diff --git a/docs/usage/embedding_different_models.ipynb b/docs/usage/embedding_different_models.ipynb
index b494ef97..2c23d506 100644
--- a/docs/usage/embedding_different_models.ipynb
+++ b/docs/usage/embedding_different_models.ipynb
@@ -18,7 +18,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-05-29 12:01:28.282\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpyeed.embeddings.processor\u001b[0m:\u001b[36m_initialize_devices\u001b[0m:\u001b[36m44\u001b[0m - \u001b[1mInitialized 3 GPU device(s): [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2)]\u001b[0m\n"
+      "\u001b[32m2025-09-01 09:27:09.198\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpyeed.embeddings.processor\u001b[0m:\u001b[36m_initialize_devices\u001b[0m:\u001b[36m46\u001b[0m - \u001b[1mInitialized 3 GPU device(s): [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2)]\u001b[0m\n"
      ]
     }
    ],
@@ -40,16 +40,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pyeed Graph Object Mapping constraints not defined. Use _install_labels() to set up model constraints.\n",
       "📡 Connected to database.\n",
-      "All data has been wiped from the database.\n"
+      "The provided date does not match the current date. Date is you gave is 2025-05-29 actual date is 2025-09-01\n"
      ]
     }
    ],
@@ -64,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -89,17 +88,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# now fecth all of the proteins from the database\n",
-    "eedb.fetch_from_primary_db(data_ids, db=\"ncbi_protein\")"
+    "eedb.fetch_from_primary_db(['P00974'], db=\"uniprot\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,13 +130,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e75dae63f3f740b2b6d95da33c196de5",
+       "model_id": "44c8916a2c7f4f9b9aa76dcb48a49eab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -157,7 +156,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -186,7 +185,7 @@
     },
     {
      "data": {
-      "image/png": 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N69WrJ4KCgtTKzZkzRwAQ58+fF0L8d82+++47ERISIry9vcWFCxc0nk+fPn1E165dlT///PPPAoA4ePCg2jl27txZ1KlTRwghRE5OjrCzsxPdunXTGSTUqFFDBAQEqGx79OiRAKA2RuXs2bMCgPj++++1Hu/IkSOiQoUKGq9XUYoKltLT08X+/fvFzp07xZQpU0Tz5s3Fnj17NJaNi4sT+/fvFz/88IPo1q2b6N27t0hKSjKoPrVq1RIAxDvvvKOyXRGIXblyRefzR40apTamqlatWqJLly5qZePj4wUAsXDhQuW2Z8cJjRs3Tvl7+vDDD5VfzEozWNL0GTVy5EiNX3ael5qaKry9vVXGjf3xxx8CgPjxxx/Vys+YMUMAEA8fPtSrzkII8fLLL4sKFSoUWZfyht1wVGxLly7F/v37cejQIcTGxuL69esqff+urq7IzMzU+3j79u3D/fv3ERQUhGvXruHatWtISEhA+/btsXnz5iK7LRTeeust7N+/H9HR0Th16hRSUlIwZcoUreXv37+Px48fo379+mr7GjRoALlcrjYGQh83b94EAK3HffDgAbKzsw0+rjZPnjzBzJkzUa1aNdjb28PDwwOenp549OgR0tPTleXi4+MRGBio81hXr17FxYsX4enpqfKoV68eACAlJUWlfN26ddWOUa9ePTx+/Bj379836BrfvHlTaznF/meNHz8eJ0+exIEDB9CwYUO15z19+hT79+9XGa909epVAIXj2Z4/x3379inPz97eHnPnzsVvv/0Gb29vtGnTBl9++SWSkpLUXufZ8SIAIJVKAQDVqlXTuP3hw4dqxwCAS5cuoXfv3ggMDMR3332nsUxJuLq6IjQ0FD179sTcuXMxadIk9OzZE+fOnVMr6+/vj9DQUAwbNgy//PILsrKy0L17dwgh9H49R0dHAIXdxs96/fXXAQAxMTE6n6+YOXbgwAGVY2oaO6fo1lO85vNef/11xMbG4ty5c9i0aRMGDhxY5JguY1OkOIiKisL8+fPh5uaGhw8fqs1Y08Td3R0jR47E5cuXlV3minMtzvXQJCsrCy4uLnqXLy+YOoCKLSgoSGd+H39/f5w9exZ5eXl6fRAoxib1799f4/4jR46gffv2RR6nbt26WnOqlGXvvvsu1qxZg/HjxyMkJESZsHLgwIF6B5oKcrkcjRo1woIFCzTufz4AMKeePXtiy5Yt+OKLL7Bu3Tq1gdJ//fUXMjIyVMYrKa7H+vXrNU7Lr1Dhv4/G8ePHo3v37ti5cyd+//13zJgxA3PmzMHBgwfRrFkzZTlbW1uN9dO2XVPAcfv2bWWy0b1795bKTeu1117D0KFDsWXLFjRp0kRn2b59++Ltt9/GlStXNAa0mvj6+uLixYvw9vZW2e7l5QVAe9CooPhbS0tLU27z8fHB3bt31coq8lJpSjUAFAYqtWvXxvjx45GQkKAM2IrDwcEBQOGXFE0eP36sLPMsDw8P5edTWFgY/P398eqrr+Lrr7/GxIkTi3zdZ6/HCy+8AB8fHwDQmJMrMTER7u7usLe31+ucnj59iitXrhT5Zao8YrBEJtO9e3fExMRg+/btat8qn5ednY1du3ZhwIABGgeEjxs3Dhs3btQrWDKUp6cnnJyccPnyZbV9ly5dgo2NjfIDypBvoYpZa9qO6+HhgUqVKhWz1up++uknDB8+HF999ZVyW05Ojtrspdq1a6sMvNakdu3aOHfuHDp27KjXOStaap515coVODk5wdPTEwD0vsY1atTQWk6x/1m9evVC586dMWLECLi4uKglMv31118REBCgkherdu3aAApv2PoE1rVr18akSZMwadIkXL16FU2bNsVXX32FDRs2FPlcfaWmpqJz587Izc1FdHS08iZoKENbSnJzcyGXy1VaH7VRBAb6lFVo0aIF9u/frxzgrXDv3j0AUP59aHP9+nW1ck2bNsWhQ4eQkZGhMshbMVFAVz61QYMG4dNPP0WDBg1KlHdN1/tbsV1XYlmFbt26oW3btvj888/x9ttvF/mZ8Pz1qFq1Kjw9PTUm1Dxx4oRB5/jTTz/hyZMnZp0daKnYDUcm884778DHxweTJk3ClStX1PanpKTg008/BQDs2LED2dnZGDNmDPr27av2ePXVV7F9+3at09ZLwtbWFp07d8auXbtUZkElJydj06ZNaN26tfIDWfFBps/0aR8fHzRt2hQ//PCDSvkLFy5g3759aonmSsrW1latteKbb75BQUGByrY+ffrg3LlzGmeWKZ7fv39/3L17F6tWrVIr8+TJE7Xuw5iYGJw+fVr58+3bt7Fr1y507twZtra2Bl3jrl274sSJEyrdM9nZ2Vi5ciX8/PwQEBCgVqdhw4Zh8eLFWL58OT744AOVfXv37lVLGRAWFgZXV1d8/vnnajOtACgzGT9+/Fhtxlbt2rXh4uJi1L/F7OxsdO3aFXfv3sXevXs1dmvqS9vf6KNHjzSeq6Kr79lW4ue7WYHCVod169bB0dFR4+9AG0VL8ffff6/2uhUqVFCmycjIyFC7pkII5WfEszfwvn37oqCgACtXrlRuy83NxZo1axAcHKyz5fN///sfZs2apfKlojgU7+8NGzaoXetTp07h2LFj6NKli17H+uCDD5CamqryftOUTfvu3btYvXo1GjdurBJM9+nTB7/88ovKcIHo6GhcuXJFme6kKOfOncP48eNRuXJljBkzRq/nlCdsWSKTqVy5Mnbs2IGuXbuiadOmKhm8T58+jc2bNysz0W7cuBFVqlRRme79rB49emDVqlX49ddf8dprrxm9rp9++in279+P1q1bY/To0ahQoQJWrFiB3NxcleUxmjZtCltbW8ydOxfp6emwt7dHhw4dlF0Kz5s3bx66dOmCkJAQvPnmm3jy5Am++eYbSKVSo69N9+qrr2L9+vWQSqUICAhATEwMDhw4oJYy4f3338dPP/2Efv364Y033kCLFi2QlpaG3bt3Y/ny5WjSpAmGDh2KrVu34p133sGhQ4fw8ssvo6CgAJcuXcLWrVvx+++/q9xcAwMDERYWhnHjxsHe3h7ffvstgMJpzwr6XuOpU6di8+bN6NKlC8aNGwd3d3f88MMPSEhIwPbt29W62RTGjh2LjIwMfPjhh5BKpZg+fToSEhKUWdyf5erqimXLlmHo0KFo3rw5Bg4cCE9PT9y6dQu//vorXn75ZSxZsgRXrlxBx44d0b9/fwQEBKBChQrYsWMHkpOTMXDgwBL/zhQGDx6MEydO4I033kBcXJxKbiVnZ2f06tVL72Mp3mPjxo1DWFgYbG1tMXDgQBw+fBjjxo1D3759UbduXeTl5eHPP//Ezz//jJYtW2LIkCHKY7z99tvIyMhAmzZtULVqVSQlJWHjxo24dOkSvvrqKzg7O+tdn2bNmuGNN97A6tWrkZ+fj7Zt2+Lw4cPYtm0bpk2bpuwyO336NAYNGoRBgwahTp06ePLkCXbs2IG///4bb731Fpo3b648ZnBwMPr164dp06YhJSUFderUwQ8//IAbN26oBWXPq1GjhtHeewsWLEBYWBiaNm2KESNGwNfXF3FxcVi5ciV8fHwwbdo0vY7TpUsXBAYGYsGCBRgzZgwqVqyIKVOmID4+Hh07doSvry9u3LiBFStWIDs7G19//bXK86dPn45t27ahffv2eO+995CVlYV58+ahUaNGGDlypNrr/fnnn8jJyUFBQQFSU1Px999/Y/fu3ZBKpdixY4dBGePLDbMOLyerZOjMkXv37okJEyaIevXqCQcHB+Hk5CRatGghPvvsM5Geni6Sk5NFhQoVxNChQ7Ue4/Hjx8LJyUllKvrzDMl4Cw0ZvE+fPi3CwsKEs7OzcHJyEu3bt1eZCq6watUqUatWLWFra6vXzLgDBw6Il19+WTg6OgpXV1fRvXt3ERsbq1LGGLPhHj58KEaOHCk8PDyEs7OzCAsLE5cuXRI1atQQw4cPVymbmpoqxo4dK6pWrSrs7OzECy+8IIYPHy4ePHigLJOXlyfmzp0rGjZsKOzt7UXlypVFixYtRGRkpHLmmhD/ZfDesGGDqFu3rrC3txfNmjXTeF30vcbx8fGib9++ws3NTTg4OIigoCCV6dK6rtmUKVMEALFkyRKxZMkSIZVKxdOnTzVey0OHDomwsDAhlUqFg4ODqF27thgxYoQyDcKDBw/EmDFjhL+/v6hUqZKQSqUiODhYbN26VeU4mjIzP3ttnqXp96dtlhT+f5apIfLz88W7774rPD09hUQiUc7Iunbtmhg2bJioVauWcHR0FA4ODqJhw4Zi1qxZIisrS+UYmzdvFqGhocLb21tUqFBBVK5cWYSGhopdu3YZVBeFvLw8MXv2bFGjRg1RsWJFUadOHZUZa0IIcf36ddGvXz/h5+en8jmxfPlyZUqLZz158kRMnjxZyGQyYW9vL1588UWV2bgK2n43zyrObDiFY8eOiVdffVVUrlxZVKhQQVStWlX873//U6ZQ0bcua9euFQDEmjVrhBBCbNq0SbRp00Z4enqKChUqCA8PD9G7d29x6tQpjc+/cOGC6Ny5s3BychJubm5i8ODBajMXFe8ZxaNixYrC09NTtGnTRnz22WciJSXF4PMvLyRCGDCtgYjIinTt2hXOzs56ZzEmItKE3XBEVGa1a9cOr7zyirmrQURWji1LRERWID09Xes0dYXSHGuiWI5FF09PT62pEyxVXl6eSpoCTaRSqUG5i8j6sWWJiMgKvPfee/jhhx90linN777z589XGcCvSUJCgkrKBmtw9OjRIlOUrFmzBiNGjCidCpFFYMsSEZEViI2NVeYm0qY0k7Fev35dmfNHm9atW2tMzGjJHj58iFOnTuks07Bhw2LnwSLrxGCJiIiISAcmpSQiIiLSgWOWjEAul+PevXtwcXEp9UUZiYiIqHiEEMjMzISvr6/WhLcAgyWjuHfvnkUtLEpERET6u337Nl544QWt+xksGYFiZfDbt2+rLOpIRERElisjIwPVqlVT3se1YbBkBIquN1dXVwZLREREVqaoITRWN8B76dKl8PPzg4ODA4KDg3HixAmtZS9evIg+ffrAz88PEokEixYtUisze/ZsSCQSlYe/v78Jz4CIiIisiVUFSz/++CMmTpyIWbNm4fTp02jSpAnCwsKQkpKisfzjx49Rq1YtfPHFFzoz2zZs2BCJiYnKx19//WWqUyAiIiIrY1XB0oIFCzBq1CiMHDkSAQEBWL58OZycnLB69WqN5V988UXMmzcPAwcOhL29vdbjVqhQATKZTPnw8PAw1SkQERGRlbGaYCkvLw+nTp1SyVBrY2OD0NBQxMTElOjYV69eha+vL2rVqoXBgwfj1q1bOsvn5uYiIyND5UFERERlk9UESw8ePEBBQQG8vb1Vtnt7eyMpKanYxw0ODsbatWsRFRWFZcuWISEhAa+88goyMzO1PmfOnDmQSqXKB9MGEBERlV1WEyyZSpcuXdCvXz80btwYYWFh2Lt3Lx49eoStW7dqfc60adOQnp6ufNy+fbsUa0xERESlyWpSB3h4eMDW1hbJyckq25OTk3UO3jaUm5sb6tWrh2vXrmktY29vr3MMFBEREZUdVtOyZGdnhxYtWiA6Olq5TS6XIzo6GiEhIUZ7naysLMTHx3NFaSIiIgJgRS1LADBx4kQMHz4cLVu2RFBQEBYtWoTs7GyMHDkSADBs2DBUrVoVc+bMAVA4KDw2Nlb5/7t37+Ls2bNwdnZGnTp1AACTJ09G9+7dUaNGDdy7dw+zZs2Cra0tBg0aZJ6TJCIiIotiVcHSgAEDcP/+fcycORNJSUlo2rQpoqKilIO+b926pbIQ3r1799CsWTPlz/Pnz8f8+fPRtm1bHD58GABw584dDBo0CKmpqfD09ETr1q1x7NgxeHp6luq5EZFlK5ALnEhIQ0pmDrxcHBBU0x22Nlw4m6g8kAghhLkrYe0yMjIglUqRnp7O5U6IyqCoC4mI3BOLxPQc5TYfqQNmdQ9AeCC77Imslb73b6sZs0REZA5RFxIRseG0SqAEAEnpOYjYcBpRFxLNVDMiKi0MloiItCiQC0TuiYWm5nfFtsg9sSiQs4GeqCxjsEREpMWJhDS1FqVnCQCJ6Tk4kZBWepUiolLHYImISIuUTO2BUnHKEZF1YrBERKSFl4uDUcsRkXVisEREpEVQTXf4SB2gLUGABIWz4oJqupdmtYiolDFYIiLSwtZGglndAwBALWBS/DyrewDzLRGVcQyWiIh0CA/0wbIhzSGTqna1yaQOWDakOfMsEZUDVpXBm4jIHMIDfdApQMYM3kTlFIMlIiI92NpIEFK7irmrQURmwG44IiIiIh0YLBERERHpwGCJiIiISAcGS0REREQ6MFgiIiIi0oGz4YiIiCxUgVwwZYUFYLBERERkgaIuJCJyTywS0/9bqNlH6oBZ3QOYDLWUsRuOiIjIwkRdSETEhtMqgRIAJKXnIGLDaURdSDRTzconBktEREQWpEAuELknFkLDPsW2yD2xKJBrKkGmwGCJiIjIgpxISFNrUXqWAJCYnoMTCWmlV6lyjsESERGRBUnJ1B4oFacclRyDJSIiIgvi5eJg1HJUcpwNR+USp+MSkaUKqukOH6kDktJzNI5bkgCQSQs/t6h0MFiicofTcYnIktnaSDCrewAiNpyGBFAJmBRf6WZ1D+AXvFLEbjgqVzgdl4isQXigD5YNaQ6ZVLWrTSZ1wLIhzfnFrpSxZYnKjaKm40pQOB23U4CM39iIyOzCA33QKUDGIQMWgMESlRuGTMcNqV2l9CpGRKSFrY2En0cWgN1wVG5wOi4RERUHW5ao3DD1dFzOsCMiKpsYLFG5YcrpuJxhR0RUdrEbjsoNxXRc4L/ptwolmY7LGXZERGUbgyUqV4w9HbeoGXYCwNTt5/H3tQdc9JKIyEqxG47KHWNOxy1qhh0APHryFIO/O85uOSIiK8VgicolY03HNWTmnKJbjgnliIisC7vhiErAkJlzik64yD2x7JIjIrIiDJaISkAxw07fDrxnE18SEZF1YLBEVAK6ZtjpwsSXRETWg8ESUQlpm2GnS3ETXxIRUeljsERkBOGBPvjrgw7Y+GYw3Bwrai0nQWGyyuIkviQiIvNgsERkJLY2Erxc1wNf9GkECYyb+JKIiMyHwRKRkRk78SUREZkX8ywRmYAxE18SEZF5MVgiMhFjJb4kIiLzYjccERERkQ4MloiIiIh0YLBEREREpAODJSIiIiIdGCwRERER6cBgiYiIiEgHBktEREREOjBYIiIiItKBwRIRERGRDgyWiIiIiHSwumBp6dKl8PPzg4ODA4KDg3HixAmtZS9evIg+ffrAz88PEokEixYtKvExiYiIqHyxqmDpxx9/xMSJEzFr1iycPn0aTZo0QVhYGFJSUjSWf/z4MWrVqoUvvvgCMpnMKMckIiKi8kUihBDmroS+goOD8eKLL2LJkiUAALlcjmrVquHdd9/F1KlTdT7Xz88P48ePx/jx4412TIWMjAxIpVKkp6fD1dXV8BOzIAVygRMJaUjJzIGXiwOCarrD1kZi7moREREZnb737wqlWKcSycvLw6lTpzBt2jTlNhsbG4SGhiImJqZUj5mbm4vc3FzlzxkZGcV6fUsTdSERkXtikZieo9zmI3XArO4BCA/0MWPNiIiIzMdquuEePHiAgoICeHt7q2z39vZGUlJSqR5zzpw5kEqlyke1atWK9fqWJOpCIiI2nFYJlAAgKT0HERtOI+pCoplqRkREZF5WEyxZkmnTpiE9PV35uH37trmrVCIFcoHIPbHQ1B+r2Ba5JxYFcqvpsSWiMqpALhATn4pdZ+8iJj6Vn0tUKqymG87DwwO2trZITk5W2Z6cnKx18Lapjmlvbw97e/tivaYlOpGQptai9CwBIDE9BycS0hBSu0rpVYyI6BkcKkDmYjUtS3Z2dmjRogWio6OV2+RyOaKjoxESEmIxx7RGKZnaA6XilCMiMjYOFSBzspqWJQCYOHEihg8fjpYtWyIoKAiLFi1CdnY2Ro4cCQAYNmwYqlatijlz5gAoHMAdGxur/P/du3dx9uxZODs7o06dOnodszzwcnEwajlDcPYdERWlqKECEhQOFegUIOPnB5mEVQVLAwYMwP379zFz5kwkJSWhadOmiIqKUg7QvnXrFmxs/mssu3fvHpo1a6b8ef78+Zg/fz7atm2Lw4cP63XM8iCopjt8pA5ISs/R+GEkASCTFgYyxsQmdSLSB4cKkLlZVZ4lS1UW8iwpmrgBqARMiu9oy4Y0N2oAo3i95//4TPV6RGS9dp29i/e2nC2y3NcDm6Jn06qmrxCVGfrev61mzBKZVnigD5YNaQ6ZVLWrTSZ1MHrgwtl3ZMk428rymHOoABFgZd1wZFrhgT7oFCAz+RgiNqmTpWLXsGUy11ABIgW2LJEKWxsJQmpXQc+mVRFSu4pJBkty9h1ZIs62sly2NhLM6h4A4L+uegXFz7O6B3BwN5kMgyUqdWxSJ0vDrmHLV5pDBYiex244KnVsUidLw65h61BaQwWInsdgiUqdokk9YsNpSKB59h2b1Kk0sWvYeiiGChCVJnbDkVmwSZ0sCbuGiUgXtiyR2bBJnSwFu4aJSBcGS2RWbFInS8CuYSLShd1wRERg1zARaceWJSKi/8euYSLShMESEdEz2DVMRM9jNxwRERGRDgyWiIiIiHTQO1hq1KgRPvnkE9y+fduU9SEiIiKyKHoHSxcvXsTXX3+NmjVrIjw8HNu3b0d+fr4p60ZERERkdgZ1w/3777/46aefYGdnh4EDB8LX1xeTJ09GXFycqepHREREZFYGBUsVKlRAr169sHv3bty6dQsTJkzA7t27ERgYiFatWmH16tWmqicRERGRWegdLEkkqnlGfHx8MG3aNFy5cgXR0dGoXbs2xo0bZ/QKEhEREZmT3nmWhNC0YlKhdu3aoV27dsjIyDBKpYiIiIgshd4tS8OHD4ejo6POMq6uriWuEBEREZEl0btlac2aNaasBxEREZFFYlJKIiIiIh0MCpa+/fZbhIaGon///oiOjlbZ9+DBA9SqVcuolSMiIiIyN72DpcWLF+P999+Hv78/7O3t0bVrV8yZM0e5v6CgADdv3jRJJYmIiIjMRe8xSytWrMCqVavw+uuvAwAiIiLQq1cvPHnyBB9//LHJKkhERERkTnoHSwkJCWjVqpXy51atWuHgwYMIDQ3F06dPMX78eFPUj4iISC8FcoETCWlIycyBl4sDgmq6w9ZGUvQTiYqgd7Dk4eGB27dvw8/PT7ktMDAQBw8eRIcOHXDv3j1T1I+IiKhIURcSEbknFonpOcptPlIHzOoegPBAHzPWjMoCvccstW7dGj///LPa9oCAAERHR+O3334zasWIiIj0EXUhEREbTqsESgCQlJ6DiA2nEXUh0Uw1o7JC72Bp6tSpaNy4scZ9DRs2xMGDBzFz5kyjVYyIiKgoBXKByD2x0LTGhGJb5J5YFMi1r0JBVBS9u+EaN26sNVgCCrvkAgMDjVIpImvFMRNEpetEQppai9KzBIDE9BycSEhDSO0qpVcxKlP0DpaISDeOmSAqfSmZ2gOl4pQj0sRoGbwbNGgAW1tbYx2OyKpwzASReXi5OBi1HJEmRmtZmjNnDtLT0411OCKrUdSYCQkKx0x0CpCxS47IyIJqusNH6oCk9ByN70EJAJm0sEucqLiM1rLUq1cvDB8+3FiHI7IahoyZICLjsrWRYFb3AACFgdGzFD/P6h7ALypUIsUKltLT03H58mVcvnyZrUlU7nHMBJF5hQf6YNmQ5pBJVbvaZFIHLBvSnGMGqcQM6ob77rvvsGDBAly+fFlle/369TFp0iS8+eabRq0ckTXgmAki8wsP9EGnABlno5JJ6B0szZs3D7Nnz8a4ceMQFhYGb29vAEBycjL27duH9957Dw8fPsTkyZNNVlkiS8QxE1RWWVsqDFsbCdMDkElIhBB6ZeqqUaMG5s2bh/79+2vc/+OPP+L999/HrVu3jFpBa5CRkQGpVIr09HS4urqauzpkBorZcABUAibFbYVdAWRtmAqDygN97996j1lKSUlBo0aNtO5v1KgRHjx4YFgticoIjpmgsoSpMIhU6d2y1KZNG9SsWRPff/89KlRQ7b0rKCjAG2+8gRs3buDIkSMmqaglY8sSKVhbtwXR8wrkAq3nHtQ6w1PRrfzXBx34t01WT9/7t95jlpYsWYKwsDDIZDK0adNGZczSH3/8ATs7O+zbt6/kNSeyYhwzQdaOy4cQqdO7G65x48a4cuUKPvnkE7i4uOD69eu4fv06XFxc8Omnn+LSpUtcG46IyMoxFQaROoNSB7i4uCAiIgIRERGmqg8REZkRU2EQqTNaBm8iIrJ+ilQY2kYjSVA4K46pMKg8YbBERKRBgVwgJj4Vu87eRUx8Kgrkes2FsXpcPoRIndEW0iUiKivKe44hRSqM56+BrBxdA6Jn6ZU64N9//0VgYCBsbNgQpQlTBxCVHYocQ89/MJbHBKNMhUFlnVGTUjZr1kyZcLJWrVpITU01Ti3JpMprNwJRcRXIBSL3xGpctkaxLXJPbLl5LylSYfRsWhUhtaswUKJyS69uODc3NyQkJMDLyws3btyAXC43db2ohMp7NwJRcTDHEBFpolew1KdPH7Rt2xY+Pj6QSCRo2bIlbG1tNZa9fv26UStIhtPWjaBYqqA8dSMQGaI4OYbYVUVU9ukVLK1cuRKvvfYarl27hnHjxmHUqFFwcXExdd2oGIrqRpCgsBuhU4CMH+hEzzE0xxBbcInKB71nw4WHhwMATp06hffee4/BkoViNwJR8SlyDCWl52j8wqFYFy2opjtbcInKEYOnt61Zs0YZKN25cwd37twxeqV0Wbp0Kfz8/ODg4IDg4GCcOHFCZ/lt27bB398fDg4OaNSoEfbu3auyf8SIEZBIJCoPRWBojbhUAVHx6ZtjCAAHghOVIwYHS3K5HB9//DGkUilq1KiBGjVqwM3NDZ988onJB37/+OOPmDhxImbNmoXTp0+jSZMmCAsLQ0pKisbyR48exaBBg/Dmm2/izJkz6NWrF3r16oULFy6olAsPD0diYqLysXnzZpOehylxqQKiklHkGJJJVd8jMqmDsrXIkBZcIrJ+Biel/PDDD/H999/jiy++wMsvvwwA+OuvvzB79mzk5OTgs88+M3olFRYsWIBRo0Zh5MiRAIDly5fj119/xerVqzF16lS18l9//TXCw8Px/vvvAwA++eQT7N+/H0uWLMHy5cuV5ezt7SGTyUxW79JkSDcCEWkWHuiDTgEyrQO32YJLVL4Y3LL0ww8/4LvvvkNERAQaN26Mxo0bY/To0Vi1ahXWrl1rgioWysvLw6lTpxAaGqrcZmNjg9DQUMTExGh8TkxMjEp5AAgLC1Mrf/jwYXh5eaF+/fqIiIiw6jxSXKqAyDh05RhiCy5R+WJwsJSWlgZ/f3+17f7+/khLM12T84MHD1BQUABvb2+V7d7e3khKStL4nKSkpCLLh4eHY926dYiOjsbcuXNx5MgRdOnSBQUFBVrrkpubi4yMDJWHJdGnG4GIio+LzRKVLwZ3wzVp0gRLlizB4sWLVbYvWbIETZo0MVrFSsvAgQOV/2/UqBEaN26M2rVr4/Dhw+jYsaPG58yZMweRkZGlVcViKaobgYiKT9GCG7HhNCSASpc3W3CJyh6Dg6Uvv/wS3bp1w4EDBxASEgKgsLvr9u3bajPNjMnDwwO2trZITk5W2Z6cnKx1vJFMJjOoPFC4nIuHhweuXbumNViaNm0aJk6cqPw5IyMD1apV0/dUSo2iG4GIjI+LzRKVHwYHS23btsWVK1ewdOlSXLp0CQDw2muvYfTo0fD19TV6BRXs7OzQokULREdHo1evXgAKZ+ZFR0dj7NixGp8TEhKC6OhojB8/Xrlt//79yiBPkzt37iA1NRU+Pto/6Ozt7WFvb1+s8yCisoMtuETlg0QIYTWJQH788UcMHz4cK1asQFBQEBYtWoStW7fi0qVL8Pb2xrBhw1C1alXMmTMHQGHqgLZt2+KLL75At27dsGXLFnz++ec4ffo0AgMDkZWVhcjISPTp0wcymQzx8fGYMmUKMjMzcf78eb0DIn1XLSYiIiLLoe/92+CWJXMaMGAA7t+/j5kzZyIpKQlNmzZFVFSUchD3rVu3YGPz35j1Vq1aYdOmTfjoo48wffp01K1bFzt37kRgYCAAwNbWFv/++y9++OEHPHr0CL6+vujcuTM++eQTthwRERERACtrWbJUbFkiIiKyPvrevw1OHUBERERUnjBYIiIiItKhWMFSfn4+Dhw4gBUrViAzMxMAcO/ePWRlZRm1ckRERETmZvAA75s3byI8PBy3bt1Cbm4uOnXqBBcXF8ydOxe5ubkqa64RERERWTuDW5bee+89tGzZEg8fPoSjo6Nye+/evREdHW3UyhERERGZm8EtS3/++SeOHj0KOzs7le1+fn64e/eu0SpGREREZAkMblmSy+UaF5m9c+cOXFxcjFIpIiIiIkthcLDUuXNnLFq0SPmzRCJBVlYWZs2aha5duxqzbkREVEIFcoGY+FTsOnsXMfGpKJAztR6RoQxOSnnnzh2EhYVBCIGrV6+iZcuWuHr1Kjw8PPDHH3/Ay8vLVHW1WExKSUSWKOpCotpCvz5c6JdISd/7d7EyeOfn5+PHH3/EuXPnkJWVhebNm2Pw4MEqA77LEwZLRGRpoi4kImLDaTz/Aa9Y4nfZkOYMmKjcM2mwRKoYLBGRJSmQC7See1ClRelZEgAyqQP++qADbG0kGssQlQcmW+5kzpw5WL16tdr21atXY+7cuYYejoiIjOxEQprWQAkABIDE9BycSEhT2c7xTUSaGZw6YMWKFdi0aZPa9oYNG2LgwIH44IMPjFIxIiIqnpRM7YGStnIc30SkncEtS0lJSfDxUX/jeHp6IjEx0SiVIiKi4vNycTConGJ80/OtUUnpOYjYcBpRF/jZTuWbwcFStWrV8Pfff6tt//vvv+Hr62uUShERUfEF1XSHj9QB2kYjSVDYahRU0x0FcoHIPbFqA8EBKLdF7olllxyVawYHS6NGjcL48eOxZs0a3Lx5Ezdv3sTq1asxYcIEjBo1yhR1JCIiA9jaSDCrewAAqAVMip9ndQ+ArY2k2OObiMoTg8csvf/++0hNTcXo0aORl5cHAHBwcMAHH3yAadOmGb2CRERkuPBAHywb0lxtHJLsuXFIxRnfRFTeGBwsSSQSzJ07FzNmzEBcXBwcHR1Rt25d2Nvbm6J+RERUTOGBPugUIMOJhDSkZObAy6Ww6+3ZdAGGjm8iMrUCudD5N2sOBgdLCs7OznjxxReNWRciMgNL/GAi47G1kSCkdhWt+xXjm5LSczSOW1LkZAqq6W6yOhIpWOqsTIODpezsbHzxxReIjo5GSkoK5HK5yv7r168brXJEZFqW+sFEpUcxviliw2lIAJWA6fnxTUSmpC3rfGJ6Dt7ZcBrfvt4cXRub53PJ4GDpf//7H44cOYKhQ4fCx8cHEgnfQETWSNsHk2K6OJfDKD/0Hd9EZCq6ZmUqjN18GkvQDF0bl/7Me4OXO3Fzc8Ovv/6Kl19+2VR1sjpc7oSsDZfDIE3YJUvmEhOfikGrjulVdrkRv8jpe/82uGWpcuXKcHdn3zWRNTNkuriu8S5UthQ1vonIVAyZbRm5JxadAmSlGsgbnGfpk08+wcyZM/H48WNT1IeISgGnixORJTFktqU58n4Z3LL01VdfIT4+Ht7e3vDz80PFihVV9p8+fdpolSMi0+B0cSKyJIpZmbpavJ9V2l/kDA6WevXqZYJqEFFp4nRxIrIkilmZ72zQr8GltL/IGTzAm9RxgDdZI8VsOEDzdHHOhiOi4ijJRIG9/yZi7ObT0LYUobEnn5hsgDcRlQ2cLk5ExlbS3G1dG/tgCZph9KYzavvMmffL4JalgoICLFy4EFu3bsWtW7eU68MppKWVv8UW2bJE1ozTxYnIGLTlbitOa3VpJcw1WctSZGQkvvvuO0yaNAkfffQRPvzwQ9y4cQM7d+7EzJkzS1RpIip9nC5OVH6Y6suRrqSSAoUBkyFT/vVZ17A0GRwsbdy4EatWrUK3bt0we/ZsDBo0CLVr10bjxo1x7NgxjBs3zhT1JCIiohIwZWuNKXK3WdIXOYPzLCUlJaFRo0YAChfTTU9PBwC8+uqr+PXXX41bOyIiIioxRRfZ8wGNYnmjqAuJJTp+Wc/dZnCw9MILLyAxsfCi1q5dG/v27QMAnDx5Evb29satHREREZVIUV1kQGEXWYG2KWh60Hcq/40H2Wp1i4lPxa6zdxETn1qiOpiSwd1wvXv3RnR0NIKDg/Huu+9iyJAh+P7773Hr1i1MmDDBFHUkIiIqVWVp4kNpLG8UVNMdMlcHJGXobjnafOIWxnaoC1sbSakN4jYGg4OlL774Qvn/AQMGoHr16oiJiUHdunXRvXt3o1aOiIiotFnTTVwfpdFFZmsjwaCg6lh44IrOckkZuTiRkIb0J3kaZ84pugUtLc9bifMshYSEICQkxBh1IaJSVpa+PRMZg7bp75Z6E9dHaS1v5OfhpFe5pPQn+PL3y0abOVca9AqWdu/ejS5duqBixYrYvXu3zrI9evQwSsWIyLSM+e2ZQReVBcae/m4pSmt5I32DrbTsPJN3CxqbXsFSr169kJSUBC8vL51rw0kkEhQUFBirbkRkIsb89lzWuiyo/CqNsT3moFh3LWLDaUigeXkjY2TF1jcoc3fWbzKYJc2c02s2nFwuh5eXl/L/2h4MlIgsnzFnxph6OjJRaSrL098VyxvJpKqtPzKpA5YNaY5OAbISz0pTBGXAf0GYwrNBmcy1dLoFjcmgMUtPnz5FeHg4li9fjrp165qqTkRkQsb69lxWuyyo/CqtsT3moi0r9v7YJLSee9AorcP6rDlZIBel0i1oTAYFSxUrVsS///5rqroQUSkw1rfnstplQeVXaY3tMTVdYwifz4ptigHtRS1VUlrdgsZkcFJKRV4lIrJOxvr2XJa7LKh80rcbyZJu4s+LupCI1nMPYtCqY3hvy1kMWnUMrece1NglbspklYqgrGfTqgipXUXtmhXVLWhp4x0NTh2Qn5+P1atX48CBA2jRogUqVaqksn/BggVGqxwRGZ+xvj2X9S4LKp/06UayVIa2Epm7ddjSFsvVxeBg6cKFC2jevDkA4MoV1eRTEonlnSARqTJWE3hZ6bIgep65buIlScFRnDGEltA6bEmL5epicLB06NAhU9SDiEqRMb49W+O4AyJ9lfZNvKQpOIrTSsTWYf2VOIM3EVknY3x7tuYuCyJLYYxB1sVpJWLrsP6KFSz9888/2Lp1K27duoW8vDyVfT///LNRKkZEpmeMb8/WNO6AyNIYKwVHcVqJ2DqsP4Nnw23ZsgWtWrVCXFwcduzYgadPn+LixYs4ePAgpFKpKepIRBauqJkvRKSZId1nuihaibS98yQo7NZ7vpXI2malmYvBLUuff/45Fi5ciDFjxsDFxQVff/01atasibfffhs+PryoRFT+cG08Ki5jDbIuSSsRW4eLZnCwFB8fj27dugEA7OzskJ2dDYlEggkTJqBDhw6IjIw0eiWJiCwV18ajkjDmIOuSjCG0lllp5mJwsFS5cmVkZmYCAKpWrYoLFy6gUaNGePToER4/fmz0ChIRWSpTZD+m8sXYg6zZSmQaBo9ZatOmDfbv3w8A6NevH9577z2MGjUKgwYNQseOHY1eQSIiS2TK7MdUfpgiazjHEBqfwS1LS5YsQU5OYfPehx9+iIoVK+Lo0aPo06cPPvroI6NXkIjIEpk7+zGVHUzBYfkMDpbc3f9rCrSxscHUqVONWiEiImtgCdmPyTIYY4A/u88sm8HdcKGhoVi7di0yMjJMUZ8iLV26FH5+fnBwcEBwcDBOnDihs/y2bdvg7+8PBwcHNGrUCHv37lXZL4TAzJkz4ePjA0dHR4SGhuLq1aumPAUiKgOY/ZgAwxauLQq7zyyXwcFSw4YNMW3aNMhkMvTr1w+7du3C06dPTVE3NT/++CMmTpyIWbNm4fTp02jSpAnCwsKQkpKisfzRo0cxaNAgvPnmmzhz5gx69eqFXr164cKFC8oyX375JRYvXozly5fj+PHjqFSpEsLCwpRdjURk3QrkAjHxqdh19i5i4lONNoaouHltqOxQDPB/vjtWMcC/OAETWSaJEMLgTw65XI4DBw5g06ZN2LFjB2xtbdG3b18MHjwYbdu2NUU9AQDBwcF48cUXsWTJEmU9qlWrhnfffVdjd+CAAQOQnZ2NX375RbntpZdeQtOmTbF8+XIIIeDr64tJkyZh8uTJAID09HR4e3tj7dq1GDhwoF71ysjIgFQqRXp6OlxdXY1wpkRkDKae1q+4WQKa89pwNlzZVSAXaD33oNZxa4pZbH990IEtRBZM3/u3wS1LQOFYpc6dO2Pt2rVITk7GihUrcOLECXTo0KHYFS5KXl4eTp06hdDQUJV6hIaGIiYmRuNzYmJiVMoDQFhYmLJ8QkICkpKSVMpIpVIEBwdrPSYA5ObmIiMjQ+VBRJalNL71M/tx+WWszNtkHUq0kG5SUhK2bNmCDRs24N9//0VQUJCx6qXmwYMHKCgogLe3t8p2b29vXLp0SWv9NJVPSkpS7lds01ZGkzlz5jD5JpEFM9Z6W/rgwNzyiQP8yxeDW5YyMjKwZs0adOrUCdWqVcOyZcvQo0cPXL16FceOHTNFHS3OtGnTkJ6ernzcvn3b3FUiomeU9rd+DswtfzjAv3wxuGXJ29sblStXxoABAzBnzhy0bNnSFPVS4+HhAVtbWyQnJ6tsT05Ohkwm0/gcmUyms7zi3+TkZJV17ZKTk9G0aVOtdbG3t4e9vX1xToOISgG/9ZOpGTvzNlk2g1uWdu/ejTt37mDhwoWlFigBhevQtWjRAtHR0cptcrkc0dHRCAkJ0fickJAQlfIAsH//fmX5mjVrQiaTqZTJyMjA8ePHtR6TiCwfv/WTqZki8zZZLoODpU6dOsHGpljjwkts4sSJWLVqFX744QfExcUhIiIC2dnZGDlyJABg2LBhmDZtmrL8e++9h6ioKHz11Ve4dOkSZs+ejX/++Qdjx44FAEgkEowfPx6ffvopdu/ejfPnz2PYsGHw9fVFr169zHGKRGQEnNZPpYED/MuPEg3wLm0DBgzA/fv3MXPmTCQlJaFp06aIiopSDtC+deuWSiDXqlUrbNq0CR999BGmT5+OunXrYufOnQgMDFSWmTJlCrKzs/HWW2/h0aNHaN26NaKiouDgwG+cRNZK8a0/YsNpSKB5Wj+/9ZMxcIB/+VCsPEukinmWiCyTqfMsEZF10/f+bVUtS0REhuC3fiIyBgZLRFSmKab1ExEVV7GCpezsbBw5cgS3bt1CXl6eyr5x48YZpWJEzzLGqt5ERETFYXCwdObMGXTt2hWPHz9GdnY23N3d8eDBAzg5OcHLy4vBEhmdKcadMPgiIiJ9GRwsTZgwAd27d8fy5cshlUpx7NgxVKxYEUOGDMF7771nijpSOaZY3+v5WQiK9b2KMz2Xg36JiMgQBidMOnv2LCZNmgQbGxvY2toiNzcX1apVw5dffonp06eboo5UThW1vhdQuL5XgVz/CZ2lsbgqEREZR4FcICY+FbvO3kVMfKpBn/fGZHDLUsWKFZW5jLy8vHDr1i00aNAAUqmUa6SRURmyvpc+A3hLc3FVIiqestpFXlbPy5QsqRfA4GCpWbNmOHnyJOrWrYu2bdti5syZePDgAdavX6+S7JGopIy9vpexgy8iMi59bo7WGHRY0k3fWphiCEZJGBwsff7558jMzAQAfPbZZxg2bBgiIiJQt25drF692ugVpPLL2Ot7cXFVIsulz80RgNUFHZZ207cGltgLYHCw9OziuV5eXoiKijJqhYgUjL2qNxdXJbJM+twcp/58HumPn1pV0GGJN31rYIm9AOZZEZdID8Ze1ZuLq1JZZymDYQ2lz83xkYZASbEPMHyyR2kw5KZP/7HEXgBm8CaLpljV+/mmd1kxmt65uCqVZdY8LqakNz1LHW9oiTd9a2CJvQAMlsjiGXN9L2MGX0SWwtrHxRjrpmdpQYcl3vStgbGHYBgDgyWyCsZc34uLq1JZUhbGxRR1c9SXpQUdlnjTtwaW2AvAMUtULimCr55NqyKkdhWLvYkQFaUsjIvRZ3yim1NFqxtvaOxxl+WJohdAJlUNgGVSB7O0lBarZSk6OhrR0dFISUmBXC5X2cf0AUREpaesjIspqoscgEW1NOiLXf/FZ0m9AAYHS5GRkfj444/RsmVL+Pj4QCKxvD9OIvqPNSbxI/2VpXExRd0crTXosKSbvrUx5hCMkpAIIQzqIvbx8cGXX36JoUOHmqpOVicjIwNSqRTp6elwdXU1d3WIlKx5hhTpp0Au0HruwSLHxfz1QYcycXNm8E/GpO/92+AxS3l5eWjVqlWJKkdEpsdFg8uH8jYuhuMNyRwMDpb+97//YdOmTaaoCxEZSVEzpADLTOJHxWNpg2HJ/Kw1Qaml0mvM0sSJE5X/l8vlWLlyJQ4cOIDGjRujYsWKKmUXLFhg3BoSkcEscbkAMi2OiyEFc3a/G6Ob1BK7WvUKls6cOaPyc9OmTQEAFy5cMHqFiKjkysoMKTKMpQyGJfMxZ4JSYwRpljrOUq9g6dChQ6auBxEZUVmaIUVE+jFnglJjBGmWnIne4DFLb7zxBjIzM9W2Z2dn44033jBKpYioZLhoMFH5Y64EpcYYI2np4ywNDpZ++OEHPHnyRG37kydPsG7dOqNUiohKprzNkCIi83W/GyNIs/RM9HoHSxkZGUhPT4cQApmZmcjIyFA+Hj58iL1798LLy8uUdSUiA3CGFFH5Yq7ud2MEaZY+zlLvDN5ubm6QSCSQSCSoV6+e2n6JRILIyEijVo6ISoYzpIjKD3Mt3GuMIM3Sx1nqHSwdOnQIQgh06NAB27dvh7v7fxfbzs4ONWrUgK+vr0kqSUTFxxlSRNajJNPmFd3vpb2GnjGCNHMFevoyeLmTmzdvonr16lwT7hlc7oSIiErKWNPmzTH9XjGTDdAcpBkyG64kxzCUvvdvvYKlf//9F4GBgbCxscG///6rs2zjxo0Nr62VY7BEREQloW3afHEDBXMkdrTGPEtGDZZsbGyQlJQELy8v2NjYQCKRQNPTJBIJCgoKSlZzK8RgiYio7DNVAKJYDFnbbDBrWgzZ2jJ463v/1mvMUkJCAjw9PZX/JyIiKk9M2eJRlpYnMsYYSUscZ6lXsFSjRg2N/yciIvOwxPWzyipTZ5bWdzr8gdgkiwsiygu9Z8MpVK9eHe3atUPbtm3Rrl071K5d2xT1IiIq00oS7Fjq+lllUWksIaLvdPgdZ+9iejcmkzUHgzN4f/7553BwcMDcuXNRt25dVKtWDUOGDMGqVatw9epVU9SRiKhMibqQiNZzD2LQqmN4b8tZDFp1DK3nHkTUhUS9nhux4bRat42ilUOfY5D+SiOzdFBNd7hXsiuyXFr2U7NlsC7vDA6WhgwZgpUrV+LKlSu4e/cu5s2bBwAYPXo0/P39jV5BIqKypCTBjqWvn1UWlUZmaVsbCXo11S9PobkyWJd3BnfDAcDjx4/x119/4fDhwzh06BDOnDmDwMBAtGvXzsjVIyIqO0rapVOWBgJbi9LKLN0pQIbVf98w+etQ8RgcLLVq1QpnzpxBgwYN0K5dO0ydOhVt2rRB5cqVTVE/IqIyo6TBjqWvn1UWlVZmacXrFJU+wFwZrMs7g7vhLl26hEqVKsHf3x/+/v5o0KABAyUiMyuQC8TEp2LX2buIiU9lN4yFKmmwY+nrZ5VFiiVEgP8SRCoYcwkRxetITPw6z+Nnh34MbllKTU3F+fPncfjwYfz+++/48MMPYWdnh7Zt26J9+/YYNWqUKepJRFoYc2YUp6ObVkmDHUtfP6usCg/0wbIhzdXeZ7L/f591CpAhJj61xO+bol7H2DMdOatSfwavDfcsIQROnTqFJUuWYOPGjZDL5czgzQzeVIqMuUQCPzhNT5GpuahgR1emZnOsn0WFNH2Z2B+bZPT3TWl8aTH28irWyqjLnTzr9OnTOHz4MA4fPoy//voLmZmZaNSokTL3Us+ePUtceWvDYInMwZhLJPCDs/QYa8FRBrbmZ63vm7K0vEpJGXW5k2cFBQWhWbNmaNu2LUaNGoU2bdpAKpWWqLJEZDhjzYwqjaR79B9jdLWEB/qgU4CMXaZmZM3vG86qNJzBwVJaWhpbT4gsgLFmRvGDs/QZI9ixxPWzyhNrft9wVqXhDA6WGCgRWQZjzYziB6d5MNixbtb8vuGsSsMZnDqAiCyDYmaUtrYICQrHsRQ1M4ofnESGs+b3jbE+O8oTBktEVspY+V/4wUllSWnlDbLm901p5Y4qSxgsEVkxxWBhmVT126tM6qD3TBx+cJIxmTPJYUkWKDaUtb9vjPHZUZ6UKM8SFWLqADI3Y+Rl4XR0Kilz/g2Zaxq/tb9vynsiWpPkWUpMTER0dDTc3d0RGhoKOzs75b7s7Gx89dVXmDlzZslqboUYLFFZUd4/OKn4zJlzyNx5g/i+sV5GD5ZOnjyJzp07Qy6X4+nTp6hatSp27tyJhg0bAgCSk5Ph6+vLDN4MloionClJsGKMQCMmPhWDVh0rstzmUS9xBiKp0Pf+rfeYpenTp6N37954+PAhkpOT0alTJ7Rt2xZnzpwxSoWJiMg6GZJz6FnGGmNkzdP4yTroHSydOnUKU6dOhY2NDVxcXPDtt99i8uTJ6NixI06ePGnKOhIRkQUrTrCi6LZ7PshKSs9BxIbTBgVM1jyNn6yDQbPhcnJU/6inTp2K6dOno3Pnzjh69KhRK/a8tLQ0DB48GK6urnBzc8Obb76JrKwsnc/JycnBmDFjUKVKFTg7O6NPnz5ITk5WKSORSNQeW7ZsMeWpEBGVKYYGK0UtFQIULhWi70w6a57GT9ZB72ApMDBQY0A0efJkTJs2DYMGDTJqxZ43ePBgXLx4Efv378cvv/yCP/74A2+99ZbO50yYMAF79uzBtm3bcOTIEdy7dw+vvfaaWrk1a9YgMTFR+ejVq5eJzoKIqOwxNFgpbredNtY+jZ8sn97B0rBhw/DXX39p3DdlyhRERkaievXqRqvYs+Li4hAVFYXvvvsOwcHBaN26Nb755hts2bIF9+7d0/ic9PR0fP/991iwYAE6dOiAFi1aYM2aNTh69CiOHVMdCOjm5gaZTKZ8ODiwqZaISF+GBiumGGPEvEFkSlaRZ2n16tWYNGkSHj58qNyWn58PBwcHbNu2Db1791Z7zsGDB9GxY0c8fPgQbm5uyu01atTA+PHjMWHCBACF3XC+vr7Izc1FrVq18M4772DkyJGQSLR/A8nNzUVubq7y54yMDFSrVo2z4YioXNM355ApZ69xGj8ZQt/ZcHovpJuTk4N9+/ahffv2cHFxUXuxw4cPIywsDPb29sWvtRZJSUnw8vJS2VahQgW4u7sjKSlJ63Ps7OxUAiUA8Pb2VnnOxx9/jA4dOsDJyQn79u3D6NGjkZWVhXHjxmmtz5w5cxAZGVn8EyIiKoPCA33QKUBWZLCi6LZLSs/ROG5JkWqgOGOMuEAxmYLe3XArVqzA119/rRYoAYCrqysWL16MVatWGfTiU6dO1TjA+tnHpUuXDDqmoWbMmIGXX34ZzZo1wwcffIApU6Zg3rx5Op8zbdo0pKenKx+3b982aR2JiKyFIljp2bQqQmpX0diqwzFGZG30DpY2btyI8ePHa90/fvx4rFu3zqAXnzRpEuLi4nQ+atWqBZlMhpSUFJXn5ufnIy0tDTKZTOOxZTIZ8vLy8OjRI5XtycnJWp8DAMHBwbhz545KN9vz7O3t4erqqvIgIiL9cYwRWRO9u+GuXr2KJk2aaN3fuHFjXL161aAX9/T0hKenZ5HlQkJC8OjRI5w6dQotWrQAUDgmSS6XIzg4WONzWrRogYoVKyI6Ohp9+vQBAFy+fBm3bt1CSEiI1tc6e/YsKleubJLuRCIi+o++3XZE5qZ3sJSfn4/79+9rnfF2//595OfnG61iz2rQoAHCw8MxatQoLF++HE+fPsXYsWMxcOBA+Pr6AgDu3r2Ljh07Yt26dQgKCoJUKsWbb76JiRMnwt3dHa6urnj33XcREhKCl156CQCwZ88eJCcn46WXXoKDgwP279+Pzz//HJMnTzbJeRARkSqOMSJroHew1LBhQxw4cEDZsvO8ffv2KdeJM4WNGzdi7Nix6NixI2xsbNCnTx8sXrxYuf/p06e4fPkyHj9+rNy2cOFCZdnc3FyEhYXh22+/Ve6vWLEili5digkTJkAIgTp16mDBggUYNWqUyc6DiIrGGU1EZEn0Th2wcuVKTJw4EVu2bMGrr76qsm/Pnj0YNGgQFixYUGSiyLKIC+lSWWWOoEXf6edERCWl7/3boDxLQ4YMwaZNm+Dv74/69esDAC5duoQrV66gf//+2Lx5c8lrboUYLFFZZI6gRbFe2PMfSorwjAN/iciY9L1/G7Q23IYNG7BlyxbUrVsXV65cweXLl1G/fn1s3ry53AZKRGWRMRc51Zex1wsjIjIWvccsKfTv3x/9+/c3RV2IyAIUFbRIUBi0dAqQGbVLzpD1wjggmIhKk94tS3K5HHPnzsXLL7+MF198EVOnTsWTJ09MWTciMgNjL3KqL1OsF0ZEZAx6B0ufffYZpk+fDmdnZ1StWhVff/01xowZY8q6EZEZmCto8XLRbwFrfcsRERmL3sHSunXr8O233+L333/Hzp07sWfPHmzcuBFyudyU9SOiUmauoEWxXpi2jj0JCgeYF2e9MCKiktA7WLp16xa6du2q/Dk0NBQSiQT37t0zScWo7CmQC8TEp2LX2buIiU81y0BdS6iDpTNX0ML1wojIUhmUwdvBQfWbZMWKFfH06VOjV4rKHkvInWMJdbAGiqAlYsNpSACVgd6mDloU64U9/3uS8fdERGakd54lGxsbdOnSRWXNtD179qBDhw6oVKmSctvPP/9s/FpaOOZZ0s0ScudYQh2sjTmDS2bwJqLSYPSklCNHjtTrhdesWaNfDcsQBkvaFcgFWs89qHV2lQSFrQZ/fdDBZDdDS6iDtWLQQkRlmb73b7274cpjEEQlZwm5cyyhDtaKi5wSERmYwZvIUJaQO8cS6kBERNaLwRKZlCXkzrGEOhARkfVisEQmZQm5cyyhDkREZL0YLJFJWULuHEuoAxERWS8GS2Ryitw5MqlqN5dM6lBqU/YtoQ5ERGSd9E4dQNoxdYB+LGEauiXUgYiILIPRUwcQlZQlTEO3hDoQEZF1YbBEVIaw5YyIyPgYLBGVEVz7jojINDjAm6gMUKx993ym8qT0HERsOI2oC4lmqhkRkfVjsERk5QrkApF7YtUWCQag3Ba5JxYFcs7lICIqDgZLRFbOkLXviIjIcAyWiKwc174jIjItBktEVo5r3xERmRaDJSIrx7XviIhMi8ESkZXj2ndERKbFYImoDODad0REpsOklERlRHigDzoFyJjBm4jIyBgsERmJJSw1wrXviIiMj8ESkRFwqREiorKLY5aISohLjRARlW0MlohKgEuNEBGVfQyWiEqAS40QEZV9HLNkoSxhsDAVjUuNEBGVfQyWLBAHC1sPLjVCRFT2sRvOwnCwsHXhUiNERGUfgyULwsHC1odLjRARlX0MliwIBwtbJy41QkRUtnHMkgXhYGHrxaVGiIjKLgZLFoSDha0blxohIiqb2A1nQThYmIiIyPIwWLIgHCxMRERkeRgsWRgOFiYiIrIsHLNkgThYmIiIyHIwWLJQHCxMRERkGdgNR0RERKQDgyUiIiIiHRgsEREREenAYImIiIhIBwZLRERERDpwNhwRURlVIBdMQUJkBAyWiIjKoKgLiYjcE4vE9P8W3vaROmBW9wAmtyUykNV0w6WlpWHw4MFwdXWFm5sb3nzzTWRlZel8zsqVK9GuXTu4urpCIpHg0aNHRjkuEZEli7qQiIgNp1UCJQBISs9BxIbTiLqQaKaaEVknqwmWBg8ejIsXL2L//v345Zdf8Mcff+Ctt97S+ZzHjx8jPDwc06dPN+pxiYgsVYFcIHJPLISGfYptkXtiUSDXVIKINJEIISz+HRMXF4eAgACcPHkSLVu2BABERUWha9euuHPnDnx9fXU+//Dhw2jfvj0ePnwINzc3ox1XISMjA1KpFOnp6XB1dS3eSRIRGUFMfCoGrTpWZLnNo17iKgFU7ul7/7aKlqWYmBi4ubkpAxoACA0NhY2NDY4fP25xxyUiMpeUzJyiCxlQjoisZIB3UlISvLy8VLZVqFAB7u7uSEpKKvXj5ubmIjc3V/lzRkZGsetARKSLoTPavFwc9DquvuWIyMzB0tSpUzF37lydZeLi4kqpNvqbM2cOIiMjzV0NIirjijOjLaimO3ykDkhKz9E4bkkCQCYtDLqISD9m7YabNGkS4uLidD5q1aoFmUyGlJQUlefm5+cjLS0NMpms2K9f3ONOmzYN6enpysft27eLXQciIk2KO6PN1kaCWd0DABQGRs9S/DyrewDzLREZwKwtS56envD09CyyXEhICB49eoRTp06hRYsWAICDBw9CLpcjODi42K9f3OPa29vD3t6+2K9LRKRLUTPaJCic0dYpQKYx6AkP9MGyIc3VWqVkzLNEVCxWMWapQYMGCA8Px6hRo7B8+XI8ffoUY8eOxcCBA5Uz1u7evYuOHTti3bp1CAoKAlA4JikpKQnXrl0DAJw/fx4uLi6oXr063N3d9TouEVk3a8xifSIhTa1F6VkCQGJ6Dk4kpGmd0RYe6INOATKrO3ciS2QVwRIAbNy4EWPHjkXHjh1hY2ODPn36YPHixcr9T58+xeXLl/H48WPltuXLl6uMLWrTpg0AYM2aNRgxYoRexyUi62WtWayNNaPN1kbC9ABERmAVeZYsHfMsEVkexZif5z/gFO0q40Prwc/DySJbXJgriah06Hv/tpqWJSIifemTxXrhgSvKbZbW2sQZbUSWxSqSUhIRGaKoMT/Ps7Q10zijjciyMFgiojLH0OzUlrhmmmJGm0yqmjxSJnXAsiHNLaYVjKg8YDccEZU5xclOrc8Ms9LGGW1EloHBEhGVOUWN+dHF0tZM44w2IvNjNxwRlTm6xvwUhWumEdHzGCwRUZmkbcyPNhIUzorjDDMieh674YiozHp+zM+NB9lYeOAqJIBK9xxnmBGRLgyWiKhMe37MT32ZC9dMIyKDMFgionKFM8yIyFAMloio3OEMMyIyBAd4ExEREenAYImIiIhIB3bDkUUokAuOISEiIovEYInMLupCotrsJEtbBZ6IiMovdsORWUVdSETEhtNqK8Rb2irwRERUfjFYIrMpkAtE7onVuHaXJa4CT0RE5RODJTKbEwlpai1Kz3p2FXgiIiJzYbBEZqPv6u6Wtgo8ERGVLwyWyGz0Xd2dq8ATEZE5MVgiswmq6Q4fqQO0JQjgKvBERGQJGCyR2djaSDCrewAAqAVMXAWeiIgsBYMlMqvwQB8sG9IcMqlqV5tM6oBlQ5ozzxIREZkdk1KS2XEVeCIismQMlsgicBV4IiKyVOyGIyIiItKBwRIRERGRDgyWiIiIiHRgsERERESkA4MlIiIiIh0YLBERERHpwGCJiIiISAcGS0REREQ6MFgiIiIi0oEZvI1ACAEAyMjIMHNNiIiISF+K+7biPq4NgyUjyMzMBABUq1bNzDUhIiIiQ2VmZkIqlWrdLxFFhVNUJLlcjnv37sHFxQUSCRd/NaaMjAxUq1YNt2/fhqurq7mrUy7xd2BevP7mxetvfqb8HQghkJmZCV9fX9jYaB+ZxJYlI7CxscELL7xg7mqUaa6urvygMjP+DsyL19+8eP3Nz1S/A10tSgoc4E1ERESkA4MlIiIiIh0YLJFFs7e3x6xZs2Bvb2/uqpRb/B2YF6+/efH6m58l/A44wJuIiIhIB7YsEREREenAYImIiIhIBwZLRERERDowWCIiIiLSgcESmV1aWhoGDx4MV1dXuLm54c0330RWVpbO56xcuRLt2rWDq6srJBIJHj16ZJTjlkfFuU45OTkYM2YMqlSpAmdnZ/Tp0wfJyckqZSQSidpjy5YtpjwVq7F06VL4+fnBwcEBwcHBOHHihM7y27Ztg7+/PxwcHNCoUSPs3btXZb8QAjNnzoSPjw8cHR0RGhqKq1evmvIUrJqxr/+IESPU/tbDw8NNeQpWzZDrf/HiRfTp0wd+fn6QSCRYtGhRiY9ZLILIzMLDw0WTJk3EsWPHxJ9//inq1KkjBg0apPM5CxcuFHPmzBFz5swRAMTDhw+NctzyqDjX6Z133hHVqlUT0dHR4p9//hEvvfSSaNWqlUoZAGLNmjUiMTFR+Xjy5IkpT8UqbNmyRdjZ2YnVq1eLixcvilGjRgk3NzeRnJyssfzff/8tbG1txZdffiliY2PFRx99JCpWrCjOnz+vLPPFF18IqVQqdu7cKc6dOyd69OghatasyeutgSmu//Dhw0V4eLjK33paWlppnZJVMfT6nzhxQkyePFls3rxZyGQysXDhwhIfszgYLJFZxcbGCgDi5MmTym2//fabkEgk4u7du0U+/9ChQxqDpZIet7woznV69OiRqFixoti2bZtyW1xcnAAgYmJilNsAiB07dpis7tYqKChIjBkzRvlzQUGB8PX1FXPmzNFYvn///qJbt24q24KDg8Xbb78thBBCLpcLmUwm5s2bp9z/6NEjYW9vLzZv3myCM7Buxr7+QhQGSz179jRJfcsaQ6//s2rUqKExWCrJMfXFbjgyq5iYGLi5uaFly5bKbaGhobCxscHx48ct7rhlTXGu06lTp/D06VOEhoYqt/n7+6N69eqIiYlRKTtmzBh4eHggKCgIq1evhijnad3y8vJw6tQplWtnY2OD0NBQtWunEBMTo1IeAMLCwpTlExISkJSUpFJGKpUiODhY6zHLK1Ncf4XDhw/Dy8sL9evXR0REBFJTU41/AlauONffHMfUhAvpklklJSXBy8tLZVuFChXg7u6OpKQkiztuWVOc65SUlAQ7Ozu4ubmpbPf29lZ5zscff4wOHTrAyckJ+/btw+jRo5GVlYVx48YZ/TysxYMHD1BQUABvb2+V7d7e3rh06ZLG5yQlJWksr7jWin91laFCprj+ABAeHo7XXnsNNWvWRHx8PKZPn44uXbogJiYGtra2xj8RK1Wc62+OY2rCYIlMYurUqZg7d67OMnFxcaVUm/LHEq7/jBkzlP9v1qwZsrOzMW/evHIdLFHZNHDgQOX/GzVqhMaNG6N27do4fPgwOnbsaMaakbEwWCKTmDRpEkaMGKGzTK1atSCTyZCSkqKyPT8/H2lpaZDJZMV+fVMd11qY8vrLZDLk5eXh0aNHKq1LycnJOq9tcHAwPvnkE+Tm5pbbdbY8PDxga2urNnNQ17WTyWQ6yyv+TU5Oho+Pj0qZpk2bGrH21s8U11+TWrVqwcPDA9euXWOw9IziXH9zHFMTjlkik/D09IS/v7/Oh52dHUJCQvDo0SOcOnVK+dyDBw9CLpcjODi42K9vquNaC1Ne/xYtWqBixYqIjo5Wbrt8+TJu3bqFkJAQrXU6e/YsKleuXG4DJQCws7NDixYtVK6dXC5HdHS01msXEhKiUh4A9u/fryxfs2ZNyGQylTIZGRk4fvy4zt9HeWSK66/JnTt3kJqaqhK8UvGuvzmOqZHRhooTFVN4eLho1qyZOH78uPjrr79E3bp1Vaau37lzR9SvX18cP35cuS0xMVGcOXNGrFq1SgAQf/zxhzhz5oxITU3V+7hUqDjX/5133hHVq1cXBw8eFP/8848ICQkRISEhyv27d+8Wq1atEufPnxdXr14V3377rXBychIzZ84s1XOzRFu2bBH29vZi7dq1IjY2Vrz11lvCzc1NJCUlCSGEGDp0qJg6daqy/N9//y0qVKgg5s+fL+Li4sSsWbM0pg5wc3MTu3btEv/++6/o2bMnUwdoYezrn5mZKSZPnixiYmJEQkKCOHDggGjevLmoW7euyMnJMcs5WjJDr39ubq44c+aMOHPmjPDx8RGTJ08WZ86cEVevXtX7mMbAYInMLjU1VQwaNEg4OzsLV1dXMXLkSJGZmancn5CQIACIQ4cOKbfNmjVLAFB7rFmzRu/jUqHiXP8nT56I0aNHi8qVKwsnJyfRu3dvkZiYqNz/22+/iaZNmwpnZ2dRqVIl0aRJE7F8+XJRUFBQmqdmsb755htRvXp1YWdnJ4KCgsSxY8eU+9q2bSuGDx+uUn7r1q2iXr16ws7OTjRs2FD8+uuvKvvlcrmYMWOG8Pb2Fvb29qJjx47i8uXLpXEqVsmY1//x48eic+fOwtPTU1SsWFHUqFFDjBo1yqg36rLGkOuv+Px5/tG2bVu9j2kMEiHK+VxeIiIiIh04ZomIiIhIBwZLRERERDowWCIiIiLSgcESERERkQ4MloiIiIh0YLBEREREpAODJSIiIiIdGCwRkdFJJBLs3LlTZ5kRI0agV69epVIfU/Hz88OiRYvMXQ2jmD17Nry9vfX63RGVNwyWiKzEiBEjIJFIIJFIYGdnhzp16uDjjz9Gfn6+sowQAitXrkRwcDCcnZ3h5uaGli1bYtGiRXj8+DEA4OLFi+jTpw/8/PwgkUhMcrNPTExEly5dAAA3btyARCLB2bNnjf465nby5Em89dZb5q5GicXFxSEyMhIrVqxQ+d0979atW+jWrRucnJzg5eWF999/X+XvT5MePXqgevXqcHBwgI+PD4YOHYp79+4p9x8+fBg9e/aEj48PKlWqhKZNm2Ljxo1GPT+ikmKwRGRFwsPDkZiYiKtXr2LSpEmYPXs25s2bp9w/dOhQjB8/Hj179sShQ4dw9uxZzJgxA7t27cK+ffsAAI8fP0atWrXwxRdfGHVV7mfJZLIyvWBuXl4egMIFi52cnMxcm5KLj48HAPTs2VPr766goADdunVDXl4ejh49ih9++AFr167FzJkzdR67ffv22Lp1Ky5fvozt27cjPj4effv2Ve4/evQoGjdujO3bt+Pff//FyJEjMWzYMPzyyy/GPUmikjDq4ilEZDLDhw8XPXv2VNnWqVMn8dJLLwkhhPjxxx8FALFz506158rlcvHo0SO17TVq1BALFy7U+bpyuVx4eHiIbdu2Kbc1adJEyGQy5c9//vmnsLOzE9nZ2UIIIQCIHTt2KP8PDWs6Kc5n3rx5QiaTCXd3dzF69GiRl5entS7Xrl0TPXr0EF5eXqJSpUqiZcuWYv/+/VrLX758WQAQcXFxKtsXLFggatWqJYQQIj8/X7zxxhvCz89PODg4iHr16olFixaplFfU9dNPPxU+Pj7Cz89P4/X76quvRGBgoHBychIvvPCCiIiIUFlnb82aNUIqlYqoqCjh7+8vKlWqJMLCwsS9e/dUXu/7778XAQEBws7OTshkMjFmzBjlvocPH4o333xTeHh4CBcXF9G+fXtx9uxZrddACCH+/fdf0b59e+Hg4CDc3d3FqFGjlPXStM6iJnv37hU2NjYqa54tW7ZMuLq6itzcXJ2v/6xdu3YJiUSi8/fctWtXMXLkSL2PSWRqbFkismKOjo7KVo6NGzeifv366Nmzp1o5iUQCqVRarNeQSCRo06YNDh8+DAB4+PAh4uLi8OTJE1y6dAkAcOTIEbz44osaW1lOnDgBADhw4AASExPx888/K/cdOnQI8fHxOHTokLKlYu3atVrrkpWVha5duyI6OhpnzpxBeHg4unfvjlu3bmksX69ePbRs2VKtW2fjxo14/fXXAQByuRwvvPACtm3bhtjYWMycORPTp0/H1q1bVZ4THR2Ny5cvY//+/VpbPWxsbLB48WJcvHgRP/zwAw4ePIgpU6aolHn8+DHmz5+P9evX448//sCtW7cwefJk5f5ly5ZhzJgxeOutt3D+/Hns3r0bderUUe7v168fUlJS8Ntvv+HUqVNo3rw5OnbsiLS0NI11ys7ORlhYGCpXroyTJ09i27ZtOHDgAMaOHQsAmDx5MtasWQOgsPs0MTFR43FiYmLQqFEjeHt7K7eFhYUhIyMDFy9e1Pic56WlpWHjxo1o1aoVKlasqLVceno63N3d9TomUakwd7RGRPp5tmVJLpeL/fv3C3t7ezF58mQhhBANGjQQPXr0MOiY+rQsCSHE4sWLRcOGDYUQQuzcuVMEBweLnj17imXLlgkhhAgNDRXTp09XlsczLUuKVcPPnDmjdj41atQQ+fn5ym39+vUTAwYMMOgcGjZsKL755hut+xcuXChq166t/Flba9OzxowZI/r06aNSV29vb7UWlKKu37Zt20SVKlWUP69Zs0YAENeuXVNuW7p0qfD29lb+7OvrKz788EONx/vzzz+Fq6uryMnJUdleu3ZtsWLFCo3PWblypahcubLIyspSbvv1119VWol27NihtUVJYdSoUaJz584q27KzswUAsXfvXp3PnTJlinBychIAxEsvvSQePHigteyPP/4o7OzsxIULF3Qek6g0sWWJyIr88ssvcHZ2hoODA7p06YIBAwZg9uzZAAoHd5tK27ZtERsbi/v37+PIkSNo164d2rVrh8OHD+Pp06c4evQo2rVrZ/BxGzZsCFtbW+XPPj4+SElJ0Vo+KysLkydPRoMGDeDm5gZnZ2fExcVpbVkCgIEDB+LGjRs4duwYgMJWpebNm8Pf319ZZunSpWjRogU8PT3h7OyMlStXqh2zUaNGsLOz03k+Bw4cQMeOHVG1alW4uLhg6NChSE1NVQ6uBwAnJyfUrl1b4zmnpKTg3r176Nixo8bjnzt3DllZWahSpQqcnZ2Vj4SEBOW4o+fFxcWhSZMmqFSpknLbyy+/DLlcjsuXL+s8H2N5//33cebMGezbtw+2trYYNmyYxr/XQ4cOYeTIkVi1ahUaNmxYKnUj0kcFc1eAiPTXvn17LFu2DHZ2dvD19UWFCv+9hevVq6fsFjO2Ro0awd3dHUeOHMGRI0fw2WefQSaTYe7cuTh58iSePn2KVq1aGXzc57tiJBIJ5HK51vKTJ0/G/v37MX/+fNSpUweOjo7o27evsitSE5lMhg4dOmDTpk146aWXsGnTJkRERCj3b9myBZMnT8ZXX32FkJAQuLi4YN68eTh+/LjKcZ4NNjS5ceMGXn31VUREROCzzz6Du7s7/vrrL7z55pvIy8tTdlFqOmdF4ODo6KjzNbKysuDj46PsEn2Wm5ubzueWlEwmU3apKiQnJyv36eLh4QEPDw/Uq1cPDRo0QLVq1XDs2DGEhIQoyxw5cgTdu3fHwoULMWzYMOOfAFEJsGWJyIpUqlQJderUQfXq1VUCJQB4/fXXceXKFezatUvteUIIpKenF/t1JRIJXnnlFezatQsXL15E69at0bhxY+Tm5mLFihVo2bKl1mBC0RpTUFBQ7NdX+PvvvzFixAj07t0bjRo1gkwmw40bN4p83uDBg/Hjjz8iJiYG169fx8CBA1WO2apVK4wePRrNmjVDnTp1tLbS6HLq1CnI5XJ89dVXeOmll1CvXj2VKfL6cHFxgZ+fH6KjozXub968OZKSklChQgXUqVNH5eHh4aHxOQ0aNMC5c+eQnZ2t3Pb333/DxsYG9evX17tuISEhOH/+vErL3/79++Hq6oqAgAC9j6MIhnNzc5XbDh8+jG7dumHu3LllIhUDlT0MlojKiP79+2PAgAEYNGgQPv/8c/zzzz+4efMmfvnlF4SGhuLQoUMACqe9nz17FmfPnkVeXh7u3r2Ls2fP4tq1azqP365dO2zevBlNmzaFs7MzbGxs0KZNG2zcuBFt27bV+jwvLy84OjoiKioKycnJJQra6tati59//hlnz57FuXPn8Prrr+tsiVJ47bXXkJmZiYiICLRv3x6+vr4qx/znn3/w+++/48qVK5gxYwZOnjxpcN3q1KmDp0+f4ptvvsH169exfv16LF++3ODjzJ49G1999RUWL16Mq1ev4vTp0/jmm28AAKGhoQgJCUGvXr2wb98+3LhxA0ePHsWHH36If/75R+PxBg8eDAcHBwwfPhwXLlzAoUOH8O6772Lo0KEqg7WL0rlzZwQEBGDo0KE4d+4cfv/9d3z00UcYM2aMMtXAiRMn4O/vj7t37wIAjh8/jiVLluDs2bO4efMmDh48iEGDBqF27drKVqVDhw6hW7duGDduHPr06YOkpCQkJSVpHbBOZBbmHTJFRPrSlDrgeQUFBWLZsmXixRdfFE5OTsLV1VW0aNFCfP311+Lx48dCiP8GXD//UEzp1+bMmTMCgPjggw+U2xYuXCgAiKioKJWyeGaAtxBCrFq1SlSrVk3Y2NiopQ541nvvvaezHgkJCaJ9+/bC0dFRVKtWTSxZskS0bdtWvPfeezrrLoQQ/fv3FwDE6tWrVbbn5OSIESNGCKlUKtzc3ERERISYOnWqaNKkibKMtmv//ADvBQsWCB8fH+Ho6CjCwsLEunXrBADx8OFDIcR/qQOepWlw9fLly0X9+vVFxYoVhY+Pj3j33XeV+zIyMsS7774rfH19RcWKFUW1atXE4MGDxa1bt7Seu67UAdrqoMmNGzdEly5dhKOjo/Dw8BCTJk0ST58+Ve4/dOiQACASEhJUXtfd3V3Y29sLPz8/8c4774g7d+4onzN8+PBi/T0SlSaJECYcFUpERERk5dgNR0RERKQDgyUiIiIiHRgsEREREenAYImIiIhIBwZLRERERDowWCIiIiLSgcESERERkQ4MloiIiIh0YLBEREREpAODJSIiIiIdGCwRERER6cBgiYiIiEiH/wOfzEf6BgryOAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -215,7 +214,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -244,7 +243,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/model_diagram.json b/model_diagram.json
new file mode 100644
index 00000000..5384637f
--- /dev/null
+++ b/model_diagram.json
@@ -0,0 +1,425 @@
+{
+    "style": {
+        "node-color": "#ffffff",
+        "border-color": "#000000",
+        "caption-color": "#000000",
+        "arrow-color": "#000000",
+        "label-background-color": "#ffffff",
+        "directionality": "directed",
+        "arrow-width": 5
+    },
+    "nodes": [
+        {
+            "id": "n0",
+            "position": {
+                "x": 0,
+                "y": 0
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "StrictStructuredNode"
+            ],
+            "properties": {}
+        },
+        {
+            "id": "n1",
+            "position": {
+                "x": 346.4101615137755,
+                "y": 199.99999999999997
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Organism"
+            ],
+            "properties": {
+                "taxonomy_id": "int - required",
+                "name": "str"
+            }
+        },
+        {
+            "id": "n2",
+            "position": {
+                "x": 2.4492935982947064e-14,
+                "y": 400.0
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Site"
+            ],
+            "properties": {
+                "site_id": "id - unique",
+                "name": "str",
+                "annotation": "str - required"
+            }
+        },
+        {
+            "id": "n3",
+            "position": {
+                "x": -346.4101615137754,
+                "y": 200.00000000000014
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Region"
+            ],
+            "properties": {
+                "region_id": "id - unique",
+                "name": "str",
+                "annotation": "str - required",
+                "sequence_id": "str"
+            }
+        },
+        {
+            "id": "n4",
+            "position": {
+                "x": -346.4101615137755,
+                "y": -199.9999999999999
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Reaction"
+            ],
+            "properties": {
+                "rhea_id": "str - required",
+                "chebi_id": "list[str]"
+            }
+        },
+        {
+            "id": "n5",
+            "position": {
+                "x": -7.347880794884119e-14,
+                "y": -400.0
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Molecule"
+            ],
+            "properties": {
+                "chebi_id": "str - required",
+                "rhea_compound_id": "str",
+                "smiles": "str"
+            }
+        },
+        {
+            "id": "n6",
+            "position": {
+                "x": 346.41016151377534,
+                "y": -200.00000000000017
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "StandardNumbering"
+            ],
+            "properties": {
+                "name": "str - required",
+                "definition": "str - required"
+            }
+        },
+        {
+            "id": "n7",
+            "position": {
+                "x": 1146.4101615137754,
+                "y": 0
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "GOAnnotation"
+            ],
+            "properties": {
+                "go_id": "str - required",
+                "term": "str",
+                "definition": "str"
+            }
+        },
+        {
+            "id": "n8",
+            "position": {
+                "x": -399.99999999999983,
+                "y": 692.820323027551
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "Protein"
+            ],
+            "properties": {
+                "accession_id": "str - required",
+                "sequence": "str - required",
+                "name": "str",
+                "seq_length": "int - required",
+                "mol_weight": "float",
+                "ec_number": "str",
+                "nucleotide_id": "str",
+                "nucleotide_start": "int",
+                "nucleotide_end": "int",
+                "locus_tag": "str",
+                "structure_ids": "list[str]",
+                "go_terms": "list[str]",
+                "rhea_id": "list[str]",
+                "chebi_id": "list[str]",
+                "embedding": "list[float]",
+                "TBT": "str",
+                "PCL": "str",
+                "BHET": "str",
+                "PET_powder": "str"
+            }
+        },
+        {
+            "id": "n9",
+            "position": {
+                "x": -800.0,
+                "y": 4.5324311181183836e-13
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "DNA"
+            ],
+            "properties": {
+                "accession_id": "str - required",
+                "sequence": "str - required",
+                "name": "str",
+                "seq_length": "int - required",
+                "go_terms": "list[str]",
+                "embedding": "list[float]",
+                "gc_content": "float"
+            }
+        },
+        {
+            "id": "n10",
+            "position": {
+                "x": -400.00000000000034,
+                "y": -692.8203230275507
+            },
+            "caption": "",
+            "style": {},
+            "labels": [
+                "OntologyObject"
+            ],
+            "properties": {
+                "name": "str - required",
+                "description": "str",
+                "label": "str",
+                "synonyms": "list[str]"
+            }
+        }
+    ],
+    "relationships": [
+        {
+            "id": "e0",
+            "type": "MUTATION",
+            "style": {},
+            "properties": {},
+            "fromId": "n3",
+            "toId": "n3"
+        },
+        {
+            "id": "e1",
+            "type": "HAS_STANDARD_NUMBERING",
+            "style": {},
+            "properties": {},
+            "fromId": "n3",
+            "toId": "n6"
+        },
+        {
+            "id": "e2",
+            "type": "SUBSTRATE",
+            "style": {},
+            "properties": {},
+            "fromId": "n4",
+            "toId": "n5"
+        },
+        {
+            "id": "e3",
+            "type": "PRODUCT",
+            "style": {},
+            "properties": {},
+            "fromId": "n4",
+            "toId": "n5"
+        },
+        {
+            "id": "e4",
+            "type": "HAS_STANDARD_NUMBERING",
+            "style": {},
+            "properties": {},
+            "fromId": "n6",
+            "toId": "n8"
+        },
+        {
+            "id": "e5",
+            "type": "ORIGINATES_FROM",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n1"
+        },
+        {
+            "id": "e6",
+            "type": "HAS_SITE",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n2"
+        },
+        {
+            "id": "e7",
+            "type": "HAS_REGION",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n3"
+        },
+        {
+            "id": "e8",
+            "type": "ASSOCIATED_WITH",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n7"
+        },
+        {
+            "id": "e9",
+            "type": "HAS_REACTION",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n4"
+        },
+        {
+            "id": "e10",
+            "type": "SUBSTRATE",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n5"
+        },
+        {
+            "id": "e11",
+            "type": "PRODUCT",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n5"
+        },
+        {
+            "id": "e12",
+            "type": "ASSOCIATED_WITH",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n10"
+        },
+        {
+            "id": "e13",
+            "type": "MUTATION",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n8"
+        },
+        {
+            "id": "e14",
+            "type": "PAIRWISE_ALIGNED",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n8"
+        },
+        {
+            "id": "e15",
+            "type": "HAS_STANDARD_NUMBERING",
+            "style": {},
+            "properties": {},
+            "fromId": "n8",
+            "toId": "n6"
+        },
+        {
+            "id": "e16",
+            "type": "ORIGINATES_FROM",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n1"
+        },
+        {
+            "id": "e17",
+            "type": "HAS_SITE",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n2"
+        },
+        {
+            "id": "e18",
+            "type": "HAS_REGION",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n3"
+        },
+        {
+            "id": "e19",
+            "type": "ASSOCIATED_WITH",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n7"
+        },
+        {
+            "id": "e20",
+            "type": "MUTATION",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n9"
+        },
+        {
+            "id": "e21",
+            "type": "ENCODES",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n8"
+        },
+        {
+            "id": "e22",
+            "type": "PAIRWISE_ALIGNED",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n9"
+        },
+        {
+            "id": "e23",
+            "type": "HAS_STANDARD_NUMBERING",
+            "style": {},
+            "properties": {},
+            "fromId": "n9",
+            "toId": "n6"
+        },
+        {
+            "id": "e24",
+            "type": "SUBCLASS_OF",
+            "style": {},
+            "properties": {},
+            "fromId": "n10",
+            "toId": "n10"
+        },
+        {
+            "id": "e25",
+            "type": "CUSTOM_RELATIONSHIP",
+            "style": {},
+            "properties": {},
+            "fromId": "n10",
+            "toId": "n10"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 9e77bcce..e35c4514 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,6 @@ shapely = "^2.0.6"
 torch = "^2.4.1"
 transformers = "^4.45.2"
 scikit-learn = "^1.5.2"
-numpy = ">=1.14.5,<2.0"
 openai = "^1.52.2"
 esm = "^3.1.3"
 rdflib = "^6.0.0"
@@ -42,7 +41,7 @@ pysam = "0.23.0"
 types-requests = "2.32.0.20250328"
 ipywidgets = "^8.1.7"
 sentencepiece = "^0.2.0"
-umap = "^0.1.1"
+umap-learn = "^0.5.7"
 
