As part of an ongoing effort to develop novel succinate dehydrogenase inhibitors (SDHIs), a range of innovative nicotinamide derivatives featuring stilbene scaffolds were synthesized and assessed for their antifungal properties. Lead compounds 6g and 6c demonstrated significant antifungal efficacies against Rhizoctonia solani and Sclerotinia sclerotiorum, with EC50 (half maximal effective concentration) values of 0.008 and 0.034 μg/mL, respectively, surpassing Boscalid (EC50 = 0.573 and 0.455 μg/mL, respectively) and Fluxapyroxad (EC50 = 0.026 and 0.037 μg/mL, respectively). Detached leaf assays further confirmed their excellent protective and curative effects, outperforming Boscalid and Fluxapyroxad. Further studies revealed that compound 6g operated through a mechanism akin to Boscalid and Fluxapyroxad. These results indicated that both 6g and 6c showed great potential as novel SDHIs for effectively managing R. solani and S. sclerotiorum, underscoring the necessity for further research to fully explore their capabilities.
Bo Luo, Yihan Hu, Yi Zhao et al.· Journal of Agricultural and...· 0 citations
Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datasets. Parameterized PINNs (ParamPINNs) further take physical parameters as inputs, allowing a single model to represent a family of PDE solutions over a parameter domain. Existing ParamPINNs, however, still face inefficient training, uneven accuracy across parameters, and overfitting to a limited set of sampled parameter tasks, which can impair generalization to unsampled parameters. To address these issues, we propose a continual-learning physics-informed neural network (CL-PINN), which treats PDE instances at different parameter values as related tasks and learns them sequentially. CL-PINN combines Bayesian-optimization-based active parameter selection, task-wise dynamic loss weighting, sparse physics-constrained replay, and an optional parameter subnetwork to improve task allocation and knowledge retention under bounded active-task capacity. It requires no observational data and is designed to solve parameterized PDEs over relatively broad parameter domains under limited computational resources. Multi-seed evaluations on five benchmarks, including one continuous function and four parameterized PDEs, show that Bayesian selection substantially reduces objective-loss queries relative to grid-greedy search, while sparse replay mitigates forgetting of earlier tasks. Under the prescribed within-case resource protocols, CL-PINN generally provides higher and more balanced solution accuracy than fixed-sampling and grid-greedy baselines. CL-PINN offers a practical route toward learning PDE solutions that generalize across physical parameters and has the potential to support reusable physics-informed surrogates for large-scale engineering parameter studies.
Xujia Chen, Xinyu Hu, Letian Chen et al.· 0 citations
