2 research outputs found
Effects of social environment and energy efficiency on preferred swim speed in a marine generalist fish, pile perch Phanerodon vacca
No full textEnergy efficiency is a key component of movement strategy for many species. In fish, optimal swimming speed (Uₒₚₜ) is the speed at which the mass-specific energetic cost to move a given distance is minimised. However, additional factors may influence an individual's preferred swimming speed (Uₚᵣₑբ). Activities requiring consistent sensory inputs, such as food finding, may require slower swimming speeds than Uₒₚₜ. Further, although the majority of fish display some form of social behaviour, the influence of social interactions on Uₚᵣₑբ remains unclear. It is unlikely that all fish within a group will have the same Uₚᵣₑբ, and fish may therefore compromise individual Upref to swim with a conspecific. This study measured the Uₒₚₜ, Upref and Uₚᵣₑբ in the presence of a conspecific (Uₚₐᵢᵣ) of pile perch, Phanerodon vacca, a non-migratory coastal marine generalist. Uₒₚₜ was significantly higher than, and was not correlated with, Uₚᵣₑբ. Fish therefore chose to swim at speeds below their energetic optimum, possibly because slower swimming allows for greater awareness of surroundings. Mean Uₚₐᵢᵣ was significantly lower than the Uₚᵣₑբ of the faster fish in each pair but did not differ significantly from the Uₚᵣₑբ of the slower fish. Therefore, faster fish appear to slow their speed to remain with a slower conspecific. Our study suggests that environmental factors, including social surroundings, may be more important than energetic efficiency for determining swim speed in P. vacca. Further studies of fish species from various habitats will be necessary to elucidate the environmental and energetic factors underpinning Uₚᵣₑբ
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
No full textThe combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)
