Running Spectral Index from Inflation with Modulations

We argue that a large negative running spectral index, if confirmed, might suggest that there are abundant structures in the inflaton potential, which result in a fairly large (both positive and negative) running of the spectral index at all scales. It is shown that the center value of the running spectral index suggested by the recent CMB data can be easily explained by an inflaton potential with superimposed periodic oscillations. In contrast to cases with constant running, the perturbation spectrum is enhanced at small scales, due to the repeated modulations. We mention that such features at small scales may be seen by 21 cm observations in the future.Comment: 7 pages, 6 figures, v2: published in JCA

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

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

Radiation-induced breast cancer incidence and mortality from digital mammography screening a modeling study

Background: Estimates of risk for radiation-induced breast cancer from mammography screening have not considered variation in dose exposure or diagnostic work-up after abnormal screening results. Objective: To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening while considering exposure from screening and diagnostic mammography and dose variation among women. Design: 2 simulation-modeling approaches. Setting: U.S. population. Patients: Women aged 40 to 74 years. Intervention: Annual or biennial digital mammography screening from age 40, 45, or 50 years until age 74 years. Measurements: Lifetime breast cancer deaths averted (bene-fits) and radiation-induced breast cancer incidence and mortality (harms) per 100 000 women screened. Results: Annual screening of 100 000 women aged 40 to 74 years was projected to induce 125 breast cancer cases (95% CI, 88 to 178) leading to 16 deaths (CI, 11 to 23), relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 cases of radiation-induced breast cancer leading to 32 deaths per 100 000 women. Women with large breasts requiring extra views for complete examination (8% of population) were projected to have greater radiation-induced breast cancer risk (266 cancer cases and 35 deaths per 100 000 women) than other women (113 cancer cases and 15 deaths per 100 000 women). Biennial screening starting at age 50 years reduced risk for radiation-induced cancer 5-fold. Limitation: Life-years lost from radiation-induced breast cancer could not be estimated. Conclusion: Radiation-induced breast cancer incidence and mortality from digital mammography screening are affected by dose variability from screening, resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may have a greater risk for radiation-induced breast cancer. Primary Funding Source: Agency for Healthcare Research and Quality, U.S. Preventive Services Task Force, National Cancer Institute

Measurements of L-shell x-ray production cross sections of W, Pt, and Au by 10-30-keV electrons

We present results from measurements of Lα x-ray production cross sections of the elements W, Pt, and Au by impact of electrons with energies in the range 10–30 keV. The cross sections were obtained by measuring Lα x-ray intensities emitted from very thin films of the studied elements deposited on thick carbon substrates. The directional and energy spreading of the electron beam within the active film and the x-ray enhancement due to electron backscattering from the substrate were accounted for by means of Monte Carlo simulation. Recorded x-ray intensities were converted to absolute x-ray production cross sections by using two different methods; the first employs measured values of the sample thickness and the number of incident electrons and estimated detector efficiencies; the second is based on a comparison between measured and calculated bremsstrahlung intensities. Experimental data are compared with the results of simple analytical formulas of common use in practical electron probe microanalysis, with calculated cross sections obtained from the distorted-wave Born approximation and with other experimental data available in the literature