Numerical modeling of the Sakuma Dam reservoir sedimentation

YesThe present study attempts to predict the reservoir sedimentation in 32 km region of the Tenryu River between the Hiraoka and Sakuma Dams in Japan. For numerical simulations of the reservoir sedimentation, the one-dimensional model of the Hydrologic Engineering Centre-River Analysis System (HEC-RAS) is used together with the inclusion of channel geometry, bed gradation curve, Exner-5 bed sorting mechanisms, fall velocity of the particle, and flow and sediment boundary conditions pertaining to modeling region. The modeling region of the Tenryu River is divided into 48 river stations with 47 reaches in the numerical simulations. The numerical model is calibrated using the available data for 48 years from 1957 to 2004. The formulae of sediment transport function, Manning’s roughness coefficient, computational increment and fall velocity have been identified for getting the best estimation of the Sakuma Dam reservoir sedimentation. Combination of obtained sensitive parameters and erodible limits of 2 m gave the best comparison with the measured bed profile. The computed results follow the trend of measured data with a small underestimation. Although Manning’s roughness coefficient has an effect on the sedimentation, no direct relation is found between the Manning’s roughness coefficient and reservoir sedimentation. It is found that the temperature of water has no effect on the reservoir sedimentation

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk