Dried tea residue can alter the blood metabolism and the composition and functionality of the intestinal microbiota in Hu sheep

Ruminant animals face multiple challenges during the rearing process, including immune disorders and oxidative stress. Green tea by-products have gained widespread attention for their significant immunomodulatory and antioxidant effects, leading to their application in livestock production. In this study, we investigated the effects of Dried Tea Residue (DTR) as a feed additive on the growth performance, blood biochemical indicators, and hindgut microbial structure and function of Hu sheep. Sixteen Hu sheep were randomly divided into two groups and fed with 0 and 100 g/d of DTR, respectively. Data were recorded over a 56-day feeding period. Compared to the control group, there were no significant changes in the production performance of Hu sheep fed with DTR. However, the sheep fed with DTR showed a significant increase in IgA (p < 0.001), IgG (p = 0.005), IgM (p = 0.003), T-SOD (p = 0.013), GSH-Px (p = 0.005), and CAT (p < 0.001) in the blood, along with a significant decrease in albumin (p = 0.019), high density lipoprotein (p = 0.050), and triglyceride (p = 0.021). DTR supplementation enhanced the fiber digestion ability of hindgut microbiota, optimized the microbial community structure, and increased the abundance of carbohydrate-digesting enzymes. Therefore, DTR can be used as a natural feed additive in ruminant animal production to enhance their immune and antioxidant capabilities, thereby improving the health status of ruminant animals

Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components