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[ { "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\nReductions in perioperative surgical site infections are obtained by a multifaceted approach including patient decolonization, hand hygiene, and hub disinfection, and environmental cleaning. Associated surveillance of S. aureus transmission quantifies the effectiveness of the basic measures to prevent the transmission to patients and clinicians of pathogenic bacteria and viruses, including Coronavirus Disease 2019 (COVID-19). To measure transmission, the observational units are pairs of successive surgical cases in the same operating room on the same day. We evaluated appropriate sample sizes and strategies for measuring transmission. There was absence of serial correlation among observed counts of transmitted isolates within each of several periods (all P >/=.18). Similarly, observing transmission within or between cases of a pair did not increase the probability that the next sampled pair of cases also had observed transmission (all P >/=.23). Most pairs of cases had no detected transmitted isolates. Also, although transmission (yes/no) was associated with surgical site infection (P =.004), among cases with transmission, there was no detected dose response between counts of transmitted isolates and probability of infection (P =.25). The first of a fixed series of tests is to use the binomial test to compare the proportion of pairs of cases with S. aureus transmission to an acceptable threshold. An appropriate sample size for this screening is N =25 pairs. If significant, more samples are obtained while additional measures are implemented to reduce transmission and infections. Subsequent sampling is done to evaluate effectiveness. The two independent binomial proportions are compared using Boschloo's exact test. The total sample size for the 1(st) and 2(nd) stage is N =100 pairs. Because S. aureus transmission is invisible without testing, when choosing what population(s) to screen for surveillance, another endpoint needs to be used (e.g., infections). Only 10/298 combinations of specialty and operating room were relatively common (>/=1.0% of cases) and had expected incidence >/=0.20 infections per 8 hours of sampled cases. The 10 combinations encompassed congruent with17% of cases, showing the value of targeting surveillance of transmission to a few combinations of specialties and rooms. In conclusion, we created a sampling protocol and appropriate sample sizes for using S. aureus transmission within and between pairs of successive cases in the same operating room, the purpose being to monitor the quality of prevention of intraoperative spread of pathogenic bacteria and viruses." }, { "role": "assistant", "content": "[\"Transmission\", \"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: Face masks and social distancing measures reduce COVID-19 spread, but they also work against other pathogens.\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}" } ]

[ { "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\nItaly has been the first-hit European country to face the outbreak of coronavirus disease 2019 (COVID-19). Aim of this survey was to assess in depth the impact of the outbreak on healthcare workers (HCW). A 40-item online survey was disseminated via social media inviting Italian HCW, with questions exploring demographics, health status and work environment of respondents. A total of 527 were invited to take part in March 2020, of whom 74% (n = 388) responded to the survey. Of these, 235 (61%) were women. HCW were mostly physicians (74%), from high-prevalence regions (52%). 25% experienced typical symptoms during the last 14 days prior to survey completion, with only 45% of them being tested for COVID-19. Among the tested population, 18 (18%) resulted positive for COVID-19, with 33% being asymptomatic. Only 22% of HCW considered personal protective equipment adequate for quality and quantity. Females and respondents working in high-risk sectors were more likely to rate psychological support as useful (OR, 1.78 [CI 95% 1.14-2.78] P = 0.012, and 2.02 [1.12-3.65] P = 0.020, respectively) and workload as increased (mean increase, 0.38 [0.06-0.69] P = 0.018; and 0.54 [0.16-0.92] P = 0.005, respectively). The insights from this survey may help authorities in countries where COVID-19 epidemic has not yet broken out. Management strategies should be promptly undertaken in order to enhance safety and optimise resource allocation." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "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 low T3 syndrome related with high BNP in cardiac patients?\n\nContext: BNP and fT3 are independently associated with exercise capacity in severely compromised HF patients.\nfter adjustment for known confounders, NT-pro-BNP was significantly associated with fT3 and low-T3 syndrome. fT3 (HR 0.58, 95%CI 0.34-0.98) and low-T3 syndrome (HR 3.0, 95%CI 1.4-6.3) were predictive for mortality after adjustment for NT-pro-BNP levels and other cardiovascular prognostic variables.\nfT3 and low-T3 syndrome are significantly related to NT-pro-BNP in patients with cardiovascular disease, but are predictors of mortality independently of NT-pro-BNP and other known cardiovascular risk parameters.\nHigher NT-pro BNP concentrations were related to lower total T3 concentrations (r = -0.294, p = 0.011) and to higher reverse T3 concentrations (r = 0.353, p = 0.002)" }, { "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: Patients taking ACE-i and ARBs who contract COVID-19 should continue treatment, unless otherwise advised by their physician\n\nAbstract:\nAims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.\nWe hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.\nMethods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).\nThe SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).\nA statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.\nAfter stratification on obesity, this increase was limited to the high risk subgroup of obese patients.\nConclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.\"]}" } ]

[ { "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 inhibited by TH1579?\n\nContext: Previously, we showed cancer cells rely on the MTH1 protein to prevent incorporation of otherwise deadly oxidised nucleotides into DNA and we developed MTH1 inhibitors which selectively kill cancer cells. Recently, several new and potent inhibitors of MTH1 were demonstrated to be non-toxic to cancer cells, challenging the utility of MTH1 inhibition as a target for cancer treatment\nWe demonstrate that in order to kill cancer cells MTH1 inhibitors must also introduce oxidized nucleotides into DNA. Furthermore, we describe TH1579 as a best-in-class MTH1 inhibitor, which we expect to be useful in order to further validate the MTH1 inhibitor concept." }, { "role": "assistant", "content": "MTH1" } ]

[ { "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: Taking vitamin D supplement can reduce the risk of becoming infected with COVID-19.\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}" } ]

[ { "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 configurable software systems, stakeholders are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and optimization processes of these systems. There are several black-box approaches to obtain this information, but they usually require a large number of samples to make accurate predictions, whereas the few existing white-box approaches impose limitations on the systems that they can analyze. This paper proposes ConfigCrusher, a white-box performance analysis that exploits several insights of configurable systems. ConfigCrusher employs a static data-flow analysis to identify how configuration options may influence control-flow decisions and instruments code regions corresponding to these decisions to dynamically analyze the influence of configuration options on the regions' performance. Our evaluation using @math real-world configurable systems shows that ConfigCrusher is more efficient at building performance models that are similar to or more accurate than current state-of-the-art black-box and white-box approaches. Overall, this paper showcases the benefits and potential of white-box performance analyses to outperform black-box approaches and provide additional information for analyzing configurable systems.\n\nCited Abstract(s):\n[1]: Many modern software systems are designed to be highly configurable, which increases flexibility but can make programs hard to test, analyze, and understand. We present an initial empirical study of how configuration options affect program behavior. We conjecture that, at certain levels of abstraction, configuration spaces are far smaller than the worst case, in which every configuration is distinct. We evaluated our conjecture by studying three configurable software systems: vsftpd, ngIRCd, and grep. We used symbolic evaluation to discover how the settings of run-time configuration options affect line, basic block, edge, and condition coverage for our subjects under a given test suite. Our results strongly suggest that for these subject programs, test suites, and configuration options, when abstracted in terms of the four coverage criteria above, configuration spaces are in fact much smaller than combinatorics would suggest and are effectively the composition of many small, self-contained groupings of options.\n\n[2]: Quality assurance for highly-configurable systems is challenging due to the exponentially growing configuration space. Interactions among multiple options can lead to surprising behaviors, bugs, and security vulnerabilities. Analyzing all configurations systematically might be possible though if most options do not interact or interactions follow specific patterns that can be exploited by analysis tools. To better understand interactions in practice, we analyze program traces to characterize and identify where interactions occur on control flow and data. To this end, we developed a dynamic analysis for Java based on variability-aware execution and monitor executions of multiple small to medium-sized programs. We find that the essential configuration complexity of these programs is indeed much lower than the combinatorial explosion of the configuration space indicates. However, we also discover that the interaction characteristics that allow scalable and complete analyses are more nuanced than what is exploited by existing state-of-the-art quality assurance strategies." }, { "role": "assistant", "content": "[1] and [2] used symbolic execution and variational execution, respectively, to analyze the behavior of interactions in configurable systems and found the insights that we consider in this work (Sec. ). We leverage those insights to create a novel white-box performance analysis that efficiently generates accurate performance models. =-1" } ]

[ { "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: [\"Chemical\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\": [\"Arsenic\", ...]}. 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\nG-protein coupled receptor agonist BV8/prokineticin-2 and STAT3 form a feed-forward loop in both normal and malignant myeloid cells. An important role of Bv8 in mobilization of myeloid cells and myeloid cell-dependent angiogenesis has been established. Recently, it has also been shown that granulocyte colony stimulating factor (G-CSF)-induced Bv8 expression is Stat3 dependent in CD11b+Gr1+ myeloid cells. However, Bv8 downstream signaling pathway(s) intrinsic to myeloid cells crucial for angiogenesis, and potentially also for development of cancers of myeloid origin, remains largely unknown. Here we show that BV8 activates STAT3, which is critical for regulating genes important for both tumor cell proliferation/survival and tumor angiogenesis, in both normal and malignant myeloid cells. Further, BV8-induced STAT3 activation requires Janus activated kinase 2 (JAK2) activity as shown by both genetic and pharmacologic inhibition. Knocking down BV8 in human myeloid leukemia cells inhibits STAT3 activity and expression of STAT3-downstream angiogenic and pro-proliferation/survival genes, leading to a decrease in tumor cell viability. BV8 shRNA expressing leukemia cells exhibit reduced STAT3 activity and tumor growth in vivo. Taken together, we have delineated a signaling pathway downstream of BV8 that plays critical roles in both the tumor microenvironment and malignant myeloid cells for angiogenesis and tumor cell proliferation/survival." }, { "role": "assistant", "content": "{\"Chemical\": []}" } ]

[ { "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 can live on your clothes and shoes\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.\nFrequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.\nThe inanimate surfaces have often been described as a source of nosocomial infections.\nHowever, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.\nThis review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.\nThe literature was systematically searched on Medline without language restrictions.\nAll reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.\nMost viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\nPersistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.\nSARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.\nThe most common coronaviruses may well survive or persist on surfaces for up to one month.\nViruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.\nAbsorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.\nThe risk of transmission via touching contaminated paper is low.\nPreventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\", \"The most common coronaviruses may well survive or persist on surfaces for up to one month.\"]}" } ]

[ { "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 B \"should be assessed\" as a possible \"adjunct to current treatments\" for COVID-19\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\": \"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: Treatment of pancreatic cancer with a combination of docetaxel , gemcitabine and granulocyte colony-stimulating factor : a phase II study of the Greek Cooperative Group for Pancreatic Cancer . To evaluate the tolerance and efficacy of front-line docetaxel plus gemcitabine treatment in patients with inoperable pancreatic cancer. ### Patients And Methods Fifty-four patients with locally advanced or metastatic pancreatic cancer were enrolled. gemcitabine (1000 mg/m2) was administered on days 1 and 8 and docetaxel (100 mg/m2) on day 8, every three weeks; rh-G-CSF (150 ig/m2 s.c.) was given prophylactically on days 9-15. ### results Seven (13%) patients achieved partial response and 18 (33%) stable disease (intent-to-treat). The median duration of response was 24 weeks, time to tumour progression 32 weeks, and overall survival 26 weeks. Performance status was improved in 33% of patients, pain in 43%, asthenia in 16%, weight gain in 28% and appetite in 27%. Grade 3-4 neutropenia occurred in 17 (31%) patients and grade 3-4 thrombocytopenia in four (4%). Six (11%) patients developed febrile neutropenia and one of them died from sepsis. ### conclusions This combination is a relatively well-tolerated out-patient regimen for patients with inoperable pancreatic cancer.\n\nsentence: Treatment of pancreatic cancer with a combination of docetaxel , gemcitabine and granulocyte colony-stimulating factor : a phase II study of the Greek Cooperative Group for Pancreatic Cancer ." }, { "role": "assistant", "content": "[[\"docetaxel\", \"gemcitabine\", \"POS\"]]" } ]

[ { "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: Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion\n\nIn the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.\n\nIntroduction\nAbbreviations and acronyms appear frequently in the medical domain. Based on a popular online knowledge base, among the 3,096,346 stored abbreviations, 197,787 records are medical abbreviations, ranked first among all ten domains. An abbreviation can have over 100 possible explanations even within the medical domain. Medical record documentation, the authors of which are mainly physicians, other health professionals, and domain experts, is usually written under the pressure of time and high workload, requiring notation to be frequently compressed with shorthand jargon and acronyms. This is even more evident within intensive care medicine, where it is crucial that information is expressed in the most efficient manner possible to provide time-sensitive care to critically ill patients, but can result in code-like messages with poor readability. For example, given a sentence written by a physician with specialty training in critical care medicine, “STAT TTE c/w RVS. AKI - no CTA. .. etc”, it is difficult for non-experts to understand all abbreviations without specific context and/or knowledge. But when a doctor reads this, he/she would know that although “STAT” is widely used as the abbreviation of “statistic”, “statistics” and “statistical” in most domains, in hospital emergency rooms, it is often used to represent “immediately”. Within the arena of medical research, abbreviation expansion using a natural language processing system to automatically analyze clinical notes may enable knowledge discovery (e.g., relations between diseases) and has potential to improve communication and quality of care.\nIn this paper, we study the task of abbreviation expansion in clinical notes. As shown in Figure 1, our goal is to normalize all the abbreviations in the intensive care unit (ICU) documentation to reduce misinterpretation and to make the texts accessible to a wider range of readers. For accurately capturing the semantics of an abbreviation in its context, we adopt word embedding, which can be seen as a distributional semantic representation and has been proven to be effective BIBREF0 to compute the semantic similarity between words based on the context without any labeled data. The intuition of distributional semantics BIBREF1 is that if two words share similar contexts, they should have highly similar semantics. For example, in Figure 1, “RF” and “respiratory failure” have very similar contexts so that their semantics should be similar. If we know “respiratory failure” is a possible candidate expansion of “RF” and its semantics is similar to the “RF” in the intensive care medicine texts, we can determine that it should be the correct expansion of “RF”. Due to the limited resource of intensive care medicine texts where full expansions rarely appear, we exploit abundant and easily-accessible task-oriented resources to enrich our dataset for training embeddings. To the best of our knowledge, we are the first to apply word embeddings to this task. Experimental results show that the embeddings trained on the task-oriented corpus are much more useful than those trained on other corpora. By combining the embeddings with domain-specific knowledge, we achieve 82.27% accuracy, which outperforms baselines and is close to human's performance.\n\nRelated Work\nThe task of abbreviation disambiguation in biomedical documents has been studied by various researchers using supervised machine learning algorithms BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . However, the performance of these supervised methods mainly depends on a large amount of labeled data which is extremely difficult to obtain for our task since intensive care medicine texts are very rare resources in clinical domain due to the high cost of de-identification and annotation. Tengstrand et al. tengstrand2014eacl proposed a distributional semantics-based approach for abbreviation expansion in Swedish but they focused only on expanding single words and cannot handle multi-word phrases. In contrast, we use word embeddings combined with task-oriented resources and knowledge, which can handle multiword expressions.\n\nOverview\nThe overview of our approach is shown in Figure FIGREF6 . Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates. We train word embeddings using the clinical notes data with task-oriented resources such as Wikipedia articles of candidates and medical scientific papers and compute the semantic similarity between an abbreviation and its candidate expansions based on their embeddings (vector representations of words).\n\nTraining embeddings with task oriented resources\nGiven an abbreviation as input, we expect the correct expansion to be the most semantically similar to the abbreviation, which requires the abbreviation and the expansion share similar contexts. For this reason, we exploit rich task-oriented resources such as the Wikipedia articles of all the possible candidates, research papers and books written by the intensive care medicine fellows. Together with our clinical notes data which functions as a corpus, we train word embeddings since the expansions of abbreviations in the clinical notes are likely to appear in these resources and also share the similar contexts to the abbreviation's contexts.\n\nHandling MultiWord Phrases\nIn most cases, an abbreviation's expansion is a multi-word phrase. Therefore, we need to obtain the phrase's embedding so that we can compute its semantic similarity to the abbreviation.\nIt is proven that a phrase's embedding can be effectively obtained by summing the embeddings of words contained in the phrase BIBREF0 , BIBREF7 . For computing a phrase's embedding, we formally define a candidate INLINEFORM0 as a list of the words contained in the candidate, for example: one of MICU's candidate expansions is medical intensive care unit=[medical,intensive,care,unit]. Then, INLINEFORM1 's embedding can be computed as follows: DISPLAYFORM0 \nwhere INLINEFORM0 is a token in the candidate INLINEFORM1 and INLINEFORM2 denotes the embedding of a word/phrase, which is a vector of real-value entries.\n\nExpansion Candidate Ranking\nEven though embeddings are very helpful to compute the semantic similarity between an abbreviation and a candidate expansion, in some cases, context-independent information is also useful to identify the correct expansion. For example, CHF in the clinical notes usually refers to “congestive heart failure”. By using embedding-based semantic similarity, we can find two possible candidates – “congestive heart failure” (similarity=0.595) and “chronic heart failure”(similarity=0.621). These two candidates have close semantic similarity score but their popularity scores in the medical domain are quite different – the former has a rating score of 50 while the latter only has a rating score of 7. Therefore, we can see that the rating score, which can be seen as a kind of domain-specific knowledge, can also contribute to the candidate ranking.\nWe combine semantic similarity with rating information. Formally, given an abbreviation INLINEFORM0 's candidate list INLINEFORM1 , we rank INLINEFORM2 based on the following formula: DISPLAYFORM0 \nwhere INLINEFORM0 denotes the rating of this candidate as an expansion of the abbreviation INLINEFORM1 , which reflects this candidate's popularity, INLINEFORM2 denotes the embedding of a word. The parameter INLINEFORM3 serves to adjust the weights of similarity and popularity\n\nData and Evaluation Metrics\nThe clinical notes we used for the experiment are provided by domain experts, consisting of 1,160 physician logs of Medical ICU admission requests at a tertiary care center affiliated to Mount Sanai. Prospectively collected over one year, these semi-structured logs contain free-text descriptions of patients' clinical presentations, medical history, and required critical care-level interventions. We identify 818 abbreviations and find 42,506 candidates using domain-specific knowledge (i.e., www.allacronym.com/_medical). The enriched corpus contains 42,506 Wikipedia articles, each of which corresponds to one candidate, 6 research papers and 2 critical care medicine textbooks, besides our raw ICU data.\nWe use word2vec BIBREF0 to train the word embeddings. The dimension of embeddings is empirically set to 100.\nSince the goal of our task is to find the correct expansion for an abbreviation, we use accuracy as a metric to evaluate the performance of our approach. For ground-truth, we have 100 physician logs which are manually expanded and normalized by one of the authors Dr. Mathews, a well-trained domain expert, and thus we use these 100 physician logs as the test set to evaluate our approach's performance.\n\nBaseline Models\nFor our task, it's difficult to re-implement the supervised methods as in previous works mentioned since we do not have sufficient training data. And a direct comparison is also impossible because all previous work used different data sets which are not publicly available. Alternatively, we use the following baselines to compare with our approach.\nRating: This baseline model chooses the highest rating candidate expansion in the domain specific knowledge base.\nRaw Input embeddings: We trained word embeddings only from the 1,160 raw ICU texts and we choose the most semantically related candidate as the answer.\nGeneral embeddings: Different from the Raw Input embeddings baseline, we use the embedding trained from a large biomedical data collection that includes knowledge bases like PubMed and PMC and a Wikipedia dump of biomedical related articles BIBREF8 for semantic similarity computation.\n\nResults\nTable 1 shows the performance of abbreviation expansion. Our approach significantly outperforms the baseline methods and achieves 82.27% accuracy.\nFigure FIGREF21 shows how our approach improves the performance of a rating-based approach. By using embeddings, we can learn that the meaning of “OD” used in our test cases should be “overdose” rather than “out-of-date” and this semantic information largely benefits the abbreviation expansion model.\nCompared with our approach, embeddings trained only from the ICU texts do not significantly contribute to the performance over the rating baseline. The reason is that the size of data for training the embeddings is so small that many candidate expansions of abbreviations do not appear in the corpus, which results in poor performance. It is notable that general embeddings trained from large biomedical data are not effective for this task because many abbreviations within critical care medicine appear in the biomedical corpus with different senses.\nFor example, “OD” in intensive care medicine texts refers to “overdose” while in the PubMed corpus it usually refers to “optical density”, as shown in Figure FIGREF24 . Therefore, the embeddings trained from the PubMed corpus do not benefit the expansion of abbreviations in the ICU texts.\nMoreover, we estimated human performance for this task, shown in Table TABREF26 . Note that the performance is estimated by one of the authors Dr. Mathews who is a board-certified pulmonologist and critical care medicine specialist based on her experience and the human's performance estimated in Table TABREF26 is under the condition that the participants can not use any other external resources. We can see that our approach can achieve a performance close to domain experts and thus it is promising to tackle this challenge.\n\nError Analysis\nThe distribution of errors is shown in Table TABREF28 . There are mainly three reasons that cause the incorrect expansion. In some cases, some certain abbreviations do not exist in the knowledge base. In this case we would not be able to populate the corresponding candidate list. Secondly, in many cases although we have the correct expansion in the candidate list, it's not ranked as the top one due to the lower semantic similarity because there are not enough samples in the training data. Among all the incorrect expansions in our test set, such kind of errors accounted for about 54%. One possible solution may be adding more effective data to the embedding training, which means discovering more task-oriented resources. In a few cases, we failed to identify some abbreviations because of their complicated representations. For example, we have the following sentence in the patient's notes: “ No n/v/f/c.” and the correct expansion should be “No nausea/vomiting/fever/chills.” Such abbreviations are by far the most difficult to expand in our task because they do not exist in any knowledge base and usually only occur once in the training data.\n\nConclusions and Future Work\nThis paper proposes a simple but novel approach for automatic expansion of abbreviations. It achieves very good performance without any manually labeled data. Experiments demonstrate that using task-oriented resources to train word embeddings is much more effective than using general or arbitrary corpus.\nIn the future, we plan to collectively expand semantically related abbreviations co-occurring in a sentence. In addition, we expect to integrate our work into a natural language processing system for processing the clinical notes for discovering knowledge, which will largely benefit the medical research.\n\nAcknowledgements\nThis work is supported by RPI's Tetherless World Constellation, IARPA FUSE Numbers D11PC20154 and J71493 and DARPA DEFT No. FA8750-13-2-0041. Dr. Mathews' effort is supported by Award #1K12HL109005-01 from the National Heart, Lung, and Blood Institute (NHLBI). The content is solely the responsibility of the authors and does not necessarily represent the official views of NHLBI, the National Institutes of Health, IARPA, or DARPA.\n\nFigure 1: Sample Input and Output of the task and intuition of distributional similarity\n\nFigure 2: Approach overview.\n\nTable 1: Overall performance\n\nTable 2: Estimated human performance for abbreviation expansion\n\nTable 3: Error distribution\n\nQuestion: What kind of model do they build to expand abbreviations?" }, { "role": "assistant", "content": "{\"answer\": [\"word2vec BIBREF0\"], \"evidence\": [\"We use word2vec BIBREF0 to train the word embeddings. The dimension of embeddings is empirically set to 100.\"]}" } ]

