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kejian

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SciRIFFEval-4096-0214-v2

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[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Before the resin bond endodontic post-cementation, various solutions are used to improve bond strength of these posts to root dentine. The purpose of this study was to evaluate the effect of the different pre-treatments (ethanol, ethylene de amine tetra acetic acid [EDTA] and EDTA + ethanol) on the bond strength of adhesively lutted tooth colored fiber post.\n\nABSTRACT.MATERIALS AND METHODS:: Forty-eight human anterior teeth (incisor and canine) were selected in this experimental study. The species were endodontically treated, and the post-space was prepared. The species were randomly divided into four groups of 12 teeth in each and were treated as follows: (i) 0.1 M EDTA, (ii) 99.6% ethanol, (iii) 0.1 M EDTA followed by 99.6% ethanol and (iv) without any treatment (control). A push-out test was carried out after 24 h. Each tooth was cut into three 1 mm-thick sections. Fracture type was assessed using stereomicroscope. The data were analyzed by one way-ANOVA followed by Scheffe post-hoc test at P = 0.05.\n\nABSTRACT.RESULTS:: Almost, in EDTA and (EDTA + ethanol) every sample failed at the interface between post and lutting cement. Predominant failure mode in ethanol and control group was mixed failure. The surface treatment methods did not have any significant effect (P > 0.05) on any group but EDTA group had the highest bond strength.\n\nABSTRACT.CONCLUSION:: Surface treatment with (ethanol, EDTA, and EDTA + ethanol) does not significantly appear to be effective in improving the bond strength of fiber posts into root dentine. The highest bond strength was observed in EDTA group and the lowest in the control group.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Ethylene di-amine tetra-acetic acid\", \"Ethanol\", \"Bond strength\", \"no significant difference\", \"The surface treatment methods did not have any significant effect (P \\u003e 0.05) on any group but EDTA group had the highest bond strength.\"], [\"Ethylene di-amine tetra-acetic acid\", \"Ethanol\", \"Bond strength\", \"no significant difference\", \"Surface treatment with (ethanol, EDTA, and EDTA + ethanol) does not significantly appear to be effective in improving the bond strength of fiber posts into root dentine.\"], [\"Ethylene di-amine tetra-acetic acid\", \"Control\", \"Bond strength\", \"no significant difference\", \"The surface treatment methods did not have any significant effect (P \\u003e 0.05) on any group but EDTA group had the highest bond strength.\"], [\"Ethylene di-amine tetra-acetic acid + Ethanol\", \"Ethanol\", \"Bond strength\", \"no significant difference\", \"The surface treatment methods did not have any significant effect (P \\u003e 0.05) on any group but EDTA group had the highest bond strength.\"]]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nVitamin D is an important factor in the relationship between calcium and parathyroid hormone.\n\nResearch Abstract:\nCONTEXT Adequate vitamin D status for optimum bone health has received increased recognition in recent years; however, the ideal intake is not known.\nSerum 25-hydroxyvitamin D is the generally accepted indicator of vitamin D status, but no universal reference level has been reached.\nOBJECTIVE To investigate the relative importance of high calcium intake and serum 25-hydroxyvitamin D for calcium homeostasis, as determined by serum intact parathyroid hormone (PTH).\nDESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of 2310 healthy Icelandic adults who were divided equally into 3 age groups (30-45 years, 50-65 years, or 70-85 years) and recruited from February 2001 to January 2003.\nThey were administered a semi-quantitative food frequency questionnaire, which assessed vitamin D and calcium intake.\nParticipants were further divided into groups according to calcium intake (<800 mg/d, 800-1200 mg/d, and >1200 mg/d) and serum 25-hydroxyvitamin D level (<10 ng/mL, 10-18 ng/mL, and >18 ng/mL).\nMAIN OUTCOME MEASURE Serum intact PTH as determined by calcium intake and vitamin D. RESULTS A total of 944 healthy participants completed all parts of the study.\nAfter adjusting for relevant factors, serum PTH was lowest in the group with a serum 25-hydroxyvitamin D level of more than 18 ng/mL but highest in the group with a serum 25-hydroxyvitamin D level of less than 10 ng/mL. At the low serum 25-hydroxyvitamin D level (<10 ng/mL), calcium intake of less than 800 mg/d vs more than 1200 mg/d was significantly associated with higher serum PTH (P = .04); and at a calcium intake of more than 1200 mg/d, there was a significant difference between the lowest and highest vitamin D groups (P = .04).\nCONCLUSIONS As long as vitamin D status is ensured, calcium intake levels of more than 800 mg/d may be unnecessary for maintaining calcium metabolism.\nVitamin D supplements are necessary for adequate vitamin D status in northern climates.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"After adjusting for relevant factors, serum PTH was lowest in the group with a serum 25-hydroxyvitamin D level of more than 18 ng/mL but highest in the group with a serum 25-hydroxyvitamin D level of less than 10 ng/mL. At the low serum 25-hydroxyvitamin D level (<10 ng/mL), calcium intake of less than 800 mg/d vs more than 1200 mg/d was significantly associated with higher serum PTH (P = .04); and at a calcium intake of more than 1200 mg/d, there was a significant difference between the lowest and highest vitamin D groups (P = .04).\", \"CONCLUSIONS As long as vitamin D status is ensured, calcium intake levels of more than 800 mg/d may be unnecessary for maintaining calcium metabolism.\"]}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nEctopic expression of Sall4, Nanog, Esrrb, and Lin28 generates induced pluripotent stem cells from mouse embryonic fibroblasts more efficiently than other factor combinations.\n\nResearch Abstract:\nInduced pluripotent stem cells (iPSCs) are commonly generated by transduction of Oct4, Sox2, Klf4, and Myc (OSKM) into cells.\nAlthough iPSCs are pluripotent, they frequently exhibit high variation in terms of quality, as measured in mice by chimera contribution and tetraploid complementation.\nReliably high-quality iPSCs will be needed for future therapeutic applications.\nHere, we show that one major determinant of iPSC quality is the combination of reprogramming factors used.\nBased on tetraploid complementation, we found that ectopic expression of Sall4, Nanog, Esrrb, and Lin28 (SNEL) in mouse embryonic fibroblasts (MEFs) generated high-quality iPSCs more efficiently than other combinations of factors including OSKM.\nAlthough differentially methylated regions, transcript number of master regulators, establishment of specific superenhancers, and global aneuploidy were comparable between high- and low-quality lines, aberrant gene expression, trisomy of chromosome 8, and abnormal H2A.X deposition were distinguishing features that could potentially also be applicable to human.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Based on tetraploid complementation, we found that ectopic expression of Sall4, Nanog, Esrrb, and Lin28 (SNEL) in mouse embryonic fibroblasts (MEFs) generated high-quality iPSCs more efficiently than other combinations of factors including OSKM.\"]}", "role": "assistant" } ]

[ { "content": "| Average distance (A) | Y | La | Gd |\n| RE3+-Eu3+ | 4.25 | 4.41 | 4.27 |\n| Eu3+-Eu3+ | 11.03 | 11.44 | 11.09 |\n\nCaption: The average distances of RE3+-Eu3+ and Eu3+-Eu3+ ion pairs.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"4.25\", \"type\": \"Other\"}\n{\"value\": \"4.41\", \"type\": \"Other\"}\n{\"value\": \"4.27\", \"type\": \"Other\"}\n{\"value\": \"11.03\", \"type\": \"Other\"}\n{\"value\": \"11.44\", \"type\": \"Other\"}\n{\"value\": \"11.09\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nBiochemical effects of Solidago virgaurea extract on experimental cardiotoxicity. Cardiovascular diseases (CVDs) are the major health problem of advanced as well as developing countries of the world. The aim of the present study was to investigate the protective effect of the Solidago virgaurea extract on isoproterenol-induced cardiotoxicity in rats. The subcutaneous injection of isoproterenol (30 mg/kg) into rats twice at an interval of 24 h, for two consecutive days, led to a significant increase in serum lactate dehydrogenase, creatine phosphokinase, alanine transaminase, aspartate transaminase, and angiotensin-converting enzyme activities, total cholesterol, triglycerides, free serum fatty acid, cardiac tissue malondialdehyde (MDA), and nitric oxide levels and a significant decrease in levels of glutathione and superoxide dismutase in cardiac tissue as compared to the normal control group (P < 0.05). Pretreatment with S. virgaurea extract for 5 weeks at a dose of 250 mg/kg followed by isoproterenol injection significantly prevented the observed alterations. Captopril (50 mg/kg/day, given orally), an inhibitor of angiotensin-converting enzyme used as a standard cardioprotective drug, was used as a positive control in this study. The data of the present study suggest that S. virgaurea extract exerts its protective effect by decreasing MDA level and increasing the antioxidant status in isoproterenol-treated rats. The study emphasizes the beneficial action of S. virgaurea extract as a cardioprotective agent.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"cardioprotective agent\", \"Captopril\", \"nitric oxide\", \"triglycerides\", \"cardioprotective drug\", \"Solidago virgaurea extract\", \"fatty acid\", \"MDA\", \"cholesterol\", \"malondialdehyde\", \"isoproterenol\", \"S. virgaurea extract\", \"glutathione\"], \"Disease\": [\"Cardiovascular diseases\", \"cardiotoxicity\", \"CVDs\"], \"Gene\": [\"lactate dehydrogenase\", \"angiotensin-converting enzyme\", \"aspartate transaminase\", \"superoxide dismutase\", \"alanine transaminase\", \"creatine phosphokinase\"], \"Species\": [\"rats\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nThe method proposed by <cite>Yuan et al. (2016)</cite> performs WSD by annotating each lemma in a text with one WordNet synset that is associated with its meaning.", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nSecond, we find that WMM2Seq outperforms <cite>Mem2Seq</cite>, which uses a unified memory to store dialog history and KB information.", "role": "user" }, { "content": "[\"Differences\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nX-linked adrenoleukodystrophy (ALD): a novel mutation of the ALD gene in 6 members of a family presenting with 5 different phenotypes. Fragments of the adrenoleukodystrophy (ALD) cDNA from a patient with adolescent ALD were amplified by polymerase chain reaction and subcloned. Bidirectional sequencing of the entire coding ALD gene disclosed a cytosine to guanine transversion at nucleotide 1451 in exon five, resulting in substitution of proline 484 by arginine. Five of nine siblings of the patient, comprising two cerebral ALD, one adrenomyeloneuropathy, one Addison only as well as the symptomatic mother (all accumulating very long chain fatty acids) carried this mutation, which was not found in the unaffected persons, in five unrelated ALD patients, and in twenty controls. We propose that this missense mutation generated the disease per se as well as the metabolic defect; the different phenotypes, however, must have originated by means of additional pathogenetic factors..", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"long chain fatty acids\"], \"Disease\": [\"X-linked adrenoleukodystrophy\", \"adrenomyeloneuropathy\", \"Addison\", \"adrenoleukodystrophy\", \"ALD\"], \"Gene\": [\"ALD\"], \"Species\": [\"patient\", \"patients\"], \"Variant\": [\"proline 484 by arginine\", \"cytosine to guanine transversion at nucleotide 1451\"]}", "role": "assistant" } ]

[ { "content": "| Sample no. | N 4 | Optical absorption edge l 0 (nm) |\n| 1 | 0.323+-0.004 | 351 |\n| 2 | 0.358+-0.004 | 362 |\n| 3 | 0.409+-0.004 | 366 |\n| 4 | 0.427+-0.004 | 366 |\n| 5 | 0.444+-0.004 | 366 |\n| 6 | 0.454+-0.005 | 393 |\n| 7 | 0.451+-0.007 | - |\n| 8 | 0.460+-0.003 | - |\n| 9 | 0.447+-0.007 | - |\n| 10 | 0.454+-0.005 | 411 |\n| 11 | 0.429+-0.007 | - |\n| 12 | 0.419+-0.007 | 409 |\n| 13 | 0.388+-0.007 | 424 |\n| 14 | 0.314+-0.006 | 444 |\n\nCaption: Fraction of tetrahedral boron (N 4) and optical absorption edge, l 0 in bismuth borate glasses\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"1\", \"type\": \"Other\"}\n{\"value\": \"0.323\", \"type\": \"Other\"}\n{\"value\": \"351\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"0.358\", \"type\": \"Other\"}\n{\"value\": \"362\", \"type\": \"Other\"}\n{\"value\": \"3\", \"type\": \"Other\"}\n{\"value\": \"0.409\", \"type\": \"Other\"}\n{\"value\": \"366\", \"type\": \"Other\"}\n{\"value\": \"4\", \"type\": \"Other\"}\n{\"value\": \"0.427\", \"type\": \"Other\"}\n{\"value\": \"366\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"0.444\", \"type\": \"Other\"}\n{\"value\": \"366\", \"type\": \"Other\"}\n{\"value\": \"6\", \"type\": \"Other\"}\n{\"value\": \"0.454\", \"type\": \"Other\"}\n{\"value\": \"393\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Other\"}\n{\"value\": \"0.451\", \"type\": \"Other\"}\n{\"value\": \"8\", \"type\": \"Other\"}\n{\"value\": \"0.460\", \"type\": \"Other\"}\n{\"value\": \"9\", \"type\": \"Other\"}\n{\"value\": \"0.447\", \"type\": \"Other\"}\n{\"value\": \"10\", \"type\": \"Other\"}\n{\"value\": \"0.454\", \"type\": \"Other\"}\n{\"value\": \"411\", \"type\": \"Other\"}\n{\"value\": \"11\", \"type\": \"Other\"}\n{\"value\": \"0.429\", \"type\": \"Other\"}\n{\"value\": \"12\", \"type\": \"Other\"}\n{\"value\": \"0.419\", \"type\": \"Other\"}\n{\"value\": \"409\", \"type\": \"Other\"}\n{\"value\": \"13\", \"type\": \"Other\"}\n{\"value\": \"0.388\", \"type\": \"Other\"}\n{\"value\": \"424\", \"type\": \"Other\"}\n{\"value\": \"14\", \"type\": \"Other\"}\n{\"value\": \"0.314\", \"type\": \"Other\"}\n{\"value\": \"444\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWe will evaluate the parser on both the full PTB (Marcus et al. 1993 ) and on a senseannotated subset of the Brown Corpus portion of PTB, in order to investigate the upper bound performance of the models given gold-standard sense information, as in <cite>Agirre et al. (2008)</cite> .", "role": "user" }, { "content": "[\"Similarities\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nSelf-attention networks (SANs) (Parikh et al., 2016; Lin et al., 2017) have shown promising empirical results in various natural language processing (NLP) tasks, such as machine translation <cite>(Vaswani et al., 2017)</cite> , natural language inference (Shen et al., 2018a) , and acoustic modeling (Sperber et al., 2018) .", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nCaffeine challenge test in panic disorder and depression with panic attacks. Our aim was to observe if patients with panic disorder (PD) and patients with major depression with panic attacks (MDP) (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria) respond in a similar way to the induction of panic attacks by an oral caffeine challenge test. We randomly selected 29 patients with PD, 27 with MDP, 25 with major depression without panic attacks (MD), and 28 healthy volunteers. The patients had no psychotropic drug for at least a 4-week period. In a randomized double-blind experiment performed in 2 occasions 7 days apart, 480 mg caffeine and a caffeine-free (placebo) solution were administered in a coffee form and anxiety scales were applied before and after each test. A total of 58.6% (n = 17) of patients with PD, 44.4% (n = 12) of patients with MDP, 12.0% (n = 3) of patients with MD, and 7.1% (n= 2) of control subjects had a panic attack after the 480-mg caffeine challenge test (chi(2)(3) = 16.22, P = .001). The patients with PD and MDP were more sensitive to caffeine than were patients with MD and healthy volunteers. No panic attack was observed after the caffeine-free solution intake. The patients with MD had a lower heart rate response to the test than all the other groups (2-way analysis of variance, group by time interaction with Greenhouse-Geisser correction: F(3,762) = 2.85, P = .026). Our data suggest that there is an association between panic attacks, no matter if associated with PD or MDP, and hyperreactivity to an oral caffeine challenge test.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"Caffeine\", \"caffeine\"], \"Disease\": [\"panic attack\", \"panic disorder\", \"panic attacks\", \"PD\", \"major depression\", \"anxiety\", \"MDP\", \"depression\", \"Mental Disorders\", \"MD\"], \"Gene\": [], \"Species\": [\"patients\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Knee pain in children with Joint Hypermobility Syndrome (JHS) is traditionally managed with exercise, however the supporting evidence for this is scarce. No trial has previously examined whether exercising to neutral or into the hypermobile range affects outcomes. This study aimed to (i) determine if a physiotherapist-prescribed exercise programme focused on knee joint strength and control is effective in reducing knee pain in children with JHS compared to no treatment, and (ii) whether the range in which these exercises are performed affects outcomes.\n\nABSTRACT.METHODS: A prospective, parallel-group, randomised controlled trial conducted in a tertiary hospital in Sydney, Australia compared an 8 week exercise programme performed into either the full hypermobile range or only to neutral knee extension, following a minimum 2 week baseline period without treatment. Randomisation was computer-generated, with allocation concealed by sequentially numbered opaque sealed envelopes. Knee pain was the primary outcome. Quality of life, thigh muscle strength, and function were also measured at (i) initial assessment, (ii) following the baseline period and (iii) post treatment. Assessors were blinded to the participants' treatment allocation and participants blinded to the difference in the treatments.\n\nABSTRACT.RESULTS: Children with JHS and knee pain (n=26) aged 7-16 years were randomly assigned to the hypermobile (n=12) or neutral (n=14) treatment group. Significant improvements in child-reported maximal knee pain were found following treatment, regardless of group allocation with a mean 14.5 mm reduction on the visual analogue scale (95% CI 5.2 – 23.8 mm, p=0.003). Significant differences between treatment groups were noted for parent-reported overall psychosocial health (p=0.009), specifically self-esteem (p=0.034), mental health (p=0.001) and behaviour (p=0.019), in favour of exercising into the hypermobile range (n=11) compared to neutral only (n=14). Conversely, parent-reported overall physical health significantly favoured exercising only to neutral (p=0.037). No other differences were found between groups and no adverse events occurred.\n\nABSTRACT.CONCLUSIONS: Parents perceive improved child psychosocial health when children exercise into the hypermobile range, while exercising to neutral only is perceived to favour the child's physical health. A physiotherapist prescribed, supervised, individualised and progressed exercise programme effectively reduces knee pain in children with JHS.\n\nABSTRACT.TRIAL REGISTRATION: Australia & New Zealand Clinical Trials Registry; ACTRN12606000109505.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"physiotherapist-prescribed exercise programme\", \"no treatment\", \"child-reported maximal knee pain\", \"significantly decreased\", \"Significant improvements in child-reported maximal knee pain were found following treatment, regardless of group allocation with a mean 14.5 mm reduction on the visual analogue scale (95% CI 5.2 \\u2013 23.8 mm, p=0.003).\"], [\"physiotherapist-prescribed exercise programme\", \"no treatment\", \"adverse events\", \"no significant difference\", \"No other differences were found between groups and no adverse events occurred.\"]]", "role": "assistant" } ]

[ { "content": "| | | r (A) | | | | CN | |\n| | | Si-O | Al-O | La-O/Y-O | O-O | C Si-O | Al-O |\n| Sum of ionic radii | | ~1.62 | AlIV 1.76 | 2.39/2.26 | 2.72 | | |\n| Sum of ionic radii | | ~1.62 | AlV 1.84 | 2.39/2.26 | 2.72 | | |\n| Sum of ionic radii | | ~1.62 | AlIV 1.9 | 2.39/2.26 | 2.72 | | |\n| Errors | | +-0.03 | +-0.03 | +-0.05 | +-0.1 | +-0.5 | +-0.5 |\n| | | | | | | | |\n| LAS1 | X | 1.62 | 1.82 | 2.34 | 2.70 | 4.1 | 4.5 |\n| LAS1 | N | 1.62 | 1.84 | 2.36 | 2.70 | 4.2 | 4.6 |\n| | | | | | | | |\n| LAS3 | X | 1.62 | 1.82 | 2.36 | 2.66 | 4.0 | 4.4 |\n| LAS3 | N | 1.62 | 1.82 | 2.36 | 2.66 | 4.1 | 4.5 |\n| | | | | | | | |\n| YAS1 | X | 1.60 | 1.80 | 2.26 | 2.62 | 3.9 | 4.5 |\n| YAS1 | N | 1.60 | 1.80 | 2.22 | 2.64 | 4.0 | 4.5 |\n| | | | | | | | |\n| YAS3 | X | 1.62 | 1.82 | 2.24 | 2.62 | 4.0 | 4.6 |\n| YAS3 | N | 1.62 | 1.82 | 2.20 | 2.64 | 4.0 | 4.4 |\n\nCaption: Interatomic distances and coordination numbers derived from the Gaussian fits of the X-ray (X) and neutron (N) data\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"2.39\", \"type\": \"Other\"}\n{\"value\": \"2.26\", \"type\": \"Other\"}\n{\"value\": \"2.72\", \"type\": \"Other\"}\n{\"value\": \"2.39\", \"type\": \"Other\"}\n{\"value\": \"2.26\", \"type\": \"Other\"}\n{\"value\": \"2.72\", \"type\": \"Other\"}\n{\"value\": \"2.39\", \"type\": \"Other\"}\n{\"value\": \"2.26\", \"type\": \"Other\"}\n{\"value\": \"2.72\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.82\", \"type\": \"Other\"}\n{\"value\": \"2.34\", \"type\": \"Other\"}\n{\"value\": \"2.70\", \"type\": \"Other\"}\n{\"value\": \"4.1\", \"type\": \"Other\"}\n{\"value\": \"4.5\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.84\", \"type\": \"Other\"}\n{\"value\": \"2.36\", \"type\": \"Other\"}\n{\"value\": \"2.70\", \"type\": \"Other\"}\n{\"value\": \"4.2\", \"type\": \"Other\"}\n{\"value\": \"4.6\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.82\", \"type\": \"Other\"}\n{\"value\": \"2.36\", \"type\": \"Other\"}\n{\"value\": \"2.66\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"4.4\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.82\", \"type\": \"Other\"}\n{\"value\": \"2.36\", \"type\": \"Other\"}\n{\"value\": \"2.66\", \"type\": \"Other\"}\n{\"value\": \"4.1\", \"type\": \"Other\"}\n{\"value\": \"4.5\", \"type\": \"Other\"}\n{\"value\": \"1.60\", \"type\": \"Other\"}\n{\"value\": \"1.80\", \"type\": \"Other\"}\n{\"value\": \"2.26\", \"type\": \"Other\"}\n{\"value\": \"2.62\", \"type\": \"Other\"}\n{\"value\": \"3.9\", \"type\": \"Other\"}\n{\"value\": \"4.5\", \"type\": \"Other\"}\n{\"value\": \"1.60\", \"type\": \"Other\"}\n{\"value\": \"1.80\", \"type\": \"Other\"}\n{\"value\": \"2.22\", \"type\": \"Other\"}\n{\"value\": \"2.64\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"4.5\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.82\", \"type\": \"Other\"}\n{\"value\": \"2.24\", \"type\": \"Other\"}\n{\"value\": \"2.62\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"4.6\", \"type\": \"Other\"}\n{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"1.82\", \"type\": \"Other\"}\n{\"value\": \"2.20\", \"type\": \"Other\"}\n{\"value\": \"2.64\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"4.4\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nUpregulation of T-cell factor-4 isoform-responsive target genes in hepatocellular carcinoma. BACKGROUND: The Wnt/beta-catenin signalling pathway regulates genes involved in cell proliferation, survival, migration and invasion through regulation by T-cell factor (TCF)-4 transcription factor proteins. However, the role of TCF-4 isoforms generated by alternative splicing events in hepatocellular carcinoma (HCC) is unknown. AIM: Here, we investigated TCF-4 isoforms (TCF-4J and K)-responsive target genes that are important in hepatic oncogenesis and tumour development. METHODS: Gene expression microarray was performed on HCC cells overexpressing TCF-4J and K isoforms. Expression level of selected target genes was evaluated and correlations were made between their expression level and that of TCF-4 isoform in 47 pairs of human HCC tumours. RESULTS: Comparison by gene expression microarray revealed that 447 genes were upregulated and 343 downregulated more than 2.0-fold in TCF-4J compared with TCF-4K expressing cells. We validated expression of 18 selected target genes involved in Wnt/beta-catenin, insulin/IGF-1/IRS1 and Notch signalling pathways in 47 pairs of human HCCs and adjacent uninvolved liver tissues. It was observed that 13 genes (CLDN2, STK17B, SPP1, AXIN2, WISP2, MMP7, IRS1, ANXA1, CAMK2N1, ASPH, GPR56, CD24 and JAG1) activated by TCF-4J isoform in HCC cells, were also upregulated in HCC tumours compared with adjacent peritumour tissue; more importantly, 10 genes exhibited a significant correlation with the TCF-4J expression level in tumour. CONCLUSION: TCF-4 isoforms (TCF-4J and K) activated different downstream target genes in HCC. The biological consequence of TCF-4J isoform expression was upregulation of genes associated with tripartite Wnt/beta-catenin, insulin/IGF-1/IRS1 and Notch signal transduction pathway activation, which contribute to the pathogenesis of HCC.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"HCC\", \"hepatocellular carcinoma\", \"hepatic oncogenesis\", \"tumour\", \"HCC tumours\", \"HCCs\"], \"Gene\": [\"T-cell factor (TCF)-4 transcription factor\", \"CAMK2N1\", \"IGF-1\", \"beta-catenin\", \"SPP1\", \"CD24\", \"CLDN2\", \"ASPH\", \"GPR56\", \"STK17B\", \"TCF-4J and K\", \"WISP2\", \"IRS1\", \"TCF-4J\", \"Notch\", \"TCF-4K\", \"AXIN2\", \"insulin\", \"ANXA1\", \"Wnt\", \"TCF-4\", \"JAG1\", \"T-cell factor-4\", \"MMP7\"], \"Species\": [\"human\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Currently, one sixth of triple-negative breast cancer (TNBC) patients who receive docetaxel (T) and epirubicin (E) as neoadjuvant chemotherapy achieve a pathologic complete response (pCR). This study evaluates the impact of adding lobaplatin (L) to the TE regimen. Here, we show data from 125 patients (63 TE and 62 TEL patients). Four patients did not complete all the cycles. Two-sided P values show that the addition of L (38.7% vs. 12.7%, P = 0.001) significantly increases the rate of pCR in the breast and the axilla (TpCR) and the overall response rate (ORR; 93.5% vs. 73.0%, P = 0.003). The occurrence of grade 3–4 anemia and thrombocytopenia is higher in the TEL group (52.5% vs. 10.0% and 34.4% vs. 1.7% respectively). These results demonstrate that the addition of L to the TE regimen as neoadjuvant chemotherapy improves the TpCR and the ORR rates of TNBC but with increased side effects.\n\nExtracted Data:", "role": "user" }, { "content": "[[\" docetaxel (T) and epirubicin (E) + lobaplatin (L)\", \" docetaxel (T) and epirubicin (E)\", \"Pathologic CR in the breast and the axilla (TpCR)\", \"significantly increased\", \"Two-sided P values show that the addition of L (38.7% vs. 12.7%, P\\u2009=\\u20090.001) significantly increases the rate of pCR in the breast and the axilla (TpCR) and the overall response rate (ORR; 93.5% vs. 73.0%, P\\u2009=\\u20090.003).\"], [\" docetaxel (T) and epirubicin (E) + lobaplatin (L)\", \" docetaxel (T) and epirubicin (E)\", \"grade 3\\u20134 thrombocytopenia and anemia\", \"significantly increased\", \"The occurrence of grade 3\\u20134 anemia and thrombocytopenia is higher in the TEL group (52.5% vs. 10.0% and 34.4% vs. 1.7% respectively).\"]]", "role": "assistant" } ]

[ { "content": "| Sample | Nitrogen content (ppm) | Bulk density (g/cm3) | Young's modulus (GPa) |\n| N-doped | 2300 | 2.217 | 73.80 |\n| N-doped | 4500 | 2.229 | 74.75 |\n| N-doped | 6800 | 2.237 | 75.80 |\n| VAD | 0 | 2.206 | 72.33 |\n| Type I | 0 | 2.210 | 72.55 |\n| Type II | 0 | 2.208 | 72.48 |\n| Type III | 0 | 2.201 | 71.42 |\n\nCaption: Density and Young's modulus of the nitrogen-incorporated silica glass and the representative pure silica glass\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"2300\", \"type\": \"Other\"}\n{\"value\": \"2.217\", \"type\": \"Other\"}\n{\"value\": \"73.80\", \"type\": \"Other\"}\n{\"value\": \"4500\", \"type\": \"Other\"}\n{\"value\": \"2.229\", \"type\": \"Other\"}\n{\"value\": \"74.75\", \"type\": \"Other\"}\n{\"value\": \"6800\", \"type\": \"Other\"}\n{\"value\": \"2.237\", \"type\": \"Other\"}\n{\"value\": \"75.80\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"2.206\", \"type\": \"Other\"}\n{\"value\": \"72.33\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"2.210\", \"type\": \"Other\"}\n{\"value\": \"72.55\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"2.208\", \"type\": \"Other\"}\n{\"value\": \"72.48\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"2.201\", \"type\": \"Other\"}\n{\"value\": \"71.42\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nA novel compound, maltolyl p-coumarate, attenuates cognitive deficits and shows neuroprotective effects in vitro and in vivo dementia models. To develop a novel and effective drug that could enhance cognitive function and neuroprotection, we newly synthesized maltolyl p-coumarate by the esterification of maltol and p-coumaric acid. In the present study, we investigated whether maltolyl p-coumarate could improve cognitive decline in scopolamine-injected rats and in amyloid beta peptide(1-42)-infused rats. Maltolyl p-coumarate was found to attenuate cognitive deficits in both rat models using passive avoidance test and to reduce apoptotic cell death observed in the hippocampus of the amyloid beta peptide(1-42)-infused rats. We also examined the neuroprotective effects of maltolyl p-coumarate in vitro using SH-SY5Y cells. Cells were pretreated with maltolyl p-coumarate, before exposed to amyloid beta peptide(1-42), glutamate or H2O2. We found that maltolyl p-coumarate significantly decreased apoptotic cell death and reduced reactive oxygen species, cytochrome c release, and caspase 3 activation. Taking these in vitro and in vivo results together, our study suggests that maltolyl p-coumarate is a potentially effective candidate against Alzheimer's disease that is characterized by wide spread neuronal death and progressive decline of cognitive function.", "role": "user" }, { "content": "{\"CellLine\": [\"SH-SY5Y\"], \"Chemical\": [\"maltolyl p-coumarate\", \"maltol\", \"reactive oxygen species\", \"p-coumaric acid\", \"cytochrome c\", \"glutamate\", \"scopolamine\", \"Maltolyl p-coumarate\", \"amyloid beta peptide(1-42)\", \"H2O2\"], \"Disease\": [\"cognitive deficits\", \"Alzheimer\\u0027s disease\", \"decline of cognitive function\", \"dementia\", \"neuronal death\", \"cognitive decline\"], \"Gene\": [\"caspase 3\"], \"Species\": [\"rats\", \"rat\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nShallow Syntax in Deep Water\n\nShallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.\n\nIntroduction\nThe NLP community is revisiting the role of linguistic structure in applications with the advent of contextual word representations (cwrs) derived from pretraining language models on large corpora BIBREF2, BIBREF3, BIBREF4, BIBREF5. Recent work has shown that downstream task performance may benefit from explicitly injecting a syntactic inductive bias into model architectures BIBREF6, even when cwrs are also used BIBREF7. However, high quality linguistic structure annotation at a large scale remains expensive—a trade-off needs to be made between the quality of the annotations and the computational expense of obtaining them. Shallow syntactic structures (BIBREF8; also called chunk sequences) offer a viable middle ground, by providing a flat, non-hierarchical approximation to phrase-syntactic trees (see Fig. FIGREF1 for an example). These structures can be obtained efficiently, and with high accuracy, using sequence labelers. In this paper we consider shallow syntax to be a proxy for linguistic structure.\nWhile shallow syntactic chunks are almost as ubiquitous as part-of-speech tags in standard NLP pipelines BIBREF9, their relative merits in the presence of cwrs remain unclear. We investigate the role of these structures using two methods. First, we enhance the ELMo architecture BIBREF0 to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context (§SECREF2). Our second method involves classical addition of chunk features to cwr-infused architectures for four different downstream tasks (§SECREF3). Shallow syntactic information is obtained automatically using a highly accurate model (97% $F_1$ on standard benchmarks). In both settings, we observe only modest gains on three of the four downstream tasks relative to ELMo-only baselines (§SECREF4).\nRecent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Our analysis indicates that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings (§SECREF18).\nOverall, our findings show that while shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.\n\nPretraining with Shallow Syntactic Annotations\nWe briefly review the shallow syntactic structures used in this work, and then present a model architecture to obtain embeddings from shallow Syntactic Context (mSynC).\n\nPretraining with Shallow Syntactic Annotations ::: Shallow Syntax\nBase phrase chunking is a cheap sequence-labeling–based alternative to full syntactic parsing, where the sequence consists of non-overlapping labeled segments (Fig. FIGREF1 includes an example.) Full syntactic trees can be converted into such shallow syntactic chunk sequences using a deterministic procedure BIBREF9. BIBREF12 offered a rule-based transformation deriving non-overlapping chunks from phrase-structure trees as found in the Penn Treebank BIBREF13. The procedure percolates some syntactic phrase nodes from a phrase-syntactic tree to the phrase in the leaves of the tree. All overlapping embedded phrases are then removed, and the remainder of the phrase gets the percolated label—this usually corresponds to the head word of the phrase.\nIn order to obtain shallow syntactic annotations on a large corpus, we train a BiLSTM-CRF model BIBREF14, BIBREF15, which achieves 97% $F_1$ on the CoNLL 2000 benchmark test set. The training data is obtained from the CoNLL 2000 shared task BIBREF12, as well as the remaining sections (except §23 and §20) of the Penn Treebank, using the official script for chunk generation. The standard task definition from the shared task includes eleven chunk labels, as shown in Table TABREF4.\n\nPretraining with Shallow Syntactic Annotations ::: Pretraining Objective\nTraditional language models are estimated to maximize the likelihood of each word $x_i$ given the words that precede it, $p(x_i \\mid x_{<i})$. Given a corpus that is annotated with shallow syntax, we propose to condition on both the preceding words and their annotations.\nWe associate with each word $x_i$ three additional variables (denoted $c_i$): the indices of the beginning and end of the last completed chunk before $x_i$, and its label. For example, in Fig. FIGREF8, $c_4=\\langle 3, 3, \\text{VP}\\rangle $ for $x_4=\\text{the}$. Chunks, $c$ are only used as conditioning context via $p(x_i \\mid x_{<i}, c_{\\leqslant i})$; they are not predicted. Because the $c$ labels depend on the entire sentence through the CRF chunker, conditioning each word's probability on any $c_i$ means that our model is, strictly speaking, not a language model, and it can no longer be meaningfully evaluated using perplexity.\nA right-to-left model is constructed analogously, conditioning on $c_{\\geqslant i}$ alongside $x_{>i}$. Following BIBREF2, we use a joint objective maximizing data likelihood objectives in both directions, with shared softmax parameters.\n\nPretraining with Shallow Syntactic Annotations ::: Pretraining Model Architecture\nOur model uses two encoders: $e_{\\mathit {seq}}$ for encoding the sequential history ($x_{<i}$), and $e_{\\mathit {syn}}$ for shallow syntactic (chunk) history ($c_{\\leqslant i}$). For both, we use transformers BIBREF16, which consist of large feedforward networks equipped with multiheaded self-attention mechanisms.\nAs inputs to $e_{\\mathit {seq}}$, we use a context-independent embedding, obtained from a CNN character encoder BIBREF17 for each token $x_i$. The outputs $h_i$ from $e_{\\mathit {seq}}$ represent words in context.\nNext, we build representations for (observed) chunks in the sentence by concatenating a learned embedding for the chunk label with $h$s for the boundaries and applying a linear projection ($f_\\mathit {proj}$). The output from $f_\\mathit {proj}$ is input to $e_{\\mathit {syn}}$, the shallow syntactic encoder, and results in contextualized chunk representations, $g$. Note that the number of chunks in the sentence is less than or equal to the number of tokens.\nEach $h_i$ is now concatentated with $g_{c_i}$, where $g_{c_i}$ corresponds to $c_i$, the last chunk before position $i$. Finally, the output is given by $\\mbox{\\textbf {mSynC}}_i = {u}_\\mathit {proj}(h_i, g_{c_i}) = W^\\top [h_i; g_{c_i}]$, where $W$ is a model parameter. For training, $\\mbox{\\textbf {mSynC}}_i$ is used to compute the probability of the next word, using a sampled softmax BIBREF18. For downstream tasks, we use a learned linear weighting of all layers in the encoders to obtain a task-specific mSynC, following BIBREF2.\n\nPretraining with Shallow Syntactic Annotations ::: Pretraining Model Architecture ::: Staged parameter updates\nJointly training both the sequential encoder $e_{\\mathit {seq}}$, and the syntactic encoder $e_{\\mathit {syn}}$ can be expensive, due to the large number of parameters involved. To reduce cost, we initialize our sequential cwrs $h$, using pretrained embeddings from ELMo-transformer. Once initialized as such, the encoder is fine-tuned to the data likelihood objective (§SECREF5). This results in a staged parameter update, which reduces training duration by a factor of 10 in our experiments. We discuss the empirical effect of this approach in §SECREF20.\n\nExperiments\nOur experiments evaluate the effect of shallow syntax, via contextualization (mSynC, §SECREF2) and features (§SECREF3). We provide comparisons with four baselines—ELMo-transformer BIBREF0, our reimplementation of the same, as well as two cwr-free baselines, with and without shallow syntactic features. Both ELMo-transformer and mSynC are trained on the 1B word benchmark corpus BIBREF19; the latter also employs chunk annotations (§SECREF2). Experimental settings are detailed in Appendix §SECREF22.\n\nExperiments ::: Downstream Task Transfer\nWe employ four tasks to test the impact of shallow syntax. The first three, namely, coarse and fine-grained named entity recognition (NER), and constituency parsing, are span-based; the fourth is a sentence-level sentiment classification task. Following BIBREF2, we do not apply finetuning to task-specific architectures, allowing us to do a controlled comparison with ELMo. Given an identical base architecture across models for each task, we can attribute any difference in performance to the incorporation of shallow syntax or contextualization. Details of downstream architectures are provided below, and overall dataset statistics for all tasks is shown in the Appendix, Table TABREF26.\n\nExperiments ::: Downstream Task Transfer ::: NER\nWe use the English portion of the CoNLL 2003 dataset BIBREF20, which provides named entity annotations on newswire data across four different entity types (PER, LOC, ORG, MISC). A bidirectional LSTM-CRF architecture BIBREF14 and a BIOUL tagging scheme were used.\n\nExperiments ::: Linguistic Probes\nWe further analyze whether awareness of shallow syntax carries over to other linguistic tasks, via probes from BIBREF1. Probes are linear models trained on frozen cwrs to make predictions about linguistic (syntactic and semantic) properties of words and phrases. Unlike §SECREF11, there is minimal downstream task architecture, bringing into focus the transferability of cwrs, as opposed to task-specific adaptation.\n\nExperiments ::: Effect of Training Scheme\nWe test whether our staged parameter training (§SECREF9) is a viable alternative to an end-to-end training of both $e_{\\mathit {syn}}$ and $e_{\\mathit {seq}}$. We make a further distinction between fine-tuning $e_{\\mathit {seq}}$ vs. not updating it at all after initialization (frozen).\nDownstream validation-set $F_1$ on fine-grained NER, reported in Table TABREF21, shows that the end-to-end strategy lags behind the others, perhaps indicating the need to train longer than 10 epochs. However, a single epoch on the 1B-word benchmark takes 36 hours on 2 Tesla V100s, making this prohibitive. Interestingly, the frozen strategy, which takes the least amount of time to converge (24 hours on 1 Tesla V100), also performs almost as well as fine-tuning.\n\nSupplemental Material ::: Hyperparameters ::: ELMo-transformer\nOur baseline pretraining model was a reimplementation of that given in BIBREF0. Hyperparameters were generally identical, but we trained on only 2 GPUs with (up to) 4,000 tokens per batch. This difference in batch size meant we used 6,000 warm up steps with the learning rate schedule of BIBREF16.\n\nSupplemental Material ::: Hyperparameters ::: mSynC\nThe function $f_{seq}$ is identical to the 6-layer biLM used in ELMo-transformer. $f_{syn}$, on the other hand, uses only 2 layers. The learned embeddings for the chunk labels have 128 dimensions and are concatenated with the two boundary $h$ of dimension 512. Thus $f_{proj}$ maps $1024 + 128$ dimensions to 512. Further, we did not perform weight averaging over several checkpoints.\n\nSupplemental Material ::: Hyperparameters ::: Shallow Syntax\nThe size of the shallow syntactic feature embedding was 50 across all experiments, initialized uniform randomly.\nAll model implementations are based on the AllenNLP library BIBREF33.\n\nFigure 1: A sentence with its phrase-syntactic tree (brown) and shallow syntactic (chunk) annotations (red). Nodes in the tree which percolate down as chunk labels are in red. Not all tokens in the sentence get chunk labels; e.g., punctuation is not part of a chunk.\n\nTable 1: Shallow syntactic chunk phrase types from CoNLL 2000 shared task (Tjong Kim Sang and Buchholz, 2000) and their occurrence % in the training data.\n\nFigure 2: Model architecture for pretraining with shallow syntax. A sequential encoder converts the raw text into CWRs (shown in blue). Observed shallow syntactic structure (chunk boundaries and labels, shown in red) are combined with these CWRs in a shallow syntactic encoder to get contextualized representations for chunks (shown in orange). Both representations are passed through a projection layer to get mSynC embeddings (details shown only in some positions, for clarity), used both for computing the data likelihood, as shown, as well as in downstream tasks.\n\nTable 2: Test-set performance of ELMo-transformer (Peters et al., 2018b), our reimplementation, and mSynC, compared to baselines without CWR. Evaluation metric is F1 for all tasks except sentiment, which reports accuracy. Reported results show the mean and standard deviation across 5 runs for coarse-grained NER and sentiment classification and 3 runs for other tasks.\n\nTable 4: Validation F1 for fine-grained NER across syntactic pretraining schemes, with mean and standard deviations across 3 runs.\n\nTable 5: Downstream dataset statistics describing the number of train, heldout and test set instances for each task.\n\nTable 6: Dataset and metrics for each probing task from Liu et al. (2019), corresponding to Table 3.\n\nQuery: What are the black-box probes used?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"CCG Supertagging CCGBank , PTB part-of-speech tagging, EWT part-of-speech tagging,\\nChunking, Named Entity Recognition, Semantic Tagging, Grammar Error Detection, Preposition Supersense Role, Preposition Supersense Function, Event Factuality Detection\", \"evidence\": [\"Recent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Our analysis indicates that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings (\\u00a7SECREF18).\"]}", "role": "assistant" } ]

[ { "content": "| System | Chemical shift (ppm) | Chemical shift (ppm) | Chemical shift (ppm) | References |\n| | Al(4) | Al(5) | Al(6) | |\n| Al2O3-P2O5 glass | +36 | +4 | -22 | [27] |\n| Al2O3-P2O5 cryst. | +40 | - | -15 | [27] |\n| Na2O-Al2O3-P2O5 glass | +40 | +15 | -10 | [38] |\n| K2O-SiO2-P2O5 glass | +44 | +17 | -17 | [29] |\n| CaO-Al2O3-P2O5 glass | +36 | +4 | -21 | [27] |\n| PbO-Al2O3-P2O5 glass | +40 | +11 | -12 | [21] |\n| MgO-Al2O3-P2O5 | +35 | +7 | -18 | [22] |\n| Amorphous alumina | +62 | +28 | -4 | [25] |\n| CdO-Al2O3-P2O5 glass | +36 | +6 | -20 | Present work |\n\nCaption: Average 27Al chemical shifts and co-ordinations found in phosphate glasses\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"+36\", \"type\": \"Other\"}\n{\"value\": \"+4\", \"type\": \"Other\"}\n{\"value\": \"+40\", \"type\": \"Other\"}\n{\"value\": \"+40\", \"type\": \"Other\"}\n{\"value\": \"+15\", \"type\": \"Other\"}\n{\"value\": \"+44\", \"type\": \"Other\"}\n{\"value\": \"+17\", \"type\": \"Other\"}\n{\"value\": \"+36\", \"type\": \"Other\"}\n{\"value\": \"+4\", \"type\": \"Other\"}\n{\"value\": \"+40\", \"type\": \"Other\"}\n{\"value\": \"+11\", \"type\": \"Other\"}\n{\"value\": \"+35\", \"type\": \"Other\"}\n{\"value\": \"+7\", \"type\": \"Other\"}\n{\"value\": \"+62\", \"type\": \"Other\"}\n{\"value\": \"+28\", \"type\": \"Other\"}\n{\"value\": \"+36\", \"type\": \"Other\"}\n{\"value\": \"+6\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nAs a result, we do not adopt their parse tree-based representation of nested entities and propose instead a linear time directed hypergraph-based model similar to that of<cite> Lu and Roth (2015)</cite> .", "role": "user" }, { "content": "[\"Similarities\"]", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nOn the Global Convergence of Training Deep Linear ResNets\n\nMain Content:\n1 INTRODUCTION . Despite the remarkable power of deep neural networks ( DNNs ) trained using stochastic gradient descent ( SGD ) in many machine learning applications , theoretical understanding of the properties of this algorithm , or even plain gradient descent ( GD ) , remains limited . Many key properties of the learning process for such systems are also present in the idealized case of deep linear networks . For example , ( a ) the objective function is not convex ; ( b ) errors back-propagate ; and ( c ) there is potential for exploding and vanishing gradients . In addition to enabling study of systems with these properties in a relatively simple setting , analysis of deep linear networks also facilitates the scientific understanding of deep learning because using linear networks can control for the effect of architecture choices on the expressiveness of networks ( Arora et al. , 2018 ; Du & Hu , 2019 ) . For these reasons , deep linear networks have received extensive attention in recent years . One important line of theoretical investigation of deep linear networks concerns optimization landscape analysis ( Kawaguchi , 2016 ; Hardt & Ma , 2016 ; Freeman & Bruna , 2016 ; Lu & Kawaguchi , 2017 ; Yun et al. , 2018 ; Zhou & Liang , 2018 ) , where major findings include that any critical point of a deep linear network with square loss function is either a global minimum or a saddle point , and identifying conditions on the weight matrices that exclude saddle points . Beyond landscape analysis , another research direction aims to establish convergence guarantees for optimization algorithms ( e.g . GD , SGD ) for training deep linear networks . Arora et al . ( 2018 ) studied the trajectory of gradient flow and showed that depth can help accelerate the optimization of deep linear networks . Ji & Telgarsky ( 2019 ) ; Gunasekar et al . ( 2018 ) investigated the implicit bias of GD for training deep linear networks and deep linear convolutional networks respectively . More recently , Bartlett et al . ( 2019 ) ; Arora et al . ( 2019a ) ; Shamir ( 2018 ) ; Du & Hu ( 2019 ) analyzed the optimization trajectory of GD for training deep linear networks and proved global convergence rates under certain assumptions on the training data , initialization , and neural network structure . Inspired by the great empirical success of residual networks ( ResNets ) , Hardt & Ma ( 2016 ) considered identity parameterizations in deep linear networks , i.e. , parameterizing each layer ’ s weight matrix as I ` W , which leads to the so-called deep linear ResNets . In particular , Hardt & Ma ( 2016 ) established the existence of small norm solutions for deep residual networks with sufficiently large depth L , and proved that there are no critical points other than the global minimum when the maximum spectral norm among all weight matrices is smaller than Op1 { Lq . Motivated by this intriguing finding , Bartlett et al . ( 2019 ) studied the convergence rate of GD for training deep linear networks with identity initialization , which is equivalent to zero initialization in deep linear ResNets . They assumed whitened data and showed that GD can converge to the global minimum if ( i ) the training loss at the initialization is very close to optimal or ( ii ) the regression matrix Φ is symmetric and positive definite . ( In fact , they proved that , when Φ is symmetric and has negative eigenvalues , GD for linear ResNets with zero-initialization does not converge . ) Arora et al . ( 2019a ) showed that GD converges under substantially weaker conditions , which can be satisfied by random initialization schemes . The convergence theory of stochastic gradient descent for training deep linear ResNets is largely missing ; it remains unclear under which conditions SGD can be guaranteed to find the global minimum . In this paper , we establish the global convergence of both GD and SGD for training deep linear ResNets without any condition on the training data . More specifically , we consider the training of L-hidden-layer deep linear ResNets with fixed linear transformations at input and output layers . We prove that under certain conditions on the input and output linear transformations , GD and SGD can converge to the global minimum of the training loss function . Moreover , when specializing to appropriate Gaussian random linear transformations , we show that , as long as the neural network is wide enough , both GD and SGD with zero initialization on all hidden weights can find the global minimum . There are two main ingredients of our proof : ( i ) establishing restricted gradient bounds and a smoothness property ; and ( ii ) proving that these properties hold along the optimization trajectory and further lead to global convergence . We point out the second aspect is challenging especially for SGD due to the uncertainty of its optimization trajectory caused by stochastic gradients . We summarize our main contributions as follows : • We prove the global convergence of GD and SGD for training deep linear ResNets . Specifically , we derive a generic condition on the input and output linear transformations , under which both GD and SGD with zero initialization on all hidden weights can find global minima . Based on this condition , one can design a variety of input and output transformations for training deep linear ResNets . • When applying appropriate Gaussian random linear transformations , we show that as long as the neural network width satisfies m “ Ωpkrκ2q , with high probability , GD can converge to the global minimum up to an -error within Opκ logp1 { qq iterations , where k , r are the output dimension and the rank of training data matrix X respectively , and κ “ } X } 22 { σ2rpXq denotes the condition number of the covariance matrix of the training data . Compared with previous convergence results for training deep linear networks from Du & Hu ( 2019 ) , our condition on the neural network width is independent of the neural network depth L , and is strictly better by a factor of OpLκq . • Using the same Gaussian random linear transformations , we also establish the convergence guarantee of SGD for training deep linear ResNets . We show that if the neural network width satisfies m “ rΩ ` krκ2 log2p1 { q ¨n2 { B2 ˘ , with constant probability , SGD can converge to the global minimum up to an -error within rO ` κ2 ´1 logp1 { q ¨ n { B ˘ iterations , where n is the training sample size and B is the minibatch size of stochastic gradient . This is the first global convergence rate of SGD for training deep linear networks . Moreover , when the global minimum of the training loss is 0 , we prove that SGD can further achieve linear rate of global convergence , and the condition on the neural network width does not depend on the target error . As alluded to above , we analyze networks with d inputs , k outputs , and m ě maxtd , ku nodes in each hidden layer . Linear transformations that are fixed throughout training map the inputs to the first hidden layer , and the last hidden layer to the outputs . We prove that our bounds hold with high probability when these input and output transformations are randomly generated by Gaussian distributions . If , instead , the input transformation simply copies the inputs onto the first d compo- nents of the first hidden layer , and the output transformation takes the first k components of the last hidden layer , then our analysis does not provide a guarantee . There is a good reason for this : a slight modification of a lower bound argument from Bartlett et al . ( 2019 ) demonstrates that GD may fail to converge in this case . However , we describe a similarly simple , deterministic , choice of input and output transformations such that wide enough networks always converge . The resulting condition on the network width is weaker than that for Gaussian random transformations , and thus improves on the corresponding convergence guarantee for linear networks , which , in addition to requiring wider networks , only hold with high probability for random transformations . 1.1 ADDITIONAL RELATED WORK . In addition to what we discussed above , a large bunch of work focusing on the optimization of neural networks with nonlinear activation functions has emerged . We will briefly review them in this subsection . It is widely believed that the training loss landscape of nonlinear neural networks is highly nonconvex and nonsmooth ( e.g. , neural networks with ReLU/LeakyReLU activation ) , thus it is fundamentally difficult to characterize the optimization trajectory and convergence performance of GD and SGD . Some early work ( Andoni et al. , 2014 ; Daniely , 2017 ) showed that wide enough ( polynomial in sample size n ) neural networks trained by GD/SGD can learn a class of continuous functions ( e.g. , polynomial functions ) in polynomial time . However , those works only consider training some of the neural network weights rather than all of them ( e.g. , the input and output layers ) 1 . In addition , a series of papers investigated the convergence of gradient descent for training shallow networks ( typically 2-layer networks ) under certain assumptions on the training data and initialization scheme ( Tian , 2017 ; Du et al. , 2018b ; Brutzkus et al. , 2018 ; Zhong et al. , 2017 ; Li & Yuan , 2017 ; Zhang et al. , 2018 ) . However , the assumptions made in these works are rather strong and not consistent with practice . For example , Tian ( 2017 ) ; Du et al . ( 2018b ) ; Zhong et al . ( 2017 ) ; Li & Yuan ( 2017 ) ; Zhang et al . ( 2018 ) assumed that the label of each training data is generated by a teacher network , which has the same architecture as the learned network . Brutzkus et al . ( 2018 ) assumed that the training data is linearly separable . Li & Liang ( 2018 ) addressed this drawback ; they proved that for two-layer ReLU network with cross-entropy loss , as long as the neural network is sufficiently wide , under mild assumptions on the training data SGD with commonly-used Gaussian random initialization can achieve nearly zero expected error . Du et al . ( 2018c ) proved the similar results of GD for training two-layer ReLU networks with square loss . Beyond shallow neural networks , Allen-Zhu et al . ( 2019 ) ; Du et al . ( 2019 ) ; Zou et al . ( 2019 ) generalized the global convergence results to multi-layer over-parameterized ReLU networks . Chizat et al . ( 2019 ) showed that training over-parameterized neural networks actually belongs to a so-called “ lazy training ” regime , in which the model behaves like its linearization around the initialization . Furthermore , the parameter scaling is more essential than over-paramterization to make the model learning within the “ lazy training ” regime . Along this line of research , several follow up works have been conducted . Oymak & Soltanolkotabi ( 2019 ) ; Zou & Gu ( 2019 ) ; Su & Yang ( 2019 ) ; Kawaguchi & Huang ( 2019 ) improved the convergence rate and over-parameterization condition for both shallow and deep networks . Arora et al . ( 2019b ) showed that training a sufficiently wide deep neural network is almost equivalent to kernel regression using neural tangent kernel ( NTK ) , proposed in Jacot et al . ( 2018 ) . Allen-Zhu et al . ( 2019 ) ; Du et al . ( 2019 ) ; Zhang et al . ( 2019 ) proved the global convergence for training deep ReLU ResNets . Frei et al . ( 2019 ) proved the convergence of GD for training deep ReLU ResNets under an over-parameterization condition that is only logarithmic in the depth of the network , which partially explains why deep residual networks are preferable to fully connected ones . However , all the results in Allen-Zhu et al . ( 2019 ) ; Du et al . ( 2019 ) ; Zhang et al . ( 2019 ) ; Frei et al . ( 2019 ) require a very stringent condition on the network width , which typically has a high-degree polynomial dependence on the training sample size n. Besides , the results in Allen-Zhu et al . ( 2019 ) ; Zhang et al . ( 2019 ) also require that all data points are separated by a positive distance and have unit norm . As shown in Du & Hu ( 2019 ) and will be proved in this paper , for deep linear ( residual ) networks , there is no assumption on the training data , and the condition on the network width is significantly milder , which is independent of the sample size n. While achieving a stronger result for linear networks than for nonlinear ones is not surprising , we believe that our analysis , conducted in the idealized deep linear case , can provide useful insights to understand optimization in the nonlinear case . 1In Daniely ( 2017 ) , the weight changes in all hidden layers make negligible contribution to the final output , thus can be approximately treated as only training the output layer . Two concurrent works analyze gradient descent applied to deep linear ( residual ) networks ( Hu et al. , 2020 ; Wu et al. , 2019 ) . Hu et al . ( 2020 ) consider deep linear networks with orthogonal initialization , and Wu et al . ( 2019 ) consider zero initialization on the last layer and identity initialization for the rest of the layers , which are similar to our setting . However , there are several differences between their work and ours . One major difference is that Hu et al . ( 2020 ) and Wu et al . ( 2019 ) only prove global convergence for GD , but our results cover both GD and SGD . In addition , Hu et al . ( 2020 ) focuses on proving the global convergence of GD for sufficiently wide networks , while we provide a generic condition on the input and output linear transformations for ensuring global convergence . Wu et al . ( 2019 ) assumes whitened data and proves a OpL3 logp1 { qq bound on the number of iterations required for GD to converge , where we establish a Oplogp1 { qq2 bound .", "role": "user" }, { "content": "This paper deals with the global convergence of deep linear ResNets. The author show that under some initialization conditions for the first and the last layer (that are not optimized !) GD and SGD does converge to a global minimum of the min squared error. The closed related work seems to be Bartlett et al. 2019 that study the convergence of GD in the case of linear networks.", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nThis class of models [1, 11,<cite> 19]</cite> focuses on learning the projections (i.e., mappings) between independently trained monolingual embedding spaces.", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: [Purpose] The aim of the present study is to examine the effects of silver spike point\ntherapy on elderly persons' chronic constipation. [Subjects and Methods] The subjects of\nthe present study were 30 elderly chronic-constipation sufferers who resided in a nursing\nhome and had other physical conditions that necessitated wheelchair ambulation. There were\nthree test groups in the present study: an silver spike point group, an acupuncture group,\nand a control group; 10 subjects were assigned to each group. The silver spike point group\nand the acupuncture group received their respective interventions, which were applied to\nboth sides of the Tiensu for 20 minutes per day, five days per week, over a four week\nperiod. Weekly bowel movement frequency was recorded, and the Constipation Assessment\nScale and Bowel Function Assessment Form were used as data-gathering instruments.