Design, bioactivity and mechanism of N'-phenyl pyridylcarbohydrazides with broad-spectrum antifungal activity.

Succinate dehydrogenase inhibitors (SDHIs) as one of the fastest-growing fungicide categories for plant protection. In this study, a series of N'-phenyl pyridylcarbohydrazides as analogues of commercial SDHIs were designed and evaluated for inhibition activity on phytopathogenic fungi to search for potential novel SDHIs. The determination of antifungal activity in vitro and in vivo led to the discovery of a series of compounds with high activity and broad-spectrum property. Especially, N'-(4-fluorophenyl)picolinohydrazide (1c) and N'-(3,4-fluorophenyl)picolinohydrazide (1ae) showed 0.041-1.851 µg/mL of EC50 values on twelve fungi, superior to positive controls carbendazim and boscalid. In vivo activity, 1c at 50 µg/mL showed 61% of control efficacy at the post-treatment 9th day for the infection of P. piricola on apples, slightly smaller than 70% of carbendazim. In terms of action mechanism, 1c showed strong inhibition activity with IC50 of 0.107 µg/mL on SDH in Alternaria brassicae, superior to positive SDHI boscalid (IC50 0.182 µg/mL). Molecular docking indicated that 1c can well bind with the ubiquinone-binding region of SDH mainly by hydrogen bond, carbon hydrogen bond, π-alkyl, amide-π stacking, F-N and F-H interactions. Furthermore, scanning and transmission electron micrographs showed that 1c was able to obviously change the structure of mycelia and cell membrane. Fluorescence staining analysis showed that 1c could increase both the intracellular reactive oxygen species level and mitochondrial membrane potential. Finally, seed germination test, seedling growth test and cytotoxicity assay showed that 1c had very low toxicity to plant growth and mammalian cells. Thus, N'-phenyl pyridylcarbohydrazides especially 1c and 1ae can be considered promising fungicide alternatives for plant protection.

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype.

Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, which is influenced by the size of structural variations and the coverage fluctuations of sequencing data. In this study, we propose a new method for calling deletions' genotypes from the next-generation data, called Cnngeno. Cnngeno can convert sequencing data into images and classifies the genotypes from these images using the convolutional neural network(CNN). Moreover, Cnngeno adopted the convolutional bootstrapping strategy to improve the anti-noisy label's ability. The results show that Cnngeno performs better in terms of precision for calling genotype when compared with other existing methods. The Cnngeno is an open-source method, available at https://github.com/BRF123/Cnngeno.