SlowInsulinCurves.ipynb

{
 "cells": [
  {
   "source": [
    "# ULTRA SLOW INSULIN (TRESIBA AND TOUJEO FOR EXAMPLE)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def objective(x, a, b, c, d, e, f, g,h):\n",
    "\treturn (a * x) + (b * x**2) + (c * x**3) + (d * x**4) + (e * x**5) + f + (g * x**6) + (h * x**7)\n",
    "\n",
    "def bilinear(xdata, xpeak=6, ypeak=1, totalDuration=24):\n",
    "        a1 = ypeak/xpeak\n",
    "        a2 = -ypeak/(totalDuration-xpeak)\n",
    "        b2 = ypeak - (xpeak*a2)       \n",
    "        return np.piecewise(xdata, [xdata <= xpeak, xdata > xpeak], [lambda x: x*a1, lambda x: a2*x + b2])"
   ]
  },
  {
   "source": [
    "## ULTRALONG"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# bilinear\n",
    "xpeak = 24\n",
    "ypeak = 1\n",
    "totalDuration = 72\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many days\n",
    "days = 1\n",
    "insulinPerDay = []\n",
    "for i in range(0,days):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    "\n",
    "\n",
    "#take tresiba data\n",
    "tresibaX = totalInsulinX\n",
    "tresibaY = totalInsulinY\n",
    "\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Insulina Basal Ideal',pad=15)\n",
    "ax.set_xlabel('Dias', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, int(max(totalInsulinX)/24) +1)) #converte de horas para dias\n",
    "plt.xlim(0, 3)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "plt.plot([number / 24 for number in totalInsulinX], 1.8*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# bilinear\n",
    "xpeak = 24\n",
    "ypeak = 1\n",
    "totalDuration = 72\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many days\n",
    "days = 7\n",
    "insulinPerDay = []\n",
    "for i in range(0,days):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=np.array(totalInsulinX), ydata=np.array(totalInsulinY))\n",
    "totalInsulinYSmooth = objective(np.array(totalInsulinX), *pars)\n",
    "\n",
    "   \n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Degludeca/Tresiba®',pad=15)\n",
    "ax.set_xlabel('Dias', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, int(max(totalInsulinX)/24) +1)) #converte de horas para dias\n",
    "plt.xlim(0, days+1)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinYSmooth)\n",
    "\n",
    "for idx,day in enumerate(insulinPerDay):\n",
    "    #if ((idx) % 3 == 0) or idx == 0:\n",
    "    #    plt.plot([number / 24 for number in day[0]], day[1], linewidth=2, alpha=0.8)\n",
    "    if idx < 3:\n",
    "        plt.plot([number / 24 for number in day[0]], day[1], '--', linewidth=2, alpha=0.5)\n",
    "\n",
    "#plt.plot(x, y_bilinear)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], 0.8*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "plt.xticks()\n",
    "plt.show()\n"
   ]
  },
  {
   "source": [
    "## NPH"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# bilinear\n",
    "xpeak = 7\n",
    "ypeak = 4\n",
    "totalDuration = 16\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many times during a day\n",
    "times = 1\n",
    "insulinPerDay = []\n",
    "for i in range(0,times):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    "\n",
    "#take nph data\n",
    "nphX = totalInsulinX\n",
    "nphY = totalInsulinY\n",
    "\n",
    "  \n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('NPH',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, 25))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "#plt.plot(totalInsulinX, totalInsulinY)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "#plt.plot(totalInsulinX, 2.5*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "for idx,day in enumerate(insulinPerDay):\n",
    "    #if ((idx) % 3 == 0) or idx == 0:\n",
    "    #    plt.plot([number / 24 for number in day[0]], day[1], linewidth=2, alpha=0.8)\n",
    "    if idx < 10:\n",
    "        plt.plot(day[0], day[1], '--', linewidth=2, alpha=0.5)\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# NPH variations\n",
    "\n",
    "# bilinear\n",
    "xpeaks = [6, 8, 10]\n",
    "ypeak = 4\n",
    "totalDurations = [18, 12, 15]\n",
    "\n",
    "xpeak = xpeaks[2]\n",
    "totalDuration = totalDurations[2]\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many times during a day\n",
    "times = 1\n",
    "insulinPerDay = []\n",
    "for i in range(0,times):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    " \n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('NPH',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, 25))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "plt.plot(totalInsulinX, totalInsulinY)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "#plt.plot(totalInsulinX, 2.5*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take nph data\n",
    "nphX1 = totalInsulinX\n",
    "nphY1 = totalInsulinY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take nph data\n",
    "nphX2 = totalInsulinX\n",
    "nphY2 = totalInsulinY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take nph data\n",
    "nphX3 = totalInsulinX\n",
    "nphY3 = totalInsulinY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Imprevisibilidade NPH',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, 25))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "plt.plot(nphX1, nphY1)\n",
    "plt.plot(nphX2, nphY2)\n",
    "plt.plot(nphX3, nphY3)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "#plt.plot(totalInsulinX, 2.5*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## LONG "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x504 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# bilinear\n",
    "xpeak = 5\n",
    "ypeak = 2\n",
    "totalDuration = 22\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many days\n",
    "days = 1\n",
    "insulinPerDay = []\n",
    "for i in range(0,days):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    "\n",
    "#take levemir data\n",
    "levemirX = totalInsulinX\n",
    "levemirY = totalInsulinY\n",
    "  \n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Detemir/Levemir®',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, int(max(totalInsulinX)) +1))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "plt.plot(totalInsulinX, totalInsulinY)\n",
    "#plt.plot(nphX, nphY)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "plt.plot(totalInsulinX, 1.2*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "\"\"\"\n",
    "ax.annotate(\"DETEMIR\",\n",
    "  xy     = (     levemirX[int(len(levemirX)/2)-5], levemirY[int(len(levemirY)/2)]),\n",
    "  xytext = (levemirX[int(len(levemirX)/2)-5], 1.5*levemirY[int(len(levemirY)/2)]), color='blue'\n",
    ")\n",
    "\n",
    "ax.annotate(\"NPH\",\n",
    "  xy     = (     nphX[int(len(nphX)/2)], nphY[int(len(nphY)/2)]),\n",
    "  xytext = (nphX[int(len(nphX)/2)], 1.2*nphY[int(len(nphY)/2)]), color='orange'\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# bilinear\n",
    "xpeak = 5\n",
    "ypeak = 1\n",
    "totalDuration = 24\n",
    "\n",
    "x = np.linspace(0,totalDuration, num=50)\n",
    "y_bilinear = bilinear(x, xpeak=xpeak, ypeak=ypeak, totalDuration=totalDuration)\n",
    "\n",
    "# smoothing\n",
    "pars, cov = curve_fit(f=objective, xdata=x, ydata=y_bilinear)\n",
    "y_poly = objective(x, *pars)\n",
    "\n",
    "\n",
    "#generate insulin curve for many days\n",
    "days = 1\n",
    "insulinPerDay = []\n",
    "for i in range(0,days):\n",
    "    offset = i*24\n",
    "    insulinPerDay.append([x + offset, y_poly])\n",
    "\n",
    "#generate combined action of these days\n",
    "noSuperpositionPct = (24/totalDuration)\n",
    "totalInsulinX = []\n",
    "totalInsulinY = []\n",
    "for i, day in enumerate(insulinPerDay):\n",
    "    xday = day[0]\n",
    "    yday = day[1]\n",
    "    offset = int(i*noSuperpositionPct*len(xday))\n",
    "    for j in range(0, len(xday)):\n",
    "        totalInsulinIndex = j + offset\n",
    "        if totalInsulinIndex < len(totalInsulinY):\n",
    "            totalInsulinY[totalInsulinIndex] += yday[j] \n",
    "        else: # continue expanding array by inserting new data\n",
    "            totalInsulinX.append(xday[j])\n",
    "            totalInsulinY.append(yday[j])\n",
    "\n",
    "#take lantus data\n",
    "lantusX = totalInsulinX\n",
    "lantusY = totalInsulinY\n",
    "\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Glargina/Lantus®',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, int(max(totalInsulinX)) +1))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "plt.plot(totalInsulinX, totalInsulinY)\n",
    "#plt.plot(levemirX, levemirY)\n",
    "#plt.plot([number / 24 for number in totalInsulinX], totalInsulinY)\n",
    "plt.plot(totalInsulinX, 0.7*np.ones(len(totalInsulinX)), '--', alpha=0.5,linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## COMPARATIVO"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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96+w6cEuA4u4sRERERERERPatswHuZuBuY8xJxpiexpj80Et3FigiIiIiIiKOznahfDtw/Rbh498MmsREREREREQkIjob4E7o1ipERERERERkvzq7kPcH3V2IiIiIiIiI7NteA5wxZiLwhbXWH7i9V1rIW0REREREpPvtqwVuPs7MkzsCty17LuINGgMnIiIiIiISEfsKcAOAypDbIiIiIiIiEkV7DXDW2rKObouIiIiIiEh07G8MXKdoDJyIiIiIiEj3298YuL2NewulMXAiIiIiIiIRsL8xcCIiIiIiIhIjOjUGTkRERERERKKvUwt5ax04ERERERGR6OtUgKPj8XA25LbGwImIHMJqa2vZsWMHHo8n2qVIF3K5XBQVFZGdnR3tUkREpJM6G+Daj4dzAROAnwE3dWlFIiISU2pra9m+fTt9+vQhLS0NY/Y3t5XEA2stTU1NbN26FUAhTkQkTnQqwO1lPNxaY0wN8Evg9S6tSkREYsaOHTvo06cP6enp0S5FupAxhvT0dPr06UN5ebkCnIhInEj4ks/fAIzvgjpERCRGeTwe0tLSol2GdJO0tDR1jRURiSOdncQkv/0moBdwK7Cqi2sSEZEYo26Thy7924qIxJfOjoHbSfikJeCEuM3ABV1akYiIiIiIiHSoswHuhHb3/UAlsNZa6+3akkRERERERKQjnZ3E5IPuLkRERERERET2rVOTmBhjjjfGHB1yf5Yx5mNjzEPGmMzuK09EROTgzZo1C2MMd9xxR9j2999/H2MMO3fuZOPGjRhjgpe8vDyOO+44Pvjgg7DXOeOMM/Z4/fnz52OMYePGjd39o4iIiACdn4XyfqAYwBgzDHgIWAxMBu7plspERES6QGpqKnfffTeVlZX73O+NN95g27ZtfPDBB2RnZ3PaaaexYcOGCFUpIiLSOZ0NcIOAJYHb5wH/s9b+ALgCOLM7ChMREekKJ5xwAqWlpdx+++373K9Hjx4UFxczduxYHnroIRobG3nrrbciVKWIiEjndHYSEwskBm6fCPwrcLsC6NHVRYmISGy77dVlLC+vjegxR/bO5pdnjjrg5yUkJPDrX/+as88+m2uuuYZBgwbt9zmti5ZrfTQREYk1nQ1wnwG3GGP+B0wDrgxsL8UJcSIiIjHrtNNO49hjj+VnP/sZzz///D73bWho4KabbiIxMZHjjz8+uP2NN94gMzN82Lff7++WekVERPamswHuWuBZ4KvA/7PWrgts/xowpxvqEhGRGHYwLWHRdvfdd3PMMcdwww03dPj4cccdR0JCAo2NjfTq1YvHH3+cMWPGhD3+17/+New5S5cu5ZxzzunWukVEREJ1dhmBpcDYDh66AfB1aUUiIiLd4Mgjj+S8887jxhtv5JZbbtnj8WeffZYxY8aQm5tLjx57jg5IT09n8ODBYduqq6u7q1wREZEOdbYFrkPW2uauKkRERKS73XnnnYwcOZI33nhjj8f69u3bqfFxIiIi0dSpAGeMyQf+H84EJkW0m73SWpvd9aWJiIh0rcGDB3PllVfywAMPRLsUERGRg9LZFrhHgQnAX4FynFkpRURE4s4vfvELnnjiiWiXISIiclCMtfvPYsaYWuBka+287i5o0qRJdv78+d19GBER6aQVK1YwYsSIaJch3Uj/xiIiscUYs8BaO6mjxzq7kPcOoL7rShIREREREZED1dkA9zPgV8aYzP3uKSIiIiIiIt2is2Pgfo6zaPcOY0wZ4Al90Frb0RIDIiIiIiIi0oU6G+Be7NYqREREREREZL86u5D3bd1diIiIiIiIiOxbZ8fAiYiIiIiISJTtswXOGLOETqz5pjFwIiIiIiIi3W9/XSg19k1ERERERCRG7DPAaeybiIiIiIhI7NAYOBERkRhjjOHFF9UJRkRE9qQAJyIih6xZs2ZxxhlnRLuMA7Zt2zbOPPPMTu9fVlbG9773PQYNGkRBQQFHHXUUDzzwAG63uxurFBGRaFCAExERiTHFxcWkpKTs9XGPxxO8/eGHHzJ9+nR69+7NM888wwcffMBNN93E66+/zoknnkhDQ0MkShYRkQhRgBMRkcPS8uXLOf3008nKyqKoqIhvfOMbVFRUAPDmm2+SnJzMrl27wp5z8803M27cuOD9OXPmcPzxx5Oenk6fPn34/ve/T21tbfDx6dOn8/3vf5/rr7+e/Px8CgsLeeCBB2hpaeGqq64iNzeX/v3789RTT4UdJ7QL5caNGzHG8NxzzzFjxgzS0tJ46KGHANi5cyfXXHMN7733Hp988gknnXQSxx9/PL/73e+47777OPXUU7nqqqu65f0TEZHo6NRC3iIiImFe/ylULInsMYvHwFd+3SUvtW3bNo477jguu+wy7r33XjweDz/72c8466yzmDt3LieddBI9evTghRde4Hvf+x4A1lqee+45fvCDHwCwZMkSZs6cyW233cYjjzxCVVUV1157LZdeemnY+LVnnnmG6667jnnz5vHKK69w7bXX8sYbb3Dqqacyf/58nnjiCS6//HJOPPFEevfuvdeab7rpJu69914effRRXC4XAA899BC//vWvKS0tpampiYceeoiJEydy/fXX89BDD/H73/+eqVOnUl5evs/XFhGR+NHpFjhjTJIxZoox5kJjzMWhl+4sUEREpKv9+c9/Zty4cfzmN79hxIgRjB07lieffJLPPvuM+fPnk5iYyIUXXsgzzzwTfM7s2bPZtGkT3/zmNwG45557uOCCC7j++usZMmQIRx99NH/+85/55z//yY4dO4LPGzVqFLfeeitDhgzhuuuuo6CgAJfLxTXXXMPgwYP5xS9+gbWWOXPm7LPmq6++mvPPP58BAwbQt29fAFauXMnMmTOD+1x++eVMmDCBzZs3M2vWLADOOecc5s2b11VvnYiIRFmnWuCMMcOBV4EBgAF8ged6gBbgye4qUEREYlAXtYRFy4IFC/jwww/JzMzc47F169Zx1FFHcdFFF/HAAw9QVlZGSUkJzzzzDNOnT6dPnz7B11i7di1///vfg8+11gZfo6ioCICxY8cGHzfGUFRUxJgxY4LbXC4XeXl5YaGvI5MmTdpjmzEGY0zw/l//+leWL1/Ou+++GzxGamoqXq93v++JiIjEh862wN0PLABygEZgBDAJ+AI4rzsKExER6S5+v5/TTz+dL774IuyyZs2a4KyVRxxxBMOHD+fZZ5/F4/HwwgsvcNFFF4W9xuWXXx72/EWLFrFmzRrGjx8f3K+1u2MrY0yH2/x+/z5rzsjI2GNbz549WbRoUfB+v379uPPOO8nJyeGee+4B4J133gkLjCIiEt86OwbuSOB4a22DMcYPJFlrFxpjfgL8ARi776eLiIjEjokTJ/KPf/yDkpKSPcJUqG9961s888wzjB49moaGBs4777yw11i2bBmDBw+ORMkduuKKK7j00kt56623mDVrFqWlpRhjePTRR3nuued4+eWXqampYfjw4VGrUUREulZnW+AMTssbQCXQJ3B7CxC9/7lERET2o7a2do+WttNPP52amhouuOAC5s2bx/r163n77be58sorqaurCz73oosuYvny5dxyyy2cddZZZGdnBx+78cYb+fTTT/ne977H559/ztq1a3nttdf47ne/G7GfbejQoVx66aVMnjyZoqIiiouLAad1sKamhuuvv57HH388YvWIiEj362wL3FJgHLAe+BS40RjjA64A1nZTbSIiIl/aRx99xIQJE8K2nXfeecyePZubbrqJU089lebmZvr378/MmTPD1l8rKSlh6tSpfPTRR9x+++1hrzF27Fg+/PBDfv7zn3P88cfj8/kYOHAg55xzTkR+rlaXXnopJSUl3HzzzZxxxhkkJSXhcrn4+te/zpw5c+jZs2dE6xERke5lWgdc73MnY04BMqy1LxljBgKvAcOBncDXrbXvd1VBkyZNsvPnz++qlxMRkS9pxYoVjBgxItplSCfU1NRQW1tLcXHxPruGtqd/YxGR2GKMWWCt3XP2KjrZAmetfTPk9npgpDEmH9htO5MARUREpNvl5OSQk5MT7TJERKQbHfRC3tbaqq4sRERERERERPZtrwHOGPMe0KnWNWvtjC6rSERERERERDq0rxa4pSG3E4FvARXAvMC2o4BewNPdU5qIiIiIiIiE2muAs9Ze3XrbGPM74AngmtAxb8aY+3GWGBAREREREZFu1tl14C4G/tjBhCV/Ar7dtSWJiIiIiIhIRw5kIe8xHWzvaJuIiIiIiIh0g87OQvkY8IgxZggwN7DtGOAnwN+6ozAREREREREJ19kA9xNgB3ANcGdg2zbg18B93VCXiIiIiIiItNPZhbz9wN3A3caY7MC22u4sTERERERERMJ1dgxckLW2VuFNRETiwaxZszjjjDO67fVvvfVWRo8e3W2vD/Dxxx9z9tln07t3b3r27Mkpp5zCK6+80q3HFBGR2HXAAU5EREQi43e/+x1XXHEFp512Gq+//jpvvfUW559/Pj/96U/50Y9+FO3yREQkChTgRETksPTb3/6WsWPHkpGRQZ8+fbj88suprq4OPv7444+TmZnJO++8w+jRo8nIyOCEE05gw4YNwcdvu+02li1bhjEGYwyPP/44AMYYXnzxxbDjlZaWcu+99wbvP/TQQwwdOpTU1FQKCws55ZRT8Hq9wcffeecd3nrrLRYuXMh1113H1KlTOfXUU3n77bf54IMPWLt2bfB4IiJy+OjsJCYiIiJBv/n0N6ysWhnRYw7PH86NR93YZa+XkJDA/fffz8CBAykrK+Pqq6/m6quv5qmnngru09LSwl133cVjjz1Gamoq3/nOd/je977Hm2++yQUXXMDSpUt57bXXeP/99wHIycnp1LHnz5/PVVddxRNPPMHUqVOprq7m3XffDdvnwQcf5KmnniItLY2GhgbWrFlDbW0tZ511Fh999BGPP/44p5xyCrNmzeqqt0REROKAApyIiByWrr322uDt0tJS7r77br761a/yxBNPkJDgdFDxer08+OCDDBs2DIAbbriBSy65BL/fT1paGpmZmSQlJVFcXHxAx960aRMZGRmcddZZZGVlUVJSwrhx48L2SUtLo6CgIHh//PjxtLS0cMoppzBjxgxyc3Pp1asXu3fvJi8v7yDfBRERiTedDnDGmLHADcBIwALLgXuttUu6qTYREYlRXdkSFi3vvvsud911FytWrKCmpgafz4fb7aaiooLevXsDkJKSEgxvAL1798bj8VBdXU1+fv5BH/vkk0+mpKSEAQMGcMoppzBz5kzOPfdcsrKygvskJiaGPWfOnDnceOONDBkyhNzcXABSU1PDul2KiMihr1Nj4IwxZwELgX7A68AbQH9goTHmzO4rT0REpOuVlZVx+umnM2LECF544QUWLFjAY489BoDb7Q7ul5QU/j2nMQYAv9+/z9c3xmCtDdvm8XiCt7Oysli4cCH/+Mc/6N+/P3fddRfDhw+nvLw8uM/u3btpaWkJ3h87dixPP/00zz//PLNnz8btdrNu3bqwVjoRETn0dXYSkzuA/2etPcFae0vgcgJwV+AxERGRuDF//nzcbje/+93vmDx5MkOHDg0LT52VnJyMz+fbY3thYSHbtm0L3t++fXvYfXDC4YwZM7jrrrtYvHgxDQ0NvPbaa8HHzz33XG644QYAfvnLXwLQo0cP/vGPf7B582Zuuukmzj777GCoFBGRw0Nnu1AOBZ7qYPtTwE+6rhwREZGuVVtbyxdffBG2bciQIfj9fu6//37OPfdc5s6dy/3333/Ar11aWkpZWRkLFy6kf//+ZGVlkZKSwowZM3jwwQeZMmUKiYmJ3HzzzaSmpgaf99prr7Fu3TqOO+448vPzee+996irq2PEiBHBfS655BI+/PBDzj77bG655ZZgq19GRgYvvvgitbW1/Oc//zmo90REROJXZ1vgdgBHdLD9CGB715UjIiLStT766CMmTJgQdvnVr37FAw88wG9/+1tGjhzJI488EjbFf2edd955nHbaaZx44okUFhby3HPPAXDfffcxcOBApk+fzvnnn8/ll19OUVFR8Hm5ubm8/PLLnHTSSQwfPpx7772XRx55hGnTpoW9/mOPPcbMmTM577zzSE5OJjk5mdNOO42xY8fyn//8B5fL9eXeHBERiTumfR/9Dncy5hbgeuAeYA7OJCZTcSY1ucda+/+6qqBJkybZ+fPnd9XLiYjIl7RixYqwliGJjh07duDz+SguLu7ybpP6NxYRiS3GmAXW2kkdPdbZLpR3APU4Ie72wLZy4JfA7790hSIiIrJPoS14IiJy+OpUgLNOM93vgN8ZY7IC2+q6szAREREREREJd8ALeSu4iYiIiIiIRMeBLOR9CfANnPXfkkMfs9YO7OK6REREREREpJ29zkJpjPnIGHNh4PaPgfuABUAp8DKwFMgHHuv2KkVERERERGSfywjMwlmoG+AK4Epr7U2AB/ijtfYsnFBX0q0VioiIiIiICLDvAHcL8GTgdl/g08DtJiA7cPs54LzuKU1ERERERERC7SvAnQ5sCdyuAAoCt8uAyYHbg3HWhBMREREREZFutq8AdxrQJ3D7XeCswO1Hgd8aY94D/g681H3liYiIiIiISKu9Bjhr7WfW2lsDd6/EWcwba+1fcMbHLQF+Bvyge0sUERGJfbfeeiujR48O3p81axZnnHFGFCsSEZFD0b5a4IKstX5rrTfk/t+ttf9nrf2jtdbTfeWJiIgcOGPMPi+zZs3q8mPecMMNfPDBB13+uq3q6+v51a9+xbhx4+jRowfDhw/nhhtuoLKystuOKSIisadT68AZY34IVFtrn263/SIg21r7p+4oTkRE5GBs27YtePu1117jiiuuCNuWlpYWtr/H48Hlcn2pY2ZmZpKZmfmlXmNvtm3bxsyZM5kyZQr33Xcfffr0oby8nBdffJEjjzyS//73v4wcObJbji0iIrGlUy1wwLXA5g62bwR+ZIw5wRjzO2PMyV1VmIiIyMEqLi4OXnJzc8O2NTc3k5uby3PPPceMGTNIS0vjoYceAuBvf/sbI0eOJDU1laFDh/K73/0Ov98ffN2HHnqIoUOHkpqaSmFhIaeccgper9NBpX0XylZ33HEHPXv2JDMzk0suuYSmpqbgY2+88QbTpk0jLy+P/Px8TjnlFFasWLHHa1x88cXcc889lJSUcPbZZzN58mSuvfZaTjrpJB599FEuvPBCPB51iBERORx0qgUOZxmBsg62bwEGAV/HWR/uaaBn15QmIiKxquLOO2lZsTKix0wZMZzim2/uste76aabuPfee3n00UdxuVw8/PDD/OIXv+APf/gDRxxxBEuXLuWKK67A5XLxwx/+kPnz53PVVVfxxBNPMHXqVKqrq3n33Xf3eYwPPviAtLQ03nnnHbZu3cqll17KjTfeyO9//3sAGhoauPbaaxk7dixNTU3ccccdnHnmmSxfvpzk5GQAZs+ezaRJkzj11FOZO3cul156KT/84Q95+umnue2221i8eDGnn346//znP7nwwgu77P0REZHY1NkAVwGMx2lxCzUR2Gqt/b4xZhDwna4rTUREpPtcffXVnH/++cH7t99+O3fffXdw24ABA/jpT3/Kn/70J374wx+yadMmMjIyOOuss8jKyqKkpIRx48bt8xiJiYn87W9/IzMzk9GjR/Ob3/yGyy67jLvuuouMjAzOOy98KdW//e1vZGdn8+mnnzJ16lQA5s2bx9e+9rXgPn/5y1949NFHyc7O5u677wbg/PPP58knn1SAExE5DHQ2wD0L/N4Y0wC8H9h2AnA/8EzgfiVwTFcWJyIisakrW8KiZdKkScHblZWVbN68me9+97t8//vfD273er1Y6yx3evLJJ1NSUsKAAQM45ZRTmDlzJueeey5ZWVl7PcbYsWPDxsVNnjwZt9vNunXrGDt2LOvWreOWW25h3rx5VFZW4vf78fv9bNq0KayGlJSU4P3vfe97TJkyhWuuuYZp06YBkJqaGuzKKSIih7bOBrhfAgOANwFfYFsC8AJwC4C1thao7eoCRUREukNGRkbwdus4t7/85S9MmTKlw/2zsrJYuHAhH374If/73/+46667uPnmm/nss8/o3bv3QdVw5pln0qdPHx566CH69OlDUlISI0eOxO12B/cZNWoUb7/9NqNGjQIgPz+fCy+8kHXr1nHllVfy1ltv8c477zBmzJiDqkFEROLLfgOcMSYBGAJcgRPWJgAGWGitXdu95YmIiHS/nj170qdPH9atW8fFF1+81/2SkpKYMWMGM2bM4LbbbqOoqIjXXnuNK6+8ssP9lyxZQkNDQzAszp07l+TkZAYNGsSuXbtYsWIFDz74ICeccAIACxcu3KMlbebMmfzqV7/iK1/5CtOnTw9uv/HGG0lISGDOnDk8+OCDzJ0790u+CyIiEg860wJngS+AkYHAptAmIiKHnFtvvZWrr76a3NxcTjvtNDweDwsXLmTr1q3cdNNNvPbaa6xbt47jjjuO/Px83nvvPerq6hgxYsReX9Pr9XLppZfyi1/8gvLycn76059yxRVXkJGRQVpaGgUFBTz88MP069ePrVu38uMf/5ikpPD/ml0uF3/5y1847bTT+PGPfxwcD1dXV0dhYSEXXHABDz/8MHl5ed36/oiISGzY7zIC1un8vwoo7P5yREREouPyyy/nscce46mnnmLcuHFMmzaNv/71rwwYMACA3NxcXn75ZU466SSGDx/OvffeyyOPPBIch9aR448/nlGjRnHCCSdwzjnnMGPGjODEIwkJCfz9739n8eLFjB49mquuuorbb789bLxbqwkTJvDmm2/y5ptv0qtXL1JSUigsLOT555/n5Zdf5tRTT+2eN0VERGKOaR2cvc+djPkK8DPgh8Ai25knHaRJkybZ+fPnd9fLi4jIAVqxYsU+W5kkspqbm9mxYwc9evQIG8f3ZejfWEQkthhjFlhrJ3X0WGcnMfkHkAosALzGmJbQB6212V+uRBEREemM1NRU+vfvH+0yREQkSjob4H7YrVWIiIiIiIjIfnUqwFlrn+juQkRERERERGTfOtsChzEmBfgWMBJnZsplwHPW2pZ9PlFERERERES6xH5noQQwxowE1gC/BY4GjgHuB1YbYzTqWUTkENeNc1dJlOnfVkQkvnQqwAEPAJ8D/a2106y104D+wCKcICciIocol8tFU1NTtMuQbtLU1ITL5Yp2GSIi0kmdDXDHAjdba2tbNwRu/wyY2h2FiYhIbCgqKmLr1q00NjaqteYQYq2lsbGRrVu3UlRUFO1yRESkkzo7Bq4ZyO1ge07gMREROURlZzsrxZSXl+PxeKJcjXQll8tFz549g//GIiIS+zob4F4FHjbGXAHMDWybDDwEvNIdhYmISOzIzs7Wh3wREZEY0NkulNfgTGLyEU6LWzPwAbAauLZbKhMREREREZEwnV0Hrhr4qjFmMDACMMBya+3abqxNREREREREQnR6HTiAQGBbC2CMGWyMSbXWagyciIiIiIhIBHQqwBlj7gRWWWufMMYY4C3gRKDGGHOqtXZedxYpIocPn99SUdtMTaOHumYP9S1e6pq91DV7qG12bte3eALbnO11zV4SEwzpyYmkJyeRnpxIWnIiGSG3Qx9LT04kPyOF0h7pFGal4JzWRERERGJfZ1vgvgVcELj9FWA8zmLe3wJ+DZzQ5ZWJyCHN7fVTtquBtTvqWbOjPni9vrKeFq9/r89LTkwgKzWJrNQkMlOTyEpx0S8/Hb/f0uj2Ud3oprzaR6PbR5PHR0OLd5+vl56cSP/8dEp7ZFBSELju4VwXZ6eSkKBwJyIiIrGjswGuJ7AlcPs04B/W2k+NMVXA/G6pTEQOCW6vnzU76ljbGtK217O2sp6NOxvw+tvWFOubl8aQokymDu7BwMJM8tJdZKW6AmHNRWaKE9pSXYkHXIPPb2ny+Ghs8dLodsJdZX0Lm3Y1sGFnI2W7Glizo453V+7A7WsLe8lJCZTkp1PSI4NBhRmM75fLEaV5FGWldsl7IyIiInKgOhvgdgElOCFuJnBTyPP19bSIBFlrWbW9jo/X7GT22p3M21BFo9sHQGKCoaRHOkOKMjllVE+GFGUxuCiTgYUZpCcf0JDcA5KYYMhMSSIzpf0xCsPu+fyWbTVNlO1qZOOuBud6p3P94ZpK3IGWvP756UwqyeOI0jwmleQzpChTLXUiIiISEZ39xPRP4FljzGogH3gjsH08gUlNROTwVV7dxOy1O/l47U5mr93FzvoWAAYWZnD+EX05sjSfYcVZlPbIIDmps6uXRF5igqFvXjp989I5dnBB2GMtXh/LymtZsHE388uq+HBNJS99vhWA7NQkJpbkcUR/J9SN75fbrYFUREREDl+d/YRxHVAG9Ad+Yq1tCGzvBfy5OwoTkdhV0+Rh7vpdwdC2vtI5JRRkJnPs4AKmDi7g2MEF9M5Ni3KlXSclKZGJ/fOY2D+PKxiItZayXY3ML9vNgrLdLCir4v1VlYATBEf1zub4oYWcPLInY/rkaKIUERER6RLGWrv/vSJo0qRJdv58DasTiTU1jR5eW1LOy59vZUHZbvzWmQDk6AH5TmgbUsCwnlmHdVCpafSwcJPTQvfphqrg+1ScncpJI4s4eWQxkwf2iOlWSBEREYk+Y8wCa+2kDh/rbIAzxvQCvg+MDGxaAfzJWrutS6oMUIATiR1ur58PVlfy0sItvLPCmeBjSFEmXxldzNQhhYzvl6swsg9VDW7eXbmD/y2v4MPVO2ny+MhMSeL4YYXMHNmT6cOKyElzRbtMERERiTFfOsAZY04G/g1sBlrXfDsKp0vl2dbat7qoVgU4kSiz1rJ4Sw0vLdzCK4vK2d3ooUdGMmeN7825E/oyuk/2Yd3KdrCaPT5mr93J/5Zv5+0V29lZ7yYpwXDMwB6cPLInJ43sSZ9DqMupiIiIHLyuCHArgP8B19iQJxhjHgBmWmtHdFWxCnAi0bG1uomXP9/KPxduYX1lA8lJCZw8sifnTezDtCGFuBLV0tZV/H7L55ureWt5Bf9bvj04hnB8v1zOP6IvZ47rrZY5ERGRw1hXBLgmYJy1dnW77UOBL6y16V1SKQpwIpHU7PHx6qJy/rlwC3PXVwFw1IB8zp3Qh6+M6aUQESHrKut5a9l2Xv58K6u215GclMApo4o5/4i+TB1cQKKWKBARETms7CvAdXYWyvnAGGB1u+1jgM+/RG0iEgXVjW6e+qSMJz7ZyM56NwMKMrj+5KGcPaEP/fK77PsY6aRBhZl8f3om3zt+IEu31vLCgs38+4tyXl1UTq+cVM6d2IfzJvZlYGFmtEsVERGRKNtrC5wxZmLI3WHAb4A/AXMD247BmdTkp9ba57qqILXAiXSfzVWNPPrxBv4xfzONbh/ThxVy5XEDmTywh8a1xZgWr4+3l+/gxQWb+WB1JX4Lk0ryOP+Ivpw+thdZqWodFREROVQdVBdKY4wfsMD+PtVZa23ilyuxjQKcSNdburWGhz5cz3+XbCPBwFnj+nDlcQMZVpwV7dKkE7bXNvOvz7fywvzNrKtsINWVwFdG9+Jrk/oqfIuIiByCDjbAlXT2ANbasoOsbQ8KcCJdw1rLh2t28tcP1zF77S4yU5L45tH9ueTYUnrlaLbDeGSt5YvN1bywYAuvLiqnrtnLsJ5ZzDq2lLPH9yEtucu+SxMREZEo6pJ14Pbx4idZa9/+Ui8SQgFO5Mvx+Py8trichz5Yz8qKOnpmp3DpsQP4xtH9yVa3u0NG6wQ0f5u9keXbaslNd3Hhkf25eHIJvbUcgYiISFzr8gBnjOkDXAJcBvRXF0qR6PP4/Pz9s8386b21lNc0M7RnJldMG8hXx/fRYtuHMGstn26o4m+zN/LW8gqMMZw6qphLji3liJI8da8UERGJQ10xCyXGmETgLOByYCawGPgz8EJXFCkiB8day+tLK7jnzVVs2NnApJI87jhnNNOHFpGg6ecPecYYjh7Yg6MH9mBzVSNPzS3j+U838Z8l2xjTJ4dZU0o5Y1wvUpLUvVJERORQsN8WOGPMMJzQdjHQADwL3IizLtzyri5ILXAinffJul38+o2VLNpczdCemdx46nBmDC9Sq8thrtHt5aWFW3l8zkbW7qinIDOFbx3dn28d05+irNRolyciIiL7cdBdKI0xHwGjgReBp621HwS2e1CAE4maFdtq+c0bK3l/VSW9clL50clDOW9iXy34LGGstXy0Zid/m72B91ZV4ko0nDexL989fhADCjKiXZ6IiIjsxZfpQjkZeBB42Fq7tMsrE5EDsmV3I7/932r+9flWslKSuOkrw/nOlFJSXeoeJ3syxnDc0EKOG1rIhp0NPPbxBv4+fzP/mL+Z08b04gfTBzOyd3a0yxQREZEDsL8WuPHAFcA3gY3Ak8BzwGbUAicSMbsb3Dz43lqe/KQMDFxybCk/OH4wOemaVVIOzI66Zh77eCNPzy2jvsXLjOFF/GD6ICaV5ke7NBEREQn40rNQGmNSga/hzDp5LJAA/BR4xFq7uwtrVYATCdHk9vHY7A385f11NLi9nDexLz86eaimiZcvrabRw5OfbOSx2RvY3ejhqAH5XHXCYI4bUqAxlCIiIlHWpcsIGGMG0zapSQ/gXWvtV750lQEKcCLO2KU3l23nV68uo7ymmZNGFPHjU4YzrDgr2qXJIabR7eX5Tzfz1w/XU1HbzOg+2Vw1fTCnjCrWLKYiIiJR0i0LeQeWFTgDuNRa+9UvUV8YBTg53G3a1cgvX1nKe6sqGV6cxa1njeKYgT2iXZYc4txeP//6fAt/+WA9G3Y2MLAwg+8fP4izJ/TBlah1BEVERCKpWwJcd1GAk8NVs8fHQx+s58H31+JKMPzo5KF8Z0qpPjxLRPn8lteXbuPB99axYlstffPSuHrGYM6d2Fe/iyIiIhGiACcS4z5cXckv/r2UjbsaOWNsL35++kiKc7Rel0SPtZb3Vu3g/rfXsHhLDf3z07l6xmDOmdCHJAU5ERGRbqUAJxKjttU0ccdrK/jPkm0MLMjgV18dzdQhBdEuSyTIWsu7K3fw2/+tZll5LaU90vm/E4dw1rjeCnIiIiLdRAFOJMZ4fH4en72R3729Gp/fcvWMwVxx3EBSkrSem8Qmay3/W76d3729hhXbahlYkME1Jw3hjLG9tYC8iIhIF1OAE4khn26o4paXl7Jqex0nDi/i1rNG0S8/PdpliXSK3295a3kF97+9hpUVdQwuyuT/ThzCGWN6adZKERGRLqIAJxIDqhvd3PGfFby4YAt9ctO49axRnDyyZ7TLEjkofr/l9aUV3P/2atbsqGdoz0yuOXEoXxmt5QdERES+LAU4kSh7Y2kFP395KdWNbq48biBXzxhCWrK6S0r88/kt/1myjQfeXs26ygaGF2dx/cxhnDSiSAuCi4iIHCQFOJEo2VXfwi9fWcZri7cxqnc295w/jpG9s6NdlkiX8/ktry4q5/63V7NxVyMT+ufy41OGMWWQJuURERE5UApwIhFmrdMq8ct/L6O22cM1Jw7hu8cP0jpacsjz+Py8uGALD7y9horaZqYOLuCGU4Yxvl9utEsTERGJGwpwIhFUWdfCLS8v5Y1lFYzrm8M9XxvH0J5Z0S5LJKKaPT6enlvGn95fR1WDm5kje3L9zGEMK9bfgoiIyP4owIlEgLWWf39Rzq2vLqPR7eO6k4dy+dQBWitLDmv1LV4e+3gDD3+4nnq3l7PH9+Hak4ZQ0iMj2qWJiIjELAU4kW62vbaZn/1rCW+v2MGE/rncc/44BhdlRrsskZixu8HNXz5cxxNzNuL1WS44sh9XzxhCcU5qtEsTERGJOQpwIt3EWssLC7Zw+2vLcXv9/PiUYVxy7AAtbCyyF9trm/nju2t57tNNJCYYvjOllO8fP4i8jORolyYiIhIzFOBEusG2miZ++s8lfLC6kqNK8/nN+WMZUKBuYSKdsWlXI/e/s5p/fb6VzOQkrjxuIJdOHUBGSlK0SxMREYk6BTiRLvbvL7Zyy8tL8fgsN546jIsnl2rxYpGDsHp7Hfe+uYq3lm+nIDOZH54wmG8c3Z+UJK2TKCIihy8FOJEuUt3o5pZ/L+PVReVM6J/Lb78+Xq1uIl1g4abd3P3GSuaur6JvXhrXnTyUr47vo+7IIiJyWFKAE+kCH62p5IYXFrGr3s01Jw7h+9MHaYZJkS5kreWjNTu5+82VLN1ay9Cemfz4lOGcNKIIYxTkRETk8LGvAKfBBiL70eT28Zs3VvL4nI0MKszgkYuPZEzfnGiXJXLIMcZw3NBCpg4u4PWlFdz31iqueHI+E/vn8pNTh3PMwB7RLlFERCTq1AInsg+Lt1Rz7d+/YH1lA7OmlPLTrwwn1aWxOSKR4PX5eXHBFu5/ew0Vtc0cP7SQH58yjNF99AWKiIgc2tSFUuQAeX1+HnxvHX94dw0FmSnc+7VxTB1SEO2yRA5LzR4fT36ykT+9v47qRg9njO3F9TOHafypiIgcshTgRA7A+sp6rvvHIr7YXM1Z43pz+1dHk5PuinZZIoe92mYPD3+4nkc/3kCL18/XJ/Xl/04cQq+ctGiXJiIi0qUU4EQ6wVrL0/M2ced/VuBKNNxxzhjOGtc72mWJSDuVdS08+N5anp23CQxcfEwJPzhhMPlaDFxERA4RCnAi+7GjrpmfvLiY91dVMm1IAfecP47inNRolyUi+7BldyP3v72GlxZuIT05icunDeDyaQPJ1GLgIiIS5xTgRPbhnRXb+cmLi6lr8XLzV4ZrUW6ROLNmex33vbWaN5ZVkJ+RzA+mD+KiY0o04ZCIiMQtBTiRDjS5fdzxn+U8M28Tw4uz+P03JjC0Z1a0yxKRg7RoczX3vLmKj9fupFdOKtecOITzj+ir9RpFRCTuKMCJtLN0aw3/9/znrK9s4IppA7jhlGGkJOnbepFDwZy1O/nNm6tYtLmagQUZ/OjkoZw+ppda1kVEJG4owIkE+PyWv364nt/+bxX5Gcnc97XxWh5A5BBkreWt5du5761VrN5ez4he2Vx/8lBOHFGEMQpyIiIS2xTgRIDy6iZ+9PcvmLehiq+MLubOc8aQp1nrRA5pPr/l1UXl/O7t1ZTtamRcv1yuP3ko04YUKMiJiEjMUoCTw96ri8r52b+W4PVbbj1rFF87oq8+vIkcRjw+Py8t3MLv31nL1uomjirN5/qZQzl6YI9olyYiIrIHBTg5bNU1e/jlv5fx0udbGd8vl/svGE9pQUa0yxKRKGnx+vj7Z5v5w7trqaxrYdqQAq6fOYzx/XKjXZqIiEiQApwclhaUVXHt379g6+4mfjhjCFfPGIxLs9GJCM4stE/PLePPH6yjqsHNSSN6ct3JQxnZOzvapYmIiCjAyeHF4/Pz+3fW8OB7a+mTl8b9F4zniJL8aJclIjGovsXL47M38NCH66lr9nL62F786KShDC7KjHZpIiJyGFOAk8PG+sp6fvT3L1i0pYbzJvbl1rNGkpXqinZZIhLjaho9PPLxeh77eANNHh9njevN1ScOYVChgpyIiESeApwc8qy1PPvpJu54bQXJSQncde4YThvTK9pliUic2VXfwl8/XM+Tn5TR4nWC3A9nDFGLnIiIRJQCnBzSdta3cOOLi3ln5Q6mDSngnvPHUZyTGu2yRCSO7apv4a8frefJOQpyIiISeQpwcsh6Z8V2bvznYmqbvfz01OHMmlJKQoKWBxCRrrGrvoWHP9rAk59sbOtaqSAnIiLdTAFODjmNbi93/GcFz87bxPDiLB64cALDirOiXZaIHKIU5EREJJIU4OSQsmhzNdf+/Qs27mrgimkDuX7mUFKSEqNdlogcBhTkREQkEhTg5JDg9fn58/vruP+dNRRlpXDf18cxZVBBtMsSkcNQVYObhz9azxNznCB32pheXDV9sNaRExGRLqEAJ3GvbFcD1/1jEQvKdnPWuN7c/tXR5KRreQARia6qBjePfLSepz4po67Fy4nDi7hqxmAm9s+LdmkiIhLHFOAkbvn9lmfmlXHnf1eSlGi44+zRfHV8n2iXJSISpqbJw5NzNvLY7A3sbvQweWAPfjhjMFMG9cAYTawkIiIHRgFO4tLW6iZ+8uIiZq/dxbQhBdx9/lh65aRFuywRkb1qdHt5dt4mHv5oPdtrWxjfL5erThjMSSOKFORERKTTFOAkrlhreWHBFm5/dTk+a/nZ6SP45lH99eFHROJGi9fHiwu28JcP1rG5qonhxVn84ITBnD6mF4la6kRERPZDAU7ixo7aZm56aQnvrNzBUQPyuff8cfTvkR7tsiLP7wdfC5gESHBBQkK0KxKRg+D1+Xl1cTkPvreOtTvqKe2RzvenD+KcCX1JTtLftYiIdEwBTmKetZZXF2/jlpeX0uzx8ZNTh3NJPC7K7W6Eum1QWw51FVBfAe4G8DSCpynkOvTSuOdtb1P465oESEhywlxi4DohCRID16G3E13O4ylZkJYLqbn7v07OALVwinQbv9/y1vIK/vjeWpZuraU4O5XLpg7gwqP6kZWqCZlERCScApzEtKoGN7e8vJT/LNnG+H653Pf1cQwqjLE1lfx+aKiEunKo3RZyXRG+rbmm4+cnpYIrDVzpgdvpgftpIbdbrwOPJ6WAteD3gs/jXIfd9oAvcB263ecBnxta6qC5GpqqA3Xt4289ISk80GX2hOxekBW4ZPeCrN6QVQypOQp7IgfJWsuHa3by0AfrmLNuF1kpSXzzmP5ceuwAemanRrs8ERGJEQpwErPeXFbBz/61hJomDz86eShXThtIUmIUuxX5PLBzDWxfBtuXwo7lsGOlE8783vB9TYITdLJ6QXbv8KATDD/FkJwV/S6Qfj+01IYEun1d74b6HU4rYnP1nq/lSnd+ruDPWRz+M+f2h8zi6P/MIjFuyZYaHvpwHf9dso3EBMPZ4/tw5XEDGdIzK9qliYhIlCnAScypafJw26vLeGnhVkb2yua+r49jRK8ILoBrrdN6tn0Z7FgWCGzLoHKV06IFTjfEwmFQNAJy+rWFtNagllHkdGc8lLkbnW6gtducrqF129paG+sq2rqK+lrCn5eYAnklkFe65yW3BFJirIVVJIo27Wrk0Y/X8/f5m2n2+DlxeBFXHjeQowbka/ImEZHDlAKcxAxrLa8vreCXryyjqsHNVScM5ocnDO7ewfw+L1QsdlrUtoeEtaaqtn2yekPPUeGXHkMgKbn76jpUWOu02tWWOwGvugx2b2y7VG0Ed134czIKOw53+QOdgKwPrXIYqmpw89QnZTzxyUaqGtyM75fLd48byMxRxZq5UkTkMKMAJzFhW00Tt7y8jLdXbGdU72x+fe5YxvTN6foD+f1OWNvwoXMpm9MWIFzpTotaz1HQc7RzXTQS0vO7vg5xtAa83RtCgl1IyKvZAtbXtn9yJvQY5ATogiHQY3DbdXJGdH4GkQhqcvt4ceEWHvloPWW7Gintkc5l0wZy/sS+pCUnRrs8ERGJAAU4iSq/3/L0vDLufmMVXr+fH500lMumDui6sW7Wws7VgcD2AWz82AkM4HzoH3AclE6FXuMhb4DGZsUan8cJcbs3wK51sGutMw5x1xqo3kzY5CvZfZx/02CoGwIFg50urgn6YCuHFp/f8uayCh76cD2LNleTm+7iG0f15zuTSynO0YQnIiKHMgU4iZo12+v46UtLWFC2m6mDC7jznDFds67b7o1tLWwbPoT67c72nH4w4HgYMA1Kp0FOny9/LIkeTxNUrW8LdDvXtl23hMz4mZjiBLrCYVA4vO06f6CzrIJIHLPWMr9sN49+tIG3lleQYAynjenFZVMHMK5fbrTLExGRbqAAJxHX4vXx4Hvr+PP7a8lISeKW00dy7sQ+Bz8gv2k3rPmf08K24UOo3uRsz+zpBLUBxzmXvFKNnzocWAsNOwNhLhDuKldD5UpnDF6rhCSnta59sOsx2FmmQSTObK5q5PE5G/n7Z5upb/FyREkel00dwMyRPaM7g6+IiHQpBTiJqM82VvHTfy5mXWUDZ4/vzS1njKRH5kF8WG6ugVWvw9KXYN27zuyQqblO69qA453AVjBUgU3CuRucUFe5ygl0latg5yqnJc/6nX1MgtM6Vzjc+R0qGtF226WuaRL76po9vDB/C4/P2cimqkb65KYxa0opXz+yHzlpanUWEYl3CnASEbXNHn7z+kqembeJPrlp/L9zRjN9WNGBvUhLHax6A5b9C9b+z1mQOqcfjDobRp4DvSdoDJscHE8zVK1rC3Wt17vWtq3xFxrsWkNd0chAi51mJJXY4/Nb3l6xncc+3sC8DVWkJyfy9Un9mDWllNICTfojIhKvFOCkW1nrDLT/5SvLqKxr4ZJjB3DdyUPJSOnkGmnuBlj9Jix7yekm6W12pvUfdTaMOhf6TlIrm3Qfn8eZPKVyBewIXCpXOttaZ8dMSIL8QVAUCHStAU9j7CSGLN1aw2OzN/DqonK8fssJw4q4eHIJxw0pJEHLEIiIxBUFOOk2a7bXcdury/l47U5G9Mrm1+eO6dygek8TrHnLaWlb/SZ4Gp3xbCPPhlHnQL+j1dIm0eVtCXTFXBkS7FZA1QaCM2MmJgfG2AWCXdFwKBwB+QM0K6ZEzY7aZp6et4ln521iZ30LAwoyuHhyCecd0ZfsVH3hICISDxTgpMvVNHm4/+3VPPlJGRnJiVw/cxjfOrr/vgfR+zxOaFv6kjO2zdMA6QUw8qsw+lzoP1kfeiX2eZqcZStCW+t2rAifPCUxJTC2bnhba13RCMgt1RcTEjEtXh9vLK3g8Tkb+XxTNRnJiZw7sS/fmVLC4KKsaJcnIiL7oAAnXcbnt/xj/mbueXMVuxvdfOOo/twwcxj5GfsYH1RbDgsedy712yEtH0ae5bS0lUyFxE52tRSJZS31zmQpO1YGumOudMJdzea2fZLSoHCo00rX2lpXOAxySxTspFst3lLNE3PKeHVROW6fn2MH9+A7k0s5cURPEtW9UkQk5ijASZeYv7GKX76yjGXltRxZmscvzxzF6D45He9srTPd/2ePwMr/OLP/DTkZJl0Gg0/UuCE5fDTXBiZNWREe7urK2/ZJSgusYxey1EHhcGdZDH3BIV1oV30Lz3+2mafnlrGtppk+uWl8e3IJF0zqR96+vogTEZGIUoCTL6Wipplfv76Cl78opzg7lZtPH8GZY3t1vKZbcw188RzMf9TpZpaWBxO+DZMudcYFiYijqdr5G2k/K2Zoi11iMvQY0tYVUwuUSxfx+vy8vWI7j8/ZyNz1VaQkJXDWuN5cPLmUMX338sWciIhEjAKcHJRmj49HP97Ag++txeu3fPe4gXx/+iDSkztoEahY4rS2Lf6HMyFJnyPgyCucmSRdaRGvXSRutdS1LUoeGu7aL1CeP8hptSsYGnIZAqnZ0atd4tKqijqe+GQjL3++lUa3j3H9cvn2MSWcMbYXqS6NSxYRiQYFODkg1lreXrGDO/6znLJdjZwyqic/O20k/Xukh+/obYHlrzjBbfNcSEqF0efDkZdBn4nRKV7kUNV+gfKdq51L1fq2dewAsnqFBLthbbeze2s5Dtmn2mYP/1q4lafmlrF2Rz256S6+Pqkf3zq6PyU9tKaciEgkKcBJp326oYp73lzJZxt3M7gok1+eOZJpQwrDd6reDAv+BgufhIZKyBsAR14O478J6fnRKVzkcOXzwO6Nge6Yq5yQ1xruWmrb9kvOdJY8aG2t6zHIud9jECTrw7m0sdbyyfpdPD23jDeXbcfntxw/tJBvH1PCCcOLNOmJiEgEKMDJfi3dWsO9b63i/VWVFGWlcPWJQ7jwyH64QpcF2LECProPlv7TuT/0VKe1beAMzaAnEmusdWZ9bQ1zra13O1dD7dbwfbN6hwS6kEteicbaHea21zbz3KebeO7TTWyvbaFPbhrfPLo/FxzZj4LMlGiXJyJyyFKAk71aV1nPb99azX+WbCMnzcX3pw/iO5NLSUsOGfdQ/gV8dC+seBVcGXDkpXDUlZDbP2p1i8iX4G50ul7uWgO71sKudc71zjXQXN22X0KSMxNmMNQNcsbe5Q+A7D5at/Ew4vH5eXv5dp6aW8acdbtwJRpOG9OLi44pYVJJXseTWomIyEFTgJM9bK1u4vdvr+HFhVtISUrg8qkDuPy4gWSnhnzbvvlT+PAeZ/HtlBw4+rtwzPfVTVLkUNZYFQh1oZd1zsXb1LZfYrIT7vIGOLNi5g90gl3+QOfLHbXcHbLW7qjj6bmb+OeCLdS1eBlenMW3jinhnAl9yEzRshciIl1BAU6Cdta38Kf31vH0XGdGu4uOKeEHJwxq6wpjLWz8yAluGz50Ft2efBUcdQWkamppkcOW3+90vaxaD7s3ONdV66Fqo3PtaWjb1yRCTt89g13eAKdbpsbcHRIa3V5e+aKcp+aWsay8lozkRM6e0IeLjilhRC/Nhioi8mUowAm1zR4e/nA9j368gWaPj68d0Y//O2kIfXIDU/xbC2vfcYLb5rmQ2ROm/B8cMQtSMqNau4jEOGuhfkcH4W4DVK1z1ocMlVHotN7llgRa8UqdYJdXqq6Zcchayxebq3l67iZeW1xOi9fPpJI8LjqmhK+MKSYlSf+eIiIHSgHuMFbT6OHJTzby6OwNVDd6OH1sL647eSiDCgOhzO+HVf91gtu2LyC7L0y9FiZcpPXbRKRrNFY5YW73BmfGzOoy53p3GdRsAetr2zchCXL6hYe63BLndm4JpPfQcggxbHeDmxcXbOGZeWVs3NVIfkZycCmCfvnp+38BEREBFOAOSxU1zTz68XqenbeJBrePGcOLuO7koYzuE+gG6ffBsn85s0ruWO50bZp2HYy9EJKSo1u8iBw+fB4nxIWGut0b24Je467w/V3pTsDL7R+4tN4ucbZnFingxQC/3zJ73U6e+qSMt1dsx0JwKYLpw7QUgYjI/ijAHUbWVdbz1w/W89LnW/BbOHNsL757/KC28Qh+Pyx7Cd6/y5mcoGAYHHcDjDoXEjX4XERiTEtdIMxtctagrN7kBLvqTVCzGZp2h++flBoIeCEhL6e/MyYvtx9kFutcF2Hbapp47tPNPPfpJirrWuiXn8a3ji7h65P6kZ+hLwxFRDqiAHcYWLylmj+/v443llWQnJjABUf244ppA8O7rKx7D97+JWxbBEWj4PifwIiztIabiMSv5lonyHUU7qo37dmCZxKdcXY5fdtCXU7ftpCX01fjfruJx+fnzWUVPPVJGfM2VJGclMAZY3tx8eRSxvXN0VIEIiIhFOAOUdZaZq/dxZ8/WMvstbvISk3iO5NLmXVsafgCq+Wfw9u3wvr3nQ8pM34OY76m4CYih76WeqeLZs0WJ9TVbG67X73ZmVkzdAweQFpeu1AXCHzZgYCXVayJVr6kVRV1PD23jJcWbqHB7WNMnxy+PbmEs8b1JtWl91ZERAHuEOPzW95cVsGf31/Hkq01FGWlcPm0AXzjqP5kha7jVrUe3r0Dlv7TWQ7guB/DkZdBUsreX1xE5HDi90FdRUiwC7TmhYa+ltrw55hEyO4dCHUhrXmh99PyNBavE+qaPbz8+Vae/KSMNTvqyUlz8fVJffnW0SWUFmi5CRE5fCnAHSJqmjy8MH8zT80to2xXIwMKMvjucQM5Z2Kf8Gma63fAB3fDgr85i+1OvgqmXK113EREDkZzDdRsdVrrajY7t1sDXu0W577fE/4cV/qeAa/97WTNytjKWsu8DVU89UkZby6rwOu3HD+0kO9MKWH60CISNOmJiBxmFODi3JrtdTzxyUZeWriVRrePSSV5XHLsAE4dXRw+k1dLHcz5I8z5A3ib4YjvwPE3Ot19RESke/j90FDZ1mJXuzWky2Yg+NVv3/N5afmB7pn9Og56Wb0OywlXttc289ynm3h23iZ21LVQ0iOdiyeX8rVJfckO7WUiInIIU4CLQz6/5Z0V23nik43MXruL5KQEzhrXm1lTStuWAmjldTutbR/cDY07YeTZMOMWKBgcldpFRKQdbwvUloeEu9YWvK1tLXot7RY8NwmQ1Tt8HF5Ov/Cgdwh31fT4/LyxtILH52xkQdlu0pMTOW9iX74zpYTBRVnRLk9EpFspwMWR6kY3/5i/mSc/KWPL7iZ65aRy0TElXHhkP3pkthu71rokwLu3O9Nsl06Dk26DvkdEpXYREfkSmmtDAt3mjsOezx3+HFdG+Bi84KVfW8g7BNb2XLKlhsfnbOTVReW4fX6mDSlg1pRSrSknIocsBbg4sLKilifmbORfn2+l2ePnqAH5zJpSysyRPUlK7GC2yPUfwFs/h4rF0HMMnHQrDD7xkP0mNp54fB5q3bXUueuo99TT7G3G7Xfj8Xlw+924fSEXf7vrwMXj9+D2uWnxtWDZ82/U0Pbv3Dr1duu20McwkGAScCW4cCW4SE5MJjkhGVdi+P3kxGRnn0RX8H7ofmlJaaS70p3rJOdaU36LRFhoV83aLeETrbS24jXsaPck43TFDC6ZEAh2uf3bbqdmR+XHORg761t4/tNNPD13ExW1zfTPT+fiySV8bVI/ctLUvVJEDh0KcDHK7fXz1vK2NXFSXQmcPb4PF08uZWTvvfyHumudE9xW/deZ4vrEW2D0+VoSoIu5fW4qmyqpaqqizl1HrccJZKGX1pDW/tLsaz6oYyaZJCdAtQtVSQnOGJjWv9XQQNd6u6O/49bHfH4fHr/HuYSEyI6CYWcZTDDUpSelB6/TXG0BL3R7hiuDrOQsMpMzyU7Odm67MoPXiZqSXaRreJrbJlupDp1Zc1Nb0Gs/4UpqTshi5/33vMRgN83WNeWemLORzzY63SvPmdCHS44tVfdKETkkKMDFmPLqJp77dBPPfbqZnfUt9M1L49vHlHDBkf3ITd9LV5em3c4Yt0//CkmpMO16OOYH4EqNbPFxzu1zs7NpJzsadwSvK5sqqWyspLKpMri9uqV6r6+RaBKDIST00tG2TFcmKYkppCSmBANZcmLyHveTE5IjGmKstfisL6y1L7SVsP11s7eZRm8jjZ7GDq+bPE3Otbep7bHA4572HxY7kOnKJDPZCXRZLue9DN4PvLfZydnkpOQ4l+QcclNzyU7OJjkx/ruHiUSM3+9MqBIMdaFLJwSu3XXhz0nO6jjYxUjAW7rV6V75yqJy3F4/04cVcvnUgRw7uId6CohI3FKAiwF+v+WjtTt5em4Z76zYjgVmDCviomNKOG5o4d778Ps8MP9v8P6d0FQNE78NJ/wcsnpGsvy4YK1lV/MuNtdtZlPtJjbXbWZ743YqGyvZ0bSDnY072d2ye4/nJZpECtIKKEovCl4XphVSmF5Ij9QeZKdkk+VqC2XqPnhgPD4PDZ4G6jxOC2W9uz7YglnvqQ9r0ax31wf3a73Ue+rxW/9eXz8tKY2clBxyU3LJSc4hOyXbuR0Ieq2hLzcl17mkOvup1U+kA9ZCc7UT7jq67C7rIOBltoW5vFLILXGu80ohrwSSI7Oe2676Fp6Zt4knP9nIzno3w4uzuGzqAM4a3zt8qR0RkTigABdFuxvcvLBgM8/M20TZrkZ6ZCRzwZH9+MZR/emXv581gNb8D968GXaudiYoOfUuKB4TmcJjlN/62dG4IxjSNtVtCgtsjd7G4L4JJsEJZGlFFKYXBkNZ+6CWl5pHglEX1FhlraXB00Ctu5bqlmpqWmqocddQ2xJyP7AteDtw8Vpvh69pMMGgl5uSS15KHrmpznVOSg55qXnO9tTA/ZQ8spOzFfpE9hXwdpdBdRm468Ofk1EYEupKwkNedp8uXyqh2ePjlUXlPPrRBlZtr6MgM4XvTC7hW8eUkJ+hFnsRiQ9xFeCOGD3aznnlFUxyCibZRUJKCiY52bkkxseHJ2stn2+u5um5Zby2eBtur5+jSvP51jH9OXV08f6/CdyxAt78Gax7B/IHwsw7YNhpMTcGobtYa6lsqmTt7rVtAa1uE5trN7O5bjNuf9ssbEkJSfTN7Ev/7P70z+pP36y+9M/qT//s/vTO6I0rUYPaD1fWWhq9jdS01ASDXnVLNbubd1PdUu1cmqvZ3bI7bHuLr6XD1zOYYLjLS2kLeK33Q2+3hkG11sphx1porHJmRt69wQl0uzc64W73RqfLpvW17Z+QFBh7VwL5AyBvgPP/XuvtlMwvUYrl47U7eeSjDXywupJUVwLnTezLpVMHMKjw4F9XRCQS4irAjU5Nsy+Ulnb8YFISCa1hLjkZ0xruUpJJcIXeD4S/5ORAEAzZFgyEbcEwISXk9VzO6znbU8KP5XL2JSmpww9lLV4fr3xRzuNzNrKsvJaM5ETOndiXbx3Tn+HFnZjlq2EnvHcnLHjc+U/r+BvhyCsOiSmg96bZ28y66nWs3r067BI6Bi01MTUsmPXL6ke/rH70z+5PcXqxWkWkSzV5m9qCXQcBb3fzbna37HauA9t8oR9IQ6QkpoQFvGDo6+B+bqrTGtg6aY3IIcnndWbQbA10wYC3Eao2QFNV+P4ZhYFQN6DtOn+gczujoNNfbK7eXsdjH2/gpc+34vb6OXF4EZdNG8DkgRonJyKxKa4C3IShQ+379/0W63Zj3S1Ytxt/i9u53+Lctx43/pYWrNvTts3dgt/tDt/W0oLf48aGPJ+u+HmNCQuPNslFnS+BXW5LE4kkpqZQ1CObooJsXKkpTsBMSQkJk+3uuxJI2Dwbs/a/GNuEGXEqCZO+jckpDAuOJiWl7bjJyXH1n461lm0N2/YIamW1ZcHxTWlJaQzOHczQvKEMyRvCkNwhlGSXUJheqC6OErOstcHunaGhrqq5Kuw69LF6T/1eXy87ObvDcJefkh8Mea2P5abkkpWcpb8POXQ01zhBbvcGqFofuL3Rua7dCqGz5yZnBkJdKeQPcoJdj8B1Vq8Ow93O+haenlvGU5+UsavBzche2Xz3+IGcPqZXx0v2iIhESVwFuO4cA2etBa/XCYXu8FDoDwZBTzA42pZAKGwNgKFBscXN7up6Vm3exZYdNSR6vfRKT6A0y0Vukg0co8V5bkv712txZgL7kvZohUxOCQl5rj3vt4bGfd0Pbkt2WjBDQmNCaIBMScG4XB2GSI/Pw8qqlayoWsHq3atZs3sNq3evDvvQ2i+rH0PzhgbD2tC8ofTN7KvWNDkseHyeYCteaKtea4tf2P3A7b3N5plgEoKTtrQfw9c+7LVespKz9Lcm8cfTHBhrtyEk5AWC3u6N4csjuNKdcNdjYKBLZkjAy+pFs9fPv7/YysMfbWDtjnr65adxxbSBfO2IfqQl629DRKJPAa4Lte9Tn5KUwLkT+3LZ1ANbe8Zuno/97y34N87F5g7GTr4O22tSoLUxJPC1tIQEyL3cbw2ZLS1OaNzHfadV0oNtbu6aEJmSgk124U0ytCRamhI8NOCmJcniSQK/K4mUtEzS0rPJzMgjO7OAnOxCUtIyQsJju1bJlJDuq4Ft7Vsgg+EySd3N5NDXOp6vqrmKmpaa8HF8gbF8rbd3t+ymprlmn6HPYMhKzmqbqTM1sDRD6+ydITN3tm7PTslWa5/ELr/PGV9Xtc5ZL7Vqg3O7Ndz52sZOk5QWHGdn8wex3F3Is2uTeasiE396IbOOHcC3J5fsfVkfEZEIUIDrAs0eZ3zbox+Hz2r1zaP70yMzpfMvVLMV3r0dFj0H6T1g+k1wxCVdPgtXZ1ivt61V0B0S8NztWg1DQmNzUx3bqsqoqN5MZfVWqmq242tpwuWDVF8iPRKzyU/IIpc0MmwyLi9trZetgTIkTH5piYmYlJS2QNdRa2Rra2LyPu6Hjo1MCWltdCWH3w/sEwyUcTS5jhxerLU0eZuCY/haW/dCZ+msbql2Zu9sdmbxrG6ppq79FPEhEkyCE/ySc5y1+VKyg0s3hK7V1/pY6H1N6CJRExruqtbDrvWB7pnr9gh3TSadNb6ebDa9yOo9nDHjjiCv3winBS8tN2o/gogcfhTgvoSqBjdPfrKRp+eWBdeVuXzaQM4c1+vA1pVpqYfZD8CcPzgzcB3zfWcx7tSc7iv+S/L5fayvWc/iysUs2bmERZWLWFe9DhsYg1CaXcrYwrGMLRjL2MKxDM4bjCuh87M+WmuxHk94eAwJjs79ttbG8DC5l9bH0JZGd8g4ydDHQ7qz4tn/ItP71Tq5zj4Co9Nq2C4wtk68s8cYyXYBsTUwJodvC5uURy2R0kV8fh+17tqw2Ttr3DXBVr5ad61zaand4/beJnMBSDJJYUGv/aL3rYu4h94P3S8lMUUBULqe3+csYL5rrdNyt2sd9dtW0lKxmjx3BQkm5DNSegH0GOx0w+wxKHB7sNOa50qL3s8gIockBbiD0Oj28shHG/jrh+upb/EyY3gRl08dwORBBzhjld8HXzzrtLrVb4dR58JJv3TWv4kxLb4WFlcuZv72+SyoWMCSnUuC66plJ2czpnAM4wrGMaZwDGMKxpCTErvhs7Os3x/e0rhH62P7Lq2tj4eMnexs91V3S/iEPK0h0tvxWmUHJDExvGtpyHVw5tawFsp2M7Tusa39TKyt4yDbvb6r3Syuao08bIWu19caAEPDXfB+64LtgcXbW6/31t2zlSvBFQxzma5MMpMzyXJlkeHKcLYlZzrbQx9LziDL1faYWgHlQGyp3M3L785hxdKF9PFvZWpeDRMyd5FVXwb1FSF7Gsjp1xbqCoa03c7pBxpvKiIHQQHuAHh8fv7+2WYeeGcNlXUtnDKqJzfMHMaQnp0f3xa0/n1nPbftS6HvkXDKndDvqC6v+WC1BrbPKj5j/vb5LNqxCLffjcEwNG8o44vGB1vYSrJL9MGnm1ifb++Bsblt5tW2CXdCQqa73UQ7LS0dz9Lavqts6yQ7IS2UXTJDa2uXVpdrjyAZnFgndCmQ1u2tLZJ7eyx0m6vd8h8dXBKSk2Evk+xIbGrxtYSFur1dat211HvqqXfXU+9xgmCDp2GfM3u2SjSJZLgyyHRlkpHsXKe70p37roy2xwK397Yt05WpNSYPI1UNbp6Ys5EnPtlIdaOHowfkc+20XhyTuxsTaLVj1xqnFW/nWgjthpyY0jZ5SsGQtla7HkMgPf+wWd9VRA6cAlwnWGt5Y2kF97y5ivU7GziyNI+ffmUER5TkHfiLVa6G/90Cq9+A3P5w0m0w6pyon6ibvc1OYNv+GZ9VfMaSyiXBwDY8fziTiidxZM8jmdhz4iHRuiadt8cMrXvMnBrSldW9l2U82m3bswXSeQ2/p3W213bLg7jd+D2erunWGrBHuGttaewoLKaEtFa6wreHtWS6XCFh0hW+f/AxV2CbK+x5e1tDUr48v/XT4GmgwdNAnbuuw5AXur3B00CDt4EGd9t1vac+2Otgf1wJLjJcGaQnpZPuSg8LeOlJbffTXel73M9wZZCRFP6YAmHsa3R7ef7Tzfz1w/VU1DYzrl8uV58wmBNHFLX9XVsL9TsCXTLXBoLdOud21YbwmTJTc/dssesxxAl8yelR+RlFJHYowO3HvPW7uOv1lXyxuZohRZnceOrw8BNyZzXshPd/DfMfg+QMOO4GOOq74ErtnsL3o8nbxKLKRcyvmO8Etp1L8Pg9JJgEhucP58ieRzKpeBITe04kO7kTC42LRID1+8PHRraGO3do8GvbHrYsSGuA9HTwWNhyIIH1JNuvHRn6mh5nO769j+s6YMbsPQQGg58rJGS6wsNkh89zBW/vM2SGvaar7bHWS4JmlwQnCDZ6GoNhsLV1r6PbjZ5GGr2NYfdbg2Gjp5FGTyNe27ku0kkJSW2BMBDq0lxpwdt729YaCNOT0klzpQWDYVpSmhaF7yYtXh//XLCVP72/li27mxjRK5urThjEV0b3IjFhH58bfF5n4fLQFrvWsXe1W8P3ze4LBYNDWuwCl9z+6pIpcphQgNuLVRV13P3GSt5ZuYPi7FSuO3ko507sc+CLeXpbYN5f4MN7wd0Aky6F6T+FjILuKXwvPH4PSyqX8Mm2T/h026cs3rkYr99LgklgZP5Ip4Wt+EgmFE0gK/kguoSKHIaCXVyDwc/TFgxbg2ZrsGy939rK6OngOe2f5/F0+Pphz/PsuW+XBksIjqMMC3Xt73e0rTP77O95ewuV7e/H2RhLay1uv3vPgOdpoNHbGAyAoUFwf9uafc2dPn5qYmowzIUGvtDr0FbBDrcnOaExw5VBamKqWpBDeHx+XvminAffX8v6ygYGFmZw1fTBnDW+N64D/RzRUh+YIXNt+GXnWmipadsvMTmwvl0Hk6lk9ox6Tx8R6ToKcO2UVzfxu/+t5p8Lt5CRksQPpg9m1pTSA1+80++DJS/Ce//P+VZtyCkw83YoHNY9hbdjrWVD7QY+Kf+EueVz+Wz7ZzR4GoKB7cheRzKp5yQmFk0kMzkzIjWJSGRYny8kMHYcAvcaMttf2m/vaL8D3KdLJudpLyGhLdQlJbUFu6QkTLLLGffocmGS2oXH0H1dLoyr7T7Bxzp43eTw+4S9TnLY63R0nO7oMuvz+4LBrsnbFBbu9hYMwx7rYN/9TSATfPtNQrCFMN2Vvkd30NaxgqFdSltDYPtxhRmuDJITD4111nx+ZwjGH95dw8qKOvrlp/G94wdx/hF9D2y26o5Y6/TuCXbHDIS6qsBad76Q5XiSMwPj7QbvOUtmev6Xq0NEIk4BLqDF6+P376zhkY82YC18