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Background: Congenital heart disease (CHD) is the most common major congenital anomaly and causes significant morbidity and mortality. Epidemiologic evidence supports a role of genetics in the development of CHD. Genetic diagnoses can inform prognosis and clinical management. However, genetic testing is not standardized among individuals with CHD. We sought to develop a list of validated CHD genes using established methods and to evaluate the process of returning genetic results to research participants in a large genomic study. Methods: Two-hundred ninety-five candidate CHD genes were evaluated using a ClinGen framework. Sequence and copy number variants involving genes in the CHD gene list were analyzed in Pediatric Cardiac Genomics Consortium participants. Pathogenic/likely pathogenic results were confirmed on a new sample in a clinical laboratory improvement amendments-certified laboratory and disclosed to eligible participants. Adult probands and parents of probands who received results were asked to complete a post-disclosure survey. Results: A total of 99 genes had a strong or definitive clinical validity classification. Diagnostic yields for copy number variants and exome sequencing were 1.8% and 3.8%, respectively. Thirty-one probands completed clinical laboratory improvement amendments-confirmation and received results. Participants who completed postdisclosure surveys reported high personal utility and no decision regret after receiving genetic results. Conclusions: The application of ClinGen criteria to CHD candidate genes yielded a list that can be used to interpret clinical genetic testing for CHD. Applying this gene list to one of the largest research cohorts of CHD participants provides a lower bound for the yield of genetic testing in CHD.

Physics-informed neural networks (PINN) can potentially be applied to develop computationally efficient surrogate models, perform anomaly detection, and develop time-series forecasting models. However, predicting small-scale features such as the exact location of shocks and the associated rapid changes in fluid properties across it, have proven to be challenging when using standard PINN architectures, due to spatial biasing during network training. This paper investigates the ability of PINNs to capture these features of an oblique shock by applying Fourier feature network architectures. Four PINN architectures are applied namely a standard PINN architecture with the direct and indirect implementation of the ideal gas equation of state, as well as the direct implementation combined with a standard and modified Fourier feature transformation function. The case study is 2D steady-state compressible Euler flow over a 15° wedge at a Mach number of 5. The PINN predictions are compared to results generated using proven numerical CFD techniques. The results show that the indirect implementation of the equation of state is unable to enforce the prescribed boundary conditions. The application of the Fourier feature up-sampling to the low-dimensional spatial coordinates improves the ability of the PINN model to capture the small-scale features, with the standard implementation performing better than the modified version.

Small cryptic invertebrates (the cryptofauna) are extremely abundant, ecologically important, and species rich on coral reefs. Ongoing ocean acidification is likely to have both direct effects on the biology of these organisms, as well as indirect effects through cascading impacts on their habitats and trophic relationships. Naturally acidified habitats have been important model systems for studying these complex interactions because entire communities that are adapted to these environmental conditions can be analyzed. However, few studies have examined the cryptofauna because they are difficult to census quantitatively in topographically complex habitats and are challenging to identify. We addressed these challenges by using Autonomous Reef Monitoring Structures (ARMS) for sampling reef-dwelling invertebrates >2 mm in size and by using DNA barcoding for taxonomic identifications. The study took place in Papua New Guinea at two reef localities, each with three sites at varying distances from carbon dioxide seeps, thereby sampling across a natural gradient in acidification. We observed sharp overall declines in both the abundance (34–56%) and diversity (42–45%) of organisms in ARMS under the lowest pH conditions sampled (7.64–7.75). However, the overall abundance of gastropods increased slightly in lower pH conditions, and crustacean and gastropod families exhibited varying patterns. There was also variability in response between the two localities, despite their close proximity, as one control pH site displayed unusually low diversity and abundances for all invertebrate groups. The data illustrate the complexity of responses of the reef fauna to pH conditions, and the role of additional factors that influence the diversity and abundance of cryptic reef invertebrates.