No abstract

Wheat stripe rust can cause considerable yield losses, and genetic resistance is the most effective approach for controlling the disease. To identify the genomic regions responsible for Puccinia striiformis f. sp. tritici (Pst) resistance in a set of winter wheat strains mainly from southwestern China, and to identify DNA markers in these regions, we carried out a genome-wide association study (GWAS) of 120 China winter wheat accessions using single nucleotide polymorphism (SNP) markers from 90K wheat SNP arrays. In total, 16 SNP loci were significantly associated with wheat stripe rust in field and greenhouse trials. Of these, three distinctive SNPs on chromosomes 1B, 4A, and 6A were identified at a site in Mianyang in 2014, where the most prevalent wheat stripe rust races since 2009 have been V26 (G22-9, G22-14). This suggests that the three SNP loci were linked to the new quantitative trait loci (QTL)/genes resistant to the V26 races. Germplasm with immunity to Pst is a good source of stripe rust resistance for breeding, and after further validation, SNPs closely linked to resistance QTLs/genes could be converted into userfriendly markers and facilitate marker-assisted selection to improve wheat stripe rust resistance.

The biomass production (BP), the leaf age (LA), and the plant height (PH) as well as the quantitative trait loci (QTLs) associated with these traits were determined for F 2:3 population derived from the cross of two contrasting maize (Zea mays L.) genotypes: 082 and Ye107. By using composite interval mapping, a total of 12 and 12 distinct QTLs were identified at Kaixian and Southwest University under deficient phosphorus. Another 9 and 8 distinct QTLs were identified at two sites under normal phosphorus, respectively. Seven coincident QTLs for two traits (BP and LA) were detected in the interval bnlg1832-P2M8/j (bin 1.05) on Chromosome 1, and four consistent QTLs for one trait (PH) were coincident in the interval umc1102-P1M7/d (bin 3.05) on Chromosome 3. These coincident QTLs in two important genomic regions were identified under different phosphorus levels and two different environments. Therefore, the above two segments one (bnlg1832-P2M8/j) identified in Chromosome 1 and the other (umc1102-P1M7/d) identified in Chromosome 3 may be used in future for marker-assisted selection and high-resolution mapping leading to map-based cloning of QTLs for agronomically important traits under phosphorus deficiency.

The losses and damage caused by landslide are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were, respectively, prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.