annotation_gui_230315_deconstruct.ipynb

annotation_gui_230315_deconstruct.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/raven44099/322cd2fedf924c6a01079b6f7ad9cd6b/annotation_gui_230315_deconstruct.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gas2YIpI-0B"
      },
      "source": [
        "#  1. preparations"
      ],
      "id": "3gas2YIpI-0B"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sTNsGyMhO7V0"
      },
      "outputs": [],
      "source": [
        "\n",
        "initial_color             = \"Green\"\n",
        "sentence_from_to          = [0, -1]\n",
        "\n",
        "doc_param = {\n",
        "    'myid'      : \"pm:18840364\",\n",
        "    'passage_first'   : 0,\n",
        "}\n",
        "memory = {}\n",
        "memory.update({'NE_join_last_click' :'', \n",
        "               'l_idP_displayed' : [],\n",
        "              'NE_sentence_id'     : None,\n",
        "               })"
      ],
      "id": "sTNsGyMhO7V0"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8eeadd62-c339-4c5c-9107-df0b4331b9d3"
      },
      "outputs": [],
      "source": [
        "#@title import libs\n",
        "import sys, copy\n",
        "import ipywidgets as widgets\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import io, json\n",
        "import math\n",
        "import functools\n",
        "import json\n",
        "import os\n",
        "from ipywidgets import Button, GridBox, Layout, ButtonStyle, HBox, VBox, Label, Text, HTML\n",
        "from collections import OrderedDict\n",
        "from tqdm import tqdm\n",
        "from urllib.request import urlopen\n",
        "from pprint import pprint\n",
        "import ast\n",
        "import asyncio\n",
        "\n",
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()\n",
        "\n",
        "!mkdir /content/session/\n",
        "pathXX = '/content'"
      ],
      "id": "8eeadd62-c339-4c5c-9107-df0b4331b9d3"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zTSjN84YVDKb"
      },
      "outputs": [],
      "source": [
        "#@title ⏭ run_word_btn\n",
        "def run_word_button_action(btn): \n",
        "    \n",
        "    '''deleted for brevity'''\n",
        "    # if d_mode_btns['mode_NE']['on'] >= 1 or d_mode_btns['mode_CR']['on'] >= 1:\n",
        "    memory['idE_member_click'] = None\n",
        "    # run_append_to_CR_dict(btn)"
      ],
      "id": "zTSjN84YVDKb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DYbFSqX0jHU"
      },
      "source": [
        "# 2. <font color ='gold'> raw data  </font>"
      ],
      "id": "7DYbFSqX0jHU"
    },
    {
      "cell_type": "code",
      "source": [
        "#@title my_doc = {...json...}\n",
        "my_doc = {'id': '3222897',\n",
        " 'infons': {'license': 'author_manuscript'},\n",
        " 'passages': [{'offset': 0,\n",
        "   'infons': {'article-id_doi': '10.1016/j.cmet.2008.08.014',\n",
        "    'article-id_manuscript': 'NIHMS73535',\n",
        "    'article-id_pmc': '3222897',\n",
        "    'article-id_pmid': '18840364',\n",
        "    'fpage': '333',\n",
        "    'issue': '4',\n",
        "    'license': '\\n          This file is available for text mining. It may also be used consistent with the principles of fair use under the copyright law.\\n        ',\n",
        "    'lpage': '341',\n",
        "    'name_0': 'surname:Banks;given-names:Alexander S.',\n",
        "    'name_1': 'surname:Kon;given-names:Ning',\n",
        "    'name_2': 'surname:Knight;given-names:Colette',\n",
        "    'name_3': 'surname:Matsumoto;given-names:Michihiro',\n",
        "    'name_4': 'surname:Gutiérrez-Juárez;given-names:Roger',\n",
        "    'name_5': 'surname:Rossetti;given-names:Luciano',\n",
        "    'name_6': 'surname:Gu;given-names:Wei',\n",
        "    'name_7': 'surname:Accili;given-names:Domenico',\n",
        "    'section_type': 'TITLE',\n",
        "    'type': 'front',\n",
        "    'volume': '8',\n",
        "    'year': '2011'},\n",
        "   'text': 'SirT1 gain-of-function increases energy efficiency and prevents diabetes in mice',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 81,\n",
        "   'infons': {'section_type': 'ABSTRACT', 'type': 'abstract_title_1'},\n",
        "   'text': 'Summary',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 89,\n",
        "   'infons': {'section_type': 'ABSTRACT', 'type': 'abstract'},\n",
        "   'text': 'In yeast, worms and flies, an extra copy of the gene encoding the Sirtuin Sir2 increases metabolic efficiency, as does administration of polyphenols like resveratrol, thought to act through Sirtuins. But evidence that Sirtuin gain-of-function results in increased metabolic efficiency in mammals is limited. We generated transgenic mice with moderate overexpression of SirT1, designed to mimic the Sirtuin gain-of-function that improves metabolism in C.elegans. These mice exhibit normal insulin sensitivity, but decreased food intake and locomotor activity, resulting in decreased energy expenditure. However, in various models of insulin resistance and diabetes, SirT1 transgenics display improved glucose tolerance due to decreased hepatic glucose production and increased adiponectin levels, without changes in body weight or composition. We conclude that SirT1 gain-of-function primes the organism for metabolic adaptation to insulin resistance, increasing hepatic insulin sensitivity and decreasing whole-body energy requirements. These findings have important implications for Sirtuin-based therapies in humans.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 1208,\n",
        "   'infons': {'section_type': 'INTRO', 'type': 'title_1'},\n",
        "   'text': 'Introduction',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 1221,\n",
        "   'infons': {'section_type': 'INTRO', 'type': 'paragraph'},\n",
        "   'text': 'The increased prevalence of obesity and diabetes, with the attendant increase in morbidity and mortality, pose a substantial therapeutic challenge. Genetic screens in lower organisms provide evidence that gain-of-function of the deacetylase Sir2 results in beneficial metabolic effects and lifespan extension. Sirtuin agonists increase metabolic efficiency in rodents through a mechanism bearing similarity with calorie restriction. However, the specificity of these compounds remains undefined.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 1717,\n",
        "   'infons': {'section_type': 'INTRO', 'type': 'paragraph'},\n",
        "   'text': 'The function of mammalian Sir2α (also known as SirT1) in metabolism is controversial. Two models of SirT1 gain-of-function in either pancreatic β cells or adipocytes and brain result in improved insulin secretion and sensitivity, respectively. But increasing SirT1 expression in liver impairs glucose tolerance, and resveratrol increases glucose production by hepatoma cells. Thus, it remains unclear whether chronic, ubiquitous SirT1 activation ultimately benefits or impairs metabolic control.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 2220,\n",
        "   'infons': {'section_type': 'INTRO', 'type': 'paragraph'},\n",
        "   'text': 'To address this question, we developed transgenic mice designed to mimic the chromosomal duplication leading to moderate gain-of-function that improves metabolism in C.elegans. In the latter organism, Sir2 function requires the FoxO1 ortholog daf-16. Thus, in our studies we sought to address whether SirT1 gain-of-function mediates its effects through FoxO1.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 2580,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_1'},\n",
        "   'text': 'Results',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 2588,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_2'},\n",
        "   'text': 'Generation and analysis of SirT1 Transgenic Mice',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 2637,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'To test the hypothesis that SirT1 participates in the regulation of metabolism, we generated BAC transgenic mice overexpressing SirT1 (SirT1 Bacterial Artificial Chromosome Overexpressor, SirBACO). SirBACO mice were born in Mendelian ratios and displayed no gross anatomical or reproductive defects. SirT1 levels were two- to threefold higher than control littermates, except in the spleen (~seven-fold higher), and distribution patterns were indistinguishable from those of the endogenous protein in all tissues examined (Figure 1A–1B). SirT1 overexpression was preserved when SirBACO mice were backcrossed onto the db/db background (Figure 1C). To probe the transgene’s function, we intercrossed SirBACO with Sirt1−/− mice. The transgene readily rescued developmental defects and postnatal lethality of Sirt1−/− mice (data not shown). These findings are consistent the generation of a mouse model of SirT1 gain-of-function.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 3575,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_2'},\n",
        "   'text': 'Altered energy balance in SirBACO Mice',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 3614,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'We carried out a metabolic characterization of SirBACO mice on a standard diet. Serum metabolite levels–including insulin and glucose–were normal (Table S1), as was the response to intraperitoneal glucose or insulin challenge tests (Figure 2A–2B). However, we detected a ~30% increase in plasma adiponectin levels (Table S1).',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 3946,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'In C.elegans and D.melanogaster, increased Sir2 mimics the effects of calorie restriction to extend lifespan. In mammals, prolonged calorie restriction decreases energy expenditure and increases metabolic efficiency by decreasing O2 consumption. To determine whether SirT1 overexpression reproduces these sub-phenotypes, we carried out indirect calorimetry experiments. SirBACO mice demonstrated a coordinate ~15% decrease in O2 consumption and CO2 production (Figure 2C and data not shown). Due to the combined fall in VO2 and VCO2, the respiratory quotient (RQ = VCO2/VO2) was unchanged (Figure 2D). In addition, spontaneous locomotor activity decreased by 33.6 ± 1.4% (Figure 2E), more markedly during the dark phase.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 4668,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'To investigate differences in basal metabolic rate, we compared the energy expenditure required for locomotor activity. SirBACO mice had decreased resting energy expenditure, but no differences at higher activity levels (Figure S1). Body weight and composition were similar in SirBACO mice and non-transgenic littermates (Figure 2F). Food intake decreased by 15 ± 1.3% in SirBACO mice (Figure 2G), consistent with the preservation of normal body weight, in the face of decreased metabolic rate and activity.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 5177,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'In response to 24-hr food deprivation SirBACO mice showed a normal drop in RQ, reflecting increased fat oxidation (not shown). These data indicate that, although hypometabolic, SirBACO mice have normal rates of fatty acid oxidation. They also exhibited lower body temperature than control littermates under fed and fasted conditions (Figure 2H), but did not display changes in blood pressure or heart rate (Figure S2), suggesting that the effect on body temperature is secondary to decreased locomotion.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 5681,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_2'},\n",
        "   'text': 'SirBACO mice are protected from insulin-resistant diabetes',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 5740,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'We next asked whether SirT1 overexpression affects the susceptibility to insulin resistance and diabetes in environmental and genetic models of these conditions. When SirBACO mice on an inbred C57BL/6J background were placed on a HFD or backcrossed onto db/db mice, they showed lower blood glucose and plasma insulin levels (Figures 3A–3B and Table S1) and better glucose tolerance (Figure 3C) than non-transgenic controls, despite similar body weight (Figure S3A), weight gain during the diet (Figure 3D), and body fat content (Figure 3E). We provisionally conclude that SirT1 overexpression does not improve glucose tolerance per se, but prevents the adverse effects of obesity on glucose metabolism.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 6445,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_2'},\n",
        "   'text': 'Catalytically inactive SirT1 fails to protect aging mice from glucose intolerance',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 6527,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'To assess the specificity of these findings, we carried out glucose tolerance tests in aging transgenic mice expressing a catalytically inactive SirT1 transgene (H355Y). Unlike SirBACO mice, SirT1H355Y transgenics showed no differences in glucose tolerance from control littermates, demonstrating that SirT1 catalytic activity is required for its insulin-sensitizing effects (Figure S3B).',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 6916,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'title_2'},\n",
        "   'text': 'Normal energy expenditure in diabetes-resistant SirBACO mice',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "  {'offset': 6977,\n",
        "   'infons': {'section_type': 'RESULTS', 'type': 'paragraph'},\n",
        "   'text': 'Based on the decrease of food intake and energy expenditure in SirBACO mice, we wanted to determine whether the transgenes ’ ability to improve glucose homeostasis in diabetes models could be explained by changes in physical activity or metabolic rate. In both the HFD and db/db model of insulin-resistant diabetes, body weight and composition were similar between SirT1 transgenic and non-transgenic mice (Figure 3D–3E). In contrast to the findings in SirBACO mice on a normal diet (Figure 2), there were no differences in metabolic rate, spontaneous locomotor activity, or food intake in SirBACO mice on a HFD or crossed onto db/db compared to littermate controls (Figure 3F-3G and data not shown). We conclude that SirT1 promotes a state of increased energy efficiency that protects against insulin resistance and hyperglycemia.',\n",
        "   'sentences': [],\n",
        "   'annotations': [],\n",
        "   'relations': []},\n",
        "],\n",
        " 'annotations': [],\n",
        " 'relations': []}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "QZCedyEGycAZ"
      },
      "id": "QZCedyEGycAZ",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title dataframe source\n",
        "tsv_string = '''\tpassage\tsentence_id\ts_position\tp_position\n",
        "SirT1_0\t0\t0\t[0, 5]\t[0, 5]\n",
        "gain_0\t0\t0\t[6, 10]\t[6, 10]\n",
        "-_0\t0\t0\t[10, 11]\t[10, 11]\n",
        "of_0\t0\t0\t[11, 13]\t[11, 13]\n",
        "-_1\t0\t0\t[13, 14]\t[13, 14]\n",
        "function_0\t0\t0\t[14, 22]\t[14, 22]\n",
        "increases_0\t0\t0\t[23, 32]\t[23, 32]\n",
        "energy_0\t0\t0\t[33, 39]\t[33, 39]\n",
        "efficiency_0\t0\t0\t[40, 50]\t[40, 50]\n",
        "and_0\t0\t0\t[51, 54]\t[51, 54]\n",
        "prevents_0\t0\t0\t[55, 63]\t[55, 63]\n",
        "diabetes_0\t0\t0\t[64, 72]\t[64, 72]\n",
        "in_0\t0\t0\t[73, 75]\t[73, 75]\n",
        "mice_0\t0\t0\t[76, 80]\t[76, 80]\n",
        "Summary_0\t1\t0\t[0, 7]\t[0, 7]\n",
        "In_0\t2\t0\t[0, 2]\t[0, 2]\n",
        "yeast_0\t2\t0\t[3, 8]\t[3, 8]\n",
        ",_0\t2\t0\t[8, 9]\t[8, 9]\n",
        "worms_0\t2\t0\t[10, 15]\t[10, 15]\n",
        "and_1\t2\t0\t[16, 19]\t[16, 19]\n",
        "flies_0\t2\t0\t[20, 25]\t[20, 25]\n",
        ",_1\t2\t0\t[25, 26]\t[25, 26]\n",
        "an_0\t2\t0\t[27, 29]\t[27, 29]\n",
        "extra_0\t2\t0\t[30, 35]\t[30, 35]\n",
        "copy_0\t2\t0\t[36, 40]\t[36, 40]\n",
        "of_1\t2\t0\t[41, 43]\t[41, 43]\n",
        "the_0\t2\t0\t[44, 47]\t[44, 47]\n",
        "gene_0\t2\t0\t[48, 52]\t[48, 52]\n",
        "encoding_0\t2\t0\t[53, 61]\t[53, 61]\n",
        "the_1\t2\t0\t[62, 65]\t[62, 65]\n",
        "Sirtuin_0\t2\t0\t[66, 73]\t[66, 73]\n",
        "Sir2_0\t2\t0\t[74, 78]\t[74, 78]\n",
        "increases_1\t2\t0\t[79, 88]\t[79, 88]\n",
        "metabolic_0\t2\t0\t[89, 98]\t[89, 98]\n",
        "efficiency_1\t2\t0\t[99, 109]\t[99, 109]\n",
        ",_2\t2\t0\t[109, 110]\t[109, 110]\n",
        "as_0\t2\t0\t[111, 113]\t[111, 113]\n",
        "does_0\t2\t0\t[114, 118]\t[114, 118]\n",
        "administration_0\t2\t0\t[119, 133]\t[119, 133]\n",
        "of_2\t2\t0\t[134, 136]\t[134, 136]\n",
        "polyphenols_0\t2\t0\t[137, 148]\t[137, 148]\n",
        "like_0\t2\t0\t[149, 153]\t[149, 153]\n",
        "resveratrol_0\t2\t0\t[154, 165]\t[154, 165]\n",
        ",_3\t2\t0\t[165, 166]\t[165, 166]\n",
        "thought_0\t2\t0\t[167, 174]\t[167, 174]\n",
        "to_0\t2\t0\t[175, 177]\t[175, 177]\n",
        "act_0\t2\t0\t[178, 181]\t[178, 181]\n",
        "through_0\t2\t0\t[182, 189]\t[182, 189]\n",
        "Sirtuins_0\t2\t0\t[190, 198]\t[190, 198]\n",
        "._0\t2\t0\t[198, 199]\t[198, 199]\n",
        "But_0\t2\t1\t[0, 3]\t[200, 203]\n",
        "evidence_0\t2\t1\t[4, 12]\t[204, 212]\n",
        "that_0\t2\t1\t[13, 17]\t[213, 217]\n",
        "Sirtuin_1\t2\t1\t[18, 25]\t[218, 225]\n",
        "gain_1\t2\t1\t[26, 30]\t[226, 230]\n",
        "-_2\t2\t1\t[30, 31]\t[230, 231]\n",
        "of_3\t2\t1\t[31, 33]\t[231, 233]\n",
        "-_3\t2\t1\t[33, 34]\t[233, 234]\n",
        "function_1\t2\t1\t[34, 42]\t[234, 242]\n",
        "results_0\t2\t1\t[43, 50]\t[243, 250]\n",
        "in_1\t2\t1\t[51, 53]\t[251, 253]\n",
        "increased_0\t2\t1\t[54, 63]\t[254, 263]\n",
        "metabolic_1\t2\t1\t[64, 73]\t[264, 273]\n",
        "efficiency_2\t2\t1\t[74, 84]\t[274, 284]\n",
        "in_2\t2\t1\t[85, 87]\t[285, 287]\n",
        "mammals_0\t2\t1\t[88, 95]\t[288, 295]\n",
        "is_0\t2\t1\t[96, 98]\t[296, 298]\n",
        "limited_0\t2\t1\t[99, 106]\t[299, 306]\n",
        "._1\t2\t1\t[106, 107]\t[306, 307]\n",
        "We_0\t2\t2\t[0, 2]\t[308, 310]\n",
        "generated_0\t2\t2\t[3, 12]\t[311, 320]\n",
        "transgenic_0\t2\t2\t[13, 23]\t[321, 331]\n",
        "mice_1\t2\t2\t[24, 28]\t[332, 336]\n",
        "with_0\t2\t2\t[29, 33]\t[337, 341]\n",
        "moderate_0\t2\t2\t[34, 42]\t[342, 350]\n",
        "overexpression_0\t2\t2\t[43, 57]\t[351, 365]\n",
        "of_4\t2\t2\t[58, 60]\t[366, 368]\n",
        "SirT1_1\t2\t2\t[61, 66]\t[369, 374]\n",
        ",_4\t2\t2\t[66, 67]\t[374, 375]\n",
        "designed_0\t2\t2\t[68, 76]\t[376, 384]\n",
        "to_1\t2\t2\t[77, 79]\t[385, 387]\n",
        "mimic_0\t2\t2\t[80, 85]\t[388, 393]\n",
        "the_2\t2\t2\t[86, 89]\t[394, 397]\n",
        "Sirtuin_2\t2\t2\t[90, 97]\t[398, 405]\n",
        "gain_2\t2\t2\t[98, 102]\t[406, 410]\n",
        "-_4\t2\t2\t[102, 103]\t[410, 411]\n",
        "of_5\t2\t2\t[103, 105]\t[411, 413]\n",
        "-_5\t2\t2\t[105, 106]\t[413, 414]\n",
        "function_2\t2\t2\t[106, 114]\t[414, 422]\n",
        "that_1\t2\t2\t[115, 119]\t[423, 427]\n",
        "improves_0\t2\t2\t[120, 128]\t[428, 436]\n",
        "metabolism_0\t2\t2\t[129, 139]\t[437, 447]\n",
        "in_3\t2\t2\t[140, 142]\t[448, 450]\n",
        "C_0\t2\t2\t[143, 144]\t[451, 452]\n",
        "._2\t2\t2\t[144, 145]\t[452, 453]\n",
        "elegans_0\t2\t2\t[145, 152]\t[453, 460]\n",
        "._3\t2\t2\t[152, 153]\t[460, 461]\n",
        "These_0\t2\t3\t[0, 5]\t[462, 467]\n",
        "mice_2\t2\t3\t[6, 10]\t[468, 472]\n",
        "exhibit_0\t2\t3\t[11, 18]\t[473, 480]\n",
        "normal_0\t2\t3\t[19, 25]\t[481, 487]\n",
        "insulin_0\t2\t3\t[26, 33]\t[488, 495]\n",
        "sensitivity_0\t2\t3\t[34, 45]\t[496, 507]\n",
        ",_5\t2\t3\t[45, 46]\t[507, 508]\n",
        "but_0\t2\t3\t[47, 50]\t[509, 512]\n",
        "decreased_0\t2\t3\t[51, 60]\t[513, 522]\n",
        "food_0\t2\t3\t[61, 65]\t[523, 527]\n",
        "intake_0\t2\t3\t[66, 72]\t[528, 534]\n",
        "and_2\t2\t3\t[73, 76]\t[535, 538]\n",
        "locomotor_0\t2\t3\t[77, 86]\t[539, 548]\n",
        "activity_0\t2\t3\t[87, 95]\t[549, 557]\n",
        ",_6\t2\t3\t[95, 96]\t[557, 558]\n",
        "resulting_0\t2\t3\t[97, 106]\t[559, 568]\n",
        "in_4\t2\t3\t[107, 109]\t[569, 571]\n",
        "decreased_1\t2\t3\t[110, 119]\t[572, 581]\n",
        "energy_1\t2\t3\t[120, 126]\t[582, 588]\n",
        "expenditure_0\t2\t3\t[127, 138]\t[589, 600]\n",
        "._4\t2\t3\t[138, 139]\t[600, 601]\n",
        "However_0\t2\t4\t[0, 7]\t[602, 609]\n",
        ",_7\t2\t4\t[7, 8]\t[609, 610]\n",
        "in_5\t2\t4\t[9, 11]\t[611, 613]\n",
        "various_0\t2\t4\t[12, 19]\t[614, 621]\n",
        "models_0\t2\t4\t[20, 26]\t[622, 628]\n",
        "of_6\t2\t4\t[27, 29]\t[629, 631]\n",
        "insulin_1\t2\t4\t[30, 37]\t[632, 639]\n",
        "resistance_0\t2\t4\t[38, 48]\t[640, 650]\n",
        "and_3\t2\t4\t[49, 52]\t[651, 654]\n",
        "diabetes_1\t2\t4\t[53, 61]\t[655, 663]\n",
        ",_8\t2\t4\t[61, 62]\t[663, 664]\n",
        "SirT1_2\t2\t4\t[63, 68]\t[665, 670]\n",
        "transgenics_0\t2\t4\t[69, 80]\t[671, 682]\n",
        "display_0\t2\t4\t[81, 88]\t[683, 690]\n",
        "improved_0\t2\t4\t[89, 97]\t[691, 699]\n",
        "glucose_0\t2\t4\t[98, 105]\t[700, 707]\n",
        "tolerance_0\t2\t4\t[106, 115]\t[708, 717]\n",
        "due_0\t2\t4\t[116, 119]\t[718, 721]\n",
        "to_2\t2\t4\t[120, 122]\t[722, 724]\n",
        "decreased_2\t2\t4\t[123, 132]\t[725, 734]\n",
        "hepatic_0\t2\t4\t[133, 140]\t[735, 742]\n",
        "glucose_1\t2\t4\t[141, 148]\t[743, 750]\n",
        "production_0\t2\t4\t[149, 159]\t[751, 761]\n",
        "and_4\t2\t4\t[160, 163]\t[762, 765]\n",
        "increased_1\t2\t4\t[164, 173]\t[766, 775]\n",
        "adiponectin_0\t2\t4\t[174, 185]\t[776, 787]\n",
        "levels_0\t2\t4\t[186, 192]\t[788, 794]\n",
        ",_9\t2\t4\t[192, 193]\t[794, 795]\n",
        "without_0\t2\t4\t[194, 201]\t[796, 803]\n",
        "changes_0\t2\t4\t[202, 209]\t[804, 811]\n",
        "in_6\t2\t4\t[210, 212]\t[812, 814]\n",
        "body_0\t2\t4\t[213, 217]\t[815, 819]\n",
        "weight_0\t2\t4\t[218, 224]\t[820, 826]\n",
        "or_0\t2\t4\t[225, 227]\t[827, 829]\n",
        "composition_0\t2\t4\t[228, 239]\t[830, 841]\n",
        "._5\t2\t4\t[239, 240]\t[841, 842]\n",
        "We_1\t2\t5\t[0, 2]\t[843, 845]\n",
        "conclude_0\t2\t5\t[3, 11]\t[846, 854]\n",
        "that_2\t2\t5\t[12, 16]\t[855, 859]\n",
        "SirT1_3\t2\t5\t[17, 22]\t[860, 865]\n",
        "gain_3\t2\t5\t[23, 27]\t[866, 870]\n",
        "-_6\t2\t5\t[27, 28]\t[870, 871]\n",
        "of_7\t2\t5\t[28, 30]\t[871, 873]\n",
        "-_7\t2\t5\t[30, 31]\t[873, 874]\n",
        "function_3\t2\t5\t[31, 39]\t[874, 882]\n",
        "primes_0\t2\t5\t[40, 46]\t[883, 889]\n",
        "the_3\t2\t5\t[47, 50]\t[890, 893]\n",
        "organism_0\t2\t5\t[51, 59]\t[894, 902]\n",
        "for_0\t2\t5\t[60, 63]\t[903, 906]\n",
        "metabolic_2\t2\t5\t[64, 73]\t[907, 916]\n",
        "adaptation_0\t2\t5\t[74, 84]\t[917, 927]\n",
        "to_3\t2\t5\t[85, 87]\t[928, 930]\n",
        "insulin_2\t2\t5\t[88, 95]\t[931, 938]\n",
        "resistance_1\t2\t5\t[96, 106]\t[939, 949]\n",
        ",_10\t2\t5\t[106, 107]\t[949, 950]\n",
        "increasing_0\t2\t5\t[108, 118]\t[951, 961]\n",
        "hepatic_1\t2\t5\t[119, 126]\t[962, 969]\n",
        "insulin_3\t2\t5\t[127, 134]\t[970, 977]\n",
        "sensitivity_1\t2\t5\t[135, 146]\t[978, 989]\n",
        "and_5\t2\t5\t[147, 150]\t[990, 993]\n",
        "decreasing_0\t2\t5\t[151, 161]\t[994, 1004]\n",
        "whole_0\t2\t5\t[162, 167]\t[1005, 1010]\n",
        "-_8\t2\t5\t[167, 168]\t[1010, 1011]\n",
        "body_1\t2\t5\t[168, 172]\t[1011, 1015]\n",
        "energy_2\t2\t5\t[173, 179]\t[1016, 1022]\n",
        "requirements_0\t2\t5\t[180, 192]\t[1023, 1035]\n",
        "._6\t2\t5\t[192, 193]\t[1035, 1036]\n",
        "These_1\t2\t6\t[0, 5]\t[1037, 1042]\n",
        "findings_0\t2\t6\t[6, 14]\t[1043, 1051]\n",
        "have_0\t2\t6\t[15, 19]\t[1052, 1056]\n",
        "important_0\t2\t6\t[20, 29]\t[1057, 1066]\n",
        "implications_0\t2\t6\t[30, 42]\t[1067, 1079]\n",
        "for_1\t2\t6\t[43, 46]\t[1080, 1083]\n",
        "Sirtuin_3\t2\t6\t[47, 54]\t[1084, 1091]\n",
        "-_9\t2\t6\t[54, 55]\t[1091, 1092]\n",
        "based_0\t2\t6\t[55, 60]\t[1092, 1097]\n",
        "therapies_0\t2\t6\t[61, 70]\t[1098, 1107]\n",
        "in_7\t2\t6\t[71, 73]\t[1108, 1110]\n",
        "humans_0\t2\t6\t[74, 80]\t[1111, 1117]\n",
        "._7\t2\t6\t[80, 81]\t[1117, 1118]\n",
        "Introduction_0\t3\t0\t[0, 12]\t[0, 12]\n",
        "The_0\t4\t0\t[0, 3]\t[0, 3]\n",
        "increased_2\t4\t0\t[4, 13]\t[4, 13]\n",
        "prevalence_0\t4\t0\t[14, 24]\t[14, 24]\n",
        "of_8\t4\t0\t[25, 27]\t[25, 27]\n",
        "obesity_0\t4\t0\t[28, 35]\t[28, 35]\n",
        "and_6\t4\t0\t[36, 39]\t[36, 39]\n",
        "diabetes_2\t4\t0\t[40, 48]\t[40, 48]\n",
        ",_11\t4\t0\t[48, 49]\t[48, 49]\n",
        "with_1\t4\t0\t[50, 54]\t[50, 54]\n",
        "the_4\t4\t0\t[55, 58]\t[55, 58]\n",
        "attendant_0\t4\t0\t[59, 68]\t[59, 68]\n",
        "increase_0\t4\t0\t[69, 77]\t[69, 77]\n",
        "in_8\t4\t0\t[78, 80]\t[78, 80]\n",
        "morbidity_0\t4\t0\t[81, 90]\t[81, 90]\n",
        "and_7\t4\t0\t[91, 94]\t[91, 94]\n",
        "mortality_0\t4\t0\t[95, 104]\t[95, 104]\n",
        ",_12\t4\t0\t[104, 105]\t[104, 105]\n",
        "pose_0\t4\t0\t[106, 110]\t[106, 110]\n",
        "a_0\t4\t0\t[111, 112]\t[111, 112]\n",
        "substantial_0\t4\t0\t[113, 124]\t[113, 124]\n",
        "therapeutic_0\t4\t0\t[125, 136]\t[125, 136]\n",
        "challenge_0\t4\t0\t[137, 146]\t[137, 146]\n",
        "._8\t4\t0\t[146, 147]\t[146, 147]\n",
        "Genetic_0\t4\t1\t[0, 7]\t[148, 155]\n",
        "screens_0\t4\t1\t[8, 15]\t[156, 163]\n",
        "in_9\t4\t1\t[16, 18]\t[164, 166]\n",
        "lower_0\t4\t1\t[19, 24]\t[167, 172]\n",
        "organisms_0\t4\t1\t[25, 34]\t[173, 182]\n",
        "provide_0\t4\t1\t[35, 42]\t[183, 190]\n",
        "evidence_1\t4\t1\t[43, 51]\t[191, 199]\n",
        "that_3\t4\t1\t[52, 56]\t[200, 204]\n",
        "gain_4\t4\t1\t[57, 61]\t[205, 209]\n",
        "-_10\t4\t1\t[61, 62]\t[209, 210]\n",
        "of_9\t4\t1\t[62, 64]\t[210, 212]\n",
        "-_11\t4\t1\t[64, 65]\t[212, 213]\n",
        "function_4\t4\t1\t[65, 73]\t[213, 221]\n",
        "of_10\t4\t1\t[74, 76]\t[222, 224]\n",
        "the_5\t4\t1\t[77, 80]\t[225, 228]\n",
        "deacetylase_0\t4\t1\t[81, 92]\t[229, 240]\n",
        "Sir2_1\t4\t1\t[93, 97]\t[241, 245]\n",
        "results_1\t4\t1\t[98, 105]\t[246, 253]\n",
        "in_10\t4\t1\t[106, 108]\t[254, 256]\n",
        "beneficial_0\t4\t1\t[109, 119]\t[257, 267]\n",
        "metabolic_3\t4\t1\t[120, 129]\t[268, 277]\n",
        "effects_0\t4\t1\t[130, 137]\t[278, 285]\n",
        "and_8\t4\t1\t[138, 141]\t[286, 289]\n",
        "lifespan_0\t4\t1\t[142, 150]\t[290, 298]\n",
        "extension_0\t4\t1\t[151, 160]\t[299, 308]\n",
        "._9\t4\t1\t[160, 161]\t[308, 309]\n",
        "Sirtuin_4\t4\t2\t[0, 7]\t[310, 317]\n",
