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Paper mulberry (Broussonetia papyrifera) is a high-quality silage protein feed material that can help address feed shortages and support livestock development. Although some studies have investigated the relationships between microbial communities and silage quality, these relationships and the underlying community assembly processes remain complex, requiring further research to clarify them. Additionally, limited research has explored the relationship between microbial community fermentation functions and silage quality. In this study, we aimed to explore B. papyrifera and wheat bran mixed silage quality driven by the characteristics of the microbial community. After 50 days of silage fermentation, high-quality and low-quality samples were selected from every mixing ratio (90:10, 80:20, and 65:35). The silage chemical composition, lignocellulose degradation enzyme activity, microbial community composition, and potential functions were used to explore the relevance between silage quality and the characteristics of the microbial community. The contents of hemicellulose, neutral detergent fiber, pH, and the activities of endoglucanase and exoglucanase were significantly affected by mixing ratios and silage quality grade. There were higher crude protein content, lignocellulose degrading enzyme activity, and lower pH, lignin, and acid detergent fiber in the mixing of 65:35 (BP65%) samples. The PERMANOVA results showed that mixing ratios had significant impacts on microbial community composition and bacterial fermentation functions. There was a higher bacterial diversity, lower fungal diversity, and better functional potentials for fermentation and lignocellulose degradation in BP65% high-quality silage. The dominant genera were Lactobacillus , Cladosporium , and Wallemia in all samples. The relative abundance of Clostridium , Rhodococcus , Turicibacter , Ralstonia , and Burkholderia was significantly higher in BP65% high-quality samples. There was a higher abundance of Wallemia in the BP65% samples than in other mixing ratios samples. Notably, silage quality showed a close relationship with Lactobacillus , Turicibacter , Romboutsia , Wallemia, and Pichia. In summary, 65:35 was a suitable mixing ratio for B. papyrifera and wheat bran silage, but high-quality silage still required the participation of multiple specific rare microbial taxa. The higher bacterial diversity and specific microbial taxa abundance could be critical for improving B. papyrifera silage quality. We expect that our findings will provide new insights into silage quality driven by the characteristics of the microbial community. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images. In the segmentation network, the backbone network was simplified, the activation function and loss function were replaced, and the 3D GAM attention mechanism was introduced. In the classification network, firstly, the CA attention mechanism was added to enhance the model's ability to capture positional information of disease features; secondly, dilated convolutions were introduced to extract richer features from the input feature maps; and finally, the fully connected layer of MobileNetV3 was modified and the idea of transfer learning was adopted to improve the model's feature extraction capability. The results of the study showed that the proposed approach achieved classification accuracies of 97.85% for AD/NC, 95.31% for MCI/NC, 93.96% for AD/MCI, and 92.63% for AD/MCI/NC, respectively, which were 3.1, 2.8, 2.6, and 2.8 percentage points higher than before the improvement. Comparative and ablation experiments have validated the proposed classification performance of this method, demonstrating its capability to facilitate an accurate and efficient automated auxiliary diagnosis of AD, offering a deep learning-based solution for it. [ABSTRACT FROM AUTHOR]

Cobalt phosphosulphide (CoPS) has recently been recognized as a potentially effective electrocatalyst for the hydrogen evolution reaction (HER). However, there have been no research on the design of CoPS‐based heterojunctions to boost their HER performance. Herein, CoPS/Co4S3 heterojunction was prepared by phosphating treatment based on defect‐rich flower‐like Co1‐xS precursors. The high specific surface area of nanopetals, together with the heterojunction structure with inhomogeneous strain, exposes more active sites in the catalyst. The electronic structure of the catalyst is reconfigured as a result of the interfacial interactions, which promote the catalyst's ability to adsorb hydrogen and conduct electricity. The synergistic effect of the Co and S dual‐site further enhance the catalytic activity. The catalyst has overpotentials of 61 and 70 mV to attain a current density of 10 mA cm−2 in acidic and alkaline media, respectively, which renders it competitive with previously reported analogous catalysts. This work proposes an effective technique for constructing transition metal phosphosulfide heterojunctions, as well as the development of an efficient HER electrocatalyst. [ABSTRACT FROM AUTHOR]