MBGu3d2iUW4xMpfGHNarlFqIL7abB4HiKaCKI2blLmpWyVfwBjZHpAbZXc4xOD+LYDoqmsHBljVgqQiDUF11tltsMTPYNv+7dD3dJpHqliSzJiLK4fw9AOVclNZygmKnQ63q7OzwSasCHLH9/j5t7kl4HSbsPSlx9ECm3HxY/zZJoP81tO8QhPmo4lET7aODHMU6O47BwaYMrby3w3B+8SSlbA0DzK3Qau5ssB9Zlsh8kScJq9+7JR4mI2C0XPBAU8GyIjwYIRcNs3y4w/+AonbbFznqZc4/MsrqUo1U0UKMwd2yCzEYN09Yx6y6CAqGoRiwRJhQLEon6yWxVUWSJTttkZCbFnSurzJyaZHR8gGtv3UFVFUamBmk0dSrZKuNHhqjn21RLLVRNRJYlIskIumET9KsEwhq1UgNZlkgNxqnXWoSTEd78xlVGjqYJRzQaNZ16sYmiyQQ0FX9ARYkFqGyWGTs+jF4zKBVqNMtG37ZOqcbQeBpJhs2VPFbHZXx2AFO3MVst/MEAKDJjE0M4Tg+j3kbWfIxOp8lla1TyJdKDaUqZKo2aTmo4jk+WuH1plYGxJJIkooUUKjsNoum+QwBRkXjs06e5eWGFnguVXJXB0STdnkuj2mBodICObiEHRHaWCozNDWLUTKqlJunhGLIiUs7WOPHYPDfeWCEQDyA4HonhKCcfnuLyK0v07A6m5ZAcHqC8XcEwdAIBP+nRJI5tYzQsuo5Lq2OSTgcIBcPUSk20oEZkIMSN1xdJjaRQAiKyINKzeri4GO0OiALjM6M06y0mT45SLzQIRwNcfukOj//MA9R2Kty6tk4wFiA9mcTruhh1gyNnpqjkG+i6xezRYV577jqf/NnzVCstFq9uc+bROWrVFo1CE8t0qdZLaKrG4MQAtmkzNjnM1loGs2MRUHx4okSlUuH4A0epZmpEkiHKuTqCJKFIAlpURVZF9LyJYXWYOT6BZVr4I37qxTblbIWe5zF7dpJkLMDOWp5yoUY8nWBwIsHSlXVss4fmlwlGA+TXygxMpRkcTRCOh8jvVIgnwyTSYbY3SiSGw2wsZBkcS6GpKjurBbaWMiRGYhg1E39Eo2uYhOIhipkqEzOjuEKXQCjA+o1NwokQzbrB0XNT3HxrgcRwGtPuoqoywbCfwbE4vZ7AyrU1kukoZrdHJBbENCx8Ph+j03Fef+Y6o2NpZh+e4dILtxDFHrJPJRoNkN0oER9N0LMtNL+K5ldwTBePHuViC01T8BwXKShj1A0KW1Xmzo4zNJYmnA7x2jcv0661efrnH2Ttxjbx4TiWYWN3HTp6l3g8SD5X5pFPnebNZ2/w8S+c5c0/v8ajnz3DxZdv8MQXH+DaS7eRVB9aQOHJLz3Im99+h63FAg9/6iTlXJ071zc5dX6aRsuklK3RrDaZPT6OFlRp1XR6Xo9QJMDY7BDNWgvH8rC7NonBCCvXtokmg1QKDabmR0FwWb+5xeSJSSzDJJqI0Ola7Cxk8WkaPacHTo/p02P4fAr59RzBcIBWUyc+muDW60vMn51k9fom8cEUuZ0in/75h3nuG6/wO2/9Kz4d/1sfWUm052u/xX//6/8vXFMkHA5Qr3aoVhvUCg0ikSCJoQiBaIDHPnecC9+9TS3fQFJlhqZSGFWd8586yc03l6kWW0zNDePTZGSfTCFTJbdR5sQDU4g+iUZVZ2Q8yc23V3jiiw/w2jcvUim3iKdDJEaT+H0KJx+dJbtepl1vs7OeY2gsheiTOPnQHLfeXqVcqHP+0aOsL+1gm10kVWJoNI3jdXn9W5f5xJceQVJ89NweL3/jMg998hhDo0lWb2SwTQvL6uFTBUZnh1m9ss7s2SmKuTqlrTKGYRD0qzzy2XNcfPEWM6dGqZZaDI+n2Li9zczpaV7/7lU8p8fP/d2nOfv4McZm3l8r64fFB10/HJJoHwK+MPgPPpR8P9IQQVRAEAEBejbEBvz4AxrHHpxleyFDudRElDw8B04+Os+F715j9uwUiiyjhfxMHh2htlMlt1NGUmQiqSCa6iOcDOM5AlbHol1r86lffpJ3nr9GOBVEr1k89IkTXHz5Fq7l8vGff5CN21uUsjXC4SCpoTjhdAhVkyhm6qiKj+hgmK07WT7xi4+ytZhj5domakBBVmTK2SqPfv4sXatLNd/kzJNH6PVcbl9c5cj5SRLpGJVCg0quRilTJT2aYO7MBLVSC8uwgL4KSDQVZnAsidE26RgG7ZqB7FPwcBmbGdpXv1RUhVqxzvDUwD2E2f3n7brByPTAPuFSLdRJDMbeNQyNahOfohAIae+rflnO1UgNx3+o4dabRr/PZBnTsBFlEUWR31XW/XAc5wORej9K/KRIqkNy7BCH+OjhkET7aODH452zwYXnbtKut5E0kd//59/ANB2czg+QiQaY4IuArMh0Gg70QAlKjE0Ns7W2w/DEENt3+iqiibEogYBCq92hkW3jj/h4+PMnWXpnjXy2yZGTY30J/q0Gkupx/NwsjYpOJVdGEGXiIxFa1Tam4RAO+ynVavgVDXqghlSq2Qqm3WPsyABmwyI5nMTrWsQSUZbvbOMJAqGAj+RIktJOjWa1wfTxESynhyxJlLI1JFFElkXCsTC63qFRajAwmqDdNgkG/XTNLoVchZHpAdLpJJtbBTrlNrPnJijlGviDAcxOh83FHKmRCIPDSdYXMwyPD6DrJsPjCarFBpsLBZ783Al2tivggC/oQwDqhSpD0yMUM1WqtSqqqJIYiJIcjrL4zho94IGnj7N8eRNBFhmdGGDx2jKe6CM9lMLr2rgIBEIKpmVTztQZmkpimT0kSaBaaDF9fBRcj2IhT3p8kJHxFN2Oy5VX7+CKHolohEqtiaaKnP34cQobZcqFFl7Pxe32KJebqJqE4MHAWBp/WGVoIsHNCyvIPpF2zdwlmlQU1Y9jdwkEfQSjQSbmxyhlyuys5RgYTdAxLGZPTHL9whI9B6LRILIGTqdLcjjF2sIW0WgIvd0hmorgeTA4HGfh5ibhiJ9wJIiud4jGI3Q6Jq7bI5YKY1RNQkmNaqlJNBHEMlxq9Tau6VCv1zlyapJ6sY3nCvgjPiLJMJpPZfnaBrZnMj0/wc5aCc2vUMrVUFQfRrdDUPUjKBJzR8fp9RwU2Ycnuty6tMm5jx2juJEnNhBn/MggG3cyTMyNcfPSMjgesl9mcCJObqGMbnXQVB9uz6NRaaPGfcwemWTxnVWGZgdYvbrJ8YeO0GwahOMqrWKbow9PU8032FjMMjyRYngixZXXV2jVm4QifpSQjNiViQ0ESA4kWbi2ysSRcSIDIUqZGuWdKlbbYnx+gJVbGQSfQCIRwTBtZo4NYxk2C5dWeernH2V9aYdu2wZRolFr4vV6nH3yKMVsk52FHEfPT5BZK9Go9+2VnX3yKN1Ol+31IlrQR6OsYzsWo1PDqJpCYbPIwGQK1wW/qnL5ldv4AiKDI0nSoylWbm0QUH1srpc5/sAUsiDTqLfodR3SkyluvrHCQ5842X8myw16tsfoTJrsRhnBczn7iVMYbYOLL9whlgjS7VjMn5tge6mE43bRNIVyvkZiIEqjquN0HebPzlDOlTH1DvF4lFK5SSigkd0uEkpEUBUZAQHbMpFFH57nMXViglajQW6rRCAYoNftcfzhWd56/hpTc4N0DRejbREMKfgiClt38gxPD6LX2rTbBv6AyujsANcv3+GrV/+/H3kS7ZfO/yOGR0colxocPzfDzkqOM48fpZZvYfcsSvkWwaCIY/XIbtWZPzPK8pVNpuZH8Ef8RJNhipkKsiwyeXyMxasr9Owea7dzDIynQIBoLMjk3BBrt7aZPj3O1bcWOX5+muUbO6QGI0hIWKaNaTv4ZImt9SwnH5xH9gkUMw0USeCJLz3A1VcXCEQ0Stkmg+MJJuaHefWrF5g+M07X6eETZB76wmneef4GEiIf+/JD/OlvPMeR89PUijX0VhdLNzj68Dy51SLhhIZpdMmvF7E9gVapwZFTU+i6ydmnT/Dmdy5jNDvYRo9IXGF0ZoTV21vMnBrjH//ff+1DG5tDEu198GMh0dJ//0PJ96MMX0Sg296dai6EB1RaZYvYaAjbsDAaXZAFJuZSgEuv6+C5IsFEhE5TZ2gsRbXYQK9bRFMBLMsmOZBgcCpJZimH63l85leeYuHtVfDA5xOpVxqkh5J0LJPpo2Os3NnkiS88jF5r43oulmGxcm2bkSMDTB0Zxen2EEQBLehn5eoaT37xQV78kwsMjKeIxAJ09A7RdJRAKIBpdAhHQ3QMC0WTkH0yqr9PGtVLDWRFZmA0QSlTwzJt0iNxEoMxsutFQrEArWoLQZb66pn1NpFYiNxGCf+ud8xQJEDXdnj9m5f43K98HNuykX3yXYm0AyRadr24T6DlN8t9I61WlyPnpu4ZA9t2aDfaxJKRfXti70fkGG0Tp+u8K+4uSdcgMRh9z7G+n+ALhDUswyYYeW/Vz4PQm8a70h2STYc4xCH+suCQRPto4McxTqZh8+JX3yKXqfDGty6TWSpDb3edIwMOIAn3aiYctEQgwkFtANEn4HY9IukAzVqHieNp8itVem6PngfxiIocUPAHFFotk67eQTddhJ5HKhHFkftePdtVi2Cs/1EZCPupFxscPT3FwvUtZMVl7tw8Wzd38Id8eK5Hz/VQNQVFUamV6/R6PUQFbMNh8sgYwbAfxS/RqrTYXi0SiAZQJB+tdgd/0IdPlZE8iUK5gqZoTB0doV5sUsk3CCf8CIgUsmUe/tQZMmt5CltlDN0hlgowPpWmmKujBlTMjomtW+hNCy2oEkmFyCyVCSY1bMthYDiKZzsgi7TqOu1Wl8GRGLFEBLfnsbORY3QqTbvRoVKsI8sKEydHqRarVLYbRGIBHM9ibG6COxfXwBH41C8/zEtfu8jjXzjLhe9cQ/JL+FyJnmMjaxp+TUH0SxQ3a7iWx+BsGMUfILtSpGd7KCGZWDiEI3TxPDj50DHarRY331gmNRYFwaOUaRKOhagX20QGFARHxqNHs2oyOpEAUQAJMstVECAY99FpdQnHNARJZPbkJDtLGeyeRyigYphdFJ9Au6YTHY5RyJTwOjAwmaRWqGDbEB+JEo36UbQQy1dWSI9GiaVi2HaX1WtZBBnmjo+Q2yxy7KF5wOHGW6t0bZfEcJhIyM/JJ07w3B+/QjqVpNZoMDwxiCNCt20xNJFkZ61ALOHH1HvYVpdQNEBP8Khm65gdG78/gGG2SCRi6M0OR85NUtiuoestAqEgqqZSytcYnx6gVm6jahKqqqBqGmrIx/rNHTwPHvzEcdZu72C0TERRIDEcpVZoYFpdVFWhUm4wPjNAva5z6twsF164SjgeQ/X72FrMMnN8DLvTZWRqgORIlLefvUYh08TnE4nGwpx8cprNOwX8QZX0eIyNhQwDI3Fi6TiO3WXx6hqeKxCMK9gtB08QmT05ytZCjo7VRUBgfDJFtWYguB6VcpOgX2J4dghB9rFyY53RiQFCsQAd02Tm1Axvf+sdhqZS6G0Pz3PwAFmE8eNjXH/5JpMnh7n1+grRVBRFk6jk6gyMpJg6Ncar33wHn99HIhmjmC8zOT9IJaujt3SGJgeoF2u4HkwfGyMxGENVZF7983cYHI1jdW38oRDrN7cJRjSSE0OomogkeNx5Z52x6RT+UAC90abT6ZEYDJPfKCPKEk6vR7tiMH1ihMRghNOPzXP78iaFjRKyIlEv6oCL3jCIpSKkxmKEkwECQY16uYnRMJBkmbU7OdIjUVp1nVDYjyBJBIN+2oaNGhDwiQqeAN2Og9N1EEQwjC5j0yka1SatcovBmQHyOwX+42v/gk/H/gtk4SNIonk2z9f/E7/6yP+OeCTCI59/gD/5/32XaDqMIsk88cUHWbq+w8TcAG+9eI25E+OUMzU8T0AJysydnWLj+ja1SpPaToOHP3+a7GoeUZMQRRnH7PLlX/80z/3hm5R3Khx9YJqLr19j7tg02bUK82fHufTKLUanUyhKgPRoDAGPKy/d4vynT9AoGURTIfJbRQqbdWZPj/cd9SVCGG2T+bPTLFxeYXRyiJXFbZIDCXBdCttFksNRJFEmGAlQLzcZHk+Qy9YYGk2xsbC9r4rfahlEwgEuv3abWCxMIBEmt14kFFTpCR7hgIoni+CJ9Jwew9Npbly4QzAS4l985b/70MbmkER7H/w4FlY/k/p7H0q+fynwfqqmAgi7JvY8z0UQRPqrRREPdz/ufgzMRBmZSHHtwmpfmxSYmkthu1DPNTjyyAxLFzZID0WoNVuMzY3gGg7ZrSLjR4cJhsKIPmjX2nSaJoJP5sxjs2wuFxmfGyC3VkEJCgSDQVbv7BAIBXjks6dp1XUyi1nSkwNMHRlmZCbN8tVNXM9FlmU8z6VV65AaiHLs4Rm21/Ik01FK2TpTR0a48Ow1Hvr0KbpWDy2k0aw0iaci+FQZJaDQrLbBg1g6wvrtbc4+eZyrry2iqBJdy0GSRNKTSVTFh6lbDE2mcV0XURSpFuvYpoPsk0gNx2lW2wSjfhqVFqFIAEXr/1i8+CcX+OQvPrrfl81qm0ji3ZJle+fZ9QIj04O06wbtlkFqOEa73iGSCL7LSL8W1N4lOdastslvlhiaShOM+NEb/e32vTI/KP6ykGLlbJXUSOInXY1DHOIQHyEckmgfDfw4xim7XsA2bP4f/+jfUy6U0Uvu90z/vmshCRKjYVrVFvGBCMXtJnRBjoFTv5tM0MCv+fBHfIzNjnL74iqDYxGMlkO7aeD2XNSwglGzSQ1FKWUbqCGRsclhzI5NvdKga9l9YkjuGzdv1ZuYRhfwUGQfumkgiBKyT8EniBidDg9++hSZ1Rx63cRsm/jDCmbHQUKg1TIRBQgnAtQyBiiADUcfGmVtOYvgCsiSTDjqp9UwiKWCiJqPnZsltIiIIIiMzA5g2Q5G00RVZJLDcZZvrIMjYDk90sMRzJZNq2zii4goyIh+aFVtZB84BgTSPuiB0egyeaJvgiMY0XC6Np2623fu4IGsgiSLyJpIKBzCMiw8QUSRRTygvN0iGJUIRvwUt/sbgmocXBu6OihBcHog+UB0RdSwRjNvEEqqhGNhXLdLzwG92abT6CGHBRzTIxiWSQzEMA0bvaljNHsggiRDzwRk0AIykiwzdWqY0lYNp9cDRBrFBj0LBudjVPMNui0Pf8KHaXTxTAglFAzdxieB5YI/4KNT7QIw88Awm8s5FF/ftp4AhJJ+JEehVW8ghHycefQoO3cymKaNqdvMn5tk+1aOerOFo4M/InL0gVkWrq5i6i6nH52jUW0SjPrptCxatQbHnjjG9efvEB+JUNquMXF0mFDETzQd5srzi3zilx9l9coaWxtFVJ+MUevwyOdOc/PiKoMjSTaWs6hBhZHRJD6fxO0rG4zOD1FYL6AqPgRVYHByhJtvLeJ5kB6KYXW7DI/HSA+nufDCVQKqgmm7yLKIZdhMHBmi1bRoVRpMHx1H1VQ8ySO3UWJoIo5fC9GxDWzdZv7cNG989yrjUyPcvLSAKvtIDSf5/K89ztd+4zmOnJnl2su3kAIKkUQIz7Kplw2i6RBDo3FuvLXKyESKWrUNgog/pCHLEs1GnZGJND1HopSvMzQVI7NcwrFtOlaP4dEYSsBHbrNMMKxx/uNnufT8NVS/TLNh8PCnzrK1lCE2EKZZbdKuWXTtvlfbUDSEEhAx2h1ULUCzohOO+bBtl4GpBBvXdxg/MsbsyQlqlRILFzf6Eo2hIHqjQ9fp4tMk/EE/rtllYGqQlVtbpIfCVLItjj12lIU3Fhk5NoBZ65DP1hmZSRMKBbDtLut3tpk7PkkopmGaXRo1g3DEj0+VUVWZQMTPwpUNHAc0VeKJL5zhmd9+HcMwiaXDOLbL7IlRSsUW4/MDXH7pDrIsM350kOVr60RiITxP4Om/8hivfP0Cdq+HT/SIDkZRNYVmo85vvPDP+HT01z66JFrjt/k//do/Z+NqkWMPTLJwdYuxmUEWr6zhej2GJgeJxUP4owq1TBNEl1AiQqvWxjFtwokoeruNpGq0q3UC0Qii4BLwa/hDfsLJILffXiM1FKVaaGB1ush+ieGJJBICW2sFTj12hNe/cYWTH5sGR0DVVBrlFpmNEjOnxmmWmiTH4mwu5pBFSI5E8SkqltnF6hiMTY2xdHudrmHj2F3igzHMjoXq9zN7cpRbby3hDwZo1Q08zyOY0JAFiWAkTMcwMOo6wYhGKVdH9MnE0lGOn5ukmKuxfnOb0w8foVJts7O8zbmPncIfUPD5Vf7Lf/qLH9rYHDoW+EnjPQ0I/miMEO7nw4Gwg2W8XzkH4+5hwt4r7H3wfkm8Pnnmef1FZP+8fzwYd/9fYbXGlReXcTsunuHiGi5r14u0qm16rsvC2yt4ksPmVgGxJ2F1LELJAENTSXqeSKNep7RZ4syjc4zMDjIwGmF4ZgjN76O8VSY5EERv2QyNp0gNRhieTLB0eQtVlklNJpFlgWK2SmWnytKlFUTANmwm5kYIRDUERaCar9OstHn1z64wMp2iUqzx1C88TGGrRMcyuXNhCaNjs7mWo1bRadV0StsVKrkaeLBxJ8vSlXXGZtK4wPSJMQbHEzTLbWKpCKIsYLRNthYy2KZNfqOE2e7g9jxs06Zd19m4kyExENvv7ptvLXH2iaP3DEEkEaKc7XtSqubqAGTXCvuE1cj0ILZpo4UUhsZTGE0TRfP1vX5W22TXCv184qG7BNouGbiHI+enicRDSJJEJBH6QARau27cW8/7CDS9aex73Lw/7Xul05tGv1734b3C3g/NavunlkD7QfrhEIc4xCEO8YOjWmjQczwatTbjx4aIJqPvu87ZXxu931rIcWmWW9gdl1ajg4fLyLEk3ZqLFPIIp314novbcdFrFq2yzurVbYZHErSrFu16E89xEQSXxECEyeNpwskgil/AbDisL2zTqNQ59uA8asCPpKl0DBPP6yFKIrIiY1kW3a6FIsmYtS49LKyOiW05mPUOruUgyyKxwRgd0yQUDTB6bBhRcukaLp4nMX58gFDQh6i5bG0UsFs9RM+jY5nkt2qYtkVmp8r2zQLhtJ9gWMN1Xdbv7LB9I48sSVSzNURJQFM1zFYXz3TxyQpN3SA4oDAwlqDdMNGbJrG4Sld38Xxu3+h7xUIJS+ys5YjFI4iKiFFzGD8ySGIwiNdzCUT8yKqE0bLJr1ap5XTqhRb1agO93cHzubTrXarVNp7kIvhdzIbLwGgcDxcPD0WTOPXALKFIiI6uo0Y8LNumnC+RX6tR2qphdLp4nku32UMNitjdLltbRYqb9f5KvOcSTflx7N050HXpWjaCBDdfXsUwO1S2GlQLNSSfyOixJPmlKnazh+f11d0GRiLIIRfdNHF7LlJIYng0giAK/c1szSW3VkZTRIyWjdVwMJsOYk+kVm3QsR30bIc3v3aVZlvHatv4fAKFTJ2moRMbDOF5Lg4uOxsFPFxEyWVzcZvNpTxGW6dcrtJomrz2R5eIDIUobVX5/H/xBJmVPNffuENmvYwv5PHM77zE8o1NattN8pkqXRye++O3yW9WcASPQFih02izuVZkbSGLXjPZuL3BwFiSYqZJYihJs1pjYDTOA08doZyvEksHcboety8uYxsu/nS0T1K2TUYnk2hBP8VMhY7uUM43uPHOAss3NzH0DoPjA9y+tsrJh49Sq+ssXV2jlq+ztbTN/KkJwqkA2a0sf/ivn2VgKElxu4wSUJg+Nkw1X6GUbzD3wASSBNn1EgOTSXZ2SrTaBk7PppqvEU+GCMejFHfqLN3YwOmZRFNxunaXJ77wANGon+xKlY3rBRIjCTq6zZ1Ly7SaOoNTaRRN49KrN9i4mWPr9g7RZJxQIogn9BBkAbttklvPo6gqha084aiM3rLptHSuPLcArsf1Nxe48cotmmULWfVhdCzUkILPLyPLIpV8m07HIpyOsrOSw7MdmnkdpB5btzbB57F2bZOO0UFSXIIBPxt3thmdHkTza2ys5rj6+iKF7Qq4Npm1HJm1PJLiY/nGDvFUiJG5NC5dXvnGJYbnE/S8HoNTaWRJolHt0Ko1uf7WMt2OhW60ufHyCiOTaYrZBoZhklnOovl9BP0+KoUWdsNk41qW5ctb/Zex6350/4CNhRyVXIM3n73OuY/NYXVNJBlC4QCe0KNWqLB8Ywer26NcaaM32hR2qvgiCsuLm4iixPbSNmMzwygKmE2Dq68usXBliZtvLqAGJbqmiWPatHW9r15rQ6XYolaoU9guc+TsCHfeXEP1Kyxf36RZbRAZCFLJ1Vi5uYVe7+C5Lj1cKrkWm8tZHMejWTUoFYpEIkF8iognSuj1Dj3XZXthh7WlHULJEKIq4g/4MDsWrtEjPZYks5whFA0wMJlie6uM6lfwhzRiyTDbKwVKWxVmTk9QyJVZubHG5PExijs1lq5t8O3fevkn9+N/AIeSaB8CfibxX30o+f5UQwLBB565e632D7FEAMfqYlhdVL+PUEDDRURWPIymzYNPH0cLBKlXaghCf6tR9vmwOxZdBI6enCAS9WNYNrVCiyPnJrny2k3iQym8bpfcdoW/+09/kT/5199h/MQIZx4/xpUXbnLq4ydolet0dJPl6zv88j/5Am9+6yqDYwnWF3LoTZ3xuSGOnp/B1Dvkd6okh6OoisrVV2/z5V//NFdevkV6NEkgoqE3TMbnh7j2ym3OPnWCpavrdNo20UQANaDQrBlE4iGGp9L7JMhBgqpZbWObNtVCo++lc2bwXsmzA/c0q20kWSQYCeyHh2IBRFHsexMLaXguBCLaPml1MB9RFHFd957yS5kK6dHku4bNNCxEUdiXjrsfe3WslRv4fD5C0cB+2Hu18/3uvyes3kbRZFyHe5wn/DhxKMV2F+26QSj2/VV1D3GInwYcSqJ9NPBjsYlWaLB8bZO3n7vG9dcW2F4o/kjzl0Pg7O6JxCYDuDYEVI18tgoexJNBAmE/mc0yYzMD1Est2rUOWkwkNZiga/dolFp0LYee21cT1ItdAikVo2wxfHQAxSeytZLHp4GqBGiV+2sCNSIiST4UWabR1vFsoAvxsQiNcpPx6TSG0aVSrON26Et69SA6GMTsmIRDIcrZxn5bAjEVo24xMp8ku1XBryp0TBts9lVfA0kFPJGZE6PcfG11/15RgWgqiOZXya1W7+0krV+v2TOjrF7J9G2BOf387nZk/zo0rNHOmSCDrACCiKO7+33dcw6sQe9DfCBIx+xg6m7foyr0yzq4ZrVAUMHrsq+2K4cFZEnCrDvg69d1D0pIxG67qCGw7tv7ElTwrL0L8IdlOrYDJkjarvQaoIQE7LZ3b94CSCER13Hxdu3z7TmsSI75qez0A4UQ+DQJu9zj0S+f5cI3rt2bjwr0IBwLEIwF6HW6lDINfCE48dhxrj13B19QpKu7iFpfc1mUZUTRIxQPUKu2iCWClHM6qeEweAKVYhPPhpEjA2QXi/hjMp2qg+SHXgfCg340n0yp1AIXFFXiYz/3MFt3tul5LpnVIlpQRguoNCo6sXgYLajQKLfRogr5zTrReBCfJtLr9IiMRGhWdEQPTjwyz9XXbtHreiiqj6Pnp7h9cZ2x+QGKOxU67Q7jR0bJLBVpNzrEhiMIoks4EGZrLUck4seToFXscPpjc9y+tIIoQGowQqNhEUoEUAWZ7eUSI7NxauU2nWaXuQdHKefqzM6PsXhjC1mSMAwdW/cYPZqkkKkSjoURPBdJluj1XHySgN7p4g9pBIIqnttja6mELMPE7DhrN7ZRgzJd18Ht9OevKEsoosTc+Sm2Vwukh2LUSk2MpomAQKtpoIVEvJ5MKOynVmrQ60IqHUIMqqhKf3N85foWg5MJLN2hlK8SifkJxSKUtisge1hGD0GAUCKEY5r4AwrVjMHM+XEs00CRfJQKNVRNRdMUEEUqpSp0eziuhyTLDI0n2bxdABd8QQgnooiCSzQeplU2EDSRSr5GaixFt25gOF1c28HquARCCqGwQrFW4zvbv8Wnw3/zoyuJ1vpdfun8P+Ljn36E73zlFXo2pAaieJJAvdSi5/QIx8J4bpdQyI/V6TI0M8DStXVEV0Dx+1DDKrViE1yBWDqK67mYrTapsRTbd4pEEirNskUoqRJLBLEdF8dyadebBCNBRqeG2VnLocZ8JFMp4gMh9LbByqVtfLJIZDgGXZvRmRE2Fnbw6NJu9lD8EvFUhGqpQSIZJ7ddQJYl5s7PUNypcvzBaQqbRYKRMFuLGVzLxhdVmD02yc03lggnQ3RqOqKq0Wo2OfngHG/82TVCKR+hWJSOYeCTZJyeQ2oghdtzGBhPktsqIfok/pfn/4cPbWwO1TnfBz8WEi3+6x9Kvj+1EPb/9eF5/V9jl/0f9GDah+hBq9zFH4FuD8bnBkmk4qze3mJwPMnmUhZFlUgPJHFch7H5ERS/RnYjj9XsIisixx+ZQRJ81IoVEiNJOq0OiZE4M8eH+PZ/ep2nfuEx/GEfkUSU1esbmGaXYEDh1ONHabc6mC2Ti89d41O/9Di9nksoGmRrKcfAaJLMap7USJzrby7xC//VZ9hZyWJ3uhw5P83yzS1OPDSH2TZp13Wa1TaO02PiyAhbK3nSwzH8QY2Fd1aZODKM0e6gBTTaDYOZ0+MYTZP8VplQzI9tWUwdnaCYqRIMa1iWjSzJaAGFO5c3SA6EkP0yQ6N9zyV7RFkoFuDWhRVOPjp3D4Fl2w6O7WA0DVIjCWzbpms69Jy7RJptO/vSadC3mfaDEli9Xg9Jkt43/r0Is/vjDsmrQxziR4/D5+qD45BE+2jgxzFOqze3Wb+zyW/9j9+gtNn3zPm+ntn3TGG8pyf1+72kHzjuXYjCXXtre+mlA+smF5KjYerFFpImEQpr9FyXRs7Yz0MOCjg6u4SXhz8h06n21Qr3SCB/WKTT6PXv2bXKIQfAMfYrhBIV6Jp96SG9vOuFFA9JE+iZHnJAwLlHqNzbJ9mCURm94SDIAp7Tj9PiPjyvTwZ0u/263dN2wBcS6bb6zJSoCrjWXl9AbDhIPWf00+55mfe8fW/zSlTEbvTu9rtPgG7/QvAJeAL717u2SQ6M430Dd9CL/f1jve/d/sC9nvfuuL2xFIR99dd+fQ+O+z0Zc8+k8UCJSf02AUgCggiBkIJeuzseIBAc8KEX7bt5yAI4/biRo2myi0VAwBeCbttDCUrYeu9AuQcK9UD0C7gmiGpfgUXy9R2FBaMSetnZnQPsE3CSD3od7z3yg9EjKTKLJdAEsAAfjE+naFR0mhWjb984KNA1+nM3kYpQ3GqSHo9R2qrvzkv25xZ+mJgbZOtGAS0u4tP8mG2Tbrt3dz4IkBwP0+10MXUbxS/RA9yui1+VCcXCmC2HVruF1epx7PEp1m9t0XNdcAVSQ1FazRZSTyI6FKNV6yD5oN1oYzVdZs4Os7aUR5FERBnMuosUEDj64CSbtzLo1S5aWEKQBLyeC1J/w9tsWSB66A2bxHCIaq4NPTjx6BS57TI9z8MnyTTrLbSITCtjMXFqiGapxcSJCXJrWUqZJsOTcerFBh2j/6wkRsJ0bYdWoU+cKmGRhz5zhhuv3sbo2IT9Go2miedBMhkknA6QWS/1VbvrXabODVHermB2uzhW/5Xg80sMzwxR3S6jGxaa349e6SApoAQVOjWb8JCfVq4DMkQHgjQqOlgQHQlhNPoqr922ixb3IXoehtF3qJIeiaC3Oxi1LoIKoYhGq9TZf+4S40FcB+plnZ7c5rnsH/Dp0K98dEm09u/xmaFf4djpY3QNi0alBZ6AYZtY9R7JiSAd3WFsbohytobZsojFA9RaLUIRleGxQZZubiEg0Gl08an01Z8VCUGQaRbbSCogQHokTr3UREAgPhQju1zedUACYghGJhJk16qc//gJbl1Y4tHPn+blr19hdDKOZXSZmB/j8msLzJ0dZeV6hlBM5ci5CTaWirSrLdSgRjQWQFEVdrayCI6ErMgIokggIOAgEY+FqZabJAai5DNlovEggixjtWxweiQmwuCJRNMRLr1wm3NPHuPyCwukBsM4kkcyHSWzWiAYCvC7N//lhzY2H3T98ON1g/efC9zvbRfjpx575tD2zu8JPKBB7Logirtrg77th/1dsJ4HIqiqAJqIrduE4xGCSZdYOkatWGf9ap745xLIkoze7uAPqQiSiBbyUyqUGZ8d4errd1D9CrV8DaUns7OWYWJ2gsUrG0RWS0yeGkUWBCzD5cj5Cd558Sb+kAiCzJnHjxLuuWytFXnt2+9w7PwMoWSQk48dYe12Fsfq0qNHPBqmmq9Q2KlR3C7y8Z97kJtvLeB0+rYxlq9tguuxenWdnucxNJHCp/rI7pRQVB+DE3EaBZ2u49DruQxN9cmv7FqBI+enWbm5hSyLBIIqiuIDBGzbRvaJ9ByXWq5JIKLgugHOPnEE27ZRFIWd5Sxj8yPYloXs81ErNTj56Bx607iHrDLbJpFECMfub92Wd2qEYsF7pNMiiRB606BrO0QSIWTl/V8de+nvl1B6PwJtL/33kkjbiwt8AOcEHwQ/CdLge5GEhzjETxKHBNohDvGDY/LYMJ1Gk5GpGKWd6l3pp721zcFzD8DdPd5nSeV+MmbfcoeLFhUxG9wl0PbSC/TJkD0CAahsNxFU0BSBaqH/EY4H/riEZTog7ZJAbj+8U+lX2KcBnoAQkOi0nN3yPUJpFVUWqex08AWga/TrEAyq1Oomumzdwy/tkSWyT8aRuv24PT5mt2/0ev/E695tj9ns3is5ttsHYtDD1fuXewQasE+giRq4PahndxMdJKQO9Ktd73EPDpR9sB77BXsHmSzvviP3jdeBde2BcCkEvfb73NM7UD+LA2neXRSA5Pfo3efx9Z42OR4eoLes/lfdniSZ6KEXDpJq7Ldd9ntkF4qEkgrtik23tZtv+0C+Po9wLEirpPeb6IG7O8buriRcajhOYaOGXuoP4D5hZkJsKEw933p3A3clAzPrpd1Cd+elDdVaC71sEh3WaORMvN257rQ9ajQRFK9PWEv0CWFAlPqPWSyqsnUzDx6omg+jrdNtesw8OMraQqb/vFggdj3ajQ5eDyRJ7NvwCqmUdlrUCyaxdJCgpmA1DZaurOP1QIuodBoW+fUa8eEQRsukkqmiBmUquQ5aVEGNuKzdzIED0VE/pVIbJQh226OeaxOIaug1m57t4ErQ02FkPk6zrNPRTRJDMdxej069g08T6LZc8hsFPM+l13FwZZlwwg+Wx9SpGBs3+h576/k7BJMKdD2MtoUoSUSjCo1KB6nnUa0baCEBn+KjVbHZvLaF7BORdajXOvvPnul2qS4WURQRvWUTiivklnNYbYgOqliSQ9fqYdUcNi7tADA0m8DuWDghAUXTaBUNAlGZVqlPgiYHY3S7FrLkgR8amf58EJU+eZ+IBchu1tEiPmyzi2WYJFMxjEYR0QPXcoikNeLpMJuLJao77T6xHwRndy57rosnfPS+u/fMH00dGcFsGmS2SiCC4IEnQSipUsu3cV1YemeDQFhD9QlkN/rSuD3HoZRdJRjQ8KkCQsjD6PSJNKPaH9foUAB/UENVfWzeKqCGweyC37QZGIsQSYcpb5dIjg6SW8/geh6rVzeJxkOYrS6S7FGrNQmoQcLpEL6ASG6jyNhckvxGmTsXN+joNsPTMayGQ73cRG9bJAdjNEp19IbD8HySZCyKY3YplOtEkyFMwyKeDOMKYDRM/EGZTrvH2tUMM8dHuPidWwyNJshuFHn8i+eo5IqMzYyS2ykTigT7L/6/BDgk0T4sHPzBvG9H7d4fZ+E90h642FssvXtr6t48fuC4DxHue5277xHJfYSje++CwgWrAXurMNPfwajZ6PUi2BAaVVm8dIdoIorrdJElH4LkUSlUUXw+rrx0nY7pYLU7pIaTzJ4c49bFVd5Zu8bjX3qQO2/cZmslixKUufDMDcaPDjJ7eoyublIpVHnnu1eZPz+L3dTJ5Wro5Q5Wx+Rzv/o0i1fWiaaCRBMRNq5vY+gdwvEAlaqJpqmsXt9hZDLF2PwQi9c2OPXIEYx2B8dyUFSFZqXF2NQAsVSYtas7+AIybs9POBYku1bANrvUyw2GptL4fBLNaovp4xOIMmgO5NeLKJpKaDiE23PwXAXHcbjzzirHH5oFIBDrEzaheJB2tUOr0UaSJDzcfScGe3AcZ58YG5kZ3A/PrhX2rwVZpLReIRAJ7EulOY6DbTp4rruvPrpHFL2Xit97EUm2ae+fm4aJbTrvSzYp2r2vrL+oKuF7kQa27eA6DlrgB1cRPdiu95PSOyTQDnGIQxzipwfrN7b59h9eQJIUsL/H2uZdG6vv8cGnAe+hSmjWvs/H4YHoYEpFL1vonf5CSgpAzwC7Y+Ma7BNSpz91hIW3Vui2dyW78CGIHlbXvkfdsJ3t0Aa0sIQjeihB8ESX2vaumFlzN+HuHqkaF7FqLmbVgiCgf++q72NvCbDn0XSvaa33Snwg/n4Tqu+jink/BO391Tb3od5HcH3vmrwrpNcEOSriNH74j/veB+3H++v7PYreI6Dape/RERa0CruD8D5mkwtrlf1zf8SHaXX31VDr2QbRoQCN/H0DJfTrFtBULGx65i6xp4KeN0Cjf48LbhcUv4jdcuk272YRHQ7RM21kWcbn91HaaVDP3GUaGzv9MoMxha1b28QTIWo7bVJjMUqlOpgQGlZoFy06stXvOxliyQCNRovpo6NU821cE9SogtBzGRqP0awa9CybaMzft9lVs8AP5m4/psbClLdblMtNMEELanhhk+xKgeiQBoJL1+iTQLiQ3SwiiKAFfVimhd2x0VQ/Ej2OPjbJrUvriEDPgmBSoJoxUAMC1YUWWlTG1B0UVcLUTYaPDpC7UwQZhiYSNEo6pe0GviCYDbC1HoIPipkSXQPwgRbwgdBFUjW6bQuv42I5LgjQ7XWxmi7I0Mjd7Vs52J8/oh/yy2UAAkkfrWqf4DJqNonJINWMjuITMZtdNE3GaNngghQR6ZkuI5NJsisV8IFrdnENl6beoVnqkBiNUt1pYGLRa0KzZhCMquglC0EG2e/Dafx0KNJtr+Wh6QMFIgk/Rr2DJvvwaRJu0SWQ8BMIKXQMk1reQtRAFAXstsvYdBLDdKhuNdDiCvGkH9vs4pNd0pMpsstl9ECbYDAAkouqBLFMHdFyCMT8FDZLtModPLeC0eyixSUiqSC5jRxdu4uqyoiiTDlT4+U/vEBwMIBeMPoktuEyOBalFWhR3qyCKOJTFDzLpZypMjAax+x0KK6XKQsVxo6NUCs0MGpt/KpGvaoj+WBgNE32TonREwPUdlwsu0ssHkA3G3hVkQ1hnU7NYv3ONv5QkJ7ZI5L6y2FC5lCd80PAz0T+zoeS7yF2sbvbGEhJGOUeakTEBSKpIA88eYr1W1kcx0Hxi3QtCCV85FfrJEfCyJpKu9BE1iS0cIhwSKPR7hAI+DhydpKd9RJd3eapX3yY1/78MsfOzyP7oFXX+fQvP8HiO6u0qgZjx4bZWcyj+lU0v8DilQ3mzs0weWyE537/DWbOjHPk7DT+iEY8FWXl6ibthk56LEE4GaHXddi8vUUoESY5FGdnOcvJx46wdHkVy3IIRUMEQhqe6yErEttLebSgRHIwyeLldT77Kx+7RyUzu1YAQUAUYWhy4F1k1EE7ZO26zsjM4D0E2R728pRkEb1pkBiKYRk2wfskwA7mF0mEKGbLaAGNSCz0rnS2ZROKBtACWt+752T6Huk2NaAgy31yzDZtTMMmkghRLdbRAtpPzO7ZXv3v78Pvh/tVXw9xiEP89OBQnfOjgR/HOG0t7vAH//LPePv5q7RLH5hx+ZFBCYrY+gckad5PnfSAJBuA4AevA+HBAK2Cca9trl2Vzw+Ce1QuvxfusxP2LtxHrB3iLyfEyC7p+Rf8ovQn5X3JyO+HxFQEx+z1DeCroGl9T6V49Ofz7hwVA32i1ReSEEQPu+miJkSs6u4zs0v+CkpfXiEY6aslKiGwTd5z3okquO/zDMTGAtR3DJSIhG32CIYk9HoP3P53SLvaZfToEJnF/L5k354tuPv7TY1IWM0943sH6rJn4ibhQ9/1wKqGRayWS2IiTHXrPZhnBUJhFcHX9+TZa/fzVAPgWH2Cbg++kEBvV9XYNd49mIG4D6PWL1cO9m0KikEB1/buPsc+UFQRwScTCGi0qi1ETcKuOf13iL3bDvtd2RMbClHP7xoIFEGN+rA7XbwuKEEBUZEwyw5IYDo6L7f/mE/5f/kjq875QucP+czgLyF1Av05sLeZcp9Glz+uEEtGya2W9iWJgf77XAJ/QCYQD1Avt+hZHpGEn2Zpl/j0+vYXu10XVZVJTyTYvlPclWbuJ/GFBOIDUcq5Oq4JgbiKGlSpbTdJTkap7DQIhGTcHtiuQyQVolU28FyXqVOjrF3JIPshPRwDV6GYK9IzIJQKoAZkBERa9TaW7eD3q2ghP7V8HUECWRYRBY/5B4+wdHkNRRVoV2z8SYXJ6RHyOxXajda+KQJFFQlF/Pze0r/50MbmUJ3zJwjvP3d1zh8WewJS7n1hLgiiuLvjK2KUe6CATxVoN3tU1lpccW7SbOjYHRfVL2LbLrFYiPkHxrj2xjIj04PERqK0GybtVotgSOHRz54hs5xj9do2lWqNgD/Am9+5QjASpJQps3p9lbkz07zwx28gIOE5Nrl1kdhAiOHJITYWdhieHGL1+ibZlQLpqSStis4zv/Mqc2emiKYDFLdqSD4JfyRAfDhBJVtlZHYYgLWbm8yfnebWxRWquTqaJnPy8aMEAtq+oX9BgOHpQdp1A6fnkF0r7KtcXn7pJscemtv35Ok4DqIsY7b7pE9mLc/ozBAAsiLvOyXYI9K83bdxOBYiENEwmuY+aSbLMnLk3tdEcadKbCBCu35XLVQWZTRNoVltU8nXGBhLou66Aoc9xwhtxF2Vzr1wf0gjt1EkHAtRydeYPjGOovWl6hRN3ifQbNMGUfy+5FSz2iYUC1AvN+/xZnoQ1XyDxFD0e+azl9dB0uyDSpIdEmiHOMQhDvHTj/UbOxx9ZJLnf+9NBFHEc939418Eggje97t1by0ki9h6D8/tm74Q2L13lyQTxIOBIu+lKhgeUmnlO31lCA+EoEg4FKTnd2gVjN2CDpjisHbz3wu6/xv7wIdfr9Pr37obcbBPtLCEqffJBaF7IO7A/fv9ePBDWwRhLy8RhN3G7nOD9/fd/aZFfpil+XutS3+UeJ/67fXh950X3yOPe/Pbm6cH8hT3dFZEPFyEA+rG3p4K8q702EFN5L2xEEQRt8n+uOyn25NeE++m77fFvVsXXARF3CfQ7nl2Dq77D6C60cTDJTSsoedMTMu6W3fnblq3C+DiuexKXe4RaG6/Sa6IEABvV1BOr/RJB6vd74M9iUXPdRGiIn7JR6feZ4okFcLJIPVsCxQRz3apbbX7j0m93yd6tdf/ypagVe0zVZnF/G7l+vVxDHf/eVaCUt+jbbiv4izsdYAj7hJpLvT6YUpARa/1iUNrV9W5uqsqeZDs1iISZrNHu3KAKdsl5ax9yb7+cx5MqOgVa99m4h60uIDZ8sABQ7/LePsUAccGV7/3RaBqIq7oYdVMrD0bfc4uW2P31RjH5gbJ3C7dU1fPdQmENOq096XdrNrd8hzbw207gIukiHdJO9fr60B+1LArw9TteEh71d8TDPWB4pP66tUuCA7kVvoEmqSBY+4+px7geHRaDp1mE0SIDAWQkDnxxCjb61laWYNIKkx5o4FpO2zf7DvA0QYU7IaNa/XtIZacOrGBELWtNkbVwqj254LRbBOIyX27dR1AgUax3ZfmlaCc6Tu6UX0+cmt1AhGFgaEU1VKFdtnADvdJ6YHBOOVSDXoOtZ1630GKDV3XRdEE1i6t4ThdcES0iEynZLJQ3kAOiYSTIWpmGzEAdttF932vnZcfH8Tvn+QQPzA87z+/P97n/KBtinedH0h7cDXm8u6FwO6157qocR9KTOjrwZsurXIXr+OC6lLaamDVHKKpAF3HYXw2Sb3WpJzTefDjJ+k0DXJbBXC71Mt1QhE/b337HdrNDtViA2yXRqlJrdBgczGDGlTodCz84SDLV9eZODaMaXtkVku8/mcXye9UiCQDrN/eYv6BKbSQSi1bIRzzY5sWt99epLBdpllusLNWYPHSKoWNPC9//S1uX1xCCyr0uj2q5Sb51SJGs0M4FeHFr7zBjTcW6OgmmdUstUKT5asb5DaKWB0LLaRitAzym0VSwzFWb2xiGTaBiJ+v/M/fQpZFuo5Ds9omHAuxeHkNgNxaAdMwyW30X6IjM4OEYyHCsb5NMtu8u/WmBvpk1p502h4GxhIoikyj2tyPSwzFkBWZWqlJcihOMBJAluV3eeAcGOurUTarbZrVNp22yejMEJFEiPQBFctasUk1f1d2X9GU3TLvc2F1AO26vq/aGYq9P+ElK9/bucEefhzql/f37SEOcYhDHOKjgaf/2mNs3sgzfXq4//HveftH4Ac+er3vkw5210IenuP2CbTdsH1CZO8Wt4fX6xMInuu+518z28FzQQn2N3483aVRaNEud/Dk/r2ucV8ePRevu3u8P0/n7nm/DuzfexBmq3fPmu7etr1H+IH4/XAXPNfrt/tg++9L/+7zD/ixfb+SznutS3+UeJ+8vfdrWz/2A+Vxb353x+bgffvjdN982SdLD1pk2etz0d3P8+C47B+9e++5Jx0HyjPdd8UdzOu95i4u2Lp17xxzwfMOpLPuEmj3Pgd36+Oa752357q49oG53HAxqtZ+GqfjUttp4Sn9+u+3e7fNviB3nwnLvdsH0t067Pcv4Dkujt3Fc116jYPjsHuP0D/XwjKeC9Wt5rufQefdbZFlCVG7L9x28VQXT3KR/f1nyOu5tEsdPMlF8cv9ewOghgQ6lV7/nt1vrr18jEp3t4/36ueC7GI2HOxan0DcSysJd98fuLBzs9C/7rj7fQJQyJSRwtDV75YTGfT1+8Vw999pjv4e8+Ujir1x9IUPzNGOi9XszwdPcrG71n46x3D7ElkREV/Qw/N5+KMyUrCfV2OnTXWnztKVFZo7bTzXpZSr4bkuwaS0+2536RRNep27/exaLu1KP/3+c9Ttj7PesvF0F090UTV593ehH9/I6Hi4tGsWSkBCr5hkN4qYjR6e66JFFLSAj8JWhV7HpYuLFpFwdBd/1Icg9DdLOpbF2Mwg+AQi0RChtB+v59JtODTyu/O95+LJLpbxAXX3P2Qcikwc4kcD733O4d7FyD3n99/zwRY3ZvleGWBJ6dv92NtNksP0jZl2YXu1iKtDebPE1u0NhmeHsC2Hwk6NQEzl4jNX6Yk9qvkaiqJi6DqiItHNWCBLFLeKNGsGnmcTioa5/fYKixdXMVo602cmWbuxhmN54MHb373G2OQQrivTbHU49bGjrF/fprjTIBCQGJ8eYGR+kJe/9jZnPnYCLaDw0h+/xcB4jLVrm/Q8l2BIIRjUkASJ8SMjFDZLZFYLNIoNzn/mLIWNIlNHRzENm2qhgU8RSQ7HMVomi5fWCMcDHH9ghtx6EUVTsDomRstkcCK121cyy1c3ESVxX+XTw2X9ZobB8QSu6xGOh9hY2EYLqIRjdyXJ4C6x9No3L3Lswdn968xqntHZISaPjuynjyRCdNomjuO8i9Q6SFA1q20kWUQLqfvXqqaQGIzek1ez2kbxv1tkey9eVqR9O2+K8v77Ax/EgcGHjYNqq++FH4cTgg+jjJ8m5wk/TW05xCEO8aPHyrUNNpcy+CLqX5g4+4GP+/gg66UPtqayW++xq//j1079MeIvSKT9pcRPsI4/SWEQCezae7T9PYKCCRW9+j4T+nu1ocu+1JaggPce6od7ttT289kt395VxfQF2Hfa0I94/+J699v4O3BPcDiAnjPovF87oC/VZXKPbcBWqfPeaXfL6tr3dZgNlt0vo9vqfe8h3nPkAXed1x6s3oFz532qsVfmfrr2uw3GN7ImakjEF1RpFw5kdM+78SNIqO3Vv+eB4t3rLOSgmr0N3Z7Xb+KeOr4Nln13dCzR6s8fmb54lN0fv1DKT7vcIRDuS5KpioLj72FaPZSgiOcKdNs9pIhAr+Vhd3a9NfdADUtYnV5f5dNiXzLUrFmggj/ko1Pp4o/dldC06nbfwbEAwXSAdtEgEg4z/sAwb3z9GqmZOK1yk/hQGJ+s0yz2J6uYkvBZsHkjCzIUmhVw+P+3d+fhkZVlwv+/tS+pVCprZd+60wvpvRuabhQQGpFpncFxYRjGQcfXccERBhV1UNDxcuDF5dUREfX6jfCOCMor4ojIYrND003v+97ppJNUKkml9i1V5/n9UUkllbUbujvVnfvDVRedc55zzlPPqaTuuutZqG910763B6vdgrKozLx6aTA4z9pdOS2SRDsbtPPhjffCkR7uzKMHfYGOdBoYmotDCwIWHbFojPLqck4e9lBUUUgiEaYgYcFcZKW60U3PyR6C/iA2iwWT0YhOp6OivoyTh04yb0kzu149QPfxXt7/z+soqShEr1d0He8iGU2y6LK56FQKm86GrzdAibuIozvaScyrJBqKkkopln5wJS89vglboRkU9Hb5WLiygTlLGympLOTQtjZ0CYhH0nQc6qTpohr2vLYf9Dri4QTlDRUc392G3WGltqWaEwc6SSWSlFWX4vP4cZQ4iEUSVNSWEIskcDeU4Tnupbalms6jHrqO9RAJRLGYDVSvaCIcjNLf7admTiVHdh6nbl7mm3Sz1YzPG8BqtdDb4cNZ4qTz8EkWrp4HwLYX97DiPYuoaSonmcz0UgsNhCksduDvC+Iqc3J8bwdFpYUAlFa7iAbj2ZU/U6kUx/eepGVpY3busNGJCs8JL2abecKhmGMTGqNX9YwEo5hGLXYwPMfaWJ1HPRQWO8Yl8YbPn+nNVjDl6+1MJVcmOseprFR6ppyNa0x2j85H52u9hRBnXyQY5cSBk9z0tev52vv+94zmMoSYdabKmeh1OZ/DIn0T9FqZbOjrmGOHEzzZhSjG7ldMOWffYGCKek5lTP0i3aNWlxg9rHb0cxibhJtueO+Y+RBP+bjhMsNzd2UG9o5fP8Wsy6y8OtnxQ8/DWmoi3j+Yc02dVYeKq2y5RDBNIjjmCQ7dB6Up1Hk4nHNkSnqV2056Xc4Kwlh0mdUrfYNgGPX6G2rfAreFwfAgaTI9ujCMLEoY9kbBrCPqyQz97e8IAwqLS0cirmWTv2n/UOMPvx70kEylIAkmp47BsEJv02VW59Wg1O1Ep+mJ4ScWGQSdQm/XZYb2GsBsN5GOp0BTdOztoeNQD6Cj75gPjDq6DmQWpMCow2jVoyUSJIIKo12XWbQgpqhfWIHepM8MKQ0k0JvItkuBLT/icxnOeTaM9C+Wx5l4MOr/Of3Dx3dl10IKNfqbDDOU17kocNnpavPiLCvApDfgLHDi7e7DqANPWzeppIaWSFMzr4pIOIbdYaO/04fRaKZjbztWk5nq5nJ2vbIfvQEMFiNmvZlUMsHelw4RDsQoLHKg0mksNhsXX72Ith3HqZ1TxfwlDTzz/71MaV0xPSf6MZgUsUiMDb95HdDY/+ZRvO39lNWV0X2yl4IiB7tePUBvV4B4PI7erEdLpxhMpkkk00SCUQa6+7EVZub+mresmUQkibPYTr83gN1hJRFNEgrF2P3GQcL+KHOXNOD3+tnywh7i0SQv/fYNCorsvPHHzQT7wwT7Q/R1+ggNhCkqd5JOaRjMRoL9QVpWNjPgDbDxT1twlRdlhoiWOHGW2Onv9lEzJzPfWjwcR9M0dLrM8M6gL0z3EQ96vY6BHj8A3g4fcxbXA2QTa51HPdnbVdlQQUmFK7sAQtAXxu7MXVig86gHT1svdqeVPRsPkkymsDmsmK2ZXmqJaDKbGEsmc7/2q5lTOS45MjppNV0Cbbj8aKlUimh46q7FsViScCBM51EPQV+Y+Kg6TnXu88mF9nxE/pPh0GKm2BxW3LWlPPfQS+itZD6Qwqi4hXMbJ53reGzc9lHPP2fM35gy2bKTnGPstSa73lTHvZ3y4+r6DtridM4xE/dqsn1TtWP2nqqJzzN63OdE12DMNbJjREc/xryGJjwPE9Rp1PFKG9ORYez5tVH/G30eMnXSRtVr9Gs1e1w693WsjT33mOcyvH3078i4zy9j9w0XGbVd0zKPtJb7s5roehOcdmwbok2cQIORa4yr79hh5UP/19TEi4hMkEDTD69RZho5Pt47OO4pqPjIUPVpzeTv2Dv+HZ3gOQ69fofXSnA4zMSG54ZLDyUWDSPtG/EmSMY17C5ztozBNnxeDaNxaO40Q+bc7toiEj4FUZVJSg1dr6zRhW7o45a9xIgaSg7rUobM5+tRc9/5PEH6TvqHFvJQWAr0mIbnTUxBwjdIzJ8YeX4JMBfqMdigrK4wc/91YHcaSYXTJAKZEV2pmMJutVFYaqV9r5e2vV0wqFBJRWmtE/fcEtCDpp/sxXtuSRLtLMjM0zBLHoz/efgPrTpTX82OStZn5lPTxvx77ANsrqFZUxMavYf7GTgRgLiGvytINBQlEUtitZjoPtHHYEIjGotQUGojEgqhVJqB3iD2QjuL3jWfoooirE4rsVgSZ0kh/Z0DWG0W0Cv0mCiudWEpsJFKDrL2ry/h8I6jdB/txl5RSPexHvo8AzQuricRjNLX7SPkS6CSaZzFNmKxFP0eP2U1xXQd7qSkvICjO46j08O7/3YVJoOJbc/uZP9bh7E7bfg6vWx7aTe9HQP0dfspKLJyaPsRlE6j+5gHT5uXtr0dJOJJTuzpYPHa+dS2VLLz9QMk4ilc5UWcONDJynVL6G7rYfGVrcxfNYeGhTUYzQaO72lHpdP09/gwGDT6u3yE/VFiwSgXX7uMdDLBYDJFsbsIb4ePYreL9sNd9HT0UTA0ob/VYeHk4S6cJQ7cjRVomsJoMmJ1WHEWF4wMubQaaT90kuE5Yw9uPQaApmk4SxxEIwmcJQ48x7yZhQWG6PVQ2ViO0WikuKIIs9mIXq/H5/UT9IUpcNqzyZtoINP1O5kc+apw7AfvVGryrxGHy072YT3oC2M0ZhZA0DSNsD8y7lgAm82Mo8iRTeKdSm+z4XnjpnOmykTD8Zx2Oh3nc48zcf6S15yYKXq9HkdRAUuvaKW0vCj7gXY4Jhr+gJSdt4yR2HCin09l30RlJ9s39pF7nJrweiOmiN0m26U0lEqPlFETlM1Jgkx8jrHXUlMMqczGmGOPm85E5ScYLTvVtSc85nT3n21TXf9U2n9s+eG4e6LzZGPySa6RLTf8GlC5rxNFbmJh0vNoudvHHj9cZtTngdzzk7uP0ftU7jEw5hza+Oswdt+Y55JzndF1UmN+HvNvxj6XSerERNeb+HNRThtOdcyEbagY95lLp4Fp+GcF5umTRlpk6N/xScoYT2f7TP+CnSFDbVfZVJR9bmanLjOXntII98VGEqZKY2f8FZ6LPMJz8V/xl/hjeE3HIKUR7Y8zd2UtKA2jyZi9d6nIYObfhszxPScGJmzXvuO+zPziSqPQaYOh6ycjSVAaphI9+qH7rWJa5n7rNeasrCcRSpEIpDE4tEwSVq/hrLCBRcs8VGb+tHRYo++IP7NAoKYR7U+A0nBW2rAV6yGtEewJE48nQa9htAIGDZ1FI+qNEQlEIK0RGt0zcwbJcE7xzozLoA//rT7DibTTFO0fxFwEyQBgg8JCKyFvHOJQUVPKQF+QQH8YNLDZTTjtBZjNFrr292IrNGGxGkmnNY7t7MBs1XP04AkuW7cSX3+YZDJFSWUJzuJCDHod/V1+TDYjAa+BrmMeiqudOMsKaTvUhS8aRTtqoKLeRd3CGvzdAfQGHZ42L0azHn/fHkoqnbgb3ERDJ2ha0kBpRRH73zrCkz/+M3ULa2leXMdgSlHiLkJv0FNYZKfAaaNyTiXP/d+XMJtNVDe7KXA5KKmxEOoNYTQaKSx2cGzPCToPdxOLJkEp9EY9R3b1cum1K/B54vR3DuBt7ycWjTEYHWTeyiaCvUFMFhM2uwVDkZlIIEo8lqDnRB+JRIrioZU5XeVO9Ho99S3V+Lx+vO39VDaW42n34ii0D60smvmg6+/1MxhPEo8m0DRFJBDG3xfA3eAmEcn04pq/shmAk0c9uEqdpIaGi9bOq87e174uH+6GCpLxJBpgMIx8D2AaSqaNNrwaqJZK0d3ZR0HRSPJqOPGTiCbHrUA6bLIhl8P7hvcPDyEd3ZNt7HVO16kecyrlTmWY5fBKqG/H6Tw/SbjNnKmGOgshTk+/J4C7vpymZTV4j/ty9k2eoJr65zNZdvLjTuV8by92Oxsx31TnPNsx5rSJNCFOx6jVKPPK0NxvWWPrOTwX12jamGOmmvdsomtMZLK2mWC70o/6cuA8Hs6pNEVBkZnu4z4sxUYS/SmMRjOaI85gUMNcYiDpS4MdDgS30qPaqTY0U04Nh9M72BF8gzUGF2VlpRzefAIAh9OOpg1iLbAT7Bz6Ej0GhgId6dGrqQ7P9zdmMI2nzU+2s6g5Uy7ZOzIHW1VrGd17+6hoKOHoruMofaZMamj4ckm9nZgvgYoqLC4DWAwkAkn0VtArHamYorSxCHtxAd1Huwj0htEbASPUzHNjs1roPNaD3mLErEthsBsIdkTR20BZoGBoDu2Zlhc90X7yk5/Q2NiI1Wpl9erVbN68ecryjz/+OAsWLMBqtbJ48WKefvrpc1TTU/ROu3fKY/oHZLqrDndRto5qex3o7GAymTI/JyHki2MqAqMTYoEIJquBkion7vpivCf78Hv8zFlRj6vCgVGvIxqKYzLrcZU5OHGwk+qackKBKHtf20d5fRmdBzs4uquNo/tO4DnqIewLYXNY0KOnv3uAsC9GKpairqWO9GCS3Rv3c2jLYRpaq/F7QzicNoxmA1F/GJSOA28dwnO0m+0v7GHfm4foOe7F6jBT7HYRiydoWljNY//7SWLhGH/82bN42nrpPdHHmr9axcEtRwj6gpTVlOLt6KdmXhUHthxBhyIajuMscVDdXImWTlM/r4rKhgoCvSF8Hj/73thPZVM5pRVOnCUFJKJJPCe8tCxroqy2gsPbjhINRimvKcNoMlLidnHiYCe7XtlNLBSn+3hPZpXNUJzUYIoCpx2b3YrPG+DIjuM4XA48bV7q59fSezIzBn54DrWqpipKKlwk4plvr/s8fvZvPYKjKNPfO+QLERoIc3DrUTztXoK+zGqdh7cfH0rQ6THbzdmkVqHLQTql5fQeG86pWe3WbFsM7xtW4LRn/93n8U/Y+2zscWOTQKNXNR3r7SaMtKEJJqYaKjpdT7kzXaex13075c9GAi0fh/XlY50S0SlmNRbiLLrQ4ryOg50sv6qVuUsb2PbsnpmPieQhjwvhMVNDYCcc3jpVeU6vrsNlJ+qBNdW1jZxa+eFP88P7T6VuulHlBsfsiw/tMwydc7gnlGnoZx3jr2OGuatrAQ2jQ4cx+51yZv/w8ESUhs4yXNcJ6j16SKyeCbZrmSTfuB5+5+FjSNgTw15qItGfAh1EfFHSSY2F75mDw24HS6YjWZc6hoMiWvUXU2GpZY3hOgAOaduwW6yAhsVlpK9tAKurgGBnEN3QR5yKOcWk4wqMYCjKtK3NoUcNAihMhYARCkotMAjWYlPmfic10KCioZDG5ZWZ+vVHQAexSBSDArMNCsoyvd8cbjuRSJJ4IpN0S2tpEgNJrA4j8xY1ojeAq8pOf3uA3hNe3O5ynE4bWhKq55Zyck83XSc8NCyoxGozEY0kiPVHwQAllUUUOmwsunT+Kb5Tn10znkT7zW9+w+23387dd9/Ntm3bWLp0Kddeey1er3fC8m+88QY33ngjn/zkJ9m+fTvXX389119/PXv27DnHNZ/cjA+xPN8ew8Mfhvonj/5vikbOLHecXZ1l1B8kTcNo0RHpS+Css0Mq8wYx6Ncory4jEk4QCyZwOGwY9AYKXHZUSuOtP27PHG8y4Cy0Eur3c2DLIdyNZfg9QbqO91BWW8JAVx86HVTWFVNa4cRSaMbT1ovnuBdUirg/xpEdR9Ab9DTMr2TldStYedUSQr4wL//mTYwmI70n+zCZDET8EdLxFLte3UssHKfQaaWyvoyaBdWkU4pUKkV/p5+B3gDL37uUvpNell25CF//AD6vn20v72bF1YsY6I3wzMMvcnDrUf7nZ8+hqRQDvhBhf5SAL4rfGyARHeTQtuNUNVZQPbeKojInrWsXsPUvuzhxsBtvh4/CcgfWQgub/7ydVDJFUYULk9XMkaEhpg6Xg1QskZnjyx9hwDPAzhd3UTOnkqomNy/85nWMZiMWk5HKxnJOHutCb9bTedSDw1VAOqWRSqUI9GeSYJ1HPQT7gySTSSxWI65iB0ajkXg0QWlNKTVzKqltqcJqtxIJxjFbzUNDT41Eg1F8Hj+JaJywP0wqlcpJ0GiahtGaSbJpmoatIPNO4m33MtDrz1lZdJjDac1unyjhNlrnUU92KOhwL67MfGe5Sa/I0GSoyWQqe62gL5xz3bHJsKAvTDQYzaxIap2459Dwog6nKuzPHR56qsNFJzPZsWF/eFy5s93z7Gye/+220UR10sbNvHv26zHa6ISxEOfKhRjnuZvcBPpC/OzfHmEwms6JaUabbGjlZPve1mNUj4bJrjV2/6it47ZP3sNtdFw2OnbLOdvItaaI5ya7xuiyo+NDpXLjxdH1mPC5jyk30XOcKh6dtDxjto0uryY/dnT75Jxj7PXIPWb0/rHXHPc8JmjjnPuQjbPJOddY4+7d2PpP0K5A9vmPfh2Mu0ejxiEq1Lhzjn1djG2/cfVU4183E5Sc4Bxqih+H6jjB6bLXUNrp9VJUYC40TLov59xD2/QOMsmtScrnSI/6DKQYSnLlMjrGbNQmOdcwM+iNY66fHPmcNe46CY2j29pBQVGhnVR4OFk0VK3ISB1VfLiuI9uMhYZs0zM03LSw1J5piwKGEnqZ/xuMWuazHZBikJQ6Dx9Ds/oXNlqIp6P4ugcYtEYoqrVDWkOLafi6B0ilNKwWIyldkjQpynRVFFRb0Rs0yhtdFNmKCSk/Sq9htOvRNA2DTUMLprAU6FFDCdJ4IIar2o6t0IBJZ8BYAAabKTMk16wYDGqYrHoiwRjYNEzKwMK1czAW6SipdaDXG+jY1w0WDbvNjLOqAKUUzopCkhGNSDDzJe1gPE5NQyU6nYbDbSMV0HC67ZQ3lNNzso9Fq+fjHwhjsetAgc5sIBKNUFhiIZ1QFJbbKSiw4e3oo9/jw1ZgYjCiYTCDNpiiwOXAasmPnmg6NcN9lVevXs3FF1/M/fffD2Q+ZNTV1fEv//IvfPWrXx1X/oYbbiASifDUU09lt1166aUsW7aMBx98cNrrBYNBioqKCAQCOJ3OM/dERrlG/xHQ6XOyzHltuJuubtQPisxzyNLIg5zr1HSAbejfMca/OZjBaIZUCjBATVMFPW29aLrMRIa2AispTWPAG0CnB3uBleomN7FgHFdlIR1HuzEYzFTXl6E0HZghNhAjOThIXUsNbQc7Ka0oIjIQwWgysGD1PHa+upfCkgKcxQUUuArRA76eAKXVpQR7fLjcpfR09qLX6Xj3hy8hEUxwcPsx+roGuPxv1wApetr9GA06EvE4zlIX+988QMuSORw70M7SKxfRdbSbeCBGcCBI05Imwv0hWlbMYf7qOTz14HNg0mMyWbhk3RIObjpCyB+kfnEdsUASZ6mDkC/MQM8ADQvrcJY7GUwOsvvlfVz+4bUEewN4OvqZv7KZtr0dzLukhd42LwXFdrwdvVQ3VVFWXUzHIQ+RYASHy8FAZz9NyxoJ+yKcONRJeWUJNS2VFFe76DraQ8AbZO+bB/jgLet58XdvkookqF1QTUGRnaomN6lkiv7uAaqa3IQGwnQd62EwkaR9fydXfmQtRrMRn9dHWWUpIX+E7hO91M2tQqXTJBIplNJwlhRic1iJBuMYzEb0ekUkEKG43IWmZeZji0cT+Hv8lFSVZlbmDEYZTKZIpzITcaZSadoPnGTFlYuB3CSGw2UnEozi6/JTXOki4g9T4MoM7zy+tx17oQ2bw4ZeD3annZ72PtDrqKovp+tYD0opLDYjJZUl6PV6IsEo6ZSW7aVnthoJB2OUVRZnk1CRYBSL3Uw0GMNZUphNog0v7DCR0QksTdPQ6/WkUilSyRRW+/gFG8auXHqqJkqURcNRjEZjdsGHyQzX6+3uf6f1PJfDS8P+MA5X7sqwMrT1/HIuYogLzbmO8+Ds3yefx0/38W7+3w//zNbnthELJjjtGGl0jKjj1I9X2pj4TAhxQTuVFTOHmcmuKvpOWEtNxIOD2RUccwwvDDB632RDVYc+Vo4bSjiK0+0g2DMUY4+ufwEwyfRXSS3JKzw55XM4H7l0ZazSXQVAUXUhg9Ek0WCCoG6AzYPPs9h6Me50E2V1RfS1BdisPU+EIOsr/oF0cpCUSqNLZSbpz67AagKz3UAylM55HelsoAbBUmjEZDAQ7kuAGSxWM4lgEr09c/u0NBjMOvR6A8lkCoNOR2WzO9MZIAbOykKULk2oO4rBocNgMJIMDFLeUERfTwCz1YjFbCaVSmPQ6zFYDfhPhqlcWEawK0RpTQn+3gAhfxRniZ1IOJZ9rVgKdSQiisrmUkL9EZKpQQqdBfym/Rdn7R6cavwwo5OjJJNJtm7dyte+9rXsNr1ez7p169i4ceOEx2zcuJHbb789Z9u1117Lk08+OWH5RCJBIjHyWx0MBt95xU/F+ZJAg5Fkkxr9AxM8hzx/TopJ/9gCkIDUqD/wnYc9FJYUEA1FCQcihL0RdAWABqW1TtJxHUd2Hc/8/denCfSGQAO9XoM09HkGqG6uYDCp0XaoDbPeRDgQxnuih4aL6nnrL1sxmvT0dseomVPDwU2HmbeqieRgkn1v7mPhxXM4fqANq9NKJJTkDw8+y+LL5tHT0ctgZJC9mw4SHQhRXl2K1+On9ZIW9m4+SGNrHSF/mJp5Vex9fQ9mqw1vhxe7w0Zvex+WAgOv/s9m9GYj5XUlvP7EW6z98Breem4HrvIiDGYDtU1V9J7s56XfvY6jyElwIISzzMn21/ZSXuHC3x+gbW87O1/ey/xVczi84yg2m4XnHt5A04JaSmtLCPSG2PHSHiqb3VxyzXJOHOggOBABLcXOl/dhMhuYs6SBcDBGv2eAN/+0hQFvkCVXtTL/4hZee3ITkUCYhRfP4/iu46y+bgXtB06ipVP0dAyQTg1y8K2jVNSVgt7IZR+8mN7ufnZu2EOBy0713GqWX9nKiX0dHN93kro5FcRDMQb6gtjsVgJ9Qfo6fay8Zilhfxidwchzv3qJ6z/3V/g8A6RTGmV15XQcOIl+XjWdRzwc3nKEeZfMBZ2OolIndoeFoC+EpilCA2H6OvtpuKiG7mMhauZWUeC0s/3FPVQ1uQFF0BdCp9dxZPsxWi9biNVuZfOz22hcXIvfE0QPlNWWEA/HiQajhIMRHM4CLHYzYX+E7qMeTBfV4Cwpx2q3EvSFMFtN9HX5sDmsHN52LLOIQk8Ae6GVgiI7mqbh8/gAHUazCVdZ5g++z+vHarfQfvAkFpsFm8NKxB+hYGjetpOHupi7rJnOI91UNbupmVNJT3svVocFszkTIWWG0OpwlhRmf2+CvhAhX5iauVUEfSH0+vGTZQxvjycTxKOJnOOHRYJRDGY9XYc9NC9uzG7vPuahqnkkMRgLx0+7B1XQFwLIXjfoC+EsKcwsWDG0+i4MzRFmnuTb4dO83vC1IsEoNod1XOJvdJkREweUE5Ud3jbxeSYWj8bRGw3Z+3kujb0H5/K65/qaYnLnIs6Dcx/rlVS6KKl00bpmIcFgkE8s/jzBjukmBZqCgtOKsc6nGFMI8c6czmKEZ2jOtXjf5CfSGUFFxx4wSeGhMCc9UWd6EzAIQc+ov9ejLztFB3yzzsjl6q/RJphoTWcGdSZmrzid5OVpsrhMxP0JdGO+PDEqY/aigc7AyI6hv/lplemFFwlGR3r2AcnBGKnAUGWHeu0ZrEOJyyQkk9pIb77hU8YBCyR8yZFmT0EikLmZ2qjP1Kk4mBwKoho2dwGde7owFRgZNKYIdgcw2HSgFOkQpEmDAXrbBnDVF+JvD5EYyowarFCgLwCDRk+bF5WAmgITob2Zmx2NRUnHR3owJhNAGiLhCBFf5kXni/nfVpufaTOaROvr6yOdTuN2u3O2u91uDhw4MOExHo9nwvIej2fC8vfccw/f+ta3zkyFxYRcTQWEolHSfoWtyILRqogEBilrKKKwsBBvdx/zlrTgchTQfuAE8y9dSF+Hh8KyEowmI8f3tNPU2kAiEqKvL4Je07Fk9QKiqSR+7wChQIyWixqxFFqpqK1g7+ZdGE0WrGYTicEUhTYHyqRR6HSQSqQoqinGoNPhafdg0AyseN8Sju/qwOKw0O8ZYNnlF+Ft78doMtF+6CTvueFddB/rYeere/B1DtC8vBmbzYyrrJBUSqOnsw+byUI8Emfe6mY2P7uDygY3S69sxeGws/uN/fR3DuBuKKegpIDy6hKUphENxojH4/SfzPwR/Ojt76eozMnOl/dTUVtKQamV3pM+Fq5qYcfL+3AW2am9qJa2ve3o9ToaW+tIpdIc39lOLBRjwaUtNLbW07pmPj5PgFgoTM38aoJ9YYxGPYMoLEYjRrMRv2eA+ovqOXnIw9V/dznRcAyz2YzDVUAymQR0VM2tYtlVizm47RhllS7c9eVEgjEsdhN+bxir3cQ1/3AFvSf7cdeX4z3ZS0NrPa6KIkorS6htqUZLpenr6sdqt/L+/3UNnrZeDEY97vpy+jz9OJwFhP0R9EY9pdUl6I16DHo9ZdWlaJpG0BdkMKnR3FqLw+WgsnHkdzsSjHLR6gXZydCDvhDFZUU0zK/FWVKYGeqa7bE3wu8L4RpKNrQsyyxWUFJRjNkc5v3/673oh64/3CPL3ViO3mhg/qq5zF81FxhJFNXNq6XAaSfsD6PX6Vh6eStte9spqykFIOyPsPLqJTnXd5YU0riwLvvzJdeuQNM0KmtHEkPmkkxSY7g3mNFoxFVWxLxVc+jr6sdZ5sRqtWSTJmXVJQAsvGReTvsE+oK4ypyUVZdltw0rqXABUD+/Nqduw8+veUkjADVzq7I9pAqctpyEi8FoHJfAyiSjCnPON9bw9rE9r0YbPu/oBBqQk0Cb6vipjK3X6J+N5pG3PaPRiNHxzt8GR59/soTfRG11Osme6dp8ImN7Gw47F4mmmUpkSQItv5yLOA/Ofaw34PVz6K0j/Ob//JGuo93vLIEmZp+JJmo/GxxMmYjIGlUfnRPUqJyG2akjGZy4sgYHGAw6koNqJIkyKulgLtKTSiusdgupRAqrxUZSxYn3DoIZGhZXoUvr8LT1U+R24CwuorPtJFF/ErPdgMVqwWS1YNTrCATDkFJUNlRwYlcnlfNK8bb107ykgVRkkEg8RO/xELoimNNYRyAUxlHioL+7l8FYmoWr5zPQ5icaj7P86lae+b8vM3/lHMJ9YWpbqwkGIpiNRiKRKIGOIAa7EX1aj9lhItAboGa+m5MHuvngF97Pc796GSNGyupcVDZVsOnJrbjnlGG2mdEbDAz0+1l62WI2Pb+F6sYqbBYbHcfaiUSTOArshAeimBwGrFYzxa4i0qk0/b0DtMxvpK9/gJLyEvzhMM3z65mzrJ5tT++mp7OX5e9ZRG97H33dfkBx8qiH8soiCkqd1M6vJDwQpqfDi8VoYcAfoLq2kkgsjslqIdQXoGlJHfVza/nLr16hsKIIb4cXo0GPrcxC87wm9m4+QHNLHXqLkcHkIPF4nHJ3Kb2eflKxNBVNpSx79zIObj5IaYmLcDzOrlcPsPCSOYRCIbwn+mlsrWfPiwe46Ip5RCNJYgNB5iybQ3FFMb3He0ij8HUHOHm8nXkrF1BeWcz2V3ZT3VyDwWJgy9PbWH7VYgrLbXgPB6icW0r3IQ+enn4a59Yy/9K59Hf76O/yc82Nl/PEA3/CZDZR2VSG3xMibdBRXunCajUQjQ4SD8ZoP9xDeU0hztIigl4/iVSKpoWNvPDwC7zrI+/G3eJm/xv7qJ9bQ0VTBbtf2o/RZuLwtiNYHTYuvmYpHQc6SSQThANhrFYjaaWY2zqHfo8fR4ENd0s1iVCMV5/aTKHTjNNdRsAzwBUfupTX/rAJS6GVhuZq7C4HRrMRb3s/tgIzkXCMZChJgdtBRU0pBoOR1GCSvW8ewWoxEukLUzq3lO0v7mLzHzaw5H0L+cg1H+Wqm67k2NZjrL/xdfQJxfp/fC9mm5XF75pHNBhn/uo5PPmff2bJ5Qt5/f9twuy04ix3YLNb8Lb1MXd5A1aHDZPJjNluxufxsfPVfSTDg7z3pneB1cDmP2+jrqkWs8WI1WElDejRCAWi6FNgLrAyZ3k9x3a2UzXXTSqpkYgm2ffGHkpqy5i/Yg7WQis7/rKPuSsbMZp1bPrTNopLXcRjcS75wAoS0QR9bT68Xf2EfBFWv285h946QiKWwOFyUOwuwuSw0bm/k/mr57LzlT3MXznndP8SnhUzOpyzq6uLmpoa3njjDdasGfkgfMcdd/Dyyy+zadOmcceYzWYefvhhbrzxxuy2Bx54gG9961v09PSMKz/Rt5N1dXUyFEMIIYQQp0WGc56ecxHngcR6QgghLmwOh4Pm5mZ27doFZEZTWCwWrrnmGp555pkZrt2F41TjvBmdVKGsrAyDwTAuKOrp6aGycuJ5fiorK0+rvMViwel05jyEEEIIIcTZdS7iPJBYTwghxIXtk5/8JLt37+ZTn/oUTz31FIsXL0Ypxfe+972ZrtqsNKNJNLPZzMqVK9mwYUN2m6ZpbNiwIecby9HWrFmTUx7g+eefn7S8EEIIIYQ49yTOE0IIId65H/3oR3z4wx/ml7/8JR/4wAfo6OjgF7/4BYsWLZrpqs1KMzonGsDtt9/OzTffzKpVq7jkkkv44Q9/SCQS4ROf+AQA//iP/0hNTQ333HMPALfeeitXXHEF3//+91m/fj2PPfYYW7Zs4ec///lMPg0hhBBCCDGGxHlCCCHEO/f444/PdBXEkBlPot1www309vZy11134fF4WLZsGc8880x2Utn29vacVc7Wrl3Lr3/9a77+9a/zb//2b7S0tPDkk09KFlYIIYQQIs9InCeEEEKIC8mMLiwwE2RSYCGEEEK8HRJDnB/kPgkhhBDidJ0XCwsIIYQQQgghhBBCCHE+kCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTMM50Bc41pRQAwWBwhmsihBBCiPPJcOwwHEuI/CSxnhBCCCFO16nGebMuiRYKhQCoq6ub4ZoIIYQQ4nwUCoUoKiqa6WqISUisJ4QQQoi3a7o4T6dm2depmqbR1dVFYWEhOp3ujJ47GAxSV1dHR0cHTqfzjJ5bTE7a/dyTNj/3pM3PPWnzcy/f21wpRSgUorq6Gr1eZsTIV6cb6+X76242kXuRH+Q+5Ae5D/lD7kV+ONv34VTjvFnXE02v11NbW3tWr+F0OuWXawZIu5970ubnnrT5uSdtfu7lc5tLD7T893ZjvXx+3c02ci/yg9yH/CD3IX/IvcgPZ/M+nEqcJ1+jCiGEEEIIIYQQQggxDUmiCSGEEEIIIYQQQggxDUminUEWi4W7774bi8Uy01WZVaTdzz1p83NP2vzckzY/96TNxUyQ113+kHuRH+Q+5Ae5D/lD7kV+yJf7MOsWFhBCCCGEEEIIIYQQ4nRJTzQhhBBCCCGEEEIIIaYhSTQhhBBCCCGEEEIIIaYhSTQhhBBCCCGEEEIIIaYhSTQhhBBCCCGEEEIIIaYhSbQz6Cc/+QmNjY1YrVZWr17N5s2bZ7pKeemee+7h4osvprCwkIqKCq6//noOHjyYUyYej3PLLbdQWlqKw+HgQx/6ED09PTll2tvbWb9+PXa7nYqKCr785S+TSqVyyrz00kusWLECi8XC3Llzeeihh8bVZ7bdt3vvvRedTsdtt92W3SbtfXZ0dnbyD//wD5SWlmKz2Vi8eDFbtmzJ7ldKcdddd1FVVYXNZmPdunUcPnw45xw+n4+bbroJp9OJy+Xik5/8JOFwOKfMrl27ePe7343VaqWuro777rtvXF0ef/xxFixYgNVqZfHixTz99NNn50nPoHQ6zTe+8Q2ampqw2WzMmTOHb3/724xeP0fa/J155ZVX+MAHPkB1dTU6nY4nn3wyZ38+te+p1EUImF3vS2dbvsV4ImOmY7/ZLp/iwdkq32LE2eR8ih1PmRJnxGOPPabMZrP6r//6L7V37171qU99SrlcLtXT0zPTVcs71157rfrlL3+p9uzZo3bs2KH+6q/+StXX16twOJwt85nPfEbV1dWpDRs2qC1btqhLL71UrV27Nrs/lUqpRYsWqXXr1qnt27erp59+WpWVlamvfe1r2TLHjh1Tdrtd3X777Wrfvn3qxz/+sTIYDOqZZ57Jlplt923z5s2qsbFRLVmyRN16663Z7dLeZ57P51MNDQ3q4x//uNq0aZM6duyYevbZZ9WRI0eyZe69915VVFSknnzySbVz507113/916qpqUnFYrFsmfe9731q6dKl6s0331Svvvqqmjt3rrrxxhuz+wOBgHK73eqmm25Se/bsUY8++qiy2WzqZz/7WbbM66+/rgwGg7rvvvvUvn371Ne//nVlMpnU7t27z01jnCPf+c53VGlpqXrqqafU8ePH1eOPP64cDof60Y9+lC0jbf7OPP300+rOO+9UTzzxhALU73//+5z9+dS+p1IXIWbT+9K5kE8xnsiY6dhvtsuneHA2y6cYcbY5n2LHUyVJtDPkkksuUbfcckv253Q6raqrq9U999wzg7U6P3i9XgWol19+WSmllN/vVyaTST3++OPZMvv371eA2rhxo1Iq88uo1+uVx+PJlvnpT3+qnE6nSiQSSiml7rjjDtXa2ppzrRtuuEFde+212Z9n030LhUKqpaVFPf/88+qKK67IBlLS3mfHV77yFfWud71r0v2apqnKykr13e9+N7vN7/cri8WiHn30UaWUUvv27VOAeuutt7Jl/vznPyudTqc6OzuVUko98MADqri4OHsfhq89f/787M8f/ehH1fr163Ouv3r1avXpT3/6nT3JPLN+/Xr1T//0Tznb/vZv/1bddNNNSilp8zNtbCCUT+17KnURQqnZ9b40E2YyxhP5EfvNdvkUD85m+RQjzmb5HDueDhnOeQYkk0m2bt3KunXrstv0ej3r1q1j48aNM1iz80MgEACgpKQEgK1btzI4OJjTngsWLKC+vj7bnhs3bmTx4sW43e5smWuvvZZgMMjevXuzZUafY7jM8Dlm23275ZZbWL9+/bg2kfY+O/7nf/6HVatW8ZGPfISKigqWL1/OL37xi+z+48eP4/F4ctqjqKiI1atX57S7y+Vi1apV2TLr1q1Dr9ezadOmbJnLL78cs9mcLXPttddy8OBBBgYGsmWmujcXirVr17JhwwYOHToEwM6dO3nttde47rrrAGnzsy2f2vdU6iLEbHtfmgkzFeOJjJmO/UR+xYOzWT7FiGJEPsWOp0OSaGdAX18f6XQ6500GwO124/F4ZqhW5wdN07jtttu47LLLWLRoEQAejwez2YzL5copO7o9PR7PhO09vG+qMsFgkFgsNqvu22OPPca2bdu45557xu2T9j47jh07xk9/+lNaWlp49tln+exnP8sXvvAFHn74YWCk3aZqD4/HQ0VFRc5+o9FISUnJGbk3F1q7f/WrX+Xv/u7vWLBgASaTieXLl3Pbbbdx0003AdLmZ1s+te+p1EWI2fa+dK7NZIwn8iP2E/kVD85m+RQjihH5FDueDuNpHyHEGXTLLbewZ88eXnvttZmuygWro6ODW2+9leeffx6r1TrT1Zk1NE1j1apV/Md//AcAy5cvZ8+ePTz44IPcfPPNM1y7C9Nvf/tbHnnkEX7961/T2trKjh07uO2226iurpY2F0KIc0xivJkjsV/+kHgwP0iMKM4k6Yl2BpSVlWEwGMataNPT00NlZeUM1Sr/ff7zn+epp57ixRdfpLa2Nru9srKSZDKJ3+/PKT+6PSsrKyds7+F9U5VxOp3YbLZZc9+2bt2K1+tlxYoVGI1GjEYjL7/8Mv/5n/+J0WjE7XZLe58FVVVVXHTRRTnbFi5cSHt7OzDSblO1R2VlJV6vN2d/KpXC5/OdkXtzobX7l7/85ew3jYsXL+ZjH/sY//qv/5r9Fl7a/OzKp/Y9lboIMdvel86lmY7xZrt8if1EfsWDs1k+xYhiRD7FjqdDkmhngNlsZuXKlWzYsCG7TdM0NmzYwJo1a2awZvlJKcXnP/95fv/73/PCCy/Q1NSUs3/lypWYTKac9jx48CDt7e3Z9lyzZg27d+/O+YV6/vnncTqd2TeqNWvW5JxjuMzwOWbLfbv66qvZvXs3O3bsyD5WrVrFTTfdlP23tPeZd9lll3Hw4MGcbYcOHaKhoQGApqYmKisrc9ojGAyyadOmnHb3+/1s3bo1W+aFF15A0zRWr16dLfPKK68wODiYLfP8888zf/58iouLs2WmujcXimg0il6f+7ZmMBjQNA2QNj/b8ql9T6UuQsy296VzIV9ivNkuX2I/kV/x4GyWTzGiGJFPseNpOe2lCMSEHnvsMWWxWNRDDz2k9u3bp/75n/9ZuVyunBVtRMZnP/tZVVRUpF566SXV3d2dfUSj0WyZz3zmM6q+vl698MILasuWLWrNmjVqzZo12f3Dy26/973vVTt27FDPPPOMKi8vn3DZ7S9/+ctq//796ic/+cm4Zbdn630bvUKTUtLeZ8PmzZuV0WhU3/nOd9Thw4fVI488oux2u/rVr36VLXPvvfcql8ul/vCHP6hdu3apv/mbv5lwSefly5erTZs2qddee021tLTkLOns9/uV2+1WH/vYx9SePXvUY489pux2+7glnY1Go/re976n9u/fr+6+++63vaRzPrv55ptVTU1NdvnyJ554QpWVlak77rgjW0ba/J0JhUJq+/btavv27QpQP/jBD9T27dvViRMnlFL51b6nUhchZtP70rmQTzGeyDVTsd9sl0/x4GyWTzHibHM+xY6nSpJoZ9CPf/xjVV9fr8xms7rkkkvUm2++OdNVykvAhI9f/vKX2TKxWEx97nOfU8XFxcput6sPfvCDqru7O+c8bW1t6rrrrlM2m02VlZWpL37xi2pwcDCnzIsvvqiWLVumzGazam5uzrnGsNl438YGUtLeZ8cf//hHtWjRImWxWNSCBQvUz3/+85z9mqapb3zjG8rtdiuLxaKuvvpqdfDgwZwy/f396sYbb1QOh0M5nU71iU98QoVCoZwyO3fuVO9617uUxWJRNTU16t577x1Xl9/+9rdq3rx5ymw2q9bWVvWnP/3pzD/hGRYMBtWtt96q6uvrldVqVc3NzerOO+/MWe5a2vydefHFFyf8+33zzTcrpfKrfU+lLkIoNbvel862fIvxxIiZjP1mu3yKB2erfIsRZ5PzKXY8VTqllDr9/mtCCCGEEEIIIYQQQsweMieaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEIIIYQQQgghhBDTkCSaEEKcJQ899BAul2umqyGEEEIIIc4wifOEmJ0kiSaEEEIIIYQQQgghxDQkiSaEyFtXXnklX/jCF7jjjjsoKSmhsrKSb37zm6d0rN/v59Of/jRutxur1cqiRYt46qmnsvt/97vf0draisViobGxke9///s5x+t0Op588smcbS6Xi4ceegiAtrY2dDodTzzxBO95z3uw2+0sXbqUjRs3AvDSSy/xiU98gkAggE6nQ6fTZev+wAMP0NLSgtVqxe128+EPf/httY8QQgghxPlK4jwhxPnIONMVEEKIqTz88MPcfvvtbNq0iY0bN/Lxj3+cyy67jGuuuWbSYzRN47rrriMUCvGrX/2KOXPmsG/fPgwGAwBbt27lox/9KN/85je54YYbeOONN/jc5z5HaWkpH//4x0+rfnfeeSff+973aGlp4c477+TGG2/kyJEjrF27lh/+8IfcddddHDx4EACHw8GWLVv4whe+wH//93+zdu1afD4fr7766ttuHyGEEEKI85XEeUKI840k0YQQeW3JkiXcfffdALS0tHD//fezYcOGKYOrv/zlL2zevJn9+/czb948AJqbm7P7f/CDH3D11VfzjW98A4B58+axb98+vvvd7552cPWlL32J9evXA/Ctb32L1tZWjhw5woIFCygqKkKn01FZWZkt397eTkFBAe9///spLCykoaGB5cuXn9Y1hRBCCCEuBBLnCSHONzKcUwiR15YsWZLzc1VVFV6vd8pjduzYQW1tbTawGmv//v1cdtllOdsuu+wyDh8+TDqdftv1q6qqApiyftdccw0NDQ00NzfzsY99jEceeYRoNHpa1xRCCCGEuBBInCeEON9IEk0IkddMJlPOzzqdDk3TpjzGZrO94+vqdDqUUjnbBgcHp6yfTqcDmLJ+hYWFbNu2jUcffZSqqiruuusuli5dit/vf8d1FkIIIYQ4n0icJ4Q430gSTQhxwVmyZAknT57k0KFDE+5fuHAhr7/+es62119/nXnz5mXn0ygvL6e7uzu7//Dhw6f9TaLZbJ7wG0+j0ci6deu477772LVrF21tbbzwwgundW4hhBBCiNlI4jwhxEySOdGEEBecK664gssvv5wPfehD/OAHP2Du3LkcOHAAnU7H+973Pr74xS9y8cUX8+1vf5sbbriBjRs3cv/99/PAAw9kz3HVVVdx//33s2bNGtLpNF/5ylfGfVs6ncbGRsLhMBs2bGDp0qXY7XZeeOEFjh07xuWXX05xcTFPP/00mqYxf/78M90MQgghhBAXHInzhBAzSXqiCSEuSL/73e+4+OKLufHGG7nooou44447st8Wrlixgt/+9rc89thjLFq0iLvuuot///d/z5ls9vvf/z51dXW8+93v5u///u/50pe+hN1uP606rF27ls985jPccMMNlJeXc9999+FyuXjiiSe46qqrWLhwIQ8++CCPPvoora2tZ/LpCyGEEEJcsCTOE0LMFJ0aOxhcCCGEEEIIIYQQQgiRQ3qiCSGEEEIIIYQQQggxDUmiCSHOO4888ggOh2PCh3SXF0IIIYQ4f0mcJ4TIZzKcUwhx3gmFQvT09Ey4z2Qy0dDQcI5rJIQQQgghzgSJ84QQ+UySaEIIIYQQQgghhBBCTEOGcwohhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMQ1JogkhhBBCCCGEEEIIMY3/H0lYAYmQAe3PAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1500x500 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"sc.pl.scatter(adata, x='n_counts', y='percent_mito', color='percent_mito', show=False, ax=ax1)\n",
"sc.pl.scatter(adata[adata.obs['n_counts'] < 10000], x='n_counts', y='percent_mito', color='percent_mito', show=False, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "adaf6323-46e7-4666-8352-82c4bb30cb2d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata = adata[adata.obs['n_genes'] < 6000, :]\n",
"adata = adata[adata.obs['n_genes'] > 400, :]\n",
"adata = adata[adata.obs['n_counts'] > 1000, :]\n",
"adata = adata[adata.obs['percent_mito'] < 0.2, :]"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "20b6f709-e70f-4908-9a5f-23083f1ce44d",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"View of AnnData object with n_obs × n_vars = 466380 × 31025\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex', 'batch', 'n_counts', 'mad_prd', 'ds', 'mad_MED', 'mad_MAD', 'mad_thr', 'n_genes', 'percent_mito'\n",
" var: 'gene_ids', 'n_cells'"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "bc81ae14-7bed-4605-b4bf-d04e7910d4ca",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/skinatlas_cellbender_raw_by_pasha_17_5_db_QC_removed.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d1eaaefb-85a5-4db3-b589-a2454d9a29af",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import scanpy as sc\n",
"adata=sc.read('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_by_pasha_15_5_QC_filtered.h5ad')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "58f5aa18-26ee-4acd-b4a0-620e5d78714d",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 183672 × 29631\n",
" obs: 'sample_type', 'Donor', 'Sanger_ID', 'batch', 'donor_lane', 'mad_prd', 'ds', 'mad_MED', 'mad_MAD', 'mad_thr', 'n_genes', 'percent_mito', 'n_counts', 'tech'\n",
" var: 'gene_ids', 'feature_types', 'n_cells'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48c47f52-295b-4c97-8b28-c57a41cf9e3e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "“multiome”",
"language": "python",
"name": "multiome"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"version": "3.10.4"
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