High-fat diet-induced obesity is characterized by low-grade inflammation, which has been linked to gut microbiota dysbiosis. We hypothesized that quercetin supplementation would alter gut microbiota and reduce inflammation in obese mice. Male C57BL/6J mice, 4 weeks of age, were divided into 3 groups, including a low-fat diet group, a high-fat diet (HFD) group, and a high-fat diet plus quercetin (HFD+Q) group.The mice in HFD+Q group were given 50 mg per kg BW quercetin by gavage for 20 weeks. The body weight, fat accumulation, gut barrier function, glucose tolerance,

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model’s CLS vector can fairly fuse sample’s each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample’s global contextual information. As the interactions among the protein chains’ local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues’ features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix.

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In this work, a robust brazed joint of carbon fibre-reinforced carbon-based (C/C) composite and TC4 alloy was produced by utilising a slice of C/C as an interlayer. The C/C slice interlayer caused microstructural and mechanical property enhancement. During brazing, massive in situ formed carbon fibres broke away from the C/C slice bundles and were distributed in a three-dimensional interlocked network. These unique fibres consumed excessive Ti to inhibit the formation of excess brittle Ti-Cu compounds in the brazing seam. This reduced the coefficient of thermal expansion effectively, consequently relieving the high residual stress in the joint interface. The average shear strength of the joint brazed with the C/C slice interlayer reached 1.65 times higher than the directly brazed one.

The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal samples, which causes a heterogeneity gap. To bridge the above-mentioned gap, we propose the deep adversarial learning triplet similarity preserving cross-modal retrieval algorithm to map different modal samples into the common space, allowing their feature representation to preserve both the original inter- and intra-modal semantic similarity relationship. During the training process, we employ GANs, which has advantages in modeling data distribution and learning discriminative representation, in order to learn different modal features. As a result, it can align different modal feature distributions. Generally, many cross-modal retrieval algorithms only preserve the inter-modal similarity relationship, which makes the nearest neighbor retrieval results vulnerable to noise. In contrast, we establish the triplet similarity preserving function to simultaneously preserve the inter- and intra-modal similarity relationship in the common space and in each modal space, respectively. Thus, the proposed algorithm has a strong robustness to noise. In each modal space, to ensure that the generated features have the same semantic information as the sample labels, we establish a linear classifier and require that the generated features’ classification results be consistent with the sample labels. We conducted cross-modal retrieval comparative experiments on two widely used benchmark datasets—Pascal Sentence and Wikipedia. For the image to text task, our proposed method improved the mAP values by 1% and 0.7% on the Pascal sentence and Wikipedia datasets, respectively. Correspondingly, the proposed method separately improved the mAP values of the text to image performance by 0.6% and 0.8% on the Pascal sentence and Wikipedia datasets, respectively. The experimental results show that the proposed algorithm is better than the other state-of-the-art methods.