Melatonin-induced myeloblastosis viral oncogene homologs alleviate fresh-cut lotus root browning during storage by attenuating flavonoid biosynthesis and reactive oxygen species.

BACKGROUND: Lotus roots (Nelumbo nucifera Gaertn.) are rich in nutrients and have ornamental and food value. However, browning has caused huge economic losses and security risks during the storage and harvesting of fresh-cut lotus. This study investigated the role of melatonin in inhibiting lotus browning, and illustrates its molecular mechanism. RESULTS: The application of melatonin effectively retarded the process of lotus browning, enhanced reactive oxygen species (ROS) scavenging enzyme activity, and inhibited the activity of polyphenol oxidase (PPO), and peroxidase (POD). Melatonin reduced flavonoid content, and decreased enzymatic activity in flavonoid biosynthesis. Transcriptome Sequencing (RNA-seq) was used to screen the genes regulated by exogenous melatonin when defending against fresh-cut lotus browning. Gene co-expression analysis (GCN) indicated that the transcription factors MYB5, MYB6, and MYB308, activated by melatonin, were negatively related to the expression of PPO and the genes related to flavonoid and phenylpropanoid biosynthesis. These myeloblastosis viral oncogene homologs (MYBs) were positively related to the expression of genes encoding the enzymes in glutathione metabolism. CONCLUSION: Melatonin retarded lotus browning by transcriptional suppression of key genes associated with flavonoid and phenylpropanoid biosynthesis through the stimulation of MYB5, MYB6, and MYB308. © 2023 Society of Chemical Industry.

Local feature acquisition and global context understanding network for very high-resolution land cover classification.

Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images. Simultaneously, we introduce a global feature learning module that utilizes multiple sets of multi-head attention mechanisms for modeling global semantic information, abstracting the overall feature representation of remote sensing images. Validation, comparative analyses, and ablation experiments conducted on three different scales of publicly available datasets demonstrate the effectiveness and generalization capability of the LFAGCU method. Results show its effectiveness in locating category attribute information related to remote sensing areas and its exceptional generalization capability. Code is available at https://github.com/lzp-lkd/LFAGCU .