        "agonists_0\t4\t2\t[8, 16]\t[318, 326]\n",
        "increase_1\t4\t2\t[17, 25]\t[327, 335]\n",
        "metabolic_4\t4\t2\t[26, 35]\t[336, 345]\n",
        "efficiency_3\t4\t2\t[36, 46]\t[346, 356]\n",
        "in_11\t4\t2\t[47, 49]\t[357, 359]\n",
        "rodents_0\t4\t2\t[50, 57]\t[360, 367]\n",
        "through_1\t4\t2\t[58, 65]\t[368, 375]\n",
        "a_1\t4\t2\t[66, 67]\t[376, 377]\n",
        "mechanism_0\t4\t2\t[68, 77]\t[378, 387]\n",
        "bearing_0\t4\t2\t[78, 85]\t[388, 395]\n",
        "similarity_0\t4\t2\t[86, 96]\t[396, 406]\n",
        "with_2\t4\t2\t[97, 101]\t[407, 411]\n",
        "calorie_0\t4\t2\t[102, 109]\t[412, 419]\n",
        "restriction_0\t4\t2\t[110, 121]\t[420, 431]\n",
        "._10\t4\t2\t[121, 122]\t[431, 432]\n",
        "However_1\t4\t3\t[0, 7]\t[433, 440]\n",
        ",_13\t4\t3\t[7, 8]\t[440, 441]\n",
        "the_6\t4\t3\t[9, 12]\t[442, 445]\n",
        "specificity_0\t4\t3\t[13, 24]\t[446, 457]\n",
        "of_11\t4\t3\t[25, 27]\t[458, 460]\n",
        "these_0\t4\t3\t[28, 33]\t[461, 466]\n",
        "compounds_0\t4\t3\t[34, 43]\t[467, 476]\n",
        "remains_0\t4\t3\t[44, 51]\t[477, 484]\n",
        "undefined_0\t4\t3\t[52, 61]\t[485, 494]\n",
        "._11\t4\t3\t[61, 62]\t[494, 495]\n",
        "The_1\t5\t0\t[0, 3]\t[0, 3]\n",
        "function_5\t5\t0\t[4, 12]\t[4, 12]\n",
        "of_12\t5\t0\t[13, 15]\t[13, 15]\n",
        "mammalian_0\t5\t0\t[16, 25]\t[16, 25]\n",
        "Sir2α_0\t5\t0\t[26, 31]\t[26, 31]\n",
        "(_0\t5\t0\t[32, 33]\t[32, 33]\n",
        "also_0\t5\t0\t[33, 37]\t[33, 37]\n",
        "known_0\t5\t0\t[38, 43]\t[38, 43]\n",
        "as_1\t5\t0\t[44, 46]\t[44, 46]\n",
        "SirT1_4\t5\t0\t[47, 52]\t[47, 52]\n",
        ")_0\t5\t0\t[52, 53]\t[52, 53]\n",
        "in_12\t5\t0\t[54, 56]\t[54, 56]\n",
        "metabolism_1\t5\t0\t[57, 67]\t[57, 67]\n",
        "is_1\t5\t0\t[68, 70]\t[68, 70]\n",
        "controversial_0\t5\t0\t[71, 84]\t[71, 84]\n",
        "._12\t5\t0\t[84, 85]\t[84, 85]\n",
        "Two_0\t5\t1\t[0, 3]\t[86, 89]\n",
        "models_1\t5\t1\t[4, 10]\t[90, 96]\n",
        "of_13\t5\t1\t[11, 13]\t[97, 99]\n",
        "SirT1_5\t5\t1\t[14, 19]\t[100, 105]\n",
        "gain_5\t5\t1\t[20, 24]\t[106, 110]\n",
        "-_12\t5\t1\t[24, 25]\t[110, 111]\n",
        "of_14\t5\t1\t[25, 27]\t[111, 113]\n",
        "-_13\t5\t1\t[27, 28]\t[113, 114]\n",
        "function_6\t5\t1\t[28, 36]\t[114, 122]\n",
        "in_13\t5\t1\t[37, 39]\t[123, 125]\n",
        "either_0\t5\t1\t[40, 46]\t[126, 132]\n",
        "pancreatic_0\t5\t1\t[47, 57]\t[133, 143]\n",
        "β_0\t5\t1\t[58, 59]\t[144, 145]\n",
        "cells_0\t5\t1\t[60, 65]\t[146, 151]\n",
        "or_1\t5\t1\t[66, 68]\t[152, 154]\n",
        "adipocytes_0\t5\t1\t[69, 79]\t[155, 165]\n",
        "and_9\t5\t1\t[80, 83]\t[166, 169]\n",
        "brain_0\t5\t1\t[84, 89]\t[170, 175]\n",
        "result_0\t5\t1\t[90, 96]\t[176, 182]\n",
        "in_14\t5\t1\t[97, 99]\t[183, 185]\n",
        "improved_1\t5\t1\t[100, 108]\t[186, 194]\n",
        "insulin_4\t5\t1\t[109, 116]\t[195, 202]\n",
        "secretion_0\t5\t1\t[117, 126]\t[203, 212]\n",
        "and_10\t5\t1\t[127, 130]\t[213, 216]\n",
        "sensitivity_2\t5\t1\t[131, 142]\t[217, 228]\n",
        ",_14\t5\t1\t[142, 143]\t[228, 229]\n",
        "respectively_0\t5\t1\t[144, 156]\t[230, 242]\n",
        "._13\t5\t1\t[156, 157]\t[242, 243]\n",
        "But_1\t5\t2\t[0, 3]\t[244, 247]\n",
        "increasing_1\t5\t2\t[4, 14]\t[248, 258]\n",
        "SirT1_6\t5\t2\t[15, 20]\t[259, 264]\n",
        "expression_0\t5\t2\t[21, 31]\t[265, 275]\n",
        "in_15\t5\t2\t[32, 34]\t[276, 278]\n",
        "liver_0\t5\t2\t[35, 40]\t[279, 284]\n",
        "impairs_0\t5\t2\t[41, 48]\t[285, 292]\n",
        "glucose_2\t5\t2\t[49, 56]\t[293, 300]\n",
        "tolerance_1\t5\t2\t[57, 66]\t[301, 310]\n",
        ",_15\t5\t2\t[66, 67]\t[310, 311]\n",
        "and_11\t5\t2\t[68, 71]\t[312, 315]\n",
        "resveratrol_1\t5\t2\t[72, 83]\t[316, 327]\n",
        "increases_2\t5\t2\t[84, 93]\t[328, 337]\n",
        "glucose_3\t5\t2\t[94, 101]\t[338, 345]\n",
        "production_1\t5\t2\t[102, 112]\t[346, 356]\n",
        "by_0\t5\t2\t[113, 115]\t[357, 359]\n",
        "hepatoma_0\t5\t2\t[116, 124]\t[360, 368]\n",
        "cells_1\t5\t2\t[125, 130]\t[369, 374]\n",
        "._14\t5\t2\t[130, 131]\t[374, 375]\n",
        "Thus_0\t5\t3\t[0, 4]\t[376, 380]\n",
        ",_16\t5\t3\t[4, 5]\t[380, 381]\n",
        "it_0\t5\t3\t[6, 8]\t[382, 384]\n",
        "remains_1\t5\t3\t[9, 16]\t[385, 392]\n",
        "unclear_0\t5\t3\t[17, 24]\t[393, 400]\n",
        "whether_0\t5\t3\t[25, 32]\t[401, 408]\n",
        "chronic_0\t5\t3\t[33, 40]\t[409, 416]\n",
        ",_17\t5\t3\t[40, 41]\t[416, 417]\n",
        "ubiquitous_0\t5\t3\t[42, 52]\t[418, 428]\n",
        "SirT1_7\t5\t3\t[53, 58]\t[429, 434]\n",
        "activation_0\t5\t3\t[59, 69]\t[435, 445]\n",
        "ultimately_0\t5\t3\t[70, 80]\t[446, 456]\n",
        "benefits_0\t5\t3\t[81, 89]\t[457, 465]\n",
        "or_2\t5\t3\t[90, 92]\t[466, 468]\n",
        "impairs_1\t5\t3\t[93, 100]\t[469, 476]\n",
        "metabolic_5\t5\t3\t[101, 110]\t[477, 486]\n",
        "control_0\t5\t3\t[111, 118]\t[487, 494]\n",
        "._15\t5\t3\t[118, 119]\t[494, 495]\n",
        "To_0\t6\t0\t[0, 2]\t[0, 2]\n",
        "address_0\t6\t0\t[3, 10]\t[3, 10]\n",
        "this_0\t6\t0\t[11, 15]\t[11, 15]\n",
        "question_0\t6\t0\t[16, 24]\t[16, 24]\n",
        ",_18\t6\t0\t[24, 25]\t[24, 25]\n",
        "we_0\t6\t0\t[26, 28]\t[26, 28]\n",
        "developed_0\t6\t0\t[29, 38]\t[29, 38]\n",
        "transgenic_1\t6\t0\t[39, 49]\t[39, 49]\n",
        "mice_3\t6\t0\t[50, 54]\t[50, 54]\n",
        "designed_1\t6\t0\t[55, 63]\t[55, 63]\n",
        "to_4\t6\t0\t[64, 66]\t[64, 66]\n",
        "mimic_1\t6\t0\t[67, 72]\t[67, 72]\n",
        "the_7\t6\t0\t[73, 76]\t[73, 76]\n",
        "chromosomal_0\t6\t0\t[77, 88]\t[77, 88]\n",
        "duplication_0\t6\t0\t[89, 100]\t[89, 100]\n",
        "leading_0\t6\t0\t[101, 108]\t[101, 108]\n",
        "to_5\t6\t0\t[109, 111]\t[109, 111]\n",
        "moderate_1\t6\t0\t[112, 120]\t[112, 120]\n",
        "gain_6\t6\t0\t[121, 125]\t[121, 125]\n",
        "-_14\t6\t0\t[125, 126]\t[125, 126]\n",
        "of_15\t6\t0\t[126, 128]\t[126, 128]\n",
        "-_15\t6\t0\t[128, 129]\t[128, 129]\n",
        "function_7\t6\t0\t[129, 137]\t[129, 137]\n",
        "that_4\t6\t0\t[138, 142]\t[138, 142]\n",
        "improves_1\t6\t0\t[143, 151]\t[143, 151]\n",
        "metabolism_2\t6\t0\t[152, 162]\t[152, 162]\n",
        "in_16\t6\t0\t[163, 165]\t[163, 165]\n",
        "C_1\t6\t0\t[166, 167]\t[166, 167]\n",
        "._16\t6\t0\t[167, 168]\t[167, 168]\n",
        "elegans_1\t6\t0\t[168, 175]\t[168, 175]\n",
        "._17\t6\t0\t[175, 176]\t[175, 176]\n",
        "In_1\t6\t1\t[0, 2]\t[177, 179]\n",
        "the_8\t6\t1\t[3, 6]\t[180, 183]\n",
        "latter_0\t6\t1\t[7, 13]\t[184, 190]\n",
        "organism_1\t6\t1\t[14, 22]\t[191, 199]\n",
        ",_19\t6\t1\t[22, 23]\t[199, 200]\n",
        "Sir2_2\t6\t1\t[24, 28]\t[201, 205]\n",
        "function_8\t6\t1\t[29, 37]\t[206, 214]\n",
        "requires_0\t6\t1\t[38, 46]\t[215, 223]\n",
        "the_9\t6\t1\t[47, 50]\t[224, 227]\n",
        "FoxO1_0\t6\t1\t[51, 56]\t[228, 233]\n",
        "ortholog_0\t6\t1\t[57, 65]\t[234, 242]\n",
        "daf_0\t6\t1\t[66, 69]\t[243, 246]\n",
        "-_16\t6\t1\t[69, 70]\t[246, 247]\n",
        "16_0\t6\t1\t[70, 72]\t[247, 249]\n",
        "._18\t6\t1\t[72, 73]\t[249, 250]\n",
        "Thus_1\t6\t2\t[0, 4]\t[251, 255]\n",
        ",_20\t6\t2\t[4, 5]\t[255, 256]\n",
        "in_17\t6\t2\t[6, 8]\t[257, 259]\n",
        "our_0\t6\t2\t[9, 12]\t[260, 263]\n",
        "studies_0\t6\t2\t[13, 20]\t[264, 271]\n",
        "we_1\t6\t2\t[21, 23]\t[272, 274]\n",
        "sought_0\t6\t2\t[24, 30]\t[275, 281]\n",
        "to_6\t6\t2\t[31, 33]\t[282, 284]\n",
        "address_1\t6\t2\t[34, 41]\t[285, 292]\n",
        "whether_1\t6\t2\t[42, 49]\t[293, 300]\n",
        "SirT1_8\t6\t2\t[50, 55]\t[301, 306]\n",
        "gain_7\t6\t2\t[56, 60]\t[307, 311]\n",
        "-_17\t6\t2\t[60, 61]\t[311, 312]\n",
        "of_16\t6\t2\t[61, 63]\t[312, 314]\n",
        "-_18\t6\t2\t[63, 64]\t[314, 315]\n",
        "function_9\t6\t2\t[64, 72]\t[315, 323]\n",
        "mediates_0\t6\t2\t[73, 81]\t[324, 332]\n",
        "its_0\t6\t2\t[82, 85]\t[333, 336]\n",
        "effects_1\t6\t2\t[86, 93]\t[337, 344]\n",
        "through_2\t6\t2\t[94, 101]\t[345, 352]\n",
        "FoxO1_1\t6\t2\t[102, 107]\t[353, 358]\n",
        "._19\t6\t2\t[107, 108]\t[358, 359]\n",
        "Results_0\t7\t0\t[0, 7]\t[0, 7]\n",
        "Generation_0\t8\t0\t[0, 10]\t[0, 10]\n",
        "and_12\t8\t0\t[11, 14]\t[11, 14]\n",
        "analysis_0\t8\t0\t[15, 23]\t[15, 23]\n",
        "of_17\t8\t0\t[24, 26]\t[24, 26]\n",
        "SirT1_9\t8\t0\t[27, 32]\t[27, 32]\n",
        "Transgenic_0\t8\t0\t[33, 43]\t[33, 43]\n",
        "Mice_0\t8\t0\t[44, 48]\t[44, 48]\n",
        "To_1\t9\t0\t[0, 2]\t[0, 2]\n",
        "test_0\t9\t0\t[3, 7]\t[3, 7]\n",
        "the_10\t9\t0\t[8, 11]\t[8, 11]\n",
        "hypothesis_0\t9\t0\t[12, 22]\t[12, 22]\n",
        "that_5\t9\t0\t[23, 27]\t[23, 27]\n",
        "SirT1_10\t9\t0\t[28, 33]\t[28, 33]\n",
        "participates_0\t9\t0\t[34, 46]\t[34, 46]\n",
        "in_18\t9\t0\t[47, 49]\t[47, 49]\n",
        "the_11\t9\t0\t[50, 53]\t[50, 53]\n",
        "regulation_0\t9\t0\t[54, 64]\t[54, 64]\n",
        "of_18\t9\t0\t[65, 67]\t[65, 67]\n",
        "metabolism_3\t9\t0\t[68, 78]\t[68, 78]\n",
        ",_21\t9\t0\t[78, 79]\t[78, 79]\n",
        "we_2\t9\t0\t[80, 82]\t[80, 82]\n",
        "generated_1\t9\t0\t[83, 92]\t[83, 92]\n",
        "BAC_0\t9\t0\t[93, 96]\t[93, 96]\n",
        "transgenic_2\t9\t0\t[97, 107]\t[97, 107]\n",
        "mice_4\t9\t0\t[108, 112]\t[108, 112]\n",
        "overexpressing_0\t9\t0\t[113, 127]\t[113, 127]\n",
        "SirT1_11\t9\t0\t[128, 133]\t[128, 133]\n",
        "(_1\t9\t0\t[134, 135]\t[134, 135]\n",
        "SirT1_12\t9\t0\t[135, 140]\t[135, 140]\n",
        "Bacterial_0\t9\t0\t[141, 150]\t[141, 150]\n",
        "Artificial_0\t9\t0\t[151, 161]\t[151, 161]\n",
        "Chromosome_0\t9\t0\t[162, 172]\t[162, 172]\n",
        "Overexpressor_0\t9\t0\t[173, 186]\t[173, 186]\n",
        ",_22\t9\t0\t[186, 187]\t[186, 187]\n",
        "SirBACO_0\t9\t0\t[188, 195]\t[188, 195]\n",
        ")._0\t9\t0\t[195, 197]\t[195, 197]\n",
        "SirBACO_1\t9\t1\t[0, 7]\t[198, 205]\n",
        "mice_5\t9\t1\t[8, 12]\t[206, 210]\n",
        "were_0\t9\t1\t[13, 17]\t[211, 215]\n",
        "born_0\t9\t1\t[18, 22]\t[216, 220]\n",
        "in_19\t9\t1\t[23, 25]\t[221, 223]\n",
        "Mendelian_0\t9\t1\t[26, 35]\t[224, 233]\n",
        "ratios_0\t9\t1\t[36, 42]\t[234, 240]\n",
        "and_13\t9\t1\t[43, 46]\t[241, 244]\n",
        "displayed_0\t9\t1\t[47, 56]\t[245, 254]\n",
        "no_0\t9\t1\t[57, 59]\t[255, 257]\n",
        "gross_0\t9\t1\t[60, 65]\t[258, 263]\n",
        "anatomical_0\t9\t1\t[66, 76]\t[264, 274]\n",
        "or_3\t9\t1\t[77, 79]\t[275, 277]\n",
        "reproductive_0\t9\t1\t[80, 92]\t[278, 290]\n",
        "defects_0\t9\t1\t[93, 100]\t[291, 298]\n",
        "._20\t9\t1\t[100, 101]\t[298, 299]\n",
        "SirT1_13\t9\t2\t[0, 5]\t[300, 305]\n",
        "levels_1\t9\t2\t[6, 12]\t[306, 312]\n",
        "were_1\t9\t2\t[13, 17]\t[313, 317]\n",
        "two_0\t9\t2\t[18, 21]\t[318, 321]\n",
        "-_19\t9\t2\t[21, 22]\t[321, 322]\n",
        "to_7\t9\t2\t[23, 25]\t[323, 325]\n",
        "threefold_0\t9\t2\t[26, 35]\t[326, 335]\n",
        "higher_0\t9\t2\t[36, 42]\t[336, 342]\n",
        "than_0\t9\t2\t[43, 47]\t[343, 347]\n",
        "control_1\t9\t2\t[48, 55]\t[348, 355]\n",
        "littermates_0\t9\t2\t[56, 67]\t[356, 367]\n",
        ",_23\t9\t2\t[67, 68]\t[367, 368]\n",
        "except_0\t9\t2\t[69, 75]\t[369, 375]\n",
        "in_20\t9\t2\t[76, 78]\t[376, 378]\n",
        "the_12\t9\t2\t[79, 82]\t[379, 382]\n",
        "spleen_0\t9\t2\t[83, 89]\t[383, 389]\n",
        "(~_0\t9\t2\t[90, 92]\t[390, 392]\n",
        "seven_0\t9\t2\t[92, 97]\t[392, 397]\n",
        "-_20\t9\t2\t[97, 98]\t[397, 398]\n",
        "fold_0\t9\t2\t[98, 102]\t[398, 402]\n",
        "higher_1\t9\t2\t[103, 109]\t[403, 409]\n",
        "),_0\t9\t2\t[109, 111]\t[409, 411]\n",
        "and_14\t9\t2\t[112, 115]\t[412, 415]\n",
        "distribution_0\t9\t2\t[116, 128]\t[416, 428]\n",
        "patterns_0\t9\t2\t[129, 137]\t[429, 437]\n",
        "were_2\t9\t2\t[138, 142]\t[438, 442]\n",
        "indistinguishable_0\t9\t2\t[143, 160]\t[443, 460]\n",
        "from_0\t9\t2\t[161, 165]\t[461, 465]\n",
        "those_0\t9\t2\t[166, 171]\t[466, 471]\n",
        "of_19\t9\t2\t[172, 174]\t[472, 474]\n",
        "the_13\t9\t2\t[175, 178]\t[475, 478]\n",
        "endogenous_0\t9\t2\t[179, 189]\t[479, 489]\n",
        "protein_0\t9\t2\t[190, 197]\t[490, 497]\n",
        "in_21\t9\t2\t[198, 200]\t[498, 500]\n",
        "all_0\t9\t2\t[201, 204]\t[501, 504]\n",
        "tissues_0\t9\t2\t[205, 212]\t[505, 512]\n",
        "examined_0\t9\t2\t[213, 221]\t[513, 521]\n",
        "(_2\t9\t2\t[222, 223]\t[522, 523]\n",
        "Figure_0\t9\t2\t[223, 229]\t[523, 529]\n",
        "1A_0\t9\t2\t[230, 232]\t[530, 532]\n",
        "–_0\t9\t2\t[232, 233]\t[532, 533]\n",
        "1B_0\t9\t2\t[233, 235]\t[533, 535]\n",
        ")._1\t9\t2\t[235, 237]\t[535, 537]\n",
        "SirT1_14\t9\t3\t[0, 5]\t[538, 543]\n",
        "overexpression_1\t9\t3\t[6, 20]\t[544, 558]\n",
        "was_0\t9\t3\t[21, 24]\t[559, 562]\n",
        "preserved_0\t9\t3\t[25, 34]\t[563, 572]\n",
        "when_0\t9\t3\t[35, 39]\t[573, 577]\n",
        "SirBACO_2\t9\t3\t[40, 47]\t[578, 585]\n",
        "mice_6\t9\t3\t[48, 52]\t[586, 590]\n",
