SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

Clitoria ternatea blue petal extract protects against obesity, oxidative stress, and inflammation induced by a high-fat, high-fructose diet in C57BL/6 mice.

This study examined the chemical compounds and bioactivity of the aqueous extract of Clitoria ternatea blue petals and investigated its beneficial effects in vivo on a mouse model of obesity and metabolic syndrome. The extract mainly contained flavonoids, and nine compounds were tentatively identified. Male C57BL/6J mice were either fed a standard diet (SD) or a high-fat, high-fructose diet (HFFD) for 16 weeks, and HFFD-fed animals were treated with 0.25%, 0.5%, and 2% (w/w) of the aqueous extract in drinking water. The aqueous extract ameliorated oxidative stress and inflammation mediators. Furthermore, the aqueous extract reduced plasma leptin, free fatty acid, low-density lipoprotein cholesterol levels and hepatic malondialdehyde content. The aqueous extract significantly reduced total cholesterol and ameliorated insulin resistance. The results demonstrated that the aqueous extract of C. ternatea blue petals contains bioactive anthocyanins that exert substantial hypolipidemic and anti-inflammatory effects by promoting reverse cholesterol transport in HFFD-fed mice.