Production of p Nuclei from r-Process Seeds: The νr Process.

We present a new nucleosynthesis process that may take place on neutron-rich ejecta experiencing an intensive neutrino flux. The nucleosynthesis proceeds similarly to the standard r process, a sequence of neutron captures and beta decays with, however, charged-current neutrino absorption reactions on nuclei operating much faster than beta decays. Once neutron-capture reactions freeze out the produced r process, neutron-rich nuclei undergo a fast conversion of neutrons into protons and are pushed even beyond the ß stability line, producing the neutron-deficient p nuclei. This scenario, which we denote as the νr process, provides an alternative channel for the production of p nuclei and the short-lived nucleus ^{92}Nb. We discuss the necessary conditions posed on the astrophysical site for the νr process to be realized in nature. While these conditions are not fulfilled by current neutrino-hydrodynamic models of r-process sites, future models, including more complex physics and a larger variety of outflow conditions, may achieve the necessary conditions in some regions of the ejecta.

Improved estimation of functional enrichment in SNP heritability using feasible generalized least squares.

Functional enrichment results typically implicate tissue or cell-type-specific biological pathways in disease pathogenesis and as therapeutic targets. We propose generalized linkage disequilibrium score regression (g-LDSC) that requires only genome-wide association studies (GWASs) summary-level data to estimate functional enrichment. The method adopts the same assumptions and regression model formulation as stratified linkage disequilibrium score regression (s-LDSC). Although s-LDSC only partially uses LD information, our method uses the whole LD matrix, which accounts for possible correlated error structure via a feasible generalized least-squares estimation. We demonstrate through simulation studies under various scenarios that g-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless of model misspecification. In an application to GWAS summary statistics of 15 traits from the UK Biobank, estimates of functional enrichment using g-LDSC were lower and more realistic than those obtained from s-LDSC. In addition, g-LDSC detected more significantly enriched functional annotations among 24 functional annotations for the 15 traits than s-LDSC (118 vs. 51).