Genome-wide simple sequence repeat analysis and specific molecular marker development of rye.

Background: Rye (Secale cereale L.) is the most widely used related species in wheat genetic breeding, and the introduction of its chromosome fragments into the wheat genome through distant hybridization is essential for enriching the genetic diversity of wheat. Rapid and accurate detection of rye chromatin in the wheat genome is important for distant hybridization.

Molecular Cytogenetic Characterization of Novel Wheat-Rye T1RS.1AL Translocation Lines with Resistance to Powdery Mildew and Stripe Rust Derived from the Chinese Rye Landrace Qinling.

Stripe rust and powdery mildew are serious diseases that severely decrease the yield of wheat. Planting wheat cultivars with powdery mildew and stripe rust resistance genes is the most effective way to control these two diseases. Introducing disease resistance genes from related species into the wheat genome via chromosome translocation is an important way to improve wheat disease resistance.

Identification of a stable major-effect quantitative trait locus for pre-harvest sprouting in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping.

A major and stable QTL cQSGR.sau.3D, which can explain 33.

Molecular Cytogenetic and Physiological Characterization of a Novel Wheat-Rye T1RS.1BL Translocation Line from L. Weining with Resistance to Stripe Rust and Functional "Stay Green" Trait.

In this study, a novel T1RS.1BL translocation line RT843-5 was selected from a cross between wheat Mianyang11 (MY11) and Weining rye. The results of MC-FISH, PCR, and A-PAGE showed that RT843-5 contained two intact T1RS.

Development and Molecular Cytogenetic Characterization of Novel Primary Wheat-Rye 1RS.1BL Translocation Lines from Multiple Rye Sources with Resistance to Stripe Rust.

Stripe rust (caused by f. sp. ) is one of the most severe diseases for wheat production.

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

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.