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

Identification of humpback whale breeding and calving habitat in the Great Barrier Reef

During the winter months, from June to September, humpback whales Megaptera novaeangliae breed and calve in the waters of the Great Barrier Reef (GBR) after migrating north from Antarctic waters. Clearly defined wintering areas for breeding and calving comparable to those identified in other parts of the world have not yet been identified for humpback whales in the GBR Marine Park (GBRMP), mainly because of its large size, which prohibits broad-scale surveys. To identify important wintering areas in the GBRMP, we developed a predictive spatial habitat model using the Maxent modelling method and presence-only sighting data from nondedicated aerial surveys. The model was further validated using a small independent satellite tag data set of 12 whales migrating north into the GBR. The model identified restricted ranges in water depth (30 to 58 m, highest probability 49 m) and sea surface temperature (21 to 23°C, highest probability 21.8°C) and identified 2 core areas of higher probability of whale occurrence in the GBRMP, which correspond well with the movements of satellite tagged whales. We propose that one of the identified core areas is a potentially important wintering area for humpback whales and the other a migration route. With an estimated increase in port and coastal development and shipping activity in the GBRMP and a rapidly increasing population of whales recovering from whaling off the east Australian coast, the rate of human interactions with whales is likely to increase. Identifying important areas for breeding and calving is essential for the future management of human interactions with breeding humpback whales

Learning discriminative sufficient statistics score space for classification

Abstract. Generative score spaces provide a principled method to exploit generative information, e.g., data distribution and hidden variables, in discriminative classifiers. The underlying methodology is to derive measures or score functions from generative models. The derived score functions, spanning the so-called score space, provide features of a fixed dimension for discriminative classification. In this paper, we propose a simple yet effective score space which is essentially the sufficient statistics of the adopted generative models and does not involve the parameters of generative models. We further propose a discriminative learning method for the score space that seeks to utilize label information by constraining the classification margin over the score space. The form of score function allows the formulation of simple learning rules, which are essentially the same learning rules for a generative model with an extra posterior imposed over its hidden variables. Experimental evaluation of this approach over two generative models shows that performance of the score space approach coupled with the proposed discriminative learning method is competitive with state-of-the-art classification methods

Foraging ecology and interactions with fisheries of wandering albatrosses (Diomedea exulans) breeding at South Georgia

Knowledge about the areas used by the foraging wandering albatross, Diomedea exulans, its prey and overlap with longline fisheries is important information not only for the conservation of this species but also for furthering our understanding of the ecology of its prey. We attached satellite-tracking devices and activity recorders to wandering albatrosses between May and July of 1999 and 2000 (years of differing food availability around South Georgia) in order to assess inter-annual variation in the main foraging areas, association with oceanographic features (i.e. fronts, bathymetry), diet and interactions with fisheries. The overall foraging patterns of the tracked birds were similar in 1999 and 2000, ranging between southern Brazil (28degreesS) and the Antarctic Peninsula (63degreesS) and between the waters off Tristan da Cunha (19degreesW) and the Patagonian Shelf and oceanic waters south of Cape Horn (68degreesW) in the South Atlantic. In 1999, wandering albatrosses spent most time in sub-Antarctic oceanic waters, their trip durations were significantly longer and they fed on fish and cephalopods (53 and 42% by mass, respectively). In contrast, in 2000, they spent more time in Antarctic waters, foraging trips were shorter and the diet was predominantly fish (84% by mass). Wandering albatrosses were associated with the sub-Antarctic Front (SAF; both years), Subtropical Front (STF; in 1999) and the Tropical Front (TF; in 2000) suggesting that this species exploits prey concentrated at oceanic fronts. Fisheries discards also seemed to provide a very good source of food. Several fish species that are targeted (e.g. Patagonian toothfish, Dissostichus eleginoides) or are available as offal/discards from commercial fisheries (e.g. the macrourids, Antimora rostrata and Macrourus holotrachys) were mainly associated with the South Georgia shelf and the Patagonian Shelf, respectively. Wandering albatross foraging areas overlapped with longline fisheries in three different regions: around South Georgia, at the Patagonian Shelf and in oceanic waters north of 40degreesS. Females commuted more frequently to the Patagonian Shelf and to oceanic areas where longline fisheries were operating. Males, on the other hand, spent more time on the shelf/shelf slope of South Georgia where they were more at risk from the local Patagonian toothfish fishery, particularly in 2000. These results emphasize that inter-annual variation in foraging preferences could lead to increased incidental mortality of this vulnerable species. Potential evidence for this is provided by a satellite-tracked wandering albatross (male; 1.8-day trip), whose diet contained a Patagonian toothfish head and a longline hook, and who spent extensive time in the water (44% of the time wet; 0.3 days of the trip) where a Patagonian toothfish longline fishing vessel was operating