        "were_3\t9\t3\t[53, 57]\t[591, 595]\n",
        "backcrossed_0\t9\t3\t[58, 69]\t[596, 607]\n",
        "onto_0\t9\t3\t[70, 74]\t[608, 612]\n",
        "the_14\t9\t3\t[75, 78]\t[613, 616]\n",
        "db_0\t9\t3\t[79, 81]\t[617, 619]\n",
        "/_0\t9\t3\t[81, 82]\t[619, 620]\n",
        "db_1\t9\t3\t[82, 84]\t[620, 622]\n",
        "background_0\t9\t3\t[85, 95]\t[623, 633]\n",
        "(_3\t9\t3\t[96, 97]\t[634, 635]\n",
        "Figure_1\t9\t3\t[97, 103]\t[635, 641]\n",
        "1C_0\t9\t3\t[104, 106]\t[642, 644]\n",
        ")._2\t9\t3\t[106, 108]\t[644, 646]\n",
        "To_2\t9\t4\t[0, 2]\t[647, 649]\n",
        "probe_0\t9\t4\t[3, 8]\t[650, 655]\n",
        "the_15\t9\t4\t[9, 12]\t[656, 659]\n",
        "transgene_0\t9\t4\t[13, 22]\t[660, 669]\n",
        "’_0\t9\t4\t[22, 23]\t[669, 670]\n",
        "s_0\t9\t4\t[23, 24]\t[670, 671]\n",
        "function_10\t9\t4\t[25, 33]\t[672, 680]\n",
        ",_24\t9\t4\t[33, 34]\t[680, 681]\n",
        "we_3\t9\t4\t[35, 37]\t[682, 684]\n",
        "intercrossed_0\t9\t4\t[38, 50]\t[685, 697]\n",
        "SirBACO_3\t9\t4\t[51, 58]\t[698, 705]\n",
        "with_3\t9\t4\t[59, 63]\t[706, 710]\n",
        "Sirt1_0\t9\t4\t[64, 69]\t[711, 716]\n",
        "−/−_0\t9\t4\t[69, 72]\t[716, 719]\n",
        "mice_7\t9\t4\t[73, 77]\t[720, 724]\n",
        "._21\t9\t4\t[77, 78]\t[724, 725]\n",
        "The_2\t9\t5\t[0, 3]\t[726, 729]\n",
        "transgene_1\t9\t5\t[4, 13]\t[730, 739]\n",
        "readily_0\t9\t5\t[14, 21]\t[740, 747]\n",
        "rescued_0\t9\t5\t[22, 29]\t[748, 755]\n",
        "developmental_0\t9\t5\t[30, 43]\t[756, 769]\n",
        "defects_1\t9\t5\t[44, 51]\t[770, 777]\n",
        "and_15\t9\t5\t[52, 55]\t[778, 781]\n",
        "postnatal_0\t9\t5\t[56, 65]\t[782, 791]\n",
        "lethality_0\t9\t5\t[66, 75]\t[792, 801]\n",
        "of_20\t9\t5\t[76, 78]\t[802, 804]\n",
        "Sirt1_1\t9\t5\t[79, 84]\t[805, 810]\n",
        "−/−_1\t9\t5\t[84, 87]\t[810, 813]\n",
        "mice_8\t9\t5\t[88, 92]\t[814, 818]\n",
        "(_4\t9\t5\t[93, 94]\t[819, 820]\n",
        "data_0\t9\t5\t[94, 98]\t[820, 824]\n",
        "not_0\t9\t5\t[99, 102]\t[825, 828]\n",
        "shown_0\t9\t5\t[103, 108]\t[829, 834]\n",
        ")._3\t9\t5\t[108, 110]\t[834, 836]\n",
        "These_2\t9\t6\t[0, 5]\t[837, 842]\n",
        "findings_1\t9\t6\t[6, 14]\t[843, 851]\n",
        "are_0\t9\t6\t[15, 18]\t[852, 855]\n",
        "consistent_0\t9\t6\t[19, 29]\t[856, 866]\n",
        "the_16\t9\t6\t[30, 33]\t[867, 870]\n",
        "generation_0\t9\t6\t[34, 44]\t[871, 881]\n",
        "of_21\t9\t6\t[45, 47]\t[882, 884]\n",
        "a_2\t9\t6\t[48, 49]\t[885, 886]\n",
        "mouse_0\t9\t6\t[50, 55]\t[887, 892]\n",
        "model_0\t9\t6\t[56, 61]\t[893, 898]\n",
        "of_22\t9\t6\t[62, 64]\t[899, 901]\n",
        "SirT1_15\t9\t6\t[65, 70]\t[902, 907]\n",
        "gain_8\t9\t6\t[71, 75]\t[908, 912]\n",
        "-_21\t9\t6\t[75, 76]\t[912, 913]\n",
        "of_23\t9\t6\t[76, 78]\t[913, 915]\n",
        "-_22\t9\t6\t[78, 79]\t[915, 916]\n",
        "function_11\t9\t6\t[79, 87]\t[916, 924]\n",
        "._22\t9\t6\t[87, 88]\t[924, 925]\n",
        "Altered_0\t10\t0\t[0, 7]\t[0, 7]\n",
        "energy_3\t10\t0\t[8, 14]\t[8, 14]\n",
        "balance_0\t10\t0\t[15, 22]\t[15, 22]\n",
        "in_22\t10\t0\t[23, 25]\t[23, 25]\n",
        "SirBACO_4\t10\t0\t[26, 33]\t[26, 33]\n",
        "Mice_1\t10\t0\t[34, 38]\t[34, 38]\n",
        "We_2\t11\t0\t[0, 2]\t[0, 2]\n",
        "carried_0\t11\t0\t[3, 10]\t[3, 10]\n",
        "out_0\t11\t0\t[11, 14]\t[11, 14]\n",
        "a_3\t11\t0\t[15, 16]\t[15, 16]\n",
        "metabolic_6\t11\t0\t[17, 26]\t[17, 26]\n",
        "characterization_0\t11\t0\t[27, 43]\t[27, 43]\n",
        "of_24\t11\t0\t[44, 46]\t[44, 46]\n",
        "SirBACO_5\t11\t0\t[47, 54]\t[47, 54]\n",
        "mice_9\t11\t0\t[55, 59]\t[55, 59]\n",
        "on_0\t11\t0\t[60, 62]\t[60, 62]\n",
        "a_4\t11\t0\t[63, 64]\t[63, 64]\n",
        "standard_0\t11\t0\t[65, 73]\t[65, 73]\n",
        "diet_0\t11\t0\t[74, 78]\t[74, 78]\n",
        "._23\t11\t0\t[78, 79]\t[78, 79]\n",
        "Serum_0\t11\t1\t[0, 5]\t[80, 85]\n",
        "metabolite_0\t11\t1\t[6, 16]\t[86, 96]\n",
        "levels_2\t11\t1\t[17, 23]\t[97, 103]\n",
        "–_1\t11\t1\t[23, 24]\t[103, 104]\n",
        "including_0\t11\t1\t[24, 33]\t[104, 113]\n",
        "insulin_5\t11\t1\t[34, 41]\t[114, 121]\n",
        "and_16\t11\t1\t[42, 45]\t[122, 125]\n",
        "glucose_4\t11\t1\t[46, 53]\t[126, 133]\n",
        "–_2\t11\t1\t[53, 54]\t[133, 134]\n",
        "were_4\t11\t1\t[54, 58]\t[134, 138]\n",
        "normal_1\t11\t1\t[59, 65]\t[139, 145]\n",
        "(_5\t11\t1\t[66, 67]\t[146, 147]\n",
        "Table_0\t11\t1\t[67, 72]\t[147, 152]\n",
        "S1_0\t11\t1\t[73, 75]\t[153, 155]\n",
        "),_1\t11\t1\t[75, 77]\t[155, 157]\n",
        "as_2\t11\t1\t[78, 80]\t[158, 160]\n",
        "was_1\t11\t1\t[81, 84]\t[161, 164]\n",
        "the_17\t11\t1\t[85, 88]\t[165, 168]\n",
        "response_0\t11\t1\t[89, 97]\t[169, 177]\n",
        "to_8\t11\t1\t[98, 100]\t[178, 180]\n",
        "intraperitoneal_0\t11\t1\t[101, 116]\t[181, 196]\n",
        "glucose_5\t11\t1\t[117, 124]\t[197, 204]\n",
        "or_4\t11\t1\t[125, 127]\t[205, 207]\n",
        "insulin_6\t11\t1\t[128, 135]\t[208, 215]\n",
        "challenge_1\t11\t1\t[136, 145]\t[216, 225]\n",
        "tests_0\t11\t1\t[146, 151]\t[226, 231]\n",
        "(_6\t11\t1\t[152, 153]\t[232, 233]\n",
        "Figure_2\t11\t1\t[153, 159]\t[233, 239]\n",
        "2A_0\t11\t1\t[160, 162]\t[240, 242]\n",
        "–_3\t11\t1\t[162, 163]\t[242, 243]\n",
        "2B_0\t11\t1\t[163, 165]\t[243, 245]\n",
        ")._4\t11\t1\t[165, 167]\t[245, 247]\n",
        "However_2\t11\t2\t[0, 7]\t[248, 255]\n",
        ",_25\t11\t2\t[7, 8]\t[255, 256]\n",
        "we_4\t11\t2\t[9, 11]\t[257, 259]\n",
        "detected_0\t11\t2\t[12, 20]\t[260, 268]\n",
        "a_5\t11\t2\t[21, 22]\t[269, 270]\n",
        "~_0\t11\t2\t[23, 24]\t[271, 272]\n",
        "30_0\t11\t2\t[24, 26]\t[272, 274]\n",
        "%_0\t11\t2\t[26, 27]\t[274, 275]\n",
        "increase_2\t11\t2\t[28, 36]\t[276, 284]\n",
        "in_23\t11\t2\t[37, 39]\t[285, 287]\n",
        "plasma_0\t11\t2\t[40, 46]\t[288, 294]\n",
        "adiponectin_1\t11\t2\t[47, 58]\t[295, 306]\n",
        "levels_3\t11\t2\t[59, 65]\t[307, 313]\n",
        "(_7\t11\t2\t[66, 67]\t[314, 315]\n",
        "Table_1\t11\t2\t[67, 72]\t[315, 320]\n",
        "S1_1\t11\t2\t[73, 75]\t[321, 323]\n",
        ")._5\t11\t2\t[75, 77]\t[323, 325]\n",
        "In_2\t12\t0\t[0, 2]\t[0, 2]\n",
        "C_2\t12\t0\t[3, 4]\t[3, 4]\n",
        "._24\t12\t0\t[4, 5]\t[4, 5]\n",
        "elegans_2\t12\t0\t[5, 12]\t[5, 12]\n",
        "and_17\t12\t0\t[13, 16]\t[13, 16]\n",
        "D_0\t12\t0\t[17, 18]\t[17, 18]\n",
        "._25\t12\t0\t[18, 19]\t[18, 19]\n",
        "melanogaster_0\t12\t0\t[19, 31]\t[19, 31]\n",
        ",_26\t12\t0\t[31, 32]\t[31, 32]\n",
        "increased_3\t12\t0\t[33, 42]\t[33, 42]\n",
        "Sir2_3\t12\t0\t[43, 47]\t[43, 47]\n",
        "mimics_0\t12\t0\t[48, 54]\t[48, 54]\n",
        "the_18\t12\t0\t[55, 58]\t[55, 58]\n",
        "effects_2\t12\t0\t[59, 66]\t[59, 66]\n",
        "of_25\t12\t0\t[67, 69]\t[67, 69]\n",
        "calorie_1\t12\t0\t[70, 77]\t[70, 77]\n",
        "restriction_1\t12\t0\t[78, 89]\t[78, 89]\n",
        "to_9\t12\t0\t[90, 92]\t[90, 92]\n",
        "extend_0\t12\t0\t[93, 99]\t[93, 99]\n",
        "lifespan_1\t12\t0\t[100, 108]\t[100, 108]\n",
        "._26\t12\t0\t[108, 109]\t[108, 109]\n",
        "In_3\t12\t1\t[0, 2]\t[110, 112]\n",
        "mammals_1\t12\t1\t[3, 10]\t[113, 120]\n",
        ",_27\t12\t1\t[10, 11]\t[120, 121]\n",
        "prolonged_0\t12\t1\t[12, 21]\t[122, 131]\n",
        "calorie_2\t12\t1\t[22, 29]\t[132, 139]\n",
        "restriction_2\t12\t1\t[30, 41]\t[140, 151]\n",
        "decreases_0\t12\t1\t[42, 51]\t[152, 161]\n",
        "energy_4\t12\t1\t[52, 58]\t[162, 168]\n",
        "expenditure_1\t12\t1\t[59, 70]\t[169, 180]\n",
        "and_18\t12\t1\t[71, 74]\t[181, 184]\n",
        "increases_3\t12\t1\t[75, 84]\t[185, 194]\n",
        "metabolic_7\t12\t1\t[85, 94]\t[195, 204]\n",
        "efficiency_4\t12\t1\t[95, 105]\t[205, 215]\n",
        "by_1\t12\t1\t[106, 108]\t[216, 218]\n",
        "decreasing_1\t12\t1\t[109, 119]\t[219, 229]\n",
        "O2_0\t12\t1\t[120, 122]\t[230, 232]\n",
        "consumption_0\t12\t1\t[123, 134]\t[233, 244]\n",
        "._27\t12\t1\t[134, 135]\t[244, 245]\n",
        "To_3\t12\t2\t[0, 2]\t[246, 248]\n",
        "determine_0\t12\t2\t[3, 12]\t[249, 258]\n",
        "whether_2\t12\t2\t[13, 20]\t[259, 266]\n",
        "SirT1_16\t12\t2\t[21, 26]\t[267, 272]\n",
        "overexpression_2\t12\t2\t[27, 41]\t[273, 287]\n",
        "reproduces_0\t12\t2\t[42, 52]\t[288, 298]\n",
        "these_1\t12\t2\t[53, 58]\t[299, 304]\n",
        "sub_0\t12\t2\t[59, 62]\t[305, 308]\n",
        "-_23\t12\t2\t[62, 63]\t[308, 309]\n",
        "phenotypes_0\t12\t2\t[63, 73]\t[309, 319]\n",
        ",_28\t12\t2\t[73, 74]\t[319, 320]\n",
        "we_5\t12\t2\t[75, 77]\t[321, 323]\n",
        "carried_1\t12\t2\t[78, 85]\t[324, 331]\n",
        "out_1\t12\t2\t[86, 89]\t[332, 335]\n",
        "indirect_0\t12\t2\t[90, 98]\t[336, 344]\n",
        "calorimetry_0\t12\t2\t[99, 110]\t[345, 356]\n",
        "experiments_0\t12\t2\t[111, 122]\t[357, 368]\n",
        "._28\t12\t2\t[122, 123]\t[368, 369]\n",
        "SirBACO_6\t12\t3\t[0, 7]\t[370, 377]\n",
        "mice_10\t12\t3\t[8, 12]\t[378, 382]\n",
        "demonstrated_0\t12\t3\t[13, 25]\t[383, 395]\n",
        "a_6\t12\t3\t[26, 27]\t[396, 397]\n",
        "coordinate_0\t12\t3\t[28, 38]\t[398, 408]\n",
        "~_1\t12\t3\t[39, 40]\t[409, 410]\n",
        "15_0\t12\t3\t[40, 42]\t[410, 412]\n",
        "%_1\t12\t3\t[42, 43]\t[412, 413]\n",
        "decrease_0\t12\t3\t[44, 52]\t[414, 422]\n",
        "in_24\t12\t3\t[53, 55]\t[423, 425]\n",
        "O2_1\t12\t3\t[56, 58]\t[426, 428]\n",
        "consumption_1\t12\t3\t[59, 70]\t[429, 440]\n",
        "and_19\t12\t3\t[71, 74]\t[441, 444]\n",
        "CO2_0\t12\t3\t[75, 78]\t[445, 448]\n",
        "production_2\t12\t3\t[79, 89]\t[449, 459]\n",
        "(_8\t12\t3\t[90, 91]\t[460, 461]\n",
        "Figure_3\t12\t3\t[91, 97]\t[461, 467]\n",
        "2C_0\t12\t3\t[98, 100]\t[468, 470]\n",
        "and_20\t12\t3\t[101, 104]\t[471, 474]\n",
        "data_1\t12\t3\t[105, 109]\t[475, 479]\n",
        "not_1\t12\t3\t[110, 113]\t[480, 483]\n",
        "shown_1\t12\t3\t[114, 119]\t[484, 489]\n",
        ")._6\t12\t3\t[119, 121]\t[489, 491]\n",
        "Due_0\t12\t4\t[0, 3]\t[492, 495]\n",
        "to_10\t12\t4\t[4, 6]\t[496, 498]\n",
        "the_19\t12\t4\t[7, 10]\t[499, 502]\n",
        "combined_0\t12\t4\t[11, 19]\t[503, 511]\n",
        "fall_0\t12\t4\t[20, 24]\t[512, 516]\n",
        "in_25\t12\t4\t[25, 27]\t[517, 519]\n",
        "VO2_0\t12\t4\t[28, 31]\t[520, 523]\n",
        "and_21\t12\t4\t[32, 35]\t[524, 527]\n",
        "VCO2_0\t12\t4\t[36, 40]\t[528, 532]\n",
        ",_29\t12\t4\t[40, 41]\t[532, 533]\n",
        "the_20\t12\t4\t[42, 45]\t[534, 537]\n",
        "respiratory_0\t12\t4\t[46, 57]\t[538, 549]\n",
        "quotient_0\t12\t4\t[58, 66]\t[550, 558]\n",
        "(_9\t12\t4\t[67, 68]\t[559, 560]\n",
        "RQ_0\t12\t4\t[68, 70]\t[560, 562]\n",
        "=_0\t12\t4\t[71, 72]\t[563, 564]\n",
        "VCO2_1\t12\t4\t[73, 77]\t[565, 569]\n",
        "/_1\t12\t4\t[77, 78]\t[569, 570]\n",
        "VO2_1\t12\t4\t[78, 81]\t[570, 573]\n",
        ")_1\t12\t4\t[81, 82]\t[573, 574]\n",
        "was_2\t12\t4\t[83, 86]\t[575, 578]\n",
        "unchanged_0\t12\t4\t[87, 96]\t[579, 588]\n",
        "(_10\t12\t4\t[97, 98]\t[589, 590]\n",
        "Figure_4\t12\t4\t[98, 104]\t[590, 596]\n",
        "2D_0\t12\t4\t[105, 107]\t[597, 599]\n",
        ")._7\t12\t4\t[107, 109]\t[599, 601]\n",
        "In_4\t12\t5\t[0, 2]\t[602, 604]\n",
        "addition_0\t12\t5\t[3, 11]\t[605, 613]\n",
        ",_30\t12\t5\t[11, 12]\t[613, 614]\n",
        "spontaneous_0\t12\t5\t[13, 24]\t[615, 626]\n",
        "locomotor_1\t12\t5\t[25, 34]\t[627, 636]\n",
        "activity_1\t12\t5\t[35, 43]\t[637, 645]\n",
        "decreased_3\t12\t5\t[44, 53]\t[646, 655]\n",
        "by_2\t12\t5\t[54, 56]\t[656, 658]\n",
        "33_0\t12\t5\t[57, 59]\t[659, 661]\n",
        "._29\t12\t5\t[59, 60]\t[661, 662]\n",
        "6_0\t12\t5\t[60, 61]\t[662, 663]\n",
        "±_0\t12\t5\t[62, 63]\t[664, 665]\n",
        "1_0\t12\t5\t[64, 65]\t[666, 667]\n",
        "._30\t12\t5\t[65, 66]\t[667, 668]\n",
        "4_0\t12\t5\t[66, 67]\t[668, 669]\n",
        "%_2\t12\t5\t[67, 68]\t[669, 670]\n",
        "(_11\t12\t5\t[69, 70]\t[671, 672]\n",
        "Figure_5\t12\t5\t[70, 76]\t[672, 678]\n",
        "2E_0\t12\t5\t[77, 79]\t[679, 681]\n",
        "),_2\t12\t5\t[79, 81]\t[681, 683]\n",
        "more_0\t12\t5\t[82, 86]\t[684, 688]\n",
        "markedly_0\t12\t5\t[87, 95]\t[689, 697]\n",
        "during_0\t12\t5\t[96, 102]\t[698, 704]\n",
        "the_21\t12\t5\t[103, 106]\t[705, 708]\n",
        "dark_0\t12\t5\t[107, 111]\t[709, 713]\n",
        "phase_0\t12\t5\t[112, 117]\t[714, 719]\n",
        "._31\t12\t5\t[117, 118]\t[719, 720]\n",
        "To_4\t13\t0\t[0, 2]\t[0, 2]\n",
        "investigate_0\t13\t0\t[3, 14]\t[3, 14]\n",
        "differences_0\t13\t0\t[15, 26]\t[15, 26]\n",
        "in_26\t13\t0\t[27, 29]\t[27, 29]\n",
        "basal_0\t13\t0\t[30, 35]\t[30, 35]\n",
        "metabolic_8\t13\t0\t[36, 45]\t[36, 45]\n",
        "rate_0\t13\t0\t[46, 50]\t[46, 50]\n",
        ",_31\t13\t0\t[50, 51]\t[50, 51]\n",
        "we_6\t13\t0\t[52, 54]\t[52, 54]\n",
        "compared_0\t13\t0\t[55, 63]\t[55, 63]\n",
        "the_22\t13\t0\t[64, 67]\t[64, 67]\n",
        "energy_5\t13\t0\t[68, 74]\t[68, 74]\n",
