Fine mapping and validation of a stable QTL for thousand-kernel weight in wheat(Triticum aestivum L.)

Thousand-kernel weight(TKW)is a measure of grain weight,a target of wheat breeding.The object of this study was to fine-map a stable quantitative trait loci(QTL)for TKW and identify its candidate gene in a recombinant inbred line(RIL)population derived from the cross of Kenong 9204(KN9204)and Jing411(J411).On a high-density genetic linkage map,24,26 and 25 QTL were associated with TKW,kernel length(KL),and kernel width(KW),respectively.A major and stable QTL,QTkw-2D,was mapped to an8.3 cM interval on chromosome arm 2DL.By saturation of polymorphic markers in its target region,QTkw-2D was confined to a 9.13 Mb physical interval using a secondary mapping population derived from a residually heterozygous line(F6:7).This interval was further narrowed to 2.52 Mb using QTkw-2D near-isogenic lines(NILs).NILs~(KN9204)had higher fresh and dry weights than NILsJ411at various grain-filling stages.The TKW and KW of NILs~(KN9204)were much higher than those of NILsJ411in field trials.By comparison of both DNA sequence and expression between KN9204 and J411,TraesCS2D02G460300.1(TraesKN2D01HG49350)was assigned as a candidate gene for QTkw-2D.This was confirmed by RNA sequencing(RNA-seq)of QTkw-2D NILs.These results provide the basis of map-based cloning of QTkw-2D,and DNA markers linked to the candidate gene may be used in marker-assisted selection.

PanGu Drug Model: learn a molecule like a human

Dear Editor,Recent achievements in large-scale pre-trained models like GPT-3 and PanGu-α have demonstrated astounding performances in many downstream tasks of natural language processing (NLP),confirming AI to be user-oriented for even industrial applications.Deep learning has been recognized as the most promising technology for pharmaceuticals,a powerful molecule pre-trained model that could economize researchers’tons of time.For the strategic application of AI capabilities to the drug discovery field,we pre-trained a model called PanGu Drug Model with 1.7 billion small molecules from ZINC20 (Irwin et al.,2020),DrugSpaceX(Yang et al.,2021),and UniChem (Chambers et al.,2013).

Adaptive evolution to the natural and anthropogenic environment in a global invasive crop pest,the cotton bollworm

The cotton bollworm,Helicoverpa armigera,is set to become the most economically devastating crop pest in the world,threatening food security and biosafety as its range expands across the globe.Key to understanding the eco-evolutionary dynamics of H.armigera,and thus its management,is an understanding of population connectivity and the adaptations that allow the pest to establish in unique environments.We assembled a chromosome-scale reference genome and re-sequenced 503 individuals spanning the species range to delineate global patterns of connectivity,uncovering a previously cryptic population structure.