Miracle-dz/UniFAR-FTUs huggingface dataset:
Crohn’s and Colitis Canada’s 2021 Impact of COVID-19 and Inflammatory Bowel Disease in Canada: Risk Factors and Medications

Abstract Inflammatory bowel disease (IBD) is a disease that results from dysregulation of the immune system and frequently requires medications that can affect the immune response to infections; therefore, it was imperative to quickly understand the risk of coronavirus disease 2019 (COVID-19) infection on persons living with IBD and how that risk may be increased by commonly used IBD medications. The IBD research community in Canada and beyond quickly established collaborative efforts to better understand the specific risk posed by COVID-19 on persons with IBD. We learned that IBD itself was not a risk factor for death or serious complications of COVID-19, and that most commonly used drug classes (with the notable exception of corticosteroids) do not increase the risk of COVID-19-related adverse outcomes. The risk factors for serious complications and death from COVID-19 appear to be similar to those identified in the wider population; those being advanced age, having pre-existing heart or lung disease, and smoking. We recommend that persons with IBD do not alter their course of therapy to avoid complications of COVID-19, though the indiscriminate use of corticosteroids should be avoided. Persons with IBD should follow the same public health recommendations as the general population to reduce their personal risk of acquiring COVID-19.
The role of electrocardiographic imaging in patient selection for cardiac resynchronization therapy

1. Cardiology Department, Clermont University Hospital (CHU) Clermont-Ferrand, Clermont-Ferrand, France; 2. CardioThoracic Unit, Bordeaux University Hospital (CHU), Pessac-Bordeaux, France; 3. IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France; 4. Division of Cardiology, Washington University, St. Louis, Missouri, USA ✉ Correspondence to: sabualrub@chu-clermontferrand.fr https://doi.org/10.11909/j.issn. 1671-5411.2021. 10.006
MicroRNA-27a Promotes Oxidative-Induced RPE Cell Death through Targeting FOXO1

Age-related macular degeneration (AMD) is a multifactor disease, which is primarily characterized by retinal pigment epithelium (RPE) cell loss. Since the retina is the most metabolically active tissue, RPE cells are exposed to consistent oxidative environment. So, oxidation-induced RPE cell death has long been considered a contributor to the onset of AMD. Here, we applied a retinal degeneration (RD) rat model induced by blue light-emitting diode (LED) and a cell model constructed by H2O2 stimulus to mimic the prooxidant environment of the retina. We detected that the expression of miR-27a was upregulated and the expression of FOXO1 was downregulated in both models. So, we furtherly investigated the role of miR-27a-FOXO1 axis in RPE in protesting against oxidants. Lentivirus-mediated RNA was injected intravitreally into rats to modulate the miR-27a-FOXO1 axis. Retinal function and histopathological changes were evaluated by electroretinography (ERG) analysis and hematoxylin and eosin (H&E) staining, respectively. Massive photoreceptor and RPE cell death were examined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL). The damage to the retina was aggravated in the FOXO1 gene-knockdown and miR-27a-overexpression groups after exposure to LED but was alleviated in the FOXO1 gene-overexpression or miR-27a-knockdown groups. Dual luciferase assay was used to detect the binding site of miR-27a and FOXO1. Upregulated miR-27a inhibited the expression of FOXO1 by directly binding to the FOXO1 mRNA 3′UTR and decreased the autophagy activity of ARPE-19 cells, resulting in the accumulation of reactive oxygen species (ROS) and decrease of cell viability. The results suggest that miR-27a is a negative regulator of FOXO1. Also, our data emphasize the prominent role of miR-27a/FOXO1 axis in modulating ROS accumulation and cell death in RPE cell model under oxidative stress and influencing the retinal function in the LED-induced RD rat model.
Actuator control for a new hybrid electromagnetic valvetrain in spark ignition engines