 [tool.poetry.group.dev.dependencies]
 mkdocstrings = {extras = ["python"], version = "^0.26.2"}
diff --git a/src/pyeed/analysis/mutation_detection.py b/src/pyeed/analysis/mutation_detection.py
index 274e168b..ce2e8443 100644
--- a/src/pyeed/analysis/mutation_detection.py
+++ b/src/pyeed/analysis/mutation_detection.py
@@ -15,7 +15,7 @@ def get_sequence_data(
         db: DatabaseConnector,
         standard_numbering_tool_name: str,
         node_type: str = "Protein",
-        region_ids_neo4j: Optional[list[int]] = None,
+        region_ids_neo4j: Optional[list[str]] = None,
     ) -> tuple[dict[str, str], dict[str, list[str]]]:
         """
         Fetch sequence and standard numbering position data for two sequences from the database.
@@ -39,7 +39,7 @@ def get_sequence_data(
         if region_ids_neo4j is not None:
             query = f"""
             MATCH (p:{node_type})-[rel:HAS_REGION]->(r:Region)
-            WHERE id(r) IN $region_ids_neo4j
+            WHERE elementId(r) IN $region_ids_neo4j
             MATCH (r)-[rel2:HAS_STANDARD_NUMBERING]->(s:StandardNumbering)
             WHERE p.accession_id IN ['{sequence_id1}', '{sequence_id2}'] 
             AND s.name = '{standard_numbering_tool_name}'
@@ -134,7 +134,7 @@ def save_mutations_to_db(
         sequence_id1: str,
         sequence_id2: str,
         node_type: str = "Protein",
-        region_ids_neo4j: Optional[list[int]] = None,
+        region_ids_neo4j: Optional[list[str]] = None,
     ) -> None:
         """
         Save detected mutations to the database as relationships between nodes.
@@ -155,9 +155,9 @@ def save_mutations_to_db(
         if region_ids_neo4j is not None:
             query = f"""
             MATCH (p1:{node_type} {{accession_id: $sequence_id1}})-[rel:HAS_REGION]->(r1:Region)
-            WHERE id(r1) IN $region_ids_neo4j
-            MATCH (r1)-[rel_mutation:MUTATION]->(r2:Region)
-            WHERE id(r2) IN $region_ids_neo4j
+            WHERE elementId(r1) IN $region_ids_neo4j
+            MATCH (r1)-[rel_mutation:MUTATION]-(r2:Region)
+            WHERE elementId(r2) IN $region_ids_neo4j
             MATCH (r2)<-[:HAS_REGION]-(p2:{node_type} {{accession_id: $sequence_id2}})
             RETURN rel_mutation
             """
@@ -172,7 +172,7 @@ def save_mutations_to_db(
         else:
             existing_mutations = db.execute_read(
                 f"""
-                MATCH (p1:{node_type})-[r:MUTATION]->(p2:{node_type})
+                MATCH (p1:{node_type})-[r:MUTATION]-(p2:{node_type})
                 WHERE p1.accession_id = $sequence_id1 AND p2.accession_id = $sequence_id2
                 RETURN r
                 """,
@@ -188,10 +188,10 @@ def save_mutations_to_db(
             # saving the mutation between the regions
             query = f"""
             MATCH (r1:Region)
-            WHERE id(r1) IN $region_ids_neo4j
+            WHERE elementId(r1) IN $region_ids_neo4j
             MATCH (r1)<-[:HAS_REGION]-(p1:{node_type} {{accession_id: $sequence_id1}})
             MATCH (r2:Region)
-            WHERE id(r2) IN $region_ids_neo4j
+            WHERE elementId(r2) IN $region_ids_neo4j
             MATCH (r2)<-[:HAS_REGION]-(p2:{node_type} {{accession_id: $sequence_id2}})
             CREATE (r1)-[r:MUTATION]->(r2)
             SET r.from_positions = $from_positions,
@@ -230,7 +230,7 @@ def save_mutations_to_db(
             db.execute_write(query, params)
 
