005_rtr_sensitivity_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon May 17 12:23:58 PDT 2021\r\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, matplotlib.pyplot as plt, pandas as pd\n",
    "pd.set_option('display.max_rows', 8)\n",
    "!date\n",
    "\n",
    "import methods\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculations for results section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/abie/.conda/envs/limetr_conda/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "data = methods.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We strongly recommend including all positive tests as a sensitivity analysis not just those required by work. I agree that differential testing may introduce a bias, but it would be better to show all the data so that we can consider the potential magnitude of that bias. There may actually be an even greater differential between HCW and other workers.  In fact, probably most non-health care workers don't get tested through employer requirements, and only know that they have COVID after becoming sick.\n",
    "\n",
    "There are two natural approaches to this, and I don't want to include either in the main text, but I will check what the results are and include them in the response to reviewers.\n",
    "\n",
    "Approach 1 is to include everyone who was tested, regardless of whether the test was required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156127"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_last_14_days == 1)\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The survey data contained 156,127 respondents who were tested (regardless of workplace requirements) in the time period we focused on, 22,594 HCWs and 133,533 non-HCWs. There were 13,637 respondents who reported a positive test for COVID-19 in the last 14 days (1,674 among HCWs and 11,963 among non-HCWs).\n"
     ]
    }
   ],
   "source": [
    "print(f'The survey data contained {len(df):,.0f} respondents who were tested (regardless of workplace requirements) '\n",
    "      f'in the time period we focused on,'\n",
    "      f' {df.exposure.sum():,.0f} HCWs and {(1-df.exposure).sum():,.0f} non-HCWs.'\n",
    "      f' There were '\n",
    "      f'{df.outcome.sum():,.0f} respondents who reported a positive test for COVID-19 in the last 14 days '\n",
    "      f'({df[df.exposure==1].outcome.sum():,.0f} among HCWs and {df[df.exposure==0].outcome.sum():,.0f} among non-HCWs).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 55s, sys: 28.2 s, total: 5min 23s\n",
      "Wall time: 5min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# set random seed for reproducibility\n",
    "np.random.seed(12345)\n",
    "rr_draws = methods.sample_rr_draws(df)\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among HCWs tested (regardless of whether the test was required), 1,674 of 22,594 (7.4%) reported a positive test in the last 14 days, while among non-HCWs tested (regardless of whether the test was required), 11,963 of 133,533 (8.96%) reported a positive test, for a relative COVID-19 prevalence ratio of 0.8 (95% UI 0.78 to 0.83).\n"
     ]
    }
   ],
   "source": [
    "print(f'Among HCWs tested (regardless of whether the test was required), {df[df.exposure==1].outcome.sum():,.0f} of {df.exposure.sum():,.0f}'\n",
    "      f' ({df[df.exposure==1].outcome.mean()*100:,.1f}%) reported a positive test in the last 14 days,'\n",
    "      f' while among non-HCWs tested (regardless of whether the test was required), '\n",
    "      f'{df[df.exposure==0].outcome.sum():,.0f} of {(df.exposure==0).sum():,.0f}'\n",
    "      f' ({df[df.exposure==0].outcome.mean()*100:,.2f}%) reported a positive test,'\n",
    "      f' for a relative COVID-19 prevalence ratio of {rr_mean:.1f} (95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>HCW - tested</th>\n",
       "      <th>HCW - positive</th>\n",
       "      <th>HCW - (%)</th>\n",
       "      <th>non-HCW - tested</th>\n",
       "      <th>non-HCW - positive</th>\n",
       "      <th>non-HCW - (%)</th>\n",
       "      <th>RR</th>\n",
       "      <th>95% UI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22,594</td>\n",
       "      <td>1,674</td>\n",
       "      <td>7.4</td>\n",
       "      <td>133,533</td>\n",
       "      <td>11,963</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.78 to 0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HCW - tested HCW - positive HCW - (%) non-HCW - tested non-HCW - positive  \\\n",
       "0       22,594          1,674       7.4          133,533             11,963   \n",
       "\n",
       "  non-HCW - (%)    RR        95% UI  \n",
       "0           9.0  0.79  0.78 to 0.83  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table version of results\n",
    "t = pd.DataFrame(index=[0])\n",
    "t.loc[0, 'HCW - tested'] = f'{df.exposure.sum():,.0f}'\n",
    "t.loc[0, 'HCW - positive'] = f'{df[df.exposure==1].outcome.sum():,.0f}'\n",
    "t.loc[0, 'HCW - (%)'] = f'{df[df.exposure==1].outcome.mean()*100:,.1f}'\n",
    "\n",
    "t.loc[0, 'non-HCW - tested'] = f'{(1-df.exposure).sum():,.0f}'\n",
    "t.loc[0, 'non-HCW - positive'] = f'{df[df.exposure==0].outcome.sum():,.0f}'\n",
    "t.loc[0, 'non-HCW - (%)'] = f'{df[df.exposure==0].outcome.mean()*100:,.1f}'\n",
    "\n",
    "t.loc[0, 'RR'] = f'{rr_mean:.2f}'\n",
    "t.loc[0, '95% UI'] = f'{rr_lb:.2f} to {rr_ub:.2f}'\n",
    "\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 2 is to include everyone (tested and not tested)\n",
    "\n",
    "This highlights the differential access to testing among HCWs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1822662"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date'])\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The survey data contained 1,822,662 respondents (including both tested and untested)  in the time period we focused on, 123,448 HCWs and 1,699,214 non-HCWs. There were 13,637 respondents who reported a positive test for COVID-19 in the last 14 days (1,674 among HCWs and 11,963 among non-HCWs).\n"
     ]
    }
   ],
   "source": [
    "print(f'The survey data contained {len(df):,.0f} respondents (including both tested and untested)  '\n",
    "      f'in the time period we focused on,'\n",
    "      f' {df.exposure.sum():,.0f} HCWs and {(1-df.exposure).sum():,.0f} non-HCWs.'\n",
    "      f' There were '\n",
    "      f'{df.outcome.sum():,.0f} respondents who reported a positive test for COVID-19 in the last 14 days '\n",
    "      f'({df[df.exposure==1].outcome.sum():,.0f} among HCWs and {df[df.exposure==0].outcome.sum():,.0f} among non-HCWs).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 44s, sys: 928 ms, total: 2min 45s\n",
      "Wall time: 2min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "random_rows = (np.random.uniform(size=len(df)) <= 100_000/len(df))\n",
    "rr_draws = methods.sample_rr_draws(df[random_rows])\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among HCWs (tested and untested), 1,674 of 123,448 (1.4%) reported a positive test in the last 14 days, while among non-HCWs (tested and untested), 11,963 of 1,699,214 (0.70%) reported a positive test, for a relative COVID-19 prevalence ratio of 1.8 (95% UI 1.52 to 2.03).\n"
     ]
    }
   ],
   "source": [
    "print(f'Among HCWs (tested and untested), {df[df.exposure==1].outcome.sum():,.0f} of {df.exposure.sum():,.0f}'\n",
    "      f' ({df[df.exposure==1].outcome.mean()*100:,.1f}%) reported a positive test in the last 14 days,'\n",
    "      f' while among non-HCWs (tested and untested), '\n",
    "      f'{df[df.exposure==0].outcome.sum():,.0f} of {(df.exposure==0).sum():,.0f}'\n",
    "      f' ({df[df.exposure==0].outcome.mean()*100:,.2f}%) reported a positive test,'\n",
    "      f' for a relative COVID-19 prevalence ratio of {rr_mean:.1f} (95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Additionally, we strongly recommend having a different reference population than all non-healthcare workers. Other high risk workers are included in the current reference group, which may have the impact of making the risk among healthcare workers appear lower. Potentially consider including major census or SOC occupations for comparison. \n",
    "\n",
    "We prefer to focus our discussion on a comparison of HCWs with all non-HCWs, but the reviewer raises an interesting additional question.  Although we choose to leave a full investigation of these occupational comparisons for future work, we cannot resist examining them briefly in this response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN     51652\n",
       "4.0     40644\n",
       "5.0     24919\n",
       "16.0    20801\n",
       "2.0     15363\n",
       "11.0     9101\n",
       "7.0      7920\n",
       "1.0      6432\n",
       "Name: Q64, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['df'][data['df'].test_required].Q64.value_counts(dropna=False).head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides HCWs, the occupation with the highest rate of required testing are (16) Other occupation, (2) education,training, and library, and (11) office and administration services (7) is food prep and serving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19954"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "rows &= (data['df'].Q64.isin([4,5,2,7]))\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19954"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.7 s, sys: 16 ms, total: 25.7 s\n",
      "Wall time: 25.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "rr_draws = methods.sample_rr_draws(df)\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A comparison of HCWs to workers in education and food services found a relative COVID-19 incidence ratio of 0.97 (95% UI 0.82 to 1.12).\n"
     ]
    }
   ],
   "source": [
    "print(f'A comparison of HCWs to workers in education and food services found'\n",
    "      f' a relative COVID-19 incidence ratio of {rr_mean:.2f} '\n",
    "      f'(95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19195"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "rows &= (data['df'].Q64.isin([4,5,16]))\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 26.4 s, sys: 316 ms, total: 26.7 s\n",
      "Wall time: 26.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "rr_draws = methods.sample_rr_draws(df)\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A comparison of HCWs to workers in occupation \"Other\" found a relative COVID-19 incidence ratio of 0.97 (95% UI 0.82 to 1.11).\n"
     ]
    }
   ],
   "source": [
    "print(f'A comparison of HCWs to workers in occupation \"Other\" found'\n",
    "      f' a relative COVID-19 incidence ratio of {rr_mean:.2f} '\n",
    "      f'(95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I bet there are also a lot of students in the group with required tests who said they do not have an occupation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24312"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "rows &= (data['df'].Q64.isin([4,5]) | data['df'].Q64.isnull())\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9652"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['df'][rows].Q64.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age_group</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
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       "    <tr>\n",
       "      <th>Q64</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4.3</td>\n",
       "      <td>17.7</td>\n",
       "      <td>23.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>8.1</td>\n",
       "      <td>16.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>23.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>22.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "age_group   1.0   2.0   3.0   4.0   5.0  6.0  7.0\n",
       "Q64                                              \n",
       "4.0         4.3  17.7  23.1  25.2  22.5  6.5  0.7\n",
       "5.0         8.1  16.8  20.6  23.1  23.5  6.9  1.0\n",
       "NaN        22.8  18.3  17.4  17.1  13.9  6.5  4.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(\n",
    "    data['df'][rows].groupby('Q64', dropna=False\n",
    "                            ).age_group.value_counts(normalize=True).unstack()\n",
    " * 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    3149426\n",
       "2.0    2532747\n",
       "NaN    1319483\n",
       "Name: D9, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['df'].D9.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    0.649789\n",
       "NaN    0.338520\n",
       "1.0    0.011691\n",
       "Name: D9, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['df'][data['df'].Q64.isnull()].D9.value_counts(dropna=False, normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also identifies an important divergence between the “non-HCW” population and the worker population---there are XXX respondents without an occupation code included in the non-HCW population.  Repeating our analysis with these respondents excluded finds :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33778"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "rows &= (~data['df'].Q64.isnull())\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr_draws = methods.sample_rr_draws(df)\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A comparison of HCWs to respondents who were tested and reported a non-HCW occupation found a relative COVID-19 incidence ratio of 0.60 (95% UI 0.55 to 0.67).\n"
     ]
    }
   ],
   "source": [
    "print(f'A comparison of HCWs to respondents who were tested and reported a non-HCW occupation found'\n",
    "      f' a relative COVID-19 incidence ratio of {rr_mean:.2f} '\n",
    "      f'(95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34167"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what about when restricted to only those who worked in last 14 days?\n",
    "\n",
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "rows &= (data['df'].D9 == 1)\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr_draws = methods.sample_rr_draws(df)\n",
    "rr_mean, rr_lb, rr_ub = methods.my_summarize(rr_draws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A comparison of HCWs to respondents who worked in last 14 days found a relative COVID-19 incidence ratio of 0.84 (95% UI 0.76 to 0.91).\n"
     ]
    }
   ],
   "source": [
    "print(f'A comparison of HCWs to respondents who worked in last 14 days found'\n",
    "      f' a relative COVID-19 incidence ratio of {rr_mean:.2f} '\n",
    "      f'(95% UI {rr_lb:.2f} to {rr_ub:.2f}).'\n",
    "      )"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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ADVAD1AA1QA1QA9QANUANhFcDz/3f//2fuGFADVAD1AA1QA1QA9QANUANUAPUADVADVAD1EB4NUDIQshEyEYNUAPUADVADVAD1AA1QA1QA9QANUANUAMRqAFClgggkvSFl/Thhx81QA1QA9QANUANUAPUADVADVAD1EA81AAhCyELaSU1QA1QA9QANUANUAPUADVADVAD1AA1QA1EoAYIWSKAGA9pG30gNaYGqAFqgBqgBqgBaoAaoAaoAWqAGqAGwqsBQhZCFtJKaoAaoAaoAWqAGqAGqAFqgBqgBqgBaoAaiEAN8BHOfIQ1H+FNDVAD1AA1QA1QA9QANUANUAPUADVADVADEaiB5y5duiRuGFAD1AA1QA1QA9QANUANUAPUADVADVAD1AA1EF4NPHfhwgVxw4AaoAaoAWqAGqAGqAFqgBqgBqgBaoAaoAaogfBq4Llz586JGwbUADVADVAD1AA1QA1QA9QANUANUAPUADVADYRXA8+dOnVK3DCgBqgBaoAaoAaoAWqAGqAGqAFqgBqgBqgBaiC8GnhO/EMAAQQQQAABBBBAAAEEEEAAAQQQCFuAkCVsQnaAAAIIIIAAAggggAACCCCAAAIISIQsVAECCCCAAAIIIIAAAggggAACCCAQAQFClgggsgsEEEAAAQQQQAABBBBAAAEEEECAkIUaQAABBBBAAAEEEEAAAQQQQAABBCIgQMgSAUR2gQACCCCAAAIIIIAAAggggAACCBCyUAMIIIAAAggggAACCCCAAAIIIIBABAQIWSKAyC4QQAABBBBAAAEEEEAAAQQQQAABQhZqAAEEEEAAAQQQQAABBBBAAAEEEIiAACFLBBDZBQIIIIAAAggggAACCCCAAAIIIEDIQg0ggAACCCCAAAIIIIAAAggggAACERAgZIkAIrtAAAEEEEAAAQQQQAABBBBAAAEECFmoAQQQQAABBBBAAAEEEEAAAQQQQCACAoQsEUBkFwgggAACCCCAAAIIIIAAAggggAAhCzWAAAIIIIAAAggggAACCCCAAAIIRECAkCUCiOwCAQQQQAABBBBAAAEEEEAAAQQQIGShBhBAAAEEEEAAAQQQQAABBBBAAIEICBCyRACRXSCAAAIIIIAAAggggAACCCCAAAKELNQAAggggAACCCCAAAIIIIAAAgggEAEBQpYIILILBBBAAAEEEEAAAQQQQAABBBBAgJCFGkAAAQQQQAABBBBAAAEEEEAAAQQiIEDIEgFEdoEAAggggAACCCCAAAIIIIAAAggQslADCCCAAAIIIIAAAggggAACCCCAQAQECFkigMguEEAAAQQQQAABBBBAAAEEEEAAAUIWagABBBBAAAEEEEAAAQQQQAABBBCIgAAhSwQQ2QUCCCCAAAIIIIAAAggggAACCCBAyEINIIAAAggggAACCCCAAAIIIIAAAhEQIGSJACK7QAABBBBAAAEEEEAAAQQQQAABBAhZqAEEEEAAAQQQQAABBBBAAAEEEEAgAgKELBFAZBcIIIAAAggggAACCCCAAAIIIIAAIQs1gAACCCCAAAIIIIAAAggggAACCERAgJAlAojsAgEEEEAAAQQQQAABBBBAAAEEECBkoQYQQAABBBBAAAEEEEAAAQQQQACBCAgQskQAkV0ggAACCCCAAAIIIIAAAggggAAChCzUAAIIIIAAAggggAACCCCAAAIIIBABAUKWCCCyCwQQQAABBBBAAAEEEEAAAQQQQICQhRpAAAEEEEAAAQQQQAABBBBAAAEEIiBAyBIBRHaBAAIIIIAAAggggAACCCCAAAIIELJQAwgggAACCCCAAAIIIIAAAggggEAEBAhZIoDILhBAAAEEEEAAAQQQQAABBBBAAAFCFmoAAQQQQAABBBBAAAEEEEAAAQQQiIAAIUsEENkFAggggAACCCCAAAIIIIAAAgggQMhCDSCAAAIIIIAAAggggAACCCCAAAIRECBkiQAiu0AAAQQQQAABBBBAAAEEEEAAAQQIWagBBBBAAAEEEEAAAQQQQAABBBBAIAIChCwRQGQXCCCAAAIIIIAAAggggAACCCCAACELNYAAAggggAACCCCAAAIIIIAAAghEQICQJQKI7AIBBBBAAAEEEEAAAQQQQAABBBAgZKEGEEAAAQQQQAABBBBAAAEEEEAAgQgIELJEAJFdIIAAAggggAACCCCAAAIIIIAAAoQs1AACCCCAAAIIIIAAAggggAACCCAQAQFClgggsgsEEEAAAQQQQAABBBBAAAEEEECAkIUaQAABBBBAAAEEEEAAAQQQQAABBCIgQMgSAUR2gQACCCCAAAIIIIAAAggggAACCBCyUAMIIIAAAggggAACCCCAAAIIIIBABAQIWSKAyC4QQAABBBBAAAEEEEAAAQQQQAABQhZqAAEEEEAAAQQQQAABBBBAAAEEEIiAACFLBBDZBQIIIIAAAggggAACCCCAAAIIIEDIQg0ggAACCCCAAAIIIIAAAggggAACERAgZIkAIrtAAAEEEEAAAQQQQAABBBBAAAEECFmoAQQQQAABBBBAAAEEEEAAAQQQQCACAoQsEUBkFwgggAACCCCAAAIIIIAAAggggAAhCzWAAAIIIIAAAggggAACCCCAAAIIRECAkCUCiOwCAQQQQAABBBBAAAEEEEAAAQQQIGShBhBAAAEEEEAAAQQQQAABBBBAAIEICBCyRACRXSCAAAIIIIAAAggggAACCCCAAAKELNQAAggggAACCCCAAAIIIIAAAgggEAEBQpYIILILBBBAAAEEEEAAAQQQQAABBBBAgJCFGkAAAQQQQAABBBBAAAEEEEAAAQQiIEDIEgFEdoEAAggggAACCCCAAAIIIIAAAgiULmRx3NSRrRu0Yf16bdj4u87/WxSuMGevNjm/v36DNu+7UvQJkhz/XNSedQs0bcwQDejVU337j9DkWUv1w7Gbygu0heOW/thmtLthh07dcxR51sPzux7RrkO5Z3do9cdTNGpgX/Xp2VeD3xqraZ9laX/OwyL7ctw7qV82OPtQ0u077blgHHHMbO7p1I6N7mPd8JNO3Clq49NBh+X5fv3buGGztmcf0aXcQp9NSnwQwX06bh7VdtN+wybtu1xQtPn/Lmj3xpLGxvz+Bm09dK3oPqw15azVvZcVoKWi2/EVBBBAAAEEEEAAAQQQQAABBEoQKF3IkrdT42omyVYmQTZ7Zy25WvSE/mFWX6U5v18mQRX6b/Rr9qHOrhuvDtXt7n0Yz3Ptz3k/obwy+szVrzf8TvLzftfE5412bc0167j/6bBD17/qorKB2i28rh1T26uGzX1MnrbMtlNfUP9Fh5Vr6UrB8ZlqWdzzze1c/yerx/Jbcm0aK5uCE5rVzPBMaqxph/L9zP0eWp/v0xevjz2trrqMW6OT9/22Le5hxPZZoLNzWynVc1xJqjt6p/yzPMfN5epu9x5vkTH1bO98TqJqDN9e5MgLzsxVa8sYJ9cao2z/hopsxRcQQAABBBBAAAEEEEAAAQQQKFkgCiFLnk5/+ZpqJlpOjhNSVLlmbdVM8w1dyjUepx9vWlKPkEOWQv1vaXdVTjDbtKvy8y/p5RYvqX7FZG/Qk1hDA7OuucMSSdEPWcKwsQYcEQpZ3KFFoip3mK8/i070KVpN1mPwCThMd/P/EvYZYD/JNTL18wPfJsMPWQp0YkZzS5iTIFtiTY3+ya8h32Z5hAACCCCAAAIIIIAAAggggECpBB57yFJweoE6ljNPttOU0e8TZeeYl9rc16msyepU3WYEH0mqN2yrbps5S6ghi/WkPTlD476/7L0cqeCm9szurGpGAJPadKbMCTLWkCU1Y7w27z+oQwcD3Q7r7E1jVk0YM1nCsrH2MdiQxdZUU3ec0blz53Tu3BmdOLhDq6e+pgYp5jilqt28MyVfRmM9hjD2mX9kupomudu2JyS6ayGhukZs9ZtSk39LZw9ZxuPAT5rWygjqEtLVe+Fey3gd0h8X7vm+CPKP6MMMo9YSEmV3BUOJqvnWNv3t+0weIYAAAggggAACCCCAAAIIIBC0wGMOWf7VL6NqKdk4ma3SfZku+l/xI+mf/VPVzLyEI7mdFp43LhsKNWR5uEmD0twn7SkZ03TE/0qavH2a1ryqqlaspGrV+2utker4hCwvz9M5v6uXAuqGHLKEaWMNOIIOWV7R/CKdK9Bfizop3ZiRklJ/ivb7u/kDWI/BFuo+83Xw3UZKcbabkK5eI/qplisAS1T1QZuV69+m9bHjppZ1MWYmJVTRyB8Cruzj2SL/wLvKcIU5iar8+kgNqO4OdOyVB2vLIxvy7II7CCCAAAIIIIAAAggggAACCBQr8HhDlvy9mvKCMXMgsY4mZBdzDYrjhlZ1TzNms6So+7LrxnonIa7J8t8OjTVOoG32Rhq+4qCu/VesgecbUQ1ZwrWxBhwRCVkk3Vur/uasI1tLfXw6QCLm0XJeX2VZFyZgyFKKfebt0eR6xro7FfoqK2erRlQ1wo/0Adp419qg3/2gQpY87Zn0ghH4pWvA2iva9lY192yWhIoauOFRDfm1y0MEEEAAAQQQQAABBBBAAAEEAggEH7KUSValytVUo6rfLT3Fs9aJufCt4/ZKvZ5sXIJSrp82+F394T2eAp2ZYy58mqT6E3e7L+8JdSaL7mnLYOME2pyZkVZLLboM1KRZX2nz7nO6E2CWhjVksafVVrOWrdQ6wK195lpd81zSZFkUOJo21oAjUiFLwUnNaW5eflNZI7Y9emZIqUKWEvb5MHuc6hnr9VTq863uOO5ry+DKxqU86er/7W3PmjneejHuBROyPMzWhNpGmFP+DdfspfubB3suG6v8xlqV9AFNRdrnCwgggAACCCCAAAIIIIAAAghYBEIIWcx1O4r/3wxZCs9/qvbGZUDJtcbp10ecs99Z+Zrx6USJqjxka5ghi1R4ZasmNk83TtaLHmvZSo3Va/I3OnbXTEtKv/BtapsF+p95KZH1ciEj0HnUp95EzOZxhCyOK/qyk7kwcKoGbChm5pFZQNZjKG4myyP3+UA/Z9Y0ZpeUV5817k9surehv2fR4ko9V8u6FrLZtOv/IEKWBz9l6nkjzKnYc4173Z97G/RmurEGTPleWlNsQz6t8gABBBBAAAEEEEAAAQQQQACBgALBhyyJtdRp6BiNG+17G/NqPfe6GpaPcC68tFAdjZDFXn2Ufin2kh2Hbix91fgo5kRVH7Y97JDF1dv86zq0bp7eHtBZTaulBgxcyjd5T3uMGTY+M1nK11Prdu3VMcCt69j1gWeyRNPGGnBEaiZL4QUtbGvOZKmk4ZGYyfKofd7fphHVjJAjradWGyGH485a9a9gBGNpr2mlZ9qQXw2XOmS5rx/eqm6Mfzn1/tr4+G3HHa17I92YgVVOry/3ftKUX0s8RAABBBBAAAEEEEAAAQQQQKBEgeBDFntnLbnqnf1htvAwq68xEyVB5mwN3V+nAWWNk+WUrlp+o+h27u3zdeg9Y/HTMja99P5hua7m8blcqJlmmR8DZDYqh65/1cUIZyzter5vvZOnO+f2advy2Rr3WiPPTAlbmXLqteqm65IUa8iS+rgXvg3X5nGELP9ZLn2yNdfsExFYk+UR+8z9bpDncp2Uum9o5sJFWrxokRYvnKkB9cxPnCqrbl9dCXzJUGlDltxNGlLJCHOS6qnfRwvd7SxaqFl9X/CEg+W7fKUApW0tIu4jgAACCCCAAAIIIIAAAgggUKzA4w1ZCk7r45bmzIh09VltLGjrfzj5f2jGS97nDdpoTC3J3+ddODepgd4t8nE3hbr0WTului7Tcc+Ace668PperV3ypZZ88aWWbj/r/fhmT7sPdHR6c2M7mzLeO+gKdaIasoRr8xhCloe7J6mh8VHKyTVHa2cJVwuVZk2W4vd5R+v7FX85l/WSq7SOn+uSeXmWZwwllTJkuZPVzxKqFb10zNNWSid9EbAha6PcRwABBBBAAAEEEEAAAQQQQCCwwOMNWVSg03NbeWaapNYbqa3X/M+W/9XJhV1U1fWxvQmyVxmkTeYHvRTm6PMORvhSxq5m7x+Sz3m/44a+6W2eqNvVcuYJVy8Lz81Ta+MypZT6b2vPv/6dd+jqF52N47Lr5Vmn5JyzEdWQJVybSIcsuQc1u3V549KZJNUfl60H/mz+j63HEGhNlkfs03HzG/U1LwkqaS2b5Lbej/W2HkNpQhbHLX3b27wk6BEBi+sYUtRhwXn5V6i1Se4jgAACCCCAAAIIIIAAAgggUJzAYw5ZJOVma1IDMyhJUGq1thq/aKN27j+kPT+s1uz+jVXRCFhsCenqtvicK/BwH3CBznz8sieksaXUU58Za5V96A8d2bVZi0e1UhVzW1uGph8yPjKo4JhmNjHbtKtet2n6JvuYzl++rP+d2q9tX45XxyrGJ8241jNxr8hrDVmSq3fU6MlT9N6UwLdpyw7ob+dBWhe+DeZSKue24dhYA46EdLXsM1KjMzOL3kZN0Iqj+b4ft5xYWz3ema25s+do7uzZmjF5uLrVK+dZs8ZeqYdWXChF1GA9hqD26dD1FT2UboQr5bp8rvO59/X3festR6t6mqGPXW3mnS0afpQiZHFcX6meaeYla130xV/WNtz3c1b0VEXjWJyXiZ0tRdeLe0HxdQQQQAABBBBAAAEEEEAAgWdX4PGHLJLuH1qgHtXNNTaKm02QpqaZm3TZfxmQ3N/1QfNyno+H9lza4TP7IVmNR23XLc+SLw7dy56i5uZ6MD7Ptbbv3G6bzKVirCFL4Ha825btstT9qTfhhCzh2FgDjmL75zze8hq86aFvyPKI59vLN9ekbaVcALaUx1Bkn44rWtqlrDGmZdV96dUAa644dGN5d5U3jjW1xWyd8q+NEkMWh65+9apnH+VfXepdsNjymnfcWKEeqcbY2lpozkn/hixP5i4CCCCAAAIIIIAAAggggAACxQhEJWRxtp2X84sWDH1FtVOMBUg9J/o2VarfXVNWH9UdT0jie7SOe4e1NLOd6qR4Qw4zBEmt+JIGzNqhK8YkFu+WDt05sERj29dSOXO2i6fNJFWs01bDPsnWNct2sQhZQrYpZcBRmpDFbiuvGnWaqtvIufr+tLEejhey+HuPOIZH7bPw0mJ1NseybDetKObTgxw5X6qr+TxbU8065hd+lBSyFF7S5x1SjDAnTa8tKyY8clzRks7m8+xqPuNY8X3mOwgggAACCCCAAAIIIIAAAggUI1C6kKWYjUP5cmHuJR35ZbOy1qzWtxu2a8/JG77rrDxip/n3LurwDmPbrE3asf+sbhf7sdDmjgr1d84f2rVto9Z+vUpr1m7SzqNXVWSZFvPpMfw/HJsYHjZNI4AAAggggAACCCCAAAIIIICApKiHLKgjgAACCCCAAAIIIIAAAggggAAC8ShAyBKPo0qfEEAAAQQQQAABBBBAAAEEEEAg6gKELFEnp0EEEEAAAQQQQAABBBBAAAEEEIhHAUKWeBxV+oQAAggggAACCCCAAAIIIIAAAlEXIGSJOjkNIoAAAggggAACCCCAAAIIIIBAPAoQssTjqNInBBBAAAEEEEAAAQQQQAABBBCIukBoIYvjpg5vWat137pvG3ZfUn7AQy/U1X3fKct43qZ9V1RY5HkPdO6XLPe+sn7TBeMjmQuv7NOmte79Z23cp6tFN9SDczu13rXvLP1+Ps/Ys0M3D20x2lynn07ck8O/zYd/6dcsY9/r9yonwL6dmzhuHtb3xjGsW7teey4F7qUc93Tyx3XuPqz7QcduF2nR/whK/7hU1g7d8PR5rTbuDeQsOW54+5O19YhuOSTHvZP6aZ13LM0xLfq/xdjaX2Nsrc/P2rBFO/ac0s0SP17bOlZrlbV+t4oj9gFz/KNLe7K0cOoYjRjQR7169tXQke/o4xU/6vTdQIPp0L0TP3rq0HqsRe5batBss/DuKf24bLbeeau/+rzWQ336DdaYdz/Vut2XnsiPAjePm/8RQAABBBBAAAEEEEAAAQSiKxBSyFJwao5a2xJkK+O+JdccrZ3/BjrwPG0dku55nr1yX317zS+AcFzRFx3s7ufYu2ul88xfUt73Q1TZ2L8toYr6r7nmF5Y4dGVxR6W6npOsHstvGQeQp13j6yjZ9XW7Ws44oQK/Q3Nc/0rd7Mbxl+2v7x76PcH1sECnZ7cy9u98bpLqjPol8El1wQnNamb0Iamxph0qJowJ1EwJXyudtUN3vn1DlQyv1Bc/1DH/TqtQFz9rp7Ku5ySqSv+Nuiep4PhMtbSMpTmmRf93G7tGx9pfo82iz09QaqVWGr36ZGAzZ78LTmluS8PNuZ/EWhq7I2AheZTyLmzR+51rGf3w1qDZfnJ6U41Ydkz3PVu4GtKJGc0tY1l0O3N7m6UGpUJd2z5F7SvbPDXseZ6r36mq32uBDt3zq2mftnmAAAIIIIAAAggggAACCCDwrAiEELIU6PiMpr4nrAk1lLn9QQAz35DFGVTUH/2L7wlwaUKWMglKrjtGO3OtTTzmkKXgT81qYgkAyiTIXn2kfgzUTWvoENGQpfTWjttr1b+CER7YXtH8c34zOhzXtaJbWXdYkFBJgze6MR9nyOIKJGx1NWb7Xb+AzD2OBX/OUHOfgCdRtUdsVyBi5xaFOes1tG6yJfBIVNn0OmrStLHqlLMEIQlV1WPJGcvsqoKQQpbC80vULT3R015qel01a9FCzepWsoQ8SarVb538s0NrpXIfAQQQQAABBBBAAAEEEEDg2RAIPmTJP6LpGcYJbUKi7MbMiBpDt+rvImb+IUuCbMktNOuoeWmP85qcUhMG6ZUAACAASURBVMxkcbWRopdnHJV3y8cbsuQfnqYmSe7Qwp5gnGgnVNPwrb5zJFxdflwhSzDWjjvK6mvOGkpRtyVXfYONu+s0wAhh7JUGabMRWFlDltSM8dq8/6AOHQx0O6yzN43pMdb+2ppq6o4zOnfunHE7rWO71mlax6pGbSSo/KtLA4QQ+TryQYZSjPqxJxjWVd/StgDEKryib/p495n6fC/Nz86ROQnJ8c9ZfTe+pSqZ+0nvpdWe5MMastjVdPwmHQjYx4M6dOisbrm6aQ24UtRs3DbleC5/KtDN3XPUtZJRF7ZmmvVnkalDRV4NfAEBBBBAAAEEEEAAAQQQQCC+BYIOWfL3T1FjV/iQqMqvj9SA6u4TTeuJu5fMGrLYleqatZCoyl2X6pI50aIUIUuqzT2jxJ7eTcu8Gz7Gy4XydWBKQ3cAkJCuXiP6qZbr5D1R1QZuks+EGmdnraFDBGeyBGct3VnXz3OJVcXe3+iO5SqWf7a+pRpGH6oP3uyZTeQTsrw8T/4TYLxjabln7W+gWTPGZUgtjFkqyTXHKtubjrl3lL9f7zVwh3X29NeV2a+6O5RJqKwhm4oIq+DYR95ZLymtNPuoGa9Yjqvwkpa+WsGYeZKq15bdML7pG7K0mXcuwNpAlv247j7U5gHl3PtKytC0w/6XgOVp//vNVaNiJVWtVENvfnPbN9Ty3x2PEUAAAQQQQAABBBBAAAEE4l4gyJAlT3sm1jPWO0nXgLVX9MNb1YyT44p6c/1dPzBLyJLUWMNGd1Fl54l+Yi0N32KclJYYstj04tAxxmUbSaozdIsRHjzGmSz/7dY7dZPcJ9jpfZWVs00jqhphUnp/bfDvpjV0iFjIEqy1pLtZGmBc3mKvNFhbPDNC8pQ9prZ73BKqatj3nm/4rMmSGsmQ5bR33Z7Ulz6S/0SPvN2T1CDRPXulct+1urJ1mCcEqtJ/vXyJC3RiprmminM9mfW641dp5sO83Ou6kpOjnJwrupFrBiOhhCx52jmqhjEbx67Gg5dqX86j14sxj4H/EUAAAQQQQAABBBBAAAEEnk2B4EKWh9kaX9sIHyq8oXW3Hfp782BVMy7RqNTnW/l+sI41ZHlRHx44rNktUlzhRWrGu9rvPGctRcjSdNoBHZ7ZUmnOS0tsGZq6z7XhY5vJ8nDnWNUzAoBKfdbqjuO+tgyubJxwp6vfN7d8Zy08jpAlaGtnAd/Vhn7p7uN0LiL7izF9JP+ApjY0Zo34XY5jncliT6utZi1bqXWAW/vMtd5Lfqz9tbXS3BMP9PDhQ/ftQa5unP1Ni9+oY1wKZFezD49Y1kdxHudDZY953hPW9fv2thz3N2uoeflNhTe01lgA2f2yzNXaXqnGDJVkdVvivwhySS9ea8iSqPRaTQL2sXXLNhq91nuZ1b0tQ4zgx1jrJrGsar3YSQPGfKQvN/ymM7f8p+eUdBx8HwEEEEAAAQQQQAABBBBAIJ4FggpZHvyUqefN8KHXN+5A5d5GDTQXBy3XU6tvWq5RkV/IciRf938Zp4auy43KqdNnZ1VQWNKaLDY1nXZE+bk7NaGeOyhIb/+ZzhYUPqaQ5YF+zqxpBAAV9MYad6Byb8MAVTHCpIo9v5ZPN62hQ4RmsgRv7S7Tu+v7u2cLlbGp8ZT9rnCj4MRstTIu1ao57Af9Y6loa8ji+8k5RrDgWjMlQaltFuh/5iVe1v4a3w+8bZoa9ftSf/qvZPvgJ42uYYR15XvrW1egck/f9a9oBFnl1fvrm94gq/CiFrU1FyFO19At1kuFHLq1+R1179RZr/rcumjoFyeMnlpDFt9++R63Xe0/veC9lKjwqn6Y2MLwDLCdraIyur2jNUcDL+xrYeYuAggggAACCCCAAAIIIIDAMyAQRMhyX9uGGpcGlSmv3qvN2RzWBVfT1GOZdZZB0ZBFjuvK6udewNRepZ+yrl4u4SOcjZBFDl1f2889syChqgasvarLj+MjnO9v1XDj0iBbuZ5aY6Ypd9Z6Fo61le2uFZ5FVR/HmiyhWBvVagm9UpvN0smCQv3P/OjmhOrK3G6NWHw/wtlevp5at2uvjgFuXceuDzyT5REhiz2tsQZ/ecSz/ov5evrbsz5Mgir2XC1z0sqdtX09H0Ndodtyb3uOK/qyk/mpQuU0cKNvyOL9KG9rEJKk+hN3G01aQ5ZEVarbKmAfO7brrAnrrfXr3Dxf1w9laf7EQerWpKYqGCGjTziT2lTv7/ZegmX2k/8RQAABBBBAAAEEEEAAAQSeLYHShyy5mzTEvJwjqa7emPapPvt0gT779FN91OcF49KQBJXrskRXPJNZAoQszkzi3GfqlOY8Ibap4Zg1mtfemKVg766Vxhl33vdDjEVczZDFtaEWtXcvRppSb6y+mdvB+CjpZPVYfssYuTztGl/HmIliV4sZJ+T/uS+O61+pm904IS/bX99ZztlzvxvkufwppW5vfTjf2ccF+mz+dPUzZtLYyqSq25Ir3pkW1pkdkZjJEpK1Wbj39N0AY0aIvZ0Wnb+qFV3dl9o4P4L6J79ZJdaZLCGtyZJUVwNmfaVlS5e6bks/X6BZozqrrumb1EhTD5hroziPMVebB5qXXtlUv9d0LXDV0QItmP6GMcspQbaULlqaYxbSA33Xv7xxuZBdHRZc9M42kUO3t01T/5491btnT/Xq1NiYeVJcyGJX6Ra+NT19/8+/e14Htq7QnNE9lGHO4CqToPTXV/nObvLdjEcIIIAAAggggAACCCCAAALPgEApQxaH7mT1K/6yCetshpSO+tzzCUCBQxbnmhwHp77oDkiSn1d989KRkkIW52oeB6aqqevSlxTVq2te1mMNWfK1/536RuhjU+PJ7ktmrGNZeOkzdTQ++cZeebi2mx/N67ij9eaaJtY+Bbif1mGx9xOSIhqyhGrt7WHuxjdV1XVpU1m9Nn+R+pV3BkpJen7Uz/JfujXskCXgpwv9o+3DjE8LKmNXu0/Oe0IRxx3v4rw+s0GKGKeo86JLxnaFurSwvcoaz6nQ5StdMS9d8nbbucCPbq7uZcyGCTNkKbyufd8s0ZIvvtSSr7brbIDlVx4cmW5chpWglIbv6ZA1S/I5Lh4ggAACCCCAAAIIIIAAAgg8CwKlC1kct/RNL/Ojca2XZAS6n6IOn5on1cWFLJLjzjZl1jLW5TBPsEsRsshxRz8MNz4px9yujDVkKVTOoo7GDJcEpb70gQ5ZZqo4T8RvrOnjCYycl9ScMKa6OG6u0RuuQCJQv/y+ltxWn/1lnOlHMmQJ2dpSrrmbNLii+9OQylWppkrGJzqN3eEfsfheLhTSTJaAIYv18hybMqYeMha+dejWmt6eS4IeHbIkKK3tAp03iAvPL1aXssYYJD6voetz/BbTlQqvfq/M+ubaLeGGLOc0v5Wxr6QGmry7qJ3jypd61Zixk9pitk77T5myDAl3EUAAAQQQQAABBBBAAAEE4l+gVCGL4/pK9XRd3uO+jOOLv+7r7/u+t5wVPVXRCD2cJ+tnXSfHxYcsUqFyVvTwLCbrOuEuTcgiqTBnpXpZLtWw+YQsUsHpj9XavFylTIoa9JyprJ2HdOzwLm1ZNEqtPdva1eSDw54A4PqKHko3+lCuy+c6n+vbx7/v52hVT+9lK23mnXXPtLCGLAnpatlnpEZnZha9jZqgFUcfPd0hdGtrsVovyXEHE8m1xijbJ2xyP986kyW5ekeNnjxF700JfJu27ID+dm5m7W/AkKVQFz5tYwRdSWowaa9cE0Ec17XqNfflXq5Lrhaf132/Ovo7Z6V6lzPCFOe+3YUk6b5+ndjIE57ZEiuq5ZA5+mbrbzp09KB+XvGBetVzf3KVO7wpLmRJUu0OmXq3mD6+N+V9rTjgXF+lQH9+1NTTXmrt7pq+Jlt//nVZly+c0oGtX2pi26rGZWk2vTT1kLuP1mHgPgIIIIAAAggggAACCCCAwDMlUIqQxaGrX72q8kb4UP7Vpd4FSS1Ujhsr1CPVPDluoTknnX/Wf1TI4lxT9IQ+bmV+NG+CbKUMWZyLkZ6Y87LKBZzJ4jyoXO2a2twTmBQ3Y6Jsg9Habi5s67iipV3KGut+lFX3pd6P8vV206Eby7t7LJyzF045u2kNHTzHZFj4PC6vwZsCJB2eBsKx9uzEdef+Ju9HazsvFao3NluBWraGLMU5mV8v22Wpe90Ra38DhiwO3VzW1bi8J1G1Rv3sCiAcV75SN7NGUrtq2VVzzRXLsTtuaGU3cxzsennWSe+aOv8e1aJXq3vW/zGPy/p/6vM91a+lM2wpLmQJNC7WryWr21L3Jxs57mXrvSZpRk1Yn+N7v2yDUfrhRoC+WLrFXQQQQAABBBBAAAEEEEAAgfgXKDlkKbykzzuYMwTS9JrPpwdZgBxXtKSz+Ty7ms84VnLIIunv3yaosesjnYMJWZwTG37TpPruj3T2n8niOirHPR3+KlMda1pCHDPwsFVSs36z9UuOd1ZJ4aXF6pxinDyX7eb76UHWbuZ8qa7m82xNNetYQeRClrCsLQfpvJu7WUMruy8ZsiXW0YTfAiwq4syHjs9US2N9GmtYEeh+6UMWKS97nOoan8STXHuwNl37f7q0qKPSjDGo0NXy6UE+h+7QlS86e56X2mSG/rRehvPfBW2f0VfNKpqXBZmBR4rqdvlA2y8/0KGpjZUSgZDFeViO2we0dFQH1UkxLM0aKpOg5HJ11WHofP161VtHPl3hAQIIIIAAAggggAACCCCAwDMlUHLI8rRz5N/TxUO/aMu6NVqzJkubf96vs7cCBw5Pe1efqeN3PNTtc4e187u1WrvhB+09d6fIGi2R9Ci8f0XHfv9B333ztVavXqvNO47qWtFlWiLZJPtCAAEEEEAAAQQQQAABBBB4ygTiP2R5ygaEw0UAAQQQQAABBBBAAAEEEEAAgadTgJDl6Rw3jhoBBBBAAAEEEEAAAQQQQAABBJ4wAUKWJ2xAOBwEEEAAAQQQQAABBBBAAAEEEHg6BQhZns5x46gRQAABBBBAAAEEEEAAAQQQQOAJEyBkecIGhMNBAAEEEEAAAQQQQAABBBBAAIGnU4CQ5ekcN44aAQQQQAABBBBAAAEEEEAAAQSeMIHQQpa8W7pw/E8dP35G1x6U0KNHPve+zu7cqA3r12vDxr26XFDCvkL4tuPeSf2yYb27DWc7xd6+054Lsf1o58KrB7TFdawb9fPxuyH0NlabOHT/9C/6zmX7nfblFMbqQJ7ddu+fVbZR55v2XNZjeCkFafvk1ETBua1aOGeO5s6eqxW/Xzf6UahrBzZro7NmN/ykE3cdQfbv0U935J7WTnM89uY8+sl8NwSBAp3bukAfz56juXOXa9f1yI5fCAcUu00c93Vmh/fnKG+/wQ9F/s2j+v7LjzRx2ED1791XQ4aP14zPN+nQ1dj+TlCkJwXntG2B871sjuYt+73It5/0L/C++KSPEMeHAAIIIBApgZBClgc/j1KdxATZEutp8p5H/xLyyOcW/qVPX7HLViZBtuTeWns/Ut3y7qfg+Ey1tCW423C2U+wtWT2W31Isf1XP+36IKruOz6am0454O/HE3yvQ2bmtlOo69hT1z3r4xB9xvB1g4bn5amvUeVqvb/V3zDsYiZrI0/GsOZo1Y6ZmzVqtQ7mhvToDv67ytHVIuvv9IOlFfXgkP6JiBWfmqrU5Hm9kRXTfz9TO/vtT62fPdNXAnNUH5S2Bxzt+T5VxwRnNa2n+HO2r9bz9ln74HPd19MtBeqlCYuDfDcrWV79P9upOaG89pT8Ov2fm/Zmluc73vRmzteZgrvf3kv++11vp7t9jUjKm+2315D/kffHJHyOOEAEEEEAgMgJBhiwO/X0mS2NeLOv+heSRIUspnkvI4jOKgU8GfZ7yhD6IxAn1E9q1p+Sw4jNkydWa11Pc7zW2Dlp8ObQZUoFfV4/3JJ2TiQi9cHJXq1ey+6Qytf0ieUvg8Y5fhI4+OrshZAnROU8nPu2kygnWP77YVSE9XeV9/jBTXm1nHVZJk3ZDPIiAm+Wufl1prj9a2NVp0WV53vkIWQJ68UUEEEAAAQSeNIHShSx5+zW/dye1yail9CTLLySBQpZgnhvlkCU1Y7w27z+oQwcD3Q7r7M3YXmQR+GTwSSuZQMdDyBJIJZpfI2R5hPaDW7p88YL+d+Girt41Z9493pN0QpZHjEcw3yo2ZJEe3Lysixcu6H8Xr8gzrMHsO16eS8gS0kgWnP5E7VLM32fKqnG/Bfo151/XrJHC3L+0fUYX1XTO2HWGHSlt9OmZ6P1+UGzIoge6dfmi/nfhgi5eeZouKXYPEe+LIZUqGyGAAAIIPIUCpQtZLH898bncJlDIEsxz/UKWb6+f1IYPBqhjxgtq3KSj+o6YoazjlqmyIQBbLxdKfXmeznn+JPSIneXu0sJRwzVy2AhNXH5Ud459o3d7tVJG3XrKaNlDE5Yd1j2HQ3cOfqUJPV7WSw0y1LrLm3p35WHLtGKHbu+Yq9HDhmvkiMn69tRdnVg7VQPaZKhh/abq3HukZq07YZn+Lj0yZHHk6sz3CzR5aG+92rKxGr/YRt37jdT0ZbuUY5ke/s/exRo73HnswzX6k2zds05zLjih1RNHuL6XOW6pjvxnGORf0e4lkzWwcwtl1K2rjGbt9PrQGfruZAD7ghva+9UUDX2tjZrUa6AWXUZq/o6LOjEnmMuFgrdxHWlpj9Nn/P7QP39t1rQ+rdS4fl+tNBYsKLx1RGs+GKLuLTPUqE5dZbzUSl37T9bi70/5jIlbyKHc09/rs0lD9UanVnqxwUtq92o/jfpgmXZb8Z1PfrBHi43ambDsiO6f26yZAzuq+Qv1lNGiswaM/0J7rvv9sl54S0dWT9Nbr7ZSkxfqqn7DJmrdeYCmLPxep73XRxiDFfg/35DlG10/sV7T+7dT03oN1LxDH2XOWKcT5r7+2avPR7nrYGTmfP3qWyQ6sWqiMp11O3y8lh02Q4nA7bq+GnRN5OvqriWa0r+zXm7o7G9zdez+lmZtOOmxd1zfrjkjB6tTrSRj1lx1tek/XKOmb/IciOPOMX03b7z6tWuuJvXrql7dxmr92kjNWLlX1yxX/+T/sVwTXK+Jkfpo02Vj+0eFLCUfn+cgVKAbe77Se4N7qJ3zfatpF2XO26GLx+eEdLmQc82CrZ++rWG9Oqt1o4Zq1rqrBoycpuW7cmR5mXua//f8j1o0vq86N2+shvUaqVW7Pprw6fc6FWCNmdI996H2Lxrleo8YmTlHP9+0vIEUnNSaSSPd7x9Ts/SXs4wdt/TL3Ez31975RqfvnlDW1H5qX8J7eMlj59D1H2Zr1OAOqmuc6CZXf0VvDjPHMF/Hlo131+nI6drsneLisim1YyivV49+gDuOO/pzwyea0Ke9WmU00Au16+mlVj2U+eFK7bUWZYjtFtzYq6VTBqtna+fPkWbqNuIT/XLxhD4O9nKh0h6nW1O5x9dp1og+6tSkgRpmtNWAqet06u4RLRvjfh8Zs2ivD0b+lV1a+s4AdW3RSPXrNlLLtq9p+IyNOmW+B/k8OxYP8vT7uDpKds0WSVClLl/ovN/bshw3ldW3suzGjJKWM074rHX1WGrMcV3bZ43U0Pa1jWNLUq2X+2ukWeP5f2jFOLe5973wgfYsdL9mM8cu09Hcc9ry0SB1blJfDRq21Kt9x+vL3dd9jv3hvsUa43yPH5apj3+66b0cSQU6tfpt9+tqxFStP2dFid37YiwqhDYRQAABBBAIR6B0IYvjts7u3a3du3Zp965sLepl/OIRKGQJ5rnWkMX2ojq2qWT8QmP+dSlB9nItNO33e5ZfAoLrbighi+PmcnW3O48hUZXa9tKrlfyu106opK5jMtW6nN/Xy5RT6xmHjJOhQl38rJ17nZLE6urWv5Oq+UxLTpAtobxenvq7JwgpNmTJO6vVbzZQOeMXQp+gq0yiKrecoh3GyVBhzpfqavx1zl5xsDZb1rkpODpdzYyZSBVf/1quTf79Q4u71jDWU/G6u9qw1VSvxSf0r0lemKP1g+qqrP9x2OqoU9u6SnF9vTRrsgRvoyCO0zp+lXu/rTENko1LTtroswuFctzcrnENjctQ/PtSpqyavb3TEpbl6ezXA9XY8xdPXyN7eiu997Pll9Q7q9TTuLwhvX1/9alpc7dtaSet4URl5xqojlvaPrqRMTXcd9/OMSj/4jvKLsWCANaQJTWjvdpX9K/NRFVs8b52OQOVwhwt6ZzqPq6EShq6yadI9NGLxjGXe12rrSfZZh1Y/w+6Jv7VsYXdVNtnOr7Zb7ue7/G5TvwrWf/iaa13cx0Cx92dmvyi0QeLrfu5NtXpv1bmAqCBX1fFhSylOz43QaFysgarvuu9wuyD83+76rVvp/rGay2tlGuy5J39WoPrF9OnhHS1nvKz+zXratyhO799oNbFrCWRVm+wsi6ZJ0jBPPe+vu1pXqLVRgvOW1Lp/3ZoTHV3XSXXe0f7nEFW4UUtauteDyS52qt6s6N5Uur18H8PL93YWWfHefdlK2OuV1Xc+ElBOQb7erXWvv99x11lT3qpmPfpBKXU7K91ZlGG0G5hTpaG1jXeyyw1n/p8B3Wsa7xmk0uxJkswxymH7v0+VS8X+VmXpJrd+6pjWffYpPX42qPx7x8L9Vp1Y40Yy3E6X5upNXroi+OenyiebaJ+J/+wpjU2zBKqafjWfwIewr9nd2jt6tVas3qN1u++4rls57HVmHVWktXOXDfK8kcs871QuqPVrxmv2bQOerNnLeNnseV1k9xIb+80f+hI99f08lyO1G7+eU+/pDz9MqqG+/ewxBc0Za+ZVsfufTHgwPBFBBBAAAEEnnCB0oUsPp3I085RNY0fwiUtfFvCc60hi/MXisRKat57tN6ZMEzdXijnCVxSG76r/easC59jKfmBNWSxp9VWs5at1DrArX3mWl0z/mjrPUl3/pJiU63WQzX1w/eV2a6mTxiRWrO9Rr47TZP7NFa68QuRPX2gNrt+l7EECa7vJalqkz4aO3GCRnSpr3QzcLE10tR97tkCxZ0MHvmoueckPKVqKw2a9KFmfTBGvRpVMIwSVbXnKl1xng85rmp51zTjBLqGRv9k/kJbqPOftDGOP1191zgX+c3XHzO8+05v2Etvz5qnORN6qJHxy7MtpbXmn3L9yVq3svqrunncSZXVoo9zrN7yGStbmSBDllLZBHOcknX8km12918EE+wql95Fiy/m6dSsFm6HhIrq8PbX2p79u3asm63ez5uLR7bRp8aUp7zDH6mlGbAkVVPrAW9rxoxpGvd6Y1UyLOwVe2l1gJMn50lFSmXneE3XjEn91DzdDD7Kqscy96fcFJycrZddgUOiqrR9R6t/yNaun9dpTs86xlilqN0n5yy/BAeueWvI4mw3uWJT9R01SZPeelWN0sx27cp4d5/y5NC1Zd1UwWWfqNqZP3mCtMK/5qudEYBU6v2NblsmMhRt2RF0TeQfnaGWRghlS2ukPhNna/6sCer5glGzZVLVdt4p5d/7U9tWfalRTY0xSWqkIfNXaPUm54LQhfrr0zaek4Q6Xd7R4uXLtHjGcLU2wyVbK3182h0yFPe6CrTwbWmPz/WKuJmlNyubtjZVa/qG6/U93Pr6LpOgUoUs/x3RzGZm8GdTjZZvavKHMzR99Ot6sbzRRkIl9VllnOj9k60J5ol1mbJq2HWMpn84RUNaVjL+Ap6oKn3X6pZz/IJ5rkIPWVwBV4nv4aUdu3zd+3OrVn8xUi2MekxpOEgLlq/S5sO35TwZDDR+CtbREnaU5vVa9DXg/UrhuQVqb64fU7OLpixcoeULZ2hkK/OPB3a1nnPaPaMg2HYdt7S+fxXPz8SUSs3Uz/n6HtrF8vp2LiBfcsgS1HE+3Kv3GpqBSaLSG3ZT5ugReqN5NZ+fhZ6QJf+oZnnquJwyek7UnHmzNem1+sb7TYLKvTJPxkvTixfte7lr1Nt8H7J31fKSwmTr8T3OGnPc0/Gtq7RkZDPD16aMgZ9q5apNOuJ8My4pZHG+pydVcf2c+ujDtzWgSbqnZsp3Wy7zQ7iCDVli9r5odec+AggggAACT5HAExSypKj1rD89U+Idd37S2LrmpQK1NO6XUly2EADeGrK4TgJcJ5aWv/AYj1PbLND/jD/a+pyk1xipH80/9N9brzeNlf1tSRmadsg4pvwj+tD867/n5M43ZElrOUt/mvP9HXf086h6nunAdUbvlHNPAU8G72/RUPNErlxHLTxjccj9Xe+YvwDbmmjGH+4w5MaqHqro6leS6k/c7dq3HNe0tIv7JM6e3k9ZztkR/2VrgnE5hj29l1ZfMc+oC3V5WXdjQUCbXpx6SPmOm1rV3fwre4razDnh3q8z17GOVQghS4k2wRyn83g8M5Gcs4XS1XbKFp3NNf8i/6829jMWbk7K0JRd5iwph278OMd9edfwscZlMvf1/WDzxKa8Oi844+mzlKtdExsZvwjb1ezDP4qePKW01Ow/zPFy6OpXXYy/ctvU6J19rmr9d0M/lXeNlU1N3t7lmdXkuPGj5ma6L1kbv/SIpd0ARe6MHSyfLmRLfllzvMWmOz+NVgPzkouaY5T9n+S4sUq9yrlfB8l1J8r9IWEOXfvqVXd4kZCuAevuPHoGWdA1kadfxxrT4BMqqs/XVzz7L7y8TD2MECql0fs65PoDanEL3z7QT2+/ouYvNVHzlmO02ZME5WnXeOMSgMRampDttg/4ugp4kh7M8Tl0c8VrnlkLaS/P0QkzCPZ5fZcuZPl7y2BPgFmxwwKdNctGUu7vk5RhBA2pL32oYwVS7saBquoK+RJVqftyz6wd3d2q4TWM981y/bQ+N7jnKqyQJUWtZx4r4T08uLFTsWuyBA5ZgnWUNewoxes18KvP/dUHP76jNk2aqFmTVhr33W1Pbef9Pl71XK+/JNUZm+1+LQfZruPmSr1uhr0pr+jjsiPUvgAAIABJREFU42aBOHxe36UJWYI5zrwdo/W8+d5Rd4w8kyHyzmt5D++sJTNkycseZ1zelagqPb/WVc+PlMta3s044U9qrGnuF/ijOB/r9wovfqaOxmvKlj5Y35s/m0vRajRqrNg1WUoMWVL0ysw/PD8vHFe+UjejblJemKz9xsSU4EKW2L0vlmI4eAoCCCCAAAJPpMCTE7Ikd9YSz0m+0ypfh6c2Nqa92tX2E+uU1tJbWkMWe/l6at2uvToGuHUduz7gTJbUdgt1yTw/zz+o9xsaU4wrDNIWzy9mD7S+r3HibmuuWcedYYc1ZElW50U5PrMR8g+9r5eMywlSX5kv56z8QCeDeXve9pwgV+i5Rr5L3RXoxMzmxol+srosdp+0Om59q74V3CfQKY0/kOvTae+uVf/yzq8lquqAjbonqeC0d92IlEZDtHD5Sq1a4b6t/GSgGhvHV7bTl7r6cKfG1TRO3lK6avkN87dn91gdeq9RaJcLlSnZ5uzJII7T4RuyOAOE3ebJr6ts8rV/cgPvdOrEcqrbvIsGjpmuxd9m68xt8+TFmXrt1WQz6CvbS9/44qvg+CzPx4OX7fi5XOVrOXlKbTHH5y+2eb95T7ZemLDLfTT7pnicbWWSlF6rubr1H6uPFq5V9unbnl+WS6p4a8iS5hwvn+GxTI23tdanfxW61tJY29v8COPGmnbY+dv3XWW9UcE1C8pe8U195yySR/3LC7ImCk57145Iaqy3Fqzw1Nuq5fM1uIHx2rKb7wXFhSzeg8q7dVb7tmdpxYKZmjKsuzLMy2cSa2n8ziBDlqCO76GyR9cygtIUdV92w3NS7Ty6/IPvKaPUlwvlae8kM3RNU581/oV2QrObGbMJ7J30Rc5/2vdOfaOGy6mvT2EW6M6Fkzr+5586fvy8buflB/Fc55GHMZPF3lGfmzO6XEP06Pfw0oxdcCFLsI4OWUOW0rxevZX3iHt5t3R273atX7ZAs94Zrh4NzdkESaozpmjIUpp2834ZozpG2JH26jL5vv1afi6VYiaL58hLPM5CnZv3iidIbjnzpO+6Hr+M8ayX4w5ZCnTGszaXTS8OWaCVxs+TVStW6NM3Gxo1m6xXv/AGrJ7jcb7l7luo4W8O1OCI3AZpzBLnzLei/xw5n6uLeZlfeevP8qLP9f1KdGos5JDF1lJzXTNPjaPO+00Tart/bifXmaDdxo+3oEKWmL0v+srzCAEEEEAAgadJ4IkJWezVMvWzz8mwQ3dWvGas/2HTS1MPy7w6OBhga8hS2oVvrTMhynb8wnvC6ryOu5H7RNBecZi2e473oTYPKOe+RCdQyJJQRSN/sJy8O2db3F6p141f8lIavy/nOW6gkOXhpkGqZMxK8fyCbgG4s6qHcdmETU0+OGIY3dWG/hXd04SNX7oebH1LNZx/+U6ooqHGQi35+60n+EVn95gzf1x/Pf97kwalGTMfnLMhfLrjHKvuxlgFeblQKWz27wriOAt8Qxbfj311wxVe3aoJTc1Lrfz6nVpX3d7boovOYnu4SUNcwVSCkmuO9euzfE7QUjKm6ahzG2vIYg3onCcQuyboBeMv2mbIosKr2jaumefSI9Pc/X9ZvdDlPX3vOhjLoAe46w1ZElUz8yffcMZxW6u6G2s5WP6KfHd9f1VxzYaw65XZp1TwYJuGV3FempKo6oM3y5zAFaA595ceBlkT+fv1Xn0jSDFmkPn21xgLz6ys4kMWx829WtCvsSoZQYZ7P4lKtRlhRCghS1DH97f3NZ9YdKad8/Xdw3h9l3y50ENtHugOt2yW4/a6W9ZcSMrQ9CO52jLICMgSn9eE33xejN7NXPceBvFc5wahhyz2ysMt74nOfQV+Dw9m7IILWYJ1zA/+9eqn6/PQcUt75/fTixV8a9xusxtBRTEhSyneJx5+96ZxSWqSzJmPnradr+9uxuu7NCFLqY8zX4emesPzXqu9a3o42y68uEAdjNkg7pAlXwemWALsYl/jlpl/nk647zzM6uu5NDbge0Ox+/R7H3c9L1FVhm7za8F4+M96DTAvibW106KL5l9SAj/d+9Xo1FjoIUt7Lfb8Vcj1Q0eT6oQZssTsfdGrzj0EEEAAAQSeNoEnJ2RJH6KtnpkhTsZCXV7U3vNXNNdJYAi6T0TIUiZdgzeZa6O4O1F4eZFnurL5l8xAIUvez5mqZVwWUGXIVs9UfGMvylnUwWPUeu4Zz18aczcNMhbaTVbHz84qe/zzrr+626sN0w/G2bNzPZBWxi/J5VoM08fz5ml+gNuny37TtX9/VGZV99oQ9iJj5dC1LzqHFrKUwubEn0Ecp3OShuVyIZ+QzFo/+Td0aN08vTOgi1rVSTcMzV/U09Rp4V8q/O9njTYW+nT2eZtPfTrXj12sTuZlHC3n6qxzAlOwIYvrmPJ14+A6zZ/4pro1r6vK5jR244SiQvuFck4+edQ/a8hSecj3vnVSeFmL2xnhg/UvnbmbNMRY1Lls2890dqcx0yahukZsLTFikf4LsiYKTmpOc+M4UlpoxJzA9Tb/k+X6/aqzw8WELI5r+qa3uc7F/2fvPLyiuN4G/KcIbKMtoAI2Enti7wU1scQaCymW2FKMGk2MNfaowZLYokaNBSs/u0axiwWDghwB+SgRcDnsnOc7W9ldFthlQYqv5+Duzty5c+e578zOPHuLltZ9P2PRmh38ffEBifOtrd8cZIW788rtmB5ela+Ik182t4hMvzA+c+lvoLzYzBCPJYuB09Miy/JyvhCaByr+tb+t/nqw6uF/ZeLErznTTngoWapMa4owF8niGHhvTjDdeg1wN/CtKnQih50uc26u4V7WnXeSxVuOpdU8X92diQoZu0Zbu3D5ERTdjy8WruWPwxdJPjvf2mqx+pLFcGKqRZI38aep6/mtvGDLIE8lizflLCV5WXf798vA9c+cWmOW3vqRLk6SpZQHS20tK3X0/Gy52++T1atWs/3cC6e8bERLHxxi9bLlrKihv/VHn9iydn41PmZ1D+s51SSIj+JfOLVEsyQu5e6KAbRq1pyoZlH0XWL6oeftxFhdSpaTU63XNdvAt3V2XXSuMvkkBISAEBACQqAhEag3kkUV8D7fXnR4ilUy2TkixNI6pImeKYcKq8W1fkiWAN6ffd4+uKjpF97M30dYx03xI2LiIUxH5+5h0Ji2iSHWAfq0783louNDjJLFnk+sTdH9mjP1mAOjgmN82dwiRYIHz2CauZtTAK2mnjDvywwz/y8mWseY0dkkgY1ySQGZaWmkpaWRnvUao/Ep662ziKj82zDnnGNdZbNvbNlD7/h9Duts+Tm9Onal8oCNN+V0GZOlnGQpfsiJbfFsjY9n+4Hr1oFdjbxOT+LgvD7WcWj8CBm3nyJjGpsHWQcjDXifby44wSdr12hren+iPj9u4eqVZCnmUcJ2c1m2bv2L6+ZRSsH4Op2kA/PobxsoVzeOA467dmJp+VAmWfzQvvc1lxyqwHH8FZV+EoftYVLA8c+sD/fawcz80vKrtSZqKiftadzszLbI65go4OB4awsMlUkWmKyU7V8JBZnp5nhLS8vmtVkqVSBZ8vYwVmcRYqYWREl2x2CaltUiEx1bhLg7r9xKFrwpn5F/1/a1PoAG8N6scw5iSyF779iyB+4qZxcykvbrYHuLtPZzLzhcK0zScDdjrbGgbvYFCYWl3P7xA/vDb8+lD+xyldL7rB0SQ0zLVsS0+5JDed6kNdXFa/Z9Yov5dsy3DeJg0t4paxlgFUduJYt/G+aedwhUd9dwL+vOO8niLcfqSlFbzDq+5rH3E9tYT51Y7DD1uWM3QXtrRK+uE2B8WtZqRNN6Nucdz++svUywDfhcZUsW78pZ+PdkexyHDPqVsgYfBu7au6r6YRuTpeDABCLMcliNo/Q3kSrJzyTd/J2STrblBHcE+Jbfl/JgWXfrDwN+aGOmkeAy+K3y4iBx9inkWzMn0RTbbyfGaluyvN472n696TDvalkrYWMK6/tahZ1NstTZdfEth4TsTggIASEgBIRADRKoP5KliR+hnaez+8YLCvLTubJxLDHWrgBq/XgOmAZqVbLZPS6KZmHhNIswTUlof7qqEImjZNFEDmTGd/OYP8/936Kt1/ivsof06nYXMt106j5gxs4bZOTnk375V8a3tDYp9wvj0/2WAUbdPgwa09nxsa1Pv5YOEzZw5u4z0lJucPjHWFrYByWcSaJT44NCTk2LtowZEaC29IX3b8HM044PQVn8OcbarcivGcOWnuV5oYIh6x4HZ35ovQlT09k08C2lPFrVy96UO/TDmRy4m81/uU85v24MMfbWF152F/KEjeJNOatoyWI4w8wo62wtup4svvjK+oBqIG3vp9ZBHgOImXUOA0bStw23i5egtp+y8fQ9nqWlcOPgjwyLtI5R49+G2Wes8L16eHKYfatJIL1/uESO1TsY0vYyyTqAqablbM5XEeqOkkXVJIgu03eR9KKA/LTLbBrTyjoWgj9Nx+93mJ4aCk9Osx6zCp35fAug9fQzTg/5FZ5cXseEQtaesdYuSv5ExS4j8Xkhypts7h2YRVfrAI26DmUD3+62TyfcmYVXCzAqCkrWVusU635oOy7gurnbnpGcK8uJtYmp6rRkwbvylT5cRR/bDCWBXZi9/x7ZBbn8e24d42wzVXk4u5AxbTsjbWXXtGPShtPce5bG0+uHWBIbaR8k+/2vzpqvUYar39vHfNFEfsya88/Iy0/jf0tjrS0e/AiN3cxz01hPXqQ1ySe7qGoSQMsRa/nfw+f8e+MgP/QrG+jUrWQxTTneeRq7rld8Dfe27nCYBUbXaT7XCowo5vGG3A986y3H6rU8c3NGKFlsG2LrkteRH6yDohtfXWHFQOs1tkn1W7JQ+pDVPW2zTwXR7av93MsqIPfpOTaMts1E5sHsQt6WM+9vPrfKetMg4t2mrGDn/r3E/zCK9raBeE0xbp3CWcnay3ibEGway/IzzylUDGTf/Ys5H1jLr6r7gW9NNahkHSLONs6Y6f6j3Wh+3HqYM2dOcHDzfEbZZzzzI6z/OktLRZNm8fJcrU6M5e8aaf/+7TL/Kv8ZFUtLm6oGvlV51l2oTPz5oYkcwbrEhzx/msShBf2tLWBNsz3apnCuu+uimzNNFgkBISAEhIAQaBAE6o9kUQUT5nDTVtYfO4R+K25bxphQstluu5H1a870SpvJW/g7SpayPG3dQpxfA2PjMf2YVWF3k2pLFn/CQkPsUyk6liOs9wruWB+g3UoWUwuXR/GMam59oDf/SuhcbpWmA7OOZ5Vr7lx8dqZ1VgtLek2r2fzP4RdQEyHD3bUMtj3cmfL2U6E1d0+ybBPcfhYnrL/wKbknmfWerYm1Yxn80YfYptz2VrJ4yMabclbaXcjAnZV97K2IVE00RES3oW2LMLtAUum6sdT2S7ThEVuH2x5yHY/Z9l5LpxkJ5rgxR5xXkgUMt1fSzzrLjykuAvUteL9NS5raHt6bBNJziXezC+mCQuyz3jjGmiq4D6tuu9ia4kRmWwdGNKf1N/1i6xIklVzKvI6JN/dYN8AmDU0M/dEGWKWXKf60HZiTYJpe3BydJHxe9mBvKp+242IovcPSLtYHWtNgwS270qdLK8L9/QnSWh/k/ML5wtrtxv155f4hHW/Kp+Ryasb7Ll3NLHGhDgq1T9Ne9ZgslmN9FD+caKs0dao36zkf1P6rsl/blQwOTmpplWe2WHR4DerBz9eszZG8SQsUJs7mfaexbqz5aqNpHW4d3yHmW66YxiAyprLB1sLNL4TwYIe6tF+rHK7hXtYdbxKYau3SZmGi4sNFpsFMK6g/DHjF0cvzteJToZS7S7raW0ZoglrRvWdXYkICUGt11muLP83ijllaPHm9X4Xck1/R3i6zHeraL5hw2zTtVbZk8bKcGHm2ayyt3MRDYFRLomyD8Voli6le7q0ZaBfTpjpTB6gcvvt0dJqZYJlavGKYb2mNQs7pefRwG7NlfLXRY/jjseOIcLUfY4bjX5TJDtN5FNCJH00j2NeQZKHQcQr4smNVNdHRKjrCInXtkgXq7rr4lkJBdiMEhIAQEAJCoIYJ1BvJotZP5rc/59CnadlDvFrbmo8XnyLDNh5Fg5UsWkas2sf8XhFlD0V+OmKGLua0eewJS626fxi0rCt+cpjFIzsQ4XSzqyG6exwbLma57d/Omwt8Y5sdp0kAbW3TOTsFkULu9S1M6xlVJhlMN3WqcLqMWcbZlzb41nI83MvMHs3KHixNx/HRcg6tqO6YLJ6xMXWx8rScFUoy23Err7i2aSoDWgQ63PybBJOOVt0nsvL0i7LuF6ZtilP4+4eRdNI7D2gZ2LwHn6+7SJYjomo8POVc3cz0vi0JcZBbJvkQHNWDycvPkOHYq8Z2DC6vZS1Z/Gk6aTP75vQiyv5A5k9wq2H8dMrdOAgGLs5tY20p4YemzdfW6ZxddlDJx2IvY0LJvUH8l71oaRdJppt8Nc06jWX56ZdOsVx4Yx2ftAmy15NZsphGDrm+kQntHMSlKpxuUzZzcdfkspYyo3eZpzZ2f15V9JAO3pSP4of8Ob2nC+uPWfHXCoZ6PCaLDW4xKYcX8Ul7fdl1wnQuqiPpHbeOS06BZrIhyeybM5A2OkexoaZpp/GsvpDpxNGrtEoeV1cNJ8ZBeuvCezJn3wmWdbdcn922ZNF8zOp98+gbXnaelLuGe1l3UEjSupG0DbQdY1WSxcTSC45en6+2unLzmn+DX8e1J8x+Hqtp9uEUtlzYRZytdUezMew2zcBUrf0W82jPDHq7fEeOWHaQVYM9HZPFNNSRF+U0H2YxKUd+ZGKf9rQIVKHyC6RNvznsS1xtH5MqaMTOMiBKLje2TKVPpK3ljeUhXhfWmfFLz+DylVK2XZ28U8i7+TtzB7Ui2F5vVukQoOfDsUs5meYipm1MPD1Xq1PXhUlsGBlDqK1MNS1ZUMi78gsjW+msXbJN3/kR9J65j5M/97B8vztKFlPLHy+u2zV7XayTwJCdCgEhIASEgBDwiUA1JItP+6t6Y+NrMu5d5vzFJP7N9eDpsuoc6yiF47gjWkb9kQfKa9JvXeTsmYskPc11fpD3sJTGomye3rzEhSv3eZ5rKNd6xTmbIv6eaJumty3zrri7WbRtYeS/tHtcPXeGxItJPMosqiTvYrKSr3Hh/D88zq6qDLb8HV99YeNNOR336ea9UsSrp/e5fvEsp09f4tazfOcHU9dNjEVkp9zk0vkr3H+ei8FxmmTXtF5/VijKfsr96xdJPHWGSzefke8ob7zOzzS2ywvuXz7HpRtPqexUKjo8yTqOgooO315xnpnI4/16HxPGgnTuXTlnOR8eZVJUIU/TeC1pPEt9zosshz5xJbmk3jSdT5e5l+HQDc7jMlee0PPyQXFWMv+cP8/1x9k1EBdGirJSuHXxPFfvPSe3ikAz5qdz79p5Es9e5qZp/5UcljdpS/LTeHD1HOev3OdlRXgdW7JohrPTfJlL5/ZFy3Wkwmu4t3VnHiPqGanP08ku8PR7wTuOlWDzYlUJuak3uXT6LJfvZnjY7c6L7E1Ji7N4eO08F/55zKs3FZ40VWRazXIaDRQWW/iX3FjEB1bpHzbhUPn9GQtIv3uF86fPcunGIzIrPsHLb/vWlyi8Tksi8cgB9u7ay8FjF0nO8qRFX+3GmGkcm7RnqTxPz+Y/T8PeG3Yl+aTdv8r5c1d4UOFJ7pxh3V0Xncshn4SAEBACQkAI1GcC9U+y1GdaXpXNjUjwantfEysUPv6DCdZxPcwDhDq2ePY1e5+2r2s2PhW+UW2sFD5h5zjb2D0dWWyaS1z+CQFPCbiRLJ5uKunqP4GS68sY9H4MbdvE0KHv9yTmWqVOSTpHvnzP2uJKTZ9Vj+v/wUgJhYAQEAJCQAgIASHwlgiIZKk10HUoEt6cZGa02t7FwjRg4cfxqZW30qg1Du4yrkM27orzTi4zcGp6C3S25uhN/IkYGu8we8g7CUUO2lsCIlm8Jdaw0hdfZVGnsm4/On1rPuzyAR2aB9m7GGqjJ3HwZXVb1DQsHFJaISAEhIAQEAJCQAh4QkAkiyeUqpWmDkXCmxNMb1Y2hkH0kI3cr6wPQbWOz5eN6pCNL8VuVNsaODm1uV3EaZsP5dd79SpIGhXtRnswIlkabdXaDqzk+XF+HvMhkfYxnqxjlvjpaN1vJrvuvbYllVchIASEgBAQAkJACAgBQCRLLYZBSV46Tx4/5vHjJ2Tkv8Vf+pQcHl08zakTp7l4M43/3uKuPcVZZ2w8LWCjT6eQ8/AiZ06e4MyFm6QV1MMgafR10BgOsIS8tCc8Nl/nXiBh1Bjq1P0xGLIecf38CY78dYhjpy9z/8XretQ60n2ZZakQEAJCQAgIASEgBOqCgEiWuqAu+xQCQkAICAEhIASEgBAQAkJACAgBISAEGh0BkSyNrkrlgISAEBACQkAICAEhIASEgBAQAkJACAiBuiAgkqUuqMs+hYAQEAJCQAgIASEgBISAEBACQkAICIFGR6BGJUvJlfl01mkJ0mgJCurJinul8CaBmTGRREW2J27vC8qP/GAkfesntI7swDdn3t7Am4ZTs+kY2Y45p97ePr2LHiNPNgyjdfRwtqQavdu0wtQKRqMRo70SSnmwajCtosfw+wv7wgq3bqwrTLHQIfJ9ZiV4Fgult3+mV6A1zgM7s+i6THvcWGNDjksICAEhIASEgBAQAkJACAgBIeANgZqVLJe/pZ0qkjGrDnH06HkemQZ7LT7E5GDLbASa6DiOZLs+zBt5tr4/Or+mTD3u2UOuNwdYUVrD0Tia+uv57Mjb22dFZXG/3EjKxuHEtBpJfA1JltLk5fRSqem3OsU6YGEpd376AJ1qIJvSXOvFfaka41LDkSlE+OmZcrjYo8NT8h9y/ugRDq8YQZSqPQuuiWTxCJwkEgJCQAgIASEgBISAEBACQkAINHICtSBZYvjukoO4sEoWtT6cCD8V7b46Q74TVJEsTjhq8YNIFvdwvZUstlwM52cTI5LFhkNehYAQEAJCQAgIASEgBISAEBAC7zyBtyZZAocsZ/O4SDSqjiy4XOgAvrxkUfJvc2TbNv6+lefUvUjJu8XfW7dz5LZF0yivkji0dQdnUwzkJO1l5dwviZs0g5933SRHASXnJn8unc0Xk+KY8/MekrJL7fsta8mSz7Pzv/PLd1OJi5vNsp1XeemmYULJy2vsWzWPr6ZMZMqX37H6wG1yHRt/GJ/xvz+2sf9qJoYXF/ht/lSmb7xu2V9JJv/sXsG86VOYPHka81bu5XpmWVnshXJ6o5B36zDbt/3NHfP0zwo5Nw6yffsZnhpyuLN/Jd99OZnPpn/PxqMPqWqG6Nf3j7FtyWhi/FW0G7ec+F3nSTM6tGR5nsO9Ayv59ovK8izk6Zl4fp71BVMmfsHcn+JJfOpYlwr5t/9mx9bD3M5zhON6LDYs19i74jtmTplI3LTv+GXvdcphef2E078tYe7nk/h0/GRmLd7Msbs5DlOHesmlOJXE3xYza8qnTJm2iB2XM/jPTUuWksyqyyaSxSlg5YMQEAJCQAgIASEgBISAEBACQuCdJ/D2JMuw7WQ928Go8ACCuy/j9hsb+/KSxfh4NX1VanqveIijiih9uILeKjV9f3li3rj09k90UWkZOHkyHbSBtGjfhc6ROlR+ofT9ehUzPwwlpFl7enzQijA/P4LazeFsnmW/ZsniF0zvjwYRpQ2nXZfudGmpR9tETauR8Ty0N8ZRyL20lEERKlQqPTGdTelC0TbR0uGLQ7ywDZfy5gif6VW0nbKQL97ToGriR/jEw1CUxOp+YWhU4XTsNZihfTvTUuePLnocuyvtBlTKoxU90Kl6s+axaSdWIaLtz+dx7QnRRdK5Wzc6NtWiahJEt4WXeW1DWu5VIWPrKFroA9E28UMXqCey7WzOvrFJls6MHtcJvbYZHbr0oEt0MGrXPJWXJMztSrhfAOFtejCofw/eDw1AHdKVbxNeWmWYkSe/9Ean6sGqh04153IsJiyrGKgPQBfWnr6DBzOgUxQhfmpaj9uFHUv+eRZ+GIxaG03PIWMYN2IgH0SoUanfZ/qRTOs+veBScI3lfcNQN9HQLKYLvTq3IlzbmvGTBhHu0F3Io7IBIlnKBZosEAJCQAgIASEgBISAEBACQkAIvNME3qpkeaWUkvJrLOF+IQxaaxMovkoWP1Sqdkw/9ByzF8lPZG5bFaomAcRM2EOKWeYUcW95H4L9Qhi3J9dc4RbJ4oc6bAjrbxdYgkDJ45+l/c3lG7blmWVZQSJz2qjQthrH9ru2dDlcXdKX8IBoph61WhuzZPFHqwqm48RNJD7M5D+Dkfz944gIaMO0Y9n2VjnFD9YwKEhF27mXKgm+CiSLSZK0ncrh5xYLpORdZsEHatT6cRxw7odVLu8KuwuZ8mw3lcOp1jFJipNZOyDIIU+Fl3vHEeUfSI/vEsm2iiVj5hm++1CHpvkEDmSZWq54KlnyOTBOj7b1VBLsY/QUk7x6APqAGL69aDo2hYz4oQQHvMes07l2dkr233wRHUBQ7G9kmBvLWCVLlVxKuL2kC4H+kXyy5T6W9jdGci7Mp5fONGaQbUwWT8pmQSuSpVyIyQIhIASEgBAQAkJACAgBISAEhMA7TeAtSxag5D5r+gaj0X/ENnOTBV8liz/NJ/yFRZ2Y6rKYwxNCUKn6sv6prZkJlN5fRk+Vml7Lk80VbpEsKjou+MciZ2xhUJzInBYBBPbfYF6Sf2ACEf5Nmbi/TJKYVxSeZmYLFRETDlq2NEsWP7Tvfc2lIltmCukbBxHoH8OM49llXVyUPB5dSODUjRe2hG5eK5AsfhFMOlB2tFDCrUWd0JZrPVI+ywolS7k8S3m4vHtZixRjKhv7a9FET+eUY+8g4L/jX9LSX8uQX9MweipZlHQ2DdSgaTONhKyyOlLyHnIx4SQmn3T0AAAgAElEQVRJ5uZBRjLO/sYv647xxKlRzENWdVej67YcS2MZq2QpdwwuXEqus6i9Cl3nn7jjmB8FHI9rjtomWTwqm4WtSJbyMSZLhIAQEAJCQAgIASEgBISAEBAC7zKBty9ZTN0sbv5ML20AUWP3kKH4KlnU9F31pExgUMyRyXrU+jiO2bskgTF1HQNVanosvW+ub4tk0RPnOqOMksXWWA2a6K9MRohr89qhDWjDmEVrWLdmbdnf6p+Y8J4KbcdFlvixtmQJHbff2krCsrj00a8M1fuj8g+mdbeP+WLeGnafukeWQ9ncB2AFkkXVm9Xm7kO2rYz8u7YvOlVXlpumzK7kX4WSxdQl6UmZ7DC1SEld168sT9PgxSF+BI3ag7UtT9lecv9glMafiClHMXgqWSjl0cbBRPj5oQlqQc+PPmP+6l2cvpvlLLxMbWPynnDp4A42LF3IN1MnMqpXG0L9/MpLlqq4vN7LOK0fYRMO4TyHkMLL32IJtEkWL8omkqUsDOSdEBACQkAICAEhIASEgBAQAkJACECdSBYo5OqCzgT6t+CLvzNJdZnCucIxWaxTEDuPyaJm8MZ0e3cSU0sWk2TRhH1OgoPIcCtZAlox55x98BVrPLzi9480qCOnm3QQJ6c2R+0XTOv2H9Clc/m/brHLLdtZJYtpHBbnh3gofJzAxm8+ZWjnaEL9LdNZB0YOYMHxarRkKTfdsosQqSSqK5YsrlM4u+SZv4tPNH5ETD5S7thMU3RPCvYjaPReCiuRLJaWMbbxZUyFLORJwnrmTRhMl6ggNE1MXDS07P89CeaWLAqvziykT1iAeXnztj0YNmoKc3/8mSmmFimuLVmq4lKwhzEVHEPR/nGE2CWLJ2WzQBbJUkmwySohIASEgBAQAkJACAgBISAEhMA7SKCOJAuQf45v2qrRxszg95/7ofNrytTjFuFhlyzLk50GvjVc/Jr3/V0HvvVBsviH8/lRFyVSeoelH6rRdV9R1pJF1Z2VyZW3EqECyaIYjRiNZTPtGF49JHH71wyM8Ef73neVhFxFLVmqECKV5WiVVP1Wp1hb/tgGvq0iT0Mis6ID0A34lfSyQzHvyZi6lgGqAGLmnHdqyeLMy8CluW3Q2AfxVTCauNjzMpDzMJHf5/Yj0k9F+28vQ0kSP3ZUo2k9kV0PCspaKhmfsKaXm+5CVUkWwwW+bhVA4ODN1rFcbKAUsrcOdWjJ4kHZrJuKZLExlFchIASEgBAQAkJACAgBISAEhIAQMBGoO8mCQk7CNN4LUBPVPAKNo2R5tp6Bpgf3Weccuo8YSPqhs3n2nxpryeIXQOu4w7yyP+xDye0ldFOp6Pj9NXOE/Hf0M/OAr7EbnpY96JvW5B5jRkwoLScesESSW8mSx+6RWrTvz+OqU4OZYo5M0qPRx1m2dft/PZIsFHAsLhKNtg9rnGYNKiF5RU+C/KOYlmDqSGTt+uXv0kLoTRKLO5lmZ7K2ZMnbxSdaFR2+u+JQv6bhdP4mTh9A07hjUPgn47V+6McdcOp+pWTtZUKEv/ctWczH0ByNLpYtzx26RhlT2TpUXzYmiydls9aXSBa3gSsLhYAQEAJCQAgIASEgBISAEBAC7yyBOpQspglksjg8uYWlq4iDZKE4kdktAlCHDWLJ0dukPLnFqfUT6RQZQYRfDbZk8VOhVUUSu2AvVx6mkHx+C5+106IJG84O2zzChlus6BmEOrQbM+PPcjclhXvnd7FwYCRadScWXLJOnOxWshh5unEwej89vWfu4H93Unj+9D7nf59F79AAosbvqyTwal6yGJ+ZxqUJIHrESo5dfESO4mFLFtME0g83MlQfQGjHKfx6IolHD5M4sWEynXUBRMRu5rG1oU/x2Zm08fen2cAfOX47hZRbJ9n4aQdahutR2ySLMYVNg4JR63swZ0cid1Oe8++9c+yc2YNw/2Z8ui/b3JJlUUc16ogh/HIuhZcvU7hxZC2fd4kkVOtvbgF17HkeBtvU1lW1ZDF1/rq9nN6BATTv9z37rz0m5V4i26d3p5kukCDbFM6elM1aayJZKglfWSUEhIAQEAJCQAgIASEgBISAEHgHCdStZDF5lvQ9jG/mj8pRsmAk4+i39GuuRmUeq8OPwMhYlh1dywhNDUoW1ftMXT6P/hG2/fgT0no4y89lOYzxAsYXJ/kxthVB1rKYyqRr2ou5fz0ta4nhVrKYnuxTODinDy3VlrFYzMfjF0jbj5dx3jztcUVRV/OShdJHxA9rjraJH+qIL0h447lkMU2p/OrSGia011vHT/FD5a+n07i1XHFsCmR8wfFv+hCtsh2vhpaxSzm29mOCbJLFjOUvvu4TRaADU7UuhpFLz2GZ1Vkh5/wyhrXQ2mNAE9qBCavOcWntQML9/NC2X8iNEm+OoYRnf3/LwEiNPU9d1FBW/jqTDgG2KZxNVVZV2Sx1JpKlotiV5UJACAgBISAEhIAQEAJCQAgIgXeTQO1LFl+4Fr0k+doFLl5LJtNl6BRfsnXdVnmdzr2rF7lyM5XcCodeKSUv9SaXziVy6fpjXjkMquuan7vPpflp3Lt2kQuXbvIk87Vz1yN3G9TaMgN5Gelk5RucRJLHu1MKeZl8ncuXrvPwZWGFeRS9fMA/Fy7wT3Jm+cFy7TsrpSDtHv9cPM/lm0/IfO3QjceWpjibx9cvc/l6Mi8Lbf26DOQ8fcCjjIr3b9vc7esbS55Xb6dRUEl9V1U2kSxu6cpCISAEhIAQEAJCQAgIASEgBITAO0ugfkuWd7Za5MAbAgGRLA2hlqSMQkAICAEhIASEgBAQAkJACAiBt0eg5iVLgJbWH/Zj4KCp7HzqpmXC2zs22ZMQqBUCxsc7+HJgfwZ+EE1QQHsWXCuplf1IpkJACAgBISAEhIAQEAJCQAgIASHQsAjUqGQxPk9g7Q8LWbRgIYsW/Upipq17R8OCIqUVApURMGacYeMia5z/sJaTaSITK+Ml64SAEBACQkAICAEhIASEgBAQAu8KgXKSJefVK35YsED+hIHEgMSAxIDEgMSAxIDEgMSAxIDEgMSAxIDEgMRAg4uBK5cv15nTKSdZMjMz+WzKFPkTBhIDEgMSA28xBsZ8Mpqhg2OF+VtkLt918l0vMSAxIDEgMSAxIDEgMdA4Y+DM6dP1R7L8+/QpYcEh8icMJAYkBiQG3mIMBGm0aPwDhPlbZC7fdfJdLzEgMSAxIDEgMSAxIDHQOGMgfsuW+iNZiouLuXzpsvwJA4kBiQGJgbcYAwu+n0+n9h2E+VtkLt918l0vMSAxIDEgMSAxIDEgMdA4YyAjI6P+SJY6K4nsWAgIASHwDhP4Y8cOevfo+Q4TkEMXAkJACAgBISAEhIAQEAINn0C5MVka/iHJEQgBISAEGh4BkSwNr86kxEJACAgBISAEhIAQEAJCwJWASBZXIvJZCAgBIVAHBESy1AF02aUQEAJCQAgIASEgBISAEKhhAiJZahioZCcEhIAQqA4BkSzVoSbbCAEhIASEgBAQAkJACAiB+kVAJEv9qg8pjRAQAu8oAZEs72jFy2ELASFQdwSUQl4mJ5F07xm5bxS35VCKsnl69zbJaXkY3KYApfAlD5OSuP8sF4P7bPAknwqyl8VCQAgIASHQwAiIZGlgFSbFFQJCoHESEMnSOOtVjkoICIH6SKCU9GOLGdVOj7aJH6omfqiDOzBh1QWy7JKkmEe7ptIjTGVer2qipfXgxZx+aSw7oNJ0EhYNp6PelsafsPbjWH0hC3s2eJBPWY7yTggIASEgBBoBAZEsjaAS5RCEgBBo+AREsjT8OpQjEAJCoGEQMD7eQGyIhvYTtnDpaTa5L25z6Ls+RPhHMHaXZcrP4n8W0UWjof2keC4/ec6DE8sY1lRFxLCtpJo9i5HHGwYRrn6fSVsu8W92Lhm3D/J9bz2a8NHsybBolqrzaRjMpJRCQAgIASHgOQGRLJ6zkpRCQAgIgVojIJKl1tBKxkJACAgBBwKlPFzeHV3IJ+zJLmtvguEqC9qpCBm1GyjgaFwztNHTOPXatqlCRvxQQlSdWXKrFEqTWdlNTfio3bxyyuZ7OgYEMmZXnmf52LKXVyEgBISAEGg0BESyNJqqlAMRAkKgIRMQydKQa0/KLgSEQMMhUMK1pUPoPXEbKQ49fyi9z/KuagJj48FwiW/bqGk+5QiFDgdmTNvIIJWagRueQ8lVlsf2ZMrWFJyyubeMHioNw+IzPcvHIX95KwSEgBAQAo2DgEiWxlGPchRCQAg0cAIiWRp4BUrxhYAQaMAEFLJPzaSjSkOvpfchfzejtSo+XJREieNRFf9NnF5FzJzzjkvL3ivZnP6qPTp1d5bfK61+PmU5yjshIASEgBBogAREsjTASpMiCwEh0PgIiGRpfHUqRyQEhEADIFD4hKM/jaCtVk3LoWu58RqUl1sYplbT7xfnVioYEpkTrSLqi4RyB1b4+Ag/j4ghWBXFx2tvYOplVJ18ymUsC4SAEBACQqDBERDJ0uCqTAosBIRAYyQgkqUx1qockxAQAvWXQDFP//6Bj1vr0Og7M3nVWdJtczTn7WSURk2XH29R6ngAb47ymV5Fm1nnypYWp3Bk4TDe0wYQ0Wkiq8+kl0317E0+ZTnKOyEgBISAEGjgBESyNPAKlOILASHQOAiIZGkc9ShHIQSEQEMgkM+VJX1oqmpG3xlbuZbp1CkIDOeY00JF1OcJFDkcjpKx2dzCpf+aFMvS/Mss7ROGrmlvZm29ims2HufjsA95KwSEgBAQAg2fgEiWhl+HcgRCQAg0AgIiWRpBJcohCAEh0CAI/Hf6K95Tt2HKn6llrU6cSp7HX+PD0Laew4Vi2wqF7J2jCFO1Z8E1k5Qp4OyMNgS2nsS+VFsTGFta26sn+djSyqsQEAJCQAg0FgIiWRpLTcpxCAEh0KAJiGRp0NUnhRcCQqChEFBe8edoPbq2U9i0bz8H9jv/HUx8bD6S1+fn0kEVSNc5h3icV0jmjd+Y0EKNvt9aHpWC8mov40LVdJjyK/td8jiw/y/+99gyL1FV+TQUbFJOISAEhIAQ8JyASBbPWUlKISAEhECtERDJUmtoJWMhIASEQBkBw2XmxQSgauLn9k/Xb701bQFJ6z/hPa0fKj9/1E0CiPhwOvufWroWGS59Szt/93momqgZuP6ZR/mUFUzeCQEhIASEQGMhIJKlsdSkHIcQEAINmoBIlgZdfVJ4ISAEGikBQ9ZDrp49zfkbqeQZq3+QNZVP9UsgWwoBISAEhMDbIiCS5W2Rlv0IASEgBCohIJKlEjiySggIASEgBISAEBACQkAINBACIlkaSEVJMYWAEGjcBESyNO76laMTAkJACAgBISAEhIAQeDcIiGR5N+pZjlIICIF6TkAkSz2vICmeEBACQkAICAEhIASEgBDwgIBIFg8gSRIhIASEQG0TEMlS24QlfyEgBISAEBACQkAICAEhUPsERLLUPmPZgxAQAkKgSgIiWapEJAmEgBAQAkJACAgBISAEhEC9JyCSpd5XkRRQCAiBd4GASbK0f78tu3ftkj9hIDEgMSAxIDEgMSAxIDEgMSAx4EMMPExOrrNHCJEsdYZediwEhIAQKCNgkizNwsLp3qWr/FWTwXutWtM8PEL4VZOfKfbatGxFZERTYegDw9YtWhLVtJkw9IFhq+gWRDePFIY+MGwZFU2LyChh6APDFs0jMXGU7+Xq35dEN2tO6+gWwtCHOIxs2sz83Sxx6H0c7tu7t+xG+y2/E8nyloHL7oSAEBAC7ghIdyF3VLxbtmXTZgYPGOjdRpLaicCaX1Yz8uPhTsvkg3cEli75mQljx3q3kaR2IvDDggV8NmWK0zL54B2Br+fMYeb0Gd5tJKmdCEyfOpXvvv7GaZl88I7AlImTWPzDIu82ktROBEaPHMWqFSuclsmH+k9AJEv9ryMpoRAQAu8AAZEsvleySBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z1hXeQgkqUuqMs+hYAQEAIuBESyuACpxkeRLNWA5rKJSBYXINX4KJKlGtBcNhHJ4gKkGh9FslQDmssmIllcgFTjo0iWakBz2UQkiwuQBvJRJEsDqSgpphAQAo2bgEgW3+tXJIvvDEWy+M5QJIvvDEWy+M5QJIvvDEWy+M5QJIvvDEWy+M6wLnIQyVIX1GWfQkAICAEXAiJZXIBU46NIlmpAc9lEJIsLkGp8FMlSDWgum4hkcQFSjY8iWaoBzWUTkSwuQKrxUSRLNaC5bCKSxQVIA/kokqWBVJQUUwgIgcZNwCPJouRwbedKVixbXsnfCuLPpmOsLVylKSSsX8nOy9ko1d2H4SUPrt0n0+A+A6Uom6d3b5Oclk+J+yRul4pkcYvFq4UiWbzC5TaxSBa3WLxaKJLFK1xuE4tkcYvFq4UiWbzC5TaxSBa3WLxaKJLFK1z1JrFIlnpTFVIQISAE3mUCHkkWYypbPm5BVLPm1r8wQvz9UGn0Dsui6PdzkmdyouQaGyaNZ/HRLM+FyZsEpjXV0GtZMqXVrLC8vybSXBNLfIaLplHyuL5+PJ1DA1A18UPVJJD3YheSkO6ZMhLJUs0KcdhMJIsDjGq+FclSTXAOm4lkcYBRzbciWaoJzmEzkSwOMKr5ViRLNcE5bCaSxQFGA3orkqUBVZYUVQgIgcZLwCPJ4nr4hgt83TqAkHEHKHZd58nn4kNMDtYwcF2q5y1fqitZlCKyn97hf7sX8VGUCpXaVbIoZOwdRwtVc4YtSSD5ZRapF39lXAs1TUftxNXHuDs8kSzuqHi3TCSLd7zcpRbJ4o6Kd8tEsnjHy11qkSzuqHi3TCSLd7zcpRbJ4o6Kd8tEsnjHq76kFslSX2pCyiEEhMA7TaB2JEsxzy/8wS8L5jL368Vs/Osmr2zNTwqSOb3rW/pr1XScsoF9p5Ot/ItJu7ib9Uvm883MOSxeu5craQ4Kp5qSRcmKZ5ja1DrF+ucqWUpv8lMnNc3H7iXT3sDFyPNdUxk05Gcuvqk6PESyVM2oqhQiWaoiVPV6kSxVM6oqhUiWqghVvV4kS9WMqkohkqUqQlWvF8lSNaOqUohkqYpQ/VwvkqV+1ouUSggIgXeMQI1LFiWbxO+7E67S03HAaCYM70kbnZqWH/3K3SIwpv3BZ11aE+HvT2hUe3rE7QQM3F7VnwhVU7oOn8y0uHEMjglBox/I+nvW0VGqKVnASMmbYoqLC3m0qg86F8lSmrycXupw4g6/htJCXqU9J/O1zQh5FgwiWTzjVFkqkSyV0fFsnUgWzzhVlkokS2V0PFsnksUzTpWlEslSGR3P1olk8YxTZalEslRGp/6uE8lSf+tGSiYEhMA7RKCmJUth4mzeVzXjk60p2MaXLbi6mN66QPqvfmgh69pdyJDIrGgNXRbesG+j5BxkcoSarktuW8ZgqbZksVWmkdR1/cpJlqJDEwlXdWPO6rkMjNJaWrwE6OkyfiWJGTImi41ebb+KZPGdsEgW3xmKZPGdoUgW3xmKZPGdoUgW3xmKZPGdYV3kIJKlLqjLPoWAEBACLgRqVrIUc3JqJLqYb7hsMyzm/eXw55hQdN2WWfbuKlmUXP69dZvUPFt/nRLykrfwSZiaDvOuWgbTrRXJopCz/SMCm6gJDOvKjN9OkfTgAVcPLGJocxVhA9bzyINGLdKSxSWoqvFRJEs1oLlsIpLFBUg1PopkqQY0l01EsrgAqcZHkSzVgOayiUgWFyDV+CiSpRrQ6sEmIlnqQSVIEYSAEBACNSpZlHQ2DdQQOHQb2TZfYkZcQtIPHdHp4yzAXSULCjnXdzB/Qiy92rWgmVaFRqcnXKOqZckCeX8MJ6iJmh4/33OYGclI2qZYQlRdWX6vassiksX380gki+8MRbL4zlAki+8MRbL4zlAki+8MRbL4zlAki+8M6yIHkSx1QV32KQSEgBBwIVCzkiWbHUO16AZsJM2pp42By1/HoIucYdm7i2QxPtnE0FA1LfvPZPWuBC7fSSWn4DLzYjS1LlkMJ6YS5R/G50edmt5guDCHGFULZp1xXu6Cz/xRJIs7Kt4tE8niHS93qUWyuKPi3TKRLN7xcpdaJIs7Kt4tE8niHS93qUWyuKPi3TKRLN7xqi+pRbLUl5qQcggBIfBOE6hRyUIpd376AJ1+LPscm7KUJrOquwb98B0W1k6SRSF76zCC1IPYnObQ/CX/MHHhtd1dCJTMHYwMVNN71WOH6aQVMuKHEqzqwaqH0pLlbZwgIll8pyySxXeGIll8ZyiSxXeGIll8ZyiSxXeGIll8Z1gXOYhkqQvqsk8hIASEgAuBmpUsYHy+g0/C1cSM+ZWrGcWU5KeQsLAfTdVtmXMm37L3N8f4XK+mw/RDPHpZwH8HJ9I0IJqJe55jQKHwWSLrRscQ5B9AzMxEzBM5u47JUnKDXyePYvyS0y5HVNFH9wPfQh4nprVC13QQS0+nU6wYyLq2mfEt1UQM20qqU4sc93lLSxb3XLxZKpLFG1ru04pkcc/Fm6UiWbyh5T6tSBb3XLxZKpLFG1ru04pkcc/Fm6UiWbyhVX/SimSpP3UhJRECQuAdJlDTkgXT+CoXVzL6vWDUTfzMs/XowroxbecDimyclUwOxbUhsIkfut5roOQxOz9tS6hfAEE6LbrANoz48RA7p7dB56el94pkSl0ly5sEpkb4Ezhsuy3XKl4rkixA/nU2jmpDcBM/tAEqVE1UNO81jxMZDi1rKsldJEslcDxcJZLFQ1CVJBPJUgkcD1eJZPEQVCXJRLJUAsfDVSJZPARVSTKRLJXA8XCVSBYPQdWzZCJZ6lmFSHGEgBB4NwlUS7J4gspYQEbyTZJup/DqjbsNSijIyiSnyNZUxEDuszvcuPGAF/nWZUohmU9TyS5yLztK7/7MwEkH3WVejWUlvEq+zOkTp7l0J51C97t0m69IFrdYvFooksUrXG4Ti2Rxi8WrhSJZvMLlNrFIFrdYvFooksUrXG4Ti2Rxi8WrhSJZvMJVbxKLZKk3VSEFEQJC4F0mUGuSpbahGlP4c1Iss46/qu09VZm/SJYqEVWZQCRLlYiqTCCSpUpEVSYQyVIloioTiGSpElGVCUSyVImoygQiWapEVGUCkSxVIqqXCUSy1MtqkUIJASHwrhFouJIlk0cPsqh6WNrar1GRLL4zFsniO0ORLL4zFMniO0ORLL4zFMniO0ORLL4zFMniO8O6yEEkS11Qb+j7VAp4fPI3Fk+fwOjYwQwbPo5p32/gyN1XDrOCNPSDLOXpsfX88vtlXnnRXaGhH7WUv+4INFjJUnfIyu1ZJEs5JF4vEMniNbJyG4hkKYfE6wUiWbxGVm4DkSzlkHi9QCSL18jKbSCSpRwSrxeIZPEaWb3YQCRLvaiGBlSIwlv8NiaGoCYqwtv0ZuS4iUwc2Z+OYWpUqmiGLbtETkOUEiXX2DBpPIuPZmEpvoETXzQjsNtyPJg5tgFVoBS1vhIQyeJ7zYhk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGdZFDiJZ6oJ6Q92n8oqEqW3QBrRg1Npr5NjGyQSU/Pv8MbktQf5NGfNHWsNr0VJ8iMnBGgauS7WXvTAzhSdpORgaan1JuRsUAZEsvleXSBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z2hSBbfGYpk8Z1hXeQgkqUuqDfQfZbeXUZPlYq2M06T5+4YipNY0kWDps0cLhQ7JHidQuL2FSycO5tvF23g6P18a2sRa5oK1ytkXj/M/hP3yXNoHWPM+IfD+05w37Sw6DGJ+w5zPaOAp6e2sPS7ucxbsplTj187FMD0tpi0i7tZv2Q+38ycw+K1e7mSZi1kQTKnd31Lf62ajlM2sO90MmAk49oh9p+8T75938U8v/AHvyyYy9yvF7Pxr5u8chiIwph2mYP7LvBvcTZJ+1az6OuvWbRqL9czS1zKIh+FQHkCIlnKM/F2iW+SxcDL+9d4kFmRVlUoykrh3u0HpOfX4TlteMmDa/epqJgluc+4l3SLRy/yqU4pqy1ZlEJeJieRdO8ZuW/sF03nKlSKyE65x50HaeRVhNl5ixr/pBS+5GFSEvef5WKooJi2nSr5qdy8lkyWw3Xetq6y12pLFk8YVrbjt7SuJC+N5JtJ3H6cSQUTjtlLUl2G1ZYsJXmkPbhJ0q3HZFZVOHsp6+6NJ3w8SePuCHyWLEoBqUnXeOjtCeCuMHW8rLoMG71ksV+T06n8a02hIDWJf5K9H3+tWpLF43LVcWC9pd37JFmUTC5tW8GKZSvZeTnb+fnLWn7l1RV2rlzOipU7uJxdxRdjFces5P3D7pVrOfqoFEpTSFhf8X6ryKrBrxbJ0uCr8G0dQCkPlnZDp+rKsrsV3XEqZGyJJci/NXPPW+6gjWmHmN4+EJ2+PQOHxNK7VTBq3Qd8fy7fXPDK1xtI/KoFgV1+5p7DLg2nphGt7srye6Uo6ZuI1YTT/+M+tIjsTOzQWHq1CEQT1JX5ibn2rj+3V/UnQtWUrsMnMy1uHINjQtDoB7L+XgnGtD/4rEtrIvz9CY1qT4+4nYBLdyElm8TvuxOu0tNxwGgmDO9JG52alh/9yt0iSx0UH55EuKoro0a0oWmrngwb1o8OoQFooz9l/wvfLlpvq5ZlP3VHQCSL7+x9kix5B5kcrmXYloxyBTHdNGwc34Fwfz9UTfxQ6dowbMFxXji05iu3UbkuiOVSVGtB3l8Taa6JJT7D5ZpS8pwj3/anpdpaxiZaWg+cx9Hn3qkW7yVLKenHFjOqnR6tiU0TP9TBHZiw6gJZDkUsfrSb6V3D7WmCogfz46mX9paDbmHUJMPSdBIWDaejXmWpwyb+hLUfx+oLti6iLiVQXnBgQgs0muHszHFZV8VH7yWLJwyLODI5HF2ACq3jn7oVc85WYqxqkmFJCgdn9SbKHmP+6N8bycoL7m/c8ZAHDl8AACAASURBVIGh95KlhKcHZtO3mcZav36og97nk+UXHMZVqwcMHWPHEz6epHHM0+G9b5JFIWP/p7T21zLyd4cToOhv4sLUzjEYoCKw5SwSKwlDajIOvS2DDwy9lywexJi35bfVaU0yRCHvn/V82kGPxnrdDmkdy6Jj6W6vycqL/UyKDiDo499xiAZbySp99U6yVF2uor8n00zlch0M0BAzM7HRtjz3SbKU3ubnzmrzdTFk6FZcbx1A4eX2jwk1xYGqG8vvOzxwVVqz7lcan66lv1rPlMPF8CaBaU019FqWXC8mR3Bf4tpbKpKl9tg2spyLODg+GHX45yS8qfjQSq59T8cADUM3vQAlh8MTm6OLnsj+NOtJW3SL5T0DCRkST0ZpFesVDyWL2g9d2684bbOvRfdYMyCUwE6LuWl6vjAkMitaQ5eFN+wXYCXnIJMj1HRdctty4pfrLuQsWQoTZ/O+qhmfbE2x51FwdTG9dYH0X/3QDMQsWZr402zoJh5ZG8kU/rOQLiotQzalVwxN1ggBQCSL72HgvWQxtU55yt3EPSweEo22iaa8ZFFesG9cJLpmsSw9/oCXWalc2jiGNqowxuwsL2TsR1HummJf4/0b0696T+/wv92L+ChKhUrtKllKebi6L3pVG8avT+RJVjbPr8QT106Hvu8akr24Z/JWshgfbyA2REP7CVu49DSb3Be3OfRdHyL8Ixi7y8qn+B8Wd9YS+N5ktl5K4fn9EyyPbYZW/xHbUisxVTXG0MjjDYMIV7/PpC2X+Dc7l4zbB/m+tx5N+Gj2lLvrLOXfrcOJ9PND9RYki0cMjSms7a2l7bhlxMfHs9X2t3UPl9LfDsMn6weg17QjbutVnuXkknHnIPO6h6CN+oIEy+8mDrHtG0NvJYvx8XoGBWnpMHkb157lkJtxh0Pf9CAsIJqpx6yFq3OGDnjwhI8naRzzdH7vi2QpfbqNURH+qJo4SxbjkzX008YwYelvZTEYH8+23Zcql841di6Dd2XwjaHXksWDGPOu/A51WoMMlRd7mRCpJir2ZxIevCQr9RKbRrdEp/+EXa7Xw9KnbB/eFHUTv1qXLFWXy0jK6j4EtRnH8t8croPxW9l70b0gciDYYN/WhGTR6cMJDxrGNtcffZWX7PgohGYRYWhqWrJQSGbKE9JyKjOwDbZaqiy4SJYqEUkCC4ECdo/UoomeWemvFaV3fqabSk2/1SmQv48JIWo6Lbju0Gxd4dXlHazedJrnuVWsN3oqWdR0//mekyXN3zuWMFV3VpqeLpRc/r11m1R7n6MS8pK38EmYmg7zrlrKVu7Ly1GyFHNyaiS6mG+47HSdyOHPMaHoui0zIzJLloDWfG1txWNe+OYk05up+eCHJAkkIVApgdqRLK95dPpP9iXcc+pyh1JA8qk/2X/mEYXWUpVkJvHX+h+ZN3s285fv4GKaU7BX3uXOlMfrh5zdd5SbWYWkntrMjwtNLcJMPe9yuX9kE0vnzeHr737mt6MPHLrgWZLU1P9eSxYli62xZb98q9xIltKbi+miimDCnsyyZrbG5+yZOoCPllxwX3S3XRCtSYufc/GPlfwwdzbfLd7AoZvZTtcudxkqWfEMs7ce8CsvWUqSWNxRTcSYvQ6/2kPBkTiiVe8z77LnrVm8kyylPFzeHV3IJ+yxSW7TARiusqCdipBRu82HYypHVEALZpws68apvIjnI52aLj/ecnfIUJMMS5NZ2U1N+KjdTnwMV00/CgQyZpdzB1jD/bUMjmjP53F9Cax1yeIZQwxnmBml59MDZQzdg3NYWpMMjc/Z2E9D6PAdTi2Uio59TpSbGPOVoXeSxcjzDf0JDBzOH47Np4qO82VTNe2/vWyBUsMMi59fYOfKBXwz+xt+2nCQW9me20xP+HiSxqG2y72ttmQx3Gf9gKZ0jPuMAWpnyWI4PYNWoeP5y4swrOxcrg5Db8rgK0OvJYsHMeZN+e2VWsm57D3DUm4t7owufCx/ZpY1NzQ+3830/kNZesHxu9/Ag7UDiWwXxxd9NbUsWTwpl4Gz06MJH38Ab0Lw9cMz7PszgXv25wATWYWCB6fYt+8sj8puhEg6sJ4l38xm7rzl/HEhzf7DqqUuKhl6wJzAdM+1j2NJWRSmnmLLoh/Ydcf0/Wsk994RNi+Zx9dz5rF08zGSy8YisFdzRW9qQrKEDv+cT5sG8VH8i7J7GROFjG0MD44i7vOPCHKVLCWZVfCAkowr7Fk5n2/mLmTt/ptkP3ZoyWJ8wT8H93HKcZiI4jQu7VrL0vlzmTNnEev3XsY2eoPp+BvT0AsiWSqKaFnuQsDAmelRaAI/YU+uyyqHj4aTU4n2D2LcnnxK7y2lu0rL6F0FDinK3la13tRlx6PuQuoQJh9yHAQGDBfm8J6qFXPOmb4sFHKu72D+hFh6tWtBM60KjU5PuEblmWQpSWfTQA2BQ7fh+BwBJST90BGdPs58UGbJou7NmicOvyoazjO3lYYPFopkKat5eeeOQO1IlhKuz2+PLvBjfne4mVKydzM2xCL/TF///11fyZAIFUFNOzAwdjA9owPRhA1izU1rXzgMVNblznQ8xtR1DNS2ZsL0EbQMUBHS6iswPmfv+BboQtsyePQEJgztSgutjs5zTpNbdm/nDke1lnktWUzlLjFQXFxM4cNf6K9ybclSysNl3QkMm8zfr6G0MJu055m8ruJZyn0XRFCyz7Kgm97cfXLwJ2MZ1aMlIaooRmy8g420+wM3UvKmmOLiQh6t6oPOtSWLWRKbWubdcRI2pcnL6a1SM2hDmvts3Sz1TrKUcG3pEHpP3EaKw2WP0vss76omMDbe3PXy0tw26MKncNR2I2varzGNX/ur0fXb4KYUptXuunFWk2HJVZbH9mTK1hSnpvCl95bRw1Tn8ZllZSi8ztLuYXSdf4m0HcMJqnXJ4glDUDLjGab7gCU3C8h8mMT1O8/IdXwWKjsC+7saZWjq1799DVvPOv5arPByx3DC1H1Y6/i9VwMMvZMsCpkXd7A2PtGpNYXycgejgjX0N/3oY7oTqDGGCq/OfE+vUDUR7QYyduxwercIRBf5MZvuVH4mmwviCR9P0thr2v2b6kmWQq4v6UHTD+ZzKW0HIzWOkkUhM34IIZ1/5FZ+Jo9uXOeuaWwj97u3L3Ufh9Vl6EUZaoCht5Kl6hjzovx2ghVdD6vJsDSZFd00NJt8mNeUUpidxvNM07vy/wqvL6G3/kMWXkzjj4+1tStZPCmXksnW2EC6/HiTgsyHJF2/w7OqLoRAyT/z6agKYsR2hx9LlGz2fBKKruMikkw3QgXX+WVQU7SaZnTuN4Sh3VoQ4h9O7C83rd/PVd8HYUxlQ18tMWNnMCpShVbXmlknini+ezytVXo6DBjDxHHD6BGpJbjDHM54eCNUE5IlaOwO/oprTmjsb5Q1ZlHI2DqMsKipHNo6ylmyVMkDDA82M7y5Cl1EBwYNHULv1hF0GDaEjqoKugu9ucXqfnp0ER8yavKXfDFuIO2CA4gYsJb71t+CGtPQCyJZyl9TZIlbAgo5u8cQ7t+UyX9VZFmKuTg3Bq2mHxueGim9+7NZsozc4b4HZ1XrK5IsxUemEKFyHJNFz2dHnL/mDcc+p7mqI4tulGB8somhoWpa9p/J6l0JXL6TSk7BZebFaDyULNnsGKpFN2AjaY4PEhi4/HUMusgZZmIWydKHdU8dEolkcRtNsrA8gdqRLFB6+ye6qIIZtcM29oTpXB5tHj9oqWl8pZJbLO2iRd9rMVds868X3mZV32DCYn/juSmcPehyZ5YsKj+0LUbz240c8w2b6SG/l6Y1s07bfnNSeLH1Y8J1H7HDQfqUp1G9JdWRLLY9WcrvKlmKOPypHl232ayd259WWsuYI1r9B0xcmUiGw6luy8f+Wq51XCH/m9UGXdORbH9iu14VcG1xD0J0fVjr0VzxRlLX9SsvWQzn+bqVmlbTTznJmtfHv6SFn4oPF920F6uqN95JFne5KWSfmklHlYZeS+8D+ewZqUXb0dp9075JMUcm6dG2mmNfUu5NrTC07kXJ5vRX7dGpu5vH97Iszef8N52I6L6EG0UKOb+/DclS7qjNPww4M4SSq/PooIqkg8PYNxp9V77Y9A+209ZdTtQGw9f3Sfh9Kxt/mEi38OYM+OGsww8QNcPQO8nieOSveXD8d7atX8TkDyOI6ruIRGvrlhpjWJjInDZqokZuI8V2Kudf5afugYT0WYNpvMeK/3nCx5M0Fe/BtqY6kiX/3Ld8qO/B0utFKDm/u0iWEq7Na4eueTuHsY0CaNrlM7b8k+P067itDPZX1zisNkNPy1AzDL2VLFXHmKflt5Mre1NTDIsOMTlUTc9Za/imXzRB5jFZVER0nsAvZzPKRHT+OeZ11NN7yQ2KlBx21rZk8aRcJVeZ31ZNdNv2RARYxx/zD6N73GauV3YhtI5Londoiae82mP+san7ElNL+BJuL+lKUFBvfrpsi+VC7qzoiz54CPGmGyEP7oMskkWNKqAlYzcnkWO6FpjlkZaYGWfsrW+U9G2MCAlk+DYH6VNW0+Xe1YhkGb2XrKOfERUYS3y69Vcu5QXbhgbTatoJXu4c4SBZPOChvGTnSL15uIZTtl+gi++zdkAoqibuJYvh7Fe0Vndm0XXbhVMh56+JRKo+ZOlty4XT/CzVSIZeEMlSLpRlQYUE8k8ys1UAgR3nkujmYvb6xjL6BQXQfMwey8BK+QeZpFfRevoZytqZGLj0XQea9ljG3f+rYn2pgfOzWqJrO49rVsNpaj1iauaodZQsajVdFt90+DXF2sIkfDJ/Fyhkbx1GkHoQm9McfjrPP0xcuKfdhUq589MH6PRj2We7kJgglSazqrsG00Xb9E8kS4WRIys8IFBbksUUpyu7awgb+YflQUh5xd7RegJ7rDQ/DJQkLeZDVTRfnXL+BTZv7xj02o/ZYXpA8aDLnUVSqOm/5qn9Jq30wXJ6qULoPXsPV1LzLb+UKSUY3hgodTgdPcDjUZIalyzKK34fpkGl0tCsyzTiTyWR/OAKf/0wmOiAEAavf1RxuVxviItPML25mnZfX3a4VgE5exkfoqanWUhUnJ1lTQWShSIufdMOnfZDZu69SUZ+ARk3djK1vRa1n8rcZbOqnG3rfZIshU84+tMI2mrVtBy6lhsmt6a8JD5Wg673aufWLhj438wWaJt+adt1+ddaYQiFj4/w84gYglVRfLz2hvXGVyH72FTa6fuy+o7lBrBOJIs7hqbWZn+OJdQ/ggHf7eVayksyHiUSH9eREP+mjNtVyZhftcBQydjDlx+0JyZMi1oTzcC5f5FiHqut5hhWW7IoGez97EM6tgonyE9Lq35fc9AqNWuKYfGJqUSr3uO7S7YHBUvo5uwZS7hrc3unqPaEjydpnDKt8IO3kkXJOs70GD0DVt2xXKPKSZYC9o0JQhPel+/3XuXpywweJW7h846BaCLGsMf24OauRC5xWH2GnpSh5hh6K1mqjjFPyu8OoOkm8xCTgzUMXJdq/o6tLkPl1Q6Gm8YyVIfTfdpvnL7xgOQrB1g8OBJt8EA2miyhkk3C1PeI6LuKu+Zzu/Yli0flKviT8YEBNO87j31XU3iZ8Yj/bYnjA10AkaN3O7TQcGVo6pbZg8Dgkew0S1frD8fqnvxi+oGjJIkfO5b/oYK8vYwL0jFiW5ZH90E2yaLrs5Z/bT/ClD5gRTc1Yd1ns/dyKvlml6BQ8saAwcMboZqRLH9SWHCUL5oFMWxLulmKmrvtBkUz42Qh+Y6SxRMeBfuZEKymx9L7Tq2gck3DNfi5lyxK7lNu304t675eksfDLSNppmrH/KuWBz3zs1QjGXpBJIvreSifKyGgkHl8Bh01/oR3nMiqv67wKD2TjJQbHN8wnd7hppl0xrEn1fYzzmvOz22LLqQn84895XVpIanH59E7WEuXxUkYqGq9ZYCrQFV7Zhx+RrGxiLQzSxgSqSPQSbL4oQruwXd/P6LgTR4pxxbSX6+h47cXzb/q/ndwIk0Dopm45zkGFAqfJbJudAxB/gHm0cjNAujNMT7Xq+kw/RCPXpq6NzmOyWLq9bCDT8LVxIz5lasZxZTkp5CwsB9N1W2Zc8YyqJ5IlkpCR1ZVSaDWJAtGnq7tR1DIKHZnKyiv9jI+VMvAdZauE0UHxhHSREvz1u3o0Nbhr4UeXUB7Fv5j+uKrusudWbKoQ5h0sEypmm7UEhcPprW5BYiaiDa9GD19OQdu22b+qhKLVwlqXLKQx87hWlTmWdXsptfc1WVLbCC6rpbxmNwW0uWGWEn7lVi1ho+3uszEYh1PpWncUbfZOC+sSLKYxsS5yW/j2xNmGqzVPEtABL2nfkms9abcOZ+KP1VPshTz9O8f+Li1Do2+M5NXnSXd/vyZx64RWnSdf8T6Q5V15waOTdGjbTG74sLUNMPiFI4sHMZ72gAiOk1k9Zl0u/BSXuxjYnQYQzc8tI8h9nYlS2UMTe3dC8nLfW0vmxnam+ss6qgmaOCvb4+h456MuVxf95G5i/CwTc+oSYbVliz28hnJvb6O4c0DCI3dbFlaIwwV0jcOIlA9jO2O47+YqujGIj5QhfH5UXvw20tjeuMJH0/SOGVayQevJIvygv3jW9B08AYe2S515SSLKQzzyH1tS2DZueH6Qj5UaYndWEm3RKdzufoMTXusqgw1ydBbyeLJeVpV+Sus0ppimPcHozR+6Lou5Z5DVRrTNjNMZ3povkvGvgm01g9m40NbgtqXLFRZLlPLyBIK83JxDkED1xd0QqcZxCbn5uZOKI0paxmoDWX0rmwU5RV/jtET1Hcd5sbnRQeYoPMjKLyN831Q25ZEqFR0nP+PR/dBFsmiIWzCIYcflxWyzyxmSLTW/N2s08fQZ+QMVuy/7XG36ZqSLEW85vhnkYTEbiZdUXjx2xD0UdMw/cbmJFk84GF8voFBKlN3QufeDaW3fqRLRd2FlFdc3z6PSbHd6RgdTnBAACF6PUEBLpKlkQy9IJLF6RSUD1UTKCXj3C9M+rAZgdZp3yw39Ho6jVzM8VSXm4vXd9g+pTMR/n6o/fxR+QXTcfxv3LH1za9ivfIqkR/7RqIzTQvq54+u+UB+2jCTjlqH7kJqDX3GT6ZzkCl/P1T+oXSa4LCPksfs/LQtoX4BBOm06ALbMOLHQ+yc3gadn5beK5IpVTI5FNfGfEy63mvKSRbzQ+bFlYx+L9g8wrrpmHVh3Zi284G9eb5IlqqjR1JUTKD2JIvJCZhunvSM3Z3Nq71jiQgcQry1ZVfR/nGE+LdgwvqjJBxPcP5LOEdyjuJRlzubZHEdH8l0xKW5KVw5sp1Vc8fTJ1qLOrAPq32cJtAdyZqXLAZOTm2ORh/HMadZ1QxcnNMaXfRX7ophWeZ0QwxK1nY+1qgZvCHN3tLHnNBwme/aqGk5/XTFednXVCJZzGkUirNTuH39DukFRkrMNztRzDztcl2251f+jfeSJZ8rS/rQVNWMvjO2ci3TdlNuy9vSIlHb7AsSHBtLKRn8Zmrh0metLWH515pkmH+ZpX3C0DXtzaytV3EtpuH4F0T6qQkO0RMeav3Tmaa9VBMSoidq0l/2QaLLF9R5ifdTOFfF0Dn/sk9FHBgXiLb9wrJFru9qimH+TQ6s38CRBy6xZO6qpiJiylFqkqF3kqWAW/vXs/HvB3ZpZsFg4MKc1mj1U1ypOHz2lqFC9rZhBKkG8qvLA53h0je0VVV8vnnC5/9qMA69kixvEpga4Y9OG1oW/yGB5nsvnS6U8GaTOWi7b3OgZ35btJ8JuipazDnFYfUZuu7a/tmhDJ5wruhQ7PlZ33gtWVwzMH/2IMYcyu82C9PCmmL45gTTmwVgEvtOZ7PhgrnbaesZJ0j4vClqla4sFkJDCVGZpvgNJDy0OXEHPCUIHk/hXGW5zlaIxnwfE9CBRdddv38cNjGm8VtsIOGf7CE7+//bOxOvJq54Af8pAlkRArgrWNeuLrVVqdR9V6q1rdXautRa69Jqq61L1Yraui+4ti5VrK+tggsoWMW6FEU5AvJQFJA8kvO9kwVMIkjIMCSBH+fkkJnM3Lnz3V/u3Hxz753dTDCFM2Sjo0cHdv5hxI5fwxHPdtDRY/x1tcirdlC1ZEl0lSzOPFQWczP1N7Ysn0Ni3w6Eh4TT/wf3XiAuuXV723CSBZ4c/pD2hkFsuJ3LpoTw6mHG7pLF9p1+MQ+KtjJKb5vz7Y5bm8Yx32ZNPVks3PxpENGadrwzYwU7j6ZyOaeIx2fm0k3rKVmaxtQLIlncwlgWvCdQQdGNDFJPHufk6XT+LXC5e/1cIlbK7l8j/ex5/sktcfsyOjat6/My8v/N4EL6NfJdG+k2r3x3PQlaAxP3llH58DaZ59O5eremY1RQfDuL9PSr3Hvk7MNnLSX/Vg6FZVXjFsyUFORTVFbVx++5EwFLCXnZF8nIvMkDtx9dNWwrq4RAPQioKVlsQza2DosgZkwSG8aaiBm1o/oJIZVZS+iliWT0tqo5WxyZfvTnSqZNXcXpR94NuXtesli5/+t8JkxJ4pJLu8dyew3xGj0jttQ8V1M9kD23acNLFiv5m4cTqenLyusu9YI1j5/fNWDovfy5PFSvcGsQ20xTJktf0dJqnPsTgCqzl9NPa5szx2Xy1epEPN/UIlksN9k5YySTfnR9kpuZi4textDqfX577vG6nuk+W66vZHmcMoOXtHG8vyfHvcH+LEke7ptAq7A45vz17DphLdjOmJZaen55zmVLj7cNxrCEPz6Jwxg7iWTPGwHOQ1oLr/BXyglSTjx7HfqiLwZNX748cIJTl+7XcO3yyK9zsb6SpU6G1kIOTH+TftOSuV91ubIdy9kLKmKoY8hqjblpKIZPDjLJpOX1bzLduobz6Fc+aKWl86y/aEiG9ZMsTzg00YThlW/IqupIa4dRwm/vt8Zgm/enARk65rmKYuJu115plVxb3gdj+HC21TLflDd8/q8B47BeksVayJU/U9ziP+XAPPvE2f2+OEjKyUvk3d/Pp3378knyfbf5V2w9eF7TGBmxuaDGELSv9IhDnxkW1J0Hbzi71Oa15xmol2TxIsasXuS/1gw1EEOs+WwdFo6hzw+4X9Y2MVSvpd/yqzz4509OutSFKScO8mUfLYY+8zh04g8y73tLsB6SxYt8Fe77hLf6TGOve0VIxsKXMRiGsdWjd5k7Syv3Nw8nyjSWDevHEWMazc6q7Suz+PY1LdEjt1W3jez7PvqTVVM/ZvXfD72besA+8a2OKBfJYr3/KwvGfsCGi24NIdb11xI+dAvetIQaUrLw5AhT2xgZtGAR7xo7VA8Vd5MsdfIosbdpvn1VS+sJe13mBbOSv30U0TUNF7IWsnmwHmP8elxHFpYcmkwbz+FCWpEs7rErS0LADwSeSZZnjXc/ZEMOKQQUE1BVsmDlwc6xxBijaW2MYdI+l+6dlptsTDBhiBtH0pm7PK4oJS8ticQ4He3G7LTPr1TTkLvVo+Pchtw9L1mgLOUTYjVtGLEylbwyK9an9zm7chjttHXccfKRZsNLFuDhMT7pqKVd/BJO5pZjfVrA+aRxdNaYGP5zTu05dQ5B7F49BNHCnS0jaaPpxIR1aeSVmym5cYxFb0dh7DKTU16JkFokC09Imd4BQ9sxbLY/i7KSB38vId5kpM8S29BM7//qJVlsXa7HmDB0m8L65L3s2+v+OnDquuPAT/5ibhctEa/O4tD1h5TmZ7BpfEcM4QNY+6JZQhuIoW2I3PhILT2n/MRejzzu27uf/7le813ZRhku5BXDSvtTpVpquzBlaxbFFrCW3eb4V28RE9aOKQcKay/gBmJokxQHE9ugbzuc1WfyqMBC2b00NiV2xajtyYK0mq/BvjKsn2Sx8mD/e7QLa8tIW13zFCxl9zi78T16aHW8Oj/NZjkbjqHlDttGxmDoOI71aXmUm0u4eXQh8SYdPT77A6++ys4S84aPN9vUFAD1kiw1JeA5XKjyGiv7GTC+NJntWcVYsFJ2+3cW9jOhbzuZQ1U/WGtKyzMOfWXoYx58ZVgvyeJNjPmYfzvShmJov6xNo7OmNQlLUrhbbqWi4Bwbx3bCYBrKLzk1CZRGGC7kRb4qr62gv15H98lbueyoCLn9+1f0N2noOOmgywTcNQWhzbXuYrwpnNZR4bSZuJ9nLSELt5LeJUYTx8SfUrlbUkHpvTQ2TuiMsdVYdt2z4k07qMaeLGUn+bS9lnZDV5F6rwyrtYL7aasY0Vpnny/NRb3UnGmgQSULTzj2YVsMGi2G9tNJcd68dpMstmHmdfCwPZb6vw3vEqPrwpTNGRSUlZN3ejWjO+rQ1yRZKOFQYhT6dhPZc6cCrKXcPrWKcXF6dKGdmHXKcQ1pSqMCpCdLrSEtHwQDAZEswVBKkkdvCKgrWWytl/1MigpF23Yqx6oe9uPMmCXvOIsHdXA8ZcA+p4ee2ISl/FXVaPZiyF2F7RHOno9Tt+ZzctEg4mxzsoSEoQsJcczZsfY8j1zvyHsDyIttVJEs9ic7rmFsnAFNCw162xMNwmJ4+4tjjgm+a8vXc0MQbV3vHnDm+1F0tw1ttA+31NK618fsuOrRRa+2NKlNstiGI6Ww6O3WGDThtImJwBBqotfHu7leH8MC1EuyVNie0hbmPBfnXDAuw0gN/ddUn0lJ+hrGxtoYhqK1x8HrzEi+5T7HSPXWzjcNxLDC1h059Pn8VZVB/Jrbnke2L/v6w6xePVm8Zfg0m+0fvkFrTQg6fQSRmhC04V0ZtzL1xeP6G4ihDYj1/km+TnDUEzqNFl2LEPTRvZjuMmzWE6SvDOsnWeyZ449FCY75n0K1GGzlHRZD34+3k1319WpAhtYHp1kxsgum6jmQouk71eVYniBqWfaGjzfb1JR8g0sW20Dq7G18/Ho0hhZhhLcMx9AiFFOXMaxOrWOerRri0FeGvuTBV4b10QtG1QAAIABJREFUkyyAFzHmS/7t5duADKGEC2tG08V2bQ7ToLd/l/vx5bE8t15Kz+KqcSRL3fmq4Nq2j+gdpUUTqifKqLVPQ9Bj9CrSvHoc8kMOTIxGG9KOaUefawhxYuEgYnW2a4XjGh3ePoFv/3T2WPOiHVRZQ08W23QD+SmLGdzBNidLKLrQUDShJl5PXMcFLxtCDStZ4MmRj2gfEkasyxMJ3SULYMl7MQ9bcFjucvTzt2hnG0pmu/ZrOzDyu3kM0dU0XAjM17cxuUs42lA9EXotkXHDWHJwGzNitWh1ffkhu7JJPUREJMuzGkTeBSOB8pucOfQr5+/WZN6D8YQkz82VgOqSxZzOou46us0+XUvPBjPFOZmcTzvH5ZziGn78ejPkrubSs5Tc41rGOS5k3qSwVL3vqhLJUnPOXdaaC7l25gTHU05z+W5pLQ1Rl+3tb2segmjncTGDrBuFtZSFZzreLpeRl3Wakyl/k3nH+TQnb3d1blcvyVLPtHlawL9ppzj5ZwY5D72Ng8ZmWN+Ten77ekmW53d/4ZrSe/+QmnKcP05f4k6JPxiaKbqRzt8njnMq7Sr5NXdgeeE5ePNhvSWLM1Hzg5uk/3WCEyfTyK4lcw3H0MLje9lcSs/kZmE9baY3EBRuo1iy1Hr8UvIup/LH8ZOcuXSHx96GITV9l31l6Gseaj2pGj+ot2RxplJ3jPma/4ZkCObCbFJP/M7J01ncK1Xhzgf1GC7kUgJ15qv0Hv+cOcmJlDNk3qlpigCXxNzemkn/qgfGznM4U8uQf3NxDlnn0jiflUPxc91MfG8HOaYbyOD8+UxuFZR6PfzUln1FksXt/Ou/8GIejvQqCm+QmX6FXMejk158kIpibmelk3H1LlWXMGtpPv/lFFI9e8OLUwiaT0WyBE1RSUaFgBBoygTUlSxWig5PJU7fh+UqTDgbKOWiqmQJlJNUOR+qShaV8x4oyaspWQLlHNXOh6+SRe18BVP66kmWYKKgLK++ShZlR21ae3s98W0jnLa16AjTOhjo9513E842Qpa8OoQ/JYtXGZSNaiQgkqVGLLJSCAgBIdC4BNSSLNb8rSTGdaSNXkuX6cd5qM7NqsaFVcvRRLLUAqYeq0Wy1ANWLZuKZKkFTD1Wi2SpB6xaNhXJUguYeqwWyVIPWLVsGhCSxZrP9gmdiYvWY4j7hBNB1hASyVJLcAX4apEsAV5Akj0hIASaBwG1JAtPb3Di5yR+2XeBfLcnbzQ9riJZlJepSBblDEWyKGcokkU5Q5EsyhmKZFHOMCAkCxXcOP4LG37ez4UgbAiJZFEeh/5IQSSLP6jLMYWAEBACHgRUkywex2nKiyJZlJeuSBblDEWyKGcokkU5Q5EsyhmKZFHOMDAki/Lz8GcKIln8Sd/3Y4tk8Z2d7CkEhIAQaDACTUuyWHl4fgcrVv3Gi57Q22DwnAmJZFFOVCSLcoYiWZQzFMminKFIFuUMRbIoZxgQksX6kAs7vufH3/5VfkJ+SEEkix+gN8AhRbI0AERJQggIASGglEDTkiwW/lv1FsbISfyq0tNHauItkqUmKvVbJ5Klfrxq2lokS01U6rdOJEv9eNW0tUiWmqjUb51IlvrxqmnrgJAsllv8+JaO6PcO1ZTFgF8nkiXgi6jGDIpkqRGLrBQCQkAINC4BkSzKeYtkUc5QJItyhiJZlDMUyaKcoUgW5QxFsihnKJJFOUORLMoZ+iMFkSz+oC7HFAJCQAh4EPCXZCnPPc2u1UtZMPsz5ixcTXJqLtWdTyx3SduXzOlb5RSmJ7Pqq8+Z+9UKki/kY3bLv5m8tJ2smD+buV+tYt/FQm40mZ4sT/g3ZQ/Jx/5xfzKTtYTsE3vYe/JfSp0szPkZ7F/zNfNmzmT+si2czq1wo0R5Lmd2rObb+bOZNWsRa3anklsNG3hyjT+SD3OxoJScE0l8vWC7Y39LMVd+W8+382Yx54ulbDx8lUcqPSVKHcnSvBiqI1maF0N1JEvzYqiKZLHVUXv28Pvlh7hWQdZHV0nZk8ypf6trQ/LT97Hu67nMnjmP77f8Te5T9+owGOpDVSRLADC0FF/h8E9LmT9rFl8uSeLo1Udu5elRUooW6yVZ6rhGWnJTOZD8N/+VF5KRvJJFc+aw6IfdXMh3b5FgzuPsju9ZMHsOi1bu5VLBDS97spRz5+9trPhqNrPnLGbd/os8cD4wwPrwCsf37OHEFQ9W5Tc4lbyHE1dLnJzML4z9J9dOsvdwBgWlOaSsX8yi7Vl18vWPZCkn9/RO1iyZz+efzmLx6t2kuTVYAMsDMvevZvHs2Xy1bBup90r57+9kDqTedTmnF/Nw2bDJvRXJ0uSKVE5ICAiBYCTgD8lScWkF8SYtbV8dzpSPPmDiwJeICjUxaPUVh0QpP8T7kVp6jxhBF1Ms/RKGEt/FhC6sA5OS7zkbZRVkrx9K+zAtbXsMZFhCX16K7sbwhO4YmsRwITMX5nfHYBzG1vxnPyushTsZF6Hl1YUZdlaPL3zPuzEawlv1ID5hEH3bG9FFvcOqi2WOcHx6iZX9TRhiXmPU5Kl8ND6ebi3DiBm4mivO9qEl50fi9bFMnD6CjmEaIjrNAMsddk/ogCGyK4PGTGTi4DfooDfwyqwUip9lp8FCXh3J0rwYqiNZmhdDdSRL82KoimQxn2dBNy2RQ7fwrDq08mDHGKI1PVmcbqvMSrjw/SDahulp12MAQwb1ItYQRuv4lVxyVocESX2oimTxM0PLnV0kttcS3SWe8RPGM+yNtoTrezAnpVgV0eK1ZPEiJsoPTSJa8wajRsTRqlNfhgzpT4/IMPTtE9l7z3lBrLjKhiFt0Wta8cqAIQzpE0ebrkMZ0k374uFC1kJOfdmbaI2JngPHMHF4X+IMWjoO/YnLtrgtP8WsThqiRu7ggcu1t+TQZNpquvJlmu2mSl2xbyHnx/6EdxrHpyPaoQ8z0vmT3+u8fje+ZKkg84cBxGha8cbwyUybMp5BnSPQmeJZ809Vg+UOye/FYQwz0eOtwQx9uxttOw1h+GuunOviUeepB/UGIlmCuvgk80JACDQVAo0vWSo49UkHjC8vJL3qDqO1iIOJrTC8uoQs290bu2QJQRs1hKRrzi4XpedZ9LKW8HeSsLVprPe3MTpSS49Pjlc3PMr/WcWgiBA0TUKyQGXmN7yuacmoLQXORqiVop22HxVv8O3lSjBf4tvX9ZjeXExakbP1VZrJD2+3JCphI3csUPHHDGK1r7DoQlXvFitF+9+jreY1vs103CqzSxZNCPoOY9iYXoRtbWX2Mt7UxfJZyhNnqFu59/Mwog1D2fLsV06DfQ3UkSzNi6E6kqV5MVRHsjQvhqpIFirJ+uZVDOHD2VbgrOusD9g9OhLD60v5x14dLqW3riVvL07lWXW4nIHhEQzZeMdeVwVLfaiKZPErw0qufdeb8E4z+KP6knKXzUMjiRiy2UWcNdglBW8lizcxYZcsLUJpPXg9/1Y3SRbwukbPu+ttvSes3N82kmhNNz47Xui8XpdzZWU80S1CXihZSk/NpIumNaN/vknVVbrk7GL6GYwMWHkNqODsF10xRI4juSqweciBCdHVbSbzpbpi3yFZDC00xI1OIqPI2U2mDtyNLlkqTvFZex2vL0ivZmEtOsDkGC1vLMm0t01KDn9IB017EnfftS9DJTlbx9Au5BnnunnUceJB/rFIliAvQMm+EBACTYNA40sWK8W3LpGV86zbt/lhNptGRmPoOo9ztpsVdsmiofOsv6ovtLaGxomP22DouYiLZni8dzxRmjdYZmtdV/8Vkzw2Am0TkSxUZvN9bx1RI7dRaPtdYftRMcaEsc/39qcnmTMW85qmPTNOVN2mdYB4uHssJv0wthRYsRbfIjMz59mQI/NDrm0YSWtNN+afddwZckgWLQNW3cLiZFl5dRlvaiLoN3MXaTmPHI0Zq5mKpxVUutxNq0av8I1akqU5MVRLsjQnhmpJlubEUB3J4hC//bQRjN7m+BFrfbCL8ZE6+n1/jUrMXFz0MoZ200lxqw4fkjymJS2HbrbXUMFSH6ojWfzJsJLs73pjaNmH2TtTyXnkuG5bzRVUVFT6tSeLNzFhlyxhscz5q0qDAE+PM721o1eprSfJvvERGN74jituTZLdTGgZ+gLJUs7xj9ti6Pw5qS5JQxF7xkZi6PWdPW4d13oTE/cWO+K4aA8TTXoGrLyBxavYd0oWzVv8eKvqKl/3RbvRJYu1mP8uZZLzsKqRYeZh9gZGR2npMe8sZio4Nb09htjZnHblVX6CT9qGOTl7VxfUffbBu4VIluAtO8m5EBACTYhA40sWmyu4wNZ5iQzp1Z3YKAP6UCMxkXr0bpJFx9srblb/6LdJlr9nxmLsuZAMs4U7awdi0A1nu6PN4SyRSjK/frWJDBeynZKFW6v7Ex4xip2FVqwPdjMhUk/8jw4uZfvGE9FCT5vYbvTo6vLqYMIQ1p0F5812MXNh8zwmJfSmZ/toWoaFEWEyER7mIVm0EUw64DJRi60L8+JBxOpD0LTQEhP3JmOmL2Nfpjpdu1WTLM2IoWqSpRkxVE2yNCOGakkWLDdZ87aeaPuwCStFu8YRre/P2pu2H41l7B9vRKOL4SXXurBrN+JMWvTd5juuEdYHBEN9qJZk8SdDa8EffPNOB8JbhKDRmOjadxSffreXLDXGn4LXPVlsNy/qigm7ZNH2Y9UNF0FR8RezO+l4dUGGfXjtugFawodtxb1Jcoklr7gOY/FoPFrvsj5eh3HwL44bKdUfm8lY2BODaYpjTeUVlvXS0TrxALaZWezD5MIHs+muTUZ4E/sOyWJsmcghl8t89eFqedPokgUrRRe2MH9iAm9260BrvQadwUS0TuOULE/YM1qP4c2V2L/2Vfm25PLTwKqnOHnDo2rHpvlfJEvTLFc5KyEgBIKMQKNLFstNkgaZMLQdyMwfdnDsTBY5RSWkfv4SRg/J4tqzwl2yQNHW4YRrBrLONiam+q+SK0vfaEKSBSy5SQwxmBi3s5AHu8cRY3yXTbmOuzxle8cTEdqBiWsOc+zoMffXsT/JLvo/bv40iGhNO96ZsYKdR1O5nFPE4zNz6aZ9XrJMPvh866uy+CZpv23mh9kTeKu9Hq3xLVa63aqrhq/ojXqSpfkwVE+yNB+G6kmW5sNQNcmChdykBCIix7C7oJDksSYiEzZi/51JGfvGG9G1H8+6wx514dFj/P7nVbu0Dpb6UDXJ4neGlRTfSOXwL8uZO+FNYvWhRPb7gauuvT8UXUme7ezdcCGLV9dIh2Tx6AXiKlkoYvtwPYYBa+3DdKtzUXmF715/kWQpZMtgPYaB68h1bcpQQeqczhjafuJMysKNlW8RHjOZ3x7ls31EJDFjdjmHSnsX+7Y5WYwt3wtoyWK5sZ7BkVo6DviUlTuOkZqVQ1FJKvM666p7shyZEoXh5cVcck7RYgdkzWNjgt7Zk8UbHtUl1CTfiGRpksUqJyUEhECwEWhsyWIt+IVhOh0JP9116SJcwq+TYzyGC+nchq94SpbKzCW8oYkmcW/Rs3Ss+ewYaWo6w4VswWS9z9ZhEcSMSWLDWBMxo3ZQNSVBZdYSemkiGb2tas4WR/Q9+nMl06au4nRxIZsH6zHGr3f+EHF8bpswr43ncCFtBM8ki5X7v85nwpQkt4aM5fYa4jV6RmwpavAwV1OyNBeGakqW5sJQTcnSXBiqJ1ls1eEWRrY0MX59EhNMJsbuqKr7Krm85HUMESPZXlVB2mupR/y14mOmrfwbrMFTH6onWfzE0Hqf3+aP46P1F12eEGjh9poBGHTD2NbwlxTverJ4GRN1S5ZKspa8hiFqAvuq500Ba/52xkS+aLiQc64h0ziS7WOCnZfWymx+6K3DNHxL9bXWcmc97xrb8OHPaxjWsg0f/PqoauO6Y5+qniyBLFmsFP48hHDtOyQ5byQ5vsKHmBJdNVzIQu66dwg3JrDR1Uo9OsLH7aqGC3lRF1RTbZpvRLI0zXKVsxICQiDICDS2ZKHkIJNNGjpN2M2dCrCW3uZ/Vo2lqy4MXcfP+B9bZwr7nCwvlixYbrEpwUT4S5PZmlFAWXkeZ1aPpLNW07Qki61r8M6xxBijaW2MYdI+l87IlptsTDBhiBtH0pm7PK4oJS8ticQ4He3G7CTPWsKhxCj07SayxwGb26dWMz5Ojy60E7NOOXqu2OdkcZMsUJbyCbGaNoxYmUpemRXr0/ucXTmMdtoeLLrgegupYQJeVcnSTBiqKlmaCUNVJUszYaimZLGJEtu8VJFR0URGJ3LArTpMYohJS5dx60nNLaGi9B5nkybQVdua8Tvu2Z/AEiz1oZqSxT8My/hjegcMrYex+sw9yqxWKu6nsXpoG4zdF6DCJcU7yYJ318i6JQtYbm1giElP98lbuFhQRnneGdaM7IQx7EWSxTbSaAujo7V0HvsTZ/PKMT+6ybEF/Wml7cqsk1UixXbDJY8tQyOIiY4mvOMMTrnMPWS5WVfsB4NkgccH3qNVWHve23WHCqyU3j7Fj2M6Ex4aRudPT2FrsVgL9jKpjZbYkT+SevcJ5YUZbJvcFYPLBMN182iYdkugpiKSJVBLRvIlBIRAsyLQ6JIFM9e3vUcPYyg6nZFwTThdhn7NoW3TeUkTSnif5Vx7bHuEcx2Sxdb5O/cI895qY7+4alqEYGw/nOXz3iW8qUx8WxWJD/czKSoUbdupHKt6MoPzM0vecRYPco5zD7HNn6InNmEpfznv5pqvb2Nyl3C0oXoi9Foi44ax5OA2ZsRq0er68kN2JTVJFqz5nFw0iDjbnCwhYehCQtCZXmHy2vM8qpqTrip/DfBfXckCNAOG6kqW5sFQXcnSPBiqKllsCPcl0joklI4fHcO9OrSQd3wRg9vrsV0PtLa5P3QdGLLkz+r5LoKlPlRVsviJoTU/ha8HdbTPyaINDUPbIoyYl9/jp/O2WUYa/s+74ULgTUx4I1lsc6jdPTyXAa219vjTtNARO2wZ86uHsdR2jlaKTn/PmJdaOmK2RQiGqF5M234VF49i0wv2Gy7RLTT0/PKsy0MBbOnWFfvBIVkwX2d7YlciQ8IIN+gxGOMY8fVBtk+PwxCip9/ybGzTJBf+9S1DOzi+55oWGtr2ncyYHlpi3j/shFwXj9rKommsF8nSNMpRzkIICIEgJ9D4ksUBrKL4NpcvZJB9t8Q5ua2V0vxb5BSU1bPBVcGD65fI+CeXEhXGdXtTvBvWJzFoYLw3m/q2jTmdRd11dJt92qNhVZWcmeKcTM6nneNyTrFLd2zn5xXF3M5KJ+PqXUqc476tpfn8l1NIWR2tW0vJPa5lnONC5k0KS90GjVcdvEH+qy5ZmgFD1SVLM2CoumRpBgzVlizmCwt4WfsSc90eL+JSDZmLyck8x9lzWeQU19DrLgjqQ7Uli/8YWnh8L5uL586TdbMQFS8pXvZkUX6NdIk8x9unhdy4lMGV3BLnI4af26LmFZYS8rIvkpF5kwdPa96kzrV1xX6dCbhv0PgT39qOX0Hx7SzS069y71F1g4X8WzkUujZY7Od6gQtX7vGk4oK9jdR19hn3E2hgHu6JB+6SSJbALRvJmRAQAs2IgL8kS1NCrK5ksVJ0eCpx+j4sV2HC2UApB3UlS/NgqK5kaR4M1ZUszYOhqpLFWsTRjzrSsvcyVSZLDZT6UFXJ0kwYetuTJVDKPBDz4R/J8mISJfvfo43+ZeafqerHZuXB8Rn00LTlw99chle9OJkm/alIliZdvHJyQkAIBAsBkSzKS0oki3KGIlmUMxTJopyhSBblDEWyKGcokkU5Q5EsyhmKZFHO0B8piGTxB3U5phAQAkLAg4BIFg8gPiyqJVms+VtJjOtIG72WLtOP87COoT0+ZD1gdlFLsjQnhmpJlubEUC3J0pwYqiNZrORvnUDXDjGEa+L49PjDeg4rDZiqzquMqCNZmhdDkSxehdoLNwpEyULZRdYmtMUQHseAYaMZNbAnbbXh9Pxwn/vjs194Zk37Q5EsTbt85eyEgBAIEgIiWZQXlFqShac3OPFzEr/su0C+n+abUU7HuxTUkizNiaFakqU5MVRLsjQnhupIFqi4fpzN63/mwIX8+s114V0VFFBbqSNZmhdDkSzKQzogJYvttCryuXhoIyuXLGbp8iQOpN31mCRY+bkHcwoiWYK59CTvQkAINBkCIlmUF6VqkkV51oImBdUkS9AQUJ5R1SSL8qwFTQqqSZagIaA8o2pJFuU5C54U1JIswUNAeU5FsihnGLCSRfmpNekURLI06eKVkxMCQiBYCIhkUV5SIlmUMxTJopyhSBblDEWyKGcokkU5Q5EsyhmKZFHOUCSLcob+SEEkiz+oyzGFgBAQAh4ERLJ4APFhUSSLD9A8dhHJ4gHEh0WRLD5A89hFJIsHEB8WRbL4AM1jF5EsHkB8WBTJ4gM0j11EsngACZJFkSxBUlCSTSEgBJo2AZEsystXJItyhiJZlDMUyaKcoUgW5QxFsihnKJJFOUORLMoZimRRztAfKYhk8Qd1OaYQEAJCwIOASBYPID4simTxAZrHLiJZPID4sCiSxQdoHruIZPEA4sOiSBYfoHnsIpLFA4gPiyJZfIDmsYtIFg8gQbIokiVICkqyKQSEQNMm0NiSpfzO32z//is+n/k536w9wKVC52NzrA+5fGQnu4784/Go4jL+TdnFrqNXeOR8hLE5P4P9a75m3syZzF+2hdO5Fc8K6clVTuw6SHrBE/47sZ6vF2x/9plK70SyKAcrkkU5Q5EsyhmKZFHOUCSLcoYiWZQzFMminKFIFuUM/ZGCSBZ/UJdjCgEhIAQ8CDSeZLHy4OSXvBmpJaZbPOPGDadfByOGtsNYn1UGlPP3zDj0LUexo8BpU2x5fXSQ96O19Jh3FptKeXzhe96N0RDeqifx7ybwZgcjuqh3WHXRlgZYcn4kXh/LhOnD6RCmIaLTDI8zbvhFkSzKmYpkUc5QJItyhiJZlDMUyaKcoUgW5QxFsihnKJJFOUN/pCCSxR/U5ZhCQAgIAQ8CjSVZyn//mPaal/jijEuvE6Bo1ziiNb1YdqUSzBl83VNLzPi9FNvyaS0iebyJ8LdWccNi+3gxr2naM+OEQ6hUncrD3WMx6YexpcDqkCwaLQNW3cJStYHK/0WyKAcskkU5Q5EsyhmKZFHOUCSLcoYiWZQzFMminKFIFuUM/ZGCSBZ/UJdjCgEhIAQ8CDSOZLFyd907GLVD2OzaSwUwpy/iVU0UHx62yZdKrn7bC2PUexx8BNbCnYyLbMnQjXex9W0p2zeeiBZ62sR2o0dXl1cHE4aw7iw4b3ZIFm0Ekw6Ue5ypeosiWZSzFcminKFIFuUMRbIoZyiSRTlDkSzKGYpkUc5QJItyhv5IQSSLP6jLMYWAEBACHgQaS7IU/jKEcE08P+W69y+pOPM5XTXt+DTF0cPFcn0VA/StmPLrI/K3jiDaNJbdhY7hQ2V7xxMR2oGJaw5z7Ogx99exP8kucvZk0UYw+aBIFo+iDuhFkSzKi0cki3KGIlmUMxTJopyhSBblDEWyKGcokkU5Q3+kIJLFH9TlmEJACAgBDwKNI1mgMvMbXtdEMXF3ob1XiiMblVxb3gdj+HC25TvnYbHcIemdcNpN+YV1gyNo9/6vPHLmuTJrCb00kYzeVuCSBjz6cyXTpq7i9CPnnCwiWTxKOfAXRbIoLyORLMoZimRRzlAki3KGIlmUMxTJopyhSBblDP2RgkgWf1CXYwoBISAEPAg0lmTBcodtI2MwdBzH+rQ8ys0l3Dy6kHiTjh6f/VEtUsBK3i/DiDLF0FrXic9Ousy/YrnJxgQThrhxJJ25y+OKUvLSkkiM09FuzE7yrCJZPIo3aBZFsigvKpEsyhmKZFHOUCSLcoYiWZQzFMminKFIFuUM/ZGCSBZ/UJdjCgEhIAQ8CDSaZLHNY/vgNCtGdsEUEoKmRQgaTTR9p24n28Wj2LJnLdzF+MgQ9N2+5Jz7PLlY8o6zeFAHwm3729PRE5uwlL+cc73Yny4kPVk8SjnwF0WyKC8jkSzKGYpkUc5QJItyhiJZlDMUyaKcoUgW5Qz9kYJIFn9Ql2MKASEgBDwINKZkcRzawuN72VxKz+RmoYdB8chb7YtminMyOZ92jss5xZhr37BRPpGJb5VjFsminKFIFuUMRbIoZyiSRTlDkSzKGYpkUc5QJItyhv5IQSSLP6jLMYWAEBACHgQaX7J4ZKAJLIpkUV6IIlmUMxTJopyhSBblDEWyKGcokkU5Q5EsyhmKZFHO0B8piGTxB3U5phAQAkLAg4BIFg8gPiyKZPEBmscuIlk8gPiwKJLFB2geu4hk8QDiw6JIFh+geewiksUDiA+LIll8gOaxi0gWDyBBsiiSJUgKSrIpBIRA0yYgkkV5+YpkUc5QJItyhiJZlDMUyaKcoUgW5QxFsihnKJJFOUORLMoZ+iMFkSz+oC7HFAJCQAh4EBDJ4gHEh0WRLD5A89hFJIsHEB8WRbL4AM1jF5EsHkB8WBTJ4gM0j11EsngA8WFRJIsP0Dx2EcniASRIFkWyBElBSTaFgBBo2gREsigvX5EsyhmKZFHOUCSLcoYiWZQzFMminKFIFuUMRbIoZyiSRTlDf6TwnGSpqKjgWna2vISBxIDEgMRAI8bAd0u/5bWXXxHmCpgvXriQN3v3FoYKGM77fC7x/fsLQwUMZ336KUMS3hWGChhO+2gqo0eMEIYKGNp+3CaOnyAMFTCcMHYsH0x+XxgqYDhy2DA+mTZNGCpgmBAfz+ezZwtDHxj+7//+rz/8iv2Yz0mWG9dvoGkRIi9hIDEgMSAxIDEgMSAxIDEgMSAxIDEgMSAxIDEgMRB0MbBuzZrAkSy2nixX/rkiL2EgMSAxIDHQiDHw7TdLeK3ny8JcAfOFXy2gb6/ewlABw7lzPmfg2/2FoQKGn834lMGDEoRNE8u+AAAGvElEQVShAoYff/gRo4aPEIYKGE5OfI+J48YLQwUMx48Zy5RJk4WhAoYjhg5j+tSPhaEChoMGxjNn5ixh6APDoqKiwJEsfsuJHFgICAEh0IwJyJwsygtf5mRRzlDmZFHOUOZkUc5Q5mRRzlDmZFHOUOZkUc5Q5mRRzlDmZFHO0B8pPDdcyB+ZkGMKASEgBJo7AZEsyiNAJItyhiJZlDMUyaKcoUgW5QxFsihnKJJFOUORLMoZimRRztAfKYhk8Qd1OaYQEAJCwIOASBYPID4simTxAZrHLiJZPID4sCiSxQdoHruIZPEA4sOiSBYfoHnsIpLFA4gPiyJZfIDmsYtIFg8gQbIokiVICkqyKQSEQNMmIJJFefmKZFHOUCSLcoYiWZQzFMminKFIFuUMRbIoZyiSRTlDkSzKGfojheYhWcqz2PfDMpZ/9+z1/fer2bDtMBfuljUS90puHVnDiq2pPLA20iHlMEJACAQNgUCWLBX3r3D+Sj4VvtI0PyT36kUyLl0nv0y9CjDYJIu1rJBblzPJzn3oO1tfy6SW/ZqKZFEcs7Xw8WZ1YEsWK6X3s7mYfoXbxRXU+G20fV+vXCQj8yaF5d6cccNvExySxUpJTgbnswuo9ERgLeV+dgYZ/9ym+GmNlD33aPDlwJYsZh7mXuVS+iWu55cFbBwGhWSxlpCTcY5rBc9FIQRAHAa0ZPGGjzfbNPi31z1Bv0mWF8WWkuvEi9J1P/WgXmoekqVoKyN1IRhMHega19n+eqmtCYPtUVy6jgxd+ieFDX4NNHP+x8kkLjxCgT3tCn7/qDXGXsu4VkM9+FwUmc+xdtIEFh8uqPni89wOskIICIFgJhC4kuUhBxNjCE/YQF5VPWm9x6YEI/owTc0v4ziSS22lYebWvpm83VpX/dg/bXgXRi/7WxXZHDySpZx/d3xMnyiNk4ue2EGLSblv8XsINw3JUkPMNiLZQJUslblH+Xp4d2LCQhxxFxLBy+NXcNrRSAEs3DvyJe+0ffZ91UX2IHHtBR5VffcbiWMwSBbrvb1Mah9G+LCtPHt+RSV3jyxmVDcTeufjXrUtezDxh7+dbcFGAggEqmQx39zPrDfbYKx6HG5IS7qPXMbp6oZ44MRh4EsWK3l7E4kN1TNy67MohMCJw8CULN7w8Wabxvk++0ey1BZbSr+ftaXbOCwb8yjNSLJoeHVBBuZqulbK8zPYMe01TKEmBq/99/k7EdXb+vKmnEOJERjf/pEcZ7u5NP8mN3KLvLtjWX6QyS11xP+Yg/+b3b6cv+wjBIRAfQgEmmSx9bL4L+sUuxcOplNYCEZXyUIp2Uc28/OmTe6vDUsZH6clZsBqrpjBcn0N74Tr6TH5F87dLqI4L4uDn/chKqw9Hx95VB88Xm0bLJKl/PwiXtfp6D5pE6k37nD19+8Y0kpDzJCfq68XXp2wChsFs2R5ccyqAKuWJANSsliusy4+EuNLiWw6fYsHxffI2j+P/qYw2ozeaReo1vs7GGfSEDf6J9Jyiym+d4n9M3sRqenM7D8bq9evA2rAS5bKW2we3gptixA3yWK5vpaECB3dJ27gzK1Ciu9lcvCLt4gJjWHcjrxaIkad1QEpWSw3WDegJeFdJ7P57G2KivO4vH8u/Vpq6PThUWxXhUCKw0CXLJW3fmFUTCiaFu6SJZDiMBAlizd8vNlGnW/u86n6Q7LUFltKv5+1pfv8WQf/mmYsWZyFV/kvP75tRNf2I465tPnN+RnsX/M182bOZP6yLZzOdekob7lL2r5kTt8qpzA9mVVffc7cr1aQfCHfKXFKuHZiB/PeMmDo9j7rdqdwrcRC3rmD7D1+pfqOkKX4Cod/Wsr8WbP4ckkSR68+cvRaKckmZcdcBui19Hx/Lckp2c7MWij+5zeSlsxjzqx5fJt0hOzGvr0U/DEvZyAEApJAYEkWKwWb3n12p7GFp2SpCWElNzYOo137CSTftalhC3fWDsBoHM626jvlQNlRprbS0n1uak2JKFoXHJKlhMNTWqNvP40TT6pO10repsFEaF5hySVvujpW7dfw/4NXsvgSsw3Pz5ZiIEqWyuxlvKmJZOyOQpfesRWc+7I7esNodj2E8oPvEa151T0Gn+wjsaWW17/JVAdWLakGtmSp4OrqeNp2m8JHb+tcJEsl15b1xhAxml3VvTKAirN81U1DxKidtZytOqsDUbJY7qwlXhvOqC2uvbTLOPZBawwvfUGaObDiMKAlS8UV1gxsRc8pHzBQ6ypZAisOA0+yeMPHm23U+d7WlGqjS5ZaY0vh9/MF6dZ03sG+7v8BVS3ZlRLo/IwAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The demographics for healthcare workers should be compared to national data about healthcare workers demographics. This data can be obtained from the CPS or census. CPS is linked here: https://www.bls.gov/cps/tables.htm\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = methods.subset_data(data, rows, exposure='hcw', outcome='test_positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43430"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what about when restricted to only those who worked?\n",
    "\n",
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "                                                 ) & (data['df'].test_required == 1)\n",
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>index</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  age_group\n",
       "0    1.0       4413\n",
       "1    2.0       7538\n",
       "2    3.0       8401\n",
       "3    4.0       8766\n",
       "4    5.0       7572\n",
       "5    6.0       2283\n",
       "6    7.0        624\n",
       "7    NaN       3833"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['df'][rows].age_group.value_counts(dropna=False).sort_index().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "     ) & (data['df'].test_required == 1) & (data['df']['Q68'] == 1)  # Physician or surgeon"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>age_group</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>44</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>65</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>71</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>61</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  age_group   pct\n",
       "0    1.0          6   2.1\n",
       "1    2.0         44  15.1\n",
       "2    3.0         65  22.3\n",
       "3    4.0         71  24.4\n",
       "4    5.0         61  21.0\n",
       "5    6.0         22   7.6\n",
       "6    7.0         22   7.6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "     ) & (data['df'].test_required == 1) & (\n",
    "    data['df'].Q68 == 1)  # Physician or surgeon\n",
    "\n",
    "def my_age_pattern(rows):\n",
    "    t = data['df'][rows].age_group.value_counts(dropna=False)\n",
    "    t = t.sort_index().reset_index()\n",
    "    t['pct'] = t.age_group / t.age_group.sum() *100\n",
    "    return np.round(t, 1).sort_index()\n",
    "my_age_pattern(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "291"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>index</th>\n",
       "      <th>age_group</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>71</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>466</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>757</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>807</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>776</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>272</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  age_group   pct\n",
       "0    1.0         71   2.2\n",
       "1    2.0        466  14.7\n",
       "2    3.0        757  23.9\n",
       "3    4.0        807  25.5\n",
       "4    5.0        776  24.5\n",
       "5    6.0        272   8.6\n",
       "6    7.0         17   0.5\n",
       "7    NaN          2   0.1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = (data['df'].date >= data['start_date']) & (data['df'].date <= data['end_date']\n",
    "     ) & (data['df'].test_required == 1) & (\n",
    "    data['df'].Q68 == 2)  # Registered nurse (including nurse practitioner)\n",
    "\n",
    "my_age_pattern(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3168"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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