{ "cells": [ { "cell_type": "raw", "id": "266252c3-9b47-4127-a80d-8c99f1770d03", "metadata": { "tags": [] }, "source": [ "Author : Aya Balbaa\n", "\n", "email: ab72@sanger.ac.uk\n" ] }, { "cell_type": "code", "execution_count": null, "id": "60515821-6a45-405f-bfe0-5d26d788218d", "metadata": {}, "outputs": [], "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": null, "id": "2ace0233-3927-4433-81b9-3e52c45b484c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "c46858c6-ef81-4d1e-aff3-7b63782e93bb", "metadata": { "tags": [] }, "outputs": [], "source": [ "#path1=\"/lustre/scratch127/cellgen/cellgeni/tickets/tic-2769/work/Sanger/gex/\"" ] }, { "cell_type": "code", "execution_count": 12, "id": "66e282b5-881d-44df-8d48-f7889af3a13a", "metadata": { "tags": [] }, "outputs": [], "source": [ "#path2= \"/output/Gene/filtered\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "0a00f4b2-c034-4c7c-84ab-c41784d4d5f0", "metadata": { "tags": [] }, "outputs": [], "source": [ "path1=\"/lustre/scratch127/cellgen/cellgeni/tickets/tic-2769/work/Sanger/gex/cellbender/\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "19d9b4bc-4303-4e47-b41c-07cb693f68dc", "metadata": { "tags": [] }, "outputs": [], "source": [ "path2= \"/cellbender_out/\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "d1fd37a7-f39f-4054-a332-1ee3081f1c3f", "metadata": { "tags": [] }, "outputs": [], "source": [ "file_path=('/lustre/scratch126/cellgen/team298/ab72/CTCL/skin_info.csv')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c6d69ac6-7775-4cf5-91fa-619818762adf", "metadata": { "tags": [] }, "outputs": [], "source": [ "samples = pd.read_csv(file_path)" ] }, { "cell_type": "code", "execution_count": 6, "id": "0755c9fb-85e5-4f8d-9db4-599f44725e54", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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irods/farmSample_typeDonorSample_idTissueSiteSex
04820STDY7388991healthy_skinS14820STDY7388991Dermisnon_lesionFemale
14820STDY7388992healthy_skinS14820STDY7388992Dermisnon_lesionFemale
24820STDY7388993healthy_skinS14820STDY7388993Dermisnon_lesionFemale
34820STDY7388994healthy_skinS14820STDY7388994Dermisnon_lesionFemale
44820STDY7388995healthy_skinS14820STDY7388995Epidermisnon_lesionFemale
........................
131CTCL3_GEX_4CTCLCTCL3CTCL3_GEX_4EpidermislesionFemale
132CTCL4_GEX_1CTCLCTCL4CTCL4_GEX_1DermislesionMale
133CTCL4_GEX_2CTCLCTCL4CTCL4_GEX_2DermislesionMale
134CTCL4_GEX_3CTCLCTCL4CTCL4_GEX_3EpidermislesionMale
135CTCL4_GEX_4CTCLCTCL4CTCL4_GEX_4EpidermislesionMale
\n", "

136 rows × 7 columns

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" ], "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", "131 CTCL3_GEX_4 CTCL CTCL3 CTCL3_GEX_4 Epidermis \n", "132 CTCL4_GEX_1 CTCL CTCL4 CTCL4_GEX_1 Dermis \n", "133 CTCL4_GEX_2 CTCL CTCL4 CTCL4_GEX_2 Dermis \n", "134 CTCL4_GEX_3 CTCL CTCL4 CTCL4_GEX_3 Epidermis \n", "135 CTCL4_GEX_4 CTCL CTCL4 CTCL4_GEX_4 Epidermis \n", "\n", " Site Sex \n", "0 non_lesion Female \n", "1 non_lesion Female \n", "2 non_lesion Female \n", "3 non_lesion Female \n", "4 non_lesion Female \n", ".. ... ... \n", "131 lesion Female \n", "132 lesion Male \n", "133 lesion Male \n", "134 lesion Male \n", "135 lesion Male \n", "\n", "[136 rows x 7 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples" ] }, { "cell_type": "code", "execution_count": 34, "id": "fcce3a97-7fec-44fb-a9ec-1a852896ec20", "metadata": { "tags": [] }, "outputs": [], "source": [ "#import os\n", "\n", "# Directory path\n", "#directory_path = '/lustre/scratch127/cellgen/cellgeni/tickets/tic-2769/work/Sanger/gex/'\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", "#sub=[item for item in sub if item not in ['logs', 'cellbender']]\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "217c97f3-a00c-455d-a71e-9e7dd82e4e3e", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os\n", "\n", "# Directory path\n", "directory_path = '/lustre/scratch127/cellgen/cellgeni/tickets/tic-2769/work/Sanger/gex/cellbender/'\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": 8, "id": "e45c4fde-7fc1-45ae-ab1e-dc992dc62fa1", "metadata": { "tags": [] }, "outputs": [], "source": [ "sample_filt = samples.loc[samples['irods/farm'].isin(sub)]\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "a220b9fb-be70-45e2-bd30-17f75138d986", "metadata": { "tags": [] }, "outputs": [], "source": [ "column_list=sample_filt['irods/farm'].tolist()" ] }, { "cell_type": "code", "execution_count": 10, "id": "2ada8a67-f82f-4ecb-8386-a6c6c4103d3f", "metadata": { "tags": [] }, "outputs": [], "source": [ "path= [path1+name+path2 for name in column_list]" ] }, { "cell_type": "code", "execution_count": 11, "id": "41131d00-0054-454c-9d99-50a19092e1c2", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_524030/2675726948.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": { "text/html": [ "
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irods/farmSample_typeDonorSample_idTissueSiteSexpath
96WSSS_SKN8090612CTCLCTCL1CTCL1_GEX_1EpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
97WSSS_SKN8090613CTCLCTCL1CTCL1_GEX_2EpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
98WSSS_SKN8090614CTCLCTCL1CTCL1_GEX_3DermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
99WSSS_SKN8090615CTCLCTCL1CTCL1_GEX_4DermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
100WSSS_SKN10827890CTCLCTCL5CTCL5_Derm_45N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
101WSSS_SKN10827891CTCLCTCL5CTCL5_Derm_45P_8N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
102WSSS_SKN10827892CTCLCTCL5CTCL5_Derm_45P_8Pr_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
103WSSS_SKN10827893CTCLCTCL5CTCL5_Epi_45N_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
104WSSS_SKN10827894CTCLCTCL5CTCL5_Epi_45P_8N_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
105WSSS_SKN10827895CTCLCTCL5CTCL5_Epi_45P_8Pr_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
106WSSS_SKN10827896CTCLCTCL6CTCL6_Derm45N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
107WSSS_SKN10827897CTCLCTCL6CTCL6_Derm45P_8N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
108WSSS_SKN10827898CTCLCTCL6CTCL6_Derm45P_8Pr_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
109WSSS_SKN10827899CTCLCTCL6CTCL6_Epi45N_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
110WSSS_SKN10827900CTCLCTCL6CTCL6_Epi45P_8N_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
111WSSS_SKN10827901CTCLCTCL6CTCL6_Epi45P_8Pr_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
112WSSS_SKN10827902CTCLCTCL7CTCL7_Derm45N_GDermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
113WSSS_SKN10827903CTCLCTCL7CTCL7_Derm45P_8N_GDermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
114WSSS_SKN10827904CTCLCTCL7CTCL7_Derm45P_8Pr_GDermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
115WSSS_SKN10827905CTCLCTCL7CTCL7_Epi45N_GEpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
116WSSS_SKN10827906CTCLCTCL7CTCL7_Epi45P_8N_GEpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
117WSSS_SKN10827907CTCLCTCL7CTCL7_Epi45P_8Pr_GEpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
118WSSS_SKN10827908CTCLCTCL8CTCL8_Derm45N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
119WSSS_SKN10827909CTCLCTCL8CTCL8_Derm45P_8N_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
120WSSS_SKN10827910CTCLCTCL8CTCL8_Derm45P_8Pr_GDermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
121WSSS_SKN10827911CTCLCTCL8CTCL8_Epi45N_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
122WSSS_SKN10827912CTCLCTCL8CTCL8_Epi45P_POOL_GEpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
123CTCL2_GEX_1CTCLCTCL2CTCL2_GEX_1DermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
124CTCL2_GEX_2CTCLCTCL2CTCL2_GEX_2DermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
125CTCL2_GEX_3CTCLCTCL2CTCL2_GEX_3DermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
126CTCL2_GEX_4CTCLCTCL2CTCL2_GEX_4EpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
127CTCL2_GEX_5CTCLCTCL2CTCL2_GEX_5EpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
128CTCL3_GEX_1CTCLCTCL3CTCL3_GEX_1DermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
129CTCL3_GEX_2CTCLCTCL3CTCL3_GEX_2DermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
130CTCL3_GEX_3CTCLCTCL3CTCL3_GEX_3DermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
131CTCL3_GEX_4CTCLCTCL3CTCL3_GEX_4EpidermislesionFemale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
132CTCL4_GEX_1CTCLCTCL4CTCL4_GEX_1DermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
133CTCL4_GEX_2CTCLCTCL4CTCL4_GEX_2DermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
134CTCL4_GEX_3CTCLCTCL4CTCL4_GEX_3EpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
135CTCL4_GEX_4CTCLCTCL4CTCL4_GEX_4EpidermislesionMale/lustre/scratch127/cellgen/cellgeni/tickets/ti...
\n", "
" ], "text/plain": [ " irods/farm Sample_type Donor Sample_id Tissue \\\n", "96 WSSS_SKN8090612 CTCL CTCL1 CTCL1_GEX_1 Epidermis \n", "97 WSSS_SKN8090613 CTCL CTCL1 CTCL1_GEX_2 Epidermis \n", "98 WSSS_SKN8090614 CTCL CTCL1 CTCL1_GEX_3 Dermis \n", "99 WSSS_SKN8090615 CTCL CTCL1 CTCL1_GEX_4 Dermis \n", "100 WSSS_SKN10827890 CTCL CTCL5 CTCL5_Derm_45N_G Dermis \n", "101 WSSS_SKN10827891 CTCL CTCL5 CTCL5_Derm_45P_8N_G Dermis \n", "102 WSSS_SKN10827892 CTCL CTCL5 CTCL5_Derm_45P_8Pr_G Dermis \n", "103 WSSS_SKN10827893 CTCL CTCL5 CTCL5_Epi_45N_G Epidermis \n", "104 WSSS_SKN10827894 CTCL CTCL5 CTCL5_Epi_45P_8N_G Epidermis \n", "105 WSSS_SKN10827895 CTCL CTCL5 CTCL5_Epi_45P_8Pr_G Epidermis \n", "106 WSSS_SKN10827896 CTCL CTCL6 CTCL6_Derm45N_G Dermis \n", "107 WSSS_SKN10827897 CTCL CTCL6 CTCL6_Derm45P_8N_G Dermis \n", "108 WSSS_SKN10827898 CTCL CTCL6 CTCL6_Derm45P_8Pr_G Dermis \n", "109 WSSS_SKN10827899 CTCL CTCL6 CTCL6_Epi45N_G Epidermis \n", "110 WSSS_SKN10827900 CTCL CTCL6 CTCL6_Epi45P_8N_G Epidermis \n", "111 WSSS_SKN10827901 CTCL CTCL6 CTCL6_Epi45P_8Pr_G Epidermis \n", "112 WSSS_SKN10827902 CTCL CTCL7 CTCL7_Derm45N_G Dermis \n", "113 WSSS_SKN10827903 CTCL CTCL7 CTCL7_Derm45P_8N_G Dermis \n", "114 WSSS_SKN10827904 CTCL CTCL7 CTCL7_Derm45P_8Pr_G Dermis \n", "115 WSSS_SKN10827905 CTCL CTCL7 CTCL7_Epi45N_G Epidermis \n", "116 WSSS_SKN10827906 CTCL CTCL7 CTCL7_Epi45P_8N_G Epidermis \n", "117 WSSS_SKN10827907 CTCL CTCL7 CTCL7_Epi45P_8Pr_G Epidermis \n", "118 WSSS_SKN10827908 CTCL CTCL8 CTCL8_Derm45N_G Dermis \n", "119 WSSS_SKN10827909 CTCL CTCL8 CTCL8_Derm45P_8N_G Dermis \n", "120 WSSS_SKN10827910 CTCL CTCL8 CTCL8_Derm45P_8Pr_G Dermis \n", "121 WSSS_SKN10827911 CTCL CTCL8 CTCL8_Epi45N_G Epidermis \n", "122 WSSS_SKN10827912 CTCL CTCL8 CTCL8_Epi45P_POOL_G Epidermis \n", "123 CTCL2_GEX_1 CTCL CTCL2 CTCL2_GEX_1 Dermis \n", "124 CTCL2_GEX_2 CTCL CTCL2 CTCL2_GEX_2 Dermis \n", "125 CTCL2_GEX_3 CTCL CTCL2 CTCL2_GEX_3 Dermis \n", "126 CTCL2_GEX_4 CTCL CTCL2 CTCL2_GEX_4 Epidermis \n", "127 CTCL2_GEX_5 CTCL CTCL2 CTCL2_GEX_5 Epidermis \n", "128 CTCL3_GEX_1 CTCL CTCL3 CTCL3_GEX_1 Dermis \n", "129 CTCL3_GEX_2 CTCL CTCL3 CTCL3_GEX_2 Dermis \n", "130 CTCL3_GEX_3 CTCL CTCL3 CTCL3_GEX_3 Dermis \n", "131 CTCL3_GEX_4 CTCL CTCL3 CTCL3_GEX_4 Epidermis \n", "132 CTCL4_GEX_1 CTCL CTCL4 CTCL4_GEX_1 Dermis \n", "133 CTCL4_GEX_2 CTCL CTCL4 CTCL4_GEX_2 Dermis \n", "134 CTCL4_GEX_3 CTCL CTCL4 CTCL4_GEX_3 Epidermis \n", "135 CTCL4_GEX_4 CTCL CTCL4 CTCL4_GEX_4 Epidermis \n", "\n", " Site Sex path \n", "96 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "97 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "98 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "99 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "100 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "101 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "102 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "103 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "104 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "105 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "106 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "107 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "108 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "109 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "110 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "111 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "112 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "113 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "114 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "115 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "116 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "117 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "118 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "119 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "120 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "121 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "122 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "123 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "124 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "125 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "126 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "127 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "128 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "129 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "130 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "131 lesion Female /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "132 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "133 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "134 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... \n", "135 lesion Male /lustre/scratch127/cellgen/cellgeni/tickets/ti... " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_filt['path']= path\n", "sample_filt" ] }, { "cell_type": "code", "execution_count": 12, "id": "1948a5d3-d330-4dbb-b916-691a750174e8", "metadata": { "tags": [] }, "outputs": [], "source": [ "sample_filt.to_csv('/lustre/scratch126/cellgen/team298/ab72/CTCL/Info_CTCL_processed_with_path_cellbender.csv')" ] }, { "cell_type": "code", "execution_count": 13, "id": "0f03aacd-49c9-40a7-924a-91bcf3998e3d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n", "--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_524030/1727816446.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_mtx(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": 13, "id": "99f9ee82-1e48-49cf-9d4d-79e3c1596139", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "outputs": [], "source": [ "concatenated_adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_by_pasha_17_5.h5ad')" ] }, { "cell_type": "code", "execution_count": 14, "id": "c53d6227-06fe-4b89-b9c8-d2281d7be75b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 737280 × 36601\n", " obs: 'sample_type', 'Donor', 'Sanger_ID', 'tissue', 'site', 'Sex'\n", " var: 'gene_ids', 'feature_types'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata" ] }, { "cell_type": "code", "execution_count": 121, "id": "f18cf14a-50bf-4e29-8896-27c16bc7565a", "metadata": { "tags": [] }, "outputs": [], "source": [ "#adata=sc.read('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_raw_by_pasha_2_5.h5ad')" ] }, { "cell_type": "code", "execution_count": 7, "id": "0d2f8c25-18be-4f20-9ec2-7c0aaf0df8c6", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata=sc.read('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_by_pasha_15_5.h5ad')" ] }, { "cell_type": "raw", "id": "0048922e-9834-4fab-a5cd-89892c86ec48", "metadata": {}, "source": [ "Doublet detection" ] }, { "cell_type": "code", "execution_count": 16, "id": "e6e631b4-45ed-4227-ad20-63917c479592", "metadata": { "tags": [] }, "outputs": [], "source": [ "concatenated_adata.obs['n_counts'] = concatenated_adata.X.sum(axis=1).A1" ] }, { "cell_type": "code", "execution_count": 17, "id": "c0c15ec6-7183-435f-8f0b-3f80800fe74b", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata = concatenated_adata[concatenated_adata.obs['n_counts'] > 1, :].copy()" ] }, { "cell_type": "code", "execution_count": 19, "id": "58ba3e99-8816-4a6c-bc07-86da94a2ad80", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_by_pasha_17_5.h5ad')" ] }, { "cell_type": "code", "execution_count": 19, "id": "273db793-9ac5-4886-94e6-7ae897f695fd", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "n_counts\n", "1558.0 81\n", "1235.0 79\n", "1648.0 77\n", "1376.0 77\n", "1646.0 77\n", " ..\n", "17368.0 1\n", "23866.0 1\n", "21090.0 1\n", "14756.0 1\n", "47844.0 1\n", "Name: count, Length: 26190, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.obs['n_counts'].value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "id": "882618be-f572-4423-9878-53ab39f13f6a", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2512786/2340965974.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": 20, "id": "cca3be30-717a-4ce8-bfae-0cf5dbcd6837", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WSSS_SKN8090612\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 = 0.4%\n", "Estimated detectable doublet fraction = 5.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.9%\n", "Elapsed time: 14.8 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.2%\n", "Estimated detectable doublet fraction = 21.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.3%\n", "\n", "\n", "WSSS_SKN8090613\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 = 0.2%\n", "Estimated detectable doublet fraction = 4.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 4.2%\n", "Elapsed time: 3.5 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.7%\n", "Estimated detectable doublet fraction = 8.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 8.2%\n", "\n", "\n", "WSSS_SKN8090614\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 = 0.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 13.6%\n", "Elapsed time: 12.2 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 = 9.8%\n", "\n", "\n", "WSSS_SKN8090615\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 = 1.1%\n", "Estimated detectable doublet fraction = 19.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 5.5%\n", "Elapsed time: 10.2 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.4%\n", "Estimated detectable doublet fraction = 39.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.1%\n", "\n", "\n", "WSSS_SKN10827890\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.37\n", "Detected doublet rate = 2.3%\n", "Estimated detectable doublet fraction = 23.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.8%\n", "Elapsed time: 17.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.3%\n", "Estimated detectable doublet fraction = 33.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 12.9%\n", "\n", "\n", "WSSS_SKN10827891\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.76\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: 10.1 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.7%\n", "Estimated detectable doublet fraction = 7.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.1%\n", "\n", "\n", "WSSS_SKN10827892\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 = 0.2%\n", "Estimated detectable doublet fraction = 3.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.4%\n", "Elapsed time: 12.2 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.0%\n", "Estimated detectable doublet fraction = 13.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 7.4%\n", "\n", "\n", "WSSS_SKN10827893\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 = 0.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.7%\n", "Elapsed time: 21.6 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.3%\n", "Estimated detectable doublet fraction = 14.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 15.9%\n", "\n", "\n", "WSSS_SKN10827894\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.37\n", "Detected doublet rate = 1.3%\n", "Estimated detectable doublet fraction = 24.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 5.4%\n", "Elapsed time: 8.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.7%\n", "Estimated detectable doublet fraction = 41.6%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 8.9%\n", "\n", "\n", "WSSS_SKN10827895\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.40\n", "Detected doublet rate = 2.1%\n", "Estimated detectable doublet fraction = 22.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.4%\n", "Elapsed time: 18.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.8%\n", "Estimated detectable doublet fraction = 26.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.5%\n", "\n", "\n", "WSSS_SKN10827896\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.41\n", "Detected doublet rate = 2.0%\n", "Estimated detectable doublet fraction = 17.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.6%\n", "Elapsed time: 32.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.8%\n", "Estimated detectable doublet fraction = 24.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 15.5%\n", "\n", "\n", "WSSS_SKN10827897\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 = 16.7%\n", "Elapsed time: 22.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.4%\n", "Estimated detectable doublet fraction = 3.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.4%\n", "\n", "\n", "WSSS_SKN10827898\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 = 0.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.8%\n", "Elapsed time: 18.0 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.0%\n", "Estimated detectable doublet fraction = 10.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.6%\n", "\n", "\n", "WSSS_SKN10827899\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.43\n", "Detected doublet rate = 0.7%\n", "Estimated detectable doublet fraction = 14.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 4.8%\n", "Elapsed time: 5.1 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.8%\n", "Estimated detectable doublet fraction = 25.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.0%\n", "\n", "\n", "WSSS_SKN10827900\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 = 0.4%\n", "Estimated detectable doublet fraction = 4.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.2%\n", "Elapsed time: 4.6 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.7%\n", "Estimated detectable doublet fraction = 17.0%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.2%\n", "\n", "\n", "WSSS_SKN10827901\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.82\n", "Detected doublet rate = 0.0%\n", "Estimated detectable doublet fraction = 0.0%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 0.0%\n", "Elapsed time: 38.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.1%\n", "Estimated detectable doublet fraction = 15.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 13.5%\n", "\n", "\n", "WSSS_SKN10827902\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.30\n", "Detected doublet rate = 4.1%\n", "Estimated detectable doublet fraction = 33.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 12.2%\n", "Elapsed time: 36.2 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 8.1%\n", "Estimated detectable doublet fraction = 50.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 15.9%\n", "\n", "\n", "WSSS_SKN10827903\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.60\n", "Detected doublet rate = 0.0%\n", "Estimated detectable doublet fraction = 0.0%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 0.0%\n", "Elapsed time: 120.3 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.0%\n", "Estimated detectable doublet fraction = 0.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.4%\n", "\n", "\n", "WSSS_SKN10827904\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.26\n", "Detected doublet rate = 8.3%\n", "Estimated detectable doublet fraction = 35.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 23.1%\n", "Elapsed time: 66.1 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.1%\n", "Estimated detectable doublet fraction = 7.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 14.3%\n", "\n", "\n", "WSSS_SKN10827905\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.40\n", "Detected doublet rate = 0.7%\n", "Estimated detectable doublet fraction = 18.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 3.5%\n", "Elapsed time: 2.7 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.3%\n", "Estimated detectable doublet fraction = 29.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 8.0%\n", "\n", "\n", "WSSS_SKN10827906\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.43\n", "Detected doublet rate = 2.6%\n", "Estimated detectable doublet fraction = 28.6%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.2%\n", "Elapsed time: 8.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.2%\n", "Estimated detectable doublet fraction = 31.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.2%\n", "\n", "\n", "WSSS_SKN10827907\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 = 0.2%\n", "Estimated detectable doublet fraction = 5.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 3.9%\n", "Elapsed time: 7.5 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.7%\n", "Estimated detectable doublet fraction = 34.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.8%\n", "\n", "\n", "WSSS_SKN10827908\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.38\n", "Detected doublet rate = 3.0%\n", "Estimated detectable doublet fraction = 20.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 14.4%\n", "Elapsed time: 35.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.0%\n", "Estimated detectable doublet fraction = 25.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 15.7%\n", "\n", "\n", "WSSS_SKN10827909\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 = 0.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.1%\n", "Elapsed time: 29.5 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.4%\n", "Estimated detectable doublet fraction = 2.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 15.0%\n", "\n", "\n", "WSSS_SKN10827910\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 = 0.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 13.0%\n", "Elapsed time: 49.5 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.7%\n", "Estimated detectable doublet fraction = 12.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 13.3%\n", "\n", "\n", "WSSS_SKN10827911\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.74\n", "Detected doublet rate = 0.0%\n", "Estimated detectable doublet fraction = 1.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 1.6%\n", "Elapsed time: 19.7 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.9%\n", "Estimated detectable doublet fraction = 17.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.9%\n", "\n", "\n", "WSSS_SKN10827912\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 = 0.7%\n", "Estimated detectable doublet fraction = 10.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 7.3%\n", "Elapsed time: 17.8 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.9%\n", "Estimated detectable doublet fraction = 33.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 14.7%\n", "\n", "\n", "CTCL2_GEX_1\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.60\n", "Detected doublet rate = 0.5%\n", "Estimated detectable doublet fraction = 18.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 2.7%\n", "Elapsed time: 5.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.2%\n", "Estimated detectable doublet fraction = 31.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 7.0%\n", "\n", "\n", "CTCL2_GEX_2\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.62\n", "Detected doublet rate = 0.2%\n", "Estimated detectable doublet fraction = 4.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 4.8%\n", "Elapsed time: 6.1 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.2%\n", "Estimated detectable doublet fraction = 4.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 4.8%\n", "\n", "\n", "CTCL2_GEX_3\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 = 16.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 3.1%\n", "Elapsed time: 6.8 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 5.8%\n", "Estimated detectable doublet fraction = 46.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 12.7%\n", "\n", "\n", "CTCL2_GEX_4\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.40\n", "Detected doublet rate = 1.0%\n", "Estimated detectable doublet fraction = 26.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 3.5%\n", "Elapsed time: 21.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.5%\n", "Estimated detectable doublet fraction = 39.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.3%\n", "\n", "\n", "CTCL2_GEX_5\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.41\n", "Detected doublet rate = 1.0%\n", "Estimated detectable doublet fraction = 19.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 5.3%\n", "Elapsed time: 27.2 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.4%\n", "Estimated detectable doublet fraction = 29.7%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 8.0%\n", "\n", "\n", "CTCL3_GEX_1\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.42\n", "Detected doublet rate = 1.1%\n", "Estimated detectable doublet fraction = 15.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.9%\n", "Elapsed time: 28.1 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.4%\n", "Estimated detectable doublet fraction = 36.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 12.0%\n", "\n", "\n", "CTCL3_GEX_2\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.44\n", "Detected doublet rate = 1.7%\n", "Estimated detectable doublet fraction = 24.3%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 7.2%\n", "Elapsed time: 15.4 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 4.4%\n", "Estimated detectable doublet fraction = 39.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.0%\n", "\n", "\n", "CTCL3_GEX_3\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.44\n", "Detected doublet rate = 1.6%\n", "Estimated detectable doublet fraction = 23.4%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.9%\n", "Elapsed time: 12.7 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.6%\n", "Estimated detectable doublet fraction = 35.5%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 10.3%\n", "\n", "\n", "CTCL3_GEX_4\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.42\n", "Detected doublet rate = 2.1%\n", "Estimated detectable doublet fraction = 35.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 5.7%\n", "Elapsed time: 9.5 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 2.5%\n", "Estimated detectable doublet fraction = 39.6%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 6.2%\n", "\n", "\n", "CTCL4_GEX_1\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 = 0.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 0.0%\n", "Elapsed time: 19.6 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.2%\n", "Estimated detectable doublet fraction = 12.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.0%\n", "\n", "\n", "CTCL4_GEX_2\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.49\n", "Detected doublet rate = 0.6%\n", "Estimated detectable doublet fraction = 7.9%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 7.6%\n", "Elapsed time: 18.7 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 1.2%\n", "Estimated detectable doublet fraction = 13.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.5%\n", "\n", "\n", "CTCL4_GEX_3\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 = 0.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 0.0%\n", "Elapsed time: 16.2 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 0.8%\n", "Estimated detectable doublet fraction = 6.8%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 11.7%\n", "\n", "\n", "CTCL4_GEX_4\n", "Preprocessing...\n", "Simulating doublets...\n", "Embedding transcriptomes using PCA...\n", "Calculating doublet scores...\n", "Automatically set threshold at doublet score = 0.34\n", "Detected doublet rate = 2.1%\n", "Estimated detectable doublet fraction = 23.1%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 9.1%\n", "Elapsed time: 14.9 seconds\n", "Threshold found by scrublet\n", "Detected doublet rate = 3.4%\n", "Estimated detectable doublet fraction = 28.2%\n", "Overall doublet rate:\n", "\tExpected = 10.0%\n", "\tEstimated = 12.0%\n", "\n", "\n" ] } ], "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": 21, "id": "5e3843e1-d357-4dc5-b57e-5ca0bb85b52f", "metadata": { "tags": [] }, "outputs": [], "source": [ "ns = np.array(list(map(len, DSs)))" ] }, { "cell_type": "code", "execution_count": 22, "id": "b7209b5f-31ba-4e37-a8cf-be9deadc2a2c", "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": 23, "id": "97e37766-7277-459c-818c-70b117f07614", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata.obs['mad_prd']=tbl['mad_prd']\n", "adata.obs['ds']=tbl['ds']\n", "adata.obs['mad_MED']=tbl['mad_MED']\n", "adata.obs['mad_MAD']=tbl['mad_MAD']\n", "adata.obs['mad_thr']=tbl['mad_thr']" ] }, { "cell_type": "code", "execution_count": 24, "id": "75c30c38-a91e-4e07-b27d-3777248ade23", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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sample_typeDonorSanger_IDtissuesiteSexbatchn_countsmad_prddsmad_MEDmad_MADmad_thr
AAACCTGAGAAGCCCA-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale09657.0False0.0548590.0743800.0455230.256471
AAACCTGAGAATGTTG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale07317.0False0.1050850.0743800.0455230.256471
AAACCTGAGCCAACAG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale02564.0False0.2073390.0743800.0455230.256471
AAACCTGAGCGTTCCG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale01501.0False0.1187270.0743800.0455230.256471
AAACCTGAGTACGTTC-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale031221.0False0.0856570.0743800.0455230.256471
..........................................
TTTGTCACATGATCCA-39CTCLCTCL4CTCL4_GEX_4EpidermislesionMale3914797.0False0.1207600.0793320.0407600.242374
TTTGTCACATTACCTT-39CTCLCTCL4CTCL4_GEX_4EpidermislesionMale391168.0False0.0585110.0793320.0407600.242374
TTTGTCAGTCCAGTAT-39CTCLCTCL4CTCL4_GEX_4EpidermislesionMale395442.0False0.1021900.0793320.0407600.242374
TTTGTCATCACTATTC-39CTCLCTCL4CTCL4_GEX_4EpidermislesionMale392733.0False0.0701750.0793320.0407600.242374
TTTGTCATCGGTGTTA-39CTCLCTCL4CTCL4_GEX_4EpidermislesionMale3910016.0False0.0534350.0793320.0407600.242374
\n", "

346056 rows × 13 columns

\n", "
" ], "text/plain": [ " sample_type Donor Sanger_ID tissue site \\\n", "AAACCTGAGAAGCCCA-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGAATGTTG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGCCAACAG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGCGTTCCG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGTACGTTC-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "... ... ... ... ... ... \n", "TTTGTCACATGATCCA-39 CTCL CTCL4 CTCL4_GEX_4 Epidermis lesion \n", "TTTGTCACATTACCTT-39 CTCL CTCL4 CTCL4_GEX_4 Epidermis lesion \n", "TTTGTCAGTCCAGTAT-39 CTCL CTCL4 CTCL4_GEX_4 Epidermis lesion \n", "TTTGTCATCACTATTC-39 CTCL CTCL4 CTCL4_GEX_4 Epidermis lesion \n", "TTTGTCATCGGTGTTA-39 CTCL CTCL4 CTCL4_GEX_4 Epidermis lesion \n", "\n", " Sex batch n_counts mad_prd ds mad_MED \\\n", "AAACCTGAGAAGCCCA-0 Female 0 9657.0 False 0.054859 0.074380 \n", "AAACCTGAGAATGTTG-0 Female 0 7317.0 False 0.105085 0.074380 \n", "AAACCTGAGCCAACAG-0 Female 0 2564.0 False 0.207339 0.074380 \n", "AAACCTGAGCGTTCCG-0 Female 0 1501.0 False 0.118727 0.074380 \n", "AAACCTGAGTACGTTC-0 Female 0 31221.0 False 0.085657 0.074380 \n", "... ... ... ... ... ... ... \n", "TTTGTCACATGATCCA-39 Male 39 14797.0 False 0.120760 0.079332 \n", "TTTGTCACATTACCTT-39 Male 39 1168.0 False 0.058511 0.079332 \n", "TTTGTCAGTCCAGTAT-39 Male 39 5442.0 False 0.102190 0.079332 \n", "TTTGTCATCACTATTC-39 Male 39 2733.0 False 0.070175 0.079332 \n", "TTTGTCATCGGTGTTA-39 Male 39 10016.0 False 0.053435 0.079332 \n", "\n", " mad_MAD mad_thr \n", "AAACCTGAGAAGCCCA-0 0.045523 0.256471 \n", "AAACCTGAGAATGTTG-0 0.045523 0.256471 \n", "AAACCTGAGCCAACAG-0 0.045523 0.256471 \n", "AAACCTGAGCGTTCCG-0 0.045523 0.256471 \n", "AAACCTGAGTACGTTC-0 0.045523 0.256471 \n", "... ... ... \n", "TTTGTCACATGATCCA-39 0.040760 0.242374 \n", "TTTGTCACATTACCTT-39 0.040760 0.242374 \n", "TTTGTCAGTCCAGTAT-39 0.040760 0.242374 \n", "TTTGTCATCACTATTC-39 0.040760 0.242374 \n", "TTTGTCATCGGTGTTA-39 0.040760 0.242374 \n", "\n", "[346056 rows x 13 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.obs" ] }, { "cell_type": "code", "execution_count": 25, "id": "24e4e5d7-2b52-47d5-ac42-ffec5f300b7d", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_db.h5ad')" ] }, { "cell_type": "code", "execution_count": 26, "id": "42af0828-be3c-4ac2-8766-47a493826c17", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata = adata[tbl['mad_prd'] != True]" ] }, { "cell_type": "code", "execution_count": 126, "id": "4d455bb1-0d71-4f66-9fa0-eb9d183ecfaa", "metadata": { "tags": [] }, "outputs": [], "source": [ "#adata.obs['mad_prd']=tbl['mad_prd']\n", "#adata = adata[tbl['mad_prd'] != True]" ] }, { "cell_type": "code", "execution_count": 27, "id": "3ca3b4d2-94be-4322-b071-f44cf9ec125a", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "filtered out 20793 cells that have less than 200 genes expressed\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/nfs/team298/ab72/miniconda3/envs/multiome/lib/python3.10/site-packages/scanpy/preprocessing/_simple.py:160: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n", " adata.obs[\"n_genes\"] = number\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "filtered out 6767 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": 28, "id": "f97a2981-b5ee-4820-a3ba-2d0727b6dece", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata.obs_names_make_unique()" ] }, { "cell_type": "code", "execution_count": 31, "id": "58b20fa2-e576-42e6-9e1d-491e0ad9aa5f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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sample_typeDonorSanger_IDtissuesiteSexbatchn_countsmad_prddsmad_MEDmad_MADmad_thrn_genespercent_mito
AAACCTGAGAAGCCCA-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale09657.0False0.0548590.0743800.0455230.25647127660.002692
AAACCTGAGAATGTTG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale07317.0False0.1050850.0743800.0455230.25647131060.000547
AAACCTGAGCCAACAG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale02564.0False0.2073390.0743800.0455230.2564716330.003510
AAACCTGAGCGTTCCG-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale01501.0False0.1187270.0743800.0455230.2564719790.039307
AAACCTGAGTACGTTC-0CTCLCTCL1WSSS_SKN8090612EpidermislesionFemale031221.0False0.0856570.0743800.0455230.25647143560.009833
................................................
TTTGTCATCAACGCTA-26CTCLCTCL8WSSS_SKN10827912EpidermislesionMale263668.0False0.0686160.1065690.0562690.3316469600.001636
TTTGTCATCACGCGGT-26CTCLCTCL8WSSS_SKN10827912EpidermislesionMale264474.0False0.1107780.1065690.0562690.3316466970.001341
TTTGTCATCCAGATCA-26CTCLCTCL8WSSS_SKN10827912EpidermislesionMale264143.0False0.0686160.1065690.0562690.33164612350.000724
TTTGTCATCGGTCTAA-26CTCLCTCL8WSSS_SKN10827912EpidermislesionMale262593.0False0.1247970.1065690.0562690.3316468740.003471
TTTGTCATCTTGTATC-26CTCLCTCL8WSSS_SKN10827912EpidermislesionMale264724.0False0.0820900.1065690.0562690.33164610610.001905
\n", "

242723 rows × 15 columns

\n", "
" ], "text/plain": [ " sample_type Donor Sanger_ID tissue site \\\n", "AAACCTGAGAAGCCCA-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGAATGTTG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGCCAACAG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGCGTTCCG-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "AAACCTGAGTACGTTC-0 CTCL CTCL1 WSSS_SKN8090612 Epidermis lesion \n", "... ... ... ... ... ... \n", "TTTGTCATCAACGCTA-26 CTCL CTCL8 WSSS_SKN10827912 Epidermis lesion \n", "TTTGTCATCACGCGGT-26 CTCL CTCL8 WSSS_SKN10827912 Epidermis lesion \n", "TTTGTCATCCAGATCA-26 CTCL CTCL8 WSSS_SKN10827912 Epidermis lesion \n", "TTTGTCATCGGTCTAA-26 CTCL CTCL8 WSSS_SKN10827912 Epidermis lesion \n", "TTTGTCATCTTGTATC-26 CTCL CTCL8 WSSS_SKN10827912 Epidermis lesion \n", "\n", " Sex batch n_counts mad_prd ds mad_MED \\\n", "AAACCTGAGAAGCCCA-0 Female 0 9657.0 False 0.054859 0.074380 \n", "AAACCTGAGAATGTTG-0 Female 0 7317.0 False 0.105085 0.074380 \n", "AAACCTGAGCCAACAG-0 Female 0 2564.0 False 0.207339 0.074380 \n", "AAACCTGAGCGTTCCG-0 Female 0 1501.0 False 0.118727 0.074380 \n", "AAACCTGAGTACGTTC-0 Female 0 31221.0 False 0.085657 0.074380 \n", "... ... ... ... ... ... ... \n", "TTTGTCATCAACGCTA-26 Male 26 3668.0 False 0.068616 0.106569 \n", "TTTGTCATCACGCGGT-26 Male 26 4474.0 False 0.110778 0.106569 \n", "TTTGTCATCCAGATCA-26 Male 26 4143.0 False 0.068616 0.106569 \n", "TTTGTCATCGGTCTAA-26 Male 26 2593.0 False 0.124797 0.106569 \n", "TTTGTCATCTTGTATC-26 Male 26 4724.0 False 0.082090 0.106569 \n", "\n", " mad_MAD mad_thr n_genes percent_mito \n", "AAACCTGAGAAGCCCA-0 0.045523 0.256471 2766 0.002692 \n", "AAACCTGAGAATGTTG-0 0.045523 0.256471 3106 0.000547 \n", "AAACCTGAGCCAACAG-0 0.045523 0.256471 633 0.003510 \n", "AAACCTGAGCGTTCCG-0 0.045523 0.256471 979 0.039307 \n", "AAACCTGAGTACGTTC-0 0.045523 0.256471 4356 0.009833 \n", "... ... ... ... ... \n", "TTTGTCATCAACGCTA-26 0.056269 0.331646 960 0.001636 \n", "TTTGTCATCACGCGGT-26 0.056269 0.331646 697 0.001341 \n", "TTTGTCATCCAGATCA-26 0.056269 0.331646 1235 0.000724 \n", "TTTGTCATCGGTCTAA-26 0.056269 0.331646 874 0.003471 \n", "TTTGTCATCTTGTATC-26 0.056269 0.331646 1061 0.001905 \n", "\n", "[242723 rows x 15 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.obs" ] }, { "cell_type": "code", "execution_count": 29, "id": "cecf36fa-a540-4e75-a2e7-fc523dc9deb4", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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eeCItWrSILr30UvrDH/5AZ599Nv3617+m2bNnNz1d2hVXXcFkr3W0ut1C3gMAQPXI2naj7W8dMWlfpV9aAwDigSDfwbT6fO6Y66FCZrNFuaLDbKeOU+ZtUYJ8mdMsq6juG5TF7KIILbcud5MmTaLe3l6aNGlSYlgx4clnQsEOkvSU6ei4XXfdtb4g8PGPf5y+//3v05133knbbbddcxKjA0BdKRdVWEzEi+IAAACAcpPUV1dhvAFAJzLAYKSdyPLly2nYsGG0bNmytvk5vG2UqzQx7+vrS7Q1xE3e9hQVZhXzKIQ99tiDiIguuOAC2nXXXXP1ux3STJanIstXqHhPlH2HRkw82iEfQWtox/66CjSz782T2HaplW1xs7F99axZs+jQQw9trTEgmLKWJwA46KsById58+bRE088QePGjaPLL7/c67Zd+od2iQcARNgh37Fk3SUnV1ebsdoaetZaM+CrzEWF2e47GfPaIc/LXjukmRTji9rNEJNGedjgC087PqPq+QhAp1DVXVcxdmtu84ov2rn2pdl1oqp1EQAAQD742n873ihrH5F2PAZAlYEg38FkEeN5Q9iJDWNRgqEmTLYrq1evzuyHVvbaKc3KIEwXbYOr/WinfASgnSlDO5WGGLul204c94A4WlFGqloXASgzZW3ny2pXmWn3NAvpd3xuWpk+oX1mbD/X7nmeB0ij1gJBHkQjG8JOnQDEdgRJjV2VJvh52Pjee+9lDrOdyp4rTe2xSHn5l4aY3e1p/C5THlah/gFQNspSf2OJsZu7LVu7BcpHq8pI1cok+lxQZso6NyurXWVD/qqtndPMGBPU77jcpE2fEH0jhNA+sy/FEahVz/Mi7S86jaqe9s0AgjxIhWwIqzYBaBa2kXv44YcTGzvbETXLrizP5tFwx+yQ94UZU/bK2in4ykea9G7WACSvcMrSfrTLwA0AUDxlabeKZsCAAa02obJ0ShlJC/pcUHbKuvhaVrvKhGxf2j3NbF8d+qs/7Vps+iS14aFtPM+jLOFJ2iHPi+4ni0wj9PFhQJAHpaQqFTfJTtvITZo0KbixK+qcWu5flsYxr4Y7RpDPGmZfX19pO4W+vj6aPHky9fb25jZAasYAxO5QaPVAJ2SQ10kDNwAAKFs/B4AG+lxQBcpaPvO0qx37DK19KWteNgufcE6ULn18bXhIGx8zP087Jw6ljPWgGf1kUX6jjw8Dgjyo08xGyBdWWYVTSaidthEKXbEu+pza0M4xyY+sxL7UNSRM3w5zIv+goRU8/PDDDYs2LtIMJooW4/N8qXHa8h26MyN2NwUAAFSVqoyhACBCnwtAq2nnPgPtywe48jlt/vP5tY+kPIgVbYvK0zLXAy3OZdHtkkAdTAaCPCCiYhqhJJHMdT+P1bRmNFKhdlpbtA7Q5W9sGLHktVKdhaQd8iHhy18S2OOBODwNy9QpPPzwwzR58uS6KJ8Hzcq7PMtlFpuT7LD3sy5AAQBAVch73GCMycUfAAAA5QO7WDsDVz6nzf88y00Zyl6V6kEzFw/ShIV5dRwQ5AER5d8I+SpvSFj8XtoV22aJ8iG2yDPCY2xsdsdQZIfEJ/a+Sb5MH9/Od74jvLe3lyZPntzPfSs616S8nTRpEvX29np3xsf6mzbv0orheZC1vIXsvCjDAhQAADSLKkwoAQAAlAP0Gc2hiLlGzHtefBuY0tBu5aYq8Wnm4kFsWJhXxwNBHtRpZiNUZKUu0wqntUWeIV8mGzVCFhrSELrTjqePrwzINIw5q79IQsttGjE+5OXAefuZhtg6m7efMWGXocwAAEAZ6fSXumJSCQAomrK0M2U5BqMs6ZEnECpBWlwbXLP6EUpMWJhXxwNBHhRCTGUsYsdvmRoBLsJr14mqNfDQBhSh9ofukCdqTDdZBqwNvuey4NuZH0JRnVGaVWr7ycvP0HCLOAKrqIFsmdoLAAAoE518ZE07CijtFBcA2oGytDPNtMMXVlnSI5RQO5shVFYlzUA4edSHZtcpzKvjgCAPCiNUjM97x2/VqNrAQw4oYuyPEeRlmD4b8oCL8NoxQ9xNCEnHpKQl9tclrpea8u9p09EVjyLyp+v/jiQquj2oSj0EAGU1PUi7/GlFmhYdZpECStaF/7RhVmm8CUAnUJYdpc20wxdWs+zIox2MbVOLjNPy5cvRvqekzGmWR30oSxsDdCDItzmtGPDHUPUGIo90zTsNmpHX3NYY++2LXFesWJH4UtcYG7LCB1Q2PvL4m9BBV8j9Zr54VXupad6r7Zo/edfpvr4+9f0AeYeBwSyoAp1WVvOMZ6elXTNoRZo2sy/NG9/Cf5FUfcwNQLtSljrZTDt8YTVDjE/T9kr3adrUItt7tO/xVGFMmEeeolyUFwjybUyrBvyxVLWByLMBz1OMb0Veh9q/evVqWrFiBS1evJiWLFlSrFERyAGV/Ku50QhJ/2bvQLGfvG2wfhBRUxcYikw3iBWgKnRSWc27X+uktAsla9q2Ik2rnI+uhf9mhQ0AAJ1MaP/B+0bXWCRWjC9izrRixQq6/vrrc/WzlTRTx8hrLBFic1m1ONBaIMi3MV3/d8RDWV50WUayNIxlnAyW0SbO6tWraciQIdTd3U0bbLBBS2xw5XlImiW5CU3/MuRPXqvtzV5gaIcwAEgLb7+aUVbLMHkooo0pMu3KkGYxpBEImvGrqBDKsisxDdrCPwAAaJSt/SorMekUIsbzvjHPzUx5t/tDhgyhGTNmtEV/0orNhXmI8Uk25xUvtAXtBwT5NoYf8dCMVb8Yd2Ugj0los9I1hjJ3xqtWrSKi9wcOq1atanp5aUYnX1T6l7luNVvckjtWAOgEmj1JKdPPeMvcr3HKlGahxAoEzYpjkv+xY7eq5QsAABCh/QqlGb+my2szU57Yd7INHTo0V39bRdk3F2qE2JxHvNAWtCcQ5NsYXvFlxS1iItPqRiI23FZPQqV/ZWxcXTb1Oc4NT+Kdd96p///Xv/419ZnsadMqTZ5nDTMPWl23iiAkLlq8+bV2TBcAXDR7klLFSVGrqWqaxdjbjDgmte2xbX9V8wUAANB+heFKpyxzhCql+YABA1ptQj+yzNerRojNWeOFtqA9gSDf5lgxXgq/RUxkWtlIuIQ7/lejlZNQ7l8Zz/l3lRN7PY2977zzDq1YsYKIiIYNG5bqTPasImyaBZjYMLPmo3y+bB1wHvELSU/X7hR7rWzpAkDRNLuso27FU8U0e/jhh6PcFx3HpLbd3o/1s9Mo05gSAJCeTmy/0qCJ8XkcyQbCyHO+DnTQFrQfEOQ7kDQTmRi/W4GcvNlOIG+hO+/42QWTyZMnU29vb6qd22kIed41IbbX0wihf/rTn2jx4sW0YsUKevfdd1Mt8sSIsFl3RYQIv64Fi7Rhu54vqm5p9ie5z1qnYvJQcyPLAwAAgHQ8/PDDNHny5GhRPg+ybpjAZN8NxBAAQKcTu3GnKu1mGe2TacfTPsucGIB2B4J8B+DqjFw7yiVFd05FiOU2zvKFtr44xpKn3dbW0HCLEHxdtmnurEgdy4gRI6i7u5uGDBlCr776atAzacJJs6s9KWyXGC/DyLprO83gMTaO1r38FUBImuW1Kz3r83nUPwz0QDuAcpwfnZiWkyZNot7e3uAxSF5k7aPb/RdSWcti2vTpxDoAQB50St2pWjxDNlNxt3mc810krr7TniXfKlyb6NL29fI5+Rc0H6R9MUCQb3OsyCaFVdeOcpf4WtSkxxVuUoUPFZP5X19Ydjd9KJpfeQm/IW6znJEXu8M8z8WYd999l4YMGUIrVqyg8847L/VCSMjZskRU+GTdlZbNEqttXGOP05HuX3nllag0yyNNY/LeVWezlMu8yzYArQDlOD+yTBqrTrPFeKJ8xpbtLMbnUa/TiPFoTwCIp1PqTjvEMykOWcX4pDlqVlx9ZxnOkHf9qjlNXy932Bdx8kEzKNLWZqZDO9T9sgJBvo3hYp3WgGk7yl2NZVGTHi3cUME1tkFIiuPkyZNTi9rNbKTsAou8FvtegFB3vl8YxMb33XffJSKiIUOG0EknnZSqXHV1dXmP9+E2+/zPK6/KIAjEHHck00f+kiRtfGIF9tDyqrnNS8hp592VoDNAOc6PNGnZiglKaLsZ6haUj1bVa7QnAKSjzHUn7eYnjaR4NqvPyRJOkXnl85uPF5q52FqGcUDatLbPafPVKlDkGLHZ488yt3FVB4J8G2MrTq1Wo0033TTIfbPRxOWQxYG0Z4hrz/B0SvKTd6IhCxp5N5KuxrfowYUWdpqOwAry8n9Okn99fX2J592G5GM7rPLyAUrsc/L/kLJz++23q9fTLAjFnCFfxK8QZNtT9bIAOhcMjvMjNi3TivhpCWlrrZuy7yRrl364KFpVr8vQnqBMgCqSpe4UVeZjN8CEPOMT45vRpucRTpHtXJJ+QdS8959UpZ8NsS9mvloWWrX4UxRVSvsqAUG+zen6vx2vZVjVko2tr5NIsjNUjI85Kz1UxHUde6OJ8Xl3gr58dK3GFxU2/x4ajhXhV6xYQZdffnlUmeB29Pb2Rv2iQfMjr/rQqkGOtjBUJLfffjt9+tOfVkX5NOmp1ZfYwX9a8lhcAgAAovidakWfm84Xals97vNRhnFpFlrdX7Q6/KJAfww6AV6+iyzzoe0styFt29ysNr0sfUea/CpCk7FH1cgz5LPkZTNp9za/FYs/oFpAkO8QuGDcigZPa2yL7iRC/I9JC+tf6BE7RcUv1D9fB5dFDJDfNXHTxXvvvUdE7x9Z84UvfEH1LyTP8hAa8hLjY94/4BOd04abd312+XfooYfSbbfdRoceeqh6P6Zcatdiz8KX/vX19dXzIcmPPBaXAAAgljzGBSHP8rYtK0WeS1vVyWSrBYRWh18kVRCQAMiCrL/NmA+HuLG7t0OfSRuWi9g5eVGE2JHH4nqRyAWWMhNT/n3pLe/l1T+2Yz8LygUE+TbFtfLeqp8wt2qAnSTshqSFXESIiYPmtqgOQ8N1jE6eZUCKmb5FACvIExGtu+66Tv9clG2A0RWxW1+KzlnSn4cb+0LiEBt9onwWXO2Pa7HLZaO01/o7efJkuv3224PKtxTfk8ovAEWAstZ5aH0yqBbNGNP6ykW7i9btGi8AiPT6G1Pmi+wzWjUGbvb42xVOqB1lb4PLbp8kVIz3aQxF/PK5WeWyyuPAKtteFiDItyGulXei919cGvMCyDxJIwwXUclluvgmxyE2hnZ62oJIkv9p42/9zWJvDNpOY82ev/71r/VrXJyPCadsAwxtt76Wb1x0Jso+6LXhZj2+R7OxiPTt6+vztj8hi12uto2nxaGHHhrUxml1r4zlC7QvWAACVSgD/GfwZbaz2RQtxoeMPTsVlENQddLW3zz7jD6xOaiVY+BmLnL60jDGjqQNZM2gk/oIX97Ie3mVp2aVy7KPA11U2fYyAUG+DXGtvHPxKi15VrikRi60ksfYxP3s+79d1j4BPrQh1u5r/kpB0ud/2kbOxivpxbhF4fK7VqvRBhtsUP+eRpD3+Z8GORhNi7XJ+ucb6NlPHgtjXV1duZ8TXFTZyKP9cbVt9u+mm25aF/5l+svvrjxotwEsKC9YAGp/kvqXrGUgrzFZiD+YeDUPtA1uUA5BJxPbNvg2fGlHRbayzSmyH+TtRtHta7PaKBvOG2+8QUT9z5BvJ7gu46Ko+VzRdaLK/X2VbS8TEOTblFAhNkmQ9InVeeATskMqeaxN1k8iajjqwhdmmkbGJexrgqQvr2IbOTngaDVcoCb64KWuROkFeetvVmzeu17SG2sDj2dIuc1rVztRYxkq2yQ160BfE9Jd7lzpr7UTeecBAGnIo060I+0Qx9DxSVJfkcX/PIX2PCZe7ZCvzaIMY7gyAgEAdDoxYnzSTvCq16W0/VfSXCLLUaDNaqNsOMOGDSOiYt/30ko6YRG2S2wQrRJVbj/KAgT5Dsa1Oi7vS1G5GTa5VkI1cS620+sSvxbQBPisjaJL2PctQLj8CaUZq/4hNvD/pUD6zjvv1O/z/2PD0AZLMXlmxdje3t7gl/RqNkix2fpVRLkNIc2gpcgBQNZBlHw+qa5o6e+qF62uKwCkoRMmJjH1vtWEtklp/PTldUh/n5fQ/tprrzW4TUvZ+qdOouh0bHY+od8GnUBovXK5S2rbXfPSKhHTz4a6yeMo0Gala9XzL4R2m6+FaG6gs4Ag36aErOwmrY67GkArhiYJlmnwNbquxipWtJbPaeJpHmJAiF1JDXDMYEzujM86kItFW0yRAinfIR8iyGu2aYOl2I6ML8i47vnyz+WmiHIbkz+xg5aiha8Ye2TYUnAKeSG1DIc/42rjtLABKCvtMDHp6/P/Mo/HscyTFJdt2jgjjZ+uvNb6e408hIoVK1bQ7Nmzc0n/rP0TSEfR6Vik/8h70KmE1qskd1r/URR5+l3UvCSETTfdtOlhAj9VHvNyfJpA0ccoVcnfjsOARJYtW2aIyCxbtqzVpgTR29triMj09vamer5WqwX5T0Sq21qt5ryXFetnGr9j7OLhxD6Txi7t+dCwtedjns0zr5L8+d73vmd6enpMT0+POeyww9Q4h9rmezbG3pg0Dg0vD9vsczFpkDaMkLBi/ZP/Jz3D2yxpi7yfxs8kd0W0V6D5VK2/bjXNLve2vml1ztV2lLluhvTbMX1FkvsYN1mxffWCBQsKD8uY9GkB+lOr1Zpah4rwH31ze4O+OpnQviBmnlrk/Dwvv/n4PWkc7ws3iy1Vanfmzp1renp6zIknntjUcKuURnmR57y7WRRV79FH5wcE+QCqOGjIIsaHCpShk+m8iR18yL/atRA/fG5DbEq6lyRQhD6XJX5Fc80119Qn+VOm7K2Krs2cRMowXAM6aWfSADDPeLietQPVPNMrj+etTbEdtYxPbF1I+4x1hwFFe1DF/rpVtGowbeubbLvSth1lwxUvzV0zF8Rj3dq++vbbb09vVIQ9Vc7zMmHTslXpmWeYKA/tC/rq98mj3c677U9LEXVfm+uEhNtJfYoV5OfMmdO0MDspfS1VjnNRNlcxLcoIBPkAOm3QkKZyNaORihUerU28M5fXQjp66V/IgEgTBrMI9kl2ucJxhduqBvS///u/TXd3t9l9z73NlClTcrcr9HnNna8saOkZmpd51A35rLZrpCwDBWlTErH12uePTZPYBcmypB3ITqf111lpVZnX6lxebUHZKKLf1/zJeyzTTEGehwuywfvCVoWNvARJoK8uz+72LMT023nMc/O0J89wi3g+lHnz5jVdkDcmnw2NRZN3uGWrf3nSznErOxDkA8CgIYwiK3LagQgXUvn/2t8Y/5LcaKJtsxo6GY72PcugLks8TjrpJENEZufd9zY9PT3mnXfeSe2XxXXMiQuXEBQ6gU0T/zyEZl5eXbb29vYWbl+SuBSz8BSTZyHw47Rij7dJm3agXKC/zpei+/Uq+Vu28JP6+phn+XXbJjdbkAf50co60Or6B6pBu/bVaeepRfhdtJ9yvuvbsJR0Pw+yzm/z9CtPW5LggnxZ2l9X2dDcNcOGLH50As0sr6A/eKlrBxP7IoYk90W+cCPtyy64e/kSNPnXFz/+otIkN0REtVqtIUz5bEjap3mhjfaSN+17b29v8Is2efj2ZSQhLw3WGDRoEHV3d9PgYRvQihUr6O23307lj+Xhhx+myZMn08MPPxxcRjR3vpe8as/H4sr7kLyV9kpbeTmZPHly9MvVtBfMuJ5PcpuUB/J530sLfWG6mDRpEtVqNert7VXzUvPD+p8m7QAoO1nKc+zLGmPDKmLM0OoXgTYrfC2cmPR0udXGB8aYdEaClpD0st+iaZeX7wEQS5r2P492m4cfQ9b+io/h7f9E5HxpZW9vr/N+HuT5Ysy0fiXNb4pk2bJlpZnHaGVDzoWLHi9lzYNWjydB5wBBvgPwiVAxE+1WN0pagxpqT6xQyHn44Ycb7mliJBeqR48e7Q0zJC2lfyHxDM0jK9yGuBs9enSDAG8HVCHPa/ztb3+jIUOG0NvvrqbFixfTH/7whyAh2MWkSZMaxNfQMqK5a8agiedRTJ1yLbJwP2x5Cx18uML12SXFmlhRyLUYwu2RfvJBfiguMd73Znuedhh8gXYgj8m2nUAXFVbedS3vSXCsfc2ahBcVTuj4oJNoZVpkEdjyshtlAYAwWiHCWtL0wXnYK8fzvrnv5MmTiYgKTaM8/U0jxrs24uVNFdplba5H9EE6ERVbFmS4aZ5t5XiymbSy7QJEOLImgCr/rM7+BEU7vqHIn9Wlxfczankv5Mzv0LA0//nPrOSxKPZ4CxJHiYT+FC/EHfc3lFC3oe74ESAyvWLDsX5N2HWS2fGAow0Rmf/5n/8JfpFfaNrycIv8CVba+iPLS152xJYTWXY1P5OeS3Kbhz1JZUO6TSpDsbaAalHl/roIspRjWxdC60OaNtE1PikDRbUFZW9brH32yJrbbruttQY1CVf/0or+IGRMVMSzml9pbQDAB/rq/Mmjvhfld15+FEGedjUjjrJdti91PfHEE0ubxpKq2JkXzRxLdFratgMQ5AOo8qChVqupL4AMfbaZuBorTRSQE3k7AfH54wrLJTLy+xyenjFioBZ+1jzJKmSEDL60NAjxk8fR/r/HHnuYyfscYD5xxFzT3d1tHn/88X5pqNmllQFf+D6xWYblc+OKV2gZc5XJvDtmn38h8Y8NK4s9Mf5q9TCpzmWJrw0nr3wBzafK/XUZKbo+xLxYvRVofVFW/0L6pzLQSYJ8mj60GbZkCTvP8UWs+zLXaVAO0FeXi6Q2sEp1OnbuUaW4Wbi9XJAH5aUZZayq5bnTwZE1bYz9SdCmm26qHjMScmSK5kZeC/1pbJIbeRwGv177v3Oh+b1arUabbrppw9Eq/OgOeRSGpOY520ze53Gwx6TYn95xO2+77bbEn/tYG2+77Tb1p+GhR4nw89NjcB0N4gpLpoHPVuknT6darUZDhqxFZuAgMmsOpiFDhtDy5csb8skeF6Idg1L7vyNFkpBHnLiOsXEdHaOlhz06iB9J5DuH31Umi8L3E1Hf8TNpwwqp75o90jbNHi3fbRl0xeeVV17p548MK/SYKJu/AIAPfoJeFPa9D2X9maz2M+ssPzvmbVoe/oF88P1cO03ZzKuM5HWURBZi/cFP3wHIn6L7iaQ2sCp1OuaoV6JqxY1TJnvLNIYpky2SZuSZHGOmpczp2Ja0ekWgClR5Fd+1c9S1gpa001Tb3UUBO5dDVuxCV+ft/3xnndxJJO0LibvvPg/XtbNN/hLBFw+X2xDbZJgh7lxx87lLWl31lSGZN8YY895775menh6z2wHTzYQZZ5menh5z1113RduVVG5d7nxpF1Je5K8xQsqzq9yFpG8oIWkW8nxMeLH1J9RNSP4m1TtfmC4/uF955QtoDVXur4ukLOU6qe0uO3nbW+b4yx3yZba1FWTpAwHodNBXh4M2JQw5X+sU7A75E044oelhl6lslsmWVpMlLZCOzQeCfADtNmhwCXghQpvruRDRL0mg84Uhr2nu+LUk4TNGLJT++bCCnu9n+HagIM+l98XdZ2uS/a7wfX6GNMS+tJUDolqtZv75z3+a7u5us+tBR5ldPvc1093dba688sp+YYbmfZK98hktT2VaxJSXkPTRrmn25pHWaTrP0LItbU0T91A3MqyQuMUcy+UqL2U/OgOE0W79dR6EtunNtqMsdnUCadKYC/LIq0aS0gPpBICfqvfVza7jMfOQTm5/OjHurT6yJibNi8yfdh+nhM7TQ9xnCQvkDwT5ANpp0JAkOvmuF7HSpgmULvf8vkTGyyV8uuxLuhYab/4SVNeLdJN294eGmZSusc/4nte+xwjoRGR+9KMfGSIyY3ff3+xwxFfU9wJo9oXYHFKepRutzOTVkSf5FRM/ez8pr9Pa7Ut3Hr72ImMt3DT55HJr4x1yhnHIL1RcdthnO+Gs5Han6v11UeTRruXhj2w78rILuEnbt7Vqh3xVykSn7cJsBlXJe5CdKvfVec4X8rahDLaBMPLKo1YL8qE0o2yGzomrVj98aYc6X30gyAfQLoOG2267LdNPubJUdF8Doh0T4RpgaAsK/J58NqlhjmnEkgQ+X3w0f+QzaexKkyexoqhLhHXd08Ko1Wqmr6/PjBs3zhCR2f7I0013d7c599xz1fDkNV+5zZK3SXZnIU06u57l6Z0l/NC0kGln2w7NTrmwlNeAwfoXunOdu43Z7W7tWrBgAQY0bUCV++uyE1uHm+VX2vA7AT7ekNeSaMVLXVtdLkKpip1VAmnaWVS9r/bNPZpF7Lyo0yhzOuTZ3pVFkA+dc+Xll+/ZrEer5k1e4+Yi/QetAy91bXPsy0NfeeUV+vSnP0233XYbTZo0KbVfWewganyhqH2Jq3w5KndvX1hqX1Kx6aab1t1Yv+y9SZMmqS9Jky/wlOHUHC9zcfmhubMvgrR2+NLK3rN/7ctd+ctZk14yw+MeC8+LkJdcElG/l+Ta65qd9r582ezbb79Nw4YNozF7/icNWm8EDRkyhIYMGaKGJ3G9lDjppaXSblda8O9ZX2Si5U2In66XzFq/XnnlFTVdXTb46oEGt1e+FObTn/50/SW2PI2sO2sbEdVfXuura0npwctCyEsftTqY9Ay3q7e3l2bPnu19US8AnU6eLz9r5YvUyvYy1bzt4OMs/mJxeS3v8PKgKi/Yq4qdnLKUdxdVTFPQubjmjc3qW/r6+vrNi3y2dRqueVBZKKK9M8bk5lcsoWU/JL5Z61HZ+pK82oUQbckVPig5rV4RqAJVXsXnu+Lz/HmtXImL2YmqHRWi4ToX2sZF+iH98+3e9aWFdBsSxzT3bBj0f0dlyPB8/oWkX4gfSWFq93hau5BH8tD/HVnT3d1tiMhsN+Nrpqenx5x99tkN4Wjlivuj+Z/l3PAkd7Fp6/sFR0jY2v/cD1858dXJLGVF80umEa+nvGz5jm0KSY8Ye1274kP9SJs2oFxUub9uZ8pWv8piT1J7GGtnSB8W6mfIDvnQ9hy0tswhn0DZaIe+Okv7WlT44ANC52JVT8eq7JCPSec0eVK0/1loVRlD318NIMgHUNVBAxfxQtzG+msFM62ya5NB7j40PNdRLi4BUIahiXO+F0DKuGVBWzjQwkpzPitP37QDjDRxDG3Ypd+1Ws384he/MD09PWbcp75gxs+9zvT09JiTTz7ZKyC4ypbNP/4/v++yKclm+ddVTlzPuxY50uaP9NuVZ658cdXPWJLi46tnaeJujIlapPLZkXbRCVSTqvbX7UJM+9Qqe8oUdlIfnibd8mjra7WaV5CX/VORNrUDrawDlnY8874d49QpVL2vTju+DPUbfEBe6ZG1nw21o6j8c/lbFkHeR9F9YBn62LKCNCk/EOQDqNqgIc1EKUZ4tM+4RELun9wlHXK2M58Quq7L8Kw4a8PRdujyZ0J2yMtwYpAvd3X5E3PWtWYn/yvvFdUx+cJ1hV2r1cw999xjenp6zITPnmHGz19oevb4d3PUUUfV78syJcPj+BZj0goY2nOx+RNaZmR4vnBD0tvnR9I7DWJsjUkHzabQ5+RLVmP8cbUbLrdpf9EAyknV+us8KEtZ9dWdVtiYd12ObYfymOQ3M92szbb97e7uVgX52HRFm/o+WfqxPMJutzzwba4B5acd+mpfX5f3fKTK5DUHKYo087YYd3nY7gu/CoK8MdnTISTtm0naOS4AEgjyAVRp0JC2I0gjPEq/tf9DrmlimHYkjRYve107vkZbEJDhSXt4WCFHumhY93KRwOVP7GQixE9uhy8sLU9C/PGFL+/Z7wsWLDA9PT1ml2PPM+PnLzQTdt09aMHGlT8yv+X/SSSVA369iE7UVbZ5WiTls+u+vZ72Rc6af9wulztpU+wiH4+LXHTxpYHLj9gwNT9BtahSf50HsWW+aJptR1J4edmTZvG8LHkSA++HfDvks/YpVSaPuCSNuYpYSCpbHoTY4xpjWLJuOACtox37am08n9afkGtVII/2rIg2LI1faeaXaeMfUwZCBfmqliFj0s3vymZPJ4/VgR8I8gFUbdCgdQQuIZZPtLI0KtogJMlv+4xLaPeJZDw8za1mkxQ47UcLn4vpodjnYo4sSWqgNX9idytb5G4iXjZcIrjPPs299Jf7c+2115qenh6z8xe+bsbPX2gmTT3ITJmyd0P+uPwKTc/QgXBMusfYEHIvyZaYePju83SNKTOuOiH9cu1Os89riwFZ4xJqa2z6g/ahav11HqQtw1Uv+0nteN7hVGEnbp5p4RLkY9PdNVaoInmUuRA/8kqnZtWRWELTQNt8kbRZB1SDduyrY8efMf6W6ddnseTRd+ZZ15vdbsTMH+39GPusID9nzpzc/CwjMWOOZo0Ni3RfFO1QFtoNCPIBVH3QoB3/YHcs8+s+QjoTOUC2YfhEM5+gx/3Twgr96Zh0Lwf13A9+1Eyo3zLOMS8Z9Qn4rvSJ2RGkTWSkQOpLG80PV1x86Vir1czFF19senp6zE6zrzLj5y80ux1wWP0lr0npkCa+SYPXmPhId74XE8eWTWmTFp7ruVB8+Rpim2tS7Drn3X6Xi3JZRQgtz+Q1X/7FliNQParYX7eivIW2U2WnWfY3M51cYYW0j3nmaegZ8qE2tWOZ840TQv0omjzDarZf2pjONd4A1aKKfbWPotu30PlK2cjTxrK2ZbHhhmySirEvRJCP8bNs5SmNPWWLQ1byjk+7pU/VgSAfQDsMGuRuVU0wcxHaeUj355xzjvqcvS+FZe5OG4Rr4bjC164lDeJddvni6LI/BBlXzW/X0T2hgq8Uu0MWALR4uBYqtPySflp/jz/+eNPT02PG/teFZvz8hWbXaceYnp4e8/TTT6vpwHdghw7mNNt8cQwR7bV72i8NfGFrtiX9ysQVvywD26R6FVunfPdC04Xf8y1oucp+0jFHWj3gJC0IgmpRtf66meUttL6XlarZmwZf2xy70JvFjTEfCPK33357kPuQ8NKWwTzjFUpIWjdjsaEs5T7vOCaNk2LugepRtb46hLL040XZkXXukcW/dqr/MX1GSLylIJ8lrfJo5/PMK8zJyp8GZbWrSkCQD6Cqg4Yk4SnkGX7dN1GU/2vHj0hbtF3ZoSKhfUa77hM6kwb7MeHHLFIk+edyw9NHXrMkvaBWhulLO19ctQWUJGGau/3iF79oxo3b0RCRGTvzErPj/keYnp4e84tf/EK1V1tA8pEUvibKamknbXD55/vus81XR1x11pX3SWG6vmv++OIb4r/mT1J9k2jtgrwnf+GRJOJrf31++uIGyk8V++si+hDtmTLuUgv1M0/7y46rT0ib7/J7aDr6dsjnQagtIe5C3RRhWxr/87Yjj3BCvudlhxavmDEXqD5V7KtbTV5toXUXcy/vtijGv7zmJ2XDN4+yf0PiPW/evPoZ8nnkU9Zn8+6zypafrbAnj/FfEXTS2LxIUgvyf/zjH82iRYvM22+/bYwxZvXq1bkZVTaqOGhIU0HkM64OWRtA8/PZQ3c0W79cAlySnTFiaYhd3J28pj3T29vrFPRc/ifFt1ar9RO4td3p9m9o+Dwsbde9K56+suDySwu3VquZ/fbf3xCR2XL6aWbL6acZIjLjxo1z7jCT5UyLkyssza0UibU4azbk0cnI3f78GClXutt7/NctsYNXrSz5ynTMNZ8tIeXRZ4Os49L+pLIRYqO1M7T+lpVO6otDqGJ/HUNIufY9m5U0LzdNIjROoe1Tu5Alr+XzIW2mJHSHfJY8CH02tN/z3UszJs6bvOttHjZq4x7f97zw1WffuAw0UtUxQLv31UWRtS2095M2MDWjv5V9Ux5um2V/Ue2R7LdDwpk3b57p7u6uv9S11W1lq8Mvkqr0R82cK5Q9LapAtCD/97//3ey1115mwIABZo011jAvvfSSMcaYGTNmmLlz5+ZuYBmo4qAhbUVMGoBr17mopQ2mfeHIMJPc8muabT73IWGFHH/B3fFPUriaEKtNerSd4Ty+Mg8WLFjgDVf66xJqXfnqy0v+vOue/UycONHs9O//YcbOvMQQkRmz79Gmp6fHXHfddd605mG40kCLh5bnWplxpS1PuyyCrbRfLrK4jkyx5UTGQUsr+bwx/fPP9XLXkPx1he8q81o4Ljt9913vO4hZmEiiqN2fRdOJfXEIVeyvNbLUnaLIoz3kfvm+J7nn9rQqPZpBmnSR99OmU4ggX6U8aLWNeadVnv4l1UdtDFAErvIa2150AlUfA7RLX11VyjDGCKnrPrfyvs+P0LlJCEX3e7H99uc+9zlDRGbGjBmF2CPppPbXVZbSPNds0thQpTFdOxEtyB911FFm6tSp5uWXXzbrrrtufQCwaNEis+222+ZuYBmo6qBBdk6xk2hX56ZV0qSzyeW10N3Kvh2s0m3oLmLfAMDXUcsw+G7dkM7f/s9FeWlDaD5ZN9oLe7W0cImxMu5aXH1Iv1337777btPd3W12/dRn64L8DoefbHp6eswNN9zgTTseT3lNpglPQ02g13a/a/5q+ZXUOWnpKO/JNIsph1r++OqWZov8HhI3WdZ8dZaHk3Q/CV8cfc/76q+8r4URYlsZ6MS+OISq9tccX91pNXnYFBu/rG1JuxKTjmnSqYgd8mXKr6zpliYuece/memZV7vk66NDF/PL2j42k6qPAdqhr5Z0eplMQ8jcRnMrr4fO00LchvYNRed3qP9yh3yRdFL7G9on+Z6z3/lf1zNloag+HriJFuRHjBhhnnrqKWOMaRgAvPTSS2adddbJ17qSUPVBg20Y0u5sC218tV3drt2sWqeodcp8N3lSp601fPwjw5d+uY4P4X7JXc5a+L5wLJqor/mpIeMujzQJ3Qnti6frvssOl908H4nI7PgfnzHj5y80Y2deYnaedYXp6ekxp512mtN/+d11Xr3ccS5/BSD9lHnOFzRci0VJZ/WHLghZ99qCjK/8hQjHafLP56+MV+hZ6z4bfPFMcpcUP9/CoLaYZp+ReZ1kWxnoxL44hCr1175yVoUymIWY+MW0a51GSL+elrzPkM/Svuad7yG2xPbJPn/ahSxxsfU4yy7XPOxoF6o+BqhSXx1ClcaPZSZt+sWOKXz30s5RWoV9qWszBHljwuaXrQg37+fss2nyWs4rkzYvFl2WmlVOy1QvqkS0IL/uuuuaF154of6/HQA8/vjjZv3118/XupJQtUEDn7zya9qRMq6GIcQ/+d21K9sKj3ynrf3r2/0uP5aQF31ae/jHt8s+yRbuD48bF/ekkJsk2Mtd0r6z4l3pr/11pRd/VmsweTxDG1Lul2txxcZrzJgxpvvI+XVBfvyca0x3d3fUpFYLQzv+JdQ/+7yMD7fdGONdrOE2aHXFFQ/NPy58aOVFu87Tw/VrAM1PeR679hyvtzFlImk3a4xf3F656KKV36QXvGr++H5VUFY6sS8OoSr9ta+e2PsxfrWCZoQb0q6VjVbmR1K7H0MeO+TzsKWofI8ZJ2R5vgplNpbY+Mh6HOMe6FR9DFCVvjoGOSYF+ZOm7Yn1J80cpZVYQX7OnDmtNqXQ/jqNv3nZk+X5kPllkWWp2f1pWepFlYgW5Pfbbz9z+umnG2PeHwD86U9/MqtWrTKHHnqoOfjgg3M3sAxUadBgK13oLlHXbmDf9wULFvTbZSr9sM/xs9KJqOFZY5LFYhm+tsPVlxa1Wq3fee1a3HyTBJ6m0i4u1LrircXDIvNAsy9p16+W7lp6ST995SMWX7oaY8zXv/51Q0Tm4wfPrh9ZM/ZzXzfd3Z8wRx11VFQ42o5m38tRtWsuv3nayPP+fUKva/c4D4v/r/1Cgl9zHQ/jyk/uTrsur2lHHbnqh+aPlga+7zzOrvTnz8pFEi6c2wW+0PzW4Hka+kzZ6MS+OISq9deu67ELfM0uw0WFm9S+lL2utio/ePh52RN7hnyeYadt21uFZm/sYrbLr7KRNl/lOCXUvet7p1P1MUCV+upYfHWkE8pxUe11bNvjmyu004JfmQR5Y4rN/2Y8F+I+xs8ylLM83v1kTDni0o5EC/LPPPOM2XDDDc2+++5rBg0aZA455BAzZswYM2LECPPiiy8WYWPLqdKgwXYyUtCS//O/mvgn/eR+y49PMOZhcKHTZ7tsyLk/PH6h4p4U3qToqe2O5zbY/+UEyyXmy3C0eEi3vkFDyIDClXbG9H9pZdLRPGnR0suGc9NNN5nu7m6zy+e+Vt8hb4X5gw46yGsD/x7yKwIZvrZg4oqn9qsFaUNIumlhy3DlYpUNTz4rw5BlP2lHuOsaLwfy6B6ZHjxtQxaBtHA1/13ueJ3lQrz8BY7r+aRyrMVDpk/Z6cS+OIQq9dcWV12NKc9J99OUbdczsv7nRWh8y07Z7E9rjybI+37R6BsHxqD5VeayofUnvB/T3If6VVZi7XONIWKer0K6NJOqjwGq2FfH4OrXq1qOQ212zWGKsCPE/6RxTKg/of422w9jsgnyReVRlct5ku3cTex8M09i62RWG6w/eYn74AOiBXljjHnjjTfM1772NXPooYea/fbbz3z1q181f/vb3/K2rTRUbdDgOhNZ63x45Qrt2OwOVbvbXTsLWz6TJNxze1zh+kTSJJv5X7nbll/jQr20WTZoLnHeFS4PR/MvqfFPuh4y+CuqMXXlsQ3n3HPPNT09PWbnL15qxs9fWP/s3DPV7Lfffon2ciHXV1ate7nQIn/J4EP71YLLPnvNZYtWD2V6uX6t4LOV3w/tZJPq5TnnnKOWDVk+ZT3itviOfnIt/PF6ZG1asGBBgzvt1wquuuNKS/ldy4uqDTQ6rS8OoWr9Na/DrjY86fkQ/2MH4662Rdqb90QjT7+aSRq708bV1y/kiTxDPqmNLLocNCveadDqSdpF4zLEx0Xach461nXdL6KtaQeqPAaoWl+dF76xfavCDx1H+MbaSWHE2BfqLk+RMaZ/ySNs3xgrlnnz5qU6Qz5p/BlLaPlI418zCQlXGwM3c4wSWwbzsiF0QyyII5Ug32lUadBgK6gUymSjK0VTX8X2NdRyx6v1xyXqabvQfWIBD1uzPRQtztoOZrk7V8ad2+LaKawJjNp97p/Pbi0tbBxC/Ant7H1nqceEI8ucTevu7m6z0wnfbBDkd/vkEaanp8esWLHCGYY85igJKb5ru+qT4uV6TisPvvuuMKRtrrR2DYBluQoJz7cYxheokkQ4/l1rU1y74Lmt1j3fmc/Lira7UKuzoemk1XOtLmGgUX2q1F9bZB1yudGuhZRXXzuR9Jz2XatrnUxoPmR9xvdcTFkIJXSHfBGE2hkb76LLa4gdZe1jXG0M/z+t7XnkZxnTDKSnin11HrSyDdDGwL4+xfW9qDjE1v+Q8LO4Kbo9kv1S2nTNukM+j/zMu0yUua/kZM27PMLO223efpU9D8tAtCD/i1/8wvtpR6o0aLANghTZ+XXrTjbCsrPl92MaGdcLF132JO2o1QRPaW9o2kj/+TW+C1froEIGK1xI9KVrqL0uIVGeQe4bMCT9+kH65xKwXeHI/JBl0Bhj/uu//sv09PSY8XOvbxDkdz34c6anp8e5o8f6lXTUiXQvy1PIrnp7n6ep69cm8nn7nKuuaOXMVfZlmrvslHXCFyfpl8Tmu+aOx88XrlZnfPZwEVyGoy0OyHKm2SjDkuf/a+VDq2ehdbSVdGJfHEKV+mtJaBsV8kwedoS0/3mEE3q9zGhpFvpM2rBi/YvNQ7lDvlkk9bWa+xD/5LtT0vgVEo7PD1cfFhtOzL2Q8JL6xrRhp6GK9b8VVH0MUOW+WiOvtr/o8q+1QSFjds2fInC1YXkuYGe1x+cmq42hz3N3eZwhn0d+5l0mqtYXlNXerPUgzXO8PFdlXt1KogX5AQMG9PusscYa9U87UrVBg6tjkOcvu3bY2sojdxn70F7CKP2zftn7/Lu01z4jdzvLXbSuCp5U8V2Tj5AXwGq28nRIEuRj7JbP2u9SzPWlobaoodnjEqKT7NPCkIsyBx98sNl9r6lm7MxLGgT5Txz2JdPT02N+97vfOePqKs8uQvLedS3pZZ8uv+VijnzGtzDmcqvZyNOX+xe6UKLB/ZD1UtY531ExSWmk1ROXzVodiq03LvHF3pPxjz0Kq9V0Yl8cQtX6a0loGS8qXK3+pbXB517WO3k9j/g2uy7naXtRxNgWIsjnGVdfWcuatlofr7kpUrzJy/+kvlOmY+jiRpY6nyVuZa4vZabqY4Cq99Wc2DFqrD+tqCNlq5cx841YN1ns4HnmKwe+suHqk3w28HDKdoZ8FSgi3mVOy5g2JWQMkRRWmkWmTiZakH/jjTcaPv/7v/9rfvazn5nu7m5z3333FWFjy2mnQYMUXS2ysmg7g13utd3aXAirMQFM3pcVVgpjmvBcq+k7X7ltIY2HJvhpu6K1OHNbtQmMS8DU0jrWbsuCBQucdnH7eXxC7IltdPlH88fatP2ESYaIGkT57s+cbLq7u81VV12l5gW3O+Q4EV/acltc+ctFde7Wflw777XFI+lOpplmmysO3Bbt6BbtGe5OO/ZHpol8ySx/jofhSksfWjr7duP5Fppc8eR28XJjsfVF+2WC70XHZaUT++IQqtZfy3KbRzmMfd7VLmaxIyQurjY9j2NR8krLNOFmuV8mkgT5PNM4xK+09yS+sW/R+RPjv8utZrNr7JHkPot9eTybtgxVqR4VRdXHAFXrq5Pw1de0G7FCns+bZtatoscYedughSnbX1e76rM1zXGZ3K0V5LOcId/uuPIsL3/LlpZJ4yXfGCDrZhztmbKkS1nJ7Qz5Bx980IwfPz4v70pFuwwaXIN2e911rEONiYD2WSlcu3aayx3n2rEw3BYtHPnyWCnoa7vR+d+kRomH7WuwpT9Ju5y5baECg6txlP9bf31CqyTklwX2+aSGk+ePqwxY3nrrLTNu3Diz05SD+gnyH//0nAYBWFsw4nb7djHL5+R1aaer05H1hIev2SUXcFz1SeaddKvli2ab9SMpD62/UmjX0lja39vb21DvNHtcz0lCFq24H1q+aPHjfmsLfvx5G5dzzjmn/pyWVu1AO/fFIVSpv9bqf1LbG+pnrD9Zw01rh6v/zMOe2LBD/UxrW55xawat3CHvu5dXmSlzfsTa1ur6noV2yrcyUJUxQJX66qy4ymrafrIomjkezqMet2K8pLkNmbvkmcfSbSftkJdjgRD7tbzJa4zp04my+p3l2ZgNDtqYKk/QZyeTmyD/3HPPmXXWWScv70pFVQcNMT8Fl8c7+I5esf74xF27o1mKqZogKP247bbbVDGWP2vd8zCSxDn7CfmpMv/LO1rfooX0T8Y/TUPEw5E2ykWPpIZXiscaMr+0eHE7ZD7ZvOP2/OIXvzBEZHbc/4h+R9Zs/5nTDRGZE088Ud2Nze2SeaG50dJaK7ta56n5z8uU/XD3rrLkOtpFlht+Twrf0g/tuw9Zp6SfrsUiXm/srnKX7dK9FPB9C1L8OZc4L+PBn5OLffw5njf23jnnnOMtN0l1oyq0c18cQtX6a1le8/IzT3dpn0lbn5pRD139SMgzWSYWecetyLTq7u4u5Az5WJvl+EUbJ+SZH2XoB7SxSh52lSFuWXCNC8AHVGUMULW+uijKVpbT7NZOSxnCyNOGosdU1r3MnzzOkC8DIXklN7SFltWiypo2R83Dz6ybdGLmAc2o72Vr58pGtCD/9NNPN3yeeuop85Of/MT09PSY3XbbrQgbW07VBg12cq8Jf75duFbgWrBgQb/GQHvG1VBwcdb6pzUsXMy11127oWu1WsMRODw+3K0Up62Iyt26BFEbb63B145McS1ayI4irdAnw5HpJ8Py5QlPb194PL80sZPHR6YHF0h5WI888ojp7u423UfOrwvxdWF+zjWmu7vbfOYznzFEH+xglmmghS/TwZY5+11bxND85H7z/JTnyWtpoeUJF7xd9UPaIX85ItPflfaaUKLVVdcihGsxR+avVuY199o7H3ziiea3jG+oiC7TXuahjDvPI1dZKTOd2BeHUMX+OqvImyXcmDCbVUd8/ucZdhq/ZFuWJyH+uvqbIsIhItPd3a2+GyUtaW3WNl4UURbzLONan5fm+awbO7h/rgXyWJtaQR72txNVHwNUra/uJELreRHtQd59fBnG9a7w8+rDWy3I59VfhqSF7FdbnbecPMqbnB+3eswNmkOql7quscYa/V4kM3HiRPPcc88VYWPLqdKgQYpSrnvGGFWE4keg8EbB1yBo1zXhUGtktImGFfY025N20moiphSrtbhxEVo28L7jBLQ4SWE4JL00NzzMpLyU4Ye4l+nH48oXIThyMmzTjocj4/eDH/zA9PT0mAn/dbYZP3+h2XL6aYbog6NrJuw6ycyYMaO+g5kvvMhyJ3esczfyCKOkxRBXWXKlo/SLi+dygUcrezxO2iKOdK/tsrfutN3hrjTzlUNt0UW6Sdqp74oXD1sK6K72RKaV/Ov6nz9vkb/e0BYnZTlxlZUy0ol9cQhV6q8trSp7acIs2k5f++Bqe5phV9GECMyu9j0GX/pK7A55vqkiFlc7nwZX35CFPO2TfsixaNr04+PKrDZlWcjIEg/pT1qauXu37FR9DFDFvjovqjbe1MirPWiGn60kKU55LDCmPUM+D/j8Lw+/qk7SGC7p2axjPFBNogX5vr6+hs9f/vIX869//asI20pD1QYNrsrLK7rvuJMkMZfjGhzzBloKpHLBQNvx7BKPk8KUgxxth6ycKNmPdh67TFNut2siJ8N0pYvmvxYf10KBLz98Qq0mqnO/eZr40pmI+onoWhzOPvts093dbXb+4qVm7MxLDBGZrgOOr++UJyIzYcKEhoUR3+52l5DK0zVp0mbdu3aIS4FW5gNfaNL84PZoaa+lscxfLY+5HXKRS8ZbhqfhEsplvOXChKtcJNnsWtiSbqUb+esLabNrIKilC7/Hd/Un2VY2OrEvDqFq/XWZKEvZ1+zQ6jq/VmWRLrQdk/9nCS8Ee4b87bffnipc1xgixobY8GL89dmXJmzfNV+fl3QtpC8PsS+PPi5rWuWR5lWt53lT9TFAp/TVvjFx1ctyUe14M8IJpcg+N68+qNU75Ks+BmsGoXntmluD9ia3M+TbmXYaNMjjW6wgFdJAuAYVLoFaEx+5X0mCKA9DE9I0W7QJkOYXTwdtZ7VrQGDvJ4n3vomPjLNmuzzLXx4jxOOnhe061kXe137+raWhK29959fz9CIiM3bGBQ075LecflpdkO/u7jZPP/10v/TR/NV+daHZ77JdK4fyORk3rQxLkdjXiXL3Wt747HXFXVsckEK1Lzz7v2+RwIbJ3fuEI5f9ruOMtDyW5VTWAR43WYalPzxerkUEWZ+T4gfKTTv113nha1O4myLFyTzcu/qZNGGmpajwXP6F5EtRceeCfFp8Y4Msdmv9ZhqRK6/yniY+2nM+v5L8z3q/Wbj6fNBZtEtfndQ2u8ae7Vjmi4hTnmOTkLCaHXYefucpyKe1p1XlOU1f2SpsvQ/tq/Mqf62oOyCOIEH+iiuuCP60I1UbNLgqhdxZqu1e9/npE86Tds1K9zUhaLomJDJcPuny7TKXz/Edz/Yej7u289aVDnZXuBTlpfCvpYlr97yWLtZ/+dfmnSs/NDHZ/i8XH3hcuVv5glHfooi8zrHhjRm7Q8PLXLkobwX53/3ud1Hlkdtq/2pHprjEZi3eNj1cO/V52vNjiZI6Tf6M7ygalyDMd3Jri0myLMp6IsuCa0e8zEet/vnqhi8deHsjz8rX8prbLV/IKsutbEd4vnD7ZN3WyluZd3p0el8cQtX66xBiyqJWl0PKc6i7JGLrjyvcMtY/2U7nZWNI3riuc5tC/QvFCvJ5vtSVt9GueyF+aPng659CbMpCWj98v8iMDb/IfquoNNL677zDbSeKHAMsWLDAbL755mbw4MFmwoQJZvHixUHP3XzzzYaIzEEHHRQVXjv01SH1LmsZzvJ8M+tPkW1QM+Kh2Z+2T8nDFt93SV6CfNXy0JVnZZ3DJfV33I1vrJQmzCLTo8xpXhWCBPmurq6gz+jRo4u2tyVUadDgm9xKodbChbKQQYX9m3T0iUSKxVIIkzbxcOWEy7czW9qr7XyX/snvPlGRC6Dy6B9NnLf4dgnLyaUVLH0CsWtBwiWU8/TncZHx1Hb/h6SPSwzffocdDNEHZ8bLF7uOmXqk6enpMd/73vfq4YbuVJb5IRcTrBtus6u8ywUauSjB71uBWArDLmQeJ/1qg9vGy43dJc6va0eucHtlnQxdeNLqhmuQ4AqL+yfT2Hdslpbu8t0C/Ltmp/RTppOrHbJ2lpFO74tDqFJ/HYKvPwp1G/JsjLuk8GNFRs3esg7uQ9p76TbJTZq4yr5DLrrmkXZ5C/I+22LtjsmHtDaFhJ/FXd7lPK800fxNGt/H+KHdy6NMdAJFjQFuueUWM2jQIHPdddeZZ5991sycOdMMHz7cLF261PtcrVYzm2yyiZk8eXJHCvLGpB8vFtk/hDxbRL3K089m13uZXq1qf9LYMW/ePOcZ8kX1bbF+FpGWWce8If7nTUg/msf4Io27NBQ17ug0cGRNAFUbNLiEVDmgtn/tSzlDKpV2PIZ9xn58E3IuBMqPFJytHVL410RNLe48XC5kJsVR3pduXaK7JrLKOPAd0pqYK/PLJxDz/2X6uZCio2txQJuAc/cyHG4vj8Ndd91lenp6zPh9DmkQ4+VO+e7ubnP99deraebL3xoTI3p7e/u9fE6mCxdh+cKEdKuFIxcL5CKKdC+/83yScZJlRtrPz6qX9Y8/IxefXBMFV97LRQFfOZUCkCssrT7LdsNV5ri9HPmrEZ4OPH72Ghf/ZTqdc845hQlaoPlUrb8OIaYsZim3eZR5zY80dSrEbZKbvOqwK05Jz8QspGS95/o/BJf7InfIa9da1eamSa+QvA1xl6Zs5WFbUjjyntY35lXO8n4GxDNhwgQza9as+vdVq1aZkSNHmgsvvND5zHvvvWd23XVX853vfMccc8wxHSnIpx0vZq2noeG53DVznJu23rdKDPd9L5sd9roV5OUOeZuOZdhkVFRaFpVnZZ0L8v7Xt8jdLFvKmEZVBIJ8AFUaNPDKoe1Ylu7mzJnTIEr5KpfcQaztmLHPuzoAuRtWirzaooEU6l3imXZGt7RHm1SEpKf009fJaYsWcgFBirquBlUKwDIvZZq48sXapeWHFi95zV7nO9F5nPhOdZnu3d3d5hOHn9hPjLfnx2918Ps/tTvvvPMawpdx8O0ul2nKn5U7sfmiiHYMjFZfent7G3ZkS1FZy3+XnfZ57lbLL5m/PGwZD37dxsW1WCDzh+evrCeuNLX3tLTS0Motf8a1wOCzXR4dJe2U8bJlk+eTrKNaHQDVokr9dVGEll3uruhyn7e/sl313c8rnFi/iwg79Lmk/5PCqNVqdUH+1ltvjQo/xC5f+GVte9PYmCbvspbbkGd94STdC7FTK095UtYyUkVWrlxpBg4caO64446G60cffbT55Cc/6XzuzDPPNNOmTTPGmCBBfsWKFWbZsmX1z8svv1y5vtpXJ2L98fVfvrDz7NuKRtoaE2aaZzoJnrYuQd6Y/I/hLHN+pKkbPretSjNfv6ppND43eVN0396ppBLkX375ZfONb3zDfOUrXzFf/vKXGz7tSNUm+LaC+kRcY3SBXe405X5aUUs7dkJOVFzHnrhEXJ8w7drxb//y86j5/zYeLtHNlXYybJk+rgUP+YwNh4fHbfMJidwf15Ed1q32KwXZIHMxmrvV8lHaGir4yt3pvb295pprrjE9PT1mwowzzZbTT+snyG85/TQz9nNfNz3/vpc57rjj+h0FJBcRtJ35/LtcdJE2a/Uh6Rcl8sgYmWfSFu0+v+fa3a3FR5YnrUzy9NIEdmmj/WsFfX5P+xWGqx5p/vuQeSLzV54r76oXPA34GfpJZd+VX7LNk8+XmU7ri0OoWn9tTDqhz+U2tPxq7uQzLv/Tkned0up7EeFp6aL9iqoIQvyVeagtLsakk3Xb3d1tenp6zE033ZQtEsZfLl32J/nXTJrZLzQrbr5wYsudvC7HP3kLQlXpo4smjzHAX//6V0NE5pe//GXD9ZNOOslMmDBBfaa3t9dssskm5n//93+NMWGC/FlnndVvfFelvjrvcpe0Cc4VdhY7WlFnsrQDZa3rIWOlZtrhE+S5O/l/LGnE/aLGYXmE14y6FBtGkvuQ8VKR49Ey1sd2IFqQv++++8zaa69ttttuO7PmmmuacePGmeHDh5thw4aZf//3fy/CxpZT1Qm+/djvUlA25oPjI2pMjJMdv0WKpVwMtH5xd5roJncNSz+1RsUlott7UsC1bjXhj6cH/yvTSAtD80Omm3aUjybMWrQXg0oh2aabFBm5e62zlXnPzz2Xgr4Uhrlfshy58obnhfXzy1/+sunu7jYfnfIZQ0R1Ud7ujrefnXff20yY0F23U/5igtsiRdqkSb5WB2T8JNpiCM9b17ErMj1lGHIRiuejPEbFFQdtYUTGzX7XXs7rEtRdv1bh91zntvuQZUaK6Tz95O5/3y9ErLukdx7I9oHHR7ZTWjkvK53YF4dQpf5aa9NkOfc9m9T2h4Qf639Me5vkXxa0eGf1J9S9qz1qRbvhKwfGNB6xF2NfrfbBDvnvfe97udmah7uQcpgWXx1KU+Zi0zwvmrFgpP3q0hVeHuHnVefbhbzGALGC/PLly01XV5e555576tc6eYd8Wn+0OXlo2GnsaGU/xW1oxjN54eoH8u7/uV9p+hbfGfLymSwCtJxrhT6TR79cVNltRl1KM87M4rbIOoO+txiiBflddtnFnHnmmcYYY9Zdd13z0ksvmX/+85/mk5/8pPnmN7+Zu4FloEoTfGP8K2a8secTSy6OSTHSPsMnoZqwyL/bHau+oyBcx2FI5C4COSixR1HIl6ZywdO65fH2ifiyc9TSUsZXipw8zq4d2jwt7F9NyJVueNys3dwubWe5tqggy0bSooiWN9ydXKzhn64Djm94sas9P77rgOPNrtOOMT09Pea6665zCqQyz7h47Cs/WjpoZVrGVyt38jkpjst01MLg/nJ3Nj/5MVKayK7lr8x3zS1Pz97exvP2pUgun8syadAGrlra+sRybXFBex+Azx7ZrnHBXxP9y04n9sUhVKW/5uXf10/5yqOvruVlo/xf85/Xa23soPmXFF6Su1ZN8mRby6+7/Cm6TXGlsbXV9YuzJKwgf8MNN+RkaXZ85TCpnwopf0kimezjQ/zzlQ3NbVa08V7ebUPSmDIPQtq3qvTXRZDXGCD2yJrf/OY3hojMwIED658BAwaYAQMGmIEDB5oXX3wxKNyq9NU+YstfUp/ajHrUSWSJr6/djGmHYvsd3wK7RfZRSTvkY+zJ49kiylzsOK1o8ggjTXqGuG3GXLbT2pKiiRbk11133XpnO3z4cPO73/3OGGPMU089ZTbffPNcjSsLVRs0aBNGbRJhG3QrAGpCodw1L4U6/t26leIYH3RIm/juVk1YlRMaHjfrnyauWzuk4Mj9kAKknIhp4rxMT26P3dUrn3WJyNIGebSG5p6ntXUndwhrdrsmmDJurl102kRelifpR61WM3vvvXeDIE9Eqig/7oD3Bfmnn366bq+0JWSXsyw/GnIBxpW/Mq81UV3WC80GV5ni7uSClh1UabvhXcKKVt5lGZN1nH/XFoK4Py4xh8dF1k/pty9ttbaJv4SV55OsN6HIONpw+cKaTIOy0ol9cQhV6q9lXdXaZ1eb7fIvJMw0doYuDvC/Ic/FhKGFl5W06SG/u+yPWcxMg6uvkfdjxXhjPhDkr7vuukQbXP2nz95YfGVE62t8z7rs8N3nfoTGV9uM4SLPMtLMHfKxdTfEnqSxQ9pw24k8xwATJkwws2fPrn9ftWqV2WSTTdSXuv7rX/8yzzzzTMPnoIMOMnvuuad55plnzMqVK4PCrFpfLb+n6bN8Ymva8pzU3ua1eF0mihhTZAknqw1y3OR7XmsbQwR56XcWmlHm0lJ0+GnGUhye1yHj6iz9a+g4JZZW53E7Ei3Ijxgxwvz+9783xhgzZswY86Mf/cgY8/4AYJ111snXupJQpUGDRQpaUgS01/iLES32vibiyXOvpcjtEgztfU0Ul+fS+wYs0p3d6St3KEshUQp3UhiVoqns8LS4WrtkWPxZHk8pxGvCsNxhKHcBS3/ttXPOOadfmsl0don08q8mPLvEbvtdO6f/3XffNRMnTmwQ47ecflqDIG+Pr5nw2dMbJv7SFtcLe7UXtrqELVkOtTzTOjFuj0x7Y4zzHQiyfCTtUrR10LV4pKUFD0sK5/zYFy1+sr4kpZdvUYa7cS0sueq0jLO972rDrHteb7g/rnCkHzIdtDSXdpaJTuyLQ6hqf621WVq99ZXLpPLqe97VZob67QsvdAKj9TE+t1nux7pzPcvjqKVZTPzThK+NwUKeC8EK8t/97nedz8n2MqmMhUxA09ruK9daWsXkPfdHXg+Jbx71qczEpqWvrPJ+Pmt5aWfyHAPccsstZvDgweaGG24wv//9781xxx1nhg8fbpYsWWKMMeaoo44yp5xyivP5kCNrJFXpq2U99tXrEL80f7X7vms+P2KeTyJte1kUsfZkzZ8Q90W3USFlwgryM2bMcPqR17wmKc6tKi+xeZcGqXGlKV8+rcHlNk18so55QvyPcVuG9qPMRAvyBx10kLnmmmuMMe83AB/72MfM1772NTN+/Hiz11575W5gGajKoEHCRUpe+eXAQh4FwSsvPxZFE4al0Jwk6roE1Vqt/8sVrRv+vyb62Y92dr311ycK8jhzcdVnlyZgajt57LPaggePv7ZzWssHLQ+0F3rKfNZ2GHNRWUsL1+5sGVfXIsPLL79senp6zPh9D/vgBa7/d3a8/d+eKb/z5y823d3d/URcfna767x1votalj0tLaxbfta9lubcD1ufZLmVRxb50p+XB63s8E5eLgTZMizFfV7OiPq/J0AK4zzPtMUtHqYso7y8SLfcJk2c4vWHx0mmgwzftRDDw5FlhruXgxmZ9rLMyzwpM53YF4dQ1f7aGLd4KN24ynHayb2sk5pfaYnZIR4arquNDvFHpk/aePJnfXF05ZfLpjR2xPgTE2cryF977bWJaRpSdo1JPnc8z0mjr5zEhJFkl1b+5P+uchfqZxWIsdlVZ5LqNviAvMcAV111ldlss83MoEGDzIQJE8yvfvWr+r2enh5zzDHHOJ9tZ0HeGH0ukZe/SfebsSjlarO08XyrKCL9ff7Hiv6tTKN58+Y1zKElPC/zoMgxTRp/8h47+NDetxhD7NgjC2nHPGnC8Y1l8loMameiBfmXXnrJPP3008YYY958803z+c9/3my//fbmP//zP01fX1/uBpaBKg0aJNqOYVtxNDGSC1raTmw+YXcJejxMKaj5BDSJ7BylndZPLgQuWLCg38s3NQHTwm3gvxaQ8ZYThJAJgxRt7Ys6uR+aYKoNCrgbLU4ybbTd9DLv5ISIi+myDHA7tB3aXNy292+55RbT09Njxh00s/5s1wHH14+psZ8tp59mxs64wPT09JjPf/7zDeVTlkluJ/dDCsi+8u1aQJDpzsuH/CvLmKwTLj9dZUnWI+lGpgNPI2una2FG2qEJ59pudG4Hf4aL/twu/j4JTWzX6r1Wd2T90o7AkvG013k5lGVXLj5pfhUhSBZFJ/bFIVS1v5ZlzlX2fGVTK9uhZVi2XS6xzGe/z+YQP2LcyjY0xFbNbZY6HuJPUj7w9jYPm0II9Z8L8jHPucpQSJnMGndXOciazyF1zuXWdY1fz1J3m0nWdinLddBI1ccAVeurfX1c0eHm4cb1jGyH5LywWW1R1njm1dbHhMW/Z0kn3/glxM+kHfLcn1a2rzFhx6Zps8poyDg97zDLjE2TpHFu2ePRaqIF+WOPPdY88MADBZhSXqo2aOBolUAKki6RXBMvrZ8W7cWoUtiTq4lS6Oe7i6Xd3E8pNLrixEXDWq1WF9hdHSgXM61oLkVdbccvD1f6L0VHe04/TwvrPw9HGxDJ41g0m/hfTaiWgrJLeObhaGFp7wng6ciF3Wuvvdb09PSYXY67sH5kjRXgrSi/yZ6fqe+a32XynmbSpEn97NPKkI2TtrNcE7i1fJNl1vqpxUvLc7kgognBsixr/mnPagtUWh3jQrhcXJBo9ZMjf4kg4+F6iaosx66jeTTbeT5o6e5aUNQWLXi5kXmjHW0k/eN5kPYliM2kE/viENqtv+b35P8ut9ozoeG7npX9knZP6ws52uTeF9+QyZgvPJd73/ciSAojZDEkTTghZcmHFeS/853vpLLDVVbywFcOQ8tulnIT668rTVx5HmJbaDsQ42fS80mT7thnQmxrRh2tIlUfA1Spr87SLhdNlnqpzUda1UdmFbT5nLVIm/PoT3z+8e+h6WIF+RNPPDEorFbMbULi4vrltOZXEWjlP0vYRfe5ZcCmWZltrALRgvwnP/lJM3jwYLPpppua+fPnm6eeeqoIu0pFlQYNHK0iy52w1o12bI0Ui7Vd1da9tpuWV1D7V+5AdQl8UkzVdipr8e3t7W04ukbb4c7jKNOAC5zaefna8T8yXaT99jt/YaQUI6WgKoVhlyisddj2mhRd5X2JtnNaO8NeEyu1MnLccce9f2TNnGsadsXb42r4S17HzrzEjJ20ryEic+2116r5ztNCDlhk2dGOL+JxlwM3VzpKwV27zvOb79CW+Z+UZ3LHuRYvzX4pMku/tfx34TqTn+eHTEseJrdJIsuwPHaK2yUXiOw1Le2l7XJxw7qXZVrmp6yPMS+LbQVF9cUXXnihIaKGgf2//vUvc/zxx5v111/frLPOOuY///M/62fLWv785z+b/fff36y11lrmIx/5iJk/f7559913G9w88MADZscddzSDBg0yW2yxhbn++uv7hb9gwQKz+eabm8GDB5sJEyaYxYsXR9lftf5aq+e+X5j4rhVhk7zualdkH+1qm7VFT194SXbmMYmPfb7oNE/jv6//8rnz2ZFGkM+7XGr++MJwhRtSp0L9SptXaex23ZN9blL/HpsvsXXS52+S+JOULmUXJFpF1efjVe6ry4bWN9vrrvrj2tiS1JYURdbw8rA7a/uYJVxXvxISVqggb0xr382R1E9oY0TND9fYJmvea/PRkOd8/hXV55YB9M/5ES3IG2PM66+/br797W+bnp4es8Yaa5htt93WnH/++ZkypNUigI+qDRosrs5WClKuHeoukdEloMvjIqQdmj21mr4b2F7XxDfXhF/aLXdsy3SQIrj1u1b7YFe99UPGX8ZB60Skv5qNmhCoibkyPPs/T2P+v9ZIcjd8QYbfl4KrdWuvy7PsuVArBdG9997bTN7ngPrLW7ecflrDTnkrztsXu/Lr3B95rj9PM4kUcuXOZy7O8p3Z8pcLPJ9cArtMO2m/jIe2GCbLgyw3HFnneLj8GBnNrRSpJbwuyjIr002miUxDbfGEL3bxOPM84vGSaSLt5/WO37duZH2X+cnzQCvPWhqVkbz74scee8x0dXWZHXbYoaEv/sIXvmBGjRpl7r//fvPrX//afOITnzC77rpr/f57771ntttuOzNlyhTzm9/8xtxzzz1mgw02MKeeemrdzZ/+9Cez9tprm7lz55rf//735qqrrjIDBw40ixYtqru55ZZbzKBBg8x1111nnn32WTNz5kwzfPhws3Tp0uA4VKm/9rUlMg9d9dbnd4x7n02ue9p3+1fzJ61NSfY283lf+uQVRlpCykzSdR43+VLXtHakRbbxacJwlUXND1lW0yxohNgT694nqPG/SbbFpFmskBAq+rnwifZV6Y9bQRHz8WZRpb667Pjaq9BxAnebtZ1LS5r2Ma8wQ9uqIkgadyURI8gn+enrd7P6nUTIhg3X2NInpseOF0JpVZ9bJkLiXsV4NZtUgjzn5ZdfNhdffLHZZpttzMCBA1P50WoRIIkqDhpcO1X55ISLndwdH9xrx2FoO0y13a48LG2Ht7RXNoT2Wd85+FKY5+Ha71yUtd+5v9wPl6hq3fP04LZwoZHHx7UQwf2U8eT5og2QfAKOjJscaHDhXIuXFJG1dJFpasVgXo4eeeQR09PTY3b91AwzduYldUHeurPH1lixfvz8heajex3RT/jWdjv7fvXARV5XmrjeOcDzwsZbE5d5Psuyy5/Tzvjn6SwFb55+svPS6pxWlmReu8q0Vn6kPzIcnr483twPLY3tZ8aMGWq85bN8sURrR2S94mK/a9e9r37LRYOy74x3kbUv/uc//2m23HJLc++99zYM7N944w3zoQ99yNx+++11t88995whIvPoo48aY4y55557zBprrNGwYP6tb33LDB061KxcudIYY8zJJ59sxo4d2xDmYYcdZqZOnVr/PmHCBDNr1qz691WrVpmRI0eaCy+8MDgeVeuveRvPr2X1z9V/hPjtcyPt1ezX/MkSp1YREq8Qt82eaCXlX+izaQX5PNHGAfZ6yLOyHrgI3Z2XFHYR+ewqb7wflOJ8iD15xiPJxqRnqypGlIk85uPNpIp9dR5uikIbR8T0+dqcqpnEtgN5tBtyHpJGgPbZl9V9aBznzp0bJcgn2SH73ZhnQ+yNyeOQ69bmpDRsVv/su+7zJ4t9Zeo/ZZqjf08mkyD/zjvvmDvuuMMcfPDBZsiQIWbkyJHRfpRBBEiiioMGTdS1lYGfl86FSGPcYh4XSGu1xh2vmhsp6Fq7tDC03efSratT0F5C6RL8ZJx4urhsu+2221QBmwuHXNzlf7X05XnEr3FxUe7C9tnH4879smG43C1YsMD09jYe73POOec0iMhWVOZlRordvBzwOJx//vnvv9D14Fl1AZ6IzIiJ0wwR1XfKyx3y48btaA477LAGe2XceZnhaegqi5oIu2DBgobFKO3IJlnutE5F2ynOn9XKHS9TNiyeFzJucnFN2uEqS7Ie8PSR9dTes+86kGmspYm8Z8s5ry/8Rcn2r6wHMo5auvEFDlsGZfrz57Q6ytNaq8vczqqJ8nn0xUcffbSZM2eOMcY09MX333+/ISLzj3/8o8H9ZpttZi677DJjjDFnnHGG+fjHP95w/09/+pMhIvPkk08aY4yZPHlyv8nCddddZ4YOHWqMMWblypVm4MCB5o477uhn1yc/+cngeFS1v+Z1WPYXafzidZa3FXnbq9mf9Exe5O2f9FtLOy3MpPjFXk+yK+m+y5ZYO11H1kh3ReaDxbXJIanehNhm/dU2f8Sg1b+YZ9OEZ4z/V6ixZaHIOprURoD05DEGaDZV6qtl3U5yUxaytEF5tF+xaZHWfdr2U87x8iJ0XBTqVxJWkLdj+LRIu/PYIa99z7OehKZ1M+tnmrC0Z2LKSJnbnjLZVVZSCfI///nPzec+9zmz3nrrmWHDhpkZM2aY++67z6xevTrar1aLABorVqwwy5Ytq39efvnlygwajOkvCEqhUopXUuiU4q4maGr+2+f5PT4xkY28nATxnbH8WSkC2zhqu6Zc4iNvDLh7/ox1Z+3QRHJrD48jF3TlLlu5c57/z+2SQiS/L+Oh7ezX/JZisW/3Mt/lLgVkGwe5+MHTz8L9HjNmjNnpSwtM1wHH9zuuRp4hbwX7nfY60IwbN87cc889TmGWpw3PM2urtvOaC/RcHHa9ENW1a1u61XYQyIUaTSS2fvN4cX/kIgC/LuuHVq61OswXArR3A2jthAxPHvUi2xrtI+u6rA88nppw72qDZFsi2y6tTmlpKo8QyirKNJO8+uKbb77ZbLfdduZf//qXMaaxL77xxhvNoEGD+j2zyy67mJNPPtkYY8zMmTPNPvvs03D/rbfeMkRk7rnnHmOMMVtuuaW54IILGtz8+Mc/NkRk3n77bfPXv/7VEJH55S9/2eDmpJNOMhMmTHDaXvX+2pj+/UOW8sfLsmwjksKOCSPWj7zrk6sdzDM8no68XeTX04aXJp9Dn/Hdd92Tftdqtbogf+211/ZLC9f3mLikQZZtXgbS1pukshRrl7ZQ73Ivw0/yOyR8VzhauPJe3n2fr9xk9RPkOx9vNlUS5I0JE/XStL1J94voO0PchLYFLrdFtCcx4Yc+m9W+0LZX3sszbbQd8ln7sjzwlY08bQh1n0f/Expu1vIY29b4+nZQfqIF+ZEjR5ohQ4aYadOmmdtvv92sWLEideBlEAE0zjrrLFUAqsqgwZjGM7256CZ3x3Phze6OlcfXcKGMi1dJO2j5Ne1FoNx/165w6Z8xjZNjed0Y0yAwJg0QeBguoZ1PrHh8ZBpIUZXbKZ/nEzbXIgJPH/mMFC218Hj6uBZitJd3ynSU932da61WM1deeaUZN25HQ0T9zoy31/iZ8VaM7zrgeDNm708bIjJXXnllv4UYLf9lfOx9V/mx3/kChDyuR4q+PD254C/FXulWW/iwbuWvV5LKkawr2kKaSwDgR7nwdJQLYL7FB37Npt0555zTL2y5sKGV46R3Vlj/tToqj4zS2g/tf16mXWVcivZlJ6+++C9/+YvZcMMNzdNPP12/ViVBvh36a0lI+XO50fodl3vutorwdl5rT0LrcUx6y3YpS15p6Z7kX5F5JePW3d1tenp66u94ku26fC4mHFfapfEryc+k50LC9d3nYcu+J8l9kv+u+pwGGW4e6Z8mPJleWfzsZPKcj7eCqgnyljRlL6mvdZXrPNvKJP+knzH+u55L618saf3O2p7Y59Ns5MkzPaQg3+x2MqavC/GryHFpjE2+eOXhT5KfWntg//rahdj0S9MXg3yJFuSvueaafrvW01AWEUCj6jvubGWUQqVvh68Vwfbbbz9V9JPit0VW+Fqt1nDGtCbq2rB548Pd2jC4UC07dW23MX/W1TlqkxpNgNR2TvPw5V/ZAPL0lW5t+JpIyv/nIiYXp6VgrNkm800Thu3zNn2lKCvFaim2yjTlbjfbbDND9MFZ8VtOP81ssudn6vft/10HHG/Gz19YF+63P+I0M2bMGHP88cc3iM1aHspwXZ2ZFPWtfy5R/pxzzukn8GviusxvaS+vWzzN+KIYf9YljsuyIwVwGxY/ysUij0/SdorzNHItJsn/7Xnw3EbtfRVyIUQ+o+WrjT/3hy8EutLKV6dcZVmrT1Uhr774jjvuMERkBg4cWP8QkRkwYIAZOHCgue+++wxReY+sqXp/nQbZzlm0RTyfe+um6LLvs1Prv3zPhrhxXXP5FxL/mDBC/Neu8/Ytxq8Ym0Op1T7YIf/tb3+7X/+atby40tM3GU3jp8udHAeFuPe54/eSJsKh6SfTIY80l31+0X2erx6mLU9V6qeLJK8xQKuooiCfpewl1bmY67F9QWg4afzU+rA8+9k83Bbpj5w/5el3KFaQP+GEE1KHndbWpLyN6essRfdPeY3dQvrm0PFDUl8YMz6KST/rb9XmwO1G5pe6pqUsIkAIVRw0yN2vfKernIzY/+0OeS5UyjOgeYNh/eHHUEiR1O4ylxNwTeTjfmlHa3D/XaIaF+9tWJrIyNNJTsx4vGTjx/+Xgqh9RsZJNp7aAoCEx8sKxDIvua1ax+A7+17GUbPJdZ43f8b+bwXaBQsWmGOOOcYQkdmse7/6C1v5C13tOfJWlLf3NtnzM2bs577eYK/WAUl7uXvXgosrb+WOd172ZfnTRHlZXmVn6lqQ4XVN3pd1RNouzznXXmTKbeALLjxttB208h6vh7z+ybS38ZH1gT/reg+BDVv6KfNY+m2fkbbyezzNrQ3yLH5ZL2SetTvLly83zzzzTMNn5513Np/5zGfMM888U3+fy/e///36M3/4wx8MUf/3uSxdurTu5tvf/rYZOnRofdfeySefbLbbbruGsA8//PB+L3WdPXt2/fuqVavMJpts0tYvdQ3B1UdwXItqsl8I9T+tHdp93i656qu01xUH6SbGXtfzMXFIg+s57bprIhWSj9J9FpuN+eAM+auvvrrB36z54LufNq9jcaWnq4zzT5K/Wjl23feVjSLiz/MvZgddUaQpT5ofoHpUra/Oo07mVVZD2qK0/sa697V3MX7H+pUmL4ruT+Q1l4152+F6qWuzxgtZymPSmKeVhJTlkLQLiUtebtK6LUN6dzItE+TLJAIkUdVBg0u45Lu/uXAvxS/teBvujouXvp3V2m5f3450PmG34fHr8tgK/rw2wZdu+UcTMXnnIMU7LvYlpbF1x8Ny7dLVhHkptNo8kXnN/Zf54FqIsc/a+HL/NGGUp4uMhxVj7V8iMltssYUhovr58fZ/e58fW8PF+i2nn2bGTtq3IWyXaMzj4zp6hNvMyxLPTy4Y12q1fqI8T0ctfeRxTvZ/bq+rnEi3WrnQ6g3/FYq1QVtc4vnsSjt5jcfDVa942Dy9tPThz2mLVTLvfO0VL6MyD/n/Whsi67atYzwtpH+yTnYKcmD/hS98wWy22Wbm5z//ufn1r39tJk6caCZOnFi//95775ntttvO7LPPPuapp54yixYtMh/5yEfMqaeeWnfzpz/9yay99trmpJNOMs8995z5xje+YQYOHGgWLVpUd3PLLbeYwYMHmxtuuMH8/ve/N8cdd5wZPnx4w4vbk6haf+1C1suQsigFtjzLr2aHvOYKj7eDsj5K/7Rnfd9D7E0KI9S/ZqHFWUv7JLIKrlaQ/+Y3v5lojyv8JLe++7ys5I0rPFcZl2MJn7+yTrjyzlVGfTamJW09CvErD7LUy07uq6tMFfvqVpcz2ZeGPsP/FmGT73tav0L6jpgwY/qtvEjqZ5L6klBcZ8inKSdpSNsO8/7Vpker61gaWmVzaPnJ0r+meaaKedhqWibIa7RKBEiiqoMGKdBJkVxONLRz3l277qSgyBtSLo7xlzS6RDspUhvTKJTJsPgz3C/5kk7u1tqu7crTBgC9vb0NQqv2CwEpBNqP9qsCbg8/x1qmBR84cYGSn2cv80LuQJYdm2Yrj7cWf5m3SXlv/55xxhn93BC9v/tdCu925zw/smbszEtM9zGnmXHjxvUTb3iacHst3Fb7nae/zEN+3yXCy0UonhbSDykmy8URV52wv4Dg9Y2XPa3cafnKy4pWx7ndmnjP46MtBslywm2xZZr7qy3KyTLqet+DTHPXoiFPG1/+yjjZNJdtiixvnYrsi//1r3+Z448/3qy33npm7bXXNp/61KfMq6++2vBMX1+f2W+//cxaa61lNthgAzNv3jzz7rvvNrh54IEHzLhx48ygQYPMRz/6UXP99df3C/uqq64ym222mRk0aJCZMGGC+dWvfhVle1X7a/4/r1uu+61AC1f2Dz7btL5Gi2NeNoYIwkl+NIOk8GSe++IUkx9JWEHe/pIrxma5KSDJbZIfGmnjFpp+2rWQMKWbpDrhsycPcSi2brru+2z1pSWniAWWTu6rq0wV++pY8iybvO6F+mufce1