Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines

Detection of subtle neurological alterations by the Catwalk XT gait analysis system

BACKGROUND: A new version of the CatWalk XT system was evaluated as a tool for detecting very subtle alteration in gait based on higher speed sample rate; the system could also demonstrate minor changes in neurological function. In this study, we evaluated the neurological outcome of sciatic nerve injury intervened by local injection of hyaluronic acid. Using the CatWalk XT system, we looked for differences between treated and untreated groups and differences within the same group as a function of time so as to assess the power of the Catwalk XT system for detecting subtle neurological change. METHODS: Peripheral nerve injury was induced in 36 Sprague–Dawley rats by crushing the left sciatic nerve using a vessel clamp. The animals were randomized into one of two groups: Group I: crush injury as the control; Group II: crush injury and local application with hyaluronic acid. These animals were subjected to neurobehavior assessment, histomorphology evaluation, and electrophysiology study periodically. These data were retrieved for statistical analysis. RESULTS: The density of neurofilament and S-100 over the distal end of crushed nerve showed significant differences either in inter-group comparison at various time points or intra-group comparison from 7 to 28 days. Neuronal structure architecture, axon counts, intensity of myelination, electrophysiology, and collagen deposition demonstrate significant differences between the two groups. There was significant difference of SFI and angle of ankle in inter- group analysis from 7 to 28 days, but there were no significant differences in SFI and angle of ankle at time points of 7 and 14 days. In the Cat Walk XT analysis, the intensity, print area, stance duration, and swing duration all showed detectable differences at 7, 14, 21, and 28 days, whereas there were no significant difference at 7 and 14 days with CatWalk 7 testing. In addition, there were no significant differences of step sequence or regularity index between the two versions. CONCLUSION: Hyaluronic acid augmented nerve regeneration as early as 7 days after crush injury. This subtle neurological alteration could be detected through the CatWalk XT gait analysis but not the SFI, angle of ankle, or CatWalk 7 methods

Nrg4 promotes fuel oxidation and a healthy adipokine profile to ameliorate diet-induced metabolic disorders

OBJECTIVE: Brown and white adipose tissue exerts pleiotropic effects on systemic energy metabolism in part by releasing endocrine factors. Neuregulin 4 (Nrg4) was recently identified as a brown fat-enriched secreted factor that ameliorates diet-induced metabolic disorders, including insulin resistance and hepatic steatosis. However, the physiological mechanisms through which Nrg4 regulates energy balance and glucose and lipid metabolism remain incompletely understood. The aims of the current study were: i) to investigate the regulation of adipose Nrg4 expression during obesity and the physiological signals involved, ii) to elucidate the mechanisms underlying Nrg4 regulation of energy balance and glucose and lipid metabolism, and iii) to explore whether Nrg4 regulates adipose tissue secretome gene expression and adipokine secretion. METHODS: We examined the correlation of adipose Nrg4 expression with obesity in a cohort of diet-induced obese mice and investigated the upstream signals that regulate Nrg4 expression. We performed metabolic cage and hyperinsulinemic-euglycemic clamp studies in Nrg4 transgenic mice to dissect the metabolic pathways regulated by Nrg4. We investigated how Nrg4 regulates hepatic lipid metabolism in the fasting state and explored the effects of Nrg4 on adipose tissue gene expression, particularly those encoding secreted factors. RESULTS: Adipose Nrg4 expression is inversely correlated with adiposity and regulated by pro-inflammatory and anti-inflammatory signaling. Transgenic expression of Nrg4 increases energy expenditure and augments whole body glucose metabolism. Nrg4 protects mice from diet-induced hepatic steatosis in part through activation of hepatic fatty acid oxidation and ketogenesis. Finally, Nrg4 promotes a healthy adipokine profile during obesity. CONCLUSIONS: Nrg4 exerts pleiotropic beneficial effects on energy balance and glucose and lipid metabolism to ameliorate obesity-associated metabolic disorders. Biologic therapeutics based on Nrg4 may improve both type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) in patients

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead