Daily calorie restriction (CR) has shown benefits on weight loss and alleviation of metabolic disorders. We investigated the effects of three CR regimens, i.e., 20% (CR-20), 40% (CR-40), and 60% (CR-60) less than the average daily calorie intake, respectively, on the metabolic parameters, gut microbiome composition, and its related metabolites in healthy mice. Compared with mice fed ad libitum (AL), CR dose-dependently reduced the body weight, and weights of liver and epididymal adipose tissues, and enhanced the insulin sensitivity, glucose tolerance, and lipid homeostasis. Moreover, expression levels of intestinal tight junction proteins (i.e., ZO-1, claudin, and occludin) were significantly promoted by CR than those of AL mice, demonstrating the CR-induced improvement of the intestinal barrier integrity. CR contributed to the enrichment of beneficial microbiota (e.g., Lactobacillus, Bacteroides, and Akkermansia) and increased propionic acid levels. Notably, CR-60 deleteriously caused liver injury, and enhanced hepatic inflammatory cytokines (i.e., IL-1, IL-6, and TNF-α) and lipopolysaccharides, which were accompanied by high levels of trimethylamine (TMA) and trimethylamine oxide (TMAO) in relation to CR-60-altered gut microbiota structure and fecal metabolome. Additionally, we found differential impacts of CR-20, -40, or -60 on amino acid absorption and metabolism. Our findings support the health-promoting benefits of 60−80% daily calorie intake on the metabolic status by regulating the gut microbiota in healthy mice. However, excessive CR caused liver injury and gut microbiota-dependent elevation of TMAO. The differential effects of CR regimens on the intestinal microbiome and fecal metabolome provide novel insights into the dietary pattern-gut microbiome interactions linked with host metabolism.
The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson's correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure, and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph. The DHMR and L 1 norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN (DDBFN). Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods. Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI, providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.
<abstract> <p>Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the <italic>Euclidean</italic> distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.</p> </abstract>
Ground source heat pumps (GSHPs) are one of the renewable energy technologies with features of high efficiency, energy saving, economic feasibility and environmental protection. In China, GSHPs have been widely used for building heating and cooling in recent years, and have shown great potential for future energy development. This paper summarizes the classification, development history, and use status of shallow GSHPs. Several typical engineering cases of GSHP technology are also specified and analyzed. Finally, promising development trends and some advanced technologies are illustrated.
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