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The plant microbiome is known to benefit host health in numerous ways, including providing protection against pathogens. Here, we provide evidence that tomato seed-associated microbiota play an important role in early seedling health. To test the importance of seed epiphytic communities for seedling susceptibility to the common bacterial pathogen, Pseudomonas syringae pv. tomato, we transplanted naturally occurring seed epiphytic microbial communities back onto seeds prior to germination and compared disease susceptibility to those that were not reinoculated after surface sterilization. We found that the epiphytic microbiome can protect seedlings against Pseudomonas syringae pv. tomato establishment and disease, and we further show that this protective effect is not due to a genotype by microbiome interaction. Using 16S amplicon sequencing, we found that these microbiomes were dominated by species of Pantoea, and we took a culturing approach to show that these isolates, including both Pantoea agglomerans and Pantoea dispersa, are sufficient to protect against Pseudomonas syringae pv. tomato. By varying concentration of first the pathogen and then the protective symbiont, we then examined the dose response of protection and found that applying a higher concentration of protective inoculum to the seeds does not correlate to a lessened Pseudomonas syringae pv. tomato load but does reduce disease burden. Instead, the most protective dose of Pantoea in terms of Pseudomonas syringae pv. tomato growth corresponds to the original density at which protective bacteria were found on seeds. Overall, our findings contribute to a broader understanding of the importance of vertically transmitted plant-associated microbes, with implications for the design and efficacy of biocontrol agents.

This article discusses a study on creating broad-spectrum resistance to bacterial blight in rice using targeted insertion. The researchers used CRISPR/Cas9 gene editing to insert effector binding elements (EBEs) into rice genes, resulting in resistance against diverse strains of Xanthomonas oryzae pv. oryzae (Xoo), the bacteria that causes bacterial blight. The study found that incorporating multiple EBEs into rice genes increased resistance to a greater number of Xoo strains. The researchers also used genome sequencing to identify novel EBEs and demonstrated the effectiveness of sequencing-guided EBE deployment in enhancing resistance. The study suggests that this programmable immunity system could help counter resistance breakdown in rice varieties. [Extracted from the article]

Cotton topping plays a crucial and indispensable role in controlling excessive growth and enhancing cotton production. This study aims to improve the operational efficiency and accuracy of cotton topping robots through a real-time and accurate cotton top bud detection algorithm tailored for field operation scenarios. We propose a lightweight structure based on YOLOv8n, replacing the C2f module with the Cross-Stage Partial Networks and Partial Convolution (CSPPC) module to minimize redundant computations and memory access. The network's neck employs an Efficient Reparameterized Generalized-FPN (Efficient RepGFPN) to achieve high-precision detection without substantially increasing computational cost. Additionally, the loss calculation of the optimized prediction frame was addressed with the Inner CIoU loss function, thereby enhancing the precision of the model's prediction box. Comparison experiments indicate that the Bud-YOLO model is highly effective for detecting cotton top buds, with an AP50 of 99.2%. This performance surpasses that of other YOLO variants, such as YOLOv5s and YOLOv10n, as well as the conventional Faster R-CNN model. Moreover, the Bud-YOLO model exhibits robust performance across various angles, occlusion conditions, and bud morphologies. This study offers technical insights to support the migration and deployment of the model on cotton topping machinery. [ABSTRACT FROM AUTHOR]