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Xinjiang is an important region due to its unique epidemic characteristics of wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici. Some previous studies on race identification were conducted in this region, but it is still unclear how temporal changes affect the dynamics, diversity, and virulence characteristics of Pst races in Xinjiang. To gain a better understanding, we compared the race data from spring and winter wheat crops of 2022 with that of 2021. Our results showed a significant change in virulence frequency in 2022. Vr10, Vr13, and Vr19 exhibited an increasing trend, with a frequency of ≥18%, while the maximum decline was observed in Vr1, Vr3, and Vr9, with a frequency of ≤−25%. It was found that Yr5 and Yr15 remained effective against Xinjiang Pst races. The race diversity increased from 0.92 (70 races out of 345 isolates) to 0.94 (90 races out of 354 isolates) in 2022, with G22G being the dominant race group. Race CYR34 became prevalent in the region in 2022, while the LvG grouped was wiped out in 2022, from both summer and winter crop seasons. HyG and SuG groups showed an overall declining trend. Overall prevalent races showed over-summering and over-wintering behaviors in Xinjiang. The number of new races occurrence frequency increased by 34% in 2022, indicating a potential change in the population structure of Pst. It is crucial to introduce newly resistant gene cultivars in this region and to establish rust-monitoring protocols to prepare for any future epidemics. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a globally significant fungal disease that seriously threatens wheat yield, particularly in China. This study investigates the genetic structure and reproductive patterns of Pst populations in Ili, Xinjiang, using 12 pairs of Simple Sequence Repeat (SSR) molecular markers. Analyses of 79 Pst isolates from either spring or winter wheat areas in Ili revealed three primary genetic clusters, indicating notable differences between populations associated with spring and winter wheat. The STRUCTURE results, complemented by UPGMA and PCoA analyses, highlight significant genetic diversity within these populations, with evidence of genetic recombination and sexual reproduction in certain areas. Pst populations in Ili exhibit a mixed mode of reproduction, predominantly sexual in Qapqal and Xinyuan D and primarily asexual within the spring wheat populations. The gene flow analysis underscores extensive inter-population communication, which facilitates the spread and adaptation of the pathogen across diverse wheat-growing environments. This study marks the first documentation of sexual reproduction in Pst within Xinjiang, providing new insights into its spread and genetic variation. These findings suggest that sexual reproduction may play a role in the regional adaptation and evolution of Pst, impacting future management strategies for wheat stripe rust in Xinjiang and potentially in broader Central Asian contexts. [ABSTRACT FROM AUTHOR]

Deep learning approaches have been widely applied for agricultural disease detection. However, considerable challenges still exist, such as low recognition accuracy in complex backgrounds and high misjudgment rates for similar diseases. This study aimed to address these challenges through the detection of six prevalent wheat diseases and healthy wheat in images captured in a complex natural context, evaluating the recognition performance of five lightweight convolutional networks. A novel model, named MnasNet-SimAM, was developed by combining transfer learning and an attention mechanism. The results reveal that the five lightweight convolutional neural networks can recognize the six different wheat diseases with an accuracy of more than 90%. The MnasNet-SimAM model attained an accuracy of 95.14%, which is 1.7% better than that of the original model, while only increasing the model's parameter size by 0.01 MB. Additionally, the MnasNet-SimAM model reached an accuracy of 91.20% on the public Wheat Fungi Diseases data set, proving its excellent generalization capacity. These findings reveal that the proposed model can satisfy the requirements for rapid and accurate wheat disease detection. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic cholecystectomy (LC) is the gold standard for treating symptomatic gallstones but carries inherent risks like bile duct injury. While the critical view of safety (CVS) is advocated to mitigate bile duct injury, its real-world adoption is limited. Additionally, significant variations in surgeon performance impede procedural standardization, highlighting the need for a feasible, innovative, and effective training approach. The aim of this study is to develop an artificial intelligence (AI)-assisted coaching program for LC to enhance surgical education and improve surgeon's performance., Materials and Methods: The authors conducted a multicenter, randomized controlled trial from May 2022 to August 2023 to assess the impact of an AI-based coaching program, surgical coaching program, on novice performing LC. Surgeons and patients meeting specific inclusion criteria were randomly assigned to either a coaching group with AI-enhanced feedback or a self-learning group. The primary outcome was assessed using the Laparoscopic Cholecystectomy Rating Form, with secondary outcomes including surgical safety, efficiency, and adverse events. Statistical analyses were performed using SPSS, with significance set at a P -value less than 0.05., Results: Between May 2022 and August 2023, 22 surgeons were initially enrolled from 10 hospitals, with 18 completing the study. No demographic differences were noted between coaching and self-learning groups. In terms of surgical performance (Laparoscopic Cholecystectomy Rating Form scores), the coaching group showed significant improvement over time (31 to 40, P =0.008), outperforming the self-learning group by study end (40 vs 38, P =0.032). Significant improvements in CVS achievement were also noted in the coaching group (11% to 78%, P =0.021). Overall, the coaching program was well-received, outpacing traditional educational methods in both understanding and execution of CVS and participants in the intervention group expressed strong satisfaction with the program., Conclusions: The AI-assisted surgical coaching program effectively improved surgical performance and safety for novice surgeons in LC procedures. The model holds significant promise for advancing surgical education., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Common bunt caused by Tilletia laevis Kühn is one of the most serious fungal diseases of wheat. The root-microbial associations play key roles in protecting plants against biotic and abiotic factors. Managing these associations offers a platform for improving the sustainability and efficiency of agriculture production. Here, by using high throughput sequencing, we aimed to identify the bacterial and fungal associations in wheat, alfalfa, and oat crops cultivated in different years in the Gansu province of China. Soil samples (0-6 cm below the surface) from infected wheat by T. laevis had significantly more bacterial and fungal richness than control samples as per the Chao1 analysis. We found some dominant fungi and bacterial phyla in infected wheat by T. laevis , such as Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Ascomycota, Basidiomycota, and Mortierello mycota . We also analyzed the chemical and enzymatic properties of soil samples after T. laevis inoculation. The total nitrogen, total kalium (TK), ammonium nitrogen, available kalium, organic carbon, invertase, phosphatase, and catalase were more in T. laevis -infected samples as compared to the control samples, while pH, total phosphorus, nitrate nitrogen, available phosphorus, and urease were more in control samples compared to T. laevis -infected samples. The results of this study will contribute to the control of wheat common bunt by candidate antagonistic microorganisms and adverse properties of soil., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shen, Delai, Liu, Chen, Li, Gao and Gao.)

Background: Stripe rust, caused by the fungus Puccinia striiformis f.sp. tritici ( Pst ), poses a significant threat to global wheat production., Objectives: This study aims to analyze the distribution of stripe rust resistance genes, characterize resistance phenotypes at the seedling stage of 137 spring and 149 winter wheat varieties in Xinjiang, China, and discern differences in resistance between spring and winter wheat varieties., Design: We used various Pst races (CYR23, CYR29, CYR31, CYR32, CYR33, CYR34) to characterize seedling resistance of spring and winter wheat varieties and to correlate resistance to the presence of wheat resistance genes ( Yr5 , Yr9 , Yr10 , Yr15 , Yr17 , Yr18 , Yr26 , Yr41 , Yr80 , Yr81 ) using molecular markers., Results: Among spring wheat varieties, 62, 60, 42, 26, 51, and 24 varieties exhibited resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Among winter wheat varieties, 66, 32, 69, 26, 83, 40 varieties demonstrated resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Molecular testing revealed that, in spring wheat, 2, 17, 21, 61, 10, 0, 10, 79, and 32 varieties carried Yr9 , Yr10 , Yr15 , Yr17 , Yr18 , Yr26 , Yr41 , Yr80 , and Yr81 genes, respectively. In winter wheat, 40, 20, 7, 143, 15, 1, 6, 38, and 54 varieties carried Yr9 , Yr10 , Yr15 , Yr17 , Yr18 , Yr26 , Yr41 , Yr80 , and Yr81 genes, respectively. Notably, winter wheat exhibited a significantly higher resistance frequency than spring wheat, particularly in the incidence of Yr9 , Yr10 , Yr17 , Yr18 , and multi-gene combinations., Conclusion: In summary, this study provides information on seedling stage resistance to stripe rust 286 Xinjiang wheat varieties, elucidates the distribution of resistance genes in this population, and offers a mechanistic basis for breeding durable resistance in wheat. varieties from Xinjiang., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lai, Shen, Yang, Fernando, Ren, Deng, Lu, Sun, Chen, Li, Wang, Gao and Li.)