Direct simulation of three-dimensional flow about the AFE vehicle at high altitudes

Three-dimensional hypersonic rarefied flow about the Aeroassist Flight Experiment (AFE) vehicle was studied using the direct simulation Monte Carlo (DSMC) technique. Results are presented for the transitional flow regime encountered between 120 and 200 km altitudes with a reentry velocity of 9.92 km/s. In the simulations, a five-species reacting real-gas model that accounts for internal energies (rotational and vibrational) is used. The results indicate that the transitional effects are significant even at an altitude of 200 km and influence the overall vehicle aerodynamics. For the cases considered, the aerodynamic coefficients, surface pressures, convective heating, and flow field structure variations with rarefaction effects are presented

Implementation of a vibrationally linked chemical reaction model for DSMC

A new procedure closely linking dissociation and exchange reactions in air to the vibrational levels of the diatomic molecules has been implemented in both one- and two-dimensional versions of Direct Simulation Monte Carlo (DSMC) programs. The previous modeling of chemical reactions with DSMC was based on the continuum reaction rates for the various possible reactions. The new method is more closely related to the actual physics of dissociation and is more appropriate to the particle nature of DSMC. Two cases are presented: the relaxation to equilibrium of undissociated air initially at 10,000 K, and the axisymmetric calculation of shuttle forebody heating during reentry at 92.35 km and 7500 m/s. Although reaction rates are not used in determining the dissociations or exchange reactions, the new method produces rates which agree astonishingly well with the published rates derived from experiment. The results for gas properties and surface properties also agree well with the results produced by earlier DSMC models, equilibrium air calculations, and experiment

Dietary Manipulation of Oncogenic MicroRNA Expression in Human Rectal Mucosa: A Randomized Trial

Author version made available in accordance with the publisher's policy.High red meat intake is associated with increased colorectal cancer (CRC) risk, while resistant starch is probably protective. Resistant starch fermentation produces butyrate, which can alter microRNA (miRNA) levels in CRC cells in vitro; effects of red meat and resistant starch on miRNA expression in vivo were unknown. This study examined whether a high red meat (HRM) diet altered miRNA expression in rectal mucosa tissue of healthy volunteers, and if supplementation with butyrylated resistant starch (HRM+HAMSB) modified this response. In a randomised cross-over design, twenty-three volunteers undertook four 4-week dietary interventions; an HRM diet (300 g/day lean red meat) and an HRM+HAMSB diet (HRM with 40 g/day butyrylated high amylose maize starch), preceded by an entry diet and separated by a washout. Faecal butyrate increased with the HRM+HAMSB diet. Levels of oncogenic mature miRNAs, including miR-17-92 cluster miRNAs and miR-21, increased in the rectal mucosa with the HRM diet, while the HRM+HAMSB diet restored miR-17-92 miRNAs, but not miR-21, to baseline levels. Elevated miR-17-92 and miR-21 in the HRM diet corresponded with increased cell proliferation, and a decrease in miR-17-92 target gene transcript levels, including CDKN1A. The oncogenic miR-17-92 cluster is differentially regulated by dietary factors that increase or decrease risk for CRC, and this may explain, at least in part, the respective risk profiles of high red meat and resistant starch. These findings support increased resistant starch consumption as a means of reducing risk associated with an HRM diet

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe