Kepler Fine Guidance Sensor Data

The Kepler and K2 missions collected Fine Guidance Sensor (FGS) data in addition to the science data, as discussed in the Kepler Instrument Handbook (KIH, Van Cleve and Caldwell 2016). The FGS CCDs are frame transfer devices (KIH Table 7) located in the corners of the Kepler focal plane (KIH Figure 24), which are read out 10 times every second. The FGS data are being made available to the user community for scientific analysis as flux and centroid time series, along with a limited number of FGS full frame images which may be useful for constructing a World Coordinate System (WCS) or otherwise putting the time series data in context. This document will describe the data content and file format, and give example MATLAB scripts to read the time series. There are three file types delivered as the FGS data.1. Flux and Centroid (FLC) data: time series of star signal and centroid data. 2. Ancillary FGS Reference (AFR) data: catalog of information about the observed stars in the FLC data. 3. FGS Full-Frame Image (FGI) data: full-frame image snapshots of the FGS CCDs

The Kepler Science Data Processing Pipeline Source Code Road Map

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization

Kepler Data Release 25 Notes (Q0-Q17)

These Data Release Notes provide information specific to the current reprocessing and re-export of the Q0-Q17 data. The data products included in this data release include target pixel files, light curve files, FFIs,CBVs, ARP, Background, and Collateral files. This release marks the final processing of the Kepler Mission Data. See Tables 1 and 2 for a list of the reprocessed Kepler cadence data. See Table 3 for a list of the available FFIs. The Long Cadence Data, Short Cadence Data, and FFI data are documented in these data release notes. The ancillary files (i.e., cotrending basis vectors, artifact removal pixels, background, and collateral data) are described in the Archive Manual (Thompson et al., 2016)

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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