Valve timings of a spark ignition engine have been limited to a narrow range of alternatives within the constraints of the fuel economy, the emissions and the dynamic performance. Variable valve timing shows promise as a new key technology for improving the fuel consumption and emissions of spark ignition engines. Several techniques have been developed to perform variable valve timing, and the method with the most potential, called a hybrid electromagnetic valvetrain actuator, is an electromagnetic valvetrain with a permanent magnet and electromagnetic coils installed together. In this study, a novel hybrid electromagnetic valvetrain with a permanent magnet and electromagnetic coils, which significantly differs from existing electromagnetic valvetrains, was designed to overcome the drawbacks of conventional electromagnetic valvetrains that have been introduced previously. This new hybrid electromagnetic valvetrain is characterized by a simple structure, simple actuation and an ultra-low actuating power. Magnetic simulation was applied to analyse the magnetic flux density of the electromagnetic valvetrain and to optimize the design parameters. Dynamic simulation results show that the proposed hybrid electromagnetic valvetrain with soft-landing control can fully satisfy the valve dynamics in spark ignition engines. Additionally, the utilization of a permanent magnet and an optimal actuating current to catch and release valves has many advantages in energy consumption for valve catching and releasing when compared with other electromagnetic valvetrains.
CircRNA circVEGFC is Highly Expressed in Gestational Diabetes Mellitus (GDM) and It is Correlated with Multiple Adverse Events

Background Circular RNA vascular endothelial growth factor C (circVEGFC) is a novel regulator of glucose metabolism, while its role in gestational diabetes mellitus (GDM) is unclear. This study aimed to detect the expression of circVEGFC in GDM and explore its clinical values. Methods This study enrolled 220 pregnant women (gestational age less than 5 weeks) with normal blood glucose level on the day of admission. The expression of circVEGFC in plasma samples of these participants was determined by RT-qPCR. The participants were divided into high and low circVEGFC level groups with the median expression level of plasma circVEGFC as the cutoff value. The development of GDM was monitored until delivery. Adverse events were also monitored. Results Compared to low circVEGFC level group, GDM-free curve analysis revealed significantly higher incidence of GDM in high circVEGFC level group. In addition, plasma expression levels of circVEGFC were also higher in GDM patients than that in non-GDM patients on the day of admission and at 1 month before and after delivery. ROC curve analysis revealed that high expression levels of circVEGFC on the day of admission showed higher sensitivity and specificity in the early diagnosis of GDM. Moreover, high circVEGFC level group showed higher incidence rates of fetal malformation and hypertension. Conclusion Therefore, circVEGFC is highly expressed in GDM, and it is correlated with multiple adverse events.
Conduction disorders as the first hallmark of isolated cardiac sarcoidosis in a highly active individual: a case report

Abstract Background Cardiac sarcoidosis (CS) is an inflammatory disease with various clinical presentations depending on the extension of cardiac involvement. The disease is often clinically silent, therefore diagnosis is challenging. Case summary We discuss the case of a middle-aged highly active individual presenting with an occasional finding of low heart rate during self-monitoring. The electrocardiogram shows a Mobitz 2 heart block; thanks to multimodality imaging CS was diagnosed and corticosteroid therapy improved cardiac conduction. Discussion To our knowledge, this is one of the first documented cases of occasional, early findings of CS in a middle-aged highly active individual who presented with cardiac conduction involvement. Despite the very early diagnosis, multimodality imaging suggested an advanced disease with no oedema detection at the cardiac magnetic resonance. Nevertheless, prompt corticosteroid therapy was able to improve clinical conduction. Although non-sustained ventricular arrhythmias were detected, electrophysiological study allowed to discharge the patient safely without implantable cardioverter-defibrillator implantation. Light-to-moderate physical activity was allowed at mid-term follow-up. A multidisciplinary evaluation should be considered to resume a high-intensity training.
Effect of electrode surface roughness on the electrical impedance of ionic polymer–metal composites

In this paper, we study the effect of electrode surface roughness on the electrochemical response of ionic polymer–metal composites (IPMCs) subjected to a time-varying voltage input. We use the linearized Poisson–Nernst–Planck model to describe the dynamics of the electric potential and mobile counterions’ concentration within the polymer. We derive a closed form solution of the three-dimensional boundary value problem by employing the method of matched asymptotic expansions. Specifically, the polymer region is decomposed into a bulk region, where mainly diffusive phenomena take place, and boundary layers in proximity of the polymer–electrode interfaces, where charge storage develops as a function of the electrode surface roughness. Leading order solutions are derived and matched on account of electric potential, counterions’ concentration, and counterions’ flux continuity. We find that IPMC charge storage is greatly enhanced by the increase in effective electrode surface area. On the other hand, bulk diffusion phenomena remain largely independent of the microscopic topography of the electrode. Thus, the hypothesis of rough electrodes is found to be very well suited in interpreting the anomalous values of IPMC capacitance which scales linearly with the electrode’s actual surface area.
Effect of Lockdown on Diabetes Care During the COVID-19 Pandemic: Result of a Telephone-Based Survey Among Patients Attending a Diabetic Clinic in Northern India

