From 6f003427d26db9ff3fb2f412f294c45f4b4e546f Mon Sep 17 00:00:00 2001 From: Giovanni1085 Date: Tue, 7 Apr 2020 15:57:06 +0200 Subject: [PATCH 1/6] Metadata overview and topic modelling notebooks for paper --- .gitignore | 1 + Notebook_CORD-19_1_overview.ipynb | 2323 ++++++++++++++++++++++++ Notebook_CORD-19_2_text_analysis.ipynb | 1712 +++++++++++++++++ 3 files changed, 4036 insertions(+) create mode 100644 Notebook_CORD-19_1_overview.ipynb create mode 100644 Notebook_CORD-19_2_text_analysis.ipynb diff --git a/.gitignore b/.gitignore index 60ddbd8..25cb52d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ credentials/ datasets_input/CORD19* datasets_output/ +figures/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Notebook_CORD-19_1_overview.ipynb b/Notebook_CORD-19_1_overview.ipynb new file mode 100644 index 0000000..1a3d129 --- /dev/null +++ b/Notebook_CORD-19_1_overview.ipynb @@ -0,0 +1,2323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CORD-19 overview" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# magics and warnings\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import os, random, codecs, json\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "seed = 99\n", + "random.seed(seed)\n", + "np.random.seed(seed)\n", + "\n", + "import nltk, sklearn\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style=\"white\")\n", + "sns.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 2.5})" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "# load metadata\n", + "\n", + "df_meta = pd.read_csv(\"datasets_output/df_pub.csv\",compression=\"gzip\")\n", + "df_datasource = pd.read_csv(\"datasets_output/sql_tables/datasource.csv\",sep=\"\\t\",header=None,names=['datasource_metadata_id', 'datasource', 'url'])\n", + "df_pub_datasource = pd.read_csv(\"datasets_output/sql_tables/pub_datasource.csv\",sep=\"\\t\",header=None,names=['pub_id','datasource_metadata_id'])\n", + "df_cord_meta = pd.read_csv(\"datasets_output/sql_tables/cord19_metadata.csv\",sep=\"\\t\",header=None,names=['cord19_metadata_id', 'source', 'license', 'full_text_file', 'ms_academic_id',\n", + " 'who_covidence', 'sha', 'full_text', 'pub_id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pub_idtitleabstractpublication_yearpublication_monthjournalvolumeissuepagesdoipmidpmcidtimestamp
00‘A ticking time bomb’: Scientists worry about ...CAPE TOWN, SOUTH AFRICA—Late on Sunday evening...2020.0NaNScienceNaNNaNNaN0.1126/science.abb7331NaNNaN2020-04-04 07:55:51.892454
11[Ten hot issues of breast cancer under the nov...NaN2020.02.0Chinese medical journal1000e00210.0376/cma.j.issn.0376-2491.2020.000232036640.0NaN2020-04-04 07:55:51.892454
22Another Piece of the Puzzle: Human Metapneumov...BACKGROUND: Each winter respiratory viruses ac...2008.012.0Archives of Internal MedicineNaNNaNNaN10.1001/archinte.168.22.248919064834.0PMC27836242020-04-04 07:55:51.892454
33Viral etiology of severe pneumonia among Kenya...CONTEXT: Pneumonia is the leading cause of chi...2010.05.0JAMANaNNaNNaN10.1001/jama.2010.67520501927.0PMC29687552020-04-04 07:55:51.892454
44Critically Ill Patients With Influenza A(H1N1)...NaN2014.04.0JAMANaNNaNNaN10.1001/jama.2014.211624566924.0PMC66894042020-04-04 07:55:51.892454
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" + ], + "text/plain": [ + " pub_id title \\\n", + "0 0 ‘A ticking time bomb’: Scientists worry about ... \n", + "1 1 [Ten hot issues of breast cancer under the nov... \n", + "2 2 Another Piece of the Puzzle: Human Metapneumov... \n", + "3 3 Viral etiology of severe pneumonia among Kenya... \n", + "4 4 Critically Ill Patients With Influenza A(H1N1)... \n", + "\n", + " abstract publication_year \\\n", + "0 CAPE TOWN, SOUTH AFRICA—Late on Sunday evening... 2020.0 \n", + "1 NaN 2020.0 \n", + "2 BACKGROUND: Each winter respiratory viruses ac... 2008.0 \n", + "3 CONTEXT: Pneumonia is the leading cause of chi... 2010.0 \n", + "4 NaN 2014.0 \n", + "\n", + " publication_month journal volume issue pages \\\n", + "0 NaN Science NaN NaN NaN \n", + "1 2.0 Chinese medical journal 100 0 e002 \n", + "2 12.0 Archives of Internal Medicine NaN NaN NaN \n", + "3 5.0 JAMA NaN NaN NaN \n", + "4 4.0 JAMA NaN NaN NaN \n", + "\n", + " doi pmid pmcid \\\n", + "0 0.1126/science.abb7331 NaN NaN \n", + "1 10.0376/cma.j.issn.0376-2491.2020.0002 32036640.0 NaN \n", + "2 10.1001/archinte.168.22.2489 19064834.0 PMC2783624 \n", + "3 10.1001/jama.2010.675 20501927.0 PMC2968755 \n", + "4 10.1001/jama.2014.2116 24566924.0 PMC6689404 \n", + "\n", + " timestamp \n", + "0 2020-04-04 07:55:51.892454 \n", + "1 2020-04-04 07:55:51.892454 \n", + "2 2020-04-04 07:55:51.892454 \n", + "3 2020-04-04 07:55:51.892454 \n", + "4 2020-04-04 07:55:51.892454 " + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['pub_id', 'title', 'abstract', 'publication_year', 'publication_month',\n", + " 'journal', 'volume', 'issue', 'pages', 'doi', 'pmid', 'pmcid',\n", + " 'timestamp'],\n", + " dtype='object')" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datasource_metadata_iddatasourceurl
00CORD19https://pages.semanticscholar.org/coronavirus-...
11Dimensionshttps://docs.google.com/spreadsheets/d/1-kTZJZ...
22WHOhttps://www.who.int/emergencies/diseases/novel...
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" + ], + "text/plain": [ + " datasource_metadata_id datasource \\\n", + "0 0 CORD19 \n", + "1 1 Dimensions \n", + "2 2 WHO \n", + "\n", + " url \n", + "0 https://pages.semanticscholar.org/coronavirus-... \n", + "1 https://docs.google.com/spreadsheets/d/1-kTZJZ... \n", + "2 https://www.who.int/emergencies/diseases/novel... " + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_datasource" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select just CORD-19" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "df_meta = df_meta.merge(df_pub_datasource, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")\n", + "df_meta = df_meta.merge(df_datasource, how=\"inner\", left_on=\"datasource_metadata_id\", right_on=\"datasource_metadata_id\")\n", + "df_cord19 = df_meta[df_meta.datasource_metadata_id==0]\n", + "df_cord19 = df_cord19.merge(df_cord_meta, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(56809, 16)" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46994, 24)" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pub_idtitleabstractpublication_yearpublication_monthjournalvolumeissuepagesdoi...datasourceurlcord19_metadata_idsourcelicensefull_text_filems_academic_idwho_covidenceshafull_text
00‘A ticking time bomb’: Scientists worry about ...CAPE TOWN, SOUTH AFRICA—Late on Sunday evening...2020.0NaNScienceNaNNaNNaN0.1126/science.abb7331...CORD19https://pages.semanticscholar.org/coronavirus-...0WHOunkNaNNaN#8463NaNNaN
11[Ten hot issues of breast cancer under the nov...NaN2020.02.0Chinese medical journal1000e00210.0376/cma.j.issn.0376-2491.2020.0002...CORD19https://pages.semanticscholar.org/coronavirus-...1WHOunkNaN3.003451e+09#615NaNNaN
22Another Piece of the Puzzle: Human Metapneumov...BACKGROUND: Each winter respiratory viruses ac...2008.012.0Archives of Internal MedicineNaNNaNNaN10.1001/archinte.168.22.2489...CORD19https://pages.semanticscholar.org/coronavirus-...2PMCunkNaNNaNNaNNaNNaN
33Viral etiology of severe pneumonia among Kenya...CONTEXT: Pneumonia is the leading cause of chi...2010.05.0JAMANaNNaNNaN10.1001/jama.2010.675...CORD19https://pages.semanticscholar.org/coronavirus-...3PMCunkNaNNaNNaNNaNNaN
44Critically Ill Patients With Influenza A(H1N1)...NaN2014.04.0JAMANaNNaNNaN10.1001/jama.2014.2116...CORD19https://pages.semanticscholar.org/coronavirus-...4PMCunkNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " pub_id title \\\n", + "0 0 ‘A ticking time bomb’: Scientists worry about ... \n", + "1 1 [Ten hot issues of breast cancer under the nov... \n", + "2 2 Another Piece of the Puzzle: Human Metapneumov... \n", + "3 3 Viral etiology of severe pneumonia among Kenya... \n", + "4 4 Critically Ill Patients With Influenza A(H1N1)... \n", + "\n", + " abstract publication_year \\\n", + "0 CAPE TOWN, SOUTH AFRICA—Late on Sunday evening... 2020.0 \n", + "1 NaN 2020.0 \n", + "2 BACKGROUND: Each winter respiratory viruses ac... 2008.0 \n", + "3 CONTEXT: Pneumonia is the leading cause of chi... 2010.0 \n", + "4 NaN 2014.0 \n", + "\n", + " publication_month journal volume issue pages \\\n", + "0 NaN Science NaN NaN NaN \n", + "1 2.0 Chinese medical journal 100 0 e002 \n", + "2 12.0 Archives of Internal Medicine NaN NaN NaN \n", + "3 5.0 JAMA NaN NaN NaN \n", + "4 4.0 JAMA NaN NaN NaN \n", + "\n", + " doi ... datasource \\\n", + "0 0.1126/science.abb7331 ... CORD19 \n", + "1 10.0376/cma.j.issn.0376-2491.2020.0002 ... CORD19 \n", + "2 10.1001/archinte.168.22.2489 ... CORD19 \n", + "3 10.1001/jama.2010.675 ... CORD19 \n", + "4 10.1001/jama.2014.2116 ... CORD19 \n", + "\n", + " url cord19_metadata_id \\\n", + "0 https://pages.semanticscholar.org/coronavirus-... 0 \n", + "1 https://pages.semanticscholar.org/coronavirus-... 1 \n", + "2 https://pages.semanticscholar.org/coronavirus-... 2 \n", + "3 https://pages.semanticscholar.org/coronavirus-... 3 \n", + "4 https://pages.semanticscholar.org/coronavirus-... 4 \n", + "\n", + " source license full_text_file ms_academic_id who_covidence sha full_text \n", + "0 WHO unk NaN NaN #8463 NaN NaN \n", + "1 WHO unk NaN 3.003451e+09 #615 NaN NaN \n", + "2 PMC unk NaN NaN NaN NaN NaN \n", + "3 PMC unk NaN NaN NaN NaN NaN \n", + "4 PMC unk NaN NaN NaN NaN NaN \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Publication years" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def clean_year(s):\n", + " if pd.isna(s):\n", + " return np.nan\n", + " if not (s>1900):\n", + " return np.nan\n", + " elif s>2020:\n", + " return 2020\n", + " return s\n", + "\n", + "df_cord19[\"publication_year\"] = df_cord19[\"publication_year\"].apply(clean_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 46991.000000\n", + "mean 2010.591581\n", + "std 8.886860\n", + "min 1951.000000\n", + "25% 2007.000000\n", + "50% 2013.000000\n", + "75% 2017.000000\n", + "max 2020.000000\n", + "Name: publication_year, dtype: float64" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.publication_year.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df_cord19.publication_year.tolist(), bins=30, kde=False)\n", + "plt.xlabel(\"Publication year\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/publication_year_all.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df_cord19[(pd.notnull(df_cord19.publication_year)) & (df_cord19.publication_year > 2000)].publication_year.tolist(), bins=20, hist=True, kde=False)\n", + "plt.xlabel(\"Publication year\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/publication_year_2000.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "publication_year publication_month\n", + "2019.0 1.0 253\n", + " 2.0 273\n", + " 3.0 272\n", + " 4.0 284\n", + " 5.0 247\n", + " 6.0 256\n", + " 7.0 232\n", + " 8.0 230\n", + " 9.0 220\n", + " 10.0 259\n", + " 11.0 240\n", + " 12.0 387\n", + "2020.0 1.0 458\n", + " 2.0 978\n", + " 3.0 2107\n", + " 4.0 240\n", + " 5.0 49\n", + " 6.0 31\n", + " 7.0 11\n", + " 8.0 5\n", + " 11.0 1\n", + " 12.0 121\n", + "Name: pub_id, dtype: int64" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# recent uptake\n", + "df_cord19[df_cord19.publication_year>2018].groupby([(df_cord19.publication_year),(df_cord19.publication_month)]).count().pub_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Null values" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46994, 24)" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "df_cord19[\"abstract_length\"] = df_cord19.abstract.str.len()" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(38645, 25)" + ] + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19[df_cord19.abstract_length>0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "38645" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(pd.notnull(df_cord19.abstract))" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "43956" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(pd.notnull(df_cord19.doi))" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28156" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(pd.notnull(df_cord19.pmcid))" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36090" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(pd.notnull(df_cord19.pmid))" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "43454" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(pd.notnull(df_cord19.journal))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Journals" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Journal of Virology 1744\n", + "PLoS One 1557\n", + "Virology 866\n", + "Emerg Infect Dis 741\n", + "medRxiv 674\n", + "The Lancet 608\n", + "Viruses 568\n", + "Arch Virol 500\n", + "Virus Research 496\n", + "Vaccine 484\n", + "Sci Rep 480\n", + "Veterinary Microbiology 443\n", + "Journal of Virological Methods 393\n", + "Journal of Clinical Virology 379\n", + "The Lancet Infectious Diseases 366\n", + "PLoS Pathog 357\n", + "Virol J 356\n", + "Antiviral Research 349\n", + "Proceedings of the National Academy of Sciences 336\n", + "Journal of Clinical Microbiology 335\n", + "International Journal of Infectious Diseases 259\n", + "BMC Infect Dis 245\n", + "American Journal of Infection Control 233\n", + "Front Immunol 205\n", + "bioRxiv 201\n", + "Front Microbiol 195\n", + "Veterinary Immunology and Immunopathology 187\n", + "Nucleic Acids Res 185\n", + "Nature 182\n", + "Journal of Hospital Infection 177\n", + "Name: journal, dtype: int64" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.journal.value_counts()[:30]" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_sub = df_cord19[df_cord19.journal.isin(df_cord19.journal.value_counts()[:20].index.tolist())]\n", + "b = sns.countplot(y=\"journal\", data=df_sub, order=df_sub['journal'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"Journal\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/journals.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sources and licenses" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# source\n", + "df_sub = df_cord19[df_cord19.source.isin(df_cord19.source.value_counts()[:30].index.tolist())]\n", + "b = sns.countplot(y=\"source\", data=df_sub, order=df_sub['source'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"Source\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/sources.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# license\n", + "df_sub = df_cord19[df_cord19.license.isin(df_cord19.license.value_counts()[:30].index.tolist())]\n", + "b = sns.countplot(y=\"license\", data=df_sub, order=df_sub['license'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"License\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/licenses.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Full text availability" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [], + "source": [ + "df_cord19[\"has_full_text\"] = pd.notnull(df_cord19.full_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# full text x source\n", + "df_plot = df_cord19.groupby(['has_full_text', 'source']).size().reset_index().pivot(columns='has_full_text', index='source', values=0)\n", + "df_plot.plot(kind='bar', stacked=True)\n", + "plt.xlabel(\"Source\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/source_ft.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# full text x journal\n", + "df_sub = df_cord19[df_cord19.journal.isin(df_cord19.journal.value_counts()[:20].index.tolist())]\n", + "df_plot = df_sub.groupby(['has_full_text', 'journal']).size().reset_index().pivot(columns='has_full_text', index='journal', values=0)\n", + "df_plot.plot(kind='bar', stacked=True)\n", + "plt.xlabel(\"Source\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/journal_ft.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# full text x year\n", + "df_sub = df_cord19[(pd.notnull(df_cord19.publication_year)) & (df_cord19.publication_year > 2000)]\n", + "df_plot = df_sub.groupby(['has_full_text', 'publication_year']).size().reset_index().pivot(columns='has_full_text', index='publication_year', values=0)\n", + "df_plot.plot(kind='bar', stacked=True)\n", + "plt.xticks(np.arange(20), [int(x) for x in df_plot.index.values], rotation=45)\n", + "plt.xlabel(\"Publication year\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/year_ft.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# full text file\n", + "\n", + "df_sub = df_cord19[df_cord19.full_text_file.isin(df_cord19.full_text_file.value_counts()[:20].index.tolist())]\n", + "b = sns.countplot(y=\"full_text_file\", data=df_sub, order=df_sub['full_text_file'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"Fulltext file\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/license_ft.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [], + "source": [ + "# load Dimensions data (you will need to download it on your own!)\n", + "\n", + "directory_name = \"datasets_output/json_dimensions_cwts\"\n", + "\n", + "all_dimensions = list()\n", + "for root, dirs, files in os.walk(directory_name):\n", + " for file in files:\n", + " if \".json\" in file:\n", + " all_data = codecs.open(os.path.join(root,file)).read()\n", + " for record in all_data.split(\"\\n\"):\n", + " if record:\n", + " all_dimensions.append(json.loads(record))" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [], + "source": [ + "df_dimensions = pd.DataFrame.from_dict({\n", + " \"id\":[r[\"id\"] for r in all_dimensions],\n", + " \"publication_type\":[r[\"publication_type\"] for r in all_dimensions],\n", + " \"doi\":[r[\"doi\"] for r in all_dimensions],\n", + " \"pmid\":[r[\"pmid\"] for r in all_dimensions],\n", + " \"issn\":[r[\"journal\"][\"issn\"] for r in all_dimensions],\n", + " \"times_cited\":[r[\"times_cited\"] for r in all_dimensions],\n", + " \"relative_citation_ratio\":[r[\"relative_citation_ratio\"] for r in all_dimensions],\n", + " \"for_top\":[r[\"for\"][0][\"first_level\"][\"name\"] if len(r[\"for\"])>0 else \"\" for r in all_dimensions],\n", + " \"for_bottom\":[r[\"for\"][0][\"second_level\"][\"name\"] if len(r[\"for\"])>0 else \"\" for r in all_dimensions],\n", + " \"open_access_versions\":[r[\"open_access_versions\"] for r in all_dimensions]\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idpublication_typedoipmidissntimes_citedrelative_citation_ratiofor_topfor_bottomopen_access_versions
0pub.1016570004article10.1290/0408056.1157800071071-2690350.83Biological SciencesBiochemistry and Cell Biology[]
1pub.1010334513article10.1289/ehp.7117155314411542-4359481.12Medical and Health SciencesPublic Health and Health Services[]
2pub.1065069390article10.1360/04we0073322147152095-92733NaNMedical and Health SciencesPublic Health and Health Services[]
3pub.1065067866article10.1360/03wb0198322147142095-92733NaNMathematical SciencesStatistics[]
4pub.1037923003article10.1331/1544-3191.44.5.594.hulisz154960461544-3191480.93Medical and Health SciencesClinical Sciences[]
\n", + "
" + ], + "text/plain": [ + " id publication_type doi \\\n", + "0 pub.1016570004 article 10.1290/0408056.1 \n", + "1 pub.1010334513 article 10.1289/ehp.7117 \n", + "2 pub.1065069390 article 10.1360/04we0073 \n", + "3 pub.1065067866 article 10.1360/03wb0198 \n", + "4 pub.1037923003 article 10.1331/1544-3191.44.5.594.hulisz \n", + "\n", + " pmid issn times_cited relative_citation_ratio \\\n", + "0 15780007 1071-2690 35 0.83 \n", + "1 15531441 1542-4359 48 1.12 \n", + "2 32214715 2095-9273 3 NaN \n", + "3 32214714 2095-9273 3 NaN \n", + "4 15496046 1544-3191 48 0.93 \n", + "\n", + " for_top for_bottom \\\n", + "0 Biological Sciences Biochemistry and Cell Biology \n", + "1 Medical and Health Sciences Public Health and Health Services \n", + "2 Medical and Health Sciences Public Health and Health Services \n", + "3 Mathematical Sciences Statistics \n", + "4 Medical and Health Sciences Clinical Sciences \n", + "\n", + " open_access_versions \n", + "0 [] \n", + "1 [] \n", + "2 [] \n", + "3 [] \n", + "4 [] " + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dimensions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [], + "source": [ + "df_dimensions.pmid = df_dimensions.pmid.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50026, 10)" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dimensions.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_doi = df_cord19[pd.notnull(df_cord19.doi)].merge(df_dimensions[pd.notnull(df_dimensions.doi)], how=\"inner\", left_on=\"doi\", right_on=\"doi\")" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(43870, 35)" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_joined_doi.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined_pmid = df_cord19[pd.isnull(df_cord19.doi) & pd.notnull(df_cord19.pmid)].merge(df_dimensions[pd.isnull(df_dimensions.doi) & pd.notnull(df_dimensions.pmid)], how=\"inner\", left_on=\"pmid\", right_on=\"pmid\")" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(960, 35)" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_joined_pmid.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "df_joined = pd.concat([df_joined_doi,df_joined_pmid])" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [], + "source": [ + "# nearly all publications from CORD-19 are in Dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(44830, 38)" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_joined.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46994, 26)" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# publication type\n", + "\n", + "df_sub = df_joined[df_joined.publication_type.isin(df_joined.publication_type.value_counts()[:10].index.tolist())]\n", + "b = sns.countplot(y=\"publication_type\", data=df_sub, order=df_sub['publication_type'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"Publication type\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/dim_pub_type.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Citation counts" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter of citations vs time of publication\n", + "\n", + "sns.scatterplot(df_joined.publication_year.to_list(),df_joined.times_cited.to_list())\n", + "plt.xlabel(\"Year\", fontsize=15)\n", + "plt.ylabel(\"Count\", fontsize=15)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/dim_citations_year.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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titletimes_citedrelative_citation_ratiojournalpublication_yeardoi
19136Epidemiology and causes of preterm birth3552118.21The Lancet2008.010.1016/s0140-6736(08)60074-4
22994Global trends in emerging infectious diseases280969.92Nature2008.010.1038/nature06536
25210Guidelines for the use and interpretation of a...2656207.85Autophagy2016.010.1080/15548627.2015.1100356
18737Biology of Natural Killer Cells235947.86Advances in Immunology1989.010.1016/s0065-2776(08)60664-1
23273Knocking down barriers: advances in siRNA deli...190547.47Nat Rev Drug Discov2009.010.1038/nrd2742
11316Antimicrobial activity of flavonoids170234.92International Journal of Antimicrobial Agents2005.010.1016/j.ijantimicag.2005.09.002
18923Coronavirus as a possible cause of severe acut...154434.22The Lancet2003.010.1016/s0140-6736(03)13077-2
24490Isolation of a novel coronavirus from a man wi...150656.44New England Journal of Medicine2012.010.1056/nejmoa1211721
73592007 Guideline for Isolation Precautions: Prev...148836.50American Journal of Infection Control2007.010.1016/j.ajic.2007.10.007
22638A newly discovered human pneumovirus isolated ...141932.75Nat Med2001.010.1038/89098
14214Antioxidant activity and phenolic compounds of...138224.72Life Sciences2004.010.1016/j.lfs.2003.09.047
3769High serum procalcitonin concentrations in pat...134940.78The Lancet1993.010.1016/0140-6736(93)90277-n
15279The structure and dynamics of multilayer networks1327NaNPhysics Reports2014.010.1016/j.physrep.2014.07.001
26171Reference sequence (RefSeq) database at NCBI: ...131578.64Nucleic Acids Res2016.010.1093/nar/gkv1189
20623A knowledge base for predicting protein locali...126129.56Genomics1992.010.1016/s0888-7543(05)80111-9
23320Interferon-inducible antiviral effectors124929.22Nature Reviews Immunology2008.010.1038/nri2314
4429Molecular profile of reactive astrocytes—Impli...119932.77Neuroscience1993.010.1016/0306-4522(93)90380-x
19885Statistical methods for detecting molecular ad...113720.37Trends in Ecology & Evolution2000.010.1016/s0169-5347(00)01994-7
29051The Influence of a Sense of Time on Human Deve...112219.77Science2006.010.1126/science.1127488
3757Molecular mimicry in T cell-mediated autoimmun...111822.32Cell1995.010.1016/0092-8674(95)90348-8
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" + ], + "text/plain": [ + " title times_cited \\\n", + "19136 Epidemiology and causes of preterm birth 3552 \n", + "22994 Global trends in emerging infectious diseases 2809 \n", + "25210 Guidelines for the use and interpretation of a... 2656 \n", + "18737 Biology of Natural Killer Cells 2359 \n", + "23273 Knocking down barriers: advances in siRNA deli... 1905 \n", + "11316 Antimicrobial activity of flavonoids 1702 \n", + "18923 Coronavirus as a possible cause of severe acut... 1544 \n", + "24490 Isolation of a novel coronavirus from a man wi... 1506 \n", + "7359 2007 Guideline for Isolation Precautions: Prev... 1488 \n", + "22638 A newly discovered human pneumovirus isolated ... 1419 \n", + "14214 Antioxidant activity and phenolic compounds of... 1382 \n", + "3769 High serum procalcitonin concentrations in pat... 1349 \n", + "15279 The structure and dynamics of multilayer networks 1327 \n", + "26171 Reference sequence (RefSeq) database at NCBI: ... 1315 \n", + "20623 A knowledge base for predicting protein locali... 1261 \n", + "23320 Interferon-inducible antiviral effectors 1249 \n", + "4429 Molecular profile of reactive astrocytes—Impli... 1199 \n", + "19885 Statistical methods for detecting molecular ad... 1137 \n", + "29051 The Influence of a Sense of Time on Human Deve... 1122 \n", + "3757 Molecular mimicry in T cell-mediated autoimmun... 1118 \n", + "\n", + " relative_citation_ratio journal \\\n", + "19136 118.21 The Lancet \n", + "22994 69.92 Nature \n", + "25210 207.85 Autophagy \n", + "18737 47.86 Advances in Immunology \n", + "23273 47.47 Nat Rev Drug Discov \n", + "11316 34.92 International Journal of Antimicrobial Agents \n", + "18923 34.22 The Lancet \n", + "24490 56.44 New England Journal of Medicine \n", + "7359 36.50 American Journal of Infection Control \n", + "22638 32.75 Nat Med \n", + "14214 24.72 Life Sciences \n", + "3769 40.78 The Lancet \n", + "15279 NaN Physics Reports \n", + "26171 78.64 Nucleic Acids Res \n", + "20623 29.56 Genomics \n", + "23320 29.22 Nature Reviews Immunology \n", + "4429 32.77 Neuroscience \n", + "19885 20.37 Trends in Ecology & Evolution \n", + "29051 19.77 Science \n", + "3757 22.32 Cell \n", + "\n", + " publication_year doi \n", + "19136 2008.0 10.1016/s0140-6736(08)60074-4 \n", + "22994 2008.0 10.1038/nature06536 \n", + "25210 2016.0 10.1080/15548627.2015.1100356 \n", + "18737 1989.0 10.1016/s0065-2776(08)60664-1 \n", + "23273 2009.0 10.1038/nrd2742 \n", + "11316 2005.0 10.1016/j.ijantimicag.2005.09.002 \n", + "18923 2003.0 10.1016/s0140-6736(03)13077-2 \n", + "24490 2012.0 10.1056/nejmoa1211721 \n", + "7359 2007.0 10.1016/j.ajic.2007.10.007 \n", + "22638 2001.0 10.1038/89098 \n", + "14214 2004.0 10.1016/j.lfs.2003.09.047 \n", + "3769 1993.0 10.1016/0140-6736(93)90277-n \n", + "15279 2014.0 10.1016/j.physrep.2014.07.001 \n", + "26171 2016.0 10.1093/nar/gkv1189 \n", + "20623 1992.0 10.1016/s0888-7543(05)80111-9 \n", + "23320 2008.0 10.1038/nri2314 \n", + "4429 1993.0 10.1016/0306-4522(93)90380-x \n", + "19885 2000.0 10.1016/s0169-5347(00)01994-7 \n", + "29051 2006.0 10.1126/science.1127488 \n", + "3757 1995.0 10.1016/0092-8674(95)90348-8 " + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# most cited papers\n", + "\n", + "df_joined[[\"title\",\"times_cited\",\"relative_citation_ratio\",\"journal\",\"publication_year\",\"doi\"]].sort_values(\"times_cited\",ascending=False).head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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titletimes_citedrelative_citation_ratiojournalpublication_yeardoi
19362Clinical features of patients infected with 20...838NaNThe Lancet2020.010.1016/s0140-6736(20)30183-5
19361Clinical features of patients infected with 20...838NaNThe Lancet2020.010.1016/s0140-6736(20)30183-5
24497A Novel Coronavirus from Patients with Pneumon...571NaNNew England Journal of Medicine2020.010.1056/nejmoa2001017
24499Early Transmission Dynamics in Wuhan, China, o...500NaNNew England Journal of Medicine2020.010.1056/nejmoa2001316
19368Epidemiological and clinical characteristics o...455NaNThe Lancet2020.010.1016/s0140-6736(20)30211-7
19369Epidemiological and clinical characteristics o...455NaNThe Lancet2020.010.1016/s0140-6736(20)30211-7
10Clinical Characteristics of 138 Hospitalized P...427NaNJAMA2020.010.1001/jama.2020.1585
19360A familial cluster of pneumonia associated wit...331NaNThe Lancet2020.010.1016/s0140-6736(20)30154-9
19359A familial cluster of pneumonia associated wit...331NaNThe Lancet2020.010.1016/s0140-6736(20)30154-9
23652A pneumonia outbreak associated with a new cor...321NaNNature2020.010.1038/s41586-020-2012-7
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" + ], + "text/plain": [ + " title times_cited \\\n", + "19362 Clinical features of patients infected with 20... 838 \n", + "19361 Clinical features of patients infected with 20... 838 \n", + "24497 A Novel Coronavirus from Patients with Pneumon... 571 \n", + "24499 Early Transmission Dynamics in Wuhan, China, o... 500 \n", + "19368 Epidemiological and clinical characteristics o... 455 \n", + "19369 Epidemiological and clinical characteristics o... 455 \n", + "10 Clinical Characteristics of 138 Hospitalized P... 427 \n", + "19360 A familial cluster of pneumonia associated wit... 331 \n", + "19359 A familial cluster of pneumonia associated wit... 331 \n", + "23652 A pneumonia outbreak associated with a new cor... 321 \n", + "\n", + " relative_citation_ratio journal \\\n", + "19362 NaN The Lancet \n", + "19361 NaN The Lancet \n", + "24497 NaN New England Journal of Medicine \n", + "24499 NaN New England Journal of Medicine \n", + "19368 NaN The Lancet \n", + "19369 NaN The Lancet \n", + "10 NaN JAMA \n", + "19360 NaN The Lancet \n", + "19359 NaN The Lancet \n", + "23652 NaN Nature \n", + "\n", + " publication_year doi \n", + "19362 2020.0 10.1016/s0140-6736(20)30183-5 \n", + "19361 2020.0 10.1016/s0140-6736(20)30183-5 \n", + "24497 2020.0 10.1056/nejmoa2001017 \n", + "24499 2020.0 10.1056/nejmoa2001316 \n", + "19368 2020.0 10.1016/s0140-6736(20)30211-7 \n", + "19369 2020.0 10.1016/s0140-6736(20)30211-7 \n", + "10 2020.0 10.1001/jama.2020.1585 \n", + "19360 2020.0 10.1016/s0140-6736(20)30154-9 \n", + "19359 2020.0 10.1016/s0140-6736(20)30154-9 \n", + "23652 2020.0 10.1038/s41586-020-2012-7 " + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same but in 2020; note that duplicates are due to SI or pre-prints with different PMIDs\n", + "\n", + "df_joined[df_joined.publication_year>2019][[\"title\",\"times_cited\",\"relative_citation_ratio\",\"journal\",\"publication_year\",\"doi\"]].sort_values(\"times_cited\",ascending=False).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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times_cited
journal
Journal of Virology77287
The Lancet39295
Proceedings of the National Academy of Sciences34907
PLoS One34850
Virology33602
Emerg Infect Dis31294
Nature22220
PLoS Pathog19189
Journal of Clinical Microbiology15821
Cell14397
Virus Research11639
Nucleic Acids Res11551
Arch Virol11359
The Lancet Infectious Diseases9952
Veterinary Microbiology9734
Vaccine9403
Clinical Microbiology Reviews9304
New England Journal of Medicine8114
Antiviral Research7949
Nat Rev Microbiol7846
\n", + "
" + ], + "text/plain": [ + " times_cited\n", + "journal \n", + "Journal of Virology 77287\n", + "The Lancet 39295\n", + "Proceedings of the National Academy of Sciences 34907\n", + "PLoS One 34850\n", + "Virology 33602\n", + "Emerg Infect Dis 31294\n", + "Nature 22220\n", + "PLoS Pathog 19189\n", + "Journal of Clinical Microbiology 15821\n", + "Cell 14397\n", + "Virus Research 11639\n", + "Nucleic Acids Res 11551\n", + "Arch Virol 11359\n", + "The Lancet Infectious Diseases 9952\n", + "Veterinary Microbiology 9734\n", + "Vaccine 9403\n", + "Clinical Microbiology Reviews 9304\n", + "New England Journal of Medicine 8114\n", + "Antiviral Research 7949\n", + "Nat Rev Microbiol 7846" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# most cited journals\n", + "\n", + "df_joined[['journal','times_cited']].groupby('journal').sum().sort_values('times_cited',ascending=False).head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Categories" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YGAhAVlYW165dw9LSEqVSSf/+/bG0tKRPnz60aNGixHsTlF1EoBH8K+jUqRN169YlLi5OY2hKrVajUCg03ue9kOhK2zX6+voAPJ8NXaksmnnwxWG4P//8E7VazebNm7l8+TL9+vWjQ4cO5OXlUVJm9by8PLy9vZkyZQpmZmaYmZkxbNgwvvvuO3bu3En79u0BNHz9/fff+fDDDzV8t7Cw0Ah2ycnJ1KxZk6NHj8rBsNCOJEmy/y/aLRwq3LFjB+XLlwfg0aNH6OvrU7FiRfbt28f58+eJjY1l0qRJfPnll7i5uWm9P0HZRQydCf4V3Llzh6SkJJo0aaJx3NLSkq1btyJJEiqVil27dtGpUyeNa7p06cKePXvk4aUtW7bQrl07KlWqROvWrQkJCQEgISGBmJgYjS9kAAsLC8LDw1GpVKjVaubOncuBAwc4deoUQ4YMwcnJierVqxMdHU1+fr7WeyhXrhx37tzhu+++Izc3FygIPrdv36Zp06YYGhrSrFkzQkNDgYIAMnDgQDIyMjR8iYqK4vbt2wCcPHkSBwcHsrOztdarzW52djbm5uZs2rQJgPT0dAYOHEhERAQnTpxg6NChtGrVivHjx+Pk5MSVK1e01iEo24gejaBMUjhHU4harcbb25v69euTkpIiH585cybz58/H3t6e3NxcunTpwujRozVsffbZZyQnJ9O/f3/UajX16tUjICAAgEWLFjFjxgy2bduGsbExH3/8sUavAMDV1ZWkpCRcXFyQJIn27dszePBgPvroIxYvXkxgYCC6urq0bt1aHsLSRmBgIP7+/vTp04fy5cujVqvp1asX48aNAwpWpc2bN48tW7agUCjw9fWVF0EANGzYUO4VFfZWgoKCXjpJr81uQEAAPj4+2Nvbo1KpsLOzw8HBgfz8fH7++Wfs7OyoUKECVapUwcfHp8Q6BGUXhVRSX10gEJRIUFAQvXv3pkGDBmRkZODg4MC6devem/0q7xqJiYn06NGDiIgIPn7LEjTqPBU65d7uXp73hZc9N9GjEQj+Av/5z3+YPHkyOjo65OfnM3LkSBFkyggiyPx9iEAjEPwFbGxssLGxedtulFny81QoxRf+e49YDCAos+Tn57Np0yZcXFxwdHTE1tYWf39/VKoCDStPT082bNjwRuqOi4tjwoQJr13e1NSUR48eFTmekpLCpEmTsLe3x97env79+xdZwlwcjo6OpKenv7Y//zSxO4YTua6vCDJlBNGjEZRZ5s6dS1paGps3b6ZSpUpkZWUxdepUZsyYgb+//xut+03J3MycOZNOnTrJy5Nv3brFwIEDqV+/Pg0aNNBa7mUbQQWCN4kINIIySWJiIvv37+fUqVPyBsQKFSowb948zp8/L1934cIFXF1d+fPPP2nUqJGsJXb79m18fX158uQJ+fn5DB48mM8++4zTp0+zZMkSateuzZ07dyhfvjyjRo1iy5Yt3Llzh969ezN9+nQNmZunT58yf/58zp8/j1KppGfPnkyePJn4+Hi8vb15+vQpqampNG7cmGXLlsn7c4ojNTWV7Oxs1Go1Ojo6NGzYkKCgICpXrgzApUuXmD9/Ps+ePUNXV5dp06ZhYWGhIRC6e/dutm/fjlqtxsjIiFmzZtGgQQM8PT0xNDTk+vXr3L9/H1NTUxYtWkTFihW12tXWTk+fPsXLy4u7d++io6NDs2bN8Pb2RkdHDKL8K5EEgjLIoUOHpH79+pV4jYeHh/TZZ59JWVlZUl5enuTs7Czt3btXys3NlWxtbaUrV65IkiRJ6enpko2NjXThwgUpNjZWatKkiXT16lVJkiTpyy+/lD7//HMpJydHevjwodSsWTPp/v37UmxsrNS3b19JkiTJz89Pmjx5spSXlyfl5ORIbm5uUmxsrLRw4UIpNDRUkiRJUqlUkp2dnXTo0CFJkiTJxMREevjwYRGfo6Ojpc6dO0vt27eXRo8eLa1bt066f/++bKNz587SiRMnJEmSpLi4OMnOzk7Kz8+X7Z0+fVoaNGiQlJWVJUmSJP3yyy+StbW13B6F96JSqSQnJycpODhYq92cnByt7bR3715p+PDhkiRJUl5enjRjxgwpPj7+pc8tISFBMjExkXb795JOrLV96fWCd4PC55aQkFDsedGjEZRJdHR0UKvVL72uZ8+e8q72Ro0a8ejRI+Lj47l37x7Tp0+Xr8vOzubXX3+lQYMGfPzxxzRt2hSAunXrUqlSJfT09KhWrRoVK1YkLS1No47o6Gi8vLxQKpUolUpZsqZdu3ZERUWxbt064uPjSUlJkTeFasPCwoLIyEguXrzIuXPnOHHiBKtWrWLz5s0olUp0dHTo1q0bAGZmZuzfv1+jfGRkJHfv3sXV1VU+lp6ezpMnT4CCzal6egXzIiYmJqSlpXHjxo1i7d66dUtrO3Xp0oWlS5cyePBgOnXqxJAhQ6hXr95Ln4egbCICjaBM0qJFC37//XcyMzPloTMoyBMza9Ysef7keXmYQsmV/Px8KlWqpDGv8eeff1KpUiUuXrwofxEX8qLEzIuUK1dOQy0gOTkZAwMD5s2bR35+PjY2NnTr1o3k5OQSJWgePnzIihUrmDVrFm3btqVt27aMHj2aGTNmEBoayoABA4qoEty4cYNPPvlEfq9Wq3F0dMTd3V1+n5KSQpUqVQCKlaBRKpXF2pUkSWs76evrc/ToUU6fPk1sbCzDhg3D29sbKyurEttKUDYRA6aCMomxsTH29vZMnz5dVi/OzMxk7ty5GBkZFdm9/zz169fXEOVMTk7Gzs7utSVULCws2Lt3L2q1GpVKxYQJEzh79iynTp1i3Lhx2NraAgXzKyVJ0FSpUoXo6Gh++OEHOSA9e/aMe/fu0bRpUz755BMUCgVRUVEAXL16lSFDhmj07CwtLTlw4ICsjrB9+3aGDBlSov/a7JbUTtu2bcPLywtLS0vc3d2xtLTk119/fa32E7z/iB6NoMwyZ84cvvvuO1xdXVEqlahUKnr27Mn48eNLLKenp8d3332Hr68v69evJy8vj4kTJ9KmTRtOnz79yn58/fXX+Pr64ujoSH5+Pra2tvTu3ZvU1FTGjRtHhQoVMDQ0pF27diVK0JQrV44NGzbg7+/Pli1bqFChAgqFAmdnZz777DMAVqxYgZ+fH4sXL0ZXV5cVK1Zo9MAsLS0ZOXIkw4cPR6FQYGhoyMqVK4v0WF5sD212tbVTkyZNOHPmDLa2tpQvX57atWszePDgV247QdlASNAIBIJ3hhelTMSGzfeDl0nQiKEzgUDwziKCTNlABBqBQPBOkp+netsuCP4mRKB5C+Tm5mJpacmIESNeq3xgYKCcG+RVOX36NHZ2dgBcvnyZ2bNnv5YdbVhZWREXF/e32izkq6++knO/FJKQkICZmRkPHjwocr29vT1Hjx4t0ebu3bv58ccfX9mXGTNmEB0d/crl/iohISG4uLjg4OBA3759mTFjhka+meLYvn07a9eu/Yc8/Hv4afcQ0ZspQ4hA8xY4evQojRs35sqVK3ICqldh4sSJODk5/WU/bt26VewX9PtEnTp16NSpU5EAdOHCBTIyMl66nPZ///tfiUm/tOHr61skQdqb5vLly6xatYqNGzcSFhZGWFgYSqWSuXPnllhu4MCBGqmpBYJ/GrHq7C2wfft2bG1tqVu3Lps3b8bb2xuAtWvXEhwcTMWKFWnbti0REREcP34cT09Pnjx5QkJCAt26dePhw4c0atSIL7/8slSSI4D8vpDk5GSWL19ORkYGXl5eODk5yZIpgIaEyooVK7h48SIpKSmYmpri6enJ7NmzefjwIampqXz00UcsW7ZMI0Xyi5w4cYI1a9agUql49OgRTk5OTJo0idOnT7N06VLq1KnDzZs3ycvLY968ebRp04YHDx7g6elJSkoKH374IQ8fPizWtpubG/Pnz2f06NHy6qldu3ZprDYLCAjg7Nmz5Ofn07RpU2bOnElMTAzHjx8nKioKAwMD3NzcCAoK4siRI6jVaj766CPmzJmDsbExgwcPpkqVKvz+++8MHDiQI0eO4ObmhpmZGUOHDqVr165cunSJ9PR03N3d6dWrF3/++afWdrKysqJFixZcv34dBwcHdu7cyfHjx9HR0eHZs2dYWVlx4MAB+flBgfyMJElyYFQqlUycOJGbN28CBdk2/f39iYyMRKlU0qpVK+bMmcOaNWt4/Pgxs2fP5sGDB3h7e5OcnExubi59+/Zl9OjRJCYmar0PbXb19PS0tteRI0cICgpCoVCgVCqZNm0a7dq1e92PjOA9R/Ro/mFu3brFhQsXsLa2xsnJiX379vH48WN++eUXQkJCCA4OJiQkhKdPn2qUy87O5sCBA/JGOygYghs3bhzjxo0jPDwcHx8f/Pz8SrUjvnbt2kyYMIG2bduyYMGCl16flJTE3r17CQgI4MCBA5ibm7Nz504iIiI09lIUhyRJbNy4kYULFxISEsLOnTtZu3atrE58+fJlhg8fTmhoKC4uLixduhQAb29vWrZsyYEDB5g5cyZ37twp1n6XLl2QJIkzZ84AkJGRQUREBP379wcKArhSqSQkJISwsDBq1qxJQEAAvXr1wsrKiqFDh+Lm5kZoaCg3btxg9+7d7Nu3j65duzJz5ky5nsqVK3Pw4MEiy3QTEhKwtLQkODiYb775Bj8/P4CXtlOjRo346aefGDNmDFWqVOGXX36Ry1lYWGgEGYBPP/2UVq1aYWVlhbOzM97e3sTFxdGhQwcAtm3bxtWrV9m3b5+ssXbw4EENG+7u7vTr10/+X4uOjpav0XYf2uyW1F6LFy9mzpw5hISEMHHixNdaFi4oO4gezT/M9u3b6d69O1WrVqVq1ap8/PHH7Nq1i9TUVKytrWVxRDc3N2JjY+Vybdq0KWJLmzTIm8Dc3FzeAT9kyBDOnTvHpk2biI+P5+bNm7Rs2VJrWYVCwerVq4mMjCQ8PJzbt28jSRLPnj0D4MMPP6RJkyYANG3alL179wIF0i0eHh4A1KtXT/5CfREdHR1cXV3Zs2cPHTp0ICwsjK5du8o9rMjISDIyMuQ5ldzc3GJ7XydOnCAuLo5+/foBBbvmC30EaNu2bbH16+rq0rVrV9n/QjmXl7XT8/bc3NzYtWsXXbt2ZefOnUybNq3Yer799lumTZvG6dOnOXv2LB4eHlhYWLBs2TKio6NxdHSUN6MWKjyvWLECgKysLM6ePUtaWhqBgYHysWvXrtGiRQut96HN7sSJE7W2V9++ffn666/p2rUrnTt3ZuTIkcW2neDfgQg0/yBZWVns27cPPT09ee4gMzOTrVu30rdvXw35EaVSqVG2QoUKRexpkwZ5XnIEkPOvlESh3Eghubm5Wuv39/fn8uXL9OvXjw4dOpCXl1eidEpWVhbOzs707NmTtm3b0q9fP44dOyaXKU72pDifSpJ66devH9bW1mRmZrJr1y7mzZsnn1Or1UyfPl3+En369Ck5OTlFbKjVakaMGMGgQYOAgnZ7XresuGcABQGgUJX4+efxsnZ63p69vT1LliwhNjaWrKysYoeZgoODqVq1Kj169MDBwQEHBwfGjBmDlZUVjx49KtI+f/75p0bvVq1WI0kSO3bskPXdHj16hL6+Po8fP9Z6H9rsltRekydPpl+/fkRFRRESEsLGjRsJDg4utv0EZR8xdPYPsn//foyMjPjll184fvw4x48f59ixY2RlZdGsWTOOHDkiryAqzYeyJMmRatWqyau/CuddXkSpVJKXlwdAtWrV+OOPP3j48CGSJHHgwAGt9Z46dYohQ4bg5ORE9erViY6OLlE65e7du2RmZjJp0iSsrKw4ffo0KpXqpUN8Xbp0YefOnQD88ccfJQ6/VK1ale7du7N8+XKUSiXm5ubyOUtLS3788Ue5zlmzZrFkyZIibVA4bFQoWRMYGFhsz6K0vEo7lS9fHgcHB6ZPn64hePk8Ojo6BAQEcP/+ffnYzZs3+fDDD6lSpQoWFhaEh4fL9zl37lyN52hoaIi5uTmbNm0CCsQ0Bw4cSERERIn3oc2utvbKy8vDysqKZ8+eMXDgQObMmcP169dL9YNHUDYRPZp/kO3btzNs2DCN3krlypUZPHgw33//PQMGDODzzz/HwMCARo0ayb86tS8gU8kAACAASURBVFGSNMjMmTPx9vamcuXKdOrUiRo1ahQpb25uzqpVq/j6669ZuXIlrq6u9OvXjxo1atCtWzety5THjRvH4sWLCQwMRFdXl9atW5conWJqakq3bt2wsbFBT08PExMTGjZsyN27d4sIVD7PnDlz8PLywsbGhlq1atG4ceMS22PQoEEMGDAAX19fjeNjx45l0aJFODs7k5+fT5MmTfD09AQK5j0WLlwIwMiRI3nw4IEsTlm7dm353Ovwqu3k4uLCrl27tK4odHFx4dmzZ4wcORKVSoVCoeA///kPGzZsQKlU4urqSlJSEi4uLkiSRPv27Rk8eDBBQUGyjYCAAHx8fLC3t0elUmFnZ4eDgwOJiYla/dJmV0dHp9j2KleuHNOnT2fq1KmyoKifn1+Jz1pQthESNO8IcXFxXLhwgS+++AKATZs2cenSJXk8XFC2kSSJdevWkZSUpDHs92/jeSmT2rVqir007wkvk6ARPZp3hPr167Nu3Tp27dol/zr08fF5224J/iF69OhBzZo1+e677962K+8MIsiUHUSgeUcwNDR8IznmBe8Hx48ff9suvHPk5asopxTBpiwgFgO8YUojyZKfn8+YMWPo06ePnH3xnyIyMlJe6hoREcH8+fP/0fpfRqtWrbTOH5w/f54vv/wSR0dH7O3tGTVqFDdu3PiHPSzK8OHD5T1CI0eO5NatW3+b7XXr1uHo6IiDgwN2dnYsWrTopZPsf0Wy6G2xbe8XIsiUIUSP5h3gwYMHnDp1iosXLxZZ1vymiYuLk5ek9ujRgx49evyj9b8uZ8+exd3dnZUrV2JmZgZAWFgYgwcP5qeffiqy2fGfpHAVIBQEhr+Ln376iWPHjrFz504MDAzIyclhwoQJrFy5kilTpmgtN3HixL/NB4HgdRCB5h+kefPmjBo1iqioKFJSUhgxYgQODg6MGDGCvLw8XFxcWLFiBSkpKSxevFiWlZk0aRKffvqpvJv72bNnGBoa4uzsLMt//PHHHxgbGzNgwAC2bt1KfHw8w4YNY/jw4WRlZTF37lzu3r3LkydPqFixIgEBAWRkZLBjxw45dXG9evU4fPgwa9as4f79+8ydO5ekpCQkScLJyYkRI0aUKFXyIqtXryYiIoLs7GyePXuGh4cHvXr1YsWKFSQlJZGamkpSUhLGxsb4+/tTs2ZNzp07h4+PDwqFgubNm2tdAr18+XLGjh0rBxkABwcH9PX15SXEO3fuZMuWLejo6PDBBx8wa9Ys6tevj6enJwYGBty4cYOHDx9iZWWFkZERJ06cIDU1lfnz52NhYYGnpyf6+vpcu3aNhw8f0rlzZ2bOnImurq5WiR9/f3+gYLPm2rVrcXNzIzAwkKysLK1SO48ePcLLy4t79+5hZGREjRo1aNSoUZEEbampqeTn55OdnY2BgQH6+vrMmjVL7j09ffqU+fPnc/78eZRKJT179mTy5Ml4eXnJkkW3b9/G19eXJ0+ekJ+fz+DBg/nss89KlALSZjc3N7dYaR9DQ0O2bdvGjh070NXVRV9fH29vbxo2bPjXP0SC9xNJ8Ebp3r27dPnyZUmSJMnExETasmWLJEmSFBcXJ5mZmUnZ2dlSQkKCZG5uLkmSJD169EiysLCQLl68KEmSJN24cUNq3769dO/ePWnPnj1Su3btpIyMDEmSJGnPnj1SmzZtpD/++EPKz8+XbG1tpfHjx0v5+fnSb7/9JjVv3lzKz8+XfvrpJ8nHx0f2adasWZK3t7ckSZK0fPlyad68ebK9UaNGSZIkSW5ubtLGjRslSZKk9PR0yd7eXgoPD5cSEhIkExMT6fjx45IkSdKhQ4ekbt26FbnvxMREafDgwdKzZ88kSZKk8PBwyc7OTq6zR48e8n189dVXUmBgoJSTkyN16tRJio6OliRJkvbv3y+ZmJhICQkJReybm5tLN2/e1Nru0dHRUs+ePaWHDx/K92ZjYyOp1WrJw8ND6t+/v6RSqaSUlBTJxMRE+uGHHyRJkqTvv/9eGjZsmCRJkuTh4SE5OTlJmZmZUk5OjuTm5iY/PxMTE9n2i++ff134/GNjY6UmTZpIv/76qyRJkrRhwwbJzc1NkiRJmjx5srR48WJJkiTpwYMHUufOnaXly5cXuaf09HRp2LBhUrNmzaQBAwZICxYskM6cOSOf9/PzkyZPnizl5eXJ/sbGxkoeHh7S+vXrpdzcXMnW1la6cuWKbM/Gxka6cOFCif5ps7tixQpp4cKFklqtliRJkr799ltpzpw5Ul5entSsWTPpwYMHkiRJ0t69e6UdO3ZofVbPU/j/tWh591JdL3g3KHxuxX1WJUmSRI/mH6ZwaKpZs2aoVCqysrI0zl++fJm6devKUiWNGjWidevWnDlzBoVCgampKYaGhvL1zZs3p3bt2gB8/PHHWFpaoqOjQ506dcjJyeHZs2dYW1tTp04dtmzZwt27dzlz5gytWrXS6mNWVhbnz59n48aNAFSqVAkXFxd+/vlnWrZsqVWq5Hk++ugjFi9ezP79+7l79y6XLl3S0G9r3769fB9NmzYlLS2NGzduUK5cOSwsLACws7PTmsZAR0enxA2fv/zyC7a2tnKPw8XFBV9fX3m+p3v37ujq6lKjRg0qVKhAly5dAKhbt67G/Tg7O1OxYkUAHB0diYiI4L///a/WektCm9TOyZMn5dc1a9bE2tq62PKVKlVi48aNJCQkEBsby5kzZxg1ahSDBg3C3d2d6OhovLy8UCqVKJVKeb6v0HZ8fDz37t1j+vTpss3s7Gx+/fVXGjRoUKIUUHF2/f39i5X2USqVWFtb4+rqSrdu3bC0tJT/XwT/TkSg+YfR19cH/l/iQ3phG1N+fn4RWRlJksjLy0NXV7eIDMqLm+CKk2nZtm0bu3btws3NDXt7e4yMjErcoFcoVfLiscId9NqkSp7n6tWrjB07lqFDh9K5c2fatWunsT9Em+zMi/Vqk50xNzfn0qVLmJiYaByfN28evXr1KjYIFbYjlK7dQFMKSJIk+b6fp7Q73rXdc7ly5TTuu7g6oGC+p02bNrRu3Zo6derQv39/zp07x8iRI3F3d5c3RxaSnJysUWfhEOnzwp5//vknlSpV4uLFiyX6V5zdkqR9AgICuHHjBtHR0axdu5Z9+/bJi04E/z60rjp78OBBqf8Efx/m5ub8/vvvXL58GSiQGDl79izt27d/bZunTp3C2dmZ/v37U79+fY4fPy7PYzwvwVKIoaEhLVu2lBOCZWRkEBoa+kr5V86ePYuZmRnDhg2jffv2RERElChTAwXzHJIkcfLkSaBgFdzzWmPPM2bMGFauXMmVK1fkYyEhIRw+fBgTExO6dOnCwYMH5fmLPXv2YGRkRL169Up9D1AwAa9SqcjJyWHv3r10794doESJn+LatCS6du0qSw49fvyYY8eOFRvAs7Oz+fbbbzV6XDdu3KBp06ZAgVTM3r17UavVqFQqJkyYwNmzZ+Vr69evr6EgnZycjJ2dnUYbFoc2u9qkfR49ekTXrl0xMjJi6NChTJo06Y0lwxO8H2jt0XTt2lXrr9VCJElCoVDw22+//e2O/VupVq0agYGB+Pj4kJ2djUKhYMGCBdSvX58LFy68ls3hw4cze/Zs+cvM3NxcXgbcsWNHpk6dio+PD82aNZPLBAQE4O3tTUhICCqVCnt7e1xcXEhKSipVnXZ2dhw5cgQbGxvUajXdu3cnLS1N1sUqDl1dXVatWsXcuXNZsmQJTZo00Zrjpm3btsyfPx9fX1+ysrLIzc2lbt26/PDDD3zwwQd88MEHDB06VEP7bc2aNVp7C9owMDBg0KBBpKen06dPH1mpuCSJH2trawYPHiyrJr8MLy8vZs6cKfc2P/zwQ43eRSFjx45FoVDg6uqKQqFArVZjZmYmq0d8/fXX+Pr64ujoSH5+Pra2tvTu3Vveo6Onp8d3332Hr68v69evJy8vj4kTJ9KmTZsSdeS02f3000+LlfYxNDRkzJgxDB06FAMDA5RK5Tu3bF7wz6JVgqYwt0dp+Cu/tgWCdxVPT095tdab5Mcff6Rp06a0atUKlUrFoEGDGD9+/L9yXuN5KZNatWuKvTTvCa8tQaMteOTl5ZUo1y4QCF6Nhg0b4uPjg1qtJjc3F2tr639lkHkREWTKDqWOGKGhoaxevZrExER++ukn1q9fT82aNRk3btyb9E8geGv8FeXmV6FDhw6EhIT8I3W9T+Tmq9AVwaZMUKoB69DQUPz8/HBycpJX4TRu3Jh169b9rTufBf8+EhMTMTU1LXbJsKenJ6ampvKEvjYuX74sL4M+ffo0dnZ2b8TXl/F3Sc94enqyYcOGYs/9WyRolh34QgSZMkSpAs3GjRuZNWsWo0ePlidTBw4ciI+PD7t27XqjDgrKPvr6+ty5c0djoUHhXp7ScOvWrXdi9eOL0jN/90745yVowsLC2LNnD7///jsrV64ssdzEiRO15rgRCP4JSjV0dvfuXY2MhYWYm5u/Ex9wwfuNUqnExsaG/fv3M3r0aACOHDlCjx495E2jarUaPz8/eeOnJEnMnz+fDz/8kOXLl5ORkYGXlxdOTk5kZWUxefJkfv/9d3Jycpg/fz5t27ZFpVJplUyxsrLCzs6O2NhY0tLSGDFiBOfPn+fq1auUK1eOoKAgjI2NOXHiBGvWrEGlUvHo0SOcnJyYNGkSXl5eQFHpmebNmxMcHMymTZvQ0dGhatWqLFq0CGNj42Lvp02bNlrbSUjQCN5XStWjqV27NteuXStyPCYmRt6VLhD8FZycnDQ2EoaGhuLs7Cy/v3TpEikpKezcuZODBw/i7OzMunXrqF27NhMmTKBt27YsWLAAgPv37zN06FD27duHq6urvMx47dq1KJVKQkJCCAsLo2bNmgQEBMh15OTksGvXLiZOnMjs2bMZMmQIYWFh1K5dm7179yJJEhs3bmThwoWEhISwc+dO1q5dy6NHj+S6N2/erPGZuHbtGgEBAaxfv579+/djZWVFUFCQ1vspCWdnZypXroylpSWff/45CxcuJDk5mRYtWgAF+m85OTkcPHiQ0NBQzp8/r7F6NC8vjwkTJvDNN98QEhLC1q1b2bhxIxcvXgQKhiCHDx9OaGgoLi4uLF26tES72tozPz8fPz8/1q9fz549exgwYAD/+9//Xv2fQlBmKFWPZvjw4cydO5fU1FQkSeLMmTOEhITw/fffl6gaKxCUFjMzM5RKJVeuXKF69eo8ffpUY9d/q1atqFKlCjt27CAhIYHTp0/L0jAvUqdOHVnCp3HjxuzZswcoSIlQnGRKIb1795bLf/DBB3Lq6Lp165KWloZCoWD16tVERkYSHh7O7du3kSSJZ8+eab2vmJgYLC0t5eAzdOhQ+Vxp76cQIUEjeF8pVaAZMGAAeXl5rFmzhuzsbGbMmIGxsTEeHh64urq+aR8F/xIcHBwICwujWrVqODo6apyLjIzE19eXYcOG0aNHDz755BPCwsKKtaOrqyu/fl5KpSTJFNCUpXneRiFZWVk4OzvTs2dP2rZtS79+/Th27FgR2ZznUSqVGhufs7OzSUpKIiEhodT3U4iQoBG8r5Rq6Ozp06cMGjSIkydPEh0dzblz5zh58qQIMoK/FUdHRw4dOsTBgweLrByLioqie/fuDBo0CDMzM44dO1aijE5xaJNMKS13794lMzOTSZMmYWVlxenTp2Vb2vzo0KEDMTExpKSkALBjxw78/f1LvB9tCAkawftKqXo0nTt3pnfv3jg5OWFhYfFSaRqB4HUwNjamQYMGVKpUCSMjI41zrq6ufPPNN9jb25OXl0fnzp3lXDzm5uasWrWKr7/+msGDB2u1P3bs2GIlU0qLqakp3bp1w8bGBj09PUxMTGjYsCF3796lbt26xUrPmJqa4u7uzogRIwCoUaMGfn5+ZGZmar2fkvwXEjSC9xGtEjTPc/DgQQ4cOMDPP/9MtWrVsLe3x8nJSawiEQgEfyvPS5kY164p9tK8J7y2BM3z2NraYmtrS3p6Oj/99BPh4eFs2rSJxo0b4+zs/Nr5OQQCgUAbIsiUHV5JyrZy5cp8/vnnrF69Gg8PD+Lj4/H19X1TvgkEZRJTU1Ps7e1xdHTU+CspR9DLcHR0JD09/W/0snji4uKYMGHCG68HQJVfujw/gnefUmudqVQqfv75Z8LDw4mMjKRq1aq4ubmJHccCwWuwefNmOfvn38HzK8neJM2bN2f58uVvvJ6hR8dwbNj+N16P4J+hVIHGy8uLY8eOkZeXR8+ePfnuu+/EogCB4A1Q0g79R48e4eXlxb179zAyMqJGjRo0atSI8ePHY2pqSkxMDJGRkRw9ehQdHR3u3r2LgYEBixYtokGDBmRkZODr68uNGzfIzc3FwsKCadOmUa5cuRIVA3x9falQoQJPnz5l2rRpLFq0iPDwcHni//r169y/fx9TU1MWLVpExYoVOXnyJAEBAejo6NCkSROio6PZtm1bseP3grJPqQJNYmIinp6eWFtbv3RTmUAgeDlDhgzRSML28ccfs2rVKqBgh/6cOXNo0qQJGzduZOnSpWzdupX58+fTsGFD1qxZQ0pKCi4uLjRq1KiI7bNnzxIeHk6tWrXw8fFh7dq1LFq0CD8/P5o1a8bChQvJz8/H09OTTZs2MWzYMCZMmMDixYtp1qwZGRkZfP755/Jin5s3b3Ls2DE++uijIqvTrly5wg8//IBCoWDAgAEcOnQIKysrpk2bxubNm2ncuDF79+6VN38K/p2UKtBs2bJFfp2YmEitWrWQJKnYTW0CgeDllDR0pm2H/smTJ+XXNWvWxNrautjyzZo1o1atWnL5o0ePAgWbXuPi4uRMq9nZ2cDLFQNq167NRx99VGxdXbp0kTe6mpiYkJaWxrlz52jQoIGsrODs7CyWN//LKfUczapVq1i3bh0qlYrDhw+zZMkSypcvz7x580TAEQj+Rkraof/8bgRtaam1lVer1QQGBtKgQQMA0tPTUSgU/PHHHyUqBlSoUOGVfFUqlUXUEl41hbagbFHqNAHBwcH4+PjIv15sbW2JjIyUN4sJBII3S9euXeXeyOPHjzl27NgrzZNaWlry/fffI0kSKpWKMWPGsHXr1tdWDNBG69atiY+Pl4V4Dx8+LAc1wb+TUvVogoODmT17Nt27d5cTTPXq1QtdXV3mzp2Lu7v7G3VSIChrvDhHAzBlyhSNHsKLeHl5MXPmTOzt7TEyMuLDDz8s8foXmTFjBr6+vtjb25Obm0unTp0YMWIEurq6r6UYoA0jIyOWLFmCh4cHOjo6mJmZUa5cOcqXL//KtgRlBKkUNG/eXLp3754kSZJkbm4uv46Pj5eaN29eGhMCgeAvsnXrVun8+fOSJElSTk6O1K9fPykyMvIte1WUjIwMadGiRVJWVpYkSZJ05coVqXPnzpJarX5p2YSEBMnExETqsdHuTbsp+BspfG4JCQnFni9Vj6Z+/fqcO3eOOnXqaBw/fPgw9evXfyMBUCAQaNKwYUN8fHxQq9Xk5uZibW39TsrvGxoaoqury2effUa5cuUoV64cy5Yte6Whs+97BaHKV6En1AHKBKUKNOPHj8fd3Z1bt26Rn59PWFgYd+/e5cCBAyxevPhN+ygQCChQgg4JCXnbbpSKyZMnM3ny5L9kQwSZskOpFgP07NmTZcuWceHCBZRKJZs3byYxMZHVq1fTt2/fN+2jQFDmmD9/viw9Y2ZmRp8+feT3hcuOS0t0dHSR/D1/hd27dzN27Ni/zd7rkKsuOWWC4P2i1Mubu3bt+k520wWC95GZM2fKr62srAgICKB58+Zv0aN3C10d5dt2QfA3ojXQrF69utRGRo8e/bc4IxAICnbi+/r6kp6eTn5+PkOHDsXZ2Rko6G1s2rQJpVJJ9erVWbRoEQCZmZlMnDiR+Ph4VCoVvr6+tG7dmqlTp1K1alWuXbtGcnIyTZs2ZdGiRZQvX54zZ87g7+9PTk4Ourq6TJ48GUtLSw1f/vjjD+bNm8cff/wBgIuLC8OGDZN92bBhA+XLl8fCwoLvv/+eq1ev0qtXL+bPn0/Hjh0B8PT0pHnz5ri5uf1TTSh4x9AaaHbt2lUqAwqFQgQageBvIjc3l4kTJ7JkyRIaN25Meno6AwYMoGHDhigUCpYuXUpISAi1atViw4YNrF69ml69evHgwQOWLl1KixYtWL9+PatWrWLDhg0A/Prrr2zatAmAzz77jMOHD/Ppp58yadIk1qxZQ/Pmzbl+/TpffPFFEamYKVOmYGtryxdffEF6ejpubm7Url2b+vXrs2zZMkJCQjA2Npb30ykUCgYOHMju3bvp2LEj6enpnDx5UqMHJ/j3oTXQFGblEwgE/xy3b98mISEBDw8P+ZhKpeK3334jLS2NTz/9VJaX+fLLL4GCOZp69erRokULAJo0aUJ4eLhc/tNPP5U3Wjdq1Ii0tDQuXLjAJ598Ig/XmZqa0rJlS86cOSOXy8zMJC4uTpagqly5Mo6Ojvz8888kJiby6aefYmxsDMDgwYNZu3YtAP369WP16tU8fvyY8PBwevbsiaGh4RtpL8H7QannaAQCwZtHrVZjZGSkIQeTmppK5cqV2bZtm8YS4WfPnnH//n2gQJ6mkOdlZwD09fWLnFOr1UWWG6vVavLy8jTeF+dfXl5eiXI4RkZG9OzZk/DwcPbs2SNyVgleLfGZQCB4szRs2BAdHR0OHDgAQFJSEnZ2dly7do2OHTvyyy+/kJqaCsC2bdv49ttvX6ueVq1acePGDeLi4gC4fv0658+fp0OHDvI1lStXpmnTpmzfvh0o0EYLCwujU6dOdOnShVOnTpGSkgIUzNc8H7jc3NzYtGkTurq6NGvW7LV8FJQdRI9GIHiH0NPTIygoCD8/P1avXk1eXh7ffPMNLVu2BOCbb76Rh8yMjY3x8/Pj5s2br1zPBx98wLJly5g7dy4qlQodHR0WL15MnTp1iI2Nla9bsmQJ3t7e7N69m9zcXBwcHORkh+7u7gwbNgx9fX2aNGmiIYdjZmZGxYoVcXV1/SvNISgjKCTpBZlVgUAgeAn37t1j//79jB07FoVCwcGDB/nhhx/YsWMHUJB6YOjQoRw+fFhj6O5lJCYm0qNHDyIiIkSStPeIlz23Ug2dFa44eZFHjx7h4uLy170UCATvFbVq1ZJVnh0cHNi+fbs8F7NkyRL++9//Mnv27FcKMs8jNmyWLbQOnZ0/f5579+4BBRn7wsLCiqwcuXXrFvHx8W/UQYFA8O6hp6enNZnZlClTmDJlyl+yLzZsli20BhodHR1mzpwpryxZsGCBxnmFQkHFihXfulSF4N3h4sWLfPvttzx58gRJkqhVqxYeHh5yuuHhw4cTEBCgNbNkccTFxTFx4kSOHz/O9u3bycjIYNSoUX/Z1xkzZtC3b186depU6jJWVlYEBgZq7OA/ffo0Pj4+GsuJ3za3b99m0aJFJCcnA1ClShUmTZpE27ZtX8teadt95syZuLq6YmZm9lr1PI/o0ZQttAYac3NzOfGRlZUVwcHBr/QFIfh3oVKp+Oqrr9i4caO8ymjfvn2MHDmSiIgIlEolUVFRf6mOgQMH/h2uApTpJbcTJkxg0qRJ9OrVCygYkfjqq6+IiIjAyMjole2Vtt2jo6P5/PPPX9l+cYgeTdmiVHM0x48f1wgyubm5xMXFkZmZ+cYcE7xfPHv2jIyMDLKysuRjDg4OzJo1i/z8fLy8vICChF/JyclYWVnJS2sBjffbtm2jT58+9OvXj23btsnXrFixAm9vbwAePHjAuHHjcHFxwd7eXpZMysvLY86cOdjb2+Pi4sKECRN4+vRpEX8HDx7MoUOHSExMpGfPnvj4+PDZZ5/Ru3dvjh49+lpt8Lx/L74fPHgwCxcuxNXVld69e7N+/XoWLlyIi4sLNjY2XL9+HSjoFbq5udG/f3+6devG9OnTAV7Jz9TUVI3n0K5dO5YtW4ZSWfDlfezYMZycnHBwcGDgwIFcvnxZbrsFCxbQp08fbG1tmTFjBiqVqlTtvnTpUlJSUpg6dSpnzpyhdevWZGRkACBJEn369JEzbgr+fZRqeXNSUhIzZsxgypQpmJqaMnDgQH799VeqVKnC+vXrhRiggCpVquDu7s6IESP44IMPaN26NR06dKBv377o6emxYMECQkJC2Lx5c4k9499++42VK1eyb98+atSoIWd0fRF3d3eGDh2KlZUVOTk5jBw5krp161KzZk3OnDnDwYMHUSgU+Pv7c/36dVq3bq21zoSEBCwtLZk1axaHDx/Gz89P7g28yNSpUzWW8WZlZZV6wjspKYkdO3Zw6dIlBgwYQFBQEJ6envj5+bF161Z8fHz44YcfmDBhAh06dODp06f06NGDK1euYGRkVGo/Z8+ezbx58/D396dNmza0a9cOOzs7KlWqxO3bt5kzZw47duygTp06xMTEMHbsWA4dOkRISAhXr15l37596OnpMWXKFA4ePFiqdp88eTL79++XxUE7duxIWFgYbm5uxMbGYmRkROPGjUvVToKyR6l6NH5+fuTm5vLBBx+wf/9+7t27x65du7CxsRH5aAQyw4YNIyoqipkzZ1KjRg3WrVuHk5OT/Mu2NMTExNC5c2dq1KgBUOxQTFZWFmfPniUwMBBHR0cGDBhAcnIy165dw8TEBKVSSf/+/Vm2bBl9+vQpMcgA6OrqysrkTZs25cmTJ1qvDQgIYN++ffKftgnx4igMCoUJBLt06QJA3bp1SUtLA2DhwoVkZGSwevVq5s2bR05Ojtw7Ka2fdnZ2nDp1isWLF/PJJ5+wZ88e+vbtS2JiIrGxsXTs2FH2wcLCgmrVqnHlyhU53YCBgQE6OjosW7ZM3jMDJbf7i7i5ubF7924Adu7c+bcOewreP0rVozl9+jTbtm3jww8/JDIykq5du9KiRQuqVKmi8Y8o+Pfyv//9jwsXLjBixAi6d+9O9+7dmTJlCnZ2a//X8AAAIABJREFUdkRFRWFtbV2kzPNbuFQqVbHHC4d7nketViNJEjt27JDz0D969Ah9fX0qVqzIvn37OH/+PLGxsUyaNIkvv/yyROVgXV1dWULlVbJAvsiL0i+5ubka5wv1xp6v90X++9//YmpqSpcuXbCxseHSpUuyzdL4efv2bfbu3cvUqVPp1KkTnTp1YuLEifKeFj09vSJlJUmSZWWe588//9SQoSmp3V+kU6dOPHv2jJiYGM6dOyerTAv+nZSqRyNJEuXLlyc/P5/Y2Fh5pU52dnaRD4/g30m1atUICgri3Llz8rHU1FQyMzMxMTEBCoJGoZZW4a9oKPghUyir0rlzZ6KiomQNrxfVhKEgVbC5ubmsSJyens7AgQOJiIjgxIkTDB06lFatWjF+/HicnJzket40VatW5erVq0iSRGZmJidOnHil8unp6cTFxTF16lR69+7N/fv3uXfvXrGaY9r44IMP2LVrF4cOHZKPPXnyhAcPHtC0aVMsLCw4deoUCQkJQEEPMjk5mZYtW2JhYUF4eDgqlQq1Ws3cuXNlKRwoud1B8/kqFAoGDRrEjBkzsLOze+39NIKyQal6NObm5qxbt46qVauSnZ1N9+7dZVnyVq1avWkfBe8B9evXZ9WqVSxdupT79++jr69PpUqV8PPz45NPPgHA2tqawYMHs2LFCqZOncrcuXPZuXMnzZo1k1eqmZqa4u7uzpAhQ6hYsaKsSPwiAQEB+Pj4YG9vj0qlkjcO5ufn8/PPP2NnZ0eFChWoUqXK/7F35vFUbf//fx1TEjdU122uq9CkQRMRpQGZhTI0x63cm/pUqFxTFI1KSXNpzlRxc4mkouk2adBMyVRXURnOcc7794ev/XPioG4j+/l4eDzOntZ+77W3vfZa6/1+veHn5/dV6sDU1BTnzp3DuHHjoKSkhKFDh+JjhDd++uknODk5wcLCAjIyMlBSUsKgQYOQnZ3NDHU1ROvWrbF3716sXbsWQUFBaNmyJaSkpODs7AxNTU0AgJeXF1xcXMDn8yEtLY2tW7dCTk4OkyZNwosXL2BpaQkiwtChQ+Ho6IjQ0FCmfFH1DlQNDS5evBje3t7Q1taGhYUFAgMDP5snGsuPS6MkaJ4+fYr//e9/ePbsGVxdXeHg4IAVK1YgNTUV27ZtQ7du3b6CqSwsLD8ScXFxiI6Oxo4dOxp9DCtB82PS0H1r9BzNtm3b0LZtW2adi4sLli1b9p/GtFlYWJomjo6OKCoqwpYtWz7peDZgs2nRqIZm7dq1GDZsmFBD8ymBXywsLM2D6mRpnwobsNm0aFRD06tXL6SlpaF79+5f2h4WlmaJqqoqVFRUhBKIAcDmzZu/uyGkpKQkpKenf9H0zLyPcIBg+f5pVEPTpk0brFixAlu3bkXnzp2FAtYAYNeuXV/EOBaW5kRDwazfC/r6+tDX1/+i55AUY3MyNiUa1dBIS0uz8TIsLN+QiIgI7N69G2JiYlBQUEBgYCDat2+PI0eOIDw8HGJiYmjbti08PT3RvXt3uLu7Q1ZWFvfv30d+fj5UVVURGBiIVq1a4erVqwgKCkJZWRkkJSXh6uqKkSNHIioqCgkJCRAIBMjNzYWSkhJsbGywf/9+ZGVlYfr06ZgxYwaioqLw999/IywsDC9fvoSXlxeePHkCMTExTJo0CVOmTEFCQgJCQ0PB4XAgLi6OJUuWYMiQId+6Glm+FcTCwvLNUVFRIWNjYzI1NWX+5s6dS0RE9+7do2HDhlFubi4REe3evZs8PT0pLS2NxowZQ//++y8REUVGRpKhoSEJBAJyc3MjW1tbqqioIC6XS+bm5hQREUFFRUWkqalJN27cICKiBw8e0NChQ+nZs2cUGRlJGhoalJubS3w+n4yMjOj3338nPp9P9+7do379+hGfz6fIyEhycnIiIqJ58+ZRYGAgERGVlJTQhAkTKCsri/T19en69etERHTu3DnatGlTo+rh+fPnpKKiQs+fP/98lcvyxWnovons0Zw8eRLjx4+HlJQUTp48KbKh4nA4MDY2/iKNIAtLc0LU0Fl6ejq0tbXRvn17AMC0adMAAEFBQTAyMmKOsbS0hL+/P3JycgBUSdxUB1SrqKiguLgYt27dQpcuXZjU0D179sSgQYNw+fJlcDgc9OvXjzlPp06doK2tDTExMXTu3BkVFRUoKysTsi0tLQ2LFy8GAMjJyTHpEiZMmAAXFxfo6upixIgRmD179uesKpYfDJENzeLFi6GlpYU2bdowD1JdsA0NC8uXRVxcXCiMoLy8HC9evKhTMYD+T04GgNBcarU8Dp/PFylBIykpWUvp40NZmg+RkJAQKu/58+dQUFDAggULYGVlhQsXLiAqKgq7du1CRERE4y+apUkhcsYtJCSEyaiZmZkp8u/evXtfzVgWlubIsGHDkJ6ejsLCQgDA4cOHsXr1aujo6OCvv/5CUVERACAyMhLy8vLo2rWryLIGDBiAJ0+eMKkBHj58iCtXrmDo0KGfZJumpiYiIyMBAG/fvsXUqVPx+PFjjB49GmVlZZg8eTK8vLxw//59IT07luZFvT2aU6dO4ZdffoG+vj7zELOwsHwZpk6dWsu9eeHChdDV1WVSMABAu3btEBAQACUlJUybNg1Tp06FQCCAoqIiwsLCapVRE0VFRQQHB8PPzw/l5eXgcDhYuXIlunfvjuvXr3+0zX/++Se8vb1hYmICIoKzszP69++PpUuXYtGiRUyPJyAggNVFbMaIlKDR1taGrq4uBg8eDA8PDyxfvpzp4XwI65HGwsLyOaiWMolPTET3Ll2+tTksjeSTJWgWLVqEwMBAHD9+nPnqqQsOh8M2NCwsLJ8VNo6maSGyoTE3N2caEDU1NaSmpqJNmzZfzTAWFhYWlqZBowI2k5KSfoiIZZbvn88ttRIcHIyuXbv+EL3qt2/fYt68edi3b1+9+9UMiPyQlJQUhIaGoqysDHw+Hz169ICHhwd++eUXkeV9DcmYzw0rQdO0aFRD07Fjxy9tB0sz4nNKrcyfP/+zlPM1KC4uRkZGxicfX1BQADc3N0RFRTH/k6GhoXB1dcXhw4dFHvc1JGM+N+zQWdOiUQ0NC8vX4NKlS1i/fj06d+6Mhw8forKyEj4+PlBRUYGuri7+/vtvtGvXDgBgbW0NFxcXnDp1Cj179sTMmTPRt29f6OvrIzMzE2vWrEFFRYVIqZXExESIiYkhOzsb0tLSCAwMhLKyMhwdHdGnTx/cuHEDRUVFsLGxwatXr3D58mWUlZVhw4YNUFVVxdu3b+Hv748HDx6Ax+NBU1MTS5YsgYSEBPr16wcnJydcuHABhYWFmDVrFuzs7ODh4YHy8nKYmZkhKioK0dHROHLkCHg8HoqLizF79mzY2dmJrJ/Xr1+Dx+OhtLSUWTd16lSoqakxy2FhYYiOjoaEhAS6du2KVatWITExkekhfYrdosqVk5PDsWPHcOjQIQgEAsjLy8PT0xPKysq4evUqVq1axcT6ODs7Y/z48V/isWH5EfhqGgUsLFS/1MrFixepV69edPfuXSIi2rlzJ9nb2xMR0ZIlS2jHjh1ERPTo0SPS09MjPp9Pbm5uzHoVFRWKjo4mImqU1EpeXh4REfn6+tKSJUuIiMjBwYFcXFyIiOjGjRukoqJCSUlJRETk7+9Py5cvJyIid3d32rdvHxERVVZW0qJFi2jbtm2MHeHh4URElJGRQX379qXy8nJ6/vw5DRgwgIiI3r17RzY2NlRUVERERNevX2e21ZR4+ZCVK1dSnz59yNDQkJYtW0axsbHE4/GIiOj06dM0btw4evPmDRERBQQE0JYtW4TK+xS7RZV76dIlsrOzo9LSUiKqkpoxMDAgIqIpU6ZQbGwsEVVJ6Hh7e9fzVPx/WAmaH5NPlqBpLAUFBVBSUvocbR5LM6G+obMOHTqgV69eAIDevXsjOjoaQFUPxsfHBzNnzkRkZCSsrKzqjBcZPHgwADQotdKnTx9mXqN3795ITExkyhg7diwAMOmTdXR0AABdunTB5cuXAVTNlWRkZDDR7uXl5UJ2VA9V9enTB1wuV6gXAgCtWrXC1q1bcfbsWWRlZSEzM7PWPnXh7u4OZ2dnXL58GVeuXEFQUBDCw8Nx4MABpKenw8DAAK1btwYAeHh4AKia86nmU+wWVW5QUBCys7MxadIk5viSkhK8efMGhoaG8PX1RXJyMrS0tLBw4cIGr42l6VJvQ5OXl4ekpCRISEhg9OjR+Pnnn4W2Hzx4EOvXr8eVK1e+qJEszYe6ZFOAqgaksrISt27dQmxsLI4cOVLn8TIyMgDQoNSKqPMAqBVYKCkpWes8AoEAwcHBUFZWBlD1gq15vhYtWjBlV5+7Jvn5+bC1tYWNjQ00NDRgYGCAM2fO1HlN1SQlJeHNmzewsrLC+PHjMX78eCxYsAC6urq4e/duLamakpISlJSU/Ge7RZUrEAhgZmbGSFQJBAIUFhaidevWmDRpEkaNGoULFy7g3LlzCAkJQXx8PFM+S/NC5IxbWloaDA0NsWLFCnh7e8PQ0BCZmZkAgGfPnmHy5Mnw9fVF3759v5qxLM0ba2tr+Pn5QVVVlRF+FMXnllr5EG1tbezZswdEBC6Xizlz5mD//v31HiMhIQE+nw8iwu3bt6GoqIi5c+dCW1ubaWT4fNEpjFu1aoV169bh0aNHzLrnz59DXFwcXbp0gZaWFhITE/Hu3TsAwKZNm7Bnz57/bLeocrW1tREXF8dI4xw6dAhTp04FAEyaNAn37t2DpaUl/Pz8UFJSgpcvX9Z7Hpami8gezcaNG9G/f38EBgZCUlISfn5+CAoKwty5c+Hs7AwJCQkEBATA0tLya9rL0gQQJbXyYUK9DzE3N8e6deuwbt26Bs/xuaVWPmTZsmXw9/eHiYkJeDwetLS0GIkYUbRr1w7q6uqYMGECdu/eDSUlJRgYGIDD4WDo0KFQVFREdna2yOOHDx8OT09PuLm54e3btxAXF0e7du2wfft2tG7dGrq6unj06BEmT54MAOjRowf8/PyQkJDwn+wWVa6srCxmz56NGTNmgMPhQFZWFiEhIeBwOFi0aBECAgKwYcMGcDgcuLi4fHeZQlm+HiIlaDQ0NLB7926oq6sDAIqKiqCnpwcFBQX06tUL/v7+bAAnCwvLZ4WVoPkx+WQJmtLSUnTo0IFZVlRUBIfDgaamJlatWvVlrGVhYWEBG0fT1BB5N4mo1mSqmJgYpk+f/sWNYmFhYWFpOnz0Z0O1Vw/LfyMnJwe9evWCmZkZ82dqavrZk0NFRUXB2dn5o20bOHDgZ7XjS+Hs7CzkvluTgoICuLu7w8TEBKamprC2tsbp06e/iB0FBQWMm+/z58/x+++/11r/OUhJSYGtrS1MTU0xYcIEzJ8/H/n5+fUek5SUhBUrVnw2G74GPEGdI/osPyj1ujdnZGTgp59+Elp3586dWt4jgwYN+vyWNQOkpaVx/PhxZrmgoADGxsbo27evULQ3y8dTVFSESZMmYf78+Vi5ciU4HA4yMzMxffp0tGzZEiNGjPis51NSUmJkYHJzc/H06dNa6/8rzUuChtPwTiw/DPU2NHPmzKnl/+/q6iq0zOFw2CybnwklJSV07doVWVlZaNOmDdzc3PD69WsAVZ4/rq6umD59OgwNDWFjYwMA2LJlC968eYOlS5fWKRMCAC9fvoSTkxPy8vIgLi6OtWvXQllZGfn5+fD29saLFy9ARDA3N6/lgcTj8bBq1Sqkp6dDXFwc6urq8PDwgKysLG7dugVvb2/weDx06dIFubm5cHd3x8mTJ9GmTRssWLAAAHD8+HEkJCRg8+bNQmWfOXMGYWFh4HK5KCoqgrm5OVxdXUVK0WhoaDC9lMLCQnTo0AH//vtvnXV58OBBDBo0SEhsU01NDRs3bmQ+nq5evVqnRA2fz0dQUBCSk5MhJycHdXV1PH78GOHh4XB0dMSAAQNw7do15OXlQVNTE35+fsjNzYWJiQmuXr2K5cuXo6CgADNnzoSPjw+zfvTo0di8eTMTEuDq6oqhQ4fCzs4OoaGhSEhIgEAgQMeOHeHl5VUrEJqVoGH5YRElKZCTk9PoP5aPp6YcSTXXrl2jIUOGUG5uLoWEhJCnpycREb1//55cXV2ppKSEEhMTycrKioiI+Hw+jRo1ih4/flyv/MjgwYMpKyuLiIj8/PzIw8ODiIjs7e1p165dRERUUlJCJiYmFBsbK2RbcHAwubi4EJfLJT6fT+7u7uTp6Uk8Ho9GjhxJKSkpRESUnp5OqqqqdPHiRbp79y6NGDGCkUaxs7Oj1NRUoWsVCATk4OBAT58+JSKi/Px86tWrF/3777/1StHMnTuX1q9fT0REWVlZNGDAAIqMjKxVv87OzrR//36R9V+fRM2hQ4fI3t6eysvLqaKigmbMmEEODg5EVCVR88cffxCfz6e3b9+StrY2paenC9XZxYsXacKECbXuc3BwMPn4+BAR0Zs3b2jo0KFUUlJC0dHR5OrqytTX4cOHadasWXXazUrQsHyPfLIEzYeKzZWVlXjz5g3k5eUhIcFqcX4OqgUWgapAPQUFBaxevRrt27eHjo4O0wvR0tLC//73P8jJyWHUqFHw9/dHZmYmCgoK0KlTJ/z66684ePCgSPkRdXV1Jo98r169kJiYiNLSUly7dg27du0CAMjJycHS0hKpqamMbAsApKamYsGCBUx0vKOjI+bNm4cHDx4AqOppAVUxHj179mTO0alTJ6SkpKB79+4oLCyEtra20LVzOBxs3boVKSkpiI2NxePHj0FEKCsrAyBaiiYtLQ1ubm4AgK5du2LYsGF11u2H0f4fUp9EzdmzZ2FmZsZEsdva2iI8PJw5dtSoURATE4OsrCy6du2K4uLiRsWIWFlZYeLEiXB3d0dsbCxGjx4NOTk5nDlzBhkZGbCysgJQFWFfXQ8fwkrQsPyINNhinDhxAvv378ft27cZT7Q+ffrAwcGBeUmyfBofztHURF1dnckjcvHiRVhbW2P79u3o27cvbG1tERERgcLCQuafvD75kZofBtUvYIFAUOtFLBAIUFlZWWtdzXIFAgF4PB7ExcVrHS8uLs78tre3R2RkJLp16wYbG5taHoylpaWwsLDAmDFjMHjwYFhZWeH06dNMmaIkYj5sQER99AwYMAA3btyAg4OD0PrDhw+jrKwMXbt2FSlR82GZHwaX1idfUx8dO3ZE7969kZKSgqioKCxduhRAVZ3WHKbicrkoLi6udTwrQcPyo1Kv19ny5cvh5uYGOTk5uLq6wtfXF//73//Qpk0beHh4/FCJlH401qxZgy1btmDMmDFYtmwZevTogYcPHwIA4z11584dRgCyMfIjNZGVlUX//v1x4MABAFVJuWJiYqClpSW0n46ODg4dOgQejweBQIADBw5gxIgRUFZWhpSUFFJTUwFU9RAePHjAvJDGjx+Pe/fu4e+//2a+1GuSnZ2Nd+/ewdXVFaNHj8alS5fA5XKZMX1R6OjoMDpnubm5uHTpUp372dra4vLlyzhx4gTTENy+fRsbN26EiopKvRI1urq6OHHiBLhcLiorK5neVGMRFxcHj8erc5uNjQ22b9+OsrIyaGhoAKiShYmIiGDuXXBwMJYsWVLrWFaChuVHRWSPJjY2FrGxsdi1axc0NTWFts2cORNXr16Fs7MztLS0YGRk9MUNbW5MnToV7u7uMDY2hpSUFFRVVTFhwgQAQJs2bdC3b18oKyszQ1qNkR/5kDVr1sDX1xdRUVHgcrkwMTGBpaUlXrx4wewzZ84cBAYGwtzcHJWVlVBXV4enpyckJCSwadMmeHl5Yd26dejWrRvatm3LfO1LSUlh/PjxePXqVZ1KzaqqqtDT04OhoSGkpKSgoqKCHj16IDs7u5aoZU28vLzg4eEBQ0ND/PLLLyK98+Tl5REeHo7Vq1cjLCwMYmJiaNmyJfz9/RmPM1ESNV27dsXTp09hbm4OGRkZdOrUCS1btqzvdgnRo0cPtGjRAhMnTsT69euFto0ePRo+Pj6YPXs2s87a2hoFBQVMz699+/Z1BkWzEjQsPyyiJnfs7Oxo+/bt9U4A7d69m5kkZfl6/PvvvzRq1CjKzc39pnasWrWKXr58SUREubm5NGTIECouLiaiKgcGCwsLun79+rc08ZM4d+4cxcTEMMt+fn4UFBT0DS1qPlRPKj/JfvatTWH5CBpyBhA5dPbgwQOMHj263kZq1KhRuH///mdv/FhEc/ToURgZGWHmzJkNKhh/aTp27Ihp06bB3Nwcv/32G1asWIGffvoJ586dg56eHnR0dDBgwIBvauOn0LNnT8TExMDExAQTJkzA69ev8dtvv31rs5oVbBxN00Lk0FllZaXQ5C7L94GNjQ0TQ/OtcXBwqDXZDlTNo1QnCPsRUVJSwu7du7+1GSwsTQaRPRoVFRWkpKTUe3BKSgpUVVU/t00sLF+EhmR/PkWuRxTLli1DWlraJx27adMm+Pr61rktKioKlpaWjATNsmXL8Pbt23rLO3ToELZt2/ZJtnwrWAmapoXIHo21tTVWrVqFgQMHMqkCanL9+nWEhITAz8/vixrIwvI5qU/253Pi7+//WcsDqjz7Nm/ejMjISMjLy4PP58PHxwfe3t5Yu3atyOOqJ/F/JNihs6aFyIZm4sSJSE9Ph52dHfT09DBw4EDIy8vj3bt3+Oeff5CcnAxra2sYGBh8TXtZWD4rNWV/gLrlelq2bAljY2OcPXsWcnJyICIYGBggODgYz549Q2hoKDgcDsTFxbFkyRIMGTIEjo6OsLe3Z1I0b9iwAQKBADIyMvDx8YGamhq2bt2KpKQklJeXo6ysDG5uboy7el28fPkSRMQEWYqLi2P+/PmM23tlZSVWr16NlJQUiIuLY+DAgfDy8kJYWBhev36NP//8EwUFBfD19UVeXh54PB4mTJiA3377DTk5OZg2bRp0dXVx8+ZNlJSUYPHixRg7dqzIcqWkpERK5yQkJNRZLyzNlIa8CY4cOUKmpqakpqZGqqqqpKqqSlZWVnTy5MnP7LfAwvJlaUj2pz65njlz5jCSNmlpaWRjY0NERPr6+oxn3blz52jTpk1EVCVVc+rUKXr58iVpaGjQnTt3iIjo77//ppkzZ1JOTg45OjpSWVkZERHFxsaSsbExERFt3LiRkaqpCZfLpYULF1KvXr3I3NycfHx86MyZMyQQCIiIaO/evWRvb09lZWXE5/Np/vz5FB0dLVSeo6MjJSUlERFReXk5OTo6UlxcHOM1lJycTERE8fHxpKenV2+59UnniKqXxtwjVoLmx+OTJWiqqZ58Li8vR0lJCeTl5euNc2Bh+Z6pT/YHQJ1yPUCV0sHq1athb2+PI0eOMMNREyZMgIuLC3R1dTFixAih+BgAuHbtGnr27InevXsDAMaNG4dx48YBqJJwOXnyJLKzs3Hz5k28f/++XtslJSWxdu1aLFmyBJcuXcKVK1fg5uYGTU1NbNiwAWlpaTAzM2NimTZs2ACgas4HqFJjuHLlCoqLixEcHMysy8zMhLq6OiQlJRlJod69e+PNmzcAILLc+fPni5TOaaheWJoXIhuaAwcOYOLEiYxkhLS0dIM53VlYvnfqk/0B6pbrAaqi48vKypCeno6rV68iMDAQALBgwQJYWVnhwoULiIqKwq5du4RyCn0o30JEuH//Pvh8PubOnYtp06ZhxIgRGDJkCHx8fOq1PSIiAgoKCtDX14epqSlMTU0xZ84cjB49GkVFRbWkc169eiWktFAtO3T48GEmALWoqAgtWrTA69evISkpycjt1LRZVLn1Sec0VC8szQuRXmcrVqxgJCeq8fT0RFFR0Rc3ioXle4PD4cDOzg7Lli2DsbExWrRogcrKSowePRplZWWYPHkyvLy8cP/+fXC5XOa4/v374/Hjx8w8SlJSEhYvXowrV66gb9++mD59OoYOHYqkpCTw+fx6bRATE8OaNWuEEp09fPgQHTp0QOvWraGpqYnY2FhGysfb2xtxcXHMvrKyshgwYADjul1SUoLJkycjKSmp3vOKKleUdE5j6oWleSGyR0N1CAXGxcXBycmpTkkRFpamjoWFBQIDA2Frawug6kt/6dKlWLRoESQkJMDhcBAQECA0tNy2bVusWbMGbm5u4PP5kJWVxfr16yEvL4+EhAQYGhpCIBBg1KhRKC4urvVxVxNLS0uUlZVh9uzZ4HK54HA46NatG3bu3AlxcXFMmjQJL168gKWlJYgIQ4cOhaOjI0JDQ5ky1qxZAz8/P5iYmIDL5cLY2BimpqbIyckReV5R5YqJidUpndOYemFpXnCorhYFVUmiLly4gDZt2jDrBg4ciBMnTqBz585fzUAWlu+FuLg4REdHY8eOHd/alCZLTk4O9PX1EZ94Gt27sO+ZH4Xq+5aUlFSnph2bWIaFpRE4OjqiqKgIW7Zs+damNAvYOJqmRb0NzYf5OlhYmis1E5+xsLB8HPU2NCtXrhTyNOPxeFi3bh1kZWWF9mPVAVgaQlVVFenp6ULze1FRUUwu++DgYHTt2hXm5uYiywgJCYGamhrGjBnzNUz+aHbu3ImHDx/WKfEPVHl46enpwcLCokEPs7qoWV9fmpoBp98CPitB06QQ2dAMGTJEyLsFqJqjefXqFV69esWsY3s9LJ+D+fPnN7jPpUuX0KNHj69gzZchIiIC+vr6iI2NxYIFCyAvL/+tTfpuEWeHzpoUIhsadqiA5Wvi7u6Onj17YubMmdi4cSMSExMhKSkJBQUFrFy5EomJibh9+zaCgoIgLi6O4cOHw8fHB5mZmeBwONDR0cHChQshISGBvn37Ql9fH5mZmTAxMcH58+dx+PBhAFVZOW1sbJCcnCzkBfX06VP4+vri/fv3ePnyJdTU1LBhwwa0aNEC/fr1g5OTEy5cuIDCwkImdoTH42HFihVIS0tDmzZt0KZNG8jJydV5fQKBAEeOHIGXlxdKS0tZoW4/AAAgAElEQVRx9OhRODk5AajqqURERKCsrAyysrIIDw9HWFgYoqOjISEhga5duzK9pLokcpSVlXHjxg2sXr0aXC4XL1++hJaWFgICAgBUBY2uWbMGZWVlEBMTg4uLC0aNGgUA2Lx5M+Li4iAuLo7u3bvD09MT7dq1E7L99OnTCAkJgUAgQKtWreDh4QF1dXWUlZXBy8sLN2/ehJycHPMRYG1tjf/9739ITk6GmJgYysrKMHr0aMTFxbEeq80U1hmA5asxdepUJiAQAIqLi2upf+fl5WHv3r1IT0+HlJQUdu3ahVu3bsHe3h7x8fGwt7fH2LFj4ebmBnl5eZw8eRI8Hg9z5szBrl274OTkBB6Ph1GjRiE4OBhcLhcHDx7Ew4cP0bNnTxw7dgwWFha1XG2PHj0Kc3NzmJmZgcfjwdLSEikpKRg/fjy4XC4UFBRw+PBh3L59G5MnT4aVlRUOHz6MrKwsxMXFobKyEg4ODiIbmnPnzqG8vBxaWlp4//49Vq5ciRkzZjDBkI8ePUJycjJkZWWRlJSEqKgoHD16FK1bt8bKlSuxf/9+KCkp4fnz51i/fj26du2KFStWYOfOnQgICMC+ffvwxx9/YNiwYXj//j309fVx+/ZtdO7cGR4eHti5cyc6derEuCNXD2WeO3cOERERkJGRwaZNm+Du7o6dO3cydj9+/BheXl44fPgwOnfujPT0dMydOxfx8fEICwsDn8/HqVOnUFpaCjs7O/Tu3RsaGhpo3bo1zp07B11dXcTFxUFTU5NtZJoxIgM2WVg+N3v37sXx48eZvz/++KPWPkpKSlBTU2NiVnr16lXnnExqaiocHBzA4XAgJSWFSZMmITU1ldk+ePBgAFUppa2trXHs2DHw+XxER0fXmc9n8eLFUFRUxPbt2+Ht7Y3CwkKUlpYy2/X19QEAffr0AZfLRWlpKdLT05lU2zIyMjAxMRF57YcOHYKJiQkkJCSgr6+P8vJyxMfHM9tVVVWZuc/09HQYGBigdevWAAAPDw/MmTMHQG2JnOoA6lWrVuHt27fYunUrfHx8UFFRgdLSUty4cQMvX77EvHnzYGZmBicnJ3A4HNy/fx+pqamwtLSEjIwMAGDKlCm4ePGiUGDlxYsXMXz4cCakobrBuH37Ns6ePYuJEydCTEwMsrKysLCwYI6zt7fH0aNHAUBIsoelecL2aFi+K8TExLB//35kZGQgPT0dAQEB0NHRwZIlS4T2EwgEQvODAoEAlZWVzHL1yxOoCjicOHEihg4dip49e9YZB7Zw4ULw+XwYGhpCT08PeXl5QkHL1VJM1eesK/xMVKLAFy9e4OzZs7hz5w4SEhIAVCkt79mzB8bGxrXs/VC2pqSkBCUlJQBES+Q4ODhAVVUVOjo6MDQ0xM2bN0FE4PP5UFZWxrFjx5jjCgoKoKioiKioqHrrsHrdh/OwRITKykpISEgI1UPN3qqJiQnWrVuHixcvorS0lFVubuawPRqW74rMzEwYGxtDWVkZzs7OmDZtGjIyMgBUvYCrX4Ta2trYv38/iAhcLhdHjx6FlpZWnWW2b98eAwYMQEBAgMgv6/Pnz2PevHkwMjICANy8ebNBSRgdHR3ExMSgoqICFRUV+Ouvv+rc78iRI9DQ0MC5c+eQnJyM5ORkREVF4e7du7h27Vqt/bW0tJCYmMioBGzatAl79uwRaUdJSQkyMjKwaNEijBs3Dvn5+Xj27BkEAgEGDBiA7OxsXLlyBQBw7949jB8/HgUFBdDR0UFkZCTTcwsPD8eQIUOEhhU1NTVx/vx5PH/+HEBVbysvLw/9+/eHrq4uIiMjGTHN2NhYplFq2bIlTE1NsXTpUkyaNKneemRp+rA9GpbvCjU1NRgaGsLKygoyMjKQlpbG8uXLAQCjR4/GunXrwOPxsHz5cqxYsQImJibg8XjQ0dHBb7/9JrJcS0tL+Pn5MerEH7JgwQLMmzcPMjIykJWVxZAhQ/Ds2bN6bZ00aRKePXsGY2NjyMvLM0NaNeFyuYiIiGAm5qvp1q0bJkyYgD179kBPT09om66uLh49esQ0ij169ICfnx/TG/qQn376CU5OTrCwsICMjAyUlJQwaNAgZGdnQ1NTExs3bkRQUBAqKipARAgKCkKnTp0wceJE5OXlwdraGgKBAF27dsWaNWuEyu7Rowe8vLzg4uICPp8PaWlpbN26FXJycnB2doavry9MTEwgJyeHNm3aCIVDWFpaMnNfLM0bkRI0LCxNBYFAAF9fX3To0IHx9GL578TFxUFWVha6uroQCAT4/fffMWLECNjZ2YGIsH37drx48eKjYoaqpUwSEk+jKytB88PQkAQNO3TG0qR59+4dhg0bhry8PEyZMuVbm9Ok6NmzJ0JDQ2FmZgZjY2P8/PPPsLa2BlDlPJGcnNyo+Ki6YONomhbs0BlLk0ZWVpaZn2D5vKioqDDxSR+SnJz8la1h+Z75aj0aPp+P3bt3w9LSEmZmZjAyMmICzJoLmzZtgqqqKiIjI4XWl5aWYuDAgXB2dv5Gln19QkJCcPr06Xr3efToEczMzGBmZgY9PT1oaGgwy/VNjn8MeXl5TOKu75F+/frVUugAqnpqS5cuhYmJCUxNTWFhYVHrufovfOt6EbASNE2Kr9aj8fb2RnFxMfbu3Qs5OTmUlpZi0aJFWLZsGVavXv21zPjmdOjQAcePH2fS3wJAQkKCkHtrc6AxcjI9evRgsmF+KZ2v9u3b4+DBg5+1zK9BUFAQWrdujRMnToDD4aCgoADW1tbo0KEDNDU1/3P537pexNihsybFV2locnJycPLkSZw/f54JSpORkYGPjw/j3vn27VuRkiL9+vXD9OnTkZaWhtLSUri4uCA+Ph4PHjzAzz//jK1bt0JGRqbR+z1+/Bj+/v548+YN+Hw+HB0dMXHiRFy6dAn+/v6QkZHB+/fvERkZiRMnTmD37t0QExODgoICAgMD0b59eyQnJyM0NBQ8Hg/S0tJwc3PDwIEDG6wLHR0dnD59Gvn5+fjll18AANHR0TA1NcWTJ08AfJocSmlpKby9vZGdnY03b96gVatWWLNmDX799VdkZ2dj6dKlKC4uRrt27UBEMDU1haWlpUh5kqioKCQkJEAgECA3NxdKSkqwsbHB/v37kZWVhenTp2PGjBkAgGPHjuHQoUMQCASQl5eHp6cnlJWV4e7uDllZWdy/fx/5+flQVVVFYGAgYmJihORkxo4d+0nPFZfLxcqVK3H58mWIiYlhwIABcHd3R6tWrTBy5EiEhYWhV69eAMAsy8jIYPr06ejSpQvy8vKwevVqzJgxA1evXsXDhw/h6ekJLpcLIoKtrS0mTZqE9evXIz8/H7m5uXj58iXU1dWhoaGB48eP48WLF3Bzc2PcomuyefNmnDlzBhUVFSgrK4OHhwf09fWxfv16FBYWoqCgAC9evECHDh2wevVqtG3blnkGxcTEoK6uXme8DgAUFhaiY8eOqKyshKSkJJSUlBASEgIFBQUAVT0SX19fFBQUgMfjwcTEBE5OTsjOzha6/kGDBkFOTg5Lly4FUDXktW3bNgQGBsLKygpXr14Fj8dDUFAQUlNTIS4ujsGDB8PT0xMSEhLYsmULTp8+DYFAgM6dO8PLywvt2rXDqVOnEBYWBnFxcYiLi8PNzQ0aGhqfdJ9ZmgD0FYiPjycrK6t691myZAn5+fmRQCCgiooKmjFjBoWFhRERkYqKCu3du5eIiMLCwmjgwIGUn59PfD6fLCws6MSJE43ej8fjkZGREd2+fZuIiEpKSsjQ0JCuX79OFy9eJDU1NcrJySEionv37tGwYcMoNzeXiIh2795Nnp6e9PTpUzI2NqaioiIiInrw4AGNGDGC3r9/X+81bty4kXx8fMjX15e5thcvXpCVlRVFRkaSk5MTERGtWrWKYmJiiIiIy+WSsbExxcfHM9cYHh5OREQZGRnUt29fKi8vp1OnTpGfnx9zLk9PT/L19SUiIhsbGzpw4AARET169Ij69+9PkZGR9ObNGxo3bhw9f/6ciIjy8/Np5MiR9OLFC4qMjCQNDQ3Kzc0lPp9PRkZG9PvvvxOfz6d79+5Rv379iM/n06VLl8jOzo5KS0uJiOjcuXNkYGBARERubm5ka2tLFRUVxOVyydzcnCIiIoiIyMHBgU6dOlVvfdWkZv1Us27dOpo/fz7xeDyqrKykJUuWkI+PDxER6ejo0N27d5l9q5ezsrJIRUWFrl27RkREWVlZpKGhQURVz+COHTuYuliwYAHx+Xxat24djRkzht6+fUulpaU0aNAgCgoKIqKqZ9vQ0LCWvc+ePaOpU6dSeXk5ERHFxMSQmZkZY/fYsWPp7du3REQ0a9YsCgkJoYqKCho+fDhdvHiRiIiio6NJRUWF8vLyapV/9+5dGjNmDA0aNIhmzpxJmzdvpqysLGa7nZ0dpaSkEBFRWVkZ2dvb099//13r+p88eUKamprE5XKJiMjFxYUiIyOF6mXXrl3k6OhI5eXlxOfz6ffff6cTJ07QsWPHaOHChcTj8YiIaP/+/eTs7ExERHp6enTr1i0iIkpJSaHQ0NB67u7/5/nz56SiosI8kyw/Bg3dt6/SoxETE4NAIKh3n9TUVBw6dEhIUmTv3r2MO+r48eMBAF26dIGKigqUlJQAAJ06dUJxcTFTTkP7ZWVl4dmzZ8wXHACUl5fj7t27UFZWRvv27dGxY0cAVcFp2traaN++PQBg2rRpAIADBw6gsLCQWQaqorSfPXsGNTW1BuvDzMwMy5Ytg5OTE44fP14rzmDx4sW4cOECtm/fjqysrEbJoRgYGKBz584IDw9HdnY2Ll++jIEDB6K4uBi3bt3C/v37AQDKysoYPnw4AAjJk9S8jvv37wOomh+ovvZOnTpBW1sbYmJi6Ny5M/OVnpKSguzsbKGgvJKSErx58wZAVQ+uOgBQRUVF6F79V1JTU+Hm5sZEy9vb22PhwoUNHicpKYn+/fvXWj927FgsXboUN27cgKamJpYvX85Eu2tpaTG98Xbt2kFHRwdA1XNW1zV17twZAQEBOHHiBLKzs3H9+nWhezh8+HCmvN69e6O4uBj37t1Dy5YtMWzYMACAubk5vL2967yGXr16ISEhAbdv38aVK1dw4cIFhIaGIiQkBBoaGrh27RrWrVuHdevWAaiaB7x37x5UVVWFrr979+749ddfcfbsWQwaNAhXrlxBUFAQCgsLmXOlpaXB3NycUUfYuHEjAMDFxQV3795lhoEFAgEz52pkZIQ5c+ZAT08PWlpaTO+XpXnyVRoadXV1PHnyBO/evRPKZVNQUABPT09s3LixQUkRSUnJOn9/SEP78fl8yMnJMWP/APDq1SvIycnhxo0b9UqBlJeX48WLFxAIBNDU1MSGDRuYbXl5efj555/rrYdq1NXVwefzce/ePfz1118IDw8X8tL5FDmUgwcP4ujRo7C3t4eJiQnk5eWRk5PDyKLUPL56XX3yJCdPnqwlPFlT/qQagUAAMzMzLF68mFkuLCxkdLpqBvDVlEz5HPD5fKH7Q/8njVLXuXg8HvNbWlpaSC6lmjFjxmDQoEFIS0tDWloaQkJCEBMTAwCNqouaZGRkwMXFBdOnT4e2tjY0NDSEgjar72FN2+uiLlkbLpcLPz8/LFmyBP369UO/fv0wY8YMbNq0CUeOHMGAAQMAVA1pVttdVFQEaWlpvHz5stb1W1tbIyYmBi9evMD48ePRsmXLWjbUrOdXr15BIBCAz+fjt99+Y7TjKioqGKmcxYsXw8bGBhcuXEBkZCT27dsn0kONpenzVbzOlJSUYGJigqVLlzKyGu/evYO3tzfk5eUhLS39UZIi/4Xu3btDWlqaaWjy8vJgbGyM27dv19p32LBhSE9PZ77uDh8+jNWrV0NTUxMXLlzA48ePAQBnz56FqakpysvLG22HmZkZAgIC0L1791p5ST5FDuX8+fOwsLCAtbU1unfvjuTkZPD5fMjKymLQoEGIiooCADx//hzp6engcDj1ypM0Fm1tbcTFxTF1dOjQIUydOrXB42rKyXwqOjo6OHToECorKyEQCHDgwAHmmakWfgSqvsirxSfrY/78+UhMTISxsTG8vb3RsmVLRnrlY7l8+TL69++PadOmYfDgwTh9+nSD97BXr17gcrk4f/48gConker/l5pISUnh0aNH2LJlC1OHPB4PT548Qe/evdG6dWv06dOH8cwrLi6Gra0tUlJS6jyvgYEBbty4gYiIiDoFR7W0tHDy5ElwuVwIBAJ4enoiPj4e2traOHr0KGPj+vXr4eHhwahnV1ZWws7ODp6enrh3795/vt8sPy5fzevMy8sLW7ZswaRJkyAuLg4ul4sxY8bg999/B4CPlhT5VKSkpLBlyxb4+/tjx44dqKysxPz586GhoYFLly4J7auqqorFixdj1qxZAKqGTAICAqCkpARfX18sXLgQRAQJCQmEhoaiVatWKCgogJOTE7Zt28YM29WFqakpNmzYUGcO+k+RQ5kxYwb+/PNPREREAAAGDBiABw8eAAACAwOxbNkyHDx4EEpKSujUqROkpaWhqKgoUp7k8uXLjapPbW1tzJ49GzNmzACHw4GsrCxCQkIaTIhXU06mR48eWL58uVAvszG4uLhg1apVMDMzQ2VlJQYMGAAPDw8AVV/UPj4+OHDgAPr168c4BTRUnqenJw4cOABxcXEYGhpCQ0NDSBW6sZiYmOD06dMwMjKCQCCAnp4eXr9+LTR89iFSUlLYvHkzvL29sXr1avTp00dkcrSQkBCsWbMG48aNg4yMDAQCAcaOHcv8z6xfvx6+vr5MA2Fubg4jIyNkZ2fXKqtFixYwMDDAP//8gz59+tTabmdnh7y8PFhaWoKIMHz4cNjb24PD4aCwsBC2trYAqjwqAwICICkpCXd3d7i6ukJCQgIcDgcrV65ssBfI0oT5GhNFzQ03Nzdmovd7YMuWLfTo0SMiqnJ+0NPTo4cPH35jq4RxcXH51iawfAdUTypnZz/71qawfATfhTNAc6KsrAyamppCc1Hfmm7dumHBggUQExMDn8/H7Nmzv6uUyAUFBUJxRSwsbBxN04JtaD4zLVu2hJmZ2bc2QwhDQ0MYGhp+azNEoqSkVO8wIwsLy48NK6rJ8l2Qk5PTqIDX7wFjY+Na83kAaqVB/lLUV1ePHz+Gk5MTTExMYGJiAgcHB1y9epXZvnz58jodX+qjqKiISbmdlJSEFStWfLrxLM0StkfDwtKE+OOPP+Dq6sqoLVy5cgXOzs5ISkqCvLw80tLSmMn7T0FfX5+J42JhaSxsQ8Py3SNKkmfNmjVo1aoVXF1dUVhYCB0dHezduxfDhw/H8ePHcebMGaFYJwCIiIjAkSNHwOPxUFxcjNmzZ8POzg5RUVFITEyEmJgYsrOzIS0tjcDAQCgrK+PRo0dYunQpysrK8Ouvv9brOSYKUVI/fD4fQUFBSE5OhpycHNTV1fH48WOEh4fjxo0bjPDsy5cvoaWlVSuB2oe8fPlSyL4hQ4Zgw4YNEBcXZ6RvFi1ahKCgIKxZswb29vYwMDAAADg6OjLLCQkJWL9+PVq2bIm+ffsy5dXUnHv79i38/f3x4MED8Hg8aGpqYsmSJZCQkMDGjRuRmJgISUlJKCgoYOXKlY2OM2NperBDZyzfPdVZGo8ePYqEhATk5OQgJSUF48aNY1yPz507h3bt2iEtLQ1AlWbXuHHjhMp5//49jh07hm3btiEmJgbr168XEnS9cuUKPD09ERsbi/79+2Pbtm0AgEWLFsHa2honT57ElClTkJub+1H2FxcXw8PDA0FBQYiOjsaWLVvg7e2N3NxcHDt2DHfu3EFsbCwOHz4sFLezb98+/PHHHzh27Bji4uKQnJzc4LDXn3/+iRUrVkBbWxvz58/H/v370a9fP8jJyWHBggX4+eefsWbNmjqVEap59eoVli5dik2bNiEqKopRyviQgIAA9OnTB1FRUYiJicHr16+xe/du5OXlYe/evYiMjERUVBRGjBiBW7dufVSdsTQt2B4Ny3ePKEmesWPHoqCgAK9evcK5c+cwZ84cREVFwcXFBVeuXKn19d+qVSts3boVZ8+eRVZWFjIzM4W+/vv06cMInfbu3RuJiYl4/fo17t+/z8gEaWhooGfPnh9lf31SP2fPnoWZmRmjFGBra4vw8HAAwKpVq5CamoqtW7fiyZMnqKioQGlpqcjYGqBq/mjs2LH4559/cOXKFURGRiI0NBRHjhypM/NhXfzzzz9QUVFhPBNtbW0ZKZuapKSkICMjg4ndqg5YVlJSgpqaGiwsLDBy5EiMHDnysyhKs/y4sA0Ny3ePKEkeMTEx6Onp4ezZs7h16xaCgoIQFhaG+Ph4DBw4EK1atRIqJz8/H7a2trCxsYGGhgYMDAxw5swZZnt9cjk1f39s4GF9Uj/Vig3V1JSGcXBwgKqqKnR0dGBoaIibN2/WK+Hz+PFjREdHY9GiRdDS0oKWlhbmz5+PadOm4e+//8bMmTNrHUMiZHoac70CgQDBwcFQVlYGUKVxx+FwICYmhv379yMjIwPp6ekICAiAjo4OlixZItJ2lqYNO3TG8t1TnyTPuHHjsGPHDqioqEBKSgrDhw/HunXrag2bAcDt27ehqKiIuXPnQltbm2lk6pOGUVBQQJ8+fZhG4s6dO4ziQmOpT+pHV1cXJ06cAJfLRWVlJaKjowFUvbQzMjKwaNEijBs3Dvn5+Xj27Fm94rRt27bF0aNHER8fz6x78+YNCgoK0Lt3bwDC0j81ZXoePXrEiKkOGTIEjx49QmZmJgDUagyr0dbWxp49exjZqDlz5mD//v3IzMyEsbExlJWV4ezsjGnTpiEjI+Oj6oylacH2aFi+G6ozjdbk8OHD9UryaGpqorCwEJMnTwZQ9fL766+/MHr06FrljxgxAhERETAwMACHw8HQoUOhqKhYpyxLTdatWwcPDw8cPnwYXbp0wa+//ipy3/Xr1yMkJIRZHjVqFNatWydS6sfS0hJPnz6Fubk5ZGRk0KlTJ7Rs2RI//fQTnJycYGFhARkZGSgpKWHQoEHIzs5G586d6zx369atsXfvXqxduxZBQUFo2bIlpKSk4OzszAxdjR07FosXL4a3tzfmzJkDd3d3nD17Fr/++isGDx4MoKoBWrNmDRYtWgRJSUkMGTKkzvMtW7YM/v7+jGyUlpYWZs2aBUlJSRgaGsLKygoyMjKQlpbG8uXL661jlqYNh+rri7OwsHxRzp8/j3///ZcJ8l2xYgVatGjBqGE3N3JycqCvr4+kpKRGzymxfHsaum/s0BkLyzekZ8+eiImJgYmJCSZMmIDXr19/ETFZFpZvCTt0xsLyDVFSUsLu3bu/tRksLF8UtkfDwvKdoqqqChMTE5iZmQn95eTk1No3Pj4ejo6OX8SOGTNmMPl8Zs+ejUePHn2R87A0XdgeDQvLd8zevXuhqKj4TW24cOEC83v79u3f0BKWHxW2oWFh+UEJDg7GyZMnIS8vj65duzLr3d3d0bNnTyZupuby06dP8eeff6KoqAhiYmKYM2cOjIyMcObMGYSFhYHL5aKoqAjm5uZwdXVlEslNnToV27Ztg729PYKDg9GvXz8cOXIE4eHhEBMTQ9u2beHp6Ynu3bvD3d0dsrKyuH//PvLz86GqqorAwMBacU0szQe2oWFh+Y6ZOnWqUBBnp06dsHnzZpw+fRoJCQmIiYmBtLS0kOpAfSxcuBATJ06Evb098vLy4OjoCB0dHezatQurVq1Ct27dUFBQgFGjRmHKlClYuXIloqKiavWs0tPTsWPHDhw5coQJPJ03bx7i4uIAVMUs7du3DxwOBzY2NoiPj2dzDjVj2IaGheU7RtTQWXp6OsaOHcsk2LOysmKka0Tx5s0bZGZmwtraGgDQvn17nD59GgCwdetWpKSkIDY2Fo8fPwYRoaysTGRZ586dg5GREWObpaUl/P39mfkjHR0dSElJAQBUVFRQXFz8kVfO0pRgnQFYWH5QaobAiYuLM78/lM+plpaplpLhcP5/9sonT56gtLQUFhYWuHPnDnr37s0oMNcXYleXQgERMaoD9cn5sDQ/2IaGheUHZOTIkYiPj0dJSQkEAgGOHz/ObFNQUGCkZQoKCnD58mUAgKysLPr06YOYmBgAQF5eHiZPnowHDx7g3bt3cHV1xejRo3Hp0iVwuVymMakpW1ONjo4O/vrrL8YbLTIystZcEQtLNezQGQvLd8yHczRA1TyLrq4u7t+/DysrK/z0009QU1PD69evAVTllVm0aBHGjx+PTp06Yfjw4cyxa9euhY+PD8LDw8HhcODv7w91dXXo6enB0NAQUlJSjHJzdnY2unTpAgMDAzg6OmLTpk1MOSNGjMC0adMwdepUCAQCKCoqIiwsrJatLCwAK0HDwsLyHcFK0PyYsBI0LCwsLCzfFLahYWFhYWH5orANzUeQk5NTS8a+ORAdHQ1bW1uYmZnByMgInp6eKCkp+dZmNYpDhw4xKZk/he9ZfmXnzp1wd3evc1tFRQU2bNgAc3NzmJmZwcTEBNu2bWuU95eqqiqKiorw9u1bTJky5XObzdIMYZ0BWOpl69atSE1NxebNm9G2bVvweDwEBATgt99+w8GDB7+1eQ1SnafmU/kR5VeICHPnzkX37t1x5MgRtGjRAq9fv4azszNKS0vh6uraqHKKi4vZhGUsnwW2oflMPH36FL6+vnj//j1evnwJNTU1bNiwAS1atEC/fv3g5OSECxcuoLCwELNmzYKdnR34fD6CgoKQnJwMOTk5qKur4/HjxwgPD4ejoyPs7e1hYGAAAELLEREROHLkCHg8HoqLizF79uwGy3v79i38/f3x4MED8Hg8aGpqMvESoigtLUVYWBiio6PRtm1bAICkpCSWLFmCxMREcLlccDgcrFq1Cunp6RAXF4e6ujo8PDwgKyuL0aNHw9jYGBcvXkRxcTFmzUlbhR4AABSjSURBVJqFa9eu4c6dO5CQkEBoaCiUlJQ+ar9q+RMAzLKCggKmTZsGXV1d3Lx5EyUlJVi8eDHGjh2LTZs24fXr1/jzzz+/K/mVT3leeDweVqxYgbS0NLRp0wZt2rSBnJxcrft25coVPHnyBNu2bWPiaxQUFBAUFIQXL17Uep7qWgYADw8PlJeXw8zMDFFRUejduzfS09OZIE1VVVWkp6fj4cOH8Pf3h4yMDN6/f4/IyEicP38eoaGh4PF4kJaWhpubW7McDWD5P4il0Tx//pwGDBhQ57ZVq1ZRTEwMERFxuVwyNjam+Ph4IiJSUVGh8PBwIiLKyMigvn37Unl5OR06dIjs7e2pvLycKioqaMaMGeTg4EBERA4ODnTq1Cmm/Orld+/ekY2NDRUVFRER0fXr1xmb6ivP3d2d9u3bR0RElZWVtGjRItq2bVu915uRkUHDhw+vd5/g4GBycXEhLpdLfD6f3N3dydPTk4iIRo0aRQEBAUREFBcXR2pqanTv3j0iIpo7dy6FhoZ+9H63bt1izl29/Pz5c1JRUaHk5GQiIoqPjyc9PT0iItq4cSP5+PgQEZG5uTnt37+fiIhyc3NJX1+fSkpKyMHBgZ4+fUpERPn5+dSrVy/6999/mXtX/bv6fGlpaTRmzBhmfWRkJBkaGpJAICA3NzeytbWliooK4nK5ZG5uThEREbXq7VOelz179tCUKVOooqKC3r9/TxYWFuTm5lar7J07d9Iff/xR730T9XzVvOYPn/eadVFz+eLFi6SmpkY5OTlERPT06VMyNjZmntEHDx7QiBEj6P379/XaRETMvXz+/HmD+7J8PzR039gezWdi8eLFuHDhArZv346srCwUFhaitLSU2a6vrw8A6NOnD7hcLkpLS3H27FmYmZmhRYsWAABbW9sGZURatWqFrVu34uzZs8jKykJmZiZznvrKS0lJQUZGBiIiIgAA5eXlDV6TmJhYvTnqASA1NRULFiyApKQkgKov45q6W+PGjQMAdO7cGW3btoWamhoAoEuXLkKyJI3dTxSSkpLQ1dUFAPTu3Rtv3rwR2v69ya98yvOSnp4OY2NjSElJQUpKCiYmJrh//36tssXExL56JH779u3RsWNHAGB6YtOmTWO2czgcPHv2jLmvLM0LtqH5TCxcuBB8Ph+GhobQ09NDXl6e0D979cu/Wv6DiGoNW30Y7EZ1yIjk5+fD1tYWNjY20NDQgIGBAc6cOQMA9ZYnEAgQHBwMZWVlAEBJSYmQFEld9OjRA5WVlcjKykK3bt2Y9RUVFXBxccGKFSsgEAiEyhEIBIytAJgXLgCmMaqLxu5Xs064XK7QMdXXW9d1iZJf+eWXX2BhYYExY8Zg8ODBsLKywunTp7+4/MqnPC8fUlN2pib9+/fH3r17wefzhfa5desWwsPDsXr16lpl1rxnjaFm3QOAjIwM81sgEEBTUxMbNmxg1uXl5eHnn3/+qHOwNB1Yr7PPxPnz5zFv3jwYGRkBAG7evAk+n1/vMbq6ujhx4gS4XC4qKysRHR3NbFNUVGRkRB49esR8ud6+fRuKioqYO3cutLW1mUaGz+fXW562tjb27NkDIgL3/7V350FV1e8Dx9+AwrihggY46hiGYqC4m4oZ4JasbomiUyqWS+mAGqi4jAJGo2ISOm5hKikNmuJSOurguMVQIVqO5oJKKasbi8KF+/n+wY/7E4OrKBe0ntd/3HPP5/Ocwx0ePuee8zzFxUyfPp0dO3bojc/U1JSpU6eycOFCcnJygLI/MOHh4Tx69AgrKysGDBjAzp070Wg0aLVaYmNj6d+/fzXP3vN58pwkJSWRnZ393Pu+auVXXuTzMmDAAPbu3UtRURFFRUUcOnSo0vd169YNW1tbVqxYQVFREQA5OTmEhobqHqar6vP1pHr16lFaWqpLSBYWFrqbAw4cOFBlnH379uX06dNcu3YNKFtpe3l5PdcqWvw7yYqmmgoLC//xpeauXbsICAhg5syZNGzYkMaNG9OrVy9u3bqld6yRI0eSlpaGj48PDRs2pHXr1jRo0ACA6dOnExwczIkTJ7C1taVnz55AWemP+Ph4hg0bhpGREb1798bCwoKbN2/qHW/hwoWEhYXh6emJRqOhX79++Pv7A2V9TQBmz579jxinTZtGgwYNdL1NioqK6N27N+vWrdPFGRERgY+PDyUlJXTp0oVFixa96OnVa+7cuSxdupS4uDgcHBxwcHCo1v6vUvmVF/m8+Pr6cuvWLTw8PJ6Z2NauXUtkZCQjR47ExMQErVaLj4+P7vdY1efrSS1btqRLly64u7sTGxtLSEgIy5Ytw9zcnH79+tGyZctK537rrbdYtmwZgYGBupX7+vXrpR/Nf5iUoKlDp06dIjc3F29vbwBCQ0MxMzNj3rx5tTrejRs3iI+PZ+7cuS80rxA1RUrQvJ6kBM0rzM7Ojr179+Lp6Ym7uzv37t1j2rRptT5eWlqawfrNCyGEXDqrQ1ZWVsTExNT5eC4uLjUWgxBCPE0SjRDV0LFjRzp06PCP72Oio6Ordann/PnzxMfHs2zZMpKSkli+fLneL9jLnTt3jlWrVnH//n2UUlhbWxMUFISdnV2V+1y4cIFNmzaxdu3a545PiJokiUaIaqqqvXJ1XL16lczMzGrtU1xczCeffMI333yjuxFi3759TJ06lWPHjlV5u3Pnzp0lyYg6JYlGiBqi1WoJDw8nNTWVgoIClFKEhobSo0cPgoODuX//Punp6Tg5OXHmzBny8vKYP38+Pj4+FBYWEhAQwPXr1ykqKiI0NPQfd4I9evSIvLy8Cg92enl50bhxY90zM/Hx8cTExGBsbEzz5s2JiIjg1q1buhVTcXExK1euJDk5mdLSUt5++21CQkJ0JYNGjBjB2bNnuXPnDt7e3rq6aJWNa2Njw/HjxystNXPt2jUWLlxIcXExSilGjx6Nn59frf4+xKtDEo0Q1fR018vWrVsTHR1NamoqWVlZxMXFYWxszMaNG9m0aRM9evQAyqoxHDx4EIA9e/Zw+PBhVqxYQVJSEhkZGURGRuLk5MTWrVuJiori22+/rTBv06ZNmTdvHv7+/rRo0YLu3bvTp08f3N3dMTU15dKlS6xcuZIffvgBGxsbtm7dyvr163F3d9eNUV7/bM+ePRgZGbF69WpWrlzJ0qVLgbLb97/77jsyMzMZPHgwo0aNoqCgoNJxJ0+eTGRkJNu2baN58+ZcuXKFSZMmceTIEbZs2YKrqysff/wx2dnZhIeHM27cOOnA+R8liUaIaqrq0lm3bt1o2rQpu3btIj09naSkpArPjpQnnMq0adMGJycnAOzt7dm9e3el75s0aRJjxowhOTmZ5ORkNm3axKZNm4iPj+fs2bM4OztjY2MDoCsBk5SUpNs/MTGRvLw8zpw5A5RVBLC0tNRtLy99Y2VlhaWlJQ8ePCA5ObnScWNjY6ssNTN48GCCgoI4f/48ffv2JSQkRJLMf5gkGiFqSGJiImFhYUyaNAk3NzdsbW1JSEjQbX+yTMvTniy7U1XZml9//ZWUlBT8/f1xcXHBxcWFwMBAPDw8OH36NCYmJhVK7Dx+/FhXrbmcVqtlwYIFurpwBQUFuuoB8P+lb56Mo6px9ZWasbe35/Dhw5w5c4azZ88SHR3Nnj17sLa21nsOxb+T/IshRA05ffo0Li4ujB8/HkdHR44ePVplWZnKSts8i4WFBevXr+eXX37RvZadnU1+fj4dOnSgT58+nD17lqysLKCsYkV5XbNyzs7OxMbG6srsLFq0iNWrV+udt6px9ZWamTNnDocOHcLd3Z0lS5bQuHHjZ1Y+EP9esqIRopqe/o4Gyopk+vr6MmfOHDw9PSkpKaF///4cOXKk0iKcXbt2JTo6mk8//fS5H5Z98803iY6OJjIykoyMDMzMzGjSpAnh4eHY2toC6L7DgbISMuHh4dy4cUM3xowZM4iIiGDEiBGUlpbSqVOnKrt0luvYsWOl41pZWVVZambGjBksXLiQuLg4TExMGDRoEL169Xqu4xT/PlKCRgjxyrh58yZDhgwhNjZWLrO9RjIyMvDz8+PIkSOV1uCTFY0Q4pVRXpFbboV+PWVnZ1eaaGRFI4R4ZTx+/Jjff/+dli1bVvkAqnj1lJaWkp2djaOjY4V+TOUk0QghhDAouetMCCGEQUmiEUIIYVCSaIQQQhiUJBohhBAGJYlGCCGEQUmiEUIIYVCSaIQQQhiUJBohxCshMTERT09Phg4dyqxZs8jPz6+VeZVSBAUFsWXLFqDs4cOwsDCGDRvG4MGD2blzp+69N27cwM/Pj+HDhzN69GhdQVEoaw43fPhwhgwZwpIlS9BoNEBZw7o5c+bw/vvvM3ToUI4ePfpCce7btw8vLy+8vb3x9fXlwoULAGzYsEEXa1RUlK7y9927d/H392f48OF4eHjw22+/6caq6lzrO/aXooQQoo7l5uaqd955R6WlpSmllPryyy/VkiVLDD7v1atX1cSJE5WTk5PavHmzUkqpHTt2KH9/f6XRaNT9+/fV0KFDVWpqqlJKqVGjRqmEhASllFKJiYnK3d1dabVadfnyZfXuu++q3NxcVVpaqgICAtTGjRuVUkpFRESokJAQpZRSf//9t3J2dlZ37typVpzXrl1T/fv3V5mZmbq5Bw4cqBITE5W3t7cqKChQjx8/Vn5+furgwYNKKaVmzZql1q9fr5RS6uLFi8rZ2VkVFhbqPdf6jv1lyIpGCFHnTp06RefOnWnXrh0A48aNY//+/ZX25alJsbGxjBkzhmHDhuleO3r0KCNHjqRevXo0bdoUd3d3EhISyMzM5Pr167qOpQMHDqSwsJCLFy9y7NgxXF1dsbCwwNjYmLFjx+p6ER09epQxY8YA0KpVK/r378+PP/5YrThNTU0JDQ3ljTfeAMDR0ZGcnBx++uknPDw8aNiwIWZmZowcOZKEhARKSkpITEzkgw8+AKBTp060a9eOkydP6j3XVR37y5JEI4SocxkZGRWqNVtbW5Ofn09BQYFB5128eDGenp4VXrtz546um2h5LBkZGbqmbk+2iLCystJte3qfzMzMSscr36c6WrduzXvvvQeUXepbsWIFrq6uZGVlVTrvvXv30Gq1FTrBls+r71xXdewvSxKNEKLOabXaCl08y9VF+2elVIVYlFIYGxtXGqP6vw6kT6+8yvepbDx48eMqLCxk9uzZ3Lp1i9DQ0BeKVd+5rmq8lyWJRghR52xsbHQdPAEyMzNp2rSp3vbXtRVLVlYW1tbWtGrViuzs7ApJpXxbVfvoG6+6bt++ja+vLyYmJmzbtg1zc/Mqx7a0tEQpxf379ytss7Ky0nuuayrWp0miEULUOWdnZ1JTU3XdQHft2oWbm1udxOLm5sbu3bspKSnh4cOHHDx4kEGDBmFtbU3btm05dOgQACdPnsTY2JgOHTrg6urK8ePHyc3NRSlFXFwcgwYN0o0XFxcHlF0iPHnyJC4uLtWKKT8/n4kTJzJkyBAiIyN1pfjd3NxISEigsLCQ4uJi9uzZw6BBg6hXrx7vvfce33//PQCXLl3i2rVr9OnTR++5rurYX5a0CRBCvBJOnDjBqlWr0Gg0tG3bloiICJo1a1YrcwcHB2NnZ8eUKVMoKSkhIiKCM2fOoNFoGDt2LFOmTAHKbm9etGgR9+7dw9TUlOXLl+Pg4ADA7t27iYmJQaPR4OTkxPLlyzEzM6OgoIClS5dy8eJFSktLmT59Ot7e3tWKb8OGDaxZs4YOHTpUeH3r1q3ExcWxf/9+NBoNbm5ufP755xgZGZGTk0NISAh//fUXRkZGBAUF4ezsDFR9rvUd+8uQRCOEEMKg5NKZEEIIg5JEI4QQwqAk0QghhDAoSTRCCCEMShKNEEIIg6pX1wEIIcTroLi4mO3bt7N//35u3rxJgwYN6NKlCzNnzqRz5861FkdKSgparZYePXrU2pwvS1Y0QgjxDI8ePcLPz4+dO3cyefJk9u7dy+bNm2nWrBl+fn78/PPPtRbLhAkTuHnzZq3NVxNkRSOEEM+wZs0abty4wYEDB7CystK9/sUXX5Cbm8vy5cs5cOBApTXEatrr+OijrGiEEEKP8tIuo0ePrpBkyi1evJhVq1ZhZGTE7du3CQgIoG/fvnTr1o0ZM2aQnp6ue6+rqyvr1q2rsP+Tr0VFRTFlyhSio6NxdnamV69eTJs2TVcJ2tXVldLSUubPn8/EiRMNeNQ1SxKNEELokZ6ezsOHD3Fycqp0e5s2bbC3tyc/P59x48bx4MEDNm/ezPbt28nLy2PChAnk5eU993xJSUlcvnyZmJgYIiMjSUlJYe3atUBZF08TExMWLFhAVFRUjRxfbZBEI4QQejx8+BAAc3Nzve/bt28fDx8+ZPXq1Tg4OODo6MhXX33FgwcPqtU8TClFeHg4dnZ2ODs74+Xlxblz5wB0/WWaNGlSa3XgaoIkGiGE0KN58+YAFUruV+bKlSvY2tpWSAAWFha0b9+eP//887nna9GiBY0bN9b9bG5ujkajqWbUrxZJNEIIoUfbtm2xtLQkNTW10u1JSUlMmzaNoqKiSrdrtVrq169f5fglJSUVfjY1Nf3He17HGwCeJIlGCCH0MDY2ZsSIEezevVv3pXw5pRQbN24kLS2N7t27c/369Qorn7t375KWlkb79u0BqF+/Pvn5+brt+fn55ObmViue2rizraZJohFCiGeYMWMGrVu3Zvz48Rw4cID09HRSUlKYNWsWycnJhIWF4eXlhYWFBYGBgVy8eJE//viDwMBAzM3NcXd3B6Br164cPHiQlJQUrly5QnBwMCYmJtWKpVGjRly9erXaCaouSaIRQohnaNSoETt27MDDw4Ovv/4aDw8PPvvsM7RaLXFxcfTs2RMzMzO2bNmCqakpfn5+fPjhhzRp0oTY2FjdjQSBgYHY29vz0UcfMWnSJLp370737t2rFcvUqVPZuXNnjTQkqy3S+EwIIYRByYpGCCGEQUmiEUIIYVCSaIQQQhiUJBohhBAGJYlGCCGEQUmiEUIIYVCSaIQQQhiUJBohhBAGJYlGCCGEQf0P6EpbCrlSCW8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# FOR jeywords distribution, TOP\n", + "\n", + "df_sub = df_joined[df_joined.for_top.isin(df_joined.for_top.value_counts()[:20].index.tolist())]\n", + "b = sns.countplot(y=\"for_top\", data=df_sub, order=df_sub['for_top'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"FOR first level\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/dim_for_top.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# FOR jeywords distribution, TOP\n", + "\n", + "df_sub = df_joined[df_joined.for_bottom.isin(df_joined.for_bottom.value_counts()[:20].index.tolist())]\n", + "b = sns.countplot(y=\"for_bottom\", data=df_sub, order=df_sub['for_bottom'].value_counts().index)\n", + "#b.axes.set_title(\"Title\",fontsize=50)\n", + "b.set_xlabel(\"Count\",fontsize=15)\n", + "b.set_ylabel(\"FOR second level\",fontsize=15)\n", + "b.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/dim_for_bottom.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "covid", + "language": "python", + "name": "covid" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebook_CORD-19_2_text_analysis.ipynb b/Notebook_CORD-19_2_text_analysis.ipynb new file mode 100644 index 0000000..8db0fe1 --- /dev/null +++ b/Notebook_CORD-19_2_text_analysis.ipynb @@ -0,0 +1,1712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CORD-19 text analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# magics and warnings\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import os, random, codecs, json\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "seed = 99\n", + "random.seed(seed)\n", + "np.random.seed(seed)\n", + "\n", + "import nltk, sklearn\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style=\"white\")\n", + "sns.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 2.5})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load metadata\n", + "\n", + "df_meta = pd.read_csv(\"datasets_output/df_pub.csv\",compression=\"gzip\")\n", + "df_datasource = pd.read_csv(\"datasets_output/sql_tables/datasource.csv\",sep=\"\\t\",header=None,names=['datasource_metadata_id', 'datasource', 'url'])\n", + "df_pub_datasource = pd.read_csv(\"datasets_output/sql_tables/pub_datasource.csv\",sep=\"\\t\",header=None,names=['pub_id','datasource_metadata_id'])\n", + "df_cord_meta = pd.read_csv(\"datasets_output/sql_tables/cord19_metadata.csv\",sep=\"\\t\",header=None,names=['cord19_metadata_id', 'source', 'license', 'full_text_file', 'ms_academic_id',\n", + " 'who_covidence', 'sha', 'full_text', 'pub_id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pub_idtitleabstractpublication_yearpublication_monthjournalvolumeissuepagesdoipmidpmcidtimestamp
00‘A ticking time bomb’: Scientists worry about ...CAPE TOWN, SOUTH AFRICA—Late on Sunday evening...2020.0NaNScienceNaNNaNNaN0.1126/science.abb7331NaNNaN2020-04-04 07:55:51.892454
11[Ten hot issues of breast cancer under the nov...NaN2020.02.0Chinese medical journal1000e00210.0376/cma.j.issn.0376-2491.2020.000232036640.0NaN2020-04-04 07:55:51.892454
22Another Piece of the Puzzle: Human Metapneumov...BACKGROUND: Each winter respiratory viruses ac...2008.012.0Archives of Internal MedicineNaNNaNNaN10.1001/archinte.168.22.248919064834.0PMC27836242020-04-04 07:55:51.892454
33Viral etiology of severe pneumonia among Kenya...CONTEXT: Pneumonia is the leading cause of chi...2010.05.0JAMANaNNaNNaN10.1001/jama.2010.67520501927.0PMC29687552020-04-04 07:55:51.892454
44Critically Ill Patients With Influenza A(H1N1)...NaN2014.04.0JAMANaNNaNNaN10.1001/jama.2014.211624566924.0PMC66894042020-04-04 07:55:51.892454
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" + ], + "text/plain": [ + " pub_id title \\\n", + "0 0 ‘A ticking time bomb’: Scientists worry about ... \n", + "1 1 [Ten hot issues of breast cancer under the nov... \n", + "2 2 Another Piece of the Puzzle: Human Metapneumov... \n", + "3 3 Viral etiology of severe pneumonia among Kenya... \n", + "4 4 Critically Ill Patients With Influenza A(H1N1)... \n", + "\n", + " abstract publication_year \\\n", + "0 CAPE TOWN, SOUTH AFRICA—Late on Sunday evening... 2020.0 \n", + "1 NaN 2020.0 \n", + "2 BACKGROUND: Each winter respiratory viruses ac... 2008.0 \n", + "3 CONTEXT: Pneumonia is the leading cause of chi... 2010.0 \n", + "4 NaN 2014.0 \n", + "\n", + " publication_month journal volume issue pages \\\n", + "0 NaN Science NaN NaN NaN \n", + "1 2.0 Chinese medical journal 100 0 e002 \n", + "2 12.0 Archives of Internal Medicine NaN NaN NaN \n", + "3 5.0 JAMA NaN NaN NaN \n", + "4 4.0 JAMA NaN NaN NaN \n", + "\n", + " doi pmid pmcid \\\n", + "0 0.1126/science.abb7331 NaN NaN \n", + "1 10.0376/cma.j.issn.0376-2491.2020.0002 32036640.0 NaN \n", + "2 10.1001/archinte.168.22.2489 19064834.0 PMC2783624 \n", + "3 10.1001/jama.2010.675 20501927.0 PMC2968755 \n", + "4 10.1001/jama.2014.2116 24566924.0 PMC6689404 \n", + "\n", + " timestamp \n", + "0 2020-04-04 07:55:51.892454 \n", + "1 2020-04-04 07:55:51.892454 \n", + "2 2020-04-04 07:55:51.892454 \n", + "3 2020-04-04 07:55:51.892454 \n", + "4 2020-04-04 07:55:51.892454 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['pub_id', 'title', 'abstract', 'publication_year', 'publication_month',\n", + " 'journal', 'volume', 'issue', 'pages', 'doi', 'pmid', 'pmcid',\n", + " 'timestamp'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datasource_metadata_iddatasourceurl
00CORD19https://pages.semanticscholar.org/coronavirus-...
11Dimensionshttps://docs.google.com/spreadsheets/d/1-kTZJZ...
22WHOhttps://www.who.int/emergencies/diseases/novel...
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" + ], + "text/plain": [ + " datasource_metadata_id datasource \\\n", + "0 0 CORD19 \n", + "1 1 Dimensions \n", + "2 2 WHO \n", + "\n", + " url \n", + "0 https://pages.semanticscholar.org/coronavirus-... \n", + "1 https://docs.google.com/spreadsheets/d/1-kTZJZ... \n", + "2 https://www.who.int/emergencies/diseases/novel... " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_datasource" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select just CORD-19" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df_meta = df_meta.merge(df_pub_datasource, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")\n", + "df_meta = df_meta.merge(df_datasource, how=\"inner\", left_on=\"datasource_metadata_id\", right_on=\"datasource_metadata_id\")\n", + "df_cord19 = df_meta[df_meta.datasource_metadata_id==0]\n", + "df_cord19 = df_cord19.merge(df_cord_meta, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(56809, 16)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_meta.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46994, 24)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pub_idtitleabstractpublication_yearpublication_monthjournalvolumeissuepagesdoi...datasourceurlcord19_metadata_idsourcelicensefull_text_filems_academic_idwho_covidenceshafull_text
00‘A ticking time bomb’: Scientists worry about ...CAPE TOWN, SOUTH AFRICA—Late on Sunday evening...2020.0NaNScienceNaNNaNNaN0.1126/science.abb7331...CORD19https://pages.semanticscholar.org/coronavirus-...0WHOunkNaNNaN#8463NaNNaN
11[Ten hot issues of breast cancer under the nov...NaN2020.02.0Chinese medical journal1000e00210.0376/cma.j.issn.0376-2491.2020.0002...CORD19https://pages.semanticscholar.org/coronavirus-...1WHOunkNaN3.003451e+09#615NaNNaN
22Another Piece of the Puzzle: Human Metapneumov...BACKGROUND: Each winter respiratory viruses ac...2008.012.0Archives of Internal MedicineNaNNaNNaN10.1001/archinte.168.22.2489...CORD19https://pages.semanticscholar.org/coronavirus-...2PMCunkNaNNaNNaNNaNNaN
33Viral etiology of severe pneumonia among Kenya...CONTEXT: Pneumonia is the leading cause of chi...2010.05.0JAMANaNNaNNaN10.1001/jama.2010.675...CORD19https://pages.semanticscholar.org/coronavirus-...3PMCunkNaNNaNNaNNaNNaN
44Critically Ill Patients With Influenza A(H1N1)...NaN2014.04.0JAMANaNNaNNaN10.1001/jama.2014.2116...CORD19https://pages.semanticscholar.org/coronavirus-...4PMCunkNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " pub_id title \\\n", + "0 0 ‘A ticking time bomb’: Scientists worry about ... \n", + "1 1 [Ten hot issues of breast cancer under the nov... \n", + "2 2 Another Piece of the Puzzle: Human Metapneumov... \n", + "3 3 Viral etiology of severe pneumonia among Kenya... \n", + "4 4 Critically Ill Patients With Influenza A(H1N1)... \n", + "\n", + " abstract publication_year \\\n", + "0 CAPE TOWN, SOUTH AFRICA—Late on Sunday evening... 2020.0 \n", + "1 NaN 2020.0 \n", + "2 BACKGROUND: Each winter respiratory viruses ac... 2008.0 \n", + "3 CONTEXT: Pneumonia is the leading cause of chi... 2010.0 \n", + "4 NaN 2014.0 \n", + "\n", + " publication_month journal volume issue pages \\\n", + "0 NaN Science NaN NaN NaN \n", + "1 2.0 Chinese medical journal 100 0 e002 \n", + "2 12.0 Archives of Internal Medicine NaN NaN NaN \n", + "3 5.0 JAMA NaN NaN NaN \n", + "4 4.0 JAMA NaN NaN NaN \n", + "\n", + " doi ... datasource \\\n", + "0 0.1126/science.abb7331 ... CORD19 \n", + "1 10.0376/cma.j.issn.0376-2491.2020.0002 ... CORD19 \n", + "2 10.1001/archinte.168.22.2489 ... CORD19 \n", + "3 10.1001/jama.2010.675 ... CORD19 \n", + "4 10.1001/jama.2014.2116 ... CORD19 \n", + "\n", + " url cord19_metadata_id \\\n", + "0 https://pages.semanticscholar.org/coronavirus-... 0 \n", + "1 https://pages.semanticscholar.org/coronavirus-... 1 \n", + "2 https://pages.semanticscholar.org/coronavirus-... 2 \n", + "3 https://pages.semanticscholar.org/coronavirus-... 3 \n", + "4 https://pages.semanticscholar.org/coronavirus-... 4 \n", + "\n", + " source license full_text_file ms_academic_id who_covidence sha full_text \n", + "0 WHO unk NaN NaN #8463 NaN NaN \n", + "1 WHO unk NaN 3.003451e+09 #615 NaN NaN \n", + "2 PMC unk NaN NaN NaN NaN NaN \n", + "3 PMC unk NaN NaN NaN NaN NaN \n", + "4 PMC unk NaN NaN NaN NaN NaN \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Text analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: add a language detection to remove all non-English" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8349, 25)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cord19[df_cord19[\"abstract\"]==\"\"].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate abstracts and titles\n", + "\n", + "# note that ~8k articles have no abstract\n", + "df_cord19[\"title\"] = df_cord19[\"title\"].fillna('')\n", + "df_cord19[\"abstract\"] = df_cord19[\"abstract\"].fillna('')\n", + "df_cord19[\"title_abstract\"] = df_cord19.title +\" \"+df_cord19.abstract" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'‘A ticking time bomb’: Scientists worry about coronavirus spread in Africa CAPE TOWN, SOUTH AFRICA—Late on Sunday evening, South African President Cyril Ramaphosa, in a televised address to the nation, declared that COVID-19, the respiratory disease spreading globally, had become a “national disaster.” The declaration allows his government to access special funding and instigate harsh regulations to combat the viral outbreak. “Never before in the history of our democracy have we been confronted by such a severe situation,” Ramaphosa said before announcing a raft of measures to curb the virus’ spread, including school closures, travel restrictions, and bans on large gatherings. So far, the official numbers seemed to suggest that sub-Saharan Africa, home to more than 1 billion people, had been lucky. The interactive map of reported COVID-19 cases run by Johns Hopkins University shows big red blobs almost everywhere—except sub-Saharan Africa.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#texts = df_cord19[(pd.notnull(df_cord19[\"title_abstract\"])) & (df_cord19.publication_year>2019)].title_abstract.tolist()\n", + "texts = df_cord19[(pd.notnull(df_cord19[\"title_abstract\"]))].title_abstract.tolist()\n", + "texts[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Topic modelling" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "import gensim, sklearn\n", + "import scispacy\n", + "import spacy\n", + "import pyLDAvis.gensim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python -m spacy download en" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "#nlp = spacy.load('en')\n", + "nlp = spacy.load(\"en_core_sci_md\")\n", + "#STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# pre-processing: use lemmas and remove stopwords, add entities\n", + "\n", + "processed_docs = list()\n", + "for doc in nlp.pipe(texts, n_process=6, batch_size=100):\n", + "\n", + " ents = doc.ents # Named entities\n", + "\n", + " # Lemmatize tokens, remove punctuation and remove stopwords.\n", + " doc = [token.lemma_ for token in doc if not token.is_punct and not token.is_stop]# token.is_alpha]\n", + "\n", + " # Remove common words from a stopword list and keep only words of length 3 or more.\n", + " #doc = [token for token in doc if token not in STOPWORDS and len(token) > 2]\n", + "\n", + " # Add named entities, but only if they are a compound of more than one word.\n", + " doc.extend([str(entity) for entity in ents if len(entity) > 1])\n", + " \n", + " processed_docs.append(doc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "pickle.dump(processed_docs, open(\"datasets_output/processed_docs_cord19_scispacy.pk\", \"wb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "processed_docs = pickle.load(open(\"datasets_output/processed_docs_cord19_scispacy.pk\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "docs = processed_docs\n", + "del processed_docs\n", + "\n", + "# Add bigrams\n", + "from gensim.models.phrases import Phrases\n", + "# Add bigrams to docs (only ones that appear several times or more). A better approach would be to use a chi_sq test.\n", + "bigram = Phrases(docs, min_count=50)\n", + "for idx in range(len(docs)):\n", + " for token in bigram[docs[idx]]:\n", + " if '_' in token:\n", + " # Token is a bigram, add to document.\n", + " docs[idx].append(token)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove rare and common tokens.\n", + "# Filter out words that occur too frequently or too rarely.\n", + "max_freq = 0.5\n", + "min_wordcount = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique tokens: 29136\n", + "Number of docs: 46994 (46994)\n" + ] + } + ], + "source": [ + "# Create a dictionary representation of the documents, and filter out frequent and rare words.\n", + "from gensim.corpora import Dictionary\n", + "dictionary = Dictionary(docs)\n", + "dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)\n", + "\n", + "# Bag-of-words representation of the documents.\n", + "corpus = [dictionary.doc2bow(doc) for doc in docs]\n", + "#MmCorpus.serialize(\"models/corpus.mm\", corpus)\n", + "\n", + "print('Number of unique tokens: %d' % len(dictionary))\n", + "print('Number of docs: %d (%d)' % (len(corpus),len(texts)))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dictionary.token2id[\"covid-19\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4min 42s, sys: 2.59 s, total: 4min 44s\n", + "Wall time: 4min 36s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "from gensim.models import LdaMulticore, LdaModel\n", + "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n", + "model = LdaModel(corpus=corpus, num_topics=params['num_topics'], id2word=dictionary, #workers=6,\n", + " passes=params['passes'], random_state=params['random_state'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0,\n", + " '0.017*\"health\" + 0.014*\"disease\" + 0.008*\"public\" + 0.008*\"datum\" + 0.007*\"model\" + 0.007*\"outbreak\" + 0.006*\"public_health\"'),\n", + " (1,\n", + " '0.031*\"cell\" + 0.018*\"mouse\" + 0.018*\"infection\" + 0.017*\"response\" + 0.012*\"immune\" + 0.010*\"expression\" + 0.009*\"t\"'),\n", + " (2,\n", + " '0.048*\"virus\" + 0.043*\"cell\" + 0.017*\"protein\" + 0.016*\"viral\" + 0.014*\"replication\" + 0.012*\"infection\" + 0.011*\"hepatitis\"'),\n", + " (3,\n", + " '0.019*\"group\" + 0.018*\"=\" + 0.015*\"study\" + 0.013*\"day\" + 0.013*\"p\" + 0.010*\"high\" + 0.009*\"level\"'),\n", + " (4,\n", + " '0.020*\"infection\" + 0.020*\"dog\" + 0.015*\"hospital\" + 0.015*\"canine\" + 0.014*\"transmission\" + 0.013*\"h1n1\" + 0.013*\"worker\"'),\n", + " (5,\n", + " '0.026*\"calf\" + 0.021*\"strain\" + 0.019*\"isolate\" + 0.017*\"rotavirus\" + 0.015*\"bovine\" + 0.014*\"diarrhea\" + 0.012*\"sample\"'),\n", + " (6,\n", + " '0.051*\"antibody\" + 0.026*\"protein\" + 0.025*\"coronavirus\" + 0.015*\"s\" + 0.014*\"sars-cov\" + 0.012*\"spike\" + 0.012*\"pedv\"'),\n", + " (7,\n", + " '0.025*\"assay\" + 0.023*\"detection\" + 0.017*\"sample\" + 0.016*\"test\" + 0.016*\"method\" + 0.013*\"detect\" + 0.012*\"virus\"'),\n", + " (8,\n", + " '0.046*\"rna\" + 0.028*\"sequence\" + 0.025*\"gene\" + 0.016*\"genome\" + 0.014*\"virus\" + 0.010*\"region\" + 0.010*\"viral\"'),\n", + " (9,\n", + " '0.032*\"virus\" + 0.027*\"human\" + 0.020*\"infection\" + 0.016*\"disease\" + 0.016*\"animal\" + 0.012*\"bat\" + 0.012*\"mers-cov\"'),\n", + " (10,\n", + " '0.048*\"patient\" + 0.021*\"case\" + 0.010*\"clinical\" + 0.010*\"severe\" + 0.008*\"acute\" + 0.008*\"95\" + 0.008*\"disease\"'),\n", + " (11,\n", + " '0.035*\"respiratory\" + 0.029*\"infection\" + 0.024*\"virus\" + 0.016*\"child\" + 0.014*\"viral\" + 0.011*\"influenza\" + 0.009*\"patient\"'),\n", + " (12,\n", + " '0.039*\"activity\" + 0.025*\"antiviral\" + 0.021*\"drug\" + 0.015*\"inhibitor\" + 0.013*\"treatment\" + 0.013*\"protease\" + 0.012*\"compound\"'),\n", + " (13,\n", + " '0.036*\"protein\" + 0.012*\"domain\" + 0.009*\"interaction\" + 0.009*\"receptor\" + 0.008*\"binding\" + 0.008*\"membrane\" + 0.008*\"peptide\"'),\n", + " (14,\n", + " '0.057*\"virus\" + 0.041*\"influenza\" + 0.033*\"vaccine\" + 0.017*\"strain\" + 0.015*\"influenza_virus\" + 0.012*\"ibv\" + 0.012*\"avian\"')]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.show_topics(num_words=7, num_topics=params['num_topics'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# plot topics (NOTE: the IDs here do not match those from the model)\n", + "data = pyLDAvis.gensim.prepare(model, corpus, dictionary)\n", + "pyLDAvis.display(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# topics over time\n", + "\n", + "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n", + "publication_years = df_local.publication_year.tolist()\n", + "dois = df_local.doi.tolist()\n", + "topics = np.zeros((len(docs),params['num_topics']))\n", + "\n", + "for n,doc_topics in enumerate(model.get_document_topics(corpus)):\n", + " for t in doc_topics:\n", + " topics[n][t[0]] = t[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.58899075, 0. , 0.02117411, 0. , 0. ,\n", + " 0.13432109, 0.03213535, 0.02725324, 0. , 0.14957163,\n", + " 0.04038033, 0. , 0. , 0. , 0. ])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topics[0,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0.58898634),\n", + " (2, 0.021172175),\n", + " (5, 0.13432088),\n", + " (6, 0.032132164),\n", + " (7, 0.027253347),\n", + " (9, 0.14957902),\n", + " (10, 0.04038254)]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_document_topics(corpus[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "df_topics = pd.DataFrame(topics)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "df_topics[\"year\"] = publication_years\n", + "df_topics[\"doi\"] = dois" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from_which_year = 1980\n", + "\n", + "grouped = df_topics.groupby('year')\n", + "df_grouped = grouped.aggregate(np.mean)\n", + "df_grouped = df_grouped[df_grouped.index >= from_which_year]\n", + "#df_grouped" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(16, 12))\n", + "plt.pcolor(df_grouped.to_numpy(), norm=None, cmap='Blues')\n", + "plt.yticks(np.arange(df_grouped.to_numpy().shape[0])+0.5, [int(x) for x in df_grouped.index.values])\n", + "plt.xticks(np.arange(df_grouped.to_numpy().shape[1])+0.5, [\"Topic #\"+str(n) for n in range(model.num_topics)], rotation = 45)\n", + "plt.colorbar(cmap='Blues') # plot colorbar\n", + "plt.tight_layout() # fixes margins\n", + "plt.savefig(\"figures/topic_model_all_time_15.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# get number of papers per topic over time\n", + "\n", + "which_topic = [6,9,10]\n", + "is_selected_topic = [0 for _ in range(len(docs))]\n", + "for d in range(len(docs)):\n", + " #d_topics = sorted(model.get_document_topics(corpus[d]), key=lambda x:x[1], reverse=True)\n", + " d_topics = model.get_document_topics(corpus[d])\n", + " for x in d_topics:\n", + " if x[0] in which_topic:\n", + " is_selected_topic[d] += x[1]\n", + " \"\"\"\n", + " if d_topics[0][0] in which_topic:\n", + " is_selected_topic.append(True)\n", + " else:\n", + " is_selected_topic.append(False)\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "# plot trends\n", + "\n", + "df_topic_intensity = pd.DataFrame.from_dict({\"year\":publication_years,\"is_topic\":is_selected_topic})" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "from_which_year = 1980\n", + "\n", + "grouped_ti = df_topic_intensity.groupby('year')\n", + "df_grouped_ti = grouped_ti.aggregate(np.mean)\n", + "df_grouped_ti = df_grouped_ti[df_grouped_ti.index >= from_which_year]" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(15, 5))\n", + "plt.plot(df_grouped_ti.is_topic.values, label=\"Coronavirus publications\")\n", + "plt.xticks(np.arange(df_grouped_ti.shape[0]), [int(x) for x in df_grouped.index.values], rotation=45)\n", + "plt.legend()\n", + "plt.tight_layout() # fixes margins\n", + "plt.savefig(\"figures/topic_model_coronavirus.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Number of topics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.models.coherencemodel import CoherenceModel\n", + "\n", + "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n", + " \"\"\"\n", + " Compute c_v coherence for various number of topics.\n", + " From: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python\n", + "\n", + " Parameters:\n", + " ----------\n", + " dictionary : Gensim dictionary\n", + " corpus : Gensim corpus\n", + " texts : List of input texts\n", + " limit : Max num of topics\n", + "\n", + " Returns:\n", + " -------\n", + " model_list : List of LDA topic models\n", + " coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n", + " \"\"\"\n", + " coherence_values = []\n", + " model_list = []\n", + " params = {'passes': 3, 'random_state': seed}\n", + " for num_topics in range(start, limit, step):\n", + " m = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary,# workers=6,\n", + " passes=params['passes'], random_state=params['random_state'])\n", + " model_list.append(m)\n", + " coherencemodel = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n", + " coherence_values.append(coherencemodel.get_coherence())\n", + "\n", + " return model_list, coherence_values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Can take a long time to run\n", + "limit=50; start=5; step=5;\n", + "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, corpus=corpus, texts=docs, start=start, limit=limit, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show graph\n", + "x = range(start, limit, step)\n", + "plt.plot(x, coherence_values)\n", + "plt.xlabel(\"Num Topics\")\n", + "plt.ylabel(\"Coherence score\")\n", + "plt.legend((\"coherence_values\"), loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Journal to topics\n", + "\n", + "Using the Author topic model in gensim to map topics to journals." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# first, we select data for the top journals\n", + "\n", + "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n", + "journals = df_local.journal.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "how_many = 20\n", + "\n", + "journal_names = df_local.journal.value_counts().index.to_list()\n", + "journal2doc = {j:[] for j in list(set(journal_names))}" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "for n,j in enumerate(journal_names):\n", + " journal2doc[j].append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 57 s, sys: 550 ms, total: 57.6 s\n", + "Wall time: 40.8 s\n" + ] + } + ], + "source": [ + "from gensim.models import AuthorTopicModel\n", + "\n", + "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n", + "%time model_j = AuthorTopicModel(corpus=corpus, num_topics=params['num_topics'], id2word=dictionary.id2token, \\\n", + " author2doc=journal2doc, chunksize=2000, passes=params['passes'], eval_every=5, \\\n", + " iterations=100, random_state=params['random_state'])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0,\n", + " '0.013*\"disease\" + 0.007*\"health\" + 0.005*\"model\" + 0.005*\"patient\" + 0.005*\"study\" + 0.004*\"datum\" + 0.004*\"risk\" + 0.004*\"SARS\" + 0.004*\"protein\" + 0.004*\"case\"'),\n", + " (1,\n", + " '0.040*\"cell\" + 0.022*\"virus\" + 0.009*\"mouse\" + 0.009*\"infection\" + 0.008*\"viral\" + 0.006*\"abstract\" + 0.006*\"culture\" + 0.006*\"response\" + 0.005*\"rat\" + 0.005*\"t\"'),\n", + " (2,\n", + " '0.019*\"virus\" + 0.018*\"cell\" + 0.018*\"rna\" + 0.014*\"protein\" + 0.007*\"abstract\" + 0.007*\"viral\" + 0.006*\"mrna\" + 0.005*\"sequence\" + 0.005*\"coronavirus\" + 0.005*\"mouse\"'),\n", + " (3,\n", + " '0.010*\"fipv\" + 0.009*\"feline\" + 0.009*\"study\" + 0.008*\"volume\" + 0.006*\"infectious\" + 0.006*\"human\" + 0.005*\"content\" + 0.005*\"index\" + 0.005*\"group\" + 0.005*\"chapter\"'),\n", + " (4,\n", + " '0.011*\"antibody\" + 0.009*\"bovine\" + 0.009*\"disease\" + 0.007*\"canine\" + 0.007*\"monoclonal\" + 0.007*\"dog\" + 0.007*\"infection\" + 0.006*\"use\" + 0.005*\"monoclonal_antibody\" + 0.005*\"bcv\"'),\n", + " (5,\n", + " '0.009*\"strain\" + 0.007*\"sequence\" + 0.006*\"p\" + 0.006*\"study\" + 0.005*\"China\" + 0.005*\"identify\" + 0.005*\"coronavirus\" + 0.005*\"=\" + 0.005*\"analysis\" + 0.004*\"high\"'),\n", + " (6,\n", + " '0.020*\"strain\" + 0.011*\"virus\" + 0.011*\"tgev\" + 0.011*\"gene\" + 0.009*\"isolate\" + 0.008*\"protein\" + 0.008*\"sequence\" + 0.007*\"acid\" + 0.007*\"amino\" + 0.007*\"porcine\"'),\n", + " (7,\n", + " '0.029*\"virus\" + 0.009*\"detection\" + 0.008*\"viral\" + 0.007*\"assay\" + 0.007*\"sample\" + 0.006*\"human\" + 0.006*\"detect\" + 0.005*\"study\" + 0.005*\"method\" + 0.005*\"pcr\"'),\n", + " (8,\n", + " '0.025*\"sequence\" + 0.019*\"gene\" + 0.018*\"rna\" + 0.010*\"probe\" + 0.008*\"region\" + 0.008*\"genome\" + 0.006*\"protein\" + 0.006*\"nucleotide\" + 0.005*\"mrna\" + 0.005*\"analysis\"'),\n", + " (9,\n", + " '0.021*\"antibody\" + 0.020*\"virus\" + 0.013*\"infection\" + 0.011*\"serum\" + 0.010*\"mouse\" + 0.010*\"elisa\" + 0.007*\"day\" + 0.007*\"test\" + 0.006*\"coronavirus\" + 0.005*\"antigen\"'),\n", + " (10,\n", + " '0.023*\"patient\" + 0.011*\"respiratory\" + 0.008*\"acute\" + 0.007*\"infection\" + 0.007*\"clinical\" + 0.006*\"lung\" + 0.005*\"pneumonia\" + 0.005*\"treatment\" + 0.005*\"severe\" + 0.004*\"study\"'),\n", + " (11,\n", + " '0.018*\"infection\" + 0.012*\"respiratory\" + 0.011*\"calf\" + 0.008*\"virus\" + 0.007*\"disease\" + 0.006*\"clinical\" + 0.006*\"study\" + 0.006*\"viral\" + 0.006*\"rotavirus\" + 0.006*\"influenza\"'),\n", + " (12,\n", + " '0.014*\"activity\" + 0.012*\"study\" + 0.008*\"compound\" + 0.006*\"extract\" + 0.005*\"antiviral\" + 0.005*\"protease\" + 0.005*\"inhibitor\" + 0.004*\"inhibition\" + 0.004*\"structure\" + 0.004*\"active\"'),\n", + " (13,\n", + " '0.032*\"protein\" + 0.009*\"membrane\" + 0.008*\"virus\" + 0.007*\"vaccine\" + 0.007*\"recombinant\" + 0.007*\"glycoprotein\" + 0.006*\"chapter\" + 0.005*\"antibody\" + 0.005*\"cell\" + 0.004*\"expression\"'),\n", + " (14,\n", + " '0.028*\"virus\" + 0.007*\"ibv\" + 0.006*\"strain\" + 0.006*\"group\" + 0.006*\"infectious\" + 0.005*\"study\" + 0.005*\"isolate\" + 0.005*\"supplementary\" + 0.005*\"pig\" + 0.005*\"influenza\"')]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_j.show_topics(num_words=10, num_topics=params['num_topics'])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 0.26377487722112225), (5, 0.7256559932323484)]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_j[\"PLoS ONE\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# topics over time\n", + "\n", + "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n", + "journal_names_reduced = [j for j in reversed(df_local.journal.value_counts()[:how_many].index.to_list())]\n", + "topics = np.zeros((len(journal_names_reduced),model_j.num_topics))\n", + "\n", + "for n,j in enumerate(journal_names_reduced):\n", + " for t in model_j[j]:\n", + " topics[n][t[0]] = t[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CQ0MZPXo0P//8M1arlezsbGJjYzlz5oytK/x3devWpWfPnrz44ots3bqV3Nxczpw5w+LFi5kyZQpjx47Fzc3tmrk6OzvTsWNHpkyZwqlTp7BYLEycOJFBgwbdqo9HRMRYHLIAzkFvn7sKTYEQMYCcnBzCwsKK7OvevTvjxo3jgw8+YOrUqYwaNYoqVarw1ltv0bx5cwAmTZrEnDlzGDZsGCdOnMDJyYmgoCA+++yzqy6wGz58OA8//DAzZszg999/x9nZmcDAQD766COCgoJKlO/IkSOZNm0a0dHRnDt3jnr16jFv3jycnZ1v7oMQEREBTFar1eroJERE7CUlJYWIiAjWbkjAz8/f0enI33i1e8/uMU99M9zuMUVKk9TUFNq3iSAhIQF//5v7Trv0/ZjqZ6bAtbydMiw557xs/FKX2uVeblbp6UWLiIiIiNwGKoBFRERExFA0B1hERETESBy1IK0ULYIrPZmIiIiIiNwG6gCLiIiIGIkJx7yVrfS8CE4dYBERERExFnWARURERIxEc4DVARYRERERY1EBLCIiIiKGoikQIiIiIkZiMjloEVzpWQWnAlhE5C5wLrfA7jHLuTvbNZ5eW2w/P/5x2q7x6t9X0a7xABqO2WD3mHveamP3mGJMKoBFREREjESL4DQHWERERESMRQWwiIiIiBiKpkCIiIiIGImmQKgDLCIiIiLGog6wiIiIiJGYcNBj0G7/Ja9GHWARERERMRQVwCIiIiJiKJoCISIiImIoDloEV4r6rqUnExERERGR20AFsIjB9O7dmzFjxlzxmNlsZs6cOZjN5huKPXz4cMaPH38z6YmIyK1mMjluKyVUAIsYjNlsZu3ateTm5hbZf+jQIZKTk+nSpQtLly51UHYiIiK3ngpgEYOJjIzE3d2dhISEIvuXL19OZGQkW7duJTo6GoC4uDjMZjNms5ng4GAOHDjAH3/8waBBg2jcuDGtWrVi1qxZ5OfnX3ads2fPMm7cOJo3b05ISAivvfYamZmZAFitVmbNmkXTpk0JCwvjk08+4ZFHHiElJYU333yT1157rUis1q1bs2nTplv0iYiIiNGoABYxGFdXVzp37kxcXJxtn8ViYdWqVfTo0eOy85OSkhg8eDAJCQnUqlWLfv36cd9997F161YWLFjA2rVr+fjjjy8bFxMTw6+//srKlSvZsGEDubm5tsJ2xYoVxMXFsXjxYtasWcMPP/xAQUEBANHR0SQkJNg61Pv27SM7O5vQ0NBb8XGIiBjPpTfBOWIrJUpPJiJy23Tr1o2dO3eSkZEBwKZNm6hYsSLBwcGXnevl5UWLFi3w8PBgz549nD59mmHDhuHu7s69997LkCFDWLFiRZExubm5rF+/nmHDhuHt7Y2Hhwdvvvkm27ZtIyMjg9WrV9OzZ0/uv/9+PDw8eP31121jGzZsSMWKFfnuu+8A+Prrr2nfvj2urq637gMRERFDUQEsYkB+fn6EhISwatUqAGJjY6/Y/QXw9fW1/Xzy5El8fHyKFKN+fn4cP368yJisrCzy8vKoXr26bZ+Pjw9ubm4cP36cjIwMqlatajv29/NMJhNRUVGsWbOGwsJC1q1bZ5uSISIidqBFcCqARYzKbDazcuVK0tPT2bt3L506dbrieaa/fWFVq1aNEydOYLFYbPuOHTuGt7d3kTHe3t64ubmRmppq25eRkYHFYqFy5cpUq1atSNGcnp5eZHx0dDRbtmxh+/btlCtXjnr16t3UvYqIiPydCmARgwoPD+f8+fNMmTKF9u3bU6FChWLHPP7441SpUoWpU6eSm5vL0aNHmTNnDlFRUUXOc3JyomPHjkydOpWTJ0+Sk5PD+PHjCQwMpEaNGjz99NMsWrSII0eOcO7cOd5///0i42vVqkXt2rWZPHkyHTt2tOt9i4iIqAAWMSgnJye6du1KfHx8iZ/76+rqyocffsjhw4cJDQ3lmWeeoXXr1rz88suXnTtixAhq165NdHQ04eHhODs7M2vWLACioqKIioqie/futGvXjnvvvdcW/5Lo6GgOHDigAlhExM5MJpPDttLCZLVarY5OQkSM5cCBA1SqVMk2v/i3337jySefJCkpiTJlygDw7bff8sknn7BkyZLrip2SkkJERARrNyTg5+dv99xLq3O5BXaPWc7d2e4xxT5+/OO0XePVv6+iXeMBNByzwe4x97zVxu4xS7vU1BTat4kgISEBf/+b+0679P2YVmcQBW6edsqw5JwtWVQ/ONcu93KzXBx6dRExpC1btvDvf/+b2bNn4+Liwrx582jUqBFlypQhOzub1NRUPvroo6suzBMRkRt3cT3a7e/GlqIGsKZAiMjt16dPH/z9/WndujXNmjUjKyuLyZMnA3D48GHMZjPVqlXT0x9EROSWUAdYRG47Nzc3xo8ff8Vjjz/+OD/++ONtzkhERIxEBbCIiIiIkZj+szniuqWEpkCIiIiIiKGoAywiIiJiII56JFlpegyaOsAiIiIiYijqAIuIiIgYiAkHdYBL0SRgdYBFRERExFBUAIuIiIiIoWgKhIiIiIiBaBGcCmARuUu1ensj1rKV7BLr4Psd7RLnVirn7mz3mJb8QrvGc3PRPzrai5tz6f8s97zVxtEpiFyVCmARERERA1EHWHOARURERMRgVACLiIiIiKFoCoSIiIiIkZj+szniuqWEOsAiIiIiYijqAIuIiIgYiYMWwaFFcCIiIiIijqECWEREREQMRVMgRERERAxEzwFWB1hE7KSgoIDjx487Og0REZFiqQAWuQ4BAQHUq1ePwMDAIlu/fv0cnVoRu3btIigoqETnnjhxgqeeeorAwEBmzJhxw9d89dVXWb9+/RWPzZw5k0ceecT2edWvX59OnToRFxdnOyctLY3AwECys7NvOAcRESnepQ6wI7bSQlMgRK7T0qVLefjhhx2dht3s3LmT7OxsfvjhB1xcbvwrITMz85rHw8PD+eCDD4CL3eJt27bx2muvUVBQQNeuXalevTpJSUk3fH0REZGSUgdYxI7i4uJ4/vnnGTVqFA0aNCAiIoIdO3YQExNDw4YNiYiIYOfOnbbzExIS6NixI0FBQZjNZvbv3287FhAQwLhx42jcuDHvv/8+eXl5jBs3jkaNGhEZGcm8efMICAgoUV4BAQF8/vnntGrVisaNG/PKK69w7tw5vvrqK0aNGkVqaiqNGjXi8OHDpKenM2TIEIKDg4mMjGTBggW2OBaLhUmTJhESEmKLk5OTw/jx40lMTGTKlCmMGzeu2HycnZ0JCwvjtddeY/r06RQWFpKSkkJAQABnzpyhsLCQd999l5CQEJo2bUr//v05evRoyf8gRERErkEFsIidbdmyhccee4w9e/YQFhZG//79efTRR9m5cydt2rRh0qRJAPz8888MGzaMESNGsHPnTnr06EG/fv04c+aMLVZOTg7btm3j+eef54MPPuDHH39kzZo1LFu2jI0bN15XXt9//z0rV64kLi6OPXv2sHLlSp566ineeust6tSpQ1JSEvfeey+DBg2iWrVqbNmyhfnz57NkyRJWrlwJwKxZs/j3v//N8uXL+e677zh//jwTJ05k1KhRBAUFMWzYMGJiYkqcU3h4OH/99ReHDx8usv/bb79ly5YtrFu3ji1btuDr68uHH354XfcrIiJXZjI5ahqEo+/8v1QAi1ynZ555hqCgoCLbihUrbMerVKmC2WzGZDIRHBzMPffcQ/fu3XF1daVFixakpKQAsHz5cjp27EjTpk1xcXEhOjqa++67j2+++cYWq3379ri5ueHh4cHq1asZOHAgvr6+VK5cmZdffvm68u7Tpw8VKlTA39+fxo0bX7GjmpyczNGjRxk+fDju7u7UrFmTvn37snTpUgDi4+MZNGgQ1atXp1y5crzzzjv06dPnBj7FiypWrAhw2bzf8uXLc+LECVatWkV6ejrjx49n/PjxN3wdERGRv9McYJHrtHjx4mvOAb5U1MHFf+ovX7687XcnJycKCwuBi4u+du3axZo1a2zH8/PzSUtLs/3u6+tr+zkjI4OqVavafq9Wrdp15e3t7W372dXVlYKCgsvOSU1N5fz58zRp0sS2r7Cw0HZPf/31F9WrV7cd8/HxwcfH57ry+LtTp04BF+8lLy/Ptj8kJISYmBiWLl3Ke++9R40aNXjjjTdo1arVDV9LRET+phR1Y6/mwIEDjBkzhl9++YUaNWowfvx4Hn/88cvO+/HHH3nnnXc4fPgwlSpV4oUXXqBr167XjK0CWMTOSrrK1dfXl969ezN06FDbviNHjhQpVP8eq1q1ahw/fpx69eoBFwtie7vUXd62bZttX2ZmJhcuXAAudrfT09NtORw6dIhNmzbxwgsv3ND1Lk1v8PX1JTU11bb/2LFjPPLIIyxdupTs7GwWL17MK6+8wp49e3B1db2JOxQRkTuBxWJh8ODB9OrViy+++IINGzbQv39/Nm/ejIeHh+28wsJCBg8ezBtvvEF0dDQ//fQTzz77LI899hgPPfTQVeNrCoSIg3Tq1Inly5ezb98+rFYrO3bsoGPHjiQnJ1/x/KeffpqPPvqIP//8k1OnTtmeqGBPjz/+OB4eHnzwwQdYLBYyMzMZPHiw7fFoUVFRfPjhh2RkZHD27FmmTZtmm9Lh5uZGTk5Oia6Tn59PQkICU6dOZejQoZf9pWHnzp289NJLpKWl4eHhgaenJ+XLl7+pp1SIiMidY/fu3eTl5dGnTx9cXV3p0KEDDzzwAGvXri1yXlZWFidPnsRqtWK1WjGZTLi4uBTbLNH/m4hcJ7PZjJNT0b87Ojs7k5iYeF1xGjVqxOjRoxk9ejQpKSn4+voybty4ItMP/q5///6kpqbStm1bKlWqREREBD/++OMN38eVuLm58dFHH/Huu+8SGhqKyWQiMjKSkSNHAvDiiy9y4cIFunbtSm5uLi1atOCNN94ALhbH48aN4/Dhw0ydOvWy2N999x2BgYG269x///2MHTuWtm3bXnZu586dOXz4MN26dePs2bPcf//9zJgxo1Q9Q1JE5E7l6DfBpaenX3asQoUKVKhQwfb7oUOHqF27dpFzatWqxcGDB4vs8/LyomfPngwfPpyRI0dSUFDAqFGjLht7WS5Wq9V6ozciIrfPvn37qFmzJp6ensDFpzqMGjWqyHQFgZSUFCIiIrgQOgJr2Up2iXnw/Y52iXOnseQX2jWem4v+0dFe9qecKf6k6/CIf4XiTxKHSE1NoX2bCBISEvD397+pWJe+H08FvkJhGS87ZVhyThdO4ZU0/YrHXnrppSKLuz/44AN++ukn5s6da9v3zjvvcP78+SKLogsLC5k8eTJ169alTZs2JCUl8fLLLzN16lSaN29+1VzUARa5Q6xYscL2P/wLFy7w+eefExoa6ui0RETkDuPoDvCiRYuKLOoGinR/AcqVK0dubm6RfefPn6dcuXJF9m3YsIGkpCTbv0Y2btyYzp07s2zZsmsWwPrruMgd4p///Ce5ubmEhoYSGRmJt7e3bWqCiIjInaJq1ar4+/sX2f5vAVy7du3LnhH/+++/88ADDxTZl56ejsViKbLPxcWl2DUjKoBF7hBeXl7MmDGDXbt2sXv3biZOnFjkEWsiIiJ3i+DgYKxWKwsWLCAvL481a9bwyy+/0Lp16yLnNWvWjF9//ZVly5ZhtVpJTk7myy+/pEOHDteMrwJYRERExEAc8xa465t24ebmxrx581i/fj2NGzdm7ty5zJ49m0qVKjF37lxbgfvggw8ya9Ysli1bRlBQEEOHDmXo0KFERkZeM77mAIuIiIhIqVOnTh2WLFly2f5BgwYxaNAg2+9hYWGEhYVdV2wVwCIiIiJGYsIxb4IrRU+y1BQIERERETEUdYBFREREDMTRj0ErDdQBFhERERFDUQEsIiIiIoaiKRAiclfa9GYkfn4399pQo6vSeqxd453aPM6u8YxMry6Wm+KgKRBoCoSIiIiIiGOoAywiIiJiICaTySHdWC2CExERERFxEBXAIiIiImIomgIhIiIiYiAmHDQFohS9Ck4dYBERERExFHWARURERIzE9J/NEdctJdQBFhERERFDUQEsIiIiIoaiKRAiIiIiBmIy4aDnAN/2S16VOsAicsPi4uKIjkY0JAkAACAASURBVI4GYNeuXQQEBBAYGGjb6tevz5NPPsnmzZuLjZWYmEiLFi1udcoiIiLqAIuI/ZQvX57ExETb7xaLhU8++YRXXnmF77//nooVK151bFBQEFu2bLkdaYqIGJreBKcOsIghpKSkEBQUxMKFC2nWrBmNGzdm4cKFLFq0iNDQUIKDg1mwYAEAhw4dok+fPjRq1Ih27doRHx9vi3P69GlefvllGjRoQLt27UhOTr7mdd3c3HjmmWe4cOECx44d47fffuPxxx9n06ZNABw4cID69evz448/smvXLoKCggAwm822fABOnTpF3bp1OXbsmH0/GBERMSQVwCIGkZ2dTXJyMps3b2bs2LFMmDCB5ORkEhISePfdd5k0aRLHjx+nb9++hIaGsn37diZNmsSECRNsXd2YmBgsFgtbtmzhww8/5Pvvv7/mNc+dO8ecOXPw8fGhdu3a1K5dm1dffZW33nqLzMxMhg0bxgsvvED9+vWLjHvqqaf4+uuvbb9/88031KtXjxo1atj/gxEREcNRASxiIIMHD8bNzY2mTZtSUFBAr169cHNzo2XLlhQUFLBixQo8PDzo378/rq6uPP7443Tu3JklS5aQm5vLpk2bePnll/Hw8OC+++7jueeeKxI/OzuboKAgGjZsyGOPPUZoaCgnTpxg4cKFlCtXDoDevXtz33338fTTT+Pp6cnAgQMvy7N9+/YcPHiQP/74A4D4+Hg6dep06z8gEREDMJlMDttKCxXAIgZyaQ6us7MzcHHOLoCT08WvAnd3d44ePUpQUJBt++KLL8jIyOD06dPk5eVRtWpVWzx/f/8i8S/NAd6zZw9LliyhfPnyBAQEcP/999vOMZlMmM1mjh8/TlRUlC2X/xsnIiKC+Ph4UlNT2b9/P+3atbPvhyEiIoalRXAiBlLc377z8vKoW7cuy5Yts+3LyMjAZDJRsWJF3NzcSEtLw9vb23bsaurWrcv7779Pz549qVGjBk888QQAOTk5TJ48maeffpr333+fVq1a4evre9n4Tp06MXnyZNzc3GjVqpWtWBcRkZukN8GpAywi/xUWFsbRo0eJi4sjPz+fY8eO0atXL5YtW4abmxtPPvkk06dPJysri5SUFD7//PNrxgsMDKR///6MHTuWv/76C4C3336bWrVqMWHCBJo3b86oUaOuOLZ58+acPn2axYsX2x61JiIiYg8qgEXExsnJifnz5/PVV1/RtGlTevToQWRkJIMHDwZg9OjRVK5cmZYtW9KrVy/CwsKKjfnSSy/h7e3N2LFj2bhxIxs2bGDcuHEAjBo1iuTkZJYuXXrZOGdnZ6KiosjPz6d58+b2vVERETE0k9VqtTo6CRERe0lJSSEiIoK1GxLw8/MvfoBclVfLGLvGO7V5nF3jiRhBamoK7dtEkJCQcNm6i+t16fvxQugIrGUr2SnDkjOdz6TM1gl2uZebpQ6wiIiIiBiKFsGJiIiIGIjeBKcOsIiIiIgYjDrAIiIiIobiqJdSqAMsIiIiIuIQKoBFRERExFA0BUJERETEQBy1CM4h17wKdYBFRERExFDUARYRERExEhOOWY9WehrA6gCLiIiIiLGoABYRERERQ9EUCBG5Kx1My+FU4Rm7xKpbo4Jd4txpTm0e5+gUHCIl87xd4/lXKmvXeHeKf67ab/eY70c/YveY9nb89AW7xjtxJteu8UCL4EAdYBERERExGHWARURERAzEZMJBHeDbf8mrUQdYRERERAxFBbCIiIiIGIqmQIiIiIgYiEnPAVYHWERERESMRR1gEREREQPRY9DUARYRERERg1EBLCIiIiKGogJYREq9Y8eOOToFEZG7xqUZEI7YSgvNARa5AwwYMIA9e/YAYLFYMJlMuLq6AtCwYUPGjh1LREQEP/zwAxUq3Nxre4cPH0758uUZNWrUTedtD4sWLWL79u3Mnj3b0amIiMhdQgWwyB1g/vz5tp+vVKCmpKQ4Iq3bIjMzE6vV6ug0RETuGiYc1Y4tPS1gTYEQuYt88cUXRERE0KBBA9566y0KCwsByM3NZcKECYSFhdGsWTNiYmI4d+7cdce3WCy88847tGnThvr169O6dWvWrFkDXCzCAwMD+fTTT2nevDlNmzZlzJgxthxycnIYNWoUjRs3pkmTJowdO5a8vDwADh06RJ8+fWjUqBHt2rUjPj4egPXr1/Phhx/y3Xff0bFjR3t8RCIiIiqARe4mv/32G2vWrCE2NpavvvqKrVu3AjB58mSSk5NZsWIF33zzDSdPnuSdd9657viffPIJycnJxMbGsnfvXnr16kVMTAz5+fkAnDt3jl9++YWNGzfy8ccfs3r1alsOY8aMIS0tjfXr17Nu3Tp+/vlnPv74Y86ePUvfvn0JDQ1l+/btTJo0iQkTJpCYmEjbtm0ZOHAg4eHhrF692n4flIiIGJqmQIjcRV555RXKlClD7dq1eeihh0hJScFqtRIbG8vnn3+Ot7c3AEOHDqVDhw6MHTsWNze3Esc3m81069aNChUqkJGRQdmyZcnJyeH8+fO2c1544QXKlCnDI488QkBAAEePHsVisbB+/XoWL16Ml5cXAP/6178A+P777/Hw8KB///4APP7443Tu3JklS5YQFBRkr49GREQucdAMCGvpmQGhAljkbuLp6Wn72dXVlby8PDIzM7lw4QL9+vW7+PDz/3BxcSE1NZX777+/xPFzcnIYN24c+/btw8/Pzzb273N0LxXZl3IoKCggKyuLvLw8qlevbjvm7+8PwLp16zh69GiRYregoIBHH330Ou5cRESk5FQAi9zlKlasiKurK7GxsdSqVQu4OJf32LFj3HvvvdcVa8yYMdx333188MEHuLi4sH//fr7++utix1WqVAlXV1fS09NtBXJiYiK///47vr6+1K1bl2XLltnOz8jIKFKsi4iI/Tg5mTA53f7vWKuTicLbftUr0xxgkbucs7MzHTt2ZMqUKZw6dQqLxcLEiRMZNGjQVcecO3eO9PT0IpvFYiE7Oxt3d3ecnJw4ceIEU6dOBbAtZrtWDu3bt2fGjBmcOXOGzMxMJk2axKlTpwgLC+Po0aPExcWRn5/PsWPH6NWrl60gdnNzIzs7234fiIiIGJ4KYBEDGDlyJFWrViU6OpqQkBCOHDnCvHnzcHZ2vuL5y5cvJywsrMi2f/9+Ro0axbZt22jYsCFms5lGjRrh5eXFwYMHi83hzTffxNvbmyeeeIIOHTpQr149+vfvT8WKFZk/fz5fffUVTZs2pUePHkRGRjJ48GAAwsPD+eOPPwgLC7PrZyIiIsZlsuoBmyJyF0lJSSEiIoLpC1bhU7V68QNKoG6Nm3u5iNxZUjLPF3/SdfCvVNau8e4U/1y13+4x349+xO4x7e346Qt2jZd+PJU+XdqTkJBgWztxoy59Pzo/MRbTPZXtlGHJWc+epGDdWLvcy81SB1hEREREDEWL4EREREQMxGQyOWahcSla3KwOsIiIiIgYijrAIiIiIgZictCLMCg9DWB1gEVERETEWFQAi4iIiIihaAqEiIiIiIFoEZw6wCIiIiJiMOoAi4iIiBiKYzrA1lK0Ck4dYBERERExFHWAReSuVNPnHqpX9XB0GrdNbl6h3WOes+TbNZ7XPW52jXervB5v31f4Lu7d0K7xAPIK7Pvn7eps/35Yr3r2eRX5naZaxTJ2jVd41t2u8eQiFcAiIiIiBuKo5wCXojVwmgIhIiIiIsaiDrCIiIiIgTjqMWgOefTaVagDLCIiIiKGogJYRERERAxFUyBEREREDESL4NQBFhERERGDUQdYRERExEAudoAdsQjutl/yqtQBFhERERFDUQEsIiIiIoaiAljEwHr37s2YMWOueMxsNjNnzhzMZvNtzkpERG6lS4vgHLGVFiqARQzMbDazdu1acnNzi+w/dOgQycnJdOnShaVLlzooOxERkVtDBbCIgUVGRuLu7k5CQkKR/cuXLycyMpKtW7cSHR0NQFxcHGazGbPZTHBwMAcOHKBVq1Zs3LjRNm7mzJkMHjwYgD/++IOePXsSFBREZGQkEydOpLCwEICsrCxGjBhBs2bNCAsLY9q0aeTn5xc7TkREbt6lN8E5YistVACLGJirqyudO3cmLi7Ots9isbBq1Sp69Ohx2flJSUkMHjyYhIQEAgICrhl7woQJBAYGsnv3bhYuXMiaNWtITEwE4I033uDs2bOsX7+e2NhYdu/ezYcffljsOBEREXtQASxicN26dWPnzp1kZGQAsGnTJipWrEhwcPBl53p5edGiRQs8PDyK/Zu8h4cHu3fvZtOmTZQvX57vvvuOxo0b89dff7F582ZiYmLw8PDA19eXIUOG2KZaXG2ciIiIvagAFjE4Pz8/QkJCWLVqFQCxsbFX7P4C+Pr6ljju2LFjeeyxx3j33XcJDg5m8ODBnDhxgrS0NADatWtHUFAQQUFBvPLKK2RlZZGbm3vVcSIiYh9aBKcCWES4uBhu5cqVpKens3fvXjp16nTF8/5v19fJyQmLxWL7/fTp07afDxw4wD/+8Q82bdpEfHw82dnZ/Otf/8LX1xcnJye2bt1KYmIiiYmJfP/998THx+Pu7n7VcSIiIvaiAlhECA8P5/z580yZMoX27dtToUKFEo2rWbMm69at4/z58xw8eJD169fbjk2bNo0ZM2ZgsVioUqUKrq6ueHp6UrVqVRo3bsx7773H2bNnycnJYcSIEYwePfqa40RExF4ctQCu9LSAVQCLCE5OTnTt2pX4+Pjreu7vG2+8wZ9//klISAgxMTF07tzZduy9997jt99+IyQkhPDwcHx8fGxPiJg6dSo5OTm0bt2aVq1aYTKZmD59erHjRERE7MFktVqtjk5CRMReUlJSiIiIYOWajVT387NLTHfX0t8ryM2z/6Pizlny7RrP6x43u8a7VZ75bI9d4y3u3dCu8QDyCuz75+3qbP//jicdOV38SdcpsGZFu8cs7VJTU2jfJoKEhAT8/f1vKtal78fynd/Fuby3nTIsuYLsv8heMdIu93KzXBx6dRERERG5rRy1IE2L4EREREREHEQdYBEREREDcdRb2fQmOBERERERB1EHWERERMRANAdYHWARERERMRgVwCIiIiJiKJoCISIiImIgWgSnDrCIiIiIGIw6wCIiIiIGokVw6gCLiIiIiMGoAywidyUnp4ubUbSc/J3dY24b3tKu8VIyz9s1HkAVT3e7x1zcu6HdY9qbq3Pp/y93Na8yjk5B5KpUAIuIiIgYiBbBaQqEiIiIiBiMOsAiIiIiBqIOsDrAIiIiImIwKoBFRERExFA0BUJERETEYErRbASHUAdYRERERAxFHWARERERA9EiOHWARURERMRgVACLiIiIiKGoABa5S8XExPDQQw/x66+/3tD4Xbt2ERQUVOx5VquVNm3a8NFHH112rLCwkLCwMFavXs3cuXN59dVXbyiX5557jgULFtzQWBERKcpkctxWWqgAFrkLnT17lnXr1vHUU0+xcOHCW3otk8lEt27dWLly5WXHtm7dSm5uLu3atWPQoEFMmzbtluYiIiJSEiqARe5CX3/9NQ8//DADBgwgPj6erKwsAOLi4jCbzZjNZoKDgzlw4AAnTpzgH//4Bw0bNqR58+bMnDnTFqewsJDp06fTokULgoODmT179hWv9/TTT3Ps2DH27dtXZP/y5cvp3Lkzbm5uzJw5k8GDBwMwc+ZMXnjhBaKiomjWrBmZmZkkJyfz3HPPERQURNu2bVm0aNEt+nRERIztYjfW5IDN0Xf+X3oKhMhdaNmyZfTt25fatWtTt25dli9fTv/+/QFISkpi3rx5NGjQgHvuuYcePXpQo0YNtmzZQlZWFj179qRmzZr4+vpy9uxZcnNz2bRpE3v37qVXr160bduWBx54oMj1KlWqRJs2bYiLi6NevXoAnDx5ku+++461a9deMccdO3YQGxuLn58feXl59OnThyFDhvDJJ59w8OBBBg4ciKenJ08++eSt/bBERMRw1AEWucv8/PPPHD9+nLZt2wLQo0cPFi1aRGFhIQBeXl60aNECDw8PUlJSSEpKYsSIEdxzzz1Ur16d+fPnExwcDICLiwv//Oc/cXFxoXHjxnh7e5OSknLF6/bo0YO1a9disVgAWLlyJcHBwdSoUeOK59epU4eHHnqI8uXLk5CQgI+PD3379sXV1ZVHH32UXr16sWLFCnt/PCIiIuoAi9xtli1bRnZ2Ni1btgQuTmPIzMxk06ZNAPj6+trOPXnyJO7u7lSqVMm2r1atWgAcPnyYsmXL4ubmZjvm5uZGfn7+Fa8bFBRElSpV2LhxI+3bt2f58uUMGzbsqnn+PY/MzEyqV69e5Lifnx/Hjx8v6W2LiEgJOWpBmqZAiMgtkZOTw5o1a5g3b16RaQpz585l4cKFREdHF3kQeZUqVcjNzeXUqVN4eXkBkJCQQEFBAZ6entd9/e7du/PVV19RtWpVzp8/T3h4+FXP/Xse1apVIy0trcjxY8eO4e3tfd05iIiIFEdTIETuIqtXr6Zq1ao0bdoUHx8f29a9e3d27tzJ+fPni5xfrVo1goKCmDJlChcuXCAtLY0JEybYpjFcr06dOpGUlMTnn39Ot27dcHZ2LtG4sLAwTp06xYIFC8jLy2P//v0sXLiQqKioG8pDRESuzslkcthWWqgAFrmLLFu27IqLxurUqcOjjz7KhAkTLjs2bdo0zpw5Q1hYGN27d6dLly43vPCsfPnytG3blm+//ZYuXbqUeJynpyfz589n48aNNGnShJdeeokBAwbQvXv3G8pDRETufAcOHKB79+7Ur1+fqKgofvrppyued+LECQYPHkzDhg0JCQlh+vTpxcY2Wa1Wq70TFhFxlJSUFCIiIli9biPV/fzsEtPVufT3CkLe3WT3mNuGt7RrvLTTF+waD6CKp7vdY94Jf953gvQs+/95V/UsY/eYpV1qagrt20SQkJCAv7//TcW69P1YvfcUXCr42CnDkss/8ydpnw0r0b1YLBbatWtHr169ePbZZ9mwYQNjx45l8+bNeHh4FDm3S5cuPProo4wcOZITJ07w3HPPMXTo0Gv+K6L+Vy4iIiJiIHfCm+B2795te0Smq6srHTp04IEHHrjs0Zr79u3j2LFjjB49Gnd3d2rUqMHChQttTzO6Gi2CExEREZHbJj09/bJ9FSpUoEKFCrbfDx06RO3atYucU6tWLQ4ePFhkX3JyMnXq1GHWrFnExcXh7u7OM888Q79+/a6ZgwpgERERESP5z5vZHHFdgGefffayQy+99BIvv/yy7fdz585RpkzRKS9ly5a9bDF3VlYWe/bsoXHjxiQkJPD7778zYMAAfHx8rjkFQgWwiIiIiNw2ixYtomrVqkX2/b37C1CuXDlyc3OL7Dt//jzlypUrss/NzQ0PDw9b8fzQQw/RpUsXvv32WxXAIiIiInKRE+DkgAbwpYVnVatWLXYRXO3atVmwYEGRfb///judOnUqsq9WrVqcP38ei8Vie3FTQUFBiXMRERERESkVgoODsVqttufDr1mzhl9++YXWrVsXOa9Zs2ZUqlSJiRMnYrFY+OWXX1i+fDkdOnS4ZnwVwCIiIiJSqri5uTFv3jzWr19P48aNmTt3LrNnz6ZSpUrMnTvXVuC6u7vzxRdfcOzYMUJDQxkwYAADBgygbdu214yvKRAiIiIiBmJy0CK4671mnTp1WLJkyWX7Bw0axKBBg2y/16hRg48++ui6YqsDLCIiIiKGog6wiIiIiIFc70sp7Hnd0kIFsIjclVycTXZ7pW3qqfPFn3Sd/LzK2jXe9pGt7BrvVvCvZN97vlUq9/jUrvFOLulr13h3CiO+tljuHJoCISIiIiKGog6wiIiIiIGY/vMfR1y3tFAHWEREREQMRR1gEREREQNxMjnoTXClpwGsDrCIiIiIGIsKYBERERExFE2BEBERETESB70JrjQ9CFgdYBERERExFHWARURERAxEb4JTB1hEREREDEYFsIgd9O7dmzFjxlzxmNlsZs6cOaxevRqz2XxL80hJSSEgIIDAwEDbVr9+fVq3bk1sbOwtvbY9tWrVio0bNzo6DRERuUtpCoSIHZjNZmJiYhg5ciTu7u62/YcOHSI5OZmZM2fi4+NDx44db0s+33//PRUqVACgoKCA+Ph4hg8fToMGDahdu/ZtyUFEREonJ5MJJwfMR3DENa9GHWARO4iMjMTd3Z2EhIQi+5cvX05kZCQ+Pj7ExcURHR0NQFxcHGazGbPZTHBwMAcOHLis6zlz5kwGDx4MwB9//EHPnj0JCgoiMjKSiRMnUlhYWKLcnJ2d6dSpEx4eHvz6668A5ObmMmHCBMLCwmjWrBkxMTGcO3cOgMzMTAYOHEijRo0IDw9nxIgRXLhwodhxVquVWbNm8cQTTxAYGEiLFi1YsGCBLY+AgADGjRtH48aNef/997FarcybN4/w8HAaNmzIgAEDSE9Pt52/e/duoqOjCQwMpF+/fpw8efJ6/khERESuSgWwiB24urrSuXNn4uLibPssFgurVq2iR48eVxyTlJTE4MGDSUhIICAg4JrxJ0yYQGBgILt372bhwoWsWbOGxMTEEuVmsVj47LPPyM/Pp379+gBMnjyZ5ORkVqxYwTfffMPJkyd55513AJg9ezbly5dn+/btrFy5kv/5n//hm2++KXbc119/zapVq1iwYAF79+5lzJgxTJo0iRMnTthyycnJYdu2bTz//PN8+eWXLFq0iI8++oidO3dSvXp1Ro4caTt327ZtzJs3jy1btnDq1Ck+/vjjEt2viIhc26VFcI7YSgtNgRCxk27dutG2bVsyMjKoUqUKmzZtomLFigQHB1/xfC8vL1q0aFGi2B4eHuzevZtNmzbRpEkTvvvuO5ycrv7315YtW2IymcjNzcVkMhEaGsrnn39O1apVsVqtxMbG8vnnn+Pt7Q3A0KFD6dChA2PHjsXDw4N///vfbNiwgZCQEFauXImTk1Ox41q2bElwcDC+vr78+eefuLq6UlBQQGZmJr6+vgC0b98eNzc33NzciI+Pp2fPntSpUweA1157jZSUFNs99OvXzzYuNDSUI0eOlOizEhERKY4KYBE78fPzIyQkhFWrVvHCCy8QGxt71e4vYCvuSmLs2LFMnz6dd999l4yMDEJDQxk3btxVY2zevJkKFSpw5MgRXn75ZXx9fXn88ceBi1McLly4QL9+/Yo8CN3FxYXU1FSGDBmCk5MTs2bNYtiwYTRs2JBx48bh6el5zXFeXl5MmDCB7du3F7me1Wq94j3/9ddfVK9e3fZ7+fLlefjhh22/V6xY0fazq6sr+fn5Jf68RERErkVTIETsyGw2s3LlStLT09m7dy+dOnW66rn/9y08Tk5OWCwW2++nT5+2/XzgwAH+8Y9/sGnTJuLj48nOzuZf//pXsfnUrFmT2bNns2rVKtsUgooVK+Lq6kpsbCyJiYkkJibapjvce++9HDx4ELPZzLp169i0aROVKlVi3LhxxY6bNm0aubm5fP/998THx/P6669f856rVKlSZM7vX3/9xdSpU0s8t1lERG6MiYtvgrvtG6VnDoQKYBE7Cg8P5/z580yZMoX27dvbnsRQEjVr1mTdunWcP3+egwcPsn79etuxadOmMWPGDCwWC1WqVMHV1RVPT88Sxb333nt54403eP/99/n1119xdnamY8eOTJkyhVOnTmGxWJg4cSKDBg0C4LPPPmP8+PGcPXuWypUrU6ZMGTw9PYsdl52djbu7O87OzmRlZfHuu+8CkJeXd8W8oqKi+OKLLzh8+DAWi4VZs2axf//+a07tEBERsQf9P42IHTk5OdG1a1fi4+Ov+5m/b7zxBn/++SchISHExMTQuXNn27H33nuP3377jZCQEMLDw/Hx8bE9IaIkunfvTlBQEMOHD6egoICRI0dStWpVoqOjCQkJ4ciRI8ybNw9nZ2dGjBhBYWEhLVu2pEmTJpw5c8a2OO1a4/7f//t/HD9+nMaNGxMVFYWXlxcBAQEcPHjwijl17twZs9lM//79CQkJIT09nQkTJlzXZyYiItdPi+DAZP37BD0RkTtcSkoKERERrN2QgJ+fv11ipp46b5c4f+fnVdbuMcU+Kvf41K7xTi7pa9d4YiypqSm0bxNBQkIC/v4395126fvxoRdn4FbRx04Zlpzl9J8cmPMPu9zLzVIHWEREREQMRU+BEBERETEQk8kxb2UrTVMg1AEWEREREUNRB1hERETEQEz/2Rxx3dJCHWARERERMRR1gEVEREQM5NKLKRxx3dJCHWARERERMRQVwCIiIiJiKJoCISIiImIgTqaLmyOuW1qoAywiIiIihqIOsIiIiIiBaBGcCmARkWLVbfOa3WOe+mGW3WOKfZxc0tfRKYjILaYpECIiIiJiKOoAi4iIiBiIyXRxc8R1Swt1gEVERETEUNQBFhERETEQLYJTB1hEREREDEYFsIiIiIgYiqZAiIiIiBiICce8la30TIBQB1hEREREDEYdYBERERED0SI4dYBFRERExGBUAIsYTO/evRkzZswVj5nNZubMmWP3a/br149ly5bZPa6IiMiNUAEsYjBms5m1a9eSm5tbZP+hQ4dITk6mS5cudr/mJ598Qvfu3e0eV0RErp/JgVtpoQJYxGAiIyNxd3cnISGhyP7ly5cTGRlJ5cqVmTFjBk888QSBgYGEhYWxcOFC23m//fYbvXv3pkGDBrRq1apIZzc2NpY2bdrQoEEDnn32WQ4dOgRAjx49+OKLL2w/T58+nU6dOhEYGMizzz7L4cOHbTG+/fZboqKiCAoKokePHhw4cOBWfhwiImJAKoBFDMbV9f+zd+dhVZXr/8ffGxkcwBFRnIdvacdKQWTwSBIOKMpQpmKlaWqSlE04ZpbmlPOspaWZlqWhouCQsyaiGEftp6fslEcQgRAUUHQz/f7w6/62jwqoJJz2NH4/3gAAIABJREFU5+W1rwvWetbz3Gtts5ube61tQ69evYiIiDBtMxqNbN68mX79+hEZGUlUVBSff/45P/zwA++++y7Tp0/n0qVL3Lhxg8GDB9OmTRuOHDnC4sWLmTFjBidPnmT//v189NFHTJ8+nbi4ODw9PXnjjTfuGENkZCTz58/n4MGD2NjYsGjRIgBOnDjB6NGjGT9+PEeOHKF37968/PLLZGdnP5RrIyJiCawMhjJ7lRdKgEUsUJ8+fThy5AgpKSkA7Nmzh+rVq+Ph4UHnzp354osvcHJyIi0tDVtbW/Ly8khPTycuLo6cnBxGjBiBra0tjz32GGvXrqVx48ZERkYSHByMq6srVlZWDBs2jKlTp1JYWHjb+oGBgTRu3Bh7e3u6du3K+fPngZsV5GeeeQYPDw+sra159tlncXZ2ZseOHQ/1+oiIyF+bHoMmYoHq169P+/bt2bx5M6+88grr16+nX79+AOTm5jJ16lRiYmKoU6cOjz/+OACFhYWkpaXh5OREhQoVTHO1bNkSgEuXLtGqVSvTdltbW1q3bn3H9R0dHU1f29jYkJ+fD0BSUhJxcXFs3rzZtD8vL4+LFy+W0pmLiIgoARaxWCEhIcyaNYvAwEB++OEH5s6dC8DMmTMpKCjgwIED2NnZkZ6ezrfffgtAnTp1SE1NpaCgACurm79A2rBhA40aNaJOnTokJyeb5jcajcydO5ewsLASx+Tk5MTLL7/Mm2++adp27tw5s4RZREQejMFw81UW65YXaoEQsVA+Pj7k5OQwa9Ys/P39qVq1KgDZ2dnY2NhgZWXF5cuXmTp1KnCzMuzq6oq9vT1Lly4lNzeXM2fOMGPGDOzs7AgICGDTpk2cOnWK/Px8li9fzoEDB7C3ty9xTEFBQXzzzTecPHkSgO+//56AgADdCCciIqVKFWARC2VlZUXv3r2ZP38+GzZsMG1/8803GTNmDO7u7jg4ONCjRw8eeeQRzp49S6tWrfjkk0/48MMPWbVqFVWrVmXs2LGmVoeRI0cSHh7O77//zuOPP266ua2kvLy8GDNmDOPGjSMpKQknJyemTJmCm5tbqZ67iIgl0yfBgaHwTneoiIj8l0pMTKRTp05E79xN/foNSmXOGu1eK5V5/ijj2L39cCAilunChUT8u3Zi9+7dNGjwYP+m3fr30eOdZVSs4VRKEZbc9YxUYmeHlsq5PCi1QIiIiIiIRVELhIiIiIglKaOb4MrTR8GpAiwiIiIiFkUVYBERERELUlafyqZPghMRERERKSOqAIuIiIhYEH0QhirAIiIiImJhlACLiIiIiEVRC4SIiIiIBTFQNp/KVo46IFQBFhERERHLogqwiEgx9LHFIveuhudbpT5nxpG5pT6nJbKibCqg5anqWp5iERERERH50ykBFhERERGLohYIEREREQtiMBjK5ia4cvQgYFWARURERMSiqAIsIiIiYkEMBrDSJ8GJiIiIiFgOJcAiIiIiYlHUAiEiIiJiQazKqAWiLNa8G1WARURERMSiqAIsIiIiYkH0GDQlwCIWKy0tjRkzZnDw4EFycnJwcnIiMDCQ0NBQrK2L/qdhwoQJODg4MHLkyNv2+fr6kpaWRoUKFQAoKCjAwcGBbt26MXbsWNN2ERGRsqIEWMRCvf322zRq1IgdO3ZQtWpVzp49y+uvv05ubi5vvfVWkcdOmjSpyP1z5syhc+fOABQWFvLjjz8yZMgQmjdvTr9+/UrtHERERO6HeoBFLNSJEyfw8/OjatWqADzyyCOMGzeOSpUqmcbs3buXwMBAXFxcCA4O5tixYwCMGTOGKVOmlGgdg8HAE088Qdu2bTl79qxp+zfffIOfnx/t2rVj8ODBJCQkAJCYmMgTTzzB8uXL8fDwoH379nzyySelddoiIhbv1k1wZfEqL5QAi1io7t27Ex4ezpQpU9i1axfp6ek89dRThIaGAnD27FlGjBjBiBEjOH78OAMHDmT48OHk5OTc0zoFBQUcPnyYo0eP4uXlBcDOnTtZsGABc+bM4fvvv8fd3Z2hQ4eSl5cHgNFo5B//+Ae7d+9m5cqVrFq1iqioqNK9ACIiYrHUAiFioaZOncrmzZvZvn07ERERXL16FXd3d9577z0eeeQRtm3bhpeXl6mVITg4mMaNG5eohzc8PBxra2tyc3MxGo24uroyYcIEunTpAtys/g4YMIBWrVoB8Morr/DFF18QGxtL48aNARg3bhz29va0aNGC3r17s3XrVnr06PEnXQ0REcthMJTNp7KVo3vgVAEWsVRWVlY888wzfPzxxxw9epR169ZRqVIlhgwZQn5+PmlpaTg7O5sd4+Ligq2tbbFzz5o1i7i4OA4cOEC3bt3Iy8vD19fXtD8pKYklS5bg5uaGm5sb7dq1Iysri6SkJACsra2pX7++aXzdunVJS0srpTMXERFLpwRYxAIdOHCADh06kJubC0CFChVo06YNEydOJDk5mStXrlCnTh1SUlLMjluwYAEXLlwo8TrVqlXjo48+4saNG4SHh5u2Ozk5MWbMGOLi4kyviIgIevbsCUBeXh6XLl0yjU9KSrotGRcREblfSoBFLJCbmxvW1taMGzfOVHVNTU1l6dKluLq6UrNmTbp3705MTAz79++noKCAyMhI1q5dS/Xq1e9pLVtbW2bMmMGhQ4f45ptvgJvtFCtXruRf//oXhYWFbNmyhaCgILOEe9asWdy4cYMzZ86wfv16goODS+8CiIhYMIPBgFUZvPQcYBEpU5UrV2bt2rXMnz+fPn36kJWVhYODA08//TSLFy8GoFmzZsyfP5/Zs2fz1ltv0bRpU5YtW0aVKlXueb1HH32UV199lY8++ghvb2+Cg4PJzMxk+PDhpKam0qhRIxYvXkyTJk1ITEwEoHr16vj6+mJjY8Mbb7xh1kIhIiLyIJQAi1io+vXrM2PGjCLH+Pj44OPjc9v26dOn3/WYPXv23HF7WFgYYWFhpu8HDBjAgAED7jrPq6++yujRo4uMT0RE7p0VZdMCUJ7aDspTLCIiIiIifzolwCIiIiJiUdQCISLlSoMGDfjpp5/KOgwRkb8sA2X0HOCHv+RdqQIsIiIiIhZFFWARERERC3LrsWRlsW55oQqwiIiIiFgUVYBFRERELIjBUEY9wOWnAKwKsIiIiIhYFiXAIiIiImJR1AIhIiIiYkEMBrBSC4SIiIiIiOVQBVhE/pLOp+Vw3fpqqczVvE6VUpnnv01+QWGpzlfhTyg5rYs/X+pzPlG7eqnO16pB1VKd789wOjGz1OfMODK31OeU0qHHoKkCLCIiIiIWRgmwiIiIiFgUtUCIiIiIWBA9B1gVYBERERGxMKoAi4iIiFgQqzJ6DFpZrHk3qgCLiIiIiEVRAiwiIiIiFkUtECIiIiIWxPC/f8pi3fJCFWARERERsSiqAMtfRn5+PqmpqTg7O5d1KCIiIuWWoYxugtNj0ET+w0svvcT7779/x30hISEsXbq02DnefvttduzYcV/rR0ZGEhIScl/Hlpb+/fvTokUL9u7de9u+8ePH06JFC2JjYwHo0aPHHcc9iIULFzJ8+PA77ktKSsLFxYWsrKxi5/H19WXXrl2lGpuIiEhpUgIs5UJISAjR0dHcuHHDbPsvv/zCjz/+yHPPPVfsHOnp6fe9fmBgIOvWrbvv40tL9erV2bp1q9k2o9HInj17sLW1NW2Liori6aeffmhx1atXj/j4eBwcHB7amiIiIn8WJcBSLnTu3Bk7Ozt2795ttn3Dhg107tyZ2rVrA7B7924CAwNxc3MjJCSE06dPAzBlyhTi4uKYNWsWkyZNAuD48eP06dOHtm3bEhwczOHDh03z+vr6MmHCBDw9PRk1ahQREREEBQUBEBERwaBBgxg7dixt27alc+fOZsnx0aNHef755/Hy8sLFxYWwsDBTZXTMmDG8+eab+Pr64ufnx9ixYxk5cqTZOXXp0oU9e/bc8Tp069aNPXv2cO3aNdO2PXv24OLigp2dnVn8t6qsqampjBgxgrZt29KhQwcWLlwIQGxsLH5+foSGhtKuXTv2799PWloa4eHheHp64u3tzeTJk8nJyTHNm5WVxWuvvUa7du3o06cPp06dAiAxMZEWLVqQmZkJwOHDh+nVqxeurq4EBASwffv2O57Pv//9b0JDQ3F3d8fX15dFixaRl5cHQHZ2Nm+99RZt27bF39+fRYsW4evrC4Cfnx/r1683zZOUlMQTTzxBRkbGHdcREZGSs+L/ngX8UF9lfeJ/UJ5iEQtmY2NDr169iIiIMG0zGo1s3ryZfv36AXDq1CnCw8MZO3YsR44coV+/frz88stkZmby7rvv4ubmRnh4OBMmTODixYsMHTqUAQMGEBsbS3h4OCNGjOD8+fOm+c+dO8fevXt57733bovn8OHDtG7dmtjYWIYNG8aUKVPIzMzk2rVrhIWF8eKLLxITE8OOHTv49ddf+frrr03HHjlyhLVr17JhwwZ69erF7t27TZXtEydOkJWVhbe39x2vQ9OmTWnWrJlZghwZGUlwcPBdr92IESOws7PjwIEDfPPNN2zcuJEtW7aYztHHx4eDBw/i5eXFa6+9Rl5eHrt27WLjxo2cOXOGadOmmeY6duwY/v7+HD58mO7duxMaGmqWIAOcPXuWYcOGMXjwYI4ePcq4ceMYO3Ysx48fNxtnNBp5+eWXady4MQcPHmTVqlVER0fz6aefAjBp0iSysrLYt28fy5YtIzIy0nRsYGAg0dHRpu+3bt2Kt7c3NWrUuOt1EBERKSklwFJu9OnThyNHjpCSkgLcrHxWr14dDw8P4GY1ODAwEC8vL6ytrQkKCqJx48Z3rD5u2bIFV1dXevbsibW1NR06dOCpp57i22+/NY3x8/OjUqVKd/y1fu3atQkJCcHa2prg4GCMRiMXL17Ezs6ODRs24O/vz7Vr1/j999+pWbMmqamppmPd3d1xdnbGwcGBtm3bUr16dfbt2wfcTOT8/f2xsbG563UICAgwtUFcvnyZkydP0rFjxzuOTUhIID4+nrFjx1KlShXq1avHihUrTNfs1nwVK1YkOTmZ+Ph4xo8fj729PY6OjowcOZKNGzdSUFAAgIeHhym+gQMHUqFCBY4cOWK2ZlRUlGmctbU1Xl5eBAQEsHHjRrNxx48f5/Lly4SHh2NnZ0ejRo0ICwvj22+/xWg0sn37dt58800cHBxo1KgRgwcPNh0bGBjI0aNHuXTpkmnNwMDAu14zEREpOYPBUGav8kJPgZByo379+rRv357NmzfzyiuvsH79elP1F27+Gjw2NpaoqCjTtry8PJKSkm6bKykpiSNHjuDm5mbalp+fT5cuXUzfOzk53TWWWrVqmb6+lawWFBRQoUIFDhw4wMqVKykoKKBly5ZkZmZSWFh4x3kNBgMBAQFERUXRpUsXtm3bxuLFi4u8Dj179mTu3LlcvnyZ6OhoOnfubNb/+0eXLl3Czs6OmjVrmrY1a9YMgN9++w17e3uqVKliGmtra4ujo6NpbP369TEajaZEs169emax161bl9TUVB555BHT9vT0dLNxAA0aNDDdoPfH2GrXrm2W7NevX5+LFy9y+fJlbty4Qd26dU37/vj0joYNG/Lkk0+yfft2PD09SUpKMrVHiIiIPCglwFKuhISEMGvWLAIDA/nhhx+YO3euaZ+TkxMvvfQS77zzjmnbuXPnzBK6P47t2rUrc+bMMW1LTEw0JYPAff0kGh8fz7x581i/fr0p0Xz11VfNxvznvEFBQTz77LMcPnyYypUr07p16yLXcHR0xM3Nje3btxMZGcno0aPvOrZOnTrcuHGDjIwMU3vA7t27yc/Pp1q1amax1KtXD6PRyO+//27qqU5ISMDGxoZq1aoBkJaWZhpfWFjIxYsXb0t2nZ2diYuLM9uWkJBw2/vg7OxMamoqRqPRlMDfGlerVi1sbW25ePGi6bhblf9bAgMD2bZtG5cvX8bPz++uPwSIiIjcK7VASLni4+NDTk4Os2bNwt/fn6pVq5r2BQcHs2HDBk6cOEFhYSExMTEEBgby448/AmBra0t2djYA/v7+7N+/n/3791NQUMCZM2d47rnnHvjRYVlZWVhZWWFnZ0dBQQHbtm3j4MGD5Obm3vWYZs2a0bx5c2bOnFniX+MHBASwevVqMjIycHFxues4Z2dn3NzcmDVrFtevXycpKYlp06ZhNBpvG1unTh28vLyYOnUq2dnZpKWlMXv2bLPkMiYmhr1795Kbm8vHH3+Mra2tWTsF3Ly2cXFxREdHk5+fT0xMDFu2bCEgIMBs3JNPPkmdOnWYPXs2N27c4Pz58yxdupSAgAAqVKhAUFAQCxcuJCsri6SkJFauXHnbOqdOnSI6Otp0g6KIiDy4MrkBroyePXw3SoClXLGysqJ3795s2bLltufytmvXjvHjxzN+/HhcXV354IMPmDRpEp6ensDNpPGzzz7jnXfeoUmTJixcuJCFCxfSrl07hg8fztChQ3n22WcfKD5vb28CAgIIDg7Gy8uLr7/+mj59+nD27NkijwsKCuKf//xniRPgLl26kJSUdFtSeSdz5swhMzOTjh070rdvX5577jl69ux5x7GzZs3CysqKLl260KNHD/7nf/7H9NSMW+e3cuVK3N3dOXToEJ988sltldfGjRuzZMkSVqxYgZubGxMnTmTixIl06NDBbJyNjQ0ff/wxv/32G97e3jz//PN06dKF119/HYBRo0ZhZ2fHU089xdChQ3FzczNrl6hWrRp///vfycnJMWtlEREReVCGwj82L4rIn+K7777js88+46uvvirrUMqNY8eO8fjjj1OpUiUAvvzySyIjI80eOTd58mSqVKnCW2+9VeJ5ExMT6dSpE8u+3IpT3XrFH1ACzetUKX7QX1B+Qen+76HCn1D+WRd/vvhB9+iJ2tVLdb5WDaoWP6iMnU7MLPU5//ZfcN7/DS5cSMS/ayd2795NgwYNHmiuW/8+Pj9tFQ6OdUopwpLLSkvhy7EDS+VcHpQqwCJ/oqysLP75z3/yySef0Lt377IOp1xZtmwZixcvJj8/n99//52vv/7aVEVOSUnh6NGjbN26lV69epVxpCIiUhb++c9/0rdvX9q0aUNAQAAnT54scnxmZiY+Pj5mj1S9GyXAIn+i3377jZCQEJydndXH+h8++OADTp8+jYeHB4GBgbi7u/PKK68AsG3bNoYNG8bQoUNp1KhRGUcqIvLXYjCAlcHw0F/3cu+50Whk+PDhdO/enWPHjhEaGsrgwYNN9/rcyfvvv3/bDdV3o6dAiPyJnnzySf7xj3+UdRjlUsOGDfnss8/uuG/gwIEMHDjw4QYkIiLlxtGjR8nNzTX9v6BHjx6sWbOG6Oho+vTpc9v4jRs3kp2dzaOPPlqi+VUBFhEREZFy5ZdffqF58+Zm25o1a8bPP/9829iEhAQWLVrE1KlTSzy/KsAiIiIiFqSsHkl2a83k5OTb9lWtWtXs0afXrl2jYsWKZmMqVapETk6O2bb8/HxGjhzJ6NGjTc+4LwklwCIiIiLy0Lzwwgu3bXvttddMj8kEqFy5Mjdu3DAbk5OTQ+XKlc22LVmyhKZNm9K1a9d7ikEJsIiIiIgFMRi4pxvSSnNdgLVr11K3bl2zfX+s/gI0b96cVatWmW379ddfCQ4ONtsWFRVFamoq3333HQBXr15l4sSJnDx5kg8++OCusSgBFhEREZGHpm7dusU+B9jDw4PCwkJWrVrFCy+8wM6dO/npp5/o0qWL2bjt27ebfR8UFMRLL71U7Adf6SY4ERERESlXbG1tWb58OTt27MDd3d307PiaNWuybNkyevTo8UDzqwIsIiIiYkGsMGDFw++BuNc1H3300Tt+gmpoaCihoaF3PGbz5s0lmlsJsIj8JTVyrER9C/0I49Li6PF68YPuQcaxRaU6H0CIiz4opTToY4vF0igBFhEREbEgZX0TXHmgHmARERERsShKgEVERETEoqgFQkRERMSCGCibT4IrRx0QqgCLiIiIiGVRBVhERETEglgZDFiVwR1pZbHm3agCLCIiIiIWRQmwiIiIiFgUtUCIiIiIWBA9B1gVYBERERGxMKoAi8g9SUhIoGHDhmUdhoiI3CfdBKcKsEUbM2YMU6ZMKeswzNxvTGfPnqVr1664uLiwfv160/YLFy7w2GOPceLEiduO+e233/jb3/5GUlISQ4YMYe3atfcVc4sWLThz5sx9HXvLsmXLePvttx9ojoULFzJ8+PA77uvfvz8tWrRg7969t+0bP348LVq0IDY2ttg1zpw5Q58+fczmXbVq1X3H/J8xltZcIiIiRVEFWP4Sdu3aRZ06ddixYweGP/yEWb9+fby9vYmIiKB169Zmx6xfv56nnnqKevXqsWLFiocdspnQ0NA/fY3q1auzdetWnn76adM2o9HInj17sLW1LdEcmZmZ5Obm/lkhioiIPBSqAAsAeXl5LFiwgI4dO+Lh4UFoaCiJiYkAxMbG4ubmZjbe19eXXbt2mb6eMGECnp6ejBo1ioiICAYNGsTYsWNp27YtnTt3Zt26daZjjx49yvPPP4+XlxcuLi6EhYWRlZVVbIw//vgj/fv3x83NDT8/P1PFdvHixSxevJjjx4/j6uqK0Wg0Oy4kJITo6Ghu3Lhhdr6bN2+mX79+gHn1sX///owZM4YOHTrQv39/AKKioujZsydt27blueeeu2u19G4xAqSkpDB48GBcXV3p1asXH330kWn+P1ZvCwsLWb58OT4+PrRt25YhQ4aQnJwMwE8//cTLL79Mhw4daN26NS+99BJJSUnFXjuAbt26sWfPHq5du2batmfPHlxcXLCzszNtu3LlCmPHjuXvf/87HTt2ZM6cOeTl5XHp0iWGDh1KVlYWLi4upKSkAPDzzz8TEhKCi4sLffr04dy5c6a5vvjiCzp37oybmxv9+/fnn//8p2nf4cOH6dmzJy4uLrz99tvk5OSY9u3bt48ePXrg5uZGQEAAmzZtKtE5iohI8W7dBFcWr/JCCbAANxOwnTt3smbNGg4cOECDBg0IDQ0tcbXv3Llz7N27l/feew+4mdy0bt2a2NhYhg0bxpQpU8jMzOTatWuEhYXx4osvEhMTw44dO/j111/5+uuvi5w/PT2dgQMH4uvrS0xMDHPmzGHp0qVs3bqVsLAwhg0bho+PD/Hx8bdVM318fLC3t2f37t2mbXv37qVixYp4e3vfcb0TJ04QHR3NkiVLOHToEOPHj2f8+PHExsYyaNAghg0bxvnz50scI8Dbb7+Nk5MTMTExTJo0iYiIiDuu/c0337B27Vo++eQTjhw5Qr169Rg3bhwAI0aMoH379hw4cICDBw9SUFDA8uXLi7x2tzRt2pRmzZqxZ88e07bIyEiCg4PNxo0ePZqrV6+yY8cO1q9fz9GjR/n444+pVasWy5cvx8HBgfj4eOrUqQPAgQMHmDJlCkeOHKF69erMnTvXdB4ff/wxCxYsICYmBh8fHwYPHkxmZiZpaWmEhYXx8ssvc+zYMTp06MCpU6cAKCgoIDw8nFGjRhEXF8fYsWOZPHkyV69eLdF5ioiIFEcJsACwadMmhg8fTsOGDbGzs2PUqFEkJSVx8uTJEh3v5+dHpUqVcHBwAKB27dqEhIRgbW1NcHAwRqORixcvYmdnx4YNG/D39+fatWv8/vvv1KxZk9TU1CLn3717N7Vr12bQoEHY2NjQqlUrBgwYwLfffltsbFZWVjz33HNmCeeGDRvo27cvVlZ3/k/Ax8eHqlWr4uDgwObNmwkMDMTT0xNra2t69OhB27ZtiYqKKnGMSUlJxMXFMWrUKOzs7GjVqhV9+/a949pbtmzhxRdf5NFHH8XGxoaRI0cycuRIAJYvX87AgQPJzc0lOTmZGjVqFHvt/iggIMCUkF++fJmTJ0/SsWNH0/60tDT27t3LhAkTsLe3x8nJibCwMLMK/n/q3bs3zZs3x87Ojk6dOpl+c7Bp0yYGDBjA3/72N2xsbBg8eDAODg7s27ePffv20aBBA5599lmsra159tlnadmyJXDz/apSpQpRUVHExcXh7u7O0aNHqVKlSonPU0RE7s7AzQTwYb/KUQFYPcBy06VLl6hXr57pe1tbW5ycnEhOTsbR0bHY452cnMy+r1WrlulrGxsb4GZlr0KFChw4cICVK1dSUFBAy5YtyczMpLCwsMj509PTzeKDm/29Fy9eLDY2uJmkLVu2zPRr+9jYWKZNm3bX8beqm7fWfuSRR4pdu6gYU1JSsLOzo0aNGqZ9zs7OxMfH37Z2Wlqa2TwODg489thjwM0Wi2HDhpGVlcUjjzxCTk4ONWvWLO70TXr27MncuXO5fPky0dHRdO7c2axifqudolu3bqZthYWF5ObmmrWQ/FG1atVMX9vY2JCXlwfc/DtVv379265HcnIyBQUFZtcYoEGDBqavP/vsM1NbSH5+Pr169WLkyJGmv0siIiIPQgmwBdm0aRNXr17lhRdeACA3N5eKFSsCUK9ePS5cuECbNm2AmzdHpaSkUKtWLSpUqGDWClFYWMiVK1fM5jaUsLEnPj6eefPmsX79epo1awbAq6++Wuxxzs7Ot/W6JiQklCg5h5sJuo+PD5GRkeTn59OpU6cSJ47Ozs6mquYtiYmJpmtVkhidnZ25ceMG6enppnVv9fX+pzp16pjtS0tL4/PPP+fFF18kPDycNWvW4OrqCsDkyZNL3AMM4OjoiJubG9u3bycyMpLRo0eb7XdycsLKyoqDBw9SqVIlALKzs7l06ZJZn3BJ3Po79UeJiYn4+/tjMBhui/vWDyc5OTmkpqYyb948CgoK+OGHHxgxYgStWrUiKCjonmIQERG5E7VAWJCrV6+ydu1aMjMzSUpK4tixY6ZfOwcHB7N06VISEhK4ceMGM2bMoEaNGri6utKoUSOMRiPR0dHk5+fz+eef33c/ZlZWFlZWVtjZ2VFQUMC2bdtWk6IVAAAgAElEQVQ4ePBgsb3GHTt2JCMjg1WrVpGbm8vp06f54osvCAgIKPHat26G27Jli+nmt5IIDg5my5YtHDlyhPz8fKKiojh27Bjdu3cvcYx169alffv2zJkzhxs3bvDzzz+zYcOGO64XEBDAmjVr+O233zAajSxatIjTp0+TnZ1NYWGh6YeWmJgYNm/efM9PZQgICGD16tVkZGTg4uJitq9u3bq4u7szffp0rl69SnZ2NmPHjmX8+PHAzd8MGI3Gu1aD//O6rV69mjNnzpCbm8unn35Keno6Pj4++Pr6cunSJb788kvy8vLYunWrqQc4Pz+fV199laioKAwGA3Xr1sVgMFC9evV7Ok8REbkzg8FQZq/yQhVgC9K7d29OnDhBly5dKCwsJDAw0JTEDR06FKPRyIABA7hy5Qqurq6sXLnS1Aoxfvx4Zs6cyfvvv09QUJCpAnmvvL29CQgIIDg4GCsrKx577DH69OnDTz/9VORx1apVY8WKFUyfPp2FCxdSrVo1hgwZctc+2jtp3749165dw8bG5ranWhTFzc2NDz/8kA8//JCkpCSaNGnC4sWLb2uLKC7GqVOnMmbMGDw9PWnevDmenp5kZGTctl6vXr1IT0833TDm5ubGtGnTcHJyYsSIEQwePJi8vDyaNm1Kv3792Lp1a7EtJH/UpUsXPvjgA4YMGXLH/bNnz2batGl06dKFvLw8PD09mTdvHnDzmcePPfYYHh4exd64GBQUREZGBiNGjCAtLY2WLVvy6aefmtpjPv74YyZOnMiMGTNo27Yt7du3B8De3p4FCxYwa9Ysxo8fj729PS+88IJZr7KIiMiDMBTey/85ReS+xcTE0K5dO6ytb/7cOXPmTJKTk5k9e3YZR/bXkpiYSKdOnYjeuZv69RsUf4DcVY12r5XqfBnHFpXqfCKW4MKFRPy7dmL37t1m90rcj1v/PobNW0P12nVLKcKSu/x7MovffLFUzuVBqQVC5CGZOHEiX3/9NYWFhZw7d44tW7bc9TFsIiIi8udRC4TIQzJ79mw+/PBDZs+ejb29PX379tVNXSIi8tBZGQxYlUE/blmseTdKgEUeklatWhX5PF0RERF5ONQCISIiIiIWRRVgEREREQtioGw+la38NECoAiwiIiIiFkYVYBERERELYjDcfJXFuuWFKsAiIiIiYlGUAIuIiIiIRVELhIiIiIhFMWAok36E8tMDoQRYRP6SWvdfTL61fanMlbHz3VKZ57+NPrpYRP6qlACLiIiIWBAryqYHtjz13ZanWERERERE/nRKgEVERETEoqgFQkRERMSCGAxlcxNc2dx4d2eqAIuIiIiIRVEFWERERMSCGCibB5KVn/qvKsAiIiIiYmGUAIuIiIiIRVELhIiIiIgFMRjK5oa0cnQPnCrAIiIiImJZlABLqRszZgxTpkwp6zDM3G9MZ8+epWvXrri4uLB+/frb9huNRhYvXkz37t1xcXHB29ubCRMmkJGRYRrTokULzpw5A4CLiws//fRTsetOmDCBmTNn3nO8f3S3cx43bhxDhgy54zHvvPMO48ePJy4ujqeeeuq+1l24cCHDhw+/r2NFROTPZ1WGr/JCLRAiRdi1axd16tRhx44dt/26KD8/n6FDh2JjY8PixYtp2rQpqampTJ48mZdeeomIiAisrc3/E4uPjy/RupMmTSq1c/hP/fr1o2/fvqSkpFCnTh3T9itXrvDdd9/x1Vdf0apVKw4cOPCnxSAiIlKWylMyLn9BeXl5LFiwgI4dO+Lh4UFoaCiJiYkAxMbG4ubmZjbe19eXXbt2mb6eMGECnp6ejBo1ioiICAYNGsTYsWNp27YtnTt3Zt26daZjjx49yvPPP4+XlxcuLi6EhYWRlZVVbIw//vgj/fv3x83NDT8/P9auXQvA4sWLWbx4McePH8fV1RWj0Wh2XFRUFP/6179YtGgRzZo1w2AwUKdOHT766CMaN27M+fPnb1vrj9XgFi1asHr1anx9fXF3d+fNN9/k2rVrgHn11mg0MmPGDNq3b28al52dfd/n/MQTT9CyZUs2b95stj0yMpIWLVrQqlUrs/cmNjYWPz8/QkNDadeuHfv37yctLY3w8HA8PT3x9vZm8uTJ5OTk3LZWUe8/wFdffUXHjh35+9//zsyZM/H19SU2NpZly5bx/PPPm8310ksv8cUXXxR5biIiIiWhBFj+VAsXLmTnzp2sWbOGAwcO0KBBA0JDQ8nNzS3R8efOnWPv3r289957ABw+fJjWrVsTGxvLsGHDmDJlCpmZmVy7do2wsDBefPFFYmJi2LFjB7/++itff/11kfOnp6czcOBAfH19iYmJYc6cOSxdupStW7cSFhbGsGHD8PHxIT4+HltbW7NjDxw4QMeOHalYsaLZ9sqVK7Nw4UKaNWtW7Pnt37+fTZs2ERERwfHjx9m0adNtYxYtWsT333/Phg0b2LdvHzk5OXz00Uf3fc4AISEht621YcMG+vXrd8fx586dw8fHh4MHD+Ll5cVrr71GXl4eu3btYuPGjZw5c4Zp06bddlxR739MTAyzZ89m4cKF7Nmzh6tXr3LhwgUAAgIC+Mc//kFycjIAqamp/PDDD/To0aPYcxMRkWL87yfBPexXeboLTgmw/Kk2bdrE8OHDadiwIXZ2dowaNYqkpCROnjxZouP9/PyoVKkSDg4OANSuXZuQkBCsra0JDg7GaDRy8eJF7Ozs2LBhA/7+/ly7do3ff/+dmjVrkpqaWuT8u3fvpnbt2gwaNAgbGxtatWrFgAED+Pbbb4uNLSMjA0dHxxKdx90MHDiQqlWr0qBBA9zd3e9YNd6yZQuhoaHUq1ePypUrM3nyZAYOHHjf5wzQs2dPUlJSOHHiBACnTp3i4sWL+Pv73/WYgIAAKlasSHJyMvHx8YwfPx57e3scHR0ZOXIkGzdupKCgwOyYot7/yMhIgoKCePLJJ7Gzs2P06NGmlpH69evj6upKdHQ0ANHR0bRv356aNWuW+NqKiIjcjRJg+VNdunSJevXqmb63tbXFycnJVNkrjpOTk9n3tWrVMn1tY2MDQEFBARUqVODAgQP4+vri7+/PwoULyczMpLCwsMj509PTzeKDm8nXxYsXi42tdu3a/P7773fcd+nSpWKPB8wSaBsbG/Lz828bk5aWZhZj7dq1ad68+X2fM9ysUgcGBhIREQHA+vXreeaZZ26rZt9ib29PlSpVTOdma2trFnv9+vUxGo23nXdR739qaqpZD3KlSpWoXr266fugoCCioqIA2Lp1K0FBQcWel4iIFM9Qhq/yQgmwPLBNmzaZ+mYBcnNzTYlUvXr1TL/Whpv9rCkpKdSqVYsKFSqYtUIUFhZy5coVs7lL+pzC+Ph45s2bx4oVK9i3bx/Lli2jQYMGxR7n7OxMUlKS2baEhIQSVXY7duzIwYMHb+t9vXbtGt27d79jO8P9qFOnjtkPDL/88guffPLJfZ/zLf369WPbtm1kZmYSHR1N37597zr2j+9DvXr1MBqNZsl/QkICNjY2VKtWzey4ot7/unXrmp3X9evXuXz5sun7bt26cfbsWeLi4vjtt9/w9fUt8bmJiIgURQmwPLCrV6+ydu1aMjMzSUpK4tixY7Rs2RKA4OBgli5dSkJCAjdu3GDGjBnUqFEDV1dXGjVqhNFoJDo6mvz8fD7//HOuXr16XzFkZWVhZWWFnZ0dBQUFbNu2jYMHDxbba9yxY0cyMjJYtWoVubm5nD59mi+++IKAgIBi1/Tz86Nhw4aMGDGC3377DbiZCL7++us4OzvTvXv3+zqX/xQQEMDHH39MSkoKV69eZc6cOSQmJt73Od/y6KOP0qxZMyZNmsTjjz9eop5luJmQe3l5MXXqVLKzs0lLS2P27Nn4+fnd1idd1Pv/zDPPsGXLFk6dOoXRaGTu3Lnk5eWZjnVwcMDHx4cPP/yQrl273rU6LSIicq/0GDR5YL179+bEiRN06dKFwsJCAgMDTcnf0KFDMRqNDBgwgCtXruDq6srKlStNvwofP348M2fO5P333ycoKAhXV9f7isHb25uAgACCg4OxsrLiscceo0+fPsU+c7datWqsWLGC6dOns3DhQqpVq8aQIUOKrIbeYmVlxYoVK1i4cCFDhgwhPT2dqlWr8vTTTzNz5kzs7Ozu61z+06uvvsr169fp3bs3N27c4KmnnmL06NFUrlz5vs75j/r168eoUaOYP3/+PcU0a9Yspk2bRpcuXSgoKKBbt26MGjXqtnFFvf9ubm6MGDGCV199lYKCAnr37o21tbWptQVutkEMHz6cMWPG3FN8IiJyd2V1P1o5ugcOQ2FJGgZFRErZr7/+io2NDQ0bNgQgJyeHNm3asH37dpo2bQrA6dOnGT58OHv27MHKqmS/sEpMTKRTp05cqPMs+db2pRJrxs53S2UeEZF7deFCIv5dO7F79+57anO7k1v/Po5Zso6aTs6lFGHJpadeZPrwkFI5lwelFggRKRNnzpwhNDSUS5cukZeXx7Jly2jYsCFNmjTh+vXr/PzzzyxYsIBevXqVOPkVEZHiWWEos1d5oRYIESkT/v7+nDlzhqCgIK5du0arVq1YunQpBoOBzMxM+vbty+OPP87LL79c1qGKiMhfjBJgESkTBoOB8PBwwsPDb9vn5ORU4o+NFhGRe6MeYLVAiIiIiIiFUQIsIiIiIhZFLRAiIiIiFsTwv3/KYt3yQhVgEREREbEoqgCLiIiIWBDdBKcKsIiIiIhYGCXAIiIiImJR1AIhIn9JJ74Io379sv2oTRGAGl2nlPqc+nhueRCGMvpUNt0EJyIiIiJSRlQBFhEREbEguglOFWARERERsTBKgEVERETEoqgFQkRERMSCGCijFoiHv+RdqQIsIiIiIhZFFWARERERC2L43z9lsW55oQqwiIiIiFgUJcAiIiIiYlGUAMtfSkJCQlmHYKa8xSMiImJlKLtXeaEE+C9myJAhuLi44OLiQqtWrXj88cdN3w8ZMoTExERatGhBZmbmA681ZswYpkwp/Y/4vF9r165l+vTpd93fokULzpw5U+w8hYWFvPnmm7Rp04aBAweWWjwuLi789NNP9z3fvWrRogWtW7fGxcWFNm3a4OXlRXh4OCkpKaYxEyZMYObMmQ8tJhERkfJAN8H9xaxYscL09ZgxY3BwcODdd//vM+MTExPLIqyHIj09ncLCwgeeJzU1lW3btrFr1y4aNmxYavHEx8c/cGz3at26dTz22GMApKWlMX36dAYMGMCmTZuoVKkSkyZNeugxiYhI2dJNcKoAW6w1a9bQqVMnXF1dmThxIgUFBQDcuHGDadOm0bFjR/7+978zYcIErl27ds/zG41GJk+eTNeuXWnTpg1dunQhKioKuJmEu7i4sHLlSjp06ICXlxfvv/++KYbs7Gzeffdd3N3d8fT05IMPPiA3NxeAX375hYEDB9KuXTu6devGli1bANixYwcff/wx+/btIzAwsNj4+vfvz9y5c3nmmWdwdXXl+eef51//+hcJCQn4+fkBEBgYyPr164u9Jt9++y1+fn64uLjQr18/fv755zvG88cK9I8//kj//v1xc3PDz8+PtWvXmsW2atUq0/cREREEBQUBN5PqYcOG0a5dO3x8fBg7dizXr18v0Xvi6OjI9OnTyc3NJSIiAjCv4p84cYJevXrh5uZGt27dzH6YSk5OJiwsDA8PDzp37mwWX2ZmJqNGjcLX15fWrVsTEBBATEwMANevX2fkyJF4eHjg7e3NiBEjSE9PByA/P59ly5bRqVMnPDw8eOONN0z7ijpORETkQSkBtlD/+te/iIqKYv369WzcuJGDBw8CMHPmTH788Ue+/fZbtm/fzqVLl5g8efI9z//ZZ5/x448/sn79en744QcGDBjAhAkTyMvLA+DatWv89NNP7Nq1i08//ZTIyEhTDO+//z5JSUns2LGDbdu2cerUKT799FOuXr3KoEGD8Pb25vDhw8yYMYNp06YRFxeHn58fw4YNw8fHh8jIyBLFuGnTJubMmcOBAweoVKkSCxcupGHDhmzduhWA/fv307t37yKvyaFDh5gyZQpTpkzh+PHjdOjQgddff73IeNLT0xk4cCC+vr7ExMQwZ84cli5dalq3KIsXL8bBwYHDhw+zadMm/t//+39s3769xO+LtbU1HTp04NixY7ftGz9+PL169SIuLo758+ezZMkSEhISyM/PJzQ0FGdnZw4cOMCKFSv46quv2LRpE3Dz70xOTg5RUVGma3Dr+nz55ZckJyezb98+du7cydWrV/nqq68AWL16NZGRkaxcuZL9+/dTs2ZN3nrrrWKPExEReVBqgbBQb775JhUrVqR58+a0bNmSxMRECgsLWb9+PatXr8bR0RGAd955hx49evDBBx9ga2tb4vlDQkLo06cPVatWJSUlhUqVKpGdnU1OTo5pzCuvvELFihX529/+RosWLTh//jxGo5EdO3bw5ZdfUqNGDQDmz58P3ExI7e3tGTx4MABPPvkkvXr14quvvsLNze2er0FgYCBNmzYFwM/Pj3Xr1t02prhrsmXLFoKCgkzrDx06lA4dOpiq2Xeye/duateuzaBBgwBo1aoVAwYM4Ntvv6Vnz55Fxmxvb8/333/Pzp07ad++PZs2bcLK6t5+jq1RowYXLly449z79u2jUaNGtGvXjri4OKysrDhx4gTnz59nw4YNWFtb06RJEwYNGsS6desIDg7mjTfewNbWFltbW5KSkqhatSqpqakAODg48NtvvxEVFYW3tzfLly83xfvNN9/w+uuv06hRIwBGjhyJm5sb586dK/I4ERF5QIay+SS4ctQBoQTYUlWrVs30tY2NDbm5uaSnp3P9+nVefvllDH/4L8Pa2poLFy6YksWSyM7OZtKkSZw4cYL69eubjv1jT+ythPJWDPn5+Vy5coXc3Fzq1atn2tegQQMAtm3bxvnz582S3fz8fFq1anUPZ/5/atWqZfra2tqa/Pz828YUd03S0tLw8PAwbbe1taV169ZFrpuenm52fgD169fn4sWLxcYcFhaGlZUVixYtIjw8nLZt2zJp0iSaNWtW7LG3ZGRk3LY+wLx585g3bx5jxowhMzOT7t27895773HhwgVycnLw9PQ0jS0oKKB69erAzZ7pqVOncvbsWZo0aYKjo6PpfX7uuee4evUqa9as4b333qNly5a8//77tGnThqSkJN59910mTJhgmvfWdS3qOBERkQelBFhMqlevjo2NDevXrzclVEajkYSEBFOVrqTef/99GjduzJIlS7C2tub06dMl+hV/zZo1sbGxITk52ZQgx8XF8euvv+Lk5MTjjz/O119/bRqfkpJilpiWtuKuSZ06dcyeqpCbm8ucOXMYPnz4Xed0dnYmKSnJbFtCQoLpfK2srEw9zwCXL182ff3zzz8TEhLCG2+8wcWLF5k2bRqTJk0y68ktSn5+PocOHWLYsGFm2/Py8vj111+ZNGkSNjY2nD59mvDwcFavXo27uzu1atXi0KFDpvG3fjAAeOutt+jVqxerV6/GysqK7777jtjYWOBmq42vry8DBw4kPT2dxYsXM3LkSL777jucnJyYMGEC3t7eZufXpEmTIo8TEZEHo5vg1AMsf1ChQgUCAwOZNWsWGRkZGI1GPvroI0JDQ+96zLVr10hOTjZ7GY1GsrKysLOzw8rKitTUVGbPng1gltjdLQZ/f38WLFhAZmYm6enpzJgxg4yMDDp27Mj58+eJiIggLy+PhIQEBgwYYEqIbW1tycrKKr0LQvHXJCAggMjISE6ePEl+fj6fffYZe/fuxd7e/q7xdOzYkYyMDFatWkVubi6nT5/miy++ICAgAIAmTZqwa9cuMjMzSUpKMt2wBvD5558zZcoUrl69Sq1atahYsaJZNb8oKSkpjB49msqVK5tuqvvjeY4bN45Vq1aRn59P3bp1sbKyolq1ajz55JPY29uzZMkSjEYj6enpDB8+nAULFgA3q/0VK1bEysqKf//73yxZssT0Pm/dupVRo0aRnp5OtWrVqFKliine4OBgFi9ezMWLF8nPz+eTTz7hhRde4Pr160UeJyIi8qCUAIuZcePGUbduXYKCgmjfvj3nzp1j+fLlVKhQ4Y7jN2zYQMeOHc1ep0+f5t133+XQoUO0bduWkJAQ2rVrR40aNfj555+LjeG9997D0dGR7t2706NHD1q3bs3gwYOpXr06K1asYOPGjXh5edGvXz86d+5sqrb6+Pjw73//m44dOz60a+Ll5cXYsWMZNWoU7u7uHDp0iMWLF2MwGO4aT7Vq1VixYgW7du3C09OT1157jSFDhtC3b18Ahg8fjr29PT4+PoSGhpo91WLs2LEUFBTw9NNP4+npSWZmJuPGjbtr7CEhIabnQIeEhFC5cmVWr15NxYoVzcYZDAbmz5/Prl27aNeuHf7+/nh6etK3b19sbW355JNPOHnyJN7e3vj7+/M///M/ptaFKVOmsGbNGlxcXHjllVcIDAwkNzeXhIQEQkNDadq0Kf7+/ri5uREfH89HH30E3OwB9/Ly4oUXXqBdu3bs2bOHTz/9lKpVqxZ5nIiIyIMyFJbGg1NFRMqJxMREOnXqRPTO3dSv36CswxGhRtfS/8CgjJ3vFj9I/hIuXEjEv2sndu/ebbon5n7d+vdx2ooIHOs4l1KEJZeWcpGxQ54tlXN5UKoAi4iIiIhF0U1wIiIiIhbEQNnckFZ+boFTBVhERERELIwqwCIiIiIWxFBGH4RRJh++cReqAIuIiIiIRVECLCIiIiIWRS0QIiIiIhbEQNnckFaOOiBUARYRERERy6IKsIiIiIgFMRgMWJXBHWmGcnQXnCrAIiIiImJRlACLiIiIiEVRC4SIiMgf1HAfUarzZRxdUKrziTwo3QSnCrCIiIiIWBhVgEVEREQsiUrAqgCLiIiIiGVRAiwiIiIiFkUtECIiIiIWxPC/f8pi3fJCFWARERERsSiqAIuIiIhYEIPh5qss1i0vVAEWEREREYuiBFhERERELIpaIESk1GRmZgJQtWrVMo5ERETuRo8BVgVYpEy1aNGC1q1b4+LigouLC66urgwePJiff/4ZgNjYWNzc3O5r7ry8PObNm4evry9t2rShQ4cOjBs3jkuXLt1xfGxsLC1atDDF4uLigoeHB2PGjCE7O7tEa/r5+XHhwgUAFi5cyPDhw+8rdhERkT+TEmCRMrZu3Tri4+OJj483JaFDhw4lPz//geZdtmwZhw4d4vPPP+cf//gHmzZtIisri7CwsLse4+DgYIolPj6e7777jgsXLjBhwoQSrZmenv5AMYuIyENiKINXOaIEWKQcsbGx4ZlnniE5OZkrV64UO/7w4cP06tULV1dXAgIC2L59u2nfiRMn8PLyomHDhgA4OjoyZswYmjZtSm5uboniqVq1Kt26dTNVpDMzMxk1ahS+vr60bt2agIAAYmJiAHj22WcBCAkJITo62jT+rbfewsPDg06dOrFnz54Sxf7LL78QEhKCq6sr/fv3Z/z48YwZM6ZEMYuIiBRHCbBIOXLlyhW++OILHnnkEWrWrFnk2LNnzzJs2DAGDx7M0aNHGTduHGPHjuX48eMAdO/enZUrV/LOO++wceNGzp8/T/369Zk2bRo2NjbFxlJYWEhCQgKbN2/G3d0dgJkzZ5KTk0NUVBTHjx+nQ4cOTJ48GYCIiAjgZkXb398fgB9++IFu3bpx5MgRevXqxbvvvlts7Lm5uYSGhtK+fXuOHDnCq6++yqZNm+7vgoqIiNyBboITKWPPP/88FSpUAMDW1pYnn3yShQsXFntcVFQUHh4epmTTy8uLgIAANm7cSNu2bXn22WepX78+GzZsYM6cOaSmptK0aVPCw8Pp3LnzHefMysoy9RwXFhZStWpVnnrqKd555x0A3njjDWxtbbG1tSUpKYmqVauSmpp61xhdXFzw8/MDbibk8+fP59q1a0XGXlBQwJUrVxg+fDjW1ta0b9/eNIeIiDw4fRKcEmCRMvfll1/y2GOP3fNx6enp1KtXz2xbgwYNiI2NNX3v4eGBh4cHAOfOneObb77h9ddfZ+vWrTRv3vy2OR0cHIiLi7vrmqmpqUydOpWzZ8/SpEkTHB0dKSwsvOv4atWqmb6+VXXOy8srMvaUlBQcHR2xtv6/f56cnZ1JS0u76zoiIiL3Qi0QIv+lnJ2dTU9cuCUhIQFHR0eys7Np06YNp0+fNu1r0qQJo0aNokGDBqae3nv11ltv8dRTTxETE8PXX39NcHBwqcdet25dfv/9d/Ly8kz7kpOT72sdERG53a1PgiuLV3mhBFiknCssLCQ5OdnslZmZib+/P3FxcURHR5Ofn09MTAxbtmwhICAAe3t7vL29GT9+PKdOnaKwsJCsrCzWr19PZmamqSp8r7Kzs6lYsSJWVlb8+9//ZsmSJWY31NnY2JCVlVXsPEXF7uLigqOjI8uWLSM3N5e4uDh27tx5X/GKiIjciRJgkXIuOzubjh07mr1mzZpF48aNWbJkCStWrMDNzY2JEycyceJEOnToAMCMGTPo0KED4eHhuLq64uPjw65du/j888+LvcHubqZMmcKaNWtwcXHhlVdeITAwkNzcXBISEgB47rnnGDp0KF9++WWR8xQVe4UKFZg3bx779u3D3d2dRYsW4eHhUaIb90RERErCUFhUA5+IyEOWk/P/2bvzuKjK9vHjH5YBJARRERFzT9wVRHDDFFByA9RUzDTXR1wrHzQplzJ5yo00NVyyXHLLJRPQNC23cteyvuVWbuwgIDszwPn94Y/zOMoOhU9cb168Xsyc+1znus+ZGe655j5zMvn111/p1KmTet8bb7xBgwYNmDlzZrHrR0RE4OHhwcEjx7C3r/9Xpir+oaxdZlRovKTzH1doPFG1REZG0K+PB8eOHaN+/fK9puW/Pn68+Wts6tYrfoUKFh8TxYzXfCqkL+UlFdHWutwAACAASURBVGAhxDPFyMiISZMmcfz4cQCuXr3KiRMn1Mq2EEIIUV7yLRBCiGeKiYkJq1atYvHixbz55pvUqlWLt956S/0uYiGEEOVUWVdme4ZOgpMBsBDimdOtWzcOHDhQ2WkIIYT4h5IpEEIIIYQQVYpBpfyUtgR87do1hg8fTocOHRg4cCBXr14tsN0PP/zA4MGDcXJyonfv3uzcubPY2DIAFkIIIYQQzxStVsuUKVPo27cvFy5cwN/fn/Hjx5OWlqbXLjo6munTpzN58mQuXrzI8uXLCQ4O5tSpU0XGlwGwEEIIIYR4ppw/fx6dTseYMWPQaDT079+fZs2acfDgQb12kZGRDBgwgN69e2NoaEi7du1wcXHh8uXLRcaXOcBCCCGEEFVIZV2VLX+bBV3d09LSEktLS/X2rVu3aNq0qV6bJk2aPHUlU2dnZ5ydndXbycnJXLx4ER8fnyJzkQGwEEIIIYT424wcOfKp+6ZNm8b06dPV2xkZGZiZmem1qVatGpmZmYXGTU1NZfLkybRv3x4PD48ic5ABsBBCCCFEFVLZ34K2bds26tatq7fs8eovgLm5OdnZ2Xr3ZWZmYm5uXmDs27dvM2XKFJo1a8ayZcswNCx6lq/MARZCCCGEEH+bunXrUr9+fb3fJwfATZs25fbt23r3/fnnnzRr1uypeBcuXGDYsGF4enry8ccfY2pqWmwOUgEWQohKYN1pWoXGS7qwukLjVWVy6WIhKp+rqyuKorBp0yZGjhzJkSNHuH79Or1799Zrd+/ePSZNmsSbb77JqFGjShxfKsBCCCGEEFWJQSX+lpCJiQkbNmzg8OHDuLi4sHbtWtasWUPNmjVZu3Yt/fv3Bx5Np0hPTyc4OBhHR0f1d+nSpUXGlwqwEEIIIYR45jRv3pwdO3Y8db+/vz/+/v4ABAYGEhgYWOrYMgAWQgghhKhC/ntltr9/u88KmQIhhBBCCCGqFBkACyGEEEKIKkWmQAghhBBCVCGVfSW4Z4FUgIUQQgghRJUiFWAhhBBCiCqksq8E9yyQCrAQQgghhKhSZAAshBBCCCGqFBkACyEK9dprr7FgwYICl/n5+RESEsKBAwfw8/MrU/w5c+YQFBRUbLtVq1YxZcqUMm1DCCHEE/4HrgT3V5MBsBCiUH5+fhw8eJDs7Gy9+2/dusWvv/7Kyy+/jLe3Nzt37qykDIUQQojSkwGwEKJQnp6emJqacuzYMb379+zZg6enJzY2Nuzbtw8fHx8A9u3bh5+fH35+fri6unLt2jXu3r2Lv78/Li4uuLu7s3r1anJyciqjO0IIIfjvleAq4+dZIQNgIUShNBoNQ4YMYd++fep9Wq2Wr7/+mhEjRhS4zpUrV5gyZQrHjh2jSZMmjBs3joYNG3Lq1Ck2bdrEwYMH2bhx49/VBSGEEOIpMgAWQhRp2LBhnD17ltjYWAC+++47atSogaura4Htra2t6dGjBxYWFly6dInk5GQCAgIwNTWlQYMGTJ06lb179/6dXRBCCCH0yABYCFEke3t7unbtytdffw3A7t27C63+AtSpU0f9+8GDB9jY2KDRaPTiRUdH/3UJCyGEKFL+leAq4/dZIQNgIUSx/Pz82L9/PzExMVy+fBlfX99C2xo89gpnZ2dHXFwcWq1Wve/+/fvUrl27yO2dOHGCjz/+WL2dk5ODmZlZOXoghBBC/JcMgIUQxerZsyeZmZksW7aMfv36YWlpWaL12rVrh62tLcuXLyc7O5t79+4REhLCwIEDi1xPURS2b99ObGwsycnJnDhxghYtWlREV4QQQlClvwENkAGwEKIEDA0NGTp0KKGhoaX6zl+NRsO6deu4ffs2bm5uvPLKK/Tu3Zvp06cXuV7Pnj3x9fXF19cXDw8PmjVrxqhRo8rbDSGEEAIAA0VRlMpOQgghKkpERAQeHh4cPHIMe/v6lZ1Ooaw7TavQeEkXVldoPCHEsyEyMoJ+fTw4duwY9euX7zUt//Vx3Y4wbOvWq6AMSy42JopJIwZUSF/Ky7hSty6EEEIIIf5+z9qchL+ZTIEQQgghhBBVilSAhRBCCCGqkMq6KptcCU4IIYQQQohKIhVgIYQQQogqpLIuSiEXwhBCCCGEEKKSyABYCCGEEEJUKTIFQgghhBCiCqmsK7M9QzMgpAIshBBCCCGqFqkACyGEEEJUJVIClgqwEEIIIYSoWqQCLIQQlSDpwurKTkGIKs+607QKjynP7f8NMgAWQgghhKhCHs2AqIwrwT07ZAqEEEIIIYSoUqQCLIQQQghRhciV4KQCLIQQQgghqhgZAAshhBBCiCpFpkAIIYQQQlQh8jXAUgEWQgghhBBVjFSAhRBCCCGqEikBSwVYCCGEEEJULTIAFqIC5ObmEh0dXdlpCCGEEKIEZAAsxBPmz59PixYtuHnzZonXmTlzJocPHwbg4sWL9OjRo0TbWbp0aZlyHDVqFJs2bSpwmYODA+3bt8fR0RFHR0fat29Pr169CAkJKdO2KkNR/RNCCFE+BpX486yQOcBCPCY9PZ1Dhw4xaNAgtm7dysKFC0u0XmJiovq3s7MzJ0+eLHadksYui507d9KyZUsA8vLy+PHHH5k8eTKtW7cu0eBcCCGE+CeTCrAQjwkLC6Nly5ZMmDCB0NBQHj58CMC+ffsYO3YsgYGBdOzYEU9PT3bu3AlAUFAQFy9eZNmyZSxcuJBz587h7OwMgJ+fn14lMykpiTZt2nD//n3mzJlDUFAQAHPmzOGNN97A3d0dLy8vdDodhw8fZsiQIbi4uNCpUycCAwPR6XSl7pOhoSHdu3enSZMmalU7NzeXtWvX4uHhgaurK6+//ro6iM/KymLWrFm4urri5ubGjBkz1GVFrQewfft2Bg4cSMeOHenSpYtehdvd3Z358+fTuXNnZs+eDcDevXvx8vLC0dGRESNGcOPGDbX9jRs38PPzw9HRkWHDhnHnzp1S910IIUQBDP57Nbi/8/cZKgDLAFiIx+3atYuhQ4fStGlT2rRpw549e9RlP/74I+3bt+fcuXNMmjSJoKAgUlJSeOedd3B2diYgIID58+frxRs0aBBhYWHq7W+++Yb27dvz/PPPP7Xts2fPsm3bNvbs2UNcXByzZ88mMDCQ8+fPs2fPHr7//nuOHj1a6j7l5ORw8OBB/vjjD1xcXADYsmULBw4c4PPPP+fEiRPUrFmTN998E3g0iI2JieH48eMcOXKE9PR0duzYUex6ly9fZsWKFaxYsYJLly6xbt06Nm/ezNWrV9Vc7ty5w/fff8+8efM4ffo0QUFBBAUFcenSJbp378706dPVtidPniQoKIizZ89So0YNPvroo1L3XQghhCiIDICF+P9++eUXoqOj8fLyAmDEiBFs27aNvLw8AGxsbPDz88PY2BhfX1+0Wm2xJ77169ePGzducPfuXQBCQ0Px9fUtsK2Liwt2dnZUr14dGxsbwsLCcHZ2JjU1lcTERKytrYmLiytRX1555RWcnZ1p164d7dq1Y+/evXzyySe0bdsWgC+//JJp06bRoEEDzMzMmDVrFhcuXODOnTtUr16d27dvEx4eTkpKChs2bGDq1KnFrteyZUv2799P06ZNSUpKIisri+eee04vZy8vL6pVq0b16tUJDQ3Fx8cHZ2dnDA0NmThxIkuWLFH3d/4bEVNTUzw8PIiIiChR34UQQojiyBxgIf6/Xbt2kZqaSq9evYBHc2cTExP57rvvAKhVq5baVqPRqG2KUr16dTw8PAgNDWXQoEH89ttvrFu3rsC2derU0Yu/d+9e9uzZg5mZGa1atSI7OxtFUUrUl+3bt9OyZUvi4uL497//jampKV27dlWXR0VF8c477+hVrI2NjYmMjOTll18mPT2dL774gnnz5tGiRQsWLFhAhw4dilyvXr16rFu3jsOHD2NtbU2rVq2e2j+P9zEhIQFXV1f1tomJCe3bt1dvW1lZ6e2PnJycEvVdCCFE0eRrgGUALAQAaWlphIeHs2HDBpo1a6bev3btWrZu3YqPj0+ZY/v6+rJ06VJMTExwd3enevXqBbYzMPjvS0N4eDihoaHs3bsXW1tbALy9vUu97Tp16rBq1Sp8fHz4z3/+ow5c69Spw/z583Fzc1Pb3rhxg0aNGvHHH3/g7u7OmDFjSExMZM2aNcyaNYtvv/22yPU+//xzfvvtN44cOYKlpSWKotCpU6dC+2hra0tsbKx6W6fTERwczJQpU0rdTyGEEKI0ZAqEEMCBAweoW7cuXbp0wcbGRv0dPnw4Z8+eJTMzs8j1TUxMSEtLK3BZ9+7dSU5OZvv27SUeSKempmJkZISJiQk6nY6tW7dy/fr1Mp0EV6NGDYKCgti+fTunT58GHg3K16xZQ3R0NLm5uaxfv56RI0eSlZVFWFgYs2fPJjExESsrK5577jm1GlvUeqmpqWg0GoyNjcnMzCQ4OJjU1FS0Wm2BeQ0cOJADBw5w9epVcnNz+eyzz/j++++xsLAodR+FEEKUgkEl/j4jpAIsBI+mPwwYMOCp+5s3b07r1q354IMPaNq0aaHrDxw4kIULF3L79m2GDRumt8zIyIiBAwcSGhpK9+7dS5TPoEGDOHfuHJ6enpiYmODk5MSAAQNK9d3Ej+vevTuDBw9m3rx5hIaG8q9//YucnBxGjhxJcnIyzZs3Z+PGjVhaWuLv709sbCz9+vUjOzubNm3asHjxYoAi1xs3bhy///473bp1w9zcnB49etCtW7dCc+7SpQuBgYHMnj2b+Ph4WrVqxZo1a/SqxEIIIcRfwUAp6aRCIYT4HxAREYGHhwcHjxzD3r5+ZacjhHiGWXeaVuExky6srtB4kZER9OvjwbFjx6hfv3yvafmvj1v2HqKunX0FZVhyMdGRjB7St0L6Ul4yBUIIIYQQQlQpMgAWQgghhBBViswBFkIIIYSoQtQrs1XCdp8VUgEWQgghhBBVilSAhRBCCCGqELkQhlSAhRBCCCFEFSMDYCGEEEIIUaXIFAghhBBCiCrEgEo6Ce7v32ShpAIshBBCCCGqFKkACyGEEEJUKXIanAyAhRDiH8Da9fUKj5l0bmWFxxTiWVLRly0W/ztkCoQQQgghhKhSpAIshBBCCFGFyJXgpAIshBBCCCGqGKkACyGEEEJUIXIKnFSAhRBCCCFEFSMDYCGEEEIIUaXIFAghhBBCiKqkkk6Ce5bmQEgFWAghhBBCVClSARZCCCGEqEIM/v9PZWz3WSEVYCGEEEIIUaXIAPgZc//+/cpOoVDPcm5CCCGEECVV7ADYwcGB9u3b4+joiKOjI05OTowfP54bN278HfmVmbu7O0ePHgWgf//+fP/995Wc0SM3b96kT58+ODo6snv3br1lv//+O8OGDVNvjxo1ik2bNpV7m+fOncPBwYG9e/c+tczBwYHff/+92BhP5jZhwgS2bdtW7tyKUpL+z58/nxYtWnDz5s2/LI+S7qPK8Nlnn+Hs7IyrqytpaWl6y3JyclixYgXu7u506NCB7t278/bbb/PgwYNi4x44cAA/P7+/Km0hhBCVyaASf58RJaoA79y5kytXrnDlyhV1MDVx4kRyc3P/6vwqRHh4OL169arsNAA4evQotra2XL58maFDh+otS0lJQafT/WXbXrRoUZmruE/m9umnnzJy5MiKSq1M0tPTOXToEIMGDWLr1q2Vmktl2blzJ4GBgZw7dw4LCwu9ZWvXruX06dNs3ryZn376if3795OamsrUqVOLjevt7c3OnTv/qrSFEEKISlXqKRAajYZBgwYRExPDw4cP2bdvH35+fvj5+eHq6sq1a9e4e/cu/v7+uLi44O7uzurVq8nJyQFAq9WyZMkSunbtiouLC2+88YZauYqJiWHq1Km4urri6empV/3Lzc1l7dq1eHh44Orqyuuvv05iYqK6PCwsDE9PT5ycnFi0aJHe4PzxarC7uzvr16/npZdeomPHjowfP56EhAQAdDodCxcupFOnTnh6erJhwwYcHBwAyMrKYtasWbi6uuLm5saMGTP0tv+4X3/9lVGjRuHs7IyXl5daKV2zZg1r1qzh0qVLODk5odVq1XUePHjAxIkTSU1NxdHRkdjYWABu3LiBn58fjo6ODBs2jDt37qjrHDt2DG9vb5ydnfHz8+O3334r9LhVr16dnj17MmvWrELfuFy/fp1x48bRvXt32rdvz2uvvUZUVFSBuT1enU1ISCAgIIDOnTvj5ubGokWLyMzMBGDVqlX8+9//ZvLkyTg6OtKvXz/1WAAcPnyYIUOG4OLiQqdOnQgMDCzxm4CwsDBatmzJhAkTCA0N5eHDh3rL9+7di5eXF46OjowYMUL91CI6OpqpU6fSs2dP2rVrx9ChQ7l27Zq63qZNm3Bzc8PFxYW1a9fqxSzqMTpnzhwWL17Mq6++iqOjI35+fvzyyy+MGTNGvR0TE0NMTAwtW7bk3r176rr79+9n8ODBBfZz69ateHp64uzszKhRo9Rcvby8uHfvHu+99x5z5sx5ar2ff/6ZLl268PzzzwNQu3Zt5syZQ+PGjdV9/OeffzJ27FicnJzo1asX27dvB2Dfvn34+PiosYp6rDk4OLBlyxbc3d3V53RGRgZQ9uf7zz//zJAhQ3B2duall17i008/LXDfCCGEKD0pAJdhAPzw4UO2bt3KCy+8QM2aNQG4cuUKU6ZM4dixYzRp0oRx48bRsGFDTp06xaZNmzh48CAbN24EYPXq1fzwww/s2bOH48ePk5mZyeLFi8nNzcXf3x87OztOnjzJp59+yo4dO9i/fz8AW7Zs4cCBA3z++eecOHGCmjVr8uabbwJw7do1AgMDeffddzl37hw1atQgJiam0D4cPHiQzZs38+233xIfH8/nn38OwCeffMJPP/1EeHg4u3bt0huobd++nZiYGI4fP86RI0dIT09nx44dT8VOTExkzJgxuLu7c+bMGYKDgwkJCSEsLIypU6cyadIkevbsyZUrVzAxMVHXq1WrFhs2bKB69epcuXIFW1tbAE6ePElQUBBnz56lRo0afPTRRwD88ssvBAQEEBgYyNmzZxkxYgTjxo0jJSWl0H6/9957REdHExISUuDyGTNm0LVrV06ePMmpU6fIy8tjw4YNheaWb9q0aeTk5HD06FG++uorfv/9dz744AN1+aFDhxg+fDjnz5/npZde4t1330VRFCIjI5k9ezaBgYGcP3+ePXv28P333+vt96Ls2rWLoUOH0rRpU9q0acOePXvUZadPnyYoKIigoCAuXbpE9+7dmT59OgDvvPMOdnZ2fPvtt5w/f54GDRoQHBwMwPHjx1mzZg0hISGcOnWKiIgINWZxj1F4VJGdM2cOZ86cQavVMmbMGAICAvjxxx8xNDRk06ZN1K1bFxcXF8LDw9X1wsLC8Pb2fqqPX375JevWrePjjz/mzJkz9OzZk/Hjx5OSksLhw4epV68ewcHBfPjhh0+t27dvXz7//HP+/e9/89VXX3Hv3j3s7e354IMP0Gg0aLVaJkyYQJs2bTh79iwhISEEBwdz+fJlvTgleaydOHGC/fv3s2/fPi5duqTuk7I+3+fOncuQIUO4ePEiK1eu5JNPPpE56EIIISpMiQbAr7zyCs7Ozjg7O9O3b1/i4uJYtWqVutza2poePXpgYWHBpUuXSE5OJiAgAFNTUxo0aMDUqVPV+aehoaH4+/tTr149zM3NWbRoEWPGjOHXX3/l3r17zJkzB1NTUxo1asTYsWPVj2G//PJLpk2bRoMGDTAzM2PWrFlcuHCBO3fucPjwYbp160b37t3RaDRMnjyZGjVqFNofPz8/bG1tqVmzJr169VIrcQcOHGDSpEnUqVOHWrVqqQMmeFRBvX37NuHh4aSkpLBhw4YCP0o+duwYNjY2jB07Fo1GQ+vWrRk9enSB829LIn+AZ2pqioeHhzog27NnD97e3nTp0gVjY2N8fHxo2LAh33zzTaGxLC0tWbx4MWvXruXq1atPLd+wYQNjxoxBp9MRExODtbU1cXFxReZ37949rly5wty5c7GwsKB27drMmjWLr776iry8PADatm1Lz5490Wg0eHt7Ex8fT3p6OjY2NoSFheHs7ExqaiqJiYkl2iY8GpRFR0fj5eUFwIgRI9i2bZu6zdDQUHx8fHB2dsbQ0JCJEyeyZMkS8vLyCAoK4t///jcAUVFRWFlZqds8ePAg3t7etGnTBlNTU2bPnq1us7jHKICHhwdt2rTBzMwMR0dHXF1dadOmDdWqVcPV1VU9ft7e3hw8eBB49Kbp/PnzDBgw4Kl+7t+/n9GjR9OqVSs0Gg3jx4+nevXqHD9+vNh9NHjwYDZu3IihoSHBwcH07t2bl156SX2DcfnyZVJSUnj99dcxMTGhRYsWbNu2jSZNmujFKcljbcyYMVhaWlK/fn1cXFzU51RZn+8WFhYcP36c06dP06hRIy5evKhWsoUQQojyKtH3AG/fvp2WLVsWurxOnTrq3w8ePMDGxgaNRqPeZ29vT3R0NPDo4/J69eqpy2xsbLCxseHgwYNkZmbSuXNndVleXp46kI2KiuKdd95h/vz5/03e2JjIyEgSEhL0qpJGRkbY2dkVmm/t2rX1YuRPCYiNjaVu3brqssdjvPzyy6Snp/PFF18wb948WrRowYIFC+jQoYNe7MTERL3+Pdn/0rKyslL/1mg06lSSqKgozp07p1dFzMnJISoqqsh4nTt3ZtSoUeog9XG//vorkyZNIjU1lRdeeIHMzEy1yl+YBw8eYGJiordP7e3t0Wq16slWtWrVUpcZGz96yOXl5aHRaNi7dy979uzBzMyMVq1akZ2djaIoRW4THlV/U1NT1bndeXl5JCYm8t133+Hp6UlCQgKurq5qexMTE9q3bw/AnTt3WLp0KdHR0eqbi/xtJiQk0KxZM3U9S0tLLC0tAYiMjCzyMQro/W1oaKium387f4Du5eXFwoULuXnzJufPn8fFxUVvHz6+f+3t7fXus7e3L/ITjse5urqq++HOnTt8+eWXTJ8+nbCwMPW5mn9MAHXKz+NK8lh7PHeNRqM+p8r6fF+xYgUrVqxgzpw5pKSk0LdvX+bNm/fUPGchhBClZ1BJV4KrlKvPFaJCLoRh8FiP7OzsiIuLQ6vVqh/x379/X/0HaWtrS0xMjDoYuXXrFt999x1OTk7UqlWL06dPq7ESExPJysoCHg2y58+fj5ubm7r8xo0bNGrUiMuXL+tVNBVFIT4+vtT9sLOzIzo6Ws0tfx4uwB9//IG7uztjxowhMTGRNWvWMGvWLL799tunYjw5CH28/xWlTp06vPbaa2olEx4NcEqynTfffJMff/xRb5pCbGwsAQEBfPHFFzg5OQGPTporbkBdr149tFot8fHx2NjYAI/6q9Fo9AbvBQkPDyc0NJS9e/eqb2AKmgbwpLS0NMLDw9mwYYPeYHXt2rXqfFlbW1u946fT6QgODmbKlClMmTKFRYsW0b9/f+DRnN/8NwN16tTR63N6ejqpqanqsqIeo6D/XCiKhYUF7u7uHD58mAsXLjBkyJAC29WrV4/IyEi9+yIiIujXr1+R8dPS0ujevTvbt2+nVatWADRq1IjZs2fz7bffcuPGDWxtbUlISCA3NxcjIyPg0dzfJ9/AleexVpbne05ODn/++ScLFy5Eo9Hw22+/ERAQwJYtW5gyZUqx2xRCCCGKU+HfA9yuXTtsbW1Zvnw52dnZ3Lt3j5CQEAYOHAjAwIEDWbduHbGxsaSnpxMcHExERATt2rXDwsKCTz75BK1WS2JiIlOmTOHjjz8GwNfXlzVr1hAdHU1ubi7r169n5MiRZGVlMWDAAM6fP8+xY8fIyclhw4YN6oltpTF48GDWr19PfHw8SUlJfPLJJ+qysLAwZs+eTWJiIlZWVjz33HMFDvBefPFFkpKS2LRpEzqdjt9++42tW7eq/S+KiYkJWq2W7OzsYtv6+vqyZ88efv75ZxRF4cyZM3h7e/Prr7+WaDvLli3j66+/Vu9LS0tDURTMzMwAOHPmDF9//bV6slRhudna2tKlSxf+85//kJaWRkJCAsuXL8fLy0tvjnNBUlNTMTIywsTEBJ1Ox9atW7l+/XqxJ8EdOHCAunXr0qVLF7WiaGNjw/Dhwzl79iw3b95k4MCBHDhwgKtXr5Kbm8tnn33G999/j4GBAdnZ2Wo//+///o8tW7ao2xw0aBChoaFcuXIFrVZLcHCwWh0u7jFaWj4+Phw6dIjff/8dT0/PAtv4+vqyZcsWfv/9d3Q6HRs3biQxMZGePXsWGdvCwgI3Nzfmzp3LL7/8gqIopKamsnv3blJSUnB1daVdu3ZYW1vzySefoNPpuH79OosXL9b79CY/h7I+1sryfDcyMuLtt99m06ZN5ObmUrduXQwNDYt9QyWEEKJkDCrx51lR4ZdC1mg0rFu3jkWLFuHm5oaJiQlDhgxh2rRpAEyePJmsrCyGDh1KdnY2PXr04K233sLExIT169fzn//8Bzc3NwwMDPD09OTtt98G4F//+hc5OTmMHDmS5ORkmjdvzsaNG9WPqFesWMHixYsJCAjAw8OjwI9yizN+/HgiIyPx8vKiZs2aeHh48NNPPwHg7+9PbGws/fr1Izs7mzZt2rB48eKnYlhZWfHpp5/y4YcfsmrVKqysrJgwYQLDhw8vdvsODg60bNkSV1dXdu3aVWTbTp06MXfuXObOnUtERAR16tRh4cKFeh8pF+WFF14gICCAoKAgAJo2bcqMGTMYP348OTk5NG7cmBEjRhAWFoaiKEXmtmzZMj744AN69+5NXl4eL730kt7c2cIMGjSIc+fO4enpiYmJCU5OTgwYMKDY7/TdtWtXgfNlmzdvTuvWrdm6dSsLFy4kMDCQ2bNnEx8fT6tWrVizZg0WFhYsXLiQ9957j9mzZ2Nvb8/w4cMJCQkhPT0dV1dXAgMDmTlzJikpKQwdOlT9WL64x2hp5X8vb8+ePTE3Ny+wACgtZwAAIABJREFUjY+PD0lJScyYMYOEhARatGjBxo0b9aaVFGbJkiWEhIQQEBBAXFwchoaGODs7s3nzZnVqS0hICIsWLaJr165Ur16dWbNm0bFjR+7evavGKc9jrSzPdwMDA1auXElQUBAhISGYmJgwYMCAEj2HhBBCiJIwUEoy4bKK+Pnnn2nUqJFaaTpx4gTvvPOO3se0QlQkb29v5syZQ9euXSs7lX+MiIgIPDw8OHjkGPb29Ss7nb+NtevrFR4z6dzKCo8phCidyMgI+vXx4NixY9SvX77XtPzXx92hR7CrZ1/8ChUsOiqSoQP7VEhfyqvCK8D/y/bu3UtmZiZBQUFkZWWxZcsWvTnHQlSUe/fucfbsWdLT00tctRdCCCFExajwOcD/y958802ys7Nxc3PD09OT2rVrl/njbSGKsmTJElauXMmiRYswNJSnoRBCCPF3kgrwY6ytrct8QpMQpbF69erKTkEIIUQVVVlXZXt2ToGTCrAQQgghhKhipAIshBBCCFGFyIUwpAIshBBCCCGqGBkACyGEEEKIKkWmQAghhBBCVCGVdVW2Z+lKcFIBFkIIIYQQVYpUgIUQQgghqpJKOgnuGSoAywBYCCH+CeSyxaIqsO6/rELjJYUHVGg88b9DpkAIIYQQQogqRQbAQgghhBCiSpEBsBBCCCGEqFJkDrAQQgghRBViQCVdCe7v32ShpAIshBBCCCGqFBkACyGEEEKIKkWmQAghhBBCVCFyJTipAAshhBBCiCpGKsBCCCGEEFWIQSVdCa5Srj5XCKkACyGEEEKIKkUGwFXEnDlzCAoKquw09JQ1p5s3b9KnTx8cHR3ZvXv3U8u1Wi1r1qyhb9++ODo64ubmxvz580lKSlLbODg48PvvvwPg6OjI9evXi93u/PnzWbp0aanzfVxRfXZ3d8fBwYEbN248tWzs2LE4ODgQERFRqpwrKreLFy/So0ePEsV5fN8KIYQQzyKZAiH+5xw9ehRbW1sOHz6MwROfp+Tm5jJx4kQ0Gg1r1qyhcePGxMXFsWjRIl577TX27duHsbH+w/7KlSsl2u7ChQsrrA+FqVGjBmFhYcycOVO9LzY2ll9//VWvXUlzrijOzs6cPHnyb92mEEKIv4YBlfOdvM/QDAipAFdFOTk5fPzxx7z44ou4urri7++vVhbPnTuHs7OzXnt3d3eOHj2q/j1//nw6d+7M7Nmz2bdvH2PHjiUwMJCOHTvi6enJzp071XXPnz/PK6+8QpcuXXB0dGTq1KmkpqYWm+Ovv/7KqFGjcHZ2xsvLi23btgGwZs0a1qxZw6VLl3ByckKr1eqtFx4ezh9//MHq1atp0qQJBgYG2NrasnjxYho2bMi9e/ee2tbjFUsHBwe2bNmCu7s7Li4uvPHGG2RkZAD6FVKtVsuSJUvo2rWr2i4tLa1cfQZ46aWXCAsL07svLCwMT0/PQnP+888/GTt2LE5OTvTq1Yvt27cDsG/fPvz8/PDz88PV1ZVr165x9+5d/P39cXFxwd3dndWrV5OTk6PGjY2NZfTo0XTq1IkxY8ao++vJx0V4eDgDBgygY8eOvPzyy5w7d67A/hR2HPO3NX78eJycnBgyZAiLFy9m1KhRZGdn07FjR86cOaO2vXjxIl27dtXLVQghhCgrGQBXQatWreLIkSN88cUXnDx5kvr16+Pv749OpyvR+nfu3OH7779n3rx5APz444+0b9+ec+fOMWnSJIKCgkhJSSEjI4OpU6fy6quvcubMGQ4fPsyff/7Jrl27ioyfmJjImDFjcHd358yZMwQHBxMSEkJYWBhTp05l0qRJ9OzZkytXrmBiYqK37smTJ3nxxRcxMzPTu9/c3JxVq1bRpEmTYvt34sQJ9u/fz759+7h06RL79+9/qs3q1av54Ycf2LNnD8ePHyczM5PFixeXuc/5unbtilar1avwHjhwAB8fnwLba7VaJkyYQJs2bTh79iwhISEEBwdz+fJl4FGleMqUKRw7dowmTZowbtw4GjZsyKlTp9i0aRMHDx5k48aNen2fNm0aP/zwA82bN2fq1KkoiqK3zdOnTzN37lzmzp3LuXPnGDt2LJMmTXrqzUVRxxFg5syZ1KlThzNnzrBw4UL27dsHgKmpKV5eXoSHh6uxQkND6d+//1PVeyGEEGVgUIm/zwgZAFdB+/fvZ8qUKTz//POYmpoye/ZsoqKiuHr1aonW9/Lyolq1alSvXh0AGxsb/Pz8MDY2xtfXF61WS3R0NKampuzZs4d+/fqRkZFBfHw8NWvWJC4ursj4x44dw8bGhrFjx6LRaGjdujWjR49m7969xeaWlJRE7dq1S9SPwowZMwZLS0vq16+Pi4tLgVXj0NBQ/P39qVevHubm5ixatIgxY8aUuc/5jIyM6Nu3rzpIvHHjBrm5ubRq1arA9pcvXyYlJYXXX38dExMTWrRowbZt29SBvrW1NT169MDCwoJLly6RnJxMQEAApqamNGjQgKlTp+rt1/79++Pi4oKJiQkzZ87kzz//5ObNm3rb/Prrr/H29qZz584YGxvTv39/OnbsqDdghaKPY1RUFBcvXmT27NmYmprSunVrhg8frq7r7e3Nt99+i06nIycnh8OHD+Pt7V2ifSiEEEIUR8opVdCDBw+oV6+eetvExIQ6deoQExNTosFjnTp19G7XqlVL/Vuj0QCQl5eHkZERJ0+e5PPPPycvL48WLVqQkpLyVEXxSYmJiXr5Adjb2xMdHV1sbjY2NsTHxxe47MGDB3q5FubxfaDRaMjNzX2qTUJCgl6ONjY22NjYAJSpz4/z9vZm0qRJvP3223z99deFVn/z+2RjY6NXGXVwcFD/fvxY5bfNP0bw9H59vE9mZmbUqFGDuLg4vXUSExN54YUX9PIo6PgUdRxjY2MxNTXF2tpaXWZnZ6dWvl1dXalWrRo//PADBgYGWFtb07Zt20L3gxBCCFEaUgH+h9q/f7/efEudTqdOC6hXrx6RkZHqMq1WS2xsLLVq1cLIyEhvKoSiKDx8+FAv9pMnnhXmypUrrFixgk8//ZTjx4+zdu1a6tevX+x6dnZ2REVF6d13//79Eg3OX3zxRU6dOkVmZqbe/RkZGfTt27fA6QxlYWtrS0xMjHr71q1brF+/vsx9flzbtm2xtLTk9OnTHDp0qMjKp62tLQkJCXqD9H379nH27FlA/1jZ2dkRFxenN2/6yf2akJCg/p2RkUFycvJTg1g7Ozt1zni+iIiIp45PUcfRzs6O7OxsEhMT1WWP708DAwMGDBjA4cOHpforhBAV7NFshMr4eXbIAPgfKj09nW3btpGSkkJUVBQXLlygRYsWAPj6+hISEsL9+/fJzs5myZIlWFtb4+TkRIMGDdBqtRw8eJDc3Fw2b95Menp6mXJITU3F0NAQU1NT8vLyOHToEKdOnSp2rvGLL75IUlISmzZtQqfT8dtvv7F161YGDhxY7Da9vLx4/vnnmTFjBrdv3wYeDbqmT5+OnZ0dffv2LVNfnjRw4EDWrVtHbGws6enpBAcHExERUeY+FxR/yZIlNGzYEFtb20LbtWvXDmtraz755BN0Oh3Xr19n8eLFehXbx9va2tqyfPlysrOzuXfvHiEhIXr7NTw8nJ9++kl9XLRt2/apedO+vr6EhoZy9uxZcnNzCQ8P58KFC0/t26KOY926denatSvBwcFkZ2dz48YN9uzZo7e+j48Pp06d4uTJkyU69kIIIURJyRSIf6ihQ4fy888/07t3bxRFwdvbWx2gTJw4Ea1Wy+jRo3n48CFOTk58/vnn6lSIuXPnsnTpUhYsWICPjw9OTk5lysHNzY2BAwfi6+uLoaEhLVu2ZNiwYcV+f62VlRWffvopH374IatWrcLKyooJEybozREtjKGhIZ9++imrVq1iwoQJJCYmYmlpSa9evVi6dCmmpqZl6suTJk+eTFZWFkOHDiU7O5sePXrw1ltvYW5uXqY+P8nb25uPP/6YiRMnFtnOxMSEkJAQFi1aRNeuXalevTqzZs2iY8eO3L17V6+tRqNh3bp1LFq0CDc3N0xMTBgyZAjTpk1T27i7u7Nw4ULu3r1Lp06dWLly5VPbdHZ25v333+f9998nKiqKRo0asWbNmqemRRR3HP/zn/8wZ84cOnfuTNOmTencubPedzW/8MIL2NjYYG5uXuoquhBCiMLJleDAQCnN5EQhhKggZ86coVOnTur85aVLlxITE8Py5cvVNpMnT8bd3Z2hQ4eWOG5ERAQeHh4cPHIMe3sZOAvxT2Ldf1mFxksKD6jQeH+FyMgI+vXx4NixY+UuBuS/PoZ9c5R6lfD6GBUZwYCXPCukL+UlUyCEEJXivffeY9euXSiKwp07dwgNDcXNzQ2AqKgojh07xuXLlyts2ooQQohH5FvQZAqEEKKSLF++nPfff5/ly5djYWHB8OHD1W+82Lx5M3v37mXBggVYWFhUcqZCCCH+aWQALISoFK1bt9a7auDjAgMDCQwM/JszEkIIUVXIAFgIIYQQoiqprPkIz9AcCJkDLIQQQgghqhSpAAshhBBCVCmVdVmKZ6cELBVgIYQQQgjxzLl27RrDhw+nQ4cODBw4kKtXr5ar3eNkACyEEEIIIZ4pWq2WKVOm0LdvXy5cuIC/vz/jx48nLS2tTO2eJANgIYQQQogqJP9KcJXxW1Lnz59Hp9MxZswYNBoN/fv3p1mzZhw8eLBM7Z4kc4CFEP8oubm5AMTGxFRyJkKIimakS63QeJGRERUa76+Q/1qW/9pWkTH/bvnbjSlg+5aWllhaWqq3b926RdOmTfXaNGnShBs3bujdV9J2T5IBsBDiHyU+Ph6AsaNHVnImQoiKZl/B8fr1Kfi7yJ9F8fHxNGzYsFwxLCwssLKyqtTXR1NTU0aOfHr706ZNY/r06ertjIwMzMzM9NpUq1aNzMxMvftK2u5JMgAWQvyjtGnThm3btmFjY4ORkVFlpyOEEOWSm5tLfHw8bdq0KXesGjVqcOTIkWLnx/6VFEXBoIC5EI9XfwHMzc3Jzs7Wuy8zMxNzc/MytXuSDICFEP8oZmZmODs7V3YaQghRYcpb+X1cjRo1qFGjRoXF+6s0bdqUTZs26d33559/4uvrW6Z2T5KT4IQQQgghxDPF1dUVRVHYtGkTOp2O8PBwrl+/Tu/evcvU7kkGiqIof2UHhBBCCCGEKK0bN26wYMECrl27Rv369Xn77bfp0qULa9euJTQ0lPDw8CLbFUUGwEIIIYQQokqRKRBCCCGEEKJKkQGwEEIIIYSoUmQALIQQQgghqhQZAAshhBBCiCpFBsBCCCEqRUWdg52dnU10dHSFxPorpaen8/PPP1d2Gs+EvLy8yk6hWH9FjhUZMycnp8JiVUVG77777ruVnYQQQjzrtFotcXFx5ObmYmhoWO6rzGm1WnJzcyv0anXZ2dmcP3+e+Ph4cnJysLKyKle8rKwsLly4QGJiIiYmJlSrVq1Ccjx8+DDNmzfHwMCg0KtClVRaWhojR46kdu3atGjRotz5waOrSH377bdcu3YNc3Pzcu9HeDT4HTx4MD/88ANDhw7FwMCgXP3Ozs7mwoULPHz4EGNj4wo5Nlqtljt37pCamkpubm6xV9IqjaysLM6ePUtcXByKopR7n2q1Ws6dO8fzzz9fQRk+uqTuvn37+PHHHzE2NsbOzq7cj8/8mBcuXKB69erUqlWrXDlmZWXxwQcf8OKLL2JoaEhOTg6GhlLLLAu5EpwQQhQjLS2NV155BUtLSxITE+nUqRNDhgyhXbt2pY6V/w911qxZ1K5dm9mzZ2NqalphOVavXp2MjAysrKx4++23ad68eZnjDR8+nNq1axMZGYm/vz8vv/xyufPcuHEjX3zxBenp6QwbNqxcg+C0tDSGDBmCk5PTU1d9Km9MKysrateuTdOmTUsdo6CYvr6+GBsbY25uXu7Bb1paGiNGjKBGjRpERkYyYcIEXnnllXLnOHr0aKytrUlOTua5557D39+frl27litufuzBgwdjY2ODgYEBN2/eZMGCBfTt27fM+2H37t2Ehoai0+l48cUXKyTHl19+mQYNGmBgYMCKFStYv3493bt3L1fMwYMH06xZM1JSUjh+/DgbNmzAxMSkzI/PO3fusG3bNtLT0/nwww8xNjYmJycHY2MZzpWWVICFEKIIOTk5TJkyhVatWrFixQpq1apFTEwMW7du5YUXXsDe3r5U8fL/6Z04cYLdu3eTlpZGly5dylUJ1ul0/Otf/6JDhw6sWLGC5s2b89NPP/Hcc8/RunXrUsfTarWMGzeO9u3bs2LFCq5fv05SUhKNGjXi4cOH5bqManZ2NgcPHgQgOjqaDh06YGBgoFbWSyorK4vevXvTp08f8v+NXbp0iQcPHpCenk7NmjVLPcjIy8vjnXfeoXHjxnzyySf06dOHpKQkkpOT1TcVpZWamsrw4cNxc3Nj/fr1fP3117i4uGBpaVnqWPDoWE+aNIm2bduycuVK7t+/T1ZWFq1atSIlJYXnnnuuzDFbt27N8uXLad++PdnZ2XzwwQd06NCh1I/xxymKwocffki9evVYvXo1/fr1w9LSknfffRcrK6syvYkEOHv2LKGhoeo0gGbNmpU5x7y8PObOnUvz5s1ZunQpAwcOVD8F6NevX5mem7m5uQQGBuLg4MCSJUuoVq0ad+7coVq1aiQkJFCrVi00Gk2p4yqKwjfffMP169c5e/YsPj4+GBoalvr5I2QOsBBCFMvQ0JD+/ftjYGBA3759GT16NN26dWPRokVcvXq1TDGNjIyYMWMG+/bt4/3330en05U5v4yMDExMTHjttdcAcHJywsrKilOnTgGln2ubkZGBsbEx/v7+ANy6dYvTp08zefJkRo8ezYkTJ8qca40aNbCwsKBevXqcOXOGLVu2APDw4cNSxYmPj8fIyIgOHToA8MYbbxAUFMTcuXMZNWoUR48eLXWFzdDQkPT0dFxdXQGYOXMm77zzDgEBAUyYMKHU/dbpdPj4+ODo6MjcuXPJysoiIiKCM2fOlCrO47RaLTVq1GDw4MHAoytgHT16lFdffZWxY8eW6dhkZmai1Wrx8fEBoEWLFnTp0oWcnBxmzJjB+fPny5yvgYEBycnJ2NraAmBiYsLIkSN57733+PDDD/nmm2+A0s+Nzd+3ZmZmhIWF8e2335Y5R0NDQ1JTU2nZsqV6X4MGDYiPjy9zTCMjIxISEujUqRMAO3fu5OLFi2zbto3JkyezceNGcnJySvXczM3NxcLCgmbNmvHRRx/x4MEDxo8fDzyqDGdlZZU536pIauZCCFEAnU5HSkoKGo2GlJQUrl+/TseOHQFo2LAhfn5+6HQ69u3bR/PmzTEzMys2XlpaGtbW1qSlpXHlyhVee+01vLy81KkF8+bNK1VVSKfTkZqaSmZmJr///jv379+nfv36ANSrV4/k5GSAEg8E8+MlJiai1WrRaDRcunSJ5557js2bNxMZGcl3333HsmXLaN68OXZ2diWKmd9vgObNm+Pi4sLo0aPZu3cvFy9e5PTp06SlpbFjx45i50Xn52hvb8/kyZM5evQoR48eJTU1lR07dpCQkMDx48d59913adq0KY0bNy5RjikpKVhYWGBgYEB8fDw//PADKSkpfPnll9y5c4cTJ07w3nvv8dlnn9GoUaMS9/vDDz/ExcUFADMzM/z8/Pjtt9+AR4O+klbt8vutKAqRkZGsWLFCfROwe/dubt++zZkzZwgKCqJRo0Y0bNiwxDG1Wi23b98mMjJSrcg2bdoUPz8/LCws+PLLL2nTpk2Z5wS3bduWGzduEBMTQ926dQEYPHgwGRkZLFiwgNatW5d4Lm/+QPnUqVOMGzcOZ2dnFi9eTGhoKAC9e/cuVW6KopCdnU2dOnXUfWFiYkKTJk2oUaNGmU7UVBSF1NRUXF1dcXJyIj4+nq5du7J582YAfvjhB2bOnEn37t3VN3AlYWRkRLVq1ahduzZZWVl89NFHTJkyhV69eqHRaDh8+HCFn1fwTyYVYCGEeEJeXh6rVq3iwYMHWFpa4u/vT1BQEFeuXFHbNGrUCCcnJ86ePUtGRkaJ4iUkJKAoChYWFixdupTmzZvTtGlTdu7cSWhoKO+//36Jz+zOjxkfH4+9vT3Lli3TmwcYExNDbm6uenvVqlX88ccfJYrXrFkzdu3aRc2aNenYsSMbNmzAzMyMxo0b4+7urlbMSpNjPhMTE27fvk1cXBzTpk0jOTmZCxcuqFXXov55P74fDQ0N6du3L5mZmdy7d49//etfmJqaUrduXXr37k2dOnXUNwAlyfHBgweYmpoyatQoli1bRmhoKEOGDMHY2JjGjRvTp08fLC0tefDgQan6nT/4zT8WjRs35tChQ9y9exdDQ8MSVT4f73etWrVYsGAB9evXR6PR8Oabb2JhYUHr1q3p27cv5ubmJCUllSpm3bp1ef3115kzZw67d+/mxIkTTJgwAQMDAzp37syNGzdKVV3UarVotVr1dps2bbhx4waHDh0iJSVF3f6gQYNo3LgxUVFRJY5naGiIoaEhGzdupE+fPtSsWZOpU6dSrVo1wsLCOHr0aKlyNDAwwMzMDF9fX1xdXTExMQEefeqRk5Oj3p47dy6nT58ucUxLS0smT56Mra0tNjY2jBs3Dng0Bahbt260a9eOmzdvlrjf8N/HUP5zqEmTJri5uZGUlKTOr5bBb8nJAFgIIR6TP5fOxsZGnWvZqlUrhg8fztSpU7l48aLatk+fPlhbW5Oenl7ieO3btycqKoomTZoAj/7JOTg4sGvXLg4dOkRAQIDewLW4mP7+/rRr146GDRuqH7fCo28dyK+qzZw5kyNHjhRaFSwo3t27d9WBff4/VUNDQ5o2bYqVlVWpc2zfvj1//vkniqLQpk0bcnJy2L9/P7dv3+bll1/m0qVLhIeHl6rPhoaGjBgxAjs7O3WeqpGREXXq1EGj0egNHoqLmX+smzVrxujRo9m/f7/6hsHIyIjnn38eY2NjMjMzS93v+/fvq/urd+/euLm5sXLlSjIyMoqtABeUY/369Zk5cyZDhw5V8zE0NOT555/HwsKiVMdm0qRJtGvXDldXVyZMmMDGjRvZsWMHnTp14q233sLZ2ZmaNWuWaIpOfrV01qxZLF68WM3NxcWFkSNHsnXrVvbu3UtsbCyGhoY899xz5ObmFjq4fjJedna2en/+XOe8vDwaNGjAtGnTMDc3Z+vWrVy4cKHUOTo5OelNgYiLi1Pnur/11lv8+OOP6pu0ksY0MjJSj4VGoyEjI0M94TUvL4/q1auXqt/5z8NevXphbm7Opk2b+Oabb1i1ahW3bt0iKCio0H6LAihCCCEURVEUnU6nrFy5UsnMzFSSkpKUDh06KKNGjVIURVFSU1OVdevWKa1atVJ27Nih/Pjjj8qcOXOUoUOHKrm5uaWO97icnBxFURTl//7v/5QxY8YUGq+wmKNHj36q3ZgxY5SwsDAlKChIGTBggKLVavW2VZp4+/fvV3bu3KmcOXNGefvtt5Xhw4eXK8eDBw8qbdu2Vbp3767ExcUpiqIoO3bsKNV+fPXVV/XaREVFKcePH1d+/vlnZc6cOYqvr+9TfS0uZv6xSUlJUUJCQhQHBwflo48+Ur766ivl7bffVgYOHFjqmAUdm5MnTyrTp09XvvjiC/W4lKXfx48fVxwcHJTw8HDl6tWryjvvvKMMHTq03DlmZWUpDx48UNLS0pSAgADl1VdfVfLy8gqN+aS33npLcXBwUN5//30lOztbvX/Pnj3KoEGDlLfeekv56KOPlICAAMXHx6fIfJ+MV9D+ys/t9u3bypo1a4p8bBaXo06nUxRFUYKDg5XFixcr77//vtK/f/9Cnz8lzTP/9WLlypXKrFmzlEGDBpW53/nHvVu3bsq9e/cURVGUO3fuFBtP6JNvgRBCiP/PwMCAAwcO8MUXX/Dxxx/Tr18/GjdujIGBAVOnTmXs2LG0bt2a/fv3c/36dXJycli/fj3Gxsbk5eU9Nde2oHhNmzZFURQmTpxIixYtqFevnnoWt62tLb6+vhgYGBQYryQxmzdvjr29Pfv37+ezzz4jJyeH3bt3o9FoCvy6pOLitWvXjri4ODZt2sStW7fQ6XRF9rmomHl5eUycOBEbGxsMDQ0JCQlRT45q06ZNof0uKF6zZs3UeC1atCAhIYEFCxbwyy+/oNPp2LRpE8bGxoWeHV/UsZ42bRqjRo2iffv2HD58mKioKMzMzFi7dm2pYxZ0vOvXr8+ZM2fIzs6mR48epXo8NmvWDEVRmDBhAn379qVmzZqsWLGCa9euodPp2LhxY6mPTZMmTdR96eDggKGhIaNHj+bo0aM8fPiQzZs3Y2RkVGjMJ33//ff07t2bbdu2ERMTQ7du3TAyMqJVq1bqtu7evYuNjQ3BwcFF7tOC4nXv3l3vo/78r9KztramU6dORT5/iouZn8O1a9dYvXo1hoaG7Nq1q9DnT0nzjI2NJTExkXv37lG7du1y9fv5559HURTeffdd6tevT15eHtbW1vJtEKVViYNvIYR4ZuRXfm7evKm0bdtWmTlzpqIoirJ8+XKla9euyty5c9W2qampSk5Ojlp5yl+3pPG6deumF68icnwy5rJly5Rhw4ap65Q3x+TkZEWr1RbZ55LEfPfddxVFUZS0tDRFUYquqJU2x+joaCUjI6NcORZ0rAtat6x55lcndTpdsVX0kvY7MjJSSU5OLvexeTzmrVu3lLi4OL18C6PVapXExERFUR7tr759+yrXr19Xbt26pXTo0EGZN2+eXpW1sLxKE6+oynlZc3w8j2+++UYZNGhQkc+f8uZZnn7nP2+KOi6iaPI2QQghAGNjY6KiohgwYACenp7Ur1+fO3fucPz4cZKSktSz1xVFoVq1ahgZGamVp4KqQkXFS0xM1ItXETkmJibqfSvDsGHD2LFjR5FflF+aHC0sLNBoNEUHzDP6AAAMOklEQVT2uSQx86+EZW5uTl5eXrEn7ZQ0x7y8POrUqUO1atXUCmBZcizoWOcrT7/zY+Z/57GxsXGRJ8EVFy+/cp6Xl4ednR1WVlbl6veT+7JJkyZqpb6omCU9wXPRokXqnOwnH/OPx/4rTxgtLubChQvVHL28vNi7d2+hz5/S5PlX9Dv/eSMXwCiHyhh1CyHEs+rChQuKoijKvHnzlG7duilLly5VkpOTlZEjRypfffVVpccrbcySzIes7BwrI97/SsxnOcf8KuSWLVuUXr16KW3btlUiIyOVjIwMRVEUtep7/fp1xdn5/7V3ryFRpX8cwL8z2jhOtppaWZRTrQax22ZhRdRm0QWTghIqCtl2K+hCbVJBl126Qy1UdDGwssBiF0K6ibFFRL6p2HYX1pVq0jalzdzELjYj5pkZn/+Lds7qeKl/nWc8Z873887JvnyfV/P0dH7PSRdr167t8sRf67wPzWx9Wt3Rs89GWDe9G0+AiYjw3xVDgXs5r127hvr6eowbNw6xsbGYNm0a6uvruy3vQzO7eh5QLx1DmWeUTL139Pl8OHr0KN68eYPZs2fj5cuXSEtLw4ABA9RTc5vNBr/fj2HDhqGwsBANDQ2dPperdd7HZLY+VQ3ON8K66f1YhPiAW56JiAyuo5cQiFavzr127Rri4uKwb98+bNu2DU6nEw6Ho9MvHq3z2FG/Hc287tY53333Hf7++29UVlZixowZSEhIwNixY7Fjxw7s3r0b6enpANDu5Qyd9dIyzyiZMjrS++EtEERkOoEvkqdPn+Lu3bv4448/MGzYsDbT40OGDMHAgQPx+PFjvH79Gunp6erzr8EbBK3z2FG/Hc287oDAM6hOpxOHDx/G5MmTsXfvXty6dQuHDx/GhAkTkJOTo/5+8Cato1NVLfOMkimjI70/Pj1NRKYihEBERARcLheWL1+O5ORkxMXFIS0tDQMHDoTVaoUQQv2yyc7ObvPq2+AvHa3z2FG/Hc287tZaD9NlZWV1OTz5Pps0rfOMkimjI/0ftH2kmIhI/16/fi3mzZsnfvrpJyGEUIdNqqqq1N8JHh7r6kUAWuexo347ysg0QseOmGngT2amjI70bnx4hIjCXlNTEwoKCtRriBobG6EoCkaNGgUA6utJi4uLsX//fgBd/3ej1nnsqN+OZl53Z8w28CcrU0ZHen98BIKIwt7Vq1eRlJQEr9eLiIgIxMfHw+Fw4O7du/j000/Ro0cPAIDdbofb7Q55Hjvqt6OZ1x0QPGwVERHR5k7knTt3qsN0CQkJmDdvHhwOR8jyjJIpoyN9OA7BEVHYS0lJwdChQ7F582b89ttvmDJlCp48eYKSkhI4nU7ExsYiKioKhYWF6NmzZ5evp5WRx4767WjmdQPmHfgzwrrp43ADTERhK/DFYbVaoSgKamtrUV5ejtraWqxevRoPHjzA5cuXce7cOdy4cQOPHz9GXl5ep1cLaZ3HjvrtaOZ1t862Wq1wuVz4+uuvUVFRgZqaGnzxxRf45JNP1M1ZIKt///6YNGmSulnraOBPyzyjZMroSB+P9wATUVgKnLi8ePECjY2NsNvt6NOnD4qKilBcXIyJEydi+fLlKCsrQ0VFBaKiopCVlYXIyMh2923KyGNH/XY087qDud1uLF26FHPnzsXChQvR1NSE6OhoVFdXqzdHBP/XflcnllrnGSVTRkf6OHwGmIjCjvj3Gqj79+8jNzcX0dHR8Pv9GDduHL7//nsAQElJCbxeL1asWIGRI0eqf7ejjYHWeeyo345mXjfwdpjuxx9/xNKlS2GxWLocpvN6vVi/fv07B/60zDNKpoyOpC1ugIkorAROUdxuN7Zu3YqvvvoKGRkZqK6uxqZNm9DY2Ig9e/ZACIGzZ88iPj4eixYtUk9bgjcGWuexo347mnndAWYd+DPCukljH3R5GhGRjtXU1IjVq1eLJUuWCLfbrX7ucrnEtGnTRGlpqWhqahI///xzu7tQQ5HHjvrtaOZ1CyGE1+sVzc3NYt26dWLXrl1CCCEOHjwosrOzxc2bN0VDQ4MQQog1a9aofx7KPKNkyuhI2uIJMBGFnfj4eNTV1eHevXtwuVxIT08HAAwaNAgDBgxAXV0d7HY7MjMzAbR/9k52Hjvqt6NZ1y3+PR2OjIxEU1MThg8fjtu3byM/Px9r165FQ0MDjhw5gubmZvTt2xc1NTU4cOBAyPKMkimjI8nBDTARGV7gmUZFUaAoCmJiYnDmzBmsXLkSBQUFGDRoEPr16weHw6HevQm0naCXmceO+u1o5nUHZ7ceplu2bBliY2NRXFwMi8WCrVu3ftTA38fkGSVTRkeSh7dAEJGhBU62KioqkJeXh9raWjidTmRmZmLKlClYtmwZ3G43UlNTAQDl5eW4cOGC+gye7Dx21G9HM687ILBB7myYrqioCCUlJRgzZgxWrFihvrQB6HzgT8s8o2TK6Ehy8R5gIjI0i8WC6upq5OTkqBuCFy9e4Pr16/D5fNi4cSNKS0tx69YtTJ48GT/88ANsNps6nCI7jx3129HM6wbaDtPl5uZiwYIFWLVqFT7//HPk5+ejsrIS3377LbxeL0pKSiCEwIgRIzo9VdY6zyiZMjpSCMh6uJiISBa32y1OnDih/nz69Ok2gySvXr0SBQUFYvHixcLtdos3b96InJwc8c0334iHDx8KRVGk5rGjfjuaed0dMevAnxHWTXLxnx1EZCgejwdz587F8+fP1c8ePXqEBw8eqD/HxsZi+vTp+PPPP3Hnzh1ERUXhxIkT8Pl82LRpE6qqqqTlsaN+O5p53Z0JDNPduXMHLpdL/byjYTqr1YqWlpaQ5hklU0ZHkosbYCIyjMbGRsyfPx8ZGRnYuHGj+vnUqVMBAFeuXFE/S05ORlpaGuLi4uD3+2G323Hs2DH07t0bMTExUvLYUb8dzbzu1vx+PwBAURR4PB7Y7XacOXMGY8eORUFBAZ49ewYAHQ7TAehw4E/LPKNkyuhIocUhOCIyBI/Hg4ULF6Kurg7Hjx9v82arf/75B3l5eXj58iWGDx+OmTNnIj8/H3/99ReKiooQEREBn8/XZvBE6zx21G9HM6+7NbMO/Blh3RR6HIIjIt3zeDyYM2cOUlNTMWLECPz666+w2WwYMmQIACAmJgYpKSlQFAXnz59HWVkZfD4fTp06pV4xFLx50TKPHfXb0czrDmbWgT8jrJu6Qfc8ekxE9H78fr9YvHix2LZtmxBCiMrKSrF+/XqRm5srSktL2/1+c3Oz8Hg8oqWlRQjx9o1MMvPYUb8dzbzuALMO/Blh3dS9eAJMRLpmsVgwfvx4zJo1C8DbYZPk5GSUlZXh3r17iI6OxuDBgwEAPp8PPXr0gM1mg8VigRCi3YmL1nnsqN+OZl438N8wXf/+/TFx4kQAwMWLF1FZWYns7GwAgN1uR2JiIg4dOoTPPvsMqampyMrKwqVLl3DlyhWMHj0aCQkJUvKMkimjI3U/boCJSPcCAz1+vx9WqxWJiYnqBuH+/fuIjo6G0+lsN1hisVhCkseO+u1o1nUHhum+/PJLbNmyRf08MjISv//+OxwOB1JSUgC8vVHil19+waRJk9C3b1/YbDZkZmaitLQUM2bMQK9evTTPk9HRKOsmfeAGmIgMo/WXf2CDUF5ejps3b8LpdCIpKalb89hRvx1lZOq1Y2CYrr6+Hhs2bGjzd2w2GyoqKlBWVoaqqiokJCRgz549qK2txZo1a9RhuqioKMyePRu9evXSPE9GR6Osm/SDG2AiMqzExEQkJSVBURTMmjWryxO27shjR/12lJGph45mHfgzwrpJX3gNGhGFjcD1RHrNk5HJjvrNDHXHlpYWLFmyBIMHD8b27dvx8OFD5Ofnw+/3Y86cOcjIyGjz+4qiwOv1wuFwwGKxtLtGTes8o2TK6Ej6ww0wERFRmHj27Bn69eun/uxyuXDy5En4fL42m7fgTZoQosPTZa3zjJIpoyPpCx+BICIiChNmHPgzyrpJX7gBJiIiCjNmGfiTnSmjI+kDN8BERERhLhwH/rojU0ZH6h58BpiIiMhkjD7wp5dMGR0pNLgBJiIiIiJT4T9biIiIiMhUuAEmIiIiIlPhBpiIiIiITIUbYCIiIiIyFW6AiYiIiMhUuAEmIiIiIlPhBpiIiIiITIUbYCIiIiIyFW6AiYiIiMhU/gfaylglXMu6+wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 10))\n", + "plt.pcolor(topics, norm=None, cmap='Blues')\n", + "plt.yticks(np.arange(how_many)+0.5, journal_names_reduced)\n", + "plt.xticks(np.arange(model_j.num_topics)+0.5, [\"Topic #\"+str(n) for n in range(model_j.num_topics)], rotation = 45)\n", + "plt.colorbar(cmap='Blues') # plot colorbar\n", + "plt.tight_layout() # fixes margins\n", + "plt.savefig(\"figures/topic_model_top_journals.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: match topics here with topics above, or directly work with the topics above for all journals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Correlated topic model\n", + "\n", + "With tomotopy." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration: 0\tLog-likelihood: -8.346382399313603\n", + "Iteration: 10\tLog-likelihood: -8.049614897804599\n", + "Iteration: 20\tLog-likelihood: -7.861775270846221\n", + "Iteration: 30\tLog-likelihood: -7.712287269309254\n", + "Iteration: 40\tLog-likelihood: -7.569671492903884\n", + "Iteration: 50\tLog-likelihood: -7.418755129125436\n", + "Iteration: 60\tLog-likelihood: -7.266204104388067\n", + "Iteration: 70\tLog-likelihood: -7.1315124964846195\n", + "Iteration: 80\tLog-likelihood: -7.026582385515885\n", + "Iteration: 90\tLog-likelihood: -6.943539002753201\n", + "Iteration: 100\tLog-likelihood: -6.877397931369281\n", + "Iteration: 110\tLog-likelihood: -6.82420183294037\n", + "Iteration: 120\tLog-likelihood: -6.783903574077231\n", + "Iteration: 130\tLog-likelihood: -6.755401073204129\n", + "Iteration: 140\tLog-likelihood: -6.734613635074426\n", + "Iteration: 150\tLog-likelihood: -6.72228827961171\n", + "Iteration: 160\tLog-likelihood: -6.714758413402493\n", + "Iteration: 170\tLog-likelihood: -6.711180300450649\n", + "Iteration: 180\tLog-likelihood: -6.709352442026201\n", + "Iteration: 190\tLog-likelihood: -6.701375375001899\n" + ] + } + ], + "source": [ + "import tomotopy as tp\n", + "\n", + "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n", + "mdl = tp.CTModel(k=params['num_topics'],min_cf=min_wordcount,min_df=2)\n", + "for doc in docs:\n", + " mdl.add_doc(doc)\n", + "\n", + "for i in range(0, 200, 10):\n", + " mdl.train(10)\n", + " print('Iteration: {}\\tLog-likelihood: {}'.format(i, mdl.ll_per_word))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 words of topic #0\n", + "[('identify', 0.02288655750453472), ('severe', 0.020880186930298805), ('disease', 0.019752908498048782), ('novel', 0.017475156113505363), ('animal', 0.017168981954455376), ('syndrome', 0.01593036763370037), ('develop', 0.014276562258601189)]\n", + "Top 10 words of topic #1\n", + "[('datum', 0.02503298781812191), ('model', 0.018405042588710785), ('number', 0.017147555947303772), ('use', 0.016597861424088478), ('review', 0.016087085008621216), ('population', 0.014717713929712772), ('approach', 0.0126600107178092)]\n", + "Top 10 words of topic #2\n", + "[('infection', 0.09805650264024734), ('virus', 0.06383290141820908), ('viral', 0.06204687058925629), ('effect', 0.020593872293829918), ('role', 0.020150918513536453), ('type', 0.019906938076019287), ('level', 0.0174102820456028)]\n", + "Top 10 words of topic #3\n", + "[('protein', 0.06114864721894264), ('mouse', 0.03082796186208725), ('cell', 0.027349276468157768), ('receptor', 0.018570024520158768), ('t', 0.01584470458328724), ('domain', 0.014638119377195835), ('binding', 0.012035859748721123)]\n", + "Top 10 words of topic #4\n", + "[('human', 0.048509370535612106), ('virus', 0.025624316185712814), ('host', 0.024341823533177376), ('new', 0.018463732674717903), ('development', 0.017501862719655037), ('potential', 0.014562818221747875), ('important', 0.014009859412908554)]\n", + "Top 10 words of topic #5\n", + "[('sample', 0.024444861337542534), ('detect', 0.02417099103331566), ('associate', 0.02329721488058567), ('test', 0.022368665784597397), ('associated with', 0.019919488579034805), ('acute', 0.019100487232208252), ('respiratory', 0.018070215359330177)]\n", + "Top 10 words of topic #6\n", + "[('virus', 0.04445461183786392), ('rna', 0.04367273300886154), ('protein', 0.03127236291766167), ('assay', 0.02548983506858349), ('acid', 0.017575422301888466), ('structure', 0.01706073246896267), ('membrane', 0.012923525646328926)]\n", + "Top 10 words of topic #7\n", + "[('study', 0.05442364886403084), ('result', 0.041000258177518845), ('control', 0.023361822590231895), ('report', 0.019801076501607895), ('high', 0.0168923307210207), ('cause', 0.015907442197203636), ('include', 0.014695273712277412)]\n", + "Top 10 words of topic #8\n", + "[('health', 0.03438343480229378), ('outbreak', 0.02547668106853962), ('risk', 0.02178904414176941), ('=', 0.02003878355026245), ('China', 0.016990220174193382), ('public', 0.016244633123278618), ('pandemic', 0.012746990658342838)]\n", + "Top 10 words of topic #9\n", + "[('gene', 0.036720674484968185), ('coronavirus', 0.03477782383561134), ('analysis', 0.030939193442463875), ('strain', 0.02780134603381157), ('sequence', 0.02733328379690647), ('virus', 0.02614613249897957), ('show', 0.021625017747282982)]\n", + "Top 10 words of topic #10\n", + "[('patient', 0.06850825995206833), ('respiratory', 0.04381217435002327), ('case', 0.02733406238257885), ('child', 0.019558753818273544), ('care', 0.013909459114074707), ('pneumonia', 0.013135542161762714), ('hospital', 0.012822361662983894)]\n", + "Top 10 words of topic #11\n", + "[('response', 0.042276300489902496), ('vaccine', 0.033726729452610016), ('antibody', 0.03061981126666069), ('immune', 0.024797115474939346), ('mechanism', 0.014047734439373016), ('sars-cov', 0.013495701365172863), ('target', 0.012178035452961922)]\n", + "Top 10 words of topic #12\n", + "[('treatment', 0.027217043563723564), ('group', 0.025998372584581375), ('day', 0.020512841641902924), ('de', 0.017317213118076324), ('lung', 0.015168738551437855), ('serum', 0.013411442749202251), ('calf', 0.012523768469691277)]\n", + "Top 10 words of topic #13\n", + "[('cell', 0.08356480300426483), ('expression', 0.02826923504471779), ('activity', 0.02641606144607067), ('antiviral', 0.020584670826792717), ('induce', 0.0186122078448534), ('replication', 0.015141669660806656), ('pathway', 0.012384104542434216)]\n", + "Top 10 words of topic #14\n", + "[('influenza', 0.036708734929561615), ('disease', 0.031555432826280594), ('method', 0.03091222234070301), ('clinical', 0.030553754419088364), ('pathogen', 0.024203011766076088), ('detection', 0.020023420453071594), ('rate', 0.016634494066238403)]\n" + ] + } + ], + "source": [ + "for k in range(mdl.k):\n", + " print('Top 10 words of topic #{}'.format(k))\n", + " print(mdl.get_topic_words(k, top_n=7))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# topics over time\n", + "\n", + "topics_corr = np.zeros((len(docs),params['num_topics']))\n", + "\n", + "for n,doc in enumerate(mdl.docs):\n", + " for m,t in enumerate(doc.get_topic_dist()):\n", + " topics_corr[n][m] = t" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "df_topics_corr = pd.DataFrame(topics_corr)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "df_topics_corr[\"year\"] = publication_years\n", + "df_topics_corr[\"doi\"] = dois" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from_which_year = 1980\n", + "\n", + "grouped_corr = df_topics_corr.groupby('year')\n", + "df_grouped_corr = grouped_corr.aggregate(np.mean)\n", + "df_grouped_corr = df_grouped_corr[df_grouped_corr.index >= from_which_year]\n", + "#df_grouped" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(16, 12))\n", + "plt.pcolor(df_grouped_corr.to_numpy(), norm=None, cmap='Blues')\n", + "plt.yticks(np.arange(df_grouped_corr.to_numpy().shape[0])+0.5, [int(x) for x in df_grouped_corr.index.values])\n", + "plt.xticks(np.arange(df_grouped_corr.to_numpy().shape[1])+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)], rotation = 45)\n", + "plt.colorbar(cmap='Blues') # plot colorbar\n", + "plt.tight_layout() # fixes margins\n", + "plt.savefig(\"figures/corr_topic_model_all_time_15.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# topic correlations\n", + "\n", + "topics_correlations = np.zeros((mdl.k,mdl.k))\n", + "\n", + "for k in range(mdl.k):\n", + " for m,c in enumerate(mdl.get_correlations(k)):\n", + " topics_correlations[k][m] = c" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 10))\n", + "plt.pcolor(topics_correlations, norm=None, cmap='RdBu_r')\n", + "plt.yticks(np.arange(mdl.k)+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)])\n", + "plt.xticks(np.arange(mdl.k)+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)], rotation = 45)\n", + "plt.colorbar(cmap='Blues') # plot colorbar\n", + "plt.tight_layout() # fixes margins\n", + "plt.savefig(\"figures/corr_topic_model_correlations_15.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Language modelling, semantic spaces and clustering\n", + "\n", + "Future work." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "covid", + "language": "python", + "name": "covid" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 495dcab479ab76feff647a1786e80213ab5497bb Mon Sep 17 00:00:00 2001 From: Giovanni1085 Date: Fri, 10 Apr 2020 17:58:43 +0200 Subject: [PATCH 2/6] Added bibliographic coupling network analysis and linked that to topic models --- Notebook_CORD-19_2_text_analysis.ipynb | 1063 ++++++++++++++------- Notebook_CORD-19_3_network_analysis.ipynb | 799 ++++++++++++++++ 2 files changed, 1503 insertions(+), 359 deletions(-) create mode 100644 Notebook_CORD-19_3_network_analysis.ipynb diff --git a/Notebook_CORD-19_2_text_analysis.ipynb b/Notebook_CORD-19_2_text_analysis.ipynb index 8db0fe1..6c3cf17 100644 --- a/Notebook_CORD-19_2_text_analysis.ipynb +++ b/Notebook_CORD-19_2_text_analysis.ipynb @@ -4,14 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# CORD-19 text analysis" + "# CORD-19 text analysis\n", + "\n", + "Add some explanation here." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -21,7 +32,7 @@ "%autoreload 2\n", "import warnings; warnings.simplefilter('ignore')\n", "\n", - "import os, random, codecs, json\n", + "import os, random, codecs, json, pickle\n", "import pandas as pd\n", "import numpy as np\n", "\n", @@ -38,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -214,7 +225,7 @@ "4 2020-04-04 07:55:51.892454 " ] }, - "execution_count": 3, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -225,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -237,7 +248,7 @@ " dtype='object')" ] }, - "execution_count": 4, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -312,7 +323,7 @@ "2 https://www.who.int/emergencies/diseases/novel... " ] }, - "execution_count": 5, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -351,7 +362,7 @@ "(56809, 16)" ] }, - "execution_count": 7, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -362,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -371,7 +382,7 @@ "(46994, 24)" ] }, - "execution_count": 8, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -382,232 +393,35 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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pub_idtitleabstractpublication_yearpublication_monthjournalvolumeissuepagesdoi...datasourceurlcord19_metadata_idsourcelicensefull_text_filems_academic_idwho_covidenceshafull_text
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22Another Piece of the Puzzle: Human Metapneumov...BACKGROUND: Each winter respiratory viruses ac...2008.012.0Archives of Internal MedicineNaNNaNNaN10.1001/archinte.168.22.2489...CORD19https://pages.semanticscholar.org/coronavirus-...2PMCunkNaNNaNNaNNaNNaN
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5 rows × 24 columns

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" - ], "text/plain": [ - " pub_id title \\\n", - "0 0 ‘A ticking time bomb’: Scientists worry about ... \n", - "1 1 [Ten hot issues of breast cancer under the nov... \n", - "2 2 Another Piece of the Puzzle: Human Metapneumov... \n", - "3 3 Viral etiology of severe pneumonia among Kenya... \n", - "4 4 Critically Ill Patients With Influenza A(H1N1)... \n", - "\n", - " abstract publication_year \\\n", - "0 CAPE TOWN, SOUTH AFRICA—Late on Sunday evening... 2020.0 \n", - "1 NaN 2020.0 \n", - "2 BACKGROUND: Each winter respiratory viruses ac... 2008.0 \n", - "3 CONTEXT: Pneumonia is the leading cause of chi... 2010.0 \n", - "4 NaN 2014.0 \n", - "\n", - " publication_month journal volume issue pages \\\n", - "0 NaN Science NaN NaN NaN \n", - "1 2.0 Chinese medical journal 100 0 e002 \n", - "2 12.0 Archives of Internal Medicine NaN NaN NaN \n", - "3 5.0 JAMA NaN NaN NaN \n", - "4 4.0 JAMA NaN NaN NaN \n", - "\n", - " doi ... datasource \\\n", - "0 0.1126/science.abb7331 ... CORD19 \n", - "1 10.0376/cma.j.issn.0376-2491.2020.0002 ... CORD19 \n", - "2 10.1001/archinte.168.22.2489 ... CORD19 \n", - "3 10.1001/jama.2010.675 ... CORD19 \n", - "4 10.1001/jama.2014.2116 ... CORD19 \n", - "\n", - " url cord19_metadata_id \\\n", - "0 https://pages.semanticscholar.org/coronavirus-... 0 \n", - "1 https://pages.semanticscholar.org/coronavirus-... 1 \n", - "2 https://pages.semanticscholar.org/coronavirus-... 2 \n", - "3 https://pages.semanticscholar.org/coronavirus-... 3 \n", - "4 https://pages.semanticscholar.org/coronavirus-... 4 \n", - "\n", - " source license full_text_file ms_academic_id who_covidence sha full_text \n", - "0 WHO unk NaN NaN #8463 NaN NaN \n", - "1 WHO unk NaN 3.003451e+09 #615 NaN NaN \n", - "2 PMC unk NaN NaN NaN NaN NaN \n", - "3 PMC unk NaN NaN NaN NaN NaN \n", - "4 PMC unk NaN NaN NaN NaN NaN \n", - "\n", - "[5 rows x 24 columns]" + "(3038, 24)" ] }, - "execution_count": 9, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_cord19.head()" + "# publications without DOI will be missing from the network clustering analysis\n", + "\n", + "df_cord19[pd.isnull(df_cord19[\"doi\"])].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# we save a list of DOIs to filter data from Dimensions for the network clustering analysis (see notebook 3)\n", + "\n", + "pickle.dump(df_cord19[pd.notnull(df_cord19[\"doi\"])][\"doi\"].to_list(),open(\"datasets_output/networks/cord-19-dois.pk\",\"wb\"))" ] }, { @@ -619,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -628,16 +442,16 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8349, 25)" + "(0, 24)" ] }, - "execution_count": 57, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -648,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -662,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -671,7 +485,7 @@ "'‘A ticking time bomb’: Scientists worry about coronavirus spread in Africa CAPE TOWN, SOUTH AFRICA—Late on Sunday evening, South African President Cyril Ramaphosa, in a televised address to the nation, declared that COVID-19, the respiratory disease spreading globally, had become a “national disaster.” The declaration allows his government to access special funding and instigate harsh regulations to combat the viral outbreak. “Never before in the history of our democracy have we been confronted by such a severe situation,” Ramaphosa said before announcing a raft of measures to curb the virus’ spread, including school closures, travel restrictions, and bans on large gatherings. So far, the official numbers seemed to suggest that sub-Saharan Africa, home to more than 1 billion people, had been lucky. The interactive map of reported COVID-19 cases run by Johns Hopkins University shows big red blobs almost everywhere—except sub-Saharan Africa.'" ] }, - "execution_count": 12, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -691,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -757,23 +571,23 @@ "source": [ "import pickle\n", "\n", - "pickle.dump(processed_docs, open(\"datasets_output/processed_docs_cord19_scispacy.pk\", \"wb\"))" + "pickle.dump(processed_docs, open(\"datasets_output/texts/processed_docs_cord19_scispacy.pk\", \"wb\"))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", - "processed_docs = pickle.load(open(\"datasets_output/processed_docs_cord19_scispacy.pk\", \"rb\"))" + "processed_docs = pickle.load(open(\"datasets_output/texts/processed_docs_cord19_scispacy.pk\", \"rb\"))" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -793,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -805,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -833,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -842,7 +656,7 @@ "20" ] }, - "execution_count": 19, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -853,15 +667,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 4min 42s, sys: 2.59 s, total: 4min 44s\n", - "Wall time: 4min 36s\n" + "CPU times: user 4min 29s, sys: 3.03 s, total: 4min 32s\n", + "Wall time: 4min 22s\n" ] } ], @@ -876,56 +690,56 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0,\n", - " '0.017*\"health\" + 0.014*\"disease\" + 0.008*\"public\" + 0.008*\"datum\" + 0.007*\"model\" + 0.007*\"outbreak\" + 0.006*\"public_health\"'),\n", + " '0.017*\"health\" + 0.014*\"disease\" + 0.008*\"public\" + 0.008*\"datum\" + 0.007*\"model\" + 0.007*\"outbreak\" + 0.006*\"public_health\" + 0.006*\"infectious\" + 0.006*\"epidemic\" + 0.006*\"system\"'),\n", " (1,\n", - " '0.031*\"cell\" + 0.018*\"mouse\" + 0.018*\"infection\" + 0.017*\"response\" + 0.012*\"immune\" + 0.010*\"expression\" + 0.009*\"t\"'),\n", + " '0.031*\"cell\" + 0.018*\"mouse\" + 0.018*\"infection\" + 0.017*\"response\" + 0.012*\"immune\" + 0.010*\"expression\" + 0.009*\"t\" + 0.007*\"disease\" + 0.007*\"induce\" + 0.006*\"role\"'),\n", " (2,\n", - " '0.048*\"virus\" + 0.043*\"cell\" + 0.017*\"protein\" + 0.016*\"viral\" + 0.014*\"replication\" + 0.012*\"infection\" + 0.011*\"hepatitis\"'),\n", + " '0.048*\"virus\" + 0.043*\"cell\" + 0.017*\"protein\" + 0.016*\"viral\" + 0.014*\"replication\" + 0.012*\"infection\" + 0.011*\"hepatitis\" + 0.007*\"mhv\" + 0.006*\"mutant\" + 0.006*\"result\"'),\n", " (3,\n", - " '0.019*\"group\" + 0.018*\"=\" + 0.015*\"study\" + 0.013*\"day\" + 0.013*\"p\" + 0.010*\"high\" + 0.009*\"level\"'),\n", + " '0.019*\"group\" + 0.018*\"=\" + 0.015*\"study\" + 0.013*\"day\" + 0.013*\"p\" + 0.010*\"high\" + 0.009*\"level\" + 0.009*\"cat\" + 0.008*\"<\" + 0.008*\"effect\"'),\n", " (4,\n", - " '0.020*\"infection\" + 0.020*\"dog\" + 0.015*\"hospital\" + 0.015*\"canine\" + 0.014*\"transmission\" + 0.013*\"h1n1\" + 0.013*\"worker\"'),\n", + " '0.020*\"infection\" + 0.020*\"dog\" + 0.015*\"hospital\" + 0.015*\"canine\" + 0.014*\"transmission\" + 0.013*\"h1n1\" + 0.013*\"worker\" + 0.013*\"contact\" + 0.012*\"pandemic\" + 0.011*\"air\"'),\n", " (5,\n", - " '0.026*\"calf\" + 0.021*\"strain\" + 0.019*\"isolate\" + 0.017*\"rotavirus\" + 0.015*\"bovine\" + 0.014*\"diarrhea\" + 0.012*\"sample\"'),\n", + " '0.026*\"calf\" + 0.021*\"strain\" + 0.019*\"isolate\" + 0.017*\"rotavirus\" + 0.015*\"bovine\" + 0.014*\"diarrhea\" + 0.012*\"sample\" + 0.008*\"detect\" + 0.007*\"coli\" + 0.007*\"study\"'),\n", " (6,\n", - " '0.051*\"antibody\" + 0.026*\"protein\" + 0.025*\"coronavirus\" + 0.015*\"s\" + 0.014*\"sars-cov\" + 0.012*\"spike\" + 0.012*\"pedv\"'),\n", + " '0.051*\"antibody\" + 0.026*\"protein\" + 0.025*\"coronavirus\" + 0.015*\"s\" + 0.014*\"sars-cov\" + 0.012*\"spike\" + 0.012*\"pedv\" + 0.011*\"n\" + 0.011*\"neutralize\" + 0.010*\"monoclonal\"'),\n", " (7,\n", - " '0.025*\"assay\" + 0.023*\"detection\" + 0.017*\"sample\" + 0.016*\"test\" + 0.016*\"method\" + 0.013*\"detect\" + 0.012*\"virus\"'),\n", + " '0.025*\"assay\" + 0.023*\"detection\" + 0.017*\"sample\" + 0.016*\"test\" + 0.016*\"method\" + 0.013*\"detect\" + 0.012*\"virus\" + 0.011*\"pcr\" + 0.011*\"diagnostic\" + 0.009*\"sensitivity\"'),\n", " (8,\n", - " '0.046*\"rna\" + 0.028*\"sequence\" + 0.025*\"gene\" + 0.016*\"genome\" + 0.014*\"virus\" + 0.010*\"region\" + 0.010*\"viral\"'),\n", + " '0.046*\"rna\" + 0.028*\"sequence\" + 0.025*\"gene\" + 0.016*\"genome\" + 0.014*\"virus\" + 0.010*\"region\" + 0.010*\"viral\" + 0.010*\"analysis\" + 0.010*\"mrna\" + 0.007*\"mutation\"'),\n", " (9,\n", - " '0.032*\"virus\" + 0.027*\"human\" + 0.020*\"infection\" + 0.016*\"disease\" + 0.016*\"animal\" + 0.012*\"bat\" + 0.012*\"mers-cov\"'),\n", + " '0.032*\"virus\" + 0.027*\"human\" + 0.020*\"infection\" + 0.016*\"disease\" + 0.016*\"animal\" + 0.012*\"bat\" + 0.012*\"mers-cov\" + 0.011*\"respiratory\" + 0.010*\"host\" + 0.009*\"specie\"'),\n", " (10,\n", - " '0.048*\"patient\" + 0.021*\"case\" + 0.010*\"clinical\" + 0.010*\"severe\" + 0.008*\"acute\" + 0.008*\"95\" + 0.008*\"disease\"'),\n", + " '0.048*\"patient\" + 0.021*\"case\" + 0.010*\"clinical\" + 0.010*\"severe\" + 0.008*\"acute\" + 0.008*\"95\" + 0.008*\"disease\" + 0.007*\"pneumonia\" + 0.007*\"covid-19\" + 0.007*\"treatment\"'),\n", " (11,\n", - " '0.035*\"respiratory\" + 0.029*\"infection\" + 0.024*\"virus\" + 0.016*\"child\" + 0.014*\"viral\" + 0.011*\"influenza\" + 0.009*\"patient\"'),\n", + " '0.035*\"respiratory\" + 0.029*\"infection\" + 0.024*\"virus\" + 0.016*\"child\" + 0.014*\"viral\" + 0.011*\"influenza\" + 0.009*\"patient\" + 0.008*\"study\" + 0.008*\"tract\" + 0.007*\"pathogen\"'),\n", " (12,\n", - " '0.039*\"activity\" + 0.025*\"antiviral\" + 0.021*\"drug\" + 0.015*\"inhibitor\" + 0.013*\"treatment\" + 0.013*\"protease\" + 0.012*\"compound\"'),\n", + " '0.039*\"activity\" + 0.025*\"antiviral\" + 0.021*\"drug\" + 0.015*\"inhibitor\" + 0.013*\"treatment\" + 0.013*\"protease\" + 0.012*\"compound\" + 0.012*\"effect\" + 0.009*\"inhibit\" + 0.008*\"target\"'),\n", " (13,\n", - " '0.036*\"protein\" + 0.012*\"domain\" + 0.009*\"interaction\" + 0.009*\"receptor\" + 0.008*\"binding\" + 0.008*\"membrane\" + 0.008*\"peptide\"'),\n", + " '0.036*\"protein\" + 0.012*\"domain\" + 0.009*\"interaction\" + 0.009*\"receptor\" + 0.008*\"binding\" + 0.008*\"membrane\" + 0.008*\"peptide\" + 0.007*\"target\" + 0.007*\"complex\" + 0.006*\"structure\"'),\n", " (14,\n", - " '0.057*\"virus\" + 0.041*\"influenza\" + 0.033*\"vaccine\" + 0.017*\"strain\" + 0.015*\"influenza_virus\" + 0.012*\"ibv\" + 0.012*\"avian\"')]" + " '0.057*\"virus\" + 0.041*\"influenza\" + 0.033*\"vaccine\" + 0.017*\"strain\" + 0.015*\"influenza_virus\" + 0.012*\"ibv\" + 0.012*\"avian\" + 0.010*\"vaccination\" + 0.009*\"pig\" + 0.009*\"infectious\"')]" ] }, - "execution_count": 21, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.show_topics(num_words=7, num_topics=params['num_topics'])" + "model.show_topics(num_words=10, num_topics=params['num_topics'])" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -935,10 +749,10 @@ "\n", "\n", "\n", - "
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