Comparative Transcriptome Analysis between Resistant and Susceptible Pakchoi Cultivars in Response to Downy Mildew.

Downy mildew caused by the obligate parasite Hyaloperonospora brassicae is a devastating disease for Brassica species. Infection of Hyaloperonospora brassicae often leads to yellow spots on leaves, which significantly impacts quality and yield of pakchoi. In the present study, we conducted a comparative transcriptome between the resistant and susceptible pakchoi cultivars in response to Hyaloperonospora brassicae infection. A total of 1073 disease-resistance-related differentially expressed genes were identified using a Venn diagram. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly involved in plant-pathogen interaction, plant hormone signal transduction, and other photosynthesis-related metabolic processes. Analysis of the phytohormone content revealed that salicylic acid increased significantly in the resistant material after inoculation with Hyaloperonospora brassicae, whereas the contents of jasmonic acid, abscisic acid, and 1-aminocyclopropane-1-carboxylic acid decreased. Exogenous salicylic acid treatment also significantly upregulated Hyaloperonospora brassicae-induced genes, which further confirmed a crucial role of salicylic acid during pakchoi defense against Hyaloperonospora brassicae. Based on these findings, we suggest that the salicylic-acid-mediated signal transduction contributes to the resistance of pakchoi to downy mildew, and PAL1, ICS1, NPR1, PR1, PR5, WRKY70, WRKY33, CML43, CNGC9, and CDPK15 were involved in this responsive process. Our findings evidently contribute to revealing the molecular mechanism of pakchoi defense against Hyaloperonospora brassicae.

A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network.

In a myriad of engineering situations, we often hope to establish a model which can acquire load conditions around structures through flow features detection. A data-driven method is developed to predict the pressure on a cylinder from velocity distributions in its wake flow. The proposed deep learning neural network is constituted with convolutional layers and fully-connected layers: The convolutional layers can process the velocity information by features extraction, which are gathered by the fully-connected layers to obtain the pressure coefficients. By comparing the output data of the typical network with Computational Fluid Dynamics (CFD) results as reference values, it suggests that the present convolutional neural network (CNN) is able to predict the pressure coefficient in the vicinity of the trained Reynolds numbers with various inlet flow profiles and achieves a high overall precision. Moreover, a transfer learning approach is adopted to preserve the feature detection ability by keeping the parameters in the convolutional layers unchanged while shifting parameters in the fully-connected layers. Further results show that this transfer learning network has nearly the same precision while significantly lower cost. The active prospects of convolutional neural network in fluid mechanics have also been demonstrated, which can inspire more kinds of loads prediction in the future.