Sishen Pill inhibits intestinal inflammation in diarrhea mice via regulating kidney-intestinal bacteria-metabolic pathway

BackgroundSishen Pill (SSP) has good efficacy in diarrhea with deficiency kidney-yang syndrome (DKYS), but the mechanism of efficacy involving intestinal microecology has not been elucidated.ObjectiveThis study investigated the mechanism of SSP in regulating intestinal microecology in diarrhea with DKYS.MethodsAdenine combined with Folium sennae was used to construct a mouse model of diarrhea with DKYS and administered with SSP. The behavioral changes and characteristics of gut content microbiota and short-chain fatty acids (SCFAs) of mice were analyzed to explore the potential association between the characteristic bacteria, SCFAs, intestinal inflammatory and kidney function-related indicators.ResultsAfter SSP intervention, the body weight and anal temperature of diarrhea with DKYS gradually recovered and approached the normal level. Lactobacillus johnsonii was significantly enriched, and propionic, butyric, isobutyric and isovaleric acids were elevated. Serum creatinine (Cr), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α) levels of the mice were reduced, while serum blood urea nitrogen (BUN) and secretory immunoglobulin A (sIgA) in the colonic tissues were increased. Moreover, there were correlations between L. johnsonii, SCFAs, intestinal inflammatory, and kidney function.ConclusionSSP might suppress the intestinal inflammation by regulating the “L. johnsonii-propionic acid” pathway, thus achieving the effect of treating diarrhea with DKYS

Importance of Dendrobium officinale in improving the adverse effects of high-fat diet on mice associated with intestinal contents microbiota

A growing body of evidence suggests that the disturbance of intestinal microbiota induced by high-fat diet is the main factor causing many diseases. Dendrobium officinale (DO), a medicinal and edible homologous Chinese herbal medicine, plays essential role in regulating intestinal microbiota. However, the extent of DO on the intestinal contents microbiota in mice fed with a high-fat diet still remains unclear. Therefore, this study explored the role of intestinal contents microbiota in the regulation of adverse effects caused by high-fat diet by DO from the perspective of intestinal microecology. Twenty-four mice were randomly distributed into the normal saline-treated basal diet (bcn), normal saline-treated high-fat diet (bmn), 2.37 g kg−1 days−1 DO traditional decoction-treated high-fat diet (bdn) and 1.19 g kg−1 days−1 lipid-lowering decoction-treated high-fat diet (bjn) groups for 40 days. Subsequently, we assessed the changes in body weight, serum total cholesterol (TC), total triacylglycerol (TG), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C) levels, and the characteristics of intestinal contents microbiota. Results demonstrated that DO exerted the modulating effect on the changes in body weight, TG, TC, LDL-C, and HDL-C levels. Besides, DO decreased the richness and diversity of intestinal contents microbiota, and altered the structure as a whole. Dominant bacteria, Ruminococcus and Oscillospira, varied significantly and statistically. Moreover, DO influenced the carbohydrate, amino acid, and energy metabolic functions. Furthermore, Ruminococcus and Oscillospira presented varying degrees of inhibition/promotion of TG, TC, LDL-C, and HDL-C. Consequently, we hypothesized that Ruminococcus and Oscillospira, as dominant bacteria, played key roles in the treatment of diseases associated with a high-fat diet DO

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view

Robust Object Tracking Based on Motion Consistency

Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation. In the appearance model, we define the position factor to represent the different importance of candidate samples in different positions using the double Gaussian probability model. The candidate sample with highest likelihood is selected as the tracking result by combining the holistic and local responses with the position factor. Moreover, an adaptive template updating scheme is proposed to adapt to the target’s appearance changes, especially those caused by fast motion. The experimental results on a 2013 benchmark dataset demonstrate that the proposed algorithm performs better in scenes with fast motion and partial or full occlusion compared to the state-of-the-art algorithms

Evaluation of toxicological effects of chemical substances by gut microbiota: The example of adenine damage to the kidney and gut

This study explored the effects of different doses of adenine intake on mice in terms of kidney function, oxidative stress and gut content microbiota to elucidate interactions between adenine-induced kidney function impairment and gut content microbiota disorder. Mice were gavaged with low-dosage adenine suspension (NML), middle-dosage adenine suspension (NMM), high-dosage adenine suspension (NMH) and sterile water (NC). Behaviour, kidney structure and function, colonic structure, oxidative stress and gut content microbiota were detected. Mice in NML, NMM, and NMH groups had significantly lower body weight, anal temperature and food intake, increased water intake, the mice had loose and deformed feces with obvious water stains through the paper. NMM mice presented significantly structural damage to kidney and colonic tissues, considerably higher BUN and Cr, MDA and lower SOD. MDA and SOD levels in NMM and NMH groups were closely associated with Cr and BUN. Moreover, different doses of adenine intake effected the mice gut content microbiota, and enriched the different characteristic bacteria. Characteristic bacteria Lactobacillus and Bifidobacterium presented significant correlations with MDA. Eventually, Lactobacillus and Bifidobacterium mediated oxidative stress pathway involved in the process of adenine-induced kidney injure in mice

Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method

Design, Recombinant Fusion Expression and Biological Evaluation of Vasoactive Intestinal Peptide Analogue as Novel Antimicrobial Agent

Antimicrobial peptides represent an emerging category of therapeutