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A major and stable QTL cQSGR.sau.3D, which can explain 33.25% of the phenotypic variation in SGR, was mapped and validated, and cQSGR.sau.3D was found to be independent of GI. In this study, a recombinant inbred line (RIL) population containing 304 lines derived from the cross of Chuan-nong17 (CN17) and Chuan-nong11 (CN11) was genotyped using the Wheat55K single-nucleotide polymorphism array. A high-density genetic map consisting of 8329 markers spanning 4131.54 cM and distributed across 21 wheat chromosomes was constructed. QTLs for whole spike germination rate (SGR) were identified in multiple years. Six and fourteen QTLs were identified using the Inclusive Composite Interval Mapping-Biparental Populations and Multi-Environment Trial methods, respectively. A total of 106 digenic epistatic QTLs were also detected in this study. One of the additive QTLs, cQSGR.sau.3D, which was mapped in the region from 3.5 to 4.5 cM from linkage group 3D-2 on chromosome 3D, can explain 33.25% of the phenotypic variation in SGR and be considered a major and stable QTL for SGR. This QTL was independent of the seeds' germination traits, such as germination index. One Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110772653, which is tightly linked to cQSGR.sau.3D, was developed. The genetic effect of cQSGR.sau.3D on SGR in the RIL and natural populations was successfully confirmed. Furthermore, within the interval in which cQSGR.sau.3D is located in Chinese Spring reference genomes, thirty-seven genes were found. cQSGR.sau.3D may provide new resources for pre-harvest sprouting resistance breeding of wheat in the future.

Stripe rust (caused by Puccinia striiformis f. sp. tritici) is one of the most severe diseases for wheat production. An important method to improve the stripe rust resistance of wheat is to introduce resistance genes from related species into the wheat genome. The 1RS.1BL wheat–rye translocation from Petkus rye has contributed substantially to wheat resistance breeding worldwide. However, given the breakdown of the stripe rust resistance gene Yr9 in 1RS, its importance for wheat improvement has decreased. In this study, we developed 166 new primary 1RS.1BL translocation lines by crossing rye varieties Weining, Baili, and Aigan with several wheat cultivars. Cytogenetic and molecular analyses indicated that all of these lines contained a pair of intact 1RS.1BL translocation chromosomes. The stripe rust resistance of these translocation lines and their wheat parents was evaluated in southwestern China during the severe stripe rust epidemics in 2015 and 2021. The results showed diverse effects of the 1RS.1BL translocations from different rye cultivars on resistance to stripe rust. The highest genetic diversity was observed in 1RS.1BL translocations derived from diverse rye varieties but in the same wheat background. The development of diverse 1RS.1BL translocation lines offers ample opportunities to introduce new variations into wheat for improving stripe rust resistance. Finally, 71 new translocation lines, including nine developed from the cross of MY11 × Aigan, four from MY11 × Baili, 40 from MY11 × Weining, 14 from A42912 × Baili, and four from A42912 × Weining. These lines showed consistent resistance to stripe rust in fields under frequent changes of the pathogen races and could be useful genetic stocks for breeding wheat cultivars with resistance to stripe rust.

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available athttp://scaiweb.cs.ucla.edu/covidsurveiller/.This article is part of the theme issue ‘Data science approachs to infectious disease surveillance’.

The traditional education lacks of individual education such as one's entrance exam for the postgraduate school, work interviews and practical aspects of entrepreneurship. Currently many popular internet education platforms whose connection is from person to the course use B2C operating mode, in which students have to choose existing courses while being ignored their requirements of the course. In the early market research, we find there are some study obstacles among students who prepare for the final exam and who prepare for the entrance exam of postgraduates. To solve the problem without using some high-paid organizations, we try to use students'idle teaching resource which matches the concept"everyone can be the teacher".In this paper we design and implement an innovative data-driven platform namedi-Class that assists students to connect with teachers who can solve their problems about studying.Particularly,these teachers actually come from the students without any limitation. Through the platform, students have access to the suitable teachers then realize resource sharing and peer-education.