{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "import os\n",
    "import json\n",
    "import scipy.interpolate\n",
    "import cModel\n",
    "\n",
    "def dfdy(t,y,system):\n",
    "    dfdy=system.M.dot(y)+system.u(t)\n",
    "    return dfdy\n",
    "\n",
    "def jacobi(t,y,system):\n",
    "    return system.M\n",
    "\n",
    "#SE post calculation\n",
    "def dfdyS(t,S,system):\n",
    "    #unwrap S to NxM where M is number of parameters\n",
    "    mS=numpy.reshape(S,(system.n,system.m))\n",
    "    mOut=system.M.dot(mS)+system.fS(t)\n",
    "    return numpy.ravel(mOut)\n",
    "\n",
    "def jacobiSE(t,S,system):\n",
    "    N=system.n*(system.m)\n",
    "    fJ=numpy.zeros((N,N))\n",
    "    #print('fJ shape {}'.format(fJ.shape))\n",
    "    for i in range(system.m):\n",
    "        fJ[i*system.n:(i+1)*system.n,i*system.n:(i+1)*system.n]=system.M\n",
    "    return fJ\n",
    "\n",
    "#SE simultaeneous calculation\n",
    "def dfdySFull(t,S,system):\n",
    "    #unwrap S to NxM where M is number of parameters\n",
    "    mS=numpy.reshape(S,(system.n,system.m+1))\n",
    "    #system.fS(y,t) is NxM matrix where M are parameters\n",
    "    y=mS[:,0]\n",
    "    mOut=system.M.dot(mS)+system.fSY(y,t)\n",
    "    return numpy.ravel(mOut)\n",
    "\n",
    "def jacobiSEFull(t,S,system):\n",
    "    N=system.n*(system.m+1)\n",
    "    fJ=numpy.zeros((N,N))\n",
    "    #print('fJ shape {}'.format(fJ.shape))\n",
    "    for i in range(system.m+1):\n",
    "        fJ[i*system.n:(i+1)*system.n,i*system.n:(i+1)*system.n]=system.M\n",
    "    return fJ\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeUnit: min\n",
      "Time unit: min\n",
      "Compartments\n",
      "redBloodCells/0:\n",
      "\ttargets\n",
      "\t\tplasma[0,1]: 0.36585365853658536\n",
      "\t\tredBloodCells[0,0]: -0.03048780487804878\n",
      "plasma/1:\n",
      "\ttargets\n",
      "\t\tplasma[1,1]: -5.455487804878048\n",
      "\t\tredBloodCells[1,0]: 0.03048780487804878\n",
      "\t\tvenous[1,2]: 4.91890243902439\n",
      "venous/2:\n",
      "\ttargets\n",
      "\t\tkidney[2,3]: 0.2152439024390244\n",
      "\t\trichlyPerfused[2,5]: 0.9048780487804878\n",
      "\t\tfat[2,6]: 1.7032520325203253\n",
      "\t\tslowlyPerfused[2,7]: 0.6128048780487805\n",
      "\t\tbrainBlood[2,9]: 0.5609756097560976\n",
      "\t\tplacenta[2,11]: 0.05274390243902439\n",
      "\t\tliver[2,12]: 0.2351219512195122\n",
      "\t\tvenous[2,2]: -4.91890243902439\n",
      "kidney/3:\n",
      "\ttargets\n",
      "\t\tkidney[3,3]: -1.2607142857142855\n",
      "\t\tplasma[3,1]: 5.042857142857142\n",
      "urine/4:\n",
      "\ttargets\n",
      "\t\tkidney[4,3]: 0.0\n",
      "richlyPerfused/5:\n",
      "\ttargets\n",
      "\t\trichlyPerfused[5,5]: -0.212\n",
      "\t\tplasma[5,1]: 0.212\n",
      "fat/6:\n",
      "\ttargets\n",
      "\t\tfat[6,6]: -0.1461712890284319\n",
      "\t\tplasma[6,1]: 0.021925693354264784\n",
      "slowlyPerfused/7:\n",
      "\ttargets\n",
      "\t\tslowlyPerfused[7,7]: -0.041020523387755095\n",
      "\t\thair[7,8]: 4.6330715639693757e-10\n",
      "\t\tplasma[7,1]: 0.0820408163265306\n",
      "hair/8:\n",
      "\ttargets\n",
      "\t\tslowlyPerfused[8,7]: 2.016428571428571e-05\n",
      "\t\thair[8,8]: -8.107875236946407e-08\n",
      "brainBlood/9:\n",
      "\ttargets\n",
      "\t\tbrainBlood[9,9]: -1.8802945578231294\n",
      "\t\tplasma[9,1]: 1.8775510204081634\n",
      "brain/10:\n",
      "\ttargets\n",
      "\t\tbrainBlood[10,9]: 0.0009602380952380954\n",
      "placenta/11:\n",
      "\ttargets\n",
      "\t\tplacenta[11,11]: -2.0069605568445477\n",
      "\t\tplasma[11,1]: 4.013921113689095\n",
      "liver/12:\n",
      "\ttargets\n",
      "\t\tliver[12,12]: -0.21189250714285712\n",
      "\t\tgut[12,13]: 0.845054945054945\n",
      "\t\tplasma[12,1]: 0.21483516483516485\n",
      "gut/13:\n",
      "\ttargets\n",
      "\t\tintestine[13,14]: 0.0016941176470588236\n",
      "\t\tgut[13,13]: -1.292436974789916\n",
      "\t\tplasma[13,1]: 1.292436974789916\n",
      "intestine/14:\n",
      "\tsource\n",
      "\t\t{'name': 'constant', 'value': 0.0486, 'unit': 'ug/min'}\n",
      "\ttargets\n",
      "\t\tliver[14,12]: 4.11530612244898e-05\n",
      "\t\tintestine[14,14]: -0.0021806020408163267\n",
      "feces/15:\n",
      "\ttargets\n",
      "\t\tintestine[15,14]: 8.066e-05\n",
      "\t\tinorganicMercury[15,16]: 0.0\n",
      "inorganicMercury/16:\n",
      "\ttargets\n",
      "\t\tliver[16,12]: 4.033e-06\n",
      "\t\tintestine[16,14]: 4.033e-05\n",
      "\t\tinorganicMercury[16,16]: 0.0\n",
      "Flows\n",
      "\tplasma:kidney[(plasma,venous):kidney]:kidneyFlow [1.412]\n",
      "\tvenous:kidney[(plasma,venous):kidney]:kidneyFlow [1.412]\n",
      "\tplasma:richlyPerfused[(plasma,venous):richlyPerfused]:richlyPerfusedFlow [1.484]\n",
      "\tvenous:richlyPerfused[(plasma,venous):richlyPerfused]:richlyPerfusedFlow [1.484]\n",
      "\tplasma:fat[(plasma,venous):fat]:fatFlow [0.419]\n",
      "\tvenous:fat[(plasma,venous):fat]:fatFlow [0.419]\n",
      "\tplasma:slowlyPerfused[(plasma,venous):slowlyPerfused]:slowlyPerfusedFlow [2.01]\n",
      "\tvenous:slowlyPerfused[(plasma,venous):slowlyPerfused]:slowlyPerfusedFlow [2.01]\n",
      "\tplasma:brainBlood[(plasma,venous):brainBlood]:brainBloodFlow [0.92]\n",
      "\tvenous:brainBlood[(plasma,venous):brainBlood]:brainBloodFlow [0.92]\n",
      "\tplasma:placenta[(plasma,venous):placenta]:placentaFlow [0.173]\n",
      "\tvenous:placenta[(plasma,venous):placenta]:placentaFlow [0.173]\n",
      "\tplasma:liver[plasma:liver]:liverInFlow [0.391]\n",
      "\tliver:venous[liver:venous]:liverOutFlow [1.928]\n",
      "\tplasma:gut[(plasma,liver):gut]:gutFlow [1.538]\n",
      "\tliver:gut[(plasma,liver):gut]:gutFlow [1.538]\n",
      "\tplasma:venous[plasma:venous]:plasmaFlow [8.067]\n",
      "Volumes\n",
      "\tplasma:plasmaVolume [1.64]\n",
      "\tvenous:plasmaVolume [1.64]\n",
      "\tkidney:kidneyVolume [0.28]\n",
      "\trichlyPerfused:richlyPerfusedVolume [7]\n",
      "\tfat:fatVolume [19.11]\n",
      "\tslowlyPerfused:slowlyPerfusedVolume [24.5]\n",
      "\tbrainBlood:brainBloodVolume [0.49]\n",
      "\tplacenta:placentaVolume [0.0431]\n",
      "\tgut:gutVolume [1.19]\n",
      "\tintestine:intestineVolume [0.98]\n",
      "\tredBloodCells:redBloodCellsVolume [1.64]\n",
      "\thair:hairVolume [0.14]\n",
      "\tbrain:brainVolume [1.4]\n",
      "\tliver:liverVolume [1.82]\n",
      "Partition coefficients\n",
      "\tkidney:kidneyPC [4.0]\n",
      "\trichlyPerfused:richlyPerfusedPC [1]\n",
      "\tfat:fatPC [0.15]\n",
      "\tslowlyPerfused:slowlyPerfusedPC [2]\n",
      "\tbrainBlood:brainBloodPC [1]\n",
      "\tplacenta:placentaPC [2]\n",
      "\tliver:liverPC [5]\n",
      "\tgut:gutPC [1]\n",
      "\tbrainBlood->brain:brainPC [3]\n",
      "\thair->slowlyPerfused:hairPC [248.7]\n",
      "\tredBloodCells->plasma:rbcPC [12]\n",
      "SE parameters\n",
      "Done simultaneous LSODA SE\n"
     ]
    }
   ],
   "source": [
    "sys=cModel.model()\n",
    "fh=os.path.expanduser('~')\n",
    "#sys.parse(os.path.join(fh,'software','src','Integra','models','cDiazepam.json'))\n",
    "sys.parse(os.path.join(fh,'software','src','PBPK','models','humanHG.json'))\n",
    "#print(sys.u(10)[sys.lut['venous']])\n",
    "sys.inspect()         \n",
    "nt=201\n",
    "tmax=24*60*365*100\n",
    "t = numpy.linspace(0,tmax, nt)\n",
    "#first column is the solution y\n",
    "#initial condition\n",
    "y0=numpy.zeros(sys.n)\n",
    "    \n",
    "doSequential=0\n",
    "doSimultaneous=0\n",
    "doIVP=0\n",
    "doIVPSimultaneous=1\n",
    "\n",
    "if doSequential:\n",
    "#sequential SE (first true solution, then parameter derivatives)\n",
    "    y0=numpy.zeros(sys.n)\n",
    "    sol = scipy.integrate.odeint(dfdy, y0=y0, t=t, args=(sys,),Dfun=jacobi,tfirst=True)\n",
    "    print('shape (y) {}'.format(sol.shape))\n",
    "    \n",
    "    #solLSODA = scipy.integrate.LSODA(dfdy,y0,0,tbound=4*3600,min_step=10,max_step=1000,jac=jacobi)\n",
    "    #sol=solLSODA.\n",
    "    sys.setY(t,sol)\n",
    "    S0=numpy.zeros((sys.n,sys.m))\n",
    "    S0=S0.ravel()\n",
    "    #print('lut {}'.format(sys.lut))\n",
    "    #print('lutSE {}'.format(sys.lutSE))\n",
    "    #fJ=sys.fSS[sys.lutSE['brainPC']]\n",
    "    #print('X shape {}\\n {}'.format(fJ.shape,fJ))\n",
    "    solSE=scipy.integrate.odeint(dfdyS, S0, t, args=(sys,),Dfun=jacobiSE,tfirst=True)\n",
    "    s1=numpy.reshape(solSE,(len(t),sys.n,sys.m))\n",
    "    print('Done sequential SE')\n",
    "    \n",
    "\n",
    "if doSimultaneous:\n",
    "#simultaneous SE\n",
    "    S1=numpy.zeros((sys.n,sys.m+1))\n",
    "    #set initial condition\n",
    "    S1[:,0]=y0\n",
    "    S1=S1.ravel()\n",
    "    solSE1=scipy.integrate.odeint(dfdySFull, S1, t, args=(sys,),Dfun=jacobiSEFull,tfirst=True)\n",
    "    sFull=numpy.reshape(solSE1,(len(t),sys.n,sys.m+1))\n",
    "    s1=sFull[:,:,1:]\n",
    "    sol=sFull[:,:,0]\n",
    "    print('Done simultaneous SE')\n",
    "\n",
    "if doIVP:\n",
    "    solIVP=scipy.integrate.solve_ivp(dfdy,[0, tmax],y0, args=(sys,), jac=jacobi,\n",
    "                                  method='LSODA', atol=1e-4, rtol=1e-8)\n",
    "    #y is n x nt (odeint nt x n)\n",
    "    sol=numpy.transpose(solIVP.y)\n",
    "    t=solIVP.t\n",
    "    print('shape (y) {}'.format(sol.shape))\n",
    "    sys.setY(t,sol)\n",
    "    solIVPSE=scipy.integrate.solve_ivp(dfdyS,[0, tmax],S0, args=(sys,), jac=jacobiSE,\n",
    "                                  method='LSODA', atol=1e-4, rtol=1e-8)\n",
    "    sraw=numpy.reshape(numpy.transpose(solIVPSE.y),(len(solIVPSE.t),sys.n,sys.m))\n",
    "    #interpolate on t\n",
    "    s1=numpy.zeros((len(t),sys.n,sys.m))\n",
    "    for i in range(sys.n):\n",
    "        for j in range(sys.m):\n",
    "            tck = scipy.interpolate.splrep(solIVPSE.t, sraw[:,i,j], s=0)\n",
    "            s1[:,i,j]=scipy.interpolate.splev(t, tck, der=0)\n",
    "        \n",
    "if doIVPSimultaneous:\n",
    "    S1=numpy.zeros((sys.n,sys.m+1))\n",
    "    #set initial condition\n",
    "    S1[:,0]=y0\n",
    "    S1=S1.ravel()\n",
    "    solIVP1=scipy.integrate.solve_ivp(dfdySFull,[0, tmax],S1, args=(sys,), jac=jacobiSEFull,\n",
    "                                  method='LSODA', atol=10, rtol=1e-2)\n",
    "    t=solIVP1.t\n",
    "    sFull=numpy.reshape(numpy.transpose(solIVP1.y),(len(t),sys.n,sys.m+1))\n",
    "    s1=sFull[:,:,1:]\n",
    "    sol=sFull[:,:,0]\n",
    "    print('Done simultaneous LSODA SE')\n",
    "    \n",
    "    \n",
    "#calculate uncertainty\n",
    "#s1 is nt x nvar x npar\n",
    "\n",
    "se=sys.calculateUncertainty(sol,s1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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76+pZ9fYe1tduZuPat9n61hZ2vr2Vwv37KEo0sL+olOKePejVt5LTRw+ielAvThtYyZB+5RR17QLFxXqmLmIikfB50ikoihPTLV0RiRhzKClVZ5SIiOSmg4cTzFu1hVdeXkHtktUcfHc7XQ4f5FBBIYmu3ajsU8kZ40cyoLo3wwdVcVr/crqVB8MxW3hXqURHJBK+pCcpKCzQGF4RiSCnuEjDzUVEJHds2lXHXxbWsmLBCrasXEvZ/r3s79qdAcMHMfz8M6ip7s2oAT3oU9Ud69JFE6h0cpH4v+fuxAoK1EsgIpFjQHGx7vCJiEjndTiRZFHtNhbNX8nqRauo37SJeDIJvftwxqQzOXvcMCad1oeSynI9a5eDIpHw4VAYj2u8r4hEUlGhRh+IiEjnsnXfQf7y6nqWz1vBW8tqKd2zi7rSLvQeNoDRH7+M888axNDqKqxbN12D57hIJHzujhXGdYdPRCKpsEBtk4iIRFsi6Sx9ayfz561k9cJV7NmwkZJEPQ2VvTht7HDOGTeU807vT9deFan33EneaHPCZ2YlwItAcbCf37r77WY2BHgIqAQWA59x9/qW9uXuxOMx9S6ISEZktH0Cigt0h09ERKJnd109c5e+xWvzVrD+tTco2bGdA0Ul9KwZwPnXfpDzzhrE6UP7pO7i6cZK3mrPHb5DwMXuvt/MCoG5ZvZH4B+BO939ITP7L+BG4O4W9+ROgYZ0ikjmZK59wihSwiciIhHh7rz8xrs8/fu5bHx1BWUH6jhQUcGQUTWMHXshF4weQHmfSigpCTtUiYg2J3zu7sD+YLEw+HHgYuBTQfl9wB2c5ILK3TFN2iIiGZLJ9gmgSEM6RUQkZHX1Dcx6cRUvPfEXEm++SX2Pcs6ePJ7zJ4xg7LDexHt014z30qR2PcNnZnFSw6KGAz8D1gK73b0hqLIJGHCy/bhDPK67eyKSOe1pn8xsOjAdoKq8LwVxJXwiIhKODdvf47En5rHs+YUU791NYc1gJn/hI1w9oYaSvr10w0ROql0Jn7sngHFmVg78HhjV2m3TL6gqKwcQV4+EiGRQe9ond58BzADo1bOfK+ETEZGOlEw6L67YzDOPz2X7K8tIxgsYNHYkV196LeNHD8S6dw87ROlEMjJLp7vvNrPngfOAcjMrCHrRBwKbm9nm6AVVnz6DPaYLKhHJgra0T40p4RMRkY6w9+BhZr2wkr8+8RdiGzdSX1nJOVd/kI9cMIo+Nf00u6a0SXtm6ewFHA4upkqBy4DvAc8D15GaCW8a8Hhr9qfLKRHJlEy2T45RoCHnIiKSRbXv7OGxWfNY/eeFFLz3HqXDarjof32MK8YPoaiqUsM2pV3ac4evH3Bf8JxMDHjE3Z80s5XAQ2b2beBV4J6T7cjdiWlSBBHJnIy1T0DqtTEiIiIZlEg6z7/6Js/OmsuupStJFBUzZPwYrrnkLM4aNQC6dAk7RMkR7ZmlcylwdhPl64BzT3V/MfVciEiGZLR9Mg3pFBGRzNn1Xj2PPfsa8/74MoVvb6a+d28mfvQSPnzecCoH94fCwrBDlByTkWf42s0d06QtIhJR8ZiGdIrkMzObCXwI2OruZzRa91XgB0Avd99uZgb8CLgKqAM+5+6vBHWnAf832PTb7n5fUD4euBcoBWYDXwleLyM55I0te3j00b9SO3cR8YMH6DZyGJd97BNccvZgCior9D5qyZpIJHwOFOjfuIhElO7wieS9e4GfAvenF5pZNXA58FZa8ZXAiOBnIql3fU40swrgdmACqUufxWY2y913BXW+CMwnlfBNAf6YxfORDvTeoQZmPDSXVY/PIVFczIj3jeHai8/i9NMGQFlZ2OFJHohEwgdguqASkQhyTEPORfKcu79oZjVNrLoT+BrHTwA1Fbg/uEM3z8zKzawfMBmY4+47AcxsDjDFzF4Aurv7vKD8fuDDKOHLCc8u3sDv/utRktu3UzN5Il/+0Nn0HNwfCiJzCS55IBr/2tx1QSUikWSgYTYicgIzmwpsdvfX7Pg2YgCwMW15U1DWUvmmJsqlE3t79wHu/u/Z7Hh5IQXV1Xzu1r9h/NihUFISdmiSh6KR8AGa9VxEREQ6AzMrA75BajhnRx53OjAdYNCgQR15aGmlhkSSB59+jb/88g94MsF5113OZy4/k8LevcIOTfJYJBI+dyCmSVtEJIIM3eETkcaGAUOAI3f3BgKvmNm5wGagOq3uwKBsM6lhnenlLwTlA5uofwJ3nwHMAJgwYYImdYmYpRu2M/Nnv6d+zVoqzhrDzZ94P9UjB2v4poQuMv8CNQmeiIiIdAbuvgzofWTZzDYAE4JZOmcBt5jZQ6Qmbdnj7lvM7Gng38ysZ7DZ5cBt7r7TzPaa2SRSk7Z8FvhJR56PtM++g4eZ8eu/8MaTz0GPHkz98se48vxRWLduYYcmAkQm4XNMz/CJSCSZ7vCJ5Dkze5DU3bkqM9sE3O7u9zRTfTapVzLUknotw+cBgsTuW8DCoN43j0zgAtzEsdcy/BFN2NIpuDtzFq7jtz9/HHbsYMSF5/KlqePpPniA/m5IpEQk4QPleyIiIhJF7n7DSdbXpH124OZm6s0EZjZRvgg448QtJKo27arjrrufZPeCVygePJgvfPFvOHvsUCguDjs0kRNEI+FzMFPGJyIRpGf4REQkcDiR5IE/vspff/UHkmZccP0UPn35mRRUVYYdmkizopHwAXoNn4hEkVI9EREBeGXdNmb+9Pck162j6uwzuekT5zNwRDXENfGgRFskEj4HzdIpIhGlZ/hERPLZngOH+e9fPc+62S/gFZV87KbruOz9o7CuXcMOTaRVIpHwGa5ZOkUkupTwiYjkHXdn9vy1zPr547BrJ6ddch7TrzmHboP66++CdCqRSPgAYpq1RUSiSH/TRUTyzta9B/nhTx5n78JXKR5Sw99++WrOGjsUiorCDk3klEUn4dNFlYhEkIF6ckVE8sib2/bxvX+ZSXLXLs7/1FXccMkZFFRWhB2WSJtFJuEzPcMnIiIiIiFa+dZOfnTHPdjhw3z5q59g3PjTNCmLdHrRSPhcd/hEJKrUOImI5IMFq9/hf751D7GSEv7pn69n+BlD9aJoyQnRSPhAGZ+IRJeGdIqI5LTnF6/nwe/fR2FlBd/4yrUMOG2w2n7JGZFJ+ExfKhGJIjVNIiI57fd/WcXT//krygZX8y83TaFy+OCwQxLJqMgkfDHd4RORqFKHlIhITvrl7FeZ+/Pf0H30adz+pcvoOmhA2CGJZFxkEj5dTolIFKltEhHJPe7OXY+8zIoHZ9HrfWfzLzdeRHHf3mGHJZIVkUn4YupBFxEREZEsSySd79/zHBv/8CzVk8/ja5+5gLheuyA5LBJTDxmuXnQRyRgzqzaz581spZmtMLOvBOV3mNlmM1sS/Fx18p2hIZ0iIjniUEOCO/7zcTb+4VlGXz2ZW79wkZI9yXmRucPn8UjkniKSGxqAr7r7K2bWDVhsZnOCdXe6+w9CjE1ERELw3qEG7vj+b9n7ylLef/0Upn34XCgrCzsskaxrc5aV0R50NKRTRDLH3be4+yvB533A60Abn8RX2yQi0tntfK+eb9z+S/a9uowrP38N0z46Scme5I323FY70oM+GpgE3Gxmo4N1d7r7uOBndmt2pnxPRLLBzGqAs4H5QdEtZrbUzGaaWc9mtpluZovMbFEikVADJSLSib29+wD/ctv/cGDdBj5+00f58JTxUFISdlgiHabNCV9me9AhZhrSKSKZZWZdgd8Bf+/ue4G7gWHAOGAL8B9NbefuM9x9grtPKIjHOyxeERHJrNp39vKtf76bxLYd3PiVj3PJ5LFQVBR2WCIdKiNZVnt70IPlTIQiIgKAmRWSSvYecPdHAdz9XXdPuHsS+Dlw7sn249kNU0Q6geB6ZquZLU8r+39mtiq43vm9mZWnrbvNzGrNbLWZXZFWPiUoqzWzW9PKh5jZ/KD8YTNTRpIBSzds5wdfv4tk/WH+7p8+zsTzRkNBZKavEOkw7U74MtGDntpPeyMREUmxVA/SPcDr7v7DtPJ+adU+AixvvO0J+0ptmOkQRaRzuReY0qhsDnCGu58FvAHcBhA83vJJYEywzV1mFjezOPAz4EpgNHBD2qMw3yP1OMxwYBdwY3ZPJ/f9dfkm7rrtLqy4iNv++TrGnDMSYhpNJvmpXf/yM9WDnopEX0IRyZjzgc8AFzeaQOr7ZrbMzJYCFwH/cNI9KdcTyXvu/iKws1HZM+7eECzOAwYGn6cCD7n7IXdfD9SSuhY6F6h193XuXg88BEwNOqguBn4bbH8f8OGsnlCOe2bBWu77159TWFXJHf/0MWrGDFXHneS1Nt/XbqkH3d23BIut6kEHXVOJSOa4+1yablZaNYmUiMgp+gLwcPB5AKkE8IhNHJvjYGOj8olAJbA7LXlMry+n6Ld/WsGcn/6asuFDuePLl9Fj6KCwQxIJXXsGMh/pQV9mZkuCsm+QGqIwjtSjLxuAL51sR+Z6LYOIRJjaJxFphpn9H1Izlz/QAceaDkwHGDRIiUw6d+cXjy9i4b2PUj7uDG7/4sWUDeh38g1F8kCbE75M96DHNBGeiESSkj0RaZqZfQ74EHCJux+Z42kzUJ1WbWBQRjPlO4ByMysI7vKl1z+Ou88AZgBMmDBBc0ql+cmv57LqN3+g7/vH843PTaawd6+wQxKJDD04JyLSEuV7ItIEM5sCfA241t3r0lbNAj5pZsVmNgQYASwAFgIjghk5i0hN7DIrSBSfB64Ltp8GPN5R55ELfvunFaz6zR8YdPH5/Mv0y5TsiTQSmYRPQzpFJIrUMomImT0IvAyMNLNNZnYj8FOgGzAnmBjqvwDcfQXwCLASeAq4OZjMrgG4BXia1LuLHwnqAnwd+EczqyX1TN89HXh6ndqr67Yx5+5H6HrmGP750+cTKy8/+UYieSYyLyMxzdIpIiIiEeTuNzRR3GxS5u7fAb7TRPlsmnj0xd3X0dpZzeWorfsOMuPf7ifeswff+PyFxCsrwg5JJJIik2VFJhARERERibT6hiTf/f5vSO7byy1fuoryIdUn30gkT0Ukz3IN6RQRERGRVvnRfc9zYNlKPvTZqxk9boRmUxZpQUQSPkhqSKeIiIiInMSjL6xg/RNzGHnFBVxz8ZkQ11TvIi2JTJalfhkRERERaclr67fz9F2P0PWMUdxy/SQoLQ07JJHIU8InIiIiIpG3ff8h7v63+7EePbj18xdToElaRFolOgmfxl6LSBSpbRIRCd3hRJJ///5vSO7Zwy1fuoqKoZqkRaS1opPwhR2AiIiIiETSj3/5Z+qWruDqz17NGeOGqzNO5BREJ+GLRyYUEZGjdEkhIhKux+auovaxZxhx+QeYesmZUBCZ10iLdAqRybJ0USUiIiIi6ZZt2MHsnzxEl9NP4+8+PlGTtIi0QXQSPmV8IiIiIhLYsf8Qd//bfcS7deO2Gy+msFdV2CGJdEqRSPiU64mIiIjIEQ2JJP/+g9/RsHsPN335aiqHDQo7JJFOKxIJH4Ap7RMRERER4K5f/4X3lizjys9cxZmapEWkXaKT8GnSFhEREZG898q6bSz//TMMvmgSH9EkLSLtFpksS/02IiIiIvmtviHJL37yKLGKCm75yAQoKws7JJFOLzIJn4hINKk7SkSko9w/ayGJ9ev5xA0X07W6f9jhiOSEyCR8GpotIpGktklEpEOsfXcvCx6aTe9zx3HReSN1cSiSIdFJ+MIOQESkCWqbRESyL5l07vrp41BUzE0fP09DOUUyKDoJn3pxRCRDzKzazJ43s5VmtsLMvhKUV5jZHDNbE/zuGXasIiICv3thJQeWreCK6y6i97DqsMMRySnRSfhikQlFRDq/BuCr7j4amATcbGajgVuB59x9BPBcsCwi0iIzm2lmW81seVpZkx1IlvJjM6s1s6Vmdk7aNtOC+mvMbFpa+XgzWxZs82PLs17wrXsPMmfmY3QZPZKPXjQadE0oklH6RolIznH3Le7+SvB5H/A6MACYCtwXVLsP+HA4EYpIJ3MvMKVRWXMdSFcCI4Kf6cDdkEoQgduBicC5wO1powzuBr6Ytl3jY+W0n/73bKiv56ZPXYD16BF2OCI5p80JX6aHTAq3+ggAACAASURBVOVXX5aIdBQzqwHOBuYDfdx9S7DqHaBPM9tMN7NFZrao/vDhDolTRKLL3V8EdjYqbq4DaSpwv6fMA8rNrB9wBTDH3Xe6+y5gDjAlWNfd3ee5uwP3k0edUXMWrmP7SwuYeM0HGTKqJuxwRHJSe+7wZXTIlPI9Eck0M+sK/A74e3ffm74uuLDyprZz9xnuPsHdJxQVFnZApCLSCTXXgTQA2JhWb1NQ1lL5pibKT5DeGbVt27b2n0HI9h48zG9mPEZs8GA+O2WsXrAukiVtTvgyPWQqz4ari0iWmVkhqWTvAXd/NCh+N+hNJ/i9Naz4RCR3tNSBlOHjHO2M6tWrV7YPl3X/9cvnsR07+OLfTKawV1XY4YjkrIw8w9eWIVNN7CMToYiIEEx4cA/wurv/MG3VLODIRAnTgMdPvq/MxyciOaG5DqTNQPo0kwODspbKBzZRntMWvfEOa2e/wMhLJjH2rKFhhyOS09qd8LV1yFT6sATQkE4Ryajzgc8AF5vZkuDnKuC7wGVmtga4NFhukat1EpGmNdeBNAv4bDBb5yRgT9AR/jRwuZn1DOY3uBx4Oli318wmBZ1Vn6UVnVGd2aGGBL/46aNYVRVfunY8FBeHHZJITmvXYOmWhky5+5aWhky5+wxgBkCf8r6uXnQRyRR3n0vz/UiXdGQsItL5mdmDwGSgysw2kZpt87vAI2Z2I/AmcH1QfTZwFVAL1AGfB3D3nWb2LWBhUO+b7n5kIpibSM0EWgr8MfjJWTMfXUDyrY3c8L+vp8vAfmGHI5Lz2pzwtWLI1Hdp5ZAp0B0+EYkmtU0i4u43NLPqhA6kYHTTzc3sZyYws4nyRcAZ7Ymxs1izZQ9LfvMUAyadw4WTRmrcvEgHaM8dviNDppaZ2ZKg7Bs03+PVMn3fRURERHJWMunc/ZPf46Ul3HTduVBWFnZIInmhzQlfpodMmTI+ERERkZz18LPLOLhyFVfe+BGqhlaffAMRyYiMzNLZXo5m6RQRERHJVVv2HOBP98+i2xmn8+ELT4dYJC5BRfJCZL5tyvdEJJLUNomItNuM/3kKDie4+YYPYN27hx2OSF6JTsIXdgAiIiIiknGr3t7N23MXcvZlExk0cnDY4YjknegkfMr4RERERHLOA796jmRZFz59yRgoaNcbwUSkDaKT8IUdgIiIiIhk1PKNu9j68mLGX3ouPQb1DzsckbwUnYRPt/hEJJLUNomItNWDv5xDsms3PnXxaIjHww5HJC9FJuHTmE4RiSK1TCIibbNk/XZ2LniVSZefS/fqfmGHI5K3opPwiYiIiEjOeOhXz9LQowefmKzXMIiEKTLfPvWii4iIiOSGxWu3smfxa7z/8kl0HdA37HBE8lp0Ej5lfCIiIiI54eFfzSFR3pNPTB6pu3siIYvMN1CTtoiIiIh0fvNWbWH/q8v4wBXnUtavT9jhiOS9iCR8piGdIiIiIp2cu/ObB56lobKK6y/Us3siUaBvoYhIS9QbJSLSan99fQsHlq3gwikTKenXO+xwRIQIJXwa0SkiIiLSebk7v/vVMzRU9eJjF4zUxZ1IREQn4VM3uoiIiEin9eKKtzm4chWXXHUeJX16hR2OiASik/Ap3xMREZFOxsz+wcxWmNlyM3vQzErMbIiZzTezWjN72MyKgrrFwXJtsL4mbT+3BeWrzeyKsM6nrdydx375NIneffnIB0bowk4kQiKT8ImIiIh0JmY2APjfwAR3PwOIA58Evgfc6e7DgV3AjcEmNwK7gvI7g3qY2ehguzHAFOAuM4t35Lm01wtL3uLQ6je4/KqJFPWqCjscEUkTmYRPr2UQkShSyyQiJ1EAlJpZAVAGbAEuBn4brL8P+HDweWqwTLD+EktdAE0FHnL3Q+6+HqgFzu2g+NvN3XnsgWegT1+uPV9390SiJjIJn4iIiEhn4u6bgR8Ab5FK9PYAi4Hd7t4QVNsEDAg+DwA2Bts2BPUr08ub2Cby5ix+k8O1a7nsQ+dRWFUZdjgi0ogSPhHJOWY208y2mtnytLI7zGyzmS0Jfq4KM0YR6fzMrCepu3NDgP5AF1JDMrN1vOlmtsjMFm3bti1bhzklyaTz5K+fxvr355r36+6eSBRFJuFT+yAiGXQvTV903enu44Kf2a3blRonEWnWpcB6d9/m7oeBR4HzgfJgiCfAQGBz8HkzUA0QrO8B7Egvb2Kbo9x9hrtPcPcJvXpFYxbMpxeuI1m7jinXTKKgsiLscESkCdFJ+MIOQERyhru/COwMOw4RyXlvAZPMrCx4Fu8SYCXwPHBdUGca8HjweVawTLD+T+7uQfkng1k8hwAjgAUddA5tlkg6Tz7wDAyq5qqJI8IOR0SaEZmET0SkA9xiZkuDIZ89m6uUPmzq4KGDHRmfiHQi7j6f1OQrrwDLSF1XzQC+DvyjmdWSekbvnmCTe4DKoPwfgVuD/awAHiGVLD4F3OzuiQ48lTaZPe8NbMMGPnTNROIVzTapIhKygpNX6RgWU+4pIll1N/AtwIPf/wF8oamK7j6D1EUbgwaP8I4KUEQ6H3e/Hbi9UfE6mphl090PAh9vZj/fAb6T8QCzpCGR5KlfP0u8ZhCX6+6eSKS1K8vK5MQIGtIpItnk7u+6e8Ldk8DP6URTnouIRM2TL7+Bb9rINdeeR6y8POxwRKQF7b2tdi8ZmBjBTZO2iEh2mVm/tMWPAMubqysiIi17YdZcCqoHcumEoWGHIiIn0a4hne7+opnVZCYUEZHMMLMHgclAlZltIjXcarKZjSM1pHMD8KXQAhQR6cSWvrWTQ2+s5YJPX4Xp7p5I5GXrGb5bzOyzwCLgq+6+q3EFM5sOTAeoKu+rIZ0ikjHufkMTxfc0USYiIqfoj0/O53BpFz40UXf3RDqDbMyUcjcwDBgHbCE1McIJ0t8lA3qGT0RERCTq9h48zJqXFjN8wul0798n7HBEpBUynvBpYgQRySXqjBIROeYPL75O0Xv7uWrymVAQmcneRaQFGU/42jwxgmZtEREREYksd+flp16maMhgzhrZP+xwRKSV2tU1k8mJEUwJn4iIiEhkvbJuOw3r1nPRtGuha9ewwxGRVmrvLJ2aGEFEREQkDzz1h3nUd+nGVe8bEnYoInIKsjFpS5voBp+IiIhINO2uq2f9y0sY+b4xdOnXO+xwROQURCbhExGJJHVGiYjwxAsrKDxQx9UXjoZ4POxwROQURCbh0zWViIiISPQcmaylbNgQRp82IOxwROQURSbhExGJJnVHiUh+m79mK7ENb3L+5LHQpUvY4YjIKYpMwqdn+ERERESi55kn/srBHuVcNb4m7FBEpA2ik/CpF11EREQkUrbvP8RbC5YyZuIYSvpUhR2OiLRBZBI+EREREYmWJ55bSrz+ENd8UJO1iHRWkUn49OJ1ERERkehIJp0FT79M99OGMXx4/7DDEZE2ikzCJyIiIiLR8dLrbxPftJkPTj4TysrCDkdE2igyCZ9u8ImIiEhnY2blZvZbM1tlZq+b2XlmVmFmc8xsTfC7Z1DXzOzHZlZrZkvN7Jy0/UwL6q8xs2nhndExzz7xMvU9K7j87JqwQxGRdohMwiciIiLSCf0IeMrdRwFjgdeBW4Hn3H0E8FywDHAlMCL4mQ7cDWBmFcDtwETgXOD2I0liWN7de5C3Fy/jzEljKO6tyVpEOjMlfCIiLdDgAxFpjpn1AD4I3APg7vXuvhuYCtwXVLsP+HDweSpwv6fMA8rNrB9wBTDH3Xe6+y5gDjClA0/lBLOeWYIlGrjmwjEQ0+WiSGcWmW+whnSKiIhIJzME2Ab8wsxeNbP/MbMuQB933xLUeQfoE3weAGxM235TUNZceSgSSWfRnHlUjhpGzZC+YYUhIhkSmYRPREREpJMpAM4B7nb3s4H3ODZ8EwB3d8AzcTAzm25mi8xs0bZt2zKxyyb9efkmCrdsYfJF46C0NGvHEZGOEZmETy9eF5FIUtMkIs3bBGxy9/nB8m9JJYDvBkM1CX5vDdZvBqrTth8YlDVXfhx3n+HuE9x9Qq9evTJ6Iun+9MRfOVTVi4vHDcraMUSk40Qm4dNFlYiIiHQm7v4OsNHMRgZFlwArgVnAkZk2pwGPB59nAZ8NZuucBOwJhn4+DVxuZj2DyVouD8o63ObdB9j6yjLOPm8MhVWVYYQgIhlWEHYAR+jF6yKSKWY2E/gQsNXdzwjKKoCHgRpgA3B9MDmCiEh7/B3wgJkVAeuAz5PqUH/EzG4E3gSuD+rOBq4CaoG6oC7uvtPMvgUsDOp90913dtwpHPPEU4txh2svOF0TLIjkiEgkfK4BnSKSWfcCPwXuTys7Mk36d83s1mD56yHEJiI5xN2XABOaWHVJE3UduLmZ/cwEZmY2ulNzOJHklWcX0OuM0xhYo8laRHJFdIZ0iohkiLu/CDTuHW9umnQREQGef/VNira+y6UXngklJWGHIyIZEpmET6MGRCTLmpsmXUREgD/94a8c7t2bC8cNDjsUEcmgyCR8IiId5WTTpKdPfV534EAHRiYiEo43d7zHztdWcs75ZxGv6Bl2OCKSQZFJ+HSDT0SyrLlp0k+QPvV5md5BJSJ54I/PLCYZizP1/BEadiWSYyKT8ImIZFlz06SfhC58RCS3uTuvzV1K1WmD6TtIk7WI5Jp2JXxmNtPMtprZ8rSyCjObY2Zrgt+tGheg1zKISKaY2YPAy8BIM9sUTI3+XeAyM1sDXBosi4jkveUbd2ObNzHhfadrshaRHNTeO3z3AlMalR2Z+nwE8FywLCLSYdz9Bnfv5+6F7j7Q3e9x9x3ufom7j3D3S1v7jit1RYlIrnv+hSXUF5dy6dmDwg5FRLKgXQlfRqc+1x0+ERERkQ6VTDrLX3qNviOHUN6/d9jhiEgWZOMZPk19LiIiItIJLF63jaItWzh30igoKgo7HBHJgqxO2tLS1Ofp056Dhk2JiIiIdLQX//QqB7p045KzqsMORUSyJBsJX6umPk+f9jwLMYiIZIZ6o0QkRzUkkqyet4zq0UPp0qcq7HBEJEuykfC1aepzPcInIiIi0nFefuNdirZv4/2TRkFhYdjhiEiWtPe1DJr6XERERKQTeunZxdR178EHzxwYdigikkUF7dnY3W9oZtUlp7ov3eATERER6RiHGhLULlzOsDOHU9KrMuxwRCSLsjppi4iIiIhEz4vL36Zk9y7eP3EkxONhhyMiWRSZhM/0EJ+IiIhIh5j33CIO9ezJB8YMCDsUEcmyyCR8mrVFREREJPvq6htY/8rrjBg7gsLKirDDEZEsi07CJyIiIiJZ9+clGynbt4cPTBwFMV0KiuS6yHzLdX9PREREOhszi5vZq2b2ZLA8xMzmm1mtmT1sZkVBeXGwXBusr0nbx21B+WozuyLbMS/6yxIOVlQycVS/bB9KRCIgMgmfiIiISCf0FeD1tOXvAXe6+3BgF3BjUH4jsCsovzOoh5mNBj4JjAGmAHeZWdZmUalvSLJhyWqGjxlKvGd5tg4jIhESjYTP9AifiESTmiYRaY6ZDQSuBv4nWDbgYuC3QZX7gA8Hn6cGywTrLwnqTwUecvdD7r4eqAXOzVbML6/aQumeXbxvwggN5xTJE/qmi4iIiLTNfwJfA5LBciWw290bguVNwJFpMAcAGwGC9XuC+kfLm9jmOGY23cwWmdmibdu2tSngBXOXcqBbd84/XcM5RfJFZBI+9aKLiIhIZ2FmHwK2uvvijjqmu89w9wnuPqFXr16nvH0y6axZ9DqDRtVQUqXZOUXyRUHYAYiIiIh0QucD15rZVUAJ0B34EVBuZgXBXbyBwOag/magGthkZgVAD2BHWvkR6dtk1JK3dlK09V3O/ugHoECXgCL5IjJ3+PQQn4hEkpomEWmCu9/m7gPdvYbUpCt/cvdPA88D1wXVpgGPB59nBcsE6//k7h6UfzKYxXMIMAJYkI2YX/rLUg6UlnKhXrYuklfUvSMiIiKSOV8HHjKzbwOvAvcE5fcAvzSzWmAnqSQRd19hZo8AK4EG4GZ3T2Q6KHfn9Xkr6D1sED36nfpwUBHpvCKT8Jnu8ImIiEgn5O4vAC8En9fRxCyb7n4Q+Hgz238H+E72IoS12/bjmzdz1kVXQVFRNg8lIhETnSGdIiIiIpIVf567gsOxOBedVX3yyiKSUyKT8OkGn4iIiEh2LJ+3nG6DB9K3uk/YoYhIB4vMkE4RkY5gZhuAfUACaHD3CeFGJCKSXVv2HGBf7QbOmzoZSkvDDkdEOlhkEj7d4BORDnSRu28POwgRkY7w/Pw1FCYTXDh+aNihiEgIIjOkU0QkmtQdJSKd29KXlhLr34/hNb3DDkVEQhCZhE+zdIpIB3HgGTNbbGbTww5GRCSb9tQdZuuqtYw+axh06RJ2OCISgsgM6RQR6SAfcPfNZtYbmGNmq9z9xfQKQSI4HaB3X81oJyKd14uvbaDsQB2Txg/XDHkieSo6d/jCDkBE8oK7bw5+bwV+T9Pvy5rh7hPcfUKXsrKODlFEJGOWzl/JgYoKxg3TcE6RfBWZhE9EJNvMrIuZdTvyGbgcWB5uVCIi2ZFMOhuWrmHIaYOI9+gedjgiEhIlfCKST/oAc83sNWAB8Ad3fyrkmEREsmLZxl0Ub9/GmeOGQTwedjgiEpLIPMNnMQ3qFJHscvd1wNiw4xAR6Qjz56/iQGExHxjVL+xQRCREWUv49HJjEckF6ooSkc5q1eLX6T6oP5X9qsIORURClO07fK1+ubEuqkREREQyY+d79exe+xYTr3w/lJaGHY6IhEjP8ImItES9USLSCb209E3K6g8ycWxN2KGISMiymfC1+HJjM5tuZovMbFEWYxARERHJO0vmreBgRSVn6XUMInkvm0M6W3y5sbvPAGYAVFYMcNPLQEVERETaLZl03ly6hppRg4n16BF2OCISsqzd4WvNy41FREREJLOWbtxFyY7tjB07BGJ6ekck32WlFdDLjUVERCTXmVm1mT1vZivNbIWZfSUorzCzOWa2JvjdMyg3M/uxmdWa2VIzOydtX9OC+mvMbFp74po/73Xqiko4//T+7TtBEckJ2er20cuNRUREJNc1AF9199HAJOBmMxsN3Ao85+4jgOeCZYArgRHBz3TgbkgliMDtwERSI6JuP5IktsWqV1ZRPrg/FX31OgYRydIzfHq5sYiIiOQ6d98CbAk+7zOz14EBwFRgclDtPuAF4OtB+f3u7sA8Mys3s35B3TnuvhPAzOYAU4AHTzWmHfsPsbf2TSZefT6UlLTj7EQkV2hgt4hIizShlIicnJnVAGcD84E+QTII8A6pkU+QSgY3pm22KShrrvyUzV36JsWJer2OQUSOikTCp8spERER6azMrCvwO+Dv3X1v+rrgbp5n6DhHX2m1bdu2Jussm7eCw+UVnDlUr2MQkZRIJHwA6LUMIiIi0smYWSGpZO8Bd380KH43GKpJ8HtrUL4ZqE7bfGBQ1lz5cdx9hrtPcPcJvXr1OiGWRNJZv7SWmtP1OgYROSY6CZ+ISASpK0pEmmOplwjfA7zu7j9MWzULODLT5jTg8bTyzwazdU4C9gRDP58GLjeznsFkLZcHZadk2cZdlO7czlln6XUMInJMNl+8LiIiIpLLzgc+AywzsyVB2TeA7wKPmNmNwJvA9cG62cBVQC1QB3wewN13mtm3gIVBvW8emcDlVCxcsEqvYxCREyjhExEREWkDd59L8wMBLmmivgM3N7OvmcDM9sTzxqur6V7dl4o+le3ZjYjkmEjc78/Ik8wiIiIieWrfwcPsWPsWI06vgdLSsMMRkQiJRMInIhJV6pASkc7g5de3UHagjnPOGBR2KCISMZFI+DQpgoiIiEjbLVuwggPdujN+RJ+TVxaRvBKJhE9EJKr0xhgR6QzWLV1D/6EDKepZHnYoIhIxSvhEREREOrGNO+s4vOVdRo0ZDAWaj09EjqeET0RERKQTm79kPfFkkol6fk9EmqCET0RERKQTW/HqahKVlQyr1usYRORE0Un49KCMiHQAM5tiZqvNrNbMbg07HhGR9kgmnY3L1zL4tGqsW7ewwxGRCIpOwicikmVmFgd+BlwJjAZuMLPR4UYlItJ2q97ZR9GuHYweXQMxXdaJyInUMohIPjkXqHX3de5eDzwETA05JhGRNlu46A3qYwWcN6pv2KGISEQp4RORfDIA2Ji2vCkoa9b+Qw1ZDUhEpD1WL3mDkr696du/KuxQRCSilPCJiDRiZtPNbJGZLTp0qD7scEREmlTfkOSd1esZMnIQlJWFHY6IRJQSPhHJJ5uB6rTlgUHZcdx9hrtPcPcJhYWFHRaciMipWPrWTsr27eXM0YM0+Z2INCsaCZ/aKBHpGAuBEWY2xMyKgE8Cs0KOSUSkTZYsfoO6ohLeN6JP2KGISIQVhB2AiEhHcfcGM7sFeBqIAzPdfUXIYYmItEnt0lq69u9Dzz4VYYciIhGmhE9E8oq7zwZmhx2HiEh7uMM7azcy9rwzoLQ07HBEJMKiMaRTRERERFqtrr6Bbvv3MGb04LBDEZGIy1rCZ2ZTzGy1mdWa2a0t1Y0nEtkKQ0RERCTn1NUd5L2iUiaO6B12KCIScVlJ+MwsDvwMuBIYDdxgZqNb3GjfvmyEIiIiIhJ5p9JRDnCo7hBd+/ehR6+eHRGeiHRi2brDdy5Q6+7r3L0eeAiY2uIWyWSWQhERERGJrrZ0lDccqmf4yGo9vyciJ5WthG8AsDFteVNQJiIiIiLHO+WO8lgywdljh3RIcCLSuYU2aYuZTTezRWa2KKwYREROpqxYkxmLSNa1qqM8/dopES9g4si+HRagiHRe2Ur4NgPVacsDg7Kj3H2Gu09w9wn9B/eFfv2yFIqISNtV9SgLOwQREeD4a6fqAZUUVVWGHZKIdALZSvgWAiPMbIiZFQGfBGY1VzleWABmWQpFRKQdYnp7jYhk3Uk7yhsrKCrUtZOItEpWrmTcvQG4BXgaeB14xN1XZONYIiIiIp3cKXWUi4iciqw9nOLus4HZ2dq/iIiISC5w9wYzO9JRHgdmqqNcRDJFsxGIiIiIhEwd5SKSLXo4RUREREREJEcp4RMREREREclRSvhERERERERylBI+ERERERGRHKWET0REREREJEeZu4cdA2a2D1gddhwhqAK2hx1EB9M554cqoIu79wo7kPbK0/YpX//N5ts5Q36ed060T2qb8ko+nne+nnNW2qaovJZhtbtPCDuIjmZmi/LtvHXO+SE455qw48iQvGuf8vjfbF6dM+TneedQ+6S2KU/k43nn8TnXZGPfGtIpIiIiIiKSo5TwiYiIiIiI5KioJHwzwg4gJPl43jrn/JBL55xL59JaOuf8kY/nnSvnnCvncSry8ZwhP89b55xBkZi0RURERERERDIvKnf4REREREREJMOU8ImIiIiIiOSo0BM+M5tiZqvNrNbMbg07nlNlZjPNbKuZLU8rqzCzOWa2JvjdMyg3M/txcK5LzeyctG2mBfXXmNm0tPLxZrYs2ObHZmYde4YnMrNqM3vezFaa2Qoz+0pQnrPnbWYlZrbAzF4Lzvlfg/IhZjY/iPNhMysKyouD5dpgfU3avm4Lyleb2RVp5ZH8LphZ3MxeNbMng+WcP2eIdmytobYpP9qmICa1T2qfOhW1T/nRPqltilDb5O6h/QBxYC0wFCgCXgNGhxnT/2fvzsOkKK89jn9PzwwMoGyKyCqouKIiIYo7igvggrhF44KGaFRMiMtNNC6AqInRqPHe64KA4IbiClEUiXG5RkVBjfuCKyACioDKOt3n/lHvYDPMwOxV0/37PM/YVW+9VX1qhj7WqXqruhr7sD/QE3gnq+2vwMVh+mLg2jA9AHgSMKA3MCO0twY+Da+twnSrsOzV0NfCuv0TsM/tgJ5helPgI2CnXN7vEMcmYboImBHimwScGNpvA84J0+cCt4XpE4EHwvRO4d95Y6Br+PdfkOTPAnABcB/weJjPh31ObGxV2AflpjzITSEm5Sflp0TEVoV9UH7Kg/yk3JSc3BT3L2MvYFrW/CXAJXH/kaqxH13KJK0PgXZhuh3Rl6MC3A6cVLYfcBJwe1b77aGtHfBBVvs6/ZLyA0wGDsmX/QaaAq8DewLfAIWhfe2/Z2AasFeYLgz9rOy/8dJ+Sf0sAB2BZ4CDgMfDPuT0Ppfdr6TFVsX9UG7Ko9wUYlJ+yuF9LrtfSYutivuh/JRH+Um5Kd59jntIZwdgTtb83NDW0LV19/lh+mugbZiuaH831D63nPbECJeedyc6a5PT+x0uz78JLASmE51hWeLuJaFLdpxr9y0sXwpsRtV/F3G7CfgDkAnzm5H7+wzJjq0mcvozmi2fchMoP4V55aeGLec/p6XyKT8pNwEJyE1xF3w5z6Py2+OOoy6Y2SbAw8Dv3X1Z9rJc3G93T7t7D6IzN3sAO8QcUp0ysyOAhe4+K+5YpPbl4me0VL7lJlB+ktySq59TyL/8pNyUDHEXfPOATlnzHUNbQ7fAzNoBhNeFob2i/d1Qe8dy2mNnZkVECeted38kNOf8fgO4+xLgWaLL6i3NrDAsyo5z7b6F5S2Ab6n67yJO+wBHmdnnwP1EQxP+Tm7vc6kkx1YTOf8ZzefcBMpP5PY+l0pybDWR85/TfM5Pyk0x73PMY1wLiW427cpPNx7uHPfY22rsRxfWHYd+HevegPvXMH04696A+2pobw18RnTzbasw3TosK3sD7oAE7K8BdwE3lWnP2f0G2gAtw3QT4P+AI4AHWfcm3HPD9FDWvQl3UpjemXVvwv2U6AbcRH8WgD78dONxzu9zkmOr4n4oN+XHfis/KT8lIrYq7ofyU47vt3JTcnJTEn4ZA4ieVPQJcGnc8VQj/onAfGAN0TjaIURjb58BPgb+mfVBNOB/w76+DfTK2s6vgNnh54ys9l7AO2Gd/wEsAfu8L9GQg7eAN8PPgFzeb2BX4I2wz+8AV4T2NJ6tXwAAIABJREFUrYkS7OzwYW4c2ovD/OywfOusbV0a9utDsp6gleTPQpmklS/7nNjYKhm/clMe5KYQk/KT8lPsMVUxfuWnPMhPyk3JyU0WVhQREREREZEcE/c9fCIiIiIiIlJHVPCJiIiIiIjkKBV8IiIiIiIiOUoFn4iIiIiISI5SwSciIiIiIpKjVPCJiIiIiIjkKBV8IiIiIiIiOUoFn4iIiIiISI5SwSciIiIiIpKjVPCJiIiIiIjkKBV8IiIiIiIiOUoFn4iIiIiISI5SwSciIiIiIpKjVPCJiIiIiIjkKBV8slFm9rmZHVxO+7tm1qeCdfqY2dw6D05EpAKlucvM/mRmY+KOR0Tqz4aOUZLIzDqb2Q9mVhB3LJJ7VPBJtbn7zu7+XNxxiIhsiLtf4+6/jjsOEak/De0Yxd2/dPdN3D29oX7hhLqb2aNl2ncL7c/VaaDSIKngExERqQadiRfJLwn6zC8C9jKzzbLaBgMfVXeDtbVvFlF9kTD6g0iVmNmOZvaZmZ2UPdTTzJqY2Xgz+87M3gN+Xma9z83sIjN7y8yWmtkDZlactfwIM3vTzJaY2Utmtmto/y8ze7jMtm42s7/Xw+6KSA4wsxFmdk+YftLMziuz/D9mdkyY3sHMppvZYjP70MxOyOo33sxuNbOpZvYjcGC97oiIVFrWkO4RZjbJzO4ys+/DUM9eWf12NLPnwvHHu2Z2VNay9T7zZtbTzN4I23owHM9cFfq3MrPHzWxROB563Mw6Zm3vOTMbZWb/Dus/bWabh2VdwhW6wjDf2szuNLOvwrYey9q91cBjwImhbwHwC+DeMr+DKuUzM+tkZo+E+L81s/8Jfdfm0Apifc7MrjazfwPLgQvNbFaZWC4ws8nV+FNKLVDBJ5VmZj2BacBv3X1imcXDgW3Cz2FEZ5rKOgHoB3QFdgVOD9vdHRgH/AbYDLgdmGJmjYF7gH5m1jL0LSRKcHfV5r6JSN6YCJxUOmNmOwFbAU+YWTNgOnAfsAVRrrkl9Cn1S+BqYFPgxfoKWkRq5CjgfqAlMAUoLWSKgH8ATxN95n8L3Gtm22etm/2ZfxV4FBgPtCbKJ4Oy+qaAO4lySmdgRel7ldneGeH9GgEXVRDz3UBTYOfQ98Yyy+8CTgvThwHvAF+VLqxGPnsZeBz4AugCdCD6nVXWqcBZYVs3A13NbMcyy3XsFhMVfFJZ+xElydPc/fFylp8AXO3ui919DtGHvayb3f0rd19MlGB7hPazgNvdfYa7p919ArAK6O3u84EXgOND337AN+4+q+zGRUQq4VGgh5ltFeZPBh5x91XAEcDn7n6nu5e4+xvAw/yUfwAmu/u/3T3j7ivrN3QRqaYX3X1quD/ubmC30N4b2AT4i7uvdvd/ERU9J2Wtu/YzT3TcUkh0PLPG3R8hKgIBcPdv3f1hd1/u7t8TFVMHlInlTnf/yN1XAJP46VhoLTNrB/QHznb378J7PZ/dx91fAlqH4vQ01i+mqpTPiE7Etwf+y91/dPeV7l6Vk1rj3f3d8F6rgAeAU8L+7ExURJZ3/Cj1QAWfVNbZwEsbuAG6PTAna/6Lcvp8nTW9nCjJQnQm7MIwnGKJmS0BOoVtAkwgJI3wenfVwxcRgXAQ9gRhKBTRgV3pMKitgD3L5KKTgS2zNpGd50SkYSh7/FEcRgy1B+aEgqfUF0RXt0plf+bbA/Pc3ctbbmZNzex2M/vCzJYRnbBuaeveH1fRsVC2TsBid/9uI/t1N3Ae0fDyR8ssq2o+6wR84e4lG3nPipTNjROAX5qZEV3dmxQKQYmBCj6prLOBzmZWdkhBqflEyaJU5ypsew7R1cGWWT9Ns4aNPgbsambdic5Y3VvhlkRENm4icJKZ7QUUA8+G9jnA82Vy0Sbufk7Wul52YyLSYH0FdLJ1HzLSGZiXNZ/9mZ8PdAhFTKnsY58Lge2BPd29ObB/aM/uXxlziK7etdxIv7uBc4Gp7r68nG1UJZ/NITrOKyznfX4kGl5aasty+qyTG939FaJ7DfcjGjqqk/UxUsEnlfU90XDK/c3sL+UsnwRcEm5Y7kg0Dr6y7gDONrM9LdLMzA43s00BwrCph4jGob/q7l/WbFdEJM9NJTr7fSXwQNbZ/ceB7czsVDMrCj8/L3MfiojkjhlEV9n+ED7vfYAjqfjetZeBNHCemRWa2UBgj6zlmxLdt7fEzFoTPd+gysLtLE8S3XPXKsS2fzn9PiMaMnppOZupaj57laig/Us4Dis2s33CsjeJjv86m1kL4JJK7spdRPcwrqni8FCpZSr4pNLcfQlwCNDfzEaVWTySaBjEZ0Q3P1f6TI67zwTOJEoK3wGzCQ90yTIB2KUq2xURKU8YVvQIcDDRiaTS9u+BQ4mGe35FNPTqWqBxDGGKSB1z99VEBV5/4BvgFqJnFXywgf7HAEOAJUS3mTxO9NwBgJuAJmFbrwBP1SC8U4E1wAfAQuD3FcT0ort/VU57lfJZuL/xSGBb4EtgLtGTP3H36UT35L0FzKLy9+LdDXQnegCfxMjWHYYskkxm1pko6W3p7svijkdERETEzGYAt7n7nXHHkjRm1oSoWO3p7h/HHU8+0xU+Sbwwtv4C4H4VeyIiIhIXMzvAzLYMQzoHEz3dsiZX8nLZOcBrKvbiV96NmSKJEb5HZgHRcNF+MYcjIiIi+W17oucWNAM+BY4L99xJFjP7nOhhNUfHHIqgIZ0iIiIiIiI5S0M6RUREREREcpQKPhERERERkRyViHv4Nt98c+/SpUvcYYhILZo1a9Y37t4m7jhqSvlJJPfkQn5SbhLJPXWVmxJR8HXp0oWZM2fGHYaI1CIz+yLuGGqD8pNI7smF/KTcJJJ76io3aUiniIiIiIhIjlLBJyIiIiIikqNU8ImIiIiIiOQoFXwiIiIiIiI5SgWfiIiIiIhIjlLBJyIiIiIikqNU8ImIiIiIiOSoRHwPnySDu+MepsP8T9Ol7T/1+Wm9n5Zlz5euW7rt7Pl1+pVZf73l661Xpl8F65S3zfVWqLip3O1VtM3y+1Ve2X3acN8qbLgSGhem2KJ5ce1uVKQWrc0fvm5O+Wm6gvyzTg5Zd3nZ3LRuW5kG1v3cV5SbKtrehuJZt8P6TRU1VzY3Vdy3cuLMTQBtmxfTqFDnpkUkOWYv/IEOLZvQpFFB3KFUWmIKvkzG+dcbX7Bk6Y8UNioglSognU7j6TSZDKTTaTIOnk5TUpIm404mnSZTkiGTTpN2x0syZDKZaFlJJhQw0XqZEo/mM9E86QwZHE9H/T2TiQ4a0plQ1DiecTLuWHjN4BD6Edocx9Kl00AmE23fHTzaTsadlEftpYusdHvhQCV6Dyj9X7ZFG1l7gBOtk4mmyfpPJruQyjqgCe9f2lr6/2HLZPVh3f/ppzxT4d/HKnF4YFX8v31V+5faUJzVlapSeVb7qvu7qA2bdN+Bv406Lbb3bwhmf72MN96bQ6owRVFhIe5p0hmgJE06naEkE+WGdMZJl6SjPJPOUJJ2Mpk0XpIm7eCZDJmQvwi5KJMuk5tKQm4qzTHpNCHlQDpNBtbmGXf/Kedk0nhpSshkyBAtt3SUp0rbo9Sxfn4qfY+y+al0ndKiakO5CSrKT5XPTWvDy/r9b+wzX9v5qSafx1zLT3HmptUFRVx68/ls275lbDGIiGR787NvuO3y0XTYqweXDx0QdziVlpiC787Jr/HGnQ+zurCQgkz0P8yMGQAeXjMYGUutbXMznNI+KSiAFIalUpgBqRQpA7PS+QJSBpiF9jCfsmgrhSlSWFgebT9lkDILfQxSKQqMqH9B1JYqjNqjTUd9U4BhWOhsACGO8JZkUgXROmEeS2X1Da+l243CDm3hjEKYj9pCfNn9wkqG/dQWlrulolhZu6m1y7DS7dg67aXTXvr7DPvIT6v81CdVzrYp0yl7WeiftWhtDGX7ltfPCyo+A5y9jYrWj/pVtP762y5//cq9T4Wq0NnLiancTVZim5s1b1L5N85Da9IZ/nr+zRSs+JGMGalwAJwJOQKi3BS1pdbmJgAPOStl4d94yElrc0EqFY2rL0hFn9NU+NxZKivvgBWEz2tWTnJsbZ8on0W5rxCivEPUz4qy3hfDSrdZifyUIjvvpCqfm7L6ZeenyuamaHrdz75ldSpdlt1uZK+7fm4q7QM/5af1clNWY9nclN1WXnzZsudrIzdFfctrq1xuqup7Vb9j5XMTVC4/tWnScM6gi0hum/HBfO4ceQep5s05/cDt4w6nShJT8L357EyKdtiOP/3mUFZnDHcnlYoOaApSRkEqRSoFBakUBSmjsCBFQUEqLDNSqVTW//mzXstrq8prRW0ikhe+/2EltnI5p196Bu1bN2NNOhPlJYNUQYoCM1Ipi/KRQUFBKspPa/OWrS3WaiVHbaxNREREatXzb83hvmvGUrjZ5lwx7EjabrdV3CFVSSIKvpKM8+O8r9n/mD6036ZT3OGIiKy1csUqrM0W7Llje9hkk7jDERERkXr09Kuf8vBf76S4Y3uuOG8Am23bsIo9SEjBt7okQ7M1K+nUsU3coYiIrCNdUsLmbVpA06ZxhyIiIiL1aPK/P+DJG+6hydZdGXnuIbTo2jnukKolEQVfSUkax2jbQvcSiUiyZNIZNm21KaT0pEAREZF8MWn62/zrlolsstN2jPzNITTr3CHukKotEQVfOp1mTUEBrTbVo+FFJFk8naHZJspNIiIi+eKuJ17n5TsepNXu3Rl+5sEUt28bd0g1koiCL5POsLqgiObNGscdiohIGU7Tpir4REREcp27c8cjr/LG3Y+x+R67c/mvD6TRFg3/lrNkFHwZJ2MpmjYuijsUEZF1mLtyk4iISI5zd26+7//48MGptN93Dy45ow+Fm7WOO6xakYiCz92xRkUUFCUiHBGRdRQ10RU+ERGRXJXJONeP+xefPz6drQ7ah/86bT9SLVvGHVatSUSF5ZkMhUWFeiiCiCSOOTQtTkSqFBERkVpWks7w59ueYv70F9h+wAH87sR9sObN4w6rViXiKMbdKSwqUMEnIgnkNG7cKO4gREREpJatLskw6uYpfPvCK/Q4+mDOPHZPbNNN4w6r1iWk4IOClK7wiUjyGFBUVBB3GCIiIlKLVq5JM+K6R1j66uvsdUI/Tjt6j5z9zt2EFHxOQYGBWdyhiIisp5HuLxYREckZP64qYfifJ/HDf97hwFMP5xeH94Li3L1fPxlHMe6kdA+fiCSQA40KlZtERERywdIVaxg+6j5WfvAR/YcczcBDdoPGuf3VcBs9ijGzcWa20MzeyWq7zsw+MLO3zOxRM2uZtewSM5ttZh+a2WGVCcIdCgpSusInIlVSQX4aYWbzzOzN8DMga1mV8xNAUYEKPhERkYbu2x9Wcfll41nx0WyOOXsQAw/tkfPFHlSi4APGA/3KtE0Hurv7rsBHwCUAZrYTcCKwc1jnFjPb6M0v7hkKCwtU8IlIVY1n/fwEcKO79wg/U6H6+QmMxrqHT0REpEFbsGwlV/xpHCvnzOWX5x7LoQfuBo3y46FsGy343P0FYHGZtqfdvSTMvgJ0DNMDgfvdfZW7fwbMBvbYaBQOKRV8IlJF5eWnDahefkJX+ETynZmdb2bvmtk7ZjbRzIrNrKuZzQijBh4ws0ahb+MwPzss75K1nXJHGZhZv9A228wurv89FMltc775gREXjya9cBG/+t1xHLB/dygqijuselMbRzG/Ap4M0x2AOVnL5oa29ZjZWWY208xmlpSUUFCgM+giUmvOC0POx5lZq9BWrfzkBoUq+ETylpl1AH4H9HL37kAB0WiBa4lGE2wLfAcMCasMAb4L7TeGfhWOMggjDf4X6A/sBJwU+opILfhkwTL+fMntZJYt4zcXHE/vvXaCwmQ8xqS+1OgoxswuBUqAe6u6rruPdvde7t6roKCAlK7uiUjtuBXYBugBzAf+VtUNZOcnUMEnIhQCTcysEGhKlFsOAh4KyycAR4fpgWGesLyvmRkVjzLYA5jt7p+6+2rg/tBXRGro/TmLue7iW0mvWsOw849j917bQx5eZKr2UYyZnQ4cAZzs7h6a5wGdsrp1DG0bDySlgk9Eas7dF7h72t0zwB38NGyz2vmpoED5SSRfufs84HrgS6JCbykwC1iSdXtL9oiBtaMJwvKlwGZUPMqg0qMPRKTy/vPZN9x46WjMUvzXRcexU8/t8/YbAaq112bWD/gDcJS7L89aNAU4MYxf7wp0A17d2PbcnZTOoItILTCzdlmzg4DSJ3hWLz9husInksfCsPCBQFegPdCM8h8WVR+xrB1uvmjRojhCEGkQZnwwn1suvY3C4kb86cJj2HaXbfL6WSEbHcBqZhOBPsDmZjYXGE70VM7GwPRolAKvuPvZ7v6umU0C3iMa6jnU3dOVCcTytOIWkeqrID/1MbMeRF+h9znwG4Ca5KcCjUAQyWcHA5+5+yIAM3sE2AdoaWaF4Spe9oiB0tEEc8MQ0BbAt2x4lEGlRh+4+2hgNECvXr28vD4i+e75t+Zw3zVjKWrdmst+fxRbdtsqr4s9qETB5+4nldM8dgP9rwaurlIUDvpeYxGpqnrJT0ChTkiJ5LMvgd5m1hRYAfQFZgLPAscR3XM3GJgc+k8J8y+H5f9ydzezKcB9ZnYD0ZXC0lEGBnQLIw/mET3Y5Zf1tG8iOWX6a5/y0F8n0Kj9lgz/7QA233aruENKhEQ8osZxLA9voBSRBsCgQEM6RfKWu88ws4eA14lGB7xBdJXtCeB+M7sqtJWebBoL3G1ms4m+NubEsJ0KRxmY2XnANKIngI5z93fra/9EcsU/XvqQJ/52N026bsXIcw+jxdad4w4pMRJR8AGkdAZdRBJK9/CJ5Dd3H040ZDzbp5TzXZ7uvhI4voLtlDvKwN2nAlNrHqlIfnrwX+/yzH/fS7MdujHynEPZpLOee5QtGQWfO6YDKhFJIMf0FGEREZGEunvqG7w0ehLNe+zMyDP70qRDu42vlGeSUfABeuq5iCSRAZbnN3uLiIgkjbsz9tFXmXXXY2z28x5c/uuDaNy2TdxhJVIiCj4HLKV7+EQkeRzy/uleIiIiSeLu/Pd9L/LBg0+w5T4/59JfHUjhZq3jDiuxElHw4TqeEpFkUmoSERFJjkzG+dud/+Kzf0yn00F788fT9ifVsmXcYSVaMgo+9NAWEUmoaExn3FGIiIjkvXTGueb2acyf9hzd+h/A70/aB2vePO6wEi8hBZ+jZyKIiIiIiEh5VpdkGPU/T/DNcy+x28C+/Oa43timm8YdVoOQjILPwXSFT0QSyXSFT0REJEYr16QZccNjLH15JnuecBinH70HNG0ad1gNRjIKPtAVPhFJLhV8IiIisfhxVQkj/jyJZf95hz6nDOCkw3tBkyZxh9WgJKLgc8AK9JROEUke1XoiIiLxWLpiDSOumsjy9z+k368GMujQHtC4cdxhNTiJKPgM15PwRCS5VPWJiIjUq8U/rmb4iLtZ/dnnHHP2IA7rsys0ahR3WA1SIgo+gFSB7uETkSRSsSciIlKfFixbyajL7mT1119z8rnHcMB+3aGoKO6wGqzEFHwFOqYSkaTSFT4REZF6MffbH7jminGkv13C6b89jr332hEKE1OyNEjJ+e3pKZ0ikkSq9UREROrFpwuWcd1lY0gvX87Z5x9Pz17bgZ7zUWPJKPgcCnQGXUQSyEBX+EREROrYB18t4cbL7oCSNL87/zi699xOF4RqSTIKPsD1vQwiIiIiInnnrc+/4X+vGIMVFnDRRcex7S7b6GRrLUpMwZfSH1VEEkm5SUREpK689tHXjBkxhtQmTblk2EA677S1ir1alpiCT39WEUks/Y9HRESk1v3f23O45+qxFLZuzaXDjqL9dlvp/7l1IDEFX0pDOkUkiZSaREREat0zMz9j0rXjadxuCy7/3RG02XaruEPKWYkp+EzVvIiIiIhIznv85Y94/Pq7KO66FSPPPYyWW3eOO6ScloiCz3D0DB4RSSI9pVNERKT2PPzsu0y/+V6abt+NkeccyqZbdYg7pJyXiIIPwAtU8omIiIiI5Kp7nvoP/77tflrsshMjzj6YJh3axR1SXtholWVm48xsoZm9k9XW2symm9nH4bVVaDczu9nMZpvZW2bWs7KBaEiniFRVBfnpOjP7IOSgR82sZWjvYmYrzOzN8HNb5d6kjoIXERHJI2MffZV/33Y/rX/egyuHHqZirx5V5rLaeKBfmbaLgWfcvRvwTJgH6A90Cz9nAbdWOhAdVIlI1Y1n/fw0Heju7rsCHwGXZC37xN17hJ+zK/0uOiElIiJSLe7O/9z3f8ya8Ahb7tOLkb85mMZbbhF3WHllowWfu78ALC7TPBCYEKYnAEdntd/lkVeAlmZWqfLdTEM6RaRqystP7v60u5eE2VeAjjV7FxV7IiIi1ZHJOH8b/xzvTXqCTgftzeW/PojCzTeLO6y8U90qq627zw/TXwNtw3QHYE5Wv7mhbeOB6Ay6iNS+XwFPZs13NbM3zOx5M9svrqBERERyXTrj/Pn2aXw6eRrdDt2PiwcfQKpVq7jDyks1vqzm7g54Vdczs7PMbKaZzYzmaxqJiMhPzOxSoAS4NzTNBzq7++7ABcB9Zta8gnXX5qd0Oq0EJSIiUgVr0hlG3vw486Y9R/ej+vL7U/fHWrSIO6y8Vd2Cb0HpUM3wujC0zwM6ZfXrGNrW4+6j3b2Xu/cCcA3pFJFaYmanA0cAJ4eTUrj7Knf/NkzPAj4Btitv/ez8VFBQUE9Ri4iINHwr16S54vpHWfTcS/Q6vh/nnrgPtummcYeV16pbZU0BBofpwcDkrPbTwtM6ewNLs4Z+bjgQnUAXkVpgZv2APwBHufvyrPY2ZlYQprcmerjUp/FEKSIiknuWry7h8r88yOJXZrH/KQP41TF7QtOmcYeV9zb6PXxmNhHoA2xuZnOB4cBfgElmNgT4AjghdJ8KDABmA8uBMyoThLnu4RORqqsgP10CNAamh697eSU8kXN/4EozWwNkgLPdvewDqdZ/j+iN6iR+ERGRXLFs5RpGjJrIj+9/SP8zjuTow3pC48ZxhyVUouBz95MqWNS3nL4ODK1OIBrRKSJVVUF+GltB34eBh6v8Jqr1RERENui7H1czYuQ9rPz0U47+zdH0P3A3aNQo7rAk2GjBV190Al1EREREpGFZ+P1KRl0xgdVzv+Kkc46lz/7doago7rAkS4IKPlV8IpJQyk8iIiLrmbf4R665fCwl3y5h8O+OY++9doTCxJQXEiTmL5JKaUyniCSRij0REZGyPl/4PX+97A5KflzOb4Ydy8/22AH0ZOtESk7BF3cAIiLlUb0nIiKyjo/mL+WGS0fDmhJ+d/5xdO+5HejiTWIlpOBzDekUkURSZhIREfnJ259/y/8OvwNLGRdcdBzddtlGxV7CJaTg00GViIiIiEiSzfzoa8aMGAPNmnLxsIFstfPWus+9AUhMwec6MyAiIiIikkj/fmcuE64aR1Hrllw67Cjab7eVir0GIjEFn/65iIiIiIgkz79mfsYDfx1P47ZbcMWwI2iz7VZxhyRVkJyCT2cIREREREQSZeorHzH5+rtpvFVnRp57GK226Rx3SFJFiRlHqXJPREREksjMWprZQ2b2gZm9b2Z7mVlrM5tuZh+H11ahr5nZzWY228zeMrOeWdsZHPp/bGaDs9p/ZmZvh3VuNp0Fl4R45Ll3+ce142m2bVeu/m1/FXsNVGIKPhEREZGE+jvwlLvvAOwGvA9cDDzj7t2AZ8I8QH+gW/g5C7gVwMxaA8OBPYE9gOGlRWLoc2bWev3qYZ9ENujeaW8x7e/3smn3Hbn6t/3ZtEvHuEOSakpMwWcFiQlFROQnOtEuktfMrAWwPzAWwN1Xu/sSYCAwIXSbABwdpgcCd3nkFaClmbUDDgOmu/tid/8OmA70C8uau/sr7u7AXVnbEonFuMde48VbJ7JZz10YNfQwmnRoF3dIUgPJuYcv7gBERERE1tcVWATcaWa7AbOAYUBbd58f+nwNtA3THYA5WevPDW0bap9bTrtIvXN3bnnwZd69bwpb7v0z/nTGgRS12TzusKSGEnNZTSfRRUREJIEKgZ7Are6+O/AjPw3fBCBcmfO6DsTMzjKzmWY2c9GiRXX9dpJn3J0bJzzPu/dNoWOfvbjs1wer2MsRiSj4VOuJSFIpP4nkvbnAXHefEeYfIioAF4ThmITXhWH5PKBT1vodQ9uG2juW074edx/t7r3cvVebNm1qtFMi2dIZ58+3P83sx55im0P345Iz+lDQutXGV5QGIREFH4DpsEpEREQSxt2/BuaY2fahqS/wHjAFKH3S5mBgcpieApwWntbZG1gahn5OAw41s1bhYS2HAtPCsmVm1js8nfO0rG2J1Lk16QxX/fcTzH3qWXY+8iAuOHV/rEWLuMOSWpSce/j00BYRERFJpt8C95pZI+BT4Ayik+aTzGwI8AVwQug7FRgAzAaWh764+2IzGwW8Fvpd6e6Lw/S5wHigCfBk+BGpc6tK0oy8YTLfvfQaPz/uUH41aE9o1izusKSWJafgizsAERERkXK4+5tAr3IW9S2nrwNDK9jOOGBcOe0zge41DFOkSlasTjP8rw+ydNZbHHDyAH55RC9o0iTusKQOJKfgU8UnIiIiIlLnvl+5huFXP8CKd96j3+lHMuiw3aG4OO6wpI4kp+CLOwARERERkRy3ZPlqRo68h+WffMrAMwcx4ODdoFGjuMOSOpScgk+X+ERERERE6syi71cxavh4Vs35ihPPGcSB++8KRUVxhyV1LDkFX0oPbRGRJNLJKBERafi+WrKCqy8bx5pF3zL4vGPYZ5+doTAxpYDUIf2VRUQ2RPWeiIg0cF8s+p4/Xz4Oli7jrGHH0GvPHaGgIO6wpJ4kpuDTiE4RSSKlJhERacg+nr+UGy4dDWvWcN6Fx9O953agkXUSJL8tAAAgAElEQVR5pUZ/bTM738zeNbN3zGyimRWbWVczm2Fms83sgfCdNRvfVk0CEZG8ZGbjzGyhmb2T1dbazKab2cfhtVVoNzO7OeSmt8ysZ3yRi4iI1L13vviWv118K57JcMFFKvbyVbX/4mbWAfgd0MvduwMFwInAtcCN7r4t8B0wpJLbq24oIpK/xgP9yrRdDDzj7t2AZ8I8QH+gW/g5C7i1nmIUERGpd7M+XsDNl95GqqiIiy86ju123VbFXp6q6V+9EGhiZoVAU2A+cBDwUFg+ATi6MhtSuSciVeXuLwCLyzQPJMo9sG4OGgjc5ZFXgJZm1q5+IhUREak/L733FaOvuJ1Gm2zCpRcOosvOW+v+qTxW7YLP3ecB1wNfEhV6S4FZwBJ3Lwnd5gIdylvfzM4ys5lmNjPMVzcUEZFsbd19fpj+GmgbpjsAc7L6VSo/rV6zpu4iFRERqWXPvfkFE0bcTqPNN+OKC4+mw44q9vJdTYZ0tiI6Y94VaA80Y/2hVRVy99Hu3svde0Xbq24kIiLlc3cHvBrrrc1PjfT9RCIi0kA8OeMTJl41huKO7bjy90eyRbet4g5JEqAmT+k8GPjM3RcBmNkjwD5Ew6QKw1W+jsC8ymxM9Z6I1JIFZtbO3eeHIZsLQ/s8oFNWv0rnJxERkaR77Pn3ePLv97JJt60Zfs5hNO/SMe6QJCFqcg/fl0BvM2tq0XjMvsB7wLPAcaHPYGBypbamik9EascUotwD6+agKcBp4WmdvYGlWUM/RUREGqyJT7/FkzfdQ/OddmDUb/ur2JN1VPsKn7vPMLOHgNeBEuANYDTwBHC/mV0V2sZWZnumik9EqsjMJgJ9gM3NbC4wHPgLMMnMhgBfACeE7lOBAcBsYDlwRuXeo5aDFhERqUV3Tn6NV+98hNY9d2X4mQdR3K7txleSvFKjL1539+FEB1jZPgX2qPLGdFAlIlXk7idVsKhvOX0dGFrl91ByEhGRBHJ3bn3wZd65bwpb7NWTy351EEVtNo87LEmgGhV8tcXRFT4RERERkcpwd26663k+fvQpOh6wF38cvD8FrVvFHZYkVCIKPtCwKRFJJqUmERFJknTG+esd05nz5L/Y+pD9uOCUfUm1aBF3WJJgySn44g5ARERERCTBStIZrv6fqSx49kV2PrwP5/5ib6x587jDkoRLTsGnik9EREREpFyrStJcecNkFr/0Gr2OPYQhx/SGZs3iDksagAQVfKr4RERERETKWrE6zYjrHmLZa2+yzy/7c8qRP4cmTeIOSxqI5BR8cQcgIlIeJScREYnR9yvXMPyaB1j+9nscevpRHNNvdygujjssaUASU/BpTKeIiIiIyE+WLF/N8CvvYeXsTznyzEEc0XdXaNw47rCkgUlMwadyT0REREQksuj7VYwaPp5Vc77ihLMH0feAXaGoKO6wpAFKTsGnik9EREREhPlLV3DVpeNIL1rEqUMHse++3aEwMYft0sAk5l+OHtoiIsmk3CQiIvXni0Xf85fLx+FLlzFk2HH8fM8doaAg7rCkAUtIwWc6pBIRERGRvDb766Vcf9kYfNUqzrvweHbpuR2kUnGHJQ1cQgo+EZFk0skoERGpD+9++S03X34HZsYFFx3P9rtso2JPakViCj6N6BQRERGRfPT67AXcPmIMqeJi/vD7gXTdeRsdHEutSU7Bp/PoIiIiIpJnXnr/KyZcOYbC5i24ZNiRdNyxq4o9qVWJKfhERERERPLJ829+yX3XjKXRFm24fNiRbNFtq7hDkhyUmIJPJzJEREREJF88NeMTHr1uPI07dWDEeQNovU3nuEOSHJWcgi/uAEREyqPkJCIitWzy8+8x9e/30nTbrRl57mE079Ix7pAkhyWm4NMlPhERERHJdROffovnbr2fTXfegSvPPpimHdvHHZLkuOQUfCIiIiIiOezOf8zi1bEP0brnLgw/sy/F7drGHZLkgcQUfLrAJyIiIiK5yN259cFXeOe+ybTp3ZPLhxxEUZvN4w5L8kRyCr64AxARERERqWXuzt/vep6PHn2K9gf05pLBB1DQulXcYUkeSUzBJyIiIiKSSzIZ59qxzzDniX+y9SH7ccEp+5Jq0SLusCTPJKbgM43pFJEEUmYSEZHqKElnuPrWp1jwzxfY6fA+DP3F3ljz5nGHJXmoRgWfmbUExgDdAQd+BXwIPAB0AT4HTnD37yqxrZqEIiKylpltT5SHSm0NXAG0BM4EFoX2P7n71HoOT0REctyqkjRX3jSFxS++Sq9jD2HIMb2hWbO4w5I8larh+n8HnnL3HYDdgPeBi4Fn3L0b8EyYFxGpN+7+obv3cPcewM+A5cCjYfGNpctU7ImISG1bsTrN5dc+wuIXX2XvX/ZnyHF7q9iTWFW74DOzFsD+wFgAd1/t7kuAgcCE0G0CcPTGtuWmp3SKSJ3pC3zi7l9Ub3UlJxEBMyswszfM7PEw39XMZpjZbDN7wMwahfbGYX52WN4laxuXhPYPzeywrPZ+oW22melEeQP2w6oSLrtqIstmvsHBpx/FqUftAU2axB2W5LmaXOHrSjQs6s6QAMeYWTOgrbvPD32+Bir1BSM6pBKROnIiMDFr/jwze8vMxpnZxh+TpuQkIpFhRCOZSl1LNGJgW+A7YEhoHwJ8F9pvDP0ws52I8tHOQD/gllBEFgD/C/QHdgJOCn2lgVmyfDWXDr+bH995n8N/fTTH9u8JxcVxhyVSo4KvEOgJ3OruuwM/Umb4prs70b196zGzs8xsppnNrEEMIiIVCmfcjwIeDE23AtsAPYD5wN8qWG9tflq5cmW9xCoiyWVmHYHDiZ5bgEUPHjgIeCh0yR7RlD3S6SGgb+g/ELjf3Ve5+2fAbGCP8DPb3T9199XA/aGvNCDf/LCKKy67k5WffM5x5wziyEN3h8aN4w5LBKhZwTcXmOvuM8L8Q0QF4AIzawcQXheWt7K7j3b3Xu7eC3QSXUTqRH/gdXdfAODuC9w97e4Z4A6iA631ZOenYp2dFRG4CfgDkAnzmwFL3L0kzM8FOoTpDsAcgLB8aei/tr3MOhW1ryf7ZNSiRYvK6yIxmL90BcMvGcPqefM5ZeggDj6wBxQVxR2WyFrVLvjc/WtgTngaHkT3ybwHTAEGh7bBwORKbVA38YlI7TuJrOGcpSejgkHAO/UekYg0KGZ2BLDQ3WfFHUv2yag2bdrEHY4AXy76gSv/OJqSbxYzZNhx7LffLlCYmG89EwFq/j18vwXuDcOmPgXOICoiJ5nZEOAL4IRKbUn1nojUonBP8SHAb7Ka/2pmPYiGmn9eZpmISHn2AY4yswFAMdCc6CnlLc2sMFzF6wjMC/3nAZ2AuWZWCLQAvs1qL5W9TkXtkmCzv17K9ZeNwVetYugFx7Prz7aDVE0fgC9S+2pU8Ln7m0Cvchb1req2TBWfiNQid/+RaBhVdtupMYUjIg2Uu18CXAJgZn2Ai9z9ZDN7EDiO6J677BFNpSOdXg7L/+XubmZTgPvM7AagPdANeJXolHc3M+tKVOidCPyynnZPqundL7/l5ivGAHDBRcez/S7bqNiTxNI1ZxEREZGq+yNwv5ldBbxB+Jqq8Hq3mc0GFhMVcLj7u2Y2iej2lxJgqLunAczsPGAaUACMc/d363VPpEpen72A20eMIVVczB9+P5CuO2+jW5Mk0RJT8OlzIiIiIknm7s8Bz4XpTynnwU/uvhI4voL1rwauLqd9KjC1FkOVOvLy+/MZf+UdFDZvwSXDjqTjjl11ECuJl5yCL+4ARETKodwkIiIAz//nS+69ZhyN2mzOFcOOZItuW8UdkkilJKbgExERERFJomkzPuGR68ZT3KkDI84bQOttOscdkkilJabg09VwEREREUmax178gCdvuJum227NyHMPo3mXjnGHJFIliSn4RERERESS5P6n3+LZW++nxc7bMeLsQ2nasX3cIYlUWWIKPn0tg4iIiIgkxYTHX+eVMQ/SuucuDD+zL8Xt2sYdkki1JKbgU70nIiIiInFzd25/6BX+c+8U2vTenct/dSBFW7SJOyyRaktMwacrfCKSSEpNIiJ5w9256Z4X+PjhJ+lwQG8uOW1/CjZrHXdYIjWSnIJPB1UikkhKTiIi+SCTcf469hm+fOKfbH3Iflxw8j6kWraMOyyRGktMwSciIiIiEoeSdIZrbn2Kr//5Ajsd3oehv9gba9487rBEaoUKPhERERHJW6tK0oy66R98++IMeh17CEOO6Q3NmsUdlkitUcEnIiIiInlpxeo0w697hO9fe519TjyMUwbuCU2axB2WSK1KTMGne/hEREREpL78sKqE4Vffz49vv8chpx/Fsf12h+LiuMMSqXXJKfj0YAQRERERqQdLl69h+JX3sPKj2Rz+66M58uDdoHHjuMMSqROJKfhEREREROraNz+sYuTwu1j95VyOP2cQB/fZDYqK4g5LpM4kpuCzlK7wiYiIiEjdmb90BVddNo6ShQs5Zegg9tu3OxQm5nBYpE7oX7iIiIiI5LwvF/3Any8fS2bpMob87nj26L0jFBTEHZZInUtEwee6g09EEkq5SUSk4Zv99VKuv2wMvmoVQy84nl17dlOxJ3kjEQWfiIiIiEhdePfLb7n5ijEAnH/+sezQoxukUjFHJVJ/ElPw6WsZRERERKQ2vf7pIm6/YjSp4mL+a9hAtu6+jQ46Je8kp+CLOwARkfIoOYmINEgvvz+f8VfeQUHz5vxp2FF03LGrij3JS4kp+EREREREasPzb37JvX8eR6M2m3PFsCPZottWcYckEpvEFHymMy4iIiIiUkPTZnzCI9eNp3GnDow8bwCtt+kcd0gisarxHatmVmBmb5jZ42G+q5nNMLPZZvaAmTWqeZgiIlVjZp+b2dtm9qaZzQxtrc1supl9HF5bxR2niIjUnkf/7wMeuXYcTbfuwlXDDlexJ0ItFHzAMOD9rPlrgRvdfVvgO2BIpbaiK3wiUvsOdPce7t4rzF8MPOPu3YBnwryIiOSAiU+/xVM33EXzHbtx1W/706JLp7hDEkmEGhV8ZtYROBwYE+YNOAh4KHSZABxdk/cQEalFA4nyEig/iYjkjPGPz+KFWybSskd3Rp3Xn2ad2scdkkhi1PQK303AH4BMmN8MWOLuJWF+LtChvBXN7Cwzm1k61EpEpJY58LSZzTKzs0JbW3efH6a/BtrGE5qIiNQGd+fWSS/z6pgH2XzP3Rl1ziEUt1NqF8lW7Ye2mNkRwEJ3n2Vmfaq6vruPBkYDbN6qvWtAp4jUsn3dfZ6ZbQFMN7MPshe6u5uZl7diKBDPAmjTtmPdRyoiIlXm7tx4zwvMfvhJ2u3fmz8N3p+CzVrHHZZI4tTkKZ37AEeZ2QCgGGgO/B1oaWaF4SpfR2BezcMUEakad58XXhea2aPAHsACM2vn7vPNrB2wsIJ1156Q6tJ1u3KLQhERiU8m41w79hnmPPFPuh6yLxeevC+pli3jDkskkao9pNPdL3H3ju7eBTgR+Je7nww8CxwXug0GJldme3pmi4jUFjNrZmablk4DhwLvAFOI8hJUOj8pOYmIJElJOsOoW55kzhP/ZMfD+3DRqfur2BPZgLr4Hr4/Aveb2VXAG8DYOngPEZENaQs8Gr7fsxC4z92fMrPXgElmNgT4AjghxhhFRKSKVpWkufKmKSx+8VV6HXsIQ47pDc2axR2WSKLVSsHn7s8Bz4XpT4mGTlWJzqGLSG0JeWi3ctq/BfpWZVvKTSIiybBidZrh1z3C96+9zj4nHsYpA/eEJk3iDksk8eriCp+IiIiISK35YVUJw6++nx/ffo9DTj+KY/vtDsXFcYcl0iAkpuCzVG18B7yIiIiI5JIly1cz/Mp7WfXRbA7/9dEcefBu0Lhx3GGJNBiJKfhERERERLIt+n4Vo4aPZ9Wcrzj+nEEc3Gc3KCqKOyyRBiUxBZ/ukxGRRFJyEhGJxfylK7jqsnGULFzIKUMHsd++3aEwMYeuIg2GPjUiIiIikihfLvqBP18+lszSZQz53fHs0XtHKCiIOyyRBikZBZ/pe/hEREREBGZ/vZTrLxuDr1rF0AuOZ9ee3VTsidRAMgo+EREREcl77375LTdffgeYcf6Fx7PDrtuAHuwnUiOJKfh0gU9EREQkf70+ewG3jxhDqriYP/x+IF133kZDwERqgU6ZiIiIiFTAzDqZ2bNm9p6ZvWtmw0J7azObbmYfh9dWod3M7GYzm21mb5lZz6xtDQ79PzazwVntPzOzt8M6N5vlX5Xz0vtfcfvlt1PYbBMuvWCQij2RWpScgk8fahEREUmeEuBCd98J6A0MNbOdgIuBZ9y9G/BMmAfoD3QLP2cBt0JUIALDgT2BPYDhpUVi6HNm1nr96mG/EuP5N79k/IjRNNqsNcMvOpqOO22t40KRWpScgk9EREQkYdx9vru/Hqa/B94HOgADgQmh2wTg6DA9ELjLI68ALc2sHXAYMN3dF7v7d8B0oF9Y1tzdX3F3B+7K2lbOmzbjE+676g6K22/JyPOPYotuW8UdkkjO0T18IiIiIpVgZl2A3YEZQFt3nx8WfQ20DdMdgDlZq80NbRtqn1tOe3nvfxbRVUM6d+5c/R1JiMnPv8fUv99Ls223ZsS5h9G8S8e4QxLJScm5wqdL9yIiIpJQZrYJ8DDwe3dflr0sXJnzuo7B3Ue7ey9379WmTZu6frs6NfHpt5h60z0032kHrvpdfxV7InUoMQWf6j0RERFJIjMrIir27nX3R0LzgjAck/C6MLTPAzplrd4xtG2ovWM57Tnrzn/M4oVbJtJ69+6MGnooTTu2jzskkZyWmIJPRCSJdC5KJL+FJ2aOBd539xuyFk0BSp+0ORiYnNV+WnhaZ29gaRj6OQ041MxahYe1HApMC8uWmVnv8F6nZW0rp7g7t0x6mdfGPkib3rtz5dmHUNyu7cZXFJEa0T18IiIiIhXbBzgVeNvM3gxtfwL+AkwysyHAF8AJYdlUYAAwG1gOnAHg7ovNbBTwWuh3pbsvDtPnAuOBJsCT4SenuDt/v+t5Pnr0KToc0JuLBx9AQetWG19RRGosMQWfiIiISNK4+4tUfF66bzn9HRhawbbGAePKaZ8JdK9BmImWyTjXjn2GOU/8k60P2Y8LTtmXVIsWcYclkjdU8ImIiIhInShJZ7j61qdY8M8X2OnwPgz9xd5Y8+ZxhyWSVxJT8Jme2iIiSaTUJCJSLatK0lx50xQWv/gqvY49hCHH9IZmzeIOSyTvJKbgExEREZHcsGJ1mhHXPcSy195k35MHcPIRvaBJk7jDEslLiSn4dBJdREREpOH7fuUahl/zACvefo9DTj+KY/rtDsXFcYclkrcSU/CJiIiISMO2ZPlqhl95Dytnf8pRZw7i8IN3g0aN4g5LJK8lpuDTLXwiIiIiDdei71cxavh4Vs35il+cPYiDDtgVioriDksk71X7i9fNrJOZPWtm75nZu2Y2LLS3NrPpZvZxeK3cl6yo4hORWrKB/DTCzOaZ2ZvhZ0DcsYqI5IKvlqxgxCVjWDlvAaeddwwH9dlNxZ5IQlS74ANKgAvdfSegNzDUzHYCLgaecfduwDNhXkSkPlWUnwBudPce4WdqfCGKiOSGLxZ9z5UXjyb97WLOGnYM++zbHQoTM4hMJO9V+9Po7vOB+WH6ezN7H+gADAT6hG4TgOeAP25se7q+JyK1ZQP5qRqUnUREKvLx/KVcf9kYbPUqhl54PLv03A5SNbmeICK1rVY+kWbWBdgdmAG0DQdbAF8DbWvjPUREqqNMfgI4z8zeMrNxlR5yLiIi63nni2+5/uJbsXQJF1ykYk8kqWr8qTSzTYCHgd+7+7LsZe7ugFew3llmNtPMZjqmL14XkVpXTn66FdgG6EF0BfBvFay3Nj/9uHx5vcUrItJQzPp4ATdfehsFRUVcfNFxbLfrtir2RBKqRp9MMysiOpi6190fCc0LzKxdWN4OWFjeuu4+2t17uXuvmsQgIlKe8vKTuy9w97S7Z4A7gD3KWzc7PzVr2rT+ghYRaQBeeu8rRl9xO0XNNuHSCwfRZeet9fA9kQSryVM6DRgLvO/uN2QtmgIMDtODgcnVD09EpOoqyk+lJ6OCQcA79R2biEhD9uwbXzBhxO002nwzhl90NB12VLEnknQ1eYTSPsCpwNtm9mZo+xPwF2CSmQ0BvgBOqFmIIiJVVlF+OsnMehANNf8c+E084YmINDxTX/mIydffTZPOHRk+tD+ttukcd0giUgk1eUrni1T8+Lq+1d2uiEhNbSA/6WsYRESq4ZHn3mXazffRbLttGHn2oWzapWPcIYlIJelLUkRENkADlUQk39077S1euO1+Wuy8IyPO7kvTju3jDklEqkAFn4iIiIiUa+xjrzJz/KNs9rNdGX5mXxpvuUXcIYlIFSWi4DPQDb8ikkxKTSKSh9ydWya9zLsTp7Dl3j/jT2ccSFGbzeMOS0SqIREFn4iIiIgkg7tzw4Tn+OSxaXTssxd/PG1/Clq3ijssEakmFXwiIiIiAkA641x7+9PMnfYs2x66H+efvC/WokXcYYlIDajgExERERHWpDNc9d9TWfTci3Q/qi/nHN8b23TTuMMSkRpKRMHncQcgIiIiksdWrkkz8obJLHn5NfY4/lDOGNQbmjaNOywRqQWJKPhEREREJB7LV5cw/NqHWPb6W/Q5ZQAnHd4LmjSJOywRqSUq+ERERETy1LKVaxgxaiI/vv8h/U4/kkGH7Q7FxXGH9f/s3XucZWdd5/vPt6pzgQTIrc2E7oQOGoyNl4BtDANHEVSSoAmOyElmlMjkEC/g4AtHDeIgMDJHcAaQY0SjZBJQCBGZocV4MIQweEugQ0JIgi1FCJNuAgmXRBBJ6K7f/LGf7uyuVHXtqt5Ve/Wqz/v12q9a61nPWvv3VNX+Vf32WuvZksaoEwWfs55LkiStri/984O86lV/zNfvuIPn/MxzOOsHvgsOPXTSYUkas04UfJLUXb4lJal/7vmnr/Pq37icB3fezb/9uR/n6d/37XDIIZMOS9IKsOCTJElaQ3Z88av8l1dcxu4v3sdP/8Jz+ddP+TZY57+EUl/56pYkSVojPn3PV3jdy/+Q3V/7Gj/zkh/nu08/FaanJx2WpBXUnYIvXjYlqXvMTJL6Yvtn7+f1v34p7NrNS176XJ74pCfA1NSkw5K0wrpT8EmSJGlF3HLnF7jkFX9E1k3zH//jc/mWb3+8xZ60RljwSZIk9dhH/vFz/NEr/4jpIx/JxS85l5M2P94rq6Q1xIJPkiSpp/7643fxJ6+5jHXHHM2vv+QcTnjC4yz2pDXGgk+S9qMmHYAkLdP7P3IHf/q6KzjshON5xX94Nsd9y+MmHZKkCbDgkyRJ6pk//7vt/MV/exuPOPlxvOrnn8VjHn/SpEOSNCHdKPi8skBSR3nlk6SDzbs+cBvv///+hCNPPYVX/twPc+RJGyYdkqQJ6kbBJ0mSpAP2x1ffxN9eehWPOe3bedULn8nhG/7VpEOSNGEWfJIkSQe5quKP3v1hPvq2/8mx3/MkXvHCH+DQb1o/6bAkdYAFnyRJ0kGsqnjT2/+a7X96NSc87Xv4tRf8AOuOPWbSYUnqCAs+SZKkg9TsbPFfL/sAd773Gh73jKfyy8//v5g66qhJhyWpQ6ZW6sBJzkyyPclMkotX6nkkaSnMTZK6aDm5adfuWf7L7/8ld773Gr717O/nV376+y32JD3MihR8SaaBS4CzgM3A+Uk2r8RzSdKolpObds/6SXySVtZyclMVvPp3/pydf/XXfNe5z+Q/nP808uhHr0a4kg4yK3WG73RgpqruqKoHgSuBcxfqPL179wqFIUn7WFJuArj3n76+KoFJWtOWnJt2fu7L3Puh63nK857Fz/zfTyWPetSqBCrp4LNSBd8G4K6h9R2tba8kFyXZlmQbAPffv0KhSNJei+amuaanVuzKd0naY8m5affXvsYzL/hRnv9vzoBHPnJFg5N0cJvYfzJVdWlVbamqLcc99jhY79TBkrph+A2pR637xqTDkSRg39x0yCPX8dxnb4HDD590WJI6bqUKvp3AiUPrG1vbvB7xyMPhkENWKBRJ2muk3DT8htS/OuH4VQtO0pq15Nz02BOOh0MPXbUAJR28Vqrg+whwSpKTkxwKnAdsXaHnkqRRmZskdZG5SdKKWZHP4auqXUleDLwPmAYuq6rbVuK5JGlU5iZJXWRukrSSVuyD16vqauDqlTq+JC2HuUlSF5mbJK0Up5+TJEmSpJ6y4JMkSZKknrLgkyRJkqSesuCTJEmSpJ6y4JMkSZKknkpVTToGknwF2D7pOCbgOOALkw5ilTnmteE44IiqWj/pQA7UGs1Pa/V3dq2NGdbmuHuRn8xNa8paHPdaHfOK5KYV+1iGJdpeVVsmHcRqS7JtrY3bMa8NbcybJh3HmKy5/LSGf2fX1JhhbY67R/nJ3LRGrMVxr+Exb1qJY3tJpyRJkiT1lAWfJEmSJPVUVwq+SycdwISsxXE75rWhT2Pu01hG5ZjXjrU47r6MuS/jWIq1OGZYm+N2zGPUiUlbJEmSJEnj15UzfJIkSZKkMbPgkyRJkqSemnjBl+TMJNuTzCS5eNLxLFWSy5Lck+TWobZjklyT5JPt69GtPUne1MZ6S5InD+1zQev/ySQXDLV/d5KPt33elCSrO8KHS3JikuuS3J7ktiQvae29HXeSw5N8OMnH2phf1dpPTnJDi/OdSQ5t7Ye19Zm2fdPQsV7W2rcnedZQeydfC0mmk9yU5L1tvfdjhm7HNgpz09rITS0m85P56aBiflob+cnc1KHcVFUTewDTwKeAxwOHAh8DNk8ypmWM4fuAJwO3DrW9Dri4LV8MvLYtnw38JRDgDOCG1n4McEf7enRbPrpt+3Drm7bvWR0Y8wnAk9vyo4B/BDb3edwtjiPb8iHADS2+q4DzWvvvAz/Xln8e+EvAA/oAACAASURBVP22fB7wzra8uf2eHwac3H7/p7v8WgBeCrwdeG9bXwtj7mxsSxiDuWkN5KYWk/nJ/NSJ2JYwBvPTGshP5qbu5KZJfzOeArxvaP1lwMsm/UNaxjg2zUla24ET2vIJDD4cFeAPgPPn9gPOB/5gqP0PWtsJwD8Mte/TrysP4D3AD62VcQOPBD4KfC/wBWBda9/7+wy8D3hKW17X+mXu7/iefl19LQAbgWuBZwDvbWPo9ZjnjqtrsS1xHOamNZSbWkzmpx6Pee64uhbbEsdhflpD+cncNNkxT/qSzg3AXUPrO1rbwe74qrq7LX8OOL4tLzTe/bXvmKe9M9qp5ycxeNem1+Nup+dvBu4BrmHwDst9VbWrdRmOc+/Y2vb7gWNZ+vdi0t4I/Aow29aPpf9jhm7HdiB6/RodtpZyE5if2rr56eDW+9fpHmspP5mbgA7kpkkXfL1Xg/K7Jh3HSkhyJPBnwC9W1T8Nb+vjuKtqd1WdxuCdm9OBUycc0opK8iPAPVV146Rj0fj18TW6x1rLTWB+Ur/09XUKay8/mZu6YdIF307gxKH1ja3tYPf5JCcAtK/3tPaFxru/9o3ztE9ckkMYJKw/qap3t+bejxugqu4DrmNwWv2oJOvapuE4946tbX8M8EWW/r2YpKcC5yS5E7iSwaUJv0O/x7xHl2M7EL1/ja7l3ATmJ/o95j26HNuB6P3rdC3nJ3PThMc84Wtc1zG42fRkHrrx8ImTvvZ2GePYxL7Xof82+96A+7q2/Gz2vQH3w639GODTDG6+PbotH9O2zb0B9+wOjDfAW4E3zmnv7biB9cBRbfkRwF8DPwL8KfvehPvzbflF7HsT7lVt+YnsexPuHQxuwO30awF4Og/deNz7MXc5tiWOw9y0NsZtfjI/dSK2JY7D/NTzcZubupObuvDNOJvBTEWfAl4+6XiWEf87gLuBbzC4jvZCBtfeXgt8Enj/0AsxwCVtrB8Htgwd598DM+3xgqH2LcCtbZ/fBdKBMT+NwSUHtwA3t8fZfR438J3ATW3MtwKvaO2PZ5BgZ9qL+bDWfnhbn2nbHz90rJe3cW1naAatLr8W5iSttTLmzsY2YvzmpjWQm1pM5ifz08RjWmL85qc1kJ/MTd3JTWk7SpIkSZJ6ZtL38EmSJEmSVogFnyRJkiT1lAWfJEmSJPWUBZ8kSZIk9ZQFnyRJkiT1lAWfJEmSJPWUBZ8kSZIk9ZQFnyRJkiT1lAWfJEmSJPWUBZ8kSZIk9ZQFnyRJkiT1lAWfJEmSJPWUBZ8kSZIk9ZQFnyRJkiT1lAWfJEmSJPWUBZ/2SvLKJH+8hP53JvnBFYjjg0n+nzEd6/Ikv9mWn55kxziOK0mSJB0MLPg0rySbklSSr7bH55P8XpJDJhDLCUnekuTuJF9J8g9JXpXkiNWORZIkSTqYWPD1WJJ1YzjMUVV1JPAdwFOAF43hmCNLcgzw98AjgKdU1aOAHwKOAr55NWORJEmSDjYWfD3TLrP81SS3AP+c5GlJ/i7JfUk+luTpQ31PTvK/2lmza4DjFjpuVd0DXANsXuB5D0vyxiSfbY83JjlsaPsLk8wk+VKSrUkeO7Tth9pZu/uT/C6QoUO/FPgK8JNVdWeL5a6qeklV3dL2PzXJNe3Y25M8b8Tv1a8m2dnGvz3JM0fZT5IkSTpYWPD10/nAs4HHA+8BfhM4BviPwJ8lWd/6vR24kUGh95+BCxY6YCvQngVcv0CXlwNnAKcB3wWcDvx62/cZwP8LPA84AfgMcGXbdhzw7tb3OOBTwFOHjvuDwLuranaBuI5gUIi+Hfgm4Dzg95LMW5gO7fetwIuB72lnDZ8F3Lm/fSRJkqSDjQVfP72pqu4CfhK4uqqurqrZqroG2AacneQk4HuA/1RVD1TVh4A/n+dYX0hyH7AT+GfgXQs8578DXl1V91TVvcCrgJ8a2nZZVX20qh4AXgY8Jckm4Gzgtqp6V1V9A3gj8Lmh4x4L3L2fsf4IcGdV/feq2lVVNwF/BvzEfvYB2A0cBmxOckhV3VlVn1pkH0mSJOmgYsHXT3e1r48DfqJdznlfK9yexuAs22OBL1fVPw/t95l5jnVcVR0FPBL4W+B9CzznY+fs/5nW9rBtVfVV4IvAhrbtrqFtNbze+p2w8FB5HPC9c8b474B/tZ99qKoZ4BeBVwL3JLly+DJTSZIkqQ8s+Pqp2te7gLdV1VFDjyOq6rcYnDU7es5MlycteMCqfwEuB85ol2HO9VkGxdfwsT4737b2nMcyOGt4N3Di0LYMrwPvB34syUK/q3cB/2vOGI+sqp9baCxDY3p7VT2txVbAaxfbR5IkSTqYWPD12x8DP5rkWUmmkxzePotuY1V9hsHlna9KcmiSpwE/utCB2gQsP8XgcssvztPlHcCvJ1nfCsJXtOffs+0FSU5rx/kvwA1tEpa/AJ6Y5N+0WUX/A/uenXs98GjgiiSPa7FsSPL6JN8JvBd4QpKfSnJIe3xPkm/b3zcmybcmeUaL5+vAvwDz3icoSZIkHaws+Hqs3cd3LvBrwL0Mzob9Mg/93P8t8L3Al4DfAN46z2HuS/JV4PMMPpbhnHbZ5Vy/yaCAvAX4OPDR1kZVvR/4TwzurbubwccpnNe2fYHB/Xa/xaCQPIXBpaN7xvAl4F8D3wBuSPIV4FrgfmCmqr4C/HA73mcZFKSvZXB/3v4c1p7zC22fb2Jwb6EkSZLUG5n/f3dJkiRJ0sHOM3ySJEmS1FMWfJJ6IcllSe5JcusC25PkTUlmktyS5MmrHaOktcfcJGnSLPgk9cXlwJn72X4Wg3tETwEuAt68CjFJ0uWYmyRNkAWfpF6oqg8xmIBoIecCb62B64GjkuzvMx4l6YCZmyRN2rpJBwBw3HHH1aZNmyYdhqQxuvHGG79QVesnHceQDQxmqt1jR2u7e27HJBcxeKedI4444rtPPfXUVQlQ0uroWH4yN0kCVi43daLg27RpE9u2bZt0GJLGKMlnJh3DclXVpcClAFu2bCnzk9QvB2t+MjdJ/bZSuclLOiWtFTuBE4fWN7Y2SZokc5OkFTVywZdkOslNSd7b1k9OckObVeqdSQ5t7Ye19Zm2fdPKhC5JS7IVeH6bEe8M4P6qetglU5K0ysxNklbUUs7wvQT4xND6a4E3VNW3AF8GLmztFwJfbu1vaP0kaUUleQfw98C3JtmR5MIkP5vkZ1uXq4E7gBngD4Gfn1CoktYQc5OkSRvpHr4kG4FnA68BXpokwDOAf9u6XAG8ksFUwue2ZYB3Ab+bJFVV4wtbkvZVVecvsr2AF61SOJIEmJskTd6oZ/jeCPwKMNvWjwXuq6pdbX3PjFIwNNtU235/6y9JkiRJWkWLFnxJfgS4p6puHOcTJ7koybYk2+69995xHlqSJEmSxGhn+J4KnJPkTuBKBpdy/g6DDwbdc0no8IxSe2ebatsfA3xx7kGr6tKq2lJVW9av78pH4UiSJElSfyxa8FXVy6pqY1VtAs4DPlBV/w64Dnhu63YB8J62vLWt07Z/wPv3JEmSJGn1HcgHr/8qcGWS3wRuAt7S2t8CvC3JDPAlBkXiSO78wj/zpa89yKHTU0wlzFYxW8Xu2aFHFbt37Wb3rt3MVrHrG7th9252zRa7d+9mdnY3u2eBXbuZLQZtBczOsnu2mN092K92zw59hdnZ3VQVtPWanWWw26BPVUENjjVbULurtUHV7kHfKrK7KIrZgrT4qwra8dousHuwTzGIbU97VZHZwTH2tlGk7c/efoPjV9VDx2n7TM3u2beG+g5uv9z7/MM1eO3de8+B9u4PMFWze5of2n/oEBnen4e2D6/s2ZbZmrtpKJR93xcYXhve72H9aqEt+27be6yafVjbQu9IzNs+auPsQkc9MLVgtMtz7OYn8F9/7bmLd5QkSdJBZ0kFX1V9EPhgW74DOH2ePl8HfmKpgfz5X9/Oe1//xxy260FmM723PXsKhZole55jz9dMUa11dioPLSdte1snzGZqn7a9X/fuM8VUoKZgqrVlaooEyBQEptry1CAwmJoipPWBJIM9M2gfHC974870IIY9/SqDPnuOUe3r3vXsWX9oW7U421MO2qZavHvibsdgqj1fa2NPJC2mPXEN78v0Q/GSh/oM4pvau5yHuuxtG/wcpvdpn6/P8Dbaz4X5ts1p2Gfb1NRC3R52jDzsoPM8D8DU9Hyt8xvxmPM+z0KmRu+dpR15v9Yff9TYjiVJkqRuOZAzfGN17Z//HVMbHsuPPe/7efAbg7NfAaYTptdNMT09zfRUmJqeYt1UmJoK66amBm1teWoKpve0JUxPh6k81D4VmGpfp9tZxKmpMD2VQRGWDFUlw5XGftoOdHk565IkSZI0gk4UfLNV3PfpHTzlzKfwA9/3HZMOR5IkSZJ6YdTP4VtRD+ya5cgH/oXHPe74SYciSZIkSb3RiYJv165ZQnH8MUdMOhRJkiRJ6o1OFHy7d+/mwel1HHvkYZMORZIkSZJ6oyMF3yzfmFrHY448fNKhSJIkSVJvdKLgq9lZdk9Nc8QjDp10KJIkSZLUG50o+GZni6ybZt26JXwOmiRJkiRpvzpR8NXsLNOHHQLTFnySJEmSNC7dKPiqOGTdOpjqRDiSJEmS1AudqLCqYGp62oJPkiRJksaoExVWVTE9PQXJpEORJEmSpN7oRMFHFdPrpi34JEmSJGmMOlHweUmnJEmSJI1fJyqsogYfyeAZPkmSJEkam04UfMwW67yHT5IkSZLGqhsFHzA13ZlQJEmSJKkXOlFlFWXBJ0mSJEljtmiVleTwJB9O8rEktyV5VWu/PMmnk9zcHqe19iR5U5KZJLckefKiURRMOWGLJEmSJI3VuhH6PAA8o6q+muQQ4G+S/GXb9stV9a45/c8CTmmP7wXe3L4uqMozfJIkSZI0botWWTXw1bZ6SHvUfnY5F3hr2+964KgkJywaiPWeJEmSJI3VSGVWkukkNwP3ANdU1Q1t02vaZZtvSHJYa9sA3DW0+47WtrCCqVjxSZIkSdI4jVRlVdXuqjoN2AicnuTbgZcBpwLfAxwD/OpSnjjJRUm2Jdk2O7ubTE8vMXRJkiRJ0v4s6bRaVd0HXAecWVV3t8s2HwD+O3B667YTOHFot42tbe6xLq2qLVW1JVNT3sMnSZIkSWM2yiyd65Mc1ZYfAfwQ8A977stLEuA5wK1tl63A89tsnWcA91fV3ft9kiqm/Mx1SZIkSRqrUWbpPAG4Isk0gwLxqqp6b5IPJFkPBLgZ+NnW/2rgbGAG+BrwgsWeoPBjGSRJkiRp3BYt+KrqFuBJ87Q/Y4H+BbxoqYFMeQ+fJEmSJI1VN06rFYPzhJIkSZKkselGwQdMeROfJEmSJI1VRwq+YjoWfJIkSZI0Tt0o+AqcplOSJEmSxqsTBV+BZ/gkHZAkZybZnmQmycXzbD8pyXVJbkpyS5KzJxGnpLXH/CRpkjpR8AHgxzJIWqb2sTGXAGcBm4Hzk2ye0+3XGXyszJOA84DfW90oJa1F5idJk9aZKmvKM3ySlu90YKaq7qiqB4ErgXPn9Cng0W35McBnVzE+SWuX+UnSRI3ywesrLhTxHj5Jy7cBuGtofQfwvXP6vBL4qyS/ABwB/ODqhCZpjTM/SZqozpzhm7bgk7Syzgcur6qNwNnA25LMmwOTXJRkW5Jt995776oGKWlNGik/mZskLUdnCj7m/79LkkaxEzhxaH1jaxt2IXAVQFX9PXA4cNx8B6uqS6tqS1VtWb9+/QqEK2kNGVt+MjdJWo5uVFnlGT5JB+QjwClJTk5yKINJD7bO6fO/gWcCJPk2Bv9Q+Ra5pJVmfpI0Ud0o+MDP4ZO0bFW1C3gx8D7gEwxmu7styauTnNO6/RLwwiQfA94B/HRV1WQilrRWmJ8kTVonJm0BmMKCT9LyVdXVwNVz2l4xtHw78NTVjkuSzE+SJqkzZ/j8VAZJkiRJGq/uFHxe0ilJkiRJY9WZgs8PXpckSZKk8epEwReqG4FIkiRJUo90ps6q6c6EIkmSJEm9sGiVleTwJB9O8rEktyV5VWs/OckNSWaSvLN9tgxJDmvrM237plECiZd0SpIkSdJYjXJa7QHgGVX1XcBpwJlJzgBeC7yhqr4F+DJwYet/IfDl1v6G1m9RlnuSJEmSNF6LFnw18NW2ekh7FPAM4F2t/QrgOW353LZO2/7MjHD6bmrKSzolSZIkaZxGqrKSTCe5GbgHuAb4FHBfVe1qXXYAG9ryBuAugLb9fuDYRQPxkk5JkiRJGquRCr6q2l1VpwEbgdOBUw/0iZNclGRbkm3gJZ2SJEmSNG5Luo6yqu4DrgOeAhyVZF3btBHY2ZZ3AicCtO2PAb44z7EuraotVbUFoLykU5IkSZLGapRZOtcnOaotPwL4IeATDAq/57ZuFwDvactb2zpt+weqqhZ/nqUFLkmSJEnav3WLd+EE4Iok0wwKxKuq6r1JbgeuTPKbwE3AW1r/twBvSzIDfAk4b5RApryoU5IkSZLGatGCr6puAZ40T/sdDO7nm9v+deAnlhJECqaml7KHJEmSJGkxnblxzks6JUmSJGm8OlTwWfFJkiRJ0jh1p+CbdACSJEmS1DOdKfim/FgGSZIkSRqrzlRZU17SKUmSJElj1ZGCr7ykU5IkSZLGrCMFH8x6SackSZIkjVVnqizP8EmSJEnSeFnwSZIkSVJPdafgc9IWSZIkSRqr7hR8kw5AkiRJknqmOwXfdGdCkSRJkqRe6EyV5Rk+SZIkSRqv7hR8VnySJEmSNFadKPis9SRJkiRp/DpR8AHEsk+SJEmSxqo7BZ+TtkiSJEnSWHWmyvL8niRJkiSN16IFX5ITk1yX5PYktyV5SWt/ZZKdSW5uj7OH9nlZkpkk25M8a5RAnLRFkiRJksZr3Qh9dgG/VFUfTfIo4MYk17Rtb6iq/zrcOclm4DzgicBjgfcneUJV7d7fk1jvSZIkSdJ4LXqGr6rurqqPtuWvAJ8ANuxnl3OBK6vqgar6NDADnL7Y88RTfJIkSZI0Vku6hy/JJuBJwA2t6cVJbklyWZKjW9sG4K6h3Xaw/wJxcOypztxOKEmSJEm9MHKVleRI4M+AX6yqfwLeDHwzcBpwN/DflvLESS5Ksi3JtqXsJ0mSJEkazUgFX5JDGBR7f1JV7waoqs9X1e6qmgX+kIcu29wJnDi0+8bWto+qurSqtlTVlsFzLH8QkiRJkqSHG2WWzgBvAT5RVa8faj9hqNuPAbe25a3AeUkOS3IycArw4UWfZylRS9IcSc5sMwPPJLl4gT7PG5px+O2rHaOktcn8JGmSRpml86nATwEfT3Jza/s14PwkpwEF3An8DEBV3ZbkKuB2BjN8vmixGTol6UAkmQYuAX6IwX3DH0mytapuH+pzCvAy4KlV9eUk3zSZaCWtJeYnSZO2aMFXVX/D/Cfgrt7PPq8BXrOUQJylU9IBOB2Yqao7AJJcyWDG4NuH+rwQuKSqvgxQVfesepSS1iLzk6SJ6szUmBZ8kg7AKLMDPwF4QpK/TXJ9kjNXLTpJa5n5SdJEjXJJ56qw3JO0wtYxuKf46Qwmk/pQku+oqvvmdkxyEXARwEknnbSaMUpam0bKT+YmScvRoTN8k45A0kFslNmBdwBbq+obVfVp4B8Z/IP1MMOzCK9fv35FApa0ZowtP5mbJC1Hdwq+SQcg6WD2EeCUJCcnORQ4j8GMwcP+J4N3z0lyHINLqO5YzSAlrUnmJ0kT1ZmCz4pP0nJV1S7gxcD7gE8AV7UZg1+d5JzW7X3AF5PcDlwH/HJVfXEyEUtaK8xPkiatO/fweU2npANQVVczZ/bgqnrF0HIBL20PSVo15idJk9SJM3wFxFN8kiRJkjRWnSj4wElbJEmSJGnculPwTToASZIkSeqZ7hR8VnySJEmSNFbdKfgmHYAkSZIk9Ux3Cj5P8UmSJEnSWHWm4POaTkmSJEkar84UfJZ7kiRJkjRe3Sn4rPgkSZIkaay6U/BNOgBJkiRJ6pnOFHye4pMkSZKk8epIwRfP8EmSJEnSmHWk4PMEnyRJkiSN26IFX5ITk1yX5PYktyV5SWs/Jsk1ST7Zvh7d2pPkTUlmktyS5MkrPQhJkiRJ0sONcoZvF/BLVbUZOAN4UZLNwMXAtVV1CnBtWwc4CzilPS4C3jxKIF7UKUmSJEnjtWjBV1V3V9VH2/JXgE8AG4BzgStatyuA57Tlc4G31sD1wFFJTljsebykU5IkSZLGa0n38CXZBDwJuAE4vqrubps+BxzfljcAdw3ttqO1zT3WRUm2Jdm2xJglSZIkSSMYueBLciTwZ8AvVtU/DW+rqgJqKU9cVZdW1Zaq2tKOv5TdJUmSJEmLGKngS3IIg2LvT6rq3a3583su1Wxf72ntO4ETh3bf2NokSZIkSatolFk6A7wF+ERVvX5o01bggrZ8AfCeofbnt9k6zwDuH7r0cz/Ps6S4JUmSJEmLWDdCn6cCPwV8PMnNre3XgN8CrkpyIfAZ4Hlt29XA2cAM8DXgBWONWJIkSZI0kkULvqr6G1jwMxOeOU//Al601EA8wSdJkiRJ47WkWTpXkpO2SJIkSdJ4dabg8yY+SZIkSRqvzhR8lnuSJEmSNF6dKPgqnuCTJEmSpHHrRMEnSZIkSRq/zhR8nuCTJEmSpPGy4JMkSZKknupMwSdJkiRJGq/uFHzO2iJJkiRJY9WZgs8PXpckSZKk8epMwSdJkiRJGq/OFHye4JMkSZKk8epMwSdJkiRJGq/OFHye4JMkSZKk8epOwWfFJ0mSJElj1ZmCT5IkSZI0Xp0p+OJFnZIOQJIzk2xPMpPk4v30+/EklWTLasYnae0yP0mapM4UfNZ7kpYryTRwCXAWsBk4P8nmefo9CngJcMPqRihprTI/SZq0RQu+JJcluSfJrUNtr0yyM8nN7XH20LaXtXewtid51qiBeIZP0gE4HZipqjuq6kHgSuDcefr9Z+C1wNdXMzhJa5r5SdJEjXKG73LgzHna31BVp7XH1QDtHavzgCe2fX6vvbMlSStpA3DX0PqO1rZXkicDJ1bVX6xmYJLWPPOTpIlatOCrqg8BXxrxeOcCV1bVA1X1aWCGwTtbi3KWTkkrJckU8Hrgl0bsf1GSbUm23XvvvSsbnKQ1bSn5ydwkaTkO5B6+Fye5pV3yeXRrW/RdrD2Gk9YBxCBJADuBE4fWN7a2PR4FfDvwwSR3AmcAWxeaGKGqLq2qLVW1Zf369SsUsqQ1Ymz5ydwkaTmWW/C9Gfhm4DTgbuC/LfUAw0lrmTFI0h4fAU5JcnKSQxlcWr51z8aqur+qjquqTVW1CbgeOKeqfMNJ0kozP0maqGUVfFX1+araXVWzwB/y0GWbi72LtSAv6ZS0XFW1C3gx8D7gE8BVVXVbklcnOWey0Ulay8xPkiZt3XJ2SnJCVd3dVn8M2DOD51bg7UleDzwWOAX48AFHKUmLaJNHXT2n7RUL9H36asQkSWB+kjRZixZ8Sd4BPB04LskO4DeApyc5DSjgTuBnANo7VlcBtwO7gBdV1e5RAvFjGSRJkiRpvBYt+Krq/Hma37Kf/q8BXrPkSKz3JEmSJGmsDmSWzrGKN/FJkiRJ0lh1ouArL+iUJEmSpLHrRMEnSZIkSRq/zhR8XtEpSZIkSePVmYJPkiRJkjRenSn4PMEnSZIkSePVmYJPkiRJkjRenSn4/FgGSZIkSRqvzhR8kiRJkqTx6k7B5xk+SZIkSRqr7hR8kiRJkqSx6kzB5/k9SZIkSRqvzhR8kiRJkqTx6kzB5y18kiRJkjRenSn4JEmSJEnj1ZmCzxN8kiRJkjRenSn4JEmSJEnj1ZmCL97EJ0mSJEljtWjBl+SyJPckuXWo7Zgk1yT5ZPt6dGtPkjclmUlyS5InjxyJBZ8kSZIkjdUoZ/guB86c03YxcG1VnQJc29YBzgJOaY+LgDePGojlniRJkiSN16IFX1V9CPjSnOZzgSva8hXAc4ba31oD1wNHJTlhXMFKkiRJkka33Hv4jq+qu9vy54Dj2/IG4K6hfjtamyRJkiRplR3wpC1VVUAtdb8kFyXZlmQb8RY+SZIkSRq35RZ8n99zqWb7ek9r3wmcONRvY2t7mKq6tKq2VNWWZcYgSZIkSdqP5RZ8W4EL2vIFwHuG2p/fZus8A7h/6NLP/fIEnyRJkiSN17rFOiR5B/B04LgkO4DfAH4LuCrJhcBngOe17lcDZwMzwNeAF6xAzJIkSZKkESxa8FXV+QtseuY8fQt40bIi8SY+SZIkSRqrA560RZIkSZLUTZ0p+OIZPkmSJEkaq84UfJIkSZKk8epMwecJPkmSJEkar84UfJIkSZKk8epMwecJPkmSJEkar84UfJIkSZKk8epMwecsnZIkSZI0Xp0p+CRJkiRJ49WZgs/ze5IORJIzk2xPMpPk4nm2vzTJ7UluSXJtksdNIk5Ja4/5SdIkdabgk6TlSjINXAKcBWwGzk+yeU63m4AtVfWdwLuA161ulJLWIvOTpEmz4JPUB6cDM1V1R1U9CFwJnDvcoaquq6qvtdXrgY2rHKOktcn8JGmiOlPwZcqLOiUt2wbgrqH1Ha1tIRcCf7miEUnSgPlJ0kStm3QAkrSakvwksAX4/v30uQi4COCkk05apcgkrXWL5Sdzk6Tl6M4ZvkkHIOlgthM4cWh9Y2vbR5IfBF4OnFNVDyx0sKq6tKq2VNWW9evXjz1YSWvK2PKTuUnScnSm4JOkA/AR4JQkJyc5FDgP2DrcIcmTgD9g8M/UPROIUdLaZH6SNFEWfJIOelW1C3gx8D7gE8BVVXVbklcnOad1+23gSOBPk9ycZOsCh5OksTE/SZq0TtzDV4TEizolLV9VXQ1cPaftFUPLP7jqQUkS5idJk3VABV+SO4GvALuBXVW1JckxwDuBTcCdwPOq6ssHFqYkSZIkaanGcUnnD1TVaVW1pa1fXoSrJwAAIABJREFUDFxbVacA17Z1SZIkSdIqW4l7+M4FrmjLVwDPWYHnkCRJkiQt4kALvgL+KsmN7bNhAI6vqrvb8ueA4w/wOSRJkiRJy3Cgk7Y8rap2Jvkm4Jok/zC8saoqSc234/CHhx5z9GMPMAxJkiRJ0lwHdIavqna2r/cA/wM4Hfh8khMA2td5P09m+MNDnZ9TkiRJksZv2QVfkiOSPGrPMvDDwK0MPkz0gtbtAuA9Ix5wuaFIkiRJkuZxIJd0Hg/8j/b5eeuAt1fV/5/kI8BVSS4EPgM878DDlCRJkiQt1bILvqq6A/iuedq/CDzzQIKSJEmSJB24lfhYBkmSJElSB3Si4Jt3Gk9JkiRJ0gHpRMEnSZIkSRq/ThR8zs8pSZIkSePXiYJPkiRJkjR+FnySJEmS1FMWfJIkSZLUUxZ8kiRJktRT3Sn44tQtkiRJkjRO3Sn4JEmSJEljZcEnSZIkST1lwSdJkiRJPWXBJ0mSJEk9ZcEnSZIkST3VjYLPCTolSZIkaey6UfBJkiRJksbOgk+SJEmSesqCT5IkSZJ6asUKviRnJtmeZCbJxSv1PJIkSZKk+a1IwZdkGrgEOAvYDJyfZPNKPJckSZIkaX4rdYbvdGCmqu6oqgeBK4FzV+i5JEmSJEnzWKmCbwNw19D6jtY2r+ndu1coDEmSJElauyY2aUuSi5JsS7LtXw45DCz6JEmSJGmsVqrg2wmcOLS+sbXtVVWXVtWWqtryhMeth0c/eoVCkSRJkqS1aaUKvo8ApyQ5OcmhwHnA1hV6LkmSJEnSPNatxEGraleSFwPvA6aBy6rqtpV4LkmSJEnS/Fak4AOoqquBq1fq+JIkSZKk/ZvYpC2SJEmSpJVlwSdJkiRJPWXBJ6kXkpyZZHuSmSQXz7P9sCTvbNtvSLJp9aOUtBaZnyRNkgWfpINekmngEuAsYDNwfpLNc7pdCHy5qr4FeAPw2tWNUtJaZH6SNGkWfJL64HRgpqruqKoHgSuBc+f0ORe4oi2/C3hmkqxijJLWJvOTpIlasVk6l+LGG2/8apLtk45jAo4DvjDpIFaZY14bjgMet4rPtwG4a2h9B/C9C/VpHx1zP3As8/xsklwEXNRWH0hy69gjXl19+B10DN3Rh3F86yo+19jyk7mpk/owBujHOPowhhXJTZ0o+IDtVbVl0kGstiTb1tq4HfPa0Ma8adJxLFdVXQpcCv34+TmGbujDGKAf40iybdIxLIe5qXv6MAboxzj6MoaVOK6XdErqg53AiUPrG1vbvH2SrAMeA3xxVaKTtJaZnyRNlAWfpD74CHBKkpOTHAqcB2yd02crcEFbfi7wgaqqVYxR0tpkfpI0UV25pPPSSQcwIWtx3I55bVjVMbd7Xl4MvA+YBi6rqtuSvBrYVlVbgbcAb0syA3yJwT9do+jDz88xdEMfxgD9GMeqjWEF85M/h27owxigH+NwDAuIbyBJkiRJUj95SackSZIk9ZQFnyRJkiT11MQLviRnJtmeZCbJxZOOZ6mSXJbknuHPwklyTJJrknyyfT26tSfJm9pYb0ny5KF9Lmj9P5nkgqH2707y8bbPm7rwQaxJTkxyXZLbk9yW5CWtvbfjTnJ4kg8n+Vgb86ta+8lJbmhxvrPdkE+Sw9r6TNu+aehYL2vt25M8a6i9k6+FJNNJbkry3rbeuzEvFsf+xtYlI4zjpe11e0uSa5Os5mcljmTU34kkP56kknRuCu5RxpDkeUM59O2rHeNiRvhdOqn9Hbip/T6dPYk49yfz/H2es33Bv01d0of8ZG7qhj7kJjj489NEclNVTezB4OblTwGPBw4FPgZsnmRMyxjD9wFPBm4dansdcHFbvhh4bVs+G/hLIMAZwA2t/Rjgjvb16LZ8dNv24dY3bd+zOjDmE4Ant+VHAf8IbO7zuFscR7blQ4AbWnxXAee19t8Hfq4t/zzw+235POCdbXlz+z0/DDi5/f5Pd/m1ALwUeDvw3rbeqzGPEsdCY+vSY8Rx/ADwyLb8c10bx6i/Ey3vfAi4Htgy6biX8XM4BbhpKN9906TjXsYYLh167W8G7px03POM42F/n+dsn/dvU5cefchP5qZuPPqQm5Ywjk7np0nkpkmf4TsdmKmqO6rqQeBK4NwJx7QkVfUhBjNqDTsXuKItXwE8Z6j9rTVwPXBUkhOAZwHXVNWXqurLwDXAmW3bo6vq+hr8Brx16FgTU1V3V9VH2/JXgE8AG+jxuFvsX22rh7RHAc8A3tXa5455z/fiXcAzk6S1X1lVD1TVp4EZBq+DTr4WkmwEng38UVsP/RvzKHEsNLYuWXQcVXVdVX2trV7P4PPAumTU34n/DLwW+PpqBjeiUcbwQuCSlveoqntWOcbFjDKGAh7dlh8DfHYV4xvJAn+fhy30t6lL+pCfzE3d0IfcBD3IT5PITZMu+DYAdw2t72htB7vjq+rutvw54Pi2vNB499e+Y572zmiXjjyJwRmvXo87g0sbbwbuYVCcfgq4r6p2tS7Dce4dW9t+P3AsS/9eTNobgV8BZtv6sfRvzKPEsdDYumSp388LGbyD2CWLjqFd2nJiVf3Faga2BKP8HJ4APCHJ3ya5PsmZqxbdaEYZwyuBn0yyA7ga+IXVCW2supKD9qcP+cnc1A19yE2wNvLT2HPTpAu+3mtnqHr52RdJjgT+DPjFqvqn4W19HHdV7a6q0xi883g6cOqEQ1pRSX4EuKeqbpx0LBqvJD8JbAF+e9KxLEWSKeD1wC9NOpYDtI7BpVNPB84H/jDJURONaOnOBy6vqo0MLj96W/v5SMtmbpq4PuQmMD89zKQHvxM4cWh9Y2s72H1+z6nX9nXPKfGFxru/9o3ztE9ckkMYFHt/UlXvbs29HzdAVd0HXAc8hcFp9nVt03Cce8fWtj8G+CJL/15M0lOBc5LcyeCSiWcAv0P/xjxKHAuNrUtG+n4m+UHg5cA5VfXAKsU2qsXG8Cjg24EPtt/LM4CtHZscYZSfww5ga1V9o13m/I8M/snqilHGcCGD+3mpqr8HDgeOW5XoxqcrOWh/+pCfzE3d0IfcBGsjP40/Nx3oTYAH8mDwTsIdDCZy2HPj5RMnGdMyx7GJfSdt+W32nbzkdW352ex7E+aHW/sxwKcZTFxydFs+pm2bO3nJ2R0YbxjcV/fGOe29HTewHjiqLT8C+GvgR4A/Zd8JTH6+Lb+IfW+iv6otP5F9JzC5g8ENyJ1+LTB4t2/PpC29GvMocSw0ti49RhzHkxhcinzKpONd7hjm9P8g3ZsYYZSfw5nAFW35OAaX7hw76diXOIa/BH66LX8bg3tkMunY5xnLJhaeGGHev01devQhP5mbuvHoQ25awjg6n59WOzd1YcBnM3gH4VPAyycdzzLifwdwN/ANBu+MXMjg2vlrgU8C7+ehIibAJW2sHx9OBsC/ZzCZxQzwgqH2LcCtbZ/f7cIvLPA0Bpdr3gLc3B5n93ncwHcymLnqlhbXK1r74xkUpzMMCqHDWvvhbX2mbX/80LFe3sa1naHZR7v8WmDfgq93Y54vDuDVDN5p3u/YuvQYYRzvBz4/9LrdOumYlzqGOX0/SMf+qRrx5xAGl3/d3nLieZOOeRlj2Az8LYN/tm4GfnjSMc8zhvn+Pv8s8LNDP4d5/zZ16dGH/GRu6sajD7lpxHF0Oj9NIjelHViSJEmS1DOTvodPkiRJkrRCLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSpp5ZU8CW5LMk9SW5dYHuSvCnJTJJbkjx5PGFK0v6ZnyR1kblJ0qQt9Qzf5cCZ+9l+FnBKe1wEvHl5YUnSkl2O+UlS91yOuUnSBC2p4KuqDwFf2k+Xc4G31sD1wFFJTjiQACVpFOYnSV1kbpI0aevGfLwNwF1D6zta291zOya5iME7WRxxxBHffeqpp445FEmTdOONN36hqtZPOo4h5idJQOfyk7lJErByuWncBd/IqupS4FKALVu21LZt2yYViqQVkOQzk45hucxPUr8drPnJ3CT120rlpnHP0rkTOHFofWNrk6RJMz9J6iJzk6QVNe6Cbyvw/Dbj1BnA/VX1sEsSJGkCzE+SusjcJGlFLemSziTvAJ4OHJdkB/AbwCEAVfX7wNXA2cAM8DXgBeMMVpIWYn6S1EXmJkmTtqSCr6rOX2R7AS86oIgkaRnMT5K6yNwkadLGfUmnJEmSJKkjLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqacs+CRJkiSppyz4JEmSJKmnLPgkSZIkqaeWXPAlOTPJ9iQzSS6eZ/tJSa5LclOSW5KcPZ5QJWlh5iZJXWV+kjRJSyr4kkwDlwBnAZuB85NsntPt14GrqupJwHnA740jUElaiLlJUleZnyRN2lLP8J0OzFTVHVX1IHAlcO6cPgU8ui0/BvjsgYUoSYsyN0nqKvOTpIlaasG3AbhraH1Haxv2SuAnk+wArgZ+Yb4DJbkoybYk2+69994lhiFJ+xhbbgLzk6Sx8n8nSRO1EpO2nA9cXlUbgbOBtyV52PNU1aVVtaWqtqxfv34FwpCkfYyUm8D8JGnV+b+TpBWz1IJvJ3Di0PrG1jbsQuAqgKr6e+Bw4LjlBihJIzA3Seoq85OkiVpqwfcR4JQkJyc5lMGNxVvn9PnfwDMBknwbg6TldQeSVpK5SVJXmZ8kTdSSCr6q2gW8GHgf8AkGM0rdluTVSc5p3X4JeGGSjwHvAH66qmqcQUvSMHOTpK4yP0matHVL3aGqrmZwQ/Fw2yuGlm8HnnrgoUnS6MxNkrrK/CRpklZi0hZJkiRJUgdY8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJElST1nwSZIkSVJPWfBJkiRJUk9Z8EmSJEn/p727DbX0POsF/r+aOamotUozguSliThVxyq0ZxMqglZaZRoh88EXEijHHkJDqxFBESI99Ej6qYoKQs7ROVhahZrGfpABUwJqSqGYNlNa2yYlZZr2nEwUE2vtl9Kmwet82Ku6Z3fPzHpm1l7PM3d/P9iwnmfds+a6eWb+7P9eLxsGpfABAAAMSuEDAAAY1OTCV1UnqurJqjpbVfdeYM0vVdUTVfV4Vb33yscEuDjZBCyVfALmdGTK4qq6Jsn9SX4mybkkj1XV6e5+Ys+aY0l+O8lPdPeXqup7NzkwwH6yCVgq+QTMbeozfLcmOdvdT3X380keSHJy35o3J7m/u7+UJN397JWPCXBRsglYKvkEzGpq4bs+ydN7js+tzu31iiSvqKoPV9WjVXXiSgYEWINsApZKPgGzmvSSzgmPeSzJa5PckORDVfWj3f1vexdV1d1J7k6Sm2666RDGADjPWtmUyCdg63zvBByaqc/wPZPkxj3HN6zO7XUuyenu/np3fz7JZ7MbYufp7lPdvdPdO0ePHp04BsB5NpZNiXwCNsr3TsCspha+x5Icq6pbquraJHckOb1vzV9l9ydUqarrsvsyhaeucE6Ai5FNwFLJJ2BWkwpfd7+Q5J4kDyf5TJIHu/vxqrqvqm5fLXs4yRer6okkjyT5re7+4iaHBthLNgFLJZ+AuVV3zz1DdnZ2+syZM3OPAWxQVX2su3fmnuNKyScYzwj5JJtgPIeVTZN/8ToAAABXB4UPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAY1ufBV1YmqerKqzlbVvRdZ9/NV1VW1c2UjAlyabAKWSj4Bc5pU+KrqmiT3J3lDkuNJ7qyq4wese0mSX0/ykU0MCXAxsglYKvkEzG3qM3y3Jjnb3U919/NJHkhy8oB170jyziRfvcL5ANYhm4Clkk/ArKYWvuuTPL3n+Nzq3H+oqlcnubG7//oKZwNYl2wClko+AbPa6Ie2VNWLkvxBkt9cY+3dVXWmqs4899xzmxwD4DxTsmm1Xj4BW+F7J+CwTS18zyS5cc/xDatz3/CSJK9M8sGq+kKS1yQ5fdCbj7v7VHfvdPfO0aNHJ44BcJ6NZVMin4CN8r0TMKuphe+xJMeq6paqujbJHUlOf+PO7v5yd1/X3Td3981JHk1ye3ef2djEAN9MNgFLJZ+AWU0qfN39QpJ7kjyc5DNJHuzux6vqvqq6/TAGBLgU2QQslXwC5nZk6h/o7oeSPLTv3NsvsPa1lzcWwDSyCVgq+QTMaaMf2gIAAMByKHwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMKjJha+qTlTVk1V1tqruPeD+36iqJ6rqk1X1t1X18s2MCnBhsglYKvkEzGlS4auqa5Lcn+QNSY4nubOqju9b9vEkO939Y0nen+R3NzEowIXIJmCp5BMwt6nP8N2a5Gx3P9Xdzyd5IMnJvQu6+5Hu/srq8NEkN1z5mAAXJZuApZJPwKymFr7rkzy95/jc6tyF3JXkA1OHAphINgFLJZ+AWR05rAeuqjcm2UnyUxe4/+4kdyfJTTfddFhjAJznUtm0WiOfgK3zvRNwGKY+w/dMkhv3HN+wOneeqnp9krclub27v3bQA3X3qe7e6e6do0ePThwD4Dwby6ZEPgEb5XsnYFZTC99jSY5V1S1VdW2SO5Kc3rugql6V5E+yG1jPbmZMgIuSTcBSySdgVpMKX3e/kOSeJA8n+UySB7v78aq6r6puXy37vSTfmeQvq+oTVXX6Ag8HsBGyCVgq+QTMbfJ7+Lr7oSQP7Tv39j23X7+BuQAmkU3AUsknYE6Tf/E6AAAAVweFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGNbnwVdWJqnqyqs5W1b0H3P/iqnrf6v6PVNXNmxgU4GJkE7BU8gmY06TCV1XXJLk/yRuSHE9yZ1Ud37fsriRf6u4fSPKHSd65iUEBLkQ2AUsln4C5TX2G79YkZ7v7qe5+PskDSU7uW3MyyXtWt9+f5HVVVVc2JsBFySZgqeQTMKuphe/6JE/vOT63Onfgmu5+IcmXk7zscgcEWINsApZKPgGzOjLXX1xVdye5e3X4tar69FyzbMh1Sf5l7iE2YIR92MMy/ODcA1wu+bRI9rAcI+zjqswn2bRII+whGWMfI+zhULJpauF7JsmNe45vWJ07aM25qjqS5KVJvrj/gbr7VJJTSVJVZ7p7Z+IsizLCHpIx9mEPy1BVZ7b4120smxL5tET2sBwj7ONqzSfZtDwj7CEZYx+j7OEwHnfqSzofS3Ksqm6pqmuT3JHk9L41p5P88ur2LyT5u+7uKxsT4KJkE7BU8gmY1aRn+Lr7haq6J8nDSa5J8q7ufryq7ktyprtPJ/nTJH9eVWeT/Gt2gw3g0MgmYKnkEzC3ye/h6+6Hkjy079zb99z+apJfnPiwp6bOsUAj7CEZYx/2sAxb3cMhZVPiWiyFPSzHCPsYIZ9ch2UYYQ/JGPuwhwsorxgAAAAY09T38AEAAHCV2Grhq6oTVfVkVZ2tqnsPuP/FVfW+1f0fqaqbtznfOtbYw29U1RNV9cmq+tuqevkcc17MpfawZ93PV1VX1SI/8WidfVTVL62ux+NV9d5tz3gpa/x7uqmqHqmqj6/+Td02x5wXU1XvqqpnL/Tx4LXrj1Z7/GRVvXrbM17KCNmUyKelkE3LMEI2JWPkk2xahhGyKbn682mWbOrurXxl943Kn0vy/UmuTfIPSY7vW/MrSf54dfuOJO/b1nwb3MNPJ/n21e23Xo17WK17SZIPJXk0yc7cc1/mtTiW5ONJvmd1/L1zz30ZeziV5K2r28eTfGHuuQ/Yx08meXWST1/g/tuSfCBJJXlNko/MPfNlXIdFZ9OEfcinBexBNm1tH1d1Nk24FovOJ9m0jK8RsmnCPhadT3Nk0zaf4bs1ydnufqq7n0/yQJKT+9acTPKe1e33J3ldVdUWZ7yUS+6hux/p7q+sDh/N7u/bWZJ1rkOSvCPJO5N8dZvDTbDOPt6c5P7u/lKSdPezW57xUtbZQyf5rtXtlyb5xy3Ot5bu/lB2P1XuQk4m+bPe9WiS766q79vOdGsZIZsS+bQUsmkhBsimZIx8kk3LMEI2JQPk0xzZtM3Cd32Sp/ccn1udO3BNd7+Q5MtJXraV6dazzh72uiu7DX1JLrmH1VPHN3b3X29zsInWuRavSPKKqvpwVT1aVSe2Nt161tnD7yR5Y1Wdy+4nvP3adkbbqKn/b7ZthGxK5NNSyKarx9KzKRkjn2TTMoyQTcm3Rj5tPJsm/1oG1lNVb0yyk+Sn5p5liqp6UZI/SPKmmUfZhCPZfXnCa7P708IPVdWPdve/zTrVNHcmeXd3/35V/Xh2f0/TK7v73+cejKuXfJqdbIIDyKbZjZBNiXz6Jtt8hu+ZJDfuOb5hde7ANVV1JLtPw35xK9OtZ509pKpen+RtSW7v7q9tabZ1XWoPL0nyyiQfrKovZPe1w6cX+Objda7FuSSnu/vr3f35JJ/NbpAtxTp7uCvJg0nS3X+f5NuSXLeV6TZnrf83MxohmxL5tBSy6eqx9GxKxsgn2bQMI2RT8q2RT5vPpk28+XCdr+z+1OCpJLfkP99k+SP71vxqzn/j8YPbmm+De3hVdt9MemzueS93D/vWfzALe+PxhGtxIsl7Vrevy+7T4y+be/aJe/hAkjetbv9wdl+HXnPPfsBebs6F33z8czn/zccfnXvey7gOi86mCfuQTwvYg2za6l6u2myacC0WnU+yaRlfI2TThH0sPp+2nU3b3txt2f1pweeSvG117r7s/jQn2W3gf5nkbJKPJvn+uS/IZezhb5L8c5JPrL5Ozz3z1D3sW7u40JpwLSq7L7F4Ismnktwx98yXsYfjST68CrRPJPnZuWc+YA9/keSfknw9uz8dvCvJW5K8Zc91uH+1x08t8d/TCNm05j7k0wL2IJu2toerPpvWvBaLzyfZtIyvEbJpzX0sOp/myKZaPTAAAACD2eovXgcAAGB7FD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDmlT4qupdVfVsVX36AvdXVf1RVZ2tqk9W1as3MybAxcknYIlkEzC3qc/wvTvJiYvc/4Ykx1Zfdyf535c3FsBk7458Apbn3ZFNwIwmFb7u/lCSf73IkpNJ/qx3PZrku6vq+65kQIB1yCdgiWQTMLcjG36865M8vef43OrcP+1fWFV3Z/cnWfmO7/jyNC3NAAAR6ElEQVSO//pDP/RDGx4FmNPHPvaxf+nuo3PPsYd8ApIsLp9kE5Dk8LJp04Vvbd19KsmpJNnZ2ekzZ87MNQpwCKrq/849w+WSTzC2qzWfZBOM7bCyadOf0vlMkhv3HN+wOgcwN/kELJFsAg7Vpgvf6ST/bfWJU69J8uXu/qaXJADMQD4BSySbgEM16SWdVfUXSV6b5LqqOpfkfyb5L0nS3X+c5KEktyU5m+QrSf77JocFuBD5BCyRbALmNqnwdfedl7i/k/zqFU0EcBnkE7BEsgmY26Zf0gkAAMBCKHwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMKjJha+qTlTVk1V1tqruPeD+m6rqkar6eFV9sqpu28yoABcmm4Clkk/AnCYVvqq6Jsn9Sd6Q5HiSO6vq+L5l/yPJg939qiR3JPlfmxgU4EJkE7BU8gmY29Rn+G5Ncra7n+ru55M8kOTkvjWd5LtWt1+a5B+vbESAS5JNwFLJJ2BWUwvf9Ume3nN8bnVur99J8saqOpfkoSS/dtADVdXdVXWmqs4899xzE8cAOM/GsimRT8BG+d4JmNVhfGjLnUne3d03JLktyZ9X1Tf9Pd19qrt3unvn6NGjhzAGwHnWyqZEPgFb53sn4NBMLXzPJLlxz/ENq3N73ZXkwSTp7r9P8m1JrrvcAQHWIJuApZJPwKymFr7Hkhyrqluq6trsvrH49L41/y/J65Kkqn44u6HldQfAYZJNwFLJJ2BWkwpfd7+Q5J4kDyf5THY/Uerxqrqvqm5fLfvNJG+uqn9I8hdJ3tTdvcmhAfaSTcBSySdgbkem/oHufii7byjee+7te24/keQnrnw0gPXJJmCp5BMwp8P40BYAAAAWQOEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEFNLnxVdaKqnqyqs1V17wXW/FJVPVFVj1fVe698TICLk03AUsknYE5HpiyuqmuS3J/kZ5KcS/JYVZ3u7if2rDmW5LeT/ER3f6mqvneTAwPsJ5uApZJPwNymPsN3a5Kz3f1Udz+f5IEkJ/eteXOS+7v7S0nS3c9e+ZgAFyWbgKWST8Cspha+65M8vef43OrcXq9I8oqq+nBVPVpVJw56oKq6u6rOVNWZ5557buIYAOfZWDYl8gnYKN87AbM6jA9tOZLkWJLXJrkzyf+pqu/ev6i7T3X3TnfvHD169BDGADjPWtmUyCdg63zvBByaqYXvmSQ37jm+YXVur3NJTnf317v780k+m90QAzgssglYKvkEzGpq4XssybGquqWqrk1yR5LT+9b8VXZ/QpWqui67L1N46grnBLgY2QQslXwCZjWp8HX3C0nuSfJwks8kebC7H6+q+6rq9tWyh5N8saqeSPJIkt/q7i9ucmiAvWQTsFTyCZhbdffcM2RnZ6fPnDkz9xjABlXVx7p7Z+45rpR8gvGMkE+yCcZzWNl0GB/aAgAAwAIofAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAY1OTCV1UnqurJqjpbVfdeZN3PV1VX1c6VjQhwabIJWCr5BMxpUuGrqmuS3J/kDUmOJ7mzqo4fsO4lSX49yUc2MSTAxcgmYKnkEzC3qc/w3ZrkbHc/1d3PJ3kgyckD1r0jyTuTfPUK5wNYh2wClko+AbOaWviuT/L0nuNzq3P/oapeneTG7v7rK5wNYF2yCVgq+QTMaqMf2lJVL0ryB0l+c421d1fVmao689xzz21yDIDzTMmm1Xr5BGyF752Awza18D2T5MY9xzeszn3DS5K8MskHq+oLSV6T5PRBbz7u7lPdvdPdO0ePHp04BsB5NpZNiXwCNsr3TsCspha+x5Icq6pbquraJHckOf2NO7v7y919XXff3N03J3k0ye3dfWZjEwN8M9kELJV8AmY1qfB19wtJ7knycJLPJHmwux+vqvuq6vbDGBDgUmQTsFTyCZjbkal/oLsfSvLQvnNvv8Da117eWADTyCZgqeQTMKeNfmgLAAAAy6HwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgJhe+qjpRVU9W1dmquveA+3+jqp6oqk9W1d9W1cs3MyrAhckmYKnkEzCnSYWvqq5Jcn+SNyQ5nuTOqjq+b9nHk+x0948leX+S393EoAAXIpuApZJPwNymPsN3a5Kz3f1Udz+f5IEkJ/cu6O5Huvsrq8NHk9xw5WMCXJRsApZKPgGzmlr4rk/y9J7jc6tzF3JXkg9MHQpgItkELJV8AmZ15LAeuKremGQnyU9d4P67k9ydJDfddNNhjQFwnktl02qNfAK2zvdOwGGY+gzfM0lu3HN8w+rcearq9UneluT27v7aQQ/U3ae6e6e7d44ePTpxDIDzbCybEvkEbJTvnYBZTS18jyU5VlW3VNW1Se5Icnrvgqp6VZI/yW5gPbuZMQEuSjYBSyWfgFlNKnzd/UKSe5I8nOQzSR7s7ser6r6qun217PeSfGeSv6yqT1TV6Qs8HMBGyCZgqeQTMLfJ7+Hr7oeSPLTv3Nv33H79BuYCmEQ2AUsln4A5Tf7F6wAAAFwdFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEEpfAAAAINS+AAAAAal8AEAAAxK4QMAABiUwgcAADAohQ8AAGBQCh8AAMCgFD4AAIBBKXwAAACDUvgAAAAGpfABAAAMSuEDAAAYlMIHAAAwKIUPAABgUAofAADAoBQ+AACAQSl8AAAAg1L4AAAABqXwAQAADErhAwAAGNTkwldVJ6rqyao6W1X3HnD/i6vqfav7P1JVN29iUICLkU3AUsknYE6TCl9VXZPk/iRvSHI8yZ1VdXzfsruSfKm7fyDJHyZ55yYGBbgQ2QQslXwC5jb1Gb5bk5zt7qe6+/kkDyQ5uW/NySTvWd1+f5LXVVVd2ZgAFyWbgKWST8Cspha+65M8vef43OrcgWu6+4UkX07ysssdEGANsglYKvkEzOrIXH9xVd2d5O7V4deq6tNzzbIh1yX5l7mH2IAR9mEPy/CDcw9wueTTItnDcoywj6syn2TTIo2wh2SMfYywh0PJpqmF75kkN+45vmF17qA156rqSJKXJvni/gfq7lNJTiVJVZ3p7p2JsyzKCHtIxtiHPSxDVZ3Z4l+3sWxK5NMS2cNyjLCPqzWfZNPyjLCHZIx9jLKHw3jcqS/pfCzJsaq6paquTXJHktP71pxO8sur27+Q5O+6u69sTICLkk3AUsknYFaTnuHr7heq6p4kDye5Jsm7uvvxqrovyZnuPp3kT5P8eVWdTfKv2Q02gEMjm4Clkk/A3Ca/h6+7H0ry0L5zb99z+6tJfnHiw56aOscCjbCHZIx92MMybHUPh5RNiWuxFPawHCPsY4R8ch2WYYQ9JGPswx4uoLxiAAAAYExT38MHAADAVWKrha+qTlTVk1V1tqruPeD+F1fV+1b3f6Sqbt7mfOtYYw+/UVVPVNUnq+pvq+rlc8x5MZfaw551P19VXVWL/MSjdfZRVb+0uh6PV9V7tz3jpazx7+mmqnqkqj6++jd12xxzXkxVvauqnr3Qx4PXrj9a7fGTVfXqbc94KSNkUyKflkI2LcMI2ZSMkU+yaRlGyKbk6s+nWbKpu7fyld03Kn8uyfcnuTbJPyQ5vm/NryT549XtO5K8b1vzbXAPP53k21e333o17mG17iVJPpTk0SQ7c899mdfiWJKPJ/me1fH3zj33ZezhVJK3rm4fT/KFuec+YB8/meTVST59gftvS/KBJJXkNUk+MvfMl3EdFp1NE/YhnxawB9m0tX1c1dk04VosOp9k0zK+RsimCftYdD7NkU3bfIbv1iRnu/up7n4+yQNJTu5bczLJe1a335/kdVVVW5zxUi65h+5+pLu/sjp8NLu/b2dJ1rkOSfKOJO9M8tVtDjfBOvt4c5L7u/tLSdLdz255xktZZw+d5LtWt1+a5B+3ON9auvtD2f1UuQs5meTPetejSb67qr5vO9OtZYRsSuTTUsimhRggm5Ix8kk2LcMI2ZQMkE9zZNM2C9/1SZ7ec3xude7ANd39QpIvJ3nZVqZbzzp72Ouu7Db0JbnkHlZPHd/Y3X+9zcEmWudavCLJK6rqw1X1aFWd2Np061lnD7+T5I1VdS67n/D2a9sZbaOm/r/ZthGyKZFPSyGbrh5Lz6ZkjHySTcswQjYl3xr5tPFsmvxrGVhPVb0xyU6Sn5p7limq6kVJ/iDJm2YeZROOZPflCa/N7k8LP1RVP9rd/zbrVNPcmeTd3f37VfXj2f09Ta/s7n+fezCuXvJpdrIJDiCbZjdCNiXy6Zts8xm+Z5LcuOf4htW5A9dU1ZHsPg37xa1Mt5519pCqen2StyW5vbu/tqXZ1nWpPbwkySuTfLCqvpDd1w6fXuCbj9e5FueSnO7ur3f355N8NrtBthTr7OGuJA8mSXf/fZJvS3LdVqbbnLX+38xohGxK5NNSyKarx9KzKRkjn2TTMoyQTcm3Rj5tPps28ebDdb6y+1ODp5Lckv98k+WP7Fvzqzn/jccPbmu+De7hVdl9M+mxuee93D3sW//BLOyNxxOuxYkk71ndvi67T4+/bO7ZJ+7hA0netLr9w9l9HXrNPfsBe7k5F37z8c/l/Dcff3TueS/jOiw6mybsQz4tYA+yaat7uWqzacK1WHQ+yaZlfI2QTRP2sfh82nY2bXtzt2X3pwWfS/K21bn7svvTnGS3gf9lkrNJPprk++e+IJexh79J8s9JPrH6Oj33zFP3sG/t4kJrwrWo7L7E4okkn0pyx9wzX8Yejif58CrQPpHkZ+ee+YA9/EWSf0ry9ez+dPCuJG9J8pY91+H+1R4/tcR/TyNk05r7kE8L2INs2toervpsWvNaLD6fZNMyvkbIpjX3seh8miObavXAAAAADGarv3gdAACA7VH4AAAABqXwAQAADErhAwAAGJTCBwAAMCiFDwAAYFAKHwAAwKAUPgAAgEH9fwPZXmhrSPFLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copy output of Thompson et al\n",
    "tscale=24*60\n",
    "fig, axs = matplotlib.pyplot.subplots(5, 3,figsize=(15,25))\n",
    "name=['arterial','adipose','brain','heart','kidney','liver','lung','muscle','skin',\n",
    "      'splanchnic','stomach','testes','excrement']\n",
    "name=['plasma','gut','hair','kidney','liver','inorganicMercury','redBloodCells']\n",
    "\n",
    "#diazepam\n",
    "max=[1.5,2.6,3,4,5,2.5,6.8,1.5,1.5,4,4.2,3,25]\n",
    "#cotinine\n",
    "max=[9]*13\n",
    "max[12]=90\n",
    "\n",
    "\n",
    "max=[1000*x for x in max]\n",
    "for i in range(len(name)):\n",
    "    row=i//3\n",
    "    col=i%3\n",
    "    fy=sol[:,sys.lut[name[i]]]\n",
    "    fe=se[:,sys.lut[name[i]]]\n",
    "    ax=axs[row,col]\n",
    "    ax.plot(t/tscale,fy)\n",
    "    ax.fill_between(t/tscale, fy-fe, fy + fe, color='red',alpha=0.1)\n",
    "    ax.plot(t/tscale,fy-fe,color='red',linewidth=1,alpha=0.2)\n",
    "    ax.plot(t/tscale,fy+fe,color='red',linewidth=1,alpha=0.2)\n",
    "    axs[row,col].set_title(name[i])\n",
    "    #axs[row,col].set_ylim([0,max[i]])\n",
    "    axs[row,col].set_xlim([0,1.1*tmax/tscale])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40334091541559786"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pow(70,0.75)/60\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3. 2.]\n",
      " [2. 4.]\n",
      " [2. 6.]]\n",
      "[[3. 2.]\n",
      " [2. 4.]\n",
      " [2. 6.]]\n"
     ]
    }
   ],
   "source": [
    "M=numpy.ones((3,2,2))\n",
    "M[0,0,1]=2\n",
    "M[1,1,0]=3\n",
    "M[2,1,1]=5\n",
    "v=numpy.ones(2)\n",
    "q=M.dot(v)\n",
    "q1=q.ravel()\n",
    "q2=numpy.reshape(q1,q.shape)\n",
    "print(q)\n",
    "print(q2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}