Online change detection in exponential families with unknown parameters

International audienceThis paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common statistical models and corresponding distance functions. Using results from convex duality, we also derive an efficient scheme to compute the exact statistics sequentially, which allows their use in online settings where they are usually approximated for the sake of tractability. This is applied to real-world datasets of various natures, including onset detection in audio signals

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

The development of a destructive sampling method designed for high quality production processes (DSM-HQ)

Destructive sampling, High quality, Monitoring,

Concise formulas for the standard errors of component loading estimates

asymptotic standard errors, principal component analysis, component loadings, rotation, information matrix, least squares,