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[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It examined the susceptibility of a number of species to COVID-19, including cats and dogs, using a small number of animals.\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\"]}" } ]

[ { "role": "user", "content": "Given an abstract from a research paper, your task is to identify and label relationships between chemical compounds and genes mentioned in the abstract.\n\nThe relationship type must be one of ['DOWNREGULATOR', 'SUBSTRATE', 'INDIRECT-REGULATOR', 'PART-OF', 'MODULATOR-ACTIVATOR', 'REGULATOR', 'INHIBITOR', 'COFACTOR', 'UPREGULATOR', 'ACTIVATOR', 'ANTAGONIST', 'NOT', 'INDIRECT-DOWNREGULATOR', 'SUBSTRATE_PRODUCT-OF', 'INDIRECT-UPREGULATOR', 'AGONIST', 'PRODUCT-OF', 'MODULATOR', 'DIRECT-REGULATOR', 'UNDEFINED', 'AGONIST-INHIBITOR', 'AGONIST-ACTIVATOR', 'MODULATOR-INHIBITOR'].\n\nPlease format your output as a JSON array. Each entry in the array should express a single relation, formatted as [\"<Entity_A>\", \"<RELATION_A_B>\", \"<Entity_B>\"]. If no relations can be found, please output an empty JSON array [].\n\nAbstract:\n\nStructure activity relationship studies of tricyclic bispyran sulfone γ-secretase inhibitors. An investigation is detailed of the structure activity relationships (SAR) of two sulfone side chains of compound (-) -1a (SCH 900229), a potent, PS1-selective γ-secretase inhibitor and clinical candidate for the treatment of Alzheimer 's disease. Specifically, 4-CF (3) and 4-Br substituted arylsulfone analogs, (-) -1b and (-) -1c, are equipotent to compound (-) -1a. On the right hand side chain, linker size and terminal substituents of the pendant sulfone group are also investigated." }, { "role": "assistant", "content": "[[\"tricyclic bispyran sulfone\", \"INHIBITOR\", \"\\u03b3-secretase\"], [\"SCH 900229\", \"INHIBITOR\", \"\\u03b3-secretase\"]]" } ]

[ { "role": "user", "content": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Il-6 trans-signaling induces plasminogen activator inhibitor-1 from vascular endothelial cells in cytokine differentiation syndrome\n\nAbstract:\nCytokine release syndrome (CRS) is a life-threatening complication induced by systemic inflammatory responses to infections, including bacteria and chimeric antigen receptor T cell therapy.\nThere are currently no immunotherapies with proven clinical efficacy and understanding of the molecular mechanisms of CRS pathogenesis is limited.\nHere, we found that patients diagnosed with CRS from sepsis, acute respiratory distress syndrome (ARDS), or burns showed common manifestations: strikingly elevated levels of the four proinflammatory cytokines interleukin (IL)-6, IL-8, monocyte chemotactic protein-1 (MCP-1), and IL-10 and the coagulation cascade activator plasminogen activator inhibitor-1 (PAI-1).\nOur in vitro data indicate that endothelial IL-6 trans-signaling formed an inflammation circuit for robust IL-6, IL-8, and MCP-1 production and promoted PAI-1 production; additionally, an IL-6 signaling blockade by the human monoclonal antibody tocilizumab blunted endothelial cell activation.\nPlasma from severe COVID-19 patients similarly exhibited increased IL-6, IL-10, and MCP-1 levels, but these levels were not as high as those in patients with CRS from other causes.\nIn contrast, the PAI-1 levels in COVID-19 patients were as highly elevated as those in patients with bacterial sepsis or ARDS.\nTocilizumab treatment decreased the PAI-1 levels and alleviated critical illness in severe COVID-19 patients.\nOur findings suggest that distinct levels of cytokine production are associated with CRS induced by bacterial infection and COVID-19, but both CRS types are accompanied by endotheliopathy through IL-6 trans-signaling.\nThus, the present study highlights the crucial role of IL-6 signaling in endothelial dysfunction during bacterial infection and COVID-19." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our in vitro data indicate that endothelial IL-6 trans-signaling formed an inflammation circuit for robust IL-6, IL-8, and MCP-1 production and promoted PAI-1 production; additionally, an IL-6 signaling blockade by the human monoclonal antibody tocilizumab blunted endothelial cell activation.\"]}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\n*Medical wastes will drastically increase due to Covid-19-outbreak.*Safe and sustainable waste management is a major concern.*Experience and lessons can be learned for medical waste management in China." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "role": "user", "content": "You will be shown a question, followed by excerpts from biomedical research papers. Please answer the question based on the provided context. Do not include any text in your response other than the answer.\n\nQuestion: What is myelin?\n\nContext: Myelination is a recent evolutionary addition that significantly enhances the speed of transmission in the neural network.\n. Myelin is only formed by fully differentiated oligodendrocytes, but the entire oligodendrocyte lineage are clear targets of the altered chemistry of the aging brain\nFormation of myelin sheaths by Schwann cells (SCs) enables rapid and efficient transmission of action potentials in peripheral axons, and disruption of myelination results in disorders that involve decreased sensory and motor functions.\nMyelin is a specialized structure of the nervous system that both enhances electrical conductance and insulates neurons from external risk factors.\npolarized oligodendrocytes form myelin by wrapping processes in a spiral pattern around neuronal axons through myelin-related gene regulation.\nMyelin - the multilayer membrane that envelops axons - is a facilitator of rapid nerve conduction.\nMyelin allows for the rapid and precise timing of action potential propagation along neuronal circuits and is essential for healthy auditory system function.\nMyelin sheaths in the vertebrate nervous system enable faster impulse propagation, while myelinating glia provide vital support to axons.\nMyelin consists of tightly compacted membranes that form an insulating sheath around axons\nMyelin is a biologically active mutilamellar that is formed by oligodendrocytes (OLs) in the central nervous system (CNS) and ensheathes axons\nThe myelin sheath is a multilayered membrane in the nervous system, which has unique biochemical properties. Myelin carries a set of specific high-abundance proteins, the structure and function of which are still poorly understood" }, { "role": "assistant", "content": "Myelin is a specialized structure of the nervous system that covers the neuron and both enhances electrical conductance and insulates neurons from external risk factors." } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Colloidal silver can cure COVID-19\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Crowded areas will increase covid-19 transmission risk.\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nHomologousrecombinantswereselectedbyPCRusingthefollowingprimers,AGN1:5'-TCGTGCTTTACGGTATCGCCGCTCCCGATT-3'inthePGKneocassette,andSGN033:5'-GGACCCGGAAGACATTTGCACGGT-3'outsidethetargetingvector." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To stop the spread of coronavirus (COVID-19), you should avoid close contact with anyone you do not live with. This is called social distancing.\n\nAbstract:\nThe Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nBayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nThis provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\", \"This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Various studies over time have shown that vitamin C contains antioxidants that can help improve the body's ability to fight diseases, including COVID-19.\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) is a pandemic caused by coronavirus with mild to severe respiratory symptoms.\nThis paper aimed to investigate the effect of nutrients on the immune system and their possible roles in the prevention, treatment, and management of COVID-19 in adults.\nMETHODS This Systematic review was designed based on the guideline of the Preferred Reporting for Systematic Reviews (PRISMA).\nThe articles that focussed on nutrition, immune system, viral infection, and coronaviruses were collected by searching databases for both published papers and accepted manuscripts from 1990 to 2020.\nIrrelevant papers and articles without English abstract were excluded from the review process.\nRESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\nFew studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\nCONCLUSION Following a balanced diet and supplementation with proper nutrients may play a vital role in prevention, treatment, and management of COVID-19.\nHowever, further clinical trials are needed to confirm these findings and presenting the strong recommendations against this pandemic." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\", \"Few studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\"]}" } ]

[ { "role": "user", "content": "You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.\n\nLiterature Review Title: Compressive Video Coding: A Review of the State-Of-The-Art \nChapter Title: Key Elements of compressed sensing \n1. Abstract of Cited Paper (BIB001): This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set /spl Omega/? A typical result of this paper is as follows. Suppose that f is a superposition of |T| spikes f(t)=/spl sigma//sub /spl tau//spl isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl Omega/| for some constant C/sub M/>0. We do not know the locations of the spikes nor their amplitudes. Then with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the /spl lscr//sub 1/ minimization problem. In short, exact recovery may be obtained by solving a convex optimization problem. We give numerical values for C/sub M/ which depend on the desired probability of success. Our result may be interpreted as a novel kind of nonlinear sampling theorem. In effect, it says that any signal made out of |T| spikes may be recovered by convex programming from almost every set of frequencies of size O(|T|/spl middot/logN). Moreover, this is nearly optimal in the sense that any method succeeding with probability 1-O(N/sup -M/) would in general require a number of frequency samples at least proportional to |T|/spl middot/logN. The methodology extends to a variety of other situations and higher dimensions. For example, we show how one can reconstruct a piecewise constant (one- or two-dimensional) object from incomplete frequency samples - provided that the number of jumps (discontinuities) obeys the condition above - by minimizing other convex functionals such as the total variation of f. \n2. Abstract of Cited Paper (BIB002): Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.\n\nLiterature Review:" }, { "role": "assistant", "content": "Compressed Sensing BIB001 BIB002 BIB003 is applicable to signals that are sparse in some domain. Sparsity is a general concept and it expresses the idea that the information rate or the signal significant content may be much smaller than what is suggested by its bandwidth. Most natural signals are redundant and therefore compressible in some suitable domain. We shall first define the two principles, sparsity and incoherence, on which the theory of CS depends." } ]

[ { "role": "user", "content": "You have to answer a biomedical question in binary format, i.e. only yes and no are the only acceptable answer formats. A list of paragraphs is provided as context to help you answer the question.\n\nQuestion: Does Serca2a bind PLN in the heart?\n\nContext: The human phospholamban Arg14-deletion mutant localizes to plasma membrane and interacts with the Na/K-ATPase.\nMoreover, PLN-R14Del did not co-immunoprecipitate with SERCA2a (as did WT-PLN),\nn this review, we attempted to highlight the functional significance of PLN in vertebrate cardiac physiology. We will refer to the huge literature on mammals in order to describe the molecular characteristics of this protein, its interaction with SERCA2a\nThere is clear evidence for direct regulatory protein-protein interactions between phospholamban (PLN) and the Ca2+-ATPase of cardiac sarcoplasmic reticulum (SERCA2a) in cytoplasmic domains\nThese results suggest that PLN modulates the apparent Ca2+ affinity of SERCA2a through intramembrane interactions, which are disrupted at long range and in concert with disruption of the well characterized cytoplasmic interactions.\nPhospholamban (PLN), a homopentameric, integral membrane protein, reversibly inhibits cardiac sarcoplasmic reticulum Ca2+-ATPase (SERCA2a) activity through intramembrane interactions.\nThe concentration of this inhibited complex is determined by the dissociation constant for the PLN pentamer (which is mutation-sensitive) and by the dissociation constant for the PLN/SERCA2a heterodimer (which is likely to be mutation-sensitive).\nThese results support the proposal that PLN inhibition of SERCA2a involves, first, depolymerization of PLN and, second, the formation of inhibitory interactions between monomeric PLN and SERCA2a.\nSLN and PLN appear to bind to the same regulatory site in SERCA. However, in a ternary complex, PLN occupies the regulatory site and SLN binds to the exposed side of PLN and to SERCA.\nCellular and biochemical studies revealed that, unlike wild-type PLN, PLN(R9C) did not directly inhibit SERCA2a.\n. Conversely, using anti-SERCA2a antibody, both PLN and acylphosphatase were co-immunoprecipitated with SERCA2a, and the PLN amount in the precipitate decreased with increasing acylphosphatase concentrations.\nReconstitution of the cytoplasmic interaction between phospholamban and Ca(2+)-ATPase of cardiac sarcoplasmic reticulum.\nPhospholamban (PLN) reversibly inhibits the Ca(2+)-ATPase of cardiac sarcoplasmic reticulum (SERCA2a) through a direct protein-protein interaction, playing a pivotal role in the regulation of intracellular Ca(2+) in heart muscle cells.\nPhospholamban (PLN) is a key regulator of Ca(2+) homeostasis and contractility in the heart. Its regulatory effects are mediated through its interaction with the sarcoplasmic reticulum Ca(2+)-ATPase, (SERCA2a), resulting in alterations of its Ca(2+)-affinity\nIn a co-immunoprecipitation of PLN with SERCA2a, the physical interaction between the two proteins was increased in PUGNAc-treated cardiomyocytes." }, { "role": "assistant", "content": "yes" } ]

[ { "role": "user", "content": "You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.\n\nContext: The YOLO framework uses a custom network based on the Googlenet architecture [19]. This network is faster than VGG-16, only using 8.52 billion operations for a forward pass. However, it’s accuracy is slightly worse than VGG-16. For single-crop, top-5 accuracy at 224\\times 224, YOLO’s custom model gets 88.0% ImageNet compared to 90.0% for VGG-16.\n\nQuestion: Are there any differences between VGG-16 and YOLO's custom framework besides size?" }, { "role": "assistant", "content": "The YOLO framework uses a custom network based on the Googlenet architecture. This architecture is faster than VGG-16 but its accuracy is slightly lower." } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:\n\n- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.\n- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.\n- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.\n\nPlease format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.\n\nAs an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:\n\n[[\"artesunate\", \"artemether\", \"COMB\"], [\"Nifedipine\", \"Labetalol\", \"Prazosin\", \"POS\"]]\n\nThe paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.\n\nIn your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.\n\nparagraph: The Role of Immunotherapy in the Treatment of Adrenocortical Carcinoma. Adrenocortical carcinoma (ACC) is a rare epithelial neoplasm, with a high tendency for local invasion and distant metastases, with limited treatment options. Surgical treatment is the method of choice. For decades, the mainstay of pharmacological treatment has been the adrenolytic drug mitotane, in combination with chemotherapy. Immunotherapy is the latest revolution in cancer therapy, however preliminary data with single immune checkpoint inhibitors showed a modest activity in ACC patients. The anti-neoplastic activity of immune checkpoint inhibitors such as anti-cytotoxic-T-lymphocyte-associated-antigen 4 (anti-CTLA-4), anti-programmed death-1 (anti-PD-1), and anti-PD-ligand-1 (PD-L1) antibodies in different solid tumors has aroused interest to explore the potential therapeutic effect in ACC as well. Multiple ongoing clinical trials are currently evaluating the role of immune checkpoint inhibitors in ACC ( pembrolizumab , combination pembrolizumab and relacorilant , nivolumab , combination nivolumab and ipilimumab ) . The primary and acquired resistance to immunotherapy continue to counter treatment efficacy. Therefore, attempts are made to combine therapy: anti-PD-1 antibody and anti-CTLA-4 antibody, anti-PD-1 antibody and antagonist of the glucocorticoid receptor. The inhibitors of immune checkpoints would benefit patients with antitumor immunity activated by radiotherapy. Immunotherapy is well tolerated by patients; the most frequently observed side effects are mild. The most common adverse effects of immunotherapy are skin and gastrointestinal disorders. The most common endocrinopathy during anti-CTLA treatment is pituitary inflammation and thyroid disorders.\n\nsentence: Multiple ongoing clinical trials are currently evaluating the role of immune checkpoint inhibitors in ACC ( pembrolizumab , combination pembrolizumab and relacorilant , nivolumab , combination nivolumab and ipilimumab ) ." }, { "role": "assistant", "content": "[[\"pembrolizumab\", \"relacorilant\", \"COMB\"], [\"nivolumab\", \"ipilimumab\", \"COMB\"]]" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nHowever,anumberofinterestingdifferencesarenotable." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "Given an abstract from a research paper, your task is to identify and label relationships between chemical compounds and genes mentioned in the abstract.\n\nThe relationship type must be one of ['DOWNREGULATOR', 'SUBSTRATE', 'INDIRECT-REGULATOR', 'PART-OF', 'MODULATOR-ACTIVATOR', 'REGULATOR', 'INHIBITOR', 'COFACTOR', 'UPREGULATOR', 'ACTIVATOR', 'ANTAGONIST', 'NOT', 'INDIRECT-DOWNREGULATOR', 'SUBSTRATE_PRODUCT-OF', 'INDIRECT-UPREGULATOR', 'AGONIST', 'PRODUCT-OF', 'MODULATOR', 'DIRECT-REGULATOR', 'UNDEFINED', 'AGONIST-INHIBITOR', 'AGONIST-ACTIVATOR', 'MODULATOR-INHIBITOR'].\n\nPlease format your output as a JSON array. Each entry in the array should express a single relation, formatted as [\"<Entity_A>\", \"<RELATION_A_B>\", \"<Entity_B>\"]. If no relations can be found, please output an empty JSON array [].\n\nAbstract:\n\n5-Amino-2-hydroxybenzoic acid 4- (5-thioxo-5H-[1,2]dithiol-3yl) -phenyl ester (ATB-429), a hydrogen sulfide-releasing derivative of mesalamine, exerts antinociceptive effects in a model of postinflammatory hypersensitivity. H (2) S functions as a neuromodulator and exerts anti-inflammatory activities. Recent data indicate that irritable bowel syndrome (IBS) is linked to inflammation of the gastrointestinal tract. In this study, we have investigated the role of a novel H (2) S-releasing derivative of mesalamine (5-amino-2-hydroxybenzoic acid 4- (5-thioxo-5H-[1,2]dithiol-3yl) -phenyl ester, ATB-429) in modulating nociception to colorectal distension (CRD), a model that mimics some features of IBS, in healthy and postcolitic rats. Four graded (0.4-1.6 ml of water) CRDs were produced in conscious rats, and colorectal sensitivity and pain were assessed by measuring the abdominal withdrawal response and spinal c-Fos expression. In healthy rats, ATB-429 dose dependently (25, 50, or 100 mg/kg) attenuated CRD-induced hypersensitivity and significantly inhibited CRD-induced overexpression of spinal c-FOS mRNA, whereas mesalamine had no effect. ATB-429-induced antinociception was reversed by glibenclamide, a ATP-sensitive K (+) (K (ATP)) channel inhibitor. The antinociceptive effect of ATB-429 was maintained in a rodent model of postinflammatory hypersensitivity (4 weeks after colitis induction). At a dose of 100 mg/kg, ATB-429 reversed the allodynic response caused by CRD in postcolitic rats. Colonic cyclooxygenase-2 and interkeukin-1beta mRNA and spinal c-FOS mRNA expression were significantly down-regulated by ATB-429, but not by mesalamine. ATB-429, but not mesalamine, increased blood concentrations of H (2) S in both healthy and postcolitic rats. Taken together, these data suggest that ATB-429 inhibits hypersensitivity induced by CRD in both healthy and postcolitic, allodynic rats by a K (ATP) channel-mediated mechanism. This study provides evidence that H (2) S-releasing drugs might have beneficial effects in the treatment of painful intestinal disorders." }, { "role": "assistant", "content": "[[\"mesalamine\", \"NOT\", \"cyclooxygenase-2\"], [\"ATB-429\", \"INDIRECT-DOWNREGULATOR\", \"c-FOS\"], [\"ATB-429\", \"INDIRECT-DOWNREGULATOR\", \"interkeukin-1beta\"], [\"ATB-429\", \"INDIRECT-DOWNREGULATOR\", \"cyclooxygenase-2\"], [\"mesalamine\", \"NOT\", \"c-FOS\"], [\"mesalamine\", \"NOT\", \"interkeukin-1beta\"], [\"glibenclamide\", \"INHIBITOR\", \"ATP-sensitive K (+) (K (ATP)) channel\"]]" } ]

[ { "role": "user", "content": "You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.\n\nContext: The focus of our study is to empirically compare different LSTM variants, and not to achieve state-of-the-art results.Therefore, our experiments are designed to keep the setup simple and the comparisons fair.The vanilla LSTM is used as a baseline and evaluated together with eight of its variants.Each variant adds, removes, or modifies the baseline in exactly one aspect, which allows to isolate their effect.They are evaluated on three different datasets from different domains to account for cross-domain variations. This paper reports the results of a large scale study on variants of the LSTM architecture. We conclude that the most commonly used LSTM architecture (vanilla LSTM) performs reasonably well on various datasets.None of the eight investigated modifications significantly improves performance.However, certain modifications such as coupling the input and forget gates (CIFG) or removing peephole connections (NP) simplified LSTMs in our experiments without significantly decreasing performance.These two variants are also attractive because they reduce the number of parameters and the computational cost of the LSTM. The first important observation based on Figure 3 is that removing the output activation function (NOAF) or the forget gate (NFG) significantly hurt performance on all three datasets. Apart from the CEC, the ability to forget old information and the squashing of the cell state appear to be critical for the LSTM architecture. Indeed, without the output activation function, the block output can in principle grow unbounded. Coupling the input and the forget gate avoids this problem and might render the use of an output non-linearity less important, which could explain why GRU performs well without it. Adding full gate recurrence (FGR) did not significantly change performance on TIMIT or IAM Online, but led to worse results on the JSB Chorales dataset. Given that this variant greatly increases the number of parameters, we generally advise against using it. Note that this feature was present in the original proposal of LSTM [14, 15], but has been absent in all following studies. Removing the input gate (NIG), the output gate (NOG), and the input activation function (NIAF) led to a significant reduc\u0002tion in performance on speech and handwriting recognition. However, there was no significant effect on music modeling performance. A small (but statistically insignificant) average performance improvement was observed for the NIG and NIAF architectures on music modeling. We hypothesize that these behaviors will generalize to similar problems such as language modeling. For supervised learning on continuous real-valued data (such as speech and handwriting recognition), the input gate, output gate, and input activation function are all crucial for obtaining good performance.\n\nQuestion: What are the eight different LSTM variants that the authors experimented with?" }, { "role": "assistant", "content": "The authors conducted the experiment with these LSTM variants of the vanilla architecture to empirically compare different LSTM variants: No Input Gate (NIG), No Forget Gate (NFG), No Output Gate (NOG), No Input Activation Function (NIAF), No Output Activation Function (NOAF), Coupled Input and Forget Gate (CIFG), No Peepholes (NP), Full Gate Recurrence (FGR)." } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nInconclusion,ourdatadefineaphysiologicroleforSam68inbonemetabolismandbonemarrowmesenchymalstemcelldifferentiation." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You will be shown an abstract from a materials science research paper. Your task is to extract all synthesis operations described in the abstract into a structured JSON format. Your output should be a JSON array. Each entry in the array should be a JSON object describing a single synthesis operation. This object should have the following fields:\n\n- \"trigger\": A verbal predicate indicating a synthesis operation. This should be a string extracted directly from the abstract.\n- \"arguments\": An array of arguments specifying aspects of the synthesis operation. Each entry in the array should be a length-2 array formatted as [<argument_text>, <argument_type>]. The `argument_text` should be a string extracted directly from the abstract. The `argument_type` should be one of the following types: [\"Atmospheric_Material\", \"Participant_Material\", \"Recipe_Target\", \"Recipe_Precursor\", \"Solvent_Material\"].\n\nFor instance, suppose an excerpt from the abstract reads:\n\nTetraethoxysilane (TEOS, 98%) and cetyltrimethylammonium bromide (CTAB, 98%) were purchased from Fluka. The MTTZ-siloxy derivative (triethoxysilyl-propyl-5-mercapto-1-methyl-tetrazole) was synthesized as previously described [38].\n\nThis excerpt includes two operations, which could be annotated as follows.\n\n[{\"arguments\": [[\"Tetraethoxysilane\", \"Recipe_Precursor\"], [\"cetyltrimethylammonium bromide\", \"Recipe_Precursor\"]], \"trigger\": \"purchased\"}, {\"arguments\": [[\"MTTZ-siloxy derivative\", \"Recipe_Target\"]], \"trigger\": \"synthesized\"}]\n\nIn your response, please include the JSON and no other text.\n\nAbstract:\n10.1016/j.apcata.2006.05.025\nUnusual CH4 dry reforming performance over Ni-based catalyst separated from nickel metal hydride battery waste\nNi-MH waste used here was collected from the crucible for producing Ni-MH battery provided from Mitsui Mining and Smelting Co. Ltd., Japan. In our previous study, Ni-based compound was successfully separated from Ni-MH waste by a series of acid and base treatments [Environ. Sci. Technol., submitted]. Namely, Ni-MH waste of 10 g was dissolved with 2 M (mol/dm3) HCl solution of 200 ml for 24 h. Then, the dissolved transition and rare-earth metal ions were precipitated at pH 12 with 2 M NaOH solution. The Ni component was extracted from this precipitate by treating with 7.5 M NH3 aq. of 200 ml, through forming a Ni-ammonium complex. After filtration, the purple filtrate was heated around 60 degC to decompose the Ni-ammonium complex and to obtain Ni(OH)2. The sample, s-NiO, was obtained by calcination of the resulting Ni(OH)2 at 1000 degC." }, { "role": "assistant", "content": "[{\"arguments\": [[\"Ni-MH\", \"Recipe_Precursor\"]], \"trigger\": \"used\"}, {\"arguments\": [[\"Ni-MH\", \"Participant_Material\"], [\"compound\", \"Participant_Material\"]], \"trigger\": \"separated\"}, {\"arguments\": [[\"Ni-MH\", \"Recipe_Precursor\"], [\"HCl\", \"Solvent_Material\"]], \"trigger\": \"dissolved\"}, {\"arguments\": [[\"NaOH\", \"Solvent_Material\"], [\"rare-earth metal\", \"Participant_Material\"], [\"transition\", \"Participant_Material\"]], \"trigger\": \"precipitated\"}, {\"arguments\": [[\"precipitate\", \"Participant_Material\"], [\"Ni\", \"Participant_Material\"]], \"trigger\": \"extracted\"}, {\"arguments\": [[\"Ni-ammonium\", \"Participant_Material\"]], \"trigger\": \"forming\"}, {\"arguments\": [[\"filtrate\", \"Participant_Material\"]], \"trigger\": \"heated\"}, {\"arguments\": [[\"s-NiO\", \"Recipe_Target\"]], \"trigger\": \"obtained\"}, {\"arguments\": [[\"Ni(OH)2\", \"Participant_Material\"]], \"trigger\": \"calcination\"}, {\"arguments\": [[\"NH3\", \"Participant_Material\"]], \"trigger\": \"treating\"}, {\"arguments\": [[\"Ni-ammonium\", \"Participant_Material\"]], \"trigger\": \"decompose\"}, {\"arguments\": [[\"Ni(OH)2\", \"Participant_Material\"]], \"trigger\": \"obtain\"}]" } ]

[ { "role": "user", "content": "You will be provided with the titles and abstracts of a collection of medical RCT reports studying a clinical question -- for instance, the efficacy of an intervention on some medical outcome. Please write a few sentences synthesizing the findings of these reports, which could serve as a conclusion section for a systematic review article. Your response should not include any other text. The RCT reports are below.\n\n\n\nTitle 1:\nA double-blind treatment study of bacterial vaginosis with normal vaginal lactobacilli after an open treatment with vaginal clindamycin ovules.\nAbstract 1:\nThe expected 4-week cure rate after conventional treatment of bacterial vaginosis are only 65-70%. In an attempt to improve the cure rate by adding probiotic lactobacilli we performed a double-blind placebo-controlled study of adjuvant lactobacilli treatment after an open treatment with vaginal clindamycin ovules. Women with bacterial vaginosis as defined by Amsel's criteria were treated with clindamycin ovules. Vaginal smears were collected and analysed according to Nugent's criteria. During the following menstruation period the women used, as an adjuvant treatment, either lactobacilli-prepared tampons or placebo tampons. The lactobacilli tampons were loaded with a mixture of freeze-dried L. fermentum, L. casei var. rhamnosus and L. gasseri. The cure rate was recorded after the second menstruation period. There was no improvement in the cure rate after treatment with lactobacilli-containing tampons compared to placebo tampons; the cure rates as defined by Amsel's criteria were 56% and 62%, respectively, and 55% and 63%, as defined by Nugent's criteria. This is the first study to report cure rates for women with 'intermediate' wet smear ratings according to Nugent's classification and this group had an overall cure rate of 44%. The cure rate of treatment of bacterial vaginosis was not improved by using lactobacilli-prepared tampons for one menstruation.\n\n\nTitle 2:\nAugmentation of antimicrobial metronidazole therapy of bacterial vaginosis with oral probiotic Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14: randomized, double-blind, placebo controlled trial.\nAbstract 2:\nThis study enrolled 125 premenopausal women diagnosed with bacterial vaginosis (BV) by presence of vaginal irritation, discharge and 'fishy' odor, and Nugent criteria and detection of sialidase enzyme. The subjects were treated with oral metronidazole (500 mg) twice daily from days 1 to 7, and randomized to receive oral Lactobacillus rhamnosus GR-1 (1 x 10(9)) and Lactobacillus reuteri RC-14 (1 x 10(9)) or placebo twice daily from days 1 to 30. Primary outcome was cure of BV as determined by normal Nugent score, negative sialidase test and no symptoms or signs of BV at day 30. A total of 106 subjects returned for 30-day follow-up, of which 88% were cured in the antibiotic/probiotic group compared to 40% in the antibiotic/placebo group (p<0.001). Of the remaining subjects, 30% subjects in the placebo group and none in the probiotic group had BV, while 30% in the placebo and 12% in the probiotic group fell into the intermediate category based upon Nugent score, sialidase result and clinical findings. High counts of Lactobacillus sp. (>10(5) CFU/ml) were recovered from the vagina of 96% probiotic-treated subjects compared to 53% controls at day 30. In summary, this study showed efficacious use of lactobacilli and antibiotic in the eradication of BV in black African women.\n\n\nTitle 3:\nClinical study comparing probiotic Lactobacillus GR-1 and RC-14 with metronidazole vaginal gel to treat symptomatic bacterial vaginosis.\nAbstract 3:\nBacterial vaginosis (BV) is particularly common in black women, and in Nigeria it is often caused by Mycoplasma, as well as Atopobium, Prevotella and Gardnerella sp. Antimicrobial metronidazole oral therapy is poorly effective in eradicating the condition and restoring the Lactobacillus microbiota in the vagina. In this study, 40 women diagnosed with BV by discharge, fishy odor, sialidase positive test and Nugent Gram stain scoring, were randomized to receive either two dried capsules containing Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14 each night for 5 days, or 0.75% metronidazole gel, applied vaginally twice a day (in the morning and evening). Follow-up at day 6, 15 and 30 showed cure of BV in significantly more probiotic treated subjects (16, 17 and 18/20, respectively) compared to metronidazole treatment (9, 9 and 11/20: P=0.016 at day 6, P=0.002 at day 15 and P=0.056 at day 30). This is the first report of an effective (90%) cure of BV using probiotic lactobacilli. Given the correlation between BV and HIV, and the high risk of the latter in Nigeria, intravaginal use of lactobacilli could provide women with a self-use therapy, similar to over-the-counter anti-yeast medication, for treatment of urogenital infections.\n\n\nTitle 4:\nTherapy of bacterial vaginosis using exogenously-applied Lactobacilli acidophili and a low dose of estriol: a placebo-controlled multicentric clinical trial.\nAbstract 4:\nThe efficacy of vaginal tablets (Gynoflor) containing 50 mg of a lyophilisate of viable, H2O2-producing Lactobacillus acidophilus (at least 10(7) colony forming units/tablet) and 0.03 mg estriol (CAS 50-27-1) for the treatment of bacterial vaginosis (BV) was tested in a multicentric, randomised, placebo-controlled clinical trial with parallel-group design. 32 non-menopausal women with positive diagnoses for BV, including intermediate cases, participated in the trial. Patients were diagnosed using the classical clinical parameters of BV according to Amsel and using microscopic analysis of the Gram-stained vaginal smear. A positive clinical diagnosis of BV required at least 2 of the following 4 clinical criteria to be positive; greyish-white, homogeneous leukorrhea; vaginal pH > 4.5; KOH test for volatile amines; presence of clue cells. Microscopic diagnosis of BV, on the other hand, was obtained if examination of the Gram-stained vaginal smear showed less than 6 lactobacilli per field of view (1000 x magnification). This corresponds to another definition of BV as \"lactobacilli deficiency syndrome\". The efficacy of the 6-day therapy with 1-2 vaginal tablets daily was evaluated using both clinical and microscopic analysis. Using Amsel's classical clinical parameters of BV, the cure rate (defined as < or = 1 of the 4 clinical criteria positive) two weeks after the start of therapy was 77% in the verum group and 25% in the placebo group. Four weeks after the start of therapy, the cure rate was 88% in the verum group and 22% in the placebo group. At both control examinations, the cure rate for the test group was significantly higher than that for the placebo group (p < 0.05, Fisher's exact test, 2-sided, significance level 0.05). In addition, the trial showed that after 6 days of treatment with the test preparation, the lactobacilli were capable of recolonising the vagina. A significant increase in the number of lactobacilli was observed in the Gram-stained vaginal smear for the patient group treated with the test preparation compared to the placebo patient group (p < 0.05, Fisher's exact test, 2-sided, significance level 0.05), two and four weeks after the start of the 6-day treatment." }, { "role": "assistant", "content": "The results do not provide sufficient evidence for or against recommending probiotics for the treatment of BV. The metronidazole/probiotic regimen and probiotic/estriol perparation appear promising but well-designed randomized controlled trials with standardized methodologies and larger patient size are needed." } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D deficiency increases the risk of COVID-19 complications and deaths\n\nAbstract:\nOptimal nutrition can improve well-being and might mitigate the risk and morbidity associated with coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nThis review summarizes nutritional guidelines to support dietary counseling provided by dietitians and health-related professionals.\nThe majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\nThirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\nDietary supplementation has not been linked to COVID-19 prevention.\nHowever, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\nThere was no convincing evidence that food or food packaging is associated with the transmission of COVID-19, but good hygiene practices for handling and preparing foods were recommended.\nNo changes to breastfeeding recommendations have been made, even in women diagnosed with COVID-19." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Dietary supplementation has not been linked to COVID-19 prevention.\"]}" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nThestructuralbasisfortheelasticyofredbloodcellsincirculationarelongrangemolecularfunctionsoftheplasmamembraneandthemembraneassociatedpartofthecytoskeleton[40-43]." }, { "role": "assistant", "content": "[\"molecular\"]" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nMassspectrometricanalysiswasperformedinthecentralservicefacilitiesoftheZMMK." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of a research paper. Classify the intent behind this citation by choosing from one of the following categories:\n\n- Method: Cites the paper for its methodology or procedure.\n- Background: Cites the paper to provide background information.\n- Result: Cites the paper for its findings or results.\n\nYour answer should be a single word from the following list of options: [\"Method\", \"Background\", \"Result\"]. Do not include any other text in your response.\n\nCitation sentence:\n\nOther categories might include ‘sex siren’, a category for masculine men who are judged for their sex appeal and physical attractiveness; ‘butch queen vogue’, a category that uses elements of vogue of hands, spins and dips; ‘femme queen’, and ‘virgin runway’, a category that is open to participants who are relatively new to the Ball scene (Dickson-Gomez et al., 2014; Galindo, 2013)." }, { "role": "assistant", "content": "Background" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"CellType\", \"Protein\", \"CellLine\", \"RNA\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nDifferential interaction of nuclear factors with the leukocyte-specific pp52 promoter in B and T cells. The leukocyte-specific, cytoskeleton-binding pp52 (LSP-1, WP-34) protein is widely expressed in multiple leukocyte lineages, including B and T lymphocytes, granulocytes, and macrophages. We previously detected a tissue-specific promoter preceding the exon encoding the N terminus of the pp52 leukocyte protein. Here we describe the functional characterization of this promoter and identification of the factors in B and T cells that regulate its activity. The pp52 promoter contains an initiator specifying the unique 5' terminus of pp52 mRNA, tandem pairs of Ets and SP1 motifs, and a lone C / EBP motif. All these motifs are essential and collectively control transcriptional activity. DNA binding studies and Ab supershift assays revealed that different combinations of factors interact with these motifs in B cells vs T cells. The Ets motifs are preferentially bound by PU-1 in B cell extracts from all stages of development, whereas a different Ets family member reacts with these motifs in T cell extracts. The C / EBP motif is bound by Ig / EBP-1 in pre-B cell and T cell extracts, but is replaced by nuclear factor-IL-6beta or a nuclear factor-IL-6beta-Ig / EBP-1 heterodimer in plasmacytoma cell extracts. Despite its reported role as a negative regulator of transcription, Ig / EBP-1 appears to exert a stimulatory effect on this promoter. These findings reveal the features controlling the pp52 promoter in B and T cells and provide the foundation for determining the regulation of this promoter in other leukocyte lineages." }, { "role": "assistant", "content": "{\"CellLine\": [], \"CellType\": [\"T cells\", \"B and T lymphocytes\", \"B cells\", \"leukocyte lineages\", \"macrophages\", \"B and T cells\", \"granulocytes\"], \"DNA\": [\"lone C / EBP motif\", \"leukocyte - specific pp52 promoter\", \"pp52 promoter\", \"promoter\", \"C / EBP motif\", \"Ets motifs\", \"tissue - specific promoter\", \"Ets and SP1 motifs\", \"5\\u0027 terminus\"], \"Protein\": [\"nuclear factors\", \"Ets family member\", \"- Ig / EBP - 1\", \"nuclear factor - IL - 6beta - Ig / EBP - 1 heterodimer\", \"N terminus\", \"pp52 leukocyte protein\", \"Ig / EBP - 1\", \"PU - 1\", \"nuclear factor - IL - 6beta\"], \"RNA\": [\"pp52 mRNA\"]}" } ]

[ { "role": "user", "content": "You have to answer a biomedical question in binary format, i.e. only yes and no are the only acceptable answer formats. A list of paragraphs is provided as context to help you answer the question.\n\nQuestion: Is flibanserin effetive for Hypoactive Sexual Desire Disorder? \n\nContext: Mechanism of action of flibanserin, a multifunctional serotonin agonist and antagonist (MSAA), in hypoactive sexual desire disorder.\nFlibanserin is a novel multifunctional serotonin agonist and antagonist (MSAA) that improves sexual functioning in premenopausal women who suffer from reduced sexual interest and desire.\nFlibanserin is a novel, non-hormonal drug for the treatment of HSDD in pre- and postmenopausal women, although the application submitted to the U.S. Food and Drug Administration by Sprout Pharmaceuticals is only for premenopausal women.\nCONCLUSIONS: In naturally postmenopausal women with HSDD, flibanserin, compared with placebo, has been associated with improvement in sexual desire, improvement in the number of SSEs, and reduced distress associated with low sexual desire, and is well tolerated.\nINTRODUCTION: Flibanserin is a mixed 5-HT1A agonist/5-HT2A antagonist that has been developed for the treatment of hypoactive sexual desire disorder in women\nBACKGROUND: Flibanserin, a novel serotonin (5-HT)(1A) agonist and 5-HT(2A) antagonist, has been shown to increase sexual desire and reduce distress in women with Hypoactive Sexual Desire Disorder (HSDD).\nHypoactive sexual desire disorder (HSDD) is the most commonly described form of female sexual dysfunction. There is currently no pharmacological therapy approved to treat HSDD, and therefore, there is an unmet medical need for the development of efficacious treatment alternatives. Flibanserin is a novel, non-hormonal drug for the treatment of HSDD in pre- and postmenopausal women, although the application submitted to the U.S.\nSexual function adverse events across flibanserin groups were generally comparable to placebo.Although these studies were not designed or powered to compare sexual function outcomes, results suggested a potential benefit of flibanserin on sexual function, particularly on female sexual desire, and provided a rationale to evaluate the efficacy of flibanserin as a treatment for female hypoactive sexual desire disorder." }, { "role": "assistant", "content": "yes" } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 2020 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: A system for object detection with hybrid attention.\n\nKeyphrases: object detection images\n\nDataset description:\nCOCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\nPASCAL Context: The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids).\nmEBAL: A multimodal database for eye blink detection and attention level estimation.\nLAG: Includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432).\n\nOptions:- A: COCO B: PASCAL Context C: mEBAL D: LAG" }, { "role": "assistant", "content": "COCO" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nRedstarin(I)showstheosteoclastsinvadingtheBmp2C/C;Bmp4C/C;Prx1::crefemursat1wkofage." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nBACKGROUND: Critically ill coronavirus disease 2019 (COVID-19) patients have frequent thrombotic complications and laboratory evidence of hypercoagulability. The relationship of coagulation tests and thrombosis requires investigation to identify best diagnostic and treatment approaches. We assessed for hypercoagulable characteristics in critically ill COVID-19 patients using rotational thromboelastometry (ROTEM) and explored relationships of D-dimer and ROTEM measurements with thrombotic complications. METHODS: Critically ill adult COVID-19 patients receiving ROTEM testing between March and April 2020 were analyzed. Patients receiving therapeutic anticoagulation before ROTEM were excluded. Rotational thromboelastometry measurements from COVID-19 patients were compared with non-COVID-19 patients matched by age, sex, and body mass index. Intergroup differences in ROTEM measurements were assessed using t tests. Correlations of D-dimer levels to ROTEM measurements were assessed in COVID-19 patients who had available concurrent testing. Intergroup differences of D-dimer and ROTEM measurements were explored in COVID-19 patients with and without thrombosis. RESULTS: Of 30 COVID-19 patients receiving ROTEM, we identified hypercoagulability from elevated fibrinogen compared with non-COVID-19 patients (fibrinogen assay maximum clot firmness [MCF], 47 +/- 13 mm vs. 20 +/- 7 mm; mean intergroup difference, 27.4 mm; 95% confidence interval [CI], 22.1-32.7 mm; p < 0.0001). In our COVID-19 cohort, thrombotic complications were identified in 33%. In COVID-19 patients developing thrombotic complications, we identified higher D-dimer levels (17.5 +/- 4.3 mug/mL vs. 8.0 +/- 6.3 mug/mL; mean difference, 9.5 mug/mL; 95% CI, 13.9-5.1; p < 0.0001) but lower fibrinogen assay MCF (39.7 +/- 10.8 mm vs. 50.1 +/- 12.0 mm; mean difference, -11.2 mm; 95% CI, -2.1 to -20.2; p = 0.02) compared with patients without thrombosis. We identified negative correlations of D-dimer levels and ROTEM MCF in these patients (r = -0.61; p = 0.001). CONCLUSION: We identified elevated D-dimer levels and hypercoagulable blood clot characteristics from increased fibrinogen on ROTEM testing in critically ill COVID-19 patients. However, we identified lower, albeit still hypercoagulable, ROTEM measurements of fibrinogen in COVID-19 patients with thrombotic complications compared with those without. Further work is required to externally validate these findings and to investigate the mechanistic drivers for these relationships to identify best diagnostic and treatment approaches for these patients. LEVEL OF EVIDENCE: Epidemiologic, level IV." }, { "role": "assistant", "content": "[\"Treatment\", \"Diagnosis\"]" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: acetaminophen can stop the new coronavirus\n\nAbstract:\nThe ongoing pandemic coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of global concern.\nEnvironmental factors such as air pollution and smoking and comorbid conditions (hypertension, diabetes mellitus and underlying cardio-respiratory illness) likely increase the severity of COVID-19.\nRheumatic manifestations such as arthralgias and arthritis may be prevalent in about a seventh of individuals.\nCOVID-19 can result in acute interstitial pneumonia, myocarditis, leucopenia (with lymphopenia) and thrombocytopenia, also seen in rheumatic diseases like lupus and Sjogren's syndrome.\nSevere disease in a subset of patients may be driven by cytokine storm, possibly due to secondary hemophagocytic lymphohistiocytosis (HLH), akin to that in systemic onset juvenile idiopathic arthritis or adult-onset Still's disease.\nIn the absence of high-quality evidence in this emerging disease, understanding of pathogenesis may help postulate potential therapies.\nAngiotensin converting enzyme 2 (ACE2) appears important for viral entry into pneumocytes; dysbalance in ACE2 as caused by ACE inhibitors or ibuprofen may predispose to severe disease.\nPreliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.\nAntiviral drugs like lopinavir/ritonavir, favipiravir and remdesivir are also being explored.\nCytokine storm and secondary HLH might require heightened immunosuppressive regimens.\nCurrent international society recommendations suggest that patients with rheumatic diseases on immunosuppressive therapy should not stop glucocorticoids during COVID-19 infection, although minimum possible doses may be used.\nDisease-modifying drugs should be continued; cessation may be considered during infection episodes as per standard practices.\nDevelopment of a vaccine may be the only effective long-term protection against this disease.\nKey Points• Patients with coronavirus disease 19 (COVID-19) may have features mimicking rheumatic diseases, such as arthralgias, acute interstitial pneumonia, myocarditis, leucopenia, lymphopenia, thrombocytopenia and cytokine storm with features akin to secondary hemophagocytic lymphohistiocytosis.• Although preliminary results may be encouraging, high-quality clinical trials are needed to better understand the role of drugs commonly used in rheumatology like hydroxychloroquine and tocilizumab in COVID-19.• Until further evidence emerges, it may be cautiously recommended to continue glucocorticoids and other disease-modifying antirheumatic drugs (DMARDs) in patients receiving these therapies, with discontinuation of DMARDs during infections as per standard practice." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Preliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.\", \"Development of a vaccine may be the only effective long-term protection against this disease.\"]}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nCoronavirus disease 2019 (COVID-19) is a pandemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high morbidity and mortality. There are very limited data on the interference of immunomodulating drugs on the risk of infection and on the course of the disease. In particular, there are no current clinical data about the interference exerted by dupilumab, a biologic drugs blocking IL-4 and IL-13, used for adult atopic dermatitis. The pathogenesis of COVID-19 is complex, characterized by an immune response mainly Th1/Th17. The hyper-activation of these cells may cause the release of pro-inflammatory cytokines that may result in lung impairment. IL-4 and IL-13 are Th2 cytokines, thus being part of a pathway not considered implicated in host defense mechanism against viral infections. Indeed, viral infections, including respiratory infections, have not been reported as a significant adverse event in clinical trials. Furthermore, dupilumab has been proved to be efficacious also in exacerbations of asthma, and it is known that viral infections can worsen asthma. Therefore, the current data seem to suggest that treatment with dupilumab should not be stopped during COVID-19 pandemic. Obviously, a careful assessment is mandatory for each individual patient and further studies are necessary to characterize the immunologic responses in COVID-19." }, { "role": "assistant", "content": "[\"Prevention\", \"Mechanism\", \"Treatment\"]" } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:\n\n- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.\n- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.\n- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.\n\nPlease format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.\n\nAs an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:\n\n[[\"artesunate\", \"artemether\", \"COMB\"], [\"Nifedipine\", \"Labetalol\", \"Prazosin\", \"POS\"]]\n\nThe paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.\n\nIn your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.\n\nparagraph: Tazarotene cream (0.1%) in combination with betamethasone valerate foam (0.12%) for plaque-type psoriasis. A combination of multiple agents is often required to achieve treatment success for plaque-type psoriasis. We report a case series of 10 patients that were treated with betamethasone valerate foam ( 0.12 % ) in the morning and topical tazarotene cream ( 0.1 % ) in the evening for a total of 12 weeks or until plaques cleared . Erythema, scale, and thickness along with an aggregate severity score were determined at weeks 4, 8, and 12. One patient was lost to follow-up. Eight of the other 9 patients experienced improvement in their disease by week 12. Two patients were clear of their psoriasis at week 4 and 4 were clear at week 8. No adverse events, including irritation were reported; the use of the corticosteroid foam may protect against potential local irritation reported with tazarotene. The combination of tazarotene cream and betamethasone valerate foam is an effective combination approach to treating localized plaque-type psoriasis.\n\nsentence: We report a case series of 10 patients that were treated with betamethasone valerate foam ( 0.12 % ) in the morning and topical tazarotene cream ( 0.1 % ) in the evening for a total of 12 weeks or until plaques cleared ." }, { "role": "assistant", "content": "[[\"betamethasone\", \"tazarotene\", \"POS\"]]" } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of a research paper. Classify the intent behind this citation by choosing from one of the following categories:\n\n- Method: Cites the paper for its methodology or procedure.\n- Background: Cites the paper to provide background information.\n- Result: Cites the paper for its findings or results.\n\nYour answer should be a single word from the following list of options: [\"Method\", \"Background\", \"Result\"]. Do not include any other text in your response.\n\nCitation sentence:\n\nComparison of testes size in lemurs and the sister taxon, the lorises, however, revealed no grade shift between the two groups of strepsirrhines in a previous study (Kappeler 1997a), making this explanation unlikely." }, { "role": "assistant", "content": "Background" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: immune system protects us against the invader and can even be helpful for its therapy. But sometimes it can turn against us.\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research paper, followed by a question about the paper. Please extract text from the paragraph to answer the question. Do not include any text in your repsonse other than the answer.\n\nParagraph: Schematic representation of viral escape mechanisms from humoral immune response against surface viral proteins: point mutations on immunodominant regions, glycosylation of functionally pivotal residues (glycan shield) of the viral surface proteins and virus association with host serum components (e.g., lipoproteins) (B) Mechanisms of interference on nAb-mediated virus neutralization by the binding of interfering non-nAbs: non-neutralizing/interfering Abs might interfere with the binding of nAbs by steric hindrance following a spatial occupancy of their epitope or a competition for the binding; otherwise the binding of non-neutralizing/interfering Abs may induce conformational changes on the viral protein, thus affecting nAb binding to the antigen. Non-neutralizing/interfering Abs are depicted in black while nAbs in yellow. The HCV genome encodes a single polyprotein of about 3,000 aminoacids that is processed by host and viral proteases into at least 3 structural (core, E1 and E2) and 7 non-structural (p7, NS2, NS3, NS4A, NS4B, NS5A and NS5B) proteins [21, 22] . In particular, the envelope type I membrane glycoproteins E1 and E2 form non-covalent heterodimers on the surface of the HCV envelope and allow clathrin-mediated virus endocytosis interacting consecutively with several entry cellular factors such as glycosaminoglycans [23] [24] [25] , low-density lipoprotein receptor [26, 27] , scavenger receptor class B type I [28] , the tetraspanin CD81 [29] , the tight-junction proteins claudin-1 and occludin, and the recently described Niemann-Pick C1-like 1 cholesterol absorption receptor [30] [31] [32] [33] [34] . The development of effective prophylactic and therapeutic approaches against this virus has been hindered mainly by its high mutation rate that gives rise to highly diversified viral variants, even within a single patient (quasispecies) [35] . Indeed, seven major genotypes, varying by up to 30% in nucleotide sequence, and several subtypes are recognized, each characterized by different clinical features such as different evolutionary rates to chronic liver diseases or different response to available antiviral therapies [21, 36, 37] . The development and use of anti-HCV mAbs capable of targeting structurally and functionally conserved regions of the highly variable viral particles are being considered as novel therapeutic tools [38] [39] [40] [41] [42] [43] . In particular, the production of potent nAbs in acute infections has been shown to correlate with viral clearance in a single-source outbreak cohort [44] . Moreover, in vaccinated chimpanzees, a sustained Ab response to envelope glycoproteins E1 and E2 correlates with reduced viremia [45] , while the passive administration of neutralizing mAbs in a uPA-SCID chimeric mouse model of infection was able to protect against challenge with a HCV quasispecies inoculum [46] . Broadly cross-neutralizing human mAbs directed against the surface E2 glycoprotein of HCV (HCV/E2) are typically directed against functionally important regions within the CD81 binding site [47] [48] [49] [50] [51] [52] [53] [54] , as well as against other critical residues highly conserved among different genotypes [55, 56] . This aspect is crucial for the possible therapeutic in vivo use of such mAbs, but it may not be sufficient since it has been recently supposed that other non-nAb populations may interfere with their neutralizing activity [39, [57] [58] [59] [60] [61] . In fact, in persistently infected individuals anti-HCV/E2 cross-nAbs are generally elicited at low titer and in a late stage of the infection, leading to a poor control of viremia, whereas quasispecies-specific neutralizing or high titer non-nAbs are elicited earlier [53, [58] [59] [60] [61] [62] . Moreover, the in vivo use of anti-HCV polyclonal immunoglobulin preparations in both chimpanzees and humans has been disappointing, and clinical studies have shown that these preparations fail to prevent recurrent infections in patients after liver transplantation [63] . At this regard, a recent paper has suggested that the effect of some of these nAbs, directed against functionally important residues involved in the viral binding to CD81 (within epitope I, encompassing aminoacid residues 412-426), could be hindered by the presence of non-nAbs binding residues within epitope II on HCV/E2 (aminoacid residues 434-446) [58] . In particular, blocking of these interfering epitope II-specific Abs not only raised the neutralizing titer of serum containing both epitope I-and epitope II-specific Abs, but also uncovered a broader cross-genotype neutralizing response [58] . However, the role (and the existence itself) of these interfering Abs in influencing HCV infection is still controversial. Some authors recently corroborated the data of Zhang et al. by in vitro neutralization assays using serum-derived HCV of genotype 4a and polyclonal Abs derived from immunized goats with different conserved peptides spanning aminoacid residues 412-419, 430-447 and 517-531 of HCV/E2 glycoprotein [64] . In particular, this group found an interfering activity exerted by the weakly neutralizing 430-447-elicited Abs on the neutralizing activity of both the 412-419 and the 517-531-elicited Abs [64] . Interestingly, according to the putative model for E2 folding, all the three aforementioned regions would lie next to each other on the glycoprotein [48] . Therefore, this structural prediction possibly supports the interfering effect of epitope II-directed Abs. However, while this predicted structure is currently the best model available, these conclusions cannot be absolutely ascertained. For this purpose, the availability of E1-E2 crystal will certainly accelerate the fine elucidation of the spatial proximities of neutralizing and interfering mAbs on the E1-E2 structure and, consequently, structure-based vaccine progress. Moreover, it is noteworthy that individuals with Abs that target the region of E2 encompassing epitope I frequently harbor Abs that recognize the region containing epitope II, thus confirming the co-immunogenicity of these epitopes [58] . Finally, it has been shown both a low prevalence (less than 2.5%) and a low titer of epitope I-reactive Abs in sera from both chronic and acute resolved infections thus supporting the hypothesis of a conformational masking by adjacent regions such as that containing epitope II [65] . In fact, Zhang et al. originally put forward the idea that once epitope II is bound to an Ab, the site of epitope I becomes masked and can no longer be recognized by specific nAbs. Indeed, depletion of Abs to epitope II in plasma from a chronically infected HCV patient and vaccinated chimpanzees recovered an otherwise undetectable cross-genotype neutralizing activity [58] . Another possibility is that the initial binding of interfering Abs to the region containing epitope II may induce conformational changes on E2 that inhibit the binding by epitope I-directed Abs, as recently suggested by Lapierre et al. for other anti-HCV/E2 Abs [66] . Conversely, these conclusions were not supported in a recent study by Tarr et al. using murine (AP33) and rat (2/69a) mAbs, as well as human immunoglobulin fractions affinity-purified on linear peptides representing distinct HCV/E2 domains clustering within the regions 412-426 and 434-446 [67] . Although confirming the previously reported co-immunogenicity of these two regions, the authors failed to demonstrate any inhibition between these two groups of Abs. Considering their results, the authors indeed suggested that interference by non-nAbs, at least to the region encompassing residues 434-446, is not a possible mechanism for HCV persistence in chronically infected individuals, as it had been originally proposed by Zhang et al. In accordance with the findings of Tarr and colleagues, Keck et al. described anti-HCV/E2 human mAbs binding conformation-sensitive epitopes encompassing also some residues within the 434-446 interfering region [56] . These mAbs are broadly neutralizing and do not lead to viral escape mutants, demonstrating the functional importance of their epitopes. The authors conclude that not all Abs directed against epitope II are interfering, but they also speculate that the interfering activity could be limited to Abs recognizing linear epitopes within it [56] . Recently, we have partly confirmed the observations of Zhang et al. using a panel of anti-HCV/E2 mAbs: the well characterized mouse anti-HCV/E2 mAb AP33, whose epitope encompasses epitope I (aminoacid residues 412-423), and a weakly neutralizing human anti-HCV/E2 mAb (named e509), whose epitope encompasses epitope II [68] . In particular, we found that e509 is able to interfere with the neutralizing activity of AP33 on genotype 1a virus (strain H77). Instead, we found that e509 does not minimally interfere with the activity of two other broadly cross-neutralizing human anti-HCV/E2 mAbs, named e20 and e137 [49, 69] . Interestingly, we found that both e20 and e137 bind also residues within epitope II, at a higher affinity compared to e509, thus displacing it from the interfering epitope and, therefore, keeping unaltered their neutralizing activity. Thus, in our opinion, the described divergent observations reported above may depend on the different Ab specificities present in the polyclonal preparations used and, probably, also on the different HCV genotypes infecting the studied patients [68] . Moreover, the different strategies adopted in isolating epitope I-and epitope II-directed Abs followed in the studies above could explain the different data obtained. In fact, immunoglobulins purified on peptides representing distinct HCV/E2 regions [67] are obviously directed against linear epitopes; these preparations are certainly different from mAbs cloned using a full-length HCV/E2 glycoprotein, which are more probably directed against conformational epitopes including also residues outside the investigated linear regions [54] . To summarize, in the HCV field several works support the existence of interfering Ab populations and hypothesize their possible role in HCV persistence, as demonstrated using human plasma-derived immunoglobulin preparations, human mAbs, and sera of animals vaccinated with recombinant HCV/E2 peptides. The possible mechanism leading to the interference is still controversial, but both direct steric hindrance and induced antigen conformational changes have been hypothesized. On the other hand, other papers do not confirm these findings, suggesting that the putative interfering epitope II may be targeted by Abs endowed with a broadly neutralizing activity. Our recent paper, using well characterized mAbs [68] , shows that the interfering Abs do exist but that their overall effect may be biased by the presence of nAbs with different binding features and by the infecting HCV genotype. Future works investigating the in vivo role of these interfering Ab subpopulations in HCV persistence will certainly be very useful. The influenza viruses circulate worldwide in animal reservoirs, especially water fowl, potentially affecting humans of any age group. Influenza viruses are classified into types A, B or C based on antigenic differences of their nucleoprotein and matrix protein. The most clinically relevant and variable type is influenza A which is divided in several subtypes, according to the antigenic characteristic of the two envelope glycoproteins, and causes epidemic and pandemic infections [70] . The yearly recurring influenza epidemics are associated with significant morbidity and mortality, particularly among risk groups (such as elderly people or those with chronic medical conditions, pregnant women and children) [71] ; the global spread of pandemic influenza viruses can cause millions of deaths [72] . Within the enveloped influenza virion eight segments of negative single-stranded RNA are protected by the nucleocapsid protein, forming the ribonucleoprotein (RNP). The first six RNA segments each code for a single protein: PB2, PB1, and PA (all constituting the RNA-dependent RNA polymerase), the hemagglutinin (HA), the nucleoprotein (NP), the neuraminidase (NA). The last two segments each code for two different proteins: the matrix proteins (M1 and M2) and the non-structural proteins (NS1 and NS2). Three different proteins (HA, NA and M2) are present on the viral envelope. The HA glycoprotein is the most abundant and it is the major target of the humoral immune response. Together with the NA transmembrane glycoprotein, HA is capable of eliciting a subtype-specific immune responses which is fully protective within, but only partially protective across different subtypes [73] . HA is synthesized as inactive precursor that transits into its active form upon cleavage by host cell proteases, and which is present on the viral membrane as homotrimers. HA trimers bind to 2,6-linked sialic acid molecules on cell membrane proteins or lipids through domains located in the globular head of each monomer. Subsequently, the viral envelope fuses by clathrin-dependent and -independent mechanisms with the endocytic vesicle membrane through the HA fusion peptide located in the stem region of each monomer. As a consequence, viral components are released into the host cell and can subvert the synthetic capabilities of the host cell for production and release of progeny particles [74] . The humoral immunity plays an important role in the host defense against influenza virus infection as most of Abs neutralize influenza viruses and, hence, limit infection [75] [76] [77] [78] . In fact, a large body of experimental works suggests that occlusion of the receptor-binding site on HA by Abs is the main mechanism of influenza viral neutralization. Less common, but more broadly nAbs may neutralize influenza virus by inhibiting fusion of the viral envelope with the endocytic-vesicle membrane [50, [79] [80] [81] [82] [83] . Aminoacid changes on HA, more frequent on the immunodominant globular head, have complex effects on viral neutralization by Abs, usually allowing the mutated variants to escape from previously generated nAbs [84] .\n\nQuestion: Why has it been difficult to develop a therapy for the Hepatitis C virus?" }, { "role": "assistant", "content": "high mutation rate" } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:\n\n- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.\n- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.\n- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.\n\nPlease format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.\n\nAs an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:\n\n[[\"artesunate\", \"artemether\", \"COMB\"], [\"Nifedipine\", \"Labetalol\", \"Prazosin\", \"POS\"]]\n\nThe paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.\n\nIn your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.\n\nparagraph: Effects of oxaprozin alone or in combination with aspirin on hemostasis and plasma protein binding. oxaprozin, a new nonsteroidal antiinflammatory agent, was studied alone and in combination with aspirin for its effects on hemostasis and protein binding in 10 healthy adults. When both oxaprozin and aspirin were given separately for seven days and in combination for five days, both drugs prolonged bleeding time and inhibited collagen- and epinephrine-induced platelet aggregation to a similar degree. The effects of the combination of oxaprozin and aspirin were not additive. The data from the protein binding study showed that oxaprozin was more than 99 per cent bound to plasma proteins. aspirin displaced oxaprozin from its binding sites. As a result, the rate of plasma clearance of oxaprozin significantly increased from 20 to 26 ml/min/kg (P less than 0.05), and the plasma half-life decreased from 45 to 40 hours. Platelet count and the humoral clotting mechanism were not affected by either drug alone or in combination. There was no clinical evidence of bleeding. One subject who received oxaprozin for 12 days and , in addition , aspirin for the last five days developed a rash that subsided after both drugs were discontinued ; one subject treated with aspirin experienced tinnitus . These data suggest that oxaprozin, like aspirin and other nonsteroidal antiinflammatory drugs, should be used with caution when administered to patients who have suffered trauma, who undergo surgery, or who have known defects in hemostasis.\n\nsentence: One subject who received oxaprozin for 12 days and , in addition , aspirin for the last five days developed a rash that subsided after both drugs were discontinued ; one subject treated with aspirin experienced tinnitus ." }, { "role": "assistant", "content": "[[\"oxaprozin\", \"aspirin\", \"NEG\"]]" } ]

[ { "role": "user", "content": "You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.\nYour response should be formatted as a `json` array. For instance, for the query \"Semi supervised image classification\", a sample response might be: [\"CIFAR-10\", \"CIFAR-100\"]. Do not include any extra text in the response other than the answer array.\n\nQuery: We show how to quantize BERT.\n\nKeyphrases: natural language processing text\n\nDataset description:\nSQuAD: The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text.\nLS3D-W: A 3D facial landmark dataset of around 230,000 images.\nOffice-Caltech-10: Office-Caltech-10 a standard benchmark for domain adaptation, which consists of Office 10 and Caltech 10 datasets. It contains the 10 overlapping categories between the Office dataset and Caltech256 dataset.\nImagewoof: Imagewoof is a subset of 10 dog breed classes from Imagenet. The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog.\nGLUE: General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI.\nCoarseWSD-20: The CoarseWSD-20 dataset is a coarse-grained sense disambiguation dataset built from Wikipedia (nouns only) targeting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating Word Sense Disambiguation (WSD) models (e.g.\nTVQA: The TVQA dataset is a large-scale vido dataset for video question answering. It is based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle).\n\nOptions:- A: SQuAD B: LS3D-W C: Office-Caltech-10 D: Imagewoof E: GLUE F: CoarseWSD-20 G: TVQA" }, { "role": "assistant", "content": "[\"SQuAD\", \"GLUE\"]" } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of a research paper. Classify the intent behind this citation by choosing from one of the following categories:\n\n- Method: Cites the paper for its methodology or procedure.\n- Background: Cites the paper to provide background information.\n- Result: Cites the paper for its findings or results.\n\nYour answer should be a single word from the following list of options: [\"Method\", \"Background\", \"Result\"]. Do not include any other text in your response.\n\nCitation sentence:\n\nS1P also regulated ERK activity in several types of the cells including smooth muscle cells (Pyne et al., 1996) and osteoblasts (Kozawa et al., 1997) in addition to 3T3 fibroblasts (Wu et al., 1995; Blakesley et al., 1997; Rani et al., 1997)." }, { "role": "assistant", "content": "Background" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can wearing a face mask protect against the 2019 coronavirus?\n\nAbstract:\nThe COVID‐19 pandemic caused by the novel coronavirus SARS‐CoV‐2 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS‐CoV‐2, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.\n\nContext: For each of these two models, we can consider two possibilities - one in whichthe decoder LSTM is conditioned on the last generated frame and the other inwhich it is not. In the experimental section, we explore these choicesquantitatively. Here we briefly discuss arguments for and against a conditionaldecoder. A strong argument in favour of using a conditional decoder is that itallows the decoder to model multiple modes in the target sequence distribution.Without that, we would end up averaging the multiple modes in the low-levelinput space. However, this is an issue only if we expect multiple modes in thetarget sequence distribution. For the LSTM Autoencoder, there is only onecorrect target and hence a unimodal target distribution. But for the LSTM FuturePredictor there is a possibility of multiple targets given an input because evenif we assume a deterministic universe, everything needed to predict the futurewill not necessarily be observed in the input.\n\nQuestion: Out of conditional and unconditional decoder blocks, which one is better?" }, { "role": "assistant", "content": "The author talk both advantage and disadvantage of conditional and unconditional decoder blocks. They also provided a strong argument in favor of using a conditional decoder but clearly no winner is mentioned. The question does not have any clear answer in this paper." } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ginger can treat a coronavirus infection\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"UnapprovedDrug\", \"Drug\", \"DrugGroup\", \"Brand\"].\n\nPlease return the output as a JSON object of the format: {\"Brand\" : [\"Aspirin\", ...], \"DrugGroup\": [\"Salicyclate\", ...], \"Drug\": [\"Phenobarbital\", ...], \"UnapprovedDrug\": [\"Heroin\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nDrug Laboratory Test Interactions: Prolongation of the template bleeding time has been reported during continuous intravenous infusion of AMICAR at dosages exceeding 24 g/day. Platelet function studies in these patients have not demonstrated any significant platelet dysfunction. However, in vitro studies have shown that at high concentrations (7.4 mMol/L or 0.97 mg/mL and greater) EACA inhibits ADP and collagen-induced platelet aggregation, the release of ATP and serotonin, and the binding of fibrinogen to the platelets in a concentration-response manner. Following a 10 g bolus of AMICAR, transient peak plasma concentrations of 4.6 mMol/L or 0.60 mg/mL have been obtained. The concentration of AMICAR necessary to maintain inhibition of fibrinolysis is 0.99 mMol/L or 0.13 mg/mL. Administration of a 5 g bolus followed by 1 to 1.25 g/hr should achieve and sustain plasma levels of 0.13 mg/mL. Thus, concentrations which have been obtained in vivo clinically in patients with normal renal function are considerably lower than the in vitro concentrations found to induce abnormalities in platelet function tests. However, higher plasma concentrations of AMICAR may occur in patients with severe renal failure." }, { "role": "assistant", "content": "{\"Brand\": [\"AMICAR\"], \"Drug\": [\"EACA\"], \"DrugGroup\": [], \"UnapprovedDrug\": []}" } ]

[ { "role": "user", "content": "You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.\n\nMain Abstract:\nIn the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do not suffice to capture the spatio-temporal network complexity. In this paper, we propose a new hierarchical coupled-geometry analysis that implicitly takes into account the connectivity structures between neurons and the dynamic patterns at multiple time scales. Our approach gives rise to the joint organization of neurons and dynamic patterns in data-driven hierarchical data structures. These structures provide local to global data representations, from local partitioning of the data in flexible trees through a new multiscale metric to a global manifold embedding. The application of our techniques to in-vivo neuronal recordings demonstrate the capability of extracting neuronal activity patterns and identifying temporal trends, associated with particular behavioral events and manipulations introduced in the experiments.\n\nCited Abstract(s):\n[1]: This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and — within functional integration — the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.\n\n[2]: An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.\n\n[3]: Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.\n\n[4]: Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using efficient optimization algorithms. Unfortunately, the MAP estimate can have a relatively large average error when the posterior is highly nongaussian. Here we compare several Markov chain Monte Carlo (MCMC) algorithms that allow for the calculation of general Bayesian estimators involving posterior expectations (conditional on model parameters). An efficient version of the hybrid Monte Carlo (HMC) algorithm was significantly superior to other MCMC methods for gaussian priors. When the prior distribution has sharp edges and corners, on the other hand, the “hit-and-run” algorithm performed better than other MCMC methods. Using these algorithms, we show that for this latter class of priors, the posterior mean estimate can have a considerably lower average error than MAP, whereas for gaussian priors, the two estimators have roughly equal efficiency. We also address the application of MCMC methods for extracting nonmarginal properties of the posterior distribution. For example, by using MCMC to calculate the mutual information between the stimulus and response, we verify the validity of a computationally efficient Laplace approximation to this quantity for gaussian priors in a wide range of model parameters; this makes direct model-based computation of the mutual information tractable even in the case of large observed neural populations, where methods based on binning the spike train fail. Finally, we consider the effect of uncertainty in the GLM parameters on the posterior estimators.\n\n[5]: We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A \"hidden state\" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.\n\n[6]: Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.\n\n[7]: Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known. It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.\n\n[8]: The author reviews network models of the brain, including models of both structural and functional connectivity. He discusses contributions of network models to cognitive neuroscience, as well as limitations and challenges associated with constructing and interpreting these models.\n\n[9]: Neural responses in visual cortex are influenced by visual stimuli and by ongoing spiking activity in local circuits. An important challenge in computational neuroscience is to develop models that can account for both of these features in large multi-neuron recordings and to reveal how stimulus representations interact with and depend on cortical dynamics. Here we introduce a statistical model of neural population activity that integrates a nonlinear receptive field model with a latent dynamical model of ongoing cortical activity. This model captures temporal dynamics and correlations due to shared stimulus drive as well as common noise. Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a multiple idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional dynamical model. We introduce a fast estimation method using online expectation maximization with Laplace approximations, for which inference scales linearly in both population size and recording duration. We test this model to multi-channel recordings from primary visual cortex and show that it accounts for neural tuning properties as well as cross-neural correlations." }, { "role": "assistant", "content": "Current network analysis approaches in neuroscience can be divided into two main classes [1] [8] . The first class comprises methods, which aim to determine functional connectivity, defined in terms of statistical dependencies between measured elements (e.g., neurons or networks), by constructing direct statistical models from the data (e.g., Granger causality, transfer entropy, point process modeling and graph based methods [7] [2] [3] [8] . The second class of methods is often based on Latent Dynamical Systems (LDS), which accommodates effective connectivity characterizing the causal relations between elements through an underlying hidden dynamical system [1] [6] [9] . Non-linear and non-Gaussian extensions of the Kalman filter, contemporary sequential Monte Carlo methods and particle filters, have also been introduced in neuroscience [5] [4] ." } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: But at the start of the pandemic, there was concern that specific medications for high blood pressure could be linked with worse outcomes for COVID-19 patients\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\"]}" } ]

[ { "role": "user", "content": "Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.\n\nPaper text:\n\nWe study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset.\nThe classifier is described by a nonlinear ReLU model and the objective function adopts the exponential loss function.\nWe first characterize the landscape of the loss function and show that there can exist spurious asymptotic local minima besides asymptotic global minima.\nWe then show that gradient descent (GD) can converge to either a global or a local max-margin direction, or may diverge from the desired max-margin direction in a general context.\nFor stochastic gradient descent (SGD), we show that it converges in expectation to either the global or the local max-margin direction if SGD converges.\nWe further explore the implicit bias of these algorithms in learning a multi-neuron network under certain stationary conditions, and show that the learned classifier maximizes the margins of each sample pattern partition under the ReLU activation.\nIt has been observed in various machine learning problems recently that the gradient descent (GD) algorithm and the stochastic gradient descent (SGD) algorithm converge to solutions with certain properties even without explicit regularization in the objective function.\nCorrespondingly, theoretical analysis has been developed to explain such implicit regularization property.\nFor example, it has been shown in Gunasekar et al. (2018; 2017) that GD converges to the solution with the minimum norm under certain initialization for regression problems, even without an explicit norm constraint.Another type of implicit regularization, where GD converges to the max-margin classifier, has been recently studied in Gunasekar et al. (2018) ; Ji & Telgarsky (2018) ; Nacson et al. (2018a) ; Soudry et al. (2017; 2018) for classification problems as we describe below.\nGiven a set of training samples z i = (x i , y i ) for i = 1, . . . , n, where x i denotes a feature vector and y i ∈ {−1, +1} denotes the corresponding label, the goal is to find a desirable linear model (i.e., a classifier) by solving the following empirical risk minimization problem It has been shown in Nacson et al. (2018a) ; Soudry et al. (2017; 2018) that if the loss function (·) is monotonically strictly decreasing and satisfies proper tail conditions (e.g., the exponential loss), and the data are linearly separable, then GD converges to the solution w with infinite norm and the maximum margin direction of the data, although there is no explicit regularization towards the maxmargin direction in the objective function.\nSuch a phenomenon is referred to as the implicit bias of GD, and can help to explain some experimental results.\nFor example, even when the training error achieves zero (i.e., the resulting model enters into the linearly separable region that correctly classifies the data), the testing error continues to decrease, because the direction of the model parameter continues to have an improved margin.\nSuch a study has been further generalized to hold for various other types of gradient-based algorithms Gunasekar et al. (2018) .\nMoreover, Ji & Telgarsky (2018) analyzed the convergence of GD with no assumption on the data separability, and characterized the implicit regularization to be in a subspace-based form.The focus of this paper is on the following two fundamental issues, which have not been well addressed by existing studies.•\nExisting studies so far focused only on the linear classifier model. An\nimportant question one naturally asks is what happens for the more general nonlinear leaky ReLU and ReLU models. Will\nGD still converge, and if so will it converge to the max-margin direction? Our\nstudy here provides new insights for the ReLU model that have not been observed for the linear model in the previous studies.• Existing\nstudies mainly analyzed the convergence of GD with the only exceptions Ji & Telgarsky (2018) ; Nacson et al. (2018b) on SGD. However, Ji\n& Telgarsky (2018) did not establish the convergence to the max-margin direction for SGD, and Nacson et al. (2018b) established the convergence to the max-margin solution only epochwisely for cyclic SGD (not iterationwise for SGD under random sampling with replacement). Moreover, both\nstudies considered only the linear model. Here, our interest\nis to explore the iterationwise convergence of SGD under random sampling with replacement to the max-margin direction, and our result can shed insights for online SGD. Furthermore, our study\nprovides new understanding for the nonlinear ReLU and leaky ReLU models.\nIn this paper, we study the problem of learning a ReLU neural network via gradient descent methods, and establish the corresponding risk and parameter convergence under the exponential loss function.In particular, we show that due to the possible existence of spurious asymptotic local minima, GD and SGD can converge either to the global or local max-margin direction, which in the nature of convergence is very different from that under the linear model in the previous studies.\nWe also discuss the extensions of our analysis to the more general leaky ReLU model and multi-neuron networks.\nIn the future, it is worthy to explore the implicit bias of GD and SGD in learning multilayer neural network models and under more general (not necessarily linearly separable) datasets." }, { "role": "assistant", "content": "We study the implicit bias of gradient methods in solving a binary classification problem with nonlinear ReLU models." } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 1997 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: I want to train a face recognition system to recognize people.\n\nKeyphrases: human face recognition images\n\nDataset description:\nORL: The ORL Database of Faces contains 400 images from 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses).\nDFW: Contains over 11000 images of 1000 identities with different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings.\nSNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity: SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.\nMegaFace: MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test set). MegaFace contains 1M images from 690K individuals with unconstrained pose, expression, lighting, and exposure.\n\nOptions:- A: ORL B: DFW C: SNIPS D: MegaFace" }, { "role": "assistant", "content": "ORL" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"Protein\", \"Chemical\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\" : [\"Dexamethasone\", ...], \"Protein\" : [\"BRCA-1\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nFurther insights into the effect of quinidine in short QT syndrome caused by a mutation in HERG.\nINTRODUCTION: The principal aim of this study was to assess the efficacy of quinidine in suppressing IKr in vitro and in modulating the rate dependence of the QT interval in the \"SQT1\" form of the short QT syndrome. METHODS AND RESULTS: Graded-intensity bicycle exercise testing was performed off drug in three patients and during oral quinidine in two patients with short QT syndrome and compared to a control group of healthy normal subjects. The in vitro effects of quinidine on currents in patch clamp technique were investigated. Off drugs QTpV3/heart rate correlation is much weaker in patients with short QT syndrome, and QTpV3 shortens less with heart rate increase compared to normal subjects. In addition to prolonging the QT interval into the normal range, quinidine restored the heart rate dependence of the QT interval toward a range of adaptation reported for normal subjects. Data from heterologous expression of wild-type and mutant HERG genes indicate the mutation causes a 20-fold increase in IC50 of d-sotalol but only a 5.8-fold increase in IC50 of quinidine. CONCLUSION: Oral quinidine is effective in suppressing the gain of function in IKr responsible for some cases of short QT syndrome with a mutation in HERG and thus restoring normal rate dependence of the QT interval and rendering ventricular tachycardia/ventricular fibrillation noninducible." }, { "role": "assistant", "content": "{\"Chemical\": [\"quinidine\"], \"Protein\": [\"IKr\", \"HERG\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In fact, there is no scientific evidence that any supplement-vitamin C, vitamin D, prebiotics, or probiotics-can help prevent or speed recovery from COVID-19\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It's very important to do what you can to reduce the risk of you and other people getting ill with coronavirus.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.\n\nContext: Each box predicts the classes the bounding box may contain using multilabel classification. We do not use a softmax as we have found it is unnecessary for good performance, instead we simply use independent logistic classifiers. During training we use binary cross-entropy loss for the class predictions.\n\nQuestion: In its loss function YoloV3 uses logistic regression with multilabel classification or Softmax over all class probabilities?" }, { "role": "assistant", "content": "The authors use binary cross-entropy loss." } ]

[ { "role": "user", "content": "Given an abstract from a research paper, your task is to identify and label relationships between chemical compounds and genes mentioned in the abstract.\n\nThe relationship type must be one of ['DOWNREGULATOR', 'SUBSTRATE', 'INDIRECT-REGULATOR', 'PART-OF', 'MODULATOR-ACTIVATOR', 'REGULATOR', 'INHIBITOR', 'COFACTOR', 'UPREGULATOR', 'ACTIVATOR', 'ANTAGONIST', 'NOT', 'INDIRECT-DOWNREGULATOR', 'SUBSTRATE_PRODUCT-OF', 'INDIRECT-UPREGULATOR', 'AGONIST', 'PRODUCT-OF', 'MODULATOR', 'DIRECT-REGULATOR', 'UNDEFINED', 'AGONIST-INHIBITOR', 'AGONIST-ACTIVATOR', 'MODULATOR-INHIBITOR'].\n\nPlease format your output as a JSON array. Each entry in the array should express a single relation, formatted as [\"<Entity_A>\", \"<RELATION_A_B>\", \"<Entity_B>\"]. If no relations can be found, please output an empty JSON array [].\n\nAbstract:\n\nBeta1 adrenergic receptor-mediated enhancement of hippocampal CA3 network activity. Norepinephrine is an endogenous neurotransmitter distributed throughout the mammalian brain. In higher cortical structures such as the hippocampus, norepinephrine, via beta adrenergic receptor (AR) activation, has been shown to reinforce the cognitive processes of attention and memory. In this study, we investigated the effect of beta1AR activation on hippocampal cornu ammonis 3 (CA3) network activity. AR expression was first determined using immunocytochemistry with antibodies against beta1ARs, which were found to be exceptionally dense in hippocampal CA3 pyramidal neurons. CA3 network activity was then examined in vitro using field potential recordings in rat brain slices. The selective betaAR agonist isoproterenol caused an enhancement of hippocampal CA3 network activity, as measured by an increase in frequency of spontaneous burst discharges recorded in the CA3 region. In the presence of alphaAR blockade, concentration-response curves for isoproterenol, norepinephrine, and epinephrine suggested that a beta1AR was involved in this response, and the rank order of potency was isoproterenol > norepinephrine = epinephrine. Finally, equilibrium dissociation constants (pK (b)) of subtype-selective betaAR antagonists were functionally determined to characterize the AR subtype modulating hippocampal CA3 activity. The selective beta1AR antagonists atenolol and metoprolol blocked isoproterenol-induced enhancement, with apparent K (b) values of 85 + /- 36 and 3.9 + /- 1.7 nM, respectively. In contrast, the selective beta2AR antagonists ICI-118,551 and butoxamine inhibited isoproterenol-mediated enhancement with apparent low affinities (K (b) of 222 + /- 61 and 9268 + /- 512 nM, respectively). Together, this pharmacological profile of subtype-selective betaAR antagonists indicates that in this model, beta1AR activation is responsible for the enhanced hippocampal CA3 network activity initiated by isoproterenol." }, { "role": "assistant", "content": "[[\"norepinephrine\", \"REGULATOR\", \"beta adrenergic receptor\"], [\"metoprolol\", \"ANTAGONIST\", \"beta1AR\"], [\"epinephrine\", \"REGULATOR\", \"beta1AR\"], [\"ICI-118,551\", \"ANTAGONIST\", \"beta2AR\"], [\"norepinephrine\", \"REGULATOR\", \"AR\"], [\"isoproterenol\", \"REGULATOR\", \"beta1AR\"], [\"isoproterenol\", \"AGONIST\", \"betaAR\"], [\"butoxamine\", \"ANTAGONIST\", \"beta2AR\"], [\"norepinephrine\", \"REGULATOR\", \"beta1AR\"], [\"isoproterenol\", \"AGONIST\", \"beta1AR\"], [\"atenolol\", \"ANTAGONIST\", \"beta1AR\"]]" } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of a research paper. Classify the intent behind this citation by choosing from one of the following categories:\n\n- Method: Cites the paper for its methodology or procedure.\n- Background: Cites the paper to provide background information.\n- Result: Cites the paper for its findings or results.\n\nYour answer should be a single word from the following list of options: [\"Method\", \"Background\", \"Result\"]. Do not include any other text in your response.\n\nCitation sentence:\n\n…3 de Lamirande and Gagnon (2002) PD98059 prevents MEK activation by Raf 4–50 50 de Lamirande and Gagnon (2002) U126 inhibits activated MEK 0.058–0.072 0.3 de Lamirande and Gagnon (2002)\nPI3K Wortmannin catalytic subunit 0.005 0.01 Fisher et al. (1998); Nauc et al. (2004) Akt Akt inhibitor…" }, { "role": "assistant", "content": "Background" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many supplements and natural or other alternative treatments are being promoted to prevent or treat coronavirus (COVID-19). None have been proven to work, but some have potential benefit.\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\", \"The inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\", \"We noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\", \"CONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.\"]}" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"PopulationGroup\", \"BiomedicalOccupationOrDiscipline\", \"Food\", \"InjuryOrPoisoning\", \"ResearchActivity\", \"AnatomicalStructure\", \"SpatialConcept\", \"ClinicalAttribute\", \"BodySystem\", \"Eukaryote\", \"HealthCareActivity\", \"Virus\", \"Organization\", \"Finding\", \"Chemical\", \"IntellectualProduct\", \"ProfessionalOrOccupationalGroup\", \"Bacterium\", \"BodySubstance\", \"MedicalDevice\", \"BiologicFunction\"].\n\nPlease return the output as a JSON object of the format: {\"Virus\": [\"HIV\", ...], \"Bacterium\": [\"MRSA\", ...], \"AnatomicalStructure\": [\"Lung\", ...], \"BodySystem\": [\"CNS\", ...], \"BodySubstance\": [\"Serum\", ...], \"Finding\": [\"Headache\", ...], \"InjuryOrPoisoning\": [\"Fracture\", ...], \"BiologicFunction\": [\"Death\", ...], \"HealthCareActivity\": [\"Biopsy\", ...], \"ResearchActivity\": [\"Clinical trial\", ...], \"MedicalDevice\": [\"Lenses\", ...], \"SpatialConcept\": [\"Camps\", ...], \"BiomedicalOccupationOrDiscipline\": [\"Forensic medicine\", ...], \"Organization\": [\"WHO\", ...], \"ProfessionalOrOccupationalGroup\": [\"Provider\", ...], \"PopulationGroup\": [\"Swimmers\", ...], \"Chemical\": [\"Gold\", ...], \"Food\": [\"Rice\", ...], \"IntellectualProduct\": [\"RPAM\", ...], \"ClinicalAttribute\": [\"Biomarker\", ...], \"Eukaryote\": [\"Dogs\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nMesenteric lipoblastoma presenting as a small intestinal volvulus in an infant: A case report and literature review A 1-year-old boy with no underlying disorder presented with non-bilious vomiting since 4 days before admission. He was referred to our hospital and was diagnosed with a small bowel obstruction due to an intraabdominal tumor. Laparotomy revealed an intestinal volvulus with a soft and lobulated tumor arising from the mesentery. The resected tumor with a small part of the small bowel was diagnosed as lipoblastoma histologically. From a literature review, mesenteric lipoblastoma with an intestinal volvulus showed different characteristics such as greater frequency of vomiting and less frequency of abdominal mass as clinical symptoms, and the size of the tumor was smaller than that of the tumor without the intestinal volvulus." }, { "role": "assistant", "content": "{\"AnatomicalStructure\": [\"small bowel\"], \"Bacterium\": [], \"BiologicFunction\": [\"lipoblastoma\", \"small bowel obstruction\", \"small intestinal volvulus\", \"intestinal volvulus\", \"soft\", \"disorder\", \"tumor\", \"lobulated tumor\", \"intraabdominal tumor\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [], \"ClinicalAttribute\": [], \"Eukaryote\": [], \"Finding\": [\"symptoms\", \"abdominal mass\", \"non-bilious vomiting\", \"frequency of vomiting\", \"diagnosed\"], \"Food\": [], \"HealthCareActivity\": [\"admission\", \"Laparotomy\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [\"literature review\", \"case report\"], \"MedicalDevice\": [], \"Organization\": [\"hospital\"], \"PopulationGroup\": [], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [], \"SpatialConcept\": [\"mesenteric\", \"Mesenteric\", \"size of the tumor\", \"mesentery\"], \"Virus\": []}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nBACKGROUND: The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic. METHODS: An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each statement. Items that may influence decisions were listed as notes to be considered for each scenario. RESULTS: Twelve statements related to baseline and annual lung cancer screening (n = 2), surveillance of a previously detected lung nodule (n = 5), evaluation of intermediate and high-risk lung nodules (n = 4), and management of clinical stage I non-small cell lung cancer (n = 1) were developed and modified. All 12 statements were confirmed as consensus statements according to the voting results. The consensus statements provide guidance about situations in which it was believed to be appropriate to delay screening, defer surveillance imaging of lung nodules, and minimize nonurgent interventions during the evaluation of lung nodules and stage I non-small cell lung cancer. CONCLUSIONS: There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 2018 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: We study the use of different syntactic dependency representations in a neural relation classification task and compare\n\nKeyphrases: relation classification\n\nDataset description:\nADAM: ADAM is organized as a half day Challenge, a Satellite Event of the ISBI 2020 conference in Iowa City, Iowa, USA. The ADAM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Age-related Macular degeneration (AMD) and segmentation of lesions in fundus photos from AMD patients.\nTHEODORE: Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes.\nUniversal Dependencies: The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages.\nPGM: PGM dataset serves as a tool for studying both abstract reasoning and generalisation in models. Generalisation is a multi-faceted phenomenon; there is no single, objective way in which models can or should generalise beyond their experience.\n\nOptions:- A: ADAM B: THEODORE C: Universal Dependencies D: PGM" }, { "role": "assistant", "content": "Universal Dependencies" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it appears pets can contract the novel coronavirus from their owners\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\", \"Humans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\"]}" } ]

[ { "role": "user", "content": "You will be shown a question, followed by excerpts from biomedical research papers. Please answer the question based on the provided context. Do not include any text in your response other than the answer.\n\nQuestion: What is HOCOMOCO?\n\nContext: HOCOMOCO: a comprehensive collection of human transcription factor binding sites models.\nWe present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source.\nWe present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods\nHOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source\nHOCOMOCO: a comprehensive collection of human transcription factor binding sites models\nWe show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias. We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch." }, { "role": "assistant", "content": "HOCOMOCO is a comprehensive collection of human transcription factor binding sites models constructed by integration of binding sequences obtained by both low- and high-throughput methods. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source." } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: A novel approach for video action recognition.\n\nKeyphrases: video action recognition\n\nDataset description:\nFineGym: FineGym is an action recognition dataset build on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity.\nTinyVIRAT: TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging.\nKinetics-600: The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively.\nUCF101: UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports).\n\nOptions:- A: FineGym B: TinyVIRAT C: Kinetics-600 D: UCF101" }, { "role": "assistant", "content": "UCF101" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus is just like the common cold.\n\nAbstract:\nGiven the scope and speed of the spread, the COVID-19 pandemic is not only complex but dangerous.\nComplicated objective factors such as the long-term existence of source of infection, difficulty in completely blocking the transmission route and a large susceptible population suggested that the COVID-19 pandemic might stay with us for long term.\nTherefore, we should be ready for a tough and long-term battle against the COVID-19 epidemic.\nThe strategy should adhere to the principle of combining emergency response with regular prevention and control measures, and all efforts should be well harnessed in a coordinated way to keep the epidemic under control." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Given the scope and speed of the spread, the COVID-19 pandemic is not only complex but dangerous.\", \"Complicated objective factors such as the long-term existence of source of infection, difficulty in completely blocking the transmission route and a large susceptible population suggested that the COVID-19 pandemic might stay with us for long term.\", \"Therefore, we should be ready for a tough and long-term battle against the COVID-19 epidemic.\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 genome evolution exposes early human infection\n\nAbstract:\nThe set of mutations observed at the outset of the SARS-CoV-2 pandemic may illuminate how the virus will adapt to humans as it continues to spread.\nViruses are expected to quickly acquire beneficial mutations upon jumping to a new host species.\nAdvantageous nucleotide substitutions can be identified by their parallel occurrence in multiple independent lineages and are likely to result in changes to protein sequences.\nHere we show that SARS-CoV-2 is acquiring mutations more slowly than expected for neutral evolution, suggesting purifying selection is the dominant mode of evolution during the initial phase of the pandemic.\nHowever, several parallel mutations arose in multiple independent lineages and may provide a fitness advantage over the ancestral genome.\nWe propose plausible reasons for several of the most frequent mutations.\nThe absence of mutations in other genome regions suggests essential components of SARS-CoV-2 that could be the target of drug development.\nOverall this study provides genomic insights into how SARS-CoV-2 has adapted and will continue to adapt to humans.\nSUMMARY In this study we sought signals of evolution to identify how the SARS-CoV-2 genome has adapted at the outset of the COVID-19 pandemic.\nWe find that the genome is largely undergoing purifying selection that maintains its ancestral sequence.\nHowever, we identified multiple positions on the genome that appear to confer an adaptive advantage based on their repeated evolution in independent lineages.\nThis information indicates how SARS-CoV-2 will evolve as it diversifies in an increasing number of hosts." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SUMMARY In this study we sought signals of evolution to identify how the SARS-CoV-2 genome has adapted at the outset of the COVID-19 pandemic.\"]}" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nInkeepingwiththeresultsfromFaxitronx-ray,trabecularbone(asterisk)andcorticalthickness(arrow)werereducedinthefemurof12-month-old+/+micecomparedwithallothergroups." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You have to answer a biomedical question in binary format, i.e. only yes and no are the only acceptable answer formats. A list of paragraphs is provided as context to help you answer the question.\n\nQuestion: Is there a relationship between B cells and Multiple Sclerosis?\n\nContext: These results suggest that RRMS patients with radiological phenotypes showing high neurodegeneration have changes in B cells characterized by down-regulation of B-cell-specific genes and increased activation status\nAlthough the exact etiology is still obscure, the leading hypothesis behind MS relapses is acute inflammatory attacks on CNS myelin and axons. This complex process involves B and T cells together with macrophages and microglia.\nIt is currently known that CD24 serves as a costimulatory factor of T cells that regulate their homeostasis and proliferation, while in B cells, CD24 is functionally involved in cell activation and differentiation. CD24 can enhance autoimmune diseases in terms of its protective role in the clonal deletion of autoreactive thymocytes\nMultiple B cell-dependent mechanisms contributing to inflammatory demyelination of the CNS have been explored using experimental autoimmune encephalomyelitis (EAE), a CD4 T cell-dependent animal model for multiple sclerosis.\nThe role of B cells in multiple sclerosis: rationale for B-cell-targeted therapies.\nInterest in CD8+ T cells and B cells was initially inspired by observations in multiple sclerosis rather than in animal models: CD8+ T cells predominate in multiple sclerosis lesions, oligoclonal immunoglobulin bands in CSF have long been recognised as diagnostic and prognostic markers, and anti-B-cell therapies showed considerable efficacy in multiple sclerosis.\nDifferential effects of fingolimod on B-cell populations in multiple sclerosis.\nUnaltered regulatory B-cell frequency and function in patients with multiple sclerosis.\nB cells are increasingly recognized as major players in multiple sclerosis pathogenesis.\nThese observations underscore the B cell's contribution to the putative underpinnings of multiple sclerosis.\nData suggesting that B cells play a role in the pathogenesis of multiple sclerosis have been accumulating for the past five decades, demonstrating that the cerebrospinal fluid and central nervous system tissues of multiple sclerosis patients contain B cells, plasma cells, antibodies, and immunoglobulins.\nB-cell-targeted treatment for multiple sclerosis: mechanism of action and clinical data.\nSubset composition and cytokine production of B cells derived from peripheral blood mononuclear cells from multiple sclerosis patients under Fingolimod treatment, untreated multiple sclerosis patients and healthy controls were analyzed by flow cytometry and ELISA.\nIn particular, antigen presentation between B cells and T cells, increased trafficking of B cells across the blood-brain barrier, and autoantibodies produced by plasma cells may contribute to the pathophysiology of autoimmune disorders such as multiple sclerosis.\nAccumulating evidence supports a major role of B cells in multiple sclerosis (MS) pathogenesis.\nFurther research is needed to elucidate the pathology of B cells and their role in central nervous system autoimmune diseases, including multiple sclerosis.\nTargeting B cells in the treatment of multiple sclerosis: recent advances and remaining challenges\nphingosine-1-phosphate receptors control B-cell migration through signaling components associated with primary immunodeficiencies, chronic lymphocytic leukemia, and multiple sclerosis" }, { "role": "assistant", "content": "yes" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Asymptomatic People Can NOT Spread COVID-19\n\nAbstract:\nBackground: Previous studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease (COVID-19) and the evidence of person-to-person transmission.\nLimited data are available for asymptomatic infections.\nThis study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers.\nMethods: Epidemiological investigations were conducted among all close contacts of COVID-19 patients (or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community.\nAsymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples.\nTheir clinical records, laboratory assessments, and chest CT scans were reviewed.\nFindings: None of the 24 asymptomatic cases presented any obvious symptoms before nucleic acid screening.\nFive cases (20.8%) developed symptoms (fever, cough, fatigue and etc.) during hospitalization.\nTwelve (50.0%) cases showed typical CT images of ground-glass chest and five (20.8%) presented stripe shadowing in the lungs.\nThe remaining seven (29.2%) cases showed normal CT image and had no symptoms during hospitalization.\nThese seven cases were younger (median age: 14.0 years; P = 0.012) than the rest.\nNone of the 24 cases developed severe COVID-19 pneumonia or died.\nThe median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases).\nThrough epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia.\nInterpretation: The asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization.\nHowever, the communicable period could be up to three weeks and the communicated patients could develop severe illness.\nThese results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests.\nFurther isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases).\", \"Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia.\"]}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nOBJECTIVES: This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse. METHODS: Twitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including Linguistic Inquiry and Word Count, supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics. RESULTS: The most common category in the coded tweets was \"personal opinions\" (66.2%), followed by \"informative\" (24.7%), \"jokes/ridicule\" (4.8%), and \"personal experiences\" (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within \"jokes/ridicule\" targeted older adults, half of which were \"death jokes.\" Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. DISCUSSION: Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "role": "user", "content": "You will be presented a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nMethods" }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: An adversary who wants to perform set membership inference on generative models.\n\nKeyphrases: generative model membership inference images\n\nDataset description:\nSWAG: Given a partial description like \"she opened the hood of the car,\" humans can reason about the situation and anticipate what might come next (\"then, she examined the engine\"). SWAG (Situations With Adversarial Generations) is a large-scale dataset for this task of grounded commonsense inference, unifying natural language inference and physically grounded reasoning.\nCODAH: The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions.\nSNLI: The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences.\nCIFAR-10: The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck).\n\nOptions:- A: SWAG B: CODAH C: SNLI D: CIFAR-10" }, { "role": "assistant", "content": "CIFAR-10" } ]

[ { "role": "user", "content": "You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.\n\nMain Abstract:\nDetection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1 AP at 142 FPS, 37.4 AP at 52 FPS, and 45.1 AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.\n\nCited Abstract(s):\n[1]: We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving. Many traditional 2D vision outputs, like instance segmentations and depth-maps, can be obtained by simply rendering our output 3D scene model. We exploit class-specific shape priors by learning a low dimensional shape-space from collections of CAD models. We present novel representations of shape and pose, that strive towards better 3D equivariance and generalization. In order to exploit rich supervisory signals in the form of 2D annotations like segmentation, we propose a differentiable Render-and-Compare loss that allows 3D shape and pose to be learned with 2D supervision. We evaluate our method on the challenging real-world datasets of Pascal3D+ and KITTI, where we achieve state-of-the-art results.\n\n[2]: We present a method for 3D object detection and pose estimation from a single image. In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box. The first network output estimates the 3D object orientation using a novel hybrid discrete-continuous loss, which significantly outperforms the L2 loss. The second output regresses the 3D object dimensions, which have relatively little variance compared to alternatives and can often be predicted for many object types. These estimates, combined with the geometric constraints on translation imposed by the 2D bounding box, enable us to recover a stable and accurate 3D object pose. We evaluate our method on the challenging KITTI object detection benchmark [2] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes. Although conceptually simple, our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation, instance level segmentation and flat ground priors [4] and sub-category detection [23][24]. Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26].\n\n[3]: In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the networks outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.\n\n[4]: State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolutional features. For the very deep VGG-16 model [19], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2 mAP) and 2012 (70.4 mAP) using 300 proposals per image. Code is available at https: github.com ShaoqingRen faster_rcnn.\n\n[5]: Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30 relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3 . Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http: www.cs.berkeley.edu rbg rcnn.\n\n[6]: Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net datasets kitti" }, { "role": "assistant", "content": "3D bounding box estimation powers autonomous driving [6] . Deep3Dbox [2] uses a slow-RCNN [5] style framework, by first detecting 2D objects [4] and then feeding each object into a 3D estimation network. 3D RCNN [1] adds an additional head to Faster-RCNN [4] followed by a 3D projection. Deep Manta [3] uses a coarse-to-fine Faster-RCNN [4] trained on many tasks. Our method is similar to a one-stage version of Deep3Dbox [2] or 3DRCNN [1] . As such, CenterNet is much simpler and faster than competing methods." } ]

[ { "role": "user", "content": "Given a short query with keyphrases about a research idea in 2020 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.\n\nQuery: I want to improve the performance of multi-domain image-to-image translation\n\nKeyphrases: image-to-image translation images\n\nDataset description:\nDailyDialog++: Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context.\nImageCLEF-DA: The ImageCLEF-DA dataset is a benchmark dataset for ImageCLEF 2014 domain adaptation challenge, which contains three domains: Caltech-256 (C), ImageNet ILSVRC 2012 (I) and Pascal VOC 2012 (P). For each domain, there are 12 categories and 50 images in each category.\nCelebA: CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age.\nSUN RGB-D: The SUN RGBD dataset contains 10335 real RGB-D images of room scenes. Each RGB image has a corresponding depth and segmentation map.\n\nOptions:- A: DailyDialog++ B: ImageCLEF-DA C: CelebA D: SUN RGB-D" }, { "role": "assistant", "content": "CelebA" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D might lower the risk of viral respiratory tract infections like COVID-19\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: SARS-CoV-2 is different from other viruses, including other coronaviruses.\n\nAbstract:\nMasks are widely discussed during the course of the ongoing COVID-19 pandemic.\nMost hospitals have implemented universal masking for their healthcare workers, and the Center for Disease Control currently advises even the general public to wear cloth masks when outdoors.\nThe pertinent need for masks arises from plausible dissemination of the SARS-CoV-2 through close contacts, as well as the possibility of virus transmission from asymptomatic, pre-symptomatic, and mildly symptomatic individuals.\nGiven current global shortages in personal protective equipment, the efficacy of various types of masks: N95 respirators, surgical masks, and cloth masks are researched.\nTo accommodate limited supplies, techniques for extended use, reuse, and sterilization of masks are strategized.\nHowever, masks alone may not greatly slow down the COVID-19 pandemic unless they are coupled with adequate social distancing, diligent hand hygiene, and other proven preventive measures." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:\n\n- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.\n- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.\n- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.\n\nPlease format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.\n\nAs an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:\n\n[[\"artesunate\", \"artemether\", \"COMB\"], [\"Nifedipine\", \"Labetalol\", \"Prazosin\", \"POS\"]]\n\nThe paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.\n\nIn your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.\n\nparagraph: Characteristics of murine model of genital infection with Chlamydia trachomatis and effects of therapy with tetracyclines, amoxicillin-clavulanic acid, or azithromycin. Following intravaginal inoculation of progesterone-treated outbred mice with Chlamydia trachomatis MoPn, 4 to 6 log10 inclusion-forming units were recovered in vaginal swabs for 21 days but all animals were culture negative after 28 days. Serum antibody titers were elevated and remained high for at least 70 days. Between 28 and 70 days, upper tract infection (inflammation and distension of the uterine horns, occlusion of oviducts with inflammatory exudate, pyosalpinx, and hydrosalpinx) was seen in > 80% of the animals. Mice were dosed orally, commencing at 7 days after infection, with minocycline, doxycycline, or amoxicillin-clavulanate. Further groups received azithromycin either as a single high dose or as lower once-daily doses. In addition, minocycline and amoxicillin-clavulanate were administered at 24 h after infection, and this early treatment prevented elevation of antibody titers whereas delayed therapy did not. Vaginal swabs from mice in all treatment regimens were culture negative except for 25% of mice receiving either early amoxicillin-clavulanate or low-dose azithromycin, which yielded low numbers (20 to 70 inclusion-forming units) of chlamydiae. Numbers of fertile mice in the early treatment regimens and their litter sizes were similar to those of noninfected controls, although 25% of amoxicillin-clavulanate-treated mice had unilateral hydrosalpinges. In comparison, 88% of untreated mice developed hydrosalpinges and only 25% conceived. Delayed dosing did not affect the outcome of amoxicillin-clavulanate therapy but did diminish the protective efficacy of minocycline such that 50% of treated mice had either unilateral hydrosalpinges or ovarian abscesses. Doxycycline and azithromycin were highly effective in restoring fertility . This model makes possible the study of both short- and long-term outcomes of chlamydial infection.\n\nsentence: Doxycycline and azithromycin were highly effective in restoring fertility ." }, { "role": "assistant", "content": "[]" } ]

[ { "role": "user", "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nMONARCH 3: Abemaciclib As Initial Therapy for Advanced Breast Cancer.\nAbstract 1:\nPurpose Abemaciclib , a cyclin-dependent kinase 4 and 6 inhibitor , demonstrated efficacy as monotherapy and in combination with fulvestrant in women with hormone receptor (HR)-positive , human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer previously treated with endocrine therapy . Methods MONARCH 3 is a double-blind , r and omized phase III study of abemaciclib or placebo plus a nonsteroidal aromatase inhibitor in 493 postmenopausal women with HR-positive , HER2-negative advanced breast cancer who had no prior systemic therapy in the advanced setting . Patients received abemaciclib or placebo ( 150 mg twice daily continuous schedule ) plus either 1 mg anastrozole or 2.5 mg letrozole , daily . The primary objective was investigator-assessed progression-free survival . Secondary objectives included response evaluation and safety . A planned interim analysis occurred after 189 events . Results Median progression-free survival was significantly prolonged in the abemaciclib arm ( hazard ratio , 0.54 ; 95 % CI , 0.41 to 0.72 ; P = .000021 ; median : not reached in the abemaciclib arm , 14.7 months in the placebo arm ) . In patients with measurable disease , the objective response rate was 59 % in the abemaciclib arm and 44 % in the placebo arm ( P = .004 ) . In the abemaciclib arm , diarrhea was the most frequent adverse effect ( 81.3 % ) but was mainly grade 1 ( 44.6 % ) . Comparing abemaciclib and placebo , the most frequent grade 3 or 4 adverse events were neutropenia ( 21.1 % v 1.2 % ) , diarrhea ( 9.5 % v 1.2 % ) , and leukopenia ( 7.6 % v 0.6 % ) . Conclusion Abemaciclib plus a nonsteroidal aromatase inhibitor was effective as initial therapy , significantly improving progression-free survival and objective response rate and demonstrating a tolerable safety profile in women with HR-positive , HER2-negative advanced breast cancer\n\n\nTitle 2:\nSuperior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group.\nAbstract 2:\nPURPOSE To compare the efficacy and tolerability of tamoxifen with that of letrozole , an oral aromatase inhibitor , with tamoxifen as first-line therapy in postmenopausal women with advanced breast cancer . PATIENTS AND METHODS Nine hundred seven patients were r and omly assigned letrozole 2.5 mg once daily ( 453 patients ) or tamoxifen 20 mg once daily ( 454 patients ) . Patients had estrogen receptor- and /or progesterone receptor-positive tumors , or both receptors were unknown . Recurrence during adjuvant antiestrogen therapy or within the following 12 months or prior endocrine therapy for advanced disease precluded enrollment . One prior chemotherapy regimen for metastatic disease was allowed . The primary end point was time to progression ( TTP ) . Secondary end points included overall objective response rate ( ORR ) , its duration , rate and duration of clinical benefit , time to treatment failure ( TTF ) , overall survival , and tolerability . RESULTS TTP was significantly longer for letrozole than for tamoxifen ( median , 41 v 26 weeks ) . Treatment with letrozole reduced the risk of progression by 30 % ( hazards ratio , 0.70 ; 95 % confidence interval , 0.60 to 0.82 , P = .0001 ) . TTP was significantly longer for letrozole irrespective of dominant site of disease , receptor status , or prior adjuvant antiestrogen therapy . Similarly , TTF was significantly longer for letrozole ( median , 40 v 25 weeks ) . ORR was higher for letrozole ( 30 % v 20 % ; P = .0006 ) , as was the rate of clinical benefit ( 49 % v 38 % ; P = .001 ) . Survival data are currently immature and not reported here . Both treatments were well tolerated . CONCLUSION Letrozole was significantly superior to tamoxifen in TTP , TTF , ORR , and clinical benefit rate . Our results support its use as first-line endocrine therapy in postmenopausal women with advanced breast cancer\n\n\nTitle 3:\nAnastrozole is superior to tamoxifen as first-line therapy for advanced breast cancer in postmenopausal women: results of a North American multicenter randomized trial. Arimidex Study Group.\nAbstract 3:\nPURPOSE The efficacy and tolerability of anastrozole ( Arimidex ; AstraZeneca , Wilmington , DE , and Macclesfield , United Kingdom ) and tamoxifen were compared as first-line therapy for advanced breast cancer in 353 postmenopausal women . PATIENTS AND METHODS The r and omized , double-blind , multicenter study was design ed to evaluate anastrozole 1 mg once daily relative to tamoxifen 20 mg once daily in patients with hormone receptor-positive tumors or tumors of unknown receptor status who were eligible for endocrine therapy . Primary end points were objective response ( OR ) , defined as complete ( CR ) or partial ( PR ) response , time to progression ( TTP ) , and tolerability . RESULTS Anastrozole was as effective as tamoxifen in terms of OR ( 21 % v 17 % of patients , respectively ) , with clinical benefit ( CR + PR + stabilization > or = 24 weeks ) observed in 59 % of patients on anastrozole and 46 % on tamoxifen ( two-sided P = .0098 , retrospective analysis ) . Anastrozole had a significant advantage over tamoxifen in terms of TTP ( median TTP of 11.1 and 5.6 months for anastrozole and tamoxifen , respectively ; two-sided P = .005 ) . The tamoxifen : anastrozole hazards ratio was 1.44 ( lower one-sided 95 % confidence limit , 1.16 ) . Both treatments were well tolerated . However , thromboembolic events and vaginal bleeding were reported in fewer patients who received anastrozole compared with those who received tamoxifen ( 4.1 % v 8.2 % [ thromboembolic events ] and 1.2 % v 3.8 % [ vaginal bleeding ] , respectively ) . CONCLUSION Anastrozole satisfied the predefined criteria for equivalence to tamoxifen . Furthermore , we observed both a significant increase in TTP and a lower incidence of thromboembolic events and vaginal bleeding with anastrozole . These findings indicate that anastrozole should be considered as first-line therapy for postmenopausal women with advanced breast cancer\n\n\nTitle 4:\n[New response evaluation criteria in solid tumours-revised RECIST guideline (version 1.1)].\nAbstract 4:\nThis paper is an overview of the new response evaluation criteria in solid tumours : revised RECIST guideline ( version 1 . 1 ) , with a focus on up date d contents\n\n\nTitle 5:\nPalbociclib and Letrozole in Advanced Breast Cancer.\nAbstract 5:\nBACKGROUND A phase 2 study showed that progression-free survival was longer with palbociclib plus letrozole than with letrozole alone in the initial treatment of postmenopausal women with estrogen-receptor (ER)-positive , human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer . We performed a phase 3 study that was design ed to confirm and exp and the efficacy and safety data for palbociclib plus letrozole for this indication . METHODS In this double-blind study , we r and omly assigned , in a 2:1 ratio , 666 postmenopausal women with ER-positive , HER2-negative breast cancer , who had not had prior treatment for advanced disease , to receive palbociclib plus letrozole or placebo plus letrozole . The primary end point was progression-free survival , as assessed by the investigators ; secondary end points were overall survival , objective response , clinical benefit response , patient-reported outcomes , pharmacokinetic effects , and safety . RESULTS The median progression-free survival was 24.8 months ( 95 % confidence interval [ CI ] , 22.1 to not estimable ) in the palbociclib-letrozole group , as compared with 14.5 months ( 95 % CI , 12.9 to 17.1 ) in the placebo-letrozole group ( hazard ratio for disease progression or death , 0.58 ; 95 % CI , 0.46 to 0.72 ; P<0.001 ) . The most common grade 3 or 4 adverse events were neutropenia ( occurring in 66.4 % of the patients in the palbociclib-letrozole group vs. 1.4 % in the placebo-letrozole group ) , leukopenia ( 24.8 % vs. 0 % ) , anemia ( 5.4 % vs. 1.8 % ) , and fatigue ( 1.8 % vs. 0.5 % ) . Febrile neutropenia was reported in 1.8 % of patients in the palbociclib-letrozole group and in none of the patients in the placebo-letrozole group . Permanent discontinuation of any study treatment as a result of adverse events occurred in 43 patients ( 9.7 % ) in the palbociclib-letrozole group and in 13 patients ( 5.9 % ) in the placebo-letrozole group . CONCLUSIONS Among patients with previously untreated ER-positive , HER2-negative advanced breast cancer , palbociclib combined with letrozole result ed in significantly longer progression-free survival than that with letrozole alone , although the rates of myelotoxic effects were higher with palbociclib-letrozole . ( Funded by Pfizer ; PALOMA-2 Clinical Trials.gov number , NCT01740427 . )\n\n\nTitle 6:\nActivity of fulvestrant 500 mg versus anastrozole 1 mg as first-line treatment for advanced breast cancer: results from the FIRST study.\nAbstract 6:\nPURPOSE To compare the clinical activity of the pure antiestrogen fulvestrant at 500 mg/mo ( double the approved dose ) with the aromatase inhibitor anastrozole as first-line endocrine therapy for advanced hormone receptor-positive breast cancer in postmenopausal women . PATIENTS AND METHODS FIRST ( Fulvestrant First-Line Study Comparing Endocrine Treatments ) is a phase II , r and omized , open-label , multicenter study of a fulvestrant high-dose ( HD ) regimen ( 500 mg/mo plus 500 mg on day 14 of month 1 ) versus anastrozole ( 1 mg/d ) . The primary efficacy end point was clinical benefit rate ( CBR ) , defined as the proportion of patients experiencing an objective response ( OR ) or stable disease for > or = 24 weeks . The primary analysis was performed 6 months after the last patient was r and omly assigned . RESULTS CBR was similar for fulvestrant HD ( n = 102 ) and anastrozole ( n = 103 ) , 72.5 % v 67.0 % , respectively ( odds ratio , 1.30 ; 95 % CI , 0.72 to 2.38 ; P = .386 ) . Objective response rate ( ORR ) was also similar between treatments : fulvestrant HD , 36.0 % ; anastrozole , 35.5 % . Time to progression ( TTP ) was significantly longer for fulvestrant versus anastrozole ( median TTP not reached for fulvestrant HD v 12.5 months for anastrozole ; hazard ratio , 0.63 ; 95 % CI , 0.39 to 1.00 ; P = .0496 ) . Duration of OR and CB also numerically favored fulvestrant HD . Both treatments were well tolerated , with no significant differences in the incidence of prespecified adverse events . CONCLUSION First-line fulvestrant HD was at least as effective as anastrozole for CBR and ORR and was associated with significantly longer TTP . Fulvestrant HD was generally well tolerated , with a safety profile similar to that of anastrozole" }, { "role": "assistant", "content": "Similarly , overall response rate and clinical benefit rate were higher for patients who received the combination therapy than for patients allocated to AI alone .\nConclusion The addition of CDK 4/6 inhibitors ( either abemaciclib , palbociclib , or ribociclib ) to an AI ( anastrozole or letrozole ) significantly improved PFS , overall response rate , and clinical benefit rate in comparison with a nonsteroidal AI alone" } ]

[ { "role": "user", "content": "You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.\n\nLiterature Review Title: Managing incomplete preference relations in decision making: A review and future trends \nChapter Title: Iterative approaches \n1. Abstract of Cited Paper (BIB001): In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree \n2. Abstract of Cited Paper (BIB002): In this paper, we present a procedure to estimate missing preference values when dealing with pairwise comparison and heterogeneous information. This procedure attempts to estimate the missing information in an expert's incomplete preference relation using only the preference values provided by that particular expert. Our procedure to estimate missing values can be applied to incomplete fuzzy, multiplicative, interval-valued, and linguistic preference relations. Clearly, it would be desirable to maintain experts' consistency levels. We make use of the additive consistency property to measure the level of consistency and to guide the procedure in the estimation of the missing values. Finally, conditions that guarantee the success of our procedure in the estimation of all the missing values of an incomplete preference relation are given. © 2008 Wiley Periodicals, Inc. \n3. Abstract of Cited Paper (BIB003): Dealing with incomplete information is an important problem in decision making. In this paper, we present a short discussion on this topic and a new estimation method of missing values in an incomplete fuzzy preference relation which is based on the modelling of consistency of preferences via a representable uninorm. \n4. Abstract of Cited Paper (BIB004): Consistency of preferences is related to rationality, which is associated with the transitivity property. Many properties suggested to model transitivity of preferences are inappropriate for reciprocal preference relations. In this paper, a functional equation is put forward to model the ldquocardinal consistency in the strength of preferencesrdquo of reciprocal preference relations. We show that under the assumptions of continuity and monotonicity properties, the set of representable uninorm operators is characterized as the solution to this functional equation. Cardinal consistency with the conjunctive representable cross ratio uninorm is equivalent to Tanino's multiplicative transitivity property. Because any two representable uninorms are order isomorphic, we conclude that multiplicative transitivity is the most appropriate property for modeling cardinal consistency of reciprocal preference relations. Results toward the characterization of this uninorm consistency property based on a restricted set of (n-1) preference values, which can be used in practical cases to construct perfect consistent preference relations, are also presented. \n5. Abstract of Cited Paper (BIB005): Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model. \n6. Abstract of Cited Paper (BIB006): Quality function deployment (QFD) is a widely-used methodology for developing a design quality aimed at satisfying the customer and translating the customer's demand into design targets. As QFD methodology is based on the voices of customers, it requires a group of individuals, or decision makers (DMs), to express their preferences. However, there can be a bottleneck since DMs may not have a perfect knowledge of the problem to be solved. Thereby, the aim of this study is to improve the effectiveness of the evaluation process in QFD. The QFD methodology is extended by introducing a new group decision making (GDM) approach that takes into account incomplete information of DMs by means of the fuzzy set theory. The proposed approach is applied to a collaborative software development process to demonstrate its potential. \n7. Abstract of Cited Paper (BIB007): In this paper, we present a new method for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency. We estimate unknown preference values based on the additive consistency and then construct the consistency matrix which satisfies the additive consistency and the order consistency simultaneously for aggregation. The existing group decision making methods may not satisfy the order consistency for aggregation in some situations. The proposed method can overcome the drawback of the existing methods. It provides us with a useful way for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency.\n\nLiterature Review:" }, { "role": "assistant", "content": "Three main iterative approaches to estimate incomplete APRs and MPRs can be found: additive consistency based approaches BIB003 BIB006 BIB007 BIB002 BIB008 , multiplicative consistency based approaches BIB001 , and its generalisation approach based on the use of uninorm operators BIB004 . 1. Additive consistency based approaches: The main additive consistency based method is due to Herrera-Viedma et al. BIB002 , which consists of an iterative procedure to estimate missing preference values followed by a choice process of the solution alternative. The iterative method to estimate missing preference values is summarised below: Given an unknown preference value p ij (i = j) the iterative procedure starts by using intermediate alternatives, x k , to create indirect chains of known preference values, (p ik , p kj ), that will be used to derive, using the additive consistency property, the local consistency based estimated values: By averaging all the local consistency based estimated values, the overall consistency based estimated value is obtained: In each iteration, the algorithm checks the set of pairs of alternatives for which preference values are unknown and can be estimated using known ones. The algorithm stops when this set is empty. Notice that the cases when an incomplete APR cannot be successfully completed are reduced to those cases when no preference values involving a particular alternative are known, which means that a whole row or column of the APR is completely missing. Because of the conflict between the additive consistency property and the unit scale used to measure preference values BIB005 , the overall consistency based estimated preferences might be greater than 1 or lower than 0, and therefore a normalisation process using the median operator is necessary . In BIB003 , an extension to deal with MPR, IVPR, and LPR is presented" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Asymptomatic People Can NOT Spread COVID-19\n\nAbstract:\nINTRODUCTION: A new coronavirus, called Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2), has emerged from China in late 2019 and has now caused a worldwide pandemic.\nThe impact of COVID-19 has not been described so far in a military setting.\nWe therefore report a case series of infected patients in a recruit school in Switzerland and the herein associated challenges.\nMETHODS: Retrospective review of COVID-19 cases among Swiss Armed Forces recruits in the early weeks of SARS-CoV-2 pandemic in the canton of Ticino, the southernmost canton of Switzerland.\nPositive cases were defined with two positive PCR testing for SARS-CoV-2 from nasopharyngeal swabs.\nSerological testing was performed with a commercially available kit according to manufacturers' instructions.\nRESULTS: The first case was likely contaminated while skiing during weekend permission.\nHe became symptomatic 4 days later, tested positive for SARS-CoV-2 and was put into isolation.\nHe showed complete symptom resolution after 48 hours.\nQuarantine was ordered for all recruits with close contact in the past 2 days, a total of 55 persons out of 140 in the company.\nSeven out of nine recruits in one particular quarantine room became mildly symptomatic.\nSARS-CoV-2 PCR was positive in one of them.\nSeven days after initial diagnosis, the index patient and the other one from the quarantine retested positive for SARS-CoV-2, although they had been completely asymptomatic for over 96 hours.\nSerological testing revealed positive for both patients.\nAll others showed negative IgM and IgG. CONCLUSIONS: Young healthy recruits often showed a mild course of COVID-19 with rapid symptom decline but were persistent SARS-CoV-2 carriers.\nThis illustrates how asymptomatic patients may be responsible for covert viral transmission.\nAn early and prolonged establishment of isolation and quarantine for patients and close contacts is essential to slow down the spread of SARS-CoV-2, especially in the confined space of a military environment." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"All others showed negative IgM and IgG. CONCLUSIONS: Young healthy recruits often showed a mild course of COVID-19 with rapid symptom decline but were persistent SARS-CoV-2 carriers.\", \"This illustrates how asymptomatic patients may be responsible for covert viral transmission.\"]}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nFor the last 50 years we have known of a broad-spectrum agent tilorone dihydrochloride (Tilorone). This is a small-molecule orally bioavailable drug that was originally discovered in the USA and is currently used clinically as an antiviral in Russia and the Ukraine. Over the years there have been numerous clinical and non-clinical reports of its broad spectrum of antiviral activity. More recently we have identified additional promising antiviral activities against Middle East Respiratory Syndrome, Chikungunya, Ebola and Marburg which highlights that this old drug may have other uses against new viruses. This may in turn inform the types of drugs that we need for virus outbreaks such as for the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Tilorone has been long neglected by the west in many respects but it deserves further reassessment in light of current and future needs for broad-spectrum antivirals." }, { "role": "assistant", "content": "[\"Treatment\"]" } ]

[ { "role": "user", "content": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 infects intestinal choroid plexus and disrupts the blood-csf-barrier\n\nAbstract:\nCoronavirus disease-19 (COVID-19), caused by the SARS-CoV-2 virus, leads primarily to respiratory symptoms that can be fatal, particularly in at risk individuals.\nHowever, neurological symptoms have also been observed in patients, including headache, seizures, stroke, and fatigue.\nThe cause of these complications is not yet known, and whether they are due to a direct infection of neural cells, such as neurons and astrocytes, or through indirect effects on supportive brain cells, is unknown.\nHere, we use brain organoids to examine SARS-CoV-2 neurotropism.\nWe examine expression of the key viral receptor ACE2 in single-cell RNA sequencing (scRNA-seq) revealing that only a subset of choroid plexus cells but not neurons or neural progenitors express this entry factor.\nWe then challenge organoids with both SARS-CoV-2 spike protein pseudovirus and live virus to demonstrate high viral tropism for choroid plexus epithelial cells but not stromal cells, and little to no infection of neurons or glia.\nWe find that infected cells of the choroid plexus are an apolipoprotein and ACE2 expressing subset of epithelial barrier cells.\nFinally, we show that infection with live SARS-CoV-2 leads to barrier breakdown of the choroid plexus.\nThese findings suggest that neurological complications may result from effects on the choroid plexus, an important barrier that normally prevents entry of immune cells and cytokines into the cerebrospinal fluid (CSF) and brain." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we use brain organoids to examine SARS-CoV-2 neurotropism.\", \"We then challenge organoids with both SARS-CoV-2 spike protein pseudovirus and live virus to demonstrate high viral tropism for choroid plexus epithelial cells but not stromal cells, and little to no infection of neurons or glia.\", \"We find that infected cells of the choroid plexus are an apolipoprotein and ACE2 expressing subset of epithelial barrier cells.\", \"Finally, we show that infection with live SARS-CoV-2 leads to barrier breakdown of the choroid plexus.\", \"These findings suggest that neurological complications may result from effects on the choroid plexus, an important barrier that normally prevents entry of immune cells and cytokines into the cerebrospinal fluid (CSF) and brain.\"]}" } ]

[ { "role": "user", "content": "You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.\n\nContext: Here we propose a methodology for creating real-world, large scale supervisedtraining data for learning reading comprehension models. Inspired by work insummarisation [10, 11], we create two machinereading corpora by exploiting online newspaper articles and their matchingsummaries. We have collected 93k articles from the CNN111www.cnn.com and 220k articles fromthe Daily Mail222www.dailymail.co.uk websites. Both news providers supplement their articles with anumber of bullet points, summarising aspects of the information contained in thearticle. Of key importance is that these summary points are abstractive and donot simply copy sentences from the documents.We construct a corpus of document–query–answer triples by turning thesebullet points into Cloze [12] style questions by replacingone entity at a time with a placeholder. This results in a combined corpus ofroughly 1M data points (Table 1).Code to replicate our datasets—and to apply this method to other sources—isavailable online333http://www.github.com/deepmind/rc-data/.\n\nQuestion: The Daily Mail part of the dataset is approximately 2x larger than the CNN section of the dataset. True or false?" }, { "role": "assistant", "content": "True. The ratio of number of articles from CNN and DailyMail is 1:2.36. Similarly, the ratio of queries from these datasets is given by 380,298:879,450 = 1:2.31. Since 2.31 and 2.36 both round down to 2, the statement is true, approximately." } ]

[ { "role": "user", "content": "You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.\nYour response should be formatted as a `json` array. For instance, for the query \"Semi supervised image classification\", a sample response might be: [\"CIFAR-10\", \"CIFAR-100\"]. Do not include any extra text in the response other than the answer array.\n\nQuery: I want to train a multi-person pose estimation and tracking model.\n\nKeyphrases: multi-person pose estimation tracking video\n\nDataset description:\nMPII: The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints.\nPoseTrack: The PoseTrack dataset is a large-scale benchmark for multi-person pose estimation and tracking in videos. It requires not only pose estimation in single frames, but also temporal tracking across frames.\nMARS: MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras.\nMuCo-3DHP: MuCo-3DHP is a large scale training data set showing real images of sophisticated multi-person interactions and occlusions.\nV-COCO: Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances.\nCOCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\nSNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity: SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.\n\nOptions:- A: MPII B: PoseTrack C: MARS D: MuCo-3DHP E: V-COCO F: COCO G: SNIPS" }, { "role": "assistant", "content": "[\"MPII\", \"COCO\"]" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nInterspecies transmissions of viruses between animals and humans may result in unpredictable pathogenic potential and new transmissible diseases. This mechanism has recently been exemplified by the discovery of new pathogenic viruses, such as the novel severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) pandemic, Middle-East respiratory syndrome-coronavirus epidemic in Saudi Arabia, and the deadly outbreak of Ebola in West Africa. The. SARS-CoV-2 causes coronavirus disease-19 (COVID-19), which is having a massive global impact in terms of economic disruption, and, above all, human health. The disease is characterized by dry cough, fever, fatigue, myalgia, and dyspnea. Other symptoms include headache, sore throat, rhinorrhea, and gastrointestinal disorders. Pneumonia appears to be the most common and severe manifestation of the infection. Currently, there is no vaccine or specific drug for COVID-19. Further, the development of new antiviral requires a considerable length of time and effort for drug design and validation. Therefore, repurposing the use of natural compounds can provide alternatives and can support therapy against COVID-19. In this review, we comprehensively discuss the prophylactic and supportive therapeutic role of probiotics for the management of COVID-19. In addition, the unique role of probiotics to modulate the gut microbe and assert gut homeostasis and production of interferon as an antiviral mechanism is described. Further, the regulatory role of probiotics on gut-lung axis and mucosal immune system for the potential antiviral mechanisms is reviewed and discussed.Key points* Gut microbiota role in antiviral diseases* Factors influencing the antiviral mechanism* Probiotics and Covid-19." }, { "role": "assistant", "content": "[\"Treatment\", \"Mechanism\"]" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nThe COVID-19 pandemic has affected different sectors of the economy in an unprecedented way, and this article is an attempt to analyze the economic effect of the outbreak in India. However, before we assess the economic cost associated with the pandemic, we economists fully consider the outbreak as a human tragedy. There has not been any econometric technique that can account the countless human sufferings that the crisis has brought. Through this article, we address several important research questions and demonstrate India's strength to stay immune to combat COVID-19 pandemic. The research questions are as follows. First, what will be the effect of COVID-19 on the Indian economy and how does it affect the different sectors of the economy? Second, how does the pandemic affect the bilateral trade relation between India and China? Third, we question the role of the public health system in dealing with the outbreak of the virus in India. This article also presents the growth projection of the Indian economy by different economic agents. We finally conclude the article by mentioning a few policy recommendations for the Indian economy." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Nicotine may play an indirect role that makes it harder for the virus to gain to access cells\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\"]}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}" } ]

[ { "role": "user", "content": "You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.\nYour response should be formatted as a `json` array. For instance, for the query \"Semi supervised image classification\", a sample response might be: [\"CIFAR-10\", \"CIFAR-100\"]. Do not include any extra text in the response other than the answer array.\n\nQuery: We propose Reconfigurable Voxels, a novel method to improve the 3D\n\nKeyphrases: 3d object detection point clouds autonomous driving\n\nDataset description:\n3DMatch: The 3DMATCH benchmark evaluates how well descriptors (both 2D and 3D) can establish correspondences between RGB-D frames of different views. The dataset contains 2D RGB-D patches and 3D patches (local TDF voxel grid volumes) of wide-baselined correspondences.\nOPUS-100: A novel multilingual dataset with 100 languages.\nnuScenes: The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore.\nScanNet: ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects.\nTHEODORE: Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes.\nKITTI: KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner.\n\nOptions:- A: 3DMatch B: OPUS-100 C: nuScenes D: ScanNet E: THEODORE F: KITTI" }, { "role": "assistant", "content": "[\"nuScenes\", \"KITTI\"]" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"UnapprovedDrug\", \"Drug\", \"DrugGroup\", \"Brand\"].\n\nPlease return the output as a JSON object of the format: {\"Brand\" : [\"Aspirin\", ...], \"DrugGroup\": [\"Salicyclate\", ...], \"Drug\": [\"Phenobarbital\", ...], \"UnapprovedDrug\": [\"Heroin\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nBehavioral responses to repeated cocaine exposure in mice selectively bred for differential sensitivity to pentobarbital. Mice from the 20th generation of three lines divergently selected for response to pentobarbital-induced sedation times [long-sedation time (LST), short sedation time (SST), and randomly bred control (RBC)] were used to study cocaine-induced behavioral sensitization. These lines showed variable degrees of locomotor activities in response to cocaine. At a low cocaine dose and long withdrawal period (10 mg/kg, twice a day for 5 days followed by a 14-day withdrawal), the LST mice showed tolerance development. In response to cocaine, the locomotor activities of the SST were not significantly different from the RBC group. At a higher dose and a shorter withdrawal period (20 mg/kg, daily for 7 days followed by a 3-day withdrawal), the SST mice showed behavioral sensitization similar to the RBC mice, but the LST mice did not develop sensitization. The different responses in locomotor activity induced by cocaine suggest that genetic factors may play a role in determining the magnitude of response to this drug. Dopamine (DA) levels did not differ significantly in either striatum (STR) or nucleus accumbens (NAC) for the cocaine-treated animals to their corresponding saline-treated controls. The affinity (Kd) of D2 in the NAC decreased significantly, without changes in density (Bmax), in the cocaine-treated SST and RBC mice. On the other hand, the density of D2 binding sites in the SST and the RBC mice in the STR was significantly increased in cocaine-treated groups without change in Kd. The LST mice did not show any changes in the Kd and Bmax in either the STR or the NAC. Taken together, these findings suggest that the changes in the Kd of D2 in the NAC and the Bmax of D2 in the STR may contribute to the differences in locomotor responses to cocaine exposure in these mouse lines." }, { "role": "assistant", "content": "{\"Brand\": [], \"Drug\": [\"cocaine\", \"pentobarbital\"], \"DrugGroup\": [], \"UnapprovedDrug\": []}" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"UnapprovedDrug\", \"Drug\", \"DrugGroup\", \"Brand\"].\n\nPlease return the output as a JSON object of the format: {\"Brand\" : [\"Aspirin\", ...], \"DrugGroup\": [\"Salicyclate\", ...], \"Drug\": [\"Phenobarbital\", ...], \"UnapprovedDrug\": [\"Heroin\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nCo-administration of nelfinavir at steady-state with a single dose of azithromycin. Co-administration of nelfinavir at steady-state with a single dose of azithromycin (2 x 600 mg tablets) results in increased azithromycin serum concentrations. Although a dose adjustment of azithromycin is not recommended when administered in combination with nelfinavir, close monitoring for known side effects of azithromycin, such as liver enzyme abnormalities and hearing impairment, is warranted. Azithromycin did not affect the prothrombin time response to a single dose of warfarin. However, prudent medical practice dictates careful monitoring of prothrombin time in all patients treated with azithromycin and warfarin concomitantly. Concurrent use of macrolides and warfarin in clinical practice has been associated with increased anticoagulant effects. Drug interaction studies were performed with azithromycin and other drugs likely to be co-administered. When used in therapeutic doses, azithromycin had a modest effect on the pharmacokinetics of atorvastatin, carbamazepine, cetirizine, didanosine, efavirenz, fluconazole, indinavir, midazolam, rifabutin, sildenafil, theophylline (intravenous and oral), triazolam, trimethoprim/sulfamethoxazole or zidovudine. Co-administration with efavirenz or fluconazole had a modest effect on the pharmacokinetics of azithromycin. No dosage adjustment of either drug is recommended when azithromycin is co administered with any of the above agents. Interactions with the drugs listed below have not been reported in clinical trials with azithromycin; however, no specific drug interaction studies have been performed to evaluate potential drug-drug interaction. Nonetheless, they have been observed with macrolide products. Until further data are developed regarding drug interactions when azithromycin and these drugs are used concomitantly, careful monitoring of patients is advised: Digoxin elevated digoxin concentrations. Ergotamine or dihydroergotamine acute ergot toxicity characterized by severe peripheral vasospasm and dysesthesia. Cyclosporine, hexobarbital and phenytoin concentrations. Laboratory Test Interactions There are no reported laboratory test interactions. Repeat Treatment Studies evaluating the use of repeated courses of Zmax have not been conducted." }, { "role": "assistant", "content": "{\"Brand\": [\"Zmax\"], \"Drug\": [\"phenytoin\", \"sulfamethoxazole\", \"didanosine\", \"indinavir\", \"cetirizine\", \"hexobarbital\", \"warfarin\", \"Ergotamine\", \"Azithromycin\", \"azithromycin\", \"Digoxin\", \"atorvastatin\", \"carbamazepine\", \"Cyclosporine\", \"dihydroergotamine\", \"nelfinavir\", \"fluconazole\", \"theophylline\", \"trimethoprim\", \"triazolam\", \"zidovudine\", \"rifabutin\", \"digoxin\", \"midazolam\", \"sildenafil\", \"efavirenz\"], \"DrugGroup\": [\"macrolide products\", \"macrolides\"], \"UnapprovedDrug\": []}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Acidosis (body fluids are too acid) is not a cause of death from covid-19\n\nAbstract:\nBACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.\nThis study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).\nMETHODS: Clinical data were collected from two tertiary hospitals in Wuhan.\nA retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).\nContinuous variables were analyzed using the Mann-Whitney U test.\nCategorical variables were analyzed by &#967; test or Fisher exact test as appropriate.\nRESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.\nThe median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z = 9.738, P < 0.001).\nMore patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967; = 22.105, P < 0.001).\nPatients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z = 3.216, P = 0.001).\nOn admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967; = 60.905, P < 0.001) and expectoration (32.1% vs. 12.1%, &#967; = 13.250, P < 0.001) were significantly higher in the death group.\nThe blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z = 10.625, P < 0.001).\nThe white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] ×10/L, Z = 7.618, P < 0.001).\nPatients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] ×10/L, Z = 8.037, P < 0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z = 10.315, P < 0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z = 5.242, P < 0.001).\nAlanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z = 2.592, P = 0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z = 7.308, P < 0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] µmol/L, Z = 6.478, P < 0.001) were significantly higher in the death group than those in the recovered group.\nC-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z = 10.206, P < 0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z = 1.219, P = 0.233).\nThe patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967; = 148.105, P < 0.001), acute cardiac injury (59.6% vs. 0.9%, &#967; = 93.222, P < 0.001), acute kidney injury (18.3% vs. 0%, &#967; = 23.257, P < 0.001), shock (11.9% vs. 0%, &#967; = 14.618, P < 0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967; = 7.655, P = 0.006).\nCONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\nMore patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\"]}" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative agent of the coronavirus disease 2019 (COVID-19) pandemic spreading around the world, causing massive distress to the world's economy and affecting healthcare systems worldwide. Although some exposed individuals have no symptoms and most symptomatic infections are not severe, COVID-19 cases span a wide spectrum, ranging from mild to critical and sometimes resulting in life-threatening complications, such as pneumonia, severe respiratory distress and cardiac problems. Currently, there is no curative drug for COVID-19 and vaccines are still under development. We are presenting here a strategy for the fast development of natural live-attenuated SARS-CoV-2 vaccines. Our proposed approach is based on screening for, identifying, analyzing and selecting naturally attenuated yet highly immunogenic SARS-CoV-2 strains, which may lead to a shorter cycle of vaccine development, as well as higher vaccine effectiveness." }, { "role": "assistant", "content": "[\"Treatment\"]" } ]

[ { "role": "user", "content": "You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.\nYour response should be formatted as a `json` array. For instance, for the query \"Semi supervised image classification\", a sample response might be: [\"CIFAR-10\", \"CIFAR-100\"]. Do not include any extra text in the response other than the answer array.\n\nQuery: Given a question, an A2S system has to identify the correct answer among a set of\n\nKeyphrases: answer sentence selection text paragraph-level\n\nDataset description:\nSQuAD: The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text.\nGLUE: General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI.\nWikiQA: The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. In order to reflect the true information need of general users, Bing query logs were used as the question source.\nProofWriter: The ProofWriter dataset contains many small rulebases of facts and rules, expressed in English. Each rulebase also has a set of questions (English statements) which can either be proven true or false using proofs of various depths, or the answer is “Unknown” (in open-world setting, OWA) or assumed negative (in closed-world setting, CWA).\nMultiRC: MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions, i.e., questions that can be answered by combining information from multiple sentences of the paragraph. The dataset was designed with three key challenges in mind: * The number of correct answer-options for each question is not pre-specified.\n\nOptions:- A: SQuAD B: GLUE C: WikiQA D: ProofWriter E: MultiRC" }, { "role": "assistant", "content": "[\"SQuAD\", \"GLUE\", \"WikiQA\"]" } ]

[ { "role": "user", "content": "You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.\n\nMain Abstract:\nIn this work, we discuss multiplayer pervasive games that rely on the use of ad hoc mobile sensor networks. The unique feature in such games is that players interact with each other and their surrounding environment by using movement and presence as a means of performing game-related actions, utilizing sensor devices. We discuss the fundamental issues and challenges related to these type of games and the scenarios associated with them. We also present and evaluate an example of such a game, called the \"Hot Potato\", developed using the Sun SPOT hardware platform. We provide a set of experimental results, so as to both evaluate our implementation and also to identify issues that arise in pervasive games which utilize sensor network nodes, which show that there is great potential in this type of games.\n\nCited Abstract(s):\n[1]: Crossmedia games are a genre of pervasive gaming where a single game instance can be played with a variety of heterogeneous devices that support different forms of players' participation and deliver different game experiences. In this article we present the PM2G initiative, a service-oriented architecture aiming to support crossmedia game development and execution. Due to their relevance in this document, content adaptation and interaction adaptation services are discussed in detail. We also present, as a case study, a game called Pervasive Wizards, which is used to validate our architecture. Finally, we present some performance results obtained in our experiments.\n\n[2]: A new generation of entertainment technology takes computer games to the streets---and ultimately beyond.\n\n[3]: \n\n[4]: Human Pacman is an interactive ubiquitous and mobile entertainment system that is built upon position and perspective sensing via Global Positioning System and inertia sensors; and tangible human-computer interfacing with the use of Bluetooth and capacitive sensors. Although these sensing-based subsystems are weaved into the fabric of the game and are therefore translucent to players, they are nevertheless the technical enabling forces behind Human Pacman. The game strives to bring the computer gaming experience to a new level of emotional and sensory gratification by embedding the natural physical world ubiquitously and seamlessly with a fantasy virtual playground. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game console Pacman, and the recent mobile online Pacman. With our novel Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor physical area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now real human players in the real world experiencing mixed computer graphics fantasy-reality provided by using wearable computers that are equipped with GPS and inertia sensors for players' position and perspective tracking. Virtual cookies and actual tangible physical objects with Bluetooth devices and capacitive sensors are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. In short, we believe Human Pacman is pioneering a new form of gaming that is based on sensing technology and anchored on physicality, mobility, social interaction, and ubiquitous computing.\n\n[5]: Pervasive games are an emerging new game genre, which includes context information as an integral part of the game. These games differ from traditional games in that they expand spatio-temporal and social aspects of gaming. Mobile devices support this by enabling players to choose when and where a game is played. Designing pervasive games can be a challenging task, since it is not only limited to the virtual game world, but designers must consider information flow from the real world into the game world and vice versa. In this paper, we describe a user study with an experimental pervasive multiplayer mobile game. The objective was to understand how the players perceive pervasiveness in the game and what the crucial factors are in the design. Based on the results, we propose initial design guidelines and compare them to other design guidelines for the pervasive games.\n\n[6]: \n\n[7]: In this paper, we've made an initial attempt to explore the three dimensions of pervasive game play in the context of people's everyday life. Using an advanced prototype of SupaFly, a pervasive game developed by the former company It's Alive (now part of Daydream), we've evaluated how people perceive and play the game in normal, everyday settings. Our evaluation focused on how the players judged the designers' attempts to incorporate the three dimensions in the game.\n\n[8]: In this article we attempt to describe and analyze the formalisms of pervasive games and pervasive gaming (PG). As the title indicates, PG consists of atomic entities that nevertheless merge into a molecular structure that exhibits emergent features during the actual game-play. The article introduces four axes of PG (mobility, distribution, persistence, and transmediality). Further, it describes and analyses three key units of PG (rules, entities, and mechanics) as well as discusses the role of space in PG by differentiating between tangible space, information-embedded space, and accessibility space. The article is generally concerned with classifying the indispensable components of pervasive games and, in addition, it lists the invariant features of pervasive game-play, meaning the epistemology that is tied to this new kind of gaming situated on the borderline between corporeal and immaterial space. Of particular interest are game rules in pervasive gaming, since they seem to touch upon both the underlying, formal structure of the game (i.e., the ontology of PG) and the actual play vis-a-vis physical and or virtual constraints (i.e., the PG epistemology).\n\n[9]:" }, { "role": "assistant", "content": "There is a large body of work regarding the pervasive games genre. The aim of the IPerG EU-funded project [3] was the investigation of the pervasive gaming experience and the implementation of a series of showcase'' pervasive games. Several other works present implementations of pervasive games, see e.g., [9] [6] . Some examples of well-known pervasive games are , , [7] , [4] . In [8] several interesting issues are raised, regarding the theory around pervasive games. In [7] the authors evaluated how people perceive and play a pervasive game in normal, everyday settings. In general, most works focus on the design issues raised by specific games; some of these works additionally try to generalize such issues regarding the design of pervasive games overall, [2] and [5] or provide surveys of existing approaches. In [1] the authors present scenarios that show the intended characteristics of pervasive multiplayer games and propose services for the development and deployment of crossmedia games, i.e., games that are played on multiple platforms with varying features." } ]

