Notebook_3_metadata_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metadata overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load metadata\n",
    "\n",
    "df_meta = pd.read_csv(\"datasets_output/df_pub.csv\",compression=\"gzip\")\n",
    "df_cord = 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'])\n",
    "df_meta.drop(columns=\"Unnamed: 0\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pub_id</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>publication_month</th>\n",
       "      <th>journal</th>\n",
       "      <th>volume</th>\n",
       "      <th>issue</th>\n",
       "      <th>pages</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmid</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>The Possible Immunological Pathways for the Va...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Electronic Journal of General Medicine</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.29333/ejgm/7850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-28 08:46:55.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A Method of Estimating Time-to-Recovery for a ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Research Square</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.21203/rs.3.rs-18190/v1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-28 08:46:55.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Preparation for the quarantine of the cruise s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>JMIR Preprints</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.2196/preprints.18821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-28 08:46:55.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Differences and similarities between Severe Ac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>European review for medical and pharmacologica...</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>2781-2783</td>\n",
       "      <td>10.26355/eurrev_202003_20551</td>\n",
       "      <td>32196628.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-28 08:46:55.291546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>From SARS-CoV to SARS-CoV-2: The response and ...</td>\n",
       "      <td>Abstract:</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Fa yi xue za zhi</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1-3</td>\n",
       "      <td>10.12116/j.issn.1004-5619.2020.01.001</td>\n",
       "      <td>32198983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-28 08:46:55.291546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pub_id                                              title    abstract  \\\n",
       "0       0  The Possible Immunological Pathways for the Va...         NaN   \n",
       "1       1  A Method of Estimating Time-to-Recovery for a ...         NaN   \n",
       "2       2  Preparation for the quarantine of the cruise s...         NaN   \n",
       "3       3  Differences and similarities between Severe Ac...         NaN   \n",
       "4       4  From SARS-CoV to SARS-CoV-2: The response and ...  Abstract:    \n",
       "\n",
       "   publication_year  publication_month  \\\n",
       "0            2020.0                3.0   \n",
       "1            2020.0                3.0   \n",
       "2            2020.0                3.0   \n",
       "3            2020.0                3.0   \n",
       "4            2020.0                2.0   \n",
       "\n",
       "                                             journal volume issue      pages  \\\n",
       "0             Electronic Journal of General Medicine     17     4        NaN   \n",
       "1                                    Research Square    NaN   NaN        NaN   \n",
       "2                                     JMIR Preprints    NaN   NaN        NaN   \n",
       "3  European review for medical and pharmacologica...     24     5  2781-2783   \n",
       "4                                   Fa yi xue za zhi     36     1        1-3   \n",
       "\n",
       "                                     doi        pmid pmcid  \\\n",
       "0                     10.29333/ejgm/7850         NaN   NaN   \n",
       "1              10.21203/rs.3.rs-18190/v1         NaN   NaN   \n",
       "2                10.2196/preprints.18821         NaN   NaN   \n",
       "3           10.26355/eurrev_202003_20551  32196628.0   NaN   \n",
       "4  10.12116/j.issn.1004-5619.2020.01.001  32198983.0   NaN   \n",
       "\n",
       "                    timestamp  \n",
       "0  2020-03-28 08:46:55.291546  \n",
       "1  2020-03-28 08:46:55.291546  \n",
       "2  2020-03-28 08:46:55.291546  \n",
       "3  2020-03-28 08:46:55.291546  \n",
       "4  2020-03-28 08:46:55.291546  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Publication years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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_meta[\"publication_year\"] = df_meta[\"publication_year\"].apply(clean_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48348.000000\n",
       "mean      2011.023331\n",
       "std          8.935132\n",
       "min       1951.000000\n",
       "25%       2007.000000\n",
       "50%       2013.000000\n",
       "75%       2018.000000\n",
       "max       2020.000000\n",
       "Name: publication_year, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.publication_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a6fc3d6d0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_meta.publication_year.tolist(), bins=30, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a6fdae150>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_meta[(pd.notnull(df_meta.publication_year)) & (df_meta.publication_year > 2000)].publication_year.tolist(), bins=20, hist=True, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48428, 13)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39154"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.abstract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45393"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.doi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26228"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.pmcid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36010"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.pmid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44793"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.journal))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Journals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Journal of Virology                                    1747\n",
       "PLoS One                                               1557\n",
       "Virology                                                865\n",
       "Emerg Infect Dis                                        741\n",
       "The Lancet                                              682\n",
       "Viruses                                                 570\n",
       "medRxiv                                                 510\n",
       "Arch Virol                                              500\n",
       "Virus Research                                          500\n",
       "Vaccine                                                 486\n",
       "Sci Rep                                                 480\n",
       "Veterinary Microbiology                                 445\n",
       "Journal of Virological Methods                          395\n",
       "The Lancet Infectious Diseases                          388\n",
       "Journal of Clinical Virology                            379\n",
       "PLoS Pathog                                             357\n",
       "Virol J                                                 355\n",
       "Antiviral Research                                      354\n",
       "Journal of Clinical Microbiology                        340\n",
       "Proceedings of the National Academy of Sciences         336\n",
       "International Journal of Infectious Diseases            278\n",
       "BMC Infect Dis                                          245\n",
       "American Journal of Infection Control                   233\n",
       "Front Immunol                                           205\n",
       "Front Microbiol                                         195\n",
       "Journal of Hospital Infection                           189\n",
       "Veterinary Immunology and Immunopathology               187\n",
       "Nucleic Acids Res                                       185\n",
       "bioRxiv                                                 177\n",
       "Biochemical and Biophysical Research Communications     170\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.journal.value_counts()[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sub = df_meta[df_meta.journal.isin(df_meta.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=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sources and licenses\n",
    "\n",
    "For CORD19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cord19_metadata_id</th>\n",
       "      <th>source</th>\n",
       "      <th>license</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>ms_academic_id</th>\n",
       "      <th>who_covidence</th>\n",
       "      <th>sha</th>\n",
       "      <th>full_text</th>\n",
       "      <th>pub_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv_medrxiv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f056da9c64fbf00a4645ae326e8a4339d015d155</td>\n",
       "      <td>There are many different methods that characte...</td>\n",
       "      <td>5814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv_medrxiv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>daf32e013d325a6feb80e83d15aabc64a48fae33</td>\n",
       "      <td>The term metapopulation was coined by Levins [...</td>\n",
       "      <td>5815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv_medrxiv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f33c6d94b0efaa198f8f3f20e644625fa3fe10d2</td>\n",
       "      <td>Antibodies are essential components of the imm...</td>\n",
       "      <td>5816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv_medrxiv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4da8a87e614373d56070ed272487451266dce919</td>\n",
       "      <td>Metagenomics can be defined as the analysis of...</td>\n",
       "      <td>5817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv</td>\n",
       "      <td>biorxiv_medrxiv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eccef80cfbe078235df22398f195d5db462d8000</td>\n",
       "      <td>: Schematic of transmission links overlaid on ...</td>\n",
       "      <td>5818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cord19_metadata_id   source  license   full_text_file  ms_academic_id  \\\n",
       "0                   0  biorxiv  biorxiv  biorxiv_medrxiv             NaN   \n",
       "1                   1  biorxiv  biorxiv  biorxiv_medrxiv             NaN   \n",
       "2                   2  biorxiv  biorxiv  biorxiv_medrxiv             NaN   \n",
       "3                   3  biorxiv  biorxiv  biorxiv_medrxiv             NaN   \n",
       "4                   4  biorxiv  biorxiv  biorxiv_medrxiv             NaN   \n",
       "\n",
       "  who_covidence                                       sha  \\\n",
       "0           NaN  f056da9c64fbf00a4645ae326e8a4339d015d155   \n",
       "1           NaN  daf32e013d325a6feb80e83d15aabc64a48fae33   \n",
       "2           NaN  f33c6d94b0efaa198f8f3f20e644625fa3fe10d2   \n",
       "3           NaN  4da8a87e614373d56070ed272487451266dce919   \n",
       "4           NaN  eccef80cfbe078235df22398f195d5db462d8000   \n",
       "\n",
       "                                           full_text  pub_id  \n",
       "0  There are many different methods that characte...    5814  \n",
       "1  The term metapopulation was coined by Levins [...    5815  \n",
       "2  Antibodies are essential components of the imm...    5816  \n",
       "3  Metagenomics can be defined as the analysis of...    5817  \n",
       "4  : Schematic of transmission links overlaid on ...    5818  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cord.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source\n",
    "df_sub = df_cord[df_cord.source.isin(df_cord.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=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# license\n",
    "df_sub = df_cord[df_cord.license.isin(df_cord.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=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full text availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cord[\"has_full_text\"] = pd.notnull(df_cord.full_text)\n",
    "df_cord = df_cord.merge(df_meta, how=\"left\", left_on=\"pub_id\", right_on=\"pub_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ca0fa90>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full text x source\n",
    "df_plot = df_cord.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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a38300bd0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NzY1z585p5StfvjwLFixg6dKltGjRggEDBuDt7U3Hjh2pXLkyy5cvZ8uWLTg7OzNgwAC6deuGv7//M6tHkZ5DiI2N5dSpU5r3gIaEhNCpUyeqVq2Km5sbAwcOJDo6Gnt7e0JDQ2ncuDH169endu3aKIrCqlWr8Pb2ZteuXcTGxuLi4lIslRJClG4qdc4zeX2pSp2DqUmZJ2d8YO/evXrTzczM+PLLL/Nd79tvv9X8v0OHDnTo0EFvvqZNm2rlfdaKdIcQEhJCxYoVcXFxoUuXLpiYmDB79mwAGjVqxIwZMwgODsbJyYnz588zf/58AExNTVm2bBk7d+7E0dGR8PBwFi9eXGwvrBBClG6GnKRLw+eUFgbdITg5OWmNia1SpYrm7kCfHj160KNHD73LXn/99Wc6nEoIIcTjyeR2QgghAAkIQgghHpCAIIQQApCAIIQQ4gEJCEIIIQAJCEIIIR6QgCCEeO48q1eMyqtMtckb04QQz538Xj1a3Ax5lenw4cP5/fffgfvvdDEyMsLExASAli1bsnz58qdSxmdJAoIQQhTAwyf8yZMnU6FCBQIDA0uwRMVPmoyEEKKIEhMTsbe359NPP8XBwYH169czefJkQkJCNHmio6O13nXw+++/884779CyZUu8vLw4dOhQSRRdiwQEIYQoBunp6VStWpVDhw7pfRfCw65cucKIESMYPHgw0dHRTJgwgdGjR5OQkPCMSqufBAQhhCgm7u7umJqaPvHdLpGRkbRo0QI3NzfKli1Lu3bt6NChA5s3F7xP42mQPgQhhCgmNWrUKFC+y5cvc/jwYZ3XZZb0KwAkIAghRDF5+HWXxsbGWm9Mu337tub/NWrUoHv37oSFhWnSEhMTsbS0fDYFzYc0GQkhxFPw6quvcuDAAa5fv86tW7dYvXq1Zpmrqyv79+9n//795Obm8vfff/P222/z888/l2CJ5Q5BCPEcys1WGfSMQFE+x7is6VPZ9oABAzhz5gw9e/akatWqDBkyhL///hu4HywWLlxIWFgY48aNo2LFiowYMYK33nrrqZSloCQgCCGeO0/rJF1cnzNr1iyt3+vUqUNsbKxWWoUKFXReq+nt7a35f5s2bWjTpk2hPv9pkSYjIYQQgAQEIYQQDxgUEE6ePImzs7Pm95s3bzJu3DicnZ1p3bo1kyZN4s6dO5rl3t7eNG/eHHt7e81PTk4OADExMfTr1w87Ozvc3d05efJkMVVJCCFEYRQoICiKwqZNmxg2bJjWMKrAwEDKlCnDnj172LVrF3fv3mXq1Kmadf7++2+2bNnCiRMnND9lypRBpVLh7+9Pz549OXr0KL6+vvj4+JCamvp0aimEEOKJChQQFixYwLp16/Dz89Ok5ebmYmxszAcffICFhQUVK1akb9++mtkA4+LiyM3NpX79+jrbO3LkCGq1mqFDh2JiYkKvXr1o2LAhO3bsKKZqCSGeR4qilHQRXnhF2ccFCgj9+/fnu+++w9bW9v8rGhuzZMkS6tWrp0n76aefaNy4MQBnzpzB0tKSIUOG0Lp1awYMGMCJEycAOH/+PNbW1lqf0aBBA86ePVvoigghnm9lypTRamEQT4daraZs2cINIC1QQLCysnpinhUrVrBnzx4mTJgAQHZ2Ns2bN2fKlCkcOHCAXr16MWLECJKTk0lPT8fMzExrfXNzczIyMgpRBSFEaVC5cmWSk5PJzc0t6aK8sHJzc0lOTqZSpUqFWr/IzyGo1WqmTZvGzz//zNdff6258vfy8sLLy0uT791332X9+vX89ttvWFhYkJWVpbWdjIyMJ04IJYQovapVq0ZiYqLOeH1RvCwtLalWrVqh1i1SQEhNTcXPz4+0tDQiIiK07iS2bt1KhQoV6Nq1qyZNrVZTrlw5rK2tWbVqlda2Ll68qBVAhBAvFmNjY+rWrVvSxRCPUaTnEMaNG0dubi5r1qzRaVa6ffs206dPJz4+HrVazbJly0hLS6N9+/Y4OTmhKAqrVq1CrVYTFRVFbGxsic/0J4QQ/2aFvkOIiYlh//79mJqaaj1+XaFCBQ4cOMCQIUO4e/cugwcP5u7du9ja2rJ8+XLKly8PwLJlywgODmb+/PnUqVOHxYsXU7Vq1aLXSAghRKEYFBCcnJw4duwYADY2No9tCzQyMmL06NGMHj1a7/LXX3+ddevWGfLxQgghniKZukIIIQQgAUEIIcQDEhCEEEIAEhCEEEI8IAFBCCEEIAFBCCHEAxIQhBBCABIQhBBCPCABQQghBCABQQghxAMSEIQQQgASEIQQQjwgAUEIIQQgAUEIIcQDEhCEEEIAEhCEEEI8IAFBCCEEIAFBCCHEAxIQhBBCABIQhBBCPGBQQDh58iTOzs6a31UqFUFBQTg6OtK6dWuWLl2qlf/bb7+lQ4cO2NvbM2HCBNLT0zXLDh8+jLu7O3Z2dvTv35+EhIQiVkUIIURRFCggKIrCpk2bGDZsGGq1WpO+cOFC4uLi2L17NxEREWzZsoWtW7cCcPDgQcLDw1mxYgW//PILKpWKadOmAZCSksKoUaP44IMPOHr0KN26dcPHx4fc3NynUEUhhBAFUaCAsGDBAtatW4efn59W+pYtW/D19aVSpUrUqVMHHx8f1q9fD8DWrVvp06cPr732GpaWlowfP57t27eTlpbG7t27ee211+jRowcmJiYMHz4clUrFb7/9Vvw1FEIIUSAFCgj9+/fnu+++w9bWVpN29+5drl+/TsOGDTVp9evX5+zZswCcP38ea2trzbK6deuiKArx8fE6yx5dVwghxLNXoIBgZWWlk5bXH2BmZqZJMzc3JzMzU7Pc3Nxcs8zIyAhTU1MyMjJ0luVtJyMjw/AaCCGEKBaFHmWUd0LPysrSpGVkZGBhYaFZnhcc4H4/hEqlwtLSUmcZQGZmpmZdIYQQz16hA0KlSpWoXr06Fy9e1KTFxcVpmpAaNmxIXFycZllCQgKKolCvXj2dZQAXL17Uan4SQgjxbBXpOQQPDw8WL15MSkoKiYmJrFixAg8PDwA8PT3ZtGkTMTExpKWlERoaiouLCxYWFri4uBATE8OOHTtQq9WsWLECY2NjHB0di6VSQgghDFekgDBmzBhee+013NzcePvtt+nRowcDBgwAoGPHjvj5+eHv70+HDh1QFIWpU6cC8NJLLxEeHs5XX32Fo6MjO3fuJDw8HFNT06LXSAghRKGUNSSzk5MTx44d0/xerlw5goODCQ4O1pvf29sbb29vvctatWqleWZBCCFEyZOpK4QQQgASEIQQQjwgAUEIIQQgAUEIIcQDEhCEEEIAEhCEEEI8IAFBCCEEIAFBCCHEAxIQhBBCABIQhBBCPCABQQgh8pGbrSpQ2ovCoLmMhBDi38S4rCkXQ/popTUI3FxCpXn65A5BCCEEIAFBCCHEAxIQhBBCABIQhBBCPCABQQghBCABQQghxAMSEIQQQgASEIQQQjxQpAfTtm3bRnBwsFZaZmYmzs7OhIeH06JFC0xMTDTL7O3tWblyJQA7duxg3rx5XL9+nVatWjFr1ixeeumlohRHCCFEERQpIHh4eODh4aH5/cyZMwwbNoxJkyYRGxtLpUqV+PXXX3XWO3/+PIGBgSxbtoxmzZoxZ84cxo4dyzfffFOU4gghhCiCYmsyUqvVTJgwgQ8//BAbGxtOnz6NjY2N3rzbtm2jS5cuODg4UK5cOcaPH8/x48eJj48vruIIIYQwULEFhDVr1mBmZsbAgQOB+3cLKSkpuLu706ZNG0aPHk1ycjJw/w7B2tpas665uTm1atXi7NmzxVUcIYQQBiqWgKBSqVixYgUffPABRkZGwP2TfIsWLfj666/58ccfMTMzY9SoUQCkp6djbm6utQ0zMzMyMjKKozhCCCEKoVhmOz148CDGxsZ06tRJkxYQEKCVZ/LkyTg7O3PlyhXMzc3JzMzUWp6ZmYmlpWVxFKdUyM1WYVzW9IlpQgjxrBRLQNizZw89e/bE2Pj/Nxzz58/Hzc1N0zSkVqsBKFeuHA0bNiQuLk6TNyMjgytXrmg1I73o/m3T6gohnn/F0mT0559/0qJFC6202NhYZs2axd27d7l79y4hISF06tSJqlWr4ubmxp49e4iOjkalUhEaGkrjxo2pX79+cRRHCCFEIRRLQEhKSqJGjRpaaSEhIVSsWBEXFxe6dOmCiYkJs2fPBqBRo0bMmDGD4OBgnJycOH/+PPPnzy+OogghhCikYmky+uOPP3TSqlSpQmhoaL7r9OjRgx49ehTHxwshhCgGMnWFEEIIQAKCEEKIByQgCCGEACQgCCGEeEACghBCCEACghBCiAckIAghhAAkIAghhHhAAoIQQghAAoIQQogHJCAIIYQAJCAIIYR4QAKCEEJLbraqQGnixVMss50KIV4c8vKmfy+5QxBCCAFIQBBCCPGABAQhhHjG8uuTKem+GulDEEKIZ0xfPw2UfF+N3CEIIYQAJCAIIYR4oMgBISIigqZNm2Jvb6/52bJlCyqViqCgIBwdHWndujVLly7VWu/bb7+lQ4cO2NvbM2HCBNLT04taFCGEEEVQ5IBw5swZ3nvvPU6cOKH56d27NwsXLiQuLo7du3cTERHBli1b2Lp1KwAHDx4kPDycFStW8Msvv6BSqZg2bVqRKyOEEKLwihwQTp8+TePGjXXSt2zZgq+vL5UqVaJOnTr4+Piwfv16ALZu3UqfPn147bXXsLS0ZPz48Wzfvp20tLSiFkcIIUQhFSkg5OTkEBsby/fff0+7du1wcXHhq6++4s6dO1y/fp2GDRtq8tavX5+zZ88CcP78eaytrTXL6tati6IoxMfHF6U4Qgjxr1eUqUeKNOw0JSUFW1tbvLy8WLRoERcuXMDf3x+V6v6Hm5mZafKam5uTmZkJQHp6Oubm5pplRkZGmJqakpGRUZTiFLvcbBXGZU0LnC6E+Pd6Xs4XRZl6pEgBoXr16qxevVrze+PGjXn33Xc5cOAAAFlZWZplGRkZWFhYANrBAUBRFFQqFZaWlkUpTrF7XscKCyGePy/C+aJITUbnzp1jwYIFWmlqtZpy5cpRvXp1Ll68qEmPi4vTNCE1bNiQuLg4zbKEhAQURaFevXpFKY4QIh/Pwwymz+vTueL/inSHULFiRf773/9Ss2ZN3n77bc6cOcO3335LUFAQDRs2ZPHixTRq1Ij09HRWrFjB4MGDAfD09CQwMJAePXrwyiuvEBoaiouLi+YOQghRvJ6HGUxfhCvoF12RAoKVlRVLlixhzpw5zJw5kypVquDv78+bb75J586dmTVrFm5ubuTm5tKvXz8GDBgAQMeOHfHz88Pf3587d+7Qpk0bpk6dWiwVEkIIUThFnsvI2dmZ7777Tie9XLlyBAcHExwcrHc9b29vvL29i/rxQgghiolMXSGEKPWehz6SF4HMdiqEKPWehz6SF4HcIQghhAAkIAghhHhAAoIQQghAAoIQQogHJCCIF5aMPBHCMDLKSLywZOSJEIaROwRR7GTOGiFKJ7lDEMVO5qwRonSSOwQhhBCABAQhnhvS1PZsyGCD/P3rmoyel7caCfGo0tjUVhr/nmSwQf7+dQGhNP7RPQ9K4x/+i07fvn8eXtcI8vdUWkmTkSgYxcB08fTp2/dyPEQR/OvuEEThGJuY4j7+e530yFDPEiiNAP3HRI6HKAq5QxBCCAFIQBBCCPGABAQhhBBAMfQh/Prrr4SGhhIfH89LL72Ej48P/fv359KlS7i4uGBubq7J6+rqSkhICADffvsty5Yt4969e3Tt2pWpU6diYWFR1OIUq1y1Su9oiVy1CmMTGVkjXkz6vvfynf93KFJAuHLlCh9++CFffPEFXbt25dSpUwwfPpzatWuTlpZG8+bN2bhxo856Bw8eJDw8nFWrVvHyyy8TEBDAtGnTmDlzZlGKU+ykI1X8G0ln9b9XkZqMkpKScHNzw8XFBWNjY5o3b46joyPHjx/n9OnT2NjY6F1v69at9OnTh9deew1LS0vGjx/P9u3bSUtLK0pxhBBCFEGRAoKDgwNTp07V/H779m2OHTtGkyZNOHPmDOfOnaNHjx60a9eOwMBA7t69C8D58+extrbWrFe3bl0URSE+Pr4oxRFCCFEExdapfO/ePfz8/HjjjSDj8BwAACAASURBVDfo2rUrFStWpG3btkRERLBlyxauXLlCUFAQAOnp6Vp9C0ZGRpiampKRkVFcxREFIHPnCCEeViwPpsXFxeHv70/Dhg2ZO3cuxsbGzJs3T7O8QoUKjB07loEDB5KdnY25uTmZmZma5YqioFKpsLS0LI7ilIjSOLWDTDsghHhYkQPC0aNH8ff3p3///owbNw4jIyMyMjJYuHAhw4YNo1q1agCo1WrKli1LmTJlaNiwIXFxcZptJCQkoCgK9erVK2pxSoycXIUQpV2RmowSEhIYOXIko0ePZvz48RgZGQFgbm7OoUOHCAsLIzMzk2vXrhEaGkrv3r0xMjLC09OTTZs2ERMTQ1paGqGhobi4uBR62KlMZyuEEEVXpDuENWvWkJaWRlhYGGFhYZr0gQMHsmjRIqZPn0779u0xNjamV69efPzxxwB07NgRPz8//P39uXPnDm3atNHqnDaUTGcrhBBFV6SAEBAQQEBAQL7Lw8PD813m7e2Nt7d3UT5eCCFEMZLZTkuB52HeeyH0kaf5XywSEEoBQ5rEJHiIZ0me5n+xSEB4wUh/ytNXGocYlzayj0uGBAQhDCRDjJ8+2cclQwKCKFGGXglKk5h4lp6HmV+f5d2SBARRogy9EpQmsdKrNHZAPw8zvz7LuyUJCEKIZ0I6oJ9/8sY0IYQQgNwhiKegNDYNCCEkIPyrPa0TtzQNCFE6SUD4F5MTtxDiYdKHIIQQApA7hBLzPIxvFkKIh0lAKCHPw/hmIUTxeREGU0hAEEKIYvC89MkVpfVBAoIQQrxAitL6IAFBlCrS9yLE0/OvCwgvQjvfv5n0vQjx9PzrAsLz0s4n/h2elwsQubMSBfGvCwji3+N5OAk+LxcgcmclCqJEA0JMTAzBwcHExsbyyiuvEBISQvPmzUuySOIFIidB8SJ4lneZJRYQVCoV/v7+DB48mNWrV7Nr1y58fHz4+eefKV++vEHbeh6uBIUQJedFPgc8y7vMEgsIR44cQa1WM3ToUAB69erF6tWr2bFjB++8845B25IrwdLreWljN8TTKnNp3BdPi6H7orSdA57XY11iAeH8+fNYW1trpTVo0ICzZ88+cd2cnBwArl69qklTp6do5UlMTMx3/UfzPi5/QfMq2WpMh8zXSb8UH4dRWZMCbftxZU5OVWn9blpMeZ/GvjA07/CQ3TppywNd9ObVt21DjnVxfS8MKfOLsC+e9d8TPN19UZS/kef9O6Qvf17evHNm3jn0UUaKoij5bvUpWrJkCSdPniQ8PFyTNn36dDIyMggJCXnsuseOHcPb2/tpF1EIIV5Ia9aswcHBQSe9xO4QLCwsyMrK0krLyMjAwsLiieva2tqyZs0aqlevTpkyZZ5WEYUQ4oWSk5PD9evXsbW11bu8xAKCtbU1q1at0kq7ePEiXl5eT1zXzMxMb3QTQgjxePXq1ct3WYm9D8HJyQlFUVi1ahVqtZqoqChiY2Nxccm/jVAIIcTTU2J9CABnz54lODiYmJgY6tSpwyeffIKzs3NJFUcIIf7VSjQgCCGEeH7IKzSFEEIAEhCEEEI8IAFBCCEEIAFBCCHEAxIQxGPl5uaWdBGeqhe9fqWNHI+SVebzzz//vKQLUdolJSVRsWJF0tPTWbFiBefOnaNp06YYGRk9dr2cnByMjbVjckxMDDdu3HjsT7Vq1Z5Zmdu1a8e1a9eoWrUqNWrUeOw216xZQ926dTEzMytQGVJTU/nnn3+4fv36Y+tn6Hbh/lPvJiYm5OTkEBUVRXJyst4Hcgyp36P0Hb9HqdVqEhMTqVSpEsATvxMFVdjv3ON4eXnRv39/nfTOnTtrJqF82gw9Hnfu3DHoeyEer1QOO505c+YT8wQEBGj9npCQwLx580hMTCQ7O1tr2ZYtWwD45ptvnrjdwYMHa/2+YsUK/vOf/3Ds2DEmT57MX3/9hbGxMW3atNEpw6VLl/jqq6+YNm0aP//8Mx999BEVKlRg0aJF2NnZAWBjY4ORkRH5HRYjIyP+/vtvze+F2ReGlPnYsWNs376dnTt3UrVqVTw8PHB3d+fll1/W+ZyhQ4dy4sQJnJ2d8fT0pGvXrpia6p+5ccOGDYSEhKBSaU8y9mj9DN0uwPfff09ISAhHjhxh5syZbN++HSMjIwYNGsTIkSMLXb+CHL88mZmZTJs2jS1btmBqasrmzZvx9/dn+fLlvPLKK1p5H93neUxMTKhSpQrt27fXejLfkOMHsGPHDqKiorhx4wY1a9bkrbfeomPHjsD9Sc+++OILFEVh3759dOrUSWvde/fuER8fz/79+7XSb926xfr16/Hz8+PPP/9k0qRJVKpUiVmzZtGgQQOdMqSlpbFr1y4uX77M0KFDiYmJoWXLljr5DDkeAM2bN6dDhw54eHjQuXNnTEy0J5IcNWqU3iBZrlw5bGxsGDx4MOXKldNZvm/fPtq3b1+gqXFSUlJYuXKl3nPLokWL9NbRwcGB69evs2DBAipVqoS/v7/eaXsyMjIwNzd/Yhng/kVC7dq1C5Q3P6UyIOT3B/SwR0+U/fv3p1KlSnTt2lXnS9O7d28ABg0a9NhtGhkZ6QQNV1dX5s+fT7169WjVqhUbNmygRo0auLm5cejQIa28Pj4+WFlZERISgqurK71796ZixYpEREQQERHxxDrpU5h9YUiZ8+Tk5PDLL7+wa9cuDh48SL169fD09KRnz55YWlpq8l27do0ffviB7du3Ex8fT/fu3fH09MTR0VFre927d2fEiBH07t2bsmWfPINKQbcL4OHhQUBAAI6Ojjg5ObF06VKsrKzw9vbWObEZUj9Djl9wcDApKSmMHTuWfv36cejQIWbNmsU///zD8uXLtfIGBQWxdetWunfvzssvv8zVq1fZtWsXrVu3xsLCggMHDjB58mT69u0LGHb8vvrqK77++mv69u1LzZo1uXz5Mps2bWLs2LGaaebXrl1LSkoK4eHh+Pr6aq1vampKp06deP3117XSR48eTXp6OsuWLaNPnz60aNECS0tLTpw4ofM3EhMTg4+PD7Vq1eLChQts3boVd3d3Zs6cSa9evQp9PAAuX75MVFQUUVFRXLlyhR49euDh4aEJoPpOyHD/zu3AgQO8/vrrfPHFFzrLu3fvTmpqKq6urnh4eDz2xV0+Pj7cunWL9u3b65xbPvjgA63fZ8+ezY4dO9i3bx9+fn7cu3cPExMTrKysmDVrls627e3t6dGjB15eXrRu3TrfMgA0a9YMOzs7PD09efPNNw1+rwwAyr+Evb29kpWVVezbbdWqlaIoivLLL78o7dq1UxRFUXJycpQWLVro5G3Tpo2SnZ2tJCQkKE2aNFHu3bun5ObmKvb29nq3nZCQoISHhyvBwcHKwoULlfPnzz/zMufJzs5W9u3bpwQEBCht27ZVevfurfj5+SlOTk5KVFSU3nWOHj2qeHp6KjY2Nkrnzp2VRYsWKenp6YqiKErLli2VnJycQpX/cdt9uH5Hjx5VHB0dNen57eeC1s+Q49e2bVvl3r17WuXJysrS/P9hgwcPVg4dOqRTx/fee09RFEX5888/FRcXF536FeT4tW3bVomNjdVKO3PmjNK5c2edvDt37sxn7+jq0KGDkpmZqSQnJyuNGzdWUlJSlOzsbL1lGDhwoLJlyxZFURTFwcFBU/aePXvmu/3CfN/Onz+vLF68WHFzc1O6dOmifPnll0pCQkK+n3Hr1i1NefT5448/lJCQEKVdu3ZK9+7dlcWLF+vdnp2dneZYP4mLi4ty9epVJTU1VWnatKkSFxenpKaman1PHxYTE6PMnTtX6dy5s9KpUyclLCxMuXDhQr71WbdunTJw4EDFzs5OGTdunHLgwAElNze3QGVTFEUp1e9Uzs3NZfny5WzevJmrV6/y0ksv4eHhwYcffqhzq9eoUSMuX77Mq6++WqBt79ixg82bN3PlyhXNdvOu0B5Wt25d1q9fz759++jQoQM5OTn897//5bXXXtO73YyMDPbt20ezZs0oX748V65c0XureOTIEUaOHEnLli2pXbs2J06cYNmyZSxZsoS2bdsWaV8YUubDhw+zY8cOdu3ahYWFBW5ubqxatYqGDRsCsGfPHgICAnB1dQUgOTmZH374gaioKM6dO0fHjh0ZPXo0tWrVYsGCBfj6+vL111/j4uLC5s2b9e5TfQq6XYAaNWpw8OBBoqKiNPtqx44dOk01halfQY9f2bJlNc1hyoOb8PT0dL23/6dPn6ZVq1ZaaXZ2dvzxxx/A/WaRmzdvapYZcvzUarVOva2trXWa6uD+VfHvv//O+vXrSU5OJiwsjPXr1+ttdsnKysLIyIiDBw/SqFEjqlSpwq1bt/Q25Z09exYPDw/g/30obdu25dq1azp5DT0eeVJSUoiOjiY6OprExETatWtHamoq/fr14/3339fbB5KVlaVzRf+wN954gzfeeIOAgAAOHTrEnDlzWLhwIS1atKBv3754enpiZGREvXr1yMrKKtAV+e3bt7GysmLPnj1YWVnx6quvolar8+1Mb9SoEY0aNWL8+PGa5rRBgwZRu3ZtNm7cqJW3cuXK9O/fn/79+2vunIKDg8nOzubAgQNPLBuU8DuVi2rJkiX88MMPjBkzhtq1a5OQkEB4eDhGRkaMGTMG+H+/QIMGDRg0aBDvvPOOpoMvz6P9AmvXrmXJkiUMHjyYOnXqkJCQwPz580lPT2fIkCFaeYOCgvj000+pUKEC06ZN48iRI6xdu5YFCxbolLdXr1707duX69evExQUxLlz5/jggw/0zvA6d+5cTdNEnqioKEJDQ/UGhILsizyfffYZgYGBVKhQgalTpz62zB988AE9evRg/vz5ODk56Sxv0qQJb731FgDe3t78+eef2Nvb884779CzZ0+tP5KxY8dqOi2Tk5MJCgpiwYIFOp3IeX06eQzZLsCECRMYN24c5cuXZ+XKlfz2228EBgaycOHCItXPkOPXo0cPxowZw8SJEwGIj49nzpw5eidvfO211wgPD8ff3x9jY2Nyc3NZunSp5gVS+/fvp06dOpr8hhy/ESNGMGnSJAIDA6lZsyYpKSnMnTsXDw8PUlNTNfnKly/P9u3bmTVrFm+99RZ79+4lNzeXyMhIVCoV48aN09puhw4dGDFiBBcvXmT48OFcunSJSZMm0aVLF50y1KpVi+PHj2v1g/z111/UqlVLJ68hxwNg8+bN7Nixg+joaN544w3c3d1ZuHAhFStWBMDFxQVfX1+dgLBw4ULWrVuHp2f+b1VTqVTs37+fqKgo9u/fj7W1NZ988gm1atXiq6++YuPGjfj4+ODk5MSQIUMYNmyYzrmla9euWr83atSIefPmER0dTdeuXUlNTeXLL7+kWbNm+ZYD7l9MJCUlcfXqVVQqld59l+fSpUtERkbyww8/oFarcXd3f+y2H1Yq+xDydO3alVWrVmldASUkJODt7c3BgweBwvULvPnmm4SFhdGkSRNN2pkzZ/jwww/Zs2fPY7eXm5ub78gTRVHYvXs3FSpUwNnZmcTERA4fPkyfPn10rsBatWpFdHS01rZyc3NxcHDg+PHjOtsuyL7IT05OTr6dZyqV6rEduA9bunTpYzsAs7KyuHv3LtWrV9c56T8sr0+nMNvNoyiKZp9mZmaSnZ2t9wrOkPoZcvxUKhWzZ88mIiKCzMxMTE1NcXd355NPPtFqAwe4cOECvr6+3L17l2rVqnHt2jWqV6/Ol19+yd27dxk5ciSLFi3Kd+LHxx2/5s2bo1KpMDIyokyZMuTk5GjuWPIGL+R15Lu6ujJ79mxsbW1p1aoVR48e5dKlSwwcOFDnO5SVlcXatWspX748ffv25cKFC2zfvh1fX1+dTtrdu3czefJkevfuTUREBMOGDWPjxo1MnjwZNzc3nf1W0OMB9/9WPTw88PDw0AqaefL6nh69kDtw4ACKomg61x81adIk9u7dS6VKlXB3d8fT05P69etrlsfExODl5ZXvdxLu799HzxcJCQnMmDGDChUqaCb2nDdvHrNmzdJ7B7tnzx62b9/Ozz//jI2NDZ6envTq1UsT8B72zTffEBkZyblz5+jatSuenp60a9fuiSPhtMpcmgOCk5MTv/zyi9Ztn0qlon379kRHR+tdJ+8PIDU1Nd9bvFatWnH48GGtP7Ls7Gxat27NsWPHAAgNDWX8+PGPHeWT1+Gb91kPX5E96tGyuLu789lnn2k1JRw5coSpU6eyfft2nfUN3Rfbtm1j/fr1XLt2jdWrVzN//nyCg4N1hvA9eveUJ28ETN4ID4CjR48+Nq++YZ/Xrl3TNHHlN0KioNstzCgxQ+oH94NydHQ0SUlJuLu7k5ycTN26dR/7mSkpKVSuXPmxf5jZ2dn88ccfJCcnU7NmTezt7TE2NiYzM5MyZcroNG0U9PglJSU9tmx5ateujaOjI4cPH8bY2BhHR0eOHDlCTk4Ozs7OHDlyRO96CQkJJCUl0apVKzIzM/P9m/rzzz+JiIjg8uXL1KhRA09PT72dpIYej6fls88+0+qcflRqaipxcXF6r+wfvhh5nIIEv86dO+Ph4YGXl5dWQNJnwIAB9O7dG1dX18J1KFPKm4zs7e0JCwtjwoQJlClThuzsbObNm6czBBDg3LlzjB49mi+++ILmzZuzePFi9u/fz5IlS3T6FRo3bszq1au1rirWrFlD48aNNb/fvXtX69/H6dChg+aW+dEvysNXaA/z9fVl5MiReHp68vLLL5OUlERkZCTTpk0r8r5YtWoVGzZsYOjQocyZMwczMzPi4+OZMWMGU6dO1crbtGlTtmzZQr9+/ahVqxZXr14lIiKC1q1bU7NmTUJDQ7l8+TK+vr5MnjyZy5cvU6ZMGSpXrszt27c1V6/Z2dnUrVuX//znP1hbW3Pr1i0mTJjAoUOHMDExQa1WY29vz6JFi6hatapWGQq63d2777+jNjMzk7/++gsbGxvq1KlDcnIyf/31F23bttU54RhSv0uXLvH++++TmppKamoq9vb29O7dmyVLltCuXTud/Xzw4EESEhJ03l+r76T3119/sW3bNq5cuUK1atVQqVQ4OzvrHWNvyPGrXbt2gYNY06ZN+eabb7SaV7Zt20ajRo108qakpDBu3DiOHj2KqakpGzdu5N1332XFihV638aV1x7/JIYcD4AuXbroPfk+HECGDx/+2L4CffK2f/nyZZ3tVqpUifLly2uCQXJyMp988gkTJ07ExsaGefPmcebMGb744gteeuklrfXzmgPXrVvHvXv32LZtG4GBgXz55Zc633uAvXv3FvjZknXr1hlUR70K3P38HPrnn3+Url27Kg4ODkrPnj2Vli1bKq6urnpHAgwcOFBZvHixolarFUVRFLVarYSHhyuDBw/WyfvXX38pDg4OiqurqzJ8+HDF1dVVcXZ2Vk6fPl2ocl6+fFlRFEVJTEzM90efgwcPKhMnTlSGDRumfPrpp8rRo0fz/QxD9oWLi4vyzz//KIry/xErN2/eVJydnXXyvv322zr1jo2NVfr27asoiqLExcUpHTt2VBRFUebMmaN8/vnnSkZGhqIoipKRkaHMmDFDmTdvnpKRkaGEhYVp9vfEiROV0aNHK9evX1cURVGuXr2qfPjhh8q4ceN0ymDIdhVFUcaNG6d89913WtuIjIxUhg8fXqT6DR8+XFm6dKmSm5urGZ2ybds2xcvLS2e7wcHBip2dndK/f3/l3Xff1fwMGjRIJ++PP/6o2NnZKZ999pmydOlSJSgoSLGzs1MiIyN18iqKYccvISFBefPNN5V27dopdnZ2yvnz5xVbW1vl4MGDOnnPnTuntG/fXnF1dVWaNm2q9OvXT2nTpo1y5swZnbwfffSRMnXqVCUtLU2zL5YuXaoMGDBAJ298fLwyYcIE5Z133lH69Omj9fMoQ46HoijKwoULle7duysbNmxQDhw4oGzcuFFxdXVVgoODlbVr1yqenp7KzJkz9e7Hx2nSpIliY2Oj2NjYKI0aNdL838bGRmnSpIkydOhQJTk5WVEURRkxYoQSFBSkGWl048YNJTg4WPHz89PZblhYmNK/f3/lwIEDioODg5Kamqp88MEHypgxY7Ty9e/fX1EURfH09FS8vLz0/uTJ2x8ODg5Kq1at9P4UVKkOCLGxsUpWVpZy6NAhZfv27crvv/+uOeE/St9wuJycHL3DzlJSUpSbN28qmzZtUpYuXaps27ZNuXPnjk6+zMxMZe3atcrYsWOV4cOHK0FBQcrGjRsfO7z1woULyp9//qlcu3bNgJoWjEqlKtC+cHJyUlQqlaIo/x8GmJWVpTg5OenkbdGihSZvnqysLK2hlnn7tm3btjp1V6lUStu2bRVFuR+EW7ZsqcmbmpqqlffevXt6v7yGbFdR7g8vfXRIa3Z2tmJnZ1ek+jk6Omr26cPlzG+Isb4TqT7u7u46w04PHTqU77BMQ46fIUFMURQlLS1NiYqKUpYtW6Z8//33yu3bt/Xma926teaY5O2L7OxsreOQx9vbWxk6dKiydu1aZePGjVo/jzLkeCiKovTq1UvnoicpKUlxc3NTFEVRrl27prRp00ZvHR5n5cqVip+fnxIXF6dkZWUp8fHxypgxY5SwsDDl/Pnzyscff6z4+/srinL/GGRnZ+uUWd93uVOnTpqLoLzl9+7d0xl2um3bNkVRFOW7777L9ydP3oVidHR0vj8FVaqbjAYPHsyePXsK9Ja1atWqceLECezt7TVpp0+f1jsNhLu7Oz/++CNvv/12vtu7du0agwYNQq1W0759e1555RVu3bpFeHg433zzDd9++y2VK1fW5I+IiODLL7/kxo0bwP0Opxo1ajB+/Hi9baLHjx/niy++4J9//tEZkvZwe+7+/fvp2LGjVueVmZkZt27d0jyE9ehIBwcHB+bOncvHH3+suR1dsWKF3uYle3t7pkyZwqRJk6hYsSK3b98mLCyM5s2bk5uby/r16zXDHcuWLcvZs2e1mgxiY2M1/797965WE0hWVpZWB2t+baqGbrd27dps27ZNa/TPpk2b9LbBGlK/KlWqcP78eWxsbDTrX7hwQe93yNjYWDNK6EmSkpJ0RtQ4OTmRnJysN78hx+/kyZP85z//wcjISJPX3d0dfTPWZGdns3r1atzc3HB1dWXTpk1s2LCB4cOH6/R/WFpacu3aNa2O3OvXr+uMsoH7AzIOHjyo05mujyHHA+Dq1as6TS0VK1YkMTERgGXLlnH37t0C9fU97JtvviEqKkozpLhevXrMmDEDV1dXxo4dy+eff0779u2B+/1/8fHxWsf78uXLVKhQQWe7arVas03lQfetsbGxzsOZeSODWrVqpbez/JdfftH8P6+fw8zMTO8DdBs3btT7AKc+pTog1K9fnz/++EPvMMxHvf/++wwfPhwvLy9N22RkZKTeL0OVKlW4fPmyztOZD5szZw7Nmzdn1qxZWp3POTk5fPzxx8ybN48pU6YA94dRzpkzhwkTJtC5c2cqV66sOWHPmjULCwsLunXrprX96dOn07hxY8aMGfPYJ3nnzJlDx44dCQkJ0bvcyMhIJyAEBQXh6+uLo6Mj6enpdOjQgfLly7N06VKd9WfMmMHYsWNxdHTEzMyMzMxM2rZty6xZs4iOjubbb78lLCwMgJEjR/Lee+/h6elJzZo1Nft49OjRJCYm4uvrqxky2L17dz766CMmTpyo6SOZO3cu3bt31ymDIduF+3/go0aNYuXKldSsWZOkpCRu3rzJV199VaT6DRs2jBEjRjBkyBDUajUbN25k5cqVekey+fj4EBwcjK+vr0478qMdfvXr12f37t306NFDk7Zr1658n5kx5PgZEsRCQkI4deqUZuRPgwYNmD17NikpKUyePFkr79tvv42vry+jRo0iJyeHX3/9lYULF+odgluvXj1SUlIKFBAMOR4A7du3Z+zYsYwdOxYrKyuuXLnCwoULad++PVlZWfz2229Urly5QH19D8vKyuLGjRtafS3Xr18nIyMDuP93ntcvMXDgQEaMGMG7775LrVq1SE5OZvXq1QwYMEBnu+3btycwMJCgoCCMjIw0o9Hyu6j18vLi888/1xyTzMxMZs2axdatWzXPqeQZMmQIvr6+vP/++xgZGXHjxg0++eQTTp8+rXkq/UlK9SijAQMG8Mcff1ClShVq1Kih1fmib1jjL7/8wvbt27lx4wZWVla4u7vrHekwcuRIDh8+zOuvv66z3bxH4du2bUtkZKTejqDk5GQGDBjA3r17AXjrrbf46KOP6NChg07ePXv2sHz5cp0OIXt7e6Kjow0agmeI3NxcTp06RVJSEjVq1KB58+aP7Xi7cuUKycnJ1KpVCysrq3zz/fbbb0RGRmry9u7dm5YtWxIXF6cZ2gj3x1UHBQWxc+dOsrOzMTU11Uw5oe/EUdDt5rl58yb79u3jxo0b1KhRgy5duui9ejW0frt27WLTpk2a0TJeXl56x7Lb29trTh553x8lnwEEhw4dwtfXl9atW2uC47Fjx1iyZEm+J4qCHr+NGzeycOFChgwZwuLFiwkICGDlypW8++67vPvuu1p527Rpww8//KC1n1JSUnB3d+fXX3/V+fyvv/5aZ1+8//77Ohcw8+fPZ9u2bXh6euoER29vb731K+jxSE1NZerUqezcuZOsrCxMTU3x9PRk0qRJxMbGEh4ezueff673KvtxFi1axJYtWxg0aBBWVlZcvXqVNWvW4Obmhre3NxMnTuSVV17RDPLYsGGDzrmlT58+Otu9e/cukyZNYt++fQCUKVMGR0dHQkND9Z5L9u/fT2BgIM7Oznh5eTF16lSqVavG9OnTde54Y2JimDhxIpUrV8bd3Z2wsDDatWtHYGAgVapUKVC9S3VAMGQs++TJk/n0008LNBwrv/lP4P9zk9jb23PixIl887Vs2ZLff/9dk/f48eN6Rwvk5OTQtm1bDh8+rJU+aNAgPv74yUsRsgAAIABJREFUY70jNvJz+vRpoqKiuHbtGrVr137sULW8ibDS0tI0zVvvvPOO3qGRBX1qO68+J0+e1IyWadGixWPvcFQqFXfu3KFatWqPHU1h6HZTU1PZt2+fJn/Hjh31/sEZWj99kpOTdU5ajxvuqW947blz59ixYwc3b97k5ZdfxtXVNd/hrKmpqcyePZv33nuP+vXrs2zZMhITE5k8ebLeJ6ELGsScnJzYvXu31hj3e/fu0bNnT60mCkMNHDhQb7qRkRFr1qzRSS/M8VCr1dy+fZuXXnpJ73fYkCf580RERGhdhLz11lu4u7sTExPDgQMHGDp0KKampsyfP5/hw4cX6A4oz82bN0lKSsLKyuqxAS8v79tvv83Vq1fx8vJ6bPNXSkoKffr04erVq/Tu3ZsZM2YUuExQygPCihUr8PHx0UmfN28eY8eO1UpzdnZm//79xXbF3aJFC70PiOlbbkjePNOmTWPr1q106tRJ5/ZeXzPX1q1b+fzzz+nWrRu1atXi8uXL7N27l3nz5unMYGnIbJn6ntpevXq1punkYUlJSfj6+nLlyhWqV69OcnIyVatW5b///a/moRtDnt8wZLsPi42NxcfHh4oVK/Lyyy+TmJjIrVu3WLlyJU2bNjW4foqisHHjRs6dO4ezs7NWE9ymTZuYPXu2zrMSOTk5HD9+nOvXr1OjRg3s7OyeOIlf3gN0D9N3ATNu3DjS09MJCQnhpZde4sKFC4SFhVGhQgW9E6Tpoy+IBQYGcvXqVcaNG6dpVs17knz69OlaeR+e7fTkyZNMnDjxsbOdFpQh3zco+Il+0aJF/PDDD4waNUrrSf7u3bvrPMn/sII8J+Pk5MShQ4cKNDMq6M52WrlyZfz8/PROgRITE0NgYCCZmZl4eXnx1Vdf0atXLyZOnKgTgPbt28eUKVOoW7cu/fr1Y+7cuVhbWzNt2jRq1qxZoLKVuoBw8+ZNTdvZ+PHjCQsL05oq+t69e0yZMkXn6v2zzz4jISGB7t276zQD5f2Bjx8/ntDQ0HynzIX/3z3Y29uzfv36fKepHjBggKYMhQkIj5vFVN/J9M0332T69OlaD9IcOnSIkJAQoqKitPIaMlumIU9t+/v7U7t2bSZNmoSJiYmmfTQhIUHTfh8cHMyUKVMMql9BtvuwIUOG0LFjR4YNG6ZJW7FiBXv37tW5Ii1I/UJCQoiMjKRly5YcPXqU4OBgOnfuzLhx4/j111/x8/PD399fs/5PP/3EZ599xr1796hUqRI3b96kSpUqTJkyRe/UFT///DOBgYHcunVLk5Zf8xLcPwHt379fqyM9LS2Nrl27au40CxPEUlNTCQ4OZteuXajVakxMTHB1dSUoKEgnMBVkttNNmzbRt29fvXcBeR5tMjJ0loCCnugNfZL/4edkTE1NUalU+T4nM23aNNLS0ujZs6dOkH247wYMn+3U1tYWb29vxo8fj6mpKUlJSQQEBJCQkKBpdsrzxhtvMG7cOE3gTE1NZdq0afz000+a1oonKXWdyuXLlyc8PJybN2/+j703D6tp7cPH7wzFOZ2QFDnO4TiOzhFFKUXKlKJIIplVhswUGlRIIZpEZR4ypjQgRYjMjpljKmOiUdlbw97q+f3Rd613r73W3u1VnPft+/3d17Wvi9XT6ll7rfUMn8/9uW9UVVWxVi7Kyspwc3Nj/R615ZWWHpZMulJJZMkCNFmoqKiAnZ2dXN8CClVVVSwZXElwCY0p4nMgCYFAwCr8MTQ0xIcPH1hti4qK0K1bN1y5cgVqamrQ0dFBTU0NqqqqWG2Li4tZhUl//PEHysrKWG3v3LmD8PBwOpatrKyMZcuWMZL+VKJ9woQJnIVKXPLUipxXEk+ePMHu3bsZx6gYen2uLy0tDbt27UKPHj1w9epVREdHIzY2FlVVVTh+/DiD9XL37l14eXnB09MTtra2UFZWRlVVFRISEuDl5QUNDQ0G0w2oTaROmTIF1tbWChVQNWvWjGUM8+XLF4ZkRFBQED2JeXl5cU5i0lBVVUVISAhEIhE+f/4MdXV1mdXV9+/fx5kzZ1BYWIinT59i165dUFNTY7BZ0tLSMG7cONaChIKSkhJrQuDzvAG1YWPJgZ4qgJs0aRJjQhAKhaxVcvv27TnfPQBYv349VFVVkZWVBQ0NDeTn5yMwMBCBgYEICQlhtKUmvKSkJNb1SU/oGRkZOHr0KL58+YKsrCycPHkS7dq149SAAmrHK8k8Z8eOHbF//35ayFESx48fZzCdVFVVsWHDBmRkZHCemwuNbkJQUVHBsWPHANTG8+XF+yVBJXjloWPHjhCLxXIHbwpPnz5V6O8C4Hz5JCG9igDkTwhcq2sHBweEhoZi6dKlaN68OWpqarB9+3bOWDEftUxFqrYptGjRAoWFhYytdUFBAR2Trq6uRkVFBQghmDFjBkuBUSAQYPHixazdXV3nlUarVq3w4sULxveanZ3NyaxR5PqEQiEdajIxMYGrqyusra2xbt06Vghy27Zt8PDwYCQUVVRU6Dj6tm3bEBMTw/idz58/1/mMSGL06NGYM2cO3Nzc0L59e+Tn52Pbtm0MXSA+k5gkFK2uVkTtdNeuXQBqw0CS+PLli8x4O5/nDVB8oOdTyQ8AV65cQXp6Ot1PLS0tBAUFcQ7cfMYCPmqnhBDOsUEkErEWep8/f0ZlZSUqKysZC4WSkhKkpKSwWIyy0OgmBElQk0F5eTnrC+WKvT569AgFBQX0ql4sFuPFixdYsGABgFqF0aCgIDg4OMDR0bHB7kMUFJlgpCFNk/v06RNu3LjBSpb37dsXSkpKqKmpgVAoxJEjR6ChoYFPnz5BKBRyxtgl1TLrUmhdvnw5ZsyYgbi4OGhrayMvLw+fPn1i7bSAWjYVRUWkYvfR0dE0FbG4uBhWVlb0pCCp00SFSLjExuo6rzSmTp2KWbNmYerUqTRrJzY2Fq6urvW6PsndnpKSEpSVlbFq1SrOfNSDBw8QERHB2S9bW1vOn/Xv3x8ZGRkKv7RLlixBs2bNsH79egarRdLchs8kRmHVqlVITk6Gjo4OI9+hpKTEmhD4qJ3m5ORg8eLFCAwMRK9evbBlyxZcvnwZW7duZSXO+TxvgOIDvbe3N5ydnREfH4927dqhoKAAWlparMlZEorWyQC1u+7i4mLW2CJJhwYUVzu9d+8eFi1ahIKCAujo6GDHjh3Q0NDAvXv3sGLFCnz58oWmAmdmZmLx4sWorKyEpqYmYmNj8euvvyI9PR1+fn68rGEbXQ5BEufOnYO/vz9DK15W7HXTpk3Yu3cvfvrpJ9TU1NADqKmpKb2SIYTg0qVLSEhIQGZmJvr164eJEyfC3Nz8m3nhNgR///03duzYweCbyxIdk0RdRSnyFFqB2lXG+fPnUVJSgg4dOsDc3JxzdV5dXY3NmzcjOTmZZsvY2dlh5syZ9ABTXFyMiooKjB07FsePH2f8vrKyMkOxlM95pREXF8dqL0sUra7rk87xUMJvXJBHFxaJRDA1NaUFEqlcVVlZGV00KU2NVXQHLA3pPuvr6yMrK4uzWIpC//79sXPnToVCpnzUTqdMmQJjY2PMnj2bzgHt2LEDt27dwt69e1nnVvR5A2rzAM7OzigrK2MN9JKLoefPn6Nz5864ffs2fd5evXrJfH7WrFmD7OxsVp3M77//Dj8/P0bbffv2ITg4mF6UUmNQ9+7dWWEkRdVOx48fD11dXYwfPx47d+7Ejz/+CAMDA/j4+MDS0hK+vr504autrS1sbGwwceJEREVFoaioCH/88QciIiLg4uKCefPmKUymadQTgo2NDW2ZJ31jpVf3/fv3R0xMDCoqKnDs2DFs3LgRISEhKC4u5qRmlZSUIDExEQkJCaisrMT48ePh4ODwTQzu6wuxWAxjY2OZCWpFqZlCoRBxcXGcu6XY2FgAim2DubazfHDv3j389ttvUFNTw82bN9G8eXNWfP17gc/16enpISwsjP6uPDw8EBISwsgfUXkoe3t7uLu7c+Y3Ll++jM2bN9PGJooM9ly7y8rKSiQlJXFWsVPhRD6TGAUzMzOcO3euQUy83NxcFuff0NCQngQp1NTUwMjIiD7ekOdNLBbj77//ljvQ9+vXD+fOnVOYGsqnTsbCwgJ+fn5o1qwZzpw5g+XLlyMwMBDq6upYsWKFQn9PGr1798a1a9fQokULfP78GVZWVlBSUsLKlSthbW3Nanvr1i00a9YMFRUVMDMzg5qaGkJDQ2WGxGShUYeM3r9/j7lz5ypE96qqqkLPnj1RWlqKf/75B0Ate4WL9QEA6urqcHFxgYuLC82csLCwwKNHjxjtCgsLOVe1L168kBmnVQTSL4hYLMaJEyc4Q0DAf6iZFNdcHjXTy8sLOTk5UFdXh0AgQMeOHXHp0iWGyYydnR2tl88FyV2YPFYWBenB7/DhwwgLC8PBgwehpqaG9+/fY/369bRufn3PC9SuwsLCwjhNz6naFT7X17ZtWwZ5oU2bNoz/SxITnJycEBAQgH379jEYJzk5OQgICGAM8NS/T58+zXrJAeDIkSOcffPy8sLt27dhZGQkMwldXV2N8+fPMyZ8yf8DbEkTPtXV165dQ1BQEGtRIRaLWe9I27Ztcf/+fQaJ4MmTJ4zFFZ/7AYCTcSRPsoWPqgEA/PDDDwgJCcG6devqrJP5/PkzBg8ejKKiIoSEhEBNTQ3e3t4YPXo0a0JQhMFIXS+VC1BTU4NAIMC2bdtgamrK+j1J6YuWLVtCJBJhy5YtDKaWomjUE4KJiQmuXLnCWQEsDW1tbbx69QpdunRBSUkJhEIhmjZtivLycpm/IxaLkZGRgcTERNy9e1emM5b0il0kEmH8+PGs5OjatWuxcuVK1jmWLVuGjRs3Mo5J/60mTZqgc+fOLHljCoGBgejXrx+LmhkQEMCiZl69ehXp6enIz8/Hli1baLc1KlkP8EuUKRJikMaOHTsQGxtLT5pjxozBX3/9hXnz5tETQn3OC9TGoVu1aoVx48bJHDD5XJ8ihAQK48aNw9OnT2FpaYk+ffpAQ0MDHz58wIMHDzBp0iRao0YgENDFa97e3vjtt98Yg6FQKMSGDRsYkzSFq1evIjExUa45C59JjEJERAQqKiqQmJhYZ3X1unXr6JXoo0ePYGdnh6ioKM6wnKurK1xdXTFmzBhoa2vj48ePSExMpN3kAH73A4BMqRZ51+fq6lqnqoEi7D5pUkf79u3phSHlaPbjjz9yymVIP9OlpaVIT0/nrGqWhIqKCudkAIA1iaqoqNRrMgAa+YTQsmVLzJ07F7q6uqxQjvTKkfIaTU5OhqWlJVxcXGSGKB48eIDExESkpqZCU1MTjo6OCA0NpVdJubm5GD9+PL5+/Yry8nJWjF4kEtHb248fP+LMmTMAauPa0kk0gUCACxcusPrA9wXhQ81UUVGBhoYGfvzxR1okzsrKiqaESuPdu3dITU2lQ1HW1tYMehtXWKO4uBhqamoyB+SysjKW+FvXrl0ZL1F9zgvUxouvX7+ucOijruvjC19fX9jZ2SEzMxOFhYXo27cvvLy8WEVxzs7OKCkpAQAWG6x58+YyxRVVVVVlxtQp8JnEKHAZL8nC27dv4eHhgby8PGRmZmLo0KHo1q0bp13luHHjoKmpSbt5aWlpITQ0VOZqXZH7wff6xo8fr5CeD1/NI6C2rmfy5Mk4ePAgTE1NsXTpUqioqHBqoXE90+PHj+cUG5QEL9ezBuQ7G/WE0KVLFwazQh4mTpyIP//8E23atIGPjw/27t0LgUCAGTNm0G127tyJpKQkvHv3DsOGDcPWrVs5HZN+/vln7N69G2VlZZg1axZr8lFWVqa51Jqamrhz5w6Ki4vx9etX2sRFsq2vry9nn1+8eIHc3FxWnFh65QPwo2Z26dIFaWlpdFzyzZs3NFVVGjdv3sTs2bNhYGCAjh074u7du9ixYweioqJYL3RVVRWCgoKQlJQEkUiEZs2awcrKCmvWrGFJKujp6SEyMhKLFi1CkyZNQAhBTEwMp1QHn/MCtUyOvLw8meJw9b0+PujZs6dcn9yffvqJLgK0t7dnJdjlYfr06ViyZAlmzJjBKpJqSF6HMtOR3DWLRCK8ePGClZNr27Ytvn79Cm1tbbx58wZArYhdQUEB57nV1dXh5+cHNTU13Lp1i5V4plCf+3H37l0kJibSE8ioUaNYGlDS7DxZ4Fv/A9QO8p07d0bLli2xatUqhISE0Ds8RfDLL78w1HuB2jyRZJ+FQiHrGqidjfSi9PPnz6xFqiLkE6CRJ5UlocjKEZBv+Td8+HCMHz8e9vb2CotBlZSUQCQSoVWrVmjZsiWePn2Kn376iZOyGhQUBG9vb4XOu2HDBuzfv5+1veXyaQWAzZs34+zZsyxq5rBhw7Bw4UJG25s3b2L+/Pk0myoiIgJNmzaFra0tK6Q1fvx4TJ8+nSEed+rUKezatYs1iAUGBuLJkydYtmwZXTUaGhqKP/74g8XMePXqFVxdXSEQCNCuXTsUFRWhdevWiImJYekvKXpeqkL22bNnuHTpEsaPH89i7UjTJ/lc378NrgQtIHvQl1XZrChSU1Ph5+eHL1++MI63adOGVcG+fPlyVFZWIjAwEHPnzoWFhQVUVFQQGxuL9PR0Rtu4uDhs3LgRBw8exB9//IGEhAQEBwfTcXZJ8L0f6enp8PT0xKhRo9CxY0fk5ubSzoI2NjZwcnLC4cOH6RwFF7g00eqjfSQWi5GXl4dOnTox5MYlIf3uisVipKamorCwkCFwKU+njQI1QXwLpiENhZ0T/gdRWVlJ/Pz8SK9evYiOjg7R1dUlHh4epLy8nNW2uLiYTJs2jfz1119EX1+fPH/+nBgZGZGHDx82qA9nzpwhenp6tMvTnj17iIGBAcnMzGS1FQgExNfXl7x8+ZIQQsj27duJn58fZ38NDQ15ObR9/fqVhIaGEnNzc6Krq0ssLS1JVFSUTJOc8vJyUlNTQwgh5O7du+TSpUuc7QwNDVlmM9XV1QzDEgoDBw4kxcXFjGOFhYWcxi2E1JrcXL9+nZw6dYrcunWLZYzC97ySzmRcHy63Mj7X971w7do1YmNjQ4yMjGiHK319fdKjR49/rQ+EEGJpaUliY2NJfHw8cXd3J//88w9xdnYm27ZtY7X9/Pkz8fX1JcXFxeTx48dk+PDhpH///pzP/eDBg8mTJ08Yx/755x8ydOhQVlu+96MucyHKaCYhIaFOoxlJREZGkhEjRpBTp06Re/fukZSUFDJixAgSHh7OalteXk68vb3Jn3/+SfT09EhOTg6xtLTkdCscNGgQ4zN06FDi6upKXrx4wdkPSRQVFcl8R74VGvWEsHbtWjJp0iRy7949UlhYSG7fvk0mTZpEVq9ezWrLx/KPD6ysrDgfyJEjR7LaLlmyhMyePZsUFRURQgjJzs4mc+fOJStWrGC1HTJkCPny5YvC/QgPD2c5kMmDQCAgcXFxJCIiggiFQpmuSjY2NuTmzZuMYzdu3OC8vn79+rH6LBAIOK0d+fSB73lludE9f/6cdYzP9ZWUlJCoqChCCCH37t0jlpaWZNy4cSQnJ4fz7ykKW1tbsmHDBhIdHU3mzZtHzp49S8aMGUP27Nkj83eo727z5s1yvzs+oBzlPn78SOzt7QkhhOTn55MhQ4Y06LxcLmhisZjTUYzP/aDOzTWBUK5qMTExrMWEIhg8eDBrQH/z5g0ZMGAAq62fnx+ZP38+ycnJIYaGhkQkEpE1a9YQFxcXVtunT5/y6ocii155NpvSdpt1oVFPCHxWpHws//hAX1+f84HkOq+RkRHtC0xBKBRy9vfMmTNk1qxZ5Ny5c+TmzZuMDxeMjIxYNn6y8M8//5B+/foRR0dHoq+vT16/fk169epFr6YkcfLkSdK7d2+yatUqsn37duLv70/69OlDTp06xWrr7u5OlixZQt+ToqIisnTpUuLu7t6gPnh4eCh8XkII52qyqqqK00KTz/UtWLCAuLi4kJqaGjJmzBgSEBBAQkNDOXcekvciJyeHpKWlkZKSEs7+6unpkerqavLu3Tvi6OhICKn1IbaysuJsr8h3t2nTJkIIIUFBQTI/0rC0tKR9gY2MjEhNTQ2pqamRuTpPTk4mEyZMIEOGDCEfPnwgnp6erOebEEKmTZtGIiIi6B0pIYRERUWR6dOns9ryuR+EEDJ27FiSlpbGOHb69Gl6QnN0dCS6urpkyZIlcj3JpWFkZMRp5SltdUlIrcUr9b1RY4ssC01jY2NeCzdFFr3UTmfNmjVkyJAhZO/evSQjI4McOHCAWFpa8vKUbtRJZZFIxNDtAGqTq1wZeT6Wf3zQvXt3xMXFMeiBiYmJ+P3331ltFRElo/Dw4UNcvHgRt27dYsQslZSUOGOGNjY28PHxUUhxMSAgAN7e3rC1tUXfvn3x66+/IiYmBmvWrKFpkRRGjhyJVq1aISUlBdevX4e2tja2bdvGmWynnMr69+9PK0SamJhg06ZNrLZ8+uDp6Yl58+bB1NQUKioq9Hklqbp8mF/1vT5FBN1KSkowf/58dOnSBYGBgcjIyMCiRYvQsWNHfP78Gfv372exT/gmaBX57ii2DB/WjJmZGWbOnImoqCjo6+sjMDAQysrKnHmMvXv34ujRo5g+fTo2btyIFi1a4PXr1wgKCmJRo319feHq6opDhw5BU1MThYWFMh3e+NwPoFYKfM6cOUhISGCZCwG1tRzZ2dlISEjAwoUL0aZNGzg5OcHOzk6uNwof7aNmzZrR2knk/6Rky8vLOQkPfOshzpw5g8TERJo8oKGhgfDwcNjY2ND5MyqXsGPHDlbdkbm5OaZPn85yvJMJhaeO/0HwWTlGR0eTkSNHktTUVNKnTx9y+fJl4ujoSDZv3sx57uzsbJKZmUkyMjIYH2ncuXOHGBoaEhsbGzJz5kxia2tLjIyMyP3791ltN2zYQOzs7Eh6ejq5f/8+OXPmDBk7diwJDg5mte3Tpw/nOWShe/funB8dHR1WW8k4bV1m8Xxw8eJFUlVVRd6+fUvu3r1LPn78KLNtffpQ13mfPHlCrl+/Tnr16sUyGb979y5nrmbKlCn06q4uGBsbk6qqKhIfH09vw0tKSki/fv3oNt7e3mTBggUkLy+PEFIbUty4cSMhhJC4uDgye/Zs1nmXLVtGFixYQD5//kwmT55Mdu7cSWJjY4mlpSVnP/h+d5WVlfTu+P3795zfAyG1q9qYmBhSVlZGcnNzibOzMxk/fjznczhs2DDy5s0bRh+Ki4tlhvGqqqrIlStXSHJyMrl+/Trdn2+B58+fk/DwcOLr60uio6PpfklDJBKR06dPExcXF9KnTx+ycuVKmXm6N2/ekCFDhhBDQ0NibW1NDAwMyIgRIzjzAkFBQWTy5Mnk/v37xNDQkLx69YrMnTuXBAQEsNpOmDCB6OjoEBMTE1aohwt8wqV9+vRh3duysjJe73Wj3iFQK0dqRVpVVQVTU1POFemsWbOgoqKCyMhIVFdXY/Xq1bTlnzSioqIQGRkJDQ0NBmuJq9ild+/eSE9Pp+0atbS0YG5uTuuMSEIRUTIKrVq1YskAywOfugVtbW3cunWLYez+4MEDRqGThYUFMjMzafE8LkjvVJYtW4asrCx06tRJZkU1nz5IQlKF88GDB/RxSdYQtQO4cOECvaKSJ0gGAC9fvpTbT0koIuh26dIlJCcnQ11dHe/evcOrV69o/ru1tTWCg4NZ5/X19cXGjRshFovh7e2NJUuWQCgUyiy+4vPd/f3335g3bx62b98OPT09JCQk4NChQ9i2bRvLkF1ZWRmzZ88GUFsdu2vXLojFYk7m0ufPn9GhQwcA/1kVq6qqclKXgVrapJaWFi20lpOTg+fPn7NYRg8ePMDatWs5q8wlnzehUAhVVVUIhUJ06NCBZZRF/VwSzZs3h5WVFaysrJCfn49NmzZh7NixnNdXWVmJ1NRUhbSP3N3dERwcjKlTp6KyshK2trYYNWoUli5dymqraD0EhQEDBmDlypVYuXIl1NXVaakdriI1U1NTLFmyBIsWLYKWlhbev3+P8PBwTpq6TCg8dfwPQ5EVKR8YGxvLjNVzobq6mly9epUcO3aMVFZWylyh8EFcXBxxdnYm165dI0+ePGF8uCAUClkMJn9/f87VYEZGBh2n1dPTI6GhocTU1JQRp6XirdIrbcmPNObMmUPi4uJkrkD59oGCv78/0dfXJxMmTKiTNURI7f2IiooiZmZmRF9fn7x9+5ZMmTKFM7no6elJRowYQYKDg8nevXvJvn376I80Kisrye7du0lcXBwhpHYXGR4eTiorK+k2knmKxMREMnDgQMY5uPIYfMHnuxszZgxJTExkHEtOTiYODg4K/a2PHz9y7jLnzZtHgoKCSHV1Nb1DiIqK4twBxcbGkh49ehAdHR2io6ND71xtbGxYbW1sbIivry+5fPmy3OeNymtQ55L8yNoZE1JLONixYwcZOXIkGTRoEE0SkAbfWD+F4uJiVk6xISgqKiKOjo6ke/fudGJ5xowZNDFFEqWlpWTBggWkR48edHtPT09e5JRGW4fw9u1bZGdn06uz6upq+Pv7w8XFhdNHmI/ln7m5OS5cuKBQdeC7d+8wa9YsCIVCCIVCxMfHw87ODtHR0RgwYACrfUpKCo4cOYKCggIcOHAAERER8Pf3Z+VC+HLNlyxZgoqKCoVtFR8+fIiEhASGz66sOC1Q6ydQUFAAbW1tmQVfw4cPx5s3b6CkpARVVVXGzoJa3b18+ZL+vhXtAx8VTqDWQvXmzZuYN28elixZgszMTHh6eqJp06YIDw9ntJ0yZQrnOZSUlOi6Bj4YMmQIYmNjoa2tTcefqQKlR48ewd3dnebpUw5lz58/h7GxMdzd3TnjzlxQ9LvjcuMj/0d6XFpwjgv5+fkwNzdn7UDz8/MxZ84cvHv3DuXl5dDQ0KDzAtK7w0GDBsHb2xvNmzfH2bNn4e4KeDOPAAAgAElEQVTujqCgIGhpaTHkK4BaOfebN2/WWW374cMHdOjQQSHvapFIhHPnziExMRHXrl1D//794ejoCAsLC5l/x8nJCfPnz1co1i8SiZCWloZRo0bh9evXWLNmDVq3bg0vLy9a56y+9RAU3r17h+LiYnTo0KFOD2aRSITS0lK0bt2at1Bho5wQnj17hsmTJ8Pe3p7WFSkrK8PSpUvx8OFDHDhwgJW4U8Tyj0JERATEYjEWL15cpw/uzJkz0bdvX8ycORNGRka4desWTpw4gd27d7NusHQiLiMjA25ubujWrZtMjSJFoYitoizk5+ejZcuWnFXN7969w7x58/Dy5Uu0bt0apaWltPqntM66vAIZKvFqbGyMGzduYPr06ZzSx1zgq8I5aNAgHDt2DBoaGrTKp1AoxJAhQ3Djxg2FziEJeWEzCtS1h4eH4+rVqzAxMcGuXbuwZ88e9O3bF0+fPsXKlSvRv39/2u/by8sL79+/x/Dhw5GSkoIePXqwCvgaitGjR2PRokWMsFZWVhY2bdqE5OTkOn8/Pz8fFhYWnIuQmpoaPHr0CO/fv4empiZ69erFWRhKTUpFRUVwdXVFUlISSktLaXkPSbi6umL+/Pm8VDqrqqpw/fp1FBQUoGPHjujbty/dj1WrVuH06dNQVlaGvb09HB0d5WpAUXBycsK9e/fq1D4CarWo/vnnHyQlJWHKlClQV1dHixYtIBAI6OR2TEwMxo0bxzKFkoRkJfLhw4fh5OQkd1EiXWQJALdv38aRI0eQn5+P0NBQHDlyRCGRSAqNMocQHh4OZ2dnhstUq1atsGvXLgQHByMiIoJll6gIQ4R68aurq/Hlyxfs3buXZXwtPeg9ePAA0dHRjMpEW1tbTm2SQ4cOYefOnfjll18QEhKC1q1bY+vWrbCxseGcELikK5SUlDhNSPgymMLCwrB7924cO3YMvr6+UFFRQXh4OAYNGsRo6+/vDxMTExw7dgwqKiooLy9HcHAw/P39ER0dzWirSDWkkpIStmzZgjt37sh82KUfdD4qnEBt9Sd136j1jqQiJMDvhaOepUuXLuH69euYM2cOfv75Z+Tn5yM6OpoRy1+wYAGaNWuGBw8eICAggDYBcnJygpmZGcN7OTMzE2fPnoWqqioGDhyIadOmyZ0Q6rPKXLx4MRYtWgRTU1N06NABHz9+xPXr11k7pfrgw4cP6NWrF7p27YrY2Fi8ePEC48ePZ+2s27dvj+LiYlroTywWQ1VVlcGAoiQjfvjhBzg7O8PS0pLFAORyCnz06BHmzJmDpk2bon379vjw4QNUVFSwbds2/Pbbb3j9+jVWr16NoUOH1rm4kwSfWP/169fpSe727du4cOEC2rRpw4jzZ2ZmYuvWrRg2bBicnJwY5lBcOH/+PJycnGipG8n7Tf6P4KD0e3Ly5EmsX78e9vb2OH/+PGpqanDixAmIRCLOfAYXGuWEcP/+fYSFhXH+bPbs2bCysmIdV8Tyj8tzty60adMG2dnZjBBPTk4Op28Cn0ScPOkKrglBEVtFCsHBwTAyMgIhBNHR0Vi/fj3U1dURHBzMmhAePXqE7du30y/TDz/8AC8vL0Y4jE8C2s/PD/Hx8Zy6TtT1ST/ofFQ4gdodhY+PD3x9faGkpEQrv0rq20i/cPL6QU10np6eOHbsGD0p6ejooGfPnrCzs6NX/U2bNuUUMLt8+TJLR18kEtETWqdOnSAQCDj7QoGy4ZS0l6wLgwYNQkJCAtLS0lBUVISePXti+fLljLCqPP0eaRkLCrt27UJ0dDT+/vtvBAQE4OHDh2jSpAlevXrFGritrKwwadIkHDhwAKampvDw8ICysjJDHp6aHH788UcMHz6ccUwe1q5dC1dXV4agXkxMDPz9/REbG6vwLlQaimofAf9JYKelpaFz587Q0tJCeXk5gy4uSX9dtGgR2rRpg4kTJ2L06NGci5odO3YAAGJjY3HmzBmcPHmS3gE5ODiwtJqAWjIMpQd2+PBhaGpqYufOnZg4ceL/3ROCWCyWGT5QU1ODWCxmHVeEISK5wj116hSMjY2hoaGBs2fPQiwWM/RVKDg7O2PmzJmYNm0axGIx4uLisHv3bs7YtKGhITZt2oQVK1bQA9uuXbs4t8fx8fE4duyYwjK2fBhMlBFOdnY2ioqKYG1tDRUVFYYpOYU///wT9+7dY8Snnz17xogTU6yuLVu21Lk1HTFiBEaMGAEXFxfaqa4u8FHhBGpXksuXL6dXaL1794aRkRHDHD0gIAAAaEMgRSAQCFjXJxaLUVlZydm+LtE16WhtXd8dVWMwZswYhrkQ5dAmy1yoW7du6NatGwghKC0tZel01TXwcsm+JyQk4PDhwxCJRDh9+jSOHj0KTU1N2NjY0BPCtm3bMG7cOCxYsAC//PILWrZsCX9/f2zcuBECgYCR26ImJWnfBAqUx4E0Xr58yXrXXF1dWZLviqI+u7A+ffrA09MTjx49grW1NQoLCxEQEMDaMf/+++9YsWIFli5dinPnziE+Ph6hoaEYMWIEnJycON/13bt3Y/fu3Rg7dixMTU2Rl5eHpUuXwt3dnaWGW1RURJ+D6ru2tjaqqqoU/wIamgX/b8DR0VFm1eGtW7fIqFGjWMcVYYhQiIyMJIMHDyavXr0ihBBy7tw5MnToUIami2TmPj09nbi6upIRI0aQ6dOnk6SkJM6+ffz4kdjZ2REDAwPy559/EjMzM2Jtbc3JbeYrXcEHAwcOJPn5+Yxq0efPn5NBgwbRbahq1gULFhB9fX3i4+NDtm7dSlavXk2MjIyIp6cn3dbX11cm+0keBAIBef78eZ0sKoFAIPMjD0VFReT+/fuc7LO//vqLuLm5kaysLIX7u3LlSuLg4EDOnj1L7t+/T06fPk3s7Ow4K0HT0tKIvr4+8fPzI9u2bSO+vr5EX1+fnDhxgm4jXQHMVdnKhUOHDpG+ffvSUhzHjx8nxsbGDF2er1+/krCwMPqZffHiBbGwsCA6Ojpk0qRJ5NOnTwpfNxeovl6+fJmWc5CUjCCEWSUsj7X39etX+n727t2bdY/z8vJksrPmz5/PqlTOzMwk06ZNq9d1RUdHk6KiIpm6R1zaR8XFxSQgIICEhYURkUhEHj16RJYtW6bQd/zx40fi4eEhkxU1ePBgluTKo0ePOOVEpk+fTsudUPfn+PHjZPLkyXX2g0KjTCqfOHECW7ZsQUREBCNU8/TpUyxatAjTp0+Hk5MT5+/KUzulYGZmhri4ODq8AwB5eXlwcnKiVyomJia4du0avLy86pTM3bJlC+bPn483b96gU6dOePjwIc0OkZWIO3v2LOLj4+Ho6MjywZWMP4aEhMDd3V1uH6S38Nu3b8euXbtQUVGBLVu2QF1dHa6urgwte654rTSov+nu7o6MjAzo6OjAyckJI0aMqDMBHBcXh7Vr19IVnhS4wkA6OjoyV2uSbW/dulVnn6nv7tWrV4iPj0dycjJatmwJR0dHjB07Vq7KrUgkQlhYGNLS0lBYWAhNTU3Y29vDzc2NpYBJ2S1K7giuXbuGgIAApKam0tclmcj//PkzK7HPlagfPHgwoqOjGXUqz549w7x585CRkQGg9pk7deoUfHx8MGDAAEybNg0tW7bE6tWrERUVBUJIg4gMDg4OcHBwQGZmJtq2bYs1a9Zgz549yMjIYDi9UWGS5ORkmWGSgoICWFlZoaKigg4FSsPc3BwxMTH0/yX9qG/fvg1jY2Noa2ujqKgI165dg5mZWb1CwBMmTMDjx48VjvVLglI7pTxP5O34CgsLkZycjKSkJJSXl2PcuHGMnCgFc3NzpKenM3KDFRUVMDc3Zz0b2dnZcHZ2xk8//YQ3b95AV1cX796948XQa5QTAlAr90wZabdt2xZFRUXIy8uDq6srFi9ezGpfUlKCpUuX4tatW1BWVkZcXBwmT56MXbt2sTT4DQ0NcfnyZcZNqKqqgrm5Oc3Y6du3L7y9vbF69WqEhoZyWv9RBSF9+vTB33//DUNDQ5l+yNIIDQ3F9u3b8cMPP8iVrvD398fq1avlDuBck8Xjx4/x448/onPnzigsLER2djZnXFJRfP78GcnJyUhISMCHDx9oRocsiqqlpSVmzpyJMWPG1Jnsk6YWfvr0CXv27EH//v1hb29PH9fR0YGKigpUVFQ47weX7AdlNZmQkIAbN25g6NChcHJyQp8+fRS8cm4YGBjg1q1bjARrTU0N+vbti9u3bwOov2yxgYEBbty4wfjevn79ClNTU/qcw4cPR1RUFLp27YqSkhL0798fR48eRa9evVBQUICxY8ciKyur3td3//59rFy5Ej/99BMiIiKQnZ0NHx8fbN68mdPTQiwW02GSO3fuYOTIkYwwSXFxMSoqKjB27FiWzLWysjLLpra+ftSKQJFJTBIU3fv48eNQVlZGQkIC5s6di507dzJCq3zor0KhEACwb98+ZGdnw9PTE1paWigpKUFoaCi0tbUZBAUKHz9+RHx8PFq2bAmxWAxzc3MoKSkp7JPRaCcEoPbis7Ky8OnTJ7Rr1w4DBgzg9DcGamPs6urqcHd3h7m5OW7duoXt27cjMzMThw4dYrRduHAhWrZsCS8vL7Ru3RplZWXYtGkTysrKsHnzZgC1sb2jR4/i3bt3aN++PevvSfoWjBkzBl++fEFubq7M6mPpuKSBgQH27NnDqiZtCCifZ3lVzdIPTn104R8+fIj4+HikpqaiZ8+ecHJyYnlXGxoa4ubNm7ycoCRRXl4OGxsbhnPWypUrceHCBfTu3Rt2dnawsLDgxSzJz89HUlISTp48CSUlJaSkpACo3y7MwcEBM2fOpBOkAJCWloYdO3YgISFB4T5xwdnZGT179mSYC23duhV37tzB7t27AdTmTSgL14yMDHh5eTH4/ZI//xaoqalR+F5SVcInT55U2L+hoqKizhoNRT1RFIXkJHb37l2ZsX5/f3+UlJRgyZIlcHR0xNWrV7F+/Xq8efMGO3fuBMCf/krtiqnhWUlJCU2aNEFNTQ0IIWjWrBnLu3rv3r3YtGkTq8Kbj09Go0wqU2jfvj3GjRunUNvr16/j4sWLUFZWpl8KFxcXzuSTr68vFixYABMTE6ioqKCqqgqGhoYIDQ2l2zg7O8PZ2RlWVlZIS0uT+7e3b9+Oa9euYeXKlZzcYS4oKl3BZ6Xk6OiIO3fucCYJAe4Hh/JbpgTa3r59i5iYGCgpKXEmoYH/uIV5eHhgzZo1WLhwIeu8w4YNQ0JCgsL3Txr5+fksBgwVgrpw4QISExOxevVqDBs2DHZ2dgpNrE2aNEHTpk3RvHlzxrnrIxRXl+haQ0CJxR0+fJhlLkShZcuWNPvl5s2bMDAwoJ/7Dx8+yBV2kxf6qM/kSEE6TCJt3ATUhr7CwsJQUFBAD4ZisRjv37/nnMD4uunxgaJSF+fOnUNaWhpdjNm8eXOsWLGCwcTjS3/lMsGqCzExMdi8eTMGDRpUbxvNRj0h8AEftVOqqjk3NxfFxcXQ0tLi3AUAoCeD8vJyFn2UeunatWuHUaNGoaamRuZgLA03NzfMnTsXM2fOZOkiSa7it2zZAlVVVRgaGnKu