Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017

Making a COVID-19 vaccine that works for everyone: ensuring equity and inclusivity in clinical trials

Coronavirus disease 2019 (COVID-19) mortality and morbidity have been shown to increase with deprivation and impact non-White ethnicities more severely. Despite the extra risk Black, Asian and Minority Ethnicity (BAME) groups face in the pandemic, our current medical research system seems to prioritise innovation aimed at people of European descent. We found significant difficulties in assessing baseline demographics in clinical trials for COVID-19 vaccines, displaying a lack of transparency in reporting. Further, we found that most of these trials take place in high-income countries, with only 25 of 219 trials (11.4%) taking place in lower middle- or low-income countries. Trials for the current best vaccine candidates (BNT162b2, ChadOx1, mRNA-173) recruited 80.0% White participants. Underrepresentation of BAME groups in medical research will perpetuate historical distrust in healthcare processes, and poses a risk of unknown differences in efficacy and safety of these vaccines by phenotype. Limiting trial demographics and settings will mean a lack of global applicability of the results of COVID-19 vaccine trials, which will slow progress towards ending the pandemic

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Local gyrification index in probands with psychotic disorders and their first-degree relatives

BACKGROUND: Psychotic disorders are characterized by aberrant neural connectivity. Alterations in gyrification, the pattern and degree of cortical folding, may be related to the early development of connectivity. Past gyrification studies have relatively small sample sizes, yield mixed results for schizophrenia (SZ), and are scant for psychotic bipolar (BP) and schizoaffective (SZA) disorders and for relatives of these conditions. Here we examine gyrification in psychotic disorder patients and their first-degree relatives as a possible endophenotype. METHODS: Regional Local Gyrification Index (LGI) values, as measured by FreeSurfer software, were compared between 243 controls, 388 psychotic disorder probands, and 300 of their first-degree relatives. For patients, LGI values were examined grouped across psychotic diagnoses and then separately for SZ, SZA, and BP. Familiality (heritability) values and correlations with clinical measures were also calculated for regional LGI values. RESULTS: Probands exhibited significant hypogyria compared to controls in three brain regions and relatives with axis II cluster A disorders showed nearly significant hypogyria in these same regions. LGI values in these locations were significantly heritable and uncorrelated with any clinical measure. Observations of significant CONCLUSIONS: Psychotic disorders appear to be characterized by significant regionally localized hypogyria, particularly in cingulate cortex. This abnormality may be a structural endophenotype marking risk for psychotic illness and it may help elucidate etiological underpinnings of psychotic disorders

Biosecurity and water, sanitation, and hygiene (WASH) interventions in animal agricultural settings for reducing infection burden, antibiotic use, and antibiotic resistance: a One Health systematic review.

Prevention and control of infections across the One Health spectrum is essential for improving antibiotic use and addressing the emergence and spread of antibiotic resistance. Evidence for how best to manage these risks in agricultural communities-45% of households globally-has not been systematically assembled. This systematic review identifies and summarises evidence from on-farm biosecurity and water, sanitation, and hygiene (WASH) interventions with the potential to directly or indirectly reduce infections and antibiotic resistance in animal agricultural settings. We searched 17 scientific databases (including Web of Science, PubMed, and regional databases) and grey literature from database inception to Dec 31, 2019 for articles that assessed biosecurity or WASH interventions measuring our outcomes of interest; namely, infection burden, microbial loads, antibiotic use, and antibiotic resistance in animals, humans, or the environment. Risk of bias was assessed with the Systematic Review Centre for Laboratory Animal Experimentation tool, Risk of Bias in Non-Randomized Studies of Interventions, and the Appraisal tool for Cross-Sectional Studies, although no studies were excluded as a result. Due to the heterogeneity of interventions found, we conducted a narrative synthesis. The protocol was pre-registered with PROSPERO (CRD42020162345). Of the 20 672 publications screened, 104 were included in this systematic review. 64 studies were conducted in high-income countries, 24 studies in upper-middle-income countries, 13 studies in lower-middle-income countries, two in low-income countries, and one included both upper-middle-income countries and lower-middle-income countries. 48 interventions focused on livestock (mainly pigs), 43 poultry (mainly chickens), one on livestock and poultry, and 12 on aquaculture farms. 68 of 104 interventions took place on intensive farms, 22 in experimental settings, and ten in smallholder or subsistence farms. Positive outcomes were reported for ten of 23 water studies, 17 of 35 hygiene studies, 15 of 24 sanitation studies, all three air-quality studies, and 11 of 17 other biosecurity-related interventions. In total, 18 of 26 studies reported reduced infection or diseases, 37 of 71 studies reported reduced microbial loads, four of five studies reported reduced antibiotic use, and seven of 20 studies reported reduced antibiotic resistance. Overall, risk of bias was high in 28 of 57 studies with positive interventions and 17 of 30 studies with negative or neutral interventions. Farm-management interventions successfully reduced antibiotic use by up to 57%. Manure-oriented interventions reduced antibiotic resistance genes or antibiotic-resistant bacteria in animal waste by up to 99%. This systematic review highlights the challenges of preventing and controlling infections and antimicrobial resistance, even in well resourced agricultural settings. Most of the evidence emerges from studies that focus on the farm itself, rather than targeting agricultural communities or the broader social, economic, and policy environment that could affect their outcomes. WASH and biosecurity interventions could complement each other when addressing antimicrobial resistance in the human, animal, and environmental interface

