Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Characteristics and Predictors of Mortality in Patients with COVID-19 Infection Outside Intensive Care

Background/Introduction The coronavirus disease 2019 (COVID-19) pandemic has affected all aspects of inpatient hospital medicine with patients admitted from level 1 (general medical wards) to level 3 (intensive care). Often, there are subtle physiological differences in these cohorts of patients. In particular, in intensive care, patients tend to be younger and have increased disease severity. Data, to date, has combined outcomes from medical and intensive care cohorts, or looked exclusively at intensive care. We looked solely at the level 1 (medical) cohort to identify their clinical characteristics and predictors of outcome. Patients and Methods This was a retrospective study of adult patients admitted to a central London teaching hospital with a diagnosis of COVID-19 from 23rd March to 7th April 2020 identified from the hospital electronic database. Any patients who required level 2 or 3 care were excluded. Results A total of 229 patients were included for analysis. Increased age and frailty scores were associated with increased 30-day mortality. Reduced renal function and elevated troponin blood levels are also associated with poor outcome. Baseline observations showed that increased oxygen requirement was predictive for mortality. A trend of increased mortality with lower diastolic blood pressure was noted. Lymphopenia was not shown to be related to mortality. Conclusion Urea and creatinine are the best predictors of mortality in the level 1 cohort. Unlike previous intensive care data, lymphopenia is not predictive of mortality. We suggest that these factors be considered when prognosticating and for resource allocation for the treatment and escalation of care for patients with COVID-19 infection.
Nudge: Accelerating Overdue Pull Requests toward Completion

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests toward completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to filter out pull requests that may be overdue but for which sufficient action is taking place nonetheless. Last, we use actor identification to understand who the blocker of the pull request is and nudge the appropriate actor (author or reviewer(s)). The key novelty of Nudge is that it succeeds in reducing pull request resolution time, while ensuring that developers perceive the notifications sent as useful, at the scale of thousands of repositories. In a randomized trial on 147 repositories in use at Microsoft, Nudge was able to reduce pull request resolution time by 60% for 8,500 pull requests, when compared to overdue pull requests for which Nudge did not send a notification. Furthermore, developers receiving Nudge notifications resolved 73% of these notifications as positive. We observed similar results when scaling up the deployment of Nudge to 8,000 repositories at Microsoft, for which Nudge sent 210,000 notifications during a full year. This demonstrates Nudge’s ability to scale to thousands of repositories. Last, our qualitative analysis of a selection of Nudge notifications indicates areas for future research, such as taking dependencies among pull requests and developer availability into account.
UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development.
On the energy of the protons producing the very high-energy astrophysical neutrinos

We study the distribution of the energy of the protons that can produce an astrophysical neutrino with a given observed energy, in the TeV–PeV range, both through pp or pγ interactions. Due to the increasing multiplicity of the pion production at high center of mass energies, the resulting average proton energies can be much larger than the often used approximate value Ep ≃ 20Eν . Also the threshold of the pγ process can lead to a pronounced increase in the values of 〈Ep 〉/Eν for decreasing neutrino energies. The results depend sensitively on the assumed proton spectrum, since steeper spectra give less weight to the lower energy neutrino tail resulting from the decays of the abundant low-energy pions. In the pγ scenarios they also depend sensitively on the spectrum of the target photons. The results are in particular relevant to relate possible characteristics of the neutrino spectrum to those of the corresponding cosmic rays that generated them. We also discuss the associated production of gamma rays at the sources.
Coronaviruses and gastrointestinal symptoms: an old liaison for the new SARS-CoV-2

The coronavirus disease (Covid-19) has caused a pandemic with more than 600,000 deaths to date. It is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the beta-coronavirus genus that also includes SARS and the Middle East Respiratory Syndrome Coronavirus (MERS). While the typical presentation is given by respiratory symptoms and fever, some patients also report gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Several studies have identified the SARS-CoV-2 RNA in stool specimens of infected patients, and its viral receptor angiotensin-converting enzyme 2 (ACE2) is highly expressed in enterocytes. In this short review, we report the frequency of gastrointestinal symptoms in infected patients and suggest possible implications for disease management, transmission, and infection control.
Combustion of n-C3–C6 Linear Alcohols: An Experimental and Kinetic Modeling Study. Part II: Speciation Measurements in a Jet-Stirred Reactor, Ignition Delay Time Measurements in a Rapid Compression Machine, Model Validation, and Kinetic Analysis

This work presents new experimental data for n-C3–C6 alcohol, combustion (n-propanol, n-butanol, n-pentanol, n-hexanol). Speciation measurements have been carried out in a jet-stirred reactor (p = 107 kPa, T = 550–1100 K, φ = 0.5, 1.0, 2.0) for n-butanol, n-pentanol, and n-hexanol. Ignition delay times of ethanol, n-propanol, n-butanol, and n-pentanol/air mixtures were measured in a rapid compression machine at φ = 1.0, p = 10 and 30 bar, and T = 704–935 K. The kinetic subsets for alcohol pyrolysis and oxidation from the CRECK kinetic model have been systematically updated to describe the pyrolysis and high- and low-temperature oxidation of this series of fuels as described in Part I of this work (PelucchiM.; NamyslS.; RanziE.Combustion of n-C3–C6 linear alcohol: an experimental and kinetic modeling study. Part I: reaction classes, rate rules, model lumping and validation. Submitted to Energy and Fuels, 2020). Part II describes in detail the facilities used for this systematic experimental investigation of n-C3–C6 alcohol combustion and presents a complete validation of the kinetic model by means of comparisons with the new data and measurements previously reported in the literature for both pyrolytic and oxidative conditions. Kinetic analyses such as rate of production and sensitivity analyses are used to highlight the governing reaction pathways and reasons for existing deviations, motivating possible further improvements in our chemistry mechanism.
Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0.
Anytime Prediction as a Model of Human Reaction Time

Neural networks today often recognize objects as well as people do, and thus might serve as models of the human recognition process. However, most such networks provide their answer after a fixed computational effort, whereas human reaction time varies, e.g. from 0.2 to 10 s, depending on the properties of stimulus and task. To model the effect of difficulty on human reaction time, we considered a classification network that uses early-exit classifiers to make anytime predictions. Comparing human and MSDNet accuracy in classifying CIFAR-10 images in added Gaussian noise, we find that the network equivalent input noise SD is 15 times higher than human, and that human efficiency is only 0.6\% that of the network. When appropriate amounts of noise are present to bring the two observers (human and network) into the same accuracy range, they show very similar dependence on duration or FLOPS, i.e. very similar speed-accuracy tradeoff. We conclude that Anytime classification (i.e. early exits) is a promising model for human reaction time in recognition tasks.
Delving Deep Into Label Smoothing

Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and the hard label. It is often used to reduce the overfitting problem of training DNNs and further improve classification performance. In this paper, we aim to investigate how to generate more reliable soft labels. We present an Online Label Smoothing (OLS) strategy, which generates soft labels based on the statistics of the model prediction for the target category. The proposed OLS constructs a more reasonable probability distribution between the target categories and non-target categories to supervise DNNs. Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets. Additionally, the proposed method can significantly improve the robustness of DNN models to noisy labels compared to current label smoothing approaches. The source code is available at our project page: https://mmcheng.net/ols/
All You Need is a Good Functional Prior for Bayesian Deep Learning

The Bayesian treatment of neural networks dictates that a prior distribution is specified over their weight and bias parameters. This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution. We argue that this is a hugely limiting aspect of Bayesian deep learning, and this work tackles this limitation in a practical and effective way. Our proposal is to reason in terms of functional priors, which are easier to elicit, and to "tune" the priors of neural network parameters in a way that they reflect such functional priors. Gaussian processes offer a rigorous framework to define prior distributions over functions, and we propose a novel and robust framework to match their prior with the functional prior of neural networks based on the minimization of their Wasserstein distance. We provide vast experimental evidence that coupling these priors with scalable Markov chain Monte Carlo sampling offers systematically large performance improvements over alternative choices of priors and state-of-the-art approximate Bayesian deep learning approaches. We consider this work a considerable step in the direction of making the long-standing challenge of carrying out a fully Bayesian treatment of neural networks, including convolutional neural networks, a concrete possibility.
Peptide Based Conjugates for Therapeutic Delivery Applications

The effect of peptide charge on the self-assembly and gelation behaviour of three octa-peptides: VEVKVEVK (VEK2), VKVKVEVK (VEK3) and VEVEVKVE (VEK1) has been investigated and characterised. The critical gelation concentration of each peptide was found to correlate with the charge modulus carried by the peptide and to be independent of the sign of the charge. Hydrogels formed were found to be transparent and stable when the peptide charge modulus is > 1. No differences in hydrogel structure or mechanical properties, as probed by TEM and SAXS and shear rheology, were found when the peptides were at the same concentration and carried the same charge modulus. These peptides were shown to form dense fibrillar network formed by ? -sheet rich single fibre which lateral aggregation is controlled by the peptide charge modulus. The increase in fibre lateral aggregation with decreasing charge modulus was found to correlate with the increase in hydrogel mechanical properties, showing that fibre lateral aggregation pays a key role in controlling the mechanical properties of these hydrogels. The release profiles from VEK1 and VEK3 at pH 7 of two hydrophilic model drug molecules, namely napthol yellow (NY) and martius yellow (MY) was analysed using UV-Vis spectrophotometry. The incorporation of the guest molecules did not affect the self-assembly of the peptide at a molecular level but did affect the level of lateral fibre aggregation observed and therefore the mechanical properties of the hydrogels. The release of each of the model compounds was monitored over time and shown to be controlled by Fickian diffusion. The guest molecule diffusion rate D was dependent on the ratio between the overall effective charges carried by the peptide, i.e.: the fibrillar network, and the overall charges carried by the guest molecules but independent from the hydrogel concentration and mechanical properties, in the concentration and guest loading range investigated. This work shows that the rate of release of small drug molecules can be manipulated, not only by changing the charges on the guest molecules, but also by manipulating the charged state of the self-assembling peptide molecule and through it the charge state of the fribrillar network. Furthermore the VEK3 system was conjugated to a series of thermo-responsive synthetic polymers which imparted a significant change in mechanical properties, assembled structures and release profiles upon heating. Observed changes when above the polymers LCST include increased mechanical strength, fibre thickening and increased diffusion coeffcients. The synthesis, and subsequent characterisation, of these materials is the first time responsive hydrogels of OEGMA copolymers has been reported.
The Size-dependent Cytotoxicity of Amorphous Silica Nanoparticles: A Systematic Review of in vitro Studies

Abstract With the increasing production and application of engineered amorphous silica nanoparticles (aSiNPs), people have more opportunities to be exposed to aSiNPs. However, the knowledge of its adverse health effects and related mechanisms is still limited, compared with the well-studied crystalline micron-sized silica. Since small differences in the physical–chemical properties of nanoparticles could cause significant differences in the toxic effect, it is important to distinguish how these variations influence the outcoming toxicity. Notably, particle size, as one of the essential characterizations of aSiNPs, is relevant to its biological activities. Thus, the aim of this systematic review was to summarize the relationship between the particle size of aSiNPs and its adverse biological effects. In order to avoid the influence of complicated in vivo experimental conditions on the toxic outcome, only in vitro toxicity studies which reported on the cytotoxic effect of different sizes aSiNPs were included. After the systematic literature retrieval, selection, and quality assessment process, 76 eligible scientific papers were finally included in this review. There were 76% of the studies that concluded a size-dependent cytotoxicity of aSiNPs, in which smaller-sized aSiNPs possessed greater toxicity. However, this trend could be modified by certain influence factors, such as the synthetic method of aSiNPs, particle aggregation state in cell culture medium, toxicity endpoint detection method, and some other experimental conditions. The effects of these influence factors on the size-dependent cytotoxicity of aSiNPs were also discussed in detail in the present review.
Doubly robust adaptive LASSO for effect modifier discovery

Abstract Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.
More religion means less science. An International comparison of the relations between religious beliefs and levels of and attitudes to scientific knowledge

This research presents the results of a comparative analysis of the links between religious practices and beliefs and levels of scientific knowledge. Based on secondary analyses of survey data in the European Union (Eurobarometers 2005 and 2010) and the United States (Pew Research Center 2018), we show that, regardless of the country, correlations suggest that the more individuals identify with a religion and the more intensely they practice that religion, the less scientifically literate they are, as measured in standard tests. Moreover, scientific representations are also related to individual religious outlook. The more individuals adhere to a religion, the less they have positive attitudes towards science. The conclusion suggests possible interpretations of theses correlations.
Racial discrimination and posttraumatic stress: examining emotion dysregulation as a mediator in an African American community sample

ABSTRACT Background: African Americans experience more severe and chronic posttraumatic stress disorder (PTSD) symptoms compared to other racial groups, and thus it is important to examine factors that are relevant for the aetiology of PTSD in this population. Although racial discrimination has been implicated as an exacerbating factor in the development and maintenance of PTSD, relatively less is known about mechanisms through which this process may occur. Objective: The purpose of this study was to examine one such mechanism, emotion dysregulation, in two independent samples of African American adults. Method: Trauma-exposed participants were recruited in a large, urban community hospital setting (initial sample n = 1,841; replication sample n = 294). In the initial sample, participants completed a unidimensional measure of emotion dysregulation and self-reported PTSD symptoms based on the DSM-IV. In the replication sample, participants completed a multidimensional measure of emotion dysregulation and a diagnostic interview of PTSD symptoms based on the DSM-5. Mediation analyses were used to test our hypotheses. Results: Across both samples, results indicated that racial discrimination was indirectly associated with PTSD symptoms through emotion dysregulation (even when trauma load was added as a covariate). Conclusions: Taken together, these results provide strong evidence that the association between racial discrimination and PTSD symptoms may be partially explained by the association between racial discrimination and worse emotion dysregulation. These findings elucidate the impact of racist incidents on mental health and identify modifiable emotion regulatory processes that can be intervened upon to enhance the psychological and social wellbeing of African Americans. HIGHLIGHTS • Experiencing more racial discrimination may negatively impact how African Americans are able to regulate emotions;• Difficulty regulating emotions may partly explain the link between racial discrimination and PTSD symptoms among African Americans.