Background The coronavirus disease 2019 (COVID-19) pandemic has aggravated the demand for diabetes care due to restrictive measures like the lockdown affecting access to healthcare services. The current study was conducted to assess the changes in medication compliance, dietary pattern, and glucose monitoring during the lockdown period as compared to the pre-lockdown period among patients living with type 2 diabetes mellitus (T2DM) attending a diabetes clinic in northern India. Methods This cross-sectional study was conducted between May and July 2020. Information regarding the sociodemographic and clinical profiles of the patients like age, sex, income, qualification, family history of diabetes, history of smoking and alcohol, type of treatment, co-morbidities, drug adherence for T2DM, changes in the pattern of diet, physical activity, blood glucose monitoring, and drug usage during and before the lockdown was collected through telephonic interviews using a structured tool. Descriptive analysis was performed, and the chi-square and Wilcoxon sign ranks tests were used to see the association between variables. Results A total of 260 patients were enrolled in the study. A higher proportion of males reported a decrease in the consumption of cereals (13.9%), eggs (56.5%), and meat and fish (92.7%) and an increase in water intake (25.8%) while a higher proportion of females reported no change in physical activity levels (77.2%) during the lockdown against pre-COVID times. There was a significant improvement in medication adherence and glycemic control during the lockdown period as compared to the pre-lockdown times. Conclusion More time for self-care, adequate counseling about glycemic goals, and knowledge of self-monitoring of blood glucose levels helped the majority of patients in adopting a healthy lifestyle and achieve better glycemic control during the COVID-19 lockdown.
Shock Acceleration with Oblique and Turbulent Magnetic Fields

We investigate shock acceleration in a realistic astrophysical environment with density inhomogeneities. The turbulence induced by the interaction of the shock precursor with upstream density fluctuations amplifies both upstream and downstream magnetic fields via the turbulent dynamo. The dynamo-amplified turbulent magnetic fields (a) introduce variations of shock obliquities along the shock face, (b) enable energy gain through a combination of shock drift and diffusive processes, (c) give rise to various spectral indices of accelerated particles, (d) regulate the diffusion of particles both parallel and perpendicular to the magnetic field, and (e) increase the shock acceleration efficiency. Our results demonstrate that upstream density inhomogeneities and dynamo amplification of magnetic fields play an important role in shock acceleration, and thus shock acceleration depends on the condition of the ambient interstellar environment. The implications on understanding radio spectra of supernova remnants are also discussed.
High-Order Joint Embedding for Multi-Level Link Prediction

Abstract Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction using pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms. Supplementary materials for this article are available online.
American Hate Crime Trends Prediction with Event Extraction

Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Crime Reporting (UCR) Program collects hate crime data and releases statistic report yearly. These statistics provide information in determining national hate crime trends. The statistics can also provide valuable holistic and strategic insight for law enforcement agencies or justify lawmakers for specific legislation. However, the reports are mostly released next year and lag behind many immediate needs. Recent research mainly focuses on hate speech detection in social media text or empirical studies on the impact of a confirmed crime. This paper proposes a framework that first utilizes text mining techniques to extract hate crime events from New York Times news, then uses the results to facilitate predicting American national-level and state-level hate crime trends. Experimental results show that our method can significantly enhance the prediction performance compared with time series or regression methods without event-related factors. Our framework broadens the methods of national-level and state-level hate crime trends prediction.
Programming for All: Understanding the Nature of Programs

Computer programs are part of our daily life, we use them, we provide them with data, they support our decisions, they help us remember, they control machines, etc. Programs are made by people, but in most cases we are not their authors, so we have to decide if we can trust them. Programs enable computers and computer-controlled machines to behave in a large variety of ways. They bring the intrinsic power of computers to life. Programs have a variety of properties that all citizens must be aware of. Due to the intangible nature of programs, most of these properties are very unusual, but important to understand the digital world. In this position paper, we describe the Nature of Programs in the form of knowledge statements, accompanied by examples from everyday life to clarify their meaning. Everything is formulated in an easily understandable manner and avoids obscure technical language. We suggest that these knowledge statements must be imparted to all teachers and school students. A great way to learn and experience the nature of programs is to develop programs yourself.
Evidence for Mixing between ICM and Stripped ISM by the Analysis of the Gas Metallicity in the Tails of Jellyfish Galaxies

Hydrodynamical simulations show that the ram pressure stripping in galaxy clusters fosters a strong interaction between stripped interstellar medium (ISM) and the surrounding medium, with the possibility of intracluster medium (ICM) cooling into cold gas clouds. Exploiting the MUSE observation of three jellyfish galaxies from the GAs Stripping Phenomena in galaxies with MUSE (GASP) survey, we explore the gas metallicity of star-forming clumps in their gas tails. We find that the oxygen abundance of the stripped gas decreases as a function of the distance from the parent galaxy disk; the observed metallicity profiles indicate that more than 40% of the most metal-poor stripped clouds are constituted by cooled ICM, in qualitative agreement with simulations that predict mixing between the metal-rich ISM and the metal-poor ICM.
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable setting. Hence, we design novel computationally efficient algorithms for the realizable setting that match the minimax lower bound up to logarithmic factors and are general-purpose, accommodating a wide variety of function classes including kernel methods, H{\"o}lder smooth functions, and convex functions. The sample complexities of our algorithms can be quantified in terms of well-known quantities like the extended teaching dimension and haystack dimension. However, unlike algorithms based directly on those combinatorial quantities, our algorithms are computationally efficient. To achieve computational efficiency, our algorithms sample from the version space using Monte Carlo"hit-and-run"algorithms instead of maintaining the version space explicitly. Our approach has two key strengths. First, it is simple, consisting of two unifying, greedy algorithms. Second, our algorithms have the capability to seamlessly leverage prior knowledge that is often available and useful in practice. In addition to our new theoretical results, we demonstrate empirically that our algorithms are competitive with Gaussian process UCB methods.
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search

Despite achieving great success, Deep Neural Networks (DNNs) are vulnerable to adversarial examples. How to accurately evaluate the adversarial robustness of DNNs is critical for their deployment in real-world applications. An ideal indicator of robustness is adversarial risk. Unfortunately, since it involves maximizing the 0-1 loss, calculating the true risk is technically intractable. The most common solution for this is to compute an approximate risk by replacing the 0-1 loss with a surrogate one. Some functions have been used, such as Cross-Entropy (CE) loss and Difference of Logits Ratio (DLR) loss. However, these functions are all manually designed and may not be well suited for adversarial robustness evaluation. In this paper, we leverage AutoML to tighten the error (gap) between the true and approximate risks. Our main contributions are as follows. First, AutoLoss-AR, the first method to search for surrogate losses for adversarial risk, with an elaborate search space, is proposed. The experimental results on 10 adversarially trained models demonstrate the effectiveness of the proposed method: the risks evaluated using the best-discovered losses are 0.2% to 1.6% better than those evaluated using the handcrafted baselines. Second, 5 surrogate losses with clean and readable formulas are distilled out and tested on 7 unseen adversarially trained models. These losses outperform the baselines by 0.8% to 2.4%, indicating that they can be used individually as some kind of new knowledge. Besides, the possible reasons for the better performance of these losses are explored.
Misspecified Gaussian Process Bandit Optimization

We consider the problem of optimizing a black-box function based on noisy bandit feedback. Kernelized bandit algorithms have shown strong empirical and theoretical performance for this problem. They heavily rely on the assumption that the model is well-specified, however, and can fail without it. Instead, we introduce a \emph{misspecified} kernelized bandit setting where the unknown function can be $\epsilon$--uniformly approximated by a function with a bounded norm in some Reproducing Kernel Hilbert Space (RKHS). We design efficient and practical algorithms whose performance degrades minimally in the presence of model misspecification. Specifically, we present two algorithms based on Gaussian process (GP) methods: an optimistic EC-GP-UCB algorithm that requires knowing the misspecification error, and Phased GP Uncertainty Sampling, an elimination-type algorithm that can adapt to unknown model misspecification. We provide upper bounds on their cumulative regret in terms of $\epsilon$, the time horizon, and the underlying kernel, and we show that our algorithm achieves optimal dependence on $\epsilon$ with no prior knowledge of misspecification. In addition, in a stochastic contextual setting, we show that EC-GP-UCB can be effectively combined with the regret bound balancing strategy and attain similar regret bounds despite not knowing $\epsilon$.