One Health WASH: an AMR-smart integrative approach to preventing and controlling infection in farming communities

While the One Health framework is now widely accepted as a strength in understanding antimicrobial resistance (AMR), its application in intervention design to prevent and control drug-resistant infections across humans, animals, and the environment remains weak. The potential for infection prevention and control measures to contribute to the AMR agenda is recognised in rhetoric, but evidence to guide action is patchy and uncoordinated. While water, sanitation, and hygiene (WASH) and on-farm biosecurity interventions are key strategies for preventing and controlling infections, they are frequently implemented separately for humans and animals. We argue for integration across these sectors to improve planning for AMR control

Intention-to-treat analysis may be more conservative than per protocol analysis in antibiotic non-inferiority trials: a systematic review

Abstract Background In non-inferiority trials, there is a concern that intention-to-treat (ITT) analysis, by including participants who did not receive the planned interventions, may bias towards making the treatment and control arms look similar and lead to mistaken claims of non-inferiority. In contrast, per protocol (PP) analysis is viewed as less likely to make this mistake and therefore preferable in non-inferiority trials. In a systematic review of antibiotic non-inferiority trials, we compared ITT and PP analyses to determine which analysis was more conservative. Methods In a secondary analysis of a systematic review, we included non-inferiority trials that compared different antibiotic regimens, used absolute risk reduction (ARR) as the main outcome and reported both ITT and PP analyses. All estimates and confidence intervals (CIs) were oriented so that a negative ARR favored the control arm, and a positive ARR favored the treatment arm. We compared ITT to PP analyses results. The more conservative analysis between ITT and PP analyses was defined as the one having a more negative lower CI limit. Results The analysis included 164 comparisons from 154 studies. In terms of the ARR, ITT analysis yielded the more conservative point estimate and lower CI limit in 83 (50.6%) and 92 (56.1%) comparisons respectively. The lower CI limits in ITT analysis favored the control arm more than in PP analysis (median of − 7.5% vs. -6.9%, p = 0.0402). CIs were slightly wider in ITT analyses than in PP analyses (median of 13.3% vs. 12.4%, p < 0.0001). The median success rate was 89% (interquartile range IQR 82 to 93%) in the PP population and 44% (IQR 23 to 60%) in the patients who were included in the ITT population but excluded from the PP population (p < 0.0001). Conclusions Contrary to common belief, ITT analysis was more conservative than PP analysis in the majority of antibiotic non-inferiority trials. The lower treatment success rate in the ITT analysis led to a larger variance and wider CI, resulting in a more conservative lower CI limit. ITT analysis should be mandatory and considered as either the primary or co-primary analysis for non-inferiority trials. Trial registration PROSPERO registration number CRD42020165040

Confidence interval of risk difference by different statistical methods and its impact on the study conclusion in antibiotic non-inferiority trials

Abstract Background Numerous statistical methods can be used to calculate the confidence interval (CI) of risk differences. There is consensus in previous literature that the Wald method should be discouraged. We compared five statistical methods for estimating the CI of risk difference in terms of CI width and study conclusion in antibiotic non-inferiority trials. Methods In a secondary analysis of a systematic review, we included non-inferiority trials that compared different antibiotic regimens, reported risk differences for the primary outcome, and described the number of successes and/or failures as well as patients in each arm. For each study, we re-calculated the risk difference CI using the Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and skewness-corrected asymptotic score (SCAS) methods. The CIs by different statistical methods were compared in terms of CI width and conclusion on non-inferiority. A wider CI was considered to be more conservative. Results The analysis included 224 comparisons from 213 studies. The statistical method used to calculate CI was not reported in 134 (59.8%) cases. The median (interquartile range IQR) for CI width by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods was 13.0% (10.8%, 17.4%), 13.3% (10.9%, 18.5%), 13.6% (11.1%, 18.9%), 13.6% (11.1% and 19.0%), and 13.4% (11.1%, 18.9%), respectively. In 216 comparisons that reported a non-inferiority margin, the conclusion on non-inferiority was the same across the five statistical methods in 211 (97.7%) cases. The differences in CI width were more in trials with a sample size of 100 or less in each group and treatment success rate above 90%. Of the 18 trials in this subgroup with a specified non-inferiority margin, non-inferiority was shown in 17 (94.4%), 16 (88.9%), 14 (77.8%), 14 (77.8%), and 15 (83.3%) cases based on CI by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods, respectively. Conclusions The statistical method used to calculate CI was not reported in the majority of antibiotic non-inferiority trials. Different statistical methods for CI resulted in different conclusions on non-inferiority in 2.3% cases. The differences in CI widths were highest in trials with a sample size of 100 or less in each group and a treatment success rate above 90%. Trial registration PROSPERO CRD42020165040 . April 28, 2020