Z0oJP5g+mLyMA/xPxO+DZf+CD34DO1dD8Rg4+XYYdEK31B2qqrmKueVz+WTbJ3xS/gnbG7cD0DezL5N7T2Zy78kcVXyUxrCJSFRZv99ZazIYIjsKfqGPhWxvfz8sqHqw3raQGHxdj8c5nqej+85z8Xj3fMzt7poJfPZmbyEyKSm4DVeSEzpb90lKCoZDkjp+jOBrhL52UlvITAp/HZPU7jFX2/G9CdCMh0bjocm6aaSFBttMk99Ng2/PwNd6u6NWxXp3PW6/e//vC0630dbQF7xODr+/t8dCt6UnpcdEy6C1lndW7OAP761l0eZqirNTufK4gXzjqP4H/gVxZ7Sub7drbbvWu3XOl8rW37ZvWn5bsMsfBPkD2iZXieHljEQOZwpwwIadDVz93EKWbq3lnAl9uH7mUPrmHeAgYb8fVvzbGedWuRKKRjoLcQ8/A7pp/Eizt5mFOxYGQ9vKqpWAM63/0b2O5phexzC592T6ZfXrluOLiBzqrM8XHv7cHvDuIxh62gfFttuEhcOOQmVbCMXrbXstr7dtX28gbHr3fCz09bs1eLZqFzb3GjyTkiApEZPkgqQEfAkGX6LBlwDeBIs3ATwJFk+CH4/x4zY+WloveGk2HpqthybcNFkPTbTQaN00GQ++BPAlgjeBkNsmeNuXYEhJSSclJYO01AzSUjJJTc0kLTWT9NRs0tOyyEzLITM5ZNkJV2Zw+YnW+wmma/4ft9Yye+0u/vDuGuZtqKIgM5krpg3komNKyEiJ0LhEr9tZpLxqXcgad4HruvLwfdN7OGEuf1DgeiD0CFynHcREbiLSJQ77APevz7fw838txZWUwN3njWXmqOIDewG/H1a+5gS3HcugYJgzxm3k2V0e3PzWz+rdq/mk3GlhW7hjIS2+FpISkhhfON5pZes1mZE9RpKogcwiIoettuDpxXqcNSXDgqDHGwiDoSEwJCSGBkRPuwDZYbh0Y73eDsJlyP6tNYWG0OBlz23d0tV2L3ymoyAYCIEGbFICNjHRGWvZ2nqZ5CIhKYlEVzIJrmSSXCnOJTkFV3IaruQ0kpNTSU5JJ7G1xTQxKdjCuanGzYfrq1i1s4mU1BSmDe/JscOLSUtLcUJvYmJYi2iH91uDc7CudvcPtPXR3RgId+sDXTHXB1rw1kPtlvB90/LCg13+AGcd27wBkFmkMXci3eiwDXANLV5u+fdSXlq4laNK87n/wvH0zk3r/AtYC6teh/fvhIolTveD6Tc5SwJ0YXja2bSTT8o/YU75HD4p/4RdzbsAGJw7ONjCNqnnJNJdmlZYREQOHcElTNqFurDA2RoQ97bN4w0Pkl5fu/tePO5m3C2NuFua8LibcLub8Lpb8Lib8Xpa8Hla8Hnc+DzODLT+dsc0Ph+JPkj0t12SfJDgh6TQ+9a5HVGtraKJiU7oC0z409YymrRHK2kwALa2orbun2jA14TxNmK89eCpw7hrMC01GE8tGItJAJNgndbWjHxMViEmqwCTVYTJKYbsYkxOMSY1PTyQtgbOJBcmaS/1hYTfeJu0SKSr7SvAHbJzDC/dWsPVz31O2a4GrjlxCFfPGNz52SWthTVvwXt3wrYvnG+aznkIRp8PiV/+LXP73Hy+43Nml8/mk/K2bpF5KXlM7j2ZKb2nMLn3ZIrSi770sURERGKVMSY4TjCWta4zWO+uD19s3hNYYL6lllp34NJcQ31TDQ3NtTQ019LUXIfH3RwWAJP8gfAX2JZukslKSCcrMZ0sk06mSSUjMY0MUshISCWdZNJMCmm4SMVFKkmkkESyTQSvD+vztmsFbR9m2wVjnxfb3Bx2P9g6GmhFxeMJaVFNBG9Hs1c3A5sDly5mzB4BzwmogVbO0PuhIbR1LGjrttD7SUmYpMR299sF29bXaN8a2q51tcPa2nc1TkqC1tfQci3ShQ65AGet5bHZG/n16yvokZHCs1ccwzEDe3T2ybD2HafFbesCyC2Br/4Jxl7wpYJb6GyRs7fOZv72+TR5m0gySYwvGs81E69hSu8pDM8f3mV98EVERKRrJJgEspKzyErOohe9Dvj5Hp+nLeC5a1lSXsFLX6xhSUUFqaktjOrnIqvQ0OyrZ7u7hlp3LTUtldS562jyNu31dQ2GrOQsclJyyEnOISclh+yU7ODt4KXdY9kp2bgSDiw0W2shGOh8zjjR1sDn8UDtDmxVGbZqE3b3Fqjeiq0px9ZUYBuqwA/Wb7AWLEmQ2gObmo9NycOm5EJyLjY5B+vKwiakYH2+jltU24dUT3gI9Tc2tYVWX2gLbQdBNsLdeElICAme4UEvOJtuUrvtewTQ0DGooRMZhQbKDiY3Cp3YKKndtmTXniG0owmYWh9X19moO6S6UO6qb+HHLy7m3ZU7OGlEEfecP65zM0xaC+vfg/fugi2fQk5/OP7HMO4bkHhw3wrWtNQwb9s85pTPYU75HLY1bAOgJLuEKb2nMKX3FI4sPpIMV8ZBvb6IiIjEt2XlNfzx3bW8vrSCjOREvjOllMunDSQ/5LOL2+cOBLqaDq+rm6ud++4aaluc7a23LXv/jJfhyth76Gu3PTclN/jYQS3/4GmC6s1QvcmZIbN6U/ilYUf4/onJkNPPWbg8tz/k9nPu5/R1Llm9IalrlqEID6bethluQyY3agt9oa2bewmGgS6+wW1hY0nbhc7220LHnO5rHGnYft62FlOvN3KTG4WGu/bLurQGzNBlXdrvmxyyNEv7fUJn4d1jeZikPV4vbAmZ1jAa5+uIwmEyBm7Oup1c+/wXVDd6uPm04XxnSun+vyFo2g2LX4CFT8L2JZDdB467AcZfdMAnhhZfC4t2LGJexTzmbpvL0p1L8Vs/ma5Mju51dDC09c3qe8A/m4iIiBy6VlXU8Yd31/CfJdtIcyVy0TElXD5tAEVZqQf9mn7rD3bvrHHXOMEuEO5ab9e6a6luqQ7bp7alFq/de6tUWlIa2cnZew17e2wP7JuWlLb3z2XuRqjZS8DbXQaNO9s9wUBWcVugy+kbHvBy+jkTsByGLUXtZ9Xd75hSd7twGRomQ2bZDU56FBog28/Cu9d9Wre7wwNou+tubQ1NSAgEx3bBLvSyt8fab08+wP1bu4m7XCSEPBYMnq5kElrXMg35nT2kA5zX5+eBd9bwx/fWMqAggz98YwKjeu9jTRO/H8o+dkLb8lecRTB7jYMjZsH4b0FSSqeO6/P7WL5rOfMq5jFv2zw+3/E5Lb4WEkwCo3uMZnLvyRzb51jGFIwhKeGQ66kqIiIiXWzN9jr+9P46/v3FVlyJCVx4ZD++e/ygA5uA7Uuy1tLobQwLfMGQt48QWNNSs89F4ZMSkvYIeNkp2cGAl52cHdbFMzs5O7jN5fNCbbkT8mq2BC6ht7eAtzn8gK50J8xl94bsvpDTp+12dm/nfkr2YRnyYpX1+0NCYEcBsLVFtKNlXkL2dbvDguWea5C2LfviD9lOyJqlfo877H5Hr9UtQgLf8E/nHZoBbld9C999agHzy3Zz/hF9ue2sUXtfY6V2Gyx6FhY+Bbs3QEoOjP06TPy2E+D2w1rLuup1wRa2BRULqPPUATAkbwhHFx/N0b2O5oieR5CV3NFAXxEREZH927izgT+/v45/LtyCMXDexL58f/ogSnrE7rALay3NvuawYLfH7ZBWvuBtdy0NnoZ9vnZoq19osAveTs4mG0O2p5ms5gaym6rJaqgiq7aClLptmNpyqKuA9l1KkzOd3letgS47cAm9nZrdfW+axK3gDLr7CYl73e7e/3N6/eKWQy/A1TR6uPDhuayvrOc3543l7Al99tzJ53Vmk1z4pHNtfVA6DSZ8G0aeBa59f6O1tX4r87Y5ge3TbZ8Gp/fvm9k3uIj2pOJJFKQVHNTPKiIiIrI3W6ubeOiDdTz/2WZ8fstXx/XmBycMYnDRofVFcftJXlpn9WwNeKHbWi81LTX7neQFnJa/7ORsslyZZCWmkmWSyAKyfH6yvW6y3I1kNdeT1VhNVuNuMvx+Mv1+Mm3g2pVFYnbvPYNdTp+21ryUzMi8UXJYOeS6UNa3eLnokXksL6/l4e9M4vihheE77FoHnz8FXzwH9RWQ2RPGf9MJbj0Gdfia1lo21W1iceViFmxfwNxtc9lavxWAgrQCjio+imN6HcNRvY6iT2YHYVFERESkG+yobebhj9bz9NxNNHt9nDa6F1edMJiRvdU65PF5nElb3LXUu+uDSzyELffQus2z57YWX8t+j5FGAlnWkuHzkeX1kGH9ZPotWX4/GX4/WQnJZCTnkJmaS3p6ARkZxWRk9yEjtz8ZuQPJyBtARkr2wU0AI4etQyrANbl9fOdvn7KgbDd//tZEZo4qdh5orIK1bzutbRs/ApMAQ06BiRfDkJP3mE2ypqWGpTuXsrhyMYt3LmbpzqVUt1QDkJWcxZE9j+SoXk5oG5gzUFOmioiISFRVNbh59OP1PDGnjPoWLyeNKOKqEwYzoX9etEuLWy2+lmCYq3fXU+8JXNztrj1OOGxw11HfVEVdoOtnnbeJpn1M+hIqyUJGQhIZCSmku9LJSM4kIyWXjLQezm1XBulJ6aS70klLSiM9KZ00l3Pduj09KfBYYB/Ns3DoOmQCXIvXx+WPf8badau5//gEjk7dAtsWQ8ViZzArOItuT/w2jPsmZDtrtXj8HlbvXs2SyiUs2bmExZWL2Vi7EXDWUBmUO4ixhWMZWzCWMYVjGJQziMSE+JtuVERERA59NY0envhkI4/N3kB1o4ejB+Rz+bSBnDi8iIQEfeEcaV6/lwZPQ9ulqYrGmk001Gymoa6choYKGpp20dC8m4aWWhq9zTQkGBoSEmg0znV9kotGY2gyFt8BHDslMWWPUBcMf0lppLnSgtvCtrdeXHtuT3elk5qYqsaLKIvfAOf3Q9V62PYFvvJFrPx8NsVNq+lh6gJ7G+gxGHqNheKx0O8obN+jqWjaweKdi1lcuZglO5ewfNfyYBN5j9QejCkcw7jCcYwpGMOoHqPITFbfZREREYkv9S1enpu3ib/N3kB5TTMDCzK4dOoAzpvYl7RkfREds7wtzsyZ1ZtClk9wrm11Ge66rTSaBJoSDI0mgcasnjTm9KExqydNmYU0pufSmJpNY1IyTd5mGr2NNHmbaPA00ORtCrs0ehqDt32289HQYMJaAoOtf662sNe+tbB1n8xAa2KmK+Q6OeOAF48/3MVXgBs50M6/9zynZW37UnDXA+AliZX+vqT0G8/gMVOoKhjI5rRMtrTsYnPdZrbUbWFL3RY21m6kqrkKgOSEZEb0GBFsXRtbOJZeGb30jYKIiIgcMjw+P68vreCRj9azeEsNeekuLjqmhG9PLvlSa8lJlLgboWod7FzjzOuwa03g9lpoqW3bLzEF8gdCwWDoMcRp1CgeA0Uj9hg6ZK3F4/fsEexaw19H28P28TSFbW99XqOnEbff3akfKzUx1Ql0HQU8VwZZyVlkuDLITskmNyU3eMlLzSMnJeewC4DxFeB6J9r5V/XEUzyaiqIhlGUV8WRZIx/tqmVoPy9JKVVsqdtCo7cx7HlF6UX0y+pHv6x+jMgfwbjCcQzNG4or8fD6xxYREZHDk7WWzzbu5uGP1vP2iu24EhL46vjeXD5tIMOKD62ZKw9L1kJDZSDMBQLdzrXO7d0bwR8Yi5eYAsWjofcE59JrPBQOh8TuGS/n9XuDYa7B20Cjp5F6Tz0N7gbqPHU0eBqod9c7YwY9dTS4G5zHPQ1h4wwbPA37bCXMdGWSk5JDXkoeuam5HYa8vJQ88lLz6JnRkyxXVlw32sRVgCsc0sMe8esjqWioCPtHTMRFSU4/+mb1DQa1vpnO7T5ZfUhJ7NwC3CIiIiKHug07G3js4w28sGAzzR4/04YUcMW0gUwbUhDXH2plL3weJ8RtWwTln0P5F85td2DYUVKa0zrXewL0Hu9cFwyFGJrzwVpLk7cpuEzE7pbdVLdUU91c7Vy3Xtrd39s6gulJ6RRnFFOcUUzP9J7B28XpxfTMcO5nuGJ3bcW4CnA9hvSwlz16Gf0y+7FoYyLvLfHxjYkTue30YzSxiIiIiMgB2N3g5tlPN/H4nI1U1rUwrGcWV584mDPG9o52adLd/H6nK2ZroCv/3Al1rYHHlQ69xjktdL0nQP9jIK8kmhUfFLfPHQx8NS017GraxfbG7VQ0VFDRUBG8vbNpJ7bdYu5ZrqxgmAsNeWMLxzIwZ2CUfiJHXAU4Y0wdsCradQAFwE7VAMRGHbFQA8RGHbFQA8RGHbFQA8RGHaqhTSzUEQs1QGzUEQs1QGzUEQs1QGzUEQs1QGzUEQs1QGzUEQs1QGzUUWKtLezogVhcPGLV3tJmJBlj5ke7jlioIVbqiIUaYqWOWKghVuqIhRpipQ7VEFt1xEINsVJHLNQQK3XEQg2xUkcs1BArdcRCDbFSRyzUEEt17E1CtAsQERERERGRzlGAExERERERiROxGOD+Gu0CAmKhjlioAWKjjlioAWKjjlioAWKjjlioAWKjDtXQJhbqiIUaIDbqiIUaIDbqiIUaIDbqiIUaIDbqiIUaIDbqiIUaIHbq6FDMTWIiIiIiIiIiHYvFFjgRERERERHpgAKciIiIiIhInFCAExERERERiRMxFeCMMT8wxmwwxjQbYxYYY6ZF+PjHGWNeMcZsNcZYY8ysSB4/UMNNxpjPjDG1xphKY8yrxpjREa7hKmPM4kANtcaYT4wxp0eyhg5qujnwb/LHCB/31sBxQy8VkawhpJZexpgnAr8XzcaY5caY4yN4/I0dvBfWGPOfSNUQqCPRGHN7yLligzHmDmNMRNe1NMZkGWPuN8aUGWOajDFzjDFHdvMx93mOMo5bjTHlgZreN8aMinAN5xpj3gz8nlpjzPSuPH5n6jDGuIwxvwmcxxqMMduMMc8aY/pHqobA47cbY1YGathtjHnHGDOlK2voTB3t9v1rYJ8bIlmDMebxDs4dc7uyhs7UEdhnqDHmJWNMtTGm0Riz0BgzIlI17OU8ao0xD3ZVDZ2sI9MY8wdjzJbA+WKVMeZHEa6hZ+B3ozzwb/GGMWZIF9ew389V3X3u7GQN3X7u3F8dkTh3dvK96PZzZ2fqaLd/t5w7D1bMBDhjzAXAA8CdwARgDvB6V/7SdEImsBS4BmiK4HFDTQf+BEwBZgBe4G1jTH4Ea9gC3AhMBCYB7wIvG2PGRrCGIGPMMcAVwOJoHB9YBfQKuYyJdAHGmFxgNmCA04ERwNXAjgiWcSTh78NEwAL/iGAN4PxuXgX8HzAc5+/1KuCmCNfxCHAK8B2c34m3cP5W+3TjMfd3jvoJcD3O78aROL8f/zPGZEWwhgyc8/d1XXjMA60jHef38/8Frr8K9APeMF0b9Pf3XqzC+d0cA0wFNgRq6NmFNXSmDgCMMefj/F6Ud/HxO1vD24SfQ06LdB3GmAE459INOP/HjgZ+DtRHqgbC34NewJmB7V19Lt1fHb/F+f/k2zj/p/w/4NfGmG9HogZjjAFeBoYAZ+N89ivDOY9mdGEN09n/56ruPnd2poZInDv3V0ckzp37qwEic+7sTB1At587D461NiYuwDzg4Xbb1gB3RameemBWDLwvmYAPODPKdVQB343CcXOAdTh/XO8Df4zw8W8FlsbA78GdwOxo19Gupp8B1UB6hI/7GvBEu21PAK9FsIY0nJP9V9ttXwDcEaEaws5ROOF+G/CzdnXWddff7r7Ok0ABTsCfHun3Yi/7jAzUMyaKNWQHajgl0u8FUAJsxfmgvhG4IZI1AI9H8m90H3U8CzwTzRo62OdhYFUU3oulwG3ttn3QXf/PdnDOGhr4exgXsi0BJzxd3o3vRdjnqiidO/f62S7C5879fsaMwLmzMzVE4tzZYR2RPHceyCUmWuCMMcnAETjfYId6CycZH86ycE5ou6NxcON0V7sQ5xd7ThRK+CvworX23Sgcu9XAQPePDcaY540xA6NQw9nAPGPM340xO4wxXxhjfhj4BjPiAse9DHjaWtsY4cN/DJxgjBkeqGUkTsD/bwRrSAISgeZ225twvi2MhgFAMSHnUWttE/AhOo+C8wEAoncuTQauBGqBLyJ87CTgOZwvF1ZE8tjtTA2cv1YbYx42xhRF8uDGmASc1q7lga56lYEuVBdEso52NWUBF+KEuEj7GDjTGNMvUMsUYDzwRoSOnxK4Dp5HrbV+oIXuPY+2/1wVjXNnVD/bHWAd3X3u3GcNETx37lFHDJ079xATAQ7n24ZEYHu77dtx/qgOZw/g/MJ+EsmDGmPGGGPqcU6kfwHOsdYuiXANVwCDgVsiedx25gGzgK/gdOMsBuYYY3pEuI6BwA+A9Tjd9h4Afo3TxSAaTsb5T++RKBz7N8BTOB/CPMAynBa5P0WqAGttHc7f5M+NMX0CX3RcBEzG6RIVDa3nSp1H2wl8ALgPeNVauyXCxz4jcC5tBn4EnGytbf9v1N1uA3ZZa/8c4eOGegO4GDgRp6vaUcC7xpiUfT6raxXhfBl5M86H9ZNxPpw9Y4w5I4J1hPoGTpB5IgrH/j+czxebAufSD4AbrbWvRej4K3G6TN5pjMk3xiQbY24E+tK959H2n6uice6Myme7A60jQufODmuIwrmzozpi4dzZoYgO+u+E9quKmw62HTaMMb/F+RZqqrXWF+HDr8L5Ji4XOA94whgz3Vq7NBIHN8YMw+k2OM1a647EMTtirX099L5xBt2vxxn39NsIlpIAzLfWto7z+tw4A72vAiI6sUvAFcBn1tovonDsC3A+CH4TJ7yNBx4wxmyw1j4awTq+DTyGM2bUByzE+TA4MYI1dETn0RCBb1CfxjmXnRWFEt7D+R0twPm7+YcxZrK1dlskDm6ciY5mBWqIGmvt8yF3lxhjFuB8eD8deClCZbR+af1va23r+fsLY8wknHNppIJLqCuAl621lVE49tXAsTh/F2XAccC9xpiN1tpub4Wz1nqMMecBjwK7cM6jbwOv7/OJX8J+PldF5NwZ5c92na4jEufO/dQQsXNnR3XEyrlzb2KlBW4nzh9u+286itjzG5HDgjHmdzjfzM2w1q6P9PGttW5r7VprbWto+ALnG5BImYzzR7vUGOM1xniB44EfBO5H8lvbIGttPU5o6NJZsjphG7C83bYVQCQn+QEg0O3pq0Snyw/APcC91trnrbVLrLVP4YTpiE5iYq1dZ609Hucb/X7W2qMAF85g62honR1V59GAkO4vY4ETrbW7Il2DtbYhcC6da629DPAAl0ewhBNwWjO2hZxLS4DfGGMi2hoZylpbjvPlRyTPpTtxxq7Gyrl0PM5EYRE/lxpj0oC7gJ9Ya1+11i621v4ReB6I2Cx71toF1trxOCGhl7X2VKAH3XAe3cfnqoidO6P92a6zdUTi3Lm/GiJ17txHHTF57mwVEwEu0MKyAKc7Q6iTic64q6gyxjyA07oww1q7Mtr1BCTQ1l89El7GmX1ofMhlPs5/LuOBqLTKGWNScWY+jMi35yFmA8PabRuK861ppF2C07X2+f3t2E3Scb7wCeUjSuezwH8y24wxeTjdW/8djTpwPvBUEHIeDfy+TuPwPI+6gL/jfAA5wVobleU/OhDpc+mfcN6D8SGXcuB3ON0Zo8IYUwD0IYLn0sBnjc+InXPplTiTIrwdhWO7ApeYOJdaa2ustZWBniWT6OLz6H4+V0Xk3Bkrn+32V0ckzp0H+V50+blzP3XE5LmzVSx1ofwt8JQx5lOcD6vfA3rjjL+KCGNMJs6YK3B+UfoHviGrstZuilAND+J0zTob2G2Maf1GqD7Q+hOJGn4N/AfYjDOo85s4061GbC04a201zgyHoXU14PxbRKQbZ+CY9wKvAptwvo27BWeq30iPV/gdzti7n+GcWCfgjF+4OZJFBCYvuRx4PjAOLBpeBX5qjNmA0xo6AWfa5ScjWYQx5hSc88RKnPPGPThdj//Wjcfc5znKGHM/8DNjzEpgNW3Toz8bwRrycVozcgP7DDbGVAMVXflBYF914Pwn+wLOtM9nAjbkXFoTmKCgu2uoxpma/FWckFKI002vL108XXwn/u/a0W5/D86/x6pI1BC43Ar8E+e9KMVp/dkB/KurathfHYH34m6crlgf4SyRcwLOJCJnR7AGjDHpwLeAu6213dLFuRN/qx/gLBtQjxNgj8fpnv6TCNbwNZyW0TKcL20fwOlS2n5Suy9Twz4/V1lrbXefOzvz2S4S58791RFoeevWc2cnasgmAufOTvxe7CAC586DdrDTV3bHBWeSho043+4vAI6L8PGn4/R3bn95PII1dHR8C9wawRoexzmZtuD88r5NN07degB1vU/klxF4HufDoBtnGtl/AiOj9POfDizCGdC7GifAmQjXcELg9/GoKP4eZAH3B35Hm3DGJN4JpEa4jq/jLHHRgvOfzB+BnG4+5j7PUThjNm4N1NOMMynB6AjXMCsS57B91YETEPZ2Lp0VoRrSccJJeeB3pBynVeHoSP9edLD/Rrp4Kuz9vBdpwJs4/5+4A3+7j+N0PY74exH4HV0dOH8sBr4RhRouwenO2bur34PO1oHTZfBvOP+3NeF8GXUDXfj/Sidq+D+cL4tbfy9uB5K7+H3Y7+cquvnc2ckaZu1vn+6ugwicOztRQ0TOnZ35N+ngORuJkWUETKAgERERERERiXExMQZORERERERE9k8BTkREREREJE4owImIiIiIiMQJBTgREREREZE4oQAnIiIiIiISJxTgRERERERE4oQCnIiIiIiISJxQgBMREREREYkTCnAiIv+/vbsHvaoM4Dj++zUYDtELDVGDCRVJFEUSBL1AU1FBBBVKlL3NmTQ1RlGD0FAoQZTg0BYUiUOLRCENUToU9OKkQ29TKNrg03CvcftTNvg3Od7PB+7ynPPc+zzT5cs591wAgIkQcAAAABMh4AAAACZCwAEAAEyEgAMAAJgIAQcAADARAg4AAGAiBBwAAMBECDgAAICJEHAAAAATIeAAAAAmQsABAABMhIADAACYCAEHwHmh7a62H//D+Ma2o+3V52BZALCqBBwAnEbbNed6DQBwioADYKm0vavtF22Pt/2p7RuLkdZ2X9udbbe3/SXJ5/PxbW0Ptj3a9kjbd9pesjDv4ra72/48f+9Dbbf+7xsE4Lwm4ABYGm2vSrI3yVdJbknyTJJNSV5bcerjSZrkziRPzMdOJtma5IYkm5PcluTNhTmvJLkxyQNJrk/ydJIjZ2EbACyxjjHO9RoA4Iy13ZVZeB1fceiCJGuTrE/yXJLHklw3xjg5n7clydtJLh1jHGu7L8llY4yb/uPz7k3yYZK1Y4yTbT9K8tsY46lV2xQArOAKHADnk0+T3LzitXnh+IYk+0/F29xnSdYkuWZh7MuVb9z2nraftD3c9vckH8znXTE/ZWeSR9semN9+efdqbAgAFgk4AM4nx8YYPyy+khxeON4k/3bryeL40cUDbdcl2ZPk2ySPJLk1s1skk1nEZYyxN8m6JNuTXJ5kT9v3znA/APA3Ag6AZfJNktvbLn7/3ZHkjyQ/nmbexsxC7YUxxv4xxndJrlx50hjj1zHG7jHGlsx+X/dk2wtXbfUALD0BB8Ay2ZFZeO1ou6Ht/UleT/LWGOPYaeZ9n9l35ta269tuyuyBJn9p+3Lbh9pe23ZDkoeTHBpjnDgrOwFgKQk4AJbGGONIkvsyewLl10neTfJ+kpf+Y97BJM8n2ZbZVbxnk7y44rQTSV5NciCzvx64KMmDq7d6APAUSgAAgMlwBQ4AAGAiBBwAAMBECDgAAICJEHAAAAATIeAAAAAmQsABAABMhIADAACYCAEHAAAwEX8CoY5YA2lLsvUAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Comparação',pad=15)\n",
    "ax.set_xlabel('Horas', labelpad=18)\n",
    "ax.set_ylabel('Absorção da Insulina', labelpad=15)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.xticks(range(0, 25))\n",
    "plt.xlim(0, 25)\n",
    "plt.ylim(0, 10)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "\n",
    "plt.plot(nphX, nphY)\n",
    "plt.plot(levemirX, levemirY)\n",
    "plt.plot(lantusX, lantusY)\n",
    "plt.plot(tresibaX[0:24], tresibaY[24:48])\n",
    "\n",
    "ax.legend([\"NPH\", \"Levemir®\", \"Lantus®\" ,\"Tresiba®\"], loc=\"center\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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