         logger.debug(
-            f"Saved {len(list(params['from_positions']))} mutations to database"
+            f"Saved {len(list(params['from_positions']))} mutations to database between {sequence_id1} and {sequence_id2}"
         )
 
     def get_mutations_between_sequences(
@@ -241,7 +241,7 @@ def get_mutations_between_sequences(
         standard_numbering_tool_name: str,
         save_to_db: bool = True,
         node_type: str = "Protein",
-        region_ids_neo4j: Optional[list[int]] = None,
+        region_ids_neo4j: Optional[list[str]] = None,
     ) -> dict[str, list[int | str]]:
         """
         Get mutations between two sequences using standard numbering and optionally save them to the database.
@@ -274,8 +274,6 @@ def get_mutations_between_sequences(
             region_ids_neo4j,
         )
 
-        logger.debug(f"Debug mode output: {sequences} and {positions}")
-
         mutations = self.find_mutations(
             sequences[sequence_id1],
             sequences[sequence_id2],
diff --git a/src/pyeed/analysis/ontology_loading.py b/src/pyeed/analysis/ontology_loading.py
index ee909636..8c1d6be8 100644
--- a/src/pyeed/analysis/ontology_loading.py
+++ b/src/pyeed/analysis/ontology_loading.py
@@ -1,4 +1,4 @@
-from typing import Dict
+from typing import Any, Dict
 