        "expenditure_2\t13\t0\t[75, 86]\t[75, 86]\n",
        "required_0\t13\t0\t[87, 95]\t[87, 95]\n",
        "for_2\t13\t0\t[96, 99]\t[96, 99]\n",
        "locomotor_2\t13\t0\t[100, 109]\t[100, 109]\n",
        "activity_2\t13\t0\t[110, 118]\t[110, 118]\n",
        "._32\t13\t0\t[118, 119]\t[118, 119]\n",
        "SirBACO_7\t13\t1\t[0, 7]\t[120, 127]\n",
        "mice_11\t13\t1\t[8, 12]\t[128, 132]\n",
        "had_0\t13\t1\t[13, 16]\t[133, 136]\n",
        "decreased_4\t13\t1\t[17, 26]\t[137, 146]\n",
        "resting_0\t13\t1\t[27, 34]\t[147, 154]\n",
        "energy_6\t13\t1\t[35, 41]\t[155, 161]\n",
        "expenditure_3\t13\t1\t[42, 53]\t[162, 173]\n",
        ",_32\t13\t1\t[53, 54]\t[173, 174]\n",
        "but_1\t13\t1\t[55, 58]\t[175, 178]\n",
        "no_1\t13\t1\t[59, 61]\t[179, 181]\n",
        "differences_1\t13\t1\t[62, 73]\t[182, 193]\n",
        "at_0\t13\t1\t[74, 76]\t[194, 196]\n",
        "higher_2\t13\t1\t[77, 83]\t[197, 203]\n",
        "activity_3\t13\t1\t[84, 92]\t[204, 212]\n",
        "levels_4\t13\t1\t[93, 99]\t[213, 219]\n",
        "(_12\t13\t1\t[100, 101]\t[220, 221]\n",
        "Figure_6\t13\t1\t[101, 107]\t[221, 227]\n",
        "S1_2\t13\t1\t[108, 110]\t[228, 230]\n",
        ")._8\t13\t1\t[110, 112]\t[230, 232]\n",
        "Body_0\t13\t2\t[0, 4]\t[233, 237]\n",
        "weight_1\t13\t2\t[5, 11]\t[238, 244]\n",
        "and_22\t13\t2\t[12, 15]\t[245, 248]\n",
        "composition_1\t13\t2\t[16, 27]\t[249, 260]\n",
        "were_5\t13\t2\t[28, 32]\t[261, 265]\n",
        "similar_0\t13\t2\t[33, 40]\t[266, 273]\n",
        "in_27\t13\t2\t[41, 43]\t[274, 276]\n",
        "SirBACO_8\t13\t2\t[44, 51]\t[277, 284]\n",
        "mice_12\t13\t2\t[52, 56]\t[285, 289]\n",
        "and_23\t13\t2\t[57, 60]\t[290, 293]\n",
        "non_0\t13\t2\t[61, 64]\t[294, 297]\n",
        "-_24\t13\t2\t[64, 65]\t[297, 298]\n",
        "transgenic_3\t13\t2\t[65, 75]\t[298, 308]\n",
        "littermates_1\t13\t2\t[76, 87]\t[309, 320]\n",
        "(_13\t13\t2\t[88, 89]\t[321, 322]\n",
        "Figure_7\t13\t2\t[89, 95]\t[322, 328]\n",
        "2F_0\t13\t2\t[96, 98]\t[329, 331]\n",
        ")._9\t13\t2\t[98, 100]\t[331, 333]\n",
        "Food_0\t13\t3\t[0, 4]\t[334, 338]\n",
        "intake_1\t13\t3\t[5, 11]\t[339, 345]\n",
        "decreased_5\t13\t3\t[12, 21]\t[346, 355]\n",
        "by_3\t13\t3\t[22, 24]\t[356, 358]\n",
        "15_1\t13\t3\t[25, 27]\t[359, 361]\n",
        "±_1\t13\t3\t[28, 29]\t[362, 363]\n",
        "1_1\t13\t3\t[30, 31]\t[364, 365]\n",
        "._33\t13\t3\t[31, 32]\t[365, 366]\n",
        "3_0\t13\t3\t[32, 33]\t[366, 367]\n",
        "%_3\t13\t3\t[33, 34]\t[367, 368]\n",
        "in_28\t13\t3\t[35, 37]\t[369, 371]\n",
        "SirBACO_9\t13\t3\t[38, 45]\t[372, 379]\n",
        "mice_13\t13\t3\t[46, 50]\t[380, 384]\n",
        "(_14\t13\t3\t[51, 52]\t[385, 386]\n",
        "Figure_8\t13\t3\t[52, 58]\t[386, 392]\n",
        "2G_0\t13\t3\t[59, 61]\t[393, 395]\n",
        "),_3\t13\t3\t[61, 63]\t[395, 397]\n",
        "consistent_1\t13\t3\t[64, 74]\t[398, 408]\n",
        "with_4\t13\t3\t[75, 79]\t[409, 413]\n",
        "the_23\t13\t3\t[80, 83]\t[414, 417]\n",
        "preservation_0\t13\t3\t[84, 96]\t[418, 430]\n",
        "of_26\t13\t3\t[97, 99]\t[431, 433]\n",
        "normal_2\t13\t3\t[100, 106]\t[434, 440]\n",
        "body_2\t13\t3\t[107, 111]\t[441, 445]\n",
        "weight_2\t13\t3\t[112, 118]\t[446, 452]\n",
        ",_33\t13\t3\t[118, 119]\t[452, 453]\n",
        "in_29\t13\t3\t[120, 122]\t[454, 456]\n",
        "the_24\t13\t3\t[123, 126]\t[457, 460]\n",
        "face_0\t13\t3\t[127, 131]\t[461, 465]\n",
        "of_27\t13\t3\t[132, 134]\t[466, 468]\n",
        "decreased_6\t13\t3\t[135, 144]\t[469, 478]\n",
        "metabolic_9\t13\t3\t[145, 154]\t[479, 488]\n",
        "rate_1\t13\t3\t[155, 159]\t[489, 493]\n",
        "and_24\t13\t3\t[160, 163]\t[494, 497]\n",
        "activity_4\t13\t3\t[164, 172]\t[498, 506]\n",
        "._34\t13\t3\t[172, 173]\t[506, 507]\n",
        "In_5\t14\t0\t[0, 2]\t[0, 2]\n",
        "response_1\t14\t0\t[3, 11]\t[3, 11]\n",
        "to_11\t14\t0\t[12, 14]\t[12, 14]\n",
        "24_0\t14\t0\t[15, 17]\t[15, 17]\n",
        "-_25\t14\t0\t[17, 18]\t[17, 18]\n",
        "hr_0\t14\t0\t[18, 20]\t[18, 20]\n",
        "food_1\t14\t0\t[21, 25]\t[21, 25]\n",
        "deprivation_0\t14\t0\t[26, 37]\t[26, 37]\n",
        "SirBACO_10\t14\t0\t[38, 45]\t[38, 45]\n",
        "mice_14\t14\t0\t[46, 50]\t[46, 50]\n",
        "showed_0\t14\t0\t[51, 57]\t[51, 57]\n",
        "a_7\t14\t0\t[58, 59]\t[58, 59]\n",
        "normal_3\t14\t0\t[60, 66]\t[60, 66]\n",
        "drop_0\t14\t0\t[67, 71]\t[67, 71]\n",
        "in_30\t14\t0\t[72, 74]\t[72, 74]\n",
        "RQ_1\t14\t0\t[75, 77]\t[75, 77]\n",
        ",_34\t14\t0\t[77, 78]\t[77, 78]\n",
        "reflecting_0\t14\t0\t[79, 89]\t[79, 89]\n",
        "increased_4\t14\t0\t[90, 99]\t[90, 99]\n",
        "fat_0\t14\t0\t[100, 103]\t[100, 103]\n",
        "oxidation_0\t14\t0\t[104, 113]\t[104, 113]\n",
        "(_15\t14\t0\t[114, 115]\t[114, 115]\n",
        "not_2\t14\t0\t[115, 118]\t[115, 118]\n",
        "shown_2\t14\t0\t[119, 124]\t[119, 124]\n",
        ")._10\t14\t0\t[124, 126]\t[124, 126]\n",
        "These_3\t14\t1\t[0, 5]\t[127, 132]\n",
        "data_2\t14\t1\t[6, 10]\t[133, 137]\n",
        "indicate_0\t14\t1\t[11, 19]\t[138, 146]\n",
        "that_6\t14\t1\t[20, 24]\t[147, 151]\n",
        ",_35\t14\t1\t[24, 25]\t[151, 152]\n",
        "although_0\t14\t1\t[26, 34]\t[153, 161]\n",
        "hypometabolic_0\t14\t1\t[35, 48]\t[162, 175]\n",
        ",_36\t14\t1\t[48, 49]\t[175, 176]\n",
        "SirBACO_11\t14\t1\t[50, 57]\t[177, 184]\n",
        "mice_15\t14\t1\t[58, 62]\t[185, 189]\n",
        "have_1\t14\t1\t[63, 67]\t[190, 194]\n",
        "normal_4\t14\t1\t[68, 74]\t[195, 201]\n",
        "rates_0\t14\t1\t[75, 80]\t[202, 207]\n",
        "of_28\t14\t1\t[81, 83]\t[208, 210]\n",
        "fatty_0\t14\t1\t[84, 89]\t[211, 216]\n",
        "acid_0\t14\t1\t[90, 94]\t[217, 221]\n",
        "oxidation_1\t14\t1\t[95, 104]\t[222, 231]\n",
        "._35\t14\t1\t[104, 105]\t[231, 232]\n",
        "They_0\t14\t2\t[0, 4]\t[233, 237]\n",
        "also_1\t14\t2\t[5, 9]\t[238, 242]\n",
        "exhibited_0\t14\t2\t[10, 19]\t[243, 252]\n",
        "lower_1\t14\t2\t[20, 25]\t[253, 258]\n",
        "body_3\t14\t2\t[26, 30]\t[259, 263]\n",
        "temperature_0\t14\t2\t[31, 42]\t[264, 275]\n",
        "than_1\t14\t2\t[43, 47]\t[276, 280]\n",
        "control_2\t14\t2\t[48, 55]\t[281, 288]\n",
        "littermates_2\t14\t2\t[56, 67]\t[289, 300]\n",
        "under_0\t14\t2\t[68, 73]\t[301, 306]\n",
        "fed_0\t14\t2\t[74, 77]\t[307, 310]\n",
        "and_25\t14\t2\t[78, 81]\t[311, 314]\n",
        "fasted_0\t14\t2\t[82, 88]\t[315, 321]\n",
        "conditions_0\t14\t2\t[89, 99]\t[322, 332]\n",
        "(_16\t14\t2\t[100, 101]\t[333, 334]\n",
        "Figure_9\t14\t2\t[101, 107]\t[334, 340]\n",
        "2H_0\t14\t2\t[108, 110]\t[341, 343]\n",
        "),_4\t14\t2\t[110, 112]\t[343, 345]\n",
        "but_2\t14\t2\t[113, 116]\t[346, 349]\n",
        "did_0\t14\t2\t[117, 120]\t[350, 353]\n",
        "not_3\t14\t2\t[121, 124]\t[354, 357]\n",
        "display_1\t14\t2\t[125, 132]\t[358, 365]\n",
        "changes_1\t14\t2\t[133, 140]\t[366, 373]\n",
        "in_31\t14\t2\t[141, 143]\t[374, 376]\n",
        "blood_0\t14\t2\t[144, 149]\t[377, 382]\n",
        "pressure_0\t14\t2\t[150, 158]\t[383, 391]\n",
        "or_5\t14\t2\t[159, 161]\t[392, 394]\n",
        "heart_0\t14\t2\t[162, 167]\t[395, 400]\n",
        "rate_2\t14\t2\t[168, 172]\t[401, 405]\n",
        "(_17\t14\t2\t[173, 174]\t[406, 407]\n",
        "Figure_10\t14\t2\t[174, 180]\t[407, 413]\n",
        "S2_0\t14\t2\t[181, 183]\t[414, 416]\n",
        "),_5\t14\t2\t[183, 185]\t[416, 418]\n",
        "suggesting_0\t14\t2\t[186, 196]\t[419, 429]\n",
        "that_7\t14\t2\t[197, 201]\t[430, 434]\n",
        "the_25\t14\t2\t[202, 205]\t[435, 438]\n",
        "effect_0\t14\t2\t[206, 212]\t[439, 445]\n",
        "on_1\t14\t2\t[213, 215]\t[446, 448]\n",
        "body_4\t14\t2\t[216, 220]\t[449, 453]\n",
        "temperature_1\t14\t2\t[221, 232]\t[454, 465]\n",
        "is_2\t14\t2\t[233, 235]\t[466, 468]\n",
        "secondary_0\t14\t2\t[236, 245]\t[469, 478]\n",
        "to_12\t14\t2\t[246, 248]\t[479, 481]\n",
        "decreased_7\t14\t2\t[249, 258]\t[482, 491]\n",
        "locomotion_0\t14\t2\t[259, 269]\t[492, 502]\n",
        "._36\t14\t2\t[269, 270]\t[502, 503]\n",
        "SirBACO_12\t15\t0\t[0, 7]\t[0, 7]\n",
        "mice_16\t15\t0\t[8, 12]\t[8, 12]\n",
        "are_1\t15\t0\t[13, 16]\t[13, 16]\n",
        "protected_0\t15\t0\t[17, 26]\t[17, 26]\n",
        "from_1\t15\t0\t[27, 31]\t[27, 31]\n",
        "insulin_7\t15\t0\t[32, 39]\t[32, 39]\n",
        "-_26\t15\t0\t[39, 40]\t[39, 40]\n",
        "resistant_0\t15\t0\t[40, 49]\t[40, 49]\n",
        "diabetes_3\t15\t0\t[50, 58]\t[50, 58]\n",
        "We_3\t16\t0\t[0, 2]\t[0, 2]\n",
        "next_0\t16\t0\t[3, 7]\t[3, 7]\n",
        "asked_0\t16\t0\t[8, 13]\t[8, 13]\n",
        "whether_3\t16\t0\t[14, 21]\t[14, 21]\n",
        "SirT1_17\t16\t0\t[22, 27]\t[22, 27]\n",
        "overexpression_3\t16\t0\t[28, 42]\t[28, 42]\n",
        "affects_0\t16\t0\t[43, 50]\t[43, 50]\n",
        "the_26\t16\t0\t[51, 54]\t[51, 54]\n",
        "susceptibility_0\t16\t0\t[55, 69]\t[55, 69]\n",
        "to_13\t16\t0\t[70, 72]\t[70, 72]\n",
        "insulin_8\t16\t0\t[73, 80]\t[73, 80]\n",
        "resistance_2\t16\t0\t[81, 91]\t[81, 91]\n",
        "and_26\t16\t0\t[92, 95]\t[92, 95]\n",
        "diabetes_4\t16\t0\t[96, 104]\t[96, 104]\n",
        "in_32\t16\t0\t[105, 107]\t[105, 107]\n",
        "environmental_0\t16\t0\t[108, 121]\t[108, 121]\n",
        "and_27\t16\t0\t[122, 125]\t[122, 125]\n",
        "genetic_0\t16\t0\t[126, 133]\t[126, 133]\n",
        "models_2\t16\t0\t[134, 140]\t[134, 140]\n",
        "of_29\t16\t0\t[141, 143]\t[141, 143]\n",
        "these_2\t16\t0\t[144, 149]\t[144, 149]\n",
        "conditions_1\t16\t0\t[150, 160]\t[150, 160]\n",
        "._37\t16\t0\t[160, 161]\t[160, 161]\n",
        "When_0\t16\t1\t[0, 4]\t[162, 166]\n",
        "SirBACO_13\t16\t1\t[5, 12]\t[167, 174]\n",
        "mice_17\t16\t1\t[13, 17]\t[175, 179]\n",
        "on_2\t16\t1\t[18, 20]\t[180, 182]\n",
        "an_1\t16\t1\t[21, 23]\t[183, 185]\n",
        "inbred_0\t16\t1\t[24, 30]\t[186, 192]\n",
        "C57BL_0\t16\t1\t[31, 36]\t[193, 198]\n",
        "/_2\t16\t1\t[36, 37]\t[198, 199]\n",
        "6J_0\t16\t1\t[37, 39]\t[199, 201]\n",
        "background_1\t16\t1\t[40, 50]\t[202, 212]\n",
        "were_6\t16\t1\t[51, 55]\t[213, 217]\n",
        "placed_0\t16\t1\t[56, 62]\t[218, 224]\n",
        "on_3\t16\t1\t[63, 65]\t[225, 227]\n",
        "a_8\t16\t1\t[66, 67]\t[228, 229]\n",
        "HFD_0\t16\t1\t[68, 71]\t[230, 233]\n",
        "or_6\t16\t1\t[72, 74]\t[234, 236]\n",
        "backcrossed_1\t16\t1\t[75, 86]\t[237, 248]\n",
        "onto_1\t16\t1\t[87, 91]\t[249, 253]\n",
        "db_2\t16\t1\t[92, 94]\t[254, 256]\n",
        "/_3\t16\t1\t[94, 95]\t[256, 257]\n",
        "db_3\t16\t1\t[95, 97]\t[257, 259]\n",
        "mice_18\t16\t1\t[98, 102]\t[260, 264]\n",
        ",_37\t16\t1\t[102, 103]\t[264, 265]\n",
        "they_0\t16\t1\t[104, 108]\t[266, 270]\n",
        "showed_1\t16\t1\t[109, 115]\t[271, 277]\n",
        "lower_2\t16\t1\t[116, 121]\t[278, 283]\n",
        "blood_1\t16\t1\t[122, 127]\t[284, 289]\n",
        "glucose_6\t16\t1\t[128, 135]\t[290, 297]\n",
        "and_28\t16\t1\t[136, 139]\t[298, 301]\n",
        "plasma_1\t16\t1\t[140, 146]\t[302, 308]\n",
        "insulin_9\t16\t1\t[147, 154]\t[309, 316]\n",
        "levels_5\t16\t1\t[155, 161]\t[317, 323]\n",
        "(_18\t16\t1\t[162, 163]\t[324, 325]\n",
        "Figures_0\t16\t1\t[163, 170]\t[325, 332]\n",
        "3A_0\t16\t1\t[171, 173]\t[333, 335]\n",
        "–_4\t16\t1\t[173, 174]\t[335, 336]\n",
        "3B_0\t16\t1\t[174, 176]\t[336, 338]\n",
        "and_29\t16\t1\t[177, 180]\t[339, 342]\n",
        "Table_2\t16\t1\t[181, 186]\t[343, 348]\n",
        "S1_3\t16\t1\t[187, 189]\t[349, 351]\n",
        ")_2\t16\t1\t[189, 190]\t[351, 352]\n",
        "and_30\t16\t1\t[191, 194]\t[353, 356]\n",
        "better_0\t16\t1\t[195, 201]\t[357, 363]\n",
        "glucose_7\t16\t1\t[202, 209]\t[364, 371]\n",
        "tolerance_2\t16\t1\t[210, 219]\t[372, 381]\n",
        "(_19\t16\t1\t[220, 221]\t[382, 383]\n",
        "Figure_11\t16\t1\t[221, 227]\t[383, 389]\n",
        "3C_0\t16\t1\t[228, 230]\t[390, 392]\n",
        ")_3\t16\t1\t[230, 231]\t[392, 393]\n",
        "than_2\t16\t1\t[232, 236]\t[394, 398]\n",
        "non_1\t16\t1\t[237, 240]\t[399, 402]\n",
        "-_27\t16\t1\t[240, 241]\t[402, 403]\n",
        "transgenic_4\t16\t1\t[241, 251]\t[403, 413]\n",
        "controls_0\t16\t1\t[252, 260]\t[414, 422]\n",
        ",_38\t16\t1\t[260, 261]\t[422, 423]\n",
        "despite_0\t16\t1\t[262, 269]\t[424, 431]\n",
        "similar_1\t16\t1\t[270, 277]\t[432, 439]\n",
        "body_5\t16\t1\t[278, 282]\t[440, 444]\n",
        "weight_3\t16\t1\t[283, 289]\t[445, 451]\n",
        "(_20\t16\t1\t[290, 291]\t[452, 453]\n",
        "Figure_12\t16\t1\t[291, 297]\t[453, 459]\n",
        "S3A_0\t16\t1\t[298, 301]\t[460, 463]\n",
        "),_6\t16\t1\t[301, 303]\t[463, 465]\n",
        "weight_4\t16\t1\t[304, 310]\t[466, 472]\n",
        "gain_9\t16\t1\t[311, 315]\t[473, 477]\n",
        "during_1\t16\t1\t[316, 322]\t[478, 484]\n",
        "the_27\t16\t1\t[323, 326]\t[485, 488]\n",
        "diet_1\t16\t1\t[327, 331]\t[489, 493]\n",
        "(_21\t16\t1\t[332, 333]\t[494, 495]\n",
        "Figure_13\t16\t1\t[333, 339]\t[495, 501]\n",
        "3D_0\t16\t1\t[340, 342]\t[502, 504]\n",
        "),_7\t16\t1\t[342, 344]\t[504, 506]\n",
