Analysis of Strong-Coupling Parameters for Superfluid 3He

Superfluid $^{3}$He experiments show strong deviation from the weak-coupling limit of the Ginzburg-Landau theory, and this discrepancy grows with increasing pressure. Strong-coupling contributions to the quasiparticle interactions are known to account for this effect and they are manifest in the five $\beta$-coefficients of the fourth order Ginzburg-Landau free energy terms. The Ginzburg-Landau free energy also has a coefficient $g_{z}$ to include magnetic field coupling to the order parameter. From NMR susceptibility experiments, we find the deviation of $g_{z}$ from its weak-coupling value to be negligible at all pressures. New results for the pressure dependence of four different combinations of $\beta$-coefficients, $\beta$_{345}, $\beta$_{12}, $\beta$_{245}, and $\beta$_{5} are calculated and comparison is made with theory.Comment: 6 pages, 2 figures, 1 table. Manuscript prepared for QFS200

Investigation of excited 0+ states in 160Er populated via the (p, t) two-neutron transfer reaction

Many efforts have been made in nuclear structure physics to interpret the nature of low-lying excited 0+ states in well-deformed rare-earth nuclei. However, one of the difficulties in resolving the nature of these states is that there is a paucity of data. In this work, excited 0+ states in the N = 92 nucleus 160Er were studied via the 162Er(p, t)160Er two-neutron transfer reaction, which is ideal for probing 0+ → 0+ transitions, at the Maier-Leibnitz-Laboratorium in Garching, Germany. Reaction products were momentum-analyzed with a Quadrupole-3-Dipole magnetic spectrograph. The 0+2 state was observed to be strongly populated with 18% of the ground state strength

Aneurismas da artéria coroidéia anterior: aspectos clínicos, anatômicos e cirúrgicos

Relatamos 5 casos de doentes portadores de aneurismas da região da artéria coroidéia anterior. Aspectos clínicos, anatômicos e cirúrgicos são discutidos

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

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

A Revision by Recalculation of Selected Single Bond Energies

iv, 49 p.In this paper a revision of bond energies was undertaken. Bond energy is the average energy required to break the type of bond in question. For a molecule of only two kinds of atoms, as an example, and only one kind of bond, the bond energy for that particular type of bond could be calculated by dividing the heat of atomization for the molecule by the number of bonds. This method for calculating bond energies makes use of thermochemicai data. Another way to obtain bond energies is by spectroscopic methods. The thermochemical method was used in this paper