[ { "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 epidemic has spawned an \"infodemic,\" with excessive and unfounded information that hinders an appropriate public health response. This perspective describes a selection of COVID-19 fake news that originated in Peru and the government's response to this information. Unlike other countries, Peru was relatively successful in controlling the infodemic possibly because of the implementation of prison sentences for persons who created and shared fake news. We believe that similar actions by other countries in collaboration with social media companies may offer a solution to the infodemic problem." }, { "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: Treatment with hydroxychloroquine cut the death rate significantly in sick patients hospitalized with COVID-19 - and without heart-related side-effects\n\nAbstract:\nHydroxychloroquine (HCQ) garnered scientific attention in early February following publication of reports showing in vitro activity of chloroquine (CQ) against COVID‐19.\nWhile studies are mixed on this topic, the therapeutic effect of HCQ or CQ still need more valid clinical evidence.\nIn this descriptive observational study, we aimed to discuss the treatment response of HCQ in COVID‐19 infected patients and 30 cases were included.\nThe demographic, treatment, laboratory parameters of C‐reactive protein (CRP) and interleukin‐6 (IL‐6) before and after HCQ therapy and clinical outcome in the 30 COVID‐19 patients were assessed.\nIn order to evaluate the effect of mediation time point, we also divided these cases into two groups, patients began administrated with HCQ within 7 days hospital (defined as early delivery group) and 7 days after hospital (defined as later delivery group).\nWe found that, the elevated IL‐6, a risk factor in severe patients were reduced to normal level after HCQ treatment.\nMore importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\nIn summary, early use of HCQ was better than later use and the effect of IL‐6 and CRP level can not be ruled out.\nThis article is protected by copyright.\nAll rights reserved." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"More importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\"]}" } ]

[ { "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 the absence of a vaccine to protect people from Covid-19, the disease caused by the coronavirus SARS-CoV-2, minimising the spread of the virus is of utmost importance.\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 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 COVID-19 pandemic brought restriction to the movement of people due to the implementation of lockdown across various regions around the world. In India, most of the patients belong to rural areas and hence were unable to come for a follow-up visit. Hence, we reached out to patients on oral anticoagulation using telemedicine with aim of communicating with the patient concerning drug compliance, titration of dose of anticoagulation, health education, and identification of high-risk patients needing referral to the nearest health facility/our institute. This study was conducted at the Hero DMC heart institute (a tertiary care center for cardiac diseases). The study design is cross-sectional and involves a comparison of the pre-COVID-19 phase with the COVID-19 phase. We asked a five-component (Likert scale) questionnaire from patients for satisfaction after the consult. All symptoms, need for hospitalization and clinical events were recorded. The events were compared in both groups. RESULTS: We contacted 628 patients through telemedicine and 600 patients gave consent for participation in the study. For comparison, we analyzed data of 614 patients in the pre-COVID-19 phase. The mean age during the pre-COVID-19 phase was 55.27 + 17.09 years and the COVID-19 phase was 56.97 + 15.09 years with males more than females in both groups. There was no significant difference in the number of patients on oral anticoagulants and novel oral anticoagulants (NOAC). However, there were higher number of patients on antiplatelets in the pre-COVID phase (p value0.01). 37% in the pre-COVID-19 phase and 40.31 % in the COVID-19 phase were noted to have out of target range INR (International normalized ratio). There was no difference in the number of bleeding or thromboembolic events seen. Patient response as assessed by a questionnaire (Likert scale) showed that >75% of patients were satisfied. CONCLUSION: Through telemedicine, we were able to approach our patients on oral anticoagulation and achieved titration of anti-coagulation, and health education similar to pre-COVID-19 times. During pandemics, telemedicine offers a promising option for patient management with chronic cardiac conditions. It also provides us an opportunity for the management of patients on oral anticoagulation involving titration of drug dosages (anti-coagulation), identification of high-risk patients, and health education." }, { "role": "assistant", "content": "[\"Prevention\"]" } ]

[ { "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: We propose an efficient way to encode rotation equivariance into modern CNNs. We evaluate our approach on\n\nKeyphrases: breast tumour classification images\n\nDataset description:\nTableBank: To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data.\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.\nLocalized Narratives: We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing.\nPCam: PatchCamelyon is an image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections.\n\nOptions:- A: TableBank B: THEODORE C: Localized Narratives D: PCam" }, { "role": "assistant", "content": "PCam" } ]

[ { "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: COVID-19 is a seasonal disease\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}" } ]

[ { "role": "user", "content": "You will be presented with the full text of a biomedical research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.\n\nPaper text:\nwe conducted a retrospective cross - sectional study using convenience sampling at the aga khan university hospital in karachi , pakistan .\nparticipants were all adult patients ( > 16 years of age ) who were hospitalized with malaria during january 2009december 2011 .\nreasons for hospitalization included intravenous antimalarial therapy , management of associated diagnoses , and complications .\nthe following data on patients were retrieved through the hospital s electronic and file records : age , sex , infecting plasmodium species , malaria diagnosis methods , co - existing conditions , results of biochemical and microbiological investigations , radiographic findings , complications , hospital course , and outcome .\nrecords showed that giemsa - stained peripheral blood smears , the malaria rapid diagnostic test ( rdt ) , or both , were used for malaria diagnosis .\nthe rdt used antibodies against p. falciparum histidine - rich protein 2 and p. vivax lactate dehydrogenase . for 45 case - patients for which results from peripheral blood smears and rdts were discordant or unreliable ,\nsurface protein - specific pcr was performed by using stored patient blood samples to identify the plasmodium species ( 2,3 ) .\nclinical syndromes were classified as severe on the basis of the world health organization s 2010 severe falciparum malaria criteria ( 4 ) .\naverages , test of independence , odds ratios with 95% cis , and analysis of variance were computed when applicable .\ncase - patients with prior co - morbid conditions were excluded from relevant subanalyses , for example , diabetes mellitus patients were excluded from hypoglycemia analysis .\nall analysis was also repeated after excluding all case - patients with associated infections and comorbid illnesses .\ncomorbidity included all conditions in the charlson comorbidity index for mortality ( 5 ) .\na total of 356 patients with malaria ( mean sd age 42 18 years ) were hospitalized in the aga khan hospital during 20092011 . among these ,\n296 ( 83.1% ) , 47 ( 13.2% ) , and 13 ( 3.7% ) were found to have p.\nvivax infection , p. falciparum infection , and mixed infections ( p. vivax and p. falciparum ) , respectively .\nthe proportion of p. vivax infection among hospitalized malaria patients increased from 75.0% in 2009 to 87.7% in 2011 ( p<0.02 ) ( figure 1 , panel a ) . *\nco - existing infections included dengue fever , urinary tract infection , enteric fever , and hepatitis c , diagnosed by appropriate serologic testing / culture . \nother conditions included chronic obstructive pulmonary disease , chronic liver disease , malignancy , and other conditions from the charlson comorbidity index ( 5 ) . \n many patients had multiple comorbidities ; therefore , the total does not sum the above . a ) proportion of hospitalized cases of plasmodium vivax ( n = 296 ) , p. falciparum ( n = 47 ) , and mixed ( n = 13 ) infections , karachi , pakistan , 20092011 .\nb ) number of hospitalized cases of p. vivax ( n = 189 ) , p. falciparum ( n = 30 ) , and mixed ( n = 10 ) infections , after excluding patients with concurrent illnesses , 20092011 .\none hundred thirty - nine ( 39.0% ) patients had at least 1 complication by world health organization criteria ( 4 ) , among which 111 ( 79.9% ) patients had p. vivax infection . in 24\n( 51.0% ) cases of p. falciparum infections and in 111 cases ( 37.5% ) of p. vivax infections , respectively , severe malaria developed ( p = 0.077 ) . as shown in figure 2\n, the proportion of severe malaria among p. vivax patients increased from 24.1% in 2009 to 43.2% in 2010 and 39.5% in 2011 ( p = 0.02 ) .\npercentage of plasmodium falciparum and p. vivax patients with severe malaria , karachi , pakistan , 20092011 .\nthe number of mixed infections ( n = 13 ) over 3 years was too small for comparison .\np. vivax and p. falciparum were responsible for comparable rates of pulmonary edema , the need for mechanical ventilation , coagulopathy , hypoglycemia , hemoglobinuria , metabolic acidosis , renal impairment , liver dysfunction , bleeding , and multi - organ dysfunction .\nthe mean platelet count for p. vivax patients was 55 , significantly lower than that of p. falciparum patients ( 67.5 ; p = 0.001 ) and those with mixed infections ( 61 ; p = 0.024 ) . * who , world health organization ; pt , prothrombin time ; aptt , activated partial thromboplastin time . alt , alanine aminotransferase . \npatients with preexisting diabetes were excluded from this count ; n = 303 . \npatients with preexisting chronic kidney disease were excluded from this count ; n = 343 .\nthe mean hospital stay was 4.1 days for p. falciparum patients , 3.6 days for p. vivax patients , and 2.9 days for patients with mixed infections .\nthe mortality rate was 2.1% for p. falciparum patients and 1.0% for p. vivax patients ( p = 0.50 ) .\nanalysis was repeated after all patients with comorbid conditions were excluded ( table 1 ) , which left 229 case - patients who had no illness other than malaria . among these , 30 ( 13% ) patients had p. falciparum infection , 189 ( 83% ) had p. vivax infection , and 10 ( 4% ) had mixed infection ( figure 1 , panel b ) . in these patients , severe malaria appeared significantly more common in falciparum versus vivax malaria ( 53% and 33% , respectively , p = 0.029 ) ; however , 79.5% of the severe cases were caused by p. vivax .\nhemoglobinuria and a higher mean creatinine level were more likely to occur with falciparum malaria than with vivax malaria ( p<0.02 ) .\na study of hospitalized malaria patients at the aga khan university hospital during 19972001 showed that 51.8% of cases were caused by p. vivax and 46.5% by p. falciparum , with mortality rates of 1.5% and 2.0% , respectively ( 6 ) .\nrecent studies from elsewhere in asia reported that 20% 40% of patients hospitalized with malaria had p. vivax malaria ( 7 ) , with mortality rates of 0.8%1.6% ( 7 ) . in our study ,\na much greater proportion of malaria cases were caused by p. vivax ( 83% ) , which was not unexpected because of the decreasing number of p. falciparum cases during the study period . despite this high incidence of p.\nvivax malaria , the mortality rate found in our study is reassuring and stable at 1.0% .\nthe higher prevalence of jaundice , anemia , and hemoglobinuria seen with falciparum malaria in our study reflect the greater degree of hemolysis caused by p. falciparum .\np. vivax has been reported elsewhere to cause a similar degree of anemia as p. falciparum ( 8) .\ndifferences in the level of endemic anemia between these study populations and may explain this discrepancy .\nsimilar to our findings , another study reported the incidence of thrombocytopenia in hospitalized patients with vivax malaria as high as 96.3% ( 9 ) .\npulmonary involvement has often been reported in complicated vivax malaria ( 7 ) , as we found in our study .\nhepatic dysfunction with jaundice has been reported in up to 57% of hospitalized p. vivax patients ( 10 ) ; our findings were similar . to estimate the true effects of severe disease with vivax malaria ,\nresearchers have recommended excluding comorbid conditions ( 7 ) and other infections ( 11 ) . in this study , excluding concurrent illness enabled a stronger association between p. falciparum and severe malaria to emerge .\nthus , p. falciparum caused a higher likelihood of specific complications such as central nervous system disturbance and hemolysis than did p. vivax .\nyet , 80% of severe malaria still occurred in patients with p. vivax malaria . limitations of the study include its retrospective design , low power , and lack of pcr diagnostics for all the samples .\nfurthermore , the study findings reflect the malaria situation at a single urban tertiary care hospital , which can not be generalized without knowing the denominator of all hospitalized malaria cases in the study area .\np. vivax is a major contributor to the disease effects of malaria , including severe malaria , in a tertiary care setting in karachi , pakistan .\nfurthermore , p. falciparum and p. vivax have similar rates for several complications ( pulmonary edema , metabolic acidosis , abnormal bleeding , renal impairment ) and death ." }, { "role": "assistant", "content": "to compare the severity of plasmodium vivax malaria with that of p. falciparum malaria , we conducted a retrospective cross - sectional study of 356 adults hospitalized with malaria ( 20092011 ) in pakistan . \n p. vivax and p. falciparum accounted for 83% and 13% of cases , respectively ; 79.9% of patients with severe malaria were infected with p. vivax ." } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:\n- Background: provides context or foundational information related to the topic.\n- Extends: builds upon the cited work.\n- Uses: applies the methods or findings of the cited work.\n- Motivation: cites the work as inspiration or rationale for the research.\n- CompareOrContrast: compares or contrasts the cited work with others.\n- FutureWork: cites the work as a direction for future research.\n\nYour answer should be a single word from the following list of options: [\"Background\", \"Extends\", \"Uses\", \"Motivation\", \"CompareOrContrast\", \"FutureWork\"]. Do not include any other text in your response.\n\nSection Title:\nNone\n\nContext before the citation:\nNote that although our feature set was drawn primarily from our prior uncertainty detection experiments (Forbes-Riley and Litman, 2011a; Drummond and Litman, 2011), we have also experimented with other features, including state-of-theart acoustic-prosodic features used in the last Interspeech Challenges (Schuller et al., 2010; Schuller et al., 2009b) and made freely available in the openSMILE Toolkit (Florian et al., 2010). • Lexical and Dialogue Features dialogue name and turn number question name and question depth ITSPOKE-recognized lexical items in turn ITSPOKE-labeled turn (in)correctness incorrect runs • User Identifier Features: gender and pretest score \u0000\u0000\u0000\u0000 deviation running totals and averages for all features\n\nCitation Sentence:\nNote that although our feature set was drawn primarily from our prior uncertainty detection experiments ( Forbes-Riley and Litman , 2011a ; Drummond and Litman , 2011 ) , we have also experimented with other features , including state-of-theart acoustic-prosodic features used in the last Interspeech Challenges ( Schuller et al. , 2010 ; Schuller et al. , 2009b ) and made freely available in the openSMILE Toolkit ( Florian et al. , 2010 ) .\n\nContext after the citation:\nTo date, however, these features have only decreased the crossvalidation performance of our models.8 While some of our features are tutoring-specific, these have similar counterparts in other applications (i.e., answer (in)correctness corresponds to a more general notion of “response appropriateness” in other domains, while pretest score corresponds to the general notion of domain expertise). Moreover, all of our features are fully automatic and available in real-time, so that the model can be directly implemented and deployed. To that end, we now describe the results of our intrinsic and extrinsic evaluations of our DISE model, aimed at determining whether it is ready to be evaluated with real users. \u0000\u0000\u0000\u0000" }, { "role": "assistant", "content": "Extends" } ]

[ { "role": "user", "content": "You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:\n- Background: provides context or foundational information related to the topic.\n- Extends: builds upon the cited work.\n- Uses: applies the methods or findings of the cited work.\n- Motivation: cites the work as inspiration or rationale for the research.\n- CompareOrContrast: compares or contrasts the cited work with others.\n- FutureWork: cites the work as a direction for future research.\n\nYour answer should be a single word from the following list of options: [\"Background\", \"Extends\", \"Uses\", \"Motivation\", \"CompareOrContrast\", \"FutureWork\"]. Do not include any other text in your response.\n\nSection Title:\nintroduction\n\nContext before the citation:\nOver the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations (Jordan et al., 2006; Graesser et al., 1999; Aleven et al., 2001; Buckley and Wolska, 2007; Nielsen et al., 2008; VanLehn et al., 2007), because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring (Chi et al., 1994; Litman et al., 2009; Purandare and Litman, 2008; Steinhauser et al., 2007).\n\nCitation Sentence:\nOver the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations ( Jordan et al. , 2006 ; Graesser et al. , 1999 ; Aleven et al. , 2001 ; Buckley and Wolska , 2007 ; Nielsen et al. , 2008 ; VanLehn et al. , 2007 ) , because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring ( Chi et al. , 1994 ; Litman et al. , 2009 ; Purandare and Litman , 2008 ; Steinhauser et al. , 2007 ) .\n\nContext after the citation:\nHowever, most existing systems use pre-authored tutor responses for addressing student errors. The advantage of this approach is that tutors can devise remediation dialogues that are highly tailored to specific misconceptions many students share, providing step-by-step scaffolding and potentially suggesting additional problems. The disadvantage is a lack of adaptivity and generality: students often get the same remediation for the same error regardless of their past performance or dialogue context, as it is infeasible to author a different remediation dialogue for every possible dialogue state." }, { "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: long-term exposure to PM2.5 air pollution may spread the coronavirus\n\nAbstract:\nThe novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115 countries.\nMany international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak on an international level, its influences, and preparedness.\nAt present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses.\nIt is widely known and acknowledged that viruses causing influenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality.\nThus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\nDespite these speculations, however, the systematic analysis in the global perspective of the relation between COVID-19 spread and meteorological parameters is unavailable.\nHere, by analyzing the region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimum range of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relative humidity, cloud cover, etc. have no effect on the virus.\nUnavailability of pharmaceutical interventions would require greater preparedness and alert for the effective control of COVID-19.\nUnder these conditions, the information provided here could be very helpful for the global community struggling to fight this global crisis.\nIt is, however, important to note that the information presented here clearly lacks any physiological evidences, which may merit further investigation.\nThus, any attempt for management, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreak must not consider the evaluation presented here as the foremost factor." }, { "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: No, COVID-19 has nothing to do with 5G\n\nAbstract:\nObjective@#To analyze application fields of 5G communication technology in Corona Virus Disease 2019 (COVID-19) epidemic prevention and control, and build a 5G intelligent medical service system for COVID-19 epidemic prevention and control in China.@*Methods@#We collected and analyzed 5G technology application cases used in the fight against COVID-19 from December 2019 to February 2020: 5G + telemedicine application cases, 5G + negative pressure ambulance cases, 5G + remote monitoring cases, 5G + artificial intelligence cases, 5G + infrared thermography temperature detection cases, 5G + big data analysis cases for epidemic prevention and control.@*Results@#Through the analysis of 5G application cases in COVID-19 epidemic prevention and control, we found out the key elements of 5G intelligent medical service system in COVID-19 epidemic prevention and control.\nBy optimizing and upgrading the internal service mode of the hospital, breaking the internal and external barriers, integrating internal and external resources, and strengthening 5G intelligent medical security, we can form a 5G intelligent medical service system for COVID-19 epidemic prevention and control, including application layer, technical service layer, network support layer and security system layer.@*Conclusion@#5G communication technology has the characteristics of faster speed, shorter time delay and denser capacity.\nIn COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient.\"]}" } ]

[ { "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\nNevertheless, podocytes have been shown to possess a differentiated amino acid transport system [73] including vesicular glutamate transporter [74, 75], and recent findings reveal more and more similarities between podocytes and neuronal cells." }, { "role": "assistant", "content": "Result" } ]

[ { "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:\nOver the last few years, the complexity of web applications has increased to provide more dynamic web applications to users. The drawback of this complexity is the growing number of errors in the front-end applications. In this paper, we present BikiniProxy, a novel technique to provide self-healing for the web. BikiniProxy is designed as an HTTP proxy that uses five self-healing strategies to rewrite the buggy HTML and Javascript code. We evaluate BikiniProxy with a new benchmark of 555 reproducible Javascript errors, DeadClick. We create DeadClick by randomly crawling the Internet and collect all web pages that contain Javascript errors. Then, we observe how BikiniProxy heals those errors by collecting and comparing the traces of the original and healed pages. To sum up, BikiniProxy is a novel fully-automated self-healing approach that is specific to the web, evaluated on 555 real Javascript errors, and based on original self-healing rewriting strategies for HTML and Javascript.\n\nCited Abstract(s):\n[1]: We present a technique that finds and executes workarounds for faulty Web applications automatically and at runtime. Automatic workarounds exploit the inherent redundancy of Web applications, whereby a functionality of the application can be obtained through different sequences of invocations of Web APIs. In general, runtime workarounds are applied in response to a failure, and require that the application remain in a consistent state before and after the execution of a workaround. Therefore, they are ideally suited for interactive Web applications, since those allow the user to act as a failure detector with minimal effort, and also either use read-only state or manage their state through a transactional data store. In this paper we focus on faults found in the access libraries of widely used Web applications such as Google Maps. We start by classifying a number of reported faults of the Google Maps and YouTube APIs that have known workarounds. From those we derive a number of general and API-specific program-rewriting rules, which we then apply to other faults for which no workaround is known. Our experiments show that workarounds can be readily deployed within Web applications, through a simple client-side plug-in, and that program-rewriting rules derived from elementary properties of a common library can be effective in finding valid and previously unknown workarounds.\n\n[2]: Despite the best intentions, the competence, and the rigorous methods of designers and developers, software is often delivered and deployed with faults. To cope with imperfect software, researchers have proposed the concept of self-healing for software systems. The ambitious goal is to create software systems capable of detecting and responding “autonomically” to functional failures, or perhaps even preempting such failures, to maintain a correct functionality, possibly with acceptable degradation. We believe that self-healing can only be an expression of some form of redundancy, meaning that, to automatically fix a faulty behavior, the correct behavior must be already present somewhere, in some form, within the software system either explicitly or implicitly. One approach is to deliberately design and develop redundant systems, and in fact this kind of deliberate redundancy is the essential ingredient of many fault tolerance techniques. However, this type of redundancy is also generally expensive and does not always satisfy the time and cost constraints of many software projects. With this article we take a different approach. We observe that modern software systems naturally acquire another type of redundancy that is not introduced deliberately but rather arises intrinsically as a by-product of modern modular software design. We formulate this notion of intrinsic redundancy and we propose a technique to exploit it to achieve some level of self-healing. We first demonstrate that software systems are indeed intrinsically redundant. Then we develop a way to express and exploit this redundancy to tolerate faults with automatic workarounds. In essence, a workaround amounts to replacing some failing operations with alternative operations that are semantically equivalent in their intended effect, but that execute different code and ultimately avoid the failure. The technique we propose finds such workarounds automatically. We develop this technique in the context of Web applications. In particular, we implement this technique within a browser extension, which we then use in an evaluation with several known faults and failures of three popular Web libraries. The evaluation demonstrates that automatic workarounds are effective: out of the nearly 150 real faults we analyzed, 100 could be overcome with automatic workarounds, and half of these workarounds found automatically were not publicly known before." }, { "role": "assistant", "content": "[1] propose a technique that automatically applies workarounds to handle API issues. The workarounds are based on a set of manually written API-specific alternative rule. The manual step makes the approach not fully automated. On the contrary, our approach does not require a manual database of workarounds to be set up. In subsequent work, the same group has proposed a way to automatically mine those workarounds [2] . However, those workarounds do not consider generic Javascript errors as we do, they target API specific errors for which workarounds have been identified or mined." } ]

[ { "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\nWeight and lean body mass change with antiretroviral initiation and impact on bone mineral density . OBJECTIVE To compare the effect that initiating different antiretroviral therapy ( ART ) regimens has on weight , BMI , and lean body mass ( LBM ) and explore how changes in body composition are associated with bone mineral density ( BMD ) . METHODS A5224s was a sub-study of A5202 , a prospective trial of 1857 ART-naive participants randomized to blinded abacavir-lamivudine ( ABC/3TC ) or tenofovir DF-emtricitabine ( TDF/FTC ) with open-label efavirenz ( EFV ) or atazanavir-ritonavir ( ATV/r ) . All participants underwent dual-energy absorptiometry ( DXA ) and abdominal computed tomography for body composition . Analyses used two-sample t-tests and linear regression . RESULTS A5224s included 269 participants : 85 % men , 47 % white non-Hispanic , median age 38 years , HIV-1 RNA 4.6 log10 copies/ml , and CD4 cell count 233 cells/μl . Overall , significant gains occurred in weight , BMI , and LBM at 96 weeks post-randomization ( all P < 0.001 ) . Assignment to ATV/r ( vs. EFV ) resulted in significantly greater weight ( mean difference 3.35 kg ) and BMI gain ( 0.88 kg/m ; both P=0.02 ) , but not LBM ( 0.67 kg ; P=0.15 ) , whereas ABC/3TC and TDF/FTC were not significantly different ( P≥0.10 ) . In multivariable analysis , only lower baseline CD4 cell count and higher HIV-1 RNA were associated with greater increase in weight , BMI , or LBM . In multivariable analyses , increased LBM was associated with an increased hip BMD . CONCLUSION ABC/3TC vs. TDF/FTC did not differ in change in weight , BMI , or LBM ; ATV/r vs. EFV resulted in greater weight and BMI gain but not LBM . A positive association between increased LBM and increased hip BMD should be further investigated through prospective interventional studies to verify the impact of increased LBM on hip BMD ." }, { "role": "assistant", "content": "{\"Intervention\": [\"antiretroviral initiation\", \"atazanavir-ritonavir ( ATV/r )\", \"tenofovir DF-emtricitabine ( TDF/FTC )\", \"abdominal computed tomography for body composition\", \"antiretroviral therapy ( ART )\", \"abacavir-lamivudine ( ABC/3TC )\", \"ATV/r\", \"dual-energy absorptiometry ( DXA )\"], \"Outcome\": [\"CD4 cell count\", \"hip BMD\", \"weight\", \"higher HIV-1 RNA\"], \"Participant\": [\"antiretroviral initiation\"]}" } ]

[ { "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: Sharing delay information in service systems: a literature survey \nChapter Title: Customers as queued entities \n1. Abstract of Cited Paper (BIB001): Experiencing Statistical Regularity.- Random Walks in Applications.- The Framework for Stochastic-Process Limits.- A Panorama of Stochastic-Process Limits.- Heavy-Traffic Limits for Fluid Queues.- Unmatched Jumps in the Limit Process.- More Stochastic-Process Limits.- Fluid Queues with On-Off Sources.- Single-Server Queues.- Multi-Server Queues.- More on the Mathematical Framework.- The Space D Useful Functions.- Queueing Networks.- The Spaces E and F.- Appendices.\n\nLiterature Review:" }, { "role": "assistant", "content": "We begin by surveying papers which treat customers as queued entities that do not react to the announcements that they receive. For the most part, this branch of the literature focuses on studying ways of accurately predicting future waiting times. This is important for two main reasons: (i) from a practical perspective, systematically making inaccurate announcements may lead to customer distrust in those announcements and, ultimately, customer dissatisfaction with the service provided; and (ii) from an analytical perspective, studying waiting times in queueing systems allows for the derivation of structural results which are useful for our general understanding of those models. In broad terms, two types of methods are typically used for predicting waiting times: Queueing-theoretic and data-based. For queueing-theoretic methods ( § 2.2), the focus is on systematically considering alternative queueing models, and studying the accuracy of various real-time delay predictors in those models. The predictors may exploit different types of information about the state of the system at the time of the announcement, for example, the queue length or the history of recent delays. Relying on data-based methods for delay prediction ( § 2.3) is relatively recent, and it usually allows for superior predictive power. For background on the analysis of queueing systems and their approximations, we refer the reader to, for example, Gross BIB001 , Billingsley , and Whitt BIB002 . For a primer on data-mining methods, we refer the reader to Tan et al. ." } ]

[ { "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: Specific immunity thresholds for sars-cov-2 estimated from unfolding epidemics\n\nAbstract:\nAs severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads, the susceptible subpopulation declines causing the rate at which new infections occur to slow down.\nVariation in individual susceptibility or exposure to infection exacerbates this effect.\nIndividuals that are more susceptible or more exposed tend to be infected and removed from the susceptible subpopulation earlier.\nThis selective depletion of susceptibles intensifies the deceleration in incidence.\nEventually, susceptible numbers become low enough to prevent epidemic growth or, in other words, the herd immunity threshold is reached.\nHere we fit epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks to estimate basic reproduction numbers (R_0) alongside coefficients of individual variation (CV) and the effects of containment strategies.\nHerd immunity thresholds are then calculated as 1-(1/R_0 )^(1/((1+CV^2 ) )) or 1-(1/R_0 )^(1/((1+2CV^2 ) )), depending on whether variation is on susceptibility or exposure.\nOur inferences result in herd immunity thresholds around 10-20%, considerably lower than the minimum coverage needed to interrupt transmission by random vaccination, which for R_0 higher than 2.5 is estimated above 60%.\nWe emphasize that the classical formula, 1-1/R_0 , remains applicable to describe herd immunity thresholds for random vaccination, but not for immunity induced by infection which is naturally selective.\nThese findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures.\"]}" } ]

[ { "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 could be detected on these surfaces for the following amounts of time: Aerosols: up to 3 hours; Copper: up to 4 hours; Cardboard: up to 24 hours; Plastic: up to 2-3 days; Stainless Steel: up to 2-3 days\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated." }, { "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: The imbalance was due to a deficit in subjects aged under 40 years, reflecting men at work and the fact that parents declined the second serum for children younger than five. Baseline-proxy (,pre-epidemic) HIA titers to the pH1N1/ 2009v were measured on sample 1 ( Table 2) , obtained from 249 subjects (103 households) recruited at the very beginning of the investigation during weeks 30 and 31 (phase A, Figure 2 ), when the epidemic activity in the cohort was still very low. Age distribution in this group was similar to that of the whole cohort (data not shown). The overall, the baseline-proxy seroprevalence rate (HIA $1/40), over all ages, was 43.4% (95%CI: 37.4%-49.6%). However the majority of positive sera had low antibody titers, at the cut off value for confirmation (i.e. = 1/40). The proportions of sera with HIA titer .1/40 were 0%, 3.0% and 24.6% in the young, middle-aged and older age groups respectively. These results indicate that pre-epidemic baseline antibody cross reactivity was stronger in the elderly ($60 yrs) and weaker in children and adolescents (,20 yrs) and adults (20-59 yrs), with highly significant differences between age groups (P,0.0001). The reverse cumulative distribution curves of HIA titers are displayed for each age group and for the whole cohort on Figure 3 . The proportion of seropositive sera (HI $1/40) steadily increased during the epidemic unfolding (phase B, W32-39) and in immediate post epidemic period (phase C, W40-44) when it reached its maximum level, then declined in the late post epidemic period (phase D, W45-52). This decline was significant enough to return the reverse cumulative distribution curve to baseline levels in the elderly. The cumulative incidence rates, obtained after subtraction of the age-specific baseline-proxy seroprevalence from the raw seroprevalence at each phase of the epidemic are shown in Table 2 (note that the cumulative incidence rates of infection represented for the group ''all ages'' were standardized according to age structure of the community). The cumulative incidence rates were much higher in children and adolescents (,20 yrs), indicating very active transmission of infection within this age group. As mentioned earlier, cumulative incidence rates peaked in phase C (W40-44), and then declined indicating some lability of the humoral immune response against the pH1N1/2009v. The age-related difference observed in the incidence rates was highly statistically significant (P,0.0001). To estimate more appropriately the decline of antibody titers occurring after the peak of the humoral response to the pH1N1/ 2009v, we considered paired-sera from the group of 264 subjects for whom the first serum sample (sample 1) was obtained just after the epidemic wave (phase C, W40-44), and the corresponding second sample was collected at the end of the survey (phase D, W45-52). Seronegation rates were 27.0% (61/226) for all age groups, 17.4% (12/69) in children and adolescents (,20 yrs), 32.3% (41/127) in adults (20-59 yrs) and 26.7% (8/30) in the elderly ($60 yrs). Differences between the seronegation rates according to age were statistically weakly significant (P = 0.0671). We then considered the 1687 individuals for whom paired sera were available and we measured the seroconversion rates according to age and to the time of first serum sample collection (phase A, B or C). Criteria of seroconversion were defined in the method section. As shown in table 3, there was a sharp decline in seroconversion rates across all the age groups, depending on whether participants were enrolled during phase A, phase B, or phase C (P,0.0001). To interpret these data, one should remember that antibodies at seroprotective levels (HIA $1/40), in serum samples 1 collected during the per epidemic phase B or early post epidemic phase C could represent either base line cross reactive antibodies or rising pH1N1/2009 specific antibodies due to a recent or ongoing infection. This ambiguity could lead to underestimation of the seroconversion rate for subjects enrolled in phases B and C. In order to solve this ambiguity, we specifically considered the group of 249 subjects in whom cross reactive antibodies were detected at the time of phase A (W30-31). The seroconversion rate of this group is the most indicative of the exposure of individuals to the whole epidemic wave. It was the highest (63,2%, P,0.0001) in children and adolescents (,20 yrs), and still significantly high in adults (39.4%, P,0.0001). We then tested in this particular group, the impact of (baseline) pre-epidemic cross reactive antibodies on the rate of seroconversion to pH1N1/2009 (Table 4) . No subject with HIA titer superior to 1/40 had evidence of seroconversion to pH1N1/2009. The seroconversion rate in individuals with a HIA titer equal to 1/40 was linked with age, being more important in children and adolescents (,20 yrs). The highest seroconversion rate (.56%) was registered in subjects with HIA titers inferior to 1/40, particularly for the under 20 years where it reached 85%. Hence, the risk of seroconversion decreased when pre-epidemic HIA titer was high after controlling for age (P,0.0001) (Figure 4) . The multivariate adjusted odds ratio for seroconversion were 0.15 (95%CI: 0.06-0.37, P,0.0001) per two-fold increase in baseline titer, 1.79 (95%CI: 1.23-2.59, P,0.003) per other household members who seroconverted, 5.33 (95%CI: 1.56-19.27, P,0.008) Figure 1 . The cohort profile and major outcomes. Figure 1 details the three phases of the protocol: i) inclusion (weeks 30-44) and serum samples S1 collection; ii) follow up for detection of ILI in households, qRT-PCR on nasal swabs and estimation of cumulative seroincidence rates; iii) end of the study (weeks 45-52) and samples S2 collection. HIA on paired sera (S1+S2) allowed estimating seroconversion rates. doi:10.1371/journal.pone.0025738.g001 Bp (baseline-proxy) seroprevalence rates were estimated on weeks 30-31 in each age group. b Cumulative incidence rates measured the raise between raw seroprevalence rates and age-specific baseline-proxy seroprevalence rate. In the group ''All ages'', cumulative incidence rates were standardized according to age structure of the community. doi:10.1371/journal.pone.0025738.t002 Data are numbers, percentages (95% confidence intervals) and ALR parameter test P value for comparison of seroconversion proportions according to time of first sample (S1) collection at inclusion, in each age group, after controlling for household selection. In the group ''All ages'', rates of seroconversion were standardized according to age structure of the community. NA: not assessed. Seroconversion was defined as a shift from seronegative at inclusion (i.e. HIA titer ,1/40) to seropositive on follow-up sample, or as a 4-fold increase of reciprocal HIA titer between first and second paired samples for sera tested seropositive on inclusion (i.e. HIA titer $1/40). for age ,20 years (vs age $60 years) and 11.35 (95%CI: 0.41-4.47, P = 0.62) for age 20-60 years (vs age $60 years). The observed and predicted seroconversion rates according to age and baseline HIA titer are displayed Figure 4 . Finally, we considered the 46 subjects who had been infected by the pandemic virus over the course of the study, verified by a positive qRT-PCR nasal swab, and for whom paired sera were available. Initial HIA antibody titers in this group were ,1/40, \n\nThe CoPanFlu-RUN cohort was set up to conduct a prospective population-based study investigating the herd immunity induced by the 2009 pandemic influenza virus and identifying risk factors for pH1N1/2009v infection from paired sera collected in an entire community. Most works published to date have used either extensive cross-sectional serosurveys on pre-and post-epidemic independent serum samples, the baseline immunity being assessed from stored frozen samples [5, 7, 8] , or non representative adult cohorts (military, health care workers, long-stay patients). Antibody titers were measured by HIA using a cut-off value set at 1/40 as classically recommended. This HIA titer at 1/40 is considered protective, i.e. conferring 50% protection against a viral challenge [20] . Our assay has introduced some changes in the experimental protocol compared to the classic one. The use of a non-inactivated viral antigen, i.e. a native virus, with nondenatured epitopes probably allows detection of antibodies to epitopes of the hemagglutinin not detected in the classic HIA test. This can induce slight differences in the sensitivity of detection of cross-reacting antibodies, but this does not modify the kinetics of Ab and the epidemiological evolution of seroprevalence and does not jeopardize the global comparability of serological results. This is confirmed by the fact that our HI assay detected seroprotective antibody titers in 93.5% and gave evidence seroconversion in 73.9% of qRT-PCR confirmed pH1N1/2009 influenza, all figures close to those reported in the literature [5, 21] . We considered that titers of .1/40, in sera collected from individuals enrolled during weeks 30 and 31 were cross reactive antibodies and not de novo antibodies triggered by the pandemic virus and hence used them as a proxy for baseline pre epidemic immunity. Several arguments support this assumption: i) the first case indicating autochthonous transmission in Reunion Island was reported by the epidemiological surveillance department of La Réunion on 21st July (week 30), i.e. the same day when inclusion started in our study cohort; ii) 7 to 15 days are required to develop an antibody response after viral infection; iii) On weeks 30 and 31, the epidemic activity due to the pandemic virus was very low in our study cohort and it became significant only after week 32. Hence, during weeks 30-31, 103 households were recruited and only 2 households reported ILI cases. Nasal swabs collected from these 2 individuals were tested qRT-PCR negative to the pandemic virus whereas one had evidence of coronavirus and rhinovirus using a multiplex RT-PCR to respiratory viruses (H. Pascalis, manuscript in preparation). In contrast, during weeks 32 to 39, 199 individuals belonging to 99 households reported ILI, among whom 60 individuals had documented infection by the pandemic virus. Our study shows that a substantial proportion of Reunion Island's population had pre-existing immunity to 2009 pandemic influenza virus with the highest baseline-proxy seroprevalence rate observed among adults aged of 60 years or more. Other studies from all continents had also reported high pre-epidemic seropositivity rates among the elderly [5, 6, 8, [22] [23] [24] [25] [26] , though large variations do exist between countries [10, 11, 23, 27, 28] . These cross reactive antibodies have been interpreted as being the residual signature of the remote exposure of these individuals to H1N1 viruses circulating before 1957 [24, 25, 29, 30] . Baseline seropositivity rates that we report in children and in younger adults (i.e. 30%-35%) were notably higher than those reported from other parts of the world [6, 8, 22, 23, [31] [32] [33] . However one should note that these baseline antibodies were of low titer, just at the level of the HIA threshold (i.e. 1/40). Several factors could have contributed to this comparatively high baseline rates found in our study: i) It may reflect the fact that the HI test used in our study was marginally more sensitive than the classic one [17] ; ii) Some individuals may have already been infected with pH1N1/ 2009 virus at weeks 30 and 31 and may have triggered an antibody response to the virus. This hypothesis seems unlikely in view of the arguments presented above and of a similar high proportion of sera titering HIA = 1/40 among 122 sera from adult patients sent for diagnostic purposes to the Regional Hospital microbiology laboratory, during the first half of 2009 (i.e. before the 2009 pandemic) (data not shown). However we cannot formally exclude this hypothesis in view of a recently reported study from Taiwan [11] that showed evidence of subclinical community transmission with proved seroconversion several weeks before report of the first documented case in the island. A similar conclusion was also drawn from Australia [34] ; iii) our serological test might detect cross-reactive antibodies triggered by recent vaccination with trivalent seasonal influenza vaccine as reported [4, [35] [36] [37] [38] [39] . However, seasonal influenza vaccines were of rather limited use in Reunion Island, especially in children and young adults; iv) Finally the high baseline titers may reflect the infectious history of the individuals to seasonal influenza viruses cross antigenic with pH1N1/2009 virus as recently suggested for seasonal 2007 H1N1 infection [40] . This serosurvey indicates that a large fraction of the Reunion Island population was infected with the pandemic virus. Younger people, have paid the main tribute to the epidemic as almost two thirds show evidence of seroconversion, confirming earlier clinical reports from the island [12] and accumulating reports from other countries [17, 32, 41, 42] and suggesting that school children have likely played the central role in the epidemic diffusion of the pandemic virus. Lower infection rates were found in adults and the lowest rates were recorded in the elderly. Based on clinical cases reported to the epidemiological surveillance services [12] , it was estimated that 66,915 persons in Reunion Island who consulted a physician were infected by the pH1N1/2009 virus during the 9 weeks\n\nQuestion: How long did the pH1N1/2009 viral outbreak last?" }, { "role": "assistant", "content": "9 weeks" } ]

[ { "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: I want to train a supervised model for\n\nKeyphrases: 3d hand human pose estimation single depth map\n\nDataset description:\nYouTube-8M: The YouTube-8M dataset is a large scale video dataset, which includes more than 7 million videos with 4716 classes labeled by the annotation system. The dataset consists of three parts: training set, validate set, and test set.\nConvAI2: The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset.\nWord Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison: The Evaluation framework of Raganato et al. 2017 includes two training sets (SemCor-Miller et al., 1993- and OMSTI-Taghipour and Ng, 2015-) and five test sets from the Senseval/SemEval series (Edmonds and Cotton, 2001; Snyder and Palmer, 2004; Pradhan et al., 2007; Navigli et al., 2013; Moro and Navigli, 2015), standardized to the same format and sense inventory (i.e.\nITOP: The ITOP dataset consists of 40K training and 10K testing depth images for each of the front-view and top-view tracks. This dataset contains depth images with 20 actors who perform 15 sequences each and is recorded by two Asus Xtion Pro cameras.\n\nOptions:- A: YouTube-8M B: ConvAI2 C: Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison D: ITOP" }, { "role": "assistant", "content": "ITOP" } ]

[ { "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: Nab-paclitaxel/bevacizumab/carboplatin chemotherapy in first-line triple negative metastatic breast cancer. Triple negative metastatic breast cancer can be difficult to treat with primarily cytotoxic options. Nab-paclitaxel has demonstrated improved PFS and tolerability compared with standard cremophor-solubilized paclitaxel ; based on this , we examined the efficacy and safety of combining weekly nab-paclitaxel with carboplatin and bevacizumab in TNMBC . ### Patients And Methods In this phase II, multicenter trial, patients with first-line TNMBC received nab-paclitaxel (100 mg/m(2)) and carboplatin (area under the curve = 2) on days 1, 8, 15, and bevacizumab (10 mg/kg) on days 1 and 15 of a 28-day cycle. The primary end point was safety and tolerability and secondary end points included PFS, ORR, and CBR. PFS was calculated using the Kaplan-Meier method. ### results Between July 16, 2007, and October 3, 2011, 34 patients were enrolled at 4 centers. Median age was 50.0 (range, 30-76) years and 77% (n = 26) of patients received previous adjuvant therapy. Median PFS was 9.2 months (95% confidence interval [CI], 7.8-25.1 months). The CBR was 94% (95% CI, 80%-99%), and ORR was 85% (95% CI, 69%-95%) for the combination. The regimen was well tolerated with the most common grade 3/4 adverse events being neutropenia (n = 18; 53%) and thrombocytopenia (n = 6; 18%), with other serous events including 1 grade 3 and 1 grade 4 thrombotic event and 1 febrile neutropenia. ### conclusion The combination of nab-paclitaxel, bevacizumab, and carboplatin as first-line treatment for TNMBC was efficacious and well tolerated. The PFS, CBR, and ORR, and tolerability of the regimen, compares favorably with other standard first-line therapies.\n\nsentence: Nab-paclitaxel has demonstrated improved PFS and tolerability compared with standard cremophor-solubilized paclitaxel ; based on this , we examined the efficacy and safety of combining weekly nab-paclitaxel with carboplatin and bevacizumab in TNMBC ." }, { "role": "assistant", "content": "[[\"carboplatin\", \"bevacizumab\", \"nab-paclitaxel\", \"POS\"]]" } ]

[ { "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: I want to implement the general interactiveness knowledge learning approach for HOI detection.\n\nKeyphrases: human-object interaction detection images\n\nDataset description:\nMetaLWOz: Collected by leveraging background knowledge from a larger, more highly represented dialogue source.\nConvAI2: The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset.\nCommonsenseQA: The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.\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.\n\nOptions:- A: MetaLWOz B: ConvAI2 C: CommonsenseQA D: COCO" }, { "role": "assistant", "content": "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\nIt is now apparent that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) will remain endemic for some time. Improved therapeutics and a vaccine may shorten this period, but both are far from certain. Plans must be put in place on the assumption that the virus and its disease will continue to affect the care of patients and the safety of staff. This will impact particularly on airway management due to the inherent risk to staff during such procedures. Research is needed to clarify the nature and risk of respiratory aerosol-generating procedures. Improved knowledge of the dynamics of SARS-CoV-2 infection and immunity is also required. In the meantime, we describe the current status of airway management during the endemic phase of the COVID-19 pandemic. Some controversies remain unresolved, but the safety of patients and staff remains paramount. Current evidence does not support or necessitate dramatic changes to choices for anaesthetic airway management. Theatre efficiency and training issues are a challenge that must be addressed, and new information may enable this." }, { "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: the drugs do not increase the risk that hospitalized patients with COVID-19 will develop severe complications or die. \n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\", \"This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.\"]}" } ]

[ { "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: Detecting Online Hate Speech Using Context Aware Models\n\nIn the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.\n\nIntroduction\nFollowing a turbulent election season, 2016's cyber world is awash with hate speech. Automatic detection of hate speech has become an urgent need since human supervision is unable to deal with large quantities of emerging texts.\nContext information, by our definition, is the text, symbols or any other kind of information related to the original text. While intuitively, context accompanying hate speech is useful for detecting hate speech, context information of hate speech has been overlooked in existing datasets and automatic detection models.\nOnline hate speech tends to be subtle and creative, which makes context especially important for automatic hate speech detection. For instance,\n(1) barryswallows: Merkel would never say NO\nThis comment is posted for the News titled by \"German lawmakers approve 'no means no' rape law after Cologne assaults\". With context, it becomes clear that this comment is a vicious insult towards female politician. However, almost all the publicly available hate speech annotated datasets do not contain context information. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 .\nWe have created a new dataset consisting of 1528 Fox News user comments, which were taken from 10 complete discussion threads for 10 widely read Fox News articles. It is different from previous datasets from the following two perspectives. First, it preserves rich context information for each comment, including its user screen name, all comments in the same thread and the news article the comment is written for. Second, there is no biased data selection and all comments in each news comment thread were annotated.\nIn this paper, we explored two types of models, feature-based logistic regression models and neural network models, in order to incorporate context information in automatic hate speech detection. First, logistic regression models have been used in several prior hate speech detection studies BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF0 , BIBREF2 , BIBREF9 and various features have been tried including character-level and word-level n-gram features, syntactic features, linguistic features, and comment embedding features. However, all the features were derived from the to-be-classified text itself. In contrast, we experiment with logistic regression models using features extracted from context text as well. Second, neural network models BIBREF10 , BIBREF11 , BIBREF12 have the potential to capture compositional meanings of text, but they have not been well explored for online hate speech detection until recently BIBREF13 . We experiment with neural net models containing separate learning components that model compositional meanings of context information. Furthermore, recognizing unique strengths of each type of models, we build ensemble models of the two types of models. Evaluation shows that context-aware logistic regression models and neural net models outperform their counterparts that are blind with context information. Especially, the final ensemble models outperform a strong baseline system by around 10% in F1-score.\n\nRelated Works\nRecently, a few datasets with human labeled hate speech have been created, however, most of existing datasets do not contain context information. Due to the sparsity of hate speech in everyday posts, researchers tend to sample candidates from bootstrapping instead of random sampling, in order to increase the chance of seeing hate speech. Therefore, the collected data instances are likely to be from distinct contexts.\nFor instance, in the Primary Data Set described in BIBREF14 and later used by BIBREF9 , 10% of the dataset is randomly selected while the remaining consists of comments tagged by users and editors. BIBREF15 built a balanced data set of 24.5k tweets by selecting from Twitter accounts that claimed to be racist or were deemed racist using their followed news sources. BIBREF5 collected hateful tweets related to the murder of Drummer Lee Rigby in 2013. BIBREF0 provided a corpus of 16k annotated tweets in which 3.3k are labeled as sexist and 1.9k are labeled as racist. They created this corpus by bootstrapping from certain key words ,specific hashtags and certain prolific users. BIBREF16 created a dataset of 9000 human labeled paragraphs that were collected using regular expression matching in order to find hate speech targeting Judaism and Israel. BIBREF7 extracted data instances from instagram that were associated with certain user accounts. BIBREF2 presented a very large corpus containing over 115k Wikipedia comments that include around 37k randomly sampled comments and the remaining 78k comments were selected from Wikipedia blocked comments.\nMost of existing hate speech detection models are feature-based and use features derived from the target text itself. BIBREF5 experimented with different classification methods including Bayesian Logistic Regression, Random Forest Decision Trees and SVMs, using features such as n-grams, reduced n-grams, dependency paths, and hateful terms. BIBREF0 proposed a logistic regression model using character n-gram features. BIBREF14 used the paragraph2vec for joint modeling of comments and words, then the generated embeddings were used as feature in a logistic regression model. BIBREF9 experimented with various syntactic, linguistic and distributional semantic features including word length, sentence length, part of speech tags, and embedding features, in order to improve performance of logistic regression classifiers. Recently, BIBREF17 surveyed current approaches for hate speech detection, which interestingly also called to attention on modeling context information for resolving difficult hate speech instances.\n\nCorpus Overview\nThe Fox News User Comments corpus consists of 1528 annotated comments (435 labeled as hateful) that were posted by 678 different users in 10 complete news discussion threads in the Fox News website. The 10 threads were manually selected and represent popular discussion threads during August 2016. All of the comments included in these 10 threads were annotated. The number of comments in each of the 10 threads is roughly equal. Rich context information was kept for each comment, including its user screen name, the comments and their nested structure and the original news article. The data corpus along with annotation guidelines is posted on github.\n\nAnnotation Guidelines\nOur annotation guidelines are similar to the guidelines used by BIBREF9 . We define hateful speech to be the language which explicitly or implicitly threatens or demeans a person or a group based upon a facet of their identity such as gender, ethnicity, or sexual orientation. The labeling of hateful speech in our corpus is binary. A comment will be labeled as hateful or non-hateful.\n\nAnnotation Procedure\nWe identified two native English speakers for annotating online user comments. The two annotators first discussed and practices before they started annotation. They achieved a surprisingly high Kappa score BIBREF18 of 0.98 on 648 comments from 4 threads. We think that thorough discussions in the training stage is the key for achieving this high inter-agreement. For those comments which annotators disagreed on, we label them as hateful as long as one annotator labeled them as hateful. Then one annotator continued to annotate the remaining 880 comments from the remaining six discussion threads.\n\nLogistic Regression Models\nIn logistic regression models, we extract four types of features, word-level and character-level n-gram features as well as two types of lexicon derived features. We extract these four types of features from the target comment first. Then we extract these features from two sources of context texts, specifically the title of the news article that the comment was posted for and the screen name of the user who posted the comment.\nFor logistic regression model implementation, we use l2 loss. We adopt the balanced class weight as described in Scikit learn. Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective. BIBREF0 , BIBREF9 \nFor character level n-grams, we extract character level bigrams, tri-grams and four-grams. For word level n-grams, we extract unigrams and bigrams.\nLinguistic Inquiry and Word Count, also called LIWC, has been proven useful for text analysis and classification BIBREF19 . In the LIWC dictionary, each word is labeled with several semantic labels. In our experiment, we use the LIWC 2015 dictionary which contain 125 semantic categories. Each word is converted into a 125 dimension LIWC vector, one dimension per semantic category. The LIWC feature vector for a comment or its context is a 125 dimension vector as well, which is the sum of all its words' LIWC vectors.\nNRC emotion lexicon contains a list of English words that were labeled with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and sentiment polarities (negative and positive) BIBREF20 . We use NRC emotion lexicon to capture emotion clues in text. Each word is converted into a 10 dimension emotion vector, corresponding to eight emotion types and two polarity labels. The emotion vector for a comment or its context is a 10 dimension vector as well, which is the sum of all its words' emotion vectors.\nAs shown in table TABREF20 , given comment as the only input content, the combination of character n-grams, word n-grams, LIWC feature and NRC feature achieves the best performance. It shows that in addition to character level features, adding more features can improve hate speech detection performance. However, the improvement is limited. Compared with baseline model, the F1 score only improves 1.3%.\nIn contrast, when context information was taken into account, the performance greatly improved. Specifically, after incorporating features extracted from the news title and username, the model performance was improved by around 4% in both F1 score and AUC score. This shows that using additional context based features in logistic regression models is useful for hate speech detection.\n\nNeural Network Models\nOur neural network model mainly consists of three parallel LSTM BIBREF21 layers. It has three different inputs, including the target comment, its news title and its username. Comment and news title are encoded into a sequence of word embeddings. We use pre-trained word embeddings in word2vec. Username is encoded into a sequence of characters. We use one-hot encoding of characters.\nComment is sent into a bi-directional LSTM with attention mechanism. BIBREF22 . News title and username are sent into a bi-directional LSTM. Note that we did not apply attention mechanism to the neural network models for username and news title because both types of context are relatively short and attention mechanism tends to be useful when text input is long. The three LSTM output layers are concatenated, then connected to a sigmoid layer, which outputs predictions.\nThe number of hidden units in each LSTM used in our model is set to be 100. The recurrent dropout rate of LSTMs is set to 0.2. In addition, we use binary cross entropy as the loss function and a batch size of 128. The neural network models are trained for 30 epochs.\nAs shown in table TABREF21 , given comment as the only input content, the bi-directional LSTM model with attention mechanism achieves the best performance. Note that the attention mechanism significantly improves the hate speech detection performance of the bi-directional LSTM model. We hypothesize that this is because hate indicator phrases are often concentrated in a small region of a comment, which is especially the case for long comments.\n\nEnsemble Models\nTo study the difference of logistic regression model and neural network model and potentially get performance improvement, we will build and evaluate ensemble models.\nAs shown in table TABREF24 , both ensemble models significantly improved hate speech detection performance. Figure FIGREF28 shows the system prediction results of comments that were labeled as hateful in the dataset. It can be seen that the two models perform differently. We further examined predicted comments and find that both types of models have unique strengths in identifying certain types of hateful comments.\nThe feature-based logistic regression models are capable of making good use of character-level n-gram features, which are powerful in identifying hateful comments that contains OOV words, capitalized words or misspelled words. We provide two examples from the hateful comments that were only labeled by the logistic regression model:\n(7)kmawhmf:FBLM.\nHere FBLM means fuck Black Lives Matter. This hateful comment contains only character information which can exactly be made use of by our logistic regression model.\n(8)SFgunrmn: what a efen loon, but most femanazis are.\nThis comment deliberately misspelled feminazi for femanazis, which is a derogatory term for feminists. It shows that logistic regression model is capable in dealing with misspelling.\nThe LSTM with attention mechanism are suitable for identifying specific small regions indicating hatefulness in long comments. In addition, the neural net models are powerful in capturing implicit hateful language as well. The following are two hateful comment examples that were only identified by the neural net model:\n(9)freedomscout: @LarJass Many religions are poisonous to logic and truth, that much is true...and human beings still remain fallen human beings even they are Redeemed by the Sacrifice of Jesus Christ. So there's that. But the fallacies of thinking cannot be limited or attributed to religion but to error inherent in human motivation, the motivation to utter self-centeredness as fallen sinful human beings. Nearly all of the world's many religions are expressions of that utter sinful nature...Christianity and Judaism being the sole exceptions.\nThis comment is expressing the stereotyping against religions which are not Christian or Judaism. The hatefulness is concentrated within the two bolded segments.\n(10)mamahattheridge: blacks Love being victims.\nIn this comment, the four words themselves are not hateful at all. But when combined together, it is clearly hateful against black people.\n\nEvaluation\nWe evaluate our model by 10 fold cross validation using our newly created Fox News User Comments Corpus. Both types of models use the exact same 10 folds of training data and test data. We report experimental results using multiple metrics, including accuracy, precision/recall/F1-score, and accuracy area under curve (AUC).\n\nConclusion\nWe demonstrated the importance of utilizing context information for online hate speech detection. We first presented a corpus of hateful speech consisting of full threads of online discussion posts. In addition, we presented two types of models, feature-based logistic regression models and neural network models, in order to incorporate context information for improving hate speech detection performance. Furthermore, we show that ensemble models leveraging strengths of both types of models achieve the best performance for automatic online hate speech detection.\n\nTable 1: Performance of Logistic Regression Models\n\nTable 2: Performance of Neural Network Models\n\nFigure 1: System Prediction Results of Comments that were Annotated as Hateful\n\nQuestion: What is their baseline?" }, { "role": "assistant", "content": "{\"answer\": [\"Logistic regression model with character-level n-gram features\"], \"evidence\": [\"For logistic regression model implementation, we use l2 loss. We adopt the balanced class weight as described in Scikit learn. Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective. BIBREF0 , BIBREF9\"]}" } ]

[ { "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 Vitamin D deficiency may increase your risk of getting the infection and doing really badly if you get it\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[≥]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}" } ]

[ { "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 function of the stard10 protein?\n\nContext: STARD10, a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) protein family, is highly expressed in the liver and has been shown to transfer phosphatidylcholine.\nStarD10 contains a steroidogenic acute regulatory protein (StAR/StarD1)-related lipid transfer (START) domain that is thought to mediate binding of lipids.\nThe steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domain is a protein module of approximately 210 residues that binds lipids, including sterols" }, { "role": "assistant", "content": "STARD10 is a lipid transfer protein" } ]

[ { "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: Recurrent mutations in sars-cov-2 genomes isolated from mink point to rapid virus-adaptation\n\nAbstract:\nSevere acute respiratory coronavirus 2 (SARS-CoV-2), the agent of the ongoing COVID-19 pandemic, jumped into humans from an unknown animal reservoir in late 2019.\nIn line with other coronaviruses, SARS-CoV-2 has the potential to infect a broad range of hosts.\nSARS-CoV-2 genomes have now been isolated from cats, dogs, lions, tigers and minks.\nSARS-CoV-2 seems to transmit particularly well in mink farms with outbreaks reported in Spain, Sweden, the Netherlands, Italy, the USA and Denmark.\nGenomic data from SARS-CoV-2 isolated from infected minks provides a natural case study of a secondary host jump of the virus, in this case from humans to animals, and occasionally back again.\nWe screened published SARS-CoV-2 genomes isolated from minks for the presence of recurrent mutations common in mink but infrequent in SARS-CoV-2 genomes isolated from human infections.\nWe identify 23 recurrent mutations including three nonsynonymous mutations in the Receptor Binding Domain of the SARS-CoV-2 spike protein that independently emerged at least four times but are only rarely observed in human lineages.\nThe repeat emergence of mutations across phylogenetically distinct lineages of the virus isolated from minks points to ongoing adaptation of SARS-CoV-2 to a new host.\nThe rapid acquisition and spread of SARS-CoV-2 mutations in minks suggests that if a similar phenomenon of host adaptation had occurred upon its jump into humans, those human-specific mutations would likely have reached fixation already before the first SARS-CoV-2 genomes were generated.\nData Summary All genome assemblies considered in this manuscript are openly available on registration with GISAID (https://www.gisaid.org).\nInformation on the included assemblies, including the accessions used in the global analysis are provided in Tables S1-S2." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SARS-CoV-2 seems to transmit particularly well in mink farms with outbreaks reported in Spain, Sweden, the Netherlands, Italy, the USA and Denmark.\", \"The repeat emergence of mutations across phylogenetically distinct lineages of the virus isolated from minks points to ongoing adaptation of SARS-CoV-2 to a new host.\"]}" } ]

[ { "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 propose an efficient second-order method for training fully-connected neural networks (FCNNs\n\nKeyphrases: fcnns images\n\nDataset description:\nLocalized Narratives: We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing.\nWiki-CS: Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks. The dataset is constructed from Wikipedia categories, specifically 10 classes corresponding to branches of computer science, with very high connectivity.\nImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nUASOL: The UASOL an RGB-D stereo dataset, that contains 160902 frames, filmed at 33 different scenes, each with between 2 k and 10 k frames. The frames show different paths from the perspective of a pedestrian, including sidewalks, trails, roads, etc.\n\nOptions:- A: Localized Narratives B: Wiki-CS C: ImageNet D: UASOL" }, { "role": "assistant", "content": "ImageNet" } ]

[ { "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: The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the \"United Airway\" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) .\n\nQuestion: What is the current understanding on viral-induced exacerbations?" }, { "role": "assistant", "content": "that viral infection increases airway inflammation which aggravates disease symptoms." } ]

[ { "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: A Review on Human Body Communication: Signal Propagation Model, Communication Performance, and Experimental Issues \nChapter Title: Galvanic Coupling HBC. \n1. Abstract of Cited Paper (BIB001): To reduce space requirements for implant electronics in in vivo telemetry applications, the purpose of this project was to develop and test a new data transmission method that utilizes the ionic properties of bodily fluids as the transmission medium. Motivated by an interest in using the new method to transmit information from a sensor which measures tension in anterior cruciate ligament (ACL) grafts, a sine wave was injected into a cadaver leg using platinum electrodes implanted into the lateral femoral epicondyle. The signal was detected by electromyogram (EMG) surface electrodes. The effect of transmission frequency, the current injected, interelectrode separation, distance of the electrodes from the joint line, and the surface of electrode placement on the signal attenuation was studied. The logarithmic relation between attenuation and frequency was constant from 2 kHz until 10 kHz. For frequencies above 10 kHz, the attenuation increased linearly at the rate of 1 dB/octave. Attenuation was inversely sensitive to both current and interelectrode separation with larger separations and currents giving less attenuation. Attenuation was significantly less for the lateral thigh surface than for the anterior surface and increased with increasing distance from the joint line for both surfaces. For the application of interest here, suitable values of transmission variables to avoid the possible negative consequences of injecting current into living tissue are a current of 3 mA injected at a frequency of 37 kHz. The values of reception variables for minimum attenuation are wide interelectrode separation (5 cm) with the electrodes placed 5 cm proximal of the joint line on the lateral surface of the thigh. With the exception of the surface which is application dependent, these values of the reception variables should also be appropriate for other applications. \n2. Abstract of Cited Paper (BIB002): Personal area networks would benefit from a wireless communication system in which a variety of information could be exchanged through wearable electronic devices and sensors. Intra-body communication using the human body as the transmission medium enables wireless communication without transmitting radio waves through the air. A human arm phantom is designed and used to reduce uncertainty in experiments with the human body. The phantom exhibits transmission characteristics similar to those of the human body at frequencies between 1 MHz and 10 MHz. A 10.7 MHz frequency modulation (FM) intra-body transmitter and receiver are developed which allow transmission of analog sine waves even in the presence of external noise. Digital data transmission at 9600 bps was also achieved using newly fabricated 10.7 MHz frequency shift keying (FSK) transmitter and receiver devices. The carrier frequency of 10.7 MHz, which is the intermediate frequency in FM radio receivers, means that a wide selection of commercial radio frequency (RF) devices is available. \n3. Abstract of Cited Paper (BIB003): Abstract Objective: In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. Materials and Methods: This article presents a detailed survey on wireless body area networks (WBANs). Results and Conclusions: We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article. \n4. Abstract of Cited Paper (BIB004): Galvanic coupling in intrabody communication (IBC) is a technique that couples low-power and low-frequency voltages and currents into the human body, which acts as a transmission medium, and thus constitutes a promising approach in the design of personal health devices. Despite important advances being made during recent years, the investigation of relevant galvanic IBC parameters, including the influence of human tissues and different electrode configurations, still requires further research efforts. The objective of this work is to disclose knowledge into IBC galvanic coupling transmission mechanisms by using a realistic 3-D finite element model of the human arm. Unlike other computational models for IBC, we have modeled the differential configuration of the galvanic coupling as a four-port network in order to analyze the electric field distribution and current density through different tissues. This has allowed us to provide an insight into signal transmission paths through the human body, showing them to be considerably dependent on variables such as frequency and inter-electrode distance. In addition, other important variables, for example bioimpedance and pathloss, have also been analyzed. Finally, experimental measurements were also carried out for the sake of validation, demonstrating the reliability of the model to emulate in general forms some of the behaviors observed in practice.\n\nLiterature Review:" }, { "role": "assistant", "content": "The galvanic coupling HBC was firstly reported in 1997 by the Japanese researchers Handa et al. BIB001 . The ECG signal from the chest was modulated into micro-Ampere electric current, coupled into human body by electrodes, and detected by a pair of receiving electrodes on the wrist. The transmitting and receiving electrodes were in direct contact with the body, resulting in galvanic coupling signal transmission. This system worked with small power consumption, only 8 W. The results suggest that data transmission with low transmission power is possible by using galvanic coupling HBC. In 1998, Lindsey et al. BIB002 tested the galvanic coupling HBC between an implantable device and an external data acquisition system. In the system, two platinum electrodes (each 0.38 mm in diameter, separated by 2.5 mm) were used to inject sinusoidal currents with frequencies of 2-160 kHz and amplitudes of 1-3 mA into the leg of a human cadaver. EMG electrodes on the surface of the leg were deployed to detect the voltage differences. The human body channel resulted in attenuation within 37-47 dB. This prototype demonstrates that it is feasible to utilize galvanic coupling HBC for data transmission between implantable device and body surface mounted sensors. Galvanic coupling HBC, also termed waveguide HBC BIB003 , utilizes the ionic fluids in body and volume conduction properties of tissues BIB002 to convey electrical signal. The simplified model is depicted in Figure 3 . A major ionic current is generated within the tissue close to the transmitter. With a separation distance to the transmitter, the current will decrease due to the impedance of human tissue. The small current induces electric potential that can be detected at the receiver by a high-gain differential amplifier. The existing experiments or prototypes are usually performed on human limb. Since the signal propagation principle is based on the ionic current, the operation frequency should be low (i.e., <1 MHz ). And in such low frequency, the signal radiation or leakage into the space can be negligible BIB005 . Compared with capacitive coupling HBC, galvanic coupling HBC operates in lower frequency and is less influenced by the environment, which makes the signal transmission much more stable and reliable. Therefore, galvanic coupling HBC is more suitable for the vital physiological signal transmission, especially for implantable device communication. Certainly, it trades off data rate. Fortunately, the data rate requirements for the vital physiological signal transmission are relatively low, for example, 75 kbps in ECG, 1.6 kbps in SpO2, and <100 kbps for both pacemaker and implantable glucose sensors BIB004 . Therefore, galvanic coupling HBC is also a promising candidate for the vital physiological data transmission among wearable and implantable devices." } ]

[ { "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: Which is the phenotype of the disease fibrodysplasia ossificans progressiva?\n\nContext: Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare genetic disorder in which heterotopic bone forms in the soft tissues.\nMetaphyseal bony outgrowths are a well-recognized feature of fibrodysplasia ossificans progressiva (FOP) phenotype,\nFibrodysplasia ossificans progressiva (FOP), a congenital heterotopic ossification (HO) syndrome caused by gain-of-function mutations of bone morphogenetic protein (BMP) type I receptor ACVR1, manifests with progressive ossification of skeletal muscles, tendons, ligaments, and joints." }, { "role": "assistant", "content": "3.\tFibrodysplasia ossificans progressiva (FOP), a congenital heterotopic ossification (HO) syndrome caused by gain-of-function mutations of bone morphogenetic protein (BMP) type I receptor ACVR1, manifests with progressive ossification of skeletal muscles, tendons, ligaments, and joints." } ]

[ { "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\nFor example, although Vieira et al. (2006) listed most mites in mixed habitat associations, we found that 12 mite taxa showed clear pool versus riffle affinities, contributing to our knowledge of the habitat associations of this group. Similarly, 13 of the opposite discrepancies between our results and the Merritt et al. (2008) scheme were for Chironomidae taxa and a perusal of the Merritt et al. (2008) references for chironomid designations indicated that assignments were often based on considerations of general group" }, { "role": "assistant", "content": "Result" } ]

[ { "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: A strategyproof mechanism cannot take every agent's valuation into consideration.\n\nKeyphrases: budget-balanced mechanism design\n\nDataset description:\nJester: 6.5 million anonymous ratings of jokes by users of the Jester Joke Recommender System.\nMovieLens: The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time.\nFFHQ: Flickr-Faces-HQ (FFHQ) consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc.\nVIST: The Visual Storytelling Dataset (VIST) consists of 210,819 unique photos and 50,000 stories. The images were collected from albums on Flickr.\nEDUB-Seg: Egocentric Dataset of the University of Barcelona – Segmentation (EDUB-Seg) is a dataset for egocentric event segmentation acquired by the Narrative Clip, which takes a picture every 30 seconds. The dataset contains a total of 18,735 images captured by 7 different users during overall 20 days.\nFLIC: The FLIC dataset contains 5003 images from popular Hollywood movies. The images were obtained by running a state-of-the-art person detector on every tenth frame of 30 movies.\n\nOptions:- A: Jester B: MovieLens C: FFHQ D: VIST E: EDUB-Seg F: FLIC" }, { "role": "assistant", "content": "[\"Jester\", \"MovieLens\"]" } ]

[ { "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\nItaly has been the first Western nation facing COVID-19 outbreak. Despite the emergency situation, all efforts have been done to preserve liver transplant (LT) activity and to minimize the impact of current scenario on transplant waiting list time and mortality. Little is known about COVID-19 consequences in transplant candidates, especially those with limited life expectancy due to the severity of their baseline disease. We report here the case of a young patient requiring inpatient care due to severe decompensated liver disease (MELD 24), justifying her referral from her local hospital to our high-volume LT unit, despite the unfavourable COVID-19 epidemiology in our Region. She was quickly listed for liver transplant (MELD 26), but 5 days later she was incidentally diagnosed with COVID-19 in the setting of our surveillance program for very sick patients and, despite her underlying condition, had an indolent course of the viral disease. Concerns about potential COVID-19 consequences in a LT candidate were overruled by the severity of liver disease (MELD 36), forcing our team to proceed with an urgent successful LT as soon as 9 days after the COVID-19 diagnosis, 2 days after the first negative SARS-CoV-2 RNA by a nasopharyngeal swab and 1 day after the confirmation of its negativity on bronchoalveolar lavage. The patient was discharged on day 9 after LT. In conclusion, to the best of our knowledge, this is the first report of a LT candidate recovering from a mild form of COVID19 and undergoing a successful LT shortly after. Aggressive care should be maintained in SARS-CoV-2-positive patients with decompensated cirrhosis in order to overcome viral infection and to proceed as soon as possible with life-saving treatment." }, { "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: Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods \nChapter Title: Noisy Teachers: Γ and Π Models \n1. Abstract of Cited Paper (BIB001): In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels.\n\nLiterature Review:" }, { "role": "assistant", "content": "Both Γ and Π models are developed on the belief that a robust model ought to have stable predictions under any random transformation of data and perturbations to the model BIB001 . This could push the decision boundary apart from training examples, and make the model insensitive to the noises on the data and the model parameters. Thus, random noises and perturbations are added into the inputs and the parameters of a student model to form a noisy teacher, and the deviation from the predictions by the teacher is minimized to train the student model. Specifically, the Γ-model has a multi-layered latent representation z (l) of each layer l, and uses an auto-encoder to obtain an estimatedẑ (l) by denoising from the corrupted z (l) . Then the sum of squared errors between the (batchnormalized) estimate and the clean latent representations over layers is minimized to train the clear student model weighted by positive hyperparameter coefficients λ l across different layers. On the contrary, Π-model BIB002 is simplified by minimizing the difference between noisy outputs. In the context of semi-supervised learning problem, given a labeled or unlabeled sample x , it is corrupted by some noise and fed into the model perturbed by random dropout and pooling schemes BIB001 . This process is run twice, yielding two versions of its outputs y and y . Then, the squared error between them is minimized to encourage the consistency between noisy outputs, combined with the classification cost on labeled examples to train the model. Unlike Γ-model that matches a clean and a corrupted representation, Π-model runs the corrupted branch twice to match noisy outputs. However, both models rely on random noises to explore their resilience against noisy inputs and perturbed models, which would be ineffective in finding a competent teacher to train the robust models. Thus, an ensemble of teachers are tracked over epochs to form a more capable teacher model, resulting in the following temporal ensembling BIB002 and mean teacher methods. BIB002 and Mean Teacher are similar to each other in tracking an ensemble of models over time to have a better teacher model. However, they differ in maintaining an exponential moving average over the predictions (temporal ensembling) by or the parameters (mean teacher) of the tracked models." } ]

[ { "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: Increased risk of noninfluenza respiratory virus infections decreases with receipt of inactivated influenza vaccine\n\nAbstract:\nWe randomized 115 children to trivalent inactivated influenza vaccine (TIV) or placebo.\nOver the following 9 months, TIV recipients had an increased risk of virologically-confirmed non-influenza infections (relative risk: 4.40; 95% confidence interval: 1.31-14.8).\nBeing protected against influenza, TIV recipients may lack temporary non-specific immunity that protected against other respiratory viruses." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being protected against influenza, TIV recipients may lack temporary non-specific immunity that protected against other respiratory viruses.\"]}" } ]

[ { "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: Hydroxychloroquine, by itself or with azithromycin, did not improve the clinical status of patients with mild to moderate COVID-19\n\nAbstract:\nBackgrounds.\nSince COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\nThis has led to a lot of controversies among associated researches.\nHence, in order to address the issue of using hydroxychloroquine in treating COVID-19 patients, we conducted a systematic review and meta-analysis.\nMethods.\nA thorough search was carried out to find relevant studies in MEDLINE, medRxiv, PubMed, Cochrane Database, China Academic Journals Full-text Database and Web of Science.\nTwo investigators independently reviewed 274 abstracts and 23 articles.\nThe trials which evaluated hydroxychloroquine for treatment of COVID-19 were included for this systematic review.\nTwo investigators assessed quality of the studies and data extraction was done by one reviewer and cross checked by the other.\nResults.\nFive trials involving 677 patients were included while conducting the meta-analysis.\nCompared with the control group, hydroxychloroquine with or without azithromycin showed benefits in positive-to-negative conversion of SARS-CoV-2 (odds ratio [OR], 1.95 [95% CI,0.19 to 19.73] and a reduction in progression rate (OR, 0.89 [95% CI, 0.58 to 1.37]), but without demonstrating any statistical significance.\nThis systematic review has also suggested a possible synergistic effect of the combination therapy which included hydroxychloroquine and azithromycin.\nHowever, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\nConclusion.\nThe use of hydroxychloroquine with or without azithromycin for treatment of COVID-19 patients, seems to be effective.\nThe combination of hydroxychloroquine and azithromycin has shown synergic effects.\nHowever, mortality rate was increased when the treatment was conducted with hydroxychloroquine." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Since COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\", \"However, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\", \"However, mortality rate was increased when the treatment was conducted with hydroxychloroquine.\"]}" } ]

[ { "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: 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] . Classical studies using neutralizing mouse mAbs identified five distinct antigenic sites (A-E) on the HA1 globular head region in the three-dimensional structure of the H3 HA molecule (A/Hong Kong/1/68) [85] [86] [87] as well as in H1 [88] and H2 subtypes [89] . During the first few days of an infection, the nAb titer is often low, while the titer of non-nAbs is higher and may play a role in the outcome of an infection, as recently observed for influenza A/2009 H1N1 pandemic virus infected patients by To et al. [90] . In particular, this group found that the amount, as well as the avidity, of non-nAbs were higher for patients with severe disease than for those with mild disease. The authors concluded that an exaggerated non-nAb response during the early stage of infection was associated with severe disease [90] . Moreover, the authors speculated that non-nAbs present in patients' sera during the early stage of infection were likely to be either preexisting or the result of a secondary heterosubtypic humoral immune response against more conserved epitopes on several influenza proteins [91] . This early humoral response can be elicited within a few days after infection, because of immune priming by previous exposure to shared viral epitopes. In fact, the matrix proteins and nucleoprotein have conserved aminoacid sequences, and therefore Abs against these proteins from previous seasonal influenza virus infection or vaccination could be induced [92] . Indeed, upon infection with influenza virus, memory B-cells can proliferate rapidly and generate a large amount of these high avidity non-nAbs, especially in patients with severe disease. This is consistent with the observation that the number of peripheral blood B-cells is higher in patients with severe disease than in those with mild disease during the early stage of infection. The mechanism of Ab neutralization interference has been indirectly speculated also by Ndifon et al., who observed that some aminoacid changes on HA actually increase the efficiency of neutralization of escape variants by previously generated Abs, even if not directly influencing their binding [93] . In detail, this group suggested that the increase in neutralizing activity after HA mutation could be the resultant of a lesser steric interference between Abs. Specifically, if there is a steric competition for binding to HA by Abs with different neutralization efficiency, then a mutation that reduces the binding of Abs with low neutralizing activity could increase the overall viral neutralization. Indeed, similarly to what has been speculated for HCV, Abs that bind to HA epitopes located at a distance from the receptor-binding site may therefore fail to occupy this site efficiently, thereby leading to a decreased viral neutralization. Moreover, it has been shown that Abs that bind to a certain HA epitope can prevent further binding of Abs to other epitopes of the same HA protein, and even to epitopes found on adjacent HA proteins. The above observations suggest that Abs that bind to low-neutralization efficiency epitopes of HA might interfere with the binding of nAbs to close high-neutralization efficiency epitopes, thereby impeding the neutralization of influenza viruses. Considering the HA structure, the binding of the interfering Abs would lie at the level of epitope C and E located far from the receptor-binding site on the globular head of the HA. However, the binding of these Abs may influence the binding of nAbs to epitopes A, B and D, located closer to the receptorbinding site [93] . At this regard changes to epitope A, B and D could be highly favored by natural selection, whereas changes to epitopes C and E could be disadvantageous to influenza viruses [93] . Similarly to HCV, but with a sounder confirm due to the availability of the crystal structure, these speculations raise the intriguing possibility that the influenza viruses may have evolved by favoring the preferential elicitation of Abs recognizing epitopes with a low-neutralization profile. Indeed, steric hindrance by Abs that bind these epitopes could greatly reduce the extent of mutation required for a virus to evade neutralization by host Abs. Consequently, a decrease in the affinity of Abs for epitopes with low-neutralization efficiency could lead to an increase in viral neutralization. This suggests a possible approach to design \"low-interference\" vaccines that could greatly diminish the impact of Ab interference. These immunogens are genetically modified from viral target only at the level of low-neutralization efficiency epitopes. Indeed, vaccine-induced Abs only recognize high-neutralization efficiency epitopes of the target and Abs induced by low-interference vaccine strain have low affinity for low-neutralization efficiency epitopes of the target circulating virus strain. Therefore, they do not interfere with Abs to high-neutralization efficiency epitopes, implying an improved neutralization. Consequently, limiting Ab-mediated interference, the target virus cannot escape from vaccine-induced Abs through small epitope changes. Alternatively, vaccines could be designed to include only those regions that correspond to epitopes with high-neutralization efficiency. Furthermore, antiviral drugs could be designed to include viral proteins carrying modifications at the level of high-neutralization efficiency epitopes; these \"decoy\" proteins would compete with virus for binding to low-neutralization efficiency Abs in a manner similar to that played by neuraminidase inhibitors. In synthesis, the availability of HA crystal structure has helped to confirm the existence and to explain the mechanisms of interference by non-or weakly-neutralizing anti-HA Abs. The recent work by To et al. [90] evidencing that a non-nAb response during the early stage of infection is associated with a severe disease, may be the first proof of the role of these interfering Abs in the course of a natural infection. The [94] . More than 8,000 cases, including almost 800 deaths, were reported during the outbreak period and increasing age and comorbidity were risk factors for severe disease and death [95] . Since 2003, only sporadic cases have been reported; however, the possibility that SARS outbreaks could reemerge naturally or be deliberately released is a public health concern. Like influenza viruses, SARS-CoV circulates in animal reservoirs, with bats that are thought to transmit the virus to small mammals with exposure to these small animals as the source of human infections [96] . The clinical disease is similar to other severe acute respiratory infections, including influenza, and the SARS case definition includes clinical, epidemiologic, and laboratory criteria [97, 98] . The basic genome organization and replicative cycle is similar for all CoVs. Gene 1 encodes all predicted replicase/transcriptase proteins, which are translated from input genomic RNA, while genes 2-9 encode structural and accessory proteins, including the envelope spike (S) protein, which are translated from separate subgenomic mRNAs. CoVs use a unique discontinuous mechanism to transcribe a series of progressively larger subgenomic mRNAs, and each contains a leader RNA sequence that is derived from the 5' end of the genome [99] . The S protein of CoVs is inserted in the envelope of the virion mediating binding and fusion events necessary for infection, and it is the major target of the humoral protective immunity [100] . Although the S protein of SARS-CoV (SARS-S) shares little aminoacid identity (approximately 20%-27%), it shares common structural features with S proteins of the other members of the Coronaviridae family. SARS-S protein is a type I transmembrane glycoprotein of approximately 1,255 amino acids in length and divided into two functional domains: S1 (aminoacid residues 15-680) and S2 (aminoacid residues 681-1,255) [101] . In many CoVs, the S protein is cleaved during biogenesis and these two functional domains are held together non-covalently; however, as in the case of human CoV 229E, the S protein is not cleaved in SARS-CoV [102] . The S1 domain forms a globular structure that mediates interaction of the S protein with its main receptor, angiotensin-converting enzyme 2 (ACE2), while the S2 domain mediates fusion and contains the putative fusion peptide and two conserved helical regions (HR1 and HR2) that upon cleavage by the endosomal protease cathepsin L form the six helix bundle fusion core [103] . Vaccine strategies aiming at blocking/limiting infection by SARS-CoV mainly focus on targeting the SARS-S viral glycoprotein [100] . Nonetheless, such a strategy poses a singular dilemma for CoVs, as previous vaccination protocols have highlighted the possibility of immune-mediated enhancement of the disease [104] . At this regard, the group of Zhong et al. investigated the role of non-neutralizing interfering Abs also in the case of SARS-CoV infection [105] . In particular, they found that two mAbs directed against the region encompassing aminoacid residues 491-510 of SARS-S (341C and 540C) act synergistically to inhibit SARS-CoV infection in vitro, while a non-neutralizing mAb (240C) whose epitope encompasses the above mentioned region, disrupted the neutralizing activity of both 341C and 540C [105, 106] . By analyzing the crystal structure of the SARS-S protein, the authors proposed a possible explanation to what observed, evidencing that the epitopes of all the mAbs are closely packed and proximal to each other but distal from the ACE2 receptor binding site [105] .\n\nQuestion: How are type A, B, and C viruses determined?" }, { "role": "assistant", "content": "antigenic differences of their nucleoprotein and matrix protein" } ]

[ { "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:\nWe describe Fibres of Failure (FiFa), a method to classify failure modes of predictive processes using the Mapper algorithm from Topological Data Analysis. Our method uses Mapper to build a graph model of input data stratified by prediction error. Groupings found in high-error regions of the Mapper model then provide distinct failure modes of the predictive process. We demonstrate FiFa on misclassifications of MNIST images with added noise, and demonstrate two ways to use the failure mode classification: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode.\n\nCited Abstract(s):\n[1]: Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.\n\n[2]: With the advent of highly predictive but opaque deep learning models, it has become more important than ever to understand and explain the predictions of such models. Existing approaches define interpretability as the inverse of complexity and achieve interpretability at the cost of accuracy. This introduces a risk of producing interpretable but misleading explanations. As humans, we are prone to engage in this kind of behavior mythos . In this paper, we take a step in the direction of tackling the problem of interpretability without compromising the model accuracy. We propose to build a Treeview representation of the complex model via hierarchical partitioning of the feature space, which reveals the iterative rejection of unlikely class labels until the correct association is predicted." }, { "role": "assistant", "content": "One interpretability method with a large impact on the field, LIME [1] , inspects single instances by perturbing the input and tracing how predictions change with the perturbation. Other interpretability methods focus closer on aggregates of inputs, such as TreeView [2] , which visualizes deep neural networks by first clustering neurons by activation patterns, then clusters these groups by prediction labels, and finally trains a predictor to predict the meta-clusters from the input data directly." } ]

[ { "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\nIn [1] and [2], a multilayer directory structure is presented to reduce the size of the directory for largescale configurations of a multiprocessor while also maintain or even improve the performance." }, { "role": "assistant", "content": "Background" } ]

[ { "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\nGroup representative strains were screened for nifH, the gene encoding the nitrogenase iron protein dinitrogenase reductase , by dot blot hybridization using a biotinylated nifH specific DNA probe that detects a very wide range of diazotroph taxa Bagwell et al. 1998; 2001; Bergholz et al. 2001 ." }, { "role": "assistant", "content": "Method" } ]

[ { "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: Recovery from Covid-19 can be a lengthy process, depending on how seriously people get the virus.\n\nAbstract:\nBACKGROUND: Most patients with coronavirus disease (COVID-19) who show mild symptoms are sent home by physicians to recover.\nHowever, the condition of some of these patients becomes severe or critical as the disease progresses.\nOBJECTIVE: The aim of this study was to evaluate a telemedicine model that was developed to address the challenges of treating patients with progressive COVID-19 who are home-quarantined and shortages in the medical workforce.\nMETHODS: A telemedicine system was developed to continuously monitor the progression of home-quarantined patients with COVID-19.\nThe system was built based on a popular social media smartphone app called WeChat; the app was used to establish two-way communication between a multidisciplinary team consisting of 7 medical workers and 188 home-quarantined individuals (including 74 confirmed patients with COVID-19).\nThe system helped patients self-assess their conditions and update the multidisciplinary team through a telemedicine form stored on a cloud service, based on which the multidisciplinary team made treatment decisions.\nWe evaluated this telemedicine system via a single-center retrospective study conducted at Tongji Hospital in Wuhan, China, in January 2020.\nRESULTS: Among 188 individuals using the telemedicine system, 114 (60.6%) were not infected with COVID-19 and were dismissed.\nOf the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system.\nThe remaining 48/76 confirmed patients with COVID-19 (63%) used the system until the end of the study, including 6 patients whose conditions progressed to severe or critical.\nThese 6 patients were admitted to hospital and were stabilized (one received extracorporeal membrane oxygenation support for 17 days).\nAll 74 patients with COVID-19 eventually recovered.\nThrough a comparison of the monitored symptoms between hospitalized and nonhospitalized patients, we found prolonged persistence and deterioration of fever, dyspnea, lack of strength, and muscle soreness to be diagnostic of need for hospitalization.\nCONCLUSIONS: By continuously monitoring the changes in several key symptoms, the telemedicine system reduces the risks of delayed hospitalization due to disease progression for patients with COVID-19 quarantined at home.\nThe system uses a set of scales for quarantine management assessment that enables patients to self-assess their conditions.\nThe results are useful for medical staff to identify disease progression and, hence, make appropriate and timely treatment decisions.\nThe system requires few staff to manage a large cohort of patients.\nIn addition, the system can solicit help from recovered but self-quarantined medical workers to alleviate shortages in the medical workforce and free healthy medical workers to fight COVID-19 on the front line.\nThus, it optimizes the usage of local medical resources and prevents cross-infections among medical workers and patients." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Of the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system.\"]}" } ]

[ { "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: My mask just needs to cover my mouth.\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\": \"CONTRADICT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\"]}" } ]

[ { "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: Other research has observed high rates of vitamin D deficiency in people with COVID-19 who experienced acute respiratory failure.\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[≥]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study." }, { "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: Some cancer treatments such as chemotherapy can weaken your immune system (cause you to be immunocompromised) and may increase your risk for severe illness from COVID-19.\n\nAbstract:\nThe ongoing outbreak of COVID-19 has been expanding worldwide.\nAs of 17 April 2020, the death toll stands at a sobering 147,027 and over two million cases, this has been straining the health care systems all over.\nRespiratory failure has been cited as the major cause of death but here we present a case about a patient who instead succumbed to severe metabolic acidosis with multiple organ failure." }, { "role": "assistant", "content": "{\"verdict\": \"NEI\", \"evidence\": []}" } ]

[ { "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\nPrevious results in this regard were controversial, showing that Treg cells impair [17] or improve [18] angiogenesis after limb ischaemia." }, { "role": "assistant", "content": "Result" } ]

[ { "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 sparked a global pandemic, affecting more than 4 million people worldwide. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause acute lung injury (ALI) and even acute respiratory distress syndrome (ARDS); with a fatality of 7.0 %. Accumulating evidence suggested that the progression of COVID-19 is associated with lymphopenia and excessive inflammation, and a subset of severe cases might exhibit cytokine storm triggered by secondary hemophagocytic lymphohistiocytosis (sHLH). Furthermore, secondary bacterial infection may contribute to the exacerbation of COVID-19. We recommend using both IL-10 and IL-6 as the indicators of cytokine storm, and monitoring the elevation of procalcitonin (PCT) as an alert for initiating antibacterial agents. Understanding the dynamic progression of SARS-CoV-2 infection is crucial to determine an effective treatment strategy to reduce the rising mortality of this global pandemic." }, { "role": "assistant", "content": "[\"Treatment\", \"Mechanism\"]" } ]

[ { "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: The sunlight has a huge impact on decaying the coronavirus\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene." }, { "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: Because the virus can be spread easily, many have resorted wearing face masks to protect themselves, as the virus is transmissible through human contact and exposure to respiratory droplets from an infected person.\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}" } ]

[ { "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: Early immune pathology and persistent dysregulation characterise severe covid-19.\n\nAbstract:\nThe coronavirus disease-2019 (COVID-19) which caused by severe acute respiratory syndrome-related coronavirus (SARS-CoV-2), is a pandemic threat to global public health.\nIt has a wide spectrum of clinical manifestations from mild to critical illness, the most serious of which is the complications of acute respiratory distress syndrome (ARDS).\nSARS-CoV-2 infection appears mild in infants and children, however, in adults, it can lead to serious consequences.\nIn this review, we highlighted the differences between the immune responses of the lung in children and adults, immune dysregulation and their possible role in clinical manifestations in COVID-19.\nThere is a reduction in population of immunocompetent cells during aging and subsequently induced ineffective inflammation in the faces of some infections.\nDysregulation in the immune system can lead to an unappropriated local and systemic immune responses and subsequently the rapid spread of the virus, leading to severe COVID-19 disease.\nTherefore, recognizing the differences in the immune responses of various hosts as well as to improve the immune system disorder should always be part of research and treatment protocols." }, { "role": "assistant", "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Dysregulation in the immune system can lead to an unappropriated local and systemic immune responses and subsequently the rapid spread of the virus, leading to severe COVID-19 disease.\"]}" } ]

[ { "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: [\"Chemical\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\": [\"Arsenic\", ...]}. 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\nInfluence of experimental type 1 diabetes on the pulmonary effects of diesel exhaust particles in mice. Epidemiologically, exposure to particulate air pollution is associated with increases in morbidity and mortality, and diabetics are especially vulnerable to effects of particles. This study was carried out to determine the respiratory effect of diesel exhaust particles (DEP; 0.4mg/kg) on mice rendered diabetic by the injection of streptozotocin or vehicle (control). Four weeks following induction of diabetes, the animals were intratracheally instilled (i.t.) with DEP (0.4mg/kg) or saline. 24h later, the measurement of airway reactivity to methacholine in vivo by a forced oscillation technique showed a significant and dose-dependent increase in airway resistance in non-diabetic mice exposed to DEP versus non-diabetic mice exposed to saline. Similarly, the airway resistance was significantly increased in diabetic mice exposed to DEP versus diabetic mice exposed to saline. Nevertheless, there was no difference in the airway resistance between diabetic and non-diabetic mice after i.t. administration of DEP. Following DEP administration there were neutrophil polymorphs infiltration of pulmonary interalveolar septae and the alveolar spaces with many macrophages containing DEP in both diabetic and non-diabetic mice. Interestingly, apoptotic cells were only found in the examined lung sections from diabetic mice exposed to DEP. Total proteins and albumin concentrations in bronchoalveolar lavage (BAL) fluid, markers for increase of epithelial permeability, were significantly increased in diabetic mice exposed to DEP compared to saline-treated diabetic and DEP-treated non diabetic mice. Superoxide dismutase activity and reduced glutathione concentration in BAL were significantly decreased in diabetic mice exposed to DEP compared to saline-treated diabetic and DEP-treated non diabetic mice. Moreover, tumor necrosis factor α (TNFα) concentrations were significantly increased in diabetic mice exposed to DEP compared to saline-treated diabetic and DEP-treated non diabetic mice. We conclude that, at the dose and time point investigated, DEP equally increased airway resistance and caused infiltration of inflammatory cells in the lung of both diabetic and non-diabetic mice. However, the occurrence of oxidative stress, the presence lung apoptotic cells and the increase of total proteins, albumin and TNFα in BAL fluid were only seen in DEP-exposed diabetic mice suggesting an increased respiratory susceptibility to particulate air pollution." }, { "role": "assistant", "content": "{\"Chemical\": [\"streptozotocin\", \"Superoxide\", \"methacholine\"]}" } ]

[ { "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: [\"Chemical\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\": [\"Arsenic\", ...]}. 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\nSynthesis of a DOTA (Gd(3+))-conjugate of proton-pump inhibitor pantoprazole for gastric wall imaging studies. Magnetic resonance imaging (MRI) is used to evaluate gastrointestinal (GI) structure and functions in humans. Despite filling the viscus lumen with a contrast agent, visualization of the viscus wall is limited. To overcome this limitation, we de novo synthesized a conjugate that covalently combines a Gd-based MRI contrast agent, encaged with a chelating agent (DOTA), with pantoprazole, which is a widely used proton pump inhibitor that binds to proton pumps in the stomach and colon. The DOTA linkage was installed at a mechanism-based strategic location in the pantoprazole molecule to minimize a possible negative effect of the structural modification on the drug. It is anticipated that by defining the wall of the stomach and colon, this compound will facilitate functional MRI of the GI tract in humans." }, { "role": "assistant", "content": "{\"Chemical\": [\"DOTA\", \"Gd(3+)\", \"Gd\", \"pantoprazole\"]}" } ]

[ { "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: Erythropoietin receptor expression and its relationship with trastuzumab response and resistance in HER2-positive breast cancer cells . Resistance to trastuzumab is a major issue in the treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Several potential resistance mechanisms have been investigated, but the results are controversial and no conclusion has been reached. erythropoietin receptor (EPOR) may function in cell growth, and expressed in various cancer cells. Because the downstream signaling pathways for EPOR and HER2 partially overlapped, we hypothesized that EPOR may play a role in the inhibition effect of trastuzumab and resistance to trastuzumab. Here, we detected the expression of EPOR mRNA and protein in HER2-positive breast cancer cell lines and tissues. EPOR expressed in SKBR3, MDA-MB-453, and UACC-812 cell lines, but not in BT474. Of the 55 HER2-positive cancer tissues, EPOR was positive in 42 samples and highly expressed (H-score ≥ 25) in 24 by immunohistochemistry. The difference between EPOR expression and Ki67 index was significant (P = 0.033), and EPOR expression also positively correlated with higher pathological stage (Spearman correlation coefficient = 0.359; P = 0.007). Exogenous EPO antagonized trastuzumab-induced inhibition of cell proliferation in HER2/EPOR dual-positive breast cancer cells. We then exposed SKBR3 cells to trastuzumab for 4 months to obtain trastuzumab-resistant SKBR3 cell line, which demonstrated higher phosphorylated EPOR level, higher EPO expression and more extracellular secretion than non-resistant parental SKBR3 cells. Downregulation EPOR expression using short hairpin RNA resensitized trastuzumab-resistant cells to this drug, and SKBR3 cells with EPOR downregulation demonstrated attenuated trastuzumab resistance after the same resistance induction. EPOR downregulation plus trastuzumab produced a synergetic action in the inhibition of cell proliferation and invasion in SKBR3 and MDA-MB-453 cell lines. Therefore, EPOR expression may be involved in tumor progression and proliferation in HER2-positive breast cancer. EPO/EPOR contributes to the mechanism of trastuzumab resistance in SKBR3 cell lines, and EPOR downregulation can reverse the resistance to trastuzumab and increase the inhibition effect of this drug.\n\nsentence: Erythropoietin receptor expression and its relationship with trastuzumab response and resistance in HER2-positive breast cancer cells ." }, { "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: Are epigenetic modifications implicated in cardiovascular development and disease?\n\nContext: Gene expression regulation through the interplay of DNA methylation and histone modifications is well-established, although the knowledge about the function of epigenetic signatures in cardiovascular disease is still largely unexplored.\nThe study of epigenetic markers is, therefore, a very promising frontier of science which may aid in a deeper understanding of molecular mechanisms underlying the modulation of gene expression in the biomolecule pathways linked to cardiovascular diseases.\nThis review highlights our current knowledge of epigenetic gene regulation and the evidence that chromatin remodeling and histone modifications play key roles in the pathogenesis of cardiovascular disease through (re)programming of cardiovascular (stem) cells commitment, identity and function.\nNotably, multiple subunits of switching defective/sucrose non-fermenting (SWI/SNF) chromatin-remodeling complexes have been identified as strong candidates underlying these defects because they physically and functionally interact with cardiogenic transcription factors critical to cardiac development, such as TBX5, GATA-4, and NKX2-5. While these studies indicate a critical role of SWI/SNF complexes in cardiac development and congenital heart disease, many exciting new discoveries have identified their critical role in the adult heart in both physiological and pathological conditions involving multiple cell types in the heart, including cardiomyocytes, vascular endothelial cells, pericytes, and neural crest cells.\nRecent studies have greatly expanded our understanding of the regulation of cardiovascular development at the chromatin level, including the remodeling of chromatin and the modification of histones. Chromatin-level regulation integrates multiple inputs and coordinates broad gene expression programs. Thus, understanding chromatin-level regulation will allow for a better appreciation of gene regulation as a whole and may set a fundamental basis for cardiovascular disease.\nGenetic and epigenetic factors are of great importance in cardiovascular biology and disease. Tobacco-smoking, one of the most important cardiovascular risk factors, is itself partially determined by genetic background and is associated with altered epigenetic patterns.\nEpigenetic modifications, including DNA methylation, histone modification (acetylation, methylation and phosphorylation) and miRNA, are critical for regulating developmental events. However, aberrant epigenetic mechanisms may lead to pathological consequences such as cardiovascular disease (CAD), neurodegenerative disease, obesity, metabolic disorder, bone and skeletal diseases and various cancers.\nCardiovascular disease pathways are now being approached from the epigenetic perspective, including those associated with atherosclerosis, angiogenesis, ischemia-reperfusion damage, and the cardiovascular response to hypoxia and shear stress, among many others. With increasing interest and expanding partnerships in the field, we can expect new insights to emerge from epigenetic perspectives of cardiovascular health.\nEpigenetic modifications are heritable alterations of the genome, which can govern gene expression without altering the DNA sequence. The purpose of this review is to render an overview of the possible mechanisms of epigenetic regulation of gene expression in response to environmental pollutants leading to cardiovascular diseases (CVD).\nFrom varied study approaches directed either toward the general understanding of the key pathway regulatory genes, or sampling population cohorts for global and gene-specific changes, it has been possible to identify several epigenetic signatures of environmental exposure relevant to CVD.\nAn understanding of chromatin remodelling in response to environmental stimuli conducive to CVD is emerging, with the promise of novel diagnostic and therapeutic candidates.\nConsolidated knowledge is accumulating as to the role of epigenetic regulatory mechanisms in the physiology of vascular development and vascular tone as well as in the pathogenesis of cardiovascular disease. The modulation of gene expression through modification of the epigenome by structural changes of the chromatin architecture without alterations of the associated genomic DNA sequence is part of the cellular response to environmental changes. Such environmental conditions, which are finally being translated into adaptations of the cardiovascular system, also comprise pathological conditions such as atherosclerosis or myocardial infarction.\nEmerging data suggest that these epigenetic modifications also impact on the development of cardiovascular disease. Histone modifications lead to the modulation of the expression of genetic information through modification of DNA accessibility. In addition, RNA-based mechanisms (e.g., microRNAs and long non-coding RNAs) influence the development of disease.\nWe here outline the recent work pertaining to epigenetic changes in a cardiovascular disease setting.\nEpigenetics may represent one of the possible scientific explanations of the impact of such intrauterine risk factors for the subsequent development of cardiovascular disease (CVD) during adulthood.\nEpigenetic mechanisms include DNA methylation, histone modification, and microRNA alterations, which collectively enable the cell to respond quickly to environmental changes. A number of CVD risk factors, such as nutrition, smoking, pollution, stress, and the circadian rhythm, have been associated with modification of epigenetic marks. Further examination of these mechanisms may lead to earlier prevention and novel therapy for CVD.\nEmerging data suggest that these epigenetic modifications also impact on the development of cardiovascular disease.\nEmerging data suggest that these epigenetic modifications also impact on the development of cardiovascular disease.\nEpigenetic alterations are associated with inflammation and cardiovascular disease in patients with chronic kidney disease.\nEmerging data suggest that these epigenetic modifications also impact on the development of cardiovascular disease\nEpigenetic mechanisms that underpin metabolic and cardiovascular diseases.\nEpigenetic regulation of cardiovascular differentiation.\nEpigenetic control mechanisms play a key role in the regulation of embryonic development and tissue homeostasis and modulate cardiovascular diseases." }, { "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: The meta-classifiers were trained to classify the following properties of the training setup\n\nKeyphrases: explainable\n\nDataset description:\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).\nGYAFC: Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs. Yahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data.\nFood-101: The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise.\nChinese Classifier: Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. This dataset of Chinese Classifiers can be used to predict Chinese classifiers from context.\n\nOptions:- A: CIFAR-10 B: GYAFC C: Food-101 D: Chinese Classifier" }, { "role": "assistant", "content": "CIFAR-10" } ]

