analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis notebook for: How much research shared on Facebook is hidden from public view?\n",
    "\n",
    "This notebook produces all results and figures in the article.\n",
    "\n",
    "Figures are plotted to the *figures/* directory.\n",
    "\n",
    "In order to re-produce the plots without interacting with the notebook use `jupyter nbconvert --execute analysis.ipynb`\n",
    "\n",
    "**Outline**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import gspread\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from gspread_dataframe import get_as_dataframe, set_with_dataframe\n",
    "from matplotlib import ticker\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib_venn import venn2, venn3, venn3_circles\n",
    "from oauth2client.service_account import ServiceAccountCredentials\n",
    "from scipy import stats\n",
    "from scipy.optimize import curve_fit\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementation of partial log binning following Milojević (2010)\n",
    "def thresh(bin_size):\n",
    "    x = 1\n",
    "    while True:\n",
    "        diff = np.log10(x+1) - np.log10(x)\n",
    "        if diff < bin_size:\n",
    "            return x +1\n",
    "        x = x + 1\n",
    "\n",
    "def partial_log_binning(data_counts, bin_size=0.1):\n",
    "    n_bins = 1/bin_size\n",
    "    binning_threshold = thresh(bin_size)\n",
    "\n",
    "    log_data = np.log10(data_counts)\n",
    "    log_index = np.log10(log_data.index)\n",
    "\n",
    "    logbins = np.linspace(np.log10(binning_threshold)+0.1,\n",
    "                          np.log10(max(data)),\n",
    "                          ((np.log10(max(data))-np.log10(binning_threshold)+0.1)//0.1)+1)\n",
    "\n",
    "    binned_xs = []\n",
    "    binned_vals = []      \n",
    "    \n",
    "    for i in range(1, binning_threshold+1):      \n",
    "        if i in log_data.index:\n",
    "            binned_vals.append(log_data.loc[i])\n",
    "            binned_xs.append(np.log10(i))\n",
    "    \n",
    "    for b in logbins:       \n",
    "            vals = (b-.05 <= log_index) & (log_index < b+.05)\n",
    "            vs = data_counts[vals]\n",
    "            if len(vs)>0:\n",
    "                n = np.ceil(10**(b+.05) - 10**(b-.05))\n",
    "                if n == 0:\n",
    "                    continue\n",
    "                binned_vals.append(np.log10(vs.sum()/n))\n",
    "                binned_xs.append(b)\n",
    "    return binned_xs, binned_vals  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.family':'sans-serif',\n",
    "    'font.size': 16.0,\n",
    "    'text.usetex': False,\n",
    "    'figure.figsize': (11.69,8.27)\n",
    "})\n",
    "\n",
    "# Seaborn styles\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Color palette\n",
    "cm = \"Paired\"\n",
    "cp3 = sns.color_palette(cm, 3)\n",
    "cp10 = sns.color_palette(cm, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Optional ###\n",
    "# Set up GSpread connection to push dataframes to Google Spreadsheets\n",
    "# Instructions can be found at https://gspread.readthedocs.io/en/latest/\n",
    "\n",
    "# scope = ['https://spreadsheets.google.com/feeds',\n",
    "#          'https://www.googleapis.com/auth/drive']\n",
    "\n",
    "# credentials = ServiceAccountCredentials.from_json_keyfile_name('My Project-d9fa71152fe8.json', scope)\n",
    "\n",
    "# gc = gspread.authorize(credentials)\n",
    "# sh =  gc.open(\"PLOS Paper - Tables\")\n",
    "\n",
    "push_to_gspread = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data, preprocessing, dropping years + bad results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "articles_csv = \"data/articles.csv\"\n",
    "responses_csv = \"data/responses.csv\"\n",
    "\n",
    "figs = Path(\"figures\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "articles = pd.read_csv(articles_csv, index_col=\"doi\", parse_dates=['publication_date'])\n",
    "all_responses = pd.read_csv(responses_csv, index_col=\"id\", parse_dates=['received_at', 'og_updated_time', 'publication_date', 'added_on'])\n",
    "\n",
    "# add year and metrics\n",
    "all_responses = all_responses.merge(articles[['year', 'AES', 'AER', 'AEC', 'discipline']], left_on=\"doi\", right_index=True, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limit responses to those articles that received some forms of engagement\n",
    "responses = all_responses\n",
    "responses = responses.replace(0, np.nan)\n",
    "responses = responses.dropna(subset=['shares', 'reactions', 'comments'], how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_shares = set(articles['AES'].dropna().index.tolist())\n",
    "all_reactions = set(articles['AER'].dropna().index.tolist())\n",
    "all_comments = set(articles['AEC'].dropna().index.tolist())\n",
    "\n",
    "any_engagement = all_shares.union(all_reactions).union(all_comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['AES', 'AER', 'AEC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of articles</th>\n",
       "      <td>61848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of URLs</th>\n",
       "      <td>618480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>--------</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of successful responses</th>\n",
       "      <td>69948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of non-zero responses</th>\n",
       "      <td>32887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of zero-responses</th>\n",
       "      <td>37061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>---------</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique URLs</th>\n",
       "      <td>32887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique queries</th>\n",
       "      <td>32887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique OG IDs</th>\n",
       "      <td>22215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique DOIs</th>\n",
       "      <td>21674.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Count\n",
       "Number of articles               61848.0\n",
       "Number of URLs                  618480.0\n",
       "--------                             NaN\n",
       "Number of successful responses   69948.0\n",
       "Number of non-zero responses     32887.0\n",
       "Number of zero-responses         37061.0\n",
       "---------                            NaN\n",
       "Number of unique URLs            32887.0\n",
       "Number of unique queries         32887.0\n",
       "Number of unique OG IDs          22215.0\n",
       "Number of unique DOIs            21674.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=[\"Count\"])\n",
    "df.loc['Number of articles', \"Count\"] = len(articles)\n",
    "df.loc['Number of URLs', \"Count\"] = len(articles) * 10\n",
    "df.loc['--------', \"Count\"] = None\n",
    "df.loc['Number of successful responses', \"Count\"] = len(all_responses)\n",
    "df.loc['Number of non-zero responses', \"Count\"] = len(responses)\n",
    "df.loc['Number of zero-responses', \"Count\"] = len(all_responses) - len(responses)\n",
    "df.loc['---------', \"Count\"] = None\n",
    "df.loc['Number of unique URLs', \"Count\"] = responses.url.nunique()\n",
    "df.loc['Number of unique queries', \"Count\"] = responses.query_id.nunique()\n",
    "df.loc['Number of unique OG IDs', \"Count\"] = responses.og_id.nunique()\n",
    "df.loc['Number of unique DOIs', \"Count\"] = responses.doi.nunique()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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>AES</th>\n",
       "      <th>AER</th>\n",
       "      <th>AEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21415.00</td>\n",
       "      <td>13825.00</td>\n",
       "      <td>10259.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.81</td>\n",
       "      <td>53.54</td>\n",
       "      <td>23.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.32</td>\n",
       "      <td>475.45</td>\n",
       "      <td>311.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12473.00</td>\n",
       "      <td>36030.00</td>\n",
       "      <td>15840.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AES       AER       AEC\n",
       "count  21415.00  13825.00  10259.00\n",
       "mean       8.81     53.54     23.99\n",
       "std      136.32    475.45    311.97\n",
       "min        1.00      1.00      1.00\n",
       "25%        1.00      4.00      2.00\n",
       "50%        2.00     16.00      6.00\n",
       "75%        4.00     46.00     15.00\n",
       "max    12473.00  36030.00  15840.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles[metrics].describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the overall coverage of articles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>% (n=61848)</th>\n",
       "      <th>% (n=21674)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AES</th>\n",
       "      <td>21415</td>\n",
       "      <td>34.63</td>\n",
       "      <td>98.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AER</th>\n",
       "      <td>13825</td>\n",
       "      <td>22.35</td>\n",
       "      <td>63.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AEC</th>\n",
       "      <td>10259</td>\n",
       "      <td>16.59</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n  % (n=61848)  % (n=21674)\n",
       "AES  21415        34.63        98.81\n",
       "AER  13825        22.35        63.79\n",
       "AEC  10259        16.59        47.33"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = articles[metrics].dropna(how=\"all\")\n",
    "df = articles[metrics].count().to_frame(\"n\")\n",
    "df[\"% (n={})\".format(len(articles))] = df['n'].div(len(articles)/100).round(2)\n",
    "df['% (n={})'.format(len(temp))] = df['n'].div(len(temp)/100).round(2)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of articles with shares, reactions, and comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = venn3(subsets= [all_shares, all_reactions, all_comments],\n",
    "          set_labels=('', '', ''),\n",
    "          subset_label_formatter=lambda x: \"{} ({:.1f}%)\".format(x, 100*x/len(any_engagement)));\n",
    "\n",
    "c=venn3_circles(subsets= [all_shares, all_reactions, all_comments], linewidth=0)\n",
    "c[0].set_lw(.9)\n",
    "c[0].set_ls('-.')\n",
    "\n",
    "v.get_patch_by_id('100').set_color(cp3[0])\n",
    "v.get_patch_by_id('010').set_color(cp3[1])\n",
    "v.get_patch_by_id('001').set_color(cp3[2])\n",
    "\n",
    "v.get_patch_by_id('110').set_color(np.add(cp3[0],cp3[1])/2)\n",
    "v.get_patch_by_id('011').set_color(np.add(cp3[1],cp3[2])/2)\n",
    "v.get_patch_by_id('101').set_color(np.add(cp3[0],cp3[2])/2)\n",
    "\n",
    "v.get_patch_by_id('111').set_color(np.add(np.add(cp3[1],cp3[0]), cp3[2]) / 3)\n",
    "\n",
    "for text in v.set_labels:\n",
    "    text.set_fontsize(12)\n",
    "for text in v.subset_labels:\n",
    "    text.set_fontsize(14)\n",
    "\n",
    "\n",
    "for text in v.set_labels:\n",
    "    text.set_fontsize(10)\n",
    "for text in v.subset_labels:\n",
    "    text.set_fontsize(12)\n",
    "\n",
    "plt.gca().legend(handles=[v.get_patch_by_id('100'), v.get_patch_by_id('010'), v.get_patch_by_id('001')],\n",
    "                 labels=[\"Shares\", \"Reactions\", \"Comments\"], prop={'size': 12});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does the breakdown of URLs per article look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Articles</th>\n",
       "      <th>Articles [%]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of URLs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12344</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7702</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1442</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Articles  Articles [%]\n",
       "Number of URLs                        \n",
       "1                  12344          57.0\n",
       "2                   7702          35.5\n",
       "3                   1442           6.7\n",
       "4                    142           0.7\n",
       "5                     29           0.1\n",
       "6                      8           0.0\n",
       "7                      4           0.0\n",
       "8                      3           0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_urls_counts = responses[['doi', 'og_id']].groupby(\"doi\").count().og_id.value_counts().reset_index()\n",
    "cov_urls_counts['%'] = 100 * cov_urls_counts.og_id.div(cov_urls_counts.og_id.sum())\n",
    "cov_urls_counts.columns = [\"Number of URLs\", \"Articles\", \"Articles [%]\"]\n",
    "\n",
    "cov_urls_counts = cov_urls_counts.set_index(\"Number of URLs\")\n",
    "\n",
    "if push_to_gspread:\n",
    "    wks = sh.worksheet(\"Coverage - Number of URLs\")\n",
    "    set_with_dataframe(wks, cov_urls_counts.round(1).reset_index())\n",
    "\n",
    "cov_urls_counts.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Articles</th>\n",
       "      <th>Articles [%]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Objects per Article</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21134</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>539</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Articles  Articles [%]\n",
       "Objects per Article                        \n",
       "1                       21134          97.5\n",
       "2                         539           2.5\n",
       "3                           1           0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = responses[['doi', 'og_id']].groupby(\"doi\").nunique().og_id.value_counts().reset_index()\n",
    "x['%'] = 100*x.og_id.div(x.og_id.sum())\n",
    "x.columns = [\"Objects per Article\", \"Articles\", \"Articles [%]\"]\n",
    "x = x.set_index(\"Objects per Article\")\n",
    "x.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which URLs were used to share articles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>FB Objects</th>\n",
       "      <th>FB Objects [%]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>landing</th>\n",
       "      <td>21515</td>\n",
       "      <td>65.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old</th>\n",
       "      <td>4927</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metrics</th>\n",
       "      <td>4348</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi</th>\n",
       "      <td>982</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdf</th>\n",
       "      <td>644</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors</th>\n",
       "      <td>296</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>related</th>\n",
       "      <td>96</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comments</th>\n",
       "      <td>76</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pmid</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pmc</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FB Objects  FB Objects [%]\n",
       "URL Type                            \n",
       "landing        21515            65.4\n",
       "doi_old         4927            15.0\n",
       "metrics         4348            13.2\n",
       "doi              982             3.0\n",
       "pdf              644             2.0\n",
       "authors          296             0.9\n",
       "related           96             0.3\n",
       "comments          76             0.2\n",
       "pmid               2             0.0\n",
       "pmc                1             0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_urls_types = responses.type.value_counts().reset_index()\n",
    "cov_urls_types['%'] = 100*cov_urls_types.type.div(cov_urls_types.type.sum())\n",
    "cov_urls_types.columns = [\"URL Type\", \"FB Objects\", \"FB Objects [%]\"]\n",
    "\n",
    "cov_urls_types = cov_urls_types.set_index(\"URL Type\")\n",
    "\n",
    "if push_to_gspread:\n",
    "    wks = sh.worksheet(\"Coverage - URL Types\")\n",
    "    set_with_dataframe(wks, cov_urls_types.round(1).reset_index())\n",
    "\n",
    "cov_urls_types.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>landing -- pdf</th>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old, landing -- pdf</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi, landing -- pdf</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old -- landing</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi, doi_old, landing -- pdf</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landing, metrics -- pdf</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old, landing, metrics -- pdf</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors, doi, landing -- pdf</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors, doi_old, landing -- pdf</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old -- landing, metrics</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Articles\n",
       "landing -- pdf                         231\n",
       "doi_old, landing -- pdf                 91\n",
       "doi, landing -- pdf                     58\n",
       "doi_old -- landing                      24\n",
       "doi, doi_old, landing -- pdf            22\n",
       "landing, metrics -- pdf                 18\n",
       "doi_old, landing, metrics -- pdf        14\n",
       "authors, doi, landing -- pdf            11\n",
       "authors, doi_old, landing -- pdf         6\n",
       "doi_old -- landing, metrics              5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of FB objects per DOI\n",
    "n_responses_per_doi = responses[['doi', 'og_id']].groupby(\"doi\")[\"og_id\"].nunique()\n",
    "\n",
    "# DOIs with multiple FB objects\n",
    "dois_with_mult_ogids = n_responses_per_doi[n_responses_per_doi>1].keys()\n",
    "\n",
    "# Responses of DOIs with more FB objects\n",
    "y = responses[responses.doi.isin(dois_with_mult_ogids)]\n",
    "\n",
    "# URL types of those articles with more than one response\n",
    "z = y[['doi', 'og_id', 'type']].groupby([\"doi\", \"og_id\"])['type'].apply(lambda x: \", \".join(sorted(x))).reset_index()\n",
    "\n",
    "# Concat URL type names\n",
    "zz = z.groupby(\"doi\")['type'].apply(lambda x: \" -- \".join(sorted(x)))\n",
    "zz.value_counts().head(10).to_frame(\"Articles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "504"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of articles where a PDF caused an extra FB object\n",
    "zz.map(lambda x: \"pdf\" in x).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did the type of shared URLs change across years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>landing</th>\n",
       "      <td>57.6</td>\n",
       "      <td>68.9</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi_old</th>\n",
       "      <td>15.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi</th>\n",
       "      <td>1.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdf</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comments</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>related</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metrics</th>\n",
       "      <td>24.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pmc</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pmid</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year      2015  2016  2017\n",
       "type                      \n",
       "landing   57.6  68.9  76.5\n",
       "doi_old   15.5  19.8   7.5\n",
       "doi        1.4   2.1   7.3\n",
       "pdf        0.6   1.5   5.4\n",
       "authors    0.3   1.2   1.7\n",
       "comments   0.1   0.2   0.6\n",
       "related    0.1   0.4   0.6\n",
       "metrics   24.3   6.0   0.5\n",
       "pmc        0.0   NaN   NaN\n",
       "pmid       NaN   0.0   NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = responses.groupby(['type', 'year']).size().to_frame('size').reset_index()\n",
    "df = df.pivot(columns=\"year\", index=\"type\", values=\"size\")\n",
    "df = df.apply(lambda x: 100*x/x.sum()).sort_values(by=2017, ascending=False)\n",
    "\n",
    "df.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = responses.groupby(['type', 'year']).size().to_frame('size').reset_index()\n",
    "\n",
    "sort_order = df.groupby(\"type\")['size'].sum().sort_values(ascending=False).index.tolist()\n",
    "year_counts = df.groupby(\"year\")['size'].sum()\n",
    "\n",
    "df['%'] = df.apply(lambda x: 100*x['size']/(year_counts[x['year']]), axis=1)\n",
    "\n",
    "\n",
    "sns.barplot(x=\"type\", y=\"%\", hue=\"year\", data=df, order=sort_order)\n",
    "sns.despine(left=True, right=True, top=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the types of shared URLs vary across disciplines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>type</th>\n",
       "      <th>authors</th>\n",
       "      <th>comments</th>\n",
       "      <th>doi</th>\n",
       "      <th>doi_old</th>\n",
       "      <th>landing</th>\n",
       "      <th>metrics</th>\n",
       "      <th>pdf</th>\n",
       "      <th>pmc</th>\n",
       "      <th>pmid</th>\n",
       "      <th>related</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discipline</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arts</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biology</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biomedical Research</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinical Medicine</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth and Space</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering and Technology</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humanities</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mathematics</th>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physics</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Fields</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Sciences</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type                        authors  comments   doi  doi_old  landing  \\\n",
       "discipline                                                              \n",
       "Arts                            NaN       NaN   NaN      NaN     1.00   \n",
       "Biology                        0.01      0.00  0.03     0.17     0.63   \n",
       "Biomedical Research            0.01      0.00  0.02     0.15     0.67   \n",
       "Chemistry                      0.05      0.01  0.02     0.17     0.63   \n",
       "Clinical Medicine              0.01      0.00  0.03     0.13     0.68   \n",
       "Earth and Space                0.02      0.00  0.04     0.20     0.58   \n",
       "Engineering and Technology     0.01      0.00  0.02     0.15     0.63   \n",
       "Health                         0.01      0.00  0.05     0.13     0.64   \n",
       "Humanities                      NaN       NaN  0.12     0.23     0.46   \n",
       "Mathematics                    0.01       NaN  0.03     0.15     0.64   \n",
       "Physics                        0.01      0.00  0.03     0.16     0.62   \n",
       "Professional Fields            0.01      0.00  0.07     0.19     0.59   \n",
       "Psychology                     0.02      0.00  0.05     0.18     0.59   \n",
       "Social Sciences                0.02      0.00  0.05     0.20     0.59   \n",
       "\n",
       "type                        metrics   pdf  pmc  pmid  related  \n",
       "discipline                                                     \n",
       "Arts                            NaN   NaN  NaN   NaN      NaN  \n",
       "Biology                        0.13  0.02  NaN   NaN     0.00  \n",
       "Biomedical Research            0.14  0.01  NaN   0.0     0.00  \n",
       "Chemistry                      0.11  0.02  NaN   NaN     0.01  \n",
       "Clinical Medicine              0.13  0.02  0.0   0.0     0.00  \n",
       "Earth and Space                0.13  0.02  NaN   NaN     0.00  \n",
       "Engineering and Technology     0.15  0.02  NaN   NaN     0.01  \n",
       "Health                         0.15  0.02  NaN   NaN     0.00  \n",
       "Humanities                     0.15  0.04  NaN   NaN      NaN  \n",
       "Mathematics                    0.12  0.04  NaN   NaN     0.01  \n",
       "Physics                        0.16  0.02  NaN   NaN     0.01  \n",
       "Professional Fields            0.10  0.04  NaN   NaN     0.00  \n",
       "Psychology                     0.12  0.03  NaN   NaN     0.00  \n",
       "Social Sciences                0.10  0.03  NaN   NaN     0.01  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_types_by_disc = responses.groupby([\"discipline\", \"type\"])['og_id'].count()\n",
    "url_types_by_disc = url_types_by_disc.reset_index().pivot(columns=\"type\", index=\"discipline\", values=\"og_id\")\n",
    "url_types_by_disc = url_types_by_disc.apply(lambda x: x.div(x.sum()), axis=1)\n",
    "url_types_by_disc.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>type</th>\n",
       "      <th>landing</th>\n",
       "      <th>doi_old</th>\n",
       "      <th>metrics</th>\n",
       "      <th>doi</th>\n",
       "      <th>pdf</th>\n",
       "      <th>authors</th>\n",
       "      <th>related</th>\n",
       "      <th>comments</th>\n",
       "      <th>pmc</th>\n",
       "      <th>pmid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discipline</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arts</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biology</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biomedical Research</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinical Medicine</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth and Space</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering and Technology</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humanities</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mathematics</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physics</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Fields</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Sciences</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type                        landing  doi_old  metrics  doi  pdf  authors  \\\n",
       "discipline                                                                 \n",
       "Arts                            1.0      NaN      NaN  NaN  NaN      NaN   \n",
       "Biology                         1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Biomedical Research             1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Chemistry                       1.0      2.0      3.0  5.0  6.0      4.0   \n",
       "Clinical Medicine               1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Earth and Space                 1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Engineering and Technology      1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Health                          1.0      3.0      2.0  4.0  5.0      6.0   \n",
       "Humanities                      1.0      2.0      3.0  4.0  5.0      NaN   \n",
       "Mathematics                     1.0      2.0      3.0  5.0  4.0      6.0   \n",
       "Physics                         1.0      2.0      2.0  4.0  5.0      6.0   \n",
       "Professional Fields             1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Psychology                      1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "Social Sciences                 1.0      2.0      3.0  4.0  5.0      6.0   \n",
       "\n",
       "type                        related  comments  pmc  pmid  \n",
       "discipline                                                \n",
       "Arts                            NaN       NaN  NaN   NaN  \n",
       "Biology                         7.0       8.0  NaN   NaN  \n",
       "Biomedical Research             7.0       8.0  NaN   9.0  \n",
       "Chemistry                       7.0       7.0  NaN   NaN  \n",
       "Clinical Medicine               7.0       8.0  9.0   9.0  \n",
       "Earth and Space                 8.0       7.0  NaN   NaN  \n",
       "Engineering and Technology      7.0       8.0  NaN   NaN  \n",
       "Health                          7.0       7.0  NaN   NaN  \n",
       "Humanities                      NaN       NaN  NaN   NaN  \n",
       "Mathematics                     6.0       NaN  NaN   NaN  \n",
       "Physics                         7.0       8.0  NaN   NaN  \n",
       "Professional Fields             7.0       7.0  NaN   NaN  \n",
       "Psychology                      7.0       8.0  NaN   NaN  \n",
       "Social Sciences                 7.0       8.0  NaN   NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_types_by_disc = url_types_by_disc.rank(method=\"min\", ascending=False, axis=1).sort_values(axis=1, by=\"Clinical Medicine\")\n",
    "url_types_by_disc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b4be2aa20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(url_types_by_disc, cmap=\"PuBu\", annot=True, cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What kind of engagement did the articles receive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>AES</th>\n",
       "      <th>AER</th>\n",
       "      <th>AEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21415.000000</td>\n",
       "      <td>13825.000000</td>\n",
       "      <td>10259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.811674</td>\n",
       "      <td>53.535335</td>\n",
       "      <td>23.989668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.323191</td>\n",
       "      <td>475.446702</td>\n",
       "      <td>311.972031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12473.000000</td>\n",
       "      <td>36030.000000</td>\n",
       "      <td>15840.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AES           AER           AEC\n",
       "count  21415.000000  13825.000000  10259.000000\n",
       "mean       8.811674     53.535335     23.989668\n",
       "std      136.323191    475.446702    311.972031\n",
       "min        1.000000      1.000000      1.000000\n",
       "25%        1.000000      4.000000      2.000000\n",
       "50%        2.000000     16.000000      6.000000\n",
       "75%        4.000000     46.000000     15.000000\n",
       "max    12473.000000  36030.000000  15840.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles[metrics].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf = articles[metrics].dropna(how=\"any\")\n",
    "\n",
    "sns.boxenplot(x=\"variable\", y=\"value\", data=pdf.melt())\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "yticks = [1, 10, 100, 1000, 10000]\n",
    "plt.yticks(yticks, yticks);\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Engagement counts\")\n",
    "\n",
    "sns.despine(top=True, left=True, right=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_order = base.dropna(how=\"any\", subset=metrics).groupby(\"discipline\").AES.mean().sort_values().keys()\n",
    "\n",
    "pdf = base.dropna(how=\"any\", subset=metrics)\n",
    "pdf = pdf.melt(id_vars=\"discipline\", value_vars=metrics)\n",
    "\n",
    "sns.boxenplot(x=\"discipline\", hue=\"variable\", y=\"value\", data=pdf, order=sort_order)\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "yticks = [1, 10, 100, 1000, 10000]\n",
    "plt.yticks(yticks, yticks);\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Engagement counts\")\n",
    "\n",
    "sns.despine(top=True, left=True, right=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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>pmid</th>\n",
       "      <th>year</th>\n",
       "      <th>AES</th>\n",
       "      <th>POS</th>\n",
       "      <th>TW</th>\n",
       "      <th>AER</th>\n",
       "      <th>AEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.590000e+02</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.616453e+07</td>\n",
       "      <td>2015.270270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>4.707921</td>\n",
       "      <td>23.214286</td>\n",
       "      <td>9.139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.057507e+05</td>\n",
       "      <td>0.524889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.482461</td>\n",
       "      <td>5.467119</td>\n",
       "      <td>29.256417</td>\n",
       "      <td>9.666719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.563409e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.576836e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.581630e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.603203e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.874286e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pmid         year  AES        POS          TW         AER  \\\n",
       "count  2.590000e+02   259.000000  0.0  60.000000  202.000000  252.000000   \n",
       "mean   2.616453e+07  2015.270270  NaN   1.266667    4.707921   23.214286   \n",
       "std    7.057507e+05     0.524889  NaN   0.482461    5.467119   29.256417   \n",
       "min    2.563409e+07  2015.000000  NaN   1.000000    1.000000    1.000000   \n",
       "25%    2.576836e+07  2015.000000  NaN   1.000000    1.000000    4.000000   \n",
       "50%    2.581630e+07  2015.000000  NaN   1.000000    3.000000   12.000000   \n",
       "75%    2.603203e+07  2015.000000  NaN   1.250000    5.000000   30.000000   \n",
       "max    2.874286e+07  2017.000000  NaN   3.000000   34.000000  159.000000   \n",
       "\n",
       "              AEC  \n",
       "count  158.000000  \n",
       "mean     9.139241  \n",
       "std      9.666719  \n",
       "min      1.000000  \n",
       "25%      2.000000  \n",
       "50%      6.000000  \n",
       "75%     12.000000  \n",
       "max     56.000000  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "artics = articles[(articles.AES.isna()) & ((~articles.AER.isna()) | (~articles.AEC.isna()))]\n",
    "artics.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do the shared URL types receive different kinds of engagement?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis by groups: Do all articles receive the same types of engagement?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product \n",
    "\n",
    "def select_nonzero_src(df: pd.DataFrame, s: bool, r: bool, c: bool) -> pd.DataFrame:\n",
    "    bdf = df.isna()\n",
    "    return df[(bdf.AES != s) & (bdf.AER != r) & (bdf.AEC != c)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_src = base[(~base.AES.isna()) & (~base.AER.isna()) & (~base.AEC.isna())]\n",
    "df_sr = base[(~base.AES.isna()) & (base.AER.isna()) & (~base.AEC.isna())]\n",
    "df_sc = base[(~base.AES.isna()) & (~base.AER.isna()) & (base.AEC.isna())]\n",
    "df_s = base[(~base.AES.isna()) & (base.AER.isna()) & (base.AEC.isna())]\n",
    "df_rc = base[(base.AES.isna()) & ((~base.AER.isna()) | (~base.AEC.isna()))]\n",
    "df_ = base[(base.AES.isna()) & (base.AER.isna()) & (base.AEC.isna())]\n",
    "\n",
    "perms = [df_src, df_sr, df_sc, df_s, df_rc, df_]\n",
    "labels = ['All counts', 'Shares & Reactions', 'Shares & Comments', 'Only Shares', 'Reactions or Comments', 'None']\n",
    "[print(len(_)) for _ in perms];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations by groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>All counts</th>\n",
       "      <th>Shares &amp; Reactions</th>\n",
       "      <th>Shares &amp; Comments</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clinical Medicine (29360)</th>\n",
       "      <td>3516</td>\n",
       "      <td>506</td>\n",
       "      <td>2032</td>\n",
       "      <td>3614</td>\n",
       "      <td>96.0</td>\n",
       "      <td>19596</td>\n",
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       "      <th>Biomedical Research (14351)</th>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>Biology (6761)</th>\n",
       "      <td>1592</td>\n",
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       "      <td>3642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology (1681)</th>\n",
       "      <td>594</td>\n",
       "      <td>46</td>\n",
       "      <td>217</td>\n",
       "      <td>223</td>\n",
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       "      <td>587</td>\n",
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       "      <th>Earth and Space (1098)</th>\n",
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       "      <td>212</td>\n",
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       "    <tr>\n",
       "      <th>Health (1077)</th>\n",
       "      <td>211</td>\n",
       "      <td>28</td>\n",
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       "      <td>503</td>\n",
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       "    <tr>\n",
       "      <th>Professional Fields (474)</th>\n",
       "      <td>149</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "      <td>78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>176</td>\n",
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       "    <tr>\n",
       "      <th>Engineering and Technology (1183)</th>\n",
       "      <td>112</td>\n",
       "      <td>15</td>\n",
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       "      <td>123</td>\n",
       "      <td>2.0</td>\n",
       "      <td>867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physics (616)</th>\n",
       "      <td>112</td>\n",
       "      <td>8</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>3.0</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemistry (338)</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
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       "    <tr>\n",
       "      <th>Mathematics (231)</th>\n",
       "      <td>29</td>\n",
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      "text/plain": [
       "                                   All counts  Shares & Reactions  \\\n",
       "Clinical Medicine (29360)                3516                 506   \n",
       "Biomedical Research (14351)              2015                 200   \n",
       "Biology (6761)                           1592                 122   \n",
       "Psychology (1681)                         594                  46   \n",
       "Earth and Space (1098)                    337                  15   \n",
       "Social Sciences (732)                     298                  13   \n",
       "Health (1077)                             211                  28   \n",
       "Professional Fields (474)                 149                   8   \n",
       "Engineering and Technology (1183)         112                  15   \n",
       "Physics (616)                             112                   8   \n",
       "Chemistry (338)                            40                   4   \n",
       "Mathematics (231)                          29                   3   \n",
       "\n",
       "                                   Shares & Comments  Only Shares  \\\n",
       "Clinical Medicine (29360)                       2032         3614   \n",
       "Biomedical Research (14351)                      938         1401   \n",
       "Biology (6761)                                   634          721   \n",
       "Psychology (1681)                                217          223   \n",
       "Earth and Space (1098)                           123          118   \n",
       "Social Sciences (732)                            100          105   \n",
       "Health (1077)                                    114          214   \n",
       "Professional Fields (474)                         62           78   \n",
       "Engineering and Technology (1183)                 64          123   \n",
       "Physics (616)                                     68           73   \n",
       "Chemistry (338)                                   16           23   \n",
       "Mathematics (231)                                 13           29   \n",
       "\n",
       "                                   Reactions or Comments   None  \n",
       "Clinical Medicine (29360)                           96.0  19596  \n",
       "Biomedical Research (14351)                         79.0   9718  \n",
       "Biology (6761)                                      50.0   3642  \n",
       "Psychology (1681)                                   14.0    587  \n",
       "Earth and Space (1098)                               2.0    503  \n",
       "Social Sciences (732)                                4.0    212  \n",
       "Health (1077)                                        7.0    503  \n",
       "Professional Fields (474)                            1.0    176  \n",
       "Engineering and Technology (1183)                    2.0    867  \n",
       "Physics (616)                                        3.0    352  \n",
       "Chemistry (338)                                      NaN    255  \n",
       "Mathematics (231)                                    1.0    156  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "for tdf, l in zip(perms, labels):\n",
    "    df[l] = tdf.discipline.value_counts().sort_values(ascending=False)\n",
    "df.index = df.index.map(lambda x: \"{} ({})\".format(x, int(df.loc[x].sum())))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
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       "            background-color:  #004d1f;\n",
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       "            background-color:  #eaf7e6;\n",
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       "            background-color:  #eef8ea;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f5fbf2;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f7fcf5;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >None</th>        <th class=\"col_heading level0 col1\" >All counts</th>        <th class=\"col_heading level0 col2\" >Only Shares</th>        <th class=\"col_heading level0 col3\" >Shares & Comments</th>        <th class=\"col_heading level0 col4\" >Shares & Reactions</th>        <th class=\"col_heading level0 col5\" >Reactions or Comments</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row0\" class=\"row_heading level0 row0\" >Social Sciences (732)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col0\" class=\"data row0 col0\" >28.96</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col1\" class=\"data row0 col1\" >40.71</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col2\" class=\"data row0 col2\" >14.34</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col3\" class=\"data row0 col3\" >13.66</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col4\" class=\"data row0 col4\" >1.78</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row0_col5\" class=\"data row0 col5\" >0.55</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row1\" class=\"row_heading level0 row1\" >Psychology (1681)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col0\" class=\"data row1 col0\" >34.92</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col1\" class=\"data row1 col1\" >35.34</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col2\" class=\"data row1 col2\" >13.27</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col3\" class=\"data row1 col3\" >12.91</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col4\" class=\"data row1 col4\" >2.74</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row1_col5\" class=\"data row1 col5\" >0.83</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row2\" class=\"row_heading level0 row2\" >Professional Fields (474)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col0\" class=\"data row2 col0\" >37.13</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col1\" class=\"data row2 col1\" >31.43</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col2\" class=\"data row2 col2\" >16.46</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col3\" class=\"data row2 col3\" >13.08</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col4\" class=\"data row2 col4\" >1.69</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row2_col5\" class=\"data row2 col5\" >0.21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row3\" class=\"row_heading level0 row3\" >Earth and Space (1098)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col0\" class=\"data row3 col0\" >45.81</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col1\" class=\"data row3 col1\" >30.69</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col2\" class=\"data row3 col2\" >10.75</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col3\" class=\"data row3 col3\" >11.20</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col4\" class=\"data row3 col4\" >1.37</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row3_col5\" class=\"data row3 col5\" >0.18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row4\" class=\"row_heading level0 row4\" >Health (1077)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col0\" class=\"data row4 col0\" >46.70</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col1\" class=\"data row4 col1\" >19.59</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col2\" class=\"data row4 col2\" >19.87</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col3\" class=\"data row4 col3\" >10.58</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col4\" class=\"data row4 col4\" >2.60</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row4_col5\" class=\"data row4 col5\" >0.65</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row5\" class=\"row_heading level0 row5\" >Biology (6761)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col0\" class=\"data row5 col0\" >53.87</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col1\" class=\"data row5 col1\" >23.55</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col2\" class=\"data row5 col2\" >10.66</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col3\" class=\"data row5 col3\" >9.38</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col4\" class=\"data row5 col4\" >1.80</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row5_col5\" class=\"data row5 col5\" >0.74</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row6\" class=\"row_heading level0 row6\" >Physics (616)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col0\" class=\"data row6 col0\" >57.14</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col1\" class=\"data row6 col1\" >18.18</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col2\" class=\"data row6 col2\" >11.85</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col3\" class=\"data row6 col3\" >11.04</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col4\" class=\"data row6 col4\" >1.30</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row6_col5\" class=\"data row6 col5\" >0.49</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row7\" class=\"row_heading level0 row7\" >Clinical Medicine (29360)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col0\" class=\"data row7 col0\" >66.74</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col1\" class=\"data row7 col1\" >11.98</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col2\" class=\"data row7 col2\" >12.31</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col3\" class=\"data row7 col3\" >6.92</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col4\" class=\"data row7 col4\" >1.72</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row7_col5\" class=\"data row7 col5\" >0.33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row8\" class=\"row_heading level0 row8\" >Mathematics (231)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col0\" class=\"data row8 col0\" >67.53</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col1\" class=\"data row8 col1\" >12.55</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col2\" class=\"data row8 col2\" >12.55</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col3\" class=\"data row8 col3\" >5.63</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col4\" class=\"data row8 col4\" >1.30</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row8_col5\" class=\"data row8 col5\" >0.43</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row9\" class=\"row_heading level0 row9\" >Biomedical Research (14351)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col0\" class=\"data row9 col0\" >67.72</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col1\" class=\"data row9 col1\" >14.04</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col2\" class=\"data row9 col2\" >9.76</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col3\" class=\"data row9 col3\" >6.54</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col4\" class=\"data row9 col4\" >1.39</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row9_col5\" class=\"data row9 col5\" >0.55</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row10\" class=\"row_heading level0 row10\" >Engineering and Technology (1183)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col0\" class=\"data row10 col0\" >73.29</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col1\" class=\"data row10 col1\" >9.47</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col2\" class=\"data row10 col2\" >10.40</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col3\" class=\"data row10 col3\" >5.41</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col4\" class=\"data row10 col4\" >1.27</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row10_col5\" class=\"data row10 col5\" >0.17</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22level0_row11\" class=\"row_heading level0 row11\" >Chemistry (338)</th>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col0\" class=\"data row11 col0\" >75.44</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col1\" class=\"data row11 col1\" >11.83</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col2\" class=\"data row11 col2\" >6.80</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col3\" class=\"data row11 col3\" >4.73</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col4\" class=\"data row11 col4\" >1.18</td>\n",
       "                        <td id=\"T_5aaecba0_e177_11e9_9d8c_c85b76303b22row11_col5\" class=\"data row11 col5\" >0.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b4516e1d0>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.fillna(0).apply(lambda x: 100*x/x.sum(), axis=1)\n",
    "     .sort_values(by=\"Biology (6761)\", axis=1, ascending=False)\n",
    "     .sort_values(by=\"None\")\n",
    "     .style\n",
    "     .background_gradient(axis=None, cmap=\"Greens\")\n",
    "     .format(\"{:,.2f}\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            color:  #000000;\n",
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       "            background-color:  #afdfa8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #e5f5e1;\n",
       "            color:  #000000;\n",
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       "            background-color:  #edf8ea;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eff9eb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f1faee;\n",
       "            color:  #000000;\n",
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       "            background-color:  #03702e;\n",
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       "            background-color:  #c2e7bb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eaf7e6;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f0f9ec;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f1faee;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f0f9ed;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f4fbf1;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f4fbf1;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f3faf0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #004d1f;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #9ed798;\n",
       "            color:  #000000;\n",
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       "            background-color:  #cbeac4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ebf7e7;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col4 {\n",
       "            background-color:  #f3faf0;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col5 {\n",
       "            background-color:  #f4fbf1;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col6 {\n",
       "            background-color:  #f0f9ec;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col7 {\n",
       "            background-color:  #f5fbf3;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col8 {\n",
       "            background-color:  #f3faf0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f5fbf3;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col0 {\n",
       "            background-color:  #00441b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col1 {\n",
       "            background-color:  #76c578;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col2 {\n",
       "            background-color:  #d7efd1;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col3 {\n",
       "            background-color:  #f3faf0;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col4 {\n",
       "            background-color:  #f4fbf1;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col5 {\n",
       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col6 {\n",
       "            background-color:  #f4fbf1;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col7 {\n",
       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col8 {\n",
       "            background-color:  #f1faee;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col9 {\n",
       "            background-color:  #f5fbf2;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col10 {\n",
       "            background-color:  #f5fbf3;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col11 {\n",
       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col0 {\n",
       "            background-color:  #00441b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col1 {\n",
       "            background-color:  #9cd797;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col2 {\n",
       "            background-color:  #d3eecd;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col3 {\n",
       "            background-color:  #eff9eb;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col4 {\n",
       "            background-color:  #f2faf0;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col5 {\n",
       "            background-color:  #f3faf0;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col6 {\n",
       "            background-color:  #eff9eb;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col7 {\n",
       "            background-color:  #f4fbf2;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col8 {\n",
       "            background-color:  #f2faf0;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col9 {\n",
       "            background-color:  #f4fbf2;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col10 {\n",
       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }    #T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col11 {\n",
       "            background-color:  #f6fcf4;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Clinical Medicine</th>        <th class=\"col_heading level0 col1\" >Biomedical Research</th>        <th class=\"col_heading level0 col2\" >Biology</th>        <th class=\"col_heading level0 col3\" >Psychology</th>        <th class=\"col_heading level0 col4\" >Earth and Space</th>        <th class=\"col_heading level0 col5\" >Social Sciences</th>        <th class=\"col_heading level0 col6\" >Health</th>        <th class=\"col_heading level0 col7\" >Professional Fields</th>        <th class=\"col_heading level0 col8\" >Engineering and Technology</th>        <th class=\"col_heading level0 col9\" >Physics</th>        <th class=\"col_heading level0 col10\" >Chemistry</th>        <th class=\"col_heading level0 col11\" >Mathematics</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row0\" class=\"row_heading level0 row0\" >Reactions or Comments (259)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col0\" class=\"data row0 col0\" >37.07</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col1\" class=\"data row0 col1\" >30.50</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col2\" class=\"data row0 col2\" >19.31</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col3\" class=\"data row0 col3\" >5.41</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col4\" class=\"data row0 col4\" >0.77</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col5\" class=\"data row0 col5\" >1.54</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col6\" class=\"data row0 col6\" >2.70</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col7\" class=\"data row0 col7\" >0.39</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col8\" class=\"data row0 col8\" >0.77</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col9\" class=\"data row0 col9\" >1.16</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row0_col11\" class=\"data row0 col11\" >0.39</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row1\" class=\"row_heading level0 row1\" >All counts (9005)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col0\" class=\"data row1 col0\" >39.04</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col1\" class=\"data row1 col1\" >22.38</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col2\" class=\"data row1 col2\" >17.68</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col3\" class=\"data row1 col3\" >6.60</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col4\" class=\"data row1 col4\" >3.74</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col5\" class=\"data row1 col5\" >3.31</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col6\" class=\"data row1 col6\" >2.34</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col7\" class=\"data row1 col7\" >1.65</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col8\" class=\"data row1 col8\" >1.24</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col9\" class=\"data row1 col9\" >1.24</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col10\" class=\"data row1 col10\" >0.44</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row1_col11\" class=\"data row1 col11\" >0.32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row2\" class=\"row_heading level0 row2\" >Shares & Comments (4381)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col0\" class=\"data row2 col0\" >46.38</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col1\" class=\"data row2 col1\" >21.41</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col2\" class=\"data row2 col2\" >14.47</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col3\" class=\"data row2 col3\" >4.95</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col4\" class=\"data row2 col4\" >2.81</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col5\" class=\"data row2 col5\" >2.28</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col6\" class=\"data row2 col6\" >2.60</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col7\" class=\"data row2 col7\" >1.42</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col8\" class=\"data row2 col8\" >1.46</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col9\" class=\"data row2 col9\" >1.55</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col10\" class=\"data row2 col10\" >0.37</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row2_col11\" class=\"data row2 col11\" >0.30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row3\" class=\"row_heading level0 row3\" >Shares & Reactions (968)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col0\" class=\"data row3 col0\" >52.27</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col1\" class=\"data row3 col1\" >20.66</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col2\" class=\"data row3 col2\" >12.60</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col3\" class=\"data row3 col3\" >4.75</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col4\" class=\"data row3 col4\" >1.55</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col5\" class=\"data row3 col5\" >1.34</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col6\" class=\"data row3 col6\" >2.89</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col7\" class=\"data row3 col7\" >0.83</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col8\" class=\"data row3 col8\" >1.55</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col9\" class=\"data row3 col9\" >0.83</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col10\" class=\"data row3 col10\" >0.41</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row3_col11\" class=\"data row3 col11\" >0.31</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row4\" class=\"row_heading level0 row4\" >None (36567)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col0\" class=\"data row4 col0\" >53.59</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col1\" class=\"data row4 col1\" >26.58</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col2\" class=\"data row4 col2\" >9.96</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col3\" class=\"data row4 col3\" >1.61</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col4\" class=\"data row4 col4\" >1.38</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col5\" class=\"data row4 col5\" >0.58</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col6\" class=\"data row4 col6\" >1.38</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col7\" class=\"data row4 col7\" >0.48</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col8\" class=\"data row4 col8\" >2.37</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col9\" class=\"data row4 col9\" >0.96</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col10\" class=\"data row4 col10\" >0.70</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row4_col11\" class=\"data row4 col11\" >0.43</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22level0_row5\" class=\"row_heading level0 row5\" >Only Shares (6722)</th>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col0\" class=\"data row5 col0\" >53.76</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col1\" class=\"data row5 col1\" >20.84</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col2\" class=\"data row5 col2\" >10.73</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col3\" class=\"data row5 col3\" >3.32</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col4\" class=\"data row5 col4\" >1.76</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col5\" class=\"data row5 col5\" >1.56</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col6\" class=\"data row5 col6\" >3.18</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col7\" class=\"data row5 col7\" >1.16</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col8\" class=\"data row5 col8\" >1.83</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col9\" class=\"data row5 col9\" >1.09</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col10\" class=\"data row5 col10\" >0.34</td>\n",
       "                        <td id=\"T_166c4020_e178_11e9_9d8c_c85b76303b22row5_col11\" class=\"data row5 col11\" >0.43</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b4fed65c0>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "for tdf, l in zip(perms, labels):\n",
    "    df[l] = tdf.discipline.value_counts().sort_values(ascending=False)\n",
    "df = df.T\n",
    "df.index = df.index.map(lambda x: \"{} ({})\".format(x, int(df.loc[x].sum())))\n",
    "df\n",
    "\n",
    "(df.fillna(0).apply(lambda x: 100*x/x.sum(), axis=1)\n",
    "     .sort_values(by=\"All counts (9005)\", axis=1, ascending=False)\n",
    "     .sort_values(by=\"Clinical Medicine\")\n",
    "     .style\n",
    "     .background_gradient(axis=None, cmap=\"Greens\")\n",
    "     .format(\"{:,.2f}\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IS0sjKCgIT09PPD098fHxIS0tja1bt5LxEv+uPXjwgEqVKhXYlpycjEqlKrC9YsWKmnrionp6BFihUBQ6L/LQoUOZNGkS9+/fJzAwkDZt2tC7d2+ioqJe6H3F20WS5BdVLe/Deyc1m48f3iMnh/SLF7FXql9nZWZw88ql0oxQCCHEG6haxcK/2rezKL2v/rds2YJSqWTNmjVaP19++SX3799nz549L3xOU1NTzZzKjyUmJnLkyBFMTU1RKBTcu3cv33F3797F3Nxc8/rp+uTimHFDV1eXAQMGsH37dvbv38+UKVOIiIjgq6++euVzizdXqSfJ9+/fx8nJKd/PqFGjAPUKOCEhIbRs2RIXFxcGDhxIZGRkaYf5bBaOT+ZLjjqu2Z2vLjnPfMlRUnIhhBDiOXp62hfa/lHjwtuLy61btzh16hSdOnXCy8tL66dXr15YWlq+1AN8bm5uHDx4UCvJ3blzp2a2inr16rFr1y6tYw4dOkRycjKNGjUC1Il2XJz2Ql1nzpx54Vh0dLTTn0GDBjFr1iwAbGxs6NevH23atCE2NvaFzy3eHqX+4N7ly5cBWLFiBSYmJpr9jz8lLlq0iNDQUMaPH4+trS0hISEMGDCAnTt3YmZmVtrh5qdQqKeCC98GiTcg+TaYWeVLkm0H9kdXX5/szExuXAijRRmGLIQQ4vXXz9uBI1fvsu9y/tVa+3s70NLJslTi2Lp1KwqFgnbt2uVr09XV5d1332Xt2rWaxLWohg8fTp8+fRg1ahQ9evTg9u3bLFiwgL59+2Jqasqnn37KiBEjGDNmDF27diU2NpZ58+ZpposD8PHxYfr06SxcuBBPT09+++03Ll68+MLXWL58ecLDwzlx4gQuLi64u7sTEhKCpaUlSqWSyMhIdu3aRf/+/V/43OLtUeojyVeuXKFy5co0a9YMV1dXzY+joyMpKSksX76ckSNH0q9fP1q3bs3y5ct5+PDhK007U+yq5Z8vWc/CAgMHB0D98J6evgG2TvUBuPN3JGnJSaUephBCiDeHvq4OoX7ufNvdhSY1KlLT0oRWTpZ838+D6R800HoQrSRt3bqVRo0aUaVKlQLb33//fXJycl7432VXV1eWL19OfHw8n3zyCSEhIfj5+TFu3DhAPb/xokWLiIqKYsSIESxcuJCOHTuybNkyzcN+3bt3p3///qxdu5aPP/6YlJQUJk+e/MLXOGDAADIyMhg8eDCXLl1i+PDhDBs2jPXr1+Pv78/3339P//79GTly5AufW7w9FKrCKtlLwKRJk7hz5w4rVqzI13bkyBEGDRrEzp07qVnzybRpw4cPJyMjo8BjykT0KVjeRr3tPRLaqed4vDVxIg+2/gJAzf/tJuyPExz6YRUAHccE4OTdvCyiFUIIUYLCw8OpV69eWYchhMhVXL+TZTKSnJaWRq9evVAqlfj4+LBs2TJUKhXXr18HoFq1alrH2NnZadpeC9YNQbecejtPXbJh3pKLsHM4KF01r6UuWQghhBDizVGqNcnZ2dlERkZiZGTExIkTsbGx4ffff2fu3Lmkp6ejr6+PgYEBBgYGWseZmJgUafqX69evk5aWVlLha3GwqIvx3XOoYsO4cuEsKj1DKF9e037r9/2oalRH38iYzLRUIs6cxC48vFRiE0IIUXoyMzNL7d8eIcTzZWZmEl5IzlXUUeZSf3BvyZIl2NjY4JBbv+vl5UVqairLli1j+PDhz6y5KkotlqOjY3GGWriYlnD3HIqcLOqWTwMHN1S1a3Nl6heo0tMxvBFF9QYNiHRpxF/HD5OWmIB1RQvMq1qVXoxCCCFKXHh4uKzMJsRrRF9f/80rt9DV1cXb21uTID/WokUL0tLSMDIyIiMjg8zMTK32hw8fvh4zW+RVLc+iIrklFwo9PYycnQFIv3yZnPR0HBo+Kbm4cf5sqYYohBBCCCFeTqkmyXfu3GHDhg35JhN/9Ei9ilCFChVQqVTExGgvzRkTE0P16tVLLc4iebw8NWgtKmLkmluXnJVF+qVLUpcshBBCCPEGKtUkOSMjgy+//JJffvlFa/9vv/2Go6Mjbdu2pVy5clor+Tx48ICTJ0/i7e1dmqE+n0llqFRLvR19AnInCXn64b0KVawwr2oNQNTFc+TkZJd6qEIIIYQQ4sWUak1ytWrV6NixI0FBQSgUCmrWrMmuXbvYvXs3ixYtwsTEhL59+xIUFISOjg6Ojo4sWbIEU1NTunfvXpqhFk21JnDvKqQlqP9buXa+RUUA7JUuJN6JJf1hCnF/X8OqZu2yilgIIYQQQhRBqT+49/XXX7N48WJWr15NfHw8NWvWZOHChbRu3RqAzz77DB0dHVasWEFqaipubm7Mnj379atJBnXJRdha9XbUcahcG/0qVdC3sSHz1i3SwtTlFQ4N3Ti/R73U5o3zZyVJFkIIIYR4zZX6YiJvlfgrsCi3NtnNDzoFA3Dzs89I2vkrALV+30+WqQmLB/cGlQp754Z0/yKwrCIWQghRzGQxESFeL2/sYiJvlUq1wdBcvZ27PDWgXXIRdg4jUzOsaqjrl29evkTmo/RSDVMIIYQQby8Z7ywZkiS/Ch0dqOal3r77F6SqZ+0ouC5ZPctFdlYWNy9fKt04hRBCiBdw7Ngx/P398fT0RKlU0r59e+bPn6+1sNemTZtwcnLKN2PVm+D777+nZcuWeHp6MmnSJB4+fPjcYx5f79M/bm5udOnShc2bN5dC5PkFBwfzww8/aF77+fkxbNiwMonlbVPqNclvnWqNIeI39Xb0SXBqT7n69VHo66PKzNQkyQ5KN05u+QmAGxfCcHRpVFYRCyGEEM904MABhg8fTteuXenbty+GhoaEh4ezdOlSTpw4wbp169DV1S3rMF/a1q1bmT9/PlOnTqVChQpMnToVMzMzJk+eXKTjly1bpnlOSqVSERcXx5o1awgICMDCwoKWLVuWYPT5LVy4kM8//1zzetq0aejoyBhocZAk+VXZ51lUJPo4OLVHx8AAw/r1STt3jvSLF1FlZGDjVA89g3JkZTySRUWEEEK8tpYtW0azZs34+uuvNfu8vb2pUaMGw4YN4/Dhw7zzzjtlGOGrOXz4MHXr1qV3794A7N69m7Cwoq9j0KBBAypWrKi1r3nz5jRp0oTNmzeXepL8tFq1apXp+79N5KPGq7JpBDq5nzUKWFRElZFB+pUr6OnrY1evAQDxN/4m9UFiqYcqhBDiNZeZBkeCYFET+MYRQlvBmdVQinPsJyQkFFjj2qxZM8aOHUvVqlW19h8/fpxOnTqhVCrp0KEDe/fu1Wo/dOgQffv2xc3NDaVSSadOndi9e7emfeHChXTt2pXAwEAaNWpE586dAcjKyiIoKIiWLVuiVCrp2rUrx44d0zr35s2b6dChA0qlEh8fHwIDAzULlD2Lg4MDERER3Lhxg9TUVC5duoSrq2uhxzyPvr4+BgYGWvuKEv+1a9cYNWoUTZo0wdnZGV9fXxYtWqR1/xMTE5kyZQpNmzalUaNGDBo0iCtXrgDg5OQEwJw5c/D19QXyl1skJCQwdepUfHx8cHFxoV+/fly4cEHTvmnTJry8vDh27BidOnXC2dmZ9957T+vPMTs7mzlz5tCyZUtN+/r161/pnr0JJEl+VQbGYNVQvX3zDGRlAPkf3oMndckANy6eK70YhRBCvP4yUmFNZ/jflxAfDmn34dYfsG0UbBoKOTmlEoaPjw+HDx9m+PDh7Nixg/j4eECdCA4fPpy6detq9f/666/x8/Nj8eLFmJmZMXbsWO7duwfA+fPnGTp0KLVr12bx4sXMnz8fIyMjxo0bp1XLfOXKFS5fvsyiRYsYM2YMAF988QUrV66kX79+LFq0iBo1ajBkyBD++OMPAE6dOsXkyZPp2LEjy5cvZ/jw4fz4448EBwcXen39+/fH0tKSkSNH0qdPH6ysrBg1alSR709OTg5ZWVlkZWWRkZFBdHQ006ZNIyUlhQ8++EDT73nxP3z4kH79+pGYmMg333zD0qVL8fLy4rvvvmP//v2AOtEeOHAgBw4c4LPPPiMoKIj09HT8/f158OABGzZsANSJcUHX/fDhQz766COOHj3KuHHjmD9/PiqVir59+2oS7cf9Jk+eTJ8+fVi6dCkWFhaMHTuWxET1gN7SpUv5+eefGTNmDMuXL6dFixZMnz6dQ4cOFfm+vYmk3KI4VPNS/0WWlQ63L4Cde/6H9/z65luiul6zN/frKiGEEMXsRIi6bK8gFzdC/Q+gfqcSD+NxcrRlyxZNslajRg3atWvHwIEDqVChglb/yZMn06FDBwAqVqxI165dCQsLo3Xr1kRERNC2bVumTZum6W9jY0OXLl04d+4crVq1AtTJYEBAAPXr1wcgMjKSTZs2MXPmTM1iYj4+PsTHx7NgwQLWrFnD2bNnMTIywt/fHwMDAxo3boy+vj76+vqFXl9sbCzm5ub8+eefWFtbs2HDhnyjwIVp1qxZvn21atVi3rx5mjUfihL/33//jb29PQsWLNCUb3h7e7Nnzx5OnTqFr68vv//+O5cuXWLdunV4eHgAUL9+fbp3787Fixc1sVhbW2vuXV6bNm0iKiqKbdu2acowmjdvTrt27QgODmbhwoUAZGZmMmHCBN577z0AKlWqRKdOnThx4gTt2rXjzJkzODs7a0b5vby8MDQ0xMjIqMj37U0kI8nFwd7ryXbuX3B6NjboWVoCaBYVsbR3xKi8+i+XG+fDZMoWIYQQT5xd95z2taUShoGBAbNmzWL//v1MmzaNtm3bcu/ePUJCQujYsSPR0dFa/d3c3DTbtra2ACQnJwPQrVs3vvvuO1JTU7lw4QLbtm1j3Tr1dWZkZGidx9HRUbN98qS6fNHHx0czapuVlcU777zDH3/8QUZGBo0aNSI1NZUPPviAoKAgzp8/z4cffqhJ5Apy8uRJevToga2tLVOmTCE2NpZ58+YBsGLFCk6cOPHMYx9btWoVGzduZNWqVXh5eWFjY8O3336r+aBQ1PidnZ354YcfMDMz4+rVq+zZs4fg4GDNCDXA2bNnMTMz0yTIoE5g9+3bV2Cy/rRTp05Rq1YtrTplAwMD2rZtq4nxsbwlJ1ZWVgCkpaUB4OHhweHDh/Hz82P16tVER0czduxYrbjeRjKSXByq5X147wR4f4JCocDI1YXk/+0hMyaGrLt30atcGQelK5ePHCD5Xjz3Y29R0ca27OIWQgjx+kiOfbX2YmZlZUXv3r3p3bs3WVlZbN26lWnTphEcHMw333yj6WdoaKjZfjyrQk5uaUhqaipffvklv/6qXmCrevXqmnKNvANFxsbGGBsba14//prfx8enwNju37+Ph4cHixcvZuXKlYSGhrJ48WLs7OyYPn06LVq0KPC4qVOnakoaFAoFUVFRrFy5kqpVqzJnzhzGjRuHl5dXgcc+5uTkpBn5dXNzo2vXrgwePJjNmzdTpUqVIsdftWpVQkJCWL58OcnJydja2uLm5oaenp7m3jx48IBKlSoVGk9hkpKSqFy5cr79lStXzjftXWF/jkOHDsXIyIiNGzcSGBhIYGAg7u7uzJ49G3t7+5eO73UnSXJxKG8NFezhQRREnQCVChQKjFzUSTJA2vnzmPn6Yq904fKRAwDcuHBWkmQhhBBq5g7qWuTC2ktYWFgYI0aMICQkBJc8ZYN6enp069aNffv2ERkZWeTzzZgxgyNHjhAaGoqnpycGBgZcvXqVbdu2FXqcmZkZCoWCH3/8scDp5iwsLADw9fXF19eX5ORkDh48SEhICGPHjuXo0aP5SigSEhK4ceMGH3/8MQqFAoCAgACuXLnC7NmzMTQ0pFOnFytnMTQ05KuvvqJPnz7MmDFDU75QlPi3bNlCUFAQ06ZNo2PHjppp5by9vbXuQ0HzUB8/fhw7Ozvs7OwKja9ChQpcu3Yt3/74+HjMzc2LfJ26uroMGDCAAQMGcOvWLfbs2cPChQv56quvWLZsWZHP86aRcovi8rjkIuU2JEYBBT+893RdshBCCAFAo37Pae9f4iE4Ojry8OFD1qxZk68tOzub6OhoateuXeTzhYWF0aJFC5o1a6ZJWh8/7FVYyaG7uzsqlYqUlBSUSqXm59ixY6xatQo9PT0WLlxIjx49AHUy2aFDB/z41YGHAAAgAElEQVT9/UlOTtZa9OQxc3NzTExMNA/OgTr59/Pz05zjZeYX9vDwoGPHjuzevZvjx48XOf6zZ89iZWXFRx99pEmQ//zzT63ZRdzc3EhKStKK+cGDBwwZMoQjR44AFBqzu7s7V69e1fpgk5GRwZ49e2jUqOjrNQwaNIhZs2YB6pryfv360aZNG2JjS/fbjdImSXJxqZa3Llld02TYoAHkfoJ8vKhI+cpVsLBWjx5HXTxPTnbpTesjhBDiNdZ4CNRu94y2oVC7bYmHYG5uztixY9m+fTv+/v5s376d06dPs3PnTgYOHMidO3f4+OOPi3w+pVLJvn372Lx5M8ePH2fBggWaGuD09PRnHlevXj3atWvHhAkTWLduHcePH+e7775j/vz52NjYoKOjg5eXF+fPn2fq1KkcO3aMXbt2sWTJEtzd3fPNYwzqZHL48OFs3LiRefPmcezYMRYtWsSECRNo06YNmZmZDBw4kFu3br3wffvss88oV64cs2bNIicnp0jxK5VKYmNjCQ4O5uTJk6xfv55hw4ahUCg096ZVq1bUr1+fsWPHsmXLFg4cOMAnn3xClSpVNA/ZlS9fnjNnznDuXP5Zs7p27YqNjQ1Dhgzhl19+Yf/+/QwZMoS7d+8yfPjwIl+fu7s769atY9myZZw4cYIffviBXbt20bZtyf8/WZak3KK4PJ0kN+yBjrExhk5OpF+6RNqFC6iyslDo6eHQ0JX7sTfJSEvldmQENnXqPvu8Qggh/hl09aHXD3DhvxD2A6TcAQtHcB8ITu9CbolASRswYAAODg6sXbuWmTNnkpycjIWFhWaBkWrVqhX5XAEBAaSnpxMYGAhAzZo1CQ4OJjAwkLNnz9KlS5dnHvvtt98SFBREaGgo9+7dw9bWlnHjxuHv7w9A48aNmTdvHqGhoWzfvp1y5crh4+NDQEDAM885dOhQDAwMWLt2LStWrMDW1pbPPvsMPz8/Ll68yJQpU8h+icErW1tb+vfvT2hoKD/99BM9e/Z8bvxdu3bl+vXr/PjjjyxbtgxbW1v8/f2JjIzkzJkzgHraveXLlzNnzhwCAwPJycnBw8ODVatWaUafR44cyYIFCzh9+jRHjx7VisvU1JR169bxzTff8NVXX5GdnY2rqyvr1q0rcDaMZxk+fDg5OTmsX7+eBQsWYGlpSf/+/Rk5cuQL36s3iUIlUywUj+ws+MYBMlKgqhI+PgzA7a++4v4P6gm3q2/ZjGHdukScOsYv36pXMmraow/e3T4qs7CFEEK8mvDwcOrVq1fWYYhioFKpNPXK4s1VXL+TUm5RXHT1wC53KpS4PyE9CXjGoiINGqJQqG991AVZVEQIIYR4HUiCLPKSJLmIHmU/4r9X/kv/X/vTZWsXJhyYwJk7Z7Q7PS65UOXAzdMA+RcVAcoZm2BVS/3gw62/LpORnlbyFyCEEEIIIYpMkuQiSM1Mxf83f2Ycn8EfcX9wNfEqu67vYsCuAay6uOpJx7x1yVHqh/f0HRzQzZ1mJS1PUb1DQ/Xk6znZWcSEXyzxaxBCCCGEEEUnSXIRhJwL4Vx8wWURc8/M5a/7f6lf2HkAuV/V5M5wocidLxkg49o1snMnGHdwfjIV3I3zMhWcEEIIIcTrRJLk58jOyWZTxKZC+2jaDStA1Qbq7ZjTkKN+QtbINU/JxYULAFjXcUK/nHp1G5kvWQghhBDi9SJJ8nOkZKaQlJFUaJ+byTefvHhccpGRDHGXgIIf3tPV08euvjMAd6NvkHI//4o6QgghhBCibEiS/BzG+sYY6RkV2sfS2PLJC626ZPXKO4YNG2rmt9SqS1a6Pel6UWa5EEIIIYR4XUiS/Bz6Ovp0rNGx0D6dauVZ690+76IiJwHQNTWlXK1agDpJVuXkAOCgfDLCfOP82WKKWAghhBBCvCpJkotgpNtIHMs7FtjmV98PF8snyS7mDmBaVb0dfVyz+3Fdck5yMhl//w1ApWoOmJhbAOq6ZFnXRQghhBDi9SBJchFUNKzI2vfWMkQ5BBsTG639493Ha3dWKJ6UXCRGQVIsUHBdskKhwF6pnuUi5X4CCTejS/AqhBBCCCFezT9pQE+S5CKqUK4CoxqN4rcPf+NfDv8CICE9gajkqPyd89Yl504FV9CiIgAOyjxTwcksF0IIIV4Dx44dw9/fH09PT5RKJe3bt2f+/PmkpKRo+mzatAknJycSEt68B8+///57WrZsiaenJ5MmTeLhw4dFPjYqKop///vftG7dmoYNG9K6dWu++OILbt26VYIRvx5Onz7NqFGjyjqMUiNJ8ktoYddCs33o5qH8HeybPNnOTZINatZEx9QU0E6S7aUuWQghxGvkwIEDDBo0CCsrK+bMmUNoaCg9evTgxx9/ZPDgwWRnZ5d1iK9k69atzJ8/n6FDhzJ9+nR27dpFUFBQkY49evQoXbp04ezZs3z88ceEhoYyYsQITp8+zYcffsi1a9dKOPqytXHjRv7OLRn9J9Ar6wDeRM1tm2u2D8Ucwq++n3YHq4agZwhZ6U8WFdHRwahhQx4ePcqjiAiyUx6ia2qCWcXKVLKz515MFNGXLpKdlYWunvyxCCGEKBvLli2jWbNmfP3115p93t7e1KhRg2HDhnH48GHeeeedMozw1Rw+fJi6devSu3dvAHbv3k1Y2PO/yU1ISGDcuHHUr1+f5cuXY2BgoGnz9fWlU6dO/Pvf/2b16tUlFrsoXTKS/BIqG1WmfqX6AJy+c5rUzFTtDnoGYNNIvR17DjLU7ZpFRXJySL94QdP98WhyZnoasVevlGzwQgghXlvpWemsvLiSLlu70PzH5ny0/SN+/utnsnNKb/Q2ISGhwLrTZs2aMXbsWKpWraq1//jx43Tq1AmlUkmHDh3Yu3evVvuhQ4fo27cvbm5uKJVKOnXqxO7duzXtCxcupGvXrgQGBtKoUSM6d+4MQFZWFkFBQbRs2RKlUknXrl05duyY1rk3b95Mhw4dUCqV+Pj4EBgYyKNHjwq9PgcHByIiIrhx4wapqalcunQJV1fXQo8B2LJlCwkJCUyaNEkrQQawsLBg4sSJeHt7k5WVBUBmZiahoaG0a9cOpVLJ+++/z7Zt2zTHxMTE4OTkxJ49exgwYAAuLi60bt2a3bt3c/XqVXr37o2LiwudO3fm/PnzmuN8fX0JDQ1l6tSpuLu74+XlxXfffUdycjLjx4/Hzc2NVq1asWmT9kJoFy9epH///ri4uNCkSRNmzJhBWlqapt3Pz49Zs2Yxf/58mjVrhouLCyNGjODOnTsABAQEsHnzZiIiInBycuLECfUg4LJly2jbti1KpZI2bdqwaNEicnJn8XrT6U6fPn16WQfxJopLjeP0ndPkqHJoWLkh1StU1+5wN0I9u4UqB2q2BnN7VOnpJG3fDoCBY3WMPTwAyMnO4fKRAwCUr2xJtQYNS/VahBBCvLy7d+9iaWn5/I7PkZaVxrD/DWPz1c0kpCfwKPsRcWlx/B7zOzeSb9DavjWK3Dn3S1JMTAxbt27lzz//RKFQYGFhgYmJCbq6unh4eFC5cmUAwsPD2bt3L2fOnGHYsGH07NmTc+fOsXbtWnr06IGxsTHnz5+nf//+vPPOO4waNQpfX18uXLjA2rVr6dWrF0ZGRpw8eZLffvsNAwMDpk2bhqenJ46OjkyZMoWffvqJoUOH4ufnR1xcHPPnz6dJkyZYW1tz6tQpRo4ciZ+fH8OHD6dGjRosWbKEjIwMmjZt+szrq1evHr/88gt79uxhy5YtVKxYkRkzZuRLfJ8WHBxMWloaEydOLLC9Tp06eHh4oKOjHn+cMGEC//3vfxk0aBD9+/cnNTWV+fPnY2VlRYMGDUhKSmLNmjUcPXqUzp07M3DgQMLDw1m/fj0HDhyge/fu9OzZkz179rBnzx78/NTfWq9evZrDhw9To0YNxo4di0KhYMWKFezevRtnZ2dGjBhBVFQUa9asoXPnzpiZmXH16lV69eqFtbU1AQEBuLm5sWbNGs6ePcv7778PqD9wHDx4EJVKxfjx4/Hy8mL9+vVERUXRvn176tSpQ1RUFDk5OXz//ffUrVuXX3/9lVmzZjFy5EgGDhxIxYoVCQoKwtLSEmdn5xf+f6+4FNfvpHyv/5Ja2LZgybklgLouuZV9K+0O9k3gSO529HFwbKZeVCRX3rrkavWd0dHVJSc7mxvnw2javU9Jhy+EEOI1sy58HWfjCn425de/f6WtQ1vaOrQt8TjGjh1LYmIiW7ZsYf/+/QDUqFGDdu3aMXDgQCpUqKDVf/LkyXTo0AGAihUr0rVrV8LCwmjdujURERG0bduWadOmafrb2NjQpUsXzp07R6tW6n87s7KyCAgIoH599be0kZGRbNq0iZkzZ9K9e3cAfHx8iI+PZ8GCBZoEz8jICH9/fwwMDGjcuDH6+vro6+sXen2xsbGYm5vz559/Ym1tzYYNG56bIAPcvn0bGxub5/YDuHLlCjt27ODf//43vXr1AqB58+akpKQwb948unbtqunbvn17hgwZAkB2djaDBw/m/fffp08fdS4QFxfH1KlTSUpKonz58gBYWVkRGBiIQqHAzc2NDRs2ULVqVU0Cb2trS9u2bbl06RI2NjYsXryYypUrExoaqrlWR0dH+vTpw6lTp/D09ARAV1eXpUuXUq5cOQAuX77Mf//7XwDs7e2pWLEit27d0oy8nzlzBltbW3r37o1CoaBx48bo6elRpUqVIt2n152UW7ykBpUaYFFOPcfxoZuH8n81Zdf4yXbuoiJ6FhYYODoCuYuK5B5jYGSMdW0nAGKvXuFR6lPlG0IIId56W65uKbR9c8TmUonDwMCAWbNmsX//fqZNm0bbtm25d+8eISEhdOzYkeho7elK3dyerB5ra2sLQHJyMgDdunXju+++IzU1lQsXLrBt2zbWrVsHQEZGhtZ5HHP/fQQ4eVL976aPjw9ZWVman3feeYc//viDjIwMGjVqRGpqKh988AFBQUGcP3+eDz/8UFOuUZCTJ0/So0cPbG1tmTJlCrGxscybNw+AFStWaEoICqKjo1PkMoLTp08D6gQ4r/fee4+EhAQiIyM1+xrmGUB7PEqvVCo1+yws1LlGUlKSZp9SqdR8q2BoaIiJiYnWyK25ubnWMSdOnKBp06bo6Oho7qWrqyumpqZaJSxOTk6aBBnUyXjekoyneXh48Pfff9OtWzdCQ0P566+/8Pf3x9fXt9D786aQJPkl6ero0sy2GQC3H94mIjFCu4NJJahUW70dfQJyf7EeTwWXnZBAZp6/aOyd1Z/KVDk5RF+6gBBCiH+WuNS4Qtvj0+JLKRI1KysrevfuTXBwMEePHiUwMJD79+8THBys1c/Q0FCz/bjU4HEymZqayvjx4/H09KRXr14sXbpUUzOcd3DJ2NgYY2NjzevExERAnSQ3aNBA8/PNN9+QmZnJ/fv38fDwYPHixVhaWhIaGkr37t1p06YNhw4VMOtUrqlTp2pqePv164efnx8rV65k5cqVzJkzR6v292m2trbExsY+sz0lJUXz4eDBgwfo6elpktXHHifBeafSMzExyXeuvPe0IAUdY2Rk9Mz+iYmJbNiwQeteNmjQgJSUFOLj4595DoVCUei8yB988AGzZ89GR0eHefPm8f777/PBBx9w4cLbkcdIucUraGHbgu3X1DXGh2IOUceijnYHey+4FwFp9+HeVbCsg5GrCw+2bgXUo8kG9vYAODR049jGHwD16nu1PLwQQgjxz2FrasvVxKvPbM+7mFVJCQsLY8SIEYSEhOCSZ35/PT09unXrxr59+7RGQZ9nxowZHDlyhNDQUDw9PTEwMODq1ataD7AVxMzMDIVCwY8//oiurm6+9sejq76+vvj6+pKcnMzBgwcJCQlh7NixHD16NF8JRUJCAjdu3ODjjz/WjMIGBARw5coVZs+ejaGhIZ06dXpmTE2bNmX//v2Eh4dTr169fO0//vgj8+fPZ9euXVSoUIGsrCwSExO1EuW7d+8C5EueS5qpqSmtW7fmo48+ytf2+F6+rC5dutClSxfu3bvHvn37WLRoEZ9//jm//vrrK533dSAjya+gqU1TdBTqW1jgfMlai4qol6guaOU9AKuatTHI/QQn8yULIcQ/T9faXQtt71anW4nH4OjoyMOHD1mzZk2+tuzsbKKjo6ldu3aRzxcWFkaLFi1o1qyZJml9PNJb2Ailu7s7KpWKlJQUlEql5ufYsWOsWrUKPT09Fi5cSI8ePQB1Ut2hQwf8/f1JTk7WGql9zNzcHBMTE/744w/NPj09Pc0DcWZmZpqR8IJ06tQJc3Nzvvnmm3ylInfv3mX16tW4urpSrVo13N3dAdi1a5dWv507d1KpUiWt0pLS4O7uzrVr13B2dtbcS2tra+bOnUtERMTzT5Dr6fszZcoUzeIilSpVonv37nz44YeFjri/SWQk+RWYG5rTsHJDwuLDCIsLIykjifIG5Z90qPbUoiKN+lGuTh0URkao0tK0Ht7T1dOjWoOGRJ4+QcKtGJLv3cWsUuVSvBohhBBlqVfdXhyPPc7BmIP52j6q+xEtbFsUcFTxMjc3Z+zYscyaNYvExES6dOmClZUVcXFx/Pjjj9y5cydfuUVhlEol+/btY/PmzVhbW3P8+HGWL18OQHp6+jOPq1evHu3atWPChAmMHDmSmjVrcvLkSUJCQhg8eDA6Ojp4eXmxaNEipk6dSocOHXjw4AFLlizB3d2dihUr5junjo4Ow4cPZ/78+VhYWODt7c0ff/xBaGgobdq04fTp0wwcOJClS5cW+IBehQoV+PrrrxkzZgy9evWib9++2NjYEBkZybJly8jOzmb27NkA1K1bl3bt2jF79mwePnyIk5MTe/fuZceOHXz55ZeFJuMlYcSIEfTq1YvRo0fTrVs3MjIyWLx4MbGxsZqHJYuifPny3L59myNHjuDs7IynpycTJ05k3rx5NG3alNu3b7N+/Xrati35B0xLgyTJr6iFXQvC4sPIVmVz7NYx2jm2e9JYqRYYWajLLaJyFxXR08PI2ZnUU6dIv3yZnPR0dHJrj+ydXYk8re5340IYzi3blPr1CCGEKBv6OvoEtQpix7UdbI3cyt20u9iZ2tG9TndaVmtZKtO/AQwYMAAHBwfWrl3LzJkzSU5OxsLCQrPASLVq1Yp8roCAANLT0wkMDASgZs2aBAcHExgYyNmzZ+nSpcszj/32228JCgoiNDSUe/fuYWtry7hx4/D39wegcePGzJs3j9DQULZv3065cuXw8fEhICDgmeccOnQoBgYGrF27lhUrVmBra8tnn32Gn58fFy9eZMqUKYWuKNimTRt++OEHli9fTlBQEAkJCVStWpUWLVrwySefaM0h/Tj+VatWkZiYSI0aNfi///s/PvjggyLfv+Li7OzM6tWrWbBgAaNGjaJcuXI0atSIOXPm5Jv3ujA9e/Zk//79DBs2jDlz5tC5c2dSUlJYt24dq1atwszMjHbt2jFu3LgSvJrSo1AV9n2HeK7we+H02K7+uqdTzU7MbD5Tu8MPPeGv3K9bJlwDk0rEzZ3Lve+XAeCwbi3GuV/L3LsZzarPPgagXvOWvPfp+NK5CCGEEC/tWTWq4s2jUqlK7cOIKDnF9TspNcmvqG7FulgaqSesPnzzMDmqp6aHqZZnKrgY9ZQ2z6pLrmhjh2nFSoB6JFk+vwghhBClRxJkkZckya9IoVDQwk5dJ3Yv/R7hCeHaHfLWJUcV8PBenrpkhUKBg1I932Tqg0TuRt8ooaiFEEIIIURhJEkuBnkfpjgU89QsFzZuoJNb+v14URFLS/RzJ1zPmyQDOCifJNAyy4UQQgghRNmQJLkYNLFugp5CnQjnS5INjME6N/G99QdkqaeNeTyanHXnDpl5pkqxV7pqtqMuhJVg1EIIIYQQ4lkkSS4GpgamNKraCIALdy+QkJ6g3eFxyUVWOtxWr+Zj5FpwyYWJuQWW9o4ARIdfJCszs+QCF0IIIYQQBZIkuZg8LrlQoeLIzSPajXkf3iuoLjlMu+Ti8Why1qNHxEZcLoFohRBCCCFEYSRJLiaPH96DAlbf01p5Tz0Pcrl69VDo6wMF1CU3dNNs3zgvJRdCCCGEEKVNkuRiUqNCDWxM1Cv0HLl5hOycPJORl7cGc3v1dvQJUKnQMTDAsEEDANL//BNVniUu7eo2QFdPXeMsdclCCCGEEKVPkuRikncquKSMJC7cvaDd4XFdcsodSFRP7fa45EKVkUH65SdlFfqGhtjUUU+CfTsygvQC1qAXQgghhBAlR5LkYuRj56PZPhhzULtRqy5ZXXKh9fDeM+qSVaocoi+dL+ZIhRBCCCFEYSRJLkaeVp4Y6BgA6tX3tNjnWVQkuvBFRQAcGj6ZCk7qkoUQQpS2gwcP4u/vj5eXFy4uLnTo0IHg4GCSkpJe+FwBAQF07NjxleJJSUlh7ty5/Otf/8LZ2RkvLy+GDBnC8ePHtfr5+vry1VdfvdJ7CQGSJBcrIz0jPK09AQhPCCcuNe5JY5X6YGCm3n68qIi1NXqW6iWtn06Sq9aoRTkTEwCiLkqSLIQQovTMnTuXIUOGYGpqysyZM1myZAmdOnVi/fr1fPjhh0RFRZVqPCqVisGDB7Njxw78/f1Zvnw5M2fORFdXl4EDB7J///5SjUf8M0iSXMzyrr6nNZqsowt2HurtO39C+gMUCgVGruoR48yYGLLu3n3SXUcX+wbqkeb7sbdIis+TcAshhBAl5NdffyU0NJRJkyYRFBRE27Zt8fb2ZujQofz000+kp6czbtw4srOzn3+yYnLq1CnOnj3L/Pnz6dmzJ15eXrRt25bFixfj7OzMokWLSi0W8c8hSXIxK3SJas1UcCqIOQ08e1ER0F5974bMciGEEG+9nPR07i1fzrX33+cvryb83b0H93/6CVUpJqRLly6lTp06DBgwIF+bjY0NY8aM4fz58xw5ol4TYOHChXTt2pXt27fTrl07lEol3bp1448//ijw/LNnz6Zx48Zk5JnVCWDQoEF8+umnBR5z7949AHJycrT26+joMHbsWLp27aq1Pz09nenTp9O4cWPc3d2ZOHEiKXkegk9JSWHmzJm0atUKZ2dnmjRpwsSJE7VKSZycnFiyZAkdOnTA1dWVnTt3AnDx4kX69++Pi4sLTZo0YcaMGaSlpWmOi4+PZ/To0Zoyld69e3Py5MkCr0u83iRJLmb25e1xLO8IwLHYY2Rm51kxzz7/fMmFLSqiXZd8tviDFUII8drISUsjapA/cf/3LY8irpL94AHpFy5w+4svufX5RFRPJYglISEhgfDwcHx8fJ7Zp23btigUCg4cOKDZd/36db777jtGjhzJwoULefToEaNHjyYrKyvf8Z07d+bBgwccPvzk29b4+HiOHz9Op06dCnxPT09PjI2NNec/d+6c5txNmzald+/eWv03b97MgwcPWLBgAZ9++inbtm0jODhY0z5u3Dj27dvHuHHjWL58OYMGDWL79u0sXrxY6zwhISH069dPk9hfvXqVvn37olAoWLBgAePHj2fnzp2MGTNGc8yECROIiopi1qxZLF68GCMjI4YNG0ZiYuIz76l4PemVdQBvo+a2zbmedJ2HmQ85G3eWxta5M1vYeoBCB1Q5miTZsEED0NODrKx8I8nmVa0pb1mFpPg4oi6eQ5WTg0JHPtcIIcTbKGHNf0h7xuhr0o4dmP3rX5Rv968SjSEmJgYAW1vbZ/YxMzOjQoUK3Lx5U7Pv4cOHrFq1ioYNGwKQnZ3NiBEjuHz5Ms7OzlrH161bl7p167J9+3Z8fX0B2LFjB2ZmZrzzzjsFvmflypUJCQlh0qRJBAcHExwcjLGxMU2aNKFPnz40b95cq3/16tWZN28eCoWCpk2bcvz4cU6cUP+7++jRIzIzM5k+fbrmw4CXlxdnz57NN+LbtGlTevbsqXkdGBhI5cqVCQ0NxcBA/aC+o6Mjffr04dSpU3h6enLmzBlGjhypubbatWuzcuVK0tLSMDc3f+Z9Fa8fybhKwDNX3zMsD1XUC4gQcxqys9AxMsLQyQmAtIsXUeX51K1QKHDILblIS04i7sbfJR+8EEKIMvFg06ZC2xM3/VxKkYCeXuFjaE+36+npaSXDVlZWAFplCHl17tyZffv2kZqaCsAvv/zCe++9h37uSrQFadKkCXv27GHlypUMHDgQBwcH9u/fj7+/P/PmzdPq6+LigkKh0Ly2s7PTlFKUK1eOFStW4OPjQ0xMDIcPH2blypVERkaSmZmpdZ7q1atrvT5x4gRNmzZFR0eHrKwssrKycHV1xdTUlGPHjgHg4eHBd999x2effcbWrVsxMDBg4sSJWFtbP/PaxOtJkuQS4FHVAyM9I6CAuuTHJRcZKRB3CcizqEhqKo8iIrS756lLltX3hBDi7ZUZV/gD2llx8SUeg42NeuXYW7duPbNPamoq9+/f10r6DAwM0MnzTefj7adriB97//33yczMZN++fVy7do0///zzmaUWeenq6tK0aVMCAgLYsmULe/bswcvLi9DQUK0ZN4yMjLSOUygUqFQqzeu9e/fSpk0bWrduzfjx4zly5AiGhoZafQAqVaqk9ToxMZENGzbQoEEDrZ+UlBTi49V/PvPnz9fUIX/++ec0b96czz//nPT09Oden3i9SJJcAgx0DWhirZ4XOfJBJDdTnnwl9eThPZ7UJRf28J7zkzZ5eE8IId5eBnbPLnEA0Le1KfEYKleujFKpZO/evfkSxsf2799PdnY2LVu2fKX3adasGb/99hu7d+/GwcEBV1fXZ/YfPXo0n3zySb79dnZ2TJ48GZVKxd9/F+3b1uvXrzN69Gi8vb05cOAAx48fZ9myZflGjQtiampKt27d2LhxY76fYcOGAWBubs6UKVM4fPgwW7Zswc/Pj19++YU1a9YUKT7x+pAkuYTkLbk4HJNnKriCkuRCHt4zLl+BKo41AbgZ/idZTw7d7VYAACAASURBVD0NLIQQ4u1g/uGHhbZbdO9eKnGMGDGCv/76i++//z5fW3x8PHPnzqVBgwY0a9bsld6nc+fOHD58mN27d/PBBx8U2rdatWocOHCAiKe+bQV10qujo0PNmjWL9L6XLl0iMzOToUOHaspCUlNTOXPmzDM/GDzm7u7OtWvXcHZ2RqlUolQqsba2Zu7cuURERJCQkEDLli3ZvXs3APXq1WPixInY2NgQGxtbpPjE60Me3CshWlPB3TxEz7q5hf/m9mBqBSm3NctT69vbo2thQfb9+/lGkkE9y0Xc9UiyMjO4eeWSpk5ZCCHE28Oi9/+zd9/RUVXbA8e/dzKZ9AQSSCGVGiAkIXRCkfpQFAuIhSLN8sD+E58o6rNjpaiA8gTk8SwogqioCEqNEHoJEHp6CAnpdWYy8/vjJkNC2gApoPuzVtaazD33zrkpsHNmn73HUfDnTvIrVI2wHBs/HqdaKk7UpyFDhvDYY4/xwQcfcPToUUaNGoWrqyvHjh1j6dKl6HQ65s6dW2fecl2GDh3Kyy+/zNGjR1mwYEGtY6dOncqGDRsYP348DzzwAN26dUNRFPbt28eyZcuYMGECfn5+Vr1up06dsLGx4b333uP+++8nKyuLZcuWkZGRYdmMV5MZM2Zw33338eSTTzJmzBj0ej2LFi0iNTWVzp074+7uTmBgIG+++SaFhYX4+PiwZcsWUlJSGD58uNVfG3F9kCC5gXg7edO+eXtOZZ0iOjWaYmMx9lp7UBQ1L/nYOshJgNwUFNdWOISHk79lC/pz5yjNzsamwg7YgNCu7PlB3bCRcOSgBMlCCPEXpNja4rfwY3J+/ImctWsxZmRg6+9H83vvxXnw4Eob0Rra448/To8ePVixYgX//ve/yc/Px8/Pj3vuuYdJkybh6up6za9hZ2dH7969yczMxN/fv9ax7u7ufPPNNyxZsoT169fz2WefAdC2bVuef/557q5jFb6i1q1b88477/Dxxx/z8MMP07JlS2666SbGjBnDa6+9RlpaGl5eXtWe26VLF1asWMH8+fN54oknsLOzo1u3brz77ruWc+bOncu7777L+++/T3Z2Nq1bt+b9998nMjLS6jmK64Niruu9BXHV5u2bx7KYZQAsHraY/r5lJWp2LoQNL6iPx34OIXeR8cknpM9X/5L2X/IpzhVWDAz6EhZOvY9SgwGvNu2YMGd+Y96GEEKIWhw/fpxOnTo19TRuOCUlJQwcOJCZM2cytpFSScTfQ339TkpOcgOqsfuef59LjxPqzku21dnhG9wZgLRzZyjKy0UIIYS4EeXk5PDxxx/z4IMPYmNjw2233dbUUxKiWhIkN6Bwz3BcbF0A2Ja07dKGAO9Q0Nqrj8ubioSGqqkYVK1wARAYFqE+MJtJiDncsBMXQgghGoidnR1ffPEFiYmJvP/++1XKtQlxvWiyIFmv13PLLbcwa9Ysy3Nms5nFixczaNAgwsPDmTJlCmfOnGmqKV4zW40tfVv1BSApP4n43Hj1gFYHvt3Vx+cPg74QG2dn7Nq1A6Do8OEq7UcDpV6yEEKIvwB7e3t27tzJli1bJE9XXNeaLEj++OOPOXv2bKXnFi5cyOLFi5k6dSpz584lLy+PyZMnk5eX10SzvHY1dt/zL2tVbTJCitqG1KGsRqQpLw/9ZV8bz6A22Durq9LxRw404IyFEEIIIUSTBMnHjh1j5cqVNG/e3PJcfn4+S5cu5bHHHuOBBx5g6NChLF26lIKCAlavXt0U06wXls161JaXvAuovamIotFYGovkXEgjO+18A8xWCCGEEEJAEwTJRqORF154gWnTplUqsXLo0CEKCwsZOnSo5Tk3Nzd69erF9u3bq7vUDaGFQwtCPEIA2Ju2l0KD2qfespIMkLgbqH3zHqj1ksvFH5bVZCGEEEKIhtLoQfJ//vMfS6ebiuLi4gCq1Er08/OzHLtRladcGEwGolPVjXo4ukOLDurjxGgwmdC1aYPGRU2pqHbzXoW8ZEm5EEIIIYRoOI0aJJ85c4ZPPvmEN954o0pXm/z8fHQ6XZXnnZycyM/Pb8xp1ruKpeC2JW+7dKC8RXVxNlw8haLR4BAWBkDJqVOU5hdUuo6bpzfNvHwASIw5jMlU2rATF0IIIYT4m2q0jnsmk4nZs2dz9913ExERUeW42WyusZuQtV2G4uLiKCoquqZ5NgQbsw0uWhfyjHlsjtvMWLexKIqCmzaAVmVjUqPXkt32DvD1VZ8wmzm5/icoC5rLufgFkp2WSnFBPtF//E4z39q7FAkhhGhYBoPhuvy/R4i/K4PBwPHjx2s8bm2jkUYLkleuXElqaipLlizBaDRanjebzRiNRlxcXNDr9RgMBmxtbS3HCwoKcClLQahLUFBQfU+73tx08SZ+OvsTF/UX0fpo6dC8A7TUwp43AfAxJuLTqRP5w4aS+M03ALTMyqLFZd9Im9zBJO5TN/pp8rLp1OkfjXsjQgghKjl+/LjU+m0ktS2oib+G+vge29ra3lgd9zZt2sT58+fp2bMnISEhhISEEBsby/fff09ISAharRaz2UxSUlKl85KSkmjdunVjTbMSvdHED4dS+L9vDvLU1wdYtSeBIv3VpThU233Pox04uKuPE9XA1z401DKuus17/iFhlqYj8VIvWQghRD2bOHEiwcHBlT46d+5Mnz59mD59epP0Lzh//jzTpk0jKysLUGOD4OBgfv3110afS0NLSEjg1VdfZejQoYSFhTF06FBeeuklUlJSmnpqDW7v3r088cQTTT0Ni0ZbSX711VcpKKicYztz5kxat27No48+SuvWrXnzzTfZtGkTDz30EKC2rty9ezePPfZYY03TIj2vhIlLo4k9f6lG8/cHU/joj9P8b1pvglo4XdH1IltFolE0mMwmtidvZ1roNDXY9e8NJ3+Bi6ehIANt8xbogoLQx8VRdOhQlb+oHJxd8G7TjvNnTpFy4hiGkmJs7ezr7b6FEEKIbt268dxzz1k+1+v1xMbGsnDhQqZNm8aGDRuws7NrtPn8+eef7Nixw/K5p6cnq1atuq7fQb4af/75J48//jj+/v5Mnz4dPz8/kpOT+eyzz7j77rv53//+R5s2bZp6mg1m9erVnDt3rqmnYdFoQXJ131R7e3uaNWtGaNnq6YQJE1iwYAEajYagoCA++eQTnJ2dGTt2bGNN0+KZbw9VCpDLJWUV8c//7eOXJwdc0dsBzeybEdYijIPpBzl44SC5+lxcda5qKbiTv6iDEndDx5E4dO2KPi6O0qwsDImJ6AICKl0rILQr58+cotRoJPn4UYK6dr+mexVCCCEqcnV1pWvXrpWe69WrFw4ODrz44ovs2rWLm266qYlmBzqdrsr8bnSZmZk888wzdO7cmaVLl1YqZDBkyBDuuOMOXn31VVasWNGEs/x7abKOe9X5v//7PyZPnsyyZcuYOXMmLi4uLF++3Oqc5PpyNj2fbSfTazweez6PXWczr/i6A/0GAlBqLmVnyk71yYAKTUUSq2kqcrBqSkVg6KWNj/ExVVMyhBBC3JiM+lL2/xbPV69F89kz2/h2zh6O7UjBZDI39dQAteLU5eLj45kxYwYRERH06NGDZ599lszMyv9H/vDDD4wZM4bw8HDCw8O577772LNnT6Ux0dHRjB8/noiICAYOHMicOXMoKSlhzZo1PP/88wD07duXjz76qNp0iz179jB+/Hi6detGZGQkr732WqV3sCdOnMicOXOYN28e/fr1Izw8nBkzZpCWlmYZc/bsWR588EF69OhBt27dmDZtGrGxsbV+TZKSknjyySfp27cvERERTJ8+vVLp2o8++ojRo0fz1ltv0a1bN+68885qr/P999+TmZnJ888/X6XSV/PmzXnuuefo27evZV+XwWBgyZIljBgxgtDQUEaNGsWPP/5YaV7BwcFs2rSJyZMnEx4eztChQ/ntt984ffo048aNIzw8nDvvvJPDhw9bzhsyZAhLlizhxRdfpHv37vTu3ZsPP/yQvLw8Zs6cSUREBIMHD2bNmjWV5hgTE8OkSZMIDw+nT58+vP7665U2tNb19Z81axZr167l1KlTBAcHEx2tlsz97LPPGD58OKGhoQwbNoyFCxdiMplq/Z7UlyYNktetW8fbb79t+Vyr1TJz5kyioqI4cOAAy5Yto23bto0+r+OpdbfBjj2fe8XXrdSiujwvuVUEaMo2KlrZVKRVcCe0OvVtLmkqIoQQfw0GfSk/LDjIzjVnyEwpoKTAyIX4PDb/L5ZNy49hbsRAuXxTfflHQUEB0dHRzJs3j1atWtGjRw8AMjIyGDduHCkpKbz77ru8+uqrHDx4kGnTpqHX6wH49ddf+de//sWgQYNYsmQJc+bMITc3l6efftoy5vDhw0ydOhUXFxfmzZvH448/zurVq3nzzTcZNGgQ06dPB9SAqbp3l7du3coDDzxAy5YtLef/9NNPPPLII5UCqu+++45Dhw7x1ltv8corrxAdHc2cOXMAtQrX9OnTKS0tZd68ecybN4+srCweeeQRSkur3490/vx5xo4dS3x8PK+88gpz5swhKSmJcePGVQq+T5w4YUlXeeqpp6q9VlRUFC1btqRz587VHr/11lv55z//iVarJgE899xzLFq0iHvuuYfFixcTERHBzJkz+fbbbyudN3v2bPr168fixYvx9vbmueee49FHH+XWW2/lww8/JD8/n2effbbSOZ988gmlpaV8/PHH3HLLLSxcuJC7776bli1bsmjRItq3b8/LL79syZM+ffo0EyZMQFEU5s+fz8yZM/n555+r3GttX/8ZM2Zw00034e/vz6pVqwgJCWHdunUsWLCAyZMns3TpUsaOHctHH33EN2UFDhpao6Vb3EhcHer+srjY29Y55nLBzYNp6dCS9KJ0diTvwGQ2obF1AJ9wSN4LyfvBWIJd+/YoDg6Yi4qqbSqitbXFr1MIcYf2kx5/jsKcbBzdml3xfIQQQlw/Dv+RSOqZnGqPndqTRtuIlrTt5tkoc9m6dSshISGVnrO3t6dv3748//zzlhXlFStWUFJSwrJly3B3Vzeih4WFMWLECH7++WfuvPNOEhISGD9+PI8//rjlWra2tjz22GPExcXRoUMHPv30U/z8/Fi4cCE2NjYAlJSUsHbtWtzc3AgoSzsMCQnB3d29yib/BQsWEBYWxvz58y3P+fn58eCDD7JlyxaGDBkCgI2NDZ9++qklnzo2NtYScF28eJG4uDgef/xxBgxQF7V8fHz46aefKCwsrPZd7c8//5zi4uJK99+rVy+GDRvG8uXLmTVrFqB2G541a1aNATCoAXerVq1qPF7RiRMnWL9+Pa+++ir33XcfAP379yc/P5+5c+cyevRoy9ibb77ZstertLSUBx98kFGjRjF+/HgALly4wIsvvkhubi6urq4AeHt789Zbb6EoChEREaxatQovLy9Lnrqvry/Dhw/n2LFjtGrVikWLFtGiRQuWLFliWQUPCgpi/Pjx7Nmzh549e9b59Q8ICMDd3Z2UlBRLKs2+ffvw9fVl3LhxKIpCr1690Gq1eHo2zu/BdZVucb3o3dqDFs41b0iwt9UwvJNXjcdroiiKZTX5YvFFjl8sq+FXnnJRWgKph1G0Why6dAGg+MQJTNXU3wyo2H1PUi6EEOKGd/zP1Gs6Xp+6d+/O6tWrWb16NXPmzKFZs2YMHjyYDz/8kMDAQMu46Ohounbtiqurq2XV2cfHh7Zt27Jzp5pW+PDDD/PSSy+Rm5vLwYMHWbt2LT/88AOAZSX5wIEDDBw40BIgg7pP6bvvvqv0XHUKCgo4duwYN998c6XnBwwYgJubW6W0juDg4EobDr29vS0pAR4eHgQFBfHSSy/xwgsvsGHDBnx9ffm///u/GtM+9+zZQ+/evS0BMoC7uzt9+/Zl9+7dlcbWtclQo9FYnUawd+9egCr3PHLkSDIzMytVIAmr0G+hRYsWAJa9YKCmcgDk5l56hzw0NNSy78re3h4nJye6lMUlAM2aNat0TnR0NJGRkWg0GsvPQdeuXXF2drb8HEDtX//q9OjRg3PnzjFmzBiWLFnCyZMnmTZtmuWPnoYmQXI1dFoNL91Wc329mf8Ixs3xyleSoYbue/69Lg2w5CWXBcFGI8VHj1a5TsUW1QlSCk4IIW54BdkltR/Pqf14fXJxcSE0NJTQ0FBGjx7N/Pnz+fXXX3nxxRcrjcvOzmb79u2W0q7lHydPniQ9Xd3bk56ezsMPP0yvXr2YOHEiK1eutJxvNqspJDk5OXh4eFzVXPPy8jCbzdWe7+7uXqlr7+X1rBVFscxBo9Hw+eefc8stt/D777/zxBNPEBkZyZw5c2oMXnNzcy2BZ0UeHh6V8qEdHR1xdHSs9T58fX1JTa35D6H8/Hzy8tR00JycHLRarSVYLVc+l4r3XF0eub197VWxqjuntlrg2dnZlhSJih/5+fmWn4PqrlHx61+d22+/nbfffhuNRsPcuXMZNWoUt99+O0eOHKl1/vVF0i1qcEdXX5zttMzfdIojyZXf/rqpQ8urvm4fnz5oFS1Gs5EdSTuYHj79UntqgMRo4PHKm/cOHcKxLP+rXMuAIBxc3SjKzSH+8EEpsC6EEDc41xYOZKYU1Hjcxb3pyn327duXu+++m2+//Zabb77ZspLn7OzMwIEDq61tWx5oPfPMM6SlpVmCKK1Wy9atW/ntt98sY52dnats9svOzubo0aN069at1rm5uLigKAoXL16sciwjI6NKIFkbHx8f3nrrLUwmEwcPHuTbb7/l888/Jzw8nJEjR1YZ7+bmRkZGxjW/LkBkZCSbN2/m+PHj1TbC+Prrr5k3bx6//vorbm5uGI1GsrOzK71O+Vyu9LWvlbOzM0OHDuX++++vcqx8pfpq3XXXXdx1111cvHiRP/74g4ULF/Kvf/2LX3755Zquaw1ZSa7F0E5e/Ph4f/a9OIzX7riUm7X8z7irvqazzpluXuov/JGMI2QWZ4KLNzQre/sqIRrMZhwqvD1S3eY9RaOxrCbnXUwnK/WvX2RcCCH+yjr3qz0ftXN/6/JVG0p52sHbb79tSZPo3r07Z8+eJTg42LLy3KFDBz7++GP27dsHwMGDBxk5ciTh4eGWTWfbt6ub18tXESMiIti2bVulFduff/7ZsmlOo6k5XHFycqJTp05VGots376dvLy8OoPscrGxsfTv35+jR4+i0Wjo1q0bb7zxBlqttsZGHt27dyc6OrpSgJ+ZmcnOnTutft1yd9xxB82aNeOdd96xfH3LZWRksGLFCrp27Yq/vz/du6ulXy+/559//tmSNtKYyn8OunTpYvk58PHx4YMPPuDUqVNWX+fy7/Ps2bMtf4B5eHgwduxY7r777lpX3OuTBMlW8HC2476eAXi6qHk0a/YnkV2or+OsmpWnXJgxE5UcpT5ZnpdccAGy4tC2bImtry+gloGr7u2IgNBLq83xR6TKhRBC3Mi6DPIlMLT6lIPQQX4Edrm6dIT64u7uziOPPEJ8fLwlZWLKlCnk5eXx4IMPsmnTJrZu3crDDz/Mrl27LDmsoaGhrF27ll9//ZWdO3fy2muv8cUXXwBQXFwMwD//+U8SExN54okn2LZtG9988w3z589nwoQJODs7WzaUbdy4scqmPYDHH3+cQ4cO8dRTT7Ft2zZWrVplKVc2cOBAq+6vXbt2ODk58dxzz1nm+sILL6AoCoMGDar2nMmTJ2Nra8vUqVPZsGEDGzZsYOrUqeh0OiZNmnRFX183NzfefPNN9u7dy3333ceaNWvYtWsXX3zxBWPHjqW0tNRSEaxjx46MGDGCt99+m6VLl7Jjxw5effVV1q9fz6OPPlrrHxUNYcaMGcTExPDkk0+ydetWNm7cyEMPPURsbGytmxUv5+rqyvnz54mKiiInJ4eePXuyYcMG5s6dy65du/j+++/56quvGD58eAPezSUSJFtJp9UwsY+62ltsMPH1nsSrvlalUnDJZaXgKuUlq7UBy/OSjenpGM+fr3IdyUsWQoi/DhsbDSP/GcrQSZ3w7dCM5t6OBHbxYOT0UAbc2/66SKmbNGkSvr6+LF68mMzMTFq1asWXX36Jg4MDzz77LE8//TQmk4nly5dbUgbmzJlD27Ztef7553n66ac5c+YM//3vf3F0dORgWS+Arl27snTpUtLT03n00UdZvHgxEydO5JlnngHUdI/+/fvz+uuvs2zZsirzGjJkCAsXLiQhIYEZM2bw0Ucfcdttt/HZZ5/VufGvnFar5T//+Q+BgYG88sorPPLII5w9e5ZPP/2Udu3aVXuOj48PX3zxBZ6ensyaNYvZs2fj6+vL119/jbe39xV/fYcNG8aXX36Jv78/CxYs4KGHHmL58uUMGDCAtWvX4u/vbxn7/vvvM378eD7//HOmT5/O/v37ee+99yxVKxpTly5dWLFiBVlZWTzxxBPMnj0bLy8vVq5ciZeX9YUO7r33Xjw8PHjkkUeIiorizjvv5KWXXmLjxo08/PDDvPfee4wYMYJXXnml4W6mAsVcW8a0qORifgl93/4DvdFEKzd7tv1rMFqbK/87w2w2c8uaW0jOT8ZV58q2e7dhc+E4fNJPHdB9CoyaT+Z/V5L21lsA+M6bi+stt1S51rKnHiErNRmdgyOPLv0KjZX/GAghhKgfNeWQCiGaRn39TspK8hXwcLbjjnA1Jywlp5jfjqXVcUb1FEWhv29/AHL1uRzJOAKencBOfTvJ0lSka+1NRQACw9TVZH1RIefPWJ/3I4QQQgghaiZB8hWa0q+15fHyqHNXfZ3yFtUA25K2gcYG/MoqWFw4BkXZ2HfsiFJWlLu6piJwWb1kyUsWQgghhKgXEiRfoc6tXOnd2h1fo4aWR/NZ+UY0v3x6hHOH0mut9Xe5nt490WnUAPhSXnJ5KTgzJO9F0emwL0t4Lz52DJO+6mbBgJAwFEX9NiYckaYiQgghhBD1QYLkq3CH1olx+XaEGLTkJhVw9kA6Py8+wsZlxzCZrAuUHbQO9PRR2zTGZsaSVpBWuV5yQuXNe2a9npLY2CrXsXN0wrtdewBSTsaiL665c40QQgghhLCOBMlXKOlEFll7MqguFD61J41jO6yvV1yx+15USpSablG2KnypwkXFvOTqK1gEhkUAYCo1knQ8xurXF0IIIYQQ1ZMg+Qod3ZYMQE2FeGLKjlujYpC8PWk72LmAV1nTkqS9UGrEIdyKzXtdKuQlH5ZScEIIIYQQ10qC5CuUfaGw1uM5dRyvKMA1gCDXIAB2pu7EUGoA/7KmIoYCuHAUrbc3Wk9PoObNez4dgrG1U9uVSr1kIYQQQohrJ0HyFXJ00dV63N7Z9oquV14KrsBQwIELB6rkJSuKYllNNiQnY0xPr3ING60tfp3VzkYZifHkZ2VWGSOEEEIIIawnQfIV6tC79g46OnstpUZTrWMqqlgKbnvydgioECRf1nkPal5NDgyNsDxOiJEqF0IIIYQQ10KC5CvUvocngaEeNR7PTClg/aLD6IuNVl2vu1d3HLQOQFlesps/uPioB6vbvFdTkBxWMS9Z6iULIYQQQlwLCZKvkMZGwy2PhBI5uh1uLdXgNl8xE2NrpNRG3c6XeCyTdfMOUJRXta7x5XQ2Ovr4qHnIZ3LOkFyQcinlIicRcpLVWslaLVDz5j0PvwCcmrsDal6ydBsXQghxNSZOnEhYWBhxcXFVjh0/fpzg4GCio6Mbf2JCNDIJkq+CjVZDxD8CmPB6X/65cBAb2tnwi5OBlc7FOLipOcsX4vNY8/5+cjPqrls8wO9SlYsdSTsq5yUnRqNxcMA+OBiAopgYzMaqq9SKohDYRV1xzs/KJDM58VpuUQghxN9YSUkJL730UlNPQ4gmJUHyNbKx0TAlMgiAdMVEeu/mNPd2BCA7rZDv3ttHRlJ+rdeoWApuW/K2WvOSzUVFlJw6Ve11KreolioXQgghro6Liwu7d+/m22+/beqpCNFkJEiuB6O7+eFir6ZDfHkkmVue7IpXa1cACnP0rP1gPymnsms839vJm/bN1a55u1N3U9yiA5TlKV9RU5FQyUsWQogbmUFfwp4fvuPzZ2awcNr9fPHC0xz+fQMmU2mjzqN79+4MHjyYd999l/RqqiqVS0pK4sknn6Rv375EREQwffr0SmkaH330EaNHj+ann35ixIgRhIaGMmbMGPbv31/pOjExMUyaNInw8HD69OnD66+/TlGRdJAVTUuC5HrgZKflvp7+AOQUGfjl5AXueCqCwC7qBj99kZEfFhzk7MGa/6EpX00uLi1mb8Yh8O2uHkg9DPoCq5qKOLt74OEXAEDisRhKq0nLEEIIcX0ylBSz+o2X2PbFci4mJVCcn8f5M6fYuOQjfvl4LmaT9ZWT6sPLL7+M0Wjk9ddfr/b4+fPnGTt2LPHx8bzyyivMmTOHpKQkxo0bR1pammVcXFwcH374IY899hgfffQRJSUlPPnkkxjL/o86ffo0EyZMQFEU5s+fz8yZM/n555956qmnGuU+haiJBMn15IG+QWjK2vB9/uc5tDoNt0wPJbiPWjKu1Gji10+P1Ni2ukr3vfKUC3MpJO/H1t8fm+bNgZorXMCl1WRDcRGpp09c620JIYRoJPt//oGUE8eqPRYbtZVTe3Y26nxatWrF008/zYYNG/j999+rHP/8888pLi5m2bJljBgxgptvvpkVK1ZQXFzM8uXLLeMKCgp4//33GTVqFIMGDeLpp5/mwoULxMbGArBo0SJatGjBkiVLGDx4MHfffTcfffQRW7ZsYc+ePY12v0JcToLkeuLv7sjwzl4AnEzLJ+r0RWxsNAyd1ImI4erqrtkMm/8Xy96f46pUnwj3DMfF1gWAbUnbMPv1unQwcVelpiL6uDiMWVnVzqNiXrJ03xNCiBvH0a2baj0es3ljI83kkgkTJhAWFsZrr71Gfn7l/TV79uyhd+/euLu7W55zd3enOAjGVwAAIABJREFUb9++7N692/KcVqulS5culs+9vdXFo/J0iujoaCIjI9FoNBiNRoxGI127dsXZ2ZmdOxv3DwMhKpIguR5N6dfa8nh51DlArToROaYdkWPaWY5F/3CW7atOYTZdCpRtNbZE+kYCkJSfRHwzn0sXTlT/sanYVKT4yJFq5+DfuQsaGxsA4g9LkCyEEDeKvMyLtR5vim6qGo2GN954g4yMDD744INKx3Jzc2nRokWVczw8PCgoKLB8rtPp0GguhRvlj01l6SPZ2dmsWrWKkJCQSh/5+fm15kML0dC0TT2Bv5Lerd3p5OPK8dRcfo+9wLmMAlq3cAIgYngAjq46/lhxHJPJzJEtSRTl6Rk2uTM2tuo/GAN8B7AhbgMA2y8eJqhFMGScUDfvmUxVNu85DxxYZQ46B0d82geTHHuM1NMnKCksxM7RsRHuXgghxLVwa+nFxaSEWo57NuJsLgkODmbatGksWbKEdu0uLfi4ubmRkZFRZXxGRgbNmjWz+vrOzs4MHTqU+++/v8qx5mVphkI0BVlJrkeKojClX5Dl8xV/xlU6Htzbm5EzwtDq1C/76X0X+GnhIUt3vn6+/SxjK+UlF+dAxknsu4SCoiY+17R5Dy61qDabTCQeq37FWQghxPUldMiI2o8Prf14Q3r00UcJCAhg7ty5lue6d+9OdHQ0mZmXVrgzMzPZuXMn3bp1s/ra3bt35+zZs3Tp0oXQ0FBCQ0Px8fHhgw8+4FQNJU+FaAwSJNez28Nb4eGkNhT5dm8iucWGSscDu3hwx9MR2DvZApAUm8X3cw9QmKunhUMLQjxCANibtpfCVhGXTkzchY2zE3bt1VJxRYcP17jTWfKShRDixtN1xK206dazhmO30bprj0ae0SV2dnZV8pInT56Mra0tU6dOZcOGDWzYsIGpU6ei0+mYNGmS1deeMWMGMTExPPnkk2zdupWNGzfy0EMPERsbS+fOnRvidoSwigTJ9cze1oZxvdWNegX6Ur7dm1RljHdrN0Y/2w3n5nYApCfksea9feSkF1m67xlMBnbZ2106KaGsXnLZ5j1Tfj76s2ernYNPuw7oHNQ6y1IvWQghbgw2Wi13zHyRm2c8jX/nUNxb+dE6ogd3PPsSQ6Y8glL2TmJT6dOnD2PGjLF87uPjwxdffIGnpyezZs1i9uzZ+Pr68vXXX1s251mjS5curFixgqysLJ544glmz56Nl5cXK1euxMvLqyFuRQirKObLyyyIa5aWW0y/t//AaDIT4O7I5pmDsNFU/cctP6uYHz86RGaKusHB0VVH8ARHHjmg/gV+d/u7+feOFVB4EdzbwhP7yf5uDamzZwPg88brNLv77mrn8P17r3NmrxpYP7zoc1w8qm6uEEIIce2OHz9Op06dmnoaQogy9fU7KSvJDcDL1Z5bw9TqFAmZhfwRe6Hacc7N7bnrmW74tHUDoDBXz9FleXQoVNMstidvv1QKLvMM5KdX3rxnRb1kkBbVQgghhBBXSoLkBlJdObjq2DvZMurJrgSFqSu9+uJSBh+ZSOuLYaQVpnHKq/2lwUm70bVujcZFradc2+Y9yUsWQgghhLh6EiQ3kK7+zYgIUEvg/HnmIrHnc2sca6uz4ZZHutAxUl19Vkw2DD85hU5pkWzXVfgWJexC0WhwCAsDoOT0aUovK+5ezr2VH85lKRbxRw5WaV4ihBBCCCFqJkFyA6q4mvx5VFytYzU2GoZM7Ei3EYHq52i46ey9nNnnillRK2FUaSpiNtfYVERRFAK7qOMKc7LJSIy/hjsRQgghhPh7kSC5Ad3SxRtvV3sA1h5IJrNAX+t4RVHoe1db+o+9lGLhGxvBJuO/MJk1kHIAjCVVmorUJDCsQl6yVLkQQgghhLCaBMkNyNZGw8S+6spwidHEV7tr7qRUUfhQf2yGpVGqlAJwMr0Hv2U/Q6mxFFIP4RAaahlba15yl0vBtOQlCyFEw5GUNiGuD/X5uyhBcgO7v1cAdlr1y7xyZzyG0uobgFxu0JAIfun4KQZNCQBnSiL5Metl9Kd3Y9OsGbrWaipH0aFDNf5AODVrTsuAIAASj8dgNBiqHSeEEOLq2draUlRU1NTTEEIARUVF2Nra1su1JEhuYO5OOu7s6gvA+dxifo05b9V5wc2DKfHJ5MfOH1Nsq9ZRTtaHsna9FwU5JZamIqXZ2RgSal6hLq9yYSwpIfVU7LXcihBCiGp4enqSnJxMYWGhrCgL0UTMZjOFhYUkJyfj6elZL9fU1stVRK2m9A9i1d5EQC0HNyq8VZ3nKIrCAL8BrClaw9qQ+Uw+Op0SgzsZ+R6seW8fNwV3A74H1NVkXWBgtdcJDItg33p1XPzhg/h3Dq12nBBCiKvj6uoKQEpKCgZ5x06IJmNra4uXl5fld/JaSZDcCDp6uxLZ1oM/z1xkf0I2hxKzCfdvVud5A3wHsObUGnIcLqDvvgqPvbdw0RhEbkYxGwtbEursh0t+EkUHD+J2++3VXsOvYwg2Wi2lRqOal3zfxPq+PSGE+NtzdXWtt/+YhRDXB0m3aCTWNhepqI9PH7SK+ndMlGM2d7nPxsf2KADFhSb2RzxNZrMOtW7es7W3p1UHtTXj+TOnKK6hrrIQQgghhLhEguRGMqSjJwHujgCsP5LKhdziOs9x1jnTzasbAEeK0ijQFnO7+2u09koFoNTGnkNhM0i46ICplk0j5XnJZrOJxGOHr/VWhBBCCCH+8iRIbiQ2GoVJkUEAGErN/G+Xdc09BvoNBMCMmSjX5mgVPTd7LqJzP7U7n1ljS0zHyRz4tuY6yJXrJUspOCGEEEKIukiQ3IjG9vDDSWcDwBfRCRQbSus8Z4DvAMvj7c29ANCkH2XQGG9CO5UdUDTs+rOY6B/OVruz2qtNO+ycnABIiJEgWQghhBCiLhIkNyJXe1vG9vAH4GKBnh8PpdR5Tmu31vg6qyXkojR61LDajJK8j8jxYbQ/9Q2Y1drLe3+OY8uXJzCZKgfKGo0NASFqybis1BRy0y/U2z0JIYQQQvwVSZDcyCZFBqEo6uPlUXF11tRUFIX+vv0ByDXpOWKnUw8k7kLbogVtlDOEHFuOYjICcGx7ChuWxGC8bJW6PC8ZIF667wkhhBBC1EqC5EbWuoUTg4PVItfHUnPZfS6zznPK85IBtjmqm/9IjAbAITwcr/T9hB9ZhK1O/XaePZjOjx8eoqTIaDmvcl5yzfnLQgghhBBCguQmMaVfkOXx8qi4Osf39O6JTqOuIG93ba4+mbQPSo04dFWDX/esEwzvW4yDi9qKMeVUNms/2E9BjtrWupmXD64t1eA8IeYQZpN17bGFEEIIIf6OJEhuAv3btaC9pzMAvx07T2JmYa3jHbQO9PTpCUCsppQ0GxswFEBaDA5dwy3jHOMOMHpmd1w87AG4mJTPmvf2kZ1WiKIoBJalXBTl5XIh3rpazUIIIYQQf0cSJDcBRVGYXLaabDLDSivKwVWschHloAbBJEZjHxyMolNXmYsOHqKZlyNj/tUdD181CM/NKGbN+/u4EJ9bKS85QfKShRBCCCFqJEFyExkd4Yebg5oa8fXuBAr1xlrHD/S9lJe83dFBfZAYjaLTYR8SAkDxsWOY9Hqc3Oy4a2Y3WrVXW18X5Rn4fu4BtLpAyzVk854QQgghRM0kSG4iDjob7uulloPLLTby3f7kWsf7u/oT5BoEwE4HBwwACZc27wGYDQZKjh8HwM5By6gnwmkT0RIAQ0kpG5efxc0zAIDk40cx6vX1fFdCCCGEEH8NEiQ3oQf6BmGjUevBfR51rkp948sN8FNTLgo0Cgfs7SA3CXKSLJv3AIoOHbI81traMOKhLnQe0AoAU6mZ/Fx1857RoCf5xLF6vR8hhBBCiL8KCZKbkG8zB0aEqF30zqQXsP10Rq3jK3Xfc7iUclFx817RwcppFBqNwqBxwfS4NUj9XFsh5UJKwQkhhBBCVEuC5CY2pV9ry+PlUbVXnOju1R0HrRocb3cs27yXEI2ttzdaLzXYLjp4qMp5iqLQe1QbBt7XAY1tK0BtjR2zNRpTqZSCE0IIIYS4nATJTaxHYHO6+LoCsOVEOmfS82scq7PR0cenDwBndDqStTaVmooAGFJSMFyovu106CA/bn4oAo2t2ua6KCeJnxbuxqgvrXa8EEIIIcTflQTJTUxRFKZEXlpNXvFnXK3jy/OSoSzl4vwRKMm3BMkAxYcP13h+u+6edLmpj+XzuIMH+eHDgxQXGK5i9kIIIYQQf00SJF8Hbgv3oYWzHQCr9yWRU1RzwFopL9nRAcylkLIfh4gKm/cO1l7eLWxopOWxyRBP6ukctTtfdsnV3oIQQgghxF+KBMnXATutDeN7q6XZCvWlfLMnscax3k7etG/eHoDd9nYUKwokRGPfuTNotUD1eckVeQa1wd7ZBQCzSX2tzJQCvnt3H1nnC8g6X0DMtmSORaWQl1l8zfcnhBBCCHGjkSD5OjG+TwC2Nmo5uBU74yitpRxc+WpysUbDXns7SIxGY2+PfceOABTFxGA21tycRNFoCOiipmeYjNk081TrJedlFvP167v58pVotn55gs0rY1k5+0+2fnmCUtngJ4QQQoi/EQmSrxOeLvaMClPrGSdlFbHxWFqNYwf6Vei+5+AASbvBZLrUVKS4mJKTJ2t9vcCwS+kZnfqU4hvcHFBrKVdkNkPMtmR2rjlzZTckhBBCCHEDa/QgWa/XM2/ePAYPHkzXrl154IEHOHr0qOW42Wxm8eLFDBo0iPDwcKZMmcKZM3+PAM3acnDhLcNxsVXTJbY52mMuzoGMEzU2FalOYOilsUmxh4gc3bbW8THbkinOl819QgghhPh7aPQgec6cOaxcuZKHHnqIhQsX4uDgwAMPPEBystqWeeHChSxevJipU6cyd+5c8vLymDx5Mnl5eY091UYX6udGj0B1RTf6XCZHU3KqHafVaIn0VTffJdnaEq/VQsKuWpuKXM7N05tmXj4AJMYcJuVUVq3jSw0mUs9WPx8hhBBCiL+aRg2S8/Ly+Pbbb3n88ccZN24c/fr1Y8GCBRiNRtatW0d+fj5Lly7lscce44EHHmDo0KEsXbqUgoICVq9e3ZhTbTIVV5M/j4qrcVyVKheJ0dj6+WHj7g7UvXkPICBUDaqLC/LJu1jzZsFyilLnECGEEEKIv4RGDZIdHBz45ptvGD16tOU5rVaLoijo9XoOHTpEYWEhQ4cOtRx3c3OjV69ebN++vTGn2mRGhHjRyk3tprfuUAoX86svy9bPt5/l8TZHe0iMRlEUS16yPj4eY1btq8OBYRGWxyZ9fK1jtToNPu2aWXUPQgghhBA3ukYNkrVaLZ07d8bNzQ2TyURiYiIvvPACiqJw++23ExcXB4C/v3+l8/z8/CzH/uq0Nhom9g0CQG808WV0QrXjWji0IMQjBIC99vYUZp2D/AtWNxUB8A8JsywPp507SodeXjWObd/TCzsH7ZXcihBCCCHEDavJqlssWrSIYcOGsW7dOh588EHatGlDfn4+Op0OnU5XaayTkxP5+TW3a/6rub+XP/a26rdm5a549Mbqy6+Vd98zKgq7HOwhcXelzXuFdeQlOzi74N2mHQApJ47R/57WdOrng1LNT0V6Qh4mKQMnhBBCiL+JJlsaHDZsGL169SI6OppFixZhMBiwt7dHqSHxtabnK4qLi6OoqKi+p9okBrd24peTeVzIK+GzDfsY3Ma5yhh/w6UV9+0O9oQf/JkLwQ+CRgMmExf//JOL//hHra/j5OMPZ05RajSyZ/Nv+PQIxr2jJzmpesyYSTpYSE6KgYzEfH77ch+BParOQwghhBDiRtGpUyerxjVZkNyxrPFFr169KCgoYOnSpcycORO9Xo/BYMDW1tYytqCgABcXlzqvGRQU1FDTbXRPu/vxy8ltAGyI0zPj1qrf0A6mDrx/5j2ySrLZ7ujAy4Wn8OjWjbPt21Ny4gSa02fo0KEDio1Nja/jVGrgzI4/ADDnZFb5wcnuU8jXr++m1GAibncBff4RQjMvx3q8UyGEEEKI60+jplukp6fz3XffVUmd6NSpE3q9Hjc3N8xmM0lJSZWOJyUl0bp1a/5OOni50L9dCwAOJWazP6HqJjwbjQ39y6pcpGm1nEqPAUOxJS/ZVFCA/uzZWl+nVXAntDo7AOIPH6hyvJmnI71uU7/2pQYTm/8Xi7mWboBCCCGEEH8FjRok5+bm8sILL7Bhw4ZKz0dFReHh4cGwYcOws7Nj06ZNlmM5OTns3r2bvn37NuZUrwtT+gVZHi+voRxceV4ywHY7G0g9VGnzXl1NRbS2tvh1UjcApsefozAnu8qYrsP8aRmgruSnnMrm6I4Ua29BCCGEEOKG1KhBctu2bRkxYgTvvPMOX3/9NVFRUfz73/9m3bp1PPPMMzg7OzNhwgQWLFjA0qVL+f3333nwwQdxdnZm7NixjTnV68LgYE+CPNTUhl+OpHI+p7jKmMhWkWhQ87W3OzhA4i4cIqzfvAeVu+/Fx1QNqjU2GgZP7IiiUV9n55rT5GdVnYsQQgghxF+FzSuvvPJKY77goEGDKCgoYNWqVaxatQqDwcBLL73E7bffDkDv3r0pLi7mq6++Yv369QQGBvLBBx/g7e3dmNO8LiiKghnYejIdkxnsbDX0K0vBKGevtWdn0jbOF13ggtaGcQV6HPtPI3Pl/zCXlIDBQPNx42p9Ha2dHYc3/QKAnaMT7Xr0qTLGyc0Oo8FE6ukcSo1mctKLaN/D06oNlUIIIYQQNxrFbDZblWBqNBpZv349u3btIj09nRdffJF9+/YREhJi2YQn6l9esYG+c/4gv8RIc0dbdj4/FHvbyhvx/nP4P3x44EMA3ssu4eYnTpDw0MMU7NgBikKH3dHY1LLx0Wwy8ck/H6AwJxsXj5Y8tHBZtcGv0VDKqjf2kJ1WCMA/HgyhfY+aaysLIYQQQtyorEq3yMrK4p577uGFF17g6NGjREVFUVBQwMaNG7n//vs5VEfeq7h6Lva2jO3hB0BWoYF1B5OrjKmUl2xjgMyzl/KSzWaKjxyp9TUUjYaALur4vIvpZKVWn3OstbVh8IRLfxBtX3WS4nzDFd2PEEIIIcSNwKogec6cOeTn5/Pbb7+xZs0ayhefP/zwQ8LCwpg7d26DTvLvbnJkUHljPJZHxXH54n9w82Baap0A2OHogClhV6WmInVt3oPL8pKPVK1yUa5V+2Z0GeirXjfPwI7Vp6y+DyGEEEKIG4VVQfLmzZt56qmn8PX1rfQ2vE6nY+rUqRw9erTBJigg0MOJoR09AYg9n8fOsxcrHVcUhQHevQDItLHh+LmNOISFWo5bs3kvoEKQnHCk9vF972qLc3O1bNyJXeeJP3qx1vFCCCGEEDcaq4Lk0tJS7Ozsqj1mNBqrrGyK+je136U60dWVgxvQ9jbL420Zh7Fxc0PXpg0AxQcP1fk9cm3Rkuat1LSOhJjDmEpLaxyrc9By07hgy+dbvohFX2y06j6EEEIIIW4EVgXJffr0YeHCheTk5FieUxQFg8HAf//7X3r27NlgExSqvm09CPZSN99tOp5GwsXCSsf7tIq0tE/cYc6HoixLXnJpTg6G+Pg6XyMwVB2vLyrk/Jna0yiCQlvQvqe6aS8/s4TodbU3LRFCCCGEuJFYFSTPmjWLtLQ0hg8fzvTp01EUhQULFjBy5EhiY2N59tlnG3qef3uKoliai5jNsGJnXKXjzjpnuturZfKO2OnIPLflipqKAASGRlge15aXXG7APe2xd1Lbhx/eksT5szl1nCGEEEIIcWOwKkgOCAjghx9+4N577yUnJ4eAgAAyMjIYPHgw33//PW3btm3oeQrgzghfmjuqQek3exLJL6mc4jDAR+1KaFYUok6vv+KmIv4hoSga9Uci4UjdQbWDi47+97RXPzHDH/89TqnBZNW9CCGEEEJcz7R1D1F5eHjwzDPPNORcRB3sbW24v1cAi7acIa/EyHf7kpgUGWQ5PqDjPbx/bi0A2zOPcFu7diiOjpgLC61aSbZzdMK7XQdST8aScjIWfXEROnuHWs/p0MuLk7vTSDh6kazzhez9NY7eo9pc030KIYQQQjQ1q4Lk77//vs4xd9555zVPRtRtYt9APt12llKTmc//jGNin0A0Ze2iW7cMwdekIVljIsqcj8lkwCE0lMLoaEpOnMRUWIjG0bHW6weGdiX1ZCymUiNJx2NoE1F7vrmiKAwaH8xXr0ZjKCll/6/xtOvmiYevc73dsxBCCCFEY7MqSJ41a1a1zyuKgk6nw9HRUYLkRuLj5sAtXbz56XAq5zIK2HoyncFl5eEURaG/ox+rihPI1Wg4fGodvuHhFEZHQ2kpxUeP4ljHJsvA0K7s+u5rAOIPH6wzSAZwcbenz51t2b7qJKZSM3+sjGXMv7pbgnchhBBCiBuNVTnJe/bsqfKxdetW3nvvPTw8PPj4448bep6igikVysEtizpX6dhA336Wx9vP/IJD1yvbvOfTPhhbO3ug7nrJFYXe5ItPWzcALsTlcviPRKvPFUIIIYS43lgVJLu4uFT58PLy4rbbbmP69Om8+eabDT1PUUG3gGaE+6kB6fZTGZy+kGc51rPTPehMak3k7VnHKlW4sGbzno3WFv8QtRFJRmI8+VmZVs1J0SgMntgRjVZdPY7+4Sw56UXW3ZAQQgghxHXGqiC5Nr6+vpw+fbo+5iKspJaDq765iINHO3qWFb2INRdx0d6Irb8/oK4kW9P4JaBLhe57MXWvPpdr7u1Ez5FBABj1JrZ8ESuNZoQQQghxQ7IqSM7Ozq7ykZmZSWxsLIsXLyYgIKCh5ykuMzLUB08XtQvimv3J5BQaLMcGOgVaHked/vFSU5H0DIwpKXVeOzDsUpAcf7jueskVRfwj0LJpLyk2i9idqVd0vhBCCCHE9cDqjnt9+/at9NGvXz/uuusuDh8+zMyZMxt6nuIyOq2GCX3UYLjIUMrXexIsxwb4DbQ83h732xU3FXFv5YedoxMAx7dvZuWsJznw648YDYY6zgQbrYYhD3REKduzF7X6NAU5JVbdkxBCCCHE9UIxW/F++Jo1a1CUypUKFEXB2dmZ3r174+Li0mATFDXLyC8hcs4f6EtNtHKzZ9u/BqO10UDKAUatv584nS1Oig0bwz8n6d77AXCf9ABezz9f4zXNJhO/LprHse2bqxwL6BLGXbNeRWtrW+fcolaf4uAmdfNe224tufnh0Ku8SyGEEEKIxmdVCbjRo0c39DzEVWjhbMftXVuxel8SKTnF/HYsjZGhPuAVyoASI3E6WwrMpRxvXoirnR3mkpI6N++d2rOz2gAZICHmMAd+/ZGeo+r+eeh1exvOHkwnN6OYM/vTOXsgnTYRLa/qPoUQQgghGluNQfLy5cutvoiiKEyePLk+5iOu0JR+QazelwTA8qhzapBso2WAcyAruQDA9tStjAkJoWj/fkqOHcek16PR6aq9XszmjbW+XszmjVYFybY6GwZP6Mi6+WpQvvXrE/gGN8POse5VaCGEEEKIplZjkPzOO+9YfREJkptOSCs3erV2Z/e5TPbEZRGTnEMXXze6+w/EIeEbijQatsX/wYTw4RTt34/ZYKDk2DEcunat9np5FzNqfb38zNqPV+TX0Z1O/Xw4HpVKYY6eP787zeCJna7o/oQQQgghmkKNQXJsbGxjzkNcg6n9gth9Tq1nvCzqHHPv6YouIJI+J/7LZidHzhadpzDYzzK+6NChGoNk1xYtyUiIq/G1XDyuLGUicnQ74o9cpDBXz7GoVNr39MKvo/sVXUMIIYQQorFdc51kgPT09Pq4jLhKwzt749vMAYCfDqWSnlcCfj0ZUHSpmce+lvmWx7XlJYcOGVHra9V1/HL2TrbcdH+w5fPN/4vFoC+9omsIIYQQQjQ2qzbu5efns3DhQvbs2YNer6/UIKKoqIjU1FSOHj3aYJMUtbPRKEyKDOStn2PRl5r4Ijqep4Z1YICDH6AGypuLDhLu7Y3x/Play8C17dGbLoP/Qczm36ocaxHYmq4jRl7x/NpEtKRtREvOHFA38u3+8Rz9xrS74usIIYQQQjQWq1aS33jjDVauXImnpyclJSVoNBratm1LdnY2KSkpvPzyyw09T1GHe3sE4GBrA8D/diVQYizF278vHUr0AOxOjUYX1gUAY0oqhrQL1V5HURT+8cjj3PbUcwR0CcepuYflmI1Wi4326jbeDbivA3aO6t9khzYlcCE+96quI4QQQgjRGKwKkrdu3cpTTz3FokWLuP/++/Hy8mL+/Pls2LCBkJAQTp482dDzFHVwc7RlTHdfQK2fvP5wKvj3tqRcFJv0pLdubhlfdLjm1WRFUQjuO4CxL73JPz9ZQUComr+cduYUKSevLlfdyc2Ofnerq8dmM/zx31hKS01XdS0hhBBCiIZmVZCcl5dHeFnXtvbt2xMTEwOAo6MjU6ZMYcuWLQ02QWG9yZGtLY+XR8Vh9u/NgMJiy3P7WuRZHhdb0XmvXPeRd1ge7//lh6ueX8e+Pvh1VAP1i8n5HNiQUMcZQgghhBBNw6og2dPT07I5LygoiKysLC5cUN+ud3d3JyPD+rJgouG083RmYAe1+sSR5Bz25TYj3MYFl7IV2/W2x8BWTXmoq6lIRa27dqe5TysATu7aUWeZuJooisKg8R3R6tQfuz0/nyPrfMFVXUsIIYQQoiFZFSQPGTKEDz74gKioKHx9ffHz82PhwoUkJSXx1Vdf0apVq4aep7DSlH5BlsfL/4xHG9CHyLKUi3MlKSjt2wBQHHMUs8Fg1TUVjYauI0YBatvqg7+tv+r5ubV0oPft6hxMRjObV8ZiNtXZGV0IIYQQolFZFSQ/9dRTtGvXjmXLlgEwa9Ys1qxZw/Dhw9m0aROPPfZYg05SWO+m9i1p08IJgF+PnienZTcGFF1KuTgf5AKAubiY4ivIJe8yaCg6B0cADv+xaF3zAAAgAElEQVS+AUNJcR1n1CxsiD+eQa4ApJ7JIWZb8lVfSwghhBCiIVgVJL/++utMmDCBjz/+GIChQ4fy448/8sEHH7B+/XpuvfXWBp2ksJ5GozC5bDW51GTmh4v+9CusUC+5Ql5ybaXgLqdzcCR0yHAAivNyOb5jyzXNccjEjmg0CgA7154hL/Pqg24hhBBCiPpmVZB8+vRpHnroIYYOHcqrr77K/v37CQoKYuTIkQQFBTXwFMWVGtPNDxd7Nfd4wTEnPBQtISUlAGxwibOMu5LNewARN49CUdQfmf0//1CpXvaV8vB1ptvNgQAYSkrZ8sWJa7qeEEIIIUR9sipI/u6779i4cSOTJ0/m0KFDjBs3jiFDhvDee+9J++rrkJOdlnt7+AOQUQwZLp0tVS5SXUopbaamXFzJ5j0AN09v2vboBcDFpAQSjlxZkH25HrcE0dxbTeFIOHqRk7vTrul6QgghhBD1xeq21P7+/jz88MOsWbOGDRs2cN999xEdHc3o0aMl3eI6NCkyiLJsBjYXtmZgecqFonC+LB/YEJ+AMSvriq7b7ZbbLY/3/7LumuZoY6thyAOdoGyeO745RVGe/pquKYQQQghRH6wOkivS6/Xo9WowYzab0Wqt6m4tGpG/uyPDOnkBsCk/iBC9HvfSUuDq85IB/DqH0jJQrcd89sBeslKvbdOddxs3Qgf5AVBcYGD7N6eu6XpCCCGEEPXB6iD55MmTfPjhh4wcOZLbb7+d77//nn79+vHjjz+ybt21rSiKhjGlnxrM7jN1QAP0K0u5OOCZbxlzpUGyoiiXVpPNZg78+tM1z7PPHW1wdrcD4NSeNOKOSN1tIYQQQjQtq4Lkm2++mTvuuINvvvmGyMhIvv76azZt2sTTTz9Nu3btGnqO4ir1aeNOR28XLuLGOZOXpUX1aR8Fs6LmOBRdYV4yQMd+N+Hg6gZAzJZNlBReW0MQnb2WQeM7Wj7f+uUJ9EXGa7qmEEIIIcS1sCpIjoiI4LPPPmPbtm28+OKLlhbV4vqmKApTy1eTzcFEFhWjMZsp0Slc8HEAoPjwEcxlaRjW0up0hA+/BQBDcRExmzdd81wDQzwI7v3/7N13lFXV9cDx7311eu+FaQwDSEdBelMQRVBsiVGjxJqY5BexJRolscSCxBQ19hiNJoooYAABpXcY2tBmmML0Xt+b19/9/XHx4Tgz1KHvz1qu9c69555z73NmrT2Hc/eOA8DS4GDDl/mnPKYQQgghxMk6riD5T3/6EyNGjECnO6ktzOIsmjoggYhAE9u8mYR6vfQ/nApud6y29cJrteLIP/GAtP+VV6PTa3vRty9ZgNd7YoF2R0belIl/sBGAnFVllOc1nvKYQgghhBAnQ6LeC5yfUc+tQ7qx1ZsF4EsFd+B7lcRPdF8yQFB4BFnDRwHQVF1FwbYtp36vQUZG3dLD117x0X7crlMPvoUQQgghTpQEyReB24elUKQk0qQG+PYl5yUqvvMnEyTDD9LBLeqalze7D44htV8UAI1VrWz9X1GXjCuEEEIIcSIkSL4IxIb4MblvItneTLKcLmLcbioiwOp/8i/vAcRlZJLQoxcAJXt3U3Oo8JTvVVEUxvy4B0Y/PQDblxZTW9pyjKuEEEIIIbqWBMkXibtGpLLVm4UCjLLZURWF3HjtnPNgPp7m5pMad9DV03yfsxcv6II7haBwP4ZP17KmeL0q3/5rP16Pt0vGFkIIIYQ4HhIkXyQGdgunOXogAKMOV9/LS/jelovdu09q3MwhwwiOjAZg39qVtDY3neKdai4ZmUBCZhgANcUt7PimpEvGFUIIIYQ4HhIkX0QuHzURt6pjqM2OQYW8xCPnTnZfsk6vZ8AkrSy5x+Vi17LFXXGrKDqFcbf1RG/QfkQ3Lyyksbq1S8YWQgghhDgWCZIvIhMHpJOnSyNIVRlgd7RdST7JIBmg74RJGExaxbwdyxbhcbtO+V4BwmIDuGxKKgAel5eVH+1HVdUuGVsIIYQQ4mgkSL6IGPU6nAmXATC2tZVWP4XSSO2cfcfOkw5A/YOC6T16HADWhnpyN6ztkvsFGHBlN6KSgwAoy21k79ryLhtbCCGEEKIzEiRfZDIGjQdolwrO09SEs6jopMf9fjq4bYsWdNmKr16vY/ztvVB02n2un5ePtdHRJWMLIYQQQnRGguSLTFD3kQCkudyEu/Tkfm/LRcnazSc9bmRSN1L6aS8GVhXkUZ67/9Ru9HuiuwUz8MpkAJw2N6s+OSDbLoQQQghxWkmQfJHZaw2mXI1EAUa22toUFVny2XIarM6THnvQ1d8rLtJF6eC+c9k1aYTG+ANQuLOW/OyaLh1fCCGEEOL7JEi+yLy+8iBbvVrp56tsjZREgc2knUutKuDjzcUnPXZa/8GEx2spM/I2raO5tusCWYNJz7jbevraq/9zALu1a14QFEIIIYT4IQmSLzKrDtT4guTL7A4U9ByM11aTU5sqWLv75PMRKzodAydfC4Dq9bJj6f9O/Ya/J7FHOJeMSgDA1uJi3dy8Lh1fCCGEEOI7EiRfZNxelW2Hg2R/VSXF5u/Ll6xHJbai4JTGv2TMBMwBgQDsXr4El8N+SuP90LDp3QkM1Za+92+opGRvfZeOL4QQQggBEiRfdC5Pj2C/2g2rquU1HtPa2iZfclDBATYW1J30+CY/f/qMnwiA3Wph35qVp3S/P2T2NzDmJ0e2Xaz4936cdneXziGEEEIIIUHyReb+MRmoip4d3u4A3GgrI/d7L++lVRfwo7c28sQXu2mxn9ye34GTpqAo2o9W9uKuSwf3nbR+UXS/NAaAljo7mxcUdun4QgghhBASJF9khqZH8udbBrBbr63GJnjctJr8qAzTzvdu3oNf3H/5eNtOJv55Nd/urzrhOUJjYsm4dCgAdaXFHNq9o8vu/zujbu6BOdAAwM4VJVQWNnX5HEIIIYS4eEmQfBGaNiCRO2++BRV4LCYKj8HhSwUXZoUEJZuAtNeptFYw459b+b//bKf+BFPDDb56mu9z9qL5XXn7AASEmBh5U6bWUGHFh/vxuL1dPo8QQgghLk4SJF+k/NKGstbfn2WBAQBtiopklqvoDC2Yo5cB8OWOcq6Ys4oFO8uPe+tEYq9LiE5NB6Bw+1bqy8u6+Akga2gc3XpHAFBfbmXbkkNdPocQQgghLk4SJF+s/MNYFBXva36/qEhmmRYIB4TnEB9qBKDe6uRXn2znnn9to7Lp2BkrFEVps5q8fcnCrrrzNnOM+UkWBrMegG2Li6grt3T5PEIIIYS4+EiQfBFr9A/1fS6NVHEf/mkYv1PlvkUeEiscfPnLIdwxLMXXb/m+Kq6cs4pPNhcfc1U5a/hoAkK1zc57Vi7Hbu36ADYk0p/Lp2kr1l6PyooP9+P1SslqIYQQQpwa/axZs2ad7ZsQZ8eBkrXsaC3D5FJ5/DOVuEbtuNEL6VUwYYdKWGIyV98wnuEZUWw91EBjqwunx8s3+6rZXFjPZanhhAWYOhxfp9fjtNko3ZeD1+MmICSUhB69uvw5YlJCKN1Xj6XBgbXRgV+Akbj00GNfKIQQQgjRCVlJvli5ndzY3IJeVblhnZc+xSo/XH/VAZV/+COOwkKGpEWw+NejeGBsBnqdtjVjQ0Edk15dzTtrCvB0sno7YOLV6PRaFortS77C6/V0+aPodArjbuuFzqDd18b5+TTX2rp8HiGEEEJcPGQl+WK1+FHCdn1KosNNv+V+mN2gdNRPVbHrvYSNGoNBr2Nk9yjGZ8Wwo6SRWosDt1dldV4tq3JrGNQtnKggc5vLTX7+NFaUUVNchKPVSkxqOpGJyV3+OP7BJlChLLcRr0elobKVHkNiUZQOn0oIIYQQ4qhkJfliZKmGbR8AMLnBTvAxFl23bJlPhaXC1+6bFMqCB0fwyKQsTHrtR2hnSSNT/raGV5fn4vxBKrZB308Ht3hBFz1Ee4MmpRCRoJXELtlbz4FNladtLiGEEEJc2M54kOzxeHj//feZPHkyAwYM4Oqrr+ajjz7yvQSmqipvvPEGY8eOpX///tx1113k5+ef6du8sBVvAK9WTU9n9KLojv6iW4XZxp1L7qSkucR3zKjX8Ytx3Vn065EM6qa9nOfyqLy6PI9r/7aWnSWNvr6x6d1J7NkbgNK9OVQXFXT1EwGgN+gYf3svvls8XvtpHq3NJ5bfWQghhBACzkKQ/PrrrzNnzhymTp3KG2+8weTJk3n++ed55513AHjttdd44403mDFjBnPmzKGlpYU777yTlpaWM32rFy7lyP92nR5Cuh19KXl1Xx3l1nLuXHInBU1tA9zuMcF8dv9wZl3bmwCTlortQFUL17++juf+txebU9uDPGjyVN812YtO32pybFoI/cZr2zkcrW7W/Df3tM0lhBBCiAvXGQ2Sv1tF/tnPfsYDDzzAsGHD+OUvf8ktt9zCe++9h8Vi4d133+XBBx/kjjvuYMKECbz77rtYrVbmzp17Jm/1wpYyAgx+vmZ0v2YMAe6O+5pMxKb3BaDaVs1dS+7iQP2BNl30OoU7R6Tx9f+NZlRmFABeFd5eU8hVf1nNhvw6ul82jOCoaAD2r1tJa1Mjp8vQqemERGnPd3BbNQU7ak7bXEIIIYS4MJ3RINlisXDdddcxceLENsfT0tKor69n48aNtLa2MmHCBN+50NBQhgwZwpo1a87krV7YAiJg6H2+pjHAS9qVtYRnWtCbPaCo6EKDtZNOJw8vMTIs9nIA6u31zPh6Bjm1Oe2GTY4I4F8zhvDyjf0I8dMyWhyqa+XHb2/kifl76TVhMgAet5udyxaftsczmvWM/UlPX3v1Jwdw2Dr5I0AIIYQQogNnNEgODQ3lqaeeonfv3m2Or1ixgri4OKqqqgBITm6b/SApKYmioqIzdZsXhwlPw4hf+1aUDf5e4ga30OP6KnrdUkHmdQ2Y07UiIvZNW3imeDBjk8YC0Oxs5u6ld5Ndld1uWEVRuOnSZJY/NIZJl8T6jn+yuZjf5QSiM2o5lXcuW4TH7Tptj5fcK4Kew7WKgtYmJ+vnHTxtcwkhhBDiwnPWs1t89tlnrF+/nrvvvhuLxYLJZMJkalucIjAwEItFyg13KZ0ervwjPLQPbv4QbvoAfrMHsq7WTjuqSbi0EuXw/4v6v77G8zF3MzFF+1cAq8vK/cvvZ0P5hg6Hjwnx483bL+WNnwzypYUraYUdfpna9Y0NHNiw9rQ+4ogbuuMfot3/3jXllOU2nNb5hBBCCHHhMJzNyRcsWMDTTz/NpEmTuO2223jzzTc7zWt7PPlui4qKsNmkiMQJUzK1JMnlzSh9HqVbbQkBdbvx0xUSflkG9esAt5vSXz/EXbNfxB5pZ3XdamxuG79Y/gtmZs5kUNigDodONcDrU+J4c0sd3+Rb2BXSl34tewD4+pOPUSNi0OlO399qGSMDyFmkZbj4+t1d9Lk6HK9HJSDcgNHvrP+NKIQQQogzrFev46v+e9aC5Pfff58XX3yR8ePHM3v2bBRFITg4GKfTicvlwmg0+vparVaCg4OPOWZqauppvOOLSMYCeG8S1OYSk5SPI6MH1nwLlJcT+cV8/vbHv/Hcxuf4NPdTXKqL2Qdn89Lol7gy5cpOhxwyAFYeqOaJL3I4VJ9Miq0Eb10F/1i6nydmXENcqF+n156KXr2gtXw3BTtqsDV52PJJLQA6g0KPIXGMuikTk/9Z/VtRCCGEEOegs7KUNmfOHF544QWmTZvGX//6V9/2ipSUFFRVpbS0tE3/0tJS0tLSzsatXpwCIuC2zyE4HkWBhL4H0Qdqf7Q0fjYXy9LlPHn5k9ze+3YA3F43D696mIX5C4867NisGL7+zWiihl7hO+bavZor56zik83FvlzZXa3nsLh2x7xulf3rK/jqtZ14Pd4OrhJCCCHExeyMB8kffPABb775JnfccQcvvPACBsORVbyBAwdiNptZvny571hTUxObN29m2LBhZ/pWL25h3bRA2RyCwc9L/OAj1esqnnoKd1UVj1z6CPf2uxcAr+rlibVPMDf36Kn6gswGfn/f9fhHay/VZVgLUK0N/Hbebm59exOH6qxd/ii7VpR2eq7iYBNFu+q6fE4hhBBCnN/0s2bNmnWmJquurub+++8nIyOD++67j6qqKiorK33/JSQkYLVaeeuttzCbzTQ0NPDUU0/hcrl47rnnMJvNZ+pWBUBQDCRdBjlzMQc58TgU7PUmVIcD+779hE6dytCEyzHpTWyq2ATAqtJVhJhC6Bfdr9NhFUXBaDBQuH0LCqDq9JT4J1HaYOM/W4rxM+rpnxyG7jj2oR+L0+Zm5b8PHLWP3qAjfWD0Kc8lhBBCiAvHGd2MuXbtWpxOJ7m5udxyyy3tzm/YsIGHHnoInU7He++9R2trKwMHDuSFF144rj3J4jRIGwXT34LP7iKmfzOt1WYcTUZaN22i7r33iLrnHu7uezd+ej9e3PIiAC9ueRG7x87dfe/udNhLRo9n7X8+wGG1MsRxgKaYseRU27G7vDz7v30s3FXBSzf0Iyvu1P6/e9zH3krhdnlOaQ4hhBBCXHgU9XRtBBUXlk1vwuJHsTcaKFoajepVwGAg9ZNP8O/bB4DPcj/jmQ3PoKL9SN3b714eHPBgp5lJVn30HlsXzgNg3IwH2GDK4i/L83Ae3iNs1Cv8Ylx3fj62OybDye0MUlWVj36/geZae6d9RtzYnQFXdDup8YUQQghxYTqj2y3EeSzpUnA7MFSvR2dUsVb4gddL6+bNhE2/HsVk4pLIS0gKTmJFyQpUVLZVbcPqsjI8YXiHgXJEfCLbFy8EVJqrKrnn3tu4ul8Ce8qbqWiy41VhY0E9S/dU0Tcp9KQyYCiKgl6vcCin433H5gAD4+/ohcGkP+GxhRBCCHHhkiBZHL+0MdB4CD9XNrZ6Iy6LAU9TE+76OoLHjwcgKyKLjNAMvi3+Fi9edtbspM5Wx6ikUe0CZXNgIDXFhdSXlWBraSahR0/SM1K5aXAS4QFGNhfV4/Ko1FmdfLq1BIvDzWWpERj1J7aqHJ0SjNvppbKgqd259AHRZA1tn/1CCCGEEBc3CZLF8VMU6DEJpTybQNM+mgr9UT06HHv3Yc7sgbl7BgAZYRn0iujF8kPL8age9tTtodxSzpikMeiUtgFuYFg4e1Zq2UzslhZ6jRqHoigM6BbO1P4JHKy2UFzfigpkFzeycGc5WXHBJEcEnMBtKyT3jiBraCwBoSYi4gOpr7Di9ag01djoOSxeciULIYQQog0JksWJ0ekh6xp0xd9i1pXQfEgLVq3r1xM6ZQr6wy9Ypoam0i+6H8uLl+P2ujnQcICi5iLGdRuHXjmytSE4Kpr8bZuxNjbQWFlBzxGj8Q8OASDU38j1AxNJCvdnU0EdDreXJpuLz7PLqGp2MCQtArPh+LdJ+AUaSegeRkqfSHR6HSX76lG9Km6Xl9S+UV34JQkhhBDifCdBsjhxBhNkXYOpbD6exsbvpYXbS+jUqSiHy0wnByczKHYQyw4tw+V1kd+Yz4GGA0zoNgGDTlu5VRQFg9HEwS0bAe1Fu/RBl/mmUhSFSxJCuWFwEiX1Ng7WWADIKWtiXnYpaZGBpEcHnfAjRCcHsW99BS6Hh7oSCz2GxOEXaDz2hUIIIYS4KEiQLE6OKRAyJxFQ/TGWIi8ehx5XWTmKnx8Bgwf7uiUEJTA0bihLDy3F6XFS1FzE7prdXJFyBUadFpRGJCaz+5uvcTns1JUWM+DKazAcrsL4nUCzgSn9E8iKDWZTYT2tTg9Wh4cFO8spqLEyJC2CANORLROqqnaaVQNAp9dhMOo4lFOHqoLD5iZ9gORKFkIIIYRGgmRx8vzDUTJG4V/xEU0HjaAqtG7aSNDoMRhjY3zdYgNjGZk4kuXFy7G5bZRaSsmuzubKlCsx6U3o9Hpcdjule3fj9XjwDwkhMatXh1NmxgZz86VJ1LQ42VfRDMCBqhY+21pCXIiZvRVNPPb5bp74Mod31xRyqK6VnnHBhPq3XyWOSgriwKZKnDY39WUWMgbH4B9satdPCCGEEBcfyZMsTl3+t9Q/dQdV27T9yKa4cNL+twxdYGCbbgWNBdyz9B6qbdUA9Ivqx+tXvE6oORRrYwNv/+IuPG43IdEx/Owvb6PTH32/8arcGn43bzdljbaj9osIMDH3gWEdbsvYt76Cb/+1D4CMQdFcdW/f435sIYQQQly4Tq5CgxDflzGe8EdfITBeK9jhrGyg8rFftOuWHpbOP6/6J/GB8QDsqt3F3Uvvpt5eT2BYOD1HjAGguaaa/K2bjjntmB7RfP2b0fx0WApHK2Bd3+rkuf/t6/Bc1tBYwmK1lw/zs2uoKW455rxCCCGEuPBJkCy6hNL/FhIeuQ+9WSvx3LR8E80fv96uX3JIMh9c9QHdgrUKd/vr9zNjyQxqWmsYOHmqr9+2RfOPa94gs4E/TOvDtAEJR+337YFqGqzOdsd1eh1Drk3ztTfOLziueYUQQghxYZMgWXQZw+THSPjpSF+74oW/4spZ165ffFA8/7zqn2SEanmV85vyuXPJnXijA0jseQkAZfv3UFWYf9xzH6tstapqK8od6T4ohqhkbStG8Z46Kg42Hve8QgghhLgwSZAsuo6iEPR/7xB+uVbBzutUKP/VPagNpe26RgdE895V79EzoicAxS3F/HTJT0kZN8LXZ/viBcc9dVrU0dPA+Rl1xHdS1lrRKQydmu5rb5xfgGzVF0IIIS5uEiSLrqXTEfPafMzRWpaI1nKVupnTwN6+JHSEXwTvTHyHflH9AKiwVvBE1asEREYAsH/dKqyNDcc17Q2DEzEfZTX5ugGJbVLE/VBKn0ji0rUiJuV5jZTuO755hRBCCHFhkiBZdDldYAgJb/yT7wrr1WywYvvzDeB2tOsbag7lrYlvMThWy61cY69hS0I5AB63m53LFh/XnDHBfsy5eQAGXcev8GXEHH2lWVEUhk7L8LU3zs+X1WQhhBDiIiZ5ksVpYYiJR2fWYV2/GVBoLWggLCgbpc918IMiHya9iUmpk8ipzaHUUkptQCu9DoWgUxXqy0oYeNW1x0wHB9AjNphr+sWjUxQMOoX4MH+qmu2oQPahBq4bmEiIX+dV9UKi/Kk42EhzrR1rk5Oo5GDC4wI77S+EEEKIC5cEyeK08Rt4KbbsTbhKy/E4dbhL8giOqobuE9oFykadkYmpE8mtzyXfWoS/XU90kxmXw054XAIxqemdzNJWRKCJsVkx3HxZMrdclozbq7K5sB6XRyW/2sJ1AxKOWokvLDaAfesqAKgvt9JnVOJR+wshhBDiwiTbLcRpoygKCbNfRR+m7fVtKgykee77sP6vHfY3683MGTeHSamT2Jfa7Du+Zv7HJ7314cHx3ck8vNViVW4N87LLjto/Li2U1H5RgBYk522tOql5hRBCCHF+kyBZnFaGyEgSXnrZ167YHIbry1mw8z8d9jfqjLw46kXG9r+a0mitkp61rIpFaz4+qfnNBj0v3tjPt3D9x6/2Ut1iP+o1Q6ceyZu8eWEhHo/3pOYWQgghxPlLtluI086UkoKnqRn7rl2oXgV7g5FQxzyUpEshov02Cp2iY1zyOLKbd2PYXwfAtpIt6HvFkxGW0a7/scSH+tNid7O9uBGH20tpg40p/TovPhIQYqax0kp9uRVHq5vgCD+iuwWf8LxCCCGEOH/JSrI4I2Ienom5Rw8AWmvM1O31g//eDmXZHfbXKTp+e8vLqBFayejkSj9mLXmchfkLT2r+hydm0e3wWItzKlm8u+Ko/Ydcm45yOFPGlv8V4nHJarIQQghxMZEgWZwROrOZhNkvo5i0/Mk1u4OxVTrh45uhvuNS0Dqdjiuuv1P7jEJWUSBPrH2Cz3I/O+H5/U16Xrihr6/9+/l7aOykAh9oL/D1PFwUxdLgIGfN0fcyCyGEEOLCIkGyOGP8evQg5tFHtYaqUL4xHG9jLXw4HSw1HV5zyejxmAO1NGyZJUHo3fDHDX/ko70fnfD8wzOi+PGQbgDUWhz88au9R+1/6TWp6PTaavK2xUW4HJ4TnlMIIYQQ5yfZkyzOKL++fbHn5OA8dEhLC2fXERxZCYWroe9NYDC16a83GLBbLJQf2IfBq8Pq56YuzMm68nUYdUZfEZLjNSQtgi+yy7A43OyraKF/chhpUR3nQjYHGLG1uKguasbt9GL005PQPeykn10IIYQQ5w9ZSRZnlKIoxD//HPooLc1aU2EgzSV+ULEDPr0DPK521wyYdA2KTvtRHVWVhqJqq7t/3f5X/pr91xNKDxfiZ+T56X187Sfm7abF3n7O7wyenILBqM29fWkxjtbO+wohhBDiwiEryeKM0wUEYM7MpHmh9hKetcqf0G5W9JYCaCyGnlPaFBsxBwRSW3KIutJivK0Orhn5I9a1ZqOikl2djcVlYXjC8OMu+pEWFURRrZX9lS20ONy02F2M7xnbYV+TnwGn3U1lfhMelxedXkdSVvipfwlCCCGEOKfJSrI4K4JGjSL8jtsB8DqhfHM0qhfY9R9YPqtd/0GTp/o+G7dX8fLolzEoBgA+3Pshz258Fq96/Bkonrr2EiIDta0dH20sZmNBXad9B01MweSnlcXe+U0JtpbOX/gTQgghxIVBgmRx1sTMnIk5KwuA1io9dbmH9/uuexU2/qNN34SsXsSmdwegaGc2g429+Mv4v2DSaYHup7mf8vt1v8ftdR/X3BGBJv4w7RJf+/HPd2Fzdvxinl+QkQFXai/8uRwetn196PgfUgghhBDnJdluIc4axWAg4NLBNM6bBx4PrTVmguLtGP09cPAbiOoBMb20voqCwWzm4OYNAKhelXFjb6R/TH+WH1qO2+vmQMMBCpsKGd9tPHpFf8z5M2OC2FfRTH6NlUabC5fHy6jM6A77RicHs3dtOW6Xl9piCz2HxWPyN3TdlyGEEEKIc4qsJIuzypyZScyjj2gNj5eynd3xuou8IGcAACAASURBVBRAhS/u07JeHJY1bCSBYdp+4D2rv8FusXB5/OX844p/EGjUMlQsPbSUh1Y8hMPjOObciqLw7HV9CPHTgt131hSws6Sxw74mfwODJqVot+n2snVx0Uk+sRBCCCHOBxIki7Mu/NZbCRozBgBXVSOV5SO1Ex4n/OcnULkbAL3BSP8rrwbA7XCw+9uvARgUO4h3Jr5DiCkEgJWlK/nlN7/E5rYdc+6YED+enNIbAK8Kj87dhdPd8d7mvmMTCQjVtnfsW1tOU82xxxdCCCHE+Um2W4izTlEUAocPo2nBAtTWVhylDZj7D8Os5oPHAfsXQe9p4BdKZGIy2xcvQPV6qa8oY+BV16LodMQExDAycSTLi5djc9sotZSyvnw9e2r3MHvrbD7a9xF5DXl0C+lGuF/b7BS940PILm6guL6VOqsTnaIwLCOy3X3q9DoMRh2HcupQVXC0ukkf2PH2DCGEEEKc32QlWZwTDJGRJPzpeV+7YuEhXLHjtYalUqvK11pPQGgYPUeMBaCltoaDWzb4rsmKyOL9Se8T4x8DwJ66PczNm8uh5kOUtJTwed7n3LzwZjZVbGozt6IoPH99XwJM2j7m11YcZH9lc4f32XtkAsGRfgAc2FxJfbm1S55fCCGEEOcWCZLFOaNNWrjmFsrXBqAmXKqdrMuDj28GZyuDrj6SDi578YI2Y6SHpfPeVe91+uKew+Pgd2t/h8vbtihIckQAj13VEwC3V+XRubtwe9pvu9AbdAyZkqY1VNi8sOCknlUIIYQQ5zYJksU5pU1auG3Z1DmmQKSW+o3SLTB3BjHJ3UjqrVXNK9u/l6qCg23GqLPV4VE7TucGUN1azfqy9e2O3355CpemaFsxdpU28e7awg6v7zE0jvC4AADyt9dQU9xyYg8phBBCiHOeBMninKIzm0mc/TKK2QxAzZvvYRv4Jwg6XBEvdzH87zcMuup7q8mL5rcZo8Jaccx5Ouqj0ym8eGM/TAbt12LOslwKaiwd9htybbqvvXG+rCYLIYQQFxoJksU5p01aOLebsj++gmf6R2DWsleQ/S8yLCsIidYC5/3r12BtbPBdHxcYd8w54gPjOzyeER3E/12RCYDD7eXxz3fj9art+w2MJio5CIDiPXWUH+w4dZwQQgghzk8SJItzUvittxI0diwAruJiqt6ZD7d8BDojALo1LzHwEu0FPa/HzY6li3zXDowZSGpIaqdjh5nCGJ44vNPz945Kp0+iFpBvLqrn35vaV9hTdApDpx5ZTd40vwBVbR9MCyGEEOL8JEGyOCcpikL8c8+ij4oCoGnePJpzbTD9TV+fPhVvYzRqhUB2LV+M26W9jKdTdDw78llfgZEf8uKl0d75yq9Br+OlG/pj0CkAvLB4P6UNre36pfSJJC5dC6bL8xop2Vd/Ek8qhBBCiHOR5EkW5yxdQADmzB40L1wIgHX9BkLvehh9dDIcXI5B8WJxm6i0BeFy2AmPSyAmVVvdjQuMY2LKRDyqhyZHE0HGIML9wmlyNuHwONhTt4cp6VPQKR3/nRgdbMbl8bK5sB6XR+VgjZXrBiSgKIqvj6IohET5c2BjJQCNla30Htm2jxBCCCHOTxIki3OaKaUb3pYWbDt3ojoc2PfsJfQXz6B47FCyiTBDKzsaEgBoqqmm3xVX+YLUUHMoo5NGc1vv27i99+1c3/16VhSvoMHRQLm1HK/qZWj80E7nHpwSztc5ldRbnRyqayU5IoDeCSFt+oRE+VNxsJHmWjvWJidRycGEx3W8gi2EEEKI84dstxDnvOiHHjqSFm7rVurefgcmzIJ+txBhtpEeVAdATVEBZfv2dDpOgDGAOWPn4G/wB+Dt3W+zpnRNp/3NBj0v3tiP7xaGn/lqL9Ut9nb9Lp+W4fu8aUFBhy/6CSGEEOL8IkGyOOfpzGYSX5l9JC3c3/+OLScHpv4dMsYzMLzc13fbV3OPOlZ6WDq/v/z3vvZv1/6WCkvnKeMGdQtnxgiteEiTzcVTX7YPwmPTQkjtp+2dri+3cnBr1fE/nBBCCCHOSRIki/OCuXt3Yh57VGu43ZQ9/Ageuwtu/hcp3VOJNGnlofO3baGpvPioY12bcS039rgRgCZHEw+vfhiXx9Vp/4cnZtEtQisesmRPJYt2tw+qh05Nh8MrzpsWFuLpoFqfEEIIIc4fEiSL80b4j39M0LhxwOG0cM89B+ZglNs+Y1CSEwAVhe2v/wa8nVfcA3h8yOP0jNDKUO+q2cWfs//caV9/k54Xbujraz81P4cGq7NNn6ikIDIHaynpmmts7F9/7IImQgghhDh3SZAszhu+tHDRh9PCffEFzYsXQ1AMvX71Dn56NwA5+Vac718H718Drw2FT26FvOXwvTzGZr2ZOWPmEGTUCoJ8uPdDlh9a3uncwzOi+PGQbgDUWpw889Xedn2GXJuOcjht3NZFRbhdRw/UhRBCCHHukuwW4ryi8/fH3COL5gULALCuX0/olGswJqRjry2lvLAYj6ojyHKQeOdeaK2FujzY/Sm4bZAxzjdWqDmU1JBUvi76GoB1ZeuYlDKJUHNoh3MPSYvgi+wyLA43+ypb6J8URlrUkUwWfkFGWurs1JZYcNo9+AeZiEvveCwhhBBCnNtkJVmcd4JGjiDipz8FwNvSQtmjj6J6PAy44We+TBTbGxJoVwBv3V+gcHWbQ1ekXMFtvW4DwOKyMHPVTBweR4fzhvgZeX56H1/7d1/spsXedi/zpdekojNoN7FtSREuh6wmCyGEEOcjWUkW56WAIZdhWbECT10d7vIKFJOR8NFjqP32XepsRuweI3H+LYSbfpCyzeOE3tPaHBoaN5QNFRuoaq2i1lZLg72BMcljOpw3LSqIolor+ytbsDjcNNtcTOgV6ztvDjBia3FRXdSM2+nF6KcnoXtYlz+/EEIIIU4vWUkW56V2aeH+9ndsO3cyODjP1ye7PrH9hQ2H2h0y6o3MHj3bt83is9zP+Krgq07nfuraS4gMNAHw703FbMiva3N+8FUpGIzar9b2pcU4WjvPnCGEEEKIc5MEyeK81SYtnMdD2cOPEBMWRJxfCwCHrOHUOQLaXhQc1+FY8UHx/Gnkn3ztP274I/mN+R32jQg08Ydpl/jaj8/bhc15ZFtFYKiZfuOTAHC0utm+7Ogp6YQQQghx7pEgWZzX2qSFKymh+kAagyLKfOez6xPaXtD9yk7HGpU0inv63gOAzW1j5sqZtLpaO+x7Td94JvbWtlkcqmtlzrIDbc4PnJiCyU8PwM5vS2ltdrYbQwghhBDnLgmSxXmtXVq4jQXEKVEEGrSX7/Y2xWDzGI5csO5VsFR3Ot7PB/ycS2MvBSC/KZ9nNz6L2u4NQG3eZ6/rQ4ifNva7awvZUdLoO+8XaGTAlVrKOLfDQ/bX7bd5CCGEEOLcJUGyOO8ZIiJI+NMLvnb1Gg99e2mlpN2qnt3ugRCRrp1sKIQPp4OtsaOhMOgMvDT6JSL9IgFYWLCQeXnzOuwbE+LHk1N6A+BV4dG5O3G4j2y76D8+Gb9AIwA5q8qwNNg7HEcIIYQQ5x4JksUFoW1aOAtR+yzojVqAuqMpGe+M5RBzeB9x1W745MfgsnU4VnRANC+Nfgmdov16PL/pefbX7++w702DkxiVqa1i51ZZeG3FkX3MJn8Dg65KAcDj9rJ1UdEpP6cQQgghzgwJksUFI3rmQ5h7aqWmvdu2kxalvaTXUldD3u59cPs8CNOCVorXw2d3gcfd4VhD4ofwiwG/AMDpdTJz5UxanC3t+imKwvPX9yXApO0/fn3FQfZVNPvO9x2TSGColglj37oKmmo63uMshBBCiHOLBMnigqEzmUic/bIvLVzc6o2+c9mL5muZLe74EgJjtIO5i2HBL8Hr7XC8u/vezYiEEQAUtxTz9PqnO9yfnBwRwGNXacG526vy6NxduD3amAaTnkuvTgXA61XZ/FVhlzyrEEIIIU4vCZLFBcXcvTuxjz8GQEirnSiXFqyW5+6jMj9P25t8+zz4rvT0zo9h6ZO0L88HOkXHn0b9idgALYvFskPL+Hj/xx3Oe/vlKVyWGg7A7rIm3ll7JBjuNSKB4Eg/AHI3V1FXbumahxVCCCHEaSNBsrjghP3oRwSNHw9ASmmV73j24gXah7i+cOt/wKAFrmx8DdbO6XCscL9wZo+ZjUHRsljM3jqbXTW72vXT6RReuKEfJoP2K/XnZbkU1GjBsN6gY8gU7UVCVNi8UFaThRBCiHOdlKUWFxxFUQgcNoymBQvwb2iiLDwYt0FPXXER0Ru2YAoIxNh3BEpcX9jzBaBC4SoIjoeEAe3GiwuMw9/gz/ry9XhVLxvKN3Bt+rX4fRdkHxYRaMKg07HuYC0er8reimZuHJSEoihEJAaRv60au8VFQ2UrqX0jCQwzn6FvRAghhBAnSoJkcUHS+ftjzsqieYG2elwbolXe8+QdxPjJZzjy8wn+0f0okWmw/3AJ6rylEN0TYnq2G69/dH8O1B+gsLkQi8vCwcaDTE6bjKIobfoNTA5jxYEaqlsclDfaiQwy0T85DEVR8A82kZ+t5Wi2NNjJGtpx9T8hhBBCnH2y3UJcsIzx8aAoJNW3oD/8Il1xZCgeRaFlyRLqP/gA+v8IJh0uR616Yd49kL+i3ViKovDMyGdICtLKTa8pW8N7Oe+162fQ63jxhn4YdFrw/OLi/ZQ2aBktMgZGE5UcpN3HnnrK8zrO1SyEEEKIs0+CZHHBavzvp6CqGL1ekuq1tGxOo57yMC1Qbfj3x1q2imE/h1EPaxd5nPCfn0DZtnbjhZhCeGXsKxh1Wv7lv23/G1sqt7Tr1zshhJ+PzQDA6vTw23m7UVUVRacwdGq6r9/G+fkdZssQQgghxNknQbK4YDkOHvR9Tq1t9mWwKIoORQVc5eWotsMFRcY/CYPv0j67rPDRjVCT227M3pG9eXzI4wB4VS+PrX6MWlttu36/GN+dzBgtGF+TV8vcbaUApPSJJC5dy6xRcbCJkr31XfKsQgghhOhaZzVI/uabbxg4cGCbY6qq8sYbbzB27Fj69+/PXXfdRX5+ficjCNE5fViY73Og00VMs7btocXfTH2gH4qfny+nMooC17wCvadpbVs9fHgdNJa0G/emHjcxOW0yADW2Gh5f/Tger6dNH7NBz0s39uPwrgue+Wov1S12FEXh8mlHVpM3LSiQ1WQhhBDiHHTWguTs7GweeeSRdsdfe+013njjDWbMmMGcOXNoaWnhzjvvpKWlfbUzIY4mZMo1bdqptU2+z5szEliWlcSSN16lrrRYO6jTw/S3IX2s1m4ugw+vB2vblWJFUZg1bBZpoVpat02Vm/jHrn+0m39gt3BmjND6NNvdPPXlHgASs8JJ6qnlVK4+1ELhjvYr0UIIIYQ4u854kOx0Onn77be54447MBgMbc5ZLBbeffddHnzwQe644w4mTJjAu+++i9VqZe7cuWf6VsV5LmjMGIKvvMLXdhj0vi0XqqLgRGXvmhX8+3cPUXZgn9bJYIZb/g2Jg7V2XR78+0ZwtP0jLcAYwCtjXsFPr6WBe3Pnm6wvW9/uHmZOzCIlUsussWRPJYt2VwBw+bQMX59NCwvwemU1WQghhDiXnPEgefXq1bz11ls8+uij3HbbbW3O7dy5k9bWViZMmOA7FhoaypAhQ1izZs2ZvlVxnlN0OhLnzCH6oYcgPo49SVHatoofcDnsLHvrb0e2PZiD4NbPICpLa5dv117mczvaXJcZnsmTlz8JgIrK42sep9Ja2aaPv0nPC9P7+dpPzc+hweokNi2E1H5RANSXW8nbUoUQQgghzh1nPEju27cv33zzDXfccUe7HLNFRUUAJCcntzmelJTkOyfEiVCMRqLuvQfvrCdx6/Wd9qsrLaYy/3sv6gVGauWrQ7SUbxSugs/vhh/sPZ7WfRrTM6cD0OBo4NHVj+Lyutr0GZYRya1DuwFQa3HyzFd7AbRMF4d/BTZ/VYjncJo6IYQQQpx9hmN36VqxsbGdnrNYLJhMJkwmU5vjgYGBWCyWY45dVFSE7btsBUJ8T2HugWP2ObBgAY2Tp7Q5Zhoxm5Rv78fgaIR9C2j46C4qL328zYr09NDpbPPfxiHbIbZXb+fpZU9ze7fb24wzPV3H0t16als9zNteRv8ID0OSAojN9KMq105zjY0Vn28nsW9A1zywEEIIITrUq1ev4+p3xoPko1FVtd3q8nc6O/59qampXXxH4kKhb67nwPJFHZ9UVVAUAj/5L92Gjybw8qHfO9kLkr6AD64Fp4XwgvmEJ3aHCU+1GeLvSX/nR//7EVaXlYWVC7mi1xWM7za+TZ+XzdHc9U8tr/I/tjYyfVQ/4n/i4uM/bEL1qpRttzP2+gEYjJ2veAshhBDizDin8iQHBwfjdDpxudr+c7XVaiU4OPgs3ZW4EKQPGkJgWHjHJxUFP6eLoBYrJQ88QOu2HxQSSRwEP/oY9If/hWPNK7D+7226pIam8ofhf/C1n1z3JKUtpW36jOsZw/UDEwGoaLLzwuL9hMUG0HOYVp7a0uBgz+ryU3hKIYQQQnSVcypITklJQVVVSkvbBhelpaWkpaWdpbsSFwKD0cg1v34Uo9mvw/N2k5HS8CBUm42Se+/DtmNH2w7pY+CGd0E5/Cuz9AnY8XGbLpNSJ3Frz1sBaHG2MHPVTJweZ5s+T03pTVSQFmz/e1MxG/LruOyaNHQG7V9Kti0pwml3n+rjCiGEEOIUnVNB8sCBAzGbzSxfvtx3rKmpic2bNzNs2LCzeGfiQpDcuy8/nf13Bl8zjZi0DOK7Z5F5+Qjf+ZxucdQF+uG1Wim+515su3PaDtB7Kkx59Uh7/oNwYHGbLjMvnUmfyD4A7K3by0tbXmpzPjzQxB+m9vG1H5+3C0OQkUtGaSvMthYXu1a0/SNRCCGEEGeeftasWbPO1uSbN29m+/bt3H///QCYTCZaWlp46623MJvNNDQ08NRTT+FyuXjuuecwf1cdTYiT5BcYRGr/wfS/YjJ9J0wia9go9AYDxTk7UYHqyDBiG1owttpoXrqUoJEjMERHHxkgYQAY/KBgJaDC/q+g23AI07JX6HV6hiUMY0H+AhweB3vq9pAWmkZmeKZviMyYIPZXNpNfY6XJ5sLp9jB9fBo5q8rwelVqS1roPTIBg0n2JgshhBBnyzm1kgzw0EMPceedd/Lee+/x8MMPExwczPvvvy97ksVpM+S6m+gzbiIALtXLtt7pOAx6vE1NFN81A3tubtsLRvwfDHtQ++y2wyc/gopdvtOJQYk8P/J5X3vW+lkUNhX62oqi8My0PoT4ae/Nvru2kLxmG/3Ga6kPHa1udiwrPh2PKoQQQojjpKi+CgpCXLw8bjfzXphF8W5tL3KEwcRl2fvRqyr6yEhSPvwX5vT0IxeoKsz/Bez4t9YOjIYZX0PkkUp6c7bN4f2c9wHoHtadj6/5GH+Dv+/8Z1tLeGSuFlz3iA3isxlD+fTpTTjtHgxmPbc/M4yAkLbpEIUQQghxZpzV7RZCnCt0Oh3dLx1KQfYWWpubsHk92JPiiS2vQbXZaFm6jODx49CHhWkXKApkToKqHK10tasVchdD7+vBrP2rx2Vxl7GlcgsV1grq7fXU2GrapIXrHR/C9pJGDtW1Umd1YjIbGJoaQVluI16PiupR6XZJ5Nn4OoQQQoiL3jm33UKIs8UcEMj1jz/tSxVX5rJzcHBfANw1NRy68y6cJSVHLtAb4Mb3IOXwy3+NxfDRdLA1AGDQGXhp9EtE+EUA8OXBL/ki7wvf5Yqi8Pz1fQg8vPf49RUH8bskDL8gIwA5q8uwNNhP6zMLIYQQomMSJAvxPSFRMVz/2NMYDr8kmudupfy7QLmykuKf3omrrOzIBUZ/+PEnEKf1oXovfHwLOK0AxAbG8sKoF1AO159+btNzHKg/Uv0vKTyAxyb31Mb3qvxu4R4GXKm9BOhxe9myqOh0Pq4QQgghOiFBshA/EJvenSm/fhTlcE7knR4bjQP7AeAqL+fQnXfhqqw8coFfKNw2DyIO71ku2QSf/hQ8WlGcYQnDeKD/AwA4PA4eXvUwFueRMuu3DU1hSKq22ry7rInNBieBodpe5P3rKmiqaT2tzyuEEEKI9iRIFqIDGYOHMu7OewCtXPpmgxt7fy2/saukRFtRrq4+ckFQDNz+JQTHa+2Dy+DLB8DrBeDefvcyLF7L9V3UXMQfNvyB796Z1ekUXrihL2aD9uv46oqDpIzWxvF6VTZ/dSQzhhBCCCHODHlxT4hOxHfPwmmzUpF3AK/HQ1VoEN2Cw1Gqa/A0NWFZvYaQqyahCwjQLvAPg4zxkPO5lhqueq+2P7n7leh0OoYnDGdRwSKsbisHGw8S4RdBnygt8A4PNGHU61h7sBaPV6UCN33dRpw2N3XlVjIGRkumCyGEEOIMkpVkIY5i9G0z6H6ZtgLc2tTIluRodL20PcTO/HyK75qBu6HhyAWxveEnn4HxcOC8+S1YpVXdi/SP5OUxL6NXtBf1XtryEntq9/guvXtkGn0TQ7XLihtx9TqcG1yFzQtlNVkIIYQ4kyRIFuIodDo9V/9yJnEZWsW8uvJSdvbrgTGzOwCO3FyKZ/wMT2PjkYuSh8DNH4JOKxbCyudh89sADIodxK8H/RoAl9fFzFUzaXI0AWDQ63jpxn4YdNpLfn8+UEZQjJZXuWBHDdWHmk/78wohhBBCI9sthDgGvcFAxuCh5G5aj6PVSnNtDYYxo4mub8bb0ICnthbrxk2ETJ6M7rvS6ZHpEJEB+xZq7bxlENkdYnvTP7o/++v2U9RcRIuzhYKmAianTkZRFKKDzbi9KpsK63F6VULDzEQ2eACw1NvJGhp3lr4FIYQQ4uIiK8lCHIfAsHCmPz4Lc2AgAHvWr6b6xzdgSkkBwJ6TQ8nd9+CxWI9c1PdGmPzS4YYKX9wHecvRKTqeHfksiUGJAKwsWckHez7wXfbguO70iA0CYH5tI4ZILfAu3ltPed73VqyFEEIIcdpIkCzEcYpMSmbqQ0+g02vbKDZ8NQ/brx7AmJwMgG3nTkruuw9v6/dStg29F8b+VvvsdcOnt0PJZkLNocweMxujTisc8mr2q2yv3g6AyaDjpRv7o1MABRZ4jwTeG+fnI5XkhRBCiNNPtlsIcQJCY2IJiYrm4JaNABTt2UXvmY/Atmy8LRbcFRXYduwk5KpJKEYtACZlBNjqoWwbeF3aFoweVxETfQkh5hDWlK1BRWVd+TquzbgWf4M/caF+WBxusosbqVG9DDD5YXKqWOodxKaHEhYTcBa/BSGEEOLCJ0GyECcoJjUdUCndm4Pq9VK4dzcDn3gK17r1eK1WXGVl2HNyCL5qEorBAIoCGVdAXb6WFs5th/3/g17X0idpBAVNBeQ35mN1WcltyOXqtKvRKTouS43gf7vKabS5qPS46ePSVrAbKlvpPTIBRVHO7hchhBBCXMAkSBbiJCT17ktTVSU1xUW4nU6KD+Zy6e9nYVu5ErW1FVdJCfZ9ewmeNAlFr9cC5R6ToXw71BeA0wJ5X6P0uYHhqVeyvHg5TY4mSlpK0Ol0XBZ3GUa9jp7xIczdVkqzXqWbV0+oR6G1ycluq43M7uGYDfqz/VUIIYQQFyTZkyzESVAUhYn3/4rk3n0BaKyqYNGn/yLx7bfQR2glpq2rVlP2m4dQXVp5agwmuPlfkDREa9cXwEfTCfJ6eWXMK5j12gt6b+x4g40V2naOy9MjuWlwEgArTU7f/KWrKxj/8kr2lktaOCGEEOJ0kCBZiJOkNxiZOvMJIhK0ILYidz/fLp5P8rvvog8LA8DyzTeUPfwIqtutXWQKhFv/C9G9tHblLvjkx2QFp/DE0CcAUFF5bPVjVLdqZa9bndq1lQaVPIOWDi7KqyO6wcM9/9qKy+M9U48shBBCXDQkSBbiFPgFBTH9t7MICNWC4tyNa9myfSPd3n8PXahWPa/l668pf/y3qB4twCUgAm6fB2HdtPahtTB3BtenX8u0jGkA1NvreWTVI1Q2Wfl6T5VvvrX+LlS07BYj7AYqGmws33vkvBBCCCG6huxJFuIU+QUGkdTrEvatXYXX46H8wF7CMrNIv/U2mhcvRnU6ceTm4iovJ2j8eO2FO3MwZE6CPfPA1Qp1edBczrBxz7CiZCX19noqrBXUWFrJyY/1zdWqg3CPQrRXh5+q0KJTMUX5MTIz+ix+A0IIIcSFR4JkIbpAcEQUkUnJHNi4FoDCHVtJHj6ShGun0rJ4MarLhWP/ftzV1QSNHasFygERkD4Gcj4HjwMqd2H0uBg68nHmH5yPy+viYPNuPPZEVOeRILhGrzLQqUdBIdaj4581tZQ02IgKMhEX4idZL4QQQoguIEGyEF0kMjEZs38ARTuzQVU5uHUTPSZPIXrCFTQvXgJuN/a9e/E0NBA4erQWzAbHQfIQ2P05qB4o2UR4UDyJvW9gefFyAEzBeTib+oHXHwC7DkK8WoBsRsGqellW3ch/t5Tw9Z5K3B4vaVFB+Bkl84UQQghxsiRIFqILxWdmYWtppjI/D6/bTUH2ZvrccAthw4bTvHgxeDzYd+fgtbQQOHKkFiiHdYPYS2DPl4AKBSvJ7DaG+vBk9tTtAcWFPqAYV+MgvnuNoFqvMsChR4dCvFfHdpMbrwK1Ficrc2t4f10hBTVWwgOMJIb5y+qyEEIIcYIkSBaiCymKQmq/QVQX5tNQUY7TZqM4Zyf9f/QTAgcOomXJEvB4sO3cieqwEzBsmBbARmVCWLJWZAQg72suH3A3a53V1Nhq0BmbiQuHpvoMAOKjAhgcF4qn1oFBVbh2cCJE+3GorhVVBY9XZX9lC3O3lbJwVzkOt4fUyEACTIaz+O0IIYQQ5w9FVVX1bN+EEBcap93Gf59+nOqifABSBwzm+kefonXtWkoe/CUczp0c9fMHiP7Vr45cuP5vsPRJ7bPeRMkNb3LLjtm0VWucwwAAIABJREFUuFoAeGbYSwyPH0d0kBlbi4sPn1yP2+lFZ1BI6BGG3t/AoSD4T3ENJQ22/2fvvsPjqA69j39ndrbvaou6JbnJxnJv4IKNjWN6DQS4AQIhBAJppNzkhiTkhpJcEi4hpEByE+JwHS4hBEJ4KaEZ29jgBrjb2Ma2LMlWW5XV9t2ZOe8fK0tWs+UqOTmf55lnZqeeXcu7vz175pwuZbJaFC4YV8T1M4ZydnkuqiprlyVJkiSpL7ImWZJOAotmpXz6DHaufpd0Ik5rXS2JtjBjrroGx5gxtL3xBpgm8XXvg6riPuus7IFlM0FPQdVqEAa+XUsYMfubvFa/BoC19e/xyTMuxu/wY7VbqNsdJtyQQJjQ1pgkXBuHfXE+OaqA6648g7QhqGyKYQowBexqiPK39fv52/oaEmmDYbkuPHZZuyxJkiRJ3cmQLEknic3pYujEKWxfuQxDz1C/52OsdgcjLr4U+8hyIm++CUIQX7MWxW7HNX169sAR8yFyAGo3gpFmRPUHxCdew8bWHWTMDB/Uf8AV5VcQaUix8tldvV67+UCM0aU5fP6TFdw4cyh5Hhv7WxK0xLM12G0Jnfd2N/HHdyvZsj+Mx25haNCNKtsuS5IkSRIgQ7IknVRun5/CkaPY8d47CCHYt3kDuaVDKVnwCWxDhxJZsiQblFetQvV4cE2ZAoqS7UO5YRuEdkImwVnNtawpGU9dopGmZBNNiSaS6zxEavQ+rx1uiDNpQSkuu8b0YUFunj2Ms8tzMUzB3lAMwxQIAXsaY7y44QB/fb+atqROWdBJjtN6Cl8lSZIkSRp8ZEiWpJPMX1iMJ5DL7g+yTSZ2f7CGoRMmk3/2HKxDSoi+/TYAsZXvYgkEcE6aBIoKYy6BmrXQug9Lqo2zdZWXPG6SRortzdtxbxtKTiqvz+um4jrTLhyGasn2iKEoCqUBFxdNKOLmWcMpyrFT15YkFE0DEE3prNnbzB/fq2R9VStOq8qwXDcW2XZZkiRJ+hckQ7IknQKFI8oxDZ39H21FmCa731/D6Bln4z/rLLTCAqJLlwIQe+cdtIICnOPHg0WDsZfDnqUQqcMTb8Jpz2GFlh3eemjLOIKJ4j6vqVoUzry4MyQfymG1MGVogBtnDuXcMfkgYG8oRsbI3se7rynOy5tq+fPaappjaUoDTvwu20l4ZSRJkiRpcJIhWZJOkbJxE2mpO0Coeh96OkXlxg+pmDMf79RpWIJBYsvfASC6bDnWIUNwjB0Lmh0qLoMd/4BEM390quyxZZtCmIrBqKbpCAQKPWt7hYDKLU0UDs/B7bP3WiZFUSj2OTl/XCE3zx5GWcBFYzRFfVsKgHja4P19LTz5XiVr9jRjtSiMyHOjqT2DtyRJkiT9M5EhWZJOEUVRGDltBjXbNhMJNZKMRjiw8yMq5p6Le8oULF4vsZXZYa2jb7+NbdhQHGPGgM0FQ6Yi1i/mnrwgRvvNda2ORvJiJQSShX1eM96WZtu7tWSSBkWjfFh6qVU+yK5ZmFjq4/oZQzlvbCGqCnsbY6QNE4CalgSvbanjqdX7aGhLMcTvINfTe/iWJEmSpNOdDMmSdAqpFgvlZ87k43WrSEYjREKNhOvrGD3jbFxTpqA6ncTeew+AyJIl2MvLsY8aBRuehsp3+G3Ah3mwBwoF9gQ3Yqg6/kQBNtOBrqSpL9mJa36EQCafVKsJAur2hNm1rp7gEDe+POcRy1mQ4+ATFYXcMmc4w3PdNMfS1IaTACQzJuurW/nT6n2s3BVCVbO1y9bDBHBJkiRJOt3IwUQkaQC01B3gz/d8i0SkDYBZV/8bc/7tJgBCv/0tjY/+IrujplH66M/xRp6HjU9zW1EBa5yOnicUCjbDjq6mMVWzfR1Mb1nI9D0Xo2Y6e6uoOLuYOZ8ahcN9dD1Y7KyP8Mzaav62vobW9q7kDvI6ND45pYRPzyhj/BDfUZ1XkiRJkgYjWZMsSQPA6fEyZMw4tq9chjBNarZvxZuXR+GIclxnntk50Ihp0vbmWzhGDMGe2kqebvCy193zhApois5033BCRgJDGKBArXMv2/NW40kFOm7yC1VH2fbefnJyXQSKXdlhsfsh12Nn/ph8bjl7OKMLPYTjGWraR/VL6yabasI8vaaKtz9qQAgYke/GpsnaZUmSJOn0JEOyJA2QnLx8AsWl7FydbYe8d/37DBk9Fn9hEa4ZMxDpFIkPPwTTJLLpAI5AknJnimJdZ7XTgX5IuC3UdR6rb+ALB/Zwk+5k8sgLcRdMIJRqodVsZk/eBkLu/RS3jcRmOjDSgt0fNrBq8wb0gjDFwUIsiqVf5dYsKhVFOVwzvZQrp5TgtFqobIoRT2d73ahvS7HkowaefK+SqqY4eR4bRTmOfodxSZIkSRoMZHMLSRpga198jhVPPwlkR+m7/v6HyBs6HCEEDT/5Kc3/+78AKFYLZXPrcRemiSgKb7tdNFlUhmFnnm8U1qrVXU9sdSOm3MCOsReyPLKHd2reYUfdLmZUXc74+jkdu6UtSTaMeIP8MzXml81nbslcAo7AUT2HjGGyZHs9f15bzTu7Gun+rlJR5OXTZ5Vx1dRSfK6ezTwa2pLUtSUp8jko8PbSnESSJEmSTjEZkiVpgAkheOv3j7FpyWsAePPyueFHP8MTCCKEoP6BH9Hy9NMAKHYrQ6/y47J8BHYPjL8a5n4DfCVQvw1WPw6bngUjdcgVlOzAJLO/RKiggpUHVrL2wy14Vo3Gl8jv2KvWu4flI58h7Gpgcv5k5pfNZ17pPEb7Rx9VLXBNS5xn11Xz7Ps11LUlu2yzayqXTCzm+hlDOWt4gOrmBD/8f1tYtqMRQXawwYUVhdx35XhK/Ee+wVCSJEmSThYZkiVpEDANgxceup/KDR8AUDhyFP/2w59gdTgQpkndvffR+uyzAKguF2V/eALX1Km9nyzaCO//AdY9AbHGrtuKJ8OsL8P4q4jrJv94bi2176ZRRLbtsKHofFD6OhuGLMFUs80nit3FzCudx/zS+cwonoHd0r9u33TDZPnORv68tpqlOxowzK5vNcNzXTRF00RSPYfWLvE7eemrcwm65QAmkiRJ0sCQIVmSBolUPM5ffvgfNFZVAjBy+gyu/Nb3UVULwjSp/f49hF94AQDV42HoHxfhnDiRg/+Fe9T2ZpKw+a/Z2uWGbV23eYthxu0w/XOEmm0s/dN2GvZFOjY3O2tZVv5nGrz7uhzm1JzMLJ7J/NJsLXOBq6Bfz62+Lclf36/mmXXVHTf7HcnXFo7mG+ef0a99JUmSJOlEkyFZkgaRSFOIp7//TaItzQBMvehyPvG5OwAQhsGB79xN28svA9kaZVtFBclNm0AI3LNmkXvHF3DPmNH1pEJkh7Ze9Th8/GbXbZoTplyPOeOLbN7sZPWLu9HT5sEDiYyp4h/5i2k2Qr2Wd2xwLPPL5jO/dD7jcsehKofvzcI0Be/uDvHM2mpe2Vx72H1VBYb4nXgdVrwOjRyHlRyHll12Ztd5HVZyHAeXO9fnOKzYNXVAbxZcuSvE71fsYX1VC3arhQvGFXLn/HLKgq4BK9PJ1ucXNkmSpNOQDMmSNMg0VO7hmR9+h0wyW+O64LO3M+2SKwEQus7+b32byGuv9X6wqlLys4fJufji3rc37sjWLG98BvSu7YUZfSFt477E8hV+qrY1d6z2BO2UXqKxyfke79S8w8etH/d66lxHbkezjFlDZuG29tJV3SHO+tFbNEZTh93neNgsao/w7LVbyXFqXYJ3R9h2dj4+ONeOcYCUp1bv456/b+mx3u+08uydszmj0Hu8T29QSW7fTug3vyW6fDlC13FNm0bu7bfhmTdvoIsmSZJ0zGRIlqRBaO/693nhofsRpgmKwhX//j1GnzUbADMeZ+es2Yh0utdjLX4/o5YtRXUcppeIWBO8vwjW/R6i9V02iYKJ7Mz7JivX5JGMdbYXPmNGIXOvHU0TDbxT8w7v1LzD2rq1ZMxM97NjVa2cVXQW80rnMa90HmXesh773PmnD3hta12fRQy4rOR67ESSGdoSOomM0ffzOUmcVstRhWqvw4pumtz8hzXoZu/nnDEiyLN3zD61T+Qkir//PlWfvw2R6vmFp+j++whcd90AlEqSJOn4yZAsSYPUxjdf5a0nHgdAs9n5tx8+SNGoM4iuWEn17bcf9tjih36K/4orjnwRPQVbns82xajf3GVTwlnOCvO77NqX27HO4bYy97rRnDGjEEVRiGfirKpd1RGaQ4nem2WU+8qZV5atZZ6cPxlN1VhX2cx1v11FjzcgJY3F1sLvPjOHhaPHdKzOGCbRpE5bMkOkfd6W0Ikc8jiS1DtCdSTVvj7RuT1jDI63uwevmsi0YQGGBl04bf3rn3rQadyBWP1b9jz4Junm3r/AKA4Ho5cvw+KTozBKknT6kSFZkgax5U8t4v2X/gaAy+fnhh/9DFat5sB37j78gYqCffRonJMn4Zg0CefkydjLy1EsfQQyIaByBax6DHZ2bcqxLzOTZbG7iCY729IOHRdk/g1jyMnr7KbNFCbbm7azvGY5y2uWs62p282C7XJsOcwtmcu80nk01A/nv17elw2vio49/w2s/jUolmyt5KT8SXzrzG8xtaCPnjyOghCClG52huzEwVB9MGB3D9Xdg3eGaErHPMHvmPleO0ODrq5Tbnae77GjqoOwfe/2l+G5zxGvF+x7K7/LpoinjIinFE2Pk9u8nZL77iFw7bUDVFBJkqRjJ0OyJA1iwjR5+dGfsnPNuwAES8q46tO3UHvzLUd9LtXlwjFxIs5Jk3BOmYxz0iS0/PyeO4Z2werfwIanQW8fdtp0sCZ6I5vilwLZ0KbZVGZdWc7EBaW9BrmGeAMralawvGY5q2tXk9B79mqhKirjg5PxmhPZGXmfkN4zWNtUG3+48A9MKZhy1M/5RBNCEEsbXWqnDw3XbUmd1XuaWLGr9xr1o2XX1I7gXNY+H5bb+dhhPbW10HpLC4k1K0k8cReJBoVEyIYws//2SbufreNuJewr79hf0+Oc0biECQtH4p47F+ekiSiadkrLLEmSdKxkSJakQS6TTvHX+79H7a4dAJSNn8T0DR+R3ra91/3VQABHRQXJzZsxo9HDnlsbUoxz8mSckyZna53Hjetsyxxvhg+ehLW/g0i2J4q69BksbfsyzfrQjnMUDPOy4Kax5JV6+rxOykixrm4dy6uztcy1scP3bNHdmYVn8seL/nhUxwyUcCLD2Q8uIZbuvQnClDIfV00tZV9TnKrmONXN2fmxtLkuOFgLnduzJjrfYz+uXiaEYZD6+GMS6zeQ2LCBxPr1pPft63VfQ9VYd+Z3ibuKsr9KdLvuhK1PUNC4HjUnB/fs2XjOmYt77lysRUXHXD5JkqSTTYZkSToNxNvCPH3PvxOuz97oVjFtBqNfWYLR0NBlP0tuLkMXLcIx5gyEaZLeu5fExk0kNm4ksWkTqZ07wThMGNM0HGPG4JycbaLhmDQJW+kQlO0vwqpfQ+1GDKHxYewq3o9ei0l2iGlVhakXDuPMS4ajHaF2UwjBx60fs7xmOe/UvMPGxo2Yoo+73A5xRuAMch25+Oy+jslv9+O3+7s89tl8eG1eLOrAtfV9fWsdX/6/D9G7tM0wGVLQwo+vHses0vE4tc6mKkIIQtE0Vc0xqprjVDUlsvP2x/VtR98LiMPatRZ62CFhujTQsxbaaGvL/p2s30Biw3oSGzdhxmJ9nl8ogj2FCr4YZFwz2D72s33u64nWcNb7D9I9sttHj8I99xw858zFOX06qr1/A9VIJ1a4oZ4PXv07ez98H9M0KB07gemXfpKC4SMHumiSNKBkSJak00TzgRr+fM+3SMaytcOzLv8UFUmD6Mp3s/0kz56N/7pr0YLBPs9hxuMkt24lsWlTR3jW6+v73B9A9fmyTTQmTcRZZMXR9jZa9Rs06yUsC3+J2szYjn39uRYW3DKJIaMD/X5eLckWvrb0a6xvWN/vY45EQcFr83aE6Bx7Tq9huvs6t9V9wvr43VUf4cn3KvmwqpW0Yy1x9ytEjewIiF6rlxvG3sAXJ3+xX2E+mTGoacnWOHevga5qjpPMHPlLxqEUYTJFtDEjsZ+xTZWUHPgYT131YY8RdpN0gU680KC1wMpfh4wkIkopbi1kQu10dFtf/+YCULgk9x2MdavIVFX1XiaHA9fMGXjmzMV9zlxsw4fL/pZPgfq9u/nrA98nFev6q5OqaVzxze9RPn1GH0dK0j8/GZIl6TRSs20Lz/34Hgw92zXbxV/5d8ads+C4zpmpryexcSPJTZtIbNhIYutWROLwo+LZyobgKFBwqLvZlzOL980byYjOG/vGTzKZ/dl52Ps5rPT/bf8/frL2J31utygWfHYfbak2dNFzGOsTRVM0cuw5nUHa7us1THfZbvd1qRXu7oVdL/Cf7/1nr9uuO+M6fjD7B8dUVsM0SBpJYukY+8Nh9ja3UNXSQk04TF2kjfpIG03xCJF0HJce54ymEBX1zYxpCHNGfRRvqu9fFExgXwF8XJpDdUERrTnFWNRCAoki/IlCPGn/UZdXszQzNLiTihILXt0ksWUfsTVr+/xbs5aW4p47B8855+CaOQuL5/D9bktHTwjBn+7+Go2Ve3rd7vB6+cLjT2K1yRp+6V+TDMmSdJrZvnIZr/7qYQBUi8Y137+fsvGTTtj5ha5n26JuyDbRSGzaSHr3nmxb0z6knH52jb2WhpzOm+vcWph556YYeeWVYO07RAK0pdu45PlLCKfDvW7/6tSv8oVJX8jeOJeJ0ZpqJZwOE06GO5ZbU620pdpoTbV2WQ6nw7Sl2hA9O5s7YewWOz6bD5+ja6j22rw8u+NZ4nq8z2O/NvVrODQHCT1BQk8Q1+PZ5cwhy3qCeCbeZZ+U0UcTDCEobIEz9gvG7BecsV8wtBHUPp6+QKHZF2R3SRF1ecXE3IUoahE5yUIcxskJpmktQjpnI4GiWipsForrnKgf7iC9c1fvB2garqlTcZ+TbZphr6iQtcwnQP2ej3nqu18/7D4XfekbjJ+/8BSVSJIGFxmSJek0tPr5Z3j32acAsLvdXH//w+SW9hyw40QxIhGSmzd3aaZhNDd32UcAjflT2TH6OjK2nI71pZkPOHtyHcHrvoKlaESf19jQsIG73r6LllQLCEFZI+TEBROnXMDdlz98XG2MDdMgko50hOlwKkw41bncI2Cns8uxTN9tcgcLW0ZQXpsNxQcnXy+Z3FQsxJ0FxN1F1OYV0uQvJuUoRKMQi+hfjb+JSauWpNmaJO3eSa1/F63OetrsTXxyy9cJJAsRCJRurY/35+wiNzak19CdsiSoCmylKrAJv6OaWaEcxlQq5G3ejxrt/cuFJS8Pz5w5uM85B/ecs9EC/W/e869OmCaNVZXsfvstti97k5bU4X81QlHILSmjcEQ5BSNGUTiinPzhI7G7/nmHV5ekg2RIlqTTkBCC13/7C7YuewsAX0EhN/zoZyiqSjoRxxPMxaJZT+r1M/v3Z2/02riR5MZNJLdtQ2QyZDQXH5dfRW3x2R37a5k4o3b/jeFiI87ps3DOmp/tu3nUqC59N8cyMZa++ltyf/0c/urW7EpFwTN/PkX33Ye1sOCkPafeZMxMR6A+NFR3LKe7bjsYtpNG8sgn7yeHxYFTc+LUnLg0J0URjZHVaYbuS1BcGSG3pg31kEFSdIuduKuQmKuIuKuImKeImKeQpC0fofTvi4ZmVfEXucgpcEKOjYRTZV8mza/WVtLe4xuKJYpr+GOothYA3Ck/5+36LMWRzpu9kgiWe8Js9VWi2RoZbpiMiQUpaxuDK5PT47q6kqba/xF7cjdS5dvC0MYEU/cqnFmpMbQm2XttuKLgmDCho2mGc9KkY+5mLp7WeXVzHdXNcQpy7Fw6sRi/q39fIAYrIQSt9bVUbd5I1ZaNVG/dRCLSdtznDRQPIX94eXt4Lqdg+EhcOXLQGOmfiwzJknSaMnSdvz34Q6q2bATA5nKRjmdr3hweL5PPv5jZ11x/UsPyoUQ6TXLHjo5mGjUfNbEleAFJZ2dfzP6WHVTs/DOuRPYGNtXlwjFhAs4J43D4o6ihDdT8eTci0/NtyTZ8OCOefw7VPfjbpib1JOFUmOpINbe9cRuGyLb/HVEnuPADk6K2EbQGZxLyu/FOKGHsvFICBZ7OMGx14dSc2HSFzPYd2S7Y2rth0xsbEUDG6skGYXcRMVdRRyhOOfpfq2p3aQSK3ASKXdl5kYtgsRtv0IHSS9/X1/zmPd7f19LxWNHC2AteRcvZjKKYqFgZn7qEsshCIrqVnUqGuliaaKpbO3IhKCHBNCPGiKQPu94zXBmKwYGcnezJ3URlYDOq0cakSsGUPYLJewTBvno39HjwzJ6NZ945eObOxVpc3K/XYtmOBr72zAbCic5h1h1WlQeunMC1Z568X2lOhmhLM9VbNrJvSzYYR0KNfe6rmiamqva6TbPZKB5dQeO+vSSjkSNe15uX3xGaC0eMomBEOZ5A3zcSn2ibGzfz/K7nqYnWUOgq5JOjPsmZhWfKpjnSMZMhWZJOY8lYlKe++w3C9b33O1x+5kyu/NY9A/YhkahvYs3/fcC2nSqC7AexaqQZUfkqZTVLUPvR9ZsAmt0OYnYbuRdcwITb78Tq8aDYbNmpr1EEB4n7Vt3HczufY8FGky+8Kth1xqc5MGRul30UFRbePJaRIywk1q/vCMSJbdtJWDzZGuGDQdhdRMxViG7tu1/q7tx+W3sYdhMscnUsO73Wo/rbWF/VwvW/X92zNw01ydVn+vjR5WfjsrrIpJLs3fABibY2giWlBMsrCEXTNEZSNEZSNHTMkzS2JUmGUgQb44yMZ3ptkiEwqfPuZW9wE3uDm4jYmyhrhCl7sqG5okZg7eM+xLrcADVnjCcxcS7WqbPIC3rJ99op8NrJ99rx2DX2hGJc/IsVpPXO56VYIlj967A4q5k9oojrxl3CwmELsaqn5kvn0UjGolRv29xRW9y8v++eSpxGmtzWJHnRBLnRBCnNwpryIWS0rv+PLBYLl972FUbNWwAWC5FQI/V7P6Zh727q9+6moXIPsZbmPq7SyeXzd2mqUTCinJz8ghP+nvSbDb/h8Y2P91h/fcX1fHfGd2VQlo6JDMmSdJr7y/3fpWbr5j63X/P9HzFs0sCOVtdYFeHtP20nVN1Z/edL72NC5WLstbUgev8Ai9itrB9WRNTZ+ZO3VTcYvz/EkNb2c6lqZ2C22VBsVlRrdq5Ybd22tU9Wa3a7zYbasa6vY6wdx6h9nqvzHKrNCtb28BmuIfna3fxoz2o+vdhKXfFcdpxxfa8DbiBMRn/8HLrmbA/FhcRdhZiW/vUsoCiCHL9KoMRHsCSnvWY4Wztsc564Ue627A/z8Bs7WL6zESGgNODk83NH8NnZw1FVhe0rl7Fk0W+7dCmWWzqUy77+HfLKhh323IYp2FfZyo519dRvriUW6v3jyXBU0uDfwJa8Tez2NmFPC8bvE0zemw3NxS29HkZKgy0lHj4sKeaDglFU20dhpxgLri613RbnHpxl/9sxPPpBFYGJPDLv1xR6/Ni03mtfT4V0PEb1+2up2vAh1bu2E2qsp6+PcltGJ7c9EOdFE7jSPXuHSVg19ub7aPS6EIpCMJZgeGOYnGQaFAVLXi7WgkK0goLsVFiAtaCAlNtFi56mua2FxtoDNFTupq2xoZdSdOVwe7JNNDpqncsJFA1B6aNG+3Dakhl+v+41ntzzvT73+ek5P+WSkZcc9bklSYZkSTqNpRNxfvW5fztszxOKqmJzOtGsNixWG5rVisWWnWs2e/ax1YZms2GxWtGsNjRbdtvBx5aD6zqWbdnl9nWd58yew2qzY7FZUQ+52c40TDa8Vc3al/ditNdEKhhMcr7EpNTzpJsEjRtzODjsddqismJMGSlrLwFPCGbsqSUveoSbjgaQYrWikEFRDQzTSsISYP3ku0g6847rvBZFx5+TJFjoIDC8mMCwQgJFLvwFLizWUxfcYimdZMYg6LZ11NLt27SB5/7rB73+Pbr9AT77s8dxerz9vkakOcme9Y3s+bCOA7vboMdwJOCz1OB1ryWZ8yF7fCE2O4LEIgajKuNM2SOYsE/gyPQ8N0CDDzaMVFg/1M2moiHEKMZMBbHlv4GqpUAIpn8sOHeTIBgRNPgVXh9VwTrL57FrKl6HhtdhxWPX2pc1PHZrx/Khjz0OjZxu+7ttWseQ7sI0MVpb0UMhjFAIPRRCDzWhh0KkGxsINdRRF2ml3kjTYrP03UTCMAm2h+LcaAJvMt3Lq3biCauGmesnmesjnOOmzWGlDZNoMk0y3o82+jYNS2EOSvskCj2IXCcmAkMYmMLEEAaGaXQ83tkQZvP+FnDsRrV2trNWhKBifz4jWudiN4dic9m56oKFjJlZhGYb3L88SYOLDMmSdBqLtjTzP3fePNDF6JNqsfQI5opqJR42SKdAQQPFglXRKbJ9jFofQ8QVVCEIO+00efu+gz4/E2VBuBJhKAizfTLoWDYPWT64vq8a62MlAF1zkrL5Sdm7Tz5S9gApm4+Mrf/B8CCbEiPgbCYQNAmU+AmOGk5gzGi8ee6OYDXY/OXeu6nZvqXP7fNu/BxnXfGpYzp3vC3N3o2N7FnfSM2OZsxemld41QZGOlYz0rEar20X+0rHsTt3NA0HTLQt+yndVk9pfe+JWVdhZwlsGKmyYaRCVYHgi68I5m/p+RH54tQg/zunqP3XANE5Kd2WhcCdNgjEdHwJHX/MIBA38MV1/HGDQMzAH89OvpiJpf1SAog4bDR5nDR5nDR7nOiW3kOxagr88SR5kTi50QS+eArdAmF3dmr1QNit0OKGFo+CqcBtr5n0FRX3FMJbU1WCEUEgCoEIBKOCYARyjvE7aUZViDjthJ12mt12wi4bSaut568p3eiqSYs3Q3NOmiZfmqacNK3eNMZhcu6fx5y3AAAgAElEQVTMj0yu+GA8NcNvQ6gaQgiEUY8w46R8oF/VxMiC4Yz0j6TcX07QceraTEunHxmSJek0ZpoGv/vS5w7bNtBXWIzbH0BPpzAyGfRMGiOdRm9f1tPpw9ZED16CgC2BV0vhtabxaCm81hReLYXHmsarpXBY9C6fw8I8GJzb50Z22ewI2nSEbsNUSBgBYiJIXOQSI0CcIHElSEIJElcDJNQghnriBlrIdTczd3aYQMUYXKOnoNj73+74ZBNCkIi0EW1uItIUItocItLU1D5vJNIUoqX2wGHPodlsFI8aQ05+ITn5+eTkF+LLLyAnvwBPMA9LP3ulSMUzVG5uYs+GRqq2NKH3MuKgU21hpH0tIx2rKLFtxeL2QflCIoHp1OyNElnzPvYPP8IW7b2/6bgNXOmD4wUe8jq0P/7NxQr781T8MYE/Cv6YwBcDfwz80Ww3fP4o2A4zCnzX62k0eZyE2oNxurdfUACEQDNTCDVJwpGgxZui1StodSu0uqHVDQk7hw2gl641+eySnq9Zqwt++BkLtbm9H6vp7cE5CoH24ByMivYgDYH2YO1KH/n5GopCxGEj7LLT5rQTdtqIOuyYR/gCKBDEnBmaclLUBdOE/BlavGl0TTDzI5O7XnLy7uwHMC12TP0AmdibCLOp4/ikI4dXZm4n4s42OwnYA9nA7CvvCM7lvnLynHmyHbMkQ7Ikne7WvPAsK59Z3Os2q8PBbb98Apev7xHShBCYho6ezmBk0u3BuXPZSGfQ06mOUG1kMujpdPv2zm3Z4N25/eC23oK5cfC4dAbTOIkj6FkUPC4LXqeK16HgcQi8NgOXVaBiQxE2dMNNLO0hlvYQTecQ033EdD8xM4Dos76tf5xqKx61CafZglGdIOwtI5ozvJc9s9HrgtvGM/rMwuO65rEQpkki0kakKUSkuYloU3vwPbjcHCLa1ISe6Uf6OUaKouLJzSUnryAbnAsKycnLBmhffiHevLxee2rJpA2qtzaze0MDlZuaSCd6/j3ZlSjD7esY6VhNmX0DViUDJdMQIxeSNMsJba5nwwuvMSK0D8sp+khMaRZCHid1fictbifpw/RCk3CmaM1NEA7GifoTmFYDhewAMQrZSUWgAIawoKORERoZNDJYcZImqIRZ63Ag2oPflN0ml65V8BkzqS+YRdTto9XdTDh3DemcdXiUFE4yWET23Fr73NJ+XQuifVt2WRWgtc+tabDFFewxBS1mwYzbIGZFjVmwxhXsCYErrqOZXV9rU4GovTM4tzlttDnsGH3UoncQoCoW3BkHiqWURM5EUKxkIs8BPX85iNsNXpx7gJS97xuHvTYv5b5yyv3ljPS1h2d/OYWuwkETniNNIRoqd2O1OympGHvKejL6VyJDsiSd5kzD4LXfPMr2FUu7rLc5nFz+799j+KSpA1Sy/jE/XsrHTzzEytYbiZs5CKEDOkZyE0Z6Q5/H2V1u7G4P0eam4wzaFhTVA6oHRfWiKO1z1YvSvg7F1eWDUbWA26Pg8QrcbhO3y8DjSuN2pvE4krgdcdxbn8DS8lHHMQ0bvezfU8b7075N2p790mIarZh6FQAlYyZy9bcXoB4pEBwlYZrE28LZANzUeEjtbyhbK9wcItoU6hjq/FioFg1PMJdULEoq3vcALE5vDulE/NiupSh4AsFsLXRePr6CQnLyC9qDdHadomrs39HC7g2N7N3QSCLSMyBpSpJhtg8Z6VjNMPsH2NU4uHKJlc3n3d0pyvct47Wwn3M3HeyP5eipDguaS0Fz6GjWJJpdx3BCSHNRZ/FywPDSpPfdlaFXSzLU3ZqdXGE81jQZaw5xez5RWx5hSy4tai4hxU8DAQ4YAfYbOVSlvTSnLESSOolMZxW2jQz/Z/sxLxaEeNHrASFQhcaFOz7PsNbxPa6/yabzujODTcmQTyuFSgv5SnZeqLRQ0G1dUOmrP75saM8IJ2nhJGM6SQsXaeEkbTpJZtyk0i6SaTdJw03c8JI0XGSEC11xYGLDUKxkLCkyIoxpNiGMekyjEUQfI072U1RVaMg1aA3GaPC30pxjkLYeOfy6rW5G+kZ2Cc4jfSMZ4hmCqpyaewJS8Rhv/v4xdq5a0XHDpsvnZ96Nn5OjI55gMiRL0j8BIQQHdmxnx6oVpGJR8oeNYNz8hadP5/4175N66xFWbyxlS+IiAISZJNW2GETPD2BFUZl22dexuYYSaU7Q1thMtDlELNyMkW5DmFGEiCDMCJhRhBkF+vm7dy8U1YLTG8ATzM0GtMJ8cnLz8OTm4c3NwxvMw+X3d7lRkd1vw5+u6ngoBESqHdTsHcbWwFXsd4UwMju6XGfsOQu44AtfRbP1cwQ80yDW2kK0qTPsRtqbQxxsEpH9EnHsz92iaV2epyc3D28wt32eXe/K8aGoKtXbNvPXB76PMHvW0Hlz8/nsw7/G5nASa22hLdRAuLGBtsYG2hrr2+fZ6VhrrN3+QEdw9uYXADm0NWk07INE1IGidK1pU8lQattEuWM1w+3rcFmyw6L/POBjwl/clHZrxZSwWqgJ5tDmsGFTDMZ4Gymzh7E6DTSHieYwsDhMVAvopsKBRA5VMT9VcT91CW97XW9PDs1kaJ7K0BI/Q0eW4C8ZhpJTDJ4i8LZPRxjavTvdMNlY08qnfrMKACdJvmB7jlUlG9ju0JhQO4+5lZ/qdYREgILzh+AscWGmzfbJwMyYHY9F5pD1KR0lmUSk0oi0jpk2MTIKhmHBME9czyoHCSEQZhhhNGAaDQi9HtNoAHHsN/KqpkAR2UkoYKomhmqS0UzSmiBlEyRtgoRNkNEEhkWgWwSGmp2rVo2gJ48CXxFFvhJK/GUMDY5giK8Uu9OZvUnaZs/eIN2PZkVCCNbsbWZPY4xitZmzSzTseSMRmoNnH/geNdt6b/t/6df+g4qz5x3z6zBQ9KYmEps3o1it2CdNouFADUYmTf7wkUd1s++JJkOyJEmDh55i49vVrPxbtnbVNFrIxF5FGPWd+yhurK6FWGyj+n3a7NtcArszic2RxKLFUZUYphFBT4dJxVtJtDVjZProBqEfFFXFE8jFk5vbER69kR149ryA15LEY03h0dKoFo3XlE+zdcu+Xs8zfv55XPSlr2MaBtGW5l7a/jZ1NH+ItjT1Gkj7S7Pa8Obl4QkeEnxz8/Hm5mbX5ebh9OYc1c/Lu9a+x5I//IZYa2c/bIUjR3Pp175NoGjIEY8XQhAPt9IWygbmcEM9baHGjiAdbqxHTx1bLaJm96AoORiGB0XNyU6W7FxV3Qyx7WakYzUjHKv5sFJQuM4BQNRVzPbh02m07qT7l63R3hCXlnyEgqAh6WFfeyg+EPehi95rFq02K6WjR1M2aTpDJ59JwbARx9T92aGEEOgZk3RCJ5M0SCd10kmDX72+k82VLdgE2ISCXRi4rSFGx/NwmI7juubJY2JTEtiUBFY13rFsU+JY1UOWlQQ2NbusZJLsX+cgbLVRWTYNPbkWOPb/GyeToqpodjvWQ4KzZrNhtWcfJ4WFDQeiqKkwU7XdeLUWtjs1mk2N4gYHmUzfv0Lk5Bfy6fsfwuZwoNlsqBZt0DQPOWjL/jArdzVSFF7PVFstbS+tQFmxi/1Fc9lbUEjc2IIQ2V+kLJqViQsvYP5nPt/vyoMTSYZkSZIGle3v1fL24u0djzvuTjeaQXWiakNRug2vbNFU3AE7Hr8dt79z7vbb8QSyc5fPhuUwTRmEECSjkc5mCB03p4U62upGmhqPOaBB9l4qlzeHWNvhhwV2+fwk2toQ/RhspS+a3Z4NvMHcHsHXE8zFm5uHw+M9KR+ghp6hakt2+OPgkFIKR446Ydc5eANhW2NDNkg31GdrpEOdtdLpxDHWKCoOFNWHonpxaybethacbSmqSxeSTrxKX79GBANuYokMqWTvNeCqRaN49BiGTpjM0ImTKR51Rkf7USNjkk7ppBPZYNsZcNuX+1rfHoI799MH/P5bzaZic2jYnBo2hwWrw5J97NA6l50WrPbsvMt6R+djq6qjxBsgUnfIVAvR+uw80j5PdK3qb9jopWm7l73DLmJHvoGZ2dNnWYNqnFwlTka3kDEs6KZKRqgYQkVHxVAVTCU7N1TliD1xDFaKqqLZ7O0B3NZjufOx/ZCg3m1b+zprxz6Onvvb7V1/SetFNKVz15/XU73jQ35l/RXVnhD17wWYusPCxolfJOQ2ycRf7/XYM2bO4fJvfvdkvESHNWhD8rPPPssTTzxBXV0dY8eO5e6772bq1MHdtlKSpONX/VEz/+/RvtsiA0xcUMrQcUE8AQcevx27+9TUlgghSMVj7Te2ZW9oO7SN78Epkzy5/TfbnE68ufkdYTcbfnM7QrEnNw+7yz3oapBOBSEEqViMcGN9e+1ztha6M0jXk4r13W76xFDwBEvw5o/GFSjH4RqKrltIJ7LBNnNIwDX1QfkRjMtnY/ikvB4BtuOxs+tjq91ywtvTH9HeFfC/l3X0OCJMqF3rJ1zpYnfJJHbk9f7vbFUMbh75IX5b7/03CwFGSiUTt6AnsvN0UiOVsJBMWUmlNNIpCxldy4ZpVcFQVQyl63L3bdnH7cG7/bFuUdEtCnp7KBeK0mvzl9OBRdOwWG1Y2/vez86tWK1WNM3GrlCclmiSs9VtxDSdbYqVmTsFUc8wGgvORE+sAvruU/szDz5K4cj+/4J4IgzKkPzCCy/wve99jy9/+ctMnDiRP/3pT3z44Ye8+OKLlJWVDXTxJEk6iUxT8NQPVhFp6v3N0hOwc9OPZp/6D+SjkIrHO4Nzc4hIqL1WurmJUNU+os2hwx7vCeaRN3TYIe2AO5tweIJ52F199x8tHVkqHmtvutG1PXTL7s2EWyL00qPcESmqD1UbhmodiqqVoahH14b46K6ldKupPRhi22to7RpWZ88a2oM1uTaHxo41dax9aW+f17jw9gmMml5w0p7DCSEE/PFiqFrVZXWyVSNS42BPzlTWpK1kDmlG5fL5ueyub1FWPgxSEUi2ZeeptvbltkOWI92Ww13Wi4yOnrSQSajoCUv7lF3OxC3oyeyymTm69yoTOgN1t7Dd6HGyu6jvvp0VYeLLRBAoiPY+T0ylfd6+bCqd89Ophnz2NTdw9rU3nNJrDrqQLIRg4cKFnHPOOdx3330AZDIZLrroIhYsWMA999wzwCWUJOlkO7CrhZd+tRE93TWtaFaVS78ymdIxgQEq2fGLNjfx+6/c2ufNdKrFwu2P/RFPQA5ycMqZJrz+XdLvPUFj2s+HkU+wJz4WI7UJYR7+i43d/xUU5fBtJq12S2ctrN3S3iyhlxrajm2WbPA9NOg6NTSrety/EhgZk5cf20jNRz3H8D5jRiHn3TIOZZAOWtNFtBH+8hmoXt11/cRr4crHSGcMdq1dRbSlGX9hMeVnzkSznoCu0oQAPdlLsG47JHxnl81wE5lQM3pzK5nmCJmWGJm2FEZEJ5WAVMICcRXVPPLrbQIrxpQRc/T+t1axP8TIULjfT6NLIO+o4VbbQ7mCcUiTk+77dIT3Ho+7HaOcmCYrMz55Ledc/9njOsfROvG3nR6nffv2sX//fj7xiU90rLNarZx77rmsWLFiAEsmSdKpMmR0gOu+dxYb3qyiensLAkFZRZAp5w0lOKTvm1ZOB55gLpPOu4gNr7/S6/ZJ510sA/JAUVW4+KfYZn2Rko9ewdloUvXqeBRLkEz0uT4Pyy2bxNRLxnfW1PZWk2u3DKrQabGqXPblyWxdeYCPVtUSC6fIyXUy/pwhjJlZNKjKeliefLj1NahaDfveBYsVRl8IBRUA2DROTrdoipLtccTqBO/h+zZXAXv71IOhQzqCSIQxGmtZsuwD1q3Zwo36P9giNPboduZsE7hTnec6a08tH44oos3ZeUZFCEY0tDKiPSCbgKm2T8ohcwWMHusFpmJ0Xa9020dVeqw//P7d1kNH8D5Yu+2PKZy1Cz4adTVtngL02GtA391DllSM6/+/zwky6EJyZWUlAMOGDeuyvqysjKqqKgzDwGKRY69L0j+7QJGbBTeNHehinBTn3nw7qmph45uvdvQZbNE0Jp9/CfNv+vwAl04iMBxmf5kgULzzAw7sKkO1jsHs1mUfZPvrvvzrXyS39PRrCmixqkxaUMqkBaUDXZTjoygwbHZ2Ot1YNHAGUJwBtOBwZg09k3ub30GLCr5k/wv3l5XQ4Ff4zNLOX9VcGZ05O2todjsIuxzkzJrJ5G98G09efnv3ddkvOIYwMIWZnTAxzfa5OMzUfT/TwDT1zslIH/I4g65n0E0dQ8+QMTIYRoaMnsEwdTKGjmlk0E2DWCrFKxurmajsYpplJ78M+GlVVUqbDEYe2M6WCXMRej1Gal2vL1P+0OGMmDz9lPyTHGrQheRoNNsnqtvdtbbI7XZjmiaJRAKPp/ehWisrK0kc613NkiRJp1DRzHMIjJ9Kc1X2DvzgsJHY3R527Nw5wCWTDjVsjo3mOgtwMUYyDz21AUQMUAgOH8uESy+lIRKlYfv2I51KkvrlgU/k89/Lr2JEWx33NK3jv6YEmL8Zyg5p8aMAubEkuR4fXHM9+8NtED58rznHQkHFgg0LR+h+TeOIiXJ3qIl/bKnh6/bvIAjzQF6Qhz9l4RsvbKBk/wpqhswBkcBId+0DOqdoCBM+dSMf7ej5JfVYjR3bvwqYQReSDzaR7qu91eHaYQ0fPvxkFEmSJOnkOeusgS6BdATjJ2bYsmI/+zYHMPSFFAyzMmnBCIJD+h7uXZKO1Vhg4YyJbKqZRdOuNfyg7hleu3k3Y95qZtY2A5sOaBo5F11Ewbe/jbVwkN9g2e6hMYJHl+zi9pX/yYPhR/miJczv/Dncf4OFUfv/ypSarfh88wi6z8Ki1pJX4mTcvGkMmzD5uPsRP1aD7sa9ZcuWcccdd/DGG290aXLx5JNP8tBDD7Ft27YBLJ0kSZIkSdLAMONx9KYmLIEgFs/peX9GNKWzsaoFb2g9ueJjVuh1RH0lVOSNZ3bxbCxH6G/5VBp0NckHg3F1dXWXkFxdXS1riiVJkiRJ+pelulzYTvMuID12jTmj82H0BcAFfHqgC3QYg66j0eHDh1NcXMxbb73VsS6TybBs2TJmzz4NG+VLkiRJkiRJp51BV5OsKAq33347DzzwAD6fj2nTpvHUU0/R0tLCLbfcMtDFkyRJkiRJkv4FDLo2yQctWrSIxYsX09LSwtixY/nOd74jh6WWJEmSJEmSTolBG5IlSZIkSZIkaaAMujbJkiRJkiRJkjTQZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEiWJEmSJEmSpG5kSJYkSZIkSZKkbmRIliRJkiRJkqRuZEg+CslkkrVr1/LKK68QDoepqakZ6CJJknSaaWhoYMuWLRiGMdBFkf6FfPOb3+Sdd97BNM2BLooknTa0gS7A6WLRokU8/vjjRKNRFEXhueee49FHHyUWi/E///M/eL3egS7iUQuFQtx///2sWbOGSCSCEKLHPtu3bx+Akkn/iiKRCH/6059YtWoVoVCIX/7ylyxdupSKigrmzZs30MU7JuFwmPvvv5/Jkydz88038/e//5177rkHwzAoKyvjD3/4A2VlZQNdTKmbtrY2EokEhYWFpNNpFi9eTG1tLeeffz6zZs0a6OIdk1AoxJ133kkgEOCyyy7jyiuvZNy4cQNdLOkI7rrrLq666irmzZuHxWIZ6OL8y5E1yf3w1FNP8bOf/Yxbb72Vv/71rx1h8uabb2bv3r08+uijA1zCY/ODH/yAlStXcvnll/Mf//Ef3H333T2m09WuXbsGuggnzV133cXSpUv/qWoia2pquPzyy3nyySfxeDxUVlaSTqfZsWMHX/ziF1m+fPlAF/GY/PSnP+W9995jyJAhmKbJww8/zJQpU1i8eDGBQICf/vSnA11EqZt169Zx7rnnsnjxYiD7PvnII4+wbNkybr31Vl599dUBLuGxWbx4MUuXLuXWW29lzZo1XH311Vx66aX87ne/o7a2dqCLd0IIIYhGox2P//GPf7Bo0SJ27949gKU6PtFolK985SvMnTuXBx54gE2bNg10kU6qTCZDS0vLQBejk5CO6LzzzhOPPfaYEEIIXdfFmDFjxJYtW4QQQjz77LPinHPOGcjiHbMpU6aI5557bqCLcVKMGTNGXHXVVeLJJ58UoVBooItzQn3uc58T48aNE7NmzRL333+/2Lhx40AX6bjdeeed4pprrhHRaFRkMpku/8fuuusucd111w1wCY/N7NmzxYsvviiEEGLNmjVizJgxYunSpUIIId5++20xffr0ASzdiWEYhtixY4dYt26dWLt2bY/pdHPDDTeIW2+9VTQ2NorW1lYxfvx48dBDDwkhhPjJT34irrjiigEu4Ymxa9cu8etf/1p88pOfFGPHjhU33XSTeOGFF0QqlRrooh2Tjz76SJx77rnikUceEUII8eCDD4qKigoxfvx4MXHiRPHee+8NcAmPXWNjo/jjH/8orr76ajFmzBhxwQUXiMcee0xUV1cPdNGOSyqVEo888oj4+9//LoQQYsWKFeKss84SFRUV4jOf+Yxoamoa4BIKIWuS+6Guro4pU6b0uq20tJTW1tZTXKITw+12U1hYONDFOCmefvppJk+ezG9/+1vmz5/P7bffziuvvEIqlRrooh23RYsWsXz5cu644w42bNjAddddx4UXXsjjjz9+2raTX716NXfccQdutxtFUbps+/SnP83OnTsHqGTHJx6PU1xcDMCyZcuw2+3Mnj0bAJvNNpBFOyHWr1/PvHnzuPLKK/nMZz7DTTfd1GW6+eabB7qIR23r1q3cdttt5OXlsXz5cgzD4PLLLwdgwYIF7N27d4BLePzS6TS7d+/m448/pqqqCqfTicvl4sc//jELFy5k3bp1A13Eo/bf//3fBINBLr/8cuLxOM888wxXX301mzZt4uKLLz5tf/EFyMvL45ZbbuH555/nH//4B1dffTVvvfUW559/PjfeeCPPP//8afnZ9rOf/Ywnn3wSXdcBuPfeeyksLOTBBx+ksbGRhx56aIBLKNsk98vw4cN5++23Ofvss3tsW716NcOGDRuAUh2/a6+9lkWLFjFjxox/ig/sQ02bNo1p06Zxzz338M477/DSSy/xgx/8gP/8z//kggsu4Morrzxt2xZC55vmLbfcwt69e3njjTd4/fXX+dWvfsW0adO4+uqrueyyy7Db7QNd1H6x2Wx9vsm3traetn+f5eXlvPXWW4wYMYJXX32V2bNnY7fbMQyDp59+mtGjRw90EY/Lj370I3JycvjhD39ISUkJqnr617s4HA7S6TQAy5cvJz8/n4qKCgBqa2vx+XwDWbxjZpom7777Li+//DJLliwhHo8ze/Zs7r33Xs4//3wcDgeJRILbbruNu+++myVLlgx0kY/K+vXr+fnPf86oUaN44403SKVSXHvttaiqylVXXcWdd9450EU8bslkku3bt7N161b27t2Lx+MhEAjw0EMP8fOf/5yHH374tPpce+211/jud7/Lpz71KTZt2kRNTQ2PPPIIl1xyCQ6Hg3vvvXegiyhDcn/ccccdfOtb3yISiTB//nwURWHz5s288cYbLFq0iPvuu2+gi9hvh75RCCFYvXo18+bNo6KiAofD0WVfRVH4zW9+c6qLeEJZLBYWLFjAggUL2LJlCz/5yU944YUXeOGFFxgyZAg333wzN91002l7Q8Q/y5vm/PnzefTRRxk/fnzHjWyKotDa2srvfvc75s6dO8AlPDZ33XUXX/3qV1m8eDFWq5U77rgDgAsuuICmpqbT/v/Xrl27+OUvf8m555470EU5YWbOnMkvfvELduzYweuvv86NN94IwJtvvsmjjz562v4tzpkzh9bWVkaNGsWdd97JFVdcQUFBQZd9nE4ns2fP5umnnx6gUh47VVXRtGykWb58OT6fr+MX4HA4jMvlGsjiHTNd11m5ciUvvfQSb7/9NplMhjlz5vDjH/+Y8847r6OC4bbbbuN73/v/7J15XE2J//+fNyEqqZDtYx9bKUtJM1Ixg6zRYDDKFo1lpqlBlixjSUhFSpqKyNjNGEv2bcYSpQxjZ+ySFiVF6v7+6Nf5lmrcQqdz3edfdc7549Wje895n/fyes/gyJEjYktWmOTkZJo1awbkVtrU1dWFIW0dHZ1ykR1XBckK0KdPH7KysvDx8eH3338HcssC1atXx93dnYEDB4qsUHHS09ML/N6hQwcAsrOzC51TBv799192797Nnj17+Pfff2nSpAmurq506dKFkydP4ufnx6VLl1i2bJnYUhVGGW+a7u7ujBgxgj59+giVmZkzZ/LgwQN0dHSYOnWqyApLh5WVFbt37+bSpUu0adNGeAFwdnbGzMyMRo0aiSvwPalbty4ZGRliy/igeHh48NNPP+Hv70/Hjh2ZOHEiAIsWLaJhw4ZMmTJFZIWlo0+fPtjZ2WFoaPif1w0fPpxRo0aVkaoPR9u2bQkJCSElJYW9e/fSu3dvINehyd/fX3jWSY0vvviC1NRUWrRowQ8//EDfvn3R19cvcE3lypUxNzeX3MtNgwYNiImJwcTEhH379tGhQwe0tLQA2Lt3L40bNxZZIcjk8iJ8v1QUiVwu5/bt2zx//hxtbW2aNGki2QxkceTk5ChFyTQsLIzdu3fzzz//oKOjI1getWnTpsB1S5cuZePGjVy4cEEkpSXH3NxcuGna2dkVedME8I8OtqYAACAASURBVPf3Z+PGjZw6dUoElSXn1atX7Ny5k6ioKFJSUtDS0qJDhw7Y29sLN04p8/DhQxISEmjevDlyuRxNTU2xJb03e/fuxc/Pj6VLl2JsbCy2nI9KcnIyurq6YstQUQx37tzB2dmZu3fv0qhRI9atW4eBgQGdO3dGS0uL4OBgSdotenp6MmDAAKHlpzhSU1OpXLmyZFrsAHbu3MnMmTOpWrUq6enpBAYGYm1tzZAhQ/j777/x9vbG1tZWVI2qIPkTJzg4mAsXLhAQEABAVFQUU6ZMYdy4cUKZUYq0adMGKysr7OzssLa2Fspwb3PgwAH+/fdfxo0bV8YKS4+Xlxf9+/dXuptm3kto06ZNAUhMTOTatWt06tRJ0i9u+/fvx9vbm3v37qGmpsbWrVvx9/dHU1MTT09PKlasKLbEEtGuXbsCw5V5meQKFSoU2TseExNTZtpKS0mHr6tXr/6RlHw8unfvXmgoNg+ZTEbVqlVp2LAhw4cPx9TUtIzVfRjkcjmJiYno6+sLf+u1a9do1qyZZBNaNjY2TJ8+ne7du4st5aNw7tw5Lly4QIcOHYRsv7+/P+bm5piZmYmsTtVuoRAJCQl4enpy/PhxMjIylGbpRlBQECtXrixQWmvYsCF9+vRhyZIlyGQyhg0bJqLC0nPy5EmFHmRSvPFMmzZNoeuqVav2kZV8OB4/foyTkxOZmZkcOnQIyHUZGDduHG3btiUgIAA9PT2RVZacvXv34ubmxsCBA3F1dcXFxQWAr776ip9//pn69esLx6TC6NGjiw22pEqnTp1K9DdJ8X7fvXt3wsPD0dTUpEuXLujr65OcnMzJkydJTk7myy+/5MGDBzg4OBAcHMwXX3whtuQSs2/fPqKjo/Hw8AByh/mWLFnCuHHjsLGxEVld6Xj58qVSVJ2Ko2XLlrx580YIkB8+fIiBgQGtWrUSWVkuqiBZAebOncvZs2cZNGgQtWvXVpoHxJYtW5gyZQqOjo7CMQMDA6ZMmUKNGjVYt26dZIPkY8eOFXtOJpOhqalJgwYNaN68edmJ+kB07dq12M+gmppagYyQubl5GasrHZ6enkBuBiGPLl26sGfPHlxcXFiyZAmLFy8WS16pCQgIwMHBgenTpxdY/jJw4EBSU1NZv3695ILkyZMnCz8/evSIGjVqFJlBfvXqlWSCyUWLFgnfqZSUFLy9vbG0tKRHjx7UqFGDlJQUjh07xuHDhyXbH5+amoqxsTHBwcFUqVJFOP769Wu+++479PT08PPzw8PDA39/f8kFyVu3bsXDw0Ow6wPQ1dXFwMCAiRMn4uPjQ48ePURUWDrGjRvHsmXLyMzMpFGjRkW21kmxsgFw/fp1Ro8ejYaGhpAcefDgAfPnzyc4OJjQ0FDq168vqkZVu4UCtG3blrlz52JnZye2lA9K27ZtWbVqVZE3w1OnTuHs7CzZ7T6Ghobk5OQAFMj85z0I5XI5MpkMMzMzAgMDJfWm7uPjQ1hYGNra2lhZWVGjRg2SkpI4efIkz549o0ePHjx69IiLFy+yevVqSax0Njc3x8vLq0iXhEOHDuHh4cHp06fLXth70qZNG4KCgvj888/Jzs7G0NCQ7du3Y2hoyNmzZ3FycpLsdwygVatWbN68uch+5KioKJycnIiLixNBWekZP3489evXF7KR+Vm6dClxcXFs2LBBBGXvh6mpKcuWLSvyO3b8+HHc3Nw4f/48p06dYtKkSZJok8mPra0ttra2fP/994XO+fn5cfjwYXbt2iWCsvejU6dOpKWlCc+zopDKy+jbjBkzhszMTFauXFmgUvj8+XMmTpyIjo4Oq1atElGhKpOsEFpaWpIs9b6Lpk2bsnfv3iKD5MjIyHIxWVpagoKCcHFxYeLEifTs2VMoLR46dAgfHx9mzJhBrVq1mDNnDj4+PsyaNUtsyQqTmJhI27ZtWbNmTQHbvtevXzNhwgS0tbXZtGkTs2fPJiAgQBJBMlCsS0JOTo7gWys16tatS3R0dJEe6xcvXhQWjUiJ2bNn8/TpUyD3ZdPLywttbe1C192+fVuSg25nzpwRZjTexsLCQpIBMuR6kcfHxxd57smTJ0LPrlwul1yfPORWNYrrYe3YsSOhoaFlrOjDoGh7nRSJjY1lxYoVheIrHR0dnJycyoWTjCpIVgB7e3vWrl2LhYWFJG8exTF+/Hi+//57Hj9+jLW1Nfr6+iQlJXHs2DFOnTol6Q1FXl5eTJgwoUC/tYGBAcOHDyc7O5uwsDD++OMPJk+ezPLlyyUVJEdGRuLt7V3I17pSpUqMGDECNzc35s2bh62tLX/88YdIKktGp06dWLlyJSYmJtStW1c4/vjxY1auXFlkkCkFhg8fzpIlS5DL5YLHenx8PP/88w+rV69mwoQJYkssMVZWVqxdu1b4PSMjo9BgZYUKFWjZsiUjR44sW3EfgDp16nDs2LEikwd79uyR7PKonj17smzZMjQ1NenWrRtVqlQhIyODI0eOsHz5cnr06EFGRgabN29+p01ceeR///sfJ0+eFDZa5uf06dPUrl1bBFXvT/369WndunWR1c7U1FT++usvEVR9GDQ0NIQX7rdJSUkpF62tqiBZAV69esXff/+NpaWlUi3d6N69O35+fqxevZpFixYJx5s3b46vr68k+7fyuHfvXrHbzBo3bszdu3eB3BvQ8+fPy1Lae1OpUiUePXpU5LlHjx4JGaHs7GzJbKqbNm0aQ4cOpXv37nz22Wfo6emRnJzM9evXqVGjBu7u7mJLLBUODg6kpqYSHBxMYGAgcrmcCRMmoK6uzogRIxgzZozYEktMt27d6NatGwAjRoxg7ty5giOJMuDs7Iy7uzv37t3D0tISXV1dEhMTOXjwIDExMaxcuVJsiaVi2rRpJCYm8tNPPyGTyahcubKwrKFXr164u7tz5MgRTp06Jcmsa95nMS0trVDSZ/v27ZJKhOTHwcGh2JamS5cuMW3aNNFt0kqLtbU1Pj4+NGzYkPbt2wvHY2Nj8fX1pWvXriKqy0XVk6wAI0aMeOc169evLwMlH5aYmBjatGlDxYoVefXqleBNK6X+3OIYNGgQNWvWxN/fv1CWa9KkSTx8+JCdO3eydetWgoODOXDggEhKS86CBQv47bffmDVrFl9++SVaWlq8ePGCw4cPs2DBAnr37s3UqVOZOnUqGRkZhISEiC1ZIV68eMH27duJjY3l+fPnaGlp0b59e+zt7Yss50uJtLQ0YmNjSUlJQVtbG2NjY6Vs4VIW9u3bR3BwMFeuXBHmF0xMTPj+++8lW9XI49atW0RFRZGcnIyBgQHt27cXWuuSkpKoVKmSZH3Jw8LCCAoKErKQcrkcHR0dnJ2dJbUg5fvvv+fOnTtA7lbLBg0aFGnlGR8fj76+Pvv27StriR+E58+fM3LkSK5evYq2traQHElNTaVVq1aEhoaKPpSoCpI/YczMzJg1axb9+/cXW8oH58yZM4wbN446depgbW2Nnp6ekFV4/Pgxq1evRktLi9GjR+Pg4FDksEd55fXr10yfPp09e/Ygk8lQV1fnzZs3yGQy+vbty9y5czl8+DDz5s0jNDS00AIVFWVLTEwM58+fF7y4r1y5QkhICKNHj6Z169Yiqys5+d0D3oVMJpPksFQemZmZpKamoqOjIxm/8XeRlpZGXFwcL168oHr16rRp00YpEiN5KMPSr+joaLZt2wbkLtywsrIq9FKtpqZGtWrVGDJkiKQ3d2ZnZ3P06NFCyREbG5ti9xuUJaoguRguX75M06ZN0dDQ4PLly++8Xoo9XF27dmXKlCmSLdW8i2vXrrFmzRrOnTsnZE1MTU1xcnKiadOmnD59mtjYWMaPHy/JZRV37tzh/PnzJCcnU6tWLdq3b0+DBg2A3O1gGhoaBayeyjs3btwgKiqKrKwswZFELpeTkZFBbGwswcHBIissOYcOHeKHH36gQ4cOhIeHA3D16lU8PDy4du0av/zyCx07dhRZZclwd3cvUa9gnr1feUaRe3x+pHi/h1yLxV9++YVXr14J37FKlSoxevRoyVkRQm7farVq1VBTU1NoIYzYWcnSMH36dCZMmCDJbYHKgCpILoaWLVuyZcsWjI2NadmypVC6efvhkHdMihYs4eHhBAYG0q9fv2L9F6W4bONTISUlpUBGyNjYWLJtCZs3b2bu3LnC9yn/bUlNTY3PP/+cX375RUSFpcPOzg4TExPmzZtX6Nzs2bO5evUqW7ZsEUGZivzk3ePfhZTv9xs2bGDRokWMHDmS3r17U6NGDRISEtizZw/r169n+vTpktuymt+CUJH/oRT/b5D7uUtPTxfaYPbt2ycM3Ddp0kRkdSUjr+e/UaNGODs7/+e15WHeS/xcdjklPDxcGEbJywApG3nDeuvWrSvyvFQfBnkkJCQQGhrKuXPnhECyQ4cOODg4YGBgILa898LPz4+QkJACWVd1dXVGjRqFm5ubyOpKTlhYGNbW1nh5ebF69WrS0tKYOXMmJ06cYPr06SUq8Zcn/v3332KHDqXkPvJfvHnzhn///ZfXr18XqABkZmZy4cIFnJycRFb4bpT1Hp+f9evXM3bsWFxdXYVjBgYGGBkZUalSJSIiIiQXJC9atEjIsOZfCKNMXLt2DWdnZ/r168ePP/7I4sWLWbt2Lerq6vj6+hIUFFSko0d5JT09XVislJ6eLrKad6MKkoshfwm0Y8eOpKWlcenSJeHD+PDhQ06dOoWtra1khxwOHz4stoSPxt27dxk2bBiZmZlYWFigr6/Ps2fP+PXXX9mxYwebNm2SrJXTunXrCAoKYuzYsfTq1YsaNWrw7Nkz9uzZQ2hoKAYGBnz77bdiyywRDx48YObMmVSrVg0TExN8fHzQ0NCge/fu3L9/n3Xr1kmyd75mzZrExcXRqVOnQucuX74sSR/h/Jw/f54ff/yRZ8+eFXm+SpUqkgiSi2t5efPmDcnJyejq6paL/sj34cmTJ0V+DiF3mU9YWFgZK3p/BgwYIPw8cOBAEZV8PJYuXYqenh59+/bl5cuXbNq0CXt7e+bPn8/06dPx9fWVVJCc3+TgXYYH/7VApayQ9re+jJDC6sTSUK9evf88n5WVVUZKPjxeXl7o6+uzbt26AoFIUlISY8aMwdvbmxUrVoiosPRs3LgRJycnfvzxR+FYjRo1aNmyJRUqVCAiIkJyQXKVKlWEIKRhw4bcv3+fzMxMNDQ0MDY2Lna5Q3nH3t6eVatWIZfLC9lSBQYGSiKA/C+WLVtGtWrVmDNnjjCgZ29vz8mTJ9m4cSNr1qwRWWHpyLOgio6OJjs7m61btxIWFka9evUKfO+kRIMGDYiJiSnSnSM6OppatWqJoOrDsmXLFmrVqoW1tbVgj/bo0SN69OjBnDlzJDWjkceFCxfw8fGhWbNmHDhwgFevXjFo0CDU1NQYMGDAO1sWyjPdunVj1apVtGzZstC5ixcv4uTkxNmzZ0VQ9n+ogmQF8PLyomHDhgX8Mc3NzTl58iQTJ07E09NT9NWJpSErK4stW7YQFRVVoFQKuQsCrly5QlRUlIgKS8+ZM2dYvHhxoUydnp4ezs7ORa6clQpPnjzB3Ny8yHNS3SzVrl07tm7dirm5OU2aNEFdXZ0TJ07QvXt3rl+/LllngXHjxvHs2TNWrlyJn5+fcLxChQp88803klwmkp+rV6/i6enJl19+yYsXL1i/fj1WVlZYWVmRk5NDQECA5D6Pp0+fxsnJCVNTU3788UeWLl0K/J9/fPXq1SVlJ5bH8OHDWbBgAXK5nB49eggVqMjISNasWSMph5+iCAgIwN/fnylTpmBtbc3MmTN5+fIlY8aMYdOmTXh7e0vSK1lNTU1IIBw/fhwdHR3atm0L5FqoVa1aVUx5JWbjxo2CP/fDhw/Zvn17gQVSeURHR6syyVJBCqsTS8PSpUsJDw+nRYsWJCYmUrlyZfT09Lh+/TpZWVmSfoBraGgU61ghk8l48+ZNGSv6cDRo0IDz588XmRE6d+6cJPutJ02ahKOjI05OToSEhDB48GCmTZvG+vXriY2NLVBWlRJqamrMmjWLyZMnExsbS2pqqtL5JNeoUQPIXdJz8+ZNcnJyUFNT46uvvpJkf/yyZcvo1asXS5Ys4c2bNyxZsgTIfeHJzMxk8+bNkgySv/nmG+7evUtQUFCBykyFChUYNmyYYFEoVXbu3ImLiwujRo3ixo0bXLt2jYULF2Jvb0/9+vVZtmyZJIPktm3bEhISQkpKCnv37qV3795A7hCiv78/HTp0EFlhyUhOThYSjjKZrMiWCzU1NbS1tcuF44oqSFYAKaxOLA379u3DyckJNzc3goKCuHTpEitXriQ+Ph5HR0dJB5JmZmYEBARgZmaGjo6OcDwlJYXAwEDJ2W7lZ8SIEcybN4/s7Gx69uyJvr4+iYmJREZGEhISUi5uLCXF2NiYvXv3cuPGDSDX9khHR4e4uDicnJwk/wDX0dHByspKbBkfnGbNmhEVFYWZmRlNmjTh9evXXL58mTZt2pCamipkjKTEjRs3hJaKt+/t5ubmknRZyWPatGk4OTkRExNDWloaOjo6mJiYFOlsJDXi4+OFrW1Hjx5FTU0NGxsbIHfV+IsXL8SUV2pmzJiBs7Mzrq6uNGrUiMmTJwPg5OSElpYWU6dOFVlhyZg4cSITJ04ECrqIlVdUQbICSGF1YmlITk6mc+fOQO6HNSIiAsideHZ2diYwMFCSARfA1KlT+frrr+natSvm5uZCafHs2bOoq6uzbNkysSWWmsGDB3P//n1CQ0ML9HxWqFCBESNGMHbsWBHVlZ46depQp04dIDeTMGnSJJEVvT8vX74kMDCQ48ePk5GRUah8KJPJhDkHKeLg4MC0adNISUlh5syZWFpaMmXKFHr37s3OnTtp166d2BJLjL6+Pjdv3hTujfm5deuW5ANKPT09vvzyS7FlfHBq167NzZs3MTU1Zf/+/RgZGQnVmlOnTr1zBqe80rhxYyIjI0lMTERfX194cQsJCaFZs2aSW5SSn6tXr4ot4Z2ogmQFmDp1Kv/88w/Dhw8vcnXitGnTxJZYKnR1dYW360aNGpGQkCBMctetW5f4+HiRFZaeevXq8dtvvxEWFsb58+e5efMmOjo6DBo0iJEjR1K7dm2xJb4Xbm5ujBkzRijh6+joYGxsLGm3hJs3bxIaGkp0dDQpKSno6+vTqVMnxo4dW2TPmhT4+eef2b17NzY2NhgYGEhyac1/0a9fPypVqsS9e/eAXBsuV1dXfvnlF9q0acOcOXNEVlhy7Ozs8PPzQ1tbmy5dugC5W8FOnTqFv78/9vb2IitUHDMzM4UrnTKZTPQhqfdh8ODBLFq0iPDwcG7fvs3ixYuB3BXPBw8elFSrRVFLUtTV1Xn+/LlwjYGBAWlpaYA0l6TkcezYMWEuKo+8JVJxcXHs2bNHRHWqZSIKU95XJ5aG6dOnc/XqVZYsWUKzZs2wtLTkm2++4bvvvmPhwoUcP35cqW3iVJQfzpw5g5OTE3p6elhaWqKnp0diYiInTpwgIyODiIgIWrRoIbbMEtOhQwd+/PFHybmNfMpkZ2cza9Ysdu7cKSy2UVNTQy6X89VXX+Ht7U3FihXFlqkQPj4+JWoHlGrlMI/ff/+dmJgYTE1NBW/1WbNmYWZmJikLyU9lSUpAQAArVqygWrVqZGVlUbFiRdTV1UlKSkJNTY2vv/6an3/+WVSNqiD5EyYhIYExY8ZQvXp1wsPD2bp1K7Nnz0ZNTY2cnBzc3d1xdHQUW6bClMTnUyaTMXLkyI8n5gPTrl27EmWEoqOjP7KiD4u9vT36+vqsWrWqQADy+vVrvvvuO7Kzs1m7dq14AkuJubk5Pj4+RQ5ZKgtv3rxhz549nDlzhoSEBGbNmkV0dDRGRkaSfLHJ49atW0RFRfH8+XO0tbXp0KFDkVZVKsoHx44do0uXLkpRrdm5cyfW1tbo6uqyY8eOd977pTrY3LVrVywsLFiwYAErV67k3r17LFu2jMuXLzN+/HjGjh0r+nNaFSQXg9RWJ5YWuVxOfHy80H4QFRVFXFwcbdq0KdZ4vrxSkgeY1LYJrly5skQZIan185qYmLBy5UqhvJ2f48eP4+LiwoULF0RQ9n7MnTuXtLQ0vL29xZbyUUhOTmbMmDFcu3aNpk2bcuPGDbZt28bKlSs5e/Ysa9euxcTERGyZKv4/SUlJhIWFFdpC+u2331KzZk2x5b0XrVu3RldXl969e9O/f38MDQ3FlqTiHRgZGREcHIyFhQWHDh3C09NTqF7/+uuvbNy4UfStpNLsEygDpLY6sbTIZDJq167Nw4cPSUhIwMjICENDQzQ1NcWWVmKkMARQWvImmpWVpk2bcunSpSKD5Dt37kh26KZ+/foEBQXRt29fjI2Ni1xmIKVeybfx9PTkxYsXHDhwQFhxDLBixQqcnJxYvnx5sWvvyxN9+/bF29ub5s2bv3MFukwmExanSIn79+8zdOhQ0tPTMTc3p1mzZjx79kyoIm7evFlY8SxFjh49yh9//MHu3bsJDw+nSZMm2NnZ0bdvX2EgWKrs3buXuLg4UlNTC52TyWQsWrRIBFXvj7a2tuCi1bhxYx49esSLFy/Q0tKiWbNmPHjwQGSFqiC5WEqyOlHK7N+/H29vb+7du4eamhpbt27F398fTU1NPD09JdN7VxwpKSnExcXx4sULdHV1MTY2luwa8fwkJCQQGhpaKCPk4OAgSZ9kNzc33NzcePXqFba2ttSqVYvk5GSOHTvGqlWrmDZtGpcvXxaul0qWaOPGjWhra5Oens7p06cLnZfJZJIOko8ePcq8efOoV6+ekFQAqFSpEqNHj5aMT7KRkZHwAmNoaChZW8//wsvLC11dXX7//fcCDh2JiYmMHj2apUuXSnYLKeQOso0dO5axY8dy48YNdu3axfbt2/H19cXU1JT+/fvTs2dPySWAFixYwIYNG6hdu3aRA3pS/qyamZkREhJCmzZtaNiwIVpaWuzdu5fBgwcTFRWFtra22BJV7RbFkTdRqihSnC7du3cvbm5uDBw4EEtLS1xcXNi+fTvXrl3j559/ZuTIkZIe5PDz8yMkJKTA1Ky6ujqjRo2SzMO7KO7evcuwYcPIzMzEwsICfX19nj17xunTp6lUqRKbN2+mYcOGYsssEflbZfLf9PNuT3nH5HK55FpllJn27duzdOlSunXrRnZ2NoaGhmzfvh1DQ0MOHz7M1KlTJdcfr6x06NCBRYsW0aNHj0Ln9u/fj4eHh2Q3rBbF69ev+euvvwgJCeH8+fMAVKlSBXt7e1xcXCSTLOnUqRNDhgyR7Dr0/+LOnTuMGDGCRo0asWHDBgIDA1mxYgV16tTh8ePHODk54erqKqpGVSa5GDp16lSiNzQpPrQDAgJwcHBg+vTpBbJAAwcOJDU1lfXr10s2SF63bh1BQUGMHTuWXr16CT7Je/bsITQ0FAMDA8k6Dnh5eaGvr8+6desKWL4lJSUxZswYvL29JZcRCg8PF1vCR+XRo0ecPXuWhIQEBgwYwOPHj2nRooVk123n0alTJ1atWoWpqakQdMhkMrKysggPD8fMzExkhYrh5uaGi4uLpNsN3kXlypWL9dRVU1OT9PKoPORyOadOnWL37t0cOnSIjIwMLCwsWL58OZaWlpw8eZIFCxZw584dQkJCxJarEBUqVJD08qv/onHjxhw4cIA7d+4A8N1331GnTh1iY2Np06YNAwcOFFmhKkgulkWLFglBckpKCt7e3lhaWgo771NSUjh27JiQLZEid+/eZcaMGUWea9WqFQkJCWWs6MOxceNGnJycCrx916hRg5YtW1KhQgUiIiIkGySfOXOGxYsXF/JE1tPTw9nZGQ8PD5GUlR5lfQjk5OSwcOFCNm3aRHZ2NjKZjC+++AJfX18ePnxIeHi4JNtj8nB3d2fo0KF89dVXtG3bFplMhp+fH7dv3yY1NZWNGzeKLVEh9uzZg6OjoxAk5+Tk0L9/f3x9fWnatKnI6j4MpqamBAYGYm5uXqCMnZaWRmBgIKampiKqe38WLFhAZGQkz549o3nz5kyYMIG+ffsKa9MBevXqxfXr1yXRJ5/H0KFDCQ8Pp3379kXONEidqlWrFmifs7Ozw87OTkRFBVEFycWQ/w1m/PjxDBkypFDw0adPH5YuXcqePXsYOnRoWUt8b+rWrUt0dHSR9lQXL16U9LDDkydPMDc3L/Jcx44dCQ0NLWNFHw4NDY1ibY5kMpmkMkLp6ens2rWLFi1aCNssL1++zOLFi7l9+zafffYZrq6u5Xpt6X/h7+/Pjh078PT0pHPnzsJ3berUqUyaNAlvb2+WLFkissrS06BBA3bt2sXatWuJioqiQYMGPHv2DBsbG0aNGiXZe4hcLufGjRtkZmaKLeWDMWXKFGELaV6bVmJiIqdPn0Ymk0n6cwgQGRlJ7969GTBgwH86HZmbm0vqxWfkyJHY29vTpUsXGjdujIaGRoHzMplMUkF/fl69esW6deu4ePFikUOJIH6VURUkK8CZM2cICAgo8pyFhQUbNmwoY0UfhuHDh7NkyRLkcjlWVlbIZDLi4+P5559/WL16NRMmTBBbYqlp0KAB58+fL/IF4Ny5c5LO3pmZmREQEICZmRk6OjrC8ZSUFAIDAyWTlU1MTGTYsGHcu3cPd3d32rdvT3x8PI6OjshkMgYOHMi///7LiBEj2Lp1K82bNxdbconZvn07rq6u9OvXr0BLU8uWLfnhhx+ErWBSIv8AZR49e/akZ8+eBY4lJSWRlJQkmSFLZed///sfv/32GyEhIURHR3Pp0iWqV69O//79GTVqlGQdZPI4fvy4QiuaLSwsykDNh2PmzJncv38fY2Njya9Ef5tZs2axe/du2rdvX27nulRBsgLUqVOHY8eO8cUXXxQ6t2fPHskNSeXh4OBAamoqwcHBBAYGIpfLmTBhAurq6owYMYIxY8aILbHUjBgxsCi8nAAAIABJREFUgnnz5pGdnU3Pnj2FrElkZCQhISGS7bWG3CxkXkbI3Nxc6Lc+e/Ys6urqLFu2TGyJChEYGEhWVhZ//PEHzZo1AyAkJIT09HQCAwOxtrYGctfK+vv7S67PGnJfXBo3blzkOT09PWEtvJSwt7dXaF5DNWRZ/qhTp46k3VT+iwoVKnD9+nWioqJITU0lJyenwHmZTMbEiRNFUld6Tpw4gYeHhySr1e/i6NGjTJ8+HQcHB7GlFIsqSFaAvMUi9+7dw9LSEl1dXRITEzl48CAxMTGsXLlSbImlZtKkSTg6OhIbG0tKSgra2toYGxujp6cntrT3YvDgwdy/f5/Q0FDWrFkjHK9QoQIjRoxg7NixIqp7P+rVq8dvv/1GWFgY58+f5+bNm+jo6DBo0CBGjhwpLIYp7xw9epTx48cLATLA4cOH0dfXFwJkgH79+jF79mwRFL4/LVq0YOfOnXTu3LnQuYMHD0oyO/52+TM7O5tRo0Yxb968Yl8IVJQP7t69K6x6LyqIHD9+vEjK3p+tW7cyZ84ccnJy0NTULNSSJtUguVq1atSvX19sGR8FbW3tcp9kVAXJCmBnZ0flypUJDg5m4cKFQobExMSE4OBgya+c1dbWxtLSEsjtRT537hzm5ubltvyhKG5ubowZM4a4uDieP3+Ojo4OxsbGhQbepIiBgQHu7u5iy3gvnj59SpMmTYTfHz16xMOHD+nTp0+B66pXr05aWlpZy/sg/PDDD4wbN474+Hihpenw4cOEhYWxd+/eYtu4yjNvt/PktZHkLSKSKrt37xbs6nJycpDJZPzxxx+FbNGkttI+jx07djBz5kzkcjkaGhqFqgFSD5KDgoKwsbFh0aJFBdrQpI6joyMBAQG0bt1a6dotxo8fT0BAAC1atCi3yR1VkKwgtra22NrakpmZSWpqKjo6OpK3b3r06BGurq507tyZSZMm8csvv+Dt7Y1cLkdHR4ewsDBat24ttkyFSUlJoVq1aqipqRXwuX57LW7eOSm/BChDWVFTU7PAsMaZM2cE94f83Lt3T7L/qy+++IJffvkFX19ffHx8kMvlBAQE0LJlS/z9/QtkzFWIS1EDQmvXri10TKpBcmBgIF27dmXhwoWS/T79F0+fPmX+/PlKFSADxMbGcv36dSwtLaldu3ahZShS3QAJ0K1bN8LCwrCxsUFPT6/IocRDhw6JpC4XVZCsIG/evGHPnj2cOXOGhIQEZs2aRXR0NIaGhv85SVue8fLyIjExkU6dOvH69WvWrFmDjY0Ns2bNYs6cOSxZsqTIh0R5xcLCgs2bN2NsbKyQz7VUeyWVpaxoZmbGzp076datG5A75FaxYkVsbGyEa7Kzs9m0aRNt27YVS+Z7Y2FhgYWFBZmZmTx//hwtLS3Jbf1SdpR5pX0e8fHxLFiwQCkDZMitZNy6dUtyg3nvQlNTk+7du4st46MwdepUnj59Sq9evcptllwVJCtAcnIyY8aM4dq1azRt2pQbN26Qnp7OwYMHWbBgAWvXri2UrZQCZ86cYcGCBZiamnLixAnS0tJwcHCgbt26ODo6MnnyZLEllohFixYJPqf5fa6VDWUpKzo7OzNs2DD69u2LXC7n5s2bjBs3TniIR0ZGEhERweXLl4v18y6PXL58maZNm6KhoVGkE8SzZ88K/C7lFgUV0qF169Zcu3atWGtMKZL/+zVgwAAWL15MZmYmbdu2LdJTWIrfNU9PT7ElfDRiYmJYuHBhoRa78oQqSFYAT09PXrx4wYEDBzAwMMDIyAiAFStW4OTkxPLlyyXpU5iVlSUEWcePH6dq1aqCofybN2+oVKmSmPJKzIABAwB4/PgxnTp1om7dugDcv3+fdevWcffuXRo2bMjQoUMl5ZP5NspSVjQ0NOTXX39l/fr1JCUlMXToUIYPHy6cX7BgARUqVMDHx0dSmWR7e3u2bNmCsbGx4ASRN8eQH2Vzf1DWl1JlYerUqbi6uiKXyzExMSlU2gYkVxXN//3KY9myZUr3XVPGSjbkztaU9wUpqiBZAY4ePcq8efOoV69eAa/TSpUqMXr0aNzc3ERUV3pat27Nli1bqFy5Mnv27MHKygp1dXWSk5MJDg4WXgakQlpaGq6urvz5558AWFlZCRvB8vrIT548ybZt21i3bp0ks/+gXGXF1q1bF5sp2b59OzVr1ix2cUp5JTw8XHgJE9sI/2Pg7Oxc5PFFixYJq6nzkMlkBAYGloUsFe9g2LBhQG7SR1mCSGX8fr2NslayASZMmICvry+1atXCyMioXL5oq4JkBcjOzi52SO/NmzcF3mKlxJQpUxg3bhy7d+9GR0dH6GPt3bs3AL/88ouY8krMsmXLuHr1Kp6enmhpaREUFMTQoUMxMDDgt99+w8DAgAcPHjBp0iRWrlwpqb/vUygrvo1UF77kd3/o2LEjaWlpXLp0SXipefjwIadOncLW1rZQUCkF0tPTCx0zMzMr9pyK8kFYWJjYEj44HTt2JCIigoiICB49ekT9+vUZMmQIw4cPl9zLdXEoayUbYP369Tx8+JDBgwcjk8mKHNzLc5wRC5lcqhFeGTJhwgSePHlCWFgYWlpaGBoasmPHDj777DPGjh1LlSpVWL16tdgyS8WLFy+4desWzZo1E4aJTp48ibGxseTK+V26dOH777/n66+/BnIdIPr168fy5cvp1auXcN2BAwfw8PDg7NmzYkktMS1btizwlp33tVWWjJCycv36dUaPHo2GhoYwpX327FmcnJyoXbs2oaGhSuuBqkLFxyYiIoL58+fTuHFjWrZsyd27d7ly5QojR45k2rRpYsv7IJiZmTFv3jx69epFdnY2hoaGbN++HUNDQ44fP46bmxvnz58XW2ap8Pf3f+c1kyZNKgMlxaPKJCtAXsn+q6++om3btshkMvz8/Lh9+zapqals3LhRbImlRktLixYtWnDx4kUSEhLo3LkzjRs3llyADLlrjhs1aiT8nvfz2+tWDQwMJOe7+ymUFZURLy8vGjZsWGDhkLm5OSdPnmTixIl4enqyatUqERWqAApYRiqCVB0iTp48ydmzZ0lNTS1UAZXJZPz8888iKSsdW7ZsoV+/fnh5eQkJA29vbzZs2MBPP/2k0Jrq8o6yVrJB/ABYEVRBsgI0aNCAXbt2sXbtWqKiomjQoAHPnj3DxsaGUaNGUadOHbEllprQ0FACAgJ48eIFMpmMbdu24evrS3p6OkFBQWhra4stUWGys7MLDBvm3SDV1Qt/zKV2Y3l7gYOylfCVldjYWFasWFFog6WOjg5OTk5MmTJFJGUq8qOIZWR+pFipWbNmDcuXL0dLSwsDAwOlaEe4e/cu7u7uBf53w4YNIzg4mPv37xdImkiVTp06sWrVKkxNTYV7u0wmIysri/DwcKHVSaqkpaWxfv16Tp8+zbNnz1ixYgVHjx6lVatWwpIzMVEFyQqir68v2QG94tiwYQPe3t5MnDgRS0tLBg0aBICDgwNTp07F19cXDw8PkVWqeJuiSvgPHjxg/vz5BAcHq0r45QgNDQ2ePn1a5LmUlJRyOajyKaLMlpF5bNiwga+//pq5c+cWmTiQIpmZmYU8x2vWrAnAy5cvxZD0wVHmSvaDBw/49ttvefnyJR06dOD8+fO8fv2aa9eu4efnR0BAAFZWVqJqVI5vykfgwIEDJbpeimbf69atY+LEiUyYMKGAa4elpSWurq6sXLlSckFyaGgoNWrUAP4vWxwSElIgk/e2T63UUJYSfkkHiUaNGvWRlHw8rK2t8fHxoWHDhrRv3144Hhsbi6+vL127dhVRnYo8Bg4cKLaEj87z58/p27ev0gTIxZH3siO1amFxKHMle+HChdSsWZO1a9dSuXJlYSjR29ubN2/eqILk8sz3339f5KBUUUh1UOrJkyfF+s/Wr1+/xH16YlO3bl0uXrxY6FhsbGyha6V8Y1GWEr6Xl1eB3/P8TitUqICuri6pqam8fv2aihUroqOjI8kgeerUqfzzzz8MHz4cbW1t9PT0SE5OJjU1lVatWinNcJGycePGDaKiosjKyhLu/XK5nIyMDGJjYwkODhZZYckxMzMjJiZGqZaJ/BfKVBlQxko25C40W7p0KZqamgUSdQDffPMNEyZMEEnZ/6EKkouhXr16PHz4kBYtWtCrVy9sbGyUbpVso0aNOHLkCJ9//nmhc2fOnKFhw4YiqCo9R44cEVtCmaAsJfz8q4D//PNP3N3dmTNnDt26dRP6JU+dOsXMmTNxcXERS+Z7oaOjw7Zt2zh69CixsbHCWur27dtjY2Oj9Fk9KbJ582bmzp0rOMXkT5CoqakVeb8srxw+fFj4uXPnzixfvpzExETatWtX5DKRvBXxUsLLy6vI2Zm3fbul7NmdlJREXFxckQOXAHZ2diKoen8qVarEq1evijyXkpJSLhaaqe7QxXD48GEuX75MZGQk27ZtIyAggM6dO2Nra6s0AfP48eP56aefSEtLw8rKCplMxt9//82BAwcIDQ1l3rx5YktUUQTKWMKfP38+P/74I1999VWB459//jkuLi74+vrSv39/kdS9HxUqVODLL7/kyy+/FFuKCgUICwvD2toaLy8vVq9eTVpaGjNnzuTEiRNMnz6dvn37ii1RYSZOnFgo0N+wYQMbNmwodK0UK6LF+XMrk2/3kSNHcHV1JTMzs8jzMplMskGylZUVvr6+GBoa8r///Q/I/XtSUlJYs2YNnTt3FlmhyidZYS5fvsz+/fvZv38/T548oXPnzvTs2ZOuXbtKOmDeuXMnPj4+BTKT1atXZ/LkyQVWBKsoPzx//pyRI0dy9erVIkv4oaGhkrOoateuHcuXL8fGxqbQuQMHDjB9+nTRTeVLQnp6Ort27aJFixbCi8zly5dZvHgxt2/f5rPPPsPV1RVjY2ORlap4GyMjIwIDA7G0tGT//v34+PgQGRkJ5M437Nmzhx07doisUjHu3btXousbNGjwkZSoKC19+vRBW1sbDw8P6tWrV6QriZRcqPKTlJTEiBEjuHv3Lg0bNuTWrVu0atWKBw8eoKOjQ0REhOhLpVRBcim4cuUK+/fv5+DBgzx48IDOnTtLYlCqOORyOXfu3CElJQVtbW2aNGmiFP6Sykx2drZSlfDHjBlDcnIyq1evplatWsLxe/fu4ezsTNOmTQsMKpZnEhMTGTZsGPfu3cPd3R1HR0fi4+Pp3bs3MpmMgQMH8u+//3LmzBm2bt1K8+bNxZasIh9mZmasWLECCwsLrl69ir29PdHR0WhoaHDu3DmcnZ0l9cKWn4cPHxITEyNkw+/cucPu3bsZMmRIge+divKDsbExgYGBfPHFF2JL+Si8evWKnTt3EhUVRUpKClpaWnTo0AF7e/tyYWcqvadpOaB+/fo0atSIxo0bc/fuXU6dOiW2pPdCJpPRpEmTAsfi4+NZunQpy5YtE0mViuKIiIigT58+SlXC9/Dw4Ntvv6Vr1640b94cXV1dkpKSuHHjBvXq1WPWrFliS1SYwMBAsrKy+OOPP2jWrBmQm4FMT08nMDAQa2trIHc42N/fnxUrVoioVsXbtGvXjq1bt2Jubk6TJk1QV1fnxIkTdO/enevXrxe72KG8ExMTw9ixY6lbt64QJKekpLBlyxYiIiJYt24dLVq0EFmlirdp0qRJsTMoykDlypX55ptv+Oabb8SWUiSqTLKCJCYmcujQIQ4cOMDZs2epXLkyXbp0oUePHlhZWVGlShWxJSrEmzdv8PHx4bfffgOgX79+uLm5CdnHzMxMwWs3MzNTcj1qnwKmpqa8evUKS0tL7OzssLa2LhcDDu/Lixcv2LFjBxcuXCA1NZXq1atjbm6OnZ2dpP6+bt26MW7cOIYMGVLg2KtXr/jzzz+FY4cOHWL27NmSf8lWNi5evIijoyPt27cnJCSEhQsXsm3bNoyMjIiNjWXAgAGS20wHuUs2dHR08PPzK/B9ev36NS4uLmRkZJTYllHFxyG/s9Tp06dZuHAhM2fOLHbgUmrtdfm5fv06UVFRpKamkpOTU+CcTCZj4sSJIinLRZVJ/g8eP37MgQMHOHDgABcuXEBTUxMbGxv8/PywtLSU1IM7Dz8/P0JCQmjfvj2ampqsW7cOTU1NJk2axOnTp5k5cyaPHj3is88+U9lTlVNOnTrF4cOH2b17N25ublSpUoWePXvSr18/TE1NxZZXarS0tHBwcMDBwUFsKe/F06dPC1RmHj16xMOHD+nTp0+B66pXry659eifAsbGxuzdu5cbN24AMH36dHR0dIiLi8PJyYlx48aJrLB0XLlyhYCAgELPrUqVKvHtt98yefJkkZSpeJu3N0DK5XJcXV2LvV6qyaytW7cyZ84ccnJy0NTULNRvrQqSyzH29vb8888/VK9enW7dujF+/HgsLCyoWLGi2NLei/379zN48GAhE7J27VrCwsJo3LgxU6dORUdHh/nz52Nvb68Ua0uVkUqVKmFra4utrS0pKSlERkZy8OBBRo8eTc2aNenfvz92dnaSG8I5efIkx48fJyMjo8iMwqJFi0RSVjI0NTVJTU0Vfj9z5gwymaxQT+G9e/cknQFSZurUqSN4qaupqTFp0iSRFb0/2tra3LlzR1hln5/79+9Lphr6KfApbIAECAoKwsbGhkWLFqGjoyO2nCJRBcnFcPnyZWQyGdWrVycuLo64uLhir5XJZOzatasM1ZWe+Pj4AjZbffv2ZfHixcycOZN+/foxY8YMyU7KfopUr16drl278ubNG168eEFcXBwbNmwQel/nzp0r+nSwIqxevRpfX1+qV69OrVq1iswoSAUzMzN27twpeM5u376dihUrFnDuyM7OZtOmTcUu81EhLrdu3eLkyZO8fPmyXJaAS4OtrS1+fn5Ur14dGxsbqlSpQmZmJkePHsXHx6dQpUOFeAwcOBC5XM4ff/yBrq4ulpaWwjm5XM7o0aPp168fAwYMEFHl+/P06VPmz59fbgNkUAXJxWJnZyepB7OivHr1qsAHMu9ne3t7ya2g/pRJTU0lMjKS3bt3Ex0djZaWFra2tsyYMQMTExMuXryIm5sbkydPZsuWLWLLfScbN27km2++Yc6cOZL/3jk7OzNs2DD69u2LXC7n5s2bjBs3TsgaR0ZGEhERweXLl5kxY4bIalW8zdatW5k9ezYAVatWLZcl4NLw448/cufOHVxdXZHJZGhoaJCZmYlcLsfS0pKffvpJbIkq/j9v3rzB1dWVgwcPMmrUqAJB8rNnz3j69CkzZszgzz//ZOnSpZKt+hoZGXHr1q0iqxvlBVWQXAyLFy8WW0KZkBeQSHVRw6eIs7Mzf/31l/Bw8/HxwcbGpkArkLGxMf369WPt2rXiCS0Bqamp2NraSj5ABjA0NOTXX39l/fr1JCUlMXTo0AKe4wsWLKBChQr4+PioMsnlkKCgIL766isWLlyoVFU1DQ0N1qxZw6VLl4iNjRUsP9u1a6fy6y5nbN68mePHj+Pt7U2vXr0KnKtZsyZ79uzh999/Z+bMmXTq1IlBgwaJpLTkXL58Wfh5wIABLF68mMzMTNq2bVtky4+hoWFZyiuEKkhWAaDyRZYQT58+ZcqUKfTp0wc9Pb1ir7OxsaFTp05lqKz0mJqacu7cOczNzcWW8kFo3bo1np6eRZ7bvn07NWvWlGz2R9l5+vSp0gXI+TEyMqJmzZo8ffpUsChUUb7Ytm0bY8aMKRQg56d///5cunSJzZs3SypItre3F5IheeZqy5YtK5QgyVsLL/ZQoipI/gQ5e/YsT548ASAnJweZTMbZs2d5+PBhoWu7d+9e1vJUvIOitn1lZWXx4sULdHV1hWNGRkZlKeu9GD58ODNmzCA5ORljY+MiMwrK8lmUQo/4p4yhoSFXr15Vmhe2/Bw6dIhly5Zx9+5dZDIZW7duJSAgAG1tbRYsWCDJRUTKyN27d4XV2v+FpaUlO3fuLANFH47w8HCxJZQI1TfiE8Tb27vQsSVLlhQ6Vh7e4lQU5vXr16xatYomTZrQv39//vzzT1xdXUlLS8PU1BQ/P7//zDCXR7777jsgd1FKREREofOqz6KKssLNzQ03Nzeys7OLfWETuwRcGvbv34+Liwv9+/dn8uTJuLm5AWBtbc2CBQuoW7cu33//vcgqVUBua8zLly/feZ1cLpfci03Hjh2Fn3/77TesrKwKJHfySEhIYNeuXQWuFwPVMpFPjKKyxf9FvXr1PpISFaXF09OTTZs2MXv2bOzt7fnyyy+pUqUKY8aMYfXq1bRt21ZyPfWKfC5Vn0UVZUHLli2Fn8trCbg09O3bF3Nzc2bNmkV2djaGhoZs374dQ0NDQkNDiYiI4PDhw2LLVAGMGTOG2rVrs3Dhwv+8bvbs2Vy/fp1NmzaVkbIPS6tWrdi8eXORPfGHDx/GxcWFv//+WwRl/4e0XkFUvDeqQEP6REZGMn36dOzt7bl48SIPHjxg+fLl9OrVCw0NDebOnSu2xBLzrs9lVlZWGSlR8akjtXKwovz7779Mnz69yHOGhoYkJCSUsSIVxTF06FC+//572rZtW2y/8bZt29i+fTtz5swpY3Xvh6OjoxD4yuVyHB0dixzYzszMLBcVG1WQXAwLFiwo0fWzZs36SEpUqChIcnKyMHBz7Ngx1NXV6dKlC5Br6ffq1Ssx5ZWKrKwstmzZQlRUFK9fvyZ/gSsjI4MrV64QFRUlokIVnwr5y7svX74kPT0dHR0dSW5YzU+dOnWIiYnh888/L3Tu0qVL1K5dWwRVKoriyy+/ZMiQIXh4eBAREYGVlRV169YlJyeHx48f8+eff3LlyhV69uzJ4MGDxZZbIjw8PIiMjEQul7Nq1Sp69+5d6LOnpqZGtWrV/nNwsaxQBcnFcOTIEYWvlclkqiBZRZnRoEEDYmJiMDExYd++fXTo0AEtLS0A9u7dS+PGjUVWWHKWLl1KeHg4LVq0IDExkcqVK6Onp8f169fJyspiwoQJYktUmLCwMIWvlclkjBw58uOJUVEq/vrrL5YvX86VK1eEFzYjIyMmTZqElZWVyOpKx7BhwwQXASsrK2QyGc+ePWPHjh0EBAQwfvx4sSWqyMecOXMwMTEhJCSEoKCgAufy3HPs7OxEUld6mjVrJmywlMlkDBo0qFwPM6t6klWokBg7d+5k5syZVK1alfT0dGG73pAhQ/j777/x9vbG1tZWbJklwtLSEjs7O9zc3AgKCuLSpUusXLmS+Ph4HB0d6dmzJy4uLmLLVIj8Pa3vQqr9rcrMX3/9xbhx42jTpg29evWiRo0aPH36lH379vH3338THBxcaMW4VPD19SU0NJSsrCyhv7pChQoMGzZMtdimHJOQkMCTJ0+oUKECderUKXLQTcpkZGQUqCDK5XIyMzO5cOGC6NlkVZD8Abh48aLKjF1FmXL+/HliYmLo0KEDHTp0AMDf3x9zc3OFrIPKG0ZGRoSEhGBubs7x48fx8PDgxIkTQO4EdGBgIPv37xdZpYpPgUGDBlG3bl38/PwKnXNxcSE+Pp5ff/1VBGUfhpSUFGGZiJaWFm3btqVGjRpiy1LxCXLjxg3c3d35559/ir1G7CSCqt1CAZ48ecKiRYs4d+5cobed169fk5OTI/o/UoXyk2eP9ujRI+rXr8/gwYNp166dcD6vhCVFdHV1efHiBQCNGjUiISGB5ORkdHV1qVu3LvHx8SIr/DgkJCRQs2ZNsWWoyMf169eLtUKzt7eXlE3a9OnTmTBhAv/73/+EY9WrV8fa2lo8USpU/H88PT1JSEhg2rRpHD16lIoVK9K1a1dOnDjB8ePHy8XGWFWQrAALFy7k9OnTDBgwgOjoaDQ0NGjXrh1//fUX169fZ8WKFWJLVJh27dopvPpXJpMRHR39kRWpUISIiAjmz59P48aNsbGx4e7du3h6evL48WOmTZsmtrz3pnPnzvj7+9OgQQOaNWuGvr4+ERERfPfdd0RGRqKvry+2xFLx4sULVq1aVegFG3JLjI8fPy6wplWF+NSoUUNYtvQ2jx8/LtI3ubyyc+dOhg4dWiBIVqGivBAbG8vs2bOxs7NDU1OTHTt2MGzYMIYNG4abmxvr168XfamPKkhWgLNnz+Lm5sbQoUOJiIjgwIEDTJkyBTc3N5ycnDh48CBfffWV2DIVYvTo0QoHySrKD1u2bKFfv354eXkJ/z9vb282bNjATz/9JPm14q6urowZM4b58+cTHh7ODz/8wOzZswkMDCQnJwd3d3exJZaKBQsWsHv3brp06cKtW7fQ0NCgcePGREdHk5iYKDn7pk+B7t27s3z5curVq1fACeKvv/7C19dXMvd6FSrKO2/evKF+/foANG7cmKtXrwrn7OzsykWfvCpIVoCMjAw+++wzAJo2bSq0VqipqTFs2LAS28WJyeTJk8WWoKIU3L17F3d39wIvOMOGDSM4OJj79+/TqFEj8cR9AGrWrMnvv/8utFUMGjSIhg0bEhcXR5s2bejUqZPICkvH8ePHcXFxYezYsaxdu5ZTp07h6+vLy5cvcXR05Pr162JLVPEWkydPJjY2ltGjR6OlpYW+vj6JiYmkp6djbGzMlClTxJaoQoVS0LBhQ65evYqpqSlNmjQhIyODW7du0bRpU7Kzs4UWPDFRBckKULduXe7fv4+pqSmNGzcmNTWV+/fv87///Y8qVaqQnJwstsRSk5yczMWLFwv1WmdkZBAbGyvJxRTKSGZmJpqamgWO5fWyKrK+VArIZLICfpkdO3YUfSXp+5KWloaJiQkAn332GWvWrAGgatWqjBo1Cm9vbzw8PMSUqOItqlatysaNGzl69Cjnzp0jLS0NHR0dOnTogLW1NWpqamJLLBFz584VLCL/C5lMxrp168pAkQoVuXz99dcsWbKEly9fMm7cONq3b8/MmTMZPHgw69atK5FT0MdCFSQrQO/evYU1vwMGDKB58+YsXrwYBwcHVq9eLUlfWoCDBw/y008/8epzu36vAAAgAElEQVTVKyFDmWcLBEg+O6ns5P+fqSif1KpVS9hk1qhRI5KTk3n69Cm1atVCT0+PZ8+eiaxQRVHIZDK6du1K165dxZby3mhqaqKtrS22DBUqCuHo6MibN2+EGYD58+fz3XffMWPGDOrWrYunp6fIClVBskJMmDCBlJQUTp48yYABA5g7dy7fffcdhw8fRlNTE39/f7EllopVq1bRunVrZs+eTUREBK9fv2b8+PGcOHECHx+fYleYqihfqHrMyy9du3bF29sbHR0dvvjiC+rXr8+qVatwcnLi119/pW7dumJLVAH07dsXb29vmjdvTt++ff/zWplMxq5du8pI2fszZcoUlUWpinJDSkpKgd/t7e2F4/r6+mzatAm5XI6amlq5eLapgmQFUFdXZ/bs2cLv7du35/Dhw9y+fZsmTZooVMoqj9y+fRtfX19atWpFp06dCAoKomnTpjRt2pTnz58TGBgorDtWIT5eXl5FZoQWLVpU4DMok8kIDAwsS2kqisHFxYX79+8TGhrKF198gbu7Oy4uLmzZsgU1NTWWLFkitkQV5Pp057lWGBoalouHswoVyoiFhUWJrhfbXlcVJCvI3r17iY6OFvoHb9y4wZIlSxg3bhw2NjYiqysd6urqQp9ro0aNuHPnDllZWVSsWBELCwtJG+YrG3kLQtLT0xU6rqJ8oKWlRVBQEK9fvwagW7du/PHHH/zzzz+0bt1a1dJUTshf1s1rrVOhQsWHJ689sGXLlvTs2bNcr6QGVZCsEFu3bsXDw6NAGU5XVxcDAwMmTpyIj48PPXr0EFFh6TA0NOTgwYOYm5vTtGlTIHeTm4WFBY8fPxZZnYr8rF+/XmwJH52cnBxu3rxJampqkX3WUtwkmEelSpWEnxs1aqQKjss5t2/fJjs7m88++4yUlBT8/Px4/Pgx3bt3Z+DAgWLLU5hJkyaV+yBExafFkSNH2L9/P5GRkaxYsQITExN69uxZbgNm1VpqBbC1tcXW1rbITUt+fn4cPnxYUj1qeZw8eZLx48fTo0cPfHx8+Omnnzh9+jSWlpYcOXKEzz//HF9fX7FlqvgEuHDhApMnTyYxMbHIAFkmk4ledlOU9u3bEx4ejpGRkULLe2JiYspImQpFOHjwIC4uLjg4ODBt2jScnZ05deoUbdu2JSYmBnd3d7799luxZapQIXkeP34sBMx///03bdq0wdbWtlwFzKogWQFMTExYvXp1kb00p0+fxtnZmbi4OBGUvT8XL17k5s2bDBw4kIyMDH7++WfBm3batGno6emJLVHFJ4C9vT0ZGRn8+OOP1KtXr0ibrfJgB6QI/v7+DBo0CAMDA1auXPnOIFnK68SVkYEDB9K0aVM8PT1JSkrC2tqaH374gfHjx7NmzRp27NhBZGSk2DJVqFAq8gLmAwcOcPHiRYyMjLC1tcXR0VFUXaogWQH69OlDly5dmDp1aqFzy5cvZ//+/ezfv18EZR+G58+fo6OjA+T6uiYlJdGwYUORVan4lDA2NmbFihVYW1uLLeWjoPqOSQcTExOCg4Pp2LEjW7ZsYc6cOezbt49GjRoRFRXF2LFjuXjxotgyVahQSh4+fMimTZsICwsjOztb9AqiqidZAUaMGMHcuXNJS0vD2toafX19kpKSOHbsGNu3b2fWrFliSywV/6+9+w6K6l7fAP4soEIAkSZSRIyJoiBFRSCKaEjU6722GPUaNUEQowbFFjuajELABEQlICBSDEbsMdHYgj0odsf8sIGxICpIFUFgd39/OO7NUiJG4Gx5PjPO7J5zYB5m3OXlu+95v8XFxZg3bx7u3bsnWxm5ePEipkyZgkGDBiEkJER2xzdRU7KwsEB5ebnQMRpdSUkJ5s6dy9eYEtHX15eNqTpy5AisrKxkPeTZ2dkwNjYWMB2R6rl9+zYOHjyIgwcP4v/+7/9gamqKMWPGYODAgUJHY5HcEGPHjsWzZ88QExODbdu2QSQSQSqVwsDAAPPmzcO4ceOEjviPhIaG4saNG1i+fLns2MtRcF999RVWr16tEHunk+qbOXMmIiIiYGlpqVIzXUNCQvgaUzKenp749ttvcerUKRw9ehRffPEFACApKQlRUVEYNmyYwAmJlN+1a9dw4MABHDp0CLdu3YK5uTkGDhyIxYsXo0ePHgozhpHtFq9BKpUiOzsbxcXF0NfXx9tvvw1NTU2hY/1jffr0wdKlS/Gvf/2r1rlffvkFq1atwvHjxwVIRuqg5k1tL1eSNTU15aZBvKSMN7jxNaZ8ysvLsXLlSly4cAG9evVCYGAgWrZsiaFDh8LBwQGBgYHQ1tYWOmaDJCQkvNb1kyZNaqIkRC+Ehobi8OHDuH//Ptq3b4+BAwdi4MCBCrs4wpXkehQVFaF169bQ0NCQ2yHG2NhY9nFbaWmp7HibNm2aPeObqqioQKtWreo8p6urK/fzETU2Hx8fhVktaCp8jSkfHR0dBAUF1Tr+008/1XlDqSILDQ2Ve/7yU1BNTU0YGhqipKQElZWVaNGiBQwMDFgkU5NLSEiAhoYGevToga5du6KiogJ79uypd0KY0O2sLJLr4e7ujtTUVDg4OMDNze2Vv8yFbi7/J5ydnREXFwc3Nze89dZbsuPl5eWIj49Hjx49BExHqm7GjBmyxw8ePICJiUmdK8jPnz9XytcXwNeYslKVOcnXrl2TPT558iQWLlyI5cuXw8vLS1bw//7771iyZAlmzZolVExSIxYWFgBeTLN41X4MIpFI8CKZ7Rb12LVrF/r37w9DQ0Ps3LnzlUXyyJEjmylZ47l27RrGjx8PLS0tuLi4wMjICIWFhcjIyIBEIsGmTZuUZuwWKbeuXbvK/iitKSMjA35+fko5ZpGvMeVT15zk9PR0ODo6KvWc5EGDBmHKlCkYNWpUrXM//fQTIiIicOTIEQGSESkuFslq7sGDB0hMTMSlS5dQXFwMPT09ODs7w9vbG1ZWVkLHIxW2bNkyPH78GABw9OhR9OzZE/r6+rWuy87ORmVlJY4ePdrMCRsHX2PKRVXnJDs7OyM8PBwDBgyode7gwYNYtGgRzp8/L0AyIsXFdosGysrKwokTJ/Ds2TNIJBK5cyKRSHYHtLKxsLDg3fUkCE9PTyQmJsqel5eX1+r51NTUhK2tLby9vZs3XCPia0y5ZGVlYeHChdDS0sLRo0chlUoxaNAgAICTkxMiIyMFTvjP9OjRA+vWrYOdnR3atm0rO3737l1ERETgvffeEzAdkWJikdwA27Ztw7JlywAAb731Vq1f5MpUJCckJGDo0KEwMTF55Z3PIpFIqYsTUmxeXl7w8vICAHz88ccIDQ1Fp06dBE7V+PLy8rBx40acPXsWT58+RZs2bdCzZ098+umnCrP1Kv2Pqs5JDgwMxIQJE/D++++jc+fOMDQ0REFBAW7evAlLS0vBez+JFBHbLRrggw8+QLdu3RAUFFTnx8HKxNbWFlu3boWDg8MreyFFIpHS3jBFyqV///5YvHixQgyPb0x37tzBJ598goqKCri7u8PY2Bj5+flIT09Hq1atsGXLFu68p2CWLFmCjIwMvPfee9i6dSu++OIL+Pv7y81JXrJkidAx/5GnT59i586duHjxIkpKStCmTRu4urpixIgRdd40S6TuWCQ3gIODA+Li4uDq6ip0FCKV5OrqivDwcPTp00foKI1q+vTpuH//PpKSkmBoaCg7XlBQAF9fX7Rv3x5r164VMCHVpEpzkonozbDdogHs7Oxw7do1FslETWTKlCn47rvvUFFRARsbmzo/0lbGWeSnT59GSEiIXIEMAEZGRpg6dSoCAwMFSkb1UaU5yTWdOHECx44dQ3l5eZ331gQHBwuUjEgxsUhugLlz52Lu3LkQi8VwcHCAjo5OrWvs7OwESPb6hg4d+lrX//zzz02UhOh/4uLiUFpaCn9//3qvUcbWH21t7XoLK5FIhOrq6mZORA1RXV2NvXv34vTp08jLy8PSpUtx/vx52NnZKe3IvvXr1yMiIgJt2rRB27Zt67y3hojksUhugJczMVetWlXrjUQqlSpV766dnZ3sZxCLxdi3bx8MDAzQr18/mJqaoqioCKdOnUJ+fj7Gjh0rcFpSFwsWLBA6QpNwcXFBVFQUXFxcYGBgIDteVFSE6Oho9O7dW8B0VJfCwkL4+vri+vXr6NSpE27evImysjIcOnQIK1euRGJiIhwdHYWO+do2b96M//73v1i+fDkLYqIGYk9yA5w5c+aVbyrK+MsuJCQE165dQ2xsrNxNG2KxGDNmzEDr1q0REhIiYEIi5ZaTk4OPP/4YlZWVcHV1hYmJCfLz83HmzBloaWlh8+bNKjnRQ5nNnz8fly5dQkJCAszMzGBvb48dO3bg3XffhZ+fHwAgKSlJ4JSvz8nJCTExMWwbJHoNXEluAFV9U9m+fTvCwsJq3dWsqamJcePGYebMmSySqdncvHkTGRkZqKqqwsu/3aVSKcrLy3Hp0iXExcUJnPD1WVpaYvfu3UhISMC5c+dw69YtGBgYYPTo0fD29ka7du2Ejkg1HDlyBF9//TUsLS0hFotlx1u2bAkfHx/MnTtXwHT/XK9evXD27FmV/X1G1BRYJNdj6tSpWLhwIWxsbDB16tS/vVYkEiE6OrqZkjUebW1t3L17t85zmZmZch8PEzWl1NRUfPXVV7L2pb9+wKWhoaHUGx2YmZlh4cKFQsegBhKLxWjVqlWd56qrq6GsH76OHz8eixcvRmFhYb331qjaCEaiN8UiuR5lZWWyVYSysjKB0zSNESNGIDw8HJWVlfDw8IChoSGePHmCQ4cOITY2FjNmzBA6IqmJhIQE9O/fH6GhoVi/fj1KS0uxZMkSHD9+HIsWLXrtG06FJpVKceLECbRr1w6dO3cG8KL1IjIyEllZWejcuTM+//xztG/fXuCkVJObmxu+//579OrVC3p6egBeLIRUVVUhOTkZLi4uAif8Z6ZNmwYASElJQUpKSq3zynRvDVFzYU+yGhOLxQgKCkJqaqrcOKAWLVrA19cXAQEBAqYjdWJvb4/o6Gh4eHjgwIEDWL16Nfbv3w8AiI+Px969e7Fz506BUzZMWVkZfH19cfnyZcyePRtTpkxBSUkJ/vOf/6CwsBCenp64d+8eHj16hB07dsDS0lLoyPQXLzeAqaqqgpOTE06cOIF+/fohOzsbJSUlSttHnpOT88pr+H+RSB5XkuvxclvShlLGGa6amppYtmwZAgICcPnyZdkOTE5OTrIVFKLmoKOjAy2tF29HHTp0wL1791BRUQFtbW04ODggKipK4IQNFxsbiz///BPr16+XbY6SkJCAvLw8BAcHY+TIkRCLxfDx8UFkZCS++eYbgRPTX3Xo0AF79uxBYmIiMjIyYG1tjfz8fAwYMACTJk2Cubm50BH/kVcVwFVVVc2UhEh5sEiuh5ub22uNyVHmj6nKysrw5MkT5OXlwdXVFdnZ2ejSpUu9fXlEjc3Z2Rnbtm2Dq6sr3n77bWhpaeH48eMYOHAgbty4oVT/Fw8ePIipU6fC09NTduzAgQNo3bo1hg8fDuDFH6hjx45lgawgRo4ciaCgIHTr1g27d++Gp6en0t6gV5+qqips3boVGRkZqKyslOutLi8vR2ZmJjIyMgRMSKR4WCTXIzg4WOVnSUokEgQFBWHLli0Qi8UQiUTo06cPIiIikJOTg+TkZJiZmQkdk9SAv78/PvvsM/j5+SE+Ph5jxozBggULsGnTJly6dAkjR44UOmKD5eTkoGvXrrLn+fn5yM7OxsCBA+U2cGjbti0KCwuFiEg1ZGVl4dGjR+jWrRsWLVqE1NTUWrskKrtvv/0WycnJ6NKlC548eYJWrVrByMgIN27cQFVVFaZPny50RCKFwyK5Hh999JHQEZpcZGQkdu7ciW+++QZ9+/aVTRCYP38+/P39ERYWhlWrVgmcktSBg4MD9u3bh5s3bwIAFi1aBAMDA1y+fBl+fn6YMmWKwAkbTltbGxUVFbLnL1fnak7oePToEVq3bt2s2ahu3bp1w5w5c2BlZQWpVIp58+bV++mFSCTCnj17mjnhm/v111/h5+eHuXPnIiYmBlevXsW6devw6NEjfPbZZ9z9kagOLJL/hlQqxc8//wxDQ0N4eHjIHffx8cGwYcOUaoWrph07dmDOnDkYNmyY3DxQW1tbBAQEcEYyNStzc3NZv6eGhsbfblGtyLp3747ffvtN1m6xZ88eaGpqYsCAAXLX7dy5U2m2s1d1q1evRnJyMoqKinDr1i107NgRRkZGQsdqVIWFhejbty+AF+/xLydcmJmZYerUqYiOjsasWbOEjEikcFgk16O6uhpz5szBoUOHMGnSJLkiOT8/H48fP8bixYtx8uRJfPvtt3IfoyqLoqIidOzYsc5zRkZGePr0aTMnInUSFxeHESNGwNTUVHassrKy1uY2WVlZCAoKwsaNG5s74j8yefJk+Pr64uHDh5BIJDh58iRGjRola126cuUKNm3ahN9//x3r168XOC0BL/5Ae7k1+pkzZzB79mzY2toKnKpxGRoayt7TbWxskJeXh8LCQhgaGsLCwgKPHj0SOCGR4lG+yq6ZpKam4tixYwgLC8P8+fPlzpmammLv3r0ICQnBgQMHsGPHDoFSvpkuXbpg165ddZ47dOiQbL4rUVMIDw9Hbm6u7LlYLIajoyP++OMPueuePn2K9PT05o73j7m7uyM6OhoVFRV4+PAhJk2ahOXLl8vOT5s2DYcOHcLChQvlbu4jxZCWlgZbW1sUFRXh2LFj2Lt3L06dOoXS0lKho72Rvn37IjIyEjdv3oS1tTWMjY2RkpICsViM/fv3w9jYWOiIRAqHK8n12L59O3x9fTFkyJB6rxk+fDiuXr2K1NRUjB49uhnTNY6AgABMmTIFjx49gqenJ0QiEX777TckJCRg3759SjV2i5RPXSPaVWVsu6enZ70FcFRUFGxsbLijpQJbs2YN4uPjUVlZKTumpaWFSZMmKe3Uizlz5sDX1xcrVqxAcnIyAgICsGzZMkRHR0MikXBXSKI6sEiux507dxq0s5KHh0e9q7GKrk+fPtiwYQMiIiKwevVqSKVSREVFwdbWFpGRkejfv7/QEYlUjqOjo9AR6G8kJSUhJiYGkydPxpAhQ2BiYoL8/Hzs3bsXGzduhJmZGSZMmCB0zNdmamqKn376SdZWMXr0aHTo0AGXL19G9+7d4ebmJnBCIsXDIrke2traePbs2Suvk0qlsk0QlJG7uzvc3d1RUVGB4uJi6OnpQVdXV+hYRESC2Lx5M/z8/DB79mzZMRMTE9ja2kJTUxMpKSlKWSQDLyZztGvXTva8d+/e6N27t4CJiBSb8lZ3Taxr165IS0uDl5fX317322+/wcbGpnlCNYHnz5/j9u3b9fbbNWQ1nYhIVTx8+BCurq51nuvdu7fS3EBKRG+ORXI9xo0bh5kzZ8LJyanefuPt27djx44dcjflKJPjx4/jyy+/RElJSZ29oCKRSKl3EiTlpOqb+JBis7a2xrlz52rNtQaAs2fPcoMlIjXCIrkeH3zwAcaOHYvAwECkpKTA09MTFhYWkEgkyM3NxcmTJ5GZmYnBgwdjzJgxQsf9R4KCgmBtbY05c+ao3O5SpBxCQ0Ohr68vdyw4OBh6enqy58o+VYCUy8SJE/H1119DLBZj8ODBMDY2xpMnT7B//37Ex8dzljCRGhFJVeV28iaye/duxMfHy3YCe6lbt2749NNPMWLECIGSvTlnZ2dERUXB3d1d6CikhiZOnPha12/atKmJkhDJCwsLQ2JiotwudJqampg4caJsnjIRqT4WyQ2Ul5eHhw8fQlNTE+bm5iqx8vr555+jZ8+eSrXlLxFRU8rJyYGlpSWKiopw+fJlFBcXw8DAAA4ODkr9vn/27Nl6z4lEIujq6qJ9+/Zyn+IQqTsWyWrs0aNHmDRpEnr06AE7Ozvo6OjUukaZV8qJiF6Xm5sbFi9ejGHDhgkdpVHZ2trK9ftLpdJa/f8aGhoYNmwYVqxYodRTm4gaC18FaiwtLQ1//vknsrOzsX379lrnRSIRi2QiUitaWlpo3bq10DEa3ffff4+5c+fi448/lvVaFxQU4PDhw0hJScG8efPQqlUrhIeHw9zcHDNnzhQ6MpHguJKsxjw8PODs7IzZs2fDxMSkzmtq3lRFRKTKUlNTsWHDBkycOBE2NjZ1btdsZ2cnQLI3M2bMGLi7u8vNf34pMjISJ0+exJYtW7BlyxZs2LABhw8fFiAlkWLhSrIae/r0KcaPH4+OHTsKHYWISCG8HOkZHBwMAHW2KCjjaMzr168jICCgznPOzs6IjY0FALzzzjt4/Phxc0YjUlgsktWYh4cHTp06Ve/gfCIidZOUlKSSs7qtra2xb98+9OnTp9a5/fv3w9LSEgCQm5tb5+o5kTpikazGPD09ERISguvXr6N79+61tqMWiUTw9vYWJhwRkQBUddFg+vTpmDNnDu7fvw8vLy8YGRmhoKAAaWlpyMjIwKpVq3Dz5k2EhYXhgw8+EDoukUJgT7Ias7W1/dvzyvqxIhHR60pJSUFKSgoePHgAKysrjB07FuPHj4eGhobQ0RrN8ePHERUVhStXrkAikUBLSwvOzs6YPn063N3dcfjwYRw9ehRLliypc9oRkbphkUxERGotJSUFK1asQMeOHWFra4s7d+4gMzMT3t7eKrl5SGVlJYqLi2FsbKxSfwQQNTYWySQbmv/06VMYGhrCwcGBA+WJSG0MHz4cXbp0QWhoqKwfOSwsDD/88APOnTsHTU1NgRM2DolEgmvXruHZs2eo61e/i4uLAKmIFBd7ktXcmjVrEB8fj8rKStkxLS0tTJo0CXPnzhUwGRFR87hz5w4WLlwod8PeJ598gri4ONy7dw82NjbChWskFy9exKxZs/D48eM6C2S21xHVxiJZjSUlJSEmJgaTJ0/GkCFDYGJigvz8fOzduxcbN26EmZkZJkyYIHRMIqImVVFRUevGZVNTUwDAs2fPhIjU6IKCgtC6dWssX74c7dq1Y5sFUQOwSFZjmzdvhp+fn9xweRMTE9ja2kJTUxMpKSkskolILb1cVVaVjsQbN25g3bp18PT0FDoKkdLgn5Jq7OHDh/WOO+rduzdycnKaORERkWJRlZnJ5ubmKCsrEzoGkVLhSrIas7a2xrlz5/Dee+/VOnf27FmYmZkJkIqIqPmFhoZCX1+/1vHg4GC5G5lFIhGio6ObM1qjmDlzJtasWQNra2vY29sLHYdIKbBIVmMTJ07E119/DbFYjMGDB8PY2BhPnjzB/v37ER8fj1mzZgkdkYioyb2c6lBzpbW+48po48aNyM/Px+jRo6GpqYmWLVvKnReJRDh//rxA6YgUE0fAqbmwsDAkJiaiurpadkxTUxMTJ05UyfmgRETqKDIy8pXX+Pv7N0MSIuXBIplkc5KLi4thYGAABwcHGBoaCh2LiIiISDAsktVMUVERWrduDQ0NDRQVFb3y+jZt2jRDKiIiamwJCQkYOnQoTExMkJCQ8LfXikQieHt7N08wIiXBIlnNdO3aFampqXBwcICtre0r79zmcHkiIuVka2uLrVu3yt7v/w43EyGqjUWymtm1axf69+8PQ0ND7Ny585VF8siRI5spGRERNZXy8nLo6OgIHYNIqbBIJiIiUnEDBgzAokWLMHDgQKGjECkNjoBTU7m5uZBKpbCwsAAA3Lt3D0lJSbhz5w46dOiAcePGoVOnTgKnJCKixvDs2bNaW28T0d/jSrKaKS0txZw5c3Dy5EkAgKenJxYuXIhx48ahpKQEBgYGKCgogLa2NpKSkuDo6ChwYiIielPx8fH45Zdf4O/vDxsbGxgbG9e6hjdqE8ljkaxmli9fjrS0NMydOxd6enqIiYnB/fv3YWZmhpiYGJiZmeH+/fvw9/eHqakp4uLihI5MRERvyM3NDaWlpZBIJPVewxv3iOSx3ULNHDlyBAEBARgxYgSAF1tTDxs2DIGBgbJtqK2srDB9+nQEBgYKGZWIiBoJN4cien0sktXMkydPYGNjI3v+8rGlpaXcdWZmZigtLW3GZERE1FQ4qYjo9bFIVjNisRgtW7aUPdfU1AQAaGnV/q/AThwiItVx7949rF+/Hunp6cjPz8ePP/6IPXv24J133sHo0aOFjkekcDSEDkBERERNKzMzEyNHjsT58+fx/vvvo6qqCsCLxZBly5Zh9+7dAickUjxcSVZDGzduhImJCYD/rRbHx8fDyMhIdk1+fr4g2YiIqPEFBwfDyckJsbGxkEgk+OGHHwAAixcvRlVVFRISEmT3qhDRCyyS1YyFhQWuXLlS69ilS5dqXWtubt5csYiIqAlduXIFa9euhYaGRq1WusGDB2PXrl0CJSNSXCyS1UxaWprQEYiIqJnp6ekhLy+vznO5ubnQ09Nr5kREio89yURERCpu8ODBCA8Px+nTp2UrySKRCLdv30ZkZCS8vLwETkikeLiZCBERkYorLy/H1KlTcebMGejo6KC8vBwmJiYoKCiAvb09Nm7cyNVkohpYJBMREamJEydO4MyZMyguLoaenh569uyJ999/Hxoa/GCZqCYWyURERGqkuroahYWFaNOmDVq0aCF0HCKFxT8diYiI1MClS5fg7e0NZ2dneHp64saNG/jyyy+xevVqoaMRKSQWyURERCouPT0dEyZMAADMnj0bEokEAPDuu+8iLi4OCQkJQsYjUkhstyAiIlJxo0aNQqdOnbBq1SpUV1fD3t4eO3bsgJ2dHdauXYt9+/Zh//79QsckUihcSSYiIlJxN2/exLBhwwC8GP32V66urnjw4IEQsYgUGotkIiIiFWdsbIxbt27VeS4rKwvGxsbNnIhI8XHHPSIiIhU3YsQIrFmzBvr6+ujXrx8AQCwW4/fff0dkZCRGjRolcEIixcOeZCIiIhUnFouxdOlS7Nq1CyKRCFKpFBoaGpBKpfjww4N3BfAAAAhOSURBVA8RFhbGcXBENbBIJiIiUhNZWVnIyMhAcXEx9PX10bNnT9ja2godi0ghsUgmIiIiIqqBPclEREQqTiKRYOvWrTh27BjKy8tlc5JfEolESEpKEigdkWJikUxERKTiQkJCkJycjG7dusHMzAwaGhxuRfQqbLcgIiJScW5ubpgwYQL8/f2FjkKkNPinJBERkYqrrKxEr169hI5BpFRYJBMREam4/v374/Dhw0LHIFIq7EkmIiJScR4eHvjmm29w//59ODg4QEdHR+68SCSCt7e3MOGIFBR7komIiFTcq2Yhi0QiZGZmNlMaIuXAIpmIiIiIqAb2JBMRERER1cCeZCIiIhXUo0cPJCcnw97eHs7OzhCJRH97/YULF5opGZFyYJFMRESkgnx8fGBqaip7/KoimYjksSeZiIiIiKgGriQTERGpuN27d9d7TiQSQVdXF9bW1ujcuXMzpiJSbFxJJiIiUnF2dnaQSCQAgL/+2n/ZgiGVSiESieDi4oLo6Gjo6uoKkpNIkXC6BRERkYqLiYmBrq4u5s+fjyNHjuDKlSs4duwYli5dCl1dXQQHByMuLg45OTlYvXq10HGJFAJXkomIiFTc0KFDMXLkSPj4+NQ6l5ycjG3btuHnn3/G7t27ER4ejuPHjwuQkkixcCWZiIhIxd29exfvvvtunec6duyIO3fuAACsrKxQXFzcnNGIFBaLZCIiIhXXuXNn/Pjjj7K+5L9KTU1Fp06dAAC3b9+GmZlZc8cjUkhstyAiIlJxp0+fxpQpU2Bubo7+/fvDyMgIBQUFOHr0KHJzc7F+/Xro6enBx8cHn376KWbOnCl0ZCLBsUgmIiJSA9evX0dsbCzOnj2LwsJCmJmZoVevXvDz80OnTp2Qnp6OS5cu4fPPP4eGBj9oJmKRTERERERUAzcTISIiUgMlJSU4c+YMnj17hrrWx0aMGCFAKiLFxZVkIiIiFZeWloY5c+agoqKizvMikQiZmZnNnIpIsbFIJiIiUnH//ve/YWhoiMDAQLRr167OnmN9fX0BkhEpLrZbEBERqbi7d+9i6dKl6NKli9BRiJQGb18lIiJScZ06dUJOTo7QMYiUCleSiYiIVNyCBQuwZMkS6Ovrw9HREdra2rWuadOmjQDJiBQXe5KJiIhUnJubG8rKylBdXV3vNbxxj0geV5KJiIhU3IIFC4SOQKR0uJJMRERERFQDb9wjIiJSQXFxccjLy5M7VllZWeu6rKws+Pj4NFcsIqXBIpmIiEgFhYeHIzc3V/ZcLBbD0dERf/zxh9x1T58+RXp6enPHI1J4LJKJiIhUUF3dlOywJGo4FslERERERDWwSCYiIiIiqoFFMhERERFRDSySiYiI1IhIJBI6ApFS4GYiREREKio0NBT6+vpyx4KDg6Gnpyd7Xlpa2tyxiJQCi2QiIiIV5OLiAgAoKyv722MaGhro1atX84YjUgLccY+IiIiIqAb2JBMRERER1cAimYiIiIioBhbJREREREQ1sEgmIlJAXbp0QXx8/Bt9j507d6JLly4oKChopFREROqD0y2IiBRQamoqLCwshI5BRKS2WCQTESkgJycnoSMQEak1tlsQESmgv7ZbrFu3Dh999BF++eUXDBo0CN27d8eoUaNw4cIFua/ZvXs3Bg0aBAcHB/j5+aGoqKjW9z116hRGjx4NBwcH9OvXD2vWrIFYLAYAXL58GV27dsV3330nu/7ChQvo2rUrNmzY0IQ/LRGR4mGRTESkBP7880+sXbsW/v7+WLduHZ4/f46AgABUV1cDAH799VcsWLAAffv2xffffw8rKyuEh4fLfY/09HT4+fnBysoKkZGR8PX1RUJCAlauXAkAcHR0xMSJE5GYmIjs7Gw8f/4cS5YsgZOTE3x8fJr9ZyYiEhLbLYiIlEBZWRkSExPh4OAAABCLxZg+fTquXbsGe3t7xMbGwsPDA4GBgQAADw8P5Obm4siRI7LvERERAUdHR6xevRoA0K9fPxgYGGDRokXw9fWFlZUVZs+ejbS0NAQFBcHe3h4PHz7E+vXroaHBNRUiUi981yMiUgJaWlqwt7eXPW/Xrh0AoLy8HOXl5cjMzES/fv3kvmbQoEGyx+Xl5bhy5QoGDBiA6upq2b9+/fpBIpHgzJkzAAAdHR2sWLECJ0+eRGxsLObNm4cOHTo0w09IRKRYuJJMRKQEWrZsKbea+/KxRCJBSUkJpFIpDA0N5b7GxMRE9rikpAQSiQRhYWEICwur9f3z8vJkj11cXGBhYYGHDx/Cw8OjsX8UIiKlwCKZiEjJGRgYQCQS4cmTJ3LH/3rjnq6uLgBg2rRp8PLyqvU92rZtK3scFxeHgoICWFtbIzAwEElJSU2UnIhIcbHdgohIyWlra8PJyQmHDx+WO37s2DHZYz09Pdja2uLevXvo3r277F+LFi0QHh6Ohw8fAgCysrIQFRWFqVOnYuXKlTh9+jS2bdvWrD8PEZEi4EoyEZEKmDFjBiZPnoxFixZhyJAhOH36dK2ieebMmfjiiy+gp6eHDz/8EIWFhYiIiICGhgY6d+4MiUSCpUuXwtLSEr6+vmjZsiWGDx+OVatWwdPTU261mYhI1XElmYhIBfTp0wfr1q3D1atXMX36dFy8eBFffvml3DVeXl6IiorC1atXMW3aNAQHB8PJyQnJycnQ0dFBSkoKLly4gOXLl6Nly5YAgAULFgAAvvrqq+b+kYiIBCWSSqVSoUMQERERESkSriQTEREREdXAIpmIiIiIqAYWyURERERENbBIJiIiIiKqgUUyEREREVENLJKJiIiIiGpgkUxEREREVAOLZCIiIiKiGlgkExERERHV8P8S8ZXZFZQpGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf = df.apply(lambda x: 100*x/x.sum())\n",
    "sort_order = pdf.index.tolist()\n",
    "\n",
    "pdf = pdf.reset_index().melt(id_vars=\"index\")\n",
    "ax = sns.pointplot(x=\"index\", y=\"value\", hue=\"variable\", data=pdf, order=sort_order, dodge=True)\n",
    "\n",
    "# # Line for all articles with 1 share\n",
    "# pdf = base[base.AES==1].discipline.value_counts().to_frame()\n",
    "# pdf = pdf.apply(lambda x: 100*x/x.sum())\n",
    "# sns.pointplot(x=\"index\", y=\"discipline\", data=pdf.reset_index(), markers=\"X\", color=\"red\", linestyle=\"--\", ax=ax)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "sns.despine(top=True, left=True, right=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# Comparison of retrieval methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove articles in Arts and Humanities\n",
    "base = articles[~articles.discipline.isin([\"Arts\", \"Humanities\"])]\n",
    "\"Removed {} articles in Arts or Humanities\".format(articles[articles.discipline.isin([\"Arts\", \"Humanities\"])].shape[0])\n",
    "\n",
    "# Unit of analysis\n",
    "disc = 'discipline'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coverage of Shares, Reactions, and Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21674\n"
     ]
    },
    {
     "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>pmid</th>\n",
       "      <th>year</th>\n",
       "      <th>AES</th>\n",
       "      <th>POS</th>\n",
       "      <th>TW</th>\n",
       "      <th>AER</th>\n",
       "      <th>AEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.184300e+04</td>\n",
       "      <td>61848.000000</td>\n",
       "      <td>21415.000000</td>\n",
       "      <td>9623.000000</td>\n",
       "      <td>43064.000000</td>\n",
       "      <td>13825.000000</td>\n",
       "      <td>10259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.721320e+07</td>\n",
       "      <td>2015.873642</td>\n",
       "      <td>8.811674</td>\n",
       "      <td>2.301465</td>\n",
       "      <td>8.923718</td>\n",
       "      <td>53.535335</td>\n",
       "      <td>23.989668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.080284e+06</td>\n",
       "      <td>0.824578</td>\n",
       "      <td>136.323191</td>\n",
       "      <td>4.804324</td>\n",
       "      <td>63.637819</td>\n",
       "      <td>475.446702</td>\n",
       "      <td>311.972031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.555500e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.624747e+07</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.701101e+07</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.815831e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.928712e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12473.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>8626.000000</td>\n",
       "      <td>36030.000000</td>\n",
       "      <td>15840.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pmid          year           AES          POS            TW  \\\n",
       "count  6.184300e+04  61848.000000  21415.000000  9623.000000  43064.000000   \n",
       "mean   2.721320e+07   2015.873642      8.811674     2.301465      8.923718   \n",
       "std    1.080284e+06      0.824578    136.323191     4.804324     63.637819   \n",
       "min    2.555500e+07   2015.000000      1.000000     1.000000      1.000000   \n",
       "25%    2.624747e+07   2015.000000      1.000000     1.000000      1.000000   \n",
       "50%    2.701101e+07   2016.000000      2.000000     1.000000      3.000000   \n",
       "75%    2.815831e+07   2017.000000      4.000000     2.000000      6.000000   \n",
       "max    2.928712e+07   2017.000000  12473.000000   186.000000   8626.000000   \n",
       "\n",
       "                AER           AEC  \n",
       "count  13825.000000  10259.000000  \n",
       "mean      53.535335     23.989668  \n",
       "std      475.446702    311.972031  \n",
       "min        1.000000      1.000000  \n",
       "25%        4.000000      2.000000  \n",
       "50%       16.000000      6.000000  \n",
       "75%       46.000000     15.000000  \n",
       "max    36030.000000  15840.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(articles[['AES', 'AER', 'AEC']].dropna(how=\"all\").shape[0])\n",
    "articles.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Ezm5+ZKd38/XG6uXv9vg540aNQqnTp3S22ZnZwcPDw9ERkZixowZCAoKMlYxDXLr1i3ExcXh3Xffhbe3N7KystC/f3+sWrUKgwcPNmtZLNmoUaPg4uKCdevWSV0Us2JATkRERDXKyc3H1eZ9pCtA9sFGP7VLly6YPXu2+HtZWRnS0tKQlJSEcePGYc+ePWjWrJkxSmmQY8eO4ciRI+Lvfn5+2L59OwICAsxWBrJcDMiJiIhIdjw8PBAREaG3rXv37nB2dsa8efNw4sQJ9Okj3c2Go6NjtfKR7WIOOREREdkMV1fXatt+//13TJkyBZGRkejWrRtmzpyJ/Hz9VJ1vvvkG//rXvxAeHo7w8HA899xz+OWXX/T2OXnyJF544QVERkbikUceQXx8PO7evYuvvvoKc+fOBQBERUUhMTERWVlZCA4Oxg8//CA+/5dffsELL7yALl26oGfPnli0aBGKi4vFx0eNGoX4+HisWLECvXr1Qnh4OKZMmYLs7Gxxn/T0dIwfPx7dunVDly5dMG7cOKSlpdX6enz11VeIjIzEkSNHMHjwYERERGDkyJG4cOFCg16jOXPmYMqUKXjttdcQERGByZMn13i+P//8Ey+//DJ69OiB8PBwPP/889XSizQaDVauXIlevXohIiICkyZNQk5Ojvh4eXk53n//fQwaNAidO3fGgw8+iNjYWNy8eVPcp1+/fli2bBlGjBiBsLAwfPjhhwbVQyoMyImIiEh2tFotVCqV+K+4uBgnT57EihUr4O/vj27dugEAcnNz8fzzz+PGjRtISEjAm2++iZSUFIwbNw5lZWUAgB9++AGzZs1C3759sX79esTHx6OwsBCvvvqquM9///tfvPjii3B3d8eKFSswdepUfPHFF3jnnXfQt29fMUD98MMPMXz48GrlPXjwIGJiYnDvvfeKz9+9ezcmTpwIjUYj7vfll18iNTUVixcvxsKFC3Hy5EnEx8cDqAhkJ0+eDLVajRUrVmDFihX466+/MHHiRKjV6lpfq7KyMsyYMQPPP/88li9fjtLSUsTExCAvL8/g10hXB41Gg+TkZIwePbrGc82cORN//PEH4uPjsWbNGjg7O2PixIm4ffu2uM+RI0eQkpKC+Ph4xMXF4eTJk3jrrbfEx+Pj4/Hxxx/jpZdewsaNG/HKK6/g+PHjWLx4sd65Nm3aJObp9+vXz+B6SIEpK0RERCQ7Bw8eRKdOnfS2OTk5ISoqCnPnzhVbyrds2YK7d+9i48aN8Pb2BgCEhYVh0KBB+O677xAdHY0//vgDL7zwAqZOnSoey8HBAbGxscjMzESHDh2wbt06tGrVCklJSbCzswMA3L17F19//TU8PT1x//33AwA6deokDuqsbNWqVQgLC8PKlSvFba1atcL48eNx4MAB9OvXD0DF4NR169aJ+e9paWnYsWMHACAvLw+ZmZmYOnUqevfuDQBo0aIFdu/ejZKSEri7u9f4WqlUKrz88sv497//DQCIiIhAv379sG3bNsTGxhr0GumOs3DhQnh6etZ6Xc6cOYPY2FixPu3bt8emTZugVCrh5eUFoCLdKDk5Gc7OzgCAixcv4ptvvhGPkZ+fj1mzZmHYsGEAKlKRMjIy8O233+qdKygoCBMnThR/f++99wyqhxQYkBMREZHsdO3aVUwTuXz5MpYuXYqoqCgkJCTA0dFR3O/kyZOIiIiAh4cHVCoVgIogNigoCMePH0d0dDQmTJgAACgsLER6ejoyMjKwf/9+ABBbVs+dO4cnnnhCDMYBYOTIkRg5cmS9ZS0uLsb58+f1BqECQO/eveHp6YlffvlFDGCDg4P1BqPed999UCqVAAAfHx8EBARg/vz5OHbsGPr06YOHH34Y06dPr7cMTzzxhPizt7c3IiIicPr0aYNfI93z6grGAaBbt254//33cfHiRfTp0wd9+vSpVu/g4GAxGAeAli1borCwUPxdd9OSnZ2N9PR0pKen4+zZs9VauQMDA/V+N7QeUmBATkRERLLj7u6O0NBQAEBoaChatGiBsWPHwtHREQkJCeJ+t2/fRmpqarXWdAC49957AVTkPcfFxeHQoUNwcHBA+/bt0bJlSwD/zCteUFAAHx+fRpX1zp070Gq1NT7f29sbRUVF4u+VA1UAEARBLINCocDmzZuRmJiIffv24csvv4STkxOee+45zJ49GwpFzZnKzZo1g4eHR7XzZmRkADDsNQJgUP1XrFiBpKQkfP/99/jPf/4DBwcHDBkyBIsWLYKTk1O9dQSAs2fPYuHChbh48SLc3d0REhJS44w5VctjaD2kwICciIiIZC8qKgrDhg3D559/jsGDB4stzm5ubnjkkUcwbdq0as/RpbW89tpryM7Oxvbt29GpUyfY29vj4MGD2Lt3r7ivm5tbtcGBt2/fxm+//YYuXbrUWTZ3d3cIgiDmbFeWm5srpnIYokWLFli8eDE0Gg1SUlLw+eefY/PmzQgPD8eQIUNqfM7du3ehVCr1AuG8vDwxrcOQ18hQXl5eiIuLQ1xcHC5cuIBvvvkGmzZtQrt27cSeiLrcuXMHkyZNQpcuXZCYmIg2bdoAABISEuocvGrsehgbB3USERGRTZg+fTrc3d2xZMkSMb2ha9euSE9PR3BwMEJDQxEaGooOHTpg9erVOHPmDAAgJSUFQ4YMQXh4OOztK9oyDx8+DOCfFvLIyEgcOnRIbwDmd999Jw6orK11GqgIBkNCQvRmXNGd486dO/UG9DppaWl4+OGH8dtvv0GhUKBLly54++23YW9vjxs3btT53J9//ln8OS8vDykpKejRo4fBr5Eh8vPz0bdvX/FGJiQkBLNnz4a/v7/eDCl1SU9PR0FBAUaPHi0G4xqNBseOHat3FVRj1cMU2EJORERENsHb2xsTJ07EsmXL8NFHH2HcuHEYO3Ysdu3ahfHjxyMmJgYODg7YuHEjUlJS8MorrwCoSHn5+uuvERwcDE9PT/z444/Ytm0bAKC0tBQAMGnSJLzwwguYNm0aRowYgVu3bmHlypUYOXIk3NzcxJSQH3/8Eb169apWtqlTp2LKlCl45ZVXMHToUNy8eRPLly8Xp1A0RLt27eDq6orZs2cjNjYWnp6e2LlzJwRBQN++fet8rm6KRW9vbyQlJcHT01Mc5GnIa2QIb29vtGnTBu+88w5KSkrQokULHDhwADdu3MCAAQMMOkbbtm3h6uqKNWvWQKPRoLS0FJ9++inS0tLE1BZBEGp8rrHqYQoMyImIiKhGfr7eTVot0yjnN7LRo0dj27ZtSE5OxjPPPAN/f398+umnePfddzFz5kwIgoBOnTph06ZNCAkJAVAxzd6bb76JuXPnolmzZggODsbWrVsxYcIEpKSkoHv37oiIiMCGDRuwYsUK/N///R98fX0xatQoTJo0CUBFyszDDz+Mt956CyNGjMCLL76oV65+/fohKSkJSUlJmDJlCry8vPDkk0/i1Vdf1RsoWhd7e3t88MEHWLp0KRYuXIiSkhIEBwdj3bp1aNeuXZ3PnTNnDhITE5Gfn4+HHnoI77//vjgriyGvkaGWL1+OhIQELFu2DLdv30ZgYCCWLVuGnj17GvR8d3d3JCYmIiEhAZMnT8Y999yDbt26YdWqVZg2bRpSU1NrXXDJmPUwNkFbX/s+ERERydqFCxckD0hIGrpFi44fPy7mjJPhjPXZYQ45EREREZGEGJATEREREUmIATkRERGRjRo6dCguXrzIdBWJMSAnIiIiIpIQA3IiIiIiIgkxICciIqJ6F1UhIn3G/MwwICciIrJxDg4OUCqVUheDyKoolUo4ODgY5VgWH5Dv27cPkZGRetu0Wi2Sk5PRt29fhIeHY+zYsbh69arePmVlZVi8eDF69eqFyMhITJs2DdnZ2Xr7FBQUYM6cOejRowcefPBBxMXFoaioyOR1IiIisiR+fn64fv06SkpK2FJOVA+tVouSkhJcv34dfn5+RjmmRS8MdPbsWYwfPx5arRbnzp0Tt69evRrr16/HjBkz0LJlSyQnJyM7OxvfffeduKrU3LlzsX//fsyePRsuLi5Yvnw5nJ2d8dVXX4krXsXExCArKwszZ85EaWkpEhISEBYWhnXr1klSXyIiIqkUFhYiJycH5eXlUheFyOI5ODjAz88PHh4eRjmevVGOYmRlZWXYsmULVq1aBRcXF70/DkVFRdiwYQNiY2MRExMDAOjWrRseffRRfPHFFxg7diz++OMP7Ny5E++99x6GDBkCAOjYsSMGDx6Mffv2YeDAgThx4gROnjyJHTt2IDw8HABw3333YcyYMfjtt9/QqVMn81eciIhIIh4eHkYLLoioYSwyZeXQoUNYv349Zs2ahZEjR+o9lpqaipKSEvTv31/c5unpie7du+Pw4cMAgBMnTgAA+vbtK+4TEBCA9u3bi/scP34cPj4+YjAOAD169ICbm5u4DxERERGRqVlkQB4aGop9+/YhJiYGgiDoPZaZmQkAaN26td72Vq1aiY9lZGTA19cXLi4ude5z//336z2uUCjQsmVLcR8iIiIiIlOzyJSV5s2b1/pYUVERHB0d4ejoqLfd1dVVHJBZXFwMV1fXas91dXXFrVu36t3HkIGdmZmZHJFORERERLUKCQkxaD+LDMjrotVqq7Wa6+i2G7qPQlFzB0Ft2ysLCAgwoLRERERERHWzyJSVuri7u6OsrKzaKPDi4mJxhhU3NzcUFxdXe66h+7i5uZmg5ERERERE1VldQN6mTRtotVpkZWXpbc/KykJgYCCAitbr3NxclJaW1rnPtWvX9B7XaDS4fv26uA8RERERkalZXUAeGRmJZs2a4aeffhK3FRQU4NSpU4iKigIAREVFQa1WY//+/eI+mZmZuHz5st4+f/75J/773/+K+5w8eRJFRUXiPkREREREpmZ1OeSurq4YOXIkVq1aBYVCgYCAAKxduxZubm4YPnw4AOD+++/H4MGDMX/+fBQVFcHDwwPLly9HcHAwHnvsMQDAQw89hPDwcMTGxmLWrFlQqVRYunQp+vbti86dO0tZRSIiIiKyIRa9UicAJCYmYuPGjXordapUKqxcuRJff/01SkpKEBkZibi4OAQFBYn7lJSUID4+Hnv27IFGo0HPnj0RFxenN4NLXl4e3nrrLRw8eBCOjo7o378/Xn/9deaQExEREZHZWHxATkREREQkZ1aXQ05ERGQLcgpKpC4CEZkJA3IiIiILcy49G0Pe/gLnMrKlLgoRmQEDciIiIguiUmuwYPtRaAEs3H4UKrVG6iIRkYkxICciIrIg24+mIb+oYh2NvDul2HE0TeISEZGpMSAnIiKyELmFSiTvSYGyTAUAUJapsGZPCvLuKCUuGRGZEgNyIiIiC7EnJQNqjX6KilqjxZ6UDIlKRETmwICciIjIQgyODISdQv+r2U4hYFBEoEQlIiJzYEBORERkIXzcnTF5UAScHSsW0nZytMeUQRHwcXeWuGREZEoMyImIiCzIs706wsfdCQDg6+6EEb06SlwiIjI1BuREREQWxN5OgYUjekEAsPDZXrC341c1kdwJWq1WK3UhiIiISF9OQQn8PF2kLgYRmQEDciIiIiIiCbEfjIiIiIhIQgzIiYiIiIgkxICciIiIiEhCDMiJiIiIiCTEgJyIiIiISEIMyImIiIiIJMSAnIiIiIhIQgzIiYiIiIgkxICciIiIiEhCDMiJiIiIiCTEgJyIiIiISEIMyImIiIiIJMSAnIiIiIhIQgzIiYiIiIgkxICciIiIiEhCDMiJiIiIiCTEgJyIiIiISEIMyImIiIiIJMSAnIiIiIhIQgzIiYhIlFNQInUR6G+8FkS2gwE5EREBAM6lZ2PI21/gXEa21EWxebwWRLaFATkREUGl1mDB9qPQAli4/ShUao3URbJZvBZEtocBORERYfvRNOQXlQIA8u6UYsfRNIlLZLt4LYhsDwNyIiIbl1uoRPKeFCjLVAAAZZkKa/akIO+OUuKS2R5eCyLbxICciMjG7UnJgFqjnxah1mixJyVDohLZrj0pGVCp1XrbVGoNrwWRzDEgJyKycYMjA2Gn0P86sFMIGBQRKFGJbNfgyEBotfrbtFotrwWRzDEgJyKycT7uzpg4IBzC378LACYODIePu7OUxbIEV3VRAAAgAElEQVRJWi0gCILetqq/k/lxCkoyNQbkRERUjaCtfx8yvj0pGagafwuCwJQVCZ1Lz8bjnIKSTIwBORGRjcstVGLdj6nQxeBaAGt/TOVAQglUTh+aMCAcANOHpKRSazDn40MAgLkfH+IUlGQyDMiJiGwcB3VaDh93Z0weFAFnR3tMHBgOJ0d7TBkUwfQhiWw7fEG8Mc0tVOKzwxckLhHJFQNyIiIbx0GdluXZXh3h4+4EAPB1d8KIXh0lLpFtyi1UYvX3Z/V6jhK/P8ueIzIJBuRERDaucqssALbKSszeToGFI3oBABY+2wv2dvyqlsJXJy5BpdEfTKHSaPHViUsSlahpODDVsvFTTkREbJW1MJFtm1f8H9hc4pLYMBlNbnMuPRtDODDVojEgJyIisVVWAFtliQDgXw91gEOVz4GDnQJDH+ogUYkaR6XWYMH2o9ACWLj9KAemWij+xaVGYdcXkfxEtm2O7+YNY6ssESpSuf5vcKTe/Pz/93ik1aVybT+ahvyiUgBA3p1S7DiaJnGJqCYMyKnB2PVFJF9+ni5SF4HIYvy7dwh8PSoCcF8PZ/z74RCJS9QwuYVKJO9JgbJMBQBQlqmwZk8KB6ZaIAbk1CDs+iJTYI8LEVkiezsF4l94BAAQP/IRq0vlqjylqW5ee05papms651FkmPXFxkbe1yIyJJFtm2OXxJGWWUqV+UpTScO5EJTlkzQarVcIJkMkluoRPTSr8WuLwBwdrTHrjnPWF1OHVkGlVqDoQk7cT2/CK183PDlzGira4EiIsujLFOJ03jauk8OncfTD7aDm7MjipRl+OaXK3j+kQekLhZVwW8+MhhX8yNjY49L01W+QSYiIPeOEj/8LxO5RcyTBiqmNHVzdsRnR9Pg5uzIKU0tFANyMhhX8yNj4mCjpmPgQaRPo9XiTGZF+tvZjGxomAQAezsF1BotnuvVEWqNlr2QFopXhQzG1fzImNjj0jQMPIiqu5pzG3dVagBAqUqN9JwCiUtkGewUAhJ2nYKdQkarHckMA3JqEK7mR8Yitx4Xc88Uw8CDSF9puQoXbuRD/fdy92qNFudv5KG0vGlpXVqtFhxuR6bGgJwaRLea38QB4VzNj5pETj0u5p4pxlSBB5E1y8ovqhY4a7VaZOUXNem4giBAENiyTKbFaIoaLLJtc0wYGG6VU0CRZZFDj4sUc/Nn5RdBo9EPPDSapgceRNaslbdbtcBZEAS08naTqEREhmNATkSS0fW4CIDV9rhIMVNMbQEGAw+yZU4O9gjx9xbzpO0UAh7w94GTA6c/JMtnfd9+RCQrkW2b47t5w6yyx0XSmWKq9qCzR50skLnHVgT5ecHJ3g4A4GRvh7Z+nmY9P1FjMSAnIsn5ebpIXYRGkWqmmKz8ohrjcaaskCWRYhVehSCgS0DFzX2XwOZQGCH3m4M6yRwYkBMRNZJUM8UwV5YsnRRjK3R83Z0xODQAvm7GGSDOQZ1kDgzIyWaZuyuV5EeqmWKYK0uWTupVeHWfSSJrIWjZD0M26Fx6Nl5K3oMPpgyyytxlshwqtQb/encnPn3lKQgC4GhvZ5bBqRqtFj/9+juKy1RwdbTHY53bGKV7Xg5yCkqsNg1KDnILlYhe+rU4tgKoCJB3zXnGKqc1tXZqjUavJ6/q72QZeEXI5kjZlUryo5spxtXJAS7NHMw2U4wpcmXlQIq8ZZ3KAagt4yq8lsVOocDUDfvEfwzGLROvCtkcqbtSSX4i20rTy2LsXFlrJ+XNdu4dJX74XyZyi8www46Fk9sqvFLhDZ5tYUBONkXSaepI1qSaiYG5sv+Q6mZbo9XiVPotAMAvV29BY+OZoJXHVkwYEG7Vq/BKhTd4tocBOdkUdqWSqXAmBmlJebN9Jfsv3FWpAQClKjWuZt82+TktnW4V3okDw612FV6paLRanMmsSLk6m5Ft8zd4lcl5MgYG5GRT2JVKJE9S3WyXlqtw/nq+3rbfruehtNy20w10YysA612FV0ej1Zo1KL6ac1vvBi89p8Bs57ZkUo4PMQfr/YRUsm/fPkRGRupt02q1SE5ORt++fREeHo6xY8fi6tWrevuUlZVh8eLF6NWrFyIjIzFt2jRkZ8vzQlMFqaapIyLTkupmO+PPQlQN1bR/b28KcweBpqAbW2HtM1kpBMFsg6ZLy1W4cCMfak3FtVdrtDh/gzd4tjAZg9UH5GfPnsXMmTOrbU9KSkJycjJefPFFLF++HHfu3MGYMWNw584dcZ8FCxZg165deO211xAfH4+0tDRMmDABarXanFUgM9N1pQJgVyqRTEiXt1xb0Ny0YNqcQSBZjqz8InEsysDObQBUNDDa+iq8tjAZg9UG5GVlZfjggw8QExMDe3v9QU1FRUXYsGEDYmNjERMTg/79+2PDhg0oLi7GF198AQD4448/sHPnTixYsABDhw7F4MGDsX79ely8eBH79u2TokpkJrquVAHW35VKRP+QIm858F5PVI2bBaFiO1FDVV6F18XRHk+GB9r8Kry2MhmD1UYihw4dwvr16zFr1iyMHDlS77HU1FSUlJSgf//+4jZPT090794dhw8fBgCcOHECANC3b19xn4CAALRv317ch+Qrsm1zHHzr31bflUpE/5Aib1m3amplIf7eXDWVGqXyKryCIMDB3s7mV+G1lckYrDYgDw0Nxb59+xATE1NtZoPMzEwAQOvWrfW2t2rVSnwsIyMDvr6+cHFxqXUfkjdXJwepi0AyIoecXzmQIm+5amKJotoWIsMF+XnByd5O/L2tn233tlQeH3J88Qs4+NZzspyMwWpvuZo3r/2PbVFRERwdHeHo6Ki33dXVFUVFFXlYxcXFcHV1rfZcV1dX3Lp1q97zZ2ZmQqmUV3dJQ4SEhODChQtSF6NJ5FAHuZDDtQgJCQEAs9fD2K+dXK6Fueqg0gJXlfaoHJb/dj0Xpfm3YN+EuFyq95OxyeX9BJj3WvhUami8mNa0fGld+SuztmsyNKIFvjh7Hfb2CtjbK/CvyBbIycpEjtQFM0BNr39NrDYgr4tWq611PmDddkP2qUtAQECjyycXhr7JLJkc6iAXcrkWUtTD2OeUw7UwVx2uZN8GsnKrbBXg7NMC7Zp7Nfq4mr9n2eC1sBxS1cMU57W2a9K+QzAOXt0pDnSe+kwf2Y3/kldt/ubu7o6ysjKUl5frbS8uLoa7uzsAwM3NDcXFxdWeW3kfIiKyPuZMH6ptsF1TB+EpFAIUCutOfdHdVOj+J2os3fgQjUYLjUYru2AckGlA3qZNm4ppgrKy9LZnZWUhMLAi5yggIAC5ubkoLS2tdR8iIrI+Zp8ysOqprDuONhqFQsC6vamyuLEw902FbupDLceliCLbNpfFjWptZBmQR0ZGolmzZvjpp5/EbQUFBTh16hSioqIAAFFRUVCr1di/f7+4T2ZmJi5fvizuQ0REVJes/KIa43FbnzdaTqQIAgVBQOq1XINSaEkeZJlD7urqipEjR2LVqlVQKBQICAjA2rVr4ebmhuHDhwMA7r//fgwePBjz589HUVERPDw8sHz5cgQHB+Oxxx6TuAZERNRYutZMcwRRrbzdcP5GHqDVomMLb6TdzLf5eaOJqOFkGZADwPTp06FQKLBx40aUlJQgMjISS5Ys0csPj4+PR3x8PJYtWwaNRoOePXsiLi4OdnZ2dRyZiKg6cwaBVDdzXgPdvNEXbuQjxN8bl7P/svl5o4mo4QQtE5SIiIgaTaPV4qdff8fA0ADs/V8mHuvchsve/23d3lRMHBgudTGsUuq1XIS39jXKsaZu+GcF8sRx/evY03KpNVrY/X2zXflnueAtPBERURMoBAFdAirWxugS2JzBOElOpdbozURSWxBedT9LZqcQkLDrFABg1tPdJS6N8TEgJyIiaiJfd+eK/92cJS4JUcU0gdFLd9a7387Z0WYoDRnCOm6LiIiIbIRao6nxZyKSL7aQExERWRA7hQILth8FALz5bC+JS0NE5sAWcmowc66CR0TmI8UCKDrKMpUk5yWqC3sryFwYkFODmX0VPCILp64UxKqteJlwqVbBy72jxA//y0RukdLs5yaqi663YsH2o7BTMGQi0+G7i8jK5RSUSF2EJtG1yErVMmsMutH/CbtOyW4qLlPTaLU4k5kNADibkc3eN4mxp4JIGgzIiazYufRsDHn7C5zLyJa6KI2mUAhYtzeVC+rYqKs5t3FXpQYAlKrUSM8pkLhEtos9FUTSYUBuI9jqIT8qtQYLth+FFsDC7UehUjO/kaxLabkKF27ki2k+ao0W52/kobScf6/MzRQ9FWqNBhMHhjP3msgADMhtAFs95Gn70TTkF5UCAPLulGLH0TSJS0TUMFn5RdBqtYjuEoToLkEAAK1Wi6z8IolLZntM0VOhy79m7jVR/fgpkTnmZ8pTbqESyXtSxJ4PZZkKa/akIO8Ob7oagj1H0mrl7QahygBxQRDQytut0ces3NpOhmFPBZH0GJDLHPMz5WlPSobYDTxhQDiAii/RPSkZUhbLqrDnSHpODvYI8feGIAgQBAF2CgEP+PvAyaHxS2ToBthycK3hdD0VlbGngsi8GJDLGFs95GtwZCDsFAocfOs5TBgQBqAiEBkUEWiW81t7yzJ7jixHkJ+X+LOTvR3a+nlKWBrbZIqeCiJqGK7UKWN1tXq0a+5Vy7PIGvi4O2PyoAi4OTkCAJwc7TFlUAR83J1Nfu7cO0ocvnQdvYNbwtfN9OczhZp6jhr7mVBrNLBTKDDr6e7VtlH9Kq9p0CWwOdc4kICup0LXgGOMngo50H2OK6+Wys82mYptf9pkrpW3G87fyAMqBeVs9ZCPZ3t1FH/2dXfCiEq/m0rVluXHOrexugCqtp6jVt5ujQpA7BQKTN2wT29b4rj+RimrrbHWGzw5CPLzQkZOAVr7eOBaXiF7KlDx2X5p7V69bR9MGihRaWybLTR8MCCXMbZ6yJu9nUJMtVj4bC/Y25n+D5MxW5alwp6j2um+4OT2RUf1UwgCugQ0h6+7M+71cLa6G22SN1to+OBfXJkL8vOCk70dAOZnypFCEKAQBEQGNjf5ueQyJoH5srWz9mnqdAOdq7aiWRNded98tpeYKmGuOvj+nfLGngoi82NTqczpWj0OX7rO/ExqElO1LFdePMQcgSB7juRLDq1oTJMgsk3W2QxCDeLr7ozBoQFs9ahCo9FCw7mKDWaqlmUpWmV1PUcdW3iz58gCaLXaajd7RES2hE1CNsLZkZe6KgXnKW4QObUsM1/WslS90SMisjVsIScig8lpTALzZYmIyFIwICcig+lalgHOGU1ERGQs1tfXTJLT5V0z5cM26cYkMA2KiGrDRXWIGobfqNRgDMQth1Q3RwzGiaguUs4WoxsgzLEJZE34rUpkxXhzRESkj4E4WSP2HRER2bjKC8+YezEaIiJiCzkRkc3jYjRERNJiQE5ERET0N5VaA3u7igSC2m5MK+9DZAwMyImIyGpVDYwSx/U3aD+i2tjbKfDk4q/q3Gf360PNVBqyFQzIiYhIMlqtVhyEV/lnQ9nbKRC9dGe9++2cHd2o8hGRdCrfSMv9ZpsBOTWYbi5ZzilLRE0lCAJSr+UCAMJb+0pcGiKyJIbccMvlZpsBOTWYnUKBBduP6i34QGRtOFcxWRJD8par7kfypNFqoRAEvRtU3TaSLwbkRGSTGIiTJTEkbxlg7rItUAgCjl2+qbetZ/sWEpWGzIUBORERETVZ1dZ7zlBCZDgG5GQ1lGUqLtlORGRCTUnlYis/UePxFpWsQu4dJX74XyZyi5RSF6XJlGUqqYtAREamC2R1/1srQRCsPp1Lq9Va/XUg28OAnCyeRqvFmcxsAMDZjGxorPgPrZxuLIxBtzw7l2snaycIAi7cyLf6YFYO5HBTQbaH/f9k8a7m3MZdlRoAUKpSIz2nAO2aezXpmFJM3Vj1xuKxzm1sftR81SXbuVw7ERHZIgbkZNFKy1W4cCMfak1Fq7hao8X5G3lo5e0GJ4fGv32lmLrR2DcWlW8mOCc8NQan2iMisgwMyMmiZeUXVcsF1Gq1yMovanIruTmZ4sZCd1MBgHPCU6NwEB4RkWVgQE4WrZW3G87fyAMqBeWCIKCVt5uEpWo4udxYEBGRZamtB6uxK1iyR0waDMjJojk52CPE31tsXbZTCHjA36dJ6SpSqHxj0bGFN9Ju5lvljQUREVkWezsFus7carTjnXk3xmjHIsNZV1RDNinIzwsZOQUoLlPByd4Obf08pS5Sg1W+sQjx98bl7L+s8sZCTpoy3zKg34qUOK5/vfsQERHVhtEAWTyFIKBLQHP8eUeJez2crXZmEt2NBQCrvbGQk6ZOi2Zvp0D00p117tPYLmMiIrItDMjJKvi6O8PX3VnqYjSJ7sYCALoENrfaGwuyHFqtFoIgiP8TEZF1YkBOZEa6mwpfN+u+uSDLoFuMJsTfW+qikEw0NZWLqLGMOTjVGtMFGZATWSHdvOOVpzvkXORE0tFqtQjx97b63gprLrtOQ3qOagvcGjPVpzUGgZbEmINTrXFgKgNyMljlgK/yUucMAs2v6gqXAFe5JOuj0WqhEASEt/atts3aCIKAM5k56BrgJ3VRbF5DroWtB4FkORiQk8EYBBKRMSkEAccu39Tb1rN9C4lKQ0QkHTZtElGDabXaagsdERERUeOwhZxsDvOvm04OeaZEpK9yznV9+dfGzL2u63hEtoIBOdkcpt4QEVWny70GUG/+NVeHJEMpy1RwdmS4WR++QkRERGbEmT3IVuTeUeLwpevoHdyS0/3WgwE5EZlV1QCitt4JBhpUk5reF41dEVWq9xhn9iBboNFqcSYzGwBwNiMbj3VuY5UzKJkLA3KyGlywQh7s7RR4cvFX9e7X2FxUkjcGs0TW4WrObdxVqQEApSo10nMK0K65l8SlslxsfiKrIQgCg3EiIiILV1quwoUb+VBrKhrS1Botzt/IQ2m5SuKSWS4G5ERERERkNFn5RWKvdscW3gAqermz8oukLJZFY8oKEZGVashy7ZymjuSu8iqvullirHXlV2vXytsN52/kITqyLQAg7WY+BEFAK283iUtmuYwekJeWluLLL7/EqVOnUFRUBC8vL3Tt2hXR0dFwcXEx9umIiBqsIfMtWzKplggHmH9NlkchCDh88brett7BLSUqjW1zcrBHiL+3+LudQsAD/j5wcmA7cG2M+srk5eVh1KhRyMzMRHBwMHx8fHD16lV8//332LJlCz799FP4+PgY85RERA0mCAJSr+UCAMJb+0pcGiIi+Qny8xIbO5zs7dDWz1PiElk2owbkS5cuRWlpKb799lsEBQWJ269evYoJEyZg2bJliI+PN+YpiYiIiMjCVO53fKwTpzysj1ED8oMHDyIuLk4vGAeAoKAgvPzyywzGiWTC1qagrC1fujHzXzP3mohsAXsiG8boyTweHh61blcqlcY+HRlAlyNrzbmyciGXQNbay99QnPuaiIhMyajNNKGhodi6dSvUarXedpVKhc2bNyM0NNSYpyMDCYKACzfybS6IskScS51IXjR/32QD+jN7EBE1hFFbyGfMmIHnnnsOAwYMQP/+/eHr64vc3Fzs27cPubm52LJlizFPR2ZSuYu9tmXOq+5HRGQLOLMHERmDUQPyjh07Ytu2bVizZg12796NwsJCeHp6omvXrpg0aRI6depkzNORmXCpcyIiIjKUbv73yrnjnBO+bkbPIQ8JCUFiYqKxD0vUpPzrqq33tbX0s5Vf/vhFQVSd7jNQeU57fi7Mp+pr3bN9C4P2s1QKQcCxyzf1ttVWJ6rQ5IB806ZNeOqpp+Dr64tNmzbVua8gCBgzZkxTT9lg+/btw4wZM3Du3Dlxm1arxdq1a7F9+3b89ddf6NKlC+bNm1dthhiyHE3JvZZLK78h6UO8qagbvyiIqmPqjbQUgoC9v/5e734DO7cxQ2lICk0OyJcuXYquXbvC19cXS5curXNfKQLys2fPYubMmdW2JyUlYf369ZgxYwZatmyJ5ORkjBkzBt999x3c3d3NWkYiQxlyY2HpNxVERESkr8kBeVpaWo0/S62srAxbtmzBqlWr4OLigvLycvGxoqIibNiwAbGxsYiJqZiCrFu3bnj00UfxxRdfYOzYsVIVm4iIiIhsjFH7tefOnYtr167V+Fh6ejomT55szNPV6dChQ1i/fj1mzZqFkSNH6j2WmpqKkpIS9O/fX9zm6emJ7t274/Dhw2YrIxERERFRk1vIz58/Lw62+/rrr9GjRw8UFhZW2+/nn3/GsWPHmno6g4WGhmLfvn3w8PCoNsg0MzMTANC6dWu97a1atcL+/fvNVUQiokbjIDwiIvlockC+ceNG7N69W1zwZO7cudX20QXs0dENX2a6sZo3b17rY0VFRXB0dISjo6PedldXVxQVFZm6aERETWbNg/Aq3zhY+2wSRETG0OSAfMGCBRgxYgS0Wi1Gjx6NN954A+3atdPbR6FQwMPDA+3bt2/q6YyiriXkDZ3JIzMzE0ql0pjFMpmQkBDx5wsXLjTp+fVpzPEN1bFjRwCNG6tgKXXQlaOx5zC0HqasQ+Vy1HeeoHbt4ehQ/c9MYweelpWrcPXK5UY9V6e217CuujTk/WMIY1yfhtbD2HWo61z1CQkJqXdGiYGd29R4fEu7Fnw/GU9j69G+fQfY29sBqPumVKVS4/LlS3rbLOVaNOU7ypKuhY5cPhfGYGi9mhyQu7u7o3v37gCArVu34oEHHoCbm1tTD2tS7u7uKCsrQ3l5ORwcHMTtxcXFBs+wEhAQYKLSmZYpPrjmPL45zsE61E3X46XVag06T9eZW4127jPvxpisbua47uY4l1zqYY7jm/IccrkO1lKPPf+rf8rAQaFt+J418Tnq6/0KCQkxW++XOd+7xmDUhYG6d++Ov/76CwcPHkRZWZneF7dSqURKSgoWLlxozFM2Sps2baDVapGVlYXAwEBxe9Xfiag6QRBwJjNHL3eZiMjaXLlyBZcuXUL59fNGO17VDAFbY8h86pxLvWZGDch//PFHzJgxA3fv3hVTPyqnh1hKq3JkZCSaNWuGn376CS+99BIAoKCgAKdOnUJsbKzEpSMiIiIiW2LUgDwpKQkPPPAA3njjDXzyyScoKyvDxIkTcejQIaxYsaLGAZ9ScHV1xciRI7Fq1SooFAoEBARg7dq1cHNzw/Dhw6UuHhEREZlYu3bt0K5dO8z/OddoxyNqLKMG5Onp6Vi5ciVCQkLw0EMPYd26dQgKCkJQUBAKCgqQnJyMRx55xJinbLTp06dDoVBg48aNKCkpQWRkJJYsWcJVOomIiIjIrIwakNvb28PV1RVARXpKRkaGOHAyKioK27ZtM+bpDDZ16lRMnTpVb5u9vT1mzJiBGTNmSFImIiIiIiLAyAF5p06d8OOPP6JHjx4ICgoCAJw+fRpRUVG4efOmMU9FDaDVahHi713ndI9ERERkXYw9MFV3TKbfmJ9RA/IJEyZg4sSJyMvLw4oVKzB48GDMmDEDvXv3xv79+9GzZ09jno4MxFkxiIiMq/LUbXXNfc0FjojIEEYNyHv37o3PPvsMV65cAQC89dZbWLRoEVJTU9GvXz/Mnj3bmKcjIiKShEIQDJ77mshUjD0wVXdMMj+jBORlZWU4ceIEAODBBx9EWFgYAMDZ2Rnx8fEoLy/HRx99hMGDB+PkyZPGOCWRVdKlDTF9iIiIiHSaHJCnp6dj/PjxYo64v78/Nm/ejNatWwMADhw4gPj4ePzxxx/w9/dv6umIrBrTh0gKzDMlIrJsTQ7Ily1bhuLiYixatAiurq5YuXIllixZgpUrV2L+/PnYtWsX3Nzc8NprryEmJsYYZSYTUqk1sLdTVNu++/WhRjtWY1RuUbaV1mVLvRZERETGxFVTjRCQnzt3DlOnThUX1PHx8cHEiRMxe/ZsfP/99xg2bBimT5+Oe+65p8mFJdOzt1Og68ytRjnWmXeNdwOma1kGYDOty5Z6Lcj6MM+UiMiyNTkgLywsRMeOHcXfQ0NDUVpaimPHjmHTpk146KGHmnoKIiIiWZBDSyBToMjYuGqqEQJytVoNR0dH8fdmzZoBAObMmcNgnCRhzFSPuo5HZG5Vp9Crbbo9TrVHRGRdjDrtYWXt27c31aGJ6mTMVA+A6R5kOTjVnvWzpJbAxt7gMQWKyPhMFpDbwqA7IpI/OaQYENWEN3hElsMoAfnSpUvh7u6ut23x4sVwc3PT2yYIApKTk41xSiKSgK6lrPLAWqZHEJG51Pb3pjE3DVL97artvAM788bHljU5IH/wwQcBAMXFxXVus0R//fVXjXnugwYNwvvvvy9BiYgsm0IQcPjidb1tdS0bLgeWlGJAZOsUgoCvz1wxyrGe6SrNZ9GYdQCkqwcZV5MD8o8++sgY5ZBEWloaAGDjxo1wdXUVt3t5eUlVJCIiIiKyMSbLIbcGFy9ehK+vL3r16iV1UYiIiIjIRtn0XG4XL15EcHCw1MUwKY1WC+CfxXR0vxMRERGRZbD5gFypVOK5555DaGgoHnnkEXz44YfQyiho1eX86v5x8B0RERGRZbHZlBW1Wo2rV6/C2dkZs2fPhr+/Pw4cOID33nsPpaWliI2NrfP5mZmZUCqVZipt44WEhFTbduHChQbt3xR1nashqpbLnHWo73wNUbls9R3TEq9FbWWq7diWeC0aWoe6ntNYja1DQ8pR9RyWdC0MLUvV41+7dg0qlcqoU1CWl5c36rmWdC1s6f1kivMakxzqABhWj/btO8De3q7a9sbMFqNSqWs8VlMY63u7qQy93jYbkAPA2rVr4e/vjzZtKt48PXr0QElJCT788EO89NJL4qqjNQkICDBTKY3PnH8MTHUuc/9BM8X55FAHcxzbXOeSQx3kdo6qx3dwcMClS5dMeg5TMPfrxHNIRw51AAyvhyXPeGNt18JmA3I7OztERUVV23PVh8cAACAASURBVN67d2989tln+P3339GhQwcJSkZyVHne2cr5/EwhIjIcp6AkIrmy2YA8OzsbBw4cwIABA+Dt7S1uv3v3LgDgnnvukapoJEO2OH83ERERGcZmA/KysjK88cYbUCqVGDNmjLh9z549CAgIwL333itd4UgPV4ckIiIiObPZgLx169Z48sknsWrVKgiCgKCgIPzwww/Yu3cvkpKSpC4eVcLWZSIiIpIzmw3IAeCdd97BmjVrsGXLFvz5558ICgpCYmIi+vfvL3XRiIiIiMhG2HRA7uTkhOnTp2P69OlSF4WITKxymlPP9i0M2o+IiMgcbDogJ6L6VQ1Qa0sXsvRAViEI2Pvr7/Xu15g5dEneantvDwq1rvdKTfWwtjpQdSq1BmfejTHq8eztbHrdSEkwICeiOikEAXv+V38gW/WL/cqVK7h06ZLRFnHRHZNT1ZG5KQTBaPMtA6aZc9kQxqyHVHWg6moLnqOX7qz3uTtnRxt8PDItBuQkMnYAxeCJiIiIDGHMln5rbOVnQE5EJmHsRVx0xyQiIvlpbEu/XFr5GZCTiKvgEREREZkfA3IiIgskl4GERJbM1tMkyHIwICeyQMznJ7kMJCSyZLUF0E8u/qrO5+1+fajBxyIyBANykhXO7EFERETWhgE5kQViPj8REZHtYEBOssKZPYiIiMjaMCCXIbmsrFi5fHKuQ9X9iExBqtX8antvN2ZFVH5OiEiuGJDLUGNXVrQ0htRDDnUALL8eZP0aO3gNaNoANq4OSURUPw4JJiIiIiKSEANyIiIiIiIJMSAnIiIiIpIQA3IiIiIiIglxUCcRUT24vDYREZkSA3IionrUFkBHL91Z5/N2zo42+FhERGS7GJATERFRg0g1rz2RXNl0QK5Wq7F161bs2LEDN2/ehL+/P55//nm88MILELj4BBERUY2kmteeSK5sOiBfs2YN1q9fjylTpiAiIgKnT5/G4sWLoVQq8dJLL0ldPCJJ1LQaIhcuIjIejkkga3flyhVcunQJqem3jHK8UE+gXTvbXvjLZgNytVqNTZs2Ydy4cZg8eTIAICoqCvn5+di4cSMDcrJZXFmRyLSM2brMYJxIHmw2IC8qKkJ0dDQGDhyotz0wMBD5+fkoKSmBi4uLRKUjIiIiskzt2rVDu3btcNdIjTe23joO2HBA7unpiTfeeKPa9p9//hn33Xcfg3EiIiILIoc0CWPXAWC6h1zYbEBek88//xzHjh3DvHnz6t03MzMTSqXSDKVquJCQEIP3vXDhgt7vQe3aGy23saxchatXLjf6+YbWo2odGlJ/Q1U9h6Gaci2MXQ9D62CK18+YDKlH+/YdYG9vV237wM4Nz4VXqdS4fPlSte2NfX8ayhzv46a8Pw1l7HqUlavM/jeK18Kwcpi6DteuXWvwcxpTDlN+tk1RB0Ce76eGnKOxdTAHQ+vAgPxv33zzDRYsWIBBgwZh5MiR9e4fEBBg+kKZgaFvlMbkNjo62JsluOM5LOP45mJoPYyZB9+U186SXnc51MPRofrXVn3zwQM1zwlvrr9RNZHDtTB3HXTPsZQ0CUuoA1BzPVRqTY3v+Zr2s9X3k6VhQA5g06ZNWLp0Kfr164dly5ZxykMiIiKyWlUH+07dsE/8OXFc/1r3I+nYfEC+fPlyrFu3DtHR0XjnnXdgb2/zLwkRERERmZFNR59btmzBunXrEBMTg9dff50t40RkU1RqTY2LtNS0H1vSiIhMx2YD8pycHCxbtgwdOnTAE088gdTUVL3HO3fuzNZykhQXDyFTq/yeeGntXr3HPpg0sMb9iOTOkBtV/k0lY7PZiPPIkSMoKyvDpUuX8Oyzz1Z7/Pjx4/D29pagZGStalrhEmj8KpcKRc09NvUNsOWy1EREjWfIjSr/ppKx2WxAPnToUAwdWn9XLZGhjLnCJSCPVS6N2cqvOx6/CMmc5DJvtBzm8CaSM5sNyOXC2K2ytR3PmjAItBzGXCK8ruMREVHN1BqN3swqao0Gdgrr+FtqyPSNcvmOZkBu5dgqWx2DQCIyFmMvEa47prlxqXPbpQu+E3adwqynu1tNMA7of/9WnroR+Gf6Rrl8RzMgJ4tgzJZ+ObTyExERke1gQE4WwZgt/XJo5SciIiLbIY92fiIiIiIiK8UWciIiMgnO7EFEZBi2kBMRERERSYgt5EREjWDK6bjkMvc1Z/YgIjIMA3Iiokawpem4iIjItBiQU71Uak2N83PXtB8DEKKmk8vc10REZBgG5FSvykH2S2v36j32waSBNe5HRES2hY03RI3HgPxv+/btw4wZM3Du3Dmpi0JERDZGDsFs1XLV1oBjqeUnkhIDcgBnz57FzJkzpS4GERHZKPZEEtk2m/5kl5WV4YMPPkBMTAzs7XlvQkRERETmZ9MB+aFDh7B+/XrMmjULI0eOlLo4RERERGSDbDogDw0Nxb59+xATEwNBEP4/e/cdFtXR9gH4twVYunQbRUGKYEGFiEQBo4i9g1GTGI2aKCbGz1RJ9I2J0TfF2JNYEo0mij3Ggr1hfLEAioKCiHQMAlKk7e58f5A9soIKuOzZXZ77unJJDsvuzM4pz5kz8wzfxSGEEEIIIS1Qix6nYWdnx3cRGicwsO6206fV8znP+qynvb4xVF2PJ8vUmPdvan3U0RYN/Ywm1iGluhq3FyxQ7VLnb72lvLGZ20ItdaituY4LOrbrp6vH9vM+SxvaoiGfpWnHhS7UAXh+mWp/pir2pSffUxWeUS7ZX39x6ztw20pLIRo2rOHv3xzfuwq16ID8RaSlpaG8vFy9Hzp3bp1NHs3wMYn1fM6zPutpr28MVdfjyTI15v2bWh91tIVzZdVzMzFUVVbhThPrkJGX16S/e5am1AFoej3UUYfamuu4UOex7VJZpTRxEACqK6uQQsd2oz+joXTxPNuQz3rRenSqqsbonqrJqS+tqkbyM8rTXMeFOven+j5TFfsSoN62wPHj8Bg1Cv/dHwMA+HCkL24fP15vXPRUiYkvWMqm8fBoWItTQN5ETk5O6v/QBjbqC3/MqGcvB/6ir1eHFymTJtXnWWV5WiYGfQP9JtdBsYepdKnz5yxIU7setS9+Ta0HH3WotxwatB8pNKRMC3dE4z9h/tB7gf2oObWEY1sVr1eHJh2fzVSPqOv3nvuaQV0clf5frK/X4GMCgMqOCzljKgtkFe/XmDI19750NOHZbRHs5VhnW0PbojZNPCZeBAXkpEXQhRy/hJC6pDI59n30/AszHdtEUwifMmfteYEsUH8w+7T3I9qFAnLSItCCFYTopieP2TkbTyj9v2LcKR3bhBBNRgE5ISqQkpKC27dvq2wiIfDvZMImDJUghBBCiHahLgNCCCGEEB0mkzN8ONIXMjnjuyjkKaiH/F9z5szBnDlz+C4G0VIuLi5wcXFR2URCxXsSQgghL0okFGB7dBIm+LvzXRTyFNRDTgghhBBCCI+oh5wQQgghhGg0xbAbxc8ioW5ll6GAnBBCXoBMLq+7gpxcDpGQHkASQoiq1A7AdS0YByggJ40gk8vrrFpGgQdp6RT7f+0V5OiYIIQQ0hgUkJMGqx1kKFbzU0XgoeqUgZQukBBCCCHahLpxCCGEEEII4RH1kBPeqTplIPWOE0IIIUSbUEBOCCEEQM2ckP+E+dPcEB7RXB1CWiY6wgkhhAB4PE+Egj/+KL77hTuisXBHtNI2Qojuoh5yQggh5BnkjGF0T9UNhZMzBqFA99K2EUKajgJyLabq7CQAZSjRFvRYmxD1eVrwHHX93nP/dlAXxwa/HyGk5aKrNyFaiB5rE0IIIbpDp3rIIyMjsWHDBuTm5sLDwwMff/wxvL29n/r6t99+G6dOnaqz/erVqzA2Nm7OoqqEqrOTKN6TEMI/GibReLRqKiFEW+lMQL53714sXLgQs2fPRpcuXfDbb79h2rRp2L9/P+zt7ev9m6SkJLz++usYOnSo0nZDQ0N1FJkQQp6Khkk0Xu1VUz8c6au0jZCWTC5nmODvDrmcQajFy87L5QwAtLoOT6MTATljDKtWrUJoaCjCw8MBAH369EFISAg2b96MiIiIOn9TXFyMnJwc9O3bF927d1d3kQkhhBBC1EIRwGp7IKvt5X8WnQjI7927h6ysLPTv35/bpqenh8DAQJw7d67ev7l16xYAwM3NTS1lJITU9eTkVBpeQAghpCXSiYA8LS0NAODoqPyY1t7eHunp6ZDJZBCJREq/u3XrFvT19fHDDz/g5MmTqKioQEBAAD777DPY2Nioq+iEtGi1J6f+J8yfgnFCdARlgiKkcXQiIC8tLQWAOhMxjY2NIZfLUV5eDhMTE6Xf3bp1C1VVVTA2Nsbq1auRkZGBH374AW+88Qb27dsHfX39Z35mWloaysvLVVuRJvDw8FD5eyYmJj7z965ubvhPmD+qpVKkJCer5DNVXY9n1cGlU6c6FwpV1EXdbfHk5z2v3RpKnW3x5Oepqg6dOrmqbEKkVCpDcvLtZ76mudrieZ/zLE0pg+L9VVl+Xdifnvc5z9PYcrh06oT/hPkDUM25SYGP40Jxs92cbdGpk2u98yae1JAyP6m52uJJnVxdEezVgDrIZEi+3bg6KHh4eDRrOyg+oyGaWo7mOEc1t4Z+JzoRkDNWM8hf8JRJS/VtnzJlCoYOHYrevXsDAHx8fODs7IzQ0FAcOnQIo0aNeuZnOjk5vVihNVhDdx49sbhZglBVaEi5FBcKQHPr0pgyaWL5geeXq/Zy7c1dh6MJz58Q+eRFUSwWNbpcmtAWL1IGVZZfldli5Ixp5THxIuVo7nNTUycJN/a4UFdbnLuVpfT/fd3acT835ViuTZ3XiQvJOUr/36dTGwCAWPRiddCFY0IVf6+JdCIgNzU1BQCUlZXB2tqa215WVgaRSFRvCkNnZ2c4OzsrbevWrRvMzMy48eWEtESqDqCel91DJBTip6PxmBncTSWfSTTL09r/eTdH9fUWtoRMMYSQlkknAnLF2PGMjAylceQZGRlP7ck+ePAgbG1t4ePjw21jjKGqqgoWFhbNWl5CNFl9QQ+l2iOEEEKaj07MrnByckKbNm1w/Phxblt1dTVOnz4NPz+/ev/mjz/+wFdffQW5XM5tO3PmDCoqKtCrV69mLzMhhBBCiDrEpubV/Hs3j+eSvBg5Y5D/O0xZ1+hED7lAIMD06dOxePFimJubo0ePHti6dSsKCwsxZcoUAEB6ejoKCgq4nOMzZ87E9OnT8cEHH2DMmDFIS0vDihUrMGjQIPTo0YPH2hBCCCGEqIZUJsfCHdH485MxWLQjGrs/GAWxqHn6Y+WMPXdy6ousGqzLT111IiAHgEmTJqGyshJbtmzBr7/+Cg8PD2zcuJFbpXPt2rXYu3cvNz68b9++WLduHdasWYPZs2fDxMQEY8eOxXvvvcdnNQghhBBCVGZHdBIKSivw09F4PCipQGR0Eib269wsn1U7YH7axFRdDqpfhM4E5AAwdepUTJ06td7fLV26FEuXLlXaFhQUhKCgIHUUjTyHuicSEkIIIbouv7gc66LiUF4lxc/H4gEAa6PiMMi7A6xMDXkuHalNpwJyor2eFkA/bzKhJk0kVOVNheL96MaCEEJIU0XF3YWs1lw5AJDJGaLi7mJi3+bpJSdNQwE5ISrS1JsKQLNuLAghhOiGEO8OWBcVB+BxUC4SCjCoewf+CvUCnrfujDajgJwQLaVYUEfxMy1JzR+ZnOHDkb7czyKh7l0sCCHax8rUEO8M6s4NW5HoizFrUHetHK4Sm5oH7452iL2bB+8OdnwXR+UoICctUu3VIbU1kK1dbm2tg66oHYBTME4IeRFyxrgJkLW3NfWpaZi/OyIvJCHzQSmsTSUI9XdXRTHVSpEpZmhPZxy6eqdZM8XwRbdqQ0gDKQJYCmQJIYRoEkXgHZ+RX2dbU4hFQiwK9YcAwKIwf60MZBWZYn4+9jhTjK7RvlYhhBBCCCEN5t3RDocixmnlUI/amWIAoLxKirVRcXhQUs5zyVSLhqwQQngllzPMDO4GuZxBSMM9nknOWL0TgOt7XVN61OTymglT2twOivH8zT2Wn9pC88gZQ1+3dnW2vegEeV1pC1tzI76L0CQtJVMMBeSEEF4pLnLafrFThycDi3O3spT+XxGMNHkVPB1oA0UQ3txj+Wt/x09rhydf16j314G2ULfa3/WVtPvo6WSrkmxV1Bb80rVMMU9DQ1YIIYQQonK1J88T0lSKTDGG+jV9yNqcKeZZKCAnjSaXM+4RHiGEEFIfkVCIn47G0+R58sLC/N1hZSoBAK3NFPM8NGRFy/GxOiQ9viOEEEKIuigyxUxfF6W1mWKehwJyLUerQzadrkzUIYQQQnSdIlOMtk5OfR4KyEmLRYF4wzR3Ngl1kTOGYC/trwchRL3k/y7XTucF/ulqMA5QQE6IVlNHL39zZ/ZQl9rlu5Cco/S72qviaXo9CCHqRecEog4UkBOixaiXnxBCCNF+FJATQghRq4YMH6KhQ81PV4ajEaILKCAnjUbj6QghL6Ihw4fo/NL8dGU4GiG6gAJy0mjqOjk3pPdG03tuqAeKNJfmWiacEEKI+lFATjRWQ5am1vTgo7mX1yYtl2KfUSwRXnsbIYQQ7aJ7mdUJIYQQQlSEMQbGaHVq0ryoh5wQQgjRMDRXR3MIqA2IGlBATgghRKeocxXe5hrLT4E4IS0LDVkhhBCiU4RCgdpy9NcOnK+k3a+zrSWTyxlmBnfjbpAIIU9HATkhWkzOGPdomxBCNInipogWMCPk+WjICiFaTN2XOV1ItSdnjMt1XXubNtVBV1BbkObAGENPJ1swxmj8N9Ea1ENOiBYTCARqveDUTrX35DZtoShvfEY+4jPylbYR9aK2IM1BcU6kYJxoEwrISaPFpubV/Hs3j+eSEKI55HJGY2UJIYQ0CQXkpFGkMjkW7ogGACzaEQ2pTM5zibSDYqhH7f9o7LduUedEQkKIbmOMoZu9NeU/b0EoICeNsiM6CQWlFfjpaDwelFQgMjqJ7yJpBcrEoFkUFzu64BFCNBENu2l5aFKnDpIzhkFdHBv0usYEhfnF5VgXFYfyKil+PhYPAFgbFYdB3h1gZWrY5PISom61L3J0wSOEEMI3Csh10JNB9rlbWdzPtTNkNLaHNiruLmRy5SEqMjlDVNxdTOzbuQklJYS8CMomoTm0vS10IYMSIdqMhqyQBgvx7gCRUHmXEQkFGNS9A08lIqRlEwgESMwu0MoAUNdoe1vUzqBEw+oIUT8KyEmDWZka4p1B3WGoX/NgRaIvxqxB3Wm4CtFKjDGVjh+nRZoIUUYZuQhpOBqyQholzN8dkReSkPmgFNamEoT6u/NdJEKaRNU9mdSbSMhjioxcQ3s649DVO9j9wSiIRdQH2FLQol+NR0cHaRSxSIhFof4QAFgU5k8n2Bao9lhZQl4UZbzRTYqMXD8fo4xcLREt+tV4FE2RRvPuaIdDEePg3cGO76IQHlA6LqJKlPFG99TOyAUA5VVSrI2Kw4OScp5LRojmooCcNImtuRHfRSCEEKKBnpWRSxuper4JIfWhgJxoPFrlkjQHusgS0jz4zsil6mM7MvoWen34GyIv0LAb0nwoICcaj9JxkeYgEAhUOkSCAnxCavCdkUuVx3ZFlRTfHbgEAPh2/yVU/DsMhxBVo4CcEEJUQNUBPiHaLMzfHVamEgDQ6oxcn247B5m85kZbJmdYsO2c2j6bbvJbFgrICSGEcO4/fMR3Eci/tLktdCEj1+3sQpy5maG07fTNDKTkFKrl8+kmv2XRviOEEEJUIP/fjA/5pS+e+SE2NQ9lFdV1JrJpm9jUPAz5cpfWL+SiC4s06UJbaHtGrh+jYuvdvi4qrtk/W5XnJ6IdKCAnhLQ4csZwJa0m0Ll6N++FgjfFAij9PvsDY/67D1KZdgblinowAIt2RGttPYCaOSbaPM9El9pCmzNyzQrxrnf7O4O6N+vnqvL8RLSHTgXkkZGRCA4ORteuXREWFobY2Prvbp9UWlqKoKAgHDlypJlLSIhu0PaxjXfuF6FSKkNidgEqpDKk3n/Y5PdSLIACQO0LoDDG4NHWUiVtwWc9iDJqC83g0sYCAZ3tlbYFdraHSxuLZv1cxfkJwAufn/hEi341js4E5Hv37sXChQsxYsQIrFq1Cqamppg2bRoyMjKe+XelpaWYNWsWsrOz1VRSQrSfNo9trKiWIjG7ADI5Q1JOzb83sx+gorrx2RP4XgBFVYs08V0Pbb/BA1R3c8R3WxBlSyb1hUhYc3yJhAJ8Nalvs35e7fOT4t+mnp/4Rot+NY5OBOSMMaxatQqhoaEIDw9HQEAA1q1bBwsLC2zevPmpfxcTE4Px48cjKUl3ex+ezOFNj774RcvO8y+zoLTO988YQ2ZBaaPfS1cWQOG7Htp8g6egqpsjPttCcX6ic9RjEn0x/m+4DwBg/kgfSP5N5dhcap+fknIKADT9/ES0i04E5Pfu3UNWVhb69+/PbdPT00NgYCDOnXt6iqLZs2fD1dUVGzZsUEcxeVE7h3ft/yf8oGXn+dfe0qTO9y8QCNDe0qTR78X3Aiiqoiv10AV8tgX1aNYv7GV3/PbuUIT2af7Ujao8PxHtohMBeVpaGgDA0dFRabu9vT3S09Mhk8nq/btt27ZhxYoVsLS0bO4iEg2kzSnFSNNJ9MTwaGup9Bi6c1srSPQa3/PF9wIoqqIr9dAF1BaaqbO9lVo+R5XnJ6JddKKFS0trHuUYGxsrbTc2NoZcLkd5eTlMTOreXbq6ujb5M9PS0lBerh1j+jw8PLifExMTeSxJ09WuA/Di9UjKLcGXh5Lw2RB3uLU2faH3agxFPbS1HRS0vR6MAUImhgyAkDFUPchBYkFOk96rmxVgaiBCeZUUZgYidLVS7/fi4eGhks/jsx7avj8pqLItJGIByqsAQ7GA2qKRtL0Oqjw/8U3b20IVnoxfnkYnAnLFeKunPWJrjkdvTk5OKn9PdWjojqHpXqQeUpkcH+3bBwD45X+Z2P3BKLUvWkHtwD/bknKcu52F3q7tYG3yYr2PSwytMH1dFJa8FggvHnIuq6oddKUefFJFHaQyOUSiGwCqIRaL4e7uTueoJtDmOqjy/KQJtLkt1EUnhqyYmtb0cJaVlSltLysrg0gkqtNzTlo2SilGAMDa1BAhXZxUcrHT9gVQFHSlHtpuR3QSyiqrAQClFdV0jmqBVHl+ItpBJwJyxdjxJ1McZmRkaG1PNlGmqtn/lFKM1GaowowJ2rwASm26Ug9tRecooqDK8xPRfDoRkDs5OaFNmzY4fvw4t626uhqnT5+Gn58fjyUjqqKq2f98p3cjhJBnoXPUi9OFvPak5dGJgFwgEGD69OnYvn07li9fjjNnzmDWrFkoLCzElClTAADp6emIi4vjt6CEd5TeTTXogqe7KPsQv+gc9eJ0Ia89aXl0IiAHgEmTJuHDDz/E/v378e6776KkpAQbN26EvX3Nsrdr165FWFgYz6UkL0IVQSClFFMNuuDpptjUPAz5chdi7+bxXZQWi85RhLRMAkbdXC3ClbT76Olky3cxNIJUJsfYb/Yh80Ep2luZqDXLyvMyAhHCF6lMjjH/3YesAvUfF0QZnaOIrqD9qeHobEtaHLFIiEWh/hAAWBTmr9agg3qWiaai7EOag85RRFfQ/tRw1EPeQlAPeV33Hz6ijBKEoCazx6hle7nMHkBNhof9H4+moRI8onMUIS0H9ZCTFosudITU4DuzB00Srh+dowhpOSggbwEUObzpgkcIqQ/fmT3osbbmoJsjQvhBAXkLIBAIkJhdQBc8Qki9KLMHUaCbI0L4QQE5IYQQhPm7w8pUAgCwNpUg1N+d5xIRQkjLQQE50Rr0KJWQ5sNnZg9CCGnpKMtKC5GYXQCPtpZ8F6PFyy8ph7WpIfJLy2Ftop3DAXShDuTpKLMHIURVKA95w1EXCCFqImcMV9JqVkC8ejcPci28F9aFOpBno2Cc6ILaKTwJf2hOQsNRQN4CMMbg0daShnvw7M79IlRKZUjMLkCFVIbU+w/5LlKjKeoAQGvrQAjRbfkl5ThyPQ35peV8F4WQBqOAvAVQ3J1q+12qNo8hr6iWIjG7ADI5Q1JOzb83sx+golp7enFq10Hxr7bVgRCi2+gpHtFWFJATraHNj74yC0rr3EwwxpBZUMpTiRqvdh2ScgoAaF8dCCG6jZ7iaRZt7khTNwrICVGD9pYmdW4mBAIB2lua8FSixtOFOhBCdFftp3gA6CmeBtDmjjR1o4CcEDWQ6Inh0dYSImHNiUkkFKBzWytI9MQ8l6zhdKEOhBDdpQtPInVNfknNOH4az/98FJAToibOtq0gEYsAABKxCB1tzXkuUePpQh0IIbrpaU/r6CkePxTj+ROzC2g8fwNQQE6ImggFAvRwsgMA9OhgB6EWPsbThToQQnSTRE8MWzPltJ12Zkb0FI8nivH8STnam1lMnWhhIELUrLxKCkN97b5A6EIdCCG6paJaiqMJ97gx5EDN0LpgL0cKytWM2qLxqIecEDXThUBWF+pACNEtNIZcc1BbNB4F5IQQoqVowhQhj1EmKM1BbdF4FJATQogWoglThCijTFCag9qi8SggJ4QQLUQTpgipizJBaQ5qi8ahgJwQQrQMLYBCSP0oE5TmoLZoHMqyQgghWiYlrwgJmfmoffIWAPBqbw0Xu1Z8FYsQjUGZoDQHtUXDUA85IYRoGVoARfPcySuq+fd+Ec8lIQBlgtIk1BYNQwE5IYRooyef/tLTYN5I5XJcz8wHAFzPyIdULue5RIQQbUMBOSGEaJnMgtJ643HK8cuPS6m5YAASswvAAFxOzeW7SIQQLUMBOSGEaBnK8as5Hj6qQO7DRwCApJwCAEDOw0d4+KiSz2IRQrQMBeSEEKJlKMev5kjMLnzK9gI1l4QQos0oICeEEC1EOX41g0dby0ZtJ4SQ+lBA935bTwAAIABJREFUTgghWohy/GoGcyMDtDY3UtrWxtwI5kYGPJWIEKKNKCBvAfJLymv+LS3nuSSEEFWyNjVESBcnWJsY8l2UFs2nY2tukq0AQK+OrfksDiFEC1FAruPkjOFKWh4A4OrdPMhpHShCdArl+OWfWChEl/bWAIAu9tYQC+nSSghpHDqT67g794tQKZUhMbsAFVIZUu8/pJX8CCFExZztWsHSRAILYwnfRSGEaCG6jddhFdVSJGYXQCZnSMqp+fdm9gNUVEv5LhohhOgcynJDCGkqCsh1WGZBKdgTQ1QYY7R4CCGEqFh+STmOXE+juTqEkCahgFyH0eIhhBDS/GiuDiHkRVFArsNo8RBCCGl+irk6ALi5OoQQ0hgUkOs4WjyEEEKaT+25OgBorg4hpEkoINdxtHgIIYQ0H5qrQwhRBQrIWwBaPIQQQpoHzdUhhKgCBeQtBC0eQgghqkdzdQghqkABOSGEEPICaK4OIeRFUUBOCCGEvACaq0MIeVEC9uRsFEIIIYQ0WnmVlIYHEkKahAJyQgghhBBCeERDVgghhBBCCOERBeSEEEIIIYTwiAJyQgghhBBCeEQBOSGEEEIIITyigJwQQgghhBAeUUBOCCGEEEIIjyggJ4QQQgghhEcUkBNCCCGEEMIjCsgJIYQQQgjhEQXkhBBCCCGE8IgCckIIIYQQQnhEATkhhBBCCCE8ooCcEEIIIYQQHlFATgghhBBCCI8oICeEEEIIIYRHFJATQgghhBDCIwrICSGEEEII4REF5IQQQgghhPCIAnJCCCGEEEJ4RAF5C8AY47sIKiGXy/kuQpMovn/GGLWFBtHWOujKPqQL9dCFOtSmzfXR5rIrKOqgreem+uhCu6gLBeQ6qrKyElVVVQAAgUAAQPsO8qysLJw/fx7V1dUAAKFQO3fX3NxcAIBMJuPaQtukpaVh06ZNuHfvHkpKSrSyLZKTk7F9+3akpKRAJpNpZR2A+o9jbbzoyWQypf/XtvNTfbSxDmlpaSgoKEBhYaHS+Unb9iltPbfWlpWVBQDcNU9Bm9oiMzMTBw4cQHFxMaqqqnSiXdRFtGjRokV8F4Ko1v379zFr1iwcO3YMqampqKiogJOTk1YdGBUVFdi1axc2bdqEM2fO4ODBg2jbti309fVhZGTEd/EarKqqCuHh4Th69Ch27NgBExMTSKVSWFtb8120BpPL5YiPj8fBgwdx9uxZrFu3jrtAtG7dmufSNVxCQgIiIyPx999/Y+3atRAKhdDT04ONjQ3fRWuw7OxsTJ8+HQkJCcjMzISxsTEsLS216tgGaurx+eefo6ysDPn5+XB0dFTqONCG+mRmZmLdunWQyWSorKyEtbU1V27GmFbUAQC++OILHDx4ED/99BOKiorw8OFDuLi4aE35ASAjIwNLly6FgYEBSktLYWtry/1OW/anyspKLFq0CH/88QeOHj0Kxhj09PS06viurq7GoUOHsGfPHhw+fBg7d+6EsbExDAwMYG5uDkC7jg11o4Bcxzx69AitWrVCp06d0K1bN0RFReHYsWPYuXMn2rVrBzMzM0gkEr6L+VSKg1UsFqNHjx4YN24cbG1tkZiYiF9++QUJCQkwMzODo6Mj30VtEJFIhODgYHTr1g0lJSWIjIzE/v37UVxcDDc3N41uCwWBQAAnJyeMGDECvXv3hlgsxr59+3Ds2DGUlJTAx8eH7yI2iKIOAwYMQH5+Ps6cOYNt27ZBIpHA09NTKy4SpqamaNWqFWxtbbFx40acPXsWJ0+eRLdu3WBoaAiRSMR3ERvk/v37EAgEiImJwY4dO3D48GE8evQIjo6OMDQ01Iog6saNG3jw4AH279+PPXv24O+//4ahoSHatm0LsVisNU9hBg0ahODgYLRp0wbR0dHYuXMnrl69CgcHB1hZWWlFHXJycpCeno6LFy9i7dq1SEpKQnl5Odzd3SEQCLQiCBSLxXjllVfg7OwMoVCIH3/8EdHR0UhLS0OfPn00uvyK71ckEqFr164YO3YsXFxcUFpaivXr1yM2NhZSqVRrzrN8oYBch8TFxeHTTz9FUFAQnJyc0K5dOwQFBcHHxwdxcXH47bffUFxcDCcnJ+5uVdPI5XLuAqA4wB0cHDBkyBBYW1sjKSkJ69evR5s2beDu7s5zaRtGX18flpaW8PPzg7e3N8zNzbFu3TrcuHEDTk5OsLOz47uI9UpNTUVJSQnMzc25E66xsTF69uyJ7t27QyqV4scff0ReXh569+4NPT09votcR0lJCQoLC1FZWQm5XA6JRAJ9fX28/PLL8PT0BGMMK1asQElJCTp37qwVT1+cnZ3h5uaGkJAQ2NnZ4dSpU/j1119hZGQEe3t7GBoa8l3E57K0tESXLl3Qr18/jBw5EvHx8bh48SJ27NgBPz8/WFpa8l3E57K3t4evry8CAwPh7++PI0eO4Ny5czh79iz69+8PiUSi8YFgVVUVRCIRxGIxOnXqhD59+qBnz57YsWMHTpw4AVNTUzg7O2vsjV5GRgaqqqrg6OgIf39/BAYGws/PD6dPn+ZuVl955RUYGBjwXdQGEYlEaNOmDXr06AF/f3+IxWJs3boVf//9Nzw9PTW2t1yxn9fe3+3s7PDyyy/Dw8MD6enp2LRpE6qrq9G7d2+lvyGPUUCuIy5fvoypU6ciPT0d3bp1g7OzM2QyGSQSCWxsbDBixAg8fPgQJ06cwJ07d+Dp6alxQXliYiLWrFmDyMhIREdHo0ePHjA0NIRUKoVQKISbmxvc3NxQUVGBVatWwcHBAW5ubnwXu47U1FRcuHABBw8ehFQqhVQq5QIMa2tr9OjRA76+vti0aRPi4+PRuXNnpUesmqCqqgqhoaG4fPkyvL29YWFhAQBcr5+NjQ26du0KBwcHrF+/Hg8ePEBQUBDPpVYWFxeHDz74AJs3b8Zvv/2G5ORkODk5ccOFbGxs4OvrCzs7O6xatQpyuRwvv/wyz6Wu6+7duzh69CjOnj2LjIwMeHh4AACMjIzQqVMnTJgwATdv3sSePXsglUrh7u6ukUF5ZmYmbt26hatXr8LIyAhGRkYwMDCAkZERBgwYADs7OyQkJGDDhg3o2rUr2rdvr3E95VlZWbh9+zaSk5NhZWXFld/W1hbDhg2DQCDAuXPn8PvvvyM4OBimpqYa11OenJyM5cuXo3///hCJRErlMzQ0hL29PcaOHYvjx4/j9OnTsLa2houLi0bVAQBiYmIwZswY+Pj4wMHBAUKhkAtoAwICYGVlhSNHjuDAgQPo3bs3LCwsNC4IvHPnDqKionDixAkwxmBoaMh1ClhZWcHLywu9evXC7t27cf78efj6+nLnYk1x8+ZNrF+/Htu3b0dUVBRMTExgamrKnYPs7e3h6ekJQ0NDrFu3DgDg6+urUe2gKSgg1wGXL1/GG2+8gdGjR6OkpAQdO3ZEt27duBOo4oTbp08fVFRU4MSJEygvL0eXLl00ZsjEtWvXMG3aNJiZmUEmkyEhIQG7du3CsGHDYGxszF2Yra2t4ebmhocPH2Ljxo3w8vKCg4MD38XnXL16FdOnT8fdu3eRlJSEgwcP4ty5c7C2toazszOAmvZo164dXnnlFWzcuBEZGRkIDAyEvr4+z6V/TCgU4sKFC7hw4QLy8vLg6emJVq1aQSgUchc1AwMDuLi4wMrKCmvWrIG+vj569uzJd9EBAElJSXjrrbfQs2dPvPrqq3BwcMDFixcRFxcHf39/GBsbA6jpkfLy8oKZmRlWrlwJW1tbeHp68lz6x65evYoZM2YgLS0NFy9exPHjx3H16lUMHz4cACCVSiESiRASEoKMjAzs2bMH1tbWcHd316hezdjYWG5ey549e3D69GmUlZWhe/fuEIlEEAqF6NChA7y9vZGSkoJff/0VwcHBsLCw0Jig/MqVK3j77bdx5MgR7NixA7m5uejXrx83PEVfXx+enp7o1KkTLl68iN27d2P06NEa11OekJCA//73v6ioqECfPn0gFAqVgnJFR86wYcNw+PBhnD9/Hj4+PrCxsdGYely+fBlvvfUWJkyYgLCwMO7cKRAIIJfLuZvVnj174sSJEzh27BhGjx4NsVisMftTbGwspkyZglu3buHWrVvYtm0b0tLSoKenx10rxGIx2rVrB39/f+zYsQPXr1/HyJEjAWhGD3N8fDxef/11tG7dGiYmJigvL8fKlStRWFiI9u3bc50fZmZmcHV1hUAgwKZNm2BjY4POnTvzWnaNxIhWu3TpEvP09GSLFy9m1dXV7P3332dvv/02k8lkTCqVcq+TyWTcz0uWLGE+Pj4sJiamzu/4UFFRwWbMmMHee+899ujRIyaTyVhiYiLr2bMn+/XXX+v9m4SEBPbmm2+yd999lz148EDNJa5fbm4uCw4OZosWLWK5ubmMMcaOHTvGhg0bxtzc3NjBgwe511ZVVTHGGPv7779Zly5d2E8//cRLmZ/l119/ZUFBQczPz4+9+uqr7N69e9zvau8zhYWF7Ouvv2bDhw9n165d46OoHLlczmQyGVu4cCGbPn06Ky4u5n63fv165uXlxZVRLpdzvysrK2OLFy9mQUFBLDExUe3lrk92djYLDg5mX3zxBcvNzWUFBQVs165dzNPTk61YsYJ7Xe3jPDw8nPn5+bG8vDzGmHId+ZKamsqCgoLYsmXLWEJCAnvw4AF75513WEBAALtw4QJjTLkOiYmJbOzYsWzYsGGsqKiIr2IrSU9PZ3379mVLly5lV65cYffu3WPp6en1vlYul7PTp0+z4OBg9vbbb7OKigo1l/bZrly5wtzc3JibmxuLiIjgttduA8XPxcXFLCAggM2dO1ft5XwaxTVv6dKlSsf3s17ft29fNn36dDWUrmEePnzIJk6cyBYuXMiys7MZY4wdPXqUjR8/noWEhLDt27dzr1Wca6Ojo1nPnj3Z999/z0uZn/To0SM2depUNnPmTFZSUsJtX7FiBXNzc2Ph4eEsISFB6W/u3bvH3nvvPTZ16lSWlZWl7iJrPM16BkUa5fLly5g8eTJee+01zJ8/H2KxGK6urrh69So3NlBBKBRyKbk++eQTeHh4YNmyZdzv+JadnY22bdvC0NAQQqEQ9vb23OSoY8eOITExEf/88w/3ek9PTwwePBgxMTFIT08HwH9qqOLiYhQUFMDHx4cbFz5gwACMGzcOADBv3jzs378fAKCnpwe5XI7evXtjxowZ2L17N27dusVb2WtTfI/m5uYwMzPDmjVrkJWVhQULFiA1NRWA8j7TqlUrhISEoLKyEvHx8QD4S/8mEAggFAqRlZUFQ0NDmJqacmUZN24cBAIBLl++zL1WwcjICEOGDIGFhQViY2MB8J/C7u7du6iursbYsWNhZ2cHCwsLDBw4EJ6entz3DIAbdgAA3333HSwsLPDFF18A0IxUcLGxsTAwMEBYWBg8PDxgaWmJL7/8EmVlZTh9+jQAKJ2r3N3dMWPGDAgEAhw6dAgA/22RnJwMPT09jB07Fj169ICDgwNSU1MRGRmJXbt24d69e9xxIxAI4O/vjwkTJiAzMxMxMTEA+K+DQn5+PsRiMSZOnIioqCh8/vnnAJT3I8XPpqamiIiIwKVLl3Ds2DE+iw0AKCgowIIFC+Dm5ob58+fD1NQUAHD+/Hns27cPe/fuRUJCgtLfdOvWDXPmzEFmZia3P/FNKBQiNzcXHTt2RJs2bQAAAwcOxKeffgonJyf8/PPP2LlzJ/daAPD29sbkyZNx4cIF7jzMp6qqKqSlpcHNzQ0mJibcvjNs2DCYmJggNjYWmzdvRkZGBvc3Dg4OGDVqFK5fv47r168D4P+6rUn4j8RIk+Xk5OCNN97Au+++yw096d+/P4RCIQ4cOABAeWdXPJoEaoLDyspKREdH13mduhkYGMDMzAxXr17ltmVkZCA1NRXr1q3Dp59+iokTJ2LJkiW4e/cu95rx48ejV69e2LBhAwB+gw/GGO7fv4+SkhJujN+jR48AAG3atEH//v0xdOhQ/Pe//8XFixcBPD7RBgUFwcDAgDtx8X2CUnyPAQEBkMlkMDMzw/Lly5GamorFixcjJSUF4eHhuHbtGvc33bt3x+jRo7FlyxaNyFNuZGTE7SuKskgkEhgaGqK4uLjev+nRowd8fHywZcsWbt4Cn6qqqlBcXMwFclKpFGZmZnjppZeQnJys9DuRSATGGPT19TFz5kzcv38faWlpPJb+sYyMDJSVlcHR0RFCoRBVVVWwtLSEi4sLdzOtoNj3X3nlFTg5OXHnMb7b4p9//kFVVRU6dOgAAPj666/x7rvvYuXKlYiIiMDMmTOxceNGrj3EYjEmTJgAfX39OoEV37KysuDm5oY33ngDEydOxF9//YXPPvsMQN2gHKgJaD09PbkOAz7PTwKBAL1794ZEIuGuF9988w1mz56NZcuW4ZNPPsGcOXPwww8/cH+jp6eH/v37w8LCAmfOnOGr6By5XI6ysjJuEiQAbs2Q7t27Y86cOXB1dcVvv/2mVF5DQ0OEhITg/v37uHnzJi9lr01PTw9GRka4c+cOGGPc/iKRSBAYGIgxY8bgzz//5G6CpFIpACAwMJC7VlCecmWacYYgTTJ8+HDMnz9faQKXvb097OzscPbsWQB1g1TFQePs7IxWrVrh0qVL9b5O3RTjrvv164e33noLY8aMwZgxY7B+/XpcunQJM2fORExMDA4fPgzg8cE9fPhwPHr0qM5CCupSe/Glnj17wt3dHcuWLYNMJuMm52zduhVt27ZFaGgozMzMsHv3bpSWlnIXPk9PT/j7++OXX37h6sU3qVQKPT09lJWV4datW+jRowd++eUXJCcnY9y4cdwTDeDxAi8hISFo3bo1CgoKeCu3oj3efvttODs7IzExEUBNfRhjSnMmFBfD2t/51KlTYWdnh3v37qmx1PVr3bo1lwO+uroaYrEYQE2wV1lZCYlEohTkKY5hPz8/5Ofn49y5c7yUW0Hx/fr4+KCwsBDHjx8HAG68r5mZGQoLC5W+f0UdRCIRFixYgPv37+P8+fNqLnldXl5eKCgoQFRUFFJSUnDs2DF899132LVrF86cOQNHR0fs3LkTe/bsAVCzTxkaGmLhwoVISUlBcnIyb2V/MoA2NDREr1694OjoiNdeew1TpkzBwYMH6w3KgZrJz0OHDsXu3bvx8OFD3q4VjDFYWFhg4sSJKC4uxp9//ok9e/bgr7/+wtdff419+/YhKioKPXr0wOHDh7m2kMvlsLKywvvvv49z587xFswqzk1CoRB2dnYIDAzEihUrkJGRAX19fe446Ny5M2bMmAGRSIQDBw6grKwMcrkcjDG4u7sjNDQUf/75J6qqqnh56qL4TENDQ/Tu3RtxcXHYuHEjCgoKkJ+fzx238+bNw2uvvYaffvoJWVlZ3Ph9oCYol8lkqKioUHv5NRkF5FqmuLgY2dnZXI+NYugDUHOgGBoaYsaMGTh//jyOHj1a73swxmBiYoKZM2ciMTFR7cFsRkYGzpw5g99//x1xcXEoLy9HYGAgtm7ditDQUHTu3Blt27bFtGnT4OLiAqAmwOratSuOHTsGuVzOBSf9+vVDbm4uNwxBnW7duoUlS5ZwPdtisRjTpk1DYWEhAgICMH36dAwYMACZmZmYM2cOXnrpJQwdOhTHjx9HaWkp16sJ1NxYSCQSiMVitV7wUlJSsG3bNixevBiRkZG4ceMGVxcTExO8/PLLuH37NoCaPN4GBgbcBLby8nIAj2/yFItPnTp1Sm3lB2oyFaxcuRLA42DP1dUVERERXEYSsViM6upqPHr0iEuBJhAIkJ2djYiICNy5cwdATWaDiooKXLlyRa11AGoWwyopKUF5eTmqq6vh7u6O5cuXw8XFBXp6ety+otj3FRd4oOZRvuI4trGxwezZs5GSkqL2OgBAWVkZAHA9gO7u7vj444/RqlUrAMpDN2ofAwBQWlrK1cPExASurq7IyclRY+lrVFZWcj/L5XI4OTlh8ODB2L17N/bt2wcnJyf07t0brVu3hp2dHb7++muYmJjgr7/+AvC4jezs7CCRSLgVGNUpKSkJGRkZSj2xABAcHIzw8HAANfv7pEmT6g3Ka98oBQUFwcHBgZcAULGfK4JSxXCVvXv3YsuWLRgyZAiCg4NhZ2cHR0dHvPfeezA1NcXJkycBPB6u6eLiAk9PTzx48EDtdUhMTMR3332n9IQ3NDQUTk5OeO+995CXlwexWMx9597e3pg+fToOHjyI9PR0CIVC7rrQq1cvlJaWQl9fX61PXRITE/H1119j2rRpWLZsGWJjY/HBBx/A29sbW7ZswYABAzBkyBBkZ2fjyy+/BFDzFFtPT4+7JtTuNGCMacTNtiahgFyLxMbGYvr06Zg4cSImTpyIBQsWoKioiDsoFf96enrCzc0Np06dQmlpaZ33URwUEokEjx49UusjyCtXruD111/Ht99+i82bN2PChAn46KOPcPHiRXTq1Anh4eGQSCQoLi6Go6Mj9PT0uAu0qakpWrduzdVTKpVCX18fo0aN4malq9ORI0ewfft2rF+/Hunp6RCLxQgODsbq1asRFBQEQ0ND+Pn5Yffu3VyKyf79+6O8vJwLABVtYW9vD6lUin/++Udt7XH16lVMnjwZhw8fRkpKCr799lssWLAAmzZt4l5jamqK+Ph4lJWVYezYsbCxscHy5cuRk5ODWbNmITc3F8DjXvI33ngDwcHBaim/QkxMDNatW4dffvmF2yYUCmFlZaX0OqFQiIqKCi4gz8rKwvvvv49r165xC02JRCK88847XK5cdYmPj8ecOXMQGhqKV199FeHh4cjIyEBAQABeeuklAI978ktKSribN5lMhuzsbLz66qvcnBCgpvfq1q1bag+gsrOz8dFHHymN07e0tMTYsWPRq1cvAI/3ldLSUhgbG3P567OysjB//nxs27YNAGBsbAw/Pz9cvHgRMplMbcdFeno6li1bxh2jQqEQxsbGCAkJQV5eHg4ePAhDQ0OYmJgAqAneLS0tMX36dFy6dAnp6enc925nZ4e+ffvi0qVLYIyp9Vz7448/YsKECcjKyuKyjwA1OeAVY68ZY0pB+aFDh7Bw4UIAj28qgJrzgI2NDXcTri4ZGRn45JNPkJGRAZFIBLlcDrlcjoCAAISHhyMpKQn29vZcWaVSKRwcHBAWFobz588jPz8fjDEIhUK0atUKHTp04Dqq1NkWZ86cwebNm7Fjxw4uKHd1dcVrr72GqqoqzJ8/H7m5uRCLxdwNSJ8+fWBsbMzthwrdu3eHra0t8vLy1FaH+Ph4vPnmm0hMTIREIsHBgwexYMECxMTE4LvvvsMXX3yBOXPmYN68eTh06BCX+UwxPl4xfFMgECg93daUrFwao3nnjBJVuX37NvPx8WGfffYZO3jwIFu6dCkLCAhgq1evZozVzMSunfkiMjKSubu7s6ioKMaY8gz62tSZoSQrK4v169ePLV68mMtQsHfvXubl5cVCQkLY4cOHuW29evViu3fv5v42IyODjR07li1btqzO+1ZWVqqnAk9QzPYfNGgQ+/TTT1laWtpz/+bMmTMsICBAKZOHIhOGIvOKOjx48IANGzaMLViwgNsHbt68yUaPHs3c3Ny4mfzXr19nw4YNY/369WOhoaFc9pj//e9/bObMmXX2Kz4y9kRFRTE3NzfWp08ftnLlSm577bLJ5XKWk5PDvLy8WGRkJHvw4AEbN24cGzJkCPe9P+0YaW43b95k3t7eLCIigu3bt4+tX7+eDRs2jPXu3ZudPn26zn7x8ccfs+DgYMZYzTE1YcIEpXoo3L9/X211ULh06RLr1q0bmzp1Krty5Qq3vXa2F8XPo0aNYrNnz2aMMZaZmclCQ0NZcHAwq66u5l6bkpKi9nocOnSIdevWjX322Wfszp07Sr+LjIxkXbt2ZW5ubuzEiRNKv9u6dSsbNGgQe/jwIWPscT0vXLjAHTfq9MMPPzA3NzcWEhLCZUh61vGZn5/PVq1axdzc3NjPP//MbeczC9exY8eYl5cX+7//+z+WkZGhVJ709HS2f/9+lpmZqfQ3MpmMrVmzho0aNYq7Nij+JiEhgZe2OHfuHHNzc2P+/v5s8eLFLDU1lfvd1q1b2dChQ9mIESOUMlnFx8ezvn37sjNnztR5P3Ve8woKClhYWBj74osvWEFBAWOsJh4ZOHAgmzlzZp3XKzI8McZYcnIyGzx4MPvzzz/rvK72cU5qiJ8fshNN8Pfff8PFxQVz586FpaUlhgwZguvXr3Pj4QQCgdJQh/HjxyM2NhYRERFwdHSss4AO+zeHqToXGcjJyUFFRQUGDBgAe3t7AMCoUaOQnp6OH3/8ET///DOMjY0RFBSELVu24Mcff8SFCxdgaWmJ//3vfxAKhZg3b16d9+Urf3e7du1gaWkJW1tb/O9//wNQM7RGUTcAuH37NjZu3IguXbpAT08Pf/31F5ycnODq6sq9RtFu6lzpsry8HPfv34e3tzcsLS3BGIOHhwdmzZqFuXPnYufOnTAwMMC0adNQVFSE9u3b4/vvv+eyx/j6+sLX1xdATY+nYtgKHxPXCgsLIRQK4e3tjb1790IgECA8PJzrUau98quBgQGSkpKwZ88elJaW4s8//4Senh6kUqlSj6A6MMYgl8sRGRkJNzc3zJ07l+vV9/Lywpw5c7Bo0SJ8/vnnCAoK4o5ZRX2ys7Mxb948lJSUYN++fVw9RCIRBAIBbGxs1FofAGjfvj1EIhGSkpKwZs0azJ49Gz169FA6N9UeJy6Xy5GZmYn/+7//Q1lZGf766y/u0b1YLFbrky/F9+vo6IjKykqcPHkSMpkMb731FjeZc/z48RCJRPjuu+/w5ZdforCwEH5+fsjOzsaBAwfg5OTE9T7XfjzPFxsbG4jFYrz22mvYunUr7O3tn5q/2srKCqGhobCzs8Po0aO57Xwc07XbgjGGS5cu4ZtvvsEHH3yA9u3bA6hpPfYOAAAgAElEQVR5qqg41yYlJUEikcDR0RF37tzBmTNn4OTkxJ1Taz895kPnzp3h6ekJGxsb7Ny5EzKZDK+//jo6dOiASZMmwczMDJs2bcKwYcMwceJEVFdXIy4uDm3atIG/v3+d91PnNa+6uhp5eXmYOHEit7hSp06dMH78ePzwww/IyMjg2iEvLw8//fQTLly4AF9fX1y7do3LYPUkdZ9vtQENWdES//zzD9LT07lxmEBNCqH09HRMnToV48ePx8GDB5UySEydOhXm5ub4/vvv67yf4oSszvHK+fn5ePjwIbcqpeIxlqmpKXx8fAAA69atQ3V1NVasWIE+ffpwiyZ069YNO3fu5B7T800mk3GZIsLDwzF58mScP38ea9euRUlJCYCaoNfJyQnl5eX48ccfsWXLFhgbG2P9+vVKaSj5UFxcjIcPH3LDNxSPSfX09PDSSy/h5Zdfxo4dOxAdHY3t27djxYoV3OPHJ/G1AA3793FtdnY2+vbti3nz5qFXr17YtWsXVq9eDeDx+FGBQAA7Ozs4Oztj27ZtKC8v5z0YFwgEEIlEyM7O5oYOKHTt2hWOjo4QCAT48MMPce3aNW4ssLW1Ne7fv48333wThYWFSsG4uucgPCkvLw9CoRDDhw9HdnY2Vq9erZQ9CXg8hlxfXx95eXmYO3cuiouLsXfvXt7aA3h8LiwoKICZmRkCAgJw4sQJbNiwQSljzZgxY/Cf//wHnTp1woIFCxASEoL58+cDAFatWqU0PIQvUqkUd+7cwcsvv4yvvvoKZmZmeOONN5Cenl5nTHlttra2GD9+vNJ4Zj4o2iInJwcikQj+/v5ISkrCt99+i8zMTACPj//CwkLs3bsXISEhCAoKwjvvvAPGGL755ptn1rW5KT5XMeeJMYYZM2ZgwYIF2L17NzZv3sxNgB8+fDg2bdqEN998E9HR0YiLi0PHjh2xbdu2OpNs1UWxD5eVlSEnJ4c7JhXb27VrB4FAoDSXxcrKCi+99BLc3NxQVFSEnj17Yvv27bzVQdvQSp0aSnHBVgQTKSkpSEpKgp+fH6ysrJCamoqIiAh07doVFhYWEIlE2LhxI9q3b8/1AlhaWsLb2xtTpkzRiJRbEokEUVFRSEtLQ2BgIJeF5Pvvv8fAgQMRGBiIzZs3w9DQEAMHDoS/vz9CQ0MxcuRIDBgwAEKhkLeL9ZOEQiHEYjHOnz+Pe/fuYe7cuSgoKMCpU6dw9+5dLFiwAHK5HH5+fhg8eDCGDh2KcePGYdy4cdyEKXUHsjdu3MDJkyfh7u4OCwsLxMfHIyoqCn369OFukj7//HO4uLhgypQpOHz4MHJycjBmzBhuDLwmUKwqqLhoX7x4EZaWlhg6dCjc3NyQnZ2NY8eOobS0lFuiWZFe686dOzAzM8OWLVt4Df7Kysq4zAqnTp3CvXv3EBQUxH3PjDEcPnwYs2bNQmpqKg4cOIDRo0fDwMAAlZWV2LdvH9q3b489e/bwWo8nJSQk4O+//8bKlSthbm6OU6dOceN8FTd0iqcWeXl5+Ouvv2BjY4Pdu3fzVo8n12y4ePEiUlJSsHHjRlRUVGD//v0oLCzkMlMBQMeOHeHv74/BgwfDx8cHo0ePRnh4OBfI8rlKquLa8fvvv8PDwwMjRoyAq6srLl68iMjISAQFBaFVq1bPXelRE64Zly5dQmpqKlavXo1Hjx7h3LlzSElJgZeXF3esGBoawtzcHB4eHujQoQP69++PBQsW8NYWiYmJyMnJQevWrQE8fjJ348YNpKWl4e2334ZMJsO2bdvw8OFDzJs3Dw8fPsSAAQPg5+eHESNGYNKkSRg0aBBv17ysrCwsXrwYQUFBsLKyQmZmJm7cuIF+/fpBX18fAoEAWVlZOHToEDe3iP2b+tDFxQWDBw/GwIEDERQUpFHXbU1HAbmGUpxIqqurIRKJ0LFjR3h6eqJLly4QCATIzc1F69atMXfuXIwYMQLDhg1DZmam0nLNQM2koieXRlaXzMxMXL9+HbGxsRAIBOjYsSMqKytx4sQJ7Nu3Dzdv3sQ333yDoqIiLF68GB4eHigoKMCePXswfPhwmJmZQSQScSdUxQGvborgqfYFTHGjlJ6ejtjYWIwZMwZ9+vRBQUEBN9xj+vTpXGpAY2NjGBkZcTdZ6q7H5cuXMWHCBPTq1Qs9evSAWCyGRCLBzZs3sXr1apw+fRorVqxAeXk5li5divbt28PCwgIbN25EQEAAd3HhU3JyMgwMDCCRSJT25/bt23O5ic3NzesNyhXDODp37swNO+DrIhETE4PJkydjwIABsLS0hJOTE7dstkQiQUlJCT755BMUFRUhIiICVlZWOHToEIyMjODt7Q0jIyNYWlriq6++0ohgvHZb3LlzB+bm5ggMDISHhweX7aJ2UK547aNHj1BZWYkNGzbwVo/Y2Fjs378f7u7u3NOi2NhYmJiYoF+/fvD19UVVVRX27dtXJyg3MDCAnZ0dOnXqhLZt20IgEEAmk6m9DpmZmYiNjcWVK1dgYmKCVq1aQSAQoFevXggMDIRIJIKdnV2TgnK+JSQkwMLCAoGBgfD19UVpaSnOnj2L5ORkdOnSBWZmZgBqrnNdunSBr68vPDw8uGseH8fFgQMHcPLkSQwcOBAymYwbTpqYmIjz588jLCwMvr6+YIxh8+bNMDc3x8yZM9GuXTsA0Ihr3j///IPTp09j5MiRAGpuQHv06MH1igPgOgrCwsJgY2MDgUCAvLw8XLhwAc7Ozkrxhibc3GkDCsg1UEJCAtasWYNffvkFly5dgqGhITp16oT27dtzB4OlpSU8PT25ExLwuFdtyJAh9WaYUKerV69ixowZiI6Oxq5du3D27FkUFhYiPDwctra2KCgoQHZ2Njp27IiVK1dyF7mSkhIcOXIEI0aMgLW1tdJ7qvvCwRhDfn4+hgwZgu7du6Nt27bcBUxRFnNzc/zxxx8YNWoU9PX1sWTJEggEAojFYlRUVMDZ2Rnm5ub1jqFVl8uXL+PNN9/E5MmTMX36dG78oSINmCKY6NatG7fSI1BzU7hr1y4EBASgY8eOai1zfT766CNs2LABY8aMgYGBAXezam5uzt2AMsbQqlUrLig/fvw4ysrKuPHufN4UATVtMW3aNIwcORKDBg2CSCSCjY0NPDw8EBkZif3792P//v0wNjbGihUruMwQBw4cQFVVFUJCQmBsbIyePXvy2vN0584d/P777/D19VW64Xd2dkbnzp259nB3d1cKyh0cHLiecicnJwwZMoS3ely+fBmTJk2Ct7c3+vbty50ju3btCi8vL0gkEggEAvj4+KC6uhp79+5VCsrrO475OM++++67OHHiBE6ePInff/8dZmZm8PT0hImJCZcuUygUok2bNujUqRMXlPfv3x/m5uYaEZSnpqYiOjoaZ86cgbGxMQQCAYyMjODs7AwvLy/uaaqPjw9KS0uVesrNzMzq7XDiKwgsKChATEwMRo4cyQ2ZEwqFsLS0xKFDhzBu3DjI5XIsWbIEenp63NBBBwcHtGrVqt61BdRNIpFg3bp1MDMz456oKuakKDqjbt68iaNHj2LKlCmwsLBAVlYW5s6di+TkZIwYMYL3fUorNfOkUdJI165dYz179mTvv/8+e/fdd1lYWBjz8fFhCQkJ3GuezB6hsHPnTjZ48GCWk5Oj1jI/KS0tjfXv358tWbKE3b59m+Xk5LCIiAj20ksvKWUmqG+m+KFDh9jAgQMblLFEHaqqqpinpycLCAhg8fHxjLHH37lcLmdZWVls0KBBLCYmho0ePZpNmDCBFRUVsVWrVjEvLy/2008/8Vl8duXKFda5c2e2fPlyVlRUxG1/MiNHfZkUYmNjWf/+/dmlS5eavZwNERERwdzc3Nj48eNZSUkJY6z+mfqK9rl37x77+OOPmaenJ9u1a5day1ofRVaer7/+mit/bYWFhSwmJobFxMTU+d3UqVPZvHnz1FHMBtm2bRtzd3dnv/zyC7ftWfvU3r172aBBg9i0adPYxYsX1VXMp1K0xZIlS1hpaelTX1f7XLt27VrWu3dvFhERwVJSUtRRzGe6e/cu8/f3Z0uWLGE3btxg6enpbOHChczT05M7Vz1JLpezS5cusdGjRzMvLy9eMo486cqVK8zX15cFBQUxHx8f5uvry2bPns0uX77MGHt8PNduizVr1rCBAwey999/XykziSYoKytjAwYMYL/++qvS9ry8PNa/f3926tQpNnr0aBYWFsYqKyvZzz//zNzc3NimTZt4KrEyxfe8ePFiFhERwaRSab3Xh99++4117dqVPXjwgOXm5rKwsDAWEhLCnQdqxyakYaiHXINUV1fj+++/R+vWrbF48WKMGjUKHTt2xMmTJ+Hq6gp3d3cAj+/8U1JSkJmZCYlEgrt372LNmjVo164dQkNDeb07PX36NOLi4vDJJ5+gY8eOMDExgaenJ/744w8YGRmhX79+AGoezd25cwc//PADhEIhbt26hd9++w22traYPHmyRtxhC4VC7N69Gzk5OTh06BB69eql1FNuamqK8+fPY/ny5Wjfvj2+/fZb2NjYwNfXFzY2Npg0aRJvPTUymQwrVqxAUlISVq9ezT2F2L59OyIjI7Fr1y7cvHkTnTt35pZA/vzzz5GcnIzr16/j999/h6mpKWbNmsVrWyi+67NnzyI3NxfV1dU4cOAAt5jSk8vcKyZytWrVCh07doSFhQUmT57M62PTlJQUvPrqqxg1ahTmz58PY2NjADU9g0VFRSgoKEDbtm3Rrl07tGvXDnfv3kVmZiZMTEzwzz//YMeOHfD29lZ7fvSnycrKwpEjR5CYmIjy8nJuWJCi9wyA0hwYd3d3mJiYIDIyEhKJBH379uWt7AkJCZgwYQLeeecdzJw5UymfeGVlJUQikVJmHsX+5+PjA6lUik2bNqF169a851A+d+4ckpKS8PHHH8PFxQXm5uZwdXXFkSNHUFRUhAEDBii1B1BTnzZt2sDBwQGMMQwfPpzX46KoqAgff/wx+vTpg0WLFv0/e+cZUOWVte3rHECRKl1ApElXBGkiigI2xIYNHUsSk2gSMzFqdBKjpppoMhN7b9h1jCG2KKCCJahg1wgoCoKKiiBIb2d/P5znCShJZt5vXs6Zd7z/KPu0tZ+1y9prr3UvZsyYgZ6eHpcvX2b37t24urrKsjZM0JY85Xv27EFXV1dj5oV085aTk8OtW7cICQmRi6kZGhqSkpLC8uXLsbe359tvv8XExAQ/Pz/atm3LiBEjNCK0Q5KhsrKSRYsWERISgo2NzQtj6caNGxw7dgx/f3/mzZsnV09tyPb0Ev8aXkbZaxBqa2vJzs6Wr+EA+fr33LlzZGdn07ZtWwICAmjXrh3ff/89u3btQltbmzZt2qCrq8uiRYvkjVBdk/vRo0dUVFTIiYI1NTVyjN/NmzflCqPSZpebm8ukSZOwt7fH2tqa5cuXq70PEnJzc+VNIzU1lYkTJ7J27Vo6d+4sy+fr64uxsTHvvfcetra2cvuIESOAxrSAzQktLS1Gjx5NZmYm3333HZ9//jnLli1j1apVBAUF8fTpUy5evMjhw4f5/vvvcXZ2lkuAt2nTBhsbG/72t7/JIQnqWmAVCgXV1dXk5OQQGRmJn58f33zzDWPHjmXr1q0YGBi8EPIgjS0nJyfeeecdQH16gGesNrW1tZiZmcnX7wsXLmT//v0UFRWhUCiIjo5m1KhRuLi48Pe//51Dhw6hpaWFoaEhOjo6TJ48WS2yNwWpIJmPjw8//PADSqWSd955p9EVPdDo7yFDhmBqatokjVtzob6+ntTUVLS0tOSwDoB169bJCYSdOnWiV69e9OvXr1HOiFKp5K233sLKyopBgwaprQ8S8vPzyc3NlalIAWxsbLCzs+P+/fvAi2Eb0uEiODiYrl27AuqdFzU1Ndy/f59Ro0bJxbnGjBmDo6Mj69evZ+rUqSxevFiu7NhwPL399ttYWVnJcc6aAOl5R0VFMXbsWAIDAxs5AwIDA9HR0eGjjz7C1tZW1seQIUMA1J4P0hB9+/aVE2RXr16NnZ1do7ltaWmJQqHgL3/5CxYWFuzdu1cjclr+k/HSQ64BePjwIS1atEBXV5ekpCQePnxI//790dbWJjU1lU2bNqFSqcjIyGDv3r1kZWXh7+9Pjx49sLa2xs/Pj/DwcGbOnKm2zPKMjAzKyspo3bo1NTU17Ny5E29vbxwdHWVZjhw5wuPHj4mJiZEntampKUOGDKFPnz7ExMQwZswYtTIVFBQUUF9fLyd45ebm8v333/Puu+/StWtXbt68ybp16wgICJATNv39/QkODpZj3p/3Jjf3oeL69etcvnwZJycnzMzMKC8vJzU1lby8PI4fP86nn37KO++8w+jRo3F1dSU1NZXk5GSioqIIDQ1l2LBhxMTEMGDAALUlP2ZkZHDr1i2Zc7i+vp7t27fTtWtXhg4dSuvWrTl58iQHDx5kwIABv+kpbwh1Hu6sra0xNzdn4cKFdOvWjQsXLrB48WLefPNNoqKi8PLyYs2aNTx9+pS+ffvKh3JHR0c6d+7MZ599JlN+qpMXWnrGCQkJGBgY8MEHH3Dv3j0SExPlWP2GnnFo7Cm3t7dXW5I5PBsDBgYGPHjwgBs3btC1a1fWr1/PypUr8fDwwM7OjvT0dOLi4rCyssLDw0POGZFklpIGnx9vzYF79+6Rm5uLpaUlxcXFnD9/njZt2sg5HgqFgpMnT/Lw4UNGjRr1Qnx4Q49/w2eiLhQXF3Pw4EF8fHzw8vKSGW/s7Oxo27Ytubm5/Pjjj/j6+soHj6Z0oa7x1BSEENja2lJdXc2iRYvo2LEjDg4OAHTu3JnQ0FCN2Sv+CLq6uqSlpXH16lV8fX3leH1pHO3du5f27duza9eul8b4vwEvDXI14+nTpyxevBgPDw+MjIyoqqoiJCQER0dHlEolBw8epHXr1sybN4933nmHoKAgVqxYgVKpJDw8HC8vLzp16iRnNasrs3zbtm1kZGTQpUsXLCwssLGxwcTEBDs7O3mxjI+Pp7S0lKFDh8rGdnFxMbq6upiZmWFoaKi2hDuVSkVVVRWTJ0/G1ta2USl1PT09oqKiMDc3x9XVlVu3bslGuZSgpq7iRE0hNTWVAwcO0LdvX3R0dHBxcSE5OZmEhARsbW2ZMGGC7Bm0trZGoVBw7NgxIiIiMDExoUWLFo1uMNRxMDp8+DA//vgjAwYMkA0IFxcXevfujVKpxMnJCXNzc06cOPG7RrkmQDJGXV1duXfvHuvWrSM3N5eJEyfypz/9CTc3Nzp37oyrqytLliyhbdu2+Pj4yKwR3t7ear2luHPnDnPnziUyMlJ+tmfOnMHMzOwFqsnfM8obQp06MjMzo76+noMHD1JQUMClS5eYNWsWr776Kn379qVTp048efKEo0ePEhQUhKmpaZMyN2cfxD+KSC1YsIDq6mp8fHxko8/Ly0uexwBHjx6loKCAmJgY+TBRXl5OdXU1LVu21IhQQAlGRkacPHmSpKQkhg0bhq6urmyU29jYYGpqytWrV7l79y5dunSR11lNSeBsCtLzbdu2Lfn5+axatQpXV1f50KRJe8UfwdHRkfLyck6ePMnly5dlmmWpqKCFhQV/+ctfGhXzeon/OV4a5GqGlpYWa9euRVtbm06dOuHp6Skb4/As4186UUsn76KiIk6dOsXQoUMbVe4D9S1MN27c4MqVK0RFRaGtrU379u3l6naSt1sq7DN8+HCEEOTn5zNlyhQKCgrw9/eXv0sdG4ZCoUBHR4e7d+/y8OFDgoKCqK+vx8DAgA4dOsgV3ywsLHBzcyMrK6uRUf6810mdKC4uJiEhgREjRqBQKNDV1SUgIIAffviBkJAQ+vTpAzzTi46ODubm5qxYsYLQ0FAcHBzUygjTEGfOnGHQoEEolUqUSiWWlpZoa2vLhTacnZ0xMzPjxIkTHDp0iKioKHR1dV+IdVQHbt26xY0bN7CyspI3KYlR5cKFC1y8eJHx48fLNwB1dXW4uLhw+fJlCgoKiIiIeCFkS11zW0dHh3379tGzZ0+ZPaVt27YEBwejq6tL69atcXV1bdIo1wTPZVFREcXFxTx48ABTU1MUCgVubm4UFhayYcMGlEolr732mmx4W1paYmRkxM6dO+nZs2ejyrvqgrTOV1RUkJSURI8ePdDT08PPz0+mJJXG/ffff099fb2cS3T37l1mzpxJy5Yt5TwkdSEvL4+ff/6ZixcvUlBQgIODAx07dmTXrl2kpKQQHR2NlpaWbJTb29tTUlLCnj17GD58uOxI0ARIY/u31htjY2Ps7OwoKipi6dKlGBgYYG1tLfdBE9ap34MkX+fOnamvryctLY09e/bQvn17uQKsu7s7SqVSDkN9if8/aM6x8r8QQgi0tbWJjIzkzp07shek4estWrRoVIpZpVJRWlqKtbU1rVq10pgTad++fblz5w4JCQkAcsgH/FoSXuLzhl/LfhcXF/P66683v8C/ARMTExITE4FfK1C2atUK+LXymoeHBzNnzqRTp0689dZbnDlzRqMW1sDAQFQqFYsXLwaeyW1jY8OuXbuYOHGi/D5p7GRlZdG2bVs55l8T4O7uzu3bt1myZIncJsmrVCrludO/f3+mTJlCWVkZ/fv3p7KyUq0GoDRGli9fzqeffsq5c+eora2VX/fx8aF///688cYbeHp6yu1SJb/q6mrg2djTlLmtUqm4f/8+SUlJcpudnV2jIkb29vZMnjyZwMBAvv/+e1auXAmor4qrhAsXLvDKK68wZswYBg4cyAcffMD169cBmDp1Kn379iUkJEQ2uqXqlF26dEFHR4fbt2+rTfamYG1tze3bt+WKzNJ4g1/j9cvLy+U16969e8yYMYPs7GyioqLUIrOECxcuMG7cOJYvX85f//pX/vznP/P111/j4ODAzJkzuX79OpMmTQKeeZGlCpAjRoygvLycq1evqlP8F6ClpUV1dTWffPIJt27davSapJeOHTsyc+ZMJk2axLfffsunn35KbGwsoH6v/m9VzpRkb1hN+tVXX2XatGl06NCBCRMm8Nlnn7F7927gWe7bf5LXX5Px0kOuRkhGXElJCd988w1dunRplOghvf7o0SN5g75z5w47duygQ4cOamUqeB4qlYpTp05RXl5Ojx49GnmMpX+lanz+/v5Mnz6dsrKyRmW/1b1AAXh5ebF582aePn0qc1dLaBh/KXnKz549y/Xr1zUmsUhaTO/du8eVK1cICQlBX18fIQTGxsbo6elx7tw5kpKSqK6uJjMzk9WrV2NgYMDEiRM14mAhecCrq6s5d+4cPj4+MkOMhIbhNM7OzrRq1YqamhqioqLUPo4UCgV37tzh0KFDZGdnY29vj42NjSyXj48P/v7+6OnpUVhYSIsWLVAqldy+fZt9+/bh5+f3wthTF4QQtGzZkoqKChITEwkODn7BSymNGclTnp+fz7Zt22jTpg0eHh7qEBt4xmDz5ptv0rNnT8aMGUNERAQrV67E0NBQTmjs0qULAQEB6OnpNQqVu3LlCkeOHGHQoEFy/K8mwMbGhoSEBM6cOUO/fv3kg5ykAyEE+/btQ0tLi+DgYN59913Ky8s5ePCgWtfZvLw83nnnHTlJcOTIkZiYmLB69WratWtHVFQUBgYG7N27lxMnThAeHi4zEV25coUTJ04QHR0t5+1oCs6ePcvf/vY3unfvjr29faMqwpJejIyMCA4OpkuXLty/f599+/Zx5MgRkpOTsbW1bZSU21yQwksqKyvZvHkzx44do7CwEDc3txduSCVPuYODA/369cPNzY3Hjx+zc+dOTp48SUpKCr1799aIveM/HS8Ncg2Ag4MDN27c4NSpU/KGJ03m/Px8Fi9ezKpVqzhx4gS7du1CoVCwePFi2VOoCRNBqqD4zTff4OHhgZOTkyybtEgdPnyY/Px8Dh06REVFhcZlZQshqK+vJzc3l6tXr+Lp6dlkcSKpX+bm5nTp0oVx48ZphA4A+SBnY2PDwoULUalUhISEyHLX19eTmZnJnDlz+OGHH7h48SKWlpasW7dODgdRd1+k39fR0WH16tXo6ekRFBTU5Psko9zd3V02xtUZJiHJnpqaSnZ2NgAJCQm4u7s3Msolys/58+eze/duTp8+zY4dO1AqlSxYsEDthwoJUn8kijlPT0+cnJx+8xlLhYw0gWoyISGBmzdv8tlnn9GxY0dcXFx4+PAhFy5cICoqipqaGvmQevHiRVatWkVVVRWpqanExsaiq6vL9OnTNUYX0twUQvDzzz9jbGyMi4vLCwZUUlIS2dnZnDhx4gUqOnWts6dPn+bcuXN89NFHODg4YGxsjLW1NcePH6e6upq+ffvi5OSEvb09iYmJ7Ny5kydPnnDmzBl27NhB69atmTx5ssboQkK7du24ceMGBw8eZNiwYY3CNhrqRbqlDA0NZdSoUZibm6Onp4eLi4tciK25IK2ZZWVljBw5kszMTK5du0abNm1wdXWVb1ek8fb8fuDs7Ez37t0ZOXIknTt3JiwsTKNCif6T8dIg1xBUVlaSlJREfX09np6ecsiHoaEhd+7ckeNJAwIC+Oabb9TKRPJbaNeuHbm5uWzevBlfX1+ZrxueLU7FxcXs27cPKysrfvjhB7VvEs9DoVCgpaWFra0ty5cvp6SkhC5dujQKv5HeJxnlUsU+TYiVlaBSqTAxMcHAwIDFixdjYmKCt7e3HIdqb2/PkCFD6NmzJ9HR0bz22msaN56EELRp0wYdHR0WL16MnZ1dk/Gvki7UHWstxbxK2LlzJ46Ojnz33XccOnSIhIQEXF1dGxnlpqamXLlyhcePH1NTU0OnTp1YuHChWtlUfgsODg788ssvbNu2jSFDhqCvr9+kt1UIgamp6QsVPNWBlJQUzp8/z/jx4+U5nJiYSHZ2NsePHycuLo5bt27RrVs3SkpK2LJlC9u3b+fOnTvY2dmxZs0ajZ8m5s0AACAASURBVNKFZBjZ2dmxb98+zp8/j5eXl+xhleS8cuUKycnJWFlZsXv3bo1YZ69evUpiYiKjRo3C2NgYlUqFsbEx165dIz09neHDh9OqVSucnZ3lEE6JhtLJyYmVK1eqXRfP/7Y0/g0NDTl+/Dht2rTB2dm5SSdZwxsMHR0dHB0d8fHxaXZjXJKlrq6Od999Fx0dHZYsWcKYMWMICwujrq6O/Px8jI2Nf9fhp1KpaNGiBSYmJvJNxkv8G/DvqC70Ev8efPjhhyIoKEhs27ZNPH369Hff27BqmSYhLS1NjBkzRkRGRsoVHqUqXykpKWL27NlydcWmqiyqG5Ks8fHxwsvLS8ybN08UFRXJr2ty9bHnZXv48KGYM2eOcHd3F5s3b/7dzzZViU0TUFpaKubMmSM8PDzE7t27G/VRU2S+f/++WLFihVCpVKKmpkbU1dWJmJgY8fXXXwshhHjw4IEYPHiw6NWrl0hJSXlh3NfW1jaaz5o2L6TnnJWVJSIjI8WAAQPk6paaogMJ6enpIjc3VwghxKFDh4S3t7e4deuWEOJZ5VZ/f38xYcIEMX/+fDFt2jTRsWNHMXfuXPnzWVlZ4vHjx/I401Rd3L9/XwQHB4sxY8aIixcvNpoXubm5YurUqWpfZ7OysmRdHD9+XHTo0EEcPHiw0VifM2eOCA8Pb/LzT548EaWlpRqhC0nmyspKMWfOHHH79m252m5lZaWIiYkRkyZNUpt8/yry8/PFkCFDRHJysvz3hg0bRJcuXYSPj48YMmSIKCkpEUJo9p73fw0vPeQaAOnk3atXL1JTU9m/fz+1tbU4OjpiYGDQZBiBJnhsmoKNjQ2GhobcvHmTzZs3Y29vj4ODA0qlEjs7O0JDQzWaIkl6zs7OzhgbG7Ny5Uru37+Pra2tXAhBUzxmkhwN4xYbQl9fH3t7e4QQrFixgtLSUtq1ayfHY/8eLZ2moEWLFri4uFBTU8OSJUuoqanB1NQUc3PzRl4ndcqfmppKXFwcw4cPl6s8RkREEBgYiK6uLgYGBoSFhREfH09CQgJubm6NPOUSi4zUF3XeUjQcE+K5XJbWrVtjYWFBcnIycXFx9O7du9FVtbr1ABAbG8uZM2cICwvD0dGR8PBwHBwc5OqJFhYWTJ06ld69exMSEkJ9fT0JCQmEh4djbGyMiYkJenp6aqNf/SNI64+RkRFBQUFs2bKFs2fPoqenh7OzM1paWhgbG9O3b1+USiW1tbVqY79YsmQJaWlphIeHy0Xf2rRpg52dnTxWTp8+TU5ODqNHj5bXsCdPntCqVSt0dXVp0aKFWulX4dfCSRUVFWzatIn4+Hi2bdtGbm4uenp6ODk50b59e2JjY7G0tMTV1VUtcv4emrIhYmNjKSgo4N69e3z33Xfs3buX8PBwevXqxYULF7h37x5hYWFqn9P/TXhpkGsAGhpVAwcOJCcnh+TkZJKTk3F0dKRNmzaNDEB1bXxNTeqmNnBnZ2ccHBwoLS1l8eLFFBQUUF1djYuLy+8WbmkuPP/8fut5ent74+7uzrp167h27Rp5eXl06dKlybg6dUCpVFJdXc2sWbOwsLCQOdEbwsTEBC8vL2xtbVm/fj1XrlwhOzubzp07NzIE1YGmDjZNtRkZGREYGIipqSmxsbFcu3aN3NxcvL29G5U5VxdsbW358ccfsbW1xc7Ojrq6OvT19WWKQKCRUZ6YmIibm9sL8xrUezCSwuJqamqorKxsFKol/hEW5ODgQLt27Th79iybNm3CzMwMIyMjuYaA9F519aOwsJD09HT69euHUqnE3NxcNuSkJFNjY2M5WVVbW5stW7YwYMAA2rRpoxGUn8+vs88/TymUwMrKigEDBnDkyBHOnj1LcnIyPj4+MtUpqJ/lJjMzk969ewPg5ubWqNCXUqnkwIEDPHnyhHHjxqFSqXjw4AGvvvoqVVVVdO7cWf4edelCMsYrKysZMGAAAGvXrkWpVHL16lVWr15NXl4e2tratGzZkurqaoKDgzUiF0eCFIpYU1PD3bt3uXPnDjY2Nujo6JCQkMDJkyexs7Pj888/580338THx4eUlBSMjY0JCwtTt/j/VXhpkDcDfs+jKk3chkZ5eHg4JiYmPHr0iMWLF3P37l1yc3MxNjZGX19fLZ7lhpP6xo0bZGdny1ykEhrGVltbW9OrVy88PDxISUkhMTGR5ORk0tPTsbOzU0vsXMN+1NXVUVxcTEVFhVzKvCk4OjrSq1cvHj58SFJSEjt37uTu3buyp7ah0aUOpKen87e//Q1vb288PDya3Aj09PTw9vYmMjKS4uJiTp06xe7duzl27Bj6+vpywYrmhHRDUl1dzenTp0lJScHR0fE3n6eOjg4+Pj4EBwejpaXFjz/+yIkTJ9i7dy/W1tbyRt/cEP+gKo2Pj6e4uJjQ0NDfnOuSUZ6YmMjWrVsJDQ3VKKpJhUJBbW0t06ZN49GjR/j5+TVK7JK8lA4ODnTt2lVmWpCoHa2srNDX11erIWJiYsLChQsxNTVtlHMg9UOiZ5P6c/nyZW7evElMTAxGRkbqEht4tk9IXvna2loeP35MdXV1k+uT5Ck3NDQkMjKS1q1bc/36dbZt28apU6coLCxER0dHLQweDWX84osvsLe3fyH5VJoje/bsoba2lpEjR5Kfn8+MGTMoLy9nwYIFaj1MiAbUf9XV1Rw/fpzHjx/zwQcfYGVlRUBAAGFhYXh7e3Ps2DF++eUXTp48SVZWFj169HiBDEBdkMZTWVkZb7zxBtu3b2fDhg3k5eUxbtw4hg4dyvDhwxk7dqxcN+TRo0fs2rWLDh06NJlM/xL/e3hpkP8vQzI8Kioq2Lp1K4cOHeLq1avU19fTtm3bRiEQDY1yV1dXIiMj6dixo1yqOi8vj86dO8tZ0M0FqfqnNKm3bt3KkSNHMDIywtPTs5Eh3lRGdkREBIMHD6aurg6VSqWWzPLn+zFlyhQ2btzItm3bCAoKwszM7DeTV0xNTenSpQtjxoxBqVRSVVXF4cOH6du3b7Pr4nlYWVlRUlLC1q1bGThw4G8m2KhUKlq3bk1wcDAxMTE4Oztjb29P+/bt5YIozYWGm8TYsWM5dOgQR48eRaVS4evr+wKVmwTJK+jn58fo0aNxdXXF0NAQT0/PF2gRmxPa2toYGhrKFXelKq9NwcDAgNDQUAoLC4mJiVG7d/95aGlpsXPnTnJycuTCYxIaJtCamJjQq1cv2rdvj4GBAVu3buXnn38mOTmZfv36qe0Gr2XLlmRlZZGfn0/Xrl3lcA1JnkuXLpGfnw9ATk4Oy5Ytw9TUlDFjxqjtIJGRkcHTp08xMzNDqVRSVlbGxIkT2bBhA+vXr8fc3FwOR2kIiSdaKvgTHR2Np6cnbdu25fr160RERKgt4U5KLH/06BEZGRkEBAQ0uVYeOHCAmpoaevbsyZ///GfKysoascI09/zIy8vj6dOnGBsby3vzhx9+yJIlS7C0tGT06NHymNLT06N9+/b06NEDDw8PysrKuH37NhUVFXTr1k2tIZkN9+Oamhpee+019PT0eOeddxg3bhwODg54eXlhaGhI69atSU5OZsWKFeTk5LB06VIAvv32W41bn/6vQyGko+BL/NshTYry8nKGDRsGPLt+v3fvHiYmJnTq1Il58+b9U99VX19PZWWl2uiFqqqqiImJwczMjPHjx1NXV0dwcDD6+vovVBR8HuqOLW34+xUVFYwYMQITExPCwsLQ09MjPDz8Dz1Jz/dBKkPdnHi+dLr096VLl5gzZw6vvPIKw4cP/119/JGumgs1NTWMHz8eAwMD3nvvPfT19WnXrl2jePjfin3VlD40xOPHj/n4449RKpV88MEHODs7/1Ofe16nzQ1p+Ze84zo6Oty4cYPx48czffp0RowY0eTnntdBWVkZSqWSkpKSJkOnmhPx8fFMmTKFhQsXEhkZKT/jmpoadu3axbx58zA2NqZ169aYm5sTGxuLjo6OWsaV5ORIT09n7969ODg4yCw2/v7+5ObmEh8fz7Rp0xg7duxv3iA9L7umzJF9+/bx6aef8sUXXzQqTCTJN3v2bJKTkzEzM6OqqooDBw6olRVm8+bN7N27lw0bNmBsbMzt27dJT08nNjaWR48esWHDBpydnWX5n98XVq1aRVxcHLt371bLjcutW7do0aJFo+qy58+fZ/r06Xz99dcEBwc3en9+fj4PHjzg7Nmz7N69GxMTE+zt7VmwYIHMaqPusKf/KvxvZ43+t6O+vl58+OGHYtSoUSI7O1tuHzNmjPD09BSXLl36w+/QhCznpKQkMWDAAHH79m0hhBA1NTXi2LFjYsqUKeKtt94SFy5cEEJoFuvC7du3RXV1tRDi1yz51atXi4EDB4onT57I70tLSxNff/212Lhxo7h69eo/9d3q0klFRYV4//33RVpamigsLJTb3377bREdHS3/rQlj5veQnp4uIiMjRWpqqhBCiOLiYhEXFyeio6NFdHS0WLFiRaP+/ScgLi5OdOnSRcybN0/cv39fbtdUXUhz9Xn2isLCQvHWW2+JDz74QNTX1/+h/JrYv1mzZglvb29x7do1IcSvfS0uLhbHjx8XcXFxIjk5WV4X1MngkZCQIEaMGCG6desmjh49KqZMmSLvFdXV1WLhwoXC3d1drFmzRlRWVv5T36lJOpk5c6YICAiQddEQ27dvF25ubmLYsGGipqZGCKFeXSQmJor+/fuL6Oho4evrKzZu3ChKS0vFkSNHRFhYmBg0aJDMgNbwGTeU2dfXV+zYsaPZZb9//77o06eP+Pvf/96o/eDBg8LLy0seU9KYr6urE4sXLxZjx44VNTU1ory8XBQVFWkEq81/K16GrPwvQ6FQsH79ejw8POTCJUePHmXdunV88cUXKJVKbt68+bseNU1IDrl48SJxcXFMnDiRU6dOsXjxYpYuXUptbS0PHz5k//79smdHE1BSUkJMTAyHDh0iOjpa9raeOXOG+/fv07dvX65du8by5cv56quvyM7O5tSpUxQXFxMSEvKH3u/m1EnDHISUlBR27drFzp07uXnzJrW1tXh4eODp6UlcXBz19fVyYpcmQTznSbp9+zabN2+mW7dunDt3jmXLlhEbG4udnR0GBgbExcURGhqKra2tGqX+5yD1zd3dnZqaGtauXUtNTQ02NjZyKNTz/dcEKBQKqqurGTduHKdPn8bS0hJTU1MMDQ3R1dVl0aJF+Pn50a5duz/8Hk2DnZ0dmZmZrF+/nqCgINq0aQOArq4u9vb2uLu7y+xPUihbc+L+/ftcvXoVOzs7nJ2dsbCwkGPAnzx5woQJE9DV1ZVrT6hUKpYtW4a+vj5eXl5/KK8m6ESK2+/QoQMXLlxg69at+Pv7N7qNNDQ0pFWrVvz1r3/VCL50JycnFAoFcXFx6Ojo8Morr+Dk5ISNjQ2Ojo4kJCSQmJhIv3790NXVlee15C0/cuQIR48epVu3bnTo0KFZZTc0NMTe3p7+/ftTU1NDQUGBXGRw165dWFlZycn80nPW0tJi2bJlDBgwACsrK1q1aqV2Vpv/Zrw0yP/NaLjx1tfXU1FRwZo1a3B3dyc0NJT9+/fz/vvv89577/HKK6/w7bffcvPmTXr16qUxE6CpxEBzc3Pi4+NZtGgRR44cobi4mE8++YT333+ffv36sW3bNjw9PWnfvr2apG4MqZJmYmIip06dIioqSq6OuHv3bo4fP87u3bu5ffs206ZN44svvsDGxobY2FiGDBmitqTT5yH+EbNbVVXFqlWrGDlyJK+88gomJiZkZ2ezfv16Ll68KBdqqKqqomvXrhrDBAO/JtLW19dTXFyMSqXC0dGRrKwsli1bxunTpwGYN28e7733HsOGDWPnzp1YW1s3YlpQN6R58XyStkSRp1AoCAgIQEdHh7i4ODIzM2VaNEkX6ipd3hCSrCqViurqau7fv8/JkydJTEwkJSUFV1dXPDw8ePr0KampqXTt2lXtycv/KszMzLC1teXOnTssX75cpi1tWIUQkI2p5oRKpeL48eOsW7cOV1dXrKys0NXVxdnZmfz8fHJycujbty8WFhaoVCq0tbXx9/eX6UtVKhWdO3fWmP3ityCNeUNDQ1xcXMjIyGDdunVYWlrSunVrDAwM5LwWKdFenca4NDePHj0qswydO3cOPz8/LC0tsba2xsnJiUOHDpGYmEhkZCQtW7aU9/za2lpSUlL4+eefmTFjRrPltTx+/Fim9LS3t6e2tpbJkydz+PBhfH195XG1Y8cOrK2tcXNzk8f8pUuXuHXrFqNGjWqUPKwpe8d/G14a5P9GSIaHtOFVVVWhr68ve5BLSkqYP38+U6dO5c0335TLydfW1jJo0CCNmAQN2VQyMzPJzs6msrISOzs7wsLCsLCwYNiwYbz55psEBwfTqlUrcnJyOHnyJIMHD8bGxkbdXQCeJdq5u7tjbW3NwYMHOX36NJGRkXTq1Al9fX2EEPTs2ZOZM2cSERFBq1atZMq0/v37qzVJUEJDRphDhw7x1VdfyUweHTp0ICQkhF69enH27FkyMjJITk7mypUr+Pv7/6FXs7nQMJF2+vTpbNmyhW3btlFZWcmsWbNwcHDglVdeYdy4cXh7e8s3RgkJCfTu3VujOH2lBKlRo0bRrl27RnzKDY1yf39/zMzMyMnJYfXq1RQUFFBXV4eTk5PaY3sbUhtOnjwZGxsbYmJi5NyDGzdusHLlSh49esSTJ0+orKykS5cutG7dWmOo3Jq6bWiKfrVt27Z07NiR6upqFi9ezIMHDygvL8fDw6MRc0xz90mhUPDw4UOOHj3KmTNnmDt3LgqFgjFjxtC6dWsyMzPZsWMHYWFhmJmZyXPI39+fkpISLl26xIgRIzRCF39EhSvBysqKrl27UlBQwKZNm7h9+zZ1dXW4ubnJ71V3BU7p9zt37szQoUPR19fnwoULJCcnv2CUx8fHs2PHDkaOHCkz92hpaeHj48OIESOabR+sra1l69atnDx5kqCgIHR0dFAqlTx48IBz585x8+ZNOnfujI+PD5mZmezatUvmqs/MzGTDhg2YmpoycuRIjRhP/+14aZD/myAtmuXl5cyaNYtNmzaxfv162rVrh7OzM+fPn+fIkSMMHz6cGTNmAHD37l22bNmCv78/Xbt2VXMPGrNfjBs3jn379hEbG0t8fDxZWVmEh4cTEhKCi4sLt27dQktLi6tXr7J06VL09PSYPHmy2r1/8Kuno7a2lhMnTlBZWcmpU6f45ZdfiIyMpHPnzvTp04fAwEC0tbV58uQJDx8+ZP78+ZiamjJ+/Hi1L04NdfHxxx9z8uRJioqKuHTpEg8ePCA8PJxWrVphbW1NREQEHTp0QEdHh9zcXIqLi+nWrVuzJ502Bcm7P2rUKACGDBmCkZER/v7+2Nvb4+rqSps2bThz5gx3797l0qVLrFq1CqVSyUcffaQR46kh6urqOHjwIDk5OfTs2bNR4mlDo9zd3Z3g4GB8fX1JSUnh1KlT7Nmzh6KiInx8fNTi3Wx42D5y5AgHDhwgKSkJV1dXnJ2d8fX1ZeTIkRgZGXHnzh3Onj3LzZs3KSkpoU+fPmqfEw37ICW5V1VV0bJlyybZYBQKBaampoSFheHu7k52djbbt2/n/Pnz/Pzzz7i6ujbrwTsjI4NVq1YRGhqKvb09+vr67N69GwMDA4YMGYK7uzuOjo5YW1tz/fp1tm/fTo8ePRoZ5SEhIQwfPlwjwqBqamrQ1tampqaGvLw8ioqKMDExafIWCZ4xDPXq1QsbGxtKSkpYuXIlGRkZHDt2DHd3d4yNjZu9Dw1Z0FauXMnOnTspKCjA398fT09PtLW1uXDhAklJSQQEBGBhYYGFhQW2traUlZXJYajw60Hk92h0/93Q0tIiPz+fpUuX8uDBA+bOnYtKpeKtt96ivLyc48ePk5GRQZ8+fQgODqakpIRNmzaxc+dOfv75Z4yMjFi7dm0jR+JLqA8vDfJ/E5RKJZWVlYwcORIhBB07dsTKygo7Ozt69OiBgYEBeXl53L59m7KyMk6cOMH69etRKBR89913GmF4SB7ACRMm0KJFC2bOnMmYMWPw9fVl0aJF5Obm0q1bNx48eMDUqVNZtGgRaWlpGBkZERsbK2dlq7svSqWSiooKBg8ezMOHD7G3t8fc3JzU1FRSUlIYMGAAWlpapKamMnLkSA4cOEB8fDwtW7Zk48aNGrE4SboYM2aMzNM7fvx4KioqOHPmDDdu3KBXr17As2qWVlZW9OjRg9atW7N3714GDBigliz/x48fv7Ah/fTTT6SmpvLVV18RFhYmGyTwjPO2rKyMdevWsWjRItLT07G0tGTjxo0aM54aQltbm+LiYo4dO0afPn0wMjJ6oeKpZCi1atUKR0dH+vXrx5AhQ7C2tqZnz54YGho2u9wND3ivvPIK586do6Kigvz8fFJTU+ViXvCsIFZQUBA9evTg0aNH5OXl0bFjRywtLdVqBDa8bfnoo49Yt24de/bs4cSJE3h6eqKvry8fdJ6X0cnJibCwMKKjo9HT06Oqqgp3d/dmM8jr6+uJjY3l73//O/n5+YSFhZGWlsbjx49p0aIFN2/exNLSUq5sbGlpybVr19ixYwehoaGYmZnJOlSnMZ6VlcXp06dxdXVFS0uLp0+fMmHCBNatW0dcXBz5+fkyF//zc1eaJ66urvTs2ZP+/ftjZWVFfn4+vr6+zW6QS3HS5eXlDB8+nJycHKqrq9HS0qJDhw5yvL4UunLkyBEMDQ1JSkqiY8eOvPrqq79bKbm54ObmhoGBAZs2bcLY2Jjx48dja2uLv7+/bJRfv36dsLAwBg0aRJ8+fejSpQvDhg3j7bfflitna3oI1H8FmjGB9P88YmNjRd++fUVubu4Lr9XV1YnExETx9ddfi27duomxY8eKjz/+WM5kljKf1Y1ffvlF9O7dWyQnJ8vZ1nFxccLNzU3s2bNHXLt2TdTX14tHjx6JQ4cOibNnz6qdqUDKem+ITZs2iX79+omsrCwhxDPZTpw4IXr27CnGjh0rs6/s2LFDrF27VuzcuVPt/XgeZ8+eFd27dxdnz56V2woLC8Xq1atFt27dxJw5c+T2qqoq+f/dunUTK1asaFZZhRAiOztbeHp6iosXLzZqX7lypQgMDBSlpaVCiF/HemlpqViyZIn44IMPhBBCXL58WeTk5Pwm+0dzQ5Kzvr7+hfkZHh4uPvzwwz/8DpVKpTGMF7W1tWLixIniT3/6k7h06ZKoqakR+/fvF6+++qoICwsTJ0+elN8r9begoECEhoaqZTw1hYqKCtG/f38xevRosXz5crFy5UoRExMjAgICxMGDB2WmjqagbgaogoICMX/+fBEaGirmz58vtx86dEgMGjRIxMTEiOTkZLk9KSlJDB8+XLi7u4u7d++qQ2QZKpVKlJeXi3HjxomwsDBx8OBBIYQQr776qhg7dqzYuHGjmDNnjujYsaOYNm2a/Lnf2tfUpYuG7FpCPJPv3XffFWPHjhWFhYXyGpWfny/S0tJEQUGBEEKIffv2iUGDBgk3NzfRp08fjdmvpTXy448/FkFBQcLNzU189NFH8r4nhBArVqwQERER4s9//nMjpjcJmtKXlxDipUH+b8QXX3whBg4c+MIG/OTJEzFr1iyxc+dOIYQQJSUljV5Xt+HREGlpacLLy0ucPn1aCCHE/v37hZubm1izZo24e/euGDFihIiLi3vhc+qa1Hfu3BFdu3YVCQkJjdrnzZsnevXq1aitvr5enDp1Svj4+IhJkyY1uXlr0uJ04sQJ4ePjIy5fviyE+HWcFBYWiilTpgg3NzfxxRdfyO+vqakRR44cEb6+vmL16tXNLm9eXp7Yt2+fEOLX56hSqcTWrVtFUFCQSEtLe2Ej/vTTT0X//v1faFe38SShoqJCTJo0SSxcuFDenIV4dpAbOHCgrJv/BDx58kT06tVLrFu3rlF7amqqePXVV0XPnj1lGsqGa9j06dPFa6+9Jh9i1Yk9e/aIyMhIkZGRIbft2LFDuLm5iePHj4uioiIhhOaMHwmSPIWFheLLL78UISEhYu7cufLrP/30k2yUnzp1SgjxTAeHDh0SH3/8scasS2lpaWLMmDFi8ODBYsWKFWLChAkyVWxpaanYtGmT6NChwz9llDc3srKyhLe3t8jMzJTbqqurxdixY8XatWuFEELcunVLrFixQvj5+YkOHTqIAQMGiLS0NPm1M2fONDqoqwu/9UwlGskPP/ywkVG+cuVK0adPHzFu3Djx4MGD5hLzJf5FaM5d8H8YRBP1lAwMDMjPz+fJkyfAr5n8rVu3pra2lu3bt1NbW9voSl8IobbM8vr6+hfaTExMUCqV5OXl8dNPP/HBBx8wbdo03nzzTVQqFTk5OTx9+vSFz6nruqtly5a0b9+e2bNnk5SUJLe3a9eOuro6Ll++LLcplUq6dOlC9+7dSU5OJiYmhtra2kbfp65+SGOlISwsLKirq+PcuXMAMl2Vqakpf/rTn1AoFBw8eFAuLqWjo0NpaSlKpZKIiIhmlR+gbdu2DBw4kJqaGoYPH862bdtQKBRERUWhra3NmjVrKCwsbNRXe3t7WrduTXV1daPvUmeYSl1dnfz/iooKiouL+emnnxg0aBAbN27kxo0b9O/fX2YhgabXA02CEILy8nIqKipkxhTpmQcEBDB8+HDy8/P5y1/+wpkzZ+Tr93PnznH8+HF0dHQ0Ir40NzeX0tJSnJycANi/fz+fffYZM2fORFdXly+//FIjmGwaomERGVNTUyZOnEhkZCRJSUnMnTsXgMjISN5++205AVWixbWzs+PLL7+U4+bVDX9/f6ZPn06rVq3YvXs3N2/exN3dHXi2/0VHRzNjxgwSEhKYPn06gBwCqG4oFApmz56Nq6urLE9NTQ0PHz7k7NmzfPnll0yZMoXly5fTt29fFixYQElJCTt27ACehT0FBQXJulDXGJPClvwdWwAAIABJREFUS6qqqti+fTvbtm3j6tWrCCEYPXo0c+fOJS4ujnXr1pGTkwPAW2+9xdChQzE2NsbCwkItcr/EH+NlDPn/AA3ZVMrLy+Usaz09PZKSksjIyJAZSODZZnj27FmEEAwePPgFyjR19UFbW5vKykp++OEH4uPjKS4uxtvbm6qqKhYvXszhw4f58MMPef3114FnSagnTpyQS2ZrAgwMDOjatSs3btxg48aNuLq64ujoSIsWLdi2bRvl5eVyiWB4ZuidP38eU1NTzM3N6du3r9o374ZsKgUFBRQVFQHPDNzy8nJWrFiBm5sbzs7Osqxnz56lsLCQoKAgTp8+ja+vL5aWlri7uzNy5MhmZ7sRDWJaCwoKuHz5Mlu2bMHc3Bw/Pz/at2/Pxo0buX79Ora2tujr65Odnc2aNWto164d/fv3b1Z5fwviH3GlFRUVLFiwAA8PD9544w0CAgKorq4mLi6OvXv3YmRkhIWFBTt37iQiIkJjaDIlPJ8DoVAoMDIyIiUlhZMnTzJ27Fi0tbWprq5GW1sbBwcH4uLiaNGiBWlpaXTq1Alzc3MUCgXp6elMmzbtD6vZ/rvRcExJ/blx4waXL18mOjqao0ePMmPGDJm1KjMzk2XLltGtWzeZd1zdeH5uV1dXY25ujpeXFyUlJRw5coTs7GzCwsJo3749JiYmMgFAfn4+U6dOlee8utl5JF1YW1vj4uLChQsXuH37NlZWVjLndsuWLXFycsLMzIxt27Zx7tw5jWEQMzExwcvLS2ZKMjAwwMvLCzc3N7Zt20ZWVhb29vZ8+eWXjBs3DhcXF7KzsykrK3thn1AnI4yURzFq1CiSkpKIj48nLS0NHR0dvLy88Pb2xtTUlNWrV1NaWkptbS1xcXG8/fbbDBgwAKVSqfYcqZdoGi8N8n8RkiFbXl7O559/zqZNm8jIyMDKygovLy9KS0tJTk4mPT2dwMBAWrRoQV5eHtu3b8fR0ZGePXuquwuNkllGjhzJ5cuXuX79Omlpafj7+xMYGEhpaSmZmZn06tULhULBzZs3WbBgAXp6esyYMUPtRmxDGBgY4Ofnx61bt9i4cSPt27cnMDAQOzs7li5dSmFhIaamptjY2JCens6OHTvo1q2bXOpcnd6OhgvslClT2Lhxo1y+2crKCk9PT8rKyli+fLnMJpGTk8PatWvp1KkTr732GkuWLMHPz0/2VDU3Z/Tzz8/AwICAgACePHnCihUrsLW1pV+/fri7u7Nz504OHDjAli1bSE5ORltbW2ZVEWpmjWjI4HH58mU+++wzysvLcXR0xNXVlR49euDr64u1tTUrVqzg0aNHFBQU4ODggLe3t8YkoDZkU0lLS+P69evcvXsXBwcH2rZty/79+0lOTmbw4MEyS8yVK1e4dOkSAwYM4OLFi1hZWeHt7Y2hoSFRUVFYWlqqpQ+SJ1P6W1dXl9jYWNLS0ti+fTtTpkxh0qRJAFy4cIGMjAzGjRunlsTZ59Fwbk+ePJmNGzeyadMmrKys8PHxwcvLi+Li4heMcn9/f6Kiopg2bZpGJDY3HE+XLl3i/v37ODg4EB4ezpUrVzh//jz6+vry+iMZ5bq6umRnZzNkyBC1G38N15b79+9z4sQJtm/fjqurK927dyc6Oppx48YxfPhw2rZtKxe8W7t2LR06dCA0NFSt8kuQWKvGjRuHiYkJn3/+OW+99RY//PADmZmZAHTo0AFvb2/MzMxYs2YNKSkp5OTk8Nprr8nrrCasUy/xIhRC0+9aNQjSpC4rK2PYsGGoVCqcnZ35+eef6dSpEx999BGenp4sW7aMuLg4SkpKsLGxobKyEgMDA77//nu0tbXVbnjAs81i1qxZ3L17ly+//BJHR0fu378ve1YzMzM5ePAgmzZtQkdHBzMzM9q0acO6devQ0dGhvr5erVnZTfE5P3z4kE8++YTU1FT++te/Eh4ezk8//cTcuXNlCsCWLVtiZGSkNl1Iv9fwd6uqqoiJiUFfX5+BAweiVCpJSkoiJSWFqVOn4ufnx7Fjx9i0aRMKhQJdXV3atWvHpk2bKC4uZsKECcyaNYuQkJBm64eEhjct27dv5/79+wQFBdG7d2/y8/NZvnw5e/bs4euvvyY6Opri4mIOHz5MeXk5lpaW9O/fXyOKgkjjqaysjNdff502bdoQHx+PQqEgIiKCd999VzY44NktwIEDBzh8+DBPnjyR36tuNOzHq6++SklJCfn5+QB069aN119/nRs3brB27VpatmzJmDFjqKqq4vDhwxgbG7Nx40aGDh2Ki4sLCxYsUHsf5s6dS1FREUZGRkyZMgVnZ2d27drFV199hYuLC4sXL8bS0pJbt27x6aefYmJiwooVK9SuC2l+l5eXM3ToUIyMjAgKCiIvL4+kpCQ+++wzoqOjKSoqYtWqVcTHxxMWFsbz/jFNWWfLysqYMGECJSUl3Lt3Dx8fHzZu3MiVK1f49ttvqaysZMKECQwePFj+bHl5OXp6ejIdqDqdHs8/w7y8PObPn8/x48dZtGiRzFgVFxfHyZMnMTAwICMjg+rqavbs2aPWtel5JCQksHTpUhYuXIizszOPHj3ik08+4dq1axgYGDBhwgSZp/7cuXM8efKE8PBw+YD70hjXXLz0kP8LUCgU1NXV8f7776Ovr8+yZcsYPXo0enp6/P3vfycnJwcPDw+ioqLo3LkzxsbG2NjY0L17d7744guNoheSSnwHBAQQFRUFIC+ejx8/5ujRowwfPpyBAwfSu3dvoqKiePPNN+U+qLuimuSxyc7O5ubNmyiVSqytrenRowe//PILsbGxtG/fnr59+xIREYGnpyf29vb06dOH2bNnq83zVFpaKh8OpMUxISGB8+fPM2/ePHr27EmHDh0YMGAAT58+Zc2aNYSGhjJ69GgiIiLo168fffv2ZcqUKSiVSubPn8+dO3d48803MTAwaNa+NKTSGzlyJFlZWWRkZGBsbIyXlxcWFhZ4eHhQUlLC8uXLsbKyws/PDy8vLzp37ixXjFNH6fLnIVXae+ONN9DW1uaNN95g1KhReHt7s2XLFu7du4ebmxtmZmYA6Ovr4+3tTffu3dm/fz9KpZKOHTuqTf6G1Gs1NTVMnDgRHR0dZs2axfjx4/H39+fHH3/kl19+ITo6moEDB5KRkcHRo0fJyMjA2dmZJUuWUF9fz/79+wkKCsLX17fZ+yF576qrqxk6dKhs1N2+fZutW7cSERFB9+7dMTExYc+ePZw+fZotW7Zw9OhRADZs2KAxtKUqlYp58+ZRW1vLokWL6NOnD9ra2pw+fZr4+HjMzc3x9/fHy8uLp0+fsn37dnR1dRtVp1W38SSNp9dff50WLVowZ84cIiIiZD5xGxsbXFxcOHPmDKmpqRgYGODm5gY8o2OVnA/qNsYlnvG0tDSEEHh4eNC5c2dyc3NZvXo1Hh4eODo6kpuby759++RCXitXrpT3PHXrQsLRo0c5f/487733HlpaWixfvpyCggLmzJlDUlISZ8+elcNX2rZtK4c6qvtw9xL/BJore/T/CgoLC0V0dLQ4cOCAEEKIhw8fis8//1y89957olOnTmLkyJHiwoULTX5WU7LNVSqVePz4sejTp4/45ptv5DYJV69eFYGBgSIxMfGFz6qbvUD6/dLSUjF8+HDRu3dv4ebmJnr37i2mT58u6urqxOPHj8Xbb78tfH19xdGjR5v8HnXo4s6dOyIgIEDs37+/Ufvy5ctFSEiITN8o9VGlUomJEyeKXr16ieLiYvn9hw8fFlOmTBF/+tOfREhIiEhPT2++TjyH2tpaMWnSJDFu3DiRm5sr0y8WFRWJ3NxcUV5eLiorK8Vf/vIX4enpKbZu3ao2Wf8IeXl5IiIi4gX9nDt3TnTs2FFMnjy5EbuHEM9YGoYMGSK++uqr5hRVxu3bt+X/S6xB6enpon///o1oDIV4xhLRvXt38c4778hthYWF4tGjR6KyslI8ePBAzJkzR3Tt2lXk5OQ0TwfEr2uP9G9tba3Yt2+fmDBhgsjPzxcqlUqkpaWJkSNHCj8/P5k94pdffhFr1qwR3333nfj+++81jra0qqpKjBo1SixdulQI8WzN+vTTT8X06dPF1KlThZeXl9izZ48QQoji4mKxefNmjdkjGiI9PV306dNHHDt2rFG7SqUSeXl5Ijs7W+Tl5YnRo0eLkJCQF8adulFRUSEiIyNFaGio8Pb2FhEREWLDhg2irq5O5Ofni3feeUd4eXnJ/auoqGi0H6pTJ039dmpqqhgwYIBQqVRi9+7dwt3dXX7mEuNQYGCgvLe/xH8ONOPI9x+Ep0+fcv36dfm0vGvXLhITE/n444+JjY3l6tWrrFq1ijVr1rzwWXWdTp/PzlcoFJiZmeHh4cHevXu5e/eu7NGBZzForVq14s6dOy98lzo9HdLv19TU8MYbb6Crq8vs2bOJi4vjjTfe4MCBA7z77rsYGBjw2WefERgYyIcffsjhw4df+D516KKsrAxPT08++eQT4uPjgV/ZOVQqFQUFBUDjio/du3enuLiYkpIS+X2GhoaUlpYSGBjIli1bGoVSNAcajqenT59y//59xowZg52dHYWFhaxfv57IyEiio6MZMmQIZWVlzJo1i/DwcA4cOKCxjCSVlZXcvXtXjsFXqVTU19fj5+fH3LlzOXLkCJs2beLWrVvyZ9LT0ykuLubhw4fU1dU1a9+Kiop4/fXXGTFiBIAcD15YWMidO3fkpHJ4Ns6cnJyYMWMGycnJHD9+HHgW75+VlcXgwYN5/fXXSUlJYf369XLhpv9tnDt3jgULFlBbWyt7Yz///HO2b9+Ojo4Obdq0QaFQ4O/vz8yZM3F2diYmJobMzEw8PT157bXXmDp1KsOGDZPj/9V92yKhvr4eXV1dHj16BMC2bdv46aefGDt2LLNnz8bU1JSvvvqK2bNnU11dzbhx4zSCTUViGZLGc2lpKXfu3JFv4KQxLjFAxcTE0Lp1a95//30iIiIIDg5Wm+wSGj7DI0eOYG1tzaZNmzhy5AguLi7s2LGDjRs3YmFhwZw5c+jRowfvv/8+P/30E61atZJvV8Q/8q3UAekmuLq6mpMnT3L9+nWKiooICAhg6dKlAGzfvp1JkybRrVs3ampqyM3NJSAggI8++ohp06apRe6X+J/jpUH+O2hqYXRwcOD999+nXbt2nD17luXLlzN37lwsLS3R19dHCMEvv/xCZmamRhge0jVVZWUlsbGx7Ny5U96MP/roI/T09Hj33XfJy8uTje0bN26gr6/f7EwdTeHGjRsUFRU1WhQzMzN59OgREydOpFu3bnh4eKCtrY2WlhZhYWFkZmZiYWHBV199Rfv27dm9e7cae/ArPD09mTFjBl26dGHGjBkcPnwYhULB0KFDqampYfny5cAzg1zShUKhoE2bNjJVpkKhoGvXrqxbt44pU6bg6OjY7P2QxtOVK1do0aIFQgj27NnD0qVLeeutt/j2228JDQ2VK9ktWbIEIyMjZs+eLVMhqntuNEXDZm1tjbe3N/v27ePhw4eNqu+5uLgA8MMPP7Bu3Tpqa2sRQvDgwQOcnZ2ZPHky2trazRom0apVK8aOHUtBQQGvvfaa3N6yZUuEEFy/fh1onNDm5OQkh97Bs7CCwMBARowYwbRp09i8eXOzHfCEEGRkZFBXVycfJrS1tamqqiIrK4vc3NxGdJh+fn7MnDmT9u3bM378eDIyMl4wvjXF6QHPQgBHjx5NTEwMWVlZLF26lE8++QQfHx9atmyJnp4e7dq1Izc3F3Nzc/lz6gwrEP+g4ZVyKc6fP4+lpSVWVlYcO3aMp0+fyvNXR0cHExMTqqqqyM/PJzAwkM8++0zth4qGIY15eXk8fvwYS0tLHBwcsLCwYP78+bi4uLBr165GRnmHDh1kikMJ6gx7asimMmXKFF555RW++eYb7t27h4ODAyUlJRQVFclz5969e1y6dImuXbsyZMgQtevhJf51vIwh/w1IcdLV1dWkpaVx8eJFtLW1adWqFcHBwVhaWrJt2zZatGjB9OnT/x973x0W1bW1/wKCVClRFAsoiDOANKV3pBdBUKoQW4T4JYoxRk2MLWr0pts7WLChgKKgNAsiogKCIogUCyCIgii9zKzfH3xzLiPmlu93r2eS+D5PnjjnzBz2PrutvfZ63wUej4fS0lI0NTVhzZo1CA8PZ105gv43dq+trQ1+fn64efMmMjMzcf36dfT29sLe3h7a2tpITU3FiRMn8OLFC9y4cQOHDx/GoEGDsGLFClbj5hoaGjB//nzIysrCwMCAuV5VVYVTp04x8n7nzp3DihUrsGTJEtjb2+O7776DjIwMDAwMYG9vj6CgIJEheQnSYzc0NDCeSGNjYygrK+PAgQOoqKhgYv6qqqqwa9cuaGhowN/fH2JiYkIp2tnEqlWrcOjQIcybNw9EhJycHOTk5GDMmDFYt24d5s2bB1NTU1y+fBmysrJwcnKCvLw86wQvYOCC/eDBA8jKykJRURE9PT2IjY2FjIwMxowZgyFDhgAAnjx5gtevX2PevHnYtWsXtLS0MGHCBGhpacHZ2fm9y+wJDCIulwtZWVmkpqYiKysLvr6+GDVqFKqrq3H06FEYGxtj9OjRzO8qKytRUFAAT09PjBw5kuEBTJo0CePGjXuv6iRiYmLQ1dWFg4MDOjo6EBMTAxMTEzg5OeHNmzfIy8tDbW0tTExMGN7FyJEjoampiezsbBQXFwuRCNlCf2Lz2bNnkZWVhfLycqioqDBypNevX8e9e/ewZMkSyMjIoKSkBDk5Ofjxxx8xb948oVMxtiDgIfD5fHz//feoqqqCi4sLOBwOnjx5gri4OIwdOxYjR45kTpGeP3+OoqIiuLq64qOPPmLmObbGt8Cj3drairCwMOzduxeXLl2CvLw8/P39AfSpUNnb2+POnTvIzs5GT08PrK2t4ejoiJCQENbn1v42w48//ojOzk588803GDJkCG7evIk7d+7AwMAAI0aMwJ07d5CcnIy7d+/i2LFjICJs3rz5g5rKHxQfDPLfgYBZHhwcjKSkJJw9exYpKSkoKiqCsbExhgwZguvXr+P27dvQ19dHe3s7fv75Z0hJSWH+/PnMxMZmiIeAyPHbb7+hu7sbv/76K9zd3dHa2oozZ85ATEwMPj4+cHFxwZMnT5Cfn4+GhgZoa2tj586drEpuvXz5kiEFuru7o7u7G62trZCWlkZbWxvi4uJgbGyM+vp6REVF4YsvvkBERARaW1uxdetWGBsbw8DAQIjlz9ZE+3Y/EBjldXV1iImJAYfDgbe3N1RVVXHs2DGcOXMGx48fx+XLlyEpKYl9+/aJBFGtPz766CMkJydjzJgx8PHxYTY+oaGhjNe+pqYGiYmJ0NXVFTrGZtvo6O8BPHLkCGJjY3H27Fk8fvwYCxYsQE9PDw4fPoxHjx5BQkIC9+/fx65duyApKYmoqChkZGRAUVERlpaWEBMTY/IQvE8I+rSUlBS0tbUhJyeHtLQ0ZGVlwc/PDwYGBigsLMSBAwcgLS2Nrq4uVFZWYsuWLRgyZAj+53/+R2iDxxYE4yItLQ3r1q1Dc3Mz7O3tYWlpiRcvXiArKws1NTWYPHkyY5SrqanBzMwMs2bNYt3g6G8ABgQE4NatWygrK8PFixeRm5uLFy9ewMzMDKWlpUhJScGwYcPw8uVL7Nq1C1JSUswpkigYTwIy7cKFC9HU1ISQkBBGptfBwQElJSU4ePAgOjs7ISEhgYcPH2Lbtm0YMmQI5s6dy3p/EsyzPB4PK1euZEIbgT5Zz4aGBtja2jJj1t7eHoWFhTh9+jRGjx4NIyMj1teK/nKfYmJiOH/+PGxtbeHp6Qlra2v09PQgNzcXRUVFsLCwgJGREVpbW1FfXw8dHR3s2LFDJKQyP+D/iPcSqf4HQn9C0KpVqyg4OJjy8vKoqamJtm/fTl5eXuTq6koNDQ1069Ytcnd3JwMDA7K1taXp06czv+9PCmEL7e3tdOLECYqIiKDDhw8z1ysrK+nbb78lMzMz2r17N3P95cuXQumx2SJH3bp1i9zc3IRSxi9YsICmTZvGpC9fs2YN6ejoEIfDoePHjzO/LSoqIldX13cSUtmA4B12dXVRRUUFZWVl0cuXL6m3t5eePn1KERERNHHiRMrIyCAiovr6etq5cyft2LFDZIhqbxOL+Hw+NTY20syZM2nZsmVC9zIyMmjJkiW0Y8cOmj59Ovn6+ooMyU6Azs5OCgoKojlz5lBKSgpVVlbS2rVracqUKTR37lzq6emhuLg4Cg0NJQ6HQ1OmTKHZs2dTT08Ptba20tSpU+nkyZOslf9dRK/29nY6duwYmZub07x584iIqKGhgVasWEF6enqkp6dHU6ZMobCwMIb8KWqpvw8fPkx6enr03XffMd9Zv349OTo60qpVq+j169f/0nPeN3p7e2nhwoUUGhpKlZWV1NPTQz09PeTs7Ew2NjYM8Xb27Nmkr69PZmZmFBISwrSDKNRBAAFZnsPh0Jo1a6itrU3o/po1a8je3p44HA65urqKTH8SoLOzk2JjY2n+/PmUnZ1NRERtbW20evVqcnZ2pu+//15obX79+jX99NNPItEG/QULIiMjadasWWRubj5gLYuJiSF3d3f69NNPqb6+nohIJNbtD/j/x1/eIO8/OAUDorW1lbZs2UJff/01HTp0SOh+SkoKeXp60uLFi4mI6MaNG3T69Gk6fvy4SBhP/ZGRkUEcDoc4HA4dPXpU6N6jR4/o22+/JUtLS9q5c+eA37K1obh9+zbp6OjQhg0bmEmGz+fT9u3bycPDg+bOnUuvXr2iuro6WrRoEenq6tKpU6eoqqqKrl+/ToGBgRQUFCRyE2xQUBDZ2dkRh8Mhc3NzWrp0KTU2NtKzZ88oIiKC9PX1KSUl5Z3PEYW6dHZ2MhskAdLT00lXV5dycnKIqK+c27ZtIx8fH/Lz86OvvvqKGQts1aGhoYEaGxuFrt26dYumTJlCeXl5TLl6e3vp6NGj5OTkxBiEnZ2d9PTpU3r69Cm1tLRQd3c3rVixgmxtbam6uvq914Xo73NLR0cHnTt3jnbu3EmJiYmM8sjJkyfJzMyM5s6dy/wmLy+PcnJy6M6dO0yfZHOOEvzt9vZ2SkhIoN27d1N2djY1NjZSbGws6ejoCBnlGzZsIBcXF4qKiqLW1lbWyk1EVFFRMaDtu7q6yNfXl7Zt28b0pytXrhCHw6HExETKzMxk2ufWrVtUUFAgMmvFu8ZlS0sLzZs3j4yMjOjMmTNCxh5Rn7JPfn4+PXz4UCT6U3/s2bOHnJycaNKkSfT06VPm+qtXr2jVqlXvNMoFYHOeFbzHrq4u8vLyIj8/P5o1axZNmjSJHB0dB/S5mJgY8vLyosDAQKqrq2Oui4Ij8AP+7/jLG+Td3d3E4/GEBuOtW7eIy+USh8Ohffv2EdHfJxwej0dbtmwhS0tLxlvbH6IwqPvjwoULZGJiQn5+fpSXlyd079GjR7R69Wpm4WAbt2/fJj09Pdq0aRN1dHQQ0d/rxOPx6MCBA+Ti4kLz5s2jN2/eUG1tLa1atYomTpxIpqam5OzsTB9//LFIeZ66urooODiY5syZQ2lpafT06VP6/vvvydXVlYKDgxl5wE8//ZSMjIxEoh36g8/nU29vL4WGhpKpqSmtWLGCKioqGMMoIiKCvvnmG+Yzj8ejtrY2am5uFpKxY6PctbW1ZGhoSL/88ouQUR4XF0f6+vqMRKPA4Ojt7aVVq1aRtbU1NTU1MWXPzs6m4OBgmj59Otna2lJJScl7rw+R8AbP29ubHBwcyNramnR1dcnV1ZXZdJ88eZLMzc1p1qxZ71yg2fRkCsojqIOzszPZ2tqSmZkZlZeXU3d3Nx0+fJh0dXVp3bp1RNTXLsuXL6eFCxeyWva2tjZydXUlT09PIQPp5cuXZGRkRPv37ycioqSkJOJwOLRjxw7i8/kUFhZGq1atGvA8tuen/pu7+Ph4unDhAt2/f5+I+pxSYWFhZGVlRZmZmQOM8v4Qhf4kQF1dHW3atIk4HA7jNBPg1atXtHr1anJzc6Ovv/76fRbzX0JXVxfl5uZSVFQUPXv2jPh8PiUlJZG3tzf5+flRTU2N0Pe3b99OX375pUicTHzAfwZ/6Rjy0tJSbNmyBdu3b8e+ffvQ09MDDocDDQ0NGBoaIi8vD42NjZg6dSokJSWZ+K6xY8di165dMDc3x9ixY4WeyVbclqBsvb29aG5uxsuXL6GgoABtbW1oaGjg/PnzqK2txejRoxkCmpKSEtTV1aGmpobg4GBWY87y8/Mxe/ZshIaGYvHixZCRkWFiAt+8eQNpaWkYGhqCx+MhOzsbubm58PLygoeHB+zt7eHg4ICpU6ciIiJCJJIXCXD37l0kJibi66+/hpWVFZSUlGBjYwNpaWnk5OQwqaU1NTVRVlaGBw8ewM/Pj+1iMzGMAoKWhoYG1NTUcOHCBZw/fx4PHjyArq4u+Hw+zp07B29vbygoKICIICUlBWlpaUaNgQ3VCDExMSgoKKCyshKnTp2CrKwstLS0ICMjg46ODsTHx0NDQwNcLpcheEpKSkJNTQ3R0dFwdHTEyJEjIS4ujoaGBhARDA0NsXTpUmhpab33+gjq1NvbiyVLlkBaWhrff/89li1bBltbW1RXVyMpKQlycnJM1tf09HQkJSUhODh4wHPYAP0vWY3H42H58uUAgF9++QVLliyBj48P1NXVISEhAS0tLaipqWHXrl1oamqCg4MDkxRLwM1how6DBg2CtrY2MjIykJ2dDXNzcygqKkJWVhZlZWUoKChAU1MTNmzYwHBaxMXFkZiYCGlpabi6ugo9j835tj+XIjQ0FKmpqTh79ixKSkqgoKAAXV1duLm5ITs7G/Hx8dDS0sLo0aPfOZZFJd66vb0dysrKMDQ0RG9vLzIyMlBTU8PEwUtLS8PY2Bj3799HT08PXF03rTH5AAAgAElEQVRdWedQCMDj8bB+/Xps374dXV1dmDlzJgYPHgwOhwM5OTnk5+cjNTUVNjY2DNHczMwMLi4urI6JD/jP4i9rkOfn5+OTTz5h9LhVVFTQ3t4OIyMjKCgoYNSoUdDU1MTx48dRWVkJR0dHRl6oqKiI0V/tL1fFFvpPrlFRUdi/fz9OnjyJ8+fPY9SoUZgyZQrGjh2LEydOoLq6WsgoV1FRweTJkxkyDBuLREFBAWbNmoXp06dj9erVkJSUZOQa79+/j4iICJiYmGDYsGEwNDQEn8/H9evXkZubC2tra4wdOxbq6uoYPnw4Q8oRlYxkN2/exLlz57By5UoMHjwY3d3dGDRoEPT09PD48WOkpaVh+vTp0NDQgJGREWbPns36xNpfheThw4fo7u4Gl8vF5MmTERAQgNbWVpSUlGDbtm3Q0tJCVlYW2traMGXKlAFlZ6sugr7s6uqK+vp6REdHQ15eHlpaWlBSUkJ+fj5KSkowcuRIjBkzhlnYb968iZKSEsycOROKiooA+pQ9BAQqwTW20NbWhv3798Pf3x92dnYQExPD8OHDweVy8eTJE1y+fBlWVlYwMTGBmJgYWltb4enpyVo7CDakgvIAff1rz549sLW1hbu7O4A+CUdxcXHU1NTg2rVrMDMzg7q6OrZv3w45OTkYGxuzrtAjJiaGUaNGQVdXFwkJCbh+/TosLCygqKgIHo+H5ORkXL58GeHh4fjiiy8gJiaG6upqnDhxgmkTUYG4uDg6OzsRGhoKBQUFfPfdd3Bzc8OFCxdQXFyMIUOGQE9PD25ubsjJycGZM2cwatQojBs3TiTIgoI1r62tDWvXrsWhQ4cQHR2NxsZGjB49Gm5ubnj16hXS0tLw5MkTIaPcxsYG3t7eImXICjJkv3jxAtXV1fD19WUMb4GSUkFBAdLS0pg+J/gdiQAh+AP+M/hLGuSVlZWIjIyEt7c3li1bBldXV7i6usLIyIgxsMXFxTF27FiMHz8ee/fuxc2bN9HW1obS0lLExMRAQUEBERERIjGYBZNrcHAwJCQkEBAQACcnJzQ0NGDLli2QlJREYGAg1NTUEB8fj9raWqiqqmLUqFEDnsMGjh49iry8PDg5OTFeTHFxcdy/fx8zZ86Eo6MjfH19IS4uDnFxccYov3nzJlJSUuDp6ckoMADsGYHvmty7u7sRHx+PESNGYOLEiYyhKyEhgVGjRuHAgQOwtrbGmDFjoKyszBrL/8GDB3j8+DHU1NQY1Yg5c+bg6NGjOHjwIHp6ejBixAgMGzYMlpaW8PX1haKiIgoKCvDmzRtUV1fD2dkZioqKrEp9CiAuLo6enh5Gm76hoYExym1tbTFmzBjEx8ejtLQU4uLiGDNmDAoLC7Fv3z6oqqpi5syZA+rAdr2ICNXV1dixYweCg4Mxbtw4JqW3srIyRo4ciX379kFfX5/5z8fHh5XNNhGhp6cHmzdvhrKyMszMzJh7dXV12LNnDxwdHWFoaIienh7mNKu6uhoRERFwcnKCl5cXtLW1MW3aNCFd/veN/uNRTEwMw4YNg4ODA2JiYvDgwQOYmJhg8uTJkJKSQklJCXg8Hnp7e5Gbm4sdO3ZAUlISmzZtEjmj6cyZM7h37x5++ukn6OjoYPDgwbh16xZevHiB4uJiKCkpMZ7ypKQk1NfXw8fHh+1iA+gb3x0dHfD390dXVxe4XC7Gjx+P5ORkpKamQktLC97e3mhqakJGRgaqq6thb28PoE97n+3N3dvjUUxMDDo6OlBSUkJubi7Onj0LLy8vJv8El8uFnJwcUlNT0dDQAGdnZ6HffsCfA39JgzwpKQmvXr3Cl19+CVVVVWah7ejoQGlpKeLj41FZWQkigoWFBbS1tZGQkIALFy5AUlISo0aNwtatWxnh/fc9qBsbG5mBKsC1a9dw+fJlrFu3Ds7OzpgwYQIGDRqECxcuIDw8HIMHD8bkyZMxdOhQ7N27FyoqKrC2tn6v5f49WFlZ4eXLl4iOjoacnBxMTU1RUVGB4OBg+Pj44JtvvmFCIASTqIGBAV6/fg0igre3N+uTksCr3NPTg6qqKhARZGVlISkpiYKCAhQXF2PEiBHQ0NAQ8sYWFxcjPDwcSkpKzLPed126u7vx7bffYvv27TA3N4eqqirmzZsHcXFxhIaGQlVVFUeOHAGPx4OGhgaUlJQgISEBAwMDmJmZwdraGklJSRATE2OkANmEYEz2PyVxdHREfX09YmJiICEhAT8/P+jo6ODmzZs4duwYoqOjce3aNSgoKODAgQMYNGjQgI3R+67XuxZtOTk5XL16FRUVFbC0tISCggJjlA8bNgwnTpzAhAkTMHnyZEhISLDmQWttbYWsrCysrKxgZ2eHzs5OJCYmQk9PD0OGDEFJSQnOnz8PLy8vDBkyhNk8qaqq4vTp0+ByudDT08P48eMhLi7O1PF94uXLlwNkUwWe2WfPnuHEiRN49OgRCgoKYGdnB2trawwdOhT19fU4evQoXr16BXV1dezdu1ckpejS09NRWVmJ8PBwSElJYceOHWhpacHMmTORlZWF27dvY9iwYdDV1cWMGTMwdepUkSp/bGwsSkpKsGXLFnh6esLOzg7Pnz9HdnY2XFxcoKWlBRMTE7x69QrHjh2DjIwMjI2Nmd+zGWoj0K2Pi4tDWloaysrKICUlBQsLC2hqauLatWs4e/Ys3N3dmbWew+FAW1tbJOQ+P+C/BDYC19nGwoULaebMmcxnPp9P9+7do+DgYNLT0yMOh0M6OjpkYmJCly9fJqI+1rydnR1FRkZSQ0MDEbFDynn06BEZGBhQYWGh0PWDBw+SoaEhQ75JTEwkLpdLe/bsocLCQpozZw41NzcTEVF2djbrhCIB+pdj1apVpKurS2vXriVjY2Nau3btANktor+TiPh8PkPqYYPYUlpaSnfv3mU+t7S0UEhICNnY2JCpqSnt2bOHOjs76c6dO+Tg4EAzZsygY8eOUXNzM+Xk5FBwcDDNnTtXJEg5jx8/plmzZpGVlRWlpqbSggUL6MGDB8z9PXv2kIGBAa1du5aePHnCXBcQw2JjY8nX15caGhpYZfoLytPZ2UlXrlyhxMRESk5OZu6vX7+eOBwObd++nXg8HjU2NlJBQQHFxcVRTk6OyKhf9CfcpaWlUVpaGiP7t2fPHrK0tKSffvqJmYt4PB4VFxeTo6MjXbx4kbVyExE9efKEJk+eTGlpaUzZNm3aRMbGxrRjxw4i6lN+cXFxoeDgYCGViLKyMnJxcWF+yxYeP35MU6dOpW3btjHXBHNrcXExGRoa0qpVqyg7O5scHBxo+vTp9OzZM+a7DQ0NIiVF964xGRcXR35+fkTURwTW09OjGzduEBHR1q1bGYWugwcPMr8RlXWDiGjt2rVM+YmIzp07R1wul2JiYigzM5N++OEHIiJ6/vw5HTx4UCTKLmiH1tZWcnd3J1dXV/L19SVTU1Py8vKiv/3tb0RElJaWRq6uruTu7i5y4hEf8N/DX9Ig37NnD02ZMoVu3LhBBQUF9PPPP5ORkRFxuVz64osvqKSkhNLT0+mTTz6h+fPnU3d3N3V1ddHly5fJzMxMSP/zfaOxsZGSkpKIqG9QCgb4pUuXyMzMjIqKiujcuXPE4XAYjfHi4mLicDiUmZkp9CxRGdT9y7FmzRricDjk7+9PtbW1zPW3F5T+n9kwALu6uigsLIyMjY3p7t271NPTQ0FBQRQSEkIxMTH01Vdfka6uLm3atIm6u7upsLCQwsLCSFdXl7hcLtnY2FBQUBCrGr4tLS3U1NREr169IqI+DXSB9raRkRE9fvxY6PsCo3zdunVCkmJERDt37iRLS0vWxgWRsAqJn58fubi4kL6+Punp6dH06dOZTex3331HHA6Hdu7cOUASkUh0xkVLSwt5eXmRmZkZcTgc8vPzYyQmV61aRVZWVjRnzhxKT0+nw4cPU2BgIAUEBLBe/oqKCpo1axYZGRnRlStXiIiopqaGFi5cSC4uLrR3714i6nMaODs7k7W1NcXExNCWLVtEpg61tbUUFhZGXl5etGfPHuZ6UVERY4wLDO7bt2+Tg4MDBQQEvFMOk20puv65Mbq6uoQ2CmVlZdTT00Ourq7M5qOrq4u+/fZbCg8Pp8TERNY3E7+Hn3/+maysrIjo7xK/graKj4+niRMnUmVlpdBv2O5XRH3z1NKlSykkJESofIGBgaSjo0MPHz4kor46eXh4kKmpKeNM+4A/N/6SISuC+L7Y2FicPHkS+fn5sLOzw+eff46FCxdi2LBh0NTURFNTE1JTUxEYGAg5OTmoq6uDw+Fg27ZtqK+vZxjO7xMyMjLgcDjo7u5GaGgoenp6YGBgABkZGcTHx+Py5ctITExEVFQUIiMjAQCFhYUoLCxEWFgYPvroI+ZZbB57Ub943P4xrg4ODmhubkZmZiaUlJSYmPJ/RBZk4+hRQkIC+vr6uH//PuLi4jBu3DhUVVVh5cqVmDJlClxcXNDd3Y0jR46gpaUFnp6e8PDwgKurK4yNjREQEIDPP/+cUYR53yTU4uJibNy4EXv37kVycjIaGhrg5OQEGxsbPHv2DOXl5Zg8eTKjKCImJobJkydj0KBBiI2NRUNDAwwNDSEvL4+KigqcO3cOz549Q1hY2IBwqv82BH1JTEwMPT09WLBgAQYNGoTVq1dj7ty5sLW1xZUrV5CcnAwTExPMmDEDDQ0NOHToEABg4sSJQhwENsdF/9CM5cuXQ0JCAqtXr0ZAQADOnz+P69evQ0tLC+Hh4SAi3Lt3D4cOHcLTp0+hpqaG/fv3sx4eoaKiAiMjI9TW1mLHjh3Q09ODvr4+o3Bx5coV8Hg8hISEQFdXFy9evEBCQgIaGxuhoaGB3bt3s14HBQUFmJubo7CwEDk5ORg0aBAkJSURFhYGb29vJowOAEaMGAF9fX0kJSXh3LlzcHNzg5ycHPMsUchM29bWhnXr1uHgwYM4f/48CgoKYGpqipEjR+LFixc4cuQIfH19oa2tjdraWpw+fRoODg4IDQ1lLWRIAMHfpv/lJQjmyp6eHmRlZeHq1avYs2cPli9fzmQ+vXHjBurq6jBz5kymnQB2x7YAYmJi2LdvH4yNjeHp6QlxcXFcunQJMTEx2LBhA7q7u1FfXw87OzumH3l5eYlE2T/gvwx29wPsIT8/nw4ePEi//fYbpaenC3kxBB6BHTt20Jw5c4Tu9fb20rVr1wbsvN8H3s4w9tlnn5GBgQHFxcURUZ9++qRJk8jJyYny8vKoq6uLioqKKCgoSCRCI4qLi4X0eN/2HL0dvvKPvJiigoqKCgoICCAOh0OmpqYDPGS//PIL6evr0+bNm4WOtAVgw2Nz+/ZtMjIyooiICFqzZg35+voSh8NhkrFUV1fT7NmzydLSkm7evElEwm3166+/Unh4ONOfXr9+TSkpKUxGwveFoqKiAdq8FRUV5OnpOeA0qKGhgTw8PCgwMJC5tnjxYgoODmbdg/k22traKCEhgb777juh0I26ujpycXEhd3d3un79OhH1tUtZWRk1NjayqvsugMD72tXVRcnJyeTo6EhGRkaUlZVFRH11+Oyzz2jKlCmMp5yozyPd1dUlEnXoj5qaGoqMjCRHR0fS1dWl1atXE5/PHzCX8vl8ysnJoc8//1wkvLD90dbWRu7u7jRjxgzauHEjrVq1ijw8PMjBwYEKCwuppaWFwsLCyMzMjNasWUOenp7k5+fHatZpwZwvKENrayutWbOG5s+fT1u2bBHK2MzhcGjWrFlMLoRHjx5RYGAgLVy4UOTGdm9vL71+/ZqsrKxo69atRER09uxZId36+fPn05dffklENMD2+IA/N/7UBrmgM/+74Q18Pp8eP35MgYGBtH79+v9a+f4d9E9MJFgQWltbafXq1aSnp0enTp0iIqLc3FwyNzcnFxcXMjc3ZyZittMb83g8OnbsGHE4HFq5ciVz/Z8Z5To6OrR79+53xtGxhbffYWVlJUVGRpKuri7DOXjbgDU2NqaVK1eyXo/+yZfevHlDRH3G0MKFC8nU1JTu3LlDRH1xwB9//PHvGuWCfwva630vfC0tLeTj40NbtmwRui6onyBtdn+eweXLl0lXV1coprw/H0FUIAg56x9mJhj/9fX15OLiQl5eXpSZmTmg3KKQpOXNmzfk5+dHQUFB5O3tTQYGBjRx4kSmLgKj3MXFhYkpf9dzRAU1NTW0YMECMjc3FyrvPzKQRMF4ErzH6Oho8vHxYbKFEvUlleFwOJSdnU2dnZ2Ul5dHn332GQUEBLCeYffZs2ekr6/PbNja2trIzc2NXFxcaMaMGaSnp0dz5sxhwuZWrVpFjo6O5ObmRmFhYeTq6krTpk0TWjPZwu+9v1WrVpGDgwNt27aNuFwu7dq1i/nu/Pnz6bPPPhO5cfAB/338aQ3ykpISSkxMZEiB/6hz8/l8unLlCj1//pz4fD5dvXqVgoKCyNfXl1UvgQCCgdra2kpfffUVhYWF0bRp0+jq1avU0NBA33zzjZBRXltbSxcvXqTo6GjKzMwUGaLa69ev6ejRozRx4kRasWIFc/0fGeUCD4ioZLAUvMOuri4qLS2l3Nxc6ujooIcPH1JoaCjZ29szKeb712v9+vX08ccfs9qPCgoKSE9Pj3744QdqaWkRupeTk0McDkeIEPj06VP6+OOPydramm7dujXgeWzWhcfjMR75jo4Oev78ORH1EW0NDAyEyHgCVFRU0MSJE+ncuXMDnsUm3v77ra2tFBcXR4aGhrR8+XLG8ycYF/X19eTm5kaWlpZ0+/bt917efwQej0fLly8nPz8/evDgAfX29tKdO3do6dKlpKenRxkZGUTUZ5QvXLiQJk2aRAkJCSyX+p+jtraWIiMjydXVVcizz3bf+VewZs0a8vb2Zuau5ORk4nK5dOjQIbpy5Qpt3LiR+a6AOEzE3npRX1/PrGmHDx+m69ev09y5c6muro66urooPz+fjI2NKSwsjDHKL126RJs3b6Z169ZRTEwMU3Y26iAYp4L/t7e3U3x8PB07dowhxN+8eZPc3NyIy+XS2rVriaivLz19+pQ8PDwGOBo+4K+BP61BPnfuXOJwOJSQkEDt7e1E9PsGxMmTJ4nD4ZC9vT1ZWVmRk5MTzZo1S6RSsLe3t5OXlxeFhITQpk2baNGiRYzHqbGxkb799lvS1dVlwlfehijUgajPexYbG/tvGeX79u1jfTNBJEwaDAoKIkdHR+a4tLGxkSoqKigwMJDs7e0ZAuG7vMpsLOJPnjwhU1NT8vPzY/o1n89n/l1QUEBGRkYDjNWnT5/SnDlziMPhsJYy/h+Bx+PRwoULydDQkAkX2rRpE02cOHGAUsft27fJ1dWVIUaKAgTvv6urix4+fEiNjY1M3z906BDp6urS+vXrqbOzk4j+Pi5qa2spKipKZMa1AB0dHRQUFDTgZPHly5e0bNkymjhxIhO+Ul1dTT///LPI1eH3IAhfedsoFyVP5tshDr29vbRx40by9vYmor+fvuzatYuIiI4fP05mZmYDQjDZrlNdXR1Dvvb29qaoqCih+3fu3GGM8v6e//5go1/dv3+fFi1axKgfCdRUBMRsBwcHOnPmDBERJSUlkYeHB7m7u9OhQ4fot99+oxkzZgh59z/gr4U/LanT19cX+fn5iI+Ph6qqKsaOHQspKal3JvfQ09ODhoYGo7nq5+eHxYsXi0QKdkF5Dx48iLKyMmzdupUhCI4bNw5AH+lFT08Pvb292L17N4YMGQJ9fX2herJJCOmv5zx48GDmXUdHRzMJZQR6ye8iek6aNIl1YhHQR8bp7u7GJ598AllZWSxduhQRERHQ1dWFtrY2VFRUMGnSJOTn5yMuLg6TJk3CiBEjhEiHxFJWtY6ODty5cwctLS3o7OzE5MmTISYmxhCkVqxYARkZGaxevRrA3/udoqIiDAwMICYmJpSghQ08evQIaWlpuHr1Knp6eqCurg4igoKCAkpLS3Hy5Ek4OzvDwsICjx8/xp49e9Db24vOzk6Ul5dj69atkJOTw6JFi1gl2pWXl6O6uhoqKiqQkpJCS0sLIiMjceTIEZw+fRo9PT3Q1NSEhYUF5OXlsXv3brS0tMDc3JzJYquoqMikkhclfWs+n4+UlBR0d3cLZQiVlZWFmpoaUlJScPHiRYwdOxbGxsawtLQUuTr8HoYMGQJjY2Pcu3cPubm5ePHiBSwsLFjX3Regfyp5AGhvb4e0tDRkZGSwf/9+FBcXIzo6GosWLcKnn34KAMjLy0NVVRXCwsIgIyPDPOt916m1tRWtra2oqqqClJQUhg4diokTJ4KIkJWVBXV1dXh6eoLP54PP52PkyJEwNzdHdHQ0ysvLMW7cOAwfPlzomWz0p3v37mHr1q2ora2Fqakpfv31VxAR1q9fj8jISOTm5uLixYtQUVGBr68vNDU10dLSgri4OLS2tkJLSwu7du1indT8AezgT2mQd3Z2YtCgQZg2bRqysrJw+fJlKCoqYty4cQOMcsG/ORwOLC0tYWlpCS0tLWaRYNMYB/4+MZ47dw4vX77Exx9/jEGDBjHlbmpqwrZt29DR0YFZs2bh4cOHuH//Pvz8/ERioeifMKe8vBxiYmJQVlYGl8uFsrLyPzXK+0MUJqeysjKcOHECCxcuhJ2dHVRUVDB69GgAwPPnz9HU1ARnZ2fcvn0bu3btgqurq5CyDVttIi8vL6Qa0d7ezqTyjoiIwOPHj7Fr1y4oKSkNWAiUlZVhZ2fHquGUn5+PTz/9FA8ePEBRURGOHj0KeXl5GBsbY9SoUdDS0kJOTg4SEhIwdepUuLq6ore3F4cOHcL58+dRVFSEoUOHIiYmhtXFrru7G5GRkYiPj4epqSkUFRURERGB3t5eBAQEoL29HWlpaWhpaYGenh4sLS0hLy+PvXv3orW1FaamppCSkhJ6pqhkGwT61IeqqqqQmpqK8ePHY8yYMcx3lJWVkZGRgY6ODjx9+hT+/v7MeBeFsf2vQGCUX716FXw+H66uriIxz/ZXU1m1ahWio6ORmpoKKysrTJgwAUSEs2fPwsTEBGvWrAER4dGjR9i9ezc0NTXh6+vLWj3u3buHTZs2YceOHThy5AizYeNyudDQ0AAAJCQkMONdMA8JjPJffvkF0tLSsLOzY6X8/aGmpgZ9fX3ExMTg4cOH6OzshIeHBxwcHKCoqAgfHx9cvXoVKSkpUFZWxpQpU+Ds7Ax/f3+Eh4fD2dmZcT6xbXt8wPvHn84g5/F4kJKSQnNzM3bs2AEiQn5+PkpLS6GiooJx48ZBUlJSyGv5e3jfi0R1dTWuXbuGM2fO4MWLF5CVlYWioiIAICsrC6WlpZgzZ45QxkoZGRkcOXIERUVFCA0NhY2NDYKCghiZKFGQ3GptbcWCBQtw8OBBHDlyBBISEtDW1oaRkdE/NcrZhsDoEHj5S0tLceLECcyfPx9Dhw4VkuQ6fvw4tmzZggULFsDU1BTd3d2se5X7Q0FBAcbGxrh79y5yc3OZE5Xy8nJs27YNEyZMAPCP+z0bdcnLy8Ps2bPh4+ODlStXIjg4GIMHD8bBgwfh4eEBJSUlqKmpgcPh4MqVK0hMTGROkRwdHeHt7Y2pU6di3rx5rJ96SUhIwNLSEpcvX0ZGRgaGDx+OiooKfPvtt3BycsLUqVNRV1eHtLQ0NDc3M0a5goICtm/fDmVlZRgZGbFS9v7on23wzJkzuHr1KsrKyqCqqgpnZ2dkZWUhJSUF6urqGDduHMTExFBeXo5Lly7h22+/xeLFi//p/CuqGDJkCOzt7ZmxLQrzlbi4ONra2jBjxgzmhEVHRwdcLhcKCgrQ1tbGoEGDkJiYiLy8PJw+fRrJyckgIuzbt4/xrL/vehQWFuKTTz4Bl8uFm5sbQkNDMXjwYGhra2PkyJFQVFTE+PHj0dvbi507d0JRURGGhoaMUa6mpgY3Nzd4e3uzNs8+f/4cf/vb32BhYQEZGRmoqalBR0cHp06dwr179+Dg4ABdXV309vZCUlKSMcpTU1OhoKCAcePGYciQIczziOi9y+B+gGjgT2eQi4uLo6urC+Hh4WhsbIS9vT08PDzw6NEjpKamQkVFBZqamkJGuSigoKAAixYtQl5eHkpKSpCcnIyxY8dCS0sLkpKSUFFRQWJiIkpKSpijakH5i4qKQETw9PTE4MGDhQx2NiFYJKZPnw4xMTEEBQVBXl4eR48ehZSUFLhcLoyNjaGsrIyYmBjU1tbCyclJZNoE6KtDR0cHNm/eDGNjYygqKuLUqVOQkJCAjY0NxMXFGW1cJSUl7Ny5E+7u7pgwYQIcHR1F7jhe4OG7e/cukpKSUFNTg4MHD0JHR0ekxoMAd+7cwaxZsxAeHo5FixZh2LBhkJeXh5iYGG7cuAFPT09m8zp27Fjo6enh8uXLOH36NJycnKCpqQk1NTUMGzaMGRdsL3ZKSkqwsbHB2bNncerUKbS2tmL27NmM5rCdnR1qa2uRlpaG169fQ09PDxYWFtDS0hKJDZ7AYGhtbUVAQADy8vLw8OFDpKenIysrCx0dHYiKimLa4dq1a8jJyUFsbCwkJSWxZMkSoU3uHxFycnIiM88K8Ouvv6KhoQF79+6Fl5cXTE1NUVdXh/T0dPD5fDg7O8PR0REvXrzAsGHDYG5ujg0bNrCWC6Gurg5Lly6Fg4MDli5dCnNzc2hoaMDa2hojR44E0BcCpaCggAkTJqC7uxs7d+6EkpISDAwMmD40dOhQVkMae3p6sH37dmRkZMDBwQEHDx6EqakpjI2NcfPmTbx48QLW1tZQUFBgnFQ+Pj7IysrC8ePHoa+vz4SfAuzq1n8Ay3gvkervGTdv3iRra+sBCgQLFy4ka2trOn369D8ler5P3L17l4yNjem7776j0tJSIiIqLCwUSl3