Datasets:

kejian

/

sciriff-tulu-none-2000-template-1-summratio-0.85

like 0

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nEffects of garlic coated tablets in peripheral arterial occlusive disease.\nAbstract 1:\nFor the first time, a weak clinical efficacy of a 12-week therapy with garlic powder (daily dose, 800 mg) is demonstrated in patients with peripheral arterial occlusive disease stage II. The increase in walking distance in the verum group by 46 m (from 161.0 +/- 65.1 to 207.1 +/- 85.0 m) was significantly higher (P < 0.05) than in the placebo group (by 31 m, from 172.0 +/- 60.9 to 203.1 +/- 72.8). Both groups received physical therapy twice a week. The diastolic blood pressure, spontaneous thrombocyte aggregation, plasma viscosity, and cholesterol concentration also decreased significantly. Body weight was maintained. It is quite interesting that the garlic-specific increase in walking distance did not appear to occur until the 5th week of treatment, connected with a simultaneous decrease in spontaneous thrombocyte aggregation. Therefore, garlic may be an appropriate agent especially for the long-term treatment of an incipient intermittent claudication.", "role": "user" }, { "content": "One small trial of short duration found no statistically significant effect of garlic on walking distance.", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nThe increase in plasma nitrite after a dietary nitrate load is markedly attenuated by an antibacterial mouthwash.\nAbstract 1:\nRecent studies surprisingly show that dietary inorganic nitrate , abundant in vegetables , can be metabolized in vivo to form nitrite and then bioactive nitric oxide . A reduction in blood pressure was recently noted in healthy volunteers after dietary supplementation with nitrate ; an effect consistent with formation of vasodilatory nitric oxide . Oral bacteria have been suggested to play a role in bioactivation of nitrate by first reducing it to the more reactive anion nitrite . In a cross-over design ed study in seven healthy volunteers we examined the effects of a commercially available chlorhexidine-containing antibacterial mouthwash on salivary and plasma levels of nitrite measured after an oral intake of sodium nitrate ( 10mg/kg dissolved in water ) . In the control situation the salivary and plasma levels of nitrate and nitrite increased greatly after the nitrate load . Rinsing the mouth with the antibacterial mouthwash prior to the nitrate load had no effect on nitrate accumulation in saliva or plasma but abolished its conversion to nitrite in saliva and markedly attenuated the rise in plasma nitrite . We conclude that the acute increase in plasma nitrite seen after a nitrate load is critically dependent on nitrate reduction in the oral cavity by commensal bacteria . The removal of these bacteria with an antibacterial mouthwash will very likely attenuate the NO-dependent biological effects of dietary nitrate\n\n\nTitle 2:\nA stepwise reduction in plasma and salivary nitrite with increasing strengths of mouthwash following a dietary nitrate load.\nAbstract 2:\nNitric Oxide ( NO ) bioavailability is essential for vascular health . Dietary supplementation with inorganic nitrate , which is abundant in vegetables and roots , has been identified as an effective means of increasing vascular NO bioavailability . Recent studies have shown a reduction in resting blood pressures in both normotensive and hypertensive subjects following ingestion of inorganic nitrate . Oral bacteria play a key role in this process and the use of strong antibacterial mouthwash rinses can disable this mechanism . Hence , mouthwash usage , a $ 1.4 billion market in the US , may potentially be detrimental to cardiovascular health . The purpose of this study was to examine the effects of different strengths of commercially available mouthwash products on salivary and plasma nitrate and nitrite concentrations following 8.4 mmol inorganic nitrate load ( beetroot juice ) . Specifically , we examined the effects of Listerine antiseptic mouthwash , Cepacol antibacterial mouthwash , and Chlorhexidine mouthwash versus control ( water ) . Twelve apparently healthy normotensive males ( 36 ± 11 yrs ) completed four testing visits in a r and omized order , separated by one week . Testing consisted of blood pressure ( BP ) , and saliva and venous blood collection at baseline and each hour for 4 h. Following baseline-testing participants consumed 140 ml of beet juice and then 15 min later gargled with 5 mL of assigned mouthwash . Testing and mouthwash rinse was repeated every hour for 4 h. Linear mixed effects models , followed by pairwise comparisons where appropriate , were used to determine the influence of treatment and time on plasma and saliva nitrate and nitrite , and BP . Plasma and salivary nitrate increased above baseline ( time effect ) for all conditions ( p ≤ 0.01 ) . There were time ( p ≤ 0.01 ) , treatment ( p ≤ 0.01 ) , and interaction ( p ≤ 0.05 ) effects for plasma and salivary nitrite . There was a treatment effect on systolic BP ( p ≤ 0.05 ) . Further examination revealed a differentiation of plasma and salivary nitrite concentration between control/antiseptic and antibacterial/chlorhexidine treatments . When examined in this manner there was a reduction in both SBP ( p ≤ 0.01 ) and mean arterial BP ( p ≤ 0.05 ) from the antibacterial/chlorhexidine treatments . These results suggest a potentially differentiating effect of different commercially available mouthwash solutions on plasma and salivary nitrite concentrations and resting blood pressure responses . This raises potential public health related questions on the appropriate widespread usage of different mouthwash formulations\n\n\nTitle 3:\nPhysiological role for nitrate-reducing oral bacteria in blood pressure control\nAbstract 3:\nCirculating nitrate ( NO3− ) , derived from dietary sources or endogenous nitric oxide production , is extracted from blood by the salivary gl and s , accumulates in saliva , and is then reduced to nitrite ( NO2− ) by the oral microflora . This process has historically been viewed as harmful , because nitrite can promote formation of potentially carcinogenic N-nitrosamines . More recent research , however , suggests that nitrite can also serve as a precursor for systemic generation of vasodilatory nitric oxide , and exogenous administration of nitrate reduces blood pressure in humans . However , whether oral nitrate-reducing bacteria participate in “ setting ” blood pressure is unknown . We investigated whether suppression of the oral microflora affects systemic nitrite levels and hence blood pressure in healthy individuals . We measured blood pressure ( clinic , home , and 24-h ambulatory ) in 19 healthy volunteers during an initial 7-day control period followed by a 7-day treatment period with a chlorhexidine-based antiseptic mouthwash . Oral nitrate-reducing capacity and nitrite levels were measured after each study period . Antiseptic mouthwash treatment reduced oral nitrite production by 90 % ( p < 0.001 ) and plasma nitrite levels by 25 % ( p = 0.001 ) compared to the control period . Systolic and diastolic blood pressure increased by 2–3 .5 mm Hg , increases correlated to a decrease in circulating nitrite concentrations ( r2 = 0.56 , p = 0.002 ) . The blood pressure effect appeared within 1 day of disruption of the oral microflora and was sustained during the 7-day mouthwash intervention . These results suggest that the recycling of endogenous nitrate by oral bacteria plays an important role in determination of plasma nitrite levels and thereby in the physiological control of blood pressure\n\n\nTitle 4:\nAntibacterial mouthwash blunts oral nitrate reduction and increases blood pressure in treated hypertensive men and women.\nAbstract 4:\nBACKGROUND Endothelial nitric oxide ( NO ) is fundamental to cardiovascular health . Dietary nitrate and nitrate from endothelial derived NO metabolism provides a significant contribution to the circulating NO pool through the nitrate-nitrite-NO pathway . A critical step in this pathway is the reduction of nitrate to nitrite by the oral microbiota . We aim ed to assess the effects of antibacterial mouthwash use on markers of nitrate-nitrite-NO metabolism and blood pressure in treated hypertensive men and women . METHODS Fifteen treated hypertensive men and women ( mean age 65 years ) were recruited to a r and omized controlled cross-over trial . The effects of 3-day use of antibacterial mouthwash on oral nitrate to nitrite reduction , salivary and plasma nitrate and nitrite , plasma cyclic guanosine monophosphate ( cGMP ) and systolic and diastolic blood pressure were compared to control ( water ) . RESULTS Relative to control , 3-day antibacterial mouthwash use result ed in decreased oral nitrate to nitrite reduction ( P = 0.02 ) , decreased salivary nitrite ( P = 0.01 ) and increased salivary nitrate ( P < 0.001 ) , and there was a trend toward a decrease in plasma nitrite concentration ( P = 0.09 ) . Use of antibacterial mouthwash over 3 days also result ed in higher systolic blood pressure ( 2.3 mm Hg ; 95 % CI : 0.5 , 4.0 ; P = 0.01 ) , but not diastolic blood pressure ( P = 0.4 ) or plasma cGMP ( P = 0.7 ) , relative to control . CONCLUSIONS Interruption of the nitrate-nitrite-NO pathway through the use of antibacterial mouthwash was paralleled by a small elevation of systolic blood pressure in treated hypertensive men and women\n\n\nTitle 5:\nDietary nitrate improves vascular function in patients with hypercholesterolemia: a randomized, double-blind, placebo-controlled study123\nAbstract 5:\nBackground : The beneficial cardiovascular effects of vegetables may be underpinned by their high inorganic nitrate content . Objective : We sought to examine the effects of a 6-wk once-daily intake of dietary nitrate ( nitrate-rich beetroot juice ) compared with placebo intake ( nitrate-depleted beetroot juice ) on vascular and platelet function in untreated hypercholesterolemics . Design : A total of 69 subjects were recruited in this r and omized , double-blind , placebo-controlled parallel study . The primary endpoint was the change in vascular function determined with the use of ultrasound flow-mediated dilatation ( FMD ) . Results : Baseline characteristics were similar between the groups , with primary outcome data available for 67 patients . Dietary nitrate result ed in an absolute increase in the FMD response of 1.1 % ( an ∼24 % improvement from baseline ) with a worsening of 0.3 % in the placebo group ( P < 0.001 ) . A small improvement in the aortic pulse wave velocity ( i.e. , a decrease of 0.22 m/s ; 95 % CI : −0.4 , −0.3 m/s ) was evident in the nitrate group , showing a trend ( P = 0.06 ) to improvement in comparison with the placebo group . Dietary nitrate also caused a small but significant reduction ( 7.6 % ) in platelet-monocyte aggregates compared with an increase of 10.1 % in the placebo group ( P = 0.004 ) , with statistically significant reductions in stimulated ( ex vivo ) P-selectin expression compared with the placebo group ( P < 0.05 ) but no significant changes in unstimulated expression . No adverse effects of dietary nitrate were detected . The composition of the salivary microbiome was altered after the nitrate treatment but not after the placebo treatment ( P < 0.01 ) . The proportions of 78 bacterial taxa were different after the nitrate treatment ; of those taxa present , 2 taxa were responsible for > 1 % of this change , with the proportions of Rothia mucilaginosa trending to increase and Neisseria flavescens ( P < 0.01 ) increased after nitrate treatment relative to after placebo treatment . Conclusions : Sustained dietary nitrate ingestion improves vascular function in hypercholesterolemic patients . These changes are associated with alterations in the oral microbiome and , in particular , nitrate-reducing genera . Our findings provide additional support for the assessment of the potential of dietary nitrate as a preventative strategy against atherogenesis in larger cohorts . This trial was registered at clinical trials.gov as NCT01493752\n\n\nTitle 6:\nSex differences in the nitrate‐nitrite‐NO• pathway: Role of oral nitrate‐reducing bacteria\nAbstract 6:\nAbstract Oral reduction of nitrate to nitrite is dependent on the oral microbiome and is the first step of an alternative mammalian pathway to produce nitric oxide in humans . Preliminary evidence suggests important sex differences in this pathway . We prospect ively investigated sex‐differences following inorganic nitrate supplementation on nitrate/nitrite levels and vascular function , and separately examined sex differences in oral nitrate reduction , and oral microbiota by 16S rRNA profiling . At baseline , females exhibit higher nitrite levels in all biological matrices despite similar nitrate levels to males . Following inorganic nitrate supplementation , plasma nitrite was increased to a significantly greater extent in females than in males and pulse wave velocity was only reduced in females . Females exhibited higher oral bacterial nitrate‐reducing activity at baseline and after nitrate supplementation . Despite these differences , there were no differences in the composition of either the total salivary microbiota or those oral taxa with nitrate reductase genes . Our results demonstrate that females have augmented oral nitrate reduction that contributes to higher nitrite levels at baseline and also after inorganic nitrate supplementation , however this was not associated with differences in microbial composition ( clinical trials.gov : NCT01583803 ) . Graphical abstract Figure . No caption available . HighlightsSex differences are apparent in nitrate‐nitrite‐nitric oxide pathway under basal conditions . Females have higher nitrite levels at baseline and after nitrate supplementation . Nitrate reduction to nitrite is dependent on oral microbiota . Females have greater oral nitrate reduction capability . This difference is not due to differences in oral microbiome community composition", "role": "user" }, { "content": "Conclusions : Although , the role of oral bacteria has been unequivocally demonstrated in the regulation of salivary NO 3 - metabolism , their influence on plasma concentration of NO species remains ambiguous .", "role": "assistant" } ]

[ { "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 response other than the answer.\n\nParagraph: Among the various multiplex NATs, multilocus polymerase chain reaction coupled with electrospray ionization mass spectrometry (PCR/ESI-MS) can simultaneously identify and subtype multiple respiratory viruses. [6] [7] [8] [9] Despite the diagnostic potential, the ability of PCR/ESI-MS to detect human enterovirus and rhinovirus in respiratory samples from patients with RTIs has not been well evaluated. Previous PCR/ESI-MS studies in patients with RTIs did not include these 2 viruses in the diagnostic panels. [6] [7] [8] [9] Here, we expanded upon these previous studies utilizing PCR/ESI-MS for respiratory virus detection. We aimed to comprehensively investigate the epidemiology of adult viral RTIs using PCR/ESI-MS and compare the diagnostic performance between PCR/ESI-MS and conventional culture methods for identifying multiple, clinically relevant, respiratory viruses, including enterovirus and rhinovirus. To conduct a comprehensive epidemiologic study that included patients with and without comorbidity, we enrolled adults (of at least 18 yr of age) with acute RTIs within 7 days of onset who were treated at a local outpatient clinic of YC hospital or the outpatient or emergency departments of National Cheng-Kung University Hospital (NCKUH), a university-affiliated medical center in southern Taiwan, between October 2012 and June 2013. Acute RTI was defined as the simultaneous occurrence of at least 1 respiratory symptom or sign (new or worsening cough, sputum production, sore throat, nasal congestion, rhinorrhea, dyspnea, wheezing, or injected tonsils) and at least 1 of the following symptoms: fever, chills, and cough. Lower RTI (LRTI) was defined as the presence of acute RTI and a new infiltrate on chest radiograph. For patients experiencing more than 1 episode of RTI, the most recent episode was counted as separate only if the patient fully recovered from the previous episode and there was a least a 3-week interval between the onset of the 2 episodes. Clinical, laboratory, and radiological data and the contact history of each patient were retrieved. Comorbidities were assessed in all patients based on the Charlson comorbidity index (CCI). 10 Steroid use was defined as the receipt of corticosteroid treatment (10 mg prednisolone or an equivalent daily dosage) for more than 2 weeks. An immunocompromised state was diagnosed if the patients met one of the following conditions: corticosteroid treatment, solid organ or hematopoietic stem cell recipient, or chemotherapy for an underlying malignancy during the past 6 months. Nasopharyngeal or throat swabs were obtained from all patients and collected in transport medium, as previously described. 11 for virus detection and identification by both virus isolation and PCR/ESI-MS. Clinical specimens were stored at 48C and transported to the study sites within 24 hours of collection. Throat swabs from 42 cases without respiratory infections during the month prior to enrollment were included as control samples for PCR/ESI-MS analysis, including 15 patients with exclusively bacterial infections (documented cases of bacteremia or urinary tract infection) who were admitted to NCKUH and 27 individuals without active infections. These subjects without active infections included 10 patients with stable chronic diseases followed up in NCKUH clinics and 17 healthy individuals whose medical information was collected using a clinical questionnaire. The study was approved by the Institutional Review Board (B-ER-101-031) of the study hospital, and all patients provided informed consent. Respiratory specimens were inoculated onto appropriate tissue cultures (Madin-Darby canine kidney, MRC-5, A549, and rhabdomyosarcoma) to isolate human influenza virus, parainfluenza virus, genus Enterovirus, cytomegalovirus (CMV), adenovirus, respiratory syncytial virus (RSV), herpes simplex viruses 1 and 2 (HSV-1 and -2), and varicella zoster virus (VZV). The isolation and identification of viruses were performed using a previously described method 11 and enteroviruses were identified by a immunofluorescence assay using a Chemicon Pan EV mix that cross-reacts with rhinovirus (Light Diagnostics, Chemicon [Millipore], MA). 11, 12 Virus Detection and Identification by PCR/ESI-MS Total nucleic acids were extracted from 700 mL of swab samples using a nucleic acid autoextractor (MagNA Pure Compact Instrument, Mannheim, Germany), and the eluate was stored at À808C until analysis. During the analyses, the extracted nucleic acids were added to both a PLEX-ID Respiratory Virus assay plate and a PLEX-ID Broad Viral I assay plate (PLEX-ID, Abbott Laboratories, Abbott Park, Illinois). The PLEX-ID Respiratory Virus assay detects human adenovirus, hCoV, hMPV, influenza A and B, parainfluenza types 1 to 3, and RSV, 6 whereas the PLEX-ID Broad Viral I assay detects human adenovirus, enterovirus, rhinovirus, BK and JC polyomavirus, parvovirus B19, HSV-1 and -2, VZV, Epstein-Barr virus (EBV), CMV, and human herpesvirus (HHV)-8. 13, 14 In this study, respiratory viruses refer to adenovirus, hCoV, hMPV, influenza, parainfluenza, RSV, enterovirus, and rhinovirus. Nucleic acid amplification and analyses of PCR products were conducted using the PCR/ESI-MS platform (PLEX-ID, Abbott Laboratories) following the manufacturer's instructions, with test turnaround time from sample to result within 6 to 8 hours. 8, 13 The PCR/ESI-MS analyses included automated PCR desalting, ESI-MS signal acquisition, spectral analysis, and data reporting. Organism identification was based on the total mass and base compositions of the PCR amplicons compared with those in the molecular signature database established by the PLEX-ID manufacturer. 6, 8, 13, 14 Samples in which PCR/ESI-MS results disagreed with culture results at the species level were reexamined by a second molecular method. For enteroviruses, rhinovirus was differentiated from enterovirus using a conventional PCR sequencing analysis with the previously described primers (Rhinovirus s1 and as) and a BLAST search. 15 \n\nAll analyses were performed with the Statistical Package for the Social Sciences version 17.0 (SPSS Inc, Chicago, IL). Continuous variables were expressed as mean values AE standard deviations and were compared using the analysis of variance test. Categorical variables were compared using the Fisher exact test or x 2 test. All biologically plausible variables with a P value 0.10 in the univariate analysis were considered for inclusion in the logistic regression model for the multivariate analysis. A P value less than 0.05 was considered statistically significant, and all tests were 2-tailed. During the 9-month study period, a total of 267 episodes of acute RTIs from 263 patients were recorded, including 96 episodes at a local clinic and 171 episodes at NCKUH (19 outpatient and 152 in the emergency departments). For convenience, each episode was counted as 1 case. Overall, 123 (46.1%) cases were male patients, and 152 (56.9%), 60 (22.5%), and 55 (20.6%) patients were 18 to 39, 40 to 59, and !60 years of age, respectively. Two-hundred and twelve (79.4%) patients presented with upper RTIs (URTIs), and 55 (20.6%) cases presented with LRTIs. Compared with patients attending the local clinic, patients attending the medical care center were older and had more comorbidities ( Table 1 ). The detailed demographic data of the 267 RTI cases and 42 control cases are presented in Table 1 . All 267 respiratory samples from each RTI case were examined for viruses by both virus isolation and PCR/ESI-MS, and the results are presented in Table 2 . For virus isolation, respiratory viruses were detected in 63 (23.6%) cases, including influenza A (48 cases, 18.0%), enterovirus (13, 4.9%), and parainfluenza virus (2, 0.7%), and no coinfection was detected. Virus isolation identified additional parainfluenza type 3 and enterovirus infections that were not found by PCR/ESI-MS in 2 samples. By PCR/ESI-MS, respiratory viruses were detected in 126 cases (47.2%). Influenza A (65 cases, 24.3%) was the most frequently identified virus, among which 36 (13.5%) cases were subtyped as pandemic H1N1/09 virus, 28 (10.5%) cases as seasonal H3N2 virus, and 1 case as influenza A matching both pandemic H1N1and seasonal H3N2. Genus Enterovirus (34, 12.7%) was the second-most frequently detected virus, including rhinovirus (25, 9 .4%), enterovirus (8, 3.0%), and 1 culturenegative case matching for both rhinovirus and enterovirus. hCoV (13, 4 .9%), hMPV (10, 3.7%), adenovirus (6, 2.2%), RSV (6, 2.2%), and parainfluenza (4, 1.5%) were detected in small proportions of cases. Simultaneous detection of more than 1 respiratory virus was observed in 11 (4.1%) patients, and rhinovirus (5 cases) was most likely to be codetected with another respiratory virus ( Table 2 ). Of note, 4 cultivated viruses identified as enterovirus because of reactivity with the Chemicon Pan EV mix were characterized as rhinovirus by PCR/ESI-MS. Further PCR-sequencing analysis of the 4 clinical specimens confirmed the existence of rhinoviruses but not enteroviruses. PCR/ESI-MS identified additional respiratory viruses in 65 culture-negative samples, mostly rhinovirus (21 samples), and a second respiratory virus in 3 culture-positive influenza A samples. Overall, the positive detection rates for any respiratory virus by culture, PCR/ESI-MS, and both methods were 23.6%, 47.2%, and 47.9% (128/267), respectively. Of 61 specimens positive by both methods, PCR/ESI-MS and culture methods reached levels of agreement of 100% at the species level for influenza and parainfluenza and 100% at the genus level for the genus Enterovirus. In the control group, only 1 (2.4%) healthy individual tested positive for a respiratory virus (rhinovirus) by PCR/ESI-MS.\n\nWith respect to herpesviruses, PCR/ESI-MS identified EBV, HSV-1, CMV, and VZV in 128 (47.9%), 25 (9.4%), 7 (2.6%), and 2 (0.7%) samples from RTI cases, with similar detection rates observed in the control group. There was no detection of polyomavirus, parvovirus B19, HSV-2, or HHV-8 virus in samples from cases with RTIs or the control group. Cases that tested positive for any respiratory virus either by culture or by PCR/ESI-MS were analyzed. The positive detection rates declined with age: 55.3%, 41.7%, and 34.5% in the 18-39, 40-59, and !60-year-old groups, respectively (P ¼ 0.02) ( Figure 1A) . A higher positivity rate was observed in patients with URTIs than that in patients with LRTIs (50.5% vs. 38.2%, P ¼ 0.10) ( Table 3 and Figure 1B ). There were similar distributions of respiratory viruses in cases from the local clinical and the medical center (Table 2) , and between patients from the 3 age groups ( Figure 1A ). Of 128 cases with identifiable respiratory viruses, non-influenza virus infection was more common in patients with LRTIs than those with URTIs (81.0% [17/21] vs. 48.6% [52/107], P ¼ 0.007). Rhinovirus (12.7%), influenza A (10.9%), and parainfluenza (7.3%) were the 3 leading respiratory viruses involved in 55 cases of LRTIs, and parainfluenza was more frequently observed in the LRTI group than in the URTI group (Table 3 and Figure 1B ). There was no seasonal variation in any individual respiratory virus over the 9-month period. Of 128 patients with identifiable respiratory viruses, univariate analysis revealed that patients with 1 of the following conditions were more likely to have non-influenza respiratory virus infections: immunocompromised state, chronic obstructive pulmonary disease (COPD), and chronic renal failure receiving dialysis (OR 5.4, 95% CI 1.2-25.5, P ¼ 0.02). Multivariate analysis demonstrated that steroid use was an independent risk factor for rhinovirus infection (OR 15.3, 95% CI 1.5-154.7, P ¼ 0.02), active malignancy was an independent risk factor for hMPV infection (OR 29.3, 95% CI 2.4-358.1, P ¼ 0.008), and COPD was an independent risk factor for parainfluenza infection (OR 229.2, 95% CI 10.5-5020.8,\n\nWhile comparing the URTI and LRTI groups, factors found to be associated with LRTI by univariate analysis included old age (!60 years), a high comorbidity index, congestive heart failure, COPD, malignancy, immunocompromised state, and detection of parainfluenza or EBV, whereas detection of influenza A was less frequently associated with LRTI. Codetection of respiratory virus was not associated with the development of LRTI. By multivariate analysis, only old age, immunocompromised state, and detection of parainfluenza remained 3 independent factors associated with LRTI (Table 3) .\n\nQuestion: What tests can simultaneously identify and subtype multiple respiratory viruses?", "role": "user" }, { "content": "multilocus polymerase chain reaction coupled with electrospray ionization mass spectrometry", "role": "assistant" } ]

[ { "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\", \"Disease\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\": [\"Naloxone\", ...], \"Disease\": [\"Hypotensive\", ...]}. 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\nSevere reversible left ventricular systolic and diastolic dysfunction due to accidental iatrogenic epinephrine overdose. Catecholamine-induced cardiomyopathy due to chronic excess of endogenous catecholamines has been recognized for decades as a clinical phenomenon. In contrast, reports of myocardial dysfunction due to acute iatrogenic overdose are rare. A 35-year-old woman whose cervix uteri was inadvertently injected with 8 mg of epinephrine developed myocardial stunning that was characterized by severe hemodynamic compromise, profound, albeit transient, left ventricular systolic and diastolic dysfunction, and only modestly elevated biochemical markers of myocardial necrosis. Our case illustrates the serious consequences of medical errors that can be avoided through improved medication labeling and staff supervision.", "role": "user" }, { "content": "{\"Chemical\": [\"Catecholamine\", \"catecholamines\", \"epinephrine\"], \"Disease\": [\"left ventricular systolic and diastolic dysfunction\", \"myocardial stunning\", \"cardiomyopathy\", \"myocardial necrosis\", \"overdose\", \"myocardial dysfunction\"]}", "role": "assistant" } ]

[ { "content": "You will be shown biomedical passages and your task is to 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 organism causes woolsorter's disease\n\nContext: Today, woolsorters' disease and other industrial manifestations of anthrax are extremely rare,\nWorking independently of their more famous counterparts (Robert Koch and Louis Pasteur), Anglo-American anthrax investigators used visual representations of anthrax bacilli to persuade their peers that a specific, identifiable cause produced all forms of anthrax-malignant pustule (cutaneous anthrax), intestinal anthrax, and woolsorter's disease (pneumonic anthrax).\nToday, woolsorters' disease and other industrial manifestations of anthrax are extremely rare, but the increasing threat of bioterrorism means that the international dread and historical lessons of this significant condition should never be forgotten.\nToday, woolsorters' disease and other industrial manifestations of anthrax are extremely rare, but the increasing threat of bioterrorism means that the international dread and historical lessons of this significant condition should never be forgotten.", "role": "user" }, { "content": "Bacillus Anthracis", "role": "assistant" } ]

[ { "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: [\"RNA\", \"CellType\", \"CellLine\", \"Protein\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nOctamer independent activation of transcription from the kappa immunoglobulin germline promoter. Previous analyses of immunoglobulin V region promoters has led to the discovery of a common octamer motif which is functionally important in the tissue-specific and developmentally regulated transcriptional activation of immunoglobulin genes. The germline promoters (Ko) located upstream of the J region gene segments of the kappa locus also contain an octamer motif (containing a single base pair mutation and referred to as the variant octamer) which has been shown previously to bind Oct-1 and Oct-2 transcription factors in vitro. To further elucidate the role of this variant octamer motif in the regulation of germline transcription from the unrearranged kappa locus, we have quantitated the relative binding affinity of Oct-1 and Oct-2 for the variant octamer motif and determined the functional role of this octamer motif in transcriptional activation. We find that, although the variant octamer motif binds Oct-1 and Oct-2 in vitro with 5-fold lower affinity than the consensus octamer motif, mutation of the variant octamer motif to either a consensus octamer or non-octamer motif has no effect on transcriptional activation from the germline promoter. We also find significant differences in activation of germline and V region promoters by kappa enhancers. Our results suggest that the germline promoters and V region promoters differ in their dependence on octamer for activation and respond differently to enhancer activation. These findings have important implications in regulation of germline transcription as well as concomitant activation of the V-J recombination of the kappa light chain locus.", "role": "user" }, { "content": "{\"CellLine\": [], \"CellType\": [], \"DNA\": [\"J region gene segments\", \"consensus octamer\", \"octamer\", \"base pair mutation\", \"non - octamer motif\", \"kappa locus\", \"Ko\", \"immunoglobulin genes\", \"kappa immunoglobulin germline promoter\", \"germline\", \"V region promoters\", \"germline promoter\", \"immunoglobulin V region promoters\", \"kappa enhancers\", \"kappa light chain locus\", \"consensus octamer motif\", \"germline promoters\", \"octamer motif\"], \"Protein\": [\"Oct - 1\", \"transcription factors\", \"Oct - 2\"], \"RNA\": []}", "role": "assistant" } ]

[ { "content": "You will be presented with a paragraph from a COVID-19 literature. Your task is to 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 categories within the array. \nYour output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nThe coronavirus 2019 disease (COVID-19) pandemic has led to food shortages, increased food prices, and loss of income. As a result, global food insecurity alerts have been issued. The pandemic threatens millions of children and adolescents and their families currently living with or at risk for development of food insecurity. The lack of consistent access to nutritious food sources is associated with chronic physical and mental health problems and death. Studies on food insecurity and eating pathology have heightened our concern about the impact the added effect of the pandemic may have on eating behaviors of children and adolescents. Here, we want to draw attention to the need for making food security and healthy eating attitudes and behaviors a global priority during the COVID-19 pandemic to guarantee the current and future health and well-being of our children and adolescents.", "role": "user" }, { "content": "[\"Prevention\"]", "role": "assistant" } ]

[ { "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: [\"Gene\"].\n\nPlease return the output as a JSON object of the format: {\"Gene\": [\"SSX\", ...]}. 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\nA novel mammalian protein, p55CDC, present in dividing cells is associated with protein kinase activity and has homology to the Saccharomyces cerevisiae cell division cycle proteins Cdc20 and Cdc4. A novel protein, p55CDC, has been identified in cycling mammalian cells. This transcript is readily detectable in all exponentially growing cell lines but disappears when cells are chemically induced to fall out of the cell cycle and differentiate. The p55CDC protein appears to be essential for cell division, since transfection of antisense p55CDC cDNA into CHO cells resulted in isolation of only those cells which exhibited a compensatory increase in p55CDC transcripts in the sense orientation. Immunoprecipitation of p55CDC yielded protein complexes with kinase activity which fluctuated during the cell cycle. Since p55CDC does not have the conserved protein kinase domains, this activity must be due to one or more of the associated proteins in the immune complex. The highest levels of protein kinase activity were seen with alpha-casein and myelin basic protein as substrates and demonstrated a pattern of activity distinct from that described for the known cyclin-dependent cell division kinases. The p55CDC protein was also phosphorylated in dividing cells. The amino acid sequence of p55CDC contains seven repeats homologous to the beta subunit of G proteins, and the highest degree of homology in these repeats was found with the Saccharomyces cerevisiae Cdc20 and Cdc4 proteins, which have been proposed to be involved in the formation of a functional bipolar mitotic spindle in yeast cells. The G beta repeat has been postulated to mediate protein-protein interactions and, in p55CDC, may modulate its association with a unique cell cycle protein kinase. These findings suggest that p55CDC is a component of the mammalian cell cycle mechanism.", "role": "user" }, { "content": "{\"Gene\": [\"protein kinase domains\", \"G beta\", \"cyclin-dependent cell division kinases\", \"Cdc4\", \"beta subunit of G proteins\", \"Cdc20\", \"p55CDC\", \"myelin basic protein\", \"cell cycle protein kinase\"]}", "role": "assistant" } ]

[ { "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:\nrelated work\n\nContext before the citation:\nA previous work along this line is Sproat et al. (1996), which is based on weighted finite-state transducers (FSTs). We propose a unified approach that solves both problems simultaneously. We believe that the identification of OOV words should not be treated as a problem separate from word segmentation.\n\nCitation Sentence:\nA previous work along this line is Sproat et al. ( 1996 ) , which is based on weighted finite-state transducers ( FSTs ) .\n\nContext after the citation:\nOur approach is similarly motivated but is based on a different mechanism: linear mixture models. As we shall see, the models provide a more flexible framework for incorporating various kinds of lexical and statistical information. Many types of OOV words that are not covered in Sproat’s system can be dealt with in our system. The linear models we used are originally derived from linear discriminant functions widely used for pattern classification (Duda, Hart, and Stork 2001) and have been recently introduced into NLP tasks by Collins and Duffy (2001).", "role": "user" }, { "content": "CompareOrContrast", "role": "assistant" } ]

[ { "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\", \"Protein\"].\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\nPotentiation of sulfonylurea action by an EPAC-selective cAMP analog in INS-1 cells: comparison of tolbutamide and gliclazide and a potential role for EPAC activation of a 2-APB-sensitive Ca2+ influx.\nTolbutamide and gliclazide block the K(ATP) channel K(ir)6.2/Sur1, causing membrane depolarization and stimulating insulin secretion in pancreatic beta cells. We examined the ability of the EPAC-selective cAMP analog 8-pCPT-2'-O-Me-cAMP-AM to potentiate the action of these drugs and the mechanism that might account for it. Insulin secretion stimulated by both 200 μM tolbutamide and 20 μM gliclazide, concentrations that had equivalent effects on membrane potential, was inhibited by thapsigargin (1 μM) or the L-type Ca(2+) channel blocker nicardipine (2 μM) and was potentiated by 8-pCPT-2'-O-Me-cAMP-AM at concentrations ≥2 μM in INS-1 cells. Ca(2+) transients stimulated by either tolbutamide or gliclazide were inhibited by thapsigargin or nicardipine and were significantly potentiated by 8-pCPT-2'-O-Me-cAMP-AM at 5 μM but not 1 μM. Both tolbutamide and gliclazide stimulated phospholipase C activity; however, only gliclazide did so independently of its activity at K(ATP) channels, and this activity was partially inhibited by pertussis toxin. 8-pCPT-2'-O-Me-cAMP-AM alone (5 μM) did not stimulate insulin secretion, but did increase intracellular Ca(2+) concentration significantly, and this activity was inhibited by 25 μM 2-aminoethoxydiphenylborate (2-APB) or the removal of extracellular Ca(2+). 8-pCPT-2'-O-Me-cAMP-AM potentiation of insulin secretion stimulated by tolbutamide was markedly inhibited by 2-APB (25 μM) and enhanced by the PKC inhibitor bisindolylmaleimide I (1 μM). Our data demonstrate that the actions of both tolbutamide and gliclazide are strongly potentiated by 8-pCPT-2'-O-Me-cAMP-AM, that gliclazide can stimulate phospholipase C activity via a partially pertussis toxin-sensitive mechanism, and that 8-pCPT-2'-O-Me-cAMP-AM potentiation of tolbutamide action may involve activation of a 2-APB-sensitive Ca(2+) influx.", "role": "user" }, { "content": "{\"Chemical\": [\"gliclazide\", \"tolbutamide\", \"cAMP\", \"Tolbutamide\", \"Ca(2+)\", \"8-pCPT-2\\u0027-O-Me-cAMP-AM\", \"2-APB\", \"bisindolylmaleimide I\", \"thapsigargin\", \"K\", \"2-aminoethoxydiphenylborate\", \"ATP\", \"nicardipine\", \"sulfonylurea\"], \"Protein\": [\"K(ir)6.2\", \"pertussis toxin\", \"phospholipase C\", \"Sur1\", \"insulin\", \"L-type Ca(2+) channel\", \"Insulin\", \"K(ATP) channels\", \"EPAC\", \"PKC\", \"K(ATP) channel\"]}", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nSafety effects of 30 Km/H zones in The Netherlands.\nAbstract 1:\nSince 1983, Dutch municipal authorities can institute a maximum speed of 30 km/h on roads or in zones within built-up areas. The safety effects of 30 km/h zones are supposed to be positive. In order to be sure about this, the Ministry of Transport has stimulated 15 municipalities to implement a 30 km/h zone and set up an evaluation of the safety effects of these zones. The evaluation research is coordinated by the Institute for Road Safety Research (SWOV). The evaluation concerns the changes in traffic flows, opinions of residents, conflicts, and accidents. This paper gives the results of the evaluation.\n\n\nTitle 2:\nSafety effects of speed reducing measures in Danish residential areas.\nAbstract 2:\nOn May 1, 1977 a new code was introduced into the Danish Road Traffic Act. The result was a change in layout and speed limits in a great number of residential streets; in most cases the streets were transformed into 30 km/h streets and in few cases into 15 km/h streets. In addition to speed signs, both types of streets were equipped with speed reducing measures. Based on experiences from a selection of experimental streets, mostly 30 km/h streets, different, but very positive effects were found. Overall there was a reduction in the mean speed in these areas of 11 km/h. On the 223 km, 30 km/h streets there was a reduction of 77 accidents and 88 casualties within a period of three years. These reductions were caused by the implementation of speed signs, speed reducing measures, and a reduction in traffic. On the basis of 44 experimental streets, where traffic was recorded both before and after the changes, the reduction in risk of casualties, i.e. the number of casualties per road user km, was 72%, while the risk of accidents seemed to be unchanged. Considering serious injuries alone, a very high reduction of 78% was found. Accidents included in the study consist of all police reported accidents, i.e. accidents with personal injury as well as damage only accidents.\n\n\nTitle 3:\nSafety impact of engineering treatments on undivided rural roads.\nAbstract 3:\nThis article presents an evaluation of the safety impacts of four engineering treatments implemented in the Autonomous Community of Madrid (Spain): highway upgrading; updating and improvement of traffic signing; repainting of pavement markings and pavement resurfacings. This evaluation was carried out using the Empirical Bayes method with a comparison group. The functioning of a methodology to test the significance of the safety impact is described. The results show that highway upgrading has a positive and significant safety impact, while the updating and improvement of traffic signing, the repainting of road markings and pavement resurfacings do not exhibit a significant impact on safety.", "role": "user" }, { "content": "The results from this review suggest that area-wide traffic calming in towns and cities may be a promising intervention for reducing the number of road traffic injuries and deaths. However, further rigorous evaluations of such interventions are needed.", "role": "assistant" } ]

[ { "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: Are gut proteobacteria associated with inflammatory disease?\n\nContext: In the IBD group, Firmicutes, Proteobacteria, Verrucomicrobia, and Fusobacteria were significantly increased,\nBacterial microbiota in Crohn's disease patients was characterised by a restriction in biodiversity. with an increase of Proteobacteria and Fusobacteria.\nthe onset of inflammatory bowel disease is associated with enteric proteobacterial infection, yet the mechanistic basis for this association is unclear.\nSimilar to the findings in inflammatory disease states, SSc patients had decreased levels of commensal bacteria, such as Faecalibacterium and Clostridium, and increased levels of pathobiont bacteria, such as Fusobacterium and γ-Proteobacteria, compared with healthy controls.\nThe colonic and cecal microbiota of healthy mice fed α-MG but no DSS shifted to an increased abundance of Proteobacteria and decreased abundance of Firmicutes and Bacteroidetes, a profile similar to that found in human UC\nSignificant increase in abundance of unusual aerobes and facultative anaerobes, including members from the phylum Proteobacteria (p- = 0.031) was also observed.", "role": "user" }, { "content": "The onset of inflammatory bowel disease is associated with enteric proteobacterial infection, yet the mechanistic basis for this association is unclear.", "role": "assistant" } ]

[ { "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\nInhibitory effects of curcumin on gastric cancer cells: A proteomic study of molecular targets. Curcumin, a natural anticancer agent, has been shown to inhibit cell growth in a number of tumor cell lines and animal models. We examined the inhibition of curcumin on cell viability and its induction of apoptosis using different gastric cancer cell lines (BGC-823, MKN-45 and SCG-7901). 3-(4,5-dimethyl-thiazol-2-yl)-2-5-diphenyltetrazolium-bromide (MTT) assay showed that curcumin inhibited cell growth in a dose- (1, 5, 10 and 30μM) and time- (24, 48, 72 and 96h) dependent manner; analysis of Annexin V binding showed that curcumin induced apoptosis at the dose of 10 and 30μM when the cells were treated for 24 and 48h. As cancers are caused by dysregulation of various proteins, we investigated target proteins associated with curcumin by two-dimensional gel electrophoresis (2-DE) and MALDI-TOF-TOF mass spectrometer. BGC-823cells were treated with 30μM curcumin for 24h and total protein was extracted for the 2-DE. In the first dimension of the 2-DE, protein samples (800μg) were applied to immobilized pH gradient (IPG) strips (24cm, pH 3-10, NL) and the isoelectric focusing (IEF) was performed using a step-wise voltage ramp; the second dimension was performed using 12.5% SDS-PAGE gel at 1W constant power per gel. In total, 75 proteins showed significant changes over 1.5-fold in curcumin-treated cells compared to control cells (Student's t-test, p<0.05). Among them, 33 proteins were upregulated and 42 proteins downregulated by curcumin as determined by spot densitometry. 52 proteins with significant mascot scores were identified and implicated in cancer development and progression. Their biological function included cell proliferation, cycle and apoptosis (20%), metabolism (16%), nucleic acid processing (15%), cytoskeleton organization and movement (11%), signal transduction (11%), protein folding, proteolysis and translation (20%), and immune response (2%). Furthermore, protein-protein interacting analysis demonstrated the interaction networks affected by curcumin in gastric cancer cells. These data provide some clues for explaining the anticancer mechanisms of curcumin and explore more potent molecular targets of the drug expected to be helpful for the development of new drugs.", "role": "user" }, { "content": "{\"Chemical\": [\"Curcumin\", \"curcumin\", \"SDS\", \"3-(4,5-dimethyl-thiazol-2-yl)-2-5-diphenyltetrazolium-bromide\", \"MTT\"]}", "role": "assistant" } ]

[ { "content": "You will be shown sections from a scientific research paper, together with a question about the paper. This is an extractive question-answering task, where you must find and extract relevant text spans directly from the paper to answer the question. Your response should strictly 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 example, for the question \"What baselines did the authors compare against?\", a sample response might be:\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: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing\n\nOne of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser significantly improves over strong baselines.\n\nBi-Layered L-PCFGs\nAs mentioned earlier, one of our lattice types is based on bi-layered PCFGs introduced here.\nIn their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.\nTo create the bi-layered L-PCFG, we again use the spectral algorithm of narayan-15 to estimate a grammar $G_{\\mathrm {par}}$ from the Paralex corpus. We use the word alignment of paraphrase question pairs in Paralex to map inside and outside trees of each nonterminals in the treebank to bag of word features. The number of latent states we use is 1,000.\nOnce the two feature functions (syntactic in $G_{\\mathrm {syn}}$ and semantic in $G_{\\mathrm {par}}$ ) are created, each nonterminal in the training treebank is assigned two latent states (cluster identifiers). Figure 2 shows an example annotation of trees for three paraphrase questions from the Paralex corpus. We compute the parameters of the bi-layered L-PCFG $G_{\\mathrm {layered}}$ with a simple frequency count maximum likelihood estimate over this annotated treebank. As such, $G_{\\mathrm {layered}}$ is a combination of $G_{\\mathrm {syn}}$ and $G_{\\mathrm {par}}$ , resulting in 24,000 latent states (24 syntactic x 1000 semantic).\nConsider an example where we want to generate paraphrases for the question what day is nochebuena. Parsing it with $G_{\\mathrm {layered}}$ will lead to the leftmost hybrid structure as shown in Figure 2 . The assignment of the first latent states for each nonterminals ensures that we retrieve the correct syntactic representation of the sentence. Here, however, we are more interested in the second latent states assigned to each nonterminals which capture the paraphrase information of the sentence at various levels. For example, we have a unary lexical rule (NN-*-142 day) indicating that we observe day with NN of the paraphrase type 142. We could use this information to extract unary rules of the form (NN-*-142 $w$ ) in the treebank that will generate words $w$ which are paraphrases to day. Similarly, any node WHNP-*-291 in the treebank will generate paraphrases for what day, SBARQ-*-403, for what day is nochebuena. This way we will be able to generate paraphrases when is nochebuena and when is nochebuena celebrated as they both have SBARQ-*-403 as their roots.\nTo generate a word lattice $W_q$ for a given question $q$ , we parse $q$ with the bi-layered grammar $G_{\\mathrm {layered}}$ . For each rule of the form $X$ - $m_1$ - $m_2 \\rightarrow w$ in the bi-layered tree with $X \\in {\\cal P}$ , $m_1 \\in \\lbrace 1, \\ldots , 24 \\rbrace $ , $m_2 \\in \\lbrace 1, \\ldots , 1000 \\rbrace $ and $q$0 a word in $q$1 , we extract rules of the form $q$2 - $q$3 - $q$4 from $q$5 such that $q$6 . For each such $q$7 , we add a path $q$8 parallel to $q$9 in the word lattice.\n\nUngrounded Graphs from Paraphrases\nWe use GraphParser BIBREF7 to convert paraphrases to ungrounded graphs. This conversion involves three steps: 1) parsing the paraphrase using a CCG parser to extract syntactic derivations BIBREF33 , 2) extracting logical forms from the CCG derivations BIBREF34 , and 3) converting the logical forms to an ungrounded graph. The ungrounded graph for the example question and its paraphrases are shown in Figure 3 , Figure 3 and Figure 3 , respectively.\n\nLearning\nWe use a linear model to map ungrounded graphs to grounded ones. The parameters of the model are learned from question-answer pairs. For example, the question What language do people in Czech Republic speak? paired with its answer $\\lbrace \\textsc {CzechLanguage}\\rbrace $ . In line with most work on question answering against Freebase, we do not rely on annotated logical forms associated with the question for training and treat the mapping of a question to its grounded graph as latent.\nLet $q$ be a question, let $p$ be a paraphrase, let $u$ be an ungrounded graph for $p$ , and let $g$ be a grounded graph formed by grounding the nodes and edges of $u$ to the knowledge base $\\mathcal {K}$ (throughout we use Freebase as the knowledge base). Following reddylargescale2014, we use beam search to find the highest scoring tuple of paraphrase, ungrounded and grounded graphs $(\\hat{p}, \\hat{u}, \\hat{g})$ under the model $\\theta \\in \\mathbb {R}^n$ : $\n({\\hat{p},\\hat{u},\\hat{g}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g)} \\theta \\cdot \\Phi (p,u,g,q,\\mathcal {K})\\,,\n$ \nwhere $\\Phi (p, u, g, q, \\mathcal {K}) \\in \\mathbb {R}^n$ denotes the features for the tuple of paraphrase, ungrounded and grounded graphs. The feature function has access to the paraphrase, ungrounded and grounded graphs, the original question, as well as to the content of the knowledge base and the denotation $|g|_\\mathcal {K}$ (the denotation of a grounded graph is defined as the set of entities or attributes reachable at its target node). See sec:details for the features employed. The model parameters are estimated with the averaged structured perceptron BIBREF35 . Given a training question-answer pair $(q,\\mathcal {A})$ , the update is: $\n\\theta ^{t+1} \\leftarrow \\theta ^{t} + \\Phi (p^+, u^+, g^+, q,\n\\mathcal {K}) - \\Phi (\\hat{p}, \\hat{u}, \\hat{g}, q, \\mathcal {K})\\,,\n$ \nwhere $({p^+,u^+,g^+})$ denotes the tuple of gold paraphrase, gold ungrounded and grounded graphs for $q$ . Since we do not have direct access to the gold paraphrase and graphs, we instead rely on the set of oracle tuples, $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ , as a proxy: $\n(p^{+},u^{+},{g^{+}}) = \\operatornamewithlimits{arg\\,max}_{(p,u,g) \\in \\mathcal {O}_{\\mathcal {K},\\mathcal {A}}(q)} \\theta \\cdot \\Phi ({p,u,g,q,\\mathcal {K}})\\,,\n$ \nwhere $\\mathcal {O}_{\\mathcal {K}, \\mathcal {A}}(q)$ is defined as the set of tuples ( $p$ , $u$ , $g$ ) derivable from the question $q$ , whose denotation $|g|_\\mathcal {K}$ has minimal $F_1$ -loss against the gold answer $\\mathcal {A}$ . We find the oracle graphs for each question a priori by performing beam-search with a very large beam.\n\nExperimental Setup\nBelow, we give details on the evaluation dataset and baselines used for comparison. We also describe the model features and provide implementation details.\n\nImplementation Details\nFor WebQuestions, we use 8 handcrafted part-of-speech patterns (e.g., the pattern (DT)?(JJ.? $\\mid $ NN.?){0,2}NN.? matches the noun phrase the big lebowski) to identify candidate named entity mention spans. We use the Stanford CoreNLP caseless tagger for part-of-speech tagging BIBREF38 . For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. We generate ungrounded graphs for these paraphrases and treat the final entity disambiguation and paraphrase selection as part of the semantic parsing problem.\nWe use the features from reddylargescale2014. These include edge alignments and stem overlaps between ungrounded and grounded graphs, and contextual features such as word and grounded relation pairs. In addition to these features, we add two new real-valued features – the paraphrase classifier's score and the entity disambiguation lattice score.\nWe use beam search to infer the highest scoring graph pair for a question. The search operates over entity-entity edges and entity type nodes of each ungrounded graph. For an entity-entity edge, there are two operations: ground the edge to a Freebase relation, or skip the edge. Similarly, for an entity type node, there are two operations: ground the node to a Freebase type, or skip the node. We use a beam size of 100 in all our experiments.\n\nAcknowledgements\nThe authors would like to thank Nitin Madnani for his help with the implementation of the paraphrase classifier. We would like to thank our anonymous reviewers for their insightful comments. This research was supported by an EPSRC grant (EP/L02411X/1), the H2020 project SUMMA (under grant agreement 688139), and a Google PhD Fellowship for the second author.\n\nFigure 1: An example word lattice for the question What language do people in Czech Republic speak? using the lexical and phrasal rules from the PPDB.\n\nFigure 3: Ungrounded graphs for an input question and its paraphrases along with its correct grounded graph. The green squares indicate NL or Freebase entities, the yellow rectangles indicate unary NL predicates or Freebase types, the circles indicate NL or Freebase events, the edge labels indicate binary NL predicates or Freebase relations, and the red diamonds attach to the entity of interest (the answer to the question).\n\nTable 1: Oracle statistics and results on the WebQuestions development set.\n\nQuestion: What latent variables are modeled in the PCFG?", "role": "user" }, { "content": "{\"answer\": [\"syntactic information\", \"semantic and topical information\"], \"evidence\": [\"In their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.\"]}", "role": "assistant" } ]

[ { "content": "You will be shown the title of a chemistry paper, together with sections excerpted from the body of the paper. The paper sections may include HTML tags like `<p>` and `</p>`; please ignore these. Your task is to write an abstract for the paper. Your response should include the abstract and no other text.\n\nTitle:\nStructure features of peptide-type SARS-CoV main protease inhibitors: Quantitative Structure Activity Relationship study\n\nPaper sections:\n\n<p>To optimize a compound to become a lead or a drug, a researcher needs to follow an easy, efficient, economical, and eco-friendly approach (e-Chemistry approach). A feasible solution to achieve these goals is to use Computer-Aided Drug Designing (CADD). CADD is a contemporary approach with a good number of benefits like cheaper, result oriented, minimizes animal testing as well as trials and errors, less time-consuming, a few advantages to mention. In recent time, the thriving branches of CADD such as QSAR (Quantitative Structure Activity Relationships), Molecular docking, Pharmacophore modelling, etc. have contributed significantly in optimization of lead and drug candidates (Baig et al., 2016;Macalino et al., 2015).</p><p>QSAR analysis involves finding mathematical correlation between the structural features of congeneric molecules with bio-activity. The main steps followed during a typical QSAR analysis are (1) Selection of an appropriate dataset (2) 3D-structure generation and their optimization using suitable technique (3) Molecular descriptor calculation followed by their pruning (4) Generation of QSAR model using a proper algorithm for feature (that is, molecular descriptor) selection and (5) Adequate validation of developed QSAR model (Masand et al., 2019a;Masand et al., 2016;Pourbasheer et al., 2015).</p><p>QSAR is an established CADD branch, which has been used successfully to identify noticeable and hidden structural patterns/features having correlation with a desired activity/property (Qualitative QSAR). In addition, it is helpful to predict the activity/property before the actual synthesis and testing of a molecule (Quantitative QSAR). Therefore, QSAR analysis is performed routinely by the researchers to get important qualitative and quantitative idea about the congeneric molecules for optimization. An adequately validated and statistically robust QSAR analysis offers in-depth knowledge about the structural patterns that have good correlation with the desired activity/toxicity/property of a drug candidate and enhanced intuition for the mechanism of drug action (Cherkasov et al., 2014;Chirico and Gramatica, 2012;Fujita and Winkler, 2016;Gramatica, 2013;Gramatica et al., 2012;Huang and Fan, 2011;Martin et al., 2012;Masand et al., 2017b;Masand et al., 2014;Masand et al., 2015b).</p><p>A good number of researchers have synthesized peptide-type compounds and tested against SAR-CoV ( Konno et al., 2013;Regnier et al., 2009;Thanigaimalai et al., 2013a;Thanigaimalai et al., 2013b). Recently, Zhang et al. (Zhang et al., 2020) synthesized and tested α-Ketoamides (peptide-type compounds) as broad-spectrum inhibitors of coronavirus (SARS-CoV). Despite these efforts, optimization of peptide-type compounds is in pipeline to get a drug candidate. Henceforth, in the present work, we have developed Qualitative cum Quantitative SAR models for a series of sixty-two peptide-type compounds for anti-SARS-CoV activity. The results could be beneficial for future optimizations of peptide-type compounds with better activity profile.</p>\n\nExperimental Methodology:\n<p>Selection of dataset: The dataset selected for the present work comprises sixty-two peptidetype compounds having moderate to high activity against SARS-CoV ( Konno et al., 2013;Thanigaimalai et al., 2013a;Thanigaimalai et al., 2013b). The selected peptide-type compounds possess Ki = 3 to 56,000 nM. The reported activity Ki values were converted to pKi (pKi = -logKi) before actual QSAR analysis. For the sake of ease and understanding the chemical space covered by the present dataset, most and least active three molecules have been depicted in figure 1. The SMILES notation for all selected molecules along with their reported activity values Ki, and pKi are present in the Table S1 in supplementary material. The structures were drawn using ChekSketch 12 Freeware (www.acdlabs.com) followed by their conversion to 3D-structures using OpenBabel 2.4. Then, the force field MMFF94 was used to optimize the structures. The 3D-structures were optimized using default settings available in TINKER. The molecules in each set were then aligned using Open3DAlign. In the next step, the optimized and aligned molecules were then used for calculation of molecular descriptors using PyDescriptor (Masand and Rastija, 2017) and PaDEL (Yap, 2011).</p>\n\nMolecular descriptor pruning:\n<p>PyDescriptor and PaDEL provided more than 30,000 molecular descriptors for each molecule in all sets. Therefore, molecular descriptor pruning was essential to remove redundant molecular descriptors. For this, molecular descriptors with high co-linearity (|R| >0.90) and nearly constant (> 95 %) were removed to avoid the inclusion of multi-collinear and spurious variables in GA-MLR (Genetic Algorithm-Multi-linear Regression) model, using objective feature selection in QSARINS ver. 2.2.2 (Gramatica et al., 2014;Gramatica et al., 2013;Masand et al., 2015a;Masand et al., 2015c).The resulting reduced molecular descriptor pool comprised of only 668 molecular descriptors only but large enough to cover 1D-to 3D-descriptor space.</p>\n\nQSAR model building and their validation:\n<p>The reduced molecular descriptor set for all the four sets comprises zero-, one-, two-and three-dimensional descriptors, charge descriptors and molecular properties, thereby covering broad descriptor space. Subjective feature selection (SFS) was executed to build the statistically acceptable GA-MLR based QSAR models using QSARINS ver. 2.2.2. The developed models were subjected to thorough statistical validation (internal and external validation) according to OECD principles; models with high internal and external predictive ability have been reported.</p><p>The general procedure for building the QSAR models is as follows:</p><p>(1) The set was bifurcated randomly, using random splitting option in QSARINS, into a training and a prediction set of 52 (i.e. 80 % training set) and 12 (i.e. 20 % prediction set), respectively. Then, the training set was used for model development, and the prediction set for the external validation, that is, to judge the predictive ability on new chemicals.</p><p>(2) QSARINS was used to build GA-MLR based QSAR models using default settings.</p><p>The selected fitness function to maximize in GA was Q 2 , this ensured the double cross-validation as well (Gramatica et al., 2014;Gramatica et al., 2013;Masand et al., 2017a). During model development it was observed that there was growth in the value of Q 2 up to six variables, but then, it had visible and significant reduction. Therefore, molecular descriptor selection was restricted to a set of six descriptors to avoid overfitting and develop easy and informative QSAR models.</p><p>The values for molecular descriptors, which are present in QSAR models, are available in the supplementary information for each molecule.</p><p>(3) One of the OECD guideline suggests to thoroughly validating a QSAR model, therefore all the models were subjected to internal and external validation, Yscrambling along with model applicability domain (AD) analysis using QSARINS. (Masand et al., 2019a;Masand et al., 2018;Masand et al., 2019b):</p><p>3 with RMSE and MAE close to zero. A QSAR model that did not satisfy above mentioned criteria was consequently excluded.</p><p>Thus, the complete procedure involving molecular descriptor calculation and their pruning, followed by subjective feature selection along with model building and validation was performed on all the four sets. It was observed that the MMFF94 optimized set resulted in development of statistically better QSAR models, which have been reported in the present work.</p>\n\nResult and Discussions:\n<p>Though, the dataset used in the present study is moderate sized but the presence of positional isomers, heterocyclic rings, etc. significantly augment the chemical space covered by the peptide-type compounds. In our previous work on QSAR analysis related to small and moderate sized datasets (Masand et al., 2015c), we have demonstrated that a QSAR model built using undivided whole dataset provides advantages like identification of maximum useful information, capturing of maximum relevant molecular descriptors, and benchmark for comparison and assessment of QSAR models constructed using divided datasets. In addition, this approach also helps to capture unrevealed structural features, which govern the bioactivity profile of congeneric molecules. Therefore, in the present work, models have been derived using divided and undivided datasets.</p><p>The derived QSAR models are as follow: The statistical parameters for developed models 1.1 and 1.2 have been presented in Table 2. The symbols have their usual meaning, which are available in the supplementary material also.</p><p>, and CCC ex (Chirico andGramatica, 2011, 2012;Consonni et al., 2009;Consonni et al., 2019;Gramatica, 2013;Gramatica et al., 2012). In short, the developed QSAR models fulfill the recommended threshold values for many internal and external validation parameters. In addition, for a better validation of derived models, the model applicability domain (AD) was assured by plotting Williams plots for models 1.1 and 1.2 (see figure 2). Therefore, these models are statistically robust and possess good external predictive ability. To add further, satisfaction of recommended threshold values for these parameters along with low correlation among the molecular descriptors point out that these models are not developed by chance (see supplementary information).</p>\n\nInterpretation of QSAR models:\n<p>The models 1.1 and 1.2 have been built using the undivided and divided dataset, respectively.</p><p>They comprise four common molecular descriptors. Therefore, the approach to develop QSAR models using undivided and divided dataset has been successful in identification of greater number of important molecular descriptors, which is useful to capture maximal information. Though we have compared the activities of the molecules of the dataset in terms of a single descriptor, we make it clear that the combined or converse effect of confounding factors/descriptors do have additional influence on the activity profile of the compounds.</p><p>A molecular descriptor with a positive coefficient in both the model is sp2O_aroC_7B</p><p>(number of sp2 hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms).</p><p>Since, in the present series of compounds, sp 2 hybridized Oxygen atoms are always present as a part of carbonyl group (>C=O), therefore it is rational to consider that this molecular descriptor also points out toward the presence of number of carbonyl groups in conjugation with aromatic Carbon atoms, that is, aromatic rings. Therefore, increasing the number of Therefore, such a combination of Nitrogen atoms should be avoided for better activity. The molecule 42 has a lower number of ringC_sp3S_9B and a higher value of APC2D3_C_N than 43.</p>\n\nConclusions:\n<p>In conclusion, statistically robust QSAR models with good external predictive ability have been developed, which have successfully highlighted a good number of molecular features. The developed models 1.1 to 2.2 satisfy the threshold values for many statistical parameters that are necessary to establish the quality and usefulness of a QSAR model. Thus, the developed QSAR models have a good balance of Quantitative and Qualitative aspects. Therefore, the developed models could be useful for future optimization of the activity profile of the molecules used in the present dataset.</p>", "role": "user" }, { "content": "In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R 2 = 0.80-0.82, Q 2 loo = 0.74-0.77).The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti-SARS-CoV activity.", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nSpontaneousmotoractivityandwalkingpatternsofADAM11-deficientmicewerealsofoundtobenormal,suggestingthatthedysfunctionfoundintherotatingrodtestwasnotderivedfrommuscleweaknessorotherperipheraldisturbances.", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "content": "You will be shown a question, followed by excerpts from biomedical research papers. Please answer the question based on the provided context. Do not include any text in your response other than the answer.\n\nQuestion: What is Neuromedin U (NmU)\n\nContext: Since the discovery of neuromedin U (NmU) from porcine spinal cord in 1985, this neuropeptide has been subsequently identified in many other species with multiple physiological and pathophysiological roles detected, ranging from smooth muscle contraction, feeding, energy balance to tumorigenesis\nNmU is also emerging to play pro-inflammatory roles involving immune cell activation and cytokine release in a neuron-dependent or neuron-independent manner.\nNeuromedin U (NMU) is a highly conserved neuropeptide that has been implicated in the stress response\nNeuromedin U (NMU) is a neuropeptide belonging to the neuromedin family. Recently, significant associations between NMU and several cancers have been reported.\nNMU may serve as a novel prognostic biomarker for HCC patients, although further validation is needed in the future.\nactivation of M2 macrophages and a type-2 inflammatory response may involve in the role of NMU in patients with HCC.\nAn increasing number of studies have demonstrated that neuromedin U (NMU) plays a pleiotropic role in the pathogenesis of asthma.\nNeuromedin U (NMU) is a bioactive neuropeptide, highly distributed in the gastrointestinal tract and the central nervous system. NMU has various physiological functions related to feeding behavior, energy metabolism, stress responses, circadian rhythmicity and inflammation\nreports indicate that the central NMU system plays an important role in the reward systems in the brain\nOBJECTIVES: Neuromedin U (NmU) is a neuropeptide with anorexigenic activity.\nNeuromedin U (NmU) is a regulatory peptide found in significant concentrations in both the brain and gut of the rat and is named according to its ability to powerfully contract the uterus.\nNeuromedin U (NmU) is a neuropeptide known for its uterocontractile effects in rodents.\nNeuromedin U (NMU) is a brain-gut peptide, which peripherally stimulates smooth muscle, increases of blood pressure, alters ion transport in the gut, controls local blood flow, and regulates adrenocortical function.\nNeuromedin U (NMU) and neuromedin S (NMS) are structurally related neuropeptides that have been reported to modulate energy homeostasis.\nThe neuropeptide neuromedin U (NMU) has been shown to have significant effects on cardiovascular, gastrointestinal and CNS functions.\nNeuromedin U (NmU), originally isolated from porcine spinal cord and later from other species, is a novel peptide that potently contracts smooth muscle. N\nNeuromedin U (NMU) is a neuropeptide known to regulate food intake and energy homeostasis that is widely distributed in the gastrointestinal tract, hypothalamus, and pituitary. A\nOBJECTIVES: Neuromedin U (NmU) is a neuropeptide with anorexigeni\nNeuromedin U (NMU) is a neuropeptide with potent activity on smooth muscle which was isolated first from porcine spinal cord and later from other species.\nNeuromedin U (NMU) is a brain-gut peptide whose peripheral activities are well-understood but whose central actions have yet to be clarified.\nNeuromedin U (NMU) is a hypothalamic peptide that has been recently found to reduce food intake, but few is known about its other functions in the central nervous system.\nNeuromedin U (NmU), originally isolated from porcine spinal cord and later from other species, is a novel peptide that potently contracts smooth muscle.\nBACKGROUND: Neuromedin U (NmU) belongs to the neuromedin family, comprising a series of neuropeptides involved in the gut-brain axis and including neuromedins B and C (bombesin-like), K (neurokinin B), L (neurokinin A or neurotensin), N, S, and U.CONTENT: Although initially isolated from porcine spinal cord on the basis of their ability to induce uterine smooth muscle contraction, these peptides have now been found to be expressed in several different tissues and have been ascribed numerous functions, from appetite regulation and energy balance control to muscle contraction a\nNeuromedin U (NMU), a neuropeptide isolated from porcine spinal cord and named because of its activity as a rat uterus smooth muscle contraction inducer, is emerging as a new player in the tumorigenesis and/or metastasis of many types of cancers.", "role": "user" }, { "content": "Neuromedin U (NmU) is a bioactive neuropeptide that is highly distributed in the central nervous system and the gastrointestinal tract. It has a number of physiological and pathophysiological roles, ranging from feeding behavior, energy metabolism, stress responses, circadian rhythmicity and inflammation.", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nAllotherantibodiesareasinthelegendtoFigure2.", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nQuantum machine learning methods have the potential to facilitate learning using extremely large datasets.\nWhile the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.\nOne of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors.\nHere, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines.\nThe algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.\nData sets used for training machine learning models are becoming increasingly large, leading to continued interest in fast methods for solving large-scale classification problems.\nOne of the approaches being explored is training the predictive model using a quantum algorithm that has access to the training set stored in quantum-accessible memory.\nIn parallel to research on efficient architectures for quantum memory (Blencowe, 2010) , work on quantum machine learning algorithms and on quantum learning theory is under way (see for example Refs.\n(Biamonte et al., 2017; Dunjko & Briegel, 2018; Schuld & Petruccione, 2018) and (Arunachalam & de Wolf, 2017) for review).\nAn early example of this approach is Quantum LS-SVM (Rebentrost et al., 2014a) , which achieves exponential speedup compared to classical LS-SVM algorithm.\nQuantum LS-SVM uses quadratic least-squares loss and squared-L 2 regularizer, and the optimization problem can be solved using the seminal HHL (Harrow et al., 2009 ) algorithm for solving quantum linear systems of equations.\nWhile progress has been made in quantum algorithms for supervised learning, it has been recently advocated that the focus should shift to unsupervised and semi-supervised setting (Perdomo-Ortiz et al., 2018) .\nIn many domains, the most laborious part of assembling a training set is the collection of sample labels.\nThus, in many scenarios, in addition to the labeled training set of size m we have access to many more feature vectors with missing labels.\nOne way of utilizing these additional data points to improve the classification model is through semi-supervised learning.\nIn semi-supervised learning, we are given m observations x 1 , ..., x m drawn from the marginal distribution p(x), where the l (l m) first data points come with labels y 1 , ..., y l drawn from conditional distribution p(y|x).\nSemi-supervised learning algorithms exploit the underlying distribution of the data to improve classification accuracy on unseen samples.\nIn the approach considered here, the training samples are connected by a graph that captures their similarity.\nHere, we introduce a quantum algorithm for semi-supervised training of a kernel support vector machine classification model.\nWe start with the existing Quantum LS-SVM (Rebentrost et al., 2014a) , and use techniques from sample-based Hamiltonian simulation (Kimmel et al., 2017) to add a semisupervised term based on Laplacian SVM (Melacci & Belkin, 2011) .\nAs is standard in quantum machine learning (Li et al., 2019) , the algorithm accesses training points and the adjacency matrix of the graph connecting samples via a quantum oracle.\nWe show that, with respect to the oracle, the proposed algorithm achieves the same quantum speedup as LS-SVM, that is, adding the semisupervised term does not lead to increased computational complexity.", "role": "user" }, { "content": "We extend quantum SVMs to semi-supervised setting, to deal with the likely problem of many missing class labels in huge datasets.", "role": "assistant" } ]

[ { "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:\nrelated work\n\nContext before the citation:\n9 We do not relate to specific results in their study because it has been brought to our attention that Hohensee and Bender (2012) are in the process of rechecking their code for errors, and rerunning their experiments (personal communication). In all our experiments, we contrasted the results obtained using machine-predicted input with the results obtained using gold input (the We examined a large space of settings.\n\nCitation Sentence:\n9 We do not relate to specific results in their study because it has been brought to our attention that Hohensee and Bender ( 2012 ) are in the process of rechecking their code for errors , and rerunning their experiments ( personal communication ) .\n\nContext after the citation:\nupper bound for using these features). We started by looking at individual features (including POS tag sets) and their prediction accuracy. We then explored various feature combinations in a hill-climbing fashion. We examined these issues in the following order: 1.", "role": "user" }, { "content": "CompareOrContrast", "role": "assistant" } ]

[ { "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: Which depression rating scales were shown to have acceptable psychometric properties for screening of poststroke depression?\n\nContext: The Center of Epidemiological Studies-Depression Scale (CESD) (sensitivity: 0.75; 95% CI 0.60 to 0.85; specificity: 0.88; 95% CI 0.71 to 0.95), the Hamilton Depression Rating Scale (HDRS) (sensitivity: 0.84; 95% CI 0.75 to 0.90; specificity:0.83; 95% CI 0.72 to 0.90) and the Patient Health Questionnaire (PHQ)-9 (sensitivity: 0.86; 95% CI 0.70 to 0.94; specificity: 0.79; 95% CI 0.60 to 0.90) appeared to be the optimal measures for screening measures.\nThere are a number of possible instruments that may help in screening for poststroke depression but none are satisfactory for case-finding.\nPreliminary data suggests the CESD, HDRS or the PHQ-9 as the most promising options.\nThe Beck Depression Inventory (BDI), Hospital Anxiety and Depression Scale-depression subscale (HADS-D), Hamilton Rating Scale for Depression (HAMD), and Montgomery-Asberg Depression Rating Scale (MADRS) were administered. The balance of sensitivity and specificity was assessed using receiver operating characteristics (ROC) analysis. RESULTS: Discriminating abilities of all the scales for major and all PSD were good (area under ROC values 0.88-0.93 and 0.88-0.92 at 2 weeks; and 0.93-0.96 and 0.89-0.91 at 1 year, respectively).\nWe compared the Beck Depression Inventory, Hamilton Rating Scale for Depression, Visual Analogue Mood Scale, proxy assessment, and Clinical Global Impression of the nursing and study personnel, together with Diagnostic and Statistical Manual of Mental Disorders, 3rd Edition, Revised diagnosis, in assessing depression after stroke in a follow-up study of 100 patients.\nThe sensitivity and specificity against the Diagnostic and Statistical Manual of Mental Disorders criteria were acceptable with the Clinical Global Impression, Beck Depression Inventory, and Hamilton Rating Scale for Depression, mostly in the range of 0.70 to 1.00.\nThe Visual Analogue Mood Scale was not a sensitive instrument (sensitivity, 0.20 to 0.60) and did not correlate with the Beck Depression Inventory during the first year after stroke.\nBeck Depression Inventory, Hamilton Rating Scale for Depression, and Clinical Global Impression assessment by professionals, in addition to the Diagnostic and Statistical Manual of Mental Disorders, 3rd Edition, Revised diagnosis, are useful in assessing depression, but none of these instruments clearly stood apart from the others.\nProxy ratings should be used with caution, and the use of the Visual Analogue Mood Scale among patients with aphasia and other cognitive impairments cannot be recommended.\nThis study evaluated the diagnostic accuracy of the Poststroke Depression Rating Scale (PSDRS), a diagnostic tool specifically devised to assess depression after stroke, in comparison to the Hamilton Depression Rating Scale (Ham-D).\nAt their optimum cut-off points, the Ham-D and PSDRS showed good sensitivity and specificity for MDL or MDL + MDDM; the PSDRS had a higher positive predictive value for MDL in respect of the Ham-D (78 vs. 59%).\nFurthermore, the diagnostic accuracy of the PSDRS was higher in respect of the Ham-D in aphasic patients.\nThe Ham-D and PSDRS are both reliable diagnostic tools for the diagnosis of PSD.\nWe suggest that the PSDRS could be a useful tool in clinical practice and in therapeutic trials.\nThe psychiatrist classified 25/71 (35.2%) patients as depressed. Using the recommended cut-point of 2 or more on the Signs of Depression Scale, the nurse and carer respectively rated 27/71 (38.0%) and 18/30 (60.0%) patients as potentially depressed. The proportion of patients correctly identified as depressed by the test (sensitivity) when rated by nurses was 64%, and the proportion of patients not depressed who were correctly identified by the test (specificity) was 61%, whereas carers achieved sensitivity 90% and specificity 35%.\nThe Signs of Depression Scale is easily completed by clinical staff, although we found the sensitivity when completed by nurses to be low. Information from carers shows potential to improve screening and it is important for nurses to value the knowledge and skills of carers in detecting depression following a stroke. Further refinement of the Signs of Depression Scale, with accompanying research, is required.\nThe VAMS was not useful in screening for depression in Chinese stroke patients while both the HADS and the GDS demonstrated satisfactory accuracy in detecting depression in Chinese stroke patients.\nAt their respective optimum cutoff values, the sensitivity of the self-rated scales varied between 80% and 90%, while the specificity was about 60%. For the observer-rated scale (Hamilton Depression Rating Scale), sensitivity was 78.1%, and specificity was 74.6%.\nThe hypothesis was supported: in contrast to psychiatric interview (68% depressed) and self-report (Beck Depression Inventory, 50% depressed), none of the patients were described as depressed in chart notes by the rehabilitation team (excluding the psychiatrists).\nAlthough there were no marked differences in the screening abilities for PSD between the scales, differences were found in factors influencing misclassification. Assessment scales with less somatic items may be recommended for the screening of PSD, particularly at the acute phase of stroke.", "role": "user" }, { "content": "The Center of Epidemiological Studies-Depression Scale, Hamilton Depression Rating Scale, and the Patient Health Questionnaire, The Beck Depression Inventory, Hospital Anxiety and Depression Scale-depression subscale, Montgomery-Asberg Depression Rating Scale, Poststroke Depression Rating Scale and Clinical Global Impression scale appeared to have acceptable psychometric properties for identifying depression in patients after stroke. Therefore, these scales can be used for screening of post-stroke depression. Signs of Depression Scale and Visual Analogue Mood Scale showed inferior psychometric properties and therefore should not be used for screening for post-stroke depression.", "role": "assistant" } ]

[ { "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\nSpectroscopic fingerprints of toroidal nuclear quantum delocalization via ab initio path integral simulations. We investigate the quantum-mechanical delocalization of hydrogen in rotational symmetric molecular systems. To this purpose, we perform ab initio path integral molecular dynamics simulations of a methanol molecule to characterize the quantum properties of hydrogen atoms in a representative system by means of their real-space and momentum-space densities. In particular, we compute the spherically averaged momentum distribution n(k) and the pseudoangular momentum distribution n(kθ). We interpret our results by comparing them to path integral samplings of a bare proton in an ideal torus potential. We find that the hydroxyl hydrogen exhibits a toroidal delocalization, which leads to characteristic fingerprints in the line shapes of the momentum distributions. We can describe these specific spectroscopic patterns quantitatively and compute their onset as a function of temperature and potential energy landscape. The delocalization patterns in the projected momentum distribution provide a promising computational tool to address the intriguing phenomenon of quantum delocalization in condensed matter and its spectroscopic characterization. As the momentum distribution n(k) is also accessible through Nuclear Compton Scattering experiments, our results will help to interpret and understand future measurements more thoroughly.", "role": "user" }, { "content": "{\"Chemical\": [\"hydrogen\", \"hydroxyl hydrogen\", \"methanol\"]}", "role": "assistant" } ]

[ { "content": "You will be shown an abstract from a research paper and a corresponding research question. Determine the answer to the research question based on the context provided. Your answer should be one of: \"YES\", \"NO\", or \"MAYBE\".\n\nAbstract:\n(BACKGROUND) Ambulatory 24-h dual-channel pharyngeal and oesophageal pH monitoring is the standard test for measuring gastro-oesophageal and gastropharyngeal reflux. Artefacts caused by the intake of food may result in falsely positive gastropharyngeal reflux, which necessitates a manual review of 24-h pH data. The purpose of the study was to investigate the influence of meals and whether leaving out meals affected the reliability of the test.\n(METHODS) Patients referred for otolaryngological complaints, suspected to have been caused by gastro-oesophageal reflux, underwent 24-h dual-channel pH monitoring. The raw unprocessed pH data were corrected by visual inspection of the 24-h tracings (corrected data), by leaving out meals or meals plus a 2-h postprandrial period.\n(RESULTS) The raw pH data were substantially influenced by artefacts of food intake and pseudoreflux. Data obtained by leaving out meals agreed best with manually corrected data. Many of the falsely positive reflux episodes could be removed, thereby inducing a 9%-18% chance of undetected reflux. When examining the fraction of time supine, manually corrected data and data leaving out meals were fully concordant and detected 79% of patients with gastropharyngeal reflux. However, leaving out meals plus a 2-h postprandrial period resulted in 21%-50% falsely negative tests.\n\nQuestion:\nArtefacts in 24-h pharyngeal and oesophageal pH monitoring: is simplification of pH data analysis feasible?\n\nAnswer:", "role": "user" }, { "content": "MAYBE", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nMultiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion.\nRecent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions.\nIn this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches.\nRather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the depth map from the cost volume.\nThe cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network.\nThrough the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.\nVarious image understanding tasks, such as semantic segmentation BID3 and human pose/action recognition BID29 BID33 , have been shown to benefit from 3D scene information.\nA common approach to reconstructing 3D geometry is by multiview stereo, which infers depth based on point correspondences among a set of unstructured images BID10 ; .\nTo solve for these correspondences, conventional techniques employ photometric consistency constraints on local image patches.\nSuch photo-consistency constraints, though effective in many instances, can be unreliable in scenes containing textureless and reflective regions.Recently, convolutional neural networks (CNNs) have demonstrated some capacity to address this issue by leveraging semantic information inferred from the scene.\nThe most promising of these methods employ a traditional stereo matching pipeline, which involves computation of matching cost volumes, cost aggregation, and disparity estimation BID5 ; BID19 ; BID14 ; BID0 .\nSome are designed for binocular stereo BID31 ; BID19 ; BID0 and cannot readily be extended to multiple views.\nThe CNN-based techniques for multiview processing BID5 ; BID14 both follow the plane-sweep approach, but require plane-sweep volumes as input to their networks.\nAs a result, they are not end-to-end systems that can be trained from input images to disparity maps.In this paper, we present Deep Plane Sweep Network (DPSNet), an end-to-end CNN framework for robust multiview stereo.\nIn contrast to previous methods that employ the plane-sweep approach BID14 ; BID5 , DPSNet fully models the plane-sweep process, including construction of plane-sweep cost volumes, within the network.\nThis is made possible through the use of a differentiable warping module inspired by spatial transformer networks BID17 to build the cost volumes.\nWith the proposed network, plane-sweep stereo can be learned in an end-to-end fashion.\nAdditionally, we introduce a cost aggregation module based on local cost-volume filtering BID26 for context-aware refinement of each cost slice.\nThrough this cost-volume regularization, the effects of unreliable matches scattered within the cost volume are reduced considerably.With this end-to-end network for plane-sweep stereo and the proposed cost aggregation, we obtain state-of-the-art results over several standard datasets.\nAblation studies indicate that each of these technical contributions leads to appreciable improvements in reconstruction accuracy.\nWe developed a multiview stereo network whose design is inspired by best practices of traditional non-learning-based techniques.\nThe plane sweep algorithm is formulated as an end-to-end network via a differentiable construction of plane sweep cost volumes and by solving for depth as a multilabel classification problem.\nMoreover, we propose a context-aware cost aggregation method that leads to improved depth regression without any post-processing.\nWith this incorporation of traditional multiview stereo schemes into a deep learning framework, state-of-the-art reconstruction results are achieved on a variety of datasets.Directions exist for improving DPSNet.\nOne is to integrate semantic instance segmentation into the cost aggregation, similar to the segment-based cost aggregation method of BID25 .\nAnother direction is to improve depth prediction by employing viewpoint selection in constructing cost volumes BID6 , rather than by simply averaging the estimated cost volumes as currently done in DPSNet.\nLastly, the proposed network requires pre-calibrated intrinsic and extrinsic parameters for reconstruction.\nLifting this restriction by additionally estimating camera poses in an end-to-end learning framework is an important future challenge.", "role": "user" }, { "content": "A convolution neural network for multi-view stereo matching whose design is inspired by best practices of traditional geometry-based approaches", "role": "assistant" } ]

[ { "content": "You will be shown sections from a scientific research paper, together with a question about the paper. This is an extractive question-answering task, where you must find and extract relevant text spans directly from the paper to answer the question. Your response should strictly 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 example, for the question \"What baselines did the authors compare against?\", a sample response might be:\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: Which dataset do they use to build their model?", "role": "user" }, { "content": "{\"answer\": [\"1,160 physician logs of Medical ICU admission requests\", \"42,506 Wikipedia articles\", \"6 research papers and 2 critical care medicine textbooks\"], \"evidence\": [\"The 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\\u0027 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.\"]}", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nJasinforSpo11-embryonicstemcells,andR.", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nGenotypingwasperformedbymultiplexPCRreactionusingthreeseparateprimers(twoAbcg8genespecificprimers,mg8-in3F5'-CCCAATGATGAATGAGACGCT-3',mg8-R95'-TTTGTCACGCTGGGCCTGG-3'andneoFforidentificationofAbcg8targetstatus).", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "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:\nEntity extraction is fundamental to many text mining tasks such as organisation name recognition. A popular approach to entity extraction is based on matching sub-string candidates in a document against a dictionary of entities. To handle spelling errors and name variations of entities, usually the matching is approximate and edit or Jaccard distance is used to measure dissimilarity between sub-string candidates and the entities. For approximate entity extraction from free text, existing work considers solely character-based or solely token-based similarity and hence cannot simultaneously deal with minor variations at token level and typos. In this paper, we address this problem by considering both character-based similarity and token-based similarity (i.e. two-level similarity). Measuring one-level (e.g. character-based) similarity is computationally expensive, and measuring two-level similarity is dramatically more expensive. By exploiting the properties of the two-level similarity and the weights of tokens, we develop novel techniques to significantly reduce the number of sub-string candidates that require computation of two-level similarity against the dictionary of entities. A comprehensive experimental study on real world datasets show that our algorithm can efficiently extract entities from documents and produce a high F1 score in the range of [0.91, 0.97].\n\nCited Abstract(s):\n[1]: We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.", "role": "user" }, { "content": ": The approximate entity extraction problem can be viewed as the approximate string matching problem which is a well-studied problem. Navarro gives a nice survey for the approximate string matching problem [1] . Here, we focus on some recent work in entity extraction.", "role": "assistant" } ]

[ { "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 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: Neratinib: an option for HER2-positive metastatic breast cancer. Metastatic human epidermal growth factor receptor 2 (HER2)-positive breast cancer is currently incurable. The primary goals of treatment are to prolong survival while optimizing quality of life. Several agents are now available in this setting , including neratinib , tucatinib , ado-trastuzumab emtansine , and trastuzumab deruxtecan . neratinib in combination with capecitabine was recently approved for the treatment of adult patients with advanced or metastatic breast cancer who have received 2 or more prior anti-HER2-based regimens in the metastatic setting. neratinib is an oral pan-HER inhibitor that binds covalently to the kinase site, providing irreversible binding. Phase 3 data showed that the combination of neratinib plus capecitabine improved progression-free survival vs lapatinib plus capecitabine. The duration of response was longer among patients in the neratinib arm. neratinib plus capecitabine was also active against brain metastases associated with refractory, HER2-positive breast cancer, and this combination is listed in guidelines from the National Comprehensive Cancer Network for this indication. When combined with fulvestrant, neratinib demonstrated efficacy in patients with HER2-positive breast cancer, regardless of their hormone receptor status. Ongoing trials are evaluating the ability of neratinib to treat brain metastases, as well as the efficacy and safety of the triplet combination of neratinib, fulvestrant, and trastuzumab in this setting.\n\nsentence: Several agents are now available in this setting , including neratinib , tucatinib , ado-trastuzumab emtansine , and trastuzumab deruxtecan .", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "content": "You are presented with multiple paper abstracts, a literature review title, and a specific chapter title. Your task is to write a focused literature review chapter that aligns with the given chapter title. The review should synthesize the content from the provided abstracts, draw connections between different works, highlight major findings, and identify any gaps in the existing research.\nLiterature Review Title: A Survey on Non-transferable Anonymous Credentials \nChapter Title: Anonymous Credentials \n1. Abstract of Cited Paper (BIB001): Previously there have been essentially only two models for computers that people can use to handle ordinary consumer transactions: (1) the tamper-proof module, such as a smart card, that the person cannot modify or probe: and (2) the personal workstation whose inner working is totally under control of the individual. The first part of this article argues that a particular combination of these two kinds of mechanism can overcome the limitations of each alone, providing both security and correctness for organizations as well as privacy and even anonymity for individuals.Then it is shown how this combined device, called a wallet, ran carry a database containing personal information. The construction presented ensures that no single part of the device (i.e. neither the tamper-proof part nor the workstation) can learn the contents of the database -- this information can only be recovered by the two parts together.\n\nLiterature Review:", "role": "user" }, { "content": "The basic idea of anonymous credentials is that users are able to anonymously prove attributes issued by an organisation. As stated above, anonymous authentication means that neither should the verifier learn any information about the user except that the user is authorised nor should he be able to link several authentications of the same user which would allow him to build profiles on authenticating users. Implementations usually access proofs of knowledge in combination with blind signature BIB001 and group signature schemes. \"Knowledge\" is only one authentication factor , but it can easily be transformed to \"possession\" by moving the secret into a smartcard, where we presume it cannot be copied from. More precisely we assume the user is able to use the credential without the credential leaving the card. The smartcard then works as a blackbox for the user and if he does not trust the manufacturer of the card or the issuing organisation, we assume the user carefully observes the communication of the card with the verifier following Chaum's and Pedersen's wallet with observer architecture BIB002 . This concept suggests each user has a personal communication device (called wallet) with a tamper-resistant chip (called observer ) either built-in or in the form of a smartcard. Now the user is able to check and prevent the information flow from the organisation to the observer and only has to trust that the observer supports all legitimate operations. The verifying organisation on the other hand only has to trust that the observer is still intact and prevents illegitimate operations (e.g. releasing the secret). To prevent abuse the tamper-resistant chip may be protected by a personal identification number (PIN) resulting in a two-factor-authentication (possession of card and knowledge of the PIN) as already known from today's cash cards.", "role": "assistant" } ]

[ { "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: [\"RNA\", \"CellType\", \"CellLine\", \"Protein\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nSynergism between the CD3 antigen-and CD2 antigen-derived signals. Exploration at the level of induction of DNA-binding proteins and characterization of the inhibitory activity of cyclosporine. We have demonstrated earlier that the crosslinkage of the CD3 / TCR complex with the CD2 antigen results in the proliferation of normal human T cells. The effect of this synergism was perceptible at the level of induction of the IL-2 gene, a process critical for T cell growth. To further understand the molecular and nuclear basis for this synergism, we have explored the induction of DNA-binding proteins in highly purified normal human T cells signaled via the CD3 and / or CD2 proteins. The effect of transmembrane signaling of T cells with ionomycin, and / or sn-1, 2 dioctanoyl glycerol, was also determined. The emergence of nuclear binding proteins was investigated using interleukin-2 sequence specific oligonucleotide probes in the electrophoretic mobility shift assay. Our studies demonstrate for the first time that CD3 antigen-derived signals and CD2 antigen-derived signals are synergistic in inducing the emergence of transcription factors that bind to the NF-AT1, AP-1, and NF-kB sites located in the promoter / enhancer region of the IL-2 gene. Moreover, cyclosporine, at concentrations readily accomplished in clinical practice, was found to inhibit the emergence of these DNA-binding proteins in normal human T cells signaled via cell surface proteins implicated in antigen-dependent T cell activation and in T cells stimulated by mobilization of cellular calcium and activation of protein kinase C.", "role": "user" }, { "content": "{\"CellLine\": [], \"CellType\": [\"T cells\", \"normal human T cells\"], \"DNA\": [\"interleukin - 2 sequence specific oligonucleotide probes\", \"IL - 2 gene\", \"NF - AT1 , AP - 1 , and NF - kB sites\", \"promoter / enhancer region\"], \"Protein\": [\"CD2 antigen\", \"CD2 proteins\", \"transcription factors\", \"nuclear binding proteins\", \"IL - 2\", \"protein kinase C\", \"DNA - binding proteins\", \"interleukin - 2\", \"CD3 / TCR complex\", \"CD3\"], \"RNA\": []}", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nAsynchronous distributed methods are a popular way to reduce the communication and synchronization costs of large-scale optimization.\nYet, for all their success, little is known about their convergence guarantees in the challenging case of general non-smooth, non-convex objectives, beyond cases where closed-form proximal operator solutions are available.\n\nThis is all the more surprising since these objectives are the ones appearing in the training of deep neural networks.\n\n\nIn this paper, we introduce the first convergence analysis covering asynchronous methods in the case of general non-smooth, non-convex objectives.\nOur analysis applies to stochastic sub-gradient descent methods both with and without block variable partitioning, and both with and without momentum.\nIt is phrased in the context of a general probabilistic model of asynchronous scheduling accurately adapted to modern hardware properties.\nWe validate our analysis experimentally in the context of training deep neural network architectures.\nWe show their overall successful asymptotic convergence as well as exploring how momentum, synchronization, and partitioning all affect performance.\nTraining parameters arising in Deep Neural Net architectures is a difficult problem in several ways (Goodfellow et al., 2016) .\nFirst, with multiple layers and nonlinear activation functions such as sigmoid and softmax functions, the ultimate optimization problem is nonconvex.\nSecond, with ReLU activation functions and max-pooling in convolutional structures, the problem is nonsmooth, i.e., it is not differentiable everywhere, although typically the set of non-differentiable points is a set of measure zero in the space of the parameters.\nFinally, in many applications it is unreasonable to load the whole sample size in memory to evaluate the objective function or (sub)gradient, thus samples must be taken, necessitating analysis in a probabilistic framework.\nThe analysis of parallel optimization algorithms using shared memory architectures, motivated by applications in machine learning, was ushered in by the seminal work of Recht et al. (2011) (although precursors exist, see the references therein).\nFurther work refined this analysis, e.g. (Liu & Wright, 2015) and expanded it to nonconvex problems, e.g. (Lian et al., 2015) .\nHowever, in all of these results, a very simplistic model of asynchronous computation is presented to analyze the problem.\nNotably, it is assumed that every block of the parameter, among the set of blocks of iterates being optimized, has a fixed, equal probability of being chosen at every iteration, with a certain vector of delays that determine how old each block is that is stored in the cache relative to the shared memory.\nAs one can surmise, this implies complete symmetry with regards to cores reading and computing the different blocks.\nThis does not correspond to asynchronous computation in practice.\nIn particular, in the common Non-Uniform Memory Access (NUMA) setting, practical experience has shown that it can be effective for each core to control a set of blocks.\nThus, the choice of blocks will depend on previous iterates, which core was last to update, creating probabilistic dependence between the delay vector and the choice of block.\nThis exact model is formalized in Cannelli et al., which introduced a new probabilistic model of asynchronous parallel optimization and presented a coordinate-wise updating successive convex approximation algorithm.\nIn this paper, we are interested in studying parallel asynchronous stochastic subgradient descent for general nonconvex nonsmooth objectives, such as the ones arising in the training of deep neural network architectures.\nCurrently, there is no work in the literature specifically addressing this problem.\nThe closest related work is given by Zhu et al. (2018) and , which consider asynchronous proximal gradient methods for solving problems of the form f (x) + g(x), where f is smooth and nonconvex, and g(x) is nonsmooth, with an easily computable closed form prox expression.\nThis restriction applies to the case of training a neural network which has no ReLUs or max pooling in the architecture itself, i.e., every activation is a smooth function, and there is an additional regularization term, such as an 1 .\nThese papers derive expected rates of convergence.\nIn the general case, where the activations themselves are nonsmooth-for instance in the presence of ReLUs-there is no such additive structure, and no proximal operator exists to handle away the non-smoothness and remove the necessity of computing and using subgradients explicitly in the optimization procedure.\nThis general problem of nonsmooth nonconvex optimization is already difficult (see, e.g., Bagirov et al. (2014) ), and the introduction of stochastically uncertain iterate updates creates an additional challenge.\nClassically, the framework of stochastic approximation, with stochastic estimates of the subgradient approximating elements in a differential inclusion that defines a flow towards minimization of the objective function, is a standard, effective approach to analyzing algorithms for this class of problems.\nSome texts on the framework include Kushner & Yin (2003) , which we shall reference extensively in the paper, and Borkar (2008) .\nSee also Ermol'ev & Norkin (1998) and Ruszczyński (1987) for some classical results in convergence of stochastic algorithms for nonconvex nonsmooth optimization.\nInterest in stochastic approximation has resurfaced recently sparked by the popularity of Deep Neural Network architectures.\nFor instance, see the analysis of nonconvex nonsmooth stochastic optimization with an eye towards such models in Davis et al. (2018) and Majewski et al. (2018) .\nIn this paper, we provide the first analysis for nonsmooth nonconvex stochastic subgradient methods in a parallel asynchronous setting, in the stochastic approximation framework.\nFor this, we employ the state of the art model of parallel computation introduced in Cannelli et al., which we map onto the analysis framework of Kushner & Yin (2003) .\nWe prove show that the generic asynchronous stochastic subgradient methods considered are convergent, with probability 1, for nonconvex nonsmooth functions.\nThis is the first result for this class of algorithms, and it combines the state of the art in these two areas, while extending the scope of the results therein.\nFurthermore, given the success of momentum methods (see, e.g., Zhang et al. (2017) ), we consider the momentum variant of the classical subgradient method, again presenting the first convergence analysis for this class of algorithms.\nWe validate our analysis numerically by demonstrating the performance of asynchronous stochastic subgradient methods of different forms on the problem of ResNet deep network training.\nWe shall consider variants of asynchronous updating with and without write locks and with and without block variable partitioning, showing the nuances in terms of convergence behavior as depending on these strategies and properties of the computational hardware.\nIn this paper we analyzed the convergence theory of asynchronous stochastic subgradient descent.\nWe found that the state of the art probabilistic model on asynchronous parallel architecture applied to the stochastic subgradient method, with and without the use of momentum, is consistent with standard theory in stochastic approximation and asymptotic convergence with probability one holds for the method under the most general setting of asynchrony.\nWe presented numerical results that indicate some possible performance variabilities in three types of asynchrony: block partitioning inconsistent read (for which the above convergence theory applies), full-variable-update consistent write (for which the above convergence theory also applies), and full-variable-update inconsistent read/write (for which no convergence theory exists).", "role": "user" }, { "content": "Asymptotic convergence for stochastic subgradien method with momentum under general parallel asynchronous computation for general nonconvex nonsmooth optimization", "role": "assistant" } ]

[ { "content": "You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.\n\nTitle: Spatial dilemmas of diffusible public goods\nArticle:\nAbstract:\nThe emergence of cooperation is a central question in evolutionary biology. Microorganisms often cooperate by producing a chemical resource( a public good) that benefits other cells. The sharing of public goods depends on their diffusion through space. Previous theory suggests that spatial structure can promote evolution of cooperation, but the diffusion of public goods introduces new phenomena that must be modeled explicitly. We develop an approach where colony geometry and public good diffusion are described by graphs. We find that the success of cooperation depends on a simple relation between the benefits and costs of the public good, the amount retained by a producer, and the average amount retained by each of the producer’s neighbors. These quantities are derived as analytic functions of the graph topology and diffusion rate. In general, cooperation is favored for small diffusion rates, low colony dimensionality, and small rates of decay of the public good.\nIntroduction:\nPublic goods dilemmas are frequently observed in microbes. For example, the budding yeast Saccharomyces cerevisiae cooperates by producing the enzyme invertase, which hydrolyzes sucrose into monosaccharides, when yeast colonies are grown in glucose-limited media( Greig and Travisano, 2004; Gore et al., 2009). Other examples include the production of chemical agents that scavenge iron( Griffin et al., 2004; Buckling et al., 2007; Cordero et al., 2012; Julou et al., 2013), enable biofilm formation( Rainey and Rainey, 2003), eliminate competition( Le Gac and Doebeli, 2010), induce antibiotic resistance( Chuang et al., 2009; Lee et al., 2010), or facilitate infection of a host( Raymond et al., 2012). In many cases, the benefits of public goods go primarily to cells other than the producer. For example, in a S. cerevisiae population subject to continuous mixing, only ∼1% of monosaccharides are imported into the cell that hydrolyzes them, with the remainder diffusing away( Gore et al., 2009). Furthermore, production of public goods typically involves a metabolic cost, which may exceed the direct benefit to the producer. In this case, absent some mechanism to support cooperation( Nowak, 2006), public goods production is expected to disappear under competition from cheaters, resulting in the tragedy of the commons( Hardin, 1968). There is growing evidence from experiments( Griffin et al., 2004; Kümmerli et al., 2009; Julou et al., 2013; Momeni et al., 2013) and simulations( Allison, 2005; Misevic et al., 2012) that spatial or group clustering can support cooperation in microbial public goods dilemmas, although this effect depends on the nature of competition for space and resources( Griffin et al., 2004; Buckling et al., 2007). These findings agree with insights from mathematical models( Nowak and May, 1992; Durrett and Levin, 1994; Santos and Pacheco, 2005; Ohtsuki et al., 2006; Szabó and Fáth, 2007; Taylor et al., 2007; Perc and Szolnoki, 2008; Fletcher and Doebeli, 2009; Korolev and Nelson, 2011) suggesting that spatial structure can promote cooperation by facilitating clustering and benefit-sharing among cooperators. However, these mathematical results focus largely on pairwise interactions rather than diffusible public goods. On the other hand, previous theoretical works that specifically explore microbial cooperation( West and Buckling, 2003; Ross-Gillespie et al., 2007; Driscoll and Pepper, 2010) use a relatedness parameter in place of an explicit spatial model, obscuring the important roles of colony geometry and spatial diffusion in determining the success of cooperation.\nDiscussion:\nOur results suggest three qualitative regimes for diffusible public goods scenarios. For λ ≪ 1, the benefits are almost all retained by producer, and production is favored whenever b/c > 1. Conversely, for λ ≪ 1, public goods are shared indiscriminately, and production is favored only if public goods are essential for survival, in which case b is effectively infinite. Between these extremes, public goods are shared locally, and the spatial arrangement of cells plays a critical role in the success of cooperation( Figure 3A). At the smaller end of this critical regime, the expansion b/c>1+λ( κ−1) /κ+O( λ2) of condition( 2), derived in Supplementary file 1, shows how the difficulty of cooperation increases with the diffusion parameter λ and the Simpson degree κ. For the hydrolysis of monosaccharides in S. cerevisiae, we estimate λ ∼ 3( ‘Materials and methods’); thus we expect the success of invertase production to be strongly affected by colony geometry. Interestingly, this diffusion length is of the same order of magnitude as those reported in other recent experiments with diffusible public goods( Julou et al., 2013; Momeni et al., 2013). Our model predicts that the advantage of cooperation decreases with colony dimensionality; for example, less cooperation would be expected in three-dimensional structures than in flat( 2D) colonies( Figure 3A). It also predicts that cooperation becomes more successful with increased viscosity of the environment and/or rate of public goods utilization, both of which would decrease λ. A more subtle question is how cooperation is affected if the public good may decay( or equivalently, escape the colony) instead of being utilized. Decay reduces the absolute amount of public goods to be shared, but also restricts this sharing to a smaller circle of neighbors; thus the net effect on cooperation is at first glance ambiguous. We show in the ‘Materials and methods’ that incorporating decay effectively decreases λ by a factor 1/( 1 + d), reflecting the smaller neighborhood of sharing, and also effectively decreases b by the same factor, reflecting the diminished absolute amount of public goods. Here d represents the ratio of the decay rate to the utilization rate. Since the critical benefit-to-cost ratio always increases sublinearly with λ, the net effect is to make cooperation more difficult( see Figure 3C). Thus decay of the public good has a purely negative effect on cooperation. Our results help elucidate recent emiprical results on microbial cooperation in viscous environments. For example, Kümmerli et al.( 2009) found that increased viscosity promotes the evolution of siderophore production in Pseudomonas aeruginosa, while Le Gac and Doebeli( 2010) found that viscosity had no effect on the evolution of colicin production in E. coli. In both cases, increased viscosity restricted cell movement, effectively leading to fewer neighbors per cell( lower graph degree). The crucial difference lies in the effect on public goods diffusion. In the study of Kümmerli et al.( 2009), the diffusion rate decreased significantly as viscosity increased, while for Le Gac and Doebeli( 2010), the diffusion rate remained large even with high viscosity. Thus the divergent outcomes can be understood as a consequence of differences in the diffusion rate, captured in our model by λ. Here we have considered homotypic cooperation—cooperation within a single population. Momeni et al.( 2013), published previously in eLife, investigate heterotypic cooperation between distinct populations of S. cerevisiae, in the form of exchange of essential metabolites. Type R produces adenine and requires lysine, type G produces lysine and requires adenine, and type C( a cheater) requires adenine but does not produce adenine. While such heterotypic cooperation is not incorporated in our model, the results are qualitatively similar, in that spatial structure promoted the cooperative strategies G and R over the cheater C. This similarity can be understood by noting that heterotypic cooperation also entails a form of second-order homotypic cooperation. For example, G-cells aid nearby R-cells, which in turn aid nearby G-cells, so the benefit produced by a G-cell indirectly aids other G-cells nearby. Thus the conclusion that spatial structure aids cooperative strategies can apply to heterotypic cooperation as well. Finally, our model can also represent the spread of behaviors via imitation on social networks( Bala and Goyal, 1998; Bramoullé and Kranton, 2007; Christakis and Fowler, 2007). Suppose an action generates a benefit b0 for the actor, and additionally generates further benefits that radiate outward according to some multiplier m, so that first neighbors receive a combined benefit mb0, second neighbors receive m2b0, and so on. Education, for example, exhibits this kind of social multiplier in its effect on wages( Glaeser et al., 2003). This effect can be captured using the parameter change b = b0/( 1 − m), λ = m/( 1 − m). For non-well-mixed social networks, the action becomes more likely to spread as the multiplier increases, and can spread even if there is a net cost to the actor( Figure 4). 10. 7554/eLife. 01169. 007Figure 4. The spread of behaviors on social networks increases with their social multipliers. In an alternate interpretation of our model, an action has benefits that radiate outward from the actor according to some multiplier m. Individual receiving a large amount of benefit are more likely to be imitated by social contacts. The likelihood of the action to spread—and the benefits to the network as a whole—both increase with the multiplier m. DOI: http://dx. doi. org/10. 7554/eLife. 01169. 007", "role": "user" }, { "content": "The natural world is often thought of as a cruel place , with most living things ruthlessly competing for space or resources as they struggle to survive . However , from two chimps picking the fleas off each other to thousands of worker ants toiling for the good of the colony , cooperation is fairly widespread in nature . Surprisingly , even single-celled microbes cooperate . Individual bacterial and yeast cells often produce molecules that are used by others . Whilst many cells share the benefits of these ‘public goods’ , at least some cells have to endure the costs involved in producing them . As such , selfish individuals can benefit from molecules made by others , without making their own . However , if everyone cheated in this way , the public good would be lost completely: this is called the ‘public goods dilemma’ . Allen et al . have developed a mathematical model of a public goods dilemma within a microbial colony , in which the public good travels from its producers to other cells by diffusion . The fate of cooperation in this ‘diffusible public goods dilemma’ depends on the spatial arrangement of cells , which in turn depends on their shape and the spacing between them . Other important factors include rates of diffusion and decay of the public good—both of which affect how widely the public good is shared . The model predicts that cooperation is favored when the diffusion rate is small , when the colonies are flatter , and when the public goods decay slowly . These conditions maximize the benefit of the public goods enjoyed by the cell producing them and its close neighbors , which are also likely to be producers . Public goods dilemmas are common in nature and society , so there is much interest in identifying general principles that promote cooperation .", "role": "assistant" } ]

[ { "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: [\"Finding\", \"Bacterium\", \"ProfessionalOrOccupationalGroup\", \"InjuryOrPoisoning\", \"Eukaryote\", \"BodySystem\", \"Virus\", \"ResearchActivity\", \"BodySubstance\", \"PopulationGroup\", \"Chemical\", \"AnatomicalStructure\", \"SpatialConcept\", \"HealthCareActivity\", \"Organization\", \"BiomedicalOccupationOrDiscipline\", \"Food\", \"ClinicalAttribute\", \"MedicalDevice\", \"BiologicFunction\", \"IntellectualProduct\"].\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\nImpact of Abnormal DNA Methylation of Imprinted Loci on Human Spontaneous Abortion Currently, there is a growing concern regarding the safety of assisted reproductive technology (ART) due to increased risk of spontaneous abortion (SA) and imprinting disorders in ART - conceived offspring. Early investigations suggested that aberrant genetic imprinting may be related to pregnancy loss; however, few studies have used human tissue specimens. Here the DNA methylation patterns of 3 imprinted genes, including maternally inherited GRB10 and the paternally inherited IGF2 and PEG3 genes, were evaluated in human chorionic villus samples by pyrosequencing and bisulfite sequencing polymerase chain reaction. The samples were divided into 4 groups: (1) SA of natural conception (NC; n = 84), (2) induced abortion of NC (n = 94), (3) SA after ART (n = 73), and (4) fetal reduction after ART (n = 86). The methylation levels and the percentages of abnormal methylation of the IGF2, GRB10, and PEG3 genes between the ART group and the NC group showed no significant difference. Both IGF2 and GRB10 genes showed higher methylation levels in the SA group compared to the non-SA group. Additionally, determining the single-nucleotide polymorphisms of 4 loci, including IGF2 rs3741205, rs3741206, rs3741211, and GRB10 rs2237457, showed that the TC+CC genotype of IGF2 rs3741211 had a 1.91-fold increased risk of SA after ART. However, there was no association between the mutant genotype of IGF2 rs3741211 and the methylation levels of IGF2 and H19, and ART might not affect the distribution of the abovementioned genotypes. It provides support for the opinion that genetic imprinting defects may be associated with SA, which might not be due to ART treatments.", "role": "user" }, { "content": "{\"AnatomicalStructure\": [\"GRB10 rs2237457\", \"GRB10 genes\", \"Imprinted Loci\", \"H19\", \"GRB10\", \"IGF2\", \"tissue specimens\", \"4 loci\", \"rs3741211\", \"IGF2 rs3741205\", \"IGF2 rs3741211\", \"imprinted genes\", \"chorionic villus\", \"mutant\", \"rs3741206\", \"PEG3 genes\"], \"Bacterium\": [], \"BiologicFunction\": [\"Spontaneous Abortion\", \"DNA Methylation\", \"SA\", \"genetic imprinting\", \"DNA methylation\", \"maternally inherited\", \"paternally inherited\", \"imprinting disorders\", \"spontaneous abortion\", \"methylation\", \"pregnancy loss\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [], \"ClinicalAttribute\": [], \"Eukaryote\": [\"human\", \"Human\"], \"Finding\": [\"abnormal\", \"no\", \"Abnormal\"], \"Food\": [], \"HealthCareActivity\": [\"ART\", \"treatments\", \"fetal reduction\", \"investigations\", \"induced abortion\", \"assisted reproductive technology\", \"pyrosequencing\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [], \"MedicalDevice\": [], \"Organization\": [], \"PopulationGroup\": [], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [\"bisulfite sequencing\", \"polymerase chain reaction\"], \"SpatialConcept\": [\"single-nucleotide polymorphisms\"], \"Virus\": []}", "role": "assistant" } ]

[ { "content": "You have to answer a biomedical question in binary format, i.e., only yes and no are acceptable answers. A list of paragraphs is provided as context to help you answer the question.\n\nQuestion: Is there any tool that facilitates the functional analysis of cis-regulatory regions in zebrafish?\n\nContext: Zebrafish enhancer detection (ZED) vector: a new tool to facilitate transgenesis and the functional analysis of cis-regulatory regions in zebrafish.\nhe cis-regulatory sequences control when, where, and how much genes are transcribed and can activate (enhancers) or repress (silencers) gene expression. Here, we describe a novel Tol2 transposon-based vector for assessing enhancer activity in the zebrafish (Danio rerio). This Zebrafish Enhancer Detector (ZED) vector harbors several key improvements, among them a sensitive and specific minimal promoter chosen for optimal enhancer activity detection, insulator sequences to shield the minimal promoter from position effects, and a positive control for transgenesis. Additionally, we demonstrate that highly conserved noncoding sequences homologous between humans and zebrafish largely with enhancer activity largely retain their tissue-specific enhancer activity during vertebrate evolution. More strikingly, insulator sequences from mouse and chicken, but not conserved in zebrafish, maintain their insulator capacity when tested in this model.\nZebrafish enhancer detection (ZED) vector: a new tool to facilitate transgenesis and the functional analysis of cis-regulatory regions in zebrafish\nZebrafish enhancer detection (ZED) vector: a new tool to facilitate transgenesis and the functional analysis of cis-regulatory regions in zebrafish.", "role": "user" }, { "content": "yes", "role": "assistant" } ]

[ { "content": "You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.\n\nTitle: Twin RNA Polymerase–Associated Proteins Control Virulence Gene Expression in Francisella tularensis\nArticle:\nAbstract:\nThe MglA protein is the only known regulator of virulence gene expression in Francisella tularensis, yet it is unclear how it functions. F. tularensis also contains an MglA-like protein called SspA. Here, we show that MglA and SspA cooperate with one another to control virulence gene expression in F. tularensis. Using a directed proteomic approach, we show that both MglA and SspA associate with RNA polymerase( RNAP) in F. tularensis, and that SspA is required for MglA to associate with RNAP. Furthermore, bacterial two-hybrid and biochemical assays indicate that MglA and SspA interact with one another directly. Finally, through genome-wide expression analyses, we demonstrate that MglA and SspA regulate the same set of genes. Our results suggest that a complex involving both MglA and SspA associates with RNAP to positively control virulence gene expression in F. tularensis. The F. tularensis genome is unusual in that it contains two genes encoding different α subunits of RNAP, and we show here that these two α subunits are incorporated into RNAP. Thus, as well as identifying SspA as a second critical regulator of virulence gene expression in F. tularensis, our findings provide a framework for understanding the mechanistic basis for virulence gene control in a bacterium whose transcription apparatus is unique.\nIntroduction:\nFrancisella tularensis is a Gram-negative, facultative intracellular pathogen and the aetiological agent of tularemia, a disease that can be fatal in humans. Although outbreaks of tularemia are thought to be rare, infections caused by F. tularensis have become a public health issue because of the potential for using this organism as a bioweapon. As few as ten organisms can constitute an infectious dose, and the pneumonic form of tularemia, which has a particularly high mortality rate, can be acquired when the organism is aerosolized[1, 2]. Relatively little is known regarding the molecular mechanisms of F. tularensis pathogenesis, in part because of the genetic intractability of this organism. Evidence suggests that the pathogenicity of F. tularensis depends on its ability to survive and replicate within macrophages. However, only a handful of genes that are required for intramacrophage survival have been identified[2–4]. One of these encodes a putative transcription regulator called macrophage growth locus A( MglA) that is responsible for controlling the expression of multiple virulence factors that are themselves required for survival within macrophages[3, 5, 6]. In particular, in F. tularensis subspecies novicida, MglA positively regulates the expression of multiple virulence genes, some of which are located on a pathogenicity island[5–7]. MglA also regulates the expression of many other genes that may or may not be implicated in virulence[5, 6]. However, the mechanism of this MglA-dependent regulation is unknown. MglA is an ortholog of the stringent starvation protein A( SspA) from Escherichia coli( the two proteins share 21% identity and 34% similarity at the primary amino acid level). In E. coli, SspA is a RNA polymerase( RNAP) –associated protein that is thought to regulate the expression of a subset of genes under conditions of stress[8, 9]. SspA in E. coli is also essential for the lytic growth of bacteriophage P1[10]. Specifically, Hansen and colleagues have shown that E. coli SspA functions as an activator of P1 late gene expression, acting in concert with a phage-encoded transcription activator called Lpa[11]. SspA homologs have also been found in a number of pathogens other than F. tularensis, and several of these homologs appear to play important roles in virulence gene regulation[12–14]. Recently, the genomes of two different strains of F. tularensis have been sequenced([15]; GenBank accession number NC_007880). One of these strains is Schu S4, a highly virulent strain of subspecies tularensis, and the other is the live vaccine strain( LVS), an attenuated derivative of one of the subspecies holarctica strains. The genome sequences reveal that F. tularensis contains an MglA-like protein, which has been annotated as SspA[15]. In addition, the sequences reveal that, unlike any other bacterial genome sequenced to date, the F. tularensis genome contains two genes, designated rpoA1( or FTT0350 in the annotated Schu S4 genome sequence) and rpoA2( FTT1442), that encode different α subunits of RNAP. The α subunit of bacterial RNAP forms a dimer in the RNAP complex( subunit composition α2ββ′ωσ). The two putative α subunits in F. tularensis differ from one another in regions that are predicted to be critical for dimer formation, promoter recognition, and activator interaction. Given the fundamental roles the α subunit plays in transcription regulation[16], this may have far-reaching implications for how gene expression is controlled in F. tularensis. Here, we show that both MglA and SspA associate with RNAP and positively control virulence gene expression in F. tularensis. Furthermore, we present evidence that the ability of MglA to control virulence gene expression is linked to its ability to associate with RNAP. We also show that the two distinct α subunits encoded by the F. tularensis genome are both incorporated into RNAP. Our results provide a framework for understanding the mechanistic basis for virulence gene control in this organism.\nDiscussion:\nWe have found that virulence gene expression is controlled by both MglA and SspA in F. tularensis. Specifically, we have shown that both MglA and SspA are RNAP-associated proteins in F. tularensis, and that MglA and SspA can interact with one another directly to form a heteromeric complex. Although MglA and SspA may also form homomeric complexes, we present evidence that the ability of MglA to associate with RNAP is dependent upon SspA. One implication of this finding is that members of the SspA protein family likely associate with RNAP as an oligomer, and presumably as a dimer[8, 19]. Our findings suggest that although MglA homomers might form in the cell, they are unlikely to associate with RNAP. Unfortunately, we were unable to determine whether the association of SspA with RNAP is dependent upon MglA, and so we do not currently know whether SspA homomers can associate with RNAP; we were unable to construct a strain of F. tularensis containing a chromosomally TAP-tagged SspA analogous to the LVS MglA-TAP strain, presumably because integration of the sspA-TAP vector into the F. tularensis chromosome results in polar effects on the expression of genes downstream of sspA that are important for growth. In support of the idea that MglA and SspA function together as a heteromeric complex, the results of our microarray analyses show that the MglA and SspA regulons are almost identical. In particular, we show that 30 genes belong to the MglA regulon and 28 belong to the SspA regulon, and that essentially the same set of genes are found in both( Figure 5; Table 1). In general, the results of our microarray analyses with the ΔmglA mutant strain are in good agreement with those of Brotcke and colleagues, who analyzed the effects of an mglA point mutation on gene expression in F. novicida[6]( see Table 1). Note that when defining the MglA regulon, we listed those genes whose expression changed by a factor of 3 or more when mglA was deleted. If we had defined the regulon by listing those genes whose expression changed by a factor of 2 or more, then the MglA regulon would be composed of more than 100 genes( Table S1). Although the molecular details of how MglA exerts its effects on virulence gene expression( or on the expression of any other target gene) are not yet known, we have presented evidence that its association with RNAP plays an important, if not essential, role. For example, because expression of the iglA, iglC, and pdpA virulence genes is downregulated in a ΔsspA mutant, and because MglA is present but is not associated with RNAP in a ΔsspA mutant, we disfavor any model whereby MglA upregulates these genes without being associated with RNAP. Accordingly, we suggest two possible models for how MglA, together with SspA, positively controls virulence gene expression in F. tularensis based on association of the MglA–SspA complex with RNAP. The first model specifies that a DNA-bound transcription activator( s) contacts the RNAP-associated MglA–SspA complex directly to stimulate transcription initiation at target promoters. Put another way, we hypothesize that the MglA–SspA complex effectively becomes a “subunit” of RNAP that serves as a target for DNA-bound transcription activators that regulate the expression of virulence genes in F. tularensis( see Figure 6A). Precedent for such a model comes from studies of bacteriophage P1 late gene expression. In particular, the P1 late promoter activator( Lpa or gp10) is a phage-encoded, sequence-specific DNA binding protein that is thought to activate P1 late gene expression through a direct interaction with E. coli SspA[11]. In this particular situation, E. coli SspA evidently functions as a co-activator of P1 late gene expression by making simultaneous contact with RNAP and DNA-bound Lpa[11]. In F. tularensis there may be Lpa-like proteins( at least functionally) that bind the MglA–SspA complex and are required for the co-activation of MglA- and SspA-dependent virulence gene expression( see Figure 6A). Such MglA- and/or SspA-binding proteins, if they do indeed exist, would be predicted to be DNA-binding proteins that bind the promoter regions of MglA- and SspA-dependent virulence genes. The second model specifies that the RNAP-associated MglA–SspA complex interacts directly with the promoter DNA of MglA- and SspA-dependent virulence genes to activate transcription initiation from the cognate promoters( Figure 6B). However, there is no evidence that any SspA homolog is capable of binding directly to DNA; in particular, E. coli SspA does not appear to bind P1 late promoter DNA[11]. Other models can also be envisioned. These include the MglA–SspA complex modifying the promoter-recognition properties of the associated RNAP holoenzyme without interacting directly with the DNA, or modifying the elongation properties of the associated RNAP. We note that the latter model would be unlikely if the MglA–SspA complex, like SspA in E. coli[8], could interact only with the RNAP holoenzyme and not with the core enzyme and therefore was not a component of mature elongation complexes. The models discussed above and depicted in Figure 6 are meant only to explain how MglA and SspA positively regulate the expression of target genes. However, MglA and SspA also appear to negatively regulate the expression of at least some target genes( Table 1;[6]). Whether those genes whose expression is negatively controlled by MglA and SspA are controlled directly, or indirectly, remains to be determined. F. tularensis is unique in that it contains two genes, designated rpoA1 and rpoA2, encoding different α subunits of RNAP. This situation is without precedent; in all other bacterial genomes sequenced to date there is only one rpoA gene. The two α subunits are 32% identical and 55% similar to one another at the primary amino acid level and differ from one another in regions that may be important for dimer formation, promoter recognition, and interaction with DNA-bound transcription activators. We have shown that both α1 and α2 are components of RNAP in F. tularensis. Because α is a dimer in the RNAP complex, there could be as many as four different species of RNAP core enzyme in F. tularensis; one composed of α1 homodimers, one composed of α2 homodimers, and two composed of α1α2 heterodimers( that differ from one another with respect to which α protomer interacts with the β subunit of RNAP and which α protomer interacts with β′). However, a subset of the predicted dimerization determinants[23] present in the N-terminal domains of α1 and α2 differ from one another, raising the possibility that α1 and α2 might exclusively form either homodimers or heterodimers. The experiments we have performed do not allow us to say anything more than that both α1 and α2 are subunits of RNAP in F. tularensis. Because α participates in promoter recognition through direct sequence-specific protein–DNA interactions[16, 24], and because α is a common target for transcription activators[25], the presence of RNAP species containing distinct α subunits might significantly affect the control of gene expression in F. tularensis. The F. tularensis RNAP complex is also unusual in that it is associated with two different members of the SspA protein family( MglA and SspA). Other bacterial pathogens where SspA homologs play roles in virulence gene control, such as Neisseria gonorrhoeae[12], Yersinia enterocolitica[13], and Vibrio cholerae[14], appear to encode only one SspA homolog. Whether the use of two SspA homologs allows for the integration of multiple environmental signals, or is related to the fact that the F. tularensis RNAP contains two distinct α subunits, or both, remains to be determined.", "role": "user" }, { "content": "The Gram-negative bacterium Francisella tularensis is an intracellular pathogen and the causative agent of tularemia . In F . tularensis , the MglA protein is the only known regulator of virulence genes that are important for intracellular survival , yet it is not known how MglA functions . F . tularensis also contains an MglA-like protein called SspA whose function is not known . In this study , we show that both MglA and SspA associate with RNA polymerase ( RNAP ) and positively regulate virulence gene expression in F . tularensis . Our study provides evidence that MglA and SspA interact with one another directly and that the association of MglA with RNAP is dependent on the presence of SspA . We also show that , unlike RNAP from any other bacterium , RNAP from F . tularensis contains two distinct α subunits . Given the fundamental roles the α subunit plays in transcription regulation , this may have far-reaching implications for how gene expression is controlled in F . tularensis . Our study therefore uncovers a new critical regulator of virulence gene expression in F . tularensis ( SspA ) , provides mechanistic insight into how MglA and SspA cooperate to control virulence gene expression , and reveals that the F . tularensis transcription machinery has an unusual composition .", "role": "assistant" } ]

[ { "content": "You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.\n\nTitle: Long-Range Intra-Protein Communication Can Be Transmitted by Correlated Side-Chain Fluctuations Alone\nArticle:\nAbstract:\nAllosteric regulation is a key component of cellular communication, but the way in which information is passed from one site to another within a folded protein is not often clear. While backbone motions have long been considered essential for long-range information conveyance, side-chain motions have rarely been considered. In this work, we demonstrate their potential utility using Monte Carlo sampling of side-chain torsional angles on a fixed backbone to quantify correlations amongst side-chain inter-rotameric motions. Results indicate that long-range correlations of side-chain fluctuations can arise independently from several different types of interactions: steric repulsions, implicit solvent interactions, or hydrogen bonding and salt-bridge interactions. These robust correlations persist across the entire protein( up to 60 Å in the case of calmodulin) and can propagate long-range changes in side-chain variability in response to single residue perturbations.\nIntroduction:\nAllostery is an essential feature of protein regulation and function. Allosteric regulation acts by linking distant sites of a protein together in such a way that information about one site is transmitted to and influences the behavior of another. Chemical modifications as subtle as the phosphorylation of a serine residue can cause dramatic changes in protein function[1], and shifts in structure as small as 1 Å have even been shown to modify behavior in a domain up to 100 Å away[2]. Traditionally, allostery has been understood as a feature of symmetric, multi-subunit proteins where the binding of a ligand to one subunit facilitates the binding of similar ligands to the other subunits, resulting in cooperative binding transitions[3]. However, allosteric behavior has now been observed within a single protein domain[4] and its definition extended to include any shift in protein structure and function at one site resulting from modification at another. Moreover, it was proposed some time ago that information regarding the binding of a ligand or other modification at one protein site could be transmitted through altered protein fluctuations, even if the protein's average structure remains unaffected[5]. Two particularly clear examples of this kind of dynamic allostery have been recently observed in the binding of cAMP to the CAP dimer and in the subsequent binding of the cAMP-activated CAP dimer to DNA[6],[7]. In the first step, the binding of cAMP to one monomer of CAP lowers the binding affinity of cAMP to the second even though no structural changes are observed, and calorimetric analysis suggests that the negative cooperativity results entirely from entropic effects[6]. The observed allosteric effect of protein fluctuations has led to the idea that allostery may be present in all proteins[8]–[10], and that functional allostery simply exploits and refines pre-existing long-range correlations and interaction networks. In fact, such networks are to be expected given the physical constraints of the densely-folded, yet fluctuating, protein. Just as in any condensed phase, significant fluctuations in this packed environment are permitted through correlated motions. Qualitative experimental evidence for long-range correlation abounds in studies demonstrating allosteric regulation, as exemplified in[1] and[2]. However, attempts to quantify these long-range correlations using NMR techniques have proven difficult[11]–[13], and much of our current understanding of correlated motions has come from analyses of molecular dynamics( MD) simulations. Traditional MD trajectories evaluated with covariance matrices and principle component analyses[14] have shed light on important features of intra-protein correlations, such as how backbone motions tend to be significantly correlated within secondary structural units[14] and how a few flexible hinge residues can cause large motions within otherwise stable folds[15]. Energy-perturbative MD simulations, such as anisotropic thermal diffusion[16] and pump-probe MD[17], have been used to observe the rapid anisotropic diffusion of an energy perturbation within the protein. However, these MD studies are limited in their ability to characterize sluggish rearrangements and have largely neglected the contributions of correlated side-chain fluctuations. Within the folded protein, side-chains are significantly less ordered than the backbone[18], and alternative side-chain configurations in protein crystals are more prevalent than previously thought[19]. The thermodynamic importance of this side-chain variability in calmodulin-ligand binding has been highlighted in Refs.[20]–[22]. In addition, the participation of side-chain fluctuations in long-range networks has been demonstrated through NMR mutational studies[9],[23]. In one MD simulation designed to incorporate data from NMR experiments, correlations were even observed between side-chains whose motions appeared decoupled from those of their backbone atoms[24]. Double mutant cycles[25] have also been applied to examine the dependence of folding and binding on interactions between specific residue pairs. While such mutational studies can demonstrate the interactions of certain residue pairs, they are experimentally demanding, making it difficult to obtain a comprehensive picture of any long-range side-chain interactions present, in particular those involving residues essential for folding stability. As an alternative, an evolutionary statistical network analysis method has been developed to determine networks of correlated residues that are common to evolutionarily related proteins[26]. Although this method has had some success in identifying allosterically-related regions within proteins[27], its robustness has been challenged in a study on artificially-generated sequences[28]. In principle, it is also limited to detecting correlated changes in residues during evolution, presumably highlighting only correlated networks with a selected function and can therefore say little about the presence or absence of other correlations. In this study, we employed an atomistically detailed model to examine the kinds of correlations that emerge among side-chain fluctuations within the natively-folded protein. The computationally inexpensive nature of our model energy function[21], together with a variety of advanced Monte Carlo sampling techniques, allowed an unprecedentedly thorough investigation of the correlations among these fluctuations that result from different types of interactions. Keeping the backbone fixed, we find that long-range correlation of side-chain fluctuations can emerge from different types of atomic interactions, that significant correlations persist across the entire folded protein, and that these correlations alone can propagate changes in structure and mobility over scales as large as 50 Å.\nDiscussion:\nThe propagation of information across long distances within the folded protein is of great importance in allosteric regulation. While backbone structural changes and fluctuations have long been studied as the bearer of this information, correlated side-chain fluctuations provide potential information conductance pathways that have often been overlooked. In this work we have examined these side-chain correlations and found that changes in fluctuations in response to even single residue perturbations, such as a point mutation or residue immobilization, are statistically significant, widely distributed, and not easily intuited from the protein's sequence or structure. The correlations emerge independently from several different sources: steric repulsions, solvation, and hydrogen bonding and salt bridges. Together, these interactions give rise to robust, statistically-significant correlations that persist across the entire spatial extent of both barstar and calmodulin. In the calculation of the mutual information values, we model the protein's backbone as a rigid structure, enabling us to investigate the degree of correlation among side-chain fluctuations alone. An understanding of the role of backbone fluctuations is necessary, however, to judge the biological relevance of these observed correlations. Interestingly, we found that significant correlations are present among the side-chains of barstar in several different backbone structures, which collectively represent the range of its typical conformational fluctuations in solution. Moreover, since the time scales characteristic of backbone and side-chain motions are likely well separated in many cases, communication via side-chain rearrangements as we have described may often occur on an effectively static backbone. However, upon larger backbone rearrangement, such as hinge motions or partial unfolding events, a significant fraction of the observed side-chain fluctuations are likely to decouple, and thus the role of side-chain correlations in allosteric regulation where large backbone rearrangements are known to occur may be limited. We also utilized a simple implicit solvent model to account for the solvent's mean field effect on the protein. While this approach allows us to focus directly on the protein's degrees of freedom, it neglects solvent fluctuations. Physically realistic solvent fluctuations are likely to influence side-chain fluctuations in the same way that side-chain fluctuations influence one another, and the reverse is also true. This potential for correlation between solvent and side-chain fluctuations suggests that fluctuations of the solvent shell may also convey information from one site on the protein to another. Indeed, we observed that even the mean field effect of solvation mediates the correlation of side-chain fluctuations, as seen in Fig. 3( c). However, more and stronger correlations were observed to arise from hydrogen-bonding and salt bridge interactions( see Figs. 3( e) & 4), and it is largely through these very effects that the solvent molecules would influence the side-chains. While we are not able to explore the resulting implications in our current work, the demonstration of allosteric effects mediated through solvent fluctuations would be quite intriguing. Finally, it is important to note that the magnitude of the correlations measured here is only a fraction of the total magnitude possible among the side-chains, since correlated fluctuations within each individual rotameric well, which are not included in our correlation metric, are sure to contribute significantly, perhaps even to a greater degree, to the overall amount of correlation. Even so, correlations amongst inter-rotameric fluctuations alone reveal much about the way side-chain fluctuations give rise to long-range correlations within the folded protein. The role of these robustly correlated side-chain fluctuations in allosteric regulation should be considered further.", "role": "user" }, { "content": "Allosteric regulation occurs when the function of one part of a protein changes in response to a signal recognized by another part of the protein . Such intra-protein communication is essential for many biochemical processes , allowing the cell to adapt its behavior to a dynamic environment . Most studies of the information conveyance underlying allostery have to date focused on the role of backbone motions in mediating large structural changes . Here we focus instead on more subtle contributions , arising from fluctuations of side-chain torsions . Using a model for side-chain bond rotations in the tightly packed environment imposed by native backbone conformations , we observed significant sensitivity of side-chain organization to small , localized perturbations . This susceptibility arises from correlations among side-chain motions that can propagate information within a protein in complex , heterogeneous ways . Specifically , we found appreciable correlations even between side-chains distant from one another , so that the effect of a minor perturbation at one site on the protein could be observed in the altered fluctuations of side-chains throughout the protein . In conclusion , we have demonstrated that the statistical mechanics of correlated side-chain fluctuations within a model of the folded protein provides the basis for an unconventional but potentially important means of allostery .", "role": "assistant" } ]

[ { "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: [\"Finding\", \"Bacterium\", \"ProfessionalOrOccupationalGroup\", \"InjuryOrPoisoning\", \"Eukaryote\", \"BodySystem\", \"Virus\", \"ResearchActivity\", \"BodySubstance\", \"PopulationGroup\", \"Chemical\", \"AnatomicalStructure\", \"SpatialConcept\", \"HealthCareActivity\", \"Organization\", \"BiomedicalOccupationOrDiscipline\", \"Food\", \"ClinicalAttribute\", \"MedicalDevice\", \"BiologicFunction\", \"IntellectualProduct\"].\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\nUndiagnosed Primary Open-Angle Glaucoma in Korea: The Korean National Health and Nutrition Examination Survey 2008-2009 To evaluate the characteristics of patients with previously undiagnosed primary open-angle glaucoma (POAG) in Korea. This study examined data from 391 subjects obtained from the 2008-2009 Korean National Health and Nutrition Examination Survey (KNHANES). The KNHANES is a population-based, cross-sectional epidemiological survey. Participants aged 19 years or older completed standardized interviews and dilated ocular examinations, including measurement of intraocular pressure, visual fields with frequency doubling perimetry, and fundus photography. Data from the 361 patients with previously undiagnosed POAG were analyzed and compared with data from the 30 patients with previously diagnosed glaucoma. A total of 92.3% of POAG cases were undiagnosed before this study. Adjusted for age and sex, the strongest risk factor for undiagnosed glaucoma was longer elapsed time since last eye doctor visit. Glaucoma patients who had not visited an eye specialist in the last 3 years were 22 times (95% confidence interval, CI, 4.49-105.64, p < 0.001) more likely to have undiagnosed disease compared with patients who had visited an eye specialist in the last month. Another significant factor for previously undiagnosed glaucoma was smaller cup-to-disc ratio (odds ratio, OR, 0.60/0.1 units, 95% CI 0.43-0.85/0.1 units, p = 0.004). The higher vertical cup-to-disc ratio of a subject's two eyes was significantly different between those with previously undiagnosed (0.69) and diagnosed (0.78) POAG (p = 0.001). The undiagnosed POAG group had a longer interval from last eye doctor visit and smaller vertical cup-to-disc ratio compared to the diagnosed group.", "role": "user" }, { "content": "{\"AnatomicalStructure\": [\"eyes\"], \"Bacterium\": [], \"BiologicFunction\": [\"glaucoma\", \"disease\", \"Glaucoma\", \"primary open-angle glaucoma\", \"Primary Open-Angle Glaucoma\", \"intraocular pressure\", \"POAG\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [], \"ClinicalAttribute\": [\"vertical cup-to-disc ratio\"], \"Eukaryote\": [], \"Finding\": [\"diagnosed group\", \"Undiagnosed\", \"cup-to-disc ratio\", \"strongest risk factor\", \"undiagnosed\", \"diagnosed\"], \"Food\": [], \"HealthCareActivity\": [\"eye doctor visit\", \"dilated ocular examinations\", \"fundus photography\", \"visited an eye specialist\", \"frequency doubling perimetry\", \"evaluate\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [], \"MedicalDevice\": [], \"Organization\": [], \"PopulationGroup\": [\"subject\\u0027s\", \"subjects\", \"Participants\"], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [\"cross-sectional epidemiological survey\", \"study\", \"analyzed\", \"population-based\", \"Korean National Health and Nutrition Examination Survey\", \"KNHANES\"], \"SpatialConcept\": [\"Korea\", \"visual fields\"], \"Virus\": []}", "role": "assistant" } ]

[ { "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 respiratory disease caused by SARS-CoV-2, also known as the new coronavirus.COVID-19 can have a wide variety of symptoms, including a sore throat.\n\nAbstract:\nCoronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has turned out to be a formidable pandemic.\nUpcoming evidence from confirmed cases of COVID-19 suggests an anticipated incursion of patients with neurological manifestations in the weeks to come.\nAn expression of the angiotensin-converting enzyme 2 (ACE 2), the cellular receptor for SARS-CoV-2 over the glial cells and neurons have made the brain a potential target.\nNeurotoxicity may occur as a result of direct, indirect and post-infectious complications.\nAttention to neurological deficits in COVID-19 is fundamental to ensure appropriate, timely, beneficial management of the affected patients.\nMost common neurological manifestations seen include dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizures.\nAnosmia and ageusia have recently been hinted as significant early symptoms in COVID-19.\nAs cases with neurological deficits in COVID-19 emerge, the overall prognosis is yet unknown.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "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: [\"Participant\", \"Outcome\", \"Intervention\"].\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\nEffects of propofol and isoflurane on haemodynamics and the inflammatory response in cardiopulmonary bypass surgery . Cardiopulmonary bypass ( CPB ) causes reperfusion injury that when most severe is clinically manifested as a systemic inflammatory response syndrome . The anaesthetic propofol may have anti-inflammatory properties that may reduce such a response . We hypothesised differing effects of propofol and isoflurane on inflammatory markers in patients having CBR Forty patients undergoing elective CPB were randomised to receive either propofol or isoflurane for maintenance of anaesthesia . CRP , IL-6 , IL-8 , HIF-1α ( ELISA ) , CD11 and CD18 expression ( flow cytometry ) , and haemoxygenase ( HO-1 ) promoter polymorphisms ( PCR/electrophoresis ) were measured before anaesthetic induction , 4 hours post-CPB , and 24 hours later . There were no differences in the 4 hours changes in CRP , IL-6 , IL-8 or CD18 between the two groups , but those in the propofol group had higher HIF-1α ( P = 0.016 ) and lower CD11 expression ( P = 0.026 ) . After 24 hours , compared to the isoflurane group , the propofol group had significantly lower levels of CRP ( P < 0.001 ) , IL-6 ( P < 0.001 ) and IL-8 ( P < 0.001 ) , with higher levels CD11 ( P = 0.009 ) and CD18 ( P = 0.002 ) expression . After 24 hours , patients on propofol had increased expression of shorter HO-1 GT ( n ) repeats than patients on isoflurane ( P = 0.001 ) . Use of propofol in CPB is associated with a less adverse inflammatory profile than is isofluorane , and an increased up-regulation of HO-1 . This supports the hypothesis that propofol has anti-inflammatory activity .", "role": "user" }, { "content": "{\"Intervention\": [\"isoflurane\", \"propofol\"], \"Outcome\": [\"IL-6 , IL-8\", \"levels CD11\", \"CD11\", \"higher HIF-1\\u03b1\", \"HIF-1\\u03b1 ( ELISA )\", \"inflammatory markers\", \"HO-1 GT ( n\", \"haemodynamics and the inflammatory response\", \"haemoxygenase ( HO-1\", \"of HO-1\", \"CRP\", \"CD11 expression\", \"IL-6\", \"IL-8\", \"promoter polymorphisms ( PCR/electrophoresis )\", \"CD18\"], \"Participant\": [\"Forty patients\", \"Cardiopulmonary bypass ( CPB )\"]}", "role": "assistant" } ]

[ { "content": "You will be presented with a citation sentence from 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\nTwo traits are orthogonal when, based on individual rankings on one of the traits, it is impossible to predict anything certain about rankings on the other trait (Allik et al., 2015).", "role": "user" }, { "content": "Background", "role": "assistant" } ]

