A novel protein elicitor (PeSy1) from Saccharothrix yanglingensis induces plant resistance and interacts with a receptor-like cytoplasmic kinase in Nicotiana benthamiana.

Previously, we reported a rare actinomycete Saccharothrix yanglingensis Hhs.015 with strong biocontrol ability, which can colonize plant tissues and induce resistance, but the key elicitor and immune mechanisms were unclear. In this study, a novel protein elicitor screened from the genome of Hhs.015, PeSy1 (protein elicitor of S. yanglingensis 1), could induce a strong hypersensitive response (HR) and resistance in plants. The PeSy1 gene encodes an 11 kDa protein with 109 amino acids that is conserved in Saccharothrix species. PeSy1-His recombinant protein induced early defence events such as a cellular reactive oxygen species burst, callose deposition, and the activation of defence hormone signalling pathways, which enhanced Nicotiana benthamiana resistance to Sclerotinia sclerotiorum and Phytophthora capsici, and Solanum lycopersicum resistance to Pseudomonas syringae pv. tomato DC3000. Through pull-down and mass spectrometry, candidate proteins that interacted with PeSy1 were obtained from N. benthamiana. We confirmed the interaction between receptor-like cytoplasmic kinase RSy1 (Response to PeSy1) and PeSy1 using co-immunoprecipitation, bimolecular fluorescence complementation, and microscale thermophoresis. PeSy1 treatment promoted up-regulation of marker genes in pattern-triggered immunity. The cell death it elicited was dependent on the co-receptors NbBAK1 and NbSOBIR1, suggesting that PeSy1 acts as a microbe-associated molecular pattern from Hhs.015. Additionally, RSy1 positively regulated PeSy1-induced plants resistant to S. sclerotiorum. In conclusion, our results demonstrated a novel receptor-like cytoplasmic kinase in the plant perception of microbe-associated molecular patterns, and the potential of PeSy1 in induced resistance provided a new strategy for biological control of actinomycetes in agricultural diseases.

Visual Sequence Algorithm for Moving Object Tracking and Detection in Images.

Objective: The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods: An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results: The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion: The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.