        "and_31\t16\t1\t[345, 348]\t[507, 510]\n",
        "body_6\t16\t1\t[349, 353]\t[511, 515]\n",
        "fat_1\t16\t1\t[354, 357]\t[516, 519]\n",
        "content_0\t16\t1\t[358, 365]\t[520, 527]\n",
        "(_22\t16\t1\t[366, 367]\t[528, 529]\n",
        "Figure_14\t16\t1\t[367, 373]\t[529, 535]\n",
        "3E_0\t16\t1\t[374, 376]\t[536, 538]\n",
        ")._11\t16\t1\t[376, 378]\t[538, 540]\n",
        "We_4\t16\t2\t[0, 2]\t[541, 543]\n",
        "provisionally_0\t16\t2\t[3, 16]\t[544, 557]\n",
        "conclude_1\t16\t2\t[17, 25]\t[558, 566]\n",
        "that_8\t16\t2\t[26, 30]\t[567, 571]\n",
        "SirT1_18\t16\t2\t[31, 36]\t[572, 577]\n",
        "overexpression_4\t16\t2\t[37, 51]\t[578, 592]\n",
        "does_1\t16\t2\t[52, 56]\t[593, 597]\n",
        "not_4\t16\t2\t[57, 60]\t[598, 601]\n",
        "improve_0\t16\t2\t[61, 68]\t[602, 609]\n",
        "glucose_8\t16\t2\t[69, 76]\t[610, 617]\n",
        "tolerance_3\t16\t2\t[77, 86]\t[618, 627]\n",
        "per_0\t16\t2\t[87, 90]\t[628, 631]\n",
        "se_0\t16\t2\t[91, 93]\t[632, 634]\n",
        ",_39\t16\t2\t[93, 94]\t[634, 635]\n",
        "but_3\t16\t2\t[95, 98]\t[636, 639]\n",
        "prevents_1\t16\t2\t[99, 107]\t[640, 648]\n",
        "the_28\t16\t2\t[108, 111]\t[649, 652]\n",
        "adverse_0\t16\t2\t[112, 119]\t[653, 660]\n",
        "effects_3\t16\t2\t[120, 127]\t[661, 668]\n",
        "of_30\t16\t2\t[128, 130]\t[669, 671]\n",
        "obesity_1\t16\t2\t[131, 138]\t[672, 679]\n",
        "on_4\t16\t2\t[139, 141]\t[680, 682]\n",
        "glucose_9\t16\t2\t[142, 149]\t[683, 690]\n",
        "metabolism_4\t16\t2\t[150, 160]\t[691, 701]\n",
        "._38\t16\t2\t[160, 161]\t[701, 702]\n",
        "Catalytically_0\t17\t0\t[0, 13]\t[0, 13]\n",
        "inactive_0\t17\t0\t[14, 22]\t[14, 22]\n",
        "SirT1_19\t17\t0\t[23, 28]\t[23, 28]\n",
        "fails_0\t17\t0\t[29, 34]\t[29, 34]\n",
        "to_14\t17\t0\t[35, 37]\t[35, 37]\n",
        "protect_0\t17\t0\t[38, 45]\t[38, 45]\n",
        "aging_0\t17\t0\t[46, 51]\t[46, 51]\n",
        "mice_19\t17\t0\t[52, 56]\t[52, 56]\n",
        "from_2\t17\t0\t[57, 61]\t[57, 61]\n",
        "glucose_10\t17\t0\t[62, 69]\t[62, 69]\n",
        "intolerance_0\t17\t0\t[70, 81]\t[70, 81]\n",
        "To_5\t18\t0\t[0, 2]\t[0, 2]\n",
        "assess_0\t18\t0\t[3, 9]\t[3, 9]\n",
        "the_29\t18\t0\t[10, 13]\t[10, 13]\n",
        "specificity_1\t18\t0\t[14, 25]\t[14, 25]\n",
        "of_31\t18\t0\t[26, 28]\t[26, 28]\n",
        "these_3\t18\t0\t[29, 34]\t[29, 34]\n",
        "findings_2\t18\t0\t[35, 43]\t[35, 43]\n",
        ",_40\t18\t0\t[43, 44]\t[43, 44]\n",
        "we_7\t18\t0\t[45, 47]\t[45, 47]\n",
        "carried_2\t18\t0\t[48, 55]\t[48, 55]\n",
        "out_2\t18\t0\t[56, 59]\t[56, 59]\n",
        "glucose_11\t18\t0\t[60, 67]\t[60, 67]\n",
        "tolerance_4\t18\t0\t[68, 77]\t[68, 77]\n",
        "tests_1\t18\t0\t[78, 83]\t[78, 83]\n",
        "in_33\t18\t0\t[84, 86]\t[84, 86]\n",
        "aging_1\t18\t0\t[87, 92]\t[87, 92]\n",
        "transgenic_5\t18\t0\t[93, 103]\t[93, 103]\n",
        "mice_20\t18\t0\t[104, 108]\t[104, 108]\n",
        "expressing_0\t18\t0\t[109, 119]\t[109, 119]\n",
        "a_9\t18\t0\t[120, 121]\t[120, 121]\n",
        "catalytically_0\t18\t0\t[122, 135]\t[122, 135]\n",
        "inactive_1\t18\t0\t[136, 144]\t[136, 144]\n",
        "SirT1_20\t18\t0\t[145, 150]\t[145, 150]\n",
        "transgene_2\t18\t0\t[151, 160]\t[151, 160]\n",
        "(_23\t18\t0\t[161, 162]\t[161, 162]\n",
        "H355Y_0\t18\t0\t[162, 167]\t[162, 167]\n",
        ")._12\t18\t0\t[167, 169]\t[167, 169]\n",
        "Unlike_0\t18\t1\t[0, 6]\t[170, 176]\n",
        "SirBACO_14\t18\t1\t[7, 14]\t[177, 184]\n",
        "mice_21\t18\t1\t[15, 19]\t[185, 189]\n",
        ",_41\t18\t1\t[19, 20]\t[189, 190]\n",
        "SirT1H355Y_0\t18\t1\t[21, 31]\t[191, 201]\n",
        "transgenics_1\t18\t1\t[32, 43]\t[202, 213]\n",
        "showed_2\t18\t1\t[44, 50]\t[214, 220]\n",
        "no_2\t18\t1\t[51, 53]\t[221, 223]\n",
        "differences_2\t18\t1\t[54, 65]\t[224, 235]\n",
        "in_34\t18\t1\t[66, 68]\t[236, 238]\n",
        "glucose_12\t18\t1\t[69, 76]\t[239, 246]\n",
        "tolerance_5\t18\t1\t[77, 86]\t[247, 256]\n",
        "from_3\t18\t1\t[87, 91]\t[257, 261]\n",
        "control_3\t18\t1\t[92, 99]\t[262, 269]\n",
        "littermates_3\t18\t1\t[100, 111]\t[270, 281]\n",
        ",_42\t18\t1\t[111, 112]\t[281, 282]\n",
        "demonstrating_0\t18\t1\t[113, 126]\t[283, 296]\n",
        "that_9\t18\t1\t[127, 131]\t[297, 301]\n",
        "SirT1_21\t18\t1\t[132, 137]\t[302, 307]\n",
        "catalytic_0\t18\t1\t[138, 147]\t[308, 317]\n",
        "activity_5\t18\t1\t[148, 156]\t[318, 326]\n",
        "is_3\t18\t1\t[157, 159]\t[327, 329]\n",
        "required_1\t18\t1\t[160, 168]\t[330, 338]\n",
        "for_3\t18\t1\t[169, 172]\t[339, 342]\n",
        "its_1\t18\t1\t[173, 176]\t[343, 346]\n",
        "insulin_10\t18\t1\t[177, 184]\t[347, 354]\n",
        "-_28\t18\t1\t[184, 185]\t[354, 355]\n",
        "sensitizing_0\t18\t1\t[185, 196]\t[355, 366]\n",
        "effects_4\t18\t1\t[197, 204]\t[367, 374]\n",
        "(_24\t18\t1\t[205, 206]\t[375, 376]\n",
        "Figure_15\t18\t1\t[206, 212]\t[376, 382]\n",
        "S3B_0\t18\t1\t[213, 216]\t[383, 386]\n",
        ")._13\t18\t1\t[216, 218]\t[386, 388]\n",
        "Normal_0\t19\t0\t[0, 6]\t[0, 6]\n",
        "energy_7\t19\t0\t[7, 13]\t[7, 13]\n",
        "expenditure_4\t19\t0\t[14, 25]\t[14, 25]\n",
        "in_35\t19\t0\t[26, 28]\t[26, 28]\n",
        "diabetes_5\t19\t0\t[29, 37]\t[29, 37]\n",
        "-_29\t19\t0\t[37, 38]\t[37, 38]\n",
        "resistant_1\t19\t0\t[38, 47]\t[38, 47]\n",
        "SirBACO_15\t19\t0\t[48, 55]\t[48, 55]\n",
        "mice_22\t19\t0\t[56, 60]\t[56, 60]\n",
        "Based_0\t20\t0\t[0, 5]\t[0, 5]\n",
        "on_5\t20\t0\t[6, 8]\t[6, 8]\n",
        "the_30\t20\t0\t[9, 12]\t[9, 12]\n",
        "decrease_1\t20\t0\t[13, 21]\t[13, 21]\n",
        "of_32\t20\t0\t[22, 24]\t[22, 24]\n",
        "food_2\t20\t0\t[25, 29]\t[25, 29]\n",
        "intake_2\t20\t0\t[30, 36]\t[30, 36]\n",
        "and_32\t20\t0\t[37, 40]\t[37, 40]\n",
        "energy_8\t20\t0\t[41, 47]\t[41, 47]\n",
        "expenditure_5\t20\t0\t[48, 59]\t[48, 59]\n",
        "in_36\t20\t0\t[60, 62]\t[60, 62]\n",
        "SirBACO_16\t20\t0\t[63, 70]\t[63, 70]\n",
        "mice_23\t20\t0\t[71, 75]\t[71, 75]\n",
        ",_43\t20\t0\t[75, 76]\t[75, 76]\n",
        "we_8\t20\t0\t[77, 79]\t[77, 79]\n",
        "wanted_0\t20\t0\t[80, 86]\t[80, 86]\n",
        "to_15\t20\t0\t[87, 89]\t[87, 89]\n",
        "determine_1\t20\t0\t[90, 99]\t[90, 99]\n",
        "whether_4\t20\t0\t[100, 107]\t[100, 107]\n",
        "the_31\t20\t0\t[108, 111]\t[108, 111]\n",
        "transgenes_0\t20\t0\t[112, 122]\t[112, 122]\n",
        "’_1\t20\t0\t[123, 124]\t[123, 124]\n",
        "ability_0\t20\t0\t[125, 132]\t[125, 132]\n",
        "to_16\t20\t0\t[133, 135]\t[133, 135]\n",
        "improve_1\t20\t0\t[136, 143]\t[136, 143]\n",
        "glucose_13\t20\t0\t[144, 151]\t[144, 151]\n",
        "homeostasis_0\t20\t0\t[152, 163]\t[152, 163]\n",
        "in_37\t20\t0\t[164, 166]\t[164, 166]\n",
        "diabetes_6\t20\t0\t[167, 175]\t[167, 175]\n",
        "models_3\t20\t0\t[176, 182]\t[176, 182]\n",
        "could_0\t20\t0\t[183, 188]\t[183, 188]\n",
        "be_0\t20\t0\t[189, 191]\t[189, 191]\n",
        "explained_0\t20\t0\t[192, 201]\t[192, 201]\n",
        "by_4\t20\t0\t[202, 204]\t[202, 204]\n",
        "changes_2\t20\t0\t[205, 212]\t[205, 212]\n",
        "in_38\t20\t0\t[213, 215]\t[213, 215]\n",
        "physical_0\t20\t0\t[216, 224]\t[216, 224]\n",
        "activity_6\t20\t0\t[225, 233]\t[225, 233]\n",
        "or_7\t20\t0\t[234, 236]\t[234, 236]\n",
        "metabolic_10\t20\t0\t[237, 246]\t[237, 246]\n",
        "rate_3\t20\t0\t[247, 251]\t[247, 251]\n",
        "._39\t20\t0\t[251, 252]\t[251, 252]\n",
        "'''"
      ],
      "metadata": {
        "cellView": "form",
        "id": "hWfUmYIg71Ye"
      },
      "id": "hWfUmYIg71Ye",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7WkX4zzTJgg2"
      },
      "outputs": [],
      "source": [
        "#@title dataframe preparations\n",
        "with open('df_my_doc.tsv', 'w') as f:\n",
        "    f.write(tsv_string)\n",
        "\n",
        "df_my_doc = pd.read_csv(pathXX+'/df_my_doc.tsv', sep='\\t', index_col=0, dtype={'sentence_id': 'Int64'})\n",
        "\n",
        "df_my_doc['s_position'] = df_my_doc['s_position'].apply(lambda x: [int(el) for el in x.strip(\"[]\").split(\",\")])\n",
        "df_my_doc['p_position'] = df_my_doc['p_position'].apply(lambda x: [int(el) for el in x.strip(\"[]\").split(\",\")])\n"
      ],
      "id": "7WkX4zzTJgg2"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uSgMTpR3_nc"
      },
      "source": [
        "# <font color='lightgreen'>3. Construct GUI: 🔘🔘🔘👢 🔘👢 </font>"
      ],
      "id": "_uSgMTpR3_nc"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zIF0IhI6oJkH",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title make_word_button 👢 🏃\n",
        "\n",
        "def make_word_button(btn_description,id_token, initial_cond):\n",
        "    i = widgets.Button(\n",
        "        description=btn_description,\n",
        "        tooltip= id_token,\n",
        "        layout={\n",
        "            'width': 'auto',\n",
        "            'border': '1px solid black',\n",
        "            'margin' : '0 0 0 0',\n",
        "            \"description_width\": \"min-width\"\n",
        "        },\n",
        "        style={\"description_width\": \"min-width\"},\n",
        "    )\n",
        "    i.style.button_color = 'green'\n",
        "    i.add_class(\"narrow_button\")\n",
        "    return i"
      ],
      "id": "zIF0IhI6oJkH"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "pLhwivCbpc-f",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title word_token_to_d_sentences_idT\n",
        "\n",
        "def word_token_to_d_sentences_idT(id_passage):\n",
        "  dff = df_my_doc.loc[df_my_doc['passage'] == id_passage]\n",
        "  sentences_as_token = [[]]\n",
        "  prev_id = 0\n",
        "  for index, row in dff.iterrows():\n",
        "      if row['sentence_id'] == prev_id:\n",
        "          sentences_as_token[-1].append(index)\n",
        "      else:\n",
        "        prev_id +=1\n",
        "        sentences_as_token.append([index])\n",
        "\n",
        "  d_sentences_idT = {key: sentence for key, sentence in enumerate(sentences_as_token)}\n",
        "  return d_sentences_idT\n",
        "\n",
        "\n",
        "d_sentences_idT = word_token_to_d_sentences_idT(doc_param['passage_first'])"
      ],
      "id": "pLhwivCbpc-f"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vMv7kOXzZ-qd",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title GUI   . \\ \\ grid_template_areas\n",
        "grid_parameters= {'rows'   : 'auto 400px auto  auto',\n",
        "                  'columns': '3% 25% 25% 17% 25%',}\n",
        "\n",
        "layout_Basic= '''\n",
        "            \"bar header header header header\"\n",
        "            \"bar main main main sidebar\"\n",
        "            \"bar main main main sidebar\"\n",
        "            \"footer footer footer footer footer\"\n",
        "            '''\n",
        "layout_idE_info = '''\n",
        "            \"bar header header header header\"\n",
        "            \"bar main main main sidebar \"\n",
        "            \"bar main main main info \"\n",
        "            \"footer footer footer footer footer\"\n",
        "            '''\n",
        "\n",
        "\n",
        "\n",
        "def make_layout(l_coordinates):\n",
        "    layout= GUI.layout.grid_template_areas\n",
        "\n",
        "    matrix = layout.split('\"')\n",
        "    matrix = matrix[1::2]\n",
        "    matrix = [row.split(' ') for row in matrix]\n",
        "    for coord in l_coordinates:\n",
        "        matrix[coord[1]][coord[2]] = coord[0]\n",
        "    \n",
        "    '''reverse back'''\n",
        "    matrix = ['\"' +' '.join(row) +'\"' for row in matrix]\n",
        "    matrix = \"\\n            \" +'\\n            '.join(matrix)\n",
        "\n",
        "    return matrix"
      ],
      "id": "vMv7kOXzZ-qd"
    },
    {
      "cell_type": "code",
      "source": [
        "#@title NE ⏭ (utils)\n",
        "\n",
        "dict_NE_pos = {}\n",
        "\n",
        "\n",
        "# def update_word_btn_lst(dict_NE_pos_sorted, word_units):\n",
        "#     '''get the list of word_buttons.'''\n",
        "\n",
        "#     print(memory['NE_sentence_id'])\n",
        "#     # lst_children = d_displaying_p[id_passage][memory['NE_sentence_id']].children\n",
        "#     lst_children = lst_new2[memory['NE_sentence_id']].children\n",
        "    \n",
        "#     '''insert the word_unit'''\n",
        "#     lst_new2[memory['NE_sentence_id']].children = lst_children[:min(dict_NE_pos_sorted)] + word_units + lst_children[max(dict_NE_pos_sorted)+1:]\n",
        "    \n",
        "#     # '''insert the word_unit'''\n",
        "#     # d_displaying_p[id_passage][memory['NE_sentence_id']].children = lst_children[:min(dict_NE_pos_sorted)] + word_units + lst_children[max(dict_NE_pos_sorted)+1:]\n",
        "\n",
        "#     '''update the dictionary of 'sentences' '''\n",
        "#     lst_id_token = [box.children[0].id_token for box in word_units]\n",
        "#     sent = d_sentences_idT[int(memory['NE_sentence_id'])] \n",
        "#     s    = sent[:min(dict_NE_pos_sorted)] + lst_id_token + sent[max(dict_NE_pos_sorted)+1:]\n",
        "#     d_sentences_idT[int(memory['NE_sentence_id'])]  = s\n",
