Heart failure is a leading cause of morbidity and mortality. Around 4% of patients with heart failure carry a pathogenic genetic aberration that causes cardiomyopathy and subsequently leads to heart failure. There are five types of primary genetic cardiomyopathies that can give rise to heart failure: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy, arrhythmogenic cardiomyopathy (ACM), restrictive cardiomyopathy (RCM), and left ventricular noncompaction (LVNC). If genetic cardiomyopathy is suspected, genomic/genetic testing is recommended because it provides the underlying cause for the diagnosis, prognostic parameters, and possibility to test family members at risk. Testing should be conducted as part of a multidisciplinary approach by a team of adult or paediatric cardiologists, geneticists, and genetic counsellors. Here we will discuss 1) different genomic testing approaches and the management of variants of uncertain significance, 2) management of patients with suspected genetic cardiomyopathy in a multidisciplinary team, and 3) the associations between genotypes and phenotypes of most commonly mutated genes such as MYH7, TNNT2, TPM1, MYBPC3, TTN, and others. In conclusion, genetic testing of patients with cardiomyopathies helps with proper diagnosis, prognosis, treatment, and identification of relatives at risk.

The aim of this article is to determine the contamination influence on the parameters of gas flow through multihole orifice (MHO) meter. The numerical investigations of the contamination influence for the MHO flow meters have not been reported in the previous researches. The air flow was steady, 3-D, and turbulent. The finite volume method was used for the purpose of numerical analyses. The main considered physical properties of air were density and dynamic viscosity. The standard $k-\varepsilon $ turbulence model was used. MHO meter with two different $\beta $ parameters was observed. Also, the influence of contamination formed in front of the MHO meter with the same $\beta $ parameters was analyzed. In order to qualitatively analyze the influence of the contamination, the 15 different combinations of contamination parameters for seven different Reynolds numbers were analyzed. The pressure drop, singular pressure loss coefficient, and discharge coefficient were analyzed. The grid sensitivity study was performed on four systematically refined numerical grids for MHO meter without contamination and the results were compared with the experimental results found in the literature. Also, the grid refinement was done for MHO meter with contamination for two different values of Reynolds number. It was found that for the same values of contamination angle, regardless of the contamination parameters ratio, the results were unchanged. Also, it was found that the contamination has an influence on the change of pressure drop values, which directly affects the change of other parameters. Pressure drop and singular pressure loss coefficient of the orifice with contamination are smaller compared to the values for a pure orifice, whereby the measurement accuracy was reduced. Also, for cases of contamination, the discharge coefficient was increased, leading to a negative measurement error. It was found that the same trend occurs regardless of the Reynolds number. It was found that the MHO meter was less sensitive to the pressure drop changes due to the increase of contamination angle in regard to the single-hole orifice meters.

Recent studies on the heritability of methylation patterns in tumor cells, suggest that tumor heterogeneity and progression can be studied through methylation changes. To elucidate methylation-based evolution trajectories in tumors, we introduce a novel computational framework for methylation phylogeny reconstruction, leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), additionally incorporating copy number information inferred independently from matched single cell RNA sequencing (scRNA-seq) data, when available. Our framework consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. We validate our approach with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Additionally, we show that the constructed phylogeny can be used to impute missing entries, which, in turn, may help reduce sparsity issues in scBS-seq data sets. Contact: cenk.sahinalp@nih.gov

Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.