Kepler-22b: A 2.4 Earth-radius Planet in the Habitable Zone of a Sun-like Star

A search of the time-series photometry from NASA's Kepler spacecraft reveals a transiting planet candidate orbiting the 11th magnitude G5 dwarf KIC 10593626 with a period of 290 days. The characteristics of the host star are well constrained by high-resolution spectroscopy combined with an asteroseismic analysis of the Kepler photometry, leading to an estimated mass and radius of 0.970 +/- 0.060 MSun and 0.979 +/- 0.020 RSun. The depth of 492 +/- 10ppm for the three observed transits yields a radius of 2.38 +/- 0.13 REarth for the planet. The system passes a battery of tests for false positives, including reconnaissance spectroscopy, high-resolution imaging, and centroid motion. A full BLENDER analysis provides further validation of the planet interpretation by showing that contamination of the target by an eclipsing system would rarely mimic the observed shape of the transits. The final validation of the planet is provided by 16 radial velocities obtained with HIRES on Keck 1 over a one year span. Although the velocities do not lead to a reliable orbit and mass determination, they are able to constrain the mass to a 3{\sigma} upper limit of 124 MEarth, safely in the regime of planetary masses, thus earning the designation Kepler-22b. The radiative equilibrium temperature is 262K for a planet in Kepler-22b's orbit. Although there is no evidence that Kepler-22b is a rocky planet, it is the first confirmed planet with a measured radius to orbit in the Habitable Zone of any star other than the Sun.Comment: Accepted to Ap

Post-translational modifications and mass spectrometry detection

In this review, we provide a comprehensive bibliographic overview of the role of mass spectrometry and the recent technical developments in the detection of post-translational modifications (PTMs). We briefly describe the principles of mass spectrometry for detecting PTMs and the protein and peptide enrichment strategies for PTM analysis, including phosphorylation, acetylation and oxidation. This review presents a bibliographic overview of the scientific achievements and the recent technical development in the detection of PTMs is provided. In order to ascertain the state of the art in mass spectrometry and proteomics methodologies for the study of PTMs, we analyzed all the PTM data introduced in the Universal Protein Resource (UniProt) and the literature published in the last three years. The evolution of curated data in UniProt for proteins annotated as being post-translationally modified is also analyzed. Additionally, we have undertaken a careful analysis of the research articles published in the years 2010 to 2012 reporting the detection of PTMs in biological samples by mass spectrometry. © 2013 Elsevier Inc

Physical Processes in Star Formation

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/s11214-020-00693-8.Star formation is a complex multi-scale phenomenon that is of significant importance for astrophysics in general. Stars and star formation are key pillars in observational astronomy from local star forming regions in the Milky Way up to high-redshift galaxies. From a theoretical perspective, star formation and feedback processes (radiation, winds, and supernovae) play a pivotal role in advancing our understanding of the physical processes at work, both individually and of their interactions. In this review we will give an overview of the main processes that are important for the understanding of star formation. We start with an observationally motivated view on star formation from a global perspective and outline the general paradigm of the life-cycle of molecular clouds, in which star formation is the key process to close the cycle. After that we focus on the thermal and chemical aspects in star forming regions, discuss turbulence and magnetic fields as well as gravitational forces. Finally, we review the most important stellar feedback mechanisms.Peer reviewedFinal Accepted Versio

The Cholecystectomy As A Day Case (CAAD) Score: A Validated Score of Preoperative Predictors of Successful Day-Case Cholecystectomy Using the CholeS Data Set

Background Day-case surgery is associated with significant patient and cost benefits. However, only 43% of cholecystectomy patients are discharged home the same day. One hypothesis is day-case cholecystectomy rates, defined as patients discharged the same day as their operation, may be improved by better assessment of patients using standard preoperative variables. Methods Data were extracted from a prospectively collected data set of cholecystectomy patients from 166 UK and Irish hospitals (CholeS). Cholecystectomies performed as elective procedures were divided into main (75%) and validation (25%) data sets. Preoperative predictors were identified, and a risk score of failed day case was devised using multivariate logistic regression. Receiver operating curve analysis was used to validate the score in the validation data set. Results Of the 7426 elective cholecystectomies performed, 49% of these were discharged home the same day. Same-day discharge following cholecystectomy was less likely with older patients (OR 0.18, 95% CI 0.15–0.23), higher ASA scores (OR 0.19, 95% CI 0.15–0.23), complicated cholelithiasis (OR 0.38, 95% CI 0.31 to 0.48), male gender (OR 0.66, 95% CI 0.58–0.74), previous acute gallstone-related admissions (OR 0.54, 95% CI 0.48–0.60) and preoperative endoscopic intervention (OR 0.40, 95% CI 0.34–0.47). The CAAD score was developed using these variables. When applied to the validation subgroup, a CAAD score of ≤5 was associated with 80.8% successful day-case cholecystectomy compared with 19.2% associated with a CAAD score >5 (p < 0.001). Conclusions The CAAD score which utilises data readily available from clinic letters and electronic sources can predict same-day discharges following cholecystectomy

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

On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles

DNA microarrays provide for unprecedented, large-scale views of gene expression and, as\ud a result, have emerged as a fundamental measurement tool in the study of diverse biological\ud systems. Statistical questions abound; but many traditional data analytic approaches do\ud not apply, in large part because thousands of individual genes are measured with relatively\ud little replication. Empirical Bayes methods provide a natural approach to microarray data\ud analysis because they can significantly reduce the dimensionality of an inference problem\ud while compensating for relatively few replicates by using information across the array. We\ud propose a general empirical Bayes modeling approach which allows for replicate expression\ud profiles in multiple conditions. The hierarchical mixture model accounts for differences\ud among genes in their average expression levels, differential expression for a given gene among\ud cell types, and measurement fluctuations. Two distinct parameterizations are considered: a\ud model based on Gamma distributed measurements and one based on log-normally distributed\ud measurements. False detection rate and related operating characteristics of the methodology\ud are assessed in a simulation study. We also show how the posterior odds of dierential\ud expression in one version of the model is related to the ratio of the arithmetic mean to the\ud geometric mean of the two sample means. The methodology is used in a study of mammary\ud cancer in the rat, where four distinct patterns of expression are possible

To Pool or Not to Pool: A Question of Microarray Experimental Design

Over 10% of the data sets catalogued in the Gene Expression Omnibus Database involve messenger RNA samples that have been pooled prior to hybridization. Pooling affects data quality and inference, but the exact effects are not yet known as pooling has not been systematically studied in the context of microarray experiments. Here we report on the results of an experiment designed to evaluate the utility of pooling and the impact on identifying differentially expressed genes. We find that inference for most genes is not adversely affected by pooling and we recommend that pooling be done when fewer than three arrays are used in each condition. For larger designs, pooling does not significantly improve inferences if few subjects are pooled. The realized benefits in this case do not outweigh the price paid for loss of individual specific information. Pooling is beneficial when many subjects are pooled, provided independent samples contribute to multiple pools.