Vascular endothelial growth factor-A 165 b ameliorates outer-retinal barrier and vascular dysfunction in the diabetic retina

Diabetic retinopathy (DR) is one of the leading causes of blindness in the developed world. Characteristic features of DR are retinal neurodegeneration, pathological angiogenesis and breakdown of both the inner and outer retinal barriers of the retinal vasculature and retinal pigmented epithelial (RPE)–choroid respectively. Vascular endothelial growth factor (VEGF-A), a key regulator of angiogenesis and permeability, is the target of most pharmacological interventions of DR. VEGF-A can be alternatively spliced at exon 8 to form two families of isoforms, pro- and anti-angiogenic. VEGF-A165a is the most abundant pro-angiogenic isoform, is pro-inflammatory and a potent inducer of permeability. VEGF-A165b is anti-angiogenic, anti-inflammatory, cytoprotective and neuroprotective. In the diabetic eye, pro-angiogenic VEGF-A isoforms are up-regulated such that they overpower VEGF-A165b. We hypothesized that this imbalance may contribute to increased breakdown of the retinal barriers and by redressing this imbalance, the pathological angiogenesis, fluid extravasation and retinal neurodegeneration could be ameliorated. VEGF-A165b prevented VEGF-A165a and hyperglycaemia-induced tight junction (TJ) breakdown and subsequent increase in solute flux in RPE cells. In streptozotocin (STZ)-induced diabetes, there was an increase in Evans Blue extravasation after both 1 and 8 weeks of diabetes, which was reduced upon intravitreal and systemic delivery of recombinant human (rh)VEGF-A165b. Eight-week diabetic rats also showed an increase in retinal vessel density, which was prevented by VEGF-A165b. These results show rhVEGF-A165b reduces DR-associated blood–retina barrier (BRB) dysfunction, angiogenesis and neurodegeneration and may be a suitable therapeutic in treating DR

Relativistic Numerical Method for Close Neutron Star Binaries

We describe a numerical method for calculating the (3+1) dimensional general relativistic hydrodynamics of a coalescing neutron-star binary system. The relativistic field equations are solved at each time slice with a spatial 3-metric chosen to be conformally flat. Against this solution to the general relativistic field equations the hydrodynamic variables and gravitational radiation are allowed to respond. The gravitational radiation signal is derived via a multipole expansion of the metric perturbation to the hexadecapole order including both mass and current moments and a correction for the slow motion approximation. Using this expansion, the effect of gravitational radiation on the system evolution can also be recovered by introducing an acceleration term in the matter evolution.Comment: 15 pages, 5 figures. Figures available by anonymous ftp at ftp://cygnus.phys.nd.edu/pub/gr/gr-qc9601017

Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array

Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. (Résumé d'auteur

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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe