From cbbc85521c418abdf22c5ab3ee0236595de7affe Mon Sep 17 00:00:00 2001
From: Greg Way <gregory.way@gmail.com>
Date: Fri, 23 Feb 2018 12:41:18 -0500
Subject: [PATCH] Update CCLE variants in Response to Reviewers (#71)

* add variants to analysis

more deeply investigate false positives and false negatives

* add CCLE variant analysis
---
 scripts/ras_cell_line_predictions.ipynb | 2003 ++++++++++++++++++-----
 1 file changed, 1600 insertions(+), 403 deletions(-)

diff --git a/scripts/ras_cell_line_predictions.ipynb b/scripts/ras_cell_line_predictions.ipynb
index 9245d42..8666b42 100644
--- a/scripts/ras_cell_line_predictions.ipynb
+++ b/scripts/ras_cell_line_predictions.ipynb
@@ -20,9 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -39,7 +37,9 @@
     "import pandas as pd\n",
     "from decimal import Decimal\n",
     "from scipy.stats import ttest_ind\n",
+    "from statsmodels.stats.proportion import proportions_chisquare\n",
     "from sklearn.preprocessing import StandardScaler\n",
+    "from Bio.SeqUtils import IUPACData\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
@@ -49,9 +49,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store protein change dictionary\n",
+    "aa = IUPACData.protein_letters_1to3_extended"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
@@ -66,10 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -82,18 +88,18 @@
      "data": {
       "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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        "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -183,7 +189,7 @@
        "3589    PDLIM4 -0.082386  0.082386"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,10 +217,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -227,18 +231,18 @@
      "data": {
       "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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        "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -364,7 +368,7 @@
        "OR4F5       0.000000  0.000000  0.000000  0.000000  0.000000  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
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     }
@@ -378,10 +382,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -402,10 +404,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -423,27 +423,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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        "\n",
        "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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@@ -519,7 +517,7 @@
        "6614     UPK3BL -0.006527  0.006527"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,10 +530,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Transform the cell line data by z-score\n",
@@ -547,10 +543,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Get the weights ready for applying the classifier\n",
@@ -560,10 +554,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Apply a logit transform [y = 1/(1+e^(-wX))] to output probabilities\n",
@@ -573,27 +565,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "data": {
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-       "\n",
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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        "\n",
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+       "\n",
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+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -661,7 +651,7 @@
        "SLK1_1    0.856927"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,27 +662,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
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-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -793,7 +781,7 @@
        "SLGP2_1   0.193174            -1     SLGP2_1        0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,10 +796,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -835,16 +821,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gway/anaconda3/lib/python3.5/site-packages/plotnine/ggplot.py:688: UserWarning: Saving 6 x 0.5 in image.\n",
+      "  from_inches(height, units), units))\n",
+      "/home/gway/anaconda3/lib/python3.5/site-packages/plotnine/ggplot.py:689: UserWarning: Filename: ../figures/cell_line/kras_cell_line_predictions.pdf\n",
+      "  warn('Filename: {}'.format(filename))\n"
+     ]
+    },
     {
      "data": {
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BT08PP/zwA/bu3av2BrOwsBDh4eHKi8cAxWpH0a1Vz0pNTUVUVBQAxadxEb3puHT8AiYm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\nR4wdO1ZjH/Xq1SvDsyCikvCq4zeYvr4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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6ecc8f38d0>"
+       "<matplotlib.figure.Figure at 0x7fa9286c2ac8>"
       ]
      },
      "metadata": {},
@@ -853,10 +847,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223363279739405778)>"
+       "<ggplot: (-9223363264073166862)>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,10 +901,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -923,18 +915,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -1173,7 +1165,7 @@
        "[5 rows x 751 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1188,10 +1180,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1204,18 +1194,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -1270,7 +1260,7 @@
        "1520      MLPH  0.102069  0.102069"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1285,10 +1275,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "ccle_df = ccle_df.loc[common_ccle_coef['feature'], ccle_df.columns[1:]]"
@@ -1296,10 +1284,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "scaled_fit = StandardScaler().fit(ccle_df.T)\n",
@@ -1310,10 +1296,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Apply a logit transform [y = 1/(1+e^(-wX))] to output probabilities\n",
@@ -1323,16 +1307,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6ecc03d518>"
+       "<matplotlib.figure.Figure at 0x7fa9287fc828>"
       ]
      },
      "metadata": {},
@@ -1346,10 +1328,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1357,7 +1337,7 @@
        "(63775, 1031)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1372,108 +1352,295 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1030, 5)\n"
+      "(1175706, 31)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gway/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (3,25,26,27,30,31) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  interactivity=interactivity, compiler=compiler, result=result)\n"
      ]
     },
     {
      "data": {
       "text/html": [
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-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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>Name</th>\n",
-       "      <th>KRAS_MUT</th>\n",
-       "      <th>HRAS_MUT</th>\n",
-       "      <th>NRAS_MUT</th>\n",
-       "      <th>BRAF_MUT</th>\n",
-       "      <th>ras_status</th>\n",
+       "      <th></th>\n",
+       "      <th>Hugo_Symbol</th>\n",
+       "      <th>Entrez_Gene_Id</th>\n",
+       "      <th>NCBI_Build</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>Start_position</th>\n",
+       "      <th>End_position</th>\n",
+       "      <th>Strand</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Variant_Type</th>\n",
+       "      <th>Reference_Allele</th>\n",
+       "      <th>...</th>\n",
+       "      <th>isCOSMIChotspot</th>\n",
+       "      <th>COSMIChsCnt</th>\n",
+       "      <th>ExAC_AF</th>\n",
+       "      <th>WES_AC</th>\n",
+       "      <th>SangerWES_AC</th>\n",
+       "      <th>SangerRecalibWES_AC</th>\n",
+       "      <th>RNAseq_AC</th>\n",
+       "      <th>HC_AC</th>\n",
+       "      <th>RD_AC</th>\n",
+       "      <th>WGS_AC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tumor_Sample_Barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>DMS53_LUNG</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SW1116_LARGE_INTESTINE</th>\n",
+       "      <th>22RV1_PROSTATE</th>\n",
+       "      <td>AGRN</td>\n",
+       "      <td>375790</td>\n",
+       "      <td>37</td>\n",
        "      <td>1</td>\n",
+       "      <td>979072</td>\n",
+       "      <td>979072</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Silent</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NCIH1694_LUNG</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27:24</td>\n",
+       "      <td>9:10</td>\n",
+       "      <td>9:12</td>\n",
+       "      <td>104:20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15:13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>22RV1_PROSTATE</th>\n",
+       "      <td>ATAD3A</td>\n",
+       "      <td>55210</td>\n",
+       "      <td>37</td>\n",
        "      <td>1</td>\n",
+       "      <td>1459233</td>\n",
+       "      <td>1459233</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Silent</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>29:49</td>\n",
+       "      <td>33:40</td>\n",
+       "      <td>30:38</td>\n",
+       "      <td>315:308</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17:31</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>2 rows × 31 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Name                                     KRAS_MUT HRAS_MUT NRAS_MUT BRAF_MUT  \\\n",
-       "DMS53_LUNG                                      0        0        0        0   \n",
-       "SW1116_LARGE_INTESTINE                          1        0        0        0   \n",
-       "NCIH1694_LUNG                                   0        0        0        0   \n",
-       "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE        0        0        0        0   \n",
-       "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE        0        0        1        0   \n",
+       "                     Hugo_Symbol  Entrez_Gene_Id  NCBI_Build Chromosome  \\\n",
+       "Tumor_Sample_Barcode                                                      \n",
+       "22RV1_PROSTATE              AGRN          375790          37          1   \n",
+       "22RV1_PROSTATE            ATAD3A           55210          37          1   \n",
        "\n",
-       "Name                                     ras_status  \n",
-       "DMS53_LUNG                                        0  \n",
-       "SW1116_LARGE_INTESTINE                            1  \n",
-       "NCIH1694_LUNG                                     0  \n",
+       "                      Start_position  End_position Strand  \\\n",
+       "Tumor_Sample_Barcode                                        \n",
+       "22RV1_PROSTATE                979072        979072      +   \n",
+       "22RV1_PROSTATE               1459233       1459233      +   \n",
+       "\n",
+       "                     Variant_Classification Variant_Type Reference_Allele  \\\n",
+       "Tumor_Sample_Barcode                                                        \n",
+       "22RV1_PROSTATE                       Silent          SNP                A   \n",
+       "22RV1_PROSTATE                       Silent          SNP                A   \n",
+       "\n",
+       "                       ...   isCOSMIChotspot  COSMIChsCnt   ExAC_AF WES_AC  \\\n",
+       "Tumor_Sample_Barcode   ...                                                   \n",
+       "22RV1_PROSTATE         ...             False            0       NaN  27:24   \n",
+       "22RV1_PROSTATE         ...             False            0  0.000008  29:49   \n",
+       "\n",
+       "                     SangerWES_AC SangerRecalibWES_AC RNAseq_AC HC_AC  RD_AC  \\\n",
+       "Tumor_Sample_Barcode                                                           \n",
+       "22RV1_PROSTATE               9:10                9:12    104:20   NaN    NaN   \n",
+       "22RV1_PROSTATE              33:40               30:38   315:308   NaN    NaN   \n",
+       "\n",
+       "                      WGS_AC  \n",
+       "Tumor_Sample_Barcode          \n",
+       "22RV1_PROSTATE         15:13  \n",
+       "22RV1_PROSTATE         17:31  \n",
+       "\n",
+       "[2 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load CCLE Variant Data\n",
+    "ccle_maf_file = 'https://data.broadinstitute.org/ccle/CCLE_DepMap_18Q1_maf_20180207.txt'\n",
+    "ccle_maf_df = pd.read_table(ccle_maf_file, index_col=15)\n",
+    "print(ccle_maf_df.shape)\n",
+    "ccle_maf_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1030, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Name</th>\n",
+       "      <th>KRAS_MUT</th>\n",
+       "      <th>HRAS_MUT</th>\n",
+       "      <th>NRAS_MUT</th>\n",
+       "      <th>BRAF_MUT</th>\n",
+       "      <th>ras_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DMS53_LUNG</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SW1116_LARGE_INTESTINE</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NCIH1694_LUNG</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Name                                     KRAS_MUT HRAS_MUT NRAS_MUT BRAF_MUT  \\\n",
+       "DMS53_LUNG                                      0        0        0        0   \n",
+       "SW1116_LARGE_INTESTINE                          1        0        0        0   \n",
+       "NCIH1694_LUNG                                   0        0        0        0   \n",
+       "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE        0        0        0        0   \n",
+       "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE        0        0        1        0   \n",
+       "\n",
+       "Name                                     ras_status  \n",
+       "DMS53_LUNG                                        0  \n",
+       "SW1116_LARGE_INTESTINE                            1  \n",
+       "NCIH1694_LUNG                                     0  \n",
        "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE          0  \n",
        "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE          1  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1494,10 +1661,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1510,18 +1675,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -1607,7 +1772,7 @@
        "TT2609C02_THYROID       0.975086       TT2609C02_THYROID  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1623,10 +1788,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Use Seaborn for the 2nd plot\n",
@@ -1644,20 +1807,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Ras Status:\n",
-      "Ttest_indResult(statistic=14.212934382489067, pvalue=6.3476266274502043e-36)\n",
+      "Ttest_indResult(statistic=14.212934382489067, pvalue=6.347626627450204e-36)\n",
       "\n",
       "BRAF Status in Ras Wild-Type Samples:\n",
-      "Ttest_indResult(statistic=7.6917803515776813, pvalue=1.1612390405220905e-11)\n"
+      "Ttest_indResult(statistic=7.691780351577681, pvalue=1.1612390405220905e-11)\n"
      ]
     }
    ],
@@ -1684,16 +1845,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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4doPOinPU/0PpgRxgZ8MmaETKbDQaw5YUraysxG6PJFPTwEFCgOpGO5E1UpKV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\n1VdfkZaWRkVFBe+9954nGcelBAYGcv78eVwuF9nZ2YA7vr82I76U5OR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BrQDvMLuV4XK5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\n2gheY/bipY3gNWYvXtoIXmP24qWN8P8D8+4Ik0sBqP0AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6eccb76dd8>"
+       "<matplotlib.figure.Figure at 0x7fa8d847dc50>"
       ]
      },
      "metadata": {},
@@ -1750,10 +1909,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Assign a label to what the predictions are given classifier scores\n",
@@ -1763,10 +1920,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Stratify cell lines based on predictions and ground truth status\n",
@@ -1779,180 +1934,1234 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "wild-type    357\n",
+       "mutant       191\n",
+       "Name: predictions, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Of wild-type Ras cell lines, how many are predicted correctly?\n",
+    "# True Negative Rate, Specificity\n",
+    "negative_ras_lines_ccle['predictions'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "wild-type    357\n",
-       "mutant       191\n",
-       "Name: predictions, dtype: int64"
+       "mutant       153\n",
+       "wild-type     36\n",
+       "Name: predictions, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Of mutated Ras cell lines, how many are predicted correctly?\n",
+    "# True Positive Rate (TPR), Recall, Sensitivity\n",
+    "positive_ras_lines_ccle['predictions'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "357 out of 393 Ras wild-type predictions are true (90.8%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Of the wild-type predictions, how many are actually wild-type?\n",
+    "# Negative Predictive Value (NPV)\n",
+    "neg_ccle_results = negative_ras_predictions_ccle['ras_status'].value_counts()\n",
+    "true_neg = neg_ccle_results[0]\n",
+    "predicted_condition_neg = neg_ccle_results.sum()\n",
+    "\n",
+    "print('{} out of {} Ras wild-type predictions '\n",
+    "      'are true ({:.1f}%)'.format(true_neg, predicted_condition_neg,\n",
+    "                                  true_neg * 100 / predicted_condition_neg))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "153 out of 344 Ras mutation predictions are true (44.5%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Of the mutated predictions, how many are actually mutated?\n",
+    "# Positive Predictive Value (PPV) -or- precision\n",
+    "pos_ccle_results = positive_ras_predictions_ccle['ras_status'].value_counts()\n",
+    "false_pos, true_pos = pos_ccle_results\n",
+    "predicted_condition_pos = pos_ccle_results.sum()\n",
+    "\n",
+    "print('{} out of {} Ras mutation predictions '\n",
+    "      'are true ({:.1f}%)'.format(true_pos, predicted_condition_pos,\n",
+    "                                  true_pos * 100 / predicted_condition_pos))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "510 of 737 Total cell lines predicted correctly (69.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_correct = true_pos + true_neg\n",
+    "print('{} of {} Total cell lines '\n",
+    "      'predicted correctly ({:.1f}%)'.format(total_correct, ccle_full_df.shape[0],\n",
+    "                                             total_correct * 100 / ccle_full_df.shape[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "56 of 191 total false positives have BRAF mutations (29.3%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Of the False positives, how many are BRAF mutant?\n",
+    "wt_ras_braf_ccle = positive_ras_predictions_ccle[positive_ras_predictions_ccle['ras_status'] == 0]\n",
+    "braf_neg, braf_pos = wt_ras_braf_ccle['BRAF_MUT'].value_counts()\n",
+    "\n",
+    "print('{} of {} total false positives '\n",
+    "      'have BRAF mutations ({:.1f}%)'.format(braf_pos, false_pos,\n",
+    "                                             braf_pos * 100 / false_pos))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Including BRAF mutants, 209 of 344 Ras mutation predictions have Ras pathway mutations (60.8%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If include BRAF mutations, how many correct\n",
+    "correct_braf = wt_ras_braf_ccle['BRAF_MUT'].value_counts()[1]\n",
+    "true_pos_with_braf = true_pos + correct_braf\n",
+    "print('Including BRAF mutants, {} of {} Ras mutation predictions '\n",
+    "      'have Ras pathway mutations ({:.1f}%)'.format(true_pos_with_braf,\n",
+    "                                                    predicted_condition_pos,\n",
+    "                                                    true_pos_with_braf * 100 / predicted_condition_pos))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Of the false positives, there are 56 BRAF mutated cell lines and 135 BRAF wild-type cell lines\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Of the false positives, there are {} BRAF mutated cell lines '\n",
+    "      'and {} BRAF wild-type cell lines'.format(braf_pos, braf_neg))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In all of CCLE, there are 97 BRAF mutated cell lines and 640 BRAF wild-type cell lines\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_braf_wildtype, total_braf_mut = ccle_full_df['BRAF_MUT'].value_counts()\n",
+    "print('In all of CCLE, there are {} BRAF mutated cell lines '\n",
+    "      'and {} BRAF wild-type cell lines'.format(total_braf_mut, total_braf_wildtype))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Ras mutations are identified in False Negatives?\n",
+    "\n",
+    "**Variant level predictions in the CCLE**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "708"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many MAF tumors also have CCLE and mutation data?\n",
+    "len(set(ccle_full_df.index).intersection(set(ccle_maf_df.index)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Of the false negaves, what RAS mutations do they harbor?\n",
+    "false_negatives_df = negative_ras_predictions_ccle.query('ras_status == 1')\n",
+    "common_neg_ccle_samples = set(ccle_maf_df.index).intersection(set(false_negatives_df.index))\n",
+    "false_neg_maf_df = ccle_maf_df.loc[list(common_neg_ccle_samples), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What about true positives?\n",
+    "true_positives_df = positive_ras_predictions_ccle[positive_ras_predictions_ccle['ras_status'] == 1]\n",
+    "common_pos_ccle_samples = set(ccle_maf_df.index).intersection(set(true_positives_df.index))\n",
+    "true_pos_maf = ccle_maf_df.loc[list(common_pos_ccle_samples), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subset to only Ras genes\n",
+    "false_neg_ras = false_neg_maf_df.query('Hugo_Symbol in [\"KRAS\", \"HRAS\", \"NRAS\"]')\n",
+    "tru_pos_ras = true_pos_maf.query('Hugo_Symbol in [\"KRAS\", \"HRAS\", \"NRAS\"]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove duplicate cell-lines. Assume 1 cosmic variant supercedes lack of cosmic\n",
+    "false_neg_dup = false_neg_ras.groupby('Tumor_Sample_Barcode')['isCOSMIChotspot']\n",
+    "\n",
+    "cosmic_false_neg = (\n",
+    "    false_neg_dup.value_counts()\n",
+    "    .reset_index(name='count')\n",
+    "    .sort_values(by='isCOSMIChotspot')\n",
+    "    .drop_duplicates(subset='Tumor_Sample_Barcode', keep='last')\n",
+    ")\n",
+    "\n",
+    "n_false_neg_cosmic = cosmic_false_neg['isCOSMIChotspot'].value_counts()[1]\n",
+    "n_false_neg_obs = cosmic_false_neg['isCOSMIChotspot'].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True     22\n",
+      "False    12\n",
+      "Name: isCOSMIChotspot, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True     0.647059\n",
+       "False    0.352941\n",
+       "Name: isCOSMIChotspot, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What is the proportion of COSMIC variant (True) to non-COSMIC variant (False)\n",
+    "print(cosmic_false_neg['isCOSMIChotspot'].value_counts())\n",
+    "cosmic_false_neg['isCOSMIChotspot'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove duplicate cell-lines. Assume 1 cosmic variant supercedes lack of cosmic\n",
+    "tru_pos_dup = tru_pos_ras.groupby('Tumor_Sample_Barcode')['isCOSMIChotspot']\n",
+    "\n",
+    "cosmic_true_pos = (\n",
+    "    tru_pos_dup.value_counts()\n",
+    "    .reset_index(name='count')\n",
+    "    .sort_values(by='isCOSMIChotspot')\n",
+    "    .drop_duplicates(subset='Tumor_Sample_Barcode', keep='last')\n",
+    ")\n",
+    "\n",
+    "n_true_pos_cosmic = cosmic_true_pos['isCOSMIChotspot'].value_counts()[1]\n",
+    "n_tru_pos_obs = cosmic_true_pos['isCOSMIChotspot'].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True     144\n",
+      "False      8\n",
+      "Name: isCOSMIChotspot, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True     0.947368\n",
+       "False    0.052632\n",
+       "Name: isCOSMIChotspot, dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(cosmic_true_pos['isCOSMIChotspot'].value_counts())\n",
+    "cosmic_true_pos['isCOSMIChotspot'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi Square = 26.111738595247864, p value = 3.222191172407547e-07\n",
+      "There is a significant difference in the expected proportion of COSMIC variants.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test if the proportions of observed COSMIC variants are significantly different\n",
+    "# between true positives and false negatives. All of these samples have Ras mutations.\n",
+    "# The question is asking if the proportion of Ras variants annotated in the COSMIC\n",
+    "# database is lower in False negative tumors (those the classifier predicted as\n",
+    "# Ras wild-type) than in True positive tumors\n",
+    "cosmic_prop_chi = proportions_chisquare(count = [n_false_neg_cosmic, n_true_pos_cosmic],\n",
+    "                                        nobs = [n_false_neg_obs, n_tru_pos_obs])\n",
+    "\n",
+    "print('Chi Square = {}, p value = {}'.format(cosmic_prop_chi[0], cosmic_prop_chi[1]))\n",
+    "print('There is a significant difference in the expected proportion of COSMIC variants.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add CCLE Variant Scores (nucleotide and amino acid) to Supplementary Data Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>genes</th>\n",
+       "      <th>og_tsg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ALK</td>\n",
+       "      <td>OG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ARAF</td>\n",
+       "      <td>OG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>OG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>OG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ERBB2</td>\n",
+       "      <td>OG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   genes og_tsg\n",
+       "0    ALK     OG\n",
+       "1   ARAF     OG\n",
+       "2   BRAF     OG\n",
+       "3   EGFR     OG\n",
+       "4  ERBB2     OG"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load TCGA PanCanAtlas Core Ras Pathway genes\n",
+    "ras_genes_file = os.path.join('..', 'classifiers', 'RAS', 'ras_genes.csv')\n",
+    "ras_core_df = pd.read_table(ras_genes_file)\n",
+    "ras_core_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>Hugo_Symbol</th>\n",
+       "      <th>Protein_Change</th>\n",
+       "      <th>cDNA_Change</th>\n",
+       "      <th>KRAS_MUT</th>\n",
+       "      <th>HRAS_MUT</th>\n",
+       "      <th>NRAS_MUT</th>\n",
+       "      <th>BRAF_MUT</th>\n",
+       "      <th>ras_status</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A101D_SKIN</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>p.V600E</td>\n",
+       "      <td>c.1799T&gt;A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.447467</td>\n",
+       "      <td>A101D_SKIN</td>\n",
+       "      <td>wild-type</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A101D_SKIN</th>\n",
+       "      <td>IGF1R</td>\n",
+       "      <td>p.A241T</td>\n",
+       "      <td>c.721G&gt;A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.447467</td>\n",
+       "      <td>A101D_SKIN</td>\n",
+       "      <td>wild-type</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A172_CENTRAL_NERVOUS_SYSTEM</th>\n",
+       "      <td>IGF1R</td>\n",
+       "      <td>p.I947I</td>\n",
+       "      <td>c.2841C&gt;T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.533951</td>\n",
+       "      <td>A172_CENTRAL_NERVOUS_SYSTEM</td>\n",
+       "      <td>mutant</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Hugo_Symbol Protein_Change cDNA_Change KRAS_MUT  \\\n",
+       "A101D_SKIN                         BRAF        p.V600E   c.1799T>A        0   \n",
+       "A101D_SKIN                        IGF1R        p.A241T    c.721G>A        0   \n",
+       "A172_CENTRAL_NERVOUS_SYSTEM       IGF1R        p.I947I   c.2841C>T        0   \n",
+       "\n",
+       "                            HRAS_MUT NRAS_MUT BRAF_MUT ras_status    weight  \\\n",
+       "A101D_SKIN                         0        0        1          0  0.447467   \n",
+       "A101D_SKIN                         0        0        1          0  0.447467   \n",
+       "A172_CENTRAL_NERVOUS_SYSTEM        0        0        0          0  0.533951   \n",
+       "\n",
+       "                                             sample_name predictions  \n",
+       "A101D_SKIN                                    A101D_SKIN   wild-type  \n",
+       "A101D_SKIN                                    A101D_SKIN   wild-type  \n",
+       "A172_CENTRAL_NERVOUS_SYSTEM  A172_CENTRAL_NERVOUS_SYSTEM      mutant  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset MAF file to Ras pathway variants and merge with CCLE classifier scores\n",
+    "ras_pathway_genes = ras_core_df['genes'].tolist()\n",
+    "all_common_lines = set(ccle_maf_df.index).intersection(set(ccle_full_df.index))\n",
+    "\n",
+    "# Subset to common cell lines\n",
+    "subset_maf = ccle_maf_df.loc[list(all_common_lines), :]\n",
+    "subset_maf = (\n",
+    "    subset_maf.query('Hugo_Symbol in @ras_pathway_genes')\n",
+    "    .loc[:, ['Hugo_Symbol', 'Protein_Change', 'cDNA_Change']]\n",
+    "    .merge(ccle_full_df, left_index=True, right_index=True)\n",
+    ")\n",
+    "\n",
+    "subset_maf.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the mean classifier scores for CCLE nucleotide variants\n",
+    "mean_nuc_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .mean())\n",
+    ")\n",
+    "mean_nuc_data.columns = ['ccle_mean_weight']\n",
+    "mean_nuc_data = mean_nuc_data.reset_index()\n",
+    "\n",
+    "# Get the sd classifier scores for CCLE variants\n",
+    "sd_nuc_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .std())\n",
+    ")\n",
+    "sd_nuc_data.columns = ['ccle_sd_weight']\n",
+    "sd_nuc_data = sd_nuc_data.reset_index()\n",
+    "\n",
+    "# Counts of CCLE variants altering amino acids\n",
+    "count_nuc_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .count())\n",
+    ")\n",
+    "count_nuc_data.columns = ['ccle_count']\n",
+    "count_nuc_data = count_nuc_data.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "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>cDNA_Change</th>\n",
+       "      <th>Hugo_Symbol</th>\n",
+       "      <th>ccle_mean_weight</th>\n",
+       "      <th>ccle_sd_weight</th>\n",
+       "      <th>ccle_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>c.181C&gt;A</td>\n",
+       "      <td>NRAS</td>\n",
+       "      <td>0.626099</td>\n",
+       "      <td>0.134239</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1505</th>\n",
+       "      <td>c.34G&gt;T</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.644210</td>\n",
+       "      <td>0.146242</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1536</th>\n",
+       "      <td>c.35G&gt;T</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.734995</td>\n",
+       "      <td>0.121861</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1532</th>\n",
+       "      <td>c.35G&gt;A</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.686906</td>\n",
+       "      <td>0.142274</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>c.1799T&gt;A</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>0.611137</td>\n",
+       "      <td>0.146628</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     cDNA_Change Hugo_Symbol  ccle_mean_weight  ccle_sd_weight  ccle_count\n",
+       "601     c.181C>A        NRAS          0.626099        0.134239          15\n",
+       "1505     c.34G>T        KRAS          0.644210        0.146242          21\n",
+       "1536     c.35G>T        KRAS          0.734995        0.121861          33\n",
+       "1532     c.35G>A        KRAS          0.686906        0.142274          44\n",
+       "582    c.1799T>A        BRAF          0.611137        0.146628          59"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Of wild-type Ras cell lines, how many are predicted correctly?\n",
-    "# True Negative Rate, Specificity\n",
-    "negative_ras_lines_ccle['predictions'].value_counts()"
+    "# Merge protein data\n",
+    "nuc_merge_on = ['Hugo_Symbol', 'cDNA_Change']\n",
+    "nuc_change_df = (\n",
+    "    mean_nuc_data.merge(sd_nuc_data,\n",
+    "                        left_on=nuc_merge_on, right_on=nuc_merge_on)\n",
+    "    .merge(count_nuc_data, left_on=nuc_merge_on, right_on=nuc_merge_on)\n",
+    ")\n",
+    "\n",
+    "nuc_change_df.sort_values('ccle_count').tail(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 53,
+   "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>HGVSc</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Hugo_Symbol</th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>SD</th>\n",
+       "      <th>count</th>\n",
+       "      <th>low_CI</th>\n",
+       "      <th>high_CI</th>\n",
+       "      <th>cDNA_Change</th>\n",
+       "      <th>ccle_mean_weight</th>\n",
+       "      <th>ccle_sd_weight</th>\n",
+       "      <th>ccle_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c.1799T&gt;A</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>0.379832</td>\n",
+       "      <td>0.222155</td>\n",
+       "      <td>453.0</td>\n",
+       "      <td>0.358573</td>\n",
+       "      <td>0.400416</td>\n",
+       "      <td>c.1799T&gt;A</td>\n",
+       "      <td>0.611137</td>\n",
+       "      <td>0.146628</td>\n",
+       "      <td>59.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>c.35G&gt;A</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.819980</td>\n",
+       "      <td>0.126311</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>0.800026</td>\n",
+       "      <td>0.839292</td>\n",
+       "      <td>c.35G&gt;A</td>\n",
+       "      <td>0.686906</td>\n",
+       "      <td>0.142274</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c.35G&gt;T</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.825700</td>\n",
+       "      <td>0.127124</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0.805609</td>\n",
+       "      <td>0.844454</td>\n",
+       "      <td>c.35G&gt;T</td>\n",
+       "      <td>0.734995</td>\n",
+       "      <td>0.121861</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>c.34G&gt;T</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.812311</td>\n",
+       "      <td>0.110061</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>0.787996</td>\n",
+       "      <td>0.833271</td>\n",
+       "      <td>c.34G&gt;T</td>\n",
+       "      <td>0.644210</td>\n",
+       "      <td>0.146242</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>c.182A&gt;G</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>NRAS</td>\n",
+       "      <td>0.778051</td>\n",
+       "      <td>0.146488</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>0.743709</td>\n",
+       "      <td>0.806067</td>\n",
+       "      <td>c.182A&gt;G</td>\n",
+       "      <td>0.645535</td>\n",
+       "      <td>0.203996</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "mutant       153\n",
-       "wild-type     36\n",
-       "Name: predictions, dtype: int64"
+       "       HGVSc Variant_Classification Hugo_Symbol      Mean        SD  count  \\\n",
+       "0  c.1799T>A      Missense_Mutation        BRAF  0.379832  0.222155  453.0   \n",
+       "1    c.35G>A      Missense_Mutation        KRAS  0.819980  0.126311  166.0   \n",
+       "2    c.35G>T      Missense_Mutation        KRAS  0.825700  0.127124  157.0   \n",
+       "3    c.34G>T      Missense_Mutation        KRAS  0.812311  0.110061   98.0   \n",
+       "4   c.182A>G      Missense_Mutation        NRAS  0.778051  0.146488   91.0   \n",
+       "\n",
+       "     low_CI   high_CI cDNA_Change  ccle_mean_weight  ccle_sd_weight  \\\n",
+       "0  0.358573  0.400416   c.1799T>A          0.611137        0.146628   \n",
+       "1  0.800026  0.839292     c.35G>A          0.686906        0.142274   \n",
+       "2  0.805609  0.844454     c.35G>T          0.734995        0.121861   \n",
+       "3  0.787996  0.833271     c.34G>T          0.644210        0.146242   \n",
+       "4  0.743709  0.806067    c.182A>G          0.645535        0.203996   \n",
+       "\n",
+       "   ccle_count  \n",
+       "0        59.0  \n",
+       "1        44.0  \n",
+       "2        33.0  \n",
+       "3        21.0  \n",
+       "4         6.0  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Of mutated Ras cell lines, how many are predicted correctly?\n",
-    "# True Positive Rate (TPR), Recall, Sensitivity\n",
-    "positive_ras_lines_ccle['predictions'].value_counts()"
+    "data_s4_file = os.path.join('..', 'classifiers', 'RAS', 'tables',\n",
+    "                            'nucleotide_mutation_scores.tsv')\n",
+    "data_s4_df = pd.read_table(data_s4_file)\n",
+    "\n",
+    "# Merge the CCLE nucleotide scores\n",
+    "data_s4_df = data_s4_df.merge(nuc_change_df, left_on = ['Hugo_Symbol', 'HGVSc'],\n",
+    "                                    right_on = ['Hugo_Symbol', 'cDNA_Change'],\n",
+    "                              how='outer')\n",
+    "\n",
+    "updated_data_s4_df = data_s4_df.sort_values(by='count', ascending=False)\n",
+    "\n",
+    "updated_data_s4_file = os.path.join('..', 'tables', 'updated_Data_S4.csv')\n",
+    "updated_data_s4_df.to_csv(updated_data_s4_file, sep=',', index=False)\n",
+    "updated_data_s4_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "357 out of 393 Ras wild-type predictions are true (90.8%)\n"
-     ]
-    }
-   ],
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# Of the wild-type predictions, how many are actually wild-type?\n",
-    "# Negative Predictive Value (NPV)\n",
-    "neg_ccle_results = negative_ras_predictions_ccle['ras_status'].value_counts()\n",
-    "true_neg = neg_ccle_results[0]\n",
-    "predicted_condition_neg = neg_ccle_results.sum()\n",
+    "# Get the mean classifier scores for CCLE variants\n",
+    "mean_protein_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .mean())\n",
+    ")\n",
+    "mean_protein_data.columns = ['ccle_mean_weight']\n",
+    "mean_protein_data = mean_protein_data.reset_index()\n",
     "\n",
-    "print('{} out of {} Ras wild-type predictions '\n",
-    "      'are true ({:.1f}%)'.format(true_neg, predicted_condition_neg,\n",
-    "                                  true_neg * 100 / predicted_condition_neg))"
+    "# Get the sd classifier scores for CCLE variants\n",
+    "sd_protein_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .std())\n",
+    ")\n",
+    "sd_protein_data.columns = ['ccle_sd_weight']\n",
+    "sd_protein_data = sd_protein_data.reset_index()\n",
+    "\n",
+    "# Counts of CCLE variants altering amino acids\n",
+    "count_protein_data = (\n",
+    "    pd.DataFrame(subset_maf\n",
+    "                 .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n",
+    "                 .count())\n",
+    ")\n",
+    "count_protein_data.columns = ['ccle_count']\n",
+    "count_protein_data = count_protein_data.reset_index()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 55,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "153 out of 344 Ras mutation predictions are true (44.5%)\n"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein_Change</th>\n",
+       "      <th>Hugo_Symbol</th>\n",
+       "      <th>ccle_mean_weight</th>\n",
+       "      <th>ccle_sd_weight</th>\n",
+       "      <th>ccle_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>p.Q61K</td>\n",
+       "      <td>NRAS</td>\n",
+       "      <td>0.626099</td>\n",
+       "      <td>0.134239</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>p.G12C</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.644210</td>\n",
+       "      <td>0.146242</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>p.G12V</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.734995</td>\n",
+       "      <td>0.121861</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>p.G12D</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.686906</td>\n",
+       "      <td>0.142274</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2272</th>\n",
+       "      <td>p.V600E</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>0.611137</td>\n",
+       "      <td>0.146628</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Protein_Change Hugo_Symbol  ccle_mean_weight  ccle_sd_weight  ccle_count\n",
+       "1538         p.Q61K        NRAS          0.626099        0.134239          15\n",
+       "612          p.G12C        KRAS          0.644210        0.146242          21\n",
+       "619          p.G12V        KRAS          0.734995        0.121861          33\n",
+       "614          p.G12D        KRAS          0.686906        0.142274          44\n",
+       "2272        p.V600E        BRAF          0.611137        0.146628          59"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Of the mutated predictions, how many are actually mutated?\n",
-    "# Positive Predictive Value (PPV) -or- precision\n",
-    "pos_ccle_results = positive_ras_predictions_ccle['ras_status'].value_counts()\n",
-    "true_pos = pos_ccle_results[1]\n",
-    "predicted_condition_pos = pos_ccle_results.sum()\n",
+    "# Merge protein data\n",
+    "merge_on = ['Hugo_Symbol', 'Protein_Change']\n",
+    "protein_change_df = (\n",
+    "    mean_protein_data.merge(sd_protein_data,\n",
+    "                            left_on=merge_on, right_on=merge_on)\n",
+    "    .merge(count_protein_data, left_on=merge_on, right_on=merge_on)\n",
+    ")\n",
     "\n",
-    "print('{} out of {} Ras mutation predictions '\n",
-    "      'are true ({:.1f}%)'.format(true_pos, predicted_condition_pos,\n",
-    "                                  true_pos * 100 / predicted_condition_pos))"
+    "protein_change_df.sort_values('ccle_count').tail(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "510 of 737 Total cell lines predicted correctly (69.2%)\n"
-     ]
-    }
-   ],
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "total_correct = true_pos + true_neg\n",
-    "print('{} of {} Total cell lines '\n",
-    "      'predicted correctly ({:.1f}%)'.format(total_correct, ccle_full_df.shape[0],\n",
-    "                                             total_correct * 100 / ccle_full_df.shape[0]))"
+    "# Convert amino acid to 3 letters\n",
+    "protein_convert = [''.join([aa[x] if x in aa.keys() else x for x in y]) \n",
+    "                   for y in protein_change_df['Protein_Change']]\n",
+    "\n",
+    "protein_change_df = protein_change_df.assign(conversion = protein_convert)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 57,
+   "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>HGVSp</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Hugo_Symbol</th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>SD</th>\n",
+       "      <th>count</th>\n",
+       "      <th>low_CI</th>\n",
+       "      <th>high_CI</th>\n",
+       "      <th>ccle_mean_weight</th>\n",
+       "      <th>ccle_sd_weight</th>\n",
+       "      <th>ccle_count</th>\n",
+       "      <th>conversion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>p.Val600Glu</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>0.379832</td>\n",
+       "      <td>0.222155</td>\n",
+       "      <td>453.0</td>\n",
+       "      <td>0.360466</td>\n",
+       "      <td>0.401139</td>\n",
+       "      <td>0.611137</td>\n",
+       "      <td>0.146628</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>p.Val600Glu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>p.Gly12Asp</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.819980</td>\n",
+       "      <td>0.126311</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>0.800135</td>\n",
+       "      <td>0.838195</td>\n",
+       "      <td>0.686906</td>\n",
+       "      <td>0.142274</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>p.Gly12Asp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>p.Gly12Val</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.825700</td>\n",
+       "      <td>0.127124</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0.805637</td>\n",
+       "      <td>0.846128</td>\n",
+       "      <td>0.734995</td>\n",
+       "      <td>0.121861</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>p.Gly12Val</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>p.Gly12Cys</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>0.812311</td>\n",
+       "      <td>0.110061</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>0.789292</td>\n",
+       "      <td>0.834025</td>\n",
+       "      <td>0.644210</td>\n",
+       "      <td>0.146242</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>p.Gly12Cys</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>p.Gln61Arg</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>NRAS</td>\n",
+       "      <td>0.778051</td>\n",
+       "      <td>0.146488</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>0.748909</td>\n",
+       "      <td>0.808280</td>\n",
+       "      <td>0.645535</td>\n",
+       "      <td>0.203996</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>p.Gln61Arg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0    135\n",
-       "1     56\n",
-       "Name: BRAF_MUT, dtype: int64"
+       "         HGVSp Variant_Classification Hugo_Symbol      Mean        SD  count  \\\n",
+       "0  p.Val600Glu      Missense_Mutation        BRAF  0.379832  0.222155  453.0   \n",
+       "1   p.Gly12Asp      Missense_Mutation        KRAS  0.819980  0.126311  166.0   \n",
+       "2   p.Gly12Val      Missense_Mutation        KRAS  0.825700  0.127124  157.0   \n",
+       "3   p.Gly12Cys      Missense_Mutation        KRAS  0.812311  0.110061   98.0   \n",
+       "4   p.Gln61Arg      Missense_Mutation        NRAS  0.778051  0.146488   91.0   \n",
+       "\n",
+       "     low_CI   high_CI  ccle_mean_weight  ccle_sd_weight  ccle_count  \\\n",
+       "0  0.360466  0.401139          0.611137        0.146628        59.0   \n",
+       "1  0.800135  0.838195          0.686906        0.142274        44.0   \n",
+       "2  0.805637  0.846128          0.734995        0.121861        33.0   \n",
+       "3  0.789292  0.834025          0.644210        0.146242        21.0   \n",
+       "4  0.748909  0.808280          0.645535        0.203996         6.0   \n",
+       "\n",
+       "    conversion  \n",
+       "0  p.Val600Glu  \n",
+       "1   p.Gly12Asp  \n",
+       "2   p.Gly12Val  \n",
+       "3   p.Gly12Cys  \n",
+       "4   p.Gln61Arg  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Of the False positives, how many are BRAF mutant?\n",
-    "wt_ras_braf_ccle = positive_ras_predictions_ccle[positive_ras_predictions_ccle['ras_status'] == 0]\n",
-    "wt_ras_braf_ccle['BRAF_MUT'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Including BRAF mutants, 209 of 344 Ras mutation predictions are Ras pathway activated (60.8%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "correct_braf = wt_ras_braf_ccle['BRAF_MUT'].value_counts()[1]\n",
-    "true_pos += correct_braf\n",
-    "print('Including BRAF mutants, {} of {} Ras mutation predictions '\n",
-    "      'are Ras pathway activated ({:.1f}%)'.format(true_pos,\n",
-    "                                                   predicted_condition_pos,\n",
-    "                                                   true_pos * 100 / predicted_condition_pos))"
+    "data_s5_file = os.path.join('..', 'classifiers', 'RAS', 'tables',\n",
+    "                            'amino_acid_mutation_scores.tsv')\n",
+    "data_s5_df = pd.read_table(data_s5_file)\n",
+    "\n",
+    "# Merge the CCLE protein scores\n",
+    "data_s5_df = data_s5_df.merge(protein_change_df, left_on = ['Hugo_Symbol', 'HGVSp'],\n",
+    "                                    right_on = ['Hugo_Symbol', 'conversion'],\n",
+    "                              how='outer')\n",
+    "\n",
+    "# Sort by the total number of mutations observed\n",
+    "updated_data_s5_df = (\n",
+    "    data_s5_df.drop(['Protein_Change'], axis=1).sort_values(by='count', ascending=False)\n",
+    ")\n",
+    "\n",
+    "updated_data_s5_file = os.path.join('..', 'tables', 'updated_Data_S5.csv')\n",
+    "updated_data_s5_df.to_csv(updated_data_s5_file, sep=',', index=False)\n",
+    "updated_data_s5_df.head()"
    ]
   },
   {
@@ -1968,27 +3177,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 58,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -2203,7 +3410,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2220,10 +3427,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 59,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2235,25 +3440,25 @@
     {
      "data": {
       "text/plain": [
-       "TKI258          388\n",
-       "PF2341066       388\n",
        "Lapatinib       388\n",
-       "TAE684          388\n",
-       "Topotecan       388\n",
        "AZD0530         388\n",
-       "Nutlin-3        388\n",
+       "TAE684          388\n",
+       "PF2341066       388\n",
+       "TKI258          388\n",
        "PD-0325901      388\n",
+       "Nutlin-3        388\n",
+       "Topotecan       388\n",
+       "Erlotinib       387\n",
        "LBW242          387\n",
        "Sorafenib       387\n",
-       "AZD6244         387\n",
-       "Paclitaxel      387\n",
-       "Erlotinib       387\n",
        "AEW541          387\n",
-       "PHA-665752      387\n",
+       "Paclitaxel      387\n",
        "17-AAG          387\n",
+       "AZD6244         387\n",
+       "PHA-665752      387\n",
        "Panobinostat    384\n",
-       "ZD-6474         381\n",
        "PLX4720         381\n",
+       "ZD-6474         381\n",
        "L-685458        376\n",
        "RAF265          349\n",
        "PD-0332991      324\n",
@@ -2262,7 +3467,7 @@
        "Name: Compound, dtype: int64"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2276,10 +3481,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 60,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2308,7 +3511,7 @@
        "Name: tissue, dtype: int64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2319,10 +3522,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 61,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# What is the cell line tissue representation?\n",
@@ -2337,16 +3538,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 62,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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xoU2bNkpDRMgY6+CFF17g77//pkuXLlSqVImqVasya9YsbG1t0ev1GAwGJX45\nO4k5FkKI0k8H5BUtlPNz3mDI+pBXqR4uf7hScfnwz0+BRcErr7yCra0tb775JgsWLKBy5cqkpKRw\n9uxZfv31V1566SUCAwPp0aNHkZ5kfhHJw4YNY9myZeh0ulxtCrJr06YNixYtolmzZnh5ebF69Wp2\n7NhBmTJlcHJy4urVqxw4cIC5c+dy/fp1li9fzuzZs3n77beZM2cOKpUKtVrN5MmTi/Q6hRBCFF/p\nhowpU/ZiwJBtMmIwfpygyrZ+jtWKhQKLgpCQEOX2fCZra2teeOEFXnjhBaZNm8bVq1ctflKmRiRX\nqlSJefPmGW2bPdI4cx8vv/wyL7/8sjJ/+fLluY45f35Gv+0aNWoo+8wcASu7rl27PtY1CSGEKNm0\n6NE+/FjP2VCwMHIWCI+7n6JQYE5BzoIgJ2tra+rUqWPRExJCCCGKo/tpEaQbIC3blP4YU+a2qdl+\nTk79J9fxxo0bR5s2bfDy8mLz5s0YDAbGjx+Pt7c3vXr14s6dOxa/xgITDb28vPIc5CHTwYMHLX5C\nQgghRHF0KKo7D7SXHr4yPHwskPERWsBHZb4y2x4YUNGqynbK2mal0J46dYrx48eza9cu7t+/T/Pm\nzVm+fDnffvsta9asYcOGDZw5cybPwf3MUeDjg4ULFwLw008/ce7cOYYPHw7AunXrCh0m9KyRmOMM\nEnNsrLjEHPu0nffoFQvw2/6ZFnkvLBGVLDHHGSTm2FhRxBwbVGXQGvLp6WYwZD0WKKBAyPwanvVt\nPGNlldr447hq1arY2NiQnp7O/fv3qVChArt376ZXr14A9O7dmxUrVjzWdRSkwKKgY8eOAMyePZv/\n/e9/yl2DXr168dJLLxk99y9K0dHRTJo0iZo1a5KSksLbb7/N2rVrWbZsmbLO3bt3mT17NiEhIdja\n2hIWFkblypXp3r07ALdu3WLs2LEEBwdTt25dfvjhB+VOx82bN+nfvz+dO3cmKCgIrVaLwWBg1KhR\n1K5dG71ez4oVK4iMjKRChQp88MEHko4ohBDPGK1BTXp+RUF2BgMqDKhVykv0ebYkyJ+zszN169al\nXr16JCUlsWrVKn766SecnZ0BKF++fJE8PjAppyAqKoqUlBTs7e0BSE1N5fr16xY/mYLkjD3OydnZ\nGR8fH7799ls6derE2bNnGTJkiLL8m2++MWoj0bdvX/r27QvA5MmTadOmDdbW1kycOBEXFxeioqII\nDQ1l3rxMjXYHAAAgAElEQVR5HDlyBLVaTVBQEFu3bmXnzp1GDReFEEKUftZx7+KqrZxr/i3nobkb\nDRoMYFDlumuQeYeg8t0wo/kPXNIpUy7r9fbt27l+/ToXL17k3r17eHt74+PjQ3x8PAD37t1TCgRL\nMqkoGDhwIG3btmXgwIFAxgds5s9PWmbssZ2dXa5lPXv2ZNq0aRw/fpz33ntPubPxzz//ULZsWfT6\n3DlSN27cwMbGBjc3NwDlDoBGo1ECkU6fPq0Mgenl5cVXX30lRYEQQjxjkips4F7audwLDJpsvRGU\nVgaA4WGXREOulMOo8u8a7aJ+mf8a79JgwNnZGY1Gg6OjI2lpabz00kv8+OOP9O3bl/DwcOVuviWZ\nNEpiQEAA8+bNIzY2lpiYGOX105AZe5wXtVpNo0aNSEtLo3bt2sr8b775hv79++e5zZ49e3jxxReN\n5hkMBtasWaNsk5iYSNmyZQEoW7YsiYmJlrgUIYQQJYgWDenZpjRDxpRqyPzZinSDBq1Bg9agfviv\nhnSDFWkGK1KzrZueY185W/x369YNvV5Phw4daNeuHePHj6dnz55YW1vj7e3Nl19+yZQpUyx+jSbH\nHPfu3ZvevXtb/ARMlTP2eMmSJbnWuXXrFqdOncLDw4Pff/8db29vzpw5Q+XKlfO9zfLnn38qGQWZ\nVq1aRZMmTWjatCkADg4OJCUlAZCcnIyDg4PR+hJzLIQQpV9M6mUMD9sU5GwoWGiGrPsJAPfTbuBg\nXV1ZrFarWb9+fa7N8srZsSSTioKIiAgCAgK4dOkSWm1WyOOT7JKYPfY4P6tXr+bdd9+levXq+Pv7\n4+npyaVLl4iIiGDWrFlcvXqVGzduMGPGDJycnLh06RJubm5Gdx6++eYbNBoNr776qjKvUaNGHDly\nhFatWnH48GEaNmxodFyJORZCiGdBRR7o4x7+bFpvg0fJ6JZowFbtau7JWYRJRcGbb77J66+/zrBh\nw57YgEiPkpaWxsyZM5XXr7zyCk5OTkoMcu/evfnqq68YNmyYcodjyZIlynoAe/fuxdvbW9lHTEwM\nmzZtokGDBvj5+eHi4sLkyZNp1aoVBw8eZNq0aTg7OxulJgohhHg2WGkqkqa9l3uBIavNgCkFQkZv\nhEwZG2g0udvJPQ0mFQV6vR4/P/P60JojZ+wxwMqVK3Ot16ZNG+Xnl156KdfynB/mw4YNM3rt5ubG\nDz/8kGs7tVrN+++/X6hzFkIIUbroUKPlEV+MsxcImbOy/Wv80KD4MakoaNu2LX///bfyjF0IIYR4\n1ugeNhw0icGASmXI/pCh4NWLSaFQYMxxphYtWnDmzBk8PDyMugJKzLEQQohnRdjl97mdcinf5Vmx\nxVlMfawwos5anG2qWuAszWPSnYK8WvqLgknMcYajoRMtEmUrMcdZwjevNmt72yqX6dZh/qNXLMD2\nP/wt8nttPWSRWfs4sGGSxBw/JDHHxooi5jgj0dC4J39GIWDC3QClt0F+YyUUjzsFJhUF5gYkmBJT\nPG7cOKPXkyZNolmzZgwdOjTPfZ4/f56NGzei0+lo1aoV/fv3R6fTsXbtWiIjI9HpdEybNg0nJyde\nf/116tatC8Brr71Gq1at8ow57t27N35+fmi1WqysrGjZsiUDBgzgr7/+YsuWLVhZWeHi4sLEiROx\nsjK5N6cQQohS4FbKDeXxgamPBfKWFWqUuYd7qXdxtqlikfM0h0mfbPmNlliYxwePiinO7vr161Sp\nUoWTJ09iMBhyHTs9PZ2vvvoKf39/ozEIfv75Z+rVq8fIkSON1q9UqRKBgcYVdF4xx5kyuyxmqlWr\nFkFBQVhZWREWFsYff/xBp06dTL52IYQQJZ/WYE+KTo8yiFHmAnO+5D9smKhR25h3chZiUlGQOVoi\nQEpKCps3b6Zq1cd79lFQTHGmvXv30qVLF44dO8a5c+eUboaZIiIisLGxISgoCJ1Ox7Bhw6hVqxZ/\n/fUX9erVw8/PjwYNGvCvf2XcBouNjWXatGm4ubkxcuRIow/8nDHHKpWKwMBAbG1tGTp0KO7u7lSq\nVElZP3v8sRBCiGeHo3U1ErT/5Jib+a0/g6ldEh9umbkV9lYO+az9ZD3W4wMfHx98fHwe64CZMcWJ\niYlG3Rzv3r2r/HzixAneeOMNypcvz44dO3IVBXFxcVy7do1PPvmEmJgYli1bRnBwMHFxcVStWpUh\nQ4awaNEiJXBo5cqVODk5sX37djZu3MjYsWOVfeWMOfb19cXJyYnIyEg++eQTPv30U2XZzZs3OXr0\n6FMb90EIIcTTo3sYXVyghyMk5mxcmNUIMb/HDiWoTUFOCQkJXL58uVDb5BVTnP2W/rhx4wC4dOkS\nMTExzJ07F8j4INbpdGzcuJGIiAg8PT2pWbMmDRo0wM7OjurVq5OcnAxkjEvQokULIKPHxJUrV2jV\nqpVyZ+DFF1/k119/NTqvnDHHmevWqFEDKysrUlNTsbW1JT4+nkWLFvHhhx9ibW1ttA+JORZCiNJP\nZ1CjM5g0ZBBgQGUoTJfE4qHQbQr0ej2XL19m8uTJhTqQKTHFkPHoYMKECTRr1gyAjRs38vfffxs1\nOExMTOTbb79Fr9dz79495UO6cePGXLx4ERcXFy5evEijRo1ISUnB2toajUbDqVOnqFIlqyFHXjHH\nycnJlClThrt375KSkoKtrS0PHjwgKCiI4cOH5/nYRGKOhRCi9NMZVGgfURRk3hHIZIko5Cep0G0K\nrKysqFWr1mO3KSiIwWDg4MGDvP3228q85s2bs3PnTuUOAGQMUNS1a1elp8C772YMQdm/f38+/fRT\nfvjhBypWrEjr1q35559/WLp0KWXKlMHa2prx47O6yuSMOdbr9UrjRZ1Ox6hRowD48ccfuXHjBhs2\nbAAyRq/q3Lmzxa9fCCFE8aVDnbsoyJVNkNen/6PbHTw6MejJMLlNgVarJSIiApVKRcWKFQt1kLxi\nirN3P8z++osvvjCa37hxYxo3bpxrn926daNbt25G85ycnIzGQwCoU6eOUbuA7HLGHKvVahYvzt0v\n/8033+TNN83rxyyEEKJku5VyB61Bna2h4GN2S8yWWZBZICSmJ1OxGAx/YFJRcPjwYV577TVsbW0x\nGAxotVq+++47WrZsWdTnJ4QQQhQLiek60pWGhpZ6HpBRWqTo0yy0P/OYFHPcvn175s2bR5cuXQDY\ntWsXM2bMYN++fUV+gkIIIURx8OGJQP5JupZj7uMNoWzc9kDF8pZzqWznZvY5msukOwVJSUlKQQDQ\nuXNnkpKSiuykSgOJOc4gMcfGLBFzvP2r9WZtr658XmKOs5GY4ywSc1wwvV6NTl9QQ8OshML82g3k\n1/agRLUpKFOmDLt27VIa1+3Zs4cyZcqYffC84o9XrFiBq6srkJEk+NZbbxEUFIRWq8VgMDBq1Chq\n167NkSNH+Prrr9FoNJQtW5bJkydjb28PgE6nY9y4cfj4+NCvXz/i4uIICAjg+vXrzJ8/X4k8vnnz\nJkuXLkWr1dK1a1e6d+8OFC5CWQghxLNBZ1CZ3iXxYV4BmBuJ/GSZVBR8+umnDBgwQIkUTktL47vv\nvrPICeSMP7axsTHKL0hNTWXixIm4uLgQFRVFaGgo8+bNo2nTprRq1QqATZs28fvvvyuBSjt27DDq\neujo6MicOXNYu3at0bHDwsIYNmwY7u7u+Pr60q5dO+zs7AoVoSyEEOLZoEeFzvCIvAGjtEJVxv+K\nfy2gMDmn4OLFi0RERGAwGKhfv36uAB9z5Rd/bGtrq3w4Z48Yzn781NRUqlevDmQULAcPHqRdu3bc\nv38fABsbG2xscudKX79+Xblr0LhxYy5cuKCsa2qEshBCiGeDTq9Gm+3xQeZnvSHHv7nuCBiyPVbI\ntW7m69yVw5EjR5g+fTrp6el4eXkRHBzMhAkTOH78OOXKlWPDhg1UqFDhsa8nLyYnGkZERLB7925U\nKhUajYaGDRta9ETyij/OngdgMBhYs2YN/fv3V7bZs2cP3333HTY2Nsr8//73v/To0YOEhIRCHb9s\n2bJKEVGYCGUhhBDPhrtpD5THB8ZtAApzKyBH6fBw0zStce+DtLQ0pk2bxtatW3F0dATgl19+ITk5\nmd9//50NGzYQEhJCUFBQoa+jICY9HFm+fDndu3fn77//5vjx4/j4+OTKE3hcmfHHa9euZezYscrj\ng8DAQKOAoFWrVtGkSROaNm2qzOvYsSOfffYZHTt2ZOvWrSQlJXHq1Cm8vLxMOnb20ReTk5NxcHDA\nwcHB5AjlTKGhoXh6euLp6UloaKg5b4cQQohi6uaDBNJ1atJ0atL12SdVIaas7dL0WfuKSbtvdKz9\n+/fj4ODAoEGD6NKlC7///ju7d++mV69eAPTu3Zu9e/da/BpNblNw7NgxJbQoJiaG9u3b895775l9\nAqbEH3/zzTdoNBpeffVVZV56erryCCHzW35UVBQJCQnMmjWLO3fuoNVqcXd3VyKTc6patSqXLl3i\n+eef59SpU/Tv3x+VSmVyhHImiTkWQojSr2aZ6py/fyPbHOOHAIVtOmDItosq9q5Gy27cuMGJEyc4\nfvw49+/fp2vXrnh7e+Ps7AxA+fLluXPnTiGP+GgmFQVVqlQxSjF0c3OjcuXKFj+ZvMTExLBp0yYa\nNGiAn58fLi4uTJ48me3bt/PHH38AGUXBBx98gIODgxLJvHPnThISEmjWrBlarZY5c+Zw7do1oqKi\n6NChA3379mXIkCEsXboUnU5H165dlVs0pkYoCyGEeHa8adWMii7tc80fG7OFXAVCgVHGKj53G2C0\nLP1BKmTr1FehQgXatWuHk5MTTk5OuLq6otPpiI+PB+DevXtKgWBJJhUF7du3Z8SIEQwfPhyA9evX\n0717d86cOQPw2O0LTIk/dnNz44cffsi17csvv8zLL7+c7767du2q/GxlZcW8efNyrVO1alUWLFiQ\na76pEcpCCCGeHRtTTxKRcCOPJdmbEGZRq3L2RsgyOtq4B98P9Y0HGWzdujUzZ85Eq9Xy4MEDbt++\nzfTp0/nxxx/p27cv4eHhdOzY8fEvJh8mFQWbN28GMr59Z7d69WpUKlWhh1EWQgghShqDQYXehJyC\nzKBgHcZt1wrcJsfr8uXLM378eDp16kR6ejrBwcH07NmT8PBwvL29cXJyUgbpsySTYo6FEEKIZ92b\nez7nXMLNPJfln1ZoMJqTX42wrctEqpe1bPfCx2Fyl8Tk5GSioqLQarXKPEt3SyxNelYyrxHmz9Ff\nFIuYY69h5sXQHlo3ifYDFj56xQLs2/IhPm1zP/4pjN/2z7TI76Rz92Cz9rHrV1+z41u9vjxt1vbz\nmnxvkffCEjHHloiNLg4xxzEjzWtjdOzziRa5DkvEV1sk5tgCf1/FMeY4Va83Ci8yrVtijgIh2zbZ\nC4Ti8v3cpKLgs88+w9/fH2dnZyU8qLCPDfKLNHZzc0OlUmFjY8Nbb72lhAlld+jQIb755htUKhUj\nR46kbt26+cYcr1ixgqtXr5Kamkrnzp3p3bs3586dY82aNVhZWWFvb8+kSZNwcHDgzJkzrF+/HrVa\nTbdu3ejatSt6vZ6lS5dy69YtpQGjo6MjO3bsYMuWLVhZWeVq9yCEEKL0O38vFkO2oZMLL+/CQaWC\nK/fjqOHg8tjnZikmFQVLliwhIiKCqlWrmnWwvCKN58/PqGxv3LjB/PnzWbBggdGYAjqdjo0bN7Jg\nwQIePHhASEgIwcHB+cYcDx8+HGtra2X8gx49euDm5kZAQAC2trb8/PPPhIeH88Ybb7B27Vr8/f1x\ncnLC39+f1q1b8/fff+Pg4MCCBQs4cOAAW7duZejQoXh5edGpUyf+/e9/m/UeCCGEKJncy1ThfMLt\nbHOyPTB4jCjj7MVFzWJQEICJ4UXPPfec2QVBdpmRxtlVrVqVtm3bcuTIEaP5N27coFq1apQpUwYX\nFxd0Op1RRgEYxxxnzk9LS6NSpUpYWVnh4uKSZ1RyamqqcvejWrVqXLhwgZs3b1K7dm0AateuzenT\nGbdqy5Urh5WVyU9bhBBClDIalQaDQZVtUiuTXq9SJuN1jCfj9bK2N7VBYlEzqSiYM2cOI0aMYOvW\nrYSHhyvT48qMNM7JxcUlVxhDYmIiDg4OyuvsccR79uxhwoQJnD592qhoWbx4MaNHj6ZevXpGb3RC\nQgLh4eFKd0N7e3uioqJITU3l3Llz3L9/n5o1a3Ls2DEAJTRCCCGEMOgLmlTKpNdlTDlfZ5+XMWXb\n/mlf3EMmffXdtm0b27Zt4/z580ZtCgrKCchLZqSxRqNh7NixLFmyxGh5XFwc1apV4z//+Q9//fUX\ntWvXxsfHh6SkJGWdpKQkpUjo2LEjHTt2ZNu2bWzdupVhw4YBMHHiRNLS0pgxYwYdOnSgRo0apKam\nEhwczOjRo5WC5L333mPlypVYWVlRvXp1KlSoQOPGjTl79qwy8JGLy6Nv6YSGhrJq1SoARo4cKemG\nQghRChnI6JZo8vp6TI85LMR+i5JJRcH333/PlStXsLe3N+tgBUUa37p1i/379xMYGEi5cuXo06cP\nkNGm4Pr166SkpPDgwQM0Gg02NjZ5xhxDVvyxtbU1tra22NjYoNPp+Pjjj+nVqxcNGjRQjlmrVi3m\nzp1LamoqQUFBeHh4ACgjIO7evdukxCiJORZCiGfAw2/4pnm4XjHpVWAqk4oCd3d3iw+VDBnP/f39\n/VGpVFhbWzNhwgTKlStntI5Go+Gtt95i5syZqFQqJVUxr5hjgODgYJKTk9FqtbRt25bKlSuza9cu\nTp8+TXJyMtu2bcPT05P+/fuzdetWjhw5glqtZvDgwVhbW3Pv3j2Cg4PRaDRUr15diTk+dOgQ//nP\nf7h9+zYzZ85kxIgR1KxZ0+LviRBCiOJJ/7BdgCLX531eBUPuzIJ8Vy0GTCoK6tatS5cuXejbty92\ndnbK/LFjx5p8oLwijVeuXGnStq1bt8411kB+McczZuTu29q5c2ejERcz9e/f32goZshoUBgYGJhr\nXS8vL5NHXxRCCFH6nI2LwWDWp3n+BUVkwl1qOlp+LIPCMqkoSE1NpXbt2pw8eVKZV1xaSgohhBBP\nQmVbJ64nJeReYM7H4cPioKK9oxk7sRyJORZCCCFM8MqP6zkdd/vRK5oi85P3YUGxZ8BIajqVkDsF\nBoOBlStXsmPHDlQqFd26dWPEiBFyt6AAPd0/NGv7ny8vLBYxxy1HLzZrH0dDJ1okDrfjyyFm7WNP\n+FSLxMhaIpa3a6fcj6cKY/tX683aXl35vEXicC3xe209xLwY7QMbJhWLmOOoqebFHJ+ZP5GeDf3M\n2sfPZwLN/tvaudvPMjHHFvidFMeYY/SqjKlAuQONcn/1LqhdwtNlUlEwdepUjh07pnT5CwsL48KF\nC4SEmPYf6icVcTx37lwSExPR6/UMHjyYFi1aGB0bYMSIEbi7u+cZcQzw66+/snPnTqysrJgwYQKV\nK1fmyJEjrF27ljt37igjRgohhHi2qPQZ0yPWMmFO8WVSUfDrr79y9OhRJdHvjTfeoFWrViYXBfBk\nIo5HjBhB1apVSUhIYMaMGbRo0cLo2NnlFXFsMBjYvn07wcHBXL58mbCwMHx9falXrx6LFi1i8mTj\n8a6FEEI8QwzAI4uCTCoKdRugmNwxMCnR0GAwGD0qUKlUjz2iU1FGHGemGlpbWxud74ULF5g2bRpf\nfPEFqampynY5I47Pnz9P48aN0Wg01K1blxs3bgDg6OioxCQLIYR4RhlUuSd9fhMFLMtjP8XkfoJJ\nRUH37t3p2bMnmzZtYvPmzbzyyiv06NHjsQ5Y1BHHAOvXr1fCjypUqMDKlSsJCgrCycmJbdu2AXlH\nHOc8nl5vckkohBCilLsQGwc6jKeCPvgLKhiM9gHR9/Po1fAUmPT4ICQkhJUrV7J161YMBgP9+vVj\n1KhRhTrQk4o4/uGHH7CyslLaCGSmGwJ4e3vz1VdfAXlHHKempnLlyhXleGq1STWTxBwLIcQzoKza\nhrS0lNwLCv0lP3dDwzLWxeNudIFFgU6nIzU1lTJlyjBmzBjGjBkDQHJycqEfHzyJiOPdu3cTERHB\nlClTlH0nJydTpkwZAE6dOqXcVcgr4jglJYWvvvoKnU7HlStXqFKliknXJjHHQghR+lVzKEd8cmoe\nSwqZVJhHbwTHYvKIusCiYNq0aXh4eDBixAij+atXryYqKqpQDQ1zsnTEsU6n47PPPsPd3Z0ZM2ag\nVqsJCAjg9OnTbNq0CTs7OxwcHJQ45Lwijq2trenSpQvTp09Ho9EwfnxGt5oLFy6wYcMGJeJ4wIAB\nNGvW7LGvXQghRMmjMmRMeSzJ+tGQ9+zi0pDwUQosCsLDwwkKCso1//3336dZs2YmFwVPKuJ469at\nueblF0+cV8QxQM+ePenZs6fRvLp16zJv3jyTzlcIIUQpZVKXxGwK2QEhL+fPn6dRo0bs2rULT09P\nhg8fTmRkJDVq1GDNmjVGQw9YQoEPzdVqtTJUcnYajcbk5+1CCCFEqWAwYdJnm3TZfjZl2zzMmzeP\njh07AhmN6OvXr8/vv/+Oh4cH69evt/glFhhz7OHhwbFjx5Rn8pkSExNp1aoVERERFj8hIYQQojh6\ndf1GzkQ/KuY4v0YFBd8y2DHqXWo6lzead/DgQb799ltiYmIYMWIEy5Ytw9fXlxYtWnDs2DE+/vhj\nNm3aZPoFmKDAxwcDBw5k6NChrFmzRulGeO/ePUaPHs3rr5sXwVvaScxxBok5NiYxx1kk5jiLxBwb\nK64xx5F37j3MFTCeb1rng6y1DHnMvvfgAeQoCgICAli3bp0SnHfnzh2cnTPGRyhfvjxxcXGFOn9T\nFFgUfPTRR7zzzjtUq1ZNiSC+cOECffr0Yfbs2RY/GVNFR0czcuRI5syZo0QZh4aG5tm7ITo6mmvX\nruHp6QmAn58f06ZNIzY2llOnTik9HQpy9+5dFixYgEajQa/XM3bsWCU2WQghxLNhZovG1HRyyDX/\nrV/2Fmo/KmBzjxeN5qU/MO7V8NNPP+Hp6YmLi4syr0KFCsTHxwMZX9ArVKhQqOOaosCiwMrKio0b\nN3Lx4kWOHTuGwWCgZcuW1KlTx+InUljVq1dny5YtSpRxfm7fvs3hw4eVoiCTu7s77u6mfZN2cnIi\nKCgItVrNyZMn2bJli0QeCyHEM+b/LkRx5lbuxweqXD882qCfjAuJ7eOGGb0+fvw4u3fv5s8//+Tk\nyZOcO3eOnj17Eh4eTvPmzQkPD1faGliSSeFFderUKRaFQHaVKlXCwcGBU6dO4ebmBmTdBXBycmLF\nihW0b9+ebdu2cf78eSIjIxk7dqyy/cmTJ9m3bx9jxozh/fffp3Xr1pw6dYq6desycuRIo2Nlb2yZ\nlJRErVq1nsxFCiGEKDZMGxCJrOIgry6JJvZG8Pf3x9/fH4B33nmHESNG0KpVK9599128vb157rnn\nWLdunYlnbroCuxC0b9+er7/+mrS0tFzLLly4wMSJE/n8888tflKmev3119myZUuB6/Tu3Zs2bdoQ\nGBjIc889l+c6qampdO3alZCQEE6ePElycnKudSIjI5k6dSorV66kcePGFjl/IYQQJUxheiDkiDIu\nsBdCAdavX0+HDh2wt7dn8+bN/P7772zevNni3RHhEUXBli1b2LNnD9WqVaNdu3b069ePnj174u7u\nzsCBA2nTpo3Rt+8nrXr16tjb23P+/HkAo0GQCsPW1pZq1aoBGQM2JSYmEhYWhp+fn5J9UKNGDUJC\nQpg5c2aujIXQ0FA8PT3x9PQkNDTUjCsSQghRbBmyAozynfR5TCZsV1wU+PigSpUqfP755yxevJhD\nhw4RFRWFvb09TZo0Mfl5fFF74403WLx4Ma6urpQtW5bY2FgcHR35559/aN++PVZWVo8c2ChnMWEw\nGBg6dKjyOmescs4REyXmWAghSr98Hx8U5kP98XosPjGPbFOg0+kIDAxkzpw5T+J8Cq1WrVpUrFgR\nvV5Pr169WLRoEdWrV6ds2bIA1KxZk8jISIKCgnjnnXce6xiXLl0iLCwMtVqNwWBQ4paFEEI8O2IT\nkjIeAZgjnw//B3k8pn8aHlkUaDQa9u4tXHeLopYzNnnGjKz+rMuWLcu1fvao5sDAjH68TZo0oUmT\nJrm2yatbY/369VmwYIH5Jy6EEKLEuns/GZWuCHasgruJD4pgx4VnUlbxK6+8wsKFC7l9+zbJycnK\nJIQQQjwrPNzcUOnIf8qrPUF+U/bttFDVudyjT+AJKDDmOFNe4xyoVCp0uqIomYQQQoji541FX3L2\n+qNijh/Kq+1AAZ+24dOHUd21fP4rPCEm5RQ8qqGeyK2H07BHr1SAXxLWmR9z/Fw1s7b/OXIJXsPM\ni6E9tG4S7QcsNGsf+7Z8iE9b80ap/G3/THpWes+sffwc/QVduuYeNbQw/rdzmtnxrV5fnjZr+3lN\nvrfIe9FuoHkxx39+XXziq82NOY5917yY46OhE+lRYeSjVyzAL3dWWSS+2hIxx5b4758lYp8trhCj\nJBog1yiJj9c/7skyeajDuLg4fvrpJ8LDw7lz505RnlO+oqOj6dOnD8eOHVNez507ly1bthAVFaW8\nzs/ly5c5e/as8vrTTz8t8HiZyzdt2sS+ffsscAVCCCFKNBNzCpRHBNm6JBZ2hMSnwaSi4Ndff6V+\n/fosWbKExYsX06BBA7Zv317U55anzHjj7AYMGJBvMFF2//zzD+fOnVNef/DBBwWu/6jlQgghnh0m\ntxEozLLMuw/FpDAw6fGBv78/e/fupUGDBgCcO3eOt99+m27duhXpyeUlr3jjJUuW8MorrygjOQKc\nOXOGdevWoVKpaN68OYMGDeI///kPDx484NChQ/j7++Pr68uyZcvYtGkTcXFx3L17l3v37jFjxgyc\nnZ0ZN26c0jPh4MGDbN++nZSUFKZMmWI0SIUQQojSLzU1PSto6HE/xLNvl+15gq6YPKY36U5Benq6\nUn++7TkAACAASURBVBBARhe99PT0IjupRzEl3njNmjVMmzaNkJAQLl68yOXLl+nTpw89e/YkMDBQ\nyTHIVK5cOT766CO8vb3zfFTg4ODA7NmzefXVV/nuu+8sej1CCCGKv8hbdzMii7VAQb0QTJzQZu0r\nKubeU722TCYVBW5ubqxfv155HRYWpnxLfxpyxhvnJS0tTfk27+HhwY0bNwrcZ2ZCo5ubG/fv38+1\nPHPo6Hr16uXal8QcCyFE6Ve3sitqHaj1GVNho4xzTpn7UeugZiXnp315gIlFwYoVK1ixYgX29vbY\n29uzYsWKp/7h98Ybb/Dtt9/mu9zGxoa4uDgAIiIiqFq1KlZWVvl2o8wedZxXL82LFy8CGQNBValS\nxWjZ6NGjOXz4MIcPH5a4YyGEKKU0qFDpDXlPumxTfusUsF6JalNQp04d/vrrLxITEzEYDDg6Ohb1\neT1S9njjvLz77rssWLAAtVpN06ZNcXd3p2zZsvz0009cvHiRCRMmFOp49+/fZ9asWUqbAiGEEM8W\nk4dOhoz2ApnfNU3oYVBcuiuaVBR06NCBP/74AwcHh1zznqSC4o0zZS5v1KgRCxca94+vVKkSISFZ\n/aIzGxEOGjRImde+fXvat2+f73IhhBDPJpNHNMynMWFJYFJRkDPSWKfTPbWsAiGEEOJpyBwaOU+m\nFAsloEAoMOb4448/JiQkhHv37lG+fFb8YnJyMoMHD37q7QqEEEKIJ+XtGRu4EBmTNcOcdgA5CoTv\nPh7Oc5WKeczxqFGjeP311xk3bhzLly9X5js5OeHsXDxaShZXPd0/NGv7ny8vND/muI67eedwPpiW\noxebtY+joRNpM9i8ONy/viw+cbgv9vnYrH3s/c8UunYyL351+1frzdpeXfm8ReJwLfF7bT3EvBjt\nAxsmFYuY46ip5sUcn5k/0SKxvub+be3c7WeRmGNL/E7MjQP/5e8As7bPy6WrsRaLLc75dfzarbvF\noigosPdBuXLleP7551m3bh1VqlShZs2a1KxZEwcHB2JiYgra1CIeFVtcWImJiezZs0d5nRmPnJ/M\n5SdPnmTFihUWOw8hhBAlT7UKjqi0BmVCazDKGijMlDVCYsa+KlV4+g34wcQuib169UKr1Sqv09LS\n6N27d5GdVFFJSkoyKgoeFY9sanyyEEKI0s/BzjYrW0CZDBmTLtukL2BS1tNnTA/3Y2NtfOP+2LFj\ntG/fnhdffJEuXbpw+fJlUlJSGDx4MN7e3gwePJiUlBSLX6NJDQ1TU1MpU6aM8rps2bJFcjKPEhkZ\nSWhoKDqdDgcHB3x9fbG2tmb06NE0btyYK1eu0KFDB/r168eJEyf45ptvSE9P57nnnmPChAls27aN\niIgI/Pz8GDRoEDt27OCVV14hJSWF77//HltbW65du8aYMWNo3LixEp8McPv2bQIDA4mOjmb48OE0\nbdr0iV+/EEKIp6dQXRLBuEtivgvzVqVKFX755RccHR0JDw9n1qxZtG/fnvr16/Pll18yd+5c1q9f\nz5gxYwpxQo9m8iiJ2R8X3L59+6kMp1ypUiXmzZtHUFAQ1atX56+//gIgNjaWt99+m5CQEPbt20d8\nfDweHh7Mnz+fkJAQUlNTiYiIoHfv3nh4eBAYGEjjxo2N9p2UlMTUqVOZOHEi27Zty3Xsu3fv4uvr\ny6xZswgLC3si1yuEEKL4KDCUqKCgIlPWzVE5VK5cWckEsrGxwcrKit27d9OrVy8Aevfuzd69ey1+\njSbdKZgwYQLt27dnyJAhAGzYsIHp06db/GQeJSYmhrVr1/LgwQPi4+OVAZBcXV2Vho/PP/880dHR\nGAwGvvzyS7RaLTExMcTFxRn1oMipVq1aqFQq3NzcSExMzLW8du3aaDQaKlSoQFpamtGy0NBQVq1a\nBcDIkSMl1VAIIUqjgrokPlxeoIJuDuSzbVJSEv7+/qxbt45///vfymdd+fLlldReSzKpKHj33Xdx\nd3cnPDwcg8HA6tWrefHFFy1+Mo8SHh6Oj48Pbdq0Mfq2njnCYbly5bhy5QoVK1Zk+f+zd95hUV3b\n/34BYSyICtgLdsGOGjVW1Jio8UZFjfkZb7BcNdcUS6zYiL2XJBYsMSZqjKLGxGi+Jmocgl2wRkAs\noKL0oIAMw8z8/uDOkaGeYQ4IZr/PM89zZp+z9+wz7ayz11qftWEDo0aNon79+qxcuRKDwZCnzHFm\ncsrSvHv3Lnq9nsTEROzs7Ez2jR8/XhgCAoFA8Irzbp8GVHZqnq19ks9JwLxr/jqfnibPtWmabH20\nWi3Dhg1j1qxZNG3aFEdHR/7++28AEhMTcXR0NGv+cpBlFAB4eHjg4eGh+ATyIzQ0lLlz5wIZ2QhX\nr17l999/p2zZstJKgZOTE99++y0RERF06tSJSpUq0blzZ1avXk2tWrWki7zRwlq2bBkDBw40ax4V\nKlRgyZIlxMTEMHr0aAXPUCAQCAQlgRUbzpGenn2pQE5qYtZjJs87YbJz7eJh1HV50aTX6xkxYgQD\nBw6Urlfdu3fn6NGjtG7dmqNHj9K9e3fzTyIfZBkFiYmJLF++nCtXrpgEGJ48eVLxCWWmatWq7Nq1\nK9/jbG1tmThxoklbjx496NGjR7ZjP//8c2nb1dVV2m7RogWQocGwZElGru+kSZOy7RcIBALBPxOH\n0nYk/J2SfYeVBYoF/7tpdSinMmk+ePAgv/zyC1FRUezatYsWLVqwYsUKRo8eTdeuXalVqxY7duwo\n+Ovmgmz3QdOmTQkNDWXhwoV8/fXXtG3bVvHJCAQCgUBQXKniWJ7E+ByMAnMVjXKIHyhb1tQoGDJk\nCEOGDMl23Pfffy/jBQpOnjLHRlq1asXVq1dp2bIl165dQ6PR8Pbbb/P7778X6uQEAoFAICgujBv/\nNWFhUfI7ZDYQ8rnSfvfdh9Ss8fKVgmWtFKhUGRaMnZ0d8fHxVKpUKU8lQIGQOTYiZI5NUULm+Oj3\n2yzqr6p+V8gcZ0LIHL9AyBznjZUhI31QDgYAq4w+/+udz9gWTU0x8tQpuH37NgCNGzcmPj6e4cOH\n07FjRzp06EDr1q2LZIJZySp9fP36dUaOHIm3tzdTpkzh+PHjAPj4+BAZGQnAr7/+ypQpU6Q+s2bN\nIiYmhq+++opp06YxdepUTp06ZfI6f/31F++88w5Pnz4FyPNYgUAgEPwD0BvyfuhePKx1eqzT9S+k\njfPrW0zIc6Xgvffe4/Llyzx+/BhHR0emTJlC+/bt+fvvv+nbt29RzTFfOnbsyIcffohWq+XTTz/l\nzTffxNXVlVu3blGjRg1CQkKwt7cnNTUVW1tbEhISqFy5Mp6entSoUUPq161bN2xsbAA4fPgwDRs2\nlF4jr2MFAoFA8OqTo6JhFg981vUAoyiRyVGWBCYWMnmuFDx//pwDBw4QHh7O0aNHOXr0KE+fPsXa\n2pr/+7//K6o5ykaj0WBrawuAm5sbwcHBADx9+hR3d3dCQ0O5e/cu9erVA6BGjRoA2NjYYG394q24\ncOECTZs2pXTp0lJbbscKBAKB4J/Bo/C47EqEBkwe5PIwOS4HRcOE+OyieS+DPFcKli5diq+vL1FR\nUaxcaepLtbKyol+/foU6ObmcO3eOiIgIHj16JOVzNm7cmK+//pqnT5/i4OCAq6sr169fp2zZsri5\nuZn0P3ToEF26dMHGxga9Xs+xY8eYNWsW58+fz/ZamY8VCAQCwT8HK50eq/SsAnhK3PUbFBlFCfK8\n5R0wYABHjx5l7NixnDp1yuRR2BoF5tCxY0eWLFnCtm3bOHv2LDExMZQpUwYbGxsuX75MkyZNaNiw\nIWFhYdy6dcvEKAgICOD27dsMGzYMgNOnT9O+fftsqoU5HWvE19eXdu3a0a5dO3x9fQv3ZAUCgUDw\nUqhds9KLegb/e6DTmz70Mh86Peh0oNNhpTNQsVK5l316gMyCSGvWWBYhXFTY2tpia2sr1S5o3Lgx\nhw8fxtXVFVtbW9LS0rh3757kPrh+/TpHjx5l8uTJkksgPDycM2fOMH/+fO7fv8+qVatyPdbI+PHj\nuXTpEpcuXRJyxwKBQPCqYjBku7hbZX3oMj1MiiNl3fdiP3p9/nUTigjZMsfFiczSx7GxsTx//pyI\niAjS0tJwdXWVLvpubm6cOnWKOnXqAFCzZk00Gg2lSmWc9pdffkmZMmUklcMZM2YwcuRI6XW8vb2Z\nOnVqrsdWqFChSM5XIBAIBMUAszMFismV3gxKnFEgV/oYMnSiM2tDjx071mT/li1b8uxvlDuWc6xA\nIBAIXnGMaYd5YXiRbSDFCcjKNigeBkSJMwoEAoFAIHgp6A1Y6XOonZzD9dwqrwNyshGKh00gT+ZY\nIBAIBIJ/OsM6LyIxPjnjSU4rAjLIpm/4v1WEDQc/ooFbLQVmaRlipaCQEDLHGSglc9zjreUWjXHq\n/2YoIr2qhNyypRLDe/ZttKh/5RqPFJGQFTLHL3j0mWUyxzeXTaZv4xkWjXEsdLmQOc5EYcgcJyck\nZ0tJNDeVMLfjn/2dmsueoqXYGgVRUVH4+voyb968HPeHhoaya9cudDodbdu2xdPTk7CwMHx9fbGx\nsaFKlSpMnDgRGxsbxo0bh7OzMwC9evWiV69e0jg//PAD/v7+fPXVV0BGcGF6ejqlSpWiTZs2DBky\nhPT0dFatWsXff/+NwWBgwoQJuLi45DgvgUAgELya1K3vzJ2bj3I/wBylwiyL9FVqVCzgrJSl2BoF\neaHVatm7dy+zZ8+WijVBhjTxyJEjadasGV988QU3btygVatW2NnZmQQNGnn69CkPHjzI1j5nzhwc\nHByk50bRo5kzZ3Lz5k0OHjzI5MmTC+fkBAKBQFA80f9PlyAvjIZBTgaCwZDNGHixz7KpKUWJ1OsN\nCQnBzs6OZcuWMW/ePO7duwdA7dq1SU7O8PekpKRIF/b09HS8vb1ZvHgxT548kcY5cOAAgwYNMhnb\nysqKJUuWMH/+fO7evQtAtWrVSE9PByA5OdnEYBAIBALBPwRZokQZgkSkp794np6e8dDpcu9XTKyC\nErlSEBcXx4MHD1i9erVU7XD58uW89tprLF68mG+++YbatWtTt25dAFasWIGDgwNXr15l8+bN+Pj4\nEBsbS3x8PA0aNDAZe8aMGTg4OBAREcHq1atZv349zs7OpKamMmHCBFJTU1m82DKfsEAgEAhKHga9\nHkNO2QfZDvzfBV73v/iDYlwAKSslZqVg586deHt7c/DgQezt7XFzc6N06dLUrl2blJQUADZt2sS8\nefPYuHEj1apV4/Tp0wDSnX2rVq2IjY0FYO/evQwdmj2Qz3hsnTp1KFWqFBqNhhMnTlC9enU2btyI\nj48PmzZtMukjZI4FAoHgH0AOioYmqwPGR177c10pKB6UmJUCLy8vaTspKYn9+/ej1+tJTEyUKiMC\nlC9fHsi4uCclJaHVajEYDNjZ2REeHi7tj4qKYvv27QDExMSwc+dOvLy8SElJoWzZsiQkJJCamopK\npcJgMEjGQrly5SQXhZHx48cLeWOBQCB4xdEkp764gBtXAwq66m+FyQqCPr9YhSKiWBsFmeWMq1ev\nzoQJEwCwt7enV69eUqbA6NGjARgxYgRLliyhVKlSqFQqpk2bxtOnT1mwYAFlypQBkC7eCxculF7n\n448/xsvLC71eLwUv6nQ6xo0bB4CHhwerVq3i8uXLaDQaRowYUWTvgUAgEAiKB57je/E8WUMFJ3tK\nl1Xl3yEf0rU6niUkkxj3jEqVyyswQ8sptkZBfnLGvXv3pnfv3iZtLVu2zFbiuVy5cqxfvz7P1zKm\nI1pbW7N2bfa8/DJlykjGiUAgEAj+mfTz6vayp1DolJiYAoFAIBAIBIWLkDkWCAQCgUAAFGP3QUlH\nCalQi2WOa9W0qP+xiHUWS/L+9udsRaSB33Kfb9EY/xf0OX0cx+Z/YB78Gr9VkXlYKmcb8t+qFvW/\nO3kKfav+16IxjkVtots7K/M/MA/UP02jZ69lFo1x8sRM+jiMsmiMX5/usFjmOGFEe4v6X9wxRZHz\nUEJeWIn/Lkvfz+PavfRt6m3RGMf+skzy+Z9KsXMfREVFsWDBgmzt+cUF5MSJEyfQaDRKTEsgEAgE\ngleeYmcUZMVgMGAwGJg4caLZfYVRIBAIBAKBfIqt+8Db25uGDRty9+5dpk2bxuzZs/nqq69Yt24d\nZcuWJTIykrS0NHx8fLCzs+O7777j+vXrAIwePZr09HTu3bvHokWLaNasGcOHD2fjxo1ER0cD8NFH\nH1GjRg1Onz7Nzz//jEqlwsPDg969ezNv3jzS09NJT09n0qRJ1KhRA29vb1xdXQkJCcHe3p5Zs2a9\nzLdHIBAIBALFKbZGAUDTpk0lDYLM1KlTh3HjxrFlyxauXLmCk5MT4eHhrFixgpiYGJYvX86qVauo\nV68eM2fOxMHBgaNHj1K/fn0mTpzI/fv32b17N+PHj+fQoUOsWLECOzs7dP+TpPT29qZ06dIEBgby\n448/SvoILVq04IMPPmDRokXcv39fklEWCAQCgeBVoFgbBU2aNMmx3VivoHLlyjx79gyNRkPjxo2l\ntpxcBhEREYSEhHD27FkASpcuzZMnT6hfvz52dnYA2NjYoNFo2Lx5M0+ePCE9PZ0KFSpIY9SvXx8A\nZ2dnkpKSpHZfX1+2bt0KwNixY4W6oUAgEAhKJMXaKLCxsZF1XI0aNfjjjz+ADMliYznlUqVKSXf/\nxgJJffr0ATLKLz9//pz79++j1WqxtbVFr9dz+fJlypUrx7Jly7h8+TK//PKL9DpWmSQpM2dyCplj\ngUAgELwKFGujQC4NGjSgTp06TJ8+HYPBILkcOnbsyJo1a2jVqhUDBgxg8+bN+Pv7A9C+fXsGDBjA\nO++8w6xZs6SYgjZt2uDn58f8+fOpU6fOyzwtgUAgEAiKlGJnFFStWpV58+ZlazdKEU+aNElqGzRo\nkLSduWCSkX79+tGvXz/p+SefZM/h9fDwwMPDw6RtzZo12Y5bsuRFzuuHH36YxxkIBAKBQFAyKfYp\niQKBQCAQCIoGIXMsEAgEAoEAKIbug1eFvvWnWtT/2N1VlsscN6xv2RxCl9NmfPaqkeYQ6DuZju+v\ntmiMc7s/U0QqWQkJWCWkfXt5WCa/+tvebyzqb10tVBH5aiU+1w4fZHfVmcP5b6co8rlaKsv7cHoH\ni/r/tXiyIrK+ln63TvzhrYjMsRIy2n1azrFojF+vLbKo/z+VEuU+iIqK4v3338fb2xtvb+9sZZIB\nPv744zz7X7p0SXq+detWkpOTcz3euP/EiRMcOnTIsskLBAKBQFDMKXErBU2aNMkxEFGOFyQ6OppL\nly7Rrl07IENTIC/y2y8QCAQCwatEiTMKspJVDtnIw4cP2bBhAwaDgVq1avHRRx/x888/ExoaSkRE\nBBMmTGDjxo3MnDmTixcvEhQUhEajISoqiqlTp1KnTh28vb2ZOXMmALdv32bBggX8/fffTJw4ERcX\nl5d1ygKBQCAQFAolzigICQnB2zvD92ZUNsxJDvmbb75hzJgxNGzYkE2bNnH+/Hn+9a9/ERAQkGtK\n4ezZszlz5gy//fYbY8aMMdmn1WqZN28ed+7c4bvvvmPOHMv8XQKBQCAQFDdKVEwBZLgPlixZwpIl\nS6QLd05yyFFRUTRs2BAAV1dXHj16lOe4RgnjypUrm0gYG2nUqBGQYYhERUWZ7PP19aVdu3a0a9cO\nX19f809KIBAIBIJiQIlbKciJnOSQq1atSlhYGA0bNiQ4OBh3d3dKlSqFXq/PcYzcJIyNhIWFAXDv\n3j2qVKlisk/IHAsEAoHgVaDEGQWZ3QfGQkY58cEHH7Bx40YAqlevTvv27UlNTSUiIoJly5YxcuRI\ns17X2tqazz//nMTExByVEQUCgUAgKOmUKKOgatWq7N69O89jjHLIderUYdmyZSb7ypYta9JmlC7u\n1auX1NaoUSNJSjnz/szHCAQCgUDwKlLiYgoEAoFAIBAUDkLmWCAQCAQCAVDC3AclCSFznIGQOTZF\nCZnjo99vs6i/qvpdIXOcCSFz/ALFZI4V+EyEzPHLodgbBcuWLcPGxoZp06bx66+/olarpX0PHjxg\n0KBBdO7cmSlTplC3bl0AnJ2d8fLywtHREYDjx4+jVqvR6/UMHTqURo0asWzZMtLT0zEYDIwbN07S\nPAD44Ycf8Pf3l+ITAJKTkxk3bhwTJkygc+fORXPyAoFAIBAUIcXaKEhJSSE5ORmtVktqaip9+vSh\nT58+QIZBsGrVKvr27cvTp09N5I/Pnz/PypUrWbp0Kffv3+f27dssWvTCatRoNEyePBknJycePnyI\nr68vCxcuBODp06c8ePAg21wOHTqUox6CQCAQCASvCsU60PDs2bO8/vrrdOrUifPnz0vtWq2WtWvX\n8umnn1KmTJls/Tp06IBeryc2NpazZ89ibW3N3LlzWbVqFUlJSahUKpycnIAMjYPMOgcHDhxg0KBB\nJuMlJCSYiCEJBAKBQPAqUqyNgjNnztClSxe6d+9OQECA1P7111/TrVs3kyX/rDg7OxMXF0d8fDyp\nqaksXLiQli1b4ufnJx1jMBjYvn07np6eAMTGxhIfH59t3H379knHCAQCgUDwqlJsjYLExETu3LnD\n6tWrWbNmDbdv3+bZs2dcuHCBx48fM2DAgDz7x8bG4uTkhL29Pe7u7gC0adOG8PBw6ZitW7fSokUL\nWrZsCcDevXsZOtQ0uO/JkyekpKRQr169XF9LyBwLBAKB4FWg2MYUBAQEMHz4cN58800gI1jwyJEj\nBAQEsHDhQhNZ4qxcvHgRKysrnJ2dad68OVeuXMHDw4OwsDCqVasGZNz929jYmBgXUVFRbN++HYCY\nmBh27txJo0aNePz4MfPnz+fx48eUKVOG2rVrU6dOHamfkDkWCAQCwatAsTUK1Go1n332mfS8devW\nrF+/npSUFFaufJEW1r59e15//XVCQkKYPXs2kOE6MJZRdnd35+LFi3h7e2NjY8OkSZOIiYlhz549\nuLm54e3tjZOTE5999pkUbAjw8ccf4+XlBUCnTp0A2LNnDy4uLiYGgUAgEAgErwrF1ijIKlFcpUoV\nFi/OPbc6N/lja2vrHEsl//jjj3m+fuZ0RCPDhw/Ps49AIBAIBCWZYhtTIBAIBAKBoGgRMscCgUAg\nEAiAYuw+KOkImeMMhMyxKUrIHP+29xuL+ltXCxUyx5kQMscvEDLHgmJvFFgqc6zX69m8eTMRERE4\nOjoyceJEVCoV69atIzw8nDJlylCvXj3Gjh1LQkICS5cuxcbGBr1ez4QJE3BxcUGj0bB+/Xri4+Op\nU6cOH374IdbWwvMiEAgEgleLYm0UKCFzfPnyZaytrVm2bBkHDx7kxIkT9OvXD4AJEybQqFEj6fUc\nHBxYtmwZ1tbWXL9+HT8/Pz777DN+//13GjZsiKenJ5s3byYwMJB27doV/RsiEAgEAkEhUqxvd5WQ\nOb558yavvfYaAK+99ho3b96UjvP19cXb25urV68CGZLHxhWA5ORkSbAorzEEAoFAIHhVKNZGgRIy\nx0lJSZQrVw6AcuXKkZSUBMDo0aNZtWoVn332GVu2bEGj0QAQERHB9OnT2bJlC82bNwcgKSkJe3v7\nbGMIBAKBQPAqUWyNAiVljpOTk4EMd4Tx4u7g4ACAk5MTderUITo6GoA6deqwYsUK5s6dy5YtWwBM\nxkhOTpbGMCJkjgUCgUDwKlBsYwqUkjlu1qwZly9fpm3btly6dImmTZsCGRf3cuXKkZqayoMHD3By\nckKr1WJrawtkrAioVCoAmjVrxqVLl6hVqxaXL1+WaikYETLHAoFAIHgVKLZGgVIyx23btuXChQvM\nnDmTSpUqMWnSJABWrVpFcnIyer2eYcOGUbZsWYKDg9m5cyfW1tYYDAbGjBkDwBtvvMG6deuYOXMm\ntWvXpm3btkX1NggEAoFAUGQUW6NASZnjjz76KFv7/Pnzs7W5urqydOnSbO0qlYoZM2bkN2WBQCAQ\nCEo0xTamQCAQCAQCQdEiZI4FAoFAIBAAxdh9UNIRMscZCJljU4TM8QuEzPELXjmZ46r/tWiMY1Gb\nhMzxS6LEuQ+WLVsmBRpGRUXx/vvv4+3tjbe3NytXriQhIcHE/z99+nSOHDkCwJMnT6RgxHHjxkn9\nTpw4YfIaP/zwAx9//LH0fPPmzXh5ebF58+bCPj2BQCAQCF4aJWqlIKvsMWAib2wkKSkJrVYLQJky\nZQgJCaF///6EhITg5uYGgJ2dHUuWZLeqnz59yoMHD0zahg4dSqdOnThz5kxhnJZAIBAIBMWCErVS\nkJvscVYaNGjAnTt3CAsLo3nz5pLw0K1bt3B1dQUgPT0db29vFi9ezJMnT6S+Bw4cYNCgQSbjOTk5\n5amLIBAIBALBq0CJWik4c+YMEydOxGAwsGHDBlxdXQkJCcHbO8MX16BBA8aMGYObmxu3bt0CMtIM\no6KiiIuLIzQ0lBEjRgCwYsUKHBwcuHr1Kps3b8bHx4fY2Fji4+PzlE8WCAQCgeBVpcQYBZlljyGj\nRkFSUlKO7gNXV1f27t2LjY0N/fr148mTJ1y5cgWdTpdN5rhVq1Zs3boVgL179zJ0qPnBfb6+vtIY\nY8eOFeqGAoFAICiRlBijICfZ47CwsByPdXFxISIigurVq6NSqXB1dWXVqlVSmWStVovBYMDOzo7w\n8HDKly8PZAQubt++HYCYmBh27tyJl5dXvnMTMscCgUAgeBUoMUZBbrLH9+/fl9wHdnZ2+Pj4YG1t\nTZUqVahSpQoAtWrVIiYmhn/9619ARjDhggULpLLLxgv6woULpfE//vhjySDYt28fZ8+eJTExkceP\nH/P5558X/gkLBAKBQFDElBijwFzZ48wXbisrK/bs2SM9d3JyYv369Xm+3ldffSVtv/vuu7z77rvm\nTlkgEAgEghJFico+EAgEAoFAUHgImWOBQCAQCARACXIflDSEzHEGr5rMsRLzEDLHGQiZ4xe8nrkL\nSwAAIABJREFUcjLHCnwmQub45VCijQKjANGDBw/4+OOP6dy5Mzt27OD27dsAhIeH8+mnn9KhQ/Yf\n7JEjRzhw4ADbt2/H2to6z/aEhAS2b99ObGwsVlZWODs7M378eCm9USAQCASCV4ESbRTY2Ngwc+ZM\nfv31V6lt1KhRAOh0Oj766CNat26dY99z587Rtm1bbty4QcuWLfNsX7NmDYMGDaJNmzZAhrGh0+kK\n67QEAoFAIHgplOhAQysrKxwdHXPcd+3aNZo0aYJKpcq2LyoqigoVKvD222+jVqvzbI+Li0Or1UoG\nAWToIFSoUEHhsxEIBAKB4OVSoo2CvFCr1XTr1i3Hff7+/nTr1o169erx+PFj0tPTc22PjY3FyclJ\n6rtgwQImTpzI5cuXi+Q8BAKBQCAoKl5Jo0Cr1fLXX39JrgN/f3+8vb0lieSzZ89y5MgR5s+fT3x8\nPEFBQbm2Ozs7ExsbK409b948OnToIFVphAyZ43bt2tGuXTt8fX2L8EwFAoFAIFCOEh1TkBsXL17E\n3d0dGxsbALp27UrXrl2BjJoJNWvWZMqUKQBERkby/fffU7Vq1RzbX3vtNezs7AgMDJRcCHq93uT1\nhMyxQCAQCF4FSrxRsHz5csLCwihdujShoaGMGjUKtVrNO++8k+PxarXaJPiwRo0a3L9/nxMnTuTY\nrtFomDJlCtu2bWP//v3Y2dnh4OBA3759C/3cBAKBQCAoSkq8UTBjxoxsbTNnzsz1eGPp5Mx8+eWX\nOR5rbFepVEybNq2AMxQIBAKBoGTwSsYUCAQCgUAgMB8hcywQCAQCgQAQKwUvDSWyFCwdozjMQYyh\n/BjFYQ5ijOI3BzGGQBYGwUuhbdu2L32M4jAHMYbyYxSHOYgxit8cxBgCOYiVAoFAIBAIBADY+Pj4\n+LzsSfwTMRgMtGvX7qWOURzmIMZQfoziMAcxRvGbgxhDIAcRaCgQCAQCgQAQgYYCgUAgEAj+hzAK\nXhJpaWmyj01KSirEmQgEAoFAkIEwCooQnU7HpUuXWL16tVkKiUuXLi3EWRUtd+7cyfVRlDx9+tSi\n/tu3b5e2Dx06JG1/8cUXFo1bUrH0/RTIIyUl5aW8bmJiYoH6zZ8/3+LX3rlzp8VjCORT4mWOSwI3\nbtzg9OnTPH78mISEBObOnUu1atWKfB737t2jXr16QMafuIODAwA3b96kWbNmssY4fPgwAwYMKPAc\npkyZQpMmTahZs2a2fRMnTizwuObi4+PDm2++SZ8+fQrU/+7du9L2pUuXGDRoEABRUVEWz+3Zs2f8\n8ssvvPfeexaPVVRY+n5CRhEyZ2dn7OzsSElJ4ejRo+j1evr164e9vX2BxoyIiECtVnP+/Plc5cyz\ncvLkSXr27AnA1atXadWqFQBHjhyhf//+ssbw9fXFysoqx33jxo3Lt//s2bNZvHgxAGvWrJEKtS1e\nvFhqz4+8DO0GDRrk2z8lJQVfX1+Cg4NxcHDg6dOnuLq6Mn78eMqWLStrDsay9JYQGhpq8RgC+Qij\noJAZPXo0bdu2pX///ri4uODj42O2QXD79m0++eSTHPfJ/aMD2LZtm/SHsnz5cml7z549sv9oLly4\nYJFRsGTJEvz9/Xny5AmtWrWiS5cuVK5c2awxFi1alK3NYDBgZWXFnDlzZI2xfPly/Pz8mD17NiNH\njjT5TMqXL2/WfCzh6tWrfPfddwCMGjWKq1evcuHCBd58803ZY/z55584OTnh5uaGj48PcXFxAHh6\netKjR498+x85cgQPDw/s7e25ceMGW7ZswdraGi8vL9zd3WXNQYn3c/369dL38Msvv6RevXpUrFiR\ndevWyf5cIcMwU6vVXLlyhaioKD755BOGDRsmu/+JEycko2Dfvn2SUXD27FnZRkHnzp1NnsfExHDo\n0KFcDYW8MH6e5nLkyBFpOygoyOSzlGOA7969m0aNGjF58mSp7ejRo+zatUuWYQNw//79bL9Xc3+r\nMTEx/PjjjznuGzhwoKwxBPIRRkEh4+npyfnz59m/fz/du3enIMkejRo1kn3RLmwSExM5c+ZMjvs6\ndeqUb/9mzZrRrFkzdDod165dY/ny5TRv3pyRI0fKnoNGo0Gr1dKlSxfatGlDqVLmf41tbW0ZNmwY\nSUlJrFy50sQwkfNeR0ZGsmXLFgwGg8n248ePzZrHrl27mDlzJs+ePcPb25tRo0axbt06s8Y4duwY\nCxYsAECr1fLll1+i0+lYsGCBLKPA399futj5+vri7e1N+fLlWbRokWyjwNL3E8DGxoZSpUqh0WgI\nCwuTip2dPn1aVn+A6dOn4+joSM+ePfH09GThwoXSRb0oad68OZBhoPj5+fHgwQPef/99OnToUGRz\nyHzhnz17ttkrceHh4YwdO9akrV+/fsyePVv2GDVq1JBtQORGqVKlcHBwKNB/p8B8hFFQyPTv35/+\n/fsTHR2Nv78/ycnJrF27ltatW8v6w1YSnU5HUlISer0+27ZcUlNTiYiIyPYDtbKykmUUADx8+BB/\nf3+Cg4Np1qwZvXr1Mus8Fi5cSEJCAgEBAWzbtg1HR0feeustGjVqJHuM4OBgduzYQfv27dm0aRM2\nNjZmzeGzzz6TtjPfFWa9Q8wPlUqFs7Mzzs7O1KxZ06wVgswY5//2229Lz/V6vay+RqMqNjYWW1tb\nqlevbjKmHCx9PyFjqTkxMZHAwEBatGghtZsTlNukSRNCQkK4desWVatWLdCdeWbDN/O2OXETjx49\nYv/+/cTGxjJ48GDZxpWR0NBQaXXQuNoBEB0dbdY4lpDbe2dtLT8Uzc7OjipVqlg0j0qVKkkrN4LC\nRxgFRUSVKlUYPHgwgwcP5sGDB/j7+8vuO2LECKKjo01+XFFRUdjZ2VGpUiXZ41hbW0tBizY2Nibb\ncqlatapFvu5PP/2U0qVL061bN/79739jZWVFWload+7ckeXnNFKpUiV69+6Ng4MDR44cITAw0Cyj\n4ODBg0ydOtVs14URFxeXAvXLyqNHj6RVhri4OLZs2SLtk3uHpdPp0Gq12NraSoaZcTVFDuXKlePQ\noUOEhobSpUsXIGPFQaPRyD4PS99PAC8vLxYtWoSdnZ10V/vo0SOzvhdjxowBMuJ4fvnlFx4+fMie\nPXto3bo1TZs2lTVG586dCQ8Pz7Yt1+gF+Oijj3BxcaFp06ZcvHiRixcvSvvkfK779++X/Vq5YYwp\nMBgMPH/+3CTGQM57+vDhQ5Pvo3GsR48eyZ7DO++8Q1JSkklMSFJSEra2tqhUKlljvIyVnn8yQryo\nkMnNFwby/WFLlixh9OjRJn7aJ0+esH37drOW8pRgy5YtFi0Hrlu3Ltc7ELnLm2fPnuXMmTNoNBra\nt29Pp06dZAc+GQkMDKRNmzZARjBanTp1gAz/vPHCmBc5ve8JCQlERkbm+Zln5caNG7nuMy5B58fp\n06c5efIkgwcPxtnZmZiYGA4ePEjPnj3p3r17vv01Gg0nTpxApVLRo0cPrK2tiY6OJjw8nNdee032\nuWRFqYBJcwJhs6LT6bhy5QpqtdrEN54XlgbTguWf68mTJ02e29nZUa9evRwDdHNj/fr1ue6T81tT\n4ru5evVqBg4caGKE3L17l0OHDpmstuXF3r17cXNzMzEOrl69yq1bt0pUMG5JQawUFDK5BVmZs6yZ\nlJSULTixWrVqJCcnmzWX3CKirayssvkOc6NLly48f/6cMmXKEBcXx969ezEYDAwdOpSqVavm23/S\npElmzTknli1bRsOGDalYsSJnz57l3Llz0j65wUsHDhyQjAJfX1/J733s2DFZRkFmP3lMTAwHDhxA\nq9VKfnC5lCtXLteMELl0794dZ2dnTp06RVxcHE5OTgwbNkz2nfHTp0+li39cXJy05GvOsq8SAZOZ\nuXPnDmq1mlu3blGrVq0CGwU2NjZUrlzZrCVvS4NpwfKVpKwpgFqtlt9++w13d3fZNxOWZvMosRoW\nGxubbVWifv36xMbGyh4jKCgo28W/VatW7NmzRxgFhYAwCgqZ3Pzl33//vewx9Hq9FLGbuU2uz9iI\nEhHR33zzDcuXLwcy7kR69epFxYoV2bBhgxTslherVq1i6tSpAOzYsYNRo0YBsGDBAubNmydrDlu3\nbpU938Lk8ePH7N+/n6ioKDw9Pfnwww/NHkOJjBB4EcBZEDZv3oyVlZUUJ5KWlkZiYiL/+c9/aNmy\npawxlAiYfPToEadPn+batWs0aNCAsLAwVqxYYdYYp0+fZs+ePeh0OkaOHMnFixdJTEyUnTUAlgfT\nQobhmhtyPldjimtm9Ho93t7eso2CjRs30qlTJ1q3bi21Xb16lYCAACZMmJBvf0vPIS/MWaDOzb1Z\nkLgVQf4Io+AlcevWLdnHduzYkS+++IJhw4bh7OxMbGwsP/zwAx07djTrNZWIiLa1tcXKyoqUlBSi\no6Ol5el9+/bJ6p+QkCBth4WFSdvm+K+zBlupVCpq1qxplgvB6GPN7G81bsth9erVPHz4kMGDB0vL\nms+ePQOKNqURyDFdVavVkpCQwA8//JBv/7lz52ZrS0pKYtGiRbKNAiUCJidMmED//v1ZtGgRpUqV\n4vPPPzd7jJ9//pkvvviClJQUPvzwQ2bNmmVyUZSDEsG08+bNy9FnHhkZadZcMmPOagdkuMWyXvxb\ntWol+4ZEiYwnV1dXdu/ezbvvvoutrS1arZZ9+/bh5uYme4xKlSqZ6EVAhnFjTjyVQD7CKCgBDBw4\nkFOnTrFx40bi4uJwdnbGw8PD7OwFSyOiIcPCDwsL48qVK7Rt21ZqN+eibikBAQEmz7VaLXfv3mXQ\noEF07dpV1hguLi5SHnfWbTnExcVRtmxZjh07xrFjx0z2mfNnqkRGSGatitTUVI4ePYparWb48OGy\nx8iKvb29WXdiSgRMfvHFF6jVaubMmUPjxo0LpN5XunRpVCoVKpWKevXqmW0QgOXBtJCRITNnzhxK\nly4ttd2+fZv169fz1Vdf5ds/60qFVqvl+vXrNGzYUPYccjMizDEuTp06ZeKW6tGjh1n/OyNGjMDP\nz49JkyZJwbDdu3c36/0dO3Ysa9asYe/evVLMjEqlkh0jIjAPEWhYyOTkxzcYDFy4cMFEKtdcnj9/\nzrlz58z6gQ4cOFCKiM46J7l/3BEREezduxeVSsXo0aMpX748kZGR/Pnnn7z77rv59n///fdxc3PD\nYDAQHBwsbYeEhLBr1y7Z55IVrVbLnDlzJNdGSSGvQFFzjIuUlBR++uknzp8/T+/evenduze2tray\n+hpXOIykpaVx48YNAgIC8Pb2ljWGEkFpmQkJCUGtVhMSEoKLi0uu4l1Zye37ZY5YTm7BtGlpadjZ\n2cka49y5c/z444/MmzePsmXLEhQUxNdff82sWbOoUaNGvv2z3s3b2dlRt25dE0M8PzZs2EDjxo3p\n3bu31Hb8+HFCQ0P5+OOP8+2/b98+Hj16xLBhw6hcuTIxMTHs27eP6tWrmyUGlRMFCR6Ni4uTjBMn\nJyezPg+BfIRRUMgo+Wep1Wq5dOkSf/75J8+ePaNp06ZmWdxK/3EbMcdAySvP2tJ85szSsPmxcOHC\nXGMp5Fw8LJWxNRITE2NRGh9k+PMDAwPp168fPXr0MNvXmtUwMV6APD09C+wKMRgMXL16FbVazaef\nflqgMYzjXLt2TXZamtLfL51OR1BQEKdPnyYiIiLPiP6sXL58mR9++AEPDw9OnDjBnDlzCrzkbVQ1\ndHJykt3n+fPnbNu2jb/++ovy5cuTmJhIixYtGDNmDGXKlMm3/8yZM7PFFRgMBmbNmpVnvEFuZA0e\nLcj3wpLPQyAP4T4oZJo2bYrBYMDGxoaIiAgpd9woECOHoKAg1Go10dHRtG/fnr///rtA/r7mzZuj\n1+sJCQmRLO7GjRsXKGBHq9Vy8eJFAgICJANFDhs2bCiQrzgzWTXd09LSuH79Os7OzrLHGD9+vMnz\n0NBQ9u/fL/vCYa5IUW6sXLkSa2trunXrRufOnalQoYLZY/zxxx+oVCoOHz7M4cOHTfbJkcFWUi0z\nODgYtVpNYGAgXbt2NUt0JqcgVCsrKw4fPlxgo6Ag8SZgeb0S4/J/vXr12L17N6NGjZLiiOTEJaxb\nt47//Oc/2Nvbc+TIEU6cOEHp0qV57bXX8PT0lDWHMmXK8Mknn2AwGEhMTKRChQpYWVkRExMjyyjI\nCXPFoJQIHgW4fv06arVa+jzmzJlj1n+oQD7CKChkNm/eTI8ePXBzc2PlypU0bNgQnU5H6dKlZUUA\nQ4bWf9++fZk9ezZly5blypUrBZrLo0ePWLFiBXXq1MHZ2Znz58+zefNmpk+fLjv/2VIDRYkCKZk1\n3eHFna2cJVEjxov/zZs38fPzo2zZskyaNElKD8yP33//XZH0yhUrVhAVFYW/vz8+Pj44ODjQrVs3\nXn/9ddkXsm3btlk0B0trJwB8++23XL9+HVdXV9544w0iIyN5//33zZqHEkGoSsSbKFGvxCh4VLFi\nRfr37y8VyrKxsZFlFERFRUmCPz/99BNffPEFKpWKGTNmyDYKjFhZWWFlZcWxY8c4c+YMVlZWLFy4\nMN9+jRo1YteuXQwbNswkSNAcMSklgkdz+jyEQVB4CKOgkHnw4IEUaevg4CDlDpvz49i5cycBAQGs\nXLmSMmXK8Pfff0tBO+awdetWJk+eTN26daW28PBwtmzZIns+lhooShRIUaKa4tWrV/Hz88PR0ZEx\nY8ZQq1Yts/rHxMRYPAcjVatWZciQIQwZMoQHDx6wbds2Nm3ahJ+fn6z+e/fuNXluFLqRG0hqae0E\ngCtXrlC9enXc3d2pW7dugeSFlSDrChC8iDeRaxQoUa8kLS2NwYMHY29vz59//sl3332Hra0tgwcP\nltXfaDzfu3ePatWqSQGL5gQJpqSkSEJftra2REVFsWzZMtlKgl5eXvj5+TFx4kTS09OxtbWlS5cu\nZtUpUSJ4VInPQyAfYRQUMpl/xJlTv8zRc7e3t+ett97irbfeIj4+Hn9/f+bOnYutra0si9+IVqs1\nMQggI9renLt3Sw0UJQqk5BV0Jrdq5Lx586hduzZ2dnZ88803gHnGibEIUk4U5Pzu3r2Lv78/ly5d\nom7dumaJIBnVGI2kpaXx559/EhQUxOjRo2WNYUntBMgo7xsZGYlarWb//v3ExMRw7do1mjZtKrtg\nldFgNBgMJtvGu+6CYmtra1bRLCXqldy6dUu609+9e7f0e5k9e7asMVq1asWCBQuIjo5mxIgRQIbI\nlDnG1gcffMBbb73FpEmTKF++PD4+PrINAsioifHee+9li1tKSUmR/Xt3cXHh3//+N5ARPKrT6Zg6\ndapZwaPFqX7MPwFhFBQyZcqUITw8HBcXF8nav3fvXoF9eo6OjgwYMIABAwbw5MkTs/sbYwkyPzfH\n8rbUQFGiQIo55aJzw1IBJAcHB7O08HNj9+7dnDt3jurVq9O1a1fee+89s/64IWcftYeHh+zMAUtr\nJxipUaOGdBG5e/cuarUaX19fNmzYIKv/2rVrzXq9nFAi3uThw4fUqlUrW70StVpt9nwiIyOpUKGC\npFQp1zgZMWIEDx48wNbWVnJdWFtbm+Wymj59Ov7+/qxYsYIOHTqY/Xnq9XquXLmCwWDA3d2d5ORk\nDh48yIULF2R/pplp0qQJTZo0wWAwmNSCkIsl9WME8hFGQSEzZswYEz9+TEwMDx8+ZPr06bLHeP78\nOadPn0av19OzZ0/i4uLYs2cPMTExZgXt/Pvf/2bevHm8/vrrUorRuXPnzPLFZyazgXLv3j1ZfbIq\ntRnvRs0VZsnKgwcPOHjwoGzXQpUqVXj27BmBgYGSoeTu7i79eeeHvb29RRkbRipXrszy5cvNDoST\ng1ytg759+7Jo0aJstRP69u0r+7W+/PJLunXrRsuWLbGysqJ+/frUr1/frKXmnIxFc+snKBFvsmnT\npmxxMrVr1zYrRqJmzZr4+vpy//59Kdjy+fPnslflIiMjqVq1KnZ2dqSkpHD06FH0ej39+vWTPYf2\n7dvTvn17NBoNFy5cwN7ennnz5tGsWTNZKYVr167Fzs6O5ORkfv31V549e0bfvn2llQs55GVYtG/f\nXvY4Go0GnU4n/U6io6P566+/ZPcXyEekJBYBOp2O0NDQAkf8L1iwgCZNmpCUlERwcDDly5dn6NCh\nZqmCGUlOTub8+fMkJCSg0+moXLky3bp1kz2fzGl/a9asYcqUKdna82Lnzp2SrzUgIIBvv/1W8rXK\nXQrMTcr27bffll3AJywsjLVr19KpUyecnJyIjY3l3LlzTJw4UVa1xd9++80k/7ugKJHamLUAU1pa\nGrdu3aJly5Y5yuXmxF9//cXJkyel72jPnj1lZ5RARvqdv78/ISEhtGnThq5du+Lq6iq7P+RdP8Gc\ni2FOpKSkyDa8zEltzQ29Xk9QUBAqlUoyHhMSEoiNjZX1/ZoxYwaLFy+mVKlSLF++nHr16lGxYkUu\nXLggO/YmJ5KTkzl79ixvvPFGvsfOmjWLpUuXYjAYGDNmDF999ZXZxuvq1aslw0Kn00mGRZcuXWT/\n5/z4448cP36c9PR0+vTpw5UrV6hatSr/+te/srnOBJYjVgoKmczKZNbW1iQkJHD+/HlAvo66RqOR\nLPvx48ezdOlSs3ykRn744QfOnj0LZGjlx8bG8uTJEwIDA2VXLMuMMUrdHDL7Wnft2mW2rxWUkbL9\n/vvvmT17tomQzBtvvMGWLVtk1WDQaDRSSdgbN26wZcsWrK2t8fLyMkspMrfUxk2bNskeI6uWgEql\n4vXXX89215wXTZs2NTECHj9+zN69e2Xfobdt25a2bdui1Wq5fPkyP/30E1999RUdOnSQfMr5oUT9\nhNyM1sWLF8u+0N++fTtXf7dc15W1tXU2oaFKlSrJ1imwsbGhVKlSaDQawsLCpBiT06dPy+oP5Brz\nIheDwUBSUhIGg4EqVaqg1+vNlvKOjY212LAICAhgw4YNaDQaRo0axfLly4UxUIgIo6CQyRwkZSw6\nc/bsWSIjI2UbBZlroZcrV85kTHPSgy5evMi6devQarVMmDBB8qvPnDlT9hhKUVBfKygjZZuWlpZN\nWa5atWqy/a7+/v5SkR1fX1+8vb0pX748ixYtMssoyM0F4ejoKHuM3IpuPX78WPYYkGHk+fv7c+7c\nOapWrVqgOva2tra0bNmS5ORk4uPjCQoKkm0UKFE/ITMFMVohIxVPSe2GgpCenk5iYiKBgYG0aNFC\najcnLiAwMFBKcc0aYCwHGxsbli5dmm0b5GtbKGFYqFQqrKysKF26NPXr1xcGQSEjjIJC5v/9v/8H\nZPw41Go1P//8M61atcLHx0f2GLnp9IN56XnGADZbW1sT/605rozMd1FRUVHSdl5Kcpmx1NcKGYGa\nxsj0Bw8eSNvmpDXm5jWT600zvmexsbHY2tpKedNKVW4rypS+X3/9lbNnz1KmTBk8PDyws7MzW1de\no9Fw7tw5/P39iYmJ4fXXX+fTTz+VJelrRIn6Ca8KH3zwAYsWLcLOzk76jT969Ij69evLHmPz5s3c\nvn0btVrNxYsXadq0KR4eHrJKnEOGC0NujE1uKGFYhIaG5vifA8oEHQtMETEFhYxOp+OPP/7gl19+\nwd3dnQEDBpj9Q1Or1XTr1s3iuRRW3QFzsNTXCspI2Y4bNy7HKP+0tDR8fX3z7b9kyRLc3NwIDQ2l\nUaNGeHp6otVqmTVrFqtWrZI1B8hZbtlgMBAaGir51wt7DC8vL15//XX69+9PrVq1+Pzzz5k/f77s\nc4AMkZp27drRsWNH4uPjCxQ7o4QM97vvvitd9KKioqTt6OhoWRUjIXuGTnBwMAaDoUAxPMWFW7du\nsXXrVtzc3Bg7dqysPlOmTJEyjQrKtWvXZFfazA2jm05QNAijoJD5z3/+Q7ly5Xj77bezfbHlug+U\nCHyCwqs7YE7k/9q1axk1ahQVK1Ys8OvNnz/fYqlkS9FoNJw4cQKVSoWHhwc2NjZER0cTHh4uO9gR\nlPlMLB1Dr9dLdQpiYmJISEhg3rx5su8oIbtaZmxsLBEREUybNk22MNSDBw/Q6/UmlSrDw8OxsbEx\nW1wqKxqNRnaq57x585g+fTr29vZ899133Lt3D3t7eypUqMCYMWMsmodcLK3NARnpmX/++Sd3796l\nSZMmdOvWzaz3UavV4ufnx40bNxg5cqSJqqPcpX8l/ruU+v8TyEO4DwoZo7/XWOHLiDm12TPHFGTF\nnJgCS/UBIO/Ifzl4eHiwaNEievbsWeCIciWkkrNG7Gdm4MCB+faPjIyU5v/06VMcHByoUqWK2UqH\nSnwmlo5hbW2Nu7s77u7uUqDgzp07efDggezl2dzUMrdu3SrbgNu+fXs2t0WlSpVYvXq1xUbg4sWL\nJdXG/NBqtdjb26PX6zl9+jS+vr7Y2NgUaexNTsqM5jJlyhSaNGlCgwYNePbsGUePHpX2yXHH2Nra\nMmzYMJKSkli5cqVJ4S65F2ljOfCc7j0LWmxLULgIo6CQMcYUZCYlJUXKQJBDXFxcrpHkSkj+moOl\nkf/u7u60aNGC7du388knn1C1alWz4wGUkEpOS0vLseJcUlKSrP7btm2T/hiXL18ube/Zs6dE39XY\n2trSsWNHOnbsSGpqqux+aWlpFqtlpqWlZSsI5eDgYJb6Z26YsyBqDOYLDQ2lbt26isWJmIMShcMs\n/R4GBwfz9ddf06FDBzZt2lSg9yE8PNwkjqAg81MiG0QgH2EUFBE5lT2WS61atYr84p8blkb+6/V6\nfv75Z+7cucN//vOfAhU2UUIq+cKFC4wcOdLEV71jxw6ioqIYMGCARWOXNJSQjbaysrJYLVOv12eT\nzE5LS1NE696cwM2ePXsyceJEkpOTJQXBmJgYs2uNWIISq2GWVkU9ePAg06ZNs6i0d/369S02TopD\nNsg/CWEUFDJKlD22NAJYSXKK/Dci5y59xowZdOrUiaVLlxb4DkwJqeS5c+eyaNEihg9fcFphAAAg\nAElEQVQfTqtWrUyq0MnBuCyq1+uzbZc0Ml/4C+q/VUIt86233jJRVoyNjeXAgQNmKSvmZuCYk8rX\nr18/evTogbW1tRSHULFiRdmy0UqgxGqYpVVRP/74Y6ysrKQUwsyIpf9XFxFoWMgMHjyYvn37Mnz4\ncMqWLVugyO7iRHR0NMnJyZQqVQqVSkV8fDxhYWG0bdtW1l1/bGysiQ59QWSOL126RLt27bK1m6Na\nBxmuAmO9htatW+fo6smN2bNn57qvJN/VWBLUlZyczMWLF6W70nbt2pkdNX7z5k1OnTpVYGXFoKAg\nvv76a1JTU3FwcGDChAlmxd0YMSo8xsfH4+joiIeHhyKy1nKZNm0a06ZNy3GfXIPYx8eHkSNHZovz\n+Prrr2W5JjJ/x+/du2dSVlzud0Sv11ssYX7nzp0CfYaCgiFWCgoZpcoeFxdOnTqVTRWxXLlyhISE\nyFJF/OWXX3ItKStX0fDQoUOSUVBQ1TpjdLeVlRURERGS8BDIW/EYNmyYxalWrxrlypXDw8PDojGa\nNWtGs2bNCtx/9+7d+Pj44OTkxP3799mxY4fZvvnjx4/j7++Pp6cnlStXJjY2ln379vHo0SOL0vPM\nQYnVMEuromb+LRXUWLTUIADzgqkFliOMgkJGibLHxuj24oClqoiWlpTNSkFV6yyN7v7hhx9eGaMg\n85L7yxSHyWnpX6vVkpCQIFtjQKVSSXENdevWLZA75+TJkyxcuFAy3GvVqkWzZs2YO3dukRkFcmtW\n5IelcR6Cfx7CKChCClr22MfHhzfffJM+ffoU4uzkoYQqIlgmc6wElt6FvUqpVsUlgjvzPFJTUzl6\n9ChqtZrhw4fLHiOzL95gMEgxMCA/v9/KyirbSp6trW2Rqkzm5B4zF0vjPIyxBAaDIdv3Xe53/MCB\nAwwePBiAP/74Q1pJ+u6772TLX69du5bRo0dny0wRFA7CKChkfvnlF1xcXGjevDkTJ05Er9ej1+t5\n4403ZN8NLF++HD8/P2bPnl1gERGlMP7pGgwGk+3M9RjyQgmZY0ullpVAiVSr4sLdu3fZuXMnsbGx\n1K5dm1GjRpklXKQkKSkp/PTTT5w/f57evXuzcuVKs1xta9eutXgO1apV4/jx4ya1F3777TdFNCWK\nEldXV1asWCHFedSqVYtly5bJjvNYsmSJtPxfUIniwMBAySj47bffJKMgODhY9nl4eHiwcOFCi7RN\nBPIRRkEh8+eff0rCPvb29ixevBiDwcD8+fNlGwVKiIgohaV/uh999BFBQUF07txZCtxKTU2VLb0K\nsG/fPovmoARKpFoVFzZv3syYMWOoX78+V65cYdu2bXkGUhYWu3btIjAwkH79+rFq1aoCZacoceEe\nO3YsW7du5cCBA1SoUIHExESaNm2qiKCQXNatWyelQxYUo/JnQeM8rK2ti8V3XAltE4F8hFFQyGQO\ntHn//feBjOVJY9S9HIKDg9mxYwft27cvsIiIUiihnmdJSVkwLUedFbkqkYIX2Nra0qRJEwBee+21\nPNUeC5M//vgDlUrF4cOHOXz4sMm+onRxlC1blokTJ2IwGEhMTMTBwQFra+si9cWbq4yZE5ZqHSjh\nIouJieHHH3/EYDCYbMfGxsqehxLaJgL5CKOgCDAW9DCmVj19+tQso+DgwYNMnTrVIhGRV4kVK1bQ\nuHFjWrVqhY2NjfSnZY50tKV4eXkpIj1dHIiMjJQqEhoMBpPnRVmdcNu2bTm2azSaIpuDkYcPH2Iw\nGKhduzY6nY5jx45x9OjRIjNOMn8GWZH7mViqdaCEi2zYsGE5br/77ruy+oMy2iYC+QijoJAZMmQI\nPj4+9OnTBycnJ2JiYjh+/DgjRoyQPUZOoinPnj3jl19+4b333lNyuiWCbdu24e/vz5UrV6hRowZd\nu3Y1K5ddCY4dO5at7c6dO0RERLy0O+2CkjWVtHPnzi9pJi/Q6XQEBQVx+vRpIiIiWL9+fZG99vbt\n27l//z6pqak0aNCAe/fu0a5duyJdSndwcLDYwLVU+VMJF1lsbCxdu3Y1q4R2VmbMmGGibWLkyZMn\nJvFVAmUQRkEh4+7uTvXq1VGr1dy+fRsnJyemTp1q1pf56tWrUgncUaNGcfXqVS5cuGASCFVS+Ouv\nv6hXrx5lypQhLi6OvXv3YjAYGDp0qOzgNmdnZwYNGsSgQYO4d+8eX375JXXr1uXTTz8t5Nm/ILPs\ndHBwMPv27aN69erFRo7aHCyVw1WS69evo1arefz4MQkJCcyZM6fIl4tDQ0NZvnw5Op0OLy8v1q9f\nn2OdjMLE3t7eYrGkrFoHBREKsxQHBwc2bNiARqOha9eudOnSxez3sly5cvz666/o9Xp69uxJXFwc\ne/bsISYmhhUrVhTSzP+5CKOgCKhWrZpZy2VZ2bVrFzNnzuTZs2d4e3szatQo1q1bp+AMi45vvvmG\n5cuXA7B+/Xp69epFxYoV2bBhg+wqdjqdjitXrhAQEMDTp09544036NKlS2FOO0euX7+On58fDg4O\njBw5kjp16hT5HJTAUjlcpRg9ejRt27alf//+uLi44OPj81L8x8b0WBsbG1xcXIrcIAAKpNmRldTU\nVMl1GRAQwLfffmuWUJhcHZW86Nu3L3379pU0WpYtW4adnR3dunWTrfmwcuVKmjRpQlJSEnPnzqV8\n+fIMHToUNzc3i+cnyI4wCkoAKpUKZ2dnnJ2dqVmzZolcITBizPdOSUkhOjqa7t27A+ZlFHh5eVGj\nRg26dOkiLSveuHEDKLpAwxkzZpCamsrgwYOpUaMGWq1WijEoaTEFuZU93rJli8WV+szB09OT8+fP\ns3//frp37/7SRHZiYmKkNNe0tLSXIuZ06tQp3njjDSCjUNeoUaPMHsPW1lZKP9y1a5fZQmFKrigY\nNVrq16+Pn58fe/bskW0UaDQaKR5h/PjxLF26tMh1Tf5JiHe2BPDo0SO2bNmCwWAgLi7OJACpKAPB\nlMBgMBAWFsaVK1dMshDMCSbr168fVlZWJCcnk5ycLLUXZaBh9erVsbKyIigoiKCgIJN9Jc2FYKkc\nrlL079+f/v37Ex0djb+/P8nJyaxdu5bWrVsrcucsl//+97+K1E9QirCwMIv6v2yhsODgYCkGqEmT\nJgwcOJBWrVrJ7v/8+XPJ4C5XrpyJJkpJM8BLAsIoKGTOnTtH+fLlTfTcb968ybNnz+jYsaOsMTIH\nghWHIDBL+PDDD9m7dy8qlUq6+4mMjKR9+/ayx8iscJeYmPhSlM4szSEvbhQHOVxjMSONRoODgwP/\n/e9/sbOzQ61WF+k8lKifYCnGC6HBYDC5KIL8C6ESQmGWMmHCBOrVq0fXrl0ZOXJkgWq+uLi4cOTI\nkWzbUPIM8JKAqJJYyMycOZPFixebBG3pdDpmz57NsmXLCjSmwWDg6tWrqNXqIg2uKy48f/6crVu3\ncuPGDSpWrCiJy4wdO9asKomWYCyolBMlTVAlODiYL7/8Mkc5XFdX1yKbx9SpU5k1a9ZLvRhD9uI/\nc+bMyZbaV9jklW0h90Ko1+sJCgpCpVJJQYsJCQnExsbSqFEjReaZH8+fP6dMmTKFMvbNmzctKp4l\nyBmxUlDI2NjYZIvizqlNDsHBwajVagIDA+natatk/Zcksha9sba2pk6dOnzwwQeydRi2bNmCm5ub\niUF0/PhxfH19mTx5sqLzzY2iVLcrbLLK4dauXZuBAweaXfbYUpQoZqQEStRPsBQl7oCVEAqzlOnT\np+e6ryDxGXfu3EGtVnPr1i2pUJVAWYRRUMjY2NhkW5qNjY01K4jn22+/5fr167i6uvLGG28QGRkp\nqSOWNHL6I7h58yYbNmzAx8dH1hjR0dHZ/jTffPNNTp8+rcQUZVHSdPDzIiUlhdKlS+Ph4YFOpyMg\nIIDLly/TuXPnIvU/F4eLMShTP8FSdDodfn5+qNVqqdR6165dGTJkSIkKsps+fTq1a9e2aIxHjx5x\n+vRprl27RoMGDQgLCxOpiIVIyfl2lVD+/e9/8/nnn9OxY0ecnJyIjY3lwoULZi37X7lyherVq+Pu\n7k7dunWLtFpbUdCsWTO+//572cenp6eTnp5u8ueo1WrRarWFMb1XngULFuDj44ONjQ2bNm3CysqK\nChUq8MUXXzBlypQim0dxuBhD8TD4vvnmG1QqFWvWrEGlUqHRaDhw4AA7duwwq07Iy2bz5s0WCyBN\nmDCB/v37s2jRIkqVKvVSXEr/JIRRUMg0atSIpUuXcunSJWJjY6lVqxYDBw6kXLlyssdYs2YNkZGR\nqNVq9u/fT0xMDNeuXaNp06Yl6q4hN6Kjo80KauvVqxeff/45Q4YMkXzgBw8elFK4BOZhbW1N6dKl\nSU9PJygoiO3btwMUeVGk4nAxLi7cuXOHJUuWSM9VKhXDhw9n1qxZL3FWL4cvvvgCtVrNnDlzaNy4\nMSkpKS97Sq80Jf+KUswxRg3XqlWLWrVqARnynGBeOk2NGjV47733eO+997h79y5qtRpfX182bNig\n/KQLkawxBZCRZmTO3c+bb75JjRo1+OOPPyTXzJAhQ2jRooWSU80TJerEFxfS0tLQarXcuHHDJLBQ\nrLy8PHKrjVLS4sIzlznPityYAhcXF+k3FRISgk6nY+rUqbi4uOQ6tqDgCKOgkMmcPpMVucFEz549\nM3leuXJlBg8ejKenp0VzexkoJf7SvHlzqlevLhkFRa06p0Sd+OKCp6cnkydPRqfTSYFhjx49omLF\nii95Zv9c2rZty9q1a3nvvfek1bB9+/bh7u7+sqdmFo0aNVK0ZkSTJk1o0qQJBoOBa9euKTau4AXC\nKChklIgizit1sTjUOzeXv/76i5MnTxIfH4+joyM9evQwK4o4ISGBlStXotfrcXZ2lgI3p06diqOj\nYyHO/NWkU6dO2USfatasmWMhLkHRMHToUE6ePMnGjRuJj4/HycmJ7t27l8iMI0vJK/3XHBEkgTyE\nTkEhk1d+s6UR1cHBwUWaR64Ex48fx9/fH09PTypXrkxsbCwHDx6kc+fOsmVP169fz+uvv24ieHTx\n4kUCAgKKTFRo3Lhx9OvXD4PBwLFjx6TtX3/9FV9f3yKZg1Js376dMWPGAHDo0CEGDRoEZPhy/4k6\nGMWRl1HMSAmyZl4FBwdjMBjMqlsQHR1t8jw0NJT9+/dTpUqVIo97+ScgVgoKGY1Gg1arpUuXLrRp\n00bRwMA9e/bILiJUXDh58iQLFy6UlM2MucZz586VbRTExMRkU0B87bXXOHz4sOLzzQ0l6sQXF+7e\nvSttX7p0STIKoqKiXtaU/vHs3LmTwYMHF7iYUXFh/fr1TJ8+HXt7e7777jvu3buHvb09Z86ckQzR\n/DAGoN68eRM/Pz/Kli3LpEmTqFevXmFO/R+LMAoKmYULF5KQkEBAQADbtm3D0dGRt956SxFFsZK4\nyGNlZZVN6tRYJMmcMV42vXr1kraTk5OlO7miUlQUvNrcunXLomJGxQWtVou9vT16vZ7Tp0/j6+uL\njY0NM2fOlD3G1atX8fPzw9HRkTFjxkgB24LCQRgFRUClSpXo3bs3Dg4OHDlyhMDAQLOMgsy650YM\nBgOpqalKTrNIqFatGsePHzep9Pjbb7+ZlY529+7dbG4Zg8HA/fv3lZpmvuzatYtWrVrRokULpk+f\nTsWKFdHpdLi5ueHl5VVk81CCyMhIqeBW5u3Hjx+/7Kn943nZxYwsxZjBEhoaSt26dQuk5Dpv3jxq\n166NnZ0d33zzDZDxe7eysipxkuIlgZL1DSuBnD17ljNnzqDR/P/27j0oquuOA/h3gYVJeIQIihIR\njbYCI8pLo2KgGDTiYwxBLUarUatOa6uxtqMJjdHECRaiEbWTKuqM7xgdi7Vam0lbXSyu6EQM0EJ9\nIGpWeayAy3MfnP5hvMMGUDB79y7w/cw4cx8L+x1G4Me55/xOE0aNGoV169Z1+q/J9lYwdMWKefHi\nxcjMzMSxY8es9i3oTNtgR2hyU1BQgLlz5wIAvL29pQmfH3zwgZKxnsmqVatgsVhQXFyMRYsWQafT\nYejQoV1+862uzBE2M7KFuLg4rFixAnV1ddJ8n4qKik5tjJSZmSlXPGoDJxrKbPr06RgyZIi0vKvl\n0HdPrHIrKirQu3dvCCFQU1MDLy+vTk+eamvk5DF7baXactOce/fuoV+/fq2udxXffvst0tLSMGDA\nAGk1x+3bt/G73/2uSxae3YEjbGZkKw0NDXBycoKbmxuAR6MHZrO5wxslOcL3e0/CkQKZ2aLK7U47\n8qWnp8PJyQkxMTGIjo5+ptnUbY2c3LhxA7dv30ZWVpYtYj6Vi4sLKisr4evrKxUE5eXlzzQ8qrTM\nzEysXLkSAwcOlK6VlpYiMzOTLWUV4gibGdlCW02+1Go1Pv/88w43+bJFrxfqOBYFMuvTpw/q6+ul\nHeh8fHwQFRXVqTbH3WlHvrS0NJSVlSE7Oxvr1q2Dl5cXYmJiMGbMmA4/Vmn5g6CoqAhffPEF+vXr\nZ9cfED/72c/w4YcfSntaVFRU4NKlS11yCZ/JZLIqCIBHXeS62lA1OR5bNPlq7/ua7Y7lwaJAZsXF\nxdi6dSvGjBkDX19f3LlzB0ePHu3UXvWPCwtnZ2e4ubmhuLgYDx8+REREhMzp5eHn54cZM2ZgxowZ\nuHPnDnbt2oXPPvsMx44d6/DnyM/Px7Fjx+Dl5YW3334bAwYMkDFxa0OGDLHa0yIgIACJiYmdKvYc\nyffXk+v1+i65uoW6n+bmZuTl5UEIgfDwcNTV1eH48ePIzc3tcm3euwLOKZBZSkoKVq5cCV9fX+ma\nXq/H5s2bO/zs+ciRI7hw4QKARzsKVlZWwt3dHSaTCatWrZIlt9xu3ryJ7OxsXL58GQMHDkRMTAxG\njhzZoY9dvXo1GhsbkZSUBH9/f6tHK0o+YzQYDDh16hSSk5MVy/AsioqKsG3bNowZM0ZqqavVajtV\nuBK1xRZNvjZt2gRXV1fU1dXBYrHAYDAgISEB48aN65KP6xwdRwpkJoSwKggAwMfHp1N/hV26dAlb\ntmyByWTCL3/5S2meQmfW+jqKgwcPQqvVol+/fnj11VeRnJwsTUDqKH9/fwDAlStXcOXKFat79nqE\ncPXqVezfvx8AsGDBAly9ehW5ublWSy27iqCgIKSlpUmPuPr374+NGzdK6+SJnpUtmnxVVlYiNTUV\nQggsWrQI27dvZz8QGbEokJlarcbt27ethrdv377dqSU5j39pqtVqq/X8XbFK7t27NzZu3NhqmP3x\nuuOOMJlM+O1vfwsA2LNnDxYuXAgAdu3ueODAAaxZswYGgwHvvfceFixYgC1bttjt/W3N3d1det5L\nZCu2aPIlhEBtbS2EEOjTpw+am5ulTeI8PT1tG5hYFMht8eLFSE9PR2BgIHr37o3y8nJpuVdH3bp1\nCxs2bJAa9Dw+Li0tlTG5PCZOnIi7d+/iwYMHCAgIgMViwZdffonTp093eAfFqqoq6bjlcqWmpiab\n522Pm5sbfH194evri5deeqlLjhAQyc0WTb6cnJyQmpoK4NEfQo+Pga65IZyjY1Egs/79+2Pz5s0o\nLi7GgwcPEBkZiaFDh3bqr3xHaNZjK7t378atW7fQ2NiIwYMHo6SkBFFRUV3um/vbb7+VOv/p9Xrs\n3LlTurdkyRIFkxE5Dls0+fr4449lyUZtY1Egs5ycHOnYyckJ1dXVuHjxIgC02q62PZ1pAezoiouL\nkZaWBovFgvnz5yMjI8Nq1ntHOMLIScsJnuz8R9S2ln/8/OpXv5KOO7PcNSMjw+rczc0NgwYNQnx8\nfJd8hOroWBTIrOUvKpVKBSEELly4AJ1O1+GioDt5PJfC2dkZgYGBnS4IAMcYOXncZe4xIQSuXr0K\njUbT6h5RT2WLJl9TpkyxOjcajcjPz8e2bdvstlV6T8IliXYihIBGo8HJkycRHByMxMRE9OrVS+lY\ndrdkyRJp4mRTU5PVyoOOzilwJEVFRdBoNPj666/x6quvYsSIESwKiL5z/fp1bN26tc0mXz90+XBX\nbCneFXCkQGYWiwVnz57FqVOnEB4ejrVr10o7nvVELZ+9d2X79u1Dfn4+goKCEB8fD51Ohzlz5igd\ni8ihyNXky2KxSDswkm2xKJDZ0qVL4e7ujilTpsDDwwMFBQXSvZ74+KCtXugAsH///g73QncEeXl5\n6NevH8LDwzFw4MAOL6ck6kkerw7q37+/tLnW/fv3AXS80diOHTusvr+MRiNKSkowbdo0G6clgEWB\n7MaPHw+VSgW9Xg+9Xi9dV6lUPbIosEUvdEewefNm6HQ6aDQaHD16FBUVFfjmm28QEhLS5fa8J5KL\nLTYz+v5EXldXV/j7+7O5lkz400tmb731lnRcU1ODF154QcE0ZEv+/v5ITk5GcnIybt68CY1Ggx07\ndrAfO9F3bNFhlHN07ItFgcwaGhqQmZmJgoICeHt7o6amBiEhIVi8eHGPbNVZUVGBrKwsCCGsjisr\nK5WO9oO8/PLLePnll7lzG1ELZrMZX3zxBf7973/DZDJBrVYjOjoas2bN4oiag+LqA5llZGQgODjY\nquPdl19+icLCQqxcuVLBZMr4xz/+0e69li1Ru6q1a9fatd0ykSP7/PPP0djYiDlz5kCtVsNoNOLw\n4cNQq9VWo6jkOFiqyay8vLzVENrEiRNx7tw5hRIpqzv84ieijsnPz7daNujq6op58+YhJSVFwVT0\nJCwKZGY2m2E2m62GykwmU49dTvPrX/+61TWTyYSqqiocOXJEgUTP5qOPPmq14kAIgZKSEoUSETke\nJyenVtdUKhU7ETowFgUye+2117B+/XrMmDFD2qv++PHjiI+PVzqaIlo2KGpsbMTp06eh0Wi63FDi\n0qVLlY5A5PDKysqQlZVldU0IgfLycoUS0dNwToEdFBQU4OzZs9Dr9fDx8UFsbCxCQ0OVjqWY+vp6\n/OUvf8HFixcxYcIETJgwoVNbSRNR19Dd5xB1RywKZLZ3794ObxHaExw4cABff/01Jk+ejLi4OA4j\nEvUAer0eDx48QK9evZ5pvxOyHz4+kNn//vc/pSM4lLNnz8LNzQ0nTpzAiRMnrO51xb0PiKh9VVVV\n2LRpE0wmE3x9fVFZWQm1Wo3f/OY3PXLvl66AIwUyW7JkCSZPntzmvTfeeMPOaYiI7Cc1NRWTJk1C\neHi4dO3q1as4deoU3nvvPQWTUXtaTw0lm3JxcYGXlxc8PT1b/SMi6s5qa2utCgIAGDFiBOrq6hRK\nRE/Dxwcye/HFFzF+/HilYxAR2Z3FYkF9fb1V99a6ujqYzWYFU9GTsCiQ2YgRI5SO0CVotVqMHj1a\n6RhEZENJSUlISUlBQkKCtCT7zJkzSE5OVjoatYNzCmSWkZFhde7m5oZBgwYhPj6eM+9bYHtgou5J\np9Ph/Pnz0pLscePGwd/fX+lY1A6OFMhsypQpVudGoxH5+fnYtm0b3nnnHYVSERHZh7+/P2bNmiWd\nWywW/POf/+RjVQfFokBmQ4YMaXUtJCSkx/b+zsnJafN6TU2NnZMQkdxu3bqFo0ePQgiBt956C0VF\nRThx4gQiIiKUjkbtYFGgAIvF0mP3PigtLW3z+pgxY+ychIjk9qc//Qnz5s2DwWBASkoKJk6ciE8+\n+QRubm5KR6N2sCiQ2Y4dO6w2zjEajSgpKcHUqVMVTKWc2bNnt7pWX1+PixcvKpCGiOTk4uKCkJAQ\nAMDhw4cxZ84chRPR07AokFl0dLTVuaurK/z9/eHh4aFQIsdgMplw+fJlnD9/HgaDQfrBQUTdR3l5\nObKysiCEQH19vdXmSGze5pi4+sDOamtrkZOTg+zsbHz00UdKx7G7K1euQKPRoLy8HKNGjUJubq7V\nfutE1H1wQ6SuhyMFdtDU1AStVoucnBxcu3YNs2fPxvLly5WOpYgNGzYgISEBKSkpeP7555GXl6d0\nJCKSSWRkJLy9vVtd1+l0CqShjmCbY5l98sknWL9+PQwGA5YtW4YBAwZgwoQJ6N27t9LRFLF3714E\nBAQgPT0daWlpqK6u7rGTLom6u/T0dOn4/fffl47/+Mc/KhGHOoAjBTIzm81wdnaGi4sLnJycrCYd\n9kQeHh54/fXX8frrr+PBgwfIzs7G+++/D7Va3SMfpxD1FM3NzUpHoA5gUSCzNWvWoL6+HlqtFlu2\nbEFJSQm++uorhIWFwdfXV+l4iurVqxemT5+O6dOn4/79+0rHISLq8TjR0M4ePnyI8+fPIzs7G6mp\nqUrHsbuGhgacO3cOzc3NGD9+PPR6PQ4dOoSKigqkpaUpHY+IbGjJkiVST4Kmpibp2Gg0YseOHUpG\no3awKCC7+vDDDzF06FDU1taiqKgInp6emDlzJoKDg5WORkR2YjQa4erqqnQMagMfH5BdNTU14ac/\n/SkAYOnSpUhNTYWLC/8bEnV3FosFV65cwblz53D79u1Wm8WRY+BPY7KrhoYG3LhxAwDg7u5u1fZ4\n8ODBSsUiIpnk5+dDo9Hg3r17qKqqwu9//3v069dP6VjUDhYFZFeBgYH461//2uoYAFasWKFULCKS\nwcKFCxEZGYmpU6ciMDAQ69atY0Hg4FgUkF2Fh4cjJiZG6RhEZAdvvvkmLl68iKNHjyI2Nhacwub4\nONGQ7ColJYVtjYl6mPLycmRnZ+PChQt46aWXEBYWhri4OKVjURs4UkB21XJOwfdxTgFR99SnTx8k\nJSUhKSkJd+7cgUajUToStYMjBWRX8+fPR0RERJv3OKeAiEhZHCkgu+rfvz9/+RMROShuiER25eXl\npXQEIlJYfX290hGoHXx8QHaVk5PT7r2xY8faMQkRya25uRl5eXkQQiA8PBx1dXU4fvw4cnNzuVOi\ng2JRQHZ1+PDhVtcuXLgAnU6HY8eOKZCIiOSyadMmuLq6oq6uDhaLBQaDAQkJCRg3bhycnZ2Vjkdt\nYFFAihBCQKPR4OTJkwgODkZiYiJ69eqldCwisqF3330XqampEEJg0aJF2L59O+yCUXgAAAieSURB\nVJ5//nmlY9ETcKIh2ZXFYsHZs2dx6tQphIeHY+3atZxnQNRNCSFQW1sLIQT69OmD5uZmGAwGAICn\np6fC6agtHCkgu/r5z38Od3d3TJkyBR4eHlb3OKeAqHtJSUlp9x6bmDkmFgVkV4cOHYJKpWp1XaVS\nITk5WYFERET0GIsCcgj19fV81kjUzezevRuLFi0CAPz5z39GYmIiAGDr1q1Yvny5ktGoHexTQHbV\ncjhx8+bN0jGHEom6n5s3b0rHly9flo7LysqUiEMdwKKAFKPX65WOQERELXD1ARERyUKn02Hnzp0Q\nQlgd37t3T+lo1A7OKSC7mjVrFvz8/AA8GkJ8fFxeXo4jR44oGY2IbKygoKDde8OGDbNjEuooFgVE\nRCSLuro6/O1vf0NlZSUCAgIwceJEqNVqpWPRE3BOAdmVVqtFYWGh1bXCwkJotVqFEhGRXDZt2gSV\nSoVRo0ahvLwcO3fuVDoSPQWLArKrrKwsBAUFWV0LCgpCVlaWQomISC5NTU1ISkpCREQEFixYAJ1O\np3QkegpONCS7cnZ2brURSlvXiKjrq6mpsdoZteU5O5g6JhYFZFfOzs7Q6/Xw8fGRrlVWVsLJiYNW\nRN1NdHQ0SktLW52rVCoWBQ6KEw3Jrq5du4Zt27Zh9OjR8PHxQWVlJXJzc7F8+XIMHjxY6XhEZGNF\nRUXSRMPAwECl49BTsCggu6urq8Ply5dRWVkJX19fREVFwd3dXelYRGRjO3fuxP379xEYGIj//Oc/\nGDt2LKZPn650LHoCPj4gu3N3d0dsbKzSMYhIZjdu3MAf/vAHAIDZbMYHH3zAosDBsSggIiJZqFQq\n1NbW4vGAtMVikc49PT0VTkdt4eMDIiKSRcsN0L6Pm6A5JhYFREREBICPD4iISCYZGRlW525ubhg0\naBDi4+PZm8RBcaSAiIhkcf36datzo9GI/Px83Lt3D++8845CqehJOFJARESyGDJkSKtrISEhT5xr\nQMpiGzkiIrIbi8UCk8mkdAxqB0cKiIhIFjt27IBKpZLOjUYjSkpKMG3aNAVT0ZNwTgEREcmioKDA\n6tzV1RX+/v7w8PBQKBE9DYsCIiIiAsA5BURERPQdFgVEREQEgEUBERERfYdFAZGdZWVlITg4GOHh\n4SguLu7Ux1ZXVyMtLe0Hvb9Op8PYsWPR3NwMAFi3bh2MRqN0f+3atThy5MgPeo/OKCgowOTJk+32\nfkTUPk40JLKzhIQELFy4EDNnzuz0x966dQtRUVGorKzs9MeazWa4uLjgF7/4BWJiYjB79mwAj3ay\nMxgMis4If+ONN7BixQrExcUploGIOFJAZFcrV65EdnY2Vq9ejbi4OMyZMwdRUVEIDQ1FYmIiqqqq\npNfu2bMHI0aMwIgRIzBy5EiUlZVh2bJlqK6uRlhYGMaOHQvgUSvZ1157DcOHD0dERATOnDkjfQ6V\nSoX09HT85Cc/wfr169HY2IijR48iMTERALBs2TIAwNixYxEWFobq6mq8/fbb2L59OwDgxIkTCA0N\nRVhYGIYNG4azZ88CANavX4+goCCEhYUhPDwc1dXVuHXrFnx9faX3/v756dOnER0djcjISIwZMwZa\nrVa6N3v2bOzatcvGX20i6jRBRHYVGxsrTp48KYQQoqKiQrqekpIiVq9eLYQQ4l//+pcYPHiwuHfv\nnhBCCIPBIBoaGkRJSYnw8fGx+nyjRo0Su3btEkIIUVhYKHx8fER5ebkQQggAYuPGjdJrNRqNeOWV\nV6w+HoAwGAzS+fz588W2bduEEEIMHz5caDQaIYQQZrNZ1NTUiAcPHggPDw9RX18vhBDi4cOHwmQy\ntcrW8vz69eti9OjRoqamRgghREFBgQgICJBeW1paKvz8/DrxVSQiObCjIZGC9u3bh4MHD8JoNKKu\nrg4//vGPAQCnTp3CvHnz0LdvXwBod2jfYDAgLy8PCxYsAPCor3xYWBi0Wq3UNW7+/PnS6+/evQs/\nP78O5xs/fjxWrVqFmTNnIiEhAcOGDYPFYsHQoUMxd+5cTJo0CVOnToWnp+cTP8/f//533LhxAzEx\nMdI1s9mMsrIy+Pn5oW/fvigrK4PJZIJare5wPiKyLT4+IFJIdnY2PvvsM5w5cwb5+fnYsGEDGhsb\nAQCig1N92ntdy9ayLQuK5557TnqPjvj000+xe/duuLq6YubMmcjMzISzszO0Wi2WL1+Ou3fvIjIy\nEt988w1cXFykyYsArN5HCIFJkyYhLy9P+qfT6aQCpbGxEWq1mgUBkcJYFBAppLq6Gi+88AJ8fHzQ\n1NSEPXv2SPemTZuGffv2oaysDABQW1uLpqYmeHl5ob6+HmazGQDg5eWFsLAw7N27FwBQVFSEq1ev\n4pVXXmnzPUNDQ1utePD09ERNTU2bry8uLkZoaChWrFiBuXPn4tKlSzAYDKioqEBsbCzWr1+PYcOG\noaCgAH379oXJZJK2yz106JD0eSZOnIgzZ86gsLBQunbp0iXp+L///S+GDx/e4a8dEcmDjw+IFJKQ\nkIADBw4gKCgI/fv3R1RUFHJzcwEAsbGxePfddxEfHw8nJye4ubnh5MmT8PPzw5w5cxAaGooXX3wR\nOTk5OHjwIJYuXYpPP/0ULi4u2L9/P3r37t3mew4ePBje3t4oLi7G0KFDAQCrVq3C+PHj8dxzz0kT\nCR9bs2YNrl27BhcXF3h7e2P37t2oqalBUlISGhoa0NzcjIiICLz55ptwcXFBRkYGJkyYgMDAQKuV\nBD/60Y9w4MABLFq0CA0NDTAajYiOjsbIkSMBPHq8kJSUJMNXmYg6g0sSiXqYw4cPQ6vVIiMjQ+ko\nAB7tnDdq1Ch89dVXVqsViMj+OFJA1MPMnj0ber0ezc3NcHJS/gliaWkpPv74YxYERA6AIwVEREQE\ngBMNiYiI6DssCoiIiAgAiwIiIiL6DosCIiIiAsCigIiIiL7zfxp/v54r9yV2AAAAAElFTkSuQmCC\n",
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hhHg4MoWjiNLT0zl06BDBwcGUKFHioc9btGgRWq3WbEpI9+7deemll9DpdOzbt48pU6YwYcIEatSoAYCXlxezZ89mwYIFGI1GunTpQq1atczipqamkpqaqhwbDAYMBsMj3qUQQgghhLiXJNBFZGdnx4ABA4iIiECv15vNZy7It99+y/79+5k4cSI6nU4pr1mzpvK6WbNm7Nmzh127dlGjRg3Onj3LlClTGD58OA0aNCA1NZWIiAgcHBx46623lPNiY2OJiopSjoOCgujTp08x3a0QQgghhLhDEuhH8NprrzFs2DCmTJmChYUFzZs3L7Dt6tWriYuLIzw8HEdHx/vG1Wg0mEwmAM6cOUO5cuV47bXXAChbtizNmzdn//79Zgm0v78/Xl5eyrGMPgshhBBCqEPmQD+ixo0bExISwvTp09m5c2e+bWJjY9mwYQPjx4/HycnJrO7GjRscOHCA27dvk5OTw549e9ixYweNGjUCwNXVleTkZA4cOIDJZCItLY0dO3ZQtWpVszgGg4FatWopX5JACyGEEEKoQ0agi0GTJk0ICQlh6tSpWFhY5HmgcMmSJVhaWtK/f3+lzNvbm+DgYHJyclixYgXnzp3DwsKC8uXLM3jwYNzd3QEoX748gwYNYvHixUyePJkSJUrQuHFjWQdaCCGEEOIJkQT6ASIjI/OU1a1bV9kk5Y4mTZqwatUq5fju+nXr1hUYv1SpUkydOvW+fWjWrBnNmjV72C4LIYQQQggVyRQOIYQQQgghCkESaCGEEEIIIQrBwnRnuQchhBBCCCHEA8kc6BeAn2uIKnHXn8idH+6reVeV+JuNq/CrMVyV2OsTI2jQZ5oqsQF+/XIwTQLuP7e9qHYvH4rXW5NVib3tv58C4FdxgCrx1yfNomX7KarE3r5uGD7eE1WJDRAXH4rxnxqqxNaUS8S3+QRVYm/eMRJA1c9j425fqBJ7T8wQ1T6LkPt5bKXrokrsTVnf4D5SnZ8xCRMGA+DnHqpK/PUJE1X7XoqLD6W19YeqxAbYmL5U1Z9fbeqNUiX2hj/HqxJXqEemcAghhBBCCFEIL/wI9N3LwWVmZqLVatFqtQB06tRJqT969ChDhw6lY8eO9OrVyyxGz549uX79OhrNv7+PNGjQgP/85z8A3L59m8WLF7Njxw4yMzOpWLEi4eHh6PV6TCYTS5cuZevWrWRkZODi4kJgYGCerbpNJhMjRowgISGBNWvWKH0UQgghhBCP1wufQK9cuVJ5HRISgp+fHz4+PnnaxcXFUbJkSeLj4+nevXueBHbUqFHUr18/32vMnj0bo9HIrFmzsLe359SpU8r5P/30Ez/99BPh4eGULVuWH374gQkTJrBkyRKzhHzz5s3FcbtCCCGEEOIRyRSOh5CVlcX27dsJDAzk9u3bHDhw4KHPPXPmDPv27ePjjz/GwcEBjUaDq6srlpa5v7tcvHiROnXqUL58eTQaDW+++SZXr17lypUrSoyrV68SGxtLjx49ivvWhBBCCCFEIUkC/RB2796NyWSiefPmNG3alC1btjz0uYmJiZQtW5YVK1YQEBDAxx9/TFxcnFLv5eXF2bNnOXfuHDk5OWzatImqVavi6OiotFm4cCFvv/029vb2xXpfQgghhBCi8F74KRwPY8uWLXh6eqLT6fD29mbcuHFcvXqVUqVKKW0mTpxoNq2jffv2vP/++6SmpnLy5EkaN27M4sWLOXHiBGFhYZQvXx53d3cMBgO1a9cmODgYjUZDyZIlGTNmDBYWFgD88ccfJCUlMXDgQP75558C+5iamkpqaqpybDAYMBgMKrwbQgghhBAvNkmgHyA1NZU//viD8eNzl5ipV68epUqVIj4+ng4dOijtQkND850DrdfrsbS05L333kOr1VKzZk2aNWvGvn37cHd3Z/ny5Rw7dowFCxZQunRp9u7dy5gxY5g5cyZ2dnZ89dVXDBkyxGw+dH5iY2OJiopSjoOCgujTp08xvQtCCCGEEOIOSaAfYOvWrRiNRiIiIpRR4Zs3bxIXF2eWQBekSpUq960/deoULVq0wNnZGYCmTZsSExPDkSNHqFSpEklJSYwdOxYAo9EI5K76MWTIELOE3d/fHy8vL+VYRp+FEEIIIdQhCfQDbN26FX9/f9q3b6+UpaamEhISwvHjx6lWrdp9z69bty4Gg4FVq1bx7rvvcvLkSXbv3k1YWBgANWvW5JdffqFFixY4Ojqyb98+kpOTqVSpEmXLlmXRokVKrIsXL/Lpp58ybdq0PPOhZcqGEEIIIcTjIQn0fSQkJJCcnEz79u3NHupzdHTklVdeYcuWLUoCPX78eLNpFlWqVGHy5MlYWloyatQo5syZQ2xsLE5OTgQFBVG7dm0gd63pa9euMWTIENLT03F2dmbQoEFUqFBBudYd6enpADg4OMg60EIIIYQQT4gk0HeJjIw0O3Z3d+e7777Lt+2YMWOU13ePEuenUqVKRERE5FtnZWVF37596du37wP7V6FCBdatW/fAdkIIIYQQQj2yjJ0QQgghhBCFIAm0EEIIIYQQhWBhMplMT7oTQgghhBBCPCtkDvQLwM81RJW460/kzhn31byrSvzNxlX41RiuSuz1iRE06DNNldgAv345mCYBU1WJvXv5ULzemqxK7G3//RQAv7L9VIm//uI8Wrafokrs7euG4eM9UZXYAHHxoRj/qaFKbE25RHybT1Al9uYdIwFU/Tw27vaFKrH3xAzBr+IAVWIDrE+aRStdF1Vib8r6BveR6vyMSZgwGAA/91BV4q9PmKja91JcfCitrT9UJTbAxvSlqn1m1ifNok29UarE3vDneFXiCvXIFA4hhBBCCCEKQRLoYnL48GH69etH586diYuLe6RYK1euZPr06UDu2s/t27cnMzOzOLophBBCCCEe0XM9hePvv/9myZIlnD59GoDy5cvzwQcf8Oqrrxb7tZYvX46vry/vvPPOI8fq3LlzMfRICCGEEEKo4blNoG/dusW4ceMIDAzE29sbo9FIYmKish33wzKZTBiNxgduXHLx4kUqV678KF0WQgghhBDPgOc2gU5KSiInJwcfHx8AtFotderUUerj4uJYvXo1V65cwdXVlX79+uHi4gJAaGgotWrV4vDhwyQmJjJ+/Hhu3rzJ0qVLuXDhAra2trzxxht07doVgMDAQFJSUggPD0ej0fDVV19hZWXF4sWL2bdvHzk5OTRr1oxevXphZWXFX3/9xZQpU+jcuTOrVq3CaDTy9ttvK6PXK1asICkpiWHDhin9jY+P55tvviEzM5NWrVrRtWtXs50PhRBCCCHE4/HcZmAVK1ZEp9MRGRnJvn37uHr1qlL3119/ER0dzcCBA4mJieHll19m3LhxZGVlKW22bt1KYGAgK1eupFq1auj1egYOHMjXX39NWFgYmzZt4pdffgEgOjoaZ2dnRowYwcqVK3FwcGDGjBlkZmYyZ84c5s6dy4ULF/j222+V+NeuXSMlJYXo6GhGjx7NsmXLOH/+fIH3c+DAAWbPns2UKVP4+eef88yzTk1N5fDhw8pXampqcb2VQgghhBDiLs9tAm1jY8PkyZPR6/XMnz+f7t27M3LkSM6fP098fDw+Pj7UqlULnU5Hp06dyMzMJCEhQTn/9ddfx9XVFY1Gg06nw8PDg6pVq6LRaKhSpQotW7bk4MGD+V77ypUr7N27l48++ggbGxvs7Ozo3Lkz27dvV9poNBq6du2KTqejRo0aVKxYkZMnTxZ4P++//z42NjaUL1+edu3amcUCiI2NpWvXrspXbGzsI76DQgghhBAiP8/tFA7IHYUeMCB3Pcjk5GTmzJnDtGnTsLW1pUGDBko7jUaDwWAgLS1NKXN2djaLdeTIEWJiYjh9+jTZ2dlkZWXRpEmTfK+bnJyM0Wikd+/eStmdudR32NnZodPplGO9Xk9GRkaB93J3f8qUKWPWVwB/f3+8vLyUY4PBUGAsIYQQQghRdM91An23MmXK8H//939ERkbSvHlzkpOTlTqj0UhqaipOTk5K2b0PG06dOhU/Pz/CwsLQ6/VER0dz+fLlfK/l7OyMVqslJibGLEl+FCkpKdja2iqv7+4r5CbMkjQLIYQQQqjvuZ3Cce7cOb777jtSUlIwmUxcuXKFTZs2UbNmTby8vIiLiyMxMZHs7GxiY2OxtLTE3d29wHjp6enY2tqi1+s5evQo27ZtK7Cto6Mjr776KlFRUdy4cQOTyURKSgoHDhwo8v1888033Lp1i3/++YcffviBFi1aFDmWEEIIIYQouud2BNra2ppjx47xww8/cOPGDWxtbalbty79+vWjdOnS9OrVi2nTpimrcISFhd13tLhv374sXLiQBQsWUKdOHTw9Pbl+/XqB7QcNGsSyZcsYOHAgN27cwGAw0KZNGxo2bFik+2nQoAH9+/cnMzMTX19f3nzzzSLFEUIIIYQQj+a5TaCdnJz49NNPC6z39fXF19c337qJEyfmKfP09MTT07PAeNHR0WbHNjY2fPTRR3z00Ud52tatW5eYmBizssjISOX1Bx98oLwuW7Ys69atA6BVq1YFXl8IIYQQQjwez+0UDiGEEEIIIdRgYTKZTE+6E0IIIYQQQjwrntspHOJffhUHqBJ3fdIsAHw176oSf7NxFX41hqsSe31iBK8GfqFKbID90UNo8XbkgxsWwc9rQvBtPkGV2Jt3jATU/cy0bD9Fldjb1w2jVdPPVYkNsGnXaFYdL9ozDA/ybrUDqn+fNgmYqkr83cuHqvpZV+t9gdz3ppWuiyqxN2V9Q53/TFMl9qFJgwHwcw1RJf76E5H4eOedylgc4uJDaW39oSqxATamL1X1e6lNvVGqxN7w53hV4gr1yBQOIYQQQgghCkESaCGEEEIIIQpBpnAAoaGhHDlyBK1Wi06nw83NjY8++oiKFSsqbU6cOMGgQYNo164dQUFBZucHBgZy5coVNJp/fx8JDw/HycmJ6OhoDh48SHp6OhUrVuTDDz/klVdeUdplZmayZMkStm/fTmZmJhUqVGDChAnY2Njw119/MWrUKPR6vdK+U6dOdO7cWcV3QwghhBBC3I8k0P8TGBiIn58ft2/fZs6cOcycOZOIiAilPi4ujpIlS7J9+3Z69uyJpaX5WzdixIg8azz/888/VKtWjZ49e+Lo6Mju3bsJDw9n9uzZlClTBoC5c+eSkZHBzJkzKVWqFKdPnzZbj7pUqVJ5lrwTQgghhBBPjkzhuIder6dFixacOHFCKcvKyiI+Pp5evXqRnZ3Nvn37HipWuXLlePvtt3FyckKj0dCsWTOcnZ05duwYkLtb4q5du+jfvz+Ojo5oNBqqVq1abNt/CyGEEEKI4icj0Pe4desW27Zto3z58krZvn37yMnJoXnz5iQkJBAXF0fTpk0LHTstLY0LFy5QqVIlAI4ePUqZMmX49ttv+emnn7C3t6djx45mG6Zcv36dbt26odPpaNCgAd26daNkyZJ5YqemppKamqocGwwGDAZDofsohBBCCCHuTxLo/1mwYAFLlizh1q1blCtXjtDQUKVuy5YtNG3aFL1ej7e3N2FhYVy+fBlHR0elTUREBFqtFoCXXnqJyZMnm8XPzMxk8uTJ+Pr64uLiAkBKSgqnT5+mUaNGLFq0iFOnThEWFkaFChXw8PDAxcWFGTNm4OLiQlpaGvPmzWP69OmMHj06T/9jY2OJiopSjoOCgujTp0+xvkdCCCGEEEISaEXv3r3x8/Pj/PnzjBs3jvPnz1OlShUuXbrEr7/+ytixYwHw8PCgdOnSxMfH8/bbbyvnDx8+PM8c6DuysrKYNGkSDg4OZlt76/V6NBoNXbp0QafTUb16dTw9Pdm3bx8eHh44OjoqSbqzszMfffQRffv25fbt22YPFgL4+/vj5eWlHMvosxBCCCGEOiSBvkeFChUICgpi1qxZNGzYkJ9++gmj0UhkZCQWFhYA3Lx5ky1btpgl0AXJysoiIiICjUbDsGHDlFFqgCpVqhSqbxqNBpPJRH6bR8qUDSGEEEKIx0MS6Hw0bNiQUqVKsWHDBuLi4nj77bfp2LGjUn/p0iWGDh1KYmIiNWrUKDBOdnY2kydPJisri1GjRuVZucPDw4Ny5cqxatUq3nvvPU6dOsUvv/yPhbvHAAAgAElEQVTCyJG5u8H9+eeflC1bljJlynDlyhW++uor6tevT4kSJdS5cSGEEEII8UCSQBfA39+fyMhItFot7du3N5vv7OjoSMOGDdmyZct9E+jDhw+zZ88erKysCAgIUMqDg4Px9vZGq9UyatQoZs+ezZo1ayhdujS9e/fGw8MDyF17evr06Vy7dg07OzsaNGhA9+7d1btpIYQQQgjxQJJAAxMnTsxT1rJlS1q2bFngOXc/yBcdHZ1vGw8PD9atW3ffa7u4uDBp0qR86zp27Gg28i2EEEIIIZ48WQdaCCGEEEKIQpAEWgghhBBCiEKQKRwvgnuWvBOPR5aNhWqxM+1lt8r85Oi1D270CFpbp6gWO7tqOdViqy2zpIzF5Mek7scRSlipfIFnVHb2k+6BeAHITz0hhBBCCCEK4YVMoENDQ1m/fn2B9Z07dyYpKekx9kgIIYQQQjwrnvgUjtDQUI4cOYJWq0Wn0+Hm5kZQUJCy3fWTsHLlStWvERgYSL9+/QrcvfBeK1asICkpiWHDhqncMyGEEEIIcT9PxQh0YGAgK1euJDo6Gjs7O2bMmPGkuySEEEIIIZ6A+Pj4fJcYfpo88RHou1lbW+Pt7c2UKVM4evQoUVFRnD17Fp1OR9OmTQkMDESny314qn379gQHB7N27VouX77Ma6+9xieffKLUx8XFsXr1aq5cuYKrqyv9+vUzG9VOTk7m008/5fTp01SvXp2BAwfi7OysxJ47dy4uLi5Mnz4dvV7P5cuX+eOPPyhTpgyDBw/G1dUVgKSkJObPn8+xY8coVaoU77zzDq1atQLgn3/+YdasWRw/fhytVstLL73EpEmTmDJlCikpKYSHh6PRaGjXrh0ffvghCxYs4JdffuHmzZuUL1+ewMBAPDw82LdvH6tXr8ZkMtG5c2fs7e0LXHtaCCGEEOJZFh8fT2RkJKGhoU+6KwV6Kkag77h16xbx8fG4urqi0Wjo2bMny5YtIzIykoMHD/Ljjz+atd+5cycRERHMnz+fI0eOEB8fD8Bff/1FdHQ0AwcOJCYmhpdffplx48aRlZWlnLtlyxb69OnD0qVLKV++PF988UWB/dq+fTsdO3ZkxYoV1KtXj6+++grI3ar7888/p1atWsTExDB06FCWLFnC77//DsDSpUupWLEiy5YtY8mSJXz44YcADBs2DGdnZ0aMGMHKlSuV8mrVqjF9+nRWrFjB66+/TkREBLdv3+a1116jU6dONGvWTBmpF0IIIYQQT8ZTkUAvWLCA999/n+DgYLKyshg0aBDVqlWjdu3aaLVaypQpQ+vWrTl48KDZee+++y729vY4ODjw6quvcvz4cSD3NxcfHx9q1aqFTqejU6dOZGZmkpCQoJzr7e1NtWrVsLKyonv37iQkJJCamppv/xo3boy7uztarZY33niDEydOAJCYmMiNGzfo0qULOp2O6tWr06pVK7Zu3QqApaUlly5dIjk5GUtLS+rUqXPf98Hb2xt7e3u0Wi0dOnQgOzubs2fPPtR7mJqayuHDh5Wvgu5FCCGEEOJeu3btolWrVtjb21OyZEkaN27M5s2bAbh06RK9evXCYDBgbW1Ns2bN2L59u9n5VapUoX///mZl33//PRYWFpw6dQqAU6dOYWFhwbJly+jfvz+Ojo6UL1+ekJAQsv+3/OCYMWMYO3YsN2/exMLCAgsLC7y9vVW//8J6KqZw9O7dGz8/P7OypKQkFixYwLFjx7h9+zY5OTlUrVrVrI2jo6Py+s40C4C0tDQaNGig1Gk0GgwGA2lpaUrZnekaAHZ2dtjY2JCWlobBYMjTv3uvk5GRoVzHyckJrfbfxT7LlClDYmIiAD179mTFihWMGjUKrVZL69at6dSpU4Hvw5o1a9i8eTOXLl3CwsKCW7duce3atQLb3y02NpaoqCjlOCgoiD59+jzUuUIIIYR4cf3yyy+88cYbNGnShOjoaBwcHNi/fz9nzpwhJycHPz8/Tpw4QUREBGXLlmXmzJn4+vqyc+fOh14M4W4jR46kQ4cOrFy5kp07dzJmzBjc3Nzo27cvgYGBnDt3jhUrVigDkvb29sV9y4/sqUig8zNv3jwqV65MSEgINjY2rFu3Ls9vOwVxcnIiOTlZOTYajaSmpuLk5KSUpaT8uyHCjRs3uHXrlln9w14nLS2NnJwcJYlOTk5W4jg4OBAcHAzAiRMnGD16NNWrV+fll1/OE+vQoUOsXr2a8ePHU7lyZTQaDe+//z4mkwkAC4v7b8rh7++Pl5eXcpzfLwJCCCGEEPf69NNPcXNzY+vWrUo+c+d5rnXr1rF37142bNhA69atAWjdujVubm5MnDiR2NjYQl+vcePGzJw5EwBfX19++uknVq9eTd++fXFxccHFxQWNRkOTJk2K6Q6L31MxhSM/6enp2NjYYG1tTVJSEhs2bHjoc728vIiLiyMxMZHs7GxiY2OxtLTE3d1dabNt2zZOnDhBZmYmMTEx1K5du9BJZ40aNbC1teXbb78lKyuL48ePs3nzZl5//XUAduzYoSTqtra2aDQaNJrct9zBwYF//vnH7H41Gg329vbk5OSwcuVK0tPTlXoHBwcuXryI0WjMty8Gg4FatWopX5JACyGEEOJBbt26xe7du+nevbvZX9Tv+Pnnn7G3t1eSZwCdTsc777zDjh07inTNO8n5He7u7pw7d65IsZ6Up3YEumfPnsyZM4e1a9fi6uqKp6cnv/3220OdW69ePXr16sW0adOUVTjCwsKUFToAfHx8mD9/PqdPn8bNzY2hQ4cWuo+WlpaMHj2a+fPn061bN+zt7enatSuvvPIKAMeOHWPBggXcuHGDkiVL0q5dO+rWrQtAp06diIqKYtmyZbRt25b333+fV199leDgYEqUKEH79u3NkmBPT0+2bdtGQEAAJUuWVB5kFEIIIYQoqsuXL2M0GqlQoUKB9WXKlMlTXrZsWS5dulSkazo4OJgdW1lZKdNjnxVPPIEuaJ0/Dw8P5s2bZ1YWEBCgvF63bp1ZXffu3c2OfX198fX1LdQ184s9aNAgszoXFxezehcXF8aPH59vnB49etCjR49865o0aZLnTxMDBw5k4MCByrG/v7/y2t7enkmTJt2330IIIYQQheHg4IBGo+H8+fP51pcuXdpsWuwdFy9epHTp0spxiRIlyMzMNGtz59m059FTO4VDCCGEEEKoy9bWlqZNmxITE0NOTk6e+ubNm3Pt2jU2bdqklGVnZ7NmzRqaN2+ulLm4uPD333+bnXv3OYVhZWXF7du3i3Tu4/LER6CFEEIIIcSTM2nSJN544w3efPNNgoODcXR05Ndff8VgMNC9e3caNWpE165dmTRpEmXLlmXWrFlcuHDBbKOTTp060a9fP8aOHUuzZs3473//y65du4rUn9q1a5Odnc2MGTNo1qwZ9vb21KxZs7hut1hYmO4s8yCEEEIIIV5IO3fuZNSoUezZswetVkudOnUYP348Pj4+pKWlERISwrp167h58yYNGjQgPDzcbPWv7OxsRowYwdKlS8nIyKBLly54eXnxwQcfcPLkSapUqcKpU6eoWrUqq1atMlvWd9CgQXz//ffKetHZ2dkMHDiQ2NhYkpOTadmypbJZ3tNCEugXgJ9riCpx15+IBMBX864q8TcbV+FXY7gqsdcnRtCgzzRVYgP8+uVgmr03VZXYO78ditdbk1WJve2/nwLgV3GAKvHXJ82iZfspqsTevm4YPt73f77hUcTFh2L8p4YqsTXlEvFtPkGV2Jt3jASgSYA6n8fdy4fSuFvBO7k+ij0xQ1T7LELu57GVrosqsTdlfYP7SHV+xiRMGAyAn7s62xyvT5io2vdSXHwora0/VCU2wMb0par+/GpTb5QqsTf8mf+zVOLpJXOgH9Jff/1Ft27dlOOPP/5Y2bL7cYqLiyMkRJ2EWAghhBBCPNhzOwc6NDSUI0eOoNVq0el0uLm5ERQUhIuLS7HEnzNnjvJ6xYoVJCUlMWzYsGKJLYQQQgghnl7P9Qh0YGAgK1euJDo6Gjs7O2bMmPGkuySEEEIIIZ5xz+0I9N2sra3x9vZmypQpHD16lKioKM6ePYtOp6Np06YEBgYqm6ycO3eOBQsWkJiYCECLFi3o27dvnpiBgYH069cPo9HI6tWrMZlMdO7cGXt7e6Kjozlw4ABLly7lwoUL2Nra8sYbb9C1a1cAdu3axZdffsnMmTOxt7fn2LFjjB49milTpuDi4sLly5eJiorir7/+QqfT8eabb9KlSxdlF0MhhBBCiEd16Z+GmIy5OyZb/K/MQnmlFhN3Hr4rVWY/Wsu8m7Q8C16IBPrWrVvEx8fj6uqKRqOhZ8+e1KhRg7S0NMaOHcuPP/7I22+/TXp6OmFhYbRp04b//Oc/ABw/fvy+sV977TU6deqUZwqHXq9n4MCBVK5cmTNnzhAWFkbVqlXx9PSkadOm7N+/n1mzZhESEsLUqVPp3r07Li4uGI1Gxo8fT/369Rk0aBDXr19n3LhxODk5mW2jKYQQQgjxKEwWTuSYUu4tBe5OqIvpWvf8F8DC4tlNQ5/rIc0FCxbw/vvvExwcTFZWFoMGDaJatWrUrl0brVZLmTJlaN26NQcPHgRg37592NjY0LlzZ/R6PXq9Hnd39yJd28PDg6pVq6LRaKhSpQotW7ZUrgMQFBTE2bNnCQkJ4aWXXqJNmzZA7vbfqampdO3aFSsrK5ycnOjYsSPbt2+/7/VSU1M5fPiw8pWamlqkfgshhBDixWDEgpwCvrKxIBvIArL/95VTyK875/0bw/waz7Iipf5paWns3buXCxcukJ6ejpOTEzVr1qR+/fpYWDw9b0jv3r3x8/MzK0tKSmLBggUcO3aM27dvk5OTQ9WqVQFISUmhfPnyxXLtI0eOEBMTw+nTp8nOziYrK8ts6+4SJUrg7e3NihUrCA4OVsqTk5O5evUqH3zwgVJmNBoxGAz3vV5sbCxRUVHKcVBQEH369CmWexFCCCHE8ycHyL7vYsZ5czplZLqAdO/O4sgPs0bys7yO8kMn0FevXmXJkiUsWbKE33//nXuXj7awsMDOzo63336boKAgPD09i72zxWHevHlUrlyZkJAQbGxsWLdunTK66+zsXKSFuvP7pWHq1Kn4+fkRFhaGXq8nOjrabE/4c+fOsXbtWnx8fIiKimLy5MlYWlpiMBgwGAxER0cXqg/+/v5mC5o/KOEWQgghxIvtzkhxkZhyk2mL3JfPdDJcFA81hWPixIlUrVqVGTNm4Ovry5o1azh58iTXr18nMzOT5ORk9uzZQ0REBJcvX8bHx4c333yThIQEtftfaOnp6djY2GBtbU1SUhIbNmxQ6l599VWuX7/O6tWruX37Nrdv336oe3BwcODixYsYjUaz69ja2qLX6zl69Cjbtm1T6rKyspg6dSr+/v4MGDCAEiVKsHz5cgCqV6+Ovb0933zzDRkZGRiNRs6fP282/SM/BoOBWrVqKV+SQAshhBDifowUflrGvVMzMina9I4iJ+5PiYcagY6Pj+e7777D29s73/o7o6avvvoqffv25fLly8yePZv4+PgizyFWS8+ePZkzZw5r167F1dUVT09PfvvtNwBsbGwYN24c0dHRfPfdd2g0Glq2bPnAe/D09GTbtm0EBARQsmRJvvrqK/r27cvChQtZsGABderUwdPTk+vXrwOwfPlyrK2teeedd9BoNAwePJhBgwbRsGFDPDw8GD16NIsXL6Zv375kZGRQtmxZ/P39VX9vhBBCCPHiyDY9aArHvx52hLkwE3mf5VHrh0qgN23aVKigjo6OjB49ukgdKi4TJ+a/DamHhwfz5s0zKwsICFBeV6pUiXHjxuU5r27dusTExCjHd0+xsLe3Z9KkSWbtPT09C5zG0qNHD7NjZ2dnZQQact+/wYMH53uuj48PPj4++dYJIYQQQjysOyPQBcm7csbDpMemYl/B42n07K4fIoQQQgghiuzOaht3e/RR4fzT5uctmX6oBDq/Edn7CQsLK1JnhBBCCCHE43Er6xQm05OZSmEBZOfcxFJbOk9dUlISH3/8MT///DMWFha88cYbzJkzB2dnZ7Kyshg8eDDLly/HwsKCgIAApk2bhqXl4x0TtjDdu5xGPhwdHc2OMzMzSU9PB3KXY8vIyAByd/zT6/VcunRJha4KIYQQQojisu9MbXJM6dw9Pvw4RopN/5vm8XL5nylh9VKe+o4dOwKwbNkyTCYTAQEB2Nra8vXXX/PZZ5+xdu1a1q9fD4Cfnx/vvPPOYx+8fah0/e7l1/bv30/nzp0ZPXo0nTp1omTJkly/fp1Vq1Yxfvx4vv32W9U6K4rGzzVElbjrT0QC4Kt5V5X4m42r8KsxXJXY6xMjaNBnmiqxAX79cjDN3puqSuyd3w7F663JqsTe9t9PAfCrOECV+OuTZtGy/RRVYm9fNwwf7/yffSgOcfGhGP+poUpsTblEWjX9XJXYm3blPo+i5vveJECdz/ru5UNV+yxC7uexla6LKrE3ZX2D+0h1fsYkTMh9RsbPPVSV+OsTJqr2vRQXH0pr6w9ViQ2wMX2pqj+/2tQbpUrsDX+OVyXug2i01cjIOnJP6b3LFBfPtfIO11qg1drm2/bEiRP85z//wc7ODoD33nuP8PBwABYuXMi0adOUfTtGjhxJSEjI05lA361///4MGzaMnj17KmUlS5akV69epKen8/HHH7N3795i7aQQQgghhCheuXOg77eisUlZ7xkKPzr9oIcQC5oCMWTIEFatWkXbtm0xmUx8/fXXtGvXjsuXL3Pu3Dnq16+vtK1fvz5nzpzh6tWrlCpVqpA9LLpCJ9B//PGHsnPfvapVq/bA9YqFEEIIIcSTp0nth3122QLrL5fuek/Jg1fYyC9pdry0LN+2mWWysbLJW+7p6UlUVJQyhbhp06aMGDGCq1evArn7b9xx5/X169ef7gS6SpUqzJ8/n9atW5vtwGcymZg7dy6VK1cu1g4Wp9DQUI4cOYJWq0Wn0+Hm5kZQUBBHjhxh/fr1REZG5jlnypTcP3sOGzZMKYuKiuLs2bN5Hq6Mi4tjxowZ9OvXT9lCfO7cuWa7G+bk5JCdnc3SpUuxt7dn0aJF7Nmzh8uXL1OqVCnatm1Lhw4dlPYpKSnMmjWLv//+m1KlStGtWzdatmxZnG+LEEIIIV5AJu4/qpxtuv/otAW5UzzMH0R8tDkfRqMRX19fOnfuzObNmwEYM2YMrVq1UuY9X716Vdkw7k5SXbJkyUe6bmEVOoGeNGkSnTp1onr16rRr144yZcqQnJzMDz/8wOnTp1m9erUa/Sw2gYGB+Pn5kZ6ezuzZs5kxYwZt2rQpsH2fPn34+OOP2bVrF02bNiUhIYGtW7cyc+ZMs3bXrl1j9erVVKpUyaw8ODiY4OBg5Xjx4sUcP34ce3t7AHQ6HaGhobi4uHDu3Dk+++wzSpcuTYsWLQCIjIykSpUqjBw5ksTERMaPH0/lypWf6l9UhBBCCPHsyyloeoeSMP9vikdxTZQGLl26xOnTp/nkk0+wsckdnh4wYABTpkwhJycHFxcXfv/9d6pVqwbA77//zksvvfRYR5+hCAl0hw4d2LdvH5MmTWLt2rVcuHCB8uXL06hRI1avXm02L+VpZm1tjbe3tzLCXBB7e3v69OnDvHnzqF69OjNmzKBbt244OzubtVu0aBEdOnRg+/btBcbKyckhPj7ebP54167//nmkUqVKNGrUiISEBFq0aMH58+dJTExk9OjR6PV66tatS6NGjdi6datZDCGEEEKIwrpReiHXM48W3OCuEWizqRkm/n260HQnic49zC+VTnLolW94D318njKDwYCbmxtz5szhs88+A2DOnDm4uLhgMBjo2bMnEyZMUDarmzhxIoGBgQXfg0qKtGhe/fr1+eabb4q7L4/VrVu3iI+Px9XV9YFtmzdvzo4dOxg0aBCVK1fOM2J98OBBzp49y4ABA+6bQP/666/cvn2bJk2a5FtvNBpJSEigbdu2AJw+fRpnZ2flKVSAqlWr8ueff+Y5NzU1ldTUVOX4zvbqQgghhBD5yUFz34cI76ycke/0jHufADSZv7gzvaMo1q5dy+DBg6lYsSJGo5FXXnmFdevWATB69GjS0tKoXbs2kDsQGRqqzoo09/PC7US4YMECYmJi0Ov11KhRg0GDBnHo0KEHnufh4cHOnTvx9vY2+1NFVlYW8+fPZ/DgwWg095srBJs3b6ZFixbo9fp86xctWoRWq1W26s7IyDBLngFsbW2VNbjvFhsbS1RUlHIcFBREnz59HnhfQgghhHgxGU0W5Nwzz7lo23cXwHRXMl2I09zd3dm4cWO+dTqdjjlz5jBnzpyi96sYFDqBNhqNREdHs3r1as6dO6dsonKHhYUFx48fL7YOFrfevXsrD/jd8aAEOiUlheXLl9OhQweWL19O06ZNlcT2u+++w8PDQ5mLU5CrV6+yf/9+IiIi8q3/9ttv2b9/PxMnTkSn0wG5m9TcvHnTrN2tW7ewtrbOc76/vz9eXl7KsYw+CyGEEOJ+LmclYTRp7l35WaWr5V1f+nbOLfRaxwLaP90KnUAPHz6cqVOn4uXlxeuvv46VlZUa/XqqzJo1i9dff53evXuTnJxMdHQ0gwYNAnKX9Tt9+jQ7d+4E4MaNG5w4cYLExEQGDhyoxIiPj6dChQpUr149T/zVq1cTFxdHeHi42a6PlStXJiUlhRs3bigJ+4kTJ/J9gFCmbAghhBCiMLKMOrJN2TzunQj/XV/6/n+5f5oVOoFevnw5Y8eOZfTo0Wr054nKzMw0O9bpdGzatIkLFy4o82v69u1L//79OXDgAA0bNmTEiBFkZ2cr54SHh9O4cWNat25tFmvLli28+eabea4ZGxvLhg0bmDhxIk5OTmZ1FSpUwM3NjWXLltGzZ0+OHj3K3r17mTxZnV3ohBBCCPHisLasxKXME/eU/jvtAop/J8K7p4boNHn/ov6sKHQCnZGRQbNmzdToyxOVmJhIp06dzMrCw8NZvHgxI0aMoESJEgA4OjoSGBjInDlzmD17dp51By0tLbGxsTGbu3zs2DGSkpJ4/fXX81x3yZIlWFpa0r9/f6XM29tbWfpu2LBhzJw5k4CAABwcHAgODpYl7IQQQgjxyHLQFLxU3R2mos+GftCZBe1E+CwodAIdEBDADz/8oDzo9iyZOHFivuU+Pj4F3s/XX3+dp+z111/PNxku6Bpubm589913+ba/81RpQZydnfn888/v20YIIYQQorBy0Dxgs5R7FWYnwodJtx/PhBE1FDqBbtKkCaNGjeLixYv4+vqabad4xzvvvFMsnRNCCCGEEOrIyWcVjsLIfyfCF0OhE+gPP/wQyF2j+Ntvv81Tb2FhQU5OzqP3TAghhBBCqMaIlhy0hT/RdGcbcBOmYt6J8FlR6AT65MmTavRDiOeO8YVbZf35l2XKfnCjItADJq26T6NnllQvvkn74v3jKcTzoDAj0AWvD31nRQ3z0sLEfBYV+p94eYBNCCGEEOLZZzRp7ptAF21TlaJtnvKsKXQCfebMmQe2qVSpUpE6I4QQQgghHo/8tvI2mQ0LP2oKfFfq/Zxl04VOoKtUqfLAuS6PMgc6NDSUI0eOoNVq0el0uLm5ERQUxJEjR1i/fj2RkZF52rdo0cJsd8GrV6/Ss2dPGjZsyMiRIx94zevXrxMdHc3evXsxmUy4ubkxfvx4pf7kyZNER0dz9OhRrKysaNWqFd26dQNg+vTpbN++HUvLf9/KOXPm4OzsTEpKCh9//LHZtW7fvk3btm356KOPAGjfvj16vV55T93d3RkzZgwA+/btIzY2ltOnT2NpaUm9evUIDAw022xFCCGEEKIokm//Q87/diIslq2778d0z/rSQHr2LWwsS6lzPZUVOoFes2ZNnrLLly+zceNGdu/ezaRJkx65U4GBgfj5+ZGens7s2bOZMWMGbdq0eejz4+Pjsba25sCBA1y9epVSpe7/Pyc8PJwqVaoQFRWFtbU1J078u6j49evXCQsLo0ePHnz22WeYTCbOnz9vdn6HDh3o3r17nrjOzs6sXLlSOb5x4wbdu3enefPmZu2mTZuGi4tLnvNv3bqFv78/devWxWQyMXfuXGbMmKEk2EIIIYQQRZVj0nHbmHfQU/3B4txkWmNRhAcYnxKFTqA7dOiQb3mPHj0YMmQI27Zt47333nvkjgFYW1vj7e3NlClTCnXeli1beOedd9iyZQvx8fEF9hng999/5+LFi3z++edotbn/I+/ebnvt2rXUr1/fbJ3oqlWrFvJOcm3btg1nZ2fc3d0fqr2Xl5fZcbt27Z7LHSCFEEII8fiV1FXgZsa9U3PNH+0rrmQ67wODFlhpSxRT9MevWNcJeOutt+jcuTNz584tlni3bt0iPj4eV1fXhz7n2LFjnDlzhpYtW5Kdnc2WLVvum0AfPnwYFxcXZs6cyf79+zEYDAQEBNCoUSOlvnLlygwfPpxz587h6urKRx99xEsvvaTE2LhxIxs3bsRgMNCuXTt8fX3zvVZcXFy+G7aMGjWKnJwcqlevTo8ePQqcQ37w4EGZXy6EEEKIYvGghwhzFcNOhHde3DMF+IVaheN+du7cqWx5/SgWLFhATEwMer2eGjVqMGjQIA4dOsSxY8d4//33zdqmp6fTokUL5XjLli14eHjg7OyMl5cXK1as4NixY7i5ueV7rdTUVH777Tf69evHgAED+OOPPwgPD2fmzJlUqFCB1NRUjh49ytixY6lWrRqrVq1iwoQJzJkzB61WS7t27ejVqxe2trYcOnSIiIgIbG1t82x3fvLkSU6cOJFnTvbEiROpWbMmWVlZfPfdd4SFhTF37lxsbGzM2iUmJrJy5UrCwsIKvI/U1FTl2GAwYDAYHvxmCyGEEOKFlLuM3YPS4rvr79qJsIDTcjdVKaiykB18ihU6gf7kk0/ylGVmZvL337Qf7ToAACAASURBVH+zY8cOQkJCHrlTvXv3NnsoEODQoUO4ubnl+xDhHVlZWWzfvp2ePXsCUK5cOWrXrs2WLVtwc3MjPj5eGR13dnZmzpw56PV6DAaDcr2GDRtSu3ZtfvvtNypUqIBer6dx48bUqlULgPfee4/Y2FiSkpKoVKkS1apVU65fr1492rZtyy+//JIngY6Li6NBgwY4OTmZlXt4eACg0+no2rUrP/30E3///TcNGzZU2pw8eZLPP/+c/v37Fzj9IzY2lqioKOU4KCiIPn36FPQWCyGEEOIFZ3yUnQhNpgJ2InzOltsoQKET6B9++CFPWYkSJXBxcWHu3LkEBgYWS8eKYs+ePdy4cYPFixezdOlSIHeE+syZM/Tu3Rtvb2+8vb3NzqlSpQq7du0qMGaVKlUK1QcLCwtM5mvAkJ2dTXz8/7N353FRVe8Dxz/DvomAuOOumBu5lrsoqblRiKGYy7eUyA01f2aitrgvaZbmbqa5kkiZuUNWmlm5Q6mZ+4KCKKjsw/z+IK6OMywDczX0eb9ety/3nnPPPcyX7OF47vPsY8iQIQW6/2EXLlzggw8+YNCgQQYvHz7M399fb8+0rD4LIYQQIi9aLNBSiADabJUIi2+w/VRVIty7dy9t2rTRC+LT0tIYOXIkhw4dMhqAtmjRgi+//JLdu3fj4+PDiRMnOH36tBLsduzYkalTp/L3339TvXp1vv76a8qUKUPFihUB2L9/P40bN8bOzo5Tp07x/fffKynqcvz2228Ayr7qHJcuXSIjI4OqVauSmZlJeHg46enp1K5dW2l///33GTBggEHg/yjZsiGEEEIIU2RRkC0c2XQG2y80RhuftnzPuXlqig3funWLY8eOMXv2bIM8yR06dGDv3r1GA2gnJycmTpzIkiVLWL58OeXKlWPs2LFUqFAByN5i8eabbzJjxgySk5OpWbMmEyZMUDJ2bNu2jc8//5ysrCzc3d3p168fbdu21XtGZGQk7dq1w9raWu/6nTt3WLx4MfHx8djY2FCzZk0++ugjnJycgOyUgYmJiSxbtoxly5Yp9z2cGk8IIYQQojDyK+WdZ9BcgJvyC6YNxy8+ChVAHz16lOnTp7N//34SEhJwc3OjTZs2hIaG0rBhwyJNaPr06Uav+/j4GM1g8XD/b775xui9+e0FrlOnDp9++mmu7Z06daJTp05G2wqS9zq31HNeXl4sXrw41/tGjhzJyJEj8x1fCCGEEMJUxrJwmLUS4VO8Mm1yAP3zzz/TsWNHypUrR2BgIGXLluXGjRtERETQokUL9uzZk+deXSGEEEII8eRdTYlTKhGq4+FtHoYtKdpUSliXUO3pajI5gH7vvffw9vZm27ZteuWr58yZQ7du3XjvvffYv3+/WScphBBCCCHMS6ezIsNIJcLH8OR//7f4LktrdI+mjMiHg4MDmzdvpmvXrgZt27dvp1evXiQnJ5ttgkIIIYQQwvzGHJvCpeSrefYx19YLY9HmymYf42ztZJ4HPGYmr0A7Ojpy8+ZNo203btzA0dGxyJMS5tWletFzcxuz41x2Tu6OFq+pMv6erK/p4jlOlbF3nJlF4+BPVBkb4MjS0TR/fa4qY/+6bgztus5WZewft78LQJeKI1QZf8fVBbT1naPK2D9tHYuPt/F3KMwhcl8oWbGeqoxtUe4MHVtPU2XsPfuzizep+fP44oB5qox9aM07qv0sQvbPYyfrPqqMvTtjI3UnqPNnzJ/TRgPQpW5oPj0LZ8ef01X7dylyXyid7furMjbArpSvVP3z62WviaqMvfPEVFXGzU+WTkNWfnmgi7CPuVAvIRYTJgfQPXr0YNy4cXh4ePDSSy8p1/fu3cv48ePx9fU16wSFEEIIIYT5ZZlaiVBX0EqERu59ypgcQM+dO5eYmBg6d+6Ms7MzZcqU4ebNmyQlJdGsWTODSoHmFhoayunTp7G0tMTa2pqaNWsSFBTE6dOnWbBgATY2Nkrfxo0b89577xEZGcn333/PtWvXsLOz44UXXuCNN97A3t4egPXr1xMZGcm9e/ewt7enTZs2DBw4UG+PN8CVK1cYOXIkzZs3Z+zYsUB29cO5c+dy9uxZbt68yQcffKBXRRCyU91t3ryZ5ORkGjVqxIgRI5RUdQDR0dGsWrWKy5cvY29vz2uvvUb37t2V9u3bt/PNN99w+/Zt3NzcGDVqFHXq1DH7ZyuEEEKIZ0f2CrQpQa7hS4E5lQifNSYH0K6urhw8eJBt27axf/9+Jahr3bo13bp1w8KikCUhTTB48GC6dOlCSkoKCxcu5NNPP+Xll182WuobsoupvPnmm9SuXZuUlBTmzJnDqlWrGDp0KADe3t68+uqrODg4kJSUxKxZs/j222/x9/fXG2fx4sXUqlXLYPw6derQo0cP5s41/CvSo0ePsmHDBiZPnkz58uVZsGABixYt4t13s/+q/MqVK8yaNYuQkBAaNWpEWloaCQkJyv2RkZFs376d0NBQqlSpQnx8/GP5jIUQQgjxdMvCMI1dQelyImgdaAq50lyc4+5C5YG2sLDA19f3iW/XsLe3x9vbmzlz8t5T+fALj9bW1nTu3JlNmzYp13KKpuTQaDRcv35d71pkZCQuLi5UrFiRq1cfbLi3trbmlVdeATAa2EZFReHj40ONGjUA6N+/P8OGDeP+/fs4OjqyadMmOnXqRLNmzQCwsrJS9pFnZWWxbt06RowYoZQUL126dJ7fqxBCCCFEQWh1FLwSocEVC6OtT++mDX2FXsrcs2cPU6dOZdiwYUydOpW9e/eac14FkpyczL59+6hevbpJ90VHR1O5cmW9a9u3b6d3797069eP8+fP06VLF6UtKSmJTZs2MWjQIJPnePHiRapVq6acV6hQASsrK65cuQLA6dOnsbCwICQkhP79+zN9+nRu3boFZFdXjI+P58qVKwwaNIg333yTlStXkpGRYfI8hBBCCCEepvt3C0duh/ahI+eFwwfHw30fXDe8J/cjryXorVu30rBhQxwdHalQoQJLliwBsmOyvn374uzsTNmyZZkyZcpj+rT0mbwCHRsbi7+/PwcPHsTNzU3ZA52QkEDz5s3ZsmUL5cqVU2OuipUrV7JmzRpsbW3x9PRk1KhRxMTEcPbsWQIDA5V+o0aN4sUXX9S799dff+Wnn34y2OrRtWtXunbtyuXLl4mKisLNzU1pW7VqFd27d9e7VlCpqal6+50hO5NJSkoKAPHx8URGRvLRRx9RpkwZli5dyrx585g2bRrx8fEA/PHHH8yfP5/09HSmTp3K5s2b9b7PnHFy+gO4u7vj7u5u8nyFEEII8WzQGqtEWORRja9Bm7IyvXPnToYOHcratWtp06YNSUlJ3LhxA4ARI0aQkJDApUuXuHnzJi+99BJVqlRhwIABRZ65KUwOoN9++23OnTtHZGQk7du3V65HRUXRr18/hgwZQkREhFkn+ahBgwbprRADxMTE5LoHOsexY8dYsGABEyZMMNi2kaNSpUpUrlyZxYsXExoaSkxMDOfOnWP48OGFmqudnR3379/Xu5acnKy8wGhra0uHDh2oVKkSAK+//jpvvvkmqamp2NraAtCzZ09KlMiu1PPKK6+wdetWgwA6PDyc5cuXK+dBQUH5ljAXQgghxLPr0v14snSaR4JmtTZhGD7lXmYqJW0M80BPmjSJ999/H29vbyD7/TtXV1eSk5PZuHEjBw4cwMXFBRcXF0aMGMHKlSv/+wH0nj17WLJkiV7wDNChQwdmzpzJkCFDzDY5czp+/Dhz5sxh3Lhx1KtXL8++Wq2W2NhY5b7Y2FjeeOMNIHtFOSsriyFDhrB48eJ8n1ulShXOnz+v/BBcu3aNjIwMPDw8lHaNkVwwOp2OihUrYm1tXaDvz9/fn3bt2innsvoshBBCiDzprMjQZhleVyWGNszgYakxDEPv37/P4cOH6dq1K56eniQlJdGmTRs+++wzYmNjSU9Pp2HDhkr/hg0bMn26ejUAclOoLByurq65trm4uBR5UuZ28uRJZs2axf/93//h5eVl0L59+3Zat25NiRIluHDhAuHh4TRt2hQAPz8/vdXuiIgIrl+/zrBhw5RrGRkZ6HQ6dDodWq2W9PR0rKyssLCwoEOHDnz88ce0bduWChUqsG7dOlq0aKG8KNipUyfWrVtH+/btKV26NBs3bqRBgwbKCnXbtm2JiIigZs2aZGRk8N133/HCCy8YfA+yZUMIIYQQpninRCdKW+ReCXDErTCzVCL8zC3A6HVLreGGkdu3b6PT6fjmm2/Ys2cPpUqV4u2336Zfv358+OGHODo66qUZdnFx4e7du0WfpIlMDqBHjRrFzJkz8fb21tvbe/fuXWbNmsXIkSPNOkFz2LhxI8nJycycOVO5Vrp0aT7//HMAjhw5wvr160lPT6dkyZK0bt2avn37AtmZPnKCWcjekmFjY6P3i8KQIUOU6oxTp2ZXE5o2bRoNGjSgUaNGBAYGMmXKFJKTk2nYsCEhISHKvR06dCAuLo5x48ah1WqpX78+o0ePVtqDgoJYvHgxb775ppKj+tH0ekIIIYQQpss7On70RT9TY+nC7KfOiS1DQkKoUqUKAB999BG1atVi8uTJJCcnk5mZqQTRiYmJyjbXx8nkAPrixYtcuHABDw8P2rdvr7xE+MMPP1CiRAmuXLmiBIgajYZPP/3UrBPObZnex8cHHx8fo23TpuVdInfixIKX5swJrB+2YsWKPO/p3r27XmGUR/Xu3ZvevXsbbXNwcGDMmDEFnp8QQgghhDkYlvnOP11dUfdTu7i4GGRKy9GgQQOsra05fvy4UrTu2LFjNGjQwOTnFJXJAfS2bduwtrbG1dWVY8eOKddztnV89913yjU1AmghhBBCCFF0X6Ye4+zdG3n0eDQAfvg8u6x3TiXCvELr4BtbjI6+t0Go0etvvfUWCxYs4OWXX8bNzY3Jkyfj4+ODs7MzvXv3ZtKkSWzYsIGbN2+yYMGCJ5LKzuQA+vz582rMQwghhBBCPEZZuuyjcP4NlIuQ9y63W9977z0SEhJ4/vnnAWjfvj1fffUVAAsXLiQ4OBgPDw/s7e0ZPnz4Y8/AAaDR6Z7FCuZCCCGEEM+2gB8X8HeeK9APGEaL+qvRylUTdm1EdRqPq41jwW/4DylUKe/k5GQiIyO5fPkyqampem0ajUbvJTjx5HUpq05qwR03stP4dbR4TZXx92R9TRfPcaqMvePMLJq9MU+VsQF+X/UOrXrlnpO8KA5s/j86tVDnr6t2H5wEqPsz077zLFXG/mHXOF72Kvj7DKbaeWIqk076qTL2lAYRqv972vz1uaqM/+u6MbT1naPK2D9tHUuXiiNUGRtgx9UFdLLuo8rYuzM20mjoJ6qMfXRR9n9j1fpsdlxdQMfWeb87VFh79k+gs31/VcYG2JXylar/Lqn1Z8zOE1NVGTc/OdUDc1PwJVbDFHVQgGC6GC/hmhxA//jjj/j7+5OQkGC0XQJoIYQQQoj/PqWk9kPMui/BlGC6mDE5gB42bBheXl4sWLAAT0/PAhf6MEVoaCinT5/G0tISa2tratasSVBQEKdPn2bBggXY2Nig0WhwdHSkTp069OzZkxo1auQ55rZt29i8eTPJyck0atSIESNGKKlS1q9fT2RkJPfu3VNSxQ0cOFBJkTJ58mT+/vtv0tPTcXNzw8/Pj06dOgFw6tQpNmzYwNmzZwGoXbs2gwcPViodZmZmsnbtWn788UdSUlJ44YUXGDJkiJIa7/bt2yxdupSTJ09iaWlJ9+7dCQh4kC9x7dq1HDp0iMuXL+Pn58fAgQPN+2ELIYQQ4pl0LumWkUqEKnnkIRrgbnoqrrbFcwtH7uv2ubh48SKhoaHUq1dPleA5x+DBgwkLC2PFihU4OTkp2Txq1qxJWFgYmzZtYt68eVStWpV3332X48eP5zrW0aNH2bBhA5MmTeLLL7/EwsKCRYsWKe3e3t4sWLCATZs28dlnn3Hu3Dm+/fZbpX3AgAF88cUXbNq0ifHjx7N27VrOnDkDZFfMeemll1i2bBlffvkllStXVnJBA2zZsoWTJ08yb948vvjiC+7evcuyZcuU9nnz5uHk5MSqVauYOXMmu3btIioqSmkvX748//vf/4wWTxFCCCGEKCwrjTVarYasR48slQ+tBq1Wg5VFoXYS/yeYHEC3atWK06dPqzEXo+zt7fH29ubixYsGbS4uLrz22mv4+PiwevXqXMeIiorCx8eHGjVq4ODgQP/+/Tl48CD3798HoEKFCjg4OCj9NRoN169fV86rVq2q/LKQU3Y7p71Jkya0adMGR0dHrK2tefXVV7ly5QpJSUkA/Prrr/j6+uLq6oqDgwP+/v78/PPPpKWlkZKSwokTJwgMDMTGxoYKFSrw0ksvsWfPHuXZPj4+NGnSRG9+QgghhBBFVd7eFZ3O4pFDgy7rwYHOPMfDY+p0GnQ6C+ys1FuIVZvJof/SpUt57bXXsLGxwcfHx2jpbjc3N7NMDrJfWNy3bx/Vq1fPtU+rVq3YtWsXqamp2NnZGbRfvHiRxo0bK+cVKlTAysqKK1euULt2bSC7nPfq1atJSUmhRIkSvPHGG3pjzJ07l4MHD5Kenk61atVo1qyZ0blER0fj6uqKs7Ozcu3hRCc6nY709HSuXbtG2bJleTQJik6n48KFC7l/IEIIIYQQ5qDLL7vGg3aNYVP+wxfjlwTzY3IA7eLiQpUqVQgODlZWYx+l1WqLPLGVK1eyZs0abG1t8fT0ZNSoUcTExBjt6+bmhk6n4/79+0YD6NTUVL2y4wCOjo6kpKQo5127dqVr165cvnyZqKgog18CxowZg1arJSYmhpiYGGxsbAyeExsby9KlS3nrrbeUa02bNmXr1q00aNAAW1tbNm/eDEBaWhoODg7Uq1ePdevWERwcTFxcHJGRkaSlpRX8g/pXfHw88fHxyrm7uzvu7u4mjyOEEEKIZ0TOCnFBuir/+FfBShE+tUwOoPv378/+/fsZM2YMnp6eRgNJcxg0aBBdunTRu5ZbAJ2QkKC8VBgWFqYEqXXr1uXDDz/Ezs5O2a6RIzk5WXmR72GVKlWicuXKLF68mNBQ/Qo5lpaWeHl5sW/fPnbs2EGPHj2Utri4OCZNmoS/vz9t2rRRrvfq1Yv79+8zduxYAPz8/Dh69CilSpUC4J133mHZsmUMHjwYFxcXOnTowI8//ljQj0kRHh7O8uXLlfOgoCCCg4NNHkcIIYQQz4bsrRSFTI/x7/JyTiXCB56ydBu5MDmAjoyMZOnSpfTr10+N+RTKL7/8Qo0aNbCzsyMgIEAviwVAlSpVOH/+PN7e3gBcu3aNjIwMPDw8jI6n1WqJjY3N9XmPtsfHxzNx4kQ6d+7Mq6++qtfXxsaGoKAggoKCADh8+DDu7u5KAF26dGkmTJig9F+9erWyrcQU/v7+tGvXTjmX1WchhBBC5EVndAtHQW6EnED5oS+fKSa/RFixYkVKliypxlxMlpiYSHh4OHv37s0zvVuHDh3Yu3cv//zzDykpKaxbt44WLVrg6JidOmX79u0kJSWh0+k4f/484eHhSvnI69evc+jQIdLS0tBqtfz+++8cOHBAab916xYTJkzA29ubXr16GTz71q1bxMXFKXubv/jiCwIDA7GwyP7oL1++zP3799Fqtfz222/s3r2b3r17K/dnZmaSnp5OVlYWWVlZpKenG90i4+7uznPPPaccEkALIYQQIi/ZAbSmYIfBS4AFbMvjKM5MXoGePHkyM2bMoHXr1ri6uqoxpzydPXuWgIAANBoNDg4O1KlTh5kzZ1KrVq1c72nUqBGBgYFMmTKF5ORkGjZsSEhIiNJ+5MgR1q9fT3p6OiVLlqR169b07dsXyH6pLzw8nHnz5qHRaChTpgzBwcFKWrndu3dz/fp1IiIiiIiIUMb8/PPPKV26NDdu3GDevHncuXMHV1dXXnnlFTp27Kj0O378OJs2bSI1NZVKlSrx7rvv6q2ML1y4UC+tXUREBH369FHmJ4QQQghRGDryCWQLvTr9L6lE+MC6deu4dOkSVapUoWHDhgZZODQajV4O5cKYPn260es+Pj74+PgUaszu3bvTvXt3o20TJ+ZemrNChQrMnj071/bAwEACAwNzba9bty4rVqwo1LwARo0axahRo3JtF0IIIYQolKx/j4fox7RFXCV+aH9I8V5vNmRyAH3v3j291d67d++adUJCCCGEEEJ9/yTeVnkrxYOxjS02J6an4mZXPOtcmBxA//DDD2rMQwghhBBCPEa2WJGuzTJsUHu5+N9o2sbCUuUHqUeje7SShxBCCCGEeOq9/M0qTt2Oy7uTuYJpI9HmkcDhz84KNMDRo0eZPn06+/fvJyEhATc3N9q0acP48eNp1KiRuecoiqhL9f9TZdwd5z4GoKPFa6qMvyfra7p4jlNl7B1nZtE4+BNVxgY4snQ0zV+fq8rYv64bQ7uuue/LL4oft78LQJeKI1QZf8fVBbT1naPK2D9tHYuPt/H3J8whcl8oWbGeqoxtUe4MHVtPU2XsPfuz02Sq+fP44oB5qox9aM07qv0sQvbPYyfrPqqMvTtjI3UnqPNnzJ/TRgPQpW5oPj0LZ8ef01X7dylyXyid7furMjbArpSvVP3z62Wv3N+ZKoqdJ6aqMm6+dOT/Ip85lll1PHWboE0OoH/++Wc6duxIuXLlCAwMpGzZsty4cYOIiAhatmzJnj17aN26tRpzFUIIIYQQ5pIFZJkS2f5bPEX5h5EeuRVVecr2O5gcQL/33nt4e3uzbds2rKwe3D5nzhy6devGe++9x/79+806SSGEEEIIoQKTAtuHiqf8e59hJcJng8kB9NGjR9m8ebNe8AzZZa5DQkKMFhMxVWhoKKdPn8bS0hJra2tq1qxJUFAQp0+fZsGCBdjY2Cilu+vUqUPPnj2pUaNGnmNu27aNzZs3k5ycTKNGjRgxYgROTk4ArF+/nsjISO7du4e9vT1t2rRh4MCBWFlZcefOHVasWEF0dDQpKSlUrFiR/v37621V8fX1xdbWFo0m+wcrp4R4joiICL7//nvu3r1L/fr1GT58uF4O7bVr17Jz504yMzNp1aoVb7/9NtbW1sq8o6KiuHDhAi1atFBKggshhBBCFIkONEXN9Ux2EP2sMbkSoaOjIzdv3jTaduPGDaW6X1ENHjyYsLAwVqxYgZOTE59++ikANWvWJCwsjE2bNjFv3jyqVq3Ku+++y/Hjx3Md6+jRo2zYsIFJkybx5ZdfYmFhwaJFi5R2b29vFixYwKZNm/jss884d+6ckss6NTWVGjVqMHfuXDZs2ECvXr2YMWOGwWfwySefEBYWRlhYmF7wvG/fPrZv386UKVP46quvKFmyJB9//LHSvnv3bn788Uc+/vhjli1bxpUrV1i3bp3S7ubmRkBAAJ06dSrS5ymEEEIIoSdLU7hD98hR6HGe9AdQeCYH0D169GDcuHHs3btX7/revXsZP348vr6+ZpscgL29Pd7e3ly8eNGgzcXFhddeew0fHx9Wr16d6xhRUVH4+PhQo0YNHBwc6N+/PwcPHuT+/ftAdrEUB4cHb4FqNBquX78OQLly5fDz86NUqVJYWFjQsmVLSpcuzdmzZws0/19//ZWOHTtSvnx5bGxs6NOnDydPniQ2NhbI/txeeeUVypUrh7OzM3369CEyMlK5v2XLljRv3hxnZ+cCPU8IIYQQokB0T/goxkzewjF37lxiYmLo3Lkzzs7OlClThps3b5KUlESzZs30VlfNITk5mX379lG9evVc+7Rq1Ypdu3aRmpqKnZ2dQfvFixdp3Lixcl6hQgWsrKy4cuUKtWvXBmD79u2sXr2alJQUSpQowRtvvGH0Wbdu3eL69etUrlxZ7/rEiRPRarXUqlWL//3vfwbtOXKyBl64cIFy5cpx6dIlve+tevXqJCYmcvv2bZNKpcfHxxMfH6+cu7u74+7uXuD7hRBCCPGMMRbIqhnYPkVbPUwOoF1dXTl48CDbtm1j//793L59Gzc3N1q3bk23bt2wsDB5UduolStXsmbNGmxtbfH09GTUqFHExMQY7evm5oZOp+P+/ftGA+jU1FRlv3MOR0dHUlJSlPOuXbvStWtXLl++TFRUFG5ubgbjpKenM3v2bDp27IiHh4dyffr06dSuXZuMjAy2bNnC+++/z6JFi3BwcKBp06Zs3LiRVq1a4e7uzoYNG9BoNKSlpSlze3jbS87XKSkpJgXQ4eHhLF++XDkPCgoiODi4wPcLIYQQ4tlyLuG2iVk4iuiR4DwxLRU3+2coD7SFhQW+vr5m367xsEGDBtGlSxe9a7kF0AkJCcpLhWFhYWzevBl48DKfnZ2dsl0jR3JyMvb29gZjVapUicqVK7N48WJCQx/k2MzIyGDmzJm4uLjw1ltv6d1Tv359AKytrenXrx8//PADf/31F02aNMHHx4f4+Hg+/PBD0tLSePXVV7Gzs1NWhx+dW3JyMoDRueXF39+fdu3aKeey+iyEEEKIvNhbWJGRkW7Y8JgqEdoW40qEBQqgr1+/zvDhw3nrrbfo3Lmz0T67du1i2bJlLF68mDJlyph1kvn55ZdfqFGjBnZ2dgQEBBAQEKDXXqVKFc6fP4+3tzcA165dIyMjQ28V+WFarVbZowzZwfOsWbOwsLBg7NixWFrm/X+45qHXUTUaDX369KFPn+xk/VeuXGHjxo1UqVIFgMqVK3P+/Hnq1q0LwLlz5yhZsqRJq88gWzaEEEIIYZoKTs7cTY03bHh4pVjFSoR2/2YcK44KtN9i7ty5nDt3Ls9MEJ06deL8+fPMm6dORSpjEhMTCQ8PZ+/evQwcODDXfh06dGDv3r38888/pKSksG7dOlq0aKFsl9i+fTtJSUnodDrOnz9PeHg4zz//PACZmZnMnj2bjIwMxo0bZ5C+79KlS/zzzz9otVrS0tJYv3496enpyt7qe/fuce3aSu+7fwAAIABJREFUNXQ6HbGxsSxcuJBXXnlF2VLi4+PDt99+S2xsLHfv3mXjxo34+Pgo42u1WtLT08nKyiIrK4v09HQyMzPN+jkKIYQQ4hn0aDYNY0cWDw5TXxJU7s1l7GK8KbpAK9Dbtm1j9OjReiurj9JoNAQHBzN//nxmzpxptgk+6uzZswQEBKDRaHBwcKBOnTrMnDmTWrVq5XpPo0aNCAwMZMqUKSQnJ9OwYUNCQkKU9iNHjiiBb8mSJWndujV9+/YF4NSpUxw6dAgbGxtef/115Z6hQ4fi7e3NnTt3WLx4MfHx8djY2FCzZk0++ugjJUC+e/cuU6ZMIS4uDicnJzp16qSsRkP2Lx5xcXGMGTMGrVZLy5Yt9Z6zadMmNm7cqJwfOHCADh06MGrUqKJ/mEIIIYR4ZmkKlAc6l2qCuYWExTy7RkEVKIC+cOGCssUgL3Xq1OHChQtFnRPTp083et3Hx0dvddYU3bt3p3v37kbbJk7MvbZ9/fr12bp1a67tXl5eLF68ONf28uXL59mu0Wjo168f/fr1M9ret29fJZgXQgghhDCboqSTy7lPY4YxiqECbeGws7MjKSkp33737t3D1ta2yJMSQgghhBDqylmBNvnIeujQFn6c/KSkpFCzZk1cXFyUa0lJSfTt2xdnZ2fKli3LlClTVPyEclegANrLyyvPVdgc3377LV5eXkWelBBCCCGEUFkh9jNrsowH1IXaJ52P999/X0m6kGPEiBEkJCRw6dIlfv75Z5YvX86aNWuK9jkUQoEC6EGDBvHFF1/kWe1vzZo1rFq1isGDB5ttckIIIYQQQh0Gq8l5HToNmjxeNsxpL/B4WXnP7fDhw+zcuZNx48Yp15KTk9m4cSNTp07FxcUFT09PRowYwcqVK1X+pAwVaA/0wIED2blzJ2+88QYLFy7k5ZdfpnLlymg0Gi5dusSuXbv4448/6N27NwMGDFB7zkIIIYQQooiGeNag3PP1cm3vs+enQoxq+Hbhxo5tjfbMSE8HB8O6F5mZmQQFBfH555+TlfUg0j59+jTp6ek0bNhQudawYcNc351TU4ELqWzYsIE2bdowb948pk2bptdWo0YNFi5cyJAhQ8w+weLI19eXRYsW4eHhwaJFi3BxcZEXAYUQQgjxn5Keqc2zvSD7lIviXloqZSlpcH3OnDk0atSItm3bsm/fvgf9793D0dFRL6Wwi4sLd+/eVXeiRphUiXDo0KEMHTqUq1evcvXqVQAqVqxIxYoVVZnc4xAaGsqff/7J559/rnwfV65cYejQoQXa9z148GCGDBlCkyZNjLYPHTq0SPObPHkyf//9N+np6bi5ueHn55dnPm4hhBBCiIKwyCM9MZC9p1lFNlaGhVTOnj3LkiVLOHr0qEGbk5MTycnJZGZmKkF0YmIiJUqUUHeiRhSqlHdxD5of5ejoyLp163j33Xef9FQMDBgwgIoVK2Jtbc2lS5eYOHEiVatWxdPT80lPTQghhBDF2IpTFzhz00glwn9plH8UTd8dPxl9afDgGMMUyfv37+fGjRtKnJORkcHdu3dxd3cnPDwca2trjh8/rixcHjt2jAYNGhR9kiYq0EuEixcvJi0tzaSBT548SVRUVKEm9bh169aNw4cP888//xi0hYaGsmPHDuX88OHDyouSc+bMIS4ujhkzZhAQEMBXX31lcP/8+fOVly9PnjzJgAED2LZtGwMHDqR///5s2bIlz7lVrVoV639LXeYUsrl+/XrhvlEhhBBCiBwmVRMs5KH99yhgFo6AgADOnj3LsWPHOHbsGCtWrKBEiRIcO3aMFi1a0Lt3byZNmkRiYiJ///03CxYseCIJLAoUQH/55ZdUqVKF0aNH88svv5CRkWG037Vr11i5ciUvvfQSLVu25Pbt22adrFpcXV3p3r270QA4L2PHjqV06dKMHz+esLAw+vfvn+89SUlJxMXFsWLFCiZNmsTatWu5du1anvfMnTuXXr16MWzYMFxdXWnWrJlJ8xRCCCGEeJQpGTMMs3IUIEd0Vh7Xs4zvsXZwcMDDw0M5SpcujUajwcPDAxsbGxYuXEjJkiXx8PCgVatWDBo06IkksCjQFo5Dhw4RERHBp59+ymeffYa1tTWenp6ULl0aW1tb7ty5w/nz57l58yZubm4MHDiQtWvXUq5cObXnbzZ+fn4EBwcTHR2tl7Db3CwsLOjXr5/yGVasWJHz589ToUKFXO/JKfMdExNDTEwMNjY2Bn3i4+OJj3/w1zDu7u64u7ur8j0IIYQQ4inxH68i6O3tzZ07d5RzZ2dnNmzY8HgenocC74H28/PDz8+P8+fPExkZyR9//MH169dJTU2lSpUqdOrUiVatWuHt7a1sOShOnJyc6NmzJ2vWrCEkJETV5zz8+dja2pKamgpk/7VFjg8++IB69R6klrG0tMTLy4t9+/axY8cOevTooTdueHg4y5cvV86DgoIIDg5W69sQQgghRDFX0IqABh69xwz7pIsbk18irFatGoMHD34qC6Z0796d7777jt9//125Zmdnp7f/++HfgswtLCws3z5arZbY2FiD6/7+/rRr1045l9VnIYQQQuSpgBUBlb7kEivrHhrmGQmmC7QHOkdqaiolS5bku+++U2s+T5StrS19+vRh8+bNyrXq1avzyy+/kJKSQlxcHNu2bdO7x8XFxWhAaw7Xr1/n0KFDpKWlodVq+f333zlw4ADPP/+8QV93d3eee+455ZAAWgghhBB5yXUfs7F9yzmr1bkced2T21GcmRRA29nZ4eDgoJfA+mnTsWNHnJyclPNXXnkFBwcH/ve//zFjxgy9VV6AXr16sWXLFgIDA1m7dq1Z56LT6QgPD2fAgAG8/vrrfPXVVwQHB/PCCy+Y9TlCCCGEeAbll2WjIFk6ipLBoxgzORIeOHAgK1asoEuXLmrM57F7tPyjpaUlS5cuVc5LlCjBhx9+qNfn1VdfVb5u3rw5zZs312t/uADLqFGjlK8bNGjAmjVr9Pp+/PHHuc6tQoUKzJ49O/9vQgghhBDCROdvJDzRleCk5BRcHQ1LeRcHJgfQrq6u/Prrr3h5efHyyy9TtmxZJT8xZOcqHj16tFknKYQQQgghzMvRyprEFCN1PtTex/xv0G5rU/ySTuQwOYAeP348kL0/Nzo62qBdAmghhBBCiP++ss4lSEo2EkCruSqtQwnQbYvxlmCNTqcr5tu4hRBCCCGEqXrO/Yq/r+deyhuKHksrW0SMrGr/9NHbz84WDlH8vOz8hirj7kxaBUBHi9dUGX9P1td0qTwq/46FsOPSfJq9MU+VsQF+X/UOrXrlvr+9KA5s/j86tZiiyti7D04CoEvZIaqMv+PGYjr4zFRl7KjI93jZa6IqYwPsPDGVSSf9VBl7SoMIVT9zgJa956oy/i+bxtCuqzrvavy4/V26VByhytgAO64uoJN1H1XG3p2xkcbBn6gy9pGl2X/L+7JbkCrj70xYTsfW01QZe8/+CXS2z79qb2HtSvlK1f/mdakbqsrYO/6cnn8nNRTgZb48d3MYa8wt4jZ2vRgv4ZqUhSPH/fv3+fzzzwkMDKRz584EBgayaNEi7t+/b+75PTGLFi1i/fr1AJw8ebJIZSLDwsKYP39+gfru27ePCRMmKOe+vr5cuXKl0M8WQgghhDAm3zR2eaWgeyTLRkFT1z2TaewALl++jJeXFyEhIZw+fRoLCwtOnz5NSEgIzz//PJcvX1ZjnmYXGhrKq6++ytWrV5VrV65cwdfXF4ChQ4fSt29fk8eNjIzk//7v//SuBQQE6GXjyIu3tzfTpqnzm78QQgghhKII6ekezfdcqHGKMZMD6HfeeQeAP//8kyNHjrBjxw6OHDlCTEwMGo2GMWPGmH2SanF0dGTdunVPehpCCCGEEI+dSavOea0yywp0/vbs2cP06dOpXbu23vXatWszZcoUdu/ebbbJqa1bt24cPnyYf/75x6Bt/vz5rF692uh9V69eZdKkSQQGBvL2228r3/OFCxdYtGgRZ8+eJSAggICAAFJTU1m/fj1z5swB4MaNG/j6+vLDDz8wePBgAgMDWbFihTK2sRXsY8eO8dZbb9G3b18WLlxIRkaGuT4CIYQQQjzLClgIpcBBsSmFWIoxkwPozMxM7O2NvzFpb2+PVqst8qQeF1dXV7p3785XX31V4HsyMzOZMmUKzz33HGvWrGHMmDGsXr2aY8eOUbVqVYYOHUrNmjUJCwsjLCwMOzs7o+McO3aMhQsX8sknnxAZGcnx48dzfeZPP/3E7NmzWbRoEefOnSMsLMzk71UIIYQQ4mGaR7diGNmakV8J71xLej+6Mm3kKM5MDqBbtWrF1KlTSUxM1LuemJjItGnTaNWqldkm9zj4+fnx999/G81pbcyZM2e4d+8effr0wdramlq1atGpUyeioqJMem7fvn2xs7OjXLly1KtXj3PnzuXat1evXri4uODi4kJAQAA//vijQZ/4+HhOnTqlHPHxeaelEUIIIcSz7dz1W3kHwGY8jAXbifdTntw3X0Qmp7GbO3cubdu2pVKlSnTo0IGyZcty8+ZNIiMjsba25osvvlBjnqpxcnKiZ8+erFmzhpCQkHz737p1i1KlSmFpaalcK1OmDGfOnDHpua6ursrXtra2pKTk/kNUunRpvWclJCQY9AkPD2f58uXKeVBQEMHBwSbNSQghhBDPDmcbW+7cTzVsUKESod6Q/27fsLN+hioR1q9fnxMnTjBv3jz2799PTEwMbm5uBAUFMXr0aDw8PNSYp6q6d+/Od999x++//55v31KlSnHr1i20Wq0SRN+8eZNSpUqpNr+4uDiqVaumfO3m5mbQx9/fn3bt2inn7u7uqs1HCCGEEMVf6ZJOJN4zEkCruT9Zx4NKhDbFtxxJoWbu4eHBvHnqFaF43GxtbenTp0+B9kJ7enri6OjIpk2beO2117h06RJ79uxRso+4uLiQkJBARkYG1mb6zWrLli3KS5tff/01bdu2Nejj7u4uQbMQQgghCkyj06F5EgWpdY/8bzFk8h7o6tWr5/rCW3R0NNWrVy/ypJ6Ejh074uTklG8/KysrJk2axJ9//smAAQOYPXs2/fr1o1GjRgB4eXlRrVo1Bg4cSGBgIKmpRn6zM1Hr1q0ZO3Ysb7/9NpUrVyYgIKDIYwohhBDi2ZbvS4QmvGRY0BcH9Y4n/QEUgckr0BcuXCAtLc1oW3JycrEppDJ9un7ZTEtLS5YuXaqcP1z4pEGDBqxZs0Y59/DwYOrUqUbHtba2ZtKkSXrXHi7IUrZsWbZu3arXPnbsWOVrHx8ffHx8lPOcvt27d8/3exJCCCGEMElRVoGL8QpyURUogE5NTSU5ORndv8v8SUlJBi+ypaam8s0331ChQgXzz1IIIYQQQphVoQuaPHpPcV5KLqQCBdCzZs1i8uTJAGg0Gjp37pxr3w8//NAsExNCCCGEEOoxWz7mZ3AlWqPT5b97/Pjx4xw7dgydTsebb77JxIkTqVGjhl4fGxsb6tSpQ8OGDVWbrBBCCCGEMI++E9Zw9kpedSMehIgFXWQ2DCpzv3P3wiG4lDBenO+/rkAr0M8//zzPP/88kL0C3b17d1XTtgnz6lL9//LvVAg7zn0MQEeL11QZf0/W13TxHKfK2DvOzKJx8CeqjA1wZOlomr8+V5Wxf103hnZdZ6sy9o/b3wWgS8URqoy/4+oC2vrOUWXsn7aOxcd7ev4dCylyXyhZsZ6qjG1R7gwdW09TZew9+ycAqPrz+OIAdbIyHVrzjmo/i5D989jJuo8qY+/O2EjdCer8GfPntNEAdKkbqsr4O/6crtq/S5H7Quls31+VsQF2pXyl6p9fL3tNVGXsnSeMv1elNg1GsnAUcTXZMFzW5dFYfJeuTc7C0aFDBy5evGi07ciRI1y5cqXIkxJCCCGEEOo6f/kWZKF3qFWJUKPD4Fl37hXfSoQmB9BDhgzJNV/y+vXrGTZsWJEn9bTZt28fEyZMKFDfmJgY3nrrLeV88ODBHD58WK2pCSGEEOIZ5Wxni0aL3oGKx6PPsi/GlQhNDqAPHTpEhw4djLa1b9+egwcPFnlS5hQaGsqOHTse2/NOnjzJgAED9K55e3szbVrB/nq2Xr16LFu2TI2pCSGEEEIo3Es6osnS5XGYcQXayPjFuRKhyQH0vXv3cq2wZ2Fhwd27d4s8KSGEEEIIoa78A99Hgl6diYfevUa2dBiRlpZGUFAQ1apVo0SJEjz33HN88cUXSntSUhJ9+/bF2dmZsmXLMmXKlMf0aekzOfSvU6cOERERvPzyywZt3377rVJy+r9u5cqVHDhwgPv371O+fHkGDx5M/fr1geytKBcuXMDW1pbffvsNd3d3hgwZorRHRUURHh5OfHw8zs7OvPrqq3Tr1o379+/z0UcfkZGRoVQLnDt3LmfOnGHHjh18/HH2S3e+vr4MHTqUb7/9ltu3b9OsWTNCQkKwtrbm5MmTzJkzR69wy7lz5/jyyy+Ji4vj+eefZ8SIEQWqmiiEEEIIkZtCp7HLKyVHEd8LzMzMpHz58uzdu5fq1atz6NAhunTpgoeHB506dWLEiBEkJCRw6dIlbt68yUsvvUSVKlUM/vZfbSavQI8aNYrly5czdOhQ/vjjD65du8Yff/zBsGHDWL58OaNHj1ZjnmZXo0YN5s+fz/r162nfvj2zZs3Sq7D422+/0bhxY9avX0/Pnj2ZNm0a9+7dA8DZ2ZkJEyawceNGRo0axapVq/j7779xdHTkgw8+oGTJkoSFhREWFkalSpWMPv+XX35h1qxZLFmyhNOnT7Nv375c5xoVFcWECRP44osvyMjIYPny5QZ94uPjOXXqlHLEx+eVlkYIIYQQzzxdIY+sPA5TxjHC0dGRyZMnU6NGDTQaDc2bN6d9+/bs37+f5ORkNm7cyNSpU3FxccHT05MRI0awcuVKM38w+TM5gB4wYACzZs1izZo1vPjii1SqVIkXX3yR1atXM3PmTAYOHKjGPM3O29sbZ2dnLC0teeWVV8jMzNQrQ16tWjXat2+PpaUlPj4+lClTht9//x2Apk2bUqFCBTQaDfXq1aNx48ZER0eb9PzXXnsNZ2dnXFxcaNq0Kf/880+ufbt160a5cuVwcHCgf//+/Pzzz2Rl6f/KGB4eTr9+/ZQjPDzcpPkIIYQQ4tlisM3iMR8FkZqaym+//YaXlxenT58mPT1dr+ZIw4YNOXHihFofUa4KtXt77NixBAcHc/DgQW7dukWpUqVo0aIFzs7O5p6faiIiItizZw8JCQloNBqSk5NJSkpS2kuXLq3Xv0yZMkr58sOHD7NhwwauXbuGTqcjLS2NihUrmvR8V1dX5WtbW1tu376da193d3e9eWVmZpKYmKg3hr+/P+3atTN6jxBCCCHEo/p2r4W7q1eu7aOmRD04KejWDCPbO+ZPMp58IiMjHXDIdSidTsfgwYOpVasWPXv25MCBAzg6OmJl9SB8dXFxeSLv3xX69UdnZ+c8S3r/l8XExLB582amTp1KlSpVsLCwIDAwkIeLMsbFxendExcXR8uWLcnIyGDGjBmEhITQsmVLrKysmD79QcJ5jcb8BeEf3o4RFxeHlZUVJUuW1Ovj7u4uQbMQQgghCi6/oPihv+wucCVCM9VG0el0DB06lNOnT7N3714sLCxwcnIiOTmZzMxMJYhOTEykRIkS5nmoCQodQJ89e5YzZ86Qmppq0NazZ88iTcrctFot6enpyvm9e/ewsLDA2dkZrVbL5s2bSUnRT+Z9/vx5fvzxR1q3bs1PP/1EbGwsTZs2JSMjg8zMTEqWLImlpSVHjx7l6NGjygq0i4sL9+7d4969e2Z70W/79u00bdoUZ2dn1q1bR+vWrbGwMHn3jRBCCCFEgRXmBUOTlhFzCbZ1Oh3Dhg3j0KFDREZGKouGtWvXxtramuPHj9OkSRMAjh07RoMGDUyfaBGZHEAnJSXh5+envPSWs2r78MqrVqs1z+zMZNmyZXq5lWvXrk3Tpk0ZOnQodnZ2+Pr6GqzevvDCC/zxxx8sWrQId3d3xo8fr/yGExQUxMcff0xmZibNmjXjhRdeUO7z8PDA29ub4OBgsrKymD276CWX27dvz7Rp04iLi8PLy4ugoKAijymEEEKIZ9vMzw6ifWQvsiaXr4vinfcjAcN4ed3Sqkb7Dx8+nAMHDhAVFaW3XdXBwYHevXszadIkNmzYwM2bN1mwYMETSWVncgA9btw4YmNj+fnnn2ndujURERG4urqydu1aoqKi2LBhgxrzLLSHt1c8auTIkcrX/v7+em3W1taMGTPG6H3dunWjW7duuY4bEhKid16pUiV8fHyU861bt+q1P/ziZYMGDfRS2K1YsQLIfulQCCGEEMJcnB1suZP4+MppPxqQ29ga1hW5ePEiixYtwtbWlipVqijX+/Xrx5IlS1i4cCHBwcF4eHhgb2/P8OHDH3sKOyhEAL1z506mTZvGiy++CECFChVo1qwZbdu2ZcyYMcydO5eNGzeafaJCCCGEEMJ8Srk4kng7Oe9O5nq3y8jmaGOVCKtUqaL3TtqjnJ2d/xOLtRpdXrM0wtHRkZ07d9KmTRtKlCjB119/rRRViYyMxN/fnzt37qgy2cdl/fr1XL16lbFjxz7pqQghhBBCqGLQWys4dz4u/445TA2m8wkxv9k8kpIlc8/C8V9m8gp0pUqVlKwQtWrVYuvWrUoAffDgQezs7Mw7wyegb9++T3oKZtWl+v+pMu6Oc9mVFTtaqLO9ZE/W13TxHKfK2DvOzKJx8CeqjA1wZOlomr8+V5Wxf103hnZdi7633pgft78LQJeKI1QZf8fVBbT1naPK2D9tHYuPd+5btooqcl8oaderqzK2bflzdGw9TZWx9+yfAKDqz+OLA+apMvahNe+o9rMI2T+Pnaz7qDL27oyN1J2gzp8xf07LLljWpW6oKuPv+HO6av8uRe4LpbN9f1XGBtiV8pWqf3697DVRlbF3npiqyrj5Mb0S4b8BcW5xtJkycBQHJgfQHTt2ZO/evfj5+TF69GgGDhzIoUOHsLGx4bfffst137AQQgghhPgv0RUu79wzFCjnpkABdM+ePZk9ezY1a9akbt26BAQEANC/f3+cnJyUNHA5G7uFEEIIIcR/nMkr0DkejaALuU+6GAfiBQqgt27dyrhx46hZsybDhw+nSZMmlCpVCgA/Pz/8/PxUnaQaQkNDadOmDV26dNG7PnjwYO7cuYOFhQUWFhZUq1aN4OBgqlatyu3btxk4cCArVqygTJkyQPa+708//ZSZM2dSt25dIDsn4YwZM1i/fj1HjhwhPDycixcvYmVlhZeXF4MHD9ZLy5JjwoQJnDx5ks2bN2NjY0NGRgZLlizh+PHj3L17F3d3dwICAvQqDgohhBBCFIZGl13Ou0Dy7PZQo/nryf0nFSiArlixIt999x1ly5ZFp9MRGxvLpUuXcu1fuXJls03wSRg/fjxNmjRBq9WyYcMG5s6dy4IFC3B1daVixYpER0fToUN2Wcro6GgqVapEdHS0EkBHR0fz3HPPYWlpSXJyMv7+/jRo0ACdTseiRYv49NNP+fDDD/WeGRkZaZA/W6vV4ubmxtSpUylbtix//fUXkydPpmzZsjz33HOP5bMQQgghxFMqC71qg4YeBMaFr0T4dEbUBSpnN2rUKGbMmEGNGjXQaDT4+flRrVo1g6Nq1apUq1ZN7Tk/NpaWlrRt25bLly8rKVXq169PdHS00ic6OppevXoRExOjdy2nKk67du1o1qwZdnZ22Nvb06NHD/766y+95yQlJREWFsYbb7yhd93Ozo7XX3+dcuXKodFoqFu3LnXq1DG4XwghhBDCVBp0aHQPHVmPHg+9aFjAQ2NwPDSeTv8ozgq0Aj169Gh69OjBqVOn8PX1ZdasWXh6eqo9tycuIyODffv24enpqVRarF+/PuvWrQMgLi4OnU5Hq1atWLFiBVqtFq1Wy99//83//vc/o2NGR0cbrNCvWrUKX19fXFxc8pxPamoqZ8+epUePHgZt8fHxSnYUAHd3d4PqikIIIYQQOS7+Ewd5VCI0t0dj5sTEZEq6POVp7GrWrEnNmjUZOHAgvXr1eqpWmh81a9YsLC0tSUtLw9bWlvfee09pq1+/PrGxsdy6dYvo6Gjq1auHjY0NlSpV4uzZs6Snp2NlZUWtWrUMxj1z5gxhYWG8//77yrXo6GguXrzIiBEjiIvLPRdjVlYW8+fPp1atWjRq1MigPTw8nOXLlyvnQUFB8kKnEEIIIXLl7GRDYoKRQirmKp7y6LA5X/wbSdvb26jynMfB5DR2q1atUmMe/ynjxo2jSZMmZGVl8eeffzJt2jRmzpxJlSpVKFWqFOXLlyc6OpqYmBjq1asHQL169YiOjiY9PV3Z//yw8+fPM2XKFIYPH67slc7MzGTJkiWEhIRgYZH7bpqcvdMJCQl89NFHymr4w/z9/fVeLpTVZyGEEELkxc3VkcRb9w0b/g1wdWA05igUI1s2bGwsjXQsHkwOoJ8lFhYW1K9fn/Lly3PkyBGlJnuDBg2Ijo4mOjpayUBSr149tm3bRlpamsEK8YULF/jggw8YNGgQrVu3Vq7funWLK1euMHVqdgL1rKzsnfyDBw9m5MiRNGnSBJ1Ox5IlS5QA3N7e3uhcZcuGEEIIIUyRs0c513bAIP2GKQF1fvuci/E26Gc6gNZqtaSnpyvnxn7L+vPPP7l06ZISPEN2sLxq1So0Gg0VK1YEoE6dOsydO5e0tDT6939QZenSpUu8//77DBgwAG9vb72x3d3d9Vb04+PjGTNmDB9//LGS5m7p0qWcPn2aqVOn4uBQPPcJCSGEEOI/SFeIQioP9380birmLwaa4pkOoJctW8ayZcuU8zp16gAwY8YMZUuFm5sbAwcOpHHjxkq/Bg0acOfOHVq1aqVcs7e3p2zZsly9elVv/3NERASJiYnZ2ubmAAAgAElEQVQGzwoLC8PS0lIvH3ROMO/i4oK1tTU3b95k+/btWFtb8+abbyr9evXqpRSzEUIIIYQoFJ3O4CVCEwcw21SKm2c2gJ4+fXqh73V3d2fr1q0G1+fNm2dwbeTIkYwcObJA45YtW1Zv3DJlyhh9jhBCCCFEkWUVLYDW8bRmec7fMxtACyGEEEI800zJx2ykm9Hg+RmJqCWAFkIIIYR4FunIewXaHDs0ntKAWgJoIYohC23x3XemyVRv7hYqjg2QpEtTZdzSgGVSiipjiydHo1X5AZlqP0A89Yq4haNAiu9/rvIkAbQQQgghxDPo8t+xeaaxU5UGEm/fp6Sr45N5fhFJAC2EEEII8QxyLmHLnfh7hg0qVSJU/Lvv2tHRVt3nqOipDaBDQ0Np06YNXbp0ybfvzZs3Wb58OSdOnMDS0pJmzZoxevRoAO7evcuSJUs4fvw4Op2OunXr8vbbb1OqVCkA5s+fz08//YSV1YOP8vPPP6d06dJ6z7hy5QojR46kefPmjB07Vrnu6+uLra2tkoO6bt26fPjhh0r7tm3b2Lp1K4mJiZQpU4b+/fvzwgsvFPpzEUIIIYQAcHF14s7Nu0Za8sj1XFhGXla0spZKhMVWZmYm77//Ph07duSdd97BysqKS5cuKe2rV68mKSmJJUuWYG1tzcKFC1myZAkTJkxQ+rzyyisMHDgwz+csXrxYLz/0wz755BM8PDwMrv/111+sXr2aadOmUatWLQ4dOsTs2bNZvny5Xv5oIYQQQgjTZYEuK+8uj8a95qxEWIxZPOkJPGlRUVE4Ozvj7++Pvb091tbW1KhRQ2m/ceMGLVq0wMnJCVtbW9q2bcvFixdNekZkZCQuLi54eXmZdN+NGzeoXLkynp6eaDQamjdvjp2dHdeuXTNpHCGEEEIIAzkvEZpyaLMeHHm1GWt/9CjGnvkA+tSpU5QrV47Jkyfz+uuv8+6773Lq1CmlvVu3bhw6dIikpCRSUlLYt2+fXlVCgF27dtG3b19CQkLYs2ePXltSUhKbNm1i0KBBuc5h4sSJ9O/fn8mTJ+utfjdt2pTMzEz++usvsrKy+OWXX7CysqJatWpm+u6FEEII8czK0kFWVuEPrVb/MPX+YuyZ38IRHx/PiRMnCA0NJTQ0lKioKKZMmcLSpUtxcnKiVq1afP/99/Tr1w8LCwuqVq3K1KlTlft79OjBm2++iaOjIzExMcyaNQtHR0datmwJwKpVq+jevTtubm5Gnz99+nRq165NRkYGW7Zs4f3332fRokU4ODjg4OBAq1atmDBhAllZWVhbWzNu3DgcHByMfh/x8fHKubu7O+7u7mb+tIQQQgjxtNChQ1eYbRaP3qP2S4f/Qc/UCnRYWBgBAQEEBAQoL+rZ2try3HPP8cILL2BlZUWnTp1wcnLir7/+AmDWrFmUKVOGjRs3EhYWRtOmTfVe8qtRowbOzs5YWlri5eVFt27dOHDgAAAxMTGcO3eObt265Tqn+vXrY21tjYODA/369cPS0lJ59u7du9m9ezfz589ny5YtTJ48mU8//ZRz584ZjBMeHk6/fv2UIzw83EyfmhBCCCGeSoVYbdZotWiysvSPf6+bvBJdjHdxPFMr0DnB88OqVq3KyZMnc73nwoULBAUFKau+PXr0ICwsjKSkJJydnQ36azQa5be548ePExsbyxtvvAFAamoqWVlZDBkyhMWLFxt9nuah3+IuXLhA06ZNqVy5MgB16tShVq1aHD9+nOrVq+vd5+/vT7t27ZRzWX0WQgghRJ7y24tsZHW6YDGvClk8/mOe6gBaq9WSnp6unGs0GqytrfX6tG/fnoiICA4fPkzDhg3Zt28f9+/fp06dOgB4enqye/duKlWqhKWlJd9//z2lSpVSguf9+/fTuHFj7OzsOHXqFN9//z1vvfUWAH5+fnpp9CIiIrh+/TrDhg0D4NKlS2RkZFC1alUyMzMJDw8nPT2d2rVrA1C7dm3Wr1/P1atXqVixImfOnOGvv/6iR48eBt+rbNkQQgghhEly9kArVFgS1hvy6Qmmn+oAetmyZSxbtkw5r1OnDrNmzdLrU6FCBcaOHcuKFStISEigUqVKTJw4EScnJwBCQkJYtmwZgwYNIisri6pVqxIaGqrcv23bNj7//HOysrJwd3enX79+tG3bFgB7e3vs7e2VvnZ2dtjY2ODi4gLAnTt3WLx4MfHx8djY2FCzZk0++ugj5dne3t7ExsbywQcfcPfuXUqWLEmfPn1o1KiROh+YEEIIIZ4ZVlYa3MqUwMnZAceS9tja2+j9Tbi5ZaRncj8pmftJKaSlpKPRFN89HE9tAD19+vQC933xxRd58cUXjbaVKVOGiRMn5nrvzJkzC/ycvn376p17eXnlupUDslfMAwMD+X/27j0uxvT/H/hrmqY0Kumgg8WyROXUOmwlpuTMis2GkFM5rGUpp41lnVphLesQqbQkabXEftehptPiU5S1uxaJqGxCB1vpYGbq90e/7m00ne6ZG6v38/Ho8XDPfc97rhnT3fu+7ut6X1OnTm3yaxBCCCGENMXe2PrzG9KwFjWJkBBCCCGEEGVRAk0IIYQQQkgz8KpYFQAkhBBCCCGkZXpnx0CTf43UmsFJ3AtlRwEAw9U+5SR+dOWPGN1xKSexz2XtwnD7LZzEBoDoS2sgGrONk9gJv6zESOv1nMS+8NsGAMAofU9O4p8vOMRp20ebr+IkNgCcu+uHLt/t5CR2xjIvjDZeyEnsc0+q51kMGb+dk/iJZ1ZgqFPT54I0R6x4NUbpzuYkNgCcLzqMEYIpnMS+KAnHgNncfF+uHfYCAM4+m/NFhzG6/WJOYp/7ew9nf5OA6r9LXP6fjrb0afxAFs7davq8LfJ2aDFDOJ49ewZXV1dIJJI33RRCCCGEEPIf9k4l0D4+Pjh37pzCfUZGRoiIiKhTB5qNsLAwbN/OTW8OIYQQQgh5u71TCXR9ZDLZm24CIYQQQgh5R7yTY6D//PNPbN++HS4uLjh16hQ6deqEzz77DJ6enjh58iQ0NDTg4+MDS0tL3L59G/fu3UOnTp2wfPlytGvXDgBw9+5dHDp0CNnZ2TAyMoK7uzsGDBiAa9eu4eTJk6iqqoKrqyt0dXURGBgIiUSC48ePIzExEWVlZbC2tsaCBQuYRVHu3r2L4OBgZGZmQiAQ4OOPP8ann36K3Nxc7N27Fw8ePAAA9O3bFwsXLmSe5+HhgbFjxyIxMRE5OTmwtLSEt7c3s58QQgghhLxe72wPdFFREZ4+fYqAgAC5lQNri4uLw7x58xAaGgp9fX0cO3YMAFBSUoKvv/4aw4YNw7FjxzBnzhxs27YN2dnZGDBgACZNmgQ7OztEREQgMDAQAHDkyBHcv38fO3bsQHBwMNTV1XHw4EEAQF5eHtatW4fhw4fjyJEjOHDgAHr37s20w8XFBSEhIfD390dhYSFCQ0Pl2hkfHw8fHx+EhITgxYsXiIqK4uIjI4QQQgghTfDOJtAA4O7uDg0NDWhqairc7+TkhE6dOkEgEGDIkCG4f/8+AODatWswMjLCyJEjwefzYW1tjYEDByIhIUFhnKqqKpw/fx4eHh7Q09ODpqYmpk2bhsuXL0MmkyE+Ph6WlpZwcnKCQCCAUChE9+7dAQAmJiawtraGQCCArq4uxo8fj7/++ksu/vjx42FkZAQtLS3Y2dkx7awtLy8Pd+7cYX7y8vKU+egIIYQQQkg93skhHACgo6NTb+Jco23btsy/NTU1UV5eDgAoKCiAsbGx3LHt2rVDfn6+wjj//PMPKioqsHLlSrnHeTwenj9/jmfPnsHU1FThcwsLCxEYGIi//voLZWVlqKqqgpaWltwxenp6CttZW2RkJA4dOsRse3p6Yv78+QpfkxBCCCGEsPfOJtA8Ho/1c/X19fHkyRO5x54+fcokwa/G1tXVhYaGBnbt2lUn8QaqK4DcunVL4WsdPXoUlZWV+P7776Grq4ukpCTs37+/2W12cXGBSCRitg0NDZsdgxBCCCGENO6dHsLBVv/+/fH06VNER0dDJpPhxo0buHr1KpOg6unp4cmTJ6isrAQAqKmpYdSoUQgKCkJBQQEA4Pnz50hKSgIAiEQi/PXXX4iLi4NUKkVpaSnS0tIAAGVlZWjVqhVat26N/Px8nD59mlWbDQ0N0aNHD+aHEmhCCCGEEG5QAq2Ajo4O1q9fjwsXLmDatGkIDAyEt7c3OnToAAAYNGgQ1NXVMW3aNMybNw8AMHPmTLz//vtYvXo1Jk+ejJUrVyI9PR1AdQ/0119/jXPnzmHGjBlYuHAhbt68CQCYOnUqHj58iKlTp2Ljxo346KOP3sybJoQQQgghTfJODeHw9f13KcwjR47I7TM2NsaZM2cUHgsA/fr1YypqAECPHj2wY8cOha+jq6uLrVvll64VCARwc3ODm5ubwudYWFhg27a6Szt37NgR3333ndxjEydOZP5du00AMHr0aIwePVrhaxBCCCGEEO5RDzQhhBBCCCHNQAk0IYQQQgghzcCrqqqqetONIIQQQggh5L/inRoDTRQb3WU5J3HPZVSPER+u9ikn8aMrf8Ro81WcxD531w8fzv+u8QNZun5wGWymfctJ7KRj3hCNqTueXhUSfqmuZT66/WJO4p/7ew+GjN/OSezEMyvg5ODb+IEsieN9UJlrzklsNZO7GG6/hZPY0ZfWAACn38eP3HdyEjv5iBdn30Wg+vs4QjCFk9gXJeGwXMPNOebWlmUAgNGWilfZVda5W76c/S6J430wUmsGJ7EB4ELZUYw2XshJ7HNP/DGq91pOYp//YzMncQl3aAgHIYQQQgghzUAJdBONHz8ejx49YvXcv/76iyl315hnz57B1dUVEokEAODj44Nz586xel1CCCGEEKJ6LXYIh4+PD9LS0sDn85nH2rdvX6ekXHM9efIEnp6eOHnyJDQ0NAAAVlZWCAgIaNLzjYyMEBERoVQbCCGEEEIId1psAg0AHh4ejdZUlslkckk2IYQQQghp2WgIxyuePHmC8ePHQywWw8PDA0uWLKlzTGlpKb7//nu4u7tj9uzZCAoKYoZcrFpVPelt+vTpcHV1RWpqKv7880+4u7szz/fw8MCpU6ewbNkyTJ48GRs2bEBJSYnc6798+ZI5/unTp1i5ciUmT56MtWvX4tmzZ1x+BIQQQgghpAGUQNfj+vXr+P7777FzZ93Z5QEBAcjPz8f+/fuxc+dO3L59G+Hh4QAAPz8/AEBoaCgiIiLQr18/hfHj4+Ph4+ODkJAQvHjxAlFRUfW2JSYmBvPnz8fRo0dhamqqsE15eXm4c+cO85OXl8fmbRNCCCGEkEa06CEcQUFBckt+Dxw4kFmKe+rUqRAKhXWeI5PJkJiYiG+//Rba2trMsfv27cOMGU0vzTN+/HgYGRkBAOzs7PDHH3/Ue6yDgwM++OADAMDMmTMxbdo05OXlwdDQkDkmMjIShw4dYrY9PT0xf/78JreHEEIIIYQ0TYtOoOfOnVtnDPSTJ08AgEluX1VUVASpVIp27doxjxkbG6OgoADNWZNGT0+P+bempibKy8vrPbZ2W7S1tSEUCpGfny+XQLu4uEAkEjHbtfcRQgghhBDVadEJdEN4PJ7Cx3V1daGuro6nT5+ic+fOAKqTbn19ffB4vHqfp4zaY55LSkpQWloKAwMDuWMMDQ0paSaEEEIIeQ1oDHQz8fl8DB48GEeOHEFJSQkKCwsRHh4OBwcHAECbNm2gpqaG3Nxclb1mQkICMjIy8PLlSxw5cgQWFhaULBNCCCGEvCEtugc6MDAQhw8fZrZbtWqF7dsbX2Z43rx5CAwMxMKFC8Hn8zFo0CBMmVK9HKympiZcXV2xZs0aSKVSrFixAgKBQKl2Ojk54cCBA8jMzETXrl3h7e2tVDxCCCGEEMJei02gfX1969135syZBh9r3bo1vvjii3qf7+bmxkxGrFF7smJgYKDcvtGjRzNjsY2NjeVeq6F2EkIIIYSQ14+GcBBCCCGEENIMlEATQgghhBDSDC12CAchhDSXpErKSVxNTqIS0kKpU2pDuEc90IQQQgghhDQDJdCEEEIIIYQ0Q4u4z7Flyxakpqbi8OHDaNOmDQDA1dW1znFSqRSVlZU4ffo0njx5Ak9PT7Rq1Qo8Hg8aGhro3LkzRo0aBTs7O7nn3bx5E4cPH0Z2dja0tLTw6aefYty4cXj+/DkCAwNx8+ZNlJWVoX379pgxYwasra3rvLZYLMbu3buxcOHCOqsjAsCuXbsQGxuL/fv347333lPRJ0MIIYQQQprrnU+gnz9/jpSUFAiFQsTHx8PZ2RkAEBERIXdcSUkJvLy84OjoKPd4aGgoNDQ0UF5ejpSUFOzfvx/Z2dmYPHkyAODRo0fw8/PDkiVLYG1tjYqKChQUFAAAysvL8cEHH2D27Nlo27YtkpKS8M0332Dv3r1yS4EXFRXh5MmT6Nixo8L38OeffzJLjBNCCCGEkDfrnR/CER8fD1NTU0ycOBFisVjhMVVVVdi5cyfee+89ZkGUV7Vq1Qr29vaYP38+IiIiUFxcDAA4ceIERowYgQEDBkBdXR2tW7dGhw4dAAAmJiaYOHEiDAwMoKamBjs7OxgZGeHevXtysQ8fPgxnZ2fo6urWeV2JRIKAgAAsXLhQmY+BEEIIIYSoyDufQIvFYjg4OEAkEiEzMxP379+vc0x4eDgePXoELy8v8Hi8BuPZ2NhAJpPh7t27AIC0tDSoqalhyZIlmDFjBnx9fZGfn6/wufn5+Xj8+LFcT/PNmzeRnZ2NESNGKHxOZGQkPvzww3p7pwkhhBBCyOv1TifQ6enpyMrKgkgkgpGREaysrBATEyN3TGpqKk6dOoUvv/wS2trajcYUCATQ0dFheqDz8vIgFouxYsUKBAYGQltbGzt37qzzvJcvX2Lbtm0YPnw4M4ZZIpHgwIEDWLhwIdTU6v5X5OTkIC4urt5e8dry8vJw584d5icvL6/R5xBCCCGEkOZ7pxNosVgMCwsLGBsbAwAcHByQmJgIiUQCAMjNzcXOnTuxYMECdO7cuUkxJRIJiouLoaOjAwDQ1NTE0KFD0aFDB2hqamLatGm4efMmysvL5Z6zdetW6OnpYd68eczjP/30E3r27IkPPvhA4Wv5+/vD3d0dWlpajbYrMjIS06dPZ34iIyOb9H4IIYQQQkjzvLOTCCUSCZMsu7u7AwBkMhmKi4uRnJyMgQMHYuvWrRg8eDCGDh3a5LjJycng8/kwNzcHAHTq1EnhsI+qqiqmHX5+flBTU8OKFSvA5/OZY37//XdkZmbiypUrAKonMmZkZODu3bv44osvmP0HDx5knrN69WrMmDEDI0eOlHs9FxcXiEQiZtvQ0LDJ74kQQgghhDTdO5tAJyUlQSqVYs+ePdDU/Hedr+DgYIjFYqSmpkIgEGDu3LlNildRUYGUlBQcPHgQkyZNYnqgR4wYgWPHjsHR0RFGRkYIDw9Hr169oKWlBalUim3btkEikWDt2rVQf2V1pC+//BJS6b8rm33zzTf46KOPmOT4hx9+kDt+5syZ8PHxUdhjbWhoSEkzIYQQQshr8M4m0DExMXB0dISJiYnc487OzvD29kZVVRUEAgGmTZtW57n79u1j/j19+nTweDwIBAJ07twZ8+fPh729PbN/6NChePbsGVatWgWZTIaePXti2bJlAIA7d+4gOTkZGhoacq/z2WefwcHBgUnCa6irq0MoFDJjsdu2bVunbbq6unIXBIQQQggh5PV6ZxPoDRs2KHy8a9euiIqKalKMM2fONOm4yZMnM3Wha+vZs2eTYwCAr6+vStpDCCGEEEK4805PIiSEEEIIIUTVKIEmhBBCCCGkGXhVNeUiCCGEEEIIIY16Z8dAk3+N7rKck7jnMnYAAIarfcpJ/OjKHzHafBUnsc/d9cOH87/jJDYAXD+4DDbTvuUkdtIxb4jGbOMkdsIvKwEAo9sv5iT+ub/3YMj47ZzETjyzAk4ODc8jUIY43geVueacxFYzuYvh9ls4iR19aQ0AcPp9/Mi97uJRqpB8xIuz7yJQ/X0cIWh8oSo2LkrCYbmGm3PMrS3VE9VHW/pwEv/cLV/OfpfE8T4YqTWDk9gAcKHsKKfnr1G913IS+/wfmzmJS7hDQzgIIYQQQghphhbRA71lyxakpqbi8OHDaNOmDQDA1dW1znFSqRSVlZU4ffo0njx5Ak9PT7Rq1Qo8Hg8aGhro3LkzRo0aBTs7O+Y5EokEhw4dwq+//go1NTWMGDEC7u7uzOIqPj4+SEtLk1tAJSIiAgBQVlaGDRs2IDs7G1KpFCYmJpg6dSpsbGyYYy9fvowffvgBBQUF6NGjB5YsWYJ27dpx8jkRQgghhJDGvfMJ9PPnz5GSkgKhUIj4+Hg4OzsD+DeJrVFSUgIvLy84OjrKPR4aGgoNDQ2Ul5cjJSUF+/fvR3Z2NlO27sSJE7h//z78/f0hkUiwbt06GBkZYcyYMUwMDw8PjB49uk7bBAIBPvvsM7Rv3x58Ph937tzB+vXrsX//fhgYGCA7Oxu7d+/Gl19+CUtLSxw9ehTbtm3Djh07VP0xEUIIIYSQJnrnh3DEx8fD1NQUEydOhFgsVnhMVVUVdu7ciffeew9TpigeD9eqVSvY29tj/vz5iIiIQHFxMYDqBVumTJkCPT09GBkZYcKECYiJiWlS29TV1dGxY0fw+Xxm6W+pVIqnT58ybf/www9hbW0NTU1NuLm54cGDB8jKymrux0AIIYQQQlTknU+gxWIxHBwcIBKJkJmZifv379c5Jjw8HI8ePYKXlxcz9KI+NjY2kMlkuHv3LkpKSlBQUIDOnTsz+7t06VInwT127BimTZsGb29vXL16tU7M1atXw8XFBStXroSVlRXMzasnKmVmZsrFFgqFMDExQWZmZrM+A0IIIYQQojrv9BCO9PR0ZGVlQSQSwcjICFZWVoiJicEHH3zAHJOamopTp07Bz8+PWUK7IQKBADo6OiguLkZZWRkAoHXr1sz+1q1b4+XLl5DJZODz+Zg5cyY6dOgAgUCAa9euYfv27diyZQuTJAPA1q1bIZFIkJqaisePHzPjpcvLy+Vi18Sved3a8vLykJeXx2wbGhrC0NCwiZ8UIYQQQghpqne6B1osFsPCwgLGxsYAAAcHByQmJkIikQAAcnNzsXPnTixYsECup7chEokExcXF0NHRgZaWFgCgtLSU2f/ixQtoaGgwSXD37t0hFAohEAhgZ2cHOzs7/O9//6sTVyAQwMbGBqmpqUhOTgZQPWykduya16p53doiIyMxffp05icyMrJJ74cQQgghhDTPO9sDLZFImGTZ3d0dACCTyVBcXIzk5GQMHDgQW7duxeDBgzF06NAmx01OTgafz4e5uTm0tbWhr6+PjIwMGBgYAAAePHiAjh071vt8NTU1NLR2jUwmQ25uLgCgU6dOyMjIYPaVlZUhNzcXnTp1qvM8FxcXiEQiZpt6nwkhhBBCuPHOJtBJSUmQSqXYs2cPNDU1mceDg4MhFouRmpoKgUCAuXPnNileRUUFUlJScPDgQUyaNAk6OjoAACcnJ5w4cQLm5uZ4+fIlTp8+jXHjxgGoruyRlpaGnj17Ql1dHSkpKbh06RI2bNgAoHqISVlZGSwsLAAAcXFxSEtLg4eHB4DqHnNvb2/cuHEDlpaWCAsLw/vvv68wQachG4QQQgghr8c7m0DHxMTA0dERJiYmco87OzvD29sbVVVVEAgEmDZtWp3n7tu3j/n39OnTwePxIBAI0LlzZ8yfPx/29vbM/ilTpqCoqAgLFixg6kDXlKyTyWQICwvDo0ePwOPxYGpqimXLlsHS0pLZHxwcjMePH0NNTQ3t27fHqlWrmDHaHTp0wJIlS7Bv3z4UFhaie/fuWLlypco/K0IIIYQQ0nTvbAJd08v7qq5duyIqKqpJMc6cOdPoMQKBAIsWLcKiRYvq7GvTpg2+/bb+5XN79OiBXbt2NRjf3t5eLmEnhBBCCCFv1js9iZAQQgghhBBVowSaEEIIIYSQZuBVNVQSghBCCCGEECLnnR0DTf41ustyTuKey9gBABiu9ikn8aMrf8Ro81WcxD531w8fzv+Ok9gAcP3gMthMq3/8uzKSjnlDNGYbJ7ETfqmepDq6/WJO4p/7ew+GjN/OSezEMyvg5ODLSWwAEMf7oDLXvPEDWVAzuYvh9ls4iR19aQ0AcPp9/Mh9Jyexk494cfZdBKq/jyMEUziJfVESDss13Jxjbm1ZBgAYbenDSfxzt3w5+10Sx/tgpNYMTmIDwIWyoxhtvJCT2Oee+GNU77WcxD7/x2ZO4hLu0BAOQgghhBBCmqFF90Bv2bIFqampOHz4MNq0aQOgevXCPXv2QENDo86xjx8/xpEjRxAYGMg8vmvXLly+fBnHjx+Hunr1xxkaGor09HRs2LBBYbzPPvsMDg4OcvElEgmWLFmCFy9e4MiRI8zjoaGhSE5ORnZ2NiZOnIiZM2eq+mMghBBCCCHN0GIT6OfPnyMlJQVCoRDx8fFwdnZm9nXt2hU7duyo8xwDAwM8ffoUz549g5GREQDg5s2bMDIyQnp6OrMgys2bN9G/f/9G49V28uRJtGnTBi9evJB73NTUFLNmzcKFCxdYv1dCCCGEEKI6LXYIR3x8PExNTTFx4kSIxeImPUdfXx/t27fHzZs3AQDPnj2DmpoaBg8ezDxWUVGBu3fvomfPnk1uy99//41Lly7h00/rjiV2cnJCv379IBQKmxyPEEIIIYRwp8Um0GKxGA4ODhCJRMjMzMT9+/eb9DwrKysmWb558yYsLS3lHktLS4NAIEC3bt2Y5zx8+BDTp0/HvHnzEBISgoqKCrmY/v7+mDVrVp1hI4QQQggh5O3TIodwpKenIysrCyKRCEZGRrCyskJMTAyzhPa9e/cwdepUueccPnwYrVq1Qq9evRAWFgagOoG2srJC9+7dcfbI/MUAACAASURBVO/ePchkMty8eRM9evQAn88HUJ1w7927F+3atUNOTg527dqFkJAQzJ8/HwAQGxsLoVCIAQMG4M8//2T9nvLy8pCXl8dsGxoawtDQkHU8QgghhBCiWIvsgRaLxbCwsICxsTEAwMHBAYmJiZBIJACqxywfP35c7qdVq1YAgJ49e+Lx48fIz89nEmhNTU20b98e9+/fx19//YVevXoxr2ViYgITExOoqanhvffeg7u7Oy5fvgwAKCkpQVhYGDw9PZV+T5GRkZg+fTrzExkZqXRMQgghhBBSV4vrgZZIJEyy7O7uDgCQyWQoLi5GcnJyo883MDCAqakpEhMTUVFRATMzMwDVPc2//fYb0tLSMGNG/TUu1dTUULN2zYMHD1BQUABvb28AgFQqRWlpKdzd3bF582Z07Nixye/LxcUFIpGI2abeZ0IIIYQQbrS4BDopKQlSqRR79uyBpqYm83hwcDDEYjHs7e0bjdGzZ0+cOnVKrqe5Z8+e2L17N/h8vtz459TUVHTu3Bn6+vrIzc3FDz/8AFtbWwBAjx49EBQUxBx7+/Zt+Pv7Y/fu3dDV1QVQnVRXVlYyPy9fvgSfz2eGiNSgIRuEEEIIIa9Hi0ugY2Ji4OjoCBMTE7nHnZ2dsXz5clhYWODevXtwdXWV2+/l5QUbGxsA1clydHQ0rKysmP0WFhYoKipCnz595JLbP/74A7t370ZZWRl0dXVhZ2eHadOmAQAEAgHatm3LHKujowMejyf32N69exEbG8tsnzp1ClOmTIGbm5sKPg1CCCGEENJcLS6B3rBhg8LHu3btitOnTwNAneT5VY6OjnB0dJR7TCgUMs+vbfbs2Zg9e3aT2tarVy+5RVQAYOnSpVi6dGmTnk8IIYQQQrjXIicREkIIIYQQwhYl0IQQQgghhDQDr6qmJAQhhBBCCCGkUS1uDHRLNLrLck7insvYAQAYrlZ3CXJViK78EaPNV3ES+9xdP3w4/ztOYgPA9YPLYDPtW05iJx3zhmjMNk5iJ/yyEgAwuv1iTuKf+3sPp213cvDlJDYAiON9UJlrzklsNZO7GG6/hZPY0ZfWAACn38eP3HdyEjv5iBdn30Wg+vs4QjCFk9gXJeGwXMPNOebWlmUAgNGWPpzEP3fLl7PfJXG8D0Zq1V/qVVkXyo5yev4a1XstJ7HP/7GZk7iEOzSEgxBCCCGEkGagBJoQQgghhJBmoCEctfz888+IjY3Fw4cPYWtrixUrVgAA/vrrrzrl78rLyzFnzhxMmDChwZhbtmxBamoqDh8+jDZt2tTZn5SUBF9fX8ydOxfOzs5y+6RSKU6dOoX4+Hg8efIEQqEQ7du3x8iRI+Hg4KDcmyWEEEIIIaxQAl2Lvr4+XF1dcePGDRQXFzOPW1lZISIigtnOzs7G4sWLMWjQoAbjPX/+HCkpKRAKhYiPj6+TIAPVC7vo6OggNja2zv5t27YhNzcXixYtQteuXcHn83Hnzh2cP3+eEmhCCCGEkDeEhnDUYmdnBxsbG2YZ7frExMSgd+/eMDIyavC4+Ph4mJqaYuLEiRCLxXX2FxYWIjU1FYsWLUJmZibu37/P7Pv999+RmpqKtWvXwtLSEhoaGuDz+bCysoK3tze7N0gIIYQQQpRGCXQzyWQyxMXFwcnJqdFjxWIxHBwcIBKJ6iTIQHWCbWxsDDs7O/Tq1Usuyb5x4wa6deuGdu3aNaldeXl5uHPnDvOTl5fXvDdGCCGEEEKahBLoZkpJSYFUKoWtrW2Dx6WnpyMrKwsikQhGRkawsrJCTEyM3DFisRgikQgA4ODggISEBEgkEgBAUVERDAwM5I6fNm0apk6dChcXF/z9999y+yIjIzF9+nTmJzIyUtm3SgghhBBCFKAEupnEYjGGDBkCDQ0N5rFFixbB1dUVrq6uiI+PZ46zsLCAsbExgOoEOTExkUmQaxLsmrHMtra2qKiowNWrVwEAurq6yM/Pl3vtY8eO4fjx45BIJHh1/RsXFxeEhoYyPy4uLly8fUIIIYSQFo8mETZDzaRAPz8/ucf37dsnty2RSJhk2d3dHUD10I/i4mIkJyfD3t4eMTEx4PF4WLXq34VCZDIZxGIxBg0ahN69e+Ps2bN49uxZo2OtAcDQ0BCGhoYqeJeEEEIIIaQhlEDXIpPJIJPJUFlZicrKSrx8+RJqampQV6/+mOLj4/Hee++hW7duDcZJSkqCVCrFnj17oKmpyTweHBwMsViMgQMHIjExEZ6ennKVPNLT0+Hr64uCggJYW1ujb9++2Lx5M+bPn49u3bpBTU0NaWlp3Lx5QgghhBDSJJRA13LixAmEh4cz25cvX8bQoUOxdOlSANXDMoYNG9ZonJiYGDg6OsLExETucWdnZyxfvhz/+9//oKamhuHDh8sl2AMHDkSHDh0QFxcHFxcXrF69Gj/99BP27t2Lp0+fonXr1jAzM4O3tzfMzMxU9K4JIYQQQkhzUAJdi5ubG9zc3Ordv2fPnibFeXXRlRpdu3bF6dOnAYCZPNjQa6irqzNjqwkhhBBCyNuBJhESQgghhBDSDJRAE0IIIYQQ0gy8qlfroRFCCCGEEELqRT3QhBBCCCGENAMl0ISRl5eHgwcPcrYMOJfx/6uxuY5PbX/9sbmOT21//bG5jk9tf/2xuY7PddvJm0cJNGHk5eXh0KFDnJ6suIr/X43NdXxq++uPzXV8avvrj811fGr764/NdXyu207ePEqgCSGEEEIIaQZKoAkhhBBCCGkG/tdff/31m24EeXtoaWmhf//+EAqF/7n4/9XYXMentr/+2FzHp7a//thcx6e2v/7YXMfnuu3kzaIydoQQQgghhDQDDeEghBBCCCGkGSiBJgpJJBLWz6VZx4QQQgh5l1ECTeTcu3cPBw4cwKxZs1jHWLRokeoaRAjhTElJyZtuAiGcO3nyJKfxFy9ezGl88nZSf9MNIG/eP//8g/j4eMTExCA7OxuWlpYtOgm+desWLC0tVR63KRclISEhKn/d/4JvvvkGX375JbMdFhYGNzc3Znv58uXYsWPHm2jaO+3zzz/HokWLMGDAgDfdFKICXl5e2LlzJ7O9e/dufPHFF8z2rFmz3vpzTGpqKm7evImSkhJoa2ujZ8+e6Nevn1Ixf/zxR0yaNElFLazr6dOnnMUmby9KoFsomUyGlJQUxMTEIDU1FYaGhhCJRCgoKMDKlSuhp6f3ppvYoHv37qFr167MdkVFBTQ1NZnt5ORkfPTRR6xib9iwASdOnFC6ja8qKiqCqakphg4diu7du6s8Ptdu374NCwsLTmLfuHFDbvvnn3+WS6Czs7M5eV0AKCwsxE8//YS5c+dy9hpspaamQigUMp/748eP8d133yErKws9evTAF198gbZt27KOv2DBAuzbtw99+/aFp6cnWrduraqmN6gmOeJKeno6xGIxFixYwDpGSkoK+vfvz2w/ffoU7dq1Y7YvXryIESNGNDvuzZs3Gz2mZ8+ezY4LAH///bfcdnJystx2WVkZq7g1fvvtt0aPsba2ZhVbKpVi48aNuH37Nj744AMYGBggOzsbP//8MywsLLBu3Tqoq1PKQt4e9G1soWbNmgWZTIbBgwdj8+bNTI/rxYsXlY5dXl6OCRMmKNxXVVUFHo+H06dPK/Uaa9askUtyZ8+ejbCwMGZ7586dnCTByggJCUF8fDzEYjFiY2MxdOhQODo6Ql9fX2WvceTIkUaPcXd3ZxV706ZNGD58OKZPnw6BQMAqRlNxURwoOjoaGRkZMDMzw5gxY1BeXo7w8HCcP3+edcJSo7i4GPfv30ffvn0BVPegS6VSZv+ECROgq6vb7LhhYWFyif3evXuhpaUFb29vREdHIyQkBMuWLWPdbhsbG/Ts2RNBQUH4/PPPMW/ePHTs2FHumPbt27OOHxsbCz09PXz44YcAqhPbb775BgUFBTA1NcVXX30FMzMz1vFr++effxAXFwexWIxHjx4p3Wu5fft2uXPI0qVL5c4xQUFBrBLo7du3K3ycx+OhrKwMFRUVSp8fa6j692jfvn1y23l5eTA0NGS2eTweDh06xCr22bNnUVJSgn379sldqDx9+hR+fn44e/YsJk6cyCq2TCZDXFxcg5/H0KFDWcUGqjtw1qxZ0+AxW7ZsYR2fvJ0ogW6h9PX1kZWVhWfPniEvLw8vX76EhoaGSmJramrK3UZ8HVT9h+LZs2cN7jcyMmp2TF1dXYwfPx7jx49HRkYGYmNj4eXlhc6dO2PhwoVyfzTYioyMxHvvvQdzc3OlY71q9+7d8Pf3x5IlS/DFF1+gR48eKn+NGjweT6Xxai5eLCwscOXKFdy7dw937txB165dsW3bNnTu3Fmp+FFRUVBXV2cS6NOnT2PQoEEAgNzcXPB4PMyYMaPZcR8/fsz8XxYVFeHWrVsICAiAkZERunXrplTyXENbWxuLFy/Gtm3bsHXrVrl9yl7snj59Wm4Igb+/P3r16gUXFxf8/PPPCAkJgY+PD+v4MpkM165dg1gsRmpqKvT19fH8+XPs2LEDH3zwAeu4iqjqHPPDDz/Ueay4uBgnT57E+fPn4ejoqJLXAVT/exQYGCi3PXXq1DqPsXX58mV4enrWOQ+2a9cOnp6eOHToEOsEWiqV4pdffql3P4/HUyqBVldXx+DBg1k/n/w3UQLdQu3evRsZGRkQi8UICAjAvn37YGtri5cvXyodm8fj4b333lNBK5v3mqpSXl4OT0/Pev9gqqIHvUuXLpBIJCgrK0N8fDyeP3+ukgR6zpw5iI2NRVpaGpycnFTaw21kZIR169ZBLBZjy5YtGDJkCDp06CB3zKhRo1jFlslkcreHX92urKxk1+j/79dff4Wvry/MzMyQlZWFxYsXY+nSpSpLVpKSkrB+/Xpmm8/nM4ljXl4eNmzYwCqBrqqqgppa9VzvtLQ0GBgYMBdvenp6KC0tVbrtjx49wvfffw+pVIo9e/bU6YFWxrNnz/D+++8DAPLz85GRkYGvv/4aurq6mDlzJhYuXMg6dlBQEBISEiCVSmFnZ4eNGzfCysoKs2bNgoGBgYrewb9UnYwCYHqbT58+jd69e2PHjh11fqeaQyaT4cKFC8y5SyKR4Pz583L7VUmVn8njx4/rHdpmbm6Ox48fs46tqalZb8+/KvD5fNbnPvLfRQl0C9alSxd06dIFs2fPRnJyMsRiMcrLy7FixQqMGTOG9dX+f12rVq0QGhrKSez8/HzExsYiNjYWGhoaGDp0KIKDg9GmTRuVxHd2doazszMePHgAsVgMLy8vvP/++xg2bBhsbGxUMobQ2toacXFxSEpKwsOHD+X2sf0joqenJ3d7WEdHR25b2c+npKSEGSrQsWNHaGhoqLSnr6CgQO6uRO3eLENDQ+Tn57OK26lTJyQkJMDR0RGJiYlMDzdQnZwqO2Y5MjISJ0+exMSJE+Hi4gI+n69UvFepqamhsrISfD4faWlpMDMzY4ayCIVCVFRUsI595swZ6OjowNPTE/b29pwPK1Klmh7RH3/8EZ06dcKGDRtUctfI3NwciYmJctu//vors/02z72QyWTMxeKrar5HhLxNKIEmUFdXx6BBgzBo0CAUFhYiNjYWMTExrBPozz77TMUtrKuiogIrV65ktsvKypjtqqoqpXvSufhjvH79ejx8+BD29vZYsWIFunTpovLXqNG5c2d4eHhg9uzZCA0NxY4dO7Bhwwb06dNHqbgxMTE4fPgwHB0d8dVXX8lN3FSGqm4DN0QqlTI9cwKBQG675jG2qqqqUFxcDB0dHQCAp6cns6+4uJj17f+ZM2di06ZNOHDgADQ0NODn58fsu3TpktKTOi9fvoytW7eiU6dOSsWpj7m5OaKiojBixAhcvHiRGQsNVPc4KnNhtGvXLsTExCAoKAgBAQGws7ODg4ODClpdraKiQu7/sbS0lNmuqqpinfzHxMQgPDwcenp6WL58udK/k7X5+vqqLNbrJpVKG5ykqOrec1USiURvugnkDaClvInKicVipKSkYNWqVXX2bd++HQMGDFD6D51YLG70GCcnJ1axJ0+ezMkERGdnZ2hra0NdXb3eW5+qKjGVn5+P+Ph4xMbGgs/nw8nJCcOHD4dQKGQdc8OGDXj8+DEWL14MKysrlbTzdXF2dpb7zGsms9b+tzLDctatWwdbW1uMHj26zr7z58/j8uXL2LRpE6vYpaWlyMnJgZmZmdz/36NHj6ClpaXUcAWZTNZgr7My1WyA6jZu3LgRT548Qfv27bFlyxamasixY8eQl5cnN0aaDalUiqtXr0IsFuP69euoqqrCxIkT8fHHHys1fOn69euNHlP7gqCpnJ2doaOjgz59+tR7Hli+fHmz474O27dvl2vzlStXYGdnJ3cM27Z7eHg0egzbC+2ioiKkp6crnFiampqK7t27K1UV5tatW0hOTsbs2bPr7AsJCYGNjQ2nc0bIm0EJdAv16olQEbYnwhUrVmDevHno1q1bnX3379/HwYMHsW3bNlaxX4f66kArW3qLy/JVNRISEhAbG8v0dA8dOlRlk6kOHToEd3d3lfU617Z79+4G9/N4PCxZsoR1/KbUaVVmDPoff/yBLVu2YMqUKbCzs4O+vj4KCgpw5coVREREYPXq1SrtaVSlnJwcPHz4EKampsxkyqtXryIsLAz5+fk4evSo0q9RVFRUpwpJSUkJBAKBSr9PNXfQYmNj8fjxY/z000+sY0VFRcHZ2VllbasRFhbW6Ll36tSprGLPnDmzTmw+nw8jIyM4ODgoPU73+PHjjR7Dtu1cCgoKQuvWrTFlypQ6+06cOIHS0lKFyW9Tbdq0CSNGjFB4sXnt2jVcuHABa9euZR2fvJ0ogW6huDwRTp8+vcExxI3tb4rGqmQA7CplAK+39JaqOTs7o3379ujXr1+9QxLYlrHjsva2osoEQPUkqPj4eJSUlKistBdXrly5gqCgILnxzgYGBpgzZw5TkaO5uE6I4uLisGfPHrRu3RrFxcXw9PTEH3/8gfv378PZ2RkjRozg5IKpsLCQWbzp1dJoqnLnzh2lev24uhPFJUUX6VKpFLm5ucxQmpY4t2XBggXYunWrwvUNnj9/jtWrV+PAgQOs48+aNQuBgYEK55hIpVJ4eHi89QvYkOajMdAtVGPJcW5uLuvYUqkUL168UDjB6cWLF3L1cdny8PAAj8erM7a0drLBNuHiqvRWUFCQXE3fVxdi2LZtm9y4bjYcHR3B4/E4WaKZy9rbM2fOlNuurKxkxoqamZmxTvprvI6VDu3s7GBnZ4dHjx4xPa7KVqNZsWJFncdqJ0RlZWVKJUQnT57El19+iQEDBuDKlSvYsWMHRowYAX9/f5XPA5DJZHJDLYyMjBQOeVFWTeWJyMjItzJpkUgkjR7D9rNv6A6WlZUVfH19lfq+ZGVl4dq1a3BxcamzLzIyEgMHDmRdRYTLu6KFhYX1Lg6mp6eHwsJCVnFrlJaW1jvPoaqqSiXVcsjbhxJoUodEIsGCBQtYJ6Bdu3bFpUuXMHLkyDr7Ll++LNeLydbJkyfrPCaRSHDhwgVERkYqNSmKq9JbFy9elEugQ0JC5BLo1NRU1m2usXTpUqVjNBVXN68uXbqEY8eOQV1dHfPnz1dqDG6N17nSoSpLOHKdEBUUFDDLeNva2gIA5s6dq9LkuaYiTHx8PCQSCWxsbKCpqQk/Pz+lVjzNycnB999/jwcPHsDU1BReXl7IycnBgQMH0LZtW6VXlpTJZLh06VKDx9jb2zc77qRJk+pNFFW10JQiHTp0UDpRPHHihNzqjLUZGhrixIkTrJNcLkufCoVCPHnyBMbGxnX25ebmQktLS6n4pqamuHXrlsJhWrdu3YKpqalS8cnbiRJoopAyydGnn34KX19flJaWKhwPWrsnkK1X/8DHxcXh+PHj0NbWhre3N+vlZAFuS2/VxkUCqqh3S01NTeXlyQDV18W9fv06jh49ipKSEri5ucHBwYGT2ruA6j/7mjsi9eHxeAgICFDpa6oiIapdGozH46FVq1YqTZ6XLl2KrKws9OnTBx4eHrC1tYWmpmadOw5sHDp0CMbGxpg0aRISEhLg6+sLHo+HxYsXK70KIVDd0//jjz/Wu5/H47FKoNmu1KesrKwspZZ9B6qHxdQ3F8HGxqbeoVhNweXY6QEDBuDIkSNYvnx5nd/TY8eOYeDAgUrFHzlyJPbu3YuVK1fKzf1JT0/Hvn37WuSwmZaAEmiikDKJS58+fbB06VIEBQXJnVANDQ2xZMkS9O7dWxVNBFA97jY0NBRSqRTu7u6s/qC9isvSW7VxkRzW17ulr68PkUgENze3t7Jero+PD7KysuDi4oJRo0ZBXV29zlAfVbZb1Z99faUbHz58iJ9++omTiyVVJEQNlYOsocyE3/z8fGhqasLY2BgmJiYqHU+dnp6O4OBgaGhowNLSElOnTsXBgwdhYmKikviampqNTm5lo6KiosFhDv/3f/+HsWPHsoqtqAycVCrFkydPcPbsWYwZM4ZV3BolJSX11pJXV1fHixcvlIoPVPcI//7778wwqD59+ij9f+rm5oaVK1diyZIlsLW1ZTp1kpKSUF5ervQiK2PGjEFOTg5WrFgBQ0NDJn5+fj7GjRvHyVAl8uZRAk04YWtrC1tbWzx69Iipj6vKW3R//PEHQkNDkZeXhylTpsDJyUllvaxz587Fxo0bcfToUbRv315uPHRcXJxSJdxycnKYZKqqqkpuWxUU9W7VjJk9ceIEwsLCWPf+cVl7+6+//gJQPazl1V4sVdzW5nqlw1fLmeXm5uLYsWO4evUqxo0bx7oHiuuEaPHixXLbioZdKSMkJATXrl1DTEwMfHx8YGhoCAcHB0ilUqUvYiQSCTQ0NABU3xkSCoUqS565tG7dOmzdulXhcIKzZ88iLCyMdQKtaEKmuro6jIyM8Omnn2LYsGGs4tYwMDDA/fv3FS76cv/+faVXPQ0KCsLZs2dhYGCAtm3borCwEP7+/hg3blyTytzVR09PDzt37kRUVBR+++035m+SjY0Nxo8fr1R1pRoeHh4YO3ZsneSfhm+8u6gKRwu1YsWKev+AVVZW4t69eyofh6fKmfc1tVQdHR1VXm2ihqpLb9XUIuZyifCGPHv2DD4+PqxvIXNZe5vrMnNc1pitrbCwEOHh4YiPj4ejoyMmT56sVC+xonbXJEQikUjphKgxhYWFSvdy13j+/Dni4uIgFouRnZ0NCwsLjB8/vk4d4aaaNGmS3OcTHByMOXPmyB2jTJUSrqpwREREIDo6Glu3bpWr4R0VFYXw8HCsX7/+ra0ZHBERgcuXL+Orr76CoaEh83heXh58fX1hY2MDV1dXVrHPnDmDn3/+GV5eXnLv/86dO9i5cyfGjh3LSVlBoPpuhqKyq6qKLRaLsWDBAk7ikzeHEugWistkqLZXZ94bGhpizJgxmDBhglJxd+3a1WgPlrILNLzqdZTe4lJVVRXc3NyaVMKQNE9JSQkiIyPxyy+/4KOPPoKbm9t/ojdUkZqFSWJiYnDjxg2lainXJz09HdHR0bh8+TKOHTvGKoaPj0+j54AtW7awig1U93ArujiXyWRMrzrb2r4hISG4evUqtm7dCl1dXZw6dQo//vgj1q9fr/Llth89eoTs7Gx88MEHSl2EAtXv3c/PD9evX4e5uTkzVOHu3bvo168fVq5cyfpO4KJFi/D5558rXF3z1q1b2Lt3L/bv369U+2uruaCLiYlBbm4uIiMjVRb7n3/+kbtY7N+/P9WBfgdRAk04oWjm/dWrV+Hv76/UzPvXrb7SW2wuADZu3Ih169Zx0MqmuX37Nvz9/fH999+zej6XtbfPnz/f6DHK9CYeOHAAw4YNU0kFGEWmTp0KPp+PTz75hFmM5FXKTGytocrlx1+VkZEBsViMhIQElJSUYMiQIXB2dlbZQjyK1Jekvo0ePnzInNNevnwJR0dHpXoV9+/fj7S0NNjY2ODMmTPYsGGDwqERzREUFIQuXbrA0dERQPWQs927d0NbWxvl5eVYs2aNSr6Hv//+O27cuMEMhbC2tlZ6bourqyvCw8OhpqZWZ59MJsOUKVManNTZFLUvfq5fv47Kykp88skn+Pjjj5W+01ITWywWIzU1Ffr6+nj+/Dn8/Pw4/R0ibw6NgW6hXr58idzcXHTs2BEAEB0dDZlMxux3cHBAq1atWMXmcub9q8rKynDnzh0UFRWhTZs26N69u9IliQBuSm/VjPPlkqJEVCaT4cmTJ4iNjYWnpyfr2FzW3v71118bPUaZBPr58+dYtWoVzMzM4OTkBEdHR5VNBgXA1Dz/5ZdfFO7n8Xishs6kpqbi2rVrTKI2ZcoUJonm8XhYv369UglRUVEREhISEBMTg8zMTFhZWcHd3R1HjhzBnDlzlL7Y9fLyws6dO5nt3bt3y90Z8vT0fCtrNdcoKSlBQkICxGIxMjIyYG5ujpcvX2L37t2s7zDUVMrx9PTEd999hzNnzuCrr75C586dmX1sLyqSk5Px8ccfM9tHjx7F3Llz8fHHHyM6OhrHjx9XSQLdp0+fOiXbCgsLERcXh08++YRVTKFQiKdPnyr8XJ89e6ZwXYGmevDgAWJjY5nzua2tLdavX49vv/0W48ePV/p7HhQUhISEBEilUtjZ2WHjxo2wsrLCrFmz5IbpkHcLJdAt1NmzZ1FYWMiMIQwICGB6PwoLC1FaWsr6RMjlzPvafv75Zxw9ehQVFRXQ1dVFUVERNDU1MW3aNIwfP5513Nd5AaBqihLRmpXrVq5cqVQvEZe1t5W51d4Uq1evZpKh2NhYHDlyBP369cOwYcPQv39/pSegqmL8tCL/93//h3HjxjHbAoGAuY19+/ZtREVFKZUQzZo1C0KhEBMnToRIJGLGtbIdVvGqv//+W247OTlZbrusrIx1bK5XafTz88O1a9dgZGSEIUOGYMWKFTA1NcXMmTNZdy4A8pVyai5G0WA/fQAAIABJREFUaxZmUnbCbFFRETNMIzs7G//88w8zMXTo0KEqv1iRyWRITk5m7tB17NiR9d8NW1tb+Pv7w8fHR+5vRnl5Ofz9/Zk65WwsXboUOjo6mDNnDuzt7ZnJp6qqxnPmzBno6OjA09MT9vb2/5m7KkQ5lEC3UL/++qtcwXt1dXUmicnJycG2bdtYnwi5nHlfIz4+HidOnMCiRYswaNAg8Pl8ZuGDwMBA6OrqwsHBgVVsri4AJBJJo2WxlB23zWUiymXt7dexbLK2tjbGjh2LsWPHIisrC2KxGP7+/pDJZHBwcFB64Q0uPHz4sM5FT02CpK+vr1TdXQDo168fUlNTERsbi6qqKohEItbDcJpClSMGuV6lMSkpCUKhECNHjoRIJFLZZEou60C3atUKJSUl0NbWxu3bt9G5c2cmWaysrFS62kyNBw8eICYmBomJiSgvL4dMJoOPjw+zKA8bM2bMwLp16+Dh4YF+/fox46tTU1PRrl07zJgxg3XscePGITExEUFBQbhz5w4cHBxgaWnJOt6rdu3ahZiYGAQFBSEgIAB2dnas//6Q/w5KoFuovLw8ubJyvXr1Yv5tZmaGvLw81rH5fD5sbGxgY2MjN/O+pKQE33zzjVIz72ucPn0aS5YskTth8/l8iEQiCIVChIaGsj6BcXkB8F8a/10fLmpvv24dO3bErFmz0KtXLxw8eBBnz55VKoFWVNWGz+ejXbt2EIlEdcrcNdWLFy/k4taevKqKJdvXrFkjN+EpNDQUlpaWKC8vV6osYX1UWX+b61Uag4ODER8fj+joaPzwww/o3bs3HBwclE5Cs7OzIRQKmclyubm52LlzJ7KystCjRw+lLqL79++PvXv3YsiQITh9+rRclZYHDx7IVc5g4+zZsxCLxcjMzETPnj0xa9Ys2NraYuHChUpXsRAKhdi6dSvi4+Nx48YN3Lt3D7q6unB3d4ejo2O99aebwtPTE3PmzEFycjJiY2Oxdu1aGBgY4MWLF8jPz1f6vNy5c2d4enpi9uzZzJyZdevWoaqqClFRUfj444+VLvFH3j6UQLdQEokEpaWlEAqFAP69hQgApaWlCle0Y0NPTw8TJ07ExIkTmZn3+/btUzqBzsnJqbfH09raWqnC+FxdAAgEAs6HgSi6rf0qtrdxuay9/To9evSImSwnkUgwZMgQpcvBKRouIJPJkJubi127dmHmzJmsqtq0adMG2dnZzNLytf8IZ2VlqeSCrE2bNpgwYQImTJjAlNzKzMzE4sWLMXToUMyfP591bJlMhgsXLjA9zxKJRG6cfu15F6qkilUa27Zty5y70tLSIBaLERAQgNLSUhw+fBgTJkyod8JoQ8LCwuQu1vbs2QMtLS14e3sjOjoaISEhWLZsGas2z5kzB4cOHcLx48dhZWUlNx76+vXrGDx4MKu4NQIDA6Gjo4NVq1bBxsZGqViKqKurY9iwYQp/H5Utqcjn82FnZwc7OztmvLZYLIa3tzf69euHr776SpmmA6huf+3XiI2NRWxsLKKiojipZkPeLEqgW6j3338fqampCk+oqampSo1nrU+3bt3QrVs3lZTH09TURHFxscITalFREXPbUllcXQBwRdFtbVX56quvmNrbNQuFvIpt7e2KiopGJzgqc+u7pKQEiYmJiI2NRUZGBqytreHh4YGBAwcq1bNVo6HvtI2NDfbs2cPqe29nZ4dDhw5h3bp1ckOJKioqEBwcrPLvYc3v6Ny5c5GUlNSkcpcNMTc3R2Jiotx27XH6qi7ZVkMVqzTW1r17d3Tv3h0eHh743//+h5iYGHh5eeHUqVPNjvX48WNmvklRURFu3bqFgIAAGBkZoVu3bqyTZ6C6F7e+HuwpU6awjlvjyy+/hFgsxrZt22BsbAwHBweIRCKV3lkoKiqCtrY2U42jsLAQkZGRuHjxIiIiIlTyGm3btsUnn3yCTz75hLk4UrW2bdvCxcUFLi4uuHPnjsrjkzePEugWytnZGf7+/lBTU4ONjQ0zhjg5ORmHDh1SqtcJqO7FzsnJQbt27ZjFSB48eIDjx4/j+vXrCiekNUe/fv3q7an54Ycf0K9fP6XiK1KTXLCtZKHKMXf1aei2NgCl7iw4OjqqZNiAIurq6li4cKHK49aYOXMmTExM4OTkhDVr1qg0uWqMubk56yFRU6ZMwdq1azFv3jxYW1sz40Jv3LgBAwMDpZOijRs3YtiwYXUuJAQCAQYPHqx0j6Wvr69Sz28I16s0+vv71/lOamhoQCQSQSQSsf4/raqqYpLDtLQ0GBgYMOPO9fT0UFpayrrNNaUma+JVVFQgIiICmZmZsLCwwMSJExWWiWuqV+/MxcbGIiwsDDweD1euXMHw4cNZT6C7e/cutm7dioKCAujo6GD16tXIyMhAaGgo+vbti02bNrFud0PatWvH2UT3mjsukZGRb3W1GcIO1YFuwaKiohAWFgaJRMJUsRAIBJg6dapSC52kpqZi27ZtKC8vh7q6Ory9vXH79m1ER0dj+PDhcHZ2VnosXmFhIVatWgVNTU3Y2toyicWVK1dQUVEBPz8/1mPOGiu9NWvWLFYnw7KyMvB4PGYGf1VVFS5evMiMfVQ2WWmIRCLBuXPn8NNPP72VJ3KuJxGmpaVx1tvZmKdPn2Lt2rUICAhg9XypVIrY2Ng6SwQ3tApnUwUHByMxMRESiYRZ2bBLly5KxWyqmuSi9jCD5uB6lUauvpOrV6/GyJEj4ejoiG+//Raampr4/PPPAVQnwKtWrUJwcDCr2F999RVGjx7N3JnYvXs3/vzzTwwaNAjJycmwt7fH9OnTVfZegOpFcWJiYvDrr79CTU0NoaGhrOKsXr0aFhYWcHR0hFgsRlxcHExMTPD5558z5VaVER0djYyMDJiZmWHMmDEoLy9HeHg4zp8/j549e2L9+vWsY+fk5OD777/HgwcPYGpqCi8vL+Tk5ODAgQNMbzeX53fyZlAC3cKVlpbi9u3bKC4uhq6uLnr06MGMi2bLy8sLw4YNg5OTEy5cuIBjx46hT58++Pzzz+ssja2MFy9eICoqCjdu3GASi759+2L8+PHQ1tZmHffVP5xubm4ICwurd39Tbdq0CYMGDcLQoUMBAIcPH0Z0dDR69+6NmzdvwtXVVanye8DrOZGXlJTg7t27zHemW7duSn3eAPcJtKLeylcpU0Xk1XJtwL/1t0+cOIG+ffuqPHGRSqVISkpSehKnTCbD9evXERsbi2vXrqF9+/ZwcnKCg4ODSn5ff//9d2RkZMDU1BQ2NjaQyWQ4d+4cTp48CW1tbezdu1fp1+ACV9/J27dvY9OmTZDJZNDQ0ICfnx/MzMwAAKdOncK9e/dYD8WaMWMGAgICoKWlhYqKCkyfPh1btmyBubk5srKysHnzZtYXco2RSCRISkpifX5xc3PD0aNHwefz8fLlS7i6uiIkJEQl4/xDQkIQHx8PCwsL3Lp1C3379sWdO3fQtWtXTJo0idVY9to2bNgAXV1dDB48GAkJCUhPTwePx2MqipB3Ew3haOGEQqHKf8EfP37M3D4dO3YsDh8+jKVLlyqdmL9KS0sLI0aMwJQpU6CmpobffvsNKSkpuHLlCkaMGKGy11HVNea9e/ewdOlSANXJz8WLF7Fq1SrmZL5nzx6lE+hDhw7B2NgYkyZNQkJCAnx9fcHj8bB48WKV/D9HREQgIiICUqmUuWuhrq6OTz/9FJMnT2Ydd9KkSUq3rSGNLb3OdqGTGp999lmdRWb4fD5TwcXV1ZV17Ffdv3+fKSGmpqamdALN5/MxYMAADBgwQG6s+A8//ID+/fvjyy+/ZB371KlTOH78ODp06IDs7GyMGzcOf/75JwBg4cKF+Oijj1jHrrlors+DBw+USowkEgnCw8MbPIbNEBoLCwsEBgYiJycHZmZmcufFAQMGYMiQIc2OWUMikTALSd27dw+amprMeOuOHTvin3/+YR0bUHyhWJsydy9kMhkzKVlDQwNCoVBlVYt+/fVX+Pr6wszMDFlZWVi8eDGWLl3KrNiorPT0dAQHB0NDQwOWlpaYOnUqDh48yHqxHfLfQAl0C7VmzZoG9/N4PGzevJlV7Nplnvh8PrS0tFSePN+8eRPffPMNXrx4AUNDQ7i7uyM4OBgWFha4dOkS8vLy4ObmppLXUtUEmYqKCujo6ACoToIAMKt59ejRAwUFBUq/Bpcn8itXruDs2bNYunQpbGxsoK6uzvSCBgQEoEOHDqwntVlbWyMzM5OZvPrPP/8gMDAQmZmZ6NGjB+bMmaPU4hUNLXQikUiUnoEfFRVV7z6pVIq1a9di69atrOMXFRUxVQMyMzPB4/Ewb948DB8+nHVMRbS1teHg4AA1NTWcPHkSKSkpSsU7d+4cNm/eDHNzc9y6dQs+Pj5wc3NTyQWFp6enXA/x/PnzcfDgQWZ79erVSvcgZ2dn17tPmfOCUChUuKx87dKibBgaGuLevXvo2rUrfvvtN7l5F8XFxUpPrq65UAQUdywoswiMRCLBkSNHmO2XL1/KbQPsJymXlJQwvfwdO3aEhoaGypJnoLrtNZ+tUCiEUCik5LkFoAS6harvNtvz/8fefYdFdW1/A/8OQ5ERUBBEsIcgiMZeQEVQiHqNijUGYwQVy5VrL0TUxBIrNlCiWBAF241iwZsYZQCDEYkimJhExEgERCPoIOIwMDPO+wcv58dQLNPOlPV5njyP+5xxz0qiw5p99l6rpAQXLlxQ6iCLWCzG1q1bmbFIJJIbA5Br4qKIQ4cOISAgAF5eXuDz+YiIiEBYWBjat2+PvLw8rFu3TuEEWl2lt6ysrJhVp99++w2urq7MDyOhUKjU4Z5q6vwg//HHHzFr1iy5FU9jY2MMGDAAHA4HFy9eVDiBPnDgACZOnMgk0JGRkSgqKsLQoUNx5coVHD58WOmDrW+izlPyMplM4fmru7zdvHmTqSn95ZdfYvny5ejXr5/KOp7JZDJmG0d6ejpatmyJkSNHKp1klJaWMiugbm5uMDY2xrhx41QRcr3vpUomJiZqrWqjDmPGjMGaNWvQoUMH3L59GytXrmTu3bp1S+mtCu3atYNYLIaPjw8GDRqk0trGAwcOlCs96OnpqXQpwpokEgnzmW5iYiI3rr6mKKlUWudnRM0xUH+pS6LbKIE2ULX/MguFQsTHxyMhIQG9e/fG559/rvDcEyZMeONYFR49esRs0xg2bBiio6OZHw5t2rRR6oepukpvDR06FOvWrUO3bt2QkpKC4OBg5t7vv/+O1q1bKxxzNXV+kOfm5jbYaaxXr17Yu3evQvMCVSt9nTp1AlD1Z/HmzZsIDw9H69at0bt3byxfvlytCbS22rBhAywtLbFs2TK11N3Nz88Hn89HSkoKUxN78+bNcHJyUsn8tVcpTU1N1VY7XJWl1HSVj48P7O3tkZOTg/HjxzPNWoCqLoXKbLMCqg4l5ubmgs/nY9GiRWjXrh18fX3Rt29fpb/MzZ07V21/NkQikdw2MZlMxoyVbZ8O1P0Z4ezsLDcGKIHWR5RAGzixWIyEhAScPn0aLi4u2LRpk9KrFF27dlV7ybaaP5iNjIxUVvcZUF/prXHjxsHa2ho5OTmYPXu23EpuaWmpUpVPqqnzg1wsFjdY7snMzAwSiUSheYGqbT/V/w/v3bsHKysr5gtF8+bN1VI6TxdMmzYNSUlJ2Lx5M9MJr1+/fipLFufNm4du3bohKChIJUlQbRUVFQgMDGTGQqFQbgwo3thH3dTZ0lydOnfuXG85S2X2m9fUvn17BAUFYerUqbhx4waSkpKwZ88erFu3Tqk90AsXLsSCBQvUUgVGne3TAfWWayTaixJoAyWVSnH58mWcOHECLVq0QGhoKLMCqKw1a9aotaICUHdlVSKRaKTDmTKlt3766ScMHjyYqcJRkyqaywDq/SB//fp1nceete8rysHBAZmZmejevTvS09PlWssLBAKl9j8DqLMKX5Mq/qyoa34/Pz/4+fkxBwcPHDiAvXv3QiwW4969e+jTp4/CcwNVW2caNWqE8+fP4/z583j58iUsLS1VUs0GgMLnKN7F27aKKdtNtb7qIJmZmcjLy4OLiwtcXV2Vml8dau8Zro+i+4hre/HiBR49eoRHjx6hWbNmSv8dra7RPnLkSEycOFGlq9GnT59Wa515dVcRItqJEmgDNWfOHLx69Qrjxo1jfgjXPmHdsmVLNkJ7J29baVW25u/bSm8pkkAfPnwY165dw5w5c1Razq8mdX6Q134MWlP1Y1BF+fv7Y/PmzbCzs8Pjx4/lDtylp6fD2dlZ4bkB1FmFr03ZL4/qnt/JyQlOTk6YNm0a0tPTkZSUhI0bN6JVq1bYtWuXwvMaGRlh4cKFMDExQf/+/WFtbQ2BQICff/4ZycnJStVTB97e2EcZb9sqpuzWsbCwMHTp0gVDhw4FAMTHxyMuLg7t2rVDXFwc/vOf/8DLy0up91A1Ve4Zro9YLEZaWhr4fD7+/vtvDBgwAIsWLVLJlh8/Pz/07dsXkZGRWLRoEebNm1en/rOiT0hSUlLUmkATw0R1oA2Un58f8+va5beqrym6J2zixIlvre2qzY9H31R6a/z48Qo/Ci0vL8ehQ4eQlpZW5zCeqqgzgX769OlbX9O8eXOF53/y5AkePHgAJycn2NvbM9f/+OMPWFhYqKSZgj559uwZUlJSlDqUt3PnTshkMixYsEDuC5BMJsOOHTvA4XCUai1948YNpKenM41CaoqMjIS7u7va6uQ+efJEqQO006ZNw7Zt22BtbQ2ZTIYpU6Ywf29v3LiBo0ePYufOnSqMWHnFxcVKN6l6E39/f1hbW2Pw4MHo3bu3XPfKaqpYeNm0aRPS0tLkKn4o+zNJnSvEtAJtmGgF2kC9qeyWskQiEWbMmNHgo35lD2yom7pKb5mbm2POnDnw9PREZGQkUlJS6iSFqnq8qg7KJMfvokWLFvUmPG5ubga7B/pN2xCsrKyUrhuekZGBiIiIOk8POBwOAgICmLrlijp79myDB9e8vLxw4sQJtSTQYrEYs2fPVupz5tWrV0zL99zcXFRUVMDDwwNA1aHZHTt2qCRWVQoODlZrIicUCiEUChEbG8t0HKz5Oa/sZ3tpaSn27t2LnJwcrFy5kqnKoyx11fSuVlFRgRkzZrzxNereh000jxJoUodAIEBycjLGjh2r0O9v1KiRwu1ctYG6S29ZWFjAzMwMJSUlTF1oVVHnB/mb9vlWU/SA4hdffIHY2FhmvHLlSrn9s9OnTzfIFZ7x48c3uDVGFdUDRCJRg9uJmjRpgoqKCoXnBqqqfNTcz16Tm5sb8vLylJr/TZR9uGphYYFnz56hWbNm+OOPP+Ds7Mzsy62srFRFiDpHnQsvV69eRVRUFPr27Ytdu3apvHeAump6A1XlPGmLiOGhBJoAqDroVF1z9tatW2jTpo3CCTSgXE1Ntqmr9JZUKsXJkyeRkJAAf39/jBw5UuWlt9T5Qf62fb4VFRUKJ9C1E5IHDx4oNI++qfllRyaTYf78+YiIiFDZ/I6OjsjMzESvXr3q3MvMzISDg4NS81dUVKCioqLeA2aVlZVKJ+hvouzfrQEDBiAsLAx9+/ZFQkKC3BOo7OxsapShYtHR0Vi4cCF69Oih8rnVXdOby+WqJW6i3SiBNnC5ublMW2CRSASpVIrQ0NAG6/0aAnWV3lq4cCF4PB62bdvGdMVSNXV+kPfp00du73xNFRUVWLt2rVre15DV3jZjZGSk0q00o0ePRnh4OGbNmgUPDw9wuVxIpVKkpaUhKioK06ZNU2r+Nm3a4NatW/U22Kn+oq6tpkyZgu+++w537tzBv/71L6buPAD8/fffzOFCbSISiRosh6mKJxZA1VmI48ePIzMzk2mn3rVrV/j7+yv1pWL37t0qX3UmRJ0ogTZQCQkJTFvgzp07IzAwEB4eHvj3v/+tdMWDr7/+WkVRskNdpbd8fHwwatQonW34cPr0afB4vDrtoysqKrB69WqVdFIkmjVo0CC8fPkSu3btwvbt22FlZYXS0lKYmJhg0qRJSnci9PPzw549eyCTyeDu7s4k6NevX0dUVJRSzXGWLl3a4N8lZUoqVuNyuQ3ui1V277m6mJmZYfv27Wqb/9GjR1i2bBk+/PBDTJ48manakpqaisWLF2PLli0KHyI8derUW1+j6BkRdR9ab6g6EdFvlEAbqAMHDsDS0hIhISEq73Cm7iYq6ta5c2e8fv0az58/h42NDYyMjJCZmYmbN2+ibdu2citR72Po0KFyj7NlMhkuXbqEvLw8uLq6Nthe/X2o84N89erVWLVqFczMzDBw4EAAVZVF1qxZAy6Xi6+++krhucVisVwN28rKSrmxMk1ayJuNGjUKvr6+yM7OZlYUXVxcVLIaOGDAADx//hwRERHYtm2bXII+efJk9O/fX+G5q7cL1VdFSFWkUilSUlKQlZXF/Lfp1q0bvL291dY1TxkcDgetWrVS2/yxsbEYMmQIAgIC5K77+voiJiYGhw8fRmhoqEJz1y7B99NPPzGfM8p6W1UoZamj2y7RfpRAG6jly5eDz+djy5YtsLe3h7e3N7y8vHR2dVSV7ty5g40bN6KsrAx2dnaYMmUKoqOj0bFjR1y9ehXFxcWYNGnSe88bFhaG/v37M41UYmJicPnyZXTp0gVRUVEQCARKr2zVbmF+6dIluYR/y5YtWLZsmUJzf/DBB1i5ciXWrl2LRo0a4aOPPsLXX38NU1NTJrFW1MCBA+V+gHp6esqNVfWDVNfUPrhZ3cinJlW0CObxeOjevbvS89SnOkH/888/UVZWBktLS7i6uiqdoLu6uiI8PBwPHz6Ek5MTFixYoNLtLUKhEF999RWePHmCnj174oMPPsDz588RExODH374AWvXrjW4LQe//vorgoOD6703duxYpZ4ozJ8/X258/fr1OtcI0SaUQBsod3d3uLu7o6SkBMnJyUhKSsKxY8fA4XBw7do1fPzxxzp9EFAZhw4dQkBAALy8vMDn8xEREYGwsDC0b98eeXl5WLdunUIJ9F9//cWUBZNIJLh06RJCQkLQrVs33L17F7t27VI6gb506RKmT5/OjGNiYuQS6IyMDKXm79ixI0JCQpimJ02bNsWqVauU/rMyefJktdav1VW1D27WbiAEqCaBVjcej6fycnUHDhxA8+bN8emnnyI5ORkHDx7E8uXLVTZ/XFwcGjdujP3798Pc3Jy5LhQKsXnzZsTFxWHmzJkqez9VmDNnjlrnl0qlDX5RNjMzU2kHWFrMIdqOEmgD17RpU4wZMwZjxoxBTk4OEhMTERcXh2PHjilciu7bb7/F9OnTlVqRZNOjR4+YpHPYsGGIjo5G+/btAVQdiqq9yvuuRCIRU7bur7/+AgB07doVQNVq2vPnz5UNvQ5VPtquufLZuXNn3LlzB76+vuDz+cx1RZM5ddev1VXr169nOwSlrFix4o33ORyOwmcOcnJycPDgQZiZmcHNza3BlVFFpaWlYePGjXLJM1D1ZWD27NlYvny51iXQ6u6M2LZtW/z888/17o2/du2ayuo26xpVbjchuoMSaMJwdnaGs7MzgoKCcP36dYXnKSkpQXBwMObNm4cuXbqoMELNqJl0GhkZwdTUVCXzWllZobCwEI6Ojvjtt9/g6urKrLIIhUK1HMJT5SpO7ZXPDz74AOnp6XLXdGE1VJdU75Ov3qbg4+PDdkjvpaF9/SUlJbhw4QKEQqHCc4vFYuZLOo/HU3lt5rKysga3hDRv3hyvXr1S6fvpgokTJ2LLli0QCATo378/bGxs8Pz5c1y9ehXfffedWkvFKSMtLY1pggMABQUFcnvFL1y4gBEjRig8/+HDh3Ht2jXMmTOnwbrqRP9QAm2gKisr8eTJE6aM1OXLl+Uev3l7eys8d2hoKFJTU7F161b069cPgYGB9daB1VZSqVRutVUikciNFX1MOXToUKxbtw7dunVDSkqK3IrZ77//jtatWysedA2FhYXMlwCZTCY3Voaur4bqosOHDyMpKQmdOnXC4cOHUVRUpFTHNE2r/YVKKBQiPj4eCQkJ6N27Nz7//HOF567991TV+8Pt7OyQnZ2Njh071rmXnZ1tkFuOevbsiQULFuDgwYNyh3xtbW0xb948pbbpBAQEyH3hV1X5UKCqZX3NBHrZsmU4duwYM46NjVUqgd69ezcOHTqE4OBgpt070X+UQBuohIQECAQCBAUFAQD27dvHdN8TCAQoLy/HmDFjFJ7f09MTXbt2xfbt2zF79uw6KzlbtmxRPHg1q73P1NnZWW7s4uKi0Lzjxo2DtbU1cnJyMHv2bLkP2dLS0gbrt74PkUiEOXPmyCXMNRuraOu+Qk3Ur9VFqampWL9+PVq3bo2HDx9i06ZNOpVAVxOLxUhISMDp06fh4uKCTZs2MduiFPW2v6eAcgn00KFDsWPHDixdulSutOe9e/ewc+dOfPLJJwrPrS5r165VqhrO2xQXF8PDwwMeHh549OgRU5lE0dJ1NWly9VrVVVvMzc0xZ84ceHp6IjIyEikpKXVqnCtago9oL0qgDVRqaiqWLFnCjI2NjZkVxsLCQmzZskWpBBqoas2anZ2NIUOGaHXDhNo2bNigtrkHDx7MVOGoSVWP5tXZaled1F2/VleVlZUxTybatm2r8P57tkilUly+fBknTpxAixYtEBoaik6dOqlkbnX+PQWqalgXFRVh6dKlsLW1ZbYrFBcX45NPPmmwqRCbfv/9d7XOX/OsQsuWLVWSOFe7e/euxuopq2shwcLCAmZmZigpKWHOuxD9RQm0gSouLpbbA/bRRx8xv3Z0dERxcbHCcz958gS7du1CaWkp1q5dq3RjFqL/1F2/VpdJJBK5LTk1xwC0ulrOnDlz8OrVK4wbNw59+vQBUHVItyZVJmGqFhQUhOHDh+PXX39lVlu7dOmitk6ihuy7777T2YYkUqkUJ0+eREJCAvz9/TGRPei1AAAgAElEQVRy5EitfdpHVIcSaAMlFoshFAqZOqY1i98LhUKIxWKF5164cCGGDx+Ozz77TKt/uOujmvsSG0KPEnWHSCSSSypkMhkz1oWtLU+ePAEApslG7Ufn2h4/ULWgUF/CnJ6ejr59+7IQUcPEYjHCw8Pf+BpDrK1cUVEht5+65v5qmUyGiooKpeZfuHAheDwetm3bRl+uDAgl0AaqXbt2yMjIqPeUfEZGhlLliN626iwWiymxVpPTp0+jVatWzH52XaHu+rW6av/+/WyHoBRd3VJUrbCwEH///TccHByYPdu//PILjh07hmfPnmldAg1UlSZVF3WeVZBKpUhOTn7j/uT6tr+9C0VLJb4rHx8fjBo1iladDQwl0AbKz88Pe/bsgZGREdzd3cHlciGVSpGeno79+/cr1VGqoeRZLBbjhx9+QHx8vMKnqfVRWVkZLCwsVDLXtGnTkJSUhOzsbPj4+GDQoEGwsbFRydzqJJVKkZSU9MbXKPrDU5epsrOethEIBEhOTsbYsWPZDqVeycnJ2LVrFxo3boyXL19ixowZ+PXXX/HXX3/Bz89PrkGRtjAxManTZluV1HlWQSKR4Pvvv2/wPofDUfgzwMzMTK1bCd+0Hz43NxfHjx9XuMU50V6UQBuofv36oaioCBEREdi2bRusrKxQWloKExMT+Pv7o3///grPXVhYiIiICOTm5sLBwQGLFi1CYWEh9u7dC2tra7lOeYYkKSkJTZs2RY8ePQBUNYLYuHEjnj9/DgcHB6xatUrpx39+fn7w8/NDbm4u+Hw+Fi1ahHbt2sHX1xfu7u4wNtbOv/I//PBDvdc5HA4KCwtRVlZmkAk0UPW4+dy5c8jMzMTLly9haWmJbt26YdSoUSr74qUp1V/S+Xw+bt26hTZt2mhtAn3q1CksX74cvXv3xrVr17B161YMGTIEe/bsMdgnaOo8q2BmZoawsDC1zP3ll1/CwcEBvr6+8Pb2VvkqfUVFBU6dOoUHDx7A0dER/v7+ePnyJQ4ePIhbt27pXP128m44MlXXcyE6RSgU4u7du8wBGVdXV2ZftKLWrFkDKysreHp64sqVK8jJyQGHw0FQUJDK2/nqknnz5mH+/PlwcnICACxatAitW7fGuHHjcOHCBZSUlKh8lUIqlSIuLg5nzpzBmjVrmM6HuqCgoABxcXG4ffs2Ro0aBX9/f7ZD0jiBQICQkBCYmJigf//+sLa2hkAgwM8//4zKykps3rxZJ54w5ObmIjExET/99BNEIhGkUimTnGorf39/HD9+HEDV9oRx48bh5MmTWp08r1mzBl9//bXa5p84caLaOoaqc+6ysjKkpKQgOTkZubm56NGjB3x9fdG7d29wuVyl59+9ezfu3buH7t27IyMjA7a2tsjLy4OXlxf8/PzUuq2GsEc7l6OIxvB4PGZFVFVycnIQHR0NU1NTuLm5wd/fH1FRUWjRooVK30fXFBUVoV27dgCAZ8+e4cGDB1i9ejWsrKwQEBAgV69ZWc+ePUNKSgqSkpLA5XIxdepUnamGUlxcjOPHj+Pq1asYMmQIoqKiDLa71+HDh9GxY0csWLBAbn+lv78/duzYgcOHD2PhwoUsRvhmCQkJ4PP5ePjwITp37ozAwEB4eHjg3//+t9b/eXz9+jXzaw6Hg0aNGml18gygTrnQ3Nxcpett1zRnzhz8+eefTHOZx48fY8eOHcjLy4Orqyvmz58Pa2trlb2fqlhYWGDEiBEYMWIE8vLykJSUhL179yIyMhLe3t4YPHiwUv+dbt68ie3bt8PGxgYjRoxAUFAQVq9eje7du6vw34JoG0qgicqJxWKm/TWPxwOPxzP45Bmoagv++vVrcLlcZGdnw9HRkUkMeTye0ifBAeDKlStISkrC33//jQEDBmDRokXMire2Ky0txX//+19cunQJAwYMwO7du2FnZ8d2WKzKyMhAREREncNJHA4HAQEBWLBgAUuRvZsDBw7A0tISISEhcHd3Zzuc91JRUYFly5Yx4/LycrkxoH0NoS5evIipU6cy4xUrVsh13FPW+fPn5bbg7d69G+bm5li8eDEuX76MmJgYhb/QqWv1ubY2bdogMDAQU6ZMQUZGBvbt24eEhASlqsGUl5czT4Ls7OzQqFEjSp4NACXQROVqt9itPQaU6xCmqzp06IBz585hyJAhuHTpktzK/+PHj9GkSROl32P79u1o2bIlBg4cCBMTE/z888/4+eef5V6jjWXsjh07hoSEBKZ7JdWEriISiRpcfW/SpIlKvnSp0/Lly8Hn87FlyxbY29vD29sbXl5eOlGtYO7cuQCqvqzIZDIMHTqU5Yjen6p3aD5+/Jip8FNaWoo//vgD+/btg52dHZydnbX6aUhNBQUF4PP5SElJgUQiUaqNd7XCwkLmv7eRkZHcGNDueudEMZRAE5VTd4tdXTV9+nSsXbsWsbGxaNmypVw91uTkZJV0aBs0aBA4HA7KysqUnkuTTp48CQsLCzx79gwRERH1vkbbVvs0wdHREZmZmejVq1ede5mZmXBwcGAhqnfn7u4Od3d3CAQCZkvRsWPHwOFwcO3aNXz88cdauy3C1dUV4eHhePjwIZycnLBgwQKdq4qi6i8qMpkMRkZGAIDs7Gw0a9aMeUrUtGlTCIVClb6fKpWVlSE1NRV8Ph8PHjxAjx49MHPmTPTp00fpfdAikQhz5syRS5hrbsnThXrn5P3RIUIDVVRUhKysLHz88cd17iUmJqJbt26wtbVlITL9V31gs6aysjKYmJjAzMyMpajYxefz3/oaQzzJnpycjOjoaMyaNQseHh5Mucm0tDRERUVh2rRpGDRoENthvpecnBwkJiYiNTUVRkZGiIuLYzukeq1ZswaNGzeGt7c3kpOTIZFIsHz5crbDeqOxY8eiX79+zPjatWtyYwBYsmSJwvN/+eWXGDp0KAYNGoRt27bBzMwM//nPfwBU/UwJCQlBdHS0wvOry+bNm3Hjxg3Y29szlTi0ca820S2UQBuo3bt3o127dvU+uvr++++Rm5uL4OBglb6nRCJhSlh99dVXKp1bF9TX3dHIyEglp8BrKy8vZ6qrNGnSBC4uLjA3N1f5+xD1O3/+PI4ePQqxWCxXbnLSpElvrD+rDR49esSs4n744YeYP38+s4orFotx/fr1eps5aYPJkyfj4MGDMDMzg1AoRHBwMA4dOsR2WG9UXTXkTZSpZvPnn39i3bp1kEqlMDU1xebNm5nSm2fOnMH9+/exdOlShedXl2+//Ra+vr6sNJjS9nrnRHGUQBuoGTNmYOfOnWjcuHGde0KhEPPmzcOBAwdU8l5//fUX+Hw+rly5AqlUCh8fH8yYMUMlc+sSPz+/eh+p2tjYwMvLC5MmTVLJ4+wLFy4gNjYWFRUVTMJlZmaGzz//HKNGjVJ6fnV49OjRW19jyHsIhUIhsrOzmacXLi4uSpeb1ITVq1fDwsJCp1Zxq9Uuq/b555/j6NGjLEakHYRCIQoLC+Ho6Cj3Z7CgoADm5uZo1qwZi9Fph/rqnb+txTrRPbQH2kCVlpY2+AO4UaNGePnypdLzp6SkMCWsXF1dIZFIEBERAXt7e6Xm1lX1tWWWSCR48uQJTp48iWPHjindRSwlJQUnT55EcHAw+vfvzzzyv3r1Kg4cOAArKyt4e3sr9R7qMGfOHOawVn0MfQ8hj8djTvUXFBTg9u3bcHJy0vo9uffv32dWcd3c3FT+VEudah9+FovFOnMY+smTJ7h9+zbzhatr164qq4TE4/Hw4Ycf1rmuzQd/AwIC3rofXBXdceurdx4aGqrV9c6J4iiBNlBWVlbIz8+vUzcUqPoBbWlpqfDcGzZsQEZGBlq0aAEvLy+EhobC3t4eAQEBBrvHF2i4LbOjoyNat26N0NBQpRPos2fPYt68eXIf2FwuF15eXuDxeIiLi9PKBPrcuXNsh6CVDh48iA8++IDZ55ycnIzw8HBYWFhAJBJhxYoVWl0uSywWM3/neTweKisrWY7o3enqYeiDBw8iISEBzZo1Yxrv7Nmzh6lPbIjUva1El+udE8VRAm2g+vXrh/3792PlypVySW1FRQUOHDigVCvvmzdvwtzcHD4+PvDy8qJHeu/A1tZWJZUzCgsLG0younfvrrZWuUQ90tPTMXLkSGYcGxuL6dOnY+TIkbh8+TKOHz+u1Qm0Lq/ibtiwge0Q3tv58+eRnp6OTZs2wdXVlbl+9+5dbN++HXZ2dlq/b14d7t69i/Hjx6ttfl2ud04URwm0gfL398fKlSsxY8YM9OjRAzY2Nnj+/DkyMzPRvHlzpQ6axMTEIDk5GXw+H0eOHEHnzp0xaNAguc5eRN7du3dV0jTEzMwML1++rPeEeWlpKdPghuiG0tJS5slFfn4+Xrx4wdQjHjx4sEoeO6uTrq7i6qoff/wRCxculEuegaqSfAsWLMDu3bsNMoH+7rvv1JpA63K9c6I4SqANVKNGjbBp0yYkJycjKysL9+/fh6WlJb744gt4e3vD2FjxPxpWVlbw8/ODn58f7t+/j8TERERHR6OsrAwHDx6En59fvXvo9F3tlTegaoXun3/+QVJSkkoOVvbs2bPBbmCHDx9Gz549lX4PojmNGjVCWVkZLCws8Oeff6J9+/bMl6DXr19r/ZdSXVzF1WVFRUVwcXGp956LiwuKioo0HJFhqK53XlJSguTkZJ2qd04UR1U4iEaIxWKkp6cjMTERt2/fxpkzZ9gOSeNWrFhR5xqXy4WdnR28vLzQpUsXpd9DIBAgJCQEZmZm8PDwYJ4spKWlQSQSYfPmzUzLWaL9du/ejbKyMgwcOBBxcXHw9fVlymHdu3cPu3btwq5du1iOkmiLwMBAbNq0qd4Dg0+ePMGXX36p9U8t1GH8+PEIDg5+Y2fGwYMHq/Q9daXeOVEcJdAGqr7V0NrU9Wj12bNntC9ajV69eoVz584hKytL7hS+n58fLCws2A5PIdWrsIZGKBRi//79uH//PlxdXTFz5kxmJevEiRMwMjLCp59+ynKURFtERUWhsLAQoaGhcmdbRCIRNm7cCEdHR8yaNYvFCNkxevToNx7m43A4aut0qu31zoniKIE2UPWthta2fv16heZmMznXBdTkpK4vvvgCsbGxzHjlypX45ptvmHHtmryEkLqEQiG++uor/PPPP+jZsyfzBCojIwPNmzfHunXrdKJ+uKqp+/PjXbbGqOKMC9EutAfaQCmaHL+L2oeE6mOoCbQmmpxUVlYiMTERv//+O7Ny27lzZ/j4+GjtIcLa5c0ePHjAUiTa5dy5c3KHvjIzM+Wqbhw6dAhTp05lIzSihXg8HjZt2oSUlBTmbIuVlRWmTJmCQYMGKXW2hTQsKCio3jr2NQ8RGnIde31Ff5uIyqkzOddlmmhyUl5ejpCQELx48QLdunXDBx98gOfPn+PkyZP44YcfsGnTJoNcgdJVx44dk0ugw8LCcOzYMWZ88eJFSqCJHGNjY/j6+sLX15ftUAzGqVOn6lwTi8X48ccfcfr0abRt25aFqIi6UQJtoJYtW/bW16hjT5hYLMasWbMQHR2t8rm1nSaanMTHx8Pa2rpOoiwUChEWFob4+HhMnjxZqfcg7KEdd+RNMjIywOPx0LFjRwBVBwe3b9+OvLw8uLq6Yv78+fWWuNR39W3fKCgoQH5+vko6etausJGcnIzjx4/DwsICixcv1upa7URxlEAbqOzsbLRq1Qre3t4ar8rw7Nkzjb6fttBEk5NffvkFCxYsqLPKzOPxMGXKFOzcuVMrE2ixWIwjR44w48rKSrmxRCJhIyytQ3VlyZscO3YM06dPZ8a7du2Cubk5Fi9ejMuXLzdY4lLfaaqjZ3p6OuLi4iCRSDBlyhQMGDBA6TmJ9qIE2kDt3LkTfD4f//vf/9CuXTv4+PjA3d1dI7UqDTUJ0ESTk6dPn6J9+/b13mvfvj2ePn2q9Huog5eXFwQCATP29PSUGw8cOJCNsLSCRCKRW3muPSak2uPHj9GhQwcAVZ8pf/zxB/bt2wc7Ozs4OzsbZPIMqL+j56+//oq4uDgUFxfjs88+g4+PD7hcripCJ1qMEmgD1b59ewQFBWHq1Km4ceMGkpKSsH//fnh4eMDf3x9NmzZlO0S9o4kmJ29LrLQ18Zo/fz7bIWglkUgk10FNJpMxY5lMZrBfRkn9ZDIZjIyMAFQ9ZWzWrBlT/aFp06YQCoVshscadXf0XLVqFSwtLTFo0CA8efIER48erfOaKVOmKPUeRPtQAm3guFwu3N3d4ezsjHPnzuH8+fPw9PRUKoGu+ei9NqlUqvC8ui4gIAAhISGYO3dug01OlCWRSPDjjz82mCjrylaIwsJCuX+Hli1bshgNe/bv3892CESHtG3bFleuXMGgQYPw008/oVu3bsy9oqIiNG7cmMXo2KPujp6DBg0Ch8NBWVmZKsIlOoISaAMmFouRlpYGPp+Pv//+GwMGDMC2bdvg5OSk1Lw1H73Xp3ofmqGxtrbGjh076jQ5cXd3V1mTkw4dOuCnn35q8H5DbX7ZduXKFWRkZGDRokUAgHnz5jFbFTgcDpYsWWKQ+wmbN28OoVCIc+fOITMzEy9fvoSlpSW6deuGUaNGGWRzGdKwgIAArFu3Dnv37oWpqancl/KrV68yhwsNTa9evbB7924MHDgQZ8+elatQkpubC1tbW6XmX7BggbIhEh1EjVQMVGRkJK5fvw5XV1f4+PigV69eVCNUzXJyct7YDcuQrVixAp9//jnc3NwAAP7+/jh+/DgA4I8//sDRo0cNsjxidWt2ExMT9O/fH9bW1hAIBPj5559RWVlJrdlJHUKhEIWFhXB0dJQ7TFxQUABzc3OD7AJLHT2JOlACbaD8/Pxgb2+PJk2aNLiPUl2tTQ3VuHHj4ODgAF9fXwwaNAhNmjRhOyStERgYiOjoaGb/5uTJkxEXFwegatvP9OnTld6nqIt27twJmUyGBQsWyP09lclk2LFjBzgcjsEeDCNEWwQEBLz1PIIhfn7pO1pyNFDz5s1T29z0YVK/w4cPIyUlBcnJyThy5Ah69OgBX19f9O7dW2Untt/Wop3D4ci1yNYWr169glQqZRLo6uQZqNqjaKiHnzIyMhAREVHn7xOHw0FAQAA9OibkHai7o+fSpUuVio/oJkqgDZSPj4/a5qYPk/pZWFhgxIgRGDFiBPLy8pCUlIS9e/ciMjIS3t7eGDx4cIMl6N6Vp6dnvddLSkpw4cIFrU1Emzdvjr/++guurq517t2/f5+pJGBoRCIRrKys6r3XpEkTVFRUaDgiQnSPujt6du7c+Y33xWKxwnMT7UUJtIG7c+cOMjMzmQNt3bp1w0cffaTUnG/7MCFAmzZtEBgYiClTpiAjIwP79u1DQkICzp49q9S8w4YNkxsLhULEx8cjISEBvXv3xueff67U/Ori7e2NPXv2YNWqVXIHeoqLi7Fv3z6lOzTqKkdHR2RmZqJXr1517mVmZsLBwYGFqAjRbZrauSoWi/HDDz8gPj7eIJ+66jtKoA2URCJBWFgYbt68iQ4dOsDGxgYFBQU4e/YsevbsiWXLlil1qPD169d4/vw5bGxsYGRkhMzMTNy8eRNt27bFkCFDVPhvorsKCgrA5/ORkpICiUSCESNGqGxusViMhIQEnD59Gi4uLti0aZPSq9vqNHbsWOTk5GD27NnMn8fnz58jJycH3bp1w9ixY9kOkRWjR49GeHg4Zs2aBQ8PD3C5XEilUqSlpSEqKgrTpk1jO0RCdI6q66cXFhYiIiICubm5cHBwwKJFi1BYWIi9e/fC2tparjsk0R+UQBuokydPoqioCHv37pV7PP706VNs2rQJJ0+eVHi18s6dO9i4cSPKyspgZ2eHKVOmIDo6Gh07dsTVq1dRXFyMSZMmqepfRaeUlZUhNTUVfD4fDx48QI8ePTBz5kz06dNHJfugpVIpLl++jBMnTqBFixYIDQ1Fp06dVBC5enG5XISGhuL27dvIysrCy5cv4eLigk8//VSulq2hGTRoEF6+fIldu3Zh+/btsLKyQmlpKUxMTDBp0iSDLQlJyPtSZ0fP/fv3w97eHuPHj8eVK1ewYcMGcDgczJ07VyUNsoh2oiocBmrmzJlYsWIF2rZtW+debm4u1q9fjwMHDig09+LFizF06FB4eXmBz+cjOjoaW7duRbt27ZCXl4d169YZZIOIzZs348aNG7C3t4evry+8vb3rbeutjFmzZuHVq1cYN24c+vTpU+9rdLEpSW5urlavoKubUChEdnY2s9XKxcVFrkQZIaRhfn5+darYVI+rf63M9rnJkycjOjoapqamEAqF8Pf3R1RUFFq0aKF07ER70Qq0gRIIBGjTpk2999q2bYuSkhKF53706BGzTWPYsGGIjo5Gu3btAFTt/S0tLVV4bl1maWmJDRs2oEOHDmp7jydPngCoqnJy+PDhOissyv6gUKfq+rXNmzdnDs7l5ubi+PHjuHXrFk6dOsVyhOzh8XhyVQMIIe9O3Qs2YrGY6WzI4/HA4/EoeTYAlEAbqKZNmyIvL6/eFeiHDx8q1cq7ZtJmZGTEfLAYujlz5jR4TyAQID4+Xum9cufOnVPq97MlIyMDW7ZsgUgkgrGxMRYvXow///wTly9fxscff4y9e/eyHSIhREc1b95crfNLpVJcvHixwTFQ94A30X2UQBsoLy8vhIeHIzQ0VK7qQVFREXbt2qVU1YPaHx4SiaTOh4uhunz5Mh48eABHR0cMHz4cIpEIJ06cwMWLF1VSvWTRokXYvn07Mw4PD8f8+fOZcWBgoFaeBj969CgCAgLg4+ODH3/8EeHh4ejatSv27dvXYBk3Qgh5F0VFRbhz5w5zZmDnzp2QSCTM/YCAAKVKZXbo0AGpqanM2NnZWW4MUAKtjyiBNlCfffYZ8vLy6lQ9uHfvHrp3747PPvtM4bnf9mHi4uKiVOy6KiYmBikpKejYsSOuXbuG+/fv4+7du/jwww+xZcsWlezxffTokdw4PT1dblxeXq70e6jD48ePMXz4cADAJ598gkOHDmHBggW0z5cQorQzZ87IlXxMS0vD6NGjAQD5+fmIj4/HrFmzFJ5/w4YNSsdIdA8l0AbK2NgYoaGh+PXXX5GVlYXS0lK4urpi4sSJ6Nq1q1Jz04dJ/VJTU7FhwwY4OjoiLy8Pc+fOxYIFC9RaSUFXzgi/fv2a+TWXy4W5uTklz4QQlcjMzMSnn37KjI2MjODv7w8AKC0txZdffslWaESHUQJt4Lp06YIuXbqwHYZBKCsrg6OjI4Cqw5SmpqZqL0Om6nqn6iIWi7F161ZmLBKJ5MYAsGTJEk2HRQjRAyUlJXLnemom01ZWVnj+/LlS8wcEBNT5rOVyubCzs4O3tzdt39BTlEAbqBMnTrz1Ncps4yD1q1l71MTEpE4tUhMTE6Xml0ql+PHHH5k5xWKxTuw/nzBhwhvHhBCiKCMjIwgEAqZs6JgxY5h7AoFA6Rr8S5curXNNIpHgyZMnOHfuHMrLy+Xek+gHSqANVH5+fr3XORwOsrKyUFZWRgm0iolEIowfP54Zy2QyZqyKWqRA1f7zn376SW6sC/vPqx+nNkQsFmsoEkKIvunUqRN+/PHHen+mXb58GR07dlRq/jcdAO/UqRM2bNhACbQeokYqhJGVlYXY2Fi8ePEC/v7+8PHxYTskvfL06dO3vkbd5ZZ0zf3795GYmIjU1FQcPXqU7XAIITror7/+QmhoKAYNGoT+/fszh+avXbuGpKQkbNiwAU5OTmp7/88+++ydnvoS3UIr0AQ5OTk4cuQIHj58iE8//RTDhg2DsTH90VA1So7fzYsXL5CSkoLExETk5+fDzc0NwcHBbIdFCNFRTk5OWLNmDWJiYnDx4kXmiZ+LiwvWrFmj1uQ5Ly9P5R1niXagFWgDlp+fj9jYWPz222/w8/PD6NGj0ahRI7bD0lsbN27E8uXLmfGxY8cwadIkZrxkyZI6B+cMhVQqxc2bN5GYmIiMjAzY2trCy8sL33//PSIjI5Vq7EMIIdUqKipQVlYGCwsLmJmZqWTOzMzMOtckEgn++ecfJCQkYPjw4fDz81PJexHtQcuMBmrHjh24fv06hg0bhnnz5sHCwoLtkPReVlaW3PjChQtyCXRD+9INQWBgIKRSKTw9PfHNN9/Azc0NAHDp0iWWIyOE6LqaZyiMjIyY5kw1rytzgDsyMrLONWNjY9jZ2WHChAnw9fVVeG6ivSiBNlApKSkwMzNDSkoKrly5Uu9rtLFjnT6hhz//x8bGBnl5eSgqKkJxcTEqKyupBTwhRCXGjx/fYElPVRzgPnDggMK/l+guSqAN1Pr169kOweDpSo1mTQgPD8eDBw/A5/Oxb98+REZGwsPDA5WVlWyHRgjRcfv372c7BKKHKIE2UG8qu0PUQyqVyu2Vqz2u2Y3PEH3wwQf44IMPMHXqVKSnp4PP50MkEmHp0qUYPnw4lYEihCiEDnATdaBDhAZMKpUiJSWFaeVtZWWFbt26wdvbW+nC8qSuoKCgt76GHgXKEwgESEpKQlJSUr37DAkh5F2UlJTg7Nmz+P3335lDhJ07d8aoUaOoSgZRCCXQBkooFOKrr77CkydP0LNnT6Yu5q1bt2Bvb4+1a9eCx+OxHSYhkEgkWLlyJTZt2sR2KIQQHVRSUoJFixbBwsICffv2ZX7epaen4+XLl9i+fTsl0eS9UQJtoPbt24dHjx7hyy+/hLm5OXNdKBRi8+bNaNmyJWbOnMlihIRUEYvFmDBhgtJdGgkhhmn//v0oLS3FggUL5J6uvn79GuHh4bCwsMCMGTNYjJDoItoDbaDS0tKwceNGueQZAHg8HmbPno3ly5dTAq1i4eHhb7zP4XAwb948DUVDCCGGISsrCytWrKizNdHIyAgTJ07EN998w1JkRJdRAm2gysrKGjxY0bx5c7x69UrDEem/hpqBiMVipKSkoKysjBJoQghRMTW19vEAABb7SURBVIFAAEdHx3rvOTo6QiAQaDgiog8ogTZQdnZ2yM7ORseOHevcy87Ohq2tLQtR6beAgAC58evXr5GYmIgTJ07A0dERU6ZMYSky9l28eLHBe1KpVIOREEL0zdsqHNFOVqIISqAN1NChQ7Fjxw4sXboUzs7OzPV79+5h586d+OSTT1iMTv9dvXoVR48ehbGxMWbNmoW+ffuyHRKrUlNT33i/U6dOGoqEEKJvKisrceTIkQbv1+xISMi7okOEBuzAgQO4cOECbG1tmVPJxcXF+OSTT+hAhZrcunULsbGxKCsrw6RJk+Dt7U0NVQghRI3edv4EAObPn6+BSIg+oQTawD1+/Bi3b99m6kB36dKlwb1iRDmhoaHIy8vDuHHjMGzYMBgb130AZGJiwkJkhBBCCHkflEAbqLlz52LXrl1sh2FQ/Pz8mF/XXnWWyWTgcDhUqo0QQtSkvLwcd+/eRWlpKZo0aQIXF5c6lagIeVe0B9pAPX36lO0QDM7+/fvZDoEQQgzShQsXEBsbi4qKClhZWaG0tBRmZmb4/PPPMWrUKLbDIzqIEmhCNKShsoGEEELUJyUlBSdPnkRwcDD69+8PLpcLqVSKq1ev4sCBA7CysoK3tzfbYRIdQwm0gaqoqMCKFSve+Jr169drKBpCCCFEPc6ePYt58+ahd+/ezDUulwsvLy/weDzExcVRAk3eGyXQBsrY2Bienp5sh0EIIYSoVWFhIbp3717vve7duyMsLEzDERF9QAm0geJyuRg2bBjbYRBCCCFqZWZmhpcvX8La2rrOvdLSUpiamrIQFdF1RmwHQAip6ra3Y8cOtsMghBC907NnT8TExNR77/Dhw+jZs6dmAyJ6gcrYGahvv/0Wc+bMYTsM8v+JxWJMmDCBytgRQoiKCQQChISEwMzMDB4eHkzjsLS0NIhEImzevBk2NjZsh0l0DCXQBiozM7PONS6Xi+bNm6NFixYsRGTYKIEmhBD1efXqFc6dO4esrCymcVjXrl3h5+cHCwsLtsMjOogSaAMVFBRU55pUKkVJSQmcnZ0REhKCZs2asRCZYaIEmhBC2CEWi6kLLHlvdIjQQB04cKDe6xUVFTh06BCio6OxdOlSDUel3+pb9a8mkUg0GAkhhBCxWIwffvgB8fHxDe6RJqQhlEATOWZmZpgyZQrtj1aDyMjIN963tbXVUCSEEGI4CgsLERERgdzcXLRo0QKLFy9GYWEh9u7dC2tra0yfPp3tEIkOoi0cpA6JRIKAgAAcPXqU7VAIIYQQpaxZswZWVlbw9PTElStXkJOTAw6Hg6CgIKrAQRRGK9CkjuTkZLRr147tMAxKTk4O+Hw+Zs+ezXYohBCiV3JychAdHQ1TU1O4ubnB398fUVFRdGCeKIUSaAMVFhYGDocjd00ikeDp06f4559/sHr1anYCMyAvXrxAcnIy+Hw+8vPz0atXL7ZDIoQQvSMWi5lmKTweDzwej5JnojRKoA1Uq1at6lzjcrno27cvevbsCSsrKxai0n9SqRQ3btwAn89HRkYGbGxsUFJSgm3btsHJyYnt8AghRO9IpVJcvHixwTEA6sxL3hvtgSb1EggE9bY9JYo7ePAgrly5AolEgn79+sHb2xudOnVCYGAgwsPD0bRpU7ZDJIQQvRMaGlrniWtt69ev11A0RF/QCjRhSCQS/PLLL0hMTERWVhbi4+PZDkmvnD9/HpaWlpgxYwYGDBhAdUcJIUQDNmzYwHYIRA9RAk3w4MED8Pl8XLlyBWVlZRg4cCDCwsLYDkvv7Ny5E4mJiTh48CD27dvHrEITQghRn+rOgw3Jzc1F+/btNRgR0Qe0hcNAlZaW4sqVK0hMTMTDhw/RqVMneHl54ciRI9i9ezdtJ1Cj6pV+Pp+PW7duQSaTYcyYMRg5ciRsbGzYDo8QQvTKxIkTcfLkSWY8a9YsREVFNXifkHdBK9AGKjAwEDweD2PGjIGXlxfTxINqP6ufsbEx+vXrh379+kEgECApKQlJSUk4d+4cbZshhBA1Ky0tZTsEogcogTZQPXv2REZGBpKSkiCTyeDl5QU7Ozu2wzI41tbWGDduHMaNG4e7d++yHQ4hhOi9tx0oJORdUAJtoFasWCFXhzguLg5ubm4QiUSorKxkOzyDIhaLcfHiRZw+fRoxMTFsh0MIIYSQt6A90ATA/3XCS01NhUQiweDBgzFr1iy2w9IrhYWFiIiIQG5uLhwcHLBo0SIUFhZi7969sLa2xtixY+Hp6cl2mIQQolfGjh2Lfv36MeNr167VGdP2OfK+KIEmcsRiMa5fvw4+n0/dCFVszZo1sLKygqenJ65cuYKcnBxwOBwEBQWhZ8+ebIdHCCF66fjx4299jb+/vwYiIfqEEmhCNGTy5MmIjo6GqakphEIh/P39ERUVRS1lCSGERWKxmOryk/dmxHYAhBgKsVgMU1NTAACPxwOPx6PkmRBCWHL//n3s3bsXgYGBbIdCdBAdIiREQ6RSKS5evNjgGACGDRum6bAIIcRgvHjxAikpKUhMTER+fj7c3NwQHBzMdlhEB1ECTYiGdOjQAampqczY2dlZbgxQAk0IIaomlUpx8+ZNJCYmIiMjA7a2tvDy8sLz58+xbNkyahxGFEJ7oAkhhBCit7744gtIpVJ4enrCy8sLbm5uAICAgACEh4dTAk0UQnugCdGQiRMnsh0CIYQYHBsbG5SXl6OoqAjFxcXU64CoBG3hIIQQQojeCg8Px4MHD8Dn87Fv3z5ERkbCw8ODEmmiFNrCQYiGTJw4ESdPnmQ7DEIIMVgSiQTp6eng8/nIzMyEra0thg8fjjFjxrAdGtExlEAToiGjR4+GnZ3dG1+zf/9+DUVDCCGGTSAQICkpCUlJSYiMjGQ7HKJjKIEmREPGjx+P0NDQN76mR48eGoqGEEIIIYqiPdCEaAiXy6UEmRBCCNEDVIWDEEIIIYSQ90AJNCEaMn78eLZDIIQQQogK0B5oQjTkzp07b31N586dNRAJIYQQABCLxbh48SJGjhzJdihEx9AeaEI0JCwsrN7rHA4H5eXlqKiowNmzZzUcFSGE6L/bt2/jwYMHcHBwgLu7O6RSKX744QecOnUKFhYWlECT90Yr0ISw6OXLlzh16hQuXryIfv36Yf78+WyHRAgheuXMmTM4fvw4Wrdujfz8fIwYMQK//fYbgKqtdX379mU5QqKLKIEmhAXVq81nz55Fly5dMHnyZLRu3ZrtsAghRO/MnDkTS5YsQYcOHfDHH38gNDQUkyZNwqeffsp2aESH0RYOQjRIIpHg+++/x3fffYe2bdtizZo16NChA9thEUKI3iotLWU+Z93c3GBsbIxx48axHBXRdZRAE6IhiYmJOHHiBJo2bYolS5aga9eubIdECCF6r/aDdlNTU3C5XJaiIfqCtnAQoiF+fn6wtLRE165dweFw6n3NkiVLNBwVIYTot9GjR6Np06bMuKSkRG4MADExMRqOiug6WoEmREMmTpzYYOJMCCFEPb755hu2QyB6iFagCSGEEEIIeQ/UiZAQDUlLS5MbFxQUyI0vXLigyXAIIcQgHDx4UG586dIlufGWLVs0GQ7RE5RAE6IhO3fulBsvW7ZMbhwbG6vJcAghxCDUTphr73fOyMjQYDREX1ACTQhLaPcUIYRoHn32ElWgBJoQltCBQkII0Tz67CWqQFU4CCGEEKLXCgsLmZVnmUwmNyZEEVSFgxANeVMtUplMhhcvXuDs2bNshUcIIXrJz88PHA6nwYSZw+HQZy95b5RAE6Ihd+7ceetrOnfurIFICCGEEKIMSqAJIYQQotcKCgqQl5eH9u3bw8HBge1wiB6gQ4SEaMiNGzewe/fueu9FRkZSKSVCCFGDpKQkzJ07F3v27EFwcDCuXbvGdkhED1ACTYiGnD17FgMHDqz3npeXF86cOaPhiAghRP+dPn0aS5cuRWxsLBYtWoRTp06xHRLRA5RAE6Ih+fn5+Oijj+q95+bmhry8PA1HRAgh+u/Zs2fo168fAKBfv354+vQpyxERfUAJNCEaUlFRgYqKinrvVVZWNniPEEKI4moe9TIyMsLr169ZjIboC6oDTYiGtGnTBrdu3WJWQmq6desW2rRpw0JUhBCi3yoqKrBs2TJmXF5eLjcGgC1btmg6LKLjKIEmREP8/PywZ88eyGQyuLu7g8vlQiqV4vr164iKisKsWbPYDpEQQvTO3Llz5cZDhw5lKRKiT6iMHSEadP78eRw9ehRisRhWVlYoLS2FiYkJJk+ejJEjR7IdHiGEEELeASXQhGiYUCjE3bt38fLlS1haWsLV1RU8Ho/tsAghRC8VFhZi586dePjwIT788EPMnz8fzZs3ZzssouMogSaEEEKI3lq9ejUsLCzg7e2N5ORkSCQSLF++nO2wiI6jPdCEEEII0Vv379/HwYMHYWZmBjc3NwQHB7MdEtEDVMaOEEIIIXpLLBbDzMwMAMDj8VBZWclyREQf0Ao0IYQQQvSWVCrFxYsXmbFYLJYbA8CwYcM0HRbRcbQHmhBCCCF6KzQ0FBwO542vWb9+vYaiIfqCEmhCCCGEEELeA+2BJoQQQggh5D3QHmhCCCGE6K2AgIA6Wzi4XC7s7Ozg7e1N+5+JQmgLByGEEEL01p07d+pck0gkePLkCc6dO4chQ4ZgzJgxLERGdBkl0IQQQggxSPn5+diwYQP27NnDdihEx9AeaEIIIYQYpNatW0MgELAdBtFBlEATQgghxCDl5eXB2tqa7TCIDqJDhIQQQgjRW5mZmXWuSSQS/PPPP0hISMDw4cNZiIroOtoDTQghhBC9FRQUVOeasbEx7Ozs4OXlBV9fXxaiIrqOEmhCCCGEEELeA+2BJoQQQggh5D1QAk0IIYQQQsh7oASaEEIIIYSQ90AJNCGEEEIIIe+BEmhCCCGEEELeAyXQhBC9s2PHDrRp0wZcLhejR49WyZxZWVlYvXo1hEKhSuarrU+fPoiMjGTGMTExOHbsWJ3XeXt7Y8SIEWqJQVlHjx5Fx44dIZVK2Q6FEELUisrYEUL0Sk5ODlxcXBASEoKRI0fC1tYWHTp0UHremJgYTJ06FUVFRbC1tVVBpP/nzJkzmDVrFh4+fAhzc3MAVYmyhYUFLly4IPfaP/74A1wuFy4uLiqNQRWkUimcnZ2xatUqTJ06le1wCCFEbWgFmhCiV7KzsyGTyTBjxgz069dPJcmzOpSXlzO/3rlzJ/z9/Znk+U3c3Ny0MnkGAC6Xi8DAQERERLAdCiGEqBUl0IQQvREYGIiRI0cCAJycnMDhcBAZGYn//Oc/cHFxAY/HQ7t27TB79my8ePGizu8/cuQIunfvjkaNGsHW1hbDhw/Hw4cPmdVnALCzswOHw0G7du2Y3/fbb79h6NChaNy4MZo0aYLx48cjLy9Pbm4Oh4NNmzYhJCQELVq0QPPmzQEAubm5SE1Nxfjx45nXent748qVK/jf//4HDocDDoeD1atXM/dqbuEoKCjAp59+Cnt7ezRq1Ajt27fHwoUL3/l+YGAgOnfuLBdrSUkJOBwOYmJi5K7HxMSgS5cuaNSoEVq2bIkVK1bU2a4xYcIEZGVl4fbt2/X+PyKEEH1gzHYAhBCiKqtWrYKbmxtCQkIQHx8PBwcHODk54auvvsL69ethZ2eH/Px8rF+/HqNHj0ZycjLze8PCwrBs2TJMnz4d69evh1gsRlJSEoqKivDJJ59g5cqV+Oabb3Dx4kU0adIEZmZmAID8/HwMHDgQTk5OiIuLg0gkwooVK+Dl5YVff/0VlpaWzHuEh4fD3d0dBw8ehEQiAQDw+XwYGxujT58+zOu+/fZbTJ48GTweD1u3bgUAtGrVqt5/5ylTpqCwsBARERGwt7dHXl4ebt68+c7339X27duxbNkyLFy4ENu2bcOff/7JJNCbNm1iXtexY0dYW1vj8uXL6Nq163u/DyGE6AQZIYTokTNnzsgAyHJzc+u9LxaLZVevXpUBkGVnZ8tkMpmspKRExuPxZDNnzmxw3kOHDskAyIqKiuSuL1y4UNa4cWPZs2fPmGt//vmnjMPhyCIiIphrAGRubm6y169fy/3+mTNnyjp16lTn/by8vGSffPLJW683btxY7n1qe9v9gICAOu8vEAhkAGSHDh2SyWQyWWlpqczCwkK2fPlyudft2bNHZm5uLisuLq4T4/jx4xt8T0II0XW0hYMQovdiY2PRvXt3WFhYwMTEBAMGDAAA3Lt3DwCQlpYGoVCI6dOnv/fcqampGDx4MGxsbJhrrq6u6Nq1K65evSr32n/961/gcDhy1x4/fgw7O7v3ft9qPXr0wNatW7Fnzx7cv3//ve+/i2vXrqGsrAwTJkyARCJh/vH19UV5eTnu3Lkj93pbW1s8fvxYofcihBBdQAk0IUSvnTlzBlOmTEGfPn3w3//+F9evX8eZM2cAACKRCADw7NkzAICjo+N7zy8QCGBvb1/nur29PZ4/f17nWm0ikYjZDqKIkydPwsfHBytWrICzszNcXV0RHx//zvffRXFxMYCqZNzExIT5x9nZGUDVNpaazMzM5A5JEkKIvqEEmhCi17777jt069YNUVFRGD58OPr27Qtra2u51zRr1gwAUFhY+N7z29jY4OnTp3Wu//PPP3Kr0gDqrD5X//6SkpL3ft9qDg4OiI6ORnFxMX755Re4uLhg4sSJePDgwTvdb9SoESorK+XmFAgEdWIEgPj4eNy4caPOP//617/kXl9SUsL8NyWEEH1ECTQhRK+Vl5fD1NRU7trRo0flxh4eHuDxeDh06FCD81TPUb1qXW3AgAHg8/lySWd2djZ+/fVXZqvIm7i4uCA3N7fe96v9Xm9iZGSE3r1745tvvoFEIqmzXaOh+61atUJBQQHKysqY1166dEnu91b/9ykoKECvXr3q/FM7Wf7777+1ttQeIYSoAlXhIITotY8//hjBwcFYt24dPDw88P3334PP58u9pkmTJvj6668REhKC169fw8/PD69fv0ZycjL8/f3Rq1cvdOzYEQAQGRmJ0aNHg8fj4aOPPsLChQtx6NAhDBkyBCtWrIBIJMLKlSvRpk0bBAYGvjW+/v37Y+3atSgoKJCrtNGxY0ccPnwYCQkJcHBwgKOjY50tJi9evMDQoUPxxRdfwMXFBZWVldi1axeaNm2KHj16vPU+AIwdOxZfffUVpk2bhhkzZuD333/HgQMH5N6nadOmWLt2LZYtW4aCggJ4e3uDy+XiwYMHOHfuHE6fPg0ejwcAePXqFe7evYuvv/76vf9fEUKIzmD7FCMhhKhS7SocEolEtnjxYpmdnZ3M0tJSNn78eNn169dlAGTfffed3O+Njo6WffTRRzJTU1NZs2bNZCNGjJA9fPiQub969WpZq1atZEZGRrK2bdsy12/fvi37+OOPZTweT2ZpaSkbO3as7O+//5abG4AsLCysTrwVFRWyZs2ayfbt2yd3vaCgQDZ8+HBZ06ZNZQBkX3/9tUwmk6/CIRKJZEFBQTIXFxeZubm5zMbGRjZkyBDZL7/88k73qx05ckT24YcfyszNzWUff/yxLCsrS64KR7Xjx4/LevfuLTM3N5dZWVnJunfvLlu1apVMLBYzrzl9+rSscePGstLS0gb+DxFCiO6jVt6EEMKyxYsXIzMzE0lJSWyHorQJEybA0tIS0dHRbIdCCCFqQwk0IYSw7PHjx/jwww9x7do1nW4+kpubi06dOuG33/5f+3ZsAkAIBFF02zC2ayN7uhqMbMIahuMQjvcq2PAzsE/13m+fA/AZT4QAl7XWas5Ze+/bp7yy1qoxhngGfs8CDQAAAQs0AAAEBDQAAAQENAAABAQ0AAAEBDQAAAQENAAABAQ0AAAEBDQAAAQO+AQimJ/hLPgAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6ec4bdd3c8>"
+       "<matplotlib.figure.Figure at 0x7fa8c2dcf630>"
       ]
      },
      "metadata": {},
@@ -2355,10 +3554,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (8757102681387)>"
+       "<ggplot: (-9223363264162180327)>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2372,10 +3571,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 63,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Write out pharm_full_df to file to plot in ggplot2\n",
@@ -2401,7 +3598,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.5"
   }
  },
  "nbformat": 4,