High methionine intake alters gut microbiota and lipid profile and leads to liver steatosis in mice.

Methionine is an important sulfur-containing amino acid. Health effects of both methionine restriction (MR) and methionine supplementation (MS) have been studied. This study aimed to investigate the impact of a high-methionine diet (HMD) (1.64% methionine) on both the gut and liver functions in mice through multi-omic analyses. Hepatic steatosis and compromised gut barrier function were observed in mice fed the HMD. RNA-sequencing (RNA-seq) analysis of liver gene expression patterns revealed the upregulation of lipid synthesis and degradation pathways, cholesterol metabolism and inflammation-related nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway. Metagenomic sequencing of cecal content demonstrated a shift in gut microbial composition with an increased abundance of opportunistic pathogens and gut microbial functions with up-regulated lipopolysaccharide (LPS) biosynthesis in mice fed HMD. Metabolomic study of cecal content showed an altered gut lipid profile and the level of bioactive lipids, including docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), palmitoylethanolamide (PEA), linoleoyl ethanolamide (LEA) and arachidonoyl ethanolamide (AEA), that carry anti-inflammatory effects significantly reduced in the gut of mice fed the HMD. Correlation analysis demonstrated that gut microbiota was highly associated with liver and gut functions and gut bioactive lipid content. In conclusion, this study suggested that the HMD exerted negative impacts on both the gut and liver, and an adequate amount of methionine intake should be carefully determined to ensure normal physiological function without causing adverse effects.

Structured AutoEncoders for Subspace Clustering.

Existing subspace clustering methods typically employ shallow models to estimate underlying subspaces of unlabeled data points and cluster them into corresponding groups. However, due to the limited representative capacity of the employed shallow models, those methods may fail in handling realistic data without the linear subspace structure. To address this issue, we propose a novel subspace clustering approach by introducing a new deep model-Structured AutoEncoder (StructAE). The StructAE learns a set of explicit transformations to progressively map input data points into nonlinear latent spaces while preserving the local and global subspace structure. In particular, to preserve local structure, the StructAE learns representations for each data point by minimizing reconstruction error w.r.t. itself. To preserve global structure, the StructAE incorporates a prior structured information by encouraging the learned representation to preserve specified reconstruction patterns over the entire data set. To the best of our knowledge, StructAE is one of first deep subspace clustering approaches. Extensive experiments show that the proposed StructAE significantly outperforms 15 state-of-the-art subspace clustering approaches in terms of five evaluation metrics.

Person Re-identification by Multi-hypergraph Fusion.

Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts.Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts.

L1-Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers.

The L1-norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the original one can be obtained by tuning the parameters contained in the mollifier. Next, a recurrent neural network is proposed to optimize the mollified function, which will converge to a local minimum. In addition, to boost the speed of the system, the mollifying process is implemented by a filtering procedure. The influence of two mollifier parameters is theoretically analyzed and experimentally confirmed, showing that one of the parameters is critical to computational efficiency and accuracy, while the other not. A large number of experiments on synthetic data show that the proposed method is competitive to the state-of-the-art methods. In particular, the experiments on large matrices and a real application in the structure from motion indicate that the memory requirement of the proposed algorithm is mild, making it suitable for real applications that often involve large-scale matrix decomposition.