 from pyeed.dbconnect import DatabaseConnector
 from rdflib import OWL, RDF, RDFS, Graph, Namespace, URIRef
@@ -105,26 +105,28 @@ def _process_relationships(
         for s, p, o in g.triples((None, RDFS.subClassOf, None)):
             subclass = str(s)
 
-            if (o, RDF.type, OWL.Class) in g:
-                # Handle direct subclass relationships
-                superclass = str(o)
-                db.execute_write(
-                    """
-                    MATCH (sub:OntologyObject {name: $subclass}),
-                          (super:OntologyObject {name: $superclass})
-                    CREATE (sub)-[:SUBCLASS_OF]->(super)
-                    """,
-                    parameters={"subclass": subclass, "superclass": superclass},
-                )
-
-            elif (o, RDF.type, OWL.Restriction) in g:
+            # Check for the more specific owl:Restriction first.
+            if (o, RDF.type, OWL.Restriction) in g:
                 # Handle OWL restrictions (e.g., RO_ in CARD)
-                self._process_restriction(g, str(o), subclass, db, dicts_labels)
+                self._process_restriction(g, o, subclass, db, dicts_labels)
+            # Only if it's not a restriction, check if it's a direct superclass.
+            elif (o, RDF.type, OWL.Class) in g:
+                # Ensure we are linking to a named class, not a blank node
+                if isinstance(o, URIRef):
+                    superclass = str(o)
+                    db.execute_write(
+                        """
+                        MATCH (sub:OntologyObject {name: $subclass}),
+                              (super:OntologyObject {name: $superclass})
+                        CREATE (sub)-[:SUBCLASS_OF]->(super)
+                        """,
+                        parameters={"subclass": subclass, "superclass": superclass},
+                    )
 
     def _process_restriction(
         self,
         g: Graph,
-        restriction_node: str,
+        restriction_node: Any,
         subclass: str,
         db: DatabaseConnector,
         dicts_labels: Dict[str, str],
@@ -133,32 +135,28 @@ def _process_restriction(
         on_property = None
         some_values_from = None
 
-        # Convert restriction_node string to RDFLib URIRef
-        restriction = URIRef(restriction_node)
-
         # Extract onProperty
-        for _, _, prop in g.triples((restriction, OWL.onProperty, None)):
+        for _, _, prop in g.triples((restriction_node, OWL.onProperty, None)):
             on_property = str(prop)
 
         # Extract someValuesFrom
-        for _, _, value in g.triples((restriction, OWL.someValuesFrom, None)):
+        for _, _, value in g.triples((restriction_node, OWL.someValuesFrom, None)):
             some_values_from = str(value)
 
         if on_property and some_values_from:
+            rel_type = dicts_labels.get(on_property, "RELATED_TO")
+            rel_type = rel_type.replace(" ", "_").replace("-", "_").upper()
+
             query_params = {
                 "subclass": subclass,
                 "some_values_from": some_values_from,
                 "on_property": on_property,
-                "description": dicts_labels.get(on_property, ""),
             }
 
-            query = """
-                MATCH (sub:OntologyObject {name: $subclass}),
-                      (super:OntologyObject {name: $some_values_from})
-                CREATE (sub)-[:CustomRelationship {
-                    name: $on_property,
-                    description: $description
-                }]->(super)
+            query = f"""
+                MATCH (sub:OntologyObject {{name: $subclass}}),
+                      (super:OntologyObject {{name: $some_values_from}})
+                CREATE (sub)-[:`{rel_type}` {{uri: $on_property}}]->(super)
             """
 
             db.execute_write(query, parameters=query_params)
diff --git a/src/pyeed/analysis/sequence_alignment.py b/src/pyeed/analysis/sequence_alignment.py
index 0ca43d02..3b2440ae 100644
--- a/src/pyeed/analysis/sequence_alignment.py
+++ b/src/pyeed/analysis/sequence_alignment.py
@@ -5,6 +5,7 @@
 from Bio.Align import PairwiseAligner as BioPairwiseAligner
 from Bio.Align.substitution_matrices import Array as BioSubstitutionMatrix
 from joblib import Parallel, cpu_count, delayed
+from loguru import logger
 from pyeed.dbconnect import DatabaseConnector
 from pyeed.tools.utility import chunks
 from rich.progress import Progress
@@ -157,9 +158,11 @@ def align_multipairwise(
             pair for pair in pairs if tuple(sorted(pair)) not in existing_pairs
         ]
 
-        print(f"Number of existing pairs: {len(existing_pairs)}")
-        print(f"Number of total pairs: {len(pairs)}")
-        print(f"Number of pairs to align: {len(new_pairs)}")
+        logger.info(f"Number of existing pairs: {len(existing_pairs)}")
+        logger.info(f"Number of total pairs: {len(pairs)}")
+        logger.info(f"Number of pairs to align: {len(new_pairs)}")
+
+        logger.info(f"Length of sequences: {len(sequences)}")
 
         with Progress() as progress:
             align_task = progress.add_task(
@@ -226,7 +229,7 @@ def _to_db(
             UNWIND $alignments AS alignment
             MATCH (p1:{node_type} {{accession_id: alignment.query_id}})-[rel1:HAS_REGION]->(r1:Region)
             MATCH (p2:{node_type} {{accession_id: alignment.target_id}})-[rel2:HAS_REGION]->(r2:Region)
-            WHERE id(r1) IN $region_ids_neo4j AND id(r2) IN $region_ids_neo4j
+            WHERE elementId(r1) IN $region_ids_neo4j AND elementId(r2) IN $region_ids_neo4j
             MERGE (r1)-[r:PAIRWISE_ALIGNED]->(r2)
             SET r.similarity = alignment.identity,
             r.mismatches = alignment.mismatches,
@@ -313,14 +316,20 @@ def _get_id_sequence_dict(
         if ids != []:
             if region_ids_neo4j is not None:
                 query = f"""
-                MATCH (p:{node_type})-[e:HAS_REGION]->(r:Region)
-                WHERE id(r) IN $region_ids_neo4j AND p.accession_id IN $ids
+                MATCH (p:{node_type})-[e:HAS_REGION]-(r:Region)
+                WHERE elementId(r) IN $region_ids_neo4j AND p.accession_id IN $ids
                 RETURN p.accession_id AS accession_id, e.start AS start, e.end AS end, p.sequence AS sequence
                 """
                 nodes = db.execute_read(
                     query,
                     parameters={"region_ids_neo4j": region_ids_neo4j, "ids": ids},
                 )
+                logger.info(f" Full query: {query}")
+                logger.info(f"The ids are: {ids}")
+                logger.info(f"The region ids are: {region_ids_neo4j}")
+                logger.info(
+                    f"Length of nodes (run query of type both region and ids): {len(nodes)}"
+                )
             else:
                 query = f"""
                 MATCH (p:{node_type})
@@ -329,11 +338,13 @@ def _get_id_sequence_dict(
                 """
                 nodes = db.execute_read(query, parameters={"ids": ids})
 
+                logger.info(f"Length of nodes (run query of type ids): {len(nodes)}")
+
         else:
             if region_ids_neo4j is not None:
                 query = f"""
                 MATCH (p:{node_type})-[e:HAS_REGION]->(r:Region)
-                WHERE id(r) IN $region_ids_neo4j
+                WHERE elementId(r) IN $region_ids_neo4j
                 RETURN p.accession_id AS accession_id, e.start AS start, e.end AS end, p.sequence AS sequence
                 """
                 nodes = db.execute_read(
@@ -341,7 +352,9 @@ def _get_id_sequence_dict(
                     parameters={
                         "region_ids_neo4j": region_ids_neo4j,
                     },
-                )
+                )  #
+
+                logger.info(f"Length of nodes (run query of type region): {len(nodes)}")
             else:
                 query = f"""
                 MATCH (p:{node_type})
@@ -349,6 +362,8 @@ def _get_id_sequence_dict(
                 """
                 nodes = db.execute_read(query)
 
