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Grain of high yield and quality is needed worldwide due to the needs of a rapidly increasing human population. However, diseases caused by some stubborn types of phytopathogenic bacteria can limit the health and yields of crops. Even worse, conventional commercial bactericides have limited efficacy against such diseases. Therefore, exploring some efficacious bactericidal alternatives is urgently needed. In this work, a new type of 3-(piperazin-1-yl)propan-2-ol modified carbazole derivatives was synthesized and assessed for their bactericidal activity. Among them, compound B16 was the optimal active molecule, giving the EC50 values of 3.11 (Xanthomonas oryzae pv. oryzae), 3.20 (Xanthomonas axonopodis pv. citri) and 3.54 μg/mL (Pseudomonas syringae pv. actinidiae). Pot experiments revealed compound B16 to be able to control rice bacterial leaf blight. Some biochemical assays illustrated that our designed compounds could destroy the integrality of bacterial cell membranes and thereby leading to leaking the intracellular protein. These findings may be regard as a reference for the design of novel membrane-targeting antimicrobial agents for managing stubborn plant bacterial diseases.

A multi-scale attention-based model (MSAM) is proposed as a surrogate model for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. Instead a thousand of full-wave simulations, the surrogate model converts the uncertain inputs to electric fields, and the output uncertainty is effectively quantified. Global feature aggregation (GFA) module and local affinity reconstruction (LAR) are presented to improve the model representation capability by Affinity calculation under different receptive fields. In addition, a new loss function is proposed to accelerate the convergence of the model for training data with a wider range of input disturbances. The effectiveness and accuracy of the surrogate model are verified by comparing the UA results with the Monte Carlo method (MCM). In comparison with existing deep learning methods, the proposed method can efficiently get higher quality predictions. Meanwhile, the Sobol indices evaluated by MSAM accord with those of MCM, and the mean square error between them is only 0.0005. However, the MCM needs to run the full-wave simulation one thousand times to converge, which is much more time consuming than the proposed surrogate model.

In the search for more efficient and versatile anti-phytopathogen agents, a series of new 1,3,4-oxadiazole thioether/sulfone analogues bearing a flexible N-containing heterocyclic pattern were elaborately prepared, and their bioactivities against plant pathogenic microorganisms were systematically evaluated. Bioassay screening results demonstrated that compounds 32 and 33 significantly inhibited the growth of Xanthomonas oryzae pv. oryzae (Xoo) in vitro (32, EC50 = 5.17 mg L−1; 33, EC50 = 1.19 mg L−1), which were significantly surpass commercial bismerthiazol (BT) and thiodiazole copper (TC). Meanwhile, pot experiments confirmed the prospective applications of compound 33 in managing rice bacterial leaf blight and its good safety toward rice plants. Further studies showed that compound 33 interfered with the formation of bacterial biofilms and inhibited bacterial virulence factors. Furthermore, an in vitro antifungal bioassay showed that compound 32 possessed remarkable growth inhibitory activity against Sclerotinia sclerotiorum (S.s., EC50 = 22.16 mg L−1) and Verticillium dahlia (V.d. EC50 = 32.78 mg L−1). These results all confirmed that the designed 1,3,4-oxadiazole compounds displayed potential for managing plant microbial diseases through targeting dihydrolipoamide S-succinyltransferase (DLST).