THE MNS1 (MIRROR NEURON SYSTEM) MODEL
We now present a high level view of the MNS1 (Mirror Neuron System 1) model in terms of the set of interacting schemas (functional units: Arbib 1981; Arbib et al. 1998, Chapter 3) shown in Figure 5, which define the MNS1 (Mirror Neuron System) model of F5 and related brain regions. As we now demonstrate, the connectivity shown in Figure 5 is constrained by the existing neurophysiology and neuroanatomy of the monkey brain. We have already introduced areas AIP and area F5, dividing the F5 grasp-related neurons into (i) F5 mirror neurons which are, when fully developed, active during certain self-movements of grasping by the monkey and during the observation of a similar grasp executed by others, and (ii) F5 canonical neurons, namely those active during self-movement and object vision but not for recognition of the action of others. Other brain regions also play an important role in mirror neuron system functioning in the macaque’s brain. Posterior parietal cortex and the cortex of caudal superior temporal sulcus (STS) have been subdivided into numerous areas mainly involved in spatial analysis of the visual environment and in the control of spatially oriented behaviour (Maioli et al. 1997).
Based on cytoarchitectonic and connectional criteria Brodmann’s area 7 (inferior parietal lobule) has been found to contain distinct regions including areas 7a and 7b and area 7ip. Area 7 reaches its highest development in primates (Cavada & Goldman-Rakic, 1989). Damage to this area can cause impairments in visually guided reaching in addition to other spatial perceptual and motor deficits (Stein 1991; Ratcliff 1991). #EO1: The posterior half of 7ip corresponds to areas VIP (Maunsell & Van Essen, 1983) and LIP (Andersen et al., 1985) and contains area AIP. Areas 7a, 7ip and 7b (to a lesser extent) are reciprocally connected with the cortex of the STS. Although the density of 7b connections with the visual motion cortex of STS is weak compared the extensive connections of 7b with somatosensory areas, the interconnections of 7b with the visual regions are established through anterior 7ip, and the transitional cortex 7ab between 7a and 7b (Cavada & Goldman-Rakic, 1989). Findings from the same study also confirm that AIP is connected with area 7b.
F5 has no direct input from visual occipital areas. Its main cortical input comes from inferior parietal lobe, and in particular area AIP and area 7b (Matelli et al., 1985; Gallese et al, 1996). The detailed connections and organization of areas F4 and F5 are reviewed by Rizzolatti et al. (1998) and Geyer et al. (2000). The area cIPS (caudal intraparietal sulcus), with projections to AIP (Sakata et al., 1997) and area 7a (Cavada & Goldman-Rakic, 1989, figure 7), has been shown to encode object properties such as the orientation of object surfaces and object axes (Sakata et al., 1997).
We conclude our brief discussion of anatomical connections by turning back to area 7ip. Area 7a receives input from LIP (Lewis & Van Essen, 2000; Andersen et al., 1990), MIP (Lewis & Van Essen, 2000; Boussaoud et al., 1990; Bota, 2001) and VIP (Lewis & Van Essen, 2000; reviewed in Maunsell 1995). Interested readers can find more details about these connections at the NeuroHomology Database Website1 (Bota, 2001). The premotor projections of these areas include the regions F2 (not shown) and F4 (Luppino et al., 1999) as reviewed in Geyer et al. (2000).
The subsystem of the MNS1 model responsible for the visuo-motor transformation of objects into affordances and grasp configurations, linking AIP and F5 canonical neurons, corresponds to a key subsystem of the FARS model reviewed above. Our task is to complement the visual pathway via AIP by pathways directed toward F5 mirror neurons which allow the monkey to observe arm-hand trajectories and match them to the affordances and location of a potential target object. We will then show how the mirror system may learn to recognize actions already in the repertoire of the F5 canonical neurons. In short, we will provide a mechanism whereby the actions of others are "recognized" based on the circuitry involved in performing such actions. (A topic of future research is to delineate the circuitry - more apparent in chimpanzee than monkey but still rudimentary compared to human - for the reverse process of imitation, going from the recognition of a novel action performed by others to the addition of that action to one's own repertoire.) The Methods section provides the details of the implemented schemas and the Results section confronts the overall model with virtual experiments and produces testable predictions.
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68mOhXzOWwevXqTz/9VEz7KNKQ/6ioKFGsefPmmzdvHjp06MaNG6dOnfrTTz9hr549e44cOTIoKOjAgQNw+i9cuNC6dWsjbSUmJk6aNAn1BAcHY69Hjx5hUywlpfnfZQPDw8MNLi1ICLEvlH/iNDAMIGfZsmWtWrUaN24c9DUwMLBPnz7Q5oEDBw4ePBhKLB7Yy9P4qkePHmXLlkW6W7duDx8+HDBgwPr162EfXLt2DVINB33t2rVbtmxBJvR+06ZNRtpyd3fv168fKo+IiNi7d29oaOigQYO6d++eJk0anWUDDS4taKdzRgj5/1D+iTEcxPWXwzCAAOqbkJBw5MgRNze32NjY58+f58yZ00j5ffv2TZkyRdr89ttvZ8+enS1bNqQ7d+5cWEuFChXWrVuXL18+ZELmpfmDDbaVK1cu8VXevHmHDBkyYcIE5OfOnVtn2UC4+/pLCxJC7A7lnySLA2q/gGEAAC2PiYnx9/evV69e3759k5KSjJdPnz79zZs3ixQpIjYzZswopaUVg+7cuSPVA9/dlLbEDEKenp4a7dgCjd6ygQaXFiSE2B3KP3FWVB4GmDZt2v79+0uWLHno0CHNP9IrPoX6ytMa7Ro/M2bMqFOnjljNb+fOnWPHjtUpU7NmzenTp69evTpNmjTbtm3T/LNQpJG2xKdA1KOzbKDBpQVtcYIIIUah/BPDOKzrL0fNYYCBAwf6+fnVr1+/T58+2AwLC4OHPW/ePI12AGC/fv2g4iI9ZMiQbNmyTZ48ecyYMW3atPH19cVmhQoVatWqJZYAXrhwYefOnYsXL75s2TIkypcvX6pUKRTWaJ8RwA7Qb2vYsGFioanFixcHBQWJ+EFISMiIESN0lg00uLSg/U4bIeS/UP6JAZxC+yXUGQaYpEWkJfc9UIt+WqN9lR9OuU4lw7RIm/nz59+3b5+0KVVrsK2hWvQLZ8yYUWfZQP2lBQkhdofyT1wBNYcBCCHEAij/RBfncv3l6IQBjKxrJ39iTQghKoTyT/4H59V+AcMAhBBiCpR/4oKoczQAIYSYDuWf/H+c3fWX40q/xaFISEioWrXqb7/9Zu8DIYSkCso/+S/USxemUaNG4eHhuXPnTn0NadOmPXr0qILHRgixC5R/QpwYnZX0fv/9d+Q0btz4o48++vDhw6ZNm+rVq5czZ87du3dLox0tW+JPXsPz58/Tp0+P3TXJrCJICHF8KP/kP9D1dzqgu/or6cEIGD58+OnTpydPnjx79uyCBQtC+3fs2IHyU6dO/eSTT3755Rf5En+DBg3SXzZQ879L9q1cuVKqoUKFCrdv30blkZGRefLk0V9FEAbH2rVrk1sJkBDiOFD+iRqB2glf1vSEbXYxWInBn2BwJb3MmTMvW7YMMv/xxx8jv23btvgUs+36+fkVKVKkZcuW8iX+DC4bqLNkn04NXl5ekH9xDPqrCPbv33/NmjUGVwK0wt9ICLEcyj9Ro+svyarpCdvsYqQSHc6cOWNwJT14/OO0QIxFTokSJUR+jhw5RI60xF/t2rX1l/LTWbLPYA0Cg6sIJrcSYGr/M0KIolD+1Y4KtV+j/dXO7v