[ { "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\nOptimization of taxane binding to microtubules: binding affinity dissection and incremental construction of a high-affinity analog of paclitaxel.\nThe microtubule binding affinities of a series of synthetic taxanes have been measured with the aims of dissecting individual group contributions and obtaining a rationale for the design of novel compounds with the ability to overcome drug resistance. As previously observed for epothilones, the positive and negative contributions of the different substituents to the binding free energies are cumulative. By combining the most favorable substitutions we increased the binding affinity of paclitaxel 500-fold. Insight into the structural basis for this improvement was gained with molecular modeling and NMR data obtained for microtubule-bound docetaxel. Taxanes with affinities for microtubules well above their affinities for P-glycoprotein are shown not to be affected by multidrug resistance. This finding strongly indicates that optimization of the ligand-target interaction is a good strategy to overcome multidrug resistance mediated by efflux pumps." }, { "role": "assistant", "content": "{\"Chemical\": [\"paclitaxel\", \"Taxanes\", \"taxanes\", \"docetaxel\", \"taxane\", \"epothilones\"], \"Protein\": [\"microtubules\", \"P-glycoprotein\", \"microtubule\", \"efflux pumps\"]}" } ]

[ { "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\nDeep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial\n\nintelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others.\nIn this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.\nThe data set of each use case contains real known benign and malicious activities samples.\nThese use cases are part of Cybersecurity Data Mining Competition (CDMC) 2017.\nThe efficient network architecture for DNNs is chosen by conducting various trails of experiments for network parameters and network structures.\nThe experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5].\nExperiments of DNNs performed well in comparison to the classical machine learning algorithm in all cases of experiments of cyber security use cases.\nThis is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy.\nThe best accuracy obtained by DNNs and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997, and fraud detection 0.972 and 0.916 respectively.\nThe accuracy obtained by DNNs varies -0.05%, +0.02%, -0.01% from the top scored system in CDMC 2017 tasks.\nIn this era of technical modernization, explosion of new opportunities and efficient potential resources for organizations have emerged but at the same time these technologies have resulted in threats to the economy.\nIn such a scenario proper security measures plays a major role.\nNow days, hacking has become a common practice in organizations in order to steal data and information.\nThis highlights the need for an efficient system to detect and prevent the fraudulent activities.\ncyber security is all about the protection of systems, networks and data in the cyberspace.Malware remains one of the maximum enormous security threats on the Internet.\nMalware are the softwares which indicate malicious activity of the file or programs.\nThese are unwanted programs since they cause harm to the intended use of the system by making it behave in a very different manner than it is supposed to behave.\nSolutions with Antivirus and blacklists are used as the primary weapons of resistance against these malwares.\nBoth approaches are not effective.\nThis can only be used as an initial shelter in real time malware detection system.\nThis is primarily due to the fact that both approaches completely fails in detecting the new malware that is created using polymorphic, metamorphic, domain flux and IP flux.Machine learning algorithms have played a pivotal role in several use cases of cyber security BID0 .\nFortunately, deep learning approaches are prevailing subject in recent days due to the remarkable performance in various long-standing artificial intelligence (AI) supervised and unsupervised challenges BID1 .\nThis paper evaluates the effectiveness of deep neural networks (DNNs) for cyber security use cases: Android malware classification, incident detection and fraud detection.The paper is structured as follows.\nSection II discusses the related work.\nSection III discusses the background knowledge of deep neural networks (DNNs).\nSection IV presents the proposed methodology including the description of the data set.\nResults are displayed in Section V. Conclusion is placed in Section VI.\nThis paper has evaluated the performance of deep neural networks (DNNs) for cyber security uses cases: Android malware classification, incident detection and fraud detection.\nAdditionally, other classical machine learning classifier is used.\nIn all cases, the performance of DNNs is good in comparison to the classical machine learning classifier.\nMoreover, the same architecture is able to perform better than the other classical machine learning classifier in all use cases.\nThe reported results of DNNs can be further improved by promoting training or stacking a few more layer to the existing architectures.\nThis will be remained as one of the direction towards the future work." }, { "role": "assistant", "content": "Deep-Net: Deep Neural Network for Cyber Security Use Cases" } ]

[ { "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 do studies show about the effect of Induced hypothermia in premature babies?\n\nContext: Therapeutic hypothermia is a recognized treatment for term infants with hypoxic-ischemic encephalopathy (HIE) in reducing rate of death or neurodevelopmental disabilities. Little is known about applications of this treatment to preterm newborns.\nRandomised studies have demonstrated the efficacy of hypothermia for the treatment of perinatal hypoxic-ischaemic encephalopathy (HIE) in term or late preterm infants.\nWe aimed to establish the feasibility and safety of mild hypothermia in preterm neonates with NEC and MODS as a prelude to a randomized trial. METHODS: This was a prospective, nonrandomized pilot study of 15 preterm infants who were referred for surgical intervention of advanced NEC and failure of at least 3 organs. Whole-body cooling was achieved by ambient temperature adjustment with or without cooling mattress\nMild hypothermia for 48 hours in preterm neonates with severe NEC seems both feasible and safe. Additional investigation of the efficacy of this therapeutic intervention in this population is warranted." }, { "role": "assistant", "content": "Randomised studies have demonstrated the efficacy of hypothermia for the treatment of perinatal hypoxic-ischaemic encephalopathy (HIE) in reducing rate of death or neurodevelopmental disabilities in term or late preterm infants. It remains unclear to what degree preterm infants were included in these studies.\nA prospective non-randomised pilot study reported that mild hypothermia for 48 hours in preterm neonates with severe NEC (necrotising enterocolitis) seems both feasible and safe." } ]

[ { "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 Jarid2 play a role in early embryo development?\n\nContext: Jarid2 Coordinates Nanog Expression and PCP/Wnt Signaling Required for Efficient ESC Differentiation and Early Embryo Development\nUnlike other PRC2-deficient embryonic stem cells (ESCs), however, Jarid2-deficient ESCs show a severe differentiation block, altered colony morphology, and distinctive patterns of deregulated gene expression. Here, we show that Jarid2(-/-) ESCs express constitutively high levels of Nanog but reduced PCP signaling components Wnt9a, Prickle1, and Fzd2 and lowered β-catenin activity. Depletion of Wnt9a/Prickle1/Fzd2 from wild-type ESCs or overexpression of Nanog largely phenocopies these cellular defects. Co-culture of Jarid2(-/-) with wild-type ESCs restores variable Nanog expression and β-catenin activity and can partially rescue the differentiation block of mutant cells. In addition, we show that ESCs lacking Jarid2 or Wnt9a/Prickle1/Fzd2 or overexpressing Nanog induce multiple ICM formation when injected into normal E3.5 blastocysts. These data describe a previously unrecognized role for Jarid2 in regulating a core pluripotency and Wnt/PCP signaling circuit that is important for ESC differentiation and for pre-implantation development.\nJarid2 Coordinates Nanog Expression and PCP/Wnt Signaling Required for Efficient ESC Differentiation and Early Embryo Development.\nConsistent with an essential role for PcG proteins in early development, we demonstrate that JARID2 is required for the differentiation of mouse embryonic stem cells.\nJumonij (JMJ)/Jarid2 plays important roles in embryonic development and functions as a transcriptional repressor.\nThus, these results demonstrate that JARID2 is essential for the binding of PcG proteins to target genes and, consistent with this, for the proper differentiation of embryonic stem cells and normal development.\nJARID2 is an accessory component of Polycomb repressive complex-2 (PRC2) required for the differentiation of embryonic stem cells (ESCs).\nJarid2 Coordinates Nanog Expression and PCP/Wnt Signaling Required for Efficient ESC Differentiation and Early Embryo Development.\nThese data describe a previously unrecognized role for Jarid2 in regulating a core pluripotency and Wnt/PCP signaling circuit that is important for ESC differentiation and for pre-implantation development.." }, { "role": "assistant", "content": "yes" } ]

[ { "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\nGlutathione peroxidases and redox-regulated transcription factors. Analysis of the selenoproteome identified five glutathione peroxidases (GPxs) in mammals: cytosolic GPx (cGPx, GPx1), phospholipid hydroperoxide GPx (PHGPX, GPx4), plasma GPx (pGPX, GPx3), gastrointestinal GPx (GI-GPx, GPx2) and, in humans, GPx6, which is restricted to the olfactory system. GPxs reduce hydroperoxides to the corresponding alcohols by means of glutathione (GSH). They have long been considered to only act as antioxidant enzymes. Increasing evidence, however, suggests that nature has not created redundant GPxs just to detoxify hydroperoxides. cGPx clearly acts as an antioxidant, as convincingly demonstrated in GPx1-knockout mice. PHGPx specifically interferes with NF-kappaB activation by interleukin-1, reduces leukotriene and prostanoid biosynthesis, prevents COX-2 expression, and is indispensable for sperm maturation and embryogenesis. GI-GPx, which is not exclusively expressed in the gastrointestinal system, is upregulated in colon and skin cancers and in certain cultured cancer cells. GI-GPx is a target for Nrf2, and thus is part of the adaptive response by itself, while PHGPx might prevent cancer by interfering with inflammatory pathways. In conclusion, cGPx, PHGPx and GI-GPx have distinct roles, particularly in cellular defence mechanisms. Redox sensing and redox regulation of metabolic events have become attractive paradigms to unravel the specific and in part still enigmatic roles of GPxs." }, { "role": "assistant", "content": "[[\"alcohols\", \"PRODUCT-OF\", \"GPxs\"]]" } ]

[ { "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\nPhotocatalytic and antibacterial response of biosynthesized gold nanoparticles Increase in the bacterial resistance to available antibiotics and water contamination by different toxic organic dyes are both severe problems throughout the world. To overcome these concerns, new methodologies including synthesis of nontoxic, human friendly and efficient nanoparticles is required. These nanoparticles not even inhibit the growth of microorganisms but are also effective in the degradation of toxic organics in waste water thus providing a clean and human friendly environment. The use of plants extracts to synthesize and stabilize noble metal nanoparticles have been considered as safe, cost-effective, eco-benign and green approach nowadays. In the present study, Longan fruit juice proficiently reduced ionic gold (Au(+3)) to gold nanoparticles (AuNPs) as well as mediated the stabilization of AuNPs. The antibacterial activity of AuNPs was carried out against both gram positive and gram negative bacteria using agar well diffusion method, followed by the determination of Minimum inhibitory concentration (MIC) values. AuNPs were found to have significant antibacterial activity against Escherichia coli with MIC values of 75μg/ml while outstanding MIC values of 50μg/ml against Staphylococcus areous and Basilus subtilus. AuNPs revealed significant photocatalytic degradation (76%) of methylene blue in time period of 55 min, indicating the effective photocatalytic property of biosynthesized AuNPs (K=0.29/min, r(2)=0.95). The considerable antibacterial and photocatalytic activities of the photosynthesized AuNPs can be attributed towards their small size, spherical morphology and uniform dispersion. Our finding suggests the possible therapeutic potential of biogenic AuNPs in the development of new antibacterial agents as well as in the development of effective photocatalysts." }, { "role": "assistant", "content": "{\"AnatomicalStructure\": [], \"Bacterium\": [\"gram negative bacteria\", \"Staphylococcus areous\", \"gram positive\", \"Basilus subtilus\", \"Escherichia coli\"], \"BiologicFunction\": [\"bacterial resistance\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [\"plants extracts\", \"(Au(+3))\", \"antibacterial agents\", \"methylene blue\", \"organics\", \"gold\", \"antibiotics\", \"fruit juice\", \"organic dyes\", \"ionic gold\"], \"ClinicalAttribute\": [], \"Eukaryote\": [\"Longan\", \"human\"], \"Finding\": [\"antibacterial activity\", \"found\", \"antibacterial\", \"antibacterial response\", \"problems\", \"stabilize\"], \"Food\": [], \"HealthCareActivity\": [\"MIC\", \"Minimum inhibitory concentration\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [], \"MedicalDevice\": [], \"Organization\": [], \"PopulationGroup\": [], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [\"methodologies\"], \"SpatialConcept\": [\"spherical morphology\", \"friendly environment\", \"waste water\", \"green approach\"], \"Virus\": []}" } ]

[ { "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: In this paper, we propose a matrix factorization approach for network embedding, inspired by GloVe, that\n\nKeyphrases: network embedding text\n\nDataset description:\nWinoGrande: WinoGrande is a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.\nDBLP: The DBLP is a citation network dataset. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources.\nSI-SCORE: A synthetic dataset uses for a systematic analysis across common factors of variation.\nBiwi Kinect Head Pose: Biwi Kinect Head Pose is a challenging dataset mainly inspired by the automotive setup. It is acquired with the Microsoft Kinect sensor, a structured IR light device.\n\nOptions:- A: WinoGrande B: DBLP C: SI-SCORE D: Biwi Kinect Head Pose" }, { "role": "assistant", "content": "DBLP" } ]

[ { "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: Chadox1 ncov-19 vaccine caused sars-cov-2 pneumonia in rhesus macaques\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the coronavirus disease 2019 (COVID-19) pandemic3.\nVaccines are an essential countermeasure and are urgently needed to control the pandemic4.\nHere we show that the adenovirus-vector-based vaccine ChAdOx1 nCoV-19, which encodes the spike protein of SARS-CoV-2, is immunogenic in mice and elicites a robust humoral and cell-mediated response.\nThis response was predominantly mediated by type-1 T helper cells, as demonstrated by the profiling of the IgG subclass and the expression of cytokines.\nVaccination with ChAdOx1 nCoV-19 (using either a prime-only or a prime-boost regimen) induced a balanced humoral and cellular immune response of type-1 and type-2 T helper cells in rhesus macaques.\nWe observed a significantly reduced viral load in the bronchoalveolar lavage fluid and lower respiratory tract tissue of vaccinated rhesus macaques that were challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated SARS-CoV-2-infected animals.\nHowever, there was no difference in nasal shedding between vaccinated and control SARS-CoV-2-infected macaques.\nNotably, we found no evidence of immune-enhanced disease after viral challenge in vaccinated SARS-CoV-2-infected animals.\nThe safety, immunogenicity and efficacy profiles of ChAdOx1 nCoV-19 against symptomatic PCR-positive COVID-19 disease will now be assessed in randomized controlled clinical trials in humans." }, { "role": "assistant", "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here we show that the adenovirus-vector-based vaccine ChAdOx1 nCoV-19, which encodes the spike protein of SARS-CoV-2, is immunogenic in mice and elicites a robust humoral and cell-mediated response.\", \"Vaccination with ChAdOx1 nCoV-19 (using either a prime-only or a prime-boost regimen) induced a balanced humoral and cellular immune response of type-1 and type-2 T helper cells in rhesus macaques.\"]}" } ]

[ { "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: We propose a deep learning model with top-down feedback attention regulation to improve object classification under\n\nKeyphrases: object classification occlusion images\n\nDataset description:\nMulti-PIE: The Multi-PIE (Multi Pose, Illumination, Expressions) dataset consists of face images of 337 subjects taken under different pose, illumination and expressions. The pose range contains 15 discrete views, capturing a face profile-to-profile.\nMIMIC-CXR: MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.\nPASCAL3D+: The Pascal3D+ multi-view dataset consists of images in the wild, i.e., images of object categories exhibiting high variability, captured under uncontrolled settings, in cluttered scenes and under many different poses. Pascal3D+ contains 12 categories of rigid objects selected from the PASCAL VOC 2012 dataset.\nMNIST-1D: A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks. The training examples are 20 times smaller than MNIST examples yet they differentiate more clearly between linear, nonlinear, and convolutional models which attain 32, 68, and 94% accuracy respectively (these models obtain 94, 99+, and 99+% on MNIST).\n\nOptions:- A: Multi-PIE B: MIMIC-CXR C: PASCAL3D+ D: MNIST-1D" }, { "role": "assistant", "content": "PASCAL3D+" } ]

[ { "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: Imbalance Problems in Object Detection: A Review \nChapter Title: Comparison with Previous Reviews \n1. Abstract of Cited Paper (BIB001): Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians. \n2. Abstract of Cited Paper (BIB002): We present a comprehensive and survey for face detection 'in-the-wild'.We critically describe the advances in the three main families of algorithms.We comment on the performance of the state-of-the-art in the current benchmarks.We outline future research avenues on the topic and beyond. Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called 'in-the-wild') through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection. \n3. Abstract of Cited Paper (BIB003): This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field. \n4. Abstract of Cited Paper (BIB004): The intelligent analysis of video data is currently in wide demand because a video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while the recent surveys of text detection and recognition in imagery focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking, and recognition in video with three major contributions. First, a generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions. Second, within this framework, a variety of methods, systems, and evaluation protocols of video text extraction are summarized, compared, and analyzed. Existing text tracking techniques, tracking-based detection and recognition techniques are specifically highlighted. Third, related applications, prominent challenges, and future directions for video text extraction (especially from scene videos and web videos) are also thoroughly discussed. \n5. Abstract of Cited Paper (BIB005): Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them. \n6. Abstract of Cited Paper (BIB006): Abstract Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. \n7. Abstract of Cited Paper (BIB007): Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms. \n8. Abstract of Cited Paper (BIB008): In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class. Assuming the positive (minority) class is the group of interest and the given application domain dictates that a false negative is much costlier than a false positive, a negative (majority) class prediction bias could have adverse consequences. With big data, the mitigation of class imbalance poses an even greater challenge because of the varied and complex structure of the relatively much larger datasets. This paper provides a large survey of published studies within the last 8 years, focusing on high-class imbalance (i.e., a majority-to-minority class ratio between 100:1 and 10,000:1) in big data in order to assess the state-of-the-art in addressing adverse effects due to class imbalance. In this paper, two techniques are covered which include Data-Level (e.g., data sampling) and Algorithm-Level (e.g., cost-sensitive and hybrid/ensemble) Methods. Data sampling methods are popular in addressing class imbalance, with Random Over-Sampling methods generally showing better overall results. At the Algorithm-Level, there are some outstanding performers. Yet, in the published studies, there are inconsistent and conflicting results, coupled with a limited scope in evaluated techniques, indicating the need for more comprehensive, comparative studies. \n9. Abstract of Cited Paper (BIB009): Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research. \n10. Abstract of Cited Paper (BIB010): Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years. \n11. Abstract of Cited Paper (BIB011): The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.\n\nLiterature Review:" }, { "role": "assistant", "content": "Generic object detection surveys BIB010 , BIB011 , BIB008 aim to present advances in deep learning based generic object detection. To this end, this set of surveys proposes a taxonomy for object detection methods, and provides a detailed analysis of some cornerstone methods that highly affect object detection literature. These surveys also provide discussions for popular datasets and evaluation metrics. From the imbalance point of view, these surveys only consider class imbalance problem with a limited provision. Additionally, Zou et al. BIB011 provide a review for methods that handle the scale imbalance. Unlike these surveys, we provide a classification of imbalance problems related to object detection and present a comprehensive review of methods that handle these imbalance problems. In addition to these generic object detection surveys, there are category specific object detection surveys BIB001 , BIB002 , BIB003 , BIB004 . Although Zehang Sun et al. BIB001 and Dollar et al. BIB002 cover the proposed methods before the deep learning era, they can be beneficial from the imbalance point of view since they present a comprehensive analysis of feature extraction methods that handle scale imbalance. Zafeiriou et al. BIB003 and Yin et al. BIB005 propose a comparative analysis of methods proposed before and after the deep learning era. Litjens et al. BIB007 discuss applications of different deep neural network based methods i.e. classification, detection, segmentation to medical image analysis problem. They provide challenges about medical image analysis with their possible solutions which include a limited exploration of class imbalance problem. These category specific object detector reviews focus on a single class and do not consider the imbalance problems in a comprehensive manner from the generic object detection perspective. Another set of relevant work includes the studies specifically for imbalance problems in machine learning BIB006 , BIB009 , , BIB012 . These studies are limited to the foreground class imbalance problem in our context (i.e. there is no background class). Generally, they cover dataset-level methods such as undersampling, oversampling and algorithmlevel methods including feature selection, kernel modifications and weighted approaches. We identify three main differences of such studies compared to our work. Firstly, the main scope of such work is the classification problem, which is still relevant for object detection; however, object detection also has a \"search\" aspect, in addition to the recognition aspect, which brings in the background (i.e. negative) class into the picture. Secondly, except Johnson et al. BIB012 , they consider machine learning approaches in general without any special focus on deep learning based methods. Finally, and more importantly, these works only consider foreground class imbalance problem, which is only one of 8 imbalance problems that we present and discuss (see Table 1 )." } ]

[ { "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 new back-projection super-resolution network that improves the performance of the conventional super-\n\nKeyphrases: image super-resolution images\n\nDataset description:\nBSD: BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by Martin et al..\nTextZoom: TextZoom is a super-resolution dataset that consists of paired Low Resolution – High Resolution scene text images. The images are captured by cameras with different focal length in the wild.\nSet5: The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models.\nSet14: The Set14 dataset is a dataset consisting of 14 images commonly used for testing performance of Image Super-Resolution models.\n\nOptions:- A: BSD B: TextZoom C: Set5 D: Set14" }, { "role": "assistant", "content": "Set5" } ]