[ { "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: [\"Participant\", \"Outcome\", \"Intervention\"].\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\nInjury and illness costs in the Certified Safe Farm study . CONTEXT The Certified Safe Farm ( CSF ) intervention program aims to reduce occupational injuries and illnesses , and promote wellness to reduce health care and related costs to farmers , insurers , and other stakeholders . PURPOSE To evaluate the cost effectiveness of CSF . METHODS Farms ( 316 ) located in a 9-county area of northwestern Iowa were recruited and randomized into intervention and control cohorts . Intervention farms received occupational health screenings , health and wellness screening , education , on-farm safety reviews , and performance incentives . For both cohorts , quarterly calls over 3 years were used to collect self-reported occupational injury and illness information , including costs to the farmers and their insurers . FINDINGS Annual occupational injury and illness costs per farmer paid by insurers were 45 % lower in the intervention cohort ( $ 183 ) than in the control cohort ( $ 332 ) . Although out-of-pocket expenses were similar for both cohorts , combined costs of insurance and out-of-pocket expenses were 27 % lower in the intervention cohort ( $ 374/year per farmer ) compared to the control cohort ( $ 512/year per farmer ) . Within the cohort of intervention farmers , annual occupational injury and illness cost savings were directly associated with on-farm safety review scores . Reported health care costs were $ 237 per farmer in the safest farms ( those farms scoring in the highest tertile ) versus $ 485 per farmer in the least safe farms ( lowest tertile ) . CONCLUSIONS Results suggest that farmers receiving the intervention had lower health care costs for occupational injuries and illnesses than control farmers . These cost savings more than cover the cost of providing CSF services ( about $ 100 per farm per year ) .", "role": "user" }, { "content": "{\"Intervention\": [\"Intervention\", \"control\", \"intervention\", \"The Certified Safe Farm ( CSF ) intervention program\"], \"Outcome\": [\"Injury and illness costs\", \"health care costs\", \"out-of-pocket expenses\", \"cost savings\", \"Annual occupational injury and illness costs\", \"annual occupational injury and illness cost savings\", \"combined costs of insurance and out-of-pocket expenses\", \"cost effectiveness of CSF\"], \"Participant\": [\"farmers , insurers , and other stakeholders\"]}", "role": "assistant" } ]

[ { "content": "You will be shown sections from a scientific research paper, together with a question about the paper. This is an extractive question-answering task, where you must find and extract relevant text spans directly from the paper to answer the question. Your response should strictly 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 example, for the question \"What baselines did the authors compare against?\", a sample response might be:\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: The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources\n\nWe introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.\n\n\n1.1em\n\n ::: \n1.1.1em\n\nScientific Entity Annotations ::: Our Annotation Process ::: Annotated Corpus Characteristics\nTable TABREF17 shows our annotated corpus characteristics. Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. The number of entities per abstract directly correlates with the length of the abstracts (Pearson's R 0.97). Among the concepts, Process and Material directly correlate with abstract length (R 0.8 and 0.83, respectively), while Data has only a slight correlation (R 0.35) and Method has no correlation (R 0.02).\nIn Figure FIGREF18, we show an example instance of a manually created text graph from the scientific entities in one abstract. The graph highlights that linguistic relations such as synonymy, hypernymy, meronymy, as well as OpenIE relations are poignant even between scientific entities.\n\nScientific Entity Resolution\nAside from the four scientific concepts facilitating a common understanding of scientific entities in a multidisciplinary setting, the fact that they are just four made the human annotation task feasible. Utilizing additional concepts would have resulted in a prohibitively expensive human annotation task. Nevertheless, there are existing datasets (particularly in the biomedical domain, e.g., GENIA BIBREF6) that have adopted the conceptual framework in rich domain-specific semantic ontologies. Our work, while related, is different since we target the annotation of multidisciplinary scientific entities that facilitates a low annotation entrance barrier to producing such data. This is beneficial since it enables the task to be performed in a domain-independent manner by researchers, but perhaps not crowdworkers, unless screening tests for a certain level of scientific expertise are created.\nNonetheless, we recognize that the four categories might be too limiting for real-world usage. Further, the scientific entities from stage 1 remain susceptible to subjective interpretation without additional information. Therefore, in a similar vein to adopting domain-specific ontologies, we now perform entity linking (EL) to the Wikipedia and word sense disambiguation (WSD) to Wiktionary.\n\nScientific Entity Resolution ::: Our Annotation Process ::: Annotation Task Tools\nDuring the annotation procedure, each annotator was shown the entities, grouped by domain and file name, in Google Excel Sheet columns alongside a view of the current abstract of entities being annotated in the BRAT interface stenetorp2012brat for context information about the entities. For entity resolution, i.e. linking and disambiguation, the annotators had local installations of specific time-stamped Wikipedia and Wiktionary dumps to enable future persistent references to the links since the Wiki sources are actively revised. They queried the local dumps using the DKPro JWPL tool BIBREF8 for Wikipedia and the DKPro JWKTL tool BIBREF9 for Wiktionary, where both tools enable optimized search through the large Wiki data volume.\n\nScientific Entity Resolution ::: Our Annotation Process ::: Annotation Procedure for Entity Resolution ::: Linkability.\nIn this first step, entities that conveyed a sense of scientific jargon were deemed linkable.\nA natural question that arises, in the context of the Linkability criteria, is: Which stage 1 annotated scientific entities were now deemed unlinkable? They were 1) Data entities that are numbers; 2) entities that are coreference mentions which, as isolated units, lost their precise sense (e.g., “development”); and 3) Process verbs (e.g., “decreasing”, “reconstruct”, etc.). Still, having identified these cases, a caveat remained: except for entities of type Data, the remaining decisions made in this step involved a certain degree of subjectivity because, for instance, not all Process verbs were unlinkable (e.g., “flooding”). Nonetheless, at the end of this step, the annotators obtained a high IAA score at 0.89 $\\kappa $. From the agreement scores, we found that the Linkability decisions could be made reliably and consistently on the data.\n\nScientific Entity Resolution ::: Our Annotation Process ::: Annotated Corpus Characteristics\nIn this stage 2 corpus, linkability of the scientific entities was determined at 74.6%. Of these, 61.7% were split into shorter collocations, at 1.74 splits per split entity. Detailed statistics are presented in Table TABREF36. In the table, the domains are ranked by the total number of their linkable entities (fourth column). Ast has the highest proportion of linked entities at 87.3% which comprises 10.4% of all the linked entities and disambiguated entities at 71.4% forming 8.5% of the overall disambiguated entities. From an EL perspective, we surmize that articles on space topics are well represented in Wikipedia. For WSD, Bio, ES, and Med predictably have the least proportion of disambiguated entities at 52.3%, 54.6%, and 55.5%, respectively, since of all our domains these especially rely on high degree scientific jargon, while WSD generally tends to be linguistically oriented in a generic sense. As a summary, linked and disambiguated entities had a high correlation with the total linkable entities ($R$ 0.98 and 0.89, respectively).\nIn Table TABREF37, the ER annotation results are shown as POS tag distributions. The POS tags were obtained from Wiktionary, where entities that couldn't be disambiguated are tagged as SW (Single Word) or MWE (Multi-Word Expression). These tags have a coarser granularity compared to the traditionally followed Penn Treebank tags with some unconventional tagging patterns (e.g., “North Sea” as NNP, “in vivo” as ADJ). From the distributions, except for nouns being the most EL and WSD instances, the rest of the table differs significantly between the two tasks in a sense reflecting the nature of the tasks. While MWE are the second highest EL instances, its corresponding PHRASE type is least represented in WSD. In contrast, while adverbs are the second highest in WSD, they are least in EL.\n\nScientific Entity Resolution ::: Evaluation\nWe do not observe a significant impact of the long-tailed list phenomenon of unresolved entities in our data (c.f Table TABREF36 only 17% did not have EL annotations). Results on more recent publications should perhaps serve more conclusive in this respect for new concepts introduced–the abstracts in our dataset were published between 2012 and 2014.\n\nAppendix: Supplemental Material\nA.1. Proportion of the Generic Scientific Entities\nTo offer better insights to our STEM corpus for its scientific entity annotations made in part 1, in Figure FIGREF40 below, we visually depict the proportion of Process, Method, Material, and Data entities per domain.\nThe Figure serves a complementary view to our corpus compared with the dataset statistics shown in Table TABREF17. It shows that the Ast domain has the highest proportion of scientific entities overall. On the other hand, per generic type, Bio has the most Process entities, CS has the most Method entities, Ast has the most Material closely followed by Agr, and Eng has the most Data.\nA.2. Cohen's $\\kappa $ Computation Setup in Section 4.1.2\nLinkability. Given the stage 1 scientific entities, the annotators could make one of two decisions: a) an entity is linkable; or b) an entity is unlinkable. These decisions were assigned numeric indexes, i.e. 1 for decision (a) and -1 for decision (b) and can take on one of four possible combinations based on the two annotators decisions: (1,1), (1,-1), (-1,1), and (-1,-1). The $\\kappa $ scores were then computed on this data representation.\nWSD Agreement. In order to compute the WSD agreement, the Wiktionary structure for organizing the words needed to be taken into account. It's structure is as follows. Each word in the Wiktionary lexicographic resource is categorized based on etymology, and within each etymological category, by the various part-of-speech tags the word can take. Finally, within each POS type, is a gloss list where each gloss corresponds to a unique word sense.\nGiven the above-mentioned Wiktionary structure, the initial setup for the blind WSD annotation task entailed that the annotators were given the same reference POS tags within an etymology for the split single-word entities in the corpus. Next, as data to compute $\\kappa $ scores, each annotator-assigned gloss sense was given a numeric index and agreement was computed based on matches or non-matches between indexes.\nA.3. Per-domain Inter-annotator Agreement for Entity Resolution\nTo supplement the overall Inter-Annotator Agreement (IAA) scores reported in Section 4.1.2 `Entity Resolution (ER) Annotation' for the EL and WSD tasks, in Table TABREF43 below, we additionally report the IAA scores for our ER tasks (i.e., EL and WSD) per domain in the STEM-ECR corpus. First, considering the domains where the highest ER agreement scores were obtained. For EL, the IAA score was highest in the MS domain. While for WSD, the IAA score was highest in the Bio domain. Next, considering the domains where the agreement was least for the two tasks. We found the the EL agreement was least for CS and the WSD agreement was least for Mat. In the case of low EL agreement, it can be attributed to two main cases: only one of the annotators found a link; or the annotators linked to related pages on the same theme as the entity (e.g., wiki:Rule-based_modeling versus wiki:Rule-based_machine_learning for “rule-based system”). And in the case of the low WSD agreement obtained on Mat, we see that owing to broad terms like “set,” “matrix,” “groups,” etc., in the domain which could be disambiguated to more than one Wiktionary sense correctly, the IAA agreement was low.\nA.4. Babelfy's Precision ($P$) and Recall ($R$) Computation for Entity Resolution in Figure\nFor the $P$ and $R$ scores reported in Figure , the true positives (TP), false negatives (FN), true negatives (TN), and false positives (FP) were computed as follows:\nTP = human-annotated entities that have a EL/WSD match with Babelfy results (for Nil, a match is considered as no result from the automatic system);\nFN = human-annotated entities that have no EL/WSD match with Babelfy results;\nTN = spurious Babelfy-created strings as entities that do not have a EL/WSD result; and\nFP = spurious Babelfy-created entities that have a EL/WSD result.\nA.5. Top 30 Wikipedia Categories for Process, Method, Material, and Data\nIn part 1 of the study, we categorized the scientific entities by our four generic concept formalism, comprising Process, Method, Material, and Data. Linking the entities to Wikipedia further enables their broadened categorization. While in Figure is depicted the rich set of Wikipedia categories obtained overall, here, in Tables TABREF44 and TABREF45, we show the top 30 Wikipedia categories for the scientific entities by their four concept types. we observe the most of the Wikipedia categories pertinently broaden the semantic expressivity of each of our four concepts. Further that in each type, they are diverse reflecting the underlying data domains in our corpus. As examples, consider the Wikipedia categories for the Data scientific entities: “SIBaseQuantities” category over the entity “Kelvin” in Che; “FluidDynamics” in Eng and MS domains; and “SolarCalendars” in the Ast domain.\n\nFigure 1: A text graph of the abstract of the article ‘The Cassini Enceladus encounters 2005–2010 in the view of energetic electron measurements’ (Krupp et al., 2012). Nodes are color-coded by concept type: orange corresponds to PROCESS, green to MATERIAL, and blue to DATA. No METHOD concepts were identified in this abstract.\n\nTable 4: The domain-independent scientific entity extraction system results per-domain\n\nFigure 3: Percentage P , R, and F1 per domain of the Babelfy system for disambiguating to BabelNet (as shaded blue bars BP, BR, and BF) and for linking to DBpedia (as shaded orange bars DP, DR, DF)\n\nTable 5: The annotated corpus characteristics in terms of its Entity Linking (EL) and Word Sense Disambiguation (WSD) annotations\n\nFigure 5: Wikipedia categories cloud on scientific entities\n\nFigure 6: Percentage proportion of scientific entities in our STEM corpus per domain and per generic concept type\n\nTable 8: Top 30 Wikipedia categories pertaining to PROCESS and METHOD scientific entities in the STEM-ECR corpus\n\nTable 9: Top 30 Wikipedia categories pertaining to MATERIAL and DATA scientific entities in the STEM-ECR corpus\n\nQuestion: How large is the dataset?", "role": "user" }, { "content": "{\"answer\": [\"6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities\"], \"evidence\": [\"Table TABREF17 shows our annotated corpus characteristics. Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. The number of entities per abstract directly correlates with the length of the abstracts (Pearson\\u0027s R 0.97). Among the concepts, Process and Material directly correlate with abstract length (R 0.8 and 0.83, respectively), while Data has only a slight correlation (R 0.35) and Method has no correlation (R 0.02).\"]}", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nIndoor gardening older adults: effects on socialization, activities of daily living, and loneliness.\nAbstract 1:\nThis study examined the effects of indoor gardening on socialization , activities of daily living ( ADLs ) , and perceptions of loneliness in elderly nursing home residents . A total of 66 residents from two nursing homes participated in this two-phase study . In phase one , experimental group 1 participated once a week for 5 weeks in gardening activities while a control group received a 20-minute visit . While no significant differences were found between groups in socialization or perceptions of loneliness , there were significant pretest-posttest differences within groups on loneliness and guidance , reassurance of worth , social integration , and reliable alliance . The results also demonstrated gardening interventions had a significant effect on three ADLs ( transfer , eating , and toileting ) . Phase two examined differences in the effects of a 5-week versus a 2-week intervention program . Although no significant within-group differences were noted in socialization , loneliness , or ADLs , the 5-week program was more effective in increasing socialization and physical functioning\n\n\nTitle 2:\nThe effects of an enhanced environment on nursing home residents who pace.\nAbstract 2:\nThis study sought to enhance the well-being of older nursing home residents who pace and w and er by enriching their nursing home environment . Visual , auditory , and olfactory stimuli were added to the nursing home unit environment to simulate two types of environments : a home environment and an outdoor nature environment . Participants seemed to prefer the enhanced environments by choosing to spend more time in them , and by sitting on benches in those environments . There was a trend toward less trespassing , exit-seeking , and other agitated behaviors being exhibited in the enhanced environment , and residents were observed to manifest more pleasure in it . Staff members and family members definitely expressed a preference for the enhanced environment over the regular one . Although effects on residents were limited , this approach offers a low-cost method for making the nursing home environment a better place for those residents\n\n\nTitle 3:\nSocial Disengagement and Incident Cognitive Decline in Community-Dwelling Elderly Persons\nAbstract 3:\nSocial engagement , which is defined as the maintenance of many social connections and a high level of participation in social activities , has been thought to prevent cognitive decline in elderly persons . Associations between a socially engaged lifestyle and higher scores on memory and intelligence tests have been observed among community-dwelling older persons ( 1 - 5 ) . Short-term interventions to foster social and intellectual engagement have enhanced cognition among nursing home residents ( 6 ) and patients with dementia ( 7 ) . In animal studies ( 8) , mature rodents exposed to complex social and inanimate environments showed better maze-learning ability than those in sparser surroundings . Social engagement challenges persons to communicate effectively and participate in complex interpersonal exchanges . Besides providing a dynamic environment that requires the mobilization of cognitive faculties , social engagement may also indicate a commitment to community and family and engender a health-promoting sense of purpose and fulfillment . Another putative benefit of social engagement is greater availability of emotional support from relatives and friends . Lack of such support can predict adverse health outcomes ( 9 ) , but its influence on cognitive decline has not been examined . Although published findings on cognitive function are suggestive , interpretation of available epidemiologic data is hampered by method ologic and conceptual shortcomings . Most studies are not longitudinal assessment s of representative population -based cohorts but cross-sectional observations of volunteers or other special sample s. Adjustment for potential confounders , such as education or health status , is often lacking . Finally , although the concept of social engagement is intuitively accessible , it has been difficult to measure this construct . Early studies viewed previous favorable socioeconomic or occupational status as synonymous with social engagement in old age ( 1 ) ; this precluded examination of more salient or malleable postretirement behaviors . Recent investigations ( 10 - 12 ) , however , have used narrowly defined indicators ( for example , marital status or specific recreational activities ) to make inferences about the effect of late-life social environments on cognition . Other investigations ( 5 ) have used composite activity scales as proxies for social engagement but have not distinguished between activities that dem and a high level of externally directed attention and those that do not . Social and solitary pursuits are also rarely disaggregated , although each may affect cognitive function differently . We determined whether a global measure of social disengagement was associated with incident cognitive impairment in a large cohort of community-dwelling elderly persons followed for 12 years . We acknowledge the potential salutary influence of solitary mental pursuits , but we focused on whether interpersonal connections and activities can prevent cognitive decline . Convincing demonstration of such an association could motivate elderly persons , their families , and their care providers to maintain active relationships . It would also justify continued funding for community-based and institutionally based social programs for elderly persons and the adoption of social policies in which activities of older persons are valued . Methods Participants The study sample was drawn from the New Haven , Connecticut , site of the Established Population s for Epidemiologic Studies of the Elderly ( EPESE ) project , described in detail elsewhere ( 13 ) . The New Haven cohort is a multistage probability sample of 2812 noninstitutionalized persons 65 years of age or older who were living in New Haven , Connecticut , in 1982 . Sample s were drawn from three housing strata : public ( income-restricted ) housing for elderly persons , private housing for elderly persons , and community housing . Women were r and omly sub sample d to achieve equal representation of both sexes . The baseline response rate was 82 % . Trained lay examiners interviewed members of the cohort at home in 1982 , 1985,1988 , and 1994 and by telephone in intervening years . Our study was approved by the institutional review board of Yale University . Measurements Cognitive Function Cognitive performance was measured during in-home interviews with the 10-item Short Portable Mental Status Question naire ( SPMSQ ) ( 14 ) . ( The original item What is the name of this place ? was changed to What is your address ? because this seemed more appropriate for community-dwelling persons . ) Correct answers received 1 point each . If respondents declined to answer 4 or more items or if answers to 4 or more items were missing , the question naire was not scored . Otherwise , a participant 's decision not to answer a question was scored as an incorrect answer and scores on missing items were imputed by assigning the mean score of the nonmissing items . As has been done previously ( 15 - 17 ) , scores were divided into three categories : high ( a score of 9 or 10 ) , medium ( a score of 7 or 8) , and low ( a score of 0 to 6 ) . The validity of the question naire as a cognitive impairment measure has been assessed in a sub sample of respondents at another EPESE site . Participants received detailed medical examinations to determine the presence and severity of cognitive impairment . When medium scores on the SPMSQ and the category of mild impairment were excluded , the question naire 's sensitivity and specificity in identifying moderate or severe impairment were 85 % and 96 % , respectively ( 18 ) . Social Disengagement A comprehensive assessment of social connections and activities was completed during in-home interviews . We examined six indicators of social engagement : presence of a spouse , monthly visual contact with at least three relatives or close friends , yearly nonvisual contact ( telephone calls or letters ) with at least 10 relatives or close friends , frequent attendance ( at least once per month ) at religious services , membership in other groups , and regular participation in recreational social activities . The Appendix provides exact questions and coding rules for these indicators . A composite social disengagement index was constructed from the six indicators . Five to six social ties received a score of 1 , three to four social ties received a score of 2 , one to two social ties received a score of 3 , and no social ties received a score of 4 . Tie refers to any type of social contact . If scores for more than two indicators were missing , the index was not scored . Emotional Support Perceived availability of emotional support from a social network was assessed with the question , Can you count on anyone to provide you with emotional supportthat is , talking over problems or helping make a difficult decision ? The adequacy of this support was assessed by the question , Could you have used more emotional support than you received ? Covariates We selected the following self-reported variables as potential confounders because they had cross-sectional associations with disengagement or impaired cognition among the cohort or because they are established risk factors for Alzheimer disease or vascular dementia ( both of which are prevalent causes of progressive cognitive decline in elderly persons ) ( 19 , 20 ) . Sociodemographic factors were age , sex , ethnicity ( white or nonwhite ) , education ( 12 years or<12 years ) , annual income ( < $ 10 000 , $ 10 000 , or not given ) , and housing . Health status indicators were the presence of physical disability , which was defined as requiring assistance from another person or special equipment with at least one activity of daily living ( walking across a room , dressing , eating , transferring from bed to chair , bathing , or using the toilet ) ( 21 ) or limitations in gross mobility ( climbing one flight of stairs or walking half a mile ) ( 22 ) ; high-risk cardiovascular profile ( compared with low risk , where high risk was defined as a measured sitting blood pressure>160/95 mm Hg or a history of physician-diagnosed stroke , diabetes , or myocardial infa rct ion ) ; visual impairment ( difficulty in reading ordinary newspaper print ) ; auditory impairment ( difficulty in hearing what a person says without seeing his face if that person talks in a normal voice in a quiet room ) ; and symptoms of depression ( a score 16 on the Center for Epidemiologic Studies Depression Scale [ 23 ] ) . Health-related behaviors were current smoking status , alcohol consumption , and level of physical activity ( described in the Appendix ) . Statistical Analysis Cognitive decline was defined as a transition to a lower SPMSQ category ( transition from high to medium or low or transition from medium to low ) during a given interval . Respondents with low SPMSQ scores at the beginning of an interval were excluded from consideration during that interval . Incidence of cognitive decline by initial level of social disengagement was estimated over intervals of three lengths:3-year intervals ( 1982 to 1985 and 1985 to 1988 ) , 6-year intervals ( 1982 to1988 and 1988 to 1994 ) , and a 12-year interval ( 1982 to 1994 ) . Approximately20 % of respondents with low SPMSQ scores in 1982 scored higher in 1985 or 1988 and were therefore included in the 1985 to 1988 or 1988 to 1994 analyses . Polytomous logistic regression was used to estimate the relative risk for cognitive decline or death by level of disengagement , controlling for potential confounders . The three outcomes maintenance of cognitive function , cognitive decline , and deathwere treated as unordered categorical variables . To examine the validity of the assumption of a linear dose-response relation between social disengagement and cognitive decline or death , disengagement was initially coded as a set of indicator variables , each corresponding to 1 point on the composite disengagement index . Inspection of the result ing coefficients suggested that modeling disengagement as an ordered categorical covariate was justified ; the addition of quadratic or cubic terms did not substantially improve model fit\n\n\nTitle 4:\nAssessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: methodological research.\nAbstract 4:\nBACKGROUND The Cochrane Collaboration is strongly encouraging the use of a newly developed tool , the Cochrane Collaboration Risk of Bias Tool ( CCRBT ) , for all review groups . However , the psychometric properties of this tool to date have yet to be described . Thus , the objective of this study was to add information about psychometric properties of the CCRBT including inter-rater reliability and concurrent validity , in comparison with the Effective Public Health Practice Project Quality Assessment Tool ( EPHPP ) . METHODS Both tools were used to assess the method ological quality of 20 r and omized controlled trials included in our systematic review of the effectiveness of knowledge translation interventions to improve the management of cancer pain . Each study assessment was completed independently by two review ers using each tool . We analysed the inter-rater reliability of each tool 's individual domains , as well as final grade assigned to each study . RESULTS The EPHPP had fair inter-rater agreement for individual domains and excellent agreement for the final grade . In contrast , the CCRBT had slight inter-rater agreement for individual domains and fair inter-rater agreement for final grade . Of interest , no agreement between the two tools was evident in their final grade assigned to each study . Although both tools were developed to assess ' quality of the evidence ' , they appear to measure different constructs . CONCLUSIONS Both tools performed quite differently when evaluating the risk of bias or method ological quality of studies in knowledge translation interventions for cancer pain . The newly introduced CCRBT assigned these studies a higher risk of bias . Its psychometric properties need to be more thoroughly vali date d , in a range of research fields , to underst and fully how to interpret results from its application", "role": "user" }, { "content": "DISCUSSION AND IMPLICATION S There is inconsistent evidence that indoor nature exposures are beneficial for older care residents .\nWe expect that successful interventions were , at least partly , facilitating social interaction , supporting feelings of autonomy/control , and promoting skill development , that is , factors not necessarily associated with nature per se .", "role": "assistant" } ]

[ { "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: [\"Participant\", \"Outcome\", \"Intervention\"].\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\nEvaluation of AMSA in children with acute leukemia . A Pediatric Oncology Group study . One hundred four children with advanced leukemia in relapse ( 74 with acute lymphocytic leukemia [ ALL ] and 30 with acute nonlymphocytic leukemia [ ANLL ] ) received AMSA ( 4'- ( 9-Acridinylamino ) methanesulfon -m-anisidide ) at a dose of 120 mg/m2/day for 5 days ( Regimen I ) or 60 mg/m2/day for 10 days ( Regimen II ) . Children with ALL were randomized between Regimens I and II ( 31 and 36 evaluable patients , respectively ) . All 29 evaluable patients with ANLL were treated on Regimen I. Eighty-eight percent of evaluable patients experienced severe or life-threatening toxicity , with no statistical differences between Regimens I and II . Bacterial or fungal infections ( considered life-threatening or fatal ) occurred in 17 children with ALL and in 7 with ANLL . Fatal cardiac toxicity occurred in one patient . Complete or partial response occurred in 25.0 % ( SE = 8.8 % ) , 28.1 % ( SE = 8.0 % ) , and 25.9 % ( SE = 8.4 % ) of evaluable patients on ALL Regimen I , ALL Regimen II , and ANLL , respectively . However , responses were of short duration ( 16-91 days ) . There was no significant difference in the duration of survival from treatment start for the two ALL regimens ( P = 0.46 ) . The median duration of survival for ANLL patients was significantly longer ( P = 0.004 ) than that of ALL patients treated on Regimens I and II combined . Eighty-two percent of the complete or partial responses ( 18 of 22 ) occurred after the first course of AMSA . At the dose schedules investigated , and in a heavily pretreated patient population , AMSA had activity in childhood leukemia . However , the high incidence of severe , life-threatening , or fatal infections meant that the quality and quantity of responses and survival was not commensurate with the toxicity , and that it would be difficult to incorporate this drug into combination chemotherapy with other myelosuppressive agents .", "role": "user" }, { "content": "{\"Intervention\": [\"AMSA\", \"AMSA .\", \"AMSA ( 4\\u0027- ( 9-Acridinylamino ) methanesulfon -m-anisidide )\"], \"Outcome\": [\"severe or life-threatening toxicity\", \"toxicity\", \"quality and quantity of responses and survival\", \"Complete or partial response\", \"activity in childhood leukemia\", \"Fatal cardiac toxicity\", \"duration of survival\", \"incidence of severe , life-threatening , or fatal infections\", \"Bacterial or fungal infections\", \"responses\", \"median duration of survival\"], \"Participant\": [\"ANLL\", \"One hundred four\", \"ALL\", \"Children\", \"Fatal cardiac toxicity\", \"74\", \"30\", \"advanced leukemia\", \"acute leukemia\", \"acute lymphocytic leukemia [ ALL ]\", \"acute nonlymphocytic leukemia [ ANLL ] )\", \"children\"]}", "role": "assistant" } ]

[ { "content": "You will be shown sections from a scientific research paper, together with a question about the paper. This is an extractive question-answering task, where you must find and extract relevant text spans directly from the paper to answer the question. Your response should strictly 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 example, for the question \"What baselines did the authors compare against?\", a sample response might be:\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: Predictive Embeddings for Hate Speech Detection on Twitter\n\nWe present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.\n\nIntroduction\nThe increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hateful and harassing speech, enforcing these rules has proved challenging, as identifying hate speech speech at scale is still a largely unsolved problem in the NLP community. BIBREF3 , for example, identify many ambiguities in classifying abusive communications, and highlight the difficulty of clearly defining the parameters of such speech. This problem is compounded by the fact that identifying abusive or harassing speech is a challenge for humans as well as automated systems.\nDespite the lack of consensus around what constitutes abusive speech, some definition of hate speech must be used to build automated systems to address it. We rely on BIBREF4 's definition of hate speech, specifically: “language that is used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group.”\nIn this paper, we present a neural classification system that uses minimal preprocessing to take advantage of a modified Simple Word Embeddings-based Model BIBREF5 to predict the occurrence of hate speech. Our classifier features:\nIn the following sections, we discuss related work on hate speech classification, followed by a description of the datasets, methods and results of our study.\n\nRelated Work\nMany efforts have been made to classify hate speech using data scraped from online message forums and popular social media sites such as Twitter and Facebook. BIBREF3 applied a logistic regression model that used one- to four-character n-grams for classification of tweets labeled as racist, sexist or neither. BIBREF4 experimented in classification of hateful as well as offensive but not hateful tweets. They applied a logistic regression classifier with L2 regularization using word level n-grams and various part-of-speech, sentiment, and tweet-level metadata features.\nAdditional projects have built upon the data sets created by Waseem and/or Davidson. For example, BIBREF6 used a neural network approach with two binary classifiers: one to predict the presence abusive speech more generally, and another to discern the form of abusive speech.\n BIBREF7 , meanwhile, used pre-trained word2vec embeddings, which were then fed into a convolutional neural network (CNN) with max pooling to produce input vectors for a Gated Recurrent Unit (GRU) neural network. Other researchers have experimented with using metadata features from tweets. BIBREF8 built a classifier composed of two separate neural networks, one for the text and the other for metadata of the Twitter user, that were trained jointly in interleaved fashion. Both networks used in combination - and especially when trained using transfer learning - achieved higher F1 scores than either neural network classifier alone.\nIn contrast to the methods described above, our approach relies on a simple word embedding (SWEM)-based architecture BIBREF5 , reducing the number of required parameters and length of training required, while still yielding improved performance and resilience across related classification tasks. Moreover, our network is able to learn flexible vector representations that demonstrate associations among words typically used in hateful communication. Finally, while metadata-based augmentation is intriguing, here we sought to develop an approach that would function well even in cases where such additional data was missing due to the deletion, suspension, or deactivation of accounts.\n\nTransformed Word Embedding Model (TWEM)\nOur training set consists of INLINEFORM0 examples INLINEFORM1 where the input INLINEFORM2 is a sequence of tokens INLINEFORM3 , and the output INLINEFORM4 is the numerical class for the hate speech class. Each input instance represents a Twitter post and thus, is not limited to a single sentence.\nWe modify the SWEM-concat BIBREF5 architecture to allow better handling of infrequent and unknown words and to capture non-linear word combinations.\n\nWord Embeddings\nEach token in the input is mapped to an embedding. We used the 300 dimensional embeddings for all our experiments, so each word INLINEFORM0 is mapped to INLINEFORM1 . We denote the full embedded sequence as INLINEFORM2 . We then transform each word embedding by applying 300 dimensional 1-layer Multi Layer Perceptron (MLP) INLINEFORM3 with a Rectified Liner Unit (ReLU) activation to form an updated embedding space INLINEFORM4 . We find this better handles unseen or rare tokens in our training data by projecting the pretrained embedding into a space that the encoder can understand.\n\nPooling\nWe make use of two pooling methods on the updated embedding space INLINEFORM0 . We employ a max pooling operation on INLINEFORM1 to capture salient word features from our input; this representation is denoted as INLINEFORM2 . This forces words that are highly indicative of hate speech to higher positive values within the updated embedding space. We also average the embeddings INLINEFORM3 to capture the overall meaning of the sentence, denoted as INLINEFORM4 , which provides a strong conditional factor in conjunction with the max pooling output. This also helps regularize gradient updates from the max pooling operation.\n\nOutput\nWe concatenate INLINEFORM0 and INLINEFORM1 to form a document representation INLINEFORM2 and feed the representation into a 50 node 2 layer MLP followed by ReLU Activation to allow for increased nonlinear representation learning. This representation forms the preterminal layer and is passed to a fully connected softmax layer whose output is the probability distribution over labels.\n\nExperimental Setup\nWe tokenize the data using Spacy BIBREF10 . We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task. We experimented extensively with pre-processing variants and our results showed better performance without lemmatization and lower-casing (see supplement for details). We pad each input to 50 words. We train using RMSprop with a learning rate of .001 and a batch size of 512. We add dropout with a drop rate of 0.1 in the final layer to reduce overfitting BIBREF12 , batch size, and input length empirically through random hyperparameter search.\nAll of our results are produced from 10-fold cross validation to allow comparison with previous results. We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets.\nSR: Sexist/Racist BIBREF3 , HATE: Hate BIBREF4 HAR: Harassment BIBREF9 \n\nResults and Discussion\nThe approach we have developed establishes a new state of the art for classifying hate speech, outperforming previous results by as much as 12 F1 points. Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1. \nUsing the Approximate Randomization (AR) Test BIBREF14 , we perform significance testing using a 75/25 train and test split\nto compare against BIBREF3 and BIBREF4 , whose models we re-implemented. We found 0.001 significance compared to both methods. We also include in-depth precision and recall results for all three datasets in the supplement.\nOur results indicate better performance than several more complex approaches, including BIBREF4 's best model (which used word and part-of-speech ngrams, sentiment, readability, text, and Twitter specific features), BIBREF6 (which used two fold classification and a hybrid of word and character CNNs, using approximately twice the parameters we use excluding the word embeddings) and even recent work by BIBREF8 , (whose best model relies on GRUs, metadata including popularity, network reciprocity, and subscribed lists).\nOn the SR dataset, we outperform BIBREF8 's text based model by 3 F1 points, while just falling short of the Text + Metadata Interleaved Training model. While we appreciate the potential added value of metadata, we believe a tweet-only classifier has merits because retrieving features from the social graph is not always tractable in production settings. Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters.\n\nError Analysis\nFalse negatives\nMany of the false negatives we see are specific references to characters in the TV show “My Kitchen Rules”, rather than something about women in general. Such examples may be innocuous in isolation but could potentially be sexist or racist in context. While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.\nDebra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.\nAlong these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:\n@LoveAndLonging ...how is that example \"sexism\"?\n@amberhasalamb ...in what way?\nAnother case our classifier misses is problematic speech within a hashtag:\n:D @nkrause11 Dudes who go to culinary school: #why #findawife #notsexist :)\nThis limitation could be potentially improved through the use of character convolutions or subword tokenization.\nFalse Positives\nIn certain cases, our model seems to be learning user names instead of semantic content:\nRT @GrantLeeStone: @MT8_9 I don't even know what that is, or where it's from. Was that supposed to be funny? It wasn't.\nSince the bulk of our model's weights are in the embedding and embedding-transformation matrices, we cluster the SR vocabulary using these transformed embeddings to clarify our intuitions about the model ( TABREF14 ). We elaborate on our clustering approach in the supplement. We see that the model learned general semantic groupings of words associated with hate speech as well as specific idiosyncrasies related to the dataset itself (e.g. katieandnikki)\n\nConclusion\nDespite minimal tuning of hyper-parameters, fewer weight parameters, minimal text preprocessing, and no additional metadata, the model performs remarkably well on standard hate speech datasets. Our clustering analysis adds interpretability enabling inspection of results.\nOur results indicate that the majority of recent deep learning models in hate speech may rely on word embeddings for the bulk of predictive power and the addition of sequence-based parameters provide minimal utility. Sequence based approaches are typically important when phenomena such as negation, co-reference, and context-dependent phrases are salient in the text and thus, we suspect these cases are in the minority for publicly available datasets. We think it would be valuable to study the occurrence of such linguistic phenomena in existing datasets and construct new datasets that have a better representation of subtle forms of hate speech. In the future, we plan to investigate character based representations, using character CNNs and highway layers BIBREF15 along with word embeddings to allow robust representations for sparse words such as hashtags.\n\nSupplemental Material\nWe experimented with several different preprocessing variants and were surprised to find that reducing preprocessing improved the performance on the task for all of our tasks. We go through each preprocessing variant with an example and then describe our analysis to compare and evaluate each of them.\n\nPreprocessing\nOriginal text\nRT @AGuyNamed_Nick Now, I'm not sexist in any way shape or form but I think women are better at gift wrapping. It's the XX chromosome thing\nTokenize (Basic Tokenize: Keeps case and words intact with limited sanitizing)\nRT @AGuyNamed_Nick Now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the XX chromosome thing\nTokenize Lowercase: Lowercase the basic tokenize scheme\nrt @aguynamed_nick now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing\nToken Replace: Replaces entities and user names with placeholder)\nENT USER now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the xx chromosome thing\nToken Replace Lowercase: Lowercase the Token Replace Scheme\nENT USER now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing\nWe did analysis on a validation set across multiple datasets to find that the \"Tokenize\" scheme was by far the best. We believe that keeping the case in tact provides useful information about the user. For example, saying something in all CAPS is a useful signal that the model can take advantage of.\n\nEmbedding Analysis\nSince our method was a simple word embedding based model, we explored the learned embedding space to analyze results. For this analysis, we only use the max pooling part of our architecture to help analyze the learned embedding space because it encourages salient words to increase their values to be selected. We projected the original pre-trained embeddings to the learned space using the time distributed MLP. We summed the embedding dimensions for each word and sorted by the sum in descending order to find the 1000 most salient word embeddings from our vocabulary. We then ran PCA BIBREF16 to reduce the dimensionality of the projected embeddings from 300 dimensions to 75 dimensions. This captured about 60% of the variance. Finally, we ran K means clustering for INLINEFORM0 clusters to organize the most salient embeddings in the projected space.\nThe learned clusters from the SR vocabulary were very illuminating (see Table TABREF14 ); they gave insights to how hate speech surfaced in the datasets. One clear grouping we found is the misogynistic and pornographic group, which contained words like breasts, blonds, and skank. Two other clusters had references to geopolitical and religious issues in the Middle East and disparaging and resentful epithets that could be seen as having an intellectual tone. This hints towards the subtle pedagogic forms of hate speech that surface. We ran silhouette analysis BIBREF17 on the learned clusters to find that the clusters from the learned representations had a 35% higher silhouette coefficient using the projected embeddings compared to the clusters created from the original pre-trained embeddings. This reinforces the claim that our training process pushed hate-speech related words together, and words from other clusters further away, thus, structuring the embedding space effectively for detecting hate speech.\n\nTable 1: Dataset Characteristics\n\nTable 2: F1 Results3\n\nTable 3: Projected Embedding Cluster Analysis from SR Dataset\n\nTable 5: SR Results\n\nTable 7: HAR Results\n\nTable 6: HATE Results\n\nTable 8: Projected Embedding Cluster Analysis from SR Dataset\n\nQuestion: how many parameters did their model use?", "role": "user" }, { "content": "{\"answer\": [\"Excluding the embedding weights, our model requires 100k parameters\"], \"evidence\": [\"On the SR dataset, we outperform BIBREF8 \\u0027s text based model by 3 F1 points, while just falling short of the Text + Metadata Interleaved Training model. While we appreciate the potential added value of metadata, we believe a tweet-only classifier has merits because retrieving features from the social graph is not always tractable in production settings. Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters.\"]}", "role": "assistant" } ]

[ { "content": "You will be presented with the full text of a biomedical research paper. Please write an abstract for this paper. Your response should only include the abstract and no additional text.\n\nPaper text:\na 59-year - old man presented with progressive dysphagia and weight loss over a period of 6 months .\nthe patient 's medical history did not reveal any granulomatous diseases such as sarcoidosis or tuberculosis . on upper gastrointestinal endoscopy ,\na tumor was detected in the distal esophagus and at the gastroesophageal junction ( siewert type i ) ; biopsies were taken .\non endoscopic ultrasonography ( eus ) a tumor with a length of 8 cm was seen in the distal esophagus including the gastroesophageal junction .\nct scanning confirmed the presence of the tumor in the distal esophagus and at the gastroesophageal junction ( fig .\nfurthermore , it was noted on both eus and ct scanning that remarkably enlarged lymph nodes ( 20 and 17 mm , respectively ) were located near the celiac trunk , which appeared to be calcified ( fig .\neus with fine - needle aspiration of these enlarged nodes was done and cytological examination showed tumor cells ( fig .\nhence , the tumor was classified as ct3n1m1a and was considered ineligible for primary surgical therapy because of the large peritruncal nodes .\nthe patient was offered induction chemotherapy to downstage the tumor in order to facilitate radical resection .\nthe patient followed a 6-course weekly treatment with carboplatin / paclitaxel , followed by another 3-weekly treatment with carboplatin / paclitaxel for 3 courses .\nresponse to induction chemotherapy was evaluated at 2 and 5 months after the diagnosis by means of ct scanning .\na clear reduction in wall thickness of the primary tumor in the distal esophagus and at the gastroesophageal junction was seen after the patient had completed 9 cycles of chemotherapy ( fig .\nhowever , the calcified part of the lymph nodes near the celiac trunk had not decreased in size ( fig .\nnevertheless , given the patient 's age , good general condition and substantial response of the primary tumor , he was offered surgical resection of the tumor .\nsix weeks after having finished chemotherapy the patient underwent transhiatal esophagectomy with peritruncal lymph node dissection , followed by gastric tube reconstruction with cervical anastomosis .\nthe calcified lymph nodes could easily be identified intraoperatively and were resected en bloc together with the resection specimen .\npostoperative recovery was uneventful , and the patient was discharged home 15 days after the operation .\nhistological examination of the resection specimen revealed a poorly differentiated adenocarcinoma with tumor - free resection margins ( pr0 ) .\nlymph node metastases were present in 3 locoregional nodes , out of 17 lymph nodes that were resected in total .\nthe enlarged calcified lymph nodes that were located at the celiac trunk did not harbor vital tumor lesions anymore .\ncalcified lymph nodes mostly involve thoracic and , less frequently , abdominal and pelvic nodes .\nnodal calcification is common in benign granulomatous diseases such as sarcoidosis and tuberculosis , in which half of the patients have been reported to have calcification of hilar and/or mediastinal nodes on ct scanning . in hematological malignancies\nit is well known that lymph nodes may calcify in response to chemo- and/or radiotherapy .\nlikewise , nodal calcification has been reported in patients with metastatic prostate , ovary and colon carcinoma that have been treated with hormonal , chemo- and/or radiotherapy .\nmoreover , calcification of lymph nodes can be identified by ct scanning as radiation - induced injury to normal tissue .\nthese so - called de novo calcifications have been described in several case reports of patients with hodgkin 's lymphoma , papillary thyroid carcinoma , and adenocarcinomas of prostate , breast , colon , ovary and lung .\nmetastatic nodal calcification can occur as a consequence of dystrophic calcium deposition in areas of necrosis .\nprecipitation can occur as a reaction to tissue damage , when unresorbed necrotic material persists for prolonged periods . in our patient imaging\nsuggested that the calcified part of the lymph nodes did not decrease in size and , consequently , did not respond to chemotherapy .\nhowever , on pathologic examination no vital tumor cells could be detected in these lymph nodes anymore . to our knowledge ,\nno published data are available on response evaluation in tumor - positive calcified lymph nodes in esophageal cancer .\nwe hypothesize that calcified lymph nodes can not decrease in size even in case of adequate destruction of tumor cells by chemotherapy .\ntheir incapacity to shrink should be kept in mind when evaluating response to neoadjuvant or induction chemotherapy .\nresponse measurement should preferably be directed at the change in size of the primary tumor in case calcified lymph nodes are present .", "role": "user" }, { "content": "untreated malignant lymph nodes that are calcified are rare . \n publications on such calcifications are restricted to case reports . \n we present a case of calcified lymph nodes in a patient with adenocarcinoma of the gastroesophageal junction that seemed to be nonresponsive to induction chemotherapy , as they did not decrease in size . \n however , on pathological examination of the resected lymph nodes no vital tumor cells could be detected anymore . \n therefore , we hypothesize that a calcified lymph node is unable to shrink , even after adequate remission on induction chemotherapy . this should be taken into account when clinical decision - making depends on the change in size of an enlarged , calcified lymph node as a measure of treatment effect .", "role": "assistant" } ]

[ { "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 D3 is showing impressive effects in buffering the severity of COVID-19 infections\n\nAbstract:\nNews and social media platforms have implicated dietary supplements in the treatment and prevention of coronavirus disease 2019 (COVID-19).\nDuring this pandemic when information quickly evolves in the presence of contradicting messages and misinformation, the role of the pharmacist is essential.\nHere, we review theoretical mechanisms and evidence related to efficacy and safety of select supplements in the setting of COVID-19, including vitamin C, vitamin D, zinc, elderberry, and silver.\nEvidence evaluating these supplements in COVID-19 patients is lacking, and providers and patients should not rely on dietary supplements to prevent or treat COVID-19.\nRather, reference to evidence-based guidelines should guide treatment decisions.", "role": "user" }, { "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Evidence evaluating these supplements in COVID-19 patients is lacking, and providers and patients should not rely on dietary supplements to prevent or treat COVID-19.\"]}", "role": "assistant" } ]