\n[Results] Following treatment, the silver spike point and acupuncture groups showed\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\nAssessment Form, compared with the control group. [Conclusion] Based on the above results,\nsilver spike point therapy can be administered to alleviate chronic constipation in\nelderly nursing home residents who cannot walk independently due to declining physical\nfunctions.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Acupuncture \", \"Control \", \"Constipation assessment scale improvement \", \"significantly increased\", \"Following treatment, the silver spike point and acupuncture groups showed\\r\\r\\r\\r\\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\\r\\r\\r\\r\\nAssessment Form, compared with the control group. \"], [\"Silver spike point\", \"Control \", \"Bowel frequency\", \"significantly increased\", \"Following treatment, the silver spike point and acupuncture groups showed\\r\\r\\r\\r\\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\\r\\r\\r\\r\\nAssessment Form, compared with the control group. \"], [\"Silver spike point\", \"Control \", \"Constipation assessment scale improvement \", \"significantly increased\", \"Following treatment, the silver spike point and acupuncture groups showed\\r\\r\\r\\r\\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\\r\\r\\r\\r\\nAssessment Form, compared with the control group. \"], [\"Silver spike point\", \"Control \", \"Bowel function improvement \", \"significantly increased\", \"the silver spike point and acupuncture groups showed\\r\\r\\r\\r\\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\\r\\r\\r\\r\\nAssessment Form, compared with the control group. \"], [\"Acupuncture \", \"Control \", \"Bowel function improvement \", \"significantly increased\", \"the silver spike point and acupuncture groups showed\\r\\r\\r\\r\\nsignificant improvement, based on the Constipation Assessment Scale and the Bowel Function\\r\\r\\r\\r\\nAssessment Form, compared with the control group\"]]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nFor gold tokenization and predicted morphology (Table 2) , we also give the performance reported in our previous work <cite>(Marton et al., 2013)</cite> .", "role": "user" }, { "content": "[\"Uses\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: People with prehypertension (120–130/80–90 mmHg) are at increased risk of progressing to hypertension. Recommendations for prehypertension include engaging in regular physical activity. We aimed to assess feasibility and acceptability and collect preliminary outcome data on ChiRunning for people with elevated blood pressure. ChiRunning is a commercially available running program based on the mindful movements of Tai Chi, which is aimed at decreasing injury by both increasing body awareness and modifying running form.\n\nABSTRACT.METHODS: We enrolled adults with elevated systolic (130–150 mmHg) or diastolic (80–100 mmHg) blood pressure in a 12-week pilot trial. Participants were randomized 2:1:1 to 8 weeks of: 1) intervention—a trainer-led ChiRunning group (n = 10); 2) active control—a trainer-led running group (n = 6); or 3) educational control—a self-directed running group (n = 6) and followed for 4 more weeks. The active control and educational control groups were combined for analysis.\n\nABSTRACT.RESULTS: This study was feasible, meeting recruitment, retention and adherence goals, and acceptable to participants. Systolic and diastolic blood pressure did not change significantly over the study for either the ChiRunning or control groups. Changes in BMI over time were significantly different from zero in the ChiRunning group (p = 0.04) but not in the control group (slope for ChiRunning −0.05 [−0.1 to −0.002] vs. control −0.01 [−0.06 to 0.04], between slope difference, p = 0.22). Self-reported running-related injury (i.e. discomfort leading to a decrease in running) was similar between groups (ChiRunning, 4 [1.2 to 8.4] vs. control, 3 [0.7 to 7.1] injuries per 100 h of running, p = 0.72) although self-reported running-related discomfort (i.e. discomfort that does not lead to changes in running) trended higher in the ChiRunning group (ChiRunning, 10 [5.4 to 16.8] vs. control, 4 [1.5 to 9] reports of discomfort per 100 h of running, p = 0.06).\n\nABSTRACT.CONCLUSION: ChiRunning appears to be a feasible and acceptable exercise program for people with elevated blood pressure. We did not find that ChiRunning had a significant impact on blood pressure or self reported injury, but did see a positive change in BMI over time. ChiRunning warrants further investigation in a larger trial.\n\nABSTRACT.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01587183\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Chi running program\", \"Control\", \"Systolic blood pressure \", \"no significant difference\", \"Systolic and diastolic blood pressure did not change significantly over the study for either the ChiRunning or control groups.\"], [\"Chi running program\", \"Standard running program\", \"Diastolic blood pressure \", \"no significant difference\", \"Systolic and diastolic blood pressure did not change significantly over the study for either the ChiRunning or control groups.\"], [\"Chi running program\", \"Standard running program\", \"Body mass index\", \"no significant difference\", \"Changes in BMI over time were significantly different from zero in the ChiRunning group (p = 0.04) but not in the control group (slope for ChiRunning \\u22120.05 [\\u22120.1 to \\u22120.002] vs. control \\u22120.01 [\\u22120.06 to 0.04], between slope difference, p = 0.22).\"], [\"Chi running program\", \"Standard running program\", \"Injury incidence \", \"no significant difference\", \"Self-reported running-related injury (i.e. discomfort leading to a decrease in running) was similar between groups (ChiRunning, 4 [1.2 to 8.4] vs. control, 3 [0.7 to 7.1] injuries per 100 h of running, p = 0.72)\"], [\"Chi running program\", \"Standard running program\", \"Systolic blood pressure \", \"no significant difference\", \"Systolic and diastolic blood pressure did not change significantly over the study for either the ChiRunning or control groups.\"], [\"Chi running program\", \"Control\", \"Diastolic blood pressure \", \"no significant difference\", \"Systolic and diastolic blood pressure did not change significantly over the study for either the ChiRunning or control groups.\"]]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nRegardless of its source, this disparity motivated us to investigate the applicability of features proposed for the disambiguation of English discourse connectives for French. As mentioned in Section 2, Pitler and Nenkova<cite> [20]</cite> have shown that the context of discourse connectives in the syntactic tree is very discriminating for the disambiguation of English discourse connectives.", "role": "user" }, { "content": "[\"Uses\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.AIMS: To conduct a patient-level meta-analysis of the EDITION 1, 2 and 3 studies, which compared the efficacy and safety of new insulin glargine 300 U/ml (Gla-300) with insulin glargine 100 U/ml (Gla-100) in people with type 2 diabetes (T2DM) on basal and mealtime insulin, basal insulin and oral antihyperglycaemic drugs, or no prior insulin, respectively.\n\nABSTRACT.METHODS: The EDITION studies were multicentre, randomized, open-label, parallel-group, phase IIIa studies, with similar designs and endpoints. A patient-level meta-analysis of the studies enabled these endpoints to be examined over 6 months in a large population with T2DM (Gla-300, n = 1247; Gla-100, n = 1249).\n\nABSTRACT.RESULTS: No significant study-by-treatment interactions across studies were found, enabling them to be pooled. The mean change in glycated haemoglobin was comparable for Gla-300 and Gla-100 [each −1.02 (standard error 0.03)%; least squares (LS) mean difference 0.00 (95% confidence interval (CI) −0.08 to 0.07)%]. Annualized rates of confirmed (≤3.9 mmol/l) or severe hypoglycaemia were lower with Gla-300 than with Gla-100 during the night (31% difference in rate ratio over 6 months) and at any time (24 h, 14% difference). Consistent reductions were observed in percentage of participants with ≥1 hypoglycaemic event. Severe hypoglycaemia at any time (24 h) was rare (Gla-300: 2.3%; Gla-100: 2.6%). Weight gain was low (<1 kg) in both groups, with less gain with Gla-300 [LS mean difference −0.28 kg (95% CI −0.55 to −0.01); p = 0.039]. Both treatments were well tolerated, with similar rates of adverse events.\n\nABSTRACT.CONCLUSION: Gla-300 provides comparable glycaemic control to Gla-100 in a large population with a broad clinical spectrum of T2DM, with consistently less hypoglycaemia at any time of day and less nocturnal hypoglycaemia.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"glargine 300 U/ml (Gla-300)\", \"glargine 100 U/ml (Gla-100)\", \"Weight gain\", \"significantly decreased\", \"Weight gain was low (\\u003c1 kg) in both groups, with less gain with Gla-300 [LS mean difference \\u22120.28 kg (95% CI \\u22120.55 to \\u22120.01); p = 0.039].\"], [\"glargine 300 U/ml (Gla-300)\", \"glargine 100 U/ml (Gla-100)\", \"annualized rate (events per participant-year) of confirmed [\\u22643.9 mmol/l (\\u226470 mg/dl)] or severe hypoglycaemia at any time of day (24 h) over the 6-month\", \"significantly decreased\", \"Annualized rates of confirmed (\\u22643.9 mmol/l) or severe hypoglycaemia were lower with Gla-300 than with Gla-100 during the night (31% difference in rate ratio over 6 months) and at any time (24 h, 14% difference).\"]]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\n<cite>Gigaword</cite> test data provided by <cite>Rush et al. (2015)</cite> is already pre-processed.", "role": "user" }, { "content": "[\"Uses\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nRecently, an attention based system<cite> (Zhong et al., 2019)</cite> utilizing both documentlevel and entity-level information achieved stateof-the-art results on WIKIHOP data set, proving that techniques like co-attention and self-attention widely employed in single-document RC tasks are also useful in multi-document RC tasks.", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nTranscription rates in S. cerevisiae range between 0.7 and 2 kb/min.\n\nResearch Abstract:\nA number of proteins and drugs have been implicated in the process of transcriptional elongation by RNA polymerase (Pol) II, but the factors that govern the elongation rate (nucleotide additions per min) and processivity (nucleotide additions per initiation event) in vivo are poorly understood.\nHere, we show that a mutation in the Rpb2 subunit of Pol II reduces both the elongation rate and processivity in vivo.\nIn contrast, none of the putative elongation factors tested affect the elongation rate, although mutations in the THO complex and in Spt4 significantly reduce processivity.\nThe drugs 6-azauracil and mycophenolic acid reduce both the elongation rate and processivity, and this processivity defect is aggravated by mutations in Spt4, TFIIS, and CTDK-1.\nOur results suggest that, in vivo, a reduced rate of Pol II elongation leads to premature dissociation along the chromatin template and that Pol II processivity can be uncoupled from elongation rate.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nDiabetic patients with acute coronary syndrome experience decreased short-term and long-term risk for bleeding events.\n\nResearch Abstract:\nBACKGROUND Although unstable coronary artery disease is the most common reason for admission to a coronary care unit, the long-term prognosis of patients with this diagnosis is unknown.\nThis is particularly true for patients with diabetes mellitus, who are known to have a high morbidity and mortality after an acute myocardial infarction.\nMETHODS AND RESULTS Prospectively collected data from 6 different countries in the Organization to Assess Strategies for Ischemic Syndromes (OASIS) registry were analyzed to determine the 2-year prognosis of diabetic and nondiabetic patients who were hospitalized with unstable angina or non-Q-wave myocardial infarction.\nOverall, 1718 of 8013 registry patients (21%) had diabetes.\nDiabetic patients had a higher rate of coronary bypass surgery than nondiabetic patients (23% versus 20%, P:<0.001) but had similar rates of catheterization and angioplasty.\nDiabetes independently predicted mortality (relative risk [RR], 1.57; 95% CI, 1.38 to 1.81; P:<0.001), as well as cardiovascular death, new myocardial infarction, stroke, and new congestive heart failure.\nMoreover, compared with their nondiabetic counterparts, women had a significantly higher risk than men (RR, 1.98; 95% CI, 1.60 to 2.44; and RR, 1.28; 95% CI, 1.06 to 1.56, respectively).\nInterestingly, diabetic patients without prior cardiovascular disease had the same event rates for all outcomes as nondiabetic patients with previous vascular disease.\nCONCLUSIONS Hospitalization for unstable angina or non-Q-wave myocardial infarction predicts a high 2-year morbidity and mortality; this is especially evident for patients with diabetes.\nDiabetic patients with no previous cardiovascular disease have the same long-term morbidity and mortality as nondiabetic patients with established cardiovascular disease after hospitalization for unstable coronary artery disease.", "role": "user" }, { "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Diabetes independently predicted mortality (relative risk [RR], 1.57; 95% CI, 1.38 to 1.81; P:<0.001), as well as cardiovascular death, new myocardial infarction, stroke, and new congestive heart failure.\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nDiscerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation\n\nMain Content:\n1 Introduction . Neural network based deep learning techniques have attracted great attention from both academia and industry in the past decade . Compared with classic machine learning models , deep neural networks have much higher expressiveness and adaptability for complicated data input , and thus have made tremendous success in various application domains , such as Computational Vision [ 29 , 56 ] , Natural Language Processing [ 54 , 20 ] , and Recommender Systems [ 32 , 24 ] . Nevertheless , since deep neural networks usually have complicated connections of hidden units , a long-standing challenge is how to decipher what ’ s inside the black box of models for understanding their intrinsic decision-making processes . Indeed , in many real-world scenarios , such as business analysis [ 33 , 57 , 49 ] and human resource management [ 50 , 44 , 46 , 44 ] , the lack of model explainability makes people less likely to be convinced when the decision-making process of the model is not understandable . This prevents a broader application of deep neural networks . While many research efforts have been made in developing explainable deep learning models [ 45 , 10 ] , most of existing works focus on post-hoc explanation [ 5 , 18 ] , i.e. , designing metrics to measure the feature relevance/contribution to the outputs of a trained model . Although these methods have * Corresponding authors . †This work was accomplished when Ying Sun was an intern in Baidu supervised by Hengshu Zhu . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . made progress on finding important features , the decision-making process of deep learning models is still not available for users . For example , users often need to guess the reason why a prediction is made from the relevant features instead of understanding the decision-making process . Indeed , understanding the intrinsic decision-making process of deep neural networks is a non-trivial task . A major reason is that deep neural networks usually involve massive feature combinations to gain expressiveness on fitting complicated functions . During this process , the effect of features and hidden units may be largely coupled with each other . This indicates that the decision logic of models is inherently buried in the massive feature combinations . Meanwhile , it is difficult for human to understand the intrinsic modeling process of deep learning in a natural manner . Therefore , a key point on improving the explainability of deep neural networks is to decouple the feature combinations and make the modeling process consistent with human decision process . In this way , the model will have an explicit decision-making process and become human-understandable . To this end , in this paper , we propose an explainable deep learning model , namely Voting Transformation-based Explainable Neural Network ( VOTEN ) . Specifically , VOTEN assumes the transformation from the input to the output to be a hierarchical voting process . During this process , lower-level concepts vote for higher-level concepts layer-by-layer in an expressive but explainable way . Instead of relying on massive feature combinations , VOTEN creatively models expressive single-valued voting functions between explicitly modeled hidden concepts to gain expressiveness on fitting complicated functions . This process is explainable for its consistency with human decisionmaking process . We first theoretically analyze the major components of VOTEN and prove the relationship and advantages of VOTEN compared with Multi-Layer Perceptron ( MLP ) , which is the basic structure of deep neural networks . The results show that MLP can be derived from VOTEN by using the inexpressive voting functions . Accordingly , we further analyze the effect of inherent votings and design efficient algorithms for pruning and explaining VOTEN . Finally , we evaluated VOTEN on multiple real-world datasets with comprehensive experiments . The experimental results demonstrate that VOTEN generally promotes powerful and explainable deep learning . Specifically , VOTEN ( 1 ) significantly raises prediction performance ; ( 2 ) exponentially decreases feature combinations ; and ( 3 ) supports efficient pruning and effective feature analysis . Meanwhile , we also visualize the intrinsic decision-making process of VOTEN through case studies , which show VOTEN is explainable and can discover meaningful latent concepts . 2 VOTEN . In this section , we introduce the technical details of VOTEN . 2.1 Structure . When dealing with complicated information , we usually aggregate them step-by-step to form complicated inference . Although deep neural networks also extract abstract features layer-by-layer , they are still difficult to be explained . A major difference is that we can induce explicit concepts , which makes us able to explain our inference in a straightforward way . For example , we can predict that “ This student may get high GPA because he/she works very hard ” . If the more explicit explanation is needed , we can further explain that “ I observed that he/she stay long in the library ” . In this process , with the observation that “ stay long in the library ” , we first infer “ hard-working ” , then infer “ high GPA ” . Moreover , when trying to make more comprehensive inference , we may get more observations for existing concepts ( e.g. , “ little missing of classes ” may also imply “ hard-working ” ) , or induce more concepts ( e.g. , “ learning-efficiency ” ) . This kind of process is straightforward and conforms with our way of understanding . However , deep neural networks can not model concepts in an explicit way . Instead , concept information are embedded in massive hidden units . As we can not understand these induced concepts , neural networks are like unified , inseparable complicated functions , even though they are actually performing information aggregation . Along this line , we believe a natural way to understand how a model decides the output is to make the intrinsic concepts explicit for human . Therefore , we propose VOTEN , whose structure overview is shown in Figure 1 . VOTEN hierarchically models a small number of explicit concepts . During inference , the higher-level concepts will be induced via information aggregation from the lower-level concepts . To achieve meaningful information aggregation , VOTEN focuses on quantifying relationship between individual concepts of different levels . For better understanding , in VOTEN , observations and concepts can be regarded as voters . Based on their own value , each voter independently votes for the higher-level concepts . The votes are aggregated to get the value estimation of each higher-level concept . These concepts will further vote for the next level . In the training process , the model learns to builds intermediate concepts and their quantitative voting functions . Formally , for a VOTEN model with D levels of concepts , we use Cdi to denote the i-th concept in the d-th layer , where C0i denotes an input feature . We refer to transformations between adjacent levels of concepts as a voting layer . In the d-th voting layer , each concept Cdi votes for each higher-level concept Cd+1j with an independent voting channel Vdi , j . Vdi , j takes the value of Cdi as the input and votes with a single-valued nonlinear function fdi , j : R → R. Then , a counting layer gets weighted average of the votes and estimates the value of Cd+1j as xd+1j = nd∑ k=1 adk , jf d k , j ( x d k ) s.t . ∀d , j , nd∑ k=1 adk , j = 1 , ( 1 ) where nd denotes the number of concepts in the d-th layer , xdk denotes the value of Cdk , adk , j denotes the weight of Vdi , j . The concepts of the last layer is regarded as the decision score , which generates the model output with o = FV OTEN ( xD ) . In particular , to assure the ability of the voting functions on effective concept transformation , VOTEN models each voting function with a voting network . The voting network can be designed in complicated ways without influencing model explainability , as long as the function is still single-valued . For example , we can adopt weight-sharing structures to reduce model complexity . 2.2 Why is VOTEN more explainable than MLP ? . In this part , we theoretically discuss VOTEN ’ s advantages over MLP . Specifically , we claim that voting expressiveness is the core proposition of VOTEN that raises explainability . Theorem 1 MLP can be derived from a subset of degenerated VOTEN models whose voting functions in the form of fdi , j ( x d i ) = w d i , jσ ( x d i ) + b d i , j , where σ is a predefined activation function , the scalars wdi , j , b d i , j ∈ R are trainable parameters . Proof . Please refer to Appendix A . From this point of view , MLP also conforms to human inference . However , MLP is still difficult to explain . Indeed , we can more easily understand an inference process with ( 1 ) fewer concepts , ( 2 ) shorter concept transformations , and ( 3 ) fewer reasoning patterns . Under VOTEN schema , we show how inexpressive voting channels make MLP disobey these three traits . Corollary 1 In MLP , votings from the same concept are linearly correlated . This means individual voters in MLP are weak in distinguishing different concepts in the next level . Therefore , MLP relies on highly complicated feature combinations of a large number of deeply tiled voters to achieve high fitting ability . During this process , necessary intermediate information is inherently modeled through combinatorial effects of hidden concepts with inexplicit meanings . Indeed , previous works have proved that human-understandable concepts are inherently mounted in the hidden units of neural network models [ 28 ] . Corollary 2 In MLP , the hypothesis space for voting distribution is limited to scaling and shifting an input distribution . This means that MLP voters can not always induce complicated concepts that have different distribution from their value . As a result , the input needs to go through a long path of transformations between similar concepts until it contributes to the output . Moreover , the deeply tiled massive concepts make each input feature has massive paths to the output , which brings a large number of possible decision-making patterns of the model . In addition , the effect of votings can easily couple and cancel each other out in the downstream calculations . As a result , separately analyzing the role of individual concepts or decision paths becomes meaningless . Different from MLP , VOTEN has far more expressive voting functions . It directly models nonlinear transformations between essential intermediate concepts without relying on massive feature combinations and naive transformations . As a result , VOTEN ’ s decision-making process is explicit with only a small number of meaningful concepts , thus is explainable . 2.3 Explaining the effect of votings . In VOTEN , individual voting channels play explicit roles in influencing the model decision . In this part , we analyze the effect of votings . First , we use a concept function gdi : Rn0 → R to represent the transformation from model inputs x0 ∈ Rn0 to Cdi , which decides the meaning of the concept . Definition 1 In VOTEN , we refer to two concept functions g and g′ as equivalent iff . there exists an invertible function Φ , so that ∀x0 ∈ Rn0 , Φ ( g ( x0 ) ) = g′ ( x0 ) . Since Φ is invertible , the outputs of two equivalent concept functions have one-to-one correspondence over all the possible inputs . Then , they can effect equally in the decision-making process . Theorem 2 In VOTEN , if replacing a concept function with an equivalent form , there exists a way to replace its voting functions so that all the downstream concept functions stay unchanged . Proof . Please refer to Appendix A . To analyze how voting channels effect on concept functions , we can reformulate Equation 1 as xd+1j = g d+1 j ( x 0 ) = nd∑ i=1 adi , j ( f d i , j ( g d i ( x 0 ) ) − Ex [ fdi , j ( gdi ( x ) ) ] ) + bd+1j , ( 2 ) where Ex [ fdi , j ( gdi ( x ) ) ] denotes the expectation of fdi , j over all the instances , b d+1 j is a sampleindependent bias . In particular , bd+1j = nd∑ i=1 adi , jEx [ fdi , j ( gdi ( x ) ) ] = Ex [ g d+1 j ( x ) ] . ( 3 ) For simplicity , we use hdi , j to denote Ex [ fdi , j ( gdi ( x ) ) ] . Notably , by adding an arbitrary bias to g d+1 j , we obtain an equivalence of the original concept . According to Theorem 2 , we can construct a model with exactly the same expression ( i.e. , equivalent concepts and the same predictions ) as the previous one . This implies that VOTEN may converge to models with differed internal bias but exactly the same decision-making process , indicating VOTEN explanation should be independent of concept bias . According to Equation 2 and 3 , voting channels ’ average only influence concept bias while the voting deviation fdi , j ( g d i ( x 0 ) ) − hdi , j reveals the effect of Vdi , j for the decision . This can be intuitively explained as each voter can vote with different basic scores and only the deviation from the basic score reflects their judgement for a specific instance . Similarly , from the global view , we reformulate the concept function as xd+1j = ∑ i a d i , jσ d i , jK d i , j ( x 0 ) + bd+1j , where K d i , j ( x 0 ) = fdi , j ( g d i ( x 0 ) ) −hdi , j σdi , j . Notably , Kdi , j ( · ) generates a distribution with mean 0 and variance 1 over all the instances . Therefore , adi , j and σ d i , j jointly decide the overall effect of votings . Specifically , the counting layer explicitly adjusts adi , j so that reliable voters have stronger influences . Meanwhile , the voter implicitly adjusts σdi , j . When the concept is less related to the target concept and can not support proper votes , they decrease σdi , j and tend to always vote the basic score to avoid disturbing the model . Otherwise , they increase σdi , j and vote confidently to lead the model to correct estimation . Based on the above analysis , we can easily design algorithms to ease both VOTEN local and global explanation , such as recognizing decision paths and quantifying the concept/feature relevance to the prediction , which can be found in Appendix B and Appendix C .", "role": "user" }, { "content": "This paper proposes a novel type of model, Voting Transformation-based Explainable Neural Network (VOTEN), which represents the transformation from the input to the output to be a hierarchical voting process. This simple structure makes the model interpretable for a human. The papers systematically analyses the advantages of VOTEN, in particular the model's explainability (global and local) and reduced complexity (pruning).", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Cold contact urticaria (CCU) is characterized by itchy wheal and flare responses due to the release of histamine and other pro-inflammatory mediators after exposure to cold. The treatment of choice is nonsedating antihistamines, dosages of which may be increased up to fourfold if standard doses are ineffective. Here, we assess the effects of a standard 20 mg dose and up-dosing to 40 and 80 mg of bilastine in reducing the symptoms of CCU and inflammatory mediator release following cold challenge.\n\nABSTRACT.METHODS: Twenty patients with CCU were included in this randomized, crossover, double-blind, placebo-controlled 12-week study. They received placebo, 20, 40 or 80 mg of bilastine daily each for 7 days with 14-day washout periods. The primary readout was change in critical temperature thresholds (CTT). Secondary readouts were changes in pruritus, levels of histamine IL-6, IL-8 and TNF-α collected by skin microdialysis and safety and tolerability of bilastine.\n\nABSTRACT.RESULTS: Bilastine 20 mg was highly effective (P < 0.0001) in reducing CTT. Up-dosing to 80 mg significantly (P < 0.04) increased its effectiveness. At this dose, 19 of 20 (95%) patients responded to treatment, with 12 of 20 (60%) becoming symptom free. Only one patient was refractory to treatment. Microdialysis levels of histamine, IL-6 and IL-8 assessed 1–3 h after cold challenge were significantly (P < 0.05) decreased following up-dosing with 80 mg bilastine. Bilastine treat-ment was well tolerated without evidence of increased sedation with dose escala-tion.\n\nABSTRACT.CONCLUSIONS: Bilastine was effective in reducing the symptoms of patients with CCU. Increased efficacy of bilastine with fourfold up-dosing was without sedation and supports urticaria treatment guidelines.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Bilastine 20 mg\", \"placebo\", \"Cold contact urticaria (CCU) change in critical temperature thresholds (CTT)\", \"significantly increased\", \"Bilastine 20 mg was highly effective (P \\u003c 0.0001) in reducing CTT.\"], [\"Bilastine updosing to 80 mg\", \"placebo\", \"Cold contact urticaria (CCU) change in critical temperature thresholds (CTT)\", \"significantly increased\", \"Bilastine 20 mg was highly effective (P \\u003c 0.0001) in reducing CTT. Up-dosing to 80 mg significantly (P \\u003c 0.04) increased its effectiveness.\"], [\"Bilastine updosing to 80 mg\", \"placebo\", \"histamine assessed 1\\u20133 h after cold challenge \", \"significantly decreased\", \"Microdialysis levels of histamine, IL-6 and IL-8 assessed 1\\u20133 h after cold challenge were significantly (P \\u003c 0.05) decreased following up-dosing with 80 mg bilastine.\"], [\"Bilastine updosing to 80 mg\", \"placebo\", \"IL-8 assessed 1\\u20133 h after cold challenge \", \"significantly decreased\", \"Microdialysis levels of histamine, IL-6 and IL-8 assessed 1\\u20133 h after cold challenge were significantly (P \\u003c 0.05) decreased following up-dosing with 80 mg bilastine.\"], [\"Bilastine updosing to 80 mg\", \"placebo\", \"IL-6 assessed 1\\u20133 h after cold challenge \", \"significantly decreased\", \"Microdialysis levels of histamine, IL-6 and IL-8 assessed 1\\u20133 h after cold challenge were significantly (P \\u003c 0.05) decreased following up-dosing with 80 mg bilastine.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Aripiprazole is a new anti psychotic with a unique receptor binding profile that combines partial agonistic activity at D2 receptor and 5-HT 1A receptor and potent antagonism at 5-HT 2A receptor. This receptor profile makes it possible for it to act as a dopamine system stabilizer. Based on various short term and long term studies, aripiprazole has been found to be effective in schizophrenia and has no significant adverse effect on QTc prolongation, prolactin, serum lipids, and has a low potential for weight gain. Present study aims to evaluate the efficacy and tolerability of aripiprazole (10-15mg/day) in the treatment of Indian patients of schizophrenia and to see its effect on QTc interval, prolactin levels, serum lipids, plasma sugar and weight gain in these patients. Outpatients with an ongoing/newly diagnosed ICD-10 Schizophrenia (n=136) were randomly assigned to 10 or 15 mg dose of Aripiprazole for a period of six weeks. Clinical response was evaluated by the Positive And Negative Symptoms Scale (PANSS), Clinical Global Impression (CGI) scale and safety was evaluated by observing spontaneously reported adverse events and changes in various laboratory parameters. Switching schizophrenic patients to aripiprazole (10/15 mg) from both conventional and atypical anti-psychotics was safe and well tolerated. Six weeks after switching to aripiprazole, patients showed improvements in PANSS scores (P< 0.001), EPS, prolactin levels and weight over the baseline levels. No difference was seen in the 10 or 15mg dose groups. One hospitalization was reported (due to hepatitis E). Common side effects reported were insomnia, somnolence, nausea, vomiting and diarrhea. Aripiprazole is a safe and effective anti psychotic in Indian patients - both in newly diagnosed, as well as, in patients not responding to or intolerant to other available typical and atypical antipsychotics.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"aripiprazole (10-15mg/day)\", \"baseline\", \"Positive And Negative Symptoms Scale (PANSS)\", \"significantly increased\", \"Six weeks after switching to aripiprazole, patients showed improvements in PANSS scores (P\\u003c 0.001),\"], [\"aripiprazole (10-15mg/day)\", \"baseline\", \"Extrapyramidal symptoms (EPS), prolactin levels and weight\", \"no significant difference\", \"EPS, prolactin levels and weight over the baseline levels\"]]", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nPersistent Homology Captures the Generalization of Neural Networks Without A Validation Set\n\nMain Content:\n1 INTRODUCTION . Generalization is what makes a machine learning model useful ; the uncertainty of its behaviour with unseen data is what makes it potentially dangerous . Thus , understanding the generalization error of a model can be considered one of the holy grails of the entire machine learning field . Machine learning practitioners typically monitor some metrics of the model to estimate its generalization error and stop the training even before the numerical convergence to prevent the overfitting of the model . Usually , the error measure or the metric relevant to the task is computed for a holdout set , the validation set . Since these data have not been directly used for updating the parameters , it is assumed that the performance of the model on the validation set can be used as a proxy of the generalization error , provided it is representative of the data that will be used in inference . One can , though , potentially overfit to this holdout set if is repeatedly used for guiding a hyperparameter search . Instead of relying on an external set , though , the question of whether it could be possible to estimate the generalization error with some intrinsic property of the model is highly relevant , and it has been barely explored in the literature . On the other hand , Algebraic Topology has recently been gaining momentum as a mathematical tool for studying graphs , machine learning algorithms , and data . In this work , we have the goal of , once having characterized neural networks as weighted , acyclic graphs , represented as Algebraic Topology objects ( following previous works ) , computing distances between consecutive neural network states . More specifically , we can calculate the Persistent Homology ( PH ) diagram distances between a give state ( i.e. , when having a specific weights during the training process ) and the next one ( i.e. , after having updated the weights in a training step ) , as depicted in Figure 1 . We observe that during the training procedure of neural networks we can measure this distance in each learning step , and show that there exists a high correlation with the corresponding validation accuracy of the model . We do so in a diverse set of deep learning benchmarks and model hyperparameters . This shines light on the question of whether the generalization error could be estimated from intrinsic properties of the model , and opens the path towards a better theoretical understanding of the dynamics of the training of neural networks . In summary , our contributions are as follows : • Based on principles of Algebraic Topology , we propose measuring the distances ( Silhouette and Heat ) between the PH persistence diagrams obtained from a given state of a neural network during the training procedure and the one in the immediately previous weights update . • We empirically show that the evolution of these measures during training correlate with the accuracy in the validation set . We do so in diverse benchmarks ( MNIST , CIFAR10 , CIFAR100 , Reuters text classification ) , and models ( MLPs in MNIST and Reuters , MLPs and CNNs in CIFAR100 and CIFAR100 ) . • We thus provide empirical proof of the fact that valuable information related to the learning process of neural networks can be obtained from PH distances between persistence diagrams ( we will call this process homological convergence ) . In particular , we show that homological convergence is related to learning process and the generalization properties of neural networks . • In practice , we provide a new tool for monitoring the training of neural networks , and open the path to estimating their generalization error without a validation set . The remainder of this article is as follows . In Section 2 we describe the theoretical background of our proposal in terms of Algebraic Topology , while in Section 3 we go through the related work . Then , in Section 4 we formalize our method . Finally , in sections 6 and 7 we present and discuss our empirical results , respectively . 2 BACKGROUND . In this section we introduce the mathematical foundations of this paper . A detailed mathematical description is included in the Supplementary Material . A simplicial complex is a set composed of points , line segments , triangles , and their n-dimensional counterparts , named simplex ( K ) . In particular , a simplicial complex must comply with two properties : 1 . Every face of a simplex is also in the simplicial complex ( of lower dimension ) . 2 . The non-empty intersection of any two simplices contained on a simplicial complex is a face of both . 0,1,2,3-simplex and non simplex examples are shown in Figure 2 . We can associate to an undirected graph , G = ( V , E ) , a simplicial complex where all the vertices of G are the 0-simplex of the simplicial complex and the complete subgraphs with i vertices , in G corresponds to a ( i−1 ) -simplex . This type of construction is usually called a complex clique on the graph G , and is denoted by Cl ( G ) . Figure 3 shows a graph clique complex Cl ( G ) example . The boundary function is defined as a map , from an i-simplex to an ( i− 1 ) -simplex , as the sum of its ( i− 1 ) -dimensional faces . A boundary function sample is shown in Figure 4 . In algebraic topology , a k-chain is a combination of k-simplices ( sometimes symbolized as a linear combination of simplices that compose the chain ) . The boundary of a k-chain is a ( k−1 ) chain . It is the linear and signed combination of chain element boundary simplices . The space of i-chains is denoted by Ci ( K ) . There are two special cases of chains that will be useful to define homology group : • Closed chain or i-cycle : i-chain with empty boundary . An i-chain c is an i-cycle if and only if ∂ic = 0 , i.e . c ∈ ker ( ∂i ) . This subspace of Ci ( K ) is denoted as Zi ( K ) . • Exact chain or i-boundary : An i-chain c is an i-boundary if there exists an ( i+ 1 ) -chain d such that c = ∂i+1 ( d ) , i.e . c ∈ im ( ∂ i+1 ) . This subspace of Ci ( K ) , the set of all such i-boundaries forms , is denoted by Bi ( K ) . Now , if we consider i-cycles not bounding an ( i+1 ) -simplicial complex , this is the definition of an i-th homology group of the simplicial complex K. The precise definition is the quotient group of Bi ( K ) module Zi ( K ) ( i.e . Bi ( K ) /Zi ( K ) , see Supplementary Material ) . The number of non equivalent i-cycles ( Figure 5 ) is the dimension of the homology group Hi ( K ) , also named Betti numbers . It is possible to compare two PDs using specific distances ( Wasserstein and Bottleneck ) . To efficiently perform this operation , due to the size of these diagrams , it is sometimes necessary to simplify them by means of a discretization process ( such as Weighted Silhouette and Heat vectorizations ) . 