0bWUnv8ePHJUuWnDZtmpi3Xx9piT+DS/npLNln5DQaWUVQfyVAQohDQfknKsXZ5wZIbiW9JUuW+Pr6du3atVKlSk2bNi1fvjwy4dy/f/9eo7cMoMGl/HSW7BNINQhESSOrCOqvBEgchFmzZv3yyy8nTpyIioqy97EQe0L5VzXqdP0l8PN/+uknJw3+G1xJb/PmzejZxVC7IUOGIHPr1q2ffPJJx44dv/3226VLlx47dkwjW+JPfym/UaNG6SzZ17JlS3wlasBXJ0+exCYSQUFB+qsI1q5dOzg4WGNoJcCMGTNa8b+0MhMnTsSPTUxM/PLLL7F59erVFi1aLFq0KDo6Wr5IEowknHNbHlijRo3y5cu3fPly03cZPHjw3Llznz17Zr2jIk4B5V+9qFz7JWABaJww+K8xtJJeay0iPV6LSK9atUokqlatKl/iz+BSfpATnSX75DVUrFhR2AqaZFYRTG4lQKdm7NixsHju3Llz+PBhbMbExEBBkXj06BHsMKlYwYIFbSz/np6emTNnNmsX2Cjm7kJcEso/If9BGAGEJAdsHSkN+RTjKjJmzNipUydYVGnTphUTKtj4qDZu3Hj//n0bN0pcA8q/SqHrr484J84S/Cd2JCgoaNq0aRpthKZAgQI+Pj7Pnz/Ply8fHGtk3rx5MzAw8PDhw3ny5MEnbIWJEydevnwZ6UKFCs2dO/frr7/etGnTwYMH//jjj65duy5btixXrlw//PDDJ598gt0XLVoUEhJy584d7P7999/Dzti6davBwhs2bMDmkSNHxJhNE5u243kjDgXlX41Q+42Q3EsBDhj8J7bn22+/TUxMfPXqldh8//79kiVLZsyYAdcfihsaGpozZ84mTZqIlydbtmyZNWvWN2/e1KpVa/r06du3b0fOqFGjoMGtWrUaOnTogwcPJk2aNGDAgA4dOsTHx+/ZsycsLCwgIAC2Re/evceNG4d6vLy8UMBg4bZt20LspVGWJ06cMKVp+5084lhQ/gnRheYRSQ7xkEia7QBiD2X97LPPNm/e7O/v36tXr2rVqkVHR7dv3z5btmxLly5Nr6Vq1apw0+G+YxdIskb7AD5TpkxI+Pr6QsuREDH8jRs3arQzLMFxHzJkCPx1XI2DBg0yWBimocgXBAcHm9I0IQLKv+qgtpmIzksBDP4TCbjRIgEPu2bNmkh06tQJmZ6eno8ePdJoxwZmz569WLFiGq2tAD9+0aJFs2bNWrBggX5tYqEEMUZSxBWyZMkiKtdo12hIrrAOFjRN1AzlX11Q+81C/lIAg/8qR7jdEGOx1pGgS5cuY8eOhdbeuHHj6dOn06ZNu3LlyunTpwMDA1esWCFc823btkGtCxYsuH79emxKz+mFhMutPY3Wvz979uzBgwebNm0qVlVu1qxZcoWlfHy6ublhX8uaJuqE8k9ICogwAL1/NTNmzJgHDx5otJMZw5/Onz+/yK9du3adOnVKlSoFud25c+dXX30VGxsL/3vr1q1FihRp0KBBSEhIr169hg0b9t13302cOBG73L17d9OmTW/fvhUB/Hnz5g0cOFCjjef/+OOPqPz9+/cjRozYvHnz48ePYU8MGjQoPDzcYOF06dKJo1q3bp2fn9/o0aPNahqZaM4OZ5M4BpR/FUHX32JgAdhmnmCDldjnNxMZ8gkS5HTTIs/JkCHDfC1STnctIi2tqQj8/f2ltDyYP2fOHJ1W/LUYLNymTZtUNk1UC+VfLVD7U4+zzxNMCCESlH9CzMDa8wQbqYQQQhSE8q8K6PorCyyAJk2aMPhPCHFeKP+uD7XfGogwgM2aoxFACFEWyj8hlgMj4Ntvv7VN8N8FdrG4Eiv/jYSoEcq/i0PX39oYeSlAqeC/y+xiWSWEEGtA+XdldLQffqq9jsTRsN6poGgpC88nIVaC8q8u2JNam4+0MPivVCWOcMWqbeCFI5xzYgMo/y4Lw/72Qi5sDP6nshIHsQAc4Rhsg9psHTVD+XdZxAQ1NAJsj41fCnBt1KO7hNgYyj8hVsGsCYKMJFKzi6+vb3IlxUMKJF6/fv3RP2AzX758DhX819ACIMQ6UP5JCsTFxZ05c6ZWrVq2bBQ9fkBAwLp16+7evevp6SmlM2fObFY9OPhz585VrlxZLJNqe0ycIEgnoUndEwTxef/+fW9v7xw5cjx8+FCnQHx8vIeHh4+Pz82bN5Hz/v379OnTlypV6sqVK8IOqFGjhoME/42fXgd5NECIM0L5dwXCwsJ27doVERHx5MkTy2pAHwp9vX79OvrTx48fx8TEFC1adNiwYRkyZMDnwoUL9dcXT0hIqFq1qliTVHFmzJjx1VdfoV2kp0+fLqXNAto/ZcqUyZMnR0ZG5smTxwqHaRJmPQuQnrym/hHs6tWr8fnixYstW7bofCWMIckkgimgU0C+pq19MSLwUhhDwwgBIeZD+XcF/P39R48e/fTpU8t2T0pK6tChQ//+/Tt27Chy0JmGhIRUqFDh119/HTlypEHphUIcPXrU9FYaNWoUHh6eO3duUwrPnz//5MmT+mmzgKpNnDgR8m/BvopjfIIgeUIi9cH/N2/eeHp6+vn5GQz+X716VSosbS5atKhv376HDx92kOC/ce2Xb9ICIMQsKP9OxvLly3v06CHPgdNfrVo1Ly8vi13/mTNnVq9evUaNGlIOOlNoALQ/MDDQiHxGR0enT59eeIr37t07fvw4jsTHx8dg4d27d5vYQSckJMBfR4Vw3wsXLiylixcvjhpOnDhx48aNAgUK1K5dW/ivKO/u7o4COJI0adKY9+PNIcXfaBzjEwTpu/upCf7v378/Pj4e2j98+PAZM2YYLClF+8Uu2CxatCj+dxh8MJucIvivf7os+F8sA7fbkiVLJkyYkD9//ps3b+pEUDZu3Ni+fftx48bhfObMmdNmR0WI6VD+nQwInq+vb6VKldDZPXv27NatW1AjTepixVu3boVVoZ+P/qtPnz4iDdewV69eMAgqVqwIJx46MWLEiOnTp0Ob4dDjq/r16+PA4OIvWLAAlsShQ4fGjh178eLFFi1arFy5cseOHahk6tSpFSpUkK81Ds1GsbCwMBz/+PHjxZLkcEA12tj1p59+Cg2T0jBxRJnmzZtv3rx56NCh6GRRJ2QVu/Ts2RO6FRQUhPIHDhyA1XLhwoXWrVsn1xbKT5o0CfUEBwdjr0ePHmETP0QU1jl+6Ir+b7TsbCcXBjBIKr1/fOI/QqJfv36meP9AbP7www8O4v1rDAX2kztjtnwQkCdPnlGjRkH+cYni9hGXruDDhw9Lly5FYvTo0UbsUbPiYYQoDuXfyYD8z50795NPPkEaAjZ79mz5t6tWrUJPBHcE3TcU2sQ60ekbHBmHGl68eCHSu3btgpXw9u3bLl269OjR4+DBg3AQhbRs27YNVghkEul27dpBTV+/fv3jjz9C8t+8eTN48GDkly5dGp9+fn5FihSRN7Fs2bJWrVrBSUI/GBgYCGsDPfjAgQOxF8RYPLCX0h06dEDTZcuWRWa3bt0ePnw4YMCA9evXwzi4du0apBreOb5au3btli1bkA+937Rpk5G2IIqoOSIiYu/evaGhoYMGDerevTv66927d+scv/5vtFj+NbIwgEGnXwfLXGqcH/xesVm4cOGFCxfit2TKlMm49y9tmtiKXbx/08+YtXF3/0//iWsJNihOuBQAwOWHK+3w4cNC+w2GrDT/Gw+zb1iLqBPKv5MB7RGJkydPQozhTMu/hVkwbNiwZs2aQavOnj1rYp0+Pj6XL18WCi0Hnn3lypVFGiqIXjVHjhyQ/Lp162pko8POnDkTHR09a9YsdH8PHjxAnwXLAFZIDi3z5s1DmRIlSuCzYMGCyJE3AQFG+SNHjri5ucXGxj5//txIpHTfvn1TpkyRNuFDw/rJli0b0p07dy6sRaMdNrhu3bp8+fJptF2ziAcYbCtXrlwiP2/evEOGDIEnh0x4Y/rHr/8bTTy3RkhNwCZF/vWvf0nppk2bzp8/f8OGDV27dhU5IqYiPjVamZE+HQ0dLTfxpNkyDAA7EheGFABAozt37sRFKDajoqJ0QlYwJXHHyeNhjRo1MjGsRYiCUP6dkqSkJPEunE7+559/XrFiRU9Pz7/++sv02po0aQKRa9mypU7fCu9ECmlKX2XIkEHEHiSgi9Bd9FlSTpYsWaQ3BeDNGGkach4TE+Pv71+vXj20hd9lpHD69Olv3rwpxQ8yZswopXFUUrE7d+5I9cDlTbEt8dNw0jT/CAaMIZ3j1/+NpgMzRWfkv7krDpgbUYfp016LPNTfTYs8BzYiPmFEwtdEQrwEqHjcPpWV6Ei48Wcl+ufNBhYArkNosxQAgOvfunVr6SAHDhyoE7KCubBnzx55PAz3silhLUKUhfLvlCxcuLBUqVLCpdYB/Q682ypVqphe25gxY9AfQRGDg4MlHYWrDUWEZSBGFL58+TJr1qxIrFixAr6OfPe6dev27t37ypUrokd7+vRptWrV4P2Eh4eL6KgAR/X+/XudpqdNm7Z///6SJUseOnRI84/6ik8hwPI09AzV1qlTR1QLH2vs2LHyAoKaNWtOnz4dXWeaNGngaWn+8Wv125I3JxD16B+//m8UkQMTkVsAFqw2ZI2Iul12saASgxaAxpwwgA0sgF69euGCWb58OaR9165dK1eulMbhGgxZaf43HmZiWIsQZaH8Ox+RkZGQPXQZ+l/duHFDPK3XGRNgHLgsJ06cmDlzZps2bdDplC9fHrIHERXxRnSgDRs2hOiiw4J2wk3RMTugylFRUW3btoUfA3WE64PuDw5N5cqVa9SoAeFs2bIlinXs2BH9Gr6qWLGitC98IzhA9evXF2MMw8LC4GGLePuiRYv69esnXl5HesiQIZMnT4algoP09fXFcVaoUKFWrVrCFoE9hI6yePHiGu0zfqTxK2AhobxG25/CDtBpa+nSpfDbkFi8eDF+qbB7QkJCRowYoX/8+r/RLPkXWCb8Vn1G4PgkJ96mnxkbPAhInz79yJEjp06d6uXl1apVK52vkgtZmVJGHtYiRFko/84HBLJYsWJVq1bV/wrK+vXXX1+4cMFcdyFdunSjRo0y+FXu3Lnh0OjnC0dZjEgaoEX+rf5UM6tWrdKvZJIWeYUa7QN7oJ/WaJ/r69QwTIs8J3/+/HLbSKrWYFvyeL6Uif5X//j1f6NZWKz91htOb/tdLK7EyPkx8WRaIwyQmJio0T6Jw13QvXv36dOnL1my5JdfftH8E3DCVwZDVmJ3KR5mYliLEGWh/Dsfa9eu1c+8cuWKjQ/j7du3adOm1RnKRxTBKSL5tgn+m3iiTDmryoYBxHv/Gq1Z6e/vX6hQIRjQ4nZ4+PChCObjs1evXqGhofKQVb169UQNUjzMxLAWIcqivPxnz569adOm4eHh/9OMu/vSpUu7dOmieHPEXty/f79BgwYqD00rjhgi8O9//1ss2EhMNAJsHwbIkyfPOC1SjjRpxMcff7xYi9jUD1kJ5PEwU8JahCiL8vLv6em5aNGiuLg4aVy6t7f38uXLS5YsqXhbxF68ePHi0KFDc+fOtfeBuBTyFwSkBQMdNpJvm+C/iVKtSBjANuMECXEQlJf/+Pj48+fPN2zYsGrVqkOHDoXHD8M2c+bMjx8/VrwtYi+yZ8/er18/ex+F65DcmkCSBaBxvEi+bYL/Zkmy3UcDEOJEKC//kHkxIdrUqVMfPXqUI0eOd+/eQSrkA7hIKoFraO9DIIphfD1AMUWgap8FmKvQ5oYBpPr5GIuoDeXl38vLa8eOHZs3b4bkZ8qUKSkpqWfPnmvXrt2wYYPibRHFiY+PP3fuXKVKlQxOA2wucXFxqK1y5cqK1OaSmLgWsDRJsMZhIvm2Cf5rLBqsZ3oYQKcJhgSIelBe/qH3tbWg6x85cuS6detWrVqF3r958+YWvPtE9LGe65+YmDhlypRJkyZFRkaK+fZTAy4A1DZ58mRFanM9TBR+CTEaoEmTJg4SybdN8N/i02vugwCLGyLESbHKs3+Ndkb6Ll26XLt2Demvv/564cKF+FS8LRVi1bC/u7v7hAkTpJfjU4mHh8fEiRONrBcsJ8XVz5x3eTQh80rZvsIIUKQqpyA1RoBZDwIIURvKy39CQsLp06dr1qwp3bTI6dmzJ1esInIiIiJgHRYoUKBKlSpZs2aVr35mEP0C9+7dO378eLVq1Xx8fKx8sAqgbwTorwVgOsmtGux6wf/Uh+LNCgMQoh6Ul3/cbGIFeh26deumeFtqw2Yj/v74448vvvjizZs3kyZNEm8zx8XFjR07NiwsDD2pWJ0sMTER327evDk4ODgoKOjRo0dS4QMHDsDpv3DhQuvWrQ3W37Vr13r16jVr1iw+Pj5Tpkzy1c/8/f3129IpgGsMDdWvX9/X17dRo0YLFixIzfK7tkTBSIC0arDGpYP/SlkAGtsuE6CzVu/fyaz5+9dffx09ejRnzpy4jMWUf1zkl9gMq8z6lydPngkTJqBrfvfu3aeffoqcOnXqrFixwim8NIfFlqP9Dx48CO982bJlgwYN6t69O7ohpFu1ajVu3Ljw8PDAwMA+ffqgk+rXrx8kHyX37t0bGhoqCm/YsGHLli3r169HF7Zp0yaD9W/fvr1x48bZsmXbuHFju3bt5KufabST9uu0pVNg27Ztt27datGihUa7AjLsD2eRf4FkBKQmACBw+ZcCFByIp2wYAObvyJEjYZiuWbMGibNnz1avXj0kJAQCj7tAvlZvly5dDK75i1sGFjb8InzVo0ePcuXKYV+dRX51TGHkC5t7xowZyI+Kipo+fXrRokVxj9y7dw82tzT1ECEpYhX537lz5+eff67RLk4jcm7fvu3h4WGNtkjqQQcE4wyJwoUL37x5E4mAgIB8+fINGTIEZtzz589z584NpYdfcuTIETc3t9jYWGTCZRHL3uCrvHnzSoXRMa1btw67a7Qz9htcpxw91/fff+/p6QlPCP2gWJ9QrH4mKtRpS748GhJnzpyJjo6eNWsWLqoHDx445kL1KaLUI3xpgiCNKwb/NY5qAcD8xeWNixAnHyYvdHrgwIFt27bFxTlnzhz5Wr0G1/zdtWvXsGHDcLt5eXmNGDEClRw4cABXtc4iv/qmsLC5Dx8+jG4WNxoq7N+/P9LYEQ117do1bdq0ivxA4vIoL//okeHxwyO5f/8+LmiRmS5dOnTlirelHqzq+i/XIs8RfxbkWfNP5ztlypSYmBh/f/969erBWUlKSpIKi/5UKnznzh3pWzg3Blvs3LkzKjl9+jQ6tfbt2wubQ8JIWwL0krBU5Kv1EPlLAa4U/Ff8RCk4HhAWMD5xiYrEggUL8ufPD4NVZ63eFi1a6K/nm6Tl7t27sKFxm9SqVQtf6S/yq28KC5sbpgNsbjQNm2DQoEFIw2QfOXLkixcv+JYNMRGrjPx/+/YtLmvIv7BDM2bMCONXWgCbOD7yblesOTZt2rT9+/eXLFny0KFDUgH5p1S4Zs2a06dPhy+SJk2abdu2af5Z/UxOcHAwPBiULFasWPPmzTWy1c+Sa0teoG7dur17975y5Yp4KPD06VMLlt91PVzypQBrGAEpar9ZjUqODRJlypSRluiVEgbX84VHBK8d6t6pUycYu/J1vOSL/CZnCoufIEqKdKZMmUw8YEIEVhn6h27o4sWLsEO9vLxw7eJyxwU6YsQIxdtSCTZ76g/Tbf78+UgsXrw4KChIdC4hISH47wYOHOjn51e/fv0+ffogMywsDP6HwcLIhDaXL1++VKlSY8aM0WjdHdgB8jWIIyIiYBF6e3u/e/dOjA+QVj+rWLGiflujRo2SF2jfvn1UVFTbtm3RjcICaN26NeVfwqwJgjQOH/y3RgBAWR49eiQc7nnz5s2ZM0fzj8EqrdWb3Jq/z549mzp1KmxZqSqdHTWGTGF9y1vfWCfEFKzy7B+99qtXr9A7a7SmLpwz2LYzZ860Rlsujy1H/MEjGapFypF6k0ladDKTK7xv3z79TDmiL5MjX/3MYFvyAmCAFhN/l9owfYIgjaEgPE51586dkXj//r2Hh4eLBf8VHP0nwHWYO3duWLewUGGbxsXFCbNYWqtXfz1fuPKJiYmPHz9u0KABcnBIOXLk6NWrV2xsrOZ/F/nVMYVDQ0OFl79o0SIY2SJmgDSOQTyAgP09aNCg7NmzK/sbiUtiFfmHx99PS9q0adF96z+7JYRYm1TOE4w7V4S1XWnon0HtT2X9mzdvlj9uh8Gks1YvcvTX83V3d0f3uGTJEpgFMAV27tyJDjM6Olpnkd/kzG6R0GmIrj8xC+XlP3369EOGDBFDXaSHvkWLFsVF3KFDB8Wbc224tA9JDRbPE5wrVy5ov5FdBNjEPY7E8ePHa9asKS+wbds2+KymtAugW0ZqUxDrjSW0gGPHjs2ZM0d48/hs2rSposdFSAooL/8igCwf6Qpy5Mjx5ZdfKt6Wa0PtJ4qQmgtJPMyeN2/euHHjDNa8d+9ejXaiJ2mOBxGmfvr0acGCBZGYOHGieHBToECBy5cva7SrRet44WI50Js3b4rm5LU57OP/uLi4hQsXarRjXwICAkTs3Szg+uOH165dOykpCb9x2bJlVjhMQpJFSfn38PD47bff0qZNmyVLlrt37yIhAlwwCDJkyHD16lUF2yKEmI6YItCyeYLxOXDgQI323U6Du6xYsaJLly7yXZ49eybkUMrZsWMHvNusWbPevn1bjIGHSyBVMnPmzORqc0zt12i7uyFaLK6hlRYFD4kQs1BS/mEOz54929/fv1ixYsLwl3j9+vX48eMVbMvloetPFEfMDmRi8F8n58OHD2LyWo0s+N+yZct//etfUGt5yaCgIGg/fOIyZcqIHDc3N2g/Ek+ePMmbN++2bdtQlciRB/8N1ubIFgAhTo3Cwf8wLZcuXfrmm28S/iExMfHdu3fKNuTaUPuJlRBhgBTnCdYfRGZw2q6qVatK16oYta7RDmrDp8HZZ1Een82bN3/06JE8Xwi8wdqo/YRYCauM/K9Xrx4n+SEC2H/o1n/77TfFCxOL0X8pQD7gXyQWLVokBf91EqKwn5+feGYvhvhpZKH+EiVKiMTatWulkL5I1KhRQ16b2AW2xdu3b8UgOP3aaAEQYg2Ul/9cuXKdOHECHsCxY8e+++47xet3eVzM9U+bNu3Ro0eNFGjUqFF4eHju3LlNKUyUQuelAFPevzf+vEBKtG7desuWLXDfjxw50rBhQ0d7798aXLt2rWTJkq7XFnFtlJf/p0+fworPkCHD999/r3jlakAEZu1uBERERKCjKVCgQJUqVbJmzWow5+9kljHV4fnz5+nTp09uJZLdu3fLe3l5YRPrdw0sXgU4NRP9WuMyEyt94E+Uz/NoMY5vBJw/f37Hjh1ibNOpU6dwSj08PN69e4ee8NWrV7BrQ0JCFGxu7dq1LVu2rFChgoJ1EnVileA/7gRc8QULFhSLVgnKly9/8eJFazTnksAIsJkFoLOoaN++fbt27VqvXr1mzZrFx8eLkKx+TlRUlMFlTA8dOoTa8F+3aNFi5cqVSE+ePDkyMjJPnjz6DaHfRFVTp05Fd+bv7y8vrF//pk2b0PchHRwcHBQU9OjRo0mTJjn4CqcWi3pqmjDLIBCjAZSdsScgIEAkXrx4YXElTjHuLzY2tn///gcPHtRo1/7BZYxrUj5OAjeOsi2OGjUKd+L+/fthYClbM1EbVpH/JUuWVKpUqVWrVrNnz5Yy0cso4g2oB5uFAfQXFd2+fXvjxo2zZcu2cePGdu3aoYx+jsFlTAcMGPDjjz9C1N+8eTN48GCNdm1fKHpyDYk1e/z8/MTLYPLC+vWjn12zZg2614iIiL1794aGhg4aNKh79+5p0qSx9ilyLoRBYJYRkMp5gi1OmF7AMVm8eHGjRo3SpUsHhydDhgy4YnUKiHcmNdpxLe7u7rCA06ZNm9wVqx9g08fDw6N+/fpoFxe/Yj+DqBKrTPtz4MABXLsdO3aEiya9/Q+vUfG21IANwgD6i4pChr///ntPT8/atWvDoUdnpJ+zb98+/WVMX716tXz58hxa5s2bp/lnHHhyDZUoUQL5BQsWRHmdwgbrF0v7oJ68efMOGTJkwoQJqESMGyA6SFEB0+0Auz9y0seRtR+sW7du+vTpGm1MXn9mX1CuXDlc8zBS8S8sWrSoZ8+eI0eOhHLrhME0egG2xMRElIQlDZMXibNnz1avXh1GBgxlOFdjxoyh/JNUYhXvv2bNmiIBQ1XKdPDb2JGxdhhAf1HRzp07I3H69Gm46e3bt79586Z+jsFlTC9fviy9MwYnJsWGjByVwfpFWgSHYYtoXOi6MkWkLXuUYFYwIMUJgjQmx+1NSZhY0oJfbQNwAZ8/f15clpcuXUpu2Uk4P3PmzFm9ejU8+4sXL967d08/DIZfqh9gw1ezZs3CP7Jhw4a4uLiBAwe2bdv2zJkzRYsWRbvSogyEWIby8g+j9eHDhzBUpZf+RYKz/qUS64UB9BcVDQ4OnjRpEsy4YsWKNW/eHJn6OQaXMQ0NDUUmOjW5H6/5pwfXb0ijfaH8/fv3+oUN1v+3S6xwavFgPYM7ym0CFEjORDArGNCiRQs7Bv/FFEMOHvbXaGczwxHmz58faRgBf/zxh4hmCXCeIfaPHz+G6y9G6sGGLqyldu3aOmGwnDlz6gfYxMyJMJRFYsGCBWgL5WFY4xSh9eQeEBBiCsrLP67LL7/8Et1Hy5YtcaFD/u/evbtlyxa+rJJ6rBQG0FlUNCwsLCIiokePHt7e3u/evROzr+vnGFzGtFq1agEBAZUrV65Ro0bdunVxDYhhzytXrgwKCtJvaNSoUR07doQyoUDFihXlhfXrR7cIK0SjfeCKAhkyZNBoVzgdMWJExowZlT0nSpGaYfmWNWHEAhDg2xSPCp6ofIKgo0ePpmaAxdu3bzXaVzxweZhS/iPZigCObAR4eXnBs3/y5EmhQoXQ482dO7dp06bSicJ5/vzzzwsWLCjW/wXiitUkEwbTD7CJwpKLj0SZMmVQyZ07d9KnT0/tJ6nEKsH/cePGyd/4hxEAgwDXq+i7SSpRPAygv6goVFmnjPDX5RhcxjRfvnww9eQ5fbQk1xAQ68HoFzZY/1At0qYzuv42QFJ3I5EAIxbAyZMnNVojDPbW0qVLoUl16tTRyMbzQ8+QwFdiU6xYj8QPP/wgEh9//LFOAD86OhrFNFo7UuT8+OOPSMA90KnEiUb+Q4/hpkOnIf8jR4785ZdfOnXqhF5OWv9XXJ/S2onS5WowDKYfYBM8evRIVDhv3jyxJBJaRLs2/anEFVFe/nGtt27deuHChStWrMCFiysbkhAYGAg3jvKvFA4yNwBxcITGGzQCjFgAwkeHM4pPaPy7d+/69+9fvHhxyP+rV6/gdGJzz549DRo0CA0NbdmyZc6cOT09PeHOion8/u///g9OMBRu3bp12Pzqq6/Wr18PdUdXAIGsUaPGmzdvMmfO7Ofnd/z4cXd3d3z7xRdfFClSJCwsDB3FR/8sE+Dg2i9At3blyhVfX9+0adNC0Tds2ABrCWcDZylXrlzv37+fPn16XFycCACgV8RZxZk0GAbTD7AJBgwYkDt3bjhRHTt2rFixInKuXr2Kdu31k4nLoLz848bGrYtLVrp1nz59Om7cuGPHjinelsqx5dwAxHnRCQZITwdMeQrw4sUL8VhHo+eXg55akpKSpEf1+JTW8pFKwj6QcuDyijGbYuSalL9t27YWLVrot6Jx4KF/Gu1w/W7duomh+6CdFv1iw7RImwbDYPoBNsHmzZulcIIA5wo2U+oPnqgc5eX/9evX6A5gnG7ZskU804Lhjzvk+fPnirdFGAYgpiOEXx4MMGIBCPUtUaLEmTNn4OgjvXXrVhSGuuuP2vvon1X7pHj+4cOH9+7diwS6ArE7Cp8/f/6zzz4Tzr2k/YUKFYLfjIYePnxYoEAB5/L+4aw3btx4586d+LRSEzonATe7v78/X3YlqUd5+cfFumnTJhinq1atio2NTZcunRjwMmbMGMXbIgLTwwDyQVVEhegPDDRoAVSuXHnOnDlwavFtZGTkhAkTypUrB22WZu/4+eefNdqFfNq2bStyJH+0Y8eO+Lx+/Toc3GnTps2bNy8gIAA5u3btevnyJRIbNmwQJePi4jTaZe+hZxrtI22RLxXQOPbQP0H//v1XrlypeLU4OQsXLtRoB7riBIrB/xrtOEo/Pz/FmyMqxCpD/3A/QPKbNWvm5eWFzffv38MgEJNjECthShjABqPQHQojA+DVdirkpPhqABg9erSUliLbglq1amm0r7wL371r164i0aFDB5GAeItEz549U//ev4Nrv0CMk1AWDw+PIVp08g0+XCDEAqwi/zExMeIaFbNS67zVTawHRwMQU9CxAEwZBKCPWClA87+v7EtPAVQy6S8hzotV5F+Cwm97OBqA2AyDl9nWrVsVbILaT4iVUFL+Z82aVadOnQwZMnz22WfiqR5o1qxZeHi4Rrusy65duxRsjhiBYQBiHEUCAFJV8nB9q1atVDLpLyFOjZLyj9v+6NGjy5Ytk7Rfox0iVK9evXnz5tWtW5fyb0sYBiBmkUoLQGOdFf8U+WmEEH0Uk/80adJ4e3uPHj36/v378vz4+Phff/01NDS0Q4cOSrVFTIdhAJIcpowBNLdCBWsT0AggxEooJv9Zs2Z9/fq1jvZLXLhwITAwUKm2iFkwDECSQ8FHAAJxmekH8N+8eePUI//5xixxPRST/7Rp0yYmJib37fv37+WL/xLbwzAAsRkGXwpILvg/bNiwGTNmiE1cpQ448t/uB0CINVBM/p8/f+7l5ZUrV66nT5/qf1uiRIlnz54p1RaxDIYBiD6mBABg2f/xxx8fPnzAjezp6amzqdHa99evX09ISChVqlSmTJk05lxms2bNktZ2Cg0N/fjjjzWyRQKjo6NT/RMJIQZQTP5x5//+++/dunWbOnWqzldubm5Dhgw5deqUUm2R1MAwADFObGzs2rVrDx06BM+7ffv2jRs3Hj9+/Pfff//mzZtXr15B73U27969e/r06UaNGsHE/+GHH/z8/EqWLLlp06a9e/cWLFhQhM3FlAAa7QI2H8mWDdy6dWvLli2R+O2334wsEoj6YRn07t173LhxgwcPHjt2rD1PkJVxhIAHUQNKjvxfsWLFvHnz0B0sXboUPYJGOx7wiy++QGdRq1atfv36KdgWSQ0MAxA5OgGAc+fOYbN169Znz55duXIl5P/evXsnT55s2LChmGpeZzMsLGzYsGGZtHTs2HHVqlWlS5eGci9evPjYsWPFihUrUqSIWAdow4YNsCeioqJw7UnTBUozBeFTLB2ks0hgYGDgzz//DBcC1aJymBcuLP+StaThQwdiZZSU/yVLlkBXRmh5+/YtfAgvL6+0adNqtMu0//nnnwq2ZRkqHL9jpAdhGIAYZObMmWPGjElMTMySJUtMTAxyJkyYAFd++PDh5cuXHzRokM7mo0ePpDuraNGir1+/PnLkCHoDDw+Pr7/+WuSjAC62unXrIg1Fl2awlyNVcv36dTc3N5GG51CyZEkcCdKoDSaCeC6gBhgGIFZFSfnHnQlHYcCAAbDrYafDFUhKSjp+/Pi0adN2796tYEOpQVW3U4rmDsMARKATAPj444/z58//4sULsXnz5s1evXq1adNm4MCB+pslSpQ4deqUWBT44cOH1atXP3r06B9//PH555/rtAJzAdKO8pp/HgdIAQCdAf/Zs2cXid9++00+jaBkFrgk+jcsLQBiPRSe9Bd6P1sLDH/I/8uXL6XVrInD4oxhgNS/okaMMH/+fIh3mTJlkD5//vyxY8fg08fExAi919ns3r370qVLYSv4+PjANW/fvn2lSpXCw8Ph5cMNqF27NiQfxX7//XdYBs2aNfvuu++wC/LFisC4/Ly8vJA4ePAgbAgkIiMjAwICkNi5c+dnn32WkJCwceNG1HDo0CFfX1+Ndl7hli1b2u/0WIXkjHU+CCBWwlpz/sdpsVLlRHHEC1cUVCL4888/p02bptGqzrhx45CYOHGi9K08DbJlyzZ8+HB5TqlSpeQrfMLp37FjBxIVKlQQOWLlX/FoQFx1P/74I6RdDALImzfv5s2bkWjUqJGQvbZaxL4u6VGkGKhjGIAojnWX/CHOghR9dQoLQISpGQBQFv03AK3dnJgegHP+mwgtAKIslH9rERcXd+7cucqVK6dLl87ex5IyOrOvU1Y1qjQvFJ8GOEVMmSDI5TF9SDLtIaIglH/NjRs35s+fv2zZsqFDh/bv3z937txRUVHz5s0LDg7u3r07cooXL25undD+KVOmTJ48OTIyMk+ePNY4bKviyKMBlJ2kNsW2VGIB2Fj15Ri/0tRgBEhxDhNRwzkhNsCm8r9379769evbskVTgLoHBgYuWrRo/Pjx7u7/OSGwAJCeOnUq8gsWLGhBnR4eHhMnToT8K32w1kJ/6LUTPQuwKq5tAZio+jY4AzqrBmscbM5/a0MLgNgexeQ/Y8aMx48fh3ymTZvWXYt+4v3790o1pywiPi+0XyCmK3CKuL0iGNR+B3wWYBcn1YUtANtH+42Q3KrBKkH61SaW54MAkkqU9P4rVqxovEBSUpKCzdmMuLi4sWPHhoWF4X4bP3583759ExMTJ02atHnz5uDg4KCgoEePHmGzV69eKHzgwAE4/RcuXGjdurW9D9wM5G9gawwFAxxW/2yjzbQAbIbBMIC9D8p2MAxAbIaSc/7fuXOnadOmkEakpU/5puNM/mOQ5GYUWbZsWatWrcaNGxceHh4YGNinTx93d/d+/fpB8iMiIvbu3RsaGjpo0KDu3btv2LBhy5Yt69evh8WwadMmGx9/atDvdPQDsHY3AuyrUqJ1lzQCHNAC0KhY2GgBENugpPz7+PgYLyPmBXNYdN4nlqwBKD1+3ZEjR5ATGxv7/PnznDlz5sqVS3yVN2/eIUOGTJgwAfkzZsxYt25dvnz58BUMhaCgINv/itSQnBEg4YBDAoV02UyVXTUM4GgWgEbFRoBO8MOsXax4WMTl4Mj/lJkyZUpMTIy/vz/Ml759+8ofYYi7Tqx5invvzp070rdv3ryxy9Fahjzab/Bbzf8GYx3HCLCSaBmplhaAzcBl5pKn2kQYBiBWxXby7+7uHh4e/t1339msRdMRmg3vX/L4RSRA5E+bNm3//v0lS5Y8dOiQ5h8TW/4p7VKzZs3p06evXr06TZo027Zt02iDIrb+MRZhSkejMybLxhaAQykTLQBbgitNLE6hBnRuQ1oAxHpYS/4bNWo0fvz4/Pnzi2H/+EyXLl1kZKSVmksNN27cWLp0KRJTp07t1q1bnjx5njx5smzZMuTMnTu3e/fuAwcO9PPzq1+/fp8+fTT/LG86f/58pBcvXhwUFJQhQwakQ0JCkNm7d+/y5cuXKlVqzJgxGq1OwA4oXLiwHX+glXCoeYJtr8euagE4JsLWVIMRoC/efCOAWAlryT8cff01Pe/cuWOl5lJD8eLFZ2qRcmABjNYiNidpEWlpfMBQLdIuUv6+ffv0Mx0cE3uW5KZltc3Ae2s3YTqurfqOGQAQqCoMoAPDAERxrCL/WbJkyZYt25dffvns2bNTp04VKFAgISGhQoUKzZs3t0ZzJDWY5VUYmZ/VtUVR4+qqL0fHAnCoH66eMIA+tACIslhF/mNjYxMTEyH8+MyQIUOlSpUOHz6cLl26du3aOd1geBfGrK4kRaz3UoCJzqiVovGizm+1aBxMC62HI8cANK4bBkgxbs8HAURBrCL/8PWfPHmyfPnyTp06Xbx48eDBg8hJmzbtn3/+aY3miAVYoP0pLs7m4BMEmYXOr3CNH2UWjm8BaFwrDGD6LckwAFEEaz37r1atWpo0aZAICAjYuHFjgQIFHjx4MGzYMCs1R0zHYqffePBfPkGQgmEAsxSIw/GUxcEtAI1rGQFmrXFAC4CkHmvJvzTI/+TJk97e3nD9neUVONfGrC7D3EijBDTDoeYGIBbjFOaUyzwLMEuh+SCApBIbvfcP7U+XLt2SJUu6dOlimxZTQ3x8/Llz5ypVquRKS/6Yq+Iphvr1E/r5qRcPeQ3JeaK2nPLPxi0SE3HqMEBqhJlhAGIxVpH/AgUKHD16VHrjX3yC+/fvW6M5ZUlMTJwyZcqkSZMiIyPz5Mlj78NRBsu0Xyewb3rw374TBBHV4oxhgNSPwKUFQCzDKvKPy6tIkSL6+W/fvrVGc8oCY2XChAnSi/6ugekdRHIlze1i5DjgSgGWYeP1BYgFOF0YIDV3lrwSDR8EEDOx1ot/W7ZsGTx4cIKMGjVqWKMtRYiIiLh27VqBAgWqVKmSNWtWpaq9d+/e8ePHq1WrluJiSI6D3I9PffDfWVYNNh0X+AlqwLnCAErJMMMAxCysIv/Pnj1r06aNTubVq1cXLlx48OBBa7SYGrp27VqvXr1mzZrFx8dnypRJyv/jjz+++OKLN2/eTJo0qVevXnFxcWPHjg0LC8M9M378+L59+yYmJo4cOXLHjh1r1qxB4uzZs9WrVw8JCSlSpAhuKuxSv359X1/fRo0aLViwwO7WjyldgymRfCMJU0q6RhhA49Lr/7oGDh4GsJL/rYgFQLNAJdhuyR/oK3xrmzVnOtu3b2/cuHG2bNk2btzYrl07KR+WSkRExLJlywYNGtS9e3ckWrVqNW7cuPDw8MDAwD59+ri7uyMxa9YsaMCGDRtgHwwcOLBt27ZnzpzZtm3brVu3WrRogXpQZ3BwsN3lX5NSYN9cV14/YXpJZ1dNB38djkg4ZhhA2Rm3dEjlgwCrHhtxKKwi/+nTp798+bK7FjH0L0OGDB4eHo457Q98+u+//97T07N27do+Pj6SjRIQEJAvX74hQ4ZMmDDh+fPn/fr1S0hIOHLkiJubW2xsLHJyakHJvn37igS8/Pz58+NbWADR0dGwDPCrHzx44DgvPepbANLShRa48qnZxanDABwE4EQ4YBjArFf8U9OE6eXl5ruGAQB1YBX5T0pK0h/69+jRo+HDh1ujuVTSuXNn6Pfp06fh1rdv3/7mzZsiXyz+C7NAo72XpkyZEhMT4+/vX69ePZQXawHLS4pEmTJlYOtA+wsXLixfE8gxseMd7uxzAziU9qs5GmHiH2HfMIAkq9IdZ4NbzwILwHoHQxwQa036+9tvvzVr1kwa9xcfHx8XF2eNtlJPcHDwpEmTatasWaxYMfmiRPL788OHD9OmTdu/f3/JkiUPHTqk8y0sG/GK4Lx58+bMmYNE3bp1e/fufeXKldKlS2Pz6dOnuXLlstkvMo5Bz8PGwX/5Ls5rATgU6vTVzFIsBwwDWBtzHwTIYQDA5bHWs/9KlSpZqWbFiYiI6NGjh7e397t37zZt2gRLZf78+chfvHhxUFAQXHmkQ0JC4PH7+fnVr1+/T58+yAkLCxs1apSoYcCAAblz54a737Fjx4oVKyKnffv2UVFRbdu2LVKkCCyA1q1bO478a5JZU9zGwX+dXZzXCOAYQOfCNmEAnQfqktVrF01V5N1C4npYS/7Pnz9/+fJl6GWdOnVmzJgBcT137hxUFo6ylVq0GOHNyxmqRdqE6y8S06ZN08kRbN68WX+CoAFaFD5W18VJH6WrOeruvFgjDGBQ7+Vib19P2jILgAEA18Yq8p82bdqyZcvWqlUrc+bM27ZtE2/Sf/PNN7Nnz5YPrXcZXOAOsWPwXyffuYwAx18UR+UYMStTEwbQf5Yv5TuI3uuTmgcBxCWxivynSZMmKSkJV9uQIUOE9rds2TI6Onr16tXWaM5exMXFLVy4UKN9TBAQECAG/zspdg/+yxOpsQDkYpycNuvkp9LgcC57RT2YYpaZGAZITuydEcveCLDe8RA7YhX5f//+/d27d588eZIxY0ZsHj58ePv27QULFnR3t900AzbAw8NjiBZ7H4irIbpjy0YDyKU9OQFQUPv1a6Y14AiYFZLRDwNYoPdOpJEcCkAE1tLjvn37btiwAfJ/8eLFnj17urm5rV279saNG1ZqjqQSxwn+yxPONR5QSI5jjgSE/X3+/PmJEyeavktCQkLVqlV/++036x2VldDRflNsMp0n9zroD9xzIrE3iFkPAuQBABXaDc7+XxvBWvJ/8ODBXLlyZcqUSVrm51//+pfjzH5DdHCo4L9OwlmMACnw4GjaD7Zu3XrgwIHRo0cbX8O6UaNG4eHhuXPn1mhH8Bw9etRWB6gMxuM90v+S3MLNRkYDuJ4GWCzkrncqjODa5o51o/HyJf6g/bt27UL/YtUWiethvYi64tXqVOgg1sClS5dat279119/bdy48bvvvjNScvfu3fLO/fnz5+nTp4cdoNF2+idOnLhx40aBAgVq165t3IywC6kcgyn+JmFrqkrhTIEjAFwSJeV/xYoVT548GTFiBJz+ixcvihl/xaS/4vP9+/cKNufsJBdctUvQ1TGD/zoJE3GQ0fgGj8EugwP2798/aNAgnMPJkyfL5f/QoUNjx47FrdqiRYuVK1fu2LEDmVOnTq1QoYK/vz++QvnIyMg8efJERUWJZa6aN2++efPmoUOHbtq0ae3atUgHBwcHBQU9evRIrIxl458mMPfv1v8L5DlqmBfItZ1aYiKKyb+bmxtum8ePH0P+0V/rT/qr0U4GrFRzLoBOcFWKu9ol6OrIwX95wsanJfUYEXv9EHRyQWn9XYzXLOfp06fe3t44dbg9R40aderUqS+++EKjdfR//PFHSP6bN28GDx6MHDFDpZ+fn7h5hfyLSgYOHNijR4+yZcsi3a1bt4cPH/bv33/NmjWQ/IiIiL1794aGhoqVsdKkSWPKUSmIKdpv9wCMC8AAgOuhmPx/+PChVKlS4um+mPQXjoKY8TcxMVEk0OMo1Zyy4Njc3d3j4uIgvaL/unfv3vHjx6tVq+bj42NwFyOx0L/++gv6nTNnTii68Zcd5MFVedzVuYKuxCAGHwSYRYrWgCksWbJk3Lhx0uaCBQuE/E+cOHH58uU5tMybNw85JUqUwGfBggWRg4T80t23b9+UKVPkBzZ79mwxkWW/fv3y5s0rrYwlxg3YEn3jKTWowfXXKLq+35MnT3CN4d/Pnz//zZs3PTw85N9u3Lixffv2uAKlddGI46Bk8B83v0hATQ1O+luvXj0Fm1MEHCpcFvQgixYt6tmz58iRI4cPH96rV6/69ev7+vpCv9FdirV65ZHSGTNm6MRCt23bJgwFuEFwp+Ah4Vs4TOXKlcPuBmOk8uCqPO56+/Zt2wddXSn4bwNg7MJcW7p0Kf4v+XB6/Gv4L3AhNWzYENYkbDjpK7s4oPHx8Xfv3pUmqfzjjz8+++yzWbNm5cuX7/Lly1I+LEvj9eCHoGeXQnoZM2aU0uKvkVbGssavMBFxhlPzzEUl2i9H/y8zcq8Z/Ard1KhRoyD/uNhgUKKzkr7CBYZ7BInRo0cbCQvJB5wSW6Kk/BcrVgyCFxgYqGCd1gYe9pw5c1avXn3t2jVIO5x+CPmtW7eg8fi2Xbt2UFnot06kVD8W2qdPnz179iQlJQ0bNgwdpZeX14gRI9DjHDhw4OnTpwZjpPLgqjzuin1tH3Rl8N8s3NzcvvnmG3Rt0Pv79+/DYxb5x48fz5AhA7Qfhzpz5swxY8YYqSRFQUq9xQDfq27dutLmJ1pg6eICq1atGqxYdLtyLx+/S2eAjjjzcOBQuE6dOqLwzp07cfXqvwKnMxm2XVA2GKBCzDIIBOKqQM8PBwbdlBQA2LJlS6tWrQ4fPiz1TgZjmfLAZ3LBTv0ALUk9Sso//iqoILoA9C/oExWs2apky5ZNo132t7AWeP/R0dHwkHARP3jwQDzO0ImUfvrpp/qxUI12cAOAv5UrVy4YCrVq1UJmcjFSeberE3eVsFnQVYXefyrFVRwSHJd///vfYhWo2NhYeMn4W8VXcjfICJDb69ev4zIrVaqUsgYBbBFcG+iUcSkK7/zPP//ExbNgwYLq1auvXLkyICCgcuXKsG5hIrRs2RIFOnbsiGsMX1WsWDEkJAQ5SAcFBcFcgCnTpk0bX19f3C8VKlRA1wzLWKO3MhasXjHZl90xEgwwiApdfxORGwRG7kTcBeg2pQAA9oKZCKtRuhH0Y5nwtS5duqT5J/CJu0m/AHpFnQAtrjcr/2K1oKT8Q/muXr36+PFjuLzwpOfPn48OSMH6rYrovwC0H0aAfMkfoBMpTS4WCgMI3jmEuVOnTjExMWvXrpVqsCxGarOgq1N7/3aca++rr77q3bv3999/jz8CV/uXX365adMm5J86dQo9mnims2HDhkOHDkFZIaVff/01nCGRDgsLg5V8+vRp9HrPnj374Ycf/Pz8fv/9d6kwCsAdt/jYYH3+9ddf8pwyZcrs3btX2oRzprPLqlWrpHQfLSINUxj9uE7h5FbGcihMvDCo/akHvROEWQoA4Opq3bq1/J41GDQVy6uKwCfsUYNRVZ0Arb1+oOuhpPxHRkbCGT169Cj8Y9xO8DyyZs2Kf3f9+vVxcXEKNqQsQlqkzgueEDr0K1euiID806dP4TDpREoNxkLF7ujHcQPII67GY6RSvk7c1emCrvZFZ55gm737lylTppo1a+7btw/+yuvXr728vER+1apVpTLNmjXDLQD7ctiwYT4+PrAGRBr/OAQeiUxaIPlQX3g/UuHUaD8htqdXr17or5YvXw7Z3rVrF0zYJ0+eSN8ajGXKA58GC2j0ArQ2+zkuj5Lyj+5PvLEGZfpJS7ly5QYMGAA7YM2aNSEhIbZZ7VeKGJtSGHaJMD8XLlyIy6t48eJQ3KioqLZt28IahQUAAxbyrxMp1Y+FilGNiYmJjx8/btCgATJxGLigcT8IUdePkaJOzT/BVY0s7nry5EmNzYOuTh38l5e0/RSBjRs3HjdunLe392effSZlyg9YDADExSNlSmncEVJm0aJFcQfpFybWhq6/UuDqHTlyJPwf2MGtWrXS/za5WKYpBaQALVEK6876d+nSpW7dukFTt2/fHhgYuHr1amxatUUJE99S9fDwGKZFnjlAizwnX758OpFS/VioRjsEJm3atEuWLIFgwxSAs96vX7/o6GiDMVJ5cFUji7tWrFjR9kFXpw7+65e0gREgxWDy588P7T906JAY9Pq3Fv1DledIabg+p06dErbjw4cPq1evrl+YEAcHfZ1G+/A3TZo03bt3nz59OvrAX375RaMdsid9lVzQVAp8JldAJ0BLlEJJ+ce/CJlELyY2YbvBne3QoUP9+vXFK+xNmza1mfxrzAwDKMWxY8fmzJmTKVMmjTYyjJ9sy9aJwNqRf/REe/bsQQKfDRs2RHOvXr3CJrq8N2/e7N69O2/evNj8/fffK1SoIEbAHDhw4Msvv4TYS2ncFOgrly5d+uLFCx8fn7dv36L7kxcWdw2xKnT9U4l47x+JSZMm+fv7FypUaNSoUWIIM7RABPPx2aVLl+SCplLg02AB/QCtXX+uS6Gk/GfJkuX//u//4ME0aNAAHRn+USkcjX4QX23btk3B5kzExkYA7gR4gbVr14bBi0aXLVtmm3ZTicsE/03cJZVDBcWLf0Bsop8SiZpaRFqM+9Noh+CJd0B00hrtQ83hw4fLa9YpQKwKtT/15MmTZ5wWKUeahuTjjz9erEX6ymDQVD7gVL+AwQAtUQQl5T8xMRH94OPHj8VIDegfXGGo/vbt2+/evatf3pYRzhQDyMoihn9rtINdbdBc6nGx4D+D53bEoRazIIQkh5LyL2ZmyJAhw7///W+oPhwsaR5AR8AuzwIIURsOtZiFPmqbDog9HkkOheU/MjKyePHi7969U7BapeBtYAS1Bf9VAhezMIh6ugLeBcQISsr/hw8fatasabr2W+km1L/i1XO3W4zrBf9VbgFwMQtCiHEUfvEPd7iyFaYSCj9RJ1zMghBiHOu+929HkhN+tXmEJhpAagv+N2nSJDVzA6D+9evXb9y4sU2bNmgIHi3kTSirKRw9evTgwYNiqcDjx4/v379fvmxgiqD8vn37Zs6cabwYF7MgrgqHkSqCC8p/ioKnnpCA6baOqoL/UrsWWwDYvUOHDjt37uzYsaNGu67un3/+afrukE8hq6BatWqQZLNa/9e//rVw4UITC6t8MQviSjjaMFJnxwXln5iCpJRqpkmTJjgPFs8BIM3JHxUVBb9Zo3339erVq8gvU6aM/qb+jpp/Fku1rF3j/M3FLPQw+KK/mLhmwoQJ+fPnh1EirVcr2LhxI4523Lhxffv2zZkzp7LH40rojCr9/fffkdO4cWPcYvgfN23aVK9ePZzAFAefAvwL3t7eYojorVu3ChYs6LDDSJ0ayr8aEQ6W/E1IVQX/9du12ALYunUrXP+7d+8GBQVFRkainnbt2v3888+//PILlEa+2aNHj4SEhPXr1x84cEDHGgDnzp1btGgRtDAgIKB48eJwrw8dOoQDg/CgAxW95969e+Eljx8/XgTJNdolA1ChwTfZuJiFPslN8pMnT55Ro0ZB/vFXSuvVCnDmly5disTo0aONjEKQvFJFjtPumDt6Gt/qjyqFug8fPvz06dP4r2fPni0eAPXs2dP44FOc/8GDB+OiFUNEUcPMmTM5jNRKUP7VjtBClwz+J7dLcu1aYAS0bNkSQrtv3z6NdqGB2NjYs2fPZsmSBXKOHJ3N0NDQvHnzwtGEYyS0UCImJga94Z49e/CtmDET8ox9IZaQ/zVr1kAjoYvHjh1DSSH/f/31FwoMGDDAYPyAi1mYhTiHgYGB0nq1Ih8np1WrVocPH5a036CjKfdKk/NE9V/CdBBSPxzK4KjSzJkzL1u27JNPPvn444+RD7sTtnKKg09ximArSM+2pkyZIo1x4TBSxaH8Wxfc7ej64V05TjxK/27ngwCBZeMBIRXCRX7x4oXkLsO5mTZtmnwTn6dOnYKCwpf94osvdCoRQXUo/fbt28uVKwfxgMrCboDYI//IkSPYEQ19/fXX0i6LFi3CpgXPDizD2RezMGV+X1gns2bNkgIAuClg6MBMkeIB+o4mlO/SpUuaf7xS/NH6BeD46ryEKeIiDkLqX5E9c+aM/qhSjXbIp5gPWCwEY7CYzuBTzf8+DuMwUqtC+f8v6HDnz58PcxV3LIxHXCi41XE5wkTFrYscC5aagPbjupSCVNY4bKXQeRzgwsF/U9o10Qj4oMXNzU1EqtE9oYNr0KAB6omPj9fZhP3n6ekJ76d8+fL37t3TOUiRvn37dp06dXAFok/Mnz8/7AnxLTrBP/744/PPP5e3jusKkgOHRm4TWA8nXczCLGCZQZilAABc/9atW8svJIMvRoqHLMIrDQgIMPjmpM5LmPb6gcmRSgvA4KhSwePHj0uWLAlTeMyYMaYMPpXyxSqCkiVhBA4jtRjK/3+BuqN3g3kO412YnLAAkEZfgHyYnBbUiR4E/bgUpHIEUrzJ5eroksF/E9s1bgGgwOHDh9++fYue/auvvhLjj+B2wM6DnwH3HR2ZzmbVqlV79+69YsUKb29v4f3jYoN3otFGNeGv5MuXL2fOnF26dIGiQ1Eg9mKIwPnz5+G+hIeH//LLLygJAT579izyL1y4gOsTlYjFBq1NKy02aMgamL60T69eveDuwx+FbO/atWvlypVPnjyRvjXoaMq9UoMFNHovYSrxmxQmNRaAwVGlGq3J6Ovr27Vr10qVKjVt2tSUwacCCPbJkydxva1Zs0aj9aNEvkMNI3UBKP//HxGfl1+Folt3nLi9DUj9g0DXwPhLAfjKV4s8E1eOfNQY0NmsUKHC3LlzRVpaDxDAN5IXgwUgpaVi06dPlzLlqwKKZ/milyQGMWtZP7iSI0eOhNHv5eWlb+4YcTRNKSC9hOmApObGNziqdPPmzTBYxYA72K/I3Lp1K6xh44NPW7ZsqdGOAunWrRtsglGjRmm0b7pGRETAH3OcYaSuAeXfVGCBwqgMCwvDfSIe7yUmJk6aNMng+NIDBw7gooR/hkvc3gf+/zH9Dld58F+eSOXSwMQpEKHmpKSkNGnSdO/eHcYWPFeol+af+LP4KrkXIyWvNLkCf//vS5iORuqNfv1Rpa21iPR4LUh88sknKQ4+1WjnjZbeOJWfNKcYRupEUP51Se6l6mXLlsEbGDduHGzSwMBAXHy4w/v166c/vnTDhg24oNevXw+LQVr51xEwK76XXCDdSOTclYL/OgmnNgKuXbtWsmRJex+FfTDF9Rfv/SOBe9nf379QoUJwOsX4socPH4pgPj67dOmS3IuRkldqsID+S5jW/c1mYkqfIN0LxJWg/OuiYydK1gCUHn7AkSNHkBMbG/v8+fOcOXMaHF8KU3TdunWwajXaV4mcd5SvQU/aiOvsgN6//r4WH6qTWgDnz5/H1ejp6WnKtDZeXl6uNJeqiWH/PHnyiAHqUo70jvjHH3+8WIv0lcEXIyWv1GABgy9hOgh82KdmKP+mAvM/JiYGzgHMeXSUSUlJ0lc640vv3LkjffvmzRu7HK0RjHvS+sjFUuPk3r9c+y071NSsFGB7YKf279//4MGDsFlNnNaGc6kSohIo//8fodniVS6RIyIBIn/atGn79+8vWbLkoUOHNP8746nO+NKaNWtOnz599erV6E+3bdumMe31FRtjQRiAaFK9VpCNgdvaqFEjaeyqKdPayOdS1ZmpRmfzb8ee38asEX/qJMX7OjWvAxDHh/L/X9CLCTdI9I958uR58uSJeLl57ty53bt3HzhwoJ+fX/369cWQk7CwsGHDholHejrjS5HZu3fv8uXLlypVasyYMRrt+z+wAxzthR/L7m2nDv7L/fhUHqpTPAtYt26d/JWBFKe1EWv1RkZGZs+eXT5TzfDhw3GDyCeu6dKliyPPb0PtTxFTtF/jDBYAxyVYDOX/vxQvXnymFikHFsBoLWJzkhaRlsYHJDe+VMwCq5PpgJj7IECj+uC/PN/BwwBJSUnnz5+Xv3iW4rQ2Qv412ldedWaqgekg3zQ4AY7jz29DBCZqv5TWsaGtdViW4oCH5BRQ/okTGPiOybfffuvIowFev36Nw8ufP7880/i0NvJJLwzOVCNttmjRwmHnt6HrbxyztF8nx9H8bH373l5H4oxQ/ol52DH4b/Eu+geveLsOaAR4eXnBiYe6FypUSMo0Pq2NPjoz1UibDju/DbXfOK5k6Ov/Fj4IMAvKP/kPpgcA7Bv8t2wX27TraBaAm5ubj48PRBryb+K0NmJH6aGvRvboSmfTSee3UTmm3OPOIp8pPgokKUL5J/8fC4YCEAnj8wTbhTZt2ly5cqVs2bImTmsjDn7lypWDBg2Sz1Tj7e2tM3GNY85vQ9ffCGrQfvm3zvJb7Ajln/wHg8Ft00s6RfDfZu06jgXQtWvXbt269e3b18RpbfpoEWmdmWp0Ng3OtGrf+W2spP0mvu74119/HT16NGfOnI0aNZKGUDjIO5AahbQ/NjZ2yJAhwpScPXv2wIEDpa/u3LlTtGhR1NC7d+9Zs2ZJT38+fPhw7Nix06dPDx8+3PKjtwiGAVKE8k/+g05w20gxjb0j+ZbtonPwNmjXEYwAeO2NGzfeuXMnPu19LI5LYmLiyJEjd+zYsWbNGiTOnj1bvXr1kJAQCLzOe4wGX3f08fEJDQ198+aNsLR69OhRvnz5n3/+WWffQYMGJbdoCAwpfBUVFTV9+nSIKCywe/fuTZ48WbLSUolSfj9Efe7cuZB/WDPz5s0LCAiQbBpYkEjjF82ZM0eYRDghMAR///33U6dOlSxZUkH5Nz08SQvAOJR/JYGx70pzpkrwoYAFOMgEQf3791+5cqW9j8LqpMb1h4MeGBgInxVqvWHDBjjr8Gvbtm175swZnfcYDb7uuGvXLujczZs3vby8RowYgUr279+PAjr7Glk05PDhwzDR1q1bhzrxfyGNHdFW165dxfxL1sZ0jRTS7ufnh1+xZcsWnCWN9h0T/EaYQdevX5fCIenTp2/YsCGsH/nrJEodrVkWgIYPApKB8q8AjRo1ws2QO3du3KtOOmeq3EsWOGwk3/GD/zq72D0M0LlzZ/segOOTM2dOjXaBZpFYsGBB/vz5Y2Njdd5jNPi6Y5KWu3fv5sqVCy6vtBazzr61a9dObtEQWA958+ZF6zALBg0ahDQc65EjR7548SJPnjyp/GkpKqUF0ogDRo8Hg0nIf2hoaO/evXX8e6g+zo+5NZuIWRaAhmGAZKD8m4fBJ3+7d++Wri35nKnJPSZ0QPSD20Zi4DqCKiWSyzeSsM0ucmx/qA4SBnBhlHrqL832jUSZMmWkB9jGX3fEfQ2XHX58p06dYmJi1q5dK69T2jfFRUNESZHOlCmTIr8oRSwTxVKlSjVt2nTHjh2wZqpXr75v376hQ4fa+Om+uSFJWgD6UP7/g4kP4ZCv/+Tv0qVLGu1UwRUqVLh9+7aYMxU2u37hTZs2oWtAOjg4OCgo6NGjR2hUqcd7Nsa5nv3b8VDF6XLwCYKcGgVH/OGWFN72vHnz5syZozH5dcdnz56hB6hbt668Np19U1w0RP4pSOX7k9bw+yUg9pD/WbNmoX/r2LGjWb64gvBBQGqg/P8HEx/CGXzyJ9508vPzgx/g5eUl5kwF+oVR4Zo1a9BKRETE3r17Q0NDBw0a1L17d/sOCRboB7cdNpJvWfDfjocq39fuDwIcgWvXrkEF7X0UBhgwYEDu3LkLFy4MPatYsaL+e4wGX3eE8/D48eMGDRogB/9yjhw50JnAy9fZV3/RELgECxYsQHrRokW9e/cWYQOkcRjiGURISAi6iOzZs1vwW6yq/eCLL76oVavWzz//DNPn+PHjqakqlfBBgMVQ/v+LKQ/h9u3bp//kr0SJEkgXLFhQvEgtYbCwaAW9A2oeMmTIhAkTnj9/jh7HRj8yecwN/tP7N3EXg+fWBmGAJ0+eLFmyBBfYJ598AnMzc+bMIv/+/fsQnokTJ44fPx46JGTGxkDnWrZsCe20fdPG2bx5s/xZu/57jAZfd4TzAN8AZxtmAUwB+Ay4wXv16qWzb4qLhuiUT6Xrb64omghMInzGx8enS5du+PDhEP7WrVuLZ53iq4SEBP3hitaWW+nmMrE8LQAB5f9/+MjoQzjjE53qYKSwqNnT01PjMJEoev+28f5tFgaAjI0aNQry7+bm1r59ezQnHmzDTh09ejTkH9/aK+yEpuE079+/H/eIXQ4gOSy+GY8dOzZnzhzRV+CzadOmih6XhRixACz7pbGxsf3799doHZh58+Y1bNjws88+69atG3LGjBnz8OFDjXbqCHyF7g7pHTt27N27F4nbt2+jQI8ePXD5WfxzUsQsi0e6H613PI4P5f+//G3CQ7jknvyhY33//r1OVQYLJ1ez3XFY7x8uBRL4hLdhViv4R86cOeOY3r+UsGoYQFx4u3fvrl69+pAhQ8TDbClf0v579+7Bh6tWrZqPjw82YbN6e3sLB+7WrVvoryVnzsQJcAwSERFx7do1lKxSpUrWrFnr16+/ePHiQYMGWe3XmwF+0cKFCzXal9cDAgIsiIjA9Q8MDMRJSEpKwmkRC4U7LBZrHvyiZVqknHPnzomEPLYh0VRLSEiIZc1ZgLkxD5WHASj//wH3vykP4Qw++cO3HTt2/Pbbb1euXHny5EmNds7UoKAg/cLoHYKDgzXaXgYFRIwBNY8YMUIYy04N+k18wrMU/kFUVBScAJEvciwA/4t4gBIdHW3wDSj8U9evX9dop7GLiYlBAp8i0D1s2DDRp8sR89s7DjZ4KSBfvnzwwGrUqFGyZEmdcabo+JADJcYl2qhRo7lz527ZsgUnTYxdHT58+MyZM0Ua5y3FCXCMjGzt2rUr7pRmzZrFx8cLF7lSpUq4OxxE/j08PIZosbiGVloUPCQF0QmMu7za8UGA6VD+/wPufxMfwuk/+QOrVq0SiYoVK0pzphp8TChvReMwrr9GoeA/mKJFysfnQi2piajjM2/evPq7wLTy8/OTl0RDWbJkMV7b69evzf11T548UTz4r5Ow6rOAEiVK7Nq16+uvvy5atOhXX30l5W/btg3+fYsWLZBu164d5B8SLtlM+B8h/yINdz/FCXCMjGzdvn1748aNYQRv3LgRDWEXHMn58+dx/Uvv2hGrIg9BqQE+CDAFyj/5D6kP/j9+/BgibTAf7mMqI+rySsTntGnTRowYoV/S398feiN2geNrsDb4oGITMobEzZs3fXx8kjsA6BOatlLwX56wahigSpUqGzZs6Nix47Fjx0qVKiUyz5w5Ex0dPWvWLJiqDx48ELF9aRedydpMmQAnuZGtY8eO/f777z09PWvXro1TjYMpUKAAtB+mWNasWa33q4kctckbHwSkCOWf2Ii4uDgxVAIiMX78eCTEIjS7d+8OCgrSaB+gStHpAwcO4BPa0Lp1a4O1bd261WB++/btpQCMoE6dOr/++qtG+1R7+fLlSLx48QKfPXv2rF+/vkYbuRWPfoB4JztLlizCJ96xY4fIDw8Pt/yXm4a15wZo0KABFLpJkyanTp0SOdB+CLk8HCUQqwMbfFBifAIckf5Ib2QrjIa+ffuePn0apxF/EHZ88uQJaqD2E6vCBwHGofyT/6BU8F8K1xuM2yPx9u3bTJkywUEUgV9kfvPNN8iHQdBbi1Ty0aNH+fPnh2AnF/z/888/UzwwuLZC3v71r38VK1ZMOqQtW7ZA9ZcuXSpK1qxZU9rl2bNnOXPmXLVqlZRz9uzZypUrWzX4L08o9SBAqHhSUpI0ys/Pz+/x48fNmzcXm3Xr1sUJv3LlSunSpbH59OlTWGaQ7ZMnT+LkrFmzRvPP21yaf3pS4xPg/J3MyNbg4OBJkybhJOMvEK2LiIsiP1NZrDQnQXx8/Llz5ypVqqQzQNJhp0BwJfggIDko/+Q/2Cb4D4cS3V9ISAh8QWi/qC0yMhI7impRGJ/ly5dft24dtF+/EgEczcyZM5cpU0a/uV9++QVKIwX/of3SV9hFPPgXh/r555+3a9cOmwEBAbdv30biiy++gLUB7ccuDx48kIL/kvZbNfivk0hlGEC896/RDsnu2rWr9MIVTgicfki+RivhUVFRbdu2xfmEBdC6dWvIPwp069YNbvqoUaM0WrMpIiLCy8srxQlwjIxsRQ09evTw9vZ+9+7dpk2bkH/16lXsm5ofaA3Onz+/Y8cOWKI3btyYMGECLsJevXqlT5/+0KFDPXv2hKlkWbWww6ZMmYI/QoyjlH/lsFMguBh8EGAQyj+xEWLOc412lD7kX8qX35bilrtz5440I/qbN2/klcCbRPlbt26NHj364sWL+nf17t27Ubl421iHTz75RL4p4t7Dhg0TwoZ6Ll++LHm3BQoUSMVvVYBUvhQAmRmnRf8rMTxTpAdokX87VotIy4emmjIBTnIjW8UjFTnbtm1bv369Wb/I2oiX2g8ePIg07JupU6dC/sVLaxcuXKhSpUqXLl3wqy2o2d3dHcaE/ntxGgeeAsH14IMAfSj/BoiLizt37hx8PkdepEdZlA3+S/n6wX8pAU9IlJcXEPEDJOAPwWcyWIlUA9x3MW5cyoHqi8f5Ug48XZEYMmQIfFOpRfTsHTp00P+Z5cuXT0hI0DlUmCOWnRDLgv86CdebJxhmjb+/vyNMdiln8eLFjRo1km55+b1fpEgRMdWB2NSZKSE1wJ5wqCkQXB4+CJBD+ddFvGsurdyjeP1Ql6pVq/7222+K15waUh/8h9s3a9YsnLdu3bqJAPuyZcvGjBkDl7F79+7Igb8Ohw+JP/74o1y5cvny5Zs5cya+ha8ZFBQkqoUzhM+HDx927twZPlOLFi1QQ0Ut8Bf1DwzNnTp1CglUhfLoSaWvnjx5Ao3Jli1b69atS5YsiZI6zynmzp0bFhaGzeHDh6MMEo8ePQoICEBH37dv37p164qGOnXqVLhwYfxfNg7+yytxMQvg7du34o1NhwIW4fTp0/XzP3z4gKsLVy8usL//d6aEBQsW1KhRQ4xpFdeSmAtBow14IPPixYu4hleuXCmqwpX/xRdfvHnzRr7Wl0NNgaAG+CBAgvKvC+zxiRMnSiv3GAR3fnh4uFnui7QL1OXo0aOpPkyFUcT7F5/oKHXy0Xsa3wUnXCSESMsLQPVFAh6YxQcmT8hjDFI++n2dnEWLFil1QlJ5zNKmyxgB4tV/hyIpKen8+fP6c3jv3LmzR48esAzq1Kmj0ZspITg4GPIPM7dVq1bjxo3DDR4YGNinT589e/b8+OOPO3bsgNIPHjxYqu3gwYMREREoL1/ri1Mg2B4+CBBQ/k1CJ9y3e/dunashxXigfJfnz5+nT59ezKX6tzkzp1qP1Hv/lvm4ttlFSsTExGTJkkUMILDNoRo8txa36zIWgKPx+vVrnOT8+fPr5MNqL1Wq1OrVq4X868+UoNHOc4DEkSNHoN+xsbG4u2HOLl++PIcWMfelICAgIF++fDprfXEKBHthVhhAupeteUS2hvL//zlw4AC8zwsXLsjfNdcP9z179gz5U6dOrVChgr+/v8F4oE7oT7w+Lna5ffu29GQhKipKZ+ZUuBfe3t6TJk0yOHmqVZGu7L//9/Ut0xMOu4uUuH///rfffiu35a19qDq9hiLt2mDBQLXh5eUFc/zJkyeFChWS5+Pvg5CXL18et3bLli0NzpQgxrSiK6hXrx7u5aSkpOTGkAr/XmetL06BYEfMsgA0yYcBnDQ8QPn/L2vXrt2yZcv69evj4uLEu0kC/XDftGnTNNpXqEWoUL8ADHmd0J94r1rsgo5GerKgP3OqiBzCnzA4eaqtT4pr8eLFC5hlc+fOtfeBpBYbrBSgNiDMPj4+N2/elORfmjUB+TDce/bsWb16dYMzJaBD2L9/f8mSJcULDpCBatWqzZgxIzw8XGfmRLlCSPaBw06BoBJS/yDALAPCoaD8/xfcruvWrcuXLx/SgYGBYh46jaFwX4kSJTTahVNz5MhhsIB+6E9nF4l9+/bpz5yq0c6LpzE0eaptToWrkj17dpxSex+FMsAC0DAMoCht2rSBrvv6+iJ948YNMWsChL9///64bOAbfPPNN1u3bsXNqDNTAox4WPb169cX002GhYWtXLkyICCgcuXKNWrUgMWAP0u8XGpwrS/HnAJBbVj8IEDayxkDAJT//5Lcu+bJTYxqpIB4uizSxl8fNzJzqsbQ5KmEyGEYQEG6du3arVs3MW6/ePHiwVqkb6XhuvozJcjXupVufJgL8jJG1vpywCkQ1IlSDwKcCMr/f6lZs+b06dNXr16dJk0a3JCaf+Y8Nxjuc3Nze//+vdhRv4DB0J98F80/cm5w5lSN3kNfjSOtDWhfYGydPHkSLpq506RYvKODI8IAJPV4e3s3btwY9yA+bdaoY06BoFoseBCgs+lcBgHl/78sW7asc+fO5cuXL1Wq1JgxYzTaUDzsAIMTo3bs2BHfrly5smLFivoFdEJ/LVu2RG3SLhAhbCIRFBSkP3NqvXr14uPjjYQK7XqSbIGReRESExNDQ0NHjhwZGRlploqbu6Njzs1ArE3//v2ld/Rtg2NOgaByzA0DOC+U//+SP3/+ffv2SZtyb1s/3Ldq1Sr5pn4BndCffBdYDNKSdAZnTk2XLp2RUKGrYsq8CO7u7rCHoOLmVm7ijg4+N4NxVNJhWRv4ALZszgGnQCCaVFgAzhUAoPwTZYiIiLh27VqBAgWqVKliwVtMyc2LYDpiufq4uDhpflZzJ2d18LkZjKDT4zj7gAA+0SB2x9wHAc4I5Z8oQNeuXevVq9esWbP4+PhMmTIlJibC1d6xY8eaNWuQOHv2bPXq1UNCQooUKWJwhtTk5kUwWFgfCH/37t1/+umnRYsW9ezZEy0OHz5cfzIGqbxZx+BQczOYCIcEEqIIFoQBnCgAQPknCrB9+/bGjRtny5Zt48aNIp4ZGBg4a9YsSPKGDRsgtwMHDmzbtu2ZM2f0Z0jF3ZLcvAgGC+u3Dh99zpw5q1evvnbt2sWLF+H0G5ycVSpv1jGkfm4Gu3QHRt4MpG9NiOm48FAAyj9RADjT33//vaenZ+3atX18fKpUqZIzZ07kw2kWCfjf+fPnj42N1Z8hFQWSmxfBYGGDBwDLQ6N9cFtYC7x//clZjVdr1bkZ7DVjKMMAhKQeC94IkL/n5YCIpasp/0QBoLtQ+tOnT8Ofbt++/c2bN0W+tIoJEmXKlMmQIYP+DKlGqjWrMBBvSWhSmq3BrGoVnJvBLkYAJwjSx1WdOWJVXC8MQPknChAcHDxp0qSaNWsWK1asefPmUv6jR4/Eosnz5s2bM2cOEvozpIqSBudF0C8s8vVfhdDJ15+MQYQNRAGzjiH1czPo9BpS2pZ2gDwMIBa0TExM/PLLL7F59erVFi1aLFq0CDaT/OUXnA354hdGaNSoUb58+ZYvX26NI1ccZ3kuSxwNs7QfboODBwA0lH+iCBERET169PD29n737p18xYQBAwbkzp0bjnjHjh0rVqyo0T5K15khddSoUZpk5kXQKRwaGipmPggJCRk8eLAI+Gu0Q/nETAkLFy7s3Llz8eLFdSZjaNq0qXifW+xo1jEoMjeDQb/BxsEAKQwA8wU/7c6dO4cPH0ZOTEyMWAcBthrOm1S+YMGCJsq/p6dn5syZrXLQhDgGLub3C9Qo/y75R9oX4Ubrs3nzZuH9SxicIVWTzLwIBgsPGzZMpxUPD49hWuSZOpMxVKtWTSpg1jEoNTdDcpFDuxgB4smfAMot5rmC4dKpUyecpbRp04q3KDXa33XlypWyZcv++eefkHlYeMi5ePFipkyZYGaJGjZu3Hj//n15K7dv34bhhUrw61CPzmZyx2ZiMddD/8JgiMKhsEwyHD8AoKJ7TMD7ypbwbJuIHVcTDwoKEotY4hgKFCjg4+Pz/PnzfPnyubm5zZ49e/z48W/evOnevfvy5ctxeIGBgb/++uvBgwdRHt8OHDhww4YNy5YtO3LkiBhCAbsKObAYVq5c2a9fv759+/7000/S5q1bt2ArhISE3LlzB3bh999/D8sjTZo0OnuhWHJjPJ0d+h7OiPzGdKV/UHXyT2xAXFzcwoULNdrYeEBAgKt25eaS4tAhGw8L+PbbbxMTE1+9eiU24aksWbJkxowZ8L+hzaGhoYMHD160aBHkH5LcunXrr7/+Gn/o9evXL1y40LBhw02bNon3OWETSAc8evRoMQjg5cuXc+fO7dChw7p166TNrVu34nqAtdG7d+9x48ZNnDjRy8tr0KBBOnvZ8VFCcgMjxLf44ceOHevfv7/F9bve8DG1oX9vGvlDHTwAQPknyuPh4TFEi70PxOEwsfe3TTAAfrlG+x6E2ISVtn379s8++2zz5s3+/v69evWCfSCmOCxXrlypUqU02rmxYRmIVXGjo6PFoWbKlEl+5GL0g6SR8k0YEBrt5AoQeFweUPp///vfkH/9vexFcgMjwKVLl2rXrg1jKJUHSQvAxdC5T+H8OMu6YpR/QhwU2xgBYtgjyJo1a82aNZHo1KkTMsXbjBLyOZjNnY9ZQkQasmTJIprT/LOupkNhcGAEjnPXrl0ff/zxtWvXUt8ELQDXxpE9fjmUf0JsigXLimuUNgLi4+M1WkmTC3mXLl3g+xYrVuzGjRtPnz4VAwLEkEapdfkIRyktEvgU0zxIu4gjl2/6+vqePXv24MGDTZs2FQsqNmvWTF6Jo+miNDBi9uzZHTp00Fnry2Ic6jcS1UL5J/YnOjr65MmT0AZnCZqlEnNFTlnthzv74MEDjXbKwnHjxuXPn1/k165du06dOqVKlcqUKdPOnTu/+uqr8PBwMaR/8eLFcXFxSGAzLCysa9euIr1mzRoxryI2161b5+fnJ+0iZkLcsmWLfBPNwTEaMWLE5s2bHz9+DGUdNGgQvtXZyxFeI5QPjMChIuHt7a1IzdR+4iBQ/omdQScbGho6cuTIyMhI0+UfnmvVqlWFB+mM2NHNlb/3KKebFnmOvxZpU0i1QB4GaNOmTXK76GwCMfuTDvrF7I58YIR4x0GRaqn9xHGg/BM74+7uHhQUBPk3a6+0adMePXrUSofkUPDlSTsyatQonP8DBw6IORNv3bqFz9KlS585c8YRQhSEpAbKP3EaIiIirl27VqBAgSpVqmTNmvX58+fp06cXT691vrL3kZqE6W8B0AKwJToDI3D+pSCTj4/PnTt3Ll68aNngR7r+xKGg/BMFiIuLGzt2bFhYGDq48ePH9+3bNzExcdKkSZs3bw4ODoZz/+jRI2z26tXLYGF5VWvXro2KisqYMWO7du2ePn0Kr8vDw6Nr1659+vSpV69es2bN0DtnypQJNUyePDkyMjJPnjz4Vv6Vnc6BJTjUe4BEk/zAiAsXLqxevRraj3TPnj3nzZtnrvef4h/taMMeictD+ScKsGzZslatWqG7DA8PDwwMhFS7u7ujA4Xkwy/fu3dvaGjooEGDunfvniZNGv3C8l6vUaNGRYsWXb58uZcWeFrDhw+HBbB9+/bGjRtny5Zt48aNsAyE/ItddL6y0zmwOnYMAyi7YKCYeNgxSW5gxKdaxFrPFmCK9kufNAKIbaD8EwWA0ickJBw5csTNzS02Nvb58+c5c+bMlSuX+Cpv3rxiRDfyc+fObbCwVFXWrFnhzW/btq158+b4FvuKCWeg999//72np2ft2rV9fHyqVKki7WLkK8fHLJ/PXhaAfMHA1FelSD1OhInan9wmIVaC8k8UYMqUKTExMf7+/vXq1evbt6+Y/l0g+j4xh4zo14wUFvTu3btcuXIvX77csWNH586dRSYSKHz69Onw8PD27dvfvHlTKm/kK6fAXAtAYw+FkBYMTH0lqoKuPHFYKP9EAaZNm7Z///6SJUuKpf/kkUy5UIm3xfQLizLSu2RFihSpU6dOWFjYu3fvOnXqJDKDg4MnTZpUs2bNYsWKNW/eXGSKHQ1+5VzILQBTrAEXCAMQAX19YpC4uLhjx45t2LDBzc2tdOnSOXLkcHd3j4qKSkhIiI+PhweVNWvW2rVrf/HFF2nSpLGsCco/UYCBAwf6+fnVr19frJML5R42bNj8+fM12hljgoKCMmTIgHRISMiIESN0CoeGhor53vHt4MGDs2XLhnSvXr1QZsuWLVITERERPXr08Pb2hk2wadMmFEbmypUrUbnOV/Y4AQrj4BaAxqIwAF1/faj9RJ+3b98+ffr0zJkz6EUzZcqEi+TIkSPCO8IVJebWhOQjZ8mSJegkK1SoINbmMBfKP1EA+YApyYkfqkUqI+UbLIwLXV5hw4YNK1euXK9ePSlHhAok+mgx+JWTIiRf0gNTRoHZ8Y0AhgFShNpPzALXw/3790+dOrVixQpo/CeffHL+/Hm4Q8hPTEwUXSXyxXUl5P/Ro0foOZ88edKsWbMOHTrkzp3brBYp/8QRuXv37nfffWfvo7A1+nrgMmEAtbn+1H5iOklJSQkJCVevXh08eHBUVNT06dO/+eYb6RJCwqBzD1PA29t748aNwcHB69atu3Xr1pQpU8x6H5XyTxyOly9fTp06dcGCBfY+EIfAkS0AjWlhAGq/DtR+IoGL4fDhwz/88MOZM2dGjx4dFBRkbg1DhgwJCAho165dYGAguk13d1NlnfKfLM+ePRs6dOjFixdz5MiBzfHjx9eoUSN79uwFCxbMmDHjhQsX4uLiqlatGh8f/+eff8Lyypcvn8Fd7P07nIyZM2cu16KS5X9MwfEfBGiUnhvAeeFQf2Iib9++Xbt2bVhYWJkyZeD3ly1btlChQskVfv/+/atXr/LkySPlxMTEQInEuL906dItW7Zs+vTpvr6+8+fPL1eunCnjASn/ydKjR4+dO3dGRkZmzZq1VKlSsbGxGu36pCtWrNBo5/2+du3ayZMnNdpxauLNdYO7uB426OBq165t7SacDicNA6jN9U8Ruv4qB6oP7bh9+/Zff/21Y8eOzz77bNiwYT4+Psb3unv37uzZs7/++muIS3R09IcPH65fv96zZ08xvQqAzzl8+PAhQ4agWMuWLevXr5+iB0X5T5ZDhw7h9GXIkAFm1MKFC8VkMrNmzdIv+cMPP4g5wA3u4mKw87Ijjm8BaP43DKA27WfYn+jz7Nmz48ePw1lPSEg4ceLEH3/84e3t7eXlVbJkyT179mTJkiW5HSH5YmEzNze3ggULVqpUaeDAgdB1XGbu7u4wAmbMmDF27FgoztOnT7Nnzw4LIDw8fPny5Rs2bFi7du348ePLlClj5MAo/8lSoUIF/Gfly5eHc9+iRQvxJ+EU65eUMg3uQoiCmGgBaOynNKp9KYDaTwxy+vTpqVOnvn//HhLesGFDSDh8/YwZM6Z4wVy9elU4nJB/OJa1atVCYujQodgRlsTz588h/z169OjSpQvuuI4dO1atWhVfde/evUOHDmPGjEEm5d9CQkJC/Pz8fv/998DAwOHDh8+fP19ncRpFdiHEXEycGd7uYQBVQe0nyVGnTh2ovnhf3yzqaxFpyMqwYcM+fPhQqVKlxMTEnDlzfv3111u2bBkwYADE/u3bt4UKFYL8i8KZMmWqXr36jRs3jNdP+U+WsmXL/vbbbydPnoSKb9q0acKECSlquQW7EGIZDv4gQG0Y/zv4L6gZRZYhrVChwu7du9+/fw/tx+V0+fJliEvr1q2hMoMGDcqQIcOkSZMgQF26dKlYseKrV69mzpw5ZMgQ43VS/pNlxIgRU6dOhQ1VrVq1/fv3m/I2hQW7EGIxtAAcCq7YS6xKmjRpJEuiRo0aq1atOnHiRGBg4OvXr9u3bx8UFLRhw4Zp06ZBd0qXLt2pU6dGjRoZr5D6lCxr167t3bu3t7f3jRs3oqOjBw8eLP82Li4OnwkJCWLQnym7EKI4Dv5OoNowaAHwzBNrUEFL0aJFR40atXTpUl9fX/if9+/f37JlC4yDzp07i8nUjUD5T5YmTZrUr18/f/78b9++HTNmDE6x9BU2Hz58qNFOPTtv3jzpLBvZhRDrwTCA46BjkPGcEwXB5QTPU/5G39dff/3VV1/98ssvixcvDg0NhR3Qr1+/yZMnT5w4cerUqca7Bcp/sixatCi5r+Sz1pu4CyFWhRaAQ6GzggMhloFL6ObNm2fOnMmbN+/Lly9v3bqFza5du5YvX16aCfjDhw/ZsmWLj48Xm/hqypQp/fv3R77xyX8o/0Qxli9f3qNHD3lOREREtWrV7HU8asPx3wlUFTzJJPU8ffq0c+fO169f12ifOOfLl+/TTz9t2LBhWFgY/H53d/ekpCR0sz/88EPTpk2h9+L9gqJFi8bGxqZ4BVL+iWLAGvX19a1UqRI05tmzZzBUqf02xvHfCSSEyHn37h3uR7Ekuk7+ihUrVq5cGRAQAGlHDrrWc+fOderUqUuXLjt27FiyZMn79+8fPnxYu3btXr16/fzzz61atRo8eHDatGlXr179+eefpzjvL+WfKAbkf+7cuZ988gnSPXv2nD17tkY7w+XEiRMvX758+PDhQoUKoQCMVnsfqYvDBwGEOAtr1qw5cuRI4cKF27VrV7Zs2VevXh04cODYsWPPnz+Pjo6eMGFCgwYNxPK+r1+/njFjxh9//AGz4Pz589OnT//22293794tpvgtU6bMpk2bRo4ciVsbhoK/v3+KnQDlnygGLl+ROHnyJFS/QoUKSMfGxtaqVQtX6vbt23GZjho1ivJvA/gggBCnoE6dOnCKfvrpp4ULF+bPnx/yHxMTg/xq1arBoZce8E+ePBm+/syZMz09PU+dOjVv3rxr164NHToUFkNISEjevHk//fTT8ePHDx8+HN6/ie+cU/6JwiQlJQUEBKxbt05s5syZs2rVqsuWLYPRis03b97Y9ehUBB8EEOLIwJuPjIxMSEiAr+/l5fXs2bMXL15AvD/++ONMmTKJOxemwF9//bVnz56lS5fmzp17xIgRDRs27Nmz55MnT7BXgQIFUKZDhw7Hjx+H/COt/xDBCJR/ojCwYUuVKlWiRAmxOWXKlHHjxi1atGjWrFkLFiyw77GpED4IIMTRiIuLu3Tp0r59++C4R0dHQ7/bt28PNwnev1Tmw4cPMA5Wr14dHBwMg6BcuXK//vorutMWLVqgjx01ahQKiJIZM2aU0mZB+SdKgut17NixuKylnG3btuHSLFiw4Pr16zXa2ID9jk6l2PhBAGe+k6BRRSTQDd6+ffvHH3+Eoru7u3/++efffPMNjAC4+7/88suWLVv69ev35s0b+E5Pnz6NiopCV1m6dOnvvvsOe6VPn97NzS0mJgYmQrZs2bJmzfrq1atHjx55e3u/f//+3bt3KY7yMwjlnyjJ0KFDixUrJq08AXr16jVs2DBcxBMnTtRol7DctGlTmzZt7HeMasTE+WiVCgNQ9jQ0g8j/EhsbO2LECPjxixYtypMnT/HixaUIf4MGDerWrXv16tUXL168fPkyS5YsEHuYCIULF4bjJNWAfHFnnTx5EiXhaNWrV+/IkSMDBgxo3ry5BYdE+SdKsnbtWp2c7lpEmgsg2REOBSDEjsDLHz58ePny5eXzxEt4eHiIsdLGiYuLu3Dhwvr16+H3JyYmrlu3DjZE//79LVtcnvJPiIrgUABC7EK6dOk+//zz1NSAG3P16tWTJk3C7Tly5EjUBgvA3d3d4jgT5Z8QdeGY7wTCrTlz5kytWrVs1iIhzgXuyi+1SDkGAwmmQ/knRHXYeCiA1Oi6deuuX7+Oah8/fhwTE1O0aNFhw4ZlzpwZTgwSCxcu1BnAnJCQULVq1d9++02pYyCESFD+CVEjNh4KkJSU1KFDh/79+3fs2FE6gJCQkAoVKvz666/ZsmUbOXIk5F9nLzg3R48eNb2VRo0ahYeH586dO/UHTIjLQ/knRL3Y7EHAzJkzq1evXqNGDXm1ffv2hfYHBgYuW7YsucOIjo5Onz69CHLeu3fv+PHj1apV8/HxMVh49+7dHLVAiIlQ/glRNbZ5ELB169bly5fr57dv375Pnz7S5tWrV3v16gWboGLFivDjscv06dMjIyPh0CO/fv36vr6+cPEXLFgAS+LQoUNjx469ePFiixYtVq5cuWPHDtQwderUChUq+Pv7W3yohKgEyj8hascGDwKg69Ls5XKKFi364sULaXPXrl0wFN6+fdulS5cePXrs2bMH8q/RTh5169YtyLxGu7REcHDw69evf/zxR0j+mzdvBg8ejPzSpUvj08/Pr0iRIpYdJCGqgvJPCPkPVn0Q4OPjc/nyZaHQcuDZV65cWdqEkKOJHDlyQPXr1q0rDWw+c+ZMdHT0rFmzPDw8Hjx4kJCQMHHixOXLl+fQMm/ePJQR80wXLFgQOeYeHiEqhPJPCPkv1nsQ0KRJE4h0y5YtderfvXu3fDIo6dsMGTKIlaMF0P7ChQsPHTpUysmSJYv0moBY+IQQYhaUf0LI/8dKFsCYMWP27NkDpQ8ODpYWJdu3b5+npycsA6nYy5cvs2bNisSKFSvg60v5devW7d2795UrV0T84OnTp9WqVZsxY0Z4eLh8bVM3N7f379+bflSEqBnKPyHkfzB9KIDG5AcBHh4eJ06cmDlzZps2bbJly1a+fHnIds2aNYOCgqTaGjZsWKdOnRIlSpQsWbJHjx7SopEa7QjBqKiotm3bFilSBBZA69atV65cGRAQULly5Ro1asA4aNmyJYp17Njx22+/xVcVK1a07LcToh4o/4QQAygeBkiXLt2oUaOS+zZ37ty7du3SyRThfbGa2QAt8m+3bNmiU37VqlWmHAkhREP5J4Qkh10mB5Tz9u3btGnTcigfIdaA8k8ISRZrPAgwnfv37zdo0IAr5xJiDSj/hJAUsEsY4MWLF4cOHZo7d65SFRJC5FD+CSEpY7oFYDA/Ojr65MmTvr6+6dOnN7HF7Nmz9+vXz4xDJISYA+WfEGISplgABsskJiaGhoaOHDkyMjLSdPlPEa4HSEhqoPwTQkzFxKEAOri7uwcFBUH+FTkGaVk/c9cDJITIofwTQswjuTCA6Q/+UfLEiRM3btwoUKBA7dq1DS4HkBzyZf2eP38urQeYmjoJUSGUf0KI2ehbAKZrf1RU1Pjx4/v27du8efPNmzcPHTp027ZtPj4+Oiv4xcXFYTMsLAwNifLYV76s3+3btydPnhwZGZknTx6DdXp7e0+aNAmbwcHBQUFBjx49wmavXr0UPxuEOCOUf0KIJVj2IAAMHDiwR48eZcuWRbpbt24PHz7s06fPgAEDdFbwW7ZsWatWrcaNGxceHh4YGIgyaEu+rJ+Xlxfk30ide/bs6devHyQ/IiJi7969oaGhgwYN6t69u5hHiBBr4OHhYe9DMBXKPyHEckQYwKz3/fbt2zdlyhRp89tvv509e/arV690VvCDcickJBw5csTNzS02Nvb58+c5c+ZMblk/g3UikStXLlFV3rx5hwwZMmHCBNSTO3fu1P1oQlwByj8hJFlMmc/H3Hf906dPf/PmTbjvYjNjxoxIX758WWcFP8h5TEyMv79/vXr1+vbtm5SUZG6dOr/C09PTgqMlxFWh/BNCUiD1k/qJfYXAt2/ffsaMGXXq1BGL9e3cuXPs2LGhoaE6K/hNmzZt//79JUuWPHTokLx1nWX9RL7BOqVv5UcuGRmEqBzKPyHEJKTH/ObaAXFxcfPnz0ciJCRk8ODBkydPHjNmTJs2bXx9fbNly1ahQgX499WqVdNZwW/gwIF+fn7169fv06cP9g0LCxMrBknL+p08eRKbSAQFBRmsMz4+XrS7ePFilBELDeMYRowYkTFjRkXPDSHOB+WfEGIe5gYDPDw8hmmRcuCp65TJly+fzgp+k7SItNxll5b1q1ixorAMRBP6daZLl26oFimHrj8hEnaWfy7mQYiTYqVlfgghtoHePyHEEij8hDg1lH9CiHlQ+AlxAews/+xHCHFk9B/P8Z4lxDWg908IMQkKPyGuBOWfEJICFH5CXA/KPyEkWSj8hLgqlH9CCCFEdVD+CSGEENVB+SeEEEJUB+WfEEIIUR2Uf0KIwnAyb0IcH8o/IURJ+LIAIU4B5Z8QQghRHZR/QgghRHVQ/gkhhBDVQfknhBBCVIfl8v/3339bNr6XI4MIIYQQ+5Iq759CTgghhDgjDP4TQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCHJpz584dOnQoMDDQAWsjxHmh/BNClCE2NnbIkCFLlixBevbs2QMHDpS+unPnTtGiRf/+++/evXvPmjULiQEDBoSFhaVNm3bYsGHDhw9v3rw5VDl9+vT16tWDQmfPnn3x4sW1atXCvuHh4Sj27t07+S6TJ08WNevkG69K1PbixYsyZcpcvXq1W7duqCd37tyvX7/GYeOYt2zZUrNmTXPrJMQZofwTQpQhQ4YMc+fOhY5CNefNmxcQEJAmTRrxFcQS6cTExDlz5qRLlw45ixYtgr4WKFBACPmuXbuwe6FChX766acjR474+vqOHDny+PHj+Grnzp0omTFjRp1dBPr5RqqSaoMF0K5dO9gr0H5kZsmSBdLep08faL8FdRLijFD+CSGKIaTdz88PTjY86bZt22ITvvXFixd9fHyuX78uCkglpU0PDw+pknLlyuETNgQ+L126FB0d/eWXX+rvotNoilXJa/v7779z5Mixffv2N2/eeHp64isc7bp16yyokxAnhfJPCFGYYcOGQf5nzZol5D80NLR3797Dhw83ZV8I88aNG5Fo2bKlRuus169f3zKt1alKp7ZOnTrNnj1706ZNXbp0gV3i5eUFX9+COglxUij/hBCFKVWqVNOmTXfs2HHkyJHq1avv27dv6NChJsp/kyZNzp07N2LEiF69emm0gt2jRw/LDkOnKp3aunbtCvmHmQL5X7lyZffu3S2rkxAnhfJPCFEeiD3kf9asWc2bN+/YsaPp64NAoaX0s2fPzp49+3//93+WHYO8Kv3aSpcuXaNGjYiIiKtXrx44cGDSpEkW1EmI80L5J4QozxdffFGrVq2ff/4ZomvxELndu3dXqlQpV65cyRWAcpcqVcri2rp16wb579Sp09dffy2NUiREJVD+CSGKERcXh8/4+Ph06dINHz4cwt+6dWvxrF18lZCQIDZRRvoU+dKnBFztJk2aSJtJSUk6zc2aNWv58uWmVKVfG8CxDRgw4OzZsxs2bNApbGKdhDgvlH9CiDLExsb2798fiX79+s2bN69hw4afffYZPGzkjBkz5uHDh0j06dMHXyEB3cXngwcP8NWIESPEJAHYHD16tHjXDlq7d+9ebEr1//jjj/i8e/durVq18C0SVapUEe/oG6/KYG0a7Qt+HTp0uHTpUpEiReT5JtZJiFND+SeEKEOGDBmWaZFyzp07JxKTtMgL65RcqkVeIG3atC9fvpTndNKi326KVRmsTRASEqKfCbPAlDoJcWoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSoDso/IYQQojoo/4QQQojqoPwTQgghqoPyTwghhKgOyj8hhBCiOij/hBBCiOqg/BNCCCGqg/JPCCGEqA7KPyGEEKI6KP+EEEKI6qD8E0IIIaqD8k8IIYSojv8HJp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Figure 5. The MNS1 (Mirror Neuron System) model. (i) Top diagonal: a portion of the FARS model. Object features are processed by cIPS and AIP to extract grasp affordances, these are sent on to the canonical neurons of F5 that choose a particular grasp. (ii) Bottom right. Recognizing the location of the object provides parameters to the motor programming area F4 which computes the reach. The information about the reach and the grasp is taken by the motor cortex M1 to control the hand and the arm. (iii) New elements of the MNS1 model: Bottom left are two schemas, one to recognize the shape of the hand, and the other to recognize how that hand is moving. (iv) Just to the right of these is the schema for hand-object spatial relation analysis. It takes information about object features, the motion of the hand and the location of the object to infer the relation between hand and object. (v) The center two regions marked by the gray rectangle form the core mirror circuit. This complex associates the visually derived input (hand state) with the motor program input from region F5canonical neurons during the learning process for the mirror neurons. The grand schemas introduced in section 3.2 are illustrated as the following. The “Core Mirror Circuit” schema is marked by the center grey box; The “Visual Analysis of Hand State” schema is outlined by solid lines just below it, and the “Reach and Grasp” schema is outlined by dashed lines. (Solid arrows: Established connections; Dashed arrows: postulated connections. Details of the ascription of specific schemas to specific brain regions is deferred to a later paper.)
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