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.

An optimisation-based iterative approach for speckle tracking echocardiography - Neda Azarmehr; Xujiong Ye; Joseph D. Howes; Benjamin Docking; James P. Howard; Darrel P. Francis; Massoud Zolgharni (Medical & Biological Engineering & Computing, 2020)
Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking.

Leveraging a Landscape-Level Monitoring and Assessment Program for Developing Resilient Shorelines throughout the Laurentian Great Lakes - Donald G. Uzarski; Douglas A. Wilcox; Valerie J. Brady; Matthew J. Cooper; Dennis A. Albert; Jan J. H. Ciborowski; Nicholas P. Danz; Anne Garwood; Joseph P. Gathman; Thomas M. Gehring; Greg P. Grabas; Robert W. Howe; et al. (Wetlands, 2019)

Standardized Measures of Coastal Wetland Condition: Implementation at a Laurentian Great Lakes Basin-Wide Scale - Donald G. Uzarski; Valerie J. Brady; Matthew J. Cooper; Douglas A. Wilcox; Dennis A. Albert; Richard P. Axler; Peg Bostwick; Terry N. Brown; Jan J. H. Ciborowski; Nicholas P. Danz; Joseph P. Gathman; Thomas M. Gehring; et al. (Wetlands, 2017)

Using extended pedigrees to identify novel autism spectrum disorder (ASD) candidate genes - Marc Woodbury-Smith; Andrew D. Paterson; Bhooma Thiruvahindrapduram; Anath C. Lionel; Christian R. Marshall; Daniele Merico; Bridget A. Fernandez; Eric Duku; James S. Sutcliffe; Irene O’Conner; Christina Chrysler; Ann Thompson; et al. (Human Genetics, 2015)

Cigarette smoking, body mass index, gastro-esophageal reflux disease, and non-steroidal anti-inflammatory drug use and risk of subtypes of esophageal and gastric cancers by P53 overexpression - Jonine D. Figueroa; Mary Beth Terry; Marilie D. Gammon; Thomas L. Vaughan; Harvey A. Risch; Fang-Fang Zhang; David E. Kleiner; William P. Bennett; Christine L. Howe; Robert Dubrow; Susan T. Mayne; Joseph F. Fraumeni; et al. (Cancer Causes & Control, 2009)