Wheat disease recognition method based on the SC-ConvNeXt network model.

When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model's recognition performance is often disrupted by complex factors in natural environments.

Gut microbiota, host lipid metabolism and regulation mechanism of high-fat diet induced mice following different probiotics-fermented wheat bran intervention.

Wheat bran (WB) was fermented by Lactobacillus rhamnosus, Lactobacillus plantarum, Lactobacillus brevis (LAB-FWB), respectively, and their corresponding mechanism of obesity alleviation via gut microbiota and lipid metabolism was investigated. Results indicated LAB-FWB reduced body weight and serum glucose, followed by an improved lipid profile in obese mice compared with WB. All LAB-FWB interventions led to an enriched steroid hormone biosynthesis.

A classification method for soybean leaf diseases based on an improved ConvNeXt model.

Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately.

Gut microbiota derived structural changes of phenolic compounds from colored rice and its corresponding fermentation property.

The influence of phenolic compound extracts from three colored rice cultivars on the gut microbiota was investigated. The results revealed that protocatechuic acid, chlorogenic acid, caffeic acid and -coumaric acid were the major metabolites after gut microbiota fermentation. The presence of phenolic compounds led to a significantly decreased ratio of and , while the abundance of decreased.

Starch acylation of different short-chain fatty acids and its corresponding influence on gut microbiome and diabetic indexes.

This study investigated correlations between gut microbiota and type 2 diabetic (T2D) indexes using either native resistant starch (RS, from high amylose maize starch, HAMS) or acylated starch via short-chain fatty acids (SCFAs) acylation. Compared to HAMS, consumption of acylated starch achieved a greater impact on the improvement of T2D indexes in term of body weight loss, fasting blood glucose, serum insulin level and amino acid metabolism. Intervention with acylated starches alleviated metabolism disorders and modified the gut microbiota.