Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

Nitrogen-doped species (NDs) are theoretically accepted as a determinant of the catalytic activity of metal-free N-doped carbon (NC) catalysts for oxygen reduction reaction (ORR). However, direct relationships between ND type and ORR activity have been difficult to extract because the complexity of carbon matrix impairs efforts to expose specific NDs. Herein, we demonstrate the fabrication of a 3D hierarchically porous NC catalyst with micro-, meso-, and macroporosity in one structure, in which sufficient exposure and availability of inner-pore catalytic sites can be achieved due to its super-high surface area (2191 cm(2)  g(-1) ) and interconnected pore system. More importantly, in-situ formation of graphitic-N species (GNs) on the surface of NC stimulated by KOH activation enables us to experimentally reveal the catalytic nature of GNs for ORR, which is of great significance for the design and development of advanced metal-free NC electrocatalysts.

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The causal relationship between CHM and liver injury is much complex, and the clinical characteristics of DILI caused by CHM differ from those caused by WM.