PET Image Reconstruction Overview - Current and Future Trends

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z06LSX6rW2lLFDH+W1Qk8+gH6HxS1/fBQuIXtqjKNL/Uczyanglo/U5B8/mBPUMw2Eu3vR64yiZGibdOZdStmx7k5Re+lYyFR8k0mdhK3rkxPTmo9rkZdn3DQUXCVc9fty8aRAdkT6hsUc8PZt7s3MHhSM0SptbwVRFV7+XU7JL5ro45MIwF/34qZf5te8iBxCuiL0Po3HPdDNxDglOE/dBunScQuRlwP/oGiG/YKfqS6bIP9ZPmg+n1igvzanm4cBoY4DrXWAJUDBB87WVyCaQ7rdmPzQLXJ5qngeHuXmXniOZ2Li6Kw5fSCstmegIJHVDtz3kiZro3b85cs9K5hO6O36K2prm02N+qsovHyOmuZKyv7D9kqX3W7+HTY9phHC9ldHUAR62XUg1jo75F4vFbN6TRm7UNyq4/R2N51PpvxULGKKQTNXFXxBxJ7LKvBderl4uN2O/AsWYEvvH3zWh5M5TlfppRCdL1jhrSXEHmWFnLLNgyOKLy+E2+W9krapCWvmcoZvHjxnm2Stbm9yWJucnPqwBjWRsjaqGuVBOajzfyFjZ3q62UUUY87EZvOG0YjeimvHSC6btKNVLfNj18QGPp59v2Mvoa2Z4YfdPDhdbvAWfKHsuh3tANikRmePcFF70VFWtvNgO0l+EVva1BlUUWCPxRag3dh4sEyUFffIfhFNmHgGLqfPrPHP18sCUpvpbbp8PNCy1p2zF3qO+sjIoYBBftf9RTrtCml22zBPWu7+Ax8TUfCuKQpqXTv9K7siWwsQolFjoOecXGIxvLXE1fa+HjalmvSY+qQKDp3s0wF09yG/xwZmliwa2J/T+9L06rvJzF3nu19+eyWV+oDD1MueEP7spqg9AX4CGJxu23NCD0Zo6mRYKxFDyE/+kSjatBvsEC8/Fugd43WcbGvJZW6tNb//rOqTTuqLOOqIVqRhRqKwrjFWp+0QNtnAmynAvN7IRkDM3iBRwIBZxBZDyfCxtziCZXlACpxtmaQiPxh+JNyhua+3Sa8NEZH89yvDIV7UImbVld4GkOpImRGe+gc9k2Wvzayk1K1OSwiwVfs6bHeoJGKXAYoWUWomdhrpfzPV/WDZw1b8fMrbyKxRRbr+sJ8DeT2VuugFoeSsTkyCPWmAILJ0A919GgTfmA+npBBROf2OgC7HFuarf8abF0d0uA0N+mWqHG/9lPmg4o8BzkhQFxOeibfEMpEfl4tixwMUUx/BF9tJbXS6+2TBCOMp66FP42DG7B6mAcJF6YkSRS6XWehRNvf1K4tfgfitOBGSJ22lkOKfeVCyU1b3XNW6xqiuLuw7AlohMDEZl1x5ElBfYB3GxfvKdAqXfu5q4olhcj0Xn1BM2jva7/vACxwW9iENxbwbmKEeVdasnprzqvWpBLAOwtNKzShR1TzIp5Z4wBuhmMO9DpnxQ+B5EsHlC6ho9uSuXh9Bi9uwI2q/yzL0twJocpW7f4PQ9MpgwF07zydNFK2pUyTQ7PUQViOC+KfVFsxHF0CgxtTxDPl2uY3HeL2QIrmwwarZfoCK3vbebHpD4Iu3iBqgnM7iuxorsca8aX2TAgU349TjMYzUue5ILMwcxVj8a3YmYas0i/jjjGoHwnluFYk9/KHmj7DapMRAQtnG0rPaR/9I82jnJGyE/n79bgeRtjPJ1AWfGiQTMPe5DRxNSNJN7Prh9NHRHjmtWsFLtjI67/NrgjREofrEHwXt4w7W4OoLahoaQYLTBdZxRFhZj21vEHsDQyMSKrKokBPs32z/bvQtQkJzYmszXUSld6jLxNvGiYuIZmG65DYsLAeWSx4EytWWrPOWii5iJAOfLsjcRWxIlddNDIWGoIg4Cjy7qCc+vxOIIw5ZXjtGmgP6hhzfVbcOemFy05JvbB1HsXKSMfyZrazbwg3cGZS6M58FZooqRA7/gvsup+ifOZB0ejC4g4LTMiCKiCJ0pLl6ke+hvjDc1d/BsPNz7VgTZJd4mfRFHGQHAr5KDjX9lkTRZT5ut/lwQFVHwUadsrmUtkcCZNnIkaNCHAhoF8YNZb3QGtwfSYOuE9+i4ent6qrdDltzjBhGTZMRjbFStT0+KwBkqPBuKzZflVP89YnGOqIhRD/KlIPDv/cWkIFEnljeTRoy2di3vTIfemJ9Gt0NQScjAgfXKyIc3VEfLWYT8vaScyxKMHkn/KFrq+FOMyxBx76g+ST5kb+li9NBef3ebsK1x9w8Asoh+v0lgtBDdZY0fkTEetU5pz1oEF24ZvbinbPLQyRHkxhz0BX9mq+mHI5Fh6lT24arDm91iOlz70afeKRkY8awgS+rvrtMj3Bhj3pXJg3cUoMYxjjRQmjjBKpnXS1Cw5I4ZnjTOHQsICoojCWU+8WQfHPIuTn3vqGLfCAlHLjP9B7cNY1uk9nX+VkWVyHhxvTQ6PpjH3WJVa1Fv4eE1IH6Ko9SCmRJSrym4KqLm9Ju0RaLay2n2i2yhHlzSVGl9R/FfjRkr9L/fcGFGJ+yTajV4RqPbsg66vAhSrApxhGJZ+Prnm7HIhdweF6359hwXXs+3eJ/8OqdS1FJ4zqCkoNx7QyxJvl/GzkONZNoTNJy2zxlJEt4MKDx9Q2hxEpCyu5iBuH1+4v5ARVrUJeW0h0keXVotcbiHRz18+WqiXMQtgKRaO6rE7wNJAZVxdamNrrgd1uhmHIP2biK6EkoaY4zFj3dVUbkdCOcSe1LhNoqOa1ayM8wdZ43hr+cgiSSSWN2de2D7ncR7amgj/mfG4sRttuyw2X05aAHiEXcCkFgTGJ5uEpgGqjFKhROfQbyYmIjolpcy6U+pSeev78DuFuAFop7h0EbNT0ljL1CHAMHOzDsK79QApN7dLu7e2X1x9YIwn+ObG5dTY7Qi/cLYheMQlXcl/96iqM+0G1WqzbXKJnJNpaD2hEPwNFd3hcG6erG65MT3RwhBICkOvjP+rzS06GjJsYjmkUulgQMN2TrR7t65HDxbkIiG6aWzNpgVegFE8rNzFoXvH0+QKy+HHzenWc87g9Akhs9vS8UTbi5dW6jYpGxz7UdNoD6tcunXn9ZVMdrwtrZtgForpgE6a3Gu+q3KWjSnbW/NaC8vfS22ha3r0tf3X+QuaAVDdabLzKIukp6JCEdYfrgcNRCLQPMTSYj+7a+7JVIiqLMOKiZh1twCvnobfQMN92EBfbOJ5Ye/0Wwoke/Jf0kVunwmheNWVzk8na6SOkVwQB6Nr70Xv5tMfs5cgX4X5+uoPslokfiUWj1pcpJ85YNL+Oq2bpLkhyD23QBHkC9M/eC6DoJ5hPj1K5HKtVBY9WfBxVLkgkZ+KeOEWKOqaH+7/i2mI5ovg/H1yoxX19kKhTQS3nuRySDQ4xfCYWRRy80zPo39FF1KBaBnx4c7lIkdDKjF5v0dLtxREHuP5tg8TX/OrcAXXQsNx8mrAI4qm2o8/hCrK9RIbVbs3dODE/Jatt82ZVCKUdKvRa0QscluC/R+KhSYqJFBN3Vybyv0AZ0GEvT9bgwuxSWhloTBp3QuH58WaP0abAmp2I/KNvDbS65yE033X9MwArSgkWKQzZcWcePs9oeJ1p2VcBUDKVxXZDAHIsHBMA6eZg1Ag3W2RwQBBS3jpE7oB9jRi6z/gy/7Vp7DJNF5Dli7KuxJbDiryCmgdmNBT6lz91oSo0PDRzCCiEKVn5kDZnPRE6f7EA0rov0PdCIPC3QYYdxhlEMyTRLWit/TrzNllYbvy+drrz0nbtplCH6UgQfURiYSCspUScdzVWDUNWSikjnKbYue390Xf1jILM033sIleLsegqxNMuSHGDlvk2z6/UF0Qxf7bT7m3+abbBACbbMspcUYgROOl3jrBjVfgHQvYUlibuMrtx6CBifHZYlb2ozX0/r8q9JMEO7PxUeCvIjugC4rzDHaOVZNwqZq5c/LB8On2CG6KZIk/Oxe6Zg+X3rc43CwrbNRNRQIJAEQSmAcvvZvFgl0e2dzUCve9OwqoIlziI98A7KqBI9o1YpgyNJ/xBFeEKo2IUNX2SqgsYJhwgKRGNdatNQLDmJyYdD0PxFtN1QG9eHm4SlkumxwM/r5Ca3fpCj6/tamhXRPQNkXA0jNMC2b0AQ62Bn2q/NfP4rGqhQzTJ9EI4IVM3SycIdxGH0r52oiSZ8/Yv1IFLQCLMbi6WdzoLYFAlp9Yl+16Ggh8Awt7E49V9Qry9ePwiO9pldREkMpl7qpO/xH08DxFQg+yW8r5IAzAV1T39NfXxkXAfv0Osb3CQcEWsRQ8jeG8RL9JH39SmlVTcdp0GN/s5XiCaRq0lPU9Wp4LVUv8oZ4d4a3mIiD6yfrXfam8MPIW1vPE3sASYq/hJTV5HsHnJFYu9dMnW+34KDtFeYAt0WVh48WWSLPWdrcT0FJCGVnE25sL9MexuFf8gOGyXpW6JkJlSovtnFVA+hMEPZfi+fbywO6XuP044tm/71XHSjHkRF4CaHFS/vqNEf6fCkStzpcRoEMfbbvM7EKtyKekWjp0KC6+mon1og45y14Ua8GCaruK9j77+Cj/FOTofby+A18mpRc+BPHiOvJwKeJa2U8iXh6858YustJGQM+jPCYAOC4VCdbRs7TpU6xrL09WuX8LV013ovZ9Wky0uqZNOhDaJIXRdTDZh6bqr9v9tMaD841JyQcTIISQKqFNrkHNQ5b1QNI/VzRLUAVRvFHTrlsZxV8CLL+U8vmoPqlyDGgEG879TX/2vDQ18i6JIeyrc+4mlz+yvJXFBb5JgVphQF8BiTuK6UpoKHALlkH4P2NnbgkAozUPyx3dZUJ3Gs2q4yzqnPZaBd7PuOmjl8jG36r4cp0+xNm8KRzliDC8uymB+W81fZ6WbB9CMXKRX+Hx4Fa0ED95zJ2M+NMqbXE3DqzCe2m0ofM2L6fFO82Npl60Bw8818TsH+N121Eg5Y59sK80TlL28zlwI1+wfoUuS57GG+gPE3PfsavmJfwlPR0qOpMpyTZctlOgvhSmCchpA57CiSbt1QBGwkx6fVAlajsiprydv3ZnGwskHr6Nc1epd8mshOn0R+PHcGstFjvsDF/Vp/eP8g6/NFI+v1mLQXm36FMzX1x2lstvLkGBX9W5dWhAFWIHfKhccC8Vf0UPFR2sipXsUhCMjYlf7e895t4s39FiAu7sIXZdNasyq9GzvAu75lfLMOvVyMqkBcHxlnPtXBZgdcx3mAs6WZm8OWZHS/JQ0HhNjXCvwZzmjXpq32Z21WD791Bd2msH+tvWU9s57NSdEQMUbNo62ArASwJrLbknWwsR6Zd5IthDpFTx0gW8I/iFM5zhKv2sDqq0vs3En7pv8wBYykZYWApSLWhqWDZn+PKWwCDzx37X1ttqJH5MN1X/tVaN8KfHHXX/XK3JXa8yaxrbDaDt3HGOG9cfJcT35Qk7R+x77KaQxAhIgN8JgJsY3oxkOIUpTyp3uvW4oMwfSGYI245e1SE4/gR2T/A7gjNx0nNHM7/4S5uKhDkiLQWY147g6HpTvP6fdQ8dgjhl0CTfwqv/6svIdVEwu9V2ZIP/6yOVgJW5gVJQ+LGRYdAMzfgpYsG/ce5YPf0LwjwjiY4CCdUao3KLRCepj32+fgHED2Uznr1d1S0GA/SIbzflFgmja0jHDlcAbe4cofxpPC7MTr8sMjOnJc2LBUZDS4TeR49pPZHuPCk6p6aU7PcnQqkFboT4ari6kHV2+3HiqPx0x6rEudwx/MgDnpj2Ppy/HrQrnWn5BmRapQKN9RbKgAi4lF+5wpjP1WOrITysEtsZRzzpNRHbRR7VrJWE7tGB6guGqwOTuj9qQrm3t1Tp/fG/RqoHY