        "    \n",
        "\n",
        "def join_NE_buttons(dict_NE_pos_sorted, btns): #arg: btns even used?\n",
        "    '''\n",
        "    '''\n",
        "    lst_descriptions = [df_my_doc.at[id_token,'word_btn'].description for id_token in dict_NE_pos_sorted.values()]\n",
        "    descrip_NE = ' '.join(lst_descriptions)\n",
        "    lst_id_token     = [df_my_doc.at[id_token,'word_btn'].id_token    for id_token in dict_NE_pos_sorted.values()]\n",
        "    new_id_token = '_'.join(lst_id_token    )\n",
        "\n",
        "    b_joined   = Button( description=descrip_NE,\n",
        "                 layout={'width': 'auto',\n",
        "                         'height': '22px'},)\n",
        "    setattr(b_joined, \"id_token\", new_id_token)\n",
        "    b_joined.tooltip = b_joined.id_token\n",
        "\n",
        "\n",
        "    b_joined.on_click(run_word_button_action)\n",
        "\n",
        "\n",
        "    # button2, button2_NER, button3  =    \n",
        "    make_word_sub_unit(new_id_token, memory,memory['NE_sentence_id'])\n",
        "    button2, button2_NER, button3 = list(df_my_doc.loc[new_id_token,['button2', 'button2_NER','button3']])\n",
        "    btn_unit    = widgets.HBox([button2, button2_NER])\n",
        "    word_unit   = widgets.VBox([b_joined,btn_unit])\n",
        "    l_ids_token = list(dict_NE_pos_sorted.values())\n",
        "\n",
        "    s_span_min, s_span_max =  df_my_doc.at[lst_id_token[0],'s_position']\n",
        "    p_span_min, p_span_max =  df_my_doc.at[lst_id_token[0],'p_position']\n",
        "    \n",
        "    for id_token in l_ids_token:\n",
        "        s_span_mi, s_span_ma =  df_my_doc.at[lst_id_token[0],'s_position']\n",
        "        p_span_mi, p_span_ma =  df_my_doc.at[lst_id_token[0],'p_position']\n",
        "        s_span_min = min(s_span_min,s_span_mi)\n",
        "        p_span_min = min(p_span_min,p_span_mi)\n",
        "        s_span_max = max(s_span_max,s_span_ma)\n",
        "        p_span_max = max(p_span_max,p_span_ma)\n",
        "\n",
        "        df_my_doc.at[id_token,'button2_NER'].style.button_color = 'green'\n",
        "        df_my_doc.at[id_token,'merged_into_NE']  = new_id_token\n",
        "\n",
        "\n",
        "    \n",
        "    \n",
        "#     df_my_doc.at[new_id_token] = {'sentence_id' : df_my_doc.at[lst_id_token[0],'sentence_id'],\n",
        "#                                  'pre_merging' : l_ids_token,\n",
        "#                                  'word_btn'    : b_joined,\n",
        "#                                  'button2'     : button2,\n",
        "#                                  'button2_NER' : button2_NER,\n",
        "#                                  'passage'     : df_my_doc.at[l_ids_token[0],'passage'],\n",
        "#                                  's_position'  : [s_span_min, s_span_max],\n",
        "#                                  'p_position'  : [p_span_min, p_span_max]\n",
        "#                                  }\n",
        "#     '''I suspect that spacy and bioportal could also directly be transferred, they might not be able to improve.'''\n",
        "\n",
        "#     update_word_btn_lst(list(dict_NE_pos_sorted.keys()),(word_unit,))\n",
        "\n",
        "#     # for id_token in list(dict_NE_pos_sorted.values()):\n",
        "#     #     df_my_doc.at[id_token,'button2_NER'].style.button_color = 'green'\n",
        "#     #     df_my_doc.at[id_token,'merged_into_NE']  = new_id_token\n",
        "\n",
        "    \n",
        "#     run_append_to_CR_dict(b_joined)\n",
        "#     memory['auto_relation_head'] = None\n",
        "#     memory['auto_relation_tail'] = None\n",
        "#     memory['auto_relation_mode'] = 0\n",
        "\n",
        "#     return new_id_token"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PDiNOa-ypIL1"
      },
      "id": "PDiNOa-ypIL1",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title df_relations / f: auto_relation\n",
        "df_rel = pd.DataFrame(columns=['sentence_id','head','relation', 'tail','rel_type', 'rel_type_id', 'more_slots'])\n",
        "\n",
        "\n",
        "entity_already_grounded =[]\n",
        "# lst_entities = ['-_1','DPP','IV']\n",
        "bol = []\n",
        "\n",
        "import asyncio\n",
        "\n",
        "def wait_for_click(dict_NE_pos_sorted):\n",
        "    future = asyncio.Future()\n",
        "    \n",
        "    def on_button_clicked(btn):\n",
        "        memory['auto_relation_head'] = btn.id_token\n",
        "        future.set_result(btn.id_token)\n",
        "\n",
        "    for id_token in dict_NE_pos_sorted.values():\n",
        "        df_my_doc.loc[id_token,'word_btn'].style.button_color = 'orange'\n",
        "        df_my_doc.loc[id_token,'word_btn'].on_click(on_button_clicked)\n",
        "    \n",
        "    return future\n",
        "\n",
        "def automatic_click(bol):\n",
        "    if sum(bol)>0:\n",
        "      sub_token_id = df_t0.loc[memory['auto_relation_tail'],'pre_merging']\n",
        "      lst_id_token     = [df_my_doc.at[id_token,'word_btn'].id_token for id_token in sub_token_id]#dict_NE_pos_sorted.values()]\n",
        "      new_id_token = '_'.join(lst_id_token    )\n",
        "\n",
        "      # df = df_rel_loaded.loc[(df_rel_loaded['tail'] == memory['auto_relation_tail'])]\n",
        "      # what if more relations defined!!!!!!?????\n",
        "      return df.squeeze()['head']\n",
        "\n",
        "\n",
        "def auto_relation(entities,dict_NE_pos_sorted):\n",
        "    print('auto_relation started')    \n",
        "    entity_already_grounded.clear()\n",
        "    bol.clear()\n",
        "\n",
        "    # for id_token in entities:\n",
        "    #     if df_my_doc.at[id_token, 'grounded_to'] != None:\n",
        "    #         entity_already_grounded.append(df_my_doc.at[id_token, 'grounded_to'])\n",
        "    #     else: entity_already_grounded.append(False)\n",
        "\n",
        "    # [ bol.append(False) if math.isnan(float(x)) else bol.append(True) for x in entity_already_grounded]\n",
        "\n",
        "\n",
        "    # if sum(bol)==1:\n",
        "    #     memory['auto_relation_mode'] = 1 \n",
        "    #     memory['auto_relation_head'] = entities[bol.index(True)]\n",
        "    #     future = asyncio.Future()\n",
        "    #     future.set_result(entities[bol.index(True)])\n",
        "\n",
        "\n",
        "    # elif sum(bol)>1:\n",
        "    #     memory['auto_relation_mode'] = 1\n",
        "        \n",
        "    #     for j in [i for i, x in enumerate(bol) if x == True]:\n",
        "    #         df_my_doc.at[entities[j],'word_btn'].style.button_color = 'pink'\n",
        "        \n",
        "    #         future = wait_for_click(dict_NE_pos_sorted)\n",
        "    \n",
        "    # else:\n",
        "\n",
        "    future = asyncio.Future()\n",
        "    future.set_result(None)\n",
        "    return future\n",
        "\n",
        "    '''???????Now one of those buttons must be pressed.\n",
        "    The function continues as \"run_word_button_action\" with \n",
        "    ['auto_rel'auto_relation_mode'''\n",
        "\n",
        "# auto_relation(lst_entities)"
      ],
      "metadata": {
        "id": "-C9EuPmnsd_o"
      },
      "id": "-C9EuPmnsd_o",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title NE ⏭\n",
        "dictionary = dict()\n",
        "btns = []\n",
        "dict_NE_pos = {}\n",
        "\n",
        "'''\n",
        "async function which \n",
        "\n",
        "'''\n",
        "async def join_NE_and_auto_rel(dict_NE_pos_sorted):\n",
        "    memory['auto_relation_head'] = await auto_relation(list(dict_NE_pos_sorted.values()),\n",
        "                                                      dict_NE_pos_sorted)\n",
        "\n",
        "    memory['auto_relation_tail'] = join_NE_buttons(dict_NE_pos_sorted, btns)\n",
        "\n",
        "    dict_NE_pos.clear() # too slow\n",
        "    memory['NE_sentence_id']=None\n",
        "    \n",
        "\n",
        "\n",
        "def join_NE(btn, memory_NE, sentence_id):\n",
        "    # lst_word_units = d_displaying_p[id_passage][sentence_id].children\n",
        "    lst_word_units = lst_new2[sentence_id].children\n",
        "    sentence = [unit.children[0].id_token for unit in lst_word_units]\n",
        "\n",
        "    pos = sentence.index(btn.id_token)\n",
        "\n",
        "    assert  (memory['NE_sentence_id'] ==None) or  (memory['NE_sentence_id'] == df_my_doc.at[btn.id_token,'sentence_id']), \"ERROR: NE must be from the same sentence.\"\n",
        "    memory['NE_sentence_id'] = df_my_doc.at[btn.id_token,'sentence_id']\n",
        "    if memory['NE_join_last_click'] != btn.id_token:\n",
        "        \n",
        "        memory['NE_join_last_click'] = btn.id_token\n",
        "        dict_NE_pos[pos]= btn.id_token\n",
        "        btn.style.button_color = 'Red'\n",
        "    \n",
        "    else:\n",
        "        dict_NE_pos_sorted =dict(sorted(dict_NE_pos.items()))\n",
        "        # assert \n",
        "        assert sorted(dict_NE_pos_sorted.keys()) == list(range(min(dict_NE_pos_sorted.keys() ), \\\n",
        "                max(dict_NE_pos_sorted.keys() )+1)), \\\n",
        "                \"ERROR: NE must be consecutive tokens.{} is not equal to min-max:{}-{}\".\\\n",
        "                format(dict_NE_pos_sorted,\n",
        "                       min(dict_NE_pos_sorted.keys() ),\n",
        "                       max(dict_NE_pos_sorted.keys() ))\n",
        "\n",
        "        asyncio.ensure_future(join_NE_and_auto_rel(dict_NE_pos_sorted))\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "TcuILVLKpX2v"
      },
      "id": "TcuILVLKpX2v",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "RYthKNNpgTD9",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title NE ⏭\n",
        "def join_NE(btn, memory_NE, sentence_id):\n",
        "    # global lst_new2\n",
        "    lst_word_units = lst_new2[sentence_id].children\n",
        "    # print(lst_word_units)\n",
        "    sentence = [unit.children[0].id_token for unit in lst_word_units]\n",
        "    print(sentence)\n",
        "    pos = sentence.index(btn.id_token)\n",
        "\n",
        "    assert  (memory['NE_sentence_id'] ==None) or  (memory['NE_sentence_id'] == df_my_doc.at[btn.id_token,'sentence_id']), \"ERROR: NE must be from the same sentence.\"\n",
        "    memory['NE_sentence_id'] = df_my_doc.at[btn.id_token,'sentence_id']\n",
        "    if memory['NE_join_last_click'] != btn.id_token:\n",
        "        \n",
        "        memory['NE_join_last_click'] = btn.id_token\n",
        "        dict_NE_pos[pos]= btn.id_token\n",
        "        btn.style.button_color = 'Red'\n",
        "    \n",
        "    else:\n",
        "        dict_NE_pos_sorted =dict(sorted(dict_NE_pos.items()))\n",
        "        assert sorted(dict_NE_pos_sorted.keys()) == list(range(min(dict_NE_pos_sorted.keys() ), \\\n",
        "                max(dict_NE_pos_sorted.keys() )+1)), \\\n",
        "                \"ERROR: NE must be consecutive tokens.{} is not equal to min-max:{}-{}\".\\\n",
        "                format(dict_NE_pos_sorted,\n",
        "                       min(dict_NE_pos_sorted.keys() ),\n",
        "                       max(dict_NE_pos_sorted.keys() ))\n",
        "\n",
        "        asyncio.ensure_future(join_NE_and_auto_rel(dict_NE_pos_sorted))\n"
      ],
      "id": "RYthKNNpgTD9"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "gTP1J7GKaBp_"
      },
      "outputs": [],
      "source": [
        "df_restore_point = df_my_doc.copy()"
      ],
      "id": "gTP1J7GKaBp_"
    },
    {
      "cell_type": "code",
      "source": [
        "#@title close()\n",
        "def close_word_btns(sentence_idT):\n",
        "  for sentence_id, sentence_idT in sorted(d_sentences_idT.items()):\n",
        "    for i, id_token in enumerate(sentence_idT):\n",
        "      if isinstance(df_my_doc.loc[id_token,'button2'], widgets.IntText):\n",
        "        # print('yes closing')\n",
        "        # display(HBox([df_my_doc.loc[id_token,'button2']]))\n",
        "        df_my_doc.loc[id_token,'button2'].close()\n",
        "        # display(HBox([df_my_doc.loc[id_token,'button2']]))\n",
        "        # print(df_my_doc.loc[id_token,'button2'])\n",
        "        # print(type(df_my_doc.loc[id_token,'button2']))\n",
        "        df_my_doc.loc[id_token,'button2'] = np.NAN\n",
        "\n",
        "      if isinstance(df_my_doc.loc[id_token,'button2_NER'], Button):\n",
        "        df_my_doc.loc[id_token,'button2_NER'].close()\n",
        "        df_my_doc.loc[id_token,'button2_NER'] = np.NAN\n",
        "      \n",
        "      if isinstance(df_my_doc.loc[id_token,'button3'], Button):\n",
        "        df_my_doc.loc[id_token,'button3'].close()\n",
        "        df_my_doc.loc[id_token,'button3'] = np.NAN"
      ],
      "metadata": {
        "id": "t5UaavB9tpXi",
        "cellView": "form"
      },
      "id": "t5UaavB9tpXi",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9PQKiMijyMq"
      },
      "source": [
        "# <font color='brown'>4 Hbox for passage"
      ],
      "id": "K9PQKiMijyMq"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "SdJ6mhEWjxey"
      },
      "outputs": [],
      "source": [
        "#@title  Hbox for passage\n",
        "def show_passage(btn):\n",
        "    global df_my_doc, d_sentences_idT\n",
