The SAMI Galaxy Survey: Data Release One with emission-line physics value-added products

We present the first major release of data from the SAMI Galaxy Survey. This data release focuses on the emission-line physics of galaxies. Data Release One includes data for 772 galaxies, about 20 per cent of the full survey. Galaxies included have the redshift range 0.004 < z < 0.092, a large mass range (7.6 < logM*/M⊙ < 11.6), and star formation rates of ~10-4 to ~101M⊙ yr-1. For each galaxy, we include two spectral cubes and a set of spatially resolved 2D maps: single- and multi-component emission-line fits (with dust-extinction corrections for strong lines), local dust extinction, and star formation rate. Calibration of the fibre throughputs, fluxes, and differential atmospheric refraction has been improved over the Early Data Release. The data have average spatial resolution of 2.16 arcsec (full width at half-maximum) over the 15 arcsec diameter field of view and spectral (kinematic) resolution of R = 4263 (σ = 30 km s-1) around Ha. The relative flux calibration is better than 5 per cent, and absolute flux calibration has an rms of 10 per cent. The data are presented online through the Australian Astronomical Observatory's Data Central

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

An empirical analysis of the retention of dissatisfied business services customers using structural equation modelling

This study extends the body of literature concerning service switching, complaint handling, dependence and commitment by investigating why dissatisfied B2B customers do not switch service providers. Specifically, it develops and tests a social exchange-based model examining how dissatisfied, but behaviourally loyal, customers act in terms of their repurchase intentions. A conceptual model, specifying a set of hypothesised relationships between dimensions of switching costs, interpersonal relationships, dimensions of complaint handling, satisfaction with complaint handling, attractiveness of alternatives, dependence, calculative commitment and repurchase intentions, was examined using AMOS 17.0 on a sample of 376 business directors/managers from responding organisations. The results show that satisfaction with complaint handling, benefit-loss costs, dependence and calculative commitment significantly increase customers’ repurchase intentions. The findings also indicate that dependence, interpersonal relationships and specific types of switching costs influence customers’ calculative commitment, and the latter influences intentions to repurchase services. The study builds on the Investment Model by including justice components, and examines the effects of different types of antecedents on calculative commitment that have previously not been examined