3 RELATED WORK . Algebraic Topology and Machine Learning The use of Algebraic Topology in the fields of data science and machine learning has been gaining momentum in recent years ( see Carlsson ( 2009 ) ) . Specifically in the case of neural networks , some works have applied topology for improving the training procedure of the models Hofer et al . ( 2020 ) ; Clough et al . ( 2020 ) , or pruning the model afterwards Watanabe & Yamana ( 2020b ) . Other works have focused on analyzing the capacity of neural networks Guss & Salakhutdinov ( 2018a ) ; Rieck et al . ( 2019b ) ; Konuk & Smith ( 2019 ) or the complexity of input data Konuk & Smith ( 2019 ) . Furthermore , recent works have provided topological analysis of the decision boundaries of classifiers based on PH and Betti numbers Ramamurthy et al . ( 2019 ) ; Naitzat et al . ( 2020 ) . Graph and topological representations of neural networks Gebhart et al . ( 2019 ) suggest a method for computing the PH over the graphical activation neural networks , while Watanabe & Yamana ( 2020a ) propose representing neural networks via simplicial complexes based on Taylor decomposition , from which one can compute the PH . Chowdhury et al . ( 2019 ) show that directed homology can be used to represent MLPs . Anonymous ( 2021 ) concurrently show neural networks , when represented as directed , acyclic graphs , can be associated to an Algebraic Topology object , specifically to a directed flag complex . By computing the PH diagram , one can effectively characterize neural networks , and even compute distances between two given neural networks , which can be used to measure their similarity . This is unlike other works Corneanu et al . ( 2019 ) ; Guss & Salakhutdinov ( 2018b ) approximating neural networks representations with regard to the input space . Relevant to our work , in Rieck et al . ( 2019b ) authors propose a complexity measure of neural networks based on persistent homology . However , we will see that their representation does not fulfill our requirements in Section 4 . Estimating the generalization and studying the learning process We are , though , specifically interested in the use of PH for analyzing the learning process , especially with the goal of estimating generalization . In this regard , the literature is perhaps more limited . Jiang et al . ( 2019 ) work on understanding what drives generalization in deep networks from a Bayesian of view . Neyshabur et al . ( 2017 ) study the generalization gap prediction from the training data and network parameters using a margin distribution , which are the distances of training points to the decision boundary . In Li et al . ( 2020 ) , authors propose an alternative to cross-validation for model selection based on training once on the whole train set , without any data split , deriving a validation set with data augmentation . Corneanu et al . ( 2020 ) try to estimate the performance gap between training and testing using PH measures . They claim . However , one can observe some caveats . The first one is that their regression fitted to predict the test error has a considerably high error , making it not usable in practice . The second caveat is that for fitting the regression one needs at least part of the sequestered testing set . In this work , motivated by the interest of having a better understanding of whether it would be possible to estimate the generalization of neural networks without a holdout set , we suggest using the topological characterization and distances concurrently proposed in Anonymous ( 2021 ) but , crucially , measured between consecutive weight updates . We will show that the evolution of this distance is similar to the one of the validation accuracy . Unlike Li et al . ( 2020 ) , we do not use any data at all . Unlike Corneanu et al . ( 2020 ) , we do not build a statistical or machine learning model ( linear regression ) for predicting the testing error . Instead , we propose a new measure , and we empirically show that it highly correlates with the validation accuracy . Note that in this work we do not work with any input data and activations , but with the parameters of the neural network themselves . The code and outputs are fully available in the Supplementary Material under a MIT License .", "role": "user" }, { "content": "This paper presents some empirical observations about the relationship of a persistent homology-based measure of learning dynamics and validation set error, during the training of deep neural nets. The paper opens with an introduction on persistent homology, then introduces an approach to study the structure of deep nets using topological data analysis (TDA) tools. Three case studies are then presented, for which a measure of change in topological structure of the network during training is compared with the validation set error. The argument of the paper is that the two measures are correlated, and therefore it may be possible to use the topological measure (which depends on the structure of the network only, and not on data) in place of the validation set error to assess the generalization performance of a deep net.", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nTo benchmark our method, we use two of the same datasets as these two papers, and repurpose the best-performing methods of<cite> Salehi and Cook (2013)</cite> and Salehi et al. (2014) for classification of the compositionality of each MWE component.", "role": "user" }, { "content": "[\"Uses\"]", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Loss-of-function mutations of EZH2, a catalytic component of polycomb repressive complex 2 (PRC2), are observed in ~\\n10% of patients with myelodysplastic syndrome (MDS), but are rare in acute myeloid leukaemia (AML)\nWe describe the use of an oligo-SNP array for genomic profiling of aCNA and cnLOH, together with sequence analysis of recurrently mutated genes, in a patient with myelodysplastic syndrome (MDS) presenting with normal karyotype and FISH result\nConditionally deleting Ezh2 in mature T cells dramatically reduced the production of BM-destructive Th1 cells in vivo, decreased BM-infiltrating Th1 cells, and rescued mice from BM failure.\nAcquired aplastic anemia (AA) is a potentially fatal bone marrow (BM) failure syndrome\nConstitutional NSD1 and EZH2 mutations cause Sotos and Weaver syndromes respectively, overgrowth syndromes with considerable phenotypic overlap.\nEZH2 mutations that cause Weaver syndrome are primarily missense variants and the rare truncating mutations reported to date are in the last exon, suggesting that simple haploinsufficiency is unlikely to be generating the overgrowth phenotype although the exact mechanism has not yet been determined\nWeaver syndrome and EZH2 mutations\nIn 2011, mutations in the histone methyltransferase, EZH2, were shown to cause Weaver syndrome\nThe identification of an EZH2 mutation can therefore provide an objective means of confirming a subtle presentation of Weaver syndrome and/or distinguishing Weaver and Sotos syndromes.\nMutations in EZH2 cause Weaver syndrome\nThese data show that mutations in EZH2 cause Weaver syndrome\nThe EZH2 mutation spectrum in Weaver syndrome shows considerable overlap with the inactivating somatic EZH2 mutations recently reported in myeloid malignancies\nEZH2 mutations as the cause of Weaver syndrome and provide further links between histone modifications and regulation of human growth.\nMutations at tyrosine 641 (Y641F, Y641N, Y641S and Y641H) in the SET domain of EZH2 have been identified in patients with certain subtypes of non-Hodgkin lymphoma (NHL)\nThe EZH2 gene was previously reported to be located on chromosome 21q22 and was proposed as a candidate gene for some characteristics of the Down syndrome phenotype\nRecent extensive mutation analyses of the myeloid malignancies have revealed that inactivating somatic mutations in PcG genes such as EZH2 and ASXL1 occur frequently in patients with myelodysplastic disorders including myelodysplastic syndromes (MDSs) and MDS/myeloproliferative neoplasm (MPN) overlap disorders (MDS/MPN).\nEZH2 is frequently overexpressed and considered to be an oncogene in cancers; nevertheless, EZH2 is considered as a candidate tumor suppressor gene in MDS due to EZH2 mutations associated with poor survival.\nIn 2011, mutations in the histone methyltransferase, EZH2, were shown to cause Weaver syndrome.\nAmong them, Sotos and Weaver syndromes are clinically well defined and due to heterozygous mutations in NSD1 and EZH2, respectively.\nWeaver syndrome and EZH2 mutations: Clarifying the clinical phenotype.\nConstitutional NSD1 and EZH2 mutations cause Sotos and Weaver syndromes respectively, overgrowth syndromes with considerable phenotypic overlap.\nThe identification of an EZH2 mutation can therefore provide an objective means of confirming a subtle presentation of Weaver syndrome and/or distinguishing Weaver and Sotos syndromes.\nWe postulated that mutations in writers of these two chromatin marks could cause overgrowth conditions, resembling Sotos or Weaver syndromes, in patients with no NSD1 or EZH2 abnormalities.\nRecent extensive mutation analyses of the myeloid malignancies have revealed that inactivating somatic mutations in PcG genes such as EZH2 and ASXL1 occur frequently in patients with myelodysplastic disorders including myelodysplastic syndromes (MDSs) and MDS/myeloproliferative neoplasm (MPN) overlap disorders (MDS/MPN).\nSomatic mutations of epigenetic gene regulators are common in patients with myelodysplastic syndromes (MDS) and correlate with some clinical and laboratory features.\nRecently, the advent of next generation sequencing (NGS) techniques has helped identify somatic gene mutations in 75-80% of MDS, that cluster mainly in four functional groups, i.e. cytokine signaling (RAS genes), DNA methylation, (TET2, IDH1/2, DNMT3a genes) histone modifications (ASXL1 and EZH2 genes), and spliceosome (SF3B1 and SRSF2 genes) along with mutations of RUNX1 and TP 53 genes.\nConstitutional NSD1 and EZH2 mutations cause Sotos and Weaver syndromes respectively, overgrowth syndromes with considerable phenotypic overlap. NSD1 mutations that cause Sotos syndrome are loss-of-function, primarily truncating mutations or missense mutations at key residues in functional domains.\nEZH2 is considered as a candidate tumor suppressor gene in MDS due to EZH2 mutations associated with poor survival.\nConsiderable phenotypic overlap between Sotos and Weaver syndromes is also evident. The identification of an EZH2 mutation can therefore provide an objective means of confirming a subtle presentation of Weaver syndrome and/or distinguishing Weaver and Sotos syndromes.\nEZH2 is frequently overexpressed and considered to be an oncogene in cancers; nevertheless, EZH2 is considered as a candidate tumor suppressor gene in MDS due to EZH2 mutations associated with poor survival.\nConstitutional NSD1 and EZH2 mutations cause Sotos and Weaver syndromes respectively, overgrowth syndromes with considerable phenotypic overlap.\nThe deletion involved multiple genes, including two tumor suppressor candidate genes (CTNNA1 and HSPA9) that are associated with MDS/AML.\nSomatic mutations of epigenetic gene regulators are common in patients with myelodysplastic syndromes (MDS) and correlate with some clinical and laboratory features.\n\nQuestion: Which syndromes are associated with mutations in the EZH2 gene?", "role": "user" }, { "content": "[\"myelodysplastic syndrome (MDS)\", \"Acquired aplastic anemia (AA)\", \"Weaver syndrome\", \"non-Hodgkin lymphoma (NHL)\", \"myeloid leukaemia\"]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nProgerin induces premature aging in stem cells.\n\nResearch Abstract:\nReprogramming somatic cells to induced pluripotent stem cells (iPSCs) resets their identity back to an embryonic age and, thus, presents a significant hurdle for modeling late-onset disorders.\nIn this study, we describe a strategy for inducing aging-related features in human iPSC-derived lineages and apply it to the modeling of Parkinson's disease (PD).\nOur approach involves expression of progerin, a truncated form of lamin A associated with premature aging.\nWe found that expression of progerin in iPSC-derived fibroblasts and neurons induces multiple aging-related markers and characteristics, including dopamine-specific phenotypes such as neuromelanin accumulation.\nInduced aging in PD iPSC-derived dopamine neurons revealed disease phenotypes that require both aging and genetic susceptibility, such as pronounced dendrite degeneration, progressive loss of tyrosine hydroxylase (TH) expression, and enlarged mitochondria or Lewy-body-precursor inclusions.\nThus, our study suggests that progerin-induced aging can be used to reveal late-onset age-related disease features in hiPSC-based disease models.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We found that expression of progerin in iPSC-derived fibroblasts and neurons induces multiple aging-related markers and characteristics, including dopamine-specific phenotypes such as neuromelanin accumulation.\", \"Thus, our study suggests that progerin-induced aging can be used to reveal late-onset age-related disease features in hiPSC-based disease models.\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: There are inconsistent findings on the effects of vitamin K on bone mineral density (BMD) and undercarboxylated osteocalcin (UcOC). The present intervention study evaluated the effect in subjects over 60-yr-old. The vitamin K group (vitamin K + vitamin D + calcium supplement; 15 mg of vitamin K2 [menatetrenone] three times daily, 400 IU of vitamin D once a day, and 315 mg of calcium twice daily) and the control group (vitamin D + calcium supplement) were randomly assigned. During the six months of treatment, seventy eight women participated (38 in the vitamin K group and 40 in the control group) and 45 women completed the study. The baseline characteristics of study participants did not differ between the vitamin K and the control groups. In a per protocol analysis after 6 months, L3 bone mineral density has increased statistically significantly in the vitamin K group compared to the control group (0.01 ± 0.03 g/cm2 vs -0.008 ± 0.04 g/cm2, P = 0.049). UcOC concentration was also significantly decreased in the vitamin K group (-1.6 ± 1.6 ng/dL vs -0.4 ± 1.1 ng/dL, P = 0.008). In conclusion, addition of vitamin K to vitamin D and calcium supplements in the postmenopausal Korean women increase the L3 BMD and reduce the UcOC concentration.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Vitamin K + supplementation\", \"Supplementation alone\", \"Mineral density\", \"significantly increased\", \"L3 bone mineral density has increased statistically significantly in the vitamin K group compared to the control group (0.01 \\u00b1 0.03 g/cm2 vs -0.008 \\u00b1 0.04 g/cm2, P = 0.049).\"], [\"Vitamin K + supplementation\", \"Supplementation alone\", \"Undercarboxylated osteocalcin\", \"significantly decreased\", \"L3 bone mineral density has increased statistically significantly in the vitamin K group compared to the control group (0.01 \\u00b1 0.03 g/cm2 vs -0.008 \\u00b1 0.04 g/cm2, P = 0.049). UcOC concentration was also significantly decreased in the vitamin K group (-1.6 \\u00b1 1.6 ng/dL vs -0.4 \\u00b1 1.1 ng/dL, P = 0.008).\"]]", "role": "assistant" } ]

[ { "content": "| T (degC) | u (mmmin-1 ) |\n| 271.0 | 0.075+-0.008 |\n| 278.0 | 0.18+-0.01 |\n| 283.5 | 0.31+-0.03 |\n| 287.5 | 0.42+-0.03 |\n| 291.5 | 0.83+-0.08 |\n| 295.5 | 1.10+-0.09 |\n| 300.0 | 2.1+-0.1 |\n| 305.0 | 3.3+-0.5 |\n| 310.0 | 5.1+-0.7 |\n\nCaption: The crystal growth rate in (GeS2)0.3(Sb2S3)0.7 supercooled melt\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"271.0\", \"type\": \"Other\"}\n{\"value\": \"0.075\", \"type\": \"Other\"}\n{\"value\": \"278.0\", \"type\": \"Other\"}\n{\"value\": \"0.18\", \"type\": \"Other\"}\n{\"value\": \"283.5\", \"type\": \"Other\"}\n{\"value\": \"0.31\", \"type\": \"Other\"}\n{\"value\": \"287.5\", \"type\": \"Other\"}\n{\"value\": \"0.42\", \"type\": \"Other\"}\n{\"value\": \"291.5\", \"type\": \"Other\"}\n{\"value\": \"0.83\", \"type\": \"Other\"}\n{\"value\": \"295.5\", \"type\": \"Other\"}\n{\"value\": \"1.10\", \"type\": \"Other\"}\n{\"value\": \"300.0\", \"type\": \"Other\"}\n{\"value\": \"2.1\", \"type\": \"Other\"}\n{\"value\": \"305.0\", \"type\": \"Other\"}\n{\"value\": \"3.3\", \"type\": \"Other\"}\n{\"value\": \"310.0\", \"type\": \"Other\"}\n{\"value\": \"5.1\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Laparoscopic surgery is associated with a more favorable clinical outcome than that of conventional open surgery. This might be related to the magnitude of the tissue trauma. The aim of the present study was to examine the differences of the neuroendocrine and inflammatory responses between the two surgical techniques.\n\nABSTRACT.METHODS: Twenty-four patients with no major medical disease were randomly assigned to undergo laparoscopic (n = 13) or abdominal hysterectomy (n = 11). Venous blood samples were collected and we measured the levels of interleukin-6 (IL-6), CRP and cortisol at the time before and after skin incision, at the end of peritoneum closure and at 1 h and 24 h after operation.\n\nABSTRACT.RESULTS: The laparoscopic hysterectomy group demonstrated less of an inflammatory response in terms of the serum IL-6 and CRP responses than did the abdominal hysterectomy group, and the laparoscopic hysterectomy group had a shorter hospital stay (P < 0.05). The peak serum IL-6 (P < 0.05) and CRP concentrations were significantly less increased in the laparoscopic group as compared with that of the abdominal hysterectomy group (P < 0.05), while the serum cortisol concentration showed a similar time course and changes and there were no significant difference between the groups. The response of interleukin-6 showed a significant correlation with the response of CRP (r = 0.796; P < 0.05).\n\nABSTRACT.CONCLUSIONS: The laparoscopic surgical procedure leaves the endocrine metabolic response largely unaltered as compared with that of open abdominal hysterectomy, but it reduces the inflammatory response as measured by the IL-6 and CRP levels.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Laparoscopic hysterectomy\", \"Abdominal hysterectom\", \"Cortisol levels \", \"no significant difference\", \"The peak serum IL-6 (P \\u003c 0.05) and CRP concentrations were significantly less increased in the laparoscopic group as compared with that of the abdominal hysterectomy group (P \\u003c 0.05), while the serum cortisol concentration showed a similar time course and changes and there were no significant difference between the groups.\"], [\"Laparoscopic hysterectomy\", \"Abdominal hysterectom\", \"IL-6 levels \", \"significantly decreased\", \"The peak serum IL-6 (P \\u003c 0.05) and CRP concentrations were significantly less increased in the laparoscopic group as compared with that of the abdominal hysterectomy group (P \\u003c 0.05)\"], [\"Laparoscopic hysterectomy\", \"Abdominal hysterectom\", \"IL-6 levels \", \"significantly decreased\", \"The laparoscopic hysterectomy group demonstrated less of an inflammatory response in terms of the serum IL-6 and CRP responses than did the abdominal hysterectomy group\"], [\"Laparoscopic hysterectomy\", \"Abdominal hysterectom\", \"C-reactive protein \", \"significantly decreased\", \"The peak serum IL-6 (P \\u003c 0.05) and CRP concentrations were significantly less increased in the laparoscopic group as compared with that of the abdominal hysterectomy group (P \\u003c 0.05),\"], [\"Laparoscopic hysterectomy\", \"Abdominal hysterectom\", \"Hospital stay\", \"significantly decreased\", \"the laparoscopic hysterectomy group had a shorter hospital stay (P \\u003c 0.05).