+                logger.info(f"Length of nodes (run query of type): {len(nodes)}")
+
         if region_ids_neo4j is not None:
             return {
                 node["accession_id"]: node["sequence"][node["start"] : node["end"]]
diff --git a/src/pyeed/analysis/standard_numbering.py b/src/pyeed/analysis/standard_numbering.py
index 4bf9a8e8..83764032 100644
--- a/src/pyeed/analysis/standard_numbering.py
+++ b/src/pyeed/analysis/standard_numbering.py
@@ -63,7 +63,7 @@ def get_node_base_sequence(
         if region_ids_neo4j:
             query = f"""
             MATCH (p:{node_type})-[e:HAS_REGION]->(r:Region)
-            WHERE id(r) IN $region_ids_neo4j
+            WHERE elementId(r) IN $region_ids_neo4j
             WHERE p.accession_id = '{base_sequence_id}'
             RETURN p.accession_id AS accession_id, e.start AS start, e.end AS end, p.sequence AS sequence
             """
@@ -113,7 +113,7 @@ def save_positions(
             if region_ids_neo4j:
                 query = f"""
                     MATCH (p:{node_type} {{accession_id: '{protein_id}'}})-[e:HAS_REGION]->(r:Region)
-                    WHERE id(r) IN $region_ids_neo4j
+                    WHERE elementId(r) IN $region_ids_neo4j
                     MATCH (s:StandardNumbering {{name: '{self.name}'}})
                     MERGE (r)-[rel:HAS_STANDARD_NUMBERING]->(s)
                     SET rel.positions = {str(positions[protein_id])}
@@ -402,7 +402,7 @@ def apply_standard_numbering_pairwise(
             query = """
             MATCH (s:StandardNumbering {name: $name})
             MATCH (d:DNA)-[e:HAS_REGION]-(r:Region)-[:HAS_STANDARD_NUMBERING]-(s)
-            WHERE id(r) IN $region_ids_neo4j
+            WHERE elementId(r) IN $region_ids_neo4j
             AND d.accession_id IN $list_of_seq_ids
             RETURN d.accession_id AS accession_id
             """
@@ -431,10 +431,11 @@ def apply_standard_numbering_pairwise(
             for row in results:
                 if row is not None:
                     if row.get("accession_id"):
-                        pairs.remove((base_sequence_id, row["accession_id"]))
                         logger.info(
-                            f"Pair {base_sequence_id} and {row['accession_id']} already exists under the same standard numbering node"
+                            f"Pair {base_sequence_id} and {row['accession_id']} already exists under the same standard numbering node \n Removing x from the list: {(base_sequence_id, row['accession_id'])}"
                         )
+                        pairs.remove((base_sequence_id, row["accession_id"]))
+                        break
 
         # remove double pairs in the list of pairs
         pairs = list(set(pairs))
@@ -442,11 +443,14 @@ def apply_standard_numbering_pairwise(
 
         # Run the pairwise alignment using the PairwiseAligner.
         pairwise_aligner = PairwiseAligner(node_type=node_type)
-        input = (list_of_seq_ids or []) + [base_sequence_id]
+        input = list_of_seq_ids + [base_sequence_id]
         if not input:
             raise ValueError("No input sequences provided")
 
-        logger.info(f"Input: {input}")
+        logger.info(f"Input: {input} with length of {len(input)}")
+        logger.info(
+            f"Length of region ids: {len(region_ids_neo4j) if region_ids_neo4j else 0}"
+        )
 
         results_pairwise = pairwise_aligner.align_multipairwise(
             ids=input,  # Combine ids for alignment
@@ -454,6 +458,7 @@ def apply_standard_numbering_pairwise(
             pairs=pairs,  # List of sequence pairs to be aligned
             node_type=node_type,
             region_ids_neo4j=region_ids_neo4j,
+            num_cores=1,
         )
 
         # logger.info(f"Pairwise alignment results: {results_pairwise}")
@@ -551,7 +556,7 @@ def apply_standard_numbering(
             # get the region objects for each of the nodes as well
             query = f"""
             MATCH (p:{node_type})-[e:HAS_REGION]->(r:Region)
-            WHERE id(r) IN $region_ids_neo4j
+            WHERE elementId(r) IN $region_ids_neo4j
             WHERE p.accession_id IN $list_of_seq_ids
             RETURN p.accession_id AS accession_id, e.start AS start, e.end AS end, p.sequence AS sequence
             """
diff --git a/src/pyeed/embeddings/base.py b/src/pyeed/embeddings/base.py
index c436937d..f37f4e93 100644
--- a/src/pyeed/embeddings/base.py
+++ b/src/pyeed/embeddings/base.py
@@ -53,29 +53,35 @@ def preprocess_sequence(self, sequence: str) -> Union[str, Any]:
 
     @abstractmethod
     def get_batch_embeddings(
-        self, sequences: List[str], pool_embeddings: bool = True
+        self, sequences: List[str], pool_embeddings: bool = True, normalize: bool = True
     ) -> List[NDArray[np.float64]]:
         """Get embeddings for a batch of sequences."""
         pass
 
     @abstractmethod
     def get_single_embedding_last_hidden_state(
-        self, sequence: str
+        self, sequence: str, normalize: bool = True
     ) -> NDArray[np.float64]:
         """Get embedding from the last hidden state for a single sequence."""
         pass
 
     @abstractmethod
-    def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_all_layers(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embeddings from all layers for a single sequence."""
         pass
 
     @abstractmethod
-    def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_first_layer(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embedding from the first layer for a single sequence."""
         pass
 
-    def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
+    def get_final_embeddings(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """
         Get final embeddings for a single sequence.
 
@@ -83,7 +89,9 @@ def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
         It falls back gracefully if certain layer-specific methods are not available.
         Default implementation uses last hidden state, but can be overridden.
         """
-        result = self.get_single_embedding_last_hidden_state(sequence)
+        result = self.get_single_embedding_last_hidden_state(
+            sequence, normalize=normalize
+        )
         return np.asarray(result, dtype=np.float64)
 
     def move_to_device(self) -> None:
diff --git a/src/pyeed/embeddings/models/esm2.py b/src/pyeed/embeddings/models/esm2.py
index 0db0b25a..546b258d 100644
--- a/src/pyeed/embeddings/models/esm2.py
+++ b/src/pyeed/embeddings/models/esm2.py
@@ -45,7 +45,7 @@ def preprocess_sequence(self, sequence: str) -> str:
         return sequence
 
     def get_batch_embeddings(
-        self, sequences: List[str], pool_embeddings: bool = True
+        self, sequences: List[str], pool_embeddings: bool = True, normalize: bool = True
     ) -> List[NDArray[np.float64]]:
         """Get embeddings for a batch of sequences using ESM-2."""
         if self.model is None or self.tokenizer is None:
@@ -70,13 +70,18 @@ def get_batch_embeddings(
 
             if pool_embeddings:
                 # Mean pooling across sequence length (axis=1)
-                embeddings.append(hidden_states.mean(axis=1)[0])
+                embedding = hidden_states.mean(axis=1)[0]
+                if normalize:
+                    embedding = normalize_embedding(embedding.reshape(1, -1))[0]
+                embeddings.append(embedding)
             else:
+                if normalize:
+                    hidden_states = normalize_embedding(hidden_states)
                 embeddings.append(hidden_states)
         return embeddings
 
     def get_single_embedding_last_hidden_state(
-        self, sequence: str
+        self, sequence: str, normalize: bool = True
     ) -> NDArray[np.float64]:
         """Get last hidden state embedding for a single sequence."""
         if self.model is None or self.tokenizer is None:
@@ -91,11 +96,16 @@ def get_single_embedding_last_hidden_state(
         with torch.no_grad():
             outputs = model(**inputs)
 
-        # Remove batch dimension and special tokens ([CLS] and [SEP])
+            # Remove batch dimension and special tokens ([CLS] and [SEP])
         embedding = outputs.last_hidden_state[0, 1:-1, :].detach().cpu().numpy()
-        return np.asarray(embedding, dtype=np.float64)
 
-    def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
+
+    def get_single_embedding_all_layers(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embeddings from all layers for a single sequence."""
         if self.model is None or self.tokenizer is None:
             self.load_model()
@@ -115,12 +125,15 @@ def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
         for layer_tensor in hidden_states:
             # Remove batch dimension and special tokens ([CLS] and [SEP])
             emb = layer_tensor[0, 1:-1, :].detach().cpu().numpy()
-            emb = normalize_embedding(emb)
+            if normalize:
+                emb = normalize_embedding(emb)
             embeddings_list.append(emb)
 
-        return np.array(embeddings_list)
+        return cast(NDArray[np.float64], np.array(embeddings_list, dtype=np.float64))
 
-    def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_first_layer(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get first layer embedding for a single sequence."""
         if self.model is None or self.tokenizer is None:
             self.load_model()
@@ -137,18 +150,24 @@ def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]
         # Get the first layer's hidden states for all residues (excluding special tokens)
         embedding = outputs.hidden_states[0][0, 1:-1, :].detach().cpu().numpy()
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
+    def get_final_embeddings(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """
         Get final embeddings for ESM2 with robust fallback.
         """
         try:
-            embeddings = self.get_batch_embeddings([sequence], pool_embeddings=True)
+            embeddings = self.get_batch_embeddings(
+                [sequence], pool_embeddings=True, normalize=normalize
+            )
             if embeddings and len(embeddings) > 0:
-                return np.asarray(embeddings[0], dtype=np.float64)
+                return cast(
+                    NDArray[np.float64], np.asarray(embeddings[0], dtype=np.float64)
+                )
             else:
                 raise ValueError("Batch embeddings method returned empty results")
         except Exception as e:
diff --git a/src/pyeed/embeddings/models/esm3.py b/src/pyeed/embeddings/models/esm3.py
index 062df27b..c5d706a5 100644
--- a/src/pyeed/embeddings/models/esm3.py
+++ b/src/pyeed/embeddings/models/esm3.py
@@ -33,7 +33,7 @@ def preprocess_sequence(self, sequence: str) -> ESMProtein:
         return ESMProtein(sequence=sequence)
 
     def get_batch_embeddings(
-        self, sequences: List[str], pool_embeddings: bool = True
+        self, sequences: List[str], pool_embeddings: bool = True, normalize: bool = True
     ) -> List[NDArray[np.float64]]:
         """Get embeddings for a batch of sequences using ESM3."""
         if self.model is None:
@@ -58,11 +58,13 @@ def get_batch_embeddings(
                 )
                 if pool_embeddings:
                     embeddings = embeddings.mean(axis=0)
+                if normalize:
+                    embeddings = normalize_embedding(embeddings.reshape(1, -1))[0]
                 embedding_list.append(embeddings)
         return embedding_list
 
     def get_single_embedding_last_hidden_state(
-        self, sequence: str
+        self, sequence: str, normalize: bool = True
     ) -> NDArray[np.float64]:
         """Get last hidden state embedding for a single sequence."""
         if self.model is None:
@@ -82,11 +84,13 @@ def get_single_embedding_last_hidden_state(
                 raise ValueError("Model did not return embeddings")
             embedding = embedding.per_residue_embedding.to(torch.float32).cpu().numpy()
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_all_layers(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embeddings from all layers for a single sequence."""
         # ESM3 doesn't support all layers extraction in the same way
         # This is a simplified implementation - might need enhancement based on ESM3 capabilities
@@ -109,12 +113,15 @@ def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
             # For ESM3, we return the per-residue embedding as a single layer
             # This might need adjustment based on actual ESM3 API capabilities
             embedding = result.per_residue_embedding.to(torch.float32).cpu().numpy()
-            embedding = normalize_embedding(embedding)
+            if normalize:
+                embedding = normalize_embedding(embedding)
 
         # Return as a single layer array for consistency with other models
-        return np.array([embedding])
+        return cast(NDArray[np.float64], np.array([embedding], dtype=np.float64))
 
-    def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_first_layer(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get first layer embedding for a single sequence."""
         # For ESM3, this is the same as the per-residue embedding
         if self.model is None:
@@ -134,18 +141,24 @@ def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]
                 raise ValueError("Model did not return embeddings")
             embedding = result.per_residue_embedding.to(torch.float32).cpu().numpy()
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
+    def get_final_embeddings(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """
         Get final embeddings for ESM3 with robust fallback.
         """
         try:
-            embeddings = self.get_batch_embeddings([sequence], pool_embeddings=True)
+            embeddings = self.get_batch_embeddings(
+                [sequence], pool_embeddings=True, normalize=normalize
+            )
             if embeddings and len(embeddings) > 0:
-                return np.asarray(embeddings[0], dtype=np.float64)
+                return cast(
+                    NDArray[np.float64], np.asarray(embeddings[0], dtype=np.float64)
+                )
             else:
                 raise ValueError("Batch embeddings method returned empty results")
         except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
@@ -166,7 +179,14 @@ def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
                             raise ValueError("Model did not return embeddings")
                         embeddings = logits_output.embeddings.cpu().numpy()
                         pooled_embedding = embeddings.mean(axis=1)[0]
-                        return np.asarray(pooled_embedding, dtype=np.float64)
+                        if normalize:
+                            pooled_embedding = normalize_embedding(
+                                pooled_embedding.reshape(1, -1)
+                            )[0]
+                        return cast(
+                            NDArray[np.float64],
+                            np.asarray(pooled_embedding, dtype=np.float64),
+                        )
                 except Exception as minimal_error:
                     raise ValueError(
                         f"ESM3 embedding extraction failed with OOM: {minimal_error}"
diff --git a/src/pyeed/embeddings/models/esmc.py b/src/pyeed/embeddings/models/esmc.py
index 1eddad4e..c2e92c0e 100644
--- a/src/pyeed/embeddings/models/esmc.py
+++ b/src/pyeed/embeddings/models/esmc.py
@@ -80,7 +80,7 @@ def preprocess_sequence(self, sequence: str) -> ESMProtein:
         return ESMProtein(sequence=sequence)
 
     def get_batch_embeddings(
-        self, sequences: List[str], pool_embeddings: bool = True
+        self, sequences: List[str], pool_embeddings: bool = True, normalize: bool = True
     ) -> List[NDArray[np.float64]]:
         """Get embeddings for a batch of sequences using ESMC."""
         if self.model is None:
@@ -107,11 +107,13 @@ def get_batch_embeddings(
                 embeddings = embeddings[:, 1:-1, :]
                 if pool_embeddings:
                     embeddings = embeddings.mean(axis=1)
+                if normalize:
+                    embeddings = normalize_embedding(embeddings)
                 embedding_list.append(embeddings[0])
         return embedding_list
 
     def get_single_embedding_last_hidden_state(
-        self, sequence: str
+        self, sequence: str, normalize: bool = True
     ) -> NDArray[np.float64]:
         """Get last hidden state embedding for a single sequence."""
         if self.model is None:
@@ -142,11 +144,13 @@ def get_single_embedding_last_hidden_state(
                 logits_output.hidden_states[-1][0][1:-1].to(torch.float32).cpu().numpy()
             )
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_all_layers(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embeddings from all layers for a single sequence."""
         if self.model is None:
             self.load_model()
@@ -177,12 +181,15 @@ def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
                 # Remove batch dimension and (if applicable) any special tokens
                 emb = layer_tensor[0].to(torch.float32).cpu().numpy()
                 # If your model adds special tokens, adjust the slicing (e.g., emb[1:-1])
-                emb = normalize_embedding(emb)
+                if normalize:
+                    emb = normalize_embedding(emb)
                 embeddings_list.append(emb)
 
-        return np.array(embeddings_list)
+        return np.array(embeddings_list, dtype=np.float64)
 
-    def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_first_layer(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get first layer embedding for a single sequence."""
         if self.model is None:
             self.load_model()
@@ -209,11 +216,13 @@ def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]
                 logits_output.hidden_states[0][0].to(torch.float32).cpu().numpy()
             )
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
+    def get_final_embeddings(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """
         Get final embeddings for ESMC with robust fallback.
 
