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

Trends in Applied Intelligent Systems

This paper presents the detection and classification part of an industrial machine for automated assembly of decorative tessellae over patterned tiles that have significant reliefs. The machine consists of two vision systems for detecting the tiles and the tessellae, and a robotic manipulator to make the assembly. One of the vision systems detects the tessellae and the other one detects and classifies the tiles, finding the positions and orientations on the tiles where tessellae have to be mounted. Lateral illumination is used to enhance shadows and characterize the relief pattern. The shadow pattern is used as the main feature for classifying a tile to a model. The method works with a variety of models, is quite robust, and achieves very good classification results, as required for an industrial application

Probabilistic Models of Object Geometry with Application to Grasping

Robot manipulators typically rely on complete knowledge of object geometry in order to plan motions and compute grasps. But when an object is not fully in view it can be difficult to form an accurate estimate of the object’s shape and pose, particularly when the object deforms. In this paper we describe a generative model of object geometry based on Mardia and Dryden’s “Probabilistic Procrustean Shape” which captures both non-rigid deformations and object variability in a class. We extend their shape model to the setting where point correspondences are unknown using Scott and Nowak’s COPAP framework. We use this model to recognize objects in a cluttered image and to infer their complete 2-D boundaries with a novel algorithm called OSIRIS. We show examples of learned models from image data and demonstrate how the models can be used by a manipulation planner to grasp objects in cluttered visual scenes.National Science Foundation (U.S.). Division of Information and Intelligent Systems (Grant No. 0546467)United States. Air Force Office of Scientific Research (STTR Contract FA9550- 06-C-0088)National Science Foundation (U.S.). Division of Computer and Network Systems (grant 0707601)National Science Foundation (U.S.) ( grant 0426838)National Science Foundation (U.S.) ( grant 0735953