e1tZG27ZtIysrK1q8eDH19vZSe3s7PX36lGbMmMFoSIsS+Hw+bdiwgUJCQhiSy7179yWtOmMAACAASURBVMjOzo6MjIxo27Zt9Pr1a+ru7qbY2FghspEoobS0lDgcDkN4/Omnn2jSpEkUExMj9L3MzExyc3MbkMVSFFFbW0uffvopTZkyhfbt28dcFyXViPLyctLR0aHZs2cL6VsTEV25coU4HA7Z2toSh8MhHx8fRq0jLy+Ppk6dSoaGhky/EwW8/W6fPn1KISEhxOFwKD09nSF4CrBhwwZydHSkFStWiIT6RX/09vbSokWLKDQ0lKqqqpiswa6urmRlZUVPnjwhPp9Pe/bsoc8//5wiIyNp48aNrMrp/dnx5Zdf0rJly4iob/1Yv349cTgc0tXVFZpb337377stBGvulStXyNvbmyHAv12W/mtzS0sLtbS00Pr160lHR0coeyrb6OrqosTERLKxsSEdHR2aOXMmEfURnNPT08nExIQWLFjAaKsL6tjd3U3r1q37MBY+gMGfzkMO9B2DnT17FkuXLoWMjAwTU+7h4YGMjAxkZmZCWVkZ6urqQhn62EB7ezs2bNgAXV1dLF26lIlHHjFiBBQVFcHj8VBRUQE5OTlYWVmhu7sb586dw7Fjx5CcnIyEhARISEhg69atInN82h9HjhxhjiNbWlpw6NAhaGhoYMyYMYiNjcXgwYPB4XBgbm6O8ePHY8aMGSLjcQL6PDTDhg1DQ0MDrl+/DmdnZ6irq+P58+c4deoUmpuboaKignv37iE6OhpKSkoICwsTqTZ4FwThK8XFxbh+/boQ0VNU+lBrayvS09MhJiaGIUOGYMKECZCQkMDdu3fx6aefwsXFBSEhIfD09ERqaipyc3NhZGSESZMmYcyYMeDxeJg6dapI9CeBB7K7uxuPHj1CVVUVtLS0YGlpiZs3byI7Oxt6enoYMWIE8+7t7OxQWlqKlpYWTJs27Xe5Ff9tVFZWoqysDMOHD2e8qDweDwcOHICNjQ1cXFwgISGBrKwsxMbGYvXq1Whvb0dnZyfc3d2Z8CF7e/sP8bH/IbxrjBYWFuLMmTO4ffs2Dh8+jIKCAixYsADLly+HvLw8YmNjERAQMCDz8fvuT4K/HRcXh7KyMnz55ZdMGfqXRcCRioyMRHFxMdzd3aGlpYXm5mYUFhYyJ69sQ0JCAiNHjsSpU6fQ1tYGRUVF+Pv7Y/DgwRg1ahS0tbVx6NAhlJWVwdLSEnJycoynXDAmROkU9QNYBMsbgv9vvMujV1NTQ0ZGRkJanh0dHUTUp8HK4XDI0tKSLl269N7K+Xt49eoVubq60unTp4WuV1dX04oVK8jJyYk4HA7Z2NjQwYMHiahPY339+vW0ceNGOnDgAKuaq/8Mc+fOpYULFxIR0d69e8nY2Jju3r1LRES+vr5kYWFBs2fPFpKuYrMeb3srBP0rOTmZrK2tmSQ5Dx48oB9//JFMTU1JV1eX7O3tKTw8nPUETP8uampq6NNPPyUPDw/69ddf2S7OAFRWVpK3tzf5+PjQ1atXqbS0lIyMjGjlypVCWur5+flCXsD+759tD9TbcpnOzs7E4XBowYIF1NzczGgPe3h4UF5e3oC+I/jMRp+qqKhgTiBycnKY/t3Y2EiTJk1iPJUCOT1B4pzw8HChrLwf8J9D/4Q37e3tTIKvrq4u2rBhA/n6+tK6deuEPM+HDh2i6dOnM5r2ooCffvqJHBwc/mlyseDgYPL392c+NzY2ikRCr/7j8enTpxQdHU0HDhwgW1tbCgoKYk7zOjs7KT09nUxNTenzzz9nPTncB4gu/tAe8v5ep/z8fHR2dkJCQgJDhw5FTU0NLl26BGlpaejp6TEemYKCAkhJScHU1BQhISGs70rb29tx5MgRaGlpwcTEBNXV1bh8+TKioqJw584daGtrw8vLCzIyMoiPj4empibMzc1hZ2cHW1tbIdY5m16n/uTG/vGI48aNg5GREXp6erB48WKsXbsW9vb2aGlpwalTpxjCUf/YWDbbRFxcHO3t7fjtt98gLS2NUaNGAQC0tbVx/fp1XL58GdOnT8ewYcNgYmKC6dOnw8bGBoGBgUKkQbbjlP9ViKpqhADKysowNzdHSkoKrly5gpiYGPj6+mLt2rWQkpJiyvrRRx8hPj4eampqsLOzExnJT+Dvcplz586FnJwcoqKiEB4eDi6XC21tbSgqKsLOzg6pqalIT0+Hnp4ehg8fztSBzVjlxsZGHD9+HI2NjaisrMTYsWMxfPhwyMvL4+HDh8jPz0djYyM2bNiAL774AhERERAXF0diYiIkJSXh5ub23sv8Z0Z/ovw333yD/fv3IyEhATk5OTA0NIS3tzeCg4Nhb2+PESNGAOgTC9i+fTvU1NTg4+PD+vim//XuNzc34+zZs1BVVYWent6A0zkBN+f169e4fv06vLy8ICcnB1lZWdbj9wVzfG9vL5qbmxkVKx0dHUZNKD09HT4+Phg8eDBGjx6NCRMmYNu2bQAAa2trVsr9AaKNP6xBTv2IRWFhYTh16hQOHz6M+vp6JgTi9u3buHTpEl68eIFRo0bh7t27iImJgZaWFr755huROSoqLy/H+fPnceXKFRw/fhzx8fHQ0NDAnDlzsHHjRlhZWcHCwgJZWVkYOnQoJk+ePOAZbNahv+TWDz/8gLi4OLx8+RIqKiqYMGECRowYgdu3b+PGjRuM3vWjR4+Qm5uL5cuXIywsTCTaQrAYxMXF4dChQzh27Bjq6+vR1dXF6Izn5uZCVVUV48aNYzSwx4wZg6FDh4oMafDfhSiqRvSHiooKLCwskJmZiY6ODgQGBkJXV1doAS8vL8eFCxfg7OwMfX19tos8AMXFxYiPj8eSJUtga2uL4cOHY8yYMQD6VBoaGxvh7++PCxcuIC4uDvb29hg6dCjzezbaQ0CYk5SURHt7O+rq6pCdnQ0ul8vIGQrmrbCwMCxZsgRiYmKorq7GiRMnYGlpCVNT0/de7j8zBCTzgIAA9PT0wMbGBuPHj0dRURFiYmKgrq4OLS0tXL16Ff7+/rh69SpDQheoqbA9vgV/e8iQITh37hyqq6vB5XKZTaigfIJ59PTp05CQkEB4eLhQudmqQ/9NUVRUFA4cOICjR49i5MiR4HA40NTUhLKyMjIzM5GZmQlvb2+IiYmhs7MTfn5+8PPzY93m+ADRxB/SIBcYbr29vViwYAEkJCSwcOFCjBs3DikpKaisrGRiGxsbG5GQkIDo6Gjk5ubio48+wk8//cQMdrYHhqSkJMaNG4c3b96gpqYG4uLimDt3Lj777DNMmTKF+Z6MjAxOnDiB4cOHw8rKisUSD4TAqzxjxgw8evQIPT09SExMRE1NDdTV1TF8+HA8f/4cKSkpaGlpQVdXF5Mw4fPPP2eMQLb1lAUTvIGBAWbMmAENDQ2kpqYiIyMDhYWF0NHRwdWrV0FETOzf2xAlQ/bfgSiqRvSHsrIyLCwscO3aNdy9exfy8vKYMGECYwCuXLkSMjIyWLNmjUiW/8GDB4iPj0dISAiGDx/OKIz09PTgxIkT2LdvHz755BNYW1ujvr6ekS5lE4K+3NbWhlu3bmHp0qWoqqpCQkICdHV1YWdnB3l5edy7dw89PT3o6upCbm4udu7cCQkJCfztb39jvQ5/ZAgM07cN6DNnzqCoqAibN2+Gl5cXzMzM0Nvbi9TUVPj7+0NJSQlDhgwBn8+HvLw8TExMsHnzZkhKSorU6Z28vDzGjh2LI0eOoK6uDqNHj4aamhpTVz6fj4qKChw/fhxmZmYise4J1imBehiPx4O5uTkkJCSwe/dujB8/HlwuF5qamlBRUUFmZiZOnDiBhIQEZiMlCs6nDxBRvP8omf8M2tvbqbi4mKKiooTUVE6cOEGOjo60YMECqqqqIqK+mLOsrCwqKSlh4r5EJd5aEAPX1tZGXV1dA1QhBPfv3r1L06ZNo5SUlPdext9D/zi+K1eu0Mcff8ykL7906RIZGxvTggULqLS0lDo6Oujrr78mc3NzsrKyEoq3ZjPGV9AP2traaMuWLbRs2TI6fPgwk6q8pqaG0tLSyNvbm4KDg2nKlCnE4XDoxo0brJX5r4yKigry9vYmX19fOn/+PNXX11NwcDB5enqKRH8iendca3l5ORkYGAipCAnKKYh/FygsvX2fLfSvR1RUFIWGhtKzZ88oMDCQHBwc6NatW0RElJKSQosXLyZjY2MKCAigJUuWfFBT+Q9AEIP89rv84YcfyNHRkfmclJREXC6XDhw4QDk5ObRixYp3Pk8U24LP51NcXBzp6emRt7c3HThwgF69ekW1tbWUlpZGISEhNH36dOYdiIIqGo/Ho6+//ppmz55Nz549IyKiM2fOkJmZGXG5XEpKSiKivtjx5ORkWrRoEc2fP3+AitIHfMDb+MMa5Bs2bCAOh0OTJk0asJDFxcWRo6MjffbZZ1RUVDTgt2xMTPX19fTy5cvfvf/2RCOQSOLz+VRWVkZBQUEUEhIicpNqV1cXBQYG0ieffEIrV64Uupeenk7GxsYUGRlJT548oe7ubnr06BEVFhaKxMZI8M5bWlrI3d2d/Pz8yNvbm3755Zd3yuUlJibSd999RxwOh3788Uci+uOQN/9MEBjl3t7eZG9vTx4eHsxix/ZGu7/x9Pr1a2pubqa2tjYiItq8eTNxOBw6e/as0G8yMjLIy8uL6urq3nt5+6O8vJxycnKErgmMwoqKCgoLC6OMjAyqr6+nqVOnkqOjI0NyJiJ68eIF830i9tvij4zS0lJasWIFRURE0IoVKxjDj8fj0YEDB8jR0ZGam5sZMu3u3buJiCgtLY309PTeue6JMm7cuEFeXl5kaGhIZmZmZGRkRG5ubhQREcHq5q6goIB27twpdK2jo4NmzJjBXH/z5g2tXbuWFi1aRP/zP/9DXC6XLly48M7nfRgTH/CP8IcJWXk7iYS5uTmamppQWFgIdXV1aGtrMyml9fT0ICsri/T0dBQWFjIZIQV4n0dFRITnz58zqby5XC5kZWUHfK9/3RYtWoTTp0/j6dOnOHv2LI4dOwYAOHr0KKtpv9+FtrY2FBYWIiMjA+rq6kwyHDExMWhqakJLSwuHDh3C48ePoa6uDi6Xy0i7iUK8NZ/Px7JlywAA27Ztw6xZs2BjY4POzk5UVVVBSkoK0tLSAAAulwt7e3soKSlh165d8Pb2hpKSEpvF/0tCEFN+/PhxDB06FAkJCcxxPJvE5v6xpUuWLMHhw4dx5MgRXL16FePHj8fkyZPx8uVL7Nq1CxISEhg0aBAqKiqwd+9eKCoqIiQkhLWQp7KyMvj7+yMxMRFdXV3g8XjQ0NBgxufgwYNx48YNVFZWwt/fH05OTkhNTcXFixcxYcIEqKqqQkFBgfk+fcg2+H9GXl4e5s+fj87OTjQ3N+PWrVvIzMyEp6cn5OTkICMjg+joaBQUFCA2NhZRUVGIjIwEAOTn5+Phw4f4+OOPIS8vz3JN/nWMHj0a7u7usLGxwciRI2Fra4sZM2YgIiKCIU++77Hd1NQET09P3Lx5E0QEc3NzAEBLSwsuXLiAYcOGwcrKCtHR0Th9+jTWrFmDKVOmID4+HhcvXsSbN2+gra3NtMOHMfEB/wx/CIP8bUYzj8eDrKws7O3tUVFRgVOnTkFdXR0aGhqQlJQE0GeU8/l8NDc3Izg4mLWFTkxMDPLy8qirq8PJkychJSUFLS2tdxrlAnR3dyM3Nxf5+fmQkJCAqakpfvnlF5EwOt7eGA0ePBimpqZobW3F+fPnoaamhokTJzL3NTU1MX78eOzYsQPKyspCcYDvu01aW1vR3t6O9vZ2Rou3qakJJ0+eRGBgIExNTVFdXY3jx49j8eLFOHnyJOLi4uDg4AAVFRVGSUZbWxvnzp2DmpoadHV132sdPqAPysrK8PT0xOzZsxllGzbHBf1vbGlnZycCAwPB4/Hg7e0NLpeLV69e4YcffoCFhQUcHR0hKyuLgwcPIjExEbdu3YKioiL279+PQYMGsZa9MikpCXfv3gWPx0NlZSUePXqEtLQ0TJo0CeLi4pCTk8P48ePx448/QlVVFSYmJnByckJ6ejpiY2Nhb28PVVVV5nl/VC4F28jLy8Ps2bMREBCAFStWYMGCBZCVlUVqaiomTJgADoeDoUOHQkVFBadOncLEiROxePFiyMjIoLy8HLt27cKYMWMQEBDwh2sDGRkZjBo1CpMnT4ahoSFGjx7NquNGRkYG1dXVqKqqQm5uLl6/fg07OzvIyMiAz+fD2toajY2N+Oqrr/D999/D2toaMjIySEpKgrq6OhoaGoSIqH+09viA9w+RN8j7K3gsW7YM+/btQ25uLvh8PnR0dODh4YH79+/jyJEjGD16NMaOHcsszEZGRkyaebYWOoE329HREa9fv8b+/fshLS39D41yLpeLqVOnIigoCAEBAbCzsxMZaUOBzOT9+/dRVFSE9vZ2qKurw9bWFs+ePcPevXsxYsQIcLlc5n1ramrCwsKC1bTfxcXF2LhxI/bu3YsLFy6gvr4eFhYWkJWVRVxcHIqKilBdXY1t27bh4sWLmDZtGvz8/PDs2TPcuXMHXl5ezKJw48YNHDt2DGZmZjAyMmKlPh/QRwpjc1zcv38fV65cYSTbAOD8+fPIy8vD5s2b4ezsDBMTEzQ1NeHatWvw9PSEjo4OnJyc4OLiAgcHB/j6+iIyMpJ1uUw9PT2GrCYrKwt3d3eUl5dj586daGhogJKSEnR1ddHT04OKigoYGRlh6NChsLOzQ2Njo0iQUP/oEBjjoaGhWLJkCZSUlJjNUHJyMpycnDB+/HgAfXOqqqoq4uLicOvWLRw9ehTp6ekAgAMHDkBCQoK1Ne//B/QOBRg269DS0oKHDx/Cx8cHp0+fRl1dHRwdHaGnp4dhw4YhOzsbDx48wBdffAFpaWkUFhbixo0b+P777xEZGclsKP5o7fAB7ECkDXLqx2j29/dHa2srJk+ejPLycuTm5jIa4/+PvTsPiKp6Hz/+HgaGfRFEdhTZRTYRARHBBUtRQyU1TdEsK82Nsqz8tGirpbmUOyKoqWi5r6Uo7jsi5o64gYjiwj7DcH9/9Jv7lerTp8UasvP6pxiY8Q7MPee5z33Oc7p168bp06dZtmwZrq6uuLu7y5ly3Sp1fU0Wum4wBgYGxMTEcP/+fVJTU/9nUG5sbIypqWm9W8D6nPB0WQpdm8m1a9eSmZnJ1q1b2bVrFx07diQ+Pp6ioiLmzZuHs7Mz/v7+8vOdnZ3r/S7+TkePHmXo0KE4OzvTokULbt68ydatW7l9+zZxcXHY2Nhw7NgxDh06hK+vL++99x6DBw8mKCiIgwcPYmRkRNeuXQG4f/8+x48fJy8vj7Fjx9YrhRL0Qx/nxZEjR3jmmWdo1aoVoaGh8oSbnZ3NwYMHeeGFFzA1NWXz5s1MnDiRCRMm4O7uzqeffkrr1q1xd3eXOxDpu3yrrq4OQ0NDAgMDKS0t5dy5czx48IAvv/ySRo0acfToUb788kskSUKj0XDgwAGioqKwt7fH0tKSzp07i84Rf9Lx48cZOnQo/fv35/XXX5d3kFYoFOTm5pKdnc2AAQPkuxAqlYrAwEDat2+PSqXCwcGBuLg43n33Xb1f3P0ZDS1w9ff3Z+PGjVRWVjJw4EAWLFjAnTt3iI2NBeDw4cNs2bIFHx8fioqKmDt3Lubm5gwdOlTvsYfwD6Sn2vXfrLa2VkpJSZGee+456caNG5IkSdLKlSulqKgoKSEhQVqxYoX8s6NGjZJ8fX2l7OxsfR1uPf9tEcrkyZMlX19facaMGQ1+166HFy3W1NRIgwYNkpKTk6W9e/dKV69elTIyMqT4+HjpySeflO7duyfV1tZKEyZMkIKCgqSlS5fq8ch/dOTIESkgIED6+OOP5R3tbty4IY0aNUpq3bq1lJeXJ2m1Wunu3bvSzZs36z33xo0bUq9evaSPPvqo3uMPHjyQF90K/z66z9TkyZOlysrKet9LT0+XIiMjpQcPHkgbN26st+Du+++/l4KDg6WjR4/q47B/5uGF5LrzvKamRpo7d67UoUMHafTo0VJVVZWkVqulZcuWyYvsfH19pf79+zeoXR//yWpra6WXX35Z8vX1lXbs2FGvG8eZM2ekiIgIKSQkREpNTZVmzZolZWZmSteuXZPHs196PeH30/3eftpw4NChQ1JycrK0bds2acGCBZKfn580efJk+XnPPPOM5OvrK7Vp00bq169fg+n2JPzzNOgMOfxY8rF48WLat29PXFwcd+/eZcuWLZiamqLVatm7dy9WVlZy+YparSYpKUnvV6W6mtaqqipSU1M5cuQIRUVF+Pr60r59e+7du/ebMuX6cubMGcrLy7G1tZUf0/VSTklJITo6Gmtra/z9/WnZsiXbtm1jz5499O7dm4iICC5dukReXh69evXSW9bjxIkTJCcnk5yczMiRI7G0tATA0tISGxsbMjMzadu2Ld7e3piYmGBhYcF3333HpEmTOH/+PPPnz0eSJGbMmFGv7El390L498nNzWXIkCEkJCQwadIkDA0N6/WKbtasGV9//TWbNm0iMzOTlJQUhg8fDvy4cPLkyZM8/fTT9c6rv1t5ebm8y6nuuHVZekNDQ4KDg6mtrWXnzp0cPXqUuLg4WrduTevWreUdOq2srOjXr1+Dy2j+ExkYGBAREcHJkyfZuHEjXl5eNGvWjLNnz/LMM8/QpEkTPD09OX78OFu3biUrK4u1a9eyY8cOTp48SePGjeVdOXWvJ/w+58+fZ+XKlTg4OMh3PXW/R0NDQ77//nssLCwYPnw4KpWK+fPnU1paSmxsLH369CEwMJDExERGjhzZINa0CP9Qer4g+FV1dXVScXGxFBkZKWdbv/rqKykqKkq6evWqVFBQIPn7+0sdOnSQJk6cWO+5+mwvpLvCLisrkxISEqSYmBgpLCxMat++vTRt2jT553SZ8pkzZzaoTHlJSYn01FNPSQsXLqz3+LZt26SAgADpwoULkiRJciagpqZGSktLk9q0aSPl5uZKkiRJ9+/fr9en/O925coVKTw8XOrVq5d8nHV1dfL/Hz9+XAoJCZE2bNggf6+2tlbKzMyUnnnmGWngwIHSxIkTRT9lQXbkyBGpZcuWkr+/v/Tiiy/KPfd/mllbv3691KlTJ6lLly5SSUmJ9ODBA+ns2bPS008/Lb3wwgt6bZV59uxZKTk5Wfruu+/kx34pU65Wq6V58+ZJnTp1kl544QXp/v378s9oNBr550Tbz0fnzp07Ut++faVOnTpJS5culUJCQqRJkybVuxt3+fJladu2bVJKSorUsWNHKSkpSYxNf9KFCxckX19fydfXV4qKipKWLl36s1bK27dvl4KDg6W8vDxJo9FI8+bNk1q0aCFNmjTpZ68n/h7CH9WgM+QKhQJzc3MaNWqEj48Pd+7c4dVXX+WTTz6hVatWqFQqVq9ejaurK2q1moSEBDljo48sga4+WqFQUFNTw5AhQ7CysmLatGn07duXEydOkJWVRVlZGW3btpUz5WlpaWg0GgIDA+UWe/pkZmaGr68v3bp1o6amhuLiYqysrNBoNGRmZuLm5kZISAhKpRKNRoNKpcLGxobU1FRiY2Np3rw5xsbGet35saqqihMnTlBWVkZ1dTVhYWH1tmOeMGECpqamvPPOO8CPnzUDAwMCAgJITEyka9euPPHEE3Ldu8h2/LsdPnyY5557jmeffZZOnTpx6NAhTp48SXBwMDY2NvU+587OzjRp0oQdO3awbt06MjMz2bFjBwYGBqSlpem1m8rVq1dZtGgRN27cwNbWlmbNmv3XTHlQUBAajYb9+/dz7NgxYmJiMDExkcc4rVb7j6xTbqhMTU3p1KkTu3btYuXKlURHR/Of//xHbq1aW1uLra0tnp6ePPHEE/Tp04eBAweK+v0/qaKigp07d6JQKGjUqBF79+7l5MmTXLp0iVatWgHg7e1Nfn4+N27cICYmBg8PDxo1asTcuXOxsLCot7hf/B2EP6pBB+Q6/v7+ODk5sX37dm7dusXEiRPRaDTk5eVx6tQpRowYwSuvvKK3Fc2nT5/GxMSkXhnDvn372L17Nx988AE+Pj5oNBoOHTqEQqHg2LFj3L9/n6ioKNq3b8+1a9c4d+4cAwcO1PstYN3vz8nJCa1Wy/jx45k3bx7t2rXDx8eHgoICVq5cibu7O15eXvKEfPLkSXJzc0lKSmoQ7c8sLCyIiIggJyeH/fv3U1lZSevWrQEYPnw4BQUFzJkzBxsbm59NZgYGBnJPe0n0jv3Xu3r1KkOHDqVnz568/fbbhIWFyYHqyZMnCQkJkYNyXUmTn58fvXv3xtDQkBYtWhATE8PEiRP1uuBOkiScnZ0JCwvj22+/5dy5c9jZ2f2moFzXnzw6Olp+PRF4/Dm/NFeZmprSoUMHTp8+zYULFwgICMDFxQUDAwP59617nq7sqCHs5/BPZm1tTWxsLNnZ2Tg6OtK2bVt8fHxYs2YNGzZsoLy8HD8/PwwMDMjMzOTJJ5/Ezs4ONzc3/P396dOnjzgXhEfiHxGQ6watvLw8vvnmG8LCwiguLmb69OmYmprKwbikhxXNpaWlDB8+HK1WKwd8ADk5OezcuZO+fftiY2PD7NmzuXTpEqNHj6awsJBvv/0WrVZLZGQknTt3pnfv3hgYGPxi26e/28N3GUpKSrh06RK7d++mbdu2hIeHk5eXx4oVKzA2NkalUvHDDz8wZ84crKyseOGFF/R+/DqWlpaEhoaSm5vLwYMHqa2tZe7cuVy4cIFZs2bh4+MD/Hpg0VDei6A/d+7cISQkhGeffRalUolCoSAkJAS1Ws2BAwd+MSiXJAkzMzNatWpFWFiYPKHrs3Wp7ricnZ0JCAhg7dq1vykoDwwMxNXVlSFDhojA40/Ky8sjKyuLli1b/tcEkm6Pjd27d7Nhwwa8vLxwdnaWg+6G1BbwcWFjY0ObNm1YvXo1paWlREdHM2HCBAoKCti7dy/p6enExcWRlZXFjRs36NixI+bm5vj6rlTobQAAIABJREFU+uqte5jw+NF7QP5Lt9r+W5bbzMyMixcvMnv2bPbs2YOFhUW9W8D6OCFUKhWhoaF069aN6upqqqurUalUlJWVcerUKQYPHsy2bdv49NNP+fDDD4mJicHIyIht27Zx9OhRSkpK6NChA6D/1oa6zJ0kSVRVVWFkZERwcDDGxsYcOnSIrKwsevToQWxsLKWlpSxevFjug9uoUSMWLVqk19vxv8TKykoOytevX8/169dZvHgx/v7+DeLiR2j4GjVqhKenp7yAUzdmhYaG/tegXHce6z5juv/q6/zWHbPu8+7s7Iyfnx/r1q37TUH5wxcUIvD4Y9RqNbNnz+arr77Czs6OwMDA/xqUP1y+smnTJnx8fHBychKZ8L+Qbgfg9evXc/z4cTw9PRkyZAjt2rXj9u3bfP3116jVas6cOUNsbCyNGzeWnyvOCeFR0GtArgteq6ur2bFjBzk5OTRt2lTuwfpTtra2REVFyfXXr776qt5vARsYGODg4IBWq2X06NFMnTqVPn364OnpSdu2bWnUqBGTJ0+mXbt2DB48GLVazY4dO5AkiU8//ZT+/fvLE6U+g8OHN2CaPHkyaWlpHDt2TN78xsjIiCNHjrBjxw6efPJJevXqRadOnYiOjiYpKalBbG7y31hZWdGqVSsuXLhARUWF/LXYtEH4Ix7uuPNrQfnD57Q+P2O6NRA1NTUcPHiQK1euYG1tjZeXF76+vv8zKH+YCDz+OKVSKa93WrBgAdbW1gQFBf3PoDw7O5tFixbRvn37et1UhEfP1taWiIgItm7dyv79+7GxsaF169Z06NABHx8feTMyXZ9xQXiU/vaA/MyZM0yaNIlu3bphaGgob22/bt06tm3bxpYtW/D398fZ2bneB143QVhYWNC0aVOaN2+u91vAD2dY6+rqqK2tJScnhw0bNtC9e3fs7e25e/cuGRkZ+Pj40K5dOwoLC0lPT6dFixby7nYNIeuk24CpT58+3Lt3Dz8/P1QqFUFBQVhaWhIQEICFhQX79u1j165dtGrVCm9vb5o3b94gNjf5X3TlK3l5eezbt6/eQk+RKRceplar/+fn+OEgSheUHzp0iNzcXAIDAxvMhlG68bG8vJzk5GRWrFjB5s2b2b17Nx07dsTX17deUN64ceOfBeXCo6O76KmoqCA1NfU3BeVxcXE8ePCAvn376n2e+Dd4OCg/ceKE3OTAzc2N8PBwevfuXe9OmSA8Kn9bQC5JEpIk8d1335GWlsaZM2dISEhgxowZVFdXM2nSJLp3787Zs2dZsWIFLVq0wNXV9X9mmPR1QugywRqNhqKiIsrKyoiMjMTR0VGu/evWrRs2NjZcvnyZjIwMDh06xJIlSwDk3tb6LlPRkSSJjz76iKqqKubPn0+3bt2Ii4ujuLiY7Oxs7ty5Q3x8PHZ2dhw4cIBvv/2W+Ph4ubc3NPxaRl35yqlTpzh48CAlJSVERkY2+OMW/j6nTp1ixowZuLu717sl/Ut+KSjfsGEDdXV19RY/6pOBgQFVVVX0798fc3Nz3njjDYKCgti+fTs7d+6kc+fOclC+YcMGLly4gLm5OV5eXuK8eIQevrixtbXF3d2diooKFi1a9D+DcjMzMzp06NBgkjf/BrqgfMuWLZw6dUruwNVQds4WHk9/W0Cuu23atGlTHBwcWLt2Lfv376empoauXbvSsWNH3NzciI6O5tixY6xcufJnQXlD8XDWaeTIkaSnp5OamsqVK1cYMmQI9vb27N27lw0bNtCjRw9iYmIwNDSksrKS0NBQpk+fjqGhYYMaXBUKBRs3bsTc3JynnnqK3NxcFi1axNtvv83OnTvZtWsXarWaAQMGyO//qaeeajDH/1vpgvLdu3dTV1dHly5dGtznS9CfY8eOMXPmTCoqKvD09PyfG/g8HESFhITg4uLCoEGDGsR5oQsCMzIyKCoqYsqUKQQFBWFgYMDp06e5cuUKW7dupUuXLvj5+eHj40Nqairm5ubyuhbhz9Mlb7RaLbdv38bc3Bw7Ozu8vLy4f//+bwrKdRrC5+rfQldTnpGRgbGxMZ07d5a/J+YM4a+gkCRJ+qv/kdLSUhYsWEDfvn3x8PDgwYMHrFu3jrS0NAoLC0lLSyMqKgqNRoORkRG3bt1i/Pjx5Ofn89lnnxEREdHgToCamhoGDBiAjY0NiYmJKJVKzM3NiY2Npba2li1btjBjxgwsLS1JT0/HysqK6upquc+4vntb/3QRrEaj4d133yU3Nxd7e3uuXLnCnTt3GDZsGG3btuXrr7+moKCAzMxMDA0N5cn+n9qLuKSkBDs7uwbT2UZoODZv3kxKSgoJCQkMHz4cX1/f//mcn55PDem8mDx5Mrm5uSxduhSVSsWnn37K+fPniYuL46uvvsLBwYH09HQaNWrE+fPn8fT0bDDH/k+nG+crKiqYMGEC+fn5mJubM378eMLDw7ly5QqzZ89m06ZNvPnmmwwcOBD4+edJ0J/CwkIcHBzEOSH85f6WDPnNmzfJyMigoKAAV1dXtm7dSs+ePTExMeHUqVNcvnyZXr16yVkES0tLoqOjOXHiBLNmzaJDhw71els3BEeOHGHjxo289dZbdOjQAW9vb5o1a0ZdXR13797FxcUFDw8PsrKySE9Pp1+/fpiZmcnPbwjdVNRqNfn5+RQVFeHk5ETLli05deoU9+/fp2XLlrzzzjv07NkTZ2dnioqKOHfuHE8++SSmpqZ6azP5qJibm+t14yKh4dHVjnt7e2Nvb8/ChQupra3Fzc0NOzu7X31uQ1n8+NPsqiRJnDhxgqqqKp566im+/vprZs+ezZQpU3jiiSfIzc3lyJEjpKamEhISQmhoqCiNeER0exhUVFTQu3dvysvLadWqFYWFhWzevJlmzZoRHByMj4+PnClv1KiR3H1FaBgsLS3FOSH8Lf6WFK2HhwcxMTGsXbuWTZs24ebmxrBhw+Te2zNmzODFF19k3rx5clDepEkTPv74YxYsWIC/v//fcZi/y/Xr1ykuLqZly5bA/2XEDAwMyMjI4MSJEyxZsoSqqip2795db9MgfXq43Gb48OEUFxdTXFzMlClT6NatG1OmTKl3rDU1NRQVFbFx40aaN28u7xoHj8dtOzHACvBjIKtSqXjw4AEvvfQSzs7O1NXVsWrVKiorK3nppZfw9vbW92H+qoe7qRw/fpyKigp8fHwYOXIkZ8+epby8nDlz5jB+/HiCg4MB5NKz5s2bExkZKb+WyAb+ebo7iO+88w5NmjRh2rRp2Nvbs3nzZt58800+/PBDAGJjYxk5ciQGBga8//77NG7cmPj4eD0fvfBT4pwQ/mp/eUCuVqtRqVS8+OKLrFmzhrq6Opo2bUphYSHOzs4kJiYiSRKzZ8/+WVDu4ODAxIkTAf3eAv6lkoaAgAAMDQ1ZtWoVgwYNQqlUyhOin58fmZmZlJaW0qNHD3r16gU0jNvYSqWS6upqnn32WaytrXnjjTcwMTGRL3p0wfi2bduYO3cuJiYm1NTUIEkSn3zyiei+IDyWDAwM0Gg0vPLKKyiVSvr378+wYcPIycnh448/xsjIiGHDhjXYoFzXL7y8vJxBgwZRU1NDfn4+4eHhfPrppwQGBnL58mXq6upo3rw5kiRx/fp1bty4QdeuXRk6dCig/1K6x01VVRXXr18nMTERe3t7SktLOXz4MAkJCZw9e5b//Oc/TJ48mdjYWEaMGIGbm5uo3xeEf6m/dOSVJAmVSsX9+/dJSUmhQ4cOmJiYsG3bNqZOnUpKSgouLi5ywDpnzhxefvll5syZ87PAVV+BrG6C0pU2VFVVYWpqiru7O61bt2bNmjU4OzvTqVMneSKrrKzE3t4eSZLqTW76DsZ1tm3bRk1NDVOmTMHBwQGArKwsvvnmG6ytrYmLiyM8PJw9e/bIF0avvPKK3GdcTNjC4+jBgwfcuHGDl156Sd5119/fH3t7e8aPH49Go2lwmfKHNxzSaDSMGjUKCwsLOfuq25kTftzgyMDAgNTUVI4ePcrevXtRKpUMHjxYfj1xbv85P01WSJLErVu3uHnzJgDLly9n+/btLFy4EFtbW3r06MEXX3zBsWPHGD58OCNGjADEhZEg/Bv9ZTXkunorrVbLyJEjqa6uZsyYMXTv3p179+6RnZ3N5cuXCQgIwN7eHk9PT6ysrMjIyKCiooJ27dr9FYf1u9+DbkHOO++8Q2pqKvPmzSM/Px93d3eeeOIJVq9ezcmTJ1Gr1Xh6epKTk8PixYtxdnaW+5U2NJcuXWLPnj1ERUVx5swZpk6dyqxZs9BqtVy5coWTJ0/StWtXevToQXx8PJGRkXrv+S4If6W6ujqKi4uZO3cuvXr1wtPTE7VajYGBAZ6entjZ2TF//ny0Wi2NGzeWL2T15eTJk1RXV9frd37lyhVWrFjB888/T9u2bbG3t5fX3ly7do3Tp0/To0cP1q5dy7Vr13B0dGThwoUNruPTP5VubU5tbS01NTWUlpZia2uLjY0NERERFBcXM378eD755BOioqIoKytj5cqV1NTUoFKpeOqpp+T5QvwtBOHf5y+LrpRKJVVVVUybNg1TU1NGjx6Np6cnAKNHjwZg/fr1zJw5kzFjxuDi4kK7du344osv6NKly191WL+L7j3oFmQGBQXRsmVLsrKy2LNnD1OmTGHhwoW8++67fPbZZ3zyySfY29vj4ODA1KlTG8SiwV/693XZ+1GjRlFeXo6lpSUffPAB3bt359SpUwwdOpT8/HycnJzqPa+hZPgF4c/6afmYgYGBXEc9e/ZsAgMDcXJykkvuQkJCUCqVrFy5EicnJwIDA/V27OXl5UyaNIl27doxbtw4+XG1Ws3Nmzepq6ur9/O1tbX88MMPfPrppyxZsoR169bJwbxCoRDZ2Efg4bU5b775JleuXKG0tJTnn3+eAQMGoFKpWLx4Mc2bN+eJJ54AflyH5Ovry+TJk3F3d28Q84UgCPrzl47C+/fvZ82aNZSXl9O/f38AubXh6NGjUSgUrF+/njFjxnDr1i369u3LK6+8Auj/lp3u1mN6ejpGRkZMmzYNV1dXADp16sSiRYt46623+Oqrr5g1axbXr1/n3LlzODk5ERYWVq+mXF90/75arebKlSvcvn0bX19f2rRpw/Tp09mzZw8ODg60adOGpk2bAmBsbIybm5uYoIXHlu68qKqqYv369SiVSgICAvD396dv375MmzaNKVOm8MYbb8hblT948IDOnTvTpUsXOaDSF3Nzc6ZPn46bmxvV1dWUlpbi7OyMubk5lpaW7Nu3j7i4OCwsLIAfy1CcnZ0pLCzk2rVruLi4yB2fdLXnwp+jW5szYMAAGjduTEJCAgAuLi6oVCr55+7fv8+aNWvw8PBg5syZKBQK3NzcRDAuCMKjLVn5acst3Y5kx44d4+rVq3Tu3BlTU1M0Gg1KpZKIiAjUajXl5eVYWVnx3nvvyVkrfQ9MuvexevVqtFotgwcPlt+fm5sbjRs3ZseOHVRUVBAfH0/jxo3x8/PDxcVF7+UdWq0W+HGSKC8vZ+jQoXz99ddkZmayYsUK7t69S1hYGF26dCEgIIDbt29TUFBAcXExH330EWZmZowaNapBltsIwp9lYGBAeXk5SUlJZGdns2nTJg4cOICJiQmJiYlUV1eTlZXF5s2bUalUHDhwgLS0NDQaDa+//rreW6ApFAqsra2RJIkJEybw7rvv0q1bN5o2bYqxsTHz58/HysoKb29vjI2NgR9bz+bm5pKQkIC9vX291xJ+v5KSEhQKBUZGRvJjmzdvZs+ePXz00Uc8+eSTtG7dWr4rXFZWhoWFBfv372f9+vVs3boVKysr0tPTUSqVIhgXBOHRZcgfzsZeuHCBRo0a4ezsTEJCAnV1dXzwwQdMmDCBL7/8EiMjIzlT/vzzz1NTUyNPHPrIKl+7do1Tp05x7tw5PD09adu2rbxtdm1tLUVFRcCPE7nu+Nq2bUu7du3YsWMHr7322s/aGuqjvOPSpUs4OTnJ2S+1Ws3zzz+PqakpkyZNwsfHh82bNzNz5kzOnTvH559/Tnl5OUOGDKGyshJbW1scHBxYvHgxBgYGYpIQHiu6c1fXMcjJyYnPPvsMU1NT3nvvPVJTUwEYPnw4zZs3Z/Xq1UyePBlXV1eaNm3KjBkz5C5DDaXjU1JSEgUFBSQnJ5Oens7AgQMpLCxk6tSpXLx4kcjISFQqFWlpaZibm+Pn56eX436cnDt3jkGDBjF37lxatWolP15QUEBNTY38O9aNn2q1mk8++QRnZ2cWL17MqVOn0Gg0dOjQoUHcSRUEoWF4JBnyn/a2Tk9PZ/HixdTW1uLt7U1ISAgODg4sXbqUvLw8unXrJg9EBgYG8mCkj4nu+PHjjBkzhiNHjpCTk8OmTZto2rQpXl5e8nvKyspCo9EQEhIid1wB5C4kffv2/VuP+Zfk5OQwYMAAHB0dCQgIAODixYusWLGCMWPG0K5dO6ytrbl37x7r169n3LhxKBQKfHx8CAkJoV27dsTGxjJ27Fi5m4qoGRceJwYGBlRWVrJq1Spu3bpFhw4diI2NxdbWlujoaA4fPszu3bsxMjKiZ8+edO/enZ49ezJ48GCeeuopeczSZ8cnXTa1qqoKlUqFm5sbvr6+HDx4kJUrV9KhQwe6detGo0aNWLt2Ldu2beP06dPY29uTmpoqj18iM/7HNW7cGJVKRdeuXdFqtajVagwNDSkpKWHjxo2EhYXh6uoq9yE3MjLizJkzrFu3jsGDB+Pl5UXz5s31fidVEISG5U8F5A+33KqsrKRPnz4YGRkxaNAgORugUCjw9fUlNDQUBwcHli1bxunTp+natevPsq9/9ySRm5vLc889R3x8PK+++iojR46kY8eOODo6ygsa3dzcOHnyJNnZ2VRVVREeHo5CoeDq1aukp6fj5+en976xlZWVjB8/nqZNm/Lmm2+i0WhQKBScP3+er7/+mpSUFGxsbFi7di3jxo1j9OjRREVF8dZbb+Hh4UHr1q1p3rw5Hh4eYpIQHmtHjx4lJSWFM2fOEBsbS0BAAJIkYWFhQVRUFIcPH2bfvn1otVqCg4OxsrLCyMhIrvHVVzD+cMenyZMns3z5cvLz87GzsyMgIIAWLVqwf/9+Vq5cSWxsLO3btyc+Pp4+ffrIFxX6vqD4pzt79ixZWVkEBAQQEhJCVVUVycnJVFdX4+Pjg5mZGQcOHCA/Px8vLy8aN24sz3GHDh3izp07JCUl1Zv3xB1IQRB0/lBAXl5ejkqlqhdAT58+nbt37zJr1iyioqJo0qQJ2dnZ7N+/n9raWnkQc3BwYP78+ajVaqKioh7le/lddBNby5Ytee2113B1dUWlUuHg4ICNjY0c0Do5OREfH8/JkyfZtGkTmZmZbN++nZUrV6JUKvnqq68wMDDQ62Y5hoaG7N69m8uXLxMWFsaMGTNwd3fH2dmZDRs2YGZmJrfcGjduHC+//DKmpqZ89tln+Pj4yLv26YhJQnhc/DQb7ObmRlBQEN9//z1lZWUEBwfLrQPNzc1p27Ythw8fZuPGjbi6utbrOa6v81uSJHkPhD59+lBYWIi5uTkbNmzg/PnzeHh4EBoaSosWLThw4AArVqwgLi6Opk2bym339H1B8U9XVVXFxx9/zNdff02TJk3w9/fHyMiIpUuXsmPHDpo0aUJ0dDTm5uZ8++23XL58GXt7e1xdXfnhhx9Yvnw5TZs2bTAdxARBaHh+d0B+9OhRZs2aRUBAAFZWVvLjS5cuxd3dne7du1NeXs6iRYtwcXEhICCAJUuWoFAo8PLyonXr1gQGBvL000/rNfC7f/8+CxcuJCkpieDgYDmgvn79Ol988QWfffYZc+bMYfPmzZiZmfHqq6/i7OxMTU0NVlZWtGnThilTpjSY8g4jIyOOHj1KamqqvEFIXV0dFy9eZOPGjaxbt45x48bx4osvotVqOXXqFNnZ2fTq1UvusCIIjxPdealWq8nNzaWqqgpDQ0N8fHzw8vIiLS2N4uJiAgICsLa2Bn4MyiMiIqisrCQ5OVlvY5TuQuLhMpVTp06Rl5fHzJkzGTx4MLGxsWRkZHDu3DmaNm0qB+WHDx9m1qxZ9OnTR+60AmIB559hZGSEvb09JSUlbNq0CWNjYwIDA+nfvz8HDhwgMzMTOzs7evfujZ2dHTt37iQjI4Ply5ezdetWDA0NmTNnjt6TN4IgNFy/KyA/evQozz77LFFRUXTp0qVeEPr9999z9+5devToQUZGBunp6UyYMIH+/fuzd+9e9uzZw5EjR/Dz8yM8PFxeIKmvCa+8vJxVq1bh6+tLSEgIRUVF7Ny5k9GjR3Ps2DGaNWtGly5d0Gg0fPfdd/j5+dGxY0fi4+OJj4+nTZs2Daa8Q6FQ4OrqysyZMzE0NKRRo0a0adMGR0dHfH19OXDgAABBQUE0b95cvqiysLBg7NixIiMuPHYeXtfywgsvkJGRwbp16ygqKqJFixYEBQXh7e3N3LlzuXnzZr2g3MLCgpiYGL11U8nNzWXhwoVERkZiZGSEWq2mb9++HD58mCZNmvD0008D4ODgQHh4OEuWLOHs2bM0a9aM0NBQPD090Wq19OjRQ5zbj0htbS2urq64uLhw69YtNm7ciLW1Nf7+/iQmJnLw4EFWrVqFnZ0dffr0ITo6mtDQUDw8POjatStvvvlmg0neCILQMP3mgPzo0aMMGTKEwYMHM27cOLkrim7C0m0lX1dXx9ixY/nPf/5Dhw4dKC8vZ/Xq1VhbW9OiRQv69OkjTxL6nCwUCgWHDh1i165d7Nmzh5UrV7Jy5Urc3NwYMmQIH3/8MTExMbRv3541a9bg6OhIaGjoz16noUx4BQUF3L9/n06dOnHmzBl2795Nq1ataN68OeHh4RQUFLBz505mzpxJTk4O1tbWLF68WOzSJzyWdOtakpKSMDIyYsSIEVhYWLBt2zauXbtGYGAgwcHB+Pj4MH/+fIqLi/Hx8am386Xudf4ukiQBMG/ePGxsbGjbti3wY8emCxcusHPnTho3bkynTp0wMjJCkiScnJwIDw9n2bJl8j4IERERdO7cWe/tGR8XD1/crV27lrNnz3LhwgWOHDmCjY0NAQEBPwvKdXcrwsLC8Pb2bjDJG0EQGq7fFJDrgvGBAwcybtw4TExM5FuqFRUVGBsbY2dnh6OjI7m5uezcuZPXXnsNS0tLLl++zN69e0lJSWHQoEENZpJQqVQEBQVx/vx5bty4gVqtZtiwYbzyyit07twZ+PG2sZmZGatXr8bNzY02bdro9Zgfpvsd6m5/2tra0rlzZ8LCwlCpVBw+fJg9e/bIQXlUVBQ9e/YkKiqK/v3789xzz8kZGzFJCI+jWbNmce/ePaZPn05YWBh2dnbs2bOHK1euUFBQQHBwsJwpnzlzprywU19KS0sxMzOjXbt2REVFUVVVRVpaGr6+vnTs2JHa2lq+/fZbzM3N5Z1D6+rq5KB8xowZmJmZERcXJ7+mvsfZx4GBgQHV1dUkJSVx9+5dYmNj6dSpEzdu3GDv3r0YGhoSFBREYmIihw4dYs2aNXKLyYfHVvG3EATh1/zPgPzEiRMkJyeTnJzMyJEjMTMzk/ur5ubm0r17dzp27ChvNlFcXMz27du5desWVVVVzJw5E0mSGD16tBxANpSBycbGhri4OAYMGEC3bt1o3769nCHTHafuAqNr167yJg/6ptv2u6KigmnTppGWlsbatWu5fv26nPkzNTWVg/KwsDCcnJywtLSkWbNm2NnZiUVewmMvMzMTc3NzEhMTqampIT09HWNjY1q0aMG2bdsoLi4mKCiIoKAgIiIieOqpp/Q2NhUVFdG5c2eUSiXh4eEApKam8tVXX1FWVkZ4eDgxMTFUV1czZ84cTE1NCQ4OloNyR0dHnnzySb2+h8dZVlYWu3fv5pNPPuGJJ54gKCiIwMBA7t+/z/r16zEzM5Mz5Zs3b+bWrVv07t1b1IoLgvCb/WpAfvXqVQYPHoyHhwdTp07F1NRU7rl6+vRphg4dSnx8PImJifKOZba2thQVFZGdnS1vzZ6WltZgSyNUKhVKpRJzc3Pgx810lEolCoWCixcv8v7772NsbMyrr77aYI5ddzv+6aefpri4GDs7O1QqFatWreLo0aMEBwcTHR2NkZERx44dIzs7m5CQEGxtbeu9jpgshMfZhg0bUCgUdOvWjWXLlpGamspHH31E3759ycrKYu/evaxfv56YmBgCAwP1uq6lsrKSe/fukZGRgaWlJcHBwQQGBlJaWsru3bspKioiPDyc2NhYqqqqmD17NmZmZgQFBaFUKpEkCTs7uwZzB/Jxc/bsWTZv3ky/fv3kcdTe3h4nJycOHTrEli1bsLe3x8/Pj/79+8v1+2IBpyAIv9WvBuRVVVWcOHGCsrIyqqurCQsLQ6lUkpuby6BBg+jevTvvvPOOvEulJEkYGRnRtm1bEhIS6Ny5M8OGDfvHlEaMHj2ab7/9luvXr7NhwwaWLFmCJEksW7aswV1QzJo1i5KSEqZMmcLAgQPp2rUrkZGRLFmyhB9++IHu3bsTGBiIoaEh27dv58GDB3Ts2FHfhy0IfzldENSqVSt5g69Ro0YxYcIEOnTogFqtZv369TRt2pTo6Gi6deum93UtFhYWBAQEUFNTw1dffYWFhQWtW7embdu2XLlypV5QHhcXR3V1NXPnzkWr1cqL5HUayhj1T/VLQXRRURHr1q0jJCQEX19fuWTT3t4ejUbD9u3byc7OxsPDAy8vL3lTIPG3EATht/rVgNzCwoKIiAhycnLYv38/SqUSIyMjnn32Wbp3785bb71Vb8t4hUJBUVERR44cITg4mCZNmvxjSiO0Wi3V1dVkZWVx7NgxtFotbdq04YsvvsDIyEjvFxQ/HdzT0tKwtbXlmWeeAX6sd3dxcSE4OJgvv/wSQ0NDwsPDCQgIoHnz5npt4SYIfyddMGVpaYmTkxOnT59mx47pn/7LAAAe/0lEQVQdTJgwAXNzcwoKCtixYwdJSUkMGzZMb1nl8vJyysvLyc/PR6VS0bhxY1q2bIlarWb27NlYWFgQFhZGdHT0z4Ly2NhYiouLyc3NJSkpSWRhHxFdF5Ta2lrKy8u5d+8eFhYWNGvWjPz8fJYuXUpUVBQODg7yc44ePSpvEpSYmKj3iztBEP6ZFJJuaf+vuHHjBpMnT+b8+fMUFxeTlJTEe++997N68MLCQnnRp26Xzn+a+/fvU11djaWlJWZmZsD/1Wzri65mv7KykpMnT9KmTRsGDx5M48aNmT59uvx3qKurA2DEiBFUV1fz1VdfyaU4oP/3IQiP0m+9SL548SK9e/cmPj6eyMhIVq5ciYGBAcuXL5fLPf7userUqVPMmTOHM2fOcOfOHZycnJg4cSIxMTEUFRWRmprK0qVLmTBhAkOGDEGtVvPhhx+yd+9eOnTowNixY7GwsJCPXZRG/Hm68bG8vJw33niD/Px8ampqcHFxYezYsUiSxJw5c8jLy2P8+PH4+Pig0Wj49NNPCQ4O5q233pIz42KcFQTh9/pNXVasrKwIDQ3lzJkzlJaWEh0dTZs2beTst0KhoLCwkJSUFB48eKDXie7PMjExwcLCQq6J1/ciVN3grlariY+P58KFC/Tp04eioiJWrVpFZGQkLi4uctCuUCjYvHkzhoaG9OrVq95riYyN8LiQJAmlUkllZSVpaWl4eHjUu1v3MBMTE0xMTNi0aRM5OTk4OjrqdV1LTk4Ozz//PH5+fjzxxBMMGDAAY2NjvL29cXZ2xtLSEh8fH2pra3+WKb927RrffPMN1tbWhISEiGD8EdJ1U+nXrx8KhYKEhASio6M5d+4cqampeHh4kJSUJG8q9+2337J//35MTEz44osv5DlPjLOCIPwh0u9w48YN6cUXX5S6dOkizZs3T3786tWrUr9+/aSuXbtKarVakiRJ0mg0v+elhV9RWVkp7dq1Sxo7dqx04cIFSZIkqbS0VHr22WelqKgo6ciRI5JWq5UkSZLOnz8v9ezZU5o2bZo+D1kQ/jK6sUWr1UpZWVmSr6+vNHPmTKm0tPS/PqempkYqKSmRLly4IJ8r+hijCgsLpZ49e0rvv/++dPfu3V/8Gd3xFRUVSe+//77k6+srpaWlSZIkSdXV1dLChQul2trav+uQ/1W2bNkixcfHS3l5efJjmzZtknx9faU9e/ZIN2/elCRJko4cOSLt2bNHOnTokF4/T4IgPD5+106dlpaWhIaGkpuby/79++Xbea+99hoPHjxg3bp1DaLe+p+svLycyspKKisr5YzfO++8w2effUZVVRX9+/fH0tJSzqidPXuW2bNnc+bMGbZt2ybfjp86darI1AiPHd16lPLycl5++WUuXrxIfn4+R44cQaPREBQUhImJyc+ep1QqMTMzw9bWVi/rWqT/n8U+fvw4+/btY8SIETRr1gyovz5E+kmGNSoqisrKSubOnSt/3apVK9FN5S+yc+dO9u/fL5efrF27ltdff52UlBTc3Nz44osviI+Px93dHXd3d1xcXOQyFTHnCYLwZ/yugBz+r3zl1KlT7Nu3j9TUVJRKpQjGH4G8vDw+/PBD5s+fz5YtW7h58yaRkZG0aNGCoqIi8vLy8Pf3x8PDA0NDQxwcHOjYsSMqlYr8/Hzq6uoIDQ1l+vTpDa4rjCA8CgqFArVaLW8y1q9fP3r06IGTkxMZGRlUVFQQEhLyi0H5T1/n76T79zIzMzl37ly9NqoPn6MKhYLS0lJefPFF8vLyePLJJ/H09OTevXvk5ubSp08f+bXEuf3n6MotH1ZcXMyePXuIi4vjwIEDvP7664wbN44XX3yR4uJiPv/8cyIiInBzc6v3PPG3EAThz/pDkbOLiwsTJ05k/PjxNGnSRK7HFMH4H3f06FFeeOEF2rRpQ9u2bcnJyWHu3LmUl5czceJEXn/9dcrLy/nwww9xdHSUdw21trbmlVde4fnnn68XhIi/hfC4unDhAqWlpbz//vvExMQAEBcXh5eXF2+//TZKpZKXX35Z3uSrIVEqlWi1Wvlr6Rfqv21tbdFoNPzwww/Aj+PtG2+8gY2NjagZf0R046NGo+H27dtYWFhgaWmJr68vtbW1pKSkkJ+fT0pKCsOHDwfg+vXrODs74+zsrOejFwThcfSHL+tdXFyYNWsW6enpIhj/k44ePcqQIUPo168fn3/+Oe+99x6zZ8+mS5curF+/npycHFxdXXnvvffw8/Nj3LhxHDp0qN5rqFQq+f8lSRJ/C+GxVVVVRWFhYb39DxQKBU899RTDhg0jIyODJUuWUFZWpucj/T/S/29m5e/vT0lJCd988w2AHGDraDQaADp37sytW7coLi5GkiRsbW3lTkoiGP9zdONjeXk5gwYNYsCAASQkJJCVlYWnpydTpkzh0qVLNGvWjOjoaOrq6jh9+jTLli3Dy8vrZ9lxQRCER+F3l6w8zMzM7B/TZ7yhOnHiBMnJySQnJzNy5EgsLS2BH+v1bWxsyMzMJCYmBi8vL6ytrWndujU5OTmsWLGCwMBAXFxcgPq34MWELTwufqnsqqamhuzsbJRKJUFBQRgbG8tBeXV1NZs2beLIkSMYGRkRERGhpyOvT3dOWllZsWHDBq5du4afnx8ODg71st66cXT16tUolUoGDRokzu1HTFfzPXLkSAB5w7QFCxbg4OBAt27d8PPz49tvv2Xfvn0sWLCAXbt2YWhoyKJFizA0NBQXRoIgPHJ/KiDXEQPTH3P16lUGDx6Mh4cHU6dOxdTUFEmS5M0pbt++zebNm4mNjcXHxwdADspPnTrFzJkz6dSpE/b29np+J4Lw6OnuutXU1JCbm0tBQQFubm40atSIgoICVq5ciYeHB87OzhgbGwNw6dIlKioqSExM5Msvv6R169a4urrq+Z38H90mM0uWLKGoqAhXV1ecnJzkMbSuro6LFy+yfPlyuXxNeDRqa2vr1e0fPnyY559/nsTERDp37kxRURELFizA0dGRbt260blzZ9zd3WnatCldu3bljTfekO8GiwSUIAiP2iMJyIU/pqqqihMnTlBWVkZ1dTVhYWH1smQTJkzA1NSUd955B/i/W/PW1tYEBQWhUCjq7QwnCI8L6f/3GS8vL2fAgAEsX76czMxMTpw4QWRkJAkJCeTl5ZGRkUF1dTWGhoZcvHiR2bNnY2try3PPPceWLVvw8fGhZcuW+n479TRr1gx7e3uWLFlCTk4O1dXVeHh4UFpayqFDh/jiiy+QJInPP/8cAwMDUTP+COi6oFRUVDBr1ixWr17Nvn37SEhIwNHRESMjIyIjI7l58ybz5s3DycmJtm3b4ufnR3h4OF5eXnJnG1EOKAjCX0EE5HpkYWFBREQEOTk57N+/n8rKSlq3bg3A8OHDKSgoYM6cOdjY2Pzs1n2jRo1o3769aH8mPHZ0n+e6ujpSUlIwNDRkzJgxxMTEsGnTJvbv309UVBT9+/fn7t27ZGdnk5aWxrFjx7CxsWHevHlotVo2btwoL/ZsSBQKBQEBAbRq1Yrs7Gy+++47li9fzpIlS8jNza23UF6c23+erpVkVVUVffr0IScnhwcPHlBUVISNjQ1+fn6YmpqiUqmIjIzk1q1bpKamYmNjg6+vb71suPhbCILwV1FID68oEvTixo0bTJ48mYKCAvr378/Bgwc5f/48s2bNIiAgQN+HJwh/uZs3b2JsbCx3RqmurmbDhg3s37+fpKQkoqOjgR93uRw1ahQuLi5Mnz4dR0dHiouLKSkpQaFQ4OXlhbGxMRMmTODIkSMsW7YMR0dHfb61X1VaWsqlS5fIycnBxMQEHx8fwsPDMTAwEAvlHwHdDsZ1dXWsXbuWTZs2MXHiRKytrZk7dy7Lli1j/PjxJCYmYmNjA/y4F8SECRN48OABGRkZen4HgiD8W4iAvIHQBeXHjx9Hq9WyfPlyfHx8xO1q4bEmSRJXrlyhe/fuzJs3Tw68Fy1axLx586ipqeGbb77B09NTDlBPnjwpB+VTpkyRu15kZWWxatUq7t27x5UrV1i4cCH+/v76fHt/mC6QFP48tVrNsGHDUKlUODs7M3nyZPl7kyZNYuXKlbz22mv06tVLDsqrqqowNjYWJUOCIPxtRMlKA2FlZUWrVq24cOECFRUV8te6LjZiQhAeRwqFAhsbG9zc3OjcuTO1tbXU1tbi5OSERqPh+PHjmJiYEB0dLWc6nZycaNWqFatXr2br1q106tQJCwsLzp49y507d/Dx8eHtt99ucKUq/80vBXzifH90lEol+/btY8eOHahUKuLi4jAzMwMgNjaWO3fusHDhQmxtbXF3d8fU1BQjIyN57BUXRoIg/B1EQN6AWFpaEhoaSl5eHvv27au30FNkaYTHka5G2tfXF41GQ1JSEiUlJcTGxhIQEEB1dTXp6ekAtGnTRg6SnJycaNmyJZcvX6ZPnz4YGBjg7e1Nx44diYyMlDOd/wTivH60fqnu/oknnuDevXts374dU1NTvL295T72sbGxlJaWMnv2bAICAvD29pafJ/42giD8XURA3sBYWVkRGhrKqVOnOHjwICUlJURGRoqJQXgsPbzgTqPRcPbsWZYvX46JiQnh4eGEhISgVqtZvHgxdXV19YJyFxcXEhISMDAwQKPRyIvvxMXrv9fDrTL37dtHTk4O+fn5eHt70759e+7du0dqaioWFhZ4enrKQXn79u2xsLCgV69eIiMuCIJeiIC8AdIF5bt376auro4uXbqIAEN47Gi1WpRKJdXV1XTo0IFr167x8ccfU1RURGpqKqampoSHhxMUFER1dTWLFy9GkiQ5KH/Yw50wxLny76TboK68vJxnn32WHTt2sGHDBrZv387OnTvx9fWlb9++3L17lwULFvwsKA8JCZEX04qgXBCEv5sIyBsoKysrYmNj5T7jIusnPG4MDAyoqalh9+7dlJWVMXLkSGxtbenYsSNFRUUsWrQIU1NT2rRpQ1BQEDU1NfLGLS1atND34QsNhG5sVCgUaDQaXn75ZQwNDXnnnXd47rnniImJYdeuXWzatInWrVuTlJTErVu3yMjIQJIkWrZsKW8sBaK1oSAI+iF6ajVguh04xcIi4XGk1Wp588032bx5M23atMHZ2Vn+3ocffgjAF198gUKhIDk5mRdeeAEXFxcSExP1dchCA5Kbm4udnR0uLi7yY1evXqWkpIRXX32V0NBQ4MeNmPz8/EhOTpa7qkyaNImysjKOHTuGpaWlvt6CIAiCTGTI/wFEZlx4HNXW1iJJEoWFhRQWFtKxY0dsbW1Rq9UolUo6derEzZs3SU9PR6vV0r59e8LCwkRZgUB5eTmvvPIK1dXVREREyI/n5+eTnp5OYmIi7u7u6Lr6mpub4+LiwuLFi/Hw8MDb25snn3yS3r17izuQgiA0CGJGEwThb6HVaut9rVKpiI+PZ9SoUSgUClJSUigrK0OlUqFWqwH44IMP6NixI4cPH8bIyEh+rtgw59/NzMyM6dOnM3r0aKqrq7l16xbw4+7HSqWSEydOAMilLABubm5y60wd3dciGBcEQd9EQC4Iwl+utrYWpVJJVVUVK1eu5PPPPyczM5MffviB2NhYJk+ezJ07dxg6dCgPHjyoF5TPnDmTZcuWye0/BcHAwAAPDw/q6up4/fXX6dKlC9evX8fPz49nnnmGefPm8d1339V7zt27d3F2dsbOzu5nryUIgqBvYqdOQRD+UrpygPLycvr27Su3prt//z6mpqYkJibyyiuvsGvXLt59913s7e1JS0vD0tKy3vbxYi3Fv9vly5c5fPgwt2/fJiQkhOjoaOrq6ti3bx9Tp06lrKyMjIwMTE1Neffdd9m1axcvvPACwcHBqNVq5s+fj6GhIcuWLROfI0EQGhwRkAuC8JfTarW88cYbFBUV8d577+Ht7U1ZWRkvvfQSx44dY/PmzTRv3pzdu3fz3nvvoVAo2Lhxo7yjovDvduzYMcaNG4elpSWVlZUUFRUxYcIEhgwZQm1tLceOHWPy5Mmo1WrS09MxNTVl/vz5LFu2DK1Wi6OjI66urixYsAAjIyO55aYgCEJDIQJyQRD+cpIk0adPHyIiInj99ddRKBR89913jBs3jnfffRdbW1vMzMyIiopi69atbNy4kRkzZoigSeDo0aMMGTKEfv36kZycjLGxMV9//TUrVqzgm2++wdXVFa1Wy/Hjx/nPf/5DXV0daWlpuLi4cPbsWSorKzExMcHPz09eECzWIAiC0NCI+3aCIPyltFotd+/epaSkBGtraxQKBWvXrmXUqFGMGDGCnj17smbNGtauXQtAx44d+fLLL1EqlT9bCCr8u5w4cYIhQ4YwaNAgxo0bh7u7Ow4ODkRFRWFtbY1Go+H8+fNUV1cTHh7Oxx9/jFKpZPDgwXJNeatWrWjRooW8gFME44IgNEQiIBcE4ZF6uIsF/LiLpq2tLW3btmXp0qUsWbKEt956izFjxjBixAiMjY2pqqqioqIC+LH7ysPPFf6dLl68yMCBAwkPD2fcuHFYWFjIC31ramq4evUqycnJ9OzZkwEDBrBmzRpCQ0P54IMPMDc3p3v37pSUlNR7TVE7LghCQyVGJ0EQHhldf3C1Ws2ZM2fYs2cPly5dAiA5ORkzMzM+/PBDhg4dyssvv4wkSRQUFFBcXIyfn5+ej15oSFQqFQ4ODty/f5/t27fLj+Xm5jJ27Fi6du1KSkoK06ZN4/bt20ybNo3jx4/TqlUrxo8fT5cuXbC1tdXzuxAEQfhtRA25IAiPhK4LSnl5OYMHD6a8vJyrV6/i4uKCv78/n376Kdu2bSM9PZ2ysjJGjBhBYWEh+/btQ61Ws2rVKlFOINSTn5/PmDFjMDAw4NVXX6VJkyY888wzJCQkMGHCBCwsLAA4fvw4AwYMYOzYsbz00kv1OvKIBZyCIPwTiIBcEIRHRq1W89xzz6FQKBgxYgSOjo7k5eUxfvx4oqOjmTVrFrm5uWRmZnLgwAFcXV3x9PTkgw8+wNDQUARPws9cunSJMWPGoNFoKCoqIjExkXfffReFQiEH3Wq1ms6dO9OlSxcmTpyo5yMWBEH4/UQ6ShCERyY/P5/CwkLefvttIiMjUSgU5OTkYGhoSHx8PBcvXiQyMpLIyEhu376Nra2tHFSJ7hfC/2vv/mOqqv84jj/Puffya5CgoCg2FDZEXG1CbG25NRWtafaHKFqQoDVcP2hcN1yE0MV0jv5IdNnSFYTLWppbmrk2WjX/qETMjSYRFkzdRYGuS7zGz3Pv9w/HXXyzvtOveeXyevwFZ/eenc8/977O+77P+3Mzqamp7N69G6fTSXh4OPPnzw/ctI3OuO/o6MBut5OamhrkqxURuT3qIReRO+b69et4PB6io6MxDIOjR49SXl7Oyy+/zCOPPMIbb7zBwYMHAYiNjQ2Ecb/frzAufyslJYXa2loSExNpaGjg2LFjABiGwcWLF3G5XMTHx5OXlxfkKxURuT0K5CJyW/57mgpAfHw8pmny008/8fXXX7N582acTifFxcWEh4fz888/4/V6AcYEcMMw7tp1y/g0e/ZsamtrsSyLd999l88//5zu7m42b97MtWvXOHDggEZlisi4pZKUiNyy0faSoaEh2traiIuLIy4ujuTkZAoKCqipqcHv9+NyuVi7di0APT09TJ48mcTExCBfvYxXqamp1NbWUlpayjvvvMO1a9eIioriyJEjOBwOtT2JyLilTy4RuSWjm6t4vV6effZZzp8/j91u59FHH8XpdFJYWEhPTw/Hjh2jv7+f9vZ2Ll26xNtvv82kSZN47LHHgr0EGcdSU1PZtWsX69atY+rUqRw8eBC73a4wLiLjmqasiMg/Gn1wzu/34/P5sNlsDA8PU1xczPDwMHl5eZw6dYpvv/2WefPm4XK5sCyL+vp6PvzwQxwOB7Gxsdx///3s3bsXh8OhaSryf7t8+TIJCQnYbDaFcREZ9xTIReQfeTwepkyZEvh/cHCQtrY23nrrLV544QXmz58PwL59+zh8+DBpaWlUVVWRkJBAR0cHHo+HmJgY0tLSME1T4UnuKN3ciUgo0EOdIvK3fv31VxYvXkxLSwuj9+5Op5M1a9Zw7tw5ZsyYEXhtcXExubm5nDt3jurqarq6ukhJSSE7O5v09HRM0wy0u4jcKQrjIhIKbC6XyxXsixCRe1Nvby9z585l4cKFgd0PlyxZwqlTp2hvbycpKYk5c+YEQnZWVhb9/f2cPHmSEydOsHDhQiIjIwPn0zQVERGRv1IgF5G/FR8fT0ZGBkNDQzz99NMMDAyQmZnJihUr+Oabbzhx4gQzZ84kOTk5UKnMysqiu7sbn8/Hk08+qRAuIiLyP6iHXERu6s+93m63my1btnDmzBkqKipYvXo1w8PD5ObmcuXKFV599VVycnIICwsLvH/0YdDRyrqIiIjcnCrkInJTpmnS39/PDz/8wNy5c3nwwQfp6emhrq6OhIQEHnjgAVatWsVnn31GY2MjSUlJYyrlo5NZFMZFRET+mb4pRWSMkZGRwN/bt2/npZde4ssvvyQlJYWSkhJycnKorq7m0KFDOBwODh8+THx8PJs2beL06dNjzqV2FRERkf9NFXIRCbAsK7Dpz549e/B4PJw9e5bW1lamT59OVlYWaWlp9Pb2Ul9fH6iUr1y5kq6uLtatW6eKuIiIyC1SIBeRANM0GRgYYM2aNfT09JCZmcnDDz9MR0cHx48fZ9asWYFQ/ttvv7F//36ioqLIzMxkyZIlmKaJZVkK5SIiIrdAA4FFZIyvvvoKr9dLTU0NGRkZAKxfv57nnnuOqqoqABYvXkxJSQlXr16lsbGRZ555JtAvrrnQIiIit0ZTVkRkjLq6Ovbs2RPoBx8aGiIsLIyBgQHy8vIYHBykrKyMnJwc3G43iYmJ2Gy2wFQVERERuTX6XVlkAvP5fH85lpGRgWEYfPHFFwCEhYUxNDREREQEy5Yt4/z58+zcuZPTp0+TlJSEzWbD5/MpjIuIiNwmBXKRCWpkZATTNBkeHqarqwuv1wvArFmzmD59OocOHaKlpQUgMF/cMAxycnJwOBxs3749cC71jIuIiNw+fYuKTEB+vz8wTWXDhg0UFBSwdOlSPv74YxITE6mqqqKpqYldu3Zx/PhxfD4fLS0tNDY2MmfOHF577TV++eUXvv/++2AvRUREZNxTIBeZQPx+P5ZlYRgGw8PD5OfnY1kWK1eu5KGHHqK6upp9+/aRnZ1NfX09v//+O1VVVWRlZfH888/j8/l48cUXsSyL+Ph4pk2bFuwliYiIjHuasiIyAXi9XqKjozEMA5vNxuDgIG63m9TUVEpKSpg9ezYAO3bs4M0338Tv97Nx40b27t3LhQsXaG1tZerUqSxduhSATz75hNjYWCZNmhTMZYmIiIQEBXKRENfc3MxHH32E0+lk5syZ+Hw+KisrOXr0KDNmzCAmJibw2vLycgzDYOfOnRiGwdq1a8nMzGTevHn8+OOP1NTUcOnSJb777jsaGhqYPHlyEFcmIiISGtSyIhLCmpubKSgoYMqUKWPaS5YvX86CBQvo6+ujp6cHuPGQJ8Arr7zC+vXr2b17N3V1dXi9XgYHBzl58iRNTU04HA4OHDhAenp6UNYkIiISajSHXCRENTc3U1RUREFBAaWlpURERARmhVuWRVNTEzt27OD69evs37+fpKQkRkZGsNtv/HBWUVFBZ2cnH3zwAaZp8scffxAVFRWYSy4iIiJ3hgK5SAgaDeP5+fk4nU4iIiLw+XyYpklfXx/33XcflmXR3NzMtm3b6O/vp6Gh4S+hfDTAj84Z16xxERGRO08tKyIh5syZMxQVFVFYWEhJScmYMN7S0sKCBQtoa2vDZrORnZ3Nli1biIyMpKioCLfbjd1ux7IsgEAYN01TYVxERORfokAuEkIuXLjAxo0bSUtLo7S0lOjoaIaGhjBNk7Nnz7JhwwYef/xxkpOTgRsb+mRnZ1NZWUlUVBRPPPEEvb292Gy2wDm16Y+IiMi/S9+0IiEkLCyM9PR0BgcHef/99wPHWlpayM/PZ9myZWzdupXIyMjAe0ZDeXl5OYsWLdLkFBERkbtMPeQiIaarq4utW7fS2dlJYWEhmZmZPPXUUyxfvpyKiooxYRzg8uXLtLa2smjRosAxy7LGVMlFRETk36NALhKC3G43r7/+Ou3t7XR3d7Nq1SpcLhd+v39MC0pXVxdOp5Pw8HAaGhrUJy4iIhIEalkRCUFJSUlUVlaSnp5OTEwM06ZNwzAMTNPE5/MBN8L4pk2b6Ovr47333sMwDHR/LiIicvepQi4Swv7cvpKbm0txcTEAFy9epKysjL6+Po4cOYLD4Rgz7lBERETuHgVykRA32r7S2dnJ6tWrWbFiBU6nk6tXr/Lpp58qjIuIiASZArnIBOB2u9m2bRsdHR1cuXKFhIQEVcZFRETuEQrkIhOE2+2mrKwMm81GfX09drtdYVxEROQeoEAuMoF4PB7i4uIwTVNhXERE5B6hQC4yAfl8Pu3AKSIico9QIBcRERERCSKVyEREREREgkiBXEREREQkiBTIRURERESCSIFcRERERCSIFMhFRERERIJIgVxEREREJIj+Ay593y5ppp3TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disc_counts = base.dropna(how=\"any\", subset=['AES', 'AER', 'AEC'])[disc].value_counts()\n",
    "\n",
    "x = base.dropna(how=\"any\", subset=['AES', 'AER', 'AEC'])[[disc, 'AES', 'AER', 'AEC']]\n",
    "x['Reactions per share'] = x['AER'] / x['AES']\n",
    "x['Comments per share'] = x['AEC'] / x['AES']\n",
    "x = x.melt(id_vars=disc, value_vars=['Comments per share', 'Reactions per share']).dropna()\n",
    "\n",
    "meds = x.groupby([\"discipline\", \"variable\"])['value'].median().reset_index().groupby(disc)['variable', 'value'].apply(lambda x: x.iloc[0,1])\n",
    "x['sort'] = x[disc].map(lambda x: meds[x])\n",
    "\n",
    "x[disc] = x[disc].map(lambda x: \"{} ({})\".format(x, disc_counts[x]))\n",
    "x = x.sort_values([\"sort\"])\n",
    "\n",
    "ax = sns.boxenplot(x=disc, y=\"value\", hue=\"variable\", data=x, palette=cm)\n",
    "\n",
    "# Scale and axes limits\n",
    "plt.yscale(\"log\")\n",
    "xmin, xmax = ax.get_xlim()\n",
    "\n",
    "# Plot additional line\n",
    "plt.hlines(1, xmin, xmax, zorder=-1, color=\"red\")\n",
    "\n",
    "# X and Y ticks & labels\n",
    "yticks = [0.1, 0.5, 1, 2, 5, 10, 100, 1000]\n",
    "plt.yticks(yticks, yticks);\n",
    "\n",
    "plt.xticks(rotation=45, ha=\"right\");\n",
    "\n",
    "# Axes labels\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Ratio\")\n",
    "\n",
    "# Remove legend title\n",
    "l = ax.legend()\n",
    "l.set_title(None)\n",
    "\n",
    "sns.despine(left=True, right=True, top=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disc_counts = base[disc].value_counts()\n",
    "x = base.groupby(disc)[['AES', 'AER', 'AEC']].count()\n",
    "x = x.apply(lambda x: x.map(lambda y: 100*y/disc_counts[x.name]), axis=1)\n",
    "x.index = x.index.map(lambda x: \"{} ({})\".format(x, disc_counts[x]))\n",
    "x.sort_values(\"AES\", ascending=False).plot(kind=\"barh\")\n",
    "\n",
    "plt.grid(False)\n",
    "plt.grid(True, axis=\"x\", linestyle=\":\")\n",
    "sns.despine(left=True, top=True, right=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'base' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-4de6840996f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdisc_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"disc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AES'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'AER'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'AEC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# x = x.apply(lambda x: x.map(lambda y: 100*y/disc_counts[x.name]), axis=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"{} ({})\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisc_counts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'base' is not defined"
     ]
    }
   ],
   "source": [
    "disc_counts = base.disc.value_counts()\n",
    "x = base.groupby(\"disc\")[['AES', 'AER', 'AEC']].count()\n",
    "# x = x.apply(lambda x: x.map(lambda y: 100*y/disc_counts[x.name]), axis=1)\n",
    "x.index = x.index.map(lambda x: \"{} ({})\".format(x, disc_counts[x]))\n",
    "\n",
    "x['AER/AES'] = 100 * x['AER'] / x['AES']\n",
    "x['AEC/AES'] = 100 * x['AEC'] / x['AES']\n",
    "x[['AER/AES', 'AEC/AES']].sort_values('AEC/AES', ascending=False).plot(kind=\"barh\")\n",
    "\n",
    "plt.ylabel(\"\")\n",
    "ticks = list(range(0, 81, 10))\n",
    "plt.xticks(ticks, [\"{:,}%\".format(int(_)) for _ in ticks])\n",
    "\n",
    "plt.grid(False)\n",
    "plt.grid(True, axis=\"x\", linestyle=\":\")\n",
    "sns.despine(left=True, top=True, right=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = \"disc\"\n",
    "\n",
    "cov_disciplines = base.groupby(col)[metrics].apply(lambda x: x.count())\n",
    "cov_disciplines['All articles'] = base.groupby(col)[metrics].size()\n",
    "cov_disciplines = cov_disciplines.sort_values(\"All articles\", ascending=False)\n",
    "\n",
    "# Column names + order\n",
    "cov_disciplines.index.name = \"Discipline\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of disciplines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Detailed look at Facebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "any_fb_counts = base.reindex(all_shares.union(am_shares))[col].value_counts()\n",
    "any_fb_counts.loc['Total'] = any_fb_counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/asura/.virtualenvs/altmetrics/lib/python3.5/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bc5d575f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = nz_resp['type'].isin([\"pmc\", \"pmid\"])\n",
    "pdf = nz_resp[~mask]\n",
    "pdf['log_shares'] = pdf['shares'].map(lambda x: np.log10(x))\n",
    "\n",
    "order = pdf.type.value_counts().keys().tolist()\n",
    "\n",
    "ax = sns.boxenplot(x=\"type\", y=\"log_shares\", data=pdf,\n",
    "                   saturation=1,\n",
    "                   order=order, palette=cm)\n",
    "\n",
    "\n",
    "medians = pdf.groupby(['type'])['log_shares'].median()\n",
    "nobs = pdf['type'].value_counts()\n",
    "nobs = nobs.map(lambda x: \"n: {}\".format(x))\n",
    "\n",
    "pos = range(len(nobs))\n",
    "for pos, label in enumerate(order):\n",
    "    plt.text(pos, medians[label]+.05, nobs[label],\n",
    "             horizontalalignment='center', color='w', weight='semibold')\n",
    "\n",
    "ticks = [1, 2, 5, 10, 50, 100, 500, 1000, 5000]\n",
    "plt.yticks(np.log10(ticks), ticks);\n",
    "\n",
    "sns.despine(left=True, right=True, top=True, bottom=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "Milojević, S. (2010). Power law distributions in information science: Making the case for logarithmic binning. Journal of the American Society for Information Science and Technology, 61(12), 2417–2425. doi: 10/bm7ck6"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "altmetrics",
   "language": "python",
   "name": "altmetrics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}