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

Towards rigorous validation of energy optimisation experiments

The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour.Mahmoud A. Bokhari, Brad Alexander, Markus Wagne

In-vivo and offline optimisation of energy use in the presence of small energy signals: case study on a popular Android library

Energy demands of applications on mobile platforms are increasing. As a result, there has been a growing interest in optimising their energy efficiency. As mobile platforms are fast-changing, diverse and complex, the optimisation of energy use is a non-trivial task. To date, most energy optimisation methods either use models or external meters to estimate energy use. Unfortunately, it becomes hard to build widely applicable energy models, and external meters are neither cheap nor easy to set up. To address this issue, we run application variants in-vivo on the phone and use a precise internal battery monitor to measure energy use. We describe a methodology for optimising a target application in-vivo and with application-specific models derived from the device's own internal meter based on jiffies and lines of code. We demonstrate that this process produces a significant improvement in energy efficiency with limited loss of accuracy.Mahmoud A. Bokhari, Brad Alexander, Markus Wagne

Antimicrobial Resistant Escherichia coli Strains Isolated From Food Animals in Pakistan.

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Optimization of pre-sowing magnetic field doses through RSM in pea

Seed pre-sowing magnetic field treatment was reported to induce biochemical and physiological changes. In the present study, response surface methodology was used for deduction of optimal magnetic field doses. Improved growth and yield responses in the pea cultivar were achieved using a rotatable central composite design and multivariate data analysis. The growth parameters such as root and shoot fresh masses and lengths as well as yield were enhanced at a certain magnetic field level. The chlorophyll contents were also enhanced significantly vs. the control. The low magnetic field strength for longer duration of exposure/ high strength for shorter exposure were found to be optimal points for maximum responses in root fresh mass, chlorophyll ‘a’ contents, and green pod yield/plant, respectively and a similar trend was observed for other measured parameters. The results indicate that the magnetic field pre-sowing seed treatment can be used practically to enhance the growth and yield in pea cultivar and response surface methodology was found an efficient experimental tool for optimization of the treatment level to obtain maximum response of interest

Growth curve in Mengali sheep breed of Balochistan

Growth, one of the most essential traits for farm animals, is defined as an increase in tissues and organs of the animals per unit time and affected by genetic and environmental factors. The growth that has sigmoid form is explained reliably by nonlinear growth models (such as Monomolecular, Brody, Gompertz, Richards and Logistic). Information about parameters of these nonlinear models enables researcher to obtain beneficial clues for selection studies. Data on 2377 Mengali sheep kept at four different research stations (Experimental Station CASVAB, Quetta, (ESC), Mastung, Noshki and Quetta) at three different locations in Balochistan were analyzed using Gompertz growth model, W(t) = A*exp(-B*exp(-k*t) with non-linear regression methodology. Body weight values for all the sheep were recorded monthly from birth to 360th days of age. Body weight averages of these sheep in each period were used to define the weight-age relationship in Mengali sheep. Determination coefficient (R2) and Root of Mean Square Error (RMSE) were used to decide whether Gompertz growth model was appropriate for the body weight - age data from Mengali Sheep. Convergence was achieved after 5 iterations. The parameters A, B, and k of Gompertz growth model were 36.924, 2.043 and 0.010083, respectively. These parameter estimates were statistically significant (P<0.01). Root of Mean Square Error (RMSE) and Determination Coefficient (R2) were 1.022, 99.17% respectively. Besides, it was determined the observed and predicted weight values at each time period in Gompertz growth model were almost similar. These results reflected that Gompertz growth model reliably explained relationship between weight and age in Mengali sheep. As a result, Gompertz growth model fitted to the body weight - age data from Mengali sheep might help us to determine an accurate feed regime, maturity age, and problems in growth and development over time

Bridging the Gap: The Moderating Roles of Institutional Quality and Compliance on the Link between CSR and Financial Performance

Corporate social responsibility (CSR) is widely acknowledged to have a significant impact on firm's financial performance, but it is yet ambiguous how institutional, cultural and national factors influence this relationship in corruption contexts. Therefore, institutional mechanisms and perceived corruption should not be considered in isolation, as this would jeopardize the company's ability to act in a socially responsible manner. Obtaining an institutional approach of corruption and using self-administered survey data collected from 632 Pakistani firms operating in manufacturing and service sectors, we investigated the impact of CSR, institutional quality and law enforcement (IQLE), and internal compliance and ethical management (ICEM) on firm financial performance. Our results found that IQLE negatively moderates and weakened the positive relationship between CSR and firm financial performance. Additionally, we discover that ICEM positively moderates and strengthened the direct relationship between CSR and financial performance. We show that improving compliance and ethics management, CSR has the potential to enhance financial performance. \textcopyright 2023 Elsevier Lt