Czps8+h3pX9L9mDCT0i6pz0ljo88OXz/ki5OMR9Yd8nngS9vQPlBqISFhFkVI2Uv7bB7IMiLHLJpukMV/lxst3Ga0Ne1GqQX5ZooAPqo8aHAJfS5BWO6Y5Z25veY6ykTuLJdh8pdBGmMaBDQuiMrnFixYUL8vX7Qqd8q6Pjwsu5vYxodj/dKEdiv48zSz33laSnv4y3JdYie/Z5HnZnNh0doqdzDzdJeCIxdptTzj+W5tkqKoDI+XJ359zJ4HO1/oao+qkUfWEL0vyO/0pW+Ynp4eM3v27H4dgste2fm4hG2b93JhR0s7mUYy7fl9mbe2XPgWq2SayWNhrBteV3nY2q9KZL7IRSFXOeLpx+3Qjpvi8ea2yTLNbeF5x8sTr/s2bK0uagt4PG2lXzxN5I55Hn/Nb1BtqtZfy7aL1w+XW94m5mmH9n9aP2Lcy3Y+rQ2yf5B9RYg9mi2h+Ox3+ZcUnus+TzuX2yRRISmO3d3dpru7OzE9XPeShPSQ9E4aqxVRVqXfIWFobnxlL6R85LkgERNf131XfdKe1zbzhIhdebdroLxUra+OJU07wp/VrmXpl1x+5k1IOHmEm7aPT+o3Yn5pGNMPxaa/r93ncy1j9DPk8yYpjtYuLf2S2n3rLjQvQ8YOWQjtE115Glsus9jpskf+n8aekLKvhYt+PJ5SCfJlpYqDBj74tf9zcV7bFS8HzFI400RquSu2Vmt8sSK/19vb2yCsG9P/WBYp5mnn2UuRTdqo7UDmApwMhzce9n8Z53POOccppvK0k+Ks3C2v7eDXhFO5M1o+n/QrBOuPTAM5QZJCsmzceXrL8HjcbLkiIjNu3Dizy3H/T/1Imk32/EzD0TVEjefIj5+/0IydeUn9M37+QjN5nwPMf/zHAebBBx9sSGOeR9JuKaJriyUyHnKgIDsSHpZMC+43v66VaXkMjPwFiLYgJcVjWd942tu6ZO/LesyvyzrKy7v2SwFZ3mU90vyRZZKnFXfHP/wZXp5knef5KCf7PC/kjiD7VwubtxsYSFSbqvbX/H9tcG3hbWGe4fN6qtWbNH6mdS/tCfFba8tjd7n5wkyyISkNZTuoPeeLW5LN9rsWZsizMix7v7u725x11lmqHyHhuNzL8VvIMy5b05CUz6Hp6HJjr7nsDk2zJFEmaxpo/7vchqQXt00bX7oWWPhzeeYzKC9V7KtjiWlH+DOyL5Lj2bxsa8aYV+v/8go3tE0KfUZzG9L+5+FPjHvbllo3cod83nkaYo8x7nKaV/nl5aeosbD2f5Jb7Z4rzmlsDi1jvnuu8UgWksYFIBwI8gFUbdDAK54m7tr7XKjmjYfcOWuM+/xw2bnKY2KMMQ3imHb0ihTIrC1yV3OtVusnXHMbpWCuXeMNuTxSRP7V3MkjXFxH6/BJiBVgtXsyPTWBUu6el3GTO7tlenKRlb9809rNBVCbnlJYtflWqzWesS7P9z7ssMNMz7/vacbP/a7ZcvppDbvk+VnyY2deUt8hb0V4fqb8xENmmp6eHvP888+rR68YY9Td0Nx2nofcVnnmuW8gYe9LEZrv5Lb+HnLIIQ1pq/0SwreoJMuyjTNfFOJ+aTvPZBmTcZflidddaRvPe81/Wx/kBFtrb7T8caUFj589WkampbbzXwtflhl+/5xzznH6gwFGdalaf+2C9zP8mmyj8oLXEx52THhpnnO5kWMLft3ltwxfc+ezKSaePr9ddrvsypLevM1y+et73pe248aNMz09PeaKK65I9CcGuQAeEueYeCXhKx9JYce48fUlecRHayNins2zLZG22L/arviQOpzGLagW7dJXh+BrC3zu5dzU5dbnT142ZSEmPlnC4P/n0b7lmUax/sT0O/PmzTM9PT1mzpw53vFHFnx9GQ8vpM9LG76cR2bpAy18PJJXn8jLunY9ti1IY5fWl4b6oT2b5jkQDgT5AKo4aLAVTwpyvFOUohj/cHf8f9fZ0jwM7Xx3KfDxcFwCKrdPO5rF9VdOBOTES4bJhXYevtzNrnVu1l8rdPOzqZM+vl3vPN21Y0MsMq1lHPkueSsY82eSdnXLtJeiMC9Lv/3tb82YMWPMbgce3k+I56I73ylPRA074+1zHz/kRNPT02OuvPJKtczwtNPKk9wRzuPEn+fppaW7LJdyMcqWM5u2fMGDPztnzpz6s/zlt/KXCbzcyt3g1r1rYisXsbgfvBxp9UYuvMj04mngekcC90+WRW5LUn2Q6S7bKO1dGHwRkddXvnCitQnWP94mFLETCTSPKvbXLrSBeBHl0jfgjxmIyzY1S7i++z6/efjac3nV7SQ/fHHy5WesbbFlQ6aPTA/elhK9v0P+8ssvj7InKX+ylOm88jDJzmaRNT4hz/vKYh7wPj60vsbmufyeZ10GraGd+mofoWXVV09i+8g095pRl2wYeY+z84hP3uOFotOT+88FeXnPxiMmLlnTrqhntfxNKuc+5Jw1jzwLqbMh17OW5bRlVz4bOsZAn5wNCPIBVG3QICsRF8L4rnS585sLXNrzsiPVnpeiIe8I7D1jTMOudykgymc0Md76Ia9zcdIlelrbpNBnw/aJhtw+uXPZirIuP/iRG9YWTUzmeaDtZtbSyfUrCJtGM2bMqNtq00CmsxSArb0usZ6XhQULFpizzjrLEJHZbKd/N0QfHEvTdcDxZvz8hQ274PnHivBcqB+93+dMT0+POfbYY+u2aYKttFXLd/krCRlnC++QpRsu7LoWfTShXX5cC0xSZJf5w9+/wMsKd+8qt3LxyT6v/WpDljEbR9dimtYGyHPYrTueHzIP5a91NPtke+B6n4Gr3rvqC08X+asBUD2q1l8n4Zsk5LXDLK8yn+b5pDjE+Bk6ccgaTl7P593WJE3g+JiOjxUscpzY3d1tenp6zH//938Hh8/7hxjbYvDViTzSkvcZoW7T+B/qhy+uvrSwbnxlLO+yp/3NM2weH/TR1aZqfXVseQupA9xtbF8Q0k756p42Pm5mnQoNK7TvSPMLAu4mqd+Koaj05G0+998K8vKlrtpzIWGE2u5Kr5i8LWrsqfkt/8973pfWnyLqY57Pho4x0CenB4J8AFUaNMiKIXeOcjfaLlZZoaSYZ0zjiqLcVaoJYXy3MhcVpYjGj0Xh13iYVsh3HcHBzwXn17hoy+POd8fyTk5bZJACLRf0uBgrhX2eHrKR5W61PJD28jRz7W7neWrLAC8Lcge2FIk1YVceL8JFYXv98MMPN0RkPrr30YaIGo6ssbvk+c55K9jb++PnL2wQ5Xfu2dfstdde5t577+1XXngZtGVCs13uoJflSOaLFZNtemn5x9FEY+sHv8eP17F+af5raSuPc+GCthTiZV7KY2t4uZBpytOSlx1fOXAtArjOgXfVW153eFq7dvRLN9qvD3ibJfPD1gmeNpoNoHpUqb8OwTUwl+Kp69mYcIpwm+RPUl2LDUuOX2LsiHGvPa/ZmhS3pPi53Phs0OLDxwXG6EcUyes9PT2mp6fHfP3rXw+Kj8ve0PQNzWtXeoeUp6Rwua0hZTM2vNhnkuIa4pfrflIc09ZLmYZ55JUtV7x8oY+uNlXqq0PLKy+bsePJmHos2/s0aHWybGj9mq+PiWlnio5/ln7IdV/Ofyzz58/3CvL8eVmO0tii+Ze2zBc533LVGdf9vMOMeSY0b/IKM0+/y9h2VA0I8gFUcdCgiZDa7njekGo76blwJcVyPjjmorv9zu2xfmoCphTR+G5ZKZ7xAb4V06Wf2pntNnzNH9dOfSneSUHSxkcuVGjpwxsrbSc6/yvPZXf92kATOeWOb9k5SoGTp7lcOJG/WuBx9e3I5h95jjz/8ONr7JE1W04/rS7Ubzb+fTGAp7NMFy1/tTLlOy6JL85YNzIN+bM2L3k6WT/4DnabZ0k7wKU4LOuZjIO0ySVGu4R+a5evPMi0lbvatYU0+5w8q9/+OkOWdfnrBllftLohFyJ4OvH6I/OB55Xr+C6ZP6CaVKm/lrjKnet6khjP60BehE4cQuuRz40rDiHpJPuLJFtCJ5Gu+LvSwJVW9h63T4unHE9If2Wbbr9rGwHkxgP5vwzbCvIXXHCBMz48Tj5CxF/X/dBr9npMmfflUcgCDU9/n13aMzE2atfS9FWu8qe5k2MG1z3pr/wbUl+TbNbG1kW0b6B5VK2vTiprrjqTRxl11TlfXWwWRYTnik9Sn6WhtXGh7UfWuMnnQ9tbn3+aGynIh7S5ebShrjyI7bNDrrs2Efj8CA2rDKS1rci+sAztS6cAQT6AKg0a+MDVtdvWNbDVhC0p+kkhTfrpOjpF87tW++DsdR4GF0Wl2Cx33HChzXW0imtHORd5fUdYuARK7QWz/Lsx/Seh2o5hbdc0jyM/IsV17IcUIOUijMwLm3ZamvEweTnQ8lh+33zcREP0/u53LsR3HXB8/bt9mSsX6+XZ8laU7+7uNp/5zGf65QUv73KhgKetVgc0Mde6mTNnTj8BRz7Lv8s042Vb272uPSvLS63WeEa+rL/SbqIPRG8bZ21QZMsA72Ct3/b+fvvt1xAujxd3b6/JHfPWjVyAcLUX/FmZxjwfZVpZt9oxQa5z/rU0t/GTR2jxRRlQLarUX3Oy/jJDey50x3esv7x9cQ3YZR30+RETvhae67pczA4NK8Ymnz1JbuWiucsfLZ35NTlO0X49IcOQ/sgwbf/b09NjzjvvPDXuvvi73Ppw+RFTL2Ld5xGudRu6qz7GBl94adJXlqEk967wtHId04a5RDKf3baPTnIPqkFV+2ofRZZJrezL9jtN+5fVprzDC/Ezj/4gtr2MtUM+r/XLebVnXJCP7buaQdZ+XJsfyv4pdrzb7P6jWW1DbHih4wD7f1Jao19OBwT5AKo2aEjaBaXtmJfimK1QUpjVdiVLwUxO+mq1Wj93mlgpxTQuQmo7lX07zaXY7Ht5rdwRzv3iwqLrpZOacMj91dJZi5Mx/c/F19LDlY5yh7s2IXctMPD01xYptDS11+21zTbbzHz801+uC+5EjUK869N1wPENgnz9+JrDTjUTdtvdEJG5+eabVdHCt2ublz9j9EkjTzPXAgf/aL/G8JUJ7q/MN2ujFb+5jVo9kuXA+mlFae0YHq094OFq5cguHMhnZNnS0l1+ZL3RFkLkzn6enlr6yrZEO6aG22aM6Ve+Zf2zz8lfwIDqUbX+2hjTr26mfV4bOGcZHPv8kOMLe80ixTPtWW2CKt2lvS7dxExOfOkWMvFx+cHTSj4r72n/a2G7fgngsjOkjNjrVpA/4YQT+vmlxUHzI0v58/kf4j5r2DHPh4TpShNNyE6qe0llLrTOhiL99ZXjJFzCVNZ6B6pFFftqSZYymKYO2o+cy8T4m9RGxbZ7rr6qmWlTlF9p2nPf8zFtXow98gz5tHEusk2Nadu1674NFjG/cHT5USRlCS9p3BCCL62bHc92AoJ8AFUaNMgBNx/0SmFYE6XkcSnWz5oQ8KRAqx0fo+3+4s9bWzTRmYt/2pEWLpulQCgFNk2Ek4Mb7taY98U8LujVao0LDFLMlAIiX9TQdvhIAZyfN87jysOxO6F5frrEYR4G94enp807V15JkVMKp2O23bZ+bezMSxpEeaIPXtwqd8kTNb7M1T5vX/66yZidDBGZCy64oF++aTbJ419k+ef5bG3ngraMu7YQJNPft5hi0cR46SevY7zM8XC08q/Z7Ftk4mVMW2STNmllgojqu+l5GdEW2Xi9kWkq66cNT6aPZic/fobbYdNKvgjXJfzLenfOOedgh3yFqVJ/zXHtYE6CTzBc95Ke9fnjs0U+L9sMX/jWX5coL8cvPj+0OMjwkyYm8nlXeEnx8oUX8ywvB6FpGZPf8lntWk9Pj+nu7o4uk77wYknrR5a8ysuGEL985Vy75oqXXIx3xV3bYRhjO+/D+fOhC9j2GT4Glfd8z0lb8swb0Fyq2ldbspTB2PbJupdtcdo67OoL5Lwpxi/ZPsh7/DnpT1Zc/VgW/7K4S/N8SD/kyjsryM+ZMyco3Fj/i0ILMylNXHbGpp/L75BraQgpK2nqc+i92HSL8Tv0HnADQT6AKg0abIWTIqoU6Fw7xrkIyAfu8hxo3nFzMY37UavV+omD2q5rKdrxVVAp8NuKbv+XIpoWF81GHp4NU4ZnryeJnxYp9HLRnovsxjSKq1yQkDu7a7X+CyFSlOTXeZrOmDGjYZBi81Daog2ieHrJvJZxJHp/J93HJuxZF9OlyG6PoLEvd+W74a04v962kwzR+6I9v77ZZpuZk046qS7KykUbLuBye6XYK9PM9bEiLc8b+UsRbQc3zz8u6Moz3u1fuxNdK6+HHHJIg032GV6GbZiuHeJykYLboon99hlbJuQz3A4rxsuweZ2U12V7IMuSLGsyn/ixPLLuaHVBxs/WUa2e8jxwDVpANahSfy2Ri4YxEwxeZzV3Sc/KOqG5S/KX+5U0oedxdMU3ZCeOjIN1y5/R7vHnkuKp+RPbPsS2K9L+EL99/mv54ko3+92eIX/88cdHlYM0hJTRPPz0hZH2fkx4SX7Y7zy/ZF0KWbzy+amlZ1K5l//za65FNV/4mmjne1aW0dj6BMpHlftqS5byF9s+pW0HfeFq7UDo+MP1vP2r9Sm+/iYtsv/L2j7ExN9nj9ZWhj7jc6cxd+7czIK8z/9Y9zFlVZZHV1mJtdP1nM9mGV6WcuSrG5bQhfRQe2Ntct3Pq16CcCDIB1C1QYO2E1buVpWiHB8ca+cyc0HMtZOaC9ByVzgXKGu1/iKz65xy34577aWR/EgK7h8XoTWxj3fm8uWevb299TC4mKo1VnwwINNIPuv7aLuBeZw0IVQLU+7Y5wsX1nY+4avVag3ir72mlQH+d8yYMYZI3wlvhXUuytu/dif8uptvb4jIDP3Yzolpw/NI7mCX+WDd8Djw8inLA3erlUnur/bhO65rtVpDuvPntCOa7HeZt7zc8PxyLaBYNzJdXLbLNOADFle7wO3h7QGPj+t/aZs2AJPlnddxVzy4mGgXNHg+S8HeXpcCvW2PQDWpWn+dNGgPGUDz+qD5J9sHC2/7ZT2QkwYtXC08bpPPZq2P4XEOib9057JF84enW2hau56TuITJEP/TkpTmxrgXQfhzNm4PPfRQXZA/5ZRTnPYnpYXP3hA/QtI7KyF1TCNkUSnWDh5XuanGuuHf5bMuf7W8lvdD6nlImXe1QTZ8+z3miC6trSiyPoHiqVpf3SqKbPd87W7eYbn8ziMs2bakbR98baHmVvuff4/pu7L0oXkJ8rHh+8acaftDX7qGPu8qByFjjTzs0fpNPv/lNsaOQV22xeLrp/Pwu8gxWzsCQT6Aqg0aarXG40f4i1M1IU82DJrINWPGDGOM6Sd0ax8r1NVqjbtlZYfJ/Zeim7xnGzJNbJcCXq3WX2h1+el7YaT8Lnc4aw2XtutWC18T5fkRHL4zybWz0q0NPpFSio8yP+V9bfe/lo47jh9viMh0HfDF+jW+w911JM3YmZeY8fMX9jveZsTEafVrXQccb8YdOMOMGzeubiPfzc1FVF7+7V/+vyw7rt3SvNzaPNGOJ3KVP55Gxph+iwcyD7U8s3WB/6rB1mvfcza+ctFK7ujnLzGt1RoXoaSwwO3XjlWS5Z0vSmg28HrM48SPtdHqJ7eZ569cAJE75F3p5TpGSOYfqBZV6q+TBq18EB/il8sfbTcqr2tc7LLuk2zUwpN9vG8wLic52vggZDDvCy/WjlBC0jokz7LakcZPnl4uG20c7BnyJ598cnQ4SddlviSlV0geprEjNN1dZYsv4vPFrSRbfGWV2+XaIS/TL6Qt8ZH0a5SQMHxu5PfYXbiy7QLVp0p9dRE0uxz76nY7EdPfh6SJyw1vr7O0vTG42mIryNsz5LPaEdou++IcOwbKAyl4a2OdpPx0oblJU9bk8zHjEB+x+RvST2expR3bliKBIB9AlQYNtpJJMUoeQSMn4saYBlFOE8r5kTKaOM2f0wRHTdiTIpg8Gsc+pwnY55xzToOoKAVFLgDa69IfeQSHJuDx86jl8R3G9N9ZKIVPbj+3RbNDxk9e08Ky8dcEZm6LHDi4Fg2kP7Vao0DN0/Xb3/52fXe8FNbtRx5Rw79rL32V58xvf9QZ9XNsXeVKDlK0OGv1gn9PWmziC03W/56enoZ0k78+4Z2t9V8eWSPFc63eafZYgVpbVLLl0tZrLb99ZcDe1xaQtCOUtHrnssmXxryNsLvUXf5pi2eufJgxY0a/X9Jo4fJfFIFqUqX+2hi/mMjbrqyTG22ArE2a5EDd9Rx3I21NGoxLv3n9tTaFDOa5HVkmWTH3Qvx07TSW6eXzM81kJslPl1vt+wsvvFDfIT9z5swgP+S90PBD0jQpHJfA61ogkeU1JAxfmtn/5eKXFk9pq/bXFydX2GnSz+VnWjdJdZ8Tsws1a9xA+ahaXx1CaDnN0ualCTPP8GLCLcKvtOHKNNDSJGk85LIjpt1Li2a/JWmHfEz+h4xHkvzL8ouxrPkbsoCdNlxXf570jM/vPOqmr2z4nimCkPES6A8E+QCqNmiQwniNCW/8Op/0uo6X0D5cWLQVzzZKtZp/F6/vPHR+brYmBGoCNT+OQxPteNztx9qtiZlJO5DlTlopjHPRL+RoGpcwKD92h7hcWJGLLNquZ25f0g5+LojI69qRJURktthiC0P0wU54+cJWuxN+7MxL+onv9mPPjdfE+fHzF5qPjtnea4uNE7fbdda4PK5G/rKCL6bIhRgu9Mtd7Fo+Jv3iQS5+ucq3Vn95vLWFAP6crFMyTbQd+CH1mNuvLXTw3fE2L7id3B+tbfAt7Mlr8tcPvoUvuTinLSZi8FBNqtZfG5M86XNNKIocULvs4vc0e0ImPbwP4s/w/izELx6my23aiVDSvRh4H+3KT/m/735oXELt1J7917/+1e+lrq5neJng97XrPhtibA551trI+3iXjZp/SddcbuTYU0sLXx2TfsnrSWWlGbjqf1IZ5PU1ye5WxQ00hyr21S6SynSaa6FtTmgdSaprMaStm644J/X5Se1laNhae2pxLd6GLhzm3VbxtjKpf5w7d67p7u72HlkTY1tIH+PLKy0d8y7PGtpO+JBylZYksd/GJ6RfzAofM7S63wwZG4BGIMgHUKVBg+s4DWNM/a90o+1+5aKXfMHkOeec4xUarbilCfRcEDbm/UoqxVV5nIr1z3amCxYs6LeT3vWyWpeoaP3iIh2/7hPr+REm2lEfLuFaE/T5rw2k+Gr9t3Hdb7/96o2adjyP9c+68S02WLHU2uMSQ+VOfx6nBx54oMF/+TLXrgOOr58RbwV3IjIb7Li3IXpfuLcCvDzWZsvppzXsorfXZ8yY4VyYsfHX8luKs5oQ7lscqdUaFzjkPfvRytucOXP6lQ3tJcRaebHIPPUtmtm6JePM64gU4+19Y0xD+NYPWR/kwoUWZ55nfIAQsvjH3bl+BWCvyQWRJGFfHqfDBXi5uxFUjyr115ykiXjMzq0i7ZL9jC/8kMm19Ju3p9J/zT/XhCfUFtcEzvdMGmS8fEdi8QmkKy00+2Lt9aXtW2+9Vd8h/9nPftYZHhcxeP4llRNfXiaVmxjsMzFlNqRe8TzSnuPpIu+50kK7rvmp1Yu0ZVaLR8gzSXZwe7ltctzkC8NlTxH1EzSfqvbVEl5P07RrLn9C+/dYN0ntTChp3LvC8cU5qf0Msc/VZkmyjLNc7XqIW+2+Ni9yceyxx9bnQ0WQtWzY+MT8KioWrf4k/cosTbmKsVeLd5r6FlPmQ9M5hjz6WvTXfiDIB1C1QYPdGawJ7UlnLLvu+XYDc1FPfnyCmzHus65dInOS/0kip9yxKwU7bVeta9e5S4i0eSB3DvOOQR5fYtNVhiE/IekihXNf2rh29MvnZD7VajXzox/9yIwZM8asN/KjhqhRdCeifjvefcfT2Be+WhF//PyFDYL8R/c+pn40jrVDllX7qw/Xy3OlEK2VDy4kay8ileIwX0TRdqNb//gvUqw/xpgG0Vsrx3yAL3+NoZVFecSQrw76joIJKTuuNPSVXc1vfl0uWvH/5Y57Y4y6mCfTQ9YbW/9k+TEmfNAOykvV+muJNklwDeDzOMYmFik0uvzNOunQ0sHlH28nXf7K9lS7ztuKLJPppGdcO/G4TUm/Fsg6mUyauC1fvrx+hvysWbPU9Jd+aHEMsV3e067JdPG5lf/70tH3nMuNHIfxa1o/r5WzkDTRnuNjAy1tkuLJ0fpFX5mQz/lEBlf5ctnmirfmLrSegnJTxb46pP1Iuh/St4TW4RBC25888bXjsc/5rrvcudrTNPFMY1NIG5ZkC9cektzaHfInnHCC112S3UluY/OPf49ps9OWR55W8q8vnNA8SWtTyDXf8zG2hcQ7hiLTBnwABPkAqjRokIIXFwR9opwVvvhLJI3Rz2/mYpa2e1UTAuUOa203OLfbXreLC9qZ6NYdf8aYD47fCbHLJdDaeMmjNrgtXPzjAqP8RYGcpMkd6dq5+VqaWEFZTrq0OHChUi52aGK2dMOf5ZMvni/c3dCP7dxPfCd6f9f8iInT+p0Lbz9jZ15Sv8Z3zm85/TQzfv7CBj932X2KmTBhgvnv//7vfvmkpZlLWHaJy76FEJtnUrCXQrzMRymiy19H8P/5bnT+v5YvrvrgipPmhpdtG6Yx73e+fNd8b2+v9zx7TdyW1/jAUgoWrnJsw3bVVV7/bFppiyI8fbW80QSOvHcXgOZRpf7ahTZJ0CY6stwm+ed7PuQZi29i5RIF006Es/rB7dLirP2flJax6aY9E5KGSRNYTfD12eEKRytb99xzjyF6/8WuX/ziF71xc5H2nnRnbdT6AxlnXvZc9SOpLvns0MZA9jneb8hFCq1OS/+T0tfnF7/mC0OLD09b35Ft2mKDz//Q8hJTfrU4g2pStb46j7IXUtZD6m/IdZebWH9i4+qKo6tdyase+9r8vEnqM5LKSJJtsekV8lJXzd8kG6XbpDi73GuL9qHhxpD12SKJ8T+07vvcp0mH0DYgpPxgHh0HBPkAqjZosOKT3CnsegmkJnbZSsaFLOsHF+PlIN53Rjy/z4+g8R1Bol23dnHb7M+0bCMkP1wk14RQn8Bo08O1iFCrfbBbmYvrXCS0tmmCtytd7P1ardZvRzF3w/OAx0cugrgWIuR17rdMdy1919t2Uv1/K65bIV0T4q37Tfb8jBk785L6sTbSL7vD3u6U32zH3evn1Wsv2eV5Z+2UO8XtdbkoYu/JndWuX4S4yotcwLB5Lo9V4vXLtSiU5qMdl8R38GlHv/A89Qn9/AW23K220CbLiWsnnlywkHVEuy/bGu3XHNydfPG0vM7LDLe96MEZKIYq9de8jIUOhPk1WyeSJm/yXtJgOmTi5QtL7p7n12MIqYuhacjbkJjwY6/HpHVIvGx/nBSWLBOh8ZX3Zdvd3d1tjjvuuGg/uC1Z4f0lL0dJaRpaH1xuXX7Lvk/uFOflXQtL2zXus8kXp6Qyyv+6yqa0S/PT9csY1w54V3xc7YAv/bUwQPWpYl+dVL5Dvss6qNVLrV3x1SmtvscQ6n+Mf7z9z8P/kLY91p8s+NrepLY2rf+uPkUT5EP6hpjxlc9P130+x3W1+668TEvRZSBtXsaU85g6F9oe+Z43Ju7lt77xDfcLonw4EOQDqNKgwRijCu7yI0UtLt7LyqkJ1Fz0lMKjFg4X61y7bOV1K7Tx3bncLhsuF9Z8u8X322+/+nOaOz65kgKftlPZno2t7YK2nYwUJHl62/jItPPt4rbp4toJzPNFfmq1/mKmfBmmdSdFfh43IjLTpk2ru+e72OU58vJFrlZwt7vhiT4Q6+2xNfZjv8td9dttt50aP1u+jOn/yw6bLkkLRlr6y18b+HbE83TW8oG/VFRLV5vm2hnz9rxzmd+aCM9/9SDzlNti6xNfsNKOe7Ev9uNxcP16RsbH5ou2M523B1o7It26fqHhyj/bdiTlKa+noYN6UE6q0l/zQXTawbr8X7pz/Z8Uls9N0vNafGRfGBJH/myInSF2Jfnn8jv2OZcfPrcukiazms3aL/ti6O3tNY8//nj9yJpjjz3W6daV1/IXSq5nfde0tMxjgpcUru+6tMMlmtnnQsR3V/214cgyrgnnIXXV+uFLY1/cZVy0BRLej2r2hE76XWGA9qGKfTW/5nOT9F0+K/10/QLIZR8PI7aOhfof6xeff2htnPQ/KW18fb0vfTW70sTJ56fvWl59l88fLsi70ijU9rS2uey17b50K8tIkfa57CrqWa19SDMOCrUxbTrJNsNXRmX7lBQuHwcUkY/tBgT5AKoyaDCmsYGzoq/9aLvB5a5ZXoG4WytaWeHOiskuAZgfIyN3f0sRbM6cOaZW67+j1SX4ua7xnbF8570mzhqjLzhYoVSKktZfLa5avKVb1y5oLrDKo260o2/sooLvI/OTD85sGfDtypZiKN81b+N07rnnms0228wQNe6KHz9/YcOOd3v0DH9J64iJ0/qdLz9i4rQG4d3unrd+yJe+HnzwwYaocQe7Lbc2bvye64ggXt60vLILVFyo1RZseBjyL19ssunP01bbaS7zwnW2PbeTu5GLSLLe2TSQcbdu+EKM60W0sr5Kv/hRTnyAyNOC2yjTVf5yhvuhlV9ZX3g+aG2Bjadtx+QuflBNqtZfa/+HPmfrhHZflmP5vzZh4PdcA2nXJCvJrlpNP6Pa5T+3P8keS9KEN3Yyxf+mbRdC0tH3nDFxP/2VaewqHz57icgsXry4/lLXY445xhumLFta/xiSpvxaUhnWwk2LKy981y2+3duuOCS5kX7J8HgfaOthkh88LC2N5f+h5VOWM5ct0n+Jr4z72gvXNVB+qtpXh7pJ+i7vJbV3IeH76lgsWf0IbUusW19/wO3h8fO1rb6w8iIkbsbkd366q/2zgvxnP/vZhvlVEfjadpdb/tc1ttOeLWo+FuOndBs7zpT9rs9t6D2XbWnQ6pLPltB8se7SLhB2GhDkA6jSoMEY4325oiZ02YabC66a8C1f7Mp3+2o741270G2l1Haka4KadjyJ3Lnu+2hxsbuDfTv6efpooukhhxzSkHauBQWertwP7Xx/Kypa4d2mtVw40eLo+iWDFNi1Y2zkOfi28dTE5eeff97suutu/WywL2S1u+S3nH5aw5E0m+z5mQbR3Qry8sgb6w93K3fPy7zhiwYyn5PyV17T8kWWcZsfWrhc/PX90kELm3+Xu/G1l6xKMXzOnDn9JuqWpHcryPSSiwC1Wv8jl5LiZ/3hnbi0QVvUkwsHNs3tQpurnZPpJ+9bO7Tjm7SFFlA9qtZfpyVpkKtNbOVOVjlhkItSrl16vO5ru2M1u+x9fka1a2DPJ2ncJrlbWPPfNym0/vnSy2dTCKETK9c1nw2h4SeViyS/a7Wa+fOf/1wX5I888kinOw0pxPOPLHcuP127rLV4uER/SejuL/7dZ0PsrjJZD+212HzmaanVw6S8dwncMo98dUVrP7Q0c/3PCd0FH1qPQDXolL6ak9SmxV7zLQhmIe96FeJPyK/AtF8Mpek382wvQv0K6VNi0926P/bYY+s75GM2F6Xpe3zjI1d5Demf87Avb9KWMa3c+vrlUH98thWNbHdC5si2nrY6H6sABPkAqjRosBVUE1h94huvWPJYliTBzSWK8+t2tz4Xu6wbO/Dnk3WXaCh3svI4yrPy7Yef762JftZfbTcy/85frpqUBnw3MW/I5c57HleeRtpZ31YE5gK7b3e363x1GT7/SP81N/ylqkSNx8/wjya4E33w0tYtp5/WT2iXO+H5Z71tJ5nRU8POdLfx4PUhKe/lLnb7rMwr1y9NXHXFlY5cuJcCtbZoov1iwRce7wh97yjgee5qM3yLZtY/7qfmtlarORfSQo7Z4h+7cCUXT5IWCOSgiueltU+K9aBaVKm/lsiBq28AH1s+peglJ0f8L5/sahMJ64dvh7zPBn68WMiEgw/+uW2uSaG0hbeH/H8ZTpoJo2avfZ6nneanzwbfDvcQO0Lvu9y++OKLdUH+0EMPTbTXIn+ZJd2GhK0JtFpey3IaYlfIrzq0+uWzNQSer1p5DH1ehs3rqPXP5WfMTvok2+R1nt/cjfTDVWdj05Hbmba+gtZS5b46ljT13VV/7LXQhSz+bKzNaZ5LgytttLgn9Qu+MGS6NrvtCCkDafJJniEfEre0O5d9fYKvf0mb1lme08pwrH9pFr1cdTb0mVDyKr8x/vBxRppFdKADQT6AKg0abOEPEVylCFarNe6A1Y5Hkc/z3d9S/LbXtR3HfJJt7ZWDCylS8mNwbrvttobjc7T4Ju2el+Kb9rEvskxa3HCdT23t42nr2m2cx0cTcmUe2zzT4i93I2q7vG252HT3Q8zQj+1siNw72F0feYa8PWveCvJSxOdu5TXfcS6u/NXOXXelpW+n+5w5cxru8WNTuIDus8u1I1+6tXnn+2WJayFBs0mWC2kjf4nrggULnAsOXHTSXqQq00D+8oXbxs+qdx1Bo+VZ0q9lrF92dz1fsLN+u462AtWiSv01Rw5ykwayaQa4migvw+CTVtm/u54JjZ9mQ4h7OYF22cAnX9w9d+t6Xpt0xU68tHj67HT5Jdvw2MmSLwzp1uX/c889Vz9Dftq0aWo6up7V7NbSwBc3uZih5b9r0cI1gdauy77SupUL2774JP3qQtucofljv0t3vvBleshyK/1w7UKNKafyfmhZjylDrnIsn03TFoFyUNW+2pjw/le2H7Htsq9uGBO2kOVqw0JI+1ySnyHXk9rdJP80v0LGEUVTRHjaS12TbJD9QWj5dPnne77Z5Y/nt7YIXmS9CSm3IWW9WYT0w9r1ohYCOxUI8gFUbdAgd/TWao0vSrTfrXBmv2uC1owZM4LEaE3c5SKYdWdFMG4D94fvvHOJby5BUPqpnYXO4y2FVrnL1oqKMg00MZc3/tyNFBU1UdPaz88Bdx29wePQ29vb8KsAGQ8ZLu9ceBrxXwBo4rHM3+7ubjN67Pj6/aEf27nfy1z5x/Wy1rU32doQvb+T3u6m33L6afVja4gahfv1tp1kxs68xGzWvZ+atlJAt2VJ/nIiKT+06/IoF99RR/zcdNevOeR7DvhRM1w05mVU/hqEDyxs2Zdh8vLs+jWJtdX656trdjHG7qyX5ZiHqS06aGmhpbmsb9qChvxVjY1n0q965EKfrx0C1aNq/TWH19+skyMXcleUnDTI+iJ/RaL5HzJh5s9rO7M0O2RacH+SJjSuXXQyDjLNZTr42oKkiRff2e97RsP1iwb5v4yLTDfNZtd3/v8999xT7+/333//uk0yHtw+1y8ffJM6FzKvZf674ijTV/aTEtlfhixC8fuu3fzWDe+fkuIvy11SPZVlTSuLvnrrsyUJrb4l+a+llfacqxxraRJrNygPVe2rQ9tw2YbFhpEHWWzI2xZpTx5hx/inpUHoc2VD2pRWkJd9WkzeSL+Snk1bB7TnYvJbxjVko0PWuuvzX7tv+8dWETrOiMmLMtabMgNBPoAqDRp4w6jthOcClDwTvre3t0Hk5CKadcNFP03MskIZF7O5DZqArAlmmojKd+xrL37lja+1z/7PRXufuCfPI+fiqyb0aTvotTPJ5aSP++s6Xsi3i5rbaO3mac13Ksuz/u0iDfdfi5sUvHt73z+Whe9iXnfz7ev/85e02h3u9gWu9ro8qma9bSc13Ld+SBFf2zG/1dZb97Odi+HGfNDx8jSQ5dL13gAumFu/7XNa+dQE86Ryzr9z9yFHsMgjdrjtvIzIRSftLHptQY7HTXv5q5bmckFMW+DRfnmjLRZoH57X2kKb61l5PJc8monXo9gJAygXVeuvXQNc32REupXfk8pv0kTBtWieNGj3hS3jyv3yDfw1EdiVbkm7+OV1+Ysw6VYTxWUauOxLsiPku+YPzyNXmKHx99178sknzbhx40xPT4+Zuu++6rM8fXj7LdPHl2e+ci7j4oqry09ppyvu2uaNpHC4G1cZ4P2KZqNWp7QyppEUb81f37M+XO5d6a+54+Mqed1XR2QdkL+cANWlSn01R7bBSW5jrhdBGWzgYcaMr5PchfiXpQ2MtTfJr7Rupe3SJivI2/fj8WdC8z+pPU+6l+V5zW3oWCXUP2OSj65Lay9/Ro4JfPbY/33jkyz2+MLU7rvG4b77rvQMGfuCD4AgH0CVBg1yl6q2610TrlzHqGhCGXdnxTB+nIzcuSonbFwccy0A2O+2cXMJ1jK+9q/rWAzXR6aVlj7GfLDbWu4sd/nrW4BI2iXscs8XQlzHkviO95D2+MRQfiyQvbZ9d4/ZZM8jVRu3nH5agzAvP/bIGSm42+Nqxs9fGHT8zb9tsUPDd368iiwb2q8buNiedMa7S4zm7yrw2TpjxowGd67yov3SwvXRfhHBF234ApTLJlmW+CKFFaZrtVrD4leS7Vp42gta5QtrZT7Y8PnClXbEjSamu+qyrFuuF7umOSYClIeq9NfaIJz/5XXCNaj1DZCTJkfSL5ffvI74wrS4JiJpbJXP8MG/zy8tXq74aTt05Q5eHibPF9fuaJdfLntC0pdf03YOu+KZdN2Vj3fffbchen+H/NSpU51+8vSUfSpPJy1eMm1DbPaliw/bN7j8kL9EsGXSF4arrMl8cuWPrx5r/iXh8k/Ws6T4yGuuOCTlG0e2ZXKRK1TcDC0zoPxUpa/WSFP+Qut7M2iGDb72LLTNDrHRd5+3XyFtYKz/objC1Po2X/vtskk7Qz62nQxJx7Ru0pQ337gqtP/SbJB9TcwvCkP8l+P60Gd5frv8TlsWedxjxwBJ932/VtRsQJ+tA0E+gCoNGmyBlzuqZ8yYoR7PIgUpO4lyHYEid+O6doZzUUw7KkT7xL7YMeTDd6Fz27lg6dsBr+0mlp26tpvaippaWvFw+NE5rqMzfEKo/Vg/5a72pHThdkg3/Jp1P2bMmAY3UjzXznh3udU+PjF/vW0nqf5PnDixIe5yB3atVuuXjzbvXGeeu8qA/e46E16WIX5sk6sOuH6pELqIw+Mpw5f5LOuvvZYUBhfc5fE9Pr/sQp+0i0/GeTnn9+WCiFzI08qnFkf+SwJ+LBS/r+UxqCZV6q99k0TfTh7uThv0Sv9cA+CkSSL3S15Pio/LP80GH2n9SrLVdc8nstdq/XfN+xZNQiYgfBIXk06ynMTGU45hJP/f//f/1c+Q32effZx+S6xfSYsVMuw0kzQ+nkvygy/c+uyS7n2iOo+Hr064/NbKa1q3Ie2C/etaIHDFQ/uVg3Xv2v3vy2uZpvy7T5jn7kLqFig3VeqrOSF9i/ZMUhuvXQ8JI7btj/E7Lb76GVN387BRS3NtIb5oG7R23DX+iy0H//Vf/1UX5KUfoensG7vF+BN7L6mMhJSX0DKlxY+Pv3z5E4J1G2uHFmboM0nu7PekXytat75yo6VRXos+nQ4E+QCqNmiQO0rlDmguVtmdxVbEkrvLpcDmEy5txeT+u86FDvn4dijbe1J8lfHTPtrOHOlG7iB2ifk+u6VQaMW/Wk0/yqS3t7fhGCFjjDfNXLvZZXiyHLjSx/XyXSIyxx//vlC+0eYfa7huX8bKr/lEdaIPjqmxAj0/Q55/5ItftTPqt9hiC2/5kue927zr7e1/lEqIP5pwm7TYJMuorJ+2jMlfXLh2drvslEdQyaOS7GTXlmVejnnZSwrL1wb40kK+U0JbHNDKny88maa+uu/6lYjrGU1oAOWnav21NuFJGhAT6QKdNqlLmlRIf6x71y6iJGFSTnSTJkq+uEo3IbtopS2+sF34wpETFi1NZJq7Jjn8WmjcpB1amsu4aOXFXvPdt4L8bpMmBduk/ZX/yziE+iuf4+20q5zz/32LBdJ/nq62T/Clsc9eze+Q/kUL21efktKS989a2mttgfzOw3YtuvDxgMs2WWb5Jh7fgoFMC1BdqtZXGxMurLue9V3X+o2k/stnT8jzWW33uU2yOw/S+BPSpku3RaVhSB+ZdM3a2N3dbT772c9G2+vKrzzj7gs7pD8LsSGtnb76FloPY+xyhSPDTBuWrw3x+c3rhRTvuV/8utbXu/wGfiDIB1C1QYOtJPLcbCnc+Xa6/v/tvX24XVV1Lj4h4YSvhISEBAiElPBxD4FyQMgxNLAxAnoELUrQiAo9aiyClVNEb+X2osf2p72Vetsae7G3RfG2txhqr72t1sd7sbXHR61oxafV+L2tonwUBIIgCSTz90fu2Hn3u8eYc6699zn7rJPxPs98ztlrzTXnmGOOueZa7xxrTBxkKc91JLm0+M5StpSBpHFJGBorNZt2fG5cUGg2mx3EPnpIaynnLa3FrubrOJwGyoX/I4nKx1hnTNgKQcye+TkdlugX2/KOd7zDzMce6ydvvjmu3XJra5NXi5wv8ZaXJMQ81oUx5VetWtVxjRbChu2S+0Rrv+YNL8e0jXs5Brym+5K48FhXSjdM5gv5r5Hr1obCmo2XxGK35MH6eIFMk8GK44/1aftZ4Fc/uIiBY75E5tT9zR8i6oe6zdcCfGAW20s9OOP/aKvWi03upULOy6Idl1kyLppNfVNa668WfsJ6edE8m3N6sfRRipIXs5Qu+KXFCuFS0jf8G6+1rsc+TZWpnf+rv/qr2Gg04sjISAwhxO9///tt7dLK475E5wfNJnLt5mutsrAdKEeqv3Ke56wfK29KPgslXu+sp5xt5+yc9ZKSE22W62Zb0b4Qkb5P3Zc0+88toFm256gn5tJc3UtZPE5S97gq8vQqX2k5PPZn6tm5ytdD2rWlebEfStqV+/qqFJouU8dGRkbaPOS7rYPPTzemo45u21Nq41XksJ7NrDy99ol2D6lStnYv0sri/CWyOGw4IV+Auj00NJvNNoJOBsTU1JQa5kFiXGtkGBKZQq5xrGeNQLNCeJSkFHGNx3Oe8EjUSRLylENa4OfMJeFcLLkxRI7cgDgEUIncXEYunA/2ldZuTCX7BGB7rrrqqhhCiMtW7fNGZ+91/p+92Vdfdl1bvrVbbm2R8lpc+UUnnaPKjscPXXlqcf9bixD8NQN/ZaDZAyZtg1LNXrW9BDhmO+aXjXNzhLxmm7zxbCrxgpFmZxrpnQujZOkFr+N2y0KZyFQ67iTJIox1HW5ejfXgX+43f4CoH+o0X/NDMn+9hTaoPdBr53IPxynkiHEk86zycYyx/Hg/YW/Y1IO7/ObQJNZLDZ+vMo61Fxf8v9QbGq/R5hHtxSZXTqpeyz5ydVi29/d///eteWhkZCTu3LmzldeySzyW6t+ST6cRJTrnsrF87lMtxI2lZzyv2YYmT0o3peWlfvMxbpeFnM1o8rHta3KlvjTQZNXOc105OUvsxjG7Uae52kI/7LBf9q3dT7uVLzXuuT6+J/SiE7m25F6mzUUsZ+4LMe249cVbyRd33YTVypXH+bT8L3/5y1vvOVXR73vpoO/XqXmk6nvddJRRWo+VLzfXs51oY7QK5B5QFdY4c+hwQr4AdXpowEkKk0a6pgg4K0Z2imjftGlTbDY7yeNUKJSUnCxLihi1iH/tOIfyQNm4fpQ9JT+HC5FkLXRocbO5Tmlz8/+90FuhVKosepR+gcByhBDispGNHXm0mO4r1l/eIuRP3nxzK4WwL5wNHsMykJRH0j+Edq/4EpnRu13bMJj7TvTC+w3IsRQBjX2A3vkcnkXK1Dz4ZdLT4sjzlxlaLHRO6OnKemE9dLvRrBA2OZtjG9W+zpFP+EsXrHJ1auVIbHjUDS+i4aKdo36oy3zND8ndeOHKMS1f7qUol8d6EUm9lEqZWhlyf5OxlXop1uoXaF71Wh3a3xREDtYz/6/1Q+rlicOGaXov6auUTvi4RUpbsrKuQgjxwx/+cMtDvtFoxB07drTao4WJ0ZDyzLdsRMvP7bDazuQ/t9saN3JNiljW+t4C9rsmg8iaKq90EQLzp/oC85e0wWpPlfA8nE/0oIW4Qb3wddo4T93nHPVAXebq2YCcnefuxRZyxJ6Uy8/D2hjsVgYus+RrOJSPj3F5mkzaPT3G/uyDk/OQx3tdSmfa3GXp9nWve10MIU3Iz8S9Mne/7rXsXvNWLSP1jDpdyMmeewZP2W+38nTbfz4/l8MJ+QLU7aHBChcjpJMWhgYJuWaz2VZGaqPQ0o1YkdAWAo43N+X8TJxrBFuqDI3c12RLxajHF2mtTvwfQ8uk4rWniNRS7/yU3rX+rhK/P0XwCyme2rwVE+Zbfdl1bWFsMHb86suua4W6SYWzsUj5NSedHM8444ykLCkdCCGORJEQD6ULGKw3Jt25b0q/arAWxyRZ3uiW/YiN4TiMMZoLXyKXdR/Q2mm1ydosmr36tVj93J6qX7JoobqsDapDcO/4uqJO83WVB33rhVJ7abPCxsgxjbDlsrUxoHkW4zmum8vjcDjWi2bqpULK4fawDKij3JhOvXhpsvG1WnmsG6tu9qjjdpbqhK+VucxqmyaHXN9sNuPHP/7x1oLr6OhovOeeezrmEbwO211q15rtatdZ/Whdb7Ub9xrRCPOczWG7LWBZmFfsFOcdi7gpsbuULlPnrXGaKl+zRytfDlqIGx6/qTGm2URJvY7ZhzrN1Snkxk6312KeEjtPjX0NpYvVJeOSj3XjUduP6xklYbBKjlmoeg+S/By+LpU/xvyGnDfeeGMcHR2Nb37zm9VyShZsS2QvOW/Nob3WPYh7fck800+ZtOcW7Xg395xU/+Sur2Lfju7ghHwB6vTQIINWCKZms6kSfSlCN0UCC6klJCB7Elf1brU2IsXfJRu1St3WJpHNZjsJISnlucuhNZrNfOgYK1mb0HI9Fvk7MTHRJluKzKzaH+iFj19CvPnNb27bNDWVkHg//IQ0MY4JCXaMN8+x51esv1wl6qWuVec8T20X6pNjvlubpubiuEsfavHl5frUIpZVrnVO+kZ+S5us67k9Wgib1GKVNv55HwP+n8ceE+iy+IDhbyw9ixe7xC4WHXDYpxhjdqPdkj0CtPO5h2TH7EUd5+tuH3b5ZUF7aEd7xvsHemtrMpS8VPHLIhLu/ELNZCS+HFR5AeA6eUNnzcNWq4Pbgb9THm5a+/k8t8+C5OX7O7YPy8G+1dqI+TQZUi9k3Lcf/ehHY6PRiOduuDA2Go34sY99rFWP3Fc1Ultb7GGbFFgLKJpM2sKFHGc9aHpCXaOOUN9WX0meKp6bVv9ZZDzrX1tUknItGyyxNdaxVb92fepcaRnc9ipfsmhjq8r90zG7UKe5moHj05o/c/ZZxX67tXFtzCBy3tzaOb63MkqJ/pmCdo/sRZ8lx1LncU4rvZ6f4/iat7zlLbHRaMQbbrgheX2pzFYZ3dpyP+7R/brPd1NO6uu5fo5h7CvteTt1v0khtRCvvTN0oyOfj3uDE/IFqNtDg/b5sUXWYcLwE2NjY21ke4q4YiLOIiktcpgJUu0cnuc6kWCzSDjtmOZ1m0rcNvxf5EzFFNfqseqWRQjrfMqr37rGIok1fWI65uyLYgj7YrbnYrcLyY5kuxD28nf1Zdd1hKTBhDHopZzVl12XDFtzzKpfMHVitdvqK9azVg6ST6jfVJgj3ny0xO64Hv5tJcv7XmsLktisA8vLHcP4jI+Pt64ZHR3taFvOAz5VJ38pI7Kmvi7ILUTh/gw8Xth70lEv1Gm+zr0sW3nxwbkk9AVfl3opt+rmsrl+LMPyIi5dDMiBXzCtF1vJl/vcHmVIka5Yb+oewWSNtkiA/6Occn/SdGPpjPuYve1ZFk1u/n3rrbfGRqMRN4y9rLU4ygs72FauyyLKNdm1dliya/9rdqn1C9dbEnqHbaUKyWSNF20M4jFrsQnnPNRpyQIytxHtRLtHlLaN9aO1B8vV7mHYD9z3qf+13476oE5zNUJbSNTO58ZS6VjrFdpY1fKUzMXWfZzRC/lr1dvLeclTZQ60ri9tD9/zeikTz2v3eyHkJyYmTDnw+qrPXdaclfo9G6HNg1Wv0c73Uoalx9L6rLJ53Keek3lu7hZ1tInZAifkC1C3hwYZUPLwngppIYSXRdbyCwETWinyi8vk35pcuVjtGANaZE/l1eJlp0JshKAvWqSuQS/gVLmaDkpSilTW6kx5B2uxy1N1rzhxOIZQHqIml3hz1xD0MDTiIZ8i7a106qn5zV6x7RhiKNXfaK/41UZqsUqrj23Ykiu1d0KVLzW0DWYtu9HC1eADuEbOa+NYSHQmzrW9E7QFOZQZN5+22sjjHGXetGlT9h4lIbJYt/4wUT/Ubb62yFvtodx6cJb/rYfpFEGcexDXXmCaQOJpHu/8m8tDOXr9tB3bp9VnEeyah6P8nwpLwvdt0QXWj8f55TdHBuD9P+WVhQnvrSiX1raSFy7JOzo6Gp/z/Je0nDW4T62+5uN4zFroSJ3HsmNs71NtzFQJK8Oe6NYY0NpUFfw8rfUj2xeS5jF2kjHc57k2Y11aPayXVFnWlw3aF33cXzj+cbzgeVyY0kioXvrCMXjUba5G5Dxmc4R0P+4nVcD36FSeXDlV5u3Sdpbeu1LnMUxbrp6SOatEzpxMOXlSZaTKZVmZkLe+4Ci1Tzyn6abf9ltaTj/qS/V5qj/7VW9OBszTrV60frfy5o5XPVd1TDn2wgn5AtTpoQEnJiGnNCLNIthCyBP4WD4+aFuxp7sJj5PKZ3n4hmCTdilCnTexLSU7JUyHPKBo5L/WhhQpb22SmVukwN8iD07cVjm5tv7CC18fQ9BDyGj/h9Abeb/opHNiCPuI+Cobu65Yf3lc/Yud+kvZWM7+LKLeGk+bNm1qi0dv6T8Vm17OaXZSMqZCCB2hpbR2jo+Pt8kok38uhE/VxO1Aj3drE+WpqakkAW/pNfelShUb8E1d64k6ztfywKoRVnhee7jGcngsp+rgPNYLJB/HOlIeN1wXwyISc/9zWRyLVatP81DHNsh4L3kB4mcsKwQJfw2A11o6EXDIMksGbgPWZ72Ilb4c3XLLLa3nkZGRkfjXf/3Xpj5iTHuNWn2HbbFk5rySn5852RFBI4u5LO6jXN+U6s5qO9oLyoz9KPLib7YvRqnHI+pC+1pF8mhx3lN1oow4J/M5KZ/bzXv3xBjbHCa0e5tmN456oU5zdRXw+NfGknV8uuSxZOimnJI5rEo9KV3k5gQBv2+VyMb1luqH7+m5+aobpK7TykVCPvcFR4l98jnrfKmec+dL56/SfKXI6aLU/qogZ3eYp5d7RK5PS5F6vtDakmubQ4cT8gWo20ODRhY2m53xz3ObNYagh5HQwtLgqiyXYXm+YmgLfui2YlVbZfKDAhLw3L4qRJ+WtEUOXBAo3RC3ijzaggG+kOKiQmpD2W51gKS7RZYfvOLEjrxDy47Plp0j8KVcyXvy5ptNYv64C16htjO1QIJxyDW7tvpa6xtJ7D0vD0kxRpPgthZjUjaR2ujY6mOr/iqLPlg2xnqvaleWrCnbxTHFG+NqZWEfoj1YdSCZ7w8T9UPd5utms9PDWv7iC4/1IC15QtBDwfBLk5yziOTcS0nq5Yx/s0zaNVYb8frUAoW0xZIjd6zZbF88LVmI0+TTIKQk12e9rGh9mZNB+52ylZQNId773vfGRqMRT7vwJTGEEO+66y5TBrYpzd5ybeH2p2THPhMyN8Z9z5/yFZpcg3bO5VrEdEo3qbZoRHWz2Un+c5utrxtwf5aSMWTJzDrEdrBuUWdcrqUHLAufUTXZsHz+0gbr5K8FedEtZS+O2Y+6zdUarHGWy5M6XrXuknJS99OqdVr3l15Qcr/KIfWsYdWX6pvUPGTNJyVllMhWep3kef3rX9+KIS/ziZWXkWpLr+0rbUupjkrK6WU+0OYqzYmlF/CCSUoWy067GfNVrsW8uffhlGw+L5fDCfkC1OmhQR5qOfa6Fuc9FQpFI7RSpJc8UMuNRurPEdK5DVtTG0ziefZyT6VmU/detuQryTc1NdUhK1+bipWfarMV2kdrA5ad0oe18WcIIS5evLiozZLEq13SsrMubiPll5y2Qd2QVfKG0B5jfvVl17XyM6F/8uabW976Ui9uIlsS4kbz2M7lY11rG5ii7jm/lGV5xWv9i3JZCwto99o5tE8rX4kupB7+EqQkVQnpVHVTaC2xrljnch51orXJPeTriTrN1zHaD+dMPMmxknAz/CKhXYPjzXqgxr+WPClZRV6pT3tQZ6ISxx17fmO5XA7/z3JbL6got7VIgfLg8dIvAHJENcqpyVAaHoDbzce0sqwXrne/+92x0WjE577i1+Lo6Gj8h3/4h+R12N+aPJacXF7peMB7NJbD+sN+1fpBI8KtcywHQ/NMxLwpj0VNPm2zX+06Tb6UjFyGHMcyrAUCzbZSbWHZsHyUiedprpPlS7XJUQ/Uba5maPeC1P2hm/JzdZd+HaOVW0Iql8hSpd6qcvZzbPNci781uSxZq+i8m36ROnJlsQ2Mjo623m+svNq9ODXv5uRl29eQKrMfdpUqM1d+6XmcE3vpdx57JdD6LidHv+9DVXTKMuScSxx74YR8Aer00CADgMmwXMqR2TmP9SqJY9unvGpT8aOtDWRzBKu1saSEGymRP+e1r9WLL1ZaO8Srv8riguVZzHWmvKKxLc95znNa/yO5jaS3llKhZEoSE/ZI8jPhn8t/8PLOzV1FNyXxyK1z2kJQSr+SBz3fJycnzb7h46V2YMmLMjLRXeLJzl97aHqw6qtSppQ3Pj7eNjZKw85om/Ba5afKwXZt2rTJHyBqirrN1zHaD+ccsoK9SFMvuFh2CJ0kJL+YadfjvUPq0rzVtZc6/Cv/c9nsHavlZQ9060XDemnRwnJpehLwBpNynRbexOoD+S2LmNwmzIe60Mqu+qLM12v61khZ1vFv/dZv7SXkr/r12Gg04p/92Z916AvtKLd4UtpfWpu1MrTrUC6uB78cxHxsd5pNp2xG0zHnZdks+82NVQ3WHg4lC1BsF1rdbPM5UoJ1qPUTy8i6xmswv6XflDyO2Ys6zdUM675mHataVoldVyHUuVxtHs9dk7oPltZbOla1/L2McS4P72lV5MLyqsiUy8f3QJYn1weve93r2kLW5GTmudaaf3LyamViXakyp/vezfNMqv4q/VNar9ZX2nNNlfJwPiyRubT8UlTtM+vZwtEJJ+QLUKeHBp5gciSZRa4ND+/dzDMXRoNJs6res3w9y1Ma4x7biSRmrv0aSZcjQS3PdyxL0yt681fRkdYOTS/yeby2AFDqmS3p0JX7NkYVsl3+rlh/eTx5883x5M03t/L0Y9NXi9TXvOuZpLc88DkhyY0EPes1t/Ev97P87WaxquoXE3gN9zXLzXsvaLatjYGqoWe0jXFz5W3bts3UV7eLfv3wsMfkDxD1Q13ma34BS51nG7debrWXRH7Z0F6crJdLJuRSLxR8XuooJSAlWd78Wjv5N3tMs/y5F06Wn/vGIppZn3gvwtj03D5uI+oMZcl5mHEKYd9zDL/ApbzEMe+b3/zm2Gg04uir39p6DtTy4f/4laQWK12zRdaD1ndoc5YOUhBZNJ2wR73l3W7VyzKzPFVI/lw7rfpz40OOaXqQ+qyvQ7AP0TYZbLOWvNy/bEtcN9q1tvhgtdcx+1GXuZpRcm+oWhbPTbk6Uue1ewsitdCZkrO0fqtMHP+5+5WWv+R+nAI/x+C9pZu+LJWpylySmidT199www1xdHQ0/sqv/EqRbaJc2pxXtX8wHz8DDNJDPvf8UNI/2K5uZEBYDi0l5WF/9UunVttT/V+1/F7G2P4EJ+QLUKeHhlLitYRwYzJeSLKU56pFZlthWHLEW7PZucmkpNSml5osExMTLcIS22aF98mF07HC/nCIDixfSHOtvFScck1P1p4AWj6xCylnfHy8rUyRccGCBVm7kJAxa7fcGk/efHOLDGdSnL3qtXAyVoiZRSedE9duuVUl2rVyrXKWL18e/9N/+k/mRsA4XnhjsYmJCdX2JiYmVN11k7TNiHF8VV3gqlpvt8S3FgaL8/SyV4O1UIKLXxiaRo7jIgv2C8oi9wDeH0D0jtf5A0T9UNf5Wnvox2P8QIsP5c1ms+MlSHvxlLx8Xu55bPP4II3X5F4s+a+Wn18GrbJK8+BCK7aD2y3nUi9oWr9o+uPf3GbWLf5m+YXMxvJKvZExNZvtIVq0diGpijqWPM1mM7797W/fS8hfc3NsNBrxT/7kT9r0IPWzLCjT5OSkqhPNNjQ7QZK8yv2Y82F/s31rXuDc51b5XC6PI83T39KDpYNcO1PjQ8rS7ISfd7R7B9om/tUWqrTjlrySh79GYWie+d3ahGP2oU5ztSB3b7DypO4lPH9Y12iLuVZ51sIi/+4GOQ/fXB3c5lJ5Sp4DrP7hOvt13yi93+XIXK3fuIwUrrnmmhhCiK95zWuydpQ6x32Sql87x3NGFRLbkq+bORGvyW1SXtLGXuYb1g/2bcputbpyHudVx7d137Geiaqg23G+v8IJ+QLU6aFBjB6JNiGgLK9q7f/SJGVrHsVcn0YCahsuhhDaCLZm0yblUykXyqLKJpq9kK4htJOsVllCsKSIeczXjRzaJqUhhHj++edXLkuIeUklnuy50DeYUvHgpdyVG1+thrQJIcSFx51iXt9oNNp0yGROzp76kZBUFvuQcaTZiBXCxiLVuyXJOaQTLhpw/eLljnXlSH4cd9biHu6RUDr2ME5/yUIG62JycrJtkWbr1q2Dvp07ukCd5usY2x+INW8e7UVNjln3LL5ee+jH41osbn6YrvqCqJWBKHnB4bZrsL4gsMprNts3nbTK00hr7aUJy+X2sJ5l7uY8qbA61gsTvzQ16aXf8sRHIhblQz1OTU3Ft7zlLbHRaMR1r31nbDQa8WMf+5hpB1o5+EWgJhfacMrutbZhHu0Y9xXfz/k6tH/LJrm83KIN264ml9aPlg1ZsltjQ/vCQBxCtPuDJjv3rZTL51NysEyYH+9fmg5RfpbF5+j6o45zdZU5C+02dV2qPIHl6atdq3nbW9eU1I15ch7HpeV2I0OJ7lN5tfttLyhpo9X/2rWW/CVy/tqv7d3r5TWveU1HmZYucrJzO3L5+Fg3+uV6eY6tUgb3d2m9mhxanhKU3Ae0fk/Zu9UfLGdJH1t1VPXgt8qvOs73ZzghX4C6PTRYZG03ceItAg+JNItUSxFzVtgXKU/Os8zdEOMakaglLrs0hnVq08oUMcgxyVOhdlCWrVu3FnvHS2ISPxfaQ8LHWGR3LqWuw3OrL7uug9hfufHVRZuzcuINYK2+qkJg9ytVtVu0f6uvtBjx1qIbL3jlbJs3qM2R7Kl9FbZt29ZqPy7cWTrBvR66+ZKnn6lX7w7HzKNu8zWDiXc5JvYYQjtJKceZSM69YEheK1QF5pE68VrtQZs99rksLgPbwjKiPqyxyG3GUBxW23HBzSKttZf11AsQ/k69uLIuUA9aH6BM1ksbl1uyeKHZj9zr5fqrr746Ni58Xjx3y/8XG41GvOWWW9r6DNuE8vP937JR1pe2QIPkr2ZzVl/zMVxwL9En9wHLpnm8a2C7thYccAzmxgnrz9IB3ws03WtlWDaqLSRp9XObBdo4RptAPXAZmo33ywPTMTjUca7OjXX5Xxur2jipUh9+8STntPGvlW/lyV2v5ekHmV0F1j0llT/VJl5M7bZfLN1hHZrsuWs1OXJyCSGfe/5J1Z+y7Zy8/YA1n3RTL89hubw5OarWjeVoz3Wc37KTbu2xtA1VbLBUjir1O/bBCfkC1O2hwQp3UkJKI6nGpJ1FjnXjWa9dV5W0xOuta3OEHoerkA1RtHA0UhYfn5qaMkn5FPGdC4mjJQ6vg3JxWVu3bm2LbZ6qa/HixV31oaQcgc7x4THmfCr+vFx38IoTzfosIh698ZcvX16kU0ylCzLa9aWbxpYuAmjlNZvNji8eMLSLFg6HQxVZ8nezOCHj0SLveXPbKravffGT0jHLgAsBcp22iS6X6Q8T9UPd5msN8jArNqi9XKQe4OXaFFmF449jSmNdmFA2/s3xqbl+HktMQloe8ywTt5O9m1MkuyYvE4QpWbRyMY/mVSR/Nc/7VPv4N7ZbZGav9pwOtX7UrpVzo+t/KZ77ht+JIyMjbbJz+dw2Lg9/pz4fl3ysA2wztomPpxZk0Jua4/prfa55mGt9k/MYtcqXa/G8ZiOsb9Zfql5rIUE7jrpA3WB+tm2pp6StWI+WH+0Dz7NcTOBrduSoB+bCXB2jTT5xiCdtPi8t17rGGosl8omMnJfzcx2p85ZsVc6Vyp7Lr91r+L6a03E3bcD7Wjfnu8nPHvK5PtHsKKXnKvdZ1n3pNVXr6YccvYwrLb/1rJqSz3rfTD2H5o5VHTelyI0HflZylMMJ+QLU6aGhhOQqiRuNeXoN/ZLauJHPWZ7fKY/8XjybOdROlQ02NU911BfmFR3J39Tmm6WyTU1NtfUN9n23scElMcHOhHou9EyOoEcv+bVbbq3kEZ/awLVkc9dSot3qf2uMaSSzVhfm6WUTUul7awNa3tDVsqNUysm3devWpD45JA9fW9JPqX0atLqslLtP8ebMExMTg76dO7pAnebrGPUXI40Asx628Ry+FOQe5kPoJML4gdoiqLUXhxhjx71Ny8v1a/Jb7bRk167H/zWyWfSc0knOExDrZXIc/3IMbk1OPmYtLGh1pMhuvo71qHlnN5vNeMUVV8R1510Qz7hq7+btN910U4cuEakFFn7pTNmEzA2sG012DD2EX4lZ5XI/aXJiG3EcWP3OurXaxv1qjWEm6QXYRlxQ4Hzc7tIFKh4TrHPLi1+rl9vEdVv2j/cDlIuJd82mHPVD3ebqFHL3ax4T2n1fgzZWrHy5+Z7PpRYbc+Wk7s1WnSXnqrTLgnVvtMrgBVCu27r/p47l5C25j1bJL5u5Sgx5LW9Ol6U2mUI39+USB4heZNHKLbELLCO1GIJ5NJvL9YU23lL1WmMOz3czbgS5haRUmdPVl/sDnJAvQJ0eGmQwCKkknk2YmOjC8BjygiPXa+Ryr4nJOSQUm81mR11IyAmphmWkZNPKkheLNWvWtI73GiO+tH5OpeSoRf6z/rg83sCS0wUXXJCst4Qo10hwLRRNCHuJfPaILyHRQwjx0JWnmuesBQIk/hcuXNhhR5YeU3aRI6m10E/d2oeVOOY82wq3KUWac7gkrZ5+pyrjTdORtpgn/ZIi5qXf+UsgazNoj1FbP9RpvtYerNFekZzjDRDxoRvtFomz3EuE1MvkLspiebuwzOgdzbLztexNX+VFmV/4tJcSLfQFnreuY51iPVo5LI/maayRHlo+rX6tLu4jTQbMj9dpxCbWi/Wfd955MYQQT7z4mhhCiO985ztN2bh+7BssM+fR3Gza8f35OqkH79GaPUn9aEtofxYppcWV1+xI87aXPNwG/ioD82k2ImXhnIRzFS/coVwpL3uWLxUyB9uF+S2kFvFYP9xWbI/o1QpNwzI56oc6zdUpaHYtx3HscT6+t2io4kWdu7eWlJ8qwzqfal/qHF5vPQdoJGcpSshBqTtXv9WOnPxae/mcJZNmTxZuuOGGODo6Gt/85jcn8+b0qOksZxOMnC1y3lz/VrFJS2d43CLOLWiL9pYMVfs5hZLxXHpPKe2TnG5KyqhqL469cEK+AHV6aMDJJUdsMcmb8mQPoZ1Es7xymfhKJY2U02TQPq3X8m3durWDjJNrLW9hbAtuTpny3rXi32vXdOu9363ndKpfRD58CR0eHo7LVp7QVV25JGT+6suua/OwrxofXgh7JPKRnEfSXYtdX2UjWdRfFbJcNgQVAlsjhZHcTvWT5GOPbU7WYkGMMet93k0aHh4uysckPobVqWLX2mJiTnepfq2yUCLJHyzqhzrN1zHqL+388Mwe1mKbch/neQbzsZe39SDP11gP5pLkWn7maBKJZr0gSbxyHmfyvxb3GwlL64UYvcW1duIxzaOcSUGtnVpZLBvmQz2wJ731IqUthKAMrFvpM6uPuH+5T1jeEEI8cfj01r34937v91Tds960tmK/YN9YXyCkCF+tTtYDt0sjrnlMWXJjm7Wxx33P5y2in8u1vlTAhQdcrGAyne0I28Rt53HC7WL9YZ+nXtglP28ey/1gySrHmIRPeew56ou6zdUp5GwRbZ6P8/1XoN1TNfTbK1WTsfQ6bb6y2q7V0ct8kCo3l6dEttRv7XrrvldSj+W1z3nl76//+q/HRqMRb7jhBrNMy840eUuvSbWhFLn2lciMc5rWP/xMWLWNqXE6aJTYJ87xOfl76X9H93BCvgB1e2iYmppSCTkhoTSyPBfuYdu2bX0n+fCFBuXjNDk52VE3y8shdrZt29ZKUoYlh0bkW9ekPHtL4thLO6y8+NWCeLRhG5nsLPFgFiIVdfipT30qnnvuuW35SjZFDaEznntqA9clp22Ia7fcGkPo9ITnMDghpOPJW8nysNfKDyG0tVv0J19L5Iha7MsSL29tU1bewLi0naWEsjzIsXw47vlLk354wm/btk0l40vuG1UXrjZt2pTVP9/nUl+LcEgfj01bX9RxvhbbSz1YM9kqCUkrPM73He2FAvPLvaOUkJYyNLlwnFkvhKmXP563rQUGLlvA4XJSLyg41jUCBNuqvczgMdYX14X1WIsBqBsrBA3bDNbBx/Ec3ou5LUx6rl+/PoYQ4ilX7H3Rf/e73x0ZrAsmkzVdigx4z2U5WFcpaLrm/hSdWQsglj61soVsxvGiySPlYhu0RRvNnrlvcFGK/8dxgfnZBsTm0Matscv2xjJZ7Yix0xmgpHy2I66DZeH/HfVE3eZqBtuuZZOa3Wt5NOCimpaHx19Jmal8XFeqfE0OXnCs0m7r/pAqY7rvA7nyc/q37ulWOXiflHItneA5IeQx1Kamu5J+LDmWa0O3ZWjllPQB6oz1HqO+AN+NfLNx3ikZX6k5uLSMqrKU9p9jL5yQL0CdHhrwphRC+eaSSHRpeauQ8aXep+Pj4x2kfLdlaRtYys23dDFBCLt+hK8pJThz3r7YHinT8hyumjBkT0lKEe8hdBL1knIhabR49Ss3vrp1XP4uOW2DSrJX9bgfHX1uPO2005J2xLovtQkmgbvZuDeVLOK6ysbN3SQeV5OTk8mxWdWLHctftWpV8XX4VQumUm9+Tjhu/SGinqjrfM3EtEZMym9++UiViQ/icp5ftvkYknZYDstpecvil2ZYNudPkQjs3ZtaLNDaj+FL+CVB8jChqek05d2G+tM887lOvk5bjJDyrBdHS/esH66PSVTNntAGLrnkknjOhWPx9PHfjo1GoxWyRmu79hLGusZFCP76QWsT26Gme5YDj7MHO97ntRdFTZ/cT6kvP1gv3I9WjP2UDbP+sD0WwYD5NTvHZJE9LI8mq2b7LKNlW9zWlP1oNsq266gn6jRXM1KLeALr/9QxC6m81v2x5Dk2J7eUX1KWdi/KyZ2r2zpWRa5uUapD7csf7b6XKwv1xf9beeXcjTfe2LWHfK/6y80fpfXK79S8X1K31Gct7uee6fqJ6Z6fUnaV+mKgtIxuZbH6wGHDCfkC1O2hwSLlSsltyddL3PjctUJc5ohOOc+k28TERBwfHy/yrC2JKV31Oit142m8adOm2Gw2u6oP9cHkr1XeKaecEkMI8biTdFI6l1asvzyu3PjqePLmm00vdE4aaY5Efa6cHOmuna8SqkaS2BPahbUhab8Wu/ppX9O1FwKm1Ma2Obm2bt3a0nFKVhzX3YSoCSG9cMV7ZGj966gn6jZfN5ud8Z7FDplkC6Ez5Ib20sKEFT4os9cwlqkRXZrXONfD5JuMIZbbemBP6QZJXLnOWggQ8OaXVtulDZY8mt7xWvld+sLHL49WvzG5z3lQDsyP/a3pUHthtbykR0dH43M2Xtb6/7rrrutok9anll5Ej9bXCygT9xkDz1t9IjbD7db0bumfbUZ+W3Yn/zP5rm1Sa8nNutPyoy7ZDvhaPMdjSNOJFQIHF1Gstls6xmOaB612f9Dq0BbptHHmqAfqNlfHqNugAO1cs2VE7nwOJfWUll2Sr7R8bQ60rrWeK0qA108nquowNxfnyii1Cb73iYd8Loa8ds8urTPVjzl71H5b18n8lJOjxPatc9Yc1c/5pGp5LHMv47fq+EB77aX9mr33S59zHU7IF6BODw34sM6bfco5bWNULZWSbhb5XjVmdlWCU9pVImOuvFIis9twI1w+xyqPMZpx7rW25xY8Uu15zjnnxMVLlnYct0hx9nwv3YQ1l7CcZWddnCTdZYPYbkj2VOqW7K1SXrc2WpI0O+hHm8R+0I5KxkhJnpzt4h4HVsrtUdGLDrRQG476oE7zdYydsdvlq7U2fgAAt1dJREFUZURsEF8UNO9068WCr9N+Y5kW8Y7yIBksf7VwGimSAlOp9zmHWrHaiee4zbzZrfyPeub2cRulLCmP5cTf/CKE+VL3lpR3OBK8KH+qf/C+irJhOayLu+++OzYajfici17Sut++/vWvN+VmPWO92BfsOanJzf2JbRGwp3pKVxaBbLVD6yPN25wXe5iEl3q1MEZWfdwPKTk59rs2RjSdpkht7csFlDu1CXPJWGZSXxsrPIa5fMvWHfVD3eZqtD2NSNbmghRKbFcrq2o9qbJ7uTY33nlx1aq/2+ftXmQvyaP1b+4avjdVaRvblEWmavfNN7/5zbHRaMSXvexlpn1YNlSqD+25hsvVfqfKlKQ9k6XksNpU2g7WcxVdlKJKeajfKs8BuTJLj0v92jNElTod3cMJ+QLU6aEhR2TlCDMMAVHVY7tqmIiULN144Y6PjydjtFfx+Jc49N3G1i7R3aZNmzpieAuRmCNxpS1ynda2XuOCl8aUZyK/NIRMLqZ8VeIfQ+po4XNQrqEFBxf3Xbde56kQSLn9BlgG+V2ViJbwL2NjY1mbrDre+xF3HssRG+Z7WC8hf3r5ymdycnLQt3NHF6jTfI0x0rXwKdqLAr7E8suRtemr/MbrY4xtxILIwS9qXBb/lfMc951fRqzrSl84rNAcIjcfR90xgYv/57xuUV4k81EWjXxn8AKB9iKJcqDO8bwVFsB6kWsC0Yr9jcQxtlVkGB0djWdf/LK4dsutcXT0uR15U7KjjNoGndr9nckbtnPWP2/2qy0kabrMeYwxCcxjIMZOkh2/4sA2WuS9pT/LPjQZUb+oU/TGZ32ibVgLVrhwg+1M6Y91zeewb/DrM5ZPKy91f3LUG3WaqwUpkrnftsn3P23O6LXsKvJa9y7tdxVZ8R443dD6yOpHnkdLdKXdy0rA99fcfMXlXn/99a2vgvF+2y8b4vmjH0A98zONljdlJ1XGnjbXaHrOPSukyk6Vm7u2ZLG523tN6jp8virRcbf3DYcOJ+QLUKeHBhkw3ZCIvYRMwZQi5qt4rpZ62FvXcEptcinnShYVchuy4m8rb9X+YWJxJkKTdJssT/tDV56aPM+pH574ubj3xx/fvuiwbdu2Nl2j3WmhoJBAL43fnhpnVhlVvszQbLh0A9oSGXMy5OLL5xJv4rx169aedCuy5hYzQti3r4Loyx8k6oe6zNdIYGve6fICwt6jYpcWIYvkdIz7HrK5DCTvcXxpISvwWn7Bw7w4BzL5J2VjOSloeTSynBchUGasm3WA8qMHvOaBm/qKQQsHw7Jz/2ovhNzvrAfMx0k7nupvzo/5RFZ5uV+75dY4un5D3LhxY4eeecFI62OxL65DdCjHsBy2A6yL69DsDfXOJD7bAtuLnOcvKljPvADE4XF44SFl82wDKRIG24we53Iv4fLwmtSiDbYJ9a2FntKgEUrcPzm5UJ6pqak28p4X+jCvo56oy1wt4Puq/NXuxSVlVKmz5LrU/cU6XuLxLMdSbStpOz+LWNdrerbQjT6tZyrOpx2zwPNB1b7AMvB3afve+MY3xkajEV/ykpeodZb0jwWrb0quy52T/iixLc7DY6NfYwtlYlvJlZd6Piopp4qurXGag5Yn9SWfdi231ZKlW9vZH+GEfAHq9NCANy5MvRBk+HJWmroJGYFkGl5fSoDPdML49hy/PUXsV1n4yMW61nTNm2Kmrl100jlZj3YhtXPkdgj9DylTktAbHtuiyVu6GMCpZHNWS8/89UOVxOOu6kKMRUJLOSW22O0mwv3afBiT3MdSsd+7LVuT1x8g6oc6zdccRgSRegmRcxqRzmEltBcxjaBE4rLUi1gjZPF6Dl/CZVsvKZoXOufTvH01/Uo7JR+XL88RTEZiPibhpb1MEqfuH+zpi21JvVhpL3Wsa/lrLaawvEweMIm+bdu22Gg04lljV8W1W26NIYR40UUXtenZ8nxHPWM+TSbMX3K/1fLyIhbmZbvTrsfxpz0/s/55XOL1/FdbaLP6OTVeNTlxDGsy8fXY5ym7RvA9peS+gPlFNvaK1xYGNP2yXfPCjrWxs6MeqNNczeNSs1Vt3KXKqFJ3qXzWc4RWhnYul79bObU5XbvHYf05fZXqk++tWpt7gTav9iKvll+bJxBvfOMb4+joaLz22muT5XUDuWdXvcaaxyy7y5Wn2Wm3Xvsl4yW3cJMaI9qcJmVqz0NaW6u2qxu9xqiH9yu5nseVdR/ptj37G5yQL0CdHhpKwzukvLdLk3j2VQldwaQXE4a5siT/xMREbDabSQKuJOzLpk2bKhH7JSF/5H/xtu0laf2ZCoVSkoSoZiJePNi1xER7CTmvJSbEU3WWpIOWHNP2W7zqRd5SL/vrrrtOtYNuFn1kMQZt2wo5U7o4UzLGpN7x8fFp21S2ahobG+v7whmXhzrlcyMjI3Hr1q2q/sbGxrKe/o76oS7zdckLhUWQN5v7vLY5ZIUc4/ypurF+7WEej2kkPP6PZLz85rmar2XvHLlO0w3mt14SkcTjewKHb2F98UsGypxbyODjDJRd23DU6h/tRR+v0byxmXRFXWMbtZAxcl8dueyaePZNd8Qz1m1okwOJV/ytkdBSnqYPbFfuBZXbnXrJQ71oefE8yo95OQwS9w3GiOc+ZH1YdmzJz4sM8j/bDMuM/Z46hvaH/cN9wP1n6Rn/MinPZD7rj+vC+xkS+ZyfQ+w46oe6zNUCtlUee1XKQKSIXOseoSH1vGDlt+apfkO7p1r3br5PlvxN1cvPN/1qH5ZTSlhbdWtzgBzXFntZd6961atiCCFeddVVbWVaeqpir/yMWAqtTp6De+kLrYyUPfE5yys8NV4wj6UTTWdShzYXWnVXtVXN1rX2WW1L9VeVevF46rejHU7IF6BODw388qltzmgly4O321jR3RCAJXWhnM1ms1K4kKpJFhBSXxj0a2NQzat+27ZtHXrEtlp1CyHLciOR3o23eGl8eElVCHdNnm487rGcbhcOUvrlxaySr0+ssSAPBL2EiCkdZ5OTk61ycuPFimE/XUlIdf66Q2tDL/VU/VLIHyDqhzrN19ZLIr/ooS3iQ72QVEg8VnkxlPzWSzneo/CFguVhuZnoluswZr5G3Fllctu5bAST49JGHNf48qG9ZOELkxVHmz3+UEaLgJVj2n4BVh+z7jUdYb3cTygfy4ztwnI/+MEPtp59Tt58czz9uRfGs84+22w76kzre82GS9rIest5Vmu6E7viMab1JY4JbJ9m22i7GE6Fy7dsBI9p7WXbQJvhRQDNzrVFOm18cvlaf+B4ZRvTyCLWI49VaxESPeutxTKsC58HuCzH7Eed5mpG6TyL0GxUm6+06/g+kZKrZDxood5ScqaQy4/3NL7Xafms4ylCOiXXdNwbLFn7UZa2qM3zENfLhLw2j+Tuvyn5+qVDbY6sYt+5srXnKE0HPAfn9KHlyeW3xrL2m+dyq84cWD6r7dp1vdwLSu6HPken4YR8Aer00JDykNeIu1RcZSThmBDk64S47DZUDcvGxywiTvJgvd3I0C9SvUrqZsEiRygyqX/ttde2/l8yfJ56TYqYl41dkdi2SPKcV/qik86Jqy+7rq2++YuOyrZ50UnnJGXsNkyOdp22r4BlHzI+eiGJc0S3hEOy+tgi6nPl9jpeqqSq8etTbRP7T+kc8zQaDfOeuHz5cvW42AB65znqgzrN1/giEoL9coChGmLsjKct13KeXN1o9/ywzMSjXGM93GttQeIQj7HsmJik1DzJNc9mBC5UYHuZmOYXQq1MrFOTkwlpaSsSwdwmbge/vKENyHFto1Quj/tXIzYxfI/WZrSLVev2LcDzs4XmXcZ9xh7ibN/aC6LWDpTL8qzW9MG2pdm4RczzOf4rdfCXH1g2zm3cZ1qZ2B60YbR3JtKYSMdzmu2yzjU71OTk8crlo75T5LxGLGHYHdSXtYGxpl/uW8fsR53magSPkV6vyZGtPLZy9aTup1ge7zeTk9OqT7uv5OTicwItbAbmydUj0OZ2rb5ekConV4cmE88f1jkN1157bRwdHY2/+qu/ql6Hf1N21E2butEnyoH23UsdVl9rNsN6KC2/qkwleXEMsYy92iq33Xpm7mbc87Wpe1PV8vdHOCFfgDo9NIjRI8lmEU9V09jYWCVP9F7j1kudfMyKTZ3btLFkU0fMU5LfIkQbjUar/fgiW7JpLLcPydUqG1xyElKbvdY1L3Ih4q2EZfD13Xqls1zdePDnPPKRhOeFg0WLFrX97tdXFyV2ZPV3r0mI615J95J48KVf0mjt27p1a6V29zPMlNUWRz1Rl/maX0LYU04ebjn0CuaVfHIdvmhjGVxvjJ0xXfkFUPJgaBzOy6SiyNdsNjvIO84rZIBWhzbOU4QmvnTgywHmFxnwvKZbyYd9hO1jr1zuR/zNievHa1Dv/PKC4TmQiNReZrUXL9YvEub8YtxsNuOf/MmftJ5VVl92XQxh7zx2++23t9kV616rg/vP8gS1Xqa1Pud8IofYFPcD2zfaA9uClo/za2NN+yKAv2Dh8nFxRBs7FpHOCx1YtxZCB/VrxYlFWbmPtd+Wjq3xjjbBNoM6Fb1gv2Be1F8VMsUx+1CXuVpDNzZXcg3f8/B4bs8EvtYqS86l6qqCkvpS1/L9I0fsVSkTZUq1udu6JB/fa6v0U+4aa95DvOENb4iNRiNedtll2TbgvJ2TK3eu2z5n2ViHLG9Krm7lKNGrBbZbTSbOm6qHddKvsanJkdNl7rgmW2kfOWw4IV+AOj004ICTlyjr5VBSiSe6pH6EzuhXqkJOi2wlpCG3wyIjheREIrEXHcy0/nKEt2yWevgJZ7RtnCqJ47eXptIwNis3vrpvbRX5qy4WVNmYVPtao0pKeYRbv6erLdb1qY1r5VyV9o+NjSXbJPswWPWm6pqcnOyq/yRZnjqO2Y06zdcaaRxjZ7xkzQtX85iWl4Nms9PrlV9GJY/8leuth22UV/LKX6mHCTKNINVi3mPbmGyUvWq4LpbXIrixbqkf8+NxiwDl5yq8P6AuUQ/cF1UXEFjnSKKjPSAZzWWxhzLajbRBI9Df+c53tv4XQh7vpawT+a0R/hZBjrBeFLH/8Jh2LT8Lok7YprXFKKt+7kMthJFmUxrBrxHWqD/WEbdf61+tXVY4Cgyxo+lebJo3T+XFOpYH+xvz4/jkhQscL9rXaGjPlm40W3LUB3Waq2cS1n2vxN5TC4r4V/LyPSJXTqnsVcD3Ret6vo/n5GCZeP7NXV+ib2te0+rQ5jFNzhI5ON/rX//61hytPSdp8mp1ae1l/aXalQPf07V6tPxav7E9VLUL/t2t3Wo6T427lGyp9lSx/1Qd3fYXH9fyll7v6IQT8gWo00NDFc9RJqu08Bi9Jo1krELYIUlmxcPHF7GJiQm1DVu3blWJP5avNJZ3VS9mbHOVcCKp/uo1ITmtEe7TnZaddXEb6c5EvRb6xoqBb3n0p9plLQxs3ry5b20sCUkz03rnlCP5SxaKum2H1L1169aiRbN+hNfR2mPV6S/79UOd5usYO72V8AUkhE7iF/MxOcgvW0w6M5HHZKHUYb0kYj388sZy40uctAPlwLKYhOf82oui9iKVe5lD2bFtk5OTbXktL1zUubYYoMWGZ6Jeu6/wQggTFKwbrAvzSzukLC3EiNSPBCkS19u2bYu/8Ru/EUMI8cSLroohhHjS+hfERqMR/+///b+qDWI/o3zcHzJPIAHLfcEvtrnYylKuyIJfXqCutXLleq1ftDZhv6PMKAsuQrD94vjCvuA24XnNplOLT+y9z+2ROZfbannIY/1M4GEbuc2ajNpGr9xObjv3Ecqf8xh2zG7Uba5GTOezYWphWAOPR75H471Gmwus+nlRuKTNqXJz16XaoP1fFakFiJL8Vl9o82xJ3dq9z7rn82/O99rXvjY2Go34whe+sKheq8yUjKX6KzlvtZ3nLSt/aV2cN/UsUXUukWsseUvKrNKe1D2gSp6Sui1br9KWkmsc++CEfAHq9NDAL21VkhVOhUlFjbxaunRp64WwV5IxRTx3u8FsLm3atEklT4W8Y5lyJKZFHKaum662Yep1k1NJh59wRtZDfslpG+LKja/u8MTHuPEr1l8eV192XVx21sUdBHkubI6kbjz1q16zadOmImJa61/cTHV0dLRVTkn4opQ9ifd4qfzTbVshpG04FTqLv+Th31ZbsUzeaNeK+1/6VQ16ETrqgzrN19ZLkEZu4TXsmSPXsme0/M8v4lrZmiycT/63PHDxOpQHk+Yhi+eRPGf5NQ80TW9a/dZLIHp1Y55t27a1xbPGazSPYW5DjLGN9GYdah5VWBa2O7Whp4DJbqxbI1ZQZitk0KmXvymevPnm1j35y1/+cpvO8TrUpbb4wjritmJ5LC/bOV6rhTVCXWukLy94sF6xb8UutIUaLEMbl/JXkwv7UHthRd1o7dbag8c1QlzbW4HHELYZ8+K4QGgLipK0xSgk5bn+lG6wjdjnvmheX9RprkZUIahz4LFvjXueazk/5s2VxXVr9z3t/mjJwKhKfGttstpX9fpez3EfWM8UvBBr9UOuzpJ2anPGNddcExuNRnzpS19a1C5um1UuX5+Tr0p/aXbHX1J1I8d0nLPy5MZkqkxrnGj9UaJXyVOify7PWqzT3gdybXZ0ByfkC1CnhwaLkBeijIm5XjxOS4h6rLOfsbGrpiqhPqqEt6lSnpama0NNWSCZjlQScqaUTK+Slp118Yxfa9mAFQpKXhSrhkqZicWY3AJATmYhxY899tjiOnsZ85s2bVI3fdQSjiMOZVVFBu4Hf7ioH+o4X7MXfAidHvJyXmyTPadzpLWc0+JfS71MiMo42rRpU6scJsq1lwlsB5O22G7Na1g7z7Jym/EY6gd/M+HK5aIerEW7lLeZ1h4sH/uK68PyLCJG2sjH8RoOHaItnCDZzTJJ/o0bN7aOrd1yaxx52bWx0WjEz3zmMx265zq1c1q/aQsveD22mfXIfzmsERP6rGPuN55n2A45hBSOQ77WGpfc51g+jjmtDs2OOLY6f7nBCyHybKJ586FcWCcuSOHCgmWHmuzYB1K3th9EbjGGZeLFN0f9UKe5GoH3Yut8aTnac6Z2vVWmVQaeS9XB4wzHtjavlaIkL983rOtK9IPnunl21+5lVr18DnVl6Sm3eNONzeA1r3nNa1rvpaW6t55pqsgncuT6UCvL6itrPxdNdutcL+2xfufGWkn5XJa2OK49D5WWadmfdQxt13oOSZWD9fnieG9wQr4AdXpokEGMJBSSTP3YaLXbxBs3pjzpJycn1Zfiqp6+QjIyKderx/Dk5GRbGdO12FA1Zv8g+9dKuJFqyXFMQv6jl/28w49s/a95/KP3u1x/8uabs18HaKR9iTf1dC2qhLDXriRVvS6Xx7KtVKx4K1lkf+nXMtZiQDf2PDEx0XFd1Y11Q/AY8nVFnebrGPfFa9bIQ+uFo9lsJ6A53rIGzcsay8NzIXQSyCwnb27KdWikMBN5TLxj+bwhptZu1hXKjfJoG7FyWeIVjPcuK369/GXSleO4o2x8PEVCYl2adyOWxaQ79pP1gmb1E+oDN3Vd99p3xkajEW+77baOvrEWV7R7qRy3PvFvNpsqOa6FCeK8bG8oR+olVc6jLiV/qWc7kuTcdq1s7GveDwDzsI1ysuyKxxuPA82Wuc5UvVoZ1rjV7ATHijYGrfuCNQ60vnXMftRtro4x7/1d1Sb7kS93js/jPUOb65mUx/xVZE6BZeD7de66En1UlZPvZVXeA1Jy8bxe5Votr6arV73qVbHRaMQXv/jFRfJKWSJfLp8mH891GrmcK0srE8vF8/i/Ng5z/a7NG3w9y5aSoaRNKVlK5Ey1MyVDyTnWQ9UQcNqiuSanE/VlcEK+AHV7aBgfH295DfFD9HR7qWvesxZZV4Vsw5AVFplehXSrooeqZN7o6GhfifGSxQPJc8UVV7SOHbz8F7qqT4u7rsVz51TFK37lxle3NovrtTwh6JectqHIe790U1lL91UXc7odc1XrmQ4v+24WrkrlkHyrVq2aMdmqJH+IqCfqNF8zKcekUwi2d6/kZ6LLekHBerQXEY3Qk3JRvhijGspFu07+1whYSRohydchkYh6YiIbY8Fz+BSOLY7lYXv4PoA6Y/2jDrBMi2SwdCXXSQx0rgNl4xdWbJu1AKHZAp5HeeTaNSed0jr2H178q3FkZCS+8Y1v7JDH0o8kjfDnlz+WDcPvoO1iPhwDrFe8d3MfIFIhdrh/pXxtnKG+eSzzYgK20wrtw/3FZbHO+R6Ai1KYh+3UslHNTrH/+MWd7y84Xi07Zvtj5wftyyDWDf/vqBfqNFfH2D7v5vKVHCvNY92/erF97T4jY1GrU3vO6FUeazznxneqHr4ndSMTzse5/mb9WeVpZGfV+1jqPvjKV74yNhqN+IpXvCJbhlYmz5m56wS5OSFVliULz0NWedZ5rU6et3lxv7QOC1oZVn9Vtc2cHrR8qcURlIPHe+l7r2bXPBbwvL9P5+GEfAHq9NBgxdPsZ+oHEdYP8jAXYkNI8W4Iv35voNqP1I3OFp10TjEB3U0sdkmldQh53q9Y9tORlgz/Uuv/q6++uq9l4/jkmOfT2aYqtiPhjnghKhUDvmriRQpctKsSWz+EdAidXu4z/gBRT9RpvpaH1hDaCS8knPlBWXsBlLwhtL/M4G+0a/nNXuHaMwNfL3VyCBgm1TAGu8iH9WFdTLbyy7/kx/YjCY9/mdhDopPbyWSulI/ENutRe9nQ9M91a30u90G+H7IuMO42t18LQcQ645dRJuxZl/L8s/qy6+LJm2+Op77019r6iv/HOPVa2637qmanUqbohO2KXzq1l0A8hu23SHHsVyRieNyxXlmXVogeTXbNTrT/2WY12+X7Atsa6gFtweoLtnfsS0zYDzzmcWFCs1VtIUVrY4xRHWc+P9cfdZqrBWj32m88zvcaKy/m0exau76kTEtGjazTyuJ7Cv7VCM5ex6U2H2j389T1VfKnyhGkPOT5Pp2yBS6X29mNbPj/y1/+8thoNOKVV16ZlRfl4b5kO2T5NBvR7EHLi7B0lhsjKWg2I8e1uVxrW2oMlPSx9X8V2yxpZwo8Fi1daHN91Xo4Hz9riW048nBCvgB1emjAQVaFjNq6dWvX5JXmiV+6CeYg00zE7e5XGhkZyYb/qOrJX+KBLqFiJK92DW/amqqn21jumtd+1bRy46s7FgJSixCHHXZYh51UCU+T6o8Sr3nJU2VBqUS+buxeyPgqsszknhHc7m5C1HDCTecc9UGd5mt8kZGkxaTm//nFgz1jMY+ASTWsr9lsdni9a6Fr2CPdkpnPcX1MwLG8+CCPfxkoc4z7FglSXlvyP5N8ch71hTpnkhb1pL3Q8AuPAL2cp6am2mJ7o45FHk2n2CeoO5EHSWUOVcSLDSiv/L3gggtiCHtDvZ190x3x9PHfjsPDw/HXf/3XO9rKC0la29Ge+HrsY24nLohwX2vjQrNxlIlJAM7HMvAXJdxP3D9an2/btq214K7ZMfa3NnZZN/JbawuXpd0j+GsQvEYL0WONdSlf7JhtE3WDDgfWIgrKifLi+OA2aPcER31Qp7kagfar2SHbNF/D5QhSpKZm6zn7x/GF91se96nytTbyvZXvjXj/SsnGv7VrWe5Uubl6q94vUv3L/6f616rX0m+J3HzdlVdeGRuNRmu/n1xZbKNYh3bv12RFO9BsKiUDzuWl1/SS17IrgTX3amOYYT1zc/0lOiqxhxLwl5aWXVo2mBpv/GzBsuMzQun9wOGEfBHq9NCQejHGsC/dpG4JdiTx2BO436R4CRGH7cD6++UVL+VLW6UObit6Ak/X4oDmiW55p2NM95L47v2Wq5/XzF901LTJXmJjVfqzyiapIewjuvu54FWVPO/WXi3vdyHUc18KlHjP9zqO0ePWUT/UZb6Wh1f28G4C4cYkIROPIXR6mssxrIdfsKRcJjetUBhyTYydzxhynD3UZSwjYS31aaQgv9zxAgHWJXKIjKxTfqnia/BllIlr7cVFI7e1xNeK7vF/bBvrgMuQcjTPYyyb+8pqG9eNckk7d+3aFUdHR+Pp510UQ9hLykv+zZs3d/Rpyu40faR0j7pgMrzZbKp2w+SvRiZwHu3rDqmLv0bAPmH5cRyhnExUSXn8glpKwGFbeIFFkwkXfbQXc5nbNM9zvkaz/5JxIGXiApC1oIi2zP3IfcFjx1Ff1GWuRmj3BA3W2Nbsn8u36upWTp4jS8n43LyE/6M+cuS51i6eL7AOvC+k6q1ap9YWrVyBRURqdeT0Z8mlyVIi95VXXhlHR0fj2NiYWo9Vd0ouLY+m/27sNEXWVtFB6rpcGSw/929JvZIHn1Ot/CULVpo9aA4jKeRsyrpGaxMDHVRS/cT20+19bH+CE/IFqNNDgxj+TKSRkRGTmOs2LnTV+q1z07nRptXWqsRqt22bjrTktA1x5cZXF+UtDU/TD692q4xuFgy0sqrEve9HmpiY6Iv3dqr8XJ5+xMXPpX7Yr2xmO11j2bpHaeEKHPVBneZrJMFi3Ee+WjYrZBnbKRK2kvBBGI/xdSgHk9L88K2RvkyWaZ7GOJawzRpBh9cz+cxt0BYe+GWA9cpfAKQIeZQFyV++3oqfqvUlkp1IWLLnMNsItk9k4Tysew4bonn54/02hBB/93d/N4YQ4tmXXBFP3nxzi5AfHl4bf/VXf7XDfriv+cUO9YH9jP3PL6qaxzXWwV8V4Is1lo9/+csBlpdJdO57hNZuK6SMdl5b6LL6HPXBIaawDZZHO49zLaSMZq9M9rOcHFpIW8jgdvPY5AUVGVfyl734tQUtn6PrizrN1YgcsaTdx7Vrc+dL6krlwfHL/2MePobXxtgZ4obzac8M1jWabkrkQnnwvHUPTdWdkl8rl9tV0r9VvIO1a3JyM170ohepc3AOVfLyday3quVpNtltudZ1CM15g/sotWjM9WG5OA+n5NMWdayyUUbtGVOTBY+l5LHaorWJkXJeScniSMMJ+QLU6aEBB/AgEhNx/SD6ppPAnM6wNTNFqud0nAspMxOplPjuh6xVNm3lZC0AnHLKKV2Xif2TCzvUa5qampr2ePS51O2YwussT/jFixd3LVc3xL4/RNQPdZuvtZcB8SYVcotJNib7JDHJHWNnSBG+F1lEosiH1zGZiV7w7MUrcjebzY4HePaIx5cHyae9kMk5biPqQvOilrzsbS9/tfAdFinMxDyWh2B9cngezMMLC1ofo+yaHaBM2P7Jyck2/WsLOtKPExMTrXaeNLrXQ37tllvjyZtvjuduuDC+8pWvNGXgPsP+Yhl5oQDtVHsJTd2f2U5KFgy4L1BWLoP7jIkZJId5fufzVhs0W+TxyjqT63j8iAzSp7whshyTdmKb0GY0HbONIsnO499amNPsGD36sX5rg9zUIpqjPqjTXC3I2RrfS0quSV0rx3kcavNiTmaLLLfGt3Zv1OphWSyCPCVr6TGtPaX3gFx5KKMWFksjdK2+Yj2X2g0+f+Vkx2ONCy+Mo6Oj8YUvfGGyHq1O7R5ftX4urwrYtrqx7dx11jEtD/ebNgasslLydjN+m81OD3ltfLLdyPHceE/ZcZW2pMa1z9FlcEK+AHV6aOiW8BNv0Rxp1U9yvOrmjUz0yeaTQnhOBwHea5if6UopuXoJNyOEtBUeRjZl7Ufq1oN+Nm8Iy8mKP19loarKZqra+KxSF48hiUmYuiZ1z+hl/GC7MaSOfIkyE3tU+ENE/VCX+VoeVJkM5gW1bdu2dczr7DGqEcZ4nr1dNU9Yi+SSFwLMr3k2S95U2Bsk6jWSXyPaJfGiAr6k4EJGCKEtZjcSliIPtwnHO7dbfjPRL/l5AQP1jHmsvuPPgvFaqUvag7rQPLoxD/ZpCO1fV0g+rU0337wvRM3qy66LZ990R8tL/vnPf35LdpaB2466jLGT5MZ2s34s4hXHgbWIoY0TbcEFX3TlN+ufy0F5WW4OPWUR0nIObUfLw+NS+pDHipV4vFikNy/qaAtzrCO0Aa0OHk8sK9bBiwJsy3wtbwztc3R9UZe5WsDkkmV7eLwqIaWNOe3eoc2TVlk8p+E1JQQw3xtTbeF5SWuPVnaqfksGa4HBKq+kH1DfuXKtvsI28pyYq5efgSzZMb9cMzIyEi+//PKkzFq9WJZm26V2nzuXk8Oyl6rXWNeVyGa1URtL2rgrscVu5EpdYy1M4XjHY1o+/L9KP1t/U/U5dDghX4A6PTT02zu2Snn9DNlSNSHxmYrVXpKE1Oz2uip5Rb/TRfxPh7f4dKWZDB+z6KRzkhu67s+pqt3nEpL8XDYSLJpnveURr4017fpeQ2d183DlGCzqNF8zSaZ502oEPZPK+FIliY9pBDZ70aJHquTFMjQCV/JjuRy3mse6XINkOV6jtQfblSKjUZcsLxO77HmL7cd2cNx1Lhc3H0XdYzskBAe+uGAdciylAzxmeXExwcp2xZ7oUj/26fDwcBy+eG+8eIwhPzIyEr///e+3Xad5oLNHl0WU40saf9Gg2Zf0FS9ioC1h+CbWI79spvQ4OTnZMR6wLv5ttY91rtkFjx3LsxzHDY9lDsnD3uVSH99jpH4m+7VFCLYvbKvmLc96wHsB60DqQJvBsceLMfi/o56o01wdYyfhKv+XXJf6bR0TWPO9JRce40UzbV7R2mHJk5KTdaI9P5fokI9rv7X7dw54fS6fpY9cGbl+SV2DC71aWQzs0/Xr18dGoxFfdOmlZj0pWAuimoyldl8VKd12Y4+9yKG10RpDPMdWkSllVzkbZNlK67X0i23K3Y80O0mNZ0ceTsgXoG4PDTlCLbWB43R6nfbqwZ7beHI6w89UaUvVDTJTdeQ+2a5zwvA0KXJcW1TolrxfdNI5ccX6y6etTVX6nkllsd9+2Q+X26/UT8JeI9a7WZziNpaWi979XIZv6lpP1GW+5pdiJp4sElkjtvFlSYurrb3Iax7L2guHkHNI9EmZzWZnzHvLw57LE3mx7ez9l4ppLeWzPHIMyxY5UOdMKJeQ7poHsSxqcLggi4zE85rumWBgIh9lFh3xcSZvuS7WL/bV+973vjg6OhqHX7h3T5m1W25t21/m//yf/9Nhs2iL/LKGdUtfaTaJ/ZqyKy38kTUvWB7c2gs165Tbp+mRx7MWgsj6KqHZbHYsDDHRzvHvtTZoCyJos1Ie2weWj3Pe1NRU2xcmmg0jASHjDevn/hU94HjhhSAOyYP9xgteeBzrcNQLdZmrEbyAp/2fO8Y2qx3Tzsn/uHiN+bgc66+0A8eTVmeO7NbKxWNamywZU3mstlgkZOpYStcWUB/8N3WNJUtp2SV6EhkuuOCC2Gg04iWXXFKpXfy7pF9KFkGwrFy9Wh2arXZTXpX8qfbz2JNjOI5Sbbbqs/qcxz3n5bFRxaZTYxKTBm2xnfvK0R2ckC9AnR4acvEqQ7C93mciBISVOOzFdMaNr5LQw3bhwoVF15SGDbKIUiQ8x8fHs6T80OGL4+rLrmv9RgK7n/Hj++3BzqF1uglF041MVa/RvhTgvisl0VPjU2yt9KsUrc5uN0BNjTesp4SM18qS66ZrXwWLxO928UDaUPVBzzF41Gm+birEYwi6lzMnifetxQRnYhtfJvC8Fa9a7J5lw3uT5hmMJLe0T37jy7MmkxbPWiNA8byluxDaFzb4pUbayC8X7KWNsbaxjtzmmlgu/s99pZGiKA+2H72KeRFBFgNQplx4EyR7pcxt27bF3/7t327lWX3ZdR0e8g888ECH7WhfTmiEfOo32yYeQxIXda3ZKfa32BqTT3iMZZZrsDw8r3ngI1LtwP7iBS6t37iteF4be2hv2uatOPaw/c1mU31uta7Be4QWqgfbjNfhlzgCbbFIGwO82FhCijlmN+o0V8eYJ5mZrE/l1Y7jXz7H9zpemORySttjjR8mB7Vr+b6t5bHKzslqlafd+7TnCp4D+H5WBVwm3n/wmJbfagvm5XxsCykSFvUxOjoan/e852XbosmYy69dn7rG6puSelFfGgGs1YXzRUqu3LhMyWYtDPDCUG4Ma8c1W+H3AEv3mv1ZbeD2a2XmdKT1j6bbquNsf4cT8gWo00MD3lQwaeRvLwQ8EmspL9NuUtWwO72SfBhqRyM0c4sDosdU26djc9sjjjii9X9JbPdSIvqAg8sWHrSUWgAQwr3XUDHLzrq4q+tK2p9bwJB9C0Lozu4mJyeTYVR6seWxsbE4MTFRvDiAtq7ZuJTTzy8++lEOJ0ufPB67qd8fKOqHOs/XTCJrYVzQg1USk2/8cGw9F4TQGc+aX5y4DtwkFGW1vJFzCwXSvhj3PdwjuYdEoUWcIxFpeVZj/dg+Jh2xLXi95g3Ox5kwxDpYdiwj1Ydaf2ttw3osUpY9o1MLxCc+/6oYQmfImm9/+9ut/kJZtYUQIVHZrrQvIiQ/lrtt27Y2ElfGhLbooS36oE2hdz7rkL9AwH7TQrpo/Y11ss3gWMAvKbCfsQ4O/yLgsSD6sMa0Np/hcf7qBMtn2bEuK8wNj0UcX9yPqGfUf+4rAymDSRBH/VCnuVpg2Zy2QIRzHJdhHdfmRjwudaEs/DcnN48/6zqtboS2mKrVoZVnkZtYrqYnvF9aZKD2G++Bmk5y0HRWUnZq4VCTUWsv3w9ZFzHGOPrc58ZGoxEbjYbZBta5Vpcln3a8hPzWdJSzU9YJPrdp8qTsEGXJ1Y/ypuSzytXkx2Mp2S2bZTtnaH3Kc6/VBtYvnytpP9qm1uZceY59cEK+AHV6aNA8oSSVbs7Ya9zlmUizxYO+NI2NjanE5iC/SuCUijcvJDrmOfDQvQsCQrT3YzNZK3XjCT8dm78edni1xYqqHuuaXcuCUem47HcM+CpJq1uOzSZbL9G7h6ypJ+o0X8fYTt41/x/pxQ/V2kOwXIN/JTWJ3IsxdhCx+PLB3rJSL5PNGvks9eHDuBZ+gsu3XvI4/EWMe/fG4dAzWBa/EFjENJapvQiJzNg+fqYSMhAJYCaIpV+5TUxypp7XtAUGJrilHs0Dm8tjT2zRmZD98veKK66Io6Oj8Zzrfj+u3XJrPPumO+LaLbfGY09/bgwhxA9+8INq/2te0FgX24KER4kxdoRu0eTVdGUtKLDuNC9xbeEIbRI9snFBBBemtBfoVL+hjGwDGhGNZXL70fZS9woe59K21KKTZT/Yl9yP2n2JxzyPQ42458UKbre2KOGoJ+o2V+dgkWPWXIf5+H+8N+DY5XK0vHxeu09hfTmk8qTmU5wTNTm1crVr+L7M93JLTu26VN+kykrpw2oLztFWmZgH24UoWfC48HnPMwl57hOrDVr9uX6yzmOZaIdWPuu3Zr+pMjT5U9daekjJlCuXZU4t1vFx/J+fBRjWYg/WaY1zrJ+fm6xrNH2gDFZbLJ062uGEfAHq9NDAA6yUBCshDqczRjsSef32pO3Xhqmoo9KwNCGEuHz58r6256STTqp8jZDTpSFsUuR6FeKd81re8SWe9SGEtli2nKp6zvMiwJLTNnQV4qdui0NVUxVbxzQyMpK8/3Sz8NdNSJ5uPfzxs3pHfVC3+brZbHYQWDHGDhKTvVmtxKSm1JPKh6SXFlear0WSn1+cNe9fJI3R2wkf/PlayYfyaF7qFpkr9WO9/IIgbWFSVv7H65jkx8QvMOwlzjJzn4rsSIxqJC7G9mZZrM06UQZ+UWPCWcoYGRmJz7nhtnj2TXfEs2+6oxUSb82aNfHTn/50q52W97iQtJpsKftCmdgm2Htc8+a2dIOe6THGtg1/tcUstGdrbwe2M7ZjtkXWNb7AamND9Ivt4vmJ+1vKQlsUWAtNvJjE4w7Hh9YeHq9yXNuElV/aeRGHxwOOUyTfcWEK9e/oDVu3bo0nnHBCXLBgQVy3bl38p3/6JzPvH//xH8cNGzbExYsXx8WLF8fnP//5yfwa6jRXW9DGmXUe7xN8r2E75nuKdh7L4Lq5XK3sfkOrQyNuNdIZZbaISGyTpS/+rek8VXZK31UgZTBhyXJZ7U61WSsbCXnUtWYn1n3TWtTBezbLpl3DZVr60WTRyun1Pm9dp9XNYyVGO1QNX6eVr+lP6xM+bpHtnNdaYBG5rXZq9WjHS/pD/lrPmZZ8jnY4IV+AOj00aC+LWpqYmIgTExNde61aJKRGmJWSaP0g4vtBjvZj4aHbWN5Vr7fI8ZIQNlXyhbDXO37RSeckSfHc9fL/yo2vTnrkc34tTYcHfNVUhewtHWvdEMgYSqfEfqcrfEy/F5+6Sf1cIPGHiPqhLvM1v2hh0mJmS7I8RpFU5ZcvfHhvNpsqQabVw2FnmFjHcYJ1WoSadVz7H9uuxa5n0lV+W2E0tJcIJg3lBUbTOyYkdPlFS9MLEogoC14vx3gBRPNW5hA2uEAi/Su/RQ4OLYQLP1J/jDG+6EUviqOjo/Hst3yozUN+1XNfFBuNRkfoFySsUwsWvNGu1kZekNDsEPsY4+qjDWBZrCueX9kuWW9cpkUia32vkd7WOGa7sOyAbTzlzY7Et8jDCwzYZsv25ToeL3x/4IUIK8yTZT+sC4uUwGstwsJRDXfeeWccGhqKt99+e/z6178et2zZEhcvXhwffPBBNf9VV10VP/CBD8SvfvWrcfv27fFXfuVX4hFHHBHvu+++4jrrMldb0EgxnmM4L9q+dr/B/FyXVr8mC9avkbxWeal29pKHn0e4rXh9juDFZw2tTL5vaP2RKpvlLZUpJSvKZumgRH9afpFNQtbg+4/V7hzZatXB91qe77SyrL625CvJn0LV+YDr1vpFazufr2ofOX3lxokmO8qYI8Qt28O6rXuHlK8Br08tFjg64YR8Aer00IA3h17TdIeu6WcICytMR7+843tpT78IQmljanPZHInd7yQLAr3WO3/RUX2XrddY9YNMgww7Mx1Ji4HM95fpIPNzCw+5OPr+IFE/1GW+1uZq9vrFB2MMqdEEgjNGfSEeyTG5HuuVa9GbGccp5tfKZ89r7QWCCVjtN+bTSHZeDGD5MYSMJaPUgS8IvCGqFu+72Wz3+OWwPVgetwF1gvLgX77PaN7FrAcsC+WTerXFFpbZ+loB5RWveI4hjzIziYx9i3ViGBuOH499x32oEfXaPRplQZuU/Dz/8OIV9iHaF5P7WrLsDsc5jjseCxaZjXJqX4FgW9AW5KsYzZZ4MUEj7K37EfcJL3Zhe7QNZdHGWDfSD9bGsnid1KWNQ0f3WLduXbz++utbv3fv3h2PPfbY+J73vKfo+meffTYuXLgw3nHHHcV11mWuTiFF+vJ8iOessWBdi7CuSy0I8vUl44bvp6l8eC/mvPgcotXN7U3VlboG7414X0rJWlJuSldWexAaManpqFt873vfa3nHNxqN+J3vfCfZBzhHWTpgPWh2jnq2YPWp9Tv3NwW2d62OXB9o9eSIZeu6EqT0ounAGovas3jJuLWQIvRTcvD5XF7HPjghX4C6PTRU3RR1f0+9erNjEu9k3CiWU+pctzL1Er99tqUqbekmtvx0pH4tulTxXMc6Swj80rK7+UIkd81sDOsjOkst2vlDRP1Qp/laXhiRiEKyjEk768G8CcQsJiZykURnUkvS1q1b1RAuWtJCdgiYfJfykFzlvCKXtEerG9vAZLQcwzZgPsnL5fGYZ31bYbt4IYD1gX0sf606U3IxEYukv1aetpjCXxlo7dm2bVs877zz4qlntoeJE3J+dHQ0XnXVVR26Z/tDb2rNA1rbYJX70JKRF2A0G2bdNJudYaE4P/4vtqktIGFZ4qHPCwaaZxkSFzwO+WVaW4BBwkfOobd/ysYxD78k43VNIsV5kQf1InJqix4YlofvWbgIYPWz9dWBNaZ5zDmqYefOnXHevHnxf/2v/9V2/Oqrr44veclLisrYsWNHPPjgg+Pf/M3fFNdbp7k6xrRXqXZMs0trTtfq0q5NkV08xmT8aEgRblgW33ty8lrjUfvKztKhJotWn6XflCyoI9Rjqr7cvSXlmMCLzZKnxFZSwLxPPfVUGyH/jW98Q71G+6KpRD9yXFvc0P5qsuZs3bLjqnphxxMuU+unlPwsi6UDC6nzJf2u2VNJXeiAUrVurV+t8dqrzI59cEK+AHV6aMh9ap1K8hA9nd65QsxVrWOmN4QcHx/PkuK9EPlMUM42wnI6iG4h2nMbuKauHVSqEtqnpL+nO/Xjy5CZ/LqkX2nBggU9l4H3mioPgo7ZgTrN1/iSgF677B0vf9Gjl8lB+ctzZdMg2eQFi+1fyseXZiHP5OWFCUMeJ+yJi/KxFx++8KQ8orVQNM1m0yT1eJED26ORnUxWCvHLfcQkpVYfewRzP0o+IVR5E1xMkhe9oJkoZllQdlzw0UgRa7Hh5M03xxXrL9/3++X/0ZzH5IXL8mBnEpb7UWsLE1cpEl3awJ7fqH/UoaZXtlktdjyPWUxM/OOY4DGs6Y/HAO9DwPak6UizPfk9MTHR9lvTjUV4871Aa7/WF6Iv0Z224a8W2gbbgWNPswdZFHF0jx//+McxhBA///nPtx1/61vfGtetW1dUxhvf+MZ44oknxp///Odmnqeffjo+/vjjrfSjH/0ohlCPuZrnHB7jJaQVzwVVCDPtqyauwyKGLZnwPol18TMGHtfysZ6sduW+asHxbtWpfX1k6Qzvc5iHz3O/WbDyWJ7ZUrY1H6GuSu9hWt8++uijLTJe5miNOLZk1PSotUPTE5ab0mPKDlN9ZNmSVg/rRRuPWt/k5EdZUrbJMlsyaGWn+p8XmXK2wjZm5SnRYU5uS3a5vptFlf0VTsgXoE4v+LmH5appJonEFME904Q8pxKv9tmQBk1cI5FfJYyNyN1NXPjpCHfDbZGUI+ZTC00lHtlzOS1evLg4b0pH06k/vN9t2rRp0LdzRxeo03zNRJrmxYsPtXxOEn8Vx17u1rFmszPkCb4gaKSwnIsxthHJMbYTrkwsskes5YXM7bNCtfDLLbYNyTv5X54htLjW/ELNoXtS9wwmMjlh/6HsqdjfVtm4+CJ9IX+18rgstjn2tP7jP/7jODo6Gtde8KJWqBrZM2btllvjBc+/OF555ZWtNljkOupd/uLLKi8I8Yatmv0zAY/50A5QHrFVTU5tM1fLG1vOY9IWZbhuHHPaVzBWv3NZmr3iONX6FMdqKhSM5LXuMamvZfhrFFxIZNm1e5S2SIf64HGCMkxNTbWNc0f36JWQf8973hOXLFkSv/a1ryXzveMd71BtfLbP1WhzeC/X8pQSWSWkGublMY33VC0PlsHjXSP3uC7Lk5/nXiT8eLHd0iHryGo364zvbVoeq16tzan6LEJbu5b1hHmshQfUVSkpjzJgf/71X/91m4f8xz/+cbMPUu1K1W+dT9lDqp6UHfJ8nOpTBD+P5uTl+lNfAKTsC2XihTOWR5M7txigzf2YrGusr3BQLkuWEpm0/rPkduThhHwB6vSCb3k7zXTC+NAWgdaPzVNLUgmB14+vArqJuZ8KI9LPUDqeZk94m5KkLYT1i4juZtPYXJqpscxJxghuattPmfxBon6oy3wtD6xIVDWbnbHN8aGXbVPz2uNwJRbZhw/38hvPpcaEyIfHNaI7R0ZaCwtafv4SgIl5TUdauUzA83UcBiTGzi8PhXi0Nt8VsjFFVjSJKGWPaI20RfvAvtAWXbRyBUL0cz//2Z/9Wduz0Mmbb45n33RH6+85F1wUL3/pSzteUOV/Lk/bjFfr+5TtsR7QHlAG1IFGJuGLMtqSyM8EtfZVCfZfjPtin2vnU/HXtTA+Ui8vaOE9gPWE7dK+RkEdo+1rZfOYRZ1o5WkLRdx/Wvx6TGxHbBN8jWVneM5RHb2ErHnve98bjzjiiHjPPfdk66mzhzzaV87rlOeWqs+RGskl45H/x/MWAYfPCVwmH8P28UI1y8bl4ti12sXyVIG0Ee8ZLJMGll9rCx/T2mFdi/2i9X9KLsv72bqG84ouR0dHW4T8j370I1V2fu6xyiwFX5MjcblO7X+WR+sfqQuB82ZOXp5/LP3zvKXJqf1lAp51JLCeKzS5c8/kjNwij1Yvy5uyBTxv5c2V4WiHE/IFqMsLfoy9hawpSTlP9enYCFYjpks89wdFaFeJA141deMB343XebdpyWkb4pLTNpjnZ4IU77fHvBZiZ8lpG4pC7+S+rKhqo1U3Pp2OjVJnIk3nGCqtQz65n+t497vfHc8555x4+OGHx6OOOir+8i//cvzmN7/ZlufnP/95vO666+KRRx4ZDzvssPiyl70sPvDAA215/u3f/i2+6EUvioccckg86qij4k033RSfeeaZtjx///d/H88666w4NDQU16xZEz/0oQ91yLN169Z4wgknxAULFsR169bFf/qnf6rUnrrM1/jwKyQwh96QpG2UyQ/c8nDPZUtisgsJfHwBZ6JWCxXC8ljysRwYlkdrH8vLsbqt0BYCjmeN9WG7RL/ywsAvO7KYx6RKE0gP7VlI85rWCFXLI1gjybkP+IsGS39sU3yNlm666abW/6svuy6efdMdce2WW1u/QwjxrLPPbpOFX0aRzMb/UXda+1A3loysH02nmt1pepX+47GAZWEsdD4vOhc94zHuL7QBSwcaSZ+zU7wWZZR6pF9Qp0zgIxnBZXA9k5OT2UUFXuxgmeSvRiJIft6YlhcneZz5wnnvWLduXXzTm97U+r179+64cuXK5Kau/+W//Je4aNGi+IUvfKGrOusyVzNyRJN2L6tKTHF+Hr9YJo5Vfi7gv1yHVVbuOuv/XL14r8mRhlwu3+dZTq2cnP6t9lkkviUX5uFFTetaboeUUSIz5x0ZGWmFrLntttvMPk0tFOT6OiUHyqLZYM6muNzUYpDW9/w8YpWL4wTl0nSSsi1LFm6fXMt18OJ6bg7D+vB6a98iS9/8v6VfSwatDsueHNXghHwB6vTQkHrZmqk0nTHoMfUzdMV0LCTk0tjY2IyFBKoSPma6EhPYvRLnJdcfeOgR6vFFJ52jLm5w/n7pbXR0tCeP7f01zE2vqddFuf3hAeMFL3hB/NCHPhT/9V//Nd57773xRS96UVy1alX82c9+1spz7bXXxuOPPz7efffd8ctf/nJ87nOfG88777zW+WeffTaefvrp8aKLLopf/epX4yc/+cm4bNmy+Pa3v72V5/vf/3489NBD44033hi/8Y1vxPe///1x3rx58VOf+lQrz5133hmHhobi7bffHr/+9a/HLVu2xMWLF8cHH3ywuD11na/Zc1zzug6h87PVGDtD37BnLF6HZVne6pr3slzPBC8T+/wypcWIbiaISZaBr9U8j/GlAMlErV6NMJTr2esX+whfbpiMRX3INZqcqCvRlxYnX65FD3xeCOG+R7KT9aR5zmMe8Z6W46vWXRzPvumOuHLjq1uEvKTR0dH46U9/Wg0xZNkd6s76QgvL4/AmOB5QD0hSMVHF/Sn2i4sEuLeA1j/4giq/rbj92A5+8cf8OGbYTtCurDAwrANeRNCIahwP2Aa8j+AY0uQdHx9XF4Dwf26LtkCF/S99JPZhlWUlvBc6esOdd94ZFyxYED/84Q/Hb3zjG/ENb3hDXLx4cWvh/TWveU38jd/4jVb+3/md34lDQ0PxL//yL+P999/fSk888URxnXWaqwU4RjS7k2Ma0aoRfFhGjpTD8ayRYzie8Z6AMrHMXBbLVApLHtYTztN8n8Fy8C/qkX9zuZYcJTJbeay+zrU9dW2uHVYYLk2nH//4x9vix/P1XFfpIpGlU24ny41/U/2ZqtPqN608+avplGXXnue4bPytjQ9LFk0v3GbWl7YwYAH7L0b9CxZNNixb6zPWoQbNZrRFq5xNOWw4IV+AOj005Ah5y2OWX5JmilSfzjTdZDcSrIMg9HMp5ak+6GQR5b2mfsTQr1Nom35/BTITnul1SvtjbNqHHnoohhDiZz/72RhjjI899lg86KCD4l133dXKs3379hhCaHnGffKTn4wHHnhgm9f8f/tv/y0uWrQo7ty5M8YY49ve9ra4du3atrpe8YpXxBe84AWt3+vWrYvXX3996/fu3bvjsccem/TSY9RpvmYylT3MtbAQ7EGM/2uEVipEDNeTIuqazWZH6BfNM97y8sb68OUN86JXfEoH2jjllx7tGvb81RYr8BomlfGFBvWJ7dDk1QhrPq8RkNYXE6xLzSPT0q/8z6T31NRU/KM/+qPWHCDPD0LKr9z46njWCze3PRtqG9laxDnrXWS1bEW+isB48bkY+5Kkr9jDGvuIyXitTZa9af3GCwJMlGv9jzqTl1ttsQD7Ge8TW7dubQvhoy1i8XiwbANltsZFiX3yGNZ0KX2qLfxheTJeMVnhcZwI6B3vf//746pVq+LQ0FBct25d/OIXv9g612g04jXXXNP6fcIJJ6g28Y53vKO4vjrN1THahLiAyS/Mx3MH/o9jFG2+FFgX1o8y58ZK7pglj1Yvtzt3Dc5frBfMx9eXtIHJSu6PVB1WuTm9lOgtlY/JbDmPNoTnHnzwwbZNXT/+8Y931MPzT+lCpkUsyznudzyeWwhJ9WNJ/2r9xgvpWh+XtFvyl8wvfC7Xf9rYsOwQwfcOrW6WidtrLUikxgDqE89p+VPjKLfguL/DCfkC1OmhQXt4nm5iuk7euyWysifzTJCUg1oAEa/13GaldUvo2T7bCfa5uleALFLx326SdQ+bztj1++umrt/5zndiCCH+y7/8S4wxxrvvvjuGEOKjjz7alm/VqlXxfe97X4wxxv/8n/9zPPPMM9vOf//7348hhPjP//zPMcYYzz///HjDDTe05bn99tvjokWLYoy9xbFF1GW+1rxN8bdFUMlx7Zhcx0Q5bqwpJLdGIGOSvCyLeMrmPNwxcdgOLFsj9TWSkz2okTSPMbbVoRF9HH5E0zmSm5r3OH+KjmXwb82DHMtBooRJW21Rg0OZpBYdOM4+24zWPyGE+O53v7vteWfZWRe3yPgQQlx6gk3Go61otlH6xQN7qWt1NYmo57JYN0zE4zX8FYKmH7QLjaTmhR60bfbAtxaxxK4sm2c7TnmQY4gXvN+kFtCssFHaeMQ9ElA2GYuiC62NExMTrTzWYoXYEJMG2uId5nPUC3WZq2PMk4wxdhLjTH5ZC90Cvm9WIQ5xXuXxwvWnytCOWW3nuvEemJPdIic1IpHL4nqr1IG/0eEG26Z5A6NMWDfqxZKF9YrAxWqtTJTHWrAQQl7iyHM4SS5Da0dOf5YNcVl83Gp/FVthWfi4gBewU3VbZfDxnH4sG9PGmtUGRGoRA+f3FCSvtjAg57V2sK608anJatk3loHPYA4dTsgXoE4PDVVeknPx4Gc6HXLIIQOXYbpTnRYvqqaDlhwzcBm0tOysiwcuQ2nq5+KPkNiTk5NtY72UxC6VZbbdR0KoFjtfWxAZHx8f9K18INi9e3e89NJL4y/90i+1jv35n/95HBoa6sh77rnnxre97W0xxhi3bNkSL7nkkrbzTz75ZAwhxE9+8pMxxhhPPvnk+O53v7stzyc+8YkYQohPPfVU/PGPfxxDCPHzn/98W563vvWtcd26dabMdd4oTiPY2ataI7Xkf36hRLIQSWVJ/MKH57Q41JJPjvOGp5oHOJKQ7C2L8jSbnWFrNG9+9uTG+5dGMGLSSFZcpGAi0vLu5evlpUMjT7H9SE6zNze+6IiuU+E/pAwhovElR7MT1jXH1ZeEL+ivf/3rW8dXbnx1PHnzza3fMo/iwioSp5quxsfH4+TkpLmpryTe/NX6okDawgskHBaJbYt1rxHl7MXP9oMLZRyyBevLeZWzPDlvdG1xin/jAhUuMGgv3uyVr8mn1Sn3Gh4f7LWP9sBt1ML9aO3PeRFzcs+7eqJO79Y5Mk0jn5DkYgLSKo/J7xR5p80hPG5Kx4ZWjzY3lZJ7Veq06rbqq0Iqsx75i4ESQhzlwfs8l6PJi6Qot0lb6Oc8LA/mkeu+9KUvtd73RkdH409+8pOkLvB3iV2jDGjDqFe8XrvGaluq77jM3GIYzlOWnVp9kLJFqzyt37FNpeOFy0zJoNmZZauoDwuYNyerdp1l91oZ1gKBYy8GSsjPts3kLNTxoUFLszGsSpXUL09/D8uhp+kKI6OlFHk/Wzzae5Fj8eLFMYTp/zplNiQrJnCVNFP3ptJFPyEn9zdce+218YQTTog/+tGPWsdmOyH/jne8Q+3D2T5f48M3E4bsEYwvw0xWNZtNM667JPY2leuEBOOXCJQrRS5iGZrXcGqMad7seD/BDSSFZMR7DXraxhhbC4PyV9Oh5R2N+dkTX/PS5vbJSw8vZGjPY9wXklfzYk/ZBetQytDyaH3BX2Jg/63c+KqO/CvWXx5D2Pv8JGOOF41Qn7zwy/OEtreA9iUBn0evf15ckDyazbIe8KsITfccbkf6Vys31V+8sITECubHsc1fSWC7tS9oWH8cpx1tjT3RJWl1SptRZqv9uECI+bT9KnBMpvofr7cWtvaXzdfnIur0bh2jTRzj/3hP1/Jyfg0lhLNWDxN2vRBffN/ge5eWl68rrYfvU1YZlixcBue3SHPWHdePwDJKvcQ1r2Aum3Wq6YN/Y9khhPhXf/VXsdFoxJGRkTgyMtL2/M660MrVCF3tekm8GMG6wHK5P9k2c32HNq3l42t4kQGfrViHaE8ldqQdRxvj9lmyptquyajVK7AW8FBvOZ1Z9yseY9q1nFL1sOyOdgyUkJ9Nm8mlUKeHhpxnTi4de+yxlfIL8TjdqapcnrpLvW606qlaWrp0afK85r0t3t91GxN1/DpkfyLlr7/++njcccfF73//+23HZ3vImrp6yLPnbQjtpBWHptCIbyYImaxjYg5fQDAhEWaFi2kq5LkWtgQJU/nNJDe2AUPDaASryJLyqpVz2tc6TPxp5H4uYX1M+DJxm/KURu96flm0yGKtDdZ1qC/No5v1wy9xGzdubDu/Yv3l8eTNN7dC1oSwN7ze+Ph46xptUYj7UVsk0ZL2lYHWfuw/+WuRu/K/2L62wa31lQTqUPtigxdS2GtdKxtf3K3NUbl+LTwVfx2gfQWA5Anec+Q82yePcSb32I44dBK3TxsDXLYWDifGzsUP7Ysc+Vpkf5qn5xLq9G6NKCETtWtSxBsex/GYItas41q+lFzaMSwXZWcCj0lIvG+V6gXnT267Voa12KEdQzmZvEzJoh3nBUONBC2R1QLOqdpxlg/vrehoiPs9WeVobcLjll1qC/gsr9aXWA7bjNbPlpyarnmcSP2WjHwt24ilM64P25vSIV+bOqfpWusPRKrvUteh7JxPe7608uTktGzK0Y5ZFbJmkJvJpVCnhwbtc2v3CK+WZsuGtuKx3g1JfsDBC/sv07yhff8PHdZ1OapsheVJzHtP7ak0Dv10xly30mGHdW8rM5m0cb8/PEDs2bMnXn/99fHYY4+N3/72tzvOyzz8l3/5l61j3/zmN2MInfPwgw8+2MrzwQ9+MC5atCg+/fTTMca98/Dpp5/eVvYrX/nKjk1d3/SmN7V+7969O65cuXLOb+rKSSPLUmQ4/+a86NHFLy5a2Bch8zj8ivaiI/XhQ7dGRGttQcJR28wS8/CLAdeBm4CGoBOimp41EpX/Z7mbzXbvZo3gFX0jMar1Dy+0CCGJ7RDdWuFysP8lv8io6V7z8vvOd74T1593XgxBD/O2+rLr4pLh89raGGMnyct1aTKw7jGPpndtQUX+1+wA68b/tfHGoXx4jrTsw7Itqx4ej0xIa4sVTMijHWlycZgizMs2hoSJHONFHykD6xIyHvel4D7mEAzWYhWHucFwO5ZcKZ066oU6zdUx6mQi/p+7hq9LEeoaOWeVn6qX7xcpclCgEdcoU4nHsXa91Va+Z2k6qwrr+tx9ghchWR8sW04XpbKX2ATLJ8e3bdvWiiE/MjLS4VSTqofvt/xcoMmAtqXJo937tbJybdbq1erAfsYvEEVfKZvX9KEtNmFZll2V2AHaFbdRy8f5NbtgfWj5tPqw7XyO28uhgPhaq12W3I5OzCpCflCbyTHq6nEXY8x6IFmpV6IOvV81YksL3VFCIi5c2BuxnPNA5vO+eDG702wJZ9NNSnm0183bndNcHDe5jXPmCt74xjfGI444Iv7DP/xDvP/++1vpqaeeauW59tpr46pVq+JnPvOZ+OUvfzmuX78+rl+/vnVevlS75JJL4r333hs/9alPxaOOOkr9Uu2tb31r3L59e/zABz7Q8aXanXfeGRcsWBA//OEPx2984xvxDW94Q1y8eLG6SZWFOr3k44teKuwME2jWPK8RhngtP9hr3qu8GMCks+Ytzy8OTLinNozEc/hMYIXpwbGpeU6jbNrCBBMU2uJCKua5Vmcqhj33nxX6ptlstry9Uwsa2D4pV3RkycFfSliLG7yhawh7ifgQQlxy2oYYQojLjz42nn7GGR36ZXJci1nPiyW5hPmE9GXP99T13FYuj/s1NXak7pTsSAbw4hUuEOHY5yRe59p5XJzh80zKM1nBCeWzFm1SutW842Ts4Eu85OHFAWt8YV9btqCFwXFCvn6o01ytkV4xpuNw43Ekc+Uc/s3VzbDIcqwX67bqtOTQyHS+r1jl4HjHdlttxfNYrty/U3rgcrRyc/mt61AWfB/g9uBflr9U9pw8qXJl7m40GrHRaMTvfe97arlaPdjWnBc616mNhxjbv8jAfFXbrNWbOl5SH5fDdVlfP2j2bBHZDIvgt9qM9Wl61+xNxlyqHr5OWyzha2OMHc/KqGu+r+V0UTIO9lfMGkJ+kJvJMeoakzbGapu6TncaGhrqe5mW93qOeJ/u1O1CSFaHNSagu0mHn3BG2+9FJ53TkWfe4UcOXM5+pW6IbG1xq9tY9TMR477Ue3+6Ujce+rJAuT+85Fs6wH1WZC+XJUuWxEMPPTS+9KUvjffff39bOT/4wQ/i2NhYPOSQQ+KyZcviW97yFnUvl5GRkTg0NBRPPPFEdS+X97///XHVqlVxaGgorlu3Ln7xi1+s1J66vOTjywPO21ooFX4pkCTEVC78ihYPm0l/jQhMEYCYLOIt9TzChJsVbgYf/rWvALTQFhpZKOfk5QVfPFBWJgtjjG1ErBUTm+OPS10lixL4hQDqXH6jPpjgxZfIlB1Zm4Oi3CMjI3HNhsvazgshH8I+Up77yAqxotkxe1LzwoxlM7x/APaVRpTLwpJGbgvJo3l1s964XPT6xxdhbqfmOYZfJkjZSD7jXIn2PTW1L14+huhhL3NLt9wf1sKPZp9yn2DintuSCkul2YzWX9o9w7Il3gPAUT/UZa4WpMirFAHFcygTZFXtl8lBvO9gvRphqBFuKF+qTn4O0YhBTU5NPgt8v+R2stzcBj5m1cnPCNwn+D/OMxaZydenZCslL0vz/MVf/EUMYe/7XKPRaPvS1eob67dG6lq6ZdJauy7VB1Y7tfGi2blWLtZXxVa4Xdp5XnDPjRmsS1sUS8mgla2Vxc9MVj3cptRXCdpinNbX1vjmfuOyqtjD/oRZQ8gPcjM5Rp095KsQ8rMlNMtsSRKbu5tU8kmtJ09WqmN8dUlM6tfhvlLyRQJ65jjqgzq95DebnXHZQ+gkYflBNwQ9rI1FJmohVbTjmIQg1za/lJRaiLbKxo1SJyYm1HawnJo3cI78RRk0oo8JfQ7lkiLShYwt8fjGhQ8pH0MCleoOX4jYY15sI0Y9bCG2Ba/HRYZrrrkmhhDiiRtf0XatxJA/efPN8eyb7oinNV7SJie+wGK/sk2yN39q0cTa1Jj72+p7i4DWdCz2p+nc2ogWx4/ME9ailPRRiiDPPT9imbmvM9jWebNiLSyTtuigyWDJph3n8YmbNWP9aI9iG9YCGepPIyEc9ULd5mrtGN6DU/mZJGQyTfNWTZWDNq+VkSLNtfGSIuj4Wm3cWSSb3PNZ7lR+qYtlTZF7Vnu5TVIuz6lamQi8l6MOsLxUOaiHXF5NJyl8+9vfbpHxjUYjfvOb30xeb9WpyYay8zGe4+R4agEl107UqVa35QWeK4tlzNm51V62uVRbUGbRlZUndQxhLVJo5Vtt4rq0/KVjmz3k8XrLBhw6ZgUhP+jN5HKo00NDySfL05V68UT1NNg0XZ74g/DwZw96icXvaW+yyP/h4eFZtTAwU571Vj1OyNcTdZqv5YHVIn+ZzBW7lPOYHz3McV61SEv09JWyNNJu27ZtbWQak4oce17kxHrx+UMj8JiA02JZp8heLktkwRcWJHqtePVcBpaVWrxg+bkNuLBgxUrPOVOwdx6WLbYk53OEL9sbnj/lZTfEEEKLhJcNXYWQH774qtY8kfsyMLdQEUJ7OAdsI+abmtoX0mlqaqqt35lY53o0z3qtf3PyavamjQEk6Hl883WNRkN9Zreei1P61sLxaGWLDbLM0jZr4cBajMvdX1L6ZLvX/udxxET//hJabi6iLnN1iljSSLkcWaiVwcQZ3s8tEhLzWfMD1sH3XUuenIw4LksXElAGXIBjnSLBZ+mcf2skIsrMOtDISTxvEZssS4ls+Ntqc6o/rPP4e/v27S0yfnR0NP7rv/6rWpYlO8tieUizLOwljvaBtqmd03SkLarwc0Lq+lSbWUbNVvCv1vac/WjXSX58PtTajPrVPOOt31i2JVPJ8VQ/pdqo5UP5fbG8HAMl5GfTZnIp1OWhQaB9+q2FxqhL3OdVq1ZVyt9tGI5Vq1bVZgNKTzOf6kjsyyY/3Vyr3UdySRurs9Vj3pILySZH/VCn+Rof8rWxx57QTDo2m80OElGLtayl0jjenKwNNiV2NMpkxYeWa7WNV/l/fPCXZDkYoOe81f5cCAzxlMeXYTwvJD6TC5bHtxZWSPOsl/5NycufTDPBLPYUY1T7N7WAEUKIy5eviGe/5UOtMDW4uSuS85xw4UgWeXJe39pCU4xR1a0mq5zXFmC4nbzoinVqiy7WBsnWFxfsrY22oIXlyXm1SxvQFlFmJKPRDtB+pX5rnFshelAOHMc4FlJEu1afthAmf7FdUofm4Z/qjxwx45idqNtcjf/jfUQj6HJklkVY8vyONo6EqDYvYnkakYmkZIm8PKdpXzPhfQ/LZnnkL3vz8/9YFhN7eC7VP5o++T6j9QfemzViVtNbShaNWNWut46x/Jo8McZ47733xkajEdf9P+7jb//2b1V75b7R6tZs2rJzLteyafyt2Tv+lucAtodU/+f6heXgeSO1UMRfhGg65Lq0v5r87GSDY9XSYU6PLAf/zzrRFlo0fWq/rXotW3OkMVBCfjZtJpdCnR4aYowz4g3fS+rFCzcVK17OMSmIBH2/ycHZSjbm0uEnnKHGg+02HbTkmKJ8tYyJPzR3FmmqLm7NhjTI/SH8QaJ+qMt8jS8gmu0hwYiEp/zGh3dJGnmVCokj4WPkJVUj0zDEDJKW2madLAvKjzG95Zh1rRbbOwTd27mbcHGiP144YO9gi1yXvPgyw17XUo+24JILK4Pt5evxRQfrwcUaTX98DBcYPvrRj7bOLTltQzx5881J/Q0PD8crr7yyVbegSl9IO/CrAcvrutFoxPHx8dhsdobb0ex9amqq5WWOC8va1yBaf+BCR65Nlq1aewYw+VSqqxhjmz2JZ7jUg8+5PEZxjwK2GZaP9Y9fJmhfBMiYzH3xwQv8Wh1IiFj3RZRDynQvvHqiLnO1BiYgcZxa+eWvNb753m6RsSxDyktdI85KSD/Nc5fr4/sMX4N1owypepigxXJKF+CwXawn7Z7KZVse8rlFAZQbnwf4XI7sRJlRRu57wRe/+MXYaDTi+c9/YRwdHY233XZbMn+q37FuthGrvWyzLLe2EIT2w2Vwv2nnsH6s25IxVw/Lb9leanFA0yfWkfK+52s022VZcfEi196U3riOlP5QJmtMW/870hgoIW89fA5qMzkLdXpo6NbrzdPgE29o2ks6eMWJHYT7XNoMtW5p/qKj9vXDvHl9K3emF4SqLKbNNPkv9SEx0etXQP4wUT/UZb7GB+IUqSVkebPZbCPHuAxMHCKGSe0Y28lTeRjHazdt2tT2oo0vULJpIxL0TGajp65GrrFXtVzHpKPIYrWvxBPb8uTDfFpsbSZkU4sHHJcf25+SzfrKANuM9WI/aImJ5hSpnHpeXLH+8qTsJ5xwQpvM7LnMMnJbtTxSjthnTn+5NqRIYKs/5RrNUzylb9Sztr+BNSZFf+jNziFrNF3gmOU24b1CZIuxc3NivjdwO7WQTlX2S+DzWqghrQ9kDGh1yUIl9q3P0/VEXeZqDSkPdIZFaDERh/n5vGXjFuHGeTh/zqOW86Ac1v0Hj7Hc1nE+lyP2NGLRko/bI+VrXtnSXq2vUEZtUcCSSQtTwnlTHvBM5mtoNpvxox/9aGw0GnHDC3659f6TW6TkLxS0crU+0eRhPaJ+LCIZ5wzNRrh87juWxbrWIoy5HL4e5WL55NlX0ymWgfrVbArzWOMP9YUEvBUCB8so+V+u1xaQtPJQXnausWwUy8jl2Z8xK2LIz3bU7aGhjl6wg0h1CNlz6MpT+1JOP8n+2ZpKvPRLPfk99Zaq3oOqEP0l47Yf98BNmzYN+lbu6AJ1ma/xBUVSiqTC80yQh2CT1PjgLAk9wnPkvtRnkcBcPsrOXvGY2LPYIvrQG7bU4QAJdfYQxxeukuutPOzVi6FEctekQvpwvUymY6xvzaveKlMLQSbX3XTTTa2NrvFZgRf1JWTNMavs+7XYVIr81jys0c74RRQ3hsXjWjggaat1DkPBxLj3RVT7qjS1eWqV+P+pxSP29JbU/H+EOtuUFcpFPP5ToXAsu5B7iWVnJbav2aG2CMSbVcvflK5LknvI1xN1matjzBPWJQRo1frkXpAjRzVyMSU/k5Q5wpWP4zm8R2lkpnYdk5BMYObk4WeWFNnPdTNpivdVTS98r5IyrLZo7bHuUVw/loH3VEsPWP7o6Gj8pUtf3no/0nShka+WfCmyldusLU7hOa2tOPdwf2h9hGWnSOhc/pSdoTw4F2t60cqV9mE7uD6Wj/uI+0rOaTZiLarxX+2rE61P8BlZ0y1fw/Jp17F+rPIdTsgXoU4PDe4h72m6Enp5z4XUj5jw/fjqYPHixQPXhSc9+Yt+/VCX+TpFksfYPpczsc0kX2reRy939MLFhC8veA3XpXnMIoGqEc3o8YznLNKNCboUOTc2NpbdXJLLthYxtMSe+1IHkuLSNn7R1LyktfbItexhjl9DiI5xoQD7C/uPPZ6xjUL8WvZy/HMvVY9LLPm1W25txZe3UipOvdgPvwCKfpF4R3tn3Uh7U4sqJaQu5rE2UhV9peoSefgLAGtPACREsF5pd4xRHWdSj/X1ANsr32e0vrEIeZRX/tc2QGfC3wpfhbrM9U8TFhvZXiyPekf9UJe5WiOyLJuzyCwcayX2KvmQ8NK8gJnsS3lca6Qn/mW5tN+sB7x3aV88afXL/cYqk/+izNy+3HVa29ELWPN8x3ZzuUzIcj8x8Yh9YtkGyytl4LObpcdmsxn/9E//NDYajXj6hr3z9O/8zu905E95W2sLGpqOUWarvZqumHjnOP0aYa71Aea1FjhYttQ4QGiLCtqY0MoVoA2lFom4Dq18thes05IJ+5f727r3WH2UkgdtCNtitRfL0Y479sIJ+QLU5aEhxs6H+JIkO3NXvW660kEHHdRzGcPDw21tEtJzkPGoPc2+5B7zvafh4eFp/dpkkPcmJ+Trh7rM1xj+he3OmsfRk1Yj1+SYRlppBB4Sy/IXiTLNq1oj8HKOABr5xuS4RuZrIT8wpA7Xkdss09IrxslP9QMS7UwoiO5KQtWkdKbFrsf+wxcnCd+BL/GlCUObCCm87MznJa/JhbGx7DW3sajWn908y/ImuZYHepWE84/l9W+FMuI+sfQwMTFhesJbfcf5eOHLkivVByg3e72L7HwPssYIf+2AZeUW5axFLMvbXwsf4Jj9qMtcHWMnyZgixTQyke/pKWKKybWUV7Hkx2M5D3mLOJXytbZYRJuMc5SViUwpi3WApDDKrZHkvIirhXPR6mF9pMhn/KvpEfOwzrT+YP2n+gDnCl6M5jIwb7PZjH/1V3/VmqdGRkbixz72MbUeJmW5bpavClmcsg+un/ve6g+t7hg7NwZm/aGOLEJeaxPKymNKk0Mrg8tJXZN6v8T8PP6xjVwe24/8b7UF69LyWAtfHJ4IPeRTdTlsOCFfgDo9NGgvcsuXL88+lM906pUYd6/i6Umz3gv+oEMGL0OPyWP51yc5IV8/1GW+lrnaIg1zpJzlYR9Cp2d0CHsfjpEQFBJWC72CMvCGkPLgbcmHnt0poj5VBtclBB+3KRUmBj+/RY8sJAlR55os2vMUegJabSgN3SHe0iUhOywvYa3ftLKQDGVy47zzzovLV+wNWbPsrItboWlC6Axbg0lsSPMgl0VatgHROX5tgHsF8CKK5JU8KU927jv+2sJa7EFdTUxMdOh/06ZNRYsslu6tRZrUAhr3mXaev17B+won0bt8VaJtZCw2jeRCaiywDNoiIcrOi3CaV62VRE7O32g0/MW/pqjLXC1A4s4i2/h/yS+/U+QlgsOoWGQqX4P1lLZJEpN9TLAhOczt4/sE6wHLknmTy5E68C+TuDj2mTTFclOkKutGK4e9uLW+lzmGy2T9SV6WWVtkx3ZrhDPrMcYY77zzzhYhPzo6Gv/iL/5CbZslg6aTVBgfy7a4TK1/rS8xLF1pZbOtlXypgOVo7bW+QuD28HluR8kiBrc1BWmjLJRrC0ecn21JZE3ZqqYf9uLnOpns1xbJUv872uGEfAHq9NCQeridjcS8p+lN/QjL0s/UTUz8lBc7lzfb2tvPVLqINehxLrGIB5163fBWNr901At1ma9zBJ+1waOEjeCQNqmycsSX5SGfS0K2iYzj4+Oth/NU6AqUxyKj2TNXaxPqkPNpoTHY807TgcjWbDazHsb4lYPmLY1kMx7T2sJ1aRtslvbLtm2dG6hq12/bti1++MMfLvrCSeZaaw5PfRnAHuC5tmLCMCmsuxJ7ynljS57ceNRsxtoLgIljJPmlry15OBwVe2NaMoh9IxGRW0hAm5UX/hjbvey1zV+xb5rNZluoIWt/AB4f2hculnwaEc/JX/Trh7rM1QIhqVKknUYion0iOWbZrVWmRQY2m50e5CVjguVj0hOJN7y3sUzYLu0YtoV1xG2w2sikK+uRvYjxuhzpyeQjOkugvNj37IXP7dX0yl7EqFtsk3Vvs/rzIx/5SBshf8cdd7Tysy7xr2WDKC/+ZoJWkye1IGNdj+WnytZ0ac0BaBdW+6xxhf9bYyE1frEelJv1k9IHgsPQ8AKf/K95z7NNYRm8EK/pR/SHfYR5uCxui7YIlNLZ/gwn5AtQp4cG7WZe5zRocpFTt+F0Dhw6LJ+vJE9p6mdZM6XbLsPHHH7CGXFo2fF79VyRkJfr+p3MLw1q2C8labaN036kiYmJQd/OHV2gLvM1vlhom1Li71SYlhD2hYmQh2IO7yJ50TOWw3swcZeLTa/JI+2x8mpe/alY8kh05jyjsW1WbHj0mtdI61QIldKFiqqe1JrXPy+QcD8gga/F9a+yWPMf/+N/jCMjI3F44xVx9WXXtcLSyOauFgF/4nD7RvEWmWD1QYnNaISupmttQYkXQ7RFCVlEsAhn/IJByJjchq64wWHqKxE5Pj4+3qrfaiPWIaGG2JPUWhxI9T/Lh7aXWgDSvsDhOrUFNfb459A7ch169nFe1qfI4S/59UNd5uoYdfJaYJF0KRILj8s5JsWwbI1g43sufp2WGw8aWZgqX1vY1urgtmtEoXat5Z2v6Z7Lx8V59NLVSHNNXpRToJGgKB9/sYDnNXLS6nerD0pkF7z73e9uI+R/+7d/u6M/sFzuXzyHusNjvNir2aVVJ7aZ9cr6y+lHG4eavfOzgXaeZUT7tMht7letzZiXvdQ1u8D+ZtvR2i/ya4tR/BULXo8yYH5twYr7W5MBbV3+Zz3x/Shny/sznJAvQJ0eGqq+DM7GdNhhc5O03B/TAQcvHLgMM5PmzQIZylKvnuMh6HHdDzlkXzihXEgp6zwT+4MOTZXzrHHMPtR5vp6YmFA3Tuw15TZ9FVksMo5JNfy9adOmjrAgTPJhWSmib82aNR0voyHoZLwQcawvJgpzYTRwrMuLhBYrHAnTVExyjUBnUlVCvIhs7OWMIVxK+lfLl9uklNu4dM1IR75FJ50TQ9gbtkb+l3T2C1/RmkvEiwr7zVpE0vRlnWPCmMu0ws6g7VlfGmzdulWNgc51afWjblPhZvCFNZW0vRLkfy1sjbUAUxrOJ6V3beNi6/4hY0/6I0XS8xcSuKBg6Sh138KFD48hX0/Uca7WvJzxf41M5L8WcS/3PLwWjyEByddrcbUtcLlWHszLxKdGcGJezpPykNc8f7EcbDvLiHMO3gckr0bIMrmIicu3yFBNXiZJtbI0wtVa4BCyNCWbXDs6OhrPvPKG1ruZlh9tjNuPcwv+xrkC9cFkq1Un64v1JuflrzZ+RA6WFX+LvrFsy9tekze1GKG1CfvIstcSQprbgH8t3aLNc4gjTQ7L/q3y8Zi2UMa6t8a+do3DhhPyBajTQ0OVz81nIg0NDQ1chhCqvajs7+ngFSdWvma/3hx1DsS199SZ3EO+nqjjfM2kshCdKQ/yEHSys4TEZdK4ajiUUlmkLj6mxUDHc0zI44ssEuM4r0ts7FR7ch7/Um9pvHI5r7Wdv05gPaGHNOpWW6RJlZ2ScXx8vEMXKKulD/GSX7vl1rjsrItjCKEtrnwIIa45/yUxhBB/7dd+Ldm/fNwKRcShiqwwSzJWun2mK41Znku8IXJJ6lZmDHmj2U3KPqTPtQWG3LgvrUNzBOKyS52FUnVywgUbzTPQMbtRl7maCTP8X4uFbhGAGjGHf1PENJOkWE+KGLNghZhguZmcw3PyF3XBxF6Mnd7vfL02fq3xbOkWvxZkvTFJyPck9ka3yEduE8tR4pXN9y+NtMXjqf6WdNVVV8XR0dF47ht+xyTkrQUllgPbq3mtY7mpRRaNoOV8JTrTZMf+iHGfLfN+OppM8pflYNthXWjAZ5VUebkxhDLheGIbZZ3wIkSqTK3d2jUovzZu2E75/qTZFsvo0OGEfAHq8tAQY8y+cM71JB620+Fl2LfUbdiSebNjcaNSmr9g8DL0mGbMy/+A+QNvq6Q1a9Ykz8+WOPHTnawHMcfsRV3ma3lgZaLMIqSqzO2lecfGxjo8VHNzJxKlEs4j5wjA5JxFVmuhdtBLLLdZKl6Dv/GLnpRuqpCBms41r1/N+1le4IRo1T49xrZoMqd0ir+rLLZwkg1dV258dTx5880d51etWqXanbYXQam3v6bXUt2n2poKR4Qp57HPNhhjbGuvtRGrtvmpJq8somj7Cojc7JmOZbK+qiwCVLUVHJvWHgXs9Vc1yXVan0lbffG8nqjbXK0RlXJfwLzac6MWr9kisbAurU4kHrFe7X9uB9aDJDbWGeM+slEIQiZF8TpMGiGH9wquL0Z9s0jUq0XysQ61fpD8+L+1QIDXoJw50pjr0vpDzmv3Q5QP+7aE8MU+OOM1t8RGoxFvuOEGU29sc9qzhyY76xrt0rK11PuT1m8pG07tEYB/MWyKVp/1BQP2Nc7z1sIb2ommM7QxdvSwPMq5H3ijdqyDy7Tai/2lyaqBZUuVZ92DWKcl9e7vcEK+AHV5aIhxboSsmQ1p0KEyPOXSgbNAhsElD+vUWyohKcbGxgZ9O3d0gTrN1/JyKwRcipgNoZ3wROKtlLTUjndDQktZ+KCdI0RL5ZIycgsD4iGkPfNYXvn8Apv7AkGuKdEvvsRVXaCw4mJjEkJyamoq+YWBlsS2tHZwmLAV6y+Pqy+7rvV72VkXx5M339zhIb9ozXPafnN/VbEF/mrASlyH1SZZgMFFkhQJb40BDlEkNscy4N+StgvBUmVRKRWKqduFJCv0DsajR9K7xM4sD3ytrSXzsIxBbYFDC//gqA/qNFdbJKwVYkMjMmVcM7GGpKLYMxOIeP+Jsd3DPUd+YtmST/OQZ1IYv1bDe1/qnqeRpRx/Xs5pxK8ct74SsDzKrX7SiNGUN7aUgfdoLIc9zJnIZRks4pkJTY24xf+5jayfEEI8ffy3Y6PRiK961atUwle7lvWSsyPRC8+BrD/ruPXb0hmCF1OYXLfGHtdhkez8LMnPc7gYxeOPveHZvtGOWEfamORFN013rH9NhygXt5vHHB9jsF5TRLtmdznb2t/hhHwB6vTQMFc95GdL6BtP/U9VN2Ldl9Jx2+cdfuTA26al/Seufv2TP0DUD3WZrzUiWeZvJOclaWFPtJTa0BTPcRlMdmrPEhxPmz3suFxNTvSo1zziNfJNS+iBjvWXPgNVJTFlI0t+Sev3M1euPN6/Q3t5T30RoLX72NPWdSXrMatO7JAl13csf8pbfGRkpONYSfx2tBH++gPHgfZ1Bx7Dl1i0Of5KhBd6uDwmroToKWmHtdDF/ajt61JiV2jH2qa33dqu7JNQRSa+R2lttr688Lm6fqjLXC0QUskiEplsRruUewQTZTzWkCREMs26FseYRkLKb8yDpCK3S/LwPZB1oN3ztHAofJ/Eelg/WBbrVSNisf2sE5YXy9LqwPx839Pu8dYCLJOUKU9mzYMY26ctXmi627x5cxwdHY1n33h7a860Yudr93ZcbEkRpxwahu1Vy8tyWzrXZLWA57R6eGxobbJir6P+8bfWds0eeezEaH/xodkT6rNk4cjSr9gMf/nAi2qYtDGo9Qnql+1Zk4XvLQ4dTsgXoE4PDbMthvx0p4ULndycW6na5qjzFx01o/UNOh100EF9KWc6Pexxc9c6J/8Mvp6o03ydinmOZLYVbmMQC/CaV6v23JHz4C/ZiBI3f8TrmGhNxcXGRQ7tZTvVrm6TtvAxOTmp9jd/yq/JlyNbU/2QShiaTGLFH37CGW15Vqy/vHWMN3bFVHXz1vHx8Y7NgrW+wIUirQ62Ec3WtI12S/SGZDp/wp7SNS804cJVjuTm8S31p0j73CIY6wXzaF7tMr7QNrWNjpHM0GwUFxO0uPZafk0H3CcSzgHtxF/064c6zdVIZoq9IcnE9wkm9dBG+Rx7HeM1XJZGMrK3Lo8Hvg6vYWIWSTWtPNYDewijbFIeLz7IeY2YlDKlrpTekbC29MO6YPJUIzV5PmbSH/Nr8fNZ96wrrQ9YF7yokLIfuf+u3XJrbFz4vHjFFVe08rG82H7tay/uQ629WDa3l+3RagND86ZnPfL/LB9fj3l4zGiLSZhf5MG2cH9pdqN9bSDn8blVawPbv6bbnF6wj9mz39IN2gkntgvWC9eJYxL1nPKmd+yFE/IFqNNDw/5GyHvy5Kksad6GqeP7e9q0adOgb+eOLlCX+RofwOWlL0UMhlAWBsQi3kqvwbpSYW6QGJPjVUPWWKFZNC8ezVNYC2uTkoFfSCUJmVgivxYTvjRZpL9G8mq2wP1pbQqcCmdSIjPGjpdjQsbLuRBCXHXuRck24qayOW/plE3m0tTUVFu7LKJf+i/1nKyFUJKvNrQQSVZZGPaFda9tbovyy6bOeH3Jsz0vcpWO/6mpKdM2U2Mi9yWPlr9KOCckMKy2NBoN9fN6x+xHXeZqgRCSMmbkmPxFO49RD6WChCATWRrZxuS0RUBinHHNo1ojI3HxAMcxh5iR61EujbjTyEUZxywXliV5UmWyHCwP94lGEko+XlzVyrX6i4Fl8nGWQauPiWDUF8LyRo4xxpe85CVx9LnnxRBCHF3/S/ENb3hDRx8wiY+yaQSztkiCdsp2I9dont7cR5YNaGNJswWUh21TA1/Dz5XacfzNX61Y/Yy64zZh3/OeAAKURdMR60UbE9zXfA/CRTerDzQ7YFtmW2Ad4Pjie6VDhxPyBajTQwPfhD1NUzqgXp7V/Uv7a7s97Y/JX/TrhzrN1xbpiKRUN57buXAzWAcf1xYGUmFwcrJopLm1AWVJ2BxOJd7jQqZ2u8EpypULL2LJnCJHNa9pXBix9G8taExNlcW915J4yoewj4RfdNI5cfVl18UV6y/vyDc8PByvuOKKrC2UkuDsVa7ZS5WvRKp67uNxrb4q7crlScnOdp1rpxbbN6ULtOmc137Kuz0nlzVerHBW8j+SOyXj1l/264c6zdUxdhJNGmnGi6tMoopto51jPibE5ByTcZiXvdzxf8nHbeDY5FwWetUyuYoEO8qn6YkX17Vxanm1M5moXavpxioD68J7H5Oqki/lbc99JPcpPsb3Mo2MZXlZT6n2yf/rfumCuHbLrfGCi8bia17zmjbdpOxAs0O2EZ5bNM96tG3NMx/bZo0LPmaNCavPWaeWfuWYZr9YPsqhlc861PZ1wOs0vQksb3aWn3WJerLag+dQVgaXa43tkv7BNnJ7HDqckC9AnR4aqsZ8rEs69thjBy6DJ0+e5m5atWpVxzEn5OuHuszX/DCPoVVKvKMtoj4X4sNKmqeutkmpRcCJV69F+lWRRSPZkZTjzTYnJiZackmd/QhBMzY2ppLFGCZFQujgBqIim7zUaLrN9aUVK7tqQm9rbVFi1VkXtP5H7/de0rZt2zqI9SqbEOfIey2MUdWvFcROrOuqLmik6u+276qmKgs33F9V69JizpdcUyo/5hWSB+1qbGysFQbKX/jribrM1QiNaOPzaLcCJsqxHCSzcEwiESdl8AaWnJ8XBDiERIydIUU0MpZDU8hv7ZlA0wcT8ZaHu0UcYv7UAogc5/I1D22tL7Au0SOf12TlfuE+TxGyLAv3j+aFrZUt/4+96EXx/EteHE/efHM8/wUvji984Qs72mrpzpKZ9SI6ZiKYvaL5awstJBLKbnmhsz2yXPK/Nk40faEsKLcVA591hHWlvjZIfV3Cedl2ZT7lvpJrsQzWQ4qgRx1bYXO4LKwzpRe2CW08WW1ytMMJ+QLU7aGhNN6opxqkA+YPXgZPfU3db2I7+LR8+fKByzCTyR8i6oe6zNf8sB9COtxMilQriWedSlXJ6268wHN1joyMtL28SSiKXJu0tlclZ1Opl7L4WmvTUyEWrX5MhRWT571ST3CW6cSLr24LTRNCaPOED6Hdax7TyZtvjqvO7vwCgpP2lUQq8R4BVUOwhNBJGLN++2kjIrNWr7S9ig5SCzT8fG/ZjeiqZBGICfnU/aB0UUnq18qSY1u3bu3Qi9XHU1P2Zs8pUsUxe1GXuRqhkX8MJNyR6MJxrIW1kN+atyyTYBY5KNfh3InlMRmI5TKRx2Sq5BVPcM373SIM5Ti2xdr4kwlXrBP7QduYUyONWc9yPcuE8uAXalo/M5kpeubzmv2gnbAesN2cl9si/1944YXxjNG9C+unjqxrnUc9aDbK907Lw9rqR832tDkLN0GX/Phba7/2tQLWKWOAxwPm0Wyav1ZAHWjtxnq0OQn7CG0V+9AaE+ytn7MzayxZxDzWp417ud5qTwpcJsvP9zmtbY52OCFfgDo9NMwlD/lab9h60NzY2NLTANO8ob6Vtb8R6f1K/qJfP9RpvmYiVpu/cxu+htBJSqc2OS1JY2Njbd6oWKZGLKZizeNvuTbnKSuEXYlzgaYzqVfqw81LrSSe5GNjYz05NaRCzFh6KdHFyMhIVx7QExMTrVjfVj4k3Fesv7yDkLeSEPnDw8Px3HPP7VpnVZLoQ2xI/uKXADw+OJ57Fe93TWfasdmwf1OJ3XJ8enlhxq9MZnPCzZEx+ct+/VCnuRrBZBYTvSF0ho7RvEbxb4w62chlT01NqUQnk14aQcryMNmnEZp4b9NIVPauFlmYTMY2MAmI0LzAtfA6KBsT25bHLt8j2DudyVPev0MjS6VeJHWt+hDY59hvmnc+6wvL/sd//Md44fOeF3/pslfEkzffHJ+z8bIYQoif/exnO+6NXIdGhGN+i+jFtjNRjl9qSDtRdjmHebm/tfGkjRO2SZaTCWbuXxxnVtgnbRzweNbK0TZ31uyR7yGpewkuyrAetMUUbaFKs0key4iUDWs2wX3Gx1Ll7e9wQr4AdXpoSE3CdU2HHOLktqdBJI+Xvz8ky/uUPXIc9UBd5uuqBJ6ESWHSTNvwFb115VzOC77b+OopEs/aGNaKtW7pJBdv2pJd++JACGotv0VopnQn5aFXVDd6HB8fN6+tsvF2qSf20qVLO44hMX/oylNb/x+84sS2fBza5qhjV3XULzLnwvWwzJs2bSryhK+y0THbglwv/cZlWKFTSj30rT7IyarpCBexShZ7qqSq46AkpeTrRn6r37hcR/1Ql7kagcQr/hUgSSv2KSQZE8baJp/sCc2e0kwWy/9IxGFeJggxxIf8xnJRDs0DHeVmol3TB5OJqA++hnWHZXFMe75PsLcx6lkjyOWYRbozWarplmXRPIY5f4ztYX9YD6hvJl6Z+JS6R0dH43m//Jp49k13xPMuvzqOjo7GHTt2mF8NYH+kCHW2G+1LCMxrfTGB7cE+Zv1wH2l1sC1yf3Pf8XHuJy4b+wJlZrtgWxNdiS41G+K+Y5ksHbANcBtQD2xLPM+zHQussazJznKjfKwTtnuHDSfkC1Cnh4ZuX6o9efLkKYS5vQBWZS+KiYmJQd/OHV2gLvO1vASgx2ou4fwuXt/i5RrCPuJY2w9BkkYqWkRjqpwqqYSkLt38UchCISq79UzGcrHuUtKV22Rdl9qolmUvidWfOlfl+Y+/mjr8hDPafq++7Lqictibvirh2o9Y/1bqJlyM1RYtDA3nzYXFkY1irbBFMe4ja7ol3tEOrflOSG30LtXsJ7V4hddMTEy0fbmg7VWg3QN4zPDiyKZNm2KM9ubXmCyiwTF7UZe5WsCLgBZ5KOe0RdqU17GUo40XDj3BxGRqHORIM/a81eTQCH0mC7nulOc614PkLeZhWTEsiEWY8vWoAwSHGNHaoBGxKc9yTY/YRl4I4Hot0p3ll+Mf/OAHY6PRiKddcFk8+6Y74lkv3BwbjUa877772mTDvsa/lk2ivnGxgvuI24Dt1bzP5XpsI+sKbcfSs0Y+p+w3Z0/a9ZqMlg1zW7WyuO+kDC22vSYHj9Vms9la1NH0xYtq2uIeI+f1zosb3DfW1w8cHsjRCSfkC1Cnh4bZ8NmsJ0/7TfIY/wNPCxYs6Gt5Q0N7QwUJGeCoF+oyX2svS1oqIaPkk+nSMnvxgMXwLznymjdf5f+rkI65uvpB7OKGkaX9YoXQCGGfl7j2XNZoNNq8xy0iF1+Ic/aQOldl4SeEztA17BUvXvOlG8H2k3gX4hv1guWXjJlcnm7Dt1h2qo25sbGxtvypOvvlGc/xfKtsttuNLjhckCT86oMXKXCTXg4/ZcXXd9QPdZmrY9w3X4s9I3GmLWrxPZ9JXCTxmIRjojO1USR7PGtyc9nYHiyTvbI172m8ViNK5XptU1mLVGUZtLKlfiT3NM9nrc18HoG6tQjLHOFo9YFGCuO1ml1gPvSk19qzbdu2uH379tYzmSyij46Oxu3bt8cYY5s9sue2tlCE3uzWohDrAstGO+Nr8XrrKwtsv2ZzKcIaiWj+rckvesW82A/WHgdow1Z7tbHIZaBsaBMlC2iWXbH+UPc8TllOq92aPPKbdYp2q41hSy8OJ+SLUKeHBm3F3JOn2Z4OWnLMwGXwNPdSrxtcaw/wjtmNuszXqXjxpYQwk1wpD2km9HhsdDNWpqamkiRerh0lYUyqbORp6anbaycnJ029lBKkKcLeKld7juvX/kAveclLerp+0UnnJM//8i//ct/7IQQ7VAkf6/dmrSl52L41W9HCRZXoQuw/ZTujo6Ox2Wy2kdfd7DOQk0sbx1X0PB2x6RuNhnvd1Rx1masFGqmFJBoTqBwqggk4tGeNDMfxr3nIC2maI/804hh/M+GneQ3zhqV4jr3oWQ+YX9MDknpyXq5DYhjlxTGvheRgnTCZyG3G+yzLg7Jw/7Fu8RjqxtK5/MW8GB6G7Qf1OTU1Fbds2RJDCHHVOc+LZ990Rxz55dfHRqMRv/jFL7aRoZZXPybNk12Op/Y50sYE1qER8ZoOUc9YPx7jfFw+jylui9VPfJyJcrRD9v7mBQxtcUKzB9YJjnvNU13k42ce1rnIhIQ56op1qI0Rtmu2VbYt7b6G/ah9WeBohxPyBajTQ4N1o/U0w2l+f712Z2c6YBbI4MnT9CV+qHfMftRlvtZI15LQGr0S1KVJSDQOa1HFQ76fKacb8bgtzWe1WYjGXknEktjppUlI+pnQd4pwF694iTVf6h2vparhFVP5rS9DteOlX2hwDPwSO+r2Kw4pq5tFMa1MHgOlIXusED053VqLSLn2oFyYt3TPBOwbf9GvH+oyVyOQgBO7YwKSyV5tM0okqjSSko/h9Zr3vQUmgmXMMIHYbO4LfxFjexgNJiKFgEOd4DhEYlAjCgXaggWXmVoE0YhfvlbTBRKhuNCLxCHLyn2KMmk6QJ1jOVYf8nFesMD+4GtXrVoVz7ziTfHsm+6I637lP8fR0dH4t3/7t7HZbHbMX5qXO7aVF3hQJ3JO9ixAstoaA5YNoN60vsHzlqc66kVbsJK8mLR+SfUdJ1wgsxZNWH8sq5aXyXDWER5HOXhPC+tLWW1ByhpHuXGI/crhdix74LHl6IQT8gWo00PDTHkHefLkydNcTuPj44O+nTu6QF3ma3lYTZFlTEz1GvYjtWFmSo5B7E2zadOm7GaqOX2UEJz99t4dHR01vchyXyl0kzTPcdThTPQVbvqqbRZrpSrPq9xGDIXCsZ1Frzh+tm3b1qEP3Iy3NHUTj77KpryDSFXsRNrPfdcvW9NsIvUlgmx27agf6jJXCzRiMQSdgOV7Volntdgxbg7JZLzmTasRXCwf3/eY2EMZ8Fok3pjow3q1MDJM5GrewagfJB01j1smWblf8Lel56ZCaGP92galrD8mogUSOhB1LuQ1Qvs6gu1L6zuc67HfZH5Zu+XW+ItX/ccYQvuXcBMTE612iy404jTlkc59xoQw94XWDl4gQRKZbUDqZCKYx4xmj9ZiEbYHiXW0VyaS0fubvwDBscTtxt+oO2sTYZSBF2NQfiTq0QMey2SPdMnP+pbyuG7My+3S9M3n2Z756wyHDifkC1CnhwYn5D158uSpP8kfHuqHuszX1kvFbEjygtfNAsB0hKfodkFg27ZtHWSoxFLvpm0z+UVAFRnQlvgZUHsmXLnxVTGEdiJ9xfrLW97xh648NYbQudGrpF4840MoJ6ir9pGVH1+g8QuPXBJb1jZH7eez9sTERLFtifyphYFc2JqSzVoHlXLtsvTkc3X9UJe5OsZ2r+oQdBIeCS6LxJWyYuwMUSLX8xhGApeJPCS5kKjDa5nUFWD9mmc+kn1IQvOmtpgHZUJ5sXzMYz0HWfGwtTAn0maNTEX5kKjkvS1YRyyPEOtMWsv/1sIK3p+QqJR+YaKaSUw5j/0isjSbzfiWt7wljo6OxtOveVc8+6Y74um/8ltt7eCNhZG0Zfm1RQCN3OaQTEgMYxvktxZSyLI3ycPycn4mxbl/UQYmgiU/fi3ChDLPLdrigLbApBHYPLbZTkQf0lfa8y6OY7Qb1iHeS7jdLIO26TS2X1uUkvuAfE2Dfc76YV36HJ2GE/IFqNNDw2x9yPbkyVM/0rxZIMP+kYaHhwd9O3d0gTrN193EBS8lKTUSsdfUz405S8NqMAHXTxlKk+iyHx7tVVMJUastgmj6XLx4cQxhH+GeSxKahtPKja9Onu9nynmkV/FYX758eddyrFq1alr7sCSJHeZkKd3At9fY8L3qtF/JN3WtJ+o0VzPBLESl9s6N5BR6NWvkFZNizea+MCMTExMdRKzkZUJb+8se+hohxkQy59XKwfazDLzIEGNsEciSB8k9KYMXB9hLmAlRJBy1BQjNQ17zdraIYa1OISGRpOb/mTTlrwMs5wIkdTVik4l6Ofayl70shhDi2te/N5590x1x7evfG0dGRuIll1zSQZDn+g/7hMltHAe8UMPt0MYBLkilFllQVvZi1who1DXKhIQ4y8/XcnvZE55tA+0WF1TYM9wiqXkTXCzfChXJdsljw1o00GwLx7TM97zQqI0POcf61xbPeKHLuv849sEJ+QLU6aFhJl9WFyzYH+Kke/K0H6QDnOjXkn8KXz/Uab6Wh2Eh0JhoYyJv0B7a+HyheTlrRGAVgm/Tpk0quVxChGt1T05OtukM5e/2WWm2hx+x0sKFC+PRJ5zUcXxo2fFqfibtl5y2ocNjvqqnfBXv9KpJNlnNEfQ5MhuvH/R4mw4dleTTSKNu7X6mdIgEh6MeqNNcrRGzTLJyKAi2TyQbNa90JPR4k1EmC62xjR6s+JvJsBSBJ/m5LvSKZZIXy8I5XAuxgr8tb1/2CsdzXAfrmu8J3HdYVqo+lE2L24/t1whYPKf11datW1WbQYJV5Jc+0/Txi+c+N67dcms8efPNrXMXXHBBq1zsc1784DJRn9guHgdI0OLCA+uEbUrAseuxjdwPch1/ZYI2qpHf2F6ugxedUmML9W2R9pq3OdaPutbGoyzcSTuxH6x7AdbH44ntEW1YjmP4HNEL97HIhr/x/qS1F+Xl+4yT8jackC9AnR4a5toLRC4tXLhw4DJ48uRpbiZ/eKgf6jJfWy+Rg0iD8PzOefEOQqbS1E0ccU7dhDzBTWe71Y+2aatFypekXq6dqb7C/uK+m2k7K+2/kgWjbkhyWRQp9aJnXaLdjo2NFb9z9LKQxX2mLRj4XF0/1GWujrGTDJXE8cTRFtnTG/9qx5jkRdu2vGWxLIwRrl2LZKbWFiQq8TgSxygbl83yMKHHJC2Wq3mka23meymTwJo3M7ZD2i/XYB9ZpDrWLXrW+oDrZSJdjmnErxZSBo+zbFu3bo07d+6Mo6Oj8TkbL2uVI1+tyf0dnYo0D2b5i57ZVl+j3lhu1jfbApPRWhuxLg5JwyGPsD14nhdqODwNe4tL/yDpnAqPg23kfkSSWlvs4L7VFlpSIXxQRiTXeXGAiXi2NZQDxygT8qgrmeetzYh58QsX7zCvw4YT8gWo00PDdMRv9eTJk6f9MflLfv1Ql/laHryncxF9zZo1PXt0a9dPp5d4L2XXaQ+dnKzTQRRrZHzd0nR62WNiEth6tp6YmIiTk5PJZ+9ex0vKq7/fdmLJmguBhdd1017Rd2l78L45OTk56Nu5owvUZa6OMXYQS+iJGoLu4czHJWnkueZlj6Q2k7qa97dcJ8eZ/OT2WAS4RhwjEcle99hG9HyW35LHIrzRgxnvtZgPw/dMTU11EKy4mCDt4sUPOSfgxQDUjRZnXCsP+wX7BstlIh3bjuFLLHITdYHHPv/5z8dGoxHPeuHmuHbLrXHF+stjCCGeMLI+NhqN+Ed/9Ect2dF+rU1ttTApGlmuLQ5wm1AveB3asubRjnnRBjmckiTLqx0XbfF/JspRBiS2RVaxTS2kEV7HXv1YnvV1CLdVsy22BfaWR32iXvA8ysHe7jHGDnmkPilbxgDuQYP52cZZPs0GHJ1wQr4AdXpoiDH2FOvSkydPnjztTf7wUD/UZb7GlwIrlcaBFzJrpshKTL2Q4BLTnFMvJKPmvY7ewFbZ+NzUC4Ha7Qa0mKT+0me5RqPRd2cM3PC1n3mr9qN4ilsLV6V9hWOjqnc42vj4+HjS4760nJI25OKzl/R5o9GY9r2lNH324yuS0jQxMTHo27mjC9Rlro6xfRPSlAcxE/UaeYf/I9E7OTmpkpwW+Y5kN4e9Ydm1v+zBH2PsqJfjeDNBq3mJMwGs5eFyJT+G0RA9MqGK+dk7mPsFr8P/mbgUudlLm/OkYs9j+ZqdIKmKfYbkKL5zsD1hGdu2bYv33HNPa+5Yfdl1MYS9m7M/96obY6PRiF/60pda9TD5rXlOow40m5LjOB64jeiljeSuRsgjoY4yWXsWaLrGJDaDi11o69z/6A3PdomLEzie2cbZyz+Efc8quPmxtoCA9Wu6xP/lvNYvTMhj/2jvGdqXIhMTE222xzaD+0BYoaO08aB9/ePv1TqckC9AnR4acjcsTyHOX3TUwGXw5KktHTB/8DJ46kj+4FA/1GW+TpFkSBrXNWZ5KlUh3JFItRYcugm/MZtSVTK95KuKftkNxpI/aMkxHeeRjLc2eX3pS1867XZSJc0kWdxtqtJ/mLeKNz3/Puyww9TrcHyh7U23Hq0FOy355/D1Q13maiaLMTHxpYWzQCJOI+Y4PyaO1c3e3xrhyASo1gatPpyHLAJbyuI2IKFqtZHrZa91TY+4UMB6yN0PuM+0OPBIFHJ4G9Sl1MvXy3Hud42LwXxM8qIdpdonZYhOtDxrLtwUG41G/MM//MO2PEgUa+GSUA721kdZNdlw8QLtUiOyNc/x1FcNlg1p44HtV9rC5bAdYj9r8eFZT0JQo+1s27at41kOF5ZEVu15T9uA2RqnTVik4sU/7EM8z+NBW0iQ9vMCGC8yyvXSDi0/yssLEw4dTsgXoC4PDTGmX/I9efLkyVN58oeH+qEu87X2oiEJieduPdDrTlKzHmZTWXMhYfiafsaBl8/mq6ZSclcjnKcjVFGKEE8tFEz3F6qzfYGuHyG4qt67xOPPUS/UZa6OMbYR2Ux+CzRCi0lY9tplD1kkGJFIswh89ixOedFzSA6NPEO55S8+q1gkqUYMstwcp5sJPiaCmdzG8xL2h+NhWwQt5tXuH1wunpd6NZIdZUJZRcfaIoCmr5QzpbaI0mw24y233NJ2r1y58dXmPVSu5b1JeMGB5cDFHewblo2PW4sn3IeaHfGCE5LhmjxWWShfbpED3/WwbHy20Dzqp6am1IW6Kl9KWl/N4LhGPXDcf20fAG2xh+0wFXt+cnKyow0oJ9fN90St/b5onoYT8gWo00PD/rapqyc9HXjoEQOXwZOnuid/gKgf6jJfl3zNNhOevIMkqpHYnIlnlyoet1rqB/E76IUB9Hi3kuYJf/gJZ/RVDtRlt+F2ZkqXSLKX1Dk5OdmTPefGfbdfDixdunRG9DVdKaV7XzyvH+oyVwuZJXsVMBmIJCGTZkgmWiEpLJIbr7GeFyzPYE0GiSGNxKpG+GvhOthzWQtbgd7I6K3L16IOOASOpT8sn3WgeUDLtRbZzXMOLgBo+uD/NS9vjHOv1cH9oi2KcLlMwktb9+zZE1/wghfE8y9+UStcTQghnrz55rhy46vjuRsuVPXMMki5qD9cdOL6MXFoGF5g0WzfsmchiHlcCDjMEJctutTIemmHXGvFdsfyNfJZ6tSeA3HBB9shYais8TsxMdE6ry3eWCGZ2F7keYP3VuCFPet6vh+w1zteoz3baF+JWLbm0OGEfAHq8tAQYz02da37S4EnT57mflqzZs2gb+eOLlCX+Tr3ybWW5oLXez8SE5bTsXAxHd75A/Nunr9g2utYctqGSvlL90foNaV03u9nUYn5PhPjdLZsYDw2NtaXfRN6Tf6iXz/UZa6OsX3DzRBsUg/PCQmoEcZMkuXGEHrBMqmJBJjkQ0Lb8liVhLyBFbaG22Z5UmP7NM9c1AkeQ49oJKPxes3rXerQvHm5z9CDmNuqhSfRvOotcpNjbLN9SF1anbnwRVrc+o9//OMxhBDXbnhhPPumO+LJm2+OIez7Qm3lfzizTWZsk+bJzmSzFtIlxvbNhVG33Ne8oICLD3KvRjk0vWj6zjmySNmYj9ukhSbCL0m0ccdJysSwOSyHtqiEX3RoZePigvV1DC6AsO2jzXP5SM5z2VoYILQPXITR9CFlaza9devW1rhy2HBCvgB1emjwkDWePHny1J/kL/n1Q53ma3wJmS4SL0V84rl+ksX8WfRsSSXhRDQiPre5Jqfh4eGuSUr/yrE9zVZb6mYRqGSM9XofEO/5mdZbyQJB1XFUkoSgdNQPdZqrhahLhb7AOOeaZzcTi5KHSXCNYMfy5X+tXCTSsA725Ob/NfIZyTnNsxnJN2wz/tY8xrUvCqRsJkSxXiT8xsfHO8hajUzm9mrnrXza/Y2Jd/w6QOQRe8H2YX+kFnHQXrR7qvbMuPqy69q85DEcHdoEli1tidF2DtHswfoiA/sO+1kLcyP361TIGklsD3I9f1GBdWrjihdacAyhbjS7lLxSL3uVc5iZFOGN12E9FsGvLUpJndqCHtcj5XLYJ02v3Kdoy3gPlCTPGLmY+Fy2Q4cT8gWo00ODE/Ke+p764l13wODb4clTxeSEfP1Ql/maH/hnw0aTvWyiOdtjW093mimPb0/7UhXyWluM6fciWK/l5Uj0bsfnbPuyph+b9XIZHl6ufqjLXB1jNMkvjWRFco7jk7Mda+/sHCJD7DsVAxtl43xMmOWISa1+7TorFI3WLiRR2fsW43xrixGptluJCXzWgxWqQ67FxQTRT0ovWtgZJqstj3yxGVyowPNMhG/dujW+9rWvjY1GI/7CC1/XJguT8dhuJKXlmBC27MGPCzTcd/ysasURZ7Lb2k9AxgvWw2XK3Ch/rdA3uGCj9TV/QaKFZtHCNOXCD3Fd2hc02vhPefzzlxyaDNrY4L7FMZdrN/Yz9iEvfqUWr6Ts8fHxtnslfi3h6IQT8gWo00ODe1d58uTJU3+Sv+TXD3WZr1MeKjOVqmxAOcjY570uVvRD9ukKEdIvvS5cuHBg/eNpX+oH2TyX5Zmu5C/59UMd52oOQaIlJsSQiLK80LFsrgtDukhiQjmEffMkkm4lRDZ7snOd2qaPmqc8j0cOm4P1aSGALHLS8v6VYxZRWBoGSPRUGp4mFY+ddaLpVmsLLyCI/lDfcu5P//RP44UXPi8+53mXtjzjMWSc/H/KWeuTzxfWM422iJGzI+5TuR/z4gbXkyN6MfRKai5L1ZFbLLL6UvOU56TpxQo5xN77WrvRJnN1S5mpvsExyIsEHIoIvw6wCHzRM9qqFjoLuUgOH+XQ4YR8Aery0BBjzE5Anjx58uSpLMmnt476oC7zdVWPr1yaKQ/1+fPnt/1GUn86QlLMxtRPj+NeiX52wpg3b97A9VM1VVkY8jQzqU5j2efpeqIuc3WMUSXNLXKe53YmrCYnJ9u8rdnDHecE652evdMlH88H2vVIcjIxKIk9p+U6zWNW8xjXSDpNRxyrXNOn5EOykL3Pc18FoM6tNDU11RE+qJTwnJqaaiOMc7HHJQ+GvcG25shYeQZZu+XWVtz41ZddF1dufHXyOiR6LfvVvqhAz28M34Mycz9abdAWPzTPbY3g5a/IJiYm2r4qsL7qsJJlE2Lr8jcV7x2/fpBFFGsfBK1NVviZkqR54vMzZWqBQP5ai0wlUTe0vmNdyv++cG7DCfkC1OmhwUPWePLkyVN/kj881A91ma9LCfkqxNggvdjrnvq1wWe/ypG+FK/HqjHB5x1+ZHd1Dx3Ws+wHHDw7vfXrRDJXTbNlk9dBJfe8qx/qMlfH2PlFGxPVQqoJQYd5kcy04pljSIfUe7yQePz8IKSg1G2RY0z+ybVIwKWIayTzmAjXypa8SNqJjjhOdoqUxHMamd+tgwOHhrHuLdYigdgGf8UnxLUVu99K4+PjHeS2Zg9CyMtmruIpL+S81Vb0gtZkStmeJv/k5KSpO2tOwmeZkvpyCwb4W+rUFpRYNiShx8fHzbjtOV7NGmu8ibClG7F9HIMcUgZD+1he+du2bWtbkJP/tVBJWjl8T0vZn5SFYwPbhPrk+h06nJAvQJ0eGpyQ9+TJk6f+JCfk64e6zNezIWSNp/qkfsSoP3jFidk8Bx56xIy0Z2hoqOcy+rX44Wl2pF7C6/iLfv1Ql7laoHnfcnxq9P5FopJJu5KNENlbm39LuSiH5n3cLaGIHrzs2Wt5QWP8aJQP8wghi/VqXrr4W/OcRvLRIhdRbrm/oLc8f1GglW3llXo1/Vnlsv5SfSDEMMcSP/bYY+NZY5tjCHsJeUnyW0LWLDvr4lZZv/mbv9n2zCme/zFGVbcYbonDleD5XLLCDbJ+5EsNLVRSalykFhRQRiSrRSatj7BOtuWxsTH1S5RSL3Im4bUvFDQbww1ZkZRnm08t/miLG0imp76KQB3k+h31zG3Geh2dcEK+AHV6aHBC3pMnT576k/wlv36oy3ztc/XsTP0gvp0o9lSXNNtizHcTDmp0dHTQt3NHF6jLXB1jbBFgWnzlVMKwEDlSvuozgUZ2yTEM8VHFe1yLYx/jvucVzRPcKiu1GWWuDCR/rc1Ypb1WSBEpB8+NjIzEGNsdIngD0JJ+4LAfSGAzqYob/Mqx1H5/Wlv42JLhzvu25h2/7MwLW1/b/cVf/EWH7JpjiOYpHcI+ByVtLwRNd7jwImOn5Es/3p8A+1ALRVRl3PDiSs42pV9lniypi8cl1sGb0mr64GO8MKDZhBWap3Qz2py+sH7NIz4nh+Tftm3boG7hsx5OyBegTg8NHkPekydPnvqTfCW/fqjLfF2yYVNp6iVUzYIFCwY+zrpN0x03f6bi8tctzV901MBl8LQvTRep3s+9EnKp10Usn6vrh7rM1anQDkJCarHl5f8cIa7FgU6lkvjkmJD8Rc9XjTicmppqIys1YlzCUWj1IZmI7ZZ7lHYNtkcjqkUObIPlPV+iQwmbk8unfWmgEcKaXVj2wrG+JcyQ9mUDfgXRbDbjBRdckJU5FbLm4osv7pAB+4j7hheRrM1vRTcWWZ0Kn1R1EUrKsoj9knj4XJYWxx8Te9pb51KyaPtMpK4tGctcV+qdQiPWrU2k8Xdp/6TC84TQ/oziX57rcEK+AHV5aIgx7vdxJD158uSpX8kfHOqHuszX/STkqyTrE2JPvSWNvKxDzPKZClHjydN0Jifk64e6zNVC3moeqql5XEhDIauQREwRh0hqad61kkre9/maXmKtI0GskccYJ17zvi7dB0XanCMtNX1ZOkgR6SX65OuttljHxVZSG2hy4oWHVFqx/vJ48uab49ott8bVl10XDz/hjBhCiIeuPLWV56STT67U5tziBu6dkCujJKyPpjshcjUiWnuWHR8f78irhTbK2VVqEUG7XuqQ41bfpcaAtiiQe17nOqbry1uWQ9vjoWQzYv/q3IYT8gWoy0NDjNFftveLdMAskMGTp7mf/CW/fqjLfD0oQt7T9KVVq1YNXAZPsyf1Y5PlQXzBMptDLlle+z5X1w91matjTIeYs8g7zZuY39Gt8BSYUqEhUsQih+XgDUMlaURg7qsbllsrVysjFaqFU8nmtqwn3tiymzQxMdGxmCJ9o8XyL+kDtgsh57X+Lv3iadFJ58QQ9sWKZ+94JONzSb4G5Nj50gcakcwx2TU9SB4udzq5KqyL5baeuzFfLuyK5EktWJSGPSp1ouVFMF7oKFns4kWDfoTI6ib5XK3DCfkC1OmhodeJyJMnT5487U3+4FA/1GW+ni5Cvh8x0PfXtHDhwmmv47DDDht4O6ukuRKeZib61lPvSRa1qixmDA8PD/p27ugCdZmrtfAm/K6dI6s0wixFSuZijGuxuoWQLqk7VYcQwjmvWEwoz9TUVOvaXhYlZ+qL/6q8Ccbn70b+XKgfLeFznXi/r1h/eVx92XUxhPYNXLUkG732oncthB8vkMrXEbnY7Lk+qErYp7ztx8bG2jYl5TGCbUCP/5xdVG2jpV8uR+TBhRncu8JKVRewquq0qr1Y5/3Lcx1OyBegLg8NMbqHvCdPnjz1KzkhXz/UZb6eqU1dc0RkHcKqeGpPhxxyyL7f8+u7B4CnzjSTcdvnUvLN4uqHuszVTITl3rNToWk45byhhQQvJYGrkoRWW1L7p+RkZiLPkqkqX1F1jzyrfCQIc/q02prywu4l8canuQUNJNrFY371Zdd1EPTLzro4rlj/y9n6S0nYycnJVn90M2dV2Z+nVKbSLz2qLIh0sy9jVSK7dNFKC2WVkk+Tox+2yk4/1ma9lh6ckNfhhHwB6vLQEKMT8p48efLUj7RmzZpB384dXaAu83VJLFcny6c3SWiO6QwL0o+wJZ4Gm6YzhMtBBx1knuvGLvfXe4a/5NcPdZmrSzYA7UdCQrGE1JupTcdRlqpkfJU0NjYWJycne/aMx+slTE+z2TTJy25kLl34KCVAc/kOX3V6yxt+JlIJl9TPhQirbg55s2nTptbGxNif+Du1QCBllxLtpeGDSvOxXrX5ukSvYn8Sqz7VntIFE8tJaGRkpHhMbtq0Katbn6t1OCFfgLo8NMQ4c153njx58jTXk3vI1w91ma/nWgz5Nq/taU6LFy8eeHs9TX8aRPz0QaQ6hdOZrbJOTk4O+pbuqIi6zNUxxizp1cvCK1/bbSiMELonSZlQ7FeIi4mJCXNjWvS0LfFE55QjjZFE7EWng0ol4e20UDXiKd/PxPvjWP01U194sV11kyYnJ6dtUSGXel0472bRiu8zMv7keGo8aXrq5uuBEJyQt+CEfAHq9NDg3liePHny1J/khHz9UJf5utuHWU+ePOWT5dU+kwtHnPaXBYZBJJ+r64e6zNXa12wle7VYhCGSmznSdSa+ep8uUrLUa7hqmk7Sd3+PMjBTX11YabpsJld+lefxksWqycnJbL5B6zqXuiH9c4skExMTg76dz1o4IV+Aujw0xBg9/qQnT5489Sn5w0P9UJf52gl5T3M6HTC/b2Wxd56n/qbpDMkzE8nn6XqiLnN1NyFrmIybreTbbJWrSqrCezQajYF5RfeSDl5xYs9lzDv8yGye1atXD7ytg0hoQ+zYWiVkU2mquvDTLbc3UxsjS5L7SUpeXzzXcUCMMQZHEjt27AhHHHFEePzxx8OiRYsGLU4Sr33ta8OHPvShQYvhcDgctUez2QyrV68etBiOCqjLfP2DH/wg/MIv/MKgxXA4HI7aw+fq+qEuc3UIIVx44YXhs5/9bDjssMPCk08+mcy7dOnS8Mgjj8yQZNUxNjYW/u7v/m7QYqgYHR0N//RP/zRoMbrGqlWrwg9/+MOurj3q2FXh33/S3bUzgSptW7NmTfje976XzNNoNMJnP/vZfog2Z8Fj9dhjjw0/+clPiq8fGRkJ9957r3l+8eLF4bHHHutBwurwuVrHgYMWwNFf/OM//uOgRXA4HI45gXvuuWfQIjjmKO67775Bi+BwOBxzAj5XO6YLn/vc51rEoZDxo6Ojat6FCxd2RcY3Go3uBayI2UrGhxBmFRl/2GGHFeVbvnx56/9uyfgQwqwm40PIt+2ggw5q/Z8j40MIHWT8yMhIV3L1Auy7mcSaNWuK8vFYLSHjjz322Nb/KTI+hJAl47GsfuETn/hE38ucC3BCfo7h6quvHrQIDofDMSdwzDHHDFoExxzFxz72sUGL4Kgr5i9Inz9g/szI4XD0EQsWZOza4RgAjjvuuI5jFnH8xBNPdFVHqacwkp79xiAI0dmM3JcQgoceemiaJQlhyWkbpr2OXvHMM8+ox8fGxoquz5HH3SBHfM9E32koWbDoFlU86GeyLMGgFkFmO5yQn2N4+OGHBy2Cw+FwzAloL2IORz+wZMmSQYvgqCue3Zk+H5+dGTkcjj5i586MXRtYs2ZNuPLKK/ssjcOxF9bXbENDQzMsiU169gPTQYj2CvTQHR4eHqAkg8Wj3/jcoEUwsXTp0uT5QX6RMZ3Et6M7DGoRZLbDCfk5hqmpqUGL4HA4HHMCP/jBDwYtgmOO4tFHHx20CA6Hw1F7fO973wt33XXXoMVwzFHcf//96vFdu3bNsCT7H9BDd/v27QOUpMYYKgu90y26CdGUI/EdcxfuIa/DCfk5htm8kYzD4XDUCb4nh2O6cNJJJw1aBIcjjXkz7wHqcDgcswnnnnvuoEWoBZxom6XYVRZ6Z7qBX5R0w1UdeKBTlnMBn//85wctwqyEW/ccwxVXXDFoERwOh2NOwPfkcDgc+y12uweow+HYv+EbBpfBQ1E4Uuj1i5I9e/b0SRLHIOHhOnU4IT/H8K1vfWvQIjgcDsecgO8G75guXHrppYMWweFwOOYEfAN2h8PhcDgcdYQT8nMMRx999KBFcDgcjjkBJ00d04V3vetdgxbB4XA45gRuv/32QYvgmKPw2OUOh8PRH/iX5zqckJ9jeOKJJwYtgsPhcMwJ3HfffYMWwTFHMTY2NmgRHA6HY07gOc95zqBFcDgcDofDkcBb3/rWQYswK+GE/BzDGWecMWgRHA6Ho/YYGRkJGzZsGLQYjjmKbdu2DVoEh8PhmBPoZpNAh6MES5cuHbQIDofDMSfw8pe/fNAizEo4IT/H8C//8i+DFsHhcDhqj3vvvTfcddddgxbDMUdx3HHHDVoEh8PhcDgcCXzlK18ZtAgOh8MxJ/B7v/d7gxZhVsIJ+TmGCy+8cNAiOBwOx5yAbxTnmC54OCSHw+HoDzZu3DhoERwOh8PhcCSwfv36QYswK+GE/BzDn/zJnwxaBIfD4ZgTuP/++wctgmOO4kc/+tGgRXA4HI45Ad/U1TFdeOCBBwYtgsPhcMwJfOELXxi0CLMSTsjPMZx00kmDFsHhcDhqj+XLl4crr7xy0GI45ihe9KIXDVoEh8PhmBM44ogjBi2CY47iqaeeGrQIDofDMSdw/PHHD1qEWQkn5OcYnnzyyUGL4HA4HLXHQw895DHkHdMG3+/F4XA4+oPHH3980CI45ig8HJLD4XD0B85T6nBCfo7h1FNPHbQIDofDMSdw7rnnDloEh8PhcDgcCYyNjQ1aBMccxUc+8pFBi+BwOBxzAj/96U8HLcKshBPycwxLliwZtAgOh8MxJ+AbbzqmCwsXLhy0CA6HwzEn8NBDDw1aBMccxdDQ0KBFcDgcjjkB35tNhxPycwz+aZ3D4XD0B5/5zGcGLYJjjuIf//EfBy2Cw+FwzAl4eDnHdGHXrl2DFsHhcDjmBA477LBBizAr4YT8HIMTSHWHD0mHY7bg6quvHrQIjjmKZ555ZtAiOBwOx5yAe8g7pgtPPPHEoEVwOByOOQFf4NTh7N8cw/Dw8KBFcPSEPYMWwOFwOBzTjOc///mDFsHhcDjmBDZv3jxoERwOh8PhcCTgoWB1OCHvcDgcDofDMYP4/ve/P2gRHA6HY07g0UcfHbQIjjmKxx9/fNAiOBwOx5zAihUrBi3CrIQT8nMMH/jABwYtgsPhcMwJ3HPPPYMWwTFH0Ww2By2Cw+FwzAksWbJk0CI45igOOeSQQYvgcDgccwIPPvjgoEWYlXBCfo7hrLPOGrQIDofDMSdw7rnnDloExxzFI488MmgRHA6HY07APeQd04XVq1cPWgSHw+GYEzjwQKeeNbhWHA6Hw+EgjI2N+YuYY9qwZ4/vF+JwOBz9wO7duwctgmOO4nvf+96gRXA4HI45gQULFgxahFkJJ+TnGL71rW8NWoQshoaGBi2CI4cD5g9aAodjoPi7v/u78LnPfW7QYjjmKObNmzdoERwOh2NO4DOf+cygRXDMUSxfvnzQIjgcDsecwKmnnjpoEWYlnJCfY7j00ksHLUIWu3btGrQIcxoHLTmm90Lis72X4XDUGMPDw2HDhg2DFsMxR/H0008PWgSHw+GYE3jiiScGLYJjjuKBBx4YtAgOh8MxJ/Dd73530CLMSjghP8fwP/7H/2j9f+yxxw5QEseg8Myj9w9aBIej9ti+fXu46667Bi2Gw+FwOByOBJ7//OcPWgTHHMU555wzaBEcDodjTuCkk04atAizEk7IzzGcdtpprf9/8pOfDFASh6OGmOfhlBx7MTw8HK688spBi+GYozjyyCMHLYLD4XDMCfzv//2/By1C7fGBD3wgrF69Ohx88MFhdHQ0fOlLX0rmv+uuu8J/+A//IRx88MHhjDPOCJ/85CdnSNKZxfbt2wctgsPhcMwJuIe8Difk5xjGxsYGLUIWS5cuHbQI+weKyGW/BbRht4dTcuzF9u3bww9+8INBi7FfoioxUEd46DaHw+HoD44//vhBi1BrfPSjHw033nhjeMc73hH++Z//OZx55pnhBS94QXjooYfU/J///OfDK1/5yvC6170ufPWrXw2XX355uPzyy8O//uu/zrDk048DD/T3JIfD4egHnnzyyUGLMCvhs8wcQx1W8h955JFBi7B/oIhc3jPtYjgcdcV99903aBH2O1QlBuqK3bt3D1oEh8PhmBPwz+B7w/ve976wZcuWMD4+Hk477bRw2223hUMPPTTcfvvtav4/+IM/CC984QvDW9/61jA8PBx+67d+K5x99tlh69atMyz59MP3J3A4HI7+wDfJ1uGE/BzD8PDwoEVwOBwOh6MrVCUG6op58+YNWgSHw+GYE/DP4LvHrl27wle+8pVw0UUXtY4deOCB4aKLLgpf+MIX1Gu+8IUvtOUPIYQXvOAFZv46w7/qdjgcjv7Aw2nrcEJ+jqEOHvJzCb4AEkI4cP6gJXA4pgXHHXfcoEXYr9ANMVBXHHPMMYMWweFwOOYE3Iu5ezz88MNh9+7dYcWKFW3HV6xYER544AH1mgceeKBS/hBC2LlzZ9ixY0dbqgM8xILD4XD0BwcccMCgRZiVcEJ+jmHjxo2t/ycnJ8PExETbeYwxv2nTpr7UOTo62nFsYmJCjWffaDRUeUZGRjryrlmzpmuZtLo3bdrUoY9SjI+Pq+VbCyBaPY1Go6OcEqxZsyasOPro1u/Fixe3/U2B8y5cuLBy/RZaXiN7nm3/bcggnykddthhYdWqVT3Xv3DhwlZ7li5d2pO9CDSdYh2Iww47rPX/smXLWv/jIg17EAmssacdL90XYtOmTdP2ufDk5GQIYZ9da+O1HxgbG2uNEavdeBz/Fxkt8NizFtOkD7Zt2xZWr16dldnRP3RDDNT1Jf/666/vODboWLXz5/viqsMxGzHoe8Nsx6//+q8PWgRHBu95z3vCEUcc0Up1ifvv4ZAcDoejP1i7du2gRZiV8LevOYYNGzaEqamp1v8hhHDFFVe0PD1Xr14dPve5z4X7778/XHnlla1NC++5555wzDHHhOOOO67tf8Q999wTzj333PAHf/AH4Yorrggh7PUglTJDCOH+++8P5557bovIuuuuu9rKWr16dfjABz4QzjzzzJaMn/vc51p/sU4pV47dd9994bjjjgv33Xdf+NrXvtYqQ47xtR/4wAfCpZde2pJbZBLZ77///nDMMce0yvra174WLr300nDfffe12oHlvfa1r21r81133dWWB7F69epwww03tGJQi75DCOGWW25py/uJT3wiPPLII2Hp0qXh0ksvbR2Xa6UfP/OZz4QdO3aEk08+Ofzbv/1bWLlyZfjxj38cHnzwwfDwww+HEPYRw0JorVy5MvzzP/9zOPvss9v+Sp6vf/3rYe3ateHP//zPW+Txww8/3EYwS1kPPvhg+Pa3vx2OPPLIsHbt2lb9U1NT4fzzz2/9DiGEqampcMopp4SHH344XHLJJeHHP/5xWLlyZbjzzjvD5s2bQwihJQeDybgQQvj6178eli1b1mpnCCFccsklIYQQPv3pT7f+F3kee+yxcOKJJ4af/vSn4fzzzw8PPvhg67pvf/vb4fzzzw9TU1OttghWrlwZbrvttvDiF7+41d7Nmze36e5LX/pSWLduXXje854XPvvZz4YnnngivOlNb2qNJbSN1atXhx/84Actmw1h78KEjD2xTRlzMg6+9rWvtRYwrrzyytY4+trXvhYeeeSRcPXVV7fsVCD2JTaEYwLHzv333x8eeuihsHz58vDQQw+12TxCbPu+++4LGzZsCFdffXVYvXp1OO+888KVV17ZNr4eeuihcOaZZ4bPfOYzYePGjR31YjulXLzPfOITnwiXXnppa4y+9rWvbekihH33FZFF9LFhw4bwrne9qyUb60Ubw3wvDCG06ebKK69s3ZMcsx/vec97sosxsxFCyMvcI8fuuuuusH379rB06dJw5plntmz/E5/4ROueoM3DH/nIR1qLTMccc0xrfrv99ttb96GxsbHw0EMPhUceeST8y7/8S3j5y1/eGstyX/rc5z4XPvOZz4Qf/OAH4ZZbbgn33HNPq86PfOQj4eqrr261AWV66KGHwle+8pUwNjYWzj333PCud70r3H777W33vzPPPLM1/j7xiU+EEPbeD7dv3x6Gh4db1732ta9t3es2btwYPvaxj7XmU7nH3H///WH79u3h0UcfDf/1v/7X1rOC3FdCCG3XiT5CCOHYY48NX/jCF8LnPve5cPzxx4c1a9aEpUuXhrvvvjsceeSR4ZJLLgl33nlna5547LHHQgghvPjFL249X4UQwimnnBJC2Dtn/vSnP23NbXfeeWf44Q9/GFatWhUee+yxsHjx4nDkkUd2zGE//elPW2XI3Ir3np/+9KfhyCOPDCHsne9uu+22VllS35133tm67vLLL2/NuXKcy7n33ntbC6rLli0LK1asCB/84AfDu971rvDpT386rF27tjVfylwpc/f5558fQtg3H3/84x8Pv/qrvxr+5m/+Jpx44okhhBC+//3vhxNPPLFVzpe+9KVw4okntur96U9/Gh577LFw7bXXhhBCa2697bbbwrp168KXvvSlsHjx4vCNb3wjbNiwIVxyySXh+uuvDzfffHP4+te/Hu69996watWqcMopp7SeYVCXDz/8cJsOsT0C0TU+54QQWnIsXrw4nH/++eHP//zPw9ve9rbwu7/7u+Ftb3tb61njrW99a3juc58bXve614V77703fOc73wk//OEPWzJ9/vOfDyeddFJ45JFHwvDwcGs8hxBaxz7/+c+HJUuWhFtuuSXcddddbWNseHg4HHPMMeFjH/tYWLJkSRgeHm6NTZnbZBwvXbo0fOUrX2kbq/is8IMf/KD17CpjR4BlLV++vHVdCKE1twvkHiS4/vrrW/cKue9IOyV8jBCZl156aes5d3h4uK0+kWvp0qVh+fLlrecEeabA89Ke1atXt+4LS5YsCUuXLlUXOB1lWLZsWZg3b17HOHnwwQfD0eAMhDj66KMr5Q8hhLe//e3hxhtvbP3esWNHLUj5L37xi+G5z31uePjhh8OPfvSjEEIICxYsCCGEsGfPnvD000+H1atXh/vuuy88++yzYenSpWHp0qXhhz/8YVi6dGn493//97Bw4cLw+OOPh+OOOy5873vfC4ccckjr2CGHHBJ2794dnn766bB79+4QYwxHHHFEmDdvXliwYEHYuXNneOKJJ8KCBQvC008/HU466aTw3e9+Nxx11FFtX4bMmzcvPPPMM+Hoo49uhYU49NBDQwgh/OxnPwsHHnhgOPDAA8PRRx8dHnvssfCzn/0szJ8/Pxx22GHh8ccfD4cffngIYe89avHixeGZZ54JBx98cFiwYEF4+OGHw+LFi8OTTz4ZjjjiiPD444+HJ598MixcuDA89dRTYcmSJeHhhx8OQ0NDYdeuXeH0008PDz/8cHjqqadadR9wwAFhz5494YADDgjLli0LO3fuDCGEcNRRR4UdO3aEnTt3hmeeeSbs2rWrpYcjjzwy7Nq1K+zZsycceOCB4ec//3mIMYYTTzwx3HfffWHBggVh8eLF4Yc//GEIYa+z0He+852wZ8+ecPjhh4eVK1eGr3/962H+/Pnh6aefDgsXLgzz5s0LCxcuDA8++GDr+kceeSTs2rUrHH744WHXrr37rx199NHhqaeeCvfdd184+uijw44dO8JTTz0VFi1a1Grnrl27wrPPPhueffbZMH/+/LB79+6wdOnS8NRTT4U9e/aEPXv2tBwdzjrrrNaXnocffng49NBDw2OPPRaeffbZcOCBB4aDDz44PP3002H+/PnhmWeeCQcddFBYtGhR2LFjR9izZ+9+b08//XQYGhoKixYtCrt27QpPPfVU2L17d1i5cmX46U9/2rKXZ599Npxyyinhxz/+cdi9e3cYGhoKBx98cDjooIPC/fffH4aGhkIIe7/+OOyww8LPf/7z8Oyzz4Z58+aFxYsXh4cffjgcffTR4dlnnw1PP/10OPXUU8ODDz4YHnrooZZsQ0ND4Wc/+1k4+OCDw+GHHx7mz5/fet6ZP39+uO+++8LQ0FBYuXJleOKJJ8Lu3bvDvHnzwo4dO8KuXbvCQQcdFBYsWBB27doV5s+fHxYtWtRWxn333Rfmz58fjj766PDjH/84rFixIuzcuTPs3LkzPPXUUy29HnrooeHpp58Oe/bsCQcffHBLj88++2w45JBDwo4dO8KiRYvCzp07w5FHHhkee+yx8PTTT4ejjz46PPnkk+GJJ54ICxcuDD//+c/D7t27w6mnnhq+/vWvhxhjOO6448IDDzzQqmvnzp2t5+mzzz47fOc73wmHHnpoeOqpp8KuXbta/Tdv3rxwwAEHhBhjWLp0aXj00UfDggULwtDQUFi4cGH44Q9/GObPnx8WLlwYHnvssbBw4cKWfhYsWBAeffTRNgeZJ554onXPkH474ogjwoMPPhhOOeWU8N3vfjccdthh4cknnwzz588PCxYsCPPmzQu7du0KQ0NDYffu3WH37t3h5z//eTjggAPCM888E0ZHR8N3v/vd8LOf/Szs3r07LFiwoDXO9uzZE37xF38xfOtb3wpDQ0Ph2GOPDdu3bw8rVqwIP/nJT8JBBx0UQtj7VfO8efPCmjVrWpzVs88+G/bs2ROGhoZa/S36P/XUU8NXv/rVnu/JcxEHxBjjoIWY7dixY0drAlq0aNGgxXE4HA6HY85h165d4dBDDw1/+Zd/GS6//PLW8WuuuSY89thj4a//+q87rpEHdIG85Pt87XA4HA5HHqOjo2HdunXh/e9/fwhhL9G8atWq8KY3vSn8xm/8Rkf+V7ziFeGpp54Kf/M3f9M6dt5554Vf/MVfDLfddltRnf5u7XA4HA6Hh6xxOBwOh8MxCzA0NBSe85znhLvvvrt1bM+ePeHuu+8O69evV69ZsGBBWLRoUVtyOBwOh8NRhhtvvDH89//+38Mdd9wRtm/fHt74xjeGJ598shXq7+qrrw5vf/vbW/lvuOGG8KlPfSr83u/9XvjmN78Z3vnOd4Yvf/nL4U1vetOgmuBwOBwORy3hIWscDofD4XDMCtx4443hmmuuCeecc05Yt25d+P3f//02YsDhcDgcDkf/8IpXvCL8+7//e7jlllvCAw88EEZGRsKnPvWpVgjJH/7wh237GJx33nnhf/7P/xl+8zd/M9x8883h5JNPDh//+MfD6aefPqgmOBwOh8NRS+xXIWs+8IEPhPe+973hgQceCGeeeWZ4//vfH9atW5e9zj+rczgcDodjZrB169bWXD0yMhL+8A//UN08XIPP1w6Hw+FwzG74XO1wOBwOx35EyH/0ox8NV199dbjtttvC6Oho+P3f//1w1113hW9961utTcks+EODw+FwOByzHz5fOxwOh8Mxu+FztcPhcDgc+1EM+fe9731hy5YtYXx8PJx22mnhtttuC4ceemi4/fbbBy2aw+FwOBwOh8PhcDgcDofD4XA49gPsF4T8rl27wle+8pVw0UUXtY4deOCB4aKLLgpf+MIXBiiZw+FwOBwOh8PhcDgcDofD4XA49hfsF5u6Pvzww2H37t2tzWkEK1asCN/85jc78u/cuTPs3Lmz9XvHjh3TLqPD4XA4HA6Hw+FwOBwOh8PhcDjmNvYLD/mqeM973hOOOOKIVjr++OMHLZLD4XA4HA6Hw+FwOBwOh8PhcDhqjv2CkF+2bFmYN29eePDBB9uOP/jgg+Hoo4/uyP/2t789PP744630ox/9aKZEdTgcDofD4XA4HA6Hw+FwOBwOxxzFfkHIDw0Nhec85znh7rvvbh3bs2dPuPvuu8P69es78i9YsCAsWrSoLTkcDofD4XA4HA6Hw+FwOBwOh8PRC/aLGPIhhHDjjTeGa665Jpxzzjlh3bp14fd///fDk08+GcbHxwctmsPhcDgcDofD4XA4HA6Hw+FwOPYD7DeE/Cte8Yrw7//+7+GWW24JDzzwQBgZGQmf+tSnOjZ6dTgcDofD4XA4HA6Hw+FwOBwOh2M6cECMMQ5aiNmOHTt2hCOOOCI8/vjjHr7G4XA4HI5ZCp+vHQ6Hw+GY3fC52uFwOByO/SSGvMPhcDgcDofD4XA4HA6Hw+FwOByDhhPyDofD4XA4HA6Hw+FwOBwOh8PhcMwAnJB3OBwOh8PhcDgcDofD4XA4HA6HYwaw32zq2gskzP6OHTsGLInD4XA4HO1YuHBhOOCAAwYtxqyAz9cOh8PhmI3wuXoffK52OBwOx2zETM/VTsgX4IknngghhHD88ccPWBKHw+FwONrhm6Ltg8/XDofD4ZiN8Ll6H3yudjgcDsdsxEzP1QdEWaJ2mNizZ0/4yU9+4p4NDkcfsGPHjnD88ceHH/3oR/5i4nD0AT437YPP1w5Hf+BztcPRX/i8tA8+Vzsc/YHP1Q5Hf+Ee8rMQBx54YDjuuOMGLYbDMaewaNEif3BwOBx9hc/XDkd/4XO1w+HoN3yudjj6C5+rHY56wjd1dTgcDofD4XA4HA6Hw+FwOBwOh2MG4IS8w+FwOBwOh8PhcDgcDofD4XA4HDMAJ+QdDseMYsGCBeEd73hHWLBgwaBFcTgcDofDocDnaofD4XA4Zjd8rnY46g3f1NXhcDgcDofD4XA4HA6Hw+FwOByOGYB7yDscDofD4XA4HA6Hw+FwOBwOh8MxA3BC3uFwOBwOh8PhcDgcDofD4XA4HI4ZgBPyDofD4XA4HA6Hw+FwOBwOh8PhcMwAnJB3OBwOh8PhcDgcDofD4XA4HA6HYwbghLzD4XA4HA6Hw+FwOBwOh8PhcDgcMwAn5B0Oh8PhcDgcDofD4XA4HA6Hw+GYATgh73A4HA6Hw+FwOBwOh8PhcDgcDscMwAl5h8PhcDgcDofD4XA4HA6Hw+FwOGYA/z/+IAxxTZnW8wAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "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": 10, "id": "1a470fa6-5ff8-41ff-a6d8-b6d63381e556", "metadata": { "tags": [] }, "outputs": [], "source": [ "#adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_dbrmv_9_5.h5ad')" ] }, { "cell_type": "code", "execution_count": 28, "id": "3bedb10e-e575-4184-aee1-98cbdee0cce2", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 312708 × 29789\n", " obs: 'sample_type', 'Donor', 'Sanger_ID', 'batch', 'n_counts', 'donor_lane', 'mad_prd', 'ds', 'mad_MED', 'mad_MAD', 'mad_thr', 'n_genes', 'percent_mito'\n", " var: 'gene_ids', 'feature_types', 'n_cells'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata" ] }, { "cell_type": "code", "execution_count": 28, "id": "6c7ce50e-b9b1-4ef6-af82-924e80bbba8e", "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": 29, "id": "bd642ab8-f77e-4474-a977-ced7235c2b54", "metadata": { "tags": [] }, "outputs": [], "source": [ "adata.write_h5ad('/lustre/scratch126/cellgen/team298/ab72/CTCL/ctcl_cellbender_raw_dbrmv_QCfiltered_17_5.h5ad')" ] } ], "metadata": { "kernelspec": { "display_name": "“multiome”", "language": "python", "name": "multiome" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 5 }