Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Lactobacillus rhamnosus GG Treatment Potentiates Intestinal Hypoxia-Inducible Factor, Promotes Intestinal Integrity and Ameliorates Alcohol-Induced Liver Injury

Abstract: Gut-derived endotoxin is a critical factor in the development and progression of alcoholic liver disease (ALD). Probiotics can treat alcohol-induced liver injury associated with gut leakiness and endotoxemia in animal models, as well as in human ALD; however, the mechanism or mechanisms of their beneficial action are not well defined. We hypothesized that alcohol impairs the adaptive response-induced hypoxia-inducible factor (HIF) and that probiotic supplementation could attenuate this impairment, restoring barrier function in a mouse model of ALD by increasing HIF-responsive proteins (eg, intestinal trefoil factor) and reversing established ALD. C57BJ/6N mice were fed the Lieber DeCarli diet containing 5% alcohol for 8 weeks. Animals received Lactobacillus rhamnosus GG (LGG) supplementation in the last 2 weeks. LGG supplementation significantly reduced alcohol-induced endotoxemia and hepatic steatosis and improved liver function. LGG restored alcohol-induced reduction of HIF-2α and intestinal trefoil factor levels. In vitro studies using the Caco-2 cell culture model showed that the addition of LGG supernatant prevented alcohol-induced epithelial monolayer barrier dysfunction. Furthermore, gene silencing of HIF-1α/2α abolished the LGG effects, indicating that the protective effect of LGG is HIF-dependent. The present study provides a mechanistic insight for utilization of probiotics for the treatment of ALD, and suggests a critical role for intestinal hypoxia and decreased trefoil factor in the development of ALD.

Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos

Abstract: Single modality action recognition on RGB or depth sequences has been extensively explored recently. It is generally accepted that each of these two modalities has different strengths and limitations for the task of action recognition. Therefore, analysis of the RGB+D videos can help us to better study the complementary properties of these two types of modalities and achieve higher levels of performance. In this paper, we propose a new deep autoencoder based shared-specific feature factorization network to separate input multimodal signals into a hierarchy of components. Further, based on the structure of the features, a structured sparsity learning machine is proposed which utilizes mixed norms to apply regularization within components and group selection between them for better classification performance. Our experimental results show the effectiveness of our cross-modality feature analysis framework by achieving state-of-the-art accuracy for action classification on five challenging benchmark datasets.