diff --git a/analysis_results.ipynb b/analysis_results.ipynb
index a62ffb8bd1b4ac1b05dc82d458082e7f2c0d48e2..7412cc0802509c6430a0072bdecbba2312a7b216 100644
--- a/analysis_results.ipynb
+++ b/analysis_results.ipynb
@@ -9,18 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.1\n",
-      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,14 +36,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The crawler found 752 Nextflow workflows with at least Nextflow file at the root.\n"
+      "The crawler found 3010 Nextflow workflows with at least Nextflow file at the root.\n"
      ]
     }
    ],
@@ -60,34 +51,9 @@
     "print(f\"The crawler found {len(dict)} Nextflow workflows with at least Nextflow file at the root.\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the time of writing there are 52 Nextflow workflows integrated on WorkflowHub."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hence, at most 6.9% of Nextflow workflows found on Github are integrated into WorkflowHub\n"
-     ]
-    }
-   ],
-   "source": [
-    "nb_wfhub = 52\n",
-    "print(f\"Hence, at most {(nb_wfhub)/len(dict)*100:.1f}% of Nextflow workflows found on Github are integrated into WorkflowHub\")"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,12 +66,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Y5tmzZ4xIJGK2bt1a5vYYhmFu3rzJiEQi5sCBA3LL5s2bp/C5Hj9+PNOzZ0/28bFjxxiRSMScOHFC4bZ37dpVZgzr1q1j2rRpw7x584Yt+/XXXxmRSMT89ddfDMMwTHZ2NuPm5sZ88cUXMusWFhYyffr0YQYOHFjq9sViMZOfn898/PHHzPLly9ly6esxdOhQuXUUvSeUiVNZ5cVU3nskPT2dcXR0ZCZPnixT79q1a4xIJGKGDRtW5v6vXr3KiEQiuc9bSSXf61KlfbaWLl0qUy8oKIgRiUTM9u3bZcr79u3L9O/fn32s6PlmmA/v5eLvz3Xr1jEikajUmCUSCVNQUMD89ddfjEgkYu7du8cuW7JkSanrlmzTqVOn5I4pYrGY8fLyYqZOncqWLVq0iHF2dmaSkpJkthcVFcWIRCLm0aNHpcYq3a+jo6PM+rm5uYybmxuzaNGictut6LjWtWtXpm3btszLly/Zsnv37jEikYjp1KkTk52dzZb/8ccfjEgkYk6dOsWWST/7P/74o8y+Nm7cyIhEIubvv/9mGIZh4uPjGZFIxGzbtk2mXnJyMtO2bVvm+++/Z8uGDRvGiEQi5uLFi2U+HwxT9Ln28vJi/Pz8mMLCQrY8KyuL8fT0ZAYPHsyWSd87x48fL3e7Jet+9tlnjLe3N5Obm8u229nZma3/4sULxt7eXu59nZWVxXTq1ImZPn06WyaRSJjx48cz7du3Zx4+fMj06tWL+eSTT5j379/LrOvs7CzzPpMq7bhd8vVNT09n2rZtK3eMePHiBePg4MDMmjWLLfvss8+YESNGsI979OjBrFixgmndujV7zDpy5AgjEomYJ0+elPnceXt7M/Pnz2cYhmHy8vIYZ2dnZuXKlYxIJGLfuxs3bmTatGnDtrki39cikYhp164dk5aWVurz8uzZM6ZXr15Mr169mOfPn9fK50/6uZd+71dWYWEhU1BQwBw6dIixs7OTeV769+8v8xlgGIbZtWsXIxKJmAcPHijcXlnHRUXHlmHDhpX5PSKNb/369Yybmxv7+4Nhio4/tra2MvtgGIYZPXo04+rqyh5/FB3fx4wZw3h7ezOZmZky64aEhDCOjo5y74/izp07x4hEImbLli0y5dLv6b1798rEqMxxV5GxY8cyIpGIycvLkymXPsfSP7FYzC5T1NatW7cq/E3KMB+Ol2X9Ff9uruj3gzKvjzIq1WVoxYoV2L9/v8zfvn372OXGxsbo1q0bDh06xGY/6enpOHXqFPr27QstraILE5cvX4aenh4++eQTme1LryCU7D5SEy5fvgxPT080btxYprx///7IycmR62ZSMquUXuZ58eJFqfvIzs7GjRs30LNnT+jr67PlPB4Pffr0wcuXL5XudlQRHA5HYbzFYz1z5gyEQiG6du0KsVjM/tnZ2cHc3Bx//fVXmfuQnlkp/n7YtWsXRCIRm6XHx8cjLS0N/fv3l9mHRCJB586dcevWLfYyqFgsxqZNm9CrVy84ODjA3t4eDg4O+Oeff2QuCUt9/PHHSj0XysRZmorGVN57JD4+Hnl5eexZailXV1dYWVmV25Zz584BAIYOHVpu3YqQXmWSkl5O9fHxkSuvypWIkp49e4bZs2ejU6dOsLOzQ5s2bTBs2DAAqPTnwtvbG+bm5jLdlaRXLAMCAtiys2fPwt3dHQ0bNpR5b0q7MJT3/gcAOzs79qwbAGhra6N58+ZlHhOU2Wbxq1bSq1Du7u4y/falr5GifZV8f0nPDksva585cwYcDgd9+vSRabuZmRlat24t13ZDQ0Ol+nc/efIEr169Qt++fWWuIujr6+Pjjz/GjRs35LouVcacOXPw8uVL/PjjjwqXX7hwAWKxGH379pVpn7a2Njp06CDTPg6HgxUrVkBfXx8BAQF4/vw51qxZAz09vSrHWVx8fDxyc3PRv39/mfLGjRvDw8ND5ky0p6cn4uLikJubi6SkJPz777/o3bs37Ozs8OeffwIoOttpaWlZ7o3Ynp6e7PdrfHw8cnJyMHr0aBgbG8tsy9nZmW1zRb+vPTw8YGhoqHD/d+/exeDBg2Fqaoo9e/YodZxTRNXPn/ReyRkzZiAmJqbMrqSK2jhx4kS4u7uzx7F58+ahsLAQ//zzD1tvwIABiI+Plzm2HTx4EI6OjhCJRGxZdR8XL126hFGjRqFdu3bs9tatW4e0tDSZ7mRA0X2jrVu3linz8/NDVlYW7ty5o3D7eXl5uHz5Mnr06AEdHR2542leXh6uX79eanzS1076/pL69NNPoaenJ/d+q+7j7o8//og2bdqwf3379q3UdqSaNWsm97t5//79pY6AWRGVeX0UqVSXIWtr63JvKg4ICMCJEyfw559/onPnzjh27Bjy8/NlXty0tDSYmZnJ3fhpamoKLS0tpKWlVSa8KklLS2Mv9xQn7YpQMiYjIyOZx9JLWLm5uaXuIyMjAwzDVGg/1UFXVxfa2toyZQKBQOZmr7dv3yIjIwMODg4Kt5GamlrmPszMzNCrVy/s3bsXEyZMwKNHj/D3338jJCSErSPtwjNt2rRSt5Oeng49PT1899132LVrF8aPH48OHTrA0NAQHA4HCxcuVHiTmqLntLJxlqaiMZX3HpG+1sW7YxSPszzv3r0Dj8dTuu3KKvllLr10rai8vHt+lPX+/Xt8/vnn0NbWZkcZ0dHRwcuXLzFlypQyP1dl0dLSQp8+ffDTTz8hIyMDQqEQBw8ehLm5Oby8vNh6b9++xZkzZ9CmTRuF2ynv/Q/Iv96A/Oesoko+59L3UGmvUcl9aWlpwdjYWKZM+n6Rvv/evn0LhmHQsWNHhTE0bdpU4frlkT5npR3vJBIJMjIyqnxDsqurK7p3744tW7Zg8ODBcsulx53SuhSV7PIkPbG1a9cu9OjRo9LD45ZF+tyX9twU78Lj6emJ9evX49q1a3jx4gWMjY1hb2/P/rifMWMGe0KrPJ6enjh06BD++ecfXLx4Efb29jA1NYWHhwcuXboEf39/xMfHY+LEiTKxVuT7uqz3x59//onU1FQEBwdXaUQWVT9/HTp0QEREBHbu3Il58+YhPz8frVq1wsSJE0sdRhIoSsiHDh2KFi1a4KuvvoKVlRW0tbVx8+ZNhISEyBzH/P39sWLFChw6dAizZ89GQkICbt26ha+//pqtU93HxZs3b2Ls2LFwc3PD0qVLYWFhAT6fj5MnT2LTpk1y2yvru6m03yppaWkQi8XYuXMndu7cqbBOWcfTtLQ0aGlpwcTERKacw+HAzMys3N9igHLHXWkSkZSUJNM919/fH+3atQNQ1PW8qt9x2traCn83l3dvpjIq8/ooUqmEQBleXl5o2LAhDh48iM6dO+PgwYNwcnKS6VNlZGSEGzdugGEYmYPM27dvIRaL5b7AipP+sC35IinzhV0WIyMjvH79Wq781atXAFBmTMoSCoXgcrkq309lGBsbw8jICFu3blW4vPgVjdKMGDECv/zyC06dOoXz589DKBTKnJ2Utm3RokVwcnJSuA1pf9MjR46gX79+mDVrlszy1NRUhV8kFRlVqLw4S1PRmMojPZCVvNdBWlbe2TMTExMUFhbi9evXCvvQSwkEAoUHtap+Zkqqymfz8uXLePXqFXbu3CkzykxmZmaV4woICEBUVBR+/fVX9OrVC6dPn8bIkSNl7vUxNjaGra0tO7xcSWU9vxVR/Dkq3g+2ul8LKbFYjNTUVJnjivT4I33/GRsbg8PhYNeuXQr7bJcsU/azJt1nacc7LpdbbcP0zZo1C/7+/jL3RZSMY926dTJnEkvz559/Ys+ePWjbti3++OMPnDhxAj179qyWOKWkz31pz03x18vJyQl6enq4ePEikpKS4OnpCQ6HA09PT2zfvh03b97EixcvSk3oipP+6L148SIuXrzIruPp6Yk1a9bg6tWryM/Pl9lWRb+vy3p/jB07Fs+ePWPPmPfr16/cmBWpieeve/fu6N69O/Lz83H9+nVs3rwZs2fPhpWVVanzXJw8eRLZ2dkIDw+XOX7fv39frq6hoSF8fX1x+PBhzJgxAwcOHIC2trZMwlHdx8Vff/0VWlpa2Lx5s8xJwtLm1CjtuwlQ/EMcKPqdw+Px0LdvX4WDzABAkyZNSo3RyMgIYrEY7969k0kKGIbBmzdvqm2ky44dO2Lv3r04ffq0TH97U1NT9neIvr5+tZ30UkZFvx8q8/ooorKZiqVvBOmNRbdv35a5NA8UHXyys7Pl3oSHDx9ml5dG+iErObLL6dOn5eoKBAKlM2hPT09cvnxZ7tLgL7/8Al1d3WoZakpPTw9OTk74448/ZOKSSCQ4cuQILCwslLqRtCRlrk6Up0uXLkhLS4NEIoGjo6Pcn6L5J0pycHCAi4sLtmzZgqNHj6J///4yl9pdXV0hFAqRkJCgcB+Ojo5sWzgcDnvWU+rs2bMVunRb2ThLU90xOTs7Q1tbG0ePHpUpj4uLU6orjrQ7y549e8qsZ2VlJfd5uXTpktwoFVVVkc9mSdIfESV/fP78889ydSv6fre2toaTkxMOHjyo8IolUPT+f/jwIZo1a6bwfVnWzeYVIX2OSv5AkI4cpAol31/Hjh0DAPYHRpcuXcAwDFJSUhS2vbJnyFu0aIFGjRrh2LFjMiOYZGdn4/fff4ezs3OVrw5IWVtbIyAgAD/99JNcVwEvLy9oaWnh6dOnpR53pF69eoW5c+eiQ4cO+Pnnn9GtWzcsWLBAbrSliny3KOLi4gIdHR0cOXJEpvzly5e4fPkyPDw82DI+n48OHTrg4sWLuHz5MvvDtX379uDxeFi7di37A7c8DRs2hI2NDX7//XfcuXMHnTp1AlD04+jdu3f44YcfYGBgIPOcVOX7uiQul4uQkBCMGDECwcHBlZ5DoiafP4FAADc3N8ydOxcAZEamKknRcYxhGJkuqsUNGDAAr169QmxsLI4ePYoePXrIJMkVOS4qQzoITPGrYrm5uXLPo9SjR4/kjlXHjh2Dvr5+qVdTdXV14e7ujrt378LW1lbh562sE5/S16FkTCdOnEB2dna1DUfbo0cP2NjYYPPmzQq7/CpL+tpU5UqwVEW/Hyrz+ihSqSsEjx49Uji8W7NmzWQyuYCAAGzZsgWzZ8+Gjo4OevXqJVO/X79+2LVrF+bNm4ekpCSIRCJcu3YNmzdvho+PT5lnOhwdHdGiRQt8//33KCwshFAoxMmTJ3Ht2jW5uiKRCL///jt2794NBwcHcDicUrPLyZMn48yZMxgxYgQmT54MQ0NDHD16FGfPnsXcuXPRoEEDZZ+mMs2aNQtjxozBiBEjMGbMGPD5fOzevRuPHj1CaGhopcbPb9asGXR0dHD06FFYW1tDT08PDRs2rNCPmN69e+Po0aOYMGEChg8fjrZt24LP5+Ply5e4cuUKfH190aNHj3K3M2LECMycORMcDkfu7IC+vj4WLlyI4OBgpKeno2fPnjA1NcW7d+9w//59vHv3jh3+rkuXLuzIPba2trhz5w6ioqKqbZzlsuIsTXXHZGhoiDFjxmDjxo1YsGABPvnkE7x8+RLh4eFKdcto3749+vbti40bN+Lt27fo0qULBAIB7t69C11dXXboxr59+2Lt2rVYu3Yt3NzckJCQgJ9++qna3tNS5ubm6NixIyIjI2FoaAhLS0tcunQJf/zxR7nruri4wNDQEF9//TWmTJkCLS0tHD16VC65AMD2r92yZQu8vb3B5XJha2tb5ogkAQEBWLx4MV69egUXFxe5BHfatGm4ePEihgwZguHDh6NFixbIz8/H8+fPce7cOSxZsqRa3ns+Pj4wMjLCggULMH36dPB4PBw6dAjJyclV3rYifD4f27dvR3Z2NhwdHdlRhry9vdG+fXsAQLt27TB48GB89dVXuH37Njp06ABdXV28fv0a165dg0gkUvozUhyXy8XcuXMxZ84cfPHFFxg8eDDy8/MRFRWFjIwMzJ49u1rbOmXKFBw9ehRXrlyRSfCbNGmCadOmYc2aNXj27Bm8vb0hFArx5s0b3Lp1C7q6upg2bRoKCwsxe/ZscDgcrF69GjweD9999x369euHmTNnYvfu3ex7TCQS4a+//sLp06dhbm4OfX19pU6aSAmFQgQFBSE0NBRffvklevfujbS0NEREREBbW1tuFBZPT092GFDp96OOjg5cXFxw4cIF2NraKhzNRxFPT0/s3LkTOjo6cHV1BVDULaxJkya4cOECunXrxt7vB1Tt+7o0wcHB0NfXx5IlS5CdnY1x48ZVaH1VP39r167Fy5cv4enpCQsLC2RkZGDHjh3g8/llzpPRsWNH8Pl8zJo1C+PGjUN+fj727NmjcBZboChZtbCwwJIlS/D69Wu5ExUVOS4qw8fHB9u3b8fs2bMxePBgpKWlISoqqtRjZ8OGDTFp0iRMmTIF5ubmOHLkCP7880/MmTOnzGR+wYIF+PzzzzF06FB89tlnsLKywvv37/H06VOcPn26zNGROnXqBC8vL6xatQpZWVlwdXXFgwcPsG7dOtjb21e5T78Uj8dDREQExo4di8DAQAwaNAhubm4QCoXIzMzEjRs3cP/+/VKHJJWSfh/9+OOP6N+/P7S0tNCiRYtKTZBZ0e+Hyr4+JVUqIShtDN5ly5bJDJ3VokULuLi4ID4+Hv7+/nI/PLS1tbFjxw6EhYVh69atSE1NRaNGjTBmzJgyh6MCil7ETZs2YenSpeyMlr1798bixYvlxhUeMWIEHj16hLCwMGRmZoJhmFI/SC1btsTPP/+M0NBQtq+ftbU1vv32W7kPaVW4ubnhhx9+QHh4OObPnw+JRILWrVtj48aN6Nq1a6W2qauri+XLl2P9+vUYO3YsCgoKMGXKlDLHpC6Jx+Nh48aN2LFjB3755RdERkaCx+PBwsICHTp0kLnJqSzdu3eHQCCAu7u7whvc+vbtC0tLS2zduhVff/013r9/DxMTE9jZ2cncILZgwQJoaWkhMjIS2dnZsLe3R3h4ONauXat0m6oSpyKqiGn69OnQ09PD7t278csvv6Bly5ZYsmQJtm3bptT63333Hezt7XHgwAEcPHgQOjo6sLGxkZnSfOzYscjKysKhQ4ewbds2tG3bFmvXrmWHTKxO33//PZYuXYpVq1ahsLAQXbt2xerVq+WuEpZkbGyMzZs3Y8WKFZg7dy50dXXZsc5L3jjo5+eHuLg47N69GxEREWAYBqdOnSrzMnTv3r2xfPlytu9tSQ0bNsT+/fuxYcMGREVFISUlBfr6+rCyskLnzp2rrWuLgYEBtmzZguXLl7MnGqQzjC5cuLBa9lEcn8/Hpk2bsGzZMmzcuBE6OjoIDAzEl19+KVMvJCQETk5O2Lt3L/bs2QOJRIKGDRvC1dWVHa6xMvz9/aGrq4vIyEjMnDkTPB4PTk5O2LFjB/tjtLo0atQII0eOVNht6IsvvoC1tTV27NiBX3/9Ffn5+TA3N4eDgwM70MC6devw999/Y9u2bWxCbmhoiNDQUAwbNgwrV67EggULABQdC5YsWYJZs2YhJycHbm5upfaXLs0XX3wBExMT7Ny5EzExMdDR0YGbmxtmzZold0ySnhVt3ry5TFeUjh074sqVKxX6US5NCNq1ayfTbaRjx47Yt2+f3Laq8n1dlqlTp0JPTw/ff/89srOzy7y3TBFVPn9OTk64ffs2Vq1ahXfv3kEoFMLBwQE//PADWrVqVWpM1tbWCA8Px5o1azB16lQYGRnBz88Po0aNkhu6HChKmvv164dNmzahcePGcme/K3JcVIanpyeWL1+OLVu2YOLEiWjUqBEGDRoEExMT9r1dnJ2dHQYMGIDw8HD8888/aNiwIebPn49Ro0aVuR8bGxscPHgQGzZswJo1a/Du3Ts0aNAAH330kdzgFCVxOBxs2LAB4eHhOHjwIDZt2gQjIyP07dsXs2bNKvPET0U1b94cv/zyC3bt2oU//vgD0dHRyM3NhaGhIVq3bo0ZM2aU+/vP3d0dX3zxBQ4dOoTo6GhIJBLs2LED7u7uFY6not8PlX19SuIwTCmzUBBSBadPn8akSZMQGRlZ7gdfnTQlTkIIIYQQVaGEgFSrhIQEJCUlYfny5dDV1cWhQ4cq1f1J1TQlTkIIIYQQVVPZKEOkflqyZAni4uJgb2+P7777rtb+yNaUOAkhhBBCVI2uEBBCCCGEEFKPqWzYUUIIIYQQQkjtRwkBIYQQQggh9RglBBrk5MmTCAwMhKurKzp27IgpU6bg8ePHcvViY2PRr18/ODo6okePHti1a5caoiWEEEIIIZqAEgINcfHiRUyZMgUtWrRAeHg4Fi1ahCdPnmD06NHIyspi68XHxyMoKAj29vbYsmUL+vfvj2XLliE6OlqN0RNCCCGEkNqKRhnSEL/++issLS2xYsUKdkQcKysrBAYG4tq1a+wY+hEREbC3t8fy5csBAB4eHkhOTsbatWsREBAgM1V5RdC954QQQojmoVH0iDIoIdAQYrEY+vr6Mh/skjM/5+fn4/Lly5gzZ45Mub+/P/bt24e7d+/CwcGh0jG8eZNVfqUK0NLiwthYH6mp7yEWS6p127UBtU+z1fX2AXW/jdQ+zVbX2weovo1mZgbVvk1SN1FCoCEGDhyIUaNGYefOnejbty8yMjKwYsUKWFtbs9OcP336FAUFBWjZsqXMujY2NgCAxMTEUhMCX1/fUvd94sQJ8Hg8aGlVbw8zHo8r829dQ+3TbHW9fUDdbyO1T7PV9fYB9aONRDNQQqAhOnTogPXr12P27NlYtmwZgKIf+tu2bYNAIAAApKenAwCEQqHMutLH0uWVZWysX6X1SyMU6qpku7UFtU+z1fX2AXW/jdQ+zVbX2wfUjzaS2o0SAg0RFxeHuXPnIiAgAN26dUNWVhY2bdqE8ePHY8+ePTAw+HBZsLT+gmX1Izx16lSZ+2cYBqmp7ysXfCl4PC6EQl1kZOSgsLDuXQ6m9mm2ut4+oO63kdqn2ep6+wDVt9HISI/uISBKoYRAQyxbtgweHh5YsGABW9auXTt4e3sjOjoao0ePhqGhIQD5KwEZGRkA5K8cVJSq+nAWFkrqbP9QgNqn6ep6+4C630Zqn2ar6+0D6kcbSe1GCYGGSExMRLdu3WTKTExM0LBhQzx9+hQA0KxZM/D5fDx+/Bje3t5svYSEBACAtbW1yuOUSApRWFioZF0OcnN5yM/PQ2Fh3RvFiNqn2VTRPh6PBy6XVy3bIoQQQqoLJQQawtLSEnfu3JEpe/36NV69egUrKysAgEAggIeHB44fP45Ro0ax9Y4dOwZzc3PY29urLD6GYZCR8Q45Oe8BKP/j6c0bLiSSuntWhNqn2aq/fRzo6upDKDShy/iEEEJqDUoINMTQoUOxdOlShISEwNfXFxkZGdi8eTP09PTQp08ftt7kyZMxbNgwLFy4EP7+/oiLi0N0dDRCQkIqPQeBMnJy3iMnJwsGBkbQ1tYBoNyPHR6PUyfPLktR+zRb9baPQV5eLrKy0sDna0NPj4YDJIQQUjtQQqAhhg4dCj6fj927d+PQoUPQ09ODo6MjVqxYgYYNG7L1XFxcsGHDBoSGhuLw4cOwsLDAwoULERgYqLLYGIZBVlYadHT0YWBgWKF1tbS4dbrfJLVPs1V3+/h8bYjFBcjKSoOurj5dJSCEEFIrUEKgITgcDgYPHozBgweXW9fHx4edubgmSCQSSCSF0NHRq7F9EqKpdHT0kJv7HhKJBDwe3U9ACKm9zp8/j82bNyMhIQFZWVlo1KgRunfvjilTpshMjhobG4uwsDAkJibCwsICo0aNwtChQ+W2FxUVhV27duH169cQiUT48ssv4e7uLlMnKysL33//PU6cOIH8/Hy4u7tj0aJFbPdooho0EwapMomk6CZiulmSkPJJPyfSzw0hhNRW6enpcHFxwdKlSxEVFYXRo0fj8OHDmD59OlsnPj4eQUFBsLe3x5YtW9C/f38sW7YM0dHRMtuKiopCWFgYhg4disjISHz00UcYP348Hjx4IFNv9uzZOH36NBYtWoSwsDC8evUKo0ePRm5ubo20ub6iKwSk2lD3B0LKR58TQoim8PPzg5+fH/vY3d0dAoEAixYtQkpKCho1aoSIiAjY29tj+fLlAAAPDw8kJydj7dq1CAgIAJfLRX5+PjZu3IgRI0Zg7NixAAA3Nzf4+/tj06ZNCAsLAwDcuHEDZ8+eRWRkJNvTQSQSoUePHjh06BA+++yzGn4G6g+6QkAIIYQQoi53/oRe5BTw755XdyRKMTIyAgCIxWLk5+fj8uXL6N27t0wdf39/vH79Gnfv3gVQNLlqZmamTHLB4/HQq1cvxMbGgmGKBm+IjY2FUCiUGTrd0tISrq6uiI2NVXHL6jdKCAgpJipqM7y82mPy5PEKl/Xo0Vkl+923bzcuXbogV15QUIDly5fAz687vLzaY9++3YiJOQovr/ZIS0tTSSy1SXLyC3h5tceZMyfLrPfgwX1MmDAKvr6d4OXVHpmZmZgyZQK+/HJGzQRKSC3E5XKgpcWV++Ny6SpVbcBNfQm9TZOBmV7QPvUDDKKmqTukUhUWFiIvLw937txBREQEunbtCisrKzx9+hQFBQVo2bKlTH0bGxsARXMoFf+3ZD1ra2u8f/8eKSkpbL0WLVrIXUm1sbFht0FUg7oMEaLAjRvxuHbtKtq161Aj+9u3bw86dvSCp6eXTPmvvx7BiRMxWLDgf7C0bILGjRvjypVLNRKTJgkNXQGJRIKVK9dCINCGnh7d4E7qNy6XAyNjPfAUDDddKJEgLTUbEkndHTK4VsvLhsFPX0Endic4hQVscYFDV5Xs7sWLFxg+fHipy0+dOlXuNrp27cr+aO/cuTNCQ0MBFN1jAABCoVCmvvSxdHlGRgYEAgF0dHRk6hkaFo1MmJaWBgsLC2RkZMjcrFx8e9JtEdWghICQEnR1ddGihTW2b99SYwlBaf799x+Ymprh448/VWsc6pCXl6d03X//fYKBA4fA1bW9CiMiRHNwuRzwuFwcSDyANzlv2HIzXTMEWAeAy+VQQqAOEgkabJkMnUsHZIpz+8xEVuBiNQVVvsjISGRnZyMhIQEbNmzAxIkTsX37dnZ5afdGFS9XVEfaVai8emWVk+pBXYYIUWD06HG4fj0OcXF/l1kvPz8fmzdHICDAD127emLo0IH4/fff2OX37t2Fj487DhzYy5aJxWKMHv05vvhiNAoLCzFwoD9evkzGwYPR8PJqDy+v9oiJOYqBA/0RHb0Hr16lsOXJyS8UxpGRkY7vvlsKP7/u6NatE8aPH4G//rrMLv/tt1/Rtasn8vI+jNIwatTn8PZ2Q0ZGBls2adJYfPfdUoX7KCgogK9vJxw79gtb9vPPu+Dl1R7R0T+zZUePHoavbycUFBSd+ZJIJNixYxsCA/uga1dPDBnSH/v27ZbZtrQ71t27t/HFF6PRrVtHmeesuEePHsDPrweWLfsaf//9F7y82iMrKws//LAVXl7tMWXKBIXrAUVXfiZNGotu3TqhVy9fLF26CO/evWWXT58+CUuXfvhSfvjwAby82uOrr+ayZc+fP4OXV3vcuHEdAPD4cSLmzJmGXr184evbCZ99NgC7dv1YagyE1KQ3OW+QnJ3M/hVPDkjN0z2+nk0GGL4OcntNBrbeQ87QpYCKRuqztLTEqVOnSv1TRuvWreHq6opBgwZh/fr1uHLlCv744w/2DH/Js/fS7xXplQKhUIi8vDy5Ez3SetLtCIVCme+k4vVKXoUg1YsSAkIU8PT0gp2dPbZtiyyz3uLFwfjll4MYMmQYvv8+DO7unli6dBEuXfoTAGBnZ4+RI8diw4Z1ePr0HwBFP36fP3+GRYtCwOPxsHz5SpiamqJLF19s2rQdmzZth6enF5YvX4kuXXxhamrKlpuamsnFUFhYiNmzp+HcubMYPz4I33yzAsbGppg7dzqb0Dg7t0NBQQHu3LkNoGic58ePE6ClxcfNm9cBFJ2Rv3//LpycXBS2lc/nw97eAdevx7Fl8fFxEAi0cf36Nbbs+vU4tGnjCD6fDwCIiFiLrVs34eOPP8WKFWHo3LkL1q0LxQ8/bJXZfkFBAUJCFqFnz15YtWodOnTwkIvh9u2bmDp1Irp27Y4FC/4HOzt7bNq0Hdra2vDz64tNm7Zj9uxghfHfv38PM2YEQSDQRkjIt5g6dSb+/vsvTJs2if2ScnJyUdi+Gzfi2DNZ168XldnbtwEABAfPQmZmJoKDF2HlyrX47LPhNDweIWVQdG9DfbivQevBJehHL2MfZ0zajJyRK4BmrdUYVcXZ2dmBx+Ph6dOnaNasGfh8Ph4/fixTJyEhAUDRPQLF/y15H0BiYiL09fXRqFEjtt6TJ0/Y423x7Um3QVSDugwRlRFcOQT9/d+Ak5NVah0OB2BUcNWa0TXA+4ELke/er9LbGDVqPObNm4n4+GtwcWkntzwu7m9cuHAOoaHr4eZW9OO1QwcPvH79Gtu2bYanZycAwIgRY3Dp0gUsXboYkyfPwO7dOzBz5lw0adIUACAStQafL4CJiQkcHBzZ7RsbG8PExAR8vkCmvKRLly7g3r07WLlyDXsPgrt7R4wYMRjbtkXC1bU9LCws0KiRBeLjr8HVtT1u3IiHkZEx+wPYy8sbd+7cQkFBAZydXUvdl7OzK44fP8Y+vnnzOvz9++LUqd/BMAw4HA5u3IjHp58WjSSRlpaGAwf2YsiQoRg/fhIAwM3NA+/fZ2HXrh8xaNDnbH9/sViMCRMmo1u37uz2i18R+fvvvzB//mwEBAzGxIlTAAD6+gZwcHAEl8uFuXnDMp+nHTu2wdjYBKtWrWWTlaZNm2HixDE4dep39OrlD2dnV2zbFonk5Bdo3NgS16/HoVcvfxw9eghPniSiZUsb3LgRD3v7NuDz+UhLS8OLF0mYNm02vLyKRsWgbkuElK60exvq/H0Nue8hjBgLTkHRyYKcLiOQ795fI3+ExcfHo7CwEE2aNIFAIICHhweOHz+OUaNGsXWOHTsGc3Nz2NvbAwBcXV3RoEEDxMTEsGWFhYU4fvw4fHx82O5APj4+iIiIwPnz59mRhpKTkxEXF4eFCxfWbEPrGU18LxINoXdsLbRePFTPzlMBvV/XVikh6NSpM0Si1ti+fYvChOCvvy5DKDSEq2t7iMVitrxduw4IC/sehYWF/5390sKiRSEYPXooZs2agg4d3NGv38BKx1XSjRvXoaenL3NDMpfLRdeu3bFz53YUFhaCx+PB2fnD2e8bN+Lg5OQi8wP/xo14NGzYCI0bW5a6LycnF2zfvgUvXyYjJycHGRkZGDp0JA4fPoAnTx5DX18fL18ms0nF3bu3IRaL0a3bxzLb6d69J44cOYRHjx7IXJGQJlElXbr0J06e/B0jR47ByJFjK/U83bwZD1/fj9lkAAAcHNrCwqIxbtyIR69e/mjTxgF8Ph/Xr8f9lxDE46uvFuP+/buIj49Dy5Y2uH49Hj17Ft3TYWhoCAuLxti8eT0yMzPQrl0HNGzYqFLxEVIfKLq3oa7f18DJfAvh2hHgvX0OACho6YKs0aFqjko5U6ZMgYODA2xtbaGjo4P79+9j69atsLW1RffuRSdvJk+ejGHDhmHhwoXw9/dHXFwcoqOjERISAu5/iZ9AIMCkSZMQFhYGExMT2NvbIzo6Gs+ePWNvUAYAJycndOnSBQsWLEBwcDAMDAywdu1aWFlZoX///mp5DuoLSgiIymT7zYD+/mVqu0KQ3Xt6+RXLMXr0OMyfPwc3bsTLLUtPT0NGRjq6dJHv2gIAb9++gaVlYwBAs2bN0bq1PW7ciEdAwKAqx1VcZmYGTExM5MpNTU0hFouRk5MDAwMDODu3w5o1K1FQUMD+qHV2dsW6dauRnf0e16/Hw8Wl9KsDQNEPaC0tLVy/Hofc3By0aiVCw4aNYG3dCjduxENPTw98Pp89U5+ZmcHGIhtbUdenjIwP/U51dHSgq6urcL8XLpyDtrY2evT4RPknpoTMzEyYmJjKlZuYmLJ9VrW1ddC6tT2uX4+Dvb0D0tPT4OjoDCcnF9y4EQ9v7y5ITk5iEx4Oh4PQ0HBERm5EaOgK5OTkQCRqjWnTZpV5pYWQ+k56b0Ndx0l/DeNFPmwywHB5yBoVCmgJ1ByZctq2bYuYmBhERkaCYRhYWVlh0KBBGDt2LASCoja4uLhgw4YNCA0NxeHDh2FhYYGFCxciMDBQZltjxowBwzDYuXMn3rx5A5FIhMjISNja2srUW716NVasWIElS5agoKAA7u7uCA8PlxuhiFQvSgiIyuS79yv3DL2WFhdisaRmAqqEzp27oFUrEbZt24K2bZ1kljVoIISRkTFWrVqrcF1j4w8/0o8cOYTbt2/C2roVwsPD4OraHtra1XNwEwqFePfunVz527dvoaWlxf7IdnJyQV5eHuLi/sbDh/fx5ZcL0LKlNfT1DRAXdw13797C1KmzytyXjs6HH8y5ubnsj96iqw/XoKdngNat7di2SW8Ce/fuHczNGxaL7c1/yw3ZsrJGkJg6dSaOHDmE6dODEBERWamz8A0aCJGaKv88vXv3Fi1afBgb29nZFadP/wF7ewfY2Ij+S6ZcsHLlt4iPj4OWlhYcHNqy9Zs1a45ly1ZALBbj1q0biIyMwLx5M3Ho0HEa/pSQeoz7+l8YLff/kAzwtZE5PgJia/krzrXVhAkTMGFC6QM1SPn4+LAzC5eGw+Fg3LhxGDduXJn1DAwMsHTpUixdqniAC6IadFMxIeUYNWo8rl37Czdv3pAp79DBDWlpqdDS4qN1a3u5P2nXlKSk5wgPD8Pnn4/A99+H4d27t9i4MVxmW1pafOTn51cqvrZtnZGd/R6XL19kyyQSCc6cOQUHh7bg8YpGrmjW7COYmppi587t0NPTh7W1DTgcDpycnPHzzz8hJycHzs6KbyguTnrfwc2b1+HqWvTF5uzcDtevx/3XFenDmXE7OwdoaWnh9Ok/ZLZx+vQf0NXVhUik3M10Ojo6WLVqLYyMDDFt2iQ2oaiItm2dce7cWZnuXXfu3MbLl8ky3ZacnFyQlPQcf/zxG1xdXdmy1NR3OHLkIGxt7RSeqdLS0oKLSzsMHToK79+/x5s3ryscIyGkbtBK/BvGi7qA9+ofAADD00La/04ir1P1XiEmpLpQQkBIOby9u8DauhWuXftLprxDBw906tQZs2dPxb59u3Ht2lVcuHAOO3f+wA7dKZFI8M03/4OVVROMGTMBjRpZYMaMuThwYB+uXr3Cbqt58+a4du1vXL16Gffv30V6eprS8RWNiNQGy5YtxpEjh3Dp0p/46qs5ePbsX4wZI3tmp23boh/zbds6sWfkpT/wTUxM0axZ83L35+zsgufPn+H161fsD2knJ2e8e/cOT5/+K/Pj2sjICAMHDsHPP/+EqKjN+Ouvy9iwYR1++eUghg4dWWoXIUX09Q2wevV66OjoYMaMoArP1DxixBikpr7DnDnT8Oef53H8+DEsWDAXzZu3hK/vh3sc2rZ1Ao/Hw/XrcewVEKHQEC1bWuP69TiZ9iUkPMKMGUE4evQw4uL+xrlzZ/Hjj1Fo3NgSVlZNKhQfIaRuEMT/BqMlPcHNLBrSuNCoEVJDzkLc3KmcNQlRH0oICCkHh8PB6NGKL3EuW/Y9+vULwMGD+zFnzjR8991SXL16mf0h+dNPP+LevTtYuHAJe8Xgk096w8enK779NgSZmZkAgAkTJqNhw4ZYsGAexo0bgT//PK90fDweD6tXr0OnTt7YvHk9FiyYi7dv3+D779fIjXgjjav4WXxn56Kz/G3bOiu1v7ZtncHj8dCiRUt27GhDQyO0aNESPB5PrmtVUNA0jBkzAcePH8OXX85AbOxpTJkyA6NGlX3ZWBGhUIiwsAgwDIOZM4MUjlddmtat7RAWFoG8vFwsWjQP69aFwtW1Pdau3QBtbW22np6ePlq1sgWHw5G5YvKhe9SH587U1JS96jJnzjSsXLkcjRo1QmjoevbKDCGk/uDfPAXhmmHsDMQFLV2Q+t1lFDZvW86ahKgXhyk52CshCjAMgzdvFN8cXFCQj7dvk2Fq2hh8fsVulKrt9xBUFbVPs6mifVX5vFQ3LS0ujI31kZr6vk6+jvW5fdJlm29vlrl5t7FeY3zh8IXanhNFcZUWk6a9fvx7F2D4bV82Gch1H4DMoMgybyBWdRvNzAxohl+iFLqpmBBCCCF1EpfLkZv0TCJhqn14U61/bsBwZSCbDOQ5f4zMyVsBHv3MIpqB3qmEEEIIqXNqahI0QdxxCNePASfvPQBA3KglMoO2UDJANAq9WwkhhBBS59TEJGh6h1dCP/rD8JjiRi2RFnIajL5xlbdNSE2ihIAQQgghdZaqJkHTO/Q99PcvYx8XWLdH+uyfwRjITxRJSG1HCQEhhBBCSAUIrp+QTQZauiJt8W8aMwMxISXRsKOk2tCAVYSUjz4nhGg23ssENIgYzz7O6T4OaUtOUzJANBolBKTKpOOt5+fnqTkSQmo/6eeERzccEqJxeMmPIPx+ILjZaQCAfDsvZI1cCXDp5xTRbPSNRKqMy+VBV9cAWVmpAACBQFvpcY8lEg4KC+vuGVNqn2arzvYxDIP8/DxkZaVCV9cAXPoBQYhG4WS+heG3fcF7+xwAIG7cChkzdwFcmoSQaD5KCEi1EAqLbqKSJgXK4nK5kEhq/4QzlUXt02yqaJ+urgH7eSGEVFx1zC3A48km5OWuLy6A4erBssnAnL00mhCpMyghINWCw+HA0NAUDRoYo7BQrNQ6PB4HhoZ6SE/PrpNnmal9mk0V7ePxtOjKACFVUNW5BQz4BpAwEgiFuhVaX3/PQvAf/QUAkBgYI33+L5CYNqlCSwipXSghINWKy+WCy1XuxiotLS50dHSQk1OoEdPSVxS1T7PV9fYRoomqOreADk8HXE4F1i/Ig+6pbdD7bSMAgOFwkTFjNyUDpM6hhECDDB8+HH/99ZfCZaGhoejduzcAIDY2FmFhYUhMTISFhQVGjRqFoUOH1mSohBBCiMpUdW4BZdbnpKXA6Nu+0Hp+ly3L7jMLBXadKr1fQmorSgg0yNdff42srCyZsh9//BG///47PD09AQDx8fEICgpC3759ERwcjLi4OCxbtgwCgQCBgYHqCJsQQgjRKNzXT2G0rBd4b56yZbmdBiF7QLAaoyJEdSgh0CA2NjZyZbNnz0anTp1gYlJ0k2JERATs7e2xfPlyAICHhweSk5Oxdu1aBAQEUP9lQgghpAy6x9ZC/+C34ORlAwAYvg7S5+xFgUNXNUdGiOrQr0MNFhcXh+fPn8Pf3x8AkJ+fj8uXL7Ndh6T8/f3x+vVr3L17V9FmCCGEEAKAf/c8DPYsYpMBiU4DpC38lZIBUudRQqDBjh07Bl1dXfj6+gIAnj59ioKCArRs2VKmnvTKQmJiYo3HSAghhGgCbtIDNNj0Bfs4394bqd9fgdimgxqjIqRmUJchDSUWi/Hbb7/B19cXenp6AID09HQAgFAolKkrfSxdrog0qVDkxIkT4PF40NKq3vxROg50yfGg6wpqn2ar6+0D6n4b63P7ymuzup6TsvZbcpmi9lVmfaWk/AuD7wex8wwUWljj/cIj4PK0VHrmtK6/R4nmoIRAQ/355594+/Yt/Pz85JaVNkuwsrMHl8bYWL9K65em5HjQdQ21T7PV9fYBdb+N1L7qWUfVSotJ2Vgr2yZuYSHwdR/wXv53Fb2ZHXjLT8DYzLBS26uM2vh6kPqFEgINdezYMRgZGcHLy4stMzQsOniVvBKQkZEBQP7KQXGnTp0qc38MwyA19X1lw1WIx+NCKNRFRkYOCgvr3jjv1D7NVtfbB9T9Ntbn9kmXlUZdz0lZcZWMSVH7KrN+eTrE/AI8vgkAkBg2RObsnyHhmwDV/J2niKrfo0ZGelU+GUjqB0oINFBubi5OnToFf39/8Pl8trxZs2bg8/l4/PgxvL292fKEhAQAgLW1dZX2q6rJmQoLJXV64idqn2ar6+0D6n4bqX3Vs46qlRaTsrFWpk2ON27B+czvAACGp4X02T9DbNYCqOHnpja+HqR+oU5rGuj06dN4//49O7qQlEAggIeHB44fPy5TfuzYMZibm8Pe3r4mwySEEEJqLb3379Hn0BH2cc7gryG2bq/GiAhRH0oINNDRo0dhaWmJdu3ayS2bPHkybt++jYULF+LKlSvYuHEjoqOjMX36dJqDgBBCCAHQ9N4dTA1bD75YXFRg54G8PjPUGhMh6kS/EDVMeno6zp8/j169einsF+ji4oINGzbg1q1bGDt2LA4cOICFCxfSLMWEEEIIANy7gk+2rodubi4AIE9HF5i5FaC+9qQeo3sINIyhoSFu375dZh0fHx/4+PjUUESEEEKIBgkbBy7DAABSGjXEueETENi8TY3cRExIbUUJASGEEELqBeHzf4B/PpxU2z5uNIxNm6gvIEJqCeoyRAghhJB6ofWJg+z/z3TzQa6ujhqjIaT2oISAEEIIIXWe95lzaHrtIgAgX1sbce1d1RwRIbUHJQSEEEIIqdMaZGSg6+kz7OMrfv2RWcZknYTUN5QQEEIIIaRO8zvyKzjMfw8cOuNuRxp4g5DiKCEghBBCSJ1l8O4NbO8//FAQ/BMNMUpICZQQEEIIIaRuevUMfdevZh+mN24KNGymxoAIqZ0oISCEEEJI3ZP7HggZAIO01KKHOtq4GDRfzUERUjtRQkAIIYSQukVcAIMVAcDDv4seavGwOegLZJs2VHNgmuP48eMICgqCj48PnJ2d4e/vj927d0MikbB1goODYWtrK/d37tw5ue1FRUWhW7ducHR0REBAAK5cuSJXJysrC4sXL4a7uztcXFwwceJEJCUlqbSdpAhNTEYIIYSQukNcAIOdX4J/9wIAoIDPx4HAAUg1MQZNQaa87du3w9LSEl9++SVMTU1x5coVfPPNN3j27BnmzZvH1mvatClWrVols661tbXM46ioKISFhWHmzJmwt7dHdHQ0xo8fj+joaNja2rL1Zs+ejTt37mDRokUwMDDAunXrMHr0aBw5cgQ6OjRnhCpRQkAIIYSQOoGb8hjC8NHgP4n/r4CLXyfOwP1GeuoNTANt2rQJJiYm7GMPDw9kZ2dj165dmDlzJgQCAQBAR0cHzs7OpW4nPz8fGzduxIgRIzB27FgAgJubG/z9/bFp0yaEhYUBAG7cuIGzZ88iMjISPj5Fo0CJRCL06NEDhw4dwmeffaailhKAugwRQgghpA7gPr8H4yUfs8kAw+UBE9cgpYV1OWsSRYonA1J2dnbIy8tDWlqa0tuJi4tDZmYm/Pz82DIej4devXohNjYWDFM0HmxsbCyEQiG8vb3ZepaWlnB1dUVsbGzlG0KUQgkBIYQQQqodl8uBlhZX5o/HU9HPjvcZMPhuILjprwAA4kYtkbnsDNBvqmr2V09du3YNRkZGMDU1ZcuePn2K9u3bw8HBAQMGDMDJkydl1klMTAQAtGzZUqbc2toa79+/R0pKCluvRYsW4JQYEtbGxobdBlEd6jJECCGEkGrF5XJgZKwHHlf15x2NUpKBtR3Be/0vAKCwYXOkLT4Bnlljle9bE7x48QLDhw8vdfmpU6eU2s6tW7dw8OBBTJ48GTweD0DRFQNHR0fY2NggMzMTe/bsweTJk7F27Vp88sknAICMjAwIBAK5ewAMDQ0BAGlpabCwsEBGRgYaNGggt1+hUIj09HSlYiSVRwkBIYQQQqoVl8sBj8vFgcQDeJPzhi23MbSBb1PfatuPIC8ffhs3ABlFPxglOg2QEbQFjFGjatsHAV6/fo1p06bB0dER48ePZ8tHjhwpU69bt24YMmQI1q1bxyYEAOTO+gNguwoVX6aoXlnlpPpQQkAIIYQQlXiT8wbJ2cnsYzMds2rdfs+Y36