[ { "role": "user", "content": "You will be shown biomedical passages and your task is to the answer the provided question. Provide a concise and accurate response and do not include any additional text in the response other than the answer.\n\nQuestion: What is the brand name for erenumab?\n\nContext: Aimovig (erenumab; erenumab-aooe in the United States) is the only US Food and Drug Administration (FDA)-approved monoclonal antibody (mAb) therapy against the CGRP receptor (CGRPR) for the prevention of migraine\nErenumab-aooe (erenumab, Aimovig®)-a fully human monoclonal antibody that inhibits the calcitonin gene-related peptide (CGRP) receptor-is approved for the prevention of migraine in adults in a number of countries\nPurpose: Erenumab-aooe (erenumab, Aimovig®)-a fully human monoclonal antibody that inhibits the calcitonin gene-related peptide (CGRP) receptor-is approved for the prevention of migraine in adults in a number of cou\nAmgen and Novartis are developing erenumab (AIMOVIG™, erenumab-aooe)-a fully human monoclonal antibody calcitonin gene-related peptide (CGRP) receptor antagonist-for the prevention of migraine.\nAimovig (erenumab; erenumab-aooe in the United States) is the only US Food and Drug Administration (FDA)-approved monoclonal antibody (mAb) therapy against the CGRP receptor (CGRPR) for the prevention of migraine.\nDiscovery of the Migraine Prevention Therapeutic Aimovig (Erenumab), the First FDA-Approved Antibody against a G-Protein-Coupled Receptor.\nIn 2018, the United States Food and Drug Administration (FDA) approved Aimovig (erenumab) for the prevention of migraine.\nPurpose: Erenumab-aooe (erenumab, Aimovig®)-a fully human monoclonal antibody that inhibits the calcitonin gene-related peptide (CGRP) receptor-is approved for the prevention of migraine in adults in a number of c" }, { "role": "assistant", "content": "Aimovig" } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nOBJECTIVES: To combat SARS-CoV2 (Covid-19), policy makers worldwide have adopted different policy alternatives, often including mitigation/suppression policies. We assessed the economic impact of such policies on dental practices in Germany using a modelling approach. METHODS: A providers' perspective within German healthcare was taken, with two provider scenarios (low/high volume practice, low/high proportion of non-statutory insurance revenue, low/high staff pool and costs; S1 and S2 scenarios) being modelled. Providers' costs were estimated in different blocks (staff, material, laboratory, others). A telephone-based survey was conducted on 24th March to 2nd April 2020 on a random sample of 300 German dentists (response: n=146) to determine the experienced dental services utilization changes in these service blocks. A Markov model was constructed, following 100 practices in each scenario for a total of 365 days. Different Covid-19 mitigation/suppression periods (90 days: base-case, 45, 135 days: sensitivity analyses) were modelled. Monte-Carlo micro-simulation was performed and uncertainty introduced via probabilistic and univariate sensitivity analyses. RESULTS: Mitigation/suppression reduced utilization of all services, the most severe for prevention (-80 % in mean), periodontics (-76 %) and prosthetics (-70 %). Within the base-case, mean revenue reductions were 18.7 %/15.7 % from the public insurance, 18.7/18.6 % from private insurers and 19 %/19 % for out-of-pocket expenses in S1/S2, respectively. If the mitigation/suppression was upheld for 135 days, overall revenue decreased by 31 %/30 % in S1/S2, respectively. In this case, 29 %/12 % S1/S2 would have a negative net profit over the course of one year. CONCLUSIONS: Covid-19 and associated policies have profound economic effect on dental practices. CLINICAL SIGNIFIANCE: Policy makers will want to consider our findings when designing governmental subsidy and safety nets with immediate and midterm economic relieve effects. Dentists may consider practice re-organization to reduce costs and maintain minimum profitability." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "role": "user", "content": "You will be shown sections from a scientific research paper, together with a question about the paper. Your task is to answer the question based on the contents of the paper. Your response should be a `json` object with two fields:\n\n\"answer\": An array of strings extracted directly from the paper which, collectively, answer the question.\n\"evidence\": An array of strings. Each should be an excerpt from the paper, in which one or more of the extracted answers can be found.\n\nFor instance, for the question \"What baselines did the authors compare against?\", a sample response might be:\n\n{\n \"answer\": [\"BERT\", \"RoBERTa\"],\n \"evidence\": [\"In our experiments, we compare the performance of our model against BERT and RoBERTa.\"]\n}\n\nDo not include any text in your response other than the json.\n\nIf the question is unanswerable given the provided excerpts, respond with the single word \"null\".\n\nPaper: Fully Automated Fact Checking Using External Sources\n\nGiven the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.\n\nText Representation\nNext, we build the representation of a claim and the corresponding snippets and Web pages. First, we calculate three similarities (a) between the claim and a snippet, or (b) between the claim and a Web page: (i) cosine with tf-idf, (ii) cosine over embeddings, and (iii) containment BIBREF1 . We calculate the embedding of a text as the average of the embeddings of its words; for this, we use pre-trained embeddings from GloVe BIBREF2 . Moreover, as a Web page can be long, we first split it into a set of rolling sentence triplets, then we calculate the similarities between the claim and each triplet, and we take the highest scoring triplet. Finally, as we have up to ten hits from the search engine, we take the maximum and also the average of the three similarities over the snippets and over the Web pages.\nWe further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN.\n\nVeracity Prediction\nNext, we build classifiers: neural network (NN), support vector machines (SVM), and a combination thereof (SVM+NN).\nThe architecture of our NN is shown on top of Figure FIGREF7 . We have five LSTM sub-networks, one for each of the text sources from two search engines: Claim, Google Web page, Google snippet, Bing Web page, and Bing snippet. The claim is fed into the neural network as-is. As we can have multiple snippets, we only use the best-matching one as described above. Similarly, we only use a single best-matching triple of consecutive sentences from a Web page. We further feed the network with the similarity features described above.\nAll these vectors are concatenated and fully connected to a much more compact hidden layer that captures the task-specific embeddings. This layer is connected to a softmax output unit to classify the claim as true or false. The bottom of Figure FIGREF7 represents the generic architecture of each of the LSTM components. The input text is transformed into a sequence of word embeddings, which is then passed to the bidirectional LSTM layer to obtain a representation for the full sequence.\nOur second classifier is an SVM with an RBF kernel. The input is the same as for the NN: word embeddings and similarities. However, the word embeddings this time are calculated by averaging rather than using a bi-LSTM.\nFinally, we combine the SVM with the NN by augmenting the input to the SVM with the values of the units in the hidden layer. This represents a task-specific embedding of the input example, and in our experiments it turned out to be quite helpful. Unlike in the SVM only model, this time we use the bi-LSTM embeddings as an input to the SVM. Ultimately, this yields a combination of deep learning and task-specific embeddings with RBF kernels.\n\nEvaluation Metrics\nThe evaluation metrics we use are P (precision), R (recall), and F INLINEFORM0 , which we calculate with respect to the false and to the true claims. We further report AvgR (macro-average recall), AvgF INLINEFORM1 (macro-average F INLINEFORM2 ), and Acc (accuracy).\n\nResults\nTable TABREF14 shows the results on the test dataset. We can see that both the NN and the SVM models improve over the majority class baseline (all false rumors) by a sizable margin. Moreover, the NN consistently outperforms the SVM by a margin on all measures. Yet, adding the task-specific embeddings from the NN as features of the SVM yields overall improvements over both the SVM and the NN in terms of avgR, avgF INLINEFORM0 , and accuracy. We can see that both the SVM and the NN overpredict the majority class (false claims); however, the combined SVM+NN model is quite balanced between the two classes.\nTable TABREF22 compares the performance of the SVM with and without task-specific embeddings from the NN, when training on Web pages vs. snippets, returned by Google vs. Bing vs. both. The NN embeddings consistently help the SVM in all cases. Moreover, while the baseline SVM using snippets is slightly better than when using Web pages, there is almost no difference between snippets vs. Web pages when NN embeddings are added to the basic SVM. Finally, gathering external support from either Google or Bing makes practically no difference, and using the results from both together does not yield much further improvement. Thus, (i) the search engines already do a good job at generating relevant snippets, and one does not need to go and download the full Web pages, and (ii) the choice of a given search engine is not an important factor. These are good news for the practicality of our approach.\nUnfortunately, direct comparison with respect to BIBREF3 is not possible. First, we only use a subset of their examples: 761 out of 993 (see Section SECREF17 ), and we also have a different class distribution. More importantly, they have a very different formulation of the task: for them, the claim is not available as input (in fact, there has never been a claim for 21% of their examples); rather an example consists of a set of tweets retrieved using manually written queries.\nIn contrast, our system is fully automatic and does not use tweets at all. Furthermore, their most important information source is the change in tweets volume over time, which we cannot use. Still, our results are competitive to theirs when they do not use temporal features.\nTo put the results in perspective, we can further try to make an indirect comparison to the very recent paper by BIBREF6 . They also present a model to classify true vs. false claims extracted from snopes.com, by using information extracted from the Web. Their formulation of the task is the same as ours, but our corpora and label distributions are not the same, which makes a direct comparison impossible. Still, we can see that regarding overall classification accuracy they improve a baseline from 73.7% to 84.02% with their best model, i.e., a 39.2% relative error reduction. In our case, we go from 66.7% to 80.0%, i.e., an almost identical 39.9% error reduction. These results are very encouraging, especially given the fact that our model is much simpler than theirs regarding the sources of information used (they model the stance of the text, the reliability of the sources, the language style of the articles, and the temporal footprint).\n\nApplication to cQA\nNext, we tested the generality of our approach by applying it to a different setup: fact-checking the answers in community question answering (cQA) forums. As this is a new problem, for which no dataset exists, we created one. We augmented with factuality annotations the cQA dataset from SemEval-2016 Task 3 (CQA-QA-2016) BIBREF7 . Overall, we annotated 249 question–answer, or INLINEFORM0 - INLINEFORM1 , pairs (from 71 threads): 128 factually true and 121 factually false answers.\nEach question in CQA-QA-2016 has a subject, a body, and meta information: ID, category (e.g., Education, and Moving to Qatar), date and time of posting, user name and ID. We selected only the factual questions such as “What is Ooredoo customer service number?”, thus filtering out all (i) socializing, e.g., “What was your first car?”, (ii) requests for opinion/advice/guidance, e.g., “Which is the best bank around??”, and (iii) questions containing multiple sub-questions, e.g., “Is there a land route from Doha to Abudhabi. If yes; how is the road and how long is the journey?”\nNext, we annotated for veracity the answers to the retained questions. Note that in CQA-QA-2016, each answer has a subject, a body, meta information (answer ID, user name and ID), and a judgment about how well it addresses the question of its thread: Good vs. Potentially Useful vs. Bad . We only annotated the Good answers. We further discarded answers whose factuality was very time-sensitive (e.g., “It is Friday tomorrow.”, “It was raining last week.”), or for which the annotators were unsure.\nWe targeted very high quality, and thus we did not use crowdsourcing for the annotation, as pilot annotations showed that the task was very difficult and that it was not possible to guarantee that Turkers would do all the necessary verification, e.g., gathering evidence from trusted sources. Instead, all examples were first annotated independently by four annotators, and then each example was discussed in detail to come up with a final label. We ended up with 249 Good answers to 71 different questions, which we annotated for factuality: 128 Positive and 121 Negative examples. See Table TABREF26 for details.\nWe further split our dataset into 185 INLINEFORM0 – INLINEFORM1 pairs for training, 31 for development, and 32 for testing, preserving the general positive:negative ratio, and making sure that the questions for the INLINEFORM2 – INLINEFORM3 pairs did not overlap between the splits. Figure FIGREF23 presents an excerpt of an example from the dataset, with one question and three answers selected from a longer thread. Answer INLINEFORM4 contains false information, while INLINEFORM5 and INLINEFORM6 are true, as can be checked on an official governmental website.\nWe had to fit our system for this problem, as here we do not have claims, but a question and an answer. So, we constructed the query from the concatenation of INLINEFORM0 and INLINEFORM1 . Moreover, as Google and Bing performed similarly, we only report results using Google. We limited our run to snippets only, as we have found them rich enough above (see Section SECREF3 ). Also, we had a list of reputed and Qatar-related sources for the domain, and we limited our results to these sources only. This time, we had more options to calculate similarities compared to the rumors dataset: we can compare against INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 – INLINEFORM5 ; we chose to go with the latter. For the LSTM representations, we use both the question and the answer.\nTable TABREF27 shows the results on the cQA dataset. Once again, our models outperformed all baselines by a margin. This time, the predictions of all models are balanced between the two classes, which is probably due to the dataset being well balanced in general. The SVM model performs better than the NN by itself. This is due to the fact that the cQA dataset is significantly smaller than the rumor detection one. Thus, the neural network could not be trained effectively by itself. Nevertheless, the task-specific representations were useful and combining them with the SVM model yielded consistent improvements on all the measures once again.\n\nConclusions and Future Work\nWe have presented and evaluated a general-purpose method for fact checking that relies on retrieving supporting information from the Web and comparing it to the claim using machine learning. Our method is lightweight in terms of features and can be very efficient because it shows good performance by only using the snippets provided by the search engines. The combination of the representational power of neural networks with the classification of kernel-based methods has proven to be crucial for making balanced predictions and obtaining good results. Overall, the strong performance of our model across two different fact-checking tasks confirms its generality and potential applicability for different domains and for different fact-checking task formulations.\nIn future work, we plan to test the generality of our approach by applying it to these and other datasets in combination with complementary methods, e.g., those focusing on microblogs and temporal information in Twitter to make predictions about rumors BIBREF27 , BIBREF3 . We also want to explore the possibility of providing justifications for our predictions, and we plan to integrate our method into a real-world application.\n\nAcknowledgments\nThis research was performed by the Arabic Language Technologies group at Qatar Computing Research Institute, HBKU, within the Interactive sYstems for Answer Search project (Iyas).\n\nFigure 1: Example claims and the information we use to predict whether they are factually true or false.\n\nFigure 2: Our general neural network architecture (top) and detailed LSTM representation (bottom). Each blue box in the top consists of the bi-LSTM structure in the bottom.\n\nTable 1: Results on the rumor detection dataset using Web pages returned by the search engines.\n\nTable 2: Results using an SVM with and without task-specific embeddings from the NN on the Rumor detection dataset. Training on Web pages vs. snippets vs. both.\n\nFigure 3: Example from the cQA forum dataset.\n\nTable 4: Results on the cQA answer fact-checking problem.\n\nQuestion: What algorithm and embedding dimensions are used to build the task-specific embeddings?" }, { "role": "assistant", "content": "{\"answer\": [\" task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN\"], \"evidence\": [\"We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN.\"]}" } ]

[ { "role": "user", "content": "You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: [\"Intervention\", \"Outcome\", \"Participant\"].\n\nPlease return the output as a JSON object of the format: {\"Participant\" : [\"patients with COPD\", ...], \"Intervention\" : [\"Atenolol\", ...], \"Outcome\" : [\"blood pressure\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\n[ Randomized cooperative studies of the surgical treatment of cancer of the thyroid gland ] ." }, { "role": "assistant", "content": "{\"Intervention\": [\"surgical treatment\"], \"Outcome\": [], \"Participant\": [\"cancer of the thyroid gland ]\"]}" } ]

[ { "role": "user", "content": "You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.\n\nMain Abstract:\nThe spread of behavior over social networks depends on the contact structure among individuals, and seeding the most influential agents can substantially enhance the extent of the spread. While the choice of the best seed set, known as influence maximization, is a computationally hard problem, many computationally efficient algorithms have been proposed to approximate the optimal seed sets with provable guarantees. Most of the previous work on influence maximization assumes the knowledge of the entire network graph. However, in practice, obtaining full knowledge of the network structure is very costly. In this work, we consider the choice of @math initial seeds to maximize the expected number of adopters under the independent cascade model. We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using @math edge queries in @math time, where @math is the network size and @math is the probability of spreading through an edge. To the best of our knowledge, this is the first result to provide approximation guarantees for influence maximization, using a sub-quadratic number of queries for polynomially small @math . We complement this result by showing that it is impossible to approximate the problem using @math edge queries for constant @math . In the end, we consider a more advanced query model, where one seeds a node and observes the resultant adopters after running the spreading process. We provide an algorithm that uses only @math such queries and provides almost tight approximation guarantees.\n\nCited Abstract(s):\n[1]: Influence maximization is the problem of selecting top @math seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is much more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory.\n\n[2]: This paper focuses on new challenges in influence maximization inspired by non-profits' use of social networks to effect behavioral change in their target populations. Influence maximization is a multiagent problem where the challenge is to select the most influential agents from a population connected by a social network. Specifically, our work is motivated by the problem of spreading messages about HIV prevention among homeless youth using their social network. We show how to compute solutions which are provably close to optimal when the parameters of the influence process are unknown. We then extend our algorithm to a dynamic setting where information about the network is revealed at each stage. Simulation experiments using real world networks collected by the homeless shelter show the advantages of our approach.\n\n[3]: In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.\n\n[4]: Identifying the optimal set of individuals to first receive information ('seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.\n\n[5]: In the well-studied Influence Maximization problem, the goal is to identify a set of k nodes in a social network whose joint influence on the network is maximized. A large body of recent work has justified research on Influence Maximization models and algorithms with their potential to create societ al or economic value. However, in order to live up to this potential, the algorithms must be robust to large amounts of noise, for they require quantitative estimates of the influence, which individuals exert on each other; ground truth for such quantities is inaccessible, and even decent estimates are very difficult to obtain. We begin to address this concern formally. First, we exhibit simple inputs on which even very small estimation errors may mislead every algorithm into highly suboptimal solutions. Motivated by this observation, we propose the Perturbation Interval model as a framework to characterize the stability of Influence Maximization against noise in the inferred diffusion network. Analyzing the susceptibility of specific instances to estimation errors leads to a clean algorithmic question, which we term the Influence Difference Maximization problem. However, the objective function of Influence Difference Maximization is NP-hard to approximate within a factor of O(n1−e) for any e > 0. Given the infeasibility of diagnosing instability algorithmically, we focus on finding influential users robustly across multiple diffusion settings. We define a Robust Influence Maximization framework wherein an algorithm is presented with a set of influence functions. The algorithm’s goal is to identify a set of k nodes who are simultaneously influential for all influence functions, compared to the (function-specific) optimum solutions. We show strong approximation hardness results for this problem unless the algorithm gets to select at least a logarithmic factor more seeds than the optimum solution. However, when enough extra seeds may be selected, we show that techniques of can be used to approximate the optimum robust influence to within a factor of 1−1 e. We evaluate this bicriteria approximation algorithm against natural heuristics on several real-world datasets. Our experiments indicate that the worst-case hardness does not necessarily translate into bad performance on real-world datasets; all algorithms perform fairly well.\n\n[6]:" }, { "role": "assistant", "content": "In fact, our results fall in the general category of work that addresses how lack of perfect knowledge about the spreading process affects the design and performance of seeding strategies [4] . This question has been studied by researchers from many other angles, including algorithmic stability and robustness [1] [5] . Some other prior work has focused on uncertainty in spreading model parameters, for example, by bounding the worst-case performance when the true edge influence probabilities are constrained to an interval [3] . Others have considered such uncertainties in dynamic settings [2] or with changing network graph [6] ." } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There are risks that come with using UV light devices, especially when using them on your skin.\n\nAbstract:\nObjective: To evaluate the hypothesis that light could reduce the lethality of COVID-19.\nMethods: Most models for projections of the spread and lethality of COVID-19 take into account the ambient temperature, neglecting light.\nRecent advances in understanding the mechanism of action of COVID-19 have shown that it causes a systemic infection that significantly affects the hematopoietic system and hemostasis, factors extremely dependent of light, mainly in the region of visible and infrared radiation.\nResults: In the COVID-19 patients hemoglobin is decreasing and protoporphyrin is increasing, generating an extremely harmful accumulation of iron ions in the bloodstream, which are able to induce an intense inflammatory process in the body with a consequent increase in C-reactive protein and albumin.\nObserving the unsaturation characteristics of the cyclic porphyrin ring allows it to absorb and emit radiation mainly in the visible region.\nThis characteristic can represent an important differential to change this process in the event of an imbalance in this system, through the photobiomodulation to increase the production of adenosine triphosphate (ATP) using red and near-infrared radiation (R-NIR) and vitamin D using ultraviolet B (UVB) radiation.\nThese two compounds have the primary role of activating the defense mechanisms of the immune system, enabling greater resistance of the individual against the attack by the virus.\nAccording to the theory of electron excitation in photosensitive molecules, similar to hemoglobin heme, after the photon absorption there would be an increase in the stability of the iron ion bond with the center of the pyrrole ring, preventing the losses of heme function oxygen transport (HbO2).\nThe light is also absorbed by cytochrome c oxidase in the R-NIR region, with a consequent increase in electron transport, regulating enzyme activity and resulting in a significant increase of oxygen rate consumption by mitochondria, increasing ATP production.\nConclusions: The most favorable range of optical radiation to operate in this system is between R-NIR region, in which cytochrome c oxidase and porphyrin present absorption peaks centered at 640 nm and HbO2 with absorption peak centered at 900 nm.\nBased on the mechanisms described earlier, our hypothesis is that light could reduce the lethality of COVID-19." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "role": "user", "content": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n \"verdict\": \"SUPPORT\",\n \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Should you wear a face mask to prevent COVID-19? Yes\n\nAbstract:\nFace masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\nSocial scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.\nIn the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).\nWe begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.\nThis item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.\nWe also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.\nWe lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.\nFrom these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\"]}" } ]