2CUfDCpcxUf3KDExEXv37qW3vnp6etDT08OkSZNkTgiSntS//fYbp5Vgfn4+fH19sXnzZhZFV3q3JF1YIxaLcffuXYwcOZJ1XmVlZQwfPhzDhw9HaWkpTp8+jXXr1uHTp08YPXo0K05LbeOPHz+OmzdvwtzcHB4eHgwOOeU1vW7dOpq+rKysjLy8PLRp04Zz4DE1NUVCQgJSU1NRXFwMAwMD+Pr6sjy16wPKApWyZtXU1ISenh5jZWxmZobg4GC66I1Su6yurkZ4eDhnBW5lZSUCAgKQmJgoM/TBd3LkCpO4u7vLrBL28/PDzz//jN69e+P169cwMzPD/v37OePrQC3DLScnB/v372e46W3cuPGbFPkpMokponbKl/7K5bhYF5SVlWFmZtYgT+VGHTLig5iYGJw8eRLz5s3DypUrsXnzZkRGRqJ///5YsGABo22/fv1w7tw5Fm+cC+fOnYO/vz+Ki4vpY0QGR76iogLJycl48+YNa/KQXlUpKl2RkpKCEydO4OnTp7C2tsbo0aNZZu7SEAqFuHv3LgQCATp27Ijff/9d5rUaGxvj8uXLjMFGJBLBzMyMUfUr+eLk5+fD1tYWTk5ODK65JOSV6UtzwKV3QU2aNEHnzp0xbNiwOsMDnz9/xunTp7F//37k5eXRq0zJSevHH3+Eg4MDxo0bJzPkCLCF4iIjI2UKxXFBIBAgJCSkTkP1ulBdXU1P/i9fvsSLFy9gZGTESIgXFxdjyZIlePDgAaytrekV/YABA6CsrIyDBw8ySBOAYqEPPqhPlbC+vj5u3ryJ9+/fw8/PD7GxsXj8+DF8fHyQlJTEam9ubs6QhwZq6Zc2NjZ1VujLAl+pi3Xr1uGff/7BsmXL4OLigmPHjmHjxo3o0KEDy5L2e+LQoUN49OgRpk2bxiKiKFKdDaBx0k7rg+rqarJ7925ibW1N9PT0iKWlJdm6dSunxeSECRPI5cuXFTrvyJEjyebNm8nr169Jbm4u4yONJUuWEDMzM+Lu7k48PT0Zn4aiqKiI7N27l4wZM4aMHDmSbNu2jZNuGR4eTnR1dUmvXr2ImZkZ0dPTI3p6eiQ6OprzvLNnzybr16+nDV/EYjFZv349mTVrFt3G1dWV9OjRg9jb2yssbvc9UVFRQU6dOkVmz55N9PX1iYuLC0lKSmLQeP/8808ye/Zscv78eYZxizx8L6E4Rc10iouLiZOTE/H29iaEEHL27Fny119/kWHDhhFjY2Py7NmzOvtw6dIlmfdHUaOXyspKcujQIbJkyRLi6upKfH19SVxcHEvSetq0aeT06dMybVy5YGFhQaqrq4lYLCampqb0ccrlUBp83fTqgr+/PzEyMiIDBgwgoaGh5P3793X+TlVVFQkICCB6enqke/fupGfPnsTHx6de4ngNQWxsLPnrr78Y0vfyhP640Oh3CC9fvqRlkSWhqORreXk5S55Clo6JkpISiwHRu3dv/P333zITrJIwNjZGYmKi4rN1PZGTk4OUlBScOnUKnTp1wp49ewDUJsIPHjyItWvX0oyimpoa3LhxA35+fpg1axYrpv/27Vs4OzujrKwM7dq1Q0FBAbS0tGiGFwD4+PjAycmJk10ijboougB7t/TixQtERkYiNzeXxR6S3GlcvHgRJ06cwPnz59G1a1fY2tpi5MiRLLkLoJZGzPc+fAuhOEldIL5mOj4+PhAIBPDy8kKHDh1gbW2NIUOGwMPDA8eOHcO5c+cYeQS+sLCwwPHjx6Guro6+ffvi1q1bKC0txejRo2m6dUFBAaZMmQKxWAwzMzO0bt0anz59wpUrV/DDDz8gNjaWU/pdUcyfPx/a2tpYunQpJk6ciJkzZ6JFixZYtWoVZ3Gah4cHampqWPLQSkpKnDL4dWH69OmYMGECb6kLCiUlJWjdujWd7G9I+IYvjI2NsWrVKpiYmLDGI+kdgyw06hzCli1bsHXrVrRr145x86R9Cy5fvowXL17A2NiYwRC4evUqVq5cyWCqANw6JuXl5ZyxcBMTE1y5cgUDBw6ss7+qqqqcAnKS4COkxoXq6mq8e/cOubm5tI47hYSEBISEhDAqo5s0aQITExMEBgZi/fr1rAlBEV14Wbr9XOCTmKXg7u6Obt26YdKkSXKZLLNnz0abNm0wevRoWprh1KlTjDZUUr8+k3KnTp1w/vx5RnX75cuX6xXvBWoTtBoaGnTSPyQkBDNmzKAH+NDQUMYAXx+vBT4YPnw4Fi1aRCuQvn79Ghs3bmQwxDZu3IhevXph/fr1jEGnuroaK1asQFhYGJ1zqQ9WrlwJHx8flJWVYdmyZZg3bx5EIpHMmgk+Ln2KgG+s/+XLl8jOzoaenh60tLTo0FVOTg4tdfJvoXnz5hg2bFi9JjIKjXqHYGFhgcDAQLkStTExMYiKisJvv/2GnJwcxMTE0APgoUOHYG9vL3dAO3/+PPbv34/r169DV1cX8fHxjJ9Tcsa6urqs5Kh07Ds2NhaXLl3CjBkzGDFP4D85g/py0ymV1fT0dGhqasLW1ha2traMCUEe1ZAQgn79+rHUQPnkU74XevfujVu3btX5oMuSsqZQX0lrChcuXJApFGdubl7n70vvEMzMzBgD/LBhw3DmzBn88ssvEAqFGDRoEKPYTvL+JSUlISwsjLFqbiiVlNJ8io+PR2VlJZSVlWFrawtvb2/6/vfv3x8nTpxgPb/U9Tk5ObEWWA3B169fIRKJWLt4CpcuXUK/fv2Qn5+vsDz0t0J8fDx8fX2hqqqKr1+/IjY2Frq6utizZw9CQ0PRt29fmgb8b+DAgQN4/Pgx5s6di9atWzMWlvJYZZJo1DuEiooK9OvXT26buLg4REdHo3///khNTcXevXtx+PBhPHr0CDt37uScTAQCAeLj43HgwAHk5eVh0qRJ8PHx4UyQdunShVMviAvUxCNdECSZKFZEMVQSwcHBdOXryJEjsW/fPrkJaVlQUlLi1IDi6wH7PTBw4EC6+lQe+OgSxcfHw8HBAWKxWGHeuiJCcfJ2eNJEAqFQSA+st2/fhpaWFs1CogYZSairq9OhrsuXLzOe/UePHrHkyPlCWVmZdueSDH1Iory8nHMyAAAtLS2UlZU1qA8AkJqailOnTqGoqAjt27eHvb29zAmXj0vft8aOHTuwbt062NnZYd++fdi2bRs0NTWRlJQEX19fXs5o3wIREREQCASM5DuRIwLJhUY9ITg4OGDLli1wc3OTKZXw6dMnejAbPnw4li1bBkNDQyQnJ7MopxR9LSUlBT179oS7uzvWrl0LNzc3zjg0wK8akg/dU1Hs3r0brVu3Ru/evfHmzRvOugRFahXkwdXVVSFd+O8FZ2dnTJo0Cd26dWPFQuu74l+3bh0cHBxgbGyscPU48B+hOAoXL15EYGAgzcLhI5fAd4C3tbXF4sWLYWJigrS0NDo39PTpU6xatYpT5ZcvLl26xGlfSYXa6gpnNjTgsH37duzbtw/jxo2DmZkZ8vLy4OnpiSVLlnAOsH369EFycjJsbGwaXHfAFwUFBbQFqJOTE0JDQ/Hrr78iKSnpX5+cAHCysPiiUU8IFy9eRHZ2NrZt28baElGhF8kHuGnTpmjWrBlCQkI46w9sbGxgb2+PhIQE2tUrKCiI828rMhApWoTWEPCZkCorK+XK+nKpIiqiC1+fRDEf+Pr6wszMDEZGRvWubJaGhoYGpk6diqqqKpnfoayJ9MuXLzh+/Di9g5RWzCWEoKysrM7kKt8Bno/XQn2wZs0aJCYmonv37iwvcepZJoTg2bNnMgf+hk4I+/fvx549exjJdGtra8ybN4/zOXz58iUuXLgAPz8/mS593wuSHijKysoghGDLli3/lckAqF/tgjQa9YRQn8ITFRUVTq8CABg3bhzS0tLw/v17jB8/HpaWljLPI0tHn4LkS9TQRLE88JkQ+CR/KSgitVyfRDEfvHv3DsnJyd+UsbF161acPXsWd+7cUVjn5fXr1zhw4ACOHz+OiooKWoJYcuB/8eIFZs6cifz8fHTr1g2RkZH49ddfOc/Hd4Dn47VQH6SkpDAWQ1yoqKiAnZ2dzIG/ofdILBazBtSuXbuyRBApUDLm/wto0aLFNyk8/G+iUSeVgVp2w4MHD2jN+T59+jCSj71798aRI0foB3jSpEk4dOgQ44GWHBDKy8tx4sQJHD16FB8/foRQKMTOnTt5x/YlUZ9E8dq1azmLWpYtW4aNGzfWuy98cfjwYYSFheHgwYPo1q0bEhMTsWHDBqxYsYKXiQiF+uwm5s2bB0dHR4WYXHwRFRVV58r60qVL2L9/P27fvg0rKytMmDAB8+bNQ3JyMiuU6Orqiu7du2PMmDHYv38/ioqKeMtVfPny5b+SxB86dChSU1N5+/B+S+zcuRP379+Hj48P2rdvj5KSEmzatAmtW7dm3CfJiICi3iXfGn369GFYYg4aNAgXLlxgtFE0mfu/gkY9Ibx//x5z5syhpaTz8/Ohrq6OPXv20KsMaZEoachLuDx8+BBHjx7FqVOnoK2tLdNw5lvh48ePOHPmDIDaknwPDw/GzwUCAfbs2aMQ5/1bQRGpZQpCoRBxcXEsHRrKkAfgJ6tNwcvLCydOnICenh6LPSEZ1lHEO5YrDJSamkqrtFLifZL0Wx0dHYwfPx4eHh40bXjAgAGcE4KhoSFu3LiBpk2boqysDCNHjsTly5frvOa6zHS+JyhlzdjYWOTk5GDJkiWskOq/NbD16tWL3g00a9aMkcug3mPJd3bLli1ITEzErl270LlzZ5w/fx7r1q3DuHHjMGvWrO/aV2lZdsm6A77J3P8VNOqQUWBgIPr164fly5ejefPmNG0uICCAFq1rSCKXEmnz9PRESkoK4uLivuuEoKmpSevTcFlMKisrw9fX97v9fS6UlZWha9eujGNdu3blDBN5eXkhJycH6urqtCzGpUuXMGHCBLqNIjsEaXTs2FGh711RzXdJHDp0CFFRUZg6dSp+/vlnvH37FhERESgvL6etKpcuXYpjx45h7NixGDduHMupShKEEJqf36pVK5mhDkmkp6fD09MTo0aNQt++fZGbm4u5c+ciICCAYYHKVUT5LWBoaMhYNEnWbvzbA9vp06d5tT969CjDu2Tw4MG0L8f3nhDqI0D3P48G1Ur/l2FsbMwql6+srCQGBgb/pR59OwQGBvJqHxAQwHncw8OjQf2YMWMGCQ0NJdXV1YQQQmpqakhkZCSZMWMGq22fPn1IYWEhefToEZkzZw4hhJDU1FTOtgKBgOzatYusW7eOdmdbvXo1L3enb4Hhw4eTx48fM449fvyYDB48mNU2KyuLdpDr0aMHSUpKIiKRiNGmPi5otra25OrVq4xjV69eJdbW1oxj/fr1I4SQb7WdaOcAACAASURBVCJ1IglpyRWuz78JgUBATpw4QbZv306OHz9OiouLZbY1MDAgFRUVjGOVlZXE2Nj4e3fz/0o06h1CixYtUFhYyMiuFxQU1FkN3Bjg7e0NoVCIDx8+sGQ5qJyHZIgpLi6OldASCASsmCYFSl++uroaqampaNWqFWeMXhGpZQpUwv7HH3/Es2fPANQarHNVriqym6CgqHQFhY8fP2LHjh2s0FV2djZrVVdcXMySGf/jjz84+fQDBgzAgAEDUFRUhGPHjiEiIgKBgYEYPnw4ndwkUiyc6upqFitHOon9/v17GBsbM44ZGxsjPz+fcezr169ITEzE6dOnMWzYMLmmTHywevXqensQf2s8e/YMLi4u+Omnn9CxY0fk5uZi/fr12L17N6doo6mpKfz9/VneJQ3J+f2/jEY9Idjb22POnDmYN28etLW1kZubi+joaIUlphuKt2/fIjs7m6YdVldXw9/fHy4uLoxipfpAEYvJ+oaYkpOTERgYiJs3byI4OJg2hJkyZQpmz57NaKuI1LJ0WysrKygpKeHNmzdo3rw5qyALqJUNSU9PR35+PrZs2UL7LnCV+isqXUHBy8sL1dXVaNOmDQoLC9GjRw8kJydz2qr++eefOHDgAB0eAoCDBw/KDT9paGjAzc0Nc+bMwaVLlxAXF0f/jIuFQ3HVAe6cVZcuXXD27FmGmc6ZM2dYbnNubm6IiYmBSCTC2rVrWf2SlmxRFPXJSYlEIqSlpWHUqFF49eoVAgIC0KpVK3h7e8tVjK0LQUFBtNcIhV27diEoKAgHDx5ktVfEu+T/Bw/8F3cnDcbXr19JaGgoMTc3J7q6usTS0pJERUXxUlesL54+fUoMDQ1JUFAQfay0tJQ4OzszDNAlQSkPSn90dXWJmZkZ8fHxodUmhw0bRuLi4hS+Fj4hJipE8fXrV2JgYED+/vtv8u7dOzJw4EC6DdV/yvie6yONGzdukL59+5K3b9+S/fv3EwMDA2JkZMQZzqLUKMvLy8mgQYMIIbXhKK6tvr6+Pq97qq+vTwQCAXn27BlxdnYmhNSGYBwdHVltHz58SAwNDcmIESOIq6srGTFiBDExMWGFkb4nrly5Qnr27ElmzpxJ/P39iaurK9HX12eFkSgMHz78m/596TCXIvDy8iKjR48mhBAyefJksnDhQrJ8+XLi5ubWoL707duXof5KSK3Cbl19fPfuHbl37x758OFDg/7+/+to1CwjRfC9Csjc3NzQq1cvTuOO4OBgvHnzhlW1unPnTmRkZGDBggW0Fk50dDR0dXVhYmKCffv2QVNTE+vWrauXxWRdISYKRkZGuHnzJq3vT+kXSap5Uv9W1JeBQkVFBVq0aAElJSXcu3cPAoGAU3Ji0qRJmDJlCqysrDBkyBDs3r0bzZs3h52dHYumu2jRIjg4ONQpXUHBzMwMWVlZEIlEGDp0KE0NNDY2Zmk1AbUKlefPn6fF+8zNzf/1sOOLFy9oMx1tbW2MGDFCLqddKBQiMzMTHz58QLt27WBubs7wQ+ADXV1dTJo0SW4baXbY4MGDkZSUhJqaGpiamuLChQto06YNTE1NG8SCGzZsGCIjIxnP3dOnT7Fw4UI6PArUFqWam5vLTezWZ7dUX1Ce6YWFhdi8eTNatWqFuXPnfhcSwPdEowwZubu7IyQkRC7NkKIX8ikg44P79+8jLCyM82ezZ8/mlBGIi4vDkSNHaC2Y3377DX/++SccHR2xYsUK6Ovr08qSfC0mFQkxUdDU1ERWVhZOnTpFy3qkpqYyCoLq464mFAoRHByMGTNmoEuXLrh16xZyc3NhaGjIkhVYtGgR5s+fjx49emD69OkYO3YsmjZtCltbW9Z5+UpX/PHHH9izZw+mTp0KVVVV3L9/HyoqKjIlytXV1eUyh/4NdOvWjeFAJxAIsGrVKk4zHa44+7p162TG2RUB3+JCyp4zLS0NnTt3hpaWFsrLyxWSgZeHqVOnYtasWZg6dSptPRobGwtXV1dGu40bN8Lc3FxmsWV9w2f1AaUnlpmZCT8/PwgEAjRv3hxr1qzB+vXr/5U+fCs0ygmBKmtXhGbIR/CMD8RiscwCHjU1NU6huNLSUlYisLq6mnZba9GiBX2cj8UkULv78PX1xZgxY+pUBfXw8MDSpUuhqqqK3bt349q1a/Dx8UFkZCTdJi8vT+45lJSUWI5bfn5+KC8vp1fXgwcPRmhoKFavXs16MYyMjHDx4kWoqKhgypQp0NXVhVAo5NwF8JWuWL58ORYtWoShQ4di/vz5mDhxIgBw2h/yRVVVFZo0aYLmzZvjw4cPaN269XfR0CkvL8fRo0c5JwS+cfa6oKyszJsO3KdPH3h6euLRo0ewtrZGYWEhAgICGpzMnTJlClRUVJCcnEzvljw8PDBq1ChGu5MnTwLAN1VWrS8yMjJw9OhRfPnyBVlZWTh58iTatWvHkDRpLGiUEwKV+OzatSusra1ZPz9y5AjrWHV1NTIyMjiLpuozi3ft2hV37tyBoaEh62e3b9/m1BWxsbHBrFmz4ObmBk1NTXz8+BHbt2/HyJEjIRAIEBAQQLNNKPlqRVFSUoKxY8cqNGBaWFjg5s2b9O6qQ4cOyMrKYhQfDR48mFFkIw2unceVK1dw8eJFemLr2rUrgoODOVdqdnZ2DDEuSgqDq9qTr3RF9+7daa/rTp06wcDAAF++fJEryaAIpC004+PjeVlofis8efKEJas8bdo0XsJ6kqhP1DgoKAhRUVGwtLTEnDlz8Pz5c7Ro0aJBmlUUFNHPkpQF54KSkhLnu/k9UFpaCi0tLZw7dw5aWlro3LkzxGIxJ5nifx2NbkIQCAR4//49gFpq5m+//cZ4oIVCITZs2MCiL/r6+tJxzqqqKqiqquLZs2eMwh8+oCSxIyIiWPFOHx8fTJ8+nfU73t7e2Lp1K9avX4/8/Hxoa2tjzJgxcHFxwZMnT9CyZUv4+PgA+I+vcEFBAT5+/Ii2bdvKFa/iE2KSl1ehwmcmJiZ4/PgxhgwZgjFjxii0G2vWrBnKysoYO50vX75ARUUFAJCbm4sNGzaAEILs7GyWLo9AIOB8iUxNTZGVlaWwdEV+fj68vb2xbNky6Ojo4ODBg/jnn3+wfv16mTpWiiAoKAheXl7Q09MDUKtF9OuvvyIgIOBfNUJp1aoVXrx4wXjusrOz631t9Rk41dXVGdIqPXr0aLBBDwC8evUKkZGRnL7jkjtjaiehoqIic8HyvcXtKHTv3h1hYWG4ceMGhgwZAqFQiPDwcPTs2fNf+fvfEo1uQgBqY8olJSUAmJQ+oNY1iCsefO7cORw7dgzFxcXYu3cvIiIiEBsbW++HxtbWFq9evYK9vT06deqEtm3boqioCHl5eXB1deWkOG7duhWurq6MWDGFXr16MVaZnz59goeHB65evYrmzZtDLBajd+/e2LJli0xzEkVDTNJ5ldLSUrx8+RKWlpb0hLBnzx4UFBTg5MmTCAoKQnV1Nezs7GBraytTd3/06NGYM2cO3Nzc0L59e+Tn52Pbtm30pPvzzz/DxMQEJSUlyMzMZCWslZWV6QlREmpqapg7d26d0hUUfH190bFjR/z8888AalfPkZGR8PPzo3WF6qOp9Pr1axal2dbWVqab1/eConF2RbFjxw7ev2NnZydzx9YQWfSVK1eiZcuWmDBhgtzQp4ODAy5cuIDevXvDzs4OFhYWDXIKawgCAwMRFBSETp06YeHChXj69CmePHnS6PIHQCPXMrK3t2d5HMsCxTARCoWwt7fHmTNnIBaLYWFhgStXrtS7Dx8+fMDly5fx6dMntGvXDgMGDJDJwzY2NsbVq1cVSrwtX74cVVVV8PX1hYaGBvLz8xEYGIjmzZsjJCSE1V7eS6iICN3p06dx/vx5mcJ5z58/R0pKClJTU9GlSxeMHj2aFdcVi8WIjIzEyZMnUVRUBC0tLVr/STrfcubMGblqspKQ5+fApf7Zt29fXL9+nfE9i0QiDBgwgF4A1EdTafTo0Vi0aBEjNpyVlYVNmzYhOTm5zvNx9VOemc6XL19kSkbExcUx4ux2dnas+/E9If28lZaW4vjx4xg9enS9JyYAMDAwQFZWlkLsHJFIhAsXLiAlJQUPHjzAsGHDYGdn96+G7/5vQ6OeEACm0mFGRgZEIhGn0qGDgwN8fHzQu3dvmJmZITExEc2aNcPQoUP/NbG4gIAAfPnyBdbW1iybP+nV8oABA5Cens5QvRQKhRg8eLDcXY2iISZp1NTUwMjISO53QQhBVlYWgoODkZOTUy99m8OHD8PJyUmhsFV9MWjQIOzcuZOhwfT69Wu4uLg0SH+moRaa0qivXer/KvLy8jB37twGGbXY29sjPDyct4x0aWkpTp8+jZSUFHz69AmjR4/mpIR/DyjCdmwsaJQhIwqSSocaGhpo0qQJwsLCkJubyxK2mjlzJpydnXHy5EmMHTsWTk5OaNKkicLcdmkoQmmTHnwoBoj0CyOL019VVcWYEEQikVxnOEVDTJS6peR5uZQ7Kdy/fx8nTpxAWloa1NTUYGNjw0h4h4SEwN3dXW4YhlqRnz9/Hk5OTjLpwJI0YD70YklMnDgRM2fOxOTJk9GhQwfk5+fjwIEDnGE8RRRaKShiockHfMx0GgN++ukn5ObmNugcAwcOxLRp0zBq1CjW8yhvodC6dWuMHDkSTZo0wf79+7F9+/Z/bUKQzq+VlpYiPT0dY8eO/Vf+/rdEo54Q+CgdDh06FKdOnYKmpiYWL16Mbt26QSAQ1EvTH6i96UCtLae5ublCYSA+nH5LS0ssXrwYy5Yto+PEmzZtkhlmWbduHVRVVZGVlcUIMQUGBrJCTJS6JQVCCNTU1BjucNnZ2Thx4gROnTqFqqoqjBgxAjExMdDV1WX9bYrDrgiXnYpXK0IH5kMvlsTMmTOhpqbGCF25ublxvqB8NJUAtoVmQ8DHTOd/DdI7PLFYjLNnzzKMk+qD27dv4+eff2bZmsqqF6qsrMT58+eRkpKCGzduwMDAALNmzaLref4NcIUtx48fz0kZ/l9How4ZGRoa4vLlywxWS1VVFczNzXH9+nVGWysrK8THx38zXXeRSISMjAykpKTg8ePHGD58OEaPHl0ns0AoFOL06dP4+PEjnJ2d8fjxY86wQHl5OXx9fZGeno7q6mooKyvD1tYW3t7enPFVPiEmiqVFoWnTpmjbti1Dn0hHRwdt2rSBlZUVTE1NOemsDS384WOmTqG4uBhqamqcWkr1gYGBAUNTKTo6mtZUoqidFhYWyMzMlBvzrw854VuY6fy3MGXKFMb/mzZtis6dO8PNzY0VDv0euHjxIk6cOIHz58+ja9eusLW1xciRI2Xucv9tVFZWon///rh9+/Z/uyu80KgnhIULF6Jly5YspcOysjJs3ryZ0XbIkCE4dOjQd3lYS0pKkJqaipSUFAiFQtjZ2cHGxgba2tqMdk+ePIGzszN+/fVXPHv2DElJSRg1ahTWrl3LqjmIi4uDra0tbbSioaEhl4c/YMAApKSkMMJDJSUlGDVqFKdBi6T0gYaGBiwsLBjSB3UV1SgpKbFCYpWVlUhKSuKkDEoncSXN1Nu3b4+8vDwcO3aM00y9qqoKQUFBSEpKgkgkQrNmzWBlZYU1a9YwisLqE2IyNTXF1atXUVFRgZEjR+L8+fMghMDExIReVFCyBPIG/frE+utrpmP//7F37nEx5f8ffxUqK7YsSVhrrXWLJbqJhO2qm9uqLFFqI7JY6eqWrBQ2EVaU3CnpQm1IabeIlnVbba6pdM9q1DaV8/uj38y3mTNTc2umU+f5eHg8dOZ05j3TzHl/Pu/L6z13LubMmYPZs2fzrDijMoLmllouWPiF7KQx0xwgh4YbGhpw5coVlJeX48yZM1KxQVJQOmTEUjrU09ODkpJSq0qH48ePx5w5c6CjowM1NTWOG4a4zTR9+/bF999/j++//x6PHj2Cl5cXfvnlFzx58oTjvICAAPj4+MDKygra2toYOnQoDh06hG3btpEcQkhICFvOQRD1SGFCTIJIDIvSAert7Y3c3Fzo6Oi0uYIXZph6SEgInj9/jujoaAwaNAgFBQXYs2cPgoODOeZqs67FPcmqNQRRaGXV6bNu+gUFBSgqKoK2tjb+++8/kXedhAjDdIDmG93ly5exe/du6OjowNraGt9++y3HTlkatDVpThQEKYkGwJ4//ezZMzx79ox0HVElaUSBWz6DtVvqSPOeBUaKQnrthiBKh15eXnz/iUt9fT2RmprKHp7i6OhIxMTEkM6bPHkye9BMy8EpWlpapHP9/PyIvXv3Es+ePSPev39P1NTUsP/x4sOHD8S6deuIsWPHEqNGjSLGjx9P+Pr6Eh8+fCCdu2TJEuLo0aMcxyIiIggHBwehXjc3Ojo6RFFRkcDn1tbWchyrr68nDAwMSOcaGhqShqSUl5dLZAgKP4XWbdu2kc6trKwkHB0diTFjxhATJkwg/vnnH0JHR4d4+PChSM8tyjAdbntOnTpFODo6Ejo6OoSXlxdfhVRJc+rUKcLAwIA4fPgwcfnyZeLgwYOEgYEBERUVJfHnunLlitiDntqTp0+fytoEiUHpkBHQLGsQGxuL0tJSeHl54caNG1KZh0AQBLKzs5GYmIirV69i8ODBsLa2xuzZs/mGpWxsbODj4wNdXV224uiDBw/g6+uLxMREjnMnTpyIuro6ABBoTqswISYdHR1kZ2dzJMIbGxuho6NDSuYJw6xZsxAfHy/QilmYYer6+vq4fv06R+6EwWDAxMQEWVlZpGsvXryY52vv0aMHVFVVYWhoyFGzX1dXB0VFRcjLy7eq0Lp27Vr07dsX69evx/Tp03Hnzh38+uuvSE9Px+nTp9t8zdxMnDgRZ8+eZVc3LVq0CKdPn251mA43z58/R0pKCpKTk1FdXY1+/fqhtrYWgYGB7VqyamZmhj179mDMmDHsY0+ePMHq1aslPlpSkJJoWaKnp4fr169z5O+oCqVDRtnZ2fDw8IChoSFu3LiBVatWISgoCO/evWNLRwhTEikM06ZNQ1NTE8zMzHDkyBGOyhNWWSf3jdHDwwMrVqyAjY0NmEwm9u7di5iYGJ7duSzxLkERJsQkaekDFkuXLsXatWuxbNkyUmyb+8a2b98+dmK+W7duHMPUIyMjOZyfgYEB/Pz84Ofnh759+6KyshI7duzAlClTeNqhqamJuLg4LFy4kN0vEBMTAz09Pairq2P37t0oLi5mz2luamrC8+fP0dTUBCUlJSgpKeHp06ckm2/duoWMjAwoKCiwHY6zs7PI08ZEGaYDNC+Crly5gqSkJBQWFmLmzJnYsGEDpk6dim7duuH8+fNYt26dQPkIURFm0pwwCFsS3REYNmwY7t+/z1YOpjKUdgjBwcEIDQ3FlClToK2tjUGDBuHYsWNwd3dnOwRhSiKFoaKiAkBzoxW3mB6/lfysWbNw/PhxxMbGQkdHB5WVlQgNDeXQkmE1bgnTVAY0l7+GhYWxpSVarpC5HZOkpQ9YsGKpmZmZHMd5vRfCDFP39vaGu7s7DAwMoKCgACaTCX19fYSEhPA8/+7duzh27BjH6tXCwgJ+fn4ICQnB/PnzsXTpUri5ueHcuXMIDAwUSDa8V69eKCsrY0tiAEB5eTk+/fRTgV9LS4QpQ26JqakpdHV1sWzZMpiampJWpvr6+qS/gaQRZdKcIAhSEt0RWb58OVRVVUnfPXFkPGQBpUNGLYfIsEIwBEFAW1ubtL0sKioS+ibbGtylm7wQ5flaDqkBADc3N57zi7kRJcTUmvSBKFo/wsI911lFRYVnqObmzZvQ09NDaWkpKisrMXDgwFarxSZNmoRbt25xJLaZTCb09PTY7+2kSZOQm5sLExMTuLi4CCQbfujQISQlJcHd3R1+fn7Yt28fwsLCYGBggNWrV4v4LghPaWmpVEo7W+PRo0dYtmwZ1NTUoKGhgeLiYlRXVyMiIoLDEQuLICXRLUlPT8e0adPEnsMgDuLKxnQkKL1DGD58OFJSUjikKjIzM3mWoZmZmWHChAmwtbWFqamp2P0I/G72VVVVfEsBc3JyEBQUxHNYPKukkfu4oDF9YUNMbUkMC7Ojys/Px4gRI/iueOXk5EjhBWHmOm/YsAGZmZkYMmQIxxAffkycOBFbt26Fp6cn+vTpg3fv3mHPnj0YP348Pn78iLNnz7JDfMLIhru6ukJRURFhYWFoamrC1q1bYWtrS2qClAZBQUEoKioilfdKSypBU1MTv/32m8QnzQ0aNAg1NTV4+/Yt+7WxmkB55VN27NgBBoMBCwsLWFtby0THiGo3/dag9A4hNzcXrq6umDRpErKzs2FiYoKbN29i37590NfX5ziXpXWSlJSEJ0+eYObMmbC1tcXUqVMFLlHkJjY2li0RrampCUdHR+Tl5eGLL77A4cOHSXosxsbGMDY2hqGhIekGxEoAcu8QWDsffrBCTIJgb2+PM2fOSFypUpRxm9bW1vD29oaOjg50dXVx+PBhDBgwAIsWLUJGRgbHuStWrMDMmTNhaWkp0DCasrIyrF27Frm5uVBSUmI3CQUGBuLly5fYtm0b9uzZg9GjR8Pb2xtaWlpil0tKE5bsiq6uLulzxKtrVpK05fyBthPhrREVFYXg4GDSGFh+O12gWVrl8uXLSE5OxieffAIbGxtYWVkJtHgQh/b6PskSSjqErKwsdkLx7du3SEpKQnFxMdTU1GBhYdFm+39xcTEuX76MM2fOoLGxkT1zVxj27t2L+Ph46OnpIScnBxoaGhg0aBCWL1+OM2fOoKSkhNR12rIRiR/COgRhQkyJiYmwsrISaosrjNaPMAgy15mFqakpXr9+DTk5OSgrK3N8+Vp7b96+fYvS0tJWQ0xOTk7IyspC//79+cqGSyN8JgwTJ07ErVu32HMmpImos7YFRU9PDzt27MCMGTOEXqgRBIGsrCwEBwcjLy+P7ehtbGxEXvS1Buv7dPHiRb7Xp9rugZIhozVr1uDOnTswMTFBamoqXFxcBP7dN2/eIDExEcnJyWhoaBBqKllL4uPjcfz4cQwdOhTPnj2DlZUVwsPD0adPH/z000+YMWMG6Xdmz56NixcvtroabWpqYnfLAs0335Y/A5ySEcKEmFivtaqqCs7OzqTHec2IFkTrp63kKK+QkSBznVmI0uAjaNOUIJPpJF2QIC4TJkzAixcvxE7gioIos7aFQUFBAdOmTRPqBs5kMpGRkYHLly8jIyMDw4cPh4+PDwYOHIhff/0VGRkZfOefi0NxcTGqqqowd+5ciV9bVlByh2BoaAgDAwMkJiZi0aJFPM/hXrFFR0cjMTER+fn5mDVrFmxsbDB16lSBYse8mDhxIu7du8f3Z1bSsiXZ2dlYvnw5lJWVSTkMVu22sJIRgu4oKisrcf/+fQDNEg979uzhcCY1NTXYunUrx2tgvY62tH5YncH8Pkq8Vo3p6enYsGEDe65zSUkJVq5cibCwMEydOrXV96AtTp8+jfDwcCxZsgSDBw9GQUEBTp48CRcXF46qGADw8vKCn5+fwDml+vp6yMnJQUFBAcXFxVBVVW2XmcqtwUro6urqonfv3hyPCTsbWVS4R6Cy4DUCVRhOnz6NR48ewdHRkfTauKVggOa5IWlpaVBRUYGlpSVsbGw4cohPnz6Fvb096XMtCezs7PD48WMYGxvDwcFBaiM72xNK7hBCQkLYjVyCrt6Sk5OxYMECWFhYSETgjnsFw10FwevmuGnTJtjZ2fGM/bJor6HhysrKOHToECorK1FfX4/t27dzPK6goMBTLlhRURH9+vVDr169kJeXB6A5Qb9161b2OaKsFrnnOmtoaJDmOrMQVlSOJX/cstrF0NAQq1evJjmEjIwMgSeecc9Ujo2NlclM5cDAQHz++edQV1cXeUEjCqKOQBWGjx8/Ij4+nj34irXQ4BeK6tatGw4dOsT3Zjx48OBW9ZHE4ezZs3j27BliY2Ph4eEBVVVV2Nvbw9bWVmIimtKGkg5BR0eHrSXPfWPjh6RFpgiCQF5eHvvG39TUxPEzL4dQVVUFf39/idohaIhJUVGRPfd31apVAlejCKL1AzR/kQsKCvDFF1+wjyUkJMDMzIzvDIfk5GSBwjrcw+Orq6tx6tQpmJmZ8byuME1TxsbGcHV1hYmJCamGnFvNtaPMVH769Clu377N931tL0QdgSoMYWFhCAkJgb6+vkClpPfu3Wv1OZWVldt1tvFXX32FjRs3Yt26dbh+/TpiYmKwd+9eWFhYwN7eXqwSXFlAyZBRS168eIGCggJSVQLry9xe0sWihEn8/PzwzTffSLSiRRRV0sbGRhw7doytyHrhwgW8e/cOzs7OpBVnTk4OVq1ahdjYWKSnpyM0NBTdunWDlZUVe8j6u3fv4OjoiKFDh7JVZquqqmBubs5uFuQeACNMWIcX1dXVsLOzw2+//UZ6bMmSJZg1axbHdY4fP45r166REuH83j9e7xuvhDe/vpf2ZPHixfDx8ZFJDoEFawQqa/XOYDAksiqeOnUq0tPTBZ6P3J4qxqJSWlqKkJAQJCUliZVglwWUdgj79+/HgQMH0L9/f44PUMsvc3tJF4uCq6srbt68CXV1ddKweGmWp23btg0PHz5EaGgoNDQ0kJubi127dmHixInw8vIinV9XVwclJSXIycnx1PrZvHkzqqurERQUxBFPr62thYeHB4YOHUraGYmrhVNRUQFzc3PcuXOH9Fh7NU1JeqayqOzatQvnzp3DrFmzSJ8jaVU75efnw8PDA0FBQRg/fjyCgoKQkZGBAwcOiDxBDgBOnjyJx48fY+XKlaTXxsvhrF27Frdv324XFWNhKS8vR3x8PC5duoTa2losWLBAalPbJAUlQ0YsYmJiEBER0aqGCCu2mJqayl7RtmTDhg1Scwjm5uYwNzeXynO1BksMjSW5MGnSJBw8eBBWVlYkh8BgMLBr1y4srqrjRAAAIABJREFUW7YMw4YNw507d1BYWIjJkyezb/7p6emIiYkhJVc/+eQTbN68GUuWLCE5BGHCOtyJ0oaGBty8eZPv+FNhmqZacz7cIaMff/yR70xlaVJdXc2WNa+pqZHqc7PYsmULrKys2A52/fr1UFFRwZYtW3D8+HGRrxsaGoqamhrExcUJ1HGvpKTEHqoki/eCyWTi+vXriIuLQ3Z2NgwMDLB+/XoYGRm1S6lre0Nph1BXVwc9PT2+j5eUlCA1NRVAs1QDd6NYTU2NWBURwtJRapKbmpp4JsV5fYA3bdqE2tpa9s105syZ2LNnD7Zu3YqdO3cCaHYa/AT1hgwZwvMmL4wWDnfhgLy8PBYuXEhqyONObrcc91lcXIzi4mJS3Jtby/79+/eora2FlpYWySFIeqayqEirkqg1nj59yp4RDgDdu3eHi4sLIiIixLour8ql1pDle7FlyxYkJydDQUEBc+fOxZYtW3hWQlEJSjuE+fPnY//+/VixYgXPBJuamhr+/PNPVFZWorGxkTR8Q0FBQeJJXl44Ozvj6NGj7J/j4uI4nENbzWeS5ttvv8XatWuxbt069kp33759MDIyIp37xx9/ICMjgz18Zfjw4di1axfHzXLAgAF4/fo1z4bAV69e8ZTy8PT0xLJly3D+/HlSWIcbQb/0rI5RYfI6vKq6oqKiUFxczPMakpypLA4JCQk4e/YsysrKcPLkSYSGhmLz5s1SG5LTr18/3Lt3j2OG8uPHj8VWzOUlXdEa7dU4KQivXr3C1q1b8e233wqc8+joUDqHYGlpiWfPnkFeXp4UX+S+we7YsQM+Pj7SNI9NW70C3D0M7Q2DwcDmzZuRmpqKhoYGKCgowNzcHP7+/qT30cDAABcvXuRI2pWVlWHBggVsiYkDBw7gr7/+wv79+zkcc319PTw8PDB8+HB4enqS7KiqqsL169dRXV3dphaONG+AHz9+hL6+Prt7moWkCxNEJSoqCufOncPSpUsRHByMa9euYcWKFRgxYoTAJbTiEhsbix07dsDW1pa9qEhMTIS3t7dY80iEla5YvXo138ZJXiFimtahtEMQNlHMYDDw9u1b0odNHO0VQeB2CNra2hzJUF7VK9KAyWTi/fv36Nu3L9969l27diE7OxsrVqyAuro6SktLcfjwYejq6mLDhg3s67i4uODFixcwMjLCZ599hoqKCrYgXUREhFg3blFugKyZAayZ0ebm5hg+fLhAz5ecnIzg4GDS7oH789ay/NXBwUG0FycCJiYmiIiIwOeff85eXFRVVcHS0pLnwKD24vfff0dSUhIqKiowYMAAWFlZtRrCFQRhpSsEaZykERxK73OESQafP38e27dvF0j3vr3pCMmm3NxcnD17FqWlpdizZw/Onj3Lczj92rVr0b17d+zcuZPji88aLgM0h94iIyORlJSEzMxMPHz4EGpqavD29oapqSnHNVubd8wveXj69Gn2DXD37t1QUVHBgQMHYGlpydMh5OTk4IcffsCkSZMwaNAg3Lt3D0eOHEF4eDipAIF71d/Q0AAmk8lzdcnr86ajowM7OzupOoT3799j4MCBAP7X76KsrCx2U5iwTJ06Fbq6uiguLsaQIUMk8rkWVrpCkMZJGsGhpEPgdePihrvxKiIiAv7+/gLp3nd2kpKSsHPnTsydOxdpaWn4+PEjEhMTwWQysW7dOo5ze/TogXXr1pGOcyMvLw9ra2uOmQq8YCUNCYJgj4xsC2FvgCEhIQgMDOSQRWcNped2CNxNb/Ly8hg6dGibU+dYNDU1oaqqSqBzJcXkyZMREhKCjRs3sr8HR48exYQJE6RmQ11dHbZv3464uDgoKCjg4sWLWLFiBSIiIsRSGXVzc8PmzZsFlq4QtHGSRjAoeWcUpSFHGN17SSNKV3N7Eh4ejkOHDkFTUxNnzpyBmpoaIiIi4ODgwL7xt9fo0Zbhue7duwsUrhP2Bvjy5UtSFzMrR8KNjo4Ompqa8ODBA3Z4SVVVled1hS1/bS/8/f3h5uYGHR0d1NbWwtDQEMrKyjh8+LDUbNi5cyfev3+PpKQkLFy4EEOGDMHUqVOxdetWsSqNhJWuWLNmDVatWoWxY8di6dKl7DGyoopWdnUo6RBE0Xw3NjZGbGysTHTvBZmdK00qKirY9eMttYTq6+vZ57TX6FFREPYGqK6ujtzcXGhra7OP3b17l+cKs6ioCG5ubmz59NLSUvTt2xeRkZGkla6g5a/tzYABAxAbG4tHjx6hqKgIampqGD9+PN+pYu3B9evXkZKSwpYj79GjBzZu3Ci2MKGw0hU6OjrIyMiAoqIiFi9ejHHjxpEaJ2kEh5IOQRRKS0vh7++Pffv2cZTGycnJsVcj7UV7SQWLytixYxEdHc2eOw00V/G0bBRjxWAFKfmMiYnB/Pnz0dDQ0C43JdYN8OHDh+wbd2s3QDc3N/zwww+wsbFhz4xOTEzkKaMdGBgIPT09eHp6okePHmAymdi1axcCAgLw66+/cpwr6/p/7s+RgoICuwfi+fPnANq/QIJF9+7d2fk41kKntrZWbOXXHj16wNjYWKiwblNTE54/f46mpiYoKSlBSUkJT58+ldp70ZmgdJWRMPCShqitrUVQUBAePHggA4tkx7Nnz+Dk5ITevXvj9evX0NTUxJs3bxAREUEKx/3333+4dOkSXr9+TYrLskJGLKlvYaul2uq/YDAYbV6Dn37O77//joSEBPbMaBsbG56KmHp6erh58yapXNbAwICtTySIWuaSJUvaPEdcuBPyrAH0rA5dFRUVZGdnt7sdQLNzfPLkCTZs2ABnZ2dcuHABwcHBGDhwoFjlnsJKV5w7dw6BgYEdolikM0BJh1BbW4tPPvlE5N9PS0tDdHQ0bt26BU1NTcTExEjQuo5Ly5V8Q0MD0tPT2Svu6dOns6UsWsIaRamjo0NakbNWzKamphgwYAByc3N5DgYC/pfkbzluMC8vjyRfAfzPebdWkQQ0f+mfPHlCOr5kyRKEh4cLJLZmZGSEU6dOcczIfvPmDRwdHdllp4sXL+b5uw0NDbh//z569OiBhw8ftvlckuLw4cMoLCyEp6cnevfujQ8fPmDPnj1QUFDAxo0bpWIDaycVExOD//77DwoKCrC2toaPj49Y301tbW3U1NSQHB+/G7yJiQlcXFzoYhEJQUmHoK+vj+zsbHh7ewu8ja+pqUFMTAxOnjyJ4uJiLFq0CAsXLuwQXafSQpSVvK6uLuLi4lptyX/27BmuXr2KAwcOcJSjtoSV9xFExI/VxV1UVER67OPHjzhy5AguXLgAR0dHnmJ8U6dOZce322Lfvn24evUq3N3doaGhgcLCQhw8eBDGxsbw8PDg+3v//PMPfvrpJzQ0NCAkJARjx45t87kkha6uLjIzMzl2NQ0NDZgyZQpPsT9J4uTkhGPHjiE2Nhbz5s0D0FywoaKiIpGCDV5/cxYtnTYLlnClLIpFOiOUdKmNjY2Ii4tDcnIyjI2NeVbpsKQVnj9/jujoaCQkJGDcuHFYv349tm/fjhUrVuCzzz6TtukypV+/fliyZAnq6+v5Jua5y3WVlZX5dg+z+Oqrr/DVV1+BIAisXLmy1XOF0XPivgEUFxfD29sbhYWFOHbsGPT19Xn+3rRp07Bw4UIYGRmRFDC5Qzvu7u5oamrCzp072eElW1vbVseyRkVF4ZdffsGcOXOwceNGqclFsFBUVMTz5885wntPnjwhlWm2B/fu3UNRURECAwNhamoKoDmXUVtbyz5HHBlsXjf91pBlsUhnhJI7hGPHjuHcuXN48+YN1NXVSY+3lL8ePXo05s6dC2dnZ3z55ZcAmleQ8fHxXc4hCLOSZ3HixAncvHkTy5YtI2kS8UraCTrLWFji4+MREBDALm3kFd5iwS/EIycnJ9b0rLKyMmzcuBH//PMPAgMDeWo/SYOoqCi2Oq26ujqKi4vZshHtPd/3xx9/REpKCs9QXmuhnbZoGUrkB6/dpZOTE7KystC/f3+SjpI0JeU7C5R0CCzMzMyQkpLS6jmbNm1CSkoKxowZg++++w4mJiYwMjLqkg6BRXh4eJsreRb8KjX4dRSLM/SGFzU1NfD390dmZib8/PwkphjLL9SopKSEUaNGkWTKf/vtN2zatAlaWloIDAzkKdgnTdLT05GcnIyKigqoqalhzpw5UpNxLy0thZmZGZKSkng+LuwqHxAulCjo73UUdWEqQWmHADRXoqSnp+Pt27fo378/pk+fTmosqq2tRWJiIs6dO4eSkhIwGAxERERI7QvU0SAIAr///jvPSXPiVMsIMvRGmBLVW7duwcvLC4MGDcKuXbvavNGcOXMG9vb2re4CWK+PX1NdQ0MD7ty5AzMzM/Y53t7euHTpEhYuXAg7Ozuev9cRShzr6urELvsUlDdv3ojVkUzTMaG0Q8jLy4OzszN69+6NQYMGobCwENXV1Th27BjfJN/Dhw9x7tw5XL58GRoaGiRdnq7A1q1bcenSJYwaNYo0aY51M83Pz8eIESP49lDIycmRKoS0tbVx69YtjoaixsZG6OnpsUs4hUlsjx49GgRBYMSIEXwrSFquEF1cXHDkyBGxQ0bFxcWYM2cOW+20rZu9tEsc8/LysHfvXpLkc1FRkdRUc1++fImwsDCe5cjSCNXY29vjzJkzrYaa6JCR8FDaITg6OmL69OlwcnJiHzt69CjS0tI4hnfwgsFgICEhAefPnxd6KAfVMTAw4Nlz0BLWDVuYkJEgs4yFKVEVNoxQUlLCM6ckLDU1Nfj2229J8tcdhYULF2Lw4MFQUVHBq1evMG3aNERHR8POzg6urq5SsWHRokXo2bMnTE1NSc5aGqGaxMREWFlZ0SEjCUNph6Cjo4Ps7GzSilRHR0cmctJUYdq0abh+/TrPoULiIMgsY1ES24IyduxYTJ8+HQ4ODiJLKJw6dQpnzpyBpqYmeyJcR2PChAnIyclBUVERNm3ahBMnTuDx48fw9fWV2uJm0qRJyMzMFKvnQBJ4eXnBz89PrMommv9BybJTFp9++iny8/M5VrHPnj0Te2pTZ8fZ2RmbN2+Gm5sbKbHO+mK1JbfBK2QkyCxjYUpUhSUpKQkxMTHw8vJCz549sXDhQsybN4+vWB0vevXqBXt7+w5dxqiqqoru3btjyJAhePHiBYBmZ9haDb+kGTp0KCoqKkhjaaVNRkaG1IYCdQUovUM4ceIEjhw5giVLlrA1a06cOIHly5dLRUqAqkycOBF1dXWtdoOyuoSFGUcpLO1VotrU1IS0tDTExsbi9u3b+Pbbb2Fvbw8tLS2xr90RWLVqFTQ0NLBu3To4ODjAxcUFSkpK2LJlC3uKXXvzyy+/ID4+HtbW1qRFhSjfPVHLTjdt2oSCggKYmJiQek64Z2LTtA2lHQLQPPgmPj6eo6moLU3+ro6w3aDtgSAlqpIQzSstLcWlS5eQlJQEOTk5JCQkSPJlyISSkhL4+vpix44dePHiBdzd3cFkMrFt27Z270NgIeleD9bN/tGjR8jIyMDixYsxePBglJSUIDo6GjNmzODZlT5z5ky+drAq22iEgKCh4UNTUxPx8uVLjmPx8fFEfX292Nc2NTUlHj9+zHHs8ePHxMyZM9k/a2lpEQRBEBMnThT5ecrKyogjR44Qc+bMIUxMTES+TkempqaGYDAYsjZDIpibmxMFBQUcx968eUPMmjVLRhZ1LSidQ6ARDmG25e/evYOjoyOGDh2Kffv2AWjWrAkMDERUVBSOHTsGFRUVkW2prKwk5SC+/vpr/Pvvv+yfRZHaAJqF165fv46LFy8iJycH06dPx08//YQpU6aIbG9HIj8/Hx4eHggKCsL48eNx4MABZGRkIDw8HF988UW7PrcgUu7i9GSUlpaScoB9+vRBdXU139958+YNYmNjUVpaCi8vL9y4cQO2trYi29CVoR1CF0KYbuG9e/di6NChCAoKYh/r27cvbty4AQ8PD4SFhbEnkAkiMMjdCDZ69GicPHmSw6ZTp05xlMIeOHAAV69ebbX8tSUPHjxAXFwcrly5gl69emH+/PnYsWOHwOMwqcKWLVtgZWXFrtxav349VFRUsHnzZhw/frxdn5u1qCDaKbc0ZcoUrF27FmvWrMGAAQNQVFSEX375hW8+IDs7Gx4eHjA0NMSNGzewatUqBAUF4d27dxzzPmgEg/I5BJr2Yfr06YiJieF5M33z5g2WLFmCGzduABBslCa30xCkRJWFoFIbY8aMgaGhIVvYTtqT6KQFq7mvJR8/foSurm67q522N//++y/8/f2RlpaGxsZGKCgoYPbs2fD39+dZ4jp37lz27k9bWxt37tzB33//DXd3d7Z8OY3gUHKH0N7bVprmxj1+K+shQ4ZwhHZEmSQmSIkqi5UrVwpUkXTt2rVWZbo7C/369cO9e/cwceJE9rHHjx93inLrTz/9FPv27QOTycS7d++goqLSar9MQUEB9PT0APxvHOyoUaM6xOhXKkJJh9De21aa5rGVr1+/xtChQ0mPvXr1iqe4G4PBwPnz50mSCvn5+exO5Zb07dsX8+fPb9MWXhVJoaGhqK2t5Qg5dQVnAACurq5Yvnw5bG1tMXDgQJSUlLDVTjsDmZmZAutsDR8+HCkpKbCwsOD4fdZoURrhoENGXYgXL16wJcDb4sCBA/jrr7+wf/9+0nhJDw8PDB8+HJ6enhy/s3r1ajx//hx9+/ZFTU0NBg0ahJs3b8LOzk6ssYqCiOZ1NX7//XckJSWhoqICAwYMgJWVFXulTGW2bNmC+Pj4VnW2WpKbmwtXV1dMmjQJ2dnZMDExwc2bN7Fv3z6+8zJo+EN5h1BRUYHKykrSilRa9dhUQldXF7dv38bSpUsRFRXV6rlMJhMuLi548eIFjIyM8Nlnn6GiogKZmZkYMmQIIiIiSINhJk2ahN9++w2lpaXYv38/Dh48iOTkZFy4cAHHjh0T2W5BRPMAyfQtUBXWIKitW7fK2hSxEERni5u3b98iKSmJPQ7WwsKC586Wpm0oGTJicfz4cezatYuttkj8f7ftyJEjaYfAAzk5Oezfvx9//vkn3+Yh1rZcQUEBkZGRSEpKQmZmJh4+fAg1NTV4e3vD1NSUZ8JWUVER/fr1Q69evZCXlwegeXUv7k1KkIokoDmXMX/+fOjq6nYZLau0tDScOHEC2dnZ0NTUlNrz2tra8tRNmjFjBrvYQBTk5eUxfPhwgc/fvn07/Pz8SBPuNmzYgODgYJHt6KpQ2iFERkYiLCwM3bt3R2pqKjw9PTvE8JKOyqZNmxATE4PGxkZcvXqV9LicnBxHnFZeXh7W1tYCd34PGzYMKSkpMDMzg5ycHF6/fo0ePXpwyCOLUqLq6emJZcuW4fz586SKpJaI2rdANXjNB/fx8Wn3+eCFhYUICgoCQRB49uwZ6T2uqakhSWELiyA6WyUlJUhNTQXQrFTAradUU1MjllPqylDaIbx//x4zZ85ERUUFdu/ejT59+sDHxwc2NjbYuHGjrM3rcFhYWMDCwgLOzs44evSoxK+/Zs0arFq1CmPHjsXSpUsxb948dOvWDVZWVuxzRKn+ELQiSdi+Baoh6/nggwcPhr6+PqqqqpCenk56jxUUFODr6yvWc4SGhqKurg5xcXHsXSjBpbOlpqaGP//8E5WVlTwXNwoKCuweGRrhoHQOwcLCAsePH0f//v2hq6uLzMxMyMvL0/LXAvDmzRtcuXIFb9++Rb9+/WBubi7UVp0fdXV1UFRUhLy8PO7fv4+amhpMmzZNAhYLjjAjQqlER5oPnpqaChMTE4lfV1idrR07dsDHx0fidnRVKO0Q9u/fj8TERJw6dQo7duwAk8mEoqIiioqKcPbsWVmb12HJycnBDz/8gEmTJrEnzd29exfh4eEwMDAAIHqClsFg4O3bt6SSQe7VpLAlqsLSXkqqsqSjzQfPzc3F2bNnUVpaij179uDs2bNwd3cXuyHw48ePqK2tZf/MZDKRn58PXV1dnuczGAwkJyejpKQETk5OePz4cZcdjys20hZPkjSJiYkEg8Eg3r17R/j7+xNr164lXr16JWuzOjQLFiwgLl++zHEsKSmJmDNnDvtnUYTlzp49S4wbN44YOXIkx79Ro0aRzl21ahVhbm5OLFq0iLC2tiZWrFhBjB07lggICBDxVf2PU6dOEQYGBsThw4eJy5cvEwcPHiQMDAyIqKgosa8taz58+ECcPXuWmDNnDqGvr0+MGzeOuH37ttTtSExMJAwMDIjdu3cTWlpaRGlpKWFiYkLs3r1brOtevnyZmDRpEjFq1CiOf/r6+jzPf/LkCaGnp0csXLiQmDBhAvHq1Sti/PjxREJCglh2dFUo7xBohGfy5MlEU1MTx7GmpiaOm7+JiQmxePFiYsyYMYS7uzvPf9wYGxsT58+fJxoaGtq0QUtLiygvLycePXpEuLm5EQRBEFeuXCGWLVsm5qsTTEm1M/DgwQPC19eXmDBhAmFhYUEcPHhQas9tbm5OPHz4kCCI5s8TQRBEQUEBMXXqVLGua2JiQpw4cYKIiYkh1q9fTzx58oRwcnIiDh8+zPN8e3t79s2fZUdWVhZhZmYmlh1dFUonlWU96JuqqKurIzc3F9ra2uxjd+/e5ej0FSVBW1VVhXnz5kFeXr7NcwUpURWlIgkQTEm1MzBu3DiMGzcOXl5e7Png/MaSSpqKigp2oyArRKShoYH6+nqxrltWVobvv/8epaWlOH36NEaPHo2ff/4ZDg4OPOdF5+fnY/bs2Rx26Ovro6ysTCw7uiqUdgh+fn7o2bMn7OzsSIO+afjj5uaGH374ATY2NuxJc4mJiQgICGCfI8qoS2NjY8TGxgoUqxekRFVUPRpB+xY6C8rKynBwcICDg4PUnnPs2LGIjo7mUBRNSEggOWJhUVNTA4PBwIABA1BYWAiCINC/f39UVVXxPF9DQwN37tzhyC88ePCgy8iYSBpKJ5U7yqBvKvL7778jISGBPWnOxsYGkydP5nmuoAlaJycnZGVloX///iShNe4dW05ODlatWoXY2Fikp6cjNDQU3bp1g6Wlpdglg8IoqdKIxrNnz+Dk5ITevXvj9evX0NTUxJs3b4TuMuZm+/btePz4McLDw+Hl5YUhQ4ZAQUGB/Xnl5vr169iwYQNsbGwQFxcHR0dHxMTEwNfXl0PfiEYwKO0Q5s6di19++UXmg747M4KMumTRWphuzpw5pGOClqiKUpFUVVUlkJIqjejU1tYiPT2dLRkxffp0fPrpp2Jdk8lkIjIyEvb29qipqcGmTZvAYDDg6+uL8ePH8/ydhw8fIjY2lm2Hra0t38UNTetQ2iFIetA3DRlhhOW8vLzg5+fH7ihtC0FLVNtLNI9GdBobG3Hs2DFYWlpCQ0MDFy5cQHV1NZYvXy5QDklcDh8+jAULFtCqBBKG0oH33NxcDB48mNSExi3BQCM6wiRoMzIysG3bNoGue+7cOQQGBoLJZHIc5yVdnpWVxSGaFx4ezhbN60oIMrxeWp/7wMBAPHr0CJaWlgCAL7/8Ert27UJVVRW8vLza/flv3LiB/fv3w9jYGA4ODvSOQEJQeodw8+ZN6OnptTpAg0Y8lixZglmzZnGEh44fP45r166RwjWbNm1CQUEBTExMoKamxtGgxD0C0cTEBC4uLpgzZ06bBQFTpkxBVlYW6urqMHv2bKSlpYEgCOjr6+PWrVsSeJXUYPHixa0+zk8iuj2YMmUKkpOTOUJEVVVVsLKywh9//CEVG549e4bY2FjEx8dDVVUV9vb2sLW1FXiHSkOG0g6BJVdBOwTBEGR2APeNW5gE7cyZM3leU05OjvTckydPRk5OjkDhhUWLFmHx4sUwMzPDrFmzcOzYMfTo0QO2trbIyclp8/dpJI+uri6uXr3KkZepqamBubk5fv/9d6na0tDQgOvXryMmJgb37t2DhYUF7O3t6QICEaC0Q1ixYgVmzpwJS0tL9OzZU9bmdHj43bBZ8LpxA+2ToPX29oaWlpZAJaqCViSJ2rdANZqamnDt2jWeSfadO3dKxQZfX1+UlJRg3bp17Klt+/btQ79+/bB9+3aRr+vh4QFbW1sYGhqKVEpeWlqKkJAQJCUl0VMTRYDSDsHU1BSvX7+GnJwclJWVOUIU9MpR+rS2A+HeeQhTogoIVpEkyM1elPnPHQ0fHx/cuHEDqqqqqK+vh7KyMvLy8mBpaYmQkBCp2MBgMLB582akpqay9a4sLCzg7+8vVsjmwIEDuHLlCiorK2Fubg5ra2uO2dH8KC8vR3x8PC5duoTa2losWLAAK1asENmOrgqlHUJrN31a3Kp1Hj16xHOFuXr1apGvyb0Def/+PWpra6GlpYWTJ09yPCZsiaqgFUldAV1dXVy4cAGVlZWIiopCaGgoTpw4gZycHISFhUnVFiaTiffv36Nv374SrS76+++/kZSUhNTUVMjJybHncrQsMWcymbh+/Tri4uKQnZ0NAwMDLFy4EEZGRmIL7HVVKF1lRN/0RSMkJARRUVHo3bs3Pn78iI8fP4LBYGDKlCliXTctLY10LCoqCsXFxaTjt2/fFrhEVZiKJKD9lVQ7Ap9//jn69u3Lfv12dnY4dOiQVG3IzMxEQUEByUlLotJp9OjRGDx4MEaMGIGoqCgcOnQIJ0+exNdff41Nmzbh5MmTSE5OhoKCAubOnYstW7bQ3ckSgNIOQVtbm+9KgA4Z8ScuLg5nzpxBXV0dLly4gODgYOzevRuVlZUSf64lS5ZAX1+fpFkvTInq0aNH4e/vL1BFEtAcOuLXt9AZGDJkCO7du4eJEyeirq4OFRUV6N69u9g6QsKwZcsWxMfHY9SoURx/E3FLvuvq6nD9+nVcvnwZf/zxB8aMGYMFCxbAwsICffr0wcGDB7FixQoMGjQIW7duxbfffkvL1kgQSr+TBw4c4Pi5uroap06dgpmZmYwsogb19fUYN24c3r17hydPngAAVq7BKYNoAAAgAElEQVRcCWNjY/Y5kkrQ/vbbb+jVqxfpuLGxMVxdXQUqURVGNA/o/H0LLi4ucHJyQlJSEubNmwd7e3vIy8tLdRDR1atX2eJzkkRfXx8DBgyApaUlvL29SSoEFhYWSEtLQ1RUlESfl6YZSjsEXiEjHR0d2NnZSVXoi2poaGjg5cuXGDZsGKqqqsBgMNCtWzeOoSSiCMtx79gaGhrAZDJ5dhOzShO55yLLycmRHIIwonmAYEqqVMbU1BTjxo1D//798eOPP+Krr74Cg8HgmXtpL+Tl5SUyYY+byMjIVpPIX375JS5evCjx56VphtIOgRdNTU18lRFpmrGzs4OdnR3i4+NhYmICZ2dn9OjRg+OLKEo1DveOTV5eHkOHDkX//v1J5/LKN/CjtLQU/v7+7LLGlvBKTguipEp1NDQ0UFtbi/r6ehgZGQFodsCKiopSeX5nZ2ds3rwZbm5uJNkYcaqM/vjjD56NbQoKClBVVYWenh6GDBki8vVpWofSVUbcN62GhgbcvHkT48ePx549e2RkFTW4d+8exowZAzk5OURGRoLBYGDZsmUkbRhhE7RNTU148OABe1azlpYWzxivMCWqwlYktaeSakfg2rVr2Lp1KyoqKtjHCK5B9O0NK38B/G8OgSRsWLVqFa5du4YJEyZAQ0MDJSUl+PPPP9nCdnl5edizZw/pM0IjGSjtELhj2PLy8vjiiy9gb29Pt69LCGGE5YqKiuDm5sZWnSwtLUXfvn0RGRlJWtUJU6IqrGgeILiSKhWxtLSEqakprK2tSc6W1yD69qCoqIjvY+LYsGrVKpiYmMDa2pp9LCUlBcnJyQgNDUV6ejp2796NxMREkZ+Dhj+Udgg0ovHnn38iKCiI56Q57uqsSZMmcSRoDx48yE7QHjt2jOPclStXYtCgQfD09ESPHj3AZDKxa9cuFBQU4Ndff23TLlaJKndFkr6+PjIyMoSSKOnMfQsTJ07E3bt30a1bN5na8fHjR468E5PJRH5+PsewGmHR0dHBrVu3OAoImpqaoKenhzt37gAAtLS0SIKWNJKB0jmEjx8/IiIiArGxsSgpKWEPb1m9erXMvywdme3bt2P06NFYs2ZNmyV7wiRo//zzT/zyyy/o0aMHgOa474YNG2BgYCCQXfxKVIWpSAKE71ugGvr6+vjjjz9gaGgoMxuuXLmCTZs24cOHDxzHVVVVkZWVJfJ1Bw4ciLi4OMybN499LD4+HmpqagCapdd55aRoJAOlHQKrnHDNmjUYNGgQCgoKcOjQIcjJyWHNmjWyNq/D8vLlS5w9e1agFbcwCVolJSWUl5dzhAzKysoE1j3iV6IqTEUSIHzfAtXo2bMnVq5cCU1NTVKSff/+/VKxITQ0FD/++CN69uyJ7OxsODs7IyQkRKzdAQD4+/tj5cqVOH78ONTV1fH27VuUl5cjLCwMjx8/xvfffy+WVhJN61D62xIXF4eoqCh2fPqbb77BN998g0WLFtEOoRU0NTXxzz//QFNTs81z16xZg1WrVmHs2LFYunQp5s2bh27dusHKyop07ty5c+Hm5gZ3d3doaGigsLAQBw8ehK2tLelcYUpUhalIAoTvW6Aaw4YNg5ubm0xtKCsrw/fff4/S0lJ2P8LPP/8MBwcHuLq6inzdyZMn4+rVq7hx4wbKysqgrq6OmTNnQllZGVVVVUhJScGAAQMk+EpoWkJph8BgMKCurs5xTF1dnRQqoOHk66+/hqOjI4yMjEgrTO5EvY6ODjIyMqCoqIjFixdj3LhxfBO07u7uaGpqws6dO9mzmm1tbeHi4kI6V5gSVWEqkgDh+xaoxqpVq9j/r6ysRJ8+fdhhOmmhpqYGBoOBAQMGoLCwEARBoH///hIp+X7//j1KS0vx9u1bMJlMlJaWQllZmZ6OJgUonVR2c3PDsGHD8NNPP6Fbt25obGzE7t278eLFCxw+fFjW5nVYWusw5tV/0F4JWkFLVIWpSAKEV1KlGvX19dixYwcuXboEJpOJ7t27w8zMDNu2bZOaDPz27dvx+PFjhIeHw8vLC0OGDIGCggJ+//13JCQkiHzdnJwc/PDDD5g8eTJ7l3n37l2Eh4cLnIuiER1KO4SCggI4OTnh33//Rf/+/VFWVoYBAwbg0KFDdPNKK5w/fx5WVlYC3TwESdDya2JTUlLCqFGjYG5uTnpMmBJVXvCrSAKE71ugGoGBgfj777+xYcMGdu5sz549bOE3acBkMhEZGQl7e3vU1NRg06ZNYDAY8PX1ZfcMiMJ3332HpUuXwsLCgn3s8uXLOHr0KN2hLAUo7RCA5tjz3bt32cNbxo8f3ykTiZJER0cH2dnZAlViCTLqkt+Oo6GhAXfu3IGZmRnpHHFLVD9+/Ah9fX3cvn2b9JgofQtUYvr06YiLi+MIoVRUVMDS0lKmI0UbGhrw999/i+UQtLW1cfv2bY78z8ePHzF58mS61FQKUPLO+fTpU46fVVVVoaqqCiUlJRlZRC1MTU0RFhYGKysrUhkn901UkARtazIXxcXFmDNnDskhiFuiyq8iCRBOSZWKMJlM0mddSUlJ5kn0qqoqLFy4UKzSXnV1deTm5kJbW5t97O7du7S0tZSgpEOwtbWFnJwceG1uevfujZCQEEyfPl0GllGDpKQk1NXVsUt0Af6yA+ImaHv37s3zuDAlqsJUJLFsFqZvgWpMnToVfn5+8PPzQ9++fVFZWYkdO3aIPc9CEogbcHBzc8MPP/wAGxsbaGhooKioCImJiQgICJCQhTStQfmQUUsaGxuRkJCAI0eOIDk5WdbmdFiEkR0QJ0F76tQpnDlzBpqamqRZv/v27cPVq1dJJarGxsbw8PDgOJe7e7q1iiSA/+xofjOjqUZlZSXc3d1x//59KCoqgslkQl9fH8HBwSShOWlSWloKIyMjsZv/WIlpVqWajY0NJk+eLCEraVqjUzkEoDneOGnSJNy7d0/WpnRYGAwG38e4Q0biJGgvXbqEDx8+YMGCBaQmuKamJuzbtw/x8fGkElVeuQpBK5K6Em/evEFlZSUGDhzYIWrzJeUQaGRHp3IIL168QEJCAjIzMxEbGytrczoso0aN4jtpjvvL3BEStMJWJAnbt0AVzpw5A3t7e0RHR/M9RxLjK1ujtXzRhw8fEBsbK5JDaG36IQt6CmL706mWWEeOHEF+fj7d2t4G3DfM6upqREZG8kzoSjpBK0qJamBgIPT09EgVSQEBATwrkgIDAzl+btm3QGWHkJaWBnt7e1y9epXn4+KOrxSEtgYn8epKFwTuRkUa2dCpdgg0olNbWwtLS0uSTMSmTZtQUFAgsQStKCWqenp6uHnzJkfYqb6+HgYGBrh7965Az9ta3wLVKC8v55k/yc/Px4gRI2RgkWShw4Oyg3YINACaBe/s7OxIdf3STNCySlS5bTAyMsKpU6c4Et5v3ryBo6OjwDpHrfUtUA1e8s9MJhO6urqUz52J27BIIx602+2CuLu7k8o47927h9mzZ5POFVZYThz4lagKI5rHj9b6FqhAYWEhvvvuOzQ2NqK2tpY0T5zJZHaKWQ/ChgdpJAu9Q+iCcEsksybNGRsbk0TSpJWgba1EVdiKpNb6Fuzt7SVms7R5+vQp/v33X7i6uuLIkSMcjykoKGDkyJFS0zJqLyQRHqQRnU7hEGpra0n6/J1VtkDStKWWKaywnKi0VqIqLML2LVCNqqqqDqP82dDQgOLiYnz++ecA0GalUFtIIjxIIzqUDhl1hGHjVEQYtUxeX0JWglaS8Ar/iFKRBDRrNXEnJlVVVSVqryxhMpkICAhAWVkZuzO4oaEBz549k1rj3X///YeAgADExcVBQUEBsbGxWLlyJSIiIsSK9UsiPEgjOpTeIXSEYeNURFy1TGklaEWpSAI6f2Jy2bJlaGpqgqqqKsrLyzF27FjEx8fD3t4ea9eulYoNmzdvRlVVFdauXYuFCxciKysLO3fuxOvXr0mT7YRB2PAgjYQhKMyECROIxsZGWZtBOQwNDYnKykqOY+Xl5YSurq5Av3/lyhVixowZ7WGawBQVFRE6Ojo8H1uxYgWxfft2gslkEgRBEPX19URAQADh4uIiTRPbjQkTJhA1NTVEXl4e4eTkRBAEQWRlZRELFy6Umg0GBgZETU0NQRAEoa2tTRBE8/vM+j8NNaG0y+0Iw8apiDBqmcIKy0kLfhVJgPhKqh0dZWVlKCsr44svvkB+fj6A5u/Cjz/+KDUbunfvzp6RQfx/kKG2tlbspHZVVRUiIyPx+vVrUl5QWvOiuzKUdggdYdg4FRFGLVOYUZfSglWRNGPGDJ6PC6OkSkW+/vprREZGYsmSJVBWVsZff/0FRUVFgeZbSApTU1OsWbMGGzZsAAC8evUKwcHBMDY2Fuu6np6eKC8vh5GRkdTHgtJQPIfQ2k2/5dxZGk74qWWGhITwrF7paJ2jbVUkCaOkSkXy8vKwZs0aHDlyBA8fPmTflD08PPDDDz9IxQZWf0BMTAz+++8/KCgowMrKCj4+PmL1e0yePBnXrl2DioqKBK2lERRKOwQa8RBELZOKCdqulpgsLS3Fhw8f8OWXX8rk+auqqqCioiKRAT3m5uY4ceIEacdPIx0o7RBKSkpw5MgRmZbfUQl+74mSkhJGjBgBNTU10mPijrqkkRzckwJ5Ic1u5Zs3b6KwsBCNjY0cx8UR2Pv111+RkpICOzs70mwHKgsTUgVKO4SOUH5HJfjpEjU2NqKqqgoBAQGkGQdU6hwVtW+BKrBky7m/si2T/tLqv9m2bRvi4uIwcuRIjli/nJxcq/LcbdHZhxt1dCi9f75//z4yMzNRXFyMoKAg+Pr6YubMmQgNDZW1aR2S1jo9s7Oz4evrS3IIVErQ8pNmrqysxKVLl3D//n2+vQ1UgNcOoa6uDj///DPi4+Px008/Sc2WhIQExMbGSjxMRXcjyxZKO4SOUH7XWdDR0cG7d+9Ix6nUOdra8BaWkiqVHQI3rIRyz549ERsbi6+++kpqz62iooLBgwdL/LqddbgRVaC0Q+gI5XedgTt37iA2NhZjx44lPebu7o6mpibs3LmTlKClEq31LVANgiAQHh6OQ4cOYfHixVi7dq3USjRZ41fnzZsHHx8frF27Fp9++inHOeLoiHXW4UZUgdI5hI5QftcZCA4ORmFhITZs2NAuqz5Z05qSKtUoLCzETz/9hJKSEgQFBUFXV1eqz8+dx2iZvyDaSUesMw036uhQ2iFwI+vyu85EZ0rQSlJJVZbExcVh+/btMDIywtatW2Wi6FtUVNTmOZLWEetMw406OpR2CKWlpfDx8cGGDRswatQo7NmzB0+ePMHOnTvpOmYxEVVYjqb9YJWU9u7dm6/MdHsPond1dZV6uXFycjKCg4PphLMUoLRDcHV1hbq6Ojw9PaGsrIzKykqEhYWhrKwM4eHhsjav08Jv1CVN+yLIzZ57kpqk4TW+U5J01uFGVIHSSeV79+7h1q1b7CTyZ599Bh8fH0ydOlXGlnVuOlOClkq0982+I9ARtbO6EpR2CMrKynj16hWGDx/OPlZcXEzfsPggiU7XtoTlaDo3TCaz1fJegH+4URBYTu/Dhw8cDXgMBoOegigFKO0QHBwc4OLigu+//x4DBw5EaWkpTp48SW8t+WBra8uz05WFIBUivXr1gr29PRYsWNAeJtJQAH4NgJLgypUr2LZtG/7991/2sfaqXqIhQ+kcAgCcO3cOSUlJqKiowIABA2BlZYV58+bJ2iwamk5Je+cQDA0N4erqiunTp5PE8ugpiO0P5R0CjWhUVFSgsrKSQxQwPz8fc+fOlbFlNILAYDCQnJyMt2/fwtnZGY8fP5ZKjmHixIm4d+9eu11fT08Pf/zxB91cKiMoGTJav349du/eDXd3d77ld/SAHP4cP34cu3btYk+kYm3JR44cSTsECvD333/DyckJQ4cORV5eHmxsbODi4oLt27fDysqqXZ978uTJ7Xp9BwcHHDhwAE5OTnTOQAZQ0iF8/fXXAIDRo0fL2BJqEhkZibCwMHTv3h2pqanw9PREYGAgz+E4NB2PgIAA+Pj4wMrKCtra2hg6dCgOHTqEbdu2tbtDOHLkSLte/+uvv8b69etx8OBB9jE6hyA9KOkQWLIUTU1NWL58uVgTmroi79+/x8yZM1FRUYHdu3ejT58+8PHxgY2NDTZu3Chr82jaID8/H7NnzwbwP+kIfX19lJWVydIsibBjxw6sW7cOenp6dNhIBlDSIbA4ffo0PSpTBNTV1VFeXo7+/fujpKQETCYTvXr1atfqERrJoaGhgTt37nDoGD148AAaGhoytEoy1NfXw9nZWdZmdFm6bdmyZYusjRCVkpISpKSkQEFBAXV1daioqGD/o6Ur+FNdXY2dO3fC3Nwcr1+/xpUrV3Djxg0oKChg/vz5sjaPpg3U1NSwZs0alJSU4PHjx6irq0NwcDDWrVuHESNGyNo8sWAwGMjJycHkyZMlMpKTRjgoXWXEr4mKjje2TVJSEmbMmIHGxkbs3r0bDAYDa9aswdChQ2VtGo0APHz4ELGxsexZ17a2tu2e8JUGNjY2yMvLQ/fu3dGzZ0+OopH21mmiobhDoKGh6Vy0dtPvCtIdsobyDoFVj11SUgInJyep1WNTmZcvXyIsLAyvX79ml56yiIuLk5FVNIJSUFCAvXv38hxw31n+ftzSFYB4g3doBIPSSWXuemxra2up1WNTGT8/P/Ts2RN2dnbo3p3SH4EuiaenJz799FMsWLBAapPSpAUtXSFbKL1DcHBwgL29Pbse+86dO8jOzsa2bduQnJwsa/M6LJMmTUJmZiY++eQTWZtCIwJaWlq4desWpYf98IOWrpAtlF4eduZ67PZk6NChqKiowOeffy5rU2hEYOTIkSguLsYXX3wha1MkDpPJhL29Pd2DICMo7RA6cz12e2JoaAhHR0dYW1vjs88+43hsyZIlMrKKpi2io6MBAF9++SUWL16M7777jjTgnup/P1q6QrZQOmR0/fp1bNiwATY2NoiLi4OjoyNiYmLg6+sLCwsLWZvXYVm8eDHP43JycuybDk3Hg9/fjUVn+PulpKRg/fr1HMUOdA5BelDaIQCdtx6bhoYfrC5zbvLz8ynfmMbavfKSrmhreBON+FDeIdCIxpUrVxAbG4u3b9/is88+g7W1NT30hiLwmknAZDKhq6vbrtLU0kBXV5ee1S1DKJ1DyMnJQVBQEAoLC0k1y3RXI39Onz6N8PBwLFmyBIMHD0ZBQQFCQ0NRW1sLR0dHWZtHw4PCwkJ89913aGxsRG1tLanXhslkdooVtJ2dHUJDQ+Hu7k6XRMsASu8QjI2NYWxsDENDQ1KJGt2cxh8zMzPs2bMHY8aMYR978uQJVq9ejevXr8vQMprWePr0Kf7991+4urqSZKgVFBQwcuRI9OzZU0bWSQZaukK2UNohTJ48Gbdv36ZL1IREW1sbt27d4njfGhsboaenh7t378rQMhpBqKqq6rSzK2jpCtlC6T3Z7NmzcfHiRTr2LSSjR4/GyZMnOcJDp06dogcOUYTO6gwA+qYvayi9Q8jOzsby5cuhrKxMqlmmQx/8efToEZYtWwY1NTVoaGiguLgY1dXViIiI4Agj0dBIi9bG4bKgx+K2P5TeIWzatAl2dnbQ1dWltdOFQFNTE7/99hvS0tJQVVWFgQMHYvr06ejTp4+sTaNphZiYGMyfPx8NDQ2dTsOI3p12DCi9Q5g0aRJyc3NlbQZlYNWpP336lO85naFSpbPC+rzzKjuloZEElHYIfn5++Oabb+gcgoCwbiT0YCFqYmpqigEDBiA3NxczZszgeQ4dVqERB0qHjMrKyuDv748DBw5ARUWFHYOUk5PDxYsXZWxdx4O1qmxth0DTcTlw4ACuXr3aqlOnoREHSjsEc3NzmJubcxyrra1FUFCQjCyiBra2trh06RLp+IwZM3Djxg0ZWEQjCF999RW++uorEASBlStXytocmk4IpUNGLUlLS0N0dDRu3boFTU1NxMTEyNqkDkVhYSGCgoJAEATS09NhZGTE8XhNTQ1evXqFjIwM2RhIIxSdVXokPT0d06ZNo3uLZASldwg1NTWIiYnByZMnUVxcjEWLFsHX15fyAl/tweDBg6Gvr4+qqiqkp6eTQg4KCgrw9fWVkXU0wtCZpUd27NgBBoMBCwsLWFtbY/z48bI2qUtByR3C8+fPER0djYSEBIwbNw52dnbYvn07EhMTSfr+NGRSU1NhYmIiazNoRKSzS4/89ddfuHz5MpKTk/HJJ5/AxsYGVlZWGDJkiKxN6/RQ0iGMHj0ac+fOhbOzM7788ksAwNSpUxEfH087BAFoamrCtWvXUFZWxhYFbGhoQH5+Pnbu3Clj62jaoqtIjxAEgaysLAQHByMvLw9aWlpYsGABbGxs2mxioxENSoaMFixYgJSUFBQVFeG7776jV7tCsmnTJqSlpUFVVRX19fVQVlZGXl4eLC0tZW0ajQB0dukRJpOJjIwMXL58GRkZGRg+fDh8fHwwcOBA/Prrr8jIyMDevXtlbWanhJI7BKC5migxMRHnzp1DSUkJGAwGIiIiaC0UAdDV1cWFCxdQWVmJqKgohIaG4sSJE8jJyUFYWJiszaNpg84sPeLp6Ym0tDSoqKjA0tISNjY2GDZsGPvxp0+fwt7envJzHzoqlHUILXn48CHOnTuHy5cvQ0NDA1ZWVnBzc5O1WR0W1hASBoOBuXPnIjU1FQ0NDTAyMsIff/wha/NoBKCqqqpTSo94e3tj3rx5fKceMhgMvHz5EuPGjZOyZV2DTuEQWDAYDCQkJOD8+fM86+xpmpk/fz58fX0xceJETJs2DXFxcejevTu+/fbbThWDpqEeZmZmiImJIYlV0kgHSuYQ+KGsrAwHBwc4ODjI2pQOjYuLC5ycnJCUlIR58+bB3t4e8vLymDZtmqxNo+niNDQ04MOHD7RDkBGdaodAIzjFxcXo378/evTogaSkJDAYDMyZMweKioqyNo2mC7N27Vrcvn0bOjo6UFNT46gm8vb2lqFlXYNOtUOgERwNDQ3U1taivr6e3bXc0NBAOwQamaKkpITp06cDaG48pZEu9A6hC3Lt2jVs3boVFRUV7GMEQdBqpxShuroaZ8+exYoVK/DXX3/B09MTn376KXbu3Mnuy6GhEQXaIXRBLC0tYWpqCmtra3TvzrlJHDRokIysohEUDw8P1NbW4siRI5g3bx60tLTQq1cv3Lt3D9HR0bI2TywYDAbOnz/Ps2nyxIkTMrau80OHjLogRUVFWLlyJS0gRlH++usvpKamory8HE+fPsXRo0fRp0+fTtGD4+3tjefPn6Nv376oqanBoEGDcPPmTdjZ2cnatC4BPXeyC6Kvr0/3G1CY+vp6yMnJITMzEyNHjoSqqirev38PBQUFWZsmNllZWYiOjoa3tzc0NDQQHh6O4OBgvHjxQtamdQnoHUIXpGfPnli5ciU0NTXRr18/jsfoiVsdH0NDQ7i4uODFixdYvnw53rx5A09PT8ycOVPWpomNoqIi+vXrh169eiEvLw9Ac2/C1q1bZWxZ14B2CF2QYcOG0Z3cFCYgIACnT5+GpaUlFixYgOfPn0NPT69T/E2HDRuGlJQUmJmZQU5ODq9fv0aPHj3w8eNHWZvWJaCTyl2Q0NBQLF++HL169ZK1KTQ0HOTk5GDVqlWIjY1Feno6QkND0a1bN1haWsLf31/W5nV6aIfQBdHV1UVWVhadVKYoo0aN4in/3L17d6iqqsLQ0BBeXl6U7fatq6uDoqIi5OXlcf/+fdTU1NBd9FKCdghdkICAAHz48AEWFhZQU1PjeIwe3t7xiYiIwLVr17B69WoMHDgQJSUlOHjwIDQ1NaGvr4/jx49DTU0NP//8s6xNFQkGg4G3b9+iqamJ4zj92Wx/aIfQBeH3xaIb06iBiYkJzp49i759+7KPVVdXY+HChUhNTcX79+9hbGyM27dvy9BK0Th37hwCAwPBZDI5jtOfTelAJ5W7IE+fPpW1CTRi8O7dO3Cv45qamlBZWQmgWf6Bqhw9ehT+/v6YM2cOqWmSpv2h3/EuCoPBQHJyMkpKSuDk5ITHjx93isamroClpSVcXV2xYsUKqKmpoaSkBL/++itmz56NmpoaBAQEQFdXV9ZmikRVVRXmzZsHeXm6RUoWdNuyZcsWWRtBI13+/vtvzJ8/H8XFxbh27Rqsra3h4OCAwYMHY+TIkbI2j6YNDAwMUFZWhmPHjiE6OhoPHz7E7Nmz8eOPPyI/Px/5+fnw9fWl5E7h5cuXePfuHcaOHStrU7okdA6hC+Lg4AB7e3tYWVlBW1sbd+7cQXZ2NrZt24bk5GRZm0fThXFyckJWVhb69+9PapqMi4uTkVVdBzpk1AXJz8/H7NmzAYBdvqivr4+ysjJZmkUjIFVVVYiMjMTr169JDVtU7zS3srKClZWVrM3ostAOoQuioaGBO3fucMSZHzx4AA0NDRlaRSMonp6eKC8vh5GREXr06CFrc/6vvbsNiqpswAB8r8qKaYIaC0ghRAxYgjJDINawtZiOgF8EmcZkiBJbDQZoA2ggxthojqA28jHSoEkpCVIyg6MR4kcmoKhjg4E/DFIBd5kQ/FpY9v2RbS8v9WqCPp499/ULnmV27/1zbs7znPOcQTV//nzREWSNhSBDcXFx0Gq1mDt3LgwGAzIzM7F3716sWrVKdDS6D2fOnMH3338PW1tb0VEGzcKFC/H1119j3rx5f3vTHcApo0eBhSBDQUFB2LFjB4qLi+Hn5we9Xo/NmzfD19dXdDS6D3Z2dujp6REdY1D9+Rz0xYsXC04ib1xUlqH8/HxER0f3G8/MzER8fLyARPRv5OXl4cCBA3jzzTcxbty4Pq8FBQUJSjU4kpKSsHr1asluuyF1LASZ0Ov1OHPmDAAgMTERmzZt6nNzU2dnJ9LT01FXVycqIt2nf9rmWqFQoGUcXXwAAAwBSURBVKKi4hGnGVwBAQGoqqqyiGc7SBELQSbu3LmDyMhI6PV6XL16FY6Ojn1eVyqVCAsLQ0xMjKCEREBqaiqampowY8YMqFSqPusJUj/7kQIWggx98MEHkr88UY6qqqqgVqv/8SxAoVBI/iE5lnz2IwUsBBm7efNmv+vYOXf7+AoNDUVZWRkPmvTQsBBkqKKiAmlpaebN0ADAZDJxR0l6LDQ3N6O4uBitra1ISkpCZWUl5s2bJzqWLLAQZCg0NBQzZ87EnDlz+u0o6eTkJCgV3cv9/Pcv9Xn2EydOIC4uDoGBgaisrMT+/fsRHh6Od999F++8847oeBaPhSBDPj4+qK2t5RPTJObPqaLe3l60trbCxsYGjo6OuHbtGvR6PTw8PFBaWio45cCEhYVhxYoVmDZtmnmfrfr6erz//vv44YcfRMezeLwxTYYCAgJw/PhxBAYGio5C/8KfB8SMjAyMGTMGWq3WvE10Xl4eLl68KDLeoGhqasLUqVMB/LXPlqenJ65fvy4ylmywEGRoxIgReO+99zBp0qR+O0ry6qPHX2lpKU6ePNnnmQHR0dF48cUXBaYaHG5ubjhw4ACCg4PNY0ePHoWrq6vAVPLBQpAhV1dXxMbGio5BD2jMmDGorq5GQECAeezIkSOwt7cXmGpwfPTRR4iJiUFpaSlu3bqFxMREHDlyBFu2bBEdTRa4hkAkMWVlZUhJScFLL70EBwcHXLlyBdXV1cjKyoJarRYdb8CuXr2KsrIyXLlyBSqVCsHBwZgwYYLoWLLAQpCRnTt33vNv3n777UeQhAbqwoULOHjwIHQ6HVQqFUJDQ+Hi4iI61oBlZGRg9erV/cZXrlyJzz77TEAieeGUkYwcOnTo/76uUChYCBLh6emJJ554ApcvX4avry/u3LkjOtIDa2lpwcGDBwEARUVFcHZ27vN6Z2cnKisrRUSTHRaCjHz55ZeiI9AgaG9vR0JCAmpqaqBUKlFUVITIyEjk5+dj0qRJouP9ayqVCqdPn4Zer0dPT0+/f1yUSiU+/vhjQenkhVNGRBITHx+PsWPHIjExEWq1GjU1NcjLy8Phw4fx1VdfiY43IOvWrUNKSoroGLLFQiCSmP/eItrPzw/V1dUwGo3w9/dHbW2t6HgD1tXVhfLycrS0tGDJkiX4+eef4efnJzqWLAy5958Q0eNk5MiRaGtr6zN27do12NjYCEo0eOrr6/Haa6+huLgYX3zxBXQ6HZYtW4b9+/eLjiYLLAQiiQkPD0dsbCzKy8thNBpx/PhxfPjhhxaxAdwnn3yClJQU7N69G8OGDcOECROQk5ODbdu2iY4mC1xUJpKYmJgYDB8+HFu3boXRaMTatWsxd+5ci3i4UWNjI0JCQgD8tXVFQEBAvzMiejhYCEQSM2TIEERFRSEqKkp0lEE3fvx41NTUwN/f3zx27tw5jB8/XmAq+eCUEZEEfffdd1i0aBGmT5+OlpYWJCcn4/bt26JjDVhcXBy0Wi3S09NhMBiQmZkJrVYLrVYrOpossBCIJKagoADZ2dmYO3cufv/9d1hbW+PSpUtYt26d6GgDFhQUhB07dsBkMsHPzw96vR6bN2/us9kdPTy87JRIYmbMmIHt27fD2dnZfNlpe3s7QkND8eOPP4qO90Byc3MRERGBsWPHio4iazxDIJKY69evw9HREcAfjz4F/ngW9v8+H1tKKisroVarkZCQYBH3UkgVC4FIYnx9fbFx40b09vaar8TJz8/HlClTBCd7cLt378a+fftgb2+PuLg4hISEYNeuXejq6hIdTVY4ZUQkMa2trYiNjUVzczNu3ryJp556CqNGjUJubi6eeeYZ0fEGrLu7GxUVFdi7dy/q6uoQHByMhQsX4vnnnxcdzeKxEIgkqLe3F+fPn8fly5ehUqng7e0NKysr0bEGXWtrKzZu3IiysjLU19eLjmPxOGVEJDFdXV1Ys2YNnnzyScyaNQunT59GRkYGbt26JTraoLl27Rq2b9+O6OhonDp1CnFxcaIjyQILgUhiUlNT0dbWhtGjRwMANBoNdDod0tPTBScbGIPBgPLycsTExECj0aC2thaJiYmoqKjgfQiPCKeMiCTG398fVVVVsLa2No/duHEDQUFB+OmnnwQme3Br1qxBeXk5lEolwsLCsGDBAt6dLAC3riCSmGHDhqGjo6NfIQwfPlxgqoG5dOkS0tPTMX36dAwbxsOSKDxDIJKYDRs24MSJE9BqtXBwcEBraytyc3Ph7++PlStXio5HEsZCIJKY7u5ubN26FWVlZdDpdLC3t8fs2bMRGxsLpVIpOh5JGAuBiIgA8CojIsnp7e1FXl4eZs6cicmTJ0Oj0SArKwtGo1F0NJI4rt4QScy2bdtQXl6O5cuXw8nJCU1NTcjJyYFCocDy5ctFxyMJ45QRkcQEBQWhoKCgzzYVTU1NeOutt3D06FGByUjqOGVEJDFdXV1wcHDoM+bg4ACDwSAoEVkKFgKRxPj4+GDTpk3mNYOenh5kZmZKerdTejxwyohIYpqamrBkyRJ0dHTAzs4ObW1tsLe3R05OjkXsdkrisBCIJKahoQEuLi44deoU2tvb4ejoCG9vb97hSwPGQiCSmKlTp6KiogIjR44UHYUsDNcQiCTG1dUVZ86cER2DLBDPMYkkaOnSpRgzZgxUKpX5MZoAsG/fPoGpSOo4ZUQkMf/voD9//vxHmIQsDQuBSEJMJhM6Ojpga2srOgpZIK4hEElEY2MjXn31VQQEBGDOnDn49ddfRUciC8NCIJKI9evXIyQkBPv378eUKVOwfv160ZHIwnDKiEgifH19cfLkSQwdOhQdHR0ICQnBsWPHRMciC8IzBCKJMJlMGDp0KADAxsaGexfRoGMhEEkET+bpYeN9CEQSYTKZ8Msvv5iLwWg09vkdADw9PUXFIwvANQQiifD09IRCofjHMwWFQoH6+vpHnIosCQuBiIgAcA2BiIjuYiEQEREAFgIREd3FQiDZ8/DwQGVlpegY/fz222/w8PBAQ0OD6CgkE7zslGTv2LFjsLGxER2DSDgWAsmenZ2d6AhEjwVOGZHs/TllZDAYsGXLFmg0Gnh5eWHhwoU4e/as+e80Gg127dpl/v1/p3Q0Gg02bNiAV155BWq1Gh0dHfDw8EBJSQnCwsIwefJkREREoK6uzvwe586dw+LFi+Hj4wMvLy+Eh4fj9OnTj+7LE/0XFgLRXWvXrkVxcTHS0tJQWloKd3d3REVFoa2t7b7f45tvvsGWLVvw+eefm6ehsrKysHz5cuzZswdWVlZITU0FANy4cQPLli3DxIkT8e2336KoqAgjR45EWlraQ/l+RPfCQiACcP36dZSUlCA5ORlqtRpubm5Ys2YNHB0dUVhYeN/vM2vWLHh7e8PLy8s8FhkZCbVaDU9PT0RHR6OhoQEGgwG3b9/G0qVLsWLFCjg7O2PixIlYtGgRLl68+DC+ItE9cQ2BCEBzczOMRiN8fHzMY0OGDIGPjw8aGxvv+32cnZ37jbm4uJh/HjVqFACgp6cH48aNQ0REBAoLC3HhwgVcunQJ9fX16O3tffAvQjQALAQiAKNHj/7bcZPJ9I8HaKPR2G/M2tq635iVldXfvm9bWxvCwsLg5uaGwMBAzJ49G3q9HitWrPiX6YkGB6eMiAA4OTnBysqqz4KvyWTC2bNn8eyzzwL448De2dlpfr25uXlAn3no0CEolUoUFBQgOjoa06ZNQ0tLi/mziR41niEQ4Y/pocjISHz66acYMWIEnn76aRQWFqK5uRlvvPEGAMDLywt79uyBWq1Gd3c3srKyoFAoHvgzbW1todPpcPjwYbi7u6O6uhrZ2dkAwIffkBAsBKK7EhISoFAokJycjBs3bsDLyws7d+40rwHEx8dj1apVWLBgAcaPH4+UlBRotdoH/rxZs2ahrq4OSUlJ6O7uhru7OzIyMpCQkIDz58/D3t5+kL4Z0f3h9tckaz09PXjhhReQn5+Pl19+WXQcIqF4hkCypdPpzDeBOTg4CE5DJB4LgWSrpKQE2dnZeP311/Hcc8+JjkMkHKeMiIgIAC87JSKiu1gIREQEgIVARER3sRCIiAgAC4GIiO5iIRAREQDgP0FvrJeEXzjbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full text x journal\n",
    "df_sub = df_cord[df_cord.journal.isin(df_cord.journal.value_counts()[:30].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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3bccb090>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full text x year\n",
    "df_sub = df_cord[(pd.notnull(df_cord.publication_year)) & (df_cord.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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overlap of CORD19 with Dimensions and WHO\n",
    "\n",
    "This is still problematically low.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets\n",
    "\n",
    "who_latest = \"datasets_input/WHO_28_03_2020.csv\"\n",
    "dimensions_latest = \"datasets_input/Dimensions_28_03_2020.csv\"\n",
    "\n",
    "df_who = pd.read_csv(who_latest)\n",
    "df_dimensions = pd.read_csv(dimensions_latest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "who_dois = df_who[pd.notnull(df_who[\"DOI\"])][\"DOI\"].tolist()\n",
    "dimensions_dois = df_dimensions[pd.notnull(df_dimensions[\"DOI\"])][\"DOI\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cord_dois = df_cord[pd.notnull(df_cord[\"doi\"])][\"doi\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42441"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cord_dois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4323866044934294"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(cord_dois).intersection(set(who_dois)))/len(set(who_dois))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.592679794520548"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(cord_dois).intersection(set(dimensions_dois)))/len(set(dimensions_dois))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2604880136986301"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(who_dois).intersection(set(dimensions_dois)))/len(set(dimensions_dois))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In December 2019, a new type of coronavirus pneumonia (COVID-19) emerged in Wuhan, Hubei, and spread rapidly to China and over 100 countries around the world. The lung injury and repair caused by COVID-19 has many similarities with the onset and progression of interstitial lung disease (ILD) . Therefore, it is difficult to distinguish between COVID-19 and some types of new-onset ILD or other causes leading to acute exacerbation of ILD. Clinicians need to comprehensively analyze the epidemic history, disease onset characteristics, clinical manifestations, image characteristics, serological andpathogenic microorganism test results to confirm diagnosis. Because of this, the article will discuss the issues related to the differential diagnosis and management of COVID-19 and ILD, and try to provide reasonable suggestions.'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abstracts = df_meta[pd.notnull(df_meta[\"abstract\"])].abstract.tolist()\n",
    "abstracts[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topic modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import gensim, sklearn, spacy\n",
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: en_core_web_sm==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm==2.0.0 in /anaconda3/envs/covid/lib/python3.7/site-packages (2.0.0)\n",
      "\n",
      "\u001b[93m    Linking successful\u001b[0m\n",
      "    /anaconda3/envs/covid/lib/python3.7/site-packages/en_core_web_sm -->\n",
      "    /anaconda3/envs/covid/lib/python3.7/site-packages/spacy/data/en\n",
      "\n",
      "    You can now load the model via spacy.load('en')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')\n",
    "STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 34min 13s, sys: 8min 20s, total: 1h 42min 34s\n",
      "Wall time: 28min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "processed_docs = list()\n",
    "for doc in nlp.pipe(abstracts, n_threads=5, batch_size=10):\n",
    "\n",
    "    # Process document using Spacy NLP pipeline.\n",
    "    #ents = doc.ents  # Named entities\n",
    "\n",
    "    # Keep only words (no numbers, no punctuation).\n",
    "    # Lemmatize tokens, remove punctuation and remove stopwords.\n",
    "    doc = [token.lemma_ for token in doc if token.is_alpha and not token.is_stop]\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": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(processed_docs, open(\"datasets_output/processed_docs.pk\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "processed_docs = pickle.load(open(\"datasets_output/processed_docs.pk\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": 83,
   "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": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 14389\n",
      "Number of docs: 39154 (39154)\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(abstracts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 24s, sys: 796 ms, total: 1min 24s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from gensim.models import LdaMulticore, LdaModel\n",
    "params = {'num_topics': 15,'passes': 3, '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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.049*\"respiratory\" + 0.047*\"virus\" + 0.041*\"infection\" + 0.023*\"viral\" + 0.023*\"child\"'),\n",
       " (1,\n",
       "  '0.048*\"rna\" + 0.028*\"sequence\" + 0.024*\"virus\" + 0.024*\"gene\" + 0.015*\"genome\"'),\n",
       " (2,\n",
       "  '0.079*\"influenza\" + 0.023*\"influenza_virus\" + 0.022*\"case\" + 0.018*\"pandemic\" + 0.012*\"virus\"'),\n",
       " (3,\n",
       "  '0.025*\"group\" + 0.023*\"diarrhea\" + 0.022*\"pig\" + 0.021*\"day\" + 0.019*\"strain\"'),\n",
       " (4,\n",
       "  '0.016*\"rat\" + 0.013*\"increase\" + 0.012*\"group\" + 0.011*\"study\" + 0.011*\"effect\"'),\n",
       " (5,\n",
       "  '0.038*\"protein\" + 0.024*\"virus\" + 0.021*\"cell\" + 0.015*\"activity\" + 0.014*\"viral\"'),\n",
       " (6,\n",
       "  '0.056*\"cell\" + 0.025*\"infection\" + 0.022*\"mouse\" + 0.019*\"virus\" + 0.018*\"response\"'),\n",
       " (7,\n",
       "  '0.026*\"calf\" + 0.025*\"assay\" + 0.024*\"sample\" + 0.021*\"test\" + 0.021*\"pcr\"'),\n",
       " (8,\n",
       "  '0.030*\"sars\" + 0.030*\"cov\" + 0.029*\"antibody\" + 0.027*\"coronavirus\" + 0.024*\"protein\"'),\n",
       " (9,\n",
       "  '0.034*\"virus\" + 0.022*\"disease\" + 0.022*\"human\" + 0.017*\"animal\" + 0.013*\"specie\"'),\n",
       " (10,\n",
       "  '0.020*\"disease\" + 0.015*\"review\" + 0.014*\"vaccine\" + 0.012*\"development\" + 0.011*\"cat\"'),\n",
       " (11,\n",
       "  '0.015*\"infection\" + 0.013*\"control\" + 0.013*\"outbreak\" + 0.013*\"transmission\" + 0.011*\"sars\"'),\n",
       " (12,\n",
       "  '0.042*\"health\" + 0.018*\"public\" + 0.013*\"public_health\" + 0.010*\"care\" + 0.009*\"disease\"'),\n",
       " (13,\n",
       "  '0.032*\"model\" + 0.015*\"method\" + 0.015*\"-PRON-\" + 0.014*\"datum\" + 0.012*\"study\"'),\n",
       " (14,\n",
       "  '0.068*\"patient\" + 0.015*\"clinical\" + 0.014*\"case\" + 0.012*\"hospital\" + 0.011*\"study\"')]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.show_topics(num_words=5, num_topics=params['num_topics'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el266001126592610082000754445\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el266001126592610082000754445_data = {\"mdsDat\": {\"x\": [0.17480745294457353, 0.25890535918842567, -0.007638262179896828, 0.21514933068003556, 0.20436990404189695, 0.007382148844037598, -0.1257829070516989, -0.21082207491229074, 0.0331296060851704, -0.08107381946468352, -0.1521174966076142, -0.09326239136060555, 0.029935445600431227, -0.1757823361407252, -0.07719995966705663], \"y\": [0.057817194718886904, -0.026674449282178866, -0.18713361038957688, 0.013949931574608793, 0.09132483302356388, -0.043727706915576654, -0.2396893937715531, 0.0914378092087572, 0.03767209609844211, 0.08730533668482866, -0.08959500426393155, 0.18426277496102048, -0.20179681155716814, 0.04117758528288572, 0.18366941462699157], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.428565979003906, 10.989014625549316, 9.008088111877441, 7.6992058753967285, 6.742216110229492, 6.573009490966797, 6.451426982879639, 6.194250106811523, 5.844677448272705, 5.543397426605225, 5.459969997406006, 4.99225378036499, 4.878842353820801, 4.024190425872803, 3.170896053314209]}, \"tinfo\": {\"Term\": [\"cell\", \"patient\", \"influenza\", \"rna\", \"protein\", \"virus\", \"respiratory\", \"health\", \"infection\", \"sars\", \"cov\", \"sequence\", \"antibody\", \"mouse\", \"gene\", \"model\", \"calf\", \"assay\", \"viral\", \"case\", \"vaccine\", \"coronavirus\", \"human\", \"group\", \"sample\", \"strain\", \"day\", \"pcr\", \"response\", \"disease\", \"macrophage\", \"prrsv\", \"cns\", \"apoptosis\", \"nervous\", \"central_nervous\", \"epithelial\", \"fiv\", \"chemokine\", \"cytokine\", \"encephalomyelitis\", \"epithelial_cell\", \"ifn\", \"spinal\", \"sclerosis\", \"multiple_sclerosis\", \"mononuclear\", \"dendritic\", \"system_cns\", \"mediator\", \"neuron\", \"demyelinate\", \"tnf\", \"prv\", \"dendritic_cell\", \"porcine_reproductive\", \"mononuclear_cell\", \"spinal_cord\", 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\"serum_igg\", \"noninfectious\", \"partnership\", \"commitment\", \"review_current\", \"advancement\", \"food_drug\", \"license\", \"subclass\", \"art\", \"chapter\", \"review_summarize\", \"advance\", \"summarize\", \"past_decade\", \"review\", \"review_discuss\", \"review_focus\", \"preclinical\", \"discuss\", \"clinical_trial\", \"feline\", \"technology\", \"biology\", \"delivery\", \"development\", \"drug\", \"decade\", \"recent\", \"application\", \"vaccine\", \"therapeutic\", \"discovery\", \"progress\", \"current\", \"research\", \"medicine\", \"strategy\", \"focus\", \"new\", \"disease\", \"effective\", \"therapy\", \"infectious\", \"agent\", \"approach\", \"potential\", \"use\", \"develop\", \"pathogen\", \"clinical\", \"include\", \"treatment\", \"abstract\", \"provide\", \"system\", \"human\", \"base\", \"nucleotide\", \"ibv\", \"orf\", \"subgenomic\", \"recombination\", \"reading\", \"codon\", \"frameshift\", \"open_reading\", \"genomic_rna\", \"rna_synthesis\", \"subgenomic_rna\", \"infectious_bronchitis\", \"negative_strand\", \"nucleotide_sequence\", \"egg\", \"rdrp\", \"pseudoknot\", \"single_strand\", \"transcribe\", \"rna_recombination\", \"frame_orf\", \"eukaryotic\", \"embryo\", \"rna_polymerase\", \"arterivirus\", \"passag\", \"rna_dependent\", \"ibv_strain\", \"complete_genome\", \"strand\", \"genome\", \"sequence\", \"rna\", \"ribosomal\", \"frame\", \"positive_strand\", \"genomic\", \"mrnas\", \"leader\", \"deletion\", \"mutation\", \"passage\", \"gene\", \"template\", \"segment\", \"chicken\", \"mrna\", \"region\", \"encode\", \"amino\", \"synthesis\", \"amino_acid\", \"transcription\", \"strain\", \"mutant\", \"clone\", \"site\", \"virus\", \"contain\", \"analysis\", \"protein\", \"isolate\", \"viral\", \"acid\", \"-PRON-\", \"dna\", \"coronavirus\", \"replication\", \"result\", \"sars_cov\", \"monoclonal_antibody\", \"mabs\", \"cdna\", \"spike_protein\", \"neutralize_antibody\", \"mab\", \"epitope\", \"monoclonal\", \"rbd\", 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\"heifer\", \"chikv\", \"ecology\", \"haemorrhagic\", \"virome\", \"domestic_animal\", \"bat_specie\", \"denv\", \"farmer\", \"spillover\", \"exotic\", \"subfamily\", \"arthropod\", \"nidovirales\", \"fox\", \"bat\", \"ecological\", \"chikungunya\", \"tick\", \"mosquito\", \"reservoir\", \"wnv\", \"bird\", \"west_nile\", \"dengue\", \"zoonotic\", \"dog\", \"rodent\", \"canine\", \"specie\", \"species\", \"livestock\", \"horse\", \"rabie\", \"diversity\", \"domestic\", \"animal\", \"mammal\", \"emergence\", \"human\", \"population\", \"endemic\", \"host\", \"parasite\", \"disease\", \"virus\", \"transmission\", \"pathogen\", \"evolution\", \"prevalence\", \"genetic\", \"origin\", \"emerge\", \"infection\", \"study\", \"identify\", \"-PRON-\", \"different\", \"viral\", \"infectious\", \"include\", \"outbreak\", \"new\", \"find\", \"factor\", \"infect\", \"rabbit\", \"disaster\", \"tourism\", \"crisis\", \"security\", \"political\", \"internet\", \"anxiety\", \"social_medium\", \"federal\", \"making\", \"stakeholder\", \"news\", \"maker\", \"psychiatric\", \"respondent\", \"rhythm\", \"leadership\", \"legal\", \"perception\", \"decision_making\", \"workforce\", \"training_program\", \"immobilize\", \"risk_perception\", \"mental_health\", \"suicide\", \"public_health\", \"health_service\", \"policy_maker\", \"public\", \"perceive\", \"attitude\", \"professional\", \"government\", \"global_health\", \"health\", \"policy\", \"sector\", \"preparedness\", \"communication\", \"student\", \"agency\", \"planning\", \"psychological\", \"service\", \"education\", \"social\", \"training\", \"plan\", \"international\", \"national\", \"emergency\", \"health_care\", \"resource\", \"global\", \"practice\", \"care\", \"program\", \"country\", \"management\", \"information\", \"survey\", \"medical\", \"research\", \"system\", \"need\", \"disease\", \"community\", \"response\", \"risk\", \"knowledge\", \"study\", \"infectious_disease\", \"surveillance\", \"provide\", \"-PRON-\", \"result\", \"admission\", \"icu\", \"intensive_care\", \"ard\", \"patient_admit\", \"intubation\", \"respiratory_failure\", \"mechanical_ventilation\", \"abdominal\", \"critically_ill\", \"sore\", \"sore_throat\", \"distress_syndrome\", \"hadv\", \"sprague_dawley\", \"dawley\", \"sprague\", \"postmortem\", \"hantavirus\", \"rrt\", \"respiratory_distress\", \"stool_specimen\", \"kawasaki\", \"hospital_stay\", \"university_hospital\", \"rrt_pcr\", \"myalgia\", \"ventilator\", \"patient_die\", \"adv\", \"admit\", \"odd_ratio\", \"medical_record\", \"biopsy\", \"chest_radiograph\", \"comorbiditie\", \"chest\", \"odd\", \"patient\", \"distress\", \"radiograph\", \"trauma\", \"retrospective\", \"sepsis\", \"corticosteroid\", \"pneumonia\", \"discharge\", \"intensive\", \"median\", \"cohort\", \"stay\", \"failure\", \"hospital\", \"cough\", \"symptom\", \"outcome\", \"risk_factor\", \"diagnosis\", \"antibiotic\", \"clinical\", \"diagnose\", \"onset\", \"case\", \"fever\", \"severe\", \"acute\", \"care\", \"illness\", \"mortality\", \"risk\", \"associate\", \"conclusion\", \"day\", \"study\", \"treatment\", \"age\", \"year\", \"respiratory\", \"infection\", \"include\", \"-PRON-\", \"disease\", \"result\", \"factor\", \"high\", \"calf\", \"vaccinated\", \"lamp\", \"cage\", \"brd\", \"neonatal_calf\", \"intake\", \"fluorescent_antibody\", \"calf_feed\", \"band\", \"biosensor\", \"fresh\", \"day_old\", \"lamp_assay\", \"commercially_available\", \"weight_gain\", \"crcov\", \"serum_sample\", \"fixation\", \"replacer\", \"sensitive_specific\", \"stool_sample\", \"milk_replacer\", \"holstein\", \"chip\", \"formalin\", \"false_positive\", \"strip\", \"limit_detection\", \"immunosorbent_assay\", \"immunosorbent\", \"immunoassay\", \"sensitivity_specificity\", \"enzyme_link\", \"pcr_assay\", \"elisa\", \"kit\", \"sensitivity\", \"pcr\", \"assay\", \"inoculation\", \"detection\", \"real_time\", \"sample\", \"bovine\", \"amplification\", \"test\", \"real\", \"nucleic_acid\", \"nucleic\", \"reaction\", \"detect\", \"sensitive\", \"specimen\", \"positive\", \"diagnostic\", \"specificity\", \"sera\", \"culture\", \"method\", \"time\", \"rapid\", \"serum\", \"antibody\", \"virus\", \"technique\", \"result\", \"develop\", \"specific\", \"high\", \"antigen\", \"abstract\", \"clinical\", \"platelet\", \"contaminant\", \"eae\", \"mosaic\", \"electrolyte\", \"edema\", \"disulfide_bond\", \"consolidation\", \"crypt\", \"exhale\", \"allergen\", \"thin\", \"idiopathic\", \"porcine_circovirus\", \"collagen\", \"ileal\", \"rhinitis\", \"exudation\", \"randomly_select\", \"postoperative\", \"pleural\", \"thoracic\", \"quercetin\", \"ast\", \"npc\", \"hemorrhage\", \"melatonin\", \"opacity\", \"film\", \"dark\", \"demyelination\", \"steroid\", \"microglia\", \"wind\", \"albumin\", \"arthritis\", \"leishmania\", \"glucose\", \"rat\", \"diet\", \"gas\", \"oil\", \"deposition\", \"lesion\", \"anesthesia\", \"muscle\", \"concentration\", \"diffuse\", \"blood\", \"transplantation\", \"lung\", \"white\", \"sign\", \"count\", \"plasma\", \"normal\", \"male\", \"female\", \"decrease\", \"temperature\", \"increase\", \"group\", \"effect\", \"treatment\", \"significantly\", \"level\", \"day\", \"dose\", \"pulmonary\", \"serum\", \"low\", \"treat\", \"change\", \"significant\", \"high\", \"body\", \"study\", \"compare\", \"exposure\", \"control\", \"abstract\", \"result\", \"disease\", \"observe\", \"associate\", \"les\", \"des\", \"hcw\", \"dan\", \"mask\", \"est\", \"par\", \"hajj\", \"pilgrim\", \"respirator\", \"ppe\", \"publisher\", \"quarantine\", \"cdv\", \"wear\", \"passenger\", \"traveller\", \"hand_hygiene\", \"airport\", \"incubation_period\", \"skill\", \"hotel\", \"cette\", \"visitor\", \"tourist\", \"destination\", \"airline\", \"personal_protective\", \"hygiene\", \"facemask\", \"airborne\", \"droplet\", \"travel\", \"healthcare_worker\", \"control_measure\", \"room\", \"air\", \"nursing\", \"contact\", \"aerosol\", \"worker\", \"transmission\", \"hand\", \"cas\", \"epidemic\", \"outbreak\", \"spread\", \"healthcare\", \"measure\", \"control\", \"isolation\", \"sars\", \"hospital\", \"case\", \"infection\", \"number\", \"risk\", \"period\", \"infectious\", \"rate\", \"time\", \"-PRON-\", \"disease\", \"respiratory\", \"result\", \"study\", \"use\", \"rsv\", \"rhinovirus\", \"asthma\", \"syncytial\", \"respiratory_syncytial\", \"hrv\", \"los\", \"las\", \"pneumoniae\", \"que\", \"parainfluenza\", \"hmpv\", \"bronchiolitis\", \"bvdv\", \"gag\", \"parainfluenza_virus\", \"del\", \"respiratory_tract\", \"metapneumovirus\", \"una\", \"por\", \"human_metapneumovirus\", \"human_rhinovirus\", \"tract_infection\", \"exacerbation\", \"para\", \"hbov\", \"son\", \"wheeze\", \"m\\u00e1s\", \"upper_respiratory\", \"tract\", \"streptococcus\", \"child\", \"nasopharyngeal\", \"con\", \"copd\", \"upper\", \"respiratory\", \"adenovirus\", \"infant\", \"hcov\", \"infection\", \"virus\", \"viral\", \"bacterial\", \"common\", \"pathogen\", \"detect\", \"acute\", \"nasal\", \"study\", \"human\", \"year\", \"age\", \"associate\", \"clinical\", \"influenza\", \"patient\", \"tran\", \"salmonella\", \"emission\", \"synthetic_peptide\", \"immunoblot\", \"raft\", \"mycobacterium\", \"calculation\", \"machine\", \"foodborne\", \"radiation\", \"cystic\", \"lipid_raft\", \"histologic\", \"query\", \"static\", \"fingerprint\", \"ppv\", \"drinking\", \"cystic_fibrosis\", \"confocal_microscopy\", \"histopathologic\", \"mycobacterium_tuberculosis\", \"molecular_dynamic\", \"infusion\", \"enterica\", \"asf\", \"cos\", \"endocrine\", \"topological\", \"prediction\", \"computational\", \"virtual\", \"algorithm\", \"modeling\", \"energy\", \"network\", \"simulation\", \"dataset\", \"hcmv\", \"model\", \"dynamic\", \"spatial\", \"numerical\", \"mathematical\", \"parameter\", \"image\", \"diffusion\", \"accuracy\", \"apply\", \"propose\", \"predict\", \"classification\", \"datum\", \"method\", \"space\", 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\"provide_overview\", \"prrsv\", \"prv\", \"pseudoknot\", \"pseudoknot\", \"psychiatric\", \"psychological\", \"psychological\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public_health\", \"public_health\", \"public_health\", \"public_health\", \"public_health\", \"publisher\", \"pulmonary\", \"pulmonary\", \"pulmonary\", \"pulmonary\", \"pulmonary\", \"quarantine\", \"que\", \"quercetin\", \"query\", \"rabbit\", \"rabie\", \"rabie\", \"radiation\", \"radiograph\", \"radiograph\", \"raft\", \"randomize\", \"randomize\", \"randomize_control\", \"randomly_assign\", \"randomly_select\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"rapid\", \"ras\", \"ras\", \"ras\", \"rat\", \"rat\", \"rat\", \"rat\", \"rat\", \"rat\", \"rat\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", 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\"replacer\", \"replication\", \"replication\", \"replication\", \"replication\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"reservoir\", \"reservoir\", \"resource\", \"resource\", \"resource\", \"resource\", \"resource\", \"resource\", \"resource\", \"respirator\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory_distress\", \"respiratory_failure\", \"respiratory_syncytial\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_tract\", \"respiratory_tract\", \"respondent\", \"respondent\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resumen\", \"reticulum\", \"reticulum\", \"retrospective\", \"retrospective\", \"retrospective\", \"retrospective\", \"retrospective\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review_current\", \"review_discuss\", \"review_discuss\", \"review_discuss\", \"review_focus\", \"review_focus\", \"review_focus\", \"review_summarize\", \"review_summarize\", \"rhinitis\", \"rhinovirus\", \"rhythm\", \"ribosomal\", \"ribosomal\", \"ribosome\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_factor\", \"risk_perception\", \"riyadh\", \"rna\", \"rna\", \"rna\", \"rna\", \"rna\", \"rna\", \"rna_dependent\", \"rna_polymerase\", \"rna_polymerase\", \"rna_recombination\", \"rna_synthesis\", \"rodent\", \"rodent\", \"rodent\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"room\", \"room\", \"room\", \"room\", \"rotavirus\", \"rotavirus\", \"rotavirus\", \"rrt\", \"rrt_pcr\", \"rsv\", \"rvp\", \"salmonella\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sari\", \"sars\", \"sars\", \"sars\", \"sars\", \"sars\", \"sars\", \"sars_cov\", \"saudi\", \"saudi\", \"saudi_arabia\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"sclerosis\", \"season\", \"season\", \"season\", \"season\", \"season\", \"season\", \"seasonal\", \"seasonal\", \"seasonal\", \"sector\", \"sector\", \"security\", \"segment\", \"segment\", \"segment\", \"segment\", \"segment\", \"segment\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive\", \"sensitive_specific\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity\", \"sensitivity_specificity\", \"sensitivity_specificity\", \"sensitivity_specificity\", \"sentinel\", \"sentinel\", \"sentinel\", \"sentinel\", \"sepsis\", \"sepsis\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sera\", \"sera\", \"sera\", \"sera\", \"serum\", \"serum\", \"serum\", \"serum\", \"serum\", \"serum\", \"serum\", \"serum_igg\", \"serum_sample\", \"serum_sample\", \"service\", \"service\", \"service\", \"service\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe_acute\", \"severe_acute\", \"severe_acute\", \"severe_acute\", \"severe_acute\", \"shedding\", \"shedding\", \"shedding\", \"shedding\", \"shedding\", \"shedding\", \"shedding\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"sign\", \"signaling\", \"signaling\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"simulation\", \"simulation\", \"single_strand\", \"sirna\", \"sirna\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"size\", \"skill\", \"small_molecule\", \"social\", \"social\", \"social\", \"social\", \"social_medium\", \"sole\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"son\", \"sore\", \"sore_throat\", \"south_korea\", \"south_korea\", \"south_korea\", \"south_korea\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spatial\", \"spatial\", \"spatial\", \"specie\", \"specie\", \"specie\", \"specie\", \"specie\", \"specie\", \"specie\", \"specie\", \"specie\", \"species\", \"species\", \"species\", \"species\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specific\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specificity\", \"specimen\", \"specimen\", \"specimen\", \"specimen\", \"spike\", \"spike\", \"spike\", \"spike\", \"spike_glycoprotein\", \"spike_protein\", \"spillover\", \"spinal\", \"spinal_cord\", \"spleen\", \"spleen\", \"spleen\", \"sprague\", \"sprague_dawley\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"stakeholder\", \"static\", \"stay\", \"stay\", \"stay\", \"stay\", \"stay\", \"stay\", \"steroid\", \"steroid\", \"stillbirth\", \"stool_sample\", \"stool_specimen\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strand\", \"strand\", \"strand\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"streptococcus\", \"streptococcus\", \"streptococcus\", \"strip\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"student\", \"student\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"subclass\", \"subfamily\", \"subgenomic\", \"subgenomic_rna\", \"substrate\", \"substrate\", \"suckling\", \"suckling\", \"suicide\", \"summarize\", \"summarize\", \"summarize\", \"summarize\", \"summarize\", \"summarize\", \"summarize\", \"summarize\", \"sur\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance_system\", \"surveillance_system\", \"surveillance_system\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"swine\", \"swine\", \"swine\", \"swine\", \"swine\", \"swine\", \"swine_industry\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"syncytial\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndromic_surveillance\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthetic_peptide\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system_cns\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"targeting\", \"tcm\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technological\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"template\", \"template\", \"template\", \"template\", \"terminal\", \"terminal\", \"terminal\", \"terminal\", \"terminal\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"tgev\", \"tgev\", \"tgev\", \"theme\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"thin\", \"thoracic\", \"tick\", \"tick\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"titer\", \"titer\", \"titer\", \"titer\", \"titer\", \"titer\", \"tnf\", \"topological\", \"tourism\", \"tourist\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract_infection\", \"training\", \"training\", \"training\", \"training\", \"training_program\", \"tran\", \"transcribe\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transmembrane\", \"transmembrane\", \"transmembrane\", \"transmembrane\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible_gastroenteritis\", \"transmissible_gastroenteritis\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transplantation\", \"transplantation\", \"transplantation\", \"transplantation\", \"transplantation\", \"trauma\", \"trauma\", \"travel\", \"travel\", \"travel\", \"travel\", \"traveller\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trna\", \"tumor\", \"tumor\", \"tumor\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"ubiquitin\", \"una\", \"united\", \"united\", \"united\", \"united\", \"united\", \"university_hospital\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper_respiratory\", \"upper_respiratory\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"vaccinated\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"ventilator\", \"vesicle\", \"vesicular\", \"vesicular_stomatitis\", \"villous\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"virion\", \"virion\", \"virion\", \"virome\", \"virtual\", \"virtual\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"visitor\", \"vitamin\", \"vlp\", \"vlp\", \"vsv\", \"wean\", \"wean\", \"wear\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight_gain\", \"west_nile\", \"west_nile\", \"west_nile\", \"wheeze\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"wildlife\", \"wind\", \"wind\", \"wnv\", \"wnv\", \"worker\", \"worker\", \"worker\", \"worker\", \"workforce\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"zoonosis\", \"zoonosis\", \"zoonotic\", \"zoonotic\", \"zoonotic\", \"zoonotic\", \"zoonotic\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 6, 11, 2, 9, 10, 13, 15, 8, 5, 12, 1, 14, 3, 4]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el266001126592610082000754445\", ldavis_el266001126592610082000754445_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el266001126592610082000754445\", ldavis_el266001126592610082000754445_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el266001126592610082000754445\", ldavis_el266001126592610082000754445_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 87,
     "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": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics over time\n",
    "# the topics of ALL the documents of our corpus\n",
    "\n",
    "df_local = df_meta[pd.notnull(df_meta[\"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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03333401, 0.03333402, 0.03333398, 0.03333421, 0.03333408,\n",
       "       0.03333404, 0.03333399, 0.03333408, 0.03333404, 0.03333401,\n",
       "       0.53332347, 0.03333399, 0.03333404, 0.03333413, 0.03333398])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.033333972),\n",
       " (1, 0.033333976),\n",
       " (2, 0.033333942),\n",
       " (3, 0.033334054),\n",
       " (4, 0.03333404),\n",
       " (5, 0.033333994),\n",
       " (6, 0.03333395),\n",
       " (7, 0.033334035),\n",
       " (8, 0.033333994),\n",
       " (9, 0.033333965),\n",
       " (10, 0.033334073),\n",
       " (11, 0.033333957),\n",
       " (12, 0.033334),\n",
       " (13, 0.53332406),\n",
       " (14, 0.03333394)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_document_topics(corpus[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics = pd.DataFrame(topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics[\"year\"] = publication_years\n",
    "df_topics[\"doi\"] = dois"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_which_year = 2000\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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "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]), 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 = 90)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Language modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import pickle\n",
    "\n",
    "processed_docs = pickle.load(open(\"datasets_output/processed_docs.pk\", \"rb\"))\n",
    "\n",
    "model = Word2Vec(processed_docs, size=256, window=10, min_count=2, workers=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('covs', 0.6251739263534546),\n",
       " ('cov', 0.5501048564910889),\n",
       " ('betacoronavirus', 0.5166511535644531),\n",
       " ('hcov', 0.48612651228904724),\n",
       " ('hcovs', 0.4802132546901703),\n",
       " ('coronaviruses', 0.48010629415512085),\n",
       " ('astrovirus', 0.4582931399345398),\n",
       " ('virus', 0.4369690418243408),\n",
       " ('scovs', 0.4323999881744385),\n",
       " ('alphacoronavirus', 0.4261910915374756)]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"coronavirus\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keyword extraction\n",
    "\n",
    "Using [RAKE](https://csurfer.github.io/rake-nltk/_build/html/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rake_nltk import Rake\n",
    "\n",
    "# Uses stopwords for English from NLTK, and all puntuation characters by default\n",
    "r = Rake(min_length=2)\n",
    "\n",
    "# Extraction given the text\n",
    "r.extract_keywords_from_text(abstracts[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['serological andpathogenic microorganism test results',\n",
       " 'provide reasonable suggestions',\n",
       " '100 countries around',\n",
       " 'interstitial lung disease',\n",
       " 'disease onset characteristics',\n",
       " 'onset ild',\n",
       " 'lung injury',\n",
       " 'image characteristics',\n",
       " 'spread rapidly',\n",
       " 'repair caused']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest.\n",
    "r.get_ranked_phrases()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(25.0, 'serological andpathogenic microorganism test results'),\n",
       " (9.0, 'provide reasonable suggestions'),\n",
       " (9.0, '100 countries around'),\n",
       " (8.5, 'interstitial lung disease'),\n",
       " (8.0, 'disease onset characteristics'),\n",
       " (4.5, 'onset ild'),\n",
       " (4.5, 'lung injury'),\n",
       " (4.5, 'image characteristics'),\n",
       " (4.0, 'spread rapidly'),\n",
       " (4.0, 'repair caused')]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest with scores.\n",
    "r.get_ranked_phrases_with_scores()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39154"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(processed_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = Rake(min_length=2, max_length=3)\n",
    "# Extraction given the list of strings where each string is a sentence.\n",
    "r.extract_keywords_from_sentences([\" \".join(d) for d in processed_docs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9.0, 'welcome adam vaughan'),\n",
       " (9.0, 'wash hand thoroughly'),\n",
       " (9.0, 'understand pandemic datum'),\n",
       " (9.0, 'trypsin cod trypsin'),\n",
       " (9.0, 'trichothecene mycotoxin deoxynivalenol'),\n",
       " (9.0, 'tolerance self rna'),\n",
       " (9.0, 'summarize presentation discussion'),\n",
       " (9.0, 'suffer cold addition'),\n",
       " (9.0, 'substitute mask alternative'),\n",
       " (9.0, 'strategy widely applicable'),\n",
       " (9.0, 'start codon suggest'),\n",
       " (9.0, 'speak italy day'),\n",
       " (9.0, 'set richard smyth'),\n",
       " (9.0, 'sensible way plan'),\n",
       " (9.0, 'scary hard stand'),\n",
       " (9.0, 'remain committed neurorehabilitation'),\n",
       " (9.0, 'rely quarantine isolation'),\n",
       " (9.0, 'pneumonia problem worldwide'),\n",
       " (9.0, 'new york dutton'),\n",
       " (9.0, 'need think containment'),\n",
       " (9.0, 'method general interest'),\n",
       " (9.0, 'measure cleavage angiotensin'),\n",
       " (9.0, 'knowledge sars high'),\n",
       " (9.0, 'join faculty worker'),\n",
       " (9.0, 'influence hbv transcription'),\n",
       " (9.0, 'individual different value'),\n",
       " (9.0, 'illustrate application pandemic'),\n",
       " (9.0, 'haq posthoc conclusion'),\n",
       " (9.0, 'febrile tachypneic hypoxic'),\n",
       " (9.0, 'explore hypothesis conserved'),\n",
       " (9.0, 'expect effort different'),\n",
       " (9.0, 'expand initial cohort'),\n",
       " (9.0, 'envoy ming emperor'),\n",
       " (9.0, 'endoplasmic reticulum glucosidas'),\n",
       " (9.0, 'discuss implication finding'),\n",
       " (9.0, 'describe pathology esophagus'),\n",
       " (9.0, 'describe management case'),\n",
       " (9.0, 'declare compete interest'),\n",
       " (9.0, 'decision retract correspondence'),\n",
       " (9.0, 'convert enzyme angiotensin'),\n",
       " (9.0, 'conflict interest reference'),\n",
       " (9.0, 'conclude derive vpl'),\n",
       " (9.0, 'complacent contingency planning'),\n",
       " (9.0, 'competitive inhibition value'),\n",
       " (9.0, 'circulate widely twitter'),\n",
       " (9.0, 'cadeia polimerase multiplex'),\n",
       " (9.0, 'bind viral replicase'),\n",
       " (9.0, 'approach problem straightforwardly'),\n",
       " (9.0, 'antiinflammatory agent steroid'),\n",
       " (9.0, 'analyze publication record')]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest with scores.\n",
    "r.get_ranked_phrases_with_scores()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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