BISON validation to in situ cladding burst test and high-burnup LOCA experiments - N. Capps; M. Ridley; Y. Yan; S. Bell; K. Kane (Annals of Nuclear Energy, 2023)

Assessing relationships between onsite wastewater treatment system maintenance patterns and system-level variables - Kyle N. Connelly; Seth J. Wenger; Nandita Gaur; Jacob M. Bateman McDonald; Mike Occhipinti; Krista A. Capps (Science of The Total Environment, 2023)

Cover Image: Volume 25 Number 3, March 2022 - Joseph A. Tobias; Catherine Sheard; Alex L. Pigot; Adam J. M. Devenish; Jingyi Yang; Ferran Sayol; Montague H. C. Neate‐Clegg; Nico Alioravainen; Thomas L. Weeks; Robert A. Barber; Patrick A. Walkden; Hannah E. A. MacGregor; et al. (Ecology Letters, 2022)

AVONET: morphological, ecological and geographical data for all birds - Joseph A. Tobias; Catherine Sheard; Alex L. Pigot; Adam J. M. Devenish; Jingyi Yang; Ferran Sayol; Montague H. C. Neate‐Clegg; Nico Alioravainen; Thomas L. Weeks; Robert A. Barber; Patrick A. Walkden; Hannah E. A. MacGregor; et al. (Ecology Letters, 2022)
Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species‐level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major radiation of organisms, offering a global template for testing hypotheses and exploring the evolutionary origins, structure and functioning of biodiversity.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The 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.

American Elm: Building in Resistance to Dutch Elm Disease - Z.-M. Cheng; N.-Q. Shi; D. E. Herman; T. K. Capps (Journal of Forestry, 1997)