[ { "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\nSuch positive imagery training has been shown to increase state positive affect in healthy young adult volunteers (Holmes et al., 2008a, 2009, 2006; Nelis et al., 2013) and dysphoric individuals (Pictet et al., 2011), and in some studies decrease symptoms of depression (Lang et al., 2012; Williams…" }, { "role": "assistant", "content": "Background" } ]

[ { "role": "user", "content": "You will be presented with the full text of a biomedical research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.\n\nPaper text:\nanophthalmic orbits following exenteration due to invasive fungal infections and invasive carcinoma cause significant psychosocial disability to the patient making an adequate and esthetically acceptable reconstructive procedure , a mandatory option .\nsuch conditions often lead to partial or total orbital loss causing significant disfigurement of the face .\nthe anophthalmic socket has been associated with many complications such as infection leading to continuous discharge or bleeding , exposure or extrusion of the orbital implant , and contracted socket .\nmeyer and zaoli 's classified four types of orbital exenteration depending on the extent of destruction involved in the surgery : \n type i : palpebral skin and conjunctiva are sparedtype ii : only the palpebral skin is spared , and the eyeball and its appendages are removed with the conjunctivatype iii : both eyelids are removed with orbital contentstype iv : the eyeball , eyelids , and appendages of the eye are removed with the involved bone structures . \n \ntype i : palpebral skin and conjunctiva are spared type ii : only the palpebral skin is spared , and the eyeball and its appendages are removed with the conjunctiva type iii : both eyelids are removed with orbital contents type iv : the eyeball , eyelids , and appendages of the eye are removed with the involved bone structures .\nonce the prosthesis is placed in an inadequately reconstructed orbit , it can get misplaced , extruded , or desired results are not obtained . moreover ,\nthe loss of the lid structures , periorbita , appendages , and sometimes the surrounding skin makes placement of an intraocular prosthesis impossible . in such cases ,\nobliteration of the orbital cavity with a robust flap and the use of external ocular prosthesis is the desired goal .\nthere was complete loss of the orbital contents and medial orbital wall along with the upper and lower eyelids with a naso - orbital fistula leading to escape of inspired and expired air through the orbital defect .\nthe exposed orbital cavity had unstable lining with history of recurrent bleed from the orbital surface .\nthe patient also gave history of recurrent upper respiratory tract infections [ figure 1 ] . on evaluation , loss of both eyelids with orbital contents and lining with a large communication with the nasal cavity was observed .\nthe patient was investigated in the form of routine blood investigations along with three - dimensional computed tomography scan of the face [ figure 2 ] .\nthe aim of the reconstruction was to release the socket contracture , give stable lining to the orbital wall , close the naso - orbital fistula , and provide supple matching soft tissue cover for anophthalmic orbit amenable for external prosthetic reconstruction .\npostexenteration large orbito naso cutaneous fistula computed tomography scan showing the defect since the patient had a naso - orbital fistula , this condition was a challenge for the anesthesiologist as induction was not possible with routine bag and mask ventilation due to the escape of inhalational agents through the fistula .\nthus , obliteration of orbital route was first accomplished with dry pad packing , and it was only then the patient could be put effectively on inhalational agents [ figure 3 ] .\nsocket contracture was released , unstable lining was excised , and the orbital defect was measured .\nthe dimensions of the defect were 5.2 cm in its depth , medial to lateral wall measured 6.4 cm , and distance between supraorbital ridge and infraorbital rim measured 3.5 cm [ figure 4 ] .\nradial artery forearm free flap ( raff ) was chosen as the preferred method of reconstruction .\nthe flap was planned and outlined on the left forearm after performing allen 's test [ figure 5 ] .\nthe central skin paddle was used to create the matching eye patch which would be subsequently used for eyelid reconstruction by an external prosthesis and the peripheral adipofascial component used for filling of the orbital defect . under tourniquet control ,\na curvilinear incision starting from the inner boundary ( central skin paddle ) of the flap was given along its axis to reach the antecubital fossa .\nthe skin incision for the flap was limited to the dimensions of the central skin paddle .\nthe skin was elevated off the peripheral component of the flap till its outer dimensions where it was deepened to incise the deep fascia .\na tunnel of adequate size was created superficial to the facial muscles for the pedicle to be anastomosed with facial artery and vein in the neck .\nadipofascial component was placed into the defect , and the central paddle was sutured with the skin margins .\ndifficult intubation after temporary patching of the fistula final defect after excision of unstable lining adipofascial flap raised on the forearm with central cutaneous paddle the patient has been followed for 6 months with complete fistula correction and well - settled flap [ figure 6 ] .\nfurther reconstructive procedures such as eyelid reconstruction and placement of orbital prosthesis are yet to be performed .\nthe patient apart from having a cosmetic deformity had persistent naso - orbital fistula leading to difficulty in respiration and repeated upper respiratory tract infections .\na successfully rehabilitated anophthalmic socket must hold and support a prosthetic device that mimics the contralateral globe or else should have a stable cover to accommodate an external prosthesis .\nstatic symmetry of the palpebral apertures , canthal angles , and superior sulcus is basic objectives .\nmovement of the socket implant and prosthesis with upper eyelid excursion is definitely desirable but impossible to attain in our case .\nvariety of flaps that had been used in the past had one or the other drawback , and history suggests that there is no ideal free flap which can be used to reconstruct the anophthalmic eye .\ntemporalis fascia has been commonly used for reconstruction of the anophthalmic socket but was avoided in our case because of its inability to provide bulk of tissue as requires in this patient , and it leads to temporal hollowness and scar alopecia making the donor site esthetically unacceptable to the patient .\ngracilis free flap not only does contour well into the defect but also has limitations of its pedicle length , an unreliable skin paddle if the required dimensions are quite small , and the use of graft with the muscle make it less cosmetically pleasing to the patient .\nlatissimus dorsi or rectus muscle free flaps can be used but are quite bulky and more often required when defects require more complex reconstructions .\ndorsalis pedis flap provides thin and supple soft tissue cover as raff , but it lacks good adipofascial component in thin patients and can lead to significant donor site morbidity if the flap is harvested according to the required dimensions without skin sparing .\nanterolateral thigh flap provides tissue cover for defects ranging from small to large with minimum morbidity but was avoided here because of dense coarse hair on the donor site and the comparatively bulky thighs in this patient which could have lead to further stages of debulking procedures before implant placement .\nadvantages of radial forearm flap lie in its thin , pliable , relatively hairless skin , efficient soft tissue , and flexible pedicle length which offer a suitable contour restoration to the anophthalmic orbit .\ncompared to lateral arm flap , forearm flaps are relatively thinner , softer , and easier to fold .\nthe pedicle was anastomosed with the facial artery and vein as it gave us an advantage that in case of flap failure , we can easily use regional temporalis fascial flap based on superficial temporal artery as an option .\nthe patient had desired successful functional and clinical outcome for the treatment of severe anophthalmic orbit as well as satisfactory naso - orbital fistula obliteration with no recurrence 6 months postoperatively , using microvascular radial forearm free flaps .\npostoperatively , we have adequate soft tissue for contoured eyelid reconstruction as well as for sufficiently deep conjunctival fornices which are necessary to keep the prosthesis in place .\nthe patient refused any further surgery for placement of intraocular prosthesis and settled for an external prosthesis in form of spectacles .\namong these spectrums of options for reconstruction , we chose adipofascial raff because the patients main concern was firstly to have an appropriate skin cover over the socket to match the face and secondly obliteration of the oro - naso - cutaneous fistula .\nthis required a flap which could not only fill the orbital cavity defect with adipofascial tissue but also provide stable skin cover along with minimal donor site morbidity .\nthe authors certify that they have obtained all appropriate patient consent forms . in the form the patient(s )\nhas / have given his / her / their consent for his / her / their images and other clinical information to be reported in the journal .\nthe patients understand that their names and initials will not be published and due efforts will be made to conceal their identity , but anonymity can not be guaranteed .\nthe authors certify that they have obtained all appropriate patient consent forms . in the form the patient(s )\nhas / have given his / her / their consent for his / her / their images and other clinical information to be reported in the journal .\nthe patients understand that their names and initials will not be published and due efforts will be made to conceal their identity , but anonymity can not be guaranteed ." }, { "role": "assistant", "content": "invasive mucormycosis is an uncommon cause of orbital exenteration . \n reconstruction of an exenterated orbit is a surgical challenge . \n the loss of eyelids , adnexal structures , and even surrounding skin causes significant facial disfigurement . \n the goal for reconstruction demands a symmetrical orbital cavity with good prosthetic rehabilitation . \n multiple reconstructive options in the form of skin grafts , local flaps , and free flaps are available . however \n , none of them provide ideal reconstruction . \n our patient not only had extensive soft - tissue loss and unstable lining but also a large naso - orbital fistula . \n reconstruction for this complex defect was done using an adipofascial radial artery flap which not only closed the fistula but also provided soft - tissue bulk and good skin match . \n radial artery forearm flap provides a simple , stable , and good reconstructive option postorbital exenteration ." } ]

[ { "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\nRecent image style transferring methods achieved arbitrary stylization with input content and style images.\nTo transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.\nHowever, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks.\nThis shortcoming resulted in generating partially and inexactly transferred style in the generated images.\n\nTo overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process.\nFirst, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps.\nSecond, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection.\nFinally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.\nRecent image style transferring methodsJohnson et al. (2016) ; BID18 improved image generating speed up to sub-realtime processing by learning a feed-forward network of a single style or several fixed stylesDumoulin et al. (2017) .\nHuang et al.Huang & Belongie (2017) proposed an adaptive instance normalization layer (AdaIN) that adaptively transforms the statistics of an encoded content feature into that of a target style feature and they achieved style transferring into arbitrary input target style.\nHowever, they did not consider multi-scaled style characteristics of an imageGatys et al. (2016) but only a single scale feature in differentiating styles inside AdaIN layer.\nLi et al.Li et al. (2017b) proposed to use cascade networks that cumulatively transfer the multi-scaled style characteristics by using a network per scale as shown in FIG0\n(a).\nThey also transformed correlation between channels of feature map by using their whitening and coloring transformer (WCT).\nHowever, their cascade scheme requires multiple feed-forward passes to produce a stylized image and it is not guaranteed that the transferred style through a network is preserved after going through the subsequent networks because of inter-scale dependency in the multi-scaled styles of an image.\nTherefore, transferring multi-scaled style without interference between scales is still remained to study.In this paper, we propose an improved feed-forward network structure ( FIG0 ) and a multi-scaled style transferring method, called total style transfer, to efficiently perform style transfer in all scales of feature maps through a single feed-forward pass.\nOur work has the following contributions.•\nTransforming both intra-scale and inter-scale statistics of multi-scaled feature map: There exist both of inter and intra-correlations in the encoded multi-scaled feature map as shown in fig.2 (\nb).\nTherefore, we match the second-order statistics, i.e., mean and covariance, of the encoded multi-scaled feature map considering the correlations not only between channels in each scale (intra-scale correlation) but also between scales (inter-scale correlation).\nOur feature transformer makes the transformed feature map closer to the target style feature map and this results in an output style closer to the target style.\nFigure 2: Correlation between channels in the multi-scaled feature map of the input image\n(a) extracted from the pre-trained VGG16 BID16 .\nThe area corresponding to each scale of feature map is divided into red lines.\nIn case of intra-scale feature transform, the diagonal rectangles on the correlation matrix are used.\nIn case of inter-scale feature transform, entire region of the correlation matrix is considered.•\nDecoder learning with multi-scaled style loss: we use a multi-scaled style loss consistent to the feature transformer, i.e., mean and covariance loss between the concatenated feature map ( FIG1 ). Using\nour multi-scaled style loss allows the decoder network to generate an output image of co-occurring multi-scale patterns which is better style expression than independently occurring scale patterns on the image that the existing methods generated.• Multi-scaled\nstyle transfer with a single feed-forward network: we use skip-connections for each decoder layer as shown in FIG0 (b) to consider\nthe transformed feature map as well as the decoded feature map. By doing this,\nthe style of scale corresponding to the layer and the transferred multi-scaled style so far are optimally merged into the next layer. Therefore, our\nmethod transfers multi-scaled style through a feed-forward pass in a single network instead of multiple feed-forward passes of cascade networks ( FIG0 ) without considering inter-scale correlation.In the remained of this paper, we review previous works closely related to this work in Sec. 2, our multi-scaled style transforming method is described in Sec. 3, the effectiveness of our method is tested and proven by a bundle of experiments in Sec. 4, and this work is concluded in Sec. 5.\nIn this paper, we proposed a total style transfer network that generates an image through a single feed-forward network by utilizing multi-scale features of content and style images.\nOur intra-scale feature transformer transfers multi-scale style characteristics of the target style image and our inter-scale feature transformer transfers even more style characteristics of inter-scale correlation into the content image.\nBy using our intra/inter scale feature transform, our total style transfer network achieved the lowest style loss among the existing feed-forward network methods.\nIn addition, we modified the feed-forward network structure by using skip-connections which make our decoder network to utilize all transformed multi-scale features.\nThis modification allowed a single feed-forward network to generate image of multi-scaled style without using multiple feedforward networks of cascade scheme, and resulted in the reduced test time by 31% and memory consumption by 4% compared to cascade network scheme." }, { "role": "assistant", "content": "A paper suggesting a method to transform the style of images using deep neural networks." } ]

[ { "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 is no approved treatment for covid-19\n\nAbstract:\nSARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.\nWith no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.\nTo date, close to half a million patients succumbed to the viral disease, COVID-19.\nThe high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.\nWe used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.\nUpon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.\nTreatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.\nMicro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.\nIn addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.\nBy modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.\nIn conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.\nThe information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.\nAt this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans." }, { "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: [\"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\nThe Origin of Selectivity in the Complexation of N-Methyl Amino Acids by Tetraphosphonate Cavitands We report on the eligibility of tetraphosphonate resorcinarene cavitands for the molecular recognition of amino acids. We determined the crystal structure of 13 complexes of the tetraphosphonate cavitand Tiiii[H, CH3, CH3] with amino acids. (1) H NMR and (31) P NMR experiments and ITC analysis were performed to probe the binding between cavitand Tiiii[C3H7, CH3, C2H5] or the water - soluble counterpart Tiiii[C3H6Py(+)Cl(-), CH3, C2H5] and a selection of representative amino acids. The reported studies and results allowed us (i) to highlight the noncovalent interactions involved in the binding event in each case; (ii) to investigate the ability of tetraphosphonate cavitand receptors to discriminate between the different amino acids; (iii) to calculate the Ka values of the different complexes formed and evaluate the thermodynamic parameters of the complexation process, dissecting the entropic and enthalpic contributions; and (iv) to determine the solvent influence on the complexation selectivity. By moving from methanol to water, the complexation changed from entropy driven to entropy opposed, leading to a drop of almost three orders in the magnitude of the Ka. However, this reduction in binding affinity is associated with a dramatic increase in selectivity, since in aqueous solutions only N-methylated amino acids are effectively recognized. The thermodynamic profile of the binding does not change in PBS solution. The pivotal role played by cation-π interactions is demonstrated by the linear correlation found between the log Ka in methanol solution and the depth of (+)N-CH3 cavity inclusion in the molecular structures. These findings are relevant for the potential use of phosphonate cavitands as synthetic receptors for the detection of epigenetic modifications of histones in physiological media." }, { "role": "assistant", "content": "{\"AnatomicalStructure\": [\"cavity\"], \"Bacterium\": [], \"BiologicFunction\": [\"binding\", \"epigenetic\", \"moving\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [\"tetraphosphonate resorcinarene cavitands\", \"N-methylated amino acids\", \"N-Methyl Amino Acids\", \"tetraphosphonate cavitand\", \"histones\", \"water\", \"cavitand\", \"solvent\", \"P\", \"phosphonate cavitands\", \"crystal structure\", \"Tiiii[C3H7, CH3, C2H5]\", \"H\", \"(+)N-CH3\", \"receptors\", \"amino acids\", \"Tetraphosphonate Cavitands\", \"Tiiii[C3H6Py(+)Cl(-), CH3, C2H5]\", \"methanol\", \"Tiiii[H, CH3, CH3]\"], \"ClinicalAttribute\": [], \"Eukaryote\": [], \"Finding\": [\"findings\", \"modifications\"], \"Food\": [], \"HealthCareActivity\": [\"probe\", \"evaluate\", \"determine\", \"profile\", \"detection\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [\"report\", \"reported\"], \"MedicalDevice\": [], \"Organization\": [], \"PopulationGroup\": [], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [\"analysis\", \"studies\", \"experiments\"], \"SpatialConcept\": [\"linear\", \"molecular structures\", \"counterpart\", \"depth\", \"between\"], \"Virus\": []}" } ]

[ { "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: Effect of nitrous oxide inhalation on pain after propofol and rocuronium injection. This prospective, double-blind, placebo-controlled study was designed to determine the efficacy of nitrous oxide (N(2)O) in alleviating the pain that followed sequential injection of propofol and rocuronium. ### methods A total of 205 adult patients (age, 18-68 years) received one of the following combinations: NaCl and 100 % O(2) (group C); 0.5 mg/kg lidocaine and 100 % O(2) (group L); NaCl and a mixture of 67 % N(2)O/O(2) (group N); or 0.5 mg/kg lidocaine and a mixture of 67 % N(2)O/O(2) (group LN). Vein occlusion was released after 1 min, and 5 ml propofol was injected over 10 s. Pain was evaluated on a visually enlarged, laminated, numeric rating (0-10) scale. The remainder of the induction dose of propofol (with a 3-ml bolus of normal saline and 0.6 mg/kg rocuronium) was then injected. The response to the rocuronium injection was assessed with a four-point scale (0-3). ### results The incidence and severity of pain from the propofol injection in groups L, N, and LN were significantly lower than those in group C (P < 0.001). Frequency and intensity of the withdrawal response were significantly less in groups N and LN than in groups C and L (no response, P < 0.001; severe response, P < 0.001). ### conclusions Pretreatment with inhaled N(2)O can reduce the pain associated with propofol and rocuronium injection . Moreover, N(2)O (with or without lidocaine) is more effective than lidocaine alone in reducing rocuronium-related withdrawal reactions associated with sequential injection of propofol and rocuronium.\n\nsentence: Pretreatment with inhaled N(2)O can reduce the pain associated with propofol and rocuronium injection ." }, { "role": "assistant", "content": "[]" } ]

[ { "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\nTo communicate with new partners in new contexts, humans rapidly form new linguistic conventions.\nRecent language models trained with deep neural networks are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do.\nWe introduce a repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently understand their partner over time.\nWe evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.\nLinguistic communication depends critically on shared knowledge about the meanings of words BID9 .\nHowever, the real-world demands of communication often require speakers and listeners to go beyond dictionary meanings to understand one another BID0 BID15 .\nThe social world continually presents new communicative challenges, and agents must continually coordinate on new meanings to meet them.For example, consider a nurse visiting a bed-ridden patient in a cluttered home.\nThe first time they ask the nurse to retrieve a particular medication, the patient must painstakingly refer to unfamiliar pills, e.g. \"the vasoprex-tecnoblek meds for my blood pressure, in a small bluish bottle, on the bookcase in my bathroom.\"\nAfter a week of care, however, they may just ask for their \"Vasotec.\"This type of flexible language use poses a challenge for models of language in machine learning.\nApproaches based on deep neural networks typically learn a monolithic meaning function during training, with fixed weights during use.\nFor an in-home robot to communicate as flexibly and efficiently with patients as a human nurse, it must be equipped with a continual learning mechanism.\nSuch a mechanism would present two specific advantages for interaction and communication applications.\nFirst, to the extent that current models have difficulty communicating in a new setting, an adaptive approach can quickly improve performance on the relevant subset of language.\nSecond, for human-robot contexts, an adaptive model enables speakers to communicate more efficiently as they build up common ground, remaining understandable while expending significantly fewer words as humans naturally do BID1 .In\nthis paper, we introduce a benchmark communication task and general continual learning framework for transforming neural language models into adaptive models that can be deployed in real-time interactions with other agents.Our key insight is that through continual interactions with the same partner in a shared context, an adaptive listener can more effectively communicate with its partner FIG0 .We\nare motivated by hierarchical Bayesian approaches to task-specific adaptation. Our\napproach integrates two core components: (i)\na loss function combining speaker and listener information, and (ii\n) a regularization scheme for fine-tuning model weights without overfitting.\nHuman language use is flexible, continuously adapting to the needs of the current situation.\nIn this paper, we introduced a challenging repeated reference game benchmark for artificial agents, which requires such adaptability to succeed.\nWe proposed a continual learning approach that forms context-specific conventions by adapting general-purpose semantic knowledge.\nEven when models based on generalpurpose knowledge perform poorly, our approach allows human speakers working with adapted variants of such models to become more accurate and more efficient over time." }, { "role": "assistant", "content": "We propose a repeated reference benchmark task and a regularized continual learning approach for adaptive communication with humans in unfamiliar domains" } ]