3Dz/f4V42daDieZh4/cSGD5cvO1tQwltAuZPlIkVzq4Rk7I9EXHFu2W8Zt3B1bpHditaWjPwQU8WU00WJGV/IUgaZYfJ5meWAokF5NgHN2bijo3B6p/jK4hLLqADx/V0KFHbn+iNPlDJwtPlGAjN8G9li5k1pKnzXJ1y6+pGd67uXj97BKmy09zbQO1pcvaAIwCYFAs1sqLPqlGWg74TFfJG0xLvirucn2Ro8dmPJOgat1bPZEhkTXyREWaVksIkS/+ruRm13emJ8BejmEvwSxTpp5YF+U0G0+yBGoQuY8J81E2A7YT4QNvNPNxuFrvzYAn9oWq1nGfQKBnJk3xyVW+TEbfrVEkGWVYKqH906GD0rCdrLWeMhZwgk4i0RCUYkqEnufR53XWCgsFpxqJQSj7vh6W8CLFNnjwOdzr7v70oSJZBl3NXTTD4o48/bEMTr1kdvAZ6xQHNVngD6QLhB0lUqj29mVdEiuTDGm+Q9Q+uUI58zN9xFtzY5sN5IkOJZZSA51X+YFF2lYEHPtVZi9ayJrXUreCOtYvX7Sx4Ei1e4PxrMZ+Zn4DpksRh7VYo/uoRAe6Tx7zyEXl9asQS4cmVFD18TjJXaDD/K3j7k0U2o8TloiDYSOtL+DpL2Rxa2lN4fbtuLm5cv4wCoO/1GpwUe16iDAIY1kaT0YIWSUVcAjcWwbBHEd/LZd5e6wX1MHRPcvSLoGEBrdHY1kYAQEaYlbb3sGm+RHQGQ/bA1Jecwd8qItVM3nHRGyYvA2aWWwixzCKPivcKBHQEHulrbIQSqQHtOUC8Ukj0egiuzzPZ/2uL0w4YNC04RNK6EPKUYkqkvKmkg1DvtQuwHrlXaiU8pd80iBX4J0RckkLhnOHbFytUnlxwYFdLPVa33da/1JIpMtwbuPq86nNWvYgsPg3IJ9jWZ4vIbz/yasp2BhbTLHSVvlb6q6IGmL8krRo3CVLxcD4FCDvttJYXJJXyaw5hqLuuq/4VDMBFk9X1VZn0ifqZX8Ng3Yq5HbO+sPHm5BKiopwg9Mjb40O1aWGc5Ux0tOjFEBYAfwZh6MrEdOA17phgXWAbP7ztBF2mTvF1EIMceWXgwsRvzmLwGhedTrwvZuk/XkbAVOPEA1pQVk+q7wIu2FOlqy6LmAsdsItShHBZxoXuifgK/3fNpLlk7XgjYeG/zKWgVohXycsVESVZohYIXDzvVNp7V/Y4SApNpf7+0SSzqTgv1XvOfjgAr7Qu+t9uifQBk45Zst0FU7sr+4np/J1vfQOcN3SAIaR3nYJoeL48ffzMkjnsJCG+bJvrGhB7T3b8KnWy4HiFmmtMX8eYw8+E/RUtMicahqTPvVcu8NnzqRwThTQlXRSH5v8gxbt5oHpEmj4H5QmIrCA6JAg+0gbvRPuT3+p5dkjbjIDes2sXr053kCFFF8YXSE4bqCLcJtzC6oFVHaTuqpDFU1sJVXvqwIKL0czUG9if0ySczLrG5c33F0z6g0tOqKZ4MxvsEbzwpvOorSKGRnvfxEm7QW+alRWseQ0v+ytkckFpMYWr4gTM7sxbmwDHbrTsaYO/rwmDCJrwK0FcfYYililybAjrh79D8TExyrPF955Ns4jwNpuaWlrjNScSECUZCRmv9ybTZFt/MmO8+Z+j8uZ2YL1UZ7bIdRtjlBU1IocBT/bkV1ztZYDZx8/BLqPzwX/q9UXv2MVIIy8wasLUKW7mgxhK0QV78l1nAiP3IjVyyfvA1VVZrSk2zcCeL8OAXY7otdcOtdtPjgfzc1lYdPHNgmJ/KU9Iv6rRTLuXLqFiuOqSoDwInYC3BlIDG8hBeu/1xNSp5h99jvU0u31tqg7eXvxzkxZmphXqy1sg6EnWNTkpWexSA+4bmAaeIeba8I3fWJoHNmiIllN/WQZz/fY0cF0KjIJdL+ZnpIArL4jBOuWHB17RrMZ+CqYIFvCIzQGJM3Eb1MklVjctBI+4bqFiXU2W1Z809KBoNvuCpfB+wLn6+ZfQRbJdm6emMF/BRsuMgFbruOrIGoQDkSKS6lFxPCbEqub+aGqRoOQI3jnQjVeSEbtlWgq7biVLr7KbrmG/B1VVkeEdlPGSkflmliM0VIawJCSuhjPpb9XfXKSQ84Hebf9H6hPFfyhW+QmiGEaUPy89IokTj9q7hDpbhjJlNgu0ffk/wJhgeKsKRQzSE4Dv9z+fhJoNoK9gLmBNEC/4kjhfA4UnvPA8FSGFKlR2ZEqRJ2ch+B2kA4KNfHxkZBn3pkFVNBOUcTMsD3hlX2NCQbtcawjB5j7HONPnLm3v9cJKnvrRCLfUvxB33tlrNc8CP9BVRD3SXOenVA4Et19KmkMJwEOuW9yDtRUySfZk8GmdSs4p4prypAl1/OTc2fznHgCUA/pu1r2XsSO0NVoPTAlguH2K9CwZWTO3lZcMAggh3fqknYyjo14BAa6THP7+94OVtOwIkOXHFTK8pRdlA/PSH/N0lFbcu6t25WB1N91VsDRMTm/83sNahDZf7CRJpOVV1rytKUJHiXDiSMQhEM0O9ZjI2AKSy4aWrJg4W+UWhXry2xyUHXqWwDkbD+w8/rjDWQzd+cPnu54oBPM9m0zlVOLkXGOmwMYqFHWv4syuXEpZE8cvT1Czg3NtreoWPS1E/mAv3UUUeUYlCymzWpO4fSWJlEkjcy0AxHRvh7ORr/BQaOwUIy7g7W6JzPAzWPYD+APJzYPauYy5vuyFxRQPZg+2WYCWpD9P8nVj1qPwaOjjv8AtMhLR+9tCUmq0KhnIBQpt+9WJI9cNC2/xNF/6bnFt7ln8Mh5Yp/KMG7HVl1pd5PEsA3A7dVtHoLkenBd+cT30ktVfIQWy0LjiDbYx/WnS2YYmLlpC9hoT/gEOhQcXWYbKCjGAODtv7siMBiI+PfPmhFfzko1GlbgPC0Re3IyOOIWuOkAasGsEyxBcIpGQ77Gvr453adDxsvgV31NJU8WcsqkL7LQYc/jIA0qmlDk+qD+4FpOgFsE9U4ShljJRYKxa/OXmBSnCGGjSTg9MF7iGaBQHm/IQw/59mwr/HGs0PGQKUKc45KIF8y44dPhB//E0T3wzpNdc9liMRXLA9/CF7FqihlQJKOmInWkWSBasa8BE+gFWe9WE+cfF2bc1d4gJcGBo6lEICp7l/eVdtg0jVJxyhMb1y/xluMZOVWloyCMp+sgNfabpix3wBNy9u2yMyFCFWL+m0SXVE2SvBhX1s5VLsarvE9K5+WeDTExPnpmSsiiWnPQ6GMuHuj644CAK194/0hbeY7AyhLEHUVQ0UvY1tqjwVVhL7QGdMLstgxs4nLI5oq56f0pZZVAdcZ9pQcUlDYs7ixGTOmc5zJBU9pfwbYZAx5iSf3sEHbF8DatmYRu0EX7gvAfikYs5TD92ekK2k4EvuEPp2nlbri8WZyaP3BSbB3Fj2DILAyyaDhSFnfwAaWC3avWHQuNudlI4HzI/5bHWr1MRYMapf9soZQlgRJM8ZIZ9S1AOor8NTKZv+DSxs9Q7X2LPvBjFjUazx0SyZg8f/pL4IOkBIt/jyGyt1RwLnZW9mXTa3RB4/SPrhJ7mir/7heyvi0sIBHplUVm4OJfArf18Invzty+1ebcOZQPcGRGwI0agJZbAcfbq6rrlVqk0WdcATX0ERrCFgP9uxJqVL/ZmxBv/NBE8yuAb0mifKf/ivkj9CUQbZutzPCzfRwuxsxkhtno7p7Tnka31mPjc3/0ZYk7VAPqZKrWtgEHo4OZ5LEo4ZySbAdZEB/mDScp5ENU1X1oU/L7RYc7P9oivPCw9Ks6JuWswTluVlCm5pLfGJEUMzYYiJJyu66Dipvl174i5ct3HmOz0bgESBlTz0SA9RrijpduuToXGE8QKcbIJ/MSxXW8ElrR7ZS0dGUaVVi8zABbB0OWD2lLo9YJurLKMsFTouYmk776NeJfSrgh2yPqlvEUd/Br6Ekh1cC531zo+BFHzeKvNF2XaGsT7kQpqUv7/UvWSFY1sanub2cIATkOQAnopfV66XdjBdPwFQfXeOfm82YTRm63uC+RoIGmZ48EhwkvfwWc/aA7RMaSpPEF9WO96O3QlrsYRx/aJfFh6xX7i065XfZf3s5YV0yPZhN1E+qcgMP4dktN9E8yjxLnObRrlTRxbYfCWJvW+TMk2tEKSCFP4bfQThCk9lcm1XdK7u55f35YY35GX8/NurmAl/K+WjeRkrERyzboMhG2TDt2sSDCvU4x3DtKNa7U4ajtCD/I+EOxaf159CMBT66rnQ5e/lvoDtIYS9cdcV0btGei5Fh3fjDqoXiZ+WaFh97TGCH/TeJcGWnq+Vlih7C4Iowz5T0kLBBEWFHSoY+eezpT4zK/TTr0iL2sWYCNiOAOmS875LwNcbjl4ly1H8OUOLW7ryZSvDA6/zOacgSOjsLU4KcGSr6H9wMZKCYJOJtPSVHuxTon8hJxrqlbPTXyGPoYVh2CKb6sjBkmI01Rx/G/oYvfbZlU3c2z2WShUwM76YDdbLKXPy2AKs1Ho9VlWwr4hkQ9eIS1+20yRmV2riEjgJGicVZazu8P4SXHR1dllHALEK8hr5ydXmraRoXd+QJZQzrVBX0D9SzarecvGks0ibx9OpqBgE57nr7Re3rrBXq7AqPvG7vpT3vcU3kBLd5dDgpF5vpDPfWtZZwPx4J29mOREf+ydy6DkxhihZ/oz2Zi6AXYFly8c5h6e+hVt0puJuHtaKTOsclI0mnnUstJWyPx+AVSVmC3ulGLnjBrvxL+sOOW59/cM0jv15NBG6Qvaww3ZFLMDMF97TpOP15RFURXn/QaN+4Ja8r2cqA5OWF59/xiETS+OwAy+6ClvpwrsySxcaUxJYXPf6WpeDqSQ06J3fgxDhiRL9+v4yj1kiXioSDLJQzO2HlowstD/oS+QHFWcW3jRQ5+lfZi3oEuydHuImGCFHLhL4kkeJ6WNM7gTwVhj7qOxnJjsKCEyZExAK1zAKw25g5TdmAeGyIvOMvCNrxNQ4gSwvtBzRK8PtaprRVHXGNPRBC6MnTCOxolLU1rALFEwrYe+fb7vMRI+M7bWXcpRXnwyp5Zpe8430O34POm4JZG0uj8KTX6FooV99hgFYD9cdeGCkgA2Rwkq6y5dm5atJpeywHcJX9PIB10x7iIxGNMSMGwJhcEteNzkE3TuTuQHqih/2rgPKv/MzlBBxf8gWvo3xNJJ/xcxp/6WZjqfiOFrOgZFgHdR8DF3XmF/4ouhpWbKmrfJugcMoo45foZk2lv9lNu89uUilyUBfTvQFpl+7u31ePJN6NfqEIR4kGw+VZ18DvTEIR29tdB2xs/XMRg2DxWp58dO7n84o4wpbhy9JY5kYPpk3HKjtGlQ9+cEbI9ubPh1iVTPYYXboisKvEqDYPPi6C8c3/79THIL1LIJjc5xnAcpVpFMMGke/luTpGJxk80Ay34jPNhGsoltZVlqoE+L9dhfzdUqGyXserUBFRZMvEbqIg5muHc4VjxX0ZCiq5aRT+xw+KesGMeATY1Vu6r/H/ISuL5V23q5FzxcX2NCUvzonAuMv4qatQsJrK7/9RbV1HO2/Ba8wZ0DVmfLOkKcs1k6wzQuIf4t3PqTxjZFTULfMmyz8K79Yq6Tr2RrJ9QqCk6SonsZCtIT5nUixfhmjSfb9plMRvon2j+J4a6UZ+R1ptblt3sBaGJtkrufIbnE0zNNtV6Xtf05RzKq+BuP2ypEA6kSsQZoz2BK+U43feyfINhoeo4LNePQSNINCRKf8yfw+AHNL/yttFOWZoaChLd1LL2Xdx65rMfM1uZJkwrcg9G+IkMxOB6fnDWoBtJKvL54BoGcA2003t6ioQvfYZvuN33f7C5EonIQNV5VapnXLb0jQ7Mv1DGaxQYwvhpt4cExTW6QFkNO9621JsM6/70z+xwT7MRvn03jdGM+zd1RvACMkCwpbhihHwx4nn4ROBNY2ik3axzjrf3eXXOFq5S7eT92uA6lT8J1KJABp080BxXPlnf+OymgNRKQVkInCwtdxX6f+bOWsMIpG/3iF9KIXY/LqYQd9q8ocH6M+sdt6cXxocpuzHrE071Zk1U1mVV2Ns7CYBqPhCDO2fX0onXEVysyZ+H3OLvjbZrEQnCHRecAPy3cO9cSk7pq8OOyfMoTsnnM+T7ebHwy7qY7APhqicX17ExPMdi5rIuTXcdI82N7dl8BK1cGmlVxX/NW+L96HiY9gHlkErIsErR0a/SUMXlngkR/NuMykf5GJGb1xuoI+VOyGdP7uey8pItAknBcIN8nQwnbpevh34YQ1yXmnZdDJs57KrnNgMH3c0Vne/RP9gXpGQhqHNgK6fmGuZIKhnsH9M8iyU6BoaID4j73H7ES9d6tZkNLmAfu6mD8A2n9KGLyMxjUjv5X22w7KnJzknQTOp/gwys6YNdwYTMum9dv6fIfs8rKH7gkEYa7CwXf6RHjPenOnv017VOIuXSC4YpFliA4fTE2wNWVDZtq+E5KmA1zphT1iGM3y0VicGB2uyX3LjyKj4KXfPW9iWRFzys8qeScCjbZQTvdye1akJiusQBMZikD04b+rEvLvYr+8atfZS0oxF4l3LWbIsa8T0q/TUoWh/6ur9wQMZOXZ4r1zJq90G4JRaI86UseJezSg2xLyvNf4PRl51xoLAXkY7yEPYGpdLNlbHdCg8AELfGtrzT3Fl7pz3kb0N+b4uWFtgEz2ahbFDjjhuHkSiZeEFuRhR7qv5/gaSQViYld3yImF0P3bKlVLLwTJPYRaZ7BEH/qrXvCerDqk2lKqlDJPRio6b20ZHeHeJtSl0lQgZRhTAnrtfdKIsFYe8/A+Cuta4Nj//BP9qyA+34F4pSfI+vjoAUa9ZbJw+RqrRfAzZdT211oJYZ3TpyrQYg9f0T1Cf8//d3aHSnx329nlN3OmHXdLTV7F2mywFtjCUnQJlq5TupBU4S/RY8F1INogDUpg8gakiwZB7q6D3BgrMwcfGuuGZq49Z6ha7AQPC71OelHjy2YWPy7GBfVyQWfglLlnWmUNe8yDdG9X0+ZG3Wz2zayhmwAbxNH+wuXOTxs6jQw7UDGAX91xkufNeCIuy+faj32v3FuU6i6Yy/5pFfIEymAjdm9GiRGracSEAoQCL79YuaBeekySsns9s6Zx9e97juUZpRmubqeQHBNyZdUAyTVaG6PP/wgxu/1pvbAAQ78RhM15wjhUnY0wKuOX5rZynqW+ntkd2AYkvx8UBl9C1N2WrH/fPp93O9s9Z6d5wiu62CoTb8yRgfuP6wJhdtDPzl86DrMi+Nd3ex8OUwjvbEVfHXWOAcbcyXFh+v3L+WblhTAZA9iVCRmzYRAKKSPnveCw3OIt843SQNQ8QXBGA7Swiv4PPgD9ANc3YGLelSqT5QAO3rBexMmHibFQEXH3UvBTqEr0UPVkDUwo2lcFehwb+0bxhlJUtDwK2o2N5QMEUsjGkqCOfm6kEmW9colD0IiS2ATFNtIbq7kzo4yq5zN8eLvGMNwvwDIiVyjX/80qz+8pVvV9Gt9cL2w3JgEyOX8XBWfb5UrNK5rUKqLHrvAIrTqNNHJ4fsQQHjSUToSdT49RjMTnBSLmJfYtJVQpUY6NCIIfH7SDmq7YNYJ3U/dNRVjUIoKxcJBgBePi4GXzcRTjJuIDjdGz+qy40NQgw4t7HFIj2WyQHHf3E5zX0rFaUJIfXwNoWGDOMv2mDPUkxl0desnXqdFBfAy1s2JqNjlDcqEn9tdOxWSVdWFeVK9GzLwsNEWZ/KrJgJe7FbMV1I7Y+HlPgcx89fL6bPhzbW1imaDmb+sV18pS+a+Sq1W/OZ90Y8ZVMv+sBOCc6/RuDw6FJgkZLAhBnKXl4wAb9YPNLRH+aQkbhKiwBgAbBg2PEIDV+sWqYPS/uC6/HD06NcMAFXra7gJ/mi6lGxe00gzuYRDJikzRrscqUmRK+nahI8sCXgE2D5T49qXqVkBQddGsMThyLprSAuUwBiBf6vASBuMbu+erX95LicWNSDRK/4B9z/PWhr3PhuVnknHRQD81Br3ijGAg4P+rK3w/y9GF4SVWzXFPQxOBW66H/av2fhDCMJxEN8jP2DCBHYYkTylAs9/Cnmq3BR+AsNN7PiIKdILrvdcVyBPZmiA66J37jo+cfyOW3myzbBW7HqOCDZMDdfT65swZoorJWmUuJikxxeMQt6jxaePnNLRWTPQo3NsJJDZaudk8sDKDtbwJUuMhPNoRQ3LAwv9CULqesfQG+xd3cCoPom9pVNRmNmeRDwOkRc5lV8056f3Q6VCwdP9n/w6q0JzeGUDy0BcVah1Osy8nFqODSWPbwpoqWV2CshcIG82EZeK60YL95gK8HZf84fLhGCYJtyl5v7DtK+V/mWNKD29IbVrwX+yYrKF0Vdzz6PmZFvH/0e1HK2yP2DNz66uZcJcFJSczqXZiHuasR4vJcESdM27dD/aoNppOn3lhO46/b5CnBtIIAtSyLX7D0+iUoi5Fh582/PlW06NsliHnGKh0zxu7nL3bdCZ2j6ODNwlP+BRZiPDMQ3B31OuA4hHbi2GcYrB98VyzwnhPwZQOvx9QTDoKAF/jf4CXZArAm29oLwiU21m6snk8AdWsbQeW0YKGUTcMsmeeoOq/q66sxVmyaEIndHPP+4nUSbqSvamvwGB7BozHI2OLhgp4+a1rOJrzv3O7mh+w0HORAadwHT7egGOGX8fNpHxGzxqlp0DtC7Y3eG2+hV/tMGc9MUdmTJq4hF4lKq1J9nFuR6IQHN0QU0p0nJpuDFvYe71Bv6S6DXf8PcOsnB1jnTulb7NCSXEfUvDXJqkZCLjMxqZsgbHaoiMJFtdkGdFBSavRaAH+PKq7YJf8u2QUz6eHsbJP5um9zKg48jCWhpAVxH0E/hDU3UKfH62FYv9HgU2EOrHUZq/vR1o58GaiOM0VWIsp2M/jQDjkhYztsoTJ8jN/zBUB5+M3gUQEbpJ/tBlKSRFoVBQ2+Syh8LjgjBsBJQE3OsnXvZ+/NtkTVzVMTxaH1sbgzb46j7BelL/3gfV8wsldyJgQNlAhWqQhxSd9qHl0XiBnMohBAaWqIa4QyFSJJ9tURcR6fRSko4nr4KK5sDcyuOSeDIyC09Y2ty915G3dQsH2ohNn9nYFBiZH9fm4vba301cVRlzU8Wa2DjeclPnEuhZ7sNRspCkMITiLzg38v0d1zZ33inpITlVWSu00SE4KSIWfYKssXWzhnm6o4Hvjq3XIKFXOxg/PfwKudV0e9vm2gb01Aw4eTYR2afYy8puosukUMnBtrcq3X7HBNESzMPjVLo3TZdS3PsGn9j/HfnJd/SYfk1pBhNpcJIbVAcuaXmUghL771B+GTmD26N33g8qfa11a9m5g9P9Cty5bqrq0p+MRF+McUgPYtnodNUj9Tkh5g61ly1xaXqzi3wAMBjRnaT/YfAxQxOobOSE3XFHAqdiJM26WCL79xpu22T8KTN201bW0Jqf03gUSh8r6TYVY7VQb6UazT4yCCfR8jHsYTvydlxvTOtoKBMXFPlkKDQOJJs6R60qc/FA1aQWpX6InMeMmmVdJ3PFHtLm2Yr5Y7ju4tFJC9BSddHlCfHZBR1LzGqU/VKqQ3aKY5WhYQLpysKGn25JJAqknembLBJasXeBReuQKmHCOO2RUt0Gk9ivyYqeKjNnVC82c4aAIPNx8vW789Mg+ZpybVdIHH9kskSBf9A0SXYHPgRoXq8n6S4uXTOACqcGMSpbuhA/BbgC8twyjQkGqdimKQ/K0GlvanuXwwlK/K8+aBxCOAQHRGV6TOdD6AXkLpGUhoNNMXq8vqzT6/YsjdMPZbAGU0hhjB4gHyTlGen1zXl0UoTQ2j0scYQ8vLYJY6FHfOC3XZb3rdMzj2TdeXiIfdfe4FN2IZkKPxDM7sr83PARLzXIXdjd9GQMq2qSuMUJkoEgU0QfYnOKuGkFzNNqHOCrmvq5/IDzf8a13n7rru6aHDebqhlewi91gXPBO0Up/xOPFlWJJ+UPtIeclIqqkB+NcBS8Dlo5MyBTqa/B0Yw0ud1Wx8LnPzh6YtfnOyMD1V3GP6pZy7gahtqTwApseslJ4uXb3IC45qdbuDwesZLwEWsHdT0VcjHGVXfy2QdYL5lajCReJz4ByiMn9pX73tqJAGFSMSGQ3k+0IhV8bW9Jl1kcfTdmRJ8+QampMaNZ9ToSPHrm4RajcCbaICpGTsQymr8MQa1bGmg+H3D5hcUyev75j2FjDXaOR2nJZLLh4J0N+RnJ8L2i1GGXvmmq0BKI23Bh9NwaDtRvByOhKQgJnkhXZv9ungxZ0mbFC1fqDUBNszg7nwllplOy7cvT8yedm+uvg2YD8y7IoxLAd6v7puW73t6elfUFP+Av5CpiVtNhPv+SVOq7dCbLHIhwY2ErRZCV42I0lzVcEtTdqRyX92vVFrv8cdpVMh+LI47CXlyJOxK6EGfMK3ukAim7mqNE9MMXYrsRp337e7St9yhHB1YXC1mAL6REo18bfYgK4e9mnzfbCZLKjke3Xv/B/kncgKm1Vo0rsg80WOrz10rtTn8bDvvsXKGennX6W4UFSZJYsWzft1oddaUgfogEyump9czMxdA1elp2V5c7/Bn6d2jtxXkNRdcdKmXENX9JUia7ChpHLn4davr+aVJqq6Z8NPAGFgpSv0ppLRKoJqN3WbMVlrHUApmvMxCWpPJ7Z0A9E2qRwqQG3lW1ZdjwdIj0NGfyb23843iLz/GqoZKTP84Owmr/6/68DiSwiYJyy5A72Q6C5Bke2B1lyCk4pM5ndaQWpDLH3Ak7KasKBHL46sSHD0/Q7n9IRhv/kVtqMupIazlJM7eEqauThL3pFCoNzr51JMvABil8vB/gNrVSyPqmbjYjiRaURvyD8UXngCFn1k0sw3DroxOqIwjX4VvrgrX/NR7v1vC2aMMacuit95q5fKOX/YgCue2enyez7hUoGnqLRVCTvivkc4kxiP94LM0US/2F2jsCJSukHZ7xItBAksOJcKJYXO2OESMJd5LvbJlLofgUBOohiDm0jFWo/Oowasra00SuDkcr8oH/Fq9H2tBCwicpw4wxoQJ7W4ZpiuiqyUJDsVEvxQjKFSMTJ0wD9Uw7+BYNpvLPcAmzHn7nYb7kc2MRweM/PimecmDmd6vtKH2w+qEHLp3F1Df8gvCFG41SJ17bEtbJl1MGcvgiRuw03nA+mDe/K+VZYxpN2h5/4heBVZy6wPURn2AMOJ5D97cCKrI5DrFi639TdF4mm2ZsPiwFlV2HoVflGMDZ1b9+ozvk2B0wAqvQ0aFk9YSNw2Rnl7Y/evZ/7E4MbiHBGTt5ZuegS47E6/j3MrHYcouMjZuprd/Y1Bv77y6GDrGVn3xOzkShQWKN9Pq8O+FQNtCCZ+9L0uqRcSnzgD+fGR6hzEZYK91wJbXwiU4L4xU8de+UIBIWpXymHrjYSZN8XMRsCBK1LmqAcexn64lJtMhc1A5NcNwTjnp0ON+bkqUQHj1fj8/H9GdILNkkBpSAoDpQqogYpiUm+nAnB5BhO/EMqMQAwr5fW+yA7pzozFnjbf+Gw4ZMVB+xYImPu3n17XlKBIrbneHTl/3kAQPhNVZ3rRUvwzoqGtfo+2en5eWxBEorwHwCtdIaYxkrQTUy4q3bUhluK7IbE2nF8cpIrCmLfoDzQAmbHR9WYmxtVyAF7Aeq10kSKjWKIKoVGqD6HYriIJEAxomyE+q+9Cm7GrMUB8Au1X9aDpzbeUu9tBUB9hgPdHfe/2SbbDkw3bPJvIwVfmGOR1AJnB5CFdfsmF+V2ZUBMtGOex0oU3zmq0QTHpHNDQVMZZxcGpkitYQM5kBh5ezOn+k+IxT6U9hfi0bNjGgV/M2Fjln45H0as5dq3E0o8hISEWt+Icxc+8a/jasoIr+9g5yMiUG2AMO2Y5DcFrDtXr7Cm0+2mYv+USOS671KjZgELtnZLAZ68CWZhYc15Sd7e35QdniV493N6+G64S/VE3dmltkKiyzV4wmZdru1V9AI886lgcSzCTpvmZIUvcWJ5cCl70zZcT3J8tzF6yg4XHlMKBNduxh4MekfdwXwUY6DwcUQ9EvBN+FnP4MtGvUYN+L6+kiU4dCpAT9sbro00aL6Rp5BmKFCCPsxvlnivNRKMeXaipGfj9hBMf1n4zKBwuplIMvDTDCnMzjewG+yHsVHp7m8eNCrMH6hM/H+jjud7iOvCAf/xospCEC6PzYzVRStLyl0NIF9SFSsxQyN4hJiYOlSxIJkMUphtg9Ckkj/oHuLBA0pu3cc7Cxal2h3pLGKcRSLm88UkI+NCZHA1nyqb7DH7IqXJUKHS5l96ev8sSrKxQNJZasysdK9eUOw6z4m0I7kzN31F/udJvjqZ2lyDVGhtK/FbOX+LP2XwT0L+W45M6qXu5ggqCQnDSe5UASmVooqv5dX6ZYJOiJMFHv2MCZ0gCLTVNuv1HF8RW9fV7aKYbkw24PQI7vv7D97F7PiPAoerfzdr+c9mHBqzMjKVRBzdm/3ah9UZtxxQLVxoNwpIKJowP9pcPGq4POb7f0PjEBNc7J9j980abxWH0DiS4Ozztj7YxCUkUCULg6KqoYO89roCDaaARw4aHgPlN5K5lfYSZCo0G3afms4GRDrsccJn3lvmruhOZM6uzoCXM+PxuhQH8jykkr0ZCIzFD40vIifLAtj8YNyLRiZID/+HxX8yiU6kX+Jlh5a0Mt85luqaAPVRmOVCAsM6nb1iNjbr0ypwrd/eqOA/4lwWh7iUNXHPoIy/5VCN3JYeFWaBVXbik2FkbGEDn53R7aRSF1cjPaxign2SshjMgoJeAeTsorpgM703zBitKQhzdnRX/cxb1AEzqtj6xbNLs7xpvKxuebpuxxVOrWlPlJMy9ETnlK0dtArmdhBkHDKnHWtKqLmbEh6gTh571sVrvWnxlTKj3GziKl7wKJvbUdTfVJSIBO2dlD/OEqO7rmbWCCSdGklnDzOtebrKtlmOQqe1geANGi+o8D0dbeHvDRjLn9KoK1HSa300rjxJAYg5TZdr0LMV+CxcSFo0MsGLeh9yRNoNH707jM8JlW1Cu2grErnmpZgHpPr9t1pGA0dgTPjn4v8TCllfjgKbLVaLPZmAy6Ttv+ip0mmZfhFYjm+vmuN8zR4ZzYXdh/MmhnH2aJiWQcK130qYvF2BIpyUSSxT2YcnDPu4H9LjlrOh7my8Omi165sV4/6RzWiK+LHUjTLIHFDxOLlDn0LRAf2YwmNsfyVRhSbAE6sxUF+QKQRxMwpMECSr/n1PnyN8CutAub6Wv6AvmN4lFqNexMhRSGc7nuXfFllIFvb0vMg16P1vj97pLOOffTqadnsl7TqYaRHzvHPGgSS6Ibd5dX6TRiRkGaEU1ppMe0+0Hy2piPa1vssBWHoKLsjbMdyQ+OUu7dRNZPBIytA4o/EimH8t6ERlIK6dBCEKbUOwEDUPrt+IEORz9f7ZE+ThSmsjntjUjzQQ6ajXtI+bJ85wstsVFAm4SfOQWhy+JDTJu3IGC05pBBhXFMu3fiVfWZXdL79RkjKmcLDwIHV+q60IfQUdnPWbktRc0LiJAlKt89RC1WbGShH5HiMoDQh2jyYho3nv7t583qIyIGIfMbL1mqdih6tLeYx9owcIBABWccP/TLQrqCKNbldkxqzt555y369Yv3xr4E5GTvJQ2okybNRAS/9MXeMXqxYATpk0aQ1fRTSbYjwraBOXE5eWalr/1cHB0IwZSV/uHgBCesVASbsSOPodDZdWC8xXANu3RJia2eMDjn5M1XjMX4aOYaT2aQtvKfOa9lvUnhI52It/mbqTcd9bgd+Z8vPhgF9zHY69x5Y7Vsnc5WFQHySq2rvMG1lLuZHNmBjfbHMUWtWZols+WFriOzStEMHfBCPCJP4Oj79kR5wRjJEVtsW6OwRlncgTVX5+V3a4d6pNOZaCAZILDHDPI7GL6HtTFkF/BMRh8O9fN6uwtsWomtPR+91peW4sfCIDFpLy2d8z46Mt3ym/R2l2vmIB3loIfUP88YAs2EZlKhe/WxZYkrmLVzuZBOAW8u3B2Sa0H/dNxbYe10gt9I1yc/Yjlh27EgNmXC7LQpx9hda/+h7SGNol6rVRkA/JEmwvJJ045qwov6P0lzsanynDnlQIZWxDLhlIXTKGiUqJDwM5twWbc7gkIMNUmGGnmeTXq7IQUOipbMkOElw6n3ktEZuMSIY774GZaNZOHcJjLjV5cVXiZJZinhlc0dqQxrhHjgmEiDET9NPK3s9EUyM2nW1t//B5YcexhpRDCzf/nHH/bJQKxQRYPIqz466EVDx/mE5YYDEO73Q1JEEscXK4MV5BGuBc7nocfiokj5utUOPuxgiZ8YbT+33llPeBBbrBm01EllJFl+o1j2ZKDw00UwQj+7PingwaTRGEyJZFXfNo3tbT4nLMklsjaJ0ZyAQOs4zpsKntpzO0YI/lpyq8LTUzT631/K2FVPp5W0mVsreSae9Y8rx13P613FYKLb6vSZQ/iBQCT799K9E7dsGBK0wum+/D4kcVpC52l4mW+3SidBoPj85WunuQRT07IP4h+42r2jOI0YogQftKW/rzvPnKLvvZ4wq/4dWTEOeYqJWtrD+Qlj7B9iGUpMg6CmTrl+6PSpdTdvoRnlX7w7BviHtI161iNU2mmngEuhk6D6S7yowsB4WjBBMyqtsqf0kjE+emwZJPKCw2otYSzI/eDcYrIvQe1HLrQT5Y7wraCD+qSBLfswUfrGzSHQOLZ2Uqu6GJD0AGpFX3wejV/vUnTgmzltfWywCbrT6pp35dKDrkCsw3p12vgkQRH9j0Z3g6wng2gpAteiba3blx+bdS1Fjv115kqpkm4hCAfovFlizelttxwSGkFrw8p1jT4RD2dqVvnyao7ckgusbBJNEBsBjXq/EdtEnDmoFXCDuoJPhgLHuqsN1DpJUDnkUuv+PxbRj0b2OVv7aqnzJNMsmwGnvO0l4tQmGsYef65j4TXWIY/ReeMG5AjW9A3tjKJsrzX/zhMoOFgy1SyMS4K3SKddAkGtZz/+LW4jwbG4p7lenePxPa+UFow8k1GfZenKFQBCWAZEqsM1WhuyeQ/raBvbxFBPLXbIgUK907qmTPZZZxSQdgo32NffA4+qkxxLQisf4LKwkbr/qy7t8XHhx1zyRPWQNjr/36zqSyrc9Qt9euau3FAW58feiu92EahrhYw7Kv0kHxVIzAEIWLGHTZjeMlATVFf7O0YJK64cWdEbOD34jImPrP/OUC/muJ+uddyiMTzZXaCioNy4kxR8+p4mvACSrYexsqShUQPCGMuv+Kctl1+KZtIEePLDxA5otlzMRF50xLcqp8UO4SFFMSOA7jN0To7c+7L1Fow9dF6inFf78lWlprkPOBXCguMsS39H8peB4GmJsSDVkbs31+O73x/nEupM2W70uhYzDDWdzx+7d+oJ6J5CA1dLsm02S9hrlPJ8FFxmtlq3OS4V6fQ5iFQDB7olXrTdtUpbuoPYk4ABZwB/+zTfDV/6El0LkqMdYRoSCqh7EZK0hz4jI0dvznWG5M8OyhBpqeodugD2zLmCoJzv8WEOPlgFecBX1vdVKpgln1ZTlzMDvOgfyzfRdTnk+RpbVElYIVqsRCsj86wIqPf+FnwjjMJBg+9kFivnlGvCjmLJizyWYnn+nAyAeg0UVDzeR6tRK32rYoH02WeoBe4gBckWrKTvRC6VGB4q+7UXWP6fxJACXFjUbUC7LxRE7CVm7fjw7r17qtHcTk6dPnldGmIcSs6UUof8JZVQL9Cvy9MMz90mO13MH55dslUmGHu+u46QAGxEaW9QEil+mMORHtXIul5J33o1c36TiDfzyuF+buVjrsZYGLT7UEQMFK0s7RVZQZt9cF09sHfDXvO4l4YIJ/IUOpBzT1o4P4NLkb1ekfZpkwYJI+2cyieImosAm3jlFuLt0sPCGeJdwTfhpPdYzJD/HfNCrW3BbgqUCTBOGJewWL2nLs4B7AMTj/YQySypzjOP7/nw91PixZ3t/AvBOYbhI90uyqiNPWMsOnNtDpupNcBg409pZoCj1OO1uzLJ5Tv/dPNxgr5B3kVrn4JoIPYMHb12oBlzBakTa3/kBZ/y1CXnUZb0sTb/ZiQhzWZCnOCml7kIiNeuJvRnCZB5oDjXYrSUr4zQNJPtdEXUBov4FxpJ9DLJ1+zcF1//Koz3zbRHRbI4Oy+B4eQ/mIiUz+82k3x5CnDs8MW/Vfy+SMnDlQfxMLvz1PKC8bBYwcGtKc06+uCKypF0MzvAT6PfDWBmfolP7NtEWsRJvG1QmyHGEngWmC8bX3k44RHaI3Y81mPHBGv5QqRCT+WgUjtC/Pl0y78xQD22bf3nSnPEtRmMLK0B/4uBxCQqA/B7VmXGmsVaQ+74z2PSO7Hu07XWLnSWn9Z79e6Hagzst1X2lXRPvKOctJE0IvqLwq7aGaUS+NOeaubQ1A+4GLrSEC+RvDTJsX3zQZff+v88C1RHjpoCEYoCAAvBS34w7m62FTJ2/H6g2dcCPtsPyyjmrLQ1eI0/Mm7JBcEEeLXX+EvBlRIKzdZS78a25C65f/xAP9Xxe589Esvrvt6nbbqtoDW1YH6skg40Oaa3N/MY8XppDIu8NW1sdfNzw0O4kZOXuS/onuA1vBkNGQvL1FSFYMA/v7rxzQ3nvFXLMKBpWWy9EbF7NIYBUpAqzLnYTrwoJPY1cy06Zh68aFF5f4tX+I41WzZt8juhpbAmnrDRz/uEbjVZFJ+kEf6jY9s+TwerqoWf8ZkZwoVobWo/U/AiNeGcpGq1uOpIpTIOTjrBjgqnIMp9/pp8uh6lx6yEaMGgNWW85bAPl6kNYiLyBFvVDaESxP8V1oHAhSo+s+dHKumdWWbZQBUhWt6oGJITw/XfDRV3ciiG1nMLdN0g6Oh+BvtaimczVkgK7gDlyqEl6ktpwUKWA0sV8P/F1kEaMiEC5IKbt9xQj/F3jiMDt0doxp2af2cmh1Gzb90YlcLJygNaclP0C+koH4TWzrtSXhB7hrmuk/VI4CC5vAeDJtV+WeqOGNZJOqQaHHhnrXkcS42a7Z1IJxSvyPOwSbYM2b5bPzXj4PGU+Rdnd9O4IfhuNqyPNMvGrZAmIzQ9OiEKVJGyidYA2wTkkMZmjXIb0PiRFDMl/wMmYFI++9fouu29RVjZW+exOwXNZVr3izev5Lm7S3arM1ZVEXVwcYB2i1bEIDWXc0FAg8Mh5WeAWL9ax5msmni9RSa2C0BhBg5NNNeoPk+kjOXpe8LTU16vkhPqcOja8EBLlct4W9zNzhzB/YvbEKHURuJOuFNn/xZYgnZbpYnY/ROLa/lc4Lfdcmt+S12wrmz7ePd4bhd3kI+mC9XeHmgpoHAAFxrdwL/coqxHfrF7NL4NcGrdG77yBQuavq5Vn2c+G8j8gwGbI9HIosAduqte2VaqWPnOhUub36Cxr1krZjVRkXMBrlxkmcdXDupL001Nyikjdt8O2XCHbuF6uUeRkYxIoHp6PHxstWBIoBFdh+j7K+tCL21/wYauYXeqQ33YXEL0jRQvA9ZtOxfqmb83zr9LajmLcy69m43lgceXB1WlEo79Fv6CxsoGwbOzbk263JsnMPnU754A7wzjE3SHMGYzxNFkmaB50SHTS2CtvhL8JkqUQXpj9en93vF9VKUxQMW2+4tib4pKdpd3lNj2/wh6z8QOqsyJ80Bftz4YtJMkXimdBjzP3j+7S2z0pxtV7HKv17EilsB27awcOA7FA+xK8e5zIbXjqhHq5Obee/My4IqmrBEmoK6GNSyWCI9t+aT+g1nA87WTEjaMIL1TQJhitTTYzs4lknEMPYmhzZHeAMSpwdelqWSa+w0PX4KusDDJ1x8WsgKoAhpqsDOmGtsTL+RkM2DFzjsvKORgKDBe1pZQtyTClk9Dn4Rx+S35mvm66RvWObiBIeG8nBTR/wHUYthBN90GhebyxH345rAyjtwi8Qgn3Jky5Q6ElyyOtNDkxBbCu9RPWzPD1E44dSVfjBiFap8E9CCZR/Q6duP8YVknMMWb4gN/GxFA2mpUU5xUnjdvIJjC4zShrKEuwDhbzrjZCJr8H1OKQnuL9JFPE+Unt5JsvSqZrVTb1u67QJCD4OwwtjQphMSVSyT6JeM1Z20wE0g6KmUgcg0NF+BEj15LJM0HMpcxDlc9LdQOWXRK39YzXtTUVMlOpCDNG+WGnIuvXK+gMNl5KCe0Y4LW90wHxRTTBtySFEOBrZyRKTlYqcF9V0lKXesqCGdJKPMcueq1LB2uWIg/uUMAV07z1dYkPeXJlB6kWFlzPH/Ys+vrc2KdtuhYBJeRTXf1O2mTFxnL+svPUEVPaQbnCY1JBr/j+aHc0V0BmcoL1hJzxo9VJQtLva/pU0bYmQ0+vHs+8iEsTHOyCiFZHg76ziU25zH4MGFX1PDn6CTBNfhBQhLlxCxdD+cVyKwx+dlSUhwval8OmTGtw05c4Ru0+yACPAF91A6lenRpbgGStT0NgesTuSp/7Is/IUuaAJJMNEXYp6PruGI1yp2CIsVWcO6rqW2X5QIOBJHaiC43vhhBSRPilfyBWnY+ONuTXyrPc3i1sfhrr5K8tG648KCGDGO5YIbIfrfLkoieoHWNfzIm0y6erycurKsldhlJT567Wp9/RKqNEz6/BVyj3tRdKnKN+KYjq3CSmntYYpOYKGTrGyYMhkNXQ9QhYmgETofe0aPJZ2GKkD1lHEaDFCvBHM2a8weJ31KeFCrxuw/x+YR8+ou6chr17J1ZkjN0zcgbAGGrKdrunGiTlIAqFGER7TnywmoRo2QJixP2Y1ORiC8AwgmxkMTFdf2h9gHaLc2O/AGZ93yzMvr+SQ4A+GXv7ZwUMHCTqlSDa3Rki8m61jz+lycERCtzeDGh1lpLfjds1XAVjcLe4Lh2yts1Z28376jg0HbAkANhtOm6u6JTI8ZNcnKn4gSDcJw9MdzZu5pPpFe3Ctp1FO2mBBGNnczdnBT8aY3PRkz0uVj0Sk6Bm5RVChWwqKGzPO7jT9JLa3LU93X6Vre456t497QN2qV9YSwzNClria7Y/wIdTHGaCrsrx27DlG2KWRrUcyGF0ASJ0exr/mZ9s3ZqYus/Y0LftdqRjWgLpFaQo7YBiDf40eDUS9bOwMtilyCpgrc7xJGKHyi9yW9sGx5aDrEHbDg4f5NYPlLeA9qVIJ8safzilZm/7yH7oMwJnnkAucj4VBYQ2cW/TdbSlkkHgKt5eeVwpUX8UnCE3h+qBkXkTJfI+V5LA9Yy4bxcLxWuEnM7HZGgXaIbCI1vxdZFeicqkDYqd93nRNuNbAM83pgiUL/dF8/cjZKBpulUxSeIia1hL2JcihgzJPjfNRiZkOZ3tCFr1w6z3YubNldhIST88R+BCmB+lM26/ti91zH3sSCaNJ/Enpw9qsRMS3AFWnJk+ac4QM5vbwdhVIThv+hCKScZwlNvH7fb55yot146A1jZGtph1+rpVEbpVL53j0S0jUDBQMzB2Z9kdl0WMlW9UEK2YgHjmzWk/Xhdviw7ack1OB8o2vgC/Zbzxm6gDXikbJdG1z6kItFrIcv4ZAzCnmqrtPhtI2IZN/O+7OnpDOsGiTSIgUm4D2P2qm452sMH4yBK24hYHBTjpHaEvRxVc+kXu9knii/jGJfal40vhVGX9Fnpuq8zps/7bgunih/8fkb79d+XL25W6d4cITkHGWSW7vSJJNkLJGkfQpCfoZyJi0izIpX9u+Ni5UnguqvoSWzE59ctR8JX/rnClEDdLZFdyPUyp4IKo2Lf1vZ1bu39JT6CoKdIFk6xGbtN8FzgAtl8oDqk+hesZkn/VJ+jC34OIxZ0OpDWG7mn6Sd1o0oJqCtCzYprmRqFmbqN6BYzp1KjpQSDxLbfxfDZkEPs1IP3Y0Fg3ikMwUSlVp4084VwtwSWsV8VS3iTrZ0CVIs3PPk6wwQeMvqozdJuiFbg2MAjXSKR0409EcdeJNDwXaBYEVHD7CkFACCLkW/3OxtyVoEAJ0bfSVRP1Im+IfzP9/cgfxo6ZBvCN1ZEHnTjgO/5eQI0hZaQ6VQPwNC/YSkK+JxywnLw5FbWzuA5SRPGN7csusY8jrk171Iw132nv2EKhISMLq0na/6KqRyCiqETmouPkYu0iqDm8d7x7b6i5Am9W1z6SVGY+shJOtxT9SPpxST/7086vzpHgV73MlpaAVPCJ5KKiG8S3GkCQNQM4My4nlzgcbce2iVL2CRUtc2/OAwEBSDeo9C8bz6Mad39rZZkf6zJp+rOQmKU8tzFr5xIfvFTKFA7cB7QUBYzHOf9MSasnrEQrxc3EPK7U/tIAFIS+kAQa1ORs5TjB+z58snTWgrE34rWbrs2xfjeey7YvWwHGRfgTogiHXuSWbYX0c1eL6g1rdqSrnEiVhNo821IEmtSsG9jIETZ8kVP2ZAcCvm0uRsCF84x0/BgbhZU0HgcxeQwYaSk8YUpRWCDtNDglSemciH24fWQOZUAdSWnLhwEeShKGNh14ajHgW9XvGm39ci6i8F5dEVuqZs573Bud0K5nQKIuFcP9R3FHb0jDmyTW1zM4sYMpHqFw+1u5oY/nNY63gCJAKYQO4/1PGu85Psgzco2byx6hSGkNEhI0riQ3eJGMr5ZZ/OGMlu6RzQrNMlQXzzVtVjyifHEdYoCi+zTffauoYWSwK2Mn+HYBZK272zxkFh2nEDkPH9lPyS4SgbeVlMIXnllhSNSzxqhOLyMrrlCRnMd25+OjsSNtp3Wqg7Iy1aI2hIyRMBN5CnP8D0xGy4AwsisriedDCNS95hp6Qzxs//KzUQttcFjoTgF6PXeaG+HtFIKed5tP7OTcWYzJJZFhJIDV/qMMGTLJxbmOeQYLDxRjK4tLDdAjhERdzCu9MnPS/b5+VxWSk+6VygY5URwTqDyfP2vfJ76FvuKd9GIJduMUNjyz7P7CN45Ifon51C2eKCVBTtdt+jbaip3FOtr7rHJairHQHp4TzYamZ1u8L5tygZLG0Uyc2VpHMkqn6AbBMm0k5tRBmV5oKEMlfr0uTyiRBpNAB3SU9tYRk92bLROz+k+QIIrGaVtYULTURMUhjrYrSevAMnTRDTzksYuqASxLoMExd0yBZabQlZcfFT0anL4SS2Xsvsg2YDCweSnhoYG9NUD4enHmOW+nkTugkWu6ypaHHGrUpmyMHegqKKmmHxxJE7DqsUvO5GqNXGxphXaxK8blrnuwIkm+N3nABhOZdGHua5aQfr4hZiLLEOcxqnvxJK6p/sCwP+PSF3AR8umu3de1/Mo8hhoBedbvTBCPfNt2vH+G5DBu6XHYrZT1xldl97WB8Gswgk1m41nmWSmkQYdjLRw2i/1JB6T+L0pMOZAICDFfRdvwh10e1de3bqWev8A/1JvedIDMzu78R5IzIKQRjqFDLRb7rXRahiElW7RMkwRtYHgd1K1RCqA8XPLNRY124Tki41lVJ7cL5DoeL52e1JBm5KRWqEBsGfmIdlYQyu7Q1U2wKhsbCEJ1GNCBYotPIb7g9VDxCir6kihTEY4ebjlHRWsqq2YUOwbIf8c05AyJscHsuk7tOmD7aljMYkxUJUbDIOqOsBbkZDjdQ9kHi7z5kN+bn4tvHGsa3HILvukPiVOX9fTSSWS8BkJsTOl8cg3eapIXlZrBxfdEpuKVN/a4M82TpzaMpSJgPLy7/l/rTAeUgecl4uoSSRsMDS9Z0UEZaZD5u/UvF6XPTiMgmNOoZMGrMc32sX8icFoCH8eiwlWwo1zu4EDT1hJ2BBAfwL5weRlVpDNzIkyITW3H5enGMbefw5L6p0GajuuMQHVMfRJfy3pDXH7Fho/SvU0IAhEgTwV6cAmiC2QdF4dGkv6tf3qLHUbhdtOnvDqSnxqtrWbN4kSLKK4zZRIIKgUKMhsPbxUaleLt373buD9jTJgcaAW7fgUfc8HAESBKc0uPHh0MZKninEz1ZQKIT/eAj8Yq82B1/mcesUIF3N43azXg7Ym7vz8iqOS1JbZbmukMZMylTiWss+YlXMQLFd9PHBXZMETgILCu1+xm6SzpyyWMau02/aXlSKWPzgnoTWylfk7K4DTKSSHJFD5cZdQo2AjgtVtdyX2UEd79aMrGgH5qmuv36yBBIDaW0yFegUv03QZcVR1vOxBPeMI4uPFPJTDl8RZZDBvsGBLgrvt1M+aEBYx0fBcT2UES9eYRNl1OUybpgtBzbFh+HMawVYJD5R7NIUsDVc5os8NJPX7UhjG/jTNHBKVzxi0owVnZNScC8TwIWnUcGOIRlEOABrZxSHAlLAlVH3RZxF8LXUSNxStkGAB9INtkZKtSc9NQ2MjewD/9eS/0J/n5uV/qiJRNFD7SJJXB/xZcrbwHsjxZGHe9Ex9Xpdo+uRDUKFCmN7Qi/L0gpz6p8ldZXFiiMZ8DuN8iqfJD5FV3jvGpm/hTKzC+CTnVbld6gayUIWxeYlyrHfK3zEjByK4SwOsEthRPrfa2/uLyq/V9Gr0cSf2m8ev7nCdZi+YRk0Zt0SyOLMo1A+nF0RZqWFps73GFQqtmep/CRBAjDUg3z82fJg/sK/VaDb57/cg4JCbAsp2xeYIdOefKpcuUZ+y8C2t932UdUOu4e97g1CCCQ67oaVLDQkvvjrBB3rflaVWn6HE7n9gRQFBu1FHwgxu8GXC+9a0jPqL4SoM6NX4YMlCSxSpC7nocdko/NsRy7NirEhlY43jf+rVHGaeyQXFntxdlMqNZciDUrK5AWv2yQeRCekI2YaIHPWAsx1wPlmrJwJ4J1sE2NS8KqlLE41RemIh+3TKR96S8alUkOXCYGxg1WfViQJuP1OIP8Mb78kTjpsidu/kv9MxU0kASBaOpqh/LpYwR9FdgWoiqySQzw32XD5/veMJeGRUY8N0ME488n2/LxckxXJ8fZNRE+aoTla/PKl4BYZBU79iKM43idAVgO0d5rTPrfFcyCZ9UmsELr6N27Y6VxwYESDKqZYrXr4Co2r26O1UZv0C2/s84o84zcSGCWw7l1Gbq99OW/FGhzLm8hFVljvNcsSboeONuBuJbi/UMNsEN5zRSvHPnvImnKaYK0sZa0FJ5DE+MNLUPbnINb40Y+AqQyMi7Fj262vAGGV7Otk9XsRHm+FU65vkOcm4XE+dbdgVahzYp8p13J1PplcBrpl1JtjTOSoQGtbKHHRif5ut9+1JFocVr3r6Zm8EJrdzp0WgnNGFb4wR+beOcVTHH+0GrIXVnxoJMViHhzamD16pP+PsfZrYZPmDs7wg0nTJZnr+Hh2Hja7K6B4hNag2KtKfopNJID6579rMeTSCaesv9FpRzsQg/3SX4gtDfwHeCz0Piw6QtZl6yL2Y++8eR9IVAXuvvS19SqqSkVR6++C1nlEPlbdxE8YsG0azxP4TS5ppBBnwsGpK7PuUoKGTrRIJy5d7omfdrPFZAj65qLhbhSbAATUB2jf3lu6nfY9WlfwZ+zG8bCKfIx5SGQV/sn05yVF3+hw29LIbIvXCtkJw8LnQblgG2TMDJhYc3VAoqkOg9q92AZCWSj4LG989dGye9ffzsMVgxbFRvqWUDe6RB943EDKocFYliKLo6w8uWVl827L3Pa+XeYsh7sRL2z6OmKZ5LD2P1qB5RrKRXSJdQ6gNSLrAvu7lZO91DKWqBUIOxhVsxmoleMsVolYBKumGj77jDjY+KJnZtC/S2ZWhtBqwt423ANQAPifsE139+0zW6Jo8iC9irGjviG0OeusbBMKvsncvAdd1SqtULf58aBM5WWYkVO23l1V+l7kXxaydoPS8MT1ZKUP+y+dUoejuXlhU2Z6mfLhZkrwVf3mb1PAKI35zDwTUPlBp/svHH7PmSm0Q6NVXnbmJKNYbFWzDakZZJJKk8xi+z/7a224I9rNLzRnwh7MqmmervVu/tbw+fwhAzyM7DT3EB/n/nKTY12ha8GMOXz+Qb/dQRRtfLcqsnrz1meUiaogXY+dVSIBD8Rgj9LGlQN4VF/fxDuKfJ9R9MgzZr49C5GBb2DXepX56j0Mz4cPkR+7y0Ux0fs07JUu1zVcrA8xACUGvmR2EPtIMg+ZuH+BiNhS4QGcDVxyOXKE3npLFz2I7Jl6LuPT8EL0VSpwfRe+EzELY+Z23aVyBD91s1WbC6QOt9H21W3JrExvSIFIR0iW8ADc02M+idLWKvWSg3r4Z8IZ+G5wOaQ2HACe6uH3dgzORXM9PLJcyeJ10rHh8/EaICJO+Ir/fpDYCKJ8ERdW01K7jGh5tSUWGiMp2ONYs7Jspx4WRImqQtCvrWzAyUL0n325rMO6y5k71c4Fwl5LUNMbYWmCaYMR1O/KYHd2ingbuhAEjKAdiOi+enG+md5LIRI323HHHFdPIbqstLPDQJsD4jPrA44BRW63LvocEhDnshNkQ7E9g6F+yzoMbXuV8W2j8RnUqxrii9j6Tp7nDZDXAMOOL2NGxI+0LzNywtcuTTAIpkxEJ5YTCpM0nKJl8q+AyPsuQGcB6jiDI89dV31YW3cRL7TiklgXsp505iFgOf4Pm3RB3PHT9V49DMUHxnRfgmEtW/HGJmtZUgLtB7cbgXY550K7B6oJMdQuM+T9qDNPx3+6nmQOLGNBh8BBBYiBwRs0Wgcr2ZWCOwbaK+BapOlUU3C0JiEeGEoGwu9CTs0biF0TEQJwjbESRRE27ma1JX1ov6F/Eg5zcpRwd+U4+85RkKjx0woqevmQVI5GaTQEagMhMZykFgxaFuClF+TPuPOCWV34RPe68Vlx/Rsxe8T5yjU8DS9uHIzh504vPq34IL8HfjHBBBnBl4uhc8uG7HQKEhnzy4sb7cCT3wnlBpEaEUdhwf8iSMQvrDDxCnuUP9i1K/f1jObcNk2nYFnOO0fHIn3u0J/fw90amFEY9Sq1/JBuXsaJ+WkvpzklLMBb1oIug4892qGAj5Bc2GsR5xjlGh8hmCjwkMbi6ZvNnavlMWV9GK762jInIxXN977V+4HsN/epHj8UCrzz3+tN6k44lCmOr6rYJreYy0jE+AHnBz7qJAB6NKuQHtWoFGjYKr+64R97C+sLQOg3ha8JovyCrVGmVhb3nIFtxLx+VnlObSq6ab7CAAgdkYr6Fo5wYaq/VydcvWjP8U1gOCFY3tN7bG6JnsfUcN9/EFYUC+4jK3VtuVzDEWQzD+qDik7SRUCSeOXPBBP89pCHbhbKI5O307tEQM/JBDBVx+EL3Nufkp7e+ko7+kLzAEV9gGnia5gL6q+agzsXVqkWvj/ohAKNP/qyjMHT+FUy7FSAdXc17hn5QqBnwx2lBf3lbbBN1jRG8ipj6xhdKcW43GUNVEDHEzNqrkJxwRy1e+s5eZWZXpr2Ob4aver6QNJQXjbuyxDQ+Ca7wiuOlAO5Zz9PsdfTcSZu9Tq3sIseqPA2Kmjkdi7P867B30r0xEpt4A4b64z1u5aWdcRl8qMLcjm2/GqPr3oYExEnjb1jf/bVLFWDQEmd59Bp+2HqDMNfyUMoliX2yCSPKUt9Tp3XP/wS52tMcOpccNBB1fSmYxSR40LPueVU/HYDZ2tZlRYRf6BHEbQCw7gVoeBkPOKqgxxg0Q9rV0/pRwb00CO1o9rSuBV9zjtNF8motIHzbcXL9R1UIVHIQPxzOzHPJ43jZqGYIyRdMxmphyMW/eJxjXkHcLF1AhxyvxktffU13gUc/I3RkLuqQPg9E3gfREZc8ruSS3aJuUQPn+MzHftzd9KRxkMmBSBmXlNPrycyBG8sqjY/Bsen9+hcHI7D7JDOJgszBbmhl/axvqzkMmOCPc9LqI7F6Hql3w/6fACYI/1N8mJHy1LMCUEBpZzWhMeErbLgb7Jmvt7HH/F+/y95zrLA+clGW3L1mkcCX/hBd84AVDgqeCRg7EiFw2HLe7zPRpBC+WHXUu1hM9eRm+UpvbcgqL/CR3Od1DpVETI2WVFwwN8KEcrRF1qWCyom5YkDFMNrKN9asYZ+tnyXe3WC0PBLf0S6zF/wc6ti0MBoSb2EWBEy/OGv4PHErhvuABepY7kjOv2x8mN3zJ6DoV8ePt3/UCV2lBdNjTtGWdzMvwqoHyeyPjvXC1UEK3Ahc5h1zLgmXZlyi8UTFu7sqsIOqAJnO8wxhIIpVr+FSv62+zA9/zh1FdUYCQVcf/zr/O3qATDfawXZe8FGiXYJvEOSGyaqLBmX9D4kh5SsSNF5RV5xdzx1C/U9uZMxLFSxFwpKC+g3fVGYfefwsIDRpeD4+yKcSO5Zi+oODJ6QgwcDcxTo5L0dwXyVtgQ6BLYRIXLG9nP9S14JQs1gAIaERiU9WsgT80757DkAswXeBVIjqDESG6OvG1X1alY8R+yvJ82Q+UR4UiaYpkFhvDH0E5vxOBE2dMqfuLD9iuDzu+ajKF8M0UswYPVSrBBROy4I2o64IERsTWzpPl2XhnW4AmCF1FJ+SgbHGIw5z+M4I9BXT/6sh5mOtp3ANLukbc+CnT2fd7nNEvCsiOvnkx7cjbVmUxeb9fymgg+EuNFq4kQ1VRnWjiHeQL5X0UERXhQ9qui9yt/8AjKcoaNuf5gTC9wL/k3q86H4AvgL2urZvyhWfQD5XnXGmgRZuqM6zmSu/pyqtOml8XxHm7rsKpXwpKRZZraEhwq46PKad13sYGcGMBYnfV7/afSCl8rsnkL48sVecxPJPBV9pJTcvtLVttPDpqCqI3j0IeW91pnfMB6tvEJv8tMMWam1GSDb+p6VN20eKQhGsK3dOiUeqNOSUlA1+3dZQ9nd1BtShNCH71f3PDEv6MLCz2Up12fbcMKrhhc9i28zP7AEbSSrgiRm+L+bwf0jJiQTHmyQg3cL1eFUV3IAj7nB7f2DgGXafbum6kegoqk/g7GGRJF0lYQQbAEmeZCQ/bVSRiAyATjLnYHEgEsjYQ6w+10fbpsTnPmQMqVKhEHu0avky7k65G/ukdXSbFGJIw0VAkHtbErx2yHmhEbXcT69hSRPBAX17CFjxhUeZZGr8qEtLUXM8027JcUvOryCtGtEomS4w9ox7oKZTafN3rKlnZeOoghytoElkInHQwAOdL2U0A5akOiJHQxTU62xFCpH9OdFQGAPXwTT3BTswWqAA1B+pn2W7RZ3XSLIwDKbHuuJUMNcN1F/wELWCE696dMbCplPDhY5TDU3XdhJBlp/CKXKdoJle8rbkBHCY4tRiC8UBh2lH2rCkfFQh+8WL/ZirDPkZSd/x4eQsqi6+mELAJ4BJM/AE60vYRDmdhqPq1YbB99NFRXz/1QjQ0foUQyZurSH9IwfPUXiSxFGt6Ts8WQ3zicLduwl1xtSBKNPs5ryPg0k7hKkQyQYYgAdI8qCxKmjLt3PEujkOGq36an/TcZyxyMtZzKFmyuWlGNXqV2GHQlAswxINlVxszf6+BA9zYB1r6ohtYxyG3ELYR/tmj3+ni9BqWurDIw4t7pl1mDGmupvju7Dv6BEcpVEc0kN4iwVkY7oIhISDStAekt/W1T/VJ4MNtY6B6eYv6cfqJ1wzQGgOTIcQfCUNW4S+r+5M4wft7eeeaa8xvZxQxWj9vEOJVpjYRuSby7NaxRLwkd/meVHpCWCEeg9Y50s6ZlaySAzyRwLZP8+K2l70SMR4SVokiFXYXXI0H0A+Alm2bVtA0HOCGb+R4lWXBPNsSVdnNpMNbTAUfbUtmt4nrGgyeznkFN+25VDqjJaabNB7cImss8HgVQIQJ3YZEkA0hgkwYsYTgSFWPOg1XE+wL9Cg49nYaNpELrca5vk8x69ay+07tuwGCpIu3icT0GGiX5asAta9I4SFqa8a5+ZScFLYkmVrXLHY1jjeXTFw8ieJXEzYdaDog+O9Rsw6IVHogeU54hS8gbkp4zeSBjRQWBVUAmpJGVUDueFEdujKA0ro9Sw2eUrkWNbuCISzM9ZGFF5VL0PsDkc0UF6TttSjhXQ/xE7us4LaweVSDLPEoXbwnBEZWYYR+ciLTI1U03nRg5xeICYicfw8xbXXkPcrwXSuHdCyb+Bl75dK46UhcKcgD4Wwj09dq52CDexSYKimDJWCUf+RaWv9liIHFc6TT467dbwPQ/0C7f5DcNKIAn7EHAmPY44QFo8CIS6tqFn1kls4LQaH2mCFNSe+uf/TfDTV92GnhWk2qS/X7bnS3YJ3dgv593VRXGxTCK16PFYuhxhdW1QyRTLEh3+QAfl/1j0TGzxEk5uT5r+Fe4gp2rHz+XxqKvUlGYZE8qgjyohvD9MPaAD09+eT19aZzvFJQcNn702Cqxk28eT2EukqNjWblKGGGd3plqgIW9naCyBQwn8INFwx120EFv5et6m+mMIZnjLYXYv0ko9emcWCxcYgNUytnfEsJyRD0v/4CqBTCenJGW1A3gAerEiTQEezOstS5jzPmRkT/g3X6JBDd6kASQ57PFotK2BktqyXn6PmyWmRwQvAl+zoEMR5EDwpQvlScLzLEe7YUa3h6/Hp2LAMt/nVB664ncy6NZ957FhwNcV7MHHOrW091zWOAEQPxcCJ6pZb8BPTIMhgwRANPtt1sJ/X7a2xqgeOVWWvQEumntZ77ESRXED3BQf7/zOyhROTWX+NSQnjQpKOyAxVx/GqU3AOCc5TWBHWBiTLbIXCdUZGzW/Alya6Yu0wxCc+iPnHI/inmVLUIBxa7Yl/iUgRlunJ7Y1Xp2Lm7Xehl2hf2NYY8zm0phQ3bC9/k6Be9W9wx3tCCkYVD1QE/CqnkvHykOFLRYGE6aJEITLhl6BWDIun6/pvZbnR2P+tM6Ftknofr+DUNGULMcB0jwbzbN6YufgMZCT2oa1zMUkpq/qoMQg9+tHcq5gWz69wN0Z/mXcV0JTkbaNS07cE4RFsC12t10J+ZhuPTV13Rv3Rt1eHyZFktS6aQp91p6pBKYzzbktbtnX2KwN+t59RUvFvlP4sth6PPkETSw7VVa3XfPlEkFrIgDQnAMB+67JRnB3q26cmwQl23g2LbeUII76J5FJm2Cnuhv107De2r+gvJLFK/UyYYg2cYS5G+4F6XtXhPdVeLF8Geq211/zyCStJw53coH3jOFJuESNvKswkyOBeBLgupkQeFCRa+BYi7kgfe6AEjvPX6YoIkQbBm1PrDFiIO+OLp1Dqy4aVIvoHQLHZWnm44+FqwdfErStqMAkgYSHMugKP/2V0/NsuiBTzEeZ/0PGABFg100nyqGfn2MpuC0Sxi03SshqrHK1RVUnUfeYm3eAAuS0BAcF129qRwnGL5S0mlnt7uad3WqLgqvXzsMInndIY9TiCO1RJeBBz92JyqBf9Yc+ZxCPnIpMPd2cGt1g2hh6l7a+h4QScEJeAEbkGMJotUtEfWSxI3zybz6nJmc2VjePok55CUVl/gX+jCzf29a0cWXip2ga9mxohbFnC+/WMMP/XpXvmg2Bl1l8hkz2aCeS1KDyNdWe2CcKcYkVIoSbMdQNaiuZnCAHYNnrPOB/0KKY4C3mNojYBpRBHawXPW0uqJj9W8N+eaFwE/iz69Zy5eeKA1CSrB+/wiqgdPVqBaOEzuUSHqgdL8/bFSAVeI72KcmENEGNUaP9WPYRGM/GlIavgBXYY6hsuS2beXL8uLNWWKr18euNH/x6r+hO8J9kVigCyh3Ejq+zxxMSwzamQJ1aUiVnwLg2ruULd6bFTjzJPspuzicr/DzlRGBMwDp5hrw4rXkHlGhg8es+q9TsvmfO3q/YdzGS+C3MTbkBq/EMk8HIdVA+pI2I22vcxCWapIXrRBjjinL6chnWtDM7XLJG4/CVvJr9qQaYitEra6de5VrUwZ7+IaWKXQTHCMdK73GOQvKMUxvwFgKeZnotoz9PzMlgNGAvjzqLPXXRY2Qh+i0Q7ILqE7kP6ZReNxgzSoHyJ3OUPsKW6HDsHMy8K0dHj+6P7C8jUciONG477MupjIX90/tAlX3Ji+axaB9lCq/wk3TGkI+8OTStoMVWZxGroP9ehkr6871c3EsD6UrM7u8eToD4RvsC6g9gEwCV0BHXW4YAla/8F/oPz+VWHR2hB9btzO4///Xv+gGZrUKdTDa3lbgnhTWMaheuFHNYd9b5toKPwFlNe4RAUIrCnSt1bGMfPCN6viSu0EN3Wit6rvpyjMnhiCSu6zNluhvw6D6YNv1vUsFBkIyABQkBnhi2Ep8+d9IFCtouHG3gGKqQD4z7RrcyZCYUXPKaNWfqJRdN9qrj++Pnr47jhec83UGTemfuiOwMlqiOSJAciva6OITwzs3XcBRgq2ycjdS53i7YZUgesd6JinTYVlMONsn2joZ+YjQN0f754VpaytERA+dJ887h0ZLWvirIJPVhNqsLggLzptqC8/nARIJbpSBfP4Xdby23yQl8S8modv2ODLdPQVJWiLdUwzQs/qshNij4fUWTrURQHa/VAeRAmGFkh6lF9Z5MkebPnt+UCZaGFNtgNucZ/AOJIh5aMxrSAacbKY5no/0qoUpASTdDMR54a5kOGYIxe0ftQ6JJGkbfjhwPuNcpYRnhGwfO8NW7bfrg/x4O6KBRzL4u4XwU6EkErku1nrjb3VsuWm1CvZBZsToiGSF1vqU0ybRiQlc0QwBkYmAZpxI3Xdr66sbU9dd56a6yHv+9/1TXq8+6A5otN3bc1BpiMHYyLrppy/TIhSnm7kfcivz92leE5B/eY9qL5+YPYVWJytES39BRug7g+YHHHVuCnhkkWEqMxl5WBisC3HPd0Ytee6NgSvx3Md8aSSATmbAUmNoiULsnaXQGkOAfjAxkWyIHRgg48s/1TKJBDCHhv+Zy0X2O/SPspgVexDixdjGpo3rBPc30K+hXmf/EDdNhGKzgcifTWp0S+ZKbC5L/FtXl6IN6H+q2TwYjPvAAz/Pl+F7NpM7qnCPONciOECEX1nJ+7Yui3QbYvxhp+2HtKjUfHZy3nNq/qwaa+c8KjseD9/B7yNsnb/4gNnoU1pMsbOC+tkFA5kqSFoC+rnIzcyx5RATqBSWNViB9SVAbwI2q6J2c7PDRwhNYDqZntZdbBjdVQiKvvFh6x5ovfedLz4ErPV+o3gKk7TEzIycZwJZnxHJfZpR+uJsQMn/RZmbVnRqrrIDvmmYnSwrYbeURQ29gOIyNI+tSuqOCz97USTgamJzOiPLA7H48Fsgv55lZZQcVL6lm7KaD13mo4g38FhNh5vLvcXduzxmkXA+UDPC3lS8AuTlDCZhGol4+wOwX4HQbz89WPxcmcsW3+EFjekQQ3qzvBr8loJClzKQbF6bfn1VV7iGyoD9eHKthj0UYxhVzRygDNkbVWxe3OsEHF0d/lSqAhoCuUM1+Vef198TH+OOJpoEn0Ep2zwtL9yIVSyDNQtwv9F7KkQaJrS+ZcKPj94X+52fmzm9ltvIOvx0G55yI0ViBuiCS2ptERPIjUokHRaFHCf2i5959811yveRnNLJjBGysjxK1jVZIV5HbnWQKyAkIfLa2CkweYSlZIAuHNyrpe5iZZw1meQToY0J1LpXKTz/ZYPR/2cjnq6EfSjmRWl8v3+C15Q4xsfLSmIxNRQa6K9zZfELFwEvZ3h/2+za90Rbr9oH5u501wrI6foD82Izbm4ReAdy7ZkmLQY/DytmpNNKrS5W8hk08rR+uyCoVv4q2yKqeQzHBbDEI2kGER2orlN5ao0NWQzIbQAzyZh0+z8Gk/Y00V59ECMy5K8quzHOvgFmXdwuoukrNC5sRhTG5mpSB3V8wVw1lbJaSsQKnLXk2esRj7QvC74IT36zjUXpWN9TFRD2J+1Pm4or3j7HJhXz75XSq3+6DiQHTqy7T83/v5BAq/ProMbt9CI6B7lF6gi/baA6ZByFF4ukS+mm6rsEbBk+uC/1I45uY0AvqQczEL+Qsllxwgu1yJeREB8hlXH5VGDi+X/DnoPoQC1EwqzW5UXUHt+mhjIAAQneLtGBbcQy4m1ZFEqQslSCvhKW9NzX0YpydyRUS/x0RZT59s+EsgcQjOT+z7MZF7zoU+vKzWdH20IST1qRzzZV6mtm4higER8lDSQVvBxDwdaJiREDlo73Om1eN3ExX8yxK0kdoJnArYwsukIcempbVsB4lqiUw9P4nteKGsbwEFSB4g+xbTYdLjS84AKatU+yI79GmVGQpmT0vQNLXRxn2v66ce4bFAo93l2iT8jXKpVcXAFmC1o7zjCWfSObxPWyBmVmqKbqrT67Ld5gdPpHc2YrgTum87C+xTw9r1cTAuMGs4bMsdy7PP/Oj64l6SZsjOhMfyIkCEKXB7dzZ/MXbr3iikB2JNfSWMlWG02G7tOT5C1z0ILEMRNn7e+DT0VQ3rakbqM1sFoeszsOLQyRPcKAC0Yx1SHHO9EBkZVEIP53Jrl9Tc39y7jc7SYgFPYm03ybe+6qXqmxqWsXZyHCxgLoUTjLpQJhDP1bj9GGOZE7dwVxmOc4wSRk7GhJtoIBt46OIT6K4h2gUfWbg6pBUeQZmYVHbfDxYSRVhQW3D8JequJX0riy4PaWb/wYkTDnwCFBoxIr1opJQluTMdP66jGtIbu9KMQek+UF0Q9EfePbujky9GH6WgN9aFhR1KH0HzLJkdtAn8NbOu0KunpNkLJ/GTTjKZYuAl7QiOYGJNY4HoBzgWl3rFpZGN7GFSo7tAeM76G7w7GlOGe9Cbu5xV2QYoWyK1ER/Ofn9Pyy2zOhERcQU9ZFCXxy6Z/XP+r4QwEKjy9VnoXX8MvZK+mxD5lPR3AQIKnuEkZLHzc3Xcg3AD7xZKKaQUfqH6mAJhuY/lBF4/6p0wtGlx3xvvJdBaMSMheIwtUv6X8r1WtAzRdp8ZdRqGTQIpo6kgaS5NTt3QPP8Pbrul9/GHRP7HJLRj4WSX5D+Fbx89yaBob5yOS29g2JOlKI8KhqhWQougA186BGpqcP7t6ajAmUecZIogDwjmZ7tWB5aJHnrkMV3OSrJSuKszoCG0CjJIb7BzKkSeCn+GeypURbkCc+pI6l1l6abz71/Wpqm1lwOmSSk807rSqrhlBirDM/C4O/36SD5apoNr8hAuUw9nN10N+N5f4ZBVgWTdcmMDUdjRxbWHWIVv8SXL9e2jB4ZuZr9iQBplV5xTjdU9QZeodnUv40Zr2c2qveBBtSYN/QtPAvFWiyJNBISWT+kFjaCTq2XzUA9fb3nGDxot5Vuu50BLGl7XBJX7H7Bizl4FSJkehiOVJUUWwuPGzEGFHCCaJ26TcsKbCR/Cg+8BvToL1qxVsX77LhqLJtUdZQFhVqYstpdMI5L1qFr43POJ2rgE+ciVdQjslzvn+n/HQwPGTyCmZbtICacAXshHcY6hL6aF33g4n1mLxqvErzx+rSYm7PYGb/hOJ55DgXNlWzJSJFHdPYnEdv8yvTI/A9BQAKHcESCFraUxvVSfOoju7vvw3xpw5ZNKtUAJXeRrsxbINzAJVoZAPri8k5Cf0P1plYp7iESsp2JTcppazPLDnb92YApHgW+D6C9MQ37QA+sksvcZEzK6p061I8qSL3aTs+/r1CArCs/3pfreFLpiAaIGH9orToQ62G5lEtOd5bh7R2RSVpTo0iaTdTEQBGhd6FxLG5b/nd6MHvqxQfgQzQWrWkahCSjDe4RphJxKB8SfUnh9ywMPonB89YfkRT0pI5wj8FSJ7VVszWaY1YZWcwCC4iEF0+bzU9DWO8csUjex+xNGjmH+1B+GRqwQMTVzFFCbYUWKg==" 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Copy</span></span></a></li><li class="cta-supplier-profile"><a href="/suppliers/Bruker-BioSpin-NMR-EPR-and-Imaging.aspx"><span class="cta-text"><span>View</span> <span>Supplier</span> <span>Profile</span></span></a></li><li class="cta-webinar"><a href="https://www.bruker.com/en/news-and-events/webinars/2020/pet-image-reconstruction-overview-current-and-future-trends.html?utm_source=Advertising&utm_medium=PaidMediaAdvertorial&utm_campaign=BBIO-Imagin" rel="sponsored noopener"><span class="cta-text"><span>Watch</span> <span>the</span> <span>Webinar</span></span></a></li> </ul> </div> </div> <div class="page-content"> <div class="article-meta"> <span class="article-meta-contents"> <span class="article-meta-left"> <span class="article-meta-author"> <span itemprop="organizer" itemscope itemtype="http://schema.org/Organization">From <a href="/suppliers/Bruker-BioSpin-NMR-EPR-and-Imaging.aspx" itemprop="url"><span itemprop="name">Bruker BioSpin - NMR, EPR and Imaging</span></a></span> </span> </span> <span class="article-meta-right"> <span class="article-meta-date">Jun 3 2020</span> </span> </span> </div> <span itemprop="location" itemscope itemtype="https://schema.org/VirtualLocation"><meta itemprop="url" content="https://www.bruker.com/en/news-and-events/webinars/2020/pet-image-reconstruction-overview-current-and-future-trends.html?utm_source=Advertising&utm_medium=PaidMediaAdvertorial&utm_campaign=BBIO-Imagin" /></span> <span itemprop="performer" itemscope itemtype="http://schema.org/Organization"><meta itemprop="url" content="/suppliers/Bruker-BioSpin-NMR-EPR-and-Imaging.aspx" /><meta itemprop="name" content="Bruker BioSpin - NMR, EPR and Imaging" /></span> <span itemprop="offers" itemscope itemtype="https://schema.org/Offer"><meta itemprop="url" content="https://www.news-medical.net/webinar/PET-Image-Reconstruction-Overview-Current-and-Future-Trends" /><meta itemprop="price" content="0" /><meta itemprop="priceCurrency" content="USD" /><meta itemprop="availability" content="https://schema.org/InStock" /><meta itemprop="validFrom" content="Jun 3 2020" /></span> <div class="clearfix"> <p><span name="summary">Image reconstruction is a fundamental part of PET allowing to generate 3D tomographic images of the tracer’s spatial distribution based on the position and timing of the detected annihilation gammas. PET reconstruction, common to other imaging modalities like CT and SPECT, apply algorithms whose mathematical details the general users may not need to understand. Nevertheless, a good knowledge on the performance characteristics of different algorithms can be highly beneficial to obtain reliable quantitative images more efficiently.