Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions.

Uncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Hollow Gaussian beam generation through nonlinear interaction of photons with orbital-angular-momentum

Hollow Gaussian beams (HGB) are a special class of doughnut shaped beams that do not carry orbital angular momentum (OAM). Such beams have a wide range of applications in many fields including atomic optics, bio-photonics, atmospheric science, and plasma physics. Till date, these beams have been generated using linear optical elements. Here, we show a new way of generating HGBs by three-wave mixing in a nonlinear crystal. Based on nonlinear interaction of photons having OAM and conservation of OAM in nonlinear processes, we experimentally generated ultrafast HGBs of order as high as 6 and power >180 mW at 355 nm. This generic concept can be extended to any wavelength, timescales (continuous-wave and ultrafast) and any orders. We show that the removal of azimuthal phase of vortices does not produce Gaussian beam. We also propose a new and only method to characterize the order of the HGBs.by N. Apurv Chaitanya, M. V. Jabir, J. Banerji, G. K. Samant

Pseudomonas aeruginosa cell membrane protein expression from phenotypically diverse cystic fibrosis isolates demonstrates host-specific adaptations

Pseudomonas aeruginosa is a Gram-negative, nosocomial, highly adaptable opportunistic pathogen especially prevalent in immuno-compromised cystic fibrosis (CF) patients. The bacterial cell surface proteins are important contributors to virulence, yet the membrane subproteomes of phenotypically diverse P. aeruginosa strains are poorly characterized. We carried out mass spectrometry (MS)-based proteome analysis of the membrane proteins of three novel P. aeruginosa strains isolated from the sputum of CF patients and compared protein expression to the widely used laboratory strain, PAO1. Microbes were grown in planktonic growth condition using minimal M9 media, and a defined synthetic lung nutrient mimicking medium (SCFM) limited passaging. Two-dimensional LC-MS/MS using iTRAQ labeling enabled quantitative comparisons among 3171 and 2442 proteins from the minimal M9 medium and in the SCFM, respectively. The CF isolates showed marked differences in membrane protein expression in comparison with PAO1 including up-regulation of drug resistance proteins (MexY, MexB, MexC) and down-regulation of chemotaxis and aerotaxis proteins (PA1561, PctA, PctB) and motility and adhesion proteins (FliK, FlgE, FliD, PilJ). Phenotypic analysis using adhesion, motility, and drug susceptibility assays confirmed the proteomics findings. These results provide evidence of host-specific microevolution of P. aeruginosa in the CF lung and shed light on the adaptation strategies used by CF pathogens.12 page(s