Computation vs. Experiment for High-Frequency Low-Reynolds Number Airfoil Plunge

We seek to extend the literature on sinusoidal pure-plunge of 2D airfoils at high reduced frequency and low Reynolds number, by including effects of camber and nonzero mean incidence angle. We compare experimental results in a water tunnel using dye injection and 2D particle image velocimetry, with a set of computations in 2D – Immersed Boundary Method and unsteady Reynolds-Averaged Navier Stokes. The Re range is from 10,000 to 60,000, based on free stream velocity and airfoil chord, chosen to cover cases where transition in attached boundary layers would be of some importance, and where transition would only occur in the wake. Generally at high reduced frequency there is no Reynolds number effect. Mean angle of attack has significance, notionally, depending on whether it is below or above static stall. Computations were found to agree well with experimentally-derived velocity contours, vorticity contours and momentum in the wake. As found previously for the NACA0012, varying Strouhal number is found to control the topology of the wake, while varying reduced amplitude and reduced frequency together, but keeping Strouhal number constant, causes wake vortical structures to scale with the reduced amplitude of plunge. Flowfield periodicity – as evinced from comparison of instantaneous and time-averaged particle image velocimetry – is generally attained after two periods of oscillation from motion onset

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 hospitalization2–4 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

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

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Contributions of intrinsic mutation rate and selfish selection to levels of de novo HRAS mutations in the paternal germline

The RAS proto-oncogene Harvey rat sarcoma viral oncogene homolog (HRAS) encodes a small GTPase that transduces signals from cell surface receptors to intracellular effectors to control cellular behavior. Although somatic HRAS mutations have been described in many cancers, germline mutations cause Costello syndrome (CS), a congenital disorder associated with predisposition to malignancy. Based on the epidemiology of CS and the occurrence of HRAS mutations in spermatocytic seminoma, we proposed that activating HRAS mutations become enriched in sperm through a process akin to tumorigenesis, termed selfish spermatogonial selection. To test this hypothesis, we quantified the levels, in blood and sperm samples, of HRAS mutations at the p.G12 codon and compared the results to changes at the p.A11 codon, at which activating mutations do not occur. The data strongly support the role of selection in determining HRAS mutation levels in sperm, and hence the occurrence of CS, but we also found differences from the mutation pattern in tumorigenesis. First, the relative prevalence of mutations in sperm correlates weakly with their in vitro activating properties and occurrence in cancers. Second, specific tandem base substitutions (predominantly GC>TT/AA) occur in sperm but not in cancers; genomewide analysis showed that this same mutation is also overrepresented in constitutional pathogenic and polymorphic variants, suggesting a heightened vulnerability to these mutations in the germline. We developed a statistical model to show how both intrinsic mutation rate and selfish selection contribute to the mutational burden borne by the paternal germline

Contributions of intrinsic mutation rate and selfish selection to levels of de novo HRAS mutations in the paternal germline.

The RAS proto-oncogene Harvey rat sarcoma viral oncogene homolog (HRAS) encodes a small GTPase that transduces signals from cell surface receptors to intracellular effectors to control cellular behavior. Although somatic HRAS mutations have been described in many cancers, germline mutations cause Costello syndrome (CS), a congenital disorder associated with predisposition to malignancy. Based on the epidemiology of CS and the occurrence of HRAS mutations in spermatocytic seminoma, we proposed that activating HRAS mutations become enriched in sperm through a process akin to tumorigenesis, termed selfish spermatogonial selection. To test this hypothesis, we quantified the levels, in blood and sperm samples, of HRAS mutations at the p.G12 codon and compared the results to changes at the p.A11 codon, at which activating mutations do not occur. The data strongly support the role of selection in determining HRAS mutation levels in sperm, and hence the occurrence of CS, but we also found differences from the mutation pattern in tumorigenesis. First, the relative prevalence of mutations in sperm correlates weakly with their in vitro activating properties and occurrence in cancers. Second, specific tandem base substitutions (predominantly GC>TT/AA) occur in sperm but not in cancers; genomewide analysis showed that this same mutation is also overrepresented in constitutional pathogenic and polymorphic variants, suggesting a heightened vulnerability to these mutations in the germline. We developed a statistical model to show how both intrinsic mutation rate and selfish selection contribute to the mutational burden borne by the paternal germline