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As a devastating wheat disease, Fusarium head blight (FHB) causes large losses in yield and quality. The purpose of this study was to detect the genetic loci for FHB resistance based on a genome-wide association study (GWAS) using two panels genotyped by the Wheat 660 K SNP array and Wheat 90 K SNP array. Phenotypic investigation results showed that 43.6–51.6% of the test varieties showed an infection type of more than 3, indicating that substantial improvement in the level of resistance was urgently required. GWAS detected 1264 SNPs associated with FHB response distributed on all wheat chromosomes. Fourteen QTL were stably detected in two environments. gFHB-1B (582.9~607.9 Mb on chromosome 1B) and gFHB-5A (577.9~596.5 Mb on 5A) were two novel resistance loci that were detected in two wheat panels. Haplotype analysis of both gFHB-1B and gFHB-5A indicated that varieties with gFHB-1B_R and gFHB-5A_R had consistently less FHB than varieties carrying a one or no resistance QTL. The application frequency of the FHB-resistant alleles gFHB-1B and gFHB-5A showed a gradually decreasing trend in FHB resistance breeding. This study identified new genetic loci for the control of FHB and provides useful information for marker-assisted selection in wheat FHB resistance. [ABSTRACT FROM AUTHOR]

In this paper, we first prove that the large deviation principle is preserved under a homeomorphism. As an application, we establish the large deviation principle for distribution dependent stochastic differential equations with Dini continuous drift, semilinear distribution dependent stochastic partial differential equations with Dini continuous drift, respectively. [ABSTRACT FROM AUTHOR]

In this paper, by using Girsanov's transformation and the property of the corresponding reference stochastic differential equations, we investigate weak existence and uniqueness of solutions and weak convergence of Euler-Maruyama scheme to stochastic functional differential equations with Hölder continuous drift driven by fractional Brownian motion with Hurst index H ∈ (1 / 2 , 1). [ABSTRACT FROM AUTHOR]

In this paper, under a one-sided Lipschitz condition on the drift coefficient we adopt (via contraction principle) an exponential approximation argument to investigate large deviations for neutral stochastic functional differential equations. [ABSTRACT FROM AUTHOR]

This paper focuses on the problems of weak center and local bifurcation of critical periods for a class of cubic Z2-equivariant planar Hamiltonian vector fields. By computing the period constants carefully, one can see that there are three weak centers: (±1, 0) and the origin. The corresponding weak center conditions are also derived. Meanwhile, we address the problem of the coexistence of bifurcation of critical periods that occurred from (±1, 0) and the origin. [ABSTRACT FROM AUTHOR]

Wheat stripe rust poses a marked threat to global wheat production. Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases. In this study, we propose a practical solution using mobile-based deep learning and model-assisted labeling. StripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds. Additionally, StripeRust-Pocket facilitates image acquisition, result storage, organization, and sharing. The underlying model employed by StripeRust-Pocket, called StripeRustNet, is a balanced lightweight 2-stage model. The first stage utilizes MobileNetV2-DeepLabV3+ for leaf segmentation, followed by ResNet50-DeepLabV3+ in the second stage for lesion segmentation. Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area. StripeRustNet achieves 98.65% mean intersection over union (MIoU) for leaf segmentation and 86.08% MIoU for lesion segmentation. Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores. To address the challenges in manual labeling, we introduce a 2-stage labeling pipeline that combines model-assisted labeling, manual correction, and spatial complementarity. We apply this pipeline to our self-collected dataset, reducing the annotation time from 20 min to 3 min per image. Our method provides an efficient and practical solution for wheat stripe rust severity assessments, empowering wheat breeders and pathologists to implement timely disease management. It also demonstrates how to address the "last mile" challenge of applying computer vision technology to plant phenomics., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Weizhen Liu et al.)