D/XzJQaGGN9Ok/obBZm2rdh6aztLRU+iqAIpmZmRg/fjx0dHSwceNG8Pn8UutyuVx8/PHHWLlyJXJzc6GjowOhUIi8vDzk5eVBW1ubrZuRkQHgw5UCoVCI5ORkuW1mZGRAKBRWOn6iHLqHgBBCCCEap+HLFLS79t9oQnxtZM3ZQ8lANcvLy8OkSZPw5s0bbN26FcbGxuWuIz3zLyUdgrTkfQCJiYnQ19dHo0aN2HpPnjyRWz8hIUFuGFNS/SghIIQQQohmkUjgd+TXD48DZkHS1F598dRBYrEY06dPx/3797F161ZYWVmVu45EIsGJEyfQqlUr9p4BV1dXNGjQADExMWy9wsJCHD9+HD4+Pmx3IB8fH2RkZOD8+fNsveTkZMTFxbHDkBLVoS5DhBBCCNEobjG/oNnTZwCAfG0dCALnAmI1B1XHhISE4MyZM5g7dy5yc3Nx/fp1dpmNjQ3S09MRHBwMPz8/NGvWDOnp6dizZw9u376N8PBwtq5AIMCkSZMQFhYGExMTdmKyZ8+eITQ0lK3n5OSELl26YMGCBQgODoaBgQHWrl0LKysr9O/fvyabXi9RQkAIIYQQzfH373A5fYJ9eKnvQPg0MAZS36sxqLrnwoWimZ5Xrlwpt2zHjh2wtbWFgYEBIiIi8O7dO/D5fDg4OGDLli3o3LmzTP0xY8aAYRjs3LkTb968gUgkQmRkpMwsxQCwevVqrFixAkuWLEFBQQHc3d0RHh5OsxTXAEoICCGEEKIReAnXgGUB7OO/3NvjvocXqENJ9Tt9+nS5dTZu3KjUtjgcDsaNG4dx48aVWc/AwABLly7F0qVLldouqT50DwEhhBBCaj3B9d/RYJk/kJMFAHjcsgVOfNpTzVERUjfQFQJCCCGE1Gq6v4bDYPcC9vEL61b4+fOBKNSinzGEVAf6JBFCCCGk1tL+dT30iiUDaNMJx4cNQn7hO/UFRUgdQ12GCCGEECKHy+VAS4sr98fl1tCssRIJsGc59HYEs0X5Hv2Bb3+HuNgEV4SQqqMrBIQQQgiRweVyYGSsBx5X/rxhoUSCtNRsSCSMgjWrgTgfeodWQPfUNiDzLVuc2zEQOdO2QaCjp5r9ElKPUUJACCGEEBlcLgc8LhcHEg/gTc4bttxM1wwB1gHgcjmqSQgK8mC03B/8h5dlirN7T8f7z0KgxamhqxOE1DOUEBBCCCFEoTc5b5CcnVxj+2sQGcQmAwyHA45zN2R+PAm5bT+usRgIqY/oHgINFB0djT59+sDR0RGenp6YOHGizPLY2Fj069cPjo6O6NGjB3bt2qWmSAkhhBAlZLyD/oqB0LkYzRZlzTsArDgJsesnagyMkPqBrhBomPDwcPzwww+YOHEinJyckJ6ejvPnz7PL4+PjERQUhL59+yI4OBhxcXFYtmwZBAIBAgMD1Rg5IYQQUoqQARDcjGUfZo5aDbELXRUgpKZQQqBBEhMTsXHjRkRGRsLLy4st79GjB/v/iIgI2NvbY/ny5QAADw8PJCcnY+3atQgICABXwQ1ihBBCiDpoFYjxydYI4O4ttuz9wAXI7T6OfqAQUoPo16EGOXjwIJo2bSqTDBSXn5+Py5cvo3fv3jLl/v7+eP36Ne7evVsTYRJCCCHlYxgM/2EnPiqWDGSODkN2/3kA3TxMSI2iBFyD3LhxAyKRCBEREfjpp5+QmZkJZ2dnLFiwAHZ2dnj69CkKCgrQsmVLmfVsbGwAFF1hcHBwULhtX1/fUvd74sQJ8Hg8aGlVb/7I43Fl/q1rqH2ara63D6j7baxq+zgcjtyY+xIJA4ZR0XCbFVRW+8prs7qXA4DPmXP46N+nRQ+0+MgZvxbiLiPYHyaK2lfWdksuq+jrzufz5NZR9etd1z+DRHNQQqBBXr9+jTt37uDRo0dYsmQJ+Hw+1q9fj9GjR+P3339Heno6AEAoFMqsJ30sXV5Zxsb6VVq/NEKhrkq2W1tQ+zRbXW8fUPfbWNn2SRgJuBxuuWXqVpn2VfU1r+r6ehnp8D577kPB1I3Q/XQsFG1V2X1VNiYDvgEkjAQGBjpyy2rq9a7rn0FS+1FCoEEYhkF2djbCw8PRqlUrAECbNm3g6+uLvXv3wtXVFUDRWS1FSisHgFOnTpW779TU95WMXDEejwuhUBcZGTkoLJRU67ZrA2qfZqvr7QPqfhur0j7pusXH4ZeOwV9bnq+y2iddVpry2qDS9QvF6Hh4H3iSovWTW9qg8Sdj5LapqH1lbbe09cujw9MBl1P6nAuqfL1V/Rk0MtIr87ufEClKCDSIoaEhzMzM2GQAABo2bIiWLVsiISEBXbt2BSB/JSAjIwOA/JWDihKLVXNALCyUqGzbtQG1T7PV9fYBdb+NVWmfonH4a9vzVZl4qtqGyq7PKRQD3w2F9fVrAAAJh4Mzn43E5xxOqdtUdl9VbVNpcy7UxOtd295TpP6pXdc9SZmsra0VljMMAy6Xi2bNmoHP5+Px48cyyxMSEspcnxBCCKkIHo8LLa0PfyXvtShNq5NHgdh97OOjff2QaWquqjAJIUqihECDdOnSBW/evMHDhw/ZspSUFDx+/Bi2trYQCATw8PDA8ePHZdY7duwYzM3NYW9vX9MhE0IIqUOk/e2FQl0YG+uzf0bGeuUmBdzCQljHfvh+2j8oAPHtXVUdMiFECdRlSIP06NEDbdq0wdSpUzF9+nQIBAJERETAxMQEgwYNAgBMnjwZw4YNw8KFC+Hv74+4uDhER0cjJCSE5iAghBBSJYr620v72nO5HEgkpY/I43ItHnpp7wAA/7Rpi9ttFY96RwipeZQQaBAej4ctW7Zg+fLlWLx4McRiMTp06IDVq1dDT08PAODi4oINGzYgNDQUhw8fhoWFBRYuXEizFBNCCKk2pfW3L41OTi58/zjNPr7ZpUcZtQkhNY0SAg1jamqK1atXl1nHx8cHPj4+NRQRIYQQUjqOhEGvozHQy8kpKrDviOSWNkDOS/UGRghhUR8SQgghhKhMjxN/oO3NotmIxQJtYP5umomYkFqGEgJCCCGEqETjxEfwuHiZfXwjcDTQ6CM1RkQIUYS6DBFCCCGkekkKgXVB6HNsI1t02rcLUjt1Rzs1hkUIUYwSAkIIIYRUH3EB9H76Evgjii36p/lH+LNzJ2ji4Nc8nmxnComEKXM0peK4XI7ccKwVWZ+QmkIJASGEEEKqBffVPxCuHQH+P9fZsoudPHC2W1cUamnWT47icy4UVyiRIC01u9wf9VwuB0bGeuCVGPJb2fUJqUma9ekkhBBCSK3EyXgNo2W9wHv7vKiAp4WzgZ/jrEML9QZWSVWZcwEoSgh43MqvT0hNooSAEEIIIVVTKIb+2tFsMiAxbAjuN7/iQf41oALzFdRGFZ1zobrXJxV39+5dHD16FC9evEBeXp7MMg6Hg40bN5ayZv1FCQEhhBCiAjXZf7zkvkr2e1clfm4u8P1I8G+fBVCUDGQsj4VRq9bA7Ws1FkdNqsp9BUS1Dh8+jPnz54PL5cLExAR8Pl9mOYeGvFWIEgJCCCGkmtVk/3EOhwMjY125fdUEfn4++m/+DkgpmmSM4fKQMW0HGLOmNR5LTajqfQVE9TZu3AgfHx+sWLEChoaG6g5HY1BCQAghhFSzmuw/rmhfNoY28G3qW237KI3XuT9hLE0GBLrImBSJgtYd6+yPi6reV0BU79WrV/j6668pGaiguvqZJYQQQtSuJvuPF9+XmY6ZyvfX+u59+Jw9xz7OmheN/NbeKt9vbUD3BdRednZ2SElJUXcYGodmKiaEEELqGR6PCy2tD38l73Uoj+iviwj8OfpDwbDFEDt0qd4gCamEL7/8EpGRkbh//766Q9EodIWAEEIIqSeqpQ/8jbPw2bsTXKao7j9t2qL50MVARq4qQiakQpydnfHxxx+jf//+MDc3l+s6xOFwcOTIETVFV3tRQkAIIYTUE1XpA88tlMD2twPAicNsMnCtvSuuDRmHCTxejcRPSHkiIyOxefNmmJiYwNLSUm6UIaIYJQSEEEJIPVOZPvD9Dh6Gw41b7OOURg0R49cLDdUwuhEhpdmxYwcCAgIQEhICHiWqSqOEgBBCCCFlsrtzF22LJQPPRHbYE+CHQi36wSVVcm6CmpwLgnzw/v17+Pn5UTJQQZQQEEIIIaRUjV6mYMD+Qx8KBs9DjHsLZNMoOwBKvy+DqIerqysSExPh6emp7lA0CqWvhBBCCFFILy0Vg3fvBb9ADAB4ZesAjF6u5qhql+L3ZWy+vZn9O/XslLpDq5Ljx48jKCgIPj4+cHZ2hr+/P3bv3g2JRCJTLzY2Fv369YOjoyN69OiBXbt2KdxeVFQUunXrBkdHRwQEBODKlStydbKysrB48WK4u7vDxcUFEydORFJSUoXiXrBgAX7++WecPHkS+fn5FVq3PqMrBIQQQgiRd/sC+q37Hg3SUgEAb8xMcXn8HPShewYUKnlfRk3MBaFK27dvh6WlJb788kuYmpriypUr+Oabb/Ds2TPMmzcPABAfH4+goCD07dsXwcHBiIuLw7JlyyAQCBAYGMhuKyoqCmFhYZg5cybs7e0RHR2N8ePHIzo6Gra2tmy92bNn486dO1i0aBEMDAywbt06jB49GkeOHIGOjo5ScQcEBEAsFmPq1KngcDhy63E4HFy7dq0anqG6hRICQgghhMjg/XMLWNgdDQryAAAZwgb4+fPBaKxnoObISE3ZtGkTTExM2MceHh7Izs7Grl27MHPmTAgEAkRERMDe3h7Lly9n6yQnJ2Pt2rUICAgAl8tFfn4+Nm7ciBEjRmDs2LEAADc3N/j7+2PTpk0ICwsDANy4cQNnz55FZGQkfHx8AAAikQg9evTAoUOH8NlnnykVd8+ePcHhVGxeDUIJASGEEEKKYxjobfwC+C8ZeNqsKfYPCkCGkSEaqzk0UnOKJwNSdnZ2yMvLQ1paGoyMjHD58mXMmTNHpo6/vz/27duHu3fvwsHBAXFxccjMzISfnx9bh8fjoVevXti2bRsYhgGHw0FsbCyEQiG8vT/Mdm1paQlXV1fExsYqnRB89913lWxx/UYJASGEEEJYeoe/h9Y/NwEAOfoG2DF6OMQ0lrvGevHiBYYPH17q8lOnlL/X4dq1azAyMoKpqSmePHmCgoICtGzZUqaOjY0NACAxMREODg5ITEwEALl61tbWeP/+PVJSUmBhYYHExES0aNFC7uy+jY0NLly4oHSMpHIoISCEEEIIAEDn7A7o7/+GfXyxXyAlAwQAcOvWLRw8eBCTJ08Gj8dDeno6AEAoFMrUkz6WLs/IyIBAIJDryy+dQTgtLQ0WFhbIyMhAgwYN5PYrFArZbSnj8OHD5dbp16+f0turLyghIIQQQqqIy+WAy/1wZlMTx6DnpL6EwfZZHwoGzESCqwjIeam+oCqA5gFQzNLSskJXARR5/fo1pk2bBkdHR4wfP15mWWn99YuXK6rD/DfbdXn1yipXJDg4uNxtUEIgjxICQgghpAq4XA6MjPXA09TRdxgGePg3DNYEgSMuGqYxr/Nn0P5iNXAnUs3BlY/mAVCtzMxMjB8/Hjo6Oti4cSP4/10xkp7hL3n2PiMjA8CHKwVCoRB5eXnIy8uDtra2XD3pdoRCIZKT5ee2yMjIkLsKURZFyU9qaipOnTqFmJgY9iZmIosSAkIIIaQKuFwOeNyicejf5LwBANgY2sC3qa+aIyuf9aNE+B3fDLx6yf4gYPjayB28CNoaMlJL8XkApM8/oDmvQW2Wl5eHSZMm4c2bN9i7dy+MjY3ZZc2aNQOfz8fjx49lbgROSEgAUHSPQPF/ExMTYW9vz9ZLTEyEvr4+GjVqxNa7ePEie5Nx8e1Jt6EMKysrhWUODg4Qi8XYsWMH3XisgIaeziCEEEJqF+k49MnZyUjLS1N3OOVyvXoNw3/8CcavPnQJkugZIWPaDkjMm6kxssop/vxrymtQm4nFYkyfPh3379/H1q1b5X5oCwQCeHh44Pjx4zLlx44dg7m5Ofvj39XVFQ0aNEBMTAxbp7CwEMePH4ePjw/749/HxwcZGRk4f/48Wy85ORlxcXHsMKRV5enpidOnT1fLtuoaukKgQQ4ePIj58+fLlY8fP15m2K/Y2FiEhYUhMTERFhYWGDVqFIYOHVqToRJCCKnFRFcvoesvx4oVtEdOlxHI7TQI0DGoVP/74utUdf3KboNUn5CQEJw5cwZz585Fbm4url+/zi6zsbGBgYEBJk+ejGHDhmHhwoXw9/dHXFwcoqOjERISAu5/XegEAgEmTZqEsLAwmJiYsBOTPXv2DKGhoew2nZyc0KVLFyxYsADBwcEwMDDA2rVrYWVlhf79+1dLm5KSkti4iCxKCDTQ1q1bZe7El15uA5SfNZAQQkg9des8uu75kX34rG07WH1/GbpcHirTC7+qffjLWl/CSGiSKTWRDvW5cuVKuWU7duyAu7s7XFxcsGHDBoSGhuLw4cOwsLDAwoUL5X5vjBkzBgzDYOfOnXjz5g1EIhEiIyNlZikGgNWrV2PFihVYsmQJCgoK4O7ujvDwcKVnKQaAq1evypXl5+fjwYMH2Lx5Mzw9PZXeVn1CCYEGatOmjcIJQwAoNWsgIYSQ+sns0V1g64dRYh61ssHtsbPRlMur9D0QivrwV3V9ADDTNUOAdYDM6E2k5ijbtcbHx6fcLj0cDgfjxo3DuHHjyqxnYGCApUuXYunSpUrHWdLw4cPlkkjpiEYdO3bEokWLKr3tuowSgjokPz9fqVkDCSGE1D+29x7Aa+9+QCwGACQ3tsDu4Z/BgS8A8KEPPgCY6ZhVePvVuT4hlbVjxw65Mm1tbVhZWcHMrOLvy/qCEgIN5Ofnh9TUVFhaWmLQoEEYN24ceDwenj59qtSsgYr4+pZ+JufEiRPg8XjQ0qreqwvS/qF1tZ8otU+z1fX2AeppI4fDkTvjK5Ew7Bm86lSV9pW1TlX7ulfX8y3djjJn0FskPsHg3XvB/e95ftGyFXYN6gNGA64ac7kc9vunrnweS74360q7agM3Nzd1h6CRKCHQIObm5pg6dSqcnJzA4XBw+vRprFmzBikpKVi8eLHSswZWlrGxfpXWL01dHzua2qfZ6nr7gJpto4SRgMvhlltWnaq7fVXdXnXHY2BQfv/qbidPs8kAnLsh5rP+eF/wpuyVagll2qdpSr4H6sNxpqbk5eWhoKAABgYGbFlMTAzu3r2Ljh07omPHjmqMrvaihECDdO7cGZ07d2Yfe3l5QVtbGz/++CMmTpzIlldmpr/yZjFkGAapqe8rGHHZeDwuhEJdZGTkoLBQUq3brg2ofZqtrrcPqPk2SvdXvK+4tJ+4KmKoSvuk6ypScntl1VVm/cqS7jcrK7fMH82Nk16g6bPnAIBcoRF0lh5D4aMdQEGVQ6gRWVm5KCgoBFDx57q2kr4HVP0ZNDLSq3c3Zc+dOxd6enrsXAM7duxg76uMiorCpk2bqm0Y07qEEgIN9+mnn2Lbtm24d+8eO0ZwebMGVpZYrJofDIWFEpVtuzag9mm2ut4+oObbqKivuCpjqO5tV3V71R2PRFJ2d6tOFy6y/7/XKxAu2pr1g1oiYercZ7Dke6A+HGdqyq1bt2Tupdy5cyf69OmDxYsX46uvvkJUVBQlBApQp7U6pPisgcWVnDWQkPpK2he55B+NYlJ/lPYeKPlX0326FcVVHe9Lqwd34XDrDgCggK+Fpx06l7MGIZrt3bt37HDsz549w7NnzzBs2DAYGBhg4MCBePTokZojrJ3oCoGGi4mJAY/Hg729vcysgaNGjWLrlJw1kJD6iMvlwMhYDzwFN1EWSiRIS80u90wr0WxlvQdUfR9DWUqLq8rvy1O74Ld5HfvwL/cOEOvqVSVUQmo9HR0dZGZmAgCuXbsGPT09ODo6AigabSg7O1ud4dValBBokLFjx8LDwwMikQhAUb//ffv2YcSIETA3NwcApWYNJKQ+4nI54HHLHuucEoK6rbT3gHTM/MqOo6+KuKr6vtR/kwKsms4+ftyyBc74doNdtUVNSO0kEomwa9cuWFpaYvfu3XB3d2fvo0hOTqahR0tBCYEGadGiBfbv34+XL19CIpGgefPm+OqrrzB8+HC2jrKzBhJSX9FY56Tke0A6Zn5Vx9Gv7rgqjWHgtDcKKCyab+Bps6bY+/lgiPn0lU/qvqCgIEycOBH9+vUDn8/H9u3b2WVnz55FmzZt1Bhd7UVHBw2ycOFCpeopM2sgIYSoGpcrO+cAjbWuejyxGMN+3IXGT/4BAIi1tHAgcADydLTVGxghNcTT0xMxMTG4c+cO7Ozs0LRpU3aZh4cH7OzoOpkilBAQQgipdmX11yeqof0+CyO278RH/z5ly84HDkW6sZH6giJEDaysrNiRF4sbMmSIGqLRDJQQEEIIqXaK+sXXZL/8eic/F33Wr4ZJSrEuR7Oi8LBJAUBd5Agh5aBTN4QQQlRG2i8+OTsZaXlp6g6nbsrPBRb3YZMBMY+HK6NnAJ+MUW9chBCNQQkBIYQQosF0ts8B4v5gH/8wdiSed/BSY0SEEE1DXYYIIYQQTcMw4N2/BJyIgODiYbb4xCc98LxZUxirLzJCiAaihEANkpOT8ejRIzg6OsLYmA7bhBBClMd9mQjh+jHgP4mXKY/r/ikuebmpKSpCiCajhEDFwsLCkJOTg6+++goAcPHiRUycOBEFBQUQCoX46aef0KpVKzVHSQghRBPo/rYBBjuDZcoYHX1wBs7GVVdLuoGYkP8kJibi6tWrSE1NxcCBA2Fubo6UlBQYGhpCR0dH3eHVOnQPgYr9/vvvsLGxYR+vWbMGtra2WL9+PSwtLbFx40Y1RkcIIURT8JIeQP+nr9jHEmML4MsdyNzyBBixRI2REVJ7FBYW4quvvoKfnx/+97//Yd26dXj16hUA4Ouvv8bmzZvVHGHtRAmBiqWkpKBZs2YAgNTUVNy6dQvTpk2Dr68vxo8fj2vXrqk5QkJIVXG5HGhpcWX+ik/IRUhVcV8/hfF8T3AYCQCA4esg45tYoPtwQNdAzdERUnts3LgRx44dw5dffoljx46BYRh2WefOnXH+/Hk1Rld7UZchFWMYhn0zxsXFgcfjoUOHDgCAhg0bIjU1VZ3hEUKqqLQJuAolEqSlZkMiYUpZkxDl8O/EQrhmODiFYgCARN8I71bFgWfSUM2REVL7HDp0CEFBQRg9ejQKCwtlljVp0gTPnz9XU2S1GyUEKtasWTOcOXOGnUrb0dGR7bv26tUrCIVCNUdICKkKRRNwmemaIcA6AFwuhxICUnlZadDdOhc6f2xliyQ6BsiYsg2M0EyNgRFSe6WkpMDZ2VnhMm1tbbx//75mA9IQ1GVIxQYPHowdO3bA3d0dMTExGDRoELssLi5O5v4CQojmKj4BlzQxIKSyTJ8/BSY5yyQDBc2d8C70OgradldjZITUbqampnj27JnCZU+ePIGFhUUNR6QZ6AqBin3++ecwNDREfHw8HB0d0bdvX3ZZXl4e+vXrp77gCCGE1CrcwkK4XbmKHidOAv91d2A4XOR2H4v3g/8HRreBmiMkpHbz8fHBpk2b4O3tDTOzoitpHA4HmZmZ2LlzJ7p27armCGsnSghqQO/evdG7d2+58qVLl6ohGkIIIbWSRIKJEZvR8NVrtqjQrCnSZ/2Mwo8c1RgYIZpj2rRpOHfuHHr16gV3d3dwOByEhobi0aNH0NLSQlBQkLpDrJWoy5CK2dnZ4ebNmwqX3b59G3Z2djUcESGEkNqo/YljMskArJ2R+c1ZSgYIqQAzMzPs378fvXv3xp07d8Dj8XD//n14e3vj559/hpGRkbpDrJXoCoGKFR/uqiSJRFKDkRBCCKmV8nOBDV+h3R8xbNGNLj3gFPwbmPQcQEzfFYRUhJmZGUJCQtQdhkahKwRqdOfOHRpliBBCSlEv5ncoFEM/bDhweB1b9NunH+NynwCAS1/RdRWPV/R+5vG47OM6995Wk59++gnp6enqDkPj0BUCFfjxxx+xY8cOAEU3skyePBkCgUCmTl5eHt6+fYuePXuqI0RCCKnV6sP8Dty3SWiweSIEd2LZsnM+Xrjc0QON1RgXUR0DvgEkjARCoa5MuVCoW6fe2+q0bNkyfP/99+jWrRsCAgLg5eUFDoeSrfJQQqACpqamaNWqFQAgKSkJTZs2lbsSIBAIIBKJMGLECHWESAghtVpdn99B6+EVGK4KBPd9WlEBl4tTn43Eebtmao2LqJYOTwdcjuz7Gqhb7211i4mJwYEDB3D06FGcOHEC5ubm6N+/P/r374/mzZurO7xaixICFfDz84Ofnx8AYPjw4fjf//4Ha2trNUdFCCGaRzq/Q50hkUDn0Ero7FsGjqRoWFGJ0AzcRdFI4D0A6lJbSanq3Pu6FmnZsiXmzp2L2bNn4/z58zh48CC2b9+OyMhIuLi4ICAgAAEBAeoOs9ahhEDFdu7cqe4QCCGE1AIG794Cc7pA9/Z5tkzczBFZ8w/CyNoGuP2g3G1wuRy2r7m0/zn1PSdEHpfLhY+PD3x8fJCRkYGjR49iy5YtWLRoESUEClBCUAMYhsGtW7eQlJSEvLw8ueU0ORkhhNRtLn/Hwe/ocaBQzJbldB2FrFGroKWjo9Q2SruvwsBAufUJqY+ysrJw/PhxHDlyBC9fvoSurm75K9VDlBCo2JMnTzBp0iT8+++/Cocg5XA4lBAQQkgdZn/7DvoePso+ZnSFyBwdiryOgUAFbnZUdF8FANgY2sC3qW+1xkyIprt06RIOHDiAkydPIjc3F05OTggJCUGvXr3UHVqtRAmBioWEhCA/Px9hYWGwtbWVG22IEEJI3dX03m10P/jLhwJ7T6RP+QEFhpUfR6hk/3MzHbOqhEhInbJu3TocPnwYycnJMDU1xdChQzFgwAC6l7MclBCo2M2bN7F06VJ88skn6g6FEEJITTq4Br22rGcfJre0QeOwP8GkZdNkY4SoSGRkJLp27YpFixbB29sbPB5P3SFpBEoIVExPTw8GBgbqDoMQQkgNanj/JrDpw0ypD1qL8OeISRhD46ETolLnzp2DiYmJusPQODQNoooNGDAAx44dU3cYhBBCaojx23dw/nkL+/hWWwfsGToEBTp0MyPRLP/++y8WL16Mvn37wt7enh1Svbjg4GDY2trK/Z07d06ublRUFLp16wZHR0cEBATgypUrcnWysrKwePFiuLu7w8XFBRMnTkRSUpLSMVMyUDl0hUDFRCIRfv31V0ycOBHdunWDkZGRXJ2PP/64wtt9//49Pv30U6SkpGD//v1wdHRkl8XGxiIsLAyJiYmwsLDAqFGjMHTo0Ko0gxBCiBJs7z3A4D17wf1vcqk080Y4OHBAhW4eJqS2ePToEWJjY+Hk5ASJRKJwcBQAaNq0KVatWiVTVrLPflRUFMLCwjBz5kzY29sjOjoa48ePR3R0NGxtbdl6s2fPxp07d7Bo0SIYGBhg3bp1GD16NI4cOQKdUkbkmj9/PoKCgtC0aVPMnz+/zDZxOBwsX75cmebXK5QQqNjs2bMBAM+fP8fZs2fllnM4HNy7d6/C292wYQMKCwvlyuPj4xEUFIS+ffsiODgYcXFxWLZsGQQCAQIDAyu8H0IIIbKk4/+XfGz08gX6RR9kkwEITXHm81FgSswTUNr6hNQ23bp1Q/fu3QEUXQm4ffu2wno6OjpwdnYudTv5+fnYuHEjRowYgbFjxwIA3Nzc4O/vj02bNiEsLAwAcOPGDZw9exaRkZHw8fEBUHRitUePHjh06BA+++wzhdu/cuUKRo4cyf6/LBxKzhWihEDFduzYUe3bTExMxO7duzFv3jx8/fXXMssiIiJgb2/PZr8eHh5ITk7G2rVrERAQAC6XvngIIaQyDPgGkDASCIUKuv7cPIf+a1dAkJ8PAMho3ATCNdfwKukQO/twmesTUgtV12+GuLg4ZGZmynQ54vF46NWrF7Zt2waGYcDhcBAbGwuhUAhvb2+2nqWlJVxdXREbG1tqQnD69GmF/yfKo4RAxdzc3Kp9m9988w2GDBmCFi1ayJTn5+fj8uXLmDNnjky5v78/9u3bh7t378LBwaHa4yGEkPpAh6cDLkd+HoBuF6+h1f4tkA4qnWRliWszl6GPcUMgqfz1aR4BoumePn2K9u3bIzc3FyKRCEFBQeyVBaDoRCYAtGzZUmY9a2trvH//HikpKbCwsEBiYiJatGghdxbfxsYGFy5cUCqWFy9ewNzcHHw+X26ZWCzGq1evYGlpWdEm1nmUEGiY3377Dffv38e6detw584dmWVPnz5FQUGB3AfOxsYGQNEHkhICQgipmuLzALS+ew+t9u9jl71qaI6do4ajlV7po8vRPAKkJr148QLDhw8vdfmpU6eqtH07Ozs4OjrCxsYGmZmZ2LNnDyZPnoy1a9eyQ65nZGRAIBDI3QNgaGgIAEhLS4OFhQUyMjLQoEEDuX0IhUKkp6crFY+vry/27t2Ltm3byi27f/8+AgMDK9VVu66jhEDFRowYUeZyDoeDH3/8Ualt5eTk4LvvvsOsWbMUDmUq/bAIhUKZcunjsj5Mvr6ln506ceIEeDwetLSqt7uRtN9sXe0/S+2rXcqLs7R+3RVdryL7VDdVvoYV3WZF+tUru+2S7avudtrfvoP++w+zj99ZWGLLhJEoqMYJKPl8Hhs3l1s/+z4XffcU/Vyp689BbT9mKCLtuy/VrVs3DBkyBOvWrZOZg0lR333pTcrFl5XWx1/Zvv+l3fgMABKJhO4hKAUlBCqm6I2ZlpaGJ0+ewMTEBM2bN1d6Wxs3boSpqSkGDBhQZr2qfphKY2ysX6X1S1PX+9NS+zRDae2oSvs05bmpDXFWJIaKxquK9jnFXUefw0fBk/w3wZi9Jw4NH4yCwtRq2b70fgMDA8WjqtQH0udAT09b3aHUGFW8Vy0tLat8FaAiuFwuPv74Y6xcuRK5ubnQ0dGBUChEXl4e8vLyoK394fXMyMgA8OFKgVAoRHJystw2MzIy5E52VlR+fj7OnTsHY2PjKm2nrqKEQMV27typsPzJkycICgrClClTlNpOUlIStm3bhoiICGRlZQEAsrOz2X/fv3/PfqBKXgmQfuDK+jCVd7BgGAapqe+VilVZPB4XQqEuMjJyUFhY92btpPbVLtJ4S1OyHcq2r6zt1vbnRpWvYXnPd0mlPf/K1C0vBmn9isakUPxpfPJjBD66e4stetauI5ouOQ3xwx+A7KptXkrR/Qb17V6D+vgcVPdn0chITy1nxEueDJUOQZqYmAh7e3u2PDExEfr6+mjUqBFb7+LFi+xNxlIJCQlyw5gWt379ekRERAAoOvk5ePDgUuvSiIuKUUKgJi1atMDYsWOxcuVKREdHl1v/+fPnKCgowIQJE+SWjRgxAk5OTvjpp5/A5/Px+PFjmTv0ExISAMiPCVxRYrFqftgUFkpUtu3agNqnGUprR1XapynPTW2IsyIxVDTe6mpfiwt/AHsi8VGxHzvXXZyQOHo6mgpUcxa7+P0G9fVeg/r0HNSGz2JVSSQSnDhxAq1atWLvGXB1dUWDBg0QExPDJgSFhYU4fvw4fHx82B//Pj4+iIiIwPnz59nfMcnJyYiLi8PChQtL3aejoyM+//xzMAyD3bt3o2fPnjAzk32vCAQCiEQihZOrEUoI1MrKygqPHj1Sqq6dnZ3cEKb37t3Dt99+iyVLlsDR0RECgQAeHh44fvw4Ro0axdY7duwYzM3NZbJyQgipK7hcjsK+5dV5ZtThxi24Rh9kH2caGOC8jxeuuneAA5dXbfshpDbJyclBbGwsgKKeCllZWfjtt98AFI2imJOTg+DgYPj5+aFZs2ZIT0/Hnj17cPv2bYSHh7PbEQgEmDRpEsLCwmBiYsJOTPbs2TOEhoay9ZycnNClSxcsWLAAwcHBMDAwwNq1a2FlZYX+/fuXGqePjw87b0FOTg47SRlRHiUEavT777+jYcOGStUVCoVwd3dXuKxNmzZo06YNAGDy5MkYNmwYFi5cCH9/f8TFxSE6OhohISE0BwEhpM7hcjkwMtYDT8HxTSKphjOtEgm8Yi/A9+SHbpU3vbvhUPdOYOiYSuq4t2/fYvr06TJl0sc7duyAra0tDAwMEBERgXfv3oHP58PBwQFbtmxB586dZdYbM2YMGIbBzp078ebNG4hEIkRGRsrMUgwAq1evxooVK7BkyRIUFBTA3d0d4eHhpc5SXNK3335bhRbXX5QQqJiiKbTz8/Px8OFDJCQkYO7cudW6PxcXF2zYsAGhoaE4fPgwLCwssHDhQuozRwipk7hcDnhc+bH9zXTNEGAdUOXtdzq8Dw4Xzn4ocPfDpT69wOSmVHnbhNR2TZo0wYMHD8qss3HjRqW2xeFwMG7cOIwbN67MegYGBli6dCmWLl2qdJyKPHz4EImJicjLy5Nb1q9fvyptuy6ihEDFFE2hra2tDSsrK0yYMAH+/v6V3ra7u7vCD2rxS2eEEFIflBzbv1r8/btMMvDQ1x+iOQeBe1HVux9CSLXJycnBpEmTcPnyZXA4HIVDm1JCII8SAhWjKbQJIUTzCLIygTWz2Md/enniZcBIiHj0tUlIbbZhwwYkJSXhp59+wrBhw7B+/Xro6+tjz549ePjwIdasWaPuEGsl6gBJCCGEFKOTkwPvsMXAq6cAgOdNrHDy4+5qjooQooxTp05h/PjxcHFxAQA0btwYnp6eWLduHdq0aYPdu3erOcLaiRKCGpCWloY1a9ZgyJAh6NmzJzuDn7LTcBNCCKkhDIN+B36BYfIzAECBQBv7PhtENxAToiGSkpLQsmVL8Hg8cDgc5OTksMv8/f1rdJI2TUJHOBVLSUnBgAEDsGnTJmRmZsLS0hKZmZnYsGED+vfvj5QUujGNEEJqA4N3bzFy2w60vv/h3qzYwcORYVi1GVIJITWnQYMG7MStpqam+Pfff9llYrGYXUZkUWdIFQsNDUVubi727duHtm3bsuU3b95kx+T97rvv1BghIYTUbTweV+ZfhdJeo8/6VWiQlvqhbPj/kOhiAVT3zcqEEJWxtbXFP//8A29vb7i7u2Pz5s346KOPIBAIEBERgdatW6s7xFqJrhCo2Pnz5zFjxgyZZAAA2rZti2nTpuHcuXNqiowQQuouA74BJEzRPARCoS6MjfUhFOoqrpuSBMzxYZMBMY+H+MFjgeFf11i8hJDqERAQgPfv3wMAZsyYgZycHAwfPhyDBw/GixcvEBwcrOYIaye6QqBimZmZsLKyUrisSZMmyMzMrOGICCGk7tPh6YDLkZ2fwMbQBr5NfWXqtbt6Dd1//Q0QiwEAWfr62D5uFCxtu8KlxqMmhFRVr1692P83bdoUJ06cYIcgdXFxgZGRkfqCq8UoIVCxJk2a4OzZs+jUqZPcsnPnzqFJkyZqiIoQQuqH4vMTmOmYySzrdTQGbleuso/TTc2wY/gQvDUzhWWNRkkIURU9PT1069ZN3WHUepQQqNiAAQOwevVqMAyDfv36wdzcHK9fv8aRI0fw008/Yfbs2eoOkRBSg7hcDrhcTrn1JBIGEglTAxEVTdijpSXbg7Qm9w/I9+8vs79/VTEMOlz+SyYZQMd+2N+nG96K36puv4QQUktRQqBi48aNw7Nnz/DTTz9h165dbDnDMBg0aBDGjh2rxugIITWJy+XAyFgPvBJDWEoYCbgc2bJCiQRpqdk18qNcKNQBl6ue/Uv7+pfWv7/aJT9B3/CVsPjnMVv02sYO5ov2Q3xvKyCumTAIIdWndevWMjMRl4XD4eDu3bsqjkjzUEKgYhwOByEhIRg1ahSuXLmCtLQ0GBkZwcPDAy1atFB3eISQGsTlcsDjKu7XXrzMTNcMAdYB4HI5NZIQcEvEVJP7V9TXH1Dc37/K+0pPBYI7wCLjw1WAO23s8WDi1xjA41XrvgghNWfy5MlKJwREMUoIVCA9PR0LFy7EgAED0LVrVwBAy5Yt0bJlS7bOmTNnEBoaipCQEBgbG6srVEKIGijq1168TN0x1Yb9l+zvX1XWjxLQ7fA6IKNoJCEJh4Nf+/RGXDtXOFAyQIhGmzp1qrpD0Hg07KgKREdH4/79++jcuXOpdTp37oyHDx/KdCMipC7hcov6pRf/U6bvPKndFL2uiv5Ueg9ABbW7eg3Df9wF3fSiZKCQp4XNQRNwrUM7MPSeJIQQukKgCjExMQgMDISWVulPr5aWFgIDAxETE4MpU6bUYHSEqF5pfeVrsl88qX4VuQeitnA4dxqdfjn2oYDLw+HpXyLFhK++oAghKnP48OFy6/Tr10/lcWgaSghU4MmTJ3B0dCy3Xps2bbBhw4YaiIiQmqWor3xN94sn1U/ZeyCKl6uDdm4eWsdEAzcXodPTe2x5soMrGi86jjdJh2j2YULqqNImHit+jwElBPIoIVCBwsLCMq8OSGlpaUEspiEtSN2l7n7pRDWUuQeiuu8BUJbtvfvw++VXNMjKkim/1dYBDyd+hQDjhkCSWkIjhNSAU6dOyZWlpqbi1KlTiImJQVhYmBqiqv0oIVABc3NzJCQkoEOHDmXWe/ToEczM1POlSUhdV3K8/9rUp51UTfHXkv2/RIKO5//ExydOytTNMjTCJfd2uNTJE21qMkhCiFpYWVkpLHNwcIBYLMaOHTvw3XffqSGy2o0SAhVwc3PD7t27MXDgQPD5ivupFhQUYM+ePXB3d6/h6Aip+0rr6040W6lzFrx6ir7rV8nMLfCyjQsspmzDrpwrdJWKEAIA8PT0xIwZM9QdRq1ECYEKjBw5EgMHDsSUKVMQEhKCRo0aySxPSUnBokWL8OTJE6xatUpNURJSd5XV151orpJzFhikvkP7xCTY/vITLN6ns/XOe3fCq8EzEGDtDNy+or6ACSG1SlJSktwkjKQIJQQq0Lp1ayxevBhLliyBr68vHBwc2EtYSUlJuH37NhiGwf/+9z/Y2tqqOVpC6i5Ffd2J5nuT8waml35Hv0O/QEtcyJbn6Opg/6AAJLaygSNNUkRIvXT16lW5svz8fDx48ACbN2+Gp6enGqKq/SghUJFBgwahVatW2Lx5M65cuYLr168DAHR1ddG5c2d88cUXcHZ2VmuMhJC6q+Q9FFISCaPZozwxDERXL6Fr9EGZ4neNGmPHsEFIMzZST1yEkFph+PDhcrMWM0zRMa9jx45YtGiROsKq9SghUCEXFxds2rQJEokEqalFE+IYGxvT5SpCiEqVdQ+FJs8FoZ2ZDiwZgK4XD7Nlb6xbw2z6jziQ9zfS8l6pLzhCSK2wY8cOuTJtbW1YWVnRQC5loISgBnC5XJiamqo7DEJIPaHoHgpAs+eCsHn4CD0OhgFZGWzZq4bmuDh1Efq1doPkdrwaoyOE1BZubm7qDkEjUUJACCF1VF2ZB8I57jr6HD4C7n9JTI5BA5zu2hlx7VxhL9BWc3SEEKL5KCEghGiMOtsvvopUNedCye2oYy4Hp9Mn4HHslw8Fbr2wr3cXPOG9r/FYCCG1X2FhIXbu3ImjR4/ixYsXyMvLk1nO4XBw7do1NUVXe1FCQAjRCHW1X3xVqWLOhVLH+69B+m9SgB/94XHlGFv2j2dXNF/8C3LvRQHZlBAQQuStWrUK27dvh52dHTp27AiBQKDukDQCJQSEEI1QF/vFVwdVzLlQcrx/qZqYy4EjYeB99hy8/rwM5OWy5Vfd2uPp0ElozqOvLUJI6Y4ePYoJEyZg1qxZ6g5Fo9CRlRCiUepKv/jqpoo5F0o+16qey6HJ02fof+AwTN++Y8vydHRx2tcHf7m7wYFGaCOElCMvL4/mGqgESgg0yPnz57F582YkJCQgKysLjRo1Qvfu3TFlyhQ0aNCArRcbG4uwsDAkJibCwsICo0aNwtChQ9UYOSGElCE/Fz2jNqD5nZuy5W19EN3vEyQIchWvRwghJXTq1Ak3b96kpKCCKCHQIOnp6XBxccHIkSMhFArx6NEjhIeH49GjR9i2bRsAID4+HkFBQejbty+Cg4MRFxeHZcuWQSAQIDAwUM0tIIQQWYbPHgMzjNE8/8OP/vd6ergbOBYdhq7D+9ubAboiRAhR0sKFCzFhwgTo6urCx8cHhoaGcnWMjIxqPrBajhICDeLn5wc/Pz/2sbu7OwQCARYtWoSUlBQ0atQIERERsLe3x/LlywEAHh4eSE5Oxtq1axEQEECTohFCagXt3Fz4/3IMbW7fBZgP93783rM7rrq7obWFKzqoMT5CiGYyMDBAixYt8O233+Lbb79VWOfevXs1HFXtRwmBhpNmuWKxGPn5+bh8+TLmzJkjU8ff3x/79u3D3bt34eDgoIYoCSHkg6b3buOLLetlyvJ09XAgoA8etrZVU1SEkLrg66+/xm+//QZfX19YW1uDz+erOySNQAmBBiosLIRYLEZCQgIiIiLQtWtXWFlZISEhAQUFBWjZsqVMfRsbGwBAYmJiqQmBr2/pI4ecOHECPB4PWlrVe3VBOqa5OsY2rwn1uX1ltbmyz0d56xVfXpl9lDbmfkX2WxKfz5NZrmgOhcpuuyJ1SltHLe/N5w/huzNKtsxnEH72dcMTbmbNx0NILVJXvy9q0smTJzFr1iyMHTtW3aFoFEoINFDXrl2RkpICAOjcuTNCQ0MBFN1jAABCoVCmvvSxdHllGRvrV2n90qhzrPOaQO2rWv2a2m5p61dmu9Jx/A0MdFQSU1Wp6z2p++4NsLkvtHNz2LKLE4PRccC3yL29GcimhIDUb3X9+6ImaGlpwd7eXt1haBxKCDRQZGQksrOzkZCQgA0bNmDixInYvn07u5zDUXwWsrRyADh16lSZ+2QYBqmp1TsREI/HhVCoi4yMHBQWSqp127VBfW6fdJkilX0+ytpmye2WV1eZuJR9/RTtq7rG8VfmuapKWyuzbmVxJAx81iwC3r0GAKQaG2HT5C8gsmxfI/snRBNU9/eFkZFemd/9ddHHH3+M8+fP0yhDFUQJgQZq3bo1AMDV1RX29vYICAjAH3/8wXYNKnklICMjA4D8lYOKEotV86O2sFCism3XBtS+qtWvqe2Wtn5VtlvVcfxr63NVURyJBIP2REP/v2SgkMfDz0OHIE+naldQCKlratv3xb///ouoqCjcuHEDjx49QsuWLXHs2DG5esoOdx4VFYVdu3bh9evXEIlE+PLLL+Hu7i5TJysrC99//z1OnDiB/Px8uLu7Y9GiRbCyslIq5t69e2PRokUQi8Xo0qWLwlGG2rRpo+QzUH9QQqDh7OzswOPx8PTpU3Tr1g18Ph+PHz+Gt7c3WychIQEAYG1tra4wCan1Svbdra1n1bhcjsy9CLW9z7HT6d8x9OwJGGR9uMJ4fuDnSLFopMaoCCHKePToEWJjY+Hk5ASJRAKGkZ8NXtnhzqOiohAWFoaZM2fC3t4e0dHRGD9+PKKjo2Fr+2EwgdmzZ+POnTtYtGgRDAwMsG7dOowePRpHjhyBjhInEUaNGgUA2LFjB3bu3CmzjGEYcDgcGmVIAUoINFx8fDwKCwvRpEkTCAQCeHh44Pjx4+wHAgCOHTsGc3Nz6lNHiALS/v4lu85IJLXnLJ0Ul8uBkbEeeJoyfPDJn+Bx7KBs2fTNePARQ3MLEKIBunXrhu7duwMAgoODcfv2bbk6ygx3np+fj40bN2LEiBHszb5ubm7w9/fHpk2bEBYWBgC4ceMGzp49i8jISPj4+AAARCIRevTogUOHDuGzzz4rN+bShholZaOEQINMmTIFDg4OsLW1hY6ODu7fv4+tW7fC1taW/cBOnjwZw4YNw8KFC+Hv74+4uDhER0cjJCSE5iAgRAFF/f3NdM0QYB2g5sjkcbkc8LiysVb0voSaYhl/Gdiyin2c2cAA8WPmwLv3BOD2ZjVGRghRVnm/G5Qd7jwuLg6ZmZkycynxeDz06tUL27ZtY8/cx8bGQigUyvRysLS0hKurK2JjY5VKCPr371/BVhKAEgKN0rZtW8TExCAyMhIMw8DKygqDBg3C2LFjIRAIAAAuLi7YsGEDQkNDcfjwYVhYWGDhwoU0SzEh5SjZ3782Kx5rRe9LqAl2d+7B4+f97OPX5mbYHPQF7BrRPCiE1CVPnz5VarjzxMREAJCrZ21tjffv3yMlJQUWFhZITExEixYt5Lps2tjY4MKFCypsCaGEQINMmDABEyZMKLeej48Pe6mNkMoq2VcdACQSBhKJfB9SohqlzY1QmzneuIUB+w+C89/bJMvQCFETxkLMp68bQtThxYsXGD58eKnLyxtlsCzKDneekZEBgUAgdw+A9IbftLQ0WFhYICMjAw0aNJDbj1AoVHro9Pnz55e5nMPhsN2byAd0hCaEyCmtr3qhRIK01GxKClSstPsaajOuuAA9Y07A49JlNhlAWx/sG9IfuZJUtcZGCFEtZYY7V1RHepNyefXKKi/pypUrcmVpaWnIzs6GUChUmHAQSggIIQoo6qsu7VfP5XIoIVCx6prHoCZoZ6YDe1fg8+hvoZ/x4Qzev27e+CjkNArubgGy1RggIfWcpaVlla4ClEV6hr+84c6FQiHy8vKQl5cHbW1tuXrS7QiFQiQny3fdzMjIUHro9NOnTyssv3TpEpYsWYK1a9cqtZ36pvZffyaEqI20r3pydrLMD1NSM4o//8nZyUjLS1N3SB8wDJyvxePTBV8AUcFsMsBwgNgunfH3yKkADWRASJ3WrFkzdrjz4koOdy79V3ovgVRiYiL09fXRqFEjtt6TJ0/khjdNSEio8tDpnp6eGDZsGL755psqbaeuoqM1IYQoicvlQEuLK/OnCf36q5vhq5cYtzkK/Q4dAU8sZsv//agZto0bjTPduwG1dB4HQkj1KT7ceXElhzt3dXVFgwYNEBMTw9YpLCzE8ePH4ePjw3YH8vHxQUZGBs6fP8/WS05ORlxcXLXcG2ltbY1bt25VeTt1EXUZIoQQJWjcHAAqIMjKAKKCEbh/NXiFHxIBePbFfs82uG2sRYkAIXVITk4OYmNjAQBJSUnIysrCb7/9BqBoHgETExOlhjsXCASYNGkSwsLCYGJiwk5M9uzZM4SGhrL7c3JyQpcuXbBgwQIEBwfDwMAAa9euhZWVVbUMJ3r16lUYGxtXeTt1ESUEhBCiBEX3VQC1s19/dePnF8D90hV4/7kayM4C77/yd8bGSPQfig6fr8Xbu1tosjFC6pi3b99i+vTpMmXSxzt27IC7u7vSw52PGTMGDMNg586dePPmDUQiESIjI2VmKQaA1atXY8WKFViyZAkKCgrg7u6O8PBwpWYpBoD169fLlRUUFODBgwc4d+4cOzEakUUJASGEVEDJ+Qpq4zwA1YZh0OHKVfj+fgo6eXkyi246OeK3Xj1h3dQDHerxVRNC6rImTZrgwYMH5dZTZrhzDoeDcePGYdy4cWXWMzAwwNKlS7F06dIKxSqlKCEQCASwsrLCtGnTKCEoBSUEhJAapajPPc1vUAvl5cD3pyjYxP8tW97MDsd69cTfTQzVExchhJTh/v376g5BI9FpHUJIjSg+tr6xsb7Mn5GxntwkaER9zB7eAWZ0lEkGnjZritgZ/wO23kWSqLX6giOEEFLt6AoBIaRGlDa2Ps1vULu0vnsP3nuigf+G/csX8HG896eIb+cCRxMHNUdHCCHy0tPTsXDhQgwYMABdu3ZVWOfMmTM4ePAgQkJC6MZiBegKASGkRpUcW5/mN6g9mv77FAP2HwLnv2Qg3dQc28aPQXw7FzVHRgghpYuOjsb9+/fRuXPnUut07twZDx8+xK5du2owMs1BVwgIIaQ+y8sBfotCwKEVMHvx/EO5nQf2jhmOl3mv1BcbIYQoISYmBoGBgdDSKv1nrZaWFgIDAxETE4MpU6bUYHSagRICQgipTxgG3Ff/gv/8IXQv7gNungTSX6P4WEnvPrKByVc/g3n9m9rCJIQQZT158gSOjo7l1mvTpg02bNhQAxFpHkoICCGkPmAYfHT7BrDJDYYP/1ZYJc3IEH96dUTuJ6MQ0Ogj4HUNx0gIIZVQWFhY5tUBKS0tLYiLza5OPqCEgBBC6jDt3Dx4nz0Hx5u3IMzIVFzJ0RuHunnihpk2wOHAkctTXI8QQmohc3NzJCQkoEOHDmXWe/ToEczM6vDcMVVACQEh9RyXy5Eb8lPRXAFE8wiy32PCxkiYvn0ntyz70ylgmtpC37kTYOOCV7c300zDhBCN5Obmht27d2PgwIHg8/kK6xQUFGDPnj1wd3ev4eg0AyUEhNRjXC4HRsZ64NFMs3XPk9sIXLkUBulpAIBCHhcvbFqjaeeRSO00DGJBA2hpcaFvrK/eOAkhpIpGjhyJgQMHYsqUKQgJCUGjRo1klqekpGDRokV48uQJVq1apaYoazdKCAipx7hcDnhc+bkBbAxt4NvUV42Rkcoye/UaTn9EAed/h4GkEACQz+djx+jhKGzthi8cvgBS3wNiiZojJYSQ6tG6dWssXrwYS5Ysga+vLxwcHGBlZQUASEpKwu3bt8EwDP73v//B1tZWzdHWTpQQEELYuQGkzHSoj6Uman33Pgbu2w8tcSFbli4U4uehg5FsZYnGaoyNEEJUadCgQWjVqhU2b96MK1eu4Pr16wAAXV1ddO7cGV988QWcnZ3VGmNtRgkBIRpEUX9/iYShGX7ru2cP0HvjGjR5dP9DGYeDp7b22Ne3J7IaNFBfbIQQUkNcXFywadMmSCQSpKamAgCMjY3BpW6x5aKEgBANUVp//0KJBGmp2ZQU1EN679/D9q+DwMkxaJKdwZa/trGD+Yo/cfzffciiG4UJIfUMl8uFqampusPQKJQQEKIhFPX3N9M1Q4B1ALhcDiUE9Yjhq5dwPX0MHf66JlOeLhTijqM93vadAP8GxmqKjhBCiKahhIAQDVOyvz+pP/TepAC7BmDIn4fklj2ztcfugN7I0dODo4FQDdERQgjRVJQQEEJILccTi9Hm9l10P3YCyM1mywv4Wnht5wLLSZGIybmCHEoUCSGEVAIlBIQQUovZPEzAp78el5lcrECgjSvu7XHRqyOsm7ojwNoZuH1FfUESQgjRaJQQEEJIbXT3EgZ/uxhGr1/Jljt4YfegvnjCzVJPXIQQQuocGoeJEEJqEd3sbLjs3gzM6CiTDLwzMcbFifOAlWeRa0DDiBJCCKk+dIVAgxw/fhxHjx7FnTt3kJ6ejqZNm+Kzzz7DkCFDZMbYjY2NRVhYGBITE2FhYYFRo0Zh6NChaoycqEN9m7OAx+Mq/H91bbc6t6mIICcbbpf/QpfTZ6GXnSOz7FJHD/zRswfamDsBPF6V91VTbSKEEKIZKCHQINu3b4elpSW+/PJLmJqa4sqVK/jmm2/w7NkzzJs3DwAQHx////buPSzKMv8f+HtmYEQOAwyoiEgKCIGOoKViKrge1naVzNByV1PLyETTr0VlLfnbNdJyXU0tDYNaLSvDSMuN1NAwt8wUT3hKDooCcobhOAPM/P5wmRgYFGSOzPt1XV4593M/93N/HBrm8zz3AdHR0Zg+fTpWrlyJ9PR0xMXFQSwWY9asWSaOgIzFmvYscLR1hEqtgkTSU+9tCgVCvbark1oNHPgIf9n2Ouxqa34v7+mInydPQeqw+9Foq5+PakP8WxERkeVjQmBB3n//fUilUs3r0NBQ1NbWYteuXVixYgXEYjHee+89BAUFYc2aNZo6BQUF2LRpEyIjI7lbn5Wwpj0L7ER2EAq0Y/Vz9sPE/hPNqk1dBCoVHvhkK/DzEdi1KL/x4Bj0f/ELnCv4Bo16XDnIWHEREZFlYUJgQVomA80CAwOhUChQUVEBFxcXHD9+HDExMVp1IiIi8MUXX+DixYsYMmSIsbpLZsCa9ixoGau7nbvZttlMWlqGR776GgOuXdeU5QwcgO//OBHS4D+hv5snYKC3zpBxERGR5WFCYOFOnToFFxcXuLm5IScnBw0NDfDx8dGq4+fnBwDIyspiQkBkaidS8Nd/vQan8t+XEYVAgJN/nIr9Y4cDAgHapv5ERESGw4TAgp0/fx7JyclYsmQJRCIRKisrAQASifYupc2vm4/rMnFi+0MGDhw4AJFIBBsb/Q436u4TG/Ud353aaX2so3XvpW+t49LVRlfavdfjlsC2thpY9QScVE2asjqJC3qu2odTwkuAlTzNISLdusPnHFkmJgQWqri4GMuWLYNMJkNUVJTWMYFAoPOc9so7ytXVoUvnt6e7T3A0RnyduUZX+9P6fH3F121/DtRqBF68jMCLlyA7l3F7EvH//PxQKEqnR2Ha0DAg45IJO0lE5qDbfg6S2WNCYIGqqqoQFRUFOzs7bNu2Dba2tgAAZ2dnAG2fBMjlcgBtnxy0lJqaesdrqtVqlJfX3LFOZ4lEt1dwkcvr0NSk0mvb5kDf8TW3p0vra3S07p3qtaf5/DvF15V223MvbZqaQ3kZnk38AJ75be/871wwF9l+vpA5OZugZ0RkjvT9+9DFxb7LNwPJOjAhsDAKhQKLFy9GSUkJdu/eDVdXV80xb29v2NraIjs7G2FhYZryzMxMAICvr2+Xrt3YaJgv7U1NKoO1bQ6MEV9nrtHV/rQ+X1/xdaufA1UTsGcD/pK4CqKmxt+LRTYQjn4EXwcPRHZfRxN2kIjMUbf6HCSLwoTAgjQ2NmL58uW4fPkyPvnkE/Tr10/ruFgsRmhoKFJSUrBgwQJN+f79+9GrVy8EBQUZucdE1ifwwiVM+2YjUC1H8xZiNQ72ODhlMgRjZ+LRIXNRkBHP+QJERGQ2mBBYkNWrV+PIkSN46aWXUF9fjzNnzmiO+fn5wdHREUuWLMHcuXMRGxuLiIgIpKenIykpCatXr+YeBEQG5pOZjVmfJ0Go1t7nITHqaZS5u0FmZ1lDnoiIyDowIbAgx44dAwD885//bHNs586dGDVqFIYNG4atW7diw4YN2Lt3Lzw8PBAbG8tdiokMzP1mLqZ9uvv3ZMDTD8eHyXB0qC9qHQwzIZ+IiEgfmBBYkMOHD3eoXnh4OMLDww3cGyICAKFSAcS/iMgvN2jKbg0eBo/1J3D2UiJqOTSIiIjMHBMCIjMkFAogFGqvDMH1qc2Pa1k5pvxrOVBeoinL6+eJk0//H6aL+PFKRESWgb+xiMyMUCiAi6s9RJzzYbYEKhXG/PhfTDqk/dTu3FAZ/vPIn+Hfk0OEiIjIcjAhIDIzQqEAIqEQX2Z9iZK63+88+zn7YWL/9neUJiNoUCLw5x/x5H+SYV9b93u5rRj7Fi3DaQ8mAkREZHmYEBCZqZK6EhS0GH/ubuduwt5YN2GTCl6/HgPefBVheVe1jlV6esP5ld241XSWS4kSEZFFYkJARGap9TwKU8yh6FFTjT+kHkbI6bNwrpRrHStzdUXahHCoJs5FpF8okHHW6P0jIjKU5ORkvPrqq23Ko6KiEBMTo3mdlpaGjRs3IisrCx4eHliwYAHmzJnT5rzExETs2rULxcXF8Pf3x8svv4xRo0YZNAbqOCYERGR2TDmPwrW0DIE/7QaKEjD/1AEIWu0pUODjh/9MCkOelxfUQgFkRu8hEZHxJCQkwMnJSfO6T58+mr+fPn0a0dHRmD59OlauXIn09HTExcVBLBZrLXeemJiIjRs3YsWKFQgKCkJSUhKioqKQlJSEgIAAo8ZDujEhICKzo2sehaHnUNhVV+GBU6fwx+8OoodCCQBouc5T2X1+kD67BV/bXUNB3S2D9YOIyJwMHjwYUqlU57H33nsPQUFBWLNmDQAgNDQUBQUF2LRpEyIjIyEUCqFUKrFt2zbMmzcPCxcuBACMHDkSEREReP/997Fx40ajxULt4zImRGS2mudRFNQWoEJRYZBruF+9CLwYjnn/72VE7NuvSQYAoM7RCRmywdi2ZBGOvPIWMOJhQCC4Q2tERNZBqVTi+PHjmDp1qlZ5REQEiouLcfHiRQBAeno6qqqqMG3aNE0dkUiEP//5z0hLS4O61VNYMg0+ISAi66RWI/Sn4wj77ntArdJ6GpAXMgr9/m8XdpYe0jwN6G2aXhIRdUl+fj6efPLJdo+npqbe8fxp06ahvLwcnp6eePzxx/HMM89AJBIhNzcXDQ0N8PHx0arv5+cHAMjKysKQIUOQlZUFAG3q+fr6oqamBoWFhfDw8LiX0EiPmBAQkdURKxR4bM9XuP/SFU1ZtYsrMgIHIXOQH3qOmoFIT1+g7HsT9pKIyHR69eqF559/HsHBwRAIBDh8+DDeeecdFBYWYtWqVaisrAQASCQSrfOaXzcfl8vlEIvFsLOz06rn7OwMAKioqGBCYAaYEBCRVbFR1GPujl3wzr3xe+GUp/HZpAeQpygCAMg4LIiIuglPT8+7PgXQZdy4cRg3bpzm9dixY9GjRw/s2LEDzz33nKZc0M7nZctyXXWahwq1dz4ZF+cQEJH1kJciYutGTTKg6CHGqTnPAS8mQiUSmbhzRETm7U9/+hOamppw6dIlzR3+5icBzeTy20s0Nz8pkEgkUCgUUCgUOus1t0OmxYSAyIiEQgFsbIRaf1qutW8MItHv1zbF2v4mtf4p9L5xHQCgFNvio2eewrUxk0zcKSIiy+Pt7Q1bW1tkZ2drlWdmZgK4PUeg5X+b5xI0y8rKgoODg9YypmQ6HDJEZCTtra3fpFKhorwWKpVhV1pwtHWESq2CRNLToNcxR36/ZWL80U+Ba7/vMrzn8Zm41dcDvUzYLyIiS/Ltt99CJBIhKCgIYrEYoaGhSElJwYIFCzR19u/fj169eiEoKAgAMHz4cDg5OeHbb7/VlDU1NSElJQXh4eEcMmQmmBAQGYmutfXde7oj0jcSQqHA4AmBncgOQoFx1/Y3NZFSialf/wcjTpzUKj888Q/47X5/E/WKiMj8LVy4EKGhofD3v/1ZmZqaii+++ALz5s1Dr163b6UsWbIEc+fORWxsLCIiIpCeno6kpCSsXr0awv/d/BKLxVi8eDE2btwIqVSq2Zjsxo0b2LBhg8niI21MCIiMrHltfXO4vrudu8n6YXBJ6zF/x99gq/x9XwFI++LUA8NwbOxw0/WLiMgCDBw4EHv27MGtW7egUqkwYMAAvPbaa1pLmA4bNgxbt27Fhg0bsHfvXnh4eCA2NlZrl2IAePrpp6FWq/Hxxx+jpKQE/v7+2L59O3cpNiNMCIjILLScz9CVuQ0uZeUYdmA78ONB2LYoPzPrKYQsTMDJix9AZcKEjIjIEsTGxnaoXnh4OMLDw+9YRyAQ4JlnnsEzzzyjj66RATAhICKT0svchhtXELpvDzwun0OfwiKtQ5cDA/DTmIfgPHwqQoRWNomaiIioA5gQEJFJdWVug3NFJcZ8GgdcPIPg1gdte+Do9EgcDhkEAJDpud9ERETdBRMCIjILnZnbIGxS4cETv2L8kTTY19ZpHbvl0QelD47H4NnrcKkoBeDwICIiojtiQkCkg1Ao0Lk/gEqlNvhqQPei9Zj7bru/gEqF+zLO4tH/fKk9NMhegpPjwvDfwT4ol7pCJpVhcG9voKj9poiIiOg2JgRErbS3XwBgvD0DOspa9hYQNqnglnkR+Oc/8HDOea1juQ+OhffLyTh1MxnlfBpARETUaUwIiFrRtV8AYNw9AzpK1/h7oJvsL6BWw/3GdQSmH8OQ8xmQlpVrHc7r54lvI/4M6dCH4e3SC7hpon4SERFZOCYERO0w9X4BndG6r5a8v0DvW4UY8sMZ4HwMIguvtTmuFgiwP+LPSH/wAaiFAkiN3kMiIqLuhQkBEZmMWKFAnwungV8uIezKAfS+ehFupWU661Z4DYBLxP8hybEUF5276RwJIiIiE2BCQERGF3LqNMb++F+4lpVDpFIBAAJbVxKKkD/QBxd8vXE58H54BvwBkX6RKM+I58pBREREesSEgIiMQ60GLv6MCR8nYtDpX3VWUQkFyPX2RsWICQiZ9Ta+uZmsGQrlacy+EhERWREmBBbk+vXrSExMxNmzZ3H16lX4+Phg//79beqlpaVh48aNyMrKgoeHBxYsWIA5c+aYoMdEgEClRu/CQoz7OBm4ch6DWhxT2tri5qjx8BkzH18qL+CqvQr1Pe0gk8oQwonCRERERsGEwIJcvXoVaWlpCA4OhkqlglrddqWb06dPIzo6GtOnT8fKlSuRnp6OuLg4iMVizJo1ywS9po5ouW9Ad9pDwL6iHIvf34beRcVa5bU9e+JE6EgcHR+Gwb2C4eMXiZKMeNRzKBAREZHRMSGwIBMmTMCkSZMAACtXrkRGRkabOu+99x6CgoKwZs0aAEBoaCgKCgqwadMmREZGQqhjbX0ynW69j0B+Fh55bwOcS1skA7Y98N+pj+BI8CA02tqarm9ERESkwW+HFuRuX+aVSiWOHz+OqVOnapVHRESguLgYFy9eNGT36B603EcgPiMe8RnxSL2Raupu3TOBvAQ2V45DfORj4MUwTTKgFNvi/KNzgYSLyAibyGSAiIjIjPAJQTeSm5uLhoYG+Pj4aJX7+fkBALKysjBkyBBTdI3uouU+Ama9h0BTE4S5GbAtL4aNsgYQNsAuNwuCnLOwzU6HqPh6m1MqnSXY9eRf0SdoEmR9fYBSE/SbiIiI2sWEoBuprKwEAEgkEq3y5tfNx3WZOLH9XW0PHDgAkUgEGxv9PlBqHitvbmPm79afjva3dXzmFmdnOFZVY0zKZ8Bvb8K5+IbWsTsNdir28sbOv85ElcQJfQzbRSIii2fJvyfIsjEh6IYEAkGnyjvK1dWhS+e3x9LGz3e2v5YWX0t2VXJMOJyKMcd+gqhJdefKYjugry8weAzg5Q/4DsNXoiuoqi80TmeJiCycJf++IMvGhKAbcXZ2BtD2SYBcLgfQ9slBS6mpdx63rlarUV5e08UeahOJhJBIekIur0PT3b5sGlFzv9rT0f62ju9u7ZqTnmXFwObF+OvBj2CrVGgda7x/NBoCHoLA3gl2rlLAXoLDwnxkuUugsrn9keLn7IeJ/SdAnXHVFN0nIrJI+v596OJi3+WbgWQdmBB0I97e3rC1tUV2djbCwsI05ZmZmQAAX1/fLrXf2GiYL+1NTSqDtW0Ine2vpcRn09AA/ytX4ZOVjWFnzgMNSjRP/VUJBbjpHwTv53eiyj0AjY0q2NgIYfe/p0ZXM+Jvz4FQ3q5v1vMgiIjMlKX8vqDuhwlBNyIWixEaGoqUlBQsWLBAU75//3706tULQUFBpuucGRMKBRAKf7+D0tk5BCqVGipV2z0hLIZajf6XMjAz+VO4lZZpHWoQi3F6WDB+DB8HRw9/LBo0HNDzkyIiIiIyLSYEFqSurg5paWkAgLy8PFRXV+O7774DAIwcORJSqRRLlizB3LlzERsbi4iICKSnpyMpKQmrV6/mHgQ6CIUCuLjaQ9SBf5v29gxoUqlQUV5rcUmBWKFA/xNHgY1r8eerp7SONYp7wOaRpfhkWD9cV98eguZoik4SERGRwTEhsCClpaVYvny5Vlnz6507d2LUqFEYNmwYtm7dig0bNmDv3r3w8PBAbGwsdyluh1AogEh4ex+AkroSAM3j39uuutRyz4Dmuu493RHpGwmhUGD2CYFtbTXw/ScY89PnsC3Jx33XrsOuXnt+QJnUFWnjwyAYOxOPDpkLZUY8UNv+6lRERERk+ZgQWBAvLy9cuXLlrvXCw8MRHh5uhB51H53ZB6BlXUvgVlyCP/3nO/hk5wAqFXTtRFHSrz9Sw0bjcmAA1EIhZHaWMfmZiIiIuo4JAZml1uP6gW4wVt+IxNVVwFebMPOrdXC7la+zTqNIhOLAYPSd8xa+FGehoO6WkXtJRERE5oAJAZmd9sb1W+pYfYMrL8LAs6fgVZaH3oWF8L2aDWl5OQDArUW1Whcp7CcuwLfSBlxytUG1oyOGuAcj0m8ykJFtmr4TERGRyTEhILOja1y/JY3VNxZRYxOGfPUxcGgm/niHeoV9eiNzkB+KI6Px6OA5uJERjyoLGvJEREREhsWEgMyWpY3VNwq1Gi6Ft+B14QQmH/weYmVDmyqNNiJU9veF2wMRSBpgjwsut5+0yHrYGbu3REREZAGYEJBF0bVHgK65BbrmILRX1xCEQoGmr63/ey961tYh6NhnwJmX8MStnDbH830G4fTgQSh3dUHuffchoO8DiPSLRFlGPMCkioiIiO6ACQFZhPb2AADazi24094CxpiH0Pr6uvrcESKlEmN+/C9kZ8/D41ahzjp1zlL0XLYd37jyaQoRERHdGyYEZBF07QEA6J5boGsOQnt1DaG967e3vwEAQK1G78IieOXeQEDdWaAsAfPPHYGtUtGmarWzC04OH4qSXr0gHPc4HguIBDLiDRUOERERdXNMCMiidGZegannILS+fuv9DZzLK+CTcQD4eCfmnT+CntVVWsdtW/z9lkcf1AwKge8fnsVnjgXIUxYDAGQikcH6T0RERNaBCQGZnEAggI3N78N77mWsfctz7nZ+Z+rqk0NJIfDfVZh04Vs45eeid1Gx5piuQUU1ThJcGeSD9BEP4GZ/L8ikMvj6PQZVRjygNFq329DHnAgiIiIyH0wIyOQkEjsIdYz374g7zS3oSt1OUSogykqHqCAbglo5RE0KwEaN4Td+RH1dOexra3HftetwLykFAPjqaKLe3gE3+vVFtu9AOPiNwtgRT+OTkgNmtVmYwf79iIiIyKSYEJDJCVuNt7/jWPtWdM0taO/8ztTV2c+GBuD7jzHxUAJsyothX1sLxzoFULMEElVTm/oj7tCWSiBAnlc/yEPGYvDDL2FH3S8oqL89cVgmlQF9BwKlbVdJMqWu/vsRERGReWJCQGah5Xj71mPt9X1+h+oWXseQo4cx4UI6nCsr4VBTA4eaWgCAX6d7d1utixT2U55FkpcNrjiq0WhrA5lUhsF+w4GMX++xVePr6ntFRERE5oUJAZG8DH0zr6DfjSvoXVSEgaVJQPZljFGp7nia0tYWCkcnOLkNgLKvPxr7D4ba0RUCO3vYO0vwbcERFDdVocHWBnKJBPcNeAiR/rNQlhGPRi4RSkRERGaCCQF1e8KmJriVlqH3rTPAtVqMPPkVnHIz4VxZCUelCqgswyN3aaPKyRENrn0g9R2J//j3wpk+jmgQ22KQ8yD8xf8vEAuEELc650ZGq1WO7nGeBBERkSnk5OQgLi4Op06dQs+ePTF16lTExMTAzs7O1F0jPWNCQJZN1QSU3YLbzVw4FufAqaoavo2XAXyPKTk/wb64AG4lpRC1uNs/rCPt9uiJK0NDcHzIIBR49kV9TzvIpDJE+kXiZkY8Gv73RZ/j6omIqDuSy+WYP38+PD09sXnzZpSVlWHt2rWoqKjA+vXrTd090jMmBGTWJBWV6FtQgAHqW8CvVzA6KxWoKIZTVRXcSkrhWFMDqFSYqePcAXdpWyEWQ9XTAT1dPHGxrxTZvSUo7tULbveH45Gh8/HDhe33tOcBx9UTEZGl+/zzzyGXy7F3715IpVIAgEgkQkxMDBYvXgxfX11r5pGlYkJAptWgBEry0PtaNpxLrsOhuhq+TZcAQSqm/nYEXr9d1qo+tJPNN4mEKHF3R1Hv3rDpF4DAgeNwqCYDGb0dUeksgcxtKCL9IvFjRrzmC73EwQkQmNcKP0RERMZ09OhRjB49WpMMAMCUKVPw2muvIS0tjQlBN8OEgEzG5twRYOMcoFaOGTqOe93l/Bp7eyjdPODqGYRLggoU9hSiyskJbp5D8NDgSHxcnIocUS1U/9tASyaVIdAvEtkZ8ajkpF4iIqJ2ZWVlITIyUqtMLBbD29sbWVlZJuoVGQoTAjIZ8U9JQK38jnXq7OxwJTAAqsBQDB/0MPaW/Re5onrUOjig3q6H5g7/0RZ3+GVSGeA3ArUN6VDV1hsjFCIiIrOUn5+PJ598st3jqampOsvlcjkkEkmbcolEgsrKSr31j8wDEwIymfqI5ehRXwnY2OK8uhRFdkC1oyPc+w7GmMGR+KToe2Ta1gECAWRSGYb7PYrCjEKU8e4+ERGRSajVagg4rLbbYUJAJqPqFwD8Yy8A4Kc2d/gfRI3yFMA7/ERERPfM09Oz3acAdyKRSCCXt32KX1VVxfkD3RAXRiciIiIiLb6+vm3mCiiVSuTm5jIh6IaYEBARERGRlrCwMBw/fhzl5eWaskOHDkGpVCI8PNyEPSNDYEJARERERFpmz54NJycnREdH48cff8TevXvxxhtvICIigk8IuiHOISAiIiIiLRKJBDt27EBcXByef/552NnZYdq0aYiJiTF118gAmBAQERERURsDBw5EYmKiqbtBRsAhQ0REREREVowJARERERGRFWNC0A3l5ORg4cKFCAkJwejRoxEXF4f6eq7nT0RERERtcQ5BNyOXyzF//nx4enpi8+bNKCsrw9q1a1FRUYH169ebuntEREREZGaYEHQzn3/+OeRyOfbu3QupVAoAEIlEiImJweLFi7lUGBERERFp4ZChbubo0aMYPXq0JhkAgClTpkAsFiMtLc2EPSMiIiIicyRQq9VqU3eC9Gf06NGIjIxss07w1KlTERISgjfffFPneRMnTmy3ze+//16vfWxJIBAAAFRqlVa5UCDsUJmh6lr6+eyreZ5vSX21plgtqa/WFKsl9VVfsRriK1nz71miO+GQoW5GLpdDIpG0KZdIJKisrLzndg39gSIUtH1Y1dEyQ9W19PONeS1r6itjNc9rWVNfrSlWY17L1OcD/PJOpsOEwEqo1eo7ftCkpqYasTdEREREZC44h6CbkUgkkMvlbcqrqqp0PjkgIiIiIuvGhKCb8fX1RVZWllaZUqlEbm4uVxgiIiIiojaYEHQzYWFhOH78OMrLyzVlhw4dglKpRHh4uAl7RkRERETmiKsMdTNyuRzTpk1Dv379EB0djdLSUrz11lsYO3YsNyYjIiIiojaYEHRDOTk5iIuLw6lTp2BnZ4dp06YhJiYGdnZ2pu4aEREREZkZJgRERERERFaMcwiIiIiIiKwYEwIiIiIiIivGhICIiIiIyIoxISAiIiIismJMCIiIiIiIrBgTAiIiIiIiK8aEgDotJSUF0dHRCA8PR0hICCIiIvDpp59CpVJp1UtLS8Ojjz4KmUyGyZMnY9euXW3a2rVrFxYtWoTQ0FAEBATgu+++a1Nny5YtCAgI0Pln1apVFh8fAFy+fBmLFi3C6NGj8cADD2D27Nk4evSo3mNrZooYs7KyEBUVhZCQEIwYMQIvvfQSysrKzDq+oqIirFu3DtOnT8ewYcMwduxYLFu2DNevX29zzerqaqxatQqjRo3CsGHD8NxzzyEvL69bxKdUKrFu3TrMmTMHISEhCAgIMNh7Z6oYz507h9deew1//OMfERwcjMmTJ+Ott95CdXV1t4gvLy8Pzz33HMLCwiCTyTT1cnJyukV8rS1evBgBAQFITEzUe2yA8eO7efOmzt+Bjz/+uEHiI+tjY+oOkOX56KOP4OnpiZdffhlubm745Zdf8Oabb+LGjRt45ZVXAACnT59GdHQ0pk+fjpUrVyI9PR1xcXEQi8WYNWuWpq19+/YBAMLDw7F3716d15s1axbGjRunVfbrr79i/fr1CAsLs/j4SkpKsGDBAvTv3x9vvPEGevTogU8//RSLFy/GZ599hqFDh1p8jNXV1Zg/fz569+6N9evXo76+Hhs2bMCiRYuwe/duCIX6vTehr/guXLiAgwcPIjIyEiEhIZDL5YiPj8esWbPw9ddfw8PDQ3PNF198ERcuXMDrr78OR0dHbN68GU899RS+/vprvW8KaOz46uvrkZSUBJlMhgceeADHjh3TazzmEGNKSgpycnLw9NNPY+DAgbh27Ro2bdqE9PR0fP755xb/M1pbW4tevXrhpZdeQp8+fVBUVIT4+HjMmzcP+/btg1Qqtej4WkpLS8O5c+f0Go+5xPfCCy9g1KhRmtcODg4GjZOsiJqok0pLS9uUrVmzRi2TydQKhUKtVqvVCxcuVM+cOVOrTmxsrHrMmDHqpqYmTVnz32/cuKH29/dXp6SkdKgPr7zyinrEiBGa6+mTsePbu3ev2t/fX52bm6spUygU6gcffFC9bt06vcTUmrFjjI+PVw8dOlRdUlKiKTt37pza399fffDgQb3E1JK+4qusrFQ3NDS0aXvw4MHqLVu2aMrOnDmj9vf3V//www+asry8PHVQUJD6008/1VtcLfvQmiHjU6vVapVKpVar1eovv/xS7e/vr7MP+mTsGHVd7/vvv1f7+/urf/nlly7H05op3sPWcnJy1P7+/uqvv/66K6HoZKr4FAqFevLkyeo9e/ao/f391QkJCfoKqU0fWjNkfJ39HUnUWRwyRJ2m605SYGAgFAoFKioqoFQqcfz4cUydOlWrTkREBIqLi3Hx4kVN2b3cdVMoFDh06BCmTJkCsVjc+QDuwtjxNTY2AgCcnJw0ZWKxGD169IDaQBuJGzvGixcvIjAwEG5ubpoymUwGFxcXHD58uAuR6Kav+CQSCWxstB+kSqVSeHh4oKioSFOWlpYGiUSi9cTK09MTw4cPR1pamj5D0/ShNUPGBwACgUDPUdyZsWPUdb2goCAAaPNvoQ+meA9bc3FxAfD7Z5A+mSq+xMREODk54bHHHtNjNG2Zw/tHpE9MCEgvTp06BRcXF7i5uSE3NxcNDQ3w8fHRquPn5wfg9ljyrjhy5Aiqq6sxbdq0LrXTGYaMb+LEiXB3d8fatWtRWFiI8vJybNmyBTU1NQb/pdaSIWNUKBSwtbVtUy4Wi5GdnX3vne4EfcVXUFCA/Px8rXOzsrIwcODANl+a/fz8uvzz3lGGjM9cGDvGkydPAgB8fX272POOMUZ8KpUKDQ0NuHnzJt544w307dsXkyZN0m8g7TB0fPn5+di+fTtiY2ONnsACxnn//v73vyMwMBCjR49GbGwsKioq9BoDWS/OIaAuO3/+PJKTk7FkyRKIRCJUVlYCuH3no6Xm183H79X+/fvRp08fjBgxokvtdJSh43NxcdFMzG2+w+zk5IRt27ZpfnkYmqFjHDBgAJKTk1FfX68ZT5+fn4/i4mLY29vrIYI702d8cXFxkEgkmDFjhqZMLpdrPeFp2V5Xf947wtDxmQNjxyiXy7Fx40aMHj0agYGBeojgzowV38svv4xvvvkGAODt7Y2PPvpI58+uvhkjvrVr12Ly5MkYNmyYnnt/d4aOTywW4y9/+QvGjh0LiUSCs2fP4v3330dGRgaSkpJ03nAh6gw+IaAuKS4uxrJlyyCTyRAVFaV1rL07NF25c1NVVYW0tDRMnTpV75P8dDFGfKWlpViyZAm8vLywfft2fPjhhwgPD8fSpUu1huYYijFifOKJJ1BTU4NVq1ahsLAQubm5WLlyJYRCocHfR33GFx8fj8OHD2PNmjVwdnbuUlv6Yqz4TMnYMTY1NSEmJgZ1dXV48803u9b5DjBmfMuXL0dSUhI2b94Md3d3PPXUU8jPz+96EHdgjPiOHTuGY8eO4cUXX9RfxzvIGPH17t0bf//73zFp0iSMHDkSUVFR+Ne//oVLly7h0KFD+guGrBYTArpnVVVViIqKgp2dHbZt26a5Q9H8Idb6DohcLgfQ9o5JZ3z33XdQKpWIiIi45zY6yljxJSQkoLKyElu3bkV4eDjGjBmD9evXo3///ti8ebMeImmfsWIcMGAA1qxZgx9++AFhYWGYPHkynJ2dERYWBnd3dz1Eops+4/vqq6+wceNGxMbGYsKECVrHJBKJ5tzW7XXl5/1ujBWfKZkixtdffx2//vor4uPj0a9fP32FopOx4+vfvz+GDh2KKVOmIDExEQ0NDUhISNBnSFqMFV9cXBzmzZuHnj17Qi6Xa9pRKBQ6/9/UF1P+PxgeHg57e3tcuHChq2EQMSGge6NQKLB48WKUlJQgISEBrq6ummPe3t6wtbVtMzY8MzMTQNfG4+7fvx8+Pj6ayX6GYsz4MjMz4ePjgx49emjKBAIB7r//fty4caMLUdyZsd/DRx55BMeOHcM333yDo0ePYsuWLbhx4wZCQkK6FEd79BlfamoqYmNjsWjRIsyZM6fNtXx9fZGTk9NmEnhmZqbBxp8bMz5TMUWM69atw759+/DOO+8YZMnflkz9Htrb28PHx+eua/rfK2PGl5OTg/fffx8jRozQ/AGATZs2YcSIEVAoFPoOz+TvH5E+MSGgTmtsbMTy5ctx+fJlJCQktLmDJhaLERoaipSUFK3y/fv3o1evXvf8Zb6oqAgnTpww+GRiY8fn6emJ7Oxs1NfXa8pUKhUuXLhgsLuTpnoPxWIx/P390adPH/z888+4du2aQcaq6zO+EydOYMWKFZg+fTpWrFih83rh4eGQy+X48ccfNWUFBQVIT09HeHi4HiO7zdjxmYIpYvzggw/w4YcfIi4uziDvW0vm8B7K5XL89ttv6N+/f9eC0cHY8e3cubPNHwCYPXs2du7cqfcx9ubw/h05cgS1tbWQyWRdC4YInFRM92D16tU4cuQIXnrpJdTX1+PMmTOaY35+fnB0dMSSJUswd+5cxMbGIiIiAunp6UhKSsLq1au1xoyfP38eeXl5ml1Pz549C+D2smsjR47Uuu63334LlUpl8OFCxo7viSeewJ49e/Dcc8/hySefhI2NDb788ktcuXIFMTEx3SLG2tpabNmyBSNGjECPHj1w5swZbN++HUuXLjXIajb6ii8rKwvR0dHw8vJCZGSkVjuOjo6aSd/BwcEYP348/va3v2HlypVwdHTEpk2b0K9fP4MkPMaOD7i9tGpdXR0yMjIA3P4y4uDgAD8/P4NMfjd2jN988w3Wr1+PqVOnYuDAgVr1PDw8dG6AZUnxbdmyBVVVVRg+fDikUiny8vKwY8cONDY2Yv78+XqNzRTxtdysqyVvb+92j1lSfG+//TYEAgGCg4MhkUhw7tw5xMfHY8iQIUZbJYq6N4HaUAudU7c1YcIE5OXl6Ty2c+dOzYdvWloaNmzYgKysLHh4eOCpp55q8yh05cqV+Oqrr9q0M3LkSHz88cdaZZGRkRAKhUhKStJTJLqZIr5ffvkF7777Ln777Tc0NTXB19cXUVFRBvugN3aM9fX1WLJkCTIyMlBbWwsfHx/Mnz/fYMuq6iu+5ORkvPrqqzrbaf0eVldX4+2338aBAwfQ0NCAUaNG4fXXXzfIUx5TxNfeNZcuXYrnn3++K+HoZOwY2/s5BgwTo7HjS01Nxb///W/89ttvqK2t1azUFh0dbZAnBKb4GW0tICAAL7/8MhYuXNiFSHQzdnxJSUn47LPPcP36ddTX16NPnz6YNGkSli1bBkdHRz1HR9aICQERERERkRXjHAIiIiIiIivGhICIiIiIyIoxISAiIiIismJMCIiIiIiIrBgTAiIiIiIiK8aEgIiIiIjIijEhICIiIiKyYtypmIjIQrXe1EgsFkMikcDHxwdjx47FzJkz4ebm1ul2MzMzkZKSghkzZsDLy0ufXSYiIjPEhICIyMKtXbsWPj4+aGxsRGlpKU6dOoUPPvgAH374ITZu3IiHHnqoU+1lZmbi3XffxciRI5kQEBFZASYEREQWbtCgQZDJZJrXU6ZMwYIFC/DXv/4VS5cuxcGDB+Hu7m7CHhIRkTnjHAIiom7I09MTr7zyCmpqavD5558DAM6fP48VK1ZgwoQJGDp0KCZMmIAXXngBeXl5mvOSk5OxfPlyAMC8efMQEBCAgIAAJCcna+r89NNPmD9/PoYPH47g4GDMnj0bP//8s3EDJCIivWFCQETUTYWHh0MkEuHkyZMAgLy8PAwcOBCvvfYaEhMTERMTg+LiYsycORNlZWUAgPHjx+OFF14AAKxatQq7d+/G7t27MX78eADAvn378PTTT8PR0RFvv/023nnnHbi4uGDhwoVMCoiILBSHDBERdVP29vZwdXVFUVERAODhhx/Gww8/rDne1NSE8ePHY8yYMdi/fz/mzZsHqVSK++67DwDg5+eHkJAQTf26ujqsWbMG48ePx3vvvacpDw8Px4wZM7BhwwYkJSUZJzgiItIbJgRERN2YWq3W/L2mpgZbt27FwYMHkZeXh6amJs2xrKysu7Z1+vRpVFRUYMaMGWhsbNQ6Nm7cOCQkJKC2thb29vb6C4CIiAyOCQERUTdVW1uLiooK+Pv7AwBefPFFHD9+HNHR0ZDJZHBwcIBAIMCzzz4LhUJx1/ZKSkoAAMuWLWu3TmVlJRMCIiILw4SAiKib+uGHH9DU1ISRI0eiqqoKP/zwA5YuXYpnn31WU0epVKKysrJD7bm6ugIAXn/9dQQHB+uscy/7HhARkWkxISAi6oby8/Oxbt06ODk5Yfbs2RAIBFCr1RCLxVr1kpKStIYOAdDUqa+v1yofPnw4JBIJMjMzMXfuXMMGQERERsOEgIjIwl29ehVNTU1obGxEWVkZTp48ieTkZIhEIrz77ruQSqUAgBEjRiAxMRGurq7o168fTpw4gT179kAikWi1N2jQIADAF198AQcHB/To0QNeXl5wdXVFbGwsVq5cicrKSkyZMgVubm4oKyvD5cuXUVZWhn/84x9Gj5+IiLpGoG4544yIiCxGcnIyXn31Vc1rW1tbSCQS+Pr6YuzYsZg1a5YmGQCAwsJCvPnmmzh+/DgaGxsxfPhwvPLKK1i0aBFGjhyJt956S1N3x44d2LlzJwoKCtDU1IS1a9fiscceAwD8+uuvSEhIwJkzZ1BTUwOpVIrAwEDMmDFDaxUjIiKyDEwIiIiIiIisGDcmIyIiIiKyYkwIiIiIiIisGBMCIiIiIiIrxoSAiIiIiMiKMSEgIiIiIrJiTAiIiIiIiKwYEwIiIiIiIivGhICIiIiIyIoxISAiIiIismJMCIiIiIiIrBgTAiIiIiIiK8aEgIiIiIjIiv1/rhZHkMvD+aMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -117,7 +83,7 @@
    "source": [
     "name = \"Nextflow workflows\"\n",
     "df[name] = 1\n",
-    "df[\"First commit\"] = pd.to_datetime(df[\"first_commit_date\"], format=f'%Y-%m-%d')\n",
+    "df[\"First commit\"] = pd.to_datetime(df[\"creation_date\"], format=f'%Y-%m-%d')\n",
     "\n",
     "fig = plt.figure() \n",
     "ax = fig.add_subplot(111)\n",
@@ -149,14 +115,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Only keeping open workflows that leaves us with 677 workflows.\n"
+      "Only keeping open workflows that leaves us with 1257 workflows.\n"
      ]
     }
    ],
diff --git a/download_corpus.py b/download_corpus.py
deleted file mode 100644
index 6a4abaf14435e9c1ea054a9559617ac1d5e127c3..0000000000000000000000000000000000000000
--- a/download_corpus.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import json
-import os
-
-#with open("./wf_crawl_nextflow.json") as json_file:
-#    crawler = json.load(json_file)
-#
-#crawler.pop("last_date")
-
-def download(path, crawler):
-    #These are the licenses that we are keeping
-    LICENSE = ["Apache License 2.0", "GNU General Public License v3.0", "MIT License"]
-
-    #Code for downloading the files 
-    index = 1
-    downloaded = 0
-    for project in crawler:
-        print(f'* {index}/{len(crawler)}')
-        if(crawler[project]["license"] in LICENSE):
-            print(f"Downloading...'{project}'")
-            downloaded+=1
-            os.system(f"mkdir -p ")
-            os.system(f'git clone https://github.com/{project}.git {path}/{project}')
-        index+=1
-        print('\n\n')
-    print(downloaded)
-    
\ No newline at end of file
diff --git a/src/crawler.py b/src/crawler.py
index cde5a055b4ba390c458927355217b3362fd14323..12f079229149ce04401e162f03940f8511775641 100644
--- a/src/crawler.py
+++ b/src/crawler.py
@@ -5,7 +5,7 @@ from datetime import datetime
 from dateutil.relativedelta import relativedelta
 
 
-g = Github("YOUR API KEY")
+g = Github("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")
 
 
 def increment_month(x):
@@ -44,6 +44,11 @@ def check_rate(val):
         time.sleep(600)
         return 0
     
+def try_and_get_thing(thing):
+    try:
+        return thing 
+    except:
+        return None   
 
 #If you give this function the json file that already exists it will continue where it last stopped
 #The parameter start needs to follow the syntax '%Y-%m-%d'
@@ -117,8 +122,8 @@ def search(name='wf_crawl.json', start = '2017-01-01'):
 
                 val=check_rate(val)
                 if(nextflow_file_in_root):
-                     #Checking if there is a License
-                     if(repo.license!=None):
+                     
+                     if(True):
                         
                         #Adding the different information to the json file
                         wf_crawl[repo.full_name] = {}
@@ -140,27 +145,29 @@ def search(name='wf_crawl.json', start = '2017-01-01'):
                         #Nb stars
                         wf_crawl[repo.full_name]['stars'] = repo.stargazers_count
                         #License
-                        wf_crawl[repo.full_name]['license'] = repo.license.name
+                        try: 
+                            wf_crawl[repo.full_name]['license'] = repo.license.name
+                        except:
+                            wf_crawl[repo.full_name]['license'] = None
                         #Commits
-                        val=check_rate(val)
-                        commits = list(repo.get_commits())
-                        val=check_rate(val)
-                        wf_crawl[repo.full_name]['last_commit_date'] = date_to_string(commits[0].commit.author.date)
-                        val=check_rate(val)
-                        wf_crawl[repo.full_name]['last_commit_id'] = str(commits[0].commit._identity)
-                        val=check_rate(val)
-                        wf_crawl[repo.full_name]['first_commit_date'] = date_to_string(commits[-1].commit.author.date)
-                        val=check_rate(val)
-                        wf_crawl[repo.full_name]['first_commit_id'] = str(commits[-1].commit._identity)
-                        val=check_rate(val)
-                        wf_crawl[repo.full_name]['nb_commmits'] = len(commits)
-                        val=check_rate(val)
+                        #val=check_rate(val)
+                        #commits = list(repo.get_commits())
+                        #val=check_rate(val)
+                        #wf_crawl[repo.full_name]['last_commit_date'] = date_to_string(commits[0].commit.author.date)
+                        #val=check_rate(val)
+                        #wf_crawl[repo.full_name]['last_commit_id'] = str(commits[0].commit._identity)
+                        #val=check_rate(val)
+                        #wf_crawl[repo.full_name]['first_commit_date'] = date_to_string(commits[-1].commit.author.date)
+                        #val=check_rate(val)
+                        #wf_crawl[repo.full_name]['first_commit_id'] = str(commits[-1].commit._identity)
+                        #val=check_rate(val)
+                        #wf_crawl[repo.full_name]['nb_commmits'] = len(commits)
+                        #val=check_rate(val)
                 else:
                     nb=-1
                     
             except GithubException as error:
                 print(error)
-                break
         #Showing the progress
         print(f'Between {first_date} and {second_date}, found {nb_result} repo(s), total repo(s): {nb}')
 
diff --git a/wf_crawl_nextflow.json b/wf_crawl_nextflow.json
index cf9575dc6998d0163f6b1d91b5d1b5832651808e..aaf9f11ff414b9db0a50a83e5937c51c6e069eeb 100644
--- a/wf_crawl_nextflow.json
+++ b/wf_crawl_nextflow.json
@@ -2,12038 +2,33116 @@
     "HadrienG/nextflow_rna": {
         "name": "nextflow_rna",
         "creation_date": "2017-01-10",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "HadrienG",
         "contributors": "https://api.github.com/repos/HadrienG/nextflow_rna/contributors",
         "description": null,
         "forks": 0,
         "stars": 0,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-01-10",
-        "last_commit_id": "340532398a54ec0bab2c4a1cb965e1a04c0ce5e4",
-        "first_commit_date": "2017-01-10",
-        "first_commit_id": "4589ffee56c8a152d9baecdb8301a2dd29cb59eb",
-        "nb_commmits": 2
+        "license": "GNU General Public License v3.0"
+    },
+    "eivindgl/nextflow-rasqual": {
+        "name": "nextflow-rasqual",
+        "creation_date": "2017-01-18",
+        "actual_date": "2025-01-14",
+        "owner": "eivindgl",
+        "contributors": "https://api.github.com/repos/eivindgl/nextflow-rasqual/contributors",
+        "description": "nextflow pipeline for rasqual eQTL analysis",
+        "forks": 0,
+        "stars": 0,
+        "license": null
     },
     "IARCbioinfo/CODEX-nf": {
         "name": "CODEX-nf",
         "creation_date": "2017-01-16",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/CODEX-nf/contributors",
         "description": "Pipeline for copy number variant calling from Whole Exome sequencing data using CODEX",
         "forks": 2,
         "stars": 2,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-03-01",
-        "last_commit_id": "3579ec0877515ff247e03e1dafe911c782412346",
-        "first_commit_date": "2017-01-16",
-        "first_commit_id": "0ca2a91487490bc79b25c2b6d6348fae1598372e",
-        "nb_commmits": 37
+        "license": "GNU General Public License v3.0"
     },
     "AndersenLab/ril-nf": {
         "name": "ril-nf",
         "creation_date": "2017-01-08",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "AndersenLab",
         "contributors": "https://api.github.com/repos/AndersenLab/ril-nf/contributors",
         "description": null,
         "forks": 1,
         "stars": 1,
-        "license": "MIT License",
-        "last_commit_date": "2017-10-16",
-        "last_commit_id": "c33ee40934f2868a352d96cb2deb8b5ccbddbdac",
-        "first_commit_date": "2016-12-14",
-        "first_commit_id": "40e4de22c26e10a5b7494cb21960002d09abcbb0",
-        "nb_commmits": 118
+        "license": "MIT License"
     },
     "robsyme/markerdev": {
         "name": "markerdev",
         "creation_date": "2017-01-25",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "robsyme",
         "contributors": "https://api.github.com/repos/robsyme/markerdev/contributors",
         "description": "A nextflow pipeline for developing markers that differentiate one set of individuals from another.",
         "forks": 0,
         "stars": 0,
-        "license": "MIT License",
-        "last_commit_date": "2017-01-25",
-        "last_commit_id": "5f615cbdf7eba3b5ac465fa7077d5071ac0fe744",
-        "first_commit_date": "2017-01-25",
-        "first_commit_id": "ff89e9353e0151e3e24f188455a8c17f8e52050e",
-        "nb_commmits": 2
+        "license": "MIT License"
     },
     "CRG-CNAG/CalliNGS-NF": {
         "name": "CalliNGS-NF",
         "creation_date": "2017-02-28",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "CRG-CNAG",
         "contributors": "https://api.github.com/repos/CRG-CNAG/CalliNGS-NF/contributors",
         "description": "GATK RNA-Seq Variant Calling in Nextflow",
-        "forks": 51,
-        "stars": 128,
-        "license": "Mozilla Public License 2.0",
-        "last_commit_date": "2021-08-25",
-        "last_commit_id": "9e7ebf04455a24877a92c87216b9629ee2b6088d",
-        "first_commit_date": "2017-02-28",
-        "first_commit_id": "b6cc78c991a894646f2ddaa4304e48adaf4f66cf",
-        "nb_commmits": 171
-    },
-    "ctmrbio/BACTpipe": {
+        "forks": 55,
+        "stars": 132,
+        "license": "Mozilla Public License 2.0"
+    },
+    "KThorellGroup/BACTpipe": {
         "name": "BACTpipe",
         "creation_date": "2017-02-27",
-        "actual_date": "2024-02-15",
-        "owner": "ctmrbio",
-        "contributors": "https://api.github.com/repos/ctmrbio/BACTpipe/contributors",
+        "actual_date": "2025-01-14",
+        "owner": "KThorellGroup",
+        "contributors": "https://api.github.com/repos/KThorellGroup/BACTpipe/contributors",
         "description": "BACTpipe: An assembly and annotation pipeline for bacterial genomics",
-        "forks": 7,
+        "forks": 8,
         "stars": 20,
-        "license": "MIT License",
-        "last_commit_date": "2022-09-06",
-        "last_commit_id": "63734656cb6ea3e8b2387a531e1d2881dccfeca3",
-        "first_commit_date": "2017-09-06",
-        "first_commit_id": "84409b618b65fd6305721c2dda3b67c60afc3090",
-        "nb_commmits": 510
+        "license": "MIT License"
     },
     "danielecook/nextflow-tutorial": {
         "name": "nextflow-tutorial",
         "creation_date": "2017-02-07",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "danielecook",
         "contributors": "https://api.github.com/repos/danielecook/nextflow-tutorial/contributors",
         "description": null,
         "forks": 0,
         "stars": 0,
-        "license": "MIT License",
-        "last_commit_date": "2017-02-15",
-        "last_commit_id": "8b6e052f939605dd91c1c2b65c649710412d6d79",
-        "first_commit_date": "2017-02-07",
-        "first_commit_id": "6e53545dab0f7aef524baf171be7650b15cda778",
-        "nb_commmits": 13
+        "license": "MIT License"
     },
     "IARCbioinfo/bamsurgeon-nf": {
         "name": "bamsurgeon-nf",
         "creation_date": "2017-02-21",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/bamsurgeon-nf/contributors",
         "description": "bamsurgeon with step of variant simulation encapsulated with nextflow",
         "forks": 1,
         "stars": 1,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2019-03-01",
-        "last_commit_id": "de9138f7dc9192b9adee5595bad9e4fe03782634",
-        "first_commit_date": "2017-02-21",
-        "first_commit_id": "a4657254417e9f09f850e4cdc45c3b8ee4aed579",
-        "nb_commmits": 7
+        "license": "GNU General Public License v3.0"
+    },
+    "mmacpherson/toy-sklearn-nf-pipeline": {
+        "name": "toy-sklearn-nf-pipeline",
+        "creation_date": "2017-02-19",
+        "actual_date": "2025-01-14",
+        "owner": "mmacpherson",
+        "contributors": "https://api.github.com/repos/mmacpherson/toy-sklearn-nf-pipeline/contributors",
+        "description": "Simple sklearn example motivated by real work to learn nextflow.",
+        "forks": 1,
+        "stars": 1,
+        "license": null
     },
     "nextflow-io/cwl2nxf": {
         "name": "cwl2nxf",
         "creation_date": "2017-03-21",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "nextflow-io",
         "contributors": "https://api.github.com/repos/nextflow-io/cwl2nxf/contributors",
         "description": "Import a CWL workflow specification to Nextflow script (experimental) ",
         "forks": 5,
-        "stars": 25,
-        "license": "Apache License 2.0",
-        "last_commit_date": "2018-08-09",
-        "last_commit_id": "099599db5917b8406f9156b09b29bc5495eb0a70",
-        "first_commit_date": "2017-03-22",
-        "first_commit_id": "f8ac4abf48d8cefaf882ac07513cf7e6ac3401db",
-        "nb_commmits": 137
+        "stars": 27,
+        "license": "Apache License 2.0"
     },
     "IARCbioinfo/nf_coverage_demo": {
         "name": "nf_coverage_demo",
         "creation_date": "2017-03-03",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/nf_coverage_demo/contributors",
         "description": "Nextflow demo plotting mean coverage over a series of BAM files",
         "forks": 3,
         "stars": 0,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2020-07-28",
-        "last_commit_id": "828c77b60dcca9d5efe97539aac9a603050e816e",
-        "first_commit_date": "2017-03-03",
-        "first_commit_id": "37b8c12774a8140448ec341e49965ffc37f981d0",
-        "nb_commmits": 25
+        "license": "GNU General Public License v3.0"
+    },
+    "rwtaylor/nf-freebayes": {
+        "name": "nf-freebayes",
+        "creation_date": "2017-03-21",
+        "actual_date": "2025-01-14",
+        "owner": "rwtaylor",
+        "contributors": "https://api.github.com/repos/rwtaylor/nf-freebayes/contributors",
+        "description": "Nextflow freebayes pipeline",
+        "forks": 0,
+        "stars": 0,
+        "license": null
+    },
+    "vborjesson/Nextflow_test": {
+        "name": "Nextflow_test",
+        "creation_date": "2017-03-30",
+        "actual_date": "2025-01-14",
+        "owner": "vborjesson",
+        "contributors": "https://api.github.com/repos/vborjesson/Nextflow_test/contributors",
+        "description": null,
+        "forks": 0,
+        "stars": 1,
+        "license": null
+    },
+    "KevinSayers/SMuFin_Work": {
+        "name": "SMuFin_Work",
+        "creation_date": "2017-03-02",
+        "actual_date": "2025-01-14",
+        "owner": "KevinSayers",
+        "contributors": "https://api.github.com/repos/KevinSayers/SMuFin_Work/contributors",
+        "description": "Repo of working Nextflow and Dockerfiles for running SMuFin in Nextflow. ",
+        "forks": 0,
+        "stars": 0,
+        "license": null
     },
     "ezorita/chip-nf": {
         "name": "chip-nf",
         "creation_date": "2017-03-03",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "ezorita",
         "contributors": "https://api.github.com/repos/ezorita/chip-nf/contributors",
         "description": "ChIP-seq pipeline (Nextflow)",
         "forks": 0,
         "stars": 1,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-03-14",
-        "last_commit_id": "31e7bed81dc7d118dd0e906a9ea9bb33f2c692b1",
-        "first_commit_date": "2017-03-03",
-        "first_commit_id": "ad73f563a4fa830e65caa703a8c766dcaf9a6c3f",
-        "nb_commmits": 16
+        "license": "GNU General Public License v3.0"
     },
     "IARCbioinfo/damage-estimator-nf": {
         "name": "damage-estimator-nf",
         "creation_date": "2017-03-02",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/damage-estimator-nf/contributors",
         "description": "Nextflow pipeline to run \"Damage Estimator\"",
         "forks": 0,
         "stars": 0,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-06-14",
-        "last_commit_id": "84add3b5e205a00efdffcf19b2916e474931b567",
-        "first_commit_date": "2017-03-02",
-        "first_commit_id": "2d547ea79c40c627e34917b53b75ca75b2e86697",
-        "nb_commmits": 26
+        "license": "GNU General Public License v3.0"
     },
     "open2c/distiller-nf": {
         "name": "distiller-nf",
         "creation_date": "2017-04-25",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "open2c",
         "contributors": "https://api.github.com/repos/open2c/distiller-nf/contributors",
         "description": "A modular Hi-C mapping pipeline",
         "forks": 25,
-        "stars": 78,
-        "license": "MIT License",
-        "last_commit_date": "2023-07-12",
-        "last_commit_id": "e3dd888279e987325b186992ef5db7c437a0d22b",
-        "first_commit_date": "2017-04-25",
-        "first_commit_id": "ae55e30e8ea44d706cb5837d8a63435b0e219fec",
-        "nb_commmits": 298
+        "stars": 88,
+        "license": "MIT License"
+    },
+    "UBC-Stat-ML/nextflow-notes": {
+        "name": "nextflow-notes",
+        "creation_date": "2017-04-12",
+        "actual_date": "2025-01-14",
+        "owner": "UBC-Stat-ML",
+        "contributors": "https://api.github.com/repos/UBC-Stat-ML/nextflow-notes/contributors",
+        "description": "A template and short tutorial on nextflow for computational statisticians",
+        "forks": 5,
+        "stars": 7,
+        "license": null
     },
     "guigolab/ipsa-nf": {
         "name": "ipsa-nf",
         "creation_date": "2017-04-10",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "guigolab",
         "contributors": "https://api.github.com/repos/guigolab/ipsa-nf/contributors",
         "description": "Integrative Pipeline for Splicing Analyses (IPSA) in Nextflow",
         "forks": 5,
-        "stars": 7,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2019-07-19",
-        "last_commit_id": "d0336118f0fcbeed8512537874e6ec383497d03a",
-        "first_commit_date": "2016-05-06",
-        "first_commit_id": "4eacd257119140b67e0874fd28540358e2ecbad3",
-        "nb_commmits": 138
+        "stars": 9,
+        "license": "GNU General Public License v3.0"
     },
     "IARCbioinfo/table_annovar-nf": {
         "name": "table_annovar-nf",
         "creation_date": "2017-04-26",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/table_annovar-nf/contributors",
         "description": "annotation of variants with annovar and nextflow",
-        "forks": 2,
-        "stars": 5,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2021-02-24",
-        "last_commit_id": "b4ba036bfbe2fbc77999a2a12bbe21d22559b4e3",
-        "first_commit_date": "2017-04-26",
-        "first_commit_id": "253541bd3b6a275653327e09034d6f78bd52f0f5",
-        "nb_commmits": 41
+        "forks": 3,
+        "stars": 6,
+        "license": "GNU General Public License v3.0"
+    },
+    "mariach/upp-n-pasta-runner": {
+        "name": "upp-n-pasta-runner",
+        "creation_date": "2017-04-19",
+        "actual_date": "2025-01-14",
+        "owner": "mariach",
+        "contributors": "https://api.github.com/repos/mariach/upp-n-pasta-runner/contributors",
+        "description": "A dockerized nextflow pipeline to run upp and pasta multple sequence algners",
+        "forks": 1,
+        "stars": 0,
+        "license": null
     },
     "IARCbioinfo/BQSR-nf": {
         "name": "BQSR-nf",
         "creation_date": "2017-04-07",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "IARCbioinfo",
         "contributors": "https://api.github.com/repos/IARCbioinfo/BQSR-nf/contributors",
         "description": "Nextflow script for base quality score recalibration of bam files using GATK",
         "forks": 1,
         "stars": 0,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2022-06-15",
-        "last_commit_id": "de3d5c3d1d9a99ff0bfa7df9682e9b52f4caea28",
-        "first_commit_date": "2017-04-07",
-        "first_commit_id": "7655addba54b79436b52c8827305f4d281cca6ff",
-        "nb_commmits": 34
+        "license": "GNU General Public License v3.0"
     },
     "evanfloden/nf-module-demo": {
         "name": "nf-module-demo",
         "creation_date": "2017-05-24",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "evanfloden",
         "contributors": "https://api.github.com/repos/evanfloden/nf-module-demo/contributors",
         "description": "Demo for Nextflow modules and components proposal",
         "forks": 1,
         "stars": 6,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2018-04-02",
-        "last_commit_id": "76c112ecd89e713be2d72047353a006fb18d37ba",
-        "first_commit_date": "2017-05-24",
-        "first_commit_id": "74e0ee30bab13ee57ca1325a09acf8175209cab9",
-        "nb_commmits": 23
+        "license": "GNU General Public License v3.0"
     },
     "nmdp-bioinformatics/flow-GFE": {
         "name": "flow-GFE",
         "creation_date": "2017-05-24",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "nmdp-bioinformatics",
         "contributors": "https://api.github.com/repos/nmdp-bioinformatics/flow-GFE/contributors",
         "description": "Run fasta files through GFE with nextflow",
         "forks": 2,
         "stars": 1,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-08-17",
-        "last_commit_id": "f085a5f642920297a76a47aed108891ac00f626a",
-        "first_commit_date": "2017-05-24",
-        "first_commit_id": "092ac1b34dd2a30c14c978cb1ae0143efbc51db4",
-        "nb_commmits": 39
+        "license": "GNU General Public License v3.0"
     },
     "nmdp-bioinformatics/flow-OptiType": {
         "name": "flow-OptiType",
         "creation_date": "2017-05-23",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "nmdp-bioinformatics",
         "contributors": "https://api.github.com/repos/nmdp-bioinformatics/flow-OptiType/contributors",
         "description": "Use nextflow to run BAM files through OptiType",
         "forks": 1,
         "stars": 4,
-        "license": "GNU General Public License v3.0",
-        "last_commit_date": "2017-08-10",
-        "last_commit_id": "58cd96e50b3a138611da57ffee7769e48ea6a807",
-        "first_commit_date": "2017-05-23",
-        "first_commit_id": "1bd552e4a87d92cafabf182f095b9879967b81ad",
-        "nb_commmits": 15
+        "license": "GNU General Public License v3.0"
+    },
+    "maxemil/ALE-pipeline": {
+        "name": "ALE-pipeline",
+        "creation_date": "2017-05-31",
+        "actual_date": "2025-01-14",
+        "owner": "maxemil",
+        "contributors": "https://api.github.com/repos/maxemil/ALE-pipeline/contributors",
+        "description": "nextflow pipeline to automate analysis using ALE (https://github.com/ssolo/ALE)",
+        "forks": 1,
+        "stars": 4,
+        "license": "MIT License"
     },
     "thanhleviet/nextflow-ratt": {
         "name": "nextflow-ratt",
         "creation_date": "2017-05-17",
-        "actual_date": "2024-02-15",
+        "actual_date": "2025-01-14",
         "owner": "thanhleviet",
         "contributors": "https://api.github.com/repos/thanhleviet/nextflow-ratt/contributors",
         "description": "using RATT with nextflow and docker",
         "forks": 1,
         "stars": 1,
-        "license": "MIT License",
-        "last_commit_date": "2017-05-18",
-        "last_commit_id": "e9635e60b83ed53d60dbb30e270ab5da64801628",
-        "first_commit_date": "2017-05-17",
-        "first_commit_id": "e007b7d706e56fab36995703aa8b197e6312d92e",
-        "nb_commmits": 5
-    },
-    "maxemil/ALE-pipeline": {
-        "name": "ALE-pipeline",
-        "creation_date": "2017-05-31",
-        "actual_date": "2024-02-15",
-        "owner": "maxemil",
-        "contributors": "https://api.github.com/repos/maxemil/ALE-pipeline/contributors",
-        "description": "nextflow pipeline to automate analysis using ALE (https://github.com/ssolo/ALE)",
-        "forks": 1,
-        "stars": 2,
-        "license": "MIT License",
-        "last_commit_date": "2021-05-31",
-        "last_commit_id": "c8f17b11dd3496420cfcb4a5c29564d2257eabf4",
-        "first_commit_date": "2017-05-31",
-        "first_commit_id": "a32ab23eaf6926e84678f55abd23efd540567505",
-        "nb_commmits": 81
+        "license": "MIT License"
+    },
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+    },
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+    },
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 }
\ No newline at end of file