[ { "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 response other than the answer.\n\nParagraph: Antibodies are generated against only three major sites on the virion: (i) the surface-exposed N-terminal ∼12 residues of the pVIII monomer lattice (Terry et al., 1997; Kneissel et al., 1999) ; (ii) the N-terminal N1 and N2 domains of pIII (van Houten et al., 2010) ; and (iii) bacterial lipopolysaccharide (LPS) embedded in the phage coat (Henry et al., 2011) . In mice, serum antibody titers against the phage typically reach 1:10 5 -1:10 6 after 2-3 immunizations, and are maintained for at least 1 year postimmunization (Frenkel et al., 2000) . Primary antibody responses against the phage appear to be composed of a mixture of IgM and IgG2b isotypes in C57BL/6 mice, while secondary antibody responses are composed primarily of IgG1 and IgG2b isotypes, with a lesser contribution of IgG2c and IgG3 isotypes (Hashiguchi et al., 2010) . Deletion of the surface-exposed N1 and N2 domains of pIII produces a truncated form of this protein that does not elicit antibodies, but also results in a non-infective phage particle with lower overall immunogenicity (van Houten et al., 2010) . FIGURE 1 | Types of immune responses elicited in response to immunization with filamentous bacteriophage. As a virus-like particle, the filamentous phage engages multiple arms of the immune system, beginning with cellular effectors of innate immunity (macrophages, neutrophils, and possibly natural killer cells), which are recruited to tumor sites by phage displaying tumor-targeting moieties. The phage likely\n\nactivates T-cell independent antibody responses, either via phage-associated TLR ligands or cross-linking by the pVIII lattice. After processing by antigen-presenting cells, phage-derived peptides are presented on MHC class II and cross-presented on MHC class I, resulting in activation of short-lived CTLs and an array of helper T-cell types, which help prime memory CTL and high-affinity B-cell responses. Frontiers in Microbiology | www.frontiersin.org\n\nAlthough serum anti-phage antibody titers appear to be at least partially T-cell dependent (Kölsch et al., 1971; Willis et al., 1993; De Berardinis et al., 1999; van Houten et al., 2010) , many circulating pVIII-specific B cells in the blood are devoid of somatic mutation even after repeated biweekly immunizations, suggesting that under these conditions, the phage activates T-cell-independent B-cell responses in addition to highaffinity T-cell-dependent responses (Murira, 2014) . Filamentous phage particles can be processed by antigen-presenting cells and presented on MHC class II molecules (Gaubin et al., 2003; Ulivieri et al., 2008) and can activate T H 1, T H 2, and T H 17 helper T cells (Yang et al., 2005a; Wang et al., 2014d) . Anti-phage T H 2 responses were enhanced through display of CTLA-4 peptides fused to pIII (Kajihara et al., 2000) . Phage proteins can also be cross-presented on MHC class I molecules (Wan et al., 2005) and can prime two waves of CTL responses, consisting first of short-lived CTLs and later of long-lived memory CTLs that require CD4 + T-cell help (Del Pozzo et al., 2010) . The latter CTLs mediate a delayed-type hypersensitivity reaction (Fang et al., 2005; Del Pozzo et al., 2010) . The phage particle is self-adjuvanting through multiple mechanisms. Host cell wall-derived LPS enhances the virion's immunogenicity, and its removal by polymyxin B chromatography reduces antibody titers against phage coat proteins (Grabowska et al., 2000) . The phage's singlestranded DNA genome contains CpG motifs and may also have an adjuvant effect. The antibody response against the phage is entirely dependent on MyD88 signaling and is modulated by stimulation of several Toll-like receptors (Hashiguchi et al., 2010) , indicating that innate immunity plays an important but largely uncharacterized role in the activation of anti-phage adaptive immune responses. Biodistribution studies of the phage after intravenous injection show that it is cleared from the blood within hours through the reticuloendothelial system (Molenaar et al., 2002) , particularly of the liver and spleen, where it is retained for days (Zou et al., 2004) , potentially activating marginal-zone B-cell responses. Thus, the filamentous phage is not only a highly immunogenic carrier, but by virtue of activating a range of innate and adaptive immune responses, serves as an excellent model virus-like particle antigen. Long before the identification of filamentous phage, other types of bacteriophage were already being used for antibacterial therapy in the former Soviet Union and Eastern Europe (reviewed in Sulakvelidze et al., 2001) . The filamentous phage, with its nonlytic life cycle, has less obvious clinical uses, despite the fact that the host specificity of Inovirus and Plectrovirus includes many pathogens of medical importance, including Salmonella, E. coli, Shigella, Pseudomonas, Clostridium, and Mycoplasma species. In an effort to enhance their bactericidal activity, genetically modified filamentous phage have been used as a \"Trojan horse\" to introduce various antibacterial agents into cells. M13 and Pf3 phage engineered to express either BglII restriction endonuclease (Hagens and Blasi, 2003; Hagens et al., 2004) , lambda phage S holin (Hagens and Blasi, 2003) or a lethal catabolite gene activator protein (Moradpour et al., 2009) effectively killed E. coli and Pseudomonas aeruginosa cells, respectively, with no concomitant release of LPS (Hagens and Blasi, 2003; Hagens et al., 2004) . Unfortunately, the rapid emergence of resistant bacteria with modified F pili represents a major and possibly insurmountable obstacle to this approach. However, there are some indications that filamentous phage can exert useful but more subtle effects upon their bacterial hosts that may not result in the development of resistance to infection. Several studies have reported increased antibiotic sensitivity in bacterial populations simultaneously infected with either wild type filamentous phage (Hagens et al., 2006) or phage engineered to repress the cellular SOS response (Lu and Collins, 2009) . Filamentous phage f1 infection inhibited early stage, but not mature, biofilm formation in E. coli (May et al., 2011) . Thus, unmodified filamentous phage may be of future interest as elements of combination therapeutics against certain drug-resistant infections. More advanced therapeutic applications of the filamentous phage emerge when it is modified to express a targeting moiety specific for pathogenic cells and/or proteins for the treatment of infectious diseases, cancer and autoimmunity (Figure 2) . The first work in this area showed as proof-of-concept that phage encoding a GFP expression cassette and displaying a HER2specific scFv on all copies of pIII were internalized into breast tumor cells, resulting in GFP expression (Poul and Marks, 1999) . M13 or fd phage displaying either a targeting peptide or antibody fragment and tethered to chloramphenicol by a labile crosslinker were more potent inhibitors of Staphylococcus aureus growth than high-concentration free chloramphenicol (Yacoby et al., 2006; Vaks and Benhar, 2011) . M13 phage loaded with doxorubicin and displaying a targeting peptide on pIII specifically killed prostate cancer cells in vitro (Ghosh et al., 2012a) . Tumorspecific peptide:pVIII fusion proteins selected from \"landscape\" phage (Romanov et al., 2001; Abbineni et al., 2010; Fagbohun et al., 2012 Fagbohun et al., , 2013 Lang et al., 2014; Wang et al., 2014a) were able to target and deliver siRNA-, paclitaxel-, and doxorubicincontaining liposomes to tumor cells (Jayanna et al., 2010a; Wang et al., 2010a Wang et al., ,b,c, 2014b Bedi et al., 2011 Bedi et al., , 2013 Bedi et al., , 2014 ; they were non-toxic and increased tumor remission rates in mouse models (Jayanna et al., 2010b; Wang et al., 2014b,c) . Using the B16-OVA tumor model, Eriksson et al. (2007) showed that phage displaying peptides and/or Fabs specific for tumor antigens delayed tumor growth and improved survival, owing in large part to activation of tumor-associated macrophages and recruitment of neutrophils to the tumor site (Eriksson et al., 2009) . Phage displaying an scFv against β-amyloid fibrils showed promise as a diagnostic (Frenkel and Solomon, 2002) and therapeutic (Solomon, 2008) reagent for Alzheimer's disease and Parkinson's disease due to the unanticipated ability of the phage to penetrate into brain tissue (Ksendzovsky et al., 2012) . Similarly, phage displaying an immunodominant peptide epitope derived from myelin oligodendrocyte glycoprotein depleted pathogenic demyelinating antibodies in brain tissue in the murine experimental autoimmune encephalomyelitis model of multiple sclerosis (Rakover et al., 2010) . The advantages of the filamentous phage in this context over traditional antibody-drug or protein-peptide conjugates are (i) its ability to carry very high amounts of drug or peptide, and (ii) its ability to access anatomical compartments that cannot generally be reached by systemic administration of a protein. Unlike most therapeutic biologics, the filamentous phage's production in bacteria complicates its use in humans in several ways. First and foremost, crude preparations of filamentous phage typically contain very high levels of contaminating LPS, in the range of ∼10 2 -10 4 endotoxin units (EU)/mL (Boratynski et al., 2004; Branston et al., 2015) , which have the potential to cause severe adverse reactions. LPS is not completely removed by polyethylene glycol precipitation or cesium chloride density gradient centrifugation (Smith and Gingrich, 2005; Branston et al., 2015) , but its levels can be reduced dramatically using additional purification steps such as size exclusion chromatography (Boratynski et al., 2004; Zakharova et al., 2005) , polymyxin B chromatography (Grabowska et al., 2000) , and treatment with detergents such as Triton X-100 or Triton X-114 (Roehnisch et al., 2014; Branston et al., 2015) . These strategies routinely achieve endotoxin levels of <1 EU/mL as measured by the limulus amebocyte lysate (LAL) assay, well below the FDA limit for parenteral administration of 5 EU/kg body weight/dose, although concerns remain regarding the presence of residual virion-associated LPS which may be undetectable. A second and perhaps unavoidable consequence of the filamentous phage's bacterial production is inherent heterogeneity of particle size and the spectrum of host cellderived virion-associated and soluble contaminants, which may be cause for safety concerns and restrict its use to high-risk groups. Many types of bacteriophage and engineered phage variants, including filamentous phage, have been proposed for prophylactic use ex vivo in food safety, either in the production pipeline (reviewed in Dalmasso et al., 2014) or for detection of foodborne pathogens post-production (reviewed in Schmelcher and Loessner, 2014) . Filamentous phage displaying a tetracysteine tag on pIII were used to detect E. coli cells through staining with biarsenical dye . M13 phage functionalized with metallic silver were highly bactericidal against E. coli and Staphylococcus epidermidis . Biosensors based on surface plasmon resonance (Nanduri et al., 2007) , piezoelectric transducers (Olsen et al., 2006) , linear dichroism (Pacheco-Gomez et al., 2012) , and magnetoelastic sensor technology (Lakshmanan et al., 2007; Huang et al., 2009) were devised using filamentous phage displaying scFv or conjugated to whole IgG against E. coli, Listeria monocytogenes, Salmonella typhimurium, and Bacillus anthracis with limits of detection on the order of 10 2 -10 6 bacterial cells/mL. Proof of concept has been demonstrated for use of such phage-based biosensors to detect bacterial contamination of live produce (Li et al., 2010b) and eggs (Chai et al., 2012) . The filamentous phage particle is enclosed by a rod-like protein capsid, ∼1000 nm long and 5 nm wide, made up almost entirely of overlapping pVIII monomers, each of which lies ∼27 angstroms from its nearest neighbor and exposes two amine groups as well as at least three carboxyl groups (Henry et al., 2011) . The regularity of the phage pVIII lattice and its diversity of chemically addressable groups\n\nQuestion: What makes filamentous phage ideal scaffold for bioconjugation?", "role": "user" }, { "content": "The regularity of the phage pVIII lattice and its diversity of chemically addressable groups", "role": "assistant" } ]

[ { "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: However, research investigating garlic's antiviral properties is limited. Though garlic is considered to be a healthy food, there's no evidence showing that eating it can prevent or cure COVID-19.\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "role": "assistant" } ]

[ { "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 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: Phase I study combining treatment with temsirolimus and sunitinib malate in patients with advanced renal cell carcinoma . Concurrent inhibition of multiple oncogenic signaling pathways might improve the efficacy of anticancer agents and abrogate resistance mechanisms. This phase I study evaluated temsirolimus in combination with sunitinib in patients with advanced RCC. ### Patients And Methods Eligibility included advanced RCC and <or= 2 previous systemic regimens. At the starting dose, temsirolimus 15 mg was administered by intravenous (I.V.) infusion once weekly, and sunitinib 25 mg was administered orally once daily for 4 weeks, followed by a 2-week rest period. ### results In the first cohort, dose-limiting toxicities (grade 3 treatment-related toxicities that lasted >or= 7 days) were observed in 2 of 3 patients. One patient experienced grade 3 rash during week 3, which led to treatment discontinuation. A second patient had grade 3 thrombocytopenia (platelet count, 48,000/microL), cellulitis, and gout during week 3 and was hospitalized; platelets recovered to 109,000/microL 4 days after discontinuation of protocol therapy. A third patient experienced rash, asthenia, diarrhea, stomatitis, constipation, fever, and rectal hemorrhage, all of which were mild in severity. The study was terminated because of dose-limiting toxicity observed at low starting doses of both agents. ### conclusion Concomitant use of I.V. temsirolimus 15 mg weekly and oral sunitinib 25 mg daily (4 weeks on, 2 weeks off) is not recommended.\n\nsentence: Phase I study combining treatment with temsirolimus and sunitinib malate in patients with advanced renal cell carcinoma .", "role": "user" }, { "content": "[[\"temsirolimus\", \"sunitinib\", \"NEG\"]]", "role": "assistant" } ]

[ { "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 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: Meta-analysis of 24-hour intraocular pressure studies evaluating the efficacy of glaucoma medicines. To evaluate efficacy and safety data of currently available ocular hypotensive medicines derived from 24-hour studies, of similar design, in patients with primary open-angle glaucoma (POAG), exfoliative glaucoma, or ocular hypertension (OH). ### design Meta-analysis of published articles evaluating patients with POAG, exfoliative glaucoma, or OH. ### methods We included articles that were randomized, prospective, single- or double-masked, comparative studies of ocular hypotensive therapies over 24 hours. Each article selected contained an untreated baseline, >or=4-week treatment period, >/=20 patients per treatment arm, and >or=6 time points not spaced >5 hours apart and used Goldmann applanation or Tonopen tonometry (supine measurements) to measure intraocular pressure (IOP). ### Main Outcome Measure Twenty-four-hour IOP efficacy. ### results This analysis included 864 separate 24-hour treatment curves from 386 patients in 28 treatment arms from 11 studies. A statistical difference in the mean diurnal pressure decrease existed between monotherapy treatments for POAG/OH patients, with bimatoprost (29%) and travoprost (27%) showing the greatest 24-hour reduction (P = 0.026). timolol 0.5% was less effective than latanoprost (24% vs. 19% reduction) but decreased the pressure at each night time point (P = 0.0003). dorzolamide showed a 19% 24-hour pressure reduction and brimonidine 0.2% a 14% one. In exfoliative glaucoma patients, latanoprost and travoprost showed higher baseline and treatment pressures, although the pressure reductions (29% and 31%, respectively) were greater generally than observed with POAG/OH. An evening-dosed latanoprost/timolol fixed combination reduced the pressure 33%, and the dorzolamide/timolol fixed combination (DTFC), 26%. However, the power to detect a difference for this specific comparison was probably low, due to the limited number of patients (n = 20) in the DTFC group. A statistical difference between evening-dosed (24%) and morning-dosed (18%) latanoprost (P<0.0001) was noted, but not between evening (27%) and morning (26%) travoprost (P = 0.074). The mean reduction of night time points was statistically lower than day time points for latanoprost (P = 0.031), timolol (P = 0.032), and brimonidine (P = 0.050), but not for dorzolamide. dorzolamide (P = 0.60), travoprost (P = 0.064), and bimatoprost (P = 0.057) did not demonstrate nighttime pressures lower than daytime ones. The mean reduction of night time points was statistically lower than that of day time points for latanoprost ( P = 0.031 ) , timolol ( P = 0.032 ) , and brimonidine ( P = 0.050 ) , but not for dorzolamide ( P = 0.60 ) , bimatoprost ( P = 0.057 ) , travoprost ( P = 0.064 ) . ### conclusions Similar relative efficacies generally exist in various classes of ocular hypotensive agents during night and day hours.\n\nsentence: The mean reduction of night time points was statistically lower than that of day time points for latanoprost ( P = 0.031 ) , timolol ( P = 0.032 ) , and brimonidine ( P = 0.050 ) , but not for dorzolamide ( P = 0.60 ) , bimatoprost ( P = 0.057 ) , travoprost ( P = 0.064 ) .", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nDeep neural networks (DNN) are widely used in many applications.\nHowever, their deployment on edge devices has been difficult because they are resource hungry.\nBinary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to 1-bit.\nWe propose an improved binary training method (BNN+), by introducing a regularization function that encourages training weights around binary values.\nIn addition to this, to enhance model performance we add trainable scaling factors to our regularization functions.\nFurthermore, we use an improved approximation of the derivative of the sign activation function in the backward computation.\nThese additions are based on linear operations that are easily implementable into the binary training framework.\nWe show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.3% with VGG network.\nOn ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of 4% and 2% top-1 accuracy respectively.\nDeep neural networks (DNNs) have demonstrated success for many supervised learning tasks ranging from voice recognition to object detection BID26 BID11 .\nThe focus has been on increasing accuracy, in particular for image tasks, where deep convolutional neural networks (CNNs) are widely used.\nHowever, their increasing complexity poses a new challenge, and has become an impediment to widespread deployment in many applications; specifically when trying to deploy such models to resource constrained and lower-power devices.\nA typical DNN architecture contains tens to thousands of layers, resulting in millions of parameters.\nAs an example, AlexNet BID16 requires 200MB of memory, VGGNet BID26 requires 500MB memory.\nLarge model sizes are further exacerbated by their computational cost, requiring GPU implementation to allow real-time inference.\nSuch requirements evidently cannot be accustomed by edge devices as they have limited memory, computation power, and battery.\nThis motivated the community to investigate methods for compressing and reducing computation cost of DNNs.To make DNNs compatible with the resource constraints of low power devices, there have been several approaches developed, such as network pruning BID17 , architecture design BID25 , and quantization BID0 BID4 .\nIn particular, weight compression using quantization can achieve very large savings in memory, where binary (1-bit), and ternary (2-bit) approaches have been shown to obtain competitive accuracy BID10 BID31 BID29 .\nUsing such schemes reduces model sizes by 8x to 32x depending on the bit resolution used for computations.\nIn addition to this, the speed by quantizing the activation layers.\nIn this way, both the weights and activations are quantized so that one can replace the expensive dot products and activation function evaluations with bitwise operations.\nThis reduction in bit-width benefits hardware accelerators such as FPGAs and neural network chips.An issue with using low-bit DNNs is the drastic drop in accuracy compared to its full precision counterpart, and this is made even more severe upon quantizing the activations.\nThis problem is largely due to noise and lack of precision in the training objective of the networks during back-propagation BID19 .\nAlthough, quantizing the weights and activations have been attracting large interests thanks to their computational benefits, closing the gap in accuracy between the full precision and the quantized version remains a challenge.\nIndeed, quantizing weights cause drastic information loss and make neural networks harder to train due to a large number of sign fluctuations in the weights.\nTherefore, how to control the stability of this training procedure is of high importance.\nIn theory, it is infeasible to back-propagate in a quantized setting as the weights and activations employed are discontinuous and discrete.\nInstead, heuristics and approximations are proposed to match the forward and backward passes.\nOften weights at different layers of DNNs follow a certain structure.\nHow to quantize the weights locally, and maintaining a global structure to minimize a common cost function is important BID18 .Our\ncontribution consists of three ideas that can be easily implemented in the binary training framework presented by BID10 to improve convergence and generalization accuracy of binary networks. First\n, the activation function is modified to better approximate the sign function in the backward pass, second we propose two regularization functions that encourage training weights around binary values, and lastly a scaling factor is introduced in both the regularization term as well as network building blocks to mitigate accuracy drop due to hard binarization. Our\nmethod is evaluated on CIFAR-10 and ImageNet datasets and compared to other binary methods. We\nshow accuracy gains to traditional binary training.\nWe proposed two regularization terms (4) and (5) and an activation term (2) with a trainable parameter β.\nWe run several experiments to better understand the effect of the different modifications to the training method, especially using different regularization and asymptote parameters β.\nThe parameter β is trainable and would add one equation through back-propagation.\nHowever, we fixed β throughout our experiments to explicit values.\nThe results are summarized in TAB1 .Through\nour experiments, we found that adding regularizing term with heavy penalization degrades the networks ability to converge, as the term would result in total loss be largely due to the regu- larizing term and not the target cross entropy loss. Similarly\n, the regularizing term was set to small values in BID29 . As a result\n, we set λ with a reasonably small value 10 −5 − 10 −7 , so that the scales move slowly as the weights gradually converge to stable values. Some preliminary\nexperimentation was to gradually increase the regularization with respect to batch iterations updates done in training, though this approach requires careful tuning and was not pursued further.From TAB1 , and referring to networks without regularization, we see the benefit of using SwishSign approximation versus the STE. This was also noted\nin , where their second approximation provided better results. There is not much difference\nbetween using R 1 versus R 2 towards model generalization although since the loss metric used was the cross-entropy loss, the order of R 1 better matches the loss metric. Lastly, it seems moderate values\nof β is better than small or large values. Intuitively, this happens because\nfor small values of β, the gradient approximation is not good enough and as β increases the gradients become too large, hence small noise could cause large fluctuations in the sign of the weights.We did not compare our network with that of as they introduce a shortcut connection that proves to help even the full precision network. As a final remark, we note that the\nlearning rate is of great importance and properly tuning this is required to achieve convergence. Table 3 summarizes the best results\nof the ablation study and compares with BinaryNet, XNOR-Net, and ABC-Net. Table 3 : Comparison of top-1 and top-5\naccuracies of our method BNN+ with BinaryNet, XNORNet and ABC-Net on ImageNet, summarized from TAB1 . The results of BNN, XNOR, & ABC-Net are\nreported from the corresponding papers BID23 BID10 BID29 . Results for ABC-NET on AlexNet were not\navailable, and so is not reported.\nTo summarize we propose three incremental ideas that help binary training:\ni) adding a regularizer to the objective function of the network,\nii) trainable scale factors that are embedded in the regularizing term and\niii) an improved approximation to the derivative of the sign activation function.\nWe obtain competitive results by training AlexNet and Resnet-18 on the ImageNet dataset.\nFor future work, we plan on extending these to efficient models such as CondenseNet BID9 , MobileNets BID8 , MnasNet BID28 and on object recognition tasks.", "role": "user" }, { "content": "The paper presents an improved training mechanism for obtaining binary networks with smaller accuracy drop that helps close the gap with it's full precision counterpart", "role": "assistant" } ]

[ { "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: [\"RNA\", \"CellType\", \"CellLine\", \"Protein\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nCharacterization of the murine cyclin-dependent kinase inhibitor gene p27Kip1. The cyclin-dependent kinase inhibitor p27Kip1 plays an important role in regulating cell-cycle progression. p27Kip1 directly inhibits the catalytic activity of cyclin / cdks (cyclin-dependent kinase) complexes and / or interferes physically with cyclin / cdks activation by CAK. Interestingly, the expression level of p27Kip1 mRNA was maximal in resting Go T-cells and rapidly declined following anti-CD3 activation. We report here the cloning of p27Kip1 gene from murine genomic DNA and the functional analysis of the promoter of the p27Kip1 gene. The gene consists of at least three exons and spans more than 5. 6 kb of DNA. Primer extension and nuclease S1 protection analysis revealed two major transcription initiation sites. The promoter region lacked a TATA box but contained potential binding sites for the transcriptional factors including two Sp1, CRE, Myb and NFkB located at positions-153,-178,-286,-875, and-1011, respectively. To analyze the regulatory mechanisms controlling p27Kip1 gene expression, we characterized the 5'-flanking region from nt-1609 to + 178. The-326 to-615 region contained positive regulatory elements.", "role": "user" }, { "content": "{\"CellLine\": [], \"CellType\": [\"resting Go T - cells\"], \"DNA\": [\"positions - 153 , - 178 , - 286 , - 875 , and - 1011\", \"- 326 to - 615 region\", \"promoter region\", \"murine genomic DNA\", \"cyclin - dependent kinase inhibitor\", \"transcription initiation sites\", \"nt - 1609 to + 178\", \"positive regulatory elements\", \"5 . 6 kb of DNA\", \"p27Kip1 gene\", \"p27Kip1\", \"murine cyclin - dependent kinase inhibitor gene\", \"TATA box\", \"5\\u0027 - flanking region\"], \"Protein\": [\"CAK\", \"NFkB\", \"cyclin - dependent kinase\", \"cyclin / cdks (cyclin - dependent kinase) complexes\", \"Myb\", \"Sp1\", \"transcriptional factors\", \"anti - CD3\", \"CRE\", \"cyclin / cdks\"], \"RNA\": [\"p27Kip1 mRNA\"]}", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nWe study SGD and Adam for estimating a rank one signal planted in matrix or tensor noise.\nThe extreme simplicity of the problem setup allows us to isolate the effects of various factors: signal to noise ratio, density of critical points, stochasticity and initialization.\nWe observe a surprising phenomenon: Adam seems to get stuck in local minima as soon as polynomially many critical points appear (matrix case), while SGD escapes those.\nHowever, when the number of critical points degenerates to exponentials (tensor case), then both algorithms get trapped.\nTheory tells us that at fixed SNR the problem becomes intractable for large $d$ and in our experiments SGD does not escape this.\nWe exhibit the benefits of warm starting in those situations.\nWe conclude that in this class of problems, warm starting cannot be replaced by stochasticity in gradients to find the basin of attraction.\nReductionism consists of breaking down the study of complex systems and phenomena into their atomic components.\nWhile the use of stochastic gradient based algorithms has shown tremendous success at minimizing complicated loss functions arising in deep learning, our understanding of why, when and how this happens is still limited.\nStatements such as stochastic gradients escape from isolated critical points along the road to the best basin of attraction, or SGD generalizes better because it does not get stuck in steep local minima still need to be better understood.\nCan we prove or replicate these phenomena in the simplest instances of the problem?\nWe study the behavior of stochastic gradient descent (SGD) BID11 and an adaptive variant (Adam) BID8 under a class of well studied non-convex problems.\nThe single spiked models were originally designed for studying principal component analysis on matrices BID12 BID3 BID5 and have also been extended to higher order tensors BID10 .\nAdaptive stochastic optimization methods have been gaining popularity in the deep learning community thanks to fast training on some benchmarks.\nHowever, it has been observed that despite reaching a low value of the loss function, the solutions found by Adam do not generalize as well as SGD solutions do.\nAn assumption, widely spread and adopted in the community, has been that SGD's randomness helps escaping local critical points [WRS + 17] .\nWhile the problem has been thoroughly studied theoretically [MR14, HSS15, HSSS16, BAGJ18], our contribution is to propose experimenting with this simple model to challenge claims such as those on randomized gradient algorithms in this very simple setup.\nIt is noteworthy that the landscape of non-global critical points of these toy datasets are studied BID0 BID2 BID1 and formally linked to the neural nets empirical loss functions BID2 BID9 .\nFor this problem, the statistical properties of the optimizers are well understood, and in the more challenging tensor situation, also the impact of (spectral) warm start has been discussed BID10 .\nWe will examine the solutions found by SGD and Adam and compare them with spectral and power methods.\nThis allows to empirically elucidate the existence of multiple regimes: (1) the strong signal regime where all first order methods seem to find good solutions (2) when polynomially many critical points appear, in the matrix case, SGD converges while Adam gets trapped, unless if initialized in the basin of attraction (3) in the presence of exponentially many critical points (the tensor case), all algorithms fail, unless if d is moderately small and the SNR large enough to allow for proper initialization.2\nSingle spiked models, and stochastic gradients\nWe propose to study algorithms used for minimizing deep learning loss functions, at optimizing a non-convex objective on simple synthetic datasets.\nStudying simplified problems has the advantage that the problem's properties, and the behavior of the optimizer and the solution, can be studied rigorously.\nThe use of such datasets can help to perform sanity checks on improvement ideas to the algorithms, or to mathematically prove or disprove intuitions.\nThe properties of the toy data sets align with some properties of deep learning loss functions.\nFrom the optimization standpoint, the resulting tensor problems may appear to be even harder than deep learning problems.\nWe observe that finding good solutions is hard unless if proper initialization is performed, while the value of stochasticity in gradient estimates seems too narrow and does not appear to compensate for poor initialization heuristics.\nEach column represents the values of those quantities along iterations of the algorithm.\nThe prefix sp. refers to spectral initialization and l.\nrefers to a decreasing learning weight scheduled in 1/ √ t.\nWe observe the value of warm starting as soon as λ is large enough.\nEven at high SNR λ = 6, randomly initialized SGD fails while spectrally initialized SGD succeeds.\nAdam drifts to a non optimal critical point in that regime, even with spectral warm start.", "role": "user" }, { "content": "SGD and Adam under single spiked model for tensor PCA", "role": "assistant" } ]

[ { "content": "You are presented with multiple paper abstracts, a literature review title, and a specific chapter title. Your task is to write a focused literature review chapter that aligns with the given chapter title. The review should synthesize the content from the provided abstracts, draw connections between different works, highlight major findings, and identify any gaps in the existing research.\nLiterature Review Title: Taking Human out of Learning Applications: A Survey on Automated Machine Learning \nChapter Title: Model Selection using Auto-sklearn \n1. Abstract of Cited Paper (BIB001): Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net. \n2. Abstract of Cited Paper (BIB002): Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that attacks these issues separately. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA's standard distribution, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection and hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance. \n3. Abstract of Cited Paper (BIB003): The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN. \n4. Abstract of Cited Paper (BIB004): The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.\n\nLiterature Review:", "role": "user" }, { "content": "As each learning problem has its own preference over learning tools , suitable machine learning models should be chosen specifically for each learning problem. Automated model selection is a very typical application of AutoML (Section 3.2). In the context of supervised classification, the purpose is to automatically select best classifiers and setup their hyper-parameters properly. An representative example of automated model selection approach is Auto-sklearn BIB004 , which is built on Scikit-Learn BIB002 package. Some widely used classifiers and their hyperparameters are listed in Table 4 . In Auto-sklearn, model selection is formulated as a CASH problem (Example 1), which aims to minimize the validation loss with respect to the model as well as its hyper-parameters and parameters. Example 1 (CASH Problem BIB004 , BIB003 ). Let F = {F 1 , · · · , F R } be a set of learning models, and each model has hyper-parameter θ j with domain Λ j , D train = {(x 1 , y 1 ) , · · · , (x n , y n )} be a training set which is split into K cross-validation folds {D 1 train , · · · , D K train } and {D 1 valid , · · · , D K valid } with D i train ∪ D i valid = D train for i = 1, . . . , K. Then, the Combined Algorithm Selection and Hyper-parameter (CASH) optimization problem is defined as valid denotes the loss that F j achieves on D i valid with parameter w j , hyper-parameter θ j and training data D i train . As discussed in Section 3.2.2, in the model selection problem, a candidate configuration comprises a classifier in Scikit-Learn and its hyper-parameters, and the search space is spanned by configurations of this kind. However, (2) is very hard to optimize. First, since there is no explicit expression for the objective, we do not know its properties, e.g., the degree of smoothness, which can be very helpful for traditional optimization problems. In addition, the decision variables, i.e., θ and F j , may not even be continuous, e.g., θ for kNN includes the number of nearest neighbors that is a discrete variable. Finally, in order to estimate the validation loss, we need to train the model F j and update its parameter w j , which is usually very expensive. In BIB003 , sequential model-based algorithm configuration (SMAC) BIB001 , a tree-based Bayesian optimization method, was used as the optimizer to solve BIB005 . Then, for the evaluator, the basic method, i.e., direct evaluation was used. Besides, meta-learning was employed as the experienced technique to get better initialization (i.e., warm-starting configuration generation in Section 6.1.3). Finally, rather than discarding models searched in the configuration space, Auto-sklearn stores them and to use a post-processing method to construct an ensemble of them. This automatic ensemble construction makes the final result more robust against overfitting. Table 11 (from Table 3 in BIB004 ) shows the performance of Auto-sklearn (denoted as \"ASK\"), where classifiers C1-15 denotes: Adaboost, Bernoulli naive Bayes, decision tree, extreme random trees, Gaussian naive Bayes, gradient boost- Figure 6 . The \"naive\" means there is no special design in the evaluator, the evaluation is directly done by optimizing parameters of learning tools on the training data. ing, KNN, LDA, linear SVM, kernel SVM, multinomial naive Bayes, passive aggressive, QDA, random forest, and linear classifier, respectively. As we can see, Auto-sklearn consistently finds the best configuration in its search space, which demonstrates the success of AutoML in model selection.", "role": "assistant" } ]

[ { "content": "You will be shown the title of a chemistry paper, together with sections excerpted from the body of the paper. The paper sections may include HTML tags like `<p>` and `</p>`; please ignore these. Your task is to write an abstract for the paper. Your response should include the abstract and no other text.\n\nTitle:\nIn silico study of 1,5-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one (Deketene Curcumin) on crystallized protein structures of SARS-CoV-2\n\nPaper sections:\nINTRODUCTION\n<p>SARS-CoV-2(severe acute respiratory syndrome coronavirus 2) which is originated from China. [1].The structure of the virus is might be pleomorphic or spherical, and it is characterised by using bears club-shaped projections of glycoproteins on its surface. This virus has a single-stranded RNA genome, covered through folded structure [2]. The symptoms of COVID-19 are more or less similar to SARS and MERS, the symptoms like fever, fatigue, dry cough, myalgia, and dyspnea. Sputum production, headache, haemoptysis, and diarrhea would be the unusual sign of the infected patients [3]. The current data shows major transmission routes are sneezing droplets and air [4]. For the diagnosis usually doctors look person travel history, symptoms related with COVID-19 and so on, the nasopharyngeal swab from the nose is the most common specimens for the detection of coronavirus. [5,6]. Nowadays every scientist and researcher is finding a way to cure this pandemic COVID-19 through developing different types of medicines. Despite this, scientists are failed to develop effective or potent drugs for the prevention of the same [7].To overcome this global challenge, Pharmacophore and computational approach can be the backbone for many researchers [8].</p><p>In this research, Deketene curcumin is used for molecular docking with the five crystallized protein structures of COVID-19(PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81). In Ayurveda, curcumin is widely used for several treatment aspects because of the therapeutic properties like anti-oxidant, antiviral, antiseptic, analgesic, antimalarial, and anti-inflammatory [9]. Although curcumin (1,7-bis (4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione) provides lots of medicinal applications but, there are some drawbacks of curcumin such as the less bioavailability of orally administered curcumin, it shows minimal buffer and plasma stability, and other factors. Therefore, Modification in curcumin is required one of the potent compound is deketene curcumin (1, 5-bis (4-hydroxy-3-methoxyphenyl)-1, 4-pentadiene-3-one) [10]. Henceforth, This particular derivative of curcumin (deketene curcumin) has been chosen for simulation against COVID-19. This paper reports screening of deketene curcumin bound directly or indirectly to five Crystallized protein structure of COVID-19 extracted from the protein data bank, by utilizing the Molegro Virtual Docker Software. Moreover, deketene curcumin inhibitors that target COVID-19 are considered as effective therapeutic agents to fight against COVID-19.</p>\n\nProtein and Ligand preparation\n<p>The three-dimensional crystal structures of Covid-19 main protease (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) determined by X-ray crystallography had been retrieved from Protein Data Bank (http://www.rcsb.org) and it was imported in the Molegro Virtual Docker (MVD 7.0 Molegro Virtual Docker, DK-8000 Aarhus C, Denmark). All the crystals structures have only one chain called (A). When these PDB id downloaded they combined with the water molecules and co-factors which manually removed in the software. Furthermore, the ligand directly obtained from pubchem database, the 2D and 3D structures of ligand-Deketene curcumin illustrated in Fig. 1. The enery minimization of compound done by using Chem bio draw 3D software then it saved in SDF format which further use in docking process with protein.</p>\n\nCavity detection and selection\n<p>Possible active site/cavities for the target main protease of COVID-19(PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) of interest had been determined with the help of Molegro Virtual Docker (MVD) [13]. Only one cavity had been selected as an active site on the basis of prior information about the active site residues. Detected cavities for all crystal structures of COVID-19 (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) are illustrate in Figure 2. From these predicated cavities the one with highest volume 131.584 Å 3 , 115.2 Å 3 ,113.664 Å 3 , 160.256 Å 3 and 52.736 Å 3 for the Mpro of Covid-19 6LU7, 5R7Z, 5R7Y, 5R80, and 5R81 respectively. The selected cavity used for ligand-protein binding study.</p>\n\nMolecular docking studies\n<p>The Molegro Virtual Docker (MVD) software has been used to perform molecular docking procedure [13].In this study, docking of deketene curcumin against COVID-19 has performed using MVD software.</p>\n\nRESULT AND DISCUSSION\n<p>To find out a potential candidate as medicine or drug, molecular studies were done through deketene curcumin on the binding pocket of protein COVID-19(PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) due to its plethora medicinal applications such as anti-oxidant, antiviral, antiseptic, analgesic, antimalarial, and anti-inflammatory. It is a fact that, Molegro Virtual Docker is one of the accurate software for simulation as it is acknowledged by many researchers. Moreover, this software has proven to be successful, very affordable as well as less time consuming [11].</p><p>The molecular docking approach was used to determine a potent inhibitor, which will show the best binding energy with the selected COVID-19 Mpro. Deketene curcumin was docked against each of 5 Mpro of Covid-19 (namely PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) using Molegro Virtual Docker (MVD) tool. With this study; it was found that Deketene Curcumin docking score with all 5 Mpro of Covid-19 was showing the highest binding affinity, as binding energy obtained in negative values indicates that the scores obtained are highly efficient. The following table 1 shows the top docking poses ranked according to their binding energy.The Moldock score and the re-rank scores of the best poses for each of the docking studies of deketene curcumin with COVID-19 are presented in Table 1. In table 1show, H-bond means the hydrogen bonding which occurs between protein and ligand. Another important factor is steric which means the interaction of energy between protein and ligand [12]. LE1 and LE3 are the ligand efficiency 1 and ligand efficiency 3 respectively [13]. Docking of Covid-19 PDB Id: 6LU7 with a ligand, the results showed seven hydrogen bonding between the Mpro and ligand atoms. The interaction residues include Cyr 145, Leu 141, Ser 144, Tyr 54, and His 41 (Figure 8a). Hydroxyl group of deketene curcumin interacts by forming H-bond with amino acid Cys 145, Leu 141, Ser 144, and His 41. Furthermore, H-bond interaction is observed between amino acid Tyr 54 and the methoxy group of a ligand. Besides, the ligand was observed with steric interactions and residue Glu 166, His 163, Met 165, Gln 189, His 41, and Asp 187 involved in the interaction (Figure 9a).</p><p>In the case of Covid-19 PDB Id: 5R7Z with a ligand, the results showed six hydrogen bonding between the Mpro and ligand atoms. The interaction residues include Glu 166, Phe 140, His 172, Cys 145, Arg 188, and His 41 (Figure 8b). Hydroxyl group of deketene curcumin interacts by forming H-bond with amino acid Glu 166, Phe 140, His 172, and Arg 188. Carbonyl group interacts with Cys 145 amino acid and the methoxy group interacts with His 41. Besides, the deketene curcumin ligand was observed with 8 steric interactions that were observed and residues His 163, Phe 140, His 142, Asn 142, Cys 145, Met 165, Asp 187 and His 164 involved in the interaction (Figure 9b ).</p><p>In the case of Covid-19 PDB Id: 5R7Y with deketene curcumin, the results showed six hydrogen bonding between the Mpro and ligand atoms. The interaction residues include Thr 25, Ser 46, Cys 145, Ser 144, and Leu 141 (Figure 8c). Hydroxyl group of deketene curcumin interacts by forming H-bond with amino acid Thr 25, Cys 145, Ser 144, and Leu 141. Methoxy group of deketene curcumin interacts with Ser 46 amino acid of Covid-19 Mpro (PDB Id: 5R7Y). In addition, the deketene curcumin ligand was observed with 5 steric interactions that were observed and residues Met 49, Ser 46, His 41, Cys 145, and His 163 involved in the interaction (Figure 9b ).</p><p>In the case of Covid-19 PDB Id: 5R80 with deketene curcumin, the results showed five hydrogen bonding between the Mpro and ligand atoms. The interaction residues include Asp 187, Tyr 54, His 41, Gln 192, and Gln 189 (Figure 8d). Hydroxyl group of deketene curcumin interacts by forming H-bond with amino acid Asp 187, Tyr 54, and Gln 189. Methoxy group of deketene curcumin interact with His 41 and Carbonyl group interact with Gln 192 amino acid of Covid-19 Mpro (PDB Id: 5R80). In addition, the deketene curcumin ligand was observed with 5 steric interactions that were observed and residues Met 49, Ser 46, His 41, Cys 145, and His 163 involved in the interaction (Figure 9d).</p><p>In the case of Covid-19 PDB Id: 5R81 with deketene curcumin, the results showed four hydrogen bonding between the Mpro and ligand atoms. The interaction residues include Asp 142, Cys 145, Ser 144, and Arg 188 (Figure 8e). Hydroxyl group of deketene curcumin interacts by forming H-bond with amino acid Asn 142 and Arg 188. Methoxy group of deketene curcumin interacts with Cys 145 and Ser 144 of Covid-19 Mpro (PDB Id: 5R81). In addition, the deketene curcumin ligand was observed with 4 steric interactions had been seen and residues Asp 142, Leu 141, Cys 145, and Met 49 involved in the interaction (Figure 9e). Overall it can be stated that deketene curcumin has a good binding affinity with COVID-19 main protease. Deketene curcumin fits well in the active sites of COVID-19(namely PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81). Figure 8 shows hydrogen bonding with COVID-19 and Figure 9 shows steric interaction with COVID-19. Ligand-protein interaction analysis of Deketene Curcumin with COVID-19 Mpro (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) which is illustrated in Table 2. The Physiochemical properties of Deketene Curcumin are illustrated in the table 3.</p>", "role": "user" }, { "content": "World Health Organization (WHO) reveals total number of coronavirus cases are 5,684,802 and 352,225 deaths till today worldwide. Coronavirus instances are nevertheless surging due to its speedy spreading through infected patients. Therefore, in order to find potent vaccine almost every researcher is doing hard work to find it. However, until today there is not any availability of effective vaccine or drug for the treatment of COVID-19. In this case, the computational approach is the good choice to identify effective drugs and could be very useful due to its low cost, less error and less time consumption. Here, Deketene curcumin has taken for docking study because of its lots of biological applications such as antiviral, antimicrobial, anti-inflammatory, antioxidant, antibiotic, and to a name of few, it is a derivative of curcumin. In this study, five main protease crystallized COVID-19 structures (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) have been taken for simulation against deketene curcumin. Required procedure for this in silico study done through Molegro virtual docker (MVD) and Molegro Molecular Viewer (MMV) used for visualization. The results showed H-bonding and steric interaction between Deketene", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nEfficacy and Safety of Memantine in Patients With Mild to Moderate Vascular Dementia: A Randomized, Placebo-Controlled Trial (MMM 300)\nAbstract 1:\nBackground and Purpose — Based on the hypothesis of glutamate-induced neurotoxicity ( excitotoxicity ) in cerebral ischemia , this study examined the efficacy and tolerability of memantine , an uncompetitive N-methyl-d-aspartate antagonist , in the treatment of mild to moderate vascular dementia . Methods — In this multicenter , 28-week trial carried out in France , 321 patients received 10 mg/d memantine or placebo twice a day ; 288 patients were valid for intent-to-treat analysis . Patients had to meet the criteria for probable vascular dementia and have a Mini-Mental State ( MMSE ) score between 12 and 20 at inclusion . The 2 primary end points were the cognitive subscale of the Alzheimers Disease Assessment Scale ( ADAS-cog ) and the global Clinician ’s Interview Based Impression of Change ( CIBIC-plus ) . Results — After 28 weeks , the mean ADAS-cog scores were significantly improved relative to placebo . In the intention-to-treat population , the memantine group mean score had gained an average of 0.4 points , whereas the placebo group mean score had declined by 1.6 points , ie , a difference of 2.0 points ( 95 % confidence interval , 0.49 to 3.60 ) . The response rate for CIBIC-plus , defined as improved or stable , was 60 % with memantine compared with 52 % with placebo ( P = 0.227 , intention to treat ) . Among the secondary efficacy parameters , which were analyzed in the per- protocol subset , MMSE was significantly improved with memantine compared with deterioration with placebo ( P = 0.003 ) . The Gottfries-Brane-Steen Scale intellectual function subscore and the Nurses ’ Observation Scale for Geriatric Patients disturbing behavior dimension also showed differences in favor of memantine ( P = 0.04 and P = 0.07 , respectively ) . Memantine was well tolerated with a frequency of adverse events comparable to placebo . Conclusions — In patients with mild to moderate vascular dementia , memantine 20 mg/d improved cognition consistently across different cognitive scales , with at least no deterioration in global functioning and behavior . It was devoid of concerning side effects\n\n\nTitle 2:\nThe Consortium to Investigate Vascular Impairment of Cognition: methods and first findings.\nAbstract 2:\nBACKGROUND The Consortium to Investigate Vascular Impairment of Cognition ( CIVIC ) is a Canadian , multi-centre , clinic-based prospect i ve cohort study of patients with Vascular Cognitive Impairment ( VCI ) . We report its organization and the impact of diagnostic criteria on the study of VCI . METHODS Nine memory disability clinics enrolled patients and recorded their usual investigations and care . A case report form included all vascular dementia ( VaD ) individual criteria for each of four sets ( National Institute of Neurological Disorders and Stroke ( NINDS-AIREN ) , Alzheimer 's Disease Diagnostic Treatment Centers ( ADDTC ) , the ICD-10 Classification of Mental and Behavioural Disorders ( ICD-10 ) , and the Diagnostic and Statistical Manual of Mental Disorders ( DSM-IV ) ) of consensus-based diagnostic criteria and for the Hachinski Ischemia Score ( HIS ) . Investigators , having completed the case report form , were asked to make a clinical judgement about the cognitive diagnosis based on the best available information , including neuroimaging . RESULTS Of 1,347 patients ( mean age 72 years ; 56 % women ) , 846 ( 63 % ) were diagnosed with dementia and 324 ( 24 % ) were diagnosed with VCI . The proportion of patients diagnosed with VaD by the diagnostic criteria was : 23.9 % ( n = 322 ) by DSM-IV , 10.2 % ( n = 137 ) by HIS , 4.3 % ( n = 58 ) by ICD-10 , 3.8 % ( n = 51 ) by ADTCC , and 3.6 % ( n = 48 ) by NINDS-AIREN . Judged against a clinical diagnosis of VaD , the sensitivity/specificity of each was : DSM-IV ( 0.77/0.80 ) ; HIS ( 0.41/0.92 ) ; ICD-10 ( 0.29/0.98 ) ; ADTCC ( 0.24/0.98 ) ; NINDS-AIREN ( 0.42/0.995 ) . Compared with a clinical diagnosis of VCI , sensitivities were lower for the diagnostic criteria , reflecting the exclusion of patients who did not have dementia . CONCLUSIONS Consensus-based criteria for VaD omit patients who do not meet dementia criteria that are modeled on Alzheimer 's disease . Even for patients who do , the proportion identified with VaD varies widely . Criteria based on empirical analyses need to be developed and vali date\n\n\nTitle 3:\nDonepezil in vascular dementia\nAbstract 3:\nObjective : To evaluate the efficacy and tolerability of donepezil in patients with vascular dementia ( VaD ) . Methods : Patients ( n = 616 ; mean age , 75.0 years ) with probable or possible VaD , according to National Institute of Neurological Disorders and Stroke – Association Internationale pour la Recherche en l’Enseignement en Neurosciences criteria , were r and omized to receive donepezil 5 mg/day ( n = 208 ) , donepezil 10 mg/day ( after 5 mg/day for the first 28 days ) ( n = 215 ) , or placebo ( n = 193 ) for 24 weeks . Results : Seventy-six percent of the patients enrolled had probable VaD. A total of 75.3 % of the 10 mg donepezil group and 80.8 % of the 5 mg group completed the study compared with 83.4 % of the placebo group . Both donepezil-treated groups showed improvements in cognitive function on the Alzheimer ’s Disease Assessment Scale – cognitive subscale compared with placebo , with a mean endpoint treatment difference , as measured by the change from baseline score , of approximately 2 points ( donepezil 5 mg , −1.65 [ p = 0.003 ] ; 10 mg , −2.09 [ p = 0.0002 ] ) . Greater improvements on the Clinician ’s Interview-Based Impression of Change – plus version were observed with both donepezil groups than with the placebo group ( overall donepezil treatment vs placebo p = 0.008 ) ; 25 % of the placebo group showed improvement compared with 39 % ( p = 0.004 ) of the 5 mg group and 32 % ( p = 0.047 ) of the 10 mg group . Withdrawal rates due to adverse events were low ( placebo , 8.8 % ; donepezil 5 mg , 10.1 % ; 10 mg , 16.3 % ) . Conclusions : Donepezil-treated patients demonstrated significant improvements in cognition and global function compared with placebo-treated patients , and donepezil was well tolerated\n\n\nTitle 4:\nProgression of impairment in patients with vascular cognitive impairment without dementia\nAbstract 4:\nLittle is known about progression , short of dementia , in vascular cognitive impairment . In the Canadian Study of Health and Aging , 149 participants ( 79.3 ± 6.7 years ; 61 % women ) were found to have vascular cognitive impairment , no dementia ( CIND ) . After 5 years , 77 participants ( 52 % ) had died and 58 ( 46 % ) had developed dementia . Women were at greater risk of dementia ( OR 2.1 , 1.0 to 4.5 ) . Of 32 participants alive without dementia , cognition had deteriorated in seven and improved in four . Half of those with vascular CIND developed dementia within 5 years , suggesting a target for preventive interventions\n\n\nTitle 5:\nEfficacy and Tolerability of Donepezil in Vascular Dementia: Positive Results of a 24-Week, Multicenter, International, Randomized, Placebo-Controlled Clinical Trial\nAbstract 5:\nBackground and Purpose — Clinical observations suggest that patients with vascular dementia ( VaD ) may benefit from treatment with cholinesterase inhibitors . This study evaluated the efficacy and safety of donepezil for relieving symptoms of dementia in VaD. Methods — Patients ( n=603 ; mean age , 73.9 years ; 55.2 % men ) with probable ( 70.5 % ) or possible ( 29.5 % ) VaD , according to criteria of the National Institute of Neurological Disorders and Stroke ( NINDS ) and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences ( AIREN ) , were r and omized to 24 weeks of treatment with donepezil 5 mg/d ( n=198 ) , donepezil 10 mg/d ( 5 mg/d for first 28 days ; n=206 ) , or placebo ( n=199 ) . Analyses were based on the intent-to-treat population . Results — At week 24 , both donepezil groups showed significant improvement in cognition versus placebo on the Alzheimer ’s Disease Assessment Scale – cognitive subscale ( mean change from baseline score effect size : donepezil 5 mg/d , −1.90 ; P = 0.001 ; donepezil 10 mg/d , −2.33 ; P < 0.001 ) . Significant improvements in patients ’ global function were seen versus placebo at week 24 ( observed cases ) , on the Clinician ’s Interview-Based Impression of Change – Plus version only for patients on donepezil 5 mg/d ( P = 0.014 ) , and on the Sum of the Boxes of the Clinical Dementia Rating only for patients on 10 mg/d ( P = 0.007 ) . Donepezil-treated patients showed significant benefits in activities of daily living over placebo on the Alzheimer ’s Disease Functional Assessment and Change Scale ( mean change from baseline score effect size at week 24 : donepezil 5 mg/d , −1.31 , P = 0.02 ; donepezil 10 mg/d , −1.31 , P = 0.02 ) . Donepezil was well tolerated . Withdrawal rates due to adverse events were relatively low ( placebo , 11.1 % ; donepezil 5 mg/d , 11.1 % ; donepezil 10 mg/d , 21.8 % ; P = 0.005 versus placebo ) . Conclusions — These data demonstrate that donepezil is an effective and well-tolerated treatment for VaD and show it may have an important place in the management of this condition", "role": "user" }, { "content": "Due to insufficient evidence , rivastigmine could not yet be recommended for the treatment of vascular dementia .\nMemantine appeared to be safe and well tolerated but did not demonstrate effectiveness across all cognitive outcomes and clinical global measures .\nConclusion : Acetylcholinesterase inhibitors and NMDA receptor antagonists , in general , displayed promise as treatments for patients with vascular dementia and vascular cognitive impairment .\nThe most effective , evidence -based treatments were donepezil and galantamine", "role": "assistant" } ]

[ { "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\nRibonucleotide incorporation, proofreading and bypass by human DNA polymerase δ. In both budding and fission yeast, a large number of ribonucleotides are incorporated into DNA during replication by the major replicative polymerases (Pols α, δ and ɛ). They are subsequently removed by RNase H2-dependent repair, which if defective leads to replication stress and genome instability. To extend these studies to humans, where an RNase H2 defect results in an autoimmune disease, here we compare the ability of human and yeast Pol δ to incorporate, proofread, and bypass ribonucleotides during DNA synthesis. In reactions containing nucleotide concentrations estimated to be present in mammalian cells, human Pol δ stably incorporates one rNTP for approximately 2000 dNTPs, a ratio similar to that for yeast Pol δ. This result predicts that human Pol δ may introduce more than a million ribonucleotides into the nuclear genome per replication cycle, an amount recently reported to be present in the genome of RNase H2-defective mouse cells. Consistent with such abundant stable incorporation, we show that the 3'-exonuclease activity of yeast and human Pol δ largely fails to edit ribonucleotides during polymerization. We also show that, like yeast Pol δ, human Pol δ pauses as it bypasses ribonucleotides in DNA templates, with four consecutive ribonucleotides in a DNA template being more problematic than single ribonucleotides. In conjunction with recent studies in yeast and mice, this ribonucleotide incorporation may be relevant to impaired development and disease when RNase H2 is defective in mammals. As one tool to investigate ribonucleotide incorporation by Pol δ in human cells, we show that human Pol δ containing a Leu606Met substitution in the polymerase active site incorporates 7-fold more ribonucleotides into DNA than does wild type Pol δ.", "role": "user" }, { "content": "{\"Chemical\": [\"Ribonucleotide\", \"ribonucleotides\", \"ribonucleotide\", \"rNTP\", \"dNTPs\", \"nucleotide\"]}", "role": "assistant" } ]

[ { "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\nDefatted Jatropha curcas flour and protein isolate as materials for protein hydrolysates with biological activity. Jatropha curcas L. protein hydrolysates were produced by treatment of a non-toxic genotype with Alcalase as well as the digestive enzymes pepsin and pancreatin. The J. curcas protein hydrolysate produced with the pepsin-pancreatin system from protein isolate had the highest TEAC value and was shown to undergo single-electron transfer reactions in the ABTS(+) reduction assay, demonstrating its antioxidant capacity. Testing of antimicrobial activity in the J. curcas protein hydrolysates against seven bacterial pathogens showed no growth inhibitory effect in Gram-negative and Gram-positive bacteria. More ACE-I inhibitory active peptides were produced in the Alcalase hydrolysates obtained from J. curcas protein isolate. The protein hydrolysate obtained with Alcalase from defatted J. curcas flour as well as from the protein isolate showed the highest inhibitory effect of ADP-induced aggregation of human platelets in platelet-rich plasma. It is expected that the information collated will facilitate new applications of proteins present in Jatropha plant.", "role": "user" }, { "content": "{\"Chemical\": [\"ABTS(+)\", \"ADP\"]}", "role": "assistant" } ]

[ { "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 the options provided. A short description of the datasets is also provided to help you.\n\nQuery: We study the performance of deep neural networks on a dataset where questions can be divided into two categories\n\nKeyphrases: natural language inference text\n\nDataset description:\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.\nSNLI: The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences.\nUNITOPATHO: Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade.\nUMDFaces: UMDFaces is a face dataset divided into two parts: Still Images - 367,888 face annotations for 8,277 subjects. Video Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.\n\nOptions: A: MIMIC-CXR B: SNLI C: UNITOPATHO D: UMDFaces", "role": "user" }, { "content": "SNLI", "role": "assistant" } ]

[ { "content": "Write an abstract for the research paper below. Your response should only include the abstract and no additional text.\n\nArticle text:\nthis paper deals with pricing of bilateral counterparty risk credit valuation adjustments ( cva s ) on interest - rate portfolios . the structure of the paper is similar to the earlier work of brigo and capponi ( 2008 ) .\nhowever , while the focus in brigo and capponi ( 2008 ) is on credit default swaps ( cds ) , here it is on interest - rate products , generalizing to the bilateral case the earlier works on unilateral cva for rates products done by sorensen and bollier ( 1994 ) , brigo and masetti ( 2005 ) and brigo and pallavicini ( 2007 ) . indeed , previous research on accurate arbitrage - free valuation of unilateral cva with dynamical models on commodities ( brigo and bakkar ( 2009 ) ) , on rates ( brigo and pallavicini ( 2007 ) ) and on credit ( brigo and chourdakis ( 2008 ) ) assumed the party computing the valuation adjustment to be default - free .\nwe present here the general arbitrage - free valuation framework for counterparty risk adjustments in presence of bilateral default risk , as introduced in brigo and capponi ( 2008 ) , including default of the investor .\nwe illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option , both with zero strike and written on the residual net value of the contract at the relevant default times .\nwe allow for correlation between the default times of the investor , counterparty and underlying portfolio risk factors .\nwe use arbitrage - free stochastic dynamical models .\nwe then specialize our analysis to interest rate payouts as underlying portfolio . in comparing with the cds case as underlying ,\nan important point is that most credit models in the industry , especially when applied to collateralized debt obligations or @xmath0-th to default baskets , model default correlation but ignore credit - spread volatility .\ncredit spreads are typically assumed to be deterministic and a copula is postulated on the exponential triggers of the default times to model default correlation .\nthis is the opposite of what used to happen with counterparty risk for interest - rate underlyings , for example in sorensen and bollier ( 1994 ) or brigo and masetti ( in pykhtin ( 2005 ) ) , where correlation was ignored and volatility was modeled instead .\nbrigo and chourdakis ( 2008 ) rectify this in the cds context , but only deal with unilateral and asymmetric counterparty risk .\nbrigo and capponi ( 2008 ) then generalize this approach for cds , including credit spread volatility besides default correlation into the bilateral case . in interest - rate products , previous literature dealing with both underlying volatility and underlying / counterparty correlation is in brigo and pallavicini ( 2007 ) , who address both plain vanilla interest - rate swaps and exotics under unilateral counterparty risk . in that work , a stochastic intensity model along the lines of brigo and alfonsi ( 2005 ) and brigo and el - bachir ( 2009 )\nis assumed , and this model is correlated with the multi - factor short rate process driving the interest - rate dynamics . netting is also examined in some basic examples .\nthe present paper aims at generalizing this approach to the bilateral case .\nin such a case one needs to model the following correlations , or better dependencies : * dependence between default of the counterparty and default of the investor ; * correlation between the underlying ( interest rates ) and the counterparty credit spread ; * correlation between the underlying ( interest rates ) and the investor credit spread ; * besides default correlation between the counterparty and the investor , we might wish to model also credit spread correlation .\nwe will model all such dependencies except the last one , since default correlation is dominant over spread correlation .\nalso , a feature that is usually ignored is credit spread volatility for the investor and the counterparty , in that credit spreads are usually taken as deterministic .\nwe improve this by assuming stochastic spreads for both investor and counterparty . summarizing ,\nto avoid confusion , given the plurality of papers we produced in recent years , we can put this paper s contribution into context with respect to our analogous earlier versions for unilateral counterparty risk or for other asset classes through table [ table : contextcrmodels ] , which is clearly meant only as a partial orientation for our work and not for the overall much broader literature .\n.part of earlier analogous literature on cva valuation with respect to inclusion of underlying asset volatilities and/or correlation between underlying asset and counterparties , along with bilateral features .\ntrs stands for total return swap [ cols=\"^,^,^,^\",options=\"header \" , ]", "role": "user" }, { "content": "the purpose of this paper is introducing rigorous methods and formulas for bilateral counterparty risk credit valuation adjustments ( cva s ) on interest - rate portfolios . in doing so , we summarize the general arbitrage - free valuation framework for counterparty risk adjustments in presence of bilateral default risk , as developed more in detail in brigo and capponi ( 2008 ) , including the default of the investor . \n we illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option , both with zero strike and written on the residual net present value of the contract at the relevant default times . \n we allow for correlation between the default times of the investor and counterparty , and for correlation of each with the underlying risk factor , namely interest rates . \n we also analyze the often neglected impact of credit spread volatility . \n we include netting in our examples , although other agreements such as margining and collateral are left for future work . * ams classification codes * : 60h10 , 60j60 , 60j75 , 62h20 , 91b70 + : c15 , c63 , c65 , g12 , g13 + * keywords : * counterparty risk , arbitrage - free credit valuation adjustment , interest rate swaps , interest rate derivatives , credit valuation adjustment , bilateral risk , credit spread volatility , default correlation , stochastic intensity , short rate models , copula functions , wrong way risk .", "role": "assistant" } ]

[ { "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: [\"RNA\", \"CellType\", \"CellLine\", \"Protein\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nDNA-binding and transcriptional regulatory properties of hepatic leukemia factor (HLF) and the t (17;19) acute lymphoblastic leukemia chimera E2A-HLF. The t (17;19) translocation in acute lymphoblastic leukemias results in creation of E2A-hepatic leukemia factor (HLF) chimeric proteins that contain the DNA-binding and protein dimerization domains of the basic leucine zipper (bZIP) protein HLF fused to a portion of E2A proteins with transcriptional activation properties. An in vitro binding site selection procedure was used to determine DNA sequences preferentially bound by wild-type HLF and chimeric E2A-HLF proteins isolated from various t (17;19) -bearing leukemias. All were found to selectively bind the consensus sequence 5'-GTTACGTAAT-3' with high affinity. Wild-type and chimeric HLF proteins also bound closely related sites identified previously for bZIP proteins of both the proline-and acidic amino acid-rich (PAR) and C / EBP subfamilies; however, E2A-HLF proteins were significantly less tolerant of certain deviations from the HLF consensus binding site. These differences were directly attributable to loss of an HLF ancillary DNA-binding domain in all E2A-HLF chimeras and were further exacerbated by a zipper mutation in one isolate. Both wild-type and chimeric HLF proteins displayed transcriptional activator properties in lymphoid and nonlymphoid cells on reporter genes containing HLF or C / EBP consensus binding sites. But on reporter genes with nonoptimal binding sites, their transcriptional properties diverged and E2A-HLF competitively inhibited activation by wild-type PAR proteins. These findings establish a spectrum of binding site-specific transcriptional properties for E2A-HLF which may preferentially activate expression of select subordinate genes as a homodimer and potentially antagonize expression of others through heteromeric interactions.", "role": "user" }, { "content": "{\"CellLine\": [\"t (17;19) - bearing leukemias\"], \"CellType\": [\"lymphoid and nonlymphoid cells\"], \"DNA\": [\"HLF ancillary DNA - binding domain\", \"nonoptimal binding sites\", \"HLF consensus binding site\", \"reporter genes\", \"t (17;19)\", \"HLF or C / EBP consensus binding sites\", \"the DNA - binding\", \"consensus sequence\"], \"Protein\": [\"chimeric HLF proteins\", \"wild - type\", \"chimeric E2A - HLF\", \"transcriptional activator\", \"PAR\", \"E2A - HLF chimeras\", \"chimeric proteins\", \"hepatic leukemia factor\", \"wild - type HLF\", \"basic leucine zipper (bZIP) protein\", \"E2A - HLF\", \"proline\", \"wild - type PAR proteins\", \"HLF\", \"acidic amino acid - rich\", \"DNA - binding and protein dimerization domains\", \"bZIP proteins\", \"acute lymphoblastic leukemia chimera E2A - HLF\", \"E2A proteins\", \"C / EBP subfamilies\", \"E2A - hepatic leukemia factor\", \"C / EBP\"], \"RNA\": []}", "role": "assistant" } ]

[ { "content": "You will be shown sections from a scientific research paper, together with a question about the paper. This is an extractive question-answering task, where you must find and extract relevant text spans directly from the paper to answer the question. Your response should strictly 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 example, for the question \"What baselines did the authors compare against?\", a sample response might be:\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: Semantic Holism and Word Representations in Artificial Neural Networks\n\nArtificial neural networks are a state-of-the-art solution for many problems in natural language processing. What can we learn about language and meaning from the way artificial neural networks represent it? Word representations obtained from the Skip-gram variant of the word2vec model exhibit interesting semantic properties. This is usually explained by referring to the general distributional hypothesis, which states that the meaning of the word is given by the contexts where it occurs. We propose a more specific approach based on Frege's holistic and functional approach to meaning. Taking Tugendhat's formal reinterpretation of Frege's work as a starting point, we demonstrate that it is analogical to the process of training the Skip-gram model and offers a possible explanation of its semantic properties.\n\nINTRODUCTION\n“Meaning is, therefore, something that words have in sentences; and it's something that sentences have in a language.” BIBREF0 On the other hand, meaning could also be something that words have on their own, with sentences being compositions and language a collection of words. This is the question of semantic holism versus atomism, which was important in the philosophy of language in the second half of the 20th century and has not been satisfyingly answered yet.\nArtificial neural networks are the state-of-the-art solution for many problems in natural language processing (and machine learning in general). They produce word representation with interesting properties, but the way they work is little understood from the perspective of linguistics or the philosophy of language.\nWe believe that by finding parallels between concepts in AI and the philosophy of language, we can better understand both areas.\nIn this paper, we present an analogy between meaning defined as truth-value potential (a reformulation of Fregean holistic and functional approach) and a variant of language representation model, therefore pointing out a possibility that its “striking syntactic and semantic properties” BIBREF1 are formed due to adhering to holistic principles.\n\nINTRODUCTION ::: Related work\nWe have found only one work concerning the philosophical aspects of neural language models BIBREF2. It is, however, concentrating on Self-Organizing Maps and Quine's version of semantic holism.\nThere are papers showing that Skip-gram with negative sampling is implicitly a factorization of a word-context matrix (e.g. BIBREF3, although this result was later contested by various authors, such as BIBREF4 and BIBREF5), or deriving the equations in an alternative way BIBREF6 (discussed more in Section SECREF3). This may tell us something about the model, but it does not answer the principal question: why should the matrix factorized in a certain way contain semantic information?\n\nSEMANTIC HOLISM AND ATOMISM ::: Atomism\n“It is a widely held view that much of the history of the philosophy of language consists of a failed attempt to make semantic atomism work.” BIBREF0\nAtomism played an important role in analytic philosophy, starting with Bertrand Russell's logical atomism and continuing with logical positivism, as exemplified in this quote by Carnap BIBREF8:\nA language consists of a vocabulary and a syntax, i.e. a set of words which have meanings and rules of sentence formation. These rules indicate how sentences may be formed out of the various sorts of words.\nFor logical positivists, words have meaning, because they refer to objects (be it physical, sensual, logical, mathematical or other). The rules of composition determine the meaning of sentences (and rule out senseless sequences of words).\nUnder this (or similar) view, the fact that words refer to the outside world is presupposed. Their references are independent of each other (that “dog” refers to dog is independent of that “horse” refers to horse). There is strong emphasis on compositionality, that reached its peak in Chomskian linguistics and is still relevant today.\nCrucially, this means that a word can have meaning on its own (e.g. by referring to something). The meaning of larger units, such as sentences, is derived by the rules of composition from the meaning of words.\n\nSEMANTIC HOLISM AND ATOMISM ::: Holism\nSemantic holism accents the interdependence of meaning. The whole (language, theory, ...) is the primary vehicle of meaning. The meaning of smaller units is derived by decomposition.\nThis view is motivated by the same word having a different meaning in a different context. Gottlob Frege has shown BIBREF9 that even such seemingly unambiguous words as numbers play distinct roles in different situations: “5 is a prime number” and “there are 5 cows on the meadow” are different at least in that the first “5” signifies a complete (abstract) object, while the second one needs to be supplemented with information that it is cattle of which there are 5 specimens, otherwise the expression would not be grammatical.\nFrege promoted what we could call sentence holism: “Only in the context of a sentence does a word have a meaning.” BIBREF10 We will later use its modern reformulation to show an analogy with certain neural language models and therefore their holistic character.\nAnother group of arguments for holism consist of variations on the theme of impossibility of knowing or using a word without being able to use other words. For example, it could be argued that a person could not correctly use the word “mammal”, without also knowing (at least some of) “bird”, “animal” and kinds of animals. Therefore the meaning of words cannot be formed in isolation.\nSomething that is harder to explain under holism than under atomism is the fact that words refer to objects. If the meaning of words is given by other words, how is it connected to the world around us? However, not all words refer to something. And even if subscribing to holism makes explaining reference harder, it may be because it is a hard problem to explain.\nAnother thing that is simpler under atomism is compositionality. While in atomism it plays a central role as one of the presupposed properties of language, holism may not need it. But it does not claim that words do not have meanining at all, only that it is derived (by some sort of decomposition) from the meaning of the whole.\n\nWORD REPRESENTATIONS IN AI\nAlthough all artificial neural networks that work with language must have some way of representing it, the most interesting representations come from neural language models. Language modelling is a task of predicting a missing word from a sequence or generating text. There is also a similar class of models that are designed specifically to produce representations of language units, which we will call neural language representation models.\nThe representations (also called embeddings) are high dimensional vectors of real numbers. They are either learned together with the rest of the network for the particular task or pretrained by a general language representation model (typically on a larger dataset not specific for the task).\nSome neural language (representation) models produce representation with semantic properties, although the task of language modeling itself is not (at least at the first sight) directly connected with semantics and no explicit semantic annotation is given to the neural network.\nThese semantic properties became popular with the invention of the word2vec software and the Skip-gram model, whose author said about it BIBREF1:\nThe model itself has no knowledge of syntax or morphology or semantics. Remarkably, training such a purely lexical model to maximize likelihood will induce word representations with striking syntactic and semantic properties.\nHowever, they did not present any explanation of the phenomenon.\nGoldberg and Levy BIBREF6 present a detailed derivation of the central equation of the Skip-gram model. In the last section they say:\nWhy does this produce good word representations?\nGood question. We don't really know.\nThe distributional hypothesis states that words in similar contexts have similar meanings. The objective [of the Skip-gram model] clearly tries to increase the [dot product of the context and the word representations] for good word-context pairs, and decrease it for bad ones. Intuitively, this means that words that share many contexts will be similar to each other (note also that contexts sharing many words will also be similar to each other). This is, however, very hand-wavy. Can we make this intuition more precise? We'd really like to see something more formal.\nWe believe that the implicit holistic component of this “hand-wavy” approach is central to the quality of Skip-gram representations and we can make the intuition more precise by analogy with the definition of the truth-value potential.\n\nWORD REPRESENTATIONS IN AI ::: Semantic properties of the Skip-Gram model\nThe Skip-gram model was introduced by Tomáš Mikolov et al. BIBREF11 as a method to efficiently train word embeddings. It exceeded state-of-the-art in various semantic tasks. The embeddings have interesting semantic properties, most notably the vector arithmetic illustrated by Figure FIGREF4 and the following equation BIBREF1:\nmeaning that starting with the word “king”, if we subtract the vector for the word “man” and add the vector for the word “woman”, the nearest vector in the embedding space will be the one that corresponds to the word “queen”. This means that queen is to woman as king is to man.\nHollis et al. BIBREF12 show that it is possible to infer various psycholinguistic and semantic properties of words from the Skip-gram embeddings. Mikolov et al. BIBREF13 also trained the Skip-gram model with phrases, resulting in even simpler and more elegant equations, such as\nMikolov et al. BIBREF11 proposed another shallow neural language model, Continuous Bag of Words (CBOW). The main difference between CBOW and Skip-gram (see Figure FIGREF6) is that while Skip-gram predicts context words from a given word, CBOW predicts a word from a given context.\n\nRELEVANT THEORIES OF MEANING\nIn this section, we discuss theories of meaning that are relevant to word representations in artificial neural networks. Notice that even though they strictly speaking do not require meaning holism, they all lean towards it quite strongly.\n\nRELEVANT THEORIES OF MEANING ::: The distributional hypothesis\nHolism is generally a better alternative in cases where there is nothing beside language itself to anchor meaning to. This is the case of neural language (representation) models. If they represent meaning at all, it must be derived from the training corpus. This may be the reason behind the popularity of the distributional hypothesis in neural language model literature. The famous saying by Firth BIBREF14, “You shall know a word by the company it keeps!”, is quoted in majority of papers concerned with vector space models of language.\nThe general distributional hypothesis states that the meaning of a word is given by the contexts in which it occurs. It is, however, worth noticing that in Firth's theory, collocation is just one among multiple levels of meaning and his text does not support the idea of meaning based on context alone.\nA more suitable formulation of the distributional hypothesis (referenced in connection to Skip-gram in BIBREF15) is found in Distributional structure BIBREF16, where it is suggested that distribution may be used for comparing meanings and that “difference of meaning correlates with difference of distribution”.\nAlthough this certainly describes a basic principle of neural language models, it is still rather vague.\n\nRELEVANT THEORIES OF MEANING ::: The use theory of meaning\nThe use theory of meaning can be summed up as “the meaning of a word is its use in the language” BIBREF17. It is associated with late Wittgenstein's concept of language game. In Philosophical Investigations BIBREF17, he writes:\nTo say “This combination of words makes no sense” excludes it from the sphere of language and thereby bounds the domain of language. [...] When a sentence is called senseless, it is not as it were its sense that is senseless. But a combination of words is being excluded from the language, withdrawn from circulation.\nThis “bounding of the domain of language” is precisely what language model does, therefore the use theory may be one way to connect language modelling and semantics.\nThat “knowledge of language emerges from language use” is also one of main hypotheses of cognitive linguistics BIBREF18.\n\nSKIP-GRAM AND TRUTH-VALUE POTENTIAL ::: The truth-value potential\nTugendhat's compact reformulation of Frege's sentence holism, the definition of meaning as truth-value potential is BIBREF21:\n[T]wo expressions $\\phi $ and $\\psi $ have the same truth-value potential if and only if, whenever each is completed by the same expression to form a sentence, the two sentences have the same truth-value.\nWe can also express this definition in the following form:\nwhere $M$ is the truth-value potential (meaning), $T$ is the truth-value of the sentence and $x(\\omega )$ is the result of completing the expression $\\omega $ by the expression $x$ to form a sentence.\nOne important aspect of this definition is that, following Frege BIBREF10, it is based on an assumption that the sentence (or rather the corresponding judgement) is the basic unit of meaning.\n\nCONCLUSION AND FUTURE WORK\nThe distributional hypothesis as an explanation for the semantic properties of neural language models should be expanded into a more detailed account. We show one possible way to do that via a Fregean approach to meaning.\nBoth the distributional hypothesis itself and Tugendhat's interpretation of Frege's work are examples of holistic approaches to meaning, where the meaning of the whole determines the meaning of parts. As we demonstrated on the opposition between Skip-gram and CBOW models, the distinction between semantic holism and atomism may play an essential role in semantic properties of neural language representations models.\nWe have demonstrated the connection between the Skip-gram model and the definition of meaning as truth-value potential. Although this is an isolated observation of an analogy between a specific model and a specific theory about meaning, it is a crucial step towards finding a theory of meaning that would correspond to the current results of NLP research, increasing our understanding of NLP and ultimately the language itself.\nThe direction of research from successful language technologies to properties of language itself offers many opportunities for inquiry, with very few being explored so far.\nMany state-of-the-art models for natural language processing use smaller units than words for their input and output. This analysis could be extended to take this into account.\nIt might also be interesting to think about the philosophy of science in technical fields dominated by machine learning, but that is far beyond the scope of this paper.\nThis work has been supported by the grant 18-02196S of the Czech Science Foundation. This research was partially supported by SVV project number 260 575.\n\nFigure 1. Examples of embeddings semantic relations according to [18].\n\nFigure 2. CBOW and Skip-gram language models according to [16].\n\nQuestion: What does Frege's holistic and functional approach to meaning states?", "role": "user" }, { "content": "{\"answer\": [\"Only in the context of a sentence does a word have a meaning.\"], \"evidence\": [\"Frege promoted what we could call sentence holism: \\u201cOnly in the context of a sentence does a word have a meaning.\\u201d BIBREF10 We will later use its modern reformulation to show an analogy with certain neural language models and therefore their holistic character.\"]}", "role": "assistant" } ]

[ { "content": "Given a short query with keyphrases about a research idea in 2017 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from the options provided. A short description of the datasets is also provided to help you.\n\nQuery: I want to retrieve the images with the same visual contents as a given partially colored sketch\n\nKeyphrases: sketch-based image retrieval images\n\nDataset description:\nViolin: Video-and-Language Inference is the task of joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip.\nSketch: The Sketch dataset contains over 20,000 sketches evenly distributed over 250 object categories.\nShoeV2: ShoeV2 is a dataset of 2,000 photos and 6648 sketches of shoes. The dataset is designed for fine-grained sketch-based image retrieval.\nQuickDraw-Extended: Consists of 330,000 sketches and 204,000 photos spanning across 110 categories.\n\nOptions: A: Violin B: Sketch C: ShoeV2 D: QuickDraw-Extended", "role": "user" }, { "content": "Sketch", "role": "assistant" } ]

[ { "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\", \"Disease\"].\n\nPlease return the output as a JSON object of the format: {\"Chemical\": [\"Naloxone\", ...], \"Disease\": [\"Hypotensive\", ...]}. 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\nGLEPP1 receptor tyrosine phosphatase (Ptpro) in rat PAN nephrosis. A marker of acute podocyte injury. Glomerular epithelial protein 1 (GLEPP1) is a podocyte receptor membrane protein tyrosine phosphatase located on the apical cell membrane of visceral glomerular epithelial cell and foot processes. This receptor plays a role in regulating the structure and function of podocyte foot process. To better understand the utility of GLEPP1 as a marker of glomerular injury, the amount and distribution of GLEPP1 protein and mRNA were examined by immunohistochemistry, Western blot and RNase protection assay in a model of podocyte injury in the rat. Puromycin aminonucleoside nephrosis was induced by single intraperitoneal injection of puromycin aminonucleoside (PAN, 20 mg/100g BW). Tissues were analyzed at 0, 5, 7, 11, 21, 45, 80 and 126 days after PAN injection so as to include both the acute phase of proteinuria associated with foot process effacement (days 5-11) and the chronic phase of proteinuria associated with glomerulosclerosis (days 45-126). At day 5, GLEPP1 protein and mRNA were reduced from the normal range (265.2 +/- 79.6 x 10(6) moles/glomerulus and 100%) to 15% of normal (41.8 +/- 4.8 x 10(6) moles/glomerulus, p < 0.005). This occurred in association with an increase in urinary protein content from 1.8 +/- 1 to 99.0 +/- 61 mg/day (p < 0.001). In contrast, podocalyxin did not change significantly at this time. By day 11, GLEPP1 protein and mRNA had begun to return towards baseline. By day 45-126, at a time when glomerular scarring was present, GLEPP1 was absent from glomerulosclerotic areas although the total glomerular content of GLEPP1 was not different from normal. We conclude that GLEPP1 expression, unlike podocalyxin, reflects podocyte injury induced by PAN. GLEPP1 expression may be a useful marker of podocyte injury.", "role": "user" }, { "content": "{\"Chemical\": [\"Puromycin aminonucleoside\", \"PAN\", \"puromycin aminonucleoside\", \"tyrosine\"], \"Disease\": [\"glomerular injury\", \"proteinuria\", \"nephrosis\", \"glomerulosclerosis\"]}", "role": "assistant" } ]

[ { "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: [\"Finding\", \"Bacterium\", \"ProfessionalOrOccupationalGroup\", \"InjuryOrPoisoning\", \"Eukaryote\", \"BodySystem\", \"Virus\", \"ResearchActivity\", \"BodySubstance\", \"PopulationGroup\", \"Chemical\", \"AnatomicalStructure\", \"SpatialConcept\", \"HealthCareActivity\", \"Organization\", \"BiomedicalOccupationOrDiscipline\", \"Food\", \"ClinicalAttribute\", \"MedicalDevice\", \"BiologicFunction\", \"IntellectualProduct\"].\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\nMALDI-TOF mass spectrometry for the identification of lactic acid bacteria isolated from a French cheese: The Maroilles In this study we identified the culturable population of mesophilic lactic acid bacteria (LAB) from a French cheese Maroilles made either with raw or pasteurized milk using MALDI-TOF mass spectrometry (MS). Samples from rind and heart of Maroilles cheese were used, the LAB were selected on MRS agar at 30°C and 197 Gram-positive and catalase-negative strains were subjected to identification by MALDI-TOF MS profiling. All strains were unambiguously identified: 105 strains from Maroilles made with raw milk (38 on the rind and 67 in the heart) and 92 strains from Maroilles made with pasteurized milk (39 on the rind and 53 in the heart). MALDI-TOF MS identification allowed identification of three genera belonging to LAB including Lactobacillus, Enterococcus and Leuconostoc. Lactobacillus was the most represented genus with seven species: Lactobacillus plantarum (L. plantarum), L. paracasei, L. curvatus, L. rhamnosus, L. fructivorans, L. parabuchneri, L. brevis found in Maroilles made with both kind of milk. The correlation between the 16S rDNA -based identification performed on selected strains and those obtained by MALDI-TOF-MS demonstrates that this fast, economically affordable, robust and reliable method for bacteria characterisation stands as an attractive alternative to the commonly-used methods and its application in food industry is discussed.", "role": "user" }, { "content": "{\"AnatomicalStructure\": [], \"Bacterium\": [\"Enterococcus\", \"lactic acid bacteria\", \"L. fructivorans\", \"bacteria\", \"Leuconostoc\", \"L. curvatus\", \"L. parabuchneri\", \"LAB\", \"L. plantarum\", \"L. paracasei\", \"L. brevis\", \"L. rhamnosus\", \"Lactobacillus\", \"Lactobacillus plantarum\"], \"BiologicFunction\": [\"catalase-negative\"], \"BiomedicalOccupationOrDiscipline\": [], \"BodySubstance\": [], \"BodySystem\": [], \"Chemical\": [\"16S rDNA\", \"MRS agar\"], \"ClinicalAttribute\": [], \"Eukaryote\": [], \"Finding\": [\"Gram-positive\"], \"Food\": [\"milk\", \"French cheese Maroilles\", \"Maroilles\", \"French cheese\", \"Maroilles cheese\"], \"HealthCareActivity\": [\"culturable\", \"MS\"], \"InjuryOrPoisoning\": [], \"IntellectualProduct\": [\"genus\", \"method\", \"genera\", \"species\", \"methods\"], \"MedicalDevice\": [], \"Organization\": [], \"PopulationGroup\": [\"population\"], \"ProfessionalOrOccupationalGroup\": [], \"ResearchActivity\": [\"MALDI-TOF-MS\", \"study\", \"MALDI-TOF mass spectrometry\", \"MALDI-TOF MS\"], \"SpatialConcept\": [], \"Virus\": []}", "role": "assistant" } ]

[ { "content": "You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.\n\nMain Abstract:\nThe development and design of visualization solutions that are truly usable is essential for ensuring both their adoption and effectiveness. User-centered design principles, which focus on involving users throughout the entire development process, are well suited for visualization and have been shown to be effective in numerous information visualization endeavors. In this paper, we report a two year long collaboration with combustion scientists that, by applying these design principles, generated multiple results including an in situ visualization technique and a post hoc probability distribution function (PDF) exploration tool. Furthermore, we examine the importance of user-centered design principles and describe lessons learned over the design process in an effort to aid others who also seek to work with scientists for developing effective and usable scientific visualization solutions.\n\nCited Abstract(s):\n[1]: \n\n[2]: \n\n[3]: \n\n[4]: We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open - enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design.\n\n[5]: Involving analysts in visualisation design has obvious benefits, but the knowledge-gap between domain experts ('analysts') and visualisation designers ('designers') often makes the degree of their involvement fall short of that aspired. By promoting a culture of mutual learning, understanding and contribution between both analysts and designers from the outset, participants can be raised to a level at which all can usefully contribute to both requirement definition and design. We describe the process we use to do this for tightly-scoped and short design exercises -- with meetings workshops, iterative bursts of design prototyping over relatively short periods of time, and workplace-based evaluation -- illustrating this with examples of our own experience from recent work with bird ecologists.\n\n[6]: Working with three domain specialists we investigate human-centered approaches to geovisualization following an ISO13407 taxonomy covering context of use, requirements and early stages of design. Our case study, undertaken over three years, draws attention to repeating trends: that generic approaches fail to elicit adequate requirements for geovis application design; that the use of real data is key to understanding needs and possibilities; that trust and knowledge must be built and developed with collaborators. These processes take time but modified human-centred approaches can be effective. A scenario developed through contextual inquiry but supplemented with domain data and graphics is useful to geovis designers. Wireframe, paper and digital prototypes enable successful communication between specialist and geovis domains when incorporating real and interesting data, prompting exploratory behaviour and eliciting previously unconsidered requirements. Paper prototypes are particularly successful at eliciting suggestions, especially for novel visualization. Enabling specialists to explore their data freely with a digital prototype is as effective as using a structured task protocol and is easier to administer. Autoethnography has potential for framing the design process. We conclude that a common understanding of context of use, domain data and visualization possibilities are essential to successful geovis design and develop as this progresses. HC approaches can make a significant contribution here. However, modified approaches, applied with flexibility, are most promising. We advise early, collaborative engagement with data - through simple, transient visual artefacts supported by data sketches and existing designs - before moving to successively more sophisticated data wireframes and data prototypes.\n\n[7]: Domain Analysis for Data Visualization (DADV) is a technique to use when investigating a domain where data visualizations are going to be designed and added to existing software systems. DADV was used to design the data visualization in VisEIO-LCA, which is a framework to visualize environmental data about products. Most of the visualizations are designed using the following stages: formatting data in tables, selecting visual structures, and rendering the data on the screen. Although many visualization authors perform implicit domain analysis, in this paper domain analysis is added explicitly to the process of designing visualizations with the goal of producing move usable software tools. Environmental Life-Cycle Assessment (LCA) is used as a test bed for this technique.\n\n[8]: \n\n[9]: An adaptive bit synchronizer is operable to extract digital data and its associated clock from a transmitted digital signal, and includes a tunable matched filter set for modifying the input signal to correct for deviations in offset and gain, which filter set includes data, transition and derivative matched filters. A sampling device samples the output of the data matched filter for making bit decision and for estimating the reliability thereof. A clock-producing device is connected with the matched filter set for producing at least two clocks, use being made of an optimum phase detector for estimating the time error between the proper clock edge and the actual clock edges. A loop filter circuit smooths the estimates of the proper clock time to generate clock signals, and a device responsive to the average square error of the clock signals varies the loop parameters of the loop filter means to minimize average square phase error.\n\n[10]: \n\n[11]:", "role": "user" }, { "content": "The information visualization community has been utilizing user-centered design processes and developing guidelines for design studies. [1] presented a methodological framework for conducting design studies. They based the guidelines and pitfalls presented on the authors' combined experiences and an extensive literature review. Lloyd and Dykes [6] provided invaluable recommendations on how to incorporate user-centered design in developing geo-visualizations by presenting the experiences they gained from multiple cases in a long-term study. Similar works have also been presented to share the experiences of adopting a user-centered design process [5] [8] [7] . On a finer granularity, in [2] aimed to specifically analyze how to better establish requirements in real-world visualization projects. These works have done an effective job of promoting user-centered design, as more and more works in information visualization [11] [5]9 [10] [4] [3] have adopted the idea.", "role": "assistant" } ]

[ { "content": "You will be shown a question, followed by excerpts from biomedical research papers. Please answer the question based on the provided context. Do not include any text in your response other than the answer.\n\nQuestion: What is the composition of the gamma-secretase complex?\n\nContext: Gamma-secretase is a widely expressed multisubunit enzyme complex which is involved in the pathogenesis of Alzheimer disease and hematopoietic malignancies through its aberrant processing of the amyloid precursor protein (APP) and Notch1, respectively.\nPresenilin (PS1 or PS2) is the catalytic component of the gamma-secretase complex, which mediates the final proteolytic processing step leading to the Alzheimer's disease (AD)-characterizing amyloid beta-peptide.\ngamma-Secretase is a membrane-embedded multi-protein complex that catalyzes the final cut of the Alzheimer's disease-related amyloid precursor protein (APP) to amyloid-beta peptides of variable length (37-43 amino acids) via an unusual intramembrane cleavage.\nThe gamma-secretase complex processes substrate proteins within membranes and consists of four proteins: presenilin (PS), nicastrin, Aph-1 and Pen-2. PS harbours the enzymatic activity of the complex, and there are two mammalian PS homologues: PS1 and PS2.\ngamma-Secretase is known to contain four major protein constituents: presenilin (PS), nicastrin, Aph-1, and Pen-2, all of which are integral membrane proteins.\nHere we describe the association of all four components of the gamma-secretase complex, namely presenilin 1 (PS1)-derived fragments, mature nicastrin, APH-1, and PEN-2, with cholesterol-rich detergent insoluble membrane (DIM) domains of non-neuronal cells and neurons that fulfill the criteria of lipid rafts.\nIn contrast, the authentic TMD of NCT is critically required for the interaction with gamma-secretase complex components and for formation of an active gamma-secretase complex.\nActive gamma-secretase is a tetrameric protein complex consisting of presenilin-1 (or -2), nicastrin, PEN-2, and Aph-1a (or -1b).\nThe gamma-secretase complex processes substrate proteins within membranes and consists of four proteins: presenilin (PS), nicastrin, Aph-1 and Pen-2.\nMaturation of exogenous NCT, gamma-secretase complex formation and proteolytic function was then investigated.\ngamma-Secretase complexes containing N- and C-terminal fragments of different presenilin origin retain normal gamma-secretase activity.\nCo-overexpression of presenilin-1 or APH-1 abrogated gamma-secretase inhibition probably through prevention of the incorporation of CRB2 into the gamma-secretase complex\nWe conclude that a PS1/Pen2/Aph1a trimeric complex is an active enzyme, displaying biochemical properties similar to those of gamma-secretase and roughly 50% of its activity when normalized to PS1 N-terminal fragment levels\nγ-Secretase is involved in the regulated intramembrane proteolysis of amyloid-β protein precursor (AβPP) and of many other important physiological substrates. γ-secretase is a multiproteic complex made of four main core components, namely presenilin 1 or 2, APH-1, PEN-2, and Nicastrin\nAmyloid precursor protein associates with a nicastrin-dependent docking site on the presenilin 1-gamma-secretase complex in cells demonstrated by fluorescence lifetime imaging.\nActivity-dependent isolation of the presenilin- gamma -secretase complex reveals nicastrin and a gamma substrate.\nCharacterization of the reconstituted gamma-secretase complex from Sf9 cells co-expressing presenilin 1, nicastrin [correction of nacastrin], aph-1a, and pen-2.\np53-Dependent Aph-1 and Pen-2 anti-apoptotic phenotype requires the integrity of the gamma-secretase complex but is independent of its activity.\nHere we show that all known gamma-secretase complexes are active in APP processing and that all combinations of APH-1 variants with either FAD mutant PS1 or PS2 support pathogenic Abeta(42) production.\nTo characterize the functional similarity between complexes of various PS composition, we analysed PS1, PS2, and chimeric PS composed of the NTF from PS1 and CTF from PS2, or vice versa, in assembly and function of the gamma-secretase complex.\nWhile beta-secretase is a classical aspartyl protease, gamma-secretase activity is associated with a high molecular weight complex. One of the complex components, which is critically required for gamma-secretase activity is nicastrin (NCT). Here we investigate the assembly of NCT into the gamma-secretase complex.\nMaturation of exogenous NCT, gamma-secretase complex formation and proteolytic function was then investigated. This revealed that the cytoplasmic tail of NCT is dispensable for gamma-secretase complex assembly and function. In contrast, the authentic TMD of NCT is critically required for the interaction with gamma-secretase complex components and for formation of an active gamma-secretase complex.\nThis revealed that the cytoplasmic tail of NCT is dispensable for gamma-secretase complex assembly and function. In contrast, the authentic TMD of NCT is critically required for the interaction with gamma-secretase complex components and for formation of an active gamma-secretase complex.\nWhile beta-secretase is a classical aspartyl protease, gamma-secretase activity is associated with a high molecular weight complex. One of the complex components, which is critically required for gamma-secretase activity is nicastrin (NCT).\nThis revealed that the cytoplasmic tail of NCT is dispensable for gamma-secretase complex assembly and function. In contrast, the authentic TMD of NCT is critically required for the interaction with gamma-secretase complex components and for formation of an active gamma-secretase complex. Neither soluble NCT lacking any membrane anchor nor NCT containing a heterologous TMD were inserted into the gamma-secretase complex.\nIn contrast, the authentic TMD of NCT is critically required for the interaction with gamma-secretase complex components and for formation of an active gamma-secretase complex. Neither soluble NCT lacking any membrane anchor nor NCT containing a heterologous TMD were inserted into the gamma-secretase complex.\nMaturation of exogenous NCT, gamma-secretase complex formation and proteolytic function was then investigated. This revealed that the cytoplasmic tail of NCT is dispensable for gamma-secretase complex assembly and function.\nHere we investigate the assembly of NCT into the gamma-secretase complex.\nCo-overexpression of presenilin-1 or APH-1 abrogated gamma-secretase inhibition probably through prevention of the incorporation of CRB2 into the gamma-secretase complex.\nThis could be demonstrated also in cell-free assays, where in addition presenilin-1, the catalytic subunit of the gamma-secretase complex, was shifted out of lipid rafts.\nIn PS1(-/-)/PS2(-/-) and NCT(-/-) fibroblasts, gamma-secretase components that still remain fail to become detergent-resistant, suggesting that raft association requires gamma-secretase complex assembly.", "role": "user" }, { "content": "Gamma-secretase is a multisubunit enzyme complex which is consists of four proteins: presenilin 1 (PS1) or presenilin 2 (PS2), nicastrin, Aph-1 and Pen-2.", "role": "assistant" } ]

[ { "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: Social distancing reduces mobility that can cause COVID-19 spread\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": "user" }, { "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": "assistant" } ]

[ { "content": "You will be presented with a paragraph from a COVID-19 literature. Your task is to 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 categories within the array. \nYour output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nAfter COVID-19 initial diffusion in Europe in March 2020, research has suggested a direct correlation between environmental pollution and contagion dynamics (i.e., environment-to-human pollution), thereby indicating that mechanisms other than human-to-human transmission can explain COVID-19 diffusion. However, these studies did not consider that complex outcomes, such as a pandemic's diffusion patterns, are typically caused by a multiplicity of environmental, economic and social factors. While disciplinary specialties increase scholars' attitudes of concentrating on specific factors, neglecting this multiplicity during a pandemic crisis can lead to misleading conclusions. This communication aims to focus on certain limitations of current research about environmental-to-human COVID-19 transmission and shows the benefit of an interdisciplinary, multi-dimensional approach to understand the geographical diversity of contagion diffusion patterns.", "role": "user" }, { "content": "[\"Transmission\"]", "role": "assistant" } ]

[ { "content": "You are presented with multiple paper abstracts, a literature review title, and a specific chapter title. Your task is to write a focused literature review chapter that aligns with the given chapter title. The review should synthesize the content from the provided abstracts, draw connections between different works, highlight major findings, and identify any gaps in the existing research.\nLiterature Review Title: Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications \nChapter Title: A. MAPPING \n1. Abstract of Cited Paper (BIB001): Abstract Mapping vegetation in crop fields is an important step in remote sensing applications for precision agriculture. Traditional aerial platforms such as planes and satellites are not suitable for these applications due to their low spatial and temporal resolutions. In this article, a UAV equipped with a commercial camera (visible spectrum) was used for ultra-high resolution image acquisition over a wheat field in the early-season period. From these images, six visible spectral indices (CIVE, ExG, ExGR, Woebbecke Index, NGRDI, VEG) and two combinations of these indices were calculated and evaluated for vegetation fraction mapping, to study the influence of flight altitude (30 and 60 m) and days after sowing (DAS) from 35 to 75 DAS on the classification accuracy. The ExG and VEG indices achieved the best accuracy in the vegetation fraction mapping, with values ranging from 87.73% to 91.99% at a 30 m flight altitude and from 83.74% to 87.82% at a 60 m flight altitude. These indices were also spatially and temporally consistent, allowing accurate vegetation mapping over the entire wheat field at any date. This provides evidence that visible spectral indices derived from images acquired using a low-cost camera onboard a UAV flying at low altitudes are a suitable tool to use to discriminate vegetation in wheat fields in the early season. This opens the doors for the utilisation of this technology in precision agriculture applications such as early site specific weed management in which accurate vegetation fraction mapping is essential for crop-weed classification.\n\nLiterature Review:", "role": "user" }, { "content": "2D or 3D maps of an agricultural field made by UAVs can provide useful information. For example, the area of the farmland, soil conditions, and status of the crops can be used model enhancements and efficiencies BIB002 , BIB001 . Therefore, mapping research continues to draw attention. [64] obtained high-resolution maps delineating the spatial variations of radiation interception using UAV images (see Figure 10 ). The generated maps allow for profitable precision agriculture tasks, such as the agronomic control of homogeneous zones and the separation of fruit quality areas.", "role": "assistant" } ]

[ { "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: You can get the COVID-19 virus in sunny, hot and humid weather.\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

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

[ { "content": "You will be presented with a citation sentence from 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\nOur results showed that the treatment response of the 5 mg/day vortioxetine group was greater than the placebo group, and the pooled ES of the studies (Katona et al. 2012; Mahableshwarkar et al. 2013; Henigsberg et al. 2012; Jain et al. 2013) demonstrated the antidepressant efficacy of vortioxetine.", "role": "user" }, { "content": "Result", "role": "assistant" } ]

[ { "content": "You will be presented with a paragraph from a COVID-19 literature. Your task is to 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 categories within the array. \nYour output should be a valid JSON array, and no additional text should be included in your response.\n\nArticle:\n\nIn December 2019, the first data emerged from Wuhan, China, of a serious acute respiratory disease caused by a new coronavirus, SARS-CoV-2 (COVID-19). In a short time, the health emergency became a global pandemic. To date, there are about 18.8 million infected people and about 700,000 deaths. There are currently no effective vaccines, and treatments are mostly experimental. The symptoms associated with COVID-19 are different, ranging from mild upper respiratory tract symptoms to severe acute respiratory distress syndrome (SARS). Data from previous coronavirus outbreaks such as SARS-CoV (2003 outbreak) and emerging epidemiological data from the current global COVID-19 pandemic suggest that there could be substantial tissue fibrotic consequences following SARS-CoV-2 infection, responsible for severe and in some cases fatal lung lesions. Some data show that even patients cured of viral infection have lung fibrotic tissue residues responsible for incorrect respiratory function even after healing. The role of antifibrotic drug therapy in patients with ongoing SARS-CoV-2 infection or in patients cured of residual pulmonary fibrosis is still to be defined and unclear; the scientific rationale for initiating, continuing, or discontinuing therapy is poorly defined. In this article, we describe the advantages of antifibrotic therapy in patients with ongoing SARS-CoV-2 viral infection to prevent the worsening and aggravation of the clinical situation, and the advantages it could have in the role of preventing pulmonary fibrosis after SARS-CoV-2 infection, and in accelerating the complete healing process.", "role": "user" }, { "content": "[\"Treatment\"]", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\n(20KBCDR).", "role": "user" }, { "content": "[]", "role": "assistant" } ]

[ { "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: [\"RNA\", \"CellType\", \"CellLine\", \"Protein\", \"DNA\"].\n\nPlease return the output as a JSON object of the format: {\"DNA\" : [\"TAL1\", ...], \"RNA\": [\"mRNAs\", ...], \"CellLine\": [\"T cells\", ...], \"CellType\": [\"monocytes\", ...], \"Protein\": [\"IL-2\", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].\n\nOnly output the JSON object and do not include any additional text.\n\nAbstract:\n\nA mammalian histone deacetylase related to the yeast transcriptional regulator Rpd3p [ see comments ] Trapoxin is a microbially derived cyclotetrapeptide that inhibits histone deacetylation in vivo and causes mammalian cells to arrest in the cell cycle. A trapoxin affinity matrix was used to isolate two nuclear proteins that copurified with histone deacetylase activity. Both proteins were identified by peptide microsequencing, and a complementary DNA encoding the histone deacetylase catalytic subunit (HD1) was cloned from a human Jurkat T cell library. As the predicted protein is very similar to the yeast transcriptional regulator Rpd3p, these results support a role for histone deacetylase as a key regulator of eukaryotic transcription.", "role": "user" }, { "content": "{\"CellLine\": [], \"CellType\": [], \"DNA\": [\"complementary DNA\"], \"Protein\": [\"mammalian histone deacetylase\", \"HD1\", \"histone\", \"Rpd3p\", \"histone deacetylase catalytic subunit\", \"histone deacetylase\", \"yeast transcriptional regulator\"], \"RNA\": []}", "role": "assistant" } ]

[ { "content": "You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.\n\nTitle: CO2 directly modulates connexin 26 by formation of carbamate bridges between subunits\nArticle:\nAbstract:\nHomeostatic regulation of the partial pressure of CO2( PCO2) is vital for life. Sensing of pH has been proposed as a sufficient proxy for determination of PCO2 and direct CO2-sensing largely discounted. Here we show that connexin 26( Cx26) hemichannels, causally linked to respiratory chemosensitivity, are directly modulated by CO2. A ‘carbamylation motif’, present in CO2-sensitive connexins( Cx26, Cx30, Cx32) but absent from a CO2-insensitive connexin( Cx31), comprises Lys125 and four further amino acids that orient Lys125 towards Arg104 of the adjacent subunit of the connexin hexamer. Introducing the carbamylation motif into Cx31 created a mutant hemichannel( mCx31) that was opened by increases in PCO2. Mutation of the carbamylation motif in Cx26 and mCx31 destroyed CO2 sensitivity. Course-grained computational modelling of Cx26 demonstrated that the proposed carbamate bridge between Lys125 and Arg104 biases the hemichannel to the open state. Carbamylation of Cx26 introduces a new transduction principle for physiological sensing of CO2.\nIntroduction:\nCO2 is the unavoidable by-product of cellular metabolism. Humans produce approximately 20 moles of CO2 per day( Marshall and Bangert, 2008). The dissolved CO2 can readily combine with water, aided by carbonic anhydrase, to form H2CO3, which dissociates rapidly to H+ and HCO3−. In any physiological solution therefore, the partial pressure of CO2( PCO2) will be in equilibrium with, and inescapably related to, the pH and the concentration of HCO3− of that solution. Regulation of PCO2 is thus a vital homeostatic function that is linked to acid-base balance. As might be expected, chemosensory reflexes regulate the frequency and depth of breathing to ensure homeostatic control of blood gases. The field of respiratory chemosensitivity has been dominated by ‘reaction theory’ which posits that pH is a sufficient signal for detection of changes in PCO2( Loeschcke, 1982). Many investigators therefore equate pH-sensing with CO2-sensing. There are several areas of the medulla oblongata which contain neurons that respond to changes in pH/CO2, especially near the highly vascularised ventral surface. For example a population of neurons highly sensitive to pH/CO2 have been described in the retrotrapezoid nucleus( RTN)( Mulkey et al., 2004, 2006; Guyenet et al., 2008) and the medullary raphé nucleus( Richerson, 2004; Ray et al., 2011). Despite the acceptance of pH-sensing as the predominant mechanism by which PCO2 is measured, there is substantial evidence for an additional and independent effect of molecular CO2( Eldridge et al., 1985; Shams, 1985; Huckstepp and Dale, 2011). For example, if pH is carefully controlled at the medullary surface, an increase in PCO2 at constant pH will still enhance breathing by as much as a pH change at constant PCO2( Shams, 1985). We have recently shown that connexin 26( Cx26) hemichannels, open in response to increases in PCO2 at constant extracellular pH and are an important conduit for the CO2-dependent, as opposed to pH-dependent, release of ATP( Huckstepp et al., 2010a). Cx26 hemichannels contribute to the chemosensory control of breathing( Huckstepp et al., 2010b; Wenker et al., 2012). Hemichannels of two closely related connexins, Cx30 and Cx32, also exhibited CO2-sensitive opening( Huckstepp et al., 2010a). Despite this evidence, widespread acceptance of direct sensing of CO2 requires a detailed molecular explanation of any putative transduction system. One possible way that CO2 can interact with proteins is via carbamylation—the formation of a covalent bond between the carbon of CO2 and a primary amine group. For example, CO2 forms carbamate bonds with haemoglobin( Kilmartin and Rossi-Bernardi, 1971) and the plant enzyme RuBisCo( Lundqvist and Schneider, 1991). Here we document an important new advance—the mechanism by which CO2 binds directly to Cx26, most probably via carbamylation of a lysine residue, to cause hemichannel opening. Our work establishes a new field of direct CO2 sensing that can be mediated by CO2-dependent carbamylation of certain β connexins. As these are widely distributed in the brain and other tissues, it is likely that direct detection of CO2 via this mechanism is important in many different physiological processes.\nDiscussion:\nEvidence from six different experimental tests supports our hypothesis that CO2 forms a carbamate bridge between K125 and R104 on the adjacent subunit to open the Cx26 hemichannel. Firstly, we demonstrated the sufficiency of the carbamylation motif to confer CO2 sensitivity by inserting it into a CO2-insensitive connexin, Cx31. Secondly, we showed that K125 of the carbamylation motif was essential for this motif to confer CO2 sensitivity on Cx31. Thirdly and fourthly, we demonstrated that the mutations K125R and R104A in Cx26( to prevent carbamate bridging at either end of the bridge) destroyed the CO2 sensitivity of this connexin. Fifthly, by exploiting glutamate as an analogue of the carbamylated K125( in Cx26K125E), we demonstrated a gain of function—Cx26K125E was constitutively open, yet had lost sensitivity to CO2. Finally, we further tested the bridging concept by demonstrating that the bridge is in effect bidirectional: the mutated hemichannel Cx26R104E, in which E104 can bridge to K125 in the reverse direction, was also constitutively open, but had no sensitivity to CO2. Although we have not directly demonstrated CO2 binding to Cx26, our extensive testing of this hypothesis through selective mutations leads us to conclude that CO2 interacts with Cx26 directly and that no other protein is required for CO2 sensitivity. This interaction is most probably via carbamylation of K125. Interestingly, the mutations Cx26K125E and Cx26K125R can be considered respectively as open-state and closed-state analogues of the wild type channel. Collectively, our data strongly suggests that CO2 binds to the intracellular surface of Cx26 and must therefore cross the membrane to reach this site. This could occur either direct diffusion through the membrane bilayer, potentially via Cx26 itself, or via other CO2 permeable channels( Boron et al., 2011). Amongst its many other functions, Cx26 can therefore be regarded as a receptor for CO2. Interestingly, this mechanism of modulation applies to both Cx30 and Cx32, which can both potentially form a carbamate bridge at equivalent residues to Cx26. In the case of Cx32 this would involve bridging to K104 rather than R104( in Cx26 and 30). Cx26 can co-assemble with both Cx30 and Cx32 to form heteromeric hemichannels( Forge et al., 2003; Yum et al., 2007). Our structural studies predict that, as Cx30 and Cx32 have K125 and either R104 or K104, carbamate bridges could form in such heteromeric hemichannels and that they should also therefore be CO2-sensitive. Carbamylation involves formation of a labile covalent bond between the carbon of CO2 and a primary amine. For this to occur the amine must be in a restricted hydration space so that it is not protonated. Some examples of physiologically significant carbamylation are known. The carbamylation of the N-terminal amines of haemoglobin contributes to the Bohr effect( Kilmartin and Rossi-Bernardi, 1971), whereby the affinity of haemoglobin for O2 is reduced in the presence of elevated CO2. However in mammalian systems no other examples of carbamylation by CO2 have been described. In C3 photosynthetic plants, the enzyme RuBisCo, that participates in the Calvin cycle and carbon fixation is activated by carbamylation of a specific lysine residue( Lundqvist and Schneider, 1991). Several microbial enzymes are also carbamylated( Maveyraud et al., 2000; Golemi et al., 2001; Young et al., 2008). Despite this precedent, the functional significance of CO2-carbamylation and its potential as a transduction principle for the measurement of CO2 has been almost completely overlooked in vertebrate physiology. The mechanism of formation of a salt bridge between a carbamylated lysine and an appropriately oriented arginine on the neighbouring subunit is a unique mechanism for modulation of an ion channel and establishes carbamylation as a mechanistic basis for the direct signalling of PCO2 in mammalian physiology. This carbamylation of a lysine to transduce the concentration of CO2 into a biological signal is somewhat equivalent to the nitrosylation of a cysteine residue by NO/nitrite. It establishes a CO2-dependent signalling paradigm in which the concentration of CO2 is signalled by ATP release via Cx26 from the chemosensory cell and consequent activation of neighbouring cells, or potentially by a Ca2+ influx through the Cx26 hemichannel( Fiori et al., 2012) to initiate a Ca2+ wave within the chemosensory cell itself and further Ca2+-dependent signalling processes.", "role": "user" }, { "content": "A number of gaseous molecules , including nitric oxide and carbon monoxide , play important roles in many cellular processes by acting as signalling molecules . Surprisingly , however , it has long been assumed that carbon dioxide – a gaseous molecule that is produced during cellular metabolism – is not a signalling molecule . Controlling the concentration of carbon dioxide ( CO2 ) in a biological system is essential to sustain life , and it was thought that the body used pH – which is the concentration of hydrogen ions – as a proxy for the level of CO2 . The concentration of CO2 is related to pH because CO2 reacts with water to form carbonic acid , which quickly breaks down to form hydrogen ions and bicarbonate ions . This close relationship has led many researchers to equate pH-sensing with CO2-sensing , and to suggest that a physiological receptor for CO2 does not exist . Recent research into structures called connexin hemichannels has challenged this view . Researchers found that when pH levels were held constant , increasing the level of CO2 caused the structures to open up , suggesting that CO2 could be directly detected by the hemichannels . Each hemichannel contains six connexin subunits , but the details of how the CO2 molecules interact with the individual connexin subunits to open up the hemichannels remained mysterious . Now Meigh et al . show that CO2 molecules bind to a specific amino acid ( lysine ) at a particular place ( residue 125 ) in one of the connexin subunits to form a carbamate group . This group then interacts with the amino acid ( arginine ) at residue 104 in a neighbouring connexin subunit to form a carbamate bridge between the two subunits . This leads to structural changes that cause the gap junction hemichannels to open and release signals that can activate other cells . Since connexin hemichannels are found throughout the human body , these results suggest that CO2 might act as a signalling molecule in processes as diverse as the control of blood flow , breathing , hearing and reproduction .", "role": "assistant" } ]

[ { "content": "You will be presented with a sentence from biomedical research text. Extract all terms that correspond to chemical entities of biological interest (ChEBI).\nReturn your identified terms in a JSON array, formatted like this: [\"Entity1\", \"Entity2\", ...].\nIf no such entities are detected, respond by providing an empty JSON array [].\n\nBiomedical context:\nFurthermore,itispossiblethatpersistenceofsomematernallyderivedmRNAtranscriptorproteinmighthaveallowedasmallnumberofCapn2-/-embryostosurvivetothemorula-stage.", "role": "user" }, { "content": "[\"mRNA transcript\", \"protein\"]", "role": "assistant" } ]

[ { "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 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: Resistance to Selumetinib (AZD6244) in colorectal cancer cell lines is mediated by p70S6K and RPS6 activation. selumetinib (AZD6244, ARRY-142886) is a MEK1/2 inhibitor that has gained interest as an anti-tumour agent. We have determined the degree of sensitivity/resistance to selumetinib in a panel of colorectal cancer cell lines using cell proliferation and soft agar assays. Sensitive cell lines underwent G1 arrest, whereas selumetinib had no effect on the cell cycle of resistant cells. Some of the resistant cell lines showed high levels of ERK1/2 phosphorylation in the absence of serum. selumetinib inhibited phosphorylation of ERK1/2 and RSK and had no effect on AKT phosphorylation in both sensitive and resistant cells. Furthermore, mutations in KRAS, BRAF, or PIK3CA were not clearly associated with selumetinib resistance. Surprisingly, selumetinib was able to inhibit phosphorylation of p70 S6 kinase (p70S6K) and its downstream target ribosomal protein S6 (RPS6) in sensitive cell lines. However, p70S6K and RPS6 phosphorylation remained unaffected or even increased in resistant cells. Moreover, in some of the resistant cell lines p70S6K and RPS6 were phosphorylated in the absence of serum. Interestingly, colorectal primary cultures derived from tumours excised to patients exhibited the same behaviour than established cell lines. Pharmacological inhibition of p70S6 K using the PI3K/mTOR inhibitor NVP-BEZ235 , the specific mTOR inhibitor Rapamycin and the specific p70S6 K inhibitor PF-4708671 potentiated Selumetinib effects in resistant cells . In addition, biological inhibition of p70S6K using siRNA rendered responsiveness to selumetinib in resistant cell lines. Furthermore, combination of p70S6K silencing and PF-47086714 was even more effective. We can conclude that p70S6K and its downstream target RPS6 are potential biomarkers of resistance to selumetinib in colorectal cancer.\n\nsentence: Pharmacological inhibition of p70S6 K using the PI3K/mTOR inhibitor NVP-BEZ235 , the specific mTOR inhibitor Rapamycin and the specific p70S6 K inhibitor PF-4708671 potentiated Selumetinib effects in resistant cells .", "role": "user" }, { "content": "[[\"Selumetinib\", \"NVP-BEZ235\", \"POS\"], [\"Rapamycin\", \"Selumetinib\", \"POS\"], [\"Selumetinib\", \"PF-4708671\", \"POS\"]]", "role": "assistant" } ]

[ { "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\n2- (2-Aminoethyl) -quinoline (D-1997): a novel agonist at 5-hydroxytryptamine1-like receptors in the canine basilar artery. This study aimed to investigate the mechanisms involved in the contractile effects produced by the novel quinoline derivative, 2- (2-aminoethyl) -quinoline (D-1997), in the canine isolated basilar artery. For comparison, the effects of D-1997 were also evaluated on rat aorta. Canine basilar artery and rat aortic rings were prepared and mounted in organ baths to record isometric tension changes. The contractile effects of D-1997 in the basilar artery were compared with those produced by 5-hydroxytryptamine (5-HT) and the 5-HT receptor agonist quipazine. Thus, 4-HT (10 (-10) -10 (-6) M), D-1997 (3.1 x 10 (-8) -10 (-4) M) and quipazine (3.1 x 10 (-7) -10 (-4) M) each caused concentration-dependent contractions of the canine basilar artery with a rank order of agonist potency of 5-HT > D-1997 > quipazine. 5-HT and D-1997 exhibited similar maximum effects which were higher than that of quipazine. Similar concentrations of D-1997 failed to produce contraction in rat aorta. The effects of D-1997 in the basilar artery were not modified by incubation with either the 5-HT2 receptor antagonist ketanserin (0.01-1 microM), the 5-HT3 and 5-HT4 receptor antagonist ICS205930 (tropisetron; 0.1-10 microM), the 5-HT1A receptor antagonist spiroxatrine (0.01-1 microM), the beta-adrenoceptor blocker with high affinity for 5-HT1A and 5-HT1B binding sites (+/-) -pindolol (0.01-1 microM), or the alpha 1-adrenoceptor antagonist prazosin (0.01-1 microM). In contrast, the D-1997-induced responses were potently and concentration-dependently antagonized by the mixed 5-HT1-like and 5-HT2 receptor antagonist methiothepin (0.01-1 microM). It is concluded that D-1997 contracts the canine basilar artery by stimulating 5-HT1-like receptors unrelated to either the 5-HT1A or 5-HT1B receptor subtypes. The compound seems to be devoid of 5-HT2 receptor agonist properties in rat aorta.", "role": "user" }, { "content": "[[\"D-1997\", \"ACTIVATOR\", \"5-HT1-like receptors\"], [\"+/-) -pindolol\", \"INHIBITOR\", \"beta-adrenoceptor\"], [\"prazosin\", \"ANTAGONIST\", \"alpha 1-adrenoceptor\"], [\"+/-) -pindolol\", \"DIRECT-REGULATOR\", \"5-HT1B\"], [\"ketanserin\", \"ANTAGONIST\", \"5-HT2 receptor\"], [\"+/-) -pindolol\", \"DIRECT-REGULATOR\", \"5-HT1A\"], [\"tropisetron\", \"ANTAGONIST\", \"5-HT3\"], [\"methiothepin\", \"ANTAGONIST\", \"5-HT1-like\"], [\"D-1997\", \"ACTIVATOR\", \"5-HT1A\"], [\"D-1997\", \"ACTIVATOR\", \"5-HT1B\"], [\"2- (2-Aminoethyl) -quinoline\", \"AGONIST\", \"5-hydroxytryptamine1-like receptors\"], [\"tropisetron\", \"ANTAGONIST\", \"5-HT4\"], [\"spiroxatrine\", \"ANTAGONIST\", \"5-HT1A\"], [\"methiothepin\", \"ANTAGONIST\", \"5-HT2 receptor\"], [\"D-1997\", \"AGONIST\", \"5-hydroxytryptamine1-like receptors\"]]", "role": "assistant" } ]

[ { "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:\nAs such it resembles the parser of the grammar development system Attribute Language Engine (ALE) of (Carpenter and Penn, 1994). In contrast to Johnson and D6rre's deduction system, though, the selective magic parsing approach combines top-down and bottom-up control strategies. The proposed parser is related to the so-called Lemma Table deduction system (Johnson and D6rre, 1995) which allows the user to specify whether top-down sub-computations are to be tabled.\n\nCitation Sentence:\nAs such it resembles the parser of the grammar development system Attribute Language Engine ( ALE ) of ( Carpenter and Penn , 1994 ) .\n\nContext after the citation:\nUnlike the ALE parser, though, the selective magic parser does not presuppose a phrase structure backbone and is more flexible as to which sub-computations are tabled/filtered. \u0000\u0000", "role": "user" }, { "content": "CompareOrContrast", "role": "assistant" } ]

[ { "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\nSub-picometer structural information of graphene hidden in a 50 pm resolved image. In a 2D self-organized crystalline structure more than 1000 unit-cells can be observed in a single image. Here we exploit the benefits from having a large number of observations of the same unit cell utilizing an image processing methodology. We obtain sub-picometer resolution data from a 50 pm image of graphene, revealing a 1% axial elongation and a 3 fold symmetry, indicating a chair conformation.", "role": "user" }, { "content": "{\"Chemical\": [\"graphene\"]}", "role": "assistant" } ]

[ { "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 the options provided. A short description of the datasets is also provided to help you.\n\nQuery: We propose a novel framework to learn compact hash codes for large-scale content-based image retrieval. In\n\nKeyphrases: image retrieval images\n\nDataset description:\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.\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.\nBlendedMVS: BlendedMVS is a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes.\nOxford5k: Oxford5K is the Oxford Buildings Dataset, which contains 5062 images collected from Flickr. It offers a set of 55 queries for 11 landmark buildings, five for each landmark.\n\nOptions: A: THEODORE B: TableBank C: BlendedMVS D: Oxford5k", "role": "user" }, { "content": "Oxford5k", "role": "assistant" } ]

[ { "content": "You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.\n\nTitle: Reprogramming to Pluripotency Can Conceal Somatic Cell Chromosomal Instability\nArticle:\nAbstract:\nThe discovery that somatic cells are reprogrammable to pluripotency by ectopic expression of a small subset of transcription factors has created great potential for the development of broadly applicable stem-cell-based therapies. One of the concerns regarding the safe use of induced pluripotent stem cells( iPSCs) in therapeutic applications is loss of genomic integrity, a hallmark of various human conditions and diseases, including cancer. Structural chromosome defects such as short telomeres and double-strand breaks are known to limit reprogramming of somatic cells into iPSCs, but whether defects that cause whole-chromosome instability( W-CIN) preclude reprogramming is unknown. Here we demonstrate, using aneuploidy-prone mouse embryonic fibroblasts( MEFs) in which chromosome missegregation is driven by BubR1 or RanBP2 insufficiency, that W-CIN is not a barrier to reprogramming. Unexpectedly, the two W-CIN defects had contrasting effects on iPSC genomic integrity, with BubR1 hypomorphic MEFs almost exclusively yielding aneuploid iPSC clones and RanBP2 hypomorphic MEFs karyotypically normal iPSC clones. Moreover, BubR1-insufficient iPSC clones were karyotypically unstable, whereas RanBP2-insufficient iPSC clones were rather stable. These findings suggest that aneuploid cells can be selected for or against during reprogramming depending on the W-CIN gene defect and present the novel concept that somatic cell W-CIN can be concealed in the pluripotent state. Thus, karyotypic analysis of somatic cells of origin in addition to iPSC lines is necessary for safe application of reprogramming technology.\nIntroduction:\nThe potential to restore pluripotency to mature somatic cells has generated new prospects in the establishment of patient-specific regenerative therapies and has also offered new options for more advanced and specific modeling of human disease[1],[2]. However, several obstacles remain prior to the therapeutic application of iPSCs, including the risk of introducing loss of genomic integrity[3],[4]. Recent studies revealed that somatic cell reprogramming introduces changes at the nucleotide level. Both cell culture length and conditions were identified as key determinants of this type of genetic variation[5],[6]. In contrast to changes at the nucleotide level, reprogramming seems to be less permissive to certain types of structural chromosome damage, such as short telomeres and double strand DNA breaks[7]. Cells with these kinds of aberrations are thought to be eliminated during the early stages of reprogramming by induction of p53-dependent apoptosis[7]. Reprogrammed cells have successfully been generated from somatic cells that undergo stable inheritance of an abnormal number of chromosomes, such as Down syndrome. This implies that aneuploidy( an abnormal number of chromosomes) is not a barrier to reprogramming[8]. However, the extent to which defects that promote the continuous reshuffling of whole chromosomes during mitosis, a condition referred to as whole chromosome instability( W-CIN)[9], interfere with efficient reprogramming of somatic cells is unknown. The molecular mechanisms that underlie chromosome segregation and that safeguard the process are highly complex and remain incompletely understood[10],[11]. In budding yeast, over one hundred genes are known to cause chromosomal instability when defective, including genes implicated in chromosome condensation, sister chromatid cohesion and decatenation, kinetochore assembly and function, spindle formation, mitotic checkpoint control and attachment error correction[12],[13]. Many more genes are expected to contribute to chromosomal stability in mammals, although only a limited number have been identified to date[9],[14]. To begin to address the impact of numerical chromosome instability, we examined the impact of two distinct W-CIN gene defects on somatic cell reprogramming. The first defect involves the BubR1 gene, which encodes a core component of the mitotic checkpoint, an intricate surveillance mechanism that acts to delay anaphase onset until all duplicated chromosomes are properly attached to spindle microtubules and aligned in the metaphase plate[15]–[18]. The role of BubR1 in the mitotic checkpoint is to bind to and inhibit Cdc20, a key activator of the anaphase-promoting complex/cyclosome( APC/C) that drives cells into anaphase by targeting cyclin B1 and securin for degradation by the proteasome[19],[20]. In addition, BubR1 functions at kinetochores to stabilize microtubule-chromosome attachments[20],[21]. While complete loss of BubR1 results in cell death by mitotic catastrophe, cells with low amounts of BubR1 are viable despite frequent chromosome missegregation and development of near-diploid aneuploidies[22]. The second defect involves the RanBP2 gene, which encodes a giant nuclear pore complex( NPC) protein with SUMO E3 ligase activity[23]. At the onset of mitosis, when the nuclear envelope disintegrates and NPCs disassemble, RanBP2 becomes an important regulator of topoisomerase II alpha( hereafter referred to as Top2a), an enzyme that decatenates the centromeric DNA regions of duplicated chromosomes[24]. Accumulation of Top2a at mitotic centromeres is dependent on sumoylation by RanBP2. Complete inactivation of RanBP2 gene expression results in cell death, but cells with low levels of RanBP2 survive and proliferate normally despite incomplete DNA decatenation, frequent chromosome missegregation and aneuploidization[24]. Here, we show that both W-CIN gene defects are compatible with reprogramming. Unexpectedly, however, the two genetic defects had contrasting effects on the genomic integrity of the reprogrammed cells, with RanBP2-insufficient MEFs generating karyotypically normal and chromosomally stable iPSCs and BubR1-insufficient MEFs almost exclusively yielding aneuploid and chromosomally unstable iPSC clones. These data indicate that aneuploid cells can be selected for or against during reprogramming depending on the genetic defect driving the chromosome number instability. Furthermore, our data reveal that W-CIN that exists at the somatic cell level can become dormant upon reprogramming, indicating that testing of both iPSCs and the iPSC-founding cells for chromosome number instability will be necessary for the safe application of iPSC technology in regenerative medicine.\nDiscussion:\nAlthough the importance of iPSC technology for regenerative therapies is broadly recognized, several hurdles to their clinical use exist, including the potential of genomic instability. Here we have examined the relationship between somatic cell reprogramming and W-CIN, a type of genomic instability associated with cancer and other human disorders. Our studies provide several important new insights that should improve the efficacy of iPSC use in future clinical applications. First, we demonstrate that W-CIN does not pose a barrier to reprogramming. Second, we show that W-CIN iPSCs are capable of differentiating into all three distinct germ layer cell types. Third, we show that although MEFs with two distinct W-CIN defects efficiently reprogram into iPSCs, they do so with highly contrasting outcomes on chromosome number integrity and stability( Figure 6): our data suggest that BubR1 hypomorphic iPSC clones preferentially originate from aneuploid MEFs, while RanBP2 hypomorphic iPSC clones preferentially stem from MEFs with normal diploid chromosome numbers. Fourth, our data uncovered the fascinating concept that a W-CIN gene defect responsible for severe aneuploidization in somatic cells can become dormant upon reprogramming. The particular W-CIN defect that revealed this concept is RanBP2 hypomorphism. We ruled out that a failure to maintain the RanBP2 hypomorphic status after reprogramming is responsible for restoring high-fidelity chromosome segregation. One possibility is that SUMO E3 ligases other than RanBP2, such as PIAS proteins, redundantly targeting Top2a to inner centromeric regions of duplicated chromosomes[40]–[42]. An alternative explanation might be that Top2a accumulates to inner centromeres in a SUMO independent fashion in pluripotent cells. Regardless of the precise mechanism by which W-CIN can be concealed in iPSCs, the phenomenon itself highlights that it will not only be important to check iPSCs for aneuploidization but also the somatic cells from which they originated. The observation that aneuploid cells within BubR1 hypomorphic MEF cultures undergo preferential reprogramming is puzzling given that BubR1 insufficiency engages the p16Ink4a-Rb and p53-p19Arf pathways[43], both of which have been shown to inhibit reprogramming[7],[28]. Perhaps activation of these tumor suppressor pathways is necessary but not sufficient for the elimination of aneuploid MEFs during the early stages of reprogramming. The observation that BubR1H/H aneuploid MEFs preferentially dedifferentiate raises the possibility that BubR1 might be a key component of a surveillance pathway that prevents aneuploid cells from reprogramming. Interestingly, in earlier studies we have shown that BubR1 levels decrease with aging in various mouse tissues[22],[44],[45]. This, together with the observation that aneuploid MEFs with low amounts of BubR1 readily reprogram into chromosomally unstable iPSCs implies that reprogramming of somatic cells from elderly individuals into karyotypically normal and stable iPSCs may be particularly challenging. It will be interesting to further explore this possibility by testing whether restoration of high BubR1 levels in somatic cells of older individuals would improve iPSC quality. Mitosis is more prone to errors when cells divide in culture as evidenced by the low rates of chromosome missegregation observed in early passage MEFs from wildtype mice[26],[46]. Although the actual cause of such aneuploidies is unknown, it is generally believed that they are induced by cell culture stress[47]. Karyotypically normal mouse ESC lines used in gene targeting experiments are known to acquire severe aneuploidy upon extensive in vitro passaging, indicating that pluripotent cells are also susceptible to cell culture-induced chromosome segregation errors[48]. Our finding that aneuploid cells emerge in iPSC cultures originating from karyotypically normal wildtype MEFs( Table 1) confirms earlier indications that reprogrammed cell lines, like ESC lines, are subject to cell culture induced aneuploidization[49]. Since aneuploidy poses a risk for negative side effects in therapeutic applications, it will be important to devise strategies to avoid it. We find that aneuploidization rates of iPSC clones derived from wildtype MEF cultures can be markedly reduced through subcloning, implying that iPSC cultures contain subsets of cells that are quite resistant to cell culture induced mitotic stress. Thus subcloning might be a pragmatic method to produce iPSC lines with high chromosome integrity.", "role": "user" }, { "content": "iPSC technology has the potential to revolutionize stem-cell based regenerative medicine and would also allow for the production of patient-specific cells for disease modeling and drug discovery . One of the primary safety concerns of iPSCs is genetic instability , which is associated with cancer and various other diseases and includes abnormalities in both chromosomal structure and number . Whereas certain structural chromosome changes have been shown to preclude somatic cell reprogramming , the effect of whole-chromosome reshuffling on this process is completely unknown . Here we show that BubR1 and RanBP2 hypomorphic MEF lines , which are highly prone to erroneous chromosome segregation due to mitotic checkpoint and DNA decatenation failure , respectively , reprogram to pluripotency with normal efficiency . However , while RanBP2 hypomorphic MEFs yielded karyotypically normal iPSC clones with generally low chromosomal instability rates , BubR1 hypomorphic MEFs almost exclusively yielded aneuploid iPSC clones with high instability rates . These data provide important new insights into the genomic integrity requirements during somatic cell reprogramming , and they establish that the safe application of iPSC technology requires screening of both iPSCs and the iPSC-founder cells for chromosome number instability .", "role": "assistant" } ]

[ { "content": "You will be presented with a citation sentence from 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\nThis is a similar idea to the concept of a course or career fitting an individual’s values and expectations (Richardson & Watt, 2005) and may be a useful framework for considering decisions to pursue teacher education despite negative comments and experiences.", "role": "user" }, { "content": "Method", "role": "assistant" } ]

[ { "content": "You will be presented with a citation sentence from 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\nFurther complicating the issue, social desirability, varied interpretations of questions, and problems in recall can impede accurate measurements of risky sexual behavior (Brewer, Garrett, & Kulasingam, 1999; Brody, 1995; Schroder et al., 2003).", "role": "user" }, { "content": "Background", "role": "assistant" } ]

[ { "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: Video question answering using an end-to-end trainable model with a heterogeneous memory to learn\n\nKeyphrases: videoqa text\n\nDataset description:\nDIPS: Contains biases but is two orders of magnitude larger than those used previously.\nMSVD: The Microsoft Research Video Description Corpus (MSVD) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence.\nTGIF: The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015.\nReQA: Retrieval Question-Answering (ReQA) benchmark tests a model’s ability to retrieve relevant answers efficiently from a large set of documents.\nSocial-IQ: Social-IQ is an unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence.\n\nOptions:- A: DIPS B: MSVD C: TGIF D: ReQA E: Social-IQ", "role": "user" }, { "content": "[\"MSVD\", \"TGIF\"]", "role": "assistant" } ]

[ { "content": "You will be shown biomedical passages and your task is to 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: In what phase of clinical trials is crenezumab? (November 2017)\n\nContext: Ongoing Phase III clinical trials via passive immunotherapy against Aβ peptides (crenezumab, gantenerumab, and aducanumab) seem to be promising.", "role": "user" }, { "content": "Phase III", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nReview: E-health interventions improve blood pressure level and control in hypertension\nAbstract 1:\nQuestion In patients with hypertension , do electronic health ( e-health ) interventions improve blood pressure ( BP ) control and other outcomes ? Review scope Included studies compared e-health interventions with usual or st and ard care or attention control in patients with adequately or inadequately controlled hypertension . E-health interventions had to use portable , Web-based , interactive wireless communication devices and focus on self-care , self-management , self-care behavior change , or education . Studies of interventions that only displayed information ( not interactive ) , were only used for follow-up , or that mostly benefitted health care providers were excluded . Outcomes included physical outcomes ( inadequate BP control , systolic [ SBP ] and diastolic BP [ DBP ] , body mass index [ BMI ] , body weight , and cholesterol levels ) , and self-care behavioral outcomes ( sodium and alcohol intake , smoking , and physical activities ) . Review methods MEDLINE , EMBASE /Excerpta Medica , Cochrane Data base of Systematic Review s , Cochrane Central Register of Controlled Trials , CINAHL Plus , PsycINFO , SCOPUS , Web of Science , Joanna Briggs Institute EBP Data base , INSPEC , China Journal Net , and WanFang Data ( all 2000 to Nov 2017 ) were search ed for English- and Chinese- language r and omized controlled trials ( RCTs ) . 14 RCTs ( n = 3998 , mean age 50 to 68 y ) , ranging in size from 44 to 778 patients , met the selection criteria . Follow-up ranged from 3 to 24 months . Studies were conducted in various setting s , including primary care clinics , community health centers , and hospitals . Intervention strategies included BP monitoring , lifestyle modification techniques , and motivation and maintenance . 9 RCTs had adequate r and omization sequence generation , 6 concealed allocation , 4 blinded outcome assessors , and none adequately reported blinding participants . Main results Results for inadequate BP control , SPB , DBP , BMI , body weight , and sodium intake are in the Table . E-health interventions did not improve total cholesterol levels at 3 to 6 months after the intervention ( 3 RCTs ) ; or alcohol intake , smoking , or physical activity at 6 months after the intervention ( 3 RCTs ) . Conclusion In patients with hypertension , electronic health interventions reduce blood pressure levels and increase likelihood of blood pressure control . Electronic health interventions vs control in patients with hypertension * Outcomes Assessment times ( after intervention ) Number of trials ( n ) RRR ( 95 % CI ) NNT ( CI ) Inadequate BP control 3 to 12 mo 5 ( 1518 ) 31 % ( 16 to 43 ) 6 ( 4 to 11 ) Mean between-group difference ( CI ) Change in systolic BP ( mm Hg ) 6 mo 12 ( 3171 ) 5.50 ( 9.85 to 1.16 ) 12 mo 3 ( 1272 ) 7.68 ( 9.95 to 5.41 ) 18 mo 2 ( 735 ) 6.26 ( 9.29 to 3.23 ) Change in diastolic BP ( mm Hg ) 6 mo 12 ( 3171 ) 3.40 ( 7.79 to 0.99 ) 12 mo 3 ( 1272 ) 3.36 ( 4.80 to 1.92 ) 18 mo 2 ( 735 ) 2.53 ( 4.72 to 0.34 ) Change in body mass index ( kg/m2 ) 6 mo 5 ( 1486 ) 0.22 ( 0.46 to 0.02 ) 12 mo 3 ( 1272 ) 0.24 ( 0.82 to 0.33 ) 18 mo 2 ( 735 ) 0.07 ( 0.77 to 0.91 ) Change in body weight ( kg ) 3 to 6 mo 3 ( 517 ) 1.08 ( 2.04 to 0.13 ) Change in sodium intake ( SMD ) 6 mo 2 ( 768 ) 0.34 ( 0.49 to 0.20 ) 12 mo 2 ( 753 ) 0.39 ( 0.54 to 0.25 ) 18 mo 2 ( 735 ) 0.30 ( 0.45 to 0.16 ) * BP = blood pressure ; SMD = st and ardized mean difference ; other abbreviations defined in Glossary . Weighted electronic health intervention event rate , RRR , NNT , and CI calculated using control event rate and relative risk in article . Blood pressure 140/90 mm Hg . Weighted event rates 42 % vs 60 % . Commentary The systematic review and meta- analysis by Ma and colleagues showed a small but clinical ly meaningful reduction in SBP and DBP in patients who received e-health re sources . However , some health outcomes , including BMI and cholesterol levels , were not improved . Despite a risk for selection bias in some of the studies and substantial heterogeneity between studies , the meta- analysis is of high quality . The interventions in the included studies varied greatly and included use of telephone calls , Web sites , and smartphone applications . The comparison treatments were either attention control or usual care , and trials did not directly compare e-health interventions . The outcomes measured also varied among studies . Because clinic BP measurements may overestimate the degree of elevation and ambulatory monitoring is costly and limited to brief periods of measurement , home BP monitoring using newer e-health re sources may provide a cost-effective alternative ( 1 ) . This is an exciting new avenue for improving BP control by allowing for more accurate and streamlined monitoring , leading to more precise and timely interventions . However , based on the heterogeneity of intervention types , the optimum method is unknown . Further studies should directly compare e-health platforms and assess patient-centered outcomes , including usability , satisfaction , affordability , and quality of life as well as BP . Although the evidence indicates that these interventions are not yet ready for routine clinical practice , there is a promising signal that they could be useful for BP management\n\n\nTitle 2:\nCost-effectiveness analysis of different hypertension management strategies in a community setting\nAbstract 2:\nSelf-management schemes and mobile apps can be used for the management of hypertension in the community , but the most appropriate patient population is unknown . To explore whether the Chinese Health Literacy Scale ( CHLSH ) can be used to screen for appropriate patients with hypertension for self-management and to evaluate the clinical effectiveness and health economic evaluation of three hypertension management schemes . This was a prospect i ve study performed from March 2017 to July 2017 in consecutive patients with primary hypertension and of 50–80 years of age from the Jinyang community , Wuhou District , Chengdu . The CHLSH was completed and the patients were classified into the high ( n = 283 ) and low ( n = 315 ) health literacy groups . The patients were r and omly divided into the self-management , traditional management , and mobile app management groups . The high-health literacy group was selected to construct the cost-effectiveness decision tree model . Blood pressure control rate and the quality -adjusted life years ( QALYs ) were determined . At the end of follow-up , the success rate of self-management was 83.4 % . The costs for 6 months of treatment for each patient with hypertension in the self-management , traditional management , and mobile app groups were 1266 , 1751 , and 1856 yuan , respectively . The costs required for obtaining 1 QALY when managing for 6 months were : 30,869 yuan for self-management ; 48,628 yuan for traditional management ; and 43,199 yuan for the mobile app . The CHLSH can be used as a tool for screening patients with hypertension for self-management . The cost-effectiveness of self-management was optimal", "role": "user" }, { "content": "In fact , the observed results partially in contrast with those of numerous r and omized controlled trials have documented a significant BP reduction combined with an intensification and optimization of the use of antihypertensive medications in patients making use of blood pressure telemonitoring plus remote counseling by a case manager , with the supervision of a doctor or a community pharmacist [ 5 , 6 ] .", "role": "assistant" } ]