</span></p> <h2>Overview</h2> <p><span name="summary">This webinar will be a guided tour to PET image reconstruction for the application-oriented user making sense of the sometimes complex reconstruction concepts. The webinar will cover the Pros & Cons and FAQs of PET image reconstruction, from analytical (FBP) to iterative methods (MLEM, OSEM and MAP), from the importance of physical corrections (scatter, attenuation etc) to reconstruction acceleration and noise reduction. In addition, the webinar will give you a glimpse of what to expect in the future with exciting insights into topics like how MRI can make better PET images and how AI can be applied to PET reconstruction.</span></p> <h2>Speakers</h2> <p><img height="200" src="https://d2jx2rerrg6sh3.cloudfront.net/image-handler/picture/2020/6/w.jpg" width="200" /></p> <h3>Dr. Charalampos (Harry) Tsoumpas</h3> <p><strong>Lecturer of Medical Imaging, University of Leeds</strong></p> <p align="LEFT">Dr Charalampos (Harry) Tsoumpas is a Lecturer of Medical Imaging at the University of Leeds and Adjunct Professor at the Icahn School of Medicine at Mount Sinai, New York. He received his Ph.D. from Imperial College in 2008 and, then as researcher at Imperial College and King’s College.</p> <p><img height="200" src="https://d2jx2rerrg6sh3.cloudfront.net/image-handler/picture/2020/6/ww.jpg" width="200" /></p> <h3>Dr. Josep Oliver</h3> <p><strong>Senior NMI Image Reconstruction Expert, Bruker</strong></p> <p align="LEFT">Dr. Josep F Oliver is working at Bruker corp. in the R&D pre-clinical division as a senior NMI image reconstruction expert. Along the last years, his research has focused on the development and implementation of image reconstruction algorithms for imaging systems based on emission tomography; specifically, PET scanners and Compton cameras.</p> <h2><span name="summary">Key Discussion Topics</span></h2> <ul> <li><span name="summary">Fundamentals of FBP</span></li> <li><span name="summary">Fundamentals of MLEM</span></li> <li><span name="summary">Improvements to MLEM</span></li> <li><span name="summary">The importance of data corrections in image reconstruction</span></li> <li><span name="summary">Image reconstruction for quantification</span></li> <li><span name="summary">Current and future trends</span></li> </ul> </div> </div> <div class="in-content-ctas-container in-content-ctas-container-bottom"> <div class="in-content-ctas in-content-ctas-bottom cta-btns-cols-3"> <ul> <li class="cta-printpdf"><a href="#" class="request-pdf-download" onclick="return false;"><span class="cta-text"><span>Download</span> <span>PDF Copy</span></span></a></li><li class="cta-supplier-profile"><a href="/suppliers/Bruker-BioSpin-NMR-EPR-and-Imaging.aspx"><span class="cta-text"><span>View</span> <span>Supplier</span> <span>Profile</span></span></a></li><li class="cta-webinar"><a href="https://www.bruker.com/en/news-and-events/webinars/2020/pet-image-reconstruction-overview-current-and-future-trends.html?utm_source=Advertising&utm_medium=PaidMediaAdvertorial&utm_campaign=BBIO-Imagin" rel="sponsored noopener"><span class="cta-text"><span>Watch</span> <span>the</span> <span>Webinar</span></span></a></li> </ul> </div> </div> </div> <h2>Other Webinars from Bruker BioSpin - NMR, EPR and Imaging</h2> <script type="text/javascript"> $(function () { BlogEngine.initItemImagesWithLinks(); }); </script> <div class='item-images-and-links'> <div class="item-images"> <div class="item col-sm-4 col-ms-6 col-xs-12 item-image-cols3-left item-image-cols2-left" data-item-id="0"> <a href="/webinar/Dynamic-Nuclear-Polarization-in-the-study-of-pharmaceuticals"> <span class="img-wrap"><img src="https://d2jx2rerrg6sh3.cloudfront.net/images/webinars/ThumbImage_5.jpg" alt="Dynamic Nuclear Polarization in the study of pharmaceuticals"></span> <p class="item-title">Dynamic Nuclear Polarization in the study of pharmaceuticals</p> </a> </div> <div class="item col-sm-4 col-ms-6 col-xs-12 item-image-cols3-middle item-image-cols2-right" data-item-id="0"> <a href="/webinar/Potency-Determination-by-qNMR"> <span class="img-wrap"><img src="https://d2jx2rerrg6sh3.cloudfront.net/images/webinars/ThumbImage_7.jpg" alt="Potency Determination by qNMR"></span> <p class="item-title">Potency Determination by qNMR</p> </a> </div> <div class="item col-sm-4 col-ms-6 col-xs-12 item-image-cols3-right item-image-cols2-left" data-item-id="0"> <a href="/webinar/High-speed-spectroscopy-New-AVANCE-NEO"> <span class="img-wrap"><img src="https://d2jx2rerrg6sh3.cloudfront.net/images/webinars/ThumbImage_8.jpg" alt="High speed spectroscopy – New AVANCE NEO"></span> <p class="item-title">High speed spectroscopy – New AVANCE NEO</p> </a> </div> <div class="item col-sm-4 col-ms-6 col-xs-12 item-image-cols3-left item-image-cols2-right" data-item-id="0"> <a href="/webinar/Is-it-really-pure-honey-Detecting-adulterations-frauds-and-quality-issues-by-NMR-based-Honey-Profiling"> <span class="img-wrap"><img src="https://d2jx2rerrg6sh3.cloudfront.net/images/webinars/ThumbImage_9.jpg" alt="Is it really pure honey? 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