[ { "role": "user", "content": "You will be shown a paragraph from a biomedical research paper, followed by a question about the paper. Please extract text from the paragraph to answer the question. Do not include any text in your repsonse other than the answer.\n\nParagraph: We detail the first European cases. As at 21 February, nine European countries reported 47 cases. Among 38 cases studied, 21 were linked to two clusters in Germany and France, 14 were infected in China. Median case age was 42 years; 25 were male. Late detection of the clusters’ index cases delayed isolation of further local cases. As at 5 March, there were 4,250 cases. Text: In the WHO European Region, COVID-19 surveillance was implemented 27 January 2020. We detail the first European cases. As at 21 February, nine European countries reported 47 cases. Among 38 cases studied, 21 were linked to two clusters in Germany and France, 14 were infected in China. Median case age was 42 years; 25 were male. Late detection of the clusters' index cases delayed isolation of further local cases. As at 5 March, there were 4,250 cases. A cluster of pneumonia of unknown origin was identified in Wuhan, China, in December 2019 [1] . On 12 January 2020, Chinese authorities shared the sequence of a novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolated from some clustered cases [2] . Since then, the disease caused by SARS-CoV-2 has been named coronavirus disease 2019 (COVID -19) . As at 21 February 2020, the virus had spread rapidly mostly within China but also to 28 other countries, including in the World Health Organization (WHO) European Region [3] [4] [5] . Here we describe the epidemiology of the first cases of COVID-19 in this region, excluding cases reported in the United Kingdom (UK), as at 21 February 2020. The study includes a comparison between cases detected among travellers from China and cases whose infection was acquired due to subsequent local transmission. On 27 January 2020, the European Centre for Disease Prevention and Control (ECDC) and the WHO Regional Office for Europe asked countries to complete a WHO standard COVID-19 case report form for all confirmed and probable cases according to WHO criteria [6] [7] [8] . The overall aim of surveillance at this time was to support the global strategy of containment of COVID-19 with rapid identification and follow-up of cases linked to affected countries in order to minimise onward transmission. The surveillance objectives were to: describe the key epidemiological and clinical characteristics of COVID-19 cases detected in Europe; inform country preparedness; and improve further case detection and management. Data collected included demographics, history of recent travel to affected areas, close contact with a probable or confirmed COVID-19 case, underlying conditions, signs and symptoms of disease at onset, type of specimens from which the virus was detected, and clinical outcome. The WHO case definition was adopted for surveillance: a confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (ECDC recommended two separate SARS-CoV-2 RT-PCR tests), irrespective of clinical signs and symptoms, whereas a probable case was a suspect case for whom testing for SARS-CoV-2 was inconclusive or positive using a pan-coronavirus assay [8] . By 31 January 2020, 47 laboratories in 31 countries, including 38 laboratories in 24 European Union and European Economic Area (EU/EEA) countries, had diagnostic capability for SARS-CoV-2 available (close to 60% of countries in the WHO European Region), with cross-border shipment arrangements in place for many of those lacking domestic testing capacity. The remaining six EU/EEA countries were expected to have diagnostic testing available by mid-February [9] . As at 09:00 on 21 February 2020, 47 confirmed cases of COVID-19 were reported in the WHO European Region and one of these cases had died [4] . Data on 38 of these cases (i.e. all except the nine reported in the UK) are included in this analysis. The first three cases detected were reported in France on 24 January 2020 and had onset of symptoms on 17, 19 and 23 January respectively [10] . The first death was reported on 15 February in France. As at 21 February, nine countries had reported cases ( Figure) : Belgium (1), Finland (1), France (12), Germany (16), Italy (3), Russia (2), Spain (2), Sweden (1) and the UK (9 -not included further). The place of infection (assessed at national level based on an incubation period presumed to be up to 14 days [11] , travel history and contact with probable or confirmed cases as per the case definition) was reported for 35 cases (missing for three cases), of whom 14 were infected in China (Hubei province: 10 cases; Shandong province: one case; province not reported for three cases). The remaining 21 cases were infected in Europe. Of these, 14 were linked to a cluster in Bavaria, Germany, and seven to a cluster in Haute-Savoie, France [12, 13] . Cases from the Bavarian cluster were reported from Germany and Spain, whereas cases from the Haute-Savoie cluster were reported from France All but two cases were hospitalised (35 of 37 where information on hospitalisation was reported), although it is likely that most were hospitalised to isolate the person rather than because of severe disease. The time from onset of symptoms to hospitalisation (and isolation) ranged between 0 and 10 days with a mean of 3.7 days (reported for 29 cases). The mean number of days to hospitalisation was 2.5 days for cases imported from China, but 4.6 days for those infected in Europe. This was mostly a result of delays in identifying the index cases of the two clusters in France and Germany. In the German cluster, for example, the first three cases detected locally were hospitalised in a mean of 5.7 days, whereas the following six took only a mean of 2 days to be hospitalised. Symptoms at the point of diagnosis were reported for 31 cases. Two cases were asymptomatic and remained so until tested negative. The asymptomatic cases were tested as part of screening following repatriation and during contact tracing respectively. Of the remaining 29, 20 reported fever, 14 reported cough and eight reported weakness. Additional symptoms reported included headaches (6 cases), sore throat (2), rhinorrhoea (2), shortness of breath (2), myalgia (1), diarrhoea (1) and nausea (1). Fever was reported as the sole symptom for nine cases. In 16 of 29 symptomatic cases, the symptoms at diagnosis were consistent with the case definition for acute respiratory infection [16] , although it is possible that cases presented additional symptoms after diagnosis and these were not reported. Data on pre-existing conditions were reported for seven cases; five had no pre-existing conditions while one was reported to be obese and one had pre-existing cardiac disease. No data on clinical signs e.g. dyspnea etc. were reported for any of the 38 cases. All hospitalised cases had a benign clinical evolution except four, two reported in Italy and two reported in France, all of whom developed viral pneumonia. All three cases who were aged 65 years or over were admitted to intensive care and required respiratory support and one French case died. The case who died was hospitalised for 21 days and required intensive care and mechanical ventilation for 19 days. The duration of hospitalisation was reported for 16 cases with a median of 13 days (range: 8-23 days). As at 21 February 2020, four cases were still hospitalised. All cases were confirmed according to specific assays targeting at least two separate genes (envelope (E) gene as a screening test and RNA-dependent RNA polymerase (RdRp) gene or nucleoprotein (N) gene for confirmation) [8, 17] . The specimen types tested were reported for 27 cases: 15 had positive nasopharyngeal swabs, nine had positive throat swabs, three cases had positive sputum, two had a positive nasal swab, one case had a positive nasopharyngeal aspirate and one a positive endotracheal aspirate. \n\nAs at 09:00 on 21 February, few COVID-19 cases had been detected in Europe compared with Asia. However the situation is rapidly developing, with a large outbreak recently identified in northern Italy, with transmission in several municipalities and at least two deaths [18] . As at 5 March 2020, there are 4,250 cases including 113 deaths reported among 38 countries in the WHO European region [19] . In our analysis of early cases, we observed transmission in two broad contexts: sporadic cases among travellers from China (14 cases) and cases who acquired infection due to subsequent local transmission in Europe (21 cases). Our analysis shows that the time from symptom onset to hospitalisation/case isolation was about 3 days longer for locally acquired cases than for imported cases. People returning from affected areas are likely to have a low threshold to seek care and be tested when symptomatic, however delays in identifying the index cases of the two clusters in France and Germany meant that locally acquired cases took longer to be detected and isolated. Once the exposure is determined and contacts identified and quarantined (171 contacts in France and 200 in Germany for the clusters in Haute-Savoie and Bavaria, respectively), further cases are likely to be rapidly detected and isolated when they develop symptoms [15, 20] . In the German cluster, for example, the first three cases detected locally were hospitalised in a mean of 5.7 days, whereas the following six were hospitalised after a mean of 2 days. Locally acquired cases require significant resources for contact tracing and quarantine, and countries should be prepared to allocate considerable public health resources during the containment phase, should local clusters emerge in their population. In addition, prompt sharing of information on cases and contacts through international notification systems such as the International Health Regulations (IHR) mechanism and the European Commission's European Early Warning and Response System is essential to contain international spread of infection. All of the imported cases had a history of travel to China. This was consistent with the epidemiological situation in Asia, and supported the recommendation for testing of suspected cases with travel history to China and potentially other areas of presumed ongoing community transmission. The situation has evolved rapidly since then, however, and the number of countries reporting COVID-19 transmission increased rapidly, notably with a large outbreak in northern Italy with 3,089 cases reported as at 5 March [18, 19] . Testing of suspected cases based on geographical risk of importation needs to be complemented with additional approaches to ensure early detection of local circulation of COVID-19, including through testing of severe acute respiratory infections in hospitals irrespectively of travel history as recommended in the WHO case definition updated on 27 February 2020 [21] . The clinical presentation observed in the cases in Europe is that of an acute respiratory infection. However, of the 31 cases with information on symptoms, 20 cases presented with fever and nine cases presented only with fever and no other symptoms. These findings, which are consistent with other published case series, have prompted ECDC to include fever among several clinical signs or symptoms indicative for the suspected case definition. Three cases were aged 65 years or over. All required admission to intensive care and were tourists (imported cases). These findings could reflect the average older age of the tourist population compared with the local contacts exposed to infection in Europe and do not allow us to draw any conclusion on the proportion of severe cases that we could expect in the general population of Europe. Despite this, the finding of older individuals being at higher risk of a severe clinical course is consistent with the evidence from Chinese case series published so far although the majority of infections in China have been mild [22, 23] . This preliminary analysis is based on the first reported cases of COVID-19 cases in the WHO European Region. Given the small sample size, and limited completeness for some variables, all the results presented should be interpreted with caution. With increasing numbers of cases in Europe, data from surveillance and investigations in the region can build on the evidence from countries in Asia experiencing more widespread transmission particularly on disease spectrum and the proportion of infections with severe outcome [22] . Understanding the infection-severity is critical to help plan for the impact on the healthcare system and the wider population. Serological studies are vital to understand the proportion of cases who are asymptomatic. Hospital-based surveillance could help estimate the incidence of severe cases and identify risk factors for severity and death. Established hospital surveillance systems that are in place for influenza and other diseases in Europe may be expanded for this purpose. In addition, a number of countries in Europe are adapting and, in some cases, already using existing sentinel primary care based surveillance systems for influenza to detect community transmission of SARS-CoV-2. This approach will be used globally to help identify evidence of widespread community transmission and, should the virus spread and containment no longer be deemed feasible, to monitor intensity of disease transmission, trends and its geographical spread. Additional research is needed to complement surveillance data to build knowledge on the infectious period, modes of transmission, basic and effective reproduction numbers, and effectiveness of prevention and case management options also in settings outside of China. Such special studies are being conducted globally, including a cohort study on citizens repatriated from China to Europe, with the aim to extrapolate disease incidence and risk factors for infection in areas with community transmission. Countries together with ECDC and WHO, should use all opportunities to address these questions in a coordinated fashion at the European and global level. provided input to the outline, multiple versions of the manuscript and gave approval to the final draft.\n\nQuestion: What were the specimen types for 21 cases?" }, { "role": "assistant", "content": "15 had positive nasopharyngeal swabs, nine had positive throat swabs, three cases had positive sputum, two had a positive nasal swab, one case had a positive nasopharyngeal aspirate and one a positive endotracheal aspirate." } ]

[ { "role": "user", "content": "You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:\n\n- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.\n- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.\n- Prevention: The content covers measures to prevent the spread of COVID-19.\n- Mechanism: The paragraph explains the biological mechanisms of the virus.\n- Case Report: The content is a report of a COVID-19 case.\n- Transmission: The paragraph discusses how COVID-19 is transmitted.\n- Diagnosis: The content is about diagnosing COVID-19 in individuals.\n\nYour classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: [\"Topic1\", \"Topic2\", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nCoronavirus disease 2019 (COVID-19) has caused a huge outbreak worldwide. Patients infected with COVID-19 most commonly present with respiratory tract symptoms and pneumonia. Gastrointestinal symptoms can also occur in these patients. Renal involvement presenting as acute renal infarct and/or acute kidney injury has been described in literature; however, these typically occur in patients with severe COVID-19. To the best of our knowledge, there are no reported cases describing abnormal renal imaging findings in a patient with only mild form of COVID-19. We, therefore, report a case of a patient with mild COVID-19 presenting with acute abdominal pain and acute renal infarct." }, { "role": "assistant", "content": "[\"Case Report\"]" } ]

[ { "role": "user", "content": "You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.\n\nLiterature Review Title: QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges \nChapter Title: R AVG R MAX \n1. Abstract of Cited Paper (BIB001): Current objective video quality metrics typically estimate video quality for short video sequences (10 to 15 sec) of constant quality. However, customers of video services usually watch longer sequences of videos which are more and more delivered via adaptive streaming methods such as HTTP adaptive streaming (HAS). A viewing session in such a setting contains several different video qualities over time. In order to express this in an overall score for the whole viewing session, several temporal pooling methods have been proposed in the related work. Within this paper, we set out to compare the performance of different temporal pooling methods for the prediction of Quality of Experience (QoE) for HTTP video streams with varying qualities. We perform this comparison based on ground truth rating data gathered in a crowdsourcing study in the context of the NGMN P-SERQU project. As input data for the models, we use objective video quality metrics such as PSNR, SSIM but also very basic inputs such as the bitrate of the clips only. Our results show that certain pooling methods perform clearly better than others. These results can help in identifying well performing temporal pooling methods in the context of HAS. \n2. Abstract of Cited Paper (BIB002): In this paper, we present a novel subjective quality model for online adaptive movie streaming service. The proposed model considers the Quality of Experience (QoE) of streaming video viewing as a cumulative evaluation process of consecutive segments that compose a story line. Under bandwidth constraint, streaming client may select lower-quality segment, pause playback for re-buffering, or both. The momentary QoE loss at these events are weighted by the content suspension level. Meanwhile such experience penalty remains to influence user's opinion for the rest of the service. If the picture becomes too noisy or the interruptions occur consistently, the user may stop watching. The proposed scheme includes two parts. First, a parametric model estimates the quality loss of a interfered segment based on its network-level packet characteristics. Second, a cumulative function integrates the impact of streaming events. Both steps demand minimum computation and can be updated in real time. A subjective test to train and validate the proposed parametric model is designed and performed. This model is fundamentally different from all existing QoE assessment schemes in that temporally cumulative viewing experience of the users, instead of simple global statistics, is evaluated. \n3. Abstract of Cited Paper (BIB003): In this paper, we investigate the impact of spatial, temporal, and amplitude resolution on the perceptual quality of a compressed video. Subjective quality tests were carried out on a mobile device and a total of 189 processed video sequences with 10 source sequences included in the test. Subjective data reveal that the impact of spatial resolution (SR), temporal resolution (TR), and quantization stepsize (QS) can each be captured by a function with a single content-dependent parameter, which indicates the decay rate of the quality with each resolution factor. The joint impact of SR, TR, and QS can be accurately modeled by the product of these three functions with only three parameters. The impact of SR and QS on the quality are independent of that of TR, but there are significant interactions between SR and QS. Furthermore, the model parameters can be predicted accurately from a few content features derived from the original video. The proposed model correlates well with the subjective ratings with a Pearson correlation coefficient of 0.985 when the model parameters are predicted from content features. The quality model is further validated on six other subjective rating data sets with very high accuracy and outperforms several well-known quality models. \n4. Abstract of Cited Paper (BIB004): This work investigates the impact of non-periodic quantization step size variation on perceptual video quality. We composed non-periodic temporal variation patterns from periodic variation patterns. We present subjective test results and examine how does the overall quality under non-periodic variation relate to the qualities of individual periodic components, and how does it relate to the instantaneous qualities at different time points. We propose two quality models based on our observations and data analysis. Such quality assessment and modeling are essential for designing video adaptation strategy when delivering video over dynamically changing wireless links. \n5. Abstract of Cited Paper (BIB005): Ever since video compression and streaming techniques have been introduced, measurement of perceived video quality has been a non-trivial task. Dynamic adaptive streaming (DASH) over hypertext transfer protocol, is a new worldwide standard for adaptive streaming of video. DASH has introduced an additional level of complexity for measuring perceived video quality, as it varies the video bit rate and quality. In this paper, we study the perceived video quality using DASH. We investigate three factors which impact user perceived video quality: 1) initial delay; 2) stall (frame freezing); and 3) bit rate (frame quality) fluctuation. For each factor, we explore multiple dimensions that can have different effects on perceived quality. For example, in the case of the factor stall, while most previous research have studied how stall duration correlates with user experience, we also consider how the stalls are distributed together with the amount of motion in the video content, since we believe they may also impact user perceived quality. We conduct extensive subjective tests in which a group of subjects provide subjective evaluation while watching DASH videos with one or more artifacts occurring. Based on the subjective tests, we first derive impairment functions which can quantitatively measure the impairment of each factor, and then combine these impairment functions together to formulate an overall user experience model for any DASH video. We validate with high accuracy the user experience model, and demonstrate its applicability to long videos. \n6. Abstract of Cited Paper (BIB006): HTTP Adaptive Streaming (HAS) has become a popular trend for multimedia delivery nowadays. Because of throughput variations, video quality strongly fluctuates during a session. Therefore, a main challenge in HAS is how to evaluate the overall video quality of a session. In this paper, we explore the impact of quality variations on the perceptual quality of a video in HAS. We propose to use the histogram of segment quality values and the histogram of quality gradients in a session to model the overall video quality. Subjective test results show that the proposed model can capture the segment quality variations and accurately predict the overall quality of a session. \n7. Abstract of Cited Paper (BIB007): In the context of HTTP Adaptive Streaming (HAS), a main challenge is how to evaluate the quality of experience (QoE) of a session. In general, the QoE of a streaming session is affected by three key factors, namely initial delay, varying perceptual quality, and interruptions. In this paper, in order to investigate the impacts of quality variations and interruptions on the QoE of a session, we propose to use histograms, which represent the frequency distributions of these recurrent factors in a session. The QoE of a session is modeled by pooling the statistics of the recurrent factors and the initial delay. Evaluation results show that the proposed model has a high prediction performance. Especially, the proposed model provides insights into the impacts of different influence factors, thus leading to suggestions to improve the quality of streaming video. \n8. Abstract of Cited Paper (BIB008): This paper describes a quality model for HTTP Adaptive Streaming. It integrates existing audio and video quality scores to a final quality estimation, factoring in quality variations over time, the recency effect, as well as location and length of buffering events at the player side. We built the model based on data gathered from more than 17 subjective quality tests. It was submitted to the ITU-T P.NATS competition; parts of it have since been released in the official recommendation ITU-T P.1203.3 as an “audiovisual quality integration module”. In the context of standardization, the model was validated on 30 subjective databases, showing high performance. Its modular approach allows its components to be re-used in other applications and combined with different temporal pooling techniques.\n\nLiterature Review:" }, { "role": "assistant", "content": "and R MAX and R AVG are maximum and average values of the individual rebuffering events during the video playback. Pesudo-random values distributed uniformly on [0, 1] and the inverse function of F(x) are used to obtain the playout interruption duration values based on which pauses of that duration are then inserted in the videos. The authors observe that compared to video quality due to higher QP values, users are more sensitive to rebuffering events with higher rate of drop of QoE with increase in R MAX , which saturates after a certain value (6-8 seconds). In contrast, initial increase in QP results in slower QoE degradation with rapid fall in QoE at higher QP values. The 3-layer RNN model is validated using RMSE using subjective test scores. Since the model uses bitstream level information, the model suffers from inherent drawbacks of bitstream models such as limited scope of applications and also limited applicability to single codec. The proposed model was evaluated using only four content types of short duration (16 secs). Xue et al. BIB003 propose a QoE model which combines instantaneous qualities and cumulative quality taking into account video segment quality, quality switching and rebuffering events. The instantaneous perceptual quality is evaluated using a linear model using QP values, and instantaneous rebuffering related degradation is modeled as the opposite of the weighted intensity of the interrupted frame. Initial loading delay related degradation is assumed to be constant and is modeled using the initial QP value which approximately represents the average quality of the video. The instantaneous qualities are then pooled using exponential decay temporal pooling (which takes into account the end user attention memory) to obtain the final QoE estimation. The model is shown to be of low complexity and stable with reasonable performance results. Since the subjective tests for model parameter estimation and subsequent validation are done using only two QP values, we will see later that, in the presence of multiple resolutions and QP values, the model performance is not that satisfactory. Guo et al. BIB005 propose a model which estimates the overall quality using a linear combination of median and minimum of the instantaneous quality as: where α and β are constants (0.68 and 0.33 respectively), and Q median and Q min are the median and minimum of the average quality. The instantaneous quality is obtained from QP values using the normalized quality vs. inverted normalized quantization stepsize (NQQ) model in BIB004 . Based on this work, the authors also observe that the qualities of the composing frequency components of a non-periodic QP varying video session can be used to estimate the overall quality of the video. Among all these frequency components (of the instantaneous qualities), the one with the worst quality has the highest impact on the final quality. Tran et al. BIB007 present a QoE estimation model considering encoded video quality and quality variation as the IFs. The quality of the encoded video is calculated for each segment considering the average QP which is then used to model the effect of encoding quality and quality variation using the histogram of bins of segment qualities and segment quality gradients respectively. The overall session quality is modeled as: where α n and β m are model parameters, N SQ (= 5 in this work), F Q n and F Q m are number of segment quality bins, frequency of segment quality bins and frequency of quality gradient bin respectively. Segment quality bins represent the encoded video quality while quality gradient bins represent quality variations. Model parameter estimation and validation are done using subjective assessment for three videos of 74 seconds consisting of 2-second length segments and nine quality levels. A comparison with previously discussed models BIB005 and BIB001 for the given dataset shows a superior performance of the proposed model in terms of PLCC and Root Mean Square Error (RMSE). As in BIB006 , the authors conclude that the effect of quality up-switching has a negligible impact on the overall QoE compared to that of quality down-switching. IFs such as rebuffering events, initial loading delay and quality switching of starting quality values are not taken into account in their model. The authors also assume that various representations are of the same resolution and VOLUME 7, 2019 frame-rate which is the case in many popular HAS applications which use multi-resolution video representation in their applications. An extension of the previous model BIB007 is presented in BIB008 , where the authors, in addition to quality degradation due to encoding and quality switching, also consider the effect of different initial quality, initial loading delay and rebuffering related impairments. The overall QoE is estimated as: where I QS is the impairment factor due to varying quality modeled using the switching amplitude and the initial quality value, I RB is the impairment factor due to rebuffering duration, and I ILD is the impairment factor due to initial delay modeled using a logarithmic function. The authors find that the impact of switching amplitude depends not only on switching amplitude but also on the starting quality. For example, for equal switching amplitude, down-switching in a low-quality region is worse than down-switching in the higher quality region. Also, rebuffering duration of 0.25 seconds or less have a negligible effect on the final QoE value, while rebuffering durations of more than 2 seconds can lead to extreme QoE degradation. Robitza et al. BIB009 describe another candidate model for ITU-T Rec P.1203 competition. It follows a similar modular approach where the pooled audiovisual per second scores, representing the media quality (Q LT ) and degradation due to initial loading delay (I ILD ) and rebuffering events (I RB ), are combined to obtain the final Audiovisual MOS (MOSAVFinal) value as: The model considers quality variations over time, recency effect, length and location of rebuffering events and encoding quality and is designed for sequences up to 5 minutes in length. The authors use simple averaging of the per-second scores into the final session quality score as other temporal pooling methods did not seem to provide increased performance gains. A similar observation was also reported in BIB002 . While the authors claim the model to be video or audio codec agnostic, the performance results for the proposed model is reported only for the mode using full bitstream information Parameter selection based on the manual count of quality changes and exhaustive brute-force optimization procedure, as used by the authors, may lead to an over-fitting of the model parameters for the given test conditions and hence the performance of the same for other datasets can help in the evaluation of the actual performance gains of the model for possible real-world applications. Also, the model performance was only evaluated on PC/TV databases and its performance for mobile video streaming scenario still remains an open question." } ]