[ { "content": "You will be shown the title of a chemistry paper, together with sections excerpted from the body of the paper. The paper sections may include HTML tags like `<p>` and `</p>`; please ignore these. Your task is to write an abstract for the paper. Your response should include the abstract and no other text.\n\nTitle:\nLate-stage C(sp²)–H and C(sp³)–H glycosylation of <i>C</i>-aryl/alkyl glycopeptides: mechanistic insights and fluorescence labeling\n\nPaper sections:\nIntroduction\n<p>Glycosylation is a biological function that regulates the structure and activity of a given peptide or protein, introducing considerable structural diversity. 1 The attachment of carbohydrates onto peptides can improve their metabolic stability, water solubility, and protect the peptide backbone from proteolytic attack, while playing key structural roles in numerous biological recognition processes. 2 As a consequence, glycopeptides have emerged as valuable vaccine candidates and therapeutics. 3 However, their chemical and enzymatic instability under physiological conditions limited the utility of O/Nglycopeptides as drugs. 4 In contrast, C-glycosidestabilized isosteres of O/N-glycosides 5possess improved metabolic stability towards acids, bases and enzymatic hydrolysis, 6 thus being promising inhibitors of cell-surface recognition events and regulators of glycoside metabolism (Fig. 1a). 7 The targeted modication of biologically active peptides is an important strategy for the elucidation of structure-activity relationships (SAR). During the past decade, the direct C-H manipulation 8 of a peptide side-chain mediated by transition metal catalysis has emerged as a powerful paradigm, 9 with key contributions from Lavilla/Albericio, 10 Chen, 11 Daugulis, 12 Wang, 13 Shi, 14 Yu, 15 and Ackermann 16 among others. 17 However, efficient methods for the synthesis of C-alkyl/aryl glycopeptides continue to be rare, 18 which contrasts with numerous examples of metal-catalyzed cross-couplings for the construction of C-aryl glycoside with two prefunctionalized substrates. 19 Within our program on sustainable C-H activation, 20 we herein disclose unprecedented C(sp 3 )/(sp 2 )-H glycosylations of amino acids, peptides and (hetero)arenes (Fig. 1b). Notable features of our ndings include (1) internal peptide-isosteric click-triazole 21 as powerful amide surrogates in various bioactive peptidomimetics, (2) modication of terminal and internal peptides by secondary C(sp 3 )-H alkenylation with remarkable chemo-and diastereoselectivities, and (3) detailed mechanistic insights by experiment and computation as well as (4) versatile uorescence labeling with structurally complex glycopeptides.</p>\n\nOptimization of reaction conditions\n<p>We initiated our studies by exploring reaction conditions for the challenging secondary C(sp 3 )-H glycosylation of triazolyldimethylmethyl (TAM) amide 1a (Table 1). Preliminary optimization indicated that 60 C was a less suitable temperature, whilst a slight increase led to the formation of product 3a in 85% yield with Pd(TFA) 2 as the catalyst, 1,4-dioxane as the solvent and AgOAc as the additive. When replacing AgOAc by other silver salts (AgTFA, Ag 2 CO 3 , and AgBF 4 ), Ag 2 CO 3 stood out, providing glycopeptide 3a in 95% yield (entries 3-5). Notably, further optimization indicated that 1,4-dioxane was the solvent of choice, and DCE, PhMe, or THF provided diminished or trace amounts of the desired product 3a (entries 6-8). When 8-aminoquinoline (AQ) was employed, which was independently utilized by Liu for the synthesis of C-alkyl glycoamino acids, 22 the reaction also proceeded efficiently, albeit under slightly modied reaction conditions. Under otherwise identical reaction conditions the use of 2-iodo-glycals provided as of yet unsatisfactory results.</p>\n\nSubstrate scope\n<p>With the optimized reaction conditions for the challenging C(sp 3 )-H glycosylation in hand, we subsequently examined its versatility. Phenylalanine derivatives 1 bearing a variety of functional groups, such as esters or aldehydes, were well tolerated, leading to the formation of products 3a-e with high diasteroselectivities (Scheme 1a). Besides the substrate 2a, the reaction of rhamnose-derived glycal 2b also occurred efficiently. In addition, primary C(sp 3 )-H bonds of deuterated or nondeuterated Ala-TAM [D 3 ]-1f and 1f were likewise converted, giving product 3g and 3h in 88% and 93% yields respectively, without racemization of the stereogenic centers (see Pages S46-S47 of the ESI ‡). The optimized glycosylation proved amenable to substrate 1h with an N-aryl substituent. Similarly, we also proved the reactivity of substrates 1h-j with 8-amino quinoline as a terminal auxiliary. We were pleased to nd that glycosylation of primary and secondary C(sp 3 )-H bonds of alanine, phenylalanine, glutamic acid and even lysine proved to viable. Interestingly, when the reaction temperature was raised to 100 C, 1,3-diene 3j 0 was isolated because of the elimination of the OTIPS group.</p><p>When deuterated L-a-aminobutyramide [D 3 ]-1l was employed as the substrate, deuterium-labeled glycopeptide 3p was obtained.</p><p>Due to the unique role of C-aryl glycosides as privileged glycomimetics, 19a we next probed the TAM-assisted C(sp 2 )-H glycosylation by palladium catalysis (Scheme 2). Hence, arenes 4a-c with electron-donating or electron-withdrawing substituents were compatible, furnishing the desired products 5a-5c in 92% to 97% yields. It was found that the heteroarenes furan and thiophene were likewise tolerated by the versatile palladium catalyst to deliver conjugate saccharides. Moreover, the uorescent label pyrene could be successively attached to the glycoside 5f.</p><p>Next, we studied the bio-conjugation to form versatile glycopeptides and the hybrids derived thereof (Scheme 3). Various terminal peptides and peptide-natural product hybrids were employed, and late-stage modied peptides 7a-g were obtained by C-H glycosylation. Thereby, the possibilities for accessing structurally complex peptides for drug discovery were showcased.</p>\n\nMechanistic studies\n<p>As shown in Fig. 2, a minor deuterium kinetic isotope effect (KIE) with deuterated and non-deuterated Ala-TAM [D 3 ]-1f and 1f (k H /k D ¼ 1.0) indicated that the C(sp 3 )-H bond cleavage is not likely a kinetically relevant step of the palladium catalysis. In order to gain further insights into the mechanism of the palladium-catalyzed C-H glycosylation DFT calculations were hence performed at the uB97X-D/6-311++G (d,p), SDD(Pd, I, Ag) + SMD(1,4-dioxane)//uB97X-D/6-31G (d), LANL2DZ(Pd, I, Ag) level of theory. 23 The calculated barrier for the initial C-H activation is 19.4 kcal mol À1 (see Fig. S1 of the ESI ‡), which is in good agreement with our previous studies. 16f Subsequent to the C-H activation, the dissociation of acetic acid and association of substrate 2a lead to the intermediate I1 0 , which could be further stabilized by Ag 2 CO 3 to afford the stable intermediate I1. 24 This process is highly exergonic by 36.8 kcal mol À1 , thus making the step irreversible. In I1 as shown in Fig. 3, C-I nalize the C-C formation process, followed by subsequent protonation to release the desired product.</p><p>In order to illustrate the robustness of our C(sp 3 )-H activation, we explored the unprecedented late-stage glycosylation of internal peptides (Scheme 4). With the sterically congested gemdisubstituted substrates 8a-8e various peptides 9a-9e were converted into value-added glycopeptides under exceedingly mild reaction conditions. When unprotected peptide Phth-Ala-Tzl-Tyr-OMe was employed, only trace amounts of product 9f were observed. Here, the peptidomimetic Tzl scaffold set the stage for expedient site-selective peptide late-stage glycosylations.</p><p>Intrigued by the unique potential of BODIPYs as biocompatible uorescent probes, 16e,25 glycosylation of different BODIPY labeled amino acids was explored for our C(sp 3 )-H activation process (Scheme 5). Hence, the C-H activation enabled the unprecedented preparation of BODIPY labeled glycoamino acids (11a-11e).</p>\n\nConclusion\n<p>In summary, we have reported a versatile C(sp 3 )-H/C(sp 2 )-H glycosylation strategy enabled by peptide isosteric clicktriazoles. Thus, the de novo synthesis of structurally complex C-alkyl glycoamino acids, glycopeptides and C-aryl glycosides was achieved by palladium-catalyzed C(sp 3 )-H/C(sp 2 )-H activation with excellent levels of regio-, chemo-and diastereoselectivities. The synthetic utility of our strategy was reected inter alia by the assembly of BODIPY uorescent labeled glycoamino acids and the racemization-free late-stage diversication of structurally complex molecules. Our approach holds major potential for the preparation of C-alkyl and C-aryl glycosyl amino acid building blocks for their subsequent use in glycopeptide assembly and molecular labeling.</p>\n\nConflicts of interest\n<p>There are no conicts to declare.</p>", "role": "user" }, { "content": "C(sp 3 )-H and C(sp 2 )-H glycosylations of structurally complex amino acids and peptides were accomplished through the assistance of triazole peptide-isosteres. The palladium-catalyzed peptidesaccharide conjugation provided modular access to structurally complex C-alkyl glycoamino acids, glycopeptides and C-aryl glycosides, while enabling the assembly of fluorescent-labeled glycoamino acids. The C-H activation approach represents an expedient and efficient strategy for peptide late-stage diversification in a programmable as well as chemo-, regio-, and diastereo-selective fashion.", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nWhile counter machines have received little attention in theoretical computer science since the 1960s, they have recently achieved a newfound relevance to the field of natural language processing (NLP).\nRecent work has suggested that some strong-performing recurrent neural networks utilize their memory as counters.\nThus, one potential way to understand the sucess of these networks is to revisit the theory of counter computation.\nTherefore, we choose to study the abilities of real-time counter machines as formal grammars.\nWe first show that several variants of the counter machine converge to express the same class of formal languages.\nWe also prove that counter languages are closed under complement, union, intersection, and many other common set operations.\nNext, we show that counter machines cannot evaluate boolean expressions, even though they can weakly validate their syntax.\nThis has implications for the interpretability and evaluation of neural network systems: successfully matching syntactic patterns does not guarantee that a counter-like model accurately represents underlying semantic structures.\nFinally, we consider the question of whether counter languages are semilinear.\nThis work makes general contributions to the theory of formal languages that are of particular interest for the interpretability of recurrent neural networks.\nIt is often taken for granted that modeling natural language syntax well requires a hierarchically structured grammar formalism.\nEarly work in linguistics established that finite-state models are insufficient for describing the dependencies in natural language data (Chomsky, 1956 ).\nInstead, a formalism capable of expressing the relations in terms of hierarchical constituents ought to be necessary.\nRecent advances in deep learning and NLP, however, challenge this long-held belief.\nNeural network formalisms like the long short-term memory network (LSTM) (Hochreiter & Schmidhuber, 1997) have been shown to perform well on tasks requiring structure sensitivity (Linzen et al., 2016) , even though it is not obvious that such models have the capacity to represent hierarchical structure.\nThis mismatch raises interesting questions for both linguists and practitioners of NLP.\nIt is unclear what about the LSTM's structure lends itself towards good linguistic representations, and under what conditions these representations might fall short of grasping the structure and meaning of language.\nRecent work has suggested that the expressive capacity of LSTMs resembles that of counter machines (Merrill, 2019; Suzgun et al., 2019; Weiss et al., 2018) .\nWeiss et al. (2018) studied LSTMs with fully saturated weights (i.e. the activation functions evaluate to their asymptotic values instead of intermediate rational values) and showed that such models can express simplified counter languages.\nMerrill (2019) , on the other hand, showed that the general counter languages are an upper bound on the expressive capacity of saturated LSTMs.\nThus, there seems to be a strong theoretical connection between LSTMs and the counter automata.\nMerrill (2019) ; Suzgun et al. (2019) ; Weiss et al. (2018) also all report experimental results suggesting that some class of counter languages matches the learnable capacity of LSTMs trained by gradient descent.\nTaking the counter machine as a simplified formal model of the LSTM, we study the formal properties of counter machines as grammars.\nWe do this with the hope of understanding to what degree counter machines, and LSTMs by extension, have computational properties well-suited for representing the structure of natural language.\nThe contributions of this paper are as follows:\n• We prove that general counter machines, incremental counter machines, and stateless counter machines have equivalent expressive capacity, whereas simplified counter machines (Weiss et al., 2018) are strictly weaker than the general class.\n• We demonstrate that counter languages are closed under complement, union, intersection, and many other common operations.\n• We show that counter machines are incapable of representing the deep syntactic structure or semantics of boolean expressions, even though they can validate whether a boolean expression is well-formed.\n• We prove that a certain subclass of the counter languages are semilinear, and conjecture that this result holds for all counter languages.\nWe have shown that many variants of the counter machine converge to express the same class of formal languages, which supports that CL is a robustly defined class.\nWe also proved that real-time counter languages are closed under a large number of common set operations.\nThis provides tools for future work investigating real-time counter automata.\nWe also showed that counter automata are incapable of evaluating boolean expressions, even though they are capable of verifying that boolean expressions are syntactically well-formed.\nThis result has a clear parallel in the domain of natural language, where deciding whether a sentence is grammatical is a different task than representing its deep syntactic or semantic structure.\nA general take-away from our results is that just because a counter machine (or LSTM) is sensitive to surface patterns in linguistic data does not mean it can build correct semantic representations.\nCounter memory can be exploited to weakly match patterns in language, which might provide the wrong kinds of inductive bias for achieving sophisticated natural language understanding.\nFinally, we asked whether counter languages are semilinear as another way of studying their linguistic capacity.\nWe concluded only that a quite weak subclass of the counter languages are semilinear, and encourage future work to address the general case.", "role": "user" }, { "content": "We study the class of formal languages acceptable by real-time counter automata, a model of computation related to some types of recurrent neural networks.", "role": "assistant" } ]

[ { "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: [\"Disease\"].\n\nPlease return the output as a JSON object of the format: {\"Disease\": [\"COVID-19\", ...]}. 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\nLinkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Linkage disequilibrium mapping in isolated populations provides a powerful tool for fine structure localization of disease genes. Here, Luria and Delbrucks classical methods for analysing bacterial cultures are adapted to the study of human isolated founder populations in order to estimate (i) the recombination fraction between a disease locus and a marker; (ii) the expected degree of allelic homogeneity in a population; and (iii) the mutation rate of marker loci. Using these methods, we report striking linkage disequilibrium for diastrophic dysplasia (DTD) in Finland indicating that the DTD gene should lie within 0. 06 centimorgans (or about 60 kilobases) of the CSF1R gene. Predictions about allelic homogeneity in Finland and mutation rates in simple sequence repeats are confirmed by independent observations.", "role": "user" }, { "content": "{\"Disease\": [\"diastrophic dysplasia\", \"DTD\"]}", "role": "assistant" } ]

[ { "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 response other than the answer.\n\nParagraph: In the past, human rhinovirus (HRV) has been identified as the virus most frequently associated with respiratory illness with 30-50% of infections annually on average, and up to 80% of upper respiratory infections during the autumn outbreaks [7] . After HRVs, coronaviruses (CoV), influenza, respiratory syncytial virus (RSV) and parainfluenza viruses (PIV) are the next most frequent. More recently an evaluation of illness in 6,266 children under ten years of age in Australia, South East Asia and Latin America emphasised both the viral heterogeneity and the impact of ARI. Of the 2,421 children who experienced 3,717 individual influenza-like Illness (ILI) episodes, rhinovirus/enterovirus was most prevalent (41. 5%). Influenza followed this (15.8%), adenovirus (ADV) (9.8%), PIV and RSV (both 9.7%), CoV (5.6%), human metapneumovirus (HMPV) (5.5%) and human bocavirus (HBoV) (2.0%). The percentage of children missing school or childcare was between 21.4% for HBoV and 52.1% for influenza [8] . We have compared the data from the two reports one from 2003 [7] and the other in 2017 [8] and found that the reports, despite being separated by 14 years, were similar, with the single exception of HBoV, discovered in 2005 (Table 1) , which we discuss later. Feng et al. [9] described in detail the distribution of ARIs causing hospitalisation by age group: they observed that RSV was predominantly observed in the young and elderly, and influenza although significant in the young was noticeably more predominant in the elderly. Interestingly they observed that co-detection of viruses tended to occur more commonly in the younger age groups, particularly those under the age of five. Rhinovirus (the \"common\" cold) HRV infections, often considered trivial can significantly contribute to missed days from work and school, though infections are typically self-limiting [7] . HRV infections throughout the year and in many cases, manifest with symptoms such as nasal congestion, rhinorrhoea, sneezing, sore throat, and cough. HRV is known to be the primary cause of ARI and a severe contributing factor in exacerbations of atopic disease, e.g., asthma as well other conditions such as chronic obstructive pulmonary disease (COPD) [10] [11] [12] [13] . HRV infections are associated with significant economic implications as well as being an important contributor to sinusitis, otitis media, bronchitis and primary pneumonia [14] [15] [16] . HRV is a considerable cause of morbidity in specific at-risk groups such as infants, the elderly, immunocompromised, and, as already mentioned, chronic respiratory diseases such as asthma, COPD and cystic fibrosis. At present, HRV is considered the number one cause of asthma exacerbations [15] [16] [17] [18] [19] . Asthma is a complex disease, characterised by chronic airway inflammation, and a history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough. Over time these symptoms can vary in their intensity [20] . Each year over 300 million people worldwide are affected by asthma: approximately 250,000 people die as a result. Many deaths are due to suboptimal long-term medical care and delay in obtaining help during severe exacerbations of the disease [21] . Treatments to prevent worsening of symptoms and other therapies for mild to moderate asthma that avert relapse, i.e., the symptoms worsen again when the treatment stops, are significant unmet medical needs. The human challenge model has been used to investigate the viral pathogenicity [22] [23] [24] [25] [26] and recent publications on the asthma challenge model have focused on how the asthmatic host responds to HRV infection. Work is ongoing as to susceptibility to viral induced asthma worsening [27, 28] innate immune dysregulation [29] and induction of innate, and type 2 responses in nasal and bronchial epithelial secretions [30] . The pathogenesis of rhinoviral infection, along with other ARIs, in exacerbations of airway disease, has been investigated extensively. Impaired host responses to virus infection, a better understanding of the mechanisms of abnormal immune responses and the potential to develop novel therapeutic targets for virus-induced exacerbations have all used the HVC model [12, [31] [32] [33] [34] . Despite previous research work on multiple small molecule antivirals, such as pleconaril which have been tested using both the experimental challenge model and field studies [35] [36] [37] , there is currently no licensed treatment for HRV infections Other compounds have been tested against HRV, such as Vapendavir (BTA798) which prevented the release of viral RNA into the target cell and demonstrated a reduction in peak viral load in the HVC model [38] . A subsequent study in asthmatics was completed and although not published the compound did have a limited effect [39] . Pirodavir an intranasal capsid-binding molecule reached phase 3 clinical trials for HRV prevention and treatment in the 1990s. Although the compound decreased viral replication and shedding, it failed to show a significant reduction in the duration or severity of symptoms [40, 41] . A Protease inhibitor, rupintrivir thats prevents cleavage of viral proteins required for replication was tested in an HRV challenge trial. Rupintrivir was well tolerated and reduced viral loads and respiratory symptoms [36] . However, in studies of natural infection, it did not significantly affect viral loads or symptom severity [42] . Treatments such as zinc-containing products are now widely discredited as demonstrated by the withdrawal of a Cochrane report and JAMA editorial [43] [44] [45] . Current treatment of HRV infections primarily consists of over-the-counter (OTC) medicines to manage symptoms. There is also no licensed vaccine, and while there has been some progress on developing multivalent vaccines [46] , development in this area is hampered by the sheer number of serotypes that need to be covered (at present over 160). Despite HRV being associated with up to 50% of adult asthma exacerbations and up to 80% of childhood exacerbations, there are no HRV-specific asthma therapies [34] . As we better understand the interaction between the virus and the host, new therapies such as the monoclonal antibodies (anti-IgE [omalizumab] and anti-IL-5 [mepolizumab]) along with small molecules carefully targeting specific immune signalling pathways, HRV-specific prophylactic treatment may become practical [47] [48] [49] [50] . In order to prevent exacerbations, the design of new therapeutics could potentially improve efficacy by both directly acting to inhibit viral replication and alleviate the symptoms of asthma and COPD [51] . Influenza virus is a well-known human pathogen and can cause severe morbidity and mortality, particularly in older patients, those with co-morbidities and in the immunocompromised. In 2009, the first pandemic virus of the 21 st century hospitalised 195,000 to 403,000 in the US alone resulting in 8,870 to 18,300 deaths by mid-2010 [52] . A World Health Organization (WHO) global pooled analysis of 70,000 laboratory-confirmed hospitalised H1N1 pandemic patients from 19 countries revealed that of the 9,700 patients admitted to intensive care units, 2,500 died, and that morbid obesity might be a risk factor for hospitalisation and/or death [52] . Obesity was confirmed as a factor associated with a higher likelihood of admission to hospital in influenzainfected patients [53] . The 2009 pandemic was considered mild. However, the classic W shaped age distribution curve of infection for a pandemic virus was observed. That is high mortality in the very young and the old, but an additional spike in death amongst the \"young and healthy\". The pandemic, as did previous outbreaks, occurred in successive waves, but despite national policies favouring the use of antiviral drugs, few patients received these before admission to hospital, and many were given antibiotics [54] . The lack of real, or perceived, \"real world\" efficacy of currently available antivirals leads to the overuse of antibiotics and the subsequent problems that may arise [55] [56] [57] . The yearly seasonal morbidity and mortality of influenza results in hospitalisation and death mainly among the high-risk groups. Each year epidemics of seasonal influenza are estimated to result in about 3 to 5 million cases of severe illness, and about 290,000 to 650,000 deaths worldwide [58] . In first world / industrialised countries, most deaths associated with influenza occur among people age 65 or older [59] . Clinics and hospitals, in many countries, can be overwhelmed during peak illness periods, and there can be substantial economic cost [60] . The virus itself has been well characterised, and the two surface proteins, the haemagglutinin (HA) and the neuraminidase (NA) are important in both vaccine and antiviral development [61] . The effects of seasonal influenza epidemics in developing countries are not fully known, but research estimates that 99% of deaths in children under five years of age with influenza-related lower respiratory tract infections are found in developing countries [59, 62] . Currently, vaccines and antivirals exist for the prevention and treatment of influenza, but both have limitations in efficacy due to the rapid evolution of the virus as it mutates on a yearly basis and the sudden unexpected emergence of pandemic influenza strains. The effectiveness of recent annual influenza vaccines (to date mostly based on the HA, and rarely the NA surface glycoproteins) has languished between 37% and 70% over successive influenza seasons. In particular, the failure of the vaccine across the winter season of 2014-2015, where the overall adjusted effectiveness was 23% [95% confidence interval 14, 31] [63] is memorable. In a mismatched year, the mortality rate is increased in the most at-risk populations [64, 65] . The problem of ensuring that the seasonal vaccine is correctly matched to the upcoming circulating strain highlights the need for rapid development of inter-seasonal/universal vaccines and also the need for a way of testing their efficiency rapidly and accurately before the lengthy and expensive mass production is engaged which takes many months [66, 67] . Antiviral drugs exist of which currently the NA inhibitor oseltamivir is most commonly used. This is active against all known NA subtypes of influenza, and one would, therefore, assume against all influenza strains. They may have decreasing effect with the emergence of resistant influenza strains in which NA protein changes preventing efficient oseltamivir binding and thus its ability to inhibit the essential activity of the viral NA. For example, one genetic mutation known as 'H275Y'a substitution of histidine for tyrosine at NA position 275 -confers an evolutionary advantage to the virus including the 2009 H1N1 influenza [68] . During the 2013-2014 influenza season, 59 (1.2%) of 1,811 influenza A(H1N1) pdm09 virus isolates in 20 of 50 US states had the H275Y oseltamivir resistance substitution. No isolates were resistant to zanamivir [69] . Although animal studies have demonstrated limited transmission of mutant viruses [70, 71] , it is thought that the rise of oseltamivir resistance may be due to community transmission [72, 73] rather than the H275Y mutation becoming fixed in the viral genome. Asystematic systematic review and meta-analysis of published data from 2000 onwards concluded that most RSV-associated child deaths occur particularly in preterm infants and in infants up to 1-year of age [62, 74] . An effective maternal RSV vaccine or monoclonal antibody could have a substantial effect on disease burden in this age group [75] . The RSV-specific monoclonal antibody palivizumab is approved for prevention of serious LRI caused by RSV in susceptible infants. Economic benefit in a UK health setting has not been shown due to the high cost and lack of benefit on serious outcomes [76] . A single-centre cohort study of 22 infants showed no difference in treatment outcomes for patients receiving palivizumab when compared to patients only receiving \"standard of care\" treatment [77] . Despite the lack of evidence for clinical benefit, post-licensure data supports the use of palivizumab for reducing RSV-associated hospitalisations in premature infants under 33 weeks and in children with chronic lung and heart diseases [78] . Importantly, palivizumab resistant mutant virus has rarely been isolated in clinical specimens [79] . The RSV treatment ribavirin is limited due to difficulty with aerosol delivery, cost and potential harm to healthcare workers, despite off-label treatment of immunocompromised patients being reasonably successful. In the immunocompromised, therapy with a concomitant immunoglobulin or palivizumab has had mixed results, probably due to the difficulty of knowing when to initiate treatment [80] . Despite the call for the accelerated development of prevention and treatment strategies for an effective RSV vaccine for children [81] , research has stalled for decades since the death in the 1960s of two subjects in a clinical study. These subjects were infected with a communityacquired RSV infection after receiving the US National Institutes for Health (NIH's) formalin-inactivated, alumprecipitated RSV candidate vaccine. In contrast to influenza for which vaccines to date have shown themselves to be moderately effective but in need of improvement, RSV vaccines require substantially more research. There is currently no licensed vaccine for RSV; the most advanced candidate vaccine recently failed to show efficacy in a field study [82] . Effective treatments are urgently required. RSV is, even amongst healthcare professionals, considered a childhood disease and other confounders have obscured the understanding of the consequences of RSV in adults. RSV is poorly understood as a disease in the elderly [83] , and while the morbidity and mortality in children are of importance, it has been clearly shown that RSV has a comparable health burden to influenza in the elderly [84] .\n\nQuestion: What virus is most commonly associated with acute respiratory infections?", "role": "user" }, { "content": "human rhinovirus", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nA double-blind, placebo-controlled trial of extract of Ginkgo biloba added to haloperidol in treatment-resistant patients with schizophrenia.\nAbstract 1:\nMany studies have indicated that excess free radical formation may be involved in the pathogenesis of patients with schizophrenia. Some investigators suggested that the use of free radical scavengers might provide improvement in schizophrenia. The aim of this study was to determine the effectiveness and to evaluate the side effects of extract of Ginkgo biloba (EGb) plus haloperidol in chronic, treatment-resistant inpatients with schizophrenia.\n One hundred nine patients meeting DSM-III-R criteria for schizophrenia completed a double-blind, placebo-controlled, parallel-group study of EGb plus haloperidol. Fifty-six of the patients were randomly assigned to receive a fixed dose of 360 mg/day of EGb plus a stable dose of haloperidol, 0.25 mg/kg/day, and 53 were assigned to receive placebo plus the same dose of haloperidol for 12 weeks. Patients were assessed using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS) at baseline, week 6, and week 12 and the Treatment Emergent Symptom Scale (TESS) for side effects at week 12.\n There was a significant reduction in both groups in BPRS total score after 12 weeks of treatment (p < .05). However, a significant reduction in total SAPS and SANS scores was noted in the EGb group (p < .05), but not in the placebo group. There was a lower SAPS total score in the EGb group than in the placebo group at the end of 12 weeks of treatment (p < .05). Of those treated with EGb plus haloperidol, 57.1% were rated as responders as compared with only 37.7% of those receiving placebo plus haloperidol when assessed by the SAPS (chi2 = 4. 111, p = .043). After 12 weeks of treatment, TESS subscore 1 (behavioral toxicity) and subscore 3 (symptoms of nerve system) were significantly decreased in the EGb group compared with the placebo group (p < .05).\n EGb treatment may enhance the effectiveness of antipsychotic drugs and reduce their extrapyramidal side effects.\n\n\nTitle 2:\n[Clinical study of shuizhi-dahuang mixture in treating schizophrenics with blood stasis syndrome].\nAbstract 2:\nA clinical study of 67 female schizophrenics was conducted. Thirty two patients of them treated with the Shuizhi (leech)-Dahuang (rhubarb) mixture mainly with low dosage of antipsychotic drugs (combined therapy group), while other 35 cases were treated with antipsychotic drugs only (control group). The result showed that their overall therapeutic effects were similar and the combined therapy group could reduce the dosages of antipsychotic drugs and its side effects, and tended to normalize the hemorheologic indices.\n\n\nTitle 3:\nComparative study of schizophrenia treatment with electroacupuncture, herbs and chlorpromazine.\nAbstract 3:\nnan\n\n\nTitle 4:\n[Therapeutic effect of shuxuening combining neuroleptics for the treatment of chronic schizophrenia--a double blind study].\nAbstract 4:\nTo assess the therapeutic effect of Shuxuening (SXN), the extractum of Ginkgo biloba761 (EGb761) in treating chronic schizophrenia.\n A double-blind, placebo-controlled multi-center research on the treatment of chronic schizophrenics with SXN was used. Five hundred and forty-five patients were randomly divided into either SXN group or the control group. Patients in the former group received SXN 120 mg three times daily. Patients in both groups received their maintenance neuroleptics throughout the 16-week research treatment.\n The patients' rating scores of brief psychiatric rating scale (BPRS) and Scale for the Assessment of Negative Symptoms reduced much greater in SXN group than those in the control group from the sixth week of treatment (P < 0.01). The effect of SXN for BPRS factors of retardation and thought disturbance was better than that of the control. SXN presented a better therapeutic effect for chronic schizophrenics than the control when rated with traditional global rating method as well, in which 44.98% marked improvement was obtained in SXN group compared to 20.98% in the control group.\n SXN combining neuroleptics, was an effective medicine for chronic schizophrenics. Moreover, it appeared few side-effects within the recommended dose range.", "role": "user" }, { "content": "Chinese herbal medicines, given in a Western biomedical context, may be beneficial for people with schizophrenia when combined with antipsychotics. Traditional Chinese medicine is also under-evaluated, but results from one pioneering study that attempted to evaluate TCM should encourage further trials.\nNote: the 45 citations in the awaiting classification section of the review may alter the conclusions of the review once assessed.", "role": "assistant" } ]

[ { "content": "Below are the Abstract, Introduction, and Conclusion sections of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary sentence and no additional text.\n\nPaper text:\n\nSelf-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.\nSelf attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context.\nTo tackle the problem, we propose a novel model called Sparse Transformer.\nSparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments.\nExtensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance. \n \nSparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation.\nIn addition, we conduct qualitative analysis to account for Sparse Transformer's superior performance.\nUnderstanding natural language requires the ability to pay attention to the most relevant information.\nFor example, people tend to focus on the most relevant segments to search for the answers to their questions in mind during reading.\nHowever, retrieving problems may occur if irrelevant segments impose negative impacts on reading comprehension.\nSuch distraction hinders the understanding process, which calls for an effective attention.\nThis principle is also applicable to the computation systems for natural language.\nAttention has been a vital component of the models for natural language understanding and natural language generation.\nRecently, Vaswani et al. (2017) proposed Transformer, a model based on the attention mechanism for Neural Machine Translation(NMT).\nTransformer has shown outstanding performance in natural language generation tasks.\nMore recently, the success of BERT (Devlin et al., 2018) in natural language processing shows the great usefulness of both the attention mechanism and the framework of Transformer.\nHowever, the attention in vanilla Transformer has a obvious drawback, as the Transformer assigns credits to all components of the context.\nThis causes a lack of focus.\nAs illustrated in Figure 1 , the attention in vanilla Transformer assigns high credits to many irrelevant words, while in Explicit Sparse Transformer, it concentrates on the most relevant k words.\nFor the word \"tim\", the most related words should be \"heart\" and the immediate words.\nYet the attention in vanilla Transformer does not focus on them but gives credits to some irrelevant words such as \"him\".\nRecent works have studied applying sparse attention in Transformer model.\nHowever, they either add local attention constraints (Child et al., 2019) which break long term dependency or hurt the time efficiency (Martins & Astudillo, 2016) .\nInspired by Ke et al. (2018) which introduce sparse credit assignment to the LSTM model, we propose a novel model called Explicit Sparse Transformer which is equipped with our sparse attention mechanism.\nWe implement an explicit selection method based on top-k selection.\nUnlike vanilla Transformer, Explicit Sparse Transformer only pays attention to the k most contributive states.\nThus Explicit Sparse Transformer can perform more concentrated attention than vanilla Transformer.\nFigure 1 : Illustration of self-attention in the models.\nThe orange bar denotes the attention score of our proposed model while the blue bar denotes the attention scores of the vanilla Transformer.\nThe orange line denotes the attention between the target word \"tim\" and the selected top-k positions in the sequence.\nIn the attention of vanilla Transformer, \"tim\" assigns too many non-zero attention scores to the irrelevant words.\nBut for the proposal, the top-k largest attention scores removes the distraction from irrelevant words and the attention becomes concentrated.\nWe first validate our methods on three tasks.\nFor further investigation, we compare our methods with previous sparse attention methods and experimentally answer how to choose k in a series of qualitative analyses.\nWe are surprised to find that the proposed sparse attention method can also help with training as a regularization method.\nVisual analysis shows that Explicit Sparse Transformer exhibits a higher potential in performing a high-quality alignment.\nThe contributions of this paper are presented below:\n• We propose a novel model called Explicit Sparse Transformer, which enhances the concentration of the Transformer's attention through explicit selection.\n• We conducted extensive experiments on three natural language processing tasks, including Neural Machine Translation, Image Captioning and Language Modeling.\nCompared with vanilla Transformer, Explicit Sparse Transformer demonstrates better performances in the above three tasks.\nSpecifically, our model reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation.\n• Compared to previous sparse attention methods for transformers, our methods are much faster in training and testing, and achieves better results.\nIn this section, we performed several analyses for further discussion of Explicit Sparse Transformer.\nFirst, we compare the proposed method of topk selection before softmax with previous sparse attention method including various variants of sparsemax (Martins & Astudillo, 2016; Correia et al., 2019; Peters et al., 2019) .\nSecond, we discuss about the selection of the value of k.\nThird, we demonstrate that the top-k sparse attention method helps training.\nIn the end, we conducted a series of qualitative analyses to visualize proposed sparse attention in Transformer.", "role": "user" }, { "content": "This work propose Sparse Transformer to improve the concentration of attention on the global context through an explicit selection of the most relevant segments for sequence to sequence learning.", "role": "assistant" } ]

[ { "content": "You are presented with multiple paper abstracts, a literature review title, and a specific chapter title. Your task is to write a focused literature review chapter that aligns with the given chapter title. The review should synthesize the content from the provided abstracts, draw connections between different works, highlight major findings, and identify any gaps in the existing research.\nLiterature Review Title: A Survey of Recent Prefetching Techniques for Processor Caches \nChapter Title: Software Prefetching \n1. Abstract of Cited Paper (BIB001): Modern processors typically employ sophisticated prefetching techniques for hiding memory latency. Hardware prefetching has proven very effective and can speed up some SPEC CPU 2006 benchmarks by more than 40% when running in isolation. However, this speedup often comes at the cost of prefetching a significant volume of useless data (sometimes more than twice the data required) which wastes shared last level cache space and off-chip bandwidth. This paper explores how an accurate resource-efficient prefetching scheme can benefit performance by conserving shared resources in multicores. We present a framework that uses low-overhead runtime sampling and fast cache modeling to accurately identify memory instructions that frequently miss in the cache. We then use this information to automatically insert software prefetches in the application. Our prefetching scheme has good accuracy and employs cache bypassing whenever possible. These properties help reduce off-chip bandwidth consumption and last-level cache pollution. While single-thread performance remains comparable to hardware prefetching, the full advantage of the scheme is realized when several cores are used and demand for shared resources grows. We evaluate our method on two modern commodity multicores. Across 180 mixed workloads that fully utilize a multicore, the proposed software prefetching mechanism achieves up to 24% better throughput than hardware prefetching, and performs 10% better on average.\n\nLiterature Review:", "role": "user" }, { "content": "Chilimbi and Hirzel [2002] present a prefetching technique that is especially useful for pointer codes developed in weakly typed languages such as C/C++. First, a temporal data reference profile is collected from the application being executed. Then, profiling is disabled, and hot (e.g., frequently repeating) data streams are extracted from the reference profile using an analysis algorithm. Only reasonably long streams are extracted to amortize the prefetching overhead. Then, instructions are inserted into the application for detecting and prefetching the hot data streams. Then, analysis is disabled, and the application runs with the prefetch instructions. Afterwards, the inserted checks and prefetch instructions are removed. After this, the entire cycle of profiling step, analysis step, optimization step, and so on, begins again. Their technique improves performance while keeping the overhead low by focusing on few hot data streams. BIB001 present a SW-only prefetching technique to reduce L1 cache misses. They first run the application with a training data set to extract annotated memory access trace. Using this, memory addresses generated by loads and stores and their contents are monitored in an offline manner to see whether a load miss address may be predicted from previous instructions (called predictor instructions). Based on this, prefetch predictors are produced of which timely, accurate, and nonredundant predictors are selected. To see timeliness of a predictor, their technique checks whether a load miss is too close (in terms of processor cycles) to a predictor instruction, since, in such a case, prefetching will not be useful. When this distance is smaller than a threshold, the predictor is not used. Also, if a large fraction of prefetched data of a predictor is redundant, it is eliminated. Based on the selected predictors, SW prefetching instructions are placed in an application's assembly code directly. Working at instruction level enables their technique to have a broad view of memory access patterns spanning over boundaries of functions, modules, and libraries. Their technique integrates and generalizes multiple prefetching approaches such as self-stride, nextline and intra-iteration stride, same-object, and other approaches for pointer-intensive and function call-intensive programs. Wang et al. [2003] present a HW-SW co-operative prefetching technique. Their technique uses compiler analysis to generate load hints, such as the spatial region and its size (number of lines), to prefetch the pointer in the load's cache line to follow for prefetching and the pointer data structure to recursively prefetch. Specifying size allows prefetching a variable-size region based on loop bounds. Based on these hints and triggered by L2 cache misses, prefetches are generated in HW at runtime. Unlike other SW techniques, in their technique, individual prefetch addresses are generated by HW and not by SW, which allows timely prefetching of data. Also, use of compiler hints allows reduced storage overhead and accurate prefetching even for complex access patterns, which is challenging for a HW-only approach. They show that while generating significantly less prefetch traffic, their technique still provides similar performance improvement as a HW-only prefetching technique. Rabbah et al. [2004] present a compiler directed prefetching technique that uses speculative execution. The portion of program dependence graph relevant to the address computation of a long latency memory operation is termed as load dependence chain (LDC). The LDCs are identified by the compiler and precomputations are statically embedded in the program instruction stream with prefetch instructions. Using speculative execution of LDCs, future memory addresses are precomputed, which are utilized for performing prefetching. For pointer-based applications, LDCs contain instructions that may miss in D-caches, and, hence, generating prefetch addresses for such applications causes large instruction overhead. To address this, their technique provisions that if a load in the LDC sees a miss, successive precomputation instructions are bypassed. BIB002 present a SW prefetching technique based on runtime sampling and fast cache modeling. Their technique randomly samples memory instructions with low sampling ratio (e.g., 1 in 100,000). The blocks in D-cache that are accessed by sampled instructions are monitored for data reuse. Further, whenever the sampled instructions are re-executed, a stride sample is recorded. Based on data reuse samples recorded over the whole execution, a statistical cache model estimates per-instruction cache performance for given cache sizes. Based on it, delinquent loads are identified and stride samples for each delinquent load are analyzed to detect regular stride patterns. On detecting a dominant stride pattern for a delinquent load, their technique computes suitable prefetch distance and inserts a prefetch instruction for such a load. Based on cache modeling, their technique also identifies whether a data block will not be reused in MLC/LLC. For such data blocks, their technique uses a special prefetch instruction that prefetches data in L1 cache without polluting MLC/LLC. On eviction, this cache block is directly written back to main memory. Thus, by using cache bypassing, their technique reduces cache pollution. For single-thread applications, their technique provides performance comparable to HW prefetching, but, with increasing core-count and resource-contention, the advantage of their technique improves.", "role": "assistant" } ]

[ { "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:\nThe RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar (Yoshinaga and Miyao, 2001). This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar (FB-LTAG1) (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) and Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) by a method of grammar conversion.\n\nCitation Sentence:\nThe RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar ( Yoshinaga and Miyao , 2001 ) .\n\nContext after the citation:\nStrong equivalence means that both grammars generate exactly equivalent parse results, and that we can share the LTAG grammars and lexicons in HPSG applications. Our system can reduce considerable workload to develop a huge resource (grammars and lexicons) from scratch. Our concern is, however, not limited to the sharing of grammars and lexicons. Strongly equivalent grammars enable the sharing of ideas developed in each formalism.", "role": "user" }, { "content": "Background", "role": "assistant" } ]

[ { "content": "You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.\n\n\n\nTitle 1:\nShared decision making and risk communication in practice: a qualitative study of GPs' experiences.\nAbstract 1:\nBACKGROUND Important barriers to the wider implementation of shared decision making remain . The experiences of professionals who are skilled in this approach may identify how to overcome these barriers . AIMS To identify the experiences and views of professionals skilled in shared decision making and risk communication , exploring the opportunities and challenges for implementation . DESIGN OF STUDY Qualitative study . SETTING Gwent Health Authority . METHOD Exit interviews using focus group methodology with 20 GPs who had been in practice between 1 and 10 years , and participated in an explanatory trial lasting 6 months . The trial interventions comprised training in shared decision-making skills and the use of risk communication material s. The doctors consulted with up to 48 patients each ( mean = 40 , half of them audiotaped ) for the study . RESULTS The GPs indicated positive attitudes towards involving patients and described positive effects on their consultations . However , the frequency of applying the new skills and tools was limited outside the trial . Doctors were selective about when they felt greater patient involvement was appropriate and feasible , rather than seeking to apply the approaches to the majority of consultations . They felt they often responded to consumer preferences for low levels of involvement in decision making . Time limitations were important in not implementing the approach more widely . CONCLUSION The promotion of ' patient involvement ' appears likely to continue . Professionals appear receptive to this , and willing to acquire the relevant skills . Strategies for wider implementation of patient involvement could address how consultations are scheduled in primary care , and raise consumers ' expectations or desires for involvement\n\n\nTitle 2:\nA shared decision-making communication training program for physicians treating fibromyalgia patients: effects of a randomized controlled trial.\nAbstract 2:\nOBJECTIVE Fibromyalgia syndrome ( FMS ) is a condition of chronic widespread pain that is difficult to control and is associated with strains in physician-patient interaction . Shared decision making ( SDM ) can be a potential solution to improve interaction . We evaluated the effects of an SDM intervention , including an SDM communication training program for physicians , in a r and omized controlled trial with FMS patients . The main objective was to assess whether SDM improves the quality of physician-patient interaction from patients ' perspective . METHODS Patients were r and omized to either an SDM group or an information-only group . The SDM group was treated by physicians trained in SDM communication and had access to a computer-based information package ; the information-only group received only the information package and was treated by st and ard physicians . All patients were offered the same evidence -based treatment options for FMS . Patients were assessed with question naires on physician-patient interaction ( main outcome criteria ) and decisional processes . Physicians filled out a question naire on interaction difficulties . Assessment took place immediately after the initial consultation . RESULTS Data from 85 FMS patients ( 44 in the SDM group and 41 in the information-only group ) were analyzed . The mean age was 49.9 years ( S.D.=10.2 ) , and 91.8 % of patients were female . The quality of physician-patient interaction was significantly higher in the SDM group than in the information-only group ( P<.001 ) . We found no differences in secondary outcome measures . CONCLUSIONS SDM with FMS patients might be a possible means to achieve a positive quality of physician-patient interaction . A specific SDM communication training program teaches physicians to perform SDM and reduces frustration in patients\n\n\nTitle 3:\n[Shared decision-making as a new quality indicator in nephrology: a nationwide survey in Germany].\nAbstract 3:\nBACKGROUND AND PURPOSE Shared decision-making ( SDM ) as a model in physician-patient interaction is gaining relevance in the German health system . By applying this model , mid- and long-term improvements are expected especially in the outcomes of chronic diseases . Up to now , there has hardly been any empirical data available in German health services research regarding the state and development of SDM . This study establishes a baseline and provides actual data on this subject based on a German-wide survey of end-stage renal disease ( ESRD ) patients . METHODS St and ardized German-wide survey of 6,614 patients with ESRD . The question naire included an instrument to measure the patients ' perceived involvement in care ( PICS ) which had been translated and vali date d before . RESULTS 82 % of the question ed patients feel their physicians facilitated involvement in decision making . 81 % of the patients actively inform themselves concerning their disease and treatment options . 69 % state that SDM has taken place . Age , years on dialysis and gender correlate with perceived involvement . CONCLUSION This paper provides a valid baseline for the prospect i ve research of SDM in ESRD . The results indicate that dialysis patients are willing to participate in the process of medical decision-making . Characteristics and preferences of the patients should be taken into account not only in everyday clinical interactions . They could be monitored systematic ally within the framework of quality management and used as potential for quality improvement", "role": "user" }, { "content": "An example of this potential difficulty , from a healthcare provider perspective , is identifying the potential conflict that may develop when a patient 's preference is different from clinical practice guidelines .\nBoth patients and health care providers have noted improvement in relationships and improved interactions when shared decision-making is in evidence .\nAlong with this improved relationship and", "role": "assistant" } ]