Sea buckthorn polysaccharide ameliorates high-fat diet induced mice neuroinflammation and synaptic dysfunction via regulating gut dysbiosis.

Currently, definitive treatment for neurodegenerative diseases without side effects has not been developed, therefore, exploring natural polysaccharides with neuroprotection to prevent the occurrences and progressions of cognitive dysfunctions has important significance. The purpose of this study was to investigate the effects of sea buckthorn polysaccharide (SBP) on high-fat diet (HFD) induced mice cognitive dysfunctions and attempted to explore its biological mechanisms. Behavior tests (Y-maze and Barnes maze) suggested that SBP effectively alleviated the HFD induced behavioral disorders, which was in accordance with the inhibition of neuroinflammation via suppressing the NF-κB pathway and amelioration of synaptic dysfunction via upregulating CREB/BDNF/TrkB pathway in mice brain. Furthermore, SBP alleviated the gut barrier impairment, inflammatory responses, and lipopolysaccharide invasion into blood circulation via regulating the gut microbiome structure, especially correcting the reduction of Ileibacterium and increase of Lactobacillus, Dubosiella, Olsenella, Helicobacter, and Ruminiclostridium_9 in HFD mice. Therefore, the reversal effects of SBP on gut dysbiosis might be the important reason for its positive effects on cognitive dysfunction induced by HFD in mice.

Seabuckthorn polysaccharide ameliorates high-fat diet-induced obesity by gut microbiota-SCFAs-liver axis.

Obesity has been reported to be associated with gut microbiome dysbiosis. seabuckthorn fruits have traditionally been used in Tibetan foods and medicines for thousands of years. Seabuckthorn polysaccharide (SP) is one of the main functional components in seabuckthorn fruits. However, the effects of SP on a high-fat diet (HFD)-induced obesity have not yet been elucidated. The purpose of this study is to explore the amelioration effect of SP on obesity induced by HFD and to reveal its mechanism of gut microbiota and its metabolites. Results showed that 12-week SP (0.1%, w/w) dietary supplementation could significantly reduce body weight gain, serum lipid level and liver triglycerides level in obese mice. Notably, the SP treatment elevated p-AMPKα and PPARα proteins expression stimulated the phosphorylation of ACC1 and inhibited the protein expression of FAS, PPARγ, and CD36 in the mice liver. Further, SP also reorganized the gut microbiome by up-regulating the proportion of Muribaculaceae_unclassified, Bifidobacterium, Rikenellaceae_RC9_gut_group, Alistipes, and Bacteroides, and down-regulating the abundance of Lactobacillus, Firmicutes_unclassified, Dubosiella Bilophila, and Streptococcus in HFD-induced obese mice. Moreover, the production of microbial metabolites short-chain fatty acids (SCFAs) in feces has also increased. In addition, correlation analysis results showed that obesity-ameliorating effects of SP were highly associated with levels of SCFAs in feces. Therefore, the regulation of SP on liver lipid metabolism may be due to the variation of the gut microbiome and raised production of SCFAs. These results indicate that SP could play the part of a potential nutraceutical for ameliorating obesity through regulation of the gut-liver axis.

Violence detection in surveillance video using low-level features.

It is very important to automatically detect violent behaviors in video surveillance scenarios, for instance, railway stations, gymnasiums and psychiatric centers. However, the previous detection methods usually extract descriptors around the spatiotemporal interesting points or extract statistic features in the motion regions, leading to limited abilities to effectively detect video-based violence activities. To address this issue, we propose a novel method to detect violence sequences. Firstly, the motion regions are segmented according to the distribution of optical flow fields. Secondly, in the motion regions, we propose to extract two kinds of low-level features to represent the appearance and dynamics for violent behaviors. The proposed low-level features are the Local Histogram of Oriented Gradient (LHOG) descriptor extracted from RGB images and the Local Histogram of Optical Flow (LHOF) descriptor extracted from optical flow images. Thirdly, the extracted features are coded using Bag of Words (BoW) model to eliminate redundant information and a specific-length vector is obtained for each video clip. At last, the video-level vectors are classified by Support Vector Machine (SVM). Experimental results on three challenging benchmark datasets demonstrate that the proposed detection approach is superior to the previous methods.