Manganese sulfate application promotes berry flavonoid accumulation in Vitis vinifera cv. 'Cabernet Sauvignon' by regulating flavonoid metabolome and transcriptome profiles.

BACKGROUND: Flavonoids are vital for the development of high-quality grapes and wine, and manganese deficiency decreases grape berry coloration. However, the effects and underlying mechanisms of action of manganese sulfate on grape metabolic profiles have not been adequately researched. In this study, three concentrations of manganese sulfate solutions, 0.5 µmol·L-1 (low, L), 5 µmol·L-1 (middle, M - the standard manganese concentration of Hoagland nutrient solution, control), and 1000 µmol·L-1 (high, H), were applied to the 'Cabernet Sauvignon' grapevine (Vitis vinifera L.) to explore the effect on berry composition. RESULTS: Manganese application improved manganese concentration effectively in grape organs. Furthermore, the concentrations of malvidin 3-O-(6-O-acetyl)-glucoside, malvidin 3-O-glucoside, malvidin-trans-3-O-(6-O-p-coumaryl)-glucoside, and peonidin 3-O-(6-O-acetyl)-glucoside increased significantly under H treatment. Weighted gene co-expression network analysis (WGCNA) revealed that the structural genes (VvDFR, VvUFGT, and VvOMT) of flavonoid biosynthesis were upregulated under H treatment, and their transcription levels correlated positively with malvidin- and peonidin-derived anthocyanin concentrations. CONCLUSIONS: This study suggested that manganese application regulates berry transcriptional and flavonoid metabolic profiles, providing a theoretical basis for improving the color of red grapes and wines. © 2023 Society of Chemical Industry.

Fusing Self-Attention and CoordConv to Improve the YOLOv5s Algorithm for Infrared Weak Target Detection.

Convolutional neural networks have achieved good results in target detection in many application scenarios, but convolutional neural networks still face great challenges when facing scenarios with small target sizes and complex background environments. To solve the problem of low accuracy of infrared weak target detection in complex scenes, and considering the real-time requirements of the detection task, we choose the YOLOv5s target detection algorithm for improvement. We add the Bottleneck Transformer structure and CoordConv to the network to optimize the model parameters and improve the performance of the detection network. Meanwhile, a two-dimensional Gaussian distribution is used to describe the importance of pixel points in the target frame, and the normalized Guassian Wasserstein distance (NWD) is used to measure the similarity between the prediction frame and the true frame to characterize the loss function of weak targets, which will help highlight the targets with flat positional deviation transformation and improve the detection accuracy. Finally, through experimental verification, compared with other mainstream detection algorithms, the improved algorithm in this paper significantly improves the target detection accuracy, with the mAP reaching 96.7 percent, which is 2.2 percentage points higher compared with Yolov5s.