@@ -222,9 +231,13 @@ def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
         """
         try:
             # For ESMC, batch embeddings with pooling is more reliable and memory efficient
-            embeddings = self.get_batch_embeddings([sequence], pool_embeddings=True)
+            embeddings = self.get_batch_embeddings(
+                [sequence], pool_embeddings=True, normalize=normalize
+            )
             if embeddings and len(embeddings) > 0:
-                return np.asarray(embeddings[0], dtype=np.float64)
+                return cast(
+                    NDArray[np.float64], np.asarray(embeddings[0], dtype=np.float64)
+                )
             else:
                 raise ValueError("Batch embeddings method returned empty results")
         except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
@@ -250,25 +263,22 @@ def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
                         )
                         if logits_output.embeddings is None:
                             raise ValueError("Model did not return embeddings")
-
-                        # Get embeddings and pool them properly
                         embeddings = logits_output.embeddings.cpu().numpy()
-                        logger.info(f"Embeddings shape: {embeddings.shape}")
-
-                        # Pool across sequence dimension to get single vector
-                        pooled_embedding = embeddings.mean(axis=1)[0]
-
-                        return np.asarray(pooled_embedding, dtype=np.float64)
-
+                        # Drop special tokens and pool
+                        embeddings = embeddings[:, 1:-1, :].mean(axis=1)[0]
+                        if normalize:
+                            embeddings = normalize_embedding(embeddings.reshape(1, -1))[
+                                0
+                            ]
+                        return cast(
+                            NDArray[np.float64],
+                            np.asarray(embeddings, dtype=np.float64),
+                        )
                 except Exception as minimal_error:
-                    logger.error(
-                        f"Minimal embedding extraction also failed for ESMC: {minimal_error}"
-                    )
                     raise ValueError(
                         f"ESMC embedding extraction failed with OOM: {minimal_error}"
                     )
             else:
                 raise e
         except Exception as e:
-            logger.error(f"All embedding extraction methods failed for ESMC: {e}")
             raise ValueError(f"ESMC embedding extraction failed: {e}")
diff --git a/src/pyeed/embeddings/models/prott5.py b/src/pyeed/embeddings/models/prott5.py
index 5e4c996e..307fe83b 100644
--- a/src/pyeed/embeddings/models/prott5.py
+++ b/src/pyeed/embeddings/models/prott5.py
@@ -47,7 +47,7 @@ def preprocess_sequence(self, sequence: str) -> str:
         return preprocess_sequence_for_prott5(sequence)
 
     def get_batch_embeddings(
-        self, sequences: List[str], pool_embeddings: bool = True
+        self, sequences: List[str], pool_embeddings: bool = True, normalize: bool = True
     ) -> List[NDArray[np.float64]]:
         """Get embeddings for a batch of sequences using ProtT5."""
         if self.model is None or self.tokenizer is None:
@@ -89,23 +89,26 @@ def get_batch_embeddings(
             )
 
             # Get encoder last hidden state (encoder embeddings)
+            # remove special pad tokens
             hidden_states = outputs.encoder_last_hidden_state.cpu().numpy()
-
-        if pool_embeddings:
-            # Mean pooling across sequence length, excluding padding tokens
             embedding_list = []
+
             for i, hidden_state in enumerate(hidden_states):
                 # Get actual sequence length (excluding padding)
-                attention_mask_np = attention_mask[i].cpu().numpy()
-                seq_len = attention_mask_np.sum()
+                seq_len = attention_mask[i].cpu().numpy().sum()
                 # Pool only over actual sequence tokens
-                pooled_embedding = hidden_state[:seq_len].mean(axis=0)
-                embedding_list.append(pooled_embedding)
+                actual_embedding = hidden_state[:seq_len]
+                if pool_embeddings:
+                    actual_embedding = actual_embedding.mean(axis=0)
+                if normalize:
+                    actual_embedding = normalize_embedding(
+                        actual_embedding.reshape(1, -1)
+                    )
+                embedding_list.append(actual_embedding)
             return embedding_list
-        return list(hidden_states)
 
     def get_single_embedding_last_hidden_state(
-        self, sequence: str
+        self, sequence: str, normalize: bool = True
     ) -> NDArray[np.float64]:
         """Get last hidden state embedding for a single sequence."""
         if self.model is None or self.tokenizer is None:
@@ -140,9 +143,16 @@ def get_single_embedding_last_hidden_state(
 
         # Get encoder last hidden state including special tokens
         embedding = outputs.encoder_last_hidden_state[0].detach().cpu().numpy()
-        return np.asarray(embedding, dtype=np.float64)
-
-    def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
+        # remove special pad tokens
+        seq_len = attention_mask.cpu().numpy().sum()
+        embedding = embedding[:seq_len]
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
+
+    def get_single_embedding_all_layers(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get embeddings from all layers for a single sequence."""
         if self.model is None or self.tokenizer is None:
             self.load_model()
@@ -181,12 +191,15 @@ def get_single_embedding_all_layers(self, sequence: str) -> NDArray[np.float64]:
         for layer_tensor in encoder_hidden_states:
             # Remove batch dimension but keep special tokens
             emb = layer_tensor[0].detach().cpu().numpy()
-            emb = normalize_embedding(emb)
+            if normalize:
+                emb = normalize_embedding(emb)
             embeddings_list.append(emb)
 
         return np.array(embeddings_list)
 
-    def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]:
+    def get_single_embedding_first_layer(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """Get first layer embedding for a single sequence."""
         if self.model is None or self.tokenizer is None:
             self.load_model()
@@ -222,18 +235,24 @@ def get_single_embedding_first_layer(self, sequence: str) -> NDArray[np.float64]
         # Get first encoder hidden state including special tokens
         embedding = outputs.encoder_hidden_states[0][0].detach().cpu().numpy()
 
-        # Normalize the embedding
-        embedding = normalize_embedding(embedding)
-        return embedding
+        if normalize:
+            embedding = normalize_embedding(embedding)
+        return cast(NDArray[np.float64], embedding)
 
-    def get_final_embeddings(self, sequence: str) -> NDArray[np.float64]:
+    def get_final_embeddings(
+        self, sequence: str, normalize: bool = True
+    ) -> NDArray[np.float64]:
         """
         Get final embeddings for ProtT5 with robust fallback.
         """
         try:
-            embeddings = self.get_batch_embeddings([sequence], pool_embeddings=True)
+            embeddings = self.get_batch_embeddings(
+                [sequence], pool_embeddings=True, normalize=normalize
+            )
             if embeddings and len(embeddings) > 0:
-                return np.asarray(embeddings[0], dtype=np.float64)
+                return cast(
+                    NDArray[np.float64], np.asarray(embeddings[0], dtype=np.float64)
+                )
             else:
                 raise ValueError("Batch embeddings method returned empty results")
         except Exception as e:
diff --git a/src/pyeed/embeddings/processor.py b/src/pyeed/embeddings/processor.py
index 693f3838..4f53a53f 100644
--- a/src/pyeed/embeddings/processor.py
+++ b/src/pyeed/embeddings/processor.py
@@ -75,6 +75,7 @@ def calculate_batch_embeddings(
         embedding_type: Literal[
             "last_hidden_state", "all_layers", "first_layer", "final_embeddings"
         ] = "last_hidden_state",
+        normalize: bool = True,
     ) -> Optional[List[NDArray[np.float64]]]:
         """
         Calculate embeddings for a batch of sequences with automatic device management.
@@ -90,6 +91,7 @@ def calculate_batch_embeddings(
                 - "all_layers": Average across all transformer layers
                 - "first_layer": Use first layer embedding
                 - "final_embeddings": Robust option that works across all models (recommended for compatibility)
+            normalize: Whether to normalize the embeddings (default: True)
 
         Returns:
             List of embeddings if db is None, otherwise None (results stored in DB)
@@ -144,7 +146,7 @@ def calculate_batch_embeddings(
         if num_gpus == 1:
             # Single device processing
             embeddings = self._process_batch_single_device(
-                gpu_batches[0], models[0], batch_size, db, embedding_type
+                gpu_batches[0], models[0], batch_size, db, embedding_type, normalize
             )
             all_embeddings.extend(embeddings)
         else:
@@ -163,6 +165,7 @@ def calculate_batch_embeddings(
                             batch_size,
                             db,
                             embedding_type,
+                            normalize,
                         )
                     )
 
@@ -184,6 +187,7 @@ def _process_batch_single_device(
         batch_size: int,
         db: Optional[DatabaseConnector] = None,
         embedding_type: str = "last_hidden_state",
+        normalize: bool = True,
     ) -> List[NDArray[np.float64]]:
         """Process batch on a single device."""
         all_embeddings = []
@@ -202,22 +206,28 @@ def _process_batch_single_device(
                     if embedding_type == "last_hidden_state":
                         # no batching for last hidden state
                         embeddings_batch = [
-                            model.get_single_embedding_last_hidden_state(seq)
+                            model.get_single_embedding_last_hidden_state(
+                                seq, normalize=normalize
+                            )
                             for seq in sequences[:current_batch_size]
                         ]
                     elif embedding_type == "all_layers":
                         embeddings_batch = [
-                            model.get_single_embedding_all_layers(seq)
+                            model.get_single_embedding_all_layers(
+                                seq, normalize=normalize
+                            )
                             for seq in sequences[:current_batch_size]
                         ]
                     elif embedding_type == "first_layer":
                         embeddings_batch = [
-                            model.get_single_embedding_first_layer(seq)
+                            model.get_single_embedding_first_layer(
+                                seq, normalize=normalize
+                            )
                             for seq in sequences[:current_batch_size]
                         ]
                     elif embedding_type == "final_embeddings":
                         embeddings_batch = [
-                            model.get_final_embeddings(seq)
+                            model.get_final_embeddings(seq, normalize=normalize)
                             for seq in sequences[:current_batch_size]
                         ]
                     else:
@@ -249,6 +259,7 @@ def calculate_single_embedding(
             "last_hidden_state", "all_layers", "first_layer", "final_embeddings"
         ] = "last_hidden_state",
         device: Optional[torch.device] = None,
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """
         Calculate embedding for a single sequence.
@@ -258,6 +269,7 @@ def calculate_single_embedding(
             model_name: Name of the model to use
             embedding_type: Type of embedding to calculate
             device: Specific device to use (optional)
+            normalize: Whether to normalize the embeddings (default: True)
 
         Returns:
             Embedding as numpy array
@@ -265,13 +277,15 @@ def calculate_single_embedding(
         model = self.get_or_create_model(model_name, device)
 
         if embedding_type == "last_hidden_state":
-            return model.get_single_embedding_last_hidden_state(sequence)
+            return model.get_single_embedding_last_hidden_state(
+                sequence, normalize=normalize
+            )
         elif embedding_type == "all_layers":
-            return model.get_single_embedding_all_layers(sequence)
+            return model.get_single_embedding_all_layers(sequence, normalize=normalize)
         elif embedding_type == "first_layer":
-            return model.get_single_embedding_first_layer(sequence)
+            return model.get_single_embedding_first_layer(sequence, normalize=normalize)
         elif embedding_type == "final_embeddings":
-            return model.get_final_embeddings(sequence)
+            return model.get_final_embeddings(sequence, normalize=normalize)
         else:
             raise ValueError(f"Unknown embedding_type: {embedding_type}")
 
@@ -284,6 +298,7 @@ def calculate_database_embeddings(
         embedding_type: Literal[
             "last_hidden_state", "all_layers", "first_layer", "final_embeddings"
         ] = "last_hidden_state",
+        normalize: bool = True,
     ) -> None:
         """
         Calculate embeddings for all sequences in database that don't have embeddings.
@@ -294,6 +309,7 @@ def calculate_database_embeddings(
             model_name: Name of the model to use
             num_gpus: Number of GPUs to use (None = use all available)
             embedding_type: Type of embedding to calculate
+            normalize: Whether to normalize the embeddings (default: True)
         """
         # Retrieve sequences without embeddings
         query = """
@@ -318,6 +334,7 @@ def calculate_database_embeddings(
             num_gpus=num_gpus,
             db=db,
             embedding_type=embedding_type,
+            normalize=normalize,
         )
 
     # Legacy compatibility methods (for backward compatibility with existing processor.py)
@@ -329,6 +346,7 @@ def process_batches_on_gpu(
         tokenizer: Union[Any, None],
         db: DatabaseConnector,
         device: torch.device,
+        normalize: bool = True,
     ) -> None:
         """Legacy method for backward compatibility."""
         logger.warning(
@@ -341,7 +359,7 @@ def process_batches_on_gpu(
 
         # Use new method
         self.calculate_batch_embeddings(
-            data=embedding_data, batch_size=batch_size, db=db
+            data=embedding_data, batch_size=batch_size, db=db, normalize=normalize
         )
 
     def get_batch_embeddings_unified(
@@ -351,6 +369,7 @@ def get_batch_embeddings_unified(
         tokenizer: Union[Any, None],
         device: torch.device = torch.device("cuda:0"),
         pool_embeddings: bool = True,
+        normalize: bool = True,
     ) -> List[NDArray[np.float64]]:
         """Legacy method for backward compatibility."""
         logger.warning("Using legacy get_batch_embeddings_unified method.")
@@ -361,17 +380,20 @@ def get_batch_embeddings_unified(
         embedding_model = ESM2EmbeddingModel("", device)
         embedding_model.model = base_model
         embedding_model.tokenizer = tokenizer
-        return embedding_model.get_batch_embeddings(batch_sequences, pool_embeddings)
+        return embedding_model.get_batch_embeddings(
+            batch_sequences, pool_embeddings, normalize=normalize
+        )
 
     def calculate_single_sequence_embedding_last_hidden_state(
         self,
         sequence: str,
         device: torch.device = torch.device("cuda:0"),
         model_name: str = "facebook/esm2_t33_650M_UR50D",
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         return self.calculate_single_embedding(
-            sequence, model_name, "last_hidden_state", device
+            sequence, model_name, "last_hidden_state", device, normalize=normalize
         )
 
     def calculate_single_sequence_embedding_all_layers(
@@ -379,10 +401,11 @@ def calculate_single_sequence_embedding_all_layers(
         sequence: str,
         device: torch.device,
         model_name: str = "facebook/esm2_t33_650M_UR50D",
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         return self.calculate_single_embedding(
-            sequence, model_name, "all_layers", device
+            sequence, model_name, "all_layers", device, normalize=normalize
         )
 
     def calculate_single_sequence_embedding_first_layer(
@@ -390,37 +413,53 @@ def calculate_single_sequence_embedding_first_layer(
         sequence: str,
         model_name: str = "facebook/esm2_t33_650M_UR50D",
         device: torch.device = torch.device("cuda:0"),
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         return self.calculate_single_embedding(
-            sequence, model_name, "first_layer", device
+            sequence, model_name, "first_layer", device, normalize=normalize
         )
 
     def get_single_embedding_last_hidden_state(
-        self, sequence: str, model: Any, tokenizer: Any, device: torch.device
+        self,
+        sequence: str,
+        model: Any,
+        tokenizer: Any,
+        device: torch.device,
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         logger.warning("Using legacy get_single_embedding_last_hidden_state method.")
         return self._get_single_embedding_legacy(
-            sequence, model, tokenizer, device, "last_hidden_state"
+            sequence, model, tokenizer, device, "last_hidden_state", normalize=normalize
         )
 
     def get_single_embedding_all_layers(
-        self, sequence: str, model: Any, tokenizer: Any, device: torch.device
+        self,
+        sequence: str,
+        model: Any,
+        tokenizer: Any,
+        device: torch.device,
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         logger.warning("Using legacy get_single_embedding_all_layers method.")
         return self._get_single_embedding_legacy(
-            sequence, model, tokenizer, device, "all_layers"
+            sequence, model, tokenizer, device, "all_layers", normalize=normalize
         )
 
     def get_single_embedding_first_layer(
-        self, sequence: str, model: Any, tokenizer: Any, device: torch.device
+        self,
+        sequence: str,
+        model: Any,
+        tokenizer: Any,
+        device: torch.device,
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Legacy method for backward compatibility."""
         logger.warning("Using legacy get_single_embedding_first_layer method.")
         return self._get_single_embedding_legacy(
-            sequence, model, tokenizer, device, "first_layer"
+            sequence, model, tokenizer, device, "first_layer", normalize=normalize
         )
 
     def _get_single_embedding_legacy(
@@ -430,6 +469,7 @@ def _get_single_embedding_legacy(
         tokenizer: Any,
         device: torch.device,
         embedding_type: str,
+        normalize: bool = True,
     ) -> NDArray[np.float64]:
         """Helper method for legacy single embedding methods."""
         # Determine model type and create appropriate embedding model
@@ -440,11 +480,17 @@ def _get_single_embedding_legacy(
         embedding_model.tokenizer = tokenizer
 
         if embedding_type == "last_hidden_state":
-            return embedding_model.get_single_embedding_last_hidden_state(sequence)
+            return embedding_model.get_single_embedding_last_hidden_state(
+                sequence, normalize=normalize
+            )
         elif embedding_type == "all_layers":
-            return embedding_model.get_single_embedding_all_layers(sequence)
+            return embedding_model.get_single_embedding_all_layers(
+                sequence, normalize=normalize
+            )
         elif embedding_type == "first_layer":
-            return embedding_model.get_single_embedding_first_layer(sequence)
+            return embedding_model.get_single_embedding_first_layer(
+                sequence, normalize=normalize
+            )
         else:
             raise ValueError(f"Unknown embedding_type: {embedding_type}")
 
diff --git a/src/pyeed/export_schema.py b/src/pyeed/export_schema.py
new file mode 100644
index 00000000..ceb21aba
--- /dev/null
+++ b/src/pyeed/export_schema.py
@@ -0,0 +1,14 @@
+from pyeed import Pyeed
+
+def main(
+    uri: str = "bolt://129.69.129.130:7687",
+    user: str = "neo4j",
+    password: str = "12345678",
+) -> None:
+    # Create a Pyeed object, automatically connecting to the database
+    eedb = Pyeed(uri, user, password)
+
+    eedb.db.generate_model_diagram(models_path="/home/nab/Niklas/pyeed/src/pyeed/model.py")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file