Abstract: Background Inflammation is causally related to atherothrombosis. Treatment with canakinumab, a monoclonal antibody that inhibits inflammation by neutralizing interleukin-1β, resulted in a lower rate of cardiovascular events than placebo in a previous randomized trial. We sought to determine whether an alternative approach to inflammation inhibition with low-dose methotrexate might provide similar benefit. Methods We conducted a randomized, double-blind trial of low-dose methotrexate (at a target dose of 15 to 20 mg weekly) or matching placebo in 4786 patients with previous myocardial infarction or multivessel coronary disease who additionally had either type 2 diabetes or the metabolic syndrome. All participants received 1 mg of folate daily. The primary end point at the onset of the trial was a composite of nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. Near the conclusion of the trial, but before unblinding, hospitalization for unstable angina that led to urgent revas...

Abstract: Background WISQARS is an interactive, web-based data query system (WBDQS) that is accessible from the internet. It includes modules for fatal and non-fatal injuries, a separate module on violent deaths, and injury costs and maps. Data come from a variety of trusted sources, including national health surveys and health data repositories. CDC created WISQARS in 1999 to meet the data needs of injury practitioners in the United States. Since that time, the audience has expanded to include researchers, policy makers, media, and the general public. Objective The purpose of this evaluation was to assess the focus, quality, usefulness, impact, and outcomes of WISQARS; and to identify gaps and areas for improvement. Data were collected through peer-reviewed and grey literature searches, google searches, an environmental scan of internal and external WBDQS, and a series of stakeholder interviews. Results WISQARS is used as a data source by NGOs, academic institutions, other U.S. federal agencies, and social media websites. Stakeholders most frequently used the fatal and non-fatal modules. The most frequently accessed data were on suicides, poisonings, homicides, motor vehicle crashes, and falls. WISQARS is most often used to respond to data requests, educate decision makers, conduct basic analyses, and teach and plan. Areas for improvement included building more capacity for data visualisations and for users to export both data and graphics, allowing for full mobile responsiveness when accessing, expanding by incrementally including additional data, and developing better support information and guidance on use. Conclusions While WISQARS has been largely a success in expanding access to U.S. injury and violence surveillance data, there are several opportunities to enhance the functionality of the system for the end user. CDC is planning to use innovations in data science to enhance WISQARS’s capacity.

Abstract: Phenolic compounds are an important class of plant secondary metabolites which play crucial physiological roles throughout the plant life cycle. Phenolics are produced under optimal and suboptimal conditions in plants and play key roles in developmental processes like cell division, hormonal regulation, photosynthetic activity, nutrient mineralization, and reproduction. Plants exhibit increased synthesis of polyphenols such as phenolic acids and flavonoids under abiotic stress conditions, which help the plant to cope with environmental constraints. Phenylpropanoid biosynthetic pathway is activated under abiotic stress conditions (drought, heavy metal, salinity, high/low temperature, and ultraviolet radiations) resulting in accumulation of various phenolic compounds which, among other roles, have the potential to scavenge harmful reactive oxygen species. Deepening the research focuses on the phenolic responses to abiotic stress is of great interest for the scientific community. In the present article, we discuss the biochemical and molecular mechanisms related to the activation of phenylpropanoid metabolism and we describe phenolic-mediated stress tolerance in plants. An attempt has been made to provide updated and brand-new information about the response of phenolics under a challenging environment.

Abstract: Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5% in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50% higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.

Abstract: Background Since the publication of the European League Against Rheumatism recommendations for the pharmacological treatment of psoriatic arthritis (PsA) in 2012, new evidence and new therapeutic agents have emerged. The objective was to update these recommendations. Methods A systematic literature review was performed regarding pharmacological treatment in PsA. Subsequently, recommendations were formulated based on the evidence and the expert opinion of the 34 Task Force members. Levels of evidence and strengths of recommendations were allocated. Results The updated recommendations comprise 5 overarching principles and 10 recommendations, covering pharmacological therapies for PsA from non-steroidal anti-inflammatory drugs (NSAIDs), to conventional synthetic (csDMARD) and biological (bDMARD) disease-modifying antirheumatic drugs, whatever their mode of action, taking articular and extra-articular manifestations of PsA into account, but focusing on musculoskeletal involvement. The overarching principles address the need for shared decision-making and treatment objectives. The recommendations address csDMARDs as an initial therapy after failure of NSAIDs and local therapy for active disease, followed, if necessary, by a bDMARD or a targeted synthetic DMARD (tsDMARD). The first bDMARD would usually be a tumour necrosis factor (TNF) inhibitor. bDMARDs targeting interleukin (IL)12/23 (ustekinumab) or IL-17 pathways (secukinumab) may be used in patients for whom TNF inhibitors are inappropriate and a tsDMARD such as a phosphodiesterase 4-inhibitor (apremilast) if bDMARDs are inappropriate. If the first bDMARD strategy fails, any other bDMARD or tsDMARD may be used. Conclusions These recommendations provide stakeholders with an updated consensus on the pharmacological treatment of PsA and strategies to reach optimal outcomes in PsA, based on a combination of evidence and expert opinion.

Abstract: Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.

Abstract: To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations --- which connect two items with one or multiple linked attributes --- are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM and RippleNet. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism.

Abstract: Animal experiments have shown that nonhuman primates, cats, ferrets, hamsters, rabbits, and bats can be infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition, SARS-CoV-2 RNA has been detected in felids, mink, and dogs in the field. Here, we describe an in-depth investigation using whole-genome sequencing of outbreaks on 16 mink farms and the humans living or working on these farms. We conclude that the virus was initially introduced by humans and has since evolved, most likely reflecting widespread circulation among mink in the beginning of the infection period, several weeks before detection. Despite enhanced biosecurity, early warning surveillance, and immediate culling of animals in affected farms, transmission occurred between mink farms in three large transmission clusters with unknown modes of transmission. Of the tested mink farm residents, employees, and/or individuals with whom they had been in contact, 68% had evidence of SARS-CoV-2 infection. Individuals for which whole genomes were available were shown to have been infected with strains with an animal sequence signature, providing evidence of animal-to-human transmission of SARS-CoV-2 within mink farms.

Abstract: Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Abstract: Behavioral and psychological symptoms of dementia include agitation, depression, apathy, repetitive questioning, psychosis, aggression, sleep problems, wandering, and a variety of inappropriate behaviors. One or more of these symptoms will affect nearly all people with dementia over the course of their illness. These symptoms are among the most complex, stressful, and costly aspects of care, and they lead to a myriad of poor patient health outcomes, healthcare problems, and income loss for family care givers. The causes include neurobiologically related disease factors; unmet needs; care giver factors; environmental triggers; and interactions of individual, care giver, and environmental factors. The complexity of these symptoms means that there is no “one size fits all solution,” and approaches tailored to the patient and the care giver are needed. Non-pharmacologic approaches should be used first line, although several exceptions are discussed. Non-pharmacologic approaches with the strongest evidence base involve family care giver interventions. Regarding pharmacologic treatments, antipsychotics have the strongest evidence base, although the risk to benefit ratio is a concern. An approach to integrating non-pharmacologic and pharmacologic treatments is described. Finally, the paradigm shift needed to fully institute tailored treatments for people and families dealing with these symptoms in the community is discussed.

Abstract: Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks where CNNs were successful. In this paper we construct appropriate CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.

Abstract: We present a new model for the distribution of free electrons in the Galaxy, the Magellanic Clouds, and the intergalactic medium (IGM) that can be used to estimate distances to real or simulated pulsars and fast radio bursts (FRBs) based on their dispersion measure (DM). The Galactic model has an extended thick disk representing the so-called warm interstellar medium, a thin disk representing the Galactic molecular ring, spiral arms based on a recent fit to Galactic H II regions, a Galactic Center disk, and seven local features including the Gum Nebula, Galactic Loop I, and the Local Bubble. An offset of the Sun from the Galactic plane and a warp of the outer Galactic disk are included in the model. Parameters of the Galactic model are determined by fitting to 189 pulsars with independently determined distances and DMs. Simple models are used for the Magellanic Clouds and the IGM. Galactic model distances are within the uncertainty range for 86 of the 189 independently determined distances and within 20% of the nearest limit for a further 38 pulsars. We estimate that 95% of predicted Galactic pulsar distances will have a relative error of less than a factor of 0.9. The predictions of YMW16 are compared to those of the TC93 and NE2001 models showing that YMW16 performs significantly better on all measures. Timescales for pulse broadening due to interstellar scattering are estimated for (real or simulated) Galactic and Magellanic Cloud pulsars and FRBs.

Abstract: Despite the critical role of the human microbiota in health, our understanding of microbiota compositional dynamics during and after pregnancy is incomplete. We conducted a case-control study of 49 pregnant women, 15 of whom delivered preterm. From 40 of these women, we analyzed bacterial taxonomic composition of 3,767 specimens collected prospectively and weekly during gestation and monthly after delivery from the vagina, distal gut, saliva, and tooth/gum. Linear mixed-effects modeling, medoid-based clustering, and Markov chain modeling were used to analyze community temporal trends, community structure, and vaginal community state transitions. Microbiota community taxonomic composition and diversity remained remarkably stable at all four body sites during pregnancy (P > 0.05 for trends over time). Prevalence of a Lactobacillus-poor vaginal community state type (CST 4) was inversely correlated with gestational age at delivery (P = 0.0039). Risk for preterm birth was more pronounced for subjects with CST 4 accompanied by elevated Gardnerella or Ureaplasma abundances. This finding was validated with a set of 246 vaginal specimens from nine women (four of whom delivered preterm). Most women experienced a postdelivery disturbance in the vaginal community characterized by a decrease in Lactobacillus species and an increase in diverse anaerobes such as Peptoniphilus, Prevotella, and Anaerococcus species. This disturbance was unrelated to gestational age at delivery and persisted for up to 1 y. These findings have important implications for predicting premature labor, a major global health problem, and for understanding the potential impact of a persistent, altered postpartum microbiota on maternal health, including outcomes of pregnancies following short interpregnancy intervals.

Abstract: The Tinker software, currently released as version 8, is a modular molecular mechanics and dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with OpenMP extensions It supports a wide variety of force fields, including polarizable models such as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field The package runs on Linux, macOS, and Windows systems In addition to canonical Tinker, there are branches, Tinker-HP and Tinker-OpenMM, designed for use on message passing interface (MPI) parallel distributed memory supercomputers and state-of-the-art graphical processing units (GPUs), respectively The Tinker suite also includes a tightly integrated Java-based graphical user interface called Force Field Explorer (FFE), which provides molecular visualization capabilities as well as the ability to launch and control Tinker calculations

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Abstract: In this paper, we show a phenomenon, which we named ``super-convergence'', where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate. Furthermore, we present evidence that training with large learning rates improves performance by regularizing the network. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. The architectures to replicate this work will be made available upon publication.

Abstract: Oncogenes are activated through well-known chromosomal alterations such as gene fusion, translocation, and focal amplification. In light of recent evidence that the control of key genes depends on chromosome structures called insulated neighborhoods, we investigated whether proto-oncogenes occur within these structures and whether oncogene activation can occur via disruption of insulated neighborhood boundaries in cancer cells. We mapped insulated neighborhoods in T cell acute lymphoblastic leukemia (T-ALL) and found that tumor cell genomes contain recurrent microdeletions that eliminate the boundary sites of insulated neighborhoods containing prominent T-ALL proto-oncogenes. Perturbation of such boundaries in nonmalignant cells was sufficient to activate proto-oncogenes. Mutations affecting chromosome neighborhood boundaries were found in many types of cancer. Thus, oncogene activation can occur via genetic alterations that disrupt insulated neighborhoods in malignant cells.

Abstract: Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first revie...

Abstract: The simple act of slowly varying the parameters of a quantum system so that it remains always in its ground state is extremely rich from an information processing point of view. For an ideal, closed system, this adiabatic evolution is equivalent to full quantum computation, and it is convenient for establishing quantum algorithms for optimization. This review presents adiabatic quantum algorithms, proves the closed-system equivalence of the adiabatic and circuit models of quantum computation, reviews the placement of adiabatic quantum computation in the more general classification of computational complexity theory, and discusses the case of ``stoquastic'' quantum evolutions.

Abstract: Mitochondria are renowned for their central bioenergetic role in eukaryotic cells, where they act as powerhouses to generate adenosine triphosphate from oxidation of nutrients. At the same time, these organelles are highly dynamic and undergo fusion, fission, transport, and degradation. Each of these dynamic processes is critical for maintaining a healthy mitochondrial population. Given the central metabolic function of mitochondria, it is not surprising that mitochondrial dynamics and bioenergetics reciprocally influence each other. We review the dynamic properties of mitochondria, with an emphasis on how these processes respond to cellular signaling events and how they affect metabolism.

Abstract: Multi-wavelength light is directed to an optic including a substrate and achromatic metasurface optical components deposited on a surface of the substrate. The achromatic metasurface optical components comprise a pattern of dielectric resonators. The dielectric resonators have nonperiodic gap distances between adjacent dielectric resonators; and each dielectric resonator has a width, w, that is distinct from the width of other dielectric resonators. A plurality of wavelengths of interest selected from the wavelengths of the multi-wavelength light are deflected with the achromatic metasurface optical components at a shared angle or to or from a focal point at a shared focal length.

Abstract: Summary Background Immunotherapy targeting the PD-1 axis has activity in several tumour types. We aimed to establish the activity and safety of the PD-1 inhibitor pembrolizumab in patients with untreated brain metastases from melanoma or non-small-cell lung cancer (NSCLC). Methods In this non-randomised, open-label, phase 2 trial, we enrolled patients aged 18 years or older with melanoma or NSCLC with untreated brain metastases from the Yale Cancer Center. Patients had at least one untreated or progressive brain metastasis between 5 and 20 mm in diameter without associated neurological symptoms or the need for corticosteroids. Patients with NSCLC had tumour tissue positive for PD-L1 expression; this was not required for patients with melanoma. Patients were given 10 mg/kg pembrolizumab every 2 weeks until progression. The primary endpoint was brain metastasis response assessed in all treated patients. The trial is ongoing and here we present an early analysis. The study is registered with ClinicalTrials.gov, number NCT02085070. Findings Between March 31, 2014, and May 31, 2015, we screened 52 patients with untreated or progressive brain metastases (18 with melanoma, 34 with NSCLC), and enrolled 36 (18 with melanoma, 18 with NSCLC). A brain metastasis response was achieved in four (22%; 95% CI 7–48) of 18 patients with melanoma and six (33%; 14–59) of 18 patients with NSCLC. Responses were durable, with all but one patient with NSCLC who responded showing an ongoing response at the time of data analysis on June 30, 2015. Treatment-related serious and grade 3–4 adverse events were grade 3 elevated aminotransferases (n=1 [6%]) in the melanoma cohort, and grade 3 colitis (n=1 [6%]), grade 3 pneumonitis (n=1 [6%]), grade 3 fatigue (n=1 [6%]), grade 4 hyperkalemia (n=1 [6%]), and grade 2 acute kidney injury (n=1 [6%]) in the NSCLC cohort. Clinically significant neurological adverse events included transient grade 3 cognitive dysfunction and grade 1–2 seizures (n=3 [17%]) in the melanoma cohort. Interpretation Pembrolizumab shows activity in brain metastases in patients with melanoma or NSCLC with an acceptable safety profile, which suggests that there might be a role for systemic immunotherapy in patients with untreated or progressive brain metastases. Funding Merck and the Yale Cancer Center.

Abstract: Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

Abstract: Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Abstract: Medicinal plants are gaining much interest recently because their use in ethno medicine treating common disease such as cold, fever and other medicinal claims are now supported with sound scientific evidences. The study on medicinal plants started with extraction procedures that play a critical role to the extraction outcomes (e.g. yield and phytochemicals content) and also to the consequent assays performed. A wide range of technologies with different methods of extraction is available nowadays. Hence, this review aim to describe and compare the most commonly used methods based on their principle, strength and limitation to help evaluating the suitability and economic feasibility of the methods.

Abstract: High-efficiency lead halide perovskite solar cells (PSCs) have been fabricated with α-phase formamidinium lead iodide (FAPbI3) stabilized with multiple cations. The alloyed cations greatly affect the bandgap, carrier dynamics, and stability, as well as lattice strain that creates unwanted carrier trap sites. We substituted cesium (Cs) and methylenediammonium (MDA) cations in FA sites of FAPbI3 and found that 0.03 mol fraction of both MDA and Cs cations lowered lattice strain, which increased carrier lifetime and reduced Urbach energy and defect concentration. The best-performing PSC exhibited power conversion efficiency >25% under 100 milliwatt per square centimeter AM 1.5G illumination (24.4% certified efficiency). Unencapsulated devices maintained >80% of their initial efficiency after 1300 hours in the dark at 85°C.