\"]]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nIn young and middle-aged adults, current or remote uses of ADHD medications increase the risk of serious cardiovascular events.\n\nResearch Abstract:\nCONTEXT More than 1.5 million US adults use stimulants and other medications labeled for treatment of attention-deficit/hyperactivity disorder (ADHD).\nThese agents can increase heart rate and blood pressure, raising concerns about their cardiovascular safety.\nOBJECTIVE To examine whether current use of medications prescribed primarily to treat ADHD is associated with increased risk of serious cardiovascular events in young and middle-aged adults.\nDESIGN, SETTING, AND PARTICIPANTS Retrospective, population-based cohort study using electronic health care records from 4 study sites (OptumInsight Epidemiology, Tennessee Medicaid, Kaiser Permanente California, and the HMO Research Network), starting in 1986 at 1 site and ending in 2005 at all sites, with additional covariate assessment using 2007 survey data.\nParticipants were adults aged 25 through 64 years with dispensed prescriptions for methylphenidate, amphetamine, or atomoxetine at baseline.\nEach medication user (n = 150,359) was matched to 2 nonusers on study site, birth year, sex, and calendar year (443,198 total users and nonusers).\nMAIN OUTCOME MEASURES Serious cardiovascular events, including myocardial infarction (MI), sudden cardiac death (SCD), or stroke, with comparison between current or new users and remote users to account for potential healthy-user bias.\nRESULTS During 806,182 person-years of follow-up (median, 1.3 years per person), 1357 cases of MI, 296 cases of SCD, and 575 cases of stroke occurred.\nThere were 107,322 person-years of current use (median, 0.33 years), with a crude incidence per 1000 person-years of 1.34 (95% CI, 1.14-1.57) for MI, 0.30 (95% CI, 0.20-0.42) for SCD, and 0.56 (95% CI, 0.43-0.72) for stroke.\nThe multivariable-adjusted rate ratio (RR) of serious cardiovascular events for current use vs nonuse of ADHD medications was 0.83 (95% CI, 0.72-0.96).\nAmong new users of ADHD medications, the adjusted RR was 0.77 (95% CI, 0.63-0.94).\nThe adjusted RR for current use vs remote use was 1.03 (95% CI, 0.86-1.24); for new use vs remote use, the adjusted RR was 1.02 (95% CI, 0.82-1.28); the upper limit of 1.28 corresponds to an additional 0.19 events per 1000 person-years at ages 25-44 years and 0.77 events per 1000 person-years at ages 45-64 years.\nCONCLUSIONS Among young and middle-aged adults, current or new use of ADHD medications, compared with nonuse or remote use, was not associated with an increased risk of serious cardiovascular events.\nApparent protective associations likely represent healthy-user bias.", "role": "user" }, { "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The multivariable-adjusted rate ratio (RR) of serious cardiovascular events for current use vs nonuse of ADHD medications was 0.83 (95% CI, 0.72-0.96).\", \"Among new users of ADHD medications, the adjusted RR was 0.77 (95% CI, 0.63-0.94).\", \"CONCLUSIONS Among young and middle-aged adults, current or new use of ADHD medications, compared with nonuse or remote use, was not associated with an increased risk of serious cardiovascular events.\"]}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nPrimPol generates short DNA replication intermediates on the leading strand during DNA replication.\n\nResearch Abstract:\nPrim-pol is a recently identified DNA primase-polymerase belonging to the archaeao-eukaryotic primase (AEP) superfamily.\nHere, we characterize a previously unrecognized prim-pol in human cells, which we designate hPrimpol1 (human primase-polymerase 1).\nhPrimpol1 possesses primase and DNA polymerase activities in vitro, interacts directly with RPA1 and is recruited to sites of DNA damage and stalled replication forks in an RPA1-dependent manner.\nCells depleted of hPrimpol1 display increased spontaneous DNA damage and defects in the restart of stalled replication forks.\nBoth RPA1 binding and the primase activity of hPrimpol1 are required for its cellular function during DNA replication.\nOur results indicate that hPrimpol1 is a novel factor involved in the response to DNA replication stress.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Through characterization of CRISPR enzymology and application development, we report here four advances integrated into SHERLOCK version 2 (SHERLOCKv2) (i) four-channel single-reaction multiplexing with orthogonal CRISPR enzymes; (ii) quantitative measurement of input as low as 2 attomolar; (iii) 3.5-fold increase in signal sensitivity by combining Cas13 with Csm6, an auxiliary CRISPR-associated enzyme; and (iv) lateral-flow readout.\n\nQuestion: List the four advances integrated into the SHERLOCKv2 platform.", "role": "user" }, { "content": "[\"four-channel single-reaction multiplexing with orthogonal CRISPR enzymes\", \"quantitative measurement of input as low as 2 attomolar\", \"3.5-fold increase in signal sensitivity by combining Cas13 with Csm6\", \"an auxiliary CRISPR-associated enzyme\", \"lateral-flow readout\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Lack of spiritual health in patients with hypertension leads to many mental, social, and physical effects, On the other hand, considering the prevalence of hypertension among the elderly, interventions to enhance their spiritual wellbeing is essential. Therefore, the aim of this study was to examine the effect of religious programs based on Islam on spiritual wellbeing in elderly patients with hypertension who referred to the health centers of Isfahan in 2014.\n\nABSTRACT.MATERIALS AND METHODS:: This study was a randomized clinical trial. The participants (52 elderly patients with hypertension) were randomly divided in to experimental and control groups. Religious program was implemented for the experimental group in eight sessions in two Isfahan health centers. Spirituality wellbeing survey (SWB) questionnaire was completed in three steps, namely, pretest, posttest and follow-up (1 month) in two groups. In the study, Chi-square test, independent t-test, and repeated-measures analysis of variance were performed for analyzing the data.\n\nABSTRACT.RESULTS:: Before the intervention, there was no significant difference between the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing of the two groups. However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P < 0.001).\n\nABSTRACT.CONCLUSIONS:: The religious program based on Islam promoted the SWB of elderly patients with hypertension; further, nurses can use these programs to promote the SWB of elderly patients with hypertension.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Religious programs\", \"Control \", \"Religious dimension improvement right after test \", \"significantly increased\", \"However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"], [\"Religious programs\", \"Control \", \"Religious dimension improvement 1 month after test \", \"significantly increased\", \"However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"], [\"Religious programs\", \"Control \", \"Spiritual existential aspect 1 month after test \", \"significantly increased\", \". However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"], [\"Religious programs\", \"Control \", \"Spiritual well-being right after test \", \"significantly increased\", \"in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"], [\"Religious programs\", \"Control \", \"Spiritual well-being 1 month after test \", \"significantly increased\", \"However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"], [\"Religious programs\", \"Control \", \"Spiritual existential aspect right after test \", \"significantly increased\", \"However in the posttest step and follow-up stage, the mean scores of spiritual wellbeing, the religious dimension, and the existential aspect of spiritual wellbeing in the experimental group was significantly higher than in the control group (P \\u003c 0.001).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVES:: Ankle sprains are among the most common sports-related injuries and are often self-treated. Over-the-counter treatment options known to reduce pain and inflammation, such as oral non-steroidal anti-inflammatory drugs (NSAIDs), may be associated with systemic side effects (e.g. gastrointestinal), while topically applied rubefacients are frequently ineffective, despite their popularity. DSG 1%, which has been clinically proven to be effective and well-tolerated for the treatment of osteoarthritis, could be a new option for the treatment of acute injuries, such as ankle sprains. This study evaluated the efficacy and safety of DSG 1% applied q.i.d in subjects with acute ankle sprain.\n\nABSTRACT.METHODS:: In a double-blind, multicenter study, 205 subjects with acute sprain of the lateral ankle (Grade l-ll) were randomized in a 1:1 ratio to DSG 1% (n=102) or placebo (n=103) applied q.i.d for 7 days. The primary efficacy outcome was pain-on-movement (POM) at 72 hours, assessed on a 100 mm visual analog scale (VAS). Secondary efficacy outcomes included ankle swelling assessed by circumference measurement compared with the non-affected ankle, tenderness measured by pressure algometry compared with the non-affected ankle, and ankle joint function assessed by the Karlsson scoring scale. Efficacy assessments were conducted at clinic visits at 12, 24, and 72 hours and 7 days after treatment initiation. All adverse events were recorded and blood samples were collected for laboratory safety analysis.\n\nABSTRACT.RESULTS:: DSG 1% treatment resulted in a significant decrease of POM mean scores when compared with placebo (57 mm vs. 21 mm respectively; p<0.0001). DSG 1% treatment also resulted in significantly greater reductions in ankle swelling than placebo. The difference in circumference between the injured and non-affected ankles for subjects treated with DSG 1% vs. placebo was 1.9 cm vs. 2.4 cm at 12 hours, 1.4 cm vs. 2.0 cm at 24 hours, 0.8 cm vs. 1.5 cm at 72 hours, and 0.2 cm vs. 0.8 cm at 7 days (p<0.0001 at all time points). In subjects treated with DSG 1%, the mean difference in tenderness between the injured and non-affected ankles decreased more rapidly than in subjects in the placebo group at 12 hours (-24.7 N/cm2 vs. -28.5 N/cm2), 24 hours (-17.8 N/cm2 vs. -25.3 N/cm2), 72 hours (-9.7 N/cm2 vs. -19.8 N/cm2), and 7 days (-2.6 N/cm2 vs. -12.1 N/cm2) (p<0.0001 at all time points). In subjects treated with DSG 1%, the mean total ankle joint function score was 24.5 at baseline vs. 25.3 for the placebo group. At 72 hours and Day 7, mean ankle joint function scores were 56.3 and 79.7, respectively, for the DSG 1% group vs. 35.6 and 47.0, respectively, for the placebo group. The differences between treatment groups were significant at all time points (p<0.0001). The safety profile of DSG 1% was similar to that of placebo.\n\nABSTRACT.CONCLUSION:: DSG 1% applied 4 times daily was significantly superior to placebo in reducing pain, swelling and tenderness and improving ankle joint function in subjects with acute ankle sprain. Overall, DSG 1% was well tolerated both systemically and locally.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"DSG 1%\", \"placebo\", \"pain-on-movement (POM)\", \"significantly decreased\", \"DSG 1% treatment resulted in a significant decrease of POM mean scores when compared with placebo (57 mm vs. 21 mm respectively; p\\u0026lt;0.0001).\"], [\"DSG 1%\", \"placebo\", \"pain-on-movement (POM)\", \"significantly decreased\", \"DSG 1% treatment resulted in a significant decrease of POM mean scores when compared with placebo (57 mm vs. 21 mm respectively; p\\u003c0.0001).\"], [\"DSG 1%\", \"placebo\", \"ankle swelling\", \"significantly decreased\", \"The difference in circumference between the injured and non-affected ankles for subjects treated with DSG 1% vs. placebo was 1.9 cm vs. 2.4 cm at 12 hours, 1.4 cm vs. 2.0 cm at 24 hours, 0.8 cm vs. 1.5 cm at 72 hours, and 0.2 cm vs. 0.8 cm at 7 days (p\\u0026lt;0.0001 at all time points).\"], [\"DSG 1%\", \"placebo\", \"ankle swelling\", \"significantly decreased\", \"DSG 1% treatment also resulted in significantly greater reductions in ankle swelling than placebo.\"], [\"DSG 1%\", \"placebo\", \"the mean total ankle joint function score\", \"significantly increased\", \"In subjects treated with DSG 1%, the mean total ankle joint function score was 24.5 at baseline vs. 25.3 for the placebo group. At 72 hours and Day 7, mean ankle joint function scores were 56.3 and 79.7, respectively, for the DSG 1% group vs. 35.6 and 47.0, respectively, for the placebo group. The differences between treatment groups were significant at all time points (p\\u0026lt;0.0001).\"], [\"DSG 1%\", \"placebo\", \"the mean total ankle joint function score\", \"significantly increased\", \"At 72 hours and Day 7, mean ankle joint function scores were 56.3 and 79.7, respectively, for the DSG 1% group vs. 35.6 and 47.0, respectively, for the placebo group. The differences between treatment groups were significant at all time points (p\\u003c0.0001).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nDetection of heterozygous mutations in BRCA1 using high density oligonucleotide arrays and two-colour fluorescence analysis. The ability to scan a large gene rapidly and accurately for all possible heterozygous mutations in large numbers of patient samples will be critical for the future of medicine. We have designed high-density arrays consisting of over 96, 600 oligonucleotides 20-nucleotides (nt) in length to screen for a wide range of heterozygous mutations in the 3. 45-kilobases (kb) exon 11 of the hereditary breast and ovarian cancer gene BRCA1. Reference and test samples were co-hybridized to these arrays and differences in hybridization patterns quantitated by two-colour analysis. Fourteen of fifteen patient samples with known mutations were accurately diagnosed, and no false positive mutations were identified in 20 control samples. Eight single nucleotide polymorphisms were also readily detected. DNA chip-based assays may provide a valuable new technology for high-throughput cost-efficient detection of genetic alterations.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [], \"Gene\": [\"breast and ovarian cancer gene\", \"BRCA1\"], \"Species\": [\"patient\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Will Sofosbuvir/Ledipasvir (Harvoni) Be Cost-Effective and Affordable for Chinese Patients Infected with Hepatitis C Virus?\nThe developed method was applied to the analysis of the two drugs after a single oral administration of Harvoni 400/90 mg film-coated tablets containing 400 mg sofosbuvir and 90 mg ledipasvir to four healthy volunteers.\nAfter the introductions of sofosbuvir (Sovaldi) and ledipasvir plus sofosbuvir (Harvoni) for the treatment of hepatitis C, employers have become very sensitive to new, and especially unforeseen, factors that significantly raise healthcare costs.\nThe single-tablet regimen of the hepatitis C virus (HCV) NS5A inhibitor ledipasvir and the HCV NS5B polymerase inhibitor sofosbuvir (ledipasvir/sofosbuvir; Harvoni(®)) was recently approved in the US and the EU.\nLedipasvir/Sofosbuvir (harvoni): improving options for hepatitis C virus infection.\nLedipasvir/sofosbuvir (Harvoni): improving options for hepatitis C virus infection.\nAlso reviewed were recent practice guidelines on the management of HCV infections, prescribing information on all HCV drugs approved by the US Food and Drug Administration, and health technology assessments of Sovaldi® and Harvoni(TM) (sofosbuvir/ledipasvir).\nLedipasvir/Sofosbuvir (harvoni): improving options for hepatitis C virus infection.\nThis review will summarize the data regarding these interferon-free regimens, including Gilead's Harvoni (sofosbuvir/ledipasvir), AbbVie's Viekira Pak (paritaprevir/ritonavir/ombitasvir with dasabuvir), and Janssen's Olysio (simeprevir) with sofosbuvir.\n\nQuestion: Which two drugs are included in the Harvoni pill?", "role": "user" }, { "content": "[\"sofosbuvir\", \"ledipasvir\"]", "role": "assistant" } ]

[ { "content": "| Element line | Bonding type | E B (eV) | E B (eV) | E B (eV) | Relative intensity (%) | Relative intensity (%) |\n| Element line | Bonding type | RM * | CS | CD | CS | CD |\n| Si2p | Si-Si | 98.8 | 99.5 | 0 | 3.9 | 0 |\n| Si2p | Si-C | 0 | 101.6 | 100.9 | 22.6 | 6.2 |\n| Si2p | Si-O | 103.3 | 0 | 0 | 0 | 0 |\n| B1s | B-N | 190.5 | 190.0 | 190.8 | 4.7 | 14.6 |\n| B1s | B-C-N | 0 | 192.1 | 192.7 | 9.2 | 7.0 |\n| B1s | B-O | 193.4 | 0 | 0 | 0 | 0 |\n| C1s | C-Si | 0 | 283.7 | 283.9 | 19.3 | 28.7 |\n| C1s | C-C | 284.3 | 284.8 | 285.0 | 26.6 | 12.9 |\n| C1s | C-B-N/C-O | 286.7 | 287.3 | 286.2 | 6.6 | 15.4 |\n| N1s | N-B | 398.4 | 398.3 | 398.2 | 2.0 | 7.8 |\n| | N-B-C | 0 | 399.4 | 400.0 | 5.0 | 7.4 |\n\nCaption: Results of deconvolution and the relative concentration of different chemical binding state for the raw materials, CS, and CD.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"98.8\", \"type\": \"Other\"}\n{\"value\": \"99.5\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"3.9\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"101.6\", \"type\": \"Other\"}\n{\"value\": \"100.9\", \"type\": \"Other\"}\n{\"value\": \"22.6\", \"type\": \"Other\"}\n{\"value\": \"6.2\", \"type\": \"Other\"}\n{\"value\": \"103.3\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"190.5\", \"type\": \"Other\"}\n{\"value\": \"190.0\", \"type\": \"Other\"}\n{\"value\": \"190.8\", \"type\": \"Other\"}\n{\"value\": \"4.7\", \"type\": \"Other\"}\n{\"value\": \"14.6\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"192.1\", \"type\": \"Other\"}\n{\"value\": \"192.7\", \"type\": \"Other\"}\n{\"value\": \"9.2\", \"type\": \"Other\"}\n{\"value\": \"7.0\", \"type\": \"Other\"}\n{\"value\": \"193.4\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"283.7\", \"type\": \"Other\"}\n{\"value\": \"283.9\", \"type\": \"Other\"}\n{\"value\": \"19.3\", \"type\": \"Other\"}\n{\"value\": \"28.7\", \"type\": \"Other\"}\n{\"value\": \"284.3\", \"type\": \"Other\"}\n{\"value\": \"284.8\", \"type\": \"Other\"}\n{\"value\": \"285.0\", \"type\": \"Other\"}\n{\"value\": \"26.6\", \"type\": \"Other\"}\n{\"value\": \"12.9\", \"type\": \"Other\"}\n{\"value\": \"286.7\", \"type\": \"Other\"}\n{\"value\": \"287.3\", \"type\": \"Other\"}\n{\"value\": \"286.2\", \"type\": \"Other\"}\n{\"value\": \"6.6\", \"type\": \"Other\"}\n{\"value\": \"15.4\", \"type\": \"Other\"}\n{\"value\": \"398.4\", \"type\": \"Other\"}\n{\"value\": \"398.3\", \"type\": \"Other\"}\n{\"value\": \"398.2\", \"type\": \"Other\"}\n{\"value\": \"2.0\", \"type\": \"Other\"}\n{\"value\": \"7.8\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"399.4\", \"type\": \"Other\"}\n{\"value\": \"400.0\", \"type\": \"Other\"}\n{\"value\": \"5.0\", \"type\": \"Other\"}\n{\"value\": \"7.4\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Objectives: The fixed dose combination of saxagliptin and dapagliflozin is a recently approved antidiabetic medication. It is marketed under the brand name Qtern.\nOBJECTIVE: To review the pharmacology, pharmacokinetics, efficacy, safety, and place in therapy of the fixed-dose combination (FDC) product, QTERN (dapagliflozin/saxagliptin) tablets.\nSaxagliptin/dapagliflozin fixed-dose combination tablets (Qtern®) are indicated in the EU for the improvement of glycaemic control in adults with type 2 diabetes mellitus (T2DM), either when treatment with metformin and/or a sulfonylurea plus a monocomponent of saxagliptin/dapagliflozin provides inadequate glycaemic control, or when the patient is already being treated with the free combination of saxagliptin + dapagliflozin.\nThis article highlights the following new drugs: brodalumab (Siliq), dapagliflozin and saxagliptin (Qtern), dupilumab (Dupixent), oxymetazoline (Rhofade), safinamide (Xadago), and sarilumab (Kevzara).\nOBJECTIVE: To review the pharmacology, pharmacokinetics, efficacy, safety, and place in therapy of the fixed-dose combination (FDC) product, QTERN (dapagliflozin/saxagliptin) table\nOBJECTIVE: To review the pharmacology, pharmacokinetics, efficacy, safety, and place in therapy of the fixed-dose combination (FDC) product, QTERN (dapagliflozin/saxagliptin) tablet\nCONCLUSION: QTERN (dapagliflozin/saxagliptin) tablets are a reasonable option for patients with T2DM not controlled on metformin, but cost, insurance coverage, and a lackluster reduction in A1C will likely limit its use until more data regarding its effects on complications of diabetes and cardiovascular outcomes become availabl\nThis article highlights the following new drugs: brodalumab (Siliq), dapagliflozin and saxagliptin (Qtern), dupilumab (Dupixent), oxymetazoline (Rhofade), safinamide (Xadago), and sarilumab (Kevzara)\nOBJECTIVE: To review the pharmacology, pharmacokinetics, efficacy, safety, and place in therapy of the fixed-dose combination (FDC) product, QTERN (dapagliflozin/saxagliptin) tablets.DATA SOURCES: Searches of MEDLINE (1946 to July 1, 2017) were conducted using the keywords QTERN, saxagl\nnd dapagliflozin + metformin was -0.27 (95% CI = -0.48 to -0.05, P = 0.0166). The combination was well tolerated when added to metformin.CONCLUSION: QTERN (dapagliflozin/saxagliptin) tablets are a reasonable option for patients with T2DM not controlled on metformin, but cost, insurance coverage, and a lackluster reduction in A1C will likely limit its use until more data regard\n\nQuestion: Which drugs are included in the Qtern pill?", "role": "user" }, { "content": "[\"saxagliptin\", \"dapagliflozin\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Diabetes is a serious chronic disease during childhood. Because of the chronic nature of the disease, self-care is necessary. Education alone is not effective in providing care. Misunderstanding by the patients regarding diabetes during the training programs render telephone follow-up after training essential.\n\nABSTRACT.MATERIALS AND METHODS:: This quasi-experimental study with two groups (experimental and control) was conducted in two phases in 2014. The study population consisted of 70 children of 10–18 years of age with type I diabetes (35 patients in the experimental group and 35 in the control group). The participants were randomly selected from the patients referring to the Sedigheh Tahereh Diabetic Research and Treatment Center in Isfahan, Iran. Data were collected using a researcher-made questionnaire on self-care and a glycosylated hemoglobin recording form. The experimental group received 12 weeks of telephone follow-up training by the center, whereas the control group received no follow-up.\n\nABSTRACT.RESULTS:: The results showed that, after intervention, the total mean score of self-care in all aspects of diabetes care for children was significantly higher in the experimental group (p < 0.001). In addition, a statistically significant difference was observed between the experimental and control groups in terms of mean glycosylated hemoglobin after the intervention (p = 0.030).\n\nABSTRACT.CONCLUSIONS:: It can be concluded that telephone follow-up by a nurse can improve total self-care and glycosylated hemoglobin in patients with type I diabetes.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"12 weeks of telephone follow-up training by the center\", \"no follow-up\", \"the mean score of self-care\", \"significantly increased\", \"after intervention, the total mean score of self-care in all aspects of diabetes care for children was significantly higher in the experimental group (p \\u003c 0.001)\"]]", "role": "assistant" } ]

[ { "content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nIn this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries. We consider the case of multi-document summarization, where the input documents are in Arabic, and the output summary is in English. Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents. Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English. We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases.", "role": "user" }, { "content": "Output Entities:\n\n{\"Generic\": [], \"Material\": [\"Arabic\", \"English\", \"Arabic documents\"], \"Method\": [\"machine translation systems\"], \"Metric\": [], \"OtherScientificTerm\": [\"lexical-syntactic forms\", \"information redundancy in multilingual input\"], \"Task\": [\"multilingual summaries\", \"machine translation\", \"multi-document summarization\"]}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nCombining Recurrent and Convolutional Neural Networks for Relation Classification\n\nThis paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.\n\nIntroduction\nRelation classification is the task of assigning sentences with two marked entities to a predefined set of relations. The sentence “We poured the <e1>milk</e1> into the <e2>pumpkin mixture</e2>.”, for example, expresses the relation Entity-Destination(e1,e2). While early research mostly focused on support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 , recent research showed performance improvements by applying neural networks (NNs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 on the benchmark data from SemEval 2010 shared task 8 BIBREF8 .\nThis study investigates two different types of NNs: recurrent neural networks (RNNs) and convolutional neural networks (CNNs) as well as their combination. We make the following contributions:\n(1) We propose extended middle context, a new context representation for CNNs for relation classification. The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part.\n(2) We present connectionist bi-directional RNN models which are especially suited for sentence classification tasks since they combine all intermediate hidden layers for their final decision. Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before.\n(3) Finally, we combine CNNs and RNNs using a simple voting scheme and achieve new state-of-the-art results on the SemEval 2010 benchmark dataset.\n\nRelated Work\nIn 2010, manually annotated data for relation classification was released in the context of a SemEval shared task BIBREF8 . Shared task participants used, i.a., support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 . Recently, their results on this data set were outperformed by applying NNs BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 .\nzeng2014 built a CNN based only on the context between the relation arguments and extended it with several lexical features. kim2014 and others used convolutional filters of different sizes for CNNs. nguyen applied this to relation classification and obtained improvements over single filter sizes. deSantos2015 replaced the softmax layer of the CNN with a ranking layer. They showed improvements and published the best result so far on the SemEval dataset, to our knowledge.\nsocher used another NN architecture for relation classification: recursive neural networks that built recursive sentence representations based on syntactic parsing. In contrast, zhang investigated a temporal structured RNN with only words as input. They used a bi-directional model with a pooling layer on top.\n\nConvolutional Neural Networks (CNN)\nCNNs perform a discrete convolution on an input matrix with a set of different filters. For NLP tasks, the input matrix represents a sentence: Each column of the matrix stores the word embedding of the corresponding word. By applying a filter with a width of, e.g., three columns, three neighboring words (trigram) are convolved. Afterwards, the results of the convolution are pooled. Following collobertWeston, we perform max-pooling which extracts the maximum value for each filter and, thus, the most informative n-gram for the following steps. Finally, the resulting values are concatenated and used for classifying the relation expressed in the sentence.\n\nInput: Extended Middle Context\nOne of our contributions is a new input representation especially designed for relation classification. The contexts are split into three disjoint regions based on the two relation arguments: the left context, the middle context and the right context. Since in most cases the middle context contains the most relevant information for the relation, we want to focus on it but not ignore the other regions completely. Hence, we propose to use two contexts: (1) a combination of the left context, the left entity and the middle context; and (2) a combination of the middle context, the right entity and the right context. Due to the repetition of the middle context, we force the network to pay special attention to it. The two contexts are processed by two independent convolutional and max-pooling layers. After pooling, the results are concatenated to form the sentence representation. Figure FIGREF3 depicts this procedure. It shows an examplary sentence: “He had chest pain and <e1>headaches</e1> from <e2>mold</e2> in the bedroom.” If we only considered the middle context “from”, the network might be tempted to predict a relation like Entity-Origin(e1,e2). However, by also taking the left and right context into account, the model can detect the relation Cause-Effect(e2,e1). While this could also be achieved by integrating the whole context into the model, using the whole context can have disadvantages for longer sentences: The max pooling step can easily choose a value from a part of the sentence which is far away from the mention of the relation. With splitting the context into two parts, we reduce this danger. Repeating the middle context increases the chance for the max pooling step to pick a value from the middle context.\n\nConvolutional Layer\nFollowing previous work (e.g., BIBREF5 , BIBREF6 ), we use 2D filters spanning all embedding dimensions. After convolution, a max pooling operation is applied that stores only the highest activation of each filter. We apply filters with different window sizes 2-5 (multi-windows) as in BIBREF5 , i.e. spanning a different number of input words.\n\nRecurrent Neural Networks (RNN)\nTraditional RNNs consist of an input vector, a history vector and an output vector. Based on the representation of the current input word and the previous history vector, a new history is computed. Then, an output is predicted (e.g., using a softmax layer). In contrast to most traditional RNN architectures, we use the RNN for sentence modeling, i.e., we predict an output vector only after processing the whole sentence and not after each word. Training is performed using backpropagation through time BIBREF9 which unfolds the recurrent computations of the history vector for a certain number of time steps. To avoid exploding gradients, we use gradient clipping with a threshold of 10 BIBREF10 .\n\nInput of the RNNs\nInitial experiments showed that using trigrams as input instead of single words led to superior results. Hence, at timestep INLINEFORM0 we do not only give word INLINEFORM1 to the model but the trigram INLINEFORM2 by concatenating the corresponding word embeddings.\n\nWord Representations\nWords are represented by concatenated vectors: a word embedding and a position feature vector.\nPretrained word embeddings. In this study, we used the word2vec toolkit BIBREF11 to train embeddings on an English Wikipedia from May 2014. We only considered words appearing more than 100 times and added a special PADDING token for convolution. This results in an embedding training text of about 485,000 terms and INLINEFORM0 tokens. During model training, the embeddings are updated.\nPosition features. We incorporate randomly initialized position embeddings similar to zeng2014, nguyen and deSantos2015. In our RNN experiments, we investigate different possibilities of integrating position information: position embeddings, position embeddings with entity presence flags (flags indicating whether the current word is one of the relation arguments), and position indicators BIBREF7 .\n\nObjective Function: Ranking Loss\nRanking. We applied the ranking loss function proposed in deSantos2015 to train our models. It maximizes the distance between the true label INLINEFORM0 and the best competitive label INLINEFORM1 given a data point INLINEFORM2 . The objective function is DISPLAYFORM0 \nwith INLINEFORM0 and INLINEFORM1 being the scores for the classes INLINEFORM2 and INLINEFORM3 respectively. The parameter INLINEFORM4 controls the penalization of the prediction errors and INLINEFORM5 and INLINEFORM6 are margins for the correct and incorrect classes. Following deSantos2015, we set INLINEFORM7 . We do not learn a pattern for the class Other but increase its difference to the best competitive label by using only the second summand in Equation EQREF10 during training.\n\nExperiments and Results\nWe used the relation classification dataset of the SemEval 2010 task 8 BIBREF8 . It consists of sentences which have been manually labeled with 19 relations (9 directed relations and one artificial class Other). 8,000 sentences have been distributed as training set and 2,717 sentences served as test set. For evaluation, we applied the official scoring script and report the macro F1 score which also served as the official result of the shared task.\nRNN and CNN models were implemented with theano BIBREF12 , BIBREF13 . For all our models, we use L2 regularization with a weight of 0.0001. For CNN training, we use mini batches of 25 training examples while we perform stochastic gradient descent for the RNN. The initial learning rates are 0.2 for the CNN and 0.01 for the RNN. We train the models for 10 (CNN) and 50 (RNN) epochs without early stopping. As activation function, we apply tanh for the CNN and capped ReLU for the RNN. For tuning the hyperparameters, we split the training data into two parts: 6.5k (training) and 1.5k (development) sentences. We also tuned the learning rate schedule on dev.\nBeside of training single models, we also report ensemble results for which we combined the presented single models with a voting process.\n\nPerformance of CNNs\nAs a baseline system, we implemented a CNN similar to the one described by zeng2014. It consists of a standard convolutional layer with filters with only one window size, followed by a softmax layer. As input it uses the middle context. In contrast to zeng2014, our CNN does not have an additional fully connected hidden layer. Therefore, we increased the number of convolutional filters to 1200 to keep the number of parameters comparable. With this, we obtain a baseline result of 73.0. After including 5 dimensional position features, the performance was improved to 78.6 (comparable to 78.9 as reported by zeng2014 without linguistic features).\nIn the next step, we investigate how this result changes if we successively add further features to our CNN: multi-windows for convolution (window sizes: 2,3,4,5 and 300 feature maps each), ranking layer instead of softmax and our proposed extended middle context. Table TABREF12 shows the results. Note that all numbers are produced by CNNs with a comparable number of parameters. We also report F1 for increasing the word embedding dimensionality from 50 to 400. The position embedding dimensionality is 5 in combination with 50 dimensional word embeddings and 35 with 400 dimensional word embeddings. Our results show that especially the ranking layer and the embedding size have an important impact on the performance.\n\nPerformance of RNNs\nAs a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence. With this model, we achieve an F1 score of 61.2 on the SemEval test set.\nAfterwards, we investigate the impact of different position features on the performance of uni-directional RNNs (position embeddings, position embeddings concatenated with a flag indicating whether the current word is an entity or not, and position indicators BIBREF7 ). The results indicate that position indicators (i.e. artificial words that indicate the entity presence) perform the best on the SemEval data. We achieve an F1 score of 73.4 with them. However, the difference to using position embeddings with entity flags is not statistically significant.\nSimilar to our CNN experiments, we successively vary the RNN models by using bi-directionality, by adding connections between the hidden layers (“connectionist”), by applying ranking instead of softmax to predict the relation and by increasing the word embedding dimension to 400.\nThe results in Table TABREF14 show that all of these variations lead to statistically significant improvements. Especially the additional hidden layer connections and the integration of the ranking layer have a large impact on the performance.\n\nCombination of CNNs and RNNs\nFinally, we combine our CNN and RNN models using a voting process. For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes. In case of a tie, we pick one of the most frequent classes randomly. The combination achieves an F1 score of 84.9 which is better than the performance of the two NN types alone. It, thus, confirms our assumption that the networks provide complementary information: while the RNN computes a weighted combination of all words in the sentence, the CNN extracts the most informative n-grams for the relation and only considers their resulting activations.\n\nComparison with State of the Art\nTable TABREF16 shows the results of our models ER-CNN (extended ranking CNN) and R-RNN (ranking RNN) in the context of other state-of-the-art models. Our proposed models obtain state-of-the-art results on the SemEval 2010 task 8 data set without making use of any linguistic features.\n\nConclusion\nIn this paper, we investigated different features and architectural choices for convolutional and recurrent neural networks for relation classification without using any linguistic features. For convolutional neural networks, we presented a new context representation for relation classification. Furthermore, we introduced connectionist recurrent neural networks for sentence classification tasks and performed the first experiments with ranking recurrent neural networks. Finally, we showed that even a simple combination of convolutional and recurrent neural networks improved results. With our neural models, we achieved new state-of-the-art results on the SemEval 2010 task 8 benchmark data.\n\nAcknowledgments\nHeike Adel is a recipient of the Google European Doctoral Fellowship in Natural Language Processing and this research is supported by this fellowship.\nThis research was also supported by Deutsche Forschungsgemeinschaft: grant SCHU 2246/4-2.\n\nFigure 2: Connectionist bi-directional RNN\n\nTable 1: F1 score of CNN and its components, * indicates statisticial significance compared to the result in the line above (z-test, p < 0.05)\n\nTable 2: F1 score of RNN and its components, * indicates statisticial significance compared to the result in the line above (z-test, p < 0.05)\n\nQuery: How does their simple voting scheme work?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"Among all the classes predicted by several models, for each test sentence, class with most votes are picked. In case of a tie, one of the most frequent classes are picked randomly.\", \"evidence\": [\"Finally, we combine our CNN and RNN models using a voting process. For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes. In case of a tie, we pick one of the most frequent classes randomly. The combination achieves an F1 score of 84.9 which is better than the performance of the two NN types alone. It, thus, confirms our assumption that the networks provide complementary information: while the RNN computes a weighted combination of all words in the sentence, the CNN extracts the most informative n-grams for the relation and only considers their resulting activations.\"]}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWith this component, we call our approach language-shaped learning (LSL) (Figure<cite> 1</cite> ). Relation to L3. LSL is similar to another recent model for this setting: Learning with Latent Language (L3) <cite>[1]</cite> , which proposes to use language not only as a supervision source, but as a bottleneck for classification ( Figure<cite> 1</cite> ).", "role": "user" }, { "content": "[\"Similarities\"]", "role": "assistant" } ]