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Barley leaf stripe, caused by Pyrenophora graminea, is an essential systemic seed-borne disease in barley worldwide. Barley is a major cereal crop in the Qinghai–Tibet Plateau, and barley production has been threatened by leaf stripe in this region, particularly in organic farming regions. Detecting the pathogen in infected barley seeds is crucial for managing barley leaf stripe. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed to detect the pathogen based on primers designed based on the sequence of the pig 14 gene (GenBank: AJ277800) of P. graminea. The optimal concentrations of MgSO4, dNTPs, and enzymes in the LAMP reaction system were established as 10.0 mM, 1.0 mM, and 8 U in a 25 μL reaction volume, respectively. The established LAMP methods for detecting P. graminea were optimally performed at 63 °C for 70 min with high reliability. The minimum detection limit was 1 × 10−2 ng·μL−1 in the 25 μL reaction system. The specificity of LAMP for P. graminea was validated with eight fungal species. All DNA extracts from P. graminea-infected barley seeds with incubation, intact, and smashed treatments were applied in LAMP and confirmed to enable the detection of the pathogen. The LAMP assay in this study could facilitate the detection of P. graminea in barley seeds onsite, provide information for seed health certificates, and help decide on seed treatment in leaf stripe management. [ABSTRACT FROM AUTHOR]

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]

Selenium (Se) is essential for the basic functions of life, but the low daily intake of Se urges us to find reliable ways to increase food Se content. Plant-growth-promoting bacteria (PGPB) have shown potential in enhancing plant growth and Se accumulation. In this study, the soils collected from a Se tailing were used to isolate Se-tolerant PGPB. The results showed that a total of three strains were identified. Strain LWS1, belonging to Priestia sp., grew well in M9 medium and exhibited typical PGP characteristics by an IAA-production ability of 24.3 ± 1.37 mg·L−1, siderophore-production ability of 0.23 ± 0.04 and phosphate-solubilizing ability of 87.5 ± 0.21 mg·L−1. Moreover, LWS1 strain tolerated selenite (SeIV) up to 90 mM by a LC50 of 270.4 mg·L–1. Further investigations demonstrated that the inoculation of strain LWS1 resulted in up to 19% higher biomass and 75% higher Se concentration in rice (Oryza sativa L.) than uninoculated treatments. Our study has provided evidence that microbial Se biofortification through inoculating with Priestia sp. strain LWS1 is an alternative way to improve Se uptake in crops and maintain human health. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases in wheat. Due to the large-scale and widely-distributed planting pattern of wheat, the directional selection pressure of the pathogen is very strong. Therefore, it is urgent to pyramid more stripe rust resistance genes in wheat cultivars to enhance resistance durability and ensure wheat production safety. In this study, two quantitative trait loci (QTL) for adult plant resistance (APR) to stripe rust, QYr.nafu-2BL and QYr.nafu-3BS, were validated and introgressed from wheat line P9897 into three Chinese elite wheat cultivars, Chuanmai 42, Xiangmai 25, and Zhengmai 9023, through marker validation. The three Chinese elite varieties were used as the female parent to cross with wheat line P9897, and they were selfed to the F6 generation. A total of 114 lines were then selected based on field agronomic traits and stripe rust resistance. Four markers (Xcfd73, Xgwm120, Xbarc87 and Xbarc133) linked with the QTL's regions were employed to screen the 114 F6 lines. Subsequently, 27 lines combining two target QTL from P9897 were selected. The combination of agronomic traits and disease resistance results showed that 13 of these selected lines had favorable application prospects. The promising lines selected in this study could enrich the genetic resources of wheat stripe rust resistance genes, as well as provide material support and a theoretical basis for the prevention and control of wheat stripe rust in China. [ABSTRACT FROM AUTHOR]