        "\n",
        "    id_passage = int(btn.description)\n",
        "    l_idP_displayed = memory['l_idP_displayed']\n",
        "    print('fresh ' + str(id_passage))\n",
        "\n",
        "    # close_word_btns(d_sentences_idT)\n",
        "    mainB.clear_output()\n",
        "    with mainB:\n",
        "      display(main)\n",
        "\n",
        "    d_sentences_idT = extract_passage_tokens(id_passage)\n",
        "    make_word_btn_column(id_passage, sentence_from_to,d_sentences_idT)\n",
        "    pprint('make_word_btn_column() complete')\n",
        "    abc                       = word_btns_to_box(df_my_doc, id_passage) \n",
        "\n",
        "    main.children = abc\n",
        "    memory['l_idP_displayed'] = [id_passage]\n",
        "\n",
        "    if btn.style.button_color != 'red':\n",
        "      d_btn_passage[int(btn.description)].style.button_color = 'red'\n",
        "    else:\n",
        "      btn.style.button_color = 'gray'\n",
        "    print('finished')"
      ],
      "id": "SdJ6mhEWjxey"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0,
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          ]
        },
        "id": "WxFn_TuTBZ2R",
        "outputId": "36000193-1275-48a5-dda4-9cb894d33aed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(Button(description='0', layout=Layout(max_width='35px', min_width='35px'), style=ButtonStyle(),…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9635b0103bbc415dbc9153d44306f4d1"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "#@title Hbox for passage (make box)\n",
        "d_btn_passage = {}\n",
        "\n",
        "def box_of_passage_selection():\n",
        "  list_btn_passage = []\n",
        "  for i in range(len(my_doc['passages'])):\n",
        "    if my_doc['passages'][i]['infons']['section_type'] not in ['SUPPL', 'REF', 'FIG']:\n",
        "      d_btn_passage[i] = Button(description=str(i),\n",
        "                          tooltip = my_doc['passages'][i]['text'],\n",
        "                           layout = {\n",
        "                                'min_width':'35px',\n",
        "                                'max_width':'35px',\n",
        "                           }\n",
        "                          )\n",
        "      d_btn_passage[i].on_click(functools.partial(show_passage,))\n",
        "      list_btn_passage.append(d_btn_passage[i])\n",
        "  \n",
        "  x = HBox(list_btn_passage,layout={\n",
        "      'width':'auto'\n",
        "  },\n",
        "  overflow='scroll')\n",
        "  return x\n",
        "  \n",
        "box_of_passage_selection()"
      ],
      "id": "WxFn_TuTBZ2R"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ve9GWf6FKlY"
      },
      "source": [
        "# 5.  the GUI    <mark>. </mark>   </code>. <mark> . </mark> . <mark> . </mark>"
      ],
      "id": "4Ve9GWf6FKlY"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ip8IfDgcMyac"
      },
      "source": [
        "## <font color=\"magenta\"> 5.2 GUI restart"
      ],
      "id": "Ip8IfDgcMyac"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJmi3Gnan8yw"
      },
      "source": [
        "### util"
      ],
      "id": "ZJmi3Gnan8yw"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-gnFlzUIjIIV"
      },
      "outputs": [],
      "source": [
        "#@title def make_word_sub_unit\n",
        "def update_df_my_doc_idE2(widget,id_token):\n",
        "    df_my_doc.loc[id_token, 'grounded_to'] = widget['new']\n",
        "\n",
        "def make_word_sub_unit(new_id_token, memory, sentence_id): \n",
        "\n",
        "    button2 = widgets.IntText(value=0, \n",
        "          layout = {'width':'40px',\n",
        "                    'min_width':'20px',\n",
        "                    'hight': \"15px\"\n",
        "                    }\n",
        "    )\n",
        "    setattr(button2, \"id_token\", new_id_token)\n",
        "    df_my_doc.at[new_id_token,'button2'] = button2\n",
        "    button2.observe(functools.partial(update_df_my_doc_idE2, id_token=new_id_token),names='value')\n",
        "\n",
        "\n",
        "    button2_NER =  Button(\n",
        "        tooltip=\"press\",\n",
        "        layout={'width': '5px', 'height': '15px',\n",
        "        },\n",
        "        )\n",
        "    button2_NER.style.button_color = 'DarkBlue'\n",
        "    setattr(button2_NER, \"id_token\", new_id_token)\n",
        "    button2_NER.on_click(functools.partial(join_NE, memory_NE=memory, sentence_id=sentence_id))\n",
        "    df_my_doc.at[new_id_token,'button2_NER'] = button2_NER\n",
        "\n",
        "\n",
        "    button3 =  Button(\n",
        "        layout={'width': '5px', 'height': '15px',\n",
        "        },\n",
        "        )\n",
        "    button3.style.button_color = 'Blue'\n",
        "    setattr(button3, \"id_token\", new_id_token)\n",
        "    df_my_doc.at[new_id_token,'button3'] = button3\n"
      ],
      "id": "-gnFlzUIjIIV"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "D798VlFPPvP_"
      },
      "outputs": [],
      "source": [
        "#@title reset variables\n",
        "\n",
        "memory.update({'NE_join_last_click' :'', })\n"
      ],
      "id": "D798VlFPPvP_"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "dXgEuFdpINnS"
      },
      "outputs": [],
      "source": [
        "#@title Text-to-button-DataFrame 👢 🏃\n",
        "\n",
        "'''by going over every word token, making a pd.DataFrame to store all information'''\n",
        "def make_word_btn_column(id_passage, sentence_from_to,d_sentences_idT):\n",
        "  for id_sent, sentence in d_sentences_idT.items():\n",
        "    for id_token in sentence:\n",
        "      word = id_token.rpartition('_')[0]\n",
        "\n",
        "      df_my_doc.loc[id_token,'word_btn'] = make_word_button(word,id_token,[initial_color])\n",
        "      df_my_doc.at[id_token,'word_btn'].on_click(functools.partial(run_word_button_action))\n",
        "      df_my_doc.at[id_token,'word_btn'].add_class(\"narrow_button\")\n",
        "      setattr(df_my_doc.at[id_token,'word_btn'], \"id_token\", id_token)\n",
        "\n",
        "\n",
        "def extract_passage_tokens(id_passage):\n",
        "    # df_my_doc.loc[:,'merged_into_NE'] = df_my_doc['merged_into_NE'].astype(str)\n",
        "  d_sentences_idT = word_token_to_d_sentences_idT(id_passage)\n",
        "    \n",
        "  return d_sentences_idT\n",
        "\n",
        "id_passage = doc_param['passage_first']\n",
        "memory['l_idP_displayed'] = [id_passage]\n",
        "\n",
        "d_sentences_idT = extract_passage_tokens(id_passage)\n",
        "\n",
        "make_word_btn_column(id_passage, sentence_from_to,d_sentences_idT)"
      ],
      "id": "dXgEuFdpINnS"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ofcrAW0GWQwm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "981e02b1-a996-400a-fb27-380792872167",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0  is last\n"
          ]
        }
      ],
      "source": [
        "#@title Add the Subbutton (2ndary )\n",
        "\n",
        "\n",
        "def word_btns_to_box(df_my_doc, id_passage):\n",
        "    lst_per_sentence = []\n",
        "    lst_new = []\n",
        "    global lst_new2\n",
        "\n",
        "    id_of_sentence = df_my_doc.iloc[0,df_my_doc.columns.get_loc('sentence_id')]\n",
        "\n",
        "    df_token = df_my_doc.loc[df_my_doc['passage'] == id_passage]\n",
        "\n",
        "    for sentence_id, sentence_idT in sorted(d_sentences_idT.items()):\n",
        "        for i, id_token in enumerate(sentence_idT):\n",
        "            row = df_my_doc.loc[id_token,:]\n",
        "            make_word_sub_unit(id_token, memory, row['sentence_id'])\n",
        "            button2, button2_NER, button3 = list(df_my_doc.loc[id_token,['button2', 'button2_NER','button3']])\n",
        "            \n",
        "            btn_unit_A =VBox([button2_NER]) # button3])\n",
        "            # btn_unit  = HBox([btn_unit_A])  # \n",
        "            btn_unit  = HBox([button2,btn_unit_A])\n",
        "            word_unit         = VBox([row['word_btn'],btn_unit])\n",
        "            \n",
        "            if row['sentence_id'] != id_of_sentence :\n",
        "                print(row['sentence_id'])\n",
        "                lst_new.append(lst_per_sentence)\n",
        "                lst_per_sentence = []\n",
        "                id_of_sentence = row['sentence_id']\n",
        "\n",
        "            \n",
        "            lst_per_sentence.append(word_unit)\n",
        "    print(str(row['sentence_id']) + '  is last')\n",
        "    lst_new.append(lst_per_sentence)\n",
        "    lst_new2 = []#\n",
        "\n",
        "    for i in range(len(lst_new)):\n",
        "      lst_new2.append(\n",
        "          HBox(lst_new[i],layout={'flex_flow':'row wrap','width':'auto','border': '1px solid black'})\n",
        "      )\n",
        "      lst_new2[i].add_class('sentence_box')\n",
        "    return lst_new2\n",
        "\n",
        "\n",
        "lst_new2 = word_btns_to_box(df_my_doc, id_passage)"
      ],
      "id": "ofcrAW0GWQwm"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ezEoWTG6boq7",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title <mark> the GUI  _     _  \n",
        "\n",
        "\n",
        "main    = VBox(lst_new2, layout={'flex_flow':'row wrap',\n",
        "                                 'width':'auto', 'grid_area':'main',\n",
        "                                 'max_height':'600px'},overflow='scroll')\n",
        "mainB = widgets.Output(layout={'flex_flow':'row wrap',\n",
        "                                 'width':'auto', 'grid_area':'main',})\n",
        "\n",
        "with mainB:\n",
        "    display(main)\n",
        "\n",
        "hbox_passages = box_of_passage_selection()\n",
        "header = VBox([ hbox_passages],\n",
        "              layout={'width'     :'auto',\n",
        "                      'grid_area' :'header'})\n",
        "\n",
        "GUI = GridBox(children=[header, mainB,\n",
        "                        ],\n",
        "        layout={   'width'         : '100%',\n",
        "            'grid_template_rows'   : grid_parameters['rows'],\n",
        "            'grid_template_columns': grid_parameters['columns'],\n",
        "            'grid_template_areas'  : layout_Basic}\n",
        "       )"
      ],
      "id": "ezEoWTG6boq7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8rAF-z1M6ib"
      },
      "source": [
        "## 5.3 the GUI"
      ],
      "id": "D8rAF-z1M6ib"
    },
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              "'I added a print statement to follow the executions. For each sentence, it prints its index. However, for some long sections, indexes are mistakenly skipped '"
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            "fresh 1\n",
            "'make_word_btn_column() complete'\n",
            "0  is last\n",
            "finished\n",
            "fresh 3\n",
            "'make_word_btn_column() complete'\n",
            "0  is last\n",
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            "fresh 2\n",
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            "1\n",
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        "#@title display: ▶\n",
        "display(HTML(\"<style>.sentence_box {  border: 1px solid white;}</style>\"))\n",
        "display(HTML(\"<style>.red_label { color:red }</style>\"))\n",
        "display(HTML(\"<style>.narrow_button { padding: 0px 2px; height:18px;border: 1px solid black;vertical-align:middle  }</style>\"))\n",
        "# display(HTML(\"<style>.narrow_button { padding: 0px 2px ; height:18px; text-align: center;display:flex;justify-content:center}</style>\"))\n",
        "display(HTML(\"<style>.number_box { padding: 0px 0px;font-size:30px; }</style>\"))\n",
        "display(HTML(\"<style>.narrow_button {height:22px;vertical-align: top;text-align:center;line-height: 1;  }</style>\"))\n",
        "display(GUI)\n",
        "'''I added a print statement to follow the executions. For each sentence, it prints its index. However, for some long sections, indexes are mistakenly skipped '''"
      ],
      "id": "drqn-LqICYrl"
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            "tooltip": "To investigate differences in basal metabolic rate, we compared the energy expenditure required for locomotor activity. SirBACO mice had decreased resting energy expenditure, but no differences at higher activity levels (Figure S1). Body weight and composition were similar in SirBACO mice and non-transgenic littermates (Figure 2F). Food intake decreased by 15 ± 1.3% in SirBACO mice (Figure 2G), consistent with the preservation of normal body weight, in the face of decreased metabolic rate and activity."
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            "tooltip": "In response to 24-hr food deprivation SirBACO mice showed a normal drop in RQ, reflecting increased fat oxidation (not shown). These data indicate that, although hypometabolic, SirBACO mice have normal rates of fatty acid oxidation. They also exhibited lower body temperature than control littermates under fed and fasted conditions (Figure 2H), but did not display changes in blood pressure or heart rate (Figure S2), suggesting that the effect on body temperature is secondary to decreased locomotion."
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