{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":61446,"databundleVersionId":6962461,"sourceType":"competition"}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Kaggle Competition Notebook: SenNet + HOA - Hacking the Human Vasculature in 3D\n\n<font size=\"4\"> This notebook is a submission for the Kaggle competition '**SenNet + HOA - Hacking the Human Vasculature in 3D.**' The purpose of this notebook is to explore the given image dataset, as well as compare and ensemble popular computer vision algorithms (UNet and Attention UNET). </font>\n\n## Introduction\n\n<font size=\"4\"> In this notebook, basic techniques will be explored for understanding and working with the competition's data. UNet and Attention UNET models will also be compared to see which may work best for this 'semantic segmentation' task. Semantic segmentation can be thought of as highlighting a picture based on different objects within the picture. </font>\n\n<font size=\"4\"> Information regarding the data and task can be found in the tournament overview. A link to this page is below. </font>\n\n<font size=\"4\"> [https://www.kaggle.com/competitions/blood-vessel-segmentation/overview](https://www.kaggle.com/competitions/blood-vessel-segmentation/overview) 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"}}},{"cell_type":"markdown","source":"![image.png](attachment:245edf4f-fd41-4535-a2eb-f65bf53c6fa9.png)","metadata":{},"attachments":{"245edf4f-fd41-4535-a2eb-f65bf53c6fa9.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0sAAAKhCAYAAACB7DUmAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABhaVRYdFNuaXBNZXRhZGF0YQAAAAAAeyJjbGlwUG9pbnRzIjpbeyJ4IjowLCJ5IjowfSx7IngiOjg0MywieSI6MH0seyJ4Ijo4NDMsInkiOjY3M30seyJ4IjowLCJ5Ijo2NzN9XX2Jc+BJAAD/OElEQVR4Xuy9DVhU173v/+01dzw2Q0iHaiExoAbUAOYEyRXxgUpEaIeQDKXi0StXL5x4wtGCscEkxSQVG19iRCyIhWKlcqShmEydhIQEgpHgwWAETAQShUTgaOAJZVrCVP7ODWf+a7/MzJ5hz+w9w4CarM/zjO61197r5fe21tpvfM9EAIVCoVAoFAqFQqFQbPgf/P8UCoVCoVAoFAqFQhFAF0sUCoVCoVAoFAqFIgJdLFEoFAqFQqFQKBSKCHSxRKFQKBQKhUKhUCgi0MUShUKhUCgUCoVCoYhAF0sUCoVCoVAoFAqFIgJdLFEoFAqFQqFQKBSKCHSxRKFQKBQKhUKhUCgi0MUShUKhUCgUCoVCoYhAF0sUCoVCoVAoFAqFIgJdLFEoFAqFQqFQKBSKCHSxNEl8/XkbWj//mk9NEt98je7zbej+G5+mUCgUCoVCoVAoHuN7JgK/Pfl83YbyP5zGIJ+0MhOB/ysEIQuCEThzOr/v9uXrd3Ow5N9P4Aam45G893H0ZzP5HE/yNd55Zhn+/bUbwPdjcPC9Umh8+SwKhUKhUCgUCoUyYaZ2sTRwAunLcvA+nxTjrqit+N2+TVh2G0/8B/+yEUuePs1uT8+swmdbw9htd/ivd/NR1clthyRvxU8DuG1SC3Qbl+GpemZ7On7xejuedr8aCoVCoVAoFAqFYsctt1hi+X4Ynn71P/CLRbfpXaZvBtH05+M4+49AaNY9hsA7+f1u0Lo3CD//Pbe9saoLOQ9z2yyDZ1F+/EPcCHwc6x+7nyyZKBQKhUKhUCgUiqe4ee8sxezGuS+6cIX8PrvQgg90B/B01F1c3vU25P3yMFpvcMnbjjtmYtm6rXj63ya2UJJkZiTWb92KjXShRKFQKBQKhUKheJxb4gMP0++6C/ctegy/OKrD3hh+5+eHUVpr94GEb75Gx5t78e9x4Zg7L4j9/XPcZux+sxNff8MfI+DrzjexO+On+Gf+2LnzQrFk9S9Remb8W1MM444PXYafP/0HNA3wB7AM4s/p5vI24s9Xr+KdHA0WMun0E+z7WIOvbeTzg5D+Gl/X+b2WfXP3trF3hQ5tXMadNy8ccRmHbeth7sKRPPNdJYbS1dz5u88zKbt22LSRMHAWpU+vxpJQ8zFMHXuh67T/6EQbdrP5zG8vWpm7YoUbLectXLYRhxzIi0KhUCgUCoVC+TZza30N747Z+JdN/2q5S/LOex/CMrX/hlmUrEbilj/gHcFX5r7+vBalWzTsBxX+S7BgukEWJz9PJAuj2s+tZeAGBs+TBdH6R5D4e/5FIJ7/+stmRNsff30QrX/Zi3XLfkoWKOK3uTr/8Ev8e2UnKdkFviELpf+9Hnn1g/x5X6O7Nh/rVjKLL3bHhLhxPh+JK9dj91/aMHid38nW8Qc8lbiMLOAcVXID7xf+H6zLP20578bAaeQReT31tv0ii0KhUCgUCoVC+XZz6306PCAEy/hN1Hegm98cPJmLf3/tc3ab+QhExfst+LjpHRxMD2YXVzfqc/CUZaVxFX/e8wf+3PuxvvR9fHyxHR+/X4r19zP7bqBj7xG8Y57/D5zAr5+u5RdJd2HZ1gOoKC/Hwa2RJMXwOUp/+Qd0jLt7dRrlx9pIe1Lxi8xN+EXUbH6/BEfzcWh6CnJLy1FR+mv8C9smwvXT+PUrfDt+EIlfkDbkJLA5LD99nhxP9q02Hy/GDe4Rxg52sTMdizNLUdfUhLrSTVj8ffYAvP/MEzhku1bkOY5Df5gOzXMHcHTfJmiCzcvWG9D94Q38F5+iUCgUCoVCoVC+C9x6i6WZsxHIb+K6+X5NJ/5cwH1dDrP/FWVk4r8s4C7c5Xs/NM8XIYf/ClxryRvoYLcGcbWN3SAsRszS2bjrzum4KyAGOWVVqNCRxVPLDjzCLh6Ajr8ctnx04r6t/4GKzMewLCoSGrLQ+N0GfsFw9QTev8xtCgn8tyqcK/81nt66FU+nR0LeR8Lj8fJ/7Mb62Egsi03F3rLdeITPufHmO2hiVkvTZ2MxaUO4YP1134PkeLIv8Af8DhG+rv8PlJrXjAl7ULY1BoG+MxEYuxXFOyzPOOLQibNkCWTPbGwsrcLBf3sMj6zaioP/sQc/5XPQ9jm6Xbp9RqFQKBQKhUKh3N7ceoulb/4/fkPAQAdaLU+OfQ7d4Xzk5Zt/J9DK5zArpG729Zr7sXiV+a7ICaRHMO8e7UXpa6fRcWM2wh8gi6cf3IXpdzD5g2j/yFz4bKyODea3GaZj2XNNZGHVQn46bJzP77YwG5rHw1z/uMJjGjwiXPDMjsRPIvlt1KKDu4HmFt2fvMlvAZqE5fydMY6ZS+Mtd+1u1BNZ8dtWYhC5WNCbH8zGffwmc7fuK/rHbykUCoVCoVAo3yFuvcVS7+do4jcRef/4OzVXT6O88DAOCX46y10k4P8bY/69Cz/ddggbzY+Rse8e/QG7n9mIn8ctw8KHNHjuTbH3dgIxU8Vvmpl+F7uwsi6uhIgcLwe/mTaLGGZRZl1x3cANtg8TZ9Ys21owOxAh/Caufi1yZ2k6vF1e+VEoFAqFQqFQKN9ObrnFUvcZ86N0wH1LQwR3NngW/SsOlnPv7oz/bcIy8x2bmTHIqW7Hx3VVOPibTVgfH2x9fO16J/685f/g0EU+beEr3PgHvzmZ/GNYZKHieW4Y7GoZ6LbeTfo+XRVRKBQKhUKhUCjOuKUWS4P1ufj3XPNtojA8kcA/EvfDQATy7xfhb3chcCn37o75F7ogEKEhIeR3P2ZNI8d8cwNf/+1r9nfjrvuhWbcVucU61DW3oCLd/BLQVbReYp7Zm4mg+eaFQyd0Z2yfgesofIT/rHYo8gR3sCbEXxrQJFyU/e1DnOZfyWIehQsJ4Dfd4L5A83tJwJ8bWm0WZV9/dNr6B4FjQ6zvhlEoFAqFQqFQKJRx3LzFUnctDpnfO3phMxKXhWLJxuOWOx+Bmb+yfiXujjBoNvOJq/l4atdp/Bez2CCLosHzh5H2yDL8c3g4/llDzmcelbvegBeZNPkteeRp/Plz85Lha/xXr/VvBt33Q+4xtcWPb7IsHFpzN+PXb36Owb8NovvNXDyVzz+u9/0URApfZ5oI14/j39Py0dT7NQY/P428J3+Fd/gsJDyORxx8JaLl/bOk/Z/jv5zclpoZmwINv7C8cSwXv36tk/SF1HPxOJ76VS2XgelYn2L7PhOFQqFQKBQKhUKx5eYtloTvHlXUomPAvAKYjsXbqvB6pu2HE0LW78Ev+HeQuo9txI8XBWHufOaPzOajlf1M9v3YeOBfuXdy7lqO9Zn84ur6aTwXF8rdHZr/CJ6r5+qZHrsbT0TxNQT/K17eyn2CnPmARPmWn2JJ+DLEbTEv3qbjkd9swjJhgybCqlT8S+dhrHuELObiNuKQ5W843Y+nNz1ms4gJfPAxixxaf7ceP37kCeicfQDiB/HYnpfCL/4+x5+f0ZC+kHo0uXif/9tJgf/2H8gx951CoVAoFAqFQqGIcss8hnfX/cH46bpfo+KDFrz+72G4y/5jCneG4emT7+Dov8VgpvmRPJ67wlKxt7oKOQ+bFwBkwbVVh7p9qVjsa7co+P5MPLK1HI2/S8F9ljqYv0dEji/9Vzxid/x03xg8XfE+jv5M3kfBZaF6HDna3fgXy98xIvUEp2DvOzqyIOR38NwVvwN/ek7Y56vo+Nx6d0yMmT/Zjderd2N9mN29ox+EYf0+HV7PduMLfhQKhUKhUCgUyneM75kI/PbtA/NO0gh/N+b7d+EuiZn/ja+/5r8wNx13/UB6mWA5fho5XqpwuZzfi7mr/8Bt/1sVrjzH/XEotq5ppA93sknHmPsso782/ONrfG1kNuT1nUKhUCgUCoVCoXDccl/Dk8UdzMSf/6S3jPn/9Lv4Y2UuFizHe2qh5AS2LqmFEoO5z6426U6+L3ShRKFQKBQKhUKhuMTtuViiUCgUCoVCoVAolEmGLpYoFAqFQqFQKBQKRYTb850lCoVCoVAoFAqFQplk6J0lCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhFh6hZLY0YYhg0wjvFpO4wkz2DkE5ONkdQ1ym97EraP8jvB9NmRPChu4KL8GYx9zWi9ZOBTtzdGw21gT27oyONIxCJ3+PbYkQH9Tc3o/Xa4hDiDbTjbpucTEkyCrUw6Y3q017dBP1ltnuzyp4JbIQ45xEUfHBXMZ1yxbXeY7PLlMDaA1opClBZWoHWQ3/dtwy7u3PS5IjNnNk/Q5cZEck5/ZzO6+uyO5c8X+93KcXbqFkvTjLhYuBqphW2wD1HG07uh3lCCy1MUu/TVOVCXtPEpDzJYg51JJWjnk85pQ2lSDmq/rc5+M5CUvx69TW3otyyUDTh7JBfbKs6RrdudAdQ+m4TSSTBrj+KSj3gCI/rbyMRDOL6zbfCk77luR8ZrbWjtvsmTDsK4dgyfQ3luLiqbvy2rJXufB3qr9+CZPTr08mmneNxWpoBuHfa+tAe13Xx6goyzEQ+Xf1OY8jjkAi76YHtJkmU+45JtSzJB35kUDGjITceBS74IDQ6Ej5Lf/W3DJu4wc8WbO7azc+Zna4hFEGTExP7aHUh/bDW27S7Enq2roU7dgYZrfGZbCdSkP+N/t3acncLH8JSI3JSN0Jo9qOzkdzGMdaLy8BkkPvUkFn9bDZ9yi9CL6u1laBnmk8Qml+98G/U7V5AtyrcTPVqObEf1F3xyUnDdjgzNZdhSffOmHGbGtcN7BZ599208G/tt8Qh7nwcC0qrQWJWGAD79rWNBGo6/X4U1C/j0BBlnIx4un2LHBHzQs7Z9K/qOASNDRkQ+noLI2AgEzOB3U24d+k5gZ54eyUffxvHjx3H09bdxNL4PO8vPcDdKHs5E4/vv2fzqX9HAb2YIFsxkS7glmbaDwG9PPjPmYtFdjdheaUD8T0PgRZZqvcefxc6v12HXvz8Is93rmypwtPgwKk5dwuiMeXjgPnPQ6ENDYTl6Z0Vgzg/4XcNt0JZ+irsj5sKLJPtrC1H3dQC+f6ECBXnVQEQM5tzJHWpm9NMaVA6HIfUHF3A0Pw/l4+rhMFyqgbb49zhw8jxG/9sPc+apoBAuL4f7cPb1IpTsewMfj3pjQYABZ7TD+Of/G4G7yblH39VjwYOzoeAPx1g3Gop00AeEwU85gI+OXcDdq9SYz1Q7NoD2kxUo/u1B1F0YxtiPgnD/Dy1njme4G3XHi1F2gNStH8MP7w+Czz9xWYa2Ezh2cTrm/rUGxQf2ofeeZIT+yO4cpr2zB1FzxCo7jBnQ9W4FKv9Ywskes3H/PG9MY/JY2dfCEOSHq9p8FLF9voPIhJwraKb+vA7Hiw6i6K1PWJnND1Lx54sw0AxtWTkqCpk+jMLrvoWYxeuK02MQftSnZfvw1kejmDFnHu7zFlTmRP6z+EMssHbyGj78tBsD1wfR8w8/hN/vzdZT+Tm3zWC4cgrVRYfxyp8acHVYicCQ2Zhh1rkTHdmXwwT19soSfHqn0FYd64zBad32EF31vn8CZYc42dz9wDyMnNZiOGw9ltxjPmYAra+WoPRwORouDcNr3oPE7rgszka8sOibJqc+4KzNHtcRjzO/G9/uaxhTzcN8P7FRk7HZSrz3cTeuDg2jf1CBBaF+UBguoe71KwiIC0JvVR4KfvsOPmPaZidvSf8XINS/VBuZYytqL+DTnr9i7Koe0xeGYBYvU+exj/jf7On4+Dhp89tGEtqCMMOpz3I46od4O8RirFSskasPcRzbvUjMEfEbh3HEgc+zbW4wcrbAnM/6SRmRYSGx4WFc9wqwtt9sK+Y4LYIzO+F8RGQ8Ytpc8jsUl5/C1WkBWHQHGYve/bu8NpnHvJAxfHiE2MIfSBlGFebO9+PkxuafwRhvV6xtvn0OreeEv3/gHkvcd1yXqI3csC2fwXM+64CpHCt4HI1l4/vj7vzBkV+N90G2rGN/RCmxly+Gv4f7BOPuVx+W4y3EI32pn61t8/Z/xkbv59CrCMEDfvzJE/UdgmTMkpgzjMORXNg2vYH/NMfzToENWyC2XFaGy15Edip+F8HQXIFjF76PRfN5HUrZq10fORvj5MbpX2R+JYJj2RCcjM+kgYK4w8wV64B469ju0jxLLk7iADtnvjIPax9diBkSMdE4SuxzaTwiSaa5TXeP9eHIBwr8nDmf32dlADW78jD2v19AcpALMWGKEbrulKBKzMbm0TKU1uuJjHQoqlBi67MacHZtRPvhVKwr02P+o2nY8GgwhsrSBY/uDaFdq0P7EJvgGO3Dh9oOksMx1KlDdeEOHLjoi7j1GocDHLrKsDO/G3OYelb4o+tgOp7RDvCZxBirtyHlhWYoIlfhF6uWQnE2B5qtZKHD5xOPQdHGDJTrw5C8aRWWTqtB7sv1+CufrfCdjv4jr+Gs4OkFY1MVdrYp4efLpAKQuCsN4ezcWo+G7enYe0GFxOzd2BhpgHZrDqrNty3tGajB82tz0KpcytWtbEbu6izU8c03XmlEeeVuHPizEaGrMhF5L9nJtjcL2lFBe3eUodoiO6YNq5GhNWD+qizS50D0lmTgeZ1ZJpzsy/fkoUUVS8pYAeXp/Uh96ZTl0SND7Xak7GuDz6PZ2LVpKUa0WXheIFMbOkuQujYPXTOXYkNOGpaiHls2F6Kdv+XP6rFsDw6dU2E56cNy70Y8v3EPGszylJD/OBQkoIQHwwdKBAQvQfg8zjCYeso7eeshbcrIqMJQeBp25ayC36U8ZFlsz7mObMphMaCnUWCrdjoLNeqQtXY3Ws3Cc1q3PWQhVpiO9Eo9q9/kSAXqXtiNDwRXADHahvLUdJTqg5G4Pg1x93bhUHoGtFe4bNZGdPud+oBUmz2uI4KU35nbnV9pQChp95pF/aj8JalDeLfaghJziM4DiKp9FhCdL7AOfsxAXpl/HCMLHscG1pZzkSGQt207wmDUZRHb5q+MiSDUv1QbveYtQSjTqFnBCA8PhA/bKLvYx+iD6HhLZR97DqmB9b+iHftx2TcWGx4PI72T8lm+H0R+xnBOnjhl7Yd4O+xirJxYI1sfIji1ez7m7MhB3TTeBkd1yNhI4oTAbxzGEQc+z7a5sY+rY6wT5WtT2cd6lj6Rgw2RwAfPZaC0zZGmbZGyV9ZH7Mejazo8s4G0+V6ix/WPY04344cn5beJHfNOIj+Pt9+Uh6Cv3IasI7zQ2fxG9PCxlNFzeLj5FwafgXpUdo1gOpMpUZeojdiV71mfFWGqxwqCs7HM3B/58weR+MH6VRaqjeY2nRLEVlsfZNqiydJhJJiUlbUKAX3FSM0RzEUE2Ng2a/8C3S8AWonehsxRcKK+Q/6VM19zNmcYh7N4w7fJEs/D/e0WSgy+8DLWo6hWaFx9qC2pgMHbn+25LHu19JGDsbEPr3CtZvPt51fj4GVT2AW/FYxsAtEvjOcS47MzXJpnyWWCcVCIwjcYi8N8BWMtma+caUTAg0H8PN8W4/kTKB/UYG2sWO4thOlm0FFsWhefY9qbozZlHOvldxL6T5q2xWSbaof4NMNIo+lAfJrpdfawVtOhmFjToY/YHA72nALTRT55MT/WFPVCvekGnxZj6PVMU1T8LlPTCL+D4Yvjpgyyr+U62f6G1BOvMR37hMti+abX9Hqa2nTgA67kL19NM634TaNNPT3H0kxRlrbcMDX9Jta07fV+NsWkW3aT/r4q6K8Fpl9Pmt68yicJI/29phvf8Ak7LuarTSlFHXyKg6l7xW6uPWz/1h019QjOF2svs8/aXtLC3i7TlwKZ3Hh/lykq86SJUwcn+73vCQ5gZZ9jOv13LsnIPqvK3F/CUL/pS0aeYtwYMvV8IVQ0ke86ten3rVxqvB77TW9uijXlvsvVLy1/MZg+ZJreFDSRrSefq5SV28sfstss14dMXw6Za3CuI2E5HFx7zbZ6uUhja+sEdh9vD87rtqO3irXVcfYr8I2Rt7PHyYfdJ7QRZz5AkGqzx3Ukw+/EbJv1h2Jbf7BiqwcW1m7XmV79jE8zkL6nmdv1TYfpmIbEnC/YHA7zPjH3JYyzI4k2sscI7WXobdN2e33w+zh9iPgfwanPsm22k+f1DlNtQZXpIn/OuHbw9Zjl5U6sca4PW9jznfpcrJ0NjpC4KoijEnFEzOfZOjeZZXTDNNTVaxoStL/n2DrTigK+ANZWbM+3IMNex/sIPw7Y9Inb51qb7Oz3owLTCk2x6TKz7azNV6tM2+JJnjmOSdVFGGcjwvInxWftuAljBVOmo7GM7Y+z2CkjfjBx1NavbpgunywwvdnKFGrrg6brvaaeXkFl18mciMi/9isuOS72mO3IhhFTy8sa0zpynEUOE/Ud1g6k52vO5gz2SMUbs25t4rk9TCyPJ7o12xs/Xsqd24nJkJGxeS4nZs/jYGK3vWz6G02vFtebviSbUuOzrQ/b2oMz23QbiTgwXu8O4ouQr9427V23zpRGxqCM/Eabsq1wss99VyioW5Mpv7PEEpyG59QdqO7Q4Jfr/PmdZIX5WQfOLopCuHCBqXyIrFL70P6Zw2sR41H9QLCqdUDYUiziLpZwzA1D5IxzaGdW9n2daEEsFi/islim+WNRhAJnO5m3Wo3o6e7D8iUP2dQTEB2LIH6brK8RvnwFWuqbuSsWo+fQcDoEyfHW/loJQGhEH/Kzt6Oypg29A0Yoff2hEL2v2ofL54G4yGA+zRHwcAR8mjusL156q+BlOV+8vX4RwvaSFvsHwoeU39XUjDrmazNvXADsvk6i9BYIjbQxACMY4a/wzQmOQPuRbOwsq0F79wCM3r7g7+KOR6FCgL8S+u42tNbrUFl4FHWDRtuvodjo0RdzSGMNBsYO5MjfdVQLQxBQk4steTqi5z4YpqngpzLX4IqO7OnDxSYiH0U/Wolszb+eUSWRE3cFy3ndthi/6EL7OPuNRpzlCpcRF1vaEKoyol1Q3+VBktPRi37+KCwIwQL7MmbyPiCjzSye1JGk3/HY2DZwD7Hb/tEbfEouKmLL/CbDDCV+yG+irw2nx4if9lv73drcixFlH3q/kHmlzcU2Gj9pQ8NcH/K/oM7PSOQwdqJHcNHQxv8ITn2W6QfUiBTKc0Yw4jJTEGpbjAPciTWu6UPa7hXEHoQxU4nwqCi0X+jmrk7LiSPOmKaAKtAfysFu4iunUH2kEJWNennny7VXGx8ZQE+HApEPC/ukQGhkFL9NkNUmO/ud7Y/wYSOcSp25w7L1BAJ27UaiOVZMpP8MU+GzN2GskBzLnMVOyfjBxdbEGKFfKRCkyURimIhjziDjrC8prpOUU1OB0sM6InPghlwdEfq1Odh+NQ2vZIZZ5TBB35E7X3M2Z7BFZryRgugi8d4aNPAfROhvqsGQJgmLGf3JtVcp7OzZHjaeR5DYK5SNbxTWPLkCfnLHZwc4tM0BHZ55ZCWiXf1t1kE/0TgghjIMj27KxC8y12FO825sP9JpnTfwGBuPo8hwG9xVItycxRJx13v8STC51xc+AoMzDJNZwWxfu1t1SniRHVzg8yCqO0nJQlRQzTRg5DrZ/Lsevf6kbVyGBaXKlw/wBhi+ArwUwvBLIGnhbWHFMjWSu3U43UeM4vwHqH5wha3zWFBh+a4qHM1cArT8CbkZSYhdK3jUxIYhDA36w28WnzRDnNfP4WApo71jA6jblgB16n5Ut5zDiGoJ4pc/xGfKQxm/C7riTCwea0PlCxlQJ6Si9Ly43oznC5GekITtZafQQgaYgKiVWC6cPzhFnvxdJvhJHH91PxJ9u1FXkI2UnyQIHmlyRUf2MDoj//adQwuRrfnXo4jA+sA7uQDitG5bWD9RKmwGf8anFJbBnJOP8VqHTX0tw/5YHyF4FG0W85CFEFKGN+8DctrsFDd0JOl3UwTTjmnkJ+h3S0s3FBEaBHjLXCy5CKvTUTJZsKlTj4BkMlHgjxmHlM8y/VAq7ezEFdyJNS4iafcq+NzNb/IolMSCRkZYG5xYHCGQBUTp2gRoni1DQwuZjs2Nwo8jZBbglr0yMlVCaXcRSTGDfSiOYyJtcgSxleqc7TgbuwObHxZ4/UTrmgKfvRljheRY5ix2SsYPcRtwRL9uG9SPpSJfS8rRqxCuJgsUPk8OjPy2lXjhuR0a+AnmWxP1Hc/P1zwVb/wRkxgIbR3z2GMfztbqkRjNL8CmwF4ZxMdoMzLHZwc4tM2Zarx48iRqXP29rIZqMmLODF+ELovA4tgUPFuUCZ/KYtQKQzvRTXXZGSx/IgVBThaetwo3abEkjiooBH5NF9DFpzn60POJAkFzmRd9fOBn/3zomJuD9ifdtlcqRrvR1R2M+cxnXuaFYHn3BVwWvgNC3K7nUjci5zIHEOcNUuAsSQsxdnWhld9mmRaGeI0edU2daGk4hcRHY+0CrBAlApZpsOb5V3D0ZBVeCtJhZ4XYQ91BmB/WjdYO22DE1r3IH+Z3+21RYU6IctyVE8Mn56ztvaRDaZcGxa8XYGtmJpLVESSw83kuoPSPQOITOXjpVeKEzwSi/KUKO30yGHD2NR3ueeo4frfraWx8QoPIMH8oRK82iSFT/u4wMxhx657Gi8VVqClOwZcHC9Eg+HqeIx15kWA7Dsu8OgBzFhjgt+RJbCSytfmtYd494XFatxWVfxAU5+2utLH2y2/z8rmxQDO+vkzmyhbPZ3ZXsZgyOnkfkNtmh7ihI0m/myLYq/TMs9t2/Sa/ZLErvx6A1elYMOLt6mN+y+fyB9kj5bOMPK8Redq93CD/b3a4E2vcwKndD6C313aB2tvZDMVCf2JhE40jJBI1vYby2Zk4cWwXNmemITE2DPcoZE703LJXRqYD3F1DAV0tjfzWBNvkgH7dbhThSbzyhO1V+wnXNek+e/PGCqdjmbPYKRk/xG2A/XtJ467FdKKurAtr95/EvudJGevUWDzX4eWT8TCT4H2NiNz1Kyy3WdVM3Hek52uu4rl4o4pRI/L0B2i/2IhqYwqWm+8kybBXZuFkj3GcXpzDyoYZo4Vx1vK3vWSOz04Qtc1pCvYunss/sqjzZMzpry9Baa35XVseIlM/Yht6gcnfTneVGG6pxRIWqLFGRSYAJ6yC7j+xH0VGDWIeZFK+ZNKvQMM7p7g/iKfvhPZgmXt/K2GwwlrPmAGtJQWojkhCJPPpQu8liIslQaasGQbe2A3NJShqjMCj0ZxiQ2M1UGiLUW1u6nAzSkvOjLsqEEScdki7g5y7AssjHFwzuKbDloQsa1ljevQPAXNmihmREpE/XYGGI2VoNTs8U/fhM4hLjCZuKE6QJg1zdDuQX9MNPZks6TsrsLe81+qYxJjnjA7hr2b/MHSjWnuGT8hhANUZCXhGa9Xd0CDxDObuIZ+2ooSPrwJDA1bP0Z8+Dq2jD1qIIFf+49GTgMVv2mBEe14CUgsFOh8ixypI+5nxTUJHfnODoThdgwbmygnJ6yrPQ5FFFCosT16B6sMlVp2N9aE6i8iLvYouUbc9D8Zig0LgJ6z9ElkIOh+auA7TK/YL2kuOObgaGuFHI65VoMiRD0i2WRqXdSTD79zFoHch8M+Mxc9iz6CoxNoO5nOozxD917lgo5Lo/2Yt/0ENNisE+iAYmguRmlSIdkcDtZTPsvLswLFyaz/09buRuqHCdqEtbIcN7sUaewzdzQ7+ppQ8u9cKbZDooajCiA1kMJcfRxz5PCmByFAxNIAhc/8Hz0BbLc++3bNXItN/0aDn4A6Un+8j7TKQycVuHDpt9YoJtUkE9s5CeQB27ba9s8Aguy5HNuIRn9Wjt8nub6FZuBljhYyxzFnslIwfnF+dLauw2vXgKeSvTYX2Ep+2oILqXiOGvrI4OXpPVKGaTzmFv5vYm1JgezeRZeK+Iz1fcxXPxBsWVTTiws7gwG9OYDo517Jsl2GvinlBCO2sRy3zR8bJAqe/Pg+H6tks+ZhlY7EhorfybKhfqidbMsdnUVyZZ8nHkzHHRzGEyrz9xJZ5myV96yovg3YmWcBa/tzA7XVXieHWWixN80fyy79GaH0Gon+SAPVPViK9OgAvFT3JC1SBxeuzEd6xH5qVKxGdWgavVWku3ZK2oM7GmoEcxCYkQcPciv5MjeLnVxB3ZSBO+1QB1gwXknpIOxJWQrNnAMkFgqszwU/iladV0G5cyf5Brdi1r2H+UyJtCSZOoyATa7Wae2ZWjHs1eI4tKwGatauhXpmB6lk52KoRvzqjiMnC0TVDOLSa1J2QgOjVezCUXIDn4p2EE18N9pVmQFG3A9mbM5CrVWLjjnWYw2fDNxYb1/RhZxJpw89Jf1aXwCs6ls+Ugy8SX8iGjzYDsUmpSCUyS9Wp8FKO+UuHtoSmZGNObRaiE1aTYxOQfTEEibYXPZ0jV/42hCH5KT8cyyC285z9F3kUCH1yPxI/y4X6sdVIJ3JQv9SF5P1pCGVsT0JHimXpeDGsEzvXkrJXpuKY9ypsDmSzWGx0xrSXnP/WrGxsTWTOl6jbnmnBWJ/PyTqatd/V0M7NxFZBfZi7Drt2hqCO9DWW+YNvK1cj9ws19qcJnlkPTsGPHfqAVJtl4LKOZPidy/giPk2DS3lJiE4qEbnLKYZtOzREH9EZNfB7KkfwXtjEUMWnYX3XfqKXJO6rYDaxj9EH0/cuJP6G2ICjWZ2kz3L9SB7YY5FnSuE/sObldZYBalw77HAr1thxWbsdx0S/qiTP7tek+EPLPCLC6GGjDj5PF2A9Hyuk44gznyeEpeDFgHqkE/mk/jwBsVltCFLLDUTu2asiLBPFeyPQcyQHWSQWF12Kxa+fjuZzCRNqkz0DqC3ToXe4BlseIzKwvKuQBXYuJKMu5zbiAZ8d62L/ns9FB3c1pn6skDGWOY2d0vGD9avkAeSa/Sq1ACNr9mON8F0aFhK/MlLQQ+JXbBKZTP9kNUq9o5HM5zql7QRevmjE2cOpAr2T30HuZZ4J+47kfM11PBFvOIgOfhqF3kF/PBojfJxMhr36q/HL9UbSbzJmEJvf3rkCG9V8nlxY2eQgoNosmyRknA7AvmfUnI3IGZ9FcW2eJRsPxhxFdA6OP+WLuq1Efoy9ERlmNYXgFYFdGMnC/Ha6q8TwPeYrD/z2rQVzS5qYldznet3GSOoZc1IPk29UsLcqRWFurRqMUDDvBogGiD5o07Ng2HoS68cFQjsky7LD5ePJT3jclQqkZ+jxy3czEcrv4sokIUX4NypcxEgKME6Tpzv2WAU51t3qXJWBHBjbG3Og84nWJ3W+s7pFkCM/9rGrGbbH6LVZ0NTHQldEgqyUD0x2n8WQ8rupgmnHKPN4wxS2w0Ub4OQr4bMTlafbNjCA6o0Z0DPxz9nYK9rnNhQ9sg145T1sfpizY3KAaP0TjiMTlY+r59vFYuPp3YjVhkBXIJj0TKUPeKIud8u4dgJbNuvxi5NkMsXvEuNmjBViY5lLsZPJdxY/XGiTM/ufCBOWK4On52tuxxsrxsbdUL8RBt0r/ALFnqnyLwnZiI3PchCzzQnjYZl4xLZuEW6tO0tCGOPxpBE4glGks3qYfGea5p8THe/QxNn1BvTXHkcl1iFOaqHE4LAsB7hwvLGzBOlrd+PsNf4K72g36kpOoDcmDPO5PRxsmROzbCbAydUde+xEqnNVZnJgbM9RoyZan9T5zuoWQY78FKQ+p8dI+cBk91kMKb+bKph2TOVCicFFG+DkK2UEE5SnuzYw1ovevlgstjx+4QAZfWbs2FH9E44jE5WPC+fra7ZDvbUCXfwTUEZ9GyrLTiF0WZjt1eGp9AFP1OVuGVf70B6zxOlCieFmjBWSYxnTZ6l8Z77pQpuc2f9EmLBcGTw9X3M33jAY9NAPdEJbeQaJjzt5T3yq/EtCNpLjswMkbdMdPCwTj9jWLcKtu1i67elD7QsZ2P7OdGz9TYqsl/YmE0XwOvw6bTr+spm/NfrzHDTMzMLxZ6IkbvtSvpXMUCFoFtU8ZZIxBmB5wTrxx0klmQ4f5ll8wYfivg2o4p/GrrBOHEjlHotSpxeiJ/4VvJIywa9P3aYYZ69A8TrmHbTbBBo7b2kMbWXIzi7A5WX7sTma6oniGW7dx/AoFAqFQqFQKBQK5SZC7yxRKBQKhUKhUCgUigh0sUShUCgUCoVCoVAoItDFEoVCoVAoFAqFQqGIQBdLFAqFQqFQKBQKhSICXSxRKBQKhUKhUCgUigh0sUShUCgUCoVCoVAoItDFEoVCoVAoFAqFQqGIcGsulsb0aK9vg36MSxqHDTDy2zAaYDAYuW27424Whks1qCwsRGl1N/iWSTNK+jHKb98KjBlhIHIW/iwyv13h+3Tb90PIt7FPnmRsAK0VxBcLK9A6yO/zJDbxx0VdTIbPT3Z/ZWFAf1Mzeg188tsAoyvZwVwGHvBbY18zWi9NkZClbHWwDWfb9HziZiLD9jwUM23mIRNFqk23wvxAGOtuBxiZDnSjva0b+tup3R7CaBhAb1sbegc8aKfuIIydt8gc3RNM8WLJiP42EtikYmy3Dntf2oPabibRhtKkJJS2sTnQV+dA/WwN2CJsjrtJXCpDRpYOI4FLEBqghOjfi9Z3o7VtwGYh1V6SBHUJ36lbgbYSqImchb/YhCTsrHRhAXirMViDnUk5qL1pk8hJ4NvYJ49hQENuOg5c8kVocCB8lPxuD2ITf1zUhed9fvL7K4bxWhtauwVBfPgcynNzUdn87VktMbraWT3ApzyAq347bsww4OyRXGyrOEe2Jh8pW+2t3oNn9ujQy6dvGuNsT2SO4UbMHGfjdvOQCTNtALVbk1DUKDa6dqJ87WqUnr+5I69NrJNE5txukjBeLMOWxxKgTs/BgbwcrGO2M8rQ9e0JSY4xdKIyIwGxj6Vje14etqeTudtjGai8eHM6bxM7PT5H16O3qQ39N+FCwhQvlvRoObId1V/wSUcsSMPx96uwZgGfdoTc4yaTEQN6A9X4uToCkYt8+Z12fFGDLUeap2SQmxga/O7999Bo/h3Lgldl9k0P2hSKPAwYGTIi8vEURMZGIGAGv/tby83pr6GZTEyqBdNk7xV49t238WzsFK3WvguMGzOUWL7zbdTvXEG2bj4BaVVorEpDAJ++aYyzPZlzDAnG2bjH8UdkvD+0Z86Nvxh5sRHVo2rELRO99HqL4hm5u8U1HZ7PrsGcp4+j8e0qHD1ehZr3juPFgBpk5OhkLvZuU8YGUJ2TjeqAbOjeexvHjx/H8bffg+5pf1Rn56DOg9d73MLjc/ReVG8vQ8swn5xCpu0g8NuTTB8aCivx3sfduDo0jP5BBRaE+sHYdgLHLk7H3L/WoPjAPvTek4zQf2qDtvQMxhaGYNY/DeCjY3VA/HosuQcY/bQGlVfmYe2jCzFjWHgcVwvzSJy2+Pc4cPI8Rv/bD3PmqaDgl4QGti4vLPqmCUfz81B+6hrGVPMw38/JLIN5zOXVEpQeLkfDpWF4zXsQfnxc7q8tROU7bfj482swDnajXxGCB/xsAxxbp7bRcoxxVgTm/AD46sNyvIUYJH2vge135QeDuGP2Qtz/Q8H5Turm+jIdC75pRunuXLzx0SjufoDkX29D9bFDKHq1GaNeQXjgPplD65fnUEbEnPh/IzCL3wXlXHgNliP/6iJsiPDj9jlpE4PhyilUFx3GK39qwNVhJQJDZmOGYEnuTD8YM6Dr3QpU/rEEFacuYRSzcf88b0xjMxn7qYVh9nR8fDwPBW8bERETBEZzDss0XELd61cQEBeE3ipyzm/fwWej3ljgQpsYHdd9HYDvX6hAQV41SKWYcycZGs7rcLzoIIre+oQ9Z36Qim+nLW7piZVxGZFDId76aBjXvQKsNmru0yo15rOnkMV6TQXeIHXc/8BM9s6mUxnbQ+pqP1mB4t8eRN2FYYz9KMhFG5Twp+E+nH29CCX7KnDmCjk/iJxP5GfGpbYyDHej7ngxyg68gY/1Y/jh/UHwYXyfjQVv4D/N8aXzH7gngtgvd5YtEjbssA6CTfyx0QVjn+Xo5f2bhW3Tp7ibbwfn8/FIX8r70kAztGXlqChk6hmF130LMYuXjdlubOLij7g8Fkf9FYmJID5Z+u7f2XirMLcpZAwfHiE+8YdTuGpUYe58P1k+wfhDRe0FfNrzV4xd1WM6W49Y3x3L0BN2Y4OcuBHkh6vafBTtI+0ZvYP0h8hKYOZmf2bawsRhr+5KfOydjLgHhIbBw8vw+/O+wqn83+KVy7OhWTyTzdI3VeBo8WGuHTPmWf16nN/yMj72R5SWn8IXw9/DfXybWPmIjBnsePO5H8Lv9+YKcGbHcvTsxPelxie2jQ1GzqbEZGyncwapscGMvqkMuh4/9kkNFrYv7+DvAWEwi1PfWIYT3T7453nDAttj2jF+jqGQOQ6YEbdxfh4Sm4g7Gpg+ErscmIY5DwThboEdScYWAV73KPDF3lNQ/CwWcwRy6nr9JZxauBGbIzibcmQnZpyORU7aM86eyFjSXlmCT+/k7M0m1jHZbFli45IDucuMRS750TgMaNj/HFpjCvDSz+7l9xH+hxL3LQvB90rzcDlkDf6Zj53OxhtzzJU7Vrt6vBlnfePmG0H4UZ+W9bu3SJkz5szDfd5CI7NieH8/trTEoOBlDfwEE5AZ86KxaKQEr3z6IFL+FydTObYp3TbHcyHR2GlnA3L659De2bJew4efdmPg+iB6/iGwXSfjjacQCRWThRJzwoPBxD+fBUsQvoA4C9lrvNKI8srdOPBnI0JXZSKSsffRPnxIBoseqVttdsfpq7ch5YVmKCJX4RerlkJxNgeardYrC2xduv3IrzQg9NE0rFnUj8pfZqC8kz/AntE2lKemo1QfjMT1aUgM1qN0w2qUtnHXgrzmMf3wIV0LQGj4EsyfNd6gFbMCbY6ZY45LDE2FOHROheWk30xbjmXkwPLkh13dcfd24VB6BrRXuGyuLwUo0hkRSc5fOk2HLc9uw868M5j+0OPYEOeFutwMFE30rtAY0ZdiOrct0SZ0liAjowpD4WnYlbMKfpfykFXYZrlyZqufMBh1WUjZd4bP16Nh+2pkaA2YvyqL5AeityQDz+vMAhlCu1aHoh37cdk3FhseD2OvsLJlEh0bwzmd45SwTIY+VOYfx8gCIpNNK6A8nYsM2W0itXbqUF24Awcu+iJuvYad6Bhqt5Nj2uDzaDZ2bVqKEW0WnteKX8JxWU9jzCMYqeyjVUufyMGGSOCD5zIsNmdP/4kcZNQoEacJtsrDSX9sYWSejr0XVEjM3o2NkQZotxIbvMZny7JBJ/7E9GUjSevDsGF3DpLJ+Qc2F6Kdv1zuWlsJAzV4fm0OWpVLkbxpFZYqm5G7Oou7eqYgkwRhfAn3F18o8X061O1P9El8eu6ArQ3b1RFq1CFr7W60St4W5uyzfYhPMrDxqYPkiEB8JXVtHrpmLsWGnDQsRT22MLLhY5loXBTiqL9isXOoA+WNfZxc2fyTyM/jfSLlIegrtyHriDUIOvMpJuaxk9hZwaTOQBIbmDPs+u5MT4SJ2o0t8uJG+Z48tKhiSXuYGLAfqS+dsty16Sf+y/rzijRseNQfPfm5KO/iM8VgZXgce/e9xskohnmiwIj2w6lYV6bHfNKnDSv80VWYji2Vfdw5djAxRMM8vh1Mzs9ahYC+YqTyV8EdjRlMLCrv5IUsMTZJ61nC9xmcjE+sDs02ZZbxjhzUTeP9ZlSHjI0CnUmMDUJUCgNKK5tZWTAY22qQr63AX5rMOh1AU2UFhqYxchfanvgcg8P5OCBE3MY5zh4029FqhA6UIfVZwZ0LqfHRHlUElj/cjLomSwms7Z+tBRJjgtmkMzsx5zsciyRinY09sRjQ02gXw8w4HZccyF1WLJqYH2GsA+2N/ng01p/fIWBaMNafPIn1i7ik7XjjaH4of6x29fhxfXs0GENl6UgV2CE73yjbY/G75d6NeH7jHjQITETI5ZYzCFXHIkDkSm3QppOo2sTZkWS8kNs2u7mQZOy0swGp/jm1d37M8yH2FhBM7GweaQCD22O2i5imlH7Tm5tiTYc+4pOEodczTVHrjpp6vuF3MPSfNG2LyTS92c8kWk2HYqznsMdvOmkaYhLC474hx8VrTMc+YTJ4vuk1vZ6mNh344AabFKvrYr7alFLcwadsYY/Pfts0wqcZbry/y7Qircr0JZ82fVRgbY8jRI65mB9rinqh3sS1jOGGqek3saZtr7OdNo28nW1a8ZtGQT6/bze3j+vLcVMPl0X6+iGRE+m/oCsXC9SmtGO9fEoCpo0xBaaLfNJ0fcT05UdHTdviUyxlymrTyx9yGQzXh0xfDvFHf9NhOqZJM73+BZdkMe/jm3ijt8v0pUDYjKyjMs1y4+xg73uCA9jz7XR+vcNUW1BlusgcxtrHOtOrn3FZLF8cN6WZ+ymjTeP1xO3LquL0xDLUb/ryOr9th8t6+uaGaair1zQksNGeY+tMKwpauYTA5r+syjT9NJOUbRaJjP7Ywsj0SdObV/kkYaS/13SDr1ueDTrxJ7at+00tlnzSt6tDXPkut5Uvu8jWV3uOpVnaQyocF1/sYdpv79NDHxw1HXuPq/RykcaUYecz7L5XuX1sn8Xij12cYmHzrT7F2lI+r8cbQ6aeL4QRgcSqdWrT7/lsMdmOR6S/Nm3iEcYfNt/OJ0j+Ck2x6TKzzerAiU8R2LaZ+8Fi23cpPU3IbkRwOW6w5eeYTv+dbF9vNB2It7PDEWafNRaPQ0yGQ2+btsfvMjXZGBa3r4WJDfZ6ud5r6ukVHMy0g+TXfsWnHY0ZvNxZGTobm6T0LOH7UuOTjR/wMrb1mxFyvNrWbxyNDfawOrHKirGNQ/n7TSueref6+/d6U24MkTUbc+39zpFPOBkHRHBk4zZ2xOvM3E6peCnGyLs5tnpkdCScX0jYibOxSCrW2cQjFlvZ2ehYalzyVCyS8iN72DIc69GC7Pmh/LHa1eO5tmabaoVOzcYa65g33u84uea+KxSIGRGZO4Btq2S8cLFtcmKnnQ1I9k8qLrJ+aGtTUmO2p5jCO0tO8FbBS+wZJlfo60QLYrGYv4rAMs0fiyIUONspeLvMrq57/APRP3qDTwkx4lJnJyKjItgr9mYUYUux/MoFXPbEM5OqHwiufCngZanIiIstbQhVkdV+UzNa+d/lQZLT0Yt+/ih4K61Xz6cxJflDpeKSDPfcG4guvdhlIkfo8O+PrEQ080tIwurtzQjaWYz17MUJ6TapFoYgoCYXW/J0ROZ9MExTwU/F97CvDafH/IF+67mtzb0YUfah9wvu2oWC6MIHfegieXXMF77euADmzpYQJemzBaZMqBEp1PmMYMRlpiDUcpiKnMNvMsxQ4of8ppw2sdjoCZgTHIH2I9nYWVaD9u4BGL19IXyCaByu6InkqwL9oRzsJnI+heojhahs1Nt93caI3sospJb7YtfudewVPRa5/bEQgNCIPuRnb0dlDfMVHSOUvv5QsP4h1wad+NPMIITOrMH2zXmoru9Ev4GI8l4VV77Lbe3D5fNAXCR/pYwn4OEI+DR3yHzRnOtTXFy0jU+rotOwnr0y2YeLTUS/in5rm8ivZ1RJ9Gy+iu4hFCoE+Cuh725Da70OlYVHUTdotNWzJ+KiKHY+Mdsf4cNGsFqT5VPOkKknd+1GBJfjBrHxAIxghLnaOdCLizMisGgul8WiXIKl0fy2Q2xlaPykDQ1zfcj/Anv+TE8yOtFjviEiZAZpgy8x/05yXE0FSg/ryPgF3LBrtzhyxyYnenbq+zwOxycxFGTsZXzIjBLhUVFov9DN3sFzOjbYM4ORfydaP2HOJP+fjsLi9RFIbPsQF4nOjOT/uoilWOQs5o7DyTjgAjZ2NONOgXxkxks7lNFqJH9Sg9OsjRjRWleD+fER4B/UlbQTx2ORVKxzEVnjkjtM0I9Y7OKmGLLnhy7OqVw43vhZB84uikK4IB/Kh7A4rA/tnwlug9j4nS/mBAEGgyDfHkkdSMcLt9rmbux01j+X4+LUjdm3xmLJE/xdj15/XzJo2qJU+TpYDElhgOErMqT4Cq2HoLyT1MEPtJMGV7fxWgdaWs5Zf8P+WB8hfLTA0wg+8FCUQoZTH/b5Zw4ZbQp+Esdf3Y9E327UFWQj5ScJ1sdhGP1MIz/huS3dUERoEOBNTHpsAHXbEqBO3Y9qkjeiWoL45Q9x5zqCKVPp4AuEcpBqkwOU8bugK87E4rE2VL6QAXVCKkrPOwlmrmBoQ+naBGieLUNDC5lazo3CjyPsB7dunNUHIH7GGVTWCkYQl/ujwvJdVTiauQRo+RNyM5IQu9b86IwHbJB5DOLV4yjQ+KL3VAG2rSb63aZDPxP4XG7rEIYG/eFneaGOh0y6/SwTQCm4PnkpHLWeqYP82yds0zn0KCKwPvBOjwZe4/lCpCckYXvZKbRcIXEmaiWWuzGH8TgT9SlP6MmZ3djjTtwQItpfBaa7KADDMPHDUbJQFNhNS4seAclkkcgfI6Rftw3qx1KRryXH6VUIV5OJCp8njSfGJme+7w4q+NzNb/IolGQaOTLC+Y2zsWEcCixaEoG6cxfIHLkR1QvJwojodWn0OXQRX7l84QyWEx07XbtNOW7GS7IwXB7TjQ+YRwxHz6HhdCAejbEGAik7cTwWScU6F5E1Lk0cV/2IufARpGhDj4On9IxkEs5+xprxc4/OD12H7dtsX+IpQshii+xwuhhyCFloLFSg5ZLDzvOffpeOF261zUOxU4jrcXHqxuw7+P9vf+aFYHl3PVklp8DPcqXCiJ5L3Yhc5M43e1SYE6JE6YVObI4QXCXt60aLIgjLmcdrJw0ysQhS4Ia3BhvXiwckB4+weo7gddgcvRr55W2IfCqMuIB0m1hmBiNuHfMj25fKkJ5RiIaYXVjOXtUcwdInMhEpdkWwswKlXRoUv/4kgvirm8bT9dyGIxidX6vBZf06BAi8nPl7GJAz4ZNqkxOU/hFIfIL5kfrqdyD2pQrEnCRt5/PdxdD0GspnZ6LmFbVlMtBbbh+sgrFm09NIHAvEM+lZKPI/is0Pk6Pd6o8SAcvIAoX81pCgevbFJOysiEXVk8EeskFfBKnXsb/NY92o3JCB0vpYvBjmaluDMD+sGw0dBiTea50mGbu60LooBPfwaefwPt3Zja38OwEszKAypoRyRgDmLCDbS57Exhhx63HcZx/43UuCPZ9iGXO0OCByfk2He56qwktqs+H2kUGb35wI3qQd/KYF0g7ZSPmU5J0uT+iJwYHdxNtNkS/pXI8bQhifuUYG12EymbCMG33o+YT8F8il5KDyD4JiTIX4TCI3fp8NNuuCTtSVdWHtfuv7FBg9gwZ+UxoZY5NNfY5w7PuuM4DeXmJnc61+09vZDMXCKNJaHkdjg/COD48yIhZxeW2ovrcZc5ZoSEuZBVQIWQjoALKgWLpGYJy3BDLHx3EosDhRg735jeia2YXqB9XQWeYW8uzE0VjkPNaRhRRZKIwLaA5ChbxxyQ43YpGkH40jBJFqA3aeaEbiMxG2Y77hDIp+fhQBpUeR7PH5oeuogkLgV3YBXYgQzBOYWKNAUJx7E8r5kWoYXjqB1uSnsdgmNBJ/3rca5YHF+N0af8l44VbbPBQ7rbgTF6XHbE9xU+4sGfQSTuYO3ksQF9uG0rJmEgy4XYbmEhQ1RuDRaPcCa1B8Cny0xag2L9zH+lCdXwFjcixC+V2yGdZjSPJ2qZXQxHWYXrFfULcBrQdXQ+PgpVRJxgZw9nAetLK/va9E5Lp18NEVopp/IdR5m4xoz0tAaqFA/kN6GBS+3N9/mRmLn8WS4FVizUffCTyTkIU65qViErjnjA7hr+bmGbpRrT3DJxzA6rwDx8qtZerrdyN1Q4W8x7Kk2iTKAKozEvCM1no1Z2iQjDj3jr9q5Q7MlS7F0IDVVgbPQOvo773cq8Fzm3yh3VfGXRF2tT/XdNhC8qz61KN/CJgzk/OXidqgsTkP6rWFaDU/FmQYwpBBAR8VMQiXZU/s8acr0HCkzFrecDNKD59BXGK0dUImAevTOoFPEzur3JqEvaeZWYMKy5NXoPpwibUOxueziL4dXgU3Q+w3RIGGd05xf4BP3wntQaIXLtMOJXx8FRgasM5U9KePQ+vQ5lyAeYxhbic+qOlm5Wq8dgpFB11YPMj1Kf3frHqzYeJ6cmo39rgTN4TMjMajEbbjRv+JQpQOctuyeVCDzYoKFJ2wxgVDcyFSkwrRPs5ZVFDda8TQV5ZGo/dEFar5lAUnY8aExyYJ33cHrdBviC8XVRixITaMJCTGBjG8g7E4sAZFlcDSMG6ypnxwCX5YUYJjqmiES8wtPTLHcGjj4rgdL4Ojkag/gdzCU1geJ3xsTspOnI9FzmMd4Dc3GIrTNWhgQhvRf1d5HoqsRdkgd1yykbs7scglP2JQIPSJX2P5+VxsIfbVzx9jvEZiztbdaFFnIpFZu07C/NBlFqixRqVDqaBv/Sf2o8ioQcyD/A4XUTychhdjmrFtK4mX18ydH0BrYTae/0SNzWznZcQLd9rmqdhpQWZcJCt8gznOTGjMdo0pXiz5Ij5Ng0t5SYhOKiGrWE9CBumnCrBmuBCalQlQJ6yEZs8Akgt+heXu+sLcddi1MwR1GSsRm5CA2JUZeCsgBwVPuHjlLSwFL/qdQPrKldhZKzOIC+tm/kjsytXI/UKN/WnMXR43MHbjQ10NtOddMKC5Kdis0ZMJLf/lKKdtIkHryf1I/CwX6sdWIz2J6OClLiTvT0Moe8XXVj8akh+dUQO/p3IQx3zpyzcWG9f0YSfZr/l5EmJXl8ArOpY50QlcmckDeyw6Tyn8B9a8vM5yldk5Em0SxReJL2STwJNBZJCKVFJnqk6Fl3I0siaCkjC2ElBPbIVMLn5ObC6rDUFqx/am0uzGb5c1YkvOCfSPudgfZrH1tArajeTYtauJPjNQPSsHWzX8TGSCNqiIeBIF6g5sJ+1IXUt0mpSLy5r92Pgwk+u67BUxWTi6ZgiHVq8kuibHr96DoeQCPBfvguSZPv0qAG+ZffqxLDTM24Ff8mXY1MH8YWbG52dlY2uixOyMSGTx+myEd+wn/VmJ6NQyeK1Kc/gIQWhKNubUZiE6YTWxoQRkXwxBojsX9Mfhj8St63DjRAbRF+lDdgeWP6Hm8+Qg7VOq+DSs79pPyk8S/ZLoRPXk3G7scCtuCCGD7fN83EpIgoa0d/vA/8aLroiMYZo/kl/+NULrMxD9E6YcZvzpQuJvSPwb5yxkHMxIQQ8ZB2OTyGT6J6tR6h2NZD6XRWrMmOjYJOX7brAmxR9a5lEtxpc36uDzdAH/vqvU2CCGL5bFBsI4I9r6ToRvGJbONCIgRvBOzzg8M8eQsnFR3I2X04IRqTaid3gFEqOFq0cpO5EYi6Ri3bJ0vBjWiZ1rSbxamYpj3quw2dEdAclxSUzubsQil/yIRxmGzUW/RmTXHqT+hHvnOjZ9D7rCfo2CTLPsbccbj8wPXcWmb6QNpK3p1QF4qch6V9x1lAjNLMYrYV3ITSd+x7xv/pN05HaF4ZWiTOt7plLxwq22eSh2WpARFxGG5Kf8cIz0I/o5bl7q/pjtGt9jvvLAb397YG41GxVQKuVM6eQh/zEUz8PUbZyhhAe7M2GctmmUudXvRP6MfkZJvtjfDhgzgnlEVjTPGRPVubM2OYB5Hto4jXukweNMZX9YmRuhcGLfE7JBqfJdlb2M9srBqU97qA4pWBtS3Fq+bWGiNjhRGbpyPnusG3FDCBO3yORjwv4sFf8EeGJcmVAZHrHzNhQ9sg145T1sJgtap+1xQTa3MxOKlyJI6VhqLPKEnbFMNCa4gju2IicOTGUfHOGpWGOHHD1LHuNO2zzcH7fs1SOxzDHfzsUShUKhUCiUKcB2sUShUCjfNr49X8OjUCgUCoUyxUyHD/OeDP+3yykUCuXbBr2zRKFQKBQKhUKhUCgi0DtLFAqFQqFQKBQKhSICXSxRKBQKhUKhUCgUigh0sUShUCgUCoVCoVAoItDFEoVCoVAoFAqFQqGIQBdLFAqFQqFQKBQKhSICXSxRKBQKhUKhUCgUigh0sXQbYbyog/b0AIx8+pZD3wztkTPQ80kKhUKhUCgUCuV2ZmoXS2NGGIYN43+GW3b6PznwcjCO8Wk5jHWi8oUz8HrQFwr2fMcyMxqITO2yjYYB9La1oXdAot5RA/o7m9HVJ36c0V53/I89VhWGOYO7cazRBX0axcsz/9hy3ZGXMyZRfk6Po/ZvC6N7c989rWO5jOnRXt8G/RTXy/iRR/vKyE/Cjjxepxgy2nFLIrTFWxYSm5ua0Wvgk55gEvxuSuxskpmsPjBjyy0rm1vFBwbbcLbtO3zJdSrGpJs07t3OTO1iqa0E6qSk8b9na75bdyMGa7AzKQe1g3xaBob6CpQ+rMFyFUkYz6F0dTq0V7g8G4ZPYe9jWagd4NMGssjKSEDsY+nYnpeH7elJZDsDlRftRtyxATTkpiI2YTUyflOIPVmryXYqXrYUxNCGUjH9WfqiwOI4NarfqCfDujz01Tki5Zl/fLluyMspkyE/OcdR+7eB1b25757WsVy6ddj70h7UdvPpScB4rQ2t3UINc35U2sYnPQFjW07taBLqFEOyHbcmNrYog/E6nQKGz6E8NxeVze6ulozobyOLLWGzPe53U2RnkwrTh8mIRQOoffZWkY0evU1t6B/lkwRXfWCy6K3eg2f26NDLp79zeHxMEvH7KRj3vm3chMfwNPjd+++hUfgr0oBZA1AcYUDLqWbELXmILEcIM5ZgeYwe1Y19bK4QQ1MN6oI1iPEnCbIAqs7JRnVANnTvvY3jx4/j+NvvQfe0P6qzc1BnWQcZ0F6YhZ1DahSffBu6V4/jKPm/pkCNobwsEtxtrzatecVOf+8XINGXzwwJQ+L5GpwWrrGcoEousJbziobsEdqHoFxP4mn5yZYzA7X/W4oFaTj+fhXWLODTk4ChuQxbqr+zQ/+3kpuiU+8VePbdt/FsrJLf4Sp6tBzZjuov+CTlO0wvqreXoWWYT95CBKRVobEqDQF8+juHx8ckEb+fgnHv28a0HQR+e/L58hzK6oDE/xuBWfwuG4bboC39FN+f9xVO5f8Wr1yeDc3imexktPXVMlT+sRBvfTSM614BmO83w/achWP4qGgPdr12HmN3h+ABv1G0nyxH8W//jNZhbywImY0ZgqWh4VINtMW/x4GT5zH6336YM08FBck3kv1H//P/YdHCmZjGHtmHhsJydHmF4f6Z3B5jpw5HT4+SMv24xctwN+qOF6PswBv4WD+GH94fBJ9/Yg+Foe0Ejl2cjrl/rUHxgX3ovScZoXdeQt3rVxCwSo357LhnQG9NBd4gx93/wEyuTBs6UPfKRczZsAYP/IBJT4Pf3QZUHO7GQ2sehg97DIMeDb8vBH6SjUeDZsDw/n5saYlBwcsa+HFNZ5kxLxqLRkrwyqcPIuV/EfleqsAvC5V47veb8c938QcRFD98EJE/PIdn3gBWxweRdg3go2NEgfHrseQe/iB7/qcCQx+VoOtHaxA5R1CpHBzZh4GXV1wQeqvyUPDbd/DZqHydjsez8pMtZyn7t8cd25bhK3eHjOHDI0SOfziFL4aVCLSTo/68DseLDqLorU9YOc4PUvG+IAJbXwlKD5ej4dIwvOY9CD/hXM6Jb4x+WoPKK/Ow9tGFmGHWsdknSLntJytIHw+i7sIwxn4UhPt/ON4zGPprC1H5uR/C7/fm95DFf2UJPr0zAnNYfyF7rpxCddFhvPKnBlwV9pmVyRmMLQzBLNIupqy6r4Pwoz4t669vfTSKGXPm4T5vQd0DzdCW/A7F5adwdVoAFt1xAUff/TsWhPLxQABTXkXtBXza81eMXdVjOlsP70exibijIR9F+ypwZmAa5jwQhLuFBTiR3TgY27rgjeQlw6hjYuc4HxDxXSndEfRNFThafBgVpy5hdMY8PHCf3QEDbaguP4QiYkusLO7sRjlpB6tT/hAz+qYyHG/3QjixJwvse44XcUcY6RsxMtaHj/0RpUS2Xwx/D/fNmwsvgUyc2qaT/kjZiI0tMtlO/EhUpzds7YiF2FypxS6YcaQWhtnT8fFx4ntvGxERE8TWJT9ucWNR7yyuzbJs1QJzbiXe+7gbV4eG0T+o4Nrl8dgqbdtSujCPmQu+aUbp7ly8Qfp19wNEl9eJrR0jtvZqM0a9gmxt0Ym+uPK8sOibJhzNz0P5qWsYU82zxsVxMH24gLt/FgHDm0wfHPiflH+OkXH9/RMoO8Tp5u4H5mHktBbDYYwPkvaWleGyF+mzwB0MzRU4duH7WDRfJOaS8rrerSB9LOH8EbNx/zxvG/uXFTPZmPcaPvy0GwPXB9HzD04XZh9IDLyCE0ROjI+x481sWzlJxgQLvM0H+eGqVlyOYvOjOV+SfQ1GW7+Z54OrVXuwt4SPM0EKfFV/AkcOHUbd59Nwz3yB7J3KScwPvfCpi7qQ5XvMOFFWjopCpt+j8LpvIWbdyc8x39VjwYOzrePFWDcainTQB4TBb2x8LLHK/BrumL0Q328tIfUzcwG+BIf278Dv7cY9Bmc+7lqs+XYiGu5uGqN9+FB7HHv3vQZj+Cr8IsaXGEEnytem4sAlXyx9IgcbIoEPnsuw3u1gzzmJooM63Igk50QB2uey8fz2PJyd8RAS16+A8nQuMgrbLB9G0FdvQ8oLzVAwx68Kg1GXhZR9Z9h8hbcRXYfrcdn8LOeVRhzT6rC3toPfAVyuLcHZUR+wIWKgBs+vzUGrcimSN63CUmUzcldnWe4mGMn55ZW7ceDPRoSuykTkvdx+If0ncpBRo0ScJpgr056BPnQZA+AjvP0QHI1E1ODsRT7NQCYeDecjELeMO/ByyxmEqmMRMC7qAkGbTqJqUzC73f9JM3pjYxEpUrlS/QoaX1GLt0sUX8wJImXqPX0zvw+V+ccxsuBxbNjkmk5F8aD85B7nMq7atkxfyc/dj565RI7rY+F3KQ8pW3Xo5+3dULudyK0NPo9mY9empRjRZuF5rYPbhKNtKE9Nx6Fuf8StT0Pi3AEcSs+wPt5o5xuhRh2y1u5Gq+RTRGTRuj0dey+okJi9GxsjDdBuzUH1NT7bjqFOHco7h/gUgwE9jTq0m3d1liAjowpD4WnYlbOK7XOWWWasTBrRwz+OwpRVXbYHh86psJz463LvRjy/cQ8azOZ8TYdnNuSh695YIr/HMad7P3bmn0R5Y5+orXnNW4LQAOI9s4IRHh4IH8HYcvZgHlpUsUQ2qxE6UIbUZ8lgyee5JbvheuS/UAPwPsDoTugjNvC6K9UHEzsiugvWo3TDasFdZCPaD6diXZke8x9Nw4ZHgzFUlo5UYXmsLPagg/TBIovyLj5zPKq7jagrqUeX4Dn5fhJLiwxeCCByYWxPk6XDSDBpf9YqBPQVIzXHKhOntilhi5I2IkTCj0R1amdHLEMdArsYQjsZR4p27MdlXyKvx8PYmOpa3OLKMLdZ0lZtUGJOeDCYZvssWILwBcxE1IyHYyvBmW1L6YIdM3UFKNIZiSlnYuk0HbY8uw07885g+kOkjXFeqMvNQNF5vgUS+uLK24/8SgNCiS2vWdSPyl9mkDaw2Q7Q44OXc1A3jfe/UR0yNhai3ex/EuM+06f2wnSkV+rZcT85UoG6F3bjA8udHF94GetRVCtsRB9qSypg8PYfd9GFi4mrkaE1YP6qLKKHQPSWZOB5nblCF2Kmwg/ziS0wM5iAYGIL8wSj+7UKHODl9AtGzi+mW+VMNG4TE1b4o4v0cUvl+Cc0ODh7Ld/hWI5i8yN2n53flO8rxmV/ovuUQHQdJH6/bT/KB/2I3XP2lW6JE1JyEvNDV3Uhw/fImJO6lowTM5diQ04alqIeWzaTfpP4oPCdjv4jr+GswE+NTVXY2aaEH/MkjV0s6SdxLuVgF/xWMHHYHz35ucjX6fDhFV6ITu3fgd/b1WHr40uhOJsDDZkXCH1Wfqz5lmKaSj4qMEXFpJlyCwpMvxf8Tn/B5/efNG2LWWd69TM+zfDNDdNQV69p6Bs+Teg5ts60oqCVS/DnvG4ug9DycqwppbiDTxFaC0wr1h039TDb33SYjmnSbI637OtlEr2m19PUpkMfsTmmodczTdvyC0zbNMWmy+we5lhrfRfz1aaUIkFdhJ5jaaYVuxtNN8g2c37UuqOmHkH7uTZnmt7sN5m+rMo0/TSTtG2EzxODPb7AdJFPmrlcrLGpm6nLXC85yfTmplhLP5xxMT/WtO110hhJWk2HYmJNGTm2+vv9u6zgLMgvzw7WPsb3U9QuvjhuSjMfK6lTcTwjP/lylrR/e1y2bTm+ojYdeF9gbN9w9n7gA67XjO6yqgS6G+o3fXmd37Zj5O1sU1T22yah6Q59cNR07D1O6JeLNKaMY7YKYPe9yu1jfWPTSdMQkxD4BGdnT5revMpsc4z095puCH1IANPmqHy+jyy2OmHreflDLsFwfcj05RDXX9t6+bJeqOdtgIErK/ddppc3TC271XZ94vZZ+iECW79N+zg/2vueQHLXG00HBO2Qkt04eNuy8YERUma82Qe4Om1kYqe7G+/vMq1IqzJ9ySRYuWSbaoWdsilPTBYjpqbfOJOFbWw1p822Z7rea+rpHS+T2q+4pDPblLJFWTZibreUHxHG6dTOjlgYnVhkIaJzl+OWrQ6d26oYtn1mYdvtydgqbduydGGOaQzffEjK1JiOCcLexQK1Kc1sexL64sqzHYPZcVsYR23g+iBm22b/kxr3Tb1Vpoz4XaYmoSqIXDME+mPlHE/kbG4Xf06Lg3h7o7fL9KWgPMZfozKF9iU/ZnLH29qrmJwYOVp0NfS2abt9n/h94m2WlqNYnew+O7858L7Vyr+setIU9Wy91de/YmyYtItvg7Sc7OyTwUVdSPrejSFTzxdcjRwk1q1Tm37PipKLndY5Eh9LzbFdGEsY32FirtD/2H2COZZkvHLk93wd3xCZxBP/+oTLYhGZF7gWa7593IQ7Sz6YH05WuILfHPPdeBYVlML0NAVUgf5QDnajvekUqo8UorJRb/dFGRUUgjvFCrJ0njOTuzvA4ueP8Gt6jDDbfW04PeZPluvNaG3if829GFH2ofcLZiVOjo1RoaGFucpgwMVzeizVaLBU1YiLzFXKK+R8RGPRXLYwXD4PxEXa3jkIeDgCPs0d1hcUvVXwGnfXwYjeyiyklvti1+517MrfOcZxX9EJUq+Dj64e7ez+Ppyu7kZi1BLbKyF25zjihszjGNgrFEIdCq9MTRp2djFDiR/ym9I6FceT8pN9nKT92+OCbcvylUCEhgj0NY2z97Od3Juec4Ij0H4kGzvLatDePQCjty/En1Yx4mJLG+Liotkr5GZU0WlYH8u88NWHi02kPEW/VSfk1zOqJOWK34WxEoDQiD7kZ29HZQ3zZUEjlL7+UIjcuZODamEIAmpysSVPR/rZB8M0FfxUNlq2RfUDgQ1wd0oNBuYq3gB6OhSIfJjpnxkFQiOj+G3XUHoLJDfjTkGdbsrOO4KPSzzKh7A4rA/tn9nfjjLiUmcnIqMibHSnCFuK5Vcu4PIwOeKzDpxdFIVwganZlicmCyXCo5zJwh+R8b7QnuHfcO9rRp1eg8RlfM9n+CPAl7hxJ+lvTQVKD+vQQnabY5Nj25SyRReR5UfuYaNzN+OWDQ5t1RU8H1sd27ZMyPle/CajD8Z2VAJbvOfeQHTp+VtRcvRlNwbf4x+I/tEbfEoM5TjbXrQkDO0XusmsQHrcN37RhXbiT4uEBjk3GnHCJ0tIOvHeGjTw7tDfVIMhTRIWi8Zb4p+kzT6k7i6ig7qKQpS+cUEw7ngoZtrJyUtpFbrxkzY0zPUh/wts4TM9yehEj4OHD5j4uHjR+BjByZFHdH5ki0JptaDpjD34/sDq6zNJ3CDxaIi/a+dcThw29sngoi5YnPmeQoUAfyX03W1ordehsvAo6gbNczgyZhAZtNQ3c3duRs+h4XQIkuNFYtVALy7OsIvrM5ZgaTS/zTDReNXXSeJsLNETn2Yg84JFEQrLvIDFI7Hm9uUmLJb8EbosAosFvwDhgGyPoQ2laxOgebaMLGBIGJobhR9HiBiVXP6uR+808ms5hxbLrxuKCA0CvLngH/AwscRGEnCGiRF/xiyMmAkl0N5lgL6tGYb4aBA7IQxhaNAffvYvoBDn9xs2wlkoBrpxVh+A+BlnUGnzxTkRvH3gR0Lwl/Zf5/GPIMGXd3Bm4nFNjTjzxIMx5oUKtFxycItc8JnQe+YGo/VSt/gkjP20rG2OH5m0CPW3OFCoQAOGmO6wA9wUIUOnonhEfvLlzOGi/buCLF/xhdJunGAGRPPEQRm/C7riTCwea0PlCxlQJ6Si9LxYQCT9+oqcy6zeRGF8g/zbJ9TJOfQoIrA+8E6JxZIKy3dV4WjmEqDlT8jNSELsWsEjMK4S/CSOv7ofib7dqCvIRspPEgSPZbgC0ycllHYDqGLGdH7LU7gpO7KItlUtmXAS2xo/oHG6C/C1MzzlnWSSMYKRUXLEMJHPbF+iCSHC8tyThV+MBuE1NWgldbATEiaW8hOlft02qB9LRb6W9FevQriaLNa4LBbHtilliy7i6THHEe7GrankdmjjpOiL2L7dRSwlGdcxMkL8T3rcZ/2HTPBtLZKkbfzFHzGJgdDWMY809uFsrR6J0Q4e2R4bQN22BKhT96Oa6GBEtQTxyx/iMxk8HDNFYPs0ShaKNragR0AyWSTyx4xHBZ+7+U0ehZIsg1k5TgKScnKEC7qQgfF8IdITkrC97BRarpBYG7USywUmqVimRnK3DqfJtMF4/gNUP7gCkXbhmIXxPzJg29vRdOGOido/U4e/7zgdKlW+EhcUvlvcWu8siWBoeg3lszNx4tgubM5MQ2JsGO5RTCACzCYLn2Hm2c5MbMy0/SWH8VMN5n0WQzNadB+iIWYJuzBiFlDt5+rRdK4by5eYnSgI88O60dph2x5jVxdaF/nD0TcQOIKxZtPTeDb/SaAkC0WiE1KeGYEICuxEL/EDW/wRnxLCOngXeyUkFqGCKzTzI9UwaE+IvOdgwNl9q7GtmpswqiJiEVlfhVqR55t7KzKgOXzOhcDWh97PlAgVvsQ92cjRqSiekZ/c4yYbeb7SiX6b5vB3GeZavz2kJIvIxCdy8NKrJ1HzTCDKX6rA+DdRVJgTorS98sTALA7Z56ADMGeBAX5Lnhynk41ruPc1nKNEwDIN1jz/Co6erMJLQTrsrBB/ycCLBPVx2BvszGDErXsaLxZXoaY4BV8eLESDy1+CYvx9gLuaKqCrpZHf8hRuyq67y/adGeKLPZ8oEDTXXj6c7hou2MmzrxstiiDMIYergkLg13TBTu/C8pg2DrAXkIT0XuIvzTrCNxpxD55Bw/lOnK0mE5IYcyztRF1ZF9buP4l9z5N+rlNj8dzxUzBx25SyRZk2wuPWmMNe0LJjTCJquh23ppBJaKMrupCDx+cILN3o6rJtFGPbioX+xNqkx32VfxAU5wVPlzCMkjLtTFQVo0bk6Q/QfrER1cYULBde3RdySYfSLg2KXy/AVkb26giyWOXzLMiPme7A9mksGPF2dsD8lgvvfNgwQOYtdnLsbOblOAnIkpM4snUhCRn3X9PhnqeO43e7nsbGJzSIDPOHQhibp4UhXqNHXVMnWhpOYXmc7V1xC/NCsPzaBVwWDjljnWhv4rcJE7Z/po5u7okCK0b0XOq2mRd817nlF0vM6lYxNIAh8y3FwTPQTmTyOTMWP4s9g6KSZhjMZfadwDMJWaizLBaCsTimG0UVZ5D4UAi3a8ESLG8qQX5bFCL5XUxwivzpCjQcKUOr2dCGm1F6+AziEqPlBYN7NXhuky+0+8qcXAXyxaJo3/GTG4IyYgVx8ALkVgonHhyKh9PwYkwztm0tROs1PmAZyWSvMBvPf6LG5kT+6oOvhgQWIH/rDlR38l45pkeXdge2VyixdX2U3ZUNJwx0oR0k6Fg+SWlAV0UeSuV+S9wdZOlUHE/IT7acJxl5vjKAovwT1g86NJegqDECj0Yz1jqA6owEPKO13iUbGiT2cO/4q04MQfEp8NEVo9p8uKEblVuTsPc0Y0MqLE9egerDJVbfGOtDdRYpX+quzjUdthDdWcolttg/ZPf4oQC/ucFQnK5BA1MsY7fleSiydMGI9rwEpBZabcMwpIdBQfrk8lyP+Pu/aNBzcAfKz/exf8yzv343Dp2W4R36v1ltUxI3Zac4g1KBD/Sf2I8iowYxD3JpIazutALdMeXnV8CYHItQJr1AjTUqMvE4YbUF2/JUiEyMsI19xOdKiR85h5wXF4bqwh2oVKxDnMXlVFDda8TQV+YgaEDviSpU8ykp23Rui1I2YovsMUeoU+YRwrmd+KCmm91nvHYKRQfr+UwHTCBuTQSD3oWJ1CS00RVdyMHjcwQWBapLBP5H+lxUYcQGMhFl44DUuP9gLDYoBP4zZkBrCbFP+1ChikZc2Bkc+M0JTCfnOpyakj7OGR3CXy3u0Y1q7Rk+QXAxZnKQOGhuvxwe1GCzogJFgphgaC5EalIh2m3XQzZohXHMRo6TgJScnCFXF5Io4eOrwNCANRbqTx+H1s5fgsjibIjMsYoaVyAx2sFg5B2F5ORe7N1B5od9BqKvPpzdV4BagR3JtX+Hfu+9BHGxbSgtE4yRNvMCCsMtv1hCWApeDKhH+koy4fl5AmKz2hCktp3UugYJdE8VYM1wITSkTE1SAqIzauD3VI7N88ShUWoy4oVhcRhvldNCsHiZEcboHyNUcCtdEZOFo2uGcGj1SqgTSFmr92AouQDPxcs3MpVmN367rBFbcqyTWHsCyGRgTk2jzZekWFgH16NXJZx4mFEiNLMYr4R1ITedtO2RlYj+STpyu8LwSlEmQgX+6Ze8m/RDgbeyV3PHrVyNLJ0Cawp2I1HkC36O6D+tw8gateXRGiYgX6yvQeWpTjL9mSzk6VQUj8hPvpwnFVm+EoyNMQPYTmyVkZM6twOJ+3/F/bFjsihPfCGbTKIzEJuUitSElUjVqfBSjoO/AzV3HXb9KgBvZaxELCkv9rEsNMzbgV/ytm/jG0lJiF2ZgbdmZWOr1B/PYi4gPK2CdiNp49rVUJPzqmflYKtG/DzFsnS8GNaJnWsZu03FMe9V2BzIZ5JJT+iT+5H4WS7Uj61GOtPnl7qQvD/N5i6iXBRhmSjeG4GeIznI2pyBokux+PXTwgfIx6OKT8P6rv2kH0kSX+Cy4pbsAtPwi7mvISUhCRpGd1oVXsx/UuCLAhjd7QxBnVl3TPkBOSh4greXaf5IfvnXCK3PILZMZPaTlUivDsBLRdbylPG/QrG6A9uJTBlbin12AGueJnFTAmW0GonDevjERwjuxvgiPiMFPXmkr0mrofnJapR6RyOZz5W0TSlbdGojdsjwo/E69Ufi1nW4cSKD7CM6y+7A8iekZDGBuOUWRMZpGlwiMo5OKhG5WyyG59voki7k4PE5AkMgtj7lDy3zeBPT5406+DxdgPV8sZLj/rRgrM/n7DWa9cfV0M7NxNZx/WQWXlHoHfTHo+wf93OAbyw2runDTsbXfk58ZHUJvKJj+UyCizGTCA3JT/nhGPGX6OdOyRubbWICF2M0e7qQ+BsSS51cL1qT4liOHkdKTk6RqQsZhKZkY05tFtH9ahKrEpB9MQSJ9n0OVmONkixqY2znlLaQ8WvTUfw2ohulORlkzClE14rd+OUyPptB0v6l/N7Wx9WsXgeQXGCeF1AYvsd85YHfvrVhHqkwKqAUvOg3YdjHNEiZnvhWPPNuj8EIBfN8qRuTMGmM7KdI64KLsTXWvdm3cZiERBntMxoMMCqUzCPXrmFoRlF6PRYfzRH9DPmU4Emd2iFbfjKPmzQc+cqADs+srcePXy1A4kzGXkkzHciJtYFp499JcYTTPrvrG572qVEilzEPxBDmgoWgPcbTuxGrDYGuYBL+uLA7MmDOGWXUIa+fkvbKyI0MqA5tgW2jY1tyFan2SNmmx/xvMsYcR0xi3PIYt3obJ0lfE41tUuOpsXE31G+EQSfnT3RI+Zo78cIdZMXSNhQ9sg145T1sflhCjp7GzZjkki5k4PZcSojdeENKxdmXEvCXRVXYpxGMOJ6w/6mMebcZt/6dJTOMwXlagUyZngr805hBZDIDgQKhmcfdXigxKGS2jwm0bolaGYHNVTdxocTgSZ3aIVt+k2oHMpDjK6y9Oj6GtQGZCyUGp3121zc87VMzZMhFAn3Ndqi3VqCLf8LCqG9DZdkphC4L8/xCicEdGTDnuNBPSXtl5ObMFiRsyVWk2iNlmx7zv8kYcxwxiXHLY9zqbZwkfU00tjkcTw166Ac6oa08g8THY+VNzqV8zZ144Q5uxFKP+aUcXI1J7uhCBm7PpSwwfzcqAc+Ud3KPyJFFoP58Gcrr/bE0zG7E8YT9T2XMu824fRZLFAplYpABxOde6c+0Uhyjin8au8I6cSB1Jfu4pTq9ED3xr+CVlKl5L41CoXw7MLSVITu7AJeX7cfm6G/jBHU6GW984ePpj4VOAreuLlRY/vSvsfjSfqSsZB5bTULa4T7EHShAMh1yppTb5zE8CoVCoVAoFAqFQplC6J0lCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiiUKhUCgUCoVCoVBEoIslCoVCoVAoFAqFQhGBLpYoFAqFQqFQKBQKRQS6WKJQKBQKhUKhUCgUEehiyVOMGWEYNjj8GZm/vkwwCrY9xtgAWisKUVpYgdZBft93ASORrcHIJyhOGSWyGuW3b5bceB9x2/6FfZhsprIuGRgNkxA3JsKYHu31bdA7axMjw2+Lew624Wybnk9QXEaG/Cbfxg3ob2pGr4FPMgx3o+4IM3bWoPcm2KrNfMCVmDPRWCrGzY55t7GPGfua0XpJaFi3GULZ29nWpMxZb0O+PYslfTda2wZw08bmthKok5Ic/krb2INQatn2FAY05KbjwCVfhAYHwkfJ7/4OoK/OgfrZGtApjDTtJcQOSzjDu2lyG6zBzqQc1Lq5oBf2YbKZyrqkGUDts56OGxOkW4e9L+1BbTefFoGR4c7qAT51e9NbvQfP7NGhl09/t9Cjt6kN/ROYSEvLz/M2brzWhtZuQZQbPofy3FxUNpsntd2o3JgF7WggwoMD4KXgd08ZtvMBl2LOBGOpGFMb84zobyMLV4F6bl8fM+DskVxsqzhHtm5PbGRvY1uTMWe9Pfn2LJa+qMGWI803z1gfzkTj++9xv1czEYlgPPsqnya/zQ/zx3kcA0aGjIh8PAWRsREImMHvplAolMliQRqOv1+FNQv49LecgLQqNFalIYBPf7foRfX2MrQM80k3uBnyMzSXYUu1YOrtvQLPvvs2no01X1EcwdBgIB5NUWNxbDBU/F7KVKBHy5HtqP6CTxJuXx9TYvnOt1G/cwXZuj35bsc3eUzbQeC3pwTDlVOoLjqMV/7UgKvDSgSGzMYMsmTTN5XheLsXwoMEIUvfDO2Ri7gjLAg+0xyfa2g7gWPaRnz8+TUYB7thnBWBOT/gijBcqoG2+Pc4cPI8Rv/bD3PmqaBgl4h9aCishWGeD65W7cHeklO4Oi0Ai4IU+Kr+BI4cOoy6z6fhnvmk7n9ii5KP4RLqXr+CgFVqzLfxngF8dKwOiE3EHQ35KNpXgTMD0zDngSDcLbyqxTwacLwYZQfewMf6MfzwfgdtGG6DtvQN/OfH3bg6NIz+zn/gnoi5+B4jj4vTMfevNSg+sA+99yQj9EfkeOZxvVfLUPnHQrz10TCuewVgvh+3umJlSM5Z8E0zSnfn4o2PRnH3Aw/C73obqo8dQtGrzRj1CsID99mGA31TBY4WH0bFqUsYnTHPkm8kcj/6n/8PixbOBFEdgZF3Obq8wnD/TG6PsVOHo6dHsSDED0z3HesK6K8tRN3XAfj+hQoU5FUDETH4UV8NKq/Mw9pHF4LthYHIrfwEPv2fCzH/Rw4uEzqULW8Ps6fj4+N5KHjbSKoIYst11i4G/XkdjhcdRNFbn7D584kNcz0k5zqwWTNOyyb6aj9ZgeLfHkTdhWGM/SgI9//Q8eVPtqxjf0Rp+Sl8Mfw93DdvruVq6VcfluMtxCN9qR9GP7WTmz3DfTj7ehFKGPu8MgyvIGIHd/J5BOdtNqDr3QpiYyWcTWA27p/nzclDzC/k2jpB2AcW1p5LUHq4HA2XSDvnkXay5ZL9ZWW47EXigCCcGJorcOzC97FoPqcfZ/2wr0tKjzZI+pkXFn3ThKP5eSg/dQ1jqnmWfBYiw973T6Ds0D5yPuOH8zByWovhsPVYcg9/jIDxZdr6oju2zfrvu3oseHA265ssY91oKNJBHxAGvzEm9pzB2MIQzOL1ZfYDpk93zF4Ir+5KfOydjLgHrDHDUbxg8aC928LFnl7BuMDFzk9xN4mXXiTJxZcgElO0bMxk5D5jzjzc580VyMq4wYgFoVysGq+j2Rh68wg+vZOrgymv8nM/hN/vzZ5PSkB7ZYkln0W27TP2fAI9fmEwi4ttz1t/w9zF/rwPD+DskQpc/eHDpM0k6dAG5fmGBVZOr+HDT7sxcH0QPf8Q9MkF35WWn4iNO/Rvgll/IWP48Aix6T+Q8duowtz5fqxfMvKvqL2AT3v+irGrekxn7VRgB38/hdLjb+Fjc7/+S4EF8/+BhsM1GCXxbpbAHXtrCvHOYABC7cY+M07jIcGxzfPzgXiuzzYxh+2frX+BxKDSd//OydA+ljqLu078X4j8+GpFyp/FbZBpTyXeM89bBonsSZ+M9jZCcCZbKZ9lkB23peTNpJ2Mi0J/lxvjGX2V/y5PNH6I4szf5Og/yA9Xtcy8c/z5Nv5pY1u2NsoyQOblZeWoKGTKGYXXfQsxSyAH+3naXR9Lz+1vB8TMZvLoLEFGRhWGwtOwK2cV/C7lIauwjX10TnW3EXUl9egSPBvZX1uCIoMXAhhLdXKuYlYgwhf4kAU+CWjhSzCHj+X66m1IeaEZishV+MWqMBh1WUjZd4Z/VG8I7VodyvcV47L/49iQEoiug1l4ftt+lA/6Yfmq1QgdKEN6DpkcsMd7jrMH89CiikXyJq6O1GcFdQzU4Pm1OWhVLiX5qxBq1CFr7W60it0yU5CJeXgwAkh88lmwBOHh/uzAb7zSiPLK3TjwZyNCV2Ui8l6yc6wT5WtT2cf1lj6Rgw2RwAfPZaC0jZMGe46uAEU6IyLJOUun6bDl2W3YmXcG0x8i8onzQl1uBorOmx90NKL9cCrWlekx/9E0bFjhj67CdGyp7GNzFd5GdB2ux2WzPkn5x4i899Z28DuAy0S/Z0d92KsxtrpaCsXZHGi2WuUyRBZW1YU7cOCiL+LWa+wWoYSxPlTnZKHOW43ERfaZPKxss1BtDGNlu3TaKYFsOXso2rEfl31jseHxMFntMtRuJzbVBp9Hs7Fr01KMaIkNaflHj5zYLINz+9SjYXs69l5QITF7NzZGGqDdmoPqa2zmOJh2aLJ0GAkmZWWtQkBfMVLdsV3GTjZmoFwfhg27c5B8bxcObC5EO29/bJtJ/43hnDxwyr7Nq5GhNWD+qiySH4jekgw8r3PwKJYrtm7PaBvKU9NRqg9G4vo0xJF2HkrPgPYKk+kLL2M9imo72UM5+lBbUgGDtz878DmXvR0SerRBlp/tR36lAaHEb9Ys6kflL4m8LU0lk2riR+mVetZ3kyMVqHthNz5wclXfXObO/G7MMfviwXQ8Y7ZDN2xb4Tsd/Udew1mBARmbqrCzTQk/X5IY7cOH2kb08I9m9RO7Z/1gBan/UX/05OeivIvL43AeLybX3rn+tw/xSQa2/R0kh4ONL2V7cOicisT+TCz3bsTzG/eggS+QlXFjH69zMR3tQHm1tQ6mvPJOYYUG9DQK2uCS7ftCMXwCf2ky69OIi2+VoLyiCpZdZALzlwpS+Eyy7dQGpX3DBn6MYaJ0QDAZY+bxsdWu/UuVzchdTeKvA1eXlp+djTv1byaf0d9J5Ocdx8gCZvx+CPrKbcg6wvXLa94ShDID46xgMi4GwoftmMAOSF/Dhf1aQCaJCiLna2Ry3yywotEz0B5sg2I2Y/TjcR4PpWzeCXb+xTLUIZChEKm4K+7/TrGTf2KwHqUbVlvimKVvhV3wY30+EP3Cvjm1QSXmCOctjOzJXlsbcR6fGKR81qW4LSVviXFR6O9yYzyjL0fxYxxO5y7y9F++Iwd10/h4M6pDxkZr++1l7xAi09S1eeiauRQbctKwFPXYwsiBl5vYPE1ybn+7YJpChl7PNEW9/CGfIlwfMn05dINP9JpeX6c2HfqITzLpNLXpwAdcvvNzCR8VmKI2nTQN8UnTNx2mY5o00+tf8GkG875eJtFqOhQTazrwvrWML6ueNEU9W28a4dOmr06atsXsMjVd59Ny6WfOyzS92c+nLXB17n3PUgPpR6PpgODYy0UaU8YxtoEW2H2v2u6z0m96c1OsQG68rNYdNfV8w+9g+OaGaair1zQk2NdzbJ1pRUEru82dc9zUw6YI33xI2qoxHevg04SLBWpTmrltQ2+btscT2Qi6Yt7XwsqL05+5XUz52/ILTNs0xabL7B5GF+s4/XxD5BJP6vqEzeD4xlb/F/NjTVEv1JsEGufazOicHPtmptq07ViXTb49jBxTigQdIkdfPllgerOV6YSIbmS2K6tKoOihftOXvL04tVlZ9vmk6c2rbA7LSH+v6YZQp0Ku95p6esfbVe1XXJKVX75A10JfEcLa7n5Ti6UeYjdXh7h62fbZyeN6h6m2oMp0ka/6Rm+X6UtBM268v8sUlcnXZecXrtq6sA8jb2ebVvym0Ubf7L7d/L7eKlNGfIHporkfbJq3TUnZi8jLWewRIsvPbH3zYr7alFLM2yXfThu/+uK4KYPYptDHhbBlip1j8UV3bPuGqWU38anXzbbNpS26EeqSsbV4O3mOMPtiredLxgvP2rstXP9t5Me2n9gHnxwfX7i4mvsuV4eNz8jQkdB+OGzjtKu2f+ODXaYV5voZ3an3mw69rLa0b+TdHFOU2R8kbJBpa5oj3xCFkZ/teMbarE0sZepIs/qfHa7KT9K/Wf2tM736GZvFQeYAKyzjC1+njQ7s7WB8v9h4JYiNrNzTqkxf8mkbpOKhLJt3YDN2sZJFOMexy3cad/l6bPxfhHExL/tt61yIwJRpkQXTj5hsU61ZUAz9jaZXi+u5fCkbdDRvEdq4y3MCEZ+VG7dlydvBuEgYJztXYzyzz8Y2bXE+d5Gnf9t4M2Jq+o01ntvI3kYWtjZqujFk6vlCqHSiEzJv/z2v1vE6YeCOsfbNVo+3C1N6Z0m1MAQBNbnYkqfD2c4+GKap4KcyLy39EZnoC+0Z/k2yvmbU6TVIXMblOz9XhL42nB7zJ0vYZrQ28b/mXowo+9D7hXX9rFBay5g+jWz7/sB61WWmPwIwgKEJPKsthtJbcF1nxp2Cq3l9uNgEzFH0W9tMfj2jSrR3y1j1C/FWwUt4e5P0TRXoD+VgN9qbTqH6SCEqG/W2Xzkh7WLuTLEwsiA6UQnunN5zbyC69PzVk0/a0DDXh/wvkO9nepLRiR72goY/wmNUaGhhLqcYcPGcHks1GixVNeIic3XwCtEPorFoLtnu60QLYrF4EXMezzR/LIpQEF0L3iBX/WD8lU9jN17dmoFSv1/jpfWB4/MtcLJNjAnm0wwKBGkykRhm1YeNbmS0a05wBNqPZGNnWQ3R0QCM3r4w3213arOS9hmA0Ig+5GdvR2VNG3oHjFD6+kPh6Jb1DGKrvqS4TlJOTQVKD+tI24Ebrn7FZmYQQmfWYPvmPFTXd6LfQPpxr4qrl2kz1IgUymNGMOIyUxDKi03hHwgfIusu0p865guNb1wARNswEVs34mJLG0JVRmLL1nMvD5Kcjl70M4f4RyDu3ho08OGkv6kGQ5okLGafApEXG8y4FHtk+Zmtb95DZNY/eoPdNn7RhfawpbC5OTo3mvSF33bEghAssD9n5jm0m6/EE1yzbQVCo6LQUt/MXckdPYeG0yFIjidys2egFxdnRHC+bEa5BEuj+W2CdLy4SfYuxCa++GJOEIlchvG3etzWkQXXbV/x8I+R2HkOF5mx6BLRxbIIbIiMQsO5C+R44g/nmhG35CGu/VI2SNqa6Mg3ZNGHy+eBuEhhLCUafDgCPs0dki/oS8tPhn+zqIhN85sMs8mYM2wE50nuoYj4MZK76/k7dqT+M2cQmRIL/sE0WyTiobTNew45cdfG/51ixKXOTkRGRdjcgVIQnS2/cgGXiQ2yfYsgfRfMD+AbhTVPruBkJScOOsOtOYGtz7o8Z3SGs3FRDFdjPDte8dvjkJ67SOtfQWQpjN1KhJP43n6hm8zOXEChQoC/EvruNrTW61BZeBR1gyQCCesaN09zPre/XZjax/CCn8TxV/cj0bcbdQXZSPlJgs0jOn4xGoTX1KB1lA/g8dEIMhucxLnj+LsevdPIr+UcWiy/bhIMNQjwdmnZMYUMYYgMCEN9wjafQ48iAusD7yQhbAIY2lC6NgGaZ8vIAoYMZ3Oj8OMIkYmPTAzDRPajZNAUtLOlRY+AZDJg8scEPExmS41k4jNMBvfPmIURs4AC2rsM0Lc1w8DolzmQ0ZW/r+U8M0qVryXAOKSblBOwBF6nK1Dr4JEdDka2SihlTwgIMtqljN8FXXEmFo+1ofKFDKgTUlF6ng8/zmxW0j5VWL6rCkczlwAtf0JuRhJi11pvm9vTr9sG9WOpyNeScvQqhKujEM7nucS0YKx/9TgKNL7oPVWAbasToN6mQz8TDJk2K5V2gVDA2ADqtpHjU/ejmvRnRLUE8csf4jPtmYitG2D4igw61zpszm0Z9sf6CP75chKgYxIDoa1jHonpw9laPRKj+cHG1djgSuyZoJ+xfqVU2MmYpKXsdhb3OKsVco63ASPX+aQ9MmxbsUxNJo46nO4jsj7/AaofXGE7OTIjahcKTBfskI4XN8ne3cBtHVlww/ZnPITFEWSy3WZEV2MNQsnCSMksoM6TxclYB1obwxD5MG8BkjboxDdkwbTfH36z+KQZMkH0k7FYkZafHP+eJGZEIU7djbcYo+cvECyPEDN6gkQ8lDNGegSX4q4cOPkH+Nr1W3knafcIRsj8TFyHAiY635ARnyRxdc7oDGfjoou4Hj8k5i6y9K+Cz938Jo9C6QWMjLg0rzSeL0R6QhK2l51CyxViI1ErsVyGWp3O7W8T7uD/nzpmBiNuHfMj25fKkJ5RiIaYXVjOXCHyjUbcgyVoON+JgGoj1vzGLoA7O9ce9irTCJY+kYlI2YPYzSYAcxaQQLXkSWyM8eyQYGh6DeWzM1Hzitoyqeotd+magg0q/yAoxlSIz1zn+AsqwdFINBSjRedL9KTGi8w+soBq19ajydCN5at4/c4jA1J3PS4Pp8DPoksjei51I3KRxPdZgtdh49Ma3JibhdSthZhzNNNyl8OWIMwPI0HlMz2S5woGAeZvS0wjgUhM3DLbpfSPQOITzI/k1u9A7EsViDn5JLcQdGSzsuxTiYBlZAJPfmvIAHb2xSTsrIhF1ZP2E5tO1JV1Ye3+k1hvvhI3egYN/Kbr+CJIvY79bR7rRuWGDJTWx+LFCCKPazW4rCc6F4iQ+TsMYCYNl3Qo7dKg+HXSdz4QGk/XcxvjmIitk0lZkAI3vDXYuN5xpFYRm4ss+QDtiQGo1qvxK7Ns3IkNMmPPRP2M9asy5up8FGc/DKPd6OpmhlMnfMZccY+y+iJzTmcwQh25jxzbnhaGeI0eB5o64dd9CsvjsuwWZDyMPK+Ryf4w0aqlrD70fEL+C+RSsuLFpNm7D/zuJYM0n2IZc/8OhBwdeZFJ3bgXqCyzEndsX4nwFRHYeeEE/M77Y7GGaIJZQC0sxkUd0BAYhbW8T8qxQRvfMKbg18Ir+JIwsbQbDR0GJN5rtQhjVxdaF4XA/B64I6TlJ8+/J4vQWA2G8pvR5dsFbbQGmx2slVgfchIP5dm8A7yJzfKbFojNiuJS3JWDCnNClCi90InNEQLf6+tGiyIIy4lpq4JC4MfocIzo0DzpZf4+D9N1b8XE5xsTmRMIkTtnlCVvB+NivGhUdIio7PRt+JDYv+gdTNbfnMxduuXofwC9zB8Sm2uNN72dzVAsjCLaHh+qxCEx+TUd7nmqCi+pze3oI4tXftMZUnP724ApvLNkRHteAlILm2HgV+OGIT0MCl/B3wZSITIujH1BrFKhQaTlsQ455xKG9Rgyr/RnxuJnsWdQVGI9B30n8ExCFuqc3oFwjPFiBYoOn3HraoI8VFievALVh0vQan70j/lwQVYCnnH3iggPc0VGMTRglc/gGWgn8jdQHiSDiILI44T1ZVVDcyFSkwrRbokxwVgc042iijNIfCiE27VgCZY3lSC/LQqR/C54L0FcbBtKywT6bS5BUWMEHo12NFLZ4pecg+d8a7D3iIMXOEnIjvzpCpwtq7DKdvAU8temQnuJT9sj2a4BVGcQ3WitMhgaJGHnXuaKmITNStnnNR22kO1qc9FjevQPAXNmislDBdW9Rgx9ZR6MDOg9UYVqPuUKxuY8qNcWWmVkGMKQQQEfFWk0K48OHCu3tllfvxupGyq4R2+Ijc0ZHcJfLc3oRrX2DJ+wZ2K2Hpq4DtMr9gvkY0DrwdXQCF/gVZEAHXYGB35zAtPXqK0TM5dig8zYwzNhP3swFhsUZPAz+xXTr5JiVEvNqa8JfJE9pwDVEUmIZF74F0OmzwWRSfWQdgfZvwKJ0SIdZpgZjUcjbMvqP1GI0kFum0UqXkyqvRO7DFGg4Z1T3B/R1XdCe7AM7Vym68jQkd/cYChO16CBUT3pS1d5HoosXXfP9pUPLkE488ghIrCI/d6AEouWqIgd6+ATHUZK5ZBlg0LfSIyWMZknNm9uKx9LG46UWds/3IxSMjbGkbLENGaDDPnJ8m856P9m9XG5LIpGov4Eckl/kpcvcXyRQioeyhojHcA8Zjq3Ex/UdLNlG6+dQtFBBwsgl+KuPILiU+CjJTqxyJ/YZ34FjMmxCGXSC9RYoyI6tIx9xAfLs6F+qZ5syY+DBr250XZMeE7gWtyWkrfTcdFVgjXY6K9D7r4a9A6QBc8AiUd7KtBjv4CzIDF3kal/rTDekPGuqMKIDbFh/A45KOHjq8DQgHVppT99HFpZ82lHc/vbhylcLCkQ+uR+JH6WC/Vjq5GelEAcqwvJ+9MQal5dE5TRaiSSCeccEnStq2wZ54al4EW/E0hfuRI7a1l3ReRTBVgzXAjNygRoyDnRGTXweyrHhWfLbRnqqEel7j1cduDfnkARk4Wja4ZwaPVK9o/Zxq7MwFuzsrE1kR0d3YeRT0A9kQ8JID9PQGxWG4LUE1jdT/NH8su/Rmh9BqJ/kgRNwkpo9nQh8TdEJ8JBL0pNIk0YFofxO6eFYPEyI4zRP0ao5aq+ra7UbFkDSC74FZbLiYssvojbvQuRTdvxvGBwEsLKNnkAuYxsE4g9pBZgZM1+rHF4VVWqXb5IfCGbDCoZiE1KRSrJT9Wp8FKOhoQGKZuVsM97NXjuaRW0G0ne2tVQEzuonpWDrRoxO/BFfEYKevKIvSSRCcVPVqPUOxrJfK4rKCKeRIG6A9tJe1LXMuXl4rJmPzayfyeMa3PywB6LPFIK/4E1L6/jrmj5xmLjmj7sJOdqfk7OXV0Cr+hY5kRRJmTrc9dh184Q1GWsJG1MIvJZjdwv1NifFiaY3JD2PhqF/kEV4pYJr1BLyN4GeXHLwkT9jHncI5+zqegExq9WQzs3E1v5OzQOCU7BjwdyEMuek4Ttn6lR/Lyzv/shZds8wWRSpCQLlxihv9pDJv/P8zJi60/A9oH/jReJ61uQiheTau8KLF6fjfCO/aSvK4nfl8FrVZr7j+3J0JFiWTpeDOvEzrWkvpWpOOa9CpuF+e7Yvm8EfhxoZBdG5sWNKiwCAUZi38JHnGTZINH/T6PQO+iPR2Ok7t6EIfkpPxwjvhb93Cl2MmzTfiaWrt6DoeQCPBcvI2DLsXFZ/u0cVXwa1nftJ+cmCb5EJodgsphVoHdYTeKGs9ok4qHMMVIcfyRuXYcbJzJI+4mMszuw/AmhQwlwMe7KQih/ol/WPgNyUPAEb0ds33IQUM30jfSd9C/jdAD2PcPfSZK0QeLDaRpcIj4cnVQCmw9nssiMTw5xMW5LyNv5uOgqZK6SX0wW0qeQm52BrB0noXhiB9Y4mZc6nbvI1P+aFH9omUcjyXHRG3XweboA612cAoamZGNObRbx29VkvpOA7IshSJRZhvjc/vbhe8xXHvjtqYO5fTimgFLs2afRM8hP0iH01VcQJ+YUzs51hJGcM0rOEXx//5aHvaVthIJ5vEnUud2EkYXRRflJ4Y5OHDEZ7bPHHdlKtMtoMMDI3BIXm1BKyceZfbrYVssjcRO1Gal6ncmDPZc0Q66/TdDWmT4bZzh4lFIKZ7K3xxU794AdszalkO6XXpsFTX0sdEVkkc7UO+bADh3hgbZaYGREJjpO63cmx5tl725i1dEAqjenojfNxT9APkHbd4qEXo2Nu6F+Iww6waNSLjNR35Vh4xPybzfpr0zHFn02qjbJnAlK+ZAnx0hHuBp3ZSLpY8583hOxZaJleFL2nvJX5m6Xzfl90KZmYOjpt7HR2c0eZ/U71H8bih7ZBrzCxSZPxEy5Y5MNUnP7W5yp/cCDGSbwjZMyMeiBAXRVVqE6RuP46oHouRIwSvVwAJl0phHn9p6gQ4rByMITQUOIOzpxxGS0zx53ZCvRLiZ4OZwgSsnHmX262FaFp2xGql5n8mDPdUGHE7R1ps9um4wz2dvjip17wI5Zm3K1CKZeVxZKDB5oqwVGRlL1O5PjzbJ3N3FLR0ImaPtOcaRXgx76gU5oK88g8fFY9xdKDBP1XRnym5B/u4hxWA9D3ylUVgJr5F4yZ5DyIU+OkY5wNe7KRNLHnPm8J2LLRMvwpOw94a/GTpSnrkZ+/QD3FTmyyOnVlaByMArhC7lDHOKsfpn690TMdC3uyZzb3+LcnMWSGIYLOJadjUPdESjOjJJ9q51CoVC+08xQIWgWjZg3FwW8ZvnCZzqfvIUxtJUhO7sAl5ftx+ZoajdC+mt2ICunBoqndiN56r8tQfkuoAjGmh1pUOi4R/6iVyZhe4MKW0tzXPh8vytMhw/zHvXNik3fkrn9zXkMj0KhUCgUCoVCoVBucW6dO0sUCoVCoVAoFAqFcgtBF0sUCoVCoVAoFAqFIgJdLFEoFAqFQqFQKBSKCHSxRKFQKBQKhUKhUCgi0MUShUKhUCgUCoVCoYhAF0sUCoVCoVAoFAqFIgJdLFEoFAqFQqFQKBSKCHSxRJk0jAYD9xeqHTFmhGHYyCduPYzDDtp/i7d70hk1wDDKb38bGdOjvb4Neme26ylYW5LwEwrHLWB3DmPC7YDxFvZbpm2G70hMvV18frL97bs4jk7G2OJh32HmbQ6LE9MZs6+vE62dfc7thTluoBvtbd3Qi1XA9IP4xbifTBscF5uHu1F3pBClhTXo9YB4bqnFkvFaG1q79XxqEtF3o7VtAJ4zL8p4BlD7bBJK2/ikGIM12JlUgnY+eWvRhtIkB+1vK4H6lm23hxHxlfaSJKhLnCn2Nqdbh70v7UFtN5+eTFgfyEHtIJ92gymLmzeZm293TmLCbYC+OmcK5KdHb1Mb+p1OcMYfw7bt2RqS8x3AVZ+/SfOVSfc3yfFfji3dZkx4bJl83xmqzoJ63xlRe+s6shqaw+csef21O5CekABN1m4c+k0WNGQ7PfcU+u0Wg8aLZdjyWALU6Tk4kJeDdcx2Rhm6DPwBBLYfJL6O+8myQfvY3I3KjVnQjgYiPDgAXgp+9wS4pRZLhmYi0OpePjWJfFGDLUeaIdAThUIR47voKwvScPz9KqxZwKdvcaYsblIokvSiensZWob5pChyjqFY+M7OV76FdjLhsWXyZeK3TI3Q+g/QYr9IHevE2RojkuOiwKw9jOcLsS1Pj8SCk6g/eRxHX2X+L0Di0H5sO9xmXWxd0+H57BrMefo4Gt+uwtHjVah57zheDKhBRo7OdpEXnAnd+++hUfh7KozPdIURDA0G4tEUNRbHBkPF750I03YQ+O0pwXDlFKqLDuOVPzXg6rASgSGzMYMs2fprC1FRewGf9vwVY1f1mL4wBLP+qQ8NhbUwzJ6Oj4/noeBtIyJipuNcYTl6Z0Vgzg/4QofboC39FHdHzIUXv4u9BXe8GGUH3sDH+jH88P4g+PwTqb/tBI5pG/Hx59dgHOyGkS+Hqb/ycz+E3+/NF2BAe2UJPr3Tml/3dQC+f6ECBXnVIA3BnDvJYQ7qMeOov6KMDaD11TJU/rEQb300jOteAZjvN4PP9GBZZnmFjOHDI0SufziFq0YV5s73c7ttGDOg9/0TKDu0j+SP4u4H5mHktBbDYeux5B7+mOE+nH29CCX7iKxGvbEgwIAz2mH88/+NwCyI6ToITA2GSzXQFv8eB06ex+h/+2HOPBUUgnbqz+twvOggit76hM2fH6TCND7PJZnZMICPjtUB8YL2m/nyHMpIViLbbg6HbTQS+zjciP9+cCF8/id3LGtr7V5YtHAm187RTtQVn8ZoCLF5Jgo5sim2rBqMBj2IWQLR99YU4p3BAITep2TT+qYKHC0+jIpTlzA6Yx4e4Peb9f79eV/hVP5v8crl2dAsnsnlieDIV776sBxvIQZJ32tA8YF9qPxgEHfMXoj7fyi4fMPaSwlKD5ej4dIwvOY9CD++GaIQ++l6twLlv8vj7Wc2ht48YvE/BlbGx/6I0vJT+GL4e7hvHvF3tkredoL8cFWbjyLGvibii6yczmCMjUFm3w/Cj/q0bH+Z9s2YMw/3eYtcrnJV38ZLqHv9CgLigtBbRez+t+/gM8Y3hG3jZVP5xxJOp5iN++d5s2UxZY+Pm9xpVhz7lkNbYSA6bD9ZgeLfHkTdhWGM/SjIVscSsQ8DzdCWlaOikMkfhdd9CzGLiZkEd+LpRO2OteeL0zH3rzXs+b33JCP0RyTDEpcqcOYKOYf4lx/fTlvGxwR9cwWOn/oa9zzgDy+DvLjqSOZGYt9H//P/We2E1Vs5urzCcP9Mbo+xU4ejp0eJffixExZ7HPsIMbtPa1A5HIbUH1zA0fw8lIvpnODMJlhbdjRO/g+m/6/hw0+7MXB9ED3/EB7Hw/rW+GPYtl2Zh8TAKzhB2sbE8rG7Q/DAbEGgI0iNBWZYWb6rx4IHZ1vlNNaNhiIyOQsI42zCiX0ySMULR3Ji7azBiAWhVh1x9k7640f2GHifX6XGfOYUJ/4tHoM5u3A2B3LkX3Llx8D5WzzSl/pxO6RiutQcwen4b4cDO+HyJOKOPcLjmXpnD6LmiGC+6ET+kzu2iJQ9egfRidVnbZig7ziN9UK8/TC9Yx8a7kjG8vsFDenQYtf7C/FvWx6Gz//oxomnCuH1y2KkPywo559UeCBChdbcN4DH43H/DAMa9j+H1pgCvPSze/mDCP9DifuWheB7pXm4HLIG/0zisLkfax9dyI5NriGIzTdOofT4W/jYLKf/Utj4ors4cJNJorMEGRlVGApPw66cVfC7lIesQm4F6jVvCUIDiNBnBSM8PBA+bM+G0K7VoWjHflz2jcWGx8Og5Pe1DzH5PKN9+FDbQXJ4Bmrw/NosVBvDkLxpFZZOO4WstbvRagAUswIRvsAHUJLJZfgSzOH9b4gMQuWdwkIN6Gm01sPkVxfuwIGLvohbr+GCHFtPDlqVS9l6Qo06Sz0sTvo7DrJqL1+bigOXfLH0iRxsiAQ+eC4DpW380Z4si5XXSeTnHcfIgsexIeUh6Cu3IetIJ5dvj1R5zIBZmI70Sj1CV2UiOVKBuhd24wPh1Q8ykSjamIFyvVknNch9uR5/5bPFdU0cvHobUl5ohiJyFX6xKgxGXRZSBLeIDbXbSboNPo9mY9empRjRZuF57QCX6YrMJoBtG5dCcTYHmq38FROFEjc+K0FtB3sogQwY5cTWDtfjsvlWdUc99jYb4SVlUwpfKK6RwahZcC1m9Ay0B9ugmO1LEka0H07FujI95j+ahg0r/NFF9LKlso8/ltH7cezd9xqM4aStMcw5jnHkKyxNhTh0ToXlRN9rFvXjWEYOqnmxY7QN5anpKNUHI3F9GuLu7cKh9Axor/D54+DsJ0NrENjPDpRXW/2P0bMmS4eRYNLurFUI6CtGquWqFGc75TtyUDeNl9uoDhkbC9Huji+ycmpED39ljfX9sj2W/i73bsTzG/egweaSGI+r+mbpQ2U+74ubVkB5OhcZlrbp0bB9NSub+auyiH0ForckA8/rOGGLx017xHxLwlbYetOx94IKidm7sTHSAO1WouNrfLaM2Je6Ng9dM5diQ04alqIeWzYTfQhlKhpPxeO2hQnYnfFKI8ord+PAn42snUUyYzcT2/i4tGF3DpLJOQeYdgrrdIChuRBZeXqEa6LAzIGl46qdzB8NxlBZOlJ5XSu8jegS2glp7zGit71WY8Ll2hKcHfVhY6M9zn2Ep6sMO/O7Mces84PpeMYcLyXax+B0nFT4YX54MJjWBQQvQfg8kVY6O+ZaBQ5UkhhA6v5FnBfqXkxH0Xmrh0qNBUIUvtPRf+Q1nBV03thUhZ1tSvgxYU/CPp3HC15OhV3wW8HIKRD9At9h7ayxz6ZdjNw+vCJmVM79WzwGS8+BxPzLFfmNQyqmS80RJMd/OxzZiV3cWapsRu7qLNSZTdgetl7mcSxBvTvKUG2RlXP5m2U9OWMLX/aePLSoYknZTOzfj9SXThGvEsFt35GK9faoELkiDHV1jTbt6GqsAdTRCGJWkdfacHYwFsujRXxcpcZL77+COOZ2zlgH2hv98WisP5cnZFow1p88ifWL+LSn8PYnY6FATgsmvlBiMU0hQ69nmqJe/pBPEa4Pmb4cusEn+Pz8Vj7F0Go6FBNr2vveCJ9m4PYd+ohPMvSfNG2LKTBd5JOXizSmlKIOPsVww3T5ZIHpzVa+nI8KTFGbTpqGuBTLxfxYu7r7TW9ustbD5r9QT0qywtSTcayXT3Gw+17l9kn114ZvbpiGunpNQ9/waULPsXWmFQVcmzxZFievdaZXP+OSLEQmKzTFpst80gap8nqrTBnxu0xNQjV9cdyUIdDTl6+mmVb8ptFGfj3H0kxRFr2J6PqbDtMxTZrp9S/4NIN5Hy92Ri9ZVf1cgmGo3/TldX7TFZmNg2tPRk6B6fcFdr8cQbu/IcfFa0zHPmFP4vim1/R6mtp04AOuLrbvZttiZL+pwHRoEzmHN9HLxRpTGm9HUjZ144NdphUC22XTaVWmL5nE0Num7fZ64Pe1MDIR07sUjnzFxhdumJp+E2va9jqnh5G3s8fpmt2323afBTH7YfYJ/fx6r6mnV3DA9UbTgZhMU+1XTILXlY3cRkib1O75IiunTNObvFmN7y8XG3LfFTbYiiv6FtUJ8Z00QTy70dtl+lJQ1Y33d5miMq06GR837RHxLSlbYc950vTmVTaHZaS/13SDjwFSdmq6MWTq+UJoNcQn1qlNv+eb6SieOovbE7U7Vk7rjpp6BHGMk/9+U4tlH4l1V4cs/bSFkyNjkyMfFpjWpRD5sfbHI6ZLYVxl87NNtUKxjBA7jjfHMy5umG2eae+2/ALTNktcZmLfOtt4KMSpj/D9t9c5E6dt4oOz9vE6cDJOcjKy+o44448R0w0zxljqkjEW2HLD1LJbbbENc1qufbLtcRQvGD+xl1N/o+nV4no2DrPnisRMS1tYOVv7L+Xf42Ow1Q4tsGVaY8Y4X3FZfra6lozpEnME6fFfjPF2cjFfbRcjuHIcjS1i9TL7hPU6lz8n68kZW7iybeIym59jOv13Pj0ON3xHMtaL8Pd6U67Qxtl5jsBWROYFotjZpTPYfmgyTQds5ltVposOZSGEk6Vrccg1pvTOkmphCAJqcrElT4ezzJczpqngp5Je8ym9RVavDunDxSYgMSaYTzMoEKTJRGKYK+WIoPqBYIXK1TNH0Y/WpmbLr2dUifZu7qqSS/2dpoAq0B/KwW60N51C9ZFCVDbqLV/38GRZHCoiV36TYTZZjQ8bcYNP2iBRnvGLLrSHLcUioXjnRiPOctfViB4ik+VLHrJZ4QdExyKI3zZjo+u+Npwe8wf6rfJtbe7FiLIPvV9wV03mBEeg/Ug2dpbVELkPwOjtC/Odf3ftTYjPgiUID7f7MVf6zPR1ogWxWCy8OjLNH4siFKRO7i1Ov4hYBJw+hy6ybfjkHP4aoUFytAqnzzNXdogdNQIxDzNXXqRtSvHwj5HYXY8m9sKXEe1nziAyJRbMgxLGT9rQMNeH/C+Q12d6ktGJHsuVNzu9u4uNLygEd0mMuNjShlAVaZugD5cHSU5HL/r5o4SI2o9/hMB+CDP8EeBLTKGTlFdTgdLDOiJ34IbFphVEB8KrV0qER0Wh/UI3e3VswrZg019fzCGGazCIXv9zQd9m7HQyQ4kf8psMCv9A+JDzuogc6yoKUfrGBcDGl+Uh9C1pWwlAaEQf8rO3o7KmDb0DRih9/aFgn02RtlMoVAjwV0Lf3YbWeh0qC4+ibtBoG4NE4qlk3J6o3Xmr4MU9X8MxMwihM2uwfXMequs70U9UqrpXxfdTHMOZQmQ814Hk/TmIHPcUq+O4avysA2cXRSGcueJqRvkQFof1of0zxpbIsTEqNLQwd6IMuHhOj6UaDZaqGnGRuYJ/hcRDRGPRXOZEESR9hDAuTochcsY5tJPypds3ydjpxkspaIiMscAWBUKJ/7fUN3N31kbPoeF0CJLjeb+TsE9n8YL1nQg1IoVy8o3CmidXsHHYVTzl3+MQ+orL8hMiw7eczhHkj//O6cPl80BcpDBGkHIejoBPcwfGv7UpXi8Tn4X1Sst/cscWmzkPibEBGMGI/ftCUjjxHXnzAju8o5AY34G609wBxqYaaO8lNi8Ug9HBfHEcdnHfGfwdVOt8y9HTElPP1D6GF/wkjr+6H4m+3agryEbKTxIEtzs9xRCGBpVQuv7Qo4sw9ZB/+86hpcX661FEYH3gncQ8CK7019CG0rUJ0DxbRgZL4vZzo/DjCIFlerIsV5EozzBM2qFU2AQkJsAoLDowwPAVcWCFndWTtOUdMzH+rkfvNPITyLelpRsKMvkM8OYCvDJ+F3TFmVg81obKFzKgTkhF6Xl+YPeAvfmRxdjiZXa/YIEsmTb6+5Jga4tS5Yv+UT6UkAlJDJgJjwEtpzrYiTITsEEWU4aBNnxoIEGIfeFThk3NiEKcuhtvnSYTb34CsDyCC4ysHkbJgCI4t6VFj4BkMpiwR0wFnK6N1zoEbSC/YX+sj3DwnoWk/ZAxXrcN6sdSka8lZelVCFeTSR2fx6GCz938Jo9CSaxrZMR1X5wosvUtg7EB1G1LgDp1P6qJHEdUSxC//CE+032kbUWF5buqcDRzCdDyJ+RmJCF2rfnRE2k7ZV7+TU9IwvayU2ghE/GAqJVY7jQETTRuu253LMyjIK8eR4HGF72nCrBtNZH1Nt24rzkJqe0wInJRH7RaB4/aOICV+WxfIlkhSniRHeaFd8DD0UAjWZwOE9/+jFkYMQsooL3LAH1bMwzx0Q4nmNI+QlDdSWoUooJqpgEj1+W176YhYyywR7FMjeRuHZhQaTz/AaofXGFZ4Ejap5N4IR6v3GSS/HscbsjPigzfcjpHcHP8HwcTI0gstX/BiSwU/EQv9MqoV5b8b6GxxQ2kY70YwosN3EXZ8MRo68WAAH9EdndZHlW3x/IZb7L4C1K0ocfBE3/jPlPuHYhwm/lWmOXi983mDv7/qWNmMOLWMT+yfakM6RmFaIjZheWyr3b7wO9eMjDwKZYxoaMEYX4YcQCyck6eKwj7zN8MmEYGYwcRzotMbm0f7iY4jSEBmLOAlLnkSWyMcRI2ZfbX0PQaymdnouYVtWUw6y23G6A8WZYLSJWn8g+Cooy5shNlHchHu9HVzbgcAwlmQQqUXurGVsGVY2NXF1qdhUv2quwIlj6RiUgnDqP0j0DiE8yPlFm/A7EvVSDm5JNcWyZsbxLMI4uV7npcHk6Bn6VMI3pIXyMXBfDpYESqDSht1MGvLRZxu8iuaWRC/dlJnG76B1piVmEfe1VInk2FxmowlN+MLt8uaMPU2MibOauHMRXiM9eRkkSYkvjN6fqGtwYb1zudHVtQBYXAj7GfMWI/5qtjerKoIPbDBedO1JV1Ye1+wfPNo2fQwG9yDKCX+WMKc61y6+1shmJhlHXyN9m2YEGuvmVwSYfSLg2KXyf2zJ9jPF3PbUwASVthUSJgGZlMkd8aMvE4+2ISdlbEoupJKTs1oOE1He55qgovqc3S75P4BLB7cduKtN3Zh3crvghSr2N/m8e6UbkhA6X1sXgx3nZZYSb5yaexeeEKIH07nvc/in3Jzt/9M8PZ+QV0IUKw4OlDzycKBMXxZQRHI9FQjBadL7FNNV5k9pEFVLu2Hk2GbixfZXtV3YocHyF80k3idIRV52ycDkYo2aH6vnT7XB8nPYTMscAG4nPxGj0ONHXCr/sUlsdl8eMXsWU59ukoXojFK+ZvyJAhUemtYC+U2WN0JCO3/FtqDiSCO/KzIO1bzucIbo7/42BiRDcaOgxIvNfqm2w5i0Jg/x0mpt45IUqUdtrWy9ztbwXfD1nyv5XGFteRF+vHo1iWhA15+3G60we9p0MQ96TArmdG4MfBhfhLdToiU+zs/UoFtmT0YuPJHCyeEcKOhTtPNCPxmQjbCwyGMyj6+VEElB5Fsrypwk1lCu8skdVpXgJSC5th4K/aGYb0MCh84SMck/R/s+SL40scQIGGd05xf9hL3wntwTLBt/qViPzpCpwtq0Cr+QMDg6eQvzYV2kt8mmFYjyFBPX5zg6E4XYMGZkI5pkdXeR6KHKyGOVRYnrwC1YdLrPWM9aE6KwHPsFcVZPaXhwmwiqEBa5sGz0BreXPZk2W5jmR5D8Zig4IEnRO8wMYMaC0pRrXAM5gJvkJL9pllOtyM0pIzzq/OzYzFz2KJQ5VY+42+E3gmIQt17IvmA6jOIPLWWhU1NEhG8nuZOz1SMjOgqyIPpfxtZrfxXoK42DaUlgnqaS5BUWMEHo22TvqCwmPRUlGB6ugwzGeDcjAWx5xBfkkbEsND2GOkbYpnEZlQ6U8g9/AZJD8aZRmc8KAGmxUVKDLrgcC8hJ6aVIh2R4P16ADapf6WhZ2vSBGauA7TK/Zbdc3Yw8HV0Dh66TWYDML+OuTuq0HvAJkcDxCf3lOBHstAo4LqXiOGvjIPvgb0nqhCNZ8yoxXKjdhJUYURG2KZz4665j+eQJ6+ZUB8b87oEP5q6Xo3qrVn+IQAybhph5StXNNhC/Ezqw716B8C5sxkbFrKTpXw8VVgaMA6q9afPg6t+eMQosiM205w2e4IxuY8qNcWWus0DGHIoICPSsIwlGHY+Cs1vizZbf3ohRQL1FijEsRJQv+J/SgyahDzIL+DtZNuYrtnkPgQbycLlmB5Uwny26IQ6dB05PkIBivs4nQBqiOSuMcJZbRP3jhJfMssT4fIOUaA5FggThBZcA5pd5B4vAKJlpfRpexTIl6Y5WQZd4isy7OhfqmebJFJ5rwghHbWo/YSSZFFTH99Hg45Wv/I9W+bGCw1BxLBTfmZkfItqTmCW+M/i9BOuBjRcKTM6q9MOWQcjEuMJh4wniBNGubodiC/phv6YQMRVQX2lvda75DIlP+tNLa47DvuzAsYpgUjMl4P7Y4C9qKszWOnxAYTczKBI1l4Wdtp7XunDi8/WwGvJ9PJQonZo0DoE7/G8vO52EJk1M/XZ7xG9LZ1N1rUmUiUs1AaG8DZw3nQXjQrauqZwsUSEdqT+5H4WS7Uj61GelICCS5dSN6fhlB+Ra+KT8P6rv1Qr0xCuYMPszHlLF6fjfCO/dCsXIno1DJ4rUqzedxAEZOFo8kDyF29EuqEBHJMAUbW7Mca8xW3sBS86HcC6eT8nbWc8BXL0vFiWCd2riVlrkzFMe9V2BzIZjmErWfNEA4x9SQlIXZlBt6alY2ticxKW7q/NjBtCqgnbSKO9/MExGa1IUhtvhriybLcQKo85lGW/Gz4aDMQnZAETcJqaOdmYqtQfsFP4pWnVdBu5GW19jXMf8pWb+MhwfGpAqwZLiS6ToCG9Ds6owZ+T+Xw77MQh32Bqzc2KRWpCSuRqlPhpRwNCZxSMtPjYn0NKk8RR2eSbmPbRjVpg2bPAJILfoXlwuASFoVkElj/f/b+Byqq697/xt99yHe8NsOPdKgWogU0oglibpAskSwoXBF6x9AOlwqPPrL0iysueWIlscEkxSQVEzUxKhbEC8WvVJZUribzdRoSGihUgl8NJoCJQFSoMjwx8MTOfMt1GpbzhDu/vc85w5wZzsw58wc0yX6tNXr2+bP3Z3/+7b3POTOI359e+FgSrNYkLH/cMWR49ik7MUhfo4ZxNAkpomvpd6Wy3/gNYluIHX5K7UBl6Ufmq6TP7kYlczuqd7Ri2N1xiViRZd467N61GM0FK4ldskg856Lkuhb78+Mm+u5MGNJLK0lCb0VJUQEKd56B6qmdWDPxnaUwZBTkYPAA0UcWGaB/movqkGSiT2fW5ERAT18FoX6yyYDQ58qwnnNTL+MnECi0tyxhadi0Zgi7iMy6X5D+51YhODlNOMijLG+6IOcrc3R4kYtX0u7aXFJ3ARpmF2ObjvdDOT+NzSlCVFMhyQe5JC5XoejyYmTKpCDZvC2H135H2kzYjDJtL3YQ/eatpf5Vgmu6/dj0uHCCB1RxW/HmZqB0m2ix5QknnRMf/OlKbGyIxGsVjrvalNgkLZlNxGFpnCB10GIsfcIKa/JPEOv2qYCyGIG2CGtGipHG5eks7LiiReVLK0gWIyiQT36cjEP2s+E4TmyQ/KKbX/VSdI4rcmOBG2LIwkZNFvqpzrrz7J8y+YLTUzEiG+x6ykLB2Ujse154qhKhxa/WW0n/soiOiI77VmATMakkCuJ7cg6WnwNNxkf92ZGLLbk5gk/j/2Q/cco7NEfk7oUpuwwvZkgtlQhhOuyrLoCqeSeKthSgRK/Gpp3rECUcVqR/wr0ztvgQO77MCwTozQbrLYvzTVk7ZIx4rWwdVIYi4g9Enn8hNikyQLWmDK+Jn7ar47Cl4jdI7N+LPJJT6HlpG/eiP+43KNvqPjc7YR3Ah4ZG6D/28+a2H3yP/sqDsD190FcrxlVQy79b4R/co3ErVGq1xy/s+o1cO97010rOtXo4N5B1eYuC+ug7qFaVh9dmfLUJbXuMtC31t20IXLv0dR2pycR0+Vsg9e2v7yrtc8cBJP/PODS+LkyYAgz37vJMBa9R0TtTTv0cgj6vAKbn3sMm0d+ko/WRTrnopBsV/7IdePPP2EImudLnCEyXLwQazh9It9z4v1940omcH8ocl80HUvjr+wTFfmcnAG16BdU5ibip+G6tR/+3Q3PVuIf2p1A+v5EZC7xB1j/l8kWg9DSV8e2Kn/rzGFtyY2Cg4sybelzHlht12Fhgxq/e34pYYZd7/X/LxpapklHp3Gc6/TzATO8PPNihgTYdDhVEE8I0DH5y7XjTX5q4PZ0byLq8RUF9NHl5PMVXm9C2PQQY1667AWu6/C2Q+vbXdxX2eXioH4nLYqZkoURRkT7IimHtQ21eLkpbRvgvhZKEajRUof5WEuIf5k+xQ+uT04nHc6bLFwIN5w9TJLcnncj5ocxx2Xwghb++T1Dkd2IC0KZXUJ1P0UJESYxwucpT+1Mon9/IjAXeIOufcvkiUHqayvh2xU/9eYwtuTEwUHGmsB5rXxU2rt2DCzeF97/GBtBcdRrG1Dgs5PfwKNT/N35smSoZlc59ptPPA8zdWSwxGIx7guC4zdiWIX7F7y6gisGanflQGQr4x/n01ZU2DbZV0y+ICud4ZAZC6ffUZghFBoPBYHznUcWsw2/yZ+B/bsniXv9K/kUx2mYV4sTzScpe/2JjC0Pg7ryGx2AwGAwGg8FgMBj3OOzJEoPBYDAYDAaDwWBIwBZLDAaDwWAwGAwGgyEBWywxGAwGg8FgMBgMhgRsscRgMBgMBoPBYDAYErDFEoPBYDAYDAaDwWBIwBZLDAaDwWAwGAwGgyEBWywxGAwGg8FgMBgMhgR3f7E0boVlVPjryt8ArEMd6LpqEUrfPayjFljHhYIU1J4W9/aUvV4Oq8Vj/d9Zxs3oaemG2R/d2uFi0mEnv23GuHdg8cP4jvJdyGOKxud7eb7lMvbcs4yRPDombN9NlOiLyuqNyQM5l/gWcfcXS7casSurCj1CURbzALq6R3B3wt2CC0dLsL3uItn6LtKN6qwsVHcLRSm6q6B9oRFmoeiMgutlMDcUe6h/OjDDeL4bw3c5UVpvdqNrQKSFAQNef20vmgaEsj9wMVmMplu04L/Nvm1M0v03CO/ix4rh7g4Yv5ld/Q7D7DaZ70IeU9BHb+dbU4qEnzqNPfcuPVVZ0FbdA86kQF9U1l0NI0LJFQkbBHIu8S3im/ca3vVGPHO04y4tVtRI2fUeWnatIFuM7yZGNOyoQeeoULxLWDpq8EyDUSgRFuXjxF9OYc0iocyYMibp/luLGZ1Hd6DhulBkfENgdmN8E2B+eveRsAGbS0gStJMgbE8Po0O48HYFqvb9EZ+MhWBRpAXn9KP45/+egNnCKZarjdAf/z2qa1txffR7+PH8eQhWkf3dp3Fc345P/noT1lsDsM5OQNQPPF8jyfgIes7UofK3h9B8aRTjP4rGQz8UnTw6gOYTlag5SGQ0j+OHD0Uj9J/4Q8NN5aj/azjiHwrhd5C6uk5WofpILdqujiJ4/qMIF1ZSnLyXg7Hk6/M4VnoAta03Ma6Zj4XhM/kTBMzn63Cs8gjqWq9ibOZ8PPJj0VLMQ/3u8L2+IbSV18Io0itGu6Gv/gwPJBB9YgQfHW8GMtZj2YPC8ZFuNNQeRsX/aMXnQZFYcv8Aai+FYO2TD8O5lxTh+rRM3NdWiop9dTg3EoSoR6LxgNhWHvQ/9lkj6m/M5+u3y7Z4HB8ePYAyIsP1UTUWLJ6LmR5uA7j3FZn+c9tv4cPPBjDy1S0M/kPkBx5k5v1gBhZ93YHqPSX440djeOARoveviO6OE92d7MBYcLSEnWpQ//tyvPvRKL4KjpzwG+qDdU2X8Nng3zD+uRkzHl6M2XeobOcwTrft7dJ+Vv4OB898jLH/CkfUfA1Udr14qJ9ciOa3byBytRYL1WKbk2tqanAtmOhHw59KsXTU4fil72PJQg2ChH1izB8bcKLiECre/ZSTY2G06Dw5/xbrleaLubfQeNTuj7wumv8zGj+6UY83d1ehbYj3p++PtMJQfgSV79/AfXMfVhzfcjErqXvhWjvm8zU40ROMeNLPCcwd0B+9jPviSFuk83L5yledTcpPsKCnvgqf3c/7tFP80MNu/YDGQj3+/MkAPjeNYviWCotiw6HiYsDZz3CjFdXv/110/DN8f/6XaC39Ld68Nhe6pbO40zzmJVfGLeh/v47IVcWfj7l4aH4IrwPJNsaIvE2wzJ2BT06QXPCeFQmp0VwfPcc7uSY6HJ/raT6iPnYfiRNnW2CE2K7q31FJrudy3H2XcMzeX+EU930T2pgfis9P7cXrVUId0Sp82XIaRw8fQfNfg/DgQocPcsiMQZzPD+lReXAfsdsYZkbNx49DqDRu7EaOWIidGiqO4M0/tOFzBXlyAnc2lY1d+1hP8vwNcjyaHL9fOEbwmJ8InmKE10Ekvn+pDmUHGkCMjShSt/s6/R973LbppV6ndXxWMN9yIFc/j1/5fAI3fmofe9KjYTxF4vi3f8IVKreLTuV8R4w7u3myNcWT/335YS3eRQY2Lg/nd0hgJdcf+1//H5Y8PEvQD6/f/uA4PDSL32PtM+DY2THSPyFGPfTLPo+Y97dGLu6ND2Yj9n7xWM2fZ7cPHbvo2Bc8UI9PQrKR/oirIdzYgLO5KMcTm3qaL7v1B9d6KOKxQtjl1bhwF/EQ0lOApRsVmwpQa45D9tOrsTyoESVvtOBvwmGKpWkHdIUG3I5ZjV8WrkbkUCXyig3cayOq2QsQvygUUEciNn4ZooT5gKdrJmNG246NeP2SBplFe7Ap0QL9tmI03BQOjzTipbWFaLDaZWxF4do96BIeZZmIc9f2mfjCWDdq8zai2hyDzPX5SJ/Tj8MbC6C/wR+23mhHrWE/SustiH0yH2uWDKP+V6T/ffxx+gi050ge1pX3I3xFPjY8uQDD5RvxTP0Qf1im/sl4V19mjBnVG3JR3W1/qdGEHr0BPUL3OMaG8KG+lxyR4KYBz2/Yi15NGjas/zmiBvZjV22/cNA9Fw4dQCe5JvvpXMSO1CDvBZGtOP0Xo0u9nNN/rNXgpH8nONnOoLRkPwbn/ZzIkIbwqweQs82AYTfv23r2FZn+q0giiI9BKNSIjFmG+PlCULvIvFzdgZLcQjQLT755PyhDhcGKxNVbiU8Z8MwL27HrwDnMeIzInR6M5pICVHws2GG8D7Vr83DwahiWP1WMDYnABy8WTNgpeP4yxEaStmfHID5+AUJp1uHkbMeg8HqguWE7pwdrPOnn6uVAayFy9p0jHkKQqd89YQi2tqCiacKBCUNoqqqDJSRiIvmJofrO2deN0CeLsPvp5bitL8RLekExcv7N5YtC6MdE+WJnDRpE/kjjseHoXlRfjSR15CLqSjk2Fm9HaY2JxMBqLuaOF5D4tr+FIONfcjErqXsXNA9Y0VzVgn6RDw43VaHCEoxIcr5cvlKis8MDEUinMTxvxElnTvmJw4LBdheftuPRD9SIIr5Ouxq6iPj6ImFwc/EzDlMvatuHeN/ijp/A6/ve4n0vNYzsFPJSjRkLiU43rIhAvzgvTYLm6FwU6C1YuLqQ+O8CGKsK8JLBrgOpNvjYrdi5H9fCSD76eRz39F9JvNfuteejFVCf3Y+811odby5wOe4A+ueIclzpGUd/ZfsmtLGvEtciSKznLED/IWLP7ftReyscKav5HLhRPF7J+Cjn8zV7cfiihly/FSkh7Xhp0160cRW4sVtfFQoKTsEUn4/dxau5PFlY3i30QQYpfcvFLvUtYazfsKcY2eT4wS3l6BH6wOWnlzugSiT1rY6D1SDKTwS5GOF0UL4TBy+HkTjQcRNFjzlPwJ+xR6pN7/Q6zeOzgvmWM/L1+5XPnXDjpxxDqC89gduLSLxwMVmCApFO5XzHFUm7ydhazv+UoAqxov9IC67ZxwEythwn+n29qVfYAVwj48KFMTqjcO3XcqguFENH/NneJjc21e/Bwf+wIpbEfeIc4YCIYWIPzj6cf0VgsLQE7qdkSnK85/myZ38Q1yMgHivs8aB4XLjL2KaRL07m21a82m67I5Qpg8fzbUmpZbbLQtn2ldE2aLwtFAhftdsOpm61NX0plD8qsyU9fcZmEoocctc40WU7nLrZ9s7nQpFwe9hou/M1v32tQmfLqejlCxx3bNfOlNne6eLrv1yaZksq7eK2b79XNKk/3L49/D7T21ttSeuO2QaFuimXS7W2nEqhftN7th2pRbYmcWeG220nK1tsX5BNufonIVMfJ0/RezaRpmx3/rLbtiL/FHec102a7fBHXIFn+Ixt+4R9xMfv2Dr3aG0Fx43cEZ7btvOvaifbZwL++tf/PNlW7wzzRap/5zqFfSf5fVwf7PVzsmltB/8iqu9ro+3tfLLvA0kNyfiKXP8p9ByHvBTOpk4+w/u1sx+csA3yh4iMH5I6dLbjoksul2lt+fZ+f33HZuo32kwivxk8vs62ooz3OwpXp+CHHJycglxf99qO60j9n/KHOL7qtTWVnbJdpl2Xq19cl6tOrp+w5WcQfdivNZ6yFWTstnV+JZRdoPFSeEqkLNOw7QvhXDn/lsoXdJ84X3DxKD7n81O2wtRi29m/C2XiKU1b04iP8Gco8i9PMUuYpPtJ8D7o8CMXn5TJV3I6c41h0wfHbMf/zMsvzk88w7Z3nnbYzyl+ZP3M+VoOJ98QEOdk7vg628kr3BEempeIj5x3Eprf585v7hj7bV+Izqd5KmmrhzakcgtFQbw7XcPVbfcfqRzH75vor2zf+Dbs/kf54tRmW9ILLQ4bfknbJHUIupDzUc7GL7eI4oK3U8n79hon242z+xsfCiXCVybbFyZHDR6R0Lfs2MRds9/WOeFbxNc+N/HjLJef8m1vX+ePcNj32butIEacdCCX86Rs7eXYM1nvXup1WsdnhfMtJ+Tql89NXs1X3OYXl9imY45dBiW+44KU3eRsrcj/PI4BFOdxgNp3e2mZbbuu0naN20PlXsf35Wui+wwX/3WZy3D+4TI28foSfJjKmOGim9t0X5pt+9vihC1GLsdTn3A/X/bkD871CIjHCh/GhbvJND5ZsmJwYAgpyx5zugMdmZyGaGGbY2YEIsPICrmvA12Ndag+YkAn2X3H0y9zeHVNJGIThlBatAP1jd0wjlihDouAintuOITL54HM1BjuTB4VonVbkRlH1/5irLjc2Y1YDVkdnyftCp9rt8iRXiOGhbMQokHwxDNq4MGIBRgeu8NtWz/tRluCFomit3UQloQ1m1cgXGn9IuTqu9rXh8SkBO4uhh1V3HKk3LiEa15/B2cEg70qJD4eIZQpasQnJQnb7lGHiCSYeb/THSWq/yjV8ER/6WdwTI0e4jvSd44WIHaxqL6gCMSnanChz823E33xL48M4drHQHqi2GeIlz2egNCOXkx8s4X02f4aA4JojyOgEdnpwTkL0G8W7t+R45oFEVDfGiC2b0XD0XLUt5uV/0LQUDfOgvjBEqFMmRmD9K05iKWq8qf+ecnInNOINuG7rcPnG2HSZWGp8AafK1ExCeg5WoRdNY3EhiOwhoSBf6NNzr+l80V4gku+oKhFPkT6NgNhCLW/hQYNwucTOc30/pxC//IQs8qIQGJGGPTnBCUNdaDZrEPmE4KUMj4op7P09GSnGNYk52N9mjgOFeKvn7lFQ2Jc2CRweWleKPnfofOuK8Qe1j4M2p/4uaAiOg8l9uon5zbXlaP6j5cAJ7mc27DjlFsoCuLd6RoyFkTiNm5zd0OlcpwKsYmOHKe0byq1w4tn0PgP+4HDhrNomyMwcTlYoY9qfiCKizBEkaCwWCaeh01C8/BiRDaW4JkDBpIbh2AJInGhcdQgj1jfCsamWdGIndWIHVsOoKGlD8NENM0cDT/O0vw0TnQ67Li2q8OI2+ohGK8LPVSSp8U6kMt5An6PPU56906v0zs+K5xveYnv+dwbXGJ7pho/FDYV+Y4UTnZTYGsv5glmfSGS/2Wl6FMu/IAGPxdp66SvJVhw+aIZy3U6LNe04zJ92naD+mwylswj20N9pP40LBX7L5nLLElQOc9lXMYmJ0aMuDwzga/PjnoZlicL2z7hab7syR/k8WVcuJtM42LJAsuXQLDKJZGQ8sQkkjBs2A7tz/JQqr+ITrMG8dokxAvH3OHdNRqk7D6FY1uXAZ1/QElBFtLW2l8PMMF0Sw21ImPz/bHe7EVnJ2nX/hmNwPoE8SNl91hGiUeQQVT6XO/rV1JfZJg4UxPIRDN0YnLgDdK6Us2cIWz5Aq2T/Dsk6i/5DKoSsH7B/Y4By4kwqMWjCyFYrXE7ufXFvzxDZY5AuOsL4CSphY9a4c0UewJLN6rXroLuhRqSaMlya14SfpLgxWT472YYiVLc+qBf9UcgNXMB9M30tYchXGgyIzPZeaEoRp2xG4bKrVg63o36lwugXZWH6o9psMn5t7J84R2++JdvhKfqEN/YiC4SV9yCMiMZ0cIAI+eDcjqbpBNf8dfPFMLlpbEhXBPpvLPTjMjsBJJ7JBgfQfP2VdDm7UcDOfe2ZhkyUh4TDnqHf/Eun+O87pssU+SjMZtx4uR+ZIYNoLmsCDk/XeV4rdFrFIxNQTFYf/IEynRhMLaWYXsused24fVomp+CyEd8becAVAk6RIbwPfTabnI5TxYf9e6FXqd3fJ6K/OlPPg8QCnxHHnlbe+N/msw9aDxzRvTZjFjhWOTjZKXSThYZoxfRdoUujOgCCujpt8Dc3QELHRfoibRfEWGTcoZaE6b8Rp1kDKgwwy/le5ove/IHeQKfO6eW+4T/pwEyeYxWofrqALaJntxY+/vRNRG+fWiu6cfa/Wew3r7CHjuHNmFTGl+uUSPyCRJc5LOGBPmFV7Kwqy4NpzZHY2EcGajJ6jZ7nihp0d+pDyKDppPT8f25E6LDpvXSEwz7u6bu0EQvRnhNL4zjSRMTKf7vFBEJ6WRbpn5X5OqLWqxG9aU+bEkQTW6HBtCpikZKGC2EInwOSRTcAQFyvXSoRiJq0QgukKDPnudYrRiv0rvpC/iC19A6ia6XbcamVOkIn6zTPgzTsWnibopwh25JpFAWI+cr3vTfDvWZAbT1WpA5x6EHzq+XLIb9e7beYDn/FmrnbkXjm9qJu4zGWmUJiGP+YqTcbMQ18zpEityY/g0OurK0+lm/JlWLxKoP0JMZiQZrDn4jvhsmgToiAZlP0Q+RoWUn0l6rQyoZUOT820L9tc85X1g+vUjyha8Tel/8y0fCkpH+aBXaPu5DZANZUBbb+6AsX7nTGRfDLjrh/nbSOD+pDyaD66ROuJlD+ORnISRGhM0JSIx4QhMRDdW4BhlbiT8K+zxy1YDqfh0q3948kcesZ1v4Da/wZWwQIz0e9He2k3/5HOd132SR91GfmRWD9HX0Q7av1mBjQTnaUncjReIJnWeUjk1hiNau4z5bxgdQv6EA1S1peCWOTBhHb2P5U1uRKHlj0ge7yeQ8+51w9/ihd4V6nd7xWcl8yxVl9fuazwPCXDnfUYKcrfug98b/VK5zQxExyci0VKLTEEZ8QotX6D6ygOrRt+C8ZQApqwXbUP8daMG10RyEi57gDhL7Sc9lJKC6uUkWfaOkhxN1DGHwU/Kfr1MyDnfzZV52d/4QLTNWBD53Ti3T+gMPsWk6qPSVaLB/f2u0A9VV50QrYQ00c6wwfWkfsC0wnj6FBqE0wagZponHoQqvsXPTgGdWFTpkGDdj2AREzaIZVo3Ef12BCzV16LI/9r7VitK1edBfFcoiYjPXYUbdflFdFnQdyoVO6RdnF2mxRkMmBnp7BUT22iJoX2shWz7UL1NfdEYOQsX6Hx9CQ2kdrNlpwp2QMJKwVWj7Uyv/B8nMJGkcqnHzNxk0SMxMQNvRGoeuhk6jut6f6aYGKdkr0HCkylEnlbFwFZ53exd0BBWlpyd+0MHSUYWK9gQ8mSwaMSeQ8xWl/TfDMvFaBO8zTnqgfn3kHNIzk0mL3kPvJqlMIw4fv3UOeqm/k2D+32SSLGyLCVmG9LReHK/tmDhubtmDvA113GuBiut3h4YsBOLO4eCrpzGD9NF9ohtBQwGx3YQ/AqZbxD/m8HfQ5Pw7WpePKMNOlDYOwEwmPea+Orxea5ycgBXji3+5wZ3uJyDxkR7HfbG4XrUO6RPzFTkf9KwzLoYNohgmA279tiy8fpaPu/B5MVCdbUQb7Q7Jbf21B1DhqMoJpX5gMdtlJdDXU+b14QNiE9p/681WVBySWcg8qsMWVR0qTjsEsXSUIy+rHD1SiYzIFTVmwt8mVDSABv05oeANXo4NkyCx/X/qMHhoJ2o/HiIxb8EwiaPDZ0UzI2/7JkvgfNRhNyt6DqxCXrkjH1hMJIepiE8Jq2TLQIdXfztMLnatHQegXVvu6IPFBJNFhVANaXBWGv4t7Rwqqhzy0LHjeTIuN3NfHPfBbjI5Tx5f9C6vVyemdXxWMt9yRa5+//K5O5zyixyyvqMEOVv7mzfExGBp6gAq6s4h87HF/K5Fy5Byvgql3UlIFHbx/tuN6hqRL3mcy0gwKxlPJjjXMXy6HNW3+G1PuLWBx/myZ3+QHSsCnjunlun9NbyYzXjzOQ30m1ZCm0Uf572Fhc/mix5vhiGjIAeDB8ixLBJkP81FdUgysoWjHHE5eCX8NDauXIldTdTACq4RM0eHFzkZVkG3NhfalQVomF2MbTru1g1UqYU4lj2Cklwi46pVSM4rw+01+7FG6u75vHXYvWsxmgtWkrazSF25KLmuxf78OGWPnYMikP1GMSIbCpD801XQ/jQLBWcjse954U6vt/V7Ux/pWxrp+7uRxSh7yj6TU2Hp+iLE9+6Hjug3Oa8GwavF9nFGnfFrVGp7sSOL6JJ80l4YwZrntMJR3+D0v8aEw1T/1EeojLOLsC2Tt89kYrApdQQ7SH+oDNqSXmTu/zVSJPOLnK8o6X8csp8Nx3Giw+QX+V/NcpKZ+kzuXpiyy/Bihi9LJQL18cgW4uNkIP4F0WthN6K1jruDFE1GPtb37yc+kSX6dUU7ZJL3bBmyR/aSfhCdrFqJnPJ/YM0b6/g7mgrq9wxdICbBeCsCT6Z6vrOc+XIRmQAUEH3nIY/IkWfQ4LViHRmOCHL+HabDvuoCqJp3omhLAUr0amzauQ5R9JiPeO9fk/GsewfqZC0yR80IzUgQLfDkfFCBzn4diXftMfyzQrTN34lfCb6memIjXonrw661xD9X5uF4yGpscXdXUdYPiKz5OlwlsiZnVYH/UaUIZG5bhzunC0j/iQ6LepHylEzMc3npN4htoXkpCzrSJ93efmS+mo9YqUQWloZNa4awi+aVXxA95VYhODlNOOgNXo4NEqjitqLy9QQMHi1GIfHBiqtp+M1zoi8BeNs3Bfjvo652UyF2835kXimB9me52Ejz5Gv9yN5PZBSecFzT78Bx2V/DFCETu6qEzSgTxoa8tVT/Jbim249Nj9OL+fy0ZrScy080bycXNCL82WKkc7/w5YvdZHKeArzXu7xenZjm8Vl+vuWKXP1+5vNJSOUXOeR8Rxmebe1/3hATm0TyozUOS+MELQQtxtInrLAm/wSxE0/HnPtF/Ve3dwTZZe7mMlKQReBLgj+uorloFXaM/F94xWN6lrGBx/myjD/IjRVTkDunku/RX3kQtqcP7tGzFSoPj8eVPz534NU1cjIokFEMbds608PjWDnoq34kYNx9X8rr+hXU561+3cLpilTH/Z2PAKFE/yMGPL+2BT85WYbMWd7JEND+2/HSZ2Shr1dZVURMP/TqqQ4/6re274H2j3EwiF7h8oSVGMdKX2X11r/pHTKxLm/UYWOBGb96f+vEe+E+EWhb+YCcDyrRWUB8OBB+phSal8YVthXAvOKXrlx80Hp2D9L0i2Eos08KBLzpmxKmwkclZRxBw6YCmLedwXpv7pkIuI1dilwfqO+NEXnc2Ngnu/nrz77o3VvbT/v4HFg/8jmfBxIZ31GEjG4CagelBCIfy/iX18jpScYfZAl07pwCpvfJkp0g6uCeHVAlc1wKr66Rk0GBjGJo237ZmSYWD47mdf0K6vNWv27hdBVgJ/dS/97KEND+2/FWZjnou9D+Jg9PdfhSv8UM80gf9PXnkPnzNEULJQpNst76t7WvChvX7sGFm8Id77EBNFedhjE1Dgv5Pb4TaFv5gJwPKtFZQOQPhJ8pheYlpW0FMK/4qitz4w5ot9WhX3hDzWruRn1NK2KfiHNeKFG86ZsSpsJHpWQcN8I4lIalPv7FfqnYnUCuD9T3PNjYJ7v568++6N1b20/7+BxYP/IlnwccGd9RhIxuAmoHpQQiH8v4l9fI6UnGH2QJdO6cAu7OkyUGIxDcasQb2zqwvHQnUmYJ+xhTiqX9AAqr+hGlLcSL62LcvF4RKCwwNlahoqoFF0bJgmmmBilpBdjy7AqET/cAxvhuMm5GV+0BVJ/uQM8YnTxFICVnK361Jg7qb4sPjo2gZ0iF2EWK3/dhMBiM7xRsscRgMBgMBoPBYDAYEtyd1/AYDAaDwWAwGAwG4x6HLZYYDAaDwWAwGAwGQwK2WGIwGAwGg8FgMBgMCdhiicFgMBgMBoPBYDAkYIslBoPBYDAYDAaDwZCALZYYDAaDwWAwGAwGQwK2WGIwGAwGg8FgMBgMCe7+YmncCgv9g5PfREYH0Hy0HNXljTCSLliHOtB11cIf4/plgXWcL1pF2644XRcgPLXHmCKozS3fIF928dG7htXip94sGD7fAWNAQ2gq6rx7mM/XkTxVjvrzZmGPPN+pHDJGfHBM2P6Gcc/a6V7WaaBlm6rc/02eHzEY3yLu/mLpViN2ZVWhRyjKYh5AV/cI/EofgagDA6jfVAj92ALEx0QiWGXBhaMl2F53kUyzCFy/itF0ixa6UZ2Vhepuuu2Ky3U+YL3Zja4B8STIU3uMKaO7CtoXGqF8OnqXcfJRJZhhPN+NYb8mGZPrMDcU+6e30YuoLSlBfUcAVzZe1xkI3UwNlpadyDnUj9CYxVgYrhb2OvNdzyE9VVnQVint7N2z9TfJTt7p1EeUjOUS5wRctqnK/d7OjxgMxpTwzXsN73ojnjna4fPCgiMQdeA2TLcW4MkcLZamxUADNVJ2vYeWXSvIljf4ep0DS0cNnmkwCiUGY6owomFHDTpHhaJPBKIOF0JW4IX338MLab5GkARe1zkF/QoQ1lEzrE/okJ22AkvnqYS9zrAc4g13z9bMTi4oGcsDMt4zGIzvMkE7CcL29DA6hAtvV6Bq3x/xyVgIFkVacE4/in/+7wmYLZxiudoI/fHfo7q2FddHv4cfz5+HYDLGW7pP47i+HZ/89SastwZgnZ2AqB94vsYV2Toqf4eDZz7G2H+FI2q+Biqp5eSNVlSfeBeffDaAka9uYfD/UWFRbDhMTeWo/2s44h8KoZWh+e0biFytxUL1CD463gxkrMeyB4U6RAyLruPkuxyMJV+fx7HSA6htvYlxzXwsDJ8pnO0Mvbau6RI+G/wbxj83Y8bDizH7n4T20jJxX1spKvbV4dxIEKIeicYDYp3Q1whPVKLmILGFeRw/fCgaof8kHCNYSD8bKo7gzT+04fNRNRYsnouZIn0o1pcAfRXoWOUR1JE+3Tf3YXy/qwrN/7kYj4QLQo10QF9Ti7pyKs8Ygn/8MGbfzx+arJerGJs5H4/8WDSZHR9Bz5k6VP72EJovjWL8R9F46IfSk0OKe58ZQlt5EyzR4fhcT/VHffU+0j8XnxrpRkPtYVT8j1Z8HhSJJfcPoPZSCNY++TCkrSWvM4eOXPsnyDQ/FJ+f2ovXq4Q2o1X4suU0jh4+gua/BuHBhQ4bUt9o/s9o/GhIj8qD+/DuRy46cfJR+y438o12Q1/9Fj60+/w/BD+nyPjRBG7qGPusEfU35iNzwQ2cJratePdTjD9A/GKusxbd647qphZGIZb5fkfi+5fqUHagAUhIRZTgR3bk/cm5Tg53/XTTL3Fc81jQU1+Fz+7n6+RlmIF5f2vk7GN8MBuxPyKnET/uOlmF6iO1aLs6iuD5j8LNAyEet+fz7enb7fmhDxZxfwSmMoc4IDLW1OBaMGlfI+wiWDrqcPzS97FkoQZB4xb0v1+H+t9X8f6PuXhofgiChHNl49utLFK2pDb7DA8kkJgmxS8/rMW7yMDG5eH8cXe5yI2tOVu2WblxwC4R74d8fguErX21k9t48GA7uf5wCPaq/fcDJLeM4YFH5sL0ztEJ/+Z1moqs77Vxfa7/4BaX951s5qn/nK7PYZzrp7CPjr3v/52Ty0pldDOW2+H6IXGO37K58sVF1JDcn71sFM2lv8Wb7sZEuXjxND+ykmuPNGIs+lHMFqVGY2M5/nQrErHisZDBYAQUD1PbKcDSjYpNBag1xyH76dVYHtSIkjda8DfhMMXStAO6QgNux6zGLwtXI3KoEnnFBu7xtmr2AsQvCgXUJDHEL0OUMAfxdI0r7uowN2xHzssdUCWSOlYvh+pCMXTbpOtASATi42MQCjUiY5aR+vgBxdRnQG2fiT/HC8TXWW+0o9awH6X1FsQ+mY81S4ZR/yuisz7u8CSC5y9DbCRJkrNjiEwLECrK9RcOHUCnJo3oOhexIzXIe0HUn5FGvLS2GF3q5bwt1B0oyS1E84hwvK8KBQWnYIrPx+7i1Qi/egCF5d0TrzI46ysOVkMhcvadc/s6xLCeHD/Uj/AV+djwZAQGS0tQajDgwxvC/T7SXt7aA+iftRwbivOxHC14Zks5eoRXXex62VU6gCiilw0rItB/aCOe19sFNqNtx0a8fkmDzKI92JRogX5bMRpuCodd8OwzJvToiU322vW3Auqz+5H3Wqvj7uRNA57fsBe95PiG9T9H1ACRrbZfOCgNpzPiU9Z43sfQKtaZFT1H8rCuxoyFtH9PxsBUsxF5EzoXZNpXiWsRP8eGnAWk/4V4aft+1N4KR8pq3sYbRX5P/aqhZidK3lVh+eqtyI4nOinciIqPpe+xerSpKhwLxT4/XxiYXfwo1mpA4do96JJqwl0dlJt1OCj4/C/Tg9H8CpXT4U2e/Y3XTY8Qely/y3fi4OUwpK/XTSwExcj7k3OdfD8L0WC1565WRz/d9GtyPrBgsN1RJydD/R4c/A8rYol9EueQnWPdqM3biGpzDDLX5yN9Tj8ObyyA/gZ/zSRczs+MMaN6Qy6qu6lmVAhdJM4PjnwnJlA5xKPtEYZgawsqmsSJbAhNVXWwkHyq4uI3FwV6CxauLiQ2XgBjVQFeMiiMb0/2cbUlZWwIH+p7yREJPOUiN7bmbNk+JPgjD7W/Pb8Fwta+2kkyHmTyv1x/qC/3lG/k7EX7k52oQvPLO1Hb4KLn8+U4fFFD8tNWbiw7XkBsZjepR9+lx6mN2jEoft3R1Dshl7uxXIzHc7yQTTYOKaMtKH25ERBy1G0y5hWIxkw5ncvOj1RhUN2sQX3HRAQSOc9Bf6gbqrlhwg4GgzEl2KaRL07m21a82m67I5Qpg8fzbUmpZbbLQtn2ldE2aLwtFAhftdsOpm61NX0plD8qsyU9fcZmEooccte44lrH1122wxk62/FPhTLla6Pt7Xyt7eAHYmnFkGtIG+8MC0XC5dI0W1JpF18YPmPbPnGcnptmO/wRd2QS4utMb2+1Ja07Zhv8mityXC7V2nIqe4XSZLhr7O1y8O29/ufJOrHLy9VZ4VwntcWKPbx9uDrf+JA/QPnKZPvCJOji617bcV2+7e3rfJHDvs8olMXQtjNczuf2pdm2vy0IdMdkG7wutirR/zqt7XdCtzh5Mnbbzou6ZLt+wlZA9nV+RQu0z5tt73zOHeG4PWy03RHp0QmPPiOhP86exbazf6eFO7bOPVpbwXFxZ2/bzr+qneybdjj9uPjYV722prJTtsu0Ga7+IluT+OLbgt64ZniZDv7F4Y9fnNpsS3qhhbQs8CWtg+iI04fgV/knnHzpzl9221bkn7J9QQtiH1VkUyqDs89fq9C56EHYd1LKESiT65Dy+cHj6xw+LSsbrxt7fHH9frnFKc+4osyfHHXSPjnHyx3btTNltne67BVM7pdTPuAYtr3ztKNOqX7ffq9oUo7k9glx6QpXR9F7Dh8gONmYwJ3jJMdkJp/D999TDvHa9sZTRL8k19v7y5Xt+iZyG/ttX4iao/1I2mqPJyqP+/j2bB9nW3Jwvu8Yd5xsJZOLpGzN6c8l9mmd9vwWCFtTfLGTVDwoyv8e+mO3nVP80H0e4/AOyZEuOvHku05jqIDr2C01H3BF4hw52by2DW0j1SVHOeVveZ0rmR9xMSHqy50PnGOdwWBMDdP4ZMmKwYEhpCx7bOKxPiUyOQ3RwjbHzAhEhgHDfR3oaqxD9REDOsnuO55+7ceXa8QM9ZHz07B0iVCmBEVgSYIKF/oGhB3TSIgGwRPvngAPRizA8NgdoaQcdQh/15Nj5v0ivQ/h2sdAemKMUOaJfDwBoR29oG/Eax5ejMjGEjxzwEB0MARLkAbhGqGGoW6cHY8gCif6Pi98Ooy4rR6C8br4XqTAiBGXZyZgyTyhTJm5DMuThW2KSoPICDXMA93oajGgvvwYmm9ZnX/ladFiLBJ1CfOSkT7rInq4u32RiE0YQmnRDtQ3dsM4YoU6LAIqkR6dUOAzTvojdUXiNm5zdzlHMNirQuLjRAcTqBGflCRsS0B1Bi0SxT42MwbpW3MQS5qxXunFhSVJiBe9ogT1Y1gaN4SeK45b9Sq1w4ozgsh22A9IywKzqIwjMIm+S6GKi0GkSAeqhJ8g88YlXHP9voW3NuUYwuXzQJRq2HEN+QyOqdFDYt3dVZK4+HywWqQIX2TT/MApz0ji0Z/E8P3MTBXHiwrRuq3IjBNX4ANO/bbicmc3YjVW9Ij0ee0WOdJrxLBwlgMrrvb1ITEpweEDBFXccqRI2dgHPOUQr20fkYD0OY1oE75XP3y+ESZdFpYKrxSpSJ4LJfX2k3qa68pR/cdLwEQ8eorvANtHSS7yBb9s7Rn3dhJwigf5/C+H9Xo/eoifLRGrl7OvsG3HqV0ViWthcxp8VxYPsvlkmxCXMc4pf8vpXNn8iObv7IEWnOeeRhH5zp1DYk4ahJdHGQzGFDGNiyULLF+SSZDKJY2TMn1n3M6wYTu0P8tDqf4iOs0axGvJBFI45g5frnHi72YYI8LIQO2MWhPm0yLl3scE060IhNu/JGaHDObho1ZwPY7ZjBMn9yMzbADNZUXI+ekqxysxVF9B5NNJ9D3xGSCJXIfIEIlpEj1frXYaBOjgNEO0w/pxOTauysKOmlZ0kslqZNJKpIjXIpTZ9OUXMSqoQiy4/RXd1iBl9ykc27oM6PwDSgqykLa2HD2OdYYT/vkM1Z8aapcvJqlmzhC2JJDUgQPLKNHt3DDSCzFqBJMdFoubTiggMsTFq7mJlH3RJ8Jbm3JQPZB/h8TXXMSgKgHrF9xPhvIA4ZNsCvDoT2Kk7R14+Bxpvdkr6if5jEZgfYLjuyMO+PMjw5y9Bur7SS6TsHFA8cX2EUjNXAB9M319cggXmszITBYmj+MjaN6+Ctq8/Wgg9dzWLENGymP8MQ5P8R1Y+yjKRX7jra0DCdWXTP6XgctXahIvQpmHlBXb4G76rhw+2mYW/ZknMeL8Ladzvk25+RFmJiFdO4B3zw4BYxfRdnYxUhJcdMhgMALONC6WSFKIVuHCVecnNdb+fnQJ20Afmmv6sXb/Gex7aSs2rdNi6TzXJYwrvlzjwnyScAZc72ZZMUhkTZwXKZS/TURjYdwAunqdJ+GcLZZEYOI3KGbFIH3dc3il8hQaK3PwxaFytFEdzY1A/GgYlj9F9L3V+ZMtdSeX6vcm0a/oVWuM96HnvLBNBooLbxnw4LMn8O+7n8Omp3RIjIuAynXAvOJyV29sAP19MVg4YSI1Ip/QYc1Lb+LYmVN4LdqAXXVSX/by12ciEbVoBD39zvozXvXwU7RSOiDY/0aKJnoxws9fgvO3noYw+KkK0fN8fx+9f8jlLv+NPnSqohHlWqW3NuWgerAgfNnmSddsWhPnMnHwA59kU4CsP9mh8TKCrisuxqN/q8XDWi1YI2E3j2s7PkfeWaSb1M9NW1dI3D3WIGqxGm2XXHx8aEDaxgHFN9trUrVIPPsBei63o8EsetJ61YDqfh0q3y7DNlJHtjaBLISFYxO4i285+4Qi3PWJx7i7RYHCXOQCvbHmijWgtg4k8vlfrj9cvvq4F0bx0zZzNz5U/CKGAt8NIXbj9zogdpt6fLTNQL/z96uc8reczpXMj3hi03QwNXWgv+MD6JN1SGFrJQZjypnWH3igQa7SV6JhSNgx2oHqqnOiOzUaaOZYYfrSnlAsMJ4+hQahNMGoGaaJJK3wGlfEdYQsQ3paN6prOmAR9lk6qlDRnoAnk78hmcj8vydkl0eNxH9dgbajNeiyLxCpLY6cQ3pmMtGoFT0HViGvXKQPkxkWVRhC6SxoVhr+Le0cKqocxzF0Gs+vKkSz1A8qhCQhO9uI13fWoGeITGDoL/7sK0PThOHVCA1TwTTimOyYz56A3rWum3WoOC04z7gFXVVlaEjIQuIsesyAZ0j7E741bsawCYiaJWU/H31mAg0SMxOc9Uf6X13vMlkTw/lYL47XOnRmbtmDvA11/Gsvi7RYoyETRnv/CMOn96PCqkPqo8IOX2gpQ7X9bwWND6GhtA7W7DTE8nscKLYp8YOJmwoapGSvQMORKoceaBuFq/D8xBfzpRDXoQBv/U0pnvzJCT5eLtTUOfp5qxWla/OgvyqUOZz7FT4vBqqzjWijqiD+2F97ABUO80oSm7kOM+r2i/yYyHUoFzrxF8VFRGfkIFScUz3ZWA6vcoiPttckIz3uHA6+ehoz1mjJFFKATM6jxkz420RIDqBBf04oEDzGt5x9SN2LVWj7UyvMtH/mPugPkVxED01CYS5ysbVqfjRi+1rQRP+4OJnQD7ccwOEW4aAbvLX1BF7ZSQq5/K+gPzFkIRFhQMm+RhhHSE4fITrdW4dBiR9ZcIes79JXpef14YPGAa6/1putqDgkoVSn+YAblJwjwifbqM6R+YwjRznnb3mdy8+PBJYkI9N8GiXk2uyUZY7jN0n7B+rgcg+PwWAEgOn9NbyYzXjzOQ30m1ZCm0Vfo3gLC5/NF73+FIaMghwMHiDHskhi+mkuqkOSkS0c5YjLwSvhp7Fx5UrsaqJZQcE1rkyqgySyZ8uwZrQcupWroF21Erq9I8gu+/U34q6NJiMf6/v3Q7syy+2v5rmiSi3EsTUmHM4ltli1Csm5e2HKLsOLGdxQidjN+5F5pQTan+ViYxbRyWv9yN6fj1junXtnfenI8eSCRoQ/Wzz5nXUOUt/Tx/DbhAFUFxegcEs5+lfswa+eEA4TYnOKENVUiORVucgj8hRdXoxM59e7if/k4CcjxUhblQUdfU3mihaVLwl/n2qODi9yvkXkWZtLdFGAhtnF2KaTur3ug8+4oM74NSq1vdhB+k77n/bCCNY8pxWOSsHrLHtk74SP5ZT/A2veWIdoqtOgCGS/8RvEthQg+afk+E9XYmNDJF6r2Mwf9xVtDha+lUv6mcXpRB9ahDefclUsRYlN45D9bDiOF6xE8ov8LwM6+RGNadLGu7OLsC1TSu+UyXXI462/KcSTP7nA9TN7BCX2eMkrw+01+7Fm4jtoErp5YiNeievDrrVk38o8HA9ZjS0L+LPdMm8ddu9ajGZSD2+zXJRc12J/ftzkSRNFfD6Ri9N/ZDHKJG3sHr9ziCLbU4gtn0zC8C0N0p8QvdsWloZNa4awi8bTL0hduVUITk4TDhJk4tuzfVRYur4I8b37if8QW+TVIHi1eNxxRj4XSfhwhBa/Wm8l+7KIrYkv9a3AJk/pgOKtrQm+2EkKz/mfINufMKSXVmKLqhUlRSSn7zwD1VM7scabeJT13QhkbluHO6cLSH+JnEW9SHnKRamTxnIJlJzjig+2wYJ8/HLeW8jh8slK5Ok1eKXUkb9ldS47P7ITg5RsFYyjWhJDDmms1z9AfYMBnf7cQGIwGJJ8j/7Kg7A9fYxbYbFYoaLf4XAzEaSvJ8HDcSl8uWYSVvrqhopU4zYlfruQswV9lWXcgz6ovsbI8RAZfdG7bU71W3HhtVX4n0tOYZ/OsSK1WiywqtT0dXgnzPpC6FrSYKjQQUPbHHfzHQUFviXGb5/h2iNVyPVfjJyPUZ2TSaW/38HoObQS/zfeRPuzcXydQZP1KolSm4rxUu8+44tsEij2J1emq58E7hXNmQptRghI/vOFQOpELp7k2gqgLO5y0VTgra0Dhj/6mpTTh6DPK4DpufewiaQcb7hrvqsAr21DdTpGuzN1PjxcvxHPmItw6mnvboowGAzfmN4nS3aC6GTHc2JUyRyXwpdrJkEHx2kfse4icragg4QnfVB9yU5c6d9IWYXna/v4VxTIYGD+uAa1LRFYHuf86I4OELLqp226m9gq8C0xfvsM156X/iLnY1Tnfi6UJuHNYK/Ipi54qXef8UU2OTz5kyvT1U8C9U1vUlFA8p8vBFIncvEk11YAZVGUiwKEt7YOGL7qy9qH2rxclLaM8L8SSHK60VCF+ltJiH+YP8Ub7prvKsBr21CderrADx+2jpphGWpFfT2wZtKrFwwGY6q4O4slxncMDVKe+w2WXt2PHPoazMos5B8ZQvrBMmQr/ZWpmRpEz74bs4lvMOowRKs9/ELfdxnmTwyG76hisGZnPlQG/hU57lW9Ng22VRdP/BQ8I/AMN+5EYXEjVM/uUT52MhgMv7k7r+ExGAwGg8FgMBgMxj0Oe7LEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhwd1bLI1ZMNzXgf4hC6zjwr5vClYLLBarUPAP6+g3sP8MxjcBkmMsY8L2uBWW70isfadyitjGjG8lbIycOqwWEj9Ev66fb4S+b3XjQrdZKEjDfIcRKKZ/sTQ+graSPKStykXBq+XYW5hLtvPwRtOIcMK9hhnG890YFg3I5oZiaF9oJEf8pRvVWVmo7haKjABixXB3B4z+G2mKmexf8vhyzXePnqosaKuE4LrViF1ZxWi6xRe9xXqzG10D954zTZbru5VTnGw8DUybH5gH0NU9QrLYdx02Rk4dI2h6gcQP0a/r55ugb2PDXjy/1wCjUP5W50KWD+4607xYsqCnvBC7TFpUnnkPhpMncIz831imhelAIXHqe9EVjGjYUYPOUaHI+IZgRufRHWi4LhTvWXzxL+aT042lowbPNNiH5XuHe1WubyvTpu/rjXjmaAcZMRmMqSVx6wm0/+XPTp8tjwsH72Ei80+h/VQ+IoXytzoXsnxw1wnaSRC2p56rdfhVuRov/m4L/vn/J+wjqH74KBJ/eBHP/xHIzYiGarQb+upzGH94MWb/k3DSjVZUv/93LIoNF45/hu/P/xKtpb/Fm9fmQrd0DG3lTbDMnYFPThxA2XtWJKRGYya51HK1EfrK3+HgmY8x9l/hiJqvgUpYJg43laP5P6PxoyE9Kg/uw7sfjWFm1Hz8OEQFcO28hQ8/G8DIV7cw+I9wxD8UgrHPGlF/Yz7WPvkwgkjdx943Y9Gjc0Gu4BkfQFuFAebIOISrhX2SjOCj481AWibuaytFxb46nBsJQtQj0XhAqIzKV/9Xvl0esuCsr8Jn9ycg6gek1H0axy/PwKKvO1C9pwR/JPI/8MijCP+qGw3HD6PiZAfGgqPxyI9FgoyPoOtkDep/X076O4qvgiOxMJxqyl5fMJZ8fR7HSg+gtvUmxjXzJ467w3y+Dscqj6Cu9SrGZs6faM860Ig6/f+L2f8cgWBO51b0G46g+WY4Fs4PwZeT9D+K8R9F46EfTmiTw139wJCE3WfgYnk9/vzJAD43jWL4lor3G+EKJ0Y6oK+pRV35H/GJeQzBP34Ys+/nD8nqQvDDBxaP48OjpO3/0YrPrRrMWxiOmaLbEG79z41/TVxz/Peorm3F9dHv4cfz5yGYdsDDNRgdQPOJStQcpH0Zxw8fikaoED92P5n3t0ZOz8YHsxH7I3rNEC68XYEq6ns3RhEcTXxH6L8zgp7nh+LzU3vxehXpa1AklkSr8GXLaRw9TGz61yA8uNDRJsVtP+z1RYfjcz31fSLz2H1EN/bj8r7P4cF+X35Yi3eRgY3Lw6kgaH77BiJXaxF107uYpXLUNV3CZ4N/w/jnZsygeemOVA6aReqxoP/9OhJbVbyvYi4eIn4eJNRF8ZSPKO593RlJuf5JPqdwePAVZ0iuqKnBtWCic42wi2DpqMPxS9/HkoUaBMn1meSbnjN1qPztITRfkohvt7JQH6mFcbbI3vaYSyB+QopONqZ48Ac+10fi+5fqUHagASRRIErC1y1krGmoOII3/9CGz0fVWLB4LhfP0vqWyj/R+LsS33XTby5W9e345K83Yb01AKvQf7l4cNs/GVu7669feBhjpHAvg+DPGeux7EHuVPf9sZL9R9rxX48+jND/xp/K6ayH5PCHZ/H+ONaH5sqzGFtMbCeOB8LkfD85/nzVMa+PKlQfqUXbVZJn55M8Kw5rf2ykOIe7YsG19/Qwzs9G+iMSOYbTZyPGSH2zRaYzNpbjT7ciESvoRXZs9pDjxVjpXOp//X8OWwnx3x8ch4dm8XusfQYcOzuGRYvDYaX2arNyY7uJ2MXnXOiKW1uY0VV7FJ3/x2Is/JFQwcg51Nd0ANSfxibPB65LxZMHW0uN01FfSOcDxvTib0r0iuFPO2BMS0OiRFyqtW+i/U0tuENjQ/iQOMeg+DUjUy9q24f4x5Dc8RN4fd9bsMavxi9Tw+gJ6NEbULFzP66FpWHDz+O4uswN25HzcgdUieS81XGwGgqRs+/cxONMEwm+hpq9OHxRg5TVW5ES0o6XNu1FG32aqyIT+vgYhJKaImOWIX7+ZMFVYTMwfPQtXBA9/bWeP4Vd3WqEU7EUcOHQAXRq0pD9dC5iR2qQ9wKZtAnHqHy1fSahRLFgsN2AHmGX9UY7ag1lqDBYkUjkXx5kwDMvbMeuA+cw47GfY0N6MJpLClDxsdDj8T7Urs3DwathWP5UMTYkAh+8WDDxVI+vbz9K6y2IfTIfa5YMo/5XBUQG7rAEVvQcycO6GjMWkvM3rIhAf/lGPFM/xB1VzYuDpnsPqluEHt04jYNHbiMyIYKbqPL634mSd1VYTuTPjrdAX7iRyGu/h+K5fmm7qxFF7BZJzBW6iNhtkZuFUl8V8tYeQP+s5dhQnI/laMEzW8rRI/idrC44PzyD0gMncHsR0XXOYzDXb0fhUYeynP1vOVQXiqHbJtjXjX9ZmnZAV2jA7RhyTeFqRA5VIq/Y8zUYacRLa4vRpV5O/Gg1lqs7UJJbiGbh7VauL/V7cPA/rIglek6cQ3ZSX9hE+mOOw4Y9xcie04+DtP+St694Pdfuq8S1CNrXBeg/VIiXtu9H7a1wEju87260y0nw2A97fXvtvr8C6rP7kfdaK/FwHjnfl7OfO7yN2eD5yxBLnWl2DOLjFyCUOpNkDjKjbUcuCvQWLFxdSOy9AMaqArxkcLxizPkDsT93DfEHtIrzkZyvOyMpl4CnnOLqK7FWAwrX7kGXpN3DEGxtQUWTOAEMoamqDpYQGsNyfabHN+L1SxpkFu3BpkQS39uK0XBTOMzJUogGaxzvt0GtIll4H5mwN4XTey85IoGMP3C5pnwnDl4OQ/p6HRYKoeMEqaOg4BRM8fnYXbwa4VcPoLC8m7OPtL6lxx1Z3/XQb9XsBSRnhZJBkUxI45chSlgbydUp2T85W3vor8/IjDGT8EYGT3lOpcadK1Vo6uVPpX56oZbo7EgLrtm/t9Lbgtc7rAiWsL093+8qHUCUPf4ObcTzekf8KtGxa+4FmUTX5m3E4YEIck0+MueN4PDGAuhvCMf9sZFXOdxLVGFQ3SQL3g5Rohw7B/2hbqjm0nynbGz2lOPFqEKs6BfbitjjOLn+dYdBca2pChfG6Ogn2EuYE/qcC13xaAsNYhdZUftqHfo5GS24cHQP2kISEEsFss8HSvZjcB4ZI9encbai+X7Y3icZW0uN0+7yAWOasU0jl0vTbNvfHhZKHhg+Y9ueutX2jvjUj8psSU+fsZnoNnd8ne3kFe6IQJftcGqa7fU/3xbKhK97bcd1+ba3rwtlin2fkS9SmZJebrHd4YuEYds7T6fZSt6310PrdZbF9PZWhyzkyvOvivt1x9a5R2srOCk04BEJmb9qtx0UtcfJV9rFFzh4+Q5/xJc4WdadsA3yRdK/D0mdOtvxXqFMuFymteUfF+T5+o7N1G+0mb7mi5TB4+tsK8r4Nvj6jtkGRccvl2ptOZWiCsWY3rPtyNhtOy/qgn1f51dC+foJWwF3zrCtaavWdvAvjpO5/uUT+UXt3fnLbtuK/FO2L2hBtn4JHXI460mSOybb4HXeijxG29vrtLbfCeqW1YWUHxI/XaGrtF2j218T2TKILT7ljvB8TdrIJzr4wO5xk/3L9pXRNmic7BNNXwpliWs4uSqcbTR4PN+2Yk8759tSfeHl32/rnNhHfONzk+2O+JwJeD0f/IsjUr44tdmW9EKLbULSL2l9xFZ2u3vsh4TdOHmKbWf/zhflfF/Ofk7XO+UU72OW059YFskcRGoy9tu+EHWJ+nLSViFXcLnHxR++6rU1lZ2yXabXKIklFybJJaVXl5xyrUJnK7DnAwFun7v+G0+R+C2zXbb7BVd2yOSxz5w8m23vfM4VOG4PGyd8jLbr7Ld3bNfOlNne6aIV8n1ximFO70QWoehkYyX+4JTrJ8Pp840PhRLhK5PtC5PjCkX6Jsj5rud+E8TjnYBcnVL9k7O1XH99QmaMccWzDM4+IJfnvjhJtu06or7ydJnt8NOO8fBapc4xFrrAyeEaf8LYZfd1KR3LyXT7vSJbUtF7jjxJMH1wzHb8z7wcftmIiwelOdwV3n9ytu63/a6sTPRp4cdewp0PyFgs8kOu7M/YzMnryPHO8GOj3da039tLy2zb7eOpjebPdRPzOU4vItm4slxsuuRCV+RsQaxJxg4yVpBz7nSV2daJ5y5c35znN67jvSJbu47TFIl8wJhepvXJEuVOwH6ZRAO1xApbHSK6ZTTUjbPjEcBwB7rOC58OI26rh2C8LrpvpfmB6MlDGKKiAYtF6a0ZFeJTVqCzpYO/WzF2EW1nFyM7g7SrECeZZ94v/RTEE+R6+koKRxC9OgIa0SszD85ZgH6zcPuRHNcsiID61gB6zrei4Wg56tvNzr8YE6JBsOi9oQcjFmB47I5Qcsb6aTfa5oWS/0U6vkI0Ye3DoP3O2rx1+FX2JVRsKsLB7xdhU6qovwRVXAwiRe2pEn6CzBuXcG1UYf0EJx0qRaVBZIQa5oFudLUYUF9+DM23rF7qwsUP50YgftQK7oyhPnQiDUuXcEd4giKwJEGFC30Dwg4JZkYgMoy4bR/pa2Mdqo8YSD2eYmcI1z4G0hNjhDJP5OMJCO3onfgCrGtfMCsasbMasWPLATS09GGYuLxmjgYq8TkuqNQO75xBfS3sB9xdPo5ZRG6MwGT/LpWCfjjZLYxefxu3lf5whRL7SeJ/zPJMzkEq4h+hxB79xE+b68pR/cdLgF0emo+gRaLYH2bGIH1rDndnUqmvK8F9ThnC5fNAlGrY0Qb5DI6p0TMgPLl3JSIB6XMa0SZ8UXr4fCNMuiwsFV7N8dhnYtHYhCGUFu1AfWM3jCNWqImdeR/jZclMFfutCtG6rciMm6J4dsr1k9E8vBiRjSV45oCBxOgQLEEahGs8XcHjXf4JcL/FOPVP3tZu+ztiwPP/shLJ3n62GGBWMsaIUK5z+TwXnpCGyLMX0U+2LZ9exN8SdMhO1uDsx/RpB9FHO5D6uIc4X7QYi8QmmJeM9FkX0WN/CkRx0bFnmay43NmN9PRkR54kaJLzsT6NyuGHjSg+5HBX1JH0acwy0SdiYj6hepyMxQMtOM/lHyt6zp1DYk4a6EuvPo3NHnM8GTtTNWjrpE+xLbh80YzlOh2Wa9pxmer/Bs2fyVgyjztZMe5zoSvytiC1IfHpIoTXF6NgbzsyitY5zV2ABYhdLGqPjPe0T/x4r6R+gus4zbgnmNbF0oPzYtB1dcDhFGK4n/aVPOI7fzfDGEQ+nRfROfEZIJNxHSJDAteW6gktsgcMOEvysfXjD9Dw6AokihYr9xSWblSvXQXdCzUkKZHhZV4SfpLg7STRgWWUZMUxMmA46diMyGwyWAjnUCIfS4B1ZARL4mOcBg1KZIj4TAKX0PiEqrR+X7B+XI6Nq7Kwo6YVnSQZRyatRIrvqpgM9b+IsElyqjVhbheflGHDdmh/lodSPemrWYN4bRLihWPSmGC6FYHw2ULRDkm64faFmxRBMVh/8gTKdGEwtpZhe+4qaLeLXhnwE+/74R3+2G9KYnZ8BM3biQ7z9qOB+OltzTJkpDwmHCRQf1Cr3Q7WU+nrDqivkH+HxG1cxKAqAesX3C+dm8kkJjVzAfTN9HXBIVxoMiMzWZgcyvWZLChTdp/Csa3LgM4/oKQgC2lr7a8JUVnUUHv+OqRiAhLPMZtx4uR+ZIYNoLmsCDk/XeX0GmVgCGy/3aPA1u76O0uLV86cQaO3nze00Hg7xijWuYI8Ny8OqaCTaws6W3u5hRFdQIFMVi0j3fjQokXiIu4qaWbzr3g5UEEVYsHtr4TiJORkssDyJRCschf1ftiIEoAc/kOis6VPJIg+Cxw6mJmEdO0A3qWJUriplJLAJ8qpyFeRjycD7d0wjpK2rtCFEV1sAD39Fpi7O2DJSEa0cG7gUWALSkgMls4bQb81AbGT3DoMJMU7EazWCOO9wvoZ9yTTuljSkKSV2HIKTfb31UUY6wqgO3KRdxgyeRa+ruuALKa8hrvLT9+b3opNW50/2f7ewRMTFIcMnRnN5/vQ2daKzCfTJi0IfCWYTKwn4UdUWc6/hdq5W3H6+G5s2ZqPzLQ4PKhS+hRtMpqIaKjGY5Dhol/6SbHfARrrxvEDHUj/9Vaoj+5xfF9BoH/I5Y72DaJHVTSiSNcV1e8TFlx4y4AHnz2Bf9/9HDY9pUNiXARUSp9qKGE+GVgG+CdkDqwYvDqAxHn23/BxpQ/NNf1Yu/8M9r1E+rlOSxKz3NATjYVxA+jqdbajtb8fXUsiYP9etDRhiNauw5bdlTjxTiU2DJejusV3f3DgSz+c8ez7ftpvKmL2qgHV/TpUvl2GbcQ/s7UJiBQ/eaL+cJP4A/c4y4H9b4FMna+LiUTUIgvCl22e1MamNfz3baTQpJJJ5tkP0HO5HQ1m0dMxuT5zqBH5hA5rXnoTx86cwmvRBuyqo3ePqd+O8HejxdC/ncTZmYwD9Lt1Ysg4IL34D2A8z4pB+rrn8ErlKTRW5uCLQ+Vo8/KXJz37rly/pfF+LFBoa6n+WlTcHXmvP2qVb2OMIp0ryXMxSNRa8GG7AV3daVhKF0Z0AXXlQ5w9TyanqcsQ7emu/RUjhoVNjrEB9PfFYKG7dC0rkwZRi9XCkwUR9G81cr7ph40m9DNVOZwnNk0HU1MH+js+gD6OxL5wU2lK8lVMMjItHeg0fIg2aiuyiy6gei624PzFAaQsc36CF1iU2cLcsAcVQVvxmrYDrx/pcAnBPgw7rfOtuNrXJ4z3vuVexr3B9L6GF6YjAypQum0nGvqEgWLcjH79TuyoU2Pb+iT+rit9fWdeHz5oHICFTCKsN1tRcaiFO90rZqXh39LOoaKqg6uHY+g0nl9ViGaJBZt7zLDIDJbRZDJhIv2oaF+BlATRXaSxEfT48TdxwufFQHW2EW00AKmuag+gwv79SR+gTzVUphGY7Pq4dQ76Bqm7eAp5VIctqjpUnHYIZekoR15WOXqELNJfuxf1jxZiQ4YOv3waqHjD5QuWLWWo7hCS+/gQGkrrYM1OQywtK6jfExazu0FDjdAwFUwjDknMZ09A75VfyBCyDOlp3aiucfifpaOK+EgCnkwWP8YQ+5cGmjlWmL60y22B8fQpNAglB+Jr1Ej81xVoO1qDLvu+0Q5UHzmH9MxkUqM01o4D0K4td1xjMcFEJkmhmkCkbaX9cI9n3/fffm5j1h3m/+3II1KQ2IoaM+FvE10eQIP+nFAgcP7Qi+O1Dn8wt+xB3oY6/lVJX31dTi4nNEjJXoGGI1UOu9OYK1yF5z09QdEkIz3uHA6+ehoz1mi5SQyHXJ9vGvAMybcN9i4ROw6bgKhZ1Ct5v71QU+eQ5VYrStfmQX+VFkjdi1Vo+1MrzLR/5j7oD9Wghx6aRCDi2YqeA6uQVy6KVxOJM1UYQsUhoUDfcr7rud8Co2ZHniZ4PxbI2Vphf73EuzHGGxmU5bno+DR01tWhITkOC7mFUQyWpp5DaVU3MuMXc+e45aYo/sYt6KoqQ0NCFhJn8bsmIy9TdEYOQg2VjhggMVK/LQuvn6W+6p+NPOdwC/rrDqD6rDvdK2QJWcCYT6OE9Cn7ySTSYwE/x2ZpqK0GUFF3DpmPCbZatAwp56tQ2p2ERBnzeZcLXVGQG82tOHzEgk3bdEhZ/2ukf3wAx51+uGQEFaWnJ57sOY/3PuZeOy75gDG9TPt3lsKz9+DYGhXeLcrl33FemYtCgwpryvYgc+IuYgQyt63DndMF0K5cCW1RL1Ke0grHvIEksmfLsGa0HLqVq6DLWoXkgkaEP1uMdNc7lm6JQ/az4TheQGR9UfpXXDhitFijIhMBrXbiXX4Oczuqd7RiWMFcTArVExvxSlwfdq2lusrD8ZDV2LJAOOgLcTl4JbIFG4k+8n6xCmmF3YjW+nG3JigC2W/8BrEtBUj+aRZ0q1ZCt7cfma/mI5b2ua8KJfpovPI0vxDWZBZjy3gVXhf9whC0OVj4Vi7S6B/EW1kAfWgR3nxKkEmufreEISNfh6sHspCcVcW9w+5KbE4RopoKkbwqF3mrVqHo8mJkBvTGlbP/aTnZR5Bd9mukTKxgXP2LyF2Qg0Eid1pWLnQ/zUV1SDKyhbN5JvukKrWQxJUJh3NJvJC+JOfuhSm7DC9mTDQ0CVXCZpRpe7GDxEXeWtpeCa7p9mNTQP7GhpJ+eEbO9/22n7uYlUCTkY/1/fuJf2a5/2XIsDRsWjOEXUSful+QfudWITg5TThI4f0he2TvhD/klP8Da95Yx9/t9sHXFcnlgpOvkJhLIzH37uwibMuUeHIxAZGdTJSGb2mQ/oTo3RO5Ps/R4cXnNNBvIsfX5nLx3TC7GNt0fFucLNkjKLH7bV4Zbq/ZjzXckysVlq4vQnzvfqIv4gN5NQhene/2VU7/41mF2M37kXmlBNqf5WIj6ZP2tX5k7yf6F55GKNW3nO967jeB5unw0yRPr8SuJn7U8WUs8Gxr+f76hFdjjHcyKMpzcUkkz1iRsuwxbsyhLHwsCVZrEpY/7nHQIDkhBz8ZKUbaKhp/WdhxRYvKl1Y4FggSyMo0bx12/zoS75J8nUaOp/2sEG3zd+JXwnF/bOQ5h5txuaUR9a197ucthAvleZO+eyb+BUC6gElfo4ZxNAkpYv35PDZ7JjaJzPWscVgaJ1QStBhLn7DCmvwTxHrI077kQlc828KMtn37cS27CNn0ydnMOGx4LgHNe6tEvz4Yg02pI9hB7Eznm9qSXmTud4z3nuv3gEQ+YEwv36O/8iBsTztWiwVWlRqi74xPHdxjb/pqwVQ1NgT9xkJYtp3BevEXuDsOIPl/xqHxdc8Jd9qh+rASfQRS+fQ1knHv6uw5tBL/N95E+7Nx/PVBHvzBh/qVMC1+6IO+6etZ5AKvvqzLfffPYoXKm+t8ucYLfOqHF/huPzcx6y+cPkmXPeUaOX+YIl93IpB2l+uzXFsBlCUg8Twd+qcE0gaekGtnKvrrbc7zRoYp0JtZXwhdSxoMFTpoqOzjxIe8+V6ZApk85kJ/bDRdfuSO6YqX6cIXfdIfRVnbgp+cLEPmLHq9H/mQcc8x7U+WxFBHmbbYooPnlCyUiNObLRhuOoF6rEO6y6RreKgficsm/6jBXYfqI9DKn+lnnfR6T5f7W78bpsUPfdC3KsSHRBpEBixvr/PlGi/wqR9e4L39PMes33D6lBFIzh+myNedCKTd5fos11YAZQlIPE+H/imBtIEn5NqZiv56m/O8kWGq9UZl9/YHOBTI5DEX+mOj6fIjd0xXvEwX/uqTu96DPu62vRhec1cXS98OhtD0cgF2/GkGtr2aM+mHKYLjNmNbhswj1u8y6jBEq2cIBQZjOvAcswwG4zvITA2iZ3+LJvyM6YUsgELnsJ/9/rZyV1/DYzAYDAaDwWAwGIx7FfZkicFgMBgMBoPBYDAkYIslBoPBYDAYDAaDwZCALZYYDAaDwWAwGAwGQwK2WGIwGAwGg8FgMBgMCdhiicFgMBgMBoPBYDAkYIslBoPBYDAYDAaDwZCALZYYDAaDwWAwGAwGQwK2WGLwjFthGbXAOi6Uv4lwfbAKhe8Qd8N2pE3zQDf6h77hPuPKmAWWMWGb4Rbr3c4VVmInmVinMloClQ5oe54qGzejp6Ub5qnSiVz704B1qANdVy1CiaGYb1BOuSs2vtuxdS/hy1hO/evupgZB7rstxNTyHVwsmWE8341hb5KXeQBd3SP4VrvCrUbsyipG0y2h/E2E60MVeoSiPD74gp9Yb3aja8AslKRRco4T02w769XTeOlnWch/+QD2bstF2tqdaLspHPwmIRHXPVVZ0FZ1CyX/8dqW3wi6UZ2VhWrFarJiuLsDxgCqwdxQDG1WLmovCztcGTqNZ4iMuxpGhB3+wbX3QiPJGG4YMOD11/aiaUAoBxjZ9qccCy4cLcH2uotki+ENgc4pU8d02HjymHu3Y+uewoexnPqX8jwX+FzM4fXcawqY4nn6d3CxZETDjhp0jgpFJVxvxDNHO9gg8a3DB1/wE0tHDZ5pMAolaZScc9cYIxPlF04jfNcpGE6ewLG338OpbDNeOtQoER/Tvxj1immI63valtOGGZ1Hd6DhulAMGBYcbzgnOTj2N9RN78C9KB8n/nIKaxYJ5W8daqTseg8tu1aQLca3k+mwsQ9j7rc+tqaTqcrF9wBTPJ4H7SQI29PDSAf0NbWoK/8jPjGPIfjHD2P2/fwhS/dpHL8cjCVfn8ex0gOobb2Jcc18LAyfqei4HfP5OhyrPIK61qsYmzkfj/xYCP3Rbuir38KHnw1g5KtbGPxHOOIfCuEOWa42Qn/896iubcX10e/hx/PnIVgltKlvxyd/vQnrrQFYZycg6gfcJaS+ATSfqETNQdqXcfzwoWiE/hN/iJd1Bub9rRGVB/fB+GA2Yn9ErxnChbcrULWvDudujCI4+lGEC/1Xgtu+UcYt6H+/DvW/r+KPYy4emh+CIOEwhetn5e9w8MzHGPuvcETN10BFl8yWq2h++wYi06NhPHUAZb/9E66MhWDR4rmYKbmkHkJbeRMs0eH4XF+Kin1EB2P3kfp4vU3gTkdWsv9IO/7r0YcR+t/4U4ebylHfQ+z78Cxe5rE+NFeexdjixZgtrtPOhC5p20TWSAvO6Ufxz/89AbOFU9zZ1RdfsGP+2IATFYdQ8e6nnA4XRmscOh4fQdfJKlQfqUXbVWLf+cS+golo/+qaLuGzwb9h/HMzZjxM+iX4ix2353D11hDbluPdj0bxVXCkw+/ttlutxUKuLQuMjXX4I/G/hx6ZBSq6W7tTSN09Z+pQ+dtDaL40ivEfReOhH0opnJjtf9Xg+a9z8ds1Cyb6HPxQHJY9MAPf/9EszBQ7G66gbn0Nvsq0y+Ue2u/m/4zGj27U483dVWgbCkLUI9H4/kgrDOVHUPn+Ddw392FnuTzomrfvZ3hg8Tg+PEr8+X+04nOrBvMWhnP+7C6uv/ywFu8iFVnfa+Pitv6DW5PatdxoRUPFEbz5hzZ8PqrGAjcx4sneHuNYAo/2I3iqz1vdTs6zrnWO4KPjzUDGeix7UNjlNhfSPFGPP38ygM9Noxi+pcKi2HDOJz3aT4axzxpR/19zkdR+Ffh5Bh4SDwFj51BbcgVBEaOYsTAb6Y8IlXqKIQF3euTauzEfmQtu4DTRCY398QcW45G5wvWcv53DOGdjP3OjBPb21z75MOwSex4LPPRViI3vz/8SraW/xZvX5kK3dExWZupH9X/l86SisVgYj2r//QCRYQwPPDIXpneO4rP7RWOoC+5zr4ROJcdcT34r1DF3Bj45QXLCe1YkpEZz+pSLL0/zFg4PtlSSU+RiQWnOcfZDYR+5tvr9vzvFnbt8P6U2djPmehdbpOxBfld8Glf8naO2WR26JvAykP6EC3s8xeaksXwy9vkHbZvKHjxQj09CRHnOrfwecrGcf7uidO7lKaZEKPM19zHCXe9unh4g3Ig+RfRVIW/tAfTPWo4NxflYjhY8s6UcPcKdZ+uNdtQa9qO03oLYJ/OxZskw6n9VgNo+ZcfpI8aeI3lYV2PGQnJ8w5MxMNVsRF55N3/3UUUmtvExCIUakTHLED+f17SlaQd0hQbcjlmNXxauRuRQJfKKDdxjYdXsBYhfFAqoIxEbvwxR/HyaOFcjXlpbjC71cmQ/vRrL1R0oyS1Es/A0lJO1fg8O/ocVsau3InEO2Tneh9pNRF5zHDbsKUb2nH4cpP1XtBR26duKCPSXb8Qz9UPCcTPaduSiQG/BwtWF+OXqBTBWFeAlg+PxrLlhO3K2GWCNJ/1cvRxoLUTOPvGd2SHUl57A7UU/x4anV0B9tgQFdt1NwoQevQG1ew+gU5NGdEDP34+811odK3sXHcVaDShcuwdd9ASVGneuVKGplz+Vtn2hltR3pAXX7O/r9rbg9Q4rgqWShqUbFYIuOf0HNaLkjRb8TThM8WRXX3zBfjxnXzdCnyzC7qeX47a+EC/pBR2PdaM2byOqzTHIXJ+PdGLfwxsLoL/BHw6evwyxkaSd2TGIj1+AUIn8LnkO9Zu1eTh4Nbw5FRkAAP/0SURBVAzLnyrGhkTggxcLUN0tbZnh08UoaFQjXRfD3SHk7P5yB1SJ1O5xsBrEdqd+sxGvX9Igs2gPNiVaoN9WjAY3r9Vdu3wOiUsWEP9uhb68HNXldbgwokbsEzHQSPRHKaY+AxqO7kX11Uiiu1xEXSnHxuLtKK0xIXzFai7WjxcQuezu7KLrzBgzqjfkOnQyNoQP9WdQekDw55zHYK7fjsKjfLJwG9eU8+U4fFGDFBK3k9olOayg4BRM8fnYXbwa4VcPoNBNjEjbWyZHSeA5buXr81a39jy7q3QAUfZcc2gjnrf7uSue4px4YBSJM6qG0EUkzhYJg7NMrChijg7ZGb3QN9lzII/lrAH6R3OQ/aiwgyIbQ4Iey/uJTqgeF2DYKb8SbtbhoDD2/DI9GM2vbETFx8L1nL+1Y5Aby/zMjbLIjAVyfeVkPYHX973F+1RqGNkpLzP1o9o+E7ctPxZb0ENkouMRHf+yE1VofnknahsM6OGrmITn3CvIt7MYzUGCzsYMKNjkGD/l/Zavo2LnflwLS8OGn8dJ5MflUF0oho7Emz3ny81beFsWosFqH4taJ9vSU06RiwUvco6zHwqYelHbPiSc7znfT6mN3Yy5HIpjy7vxyutxJRBz1Ald81AZPrwhOIOXY7krw2S+wc0/uBwVgcHSEtT2CwcpHuV3k4vl/NsVBXMv2ZhyQdbXZMZ7j+N5oLBNJ3dMtsHrJqFAMdreXqe1/a6LL5ne3mpLWnfMNvg1X6ZcLtXacip7uW2547bhM7btqUW2JnETt9ttBzPybW8bhbKty3Y4davtnWGhSPnKaBs03hYKhK/INeScpi+F8kdltqSnz9jE1XLtVgjtCgwez7et2NNuu0O2pWTl5dtv65zYd8dm+txkuyM+xx2m92w7MnbbzovEtO/r/Iov3jH2274QHb/zl922pK2C3F/32o7rdLbjn3KHeL7qtTWVnbJdptdwsq2znbzCH+K4fsKWn1pmuywUnaF6TLO9/mdRg1wdxbazf+eL1yp0toLjE4rn4Pad5Pd9cZLoq1QwPr326TLb4aeJjIJar1XqbPku19vhrn2V17Udqv8ksbxydvXBFy6XptkKT4kuMA3bvhD0f/u9okkycfsEn6BwfmHvsxsmnfM18ZN+o80k8pPB4+tsK8pEuhP68cWprbZ/3XrCNmjvAmd34v/XhTLFvo9TLdXBZts7n3NHOG4PG9345G3b2RfSbDv27Lblrym2nXzvQ1vTsWJbfkaO7fBHDp198X6Z7Xdl9FNsK0jV2ba/IZRPdpEapKF6TRLr7vNTtkKRLxGt2Jq2ptkO/oU/g9NR0XtO9VF/X5F/yvYFLUj5M4njFbpK2zWhKB3XRI6XW0Q2vGM7/2qabfvbvM25dt/4kNvm+Mpk+8Iktrgzk2zJySWXo0QoilvP9fmkW9dcQ3JBwUSu4WP/8EfcEdk4J0La3nnacT5FSax4YkKvn1bacqhNJ/yVjCn5WtvBD+5w/bbbTTaGaC511eNwu+1kZQvnT1x7LvmcXj9hW1EMBiI3usK1b/dVubFAUb5wiQ0FMnN+JPRXSh9OY7HxFOcvTjLSfSK/mYTH3MvL56yz2yQ2tRM6U+q3Tn38muzLcImvrx0+xCEzb6F2c54L3LFdO1Nme6eLb0cup8jFAtcvpTnHyQ8FnPIc1YH7fD/lNubad5ZPqh3PsaV0vBL640XuC8gcVWJMUZyHpOxnh8YDzevisZzL9aL6ZeQnDUzKxfLXOCM791ISUy4o0qun8Z4iMZ4Hkul9sqTSIDJCzf2KVleLAfXlx9B8y+r8yx8hGgSLXuV5MGIBhsfuCCWCh+PWK724sCQJ8RquyKN+DEvjhtBzxcMtu5kRiAwjq/a+DnQ11qH6iAGdZPcdt79IMoRrHwPpiTFCmSfy8QSEdvRi4hsKLrJiVjRiZzVix5YDaGjpwzARSTNHA5XTq0vSWD/tRtu8UPI/kfG88LlC1unWPgwKd0VURBehRLZ+cqy5rhzVf7wE2Psw1I2z0CJxiVCmzIxB+tYcxE7c4NFALV6Rz1Tjh8KmO9QhortDYUSPuI3b3B2JIVw+D0Sphh3yks/gmBo9A/ydl/CENESevQh6Y8Ty6UX8LUGH7GQNzn5M75CS69uB1McjaGUuWDFI6khZ9tjEo25KZHIaooVtDq/tSpC5JiomAT1Hi7CrppH0YwTWkDDwT4qtuNzZjViNFT2i/l67RY70GjHMXe0jQSpoFkRAfWuA1N2KhqPlqG83u/xijhXG+kLk1YZh95513N0jDmr3caLDYYdMXR1G3FYPwXidWiESsQlDKC3agfrGbhhHrFATO0r7pAW3bwNt3aH49YndWKNNQHr+blTuSkDba3WcHSn0iUp8PP2I7iLSsv1OljvU9zuOkz7PQBhCJ/xRg/D5pBtmem/Kiqt9fUhMSuDuDNtRxS1Hyo1LuDbxPryLP8+NQPyoFaJsIo3mByI5VU5PNjUPL0ZkYwmeOWDAhb4hWIKIXF48UvM6R8nEreL6FOtWYNFiLBIrd14y0mddRM+kJz/ycT6ZAMbKEi3WaAxoOC+01NeIerMOmU+42EQmhrj8mkD0LNZjWBLWbF6BcKHoms+D1eKTJ+NPbvSE7FigKF+4xIaAe5kl8DQWX+9HD4nHJWIfiiD5gr5h4Q7ZfK3C0iXi8UCN+KQk9FwacDyxU+C3Tn0c6iNtpJF6hTIlKAJLElQkvoVfFPA4b+FtmZkqnguoEK3bisw4UTtuc4p8LPibc5zxJt8TAm1jdyiOLS/lp3iT+wIxR/WEoth0w4gRl2cmYMk8oUxRL8PyZGGbokR+V7y6RsHcS0lMSeFWr0rH+6llWhdL1o/LsXFVFnbUtKKTJK/IpJVIkZoL+4hllIwUc8OI+4tRI5jssFjcL5aGDduh/VkeSvUX0WnWIF5LJh/CMWlMMN2KQLj95Uw7xNjhniZjQTFYf/IEynRhMLaWYXvuKmi3GzCsIFC4vo2RRVonkXHiY0ZkNlmg0RPGR9C8ndSXtx8N5NhtzTJkpDzGXcvxdzOMarWTg08tVEfk3yGxvBcxqErA+gX38xOCeXFIRTsu37Cgs7WXWxjRBRRIQFlGuvGhhUxeJL/UaYHlS5JQVS69IeVgYZPivV3lr1Fn7IahciuWjnej/uUCaFflofpj6lu8TNabvU797RyNwPoEmUWCHJZuVK9dBd0LNWjrJEvxeUn4SYJr4AzggjkSGTPPob7J/k4Bgdo9iHzEMnUOQEUWppEh1AoapOw+hWNblwGdf0BJQRbS1jpebXEmDFEkI0ZnahEtSmqqx5OQMtqLq0Kz6gUJWPoE/cSQiSZJxI8L5biwAPkfr+vIMJcBlQyKoZ4md4EgZjNOnNyPzLABNJcVIeenq5xedZXD6xwlE7e+5jxZZtNlrhgVVCFksfyVUJxAQZxPIpCxEoGMnMXQ/7GFmzD3n21E6Bpn/+SQiSFOj2rSR6E8tfiiMweyY4GifDG1SOuTlEVfQXBFPl9rEPqAsCmgUpOMf/u2Q2eK/VaAxlcEmTwLRTtqTZhjUeBx3kJtqYbaQ788oyAW/Mw5zniT7z3ji439J3DySzHVc1S/YlNyLCCLP9EOX+T37hoFcy8FMeUdd3G8F3Gf8P80YMGFtwx48NlTeE1r7/QQUZ6wGQA00YsRXnMJ/UgQPWEYwuCnKkSn0/eypehDc00/1u4/g/X2lfDYObQJm9JEY2HcANp6Lcic40jN1v5+dC1ZDPt3nqUJQ7R2HffZMj6A+g0FqG5JwysZzineFU1ENFTjGmRsXYdIYZ8TfQZU9+tQ+fbmiYmC9WwLv0GZvxgpNxtxzUyuF/kc/VskmJJFVCSiFhEnX7YZm1Ld1R6DRK0F1e0GhHenIX032RVEFlBXzuDs+X+gM3U19rlOejjIojRaheqrA9gmuqPH6X8iZH2xq7Jr1BEJyHyKfkibLTuR9lodUs9s5mS6E6LDpvWBzK4kcs6/hdq5W9H4pnZiImCsdR0dYrDm6eeQOb4Az28sREXEMWx5nJzNPU25jeVPbUWi20FMjcgnyOKJfNbQOH0lC7vq0nBqs/OTU8qDETHo76N3vyMcPnNrBEbi10sl7lRPDRpELVaj+lIftiSIZBwaQKcqGik01H2dSyhhVgzS19EP2b5ag40F5WhL3Y0UBf33OkfJxK1vOU8BV+hd7SRHrhkbIHaPQeyk5KMkzl3h4zdQsaJO1SH70DE09QXDaIhG9snJdcrFEK/HXhjHkxwLLfq3Q6iaQwKdHX3RmQO5scDSpCRfTC2S+jR348MBOJ7UOaEk95I8YyTLonkOnRn7OqB6OIl4FP02C0Gx3wrQ+BpowbXRHIRPxK8Vg2RsSVxCL5Kbt9C5wAiar5iRPU8UoPRv3wSRRZSseRXGgtKcExI6Wb/Ej51Rnu894b2NA0Vg5J+M/3NUuiBwxSpSv7Kx3A10LL95EYOjJINM2J3mevLfArrti/zeXqNg7iUbU96iYLyfBqbxyZIaoWEqmEYcr3qYz56APpB/n2UR/0pG9WnHl3KHT+9HhVWHVPGXfUlatYhe1dHMscL0pd1hLTCePoUGoTTBqBmmiSdAaiT+6wq0Ha1Bl72e0Q5UHzmH9MxkLnFLYe04AO3acsc1FhNMFhVCNXzYcH+Txd3vxD+qwxZVHSpEfbN0lCMvqxw99AISpFFjJvxtohsDaNCfEwqEkGVIT+vF8doOWIR+mFv2IG9DneO1wYCiQUr2CjQcqXL0d3wIDYWr8Lzorlh0fBo66+rQkByHhVzCjcHS1HMorepGZvxi7hwpYtN0UOkr0WBXB9V/1TnRok+hXb3yhRE0FBD59Q4bmG4Rf57D30WJzVyHGXX7HTKNW9B1KBc61y/jmv/3hA3cIjqHJmCVacThf7fOQT/xjVQX5ujw4tNh0O+r4e+2zUrDv6WdQ0WVw+70b9A8v6oQzTT2bhrwDNl2yGzGsAmImiXtxZrULKS3l6GiRWjfSmy6pwqdGT9B/KTFWBw2ndmDjFlCMYBEZ+QgVGx/6luldbBmpyFW2KUIp7iWw4qeA6uQV+7QpcVE/EdF7G8f+aQQ21txjhKQi1tv61PKTVGuoX5cVYaGhCwkTrKlsjinWMz2uFIQKzfPofpAHfodl7hnZhIydWZUbNuDBrJwSpFwXdkYsutxIrZJ7NcWQfsa/8QqsCjXmSQyY4FX+WKqiCGT/wgDSvY1wjhCFg4jfdDvrcOg2xsKyvK1Xqwzkscq6qzYkBYn7CAo9lsBLr66UV0jiumOKlS0J+DJZOpIcvMWfi5woabOIdetVpSuzYP+qlCWwXMseJlz6KuM8/rwQeMAd771ZisqDolumnqZ7z3itY3tiMdcLwmk/JPwf46qmh+N2L4WNNE/7EsWqcMtB3BYpH6/YnNWMp5McPbV4dPlqL7Fb3sjvyMXe99n2bmXbEx5j+Lx3qvx3Dum9TW82JwiRDUVInlVLvJWrULR5cXI9PdmgJigCGS/8RvEthQg+aeroP3pSmxsiMRrFY6nLXQCl/1sOI4XrETyi/SXfsKQUZCDwQNZSMsiCeqnuagOSUa2cDZHXA5eCT+NjStXYlcT72Sq1EIcW2PC4dyV0JK+JOfuhSm7DC9muHcGVcJmlGl7sSOLJL+1tL0SXNPtx6bH+eOm81V4ptko/ZTHqW9Z0K1aCd3efmS+mo9YekFYGjatGcIuUrfuF6Tu3CoEJ6fx13KQpP5sGbJH9kK3kuiGXJ9T/g+seWOdSDeBxUlHWUSmlQV4d3YRtmWKbgXEJRFdW53egV34WBKs1iQsf1xSEzwxm/HmcxroNwl1r30LC5/NF722ocCuXvtCGDJfLiJBW0CO5xEfXok8gwavFevIUE+Ytw67dy1GM6kvjcikXZmLkuta7M+Pm+ibJiMf6/v3k2NZol8VcmbSOdT/IluI/xG/+cUqpBV2I1rrPnA0uj347RPteKb4NIbHebuvGS3n7K4j/pFc0IjwZ4v5d8vp4orTIzm2Npe0WYCG2cXYpnNzu0azAi+WrYP16EYk/wvR208LoA/djGNbkyT9VhWiVvSdPK8R65rEH+dbkcUoe8qLhCIR155RIXbzfmReKYH2Z7nYSHSpfa0f2ftJDLrp4yRbKspRYmTi1uv6FBKTg5+MFBPd0lyThR1XtKh8aQWRZjLycU7iKl+HqySukrOq+O+2ycSK9foHqG8woFPhRCWaTDgXWlXYkCnth7IxxOmxGJENdj1moeBsJPY977gDHEgU5UZ3yI0FXuaLqSEM6aWVZFHXipKiAhTuPAPVUzuxxu33WZTka2BNTgT09DUmmsc2GRD6XBnWi7vmhd/yOOdHGl+6vSPILvv1xKJbbt7C2TJ7BCX2uUBeGW6v2Y814u9seMJjLHibcyKQuW0d7pwuIPUQeYp6kfKUVjhG8Dbfe8RbG1Ncx1wvCaj8k/F7jhqhxa/WW0n/SK4jOX9H3wpsEqnfv9jUIOUlwRc4/16FHSP/F14R1S8v/+Rc7HWfZede8jHlNUrGe6/Hc+/4Hv2VB2F72rBaLLCqlDyi9gP6GJwYzZt3iSdeSfNmksG9qmGFypvr3FzTtW8lGh47I/tKHte3cRURVUKBSl4dsZLrycRC8vqpwBcdKUVB3b7YVe4azofpaxZu/Iteb50ZYB/31270+jFyvZRv+GIj4oc0jgNuUy/xKW79xVMMKsXbHCVnfx9ynhRmfSF0LWkwVOigoW2SBbeiOn2M8ymJFXcoiaEA6VER/uZGT37ob77wF3qH16lPQ9DnFcD03HvYJHoY5Ip0PHej4l+2A2/+GVselz7HZ7+1I6Mv2XlLAMY5j7EQiJxjJwCycvhoY78JlPxumPI5qr+xKZOjfJHf62uU2GAKctBdGe8J0/treAJUuVPmhHZowvFysPPpLngQcQRvr5O8ZgRf9Mdg6aMK7mPSvrlTIFe3jHJpQEy5AUT4oiOlKKjbF7vKXcP5sAf/otcHXMX+2o1e7843fLER8cMpsamXTNnTK094ikGleJuj5OzvQ86ThbaptE4f43xKYsUdSmJoKvToDn9zoyc/9Ddf+IO1D7V5uShtGeF/VYtMrIyGKtTfSkL8w/wp7lASz7LneOO3dmT0JTtvCcA45zEWApFz7ARAVn9s7DeBkN8DUz5H9Tc2ZXKUL/J7fY0SG0xBDror4z3hriyWGFKosOTpImQE5mkyg8Fg+MZMDaJnT+VMgfGtRxWDNTvzoTLwr4NxryS1abCtuhhLfVqIzkAo/W7oDKEoBfPb6SXgNmYw7l3uymt4DAaDwWAwGAwGg3Gvw54sMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWSwwGg8FgMBgMBoMhAVssMRgMBoPBYDAYDIYEbLHEYDAYDAaDwWAwGBKwxRKDwWAwGAwGg8FgSMAWS0oYt8IyauH/SjUsGD7fAaOFO3JPY52QmTBmgWVM2P6uwNnNKhS+nTjZmMEzOoDmo+WoLm+EUan5rSQ+LPe4r9zqxoVus1CQR9Y3xPXdCzlufARdddRudei6JewTQ2WcChs59V0ZLO4YdqyWu+wL34Tc5Q3jZvS0dMMcSJ0GWkdTIeM3HbGOv4X6YYslJdxqxK6sYjTRAXz0ImpLSlDfca+vlrpRnZWF6m6+1FOVBW2VUJiEFcPdZHKkfB72zYCzWxV6hKIs5gF0dY8QbUwX/uqd2ljwS4bAAOo3FUI/tgDxMZEIlvyD/mYYz3djWHTzwNxQDO0LjeTIvYuxYS+e32uAUSh7Rt43nOrzmOMm6yvwWNBWshEHr4YhNmYBQtXCbjHdVQGwkUTMifuuCOfcypDiWzqmTGIETS948oWpj51vQu7yigEDXn9tL5oGhLLXTEN+91vGbx9OOnbVz7TPrQIPWyx5S8gKvPD+e3ghTWo0/6ZiRufRHWi4LhS/q1xvxDNHO8i0bbpgeg88t2G6tQBP5mixNC0GGmGvM0Y07KhB56hQ/IYQmX8K7afyESmU/cVtfZNy3HToy4LbJisSf56DxLQERM4UdgccFnPTA9Mzzzcz19xVFuXjxF9OYc0ioew106Bzv2X8luOqn2mfWwWeoJ0EYXt6oK/InKhEzcE/4pOxECyaewuNRz/DAwnzECycYrnaCH3l73DwzMcY+69wRM3XQCUs64abytH8n9H40ZAelQf34d2PxjAzaj5+HCK6hUxf5zhZheojtWi7Oorg+Y8iXBj3Ld2ncfzyDMz7WyN3vfHBbMT+yH5NDep/X07qHMVXwZFYGC6M2JaraH77BiJXa7FQPYS28loYZycg6ge8PPXvXUTXRfHnH3hQ1B/z+TocqzyCutarGJs5H4/8WHqhZT5fA8NgOGIjheOj3dBX/wl/j4yD/RJzew1OD4Tin+eH8GW3dY/go+PNQMZ6LHsQ+PLDWryLDGxcHi4ct0P7U48/fzKAz02jGL6lwqLYcHDa9KDHydB6mmCJDsfn+lJU7KP2vY/YjuhBZBq3trUSvzjSjv969GGE/jf+XE63PcFY8vAsBNEdY31orjyLscWLMVvqicHoEC68XYEqrm3iW5EWnNOP4p//ewJmC6dw7R//PaprW3F99Hv4sSAf5xf6dnzy15uw3hqAVbCvp2s4iI56ztSh8reH0HxpFOM/isZDPxQJ51aH7vVuudGKhoojePMPbfh8VI0Fi+dipuRtDWrjS3jg3xJgeUfQuXkcP3woGqH/JJxCUWBHjz460gF9TS3qymn9Ywj+8cOYfT9/iLPRX8MR/xDvj0R69NRX4bP7RfqT6Y/S+LDj1odIO9Un3sUnnw1g5KtbGPx/RL5sh4upt/Ch/Zx/8LKPfdaI+hvzkbngBk6XHkDFu59i/IHFeGSu86zdm1g+QXw3Plq0XDMTPR69jPviiH2oQ8votfk/I/H9S3UoO9AAJKTih9eIj7ZZXeLTTc5S4Bucz9vrc5fj/g8pfY2hq6YG14LJcVH3LB11OH7p+1iyUMPHqyvi3C+WhbPJH/G/7LHQ55w/J/jiImouhSB72SiaS3+LNyXGBw537biLOXvf06NhPHUAZb/9E67Q/OEx7khuTcvEfW1Ut3U4NxKEqEei8YDgbHJxYR+HFn3dgeo9JfgjGcceeITE5VfdaDh+GBUnOzAWHK04Dvn6SK78+jyOEf+tbb2Jcc18kT9I4MF/FNXn9noPYwrBfQwJY8j8UHx+ai9er2rF50GRWBKtwpctp3H08BE0/zUIDy7k7WkleeDY+2YsenSuI8bHB9BWYYCZjJmSY5W/Ohy3wPiX06g5zM89HnhkPm6f1WM0jh9nnXCTa/hj7nzUPe70pih3+W1rC/rfr0Ptvx8Q+j0XpneOOuV5uT4pHtc4vZ3D+MNknP8nwSdk5hUTTFN+d5bRnq/JvPRGPd7cXYW2IT4ffH+kFYbyI6h8/wbum/uw09zA47yCQn216t9RSY5zcXDfJeLvf1cYSxJM8l1nGyoZyz3Fj13Ha598GDNF+vn+Z8S/XOZWD37pQ+x6HO8IE/M/ko9vkHlONMmngmyBQHIomDIs3ajYRF+RiUP206uxPKgRJTtr0KDvhUk4xdywHTkvd0CVuBq/XB0Hq6EQOfvOTTy+M/UZ0FCzF4cvapCyeitSQtrx0qa9aLM/Xx3rRm3eRlSbY5C5Ph/pc/pxeGMB9Df4w9Yb7ait34OD/2FFLLk+cQ7ZOd6H2rV53Csgy58qxoZE4IMXC1DdLfXQ0IQevQE9gsDB85chPt7+iUPoSAvq+29jBnfUip4jeVhXY8bCJ/OxYUUE+ss34pn6Ie6oKxqVBdX1HfxjTIK1uxGl+jr8z/Mjwp4RnK+vgykojGx7V7d71IiKjwFdn4UuIn1YJASiix4zY8yo3pDrRicUXi+1ew+gU5NG7LsC6rP7kfda68TdBI+2Valx50oVmnq5UwnE8WtJfUdacM3+3mtvC17vsCJYKpA43ypArVnkW2+04G/CYYqlaQd0hQbcjiHtF65G5FAl8opJcJJjqtkLSN9DiToiEUtsGSXkC0/XkB6hbcdGvH5Jg8yiPdiUaIF+WzEabnIHZXzRjd77qlBQcAqm+HzsLl6N8KsHUFje7eHxtRkfvFGM5qDlXL9jxwwo2FSOHrvSZeJhwo/K+xG+gvjRkwswLPYjIk/e2gPon7UcG4rzsRwteGYLqV94xYHGY22fPXopFgy2O+LDc3+892FnH1oO1YVi6LYJ9giJIDEYg1Ci28gYkU7FqMKxUHzOfJEz3azDwXoLYoksv0wPRvMrG1HxsV3zXsbyA1Y0V7WgX/TO9nBTFSoswYikQinQa0P5Thy8HIb09TqygCES0NzVPsTrTlHO8uwbTvU5IcpxkvoKQ7C1BRVNfdzZPENoqqqDhdhgks4pI414aW0xutS8LMvVHSjJLUQzTW1CGxOxEB8xeaFkZ7QFpS83AkIOua0vRIE4Pjy14y7mOIZQX3oCtxf9HBu43FXiXK8EFw7Zc10uYkdqkPeCPS/IxwWne0MZKgxW0pWtJF8Z8MwL27HrwDnMeIzIQP2vpMDhfzL+wte3H6WC/65ZMoz6X5F8KDaRGBn/ka3P4/Xu9OwSQ0/GwFSzEXkTehbGkH2VuBZBdJCzAP2HCvHS9v2ovRVOxntezxvtOTtsBoaPvoULdqUTrOdPYVe3GuF0iHTFbx2SySOJ+Y31Zm7ukJ2oQvPLe/CBuycY7nKNRx+VQiZHUzzlLn9tLfS7QE+OT/R7J2obRHnepU+xVgMK1+5Bl30c8jgOuDA2hA/J5HqQs4v8vMKJacrvzjIK+froXlRfjSTjbC6irpQTP92O0hoTsdlqTqfHC8jcQLCx53kF4aYBz28gvjonDRvW/xxRA/uxq/SMKF97O3ZK+a6zDZWM5Z7ixwmRfqTmVl7Hrtx4R48L878Ne4qRTeY5B6lskk7iI7Zp5IuT+bYVr7bb7ghlCt2XlFpmu0wLX/fajuvybW9f5w7x2PcZ+eLl0jRb0sstojqGbe88nWYref82V7r9XtGkNrh9e/h9pre32pLWHbMNfs0f4/j6js3Ub7SZRPsGj6+zrSjr4gvDZ2zbU7fa3hmmhS7b4dQ02+GPuCPOfH7Ktj2DnPe5UDa9Z9uRsdt2nheNR9jX+ZVQFvNVu+0gvZ5rh/ZVaztcut+24oUWG1fF31tsJamkPnqtbN3OcnJ6KxX6Mwleh+I+cXoqeo9vV+DOX3bbVuSfsn0hlJ3h23v9z6IrOL0V287+nWwrsC3nH3YZ6bVPl9kOP62zHe/ld12r1NnyjwsnuyDlW4PHRb5F+cpoGzSK5KP6JnZt+lIof1RmS3r6jM0kFDk8XkP7vNlhb8LtYaPtjuBHcr7oVu9vfCiUCF+ZbF+YxDWI4XVe4KST27bzr2ptBSf5fbIyUJ9JLbI1iTs93G47WdnC2/mOyTZ4XXzQaHt7ndb2O8FMk/3KuU8e++NtfHxN+ptB/OFToUz5msiTr7Ud/MDeQ6oTRwxJM/kcqbxAc8BE37yVleqJyOWwrYucSvTqlOcEGe3+KZezFPiGU30ec5yETq+fsOVnkNiyt288ZStwqws+l+VUCIEsQOPTUyxMgsZnqksOuU1zpnh88KEdru/rbCevCGUK7Z84dzjB68cp1wl5wZG7FcTFuhO2Qb5I7PkhqdOR6yiXy7SOfCfjL1L+y+mi0lkXE8j4j2x9sv7nTs8uucbJfrxeD/7F4fVfnNpsS7KPf5QvaR3CGEgsev7VNNv2t+2OecfWucfh35PwV4eCjzvlAOInBU6x4srk2JH3URdkcrSU3E65y19bS/Wb7hP1+1qFziXXCPvEuUbpuCaRi9zOKySZrHNZHXmb351kpPoi8S4eZ8lcsNBJRpOtaavItz3OKwQ/dtInv28iX3srrwLflctZiuJHcjwhTJpbeRm7iubo+22dE8fJ+Z+bJuZigWAanyxZMTgwhJRljzndeQxPSEO0sI2hbpwdjwCGO9B1Xvh0GHFbPQTjddE9CM0PRHWEIYpUYLHQJaQVlzu7Eashq2779eRz7RY50mvEMH8BEKJBsPg9kSAVNAsioL41QK5rRcPRctS3m737hRv6ZGPbaUTu3oNM+rSKYP20G23zQsn/ov5cIUtpax8Gpe4izVyG5cl96PqU9oX8fzYJS9cnILP7Q1wmK3Qr+b85YTmWzPShbq+w4mpfHxKTEiC6LwNV3HKk3LiEax7eBVaHiK4Ii0AkbuM2vfOgwLbUFyLPXkQ/2bZ8ehF/S9AhO1mDsx/TuyVDuNwOpD5O6piEtG9FJot8izKTyBNGROgjbTfWofqIAZ1k9x1PdvZ4TSRiE4ZQWrQD9Y3dMI5YoSZ9VnG+pdAXXdA8vBiRjSV45oABF/qGYAnSIFwj7pUraiQ66USNJcvi0HNpABYFMnB+lKBFovjLPWFJWLN5BbgXNlUaREaoYR7oRleLAfXlx9B8y6o4Njz1x2sfHuojuk/D0iVCmRIUgSUJKlJ3AL5p65IXgtUOpXgfbxFIzAiD/pzwze+hDjSbdch8QrClEr065TkXFOUsT77hJ/OSSZ5rRJvQveHzjTDpsrDU+a0WgSFc+xhIT4wRyjyRjycgtKOX/4EJpYQkYMk8YZuifgxL44bQc4X2yJ92NCR3CZuUmWr8UNh0h1Oum3m/e1u5g1w/8QSN2JP6jEYUhw/OWYB+s3BbV4m/uPjvgxELMDx2Ryi5oMR/PNXnw5hpvdKLC0uSEC/ONU7241GpHZqcQfUS9gPHODSLjikjMHFjkArxKSvQ2SK8jTF2EW1nFyM7Q2qMIPipQ+v1fvSQMXCJeFAkcZAujPfK8N5HZXM0xUPu8tfWkv2OSBD1m4zN54Eo1bAjN5LP4JgaPWRcpqO79+OaM27nFd7gQUcBmU+pRTmA6HwGmZuGTuQU0t/5JE+aOU+VmVeMYLBX5ZK7VYhNTBK2vZc3IL7r51zAGS9jV86HZ0UjdlYjdmw5gIaWPgyTdKKZoxHmYoFhGhdLFli+JA6qcgkQUp4YMP5uhjGIfDovonPiMwAVmTRHhogWS27h27De7BVdTz6jEVifIPFKjh2y0Kleuwq6F2rQ1knS1bwk/CTBjdGkGB9BQ/EOXEjbiS2PO7zRMkq8dowkR7EsnWZEZpPEKJzjjIpMZhLQfPES8fl2NDxMnFtDF1BkAXEDuHbpHFJSHuMGDu/r9gZej5Fh4uxMIMkg1JckRVFi23lxSEU7Lt+woLO1l1sY0QUUyETYMtKNDy1kwJD8QqUC3yIMG7ZD+7M8lOpJ22YN4rVk4BaOucPzNRqk7D6FY1uXAZ1/QElBFtLW2h/9+uiLMZtx4uR+ZIYNoLmsCDk/XYWXDJ6ydRg04kkeQU0GBdy+TQYpeRk4PyKTE3fyWD8ux8ZVWdhR04pO4oORSSuR4kVoeOqP1z5MfSiCDEBC0Y5aE+Z+UhggfIm38FQd4hsb0UXihVtMZCQjWkjefutVUc7y5Bv+EoHUzAXQN9PXaIdwocmMzGTnCaADE0y3SCzbvzhoh8gSPmqFV5abRRY1wiYPWXCQLvE3ywLYzj2G3/7iir9jng/XczE0l/ikUOYR2897VE9okT1gwNkhqqMP0PDoCudFhQh/dSidK0lZ8gaBO7z3UbkcLYuftpbvN+0T+XdInBsvYlCVgPUL7udzjdfj2vTiS373B8/zCqpPNdQufqWayX+5g+KtvIHw3UDnIG9iV9aHg2Kw/uQJlOnCYGwtw/bcVdBuN2DYp4WcNPcJ/08DGkQtVqOaTHy3pToGVfoEoYsMvBxzIxA/ehvLn9qKRK8SkB2ScKJVuBOiw6b10lYU1vVOWM6/hdq5W9H4pnZiIDbWKk/ew4Y9qMBmHHvKebKgiYiGalyDjK3rJv/ilBvUZHGQfqAbDXM6ELVMR+ShC6jFqP7YAJxdgOVreG/ypW7lCLa61IctCaI+DQ2gUxWNFKl3SuVQZNsYJGotqG43ILyb6GE32RVEFlBXzuDs+X+gM3U19kneKeDtXn3V2bes/f3Et+zLpT401/Rj7f4zWG9/MjF2Dm3CpjRKrlEj8gmy4COfNWRxcuGVLOyqS8OpzTGyvuiWWTFIX0c/ZPtqDTYWlKMtdTdSXCa+PAPo7yfD0RxHGjRe7Ybq4RVEK/LxgOjFCK/phXE8aWIiz/89G9Izsoi98JYBDz57Cq9p7VlsiCxMhE1CMFmoTAoq15m4u/5468PzFyNloAXXRnMQPqELKwaJ3ROXBD4KxPgUb2HJSH+0Cm0f9yGygSwmiu2+SfxERq9yKMtZnnxDOhd6gyZVi8SqD9CTGYkGaw5+I37i50Q0FsYNoK3Xgsw5jqUOF59LFsP1e/EeGejn3oN3fK93CIOfqhCdTpOSOnDt+ImiuFCM//7iir9jni/Xa7hccwn9SBA98Rfbz93zdg+Q8SFDZ8bB830IH2hF5pNFE/I4478OuRxAcyVIrhT2YYzE2ACddirF+1jg9eYuR8u37K+tJds3d+ND0m/+yVYkohZZYFm2GZtSPcjj1bg2vUztfMoVuXkF9ZERNF8xI3ue3VeB/s528u8CbttbeZX4ruecFfgcpDx2lfpwGKK167jPlvEB1G8oQHVLGl7JcFerd0zrDzxE6/IRZdiJ0sYBmEctMPfV4fVao+NR8qw0/FvaOVRUdcBiXxEOncbzqwrRbP/SvAyxmeswo24/Guzfcxu3oOtQLnQevqxL70yrTCMw2du8dQ56+zfxZKCr7e21kdi9R4dw14n8ozpsUdWh4rTjS3eWjnLkZZWjx50wITFYuqARFfXA8jg6gBD5Hl2GH9ZV4bgmGfH8Lt/qlsFidjhfdEYOQvWVIj0OoaG0DtbsNMQKu7xCoW2j49PQWVeHhuQ4LOT0SfSReg6lVd3IjF/MnSNFbJoOKrG8ox2orjonGsTI0mGOFaYv7X20wHj6FBqE0gSjZocfyF1z04BniPwOHZkxbAKiZvHJRKkvOvRuRc+BVcgrd+jIYjLDogqT/rszHCo0VFWhy/5qJNFpRZ0VG9LiuKKsDIu0WKMxoFpvP4H0sbYI2tdayJYaoWEqmEYcGdR89gT0InuFz4uB6mwj2mi4kP731x5Ahb0quf5468Mhy5Ce1o3qGlF9HVWoaE/Ak8mOQUUZRA5vflrWp3jTIDE9jvuhhnrVOqRPrOPl9SqHspzl2Te8Q0JfJB+lx53DwVdPY0ZmsodBW43Ef12BtqM1DllofB45h3RynVeWU50jce2w//Dp/aiw6pD6KC0pb0ec66YCz3HhLf77iyv+jHkUpdc76dmea0Qx5Gw/34gmi3aTfifJAyuQkuBush4AHT6ahg0qkfw0l1aRMUd2vSKOHR9iwWOOlsdfWyNGh00RBpTsa4RxhCyKRvqg31uHwYlFjgYp2SvQcESUa+h8oXAVnueeHvkyrvnLdOR3X5GbixAf+T91GDy0E7UfD5F+WDDcsgeHz4oczVt5Ffiu55wVgPhxmlvxKItdeR+2dhyAdm25w/8sJpgsKoRqAudg0/treGE67KsugKp5J4q2FKBEr8amnesQJRzmnOTZMqwZLYdu5SroslYhuaAR4c8WK3+3ct467N61GM0FK5GWlQXtylyUXNdif36c+7s/cTl4JbIFG0mbeb9YhbTCbrI6dX5KJM0ImmoMMI424pmfrUTyv9g/ZAJN7RgUgew3foPYlgIk/zQLulUrodvbj8xX8xHrVpgwPJG2ANaZyY5388PisHyWFZGpCY6FpU91uyMMGfk6XD2QheSsKu47Q056XEV0srIA70YWo8zl6ZlyFNo2LgnZJLmKv3+08LEkWK1JWP64h47FbMabz2mg37QSWmL3tLVvYeGz+aJH26SPBTkYJH1MyyKLhZ/mojokmbQlgvpB+GniByuxq4kmMplr5ujwItcm6c/aXOJrBWiYXYxtOmFFK+uLrnpXIXbzfmReKYH2Z7nYSHSkfa0f2fuJTV0X4hMswLZnI6Cnj6ipTjcZEPpcGdbbzSQnA+dHxYhsoH5E2iO+VHA2Evue5+/gxOYUIaqpEMmrcpFH/KDo8mJkilxA9cRGvBLXh11rid+vzMPxkNXYwt/8Isj0x2sfdvYhLXf+CLLLfo0Ur2bccch+NhzHiU6SX3Tzq0qu+Bhv6mQtMskgEZohil2CnF5lUZSzZHxDMe70RSd+STDeisCTqZ6fnqpSC3FsjQmHc0l8kv4m5+6FKbsML2Z4uchdkI9fznsLOat4G+TpNXildPPEHW/5diRy3RTgOS68x29/ccXnMU9A9noJPTvFEM01K7GxIRKvVTjs5xMxZDGhMmNYq3XznTkev3VIX/UpLUKonsjP+V8u9PO2YptHu06OHa9jQSZHy+KvrYkt00sryeS8FSVFBSjceQaqp3ZijWjcduoTHX/pfGF2EbZl0rHQl3HNH6Yvv/uG/FxEFbcVla8nYPBoMQrJXLniahp+81yycJTgrbwKfFcuZ/kVP5PmVgIKY1fOh1UJm1Gm7cUO4lt5a6leS3BNtx+bHhdOCADfo7/yIGxPD3RlKA6QG3XYWGDGr97f6vzEwmqBZUyl6DGzO6xkRW6dqaavaiqDtmklbSq+wAvGSN3j38C6CVSPpPLAfVkuALZ1C/d6ghUqD/L60h+P1yhs0ytf9MGmcv2SlYG2SYZf13elKVYLuVblhfyuyPXH2/5OZazKEcB481uvCvXgi88rwdq+B9o/xsEgej3CIwpiRRG0njHaJTf9DlQ79xh++4sr/saRr9d7yDXeMwT9xkJYtolea/JAIHQYEDv44qP+6M0fW7vO26jO8wpgeu49bBI/qJbrUwBz55QxjTJ6nleQj2i/9ewepOkXw1Cmc34C6aW8Dt8dQcOWPBjz/4wtXiwqApuDvItdWR+ewrw/rU+WrH1V2Lh2Dy7cFJ4Tjg2gueo0jKlxWMjvcUCN4edkWhXipUFpm1MVIHSS+k2sm0D1GFDHC4Bt3RJEAklGXl/64/EahW16ZSIfbCrXL1kZaJtuBmGafPxyMbn+eNvfqYxVOQIYb37rVaEefPF5j1jMMNPXcerPIfPnacoWShQFsaIIWo+nfgeqnXsMv/3FFX/jyNfrPeQa5ZCJkdmC4aYTqMc6pCuZbBECocOA2MEXH/VHb77aytqH2rxclLaM8L8+RiakRkMV6m8lIf5h/pQJ5PoUwNw5ZUyjjO7ysrlxB7Tb6tAvvPVmNXejvqYVsU/ETX5V00t5/fXdwOQg32JX1oenMO9P85MlC4yNVaioasGFUbJgmqlBSloBtjy7YvL3fRgMBoNxT2JpP4DCqn5EaQvx4roY4bVSBmM6GYB+y040zIjDpuee4//APGNKsF5tRHVFFfSXLWSaq0JkXBqZtxGde/nbRQyFjJvRVXsA1ac7uD/6qgqJQErOVvxqTRzUAZsrm9FWUojh7BNYo3SxEjC+ebE7/a/hMRgMBoPBYDAYDMY3gOn9gQcGg8FgMBgMBoPB+IbAFksMBoPBYDAYDAaDIQFbLDEYDAaDwWAwGAyGBGyxxGAwGAwGg8FgMBgSsMUSg8FgMBgMBoPBYEjAFksMBoPBYDAYDAaDIQFbLDEYDAaDwWAwGAyGBNO7WBq3wkL/GK0UYxZYxoTtcTN6Wrphpn8tmmIlx0bFHzd1eIK2PTKAnu4BmC0u13Nyubbh+HB/tXqasE51e0Jfp7NP9wLWoQ50XbUIpW8ht7pxoVv4c98M5dB4cM0HSvmOxhKDwWAwGN8lpnex1F0FbVYVeoSimJ6qLGiruvnCgAGvv7YXTQN80dxQTK4jxyc+q5C8qgD13comv9bLNXjmZ6ug3ViMgweKsY5uF9Sg3345J5e4fudPtSDWJMwD6OoegY9TLQm6Ue2pvUBwqxG7sorRdEsofxuZZBcLLhwtwfa6i2TrXsQM4/luDNtvFviAsWEvnt9rgFEoMxRCY/+FRmIBH/guxBKDwWAwGN9x7s3X8Bbl48RfTmHNIqFMidkKw1/+jHbh0/ibxWj4VQma5WY5Nw14qagRUc+dQPt7p3DsxCk0/vkEXolsREGxgZ8kPb51ot72k1uRiBi8cNLR1pbHuZomc70RzxztuEcn4N9hJtlFjZRd76Fl1wqydS9iRMOOGnSOCkUfiMw/hfZT+YgUygwGg8FgMBgM/wnaSRC2p54vLqKmGcj87wmYLeyy8+WHtXgXGdi4PBwY7Ya++hzGH16M2f8EjH3WiPob87H2yYcxUzhfNTcawV3luKDORspDKmGvKxa07X8RXalleO3f5gj7CP+HGj9+YjG+V30A1xavwT//SNhPsVxF89s3ELlai4UeZtaW7tM4rm/HJ3+9CeutAVhnJyDqB8Kxq43QV/4OB898jLH/CkfUfA1U4mXp6ACaT1Si5uAf8Yl5HD98KBqhpJ/ACD46ThSUlon72kpRsa8O50aCEPVINB4QujjcVI7m/4zGj4b0qDy4D+9+NIaZUfPx4xCRDsZH0HWyCtVHatF2dRTB8x9FuL0vEv3zSt6xECyaewuNRz/DAwnzMINce+x9MxY9OhcTEowPoK2CLEQj4xztumA+X4djlUdQ13oVYzPn45Efi070JD+G0FbeBEt0OD7XUx1Rme4jMs9DMBHAnV2o3ur/Go74h0L4cy4HY8nX53Gs9ABqW29iXDMfC8MF73LxP44brah+/+9YFBs+0U+5PvScqUPlbw+h+dIoxn8UjYd+KLKRHa6tt/DhZwMY+eoWBv/By8gfc+cnk+H61Gbl5ePq/Azff3gcH1Xsxe63Psb4A4vxSPgYkamWyPQf6Boldlw8FzM5O0vo1KU9XmczMO9vjZzfGR/MRiyNG7e2IvtranAtmOhfQ8s8lo46HL/0fSxZqEEQLcv5ngvc+cd/j+raVlwf/R5+LNhdzi8mGOlGQ+1hVPyPVnweFIkl9w+g9lKIU25xxa2M9lhKj4bx1AGU/fZPuELjY0KvhHEL+t+vQ/3vq3g/wVw8ND+E67tSmR1+dhP3zX0Y3++qIjmA2tNxkkdfZDAYDAaD4TMepiV3kbEhfEgmvIMKXkuyjHl4CW68Fz3tEXgyLULYISIoBuvPnMH6JULZS1SzFyB+USigjkRs/DJECfNbc8N25LzcAVXiavxydRyshkLk7DvneCVspBEvrS1EgzUO2U+vxvKgVhSu3YMu0eOpC4cOoFOTRo7nInakBnkvCE/ACKY+Axpq9uLwRQ1SVm9FSkg7Xtq0F232E8a6UZu3EdXmGGSuz0dmjBnVG3JR3S2tJ2d5l0N1oRi6bY72yCwZFZsKoR+zy9uIkp01aND3wkQOq8JmYPjoW7ggesJnPX8Ku7rVCA8TdjhhRc+RPKwr70f4inxseHIBhss34pn6If6wrPwm9OgNqN1r19EKqM/uR95rrdyTJHd2oXqr7aMSEwlutKPWsB+l9RbEPpmPNUuGUf+rAnKcOyztf6Ze1LYPCXYU+lBjxkJy/YYVEegX94For23HRrx+SYPMoj3YlGiBflsxGm4Kh8WowrEwPgahUCMyZhni5wuTXM5PitGlXs7rXd2BktxCNI/wh13h+mSXj5P/DCoOGXCH2jUJ0L9YhJd2HMCFmY8RvVKdlaCgvFvoj6DTncVoDuLbix0zoGBTOXoEv+Tqr9+Dg/9hRSzxu0R678GjrcIQbG1BRZNdqZQhNFXVwRISwS04ZWPFBUvTDugKDbgdQ84vXI3IoUrk2Z8Oy/gFx00Dnt+wF73k+Ib1P0fUwH7squ0XDkrDyUjiwRrPxwdaXWUcQn3pCdxe9HNs4NoU65X6QS4K9BYsXF1Irl8AY1UBXjLYjSgv87CetHfIHisRGCwtQanBgA9v2M+Q80UGg8FgMBh+YZtOPiqzJaXm20rKymy/c/mUrE+zJZV28ecNn7FtT91qe2eYL5re3mpLevqMzcQXeYynyDn5treNNtvtrlMu9bXYvqDncPWU2S5zFyjEpW2P0P6I5fq613ZcR2S6LpQp9n1ETsq1Cp0tp6KXL3DcsV07U2Z7p+s22e6yHU5Ns73+Z7ot8FW77aBInsulRE8vt5Cr7Azb3nk6zVbyPn8Np6ui92yiGmx3/rLbtiL/lEgnQn1fk/YydLbjn9IDAl8bbW/na20HP+Bb+OJkvm3Fq+2i9vh9SRN6vWM7/2qabfvbdoXdsXXu0doKTgoddsX0nm1HapGtSWzM4XbbyUreZrLyS+mI61Ox7ezfhbKrXQic3gT/4tpYd8w2+DVX5LhcqrXlVAp2kfIBcZ20Dxm7befFQgr7Or+iBSrjZts7n3NHOG4PG213RO05Q893bo+Tx8lPbLbB48QWe5xtYccpRjj51zn5YecbaY7+UbrKbCvWnbAN8gVOpwXHxTa7TezqsKOUzmRtRWK0IIP4if0arizoSEGsTOIro23QKGpNiI2mL2lBzi8Ev5To46TcYoeTxyU+vuq1NZWdsl2mzQh6PnmFP8Rx/YQtX5Rz7hj7bV+IRKL6Sdpqb09GZtq/DBcdcftE8SbriwwGg8FgMPzhLjxZCsXC+GWId/ksdH0vz5WBGhTl5WEj/azNQtomA0J/vQfZEcLTBKf6IhAsXEbvvE7br1UNdePsOBFouANd54VPhxG31UMwXqf3modw+TyQmRrDn8+hQrRuKzLjHK/NqENEr9DMvN/xepsdzQ9E+8IQFQ1YLPROsxVX+/qQmJTg9N0cVdxypNy4hGuu34kZ6kMn0rBU/HQtKAJLElS40Ed/XcOKwYEhpCx7zEmG8IQ0kCYFVIhPWYHOlg7+Dv/YRbSdXYzsDImneQTrp91oS9AiUfRqFsKSsGbzCoR7Ib+TjsIiEInbuO3NDySEaBDMvwvF8WDEAgyP3RFKnuH6MC+U/C+y8xXSe2sfBrmHBpGITRhCadEO1Dd2wzhihZrIqBK155khXPsYSE8U+wmp9fEEhHb0KvwRBw1UovfKVMSAUbNESg+PQPxNM9GaHRXxA7HN1IhPSkLPpQHHkxknnSmwVUQC0uc0ok34wZLh840w6bKwlMolGysSzCR2DiOX9JFzG+tQfcRA/Be4I4pv934xgsFeFRIfn9xHt1AZQXxVHB8zY5C+NQexE81oSJvCJmWmGj8UNikq4lehxJ79pH/NdeWo/uMlwCUfuZV5xIjLMxOwZB5/iGPmMixPFrYJ8r7IYDAYDAbDH+7CYikCsU8kYKnLJ1b0lSJJInLw64pKlNFP5Qk0vncCL2Tw73mp5sS51LeAn8CRiUe0qhuDbt5IsZIFhq+/GizJ380wBpFP50V0TnwGoErQITKENmSC6ZYaandfjvAb0p8vyaQ6TLwSIajvJxM2icUElTcijBxzRq0JExYOfH3BdKYthpQdi1FSfEKL7AEDzhI9Wz/+AA2PrnBeDImwjJIZnFo1eQHI4aX8dwmuD2NkQeNkZzMis8lihjtDg5Tdp3Bs6zKg8w8oKSCL+7WOV9rkoX4SgXDXGwhksRI+aoWyJZ23aBD6gLApoFITK9++TZZFUiixVQRSMxdA30xfWxvChSYzMpOFBaBsrExm2LAd2p/loVRPzjVrEK9NQrxwTB7p2FPNnCFsSUBlVKvd+KoCxkfQvH0VtHn70UD6d1uzDBkpjwkHFSDZvgozRDvkfZHBYDAYDIY/3JvfWZJCpUZoCJnsCB9ld+kXI1FrQf3pjskTPss5VPyiEE2BvPs6NwLxo2FY/tRWbNrq/MnmnhxFY2HcCH/nVwz9G1MBWbRpELVYjbZL4u+JEIYG0KmKRpTrd4jmL0bKgOsTJysGrw4gcR79XTW+Pv4pkwPLpxfRJWxzBMUhQ2dG8/k+dLa1IvPJNKenDWI00YsR/nEvjOK769zfq6EK8FL+qSIkFOHC5gRERjuaiGioxmOQ4WJj+kmZeAqgRuQTOqx56U0cO3MKr0UbsKvOpV9uoX4ygK5e59WVtb8fXUsi8KBQDiwjMBqdndDY1wHVwxHEKlIos5UmVYvEsx+g53I7GsyipzSyseJKH5pr+rF2/xnse4mct06LpfO8WQ5EImrRCHom/l4Aj/Gqh9/pp/Fxk8SHS7gq/ltoVw2o7teh8u0ybKP90iaQhaBwTAlS7Y/3oee8sE1Q5osMBoPBYDB85ZuzWPIJFWKf+g1SPi7BM+UdGBbmgtabHajetged2q3IlH5bTDmjZpjsE6dZafi3NLIIq+qAxb5v6DSeX1WIZu7L/Wok/usKXKipQ5d9gXKrFaVr86C/KpT9JDojB6H6SjTYn6aND6GhtA7W7DTECrsmCFmG9LRuVNc45LV0VKGiPQFPJvNT5GhdPqIMO1HaOAAzmSSa++rweq1x0mIimkyKTfqd5NoVSEnwcC9+kRZrNGQSqbcLaIGxtgja11rIlpfye0JsF2+hr3vN68MHpM9UL9abrag41CIcJDyqwxZVHSpOOx5ZWjrKkZdVjh7qYzcNeIbY3NEHM4ZNLq/BTcJMFozCpuAnbUdrHH4ySnz2yDmkZya7Wbz4j/5IlaM94rcVdVZsSIsTdkxGka00yUiPO4eDr57GjDVax+ubsrHiigaaOVaYvrQvdojfnD6FBqEkjwaJmQnOOiXtVde7rITEcPHRi+O1DhnNLXuQt6FO2auQmjBEjZnwtwmRB9CgPycUFBCShOzs/z97/wMVxZXn/ePvWfPtrDPNQ6aJDkQDaEQNYjZofiI5MLgizDYy0ywrfHXl6AMnHnniSOIEkwzmjxj/JAbFAXFgcGXlyMqjsVcmJEwgOBJ8NCQCJiKJwkaarwZOnO4nrD3h2Bu2f/dWVXdXN9VV1dAQTe7rnNa6datu3c/fe29VdWPC69ur0NVvJf7Rjwt7S9AoDi8lXxwZxIVD+2C87L5IZDAYDAaDoY7v+WKJoI3GprJXEduzB1m/WIH4v1+BxJw96Il+FSWbo8f+ig0lOgOvhJxEzooV2NFIJyNkkvtsCVYPlcKwIgUG+sdzcxsQ8mwBkoTXDDXL8nAkfRCFmSugTyH1WSW4vboIq8f4q3yjmLUWu3YsQFMukZO0n7giF++EFaDkKffvv/C491efsgKGPYNIL/ktEhwz8mAD9lbmQtO0HfmbclFo1GLD9rUIF6qdRJJFkIYsCvR6/jsp3pgSivQ3ChBWn4v4X5Br/iINuWfDsPd5PekNwaf+e2GUXXwlFKlb1uLOyVzoSRv6/CtIeEov1BE4GV5FVDOVIQ0GTm89SH0tG1HUoWYY8OJzOhg3EB9Yk0nayEX99AJsMXh7NBaN9GdDcJTIHP+i8Kt+1E9Wm3HQ4SeZe2BOL8GLyRO1VAJWZ4TCuEbwW/qdwOdKsE5O7apsRXxsZRwGbumQ9KT794WUYsWdYCTnZqBvXxoS0zJh+EUmKgPjkS7UqkGb/FuU669gG7kWvV7iC4NY/ZzIrqPg+5g+uMcZHxmlf8XqN9YiQs2T7eBEbFjdjx30ev9E+p1ZgYD4RKFSDRpEPX0Ev4vpRWVBLvI2laJn+W785kmhmqLki7ZefFjXAONF9gUmBoPBYDDGwo/orzwI299/6OteZCaqFf9NoonCZoV1WOP9WlxfbNDQ7ySo/uK/b9DXhaC2fdpfG+mvVqK/9K66uI3rNcjJteA3720WPe3phzEnD9YtPvwcO339kExIvX2Hy6f+f1dQGUa86c0PNp4EPwE6Ufb3W4E3+T/APBa9j8tWSrHiwbj9Yix5QC4+lBhP3vGMPdhwYWcK/n3hCew1eCyc5XyRwWAwGAzGmPj+P1kSM0X9hGzcaOh3q2SuxfVlIifApAu+tE/7KzHJsnVXIGfNbly4KbzDONyLpoqTMC2LxlxuB5kIWqwYaDyGWqxFki9PyKZ6XyhRfOr/dwWVwdvk1B82ngQ/8WQseh+XrZRixYNx+8VY8oCX+FDFmPMO/TtNKXi+upt/DZAsuiwXq1DdHIql0RJPGOV8kcFgMBgMxpj4YT1ZYowBK0wNFSiraMYF+iMMU3VISMzFpmeXI4SbsPbCuGk76u+PxobnnuP/WCnjHqMbtVm7gReO+e91UIZ/uNWG2gMVOHq+n0SiBrpZ0Vi/+bdefgSDwWAwGAyGv2GLJQaDwWAwGAwGg8GQ4If1Gh6DwWAwGAwGg8FgqIQtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAZj0rGix1iDCzdtQpnBYDAYdyPf3WJp2IqB7jb09FthGxH23XMQGc63wWQVilLc6sSFTotQmACIHq3DwvbdhI30yzpBk4ARG6z93ejo7r87ZZ8IqD6HJD4TKT/Vs8iGNnK9McfqOG3mdm1ffJ5edzz9loPYhOawrt7BeziH3R3YrMSmXtIFrbvr9TuWfHe5Btsu6LBwhkbYwWOz9qPnfCdMgxOQP+X6SevGk08sbTAePgel0Y6ztSN/0c8PJYf/AFCMVbd8rGL+5GCi8jgdSyYgzBjfPyZ/sTQyiJbCLCSmZCL3tVLsycsk21l4o3FQOOAuxtKLjk4yMRKKGPoI1YWFqG1zRLsFJjLIDYiSv6l+D57fUweTUPY3XRVp0Fd0CqWJYLRMoxl9jKW+APoXGhQHTl8ZaNyOnJQUGPJ24+BreTCQ7ZzCMxj4nk9WOX2mEVt7fibS9p0VIht2ojKtAI23uIJPjN9m9NppqBRE9cnnbzVgxxj77R0reqpyof9lFrbtLsX+l8k2yWFlZyfwpsj3mkE0vkBsmlsD0yif4OsctleD7WYnOnon1xa+5zsrWmpOInZlIrTCHli7UZubAn1WAfYc2ofC3DQkrtmHC370Xa6fv8yF8bqwQwRX50s+8RwPddEIv7UbR1vlZp+CrcU5LGUFsX0VuoaEQxh3B572VURFrIrz8aj5kwwTksf5sWRHvfe553eRSxh3J5O8WLKiqzQPO8x6lJ9+F3XHj+EI+b+hRA/zvjwSZHf5Ev+LBjxzuI1IIRC4HC+89y5eSHQMdybUb6tCuyjph2WfQOuJbIQJ5XuP0TKNRs0x48d2sRRb91mQWnIazaeJ7xyn/5cg1VyErYc6fUjq9yiRm1H35/fRKv48Gy1UTjCWQQxoIhAeLJRV8n20ma21BLkNC7DrxGkcOcbL1FCSCFPhHjSxcXXsXK/CtsPdQmHsWNuq8Ez9RN2e8hNkotjSFoNF0Y6nSjZ0lOajfv6rqDtFfIr61ekTOJJswvN7G1xjjl/oR/ELFegZ7w0mz/EQGixK0qP+j82K/Y3dfMyVw94/jV3zG/C/dvpbTsa4GGVfPzNq/nT3cU/kEsakMGU7QdieeK7W4DelWrz4h034u/8h7CNoHnwMsQ9+hOf/CGQmR5CUy2M5X4Mj5YdQc+YqhqfOxqMPO4KqHy2ljbDODsKNE3vwesUZ3JgShoURGnzVfBKHDx5C039MwUNzIxD0t/wZA42laPrPCPys34jy/XvxzsdDGPlZBB550P0VCG/XtHaexFFjKz75j5uw3eqFbXoMwn9K+1ENE93+m04YK9/Ch5/1YvCbW+j7awgWPxLIn9diw7yoEPVyRYTghrEYZXv/iE+G70P47FkIcO+mk68+rMY7WIa0H7VwctV+cAv3zZzvLtfIIDqOV6DyUDVarg4hYPZjCBHnp6F+XDhVhoq9NTh3ndRHkPqf0P3SMrnh5ZjhzxpQe302Uudcx8nifSh751OMPLAAj86cKpzI410XnvTi5LOlCPhNOXKeEB3ztzo8GqNDRyFxnl8l4xGheevVBhjL/4D9py9i+L9DiA510Ai3Bpy+cL0Wb+6qQEv/FIQ/GoEfD55BXekhlL933U2HnA0v349537ahcnch/vjxMB54lOjom07UHz2IsuNtGA6IGNV3f9rZoc81K+fDXYMEzgbnMDJ/AaYL/o7rZ1D53teC36m83iCRp5rI8y9CPP2kF9WXAsk1Z+NG9V58/Pd5WDtfCytpu77sEN78txbcGNJizoKZmCp528U3m3nX1yA+PtoEJK/DkoccPp+MnKUhyrJbr6Lp1HWErdJjLm1uxIqe92pQ+68V/HUwE4/MDsQU7kRBTzPvxyfH9qHkXRtilkWM0vdX56pQG5SBbcvI9QWmPLgAUX+nwwMPzMQDvvigMx8NY2r4bDwcqD5ulewgd32KV31zOv0MDywYwYeHiR6oP9h0mDU3xD/tj8KKa+8a8d/LMhByugrXogxYHMJbxFE3FM3bnkNGL1SvNY2X8FnfXzByw4L7iV/c11GFur4QRIWJ5fsTvg6LhqNLltYqnOwNwt8RX+DKMn3nbReGH1+qQcm+ehAnIXb0iE9rL5qqT+Kz/2c+5v5MIqg/q8eOrlCs/+fH8AC34yYuHDbigYwCxM3kdhA0eGB+NJY8EIAHHia6FfZ617tynHN5xLoMG4LrcORqJFJjQgTfF+qGovm4EvCmB+nxkFT89L9gebMOt/9BiLdR8PY0zU5H0qPCAX+jQcgjGnz+uwvQ/mMiwv/Wexwq+RwG22Cs+D3Kq4Ucdt8lHHHkAsGvfzz7K5wp/h3evDYThkXT1OUEH+YaYmykv0f+z39h4fxpovaq0RMQjUem8Xts3cQWZ4cxbwHN1QQqQ1U1akqJ/SzDCHh4PqbT8VjAcrEOx8oOcGMq1cHcCJ3ThqMY49jv1b4EzgZH/xWVRMdfDP0IDzv9yxGr6fjZJ1UkNg6PvqZbPuZ1wc2fHG17y2mO85IiYDpBfOJ3f8Lnw4FEZz7mPEF31WducmN8QG8tPgkU+aIIqVziGGPU5zbG9wVxmplwBj5tgykxEbESfqXVv4nWN/XCKwk2dB3KwtoqC+auzMb6lZEwV+Ugq9RxJ9qMLmMdqveW41ror7A+Yw56DuThpa1FqL4VgoRVmYgarEJOQZ3ztQgzSUj1VdtR+I4GS1dtRvpiK4x5OSi76Lhv4nHN5aHoKc3BM7X9XK1m+hwsnhdEOhqGqMVLEM6NqXw/usz0AJK0FkciiEgQFrkEi2cLklxvRXVrv9BvlXLt2Yd2XSLSn14O7dkiZO08I39353wpDn6kI3JvxuqFAziaWwDnk+XhTlRn5aDSQgbGddlIjbSgcn2m6yneSDeqN+Si2hKN9bsLkD6jB/s3laKLXtCLTG7IHXOzBvtrrYgisv46KQBNr1B9O54lyOt7FDc7ceFWIhLiJfqg02Pnn99Eko4vWuq3IuPlNmhiV+HXq5ZCc6EAhi0evnB4DyqvhhGdZCL881LiK1tRXGVGyPJVo3TI2bCuBGV1NsQSHS+dUodnXtiKHfvO4f7Hif9R2QpzvcvmLzt7Y7gfH5KBrU/8qqT5isjvVFzvZh2eX78HV0j9+nW/QnhvEXZU9wiVGkQ8dQR7DcFAdwVyc0/AvDgbuwpWIeTqPuQ55fJAtc189AUxirKLsaBlWyZyjVbMXZVHfGMOTBW5eKnOESy8nsq2F+FaMNHDr6Jdr0iJCAqdA83Zt9B0XXwFMuGLjkGYpA9Gw1aXh4y955x94vPRHmfcJgS24qUNe9DicFKPuE0icXkwR/T6lIIduOsTn7ct5mMAZ8TXV9A3p9PTKN53DLfn0fz6OCy1W5Eneuozrva9EWzAi799HLV7Kvj8I4WCXgJmL+EXRdMjsXjxHASRSZxOY0VlbZsz/m2dDSg21uDfzzvsPojztTUwT6GPTZViV7Bd6XbsvxyMpHWG0YuCkX7UF+ShKVCP1IVSHkT0d70HtpkhJG86CEb4fA1a3qqDyXEhytRgRD05x+mH8n6lNq8EI/WlAiyuJznQOf55Im9D6fGQMHUOIiK70fO5WAi13IaNO006DpXyOp/D9qFnhiiHFZ925QLOr4/h9b1v8X67jNpbXU7wZa4hRhNoQ8+hZlxzPMUjY8lR0t7rjVeEHcC1xgpcGKYjKIHEddYaIsO0pVhfkI2laMYzdDwW8pu1cRuxdyeCVuZj19NLcdtI+mJ09NUDpRwiM/Z7sy+9viGPLIYjif7yViGsvxxZHrKbqguxvzfUec396/NQf1OodIPXLTd/oiiOLf2oLRZyEufbhcj1zHkyOXeA6IrT3XIa16HoKy6Ec4iTQCqXqMkPjO8p9knkcnGifeupAaEkw8Bp+9Zl+fZGs1Cm3G6170/Otp8y0UKH/eCyRPv+P9/hqihfnthoj3uh2X5bKNu/om3ssp//hi/Sa8dlH7P3fcuXKXf+vMu+PPuE/UtaML9r35ZMjnc2QBD2tQtt2D8uscc9fdru6hbfj4MfC0WuvNn+tkhE86nNrnNUyvX6+6JOcOcU2M9+LZQ94OR6udnu0sQd+/nXXHrmrp//rksvBDe5ufaL7O1Ovdyxm2+Y7Xec5dEyjcaL3GuPuOm77+hae1xxB19Qo28xo3TvhW9JX5IN9qOfCmXKtyb7qWy9ff8HvJY4nb3W6tLZjRP2PDcdm+2Nm13+xctCfIcrEb79kMhLrnFFKBMul+jt2Uc5I06Inbk+GDbb95eU2P/g/JywX6bHc+d62MhNX0rXu2Nv36235zr6z3Gb+JF+lM65frzxoVAifGO2f2l2eZ8bam2m6At8/x1xxtnP4UdKsnvU3zH12L8UXYfGQtxmGT1Jctved+pVe3Zyov0f0jfaC8tO29u/EJ3z7RX7UQOx9RdCmeLYJ6h4dNwO2N9+OtFe+B7fzu138+3LxT5K4Pbt5vfJ2oG7lkcMfHPF3kj9hTavpG9OZ2vtxz/nqziITpcbyu3X6PZ42x8FLzufs27b298w2NcS+/LS8HUO2yvphcLpxuEflG9o7Ll84HKx3n6wuMi+3DFefN1sL3SMFZzscrErZTvhmtTnSK55e7PevvVoj1u9J6P6SLndY397+1r78mV6+9qNBfY/nPrQ3ifOB4p+pZxXnP0k23c+LLJnZJTwNnPU+ZKfvcQ31Y/3cV5saxdfHifXdoxJUnIo5nWpHMbvc88FHn5NUJMTfJlruMP30eG/VMdbi0vsWx2xZKf2W+uy6R2zve8LsUbJ+Wv19j8IZqG6zTsh0p15wP6l5HVVxAqnD5mxX8q+35jsfSaRsmhskfza+BUt8Lb1vGbf0WyPazpikdetWDdec5qU7b44Zs9eRvyXbivFBpcDPOq5uJafk46KU64f8vmB8f1kUp8sUe6oeE/a9vkVXFgYh8XCXVoO7eNYFN2Prs9dd8I0WuHdAsL9U8h28E9dd4KnhSIMgzCLvkejiY5EmOh5tSbm50i9fgnXyDG2TzvRMiuI/N+GjvPC53MLqehGn5cbN76iVi5toFMKIhOV4zZuy/3Agu6nzlc0iFQIcJ5uw9XubsTGxbjdIddEL0WCIDemRSBqWgO2bdqH+uZuDJBu6GbooPH6XN8HAnUIELUToHUJPiZ922y4I2x6pb8b7UjEooVCmTIlFAtjNLjQ3SvsIGh/4tIZ8Z37EYwgx91R6BAyGxiwiO6XEZsECJv0eCAUOpEdH5oxBz0W/hbZhNlZuMu32Plx3O1Sh/frDaLvigaxT4RyVTxaLI6LE7Zd6OYvQFhDIZ7ZV0f02Q/rFKIrnUwnVNhsMmLPgSaU6Az018ba0FRTiso/XgI8cpKbniTRIix9O468+y5q3sjGInyEyk2Zrh+t6O/E2RGiywGRPG0m3Nb2w/SF6P6jW9wGIzwCsFqpf9hwub0TUTobuhznk8+1W6TmigkD5AhZO9DrQ49YcQxMjUTS5gxEEdHU6VtH9CBsUmaGYvGQYEu/tO8NLRY9/SpizxeictQXv5X1IsnUJVga342OT2l75P+zcVi0LgapnR/iMvF/G/m/KWYpFk4l2ypj1912ArZeHN+Si8qQV7Fz3ZzR9Z7wj1FcaOcg9dVjaH7vGHatWwK0VyA3U/TjRyr9Sm1e0cRsxCtPtmLHobZRd8UnOibbq/ORk5XFf9JSkFWnw87XMuB6AdBDDsW8LpXDNIiK9cxhHn5NUJMTfJ1ruCBxs0yHlnb6VNaKyx9ZsNRgwFJdKy7TJzzXaSzFY+Es7mByIR3CQrWw9Haio7kOtaVH0HTL5vwVuPDIGHQdzseOqgb+VzgDgxHi+Z4wh4pYGcvYP5XIGkxcsJu011CDykN1xC7u87qEJY+7+X7YEzEIarui+CNXymOLh+2mavGgsKkYG4MmXJ4a49IzRUvzgrCtEtX5gfG9Y1IXSw/NikTH1d5RiZmD+2lIvsY6RLLxzGASGmLIZJXs4CcTYyMs0PXSA8dUOmHmBxLumsP9uNb+EdqdHwuZFJFAFw4fLxMll3essH5F5A52vyJdKAQ5BtApkVh3/BhKDMEwnSnB1swU6LfWTfivy/ms77BQxPb2uL9uJcL509JfW2AKJQsffrcTrS4YA8OKSy2/MGF2DpyDxU/GYJHzE+1loPQVM8y3tNB6tKWZer+wJSJyI44dL0JqcC+aSvKR8YsU0SsrHqi02WTEHsfIIJq20l8bK0I9ucZt3RIkJzwuVI4BMmnSzSGT7qd34fcnXsXiziLUnic5jPrgFPJxk6eXTE4NJAdJZj8P+Li13bwiOp98hkKxLkb4XoOcHej1tVqvk/Vx63ui29dGY8PzibjwSgkuuIWLCr1IosHCJTFo+ugSmeu3on4+WRgR2y+N/wg9ZMJ67dI5JBA/oJPfccVubxusYUsQcLYGjZKvHbmg+QimQclXt7gJ85MGbNh1BHU7otG+7wg6aAyN26880SLqqd8i4WIhys66yzbRMRmVsR0lZeX85+hpNB/fjoQZQqUUinndhxwmxt85QYKwJ8iMvLUTJvqjHp/ThRFdQAFdPVZYOonPJMcjQjiW/iBOTkoatlWdQTvxzbC4FUgQrf+0ybtQV74Zi0Y6USv8Cqf0q5QqYmUMY/9A3VbuV0CLjaQtiw6L9WThINQ5CPK82RRIFj2OGy1y+DK2eKIUG5I5S4P7vScNScaVHxj3NJO6WNLFJCK2+YTkQGKqyYXh0EfcQkoXsQAh5y/B/XXSfvR9qkHELPqe8djo6ff4HsP1brQLv/ClC42AZiQSyZs3Y4PHJ0F8N2IcTJRc3tEhfIEWLZc8fmGqv9cpN08wIvRrsWlXOY69XY71A6WobJ7YwPdZ39Ni8PPIM/h3qZ/5vF6DZzJL0EWNO3sBEnqFp2ZObOgji/TYWZPzm4STbufAILc7shwjvkyewhA+b5AbvMWYrnr5DdhpkUha+xxeKT+BhvIMfHmgFC1Sd1VV2mxcseeL7FfrUNljQPmpEmwhbafrY8hAKtSpxoqu2lI0dXtcQ7sAUfNtGLAQHXJPYYKx9KnR8qRHe0wkJCGTiwgN7swjE2aP8zdsXu6S15sdaAzcJDHgMRN3LE7Hnesmun2C5omNeDH1El7aSSZvTlWr1IsEWjL2JJEJa31bK8KXRJLpDV1ALcDZi3XoODsHS6P56c+4YjdyLTY8tx1vbgSKtwjf+/SCJiICi7p70eeYmNIfHyitQ4/HjQXN/GhE2QZhpm2N268koAvT3+rRvmcP6kV/HHfsNrTCTMOde/runfunkoUNmVTzHxUzVsW8HoG50YP80y8RPe2twpYX/JITFIiMR6q1De11H6Jl2RJuYUQXUF0fNeP8R71IIP7IY8WFt+rw0LPH8Ptdz2HDUwbERodC4+ET2tAYpD5VgJ30Vzifn4PqnTUe/kpRGyu+jP3daKrqwZqi09j7EmlnrR6LZo1eOrdfdf9uoq2nBx0LQ+H4fRZZ1I4tnijFBq2/2Y0+t7ZoXAubKpn8ORzjbmFyX8MLNpCERAeS7ajvFpLaiAU9xu3YVqPFlnVx/Mp/nh6rdSSJnXQF3cDJIpTZDFj2mLBjLDSXuF7toF/CLa6BLT0RUbT8mAGbNDUoE13T2laKrDQy6InnRUMWmGXuvNAvjFq9BfdEySVDRHIGgozlqHdc0kNuW9s+6NeUosPRZ6uZDMwaBOnEg6+MTE7UHCNCrb6dBCO1gDjP4Ty8YeyGVbCBtbsOb7xQg4CNOVhE7yoGLkFSYicqq9pcx7RVoKw1Bivj3e8HTRiTbWf6asSsbnzQ0MvJbLt5BmUHmoVKNegQmxqDlsNVLj/oP4nKWs/73jZ07UtBVqlIt2Zid00wgiTnaipt5rMviPBFdl0wwofN+ItjLmDtRb3xnFBQixZB6MSO7UW44FgDksXZAJdbIvHzGOJj0xLxj4nnUFbh0hPV5/MpeWhSeOLgICp1Le6vKRLFrRUdBzJh4L5IrGAHLgau4Gi1q97SvBtZ62v4V2HGo2/KRLfPoUHU09ux/ouTqBa9PSuvFxGW/+vSPSUwEovmNKCsFmRhxE9qtI8twYM1FTiqi8dixzzHD7Ebkl6AF4Mb8PphmS99z4hBwoxWdF0VyvSth85SFB44A4uj32SRdOFQOZoiE7F4Gin7wa+k0ERvxqvpPag0ihQ95vGwH6bPtYiK8HOuVczrWsT+vwb0HdiO6ov9ZCyykpjcjYNnFRZifskJShDfW9aLsppzSH18Ab9r3hIknK9AcWccYoVdXG4J1pDFpivvWs4eg9Fp20HU56bgeaPLJuZb5NgZo5+4UZRiRdXY72ZfHXQzbDB/5VQWTCdPoF4oOTDVlrquOdSGykPnkJQaT86Ww9exxQOl2JgWj5Ux7v4zcJIsDNX83SZxLvkO5nCMu4NJ/85SSPpuHFmtwTv5mYj/+xWIX5GJvDoNVpfsRqrjMfyUUKS/8SqimnMR/4sU6H+xAjn1YdhZthER4/kujT4Dc9/KRCL9Q3grcmEMysebTwl3ddyumQZDygoY9vQg9bVsRDnybXQGXgk5iZwVK7CjUeruSzTSnw3B0Vwi14sSv2w2UXLJMWstdu1YgCbSp8SUFCQSud8JK0CJIDd9b71EfwXb6Hvja9KIbgpxzVCEDU9w1QQFmTjUHOOBGn17MsOAnSVroanLJ/Yj1yL+o8+vg2Z1CXamO2Y7ZNB8tgSrh0phWEF0zLU7iPSS3yJhktZKk2/nUKRuWYs7J3M5vejzryDhKb1Qpw5t8m9RLviBgXwSXxjE6uc82yAT2I1FSP28EPpfZnLfNdDv7EF6EbGZN7nU2GwsvuDEB9mDE7FhdT92UBn/ifh6ZgUC4hOFSvWEZBThd8sG8fp6Xp544mdZpYNI3U9yGCeSuw9SfcbnNiDk2QIkyb1qJEYct1y+ykThF3oUZUcTKyjZgb9++uAeZwxklP4Vq99Yy/vfuPRNmej2BehrQm+Qc4Qih6xeeHTJ2VjXU0Tq0lDtfKgejCcT58A2VfT9kOBoLJ1mQ9iyGNeddr/EbjCSdu9C7PlteEk0qXInFMvSQ1F/1tFBet1dSB4oQYYQJ/G/yMHrA3r8frdBmGj6wa+8EPFUEbaInxipsaHUeDjYgy76fbZ5fNF/KOd1uugrfz0GfYcLkLcpF2VXE/HqcwpfSvFTTlAiKo7kJFs0nH9Xa8oCLHrSBlv8zxElenUwKiMf4Y15iE/JRBYZr/MvL0Cq48ETvfn0cj6CjLnE97NI/Qr+u14FDv/wQCFWFMf+UfYNRnJuBvr20WPJousXmagMjEc6f7ST9Of+GQMvEBuRa3Jt6ovwYrLS4DuGscUNpdjQIeElof0U6s8p2Db4z3hFYZgclUv8kh8Y9yI/or/yIGxPOjarFTaNFqLvTo5m2Eom36PfRfaVrgMr8L/wJv9HPGmbU2SuS+tHNNDKdmyc+EkuX6CvyRChpL/ASb8zZrVB461+IhmLvm3kHJvCOWqOmWi+AzuPC84PiJsovRozUTabjNhTK6MSQsyQoPFuXyrzMJFnHNfiXm+jry1JNaGkLyWdj1ffE92+DLJ68Qe07xMZu8OdqMxpQFR5AWLFr35xPkPdSiYX+8GvVOODDQdqc7AD2/H71eIfWvAzcj5HnwCIdGY7uxuJxgWoK/GyoHDgr5zgJ5TmRlw9ncOo9E3ZWBnD2C87lxCgfaQ/wuDzfMIfOUkuNvwV1xOdHxh3Fd/pYmkycVssMRgMBoPB8B/WNpTlNGPREbL4U/PqlJ+xNGzD2oZIlGxfC/oWoM3SidotW3FBf2RiF28MBuN7zw9nsXQ4C/tRgCOO1+4YDAaDwWB8PxixoKN6HypPtnF/xFUTGIqEjM34zepoaH19usFgMBgifjCLJQaDwWAwGAwGg8HwhUn/gQcGg8FgMBgMBoPBuBdgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYGgs2K6xWm1D4DrjViQudFqEwTsYji4p+2KxW2EaEwoRgxcD5NpisQpEy1Iumw6WoLG2A6Ts0kxvDRM9j7ct37W8TioT9Jhjb0ET7JGHEBuuQw2YSMo4MoqOG+mgNOm7J7PteM/m2Z/gA58OuWJmUuLlXmICcPFH6nfgx+IcKy18TgfVqA2pLyThY34u7adbDFktjwFJfAP0LDfDTcsVnTPV78PyeOpiE8ngYjyzK/RhE4wtpqOwUin7AdrMTHb2i3g59hOrCQtS2OTJWL2o35ME4PAeLI8MQoBF2f8d0VaRhR/2gUPKN79rfvDHKFmNhlP38y+g+dqIyzb8+KcmtBuxIq0AX3R4loxUthTnYfzUYUZFzEKT1tu97zgTb/vuOX+JPDs6HC9DILdwnKW7uSiwwne/EwLBQJPg/J1P9OnTtT/w/BjME7ur8NdpnlRnLOX7mahVy8+pwe84SRIVpcZdM3zjYYukeJCz7BFpPZCNMKH9XfBf9sLZV4Zl60fIscDleeO9dvJDomF3ehvnWHKzM0GNRYiR0wl6G/xlli7Ewyn7+xS99HC+jZLTittmG2F9lIDYxBmFTve37njPBtv++c1f49g8CE+q3VaF9SCgyGJS7On+NxWfvAj+/bYVpjh7/pI9B7MJgYefdwZTtBGF7cqCvmhyvQOWharRcHULA7McQIvY1+grVsXJU7f8jPrGM4MFHIhD0t466ThgrP8OP54/g47I92PXWRYw8sACPhgyj63Q1yn/3v9ExFIh5C2ZiKl0GCsc/sGAEHx7eh5J/OYMvhrSY46gnDDSWouk/w/DjSzUo2VcPxCxD+E/oud77MfxZA2qvz0bqnOs4WbwPZe98yvdjpvsMx3K+BkfKD6HmzFUMT52NRx92CNqPltJGWCNCcMNYjLK95BrD9yF89izXkxCip67TNUSmA2i6NISRn0XgkQf5SmvnSRxtsWFeVAis56twrCsAiyNEywJLG4yHL+O+aNLnKaSsQpY1K+djWE1bIsT94Ho2Qhz9zydRdXAv3vl4GA88Ohu3zxoxFL0OSx7iTpG3v4S9bth0mDU3hLMXtVVN4yV81vcXjNyw4P75CzD9b6kuq2GaHoPwr8+g8tg7+OSzXgx+cwt9/58G8+b+FS2HGjAc8Rimi8xjaijFn26FIcppExFEjp73alD7rxW87TATj8wOBC++CtsRLBfrcKzsAKrP3MR9M+cjoLcWnwSmI+lR74nVm7+o8jdOr1Wkz6VE90P4JiAMc0OEekfczP4KZ4p/hzevzYRh0TQFOXm89UnaFlyVd7+X7Mewy34/5dutffcjdHwk/vwVD8UQ/fKtyMSVO9J9HMTHR5uAxFTc10LtV4Nzg1MQ/mgEHhDfxpLLQ1IM9ePCqTJUcP5AclCYFeeMQ/i7/xmD6ZzPCDL+DdXBH/F/PunFDfMQBrqJbPO/RpPnPoe8Mv0YS+7iYvZyABZ+ex5HiC9R/xzRzXb5ioCsjhVzuEMXRLfXST2JvRDar1GI9CLYvuk/I/CzfiPK9/M5ZGr4bDwcKDaMO444ozEx/N8hmEtylxr/Vco1DugrIcbyP2D/6Ytc++GzddCI6mWvP46xTslOcvHnyZhtab2KplPXEbZKj7laIW6SXblcSfduDJJxpKoaNaVU1mEEPDwf08U+IdOPsfg5xXr9DOrLDuHNf2vBDY9xn17P2/jqBucnb+FDx5jy1xAsfiTQD3MAT6h+L+GBf4yB9W1hXJHKO0o+JTsGk3OrqnAtgMSbaIi3ttXg6KUfY+FcGfs54K7vZZyhyPSP9+n7MesvDVx8mx5KR9TPSIVSrpXLJ2rtaCPXUJoHyMmmYuzij1OIafF8icD7Np3DCntU506CN9m9+CyHtziUO2c8cSaBt5iguqj9Uyc++Y+bsN3qxYBGpJe7ABmRJoDhTlRn5eBgbyiS1mUjddYgDubkwnhdqB9swEtrCtChXYr0p1dhqbYNhZl5aHK8vTTcjw+Np1F2oA53Ylfh13GA8cV8vLRtHy5MfRyp65ZDe7YQuaWd/LuOwvHFhUXom/UrrF+XiJCr+5CxpQ4Dwju85u461Jdux/7LwaRPBjIokJ0e/Yiy1SFvzW50iJ+23qzB/lorolZm49dJAWh6JQdlFx1vWNrQdSgLa6ssmEvq1y8PRU9pDp6p7Rfqzegy1qF6zz606xLJNWi/i5C18wz4S1jQsi0Hr1/SITV/NzbEWmHcUoD6m1wlbNdbUd3az8moe8CGpopm9IjeSR5orECZNQBh1M/UyCKg2JYH4n6QkEEXkTGn1oKoVZuRHqtB08u78YH4LoVg/0pLJLFVNpJm9Ljb32Gvfcdwex6xV8bjsNRuRd7hbq46YDb/aBbTI7F48RwEcX3iddllJpuBoWR/JIKgRVjkEiyeR5KSJhiamyQBtolemBg+B+OBTmhmSt25oLrPRK7Rirmr8vDrVXNgqsjFS3UOJ1SyHdGZMQ8ZezsRtJzYfmUo+ooLUd0jVEoi+EtpD0K4c+ZgwM1fCHL+NtKN6jVZ3CtcS58qwPpY4IMXc1HZKdRzej2G1/e+BdtiEjfLqNxKcsr3SdoWCn4v2Q+R/Qi03cWLHZ9oBA02o7bnNu7napXiyh3pPvJcOOCwXyaiBquQ9UId0YiADzHDYe1E2YZcVFuiueOXTmlA4RvN+ItQ7SajhkwqiY/SbgXNozKGkkW2xD56mkI/xpK7uJitK0Kx4EurFw6g9jek73yIERR8USmGqS8Kuli/uwDppH7/plJ0SerO3facPFV7cPAjHRJIDkkIbMVLG/agxcu7TtbGbXycrczHrqeX4jaJu5eMHv4r64vecw3FUr8VGS+3QUPHmlXRsNXRuD5HWuaRvb6PY52vdpLzbRfjtKUM8rr3oLsCWWv2oWfaUqwvyMZSNOMZ6hOO130UdKXGz0fNFcg1c3NPwLw4G7sKVnHjfp5jXqAwvrohxKZzTJlNLy4wrjmAFBZ88EYBmqYIPjFch9wNotjxsFdqpAWV6zNdeV5xDA5GgK0ZZY0uH6c3LBoramAlY6ekC4lRHGfk7cj5dO1u7P/fNq5/sTPITiU7yuYTX+yoMA8Y0xjqnr+UZHGfL/FQ3/7wumBgn3KnjOzefFYuDr2do5Cn5OPME4+YWBkJc1UOsoTjubF/XhCgJYtXMv7Pna7okZOLfRK5/W6+PS7/XfttoUwxf3DEfvR9E7d9uVhvzyi7wm076DuabV++u9V+hxYGTtu3LltrP/UFV8XR/kaiPaNcdE5HiX352mP2PrrNHa+37/+z6Irfmuynssm+D7gWyTUT7XEvN/PtC1wrM9hzj/J9csDtO87vM5/abI9be8Te9y1X5Og7utYeV9zBF8zv2rcl77KfdxOU39f+DS102A8uS7S//r7oAK6vBfazX9MCrd9of/sGV8Nxe8BkvyNcj7v+06ftZq7Ey3PwY65AcJdPlSzittZ6b8sTt3NNJ+y5njJ/ccyeS+R0tEftv/w1wZYC3D4P+x7/nKvi+ZjY01BuvyYUuWs69MzB69LVZ1rebH97QCgS7vx5l0hGUv5gl3159gn7l0LZkzumHvuXIjm48zc7zlew3Tet9v3J2W4+ar9N9yXat54SdUoM9Y1l+fZGRwcpA6324+XNXB8V/e3bO3Zzj8lu9qhfXiLUS+mVICunQp8oo2yh5PeS/fC0n4gbJ+xbk4ktHXGg1L4E3vzFzX7UZiKfUYoZT748TnKUh1/TvBW3rMR+mSt5yjhgf/tpT5lH71Pqh79yF5d3HTlUwe7qYrjI3u5sn/jmDbMzd7njrpfR8vA6KXxPbHAX9Pi8E6KYMg/Yv3T4wVh8UZxrvr1iP2rwiGPHPkG9ctdXGuvGbSfCaN/2wC+2dMTFaFt51b0nd8z2vi/EneDHmT8IXVfSlZSfK80VON288SFfQfnGbP/S7GiByuJ9fB0NPd59TJGyj29zAE94/br7xG37+df07j7hoSeas51jmYoxmJazk0lecvRbOMdb/nRDYZxRsqNXn1ac83nLJ77ZkRv3vc0DxjSGesaECp8UXZ9Cfds5L/A5d8rJPtpnleJQ6hxVecprnHnAyeeRj7i5kSun0hzsqaO7hUl8smTD5fZOJCXFk7WrC118NtYlhpKtfly7CCTFRvIVAmFPxCCo7YroRwR00Igeo2rI4jN8mk4oEUJCsfimBbeFIjAHUQtEV5xC6pfpcKG7V9hB0P1UdFelH5fPkzY1A+g43+b89A1r0dUruisQqEOA6Jl1gNbVB9unnWiZFUT+d53f8bmFVHSjT3TzTRso6ldwKMJIr29zd9vIyjqmH8X521Db0AnToA1aUq+RfEYeitjkYBjPCd/g7G9Dk8WA1CepRCplcULaSvXWljy2L3rQFb0UC8XGnRWPJHr3iIO3f5TOhi5RX67dIjVXTBgQjqL21QpPfzlmEnsN2XBHKI4FTczPkd7bjPOc7sn1z51DbEYiQrja0WhC5yCI6K6H9K+J/jrZHy8BHr8m5NV2gyZcnhqDhbP4Kg7tEiyNF7Yl4PwlRo9YkRsjOA6rNy539VHG3zBFA92cUGhv9RLdnkH94VLUtlo8fgHJQ68EOTlV9ckDdX4/uh+S0Kc1W04ibNdupAo+pDau1OBmv6k/8T3+ndjQR/YnLHnc7c5sWHwiIoTtsaGyH37IXQ8RPxgY5iNM3u4qYnhaBKKmNWDbpn2ob+7GgJV0cQbJ2Yrv9wi4yROMcKJEq1Xy1irCI2PQdTgfO6oaiHyDsAUGw/HWzJh8UZxr+jtxdoSMSwOi89tMuK3th+kLXpPer6881o3XTmoYty1lkNP9KDQ6hIVqYentREdzHWpLj6Dplk3IT0q6EvDwc6W5gm7+AoQ1FOKZfXVkrO+HdYoOITpHC76MrzL4YQ7gjhaxT4hkJuWFS6LRdakXVqKnq93diI2LcdOThoy5Cdcv4doQaVpxDCaQcuqMBrQIQ/zA+QaYDWlY5M12YmTHGZV2dNOZijmfbD7xzY6aJ36OVG/zgDGOoS7Uzl9l8Cl3jsGHZeNQCuU8JR9n7tg+v4ILC+OwWJyPtI9jUXQ/uj6XzvF3E5O4WLLC+hVJKHR1I4kZ5luhCJkuFB2Q4AoZ12Q5GFpx9BJoUvM+6NB+kH/7P0J7u+vTp4nBujk/4RxECesQicZhEjyi89vbLQhLJ4EjHCOPDgm7TuDI5iVA+7+hMDcNiWu8PY4l68NlBixuaEAHmaxzyS85HhFc0Pgui/e25OFk1mrcJowkOkULW97+tptX3PrSPhSKdTGud3gnhKlxSNL34p2z/cQuH6Hl7AIkxIgjVsTIIJq2pkCfVYR60r/buiVITnhcqFTB1xaYiMN56uF+GQGldecDZGFRuSYFhheq0NJO0vKsOPw8RjzoSqAg51j6NH6/FyB9qy/YhguJ27HpCVfw+q19WXyNGS95jZQd37EaG2PJQ37KXV7triKGp0Ri3fFjKDEEw3SmBFsziY9tdb327E+0ybtQV74Zi0Y6UftyLvQpWai8yCfJcfsKjeMp5ON2fi80MQaEBfKa9H59NWPd+OykhnHbUgY53Xtiu1iKnJQ0bKs6g/brZJoXtwIJzvSkpCspqP4U5gqRG3HseBFSg3vRVJKPjF+kiF4x9m18HQtj879g6Dwm41oiE27fJj7B6yks2GPc0v6EtMffqJO2Nym7LYRCsSx1DoxN9HXSflxotCA13n2C7xXZcWaC7CibT3y0o9w8YCxjqBsqZFHCp9zpuw/Lx6EUKvKUbJy5w/nnTOLjQplHiwCyw9sNsbuJ+4T/JwEdwhdoUdndiy3LRMFJ/17BiBbaqRGYG92LlitWpM5wTZBsPT3oWLgAjt8H8B2yQqe2c97pF+7QLPT2G25hCJ9H+rRkIzYskw58i/C/N3ShEdCM6JC8eS1pbaxoEfYkGZjJZzVJRBdeScOOmkSc2CiR2ILjkfRYBVoudiOsniS/AscxY5DFrS0bVr+mLpFyMlfROyhxrjvqw73o6aXpmkKSRoQGdwIN2LDOlyTkH6ISDTAXt6EnuAfGeAM2eYw5Tq7WobLHgPJTG52LRNvZZn5DDfTu9E2SUIaI9p0DXz/6PiX/zeFLnugiFiCE6m6E6M6xMKV/44TkD63Ml9sdWM+/heqZm9Hwpt55V89UrZB8FOQcS58U/V46h45ioG43yrARR55y9z3/xJUSyjHjDu/XlVfd8xqXt8a1XPK1HxQ/5C5Zu6uN4WBE6Ndyn00jvahdn4vK5kS8kuxx18oPaENjkPoU/RCdN29H4s4aLDtNfHq8vsg9ZbqNpU9tRqzMXXdv15cf68ZvJzX4x5be8ap7oZ6HjF1v1eGhZ09gp96RdPtFP0+sNC8Qym6onCtMi0TSWvoh21erkJNbipZlu5DA5WUfxtcxMLZcRcbLHjIFneHyCdPVTmjmLydaEvR0qRubYkR97O9FuyYCCcHCNWXHYB7dMj1iKz5AV2oY6m0ZeHWhUKGA/DgzgXaUzSe+2dFtHhCtxwbHWmksY6gbyrJodfR7Tu7YRt0Z8SV3+iK7UhxKoXIMko0zF3w+uoQexIhyBJ0baRCRNFo3dxuT+gMPEckZCKorR73jO45W4gxb0vD6WTo0aBH7D8vRcrgKHY4vJA61ofLQOSSlxpNQHCuDKCs+6VydW9sqUNYag5Xx3lokK/b05ag/VOHqx0g/6vNS8LyXFfMoHiOTcU0Nyk66vsxpbStFVhpZ+au5bXizDs+k5Ln0NGLBgBnurxu6oUNsUjT3JdhazVokOWNlLLKI2zIgVvw6mRyPJWK9hkzAHTKPWNFRQWwtirGo1LW4v6ZIJBc55kAmDF6/EOgFy/8lCVjYVsvCeKRaTqKQ+FN6whLvd05JQgsfNuMvjjxJfLTeeE4oqGBaPFbGdKKyqs3Zx4GTpai8xW9LMk+P1TqiO6MzMEiizod+ZzPZUoYmYY15EGaHTm6dg1Hpbzopyam2T2JbjNfvCfTu19bqMOzabUCIY4LnYKzt++QvvscMHYA1RlFeo3mr4px3H1PF2GJ33LlLwe5KMWxr2wf9mlLX9a1mmK0aBOmkBvvxMIj6XCKXs5+A+RYZR2YE83fux+uL0xLxj4nnUFbhimP0n8TzJC83cV8gl7++/FjnBzs5kPPtcdrSOwq6d0OLoGANzINUbh7L2WMwir6EL68rKZTmCjZ07UtBVqnLdlazBVYN6R91Q5/HVwo533EtNYzJ/zSorxD5BPG3shob1idGc0VOT+I8Q32muAa29ERE0bKKMZhDF4+k6HPY/9pJ3E/0JV7MWXvbvP7tLqVxxv92VMgnY7GjeB6wMo70gGdMY6gbyrJoZkcgqrsZjVdJ9I3YMNC8DwdF92F9yp2qZBf7rHIc8ojPUcpTCnHmiSMfiWJi4GQRymwGLHtM2HEXM7m/hjdrLXb9Ngzv5K5AYkoKEn+Zh5bZ2/GbZN7AmmV5OLLajIOZK6An9fGZe2BOL8GLQv3YiCSr4kFsI+0Z0lKgL7yC1KLfIkGmSbd+pKUhcUUu3pmejy2pKle/U0KR/sariGrORfwv0mBIWQHDnh6kvpaNKDUzqBkGvPicDsYNpM9rMqEn16+fXoAtBu/X18brkTpkQVByjNt3SsYiC9cWGfzCSZB7+37KKOgj5OJ8ksyJzClU5kwYZ23GFvHTFGr/HQvQRO1P+qJfkYnCL/Qoyo5WPbHUJWdjXU8ROTdN9Ateaogkga+BaUiPJLnvYAUnYsPqfuwgvmL4J6KvzAoExCcKlWogCealIqR+Xkh8mOohBdsG/xmv6IVqKTh/KUBYPfUX4qPEZ3LPhmHv8667XLJEZ+CVsGbkrCCJ659IXOV1IkIvfWfNiZKcKvo0yhbj9XsyCWusqiM2asAzv1yB+L93fMigQHPzGNofi7/4HDORG/EmF6/C8Wvewtxns7FYqB4rY4ld/+QuGbsrxLAmZiNK9FewjfhV1hpy/bRCXDMUYcMT9GR/EozUl/l8k5iWhSziC1l1OuwsMHATk/H7Ipn8PFuC1UOlMJC4omNHfG4DQp4tEL4DonB9X8a6sdiJoOjb47SldxRk9yAqIx/hjXlkXMgkx6Yg//ICpIrTk4KupHDTHznHfa6gQdRGIQf/MhM5dNzf2YP0ImJ7egPG5/E1GunPhuAo6V/8i65fPZVlTP43B1ueDYWRvg5G/W1DHYKeK8E6h67E9qJ6oj4TVoASxxN4NWMwB53Yx8F0KxQrl7k/Vbxm3Iajzl/X80BpnPG7HRXyyRjmSXQekLRaS8aYOCQ8ITLEWMZQD5RkQagev1lnI36UhngSs9u6l2ODaF7gU+5UlH20zyrGocQ58nlKIc48cYsJmo9WIKc+DDvLXG+33M38iP7Kg7A9qdiGiCnodzuklMS9KmCDxlu9Wgbr8PyaZvz8eAlSp6l/rcmJP/oxTB9Da4ioPlzXgb/0QPGlreFzKE6rQ9TxN5HkPc95xUYUbdNo6evTXqH2t02VP8bfDNTm4BlLPk48rSIJcvry0V88obYnA5P0KwheGMs5DugrDzYffU2NnGOVY6x+r4aJbp/ia/z5M17FjKVdf/RFwe6yMTxRupCAyzdTZPxzvL5C42qYnO8lRpSuPyljnRLjsaUMiroXoXZc8KorKZT0J2f7ydS9j/6npAfFegVd21p3Q//HaNSJXjvjnhhuyIVly2nXAk0KFeOM3+0oV+9PO45lDPVkvP3x5fwxXEtNHI5C6Tq++rhCProb+c4WS5OCeLGk/obdDxjiwINWDDTsRu5gJhp+G6f6ic/djG3IQj6XUJl3DGElR5A++V+ZYjAYDAbju8VqgcU6iMbX8jGw+jS2xItG+JE2lKV8hIR3N0s/GWAwfsBM7mt4k80UDYJmuP+8J0MG6yUczc/Hwd4YlG/+fiyUKAMN25FX0ADNs7vZQonBYDAYP0isnVXIzy/BtSeLsEm8UKLYwpBQspYtlBgMCb7fT5YYDAaDwWAwGAwGY4x8v58sMRgMBoPBYDAYDMYYYYslBoPBYDAYDAaDwZCALZYYDAaDwWAwGAwGQwK2WGIwGAwGg8FgMBgMCdhiicFgMBgMBoPBYDAkYIslBoPBYDAYDAaDwZCALZYYDAaDwWAwGAwGQ4LJXSzZrLBabULh3sQ2ZIVtRChMBCp0NOF9GAsjtnvetj5B5fWzHdzseqsTFzotQoHHcr4GlaWlqD3vvv+ehvj7QHcbunoH/e/TnI28+OT3xV9HLOhq7oRFTnfDJKfcxaIq5jNxTlQjr4i7MldOMLb+NnRctfIFNX5O/WNY2J4MJiB3TipDvWg6XEpycQNM34MUcs/hlg/uMl+SG3O8MZn5ebJj/XvEpC6WLPUF0L/QgHt3qteJyrQ0VHYKxQnAU0e2m53o6BVrbOL7MCY6K+5x28phw0BnG0xi4W41YEdaARpvCeVx425XU/0ePL+nDia+CGvzdmQc6EFQ5ALMDdEKe+9lrOipyoX+l1nYtrsU+18m2ylZKDvrRw+iPkl0WnxWmDiK8dlfLTCd78TA3TbQ9Nbh9Z170NgrlCXoqkjDjvpBoXS3Qf1ePo7ccqIKeV3cpblyQrHiwuFCbK35iGwRVPg59Q99xUQpaTJy52TSi9oNeTAOz8HiyDAEaITdDAEJe/sZt3xwt/kS158KdAlFNUxmfp7YWP9+w17Du8uxtlXhmXrHlJnx3WBB++FtqP9CKE4CYdkn0HoiG2FC2TZkge1JA9ITl2PRrHt/hLa1liC3YQF2nTiNI8eO4cjx02goSYSpcA+a/DrQ2mDcswct427ThPptVWgfEop3C/OycezPJ7B6nlD+vvNDk9dntEjY8S6adywnW3cDk587J5bbMN+ag5UZeixKjIRO2Mtw8H2zN4PBM2U7QdiecIY/a0Dt9dlInXMdJ4v3oeydTzHywAI8OnMqf8BQJ4yV5zAyfwGm/y2/C9fPoPK9rzEvKgQa9KOltBHW2UG4cWIPXq84gxtTwrAwQoOvmk/i8MFDaPqPKXhobgSCHOePWNHzXg1q/7UCNWeuYhgz8cjsQEzhKoX2IkJww1iMsr1/xCfD9yF89iwvd4wG8fHRJiAxFfe10ONrcG5wCsIfjcAD4uPpY/pj5ajaT9qzjODBR0T9oQy2wVhVjZpSWj+MgIfnY/pP+CqHjtasnI+vG0tR03gJn/X9BSM3LLif04vKPsjhD50MdqK++iDK/kWwwU96UX0pkOu3YE13RgbRcbyKXLMU73w8hG8CwjA3xP1I+prZkfJDfJ+mzsajD/PDvbXzJI5evh+z/tKA8v17YXooHVE/IxVcmxWoPFSNlqtDCJj9GNweugz148KpMlRQHV0n9RGkXtAzxXKxDsfKDnB+OPzfIZgboRN0IIbqoxbvf9KLG+YhDNzS8L5ovYqmU9cRlhQB04l9KPndn/D5cCDmLZiJqaJbENarDTCW/wH7T1/krhE+WweN5C0Kwa7J67DkIUHmFhu5VgCu1VbA2Orwg25Yp8cg/KfkFAX5rSR26ssO4c1/a8GNIS3mOPsmYWMf/XSA+GbTf0bgZ/1GzibvfDyMqeGz8XCgOif86hzxhaAMbFsWIuwhyejBBYj6Ox0eeGAmHnC4hkws8X4RgIXfnscRkk+qz9zEiG62y6++/AhVTbOxIeNTlJzU4Jd6kW/SOk9/9XYtLi+9hQ8/68XgN7fQ99cQLH4kkD9HxGidDGHkZxF45EFBJ1w7n+HHs7/CmeLf4c1rM2FYNI2r8ub7NuI/R96zYN5jM0n+ExjpRUtZHSxh0QgZGZ0zHX5N9XHfzPkI6K3FJ4HpSHrU5RzersehEDcuVPiRYk6nfn8JD/xjDKxvS7chzolTJdrzHmOimHqQ2PZQA4aJLNNFacfUUIo/3QpDlFh+B7J5kkdWjwTZ+vHkL3Ju1+kalP/uAJouufsZ9cPa/xB8VPDz9CVDaKI+J5GHvvqwGu8gGTlLhVhU6pcn3PFSuV0hd67SY67QLmfDo/+Kyuoz+GLoR3hYPN7IyMqh0F/veVAarzajfnvsHXziyAP/nyAPX+uG7Ngi01/FnCag5Fey+lKYn8iNWfJ534u9+VPdUbCZXB/d8oGnLynJ7oGc36ka45wxSvpJx/8wK84Zh/B3/zMG04VDPBlPfracr8KxrgAsJv7kxELG6cOXcV800RF1MhndjYp1gndfUjdP8DW+7lUmX6SbNdhfa0XUymz8OikATa/koOyi8MLmcD8+NLaiT/yqi/kKqlv7wR9hRpexDtV7y3Et9FdYnzEHPQfy8NLWIlTfCkHCqkxEDVYhp4BMJLjjLWjZlolcoxVzV+Xh16vmwFSRi5fqHI88hfb27EO7LhHpTy+H9mwRsnae4V9h8MKFA47j+etlveC4HmGwAS+tKUCHdimpX4UoWx3y1uxGh6PB7gpkrdmHnmlLsb4gG0vRjGc2laJL4vWegNlLEBVGHHd6JBYvnoMgUYzK9kEWP+jkZh2eX78HV0j9+nW/QnhvEXZU9wiVEox0o3pNFvZfDcbSpwqwPhb44MVcVHY6XtS1oetQFtaW9iBkeTbWr5yDgdIcPFPbz9deb0V17W7s/982RK3ajNgZZOdwJ6qzclBpiUTqumykRlpQuT7T1Sa95oZcVFuisX53AdJn9GA/1bMghLVxGzL2diJoZT52Pb0Ut43Ej4xSj8K1CF8cCWqGoHlLsHieOPn3o7b4GG7PI77I6akQuaWdgq8STddvRcbLbdDEriJ6joatLo9c85yzXg5OZs7vNdx1XX6wBOF0nu4hfxKR72BOLozX+fOpn+XmnoB5cTZ2FaxCyNV9yHP2TbDx9gI0TRH8dLgOuRtc+lHyU3N3Heqr9uDgRzoSd5uRENiKlzaof4ITFDoHmrNvoem6WBsahETHIMwxDijEEqejuiIUC/lk9cIB1P6G2Lybr+fRYNFTRdhkrcDrkvYVkLuWhkx2iA8EEV8IiyQ+MNsxmLjD62Q7Ct/RYCnRSfpiK4x5NL8JHeby2zG8vvct2BYTn1gWTHYKvl9lwVwiw/qVkTBX5SBLsJUm+H4MHH4LF0R6tZ0/gR2dWoTQ0z1y5gDxY86vuTgKRV9xIdxD0+N6y0PRI4o1pbhxR4UfKeZ0igUfvCHThhiP9rgY21LH63PVUuCMlxjTBENzk0zo20SKHD4H44FOaGZSRXqilCflc5ZivVL8ytqB9i0Hr1/SITV/NzbEEj/bUoD6m9yZnB9Wd5v5AmWoGcUvNwBCHqK5Tpyn3FDqlyeyuV0ud7qgudiQV4fbkaR/easQ1l+OLLcx3Lus48uDnsjHIgJDSf4V5QEZebyOLQr9Vc5pCvGrpC8uz+Wh3hbNxdrSKWfccqrSmCWf99XZ26GDg72hSCI6SJ016G4zpTmUVxRk90De75RkJVg7USbEKK/LBhS+0Yy/CNVSjDc/6x6woamiGT2i72gNNFagzBqAMKpsn3Sn4O8q5wnq4+sexz6JmE9ttsetPWLv+1bYQeg7utYeV9zBFwZO27cu22x/e4AvcnxcYo97+rTdzBU67AeXJdr3//kOV6J8eWKjPe6FZvttoWz/iraxy37+G754x9Rj/9JZScp/3mWP2+ze3uvviw7g+lBgP/u1UHZD4vhvWu37RX2+Vmaw5x418QUBbt9xYd8ds73vC/7qPCb7qbV6+x8EFXA6csorlB364VDugxLj08kde/tuvYeMt+3nX9O79duNb+/YzT0mu9nD7stLHEK/a9+2LN/eKD55oNV+vLzZ/iXZlPIbbl/+uy67E6gcy7NPcOfwfS6ytzvPIX24YbbfEcqXixPteSdECjMP2L8UfGY0A/a3n060H/xYKFK49tfaj38ulClfHLNnLyuxX6bb316xHzVk2099wdXwOPa5u4cAr3fHNZT84Pa7+fblr7USqVxw+3bz+7jj3/iQr6B8Y7Z/aXYczV9LyoZq/ZTqL+7lZtH1eR0Vvie2iBy37X2nXrVnJyfa/yF9o72w7LS9/Qv3c5ViScovLhfr7RnlV/gCzR0Oe9w4Yd+aTGLkBlfjkVdUxC2nM/kY43SSfcytP6N90sNnuH0evn+bxHOyw0/4eNt6ynFhIf4c/eLOF/pF8wA9T+xzXFuJrvNprCWT/ChWtbCvnfq/Qty4o8KPxP1zIJHT5dpwiwVxe1w8GexHP6UVAt9csTeWnLBf5uRzjykuz4lsfucDkW0kkM2TCjlLqV4pfuXtQOXa6PJlwu0Bk1tuc+YKLgakfMKVh8THK/bLE6Xc7jV3inziG5O9zyRStDCeNX5FC/KyKvVXPg96wPVLLhYptD/KecDb2KKqv3I5TSl+FfRFc1pGmdAWxx37tdMl9rc7SIMqxizlvC9hbw+ovJ5jt/mDI/aj7/MXUZX3pfKBguyjkPU7ZVm/PJ49ypZ9R7NdY44n/sjPdBzO1ov0y5f3f8D3Qkl3brlB0d+pPlXkZrXxdY8z+U+WAnUIEL3HEKD1/a1fjdZ1v+L+KWQ7+Kdw3uudFoowDMIsfLdAEzoHQehHz/k2NNWUovKPlwCPX07RBoruFAfT82/jtsSTHgdux0/9iejuST8unwfCNQPoINdzfPqGtejqFe6kanQIC9XC0tuJjuY61JYeQdMtm8+/5uK9D8qMTyeD6LuiQewToVwVjxaL4+KEbQmIjXRzQqG91Yuu82dQf7gUta0Wp8y2TzvREqNHrNgVguOweuNyOB8Wu/mNDVe7uxEbF+OyO0ETvRQJ1y/hGrX9tAhETWvAtk37UN/cjQEroJuhg0ZoIzwyBl2H87GjqoH/JbbAYHi86aACHdGTsEmZqsWDwib6O3F2hOhowOUHHW0m3Nb2w/TFeO+72HC5vRNROhvRp6v9a7dIzRUTBsgRuvkLENZQiGf21eFCdz+sU3QI0Ym9RINFC0fbsOtSL/8EUY2f6n4q8rtghEcAVqvi7T8BLcLSt+PIu++i5o1sLMJHqNyUiZzCMxjgrqEilige+eQh4tsDw3eEkogZGdiyESjefVJoX4zKa6lAEx2JMFF/NDE/R6rDJzncfcb2+RVcWBiHxWLf1z6ORdH96Pqc6lKDKGKX9uY2/o7n8EdoObsA6cli2wkMmnB5agwWzhLKFO0SLI0XtglcrM0KIv+75Oz4nLRs60YfvfmtEDejUfAjVWhH5ZOFS6KV26AxBpI3FgplytRIJG3OQJQ4MQhQW6T3NuM8d5OfxM65c4jNSHTlGA/k8qRSzpKvV45feTuEISqmH8X521Db0AnToA1akqO92ijQ0yfE/iVGRb88UcjtqphKxpdgkiq7yfUaalB5qA7tZPcdrg05Wf2RB10ox6I6vI8tKvUrk9MU41dWX3yeS10WSQsCGkQYNiM1mgSM2jFrXHmf10FSUjyJche6+GysS6Q5YDy52Me4kPU7Aa+y2tBH+pOw5HFRPelBfCLIIdL4Iz8jFLHJwTCeE36koZ/kJosBqU/SXvimO3X+Lp/ffYmve53v4ZuFIkYG0bQ1BfqsItS3f4TbuiVITnhcqJwIzDCTxGfu/wjt5HqOT58mBuvm/IRzVtvFUuSkpGFb1Rm0XyfBFbcCCWJfnGjGrRMqoxZaj4WFZur9wpYE1k5UrkmB4YUqtLSbgFlx+HmMS2jrEMkCZAGsPsSssH5FdBcsjnKC9idkciMs6qZEYt3xYygxBMN0pgRbM4nMW+ucE2Vt8i7UlW/GopFO1Aq/xFbpeF3KH3xtgWkK+Yj8oL29l0zYDAgL9GX6LQUvv+3mFVHb5DMUinUxwqsPkRtx7HgRUoN70VSSj4xfpIheIaLoEPSAsCmg0QYAt29Prp9yk60YpD69C78/8SoWdxah9jztgXIs+UqIoQBbplRhR43jlRUH/rtWWGCQsCXA3cjwfvOF8/2ZwR5fFNcigOxwTEA0T+rJJL8OZ0m3bRc/QP1jy90n4Q6oz2m1HnGkwf2iHdz1hvtxTSRne7uFLFxjSOwQFOJmNPJ+pA4iv8dXwLRkwqjYhqS8MkyNQ5K+F+9QRQqLzoQYKUUSFPKkUs6Sr1cRv7J20CFh1wkc2bwEaP83FOamIXGNt1clCdPIAl3Y5HH3Lxcq+uWJQm5Xw0DdVu5XMYuN5FoWHRbryQROqJOX1R950IWaWFSD97FlDPr1QDF+ZfUlPXY7mdAxywGvgwCNN2nHk4t9iwt5v1PCixykTLKfNP7Iz4SQZQYsbmhABxlTBs43wJwcjwhuQeib7tT5u0J+9yG+7nXuE/6/OyATjVF3+UbGEaRX61DZY0D5qY2CM5FEdbaZ35gQwhA+jwTRko3YsEwqGVjR8lYdHnr2BHbqHS7aP7k/RzxunVAZB3Ghx4r0Wa4h2HSV3umYwxc8sJ5/C9UzN6PhTb1z0DZVuzKYLmIBQqquwDQS5+wT//dBSOhK/mCADuELtKi81I1NMaK7ZP29aNdEIMH5FYRgROjXcp9NI72oXZ+LyuZEvJLM90IbSibpT9EP0UHzdiTurMGy00QvXO04mRmKxUO3sfSpzYj1+YmVEjqERGhwJ9CADetkJibTIpG0ln7I9tUq5OSWomXZLiRwE9NBmOgfCRH9sp6puw2a+XGk9Yn2Uyu6aqsw8NhGJEWK7KtdgKj5Nrxjob6hFEv0DXUfmRKM1IKN+GB9PqoN4hsEytdSS08/vYMX6hoQr3e7fFJiDOF9/xJ6ECPyu370fapBRJLgyFOikWywYP/5boT0nkFCUp7H5FeA+txNMjgO0UWbsI9ri/wnhKYuNAKaER2SN68lUntDPm7ckfMjgqqc3oueHrJvhqgNkk8085dzbXi182yy2LnZgGsWIovDTQn0byuBTkok7ihHJRpgLm5DT3APjPEGbBKd54ZCnlTKWfL1KuNX1g5ahD1JJrHks5rE04VX0rCjJhEnNoqfGgj09oB+x8v15NzDv5yo7ZcLpdyuTDeaqnqwpug01jmeEA6fQ4uwyeNd1vHnQReqYlEl3sYWpf4q5TR18etNXxGYGz2Ips8tZOwWOT792ztTyCJqQscsB8LY3d2LLeInXPRvJ43Qhdx4877auFDjd3LwsVJ51V0OW08POrwtl/yVn4PjkfRYBVoudiOs3kLGNMf1fRvHlP2dPutUyO8UlfF1r3N3PVmij0VndeODhl4SOMTxbp5B2YFxLG50wQgfNuMvjtxt7UW98ZxQmAh0SEhfjvpDFehwvHYz0o/6vBQ8z622tQgK1sA86Ap3y9ljMHr5AqITy//l9KGa4UF0efubMOPWiQ6xqTFoOVzlkrH/JCprvacwLbmmxjwIs0OGW+dgFP9dgXl6rNaRyYnRccffSgbcfOh3NpMtaSKSMxBkLEe94xSq5+Ia2NITEUWKtrZ90K8pdfXRaobZqkGQjg7pg6jPJTZxXg8w3yL9nxHsvHsjhZWbxKtkWiL+MfEcyiraXLYjeno+JQ9NSvZWQVTqWtxfUySS34qOA5kwcF+utKFrXwqySl3XtpotsGqIfKL5rlHsp6RvZTU2rE+MJoUx+qmYm+dQua8GZE0tAWkfndixvQgXHG5AJpIDzSWobIvEz7m7/UqxNEaCDXjluQgcPXlG2EFRey2iQ0e9NzgZBKE9fFISh++fdPniwMkilNkMWPaYsIMQsUwPs3E7ylqXIzVeatFCmBaPlTGdqKxy2X3gZCkqb/HbHI+RBYKmBmWi61nbSpGVVoouMibKx4003v2IoCqna1BfIdOGNwKXICnxCo5Wu+S1NO9G1voa598nG8XCeKRaTqLw0DmkJyzxfjdfKU8q5SyFevn4VbDDzTo8Q/KI61wLBsxA+DQvKz8NiUVRHpLyLwdK/fJEMbcLeM+dOuhm2GD+yqlomE6eQL1QUpLVH3nQicpYlEd+bPFVv6NQiF95fWkR+w/LcaGqxuVXt86geE0WjFfJth/HLLmxkhu760RjN4mt2i1peJ37G3vjyPs+xYWC36mA3njRiOcgQ20kzs55zyl+yM88ZA6WFI360u2o1axFknOt5qPuVPq79/zuY3zd49xlr+GFInXLWtw5mQv9ihXQ519BwlN6oW4MBCdiw+p+7EhLgeGf0pCYWYGA+EShcmLQLMvDkdVmHMwk/U8j11yRi3em52NLKn9nKiojH+GNeYhPyURWSgryLy9AqudNDxG65Gys6yki+kjz+JUvGSytqNx2BgNSUesHnWiTf4ty/RVso22QT+ILg1j9nIydojPwSlgzclaQwPoncnxeJyL0IqGnhCL9jQKE1eci/hcp0P8iDblnw7D3edfdylHMWotdOxagKXcFEokeOT2HFaDkKb5dTcxGlAh9zFpD5EwrxDVDETY8QWuDkfpyPlls5ZL9WcQOK5BVp8POAgNJN1IEIznbgKv70hCfVgGZ3/0TQQamZ0uweqgUBiI31VN8bgNCni1AEv01v/Eilp/4mX5FJgq/0KMoO5okaw2iNhYh9fNC6H+ZiRxybf3OHqQXZSNKdLd9dUYojPQVGtq3DXUIeq4E6wSz+Oqnnti++AC19XVo9zLIhmQU4XfLBvH6+hWI/3vyob5ROojU/bshhIpiLI0V6r87E92DQ/la0Uh/NgRHib7jX5T5tUx9Bua+lSnYJBfGoHy8KfikJJzvv4qoZofvr0BOfRh2lrmeaHBEkoFNSwb/ZT9HlNe7vmSwfEmwe0oaDMRu2wb/Ga+IQ9PtevSYFTDs6UHqa8Q3iErk40YaOT9Sl9PnYMuzcm14g4+x9ME9XIzpiSwZpX/F6jfWuuvOjUgyodDANKRHEveevxeU8qRSzlKql41fBTvMMODF53QwbiB9W5PJ+Vn99AJsMXiJiznZ+PWst5DB+QTJdUYdXin28C8HCv0ahVJuV8ydpD43A32kPjGNLBp+kYnKwHikC7WKsvohDzpRG4uyKIwtvurXE4X4VdIXl+fSB1FI8xzJD/FZJbi9ugiruacr/hizVIyVVAe/DcM7VAd07P5lHlpmb8dvkvnRd8x536e4UPA7NURuxJvc9YR+rnkLc5/NlnmVb/z52YE2Xo/UIQuCkmPcntz7pDuV/u49v/sYX/c4P6K/8iBsf3+RfaVrguCuaYPGy+sgNtIhm0ZLX2v3P237EP/v0Wh4XeYPE/pDJ762QR+12zTQyglNXwkgvfb6XrUEcq/dqLIDfQVhwl47IFC5h4ncE+R/VH7bVC++RPU54qnzTpT9/VbgzfexiUy+5PQ3oX5KEexDDOTdBgo29CvjuFbXgRX4X3gTrc9Gu15t8UVvY/B9r6hpS9I3BFTpQb0fqWXMbajJLQIDtTl4xpKPE08rrsYEPSjkOCVdK9TLxq+cHXz1VXr8MFWvOqeU7ZcnPujfG7K2VyGrbH/lfF0KNfGjgNLY4pN+pRhP/CrVT/CY5WC8NpfEx/Nk+6CGsfRTjX/56rNifO2TZH98yO/j6es9wvf7Bx4cTJn4oB8Fd03vjkqdeKL8aqC/B7FLIonry+APnfjaBp0QKwlNBw8fByiNjJ5V2WEcA6IqqNwT6H9Ufq9qpfpU0Lmc/ibSTzkE+8jaQMGGfsVf1xrLJGgMvu8VNW3J+cYY9CAbhyoZcxsqcottyAJr/xnU1gKr1T4m5fSgYEglXSvUy8avnB18tRE93genlO2XJ2pyuwKytlchq2x/5XxdCjXxo4DS2OKTfqUYT/wq1U/wmOVgvDaXxMfzZPughrH0U41/+eqzYnztk4r+yOppPH29R/hhLJZ+YAREb8SWZIVH1owfMPcjiL5DL/MDhowxoA1GhPaHpNR7y48GGrYjr6ABmmd3I13d7xcwGAzGDxQ2TxDzw3gNj8FgMBgMBoPBYDB8hD1ZYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkGByF0u3OnGh0yIU7kFGLOhq7oRlRCjfC9issFptQuEuYMQ2uf2h1xuywjaRNvO3jifbzyZDRwSbdZzX8NCLTanPnFx3ke/frdxreXmyc8ikYcXA+TaYrELRn/g7p0xSzlDEI/cq5oSJZJj0ZVjYHg/3+jyJwfgeMqmLJVP9Hjy/pw4moXy3Y7vZiY5eUdLqrcPrO/egsVco+5lR1/MDlvoC6F9owF2TejsrZPvjdx3casCOtAI03hLKE4DfdTyhfmbDQCeZkIk76w8dWXrR0TlIWvfGIBpfSENlp1AcC2566URlmkJ7nFwV6BKKDGnutbyslEPuWYY+QnVhIWrbJmC15O+cMgl5VQ3uuVdFTphAuirSoK8Y/8XvuXhkMH4ATOpiKSz7BFpPZCNMKN/tWNuq8Ey9KGXNy8axP5/A6nlC2c+Mut4PEKYDwoT6mQXth7eh/guh6C++aMAzh9swETfFnUxw/P1Qudfy8veWwOV44b138UKiVtjhR1js3DOweGQw7j6mbCcI2xOOtfMkjrbYMC8qBJqhThgrP8MDC0bw4eF9KPmXM7hh02HW3BBM5ZZw/WgpbYQ1IgQ3jMUo2/tHfDJ8H8Jnz0KAhmuOZ6gXTcfKUbWf1FtG8OAjEQj6W7LfRvYfasBwxGOYPpU/lGJqKMWfboUh6mEyII0MouN4FWr/tRTvfDyEbwLCMDeEP3igsRQ1jZfwWd9fMHLDgvvnL8D0O7TP5zBCt/9GRfsEy/kaHCk/hJozVzE8dTYeFfZ7Ink9KgdBqQ3r1QYYy/+A/acvYvi/Q4iOdNAIy+DhzxpQe3021qycD66bQ/24cKoMFXtrcO76EAJI/0N+wh06mhEret6rIfqp4K+NmXhkdiCmcJUq7TPYifrqgyij9p0ShoU/6UX1pUBXf0RMiA6sV9F06jrCkiJgOkH87Hd/wufDgZi3YKZ//IwwSscEOZtwDLbBWPF7lFcLernvEo6897UoNgQ/E66h1J7lYh2OlR1A2TufcvVzI3SCncRQWWvx/ie9uGEewsAtDX89RR0RZHyBi2tjKz75j5uw3eqFbXoMwn/Kn+bCimvvGjEUnY6ffVKFkn2H0XKV+N9s4n8OU0rIjetnUCmpl0F8fLQJSF6HJQ8Jxzp9m9qQ9D/MinPGIfzd/4zBdOEQF1QX1TCJ++rISTHE9qRI/bHpPyPws34jyvfv5XLEyM8i8MiDYseQ802VviVC8ZpCH388+yucKf4d3rw2E4ZF07gq6X6QHFdVhWsBRE4ddxiHta0GRy/9GAvn6jAszstCvbxM8nqjqPNHARoLVdWoKaWxNYyAh+djujgnyeSQKSQujrxnwbzHZjr7jpFetJTVwRIWzfuWTPuc714OwMJvz+NI8T5Un7mJEd1s5zjgQDb/cONIBSoPVY/2aYrqnOuu29G+MIyp4bPxcKAX5yF41btb7Kj0S7kc5cgZq/SYK8iqmPNkGKt+3XOvRE7wgOvj0X9FJZHpi6Ef4WGnzOp04v184KsPq/EOkpGz9EeKMUdt4s1WbvMkeiKRv+t0Dcp/dwBNl6RzEIPBmFhUpjL/YLveiurWfv5VneF+fGg8jeJ9x3B73q+wPuNxWGq3Iu9wN3csYEaXsQ7Ve/ahXZeI9KeXQ3u2CFk7z7juXg824KU1BejQLiX1q7BU24bCzDw0DZI6TTA0N8lCqE30ssbwORgPdEIzM5gkoG5Ur8nC/qvBWPpUAdbHAh+8mIvKTv5FooDZSxAVRjLy9EgsXjwHQTQ3cX1uRR99L1mpfSJl16EsrK2yYO7KbKxfHoqe0hw8U9vPH+uB5PU821gZCXNVDrJKO52vO1nqtyLj5TZoYlfh16uWQnOhAIYtZKIg1LtBZd6Qi2pLNNbvLkD6jB7s31SKLsnHARa0bMtErtGKuavySNtzYKrIxUt1VLkUFfa5WYfn1+/BFVK/ft2vEN5bhB3VPULlaFTpQEKPnA6IzLbFvA5wJg8Ze885dUQHwtpiwc+4fhYi16nDcfqZBIo24fSyDz0zRHopPu0RG4KfEdzbi4atzl0+a+M2Uu5E0Mp87Hp6KW4b8/CSUapzWoQvjgRVcdC8JVg8zzU5lteRvC9ops8hbQWR5sMQtXgJwgO53ZKYqguxvzcUqeuykUT9b30e6m8KlR5yc5iveNWLG9ZOlAm+zdloSgMK32jGX4Tq0fB27zILRQrX/hVSw2PurkN91XYUvqPB0lWbkb7YCmNeDsouOjxDKT5V+JYHitfk+ngMr+99i/f3ZUq5JhgBtmaUNTryKqUfjRU1sAaGcvZ3y8sqZZLTm3p/JHRXIGsNiYVpS7G+IBtL0YxnaE5y2Fghh2iC78fA4bdwQZTwbOdPYEenFiFUNQrtc7LXFaG41oooIu/qhQOo/Q3xI6e6BH2U9iBkOdXHHAyI889wJ6qzclBpiXT69MGcXBiv89W+5Vx33fK+sAcHP9IhgfhCQmArXtqwBy2SyV1B726xo8IvlXKUB0o5yjvj1K8PUP0Y8upwO5L0MW8VwvrLkVXgyMvKOpE/X4xyzMnZyj0eae7NweuXdEjN340NsSQfbClw5UwGgzE52CcR86nN9rinT9vNtDBw2r512Vr78c+5Kp6PS+zLDeX2a1yhw35wWaL99fdvcyUO7pwC+9mv+eLlYr09o+wKXxDoO5ptX7671X6HbN/5YJd9ueN6jnL2CfuXtPDtHbu5x2Q3f8tVcfQdXWtfXtIhlIT+FrvK/PU3298e4Iuy7ZvftW9L3mU/L+q+Y1/7N0LZA+nr5dsbHReg3G6170/Otp8yke1viY6SDfajn/JVHN+a7Key9fb9H1ANSOm8yN7ulJno4IbZfkekAzF3TD32L0X9v/PnXfa4zQ55lexzx96+W2/PPUo76uC2/fxreld/JBilAyU9fnvFftTgoYNvrtgbS07YL9NzuD55+NkXx+zZy0rsl7nC+P3MTceKNpHSC7/P3U6Cn3HyEXt/QSsEHPuEJi4XJ9rzTghOSTEP2L/04mOkcfvbTyfaD34sFCmKOlLyBQKJXTm7Oq67/DVeZw7EenST24G4Xbd63m4OOb48TtqRaDtOJIM77udzcO27jqd6jcs+Zu8TxQeV2xnj3PEy8anCtzxRd00PWynFiOmEPTeZyOVokyu78tDoHKEsk5LeVPvjHbO97wvxxUisrNXb/8ClADU5hD9m6ynH9YRzjgvnyLYvyL72iJu+uXgvF+Kd6tFTHwOt9uPlzZw9br+bP8rvuH1uPq0257rrlvOFl5tFbfMxVPie2NAuZPXO9cM9dnzL3ePLUV4Zp37dfFfKN8V8Y7L3mUQyf0P8msjQ+BUtqIhV2fMFeznGLpo/ZWJOzlajZdpof/sGV+C4PWDyOmYzGIyJYVKfLI1GB634LvTMUCwesuGOUKRoA4Xn7ZTgUIThNm5zd8f6ce0ikBQbyVU5CHsiBkFtV7gvR2qe+DlSe5txnrthY0PXuXOIzUhECC1O0UA3JxTaW73oOn8G9YdLUdtq8emXdOTat33aiZZZQeT/NnScFz6fW0hFN/q83GT1xPb5FVxYGIfFokf50D6ORdH96PrcSlTQjXYkYtFCoY4yJRQLYzS40C3xTd5pEYia1oBtm/ahvrkbA6QJ3QwdNF7ej9GEzkEQ0XMP6XtTTSkq/3gJ8NCPd/sMou+KBrFPhHJVPFosjosTttWhqMf+TpyFHrFiHUyNRNLmDEQ5u+bhZ1O1eFDYdDAeP3ND0SZSetEgKtaLXqh8I+TYAZH8bSbc1vbD9AV/7zE8MgZdh/Oxo6oBXb2DsAUGw+MtIhXI60iNL6ghYcnjoqdZMnr0CRv6evtHtx2fiAhhe6xooiMRJooPTQyJ+euXcG1IRXwKePctaeSuyeNuK8UYCY1B0owGtAjfPR843wCzIQ2LJHxErUxy+OSPGh3CQrWw9Haio7kOtaVH0HTLJuRhNTmExA4ptze38Xf4hz9Cy9kFSE8WzpFtXyBQhwCRvh8ivj4wzI9CnG5jSH4R6yM4Dqs3Lid53obL7Z2I0pHc79A7+Vy7Rc67YsIAPdbHnDsK3U9FPh2McOLQVqu0HXzNA77l7vHlqK4DKxD/96LPJv6JzLj16wtTiYzBpJvdpJ2GGlQeqiO5Grgj8gXZWFVxvpNZ8UiViTn1tgpDVEw/ivO3obahE6ZBG7SkX6r9h8Fg+IXveLE0Hsww3wpFiOeXEcjAF+JYcE2NQ5K+F++c7XcOogkxQla2dqJyTQoML1ShpZ1M1WbF4ecx4sFBBTLtW4fILGWYTLTbP0K782NBWDqZHHJHKMO1MTOYTI3EaBFAdnAD5tcWmEKDR7Wn1QU7B3s3pkRi3fFjKDEEw3SmBFszU6DfWocBqWQ/MoimraQ+qwj1pO+3dUuQnPC4UKkGah8ttB4DgGbq/cKWOhT1SHWg1bpNkv2LCj8To2gTH/VC25tCPm7y95IJtAFhgfxERJu8C3Xlm7FopBO1L+dCn5KFSuerYn5g3L7gIkg8GaF406NPWGH9CgjQeHgBKTu+QzNWwgI9LDn1J8TX+AmUYnyOEblrSqGca0KxLHUOjE30tah+XGi0IDXeffHvwB8y+eKPtoulyElJw7aqM2i/TmSPW4EEZxpWFyuaJ/VI760DTcO2ix+g/rHlzsm3fPvKcPrQarzkF97vbDeviPROPkOhWBcjvOLqS84dJ/7LA/7PUVEbT6PhtOjzhp7zsXHr1wcG6rZC/8ssFBtJGxYdFuvjsFioU4Nv58vHnHpb6ZCw6wSObF4CtP8bCnPTkLjG22ucDAZjoriHF0sRmBvdi44r7lnD1tODjoWhcHy/MyrRAHNjG3raPoAx2nUHy3r+LVTP3IyTR3dh0+ZspCZG4yGN7xnIW/u60AhoRiKRvHkzNnh8Embxxyihi1iAkPOX4P4tn370fapBxKxgYDZZnPWK7zhTbOi72ovYWd5+SycYEfq12LSrHMfeLsf6gVJUNkvIfbUOlT0GlJ8qwRbS53R9DBn4hDpVhCF83iC6etzbNl317adVFfVIdXCT6IC7rezCf39vQ52fOVG0CW1vkL/zL6KnvVXY8oB72kq/Vzda/vRo18JDGxqD1KcKsPM4mYg8PwfVO2s8/GYcjNsXXLRfFb6LIOCmR7JI4J76ihnhJ1vykAVXhAYXiI7FcG0L26Mh15ohbDog1/JctPX0e3xH43o32jURCCfhpxifY0TumlKoyTW6ZSQ3nf0AXZdbUW8h2+InnyKUZVKnN3X+aMWFt+rw0LPH8Ptdz2HDUwbERodC41wUqswhU6KRbLCg6TzRU8sZJCTFk+UdRal9ZTh9XLwCkziXcH9jiFqI97s78wyj9L5hM30y4kBlzvUD/skDE5CjppLFV6DoQxZIFP/oVw3daKrqwZqi09j7Ejl/rR6LZnne0pLD9/PdYs6WgQSPmFNvKy3CnjRg9Utv4sjpE9gZUYcdNeLvQzEYjInmHl4saRH7D8vRcrgKHY6J6VAbKg+dQ1JqPHfXimNhPFItJ1FI9qevjBMGUXK2Lhga8yDMjiR96xyM9YNCQYTl/8IqN+n20j4eM2CTpgZlJ12TQ2tbKbLSStElN/8TX2+eHqt1ZKIqamPgZBHKbAYse4wUApcgKbETlVVtznOsbRUoa43ByninBpzY2vZBv6bUpS+rGWarBkE616TbCdFP+LAZf3GM6dZe1BvPCQU16BCbGuNun/6TqKz1WNVIIdaBkh45HVzB0WqXDizNu5G1vsZPf6dCpZ85ULQJae//NaDvwHZUX+wnkwIrBkh/D571cp90WiL+MfEcyipc7VE9Pp+ShybuS76DqM9NwfNGl37Mt4iOZ4x+uiXGavFhsqbWF4Ysrnjygqm2FPWOrnrqkb7mMqsbHzT0crLabp5B2YFm7lAl6E0LjbHcve2KczJ3n4lMCzRo+dMZ/g91WrphPFA1+m8yNZeg0vF3b0b6UV9cA1t6IqJoWSk+x4rcNaVQk2t08UiKPof9r53E/av13l9PVJRJSW+++KMWQcEamAddOcFy9hiMzi+vq88hEWRiajZuJ3G2HKnxziyv0L4KHPpwymOFqTof+p3NZIv4Xepa3F9T5PK7ESs6DmTCIPwghk85d1yMLQ9I4+8cJcM49aseHXQzbDB/5UxiMJ08gXqhpMwYzhfHHMlxrtuXPtjqZh2eIXp0yW/BgBkIn8aPPLbLJO5JDp2IJ5UMBsPFPbxYAjTL8nBktRkHM1dAn5KC+Mw9MKeX4MVk8RQ2EkmrtTANxSHhCVGyj87AK2HNyFmRgqx/SkFiXici9O6vpuiSs7Gupwj6FWmiX0fyxEv7U0KR/sariGrORfwv0mBIWQHDnh6kvpaNKC9jzqjrubWRAv0vViCnPgw7yzYigntnmQxqz5Zg9VApDEQOPXeNQaSX/BYJo2bxRF8xG1Giv4JtaUTmNWlITCvENUMRNjwhHCAmOBEbVvdjBznW8E/k2MwKBMQnCpXq0Cb/FuXC9Qzkk/jCIFY/pxdqpZHXgZQeeR2kD+5x6iCj9K9Y/cZaQUfjR52fOVC2iSZ6M8pfj0Hf4QLkbcpF2dVEvPpcPF85Cvf2qB7jcxsQ8mwBkrg7/MFIfTkfQcZcYs8sZJHrZdXpsLPAMHohxxGM5GwDru5LQ3xahbq7zmp8gcZTyEkSTyuwo9ExoRhN+nP/jIEXiF7SBP/TF4n0GIrULWtx52QusT/Rdf4VJDwl7y9OIjfized0MG4g59G217yFuc9my7wmo8GidflYfKWI6HUF4rOqELBK4nh9Bua+lUn6mkb6lAtjUD7efErIE4rxOUbkrimFqlxD/GhlHAZu6ZD0pMx7aIoyKenNN3+MyshHeGMe4lMyybEpyL+8AKkiUVXnkEgy6daSieSynyNK9PqYUvuKcPooQFi9Qx9pyD0bhr3P64lGCbPWYteOBWjKXSHYKxOFX+hRlB3NLdR9yrnjwtc8II9/c5QM49Svekjey81AH8l7iWlksfWLTFQGxiNdqFVmLOcTvfxDHEy3QrFymTjmfLDVDANe5PIa0euaTC4f1E8vwBYD/5jZfKUZtXXv45r3lMtgMPzAj+ivPAjb9y70sb3VBg397oqvkxSbFVabxvlawIQwTK4xMs5r0DZI8vV8j9yJL3L4oi/uWJL2Zf62hyL+aIOipMeJtqWvfibXH3onUNSG7exuJBoXoK5EZnJD2xsm7XnRo40o2TZFxkfGi7/sSKB9pa/m+P2Lyr7aSAb6pfT/hTfR+mw073tUt95EV4pPlfh0TSmUYsQXximTL/7IHauRkXWcvqfYvhoU9MG9+ktfN5O6hh/9Ugm/5YEJyFGyjEe/PkDbIQEyZjv4cr6tdTf0f4xG3ZvC4s8D1baaRP9hMBijuaefLDmZQpPzGJMIHUD9MbGQgyb48V6DtiGXUH2Rwxd9cceOs+/+aIOipMeJtqWvfualP5aGbdBvqUGP8HaQzdKJ2qoziHoyWv4uMG1PRo90IB33BEkOf9mRMGGDvq82UovSJE0pPseC0jWlUIoRXxinTL74I3esXLfH6XuK7atBQR8a+l0cb9eYKL+UwB95YKJylCzj0a8P0HbGYwdV51stsAx2w1h7Dqm/SpRcKFFU22oS/YfBYIzm+7FYYjDuIXTJz2FXdDf2Z/E/o6vPKUVf8pt4M8PHX2NkTCzaYERoffv1xnHzXVyTwfCA5ajxYe2sQn5+Ca49WYRN8X5Y4TEYjO+U78dreAwGg8FgMBgMBoPhZ9iTJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBgMBoPBYDAYDAnYYonBYDAYDAaDwWAwJGCLJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBgMBoPBYDAYDAnYYonBYDAYDAaDwWAwJGCLJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBgMBoPBYDAYDAnYYonBYDAYDAaDwWAwJGCLJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBgMBoPBYDAYDAnYYonBYDAYDAaDwWAwJGCLJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBgMBoPBYDAYDAnYYonBYDAYDAaDwWAwJGCLJQaDwWAwGAwGg8GQgC2WGAwGg8FgMBgMBkMCtlhiMBg/PG6eQ62xG1ahOPnYYGqoQFO3TSgzGAwGg8G4G/luFksjNlh6O9HR3Q/rBMwVbENW2EaEggdydWPiVicudFqEgi9YYTrfhgG1s7URC7qaO2HxZ989IXaxDnk3iM3qZ91JYbPC6men8GpzBXnvaoatGOhuQ0//JNjEH3C6Vt9Xv8epGxY0HSiCLSISWlqU8wOiZ+uwsC2GntPfzecwqXoR0rJoELZAB2NRHQaEPV4ZGURHTSkqS2vQcUvYN0Eo6d3W34aOq9/dEvO7h8QdydsmGRVMto5oXpZMmTIxZ7MOwtTZCdOgQpypzsfKeplQqKzOfvraFz/03VueGCsTMA7e1fg0j5o8X7NZ+9FzvpvM00bbguZKGl+Oz8SNVwoo+d6Y56gMMZO+WBpo3I6clDTkbd+Hg7vzkfGLFLxU2wv/pYVOVKalQb/nnESbtK4AjaonHDYMdJKglPEzU/0ePL+nDiahrJqhj1BbWIjqNpUR31uH13fuQWOvUJ4IbjVgR1oFuoSiO4NofCENlZ1C0S9YyIKxEwOiQLfUF0D/QgOp8Re8P0j2u7MCeq/y3qWQiXNLYRYSUzKR+1op9uRlku0slJ29y5Mh0bWB2GFH4+h+2m52oqNXvF/GZv6g+yQqBzKQsFAoy/hBVwXJJRXuHeFzWAoMebtx8LU8GMh2zt5z0jcyBuvwEpElq6Jb2CEiNB4rp9ag6bJQlsRK7J2D/VeDERU5B0Hc6s4/+K53Ky4cLsTWmo/I1g+DUToiebua5O1aZ972zGGTrSM+L++oHxTKIrh87jHeWbtRm5uCxF/mYNu+fdiWk0a2t8LoZXHXdSgT+l9uQ5NSehmll0mGxrBj3PC1L4o2VUYqT6hnMsbByUR53uSJT/OoyfA1YZzVZ+Zjz6Hd2JqZhpzCMxhw5nhhnin6JK6gY0LFhN/Q8sTd90brfsxzVIYbk7tY6j+JHfssSK98F8eOHcORYyfQcCIfQbX5qJWdMPiOrXk3XpeYmPmGBe2Ht6H+C6EoQVj2CbSeyEaYUFZN4HK88N67eCFR5exnXjaO/fkEVs8Tyt8LTKjfVoX2IaHIUMCKrtI87DDrUX76XdQdJzFE/m8o0cO0JxdlF7+jiYoiNnQ0NWDunDm4UN86agJgbavCM/WTl8p7WhuA+GjfY5Zgu1iKrSSHpZacRvNpov/j9P8SpN7YjbzSTiKpOwNn69BO5A6qa0bXqMVUMBbGaHG0WW6SZcVtsw2xv8pAbGIMwqYKu/2A73rXImHHu2jesZx/IvcDYJSORuVtzxx2F+uITADrC/JRH5aPuvf5MfjYu++j7rmfwphXgPqbwnEOhs+hqSEYi+ZcQdNZicWYGF/Hs4nE174o2nSi+b6Ng8rzJk98mkdNgq8NnCwg42wGjr19gsxTSZy8XYJkUxFqz7tn+NVvvo/WPwuf94+h6Ikr2Jq1DS3f2Sp3tO7HPEdluDFlO0HYnnBsn9ah6MsYPP/P8+Ec838yCwufmIWp/2M6pv+PKfw++trJ8QpUHqpGy9UhBMx+DCFCXFg7T+Lo5QAs/PY8jhTvQ/WZmxjRzcbcEEeLg/j4aBOiVqfjkyMnEZCkxyPOmKJ1l/DAKj3mOvYN9aLpWDmq9v8Rn1hG8OAjEQj6W1rRj5bSWrz/SS9umIcwcEuDeVEh0HAnueD602Jz1Q3148KpMlTsrcG566TvEaTvP+EO9YC2Xw3T9BiE/1TYJSM3hjphrDyHkfkLMP1v6bmNsEaE4IaxGGV7Sd+H70P47FkIEHfQq2wCzr7S8wMxL8yKc8Yh/N3/jMF04RAXVlx714ih6HT87JMqlOw77NFH0veqKlwLIPLouBM4rG01OHrpx1g4VwfBujycPG/hw896MfjNLfT9NQSLHwnE8GcNqL0+G6lzruMksW/ZO59i5IEFeHSm+yzRcr4GR8oPoebMVQxPnY1HH3Ya2QPeH5C8DkseEnY5+PIjVJGqVEHegcZS1P4H3w8esjiprcBnP+FtROub/jMCP7teizd3VaClfwrCH43AjwfPoK70EMrfu477Zs7HIw+KjDDYBmNVNWpKqQ2GEfDwfEwX/MHZXr8R5fv34p2PhzE1fDYeDvT0MoGrNfhNqRYv/mET/u5/CPsImgcfQ8LDPXjpsAXJhgUIIPv4tsPw40s1xFb1QMwyhJPruvR2k+vrjzsqyHFEvyHCNTkfrELtv5aS/gzhm4AwZ2wpx54Xhi+g9nULEooz8UBVLb5OMOBRQcW0nzWNl/BZ318wcsOC+zn/Hm0z69UGGI/+Kyqrz+CLoR/hYQ9ft1ysw7GyA5y/DP93COZGePibEws6qmtwf/xmxIYLR3j4gZivPqzGO0hGztIQUurFyWdLEfCbcuQ8IfK3v9Xh0fgwmIhPfCWSjcZ4034jIp/djUevHEDHg2TBE+beqwf+6yYOG61ITH8MDwj7nHAx8kf8H0cO6v4rHoohcnN13mObt9P9mPWXBs6vTA+lI+pnfJ0DWb0npuK+FppXSA4b5H38AUHX9DxxjFivn0F92SG8+W8tuDGkxZwFMzFV5hacbNzK5T8uX5KcNzsIN07swesVZ3BjShgWRmjwVfNJHD54CE3/MQUPzXXpYXR8DWHkZxHu8Unw1idpHYny9t9I5zBPHVG8y60yl3uFz8um2elIelSkS4r1KppOXUeYMN5Z/1yEZ9qXoeQNA0JEbjh1djwW20/it+eCsCY+1Bk3tg+P4dWvE7E/U4OyE1ZnbpFm9HimPiYJMnlSksFO1FcfRNm/CH7wk15UXwrEmpV0bjG6L1z7Fb9HOckf3PH3XcKR974Wxm1lm1LkcpB7npDAm3wK4yAvD2HEip73akheruB9CDPxyOxAlz5Vzzt4OFnK/4D9py9ytgmfrYNGFLdy9aPjSjxuUV16mTfJ2NhtHsXp5DM8sGAEHx7ehxJqY5sOs+aGCLnF3b7y/REQ50s615l5Cw2HyTUc+dQNG/7rb2YgLjEejwQKGv4bMqm5XoX/8//ohTiTmFf8zVQE/V08Fg5UoPjaY8j4/00TKkQIsv14/gg+LtuDXW9d5Oc3IcPoOl2N8t/9b3QMkf4586iELzv0I/Td5Xv/Jal7m+cclTEmZIY1/6MJi0BUdx3qL7ovu7VzYhA1QzDjcCeqs3JQaYlE6rpsJM3owcGcXBiv89W2662oritCca0VUSuzsXrhAGp/k4tqz7dcFm/ErrVWvL67TvTo1IPBBry0pgAd2qVIf3oVomx1yFuzGx3cDXotwhdHIozERdC8JVg8T9rRuP609vN3lEe6Ub2B9MUSjfW7C5BO+r5/Uym6JG/4m9FlrEOXWSh6yJ0aaUHl+kxUdgp3Mob78aGxFX3co3r+3Oo9+9CuSyR9Xw7t2SJk7TxDhk4BWdkI1k6UCX2l9UunNKDwjWb8Raj2hqm6EPt7Q5222b8+T7gjGYwAWzPKGsWG6EdjRQ2sgaGjdachgyfRbxDRc1gk0e9s0UB/swb7Bfv+OikATa/koOyi446ODV2HsrC2yoK5pH798lD0lObgmdp+oX7smIlvVnc7DEKxoq/VZSNaX394DyqvhhH5MxH+eSlyCraiuMqMkOWrOF88mlsA5xsx3RXIWrMPPdOWYn1BNpaiGc9QfxBet+Daq9qDgx/pkLBqMxICW/HShj1e70oNfNoGU2IiYj3mRBRNPPGD681oF+4Oc22Xbsf+y8FIWmfgJksDxjxkHOghfSV6WxmKvuJCFNfV4cPrglNQ/12Txb3ytfSpAqyPBT54Mdfpg6pjzwNrKxl4H1uO2BlxSNIPoum8y1YBs5cgigbZ9EgsXjwHQRJBZm3cBkNeHW5HrsKv81YhrL8cWQV1zidUtD5jbyeCVuZj19NLcZvI+ZLR251wE65d1iJo+hiGjZuduHArEQnxEgbQxiEhkQxsbaLrdpMJj0WP2MhQLEudg/pzH4168oSZoYi9acFtoeiGECPOHLQ4lB/YPWJ7qbYNhZl5aBIuzdmpdjf2/28boohfxc7g94uR0/uFA468komowSpkveDStVuMEP/OzT0B8+Js7CpYhZCr+ySfrvEIcVvq8L85GBDHrVL+c+S8veW4FvorrM+Yg54DxM5bi1B9K4TED9/XHJFf8PG1HYXvaLCU6CF9sRXGPJpLHElQPpdI60iUt73kMDcdeV5jZSTMVTnIcupJRS73E9fazyFKnwiP9TpHWKIeUY1toqefVlx45wxil8cg5Ek90m824IJsinUfz3yKSYU8OYqbdXh+/R5cIfpav+5XCO8two7qHqGS4t4X/njS/gzR8cWnXeO2Cpsq5SBZ5OSTGwedWNCyLRO5RivmrsrDr1fNgakiFy/VCfr0ad5BWqvfiowtdbAtJrKsWgqcIePCXtfXFrj6l9ugieXrNRcKYCDHu8eVt3HLy7xJwcZu8yhurnMaxfuO4fY8GuuPw1K7FXmHHQONu33l+0Pg5jp5MA6L5jrbq1BvvEJakkIDXaRoTkohbbSc1yFqfrCwwxtaLErVA42tEHukE0G2sgN1uEP1GwcYX8zHS9v24cLUx0nuo/FfiFyP/OD0ZQrXhlTfpXXvplvG2LFPMuYPy+15+kT7csNa+7bXjtkbrwwINTy33823L3+t1X5HKFO4fbv5feZTm+1xa4/Y+77l6yiXi/X2jPIrQqnDfnBZov3gx2Tz2wH720/r7VtPOa5B6zbb3xaK18oM9tyjJr4gwO077thHzxfa8gLXn6dP2820MHDavnVZkb3d2bc7dvMNs/2OqK8uRP0kcO3kv2u/zRc57vx5l3159gn7l7TAte3oO3/u6++LjubqC+xnv+aLSrJ9eTx7lJ77jmbb45aV2C8LZXd4XUid47CN/Ytj9uxkcr5DXtMJe27yLnv7N0J5FO72oEjZt+/oWntccQdfML9r30baPC9WlLBP+jq8rnILSux/KPH4FLjLe7k40XUdDnf7c/Vi+W+csOeJdE46Ym/cnGjf/2fhiDtme98XnGcImOyn1urtfxAuwbX3crNIn/z1Ct8TC+eCHu/yZU/IuU959FXc9jet9v3J2fZTXwhlCrdP1Oa3xF97THazh+6Xl/AdVo49KYhO8kUyfVpuzzCU26/xJQ6uXTe9u8eG/RuTvc8k0gntN/Gbxq/4IpU174TYiQbsX/rgc/aPS4gfZNsLPf2DfArXiXyCHueIdQm+PLHRTQ4ab07dUB918xUBLm69xRzF3QcpnM7L3HUujkMpO0nhTe9ueUXQtUNfnF8J53Dnv/Eht83xjdn+pdnlzW5w8ufbG8XKG2i1Hy9v5vIb15Zc/hP65owtAqfvF5pd53xFdUlyg2B7rq/Zx9z04Namilyi6JsS/iTWEW9fD7lvC7HIpWIJnXvkcnl4/8jYXDTKd//wxmZ7hrNvo/3IjW8/dJfDw1/dfFkSd734FJMKedKdO/b23XqPse22/fxrelFsivsidTy/T/p4ymibqslB7n4iQlG+0dfj/E6Ua+6Yeuxfii5P/Thu8xjmHd9esR81GOxHPxXKlG+u2BtLTtgv0/a/JX1J9qj/lvQ3W2/f/wEfe5yssuOWhK8p6MBNXk6etfbjn3NVPCT3LneOG+72UuqP1FyH7vM+13FhfvdVe/batfZfGbLtf/hA3H9PnxEhl9MF2cTjcPsbJH7FsdVBZF1L8hZfGH0dj/bdfW+07j19iTE2JvXJEkUXsxG/e/d9NJQUIGGWCU0FOdDnVqGHuwtiw+X2TkTpbOg634YO4XPtFqm5YnL9alSgDgGiu2MPhc7BwPAdoSRiSjBSCzYCFbtHv4+Nflw+D4RrBpzXoZ++YS26ese4Cp8WgahpDdi2aR/qm+kvqBB5Z+ig8frugQMbrnZ3IzYuBuL7SpropUi4fgnXvLzLrA0UHR0cijDcxm3uTo2SbDb0kf8Tljzu9sQnLD4REcK2N0ad80QMgtqugHurf1Y8Umc0oEX4CsbA+QaYDWlYpPCW1ig87Bugdb3XZ/u0Ey2zgsj/Lrk6PreQim70eXuYQODvzHt85gUJtT6g/YlL/ika3I9gBDnfttEhZDaR2yLc0tLoEBaq5X/5sbkOtaVH0HSLaF/8pFP3U5E+gxFODGC1er+nfMfbU1JqVc87seK2B024PDUGC2cJZcrUJVgaL2xTiDy6OaHQ3uol8XcG9YdLUdtqce+v2thzMNiKpovLkeR4GhNJfAQNuODLdxSnEt8OJnrtJrZuqEHloTq0k90OXYRHxqDrcD52VDUQ/x6ELTAY8m8GkugepccgzPX0D/KZ6/lens0Gb9LahkV2G+lEY50Wq5Mj+bIuBglPtKHpvON251jpx7WLQFKs0K6AWxxSPOzkC255ZarI3z3QzV+AsIZCPLOvDhforwJOIf6vkz6ai9sYPWJFr+giOA6rNy5HiA/5T6N1tX8/8VcE/9R1zjSaAwdhFh8fHen2JEUT83OkCm2ONZf4gu3zK7iwMA6LxXJrH8ei6H50fe7yF++5XB3aMPr0y8N/I8Pc9MnhLX/Y/otGhRPLWZLHE4m9hNwWEa8HGlrR4zX/uONTTKrJk04G0XdFg9gnQoUyRYvFcXHCtidSx2sQFevteC8o5CBZfJJPGg3Js0Ek9nuIjzbRX8b84yWXLX2Zd/R34iyIXR0/bkOZGomkzRmIos7S303kSsQicf2UUCyM0ZAYF/26lI/jlu860JGYEDYpM0OxeMh77vXeH+m5TkiM8lyHon3MgF8/vRkvrg7FhT0FqL4sI6NqiG1EsaAhHQufJkoQIURWb28bjJfBOjz/9ysQL/qUkfGEocykL5YcaEJJgK4twN5TR7BpSg3219Nn/FZYvyLuffMK2ts/cn2GQrEuZozvW84wYAtZLxW/XAOTW2CaYSaLMHO/6Drk06eJwbo5P3EbOFQzJRLrjh9DiSEYpjMl2JqZAv1WmdcAnfByhwWLR1QCmZgH+Tho8ijJxl8vgEapGFL2/k46T5B4UKeQSVmIM4nxrxsZm+gj/X5caLQgNd59UjderENkFjNMJowiudrbLQhLJ5NF4RgpQsjgvehJj0+keAD1P/THAOgvP26rOoP268S+cSuQMI5LPjQrEh1XvfxyJNGJ6WYk5nr7FufXFpi0Wo8YIos98Q5rJyrXpMDwQhVa2sm0e1Ycfh4zPh3RHzi4gDN4PkVIzivyUDlkVfhRA3cG6rZC/8ssFBuJrS06LNaTyadQR9Em70Jd+WYsIguU2pdzoU/JQqXXH7sIQsiMXphG/V53KKI8/YN8osSvsIWFIra3R3gV1hMbvuwfROws3gC28w0w2gZRnOMYlDKxgwxKLZ4/cGEhkzmfEhuN7VCETBeKDtzicJKI3Ihjx4uQGtyLphLhl00drwZ5wMUtWehIi+rv/OciLNAjK3CLP77NseYSX+CuMTOYTI/EaBFAdshOLn3kwVnRo3x30RNz8KBQz00g52vQftXLu3T0ZoomAuHcW0b9OFvfDTRvR6JjUpV3EgNDdWhUGba+xKRveZL6vxZaj4WXZur9wpYnvh4vjVIOkmPc48DIIJq2krlEVhHqiY/e1i1BcsLjQiXBl3mH5DgggtaHBo/yf60uWP6mmAL+HgvVM/a5DkUzg4+r2NXbUbI5AJXlzcqvXt7oRztZrKppf9KZpscrp0+jQfTZEC3UMWSZ1MUS98XXVo/BdEow5j2mxbVb9A1MMuBHaHBnngEbNm/2+NA7kGMjJL0AL2prsK1C/J2BMITPsyJkycbR11odPfqOnGqCEaFfi027ynHs7XKsHyhFZbPSoKhD+AItWi55fPmjvxftzgHMF5Rk4/V8gUy8xdh6etAhbHvDc7DlzlkYCsd3HHXL9Ig9+wG6Lrei3ib6eWY/oQuNgGYkEsmecpFPgvipyRgIIAPCKMa0aqZYceGtOjz07DH8ftdz2PCUAbHRodCMY+Kni0lEbPMJNI56Skpiq/4EjE8Q3Ut8p5Rj9gIk3LyEa+JMP9KNrvPCNsF6/i1Uz9yMk0d3YdPmbKQmRuMhzXgmdHTBPIj03x5zS84N+9cirKEBHap00Y2mqh6sKTqNvS8RO6/VY9Gs0VNZbWgMUp8qwM7jpP3n56B6Z430O+NkURT+GHDtuvSkXpZpMfh55Bn8u9TPNN+sw783RyMpnk6Lbeg6dw5z15W4y338OaR21+GsKISsZGAdiF6AcKGsTATmRvei44q7XTzjcNKYRm96PYdXyk+goTwDXx4oRYvEk3BdxAKEXLzifsPK+fet/J3/XPT0e7wlcL3b2eZE5hIHnNznL3n4Yj/6PtUgYtY4BBsDc2P1sBpPur636sSKjpMnYTUkIooW+9vQdHM5H0si//39Wp1wI0wd6mLS1zxJx7ZBdPGvojgxXfW2iqPxMsg/MRTR094qbKlBXQ6Sxg/jwNU6VPYYUH6qBFuIb6brYxAmfurCoXLeITUOEJx/X43W93q+zWJDH5krOG4E+Y7/x0L18LnF7akYwfrpRzJzHTJuVZA8RhZ1YrTTyOru5iBZfstAFrb11Q2YmxpPPNUf0Jt7wqYDkjfHvGydouGeYos/ym8+MSiTulgKmnoblTt3u/1NB2t3DSqNQHrsAq4clboW99cUgXvQRBkhifxAJgxevzishmAkbc/H3LqTIJcS0CEhfTnqD1Wgw5EYRvpRn5eC5z3ujlotEklHAlvbPujXlLras5phtmoQpFNeekUkZyDIWC6Sm/SluAa2dGEA8wll2aISDdCIrzfUhsqKc97vOAmYakvdzzl0DkkkMTjvnOrikRR9DvtfO4n7VSUMC5kwCZtqeMyATZoalJ10zTitbaXISitF19gdhCNkViQ09PUTqqIRC3qq96HMdRkf0SIoWAPzoGtUspw9BqPEQkc1wQYyWALFW7ajvlto10b6adyOrYeBF57Ve1/kB8YhPd2E17dXoavfSnROBoS9JWgUGZzePdSYyWDgmNDeOgej1MJALfQHDm7GISE+2C05a6MTkTrjHFqcP9pBsPxfWCWfwOqgm2GD+StHDFphOnkC9UKJvmZTn0v82ugylPkW0c2M0XdHHUQtWY6O9m7Skq/Q13qJAQ7n4Q0jOZ/2lwxcFrIAemNLFbD5OSTRQBg6h/pGknMSI93lDk5EUrL7D1z0dXciIeFxH27OaBH7D8vRcrjKFdtScagWr3pXgiwI96Ugq7TNeb7VTGJZQ/QuJcw8PVbryKTPaSdix+p86Hc2c3bwb/4T0VyCSsffY/FsU20uUdSRTA5zyC26xsDJIpTZDFhGFu1qsF0mfST2VX5DQR7NE9l4ZVkbtm4hY9RNQcDhfnSU5mPrxUS8so5/C6CnoQbXlv0cscEi3yWfqGQD5tIbYYqTXF9i0tc8qUNsaoy7//efRGWt63x3SLz8vwb0HdiO6ov9xE5WDDTvxsGzSiOd2KZKOUgOtfLJ+BDJy+HDZvzFefle1BvPCQUf5x2BS0heuoKj1a64tRB9ZK2v4V/h5eo7UVkliuu2CpS1xmAldyNIPa550wSMhT4QYchGeN12FDf0wkLsbyFzzterTTI334Nxv6UBO0iu6HHodKgXRrIICtPHS7++R/8wbH8bqjfloHh4LV7M8NdjM2L7BRq0/OkM/3f8LN0wHiBjOF/pFbVzVoZ6JvfX8OLzcGRjMJq2pAmvpqyAPr8ZIc+WYNMTQvKatRa7dixAU+4KJNI/9rUiE4Vf6FGUHa04kZdFtxwv/na5WxuaZaQ/q804mEn6wf1RsVy8Mz0fW1Idd/yCkZxtwNV9pL9pFV7uVLvQxGxEif4KtqWRScQa0l5aIa4ZirDhCeEAOcRyp6TwfQkrQMlTY3uNTVG2yI148zkdjBuE+jVvYe6z2YqvFqQ/988YeCGFP4fKpy/Ci8niJEonc3Ew3QrFymVKCSMa6c+G4CiROf5Flb/+NCUU6W+8iqjmXMT/Ig2GlBUw7OlB6mvZiBqXgxCdPZmDV6K7sWMN6c+KLBwNXIVNc4TKMRCVkY/wxjzEp2Qii9g0//ICpI7zrcSQ9N3Erhq8k5/Jx9AvMpFXp8Hqkt1kASIcJIkGUU8fwe9ielFZkIu8TaXoWb4bv3lSqKZEZ+CVsGbkrCD++0/EB/M6EaEfe4d7yMLTrNdLfGctFLHJwTD+kZ8o65Kzsa6niMR6msQv65EYzM1AH4nBxLRMGIi8lYHxSBdquQXMy/lkop1L6rOInlcgq06HnQUGrwsHTQyZwH7egAve5ldyzDBgZ8laaOrIRH8F9ZMUGPLroFldgp3pfGxZzzegKZJMhke5P7FBXByu1Tbw3/2gf8emkSxil/k2CXGLbeJX8Zl7YE4v8YhDZeT1rgSRZWMRUj8vhP6XmcghOU+/swfpRSQOpe5UcnFbgLB6GrfkWBK7uWfDsPd5YYHv5/znRJ+BuW9lCmNJLoxB+XjT0aaKXKKsI4Uc5nYNKvcK5NSHYWfZRkSovKNrvtKM2rr3cW3c8x+y4Nlcjjeje1CYQ/yG5o+UXBT2ROPNss38d1ZGunGhwYb0pLjR421oDJJmnIFR8Q9g+xaTvuZJbfJvUS6MswbySXxhEKuf0wu1o9FEb0b56zHoO1xA8l4uyq4m4tXnxF/W9MTTpko5SB5l+RR8KDgRG1b3YweV959IHzIrEBCfKFTSfObLvIOMz2S+lT64BwaSu/TENhmlf8XqN9YK/sjXrx4qddYb9gwiveS3SFCdXkbPmyZiLFRNsAF7K3OhadqOfGL/QqMWG7avlXmar8Gi58vx4vRm5KURm9A4SctDy/xXR+Wj2q1CfUoaMrZUoS/mVZwsXyv5i5Njg/RlXT4WXyki9iDXyapCwCq5eZpvc1aGen5Ef+VB2J5UuMe+9JGg6Au7nnDHTNXSV90nFvo6iNUGDX2X1x9Orqq9TpT9fSECyk5DuKHnhMpNFDM5fRmj7Db6vj2xjdQ5ttbd0P8xGnVvyjzp8Af0bs6IvA/dDVBd2TT+92Of26UTdDd72XBhZwr+feEJ7DWIRkIb0avt7tOrUlxw+phC9OHti+QiLHV5KLTk4XfZ41gNj1NPAyfzsGM4H79fN8a7kP7OW2PF1zikx5PM4M1O/sp/XQdW4H/hTbQ+G81fk/qGty5ORi5RkHuy8es44wVfYtL3fEb9n4jg7e/SifHIfbazu5FoXIC6Eu83VaQYj87GPQ4oyetrPlDKXxMwDoxbB2PFc+y7XoOcXAt+895m+SfXd0uOZXznfHc/8EAf7StEDHfMZASV8B6n34JBqT0SgJbuSzBhAcIkngZQuSetL2OUXTJ5WC2wDHbDWHsOqb9KnNiFEoUupCc96/oO1dVEdNO3dunf6kjB89Wi18cuVqG6ORRLoz2mC3Qwuwv1qhQXnD5UTkR1hpLxLZQo49RTSEbJ2BdKFH/nrbHiaxzS42Xs5Nf850Dppttk5BIFuSebCdGzB77EpM95kvN/5RMsDdug31KDHuGBmM3SidqqM4h6MtqnhRJlPDob9zigJK+v+UApf03AODBuHYwBW3cFctbsxgXnq6e9aKo4CdOyaMzl93jnbsmxjO+c7+zJ0g+aWw14Y8sZhGz+LdbF+Jqu716srfuQV9GDcH0eXlwbOfo1DsZ3y6021B6owNHz/bAS6+hmRWM98cH06Alf1jIYk07X4SzsRwGOjPdVPsa9zYgFHdX7UHmyjfu+lSYwFAkZm/Eb+mNHbBL8A8AKU0MFyiqacYH+oMxUHRISc7Hp2eUIYfZnqIQtlhgMBoPBYDAYDAZDgu/sNTwGg8FgMBgMBoPBuJthiyUGg8FgMBgMBoPBkIAtlhgMBoPBYDAYDAZDArZYYjAYDAaDwWAwGAwJ2GKJwWAwGAwGg8FgMCRgiyUGg8FgMBgMBoPBkIAtlhgMBoPB+IFiu1wH49lBCH+yk8FgMBgefP8XS8NWWIeF7bEwYkFXcycsI0LZ30x0+0rc6sSFTuFPm08WIzZY6R+HY9wT2KxW2CbbP20kbq33mI+o6TPNR45D6PG+5qaxnEOw9beh46pVKCljG/oObH6X873UyUg3al8+h4DHgvk/Ik79i8g56jOeMfQexzbYi67z3RiY7Hx0L+ZAObhx392v/BJPHvMJX3PdKCZ6TjTBMeYmv9iHxjvXtLTBePgcJnW2+F3MT73wvV8sdVWkQV/RKZSUsd3sREevyDi9dXh95x409gplfzPR7Stgqt+D5/fUwSSUJ4VbDdiRVoEuoaiMBabznRiYtAHbhoHONpjujhgdI/6SYRCNL6ShUn0IeaDGdqOPsdQXQP9Cw+Qm5nGips80H+2oH+S2ueN9yE2UsZxD/4L9hcOF2FrzEdlSQycq05Rs7v+YHJV77yrU6GQsfLe5xtpcg8onDEjQ8WXOv4icoz4++9z3gJtn8MaaFBg2bcf+Q7uxNTMF+q0nYRrHPNwXJiwHWnrR0fkdPEnsrBjlV4kpJB/W9o6vL27zCV9z3eg8NtFzoomNMXf53Xxo1FzTxxyui0b4rd042jp5nvOdzE+9wF7D88DaVoVn6kWmmZeNY38+gdXzhLK/mej2FQjLPoHWE9kIE8p3JybUb6tC+5BQnHAsaD+8DfVfCMV7krtFBjW2m2z73h2YB/sRO2syIk+LhB3vonnHcrLlL/xvs1G59wfBdxmnVrSfaUPSksf5p0oOIjej7s/vo1X8eTZaqPyh0A/jy0Uwp5ej4dQxHDl2DMfePoFXAmuwtbpbOOYe5YsGPHO4TeViwt8Y8HuxXx3NQ0BtPiov+msC7muuG53HJmVONGExJiP/qLmmrzlcg0VJetT/sXnSfOdump9O2U4Qticca+dJHL18P2b9pQHl+/fC9FA6on5GKkYG0XG8ApWHqtFydQgBsx9DiNjSQ/24cKoMFXtrcO46qY8g9T8R6gjWqw0wlv8B+09fxPB/hyB8tg4aYRn41YfVeAfJyFkawl+/xYZ5USHOwWGgsRRN/7kAj4ZouO2axkv4rO8vGLlhwf3zF2D6nU4YK89hhG7/rXDSUC+ajpWjav8f8YllBA8+EoEgoY6XMQALvz2PI8X7UH3mJkZ0szE3ZCp/gCdD4vb70VLaCOvsINw4sQevV5zBjSlhWBihwVfNJ3H44CE0/ccUPDTXdT0KJ//Rf0Vl9Rl8MfQjPDx7FgJEo5/lfA2OlB9CDenLfTPn48cdFU6Z3XUiXD8iBDeMxSjbS+Qbvo/oU317kjjtR9sLxLwwK84Zh/B3/zMG04VDvMrA6ectfPhZLwa/uYW+v4Zg8SOB8udQiE91na5B+e8OoOnSEEZ+FoFHHhT1z6vPUR3U4v1PenHDPISBWxqnv1ivn0F92SG8+W8tuDGkxZwFMzHVh9sNLr1dxfDU2Xj0Yd7JxxwXXH0Vav+1FO98PIRvAsIEP/Mug3KbVpj+fBJVB/eSNofxwKOzcfusEUPR67DkIeEYD8ZiOydejhn+rAG112cjdc51nCRxVPbOpxh5gPjYTPc48qZTT2ykj0f+z39h4fxpmMLtoTqqRk9ANB6Zxu+xddfhyNlhzFsg2JvK5SWv8HkjDD++VIOSffVAzDL8rJ/v85qV8+HopeViHY6VHeDyAI2VgN5afBKYjqSftaLs9w9g9W+WYfrfKPiqCE4vQ9HI+uklIb94yi3E8Mz78cmxfSh510a6FoGvSX9r/0Okf3EOozE58xYaDn+GB2KI7TCIj482AYmpuK+F5gGSdwenIPzRCDygZFeZ3EjxFkOSuVd0nhNvY4GNXPdQA4ZJebrIRUwNpfjTrTBEPWxRldu8919BJwQpnwinffPappc45fT7GX48+yucKf4d3rw2E4ZF0+gJMv4u2F1JPjeuoOnNywhfvxqP/pTf44g7sQ+LURpDed/wQ98lfGfs+dCRY30Yl22X8afSASz6zWrMdVzjb6bi4agn8EhAIILIhYfOV+FYVwAWRwiP5Sjcq0qXcV90BO7/VN01velFMQeOQdecHoyt+OQ/bsJ2qxe26TEIJ7b33XfHoNMvP0IVCaFU0bgPLfHPW9UovrEQ62NC+H2DRIdV1agppdccRsDD8zFdNN9Tmk9QWdxynbf2vOQxKR+XG2d43UWQ/G/kxm86bk4Nn42HA6UDTynGOBR14N0uYvndrsXJK8w1uXmth+wP31LIoUTmn/4XLG/W4fY/6F1xIQs/zpoEP+MQ/JYfawgkbr2Nf262cJy3YAQfHiZj27+Q+bFNh1lzQ9zmYS5bqZyfqsSHqd74sV1vRXXtbuz/3zZErdqM2Blk53AnqrNyUGmJROq6bCTN6MHBnFwYr/Pn0HeqqzfkotoSjfW7C5BO6vdvKkWXsLS11G9Fxstt0MSuwq9XLYXmQgEMW+r4x44ecNdv7Xd75GsmE6QPr/ONBcxegqgw4gHTI7F48RwEUd0O9+NDklz6HI8qBxvw0poCdGiXIv3pVViqbUNhZh6a+Ddr+GvUFaG41oqoldlYvXAAtb8h/fd2M8qtfTO6jHWo3luOa6G/wvqMOeg5kIeXthah+lYIElZlImqwCjkFLvmsjdtgyCPOG0nkz1uFsP5yZInqB4x5yDjQg5Dl2Vi/MhR9xYUornPJ7K4T4fp79qFdl0jkWw7t2SJk7TzjvJOg1N4orJ0oE+zH6WtKAwrfaMZfhGqKrAyaEMxdHIkgaBEWuQSLZwtJX1ZuMjHaloPXL+mQmr8bG2KtMG4pQP1NrlLB57QIJ9ejbhA0j1xvnpAwuyuQm3sC5sXZ2FWwCiFX9yGvtFPl6wM2dB3KwtpSh97mYKA0B8/U9vO1Y42LNVnYfzUYS58qwPpY4IMXc1HZSXvkRQalNomVu0i/cmotXD/SYzVoenk3PpC58zQW27khd8zNGuwX4ujXSQFoeiUHZc47kIJOqyyYS+rXLw9Fj0innmgCbeg51Ixrjve1ic6PEl9/vfGKsAO41liBC8O0J8p5heaN+tLt2H85GEnrDJIDBxcrezsRJIqV6h6hUqfHK8c2I2qqgq9K0VOFHcW9CHfIfSAHzxuFBCTEcNn2IlwLTsT6X0Vz8tD+Vneb+UO4mMyDcVgUk9urUG+8Qs52ceGAIw/weSfrBQW7euTGKFsd8tbsRocjNcjEkGTu9URuLNAEQ3OTTJTbHBYiDJ+D8UAnNDODSUE5t/H9z0O9zaGXM+79J3jVCUHSJ2THC29xSseEY3h971uwLSb+t4z238PfV0bCXJWDLGcOUiGfJ4P96LGFIUg011dCaQwdV9+3F6BpiuA7w3XI3eAa58eXD4V++zIua0IRNqsb7xg7YRV/x0M3B4ui+e936R6woamiGT2i+gGSQ8qsAQgjByhfU35c4JDLgWPQtWb6HOJnQcT1yOR38RKEC+sJ3313DDr1BtFfkOZ+fpvkiKw1+9AzbSnWF2RjKZrxDI1xx/xLxXzCLdfJteclj7n7uJLvCrqr2oODH+nIHG0zEgJb8dKGPWgRpSKfUNKBQp5yk1+MeK4pJbtiDiVMnYOIyG70fO6QXgk+trvE3eH64Rhr5Mc/N1tw551G8b5juD2Pzo8fh6V2K/IOuxzO5/mpL9gnEfOpzfa4tUfsfd8KOwi33823L3+t1X5HKFO4fbuFfQOn7VuXFdnbnefcsZtvmO13aPnbDvvBZIP96Kd8Dce3JvupbL19/wd8i5eLE+1xxR3cNnf9p0/bzVyJh9ZvPTUglIRjhOM5uOtvtr8tHHK5WG/PKLvCFwT6jmY7+yslI3dOufs5TtzaJ/IsS7Tv/7NLG1+e2GiPe6HZflso27+ix++yn/9GKH9jsveZnLWk3GrfT9pr/ErYTs62n/qCr+Lg9rlkdtcJf/3X3xe1x/WvwH72a7Ktoj1PvjxOdONhX6qvuGUl9stCWVYGDtovlw04ZM+hx2+0v32Dq+G4PWDifYZuK/mcfcD+9tOJ9oP/f/b+BiqKK9/3h7/zd257nDQPM82YgcQBNKIJYE6QPCJZMHJBmNOEmeYw4tULSy+uuOTGgcQJ5gWTjDi+JAYlA+LA4JUjjxw5mnBlhoSJDI5EL4ZEwIlAVDhKs2Jgxem+4dATlv0Pp5+9q6rpqu7qruqmQZPsz1poVe2q/fJ723tX7ar+iNvh4OT0+gfCHuFLk+0zkzgHD5jete1IKrKdERveyHnbiao222dkU85mFOv4FfGDAaPNJLpm6FiOLbncbruubVDM03jSlp9GbEskVtuN47Z8YhPifCT4ojsXXM+Rkwlt35RvUpk611U41mX3DQl8XLC3g+a/vazctt1QZbvOHemzHTPk8LatNq680iaRpcSX5HxlXM5XPNuqM1wZcjqaajfNz8mHCeI4KOeT9JjDJ2XyEPTq0JGrzq5XGmz5x4zCHg937AR/jKu7Bx/i0sWx1xlPfQHd+8seSXy/8/4eW3LeSc7HZNskjm0EWldpbL9ju3663PbHbnqNskzkbEKpv5DzU75eObYTV4V9CnfMKYZw9kRsjBOvcvtc4NJFcZjA6cBQYDtYXm77vejvnGDHEhsXkPSh06i71HbGbR2/0U/ZznTjIVdvb/plyucf2I5t0RN/M9jytu2xvf1unyR/LqbkOGKKPcbYY4RimT70C5IY6JOsCR+Vy+rQW9v1Wqa0XEm/P2777KOjtu1p2bZj9kvumGxDN8Q142X8e6HJasYT4linlB9vezJ9j10+KuTpKjvep0veE/miCC5/Fx87abti91OFOnuOU9L2u7bFcwz3HEN5JP6uCO/brvFN3Ne47/9c6+9k78Smku19uFyfyx3zpr7umdUnSxyBOgTwq14IVlzp6kG0jszeOzrRLfxdv01S+owYoafMj0D0/Bbs2HoAzW30izSA7kEdNDSP4X50IQXLl9ETBeaEYlmcBhf7Z+KLCcO4fglIjY8U9nnCHo9DUGcfplbbS9oIPBC6GCMTd4Q9ZTRax23VuXPIdvAPuLvDHPNDEYZRmOx3++eRfTLpH+knsmupR83hJiIT4A692zVqxJV5cVi2kDuTZ94KrEwUtt2gDZwqjZRNyxvHOL0b4XV+VgwNDmOV05r4sMQURAjbHJ7a4A6P14QhOm4YZUU70NDSA+OoFVrSDs5m1NicDLqHoxDWUoJnDjQR2xqGZY4OITpxq9xj/bgH7XF6xIvv4AYnYN2WZAgLD7z3C2IXusWh0N6mX2o6i+YjFWg4b/bwdSHlPK03BtAbsxLLROrHwkSk0idd7vBFd2px8qMArUOAnEwXBpH/HW3pvmomCf0Ysj9kkRCK2CQd2rvoXSgLrnxoxkqDASt153GF3p2+2YNzSORtW21c0f1AYtcS5HxFK+crnmzVDS46ikH8vA/RO/WE0MmHJcj7ZEick08SJHnMu899WzmGcaUDCNeMOPRB/oYmtOgl5dE7g9PxIQ5PfQFBE/cTZA22oYPTP7HzCxcQn53i8DGC29gm1D8jSRzbNYgwFCAjxnGNokwkNqGyv5BFR8oSNgnWq324uCwBseIYon0My2OG0XvVcdfUffvcYXWNGcJTh1jRn/0JhDp8qbuG+FuosE3RIjYhAb2XB4m3+ikeetsvz4/Dhqp3cf50OV5KDYOxpRjZP8tHwxV7nUMRnxGMxgvCi/nDnWg1G5DxhMgqPJTpfb8gjYE8vtmJLL7YrrcyRRP+539djUT6l56JtTs6EbGrChvsxWh0CAvVwjzYg+62JjRUHEXrbbuNqhxPiPGYnzKq5SmRXTDCSYUsFg/ydvEx0dN0j3VWF6d8RU0M9S/e9n9Se8cC0q+PWcFZnI/jXbXM/mRJggWWz4lKbvWhq+tDx99YKDbECUsS5kRiw4njKDcEw3i2XPgiTRNGqOF8YYYxNBhB9DwRWl2wV5MT9Zhguh2KkKkFtwIkYITYFTbLjDRth/5nuShrJHIz6xCrJ44tpHHy0WolgYU61lzpAfV4nR+v3wCN0wlkn1urKuCxDW7wfI0Oq/acxNGCFUDXv6IkPxMp6+1LOlTYnByRW3D8RCkyggfRWl6E7J+m4+Um2VG5C5Yxch6ZAKsXu4o6WnpQQ7/U9EItmQCQrmthAn4SJx5sOKOcp3w9yb7bhdW+6c4fcHWdIB26uC1dZoRlkc5cOMeZsMdJ1DxPAvLYh2i/SidGdAIF9A5YYO7phCUtke90/RFXVPuKJ1t1g+4+x80TDh108y0Y/1LY9Yg6n/QeGhvJv8NifXyIIQ0ZcC6+j3S7hGn4EIenvoAyLwGp+kG8c26Y2AbR8bkorIpzHly6g9ZfC60HW/ce//UXnL0vCCaaFqNFADngcVDmicAgMggy4jOiNwmBixH7RByWi/7C1IpRBnV11yHo+8KmgEZLLHJ8nNjOTMRDL5gXigh9DraVn8bxPA0qy1qmbqqFJBkQ29KCbjIhHelogYnGELcDPSne9wvK+M9OZmqsI/rAQ2U2GSoHYYnonS/rpQpsIpOoHbVn0XWTxOyE1Vg1pUbvY5fn/JSZEb+juPhYDOyvenmu80zEKRHTiqG+4EP/5w5/j3eduMuTJeJ4ERrcWWrA5oICpz/R3RUyU6fBauueKhz/YxU2jlSgpo1IcxFR5OBlXJe8U2HF0LVB2a9M0cGOM1a1Sy85IrAkZhDdfVJNWgcG0L0sFG7ef59B+tFaO4D1paex/2Uisxw9li8UDfGofG4R+YjXzk72o7dD2PYWr/Pj9XuR6EMMJy9hW7ENsqi5RouwJwxY9/IbOHr6JHZHNGFXPX2qoNbmZJgfidSc5/Bq1Um0VGXjszcr0K7iSzK6iCiEXOqDUXw3y+NvTSnX0dLxFuoWFODUsT3YWpCHjJQYPKDxFGGU89SFRkBD68lfwDMxiAG3D2l90Z1/4Oo6GYk0l7YUYJX4zpKYyERkWDrR1fQB2pNWcBMjOoHq/bANHR8OYtUK4W6dl3FFFnrH61Y/hiR5DGPoY2FTgjtbdcPHgzI6isQSVVXTITxK6/Lk3fLxhyKf9IUwhC+1IGTFFhd9bF7HvzfF4aMPOXDTFwhEpxhgOtOJgc730Zjo+CS2MjS2j/JPJ8WIfxfLa/zXX3AxpOMy7K+88VB70iBioWu/pgr6/sHifhglxuQZX/pQdXUfJfWQZmTs74Tm4VBisTMRDxW4eRY1Mr8pExJF2nJrlAxZBYITkfroBbRf6sfFZivW6aVPYjzhfb+gjP/sZBbGOpHEjxN7UFlnf//HgotvNeGBZ4/jd3uew+anDIiPCYVm6smomvGEGKX8lJkRv/OIUp1nIk5J8RxDLTCR+SN9kquOIIQ4r0whNi6dbHvZ/7nD3+NdJ+7yZIkoJiMHc+tL0Wx/p3HSgu4318IgvEBn7TwAPZlpdts7VAuZWVs0CNKR7jdwBVJTelBT2zn1EqalsxqV5+PwZKJrL6lZFIHo/jacoT/YRRQ20nYAh9qERDHm/yt9qXMKLeL/KRntR2od9RnrRM3hC0jNSCSuPNuQbuRBK0yf2wOaBcZTJ9Es7CEwAVlZRry2sxa9w8SZ6Fdk9pfjjK8zbR/yo46naaxy6JfKq/qCaPav0IYpzKQ8YVPpmltNeCa9UGRTZoyQ3i18Pq8hJZuzYzHb87ei90A6citEdmYi9dEEI0gYBVoGO93/RsxSPdbpmlDTaC+Q1LeuCPrd7j/BqVRHOmjRkKhlstvp7QtoFH6/R4yjDSra/WgKNmpIPU8JJ9D0aqI7t/r1RXfuUHOOiEcN2KqpR6W9rgRLZwVyMyvQ67bTiMTypEFU1l9AxmNR/KGlK7CqoxplPQmIFw55G1dkmZ+IJ+OkeYycIgN757v4CrYqy+16Jx2VozkuE/H8h7AUiTDkIbxpJ8paBmEes8DcX4/X6oyebxTIIvXJVVnJaD5c7YiNk8NoLkzH89zTI2Uf4nAbexX6AjvLyITYfAolJCZnrVrhxV17PrZfrK135H/7LMrW56LxmrDvNer7C7GfymKPISJ7HzlVikqrAUmPCge8JpiIKxjtl9UPTFT3oWJU1r1RbDvDp4ifWrGRTHoo/oqHqgnWwHJqL/GLQYc9mvvRWNUE6BOwRDhE7T4+NYb7OEKDhgxs3d2okcOHfkERtXYyZnbISpbZGOuQMnJyENRUgWZuCbEWQUTuplFHP2o+dxyNoo/dKI8nxCjnx+Oh75kRv/OEUp39Hadk2u4xhg7DeFWLaPHTQPr7eG5/tysY4VEatP/pLP9juNSH3iTjRz7Rt/7PHf4e7zpx1ydLWJiDPbui0Jq/Gin0h7lWr0XJDT1K82I4JWnitqBc34cdmaSjXZ9JzinBdUMpNj9OLyaG82w51o1VwLA6Hfr01TDsG0VW+UvydxRD9fjVBiuO5WcicXUmdvQnY7NeSBPQpeVhw0ApqUem7FddNEmFOLrOhENrV5Py0pG4dh9MWeV4Mc0/4cM7gpGWn42hA1QupOP46VrUBCYiS0iljyCjnz6K38YNoqY4H4VbKzCQvBe/ekJI9hof8ovcgjee06FxM5EX0W/K+rew5Nk80VItpTZQYpD1bAjR22okvki/7qRwzYMGvMiVmQ7D+rVEl/lovr8Y2wzCnSAFm+PqlGfANZJ/YmY1Bmi7t5Qi42oJ9D9bi03EFvW7B5BVmodoYcnF9cYdOCZ8ecmFOaHIer0YYc35SPwpufanmcg/F4b9z+uJBbtBqY4x2Xg1rA2biN3n/iIdKYU9iJDc1XRuA0EpT7rMqawIQY2knumZMKSvRePCAmxbTBPl8EV3cqg5xwlOpr9GdBuVKa0r9f0BZPyG6MRDcIxOIA5vjcHyGOGkOVFY/oQV1sSfIHpqaYOXcUUWMnl4WbAZTpbp2DH63/GqU7xRtFU59EVYN1rM/aCjgS7XuKpH1cvJ7m3JmWAD9tfkQ9O6E0Vb81HSqMXmnTkIF5LV4aozSWykvk7a8s79RdiWQdui7EOKsddjX2AnkkzaNDCO6ZEqfndEBVz9s0ZRYo/tueUYX1eKdeJ317xEub+Q8VM5JPZOY8hqbGoOw+7KLaqXfckRlpaN8Jbzki+6oZ/Yvf29EvvfVuGrfyr6UBdU1n1ddiga6VI6ot/EzU0Ieq7c8S7LtOOhl8xLwNbyLQhpKyRlCTL4RRFa7y/Abwvs/QSPNlGPjNtmhJNJhFc3HHzpF5RQI2sqq5BTRFarseuM+2g7K2OdhdnYajCjspqPIdHZRQg/U0j6n7XIJWUWXYlChliNiuMJKYr5KfU9M+R3nlCqs//ilLu2e4ihowNkoqNHvOh3QU0d1Xim1SjxCQcaLN9QhNi+UtKXknJyaxGwRqQvX/o/t/h7vOuE8KGHe4I7X4zbxsWfORHz1R3bOEl395Uo2x1yrduLZwCl+swyVHaydXE5dsfW8ZsU2/bT4q+teIGv+amQl9s2eMDjNSrL9MpsvpSzsxHbH58yOL7o4wl6vezX2tzjsY4+2r1Su++Me053xhfd+Q1ZnfgJf8QVNTr3JZ7QunlpS1M4l3PjuC0vTfplNJ9Rast09aWQP/1ilvNXvLzCF10o4c88fYgh7rlju1KeYzvo9PXEGUO27tIvZinFkpmIhx6hdfZUJ+6LW05fTPMWv+pUwF95zoQ/KKDY/3hZJ2/7M1lmQkce8LcMvMFdDKXH7V+otNP1uvsv/6nGX21xuX6a410Rd//JkghNoJa+7yjPHA33pR+3X8nQkGvdXjwDKNVnlqGyc60L/YZ9Op6v6+eXEkxaYb5Ui7q2UKyM8eXu0DTyUyEv+TZ4xuM1Ksv0ymzmydjZpBHG4RQsF91tcQu93suXMz3W0Ue7V2q3Rus53RlfdOc35HTiL/wRV9To3Jd4Quvmw4u+1v5qbFq/FxdvCU9CJwbRWn0KxqQY0fKiaaDUlunqy03+1jEzLMNn0dAArJPePvYOX3ShhD/z9CGGuEeD6ILj2Jbi87MM71BRd6VYMhPx0CO0zrJ1ssAyOoqBhpNoTvLm/TgZ/KpTAX/lORP+oIBi/+Nlnbztz2SZCR15wN8yUIPHGGrpRGPjYmzIEH8hYxSfDURi+aPTjB9+aYu/x7tSvkNnTMI245vI7U40vFmNYx3DJLRroFsYg40FLyHL189M+ju/bwITo+gdJoOOpX5cnsBgzBgWGFuqUVndhov0ZfJ5OqxKycfWZ5MRMosDIn9jbChESfNcxD71HLZyP9DJ+HrQj4bcvcALx6e15HHWsVxAZX4Vrofp8cuXchDxLe4CGd8MvI+hZhh7LAihH6EQjtxVZnB8yiZLDAaDwWAwGAwGgyHDPbUMj8FgMBgMBoPBYDDuFdhkicFgMBgMBoPBYDBkYJMlBoPBYDAYDAaDwZCBTZYYDAaDwWAwGAwGQwY2WWIwGAwGg8FgMBgMGdhkicFgMBgMBoPBYDBkYJMlBoPBYDC+ZlivNKHx3CiEnxb++mLuROORCzALuwwGg3Gv8fWeLN3uwcWeuxhi73b5vmC1wGKZRvc6aUZvWw/M9BeSv45MWmEZs8Aq1N8q2vYJLj/38rRaiLzlkp3qcU/aknMdXbBgpKMTRouwO0086oLaLUl3/MkIdYIcnxC271GsllEYe3pgHJ2m3d3jcHYv0Zfobzrx5+vIdGOuHJP9aHjlAgIeDeZ/DNJDGZIY5BS/XfTki//QODHcj+7+YdfrhRji7o/zAV0Mwm/vxbHz3snIbbyQxGSnGCVTH6/9kOYxOojenkGYZWRO6+VcxtTfPR6fvjHMZn86E/49C0x77PMt42v9o7TG2rXIbdHj+Mk8hAnHZpOZL9+KETKwsobFIUwnHJom5sZCGNpS0FRpgE9ZXqtFbn4LMqpOYt1S4djXidEmPL++DT85UY6M4B5U/tftwBt/xtbHhXRv4fIbxoa/FCBaOORgFM1bc/F+ynHsz3L6NWxJPe6+LcviVEcXxs7i9bWlwPMn8ULKdH8h27MuOLut6Bf2BOYtxtY9pVgn/Dp375ur8T/xBs4/G8Pt31NYyOC2qAiV14CwB4nnmcmkCaT+r5P6L/PjT/+bB9Ft1CI6RhhEzwhKcYm3+9ed1DVFZIHv8edryLRjrgyWMzug/1CPtpcTOD27L8MpBknit7yetEtz8MbreYgOFA54YOTMTuw4cAHGecEIm2eBcdSKsKQi7Hk5GSFzyAmXKpC4vYk/WYZ1dn8n56X822I0vaGHOm/wEC+4MoHf0ZjsHKPk6qMJRsaze/GCPlQ44B7rlVpsf6Ee3UTKEUTQI7fIgJzIq7w0DxFcxfl6NXBny2C4R+PTN4zZ7E9nwr9nHj+Mfb5t0MkS415lxPbHp1Nshz4Sdv2A6e0CW8LTp20mYf9bx8hp2/akAtsfR+hOt+1Q0jTly+VXbrsi7Erh9bf9ba4wKZJ63KPMah0960LObsc/KLflJBXZzggHr5Sl2BLKuvmde4mviB0U6G05e9tspq+EYwTTn/fYctIKbGf8Kd+PymfBv72LS/esXmYJ/8fccdu5F1JsJe+NC/ueyvAQg+TSvhq3dZVl2xKK3iWleOYOsTVqv29fFZ35xYDtbWrr5d22O8KhKTzFky/P2w56FWs8xAvqA+5islya8aRte5rBduxjYd8dn5L6p2XbDv5ZLK8RW8febFtygbeyZ3xT+HqOqfww9vmWMWcnQZg3zQ6jnWisrUN9xR/wV/MEAn78MO6/j0+y9JzCsSsBWPZVB46WHUDd2VuY1C3CkpB5/AlOcOe3W7E0OgSasR401nyC7z08iY8q92HPW5cw+f0oPBIygd7Tdaj67b+heywQS6MWYJ598eGkBQPv1aPhX6pRf/YaJrAADy0KBL0hZsfcUY+jVYdJ+i18d8HD+F53NVr/g+arkS3/+1GT+ODIAZT/r7P41KrDwiUhjvIInvKTMoz2igb8+a+D+NQ0hpHbGr4cmjQ5iu4T1ag5XIf2a2MIWPQoQsS34zykT3zSgoabi5Cx+CZOERlXvvMxL6cFgoyV2sGlX8Dkw1G4/x/4SzDag+a6Q6ik584Jw7LvXsbR974QycXp/JtnUWNPFw455EL0MG8RHvmx5/uLlmstaKz6PQ6evoSJ/wxB+CIdNFN6pe2vJXqtwDsfjeHLgDCHDVmuofXtmwhbo8cS7Sg+OtYKpG3Aigf4ZIwNovV4FWoPUvucxA8fikCQvd6UsWFcfLsS1ftJ+gSxpzALLjSO4R//RxzuF05xYMH1dxthXJSF1Eec2iOph5MtC6e4lwm1jTOwLJiLvx4nOnrXirikCNAWcnI59i+oqTuLG2PfwY8XLUQAl6HCNXKytNcxNQLGk+Sa3/4JV2mbp3yI5lkH4/1xCP8B3Sco2Kb7+snoQoTdbtc/+TBXZ4pmQQQCuitwUZuFVQ9p8PkHdXgHScj8TjuqDu5Hw/u3OR976Id2iRI8xB+Khdhmc+VhvPGv7fh0TIvF4nhB8NZOKZa/lOKZriSUv27g77YLzFuUiGXj1Xjjk0eR/f+dzx/0KD9BhxEh+LSxDJWcDX6X6IuXIWdDjefx13+/BevtQVgFvYycqSAxJgzfu1yP8gPNIIpHOGmze13wmC814Xjlm1yMoHaxJEJHYqOHuOQGXi9p2LQyRDgi4KmtvsRzgse4QKH6r/4dqkibXWKVOx/x2E/I6MQpbniOuUQGtbW4HkB0Jbotbemsx7HL38OyJVTmzvSh9Y0rCN+4Do8IfifnHzxOMUgSj2Xi0/+jQQjxpau/vQjtP6cgXBz7JAzi1LMVCPhVFTY9LvKBf9DhkTgdukv+APw8DQ+JK+MU8yT8Fw1MH1Vj4EfrEB/u2mJXPMSLzz5ELUnK4GKyU4ySpAkERiHkizrUfBaD7McFP3TBgvbSF9GdVI7d//ygcIzw/2jx4yei8J2aA7getQ7/+CPhOIeH+C+DvL+RXFzGRTKxx1sbFcUNO+7Kl0Uhjorh408EfjTcyMVl2idP/ijCEZftvr7oc5wt+y3euL4AhuW8HtzFW3NHLY73BiCWPt6zw737dgXfjYnA3I+96U/5Ojb8ewhiH7I/TrWgt6Ean9zn6NvcyUfse3NI/Dn6nhlLH13giImTg2ivbII5LEY6TqMIbRePt27I9DuK4xI1/YYkrmnRO+U/vsSgbx9idcw8/dXIXX8AA/NXYmNxHlaiDc9srUCvsI7XevM86ppKUdZgQfSTeVi3bAQNv8pHnZvlHNz554f5F1wnhvFB42lUvtmEO/Fr8MsEoPHFIry84wAuznsMGRuSoT1XgvyKHuGFWDPad6xFfqMFS9YU4pdrFsNYnY+Xm0a5VMpIYyGy3xxASHIeNj4ZiqGyEpQ1NeGDm/wCaLnyyw4cx/jSn2Nj9mMwN2xH4RFH5ZXyk6JFeGwkwojBBy1dgdilgtNP9KAudxNqzJGkTXlIfXAAhzblo/Emd9FU+qHBUKSS9IyFo9J0yq16HBRk/MvUALS+ugmVl4Q1t0rt4NLPY8i+9vpWE57fuA99uhRs3PBzhA+WYlfZaSe5iM6nmPoc6eTf3sO5yKk1Ywmpz8bkUAxUbMIzDcNcqhzm5u3I3tYEayzR85qVwFki1/0X+Pwm+1G3PhcHrwVj5VPF2BgPvP9iPmp6hPZ5YrQFL68vRrd2JbKeXoNoaxMK1+9Ft109lh5Ubib2aI7h0lfOaUHJ6234m5A8HSS2pCgTE3obm1C5sxTXg4ncfx7DLV2hS3MMhU0YjyRyKVyDsOEq5BaTIO3hGo+y5BhGQ5lgC087+xCfZ6+J21G0Pc/18x3LhEi3HRU49KEOq9YUcPHjWH4xmu0urRB/aHp+/kmYYvOwp3gNQq4dQOFUW723UzvXuy4gWp+CMJkeJ+Lp0zj5dCS/4+TbGZFm1GxcK7JdXt51+w6gi/hbFqePUuTuPku6djJ5vH8xiRNBJHSEITp2BcKFft/U34Tmip04eCWY6MXAT84VdEHTs/f3IOjJIux5eiXGSex6uZEK0k1c8haltnodzwVbfqUTGno+sWXNxWIYiG1P2RcXq4j+H3QTq2R9RKmfEHSysxitc4RBWspAAADGAklEQVS4MdGE/M3ErsRh3W3MDUaAtQ2VZ8Sd3DDOVNfDEhgqL9vRYQxYwxDky5ofuXgsyzisdsHKcasHF2+nYFUijSJO6PTY/Zc3kOpV/YIRHkH6SPN0o4EzTjHKAybrHWFLhsk+9J4PxZMpMkv15kRiw+nT2LBM2PcB9/4m9A1kXLSrbBDh9tjz5iY8L6Qrj2U8xw2Kp/JdUIqjTnDxp3YnSt7RYCWJy1mxFjQWUvsXSuds8jhe2/8W3w8l0TXfnuOt7vtWtFa3YUD0zs3ImWpUWgIQRpzGY3/6ZCRMtZuQK4odtI51/WIjsWDovMNu1MpHEzwXI0fewkWRGVs7TmJXjxYhckvZhThXVlKKoYWkj92QwvU7tE8esbfNaVyyUtuJkrWFaLUXr7LfcO77HfgQg76NCE+YZoc7JtvQDfHDSqPt7Ry97ffCCg3ucWbOUduQaKnKlTK9LbuqT9iTInn8yT3iz7G9fYNL4uh6PUV6bXe5LTnnuG1I2L1jHLB9JlpBcOcve2wJ9sfpdFlAWp4kP/6Y47G6XPknrnJJPB+R8gxVtut0W0V+rrgudxl/t8iW/JvzkiUO3LG9/DG67byEwvT+UduxPxv5bRkZDx3LcSyTUWqHZCnFHVvXXr0t/xifN8+4reM3eie5OC2voEsh7Ommd2070vbYOiQV5o91fSnsi/mqz3bM4LRk4ss+25nyk7YrNI+v7thMA0bJcifavuRycfvs9ZE+ir5eaXBqi3DsBH/ssxN5LrIfOpbnfsmHoL/sglLb78vLpX+vF9iyRXKR2JKiTPh6v/Zn8QmEL422IaPomLC05czndEfmGiVZytnCjeO2vKn2SuWnZHtq6uduWYBEPnbo8pkk4lNC9txyr1faRPq5Q2xR5F9q4s/rH/A7lC9Nts9MQm7e2ukUrj7sDq58J/nRmJScd9L2Gbcno0NOR8W2c18I+2LfEnCVC8GjLvhrCk+KnNY0Yvtsqp3q20SRW4an2FbB9lTH86+IbJyXUn1F9Juntx18n7ZcLlbxxxzykpEvwWM/IVwjFwPtcYNrq6eYS30qjfiUPZ3Ydb4nu+JkI405XBmGAttB5zhTXmrbbhD5gCT+8Xp07n8+O0HyEvTw2XtO+Z3o5nUmY2eKSMp2hdqJ+77QGUHuxU71o3/F4pjMnzdlq7TezvH6iw9shwyCnXzRbXvbKb9z1AZlZK6MvHzl8ORvnG6dYw+xGbGNqLFRT3HDs787oRBHneH8P4/4qcj+5Xxd0s8oxlvetx0xSOzrgszs9snl71iuzTEujMfEfYckRkljnKJ+pnzBqc+xxx0hFrjA1Y3U+y+ihkriFi2bjIErpWNgOu6wj/m48r3tN5z9wtsY9C1kdp8saXQIC9XCPNiD7rYmNFQcRettq/SLHIE6BIjuwD4QuhgjEx7u+EjQQSN67K8hU+Lw+aLbWyGhiL1lxriwqyF5B5EZ9EBHJ1rrK1Dzh8vA1GzeiCvz4rBsobBPmbcCKxOFbVl00Ipfil1Ayhuzgqu9T/k5Y8WVrh5E66zoJXXuFv6u3yYpfUaMCOmpqYmSOwe6xDxsEN8Rc5JxgFYkIw4P7ZAwiqE+DeIfF99t0yI2IUHYVsb6cQ/aFwaR/x3t6b5qJgn9GJK7sTXcg3PQI158F29eJFILshFNGz1HA93iUGhvDxIZnUXzkQo0nDer+OrLMK50EHvRjDjqQf6GJrToHaR3qKwYIv+vWvGY5E5LWGIKIoRtd2jDIhEbu0L6FxnmdHfHgVqZaAOdcpgXirBgYKSfnN9Sj5rDTegih++I2i65RkmWHE62ME+LHwqbUlTYnor6eWSwFkW5udhE/9ZnImVzE4Je2osssfnpfiDSj4bYtrBJUYg/uoejENZSgmcONOEi/bLXHB1CdHxuyjqhL8yuRqLoz3Hnl6DYRiuu9fcjPiFOIj9NzEqsunkZ18eEAwSJDoOJTElEG1d6UiCRC0FBF+GRceg9UoRdtS3E/kdhDQyGm9XQPqC2rV7E8+F+Uv8ULBfb8pxQLIvTEF0Okh25WKVBdLxrrHL2K4/9BIeGlOsaA3svD07dufcYcxcmIuPBFrT38LsjHS0wGTKx3KO8nfpNivBEURpr+KeAnuiqK+J9iv5lpiO3SYfdv8kGXTQZsMgpP/GTRKtcnzC7cE83xfXj6hgkpLqjBa/Z25u7FvrMElw3lGJrImmZJgRLnPKzP6GVlbmfUPS3pVFYKtYjsZnU+R+iV3hqr2yjnuOGV/6uZhznhCaG2KHI/jVxP0GGk6+L+xnleBuK+LRgNF4QnGaYtNtsQMYTkijHYb3ah4vLEhArHuZoH8PymGH0XhU//nWPevloELsqGV1tnfwT7YkP0X4uCllp4vjgzGJER4l0Q+JWbJJOiFvDuH4JSI0XVh8IhD0eh6DOPhh97Tec8SkGfbuY1cmS9VIFNqVnYkftWXQRJw9LWI1VnmxoJpkcRev2dOhzS9Hc9SHGdSuQtuoxIZHwhRlGrVY6wCB7c119UR1+yc8Cy+dEjrf60EXqPPU3FooNcbQT49MD6KhiVjDBdFsLrZNDaebNFbaUsYyRyDdBAoK4PV1mhGWRYCCcI0FWjiIsPahZnw7DC7Vo7zKSIJCAn8SpMTLaFvLvsLgeH2JIE4cNi+8jIcmNbMl+gLDpjh8ujMHyJ+Kkf48vdjPp8EEmAiNN26H/WS7KGsn5Zh1i9aSDENJkUZKlVyjbntf1cyY0Gy9VVqGc/lUdR8u7x/EC6TDVohh/Irfg+IlSZAQPorW8CNk/TZ9ayqKskxhsPn0aLaK/V7lPCAYj/GENuq65Wa439dlZXn5hwU43LrT3kfxVTIa8REkX2rQ9aKoqwPLJHjS8kg99ei5q7Mtmps0MtJXacmiwi39odcHCzTYfY5VSP8GhQ9D3hU0BjZZEhfFxEjfUEIqkjMVobKXLX4dx8YwZGYnSwZGEwCAykTHiMxKvJAQuRqxznHkiBmHimx0yRGfv5H2K/h07jbYTO7FKeC1Hu9gpP/tXFsPIYHVwwO1yPu8/S2yBibraHO+iUQgZxErqR/8ileJ9Arba21t5FKfeexe/zYvk2zUvGNFO+XFffCSTiwhND4bcujGxaXXKlkXR3+4PkgyG6dhBE2jB+JdkU5WNesYbf/dlHBdGbFbCvPtIC9z7upo+MCTJgNiWFnSTPLjBfVoiImSWOnN5LQgmXipGiwBywEL0pgZv5KN5Qo+swSacI7ZivfQ+mh9NRrxTqJMSDNINS6A3UxxxKxQhzi9EB+oQwt3A9lcs9TIGfQuZxcmSBRffasIDzx7H7/Y8h81PGRAfEwqNLx2jP7jWhJoBA6reLse2ggJk6UlQFHcqi6Kw6haZmXO3BwQm+9HbIWx7i1/yIw4SocGdpQZsJnWW/iWTDlSH8CitcEdCBB2QzYicwxC+dBS9A9KgYbwm3J6gcB27E5OOXkUXGgHNZCTSXNpTgFXip3B25ORIsHfOlo63ULegAKeO7cHWgjxkpMTgAY2agEjbYkHIii0u9di8jq7x5WV/8ZpUttaBAXQL2/7Ca5lw9KO1dgDrS09j/8vk3Bw9li/0NLUiKMjSO5Rsz4f6OaPRIiiQDHiFP41Mx+gelfFnfiRSc57Dq1Un0VKVjc/erED7mDqdaER14/6Ecd+SeD0sjacc775NQeq0fy22cy9V8fJrv+z0gubwILo0EQhXPydUgTpdaEPjkPFUMXafIJO/5xejbnc9BoS06TEDbaW2PCi9k0qMD0PEX+MX0o8HR2BJzCh/d1rEQNd5YcsNSv0ExyiMRulI2djfCc3DoaSl6tAl6RF/7n30XjmPZms2Vnl6/2XeYkQs7idlCvvTZO48sd2qnKzMj8NPIs/if0+9ECjiZj2eWVuOXq8mD8MwXtUiWvzC/owRgAAZP/VMFOL1FjSc6nSdAFsuoPIXhTgjIwpv8OhvV+nKERETgxjoj8QSatqqbFQZdf7u2zhuYNj+/pDAzX6Pvq6qDwxOROqjF9B+qR8Xm8ngPkl+cK+LiEJIx2Wntgxj6GMNIhbyFQjQyVTESdGq4+GcGKQZzGjtIG1sP4uMJ1OcJrrO9GNEYjvC06KpuDWI7j5p58GNO5aF4gE/xlKvYtC3kFmcLJGBTrAGplFHZ2U+dxyNt4Sd2YY4R/iECX+z26BlEM2NF4QdQmACsrKMeG1nLXqHyYCPfgVtfznOeHfjy8E08rOYHY4SnZGDufWlaLbf4Zq0oPvNtTAILytGpGUjqKnKkU7a1bAtE6+dcxoR+wUd4jPi0H6kFt32QcrwKdQ0iMqiy30W9uP9lkFYyADceussKt9sExIJjxqwVVOPylOOW3aWzgrkZlbId7aBK5Ca0odjdZ1cfhRz217kbqwHHTvQO8ka0yhM9sH+7QtolOvQXdBhVVYymg9XO9oyOYzmwnQ8LzxdiE4xQNMoku1YJ2qqL/jpyYwIb2XCoYPuQStMn08ZNIynTqJZ2JNFQZbe4tn2fKifX1GKP1b0HkhHboVDFhaTGRZNMIJoT+eTTng0j+fh1aRObN9Wge5bwslWMnCvKMLLH+uxNYO/LcvJT2xf1P7K6mHNSpH5DS8PjJkd9i+Lki5G0ZxP7L7R0VbTbSK3B6VPbsRxyVv81lY7nC33oKZWpL/OalSej8OTiXQArkX8fzNg6M2dqLs0TOKvBSPE1g+dU/BepX5CoFEcN0gMrKy3YmOKF7+noyMDv5gLOPibU5ibkajw2zDBWJYY7DpAmlWCkVFcABwpxOuN/Q6Z9zfh9RfqEbBlk3dLeEYH0EuXBE/9dp8FA/UHUHNumjMQv6FB9FO/xqpLJXiGxIgRuxvfIn3Atr3o0hdAcGMfUOFvt0Sxh/b51eVojstEPP1onEobdY86f+fxcRzXVo6aTqGCanxdVbwl44/UGO7jNQ2aHKS6exCyVI91OjKhFOU1cqoUlVYDkh7l90MWRkJzrgXt1NwmzRioO4DKqdO9kQ9PBJl4mBp3kviTjFVxSiOEUVSWnZr6oINL3PqnZOkYi447Dl9AKokT9Ay/xVKvYtC3j1ldhhedXYTwM4VITF+L3PR0FF2JQsbdetIXnILN64axKzMdhl9kImVtNQISU4RECgmOTx/Fb+MGUVOcj8KtFRhI3otfPSEke40v+QUjLc+AawcykZhZzd/FWJiDPbui0Jq/GimZmdCvXouSG3qU5sXwg3aa/lIY3qHpRMYpPytE+6Kd+FXazNyx06a9hCp9H3ZQOZK/lBdGse45vZBKCUXGthzcOZVP6roa+qI+rHpKlD4nFFmv/xrRbflI/GkmDOmrYdg3gIzf5CFaNsaQ4PFsObJG98GwOh16cn52xd+x7vUc/hF8TDZeDWvDJpKW+wtSn8IeROjVGZkmqRBH15lwaC2pJ5Ftyup8vHN/EbbZf5E1cgveeE6Hxs1C+vq3sOTZPO+WkqnBa5lQiK3kZ2OI2EpKJpk8/3QtagITkSWkyqMgS2/xaHu+1M+/eI4/xD+3lCLjagn0P1vLvbuh3z2ArFIicyoLn3RiR4vogiq8ETOAkk3p/DtNP92EkoEYvFFZ4Hg/TOzbVH7U/sKKUf6UF0GS2n/IKWL/q7HrjLvJjJIuyED4lSLSAeeT9Fwiq9X8eyzF9h9dlIlL3uKPtkrgbXndWMWULRv2jSKr/CWsEkKfJqYAVa/FYehIMYm/+ai8loJfP6fw0qhiP8GzLjsUjXT5Lzkvkb5L91w5NnjVFDooSoDxdiieTFIedYeRAVJ4y3nJ18BmnQcN2F2eA01TERfbqV3ri5qgWVeO3c4/wq3AyLkmjK/Ti+KOGVfaWtBwlkzEhCN3HW0Mtlb+GvED+5D7U769KZv2YSDm1ygvEPpfn1DyN0JkNn4yWkx8hcaeTOy4qkfVy8nEaggqbdQ9KsoX4dM4Tp+NJW+tJfnTMUs+GoOK8IYnX1cZb7WJemSMmRGUFue6gsWOJC8SG4juNjWHYXfllil70zyxCa/G9GPXeqLX1bk4FrgGWxfzad7KhyOSTNA0Zozo9SpuGkRic9IodhBZ0vihL+lDRqkobonHJeScxLX7YMoqx4v2MZ3fYql3Mehbh/Chh1nlzvi4bZz/0Mfd56s7tvEv3FRG9PUWHuFLJ6fFn1XxAj/nd+cLz3Kk6XdcypwhxHL05UtJlC9Je7wxjDsezveUpgTXFg+yU0r3J97KhOCT3qcjLxk81WFW7VIGxfijJHMfdCJGTftnS0ZK5XCymuEvIvm9rZ5s2akc6VfDPOC2n5B+VWo6bbnz/h5bstNXrdxzx3alPMd20OnLfXeN6cSP8Q9sh7Kdvnx2r+Np3DAN5PxN8rU1Kmd3/uiHOnnj72rHcZIvzdHY6W0VpxlvJdC8phHP1MuHftFOxQ8dc1/DE74QqaQ/FeOO6cZS72LQt4vZ/RqegEardp3wLDBH42adNv3tgnQ8XycsMZi0wnypFnVtoVgZ4/Z+ggf8nR+Ro8J6a5ru3Xsd08CtHL2Arp33xjA0Hs73lKYE1xYPslNK9yfeyoTgk96nIy8ZPNVhVu1SBsX4oyRzH3QiRk37Z0tGSuVwsprhLyL5va1ubNncsgP6bfUYEFYQWc09aKg9i+gnYtzfIbajMr751BaLGebRfjQ2XEDGz5Xeb7CjQXTBcWxLUXf2jDOd+KGNw9aTxYi/R5qiCn/0dzIo+huVs7t0P9TJG3/3aRxHY6cv10wj3kqgeU0jninLx8otTx45cxwNyEGqN+/9KOlPxbjD51jqUwz6dnFXJktfD3RY9dyvsfxaKbLpEoPVmcg7PIzUg+XSTxWrxt/53cPM1SLifj8FNwaDwfADurTnsCemHwdzhSVjmyowlPYG3sieTgCeiyD67oL6D4C6YOmpRVFROa4/IXy+msEQM0/39e5PtcGI0E7DQb5WDOPMK/nY8ae52CZ8ft8jZAIU9KD0ZwXuBiwGKfMd+nhJ2GYwGAwGg8FgMBgMhgB7ssRgMBgMBoPBYDAYMrDJEoPBYDAYDAaDwWDIwCZLDAaDwWAwGAwGgyEDmywxGAwGg8FgMBgMhgxsssRgMBgMBoPBYDAYMrDJEoPBYDAYDAaDwWDIwCZLDAaDwWAwGAwGgyHDN2uyZLXAYrEKO7PIpPXulEuwjllgnRR2vOV2Dy72CD9n/21lgtjMhLA9S0xLZ75A23h3zNO/UD8bczTEOtyJ7msWYe8uMTmK7voK1FTUo/u2cEzM3YpJDP+ipOd7lHvCR74JTJrR29YDsx/jNtMNg/H14Rs1WTI3F0P/QgtmffjfU313ykUPajIzUdMj7HqJsXkfnt/XBKOw/22ktzoT+mofBegTVGfFODOLAy7axl3No8LeTGCGsaMHIzM96bzdgl2Z1ejldiy4eKQE2+s/JFt3CwvaSzbh4LVgREcuRpBWOCzirsWkrxPmQXT3jOLenVIq6/ne5F7wES+5R2zBeqsH3YMirx1swmu79+HMoLA/bbzXjUudpo0VIz2dMLLgxGAowpbhfYsJyzuJ8yfzECbsM2YB8yhGNBEIDxb2vxEY0byjFl1jwu6soMWqXe+ibVcy2bpbWDBusiL+59mIT4lD2DzhMMM7brTgmSOd9/CA/uuq53vBR7zkHrEFS2ctnmkW3UZcmofjfzmJdUuF/WnjvW5c6jRtzOg6sgPNN4RdBoPhljk7CcL27ECXM5yoRs3hOrRfG0PAokcRwkULK4wtR/CnWwsQHS6Ej4l+tFY14/OQKPw48BbaK87AEhGCTxvLULn/D/ireRI/fCgCQf8gnP5JCxpuLsL6Jx+GvT8zd9TjaNVh1J+9hol5i/DIjx2haeRMBVr/Iwzfu1yP8gPNQFwSwu8jCWODaD1ehdqDrmVwjPague4QKv/XWXw6JwzL7htE3eVASbkSSJt7T9ej6rdvovXyGCZ/FIGHfqgREgmeypu0YOC9ejT8SzXfBizAQ4sCMYdLHMVHx1qBtA1Y8QB3QLnuIiw9p3Cs3Yql0SHQjPWgseYTfD9qEh8cOYBy2jarDguXhGCe3JRaOP97iz7H2bLf4o3rC2BYPp9L8iTzacmCMtqJxto61FfQ9AkE/Phh3E91RvBFn59/UId3kITM77Sj6uB+NLx/G99d8LC0Tm5tVpDhlblY+LcW7nrjA1mI/hFJGBvGxbcrUb2/HhdukmsiyDX3WTFQtx8f/ddC5DwskomARB/CMb5NUXgkhBxRqSPzpSYcr3wTdWdvcW0JGGzAXwOzkPqIo0y3OnKnV3d6485/Cx98MojRL29j6O8hiH0okM9LUVcR+NFwIye3dz6awLzwRcTPxbZglyG5fiIQS8MsuNA4hn/8H3G4nyTTPBr+nS+P10MAln3VgaNlB7i2T+oWYUmIyCMFX6r73QGuvO8/sgCmPx7BJ/fFIfwHwjnOuLMdrt1/wP/56yA+NY1hpP/veCBuIQL4q6awx6SMxTdxitSr8p2PMfl9os8FQr24fC5g8uEo3G+385tnUfPeF4IdDPNxb1EQPj25D69VCzEnQoPP207hyKHDaP33OXhgidqYIeQnjqMT30X4IlJ3kegl+CE/y7UWNB77F9TUncWNse/gx0I6p7fG8/jrv9+C9fYgrPc7dMFdU/V7HDx9CRP/GULy1EEj2Lnd75Z+1YmavSX4A6dP4mNfkth8jMTmE52YCIjg7dpKdHi4BRPEB+8XmYOxpQJ/uh2GaHF8csaNnr/jzu89xIopWXmjSwneXy/2EQ4P9fMlftrjhceYJBsLhTQRvtqCLIox232soHKoP3MZnwz9DZOfmjGX+uYdqZ+6k5U39fQmfsnWieqAa2ct8c0KEtPG8GVAmDTmEeRjPbWlBvzZbte3NZJ+R4Iam14wF389TvT/rpWIIgLz1MTaafYPFhInmysP441/bcenY1osjlogP1ZhMPzA7JrWRA/qcjehxhyJjA15SH1wAIc25aPxJk3UIOxRHbr3VaBdeCxsPFWK18YiEBtKHcSE3sYm1O0sRuuclch6eg2iJ5qQv7kCvbK3oazoPZyLnFozljyZh41PRsJUuwm5FT1Tj/hN/U1ortiJg1eCkbrBgCU0AIy24OX1xejWCmVYm1C4fi+67WXcasLzG/ehT5eCjRt+jvDBUuyqGxAS5TCjfccmvHZZh4yivdgcb0HjtmI03xKSufIK0WyN4cpbOeesqDx67VrkN1qwZE0hfrlmMYzV+Xi5yc2SKqW6O2G9eR5154d5eUwM44PG0yg7cBzjS3+OjdmPwdywHYVH+rlzXeDOP47X9r8Fa+wa/DKJPipxknlyKAYqNuGZhmH+GlWy8FD//mrkrj+AgfkrsbE4DyvRhme2Ev0Ly7/c69OdfAU6KnDoQx1WrSnAumUjOJZP6mQXsUebFWTYsBcH/82KaHJ9/IPk4GQ/6jbno84cg417i5FFrjlI62nRIOKpo9hvkH+sJNGHAG3TBzeFyqrQ0UhjIbL39yAomdp8KIbKSiA1TwUdyerVg940IVgSG4kgaBEWuQKxi4ReVI2uavdNyX1V4Hm8vHnflO+T0QMqBRnyemtByett+JuQTKF51PWbuG1Odk2lKGuwIJq0i+qx4Vfk+inRWNBL2kl9ieopK16D1ld2oq65Cb18Fq442eNKbSdK1haildqG0O4w0tygpaTdsaEuE6UpbtXjoFCvX6YGoPXVTai8JGiZk/d5DImXMJr6RHYgxL39VbgeSnW+GANvFuLl7aWoux1CZLcW0aO12FTcRLREUYoZQn77DqCLxLCsp5OhPVeK3N1n3dzNn35+ljM7YChswngksafCNQgbrkKuUF/N/YsRuzQI0JJJS+wKhAtjenPzdmS/0glNPLlmzUpoLhbDsM3eRru+y1HZZEU80efKOU145oXt2HXgAuY+RuRE5VySz8tZEwzNLTKg7LRfTZi4gMY3e6BZoPCI142eZf3eKVZkRJpRs3EtanrsHu2tLp3x/nqxj9jrd2gwlMRHUr+Fo5JY5j5+uvEBilJMchsL+WQxvtqCC2pitodYEbBoBaKpwu+PJPpejCA6/HDyUzlZSesZA2sTjcUXJPFcjDfxS7ZOVLbrc7nloSufKsbGeOD9F/NF9ibE+ooBhHD9wWKMTMV6LcLFdr3UzURJpU1X7izF9WAyJvp5DMlZRaydbv9Ars/PPwlTbB72FK9ByLUDKBSN7RgMv2ObRcbfLbIl/+a87Y6wT+GO7XUcGzqWx58zctq2Xb/H1jEuJNi6bYeSUmz5x4zCPmXc1vEbvS3/BH/M9HaBLeHp0zYT3aHXJxXZznA7AuPnbQfT8mxvC1lcKUuxJbzSJqnP9UqDUxnCMa6MO7auvaQ8mTpMlesCrfcW2x8/FXYJ4yNG252v+G2ad3ZlH7/Dccd2/XS57Y/dfMPvGAdsn03JgOz/ZY8tocBeFi+TQx9xOwp1d8VVXjm2E1e5JJ6Pym3JhirbdWFXgtz5pndtO9LEOiMIx7q+pDvKsvBY/zsm29ANsZSNtrdz9Lbfd/N77vTpSb6u19wh+kyxbX97hNtTsllOhjlHbUNCGzg42ZTauqaO3bGZPjVNtdMdEn0I0PrZ66Kooy8F+77BpfBwNi/KQ0lHcmUo6I1PL7D9USiCw2tdjdj++HSKreQ9vmKfnRDiALfHQ2NDQlK57Yqwz+VRxmcop4crZXpbdpWge+NJW75zu+kxkf84w10vsR0hPk3FK77O7q6nyNVr6FjOVL15eTvJjujUYQe8jx/8i0MSn53cYkt4oY1EHoHPaR6kbZyPEdGriBmv/Vl0AleHYtu5L4R9J6ad35dG25BRlE7tlLT5zOfCvqS9hK9InmkG27GPhX3KV8R+8vS2g+/zcuDletw2xO0RvvqA1INcI1LXlXK9LU+IJ1ydRWXceX+PLTnvpO0zYd8zrnqW0yt3rOhdh14ItFxHOd7rUor314t9hMYt5/qZ3j9qO/ZnXkauPqnCB5RiEpfuRSz0wRac8SVmS2IFgTvH7qMUJz91kdVXfbZjBqfYaz8m3/1KdONTnb4ishww2kyia2hsSS4XzqFx3XkMNHLedqKqTbBHlfFLhU1L/F9NrJ1m/8DV6/UPuG2OL022z0zy9sBg+INZfLJkxZWuHkTrrOjt6ES38Hf9NknpM2JEOCsspwgbPy5H/rZqaJ/NRzy9uzWFBsuXhQrbFC1iExLQe3nQ5a6o9WofLi5LQKxOOEDRPoblMcPovSo6W/cD0R2VYVzpAMI1I1P1o39DE1r0DtI7vaMY6tMg/nHXOrgnDNFxwygr2oGGlh4YR63QBodCw61h4cvLSIrkzuTRIMJQgIwYvuGa0MUIIucNkHq00q8x/eEyIPtFHqW6q0EHrXA3j2NBKGLHrLgj7LoiPd/6cQ/aFwaR/x3ld181k4R+DHF3IpVl4bH+Gh3CQrUwD/agu60JDRVH0XrbKv2ynIw+PcmXQ3KNBgFTSepsFoE6BPBrknjmRyB6fgt2bD2A5rZ+jBBz0z2oE9o5XTzoaNSIK/PisGwhl8KjXYGVicI2QVlHFKcyPOrNDV7rKhjhEYDFQn3TiiGi81UrHhOlk1okpoCc4h4nPTxAfGdkgrde640B9MasxDJxPAmNQyp9IiDLMK5fAlLjxbZD6vB4HII6+7z7KIpTvQK04qCkDo3WIYm5c8h28A9I5BGYH0o0NAqT8M6YmpihDRQJgugyDOMYFz/dEjHt/OaR/WBgpJ/YWks9ag43oYscviMbxwjD/SQ9hcR6YZ8yJxTL4jS42C96w56UOfU0j8oEodCJRPvAg4sxYOZvZWvifoKswTZ0cDZO/PnCBcRnpyCES/URiV6tuNbfj/iEOIdeCBpic6tuXsZ10ft83uhSDt+u52NZamqipH66xDxsSBH1Z07xU50PeIhJ042Fam1hCt9itjhWqEYsq+EenJskchxxlNndacS4dhjGGyqfd3hbJ6J73eJQaG8PkraeRfORCjScN0/FWC7Wx+kRLw43wQlYtyVZpd2rt2mx/6uKtdPqH0jSw1EIaynBMweaiB0MwzJHhxCd42wGw9/M4mTJAsvnxJFu9aGr60PH31goNsSJHgGTQBj9uBUDo4uxPMp5UKFD0PeFTQGNlnSX4+PEraVYxkivuCCYXCGGdK7kgN3hXDHBRIKqaVhUP/I3pInDhsX3kTJouhZa6ZJgaObNFbbk0GHVnpM4WrAC6PpXlORnImW9fRmCfH5TTI6idXs69LmlaCb1GNetQNqqx4REZ5TqPvNwMp8gHayo/K4uM8KySOfKnaEkC8/1t16qwKb0TOyoPYuum6TTTliNVeJ5qwsK8lVEpc06MycSG04cR7khGMaz5di+luhwexNG3A0O/cUXZhi1Wqd6aTBXdEBZR3J40ps83utKDC/3AI2ThMm+26VuCnDtJoNMZ9lo3NoGtZ1QhNCXo8SQAU2IxxsIdxmvYoYK/JDfSNN26H+Wi7JGYmtmHWL1CYgV0mShdhwa7GKPWl2w9wNaO/MSkKofxDvnhon9f4j2c1FYFefcv0wH3mbDgp3y1N5H2uF+Ijp7uPEpj/jBB6YbC722BR9j9nSh9ZxD/sRldg2SSboBYYEz1PtaelCzPh2GF2rR3kWmrgsT8JM4R5CVj3ne4JtNq4m10+sfCJFbcPxEKTKCB9FaXoTsn6a7fz2BwfADszhZIgE2QoM7Sw3YXFDg9Oe402HtqcXBSynYXRCAsr2nnILqKIxGaeAx9ndC83Co06SIlBYRhZCOy5C+TTSMoY81iFjobp16GMKXWhCyYotrHdfRtbg0fRS9A9JRovGa0qenyZVPGLDu5Tdw9PRJ7I5owq56uhg5AktiRvk7+2Lsv4tzrQk1AwZUvV2ObaQOWfo4Enj5U1xRqvvMowuNgGYyEmnO5ZO/VVNPO9zJQqn+Flx8qwkPPHscv9vzHDY/ZUB8TCg0HgchCvJVRJ3NyhOMCH0Otu6pwvE/VmHjSAVq2jzMLgh0AOCM1Zt+lt7RvdWPIcldZWrzwiZBnY7kcKc3OXzRlRhe7hevSe8cWwcG0C1sewsXDy71wSiOJ+YefOD2U8DUdgbR3SfVGVeHZaGwf09l2gQGudrR5DQGV17FDBVMO79+tNYOYH3paex/mdhZjh7LF7qflnMsIhOZQemdayJ5DBF7iF/o+7c7o1MMMJ3pxEDn+2hMNGCVP+dKxGbDo7Rov+zkE8OD6Lonvn7J18/laQz9HTC3fukvH/A+Fk7htS1MJ2ZPA+5pGn13yLlM4jPiVQx+xNLxFuoWFODUsT3YWpCHjJQYPKBxyFU25jn9Vp1nfLNp5Vg73f5BYH4kUnOew6tVJ9FSlY3P3qxAu8sTVQbDP8zqBx6iM3Iwt74Uzfb3/Sct6H5zLQz2F/Mm+9GwrwWxT+dhVVYBXpxTizKn34dpPFyNbrtDDJ9CZb0VG0mQcGGpHut0pKM/ZS8MGDlVikqrAUmPCgdc0JFyk9EsLmNyGM2F6Xieu2uhQ3xGHNqP1ErqUNPgNBgXc6sJz6QXitpsxogJCJ9Pe2ot4v8pGRdr6x353T6LsvW5aLxGtsngOXzChL/Z459lEM2NF4QdZ5TqPgs8asBWTT0qRTK3dFYgN7MCvVTBHmWhVH8tgoI1MI06ZG0+dxyN9o9DyKIgXxUo2qwM1s4D0K+vcJRpMcFk0SBI57nT1CyKQHR/G87QHyokndpI2wEcahMS1TA/EU/G9aCmthMWoaMaOUUGJrf5bQ4lHcnhUW92zKQTFjZ90pUUOrDVNFY5yhzrRE31Bd/vkkaSwVNoE0r2t8A4SgaIo/1o3FePIbcDf952JL5O63D4AlIzEom1+gm6RG1hP95vGeR0Zr11FpVveqN0J7yKGSqYdn466B60wvT5VAYwnjqJZmFvijEzTPbBVeAKpKZI7djSWY3K83F4MnEakl+WiAzzKZQQHWatWiGxJctg57R/wyYiLRtBYpul8ausHtasFEQLh+4mXP2aRPUjumzYlonXzrlr9/R9wKdYOE1b8CVmy2L+v1NlKjI/Bf+ccgGV1Y560rHB8yRutnoR9xQR1YneXNOYRh2yun0BjeLxkn0M1DilcBjriqDf3Ua2HFjM4j0pPtm0Yqydbv9gRe+BdORWiGzCRPofTTD/G2iTo7h4+AAar7hvF4PhLbP7NbyFOdizKwqt+auRkpkJ/eq1KLmhR2leDNdxDRzZiWOLirA5iVp8MFJfyAMO73V8LY2wLjsUjfTRc2Y6Ejc3Iei5cmyQLqnmmROKrNd/jei2fCT+NB36n67GpuYw7K7cgggP66U1SYU4us6EQ2tXQ0/qmLI6H+/cX4RtGfxtFG3aS6jS92EHKZ/WIeWFUax7Ts+lyfKgAS8+p0PjZnL++rWkzflovr8Y24QvonHlZY2ihJaXTtqUW47xdaVYR9doB6dg87ph7KJl/YLUZW01AhJTuOvkUKr7jCOReSYM6ath2DeAjN/kIZoqWI0sPNQ/OrsI4WcKkZi+FrlEVkVXopAhp3sRHuWrBgWblUMTtwXlgo3kriftyCzBdUMpNj8unOCOUD1+tcGKY/mZSFydiR39ydjswbRcIRPOl0uRcbWEtJXKPx07Rv87XhXnoaQjORT0BsQg69kQUu/VSHyR/wqaL7qSELkFb3BlCraw/i0seTbP8/Itj5B4UlZFJopnUVKUj8Kdp6F5aifWuX1nyckeqe2s3QdTVjleTPPbVIkQioxtObhzKp/IlZRT1IdVT3mldClexgxFpp1fMNLyszF0gPoBGbD+dC1qAhORJaRyxGTj1ZBT2ETav+sMtR4ySH+2HOvGKmBYTWI3Z6OjyCp/aZpPgyKxKksD45geqU9Ijf164w4cm/rCl4+IYwWxFy5+hRWj/ClvDH8GofV7KQzv2Ov3s0K0L9qJX3mw5+n6gNex0B+24EPMdkaXlocNA6Xk2kzRFzU9Ia0nNz7Jb0HIs8Ue3ov0Dpc6UVmFtRFZEdn+guizsAcRepGtcbG+GGHN9jFQJvLPhWH/83pSWwrxzTwDrhHfTMysdlqFI+CTTSvH2un1DxpEbxH6uZ+txSYia/3uAWSVkj6Mju2sg/igqQWNl2bpJjHjW8F36FcehO1ZxTpmgXWeli5tVUkPKv/rduCNP2MrCbT0etD3M9S8KEqXXZHw4NW7K/RxtcUKjbsyuHRSBfHvwnhCVX5u0v1d1mxAZT6pISqSqfN0ZEGwEmFYNd7YDsEPMvHaZu+mHtTYvCcdyeFDe3zSlRh/ypDehZTkMYzG3HyYnnsXm2UeTk9xN/XoK1ydvYgZSvghP69ith26TMzqhY0qMNKwCc+Yi3DyafHIbBTNm/Nh3nZa/sabD/jU1lnE6/pN1wf84UM+2ILXMdsfcEsbST395XtKqJGLL2MgJ7yyGZWxdtr9g7d9GIPhI7P7ZEmEJnB6AYxerzro0mDpbZCYQ4OdhzK4dC8aoCo/N+n+Lms2oDJ3p+DpyIJAO1yvbccPMvHaZu+mHtTYvCcdyeFDe3zSlRh/ydDaj7rctShrG+W/uEQGb8amajTcTkDsw/wpbrmbevQVrs7TEbwTfsjPq5hthw6kpmVAPNYxMyzDZ9HQAKxzvoU9aYRxOAXLlwr7fsCnts4iXtdvuj7gDx/ywRa8jtn+gNZztiZKFDVy8WUM5IRqm/Ei1k67f/C2D2MwfOSuPVnynn405O4FXjiufgkVg8FgCFivtaCmsppby26FBmExKdj67HOI9+YrTIyvJcaGQpQ0z0XsU89hK/dDyyImRtE7rEH0Un8ur2Qwvr2wWMv4pvE1miwxGAwGg8FgMBgMxuxx15bhMRgMBoPBYDAYDMa9DJssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDLM3mRp0grLmAXWSWHfCStJs1iFnXuByVF011egpqIe3bc9HPsmoqArDnqOO4VNmtHb1gOzp+sZdw9Ov/eSs02DCVHcuEfsznKtBQ0VJE40D0K1lGk7JoTtWcTcUU/iWQUaOszCEflj9wI+yfVewEp0e091bncRp9hjHe5E9zWLsEdw08feqzbpV5z7XV9jwkzEQX/bsJoxxr2OpzHQdPBX/+yufm7sio7BOX3c7sHFHgU/u0v91d1k9iZLc6y4UrEWuRU9Lh2d9dxe6DdW4/oM2J1vWNBesgkHrwUjOnIxgrTujvmKGcaOHozcq8Z2uwW7MotxxtOEsKca+hdaSEtkGGzCa7v34cygsM+4t+D0W41eYXcmsN7qQffgzA9seqszsat5lN+5F+zuWi3yC5swvngFosO00AiHJZgH0d0zKomDtB366h5hb3awtO1E9psDCIqMwpIQPqDJHZseVoz0dMI4XVNQI9d7FHNzsftYKcNs+c5dQRJ7LLh4pATb6z8kW/y+XB/rf5v0jRnXi1O/63NMmHYcdB2feGvDiqgZY9zreBoDTQd/9c80n5+V4qJknDmMxs3ErgpPYUQ4wjF2Fq9l7sNF4ojG5n14fl8TjELSvdJf3W1mcRmeFvFPFyG6ZR8a+oVDlMl+NBy+gIxnt2D53YuDTlgwbrIi/ufZiE+JQ9g8d8d8xYjmHbXoGhN2v2kszcPxv5zEuqXCPuNbh6WzFs80T4Xb2eFesLtxC4yL9fiFPg7xy4KFg07caMEzRzqFAeLdwzpmhvUJA7JSkrF8IT/9kDs2PczoOrIDzTeEXV9RI9dvCHfFd+4KWqza9S7adiWTLYp8H+t/m/SNr41eph0Hv+Hjk28LwTFY+eAF9F4V9imjPfjgFvl/8DKui/V7tQetkSuwLBAIyzuJ8yfzECYk3Sv91d1mzk6CsD3zzFuIZf+f89jRYEHaP0UhgEzVjMdfwK7/yMGe//ko7PMP+sj9aNVh1J+9hol5i/DIj+2zqGG0V9TBeH8cwn8gHBrrQWPNJ/h+3EIEkN2RMxVo/Y8wfO9yPcoPNANxSQi/jz9VwtggWo9XofbgH/BX8yR++FAEgv6BHqf5/QH/56+D+NQ0hpH+v+OBh79Aq/MxoTy3+XBpfN2+t+hznC37Ld64vgCGhz4lx97CB58MYvTL2xj6ewhiHyIWSjBfasLxyjdR+c7HmPjPECyJ0GEOlyIDXa5woho1h+vQfm0MAYseheSmm2K9LmDy4Sjcbz928yxq3vsCS6NDoLFcQ+vbNxG2Ro8l9jyJkzXXHULl/zqLT+eEYdl9g6i7HIj1Tz48pbcpJPlTnZ2BZVEQPj25D69VC9dHaPB52ykcOXQYrf8+Bw8scdSP12EEfjTciKqD+/HOR2OY/FEEHvqh0FHKyXX5fC7Jne2YO2pxvDcAsUSmU5g70XjkCr4bQ8qmglaSqTOezleSMWfLRC4L5uKvxw+g/F0rMdUIJ1mqtXexrCYwL3wRfhwoGlSMDePi25Wo3k9sYSIQS8MsuNA4hn/8H3G4XzjFk7248ykLaU9z5WG88a/t+HRMi8VRCzCP+DQ9v/7MZXwy9DdMfmrGXLsMJi0YeK8eDf9SzesHC/DQokDexu3tiprEB0eIPKidWXVYuCSEy9OO3Ufqzt7Cdxc8jIDBBvw1MAupjxDBy9ldRAg+bSxDJdf27yJ8EZGbeLw1Smyg+neoqhPs8ruXcXRKR/K4szHa7oY/9eCv/34L1tuDGNFE4ZEQaS6WnlM41nh+6hyroNvPP6jDO0hC5nfaOT02vH+ba9+UzVP8Zp8W9DZUo/G8XUf9sNwfBct7zscEu1NRrrxMqA4a8Gd73LytcS9XD2Wokav1WgvRmxlLH13gyH9yEO2VTTCHxfB5UV3X1qG+gtr4BAJ+/DDuF/UNSvHXnd45nbZbJW3jfYav58QnLWi4ucgRKz34gbzvKMUB97GELl1srPo9Dp6+xLUpfJEOGg+3SD2dz7XzSgCWfdWBo2UHOB+c1C3CkhCXHsCBQuzhdPvvpA/84Q3SHuc+dj6MLnaqbJN8Pedi4d9aOF8yPpCF6B95vkYp/sjrRbjWCSWZux3fOPW7fExIw6aVIXyym3jrgi9x0A53rev4xG7DGYtv4hTRPfWRye8T+14g1b3btjljb2tqBIwnibx/+ydcpfYx1SYlm7f7GOn7bjbgjT3VaB+eg/BHIvC90bNoqjiMqvduusZQDzFgKj9PfamYzz5ELRkDZa0YQysdh7jRt6IPevIRK+mXD7dgIuJR3C8StbGlAn+6HYZod/LlCMR3b5/CCfMTMPwjP8a0dPz/8Nr/+wQ2B57BDd06xIfzEW7gnf348EdrseHx+ZJ4Zp1Of/UNQ87VZhRdRhG2TtSips1MDLcJlfVabHvBAH4Ia0Xv4Vzk1Jqx5Mk8bHwyEqbaTaKleyb0Njah18Tt8EwM44PGPpLCY+pvQnPFThy8EozUDQbHYF/MaAteXl+Mbu1KZD29BtHWJhSu34tuOnXWkE4yNhJh5LqgpSsQGxtKgorMMZl8Vmo7UbK2EK3CqiC+bsfx2v63YI1dg18mBU/lHwQtwshMPnYRX0HLmR3I3t+DoCeLsOfplRhvLMTLjfaMnJjoQV3uJhwaDCVtzEPGwlEc2pSPxptCuqp6nceQ+PGsqQ9154clj1qnuNWE5zfuQ58uBRs3/Bzhg6XYVTcgJMogyZ/XWd3+KlwP/Tk2Zi/GwJukbdtLUXc7BKvWrEX0aC02FZNBDXexoMPanSh5R4OVawqQFWtBY+EmVF4S7m3IyVXBdnTft6K1ug0DojXSI2eqUWkJQBj1b0GmNeZIZBCZpj44IJWpM07nZ0SaUbNxLWp6BAkqypiXS+XOUlwPJnL9eYxwd1WMSnuv3YdDH+qILAuwKvA8Xt68D+12YVp6ULk5H3XmGN4W5rSg5PU2/E1I5vDkDwRZn+qvRn7+SZhi87CneA1Crh1AoSDrgEX8UincH0l8ZTGCuPhpRvuOtchvtGDJmkL8cs1iGKvz8XKTYJRcu06j7MBxjC+ldvIYzA3bUXjE8Rh6hPgE5yPJVL+hGCorgcQM5exu3wF0EbvNejoZ2nOlyN191nGHjLPrAxh4UGTXZafd+4GCjdF2xy4NArSkE4tdgSX3u3YcmvsXS84J5/swno6KKT2uWzaCY/nFsK8wnK59Ss/XcHHMoSNaD7ljSvlQBJlUDCCE08tijFRswjMNwyRNi3Bx3FzqZqKk4Euq5Bo8FyNH3sJF0ZoYa8dJ7OrRIoSGB2KvueuJruevxMbiPKxEG57ZWoFewT89x19PbSSpN8+72Az1mQ9uyt2L9ewH8r6jFAfkY4m5eTuyX+mEJp7EyDUxsDZR/7kgqacY6fkroblYDMM2R1zm2tlUirIGC6KJ/VMbbfgViS3ilSJiVMQeKqe6ftIK2T7WN5vk6tmwFwf/zYpo4kvxDypfoxR/5PXiCidDIjOuXyIyxFmxzJ3iR3IoBkR25BEP8dYFb+OgGDfjE45b9Tgo6P6XqQFofZX2yfYa+NK2YTSUCfLm6lWCfG/Hekf2oeZaGNHpWoRfrSDjiO0oqzURP13jGkMVYoBiXyrHWBvKXmkBBB+jccPRBmWfUvQRTTA0t2rR0CmqxMQFNL7ZA80C5SfsYcR/Bjp7psob6u9B/AoDVsUFo/lKn3B0GFfOW7GK6J0ijmc+91ffRGx3g74qW05ase21Yr0t/5hROEgYOW3bnlRkO2MS9inj520H0/Jsb3OnddsOJaXYDn3EpfBw15Tbrgi7V8pSbAmvtNnuCPtyXK80SMslcMdO2I+N2P74tFM5MseulOlt2ZV9wh7P0LE8W/Le83z5XN1ybCeuckkiaDsKbH8cEXYJtN6FJ0UHTCO2z74Utp0Yf7fIllD0rm1c2KeY3j9qO/Znvv7q6iUt3/ZRuS3h6dM2TvSS9Du2rr1OeiIld/xG7zjfGcn1vM4O/sWhkc9ObrElvNDmqP/n9Pw9tg6hvZwO847bhr7i9yl3/rLHlpx30vYZ3ZGTK3fMk+0YbW/n6UX64/cPvs/Xi8o0+TeCfAS4Y3aZOWF6u8BFB6519CBjQS6v/VmcgzO+2DtvpyXv8fl+doLo3ald1BYSRHko+YOcT3Htf/0DYY/wpcn2mclxBpde1i3s8dwxDtg+EzWXyiuhQGxzTjol8ko2VNmu0+0vBV3e4FJ4OP2m2La/LQhZInMZ+XLpxbZzX9AdObvmj3m2a082RpDo2A0y57jK+A7xMUfbvLVPNefL6cj5mGI+pndtO5xlMnLedqKqjfcD2VgqRdGXKIpy5XU3ZQvC/lRMv2OyDd0QX038P0dv+73QVI/xV6GNXP1l9Gmvi3O6Rz8guOpFKQ7I2PpXfbZjBid/sR+TujrPVySPNIPt2MfCPuUraYzk6pVzVBKXub6mStrX2FETezi7n2qrvK04y0PJJuXqqWjHSvGH4KoXJzj5Osnwyz7bmfKTtitUNdSO0kg/JzZ04VgXtTVJ/JLKhivbQ7yVIMlHKQ7KQa+R9l1yMh06luOQh1LbnJGT943jtjwnm1bs+8Q6/fSkrVDSLpPtTIFo7KEiBnjqS12gMSlJrk8SfEyFT6nxES4+iOPH+06x0RNftNlKpsZW1D5z+PqSMXh2DhljuZwj6Focz3zor76JzPqTJY7IPLyo70NznwG/ygkVDpIZ7dU+XFyWgFidcICifQzLY4bRe1X2Pog8uh/I38HkILPoDiBcM4Lujs6pv6EJLXoH3d1RlmMY1y8BqfH8bNxO2ONxCOrsc7wcBx204tm4G8Ij49B7pAi7altIPUZhDQyG/OoGK6509SA1NVHyJEKXmIcNKVSWauulllEM9WkQ/7hDT/SOcWxCgrCtDo3WoZG5c8h28A8c9Z8fijBSjkm0hlYTE4kw0RoYTdxPkHFTvM5WKldl2wlFfFowGi8ILyUOd6LVbEDGE7RevEyjdVb0imzi+m2S0meUvgjJYcW1/n7EJ8RJdKCJWYlVkjoqow0U5+AjEnsPRngEYLHQNlsxRGx61YrHJP4QlpgCcoqASn9w8indw1EIaynBMweacLF/GJY5OoToxGe4ogldjCBS3gDJv5V+8eoPlwHJ15CcfGVBKGLHrLhDt0eNuDIvDssWcik82hVYmShsu0Ei32BqZ+MY5+4kytm1BtHx7u3ab/HJHRIZaxAwVXXv7dO7892hnI/14x60x+kRL5ZJcALWbUkGv3hICX/5EtEdiUldbZ38XdSJD9F+LgpZaYJ+NTqEhWphHuxBd1sTGiqOovW2deprXJ7i7/TbKEXZD3xDYuvDPTg3Sdo+4tBbd6cR49phGG/I9HLD/ehCCpYvE/Ypc0KxLE5D/Fv0pYBAHQJEcfkB0paRCc5DnVATe3xBpW1L6qnWHzzEHzVQmYPYiViG8yKRWpCNaKIazo4WBpH/HXXovkqs1dqPIYU78r7EWzHu46AXOOk+QOtwCN/a5iTveVr8UNhUjfY+h32RccVc0v8FTeVJZLSIuIBZeK6iEAM43Palbgh07pNE/YGiT6nzETr2yRpsQwcnR2LDFy4gPjtFXewJjMTyyAvopg+RblL7TOTru3QFVt3uxBWSp7XnA7TGrcQyD6tpZXHbX30zuTuTJSLYB0JJIH+QGLbI+SxjRHMLgomJi9EigBzwaLBeYYKJBEnT8Ifo6nL8DWnisGHxfcQU1ULzCUXI1EsfAiSghHgTYAW0aXvQVFWA5ZM9aHglH/r0XNTYl51JsMDyOQlUGneB0r/14vPTQuvkSJp5c4WtmSEsMEjYEphHg6L7AK/GdkKSDIhtaUE3yWOkowWmtEREcPbHy9R6q09iE11jodgQJ7d0iD8/LFhaGg3cQb50QjOGG1sh+9wyUg4f/SFyC46fKEVG8CBay4uQ/dN0x5I6OSZH0bo9HfrcUjST/Md1K5C26jEhUQVfmGHUOn8FjXSO7txAEe/tenbikxy+2af6892hnA8nE63Gizyd8Z8vaZ7Qk0FFE84Nkzpfeh/NjyZPTXCslyqwKT0TO2rPousmKS9hNVaJ5sme4u/02yhiun6gFuovc8ifWG9dg2TgZSCxVcar6fmhpD8Wdu1odcFuJkNKqIk9vuCLbfvLHxSQjVEOODuaGMZ1cR26zAjLinORuwvexttZZlptmyWUYoBPzCcTPmGTR9QfKPqUSh+Zl4BU/SDeoYFNuAm0Ks4pXrolmEzOdGjv6odloB/GpBX8RGxOFJYn9mPgqhXXr5DJ14pIp3YwnLlLkyV5dBFRCOm4DOnbMMMY+liDiIV0fWYQQuj6YzGT3k4AwhC+1IKQFVuwuaBA+rdO7r0Rd0RgScwgmbFLB0nWgQF0LwvFA8K+N2hD45DxVDF2nziNlucXo253vZMsKDqER2mld/so9HcQuIGFinqRiYjLXQkiR3movEbROyDNz3htZj8bOTDs9JTvZj+6NBEId7NMV9l2CMGJSH30Atov9eNisxkZSfanb2QiGaHBnaUGV5sokLt7zOug/bLTYv3hQUcdvZKxO6Zr73y7Ll6T2gpnC8L2tPxhfiRSc57Dq1Un0VKVjc/erEC7uycB15pQM2BA1dvl2EbyztLHkUGbkKYGepf3Vj+GJPlT/QqbXkP9ZJS/+ylioOu8sOWKKhubEby3T+/Od4dyPpxMLvXBKL47S3/fQ/XvhKjwJbXMiUGawYzWDhIr2s9iVar96bsFF99qwgPPHsfv9jyHzU8ZEB8TCo3TRMxd/FVqIx38OGN113yf/MCHOMA9FQnGyqec9UbKjJHx6kVkAOb8hSx655vEjviFU9/F8gI1sccXfLFtf/mDAlSGt4gMpSFl6vdrdKER0ExGIs2lDgVYJX464Q5v4u0sM+22ueCPsZ4YdTHAawYHpO8li/sDRZ9S7yPRKQaYznRioPN9NCYasErtXIkQ9ngi8HEPznzYiYzHooSjGixbEYfmy6fQ26nDypiZ7L++GdxTkyUs1WOdjnQmpxwvBY6cKkWl1YCkR+leMOlYNWj/01n+B9fM/Wh8s9bL79HrsCorGc2Hq9FtN+LJYTQXpuN5r+7UaBH/T8loP1LryGesEzWHLyA1I5GUooSZdLbCJkbRnE/Kb3S023SbRFz65E3YFxORlo2gpio020+3DKJhWyZeO0ejtIp6zQtF2MJ+vN8yCAuRo/XWWVS+2cad6ooO8Rlx0vyGT6GmwalH8Ddt5ajpFCZoVD9l9bBmpSCaP+KKou1QSFtSY7iPFTRocpAqWqkYnZGDufWlDplOWtD95loY3LxEy+mgUaQD5zp6JWN3TN/eaZDViOtJbaH6gujupy/+YEXvgXTkVnRybaNYTMSeNcRexeMw8/+dSgcZUIZPmPA3+5yb2Gxz4wVhRwXzE/FkXA9qah1ljpyqQM1tftt7iJ/8NwOG3tyJukvDxBctGGnbi0PnPNxnVmVjKhkzw2SXjQq8tU9vz3eHYj52mUzFLguMdUXQ724jWw4sZunNFjGKvuQFEUl6mBp3ovJ8MjIS7caoRVCwBqZRR8wynzuORvoJXQ6F+KvQRs2iCET3t+EM/XFVMqAbaTuAQ27DqUo/EPuOL3Fgfgr+OeUCKqsd/kLj9vPphWidareIwBVITZH6l6WzmsgxDk8mejEyE6Ece3zDF9v2lz9I9eIEJ8M+HKtzyNBMYkruxnp++fujBmzV1KNSFD8snRXIzaxAr8dKqIy3fkU8PlGBz21zhz/GemKUYoCPaC4Qmxb3SaL+QIVPqfaRZYnIMJ9CCRnHZa1a4Ui/Rco/UA+ne9lS6JK7QaIbUu7yGEfO2kdXILalHjUWYWmeJ7zsr76J3FuTpTmhyHr914huy0fiT9Oh/+lqbGoOw+7KLcJyKQ2WbyhCbF8pDKtXIzG3FgFr8hDLXaweTVIhjq4z4dDa1dBnZiJldT7eub8I2zK8m11L8klPR+LafTBllePFNKXOJQZZz4bgWD5pw4v0qzTByHiliAwY8pGSmYvc9NXIbdJhd7H9K4FOLMzBnpfC8A65PoWUm/KzQrQv2olfCeUq1ysUGdtycOdUPvREjvqiPqx6Si+kuaJNewlV+j7syEyHgfylvDCKdc+5P98v6LOx5K21RB6ZpI75aAwqwhtPSd/DkqBoOzzaRD0yiOMHpcVJ7yhSme6KQiuVKVfmWpTc0KM0L0a+cxefT3VAbSisGOVTdfROxvL4wd4jt+CN53Ro3CzY+vq3sORZaR7e+4MG0VtKkXG1BPqfrcUmYhP63QPIKs1DtCBrXVoeNgyUkrZn8l/LCk7B5nXD2EVt6BekjLXVCEhM4U9WBZnUvSyUmZ4JA5H5jtH/jlenYYaamAJUvRaHoSPFKNyaj8prKfj1cx5eglJpY4rEZOPVkFPYRHS664ynXk7EdOxTzfnuUMqHk0kxwprtMslE/rkw7H9eT4YnlGCk5Rlw7UAmEjOrZZ6UExR9yQsiycRGa8ZI0k8QLVpiGZ1dhPAzhUhMX0viazqKrkQhYyp7hfir1MZQPX61wUriOWkjsfcd/cnY7M4uVfiBi+/4FAe0iH+2HOvGKsg1fNxOzG9ByLPFSHW+Y88hPV9PZGDYN4qs8pe8uostQUXs8QlfbNsP/uCqF2d4GWaN7puSYXbF37Hu9Rw+PkjiB41hVMYDyPgNiZseK6Ecb/2L8/hEBT63zR3+GeuJ8RwDfGRxHn658C1kc30SiRuNOrxaZu8PVPiUah+JxKosDYxjeqRy71nzWG+8j4bmJnR5mvRxS+6ssC7mf0dpCvo7TPPJTNa+NM8dvvRX30C+Q7/yIGzfW0xYiJO6vlPgV+hSCosVGrrOeDpBx1/5EKwWC6xz1LebPuKHu3L9WC8OLj9SnLvfHfATvW+uxv/EGzj/bAxvB1Qe3hQ5Tdvhlk3MU1+mRx3cK6ixBV/shcp6UkOar1JY/rAhf8YGerdM1Fbrub1IaYxCU7mbGxV2ZiM+ucEX+/TmfHco5uMHmcy0L3HxVeO+DYrx1196n6VYysEt0SY+qrYser7VC59Wwpe4ohJfbNtf/uARJRl6Gzft+HrdbHKP11EpBvgEtfEJGrrcZKpkDyp8ZKRhE54xF+Hk09Od4TF84d6dLDG+tUgmSwzGDGFu2YGclkiU78wB/a1iq7kHDdu246L+KH63brpv/jIYDAaDMT2sY2bydxk1hccRVn4UWaxruivcW8vwGAyKNhgR2pn92h6DoUt7Dnti+nEwdzUS/+tq6DdVYCjtDbyRzXojBoPBYNx9Rlp2orC4BZpn97KJ0l2EPVliMBgMBoPBYDAYDBnYkyUGg8FgMBgMBoPBkIFNlhgMBoPBYDAYDAZDBjZZYjAYDAaDwWAwGAwZ2GSJwWAwGAwGg8FgMGRgkyUGg8FgMBgMBoPBkIFNlhgMBoPBYDAYDAZDBjZZYjAYDAaDwWAwGAwZZneyNGmFxWIVdr6+WC0WWCeFHYZvWC3fCFvwCLX3MQVbmSBy8EkMFox0dMJoEXb9zT2iH6uS/HxhchTd9RWoqahH922yf6/ZonP9nFETR6ldTQjbM8U3JJ4zvMTnmDW7zEjs8Af+8E01fcs3hW/oWGE69kmvpfqX/bPb1mz0AbOJCjuYybH57E6Weqqhf6EFZmHXN8wwdvRgRGIEVoz0kIHj9DJWySjOvJCJmh5hVxG5+jLMzcV+sIV7nNst2JVZjDNyA16B3upM7GoeFfa8YOxD1JWUoKFzZmZL3urHeqsH3YPT06ZrHj2oyfTG19RgQXvJJhy8FozoyMUI0t5rtuhaPxdUxFFqV/pqvwrOFb/Ec8bXDZ9j1gwyO7HDP3jvmzLjGxV9yzeFmY/Pd2OMNh375K/Vu/sTbGtW+oBZRNkOvB2be8fXcBmeEc07atE1JuxymNF1ZAeabwi79xRy9WUwpklgMl547128kCI3mp59LJ21eKbZKOz5hj/yUMaCcZMV8T/PRnxKHMLmCYfvGe71+jEY9x6zEzvuFvfy+OabwNdtjBaDrX/5M85zf8fxQiQQX3Bc2Cd/z8YI5zH8yZydBGF75vnsQ9ReDkTWijG0lv0Wb5y+hIn/DEH4Ih004mnb2CBaj1eh9uAf8FfzJH74UASC/oEe70FjzVv44JNBjH55G0N/D0HsQ2Nor2jAn/86iE9NYxi5rcHS6BBoaD50OcuJatQcrkP7tTEELHoUIcLY0tJzCseuzMXCv7Wg6uB+GB/IQvSP+DQJkxYY/3IKtYf2452PJvD9RxZh/FwjxmI2YMUDwjmjnWisrUN9Ba3vBAJ+/DDuv48cl61voOdrVDBypgKt/xGG712uR/mBZiAuCeFceW7kJmC+1ITjlW+i8p2PObkvidBhjpDmSVYcXHotGv6lgshhDF8GhGFJiDCS49r5Cb636HOcpXq9vgCG5fO5JHNHPY5WHUb92WuYmLcIj/yYz3TikxY03FyEjMU3carsAFenye9H4ZEF0tGhu+uBYaL3M7AsmIu/Hj+A8netRAwRmEfq2Xu6HlW/fROtl8cw+aMIPPRDzhrkUZCZp3TOhtqtDnsj8Loh7QghRyzX0Pr2TYSt0WOJUG27DurO3sJ3FzyMgMEG/DUwC6mPiIXtwHLzLJorD+ONf23Hp2NaLI5agHmcr9D218F4fxzCf2AvNwI/utmAN/ZUo314DsIficD3Rs+iqeIwqt67yZVnl4VS3e36Wf/kw+A04kH/9Lr6M5fxydDfMPmpGXMfjsL9gozc60+KfB6j+OhYK5CSge+2l6Fyfz0ujPLt+r5YpUq2a4ez0z/g/9hjRf/f8UDcQnzXua0Ed/W2XmvB0f/z/2LZw/MF3+H1MBAQg4fm80es/U04em4CS6McspXgMb651i+Av8qBijj6+Qd1eAdp2LQyhD+gQkYW0rbGqt/joLu4PNqD5rpDqPxfZ/HpnDAsu28QdaQeYrkp4dYezCQeHunCd6MeRpAgNPP5Whz/EESO30FvbS2uBxBb1/FpFEtnPY5d/h6WLeHjmCNv3re+113t8EWKJxkIMez7UZP44AiJJ7SNVh0WLgkR/E0dXscrIVWCG/vgbO89M5Y+usBhV5ODaK9sgjkshm/LDLTRU8yS7Uf/gZZzAZOiOAASx2re+0ISb5T05T72SfE1dvDxzrt+VDHmU8iYYeC9etT97oAwZlgA0x+P4JP7+FjN+2YSMr/Tzsms4f3bktgshdqNzPjG3rekRsB4kujyt3/C1YlA4itSGSn6tMC0bYsyNoyLb1eimsr6JkmPIOn2MQ251mO/7EHmLn2RGJU27VYO3PVOY7Qf30br4fP4z0dJLPov/PVUxw29AY64P9GP1qpzmIgitkab4aH+9jpKx0eTvH2mOcaRZhLPjp/9DzzwSCgCpgZmSlhw/d1GGBe5jiFU2ZlbnZLjKmKuC57kQPBkj96PSVSMzf2IjNvMMGNtKHulhUyF1+CXa2Iw3liI/IoeTK1EHG3By+uL0a1diayn1yDa2oTC9XvRTVcbacgAPzYSQdAiLHIFYhdRrWoRTo6Fkc2gpeTYUkHQEz2oy92EGnMkMjbkIfXBARzalI/GmzSRBIeb51HXsBcH/82K6DUFiH+QPy7Fgt6KTdjUYObOyYrXoPWVvXhffAeivxq56w9gYP5KbCzOw0q04ZmtFeilj3Rl66twjQpMZDDWXLETB68EI3WDgR+Ie5IbwXJmB7L39yDoySLseXolJ/eXG4WlFAqywmQ/6tbnckuDVj5VjI3xwPsv5qOmR9DaxDA+aDyO1/a/BWss0WtSMDloRe/hXORUDCAkOQ8bn1yMESLLZxqG+Wsot+pxsMGC6Cfz8MvUALS+ugmVl+yWIFxfa8YSkr4xORQDkutN6G1sQuXOUlwPTsHGn8cQKZvRvmMTXrusQ0bRXmyOt6BxWzGabwmXOMPJrBDN1hhOZivnnJXIzFmmK7WdKFlbiFZBbJwNnR922C6B6uaDm/JL40aIzDkdcPIIxVBZCeoGhEQ5iJ3k55+EKTYPe4rXIOTaARRO+Qrf/l4Tt8PbxJF9qLkWRnS4FuFXK7CpeDvKak1E/muwbtkIjuUTWfhSdwX9ByxagWjqgPdHIjZ2sTDYVdKfFPk8eC6+eQBduhSig7WIHq1F7gukAxfSFG1XjOCPU7EilnRKQpIDp3o/GQlT7SbkCnLXBFoxcLgN1+3rookcjxE9vHamTzgAXD9TjYsT1Otl8GRTquonoBRHxTjJKCPSjJqNax3+SzA3b0f2K53QcPmthOZiMQzbRHK+1YTnN+5DH9HDxg0/R/hgKXZ5NF5nPMsVuiiET9SipL6fOxuWCzi2+zyCHqd+HYwAaxsqzwhpHMM4U10PS2AoF+8533rTHmt43yprEtmzkp1wMew0yg4cx/jSn2Nj9mMwN2xH4RFxmZ5QaJ9svJLBQxzXBM/FyJG3cFG0DsXacRK7erQIoSF3BtqoFLNk+1GunPMYEvdnpj5JvFHUl8fYJ8XX2KGmH/U+5vNjhvxG0q9NjRl2oq7ZEas5Oipw6EMdVpFznGOzFDfjG45hNJQJunw6GdpzJZIYIPXpGFibqC4vyMpw2rZF+4jN+agzx2Dj3mJkkfSDdEzDiUWhX/Zg84qosGmPcpAbo2m0uHO1Go6QTiaBdU2oE8f9vja81mlFgAqbkR8fSbF0VqDwgBmxhgTY59x+wZOdedSpcsx1gZOD+/GUUh/j3XhKxdjc39hmk4/KbQlJeba3bwj7lPHztoNp5JiR371eabDlHxN2BLhjJ+zHum2HkgpsfxwRdjlGbH98OsV26CNhlzD+bpEt+TfnbXeEfQp3bC9/zPR2gS0h56ht6Cs+TRbjSVt+2h5bx7iwT7lx3JafJCrrjsk2dMMk7FCMtrdz9Lbfdwu7cvVVvMYzV8pSbAmvtEnapiQ3ek3hSVElTCO2z77kN5VkZfvqjs00YLSZRLIaOpZjSy4XKjxy2rY9Kcd24iq/y2F617Yjqch2RtzMkfO2E1Vtts/Ippz8aZ4JZUKe9Hpn2QvHurh6U7mm2F77s/gEemyL7Y+fCruE8RGj7Y4bHVP5ZFf2CXuUO7brp8ttf+zm87xSpndKp3XMk9rQ06dt4iZSOW9/W5AzJxdB918Kdu5i+6LzneDyf/0DYY/wpcn2mcmuJb79djvkbEKsw09P2gqTim3nvhD2SS3PFKTYDv6FP0Op7pJ0Jf0TuPPtuqMo6s8VlzzkdEzlKPInRdt1wTVWSNrK6czJbiUxivhqnn7qenrt9rJy23ZDle06d6TPdsyQI9WzCCWbkqufCyriKGcPgiy59hW9axOr4s5f9tiS805yvmj7isg5zWA79jGXxPMV386D79Na3bF17dU7xZdxW8dv9C425BZFuYr2b4zbrpRnS8sjcTcvrdx2xW6DQmzmbEnOt7hjDntWtBO5GEbknDylVwUU2ycXr1zxHMd5PTjihaAXIcb7vY0qYhZnW879KFeOU59HbdZuKyr0xeXrNva5wp3vZezgfMSpH512zJcbM9BjzrFaUu4d4kuiPFyQiQlyuqQ+kkR8hG5/ReOQk4ztx6TmJeAP2yq1dU3ZAekzPjUJfS/Vhft+WWnsIifzKZRsWpUcaP2k9vrZCaJzuz3RMp4utx16msRIwTSuVxlseUKdFWO6XB0F+6Q6Hf+g3JaTTWzmcyHJK3jbkLMdJTtT1KmnmCuDx/GUYh+j7FuSdDVjcz8z+0+WAuOwbKGwTdE+huUxw+i9SmePw7jSAYRrRtDd0Tn1NzShRe+gdMbpGSuudPUgWmdFryif67dJSp8RI8JZCNR5fNxpvTGA3piVWEbvHthZmEhm4MI2RaNDWKgW5sEedLc1oaHiKFpvWz1/kcOXa5zR/UByh0lJbuGRceg9UoRdtS3k2CisgcHgV1GpkNUcDXSLQ6G9PUjOOYvmIxVoOG92qq8OWmGFIcX6cQ/a4/SI1wkHKMEJWLclGcLCIBf5B2gdJ3PXLwwi/zvq1H3VTBL6MSS6A6cNFCsnDNFxwygr2oGGlh4YR63QBodCI6tjXmYZSZHCPkWDCEMBMmJonsO4fglIjRenkxIej0NQZx+8Xh0/asSVec62vwIrE4VtGXQPRyGspQTPHGjCxf5hWOboEKLzcNtJe5/DJojO5iIYQVM6IdcuAkbMoluHalGlfylq9acGiY7nidqo1s+9wHq1DxeXJSBWbLeSGBWK2CQd2rvoHTcLrnxoxkqDASt153GF3pG72YNzSJTqeQo/2pTHOCrGimv9/YhPiJM8ydCQuLbq5mVcp3fihvvRhRQsX8anccwJxbI4DbG7QbIziqE+DeIfD+XTOLSITUgQtpVRlivdT8Dm50JQU5yP186n4Fc5ovJI3M14sAXtwsu7Ix0tMBkysZzGMDnfmif2LbV2Io1hWEB0PWbFHWHXE6raR5DGK2eU4rgG0UTmXW2d/N3YiQ/Rfi4KWWlUTjPQRrUxS6EfdUFRXz7EPje4jx0CTv3odP1TdswQGicdM1Ak5Wr4JxRe46TLeVr8UNjEMIlDk8QuRhy20N1pxLh2GMYbss+Wpmdb8yMQPb8FO7YeQHNbP0aIyese1Al9r6d+2R9jPg827bUceELiUhB27kPQh6iWjz/E3+IMyErU4dwlujKC1Pk8kMTFQ7U241RHAcuFCuS/2Ies0mLE828u+Be3dqZCp55irgsK4ynFPsY7VI3N/czsT5bmE6MRNnm0CCAdjMVCOxQTTERZpuEP0dXl+BvSxGHD4vtUOg7FAsvnRKC3+iT5dI2FYkOc+DG2ZyxjZFSn1TidT/ZFxmK9VIFN6ZnYUXsWXWSwFJawGquoD3nAl2s8oyw3bdoeNFUVYPlkDxpeyYc+PRc1l6jMVcjK0oOa9ekwvFBLBonE/Rcm4CdxnissLzv1cNdPkEAkrlOXGWFZJAgJ57iiw6o9J3G0YAXQ9a8oyc9Eynr7UgBnqMy00Mo6PoWmhyLkfmHXDhkYhKgcQEn4wgyjVutiS3M9CShyC46fKEVG8CBay4uQ/dN0vNzk5UzDH/iqf6/15y3+8XMxXL0XBBNLEiOOUbQjJKO686TTHyMDiqt0YkQnUEDvgAXmnk5Y0hIRwZ3pjB9tymMcFcPLKCxY2iI6sQ7COMbpUilqm6FkYs2nTKHVBWNkgtZK3lc08+YKW8qokStFG/UYlo2Owvr4YwiTDMBDkZSxGI2tdPnMMC6eMSMjUeiYFX3L/3bijNr2eUY5jmue0CNrsAnnyJjNeul9ND+aLNyQmoE2+hKz1KAm37sS+6bvn5wdKIwZZgUq4znkT2wLXYPQkEF/WKD8SGpatjUnEhtOHEe5IRjGs+XYvjYd+u1NGOFuqHnql5Vtflr4IAeOhTFIAr0BZkHX2T5uYkQnUCADe8toDz6w6BG/lJ44PZs502dF/LJhNDa6WUI9Y6iJFx5irgvyfcQUVA8e+xjvuBt+9l3h/9ljcIBby8w/1aAMY+hjMgNNpes4NQhfSpS4Ygs2J0nF4B3EUCM0uBNowOYN8oM6NffXdaER0NTSuwMJjsHPxCAGyESYr50FF99qwgPPnsRuvb2bHFb4BKUv1ygRpkpu2tA4ZDxF/4iTtO1Eyu56JJ3eoigrS8dbqFtQgJY39FMDNGOd5wGALiIKIVR2k0R29kEP97sspB6ByrrlZD+pQ1pBDmmdN2gR9gQJhORvHZX1q5nYVZ+Ck1ucnTwCS2JG0XrVjKyFdj0Q6G8TzCFOr6Hpg2jvsyDjQcew1DowgO5lUaDvD1JHd8bqLuLRu123SCcwRrQ1dYeJ2j75bzG/J8v8SKTm0D+yfa0Wm/Ir0J60B6tk7lJ5gzd190n/PuvPG5T93Ft4u72MAcSJJjziGEWITESGpQpdTcFEF3q8So+RCVRvYxs6LINYtcZdh6JsU6rxGEfF6BAepUXN5X5sjRPVa3gQXcTGV9HT50Vh1WAbro9lI2TKrqwYujZIOnJee+FLR3GRTAazFjrqbbxGbzl6Ml4HquQK4o+v1wIFxcho2IeazuOkzo7ydETW8dXvozcjDM3WbPzafpdyEan/rRZcNxNbs7vyZD96O8j/XPX8byfOKLdPzXNOFXF8TgzSDGYc7OhHyOBZrEotFHxyBtroa8wKDHKsHrBDYv8UivoSmKHY557px3zZfs9MBtdkzOAik5mEe7oyjpVPFSBe7QBy2rYVjAh9Dve3dXIQDRvzUdOWglfTaC7u+mVlm/dhLYQDX+TAEYl4vQU155sQ0pOC1D3kEJFP0tXTONfxd3QlrcF+Tr/Ti+lZW57D1oeTgU078HLoUezPcrWvmUGdTt3GXBcUxlPU5xX6GG/GJMpjc/8z+0+WNBdQU90Ji7CEZ+RUKSqtBiQ9Svd0WJWVjObD1ei2v6g1OYzmwnQ8L7mrZCYzS2FThMXsGMBFZ+Rgbn0pmu3vk09a0P3mWhjcvQQtx6Mp2KhpQs0pIROaR3UVmqe0oUVQsAamUYc7m88dR6PLBwXE9VVxzcQo6Ti8+e6/ktxG0ZxPthvtwgBMt0n5D/IzfSVZUSPWmEZhsi+7un0BjUq/s7FUj3U6IrupMi1kgF0E/e42sqWCRw3YqqlHpV32BPoSZG5mBXrdKfBWE55JLxS1w4wRExnozRc57xRaxP9TMi7W1jtkdvssytbnovEa3eHT24/UOtLHOlFz+AJSMxKJxIkpL4pAdH8bzlwjLSKDgZG2AzjUxp/qwvxEPBnXg5pase1XoOY2v+2KFb0H0pFb4TjfYiJ2pCE6c8Rkn/Gm7qr1b/6/U3X1SX8UcR4q8Iufi7Hbraje0hhFicTypEFU1l9AxmNR/KGlK7CqoxplPQmIFw65omxTqvEYR6VEpGUjqJHErSkZkdhQVg9rVgqi6X7gCqSmSG3T0lmNyvNxeDKR1kqH+Iw4ab2HT6GmQTqM4X7rpmdUXu4q5Go5U4HXLGSgZUjGupdScPFArfSjN7pEpMZcwMHfnMJcIq+pSXhgArKyjHhtJzl/mHTO9Ktc+8txRtRr+sNOLIOd7n9LTJXdKKGu/4sgAxhT406in2RkJDqCgd99weuYJTAvFGEL+/F+yyB3nfXWWVS+KQouivryMfZ5GTtc8UPMjySDz9AmlOxvgXGUtG20H4376jHkhwmeeHyjyPwU/HPKBVSKYgT12edJ/9jq7oNHBF9ty9p5APr1FQ65WUwwWTQI0pE8PPbLasd8PqJaDq5jyojYFHTV16M5MQZLuIkRjfsXUFbdg4xYe5D3Q0zXxmDzS3p8Vr3X8dGLWyS+H6jHgBcq9xZV8cJdzHVBYTyl2Md4OZ5SHJv7n9mfLC3Owy8XvoXs9EwY0lcjt1GHV8u2TN2F0SQV4ug6Ew6tXc39wFbK6ny8c38RtmXYZ50xyHo2BMfyVyPxxbPCwDsYaXkGXDuQicTMam6dKRbmYM+uKLSS81LoD3WtXouSG3qU5sWon3nSR8tlRWSgkY9Err5r0biwANtEd7+is4sQfqaQpK9Fbno6iq5EIUNyY9m1vorXmM+jZsdZjHiheM9yC0bGK3w7UjJzSZlE7k067C428M6sJKuYbLwa1oZNq0kH9ot0pBT2IELv7u65wJxQZL1ejLBmIrufpkP/00zknwvD/ucdTyc8wl3/a0S30et5WzHsG0DGb/IQ7U4uDxrw4nM6NG5Oh2H9WtKOfDTfX4xtBvm7NZzMskZRQmVG9JCYW47xdaVYJ9w9kciUpq/dB1NWOV5ME0JgqB6/2mAluiV2tzoTO/qTsVnPJ7lCOoWXS5FxtYTkRduTjh2j/x2vuj1fg+gtwvk/W4tNmUSGuweQVUrab79jOR28qbsK/evS8rBhoJTIPBN19HUeH/Tnkoca/OHnYiT1pna7Gpuaw7C70hGjKNEJRFjWGCyPEUqZE4XlT1hhTfwJoj3cwVS0KbUoxFEJYhmRMrnYEFaM8qfsOiQd3bPlWDdWAQPRsZ7T1Siyyl/CKqFa2rSXUKXvww5ihwbyl/LCKNY9JzUYE5ksPtNqlJe7klzNZ3HwwAA2FuVwy+80MXn41eNteI0MTBwDfdohJ8B4OxRPJonvhhJfefoofhs3yL3vVLi1AgPJe/GrJ4Rkih/s5HrjDhwTfUFQgkq7UUK5/yNEkomZlgw4k5xszd++4HXMshOKjG05uHMqn9SBtKOoD6ueEl+kpC/vY59PsUOG6cf8YKSWVWGr5ixKikjbdp6G5qmdWDetdylkxjeKSH2a+mxifgtCni32/F6Hj7aliduCciE+5K4ndptZguuGUmx+nCQq9MuqbN5n1MhBbkxJiCGTehJ9Vq14bMp/ljyWAKs1ASsfd3iUP2K6JqYAb2wByrbxE07rjffR0NyELg8T22mjKl64i7mucHJwO55S7mO8GpOoGJv7m+/QrzwI27MLmTlaJogItW7COE23WKGha5u96GzksI5ZYJ1Hl1YJB3zAaiF5aNznoZQuh9trOg8g8X/HoOW1ZGJiXqIgN65M+ljUzYDOo6ysFlisGvc6cwd9FEta4v79IAXo9ZNelOut7Sid70db5PBWHt62f6aYjv5nof7+8HMJ07VbT/jLpmg+nuKoE1RG5GT3ZSrpmKs3yUJmKW33/tVofuy0sOzGA9OQq/X8Xuj/EIMm0ZJQDnq3UtImKy7uTsf/XnYS+w3SQYtvdjKK5s35MG87jQ0K94n8YjfTtI973hfU6muWYocL05G/S9uG0ZibD9Nz72Lz3fi9UOrTE0SGKpa/q8GjbXmSm5JMp2nzivhZDi7MdP1nEE86dRtz3aEkB1/HEW7wZeztC7P/ZMnOHAVh0fRA/xidhuQzXUFSxXvKQyldDnfXjAwPIH5FpDrDdEZBblyZHjo8j7KiBumLIKkTTqeTpdd7U663tqN0vh9tkcNbeXjb/pliOvqfhfr7w88lTNduPeEvm6L5eNFoKiOPZSrpmKu3XPooPhuIxPJHVUQtX+RqMcNMlzQ1XEDGz1OcYiP9HZd0PF/Xzy/xIJ21+VIt6tpCsTLG9e6uT3YyaYRxOAXLuZe6FfCH3UzTPu5tX/BCX7MUO1zwVf7WftTlrkVZ2yj/tVDSNmNTNRpuJyD2Yf6UWYf6tB8nCB5ty5PclGQ6TZtXxM9ycGGm6z+DyOrUY8z1gJIcfB1HuMGXsbcv3L0nSwy3WAZ7MB4s/Go2g8Fg3POYYeyxICTGzQ8WThPL+QMorB5AuL4QL+ZEupZxuxMNb1bjWMcwLCRVtzAGGwteQhb3MwB+gL5HOqxB9FLXyRfDB2ZaX3cR67UW1FRWo/GKBVbStrCYFGx99jnE++m7GwzGbKAYc79lsMkSg8FgMBgMBoPBYMhw95bhMRgMBoPBYDAYDMY9DJssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDKwyRKDwWAwGAwGg8FgyMAmSwwGg8FgMBgMBoMhA5ssMRgMBoPBYDAYDIYMbLLEYDAYDAaDwWAwGDLcW5MlqwUWi1XY+eZhHbPwv+r9deB2Dy72mPntSSssXtTdalE418v8vp5YMNLRCaNF2GUwnJh2PBD76N2E+vM3JG5LYtc9Il9zRz1qKirQ0CFTl7sdS9XofoL06xPC9kzxdbXBb8iY52s1tmF8veFi3uz7zD01WTI3F0P/Qgvuge5/2lhv9aB7UNySHtRkZqKmR9i9xzE278Pz+5pgpDu3W7ArsxhnbnNJCozizAsK7fQqv68pYx+irqQEDZ3flNmSFSM9ZPL3TXBOP+Pq667MRDyQ+OjdpKd6xuO2GhlPH2nsuhfka2nbiew3BxAUGYUlIVrhqIi7HUtV6L63OhP66hnu+GbBBmcCb8c8s+MH3vL1GtswvuZwMa8avcLubMGW4c0Qls5aPNN814cxPhOWdxLnT+YhTNhneElgMl547128kCIzwPlaYkbXkR1oviHsMqZQ4+szEQ++TT56N+LpvSBf65gZ1icMyEpJxvKFGuEo49vK131cwWB8XZmzkyBszw6jnWisrUN9xR/wV/MEAn78MO6/j0+a+KQFDTcXYf2TD2MePTA2jItvV6J6fz0u3BxDQMSjCBHOpViutaCx6vc4ePoSJv4zBOGLdNC4mf6NnKlAw7+HIPahQOGIBb0N1fjkvjiE/0A4cvMsmisP441/bcenY1osjlqAeaL86HKIo1WHUX/2GibmLcIjP5YfCNOy6s9cxidDf8Pkp2bMfTgK9//DKD461gqkZOC77WWopG0anYPwRyLwfXEfODmK7hPVqDlch/ZrpM2LSJs9jbfdyNNKZHP0PTOWProAU9lPDqK9sgnmsBg+T66sWjT8SwXe+WgMXwaEYUkIJ3lYek7hWLsVS6NDoLFcQ+vbNxG2Ro8lQl042R/7F9TUncWNse/gx4sWIoAryILr7zZiLCYLP/prLcoPHHFth7v8VOqSY2wQrcerUHuQtnsSP3woAkH/wCdxdb8yFwv/1oKqg/thfCAL0T9yumYiEEsX3EbLkU/w/ThSd3rhpAUD79UTeVTzOsYCPLQoEHNoGobRXnEGlogQfNpI9Ufz+C6pp73dztDz62C8n7cve52WftWJmr0l+MNHE/j+I0QmX/ag+dghVJ7oxERAhNSmPPgKh1J7CEpyNV9qwvHKN1H5zsdc+pIIndBeMbQtDfjzXwfxqWkMI7c1vF3QJG/tVa3NcUd4X2r9jyg8EiIcEXRU97sD5HoqwwUw/fHIlB/z50fgRzcb8MaearQP8z72vdGzaKo4jKr3buK7Cx7GQz8UKc1DG3i9BWDZVx04WnYAdWdvYVK3aKrO8r7OJU0xrXigZOd2eY31oLHmE3xv0ec4W/ZbvHF9AQzL5/MnTiG1SQ7hOt5mVNr4KLHZOmKz/+ssPp0ThmX3DaLucqAjbhPc251QxoK5+OvxAyh/14q4pAjuOne26FbGHmxpql1Rk/jgCCmH1tWqw8IlIY6YTmzJ+JdTqD20X7ClRRg/R2PXBqx4QF6+HvMjOPqIW5ydfa+7Wmq/zri1Pb5/ajxvb3c/LGK92bHH0tQIGE+Sev32T7hKY4FT3+VWH1ZiX4fP4z8ffRhB/4U/l8q7oZfY/MPz+Vgw0Y/WqnOYiCJyd27GZx+ilug+a8UYWqndycSZzz+owztIw6aVIfwBFTFDsT9QYYN2zB21OE7aE0vsaQozia1HruC7McSfaCMVYq1SnHQ3NlCKaS5jHg99kLwfqPRpGX/zts91f766WMZdLztmUBt3iI6qf4cqcj2n8+9eJmOcLySy9ThGI3bXe7oeVb99E62XxzD5owhpP+CE+/oSvMzLN/uxwthyBH+6tQDR4UI7OF9sxuchUfhxoDgHN3gc0/AoxiwV/uoRD77F+wLpr4cbubEajcPzwheRtolkOTUPoHZBYluYBRcax/CP/yMO9wunzAYeXGMG6K9G7voDGJi/EhuL87ASbXhmawV65dYzT/ajbnM+6swx2Li3GFkPDuAgPVdY1WRu3o7sVzqhiV+DX65ZCc3FYhi2kYkAn+yCqb8Jdf0mYY9iwdD5JvTaD5G65eefhCk2D3uK1yDk2gEUVvQQc6VY0Xs4Fzm1Zix5Mg8bk0MxULEJzzQMc6nOBCxagegwYk33RyI2djGCRHq/+OYBdOlSkPX0WkSP1iL3BVGdJ3pQl7sJNeZIZGzIQypp86FN+Wi8KaQ740GemuC5GDnyFi6KBGLtOIldPVqEBJMdKt/1uTh4LRgrnyrGxnjg/RfzUdMjtPjmedSdHxbaL8VyZgcMhU0YjySyL1yDsOEq5BZLZW+sK8HBwdCpdhzcWIjmW0KiE1JdxsDaVIjs/Rdky+YYbcHL64vRrV1J5LgGK7WdKFlbiNZRPpmre8NeHPw3K6LXFCD+QXLQ0oPKzYVonIjhr5nTgpKdtWhu7ANvAma071iL/EYLlqwpJPVYDGN1Pl5uEjIlZ/U2EhvaZ9dfMrTnSpG7+yyxJDn48+32xdWpqRyVTVbEkzqtnNOEZ17Yjl0HLmDuYz/HxtQAtJbko/KS0GolX1Fsj7JcqR6z9/cg6Mki7Hl6JcYbC/Fyo729YrQIj40ENemgpSsQu1TonJzsNSPSjJqNa6dsyAUfbI767Qc37RImA0jid1RHVK9Z8Rq0vrITdc0OOdPzm4/sQ821MFKntQi/WoFNxdtRVmtCSPIarFs2gmP5xWi2N1PB53i9laKsgZRJfJ9e3/ArEpf6+XRPvm7H53jgZOfR1iYUrt+LbkEcEnlNDOODxuN4bf9bsMYSfSdRJ3dGapMc3HV2m1Fh47ea8PzGfegj6Rs3/Bzhg6XYVTcgJPJ4tju+jMqdpbgeTPL4eQyxLs+2KCs/BVvi23UaZQeOY3wp8a/sx2Bu2I7CI4LiBFva1GAW2dJevD8mJBNc5espP9LxkzrTJXMhyaSPeDIUQ2UlKGsS268THv1Hw/mao90rEG6/z+fCMBrKhHpxOitB/lTfpdBXarS4c7UaZ/q4UwlkYFJHbOBwG67b30Hpa8NrnVYEUEXJMdaGsldaQAIbp2+qO3H5ElTEDMW+XYUNitF934rW6jYMiN6pGTlTjUpLAMKoLSnEWs9xUhgbVNj1vhgjorGBckwT47kPko8j6nza2d+87XO584kOuNhCdIKzrud7imWexwxq4w7R0YMinZedFslWaYxGZbsJr13WIaNoLzbHW9C4jfQDbsYknuvrfV6K9iNbbw3CHtWhe18F2gVBGk+V4rWxCMSGynQ0LiiNaVTELG/HpM4o+BbXX9fuw6EPdVhF4vCqwPN4efO+qfby4xx+HjA1znm9DX8TkmcV22xyx2QbumESdihG29s5etvvu/k909sFtoSnT9u4M0ZO27Ynldq6vuKSCHdspk9Ntjt0/6tu26E0g+3Yx3wKx1ckrzy97eD7d4QDUq6UpdgSyoSCOEZsf3w6xXboI36PK/v1D/gdypcm22cmIS/Tu7YdaXtsHeP8LodwrOtLYd8JLj9JeaTOSSm21/4syuTL87aDSQW2P47wu+PvFtmSf3OetNQBd2yv9NgUHuV5x9a1V2/b/raQubCff8LI735F5DlgtJmm5GuzDR3LsSWX83V21YWjnrYvjbYho2s7znxOd3i5Ordj6Fieox3i/L7qsx0z5NnevkETBOzHhKo6c6VMb8uu7BP2eMT5c3XPOWobErXtsxMk3alO9FhCUrntirB/xzhg+0zUrDt/2WNLKBBkIKc/rh3FtnNfCPsS+PMl9pVz3DbE75I2fkDSiQ2LmnGlXG/LOyY0WsFXFNujQq7UJwpP2pVKMI3YPnNjz87+QuHaVPSuTewWVGbJeSdtnwn7EryxOQFaxykbNp605Tv7IT0mkjPn52K5fHrSVijRkcl2piDFdvAv/BlKPidnS5z9VTkUx50j8XVXXM9RjgfXKw22fLs9CHDHBB+WyIuzxRzbiatckhukNsnBXWf3ASUbF2KIpE7jto7f6B31ULQ7mTIISrboIj8FW5KVx0fltmRDle063ZazpRvHJbakKF9xflR3aU7t5o6J7NcJLn8F/3G1Gyfk6kXakTcVB4i8FfpKLpbYy6D5PV1uO/S0IzZdrzI44pIzRAYJSU7tHhdkIVwi7nsV26xYXxU26AJ/vcPupe1XirUebZOOA5KKbGfEl4+ct52oauPaI7EhAXFMc0733AcJ58vEEa98WkXfIIFLc9LJl322M+UnbVe4bGXKcIplnscMMtdzbfAUd/hjU7JTHKPRMrbY/vgpl8IxPmLkx5NyKNZXfV6K9uOx3sLYhvZRVCZ6p3MV8GhPKmKW12NSZ1T4VsIrbaK8+HFGyXt8peXGOVQe4nHbbDG7T5Y0OoSFamEe7EF3WxMaKo6i9bZV/isq8yMQPb8FO7YeQHNbP0bIRFf3oA4a+vxwuB9dSMHyZfypHHNCsSxOg4v9g8IB79A9HIWwlhI8c6CJ5DEMyxwdQnT87N36cQ/aFwaR/zvR3SH8XSVTX2s/hhyTdFVoA0W35+bdN/X4mN5huNLVg2idFb32Msjf9dskpc+IEeEsCR7lqUF0QgK62jr5uyETH6L9XBSy0kLpHpGXBrrFodDeHiTlnUXzkQo0nDer+6LNvFCEBQMj/aSOLfWoOdxE9AHcEV27asVjorYBYY/HIaizz/Vl6eEenJskdRpxtLm704hx7TCMN+Tucw3j+iUgNT5S2OdxyT9Qh4CpZ81WDA0Ou9QpJC4FEcI2RRO6GEEk/wFSh9b6CtT84TLgJA+J/oKJHDCOcbkno3KQa+3L46j8gVDoRCtDHnhwMQbMwi1Cj7pV0R4Vcg2PjEPvkSLsqm1B7+AorIHBsK9iUsaKa/39iE+I4+5U2tHErMSqm5dxXXSHforp2BzBemMAvST/ZeICQ+OQSp8citGK/IqUORfBCJq6K0/8ehERi5l6hUqfk9gS0ROxk5GJO8Le9HAfD4ZxpYPoSDPi0B/5G5rQEl1J71Q70JH8hM1p4N7GRzHUp0H840IM4dAilsSZKVT6s6QMgte2qMqWnOSxIBSxY1ZQzcna0sJEV1uS4D4/jBpxZV4cli3kUnjmrcDKRGHbBR/8xy1O9ZqnxQ+FTTV9JY0bYec+BH02Y/n4Q/wtzoCsRB3OXaJ3t4kdngeSJDp3ItCp3drHsDxmGL1XnZ+eqGizYn1V2KALoYhPC0bjBeELBMMkvpsNyHhC8DaFcYkn2+TGBnF6xIviOIITsG5LMoRFh16hpg/yBYm/edvn0vNB2ijWybxIpBZkI1qUrftYRlAxZvAu7pDxTbxD58pjtDBExw2jrGgHGlp6YBy1QkvK4MaTcnisr3d5KdqPwtgyLKcIGz8uR/62amifzUe82HkU8GhPijHLhzGpM2rG/LofiGwlGOFkEGOx0NghP84JS5SO22aLWZ0sWS9VYFN6JnbUnkXXTdLohNVY5S4Gz4nEhhPHUW4IhvFsObavTYd+exNGqJC/MMMYSgZA/JlTaHXBvg9iIrfg+IlSZAQPorW8CNk/TZ96XGkZI/9PkEF614fomvozIyyLDNC5M/yBBZbPiYxu9YnKIH9jodgQ51iTK0ZJnpon9MgabMI50udZL72P5keTHUHd0oOa9ekwvFCL9i4yxViYgJ/EuVOGlJGm7dD/LBdljaR+Zh1i9QmIFdLsBDkNhuiAM8Q+sBBDdTmH/Inb3DUIDemwwwLlhoQmmG6HIsR5saq7/Dl42QZonKRI9qcmL5OjaN1ObCy3FM2kDuO6FUhb9ZiQOPt41q2K9qiQqzZtD5qqCrB8sgcNr+RDn56LmkvOAxx38HUICxaPEghkohLkbgI5DZujcH6o1Tj5AtlXPcFzxnufmz2onZN/h0X1In9DmjhsWHyfm8nSTEPrpIXWSd6aeXOFLYLX/szjtS3ea7ZE263VuuQ3160R+eA/vqCmr1wYgyScx5WbFnSd7eMmRnQCBTI5sYz24AMLGSgv5U+VZT6ZrAmbPFoEkGbxAx4xKtqsWF8VNihDSJIBsS0t6CZljHS0wJSWiAhhcKvUj3qyTXk78pHZ6oO89VFZ2/YONWMG9yjrXHmMpsOqPSdxtGAF0PWvKMnPRMp6x2sdzniur3d5KdqP0thyTiiiH7diYHQxlkc5+Y4nlOxJMWZNv3/0aszvAl++x3HOLPJd4f9ZwIKLbzXhgWdPYrfervBhEgCFTVmCEaHP4f62Tg6iYWM+atpS8GpcFFYNtuH6WDZCpu6okVnotUHEL5P/dlEACbbCglMHznFhfiRSc+gf2b5Wi035FWhP2kOUGwHNpA5pBTmYuS8jkcF+hAZ3Ag3YvEGNNamQ55wYpBnMONjRj5DBs1iVWjjVqVk63kLdggK0vKGfOmasc+PtEvrRWjuA9aWnscF+p2niAtqFTTtd18gMTTR4sQ4MoHtZFB4Q9qfg7s6OY+VTBYhXNVCJwJKYQbT3WZDxoKOLdps/hw7hUVrUkM5/W5LjiRS9i9oNoY7XmlAzYEDV21scnei5Nn5j1lHSrYr2qJSrNjQOGU/RP9Letp1I2V2PpNNEBkK6e4Q6XO7H1jjRU77hQXRpIrBK5pUZJZujAyJnrCIf1UVEIaS2D8bJhCkdwUwGc4NkMCTseoeyzzmHjNkjDOFLSWexYgs2J8l3S97XLQghD5KBgLDHMenuBoMctE6juDhgQdZCh+8Zr9E79ov5Ha/92YE3tuh7/OLR0ZhObQnEloRjmBjEALElNYMAFxaRPulWC66bSR9hd9nJfvR2kP8F0Ujx3n98gtZLsa+MRLzegprzTQjpSUHqHnKI9B1JV0/jXMff0ZW0Bvvd3YGnDA5giMQmx5PAYQx9rEFEqnMjVLR5nnJ9FW1QjuBEpD5ajfZL/QhrNiOj2F6+unGJW9uUi0ncbz6RawI1ijFNgk99kA8+7a2Pytk2gf62Euhg25NtcKgbM7iH9vlk4H/VTHTuqMBA13nyL69zzp8Vx2hahD1BJoTkbx3V+6uZ2FWfgpNbRLbIoaa+avPicWs/Kupt7anFwUsp2F0wil17TyG2nPiGoswJSvakGLO8HZM6o8633MOXX0N8XzzO4cZ6d2G6NItPlrQICtbANOro4s3njqPRzUtx1s4D0JPZerd9OYLFBJNFgyAdCZCBK5Ca0oOa2k5YhMd5ls5qVJ6Pw5OJIm8WEbIwEppzLWgnE3lMmjFQdwCV9nf/SDDuPZCO3ApRfiYzLJpgBNF4/KgBWzX1qDw1dQEprwK5mRXodX+zlDTw/07lp4bojBzMrS9Fs72YSQu631wLg+zLsurkGZGkh6lxJ5FNMjISHZ0LDeIa0yhM9vrdvoDGqbfePaGD7kErTJ/bByYWGE+dRLOwZ8fYUOFox1gnag5fQGpGIrnaifkp+OeUC6isdsgew6fwfHohWmVtQ4v4f0pG+5Fah214yl8gwpCH8KadKGsZhJkEeXN/PV6rMzoG2UQe4RMm/G2qWYNobrwg7Mw2yrpVbI+iXEfRnJ+O5xsdNm26Tcp70PWurhiL2TEgjUjLRlBjlcheh9FcVg9rVgqihUNilGxOsygC0f1tOHONlEE6/JG2AzgkHitEkqAd2oSS/S0wjpKJxGg/GvfVY0i8BMlLvPM5D6jxda/igQ6rspLRfLjaYedUvoVEZ6IXdL2D2HiUBu1/OgszrYeZyO/NWi9+r0KH+Iw4qe8Rm6ppEE3bvPZnikpbFMnP9/gl8GgKNmrIYMIe06neq4kt+zRTIgQmICvLiNd2EnkOE9ukX3DaX44zHvLz1n98QmVfGRGbgq76ejQnxmAJN7CKxPKkCyir7kFGbBR3jls0F1Aj0vfIqVJUWg1IepTfF6PYZsX6qrBBWch1qTFortiJBk0OUqfGXkqxVsE2l+qxTkfsaCqd9Id1RdDvbiNbKmKaGLV9kCSO+ODT3voop5M+HKtznG9u24vcjfUqf4NM3ZjBPaTP/28GDL25E3WXholvWYgc9+LQOZFzKY3RbjXhGdI+h92ZMWIiE+/5ciMGhfp6lZeC/SjVm0xeGva1IPbpPNIfFODFObUoE8e5iVEyuemRn4Ao2ZOKmKXcP5ph7HD3+4vejfnliE4xQCOOF3SsV31hWk85fWVWl+FFZxch/EwhEtPXIjc9HUVXopAhPxGHJm4LyvV92JFJJjHrM5GSWYLrhlJsfpymEud5thzrxipgWJ0OffpqGPaNIqv8JaxyM1rWPLEJr8b0Y9f61UhcnYtjgWuwdepGlAbRW0qRcbUE+p+txSZSpn73ALJK8xBNO445och6/deIbstH4k8zYeDKG0DGb0i6G63p0vKwYaAU+tWZU1/OUmRhDvbsikJr/mrS3kxy7VqU3NCjNC9G1jhUyTOSBHMtceaknyBafBcpJhuvhrVhE5Ff7i/SkVLYgwi9G2VICEZafjaGDlCdEKf56VrUBCYiS0i1k/Xcf8fIC0SOpB2c7vSleDFNTjlSXRqI7BPzWxDybLHb9wc0SYU4us6EQ2tXE92T89fugymr3E3+AsEG7K/Jh6Z1J4q25qOkUYvNO3MQLiQjOAWb1w1jFynf8AtS57XVCEhMERJnH0XdKrVHUa7ByHiliAxc8ol+ckkZq5HbpMPuYgPpKuQges8z4BrRe2JmNfd+g8ReSR1TVufjnbBilD/lxo6UbC5Uj19tsOJYPimD+M2O/mRs1gtpHMFILasinctZlBTlo3DnaWie2ol1buxEFV76nBxqfN2XeCCxc+pHVL73F2Fbhq+PHTRYvqEIsX2lxCZIHMytRcCaPC+WwxCrSnsJVUJcpjaV8sIo1j0nVpL3/qzGFl3k53P8EqDLvMv4MhPTaUxfi8aFBdjm4eGEZ0gf8vRR/DZuEDXFxDa3VmAgeS9+9YSQLIe3/uMTKvvKGDJwIsMf8fsBSx5LgNWagJWPK3jC4jz8cuFbyObkSHTXqMOrZY672RIU26xcX2UblEebqEfGmBlBaXGSJ9GeY62CbXJjg2KENdOxAakvGR/knwvD/ueFJ56KMU2Eij7INY744tPe+ih/ftbovimdZFf8Hetez5HXsQvqxgye0MQUoOq1OAwdKSa+lY/Kayn49XOiFwKVxmgPGvDiczo0bibtXb+WyC8fzfcXY5tBLpYq1NfLvJTtx329B47sxLFFRdicRK2J9H8v5AGH9zq+vGc+j5odZzEi56KK9qQiZin1j5MDaN5RiytunhZ5M+aXJXIL3uBkLfSB69/Ckme967P8xXfoVx6E7VnDarHAqtHSpb7KcI+0rdC4e9xrJTNiqwZaVZmpYILkN+khP6V0P0EfcVvnqZORV/J0ZhryU/MYntaNvnCs/KieQOsyQeoi/sa+J5Rswxl6V0x83s16bMo341fvFTju5HJ5kmaprcMM41G3atpDUZArV8Yc1zXh3qB+SQZhOj7r3GYMozE3H6bn3sXmGOGQj3jjc7OKt3Y+G6jxE2/9meC1LU7HlgSmFT/FuNimFRd3p+N/LzuJ/QbxzMQVr/zHV/wgK49QmyCDJrX5K7ZZqb5+jtVKdqBom3RsQCYW04mjHLPZB3nro9O0oWnZuZN/Wc/tRUpjFJrKnW7weRqjeRlLPdbX27zU2I+3Y8vOA0j83zFoeS2Zn5zL4cmevIhZsv3jrVN4ZqsZv1RYuj/tGOulrGeC2f0angBtsGqhzaGO7EFAVAE+a0AGagye8lNK9xMa0ma1xXglT2emIT9aRyXD9cq4aV286SCUbEOEtb8am9bvxcVbwuKqiUG0Vp+CMSkGS/gjPFyeM69ftbjTrer2UBTkypUxzQ5ejS1M4avNWftRl7sWZW2j/Nd0SAA1NlWj4XYCYh/mT5kO3vjcrOKFnc8aavzEW38meG2L04hfdqYVP6egv2mSjufr+vmlSsQ2zZdqUdcWipUxnidKFK/8x1f8ICuPUJvwIn/FNivV18+xWskOFG2Tjg2mO1GizGYf5K2PTtOGfLVzc8sO6LfVY0BY0WU196Ch9iyin4hxXQnhaYzmZSz1WF9v81JjP17KdmR4APErIt1PlChu7cm7mCXbP346jN6kFYrvOE87xnop65ngrjxZYjBmHwuMLdWorG7DxTEywZinw6qUfGx9Nlndy5L3HN+09qjDeq0FNZXVaLxigRUahMWkkDY/h3hf3j9lMPzJ7U40vFmNYx3DxDs10C2MwcaCl5AV43Eow2AwlJg0o7vuAGpOdfI/uh8YilXZBfjVuhhov8H9nRKWwR6MB8cgxNcQM82YZb3VA6MmBhHzhQPfYNhkicFgMBgMBoPBYDBkuCvL8BgMBoPBYDAYDAbjXodNlhgMBoPBYDAYDAZDBjZZ+v+3dz5QTV35vv/OY156nYZHJ1QHWgtoRS2gd5AukS4oXBF6Q5kJlyssffL0wapPXjtinWLbwbYjtmprVTogDgw+GVly5Wqba1paZqBQKD4tVqCtQFUYJbxaWHWSN1wzZZlXbt7e55yQk+QkOQlBHbs/a0Vz/u2z9+/3/f323udPYDAYDAaDwWAwGAwJ2GSJwWAwGAwGg8FgMCRgkyUGg8FgMBgMBoPBkIBNlhgMBoPBYDAYDAZDAjZZYjAYDAaDwWAwGAwJbu1kadIM07jJ7sP9JX4xwj5O60WYTeTYCWGBwC2bhQVvMZNjfT74DsFdGyZEtpFj25Eu9FwyCUt3ADLq/L1FhnZpbLi03XS1/33wzd2QH/4WEectxp0B9Ymo351p3PbrXO65zQKZyfxHyx4bQl/vEIzfx0AgWhsd6MLgyG3uX6gfpuxP6nSmC/o7aHhk1aDT5/ummZmMRYFbO1nqrYY6K8vuk5qRhZ26MWEHgrDPzmajsMKBidOo/GdybHWvsGIMzS+Q/RtFZXiBsbEE6hea4OJsfxO4a0Nftcg215uwM6sEzdf5RRiH0NM7BltYmXD2cCm21Z8j32YCM0Z7SbLxxtiOdWZM4Vm7fGzUWEPFgWlr/67zjRH6M70YFQ0I74b8IIb+xfWeoTu/NXZ5i3FHQH1i63c9M12tGRqLoN57WtQ/2Rg8nAvNoXOS224ZM5T/zBdqseVnGVAXlODA/hKso98LazF4Jw3SZ4rJMXSU5pFxYS4KX63AnqJc8j0PbzTfplxAx6PW/D9+DnWlpWjo8pcjfBgPOSIxpuY+d1GfJYtbMBa5DY/hafDbjz5Ep/VTsw6mQyXQjgibBTpOtmJU+C7G1K6D9ns2aZ4xrjRhy+Eu0cRIieSdH6B150rybSYwovvwdjReERYZjDsKPRq316J7XFi8CzF1kYFYo15YYjBmjulqLfQxNWJaP0a3492syQGcbTIjOy0RCmHVXcM1HV4qbkLEc8fQ+cEJHDl2Ak0fHsMr4U0oLNHd5QNgE/oqirDToEbVqQ+gO34MR8j/TeVqGPYXoab3Ng/8glbihT9+gBdS/TU68td4yGFMTT+VGqiErQz/ELCDIHyfeb4+h9oWIPO/x2OOsApBwbj5SS0u/iQbifNJ6qP7XP0Rkg3tGI9eg7//ibAfxwga99TC9OPZ+OYnj6FgRShZZ8LlD7TQz89G2iOuRWw8U48jVYdQ33YJE7Pm45GH+H0nvmxCw9X5yFxwFSfL9qPy/S8weV80Hpk7i9vOMTmGnuO1aPh9Bd7/dBzfBoZjYaiwfbwX2povcV/0JD45vB/l/6sNX5lVmLcwFLOsU9FJEwb/WI+63+4nx0/gvkfmwvDeYXx5bzwifmzdh56jGjWH6tBxaRyB85ciVGZMWtuw9snFENWa45tP6vB5kGAb0yW0vHMV4avVeGDwJI5qO/H5n67BfH0I5jl8XUabK9Dwp1DEPRwEUy/Z58I9WPRdF2p2l+Jdru6kXt/2ovHoQVQe78JEYOSULTlc2moEHRUN+PDzIXxlGMfodQUWxYTynZ27tovqHHGtCUf+aMSipXNtneTkEDoqSScSHuveXuYhtBxqwkTkUswRGUnfVIE/XA9HjNAGVzrh618HvWAnDqvv4+chUFjlxDg577Eq1B54F58bJ3H/w5EI/jthG8F0qQnaqt/hwKnzmPiPUETMV0Eh6EbsCx7SmTRUT+nGye9EZ/qPTqL24F5BZ/Nxo12L8dj1WP4AV4AdsrRPcGkTkW8WKql9mmGKDMVX2jJU7pVurxheX4FY8t0ZHCHnr3OyOUGInYbfV/Pnx1w8PD8IAWST8UwtjvUFIi5S1C0Yu6A9fAE/jCXnpTt5sP8UnC/fxidfDmHs2+sY/itv92nbyAEz8feR//3/sGTxbK4NVl0NBsbi4dn8GvOADkfaJ7Aomo8PTiNHf4+aujZcGf8BHppP9EY2cGV5EQ9UT/XNn+HL4T9j8isj7lkcjTmCLVzVn9pYNxyKmHChMM5Of8BfSPnWJho7a3FyKBh/T/xCcVVfvq1EI3PvwefHSK78wIz4lEhOu8bzOhyrfIto4Bp+OHcxAocabHmLMj6Cs+9UonpvPU5fJTmCxHHovfwmJ9xoxgkhhn+0eBKfVu7BrrfP8/4NnUDfqTpU/eZf0TMeRHwx15bPCZ7ituXfw/Gjz+pRvr8RpJGIoHWVq0UrnvoEN+V5nbsl+rEr40osELWb9iXvI13odwlu6ueL1pwICsU9/XvR8cNsJD88pXCgX4tdHy3G/9jyKIJp3cZIzNfWob6C2mECgQ8txhyRNkxX29BYeQhv/ksHvnJoE8VVfTgbdpht/RSB9y3VB1ljl//IRre6c619GyZ07HsRPSnleO2fHhTWEf6TEg89Fo0f1OzHZfGYyJ2evNX1jMVBJH4yokXVAb5PmhUxHw8FiXwp5lI9flmhxIu/ewZ//1+EdQTF/UuRcP85PP8ukJseOeULTzryKUbHSHzUkfig47iAcCy5dwh1nwUJfaz9GEBW+1xqk5blw3jIEakxtRirX+d/g7ay3+DNy3OhWTbbvVb9rAVf+jw5uW3en5s4u+sfyEbMvQ6xSGJJ31SPd8l+Dz8y29Y/TgNRU28T4wMYHApDZKRIDYpEZK9R4Wijwy34C2TgYtQgW+3NnNmMvkN5WFcxiNCV+djw5AKMVhRgS4PoVta1ehxoMCHmyXz8Ii0QLa8UoPK8cObJAdStzcOBSyFY8VQJNiQAH79YaLvKMTGCT7SnULb/GG4s+jk25PwUxoZtKDo8wG/nrpYUoFBLyl+9GdkJCrS8vAN1jTr0GYRdJnpRl1eAGmMUMtfnI+3BQRwsKIT2qrB9BlDMWYC4RcGAkkwU4pYjQhiPG4ho6wb4ipmvdqJOV45KnRkJpO4rAnTY8sI27Nx/Gvf8lLSV2qq0UKatlIiIiwIdcwUvWk7OLQSFF21XhNyD0cNv46zo8pr5zAns7FUiNERY4QpFCBTXyCSuS3TwxGlo3+qFYi49WNBJrRELiQ42PBkFQ20B8ip6BQ0a0KcV+YzC+b6fbHHBWBNeWluERnMssp9eTezXhqK1u9Ej3MozNm5DzstdUCSsxi9Wr4DibAk0W21XD8W+4DFhuNOhDlPwOitoMIp0thsfe7pL4k77jjZZGYZBx9iZgrdP3Y4StASs4NobM6FD4cYK9Ll4aoHX1z7sLBtChLX8twrwvNb6yIURHdtzudhZuLqI2GgB9NWFeEl4bFd1nxkt1a0YFD2nPNpcjUpTIMKpuDj7l6BHKdTHrLOzvx2KUCwk+gwmOg2PIvqcL8pHfrMROU2QGYOHWnHZWmdig6PEbq839wsrgMukDWcnaE2IV5u3Q1Okw40oopGi1QgfqUKecIXZ23gInL+cn/TMiUJc3AIEcwHoXvcqhQk1DV1TmjT3NqFMW49/O2P10RjONNTDEMCf0F19rRqp3LEPl0NSseHnsVwbR7VFyNnbi2AuP4dhuKwUdYPcATw0r2wsRJ0xFht2lyCb5IgDz7jSlXvNOCHk78q3dLhJ4zAR0L5YjJe278fZWT8lOWkllO2lKJzKA/LitrFiBw5cCEHaeg3feXujRYqQFw+SvjGN5MXMeWP2edGhvBXKLpTmFqFFaKbXudvaj5Xuw/A8sn19KkIv7UcOadeo1HsAHvK2LK15iBWiPiSsjEVLSyfJbjYGO5sAdRIi6ThroBp5a/djcPYKbCjJxwq0YgvVhvVuFNleWHgChrh87CpZzbWpaMqXQn1cjA04G3aOTPmdQn37yVUpp3nSnbT27ZjsR19nGJ5MDRNWiAiIwvpTp7B+ibDswf9e63qm4qB2Dw6eUyGZaDA5qBMvbdyDDlG+EjP6RRf0qalIcDIMGT2o30Tnm2rBZu5zFsWnGL2mw/Mb9qBfRfyz/ueIGCJ9k10ish8DeGyfW21OfzwkC86vx/D63rdhjiO2SKF52oNWp62FWJh1NKfz43dv+zxZua1hNw78q5kb6ySIritYGT1ZgsImJdI0Uc5x5iuWW8mn5ZbEFLVl3bp1lnzh83PNZsvxnhvCDgS6z9OnLAbDB5btKcWWZoOw3nLT0r1bbSk8rrcY3tlsSSzrEdaPWt57OtWy7Z1RYdkBp3IIo52W41Wtlq/JV66sdUcsw9/xmyjDR9fZyv/upsUwSM7psH1lubB99JRlW8o6y/GL/CIHacNKTZXlMv2uP2EpTN9lOSNqIrcuJdVy8FN+8cYHxZaVr3aSFtrg1u22X+cKrg3UZsKymAtlIttwdd1sec9qKquthUUK3d/adt42xyzD3BLhu08sB1M0lqP9wjLhQrnakn9Uzy94spXgK2u7KR7bblfnm5Yzr4p9bdOEHG5+tMuuvTc/3mVZmX+C0wF/Hged3Oi0HEjPt7zDFd9D2m5fd/6YcssFYdGRy5UaS06lyFikvpdPlVveo3r/jpSXTmz5hbCJ8p3e8k6+2nLgY94aYl/w2NvPzu9SOrtyzE5njnjUPo0dxzKFdd3fku92vuHtU2jVAscN4i/X/uHOL1Vna/mEm/pBy9ei7ZwPN1t9yNvL1j57+1H729dHWOdSL7QNovggTNtGTtjXmZa/razcss2aLyz9lqOadZZ3rnALFsu3esuwXlT4t0STpI7N39AF7+OBa49YU550T8+XbrPJhTJS97J9lpUvtBLvEv7SailNIe23ttVtfXmNvP6hw3Z6Lmt7Kdz5HfPWPkv3lA9InvnKYLkp8okY95pxgCtbZG9C9xuplpwqUdz2kHxuzYNy4/blVlJLG95qkebAxOIPeBsLGD4+Yjn6Ib8/9YN9bqG6zJ/Km7xuvcjdnB1IGz4SndFNPpLTZzlpzetYIXD6EumTs781JxNuGizDV8SeJXVep7b8zhqetA5vfMIvUL41WL42CDWk5/Y0NpDoH131p+51J6F9R7jyXPcnYjz532td35I44Puv0j9K28DOtu7g6uomZ/lUNyF3SvRfNg3wPrTmbo/t86BN6/7i/llOXNnBjanzLaXl5ZbfiT7tVj8KfrUbnxLcatVrLdA+yyGHW9dx5uRtL7fPk5fb7Ptkvs58LH59YrPlHzeTurkJNV+4DXeWopH59Gb8wvrR3IPG7aVouSZstkJm99np/dA2C1edxk+jsTmarJO46uIG8xe96IhXI0F8MyokEWs2rYTwMAEQpEIgfzeQI1Ap2jlAAdWCMCivD6HvTBsaD1egodPo8KsbKiitT0pR5oYhbtyMm+Sr+cog+mJXYIl4ehsWj7Sp2bAZF7p7EaMyk/K70CN8Ll8nW/r1ku9t3VKClLZHzIgtSOWhEpnngQcXYNAoXGqRZSsx3rZdgbjklehuFa50T5xDR7t8TSjiH0f2UCv4i+LknKdPIyEnldOB+WI/zi5JRJxYJ8qfYlnsCPouurr8644RXDgDZKZECcsUBSI1m5EZS8QwMoBupGKZ9UohJSAMS+IVODswJKyQj6TO5iWJdOYCN9rnYmdeMPnf5puei8Ty5gEMW69g2qEg7RH7Qom4xET0fTZkd2XYjkXRWORY59nn0CdcSVOELUAwseUgOXdLfQVq3v0MmNJTGBLSQ6A9Lbx0PkL2MWqQ+RjVKW//CMWore7kMzyhRN+Q/dVij/jVRiQ3pKjQ0U3vPJtw4ZwRKzQarFB14gJt89VetCMJS+ZxOwOzwhAeAu6XoXqa6lFzSEd0A9zkbDC9eKB41P2s5ViRNICeL6gHyf/tiVi2Ph6ZvZ/gwgQ5nvzfEk90Z32eyG19eZQkp0wxpseFWfG29lKU9JzCd8rsSMTMbsL2Z/ajsXUAo6QqqgdVUIh8Isa9ZqQgZYmeh1IQ+UTMFhkklPjsmhE36He5cav6MX+lmMNbLfJ5MS0tye6qqCopH+u5uw4juHweSEsQ5xYg/NF4BHf1Y+otIW9yN8cCxESLzkjaRbXqnI9867O8jxVCUCIyyTigpZ3fwXymCdoHSX9ulbhChfAwJYxDvehp1aGh4gharpun+hzV4miEN5Viy34daccITAEqhKp4z8gaG3iBHN3ZaV8SW91dI9P/3uiaY6bjIAQRkSTrmVz3p+I84QqPOcunuo1huF+BhEed+y+3uGufB2064+tYMBgL45YjTvSxPinE4zA+JXjWqjdaIH3WJLHbqCiuu/S4oRyB/grNbnwekdfnyc1t9n0yjxn6hiLk1YVg1+513F07f3IbJkthiHksHsuET1r+m9i1xoDX6xx/9YYMvDI1MDQ0cY/ZmEiS7EjXIFkcIDIwjZMkq1SIBO0lpl7UrM2A5oVa4mziqnmJeDxe/mBE+vxkeUqIJpi+IW6+1o/u7nO2z3gY1sfbnpX2DTNuuM5L/sdrW3nfdsVjajLh0aGdzKHN5z9G49KV9p2dO2YlIk09hPfpwcLAMjmeP5jz09wQkiLEkMEGWeEuubvGAMN1JZSihGPHX4zQh4WQhGWPUhWC0Qk6zfYOzzrzHq7MCZK8xL7pNiI8myQuYR97VAi+T/gqoFCS4dqNGw6xLWKOcOt9ClLnIBNufEu+To6hZVsG1Hn70EjOfUO1HOnJP+V3EwhN0SCuqQk9ZOA+SnKEIV14PIezP/l3RFz3cxhWxGP9gntd18dLvLcRTfxkJtDZC/040eBF2knQzgToGzTB2NsFE22DsO+obhvUP8tDmZaUa1QhTk0GCcI2yrTigeBZ9wosWR6PlnOfkTFtJxoXk4kR8cOKpHMYJB3d5c9OI5n4xOpDT/V1gsaBUumk23vEK+gjSMePoVwTAn1bObblEk1sc/F4mAzNTAuf4tZbLfJ5MZCOUCSh5YUh1PElBTKACBUu0vlGCIgr7KAXBpzb5Vuf5UusUC3EkAErf0GAv8AVl5k0NZkxn69AQUYWtte2oZvoMTxxFZLFXU7UJhw7vg+ZIUNoKS9GzhMZU48bSedMH/GH7kLCEKnoxbBwfdgR20+pz5T/vcDP/RflgXlR6Lk0JJ2buZ+G5rd4zFk+x6hzf62YdY/wzXs8atMJX8eC9mNq+gl31wf4O0dSeweQj7jO3UNQxGsQHsT7TH6fNx1tD+GsMRzps06jYQZ+PfH2v7NECF8UDfNXBiIVB5aosUalQ+OZATSfHMSGTO9//UYVGY3Q82RGKu5YRYHnCdOZt1E3dzNOHt2FZzbnIzM1Fg8o5A+eJc9v7MUnUxc4iAgiFbi5SIONmzc7fORd4VLNi4RiqB/DTr8aNITB8wqEk8RxK/DeVj60PSAW6RojWogmujvakPlkqsNg2z0xqWQC3tyFwa6PoU2yTb45P535DOInlOlVjuEvFIicR+0XjFDHuzRER66DNxILY8f4K6dirH8/Zj6ZqA19hst27xSZMUw6i4R54dxSIEnuTriQrSqMaIDqTFjmmCD+d7wo7AVcmZNRSHfyzWYki+8ETDEGvd6+gvqBLigWhzl0bCIuOlwxo3UeiMJCaoJLOtQMalD1Tjm2knNmq0kn4HCFDCFJSFt6Gh3nB3C20Si6kxeOiEUmhC7f5FT3jWsk3hfwEe9tRIhKQqapC926T9CRspzrJGhn0neuFWfODSF5ubUNA2ipHcTafaew9yVS5jo1ls1zGAJMMx48654MMuJTkUY6usauTkSQutGpzZLl0Wg/r0NP+wKsiLV6V0Z9HaF34a8NYNguDuj5ha9ThCBSvQ7P7KrCsfeqsGG0AjWtErlFjmamg4y4dcZbLaoQEa10vqND/+YXl+NpbhlCT799+82Dg+hZEgaJ33KRyQBG7cYYZlwaGJBol299lk+xQlA8loUN13RoH+AvcKVx711QTDj7tg4PPHsMv931HDY+pUFCbBgUjv3g7CikrXsOr1SdQFNVDr5+qwIdxH+exgZ0cO2I2dWwwS+6i0aC2oSGk13Oad5E/2RKEfgx4Ez53wt8igP3qEieSWg9gWbHp4wI+vrCqZ+K95izfI7RMW7wLkZ/Sf5P5dsjU5t2TH8sKAt/50juSSr6nrpjnUnZ9Ckaiuw+bzrajsKap5/DC2WbgOoiVJ6X6B+mwe2dLNHENNKFykNNiEmKlRhQhSE9Jxra14pRiXVIE99WlcsifsJVM/Xb5Cbo64qhfq3VeXImAU2YCsMYDNaEev00tN78/Y8oIvwwHUr3NkE/Rjq7sQFo99RjWCTOmMx1uKeezPKtVZw0oeetXGisL9BdO42a/fWu/87CUg2eebANB/aQc1izrHkMZ/fuQI1Cg/RYYZ0U40Zb26aJXFuZjLaGeGy7BJEpahi0O1DZuRLJ8aLp8wRJdg5/J8eJJSRojSdReug0spOX2ybfVp2ctF3WGz25D5VmDVKW0qUQMoBRoOMPbTDS9hmJH9+qRR/dJIkSCf+4Emdr69FjTdrX21C2Ng/aS+R70HKkpfaiprYLJsFepq5q0qZ4PJnER0LovCgo2pvQQU04acRg3X5UurjqiKWp2KAQ1Z/asboKjdO5bEp1pahHpcgmpq4K5GVVoM+Fc7SHqm3tHTmJynozNpBJs0uuicrn6lyOxvgsJMwmy0RPERMG/NkqF9MQGrWnhQUrKiSkxXIv6zYoSI6YunuvQnL2SjSK6zM5gsaiDDzv6mV/DiMZLAlf5eCDjWhSX5YyRGxzGpk/jeZXLVqO5DPVKOtNRIKwirZB9aAZhm+mDAD9yRNoFJasuIwHVxj/75TmPOueEETqu6AJlQ0gEyNhArV0Oe6vr8ZRVRLipsaU8uprx+wkPBlvHwejJ8lE6Dr/nWLu2g/12gqbH00GGEwKBKskpoWyNDMNZMStM95rMTI9B8E6Er9Wt5B2NGzNwuvt9OILn1s6DtfayhvvQg3JaWmZSeRsvjKGyrKTU3fs3LVLdt4Wa82nWCEERCEh3QjtjnJoY8WPzSkRHKKAYcx2QcrYfgzaqcG2GX37M5BXIfKVgcS3IgTBVDoexgaK+ZGIGWhFM/0j7WSsMtq6Hwdb+T2d8IvuFIh56tdIPl+KLaTOo4JNzNeIb7fuRrd6MzK5OxMz5X8v8CkOPBCiIYN3oGzrDjQOCD6l/R7Jbdvrldi6XrhY7iln+RijCZnx9jYl/VdNg8PFTtl40qaN6Y6HvMbfOXJ2Kv4plUzmq232prZ7PqNI9HqN3D7PD9p+UIMXnw6Bdi8Zn9lMO21uw2RJh//5D6uQRD+rMqApqoYh/U28mSN9f1KZokE2kYn13RKvCQhD9hslCG8sRNITGVA/kYXC9nDsfd76yyoeiM3BK+GtKCB1zfvnDKQW9SJSbf88pXtCkFZWRTqJNpQWF6JoxykontqBNeK7FPPWYdfOaLQUrkIq/YNiq3JRekWNffmxXHIwX/kYDY06dEsEGgdtY3kV1gTUo+AJwbZPFOD1UTV+W7NJeCxJAtq20JOkbauws9kPqvJoqxCk52twaX8WkrKq+StDHtouSRRJlgojRtVqLBPfNjd2omZ7G0bdjhejyMBFAf24Gmncuy0CnE5+jZhWq05WoaAxHK9VWu2nwLL1xYjr3wcNsVdSXi0CV+e7fcRIkVKEI9ljKM1dBXVGBjmmHDfW7MMabtJPksKz5VgzXkHKI+fLWAXNnjHix19N3e1SPFaAV2IHsHMtjZU8HA1ajWcW8NucoI8qlRUjWEvqn5EFTUYutPM2Y6ur/eVgZxNaJq3jIDJfzUeMCxuvIXGspY9iZpH2btQh+LlyrHcXLlE5eHyshPvj1Br6yMJFNapeEv7OV0gqNq4ZwU5Sluafs5CaW43ApFTuMDHKJDUyyaQ/OD3eLkdw9l9jwEFqf6Kt1FWFeH9OMbZmOl8x5olF9rOhOEq0mPRim6yLKb7YiBKTqCaBHYtlscJOAdFY9pgZ5qTHETOlaRIvhTkYJvGSmkU6zCdyUROURPKhA67iQQJVej7WD+4jcZaFOvoIuUfdU0LwWOoCmGeJ3qUKicWK2WaEp4htLrO+dpCJxEv7kHmxlMQAtV8Gto/9V7xCzGNFEb8J5ep+bCc6yFtLyy7FZc0+bHxU2EGMTM34jue4lcJrLdK8+KtwvE/zIrFJ6s+K0DF/B36Zzp/ErjyaW3L3wJBdjheF7b4RhY0pY9hOyqPxqy7tR+Y+F+2Skbfda01+rFDoBQHzdROyn0y067djcooR0VxEcl4u8ki9iy9EI3Mq35DJxyZBWz/LRQFt02uDyN5Hzke1zdXHzdggTI1frjeTfED6K9KG7QMrsVGkSzv8pTtlLJ6p/DUSBvcgT+jLUwv2YDD21yjfbLPtzPjfG3yLA0+EZu8m7VLg/eJcYYyYiyKdAmvKdyPTOmbymLN8q5sy/VeoEvIM1X/qC2NY85wrh3vGvTYpfhoPeYvfc6S9vbm+v7AJoc+W2L0zLa/P84+2VZrd+M1jndhSYrv4M11+QH/lQfh+90MfgSKOdfkeiTvoIxBmBZRKHyRLnWU3YRmBNq8Qhuc+wEaHi+7mcRMZlJA6+hoZ9G6dyQwFfQ/A7py3EB9tJb/txH4FRTBtFf2UKqVrP5L+LRZNrwuDbReMNhRgi7EYJ552MYqfjk6k8OST6WhLAvpsu1kxDQ1JQW0y6a6Ovaj8h23Amx/iGTKIpb6kL0C406BRWwRNayp09A/oURtMurA5Zz9SnKu/z+GJWxUTHm3kO+7t6SIevMWPupfjfyc8nd8bP05XM3LwJW590KJbW/pL22M6PL+2FY8fL0cmmQR7Yzv5eVuEn2PFY87zdD5/ad+fupNTlr/8Px383H9ZkdWPefKbzzHqv9zha3/sU1x5g5/byUHtPUHs7Tf932Zti7gj3lm6ZVDh+ZoMqdh9Ua15AHV5uShrHeN/BYUIQK+rRsP1RMQt5ncRowiaZnAEUKHeZnH5aCvPbSfBYzRhtPkYGiQeyxwdGUQC906FNOZxI0wjbWhoANbYX+KxZzo6kcKTT3zVlgtocvFjcTzUJl4USn3plQapDVzZnLPfNBp0q2LCSxt5g7Q93ceD1/hR9177n+Lp/N74cbqakYMvceuDFt3acia07aXtPOdtCfwcKx5znqfz+Uv7/tSdnLJmwv/e4uf+y4qsfsyT33yOUf+1x9f+2Ke48gY/t5OD2tuv+r/N2hbx/Zos3Q4UUVizIx8KXSHU9PEteju/Q4WtNSUeH5lhODKC5pcLsf0P92DrqzlOj2UGxm7C1nRXj1iRyVTTDhSVNEHx7G5ku/1VGob33IPgB0MQ7M2PB81SIXLOTPYGdzvu44HBkA0ZmAQ/KPVzvAwGg8H4fj2Gx2AwGAwGg8FgMBgyYXeWGAwGg8FgMBgMBkMCNlliMBgMBoPBYDAYDAnYZInBYDAYDAaDwWAwJGCTJQaDwWAwGAwGg8GQgE2WGAwGg8FgMBgMBkMCNlliMBgMBoPBYDAYDAluz2Tp2mk0aAdgEhbvPIzoqavF2TFhkcFg8NzW2GVxyWAwGAwG49ZyGyZLRrS8tQ/myCgoJ80wjZtgnhQ2OTJhgmlC+G6FrBsd6MLgiJvjKLTskQH0DIw4l0Ewm0jZ5NyOH75MFWLmjWJn7WmYub1vIbTeJtdnNU/V0QHOlre8tr4j+N7Z9l4yOYae+grUVNSj57qw7m7CU4w44FIffkEUu3TRneYcYpfWy87f7qTqss23IC7NpG7uKjdpRF9rL4x3hD98wFP7CDQ3uqzz9V6c7TUKC9K4Pd4fSPlgfAgth2keaILeB3HccX4i3BF1kqEXt3gZL9NCRq40j3Sh59IMXeqhtpIYazDc4OCzGdW81FjhLh4/zHj+kBqfi/Fy7HKnc+snSwMnUTOag+Ql5HvAGJq3ZqGyUyoZD6BubS5qzgvbiKg7SvOQmpGLwlcrsKcol3zPwxvNzpeZR5t3oCAjA5qi3Tj4ahE05HtBaRtGp5xGzvtCFtRZzp+aXn4PxWNqZLbr0OF+XOB/equhfqGJDEul6EWNqI520OOyqtEnLN7xcPW1t31qRhZ2Ngx5MRA2EU0U4MClEMRELUAwN4K/yyB20hDb7Gx2VoT5Wi96hsTr3ejDH4hjl+JGc33VxKfV1orw9bLz9xMkPl88Cb3UuMVNm2c6Lo2NJW7ijzCkw+uv7UHzkLDslhn2hw94bJ+QG13VWd+4B8/v0UEvLDvj/nhfcNK5kw+G0LCxCNqJBYiLCkegQlgtm9vvp1seyzLxrBcxZoz2dkEv3tmreJkm15uwM6sEzS4HvSacPVyKbfXnZuTOOGerqZznI8Yh9PSO3fqLtLcLO5/NpOalxgozMX4wQn+mF6O3fdI88/nDvo+XiH2P8fi3xS2fLA12NgFJsQjnlsKQkB4G7elzzsnhQicaJ9RIe4z2fCb0VRRhp0GNqlMfQHf8GI6Q/5vK1TDsLyKCsB1tPl+BbfuNyCw/hdZTZL/j9P9yZBr2YduhXrvzJGw+hs6PPrT7PPOosDEgGsuSetHSzp75mTk0+K3Y/keLENhQbJsge8SEGwYzEn6eg4TUeITPElbfNZjR09KEhQsW4Gxjp9OAxdRViy2Nroet/sY+dr1nzZsiX394BL9U1mPbYfuY9NTm2x6Xi/Jx7KMTWLNIWP6eEZ5/Ap0n8n3WgC846dzJBzdguL4AT+aosSw1Ciph7d8StzqWZwYjug9vR+MVYZFyR8WLEsk7P0DrzpX8nfE7kStN2HK4a0Ymc99vpMYKMzF+0KNxey26x4XF7w0SsX+XEbCDIHy/BdB3DupxT9JmJEQEcGsCH1DgyuttUPxTKiL+jlvFMfjOa2hbvBHPxM8GLtXjlxVKvPi7Z/D3/0XYgaC4fykS7j+H598FctMjocAQTj5bgcBfVqHgUVE6/DsVHolXoaeU7PjzdDw8y4TLH2ihn5+NtEdcpc0A3Pvn83hzIBR5j4eRJWlMl5qgPfp71NS14cr4D/DQ/HnClc0RdFQ0wxQZiq+0Zajc+y4+n/ghIqa2C4z1orHuICr/Vxu+CgjHknuHUPdZENY+uRjOsTuGT4+2AOnrsfwBYZWVr8+hlmzK/O/xmEMWR5sr0PCnUMQ9HMRvpxPOhmp8eW88In7Mb2/590j85GoD3txVjY6RAEQ8EokfjbVBV3EIVX+8ih/OXYyH7xdVdqwL2to61FeQthgnEPjQYsy5l99k6j2JoxcCseS7MzhSth91bdcwqZqPhaEuMpBDfTmUxDbX61D21RJsiA/l19Hb5MerUXOoDh2XxhE4fylCqcvGe6GteRf/+/MhfGUYx+jAX/FAPDmeHkMfyTlWhdoDtJ6TuP/hSAQL2uLreQ/m/bkJVQf2Qv9ANmJ+Qja4Os/UMZ7bZjxTjyNVh1DfdgkTs+bjkYdE2nJTvksmzqLhdSOSy3JxX20D/pKswSOCO6n/6ps/w5fDf8bkV0bcszgac/7OWR+u9cljPK/Dscq3UPn+F5j4j1AsjFS50Lpz7Er6UOCbT+rwPtJRsIL6UUK3/0mJiMDrqKoawfL/JjreTZt55MXlFGaihUOd+I+lixH8n/lVXGz0EX8uns0fPzGAlqp2TERHI/BPTWi4Oh+ZC67iJPE1tcvkfdF4ZK7ga053pzHJ2Ztf5VpvQrtTM/HDDpoD6nF6jI+z+8Q5wBE3+qVwPq36HQ6cOs/5LGK+CgrhspenuJ/4km/fVH6ZNEH/0UnUHtyL9z+dwH2PzMeNdi3GYyVyDIGLhQ4zFsWEknxLkHP8NGJLUuc3RT4YbUPNsffx+ZdDGPv2Oob/jwKLFv4VHYeaMBG5FHNEIapvqsAfrocjRhyXHDL95MovXmpsjoPv3cayr3WSgJ6n5d/D8aPP6lG+vxGIT0EEzd9uynDWC/VlLRp+X0H8PY5vA8MFX9H+rgEfWvPxdeIHqhGJePGkX65fGtFy+ZlqalbEfDwUZGu06WobGisP4c1/6cBX40osiJ6LWfR40yW0vHMV4WmR0J/Yj/Lf/AEXJ4KwyLqdwPlFiA9n7Unn7b5T9aj6zVto+Wwckz+JtO8TRXC2Go9F3o8/c10ewVX7ufpoO/H5n67BfH0I5jkkZsl4wGttedKEH/o6R3zu+6w+W63GQqVzPyG/fyK4Gp9IjRUW/wUtPowfOFy1hzvP2/jEmov+Ks7DDrg7h1QfQzRf88e/iGLqS/xo/jdoK/sN3rw8F5plZJxsh3/yh7vxg62P/3/SsS8jHv+WuMVV1uPyBSWCxb2FKh7Jj3ah5YzoGvLkAM42k0FYShS3OPpFF/SpqUiwzzkcSvWb6HxTzV8putaLs9dTkZwksaNKjdc+ehNpXlx2VIVFkpMbXF7lMTVvh6ZIhxtRq/GLotUIH6lCXolOuBpuQJ9Wh7o9+9GtSkX20yuhbN+HvNfabOVd0+H5DXvQT7ZvWP9zRAztw866QWHj9DAMkHMPGIQlignDnTr0Cavo9sbDe1BzKRyZ63MRcbECBSXbUFZrQOjK1VizZBRHC0vQaL2AP1CNvLX7MTh7BTaU5GMFWrHlmQr0CbebzVc7Uafbh7IGE2KezOeOb/hlIakDv102k0Cw4h7++0Qv6vIKUGOMInXMR9qDgzhYUAjtVbJNQRJnXBTCiauDFy1HXFwYn+jGmvDS2hL0KFcQm69GjFmHorW70SMYnatnw24c+FczYlaTgf+DZKW781iPcds2M/oO5WFdxSCxXT42PLkAoxUF2NIwwm/2UL4rTJ0kUS1dSeqYiDT1GIkRoTxC4PzliKGNnxNF2r6A2EzYIMK9PvntOXt7EfxkMXY9vQI3tEV4Sevqjo1E7E4X+ljsLAUEb3O4a7MVT3Fph0KJmxer0dwvLJNB3dk6EhuHWnHZ+lhufyte7zIj0Jo2rtXjgODrX6QFouWVAlRa73ZOjOATMqAZtj5mwemtCI3mWE5vKwLa7PRGOfuWNQfkImasFnkv2HzghIfyjI3bkPNyFxQJxKerV0BxtgSarbbyPMW9PfSOfQEKGoxcLGQnKNDy8m587OaqKBcLnSPC3UAZx08ztiR1LvZBUBhZH4Vg0gOER5E8sIh00ooQKK6RAX2XyMoTp6F9qxeKuSHCCmfc+skhr6xQdqE0twgtNFx80ZgId7HsTZ0cc50jXM6v2IEDF0KQtl5DBqdkpTdlkH65bm0e99jSiqdKsCEB+PjFQuHJDiUixPmY+oEe4xAvcvTbWLsHB8+pkEw0lRzUiZc27rE9dkv6ocLCEzDE5WNXyWqEXtqPogrx3ekRNJQdw41FP8cGrs8tRaFouzg+rNrbWTaECKK9DSvDMPhWAZ6fyoFGdGwvwOufqZBZvBsbE0zQbiV94jVhsxSDtW7Kc2x/LMy6IpKD+XcwFXMWELsFE1OSCX3cckTQcba32vLkz2n3dY7MTN9H8ap/cjc+kRor+Dh+sLbn4FAYiaF8ZM4bs7VHKHMqF82nDpHAXS6hOPYxFEO/Le9y24/h9b1vwxxHdJTin5zm2FZP4wcbLmKfw308/k1huaX0WA6mbLa8NyosCtz4Y4klsfgDyw1h2fJpuWVl/gnL18LihbJUy7Z3HA6SghyX+PQpi0FYdM2o5b2nUy05m/dZfldebvsc77HVgeKpvG/1lmG96IhvOy0HSPuav6ELtK2pltc/FG0fPWXZllJiaf8LXbhp6d6tthQe1XObeG5YzryqdnNOvszCElGdrZ+SfEtiSrnlgrAntVliWY+wROHbfPBTfonb/monqYXAVycsRVN1oxgszZtTLQc+Eva4abAMXxHXSm95Z53a8jvhFIZ3NlsS1x2xDH/HL1MulKktOVX9wpID1Lai+lq+vWH5+tMjlm3pOZajwiE3Pii2rBTXkcCt221dZ98myuVKjYNNhXXH+XVS9fR0Ho9tM3xg2Z5SbGkWm2e003K8qpXTsOd2SEHsX5xqKf2joJ8vqiw5mirLZX6Jg6uXnY95fUzZw60+eQ0UnRDFlWHU8vW3wncnJGKX82G+pdRRi+RTul6sP4d6fXfTckPfYzm6WW1ZZ6cPz23mkB3nPF8fz7estNaFxuDT5ZaDT2umdHa5SmPJFzQj5evho+tsbeFi2GYHqq2cSnEbblounyq3vNdD2yCRAwQfOOZAK27L+46Ul07q/YWwifIdicN8teXAx7ySPMU91z6r7fQnLIXpuyxnRNWzXDlmKRT7ygFvj592bBGcdO7gAylt3vxol51Gbn68y65Pscezn7g62fmF6oLoSmiHNxqTwqmNMurkKdc5wmnj5VY7X3gqw87fJG4Ng3qLwSE2VpZb6+2cj+18JVe/dnXky7TmBK4+b3zCfef41mD52iDszZ1rneX4RX6Rg+gx30W/yJUlpV+yrpvLg9QHmyzvfcVt4bgxqrfcFLVfjMfyvuu3HNXkW965wm/isK6zukAit3mjLU/+9Ec82jHdvs8ulnnNW/XjVf/kYXxi1ZF9XnNeJ8d+dmNVguHjI5ajH1qPoW2wz0WOeMol9jYREOtCSudO+CF/yBg/2HKWq9h3H49/S9yGm2FkTmm9IiKgTFIj+4sm8K8hCO8spMdDeBCL46bDMS4xm3FT+OoJZTi9kkevKggfuxmxDGaFIZxM6umv8/U01aPmkA7dZLW4rsog0dWFELI/buAGd8VgDMP9CiQ8GsZt4lEiLjFR+O4a/kqIw4dekfIW5b229gbQK/whCJ66a6xC6HzSNqNwHUGhQniYEsahXvS06tBQcQQt1832v3QSpEKg6B75A2ELMDrhzhs6/M9/WIUk+snIQu72LkTurMJ67oaiGRe6exGjMqPvDLGv8Ll8nWzp12OUO96REVw4A0QoRqf2p5/hCSX6hqxXwwl29ZR5HjdtM3/Ri454NRLEdy1DErFm00qiYV/aQRjrRMv5lUiz3iWNSkImmnD2Ar8oCw/6jIiKR9/hYuysbSL2GYM5KATun7Zwjl2iRix01CL5LHR8Lo/QsE3w9aoMqDdsx9nIX6P8Kf7uMYc/2ixBaHwqwtvPgd6zNX1xDn+O1yA7SYX28/TqJ9FMJ5AijkMHXwcqXd2O5vVmvQPOo0CkZjMyY21xb5cDZolizgkP5Y0MEP+lYpn1BzYoAWFYEq/A2QHv36A3XxlEX+wKLBFVD/OSkEbvtsrA8/HTjy1fUcQ/juyhVpwR+pS+06eRkJNq16c44tpPI7h8HkhLEPsFCH80HsFd/dyPXXitMZm4q5OsXOeI6se+l0H6CNWCMCivDxF/tqHxcAUaOo3yf+1Krn7t6hiCiEhiUxN/uVu1OBrhTaXYsl9HjhmBKYD0UypxRKmIzYSvlFlK3C98lWRRNBY56nf2OfRxdz7CERM/grLi7Who6oV+zAwl6cMVIq064RQPsUiYJZQ30ov2SaKBUZute7r0uKEcgf6KS495oS1P/vR/PM5I3yfgVf8kZ3ziEXn2S0tLIiM1G6qkfKxPlRvbnnOJPBx07oJp5Q8Z41vPeBmPdzC3eLIUjNAHh6B3jJJZy5GcMoSPac82cQ4d7QvwZIpNfA/Mi0LPJRe/kib++eLwMCQMDRKH84uOOP6U4v0kkS17LN72iQ0RiYkkJiOpj5vEOKrbBvXP8lCmPYduowpx6kTECds8Y4DhuhJKh+BXzBI/lCRNKEkidvWmnyjvO2JvoD+cUUAmNNtr29BNEn944iokT/uUoh94qMwhXRMZeEdas64Jpm/Iea/1o7ub2Nf6GQ/D+nhXk1pqU/LviGh/8hlWxGP9gnul9ePTeewxjROdKBUu9vWt/NF2MhhAG57PsE4wilBD9Hu0Vf7P23jSpzJ9F3RVm7FsshcNLxdCnZGHmvNSz99QXMQuwhDjqEXyiZEYcE/9wMOH5dg4m+h4USRUoviS22ZPcekEifMUdOLCVRO62/q5gQUdgIAM0ExjvfjERDp7n15Al45h3/FQ3l+M0IeFEE/Yo1SF+DS5kNYtWZbZHs/HTz+2fGYWfYxzCO+3kwEl16dEIzne1aTXE9QvRDOOFwDIoDKU9D2c5WdMY67wJdc54mUZpl7UrM2A5oVadHSTYd28RDwe70Un4A/9Rm3CseP7kBkyhJbyYuQ8kYGXdESHvjKHPjQlhug3yIQb39LvKiTvOoEjm5cD3f+C0sIspK6tQJ+rFElR3etQngqq2UJ5tP0B5COydXf3EJnYaxAe5MZjsrXlyZ/+j8eZ6PuseNM/+Wd8Is9+gYrpZC4ZueSW4Dn2pze+vfv4ofD/LSIMEUuBjqskwB4VP2epwLJMDV4v68Tg7EE0LlVDJ9qsIokh4a0TaM5PRKbDIExfX4iCawVo+lUiFLPj8XhUBf6tsQAJOeLyCVfrsaVQj42nSrBM5mBg9Go/QqPXkXQnxQBaagexdt8prLdeKZs4jQ7hq2fCEbFoDGcHTcieZ0uv+kt0YLiAX5gGgaQDcnq4VF4PKoEJZ9/W4YFnT+A1tdUaI6SDE776g6h1eCYpF2V1vUh4NpYogiSPSAVuBmmwcb3crEdtShLa8k3YmCKd0Jyft/V8Hudj7FFFRiO0th/6yUREWgfxdBJP8rqSJkGv2zGCs81jyP7VMWyMF3W9V06i6MUm9Gwik3yPGpanT2VYPDKfoh8ij9YdSH2tHimnNiFS2G7DVez6QEAU1jydCPVb9ehL2owYri3y2+w+LqWIQoLahJpOHUJ7U5G2i6wKIAOQi6fQfuav6E5Zjb3eTL6miMTC2DG0XDSSGBbVhv79iQAy6fG6T/VQ3nwy4B9qxeXxHIROXa0zY/jSEBKW8L9P503c03e/FFS3ILoV1mFiCINDNCN7xvPx04+t6RCTqoGhrAuDIYPQJmnwjHzBOED9MoSOfhPpf2zaNA8OomdJNPh30WdKY67wnOs8412+NJ15G3VzN6PJ+o4wQV/nbubggAz9ymJ2FNLW0Q/5fqkWBYUV6EjZhWR+q3dcpHc4EoklBKh+B6IQM7VCifDHyGSGfNbQfvCVLOysT8WJTfZ3Bqb4YojEQ7x9eUPW8sIQN34DK57ajASZYxAeudry7E9/x6P/+z575PVP/hqfeLZfRLQSNWSSulV895/+fa1JuRfNZOSSoGDnO+DEpv7FU1sHoJ3W+Pbu45Y/hhezfCV6ugeIvB2gj9wYT6K0og3Jafa3ORGiwdbNQNnWHWgcEMJ50ohB7Q5sr1di63oyUeJWhiCzhOx4uAhvaMk5hLtIpgEd3nihHoGbCmRPlOhjcpe+ADIfc5EUyUBA9aAZhm+sLTFBf/IEGoUlz6iQkBmPjsO16LG+ED1yEjUN/hk+hM6LgqK9CR30ohu1Vd1+VArvXHqPEsEhChjGbHUzth+D1t2Lrl6jRMK6dQjWVaCRewSCaCVzHe6p34dGa70nTeh5Kxcaly8IqpCcvRKNh6ptNp0cQWNRBp53c/XR+/M4sEiNNSodarTWAogW6oqhfq2V07nX5Q80oeFaIpKTQrjb6FOf2FSSYEnCEv+0uvH/TuncHk/6HENjIbHLVJ0Bw3Xi3wedr/xacRm7PqBIyiODVx0qTwrnl91mibicGEOfh79tERmXiu76ejQmxWIh16lHYVnKaZRV9yIzLprbx3uIZv9xJc7W1tv0dr0NZWvzoL0kLHuFh/KCliMttRc1tV223NZVjcrOeDyZxA8SvIr7panYoCC6tfqA6rK6Co3S4wRnZBw/7diy4lLnblgi9CmHTiM7ebmsCaA0vF/scvV4F2pIuWmZSSTSeKatMa/a6Fuus8e7MugdIIVhDAZrHa+fhnbqF4BsmIwuMoQM/brHjL79GcirEB1vMMKkIDnLbsDgBdfqbTmI0285GuOzkEB/XOyaDlsyikTaNdLflUHEbDd1vV7vEA+i8man4p9ST6Oy2lZ/2uc/T87RIu5Lx402GwvI05Znf/otHq34u++bwpv+yV/jE8/2i0zPIWMUkuOmmjuEhq1ZeL1dPG4jmrQe74SMXEIff5s3gI+bhjidmK+1ofKtVm5X/+Gprb6Nb13GviQmDNbvR83fyJ/nueWTJUU8Ca6LTTgr1hYlgF49MUM/vhKZEr9mF5q9G0fWKPB+ca7wiE4uinQKrCnfbX+36UENXitfB4WOBOwq/nEedbEOijXleC3biyviI514f2Id0sTPV9sRgvTCHAzvz0JqFgn+J3JRE5SEbGGrHJTpv0KVuh/bszKgIZ/UF8aw5jm1sHV6KB4rwCuxA9i5ltoqD0eDVuOZadywiskpRkRzEZIycpGXkYHiC9HIdDWP9JV5OXhGYySdifCLgfPWYdfOaLQUriI2ziL+zEXpFTX25dM7T9IoUoqITgw4mEv8To5JXVWI9+cUY2umG9/7cB47AsKQ/UYJwhsLkfREBtRPZKGwPRx7nxeuwHpZ/iAZ7BrUaomJPf27ZCHQvst3RKr0fKwf3EfKy5L4tSJP+gxB5svFCNYWku15xKerkKdT4bUSDZ+wJXAZu75AbJb5tAaG+mrul67ktlkyLo2dqNnehlF3zopNJG03I3n5T6dsvvCniTCbE7HiUVleloTTW/YYSqneSFwk5ZXjxpp9WOMyb7jHfXmko322HGvGK6Ch730Rn2n2jCG7/FdIFpzmVdyTnLu+jNcAfWdQQ2JbO28ztsrNE3KOn25sEdzr3B1RZECgIH2K9e/1+Y5dXqF+yd0DQ3Y5XkwXRcs0NOZLG33KdQ54VUZsDl4Jb0UB0V7eP5P+qqgXkWpxJ0ByTr4Gl0jOScqq5t6xscezft2jQMymfci8WAr1z3JRQPpM9WuDyN6XjxjrXQ1vicrB42Ml3B9E19DHuC6qUfWS8HeYyFjixedU0G4k/fPaXOKbQjTOKcFWjRv7qouxxlV5Du2nfX5SYRNCny2xvedHbRx6kth4FXY2iwaeMrXl0Z9+iEc7/Nz32fCuf/LX+ESW/X4Vjvdpe8h5Un9WhI75O/DLqTwQi+xnQ3GUbE96UfTLxyI85xLSN25dh5snC4m9yD7F/Uh+yj/jQjHu2+rt+NZT7EthxIXWJjS0+ecC7EzzA/orD8L3W4ZRV4RSYxF+k+/b6N1sMsGsUHp+zIXeHjUroPT6eRgTevYWoGP5EWxNcZ64OULfhSIncf/ipzumblv7lK5uKbJt72e4981meXFezqZmKLz0i9fncYQ+MkW6CVe35Kddvg940ifnU/qYl4y7rtON3enhIi679iPp32LR9Lp1UHIb8FFvLvFUns+5TZrpxrWc42+H9kcbCrDFWIwTT/vpyo6//ewP/FEnb8rwh/amWwbNs5PTq4NRWwRNayp0lWQATuvj6lEqX+zrrjwK3T5B6j9Tfb6MOvs9Hmeo7/Omf/Lb+ESm/dz1qx7xR9z6Aw/1mHY77xJuy2SJwWAw/MXoyUKUBezAXm/uHDPuaszjRvL5DDVFxxBefgTZvr0ywbiLsZssCesYDAZDitvw0+EMBoPhPwJjN2FrOpsoMWyMNu1AUUkTFM/uZhMlhjSzVIj05x/ZZjAYdy3szhKDwWAwGAwGg8FgSMDuLDEYDAaDwWAwGAyGBGyyxGAwGAwGg8FgMBgSsMkSg8FgMBgMBoPBYEjAJksMBoPBYDAYDAaDIQGbLDEYDAaDwWAwGAyGBGyyxGAwGAwGg8FgMBgSsMkSg8FgMBgMBoPBYEjAJksMBoPBYDAYDAaDIQGbLDEYDAaDwWAwGAyGBGyyxGAwGAwGg8FgMBgSsMkSg8FgMBgMBoPBYEjAJksMBoPBYDAYDAaDIQGbLDEYDAaDwWAwGAyGBGyyxGAwGAwGg8FgMBgSsMkSg8FgMBgMBoPBYEjAJksMBoPBYDAYDAaDIQGbLDEYDAaDwWAwGAyGBGyyxGAwGAwGg8FgMBhOAP8ffaVhCxmt1+oAAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"![image.png](attachment:ec164fe6-8b6c-4e15-b4be-ec7be3b82349.png)","metadata":{},"attachments":{"ec164fe6-8b6c-4e15-b4be-ec7be3b82349.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## Summary of Tournament Overview\n\n<font size=\"4\"> Imagine having a special camera capable of capturing 3D images inside a human kidney. However, the focus is not on observing the entire kidney; rather, it is on the intricate network of blood vessels within the kidney. Scientists aim to understand these blood vessels, including their visual characteristics, arrangement, and organization within the kidney. </font>\n\n<font size=\"4\"> Currently, scientists must manually trace these vessels, which is a time-consuming process. In this competition, Kaggle users are asked to build models capable of examining 3D kidney images and accurately outlining the blood vessels. This can be thought of as creating software that can accurately trace the blood vessels in a given kidney image. This is important because understanding these blood vessels can yield valuable insights. For example, scientists could simulate blood flow, oxygen transport, or medication distribution through these vessels. They could also observe how these blood vessels change in response to factors like age, size, or shape. </font>\n\n<font size=\"4\">To summarize, this competition asks Kaggle users to create models that can identify and outline blood vessels in 3D kidney images. This may assist scientists to gain deeper insights into the structure of blood vessels and their impact on our bodies. </font>","metadata":{}},{"cell_type":"markdown","source":"## Code\n\n<font size=\"4\"> To begin, import the required packages. </font>","metadata":{}},{"cell_type":"code","source":"!pip install segmentation-models","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:15:50.580193Z","iopub.execute_input":"2024-11-17T19:15:50.580634Z","iopub.status.idle":"2024-11-17T19:16:05.247116Z","shell.execute_reply.started":"2024-11-17T19:15:50.580589Z","shell.execute_reply":"2024-11-17T19:16:05.246055Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport random\nimport time\nimport gc\nimport numpy as np\nimport tifffile\nimport cv2  \nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, EarlyStopping\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.layers import (Input, Conv2D, UpSampling2D, Concatenate, Activation, Multiply, MaxPooling2D, Conv2DTranspose, concatenate)\nimport tensorflow.keras.layers as layers\nfrom tensorflow.keras.models import Model\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras.optimizers import Adam\nimport pandas as pd\n\nos.environ[\"SM_FRAMEWORK\"] = \"tf.keras\"  # Force segmentation_models to use tf.keras\nfrom segmentation_models import Unet\n\n# Disable cuDNN autotuning to avoid potential convolution algorithm mismatches and precision errors on GPU.\ntf.config.optimizer.set_experimental_options({\"cudnn_autotune\": False})\n\nfrom tensorflow.keras.mixed_precision import set_global_policy, Policy\n\n# Set mixed precision policy\npolicy = Policy('mixed_float16')\nset_global_policy(policy)\n\n\n# Set random seed for reproducibility\nnp.random.seed(55)\ntf.random.set_seed(55)\n\n# Record the start time\nstart_time = time.time()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:16:05.249146Z","iopub.execute_input":"2024-11-17T19:16:05.249511Z","iopub.status.idle":"2024-11-17T19:16:19.116780Z","shell.execute_reply.started":"2024-11-17T19:16:05.249474Z","shell.execute_reply":"2024-11-17T19:16:19.115730Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\"> The data is provided in a Kaggle notebook environment. The following code displays the unique paths available in the environment. </font>","metadata":{"execution":{"iopub.execute_input":"2024-01-14T19:21:15.459062Z","iopub.status.busy":"2024-01-14T19:21:15.458535Z","iopub.status.idle":"2024-01-14T19:21:15.468074Z","shell.execute_reply":"2024-01-14T19:21:15.466103Z","shell.execute_reply.started":"2024-01-14T19:21:15.459021Z"}}},{"cell_type":"code","source":"# Initialize an empty set to store unique folder paths\nunique_folders = set()\n\n# Specify the root directory\nroot_directory = '/kaggle/input'\n\n# Iterate through all directories and subdirectories\nfor dirpath, dirnames, filenames in os.walk(root_directory):\n    for dirname in dirnames:\n        folder_path = os.path.join(dirpath, dirname)\n        unique_folders.add(folder_path)\n\n# Convert the set to a list and sort it\nunique_folders_list = sorted(list(unique_folders))\n\n# Print the sorted unique folder paths\nfor folder_path in unique_folders_list:\n    print(folder_path)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:16:19.117921Z","iopub.execute_input":"2024-11-17T19:16:19.118467Z","iopub.status.idle":"2024-11-17T19:16:39.702459Z","shell.execute_reply.started":"2024-11-17T19:16:19.118431Z","shell.execute_reply":"2024-11-17T19:16:39.701461Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\"> Viewing the available directories in the environment provides insight regarding the structure of the data that has been provided. Let's look at some files in the directories as well. Samples image and label file names can be viewed in the kidney_1_dense and kidney_2 directories.  </font>","metadata":{"execution":{"iopub.execute_input":"2024-01-14T19:24:17.479660Z","iopub.status.busy":"2024-01-14T19:24:17.478826Z","iopub.status.idle":"2024-01-14T19:24:17.492429Z","shell.execute_reply":"2024-01-14T19:24:17.489637Z","shell.execute_reply.started":"2024-01-14T19:24:17.479595Z"}}},{"cell_type":"code","source":"# Directories to explore\ndirectories = [\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_dense/images',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_dense/labels',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_2/images',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_2/labels'\n]\n\n# Function to list first five files in a directory\ndef list_sample_files(directory, num_files=5):\n    files = os.listdir(directory)\n    return sorted(files)[:num_files]\n\n# Iterate through the directories and list the first five files\nfor directory in directories:\n    print(f\"Files in {directory}:\")\n    files = list_sample_files(directory)\n    for file in files:\n        print(f\"  {file}\")\n    print(\"\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:16:39.705108Z","iopub.execute_input":"2024-11-17T19:16:39.705791Z","iopub.status.idle":"2024-11-17T19:16:39.722055Z","shell.execute_reply.started":"2024-11-17T19:16:39.705744Z","shell.execute_reply":"2024-11-17T19:16:39.721160Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\"> **Observing Folder Structure:**\n  By examining the structure of the directories, a common pattern can be identified across the folders. Each kidney type is organized into subcategories, each containing two key directories:\n  - <font size=\"4\">An \"image\" directory: This directory contains the original kidney images.\n  - <font size=\"4\">A \"label\" directory: This directory contains the corresponding masks for those images.\n  - <font size=\"4\">The filenames in each directory match, which helps with pairing images and masks (e.g., \"0000.tif,\" \"0001.tif,\" etc.).\n  - <font size=\"4\">Note that kidney_3_dense only includes the label images (masks). For this reason, kidney_3_dense was not included in the training dataset.\n  ","metadata":{"execution":{"iopub.execute_input":"2024-01-15T04:59:13.394790Z","iopub.status.busy":"2024-01-15T04:59:13.394364Z","iopub.status.idle":"2024-01-15T04:59:13.427936Z","shell.execute_reply":"2024-01-15T04:59:13.426467Z","shell.execute_reply.started":"2024-01-15T04:59:13.394759Z"}}},{"cell_type":"markdown","source":"<font size=\"4\"> **Visualizing Kidney Data:**\n  To gain insights into the data, the images and their associated masks can be visualized. Overlay representations for each distinct kidney are also displayed. To accomplish this, the following steps are performed through the code below:\n  - <font size=\"4\">Iterating through each subcategory directory.\n  - <font size=\"4\">Loading an image and its corresponding mask.\n  - <font size=\"4\">Generating an overlay of the mask onto the image.\n  - <font size=\"4\">Displaying the image, mask, and mask overlay side by side for enhanced comprehension.\n  - <font size=\"4\">Additionally, copies of the images with aesthetic enhancements, such as \"equalized\" images and color, have been added to improve the visual appeal of the representations.\n","metadata":{}},{"cell_type":"code","source":"# Define the base directory path\nbase_path = \"/kaggle/input/blood-vessel-segmentation/train/\"\n\n# Define a list of kidney types\nkidney_types = [\"kidney_1_dense\", \"kidney_1_voi\", \"kidney_2\", \"kidney_3_sparse\"]\n\n# Gradient function to create a radial gradient\ndef create_radial_gradient(size, center, max_radius, background_color, mask_color):\n    gradient = np.zeros((size[0], size[1], 3), dtype=np.uint8)\n    for y in range(size[0]):\n        for x in range(size[1]):\n            distance = np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2)\n            normalized_distance = distance / max_radius\n            gradient[y, x, :] = [int((1 - normalized_distance) * mask_color + normalized_distance * background_color)] * 3\n    return gradient\n\n# Iterate through each kidney type\nfor selected_kidney_type in kidney_types:\n    print(f\"Displaying images for: {selected_kidney_type}\")\n\n    # Define the image and label directories for the selected kidney type\n    image_directory = f\"{selected_kidney_type}/images\"\n    label_directory = f\"{selected_kidney_type}/labels\"\n\n    # Construct the full file path for the image directory\n    image_path = os.path.join(base_path, image_directory)\n\n    # Construct the full file path for the label directory\n    label_path = os.path.join(base_path, label_directory)\n\n    # Check if the file exists\n    if os.path.exists(image_path) and os.path.exists(label_path):\n        # List all files in the image directory\n        image_files = os.listdir(image_path)\n\n        # Randomly select an image file\n        selected_image_file = random.choice(image_files)\n        image_file_path = os.path.join(image_path, selected_image_file)\n\n        # Load the original grayscale image\n        image_gray = cv2.imread(image_file_path, cv2.IMREAD_GRAYSCALE)\n\n        # Load the corresponding mask\n        mask_file_path = os.path.join(label_path, selected_image_file)\n        mask = cv2.imread(mask_file_path, cv2.IMREAD_GRAYSCALE)\n        \n        # Apply histogram equalization to enhance contrast\n        image_equalized = cv2.equalizeHist(image_gray)\n        mask_equalized = cv2.equalizeHist(mask)\n\n        # Overlay the mask on the original image\n        original_overlay = cv2.addWeighted(cv2.cvtColor(image_gray, cv2.COLOR_GRAY2BGR), 0.7, cv2.applyColorMap(mask, cv2.COLORMAP_JET), 0.3, 0)\n\n        # Create a radial gradient for the background\n        gradient_size = mask_equalized.shape\n        gradient_center = (gradient_size[1] // 2, gradient_size[0] // 2)\n        max_gradient_radius = min(gradient_center[0], gradient_center[1])\n        radial_gradient = create_radial_gradient(gradient_size, gradient_center, max_gradient_radius, 220, 255)\n\n        # Create a mask for the background\n        background_mask = (mask_equalized == 0)\n\n        # Apply the radial gradient to the background\n        color_enhanced_mask = cv2.applyColorMap(mask_equalized, cv2.COLORMAP_JET)\n        color_enhanced_mask[background_mask] = radial_gradient[background_mask]\n\n        # Overlay the equalized mask on the equalized image\n        equalized_overlay = cv2.addWeighted(cv2.cvtColor(image_equalized, cv2.COLOR_GRAY2BGR), 0.7, cv2.applyColorMap(mask_equalized, cv2.COLORMAP_JET), 0.3, 0)\n\n        # Create the figure for displaying images\n        plt.figure(figsize=(18, 8))\n\n        # Displaying original set: image, mask, overlay\n        plt.subplot(2, 3, 1)\n        plt.imshow(cv2.cvtColor(image_gray, cv2.COLOR_GRAY2RGB))\n        plt.title(f'Original Image - {selected_kidney_type}')\n        plt.axis('off')\n\n        \n        \n        plt.subplot(2, 3, 2)\n        plt.imshow(mask, cmap='gray')\n        plt.title(f'Original Mask - {selected_kidney_type}')\n        plt.axis('off')\n\n        plt.subplot(2, 3, 3)\n        plt.imshow(cv2.cvtColor(original_overlay, cv2.COLOR_BGR2RGB))\n        plt.title(f'Original Overlay - {selected_kidney_type}')\n        plt.axis('off')\n\n        # Displaying equalized set: image, mask, overlay\n        plt.subplot(2, 3, 4)\n        plt.imshow(cv2.cvtColor(image_equalized, cv2.COLOR_GRAY2RGB))\n        plt.title(f'Color Enhanced Image - {selected_kidney_type}')\n        plt.axis('off')\n\n        plt.subplot(2, 3, 5)\n        plt.imshow(cv2.cvtColor(color_enhanced_mask, cv2.COLOR_BGR2RGB))\n        plt.title(f'Color Enhanced Mask - {selected_kidney_type}')\n        plt.axis('off')\n\n        plt.subplot(2, 3, 6)\n        plt.imshow(cv2.cvtColor(equalized_overlay, cv2.COLOR_BGR2RGB))\n        plt.title(f'Color Enhanced Overlay - {selected_kidney_type}')\n        plt.axis('off')\n\n        plt.show()\n    else:\n        print(f\"File not found: {image_path} or {label_path}\")\n\n\ngc.collect()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:16:39.723572Z","iopub.execute_input":"2024-11-17T19:16:39.724113Z","iopub.status.idle":"2024-11-17T19:17:37.037744Z","shell.execute_reply.started":"2024-11-17T19:16:39.724068Z","shell.execute_reply":"2024-11-17T19:17:37.036858Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size='4'> The machine learning models require the images to be the same shape. The code below checks the image size dimensions for each kidney category.","metadata":{}},{"cell_type":"code","source":"def get_image_dimensions(image_directory):\n    # Define the path to the 'images' subdirectory\n    images_path = os.path.join(image_directory, 'images')\n    \n    # Check if the 'images' subdirectory exists\n    if not os.path.exists(images_path):\n        print(f\"'images' subdirectory does not exist in {image_directory}\")\n        return set()\n\n    image_files = os.listdir(images_path)\n    unique_shapes_and_channels = set()  # To collect unique image shapes and channels\n    for image_file in image_files:\n        if not image_file.lower().endswith(('.tif', '.tiff')):\n            # Skip non-TIFF files\n            continue\n\n        full_image_path = os.path.join(images_path, image_file)\n        try:\n            image = cv2.imread(full_image_path, cv2.IMREAD_UNCHANGED)  # Use IMREAD_UNCHANGED to ensure TIFF files are read correctly\n            if image is not None:\n                if len(image.shape) == 3:\n                    height, width, channels = image.shape\n                else:\n                    height, width = image.shape\n                    channels = 1  # A single channel for grayscale images\n                shape_and_channels = (height, width, channels)\n                unique_shapes_and_channels.add(shape_and_channels)  # Collect unique shapes and channels\n            else:\n                print(f\"Failed to load image: {image_file}\")\n        except Exception as e:\n            print(f\"Error while processing {image_file}: {str(e)}\")\n    return unique_shapes_and_channels\n\n# Example usage\nimage_directories = [\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_dense',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_2',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_voi',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_3_sparse'\n]\n\nfor image_directory in image_directories:\n    print(f\"Getting unique dimensions and channels for images in: {image_directory}\")\n    unique_shapes_and_channels = get_image_dimensions(image_directory)\n    \n    if unique_shapes_and_channels:\n        print(f\"Unique Image Shapes and Channels in {image_directory}:\")\n        for shape_and_channels in unique_shapes_and_channels:\n            print(f\"Shape and Channels: {shape_and_channels}\")\n    else:\n        print(f\"No valid TIFF image files found in {image_directory}\")\n\ngc.collect()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:17:37.039050Z","iopub.execute_input":"2024-11-17T19:17:37.039402Z","iopub.status.idle":"2024-11-17T19:25:30.454120Z","shell.execute_reply.started":"2024-11-17T19:17:37.039366Z","shell.execute_reply":"2024-11-17T19:25:30.453270Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\"> **With the code above, the following information is able to be determined:**\n\n- <font size=\"4\"> **Images Shape Consistency:** For each directory, the images share the same shape. Each directory has unique image shapes, so the images will need to be resized to ensure that all images are the same size for training.\n\n- <font size=\"4\"> **Grayscale Images:** The images are grayscale (black and white), which is important as this will affect the architecture of the models.\n\n- <font size=\"4\"> **Basic Information for needed for computer vision models:**\n  - <font size=\"4\"> **Directories of Image and Label Files:** It is important that the image and label files are organized into directories suitable for the task. For image segmentation tasks, corresponding labels are needed to define the ground truth for the images. These labels specify which parts of the image correspond to different classes or objects.\n\n  -  <font size=\"4\">**Image Size:** This refers to the dimensions of the image (width and height in pixels.) The model needs to know the input size to correctly process the images.\n\n  -  <font size=\"4\">**Number of Channels:** It's important to determine whether the images are grayscale (single-channel) or color (typically three-channel RGB). This information is needed for setting up the input layer of the model.\n\n","metadata":{}},{"cell_type":"markdown","source":"# Preprocess Data","metadata":{}},{"cell_type":"markdown","source":"<font size=\"4\"> **The code below does the following to preprocess the images for training:**\n\n- <font size=\"4\"> **Define Image Directories**: `image_paths` is a list that contains the paths to the directories where training images are stored. \n\n- <font size=\"4\"> **Image Preprocessing Functions**: `preprocess_image` is a function that loads and preprocesses an image from a given file path. It reads a TIFF image, normalizes pixel values, adds a channel dimension (grayscale), and resizes the image to a fixed size (256x256 pixels). A similar function is used to preprocess the masks.\n\n- <font size=\"4\"> **Load and Preprocess Data**: The code loads and preprocesses both images and corresponding masks from the specified directories. It retrieves all image and mask file paths within the specified directories. Then, it applies the preprocessing functions to each image and mask, creating arrays of preprocessed images and masks.\n","metadata":{}},{"cell_type":"code","source":"# Define the paths to the image directories\nimage_paths = [\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_dense',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_2',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_1_voi',\n    '/kaggle/input/blood-vessel-segmentation/train/kidney_3_sparse'\n]\n\n# Function to preprocess an image\ndef preprocess_image(data_folder):\n    image = tifffile.imread(data_folder)\n    if image.ndim == 2:  # Handle grayscale images\n        image = image[..., np.newaxis]  # Add a channel dimension\n    mean = np.mean(image)  # Normalize the pixel values\n    std = np.std(image)\n    image = (image - mean) / std\n    image_tensor = tf.convert_to_tensor(image, dtype=tf.float32)\n    image_tensor = tf.image.resize(image_tensor, [256, 256])\n    return image_tensor\n\n# Function to preprocess a mask\ndef preprocess_mask(path):\n    mask = tifffile.imread(path)\n    if mask.ndim == 2:  # Handle grayscale masks\n        mask = mask[..., np.newaxis]  # Add a channel dimension\n    mask_tensor = mask / 255.0  # Normalize the pixel values\n    mask_tensor = tf.image.resize(mask_tensor, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n    return mask_tensor\n\n# Load file paths for images and masks\nimage_files = []\nlabel_files = []\n\n# Get all files in the image and label folders for each path in image_paths\nfor image_path in image_paths:\n    images_folder = os.path.join(image_path, 'images')\n    labels_folder = os.path.join(image_path, 'labels')\n\n    # List and sort the files\n    images = sorted([os.path.join(images_folder, f) for f in os.listdir(images_folder) if f.endswith('.tif')])\n    labels = sorted([os.path.join(labels_folder, f) for f in os.listdir(labels_folder) if f.endswith('.tif')])\n    \n    # Ensure the number of images and labels match\n    if len(images) != len(labels):\n        raise ValueError(f\"Number of images and labels do not match in {image_path}\")\n\n    image_files.extend(images)\n    label_files.extend(labels)\n\n# Ensure that the number of images and masks match overall\nif len(image_files) != len(label_files):\n    raise ValueError(\"The number of images and masks do not match!\")\n\n# Split into train and validation sets with 20% for validation\nimage_files_train, image_files_val, label_files_train, label_files_val = train_test_split(image_files, label_files, test_size=0.2, random_state=42)\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:30.455618Z","iopub.execute_input":"2024-11-17T19:25:30.456042Z","iopub.status.idle":"2024-11-17T19:25:30.522088Z","shell.execute_reply.started":"2024-11-17T19:25:30.455997Z","shell.execute_reply":"2024-11-17T19:25:30.521410Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">**Data Generator:** Instead of loading the entire dataset into memory at once (which may cause memory issues), a data generator is used to load smaller batches of images and masks during each training step. A batch size of four was chosen for this expirment. Larger batch sizes caused the models to throw a memory error. ","metadata":{}},{"cell_type":"code","source":"# Define the batch size\nbatch_size = 4\n\n# Define the data generator\ndef data_generator(image_files, label_files, batch_size):\n    num_samples = len(image_files)\n    \n    # Generate batches of data\n    while True:\n        for offset in range(0, num_samples, batch_size):\n            batch_image_files = image_files[offset:offset + batch_size]\n            batch_label_files = label_files[offset:offset + batch_size]\n            \n            # Preprocess images and masks dynamically\n            batch_images = np.array([preprocess_image(f) for f in batch_image_files])\n            batch_masks = np.array([preprocess_mask(f) for f in batch_label_files])\n            \n            yield batch_images, batch_masks\n\n\n\n# Create datasets for training and validation using the generator\ntrain_dataset = tf.data.Dataset.from_generator(\n    lambda: data_generator(image_files_train, label_files_train, batch_size),\n    output_signature=(\n        tf.TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32),\n        tf.TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32)\n    )\n)\n\nval_dataset = tf.data.Dataset.from_generator(\n    lambda: data_generator(image_files_val, label_files_val, batch_size),\n    output_signature=(\n        tf.TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32),\n        tf.TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32)\n    )\n)\n\n# Prefetch the dataset to improve performance\ntrain_dataset = train_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\nval_dataset = val_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n\n# Optional: Garbage collection after dataset creation\ngc.collect()\n\n# Continue with model creation and training as before","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:30.523034Z","iopub.execute_input":"2024-11-17T19:25:30.523313Z","iopub.status.idle":"2024-11-17T19:25:31.574347Z","shell.execute_reply.started":"2024-11-17T19:25:30.523282Z","shell.execute_reply":"2024-11-17T19:25:31.573439Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- <font size=\"4\"> **Subset Selection**: The dataset is into a 80% training dataset and 20% validation dataset.    ","metadata":{}},{"cell_type":"code","source":"# Total number of samples in your dataset\ntrain_dataset_size = len(image_files_train)  # Total number of training samples\nval_dataset_size = len(image_files_val)  # Total number of validation samples\n\n# Calculate steps_per_epoch and validation_steps\ntrain_steps_per_epoch = train_dataset_size // batch_size\nvalidation_steps_per_epoch = val_dataset_size // batch_size","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:31.575784Z","iopub.execute_input":"2024-11-17T19:25:31.576161Z","iopub.status.idle":"2024-11-17T19:25:31.581264Z","shell.execute_reply.started":"2024-11-17T19:25:31.576120Z","shell.execute_reply":"2024-11-17T19:25:31.580319Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">**Visualize Sample Images**: View a sample of images in the train_dataset and val_dataset to ensure the images and masks are aligned correctly.","metadata":{}},{"cell_type":"code","source":"# Function to plot sample images and masks\ndef plot_samples(dataset, num_samples=1):\n    # Create an iterator for the dataset\n    iterator = iter(dataset)\n    \n    for _ in range(num_samples):\n        # Get the next batch from the iterator\n        images, masks = next(iterator)\n        \n        # Plot the first image and mask in the batch\n        for i in range(batch_size):\n            fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n            \n            # Image\n            ax[0].imshow(tf.squeeze(images[i]), cmap='gray')\n            ax[0].set_title(\"Image\")\n            \n            # Mask\n            ax[1].imshow(tf.squeeze(masks[i]), cmap='gray')\n            ax[1].set_title(\"Mask\")\n            \n            plt.show()\n\n# Call the function to plot samples from the train dataset\nplot_samples(train_dataset, num_samples=1)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:31.586646Z","iopub.execute_input":"2024-11-17T19:25:31.587279Z","iopub.status.idle":"2024-11-17T19:25:33.911865Z","shell.execute_reply.started":"2024-11-17T19:25:31.587239Z","shell.execute_reply":"2024-11-17T19:25:33.910907Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">The file names of images and masks can also be printed to confirm alignment.","metadata":{}},{"cell_type":"code","source":"# Print first 5 image and mask file pairs to verify alignment\nfor img_file, mask_file in zip(image_files[:5], label_files[:5]):\n    print(f\"Image: {os.path.basename(img_file)} - Mask: {os.path.basename(mask_file)}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:33.913141Z","iopub.execute_input":"2024-11-17T19:25:33.913510Z","iopub.status.idle":"2024-11-17T19:25:33.919238Z","shell.execute_reply.started":"2024-11-17T19:25:33.913463Z","shell.execute_reply":"2024-11-17T19:25:33.918134Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- <font size=\"4\"> **Imbalanced data check**: The masks are imbalanced, with 99% class 0 (background) and less than 1% class 1 (blood vessels). For this reason, a custom loss funtion will be defined which is better for training with imbalanced datasets (BCE Dice Loss, described below).","metadata":{}},{"cell_type":"code","source":"# Function to calculate the class distribution (Background and Blood Vessels) with a threshold\ndef calculate_class_distribution(dataset, num_batches=None, threshold=0.5):\n    class_0_count = 0\n    class_1_count = 0\n    total_batches = 0\n    \n    for batch_images, batch_masks in dataset:\n        # Apply the threshold to binarize the masks\n        binarized_masks = (batch_masks >= threshold).numpy().astype(int)\n        \n        # Flatten the masks to 1D array\n        flattened_masks = binarized_masks.flatten()\n        \n        # Count class 0 (Background) and class 1 (Blood Vessels)\n        class_0_count += np.sum(flattened_masks == 0)\n        class_1_count += np.sum(flattened_masks == 1)\n        \n        total_batches += 1\n        if num_batches and total_batches >= num_batches:\n            break\n    \n    return class_0_count, class_1_count\n\n# Call the function to calculate the class distribution on the training dataset\nclass_0_count, class_1_count = calculate_class_distribution(train_dataset, num_batches=100, threshold=0.5)\n\n# Calculate the ratio of class 1 to class 0\nif class_0_count == 0:\n    print(\"No background pixels found in the dataset.\")\nelse:\n    class_ratio = class_1_count / class_0_count\n\n    # Print the results\n    print(\"Class 0 (Background) Count:\", class_0_count)\n    print(\"Class 1 (Blood Vessels) Count:\", class_1_count)\n    print(\"Class 1 to Class 0 Ratio:\", class_ratio)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:33.920597Z","iopub.execute_input":"2024-11-17T19:25:33.920913Z","iopub.status.idle":"2024-11-17T19:25:58.917720Z","shell.execute_reply.started":"2024-11-17T19:25:33.920880Z","shell.execute_reply":"2024-11-17T19:25:58.916779Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Loss, Evaluation Metric and Parameters for Each Model\n\n<font size=\"4\">**Dice Coefficient (Custom Metric):** The `dice_coefficient` function computes the Dice coefficient, which is a similarity metric used to evaluate the overlap between two binary images (e.g., predicted and ground truth masks). It is a measure of how well the predicted mask aligns with the true mask.\n\n<font size=\"4\">**BCE Dice Loss (Custom Loss):** The `bce_dice_loss` function combines binary cross-entropy loss (`bce`) and the Dice coefficient defined below. This combined loss function is often used in image segmentation tasks to balance the trade-off between pixel-wise accuracy and segmentation quality.\n    ","metadata":{}},{"cell_type":"code","source":"# Define the Dice Coefficient\ndef dice_coefficient(y_true, y_pred):\n    y_true_f = K.flatten(tf.cast(y_true, tf.float32))\n    y_pred_f = K.flatten(tf.cast(y_pred, tf.float32))\n    intersection = K.sum(y_true_f * y_pred_f)\n    return (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())\n\ndef bce_dice_loss(y_true, y_pred):\n    bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n    dice = dice_coefficient(y_true, y_pred)\n    total_loss = bce - K.log(dice)\n    return total_loss\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:58.918894Z","iopub.execute_input":"2024-11-17T19:25:58.919180Z","iopub.status.idle":"2024-11-17T19:25:58.925838Z","shell.execute_reply.started":"2024-11-17T19:25:58.919149Z","shell.execute_reply":"2024-11-17T19:25:58.924906Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- <font size=\"4\">**Early Stopping:** Stops training when validation loss stops improving, preventing overfitting. The patience parameter waits 5 epochs before stopping, and restore_best_weights=True ensures the model reverts to the best weights.\n\n- <font size=\"4\">**Learning Rate Schedule:** Gradually decreases the learning rate each epoch. The rate starts at 0.001 and decays each epoch by multiplying by 0.96, helping the model converge smoothly.\n    \n- <font size=\"4\">**ReduceLROnPlateau:** Lowers the learning rate when validation loss plateaus to help the model continue improving. If no improvement is seen for 3 epochs, the learning rate is reduced by a factor of 0.2, with a minimum limit of 0.00001.","metadata":{}},{"cell_type":"code","source":"# Early stopping callback to monitor validation Dice coefficient\nearly_stopping = EarlyStopping(\n    monitor='val_loss',  # Monitor the validation Dice coefficient\n    patience=5,                      # Number of epochs with no improvement after which training will be stopped\n    mode='min',                      # Mode should be 'min' as we want to minimize validation loss\n    min_delta=0.01,                  # Minimum change to qualify as an improvement\n    restore_best_weights=True        # Restore model weights from the epoch with the best value of the monitored quantity\n)\n\n# Define the learning rate schedule as a callback\nlr_schedule = tf.keras.callbacks.LearningRateScheduler(\n    lambda epoch, lr: 0.001 * 0.96 ** (epoch / 100),\n    verbose=1\n)\n\n# Define the ReduceLROnPlateau callback\nreduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n    monitor='val_loss', \n    factor=0.2,\n    patience=3, \n    min_lr=0.00001\n)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:58.927146Z","iopub.execute_input":"2024-11-17T19:25:58.927483Z","iopub.status.idle":"2024-11-17T19:25:58.935974Z","shell.execute_reply.started":"2024-11-17T19:25:58.927437Z","shell.execute_reply":"2024-11-17T19:25:58.935064Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Pre-Trained U-Net Model\n<font size=\"4\"> The first model in this expiriment is a pre-trained U-Net. The code below does the following:\n\n<font size=\"4\">**Defines a pre-trained U-Net model for image segmentation:** The U-Net architecture is adapted with a ResNet50 backbone that is pre-trained on ImageNet. It supports binary segmentation tasks and has been modified to work with grayscale images by converting the input to RGB format. The model is compiled with the custom BCE Dice loss function and the Dice coefficient metric defined above.\n\n<font size=\"4\">**Adjusting for grayscale input:** The model is originally designed for RGB images, but is adjusted to accept grayscale images by replicating the single channel into three channels to mimic RGB.\n\n<font size=\"4\">**Fitting the model:** The model is trained for 25 epochs with early stopping and learning rate adjustments to minimize validation loss. A checkpoint callback is implemented to save the best model during training.","metadata":{}},{"cell_type":"code","source":"# Function to create a pre-trained U-Net model\ndef create_pretrained_unet(input_shape):\n    # Load a pre-trained ResNet50 backbone from ImageNet and adapt it for U-Net\n    unet_model = Unet(backbone_name='resnet50', encoder_weights='imagenet', input_shape=input_shape, classes=1, activation='sigmoid')\n    return unet_model\n\n# Function to adjust for grayscale input\ndef adjust_input_for_grayscale(model, input_shape):\n    # Replace the input layer to accept grayscale images\n    input_layer = layers.Input(shape=input_shape)\n    grayscale_to_rgb = layers.Concatenate()([input_layer, input_layer, input_layer])  # Convert grayscale to RGB\n    output = model(grayscale_to_rgb)\n    grayscale_model = Model(inputs=input_layer, outputs=output)\n    \n    return grayscale_model\n\n# Adjusted for grayscale images\ninput_shape = (256, 256, 1)\nunet_model = create_pretrained_unet((256, 256, 3))\nunet_model = adjust_input_for_grayscale(unet_model, input_shape)\n\n# Compile the model\nunet_model.compile(optimizer='adam', loss=bce_dice_loss, metrics=[dice_coefficient])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:25:58.937207Z","iopub.execute_input":"2024-11-17T19:25:58.937603Z","iopub.status.idle":"2024-11-17T19:26:02.092358Z","shell.execute_reply.started":"2024-11-17T19:25:58.937560Z","shell.execute_reply":"2024-11-17T19:26:02.091350Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define the ModelCheckpoint callback\ncheckpoint_unet = ModelCheckpoint(\n    'best_model_unet.keras',  # File path where the model will be saved\n    monitor='val_loss',  # Metric to monitor\n    mode='min',  # We want to minimize validation loss\n    save_best_only=True,  # Only save the best model\n    verbose=1\n)\n\n# Train the enhanced ResNet model\nepochs = 25\n\n# Train the U-Net model and store the history\nhistory_unet = unet_model.fit(\n    train_dataset,\n    validation_data=val_dataset,\n    epochs=epochs,\n    steps_per_epoch=train_steps_per_epoch,\n    validation_steps=validation_steps_per_epoch,\n    callbacks=[early_stopping, reduce_lr, lr_schedule, checkpoint_unet]\n)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T19:26:02.093554Z","iopub.execute_input":"2024-11-17T19:26:02.093876Z","iopub.status.idle":"2024-11-17T21:25:06.918587Z","shell.execute_reply.started":"2024-11-17T19:26:02.093842Z","shell.execute_reply":"2024-11-17T21:25:06.915127Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Extract the best epoch results for U-Net\nbest_epoch_unet = history_unet.history['val_loss'].index(min(history_unet.history['val_loss']))\n\n# Store the results\nfinal_results_unet = {\n    \"Best Epoch\": best_epoch_unet + 1,  # Convert to 1-based indexing\n    \"Training Loss\": history_unet.history['loss'][best_epoch_unet],\n    \"Validation Loss\": history_unet.history['val_loss'][best_epoch_unet],\n    \"Training Dice Coefficient\": history_unet.history['dice_coefficient'][best_epoch_unet],\n    \"Validation Dice Coefficient\": history_unet.history['val_dice_coefficient'][best_epoch_unet]\n}\n\n# Print the results\nprint(f\"Best Epoch (U-Net): {final_results_unet['Best Epoch']}\")\nprint(f\"Training Loss (Best Epoch): {final_results_unet['Training Loss']}\")\nprint(f\"Validation Loss (Best Epoch): {final_results_unet['Validation Loss']}\")\nprint(f\"Training Dice Coefficient (Best Epoch): {final_results_unet['Training Dice Coefficient']}\")\nprint(f\"Validation Dice Coefficient (Best Epoch): {final_results_unet['Validation Dice Coefficient']}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:06.921254Z","iopub.execute_input":"2024-11-17T21:25:06.922837Z","iopub.status.idle":"2024-11-17T21:25:06.939597Z","shell.execute_reply.started":"2024-11-17T21:25:06.922782Z","shell.execute_reply":"2024-11-17T21:25:06.938687Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">The model performed well, with a final validation loss of 0.083 and Dice Coefficeint of 0.923. A sample of the predictions can be visualized to ensure the model is predicting the masks as expected. ","metadata":{}},{"cell_type":"code","source":"# Function to visualize original image, ground truth mask, and predicted mask\ndef visualize_predictions(model, val_dataset, num_samples=3):\n    # Get a batch of data from the validation dataset\n    for images, masks in val_dataset.take(1):\n        # Predict using the trained model\n        predictions = model.predict(images)\n        \n        # Loop through the first few samples\n        for i in range(num_samples):\n            # Extract the image, true mask, and predicted mask\n            image = images[i].numpy().squeeze()  # Remove extra dimensions if necessary\n            true_mask = masks[i].numpy().squeeze()\n            predicted_mask = predictions[i].squeeze()\n\n            # Create a subplot with 3 columns: Image, True Mask, and Predicted Mask\n            fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n            \n            # Original image\n            ax[0].imshow(image, cmap='gray')\n            ax[0].set_title(\"Original Image\")\n            ax[0].axis('off')\n            \n            # Ground truth mask\n            ax[1].imshow(true_mask, cmap='gray')\n            ax[1].set_title(\"True Mask\")\n            ax[1].axis('off')\n            \n            # Predicted mask (use a threshold to binarize the prediction)\n            ax[2].imshow(predicted_mask > 0.5, cmap='gray')  # Apply a threshold of 0.5\n            ax[2].set_title(\"Predicted Mask\")\n            ax[2].axis('off')\n\n            plt.show()\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:06.941098Z","iopub.execute_input":"2024-11-17T21:25:06.941466Z","iopub.status.idle":"2024-11-17T21:25:06.975789Z","shell.execute_reply.started":"2024-11-17T21:25:06.941424Z","shell.execute_reply":"2024-11-17T21:25:06.974890Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Call the visualization function\nvisualize_predictions(unet_model, val_dataset, num_samples=4)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:06.976890Z","iopub.execute_input":"2024-11-17T21:25:06.977204Z","iopub.status.idle":"2024-11-17T21:25:14.100301Z","shell.execute_reply.started":"2024-11-17T21:25:06.977155Z","shell.execute_reply":"2024-11-17T21:25:14.099393Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()\nK.clear_session()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:14.101683Z","iopub.execute_input":"2024-11-17T21:25:14.102007Z","iopub.status.idle":"2024-11-17T21:25:15.089193Z","shell.execute_reply.started":"2024-11-17T21:25:14.101974Z","shell.execute_reply":"2024-11-17T21:25:15.088090Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">The predicted masks appear to align correctly with the true mask images.","metadata":{}},{"cell_type":"markdown","source":"# Comparing U-Net to Attention U-Net\n<font size=\"4\">The Attention U-Net is a newer algorithm. It enhances the U-Net architecture by introducing attention mechanisms, allowing the model to focus on the most relevant features during segmentation. This refinement is particularly effective for complex or noisy images. The architecture uses an encoder-decoder structure with attention blocks between corresponding layers in the contracting and expansive paths to guide the model’s focus.\n\n<font size=\"4\">**Differences from U-Net:**\nThe key difference between U-Net and Attention U-Net is the use of attention blocks. While the standard U-Net employs simple skip connections to transfer feature maps from the contracting path to the expansive path, the Attention U-Net selectively emphasizes important features through attention mechanisms, improving segmentation performance on complex tasks.\n\n<font size=\"4\">**Diverse Modeling Approaches:**\nBoth U-Net and Attention U-Net offer different approaches to segmentation tasks. U-Net is a robust general-purpose segmentation model, while Attention U-Net is more specialized, applying attention to enhance performance on complex or noisy datasets. Comparing these models allows for an evaluation of how different architectural strategies perform on the same task. The unique architechures could also be combined for ensemble predictions. ","metadata":{}},{"cell_type":"code","source":"# Function to create Attention Gate\ndef attention_gate(x, g, inter_shape):\n    # Upsample g to match the shape of x\n    g = UpSampling2D(size=(x.shape[1] // g.shape[1], x.shape[2] // g.shape[2]))(g)\n    \n    theta_x = Conv2D(inter_shape, (1, 1), strides=(1, 1), padding='same')(x)\n    phi_g = Conv2D(inter_shape, (1, 1), strides=(1, 1), padding='same')(g)\n    \n    add_xg = layers.Add()([theta_x, phi_g])\n    relu_xg = Activation('relu')(add_xg)\n    psi = Conv2D(1, (1, 1), strides=(1, 1), padding='same')(relu_xg)\n    sigmoid_xg = Activation('sigmoid')(psi)\n    \n    attention = Multiply()([x, sigmoid_xg])\n    return attention\n\n# Function to create a pre-trained Attention U-Net with a complete decoder\ndef create_pretrained_attention_unet(input_shape):\n    # Load a pre-trained ResNet50 backbone\n    backbone = ResNet50(weights=\"imagenet\", include_top=False, input_shape=input_shape)\n    \n    # Encoder output from the ResNet50 backbone\n    conv_output = backbone.output\n\n    # Decoder: progressively upsample and add attention gates\n    up1 = UpSampling2D(size=(2, 2))(conv_output)\n    att1 = attention_gate(backbone.get_layer('conv4_block6_out').output, up1, inter_shape=256)\n    concat1 = Concatenate()([att1, up1])\n    \n    up2 = UpSampling2D(size=(2, 2))(concat1)\n    att2 = attention_gate(backbone.get_layer('conv3_block4_out').output, up2, inter_shape=128)\n    concat2 = Concatenate()([att2, up2])\n\n    up3 = UpSampling2D(size=(2, 2))(concat2)\n    att3 = attention_gate(backbone.get_layer('conv2_block3_out').output, up3, inter_shape=64)\n    concat3 = Concatenate()([att3, up3])\n\n    up4 = UpSampling2D(size=(2, 2))(concat3)\n    att4 = attention_gate(backbone.get_layer('conv1_relu').output, up4, inter_shape=32)\n    concat4 = Concatenate()([att4, up4])\n\n    # Add one more upsampling to reach the desired output size of 256x256\n    up5 = UpSampling2D(size=(2, 2))(concat4)\n    \n    # Final convolution layer to reduce channels to 1 and apply sigmoid activation\n    output = Conv2D(1, (1, 1), activation='sigmoid')(up5)\n\n    # Create the complete model\n    model = Model(inputs=backbone.input, outputs=output)\n    return model\n\n# Adjust for grayscale images\ninput_shape = (256, 256, 1)\nattention_unet_model = create_pretrained_attention_unet((256, 256, 3))\nattention_unet_model = adjust_input_for_grayscale(attention_unet_model, input_shape)\n\n# Compile the model\nattention_unet_model.compile(optimizer='adam', loss=bce_dice_loss, metrics=[dice_coefficient])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:15.094374Z","iopub.execute_input":"2024-11-17T21:25:15.094720Z","iopub.status.idle":"2024-11-17T21:25:17.184230Z","shell.execute_reply.started":"2024-11-17T21:25:15.094678Z","shell.execute_reply":"2024-11-17T21:25:17.183439Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Re-initialize early stopping and learning rates to reset the callback results\nearly_stopping_att_unet = EarlyStopping(\n    monitor='val_loss',  # Monitor the validation Dice coefficient\n    patience=5,                      # Number of epochs with no improvement after which training will be stopped\n    mode='min',                      # Mode should be 'min' as we want to minimize validation loss\n    min_delta=0.01,                  # Minimum change to qualify as an improvement\n    restore_best_weights=True        # Restore model weights from the epoch with the best value of the monitored quantity\n)\n\n# Define the learning rate schedule as a callback\nlr_schedule_att_unet = tf.keras.callbacks.LearningRateScheduler(\n    lambda epoch, lr: 0.001 * 0.96 ** (epoch / 100),\n    verbose=1\n)\n\n# Define the ReduceLROnPlateau callback\nreduce_lr_att_unet = tf.keras.callbacks.ReduceLROnPlateau(\n    monitor='val_loss', \n    factor=0.2,\n    patience=3, \n    min_lr=0.00001\n)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:17.185434Z","iopub.execute_input":"2024-11-17T21:25:17.185805Z","iopub.status.idle":"2024-11-17T21:25:17.192866Z","shell.execute_reply.started":"2024-11-17T21:25:17.185762Z","shell.execute_reply":"2024-11-17T21:25:17.191906Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define the ModelCheckpoint callback\ncheckpoint_att_unet = ModelCheckpoint(\n    'best_model_att_unet.keras',  # File path where the model will be saved\n    monitor='val_loss',  # Metric to monitor\n    mode='min',  # We want to minimize validation loss\n    save_best_only=True,  # Only save the best model\n    verbose=1\n)\n\n# Fit the model\nhistory_att_unet = attention_unet_model.fit(\ntrain_dataset,\nvalidation_data=val_dataset,\nepochs=epochs,\nsteps_per_epoch=train_steps_per_epoch,\nvalidation_steps=validation_steps_per_epoch,\ncallbacks=[early_stopping_att_unet, reduce_lr_att_unet, lr_schedule_att_unet, checkpoint_att_unet]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-17T21:25:17.194253Z","iopub.execute_input":"2024-11-17T21:25:17.194589Z","iopub.status.idle":"2024-11-18T02:00:50.005264Z","shell.execute_reply.started":"2024-11-17T21:25:17.194548Z","shell.execute_reply":"2024-11-18T02:00:50.004211Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Extract the best epoch results for Attention U-Net\nbest_epoch_attention_unet = history_att_unet.history['val_loss'].index(min(history_att_unet.history['val_loss']))\n\n# Store the results for Attention U-Net\nfinal_results_attention_unet = {\n    \"Best Epoch\": best_epoch_attention_unet + 1,  # Convert to 1-based indexing\n    \"Training Loss\": history_att_unet.history['loss'][best_epoch_attention_unet],\n    \"Validation Loss\": history_att_unet.history['val_loss'][best_epoch_attention_unet],\n    \"Training Dice Coefficient\": history_att_unet.history['dice_coefficient'][best_epoch_attention_unet],\n    \"Validation Dice Coefficient\": history_att_unet.history['val_dice_coefficient'][best_epoch_attention_unet]\n}\n\n# Print the results for Attention U-Net\nprint(f\"Best Epoch (Attention U-Net): {final_results_attention_unet['Best Epoch']}\")\nprint(f\"Training Loss (Best Epoch): {final_results_attention_unet['Training Loss']}\")\nprint(f\"Validation Loss (Best Epoch): {final_results_attention_unet['Validation Loss']}\")\nprint(f\"Training Dice Coefficient (Best Epoch): {final_results_attention_unet['Training Dice Coefficient']}\")\nprint(f\"Validation Dice Coefficient (Best Epoch): {final_results_attention_unet['Validation Dice Coefficient']}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:50.006603Z","iopub.execute_input":"2024-11-18T02:00:50.006894Z","iopub.status.idle":"2024-11-18T02:00:50.015046Z","shell.execute_reply.started":"2024-11-18T02:00:50.006863Z","shell.execute_reply":"2024-11-18T02:00:50.014155Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">The attention Unet performed significantly worse than the traditional Unet well with 0.283 validation loss and 0.77 Dice coefficient. Sample predictions for this model can be visualized as well.","metadata":{}},{"cell_type":"code","source":"# Call the visualization function\nvisualize_predictions(attention_unet_model, val_dataset, num_samples=4)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:50.016184Z","iopub.execute_input":"2024-11-18T02:00:50.016781Z","iopub.status.idle":"2024-11-18T02:00:56.341346Z","shell.execute_reply.started":"2024-11-18T02:00:50.016748Z","shell.execute_reply":"2024-11-18T02:00:56.340415Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()\nK.clear_session()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:56.342905Z","iopub.execute_input":"2024-11-18T02:00:56.343723Z","iopub.status.idle":"2024-11-18T02:00:57.991653Z","shell.execute_reply.started":"2024-11-18T02:00:56.343688Z","shell.execute_reply":"2024-11-18T02:00:57.990681Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Results\n<font size=\"4\">A dataframe can be created to compare the final results of each model.","metadata":{}},{"cell_type":"code","source":"final_results_ensemble = {\n    \"Training Loss\": ((history_unet.history['loss'][best_epoch_unet] + history_att_unet.history['loss'][best_epoch_attention_unet]) / 2),\n    \"Validation Loss\": ((history_unet.history['val_loss'][best_epoch_unet] + history_att_unet.history['val_loss'][best_epoch_attention_unet]) / 2),\n    \"Training Dice Coefficient\": ((history_unet.history['dice_coefficient'][best_epoch_unet] + history_att_unet.history['dice_coefficient'][best_epoch_attention_unet]) / 2),\n    \"Validation Dice Coefficient\": ((history_unet.history['val_dice_coefficient'][best_epoch_unet] + history_att_unet.history['val_dice_coefficient'][best_epoch_attention_unet]) / 2)\n}\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:57.999266Z","iopub.execute_input":"2024-11-18T02:00:57.999994Z","iopub.status.idle":"2024-11-18T02:00:58.015954Z","shell.execute_reply.started":"2024-11-18T02:00:57.999957Z","shell.execute_reply":"2024-11-18T02:00:58.014936Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Create the DataFrame using the results dictionaries\nresults_df = pd.DataFrame({\n    'Training Loss': [\n        final_results_unet['Training Loss'], \n        final_results_attention_unet['Training Loss'], \n        final_results_ensemble['Training Loss']\n    ],\n    'Validation Loss': [\n        final_results_unet['Validation Loss'], \n        final_results_attention_unet['Validation Loss'], \n        final_results_ensemble['Validation Loss']\n    ],\n    'Training Dice Coefficient': [\n        final_results_unet['Training Dice Coefficient'], \n        final_results_attention_unet['Training Dice Coefficient'], \n        final_results_ensemble['Training Dice Coefficient']\n    ],\n    'Validation Dice Coefficient': [\n        final_results_unet['Validation Dice Coefficient'], \n        final_results_attention_unet['Validation Dice Coefficient'], \n        final_results_ensemble['Validation Dice Coefficient']\n    ]\n}, index=['unet', 'attention_unet', 'ensemble'])\n\n# Display the DataFrame\nresults_df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:58.022124Z","iopub.execute_input":"2024-11-18T02:00:58.022569Z","iopub.status.idle":"2024-11-18T02:00:58.056750Z","shell.execute_reply.started":"2024-11-18T02:00:58.022534Z","shell.execute_reply":"2024-11-18T02:00:58.055968Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\">The traditional Unet performed best with a validation loss of 0.09 and validation Dice coefficient of 0.93. The final attention Unet model resulted in a validation loss of 0.283 and validation Dice coefficient of 0.776. The ensemble resulted in a validation loss of 0.189 and validation dice coefficient of 0.852.\nDue to the significant gap in performance, for this task it would be best to use the traditional Unet model alone without ensembling the two models. Perhaps exploring different architectures or ensembling three or more models could be explored further.\n","metadata":{}},{"cell_type":"markdown","source":"# Issue with test predictions\n\n<font size=\"4\">Although the model performs well on the validation set, predictions of the test set yield blank masks (ie all RLE values of '1 0'). Upon review, it appears the test data set is comprised of a single image that is duplicated for the entire set. The image contains an empty mask, which is why the test predicions contain only predictions of '1 0.' This appears to cause an issue with submission. The images in the test dataset can predicted and visualized with the code below. Also the submission dataframe with RLE encoding is created.","metadata":{}},{"cell_type":"code","source":"# Function for Run-Length Encoding (RLE)\ndef rle_encode(mask):\n    pixels = mask.flatten()\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\n\n# Function to preprocess test images\ndef preprocess_test_image(image_path):\n    image = tifffile.imread(image_path)\n    if image.ndim == 2:\n        image = image[..., np.newaxis]  # Add a channel dimension for grayscale\n    image = (image - np.mean(image)) / np.std(image)  # Normalize\n    image_tensor = tf.convert_to_tensor(image, dtype=tf.float32)\n    image_tensor = tf.image.resize(image_tensor, [256, 256])  # Resize to match training size\n    return image_tensor\n\n# Function to predict masks for test images\ndef predict_test_images(test_image_paths, models):\n    predictions = []\n    for image_path in test_image_paths:\n        image = preprocess_test_image(image_path)\n        image = tf.expand_dims(image, axis=0)  # Add batch dimension\n        ensemble_pred = np.mean([model.predict(image) for model in models], axis=0)  # Ensemble prediction\n        prediction = (ensemble_pred > 0.5).astype(np.uint8).squeeze()  # Threshold and squeeze to remove extra dimensions\n        predictions.append(prediction)\n    return predictions\n\n# Load test image paths\ntest_image_paths = []\ntest_image_ids = []\n\ntest_dir = '/kaggle/input/blood-vessel-segmentation/test/'\nfor dataset in os.listdir(test_dir):\n    dataset_dir = os.path.join(test_dir, dataset, 'images')\n    for image_file in sorted(os.listdir(dataset_dir)):\n        test_image_paths.append(os.path.join(dataset_dir, image_file))\n        test_image_ids.append(f\"{dataset}_{image_file.split('.')[0]}\")\n\n# Get predictions for test images\nmodels = [unet_model, attention_unet_model]  # Your trained ensemble models\npredictions = predict_test_images(test_image_paths, models)\n\n# Prepare submission data\nsubmission_data = []\nfor image_id, mask in zip(test_image_ids, predictions):\n    if np.sum(mask) == 0:  # If the mask is empty, submit \"1 0\"\n        rle = '1 0'\n    else:\n        rle = rle_encode(mask)\n    submission_data.append([image_id, rle])\n\n# Create submission dataframe\nsubmission_df = pd.DataFrame(submission_data, columns=['id', 'rle'])\n\n# Save to CSV\nsubmission_df.to_csv('submission.csv', index=False)\n\nprint(\"Submission file created successfully!\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:00:58.057869Z","iopub.execute_input":"2024-11-18T02:00:58.058263Z","iopub.status.idle":"2024-11-18T02:01:13.515506Z","shell.execute_reply.started":"2024-11-18T02:00:58.058199Z","shell.execute_reply":"2024-11-18T02:01:13.514588Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:01:13.516885Z","iopub.execute_input":"2024-11-18T02:01:13.517304Z","iopub.status.idle":"2024-11-18T02:01:13.527196Z","shell.execute_reply.started":"2024-11-18T02:01:13.517259Z","shell.execute_reply":"2024-11-18T02:01:13.526285Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<font size=\"4\"> The original test images can also be vizualized by giving the discrete path to each file. This does appear to confirm that the test image data set is comprised of duplicates of one single image that yields an empty prediction ('1 0'). It is unfortunate that this issue exists in the environment with the test data set. Fortunatley, the models perform well on the validation images and work as expected.","metadata":{}},{"cell_type":"code","source":"# Visualize Original Test Images\ndef visualize_original_images(image_paths):\n    fig, axes = plt.subplots(1, len(image_paths), figsize=(12, 6))\n    \n    for i, image_path in enumerate(image_paths):\n        image = tifffile.imread(image_path)\n        axes[i].imshow(image, cmap='gray')\n        axes[i].set_title(f'Original: {os.path.basename(image_path)}')\n        axes[i].axis('off')\n    \n    plt.show()\n\n# Example usage with the same paths displayed\nexample_paths = [\n    '/kaggle/input/blood-vessel-segmentation/test/kidney_5/images/0001.tif',\n    '/kaggle/input/blood-vessel-segmentation/test/kidney_5/images/0002.tif',\n    '/kaggle/input/blood-vessel-segmentation/test/kidney_5/images/0000.tif',\n    '/kaggle/input/blood-vessel-segmentation/test/kidney_6/images/0001.tif'\n]\n\nvisualize_original_images(example_paths)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:01:13.528506Z","iopub.execute_input":"2024-11-18T02:01:13.528941Z","iopub.status.idle":"2024-11-18T02:01:14.186311Z","shell.execute_reply.started":"2024-11-18T02:01:13.528896Z","shell.execute_reply":"2024-11-18T02:01:14.185363Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Conclusion\n<font size=\"4\"> The traditional Unet algorithm perormed best for this task. Ensembling would not be beneficial as the results of the traditional Unet alone yielded the best performance. Unfortunatley, the duplicate images in the test dataset cannot be used for evaluation.\n    \n<font size=\"4\">**Key Takeaways**:\n\n- <font size=\"4\">**Understanding Data Structure:**\n  <font size=\"4\">Understanding the structure and shape of the images, directories, and types of image files was an important factor in this experiment. Often, similar models did not perform well depending on how the images were preprocessed. I experimented with different computer vision libraries to create the datasets. I tested memory-efficient approaches, such as image generators (both built-in generators and custom ones). The keras dataloader worked best for this experiment. Perhaps further experimentation can be done to more efficiently preprocess the images to train on larger datasets. It is particularly important to note that, due to memory constraints, the models in this experiment were run with batch sizes of 4. These may be considered a smaller batch size and causes longer training times.\n    \n\n- <font size=\"4\"> **Data Augmentation:** \n  <font size=\"4\"> Data augmentation was not used in this experiment. Attempts to incorporate data augmentation resulted in poor model performance. Perhaps additional expirmentation can be explored to implement image augmentation.\n      \n      \n- <font size=\"4\"> **Importance of Choosing an Appropriate Loss Function:**\n<font size=\"4\"> When working with datasets that have a severe class imbalance, such as a high proportion of background pixels compared to blood vessel pixels, selecting the right loss function is an important factor. In this case, BCE Dice loss was used. This loss function is well-suited for binary segmentation tasks with a class imbalance. </font>\n      \n ","metadata":{}},{"cell_type":"markdown","source":"## Feedback and Comments\n<font size=\"4\">Thank you to anyone who has taken the time to review this notebook. Comments, feedback, corrections etc. would be greatly appreciated. Thank you also to the Kaggle community and participants in this competition who have shared their ideas. The ability to review the submissions of others greatly helped with the completion of this notebook. ChatGPT was also used to assist with this notebook.","metadata":{}},{"cell_type":"code","source":"# Record the end time\nend_time = time.time()\n\n# Calculate the elapsed time in seconds\nelapsed_time_seconds = end_time - start_time\n\n# Calculate hours, minutes, and seconds\nhours, remainder = divmod(int(elapsed_time_seconds), 3600)\nminutes, seconds = divmod(remainder, 60)\n\n# Print the elapsed time in hours, minutes, and seconds\nprint(f\"Notebook run time: {hours} hours, {minutes} minutes, {seconds} seconds\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-18T02:01:14.187639Z","iopub.execute_input":"2024-11-18T02:01:14.187956Z","iopub.status.idle":"2024-11-18T02:01:14.194123Z","shell.execute_reply.started":"2024-11-18T02:01:14.187921Z","shell.execute_reply":"2024-11-18T02:01:14.193278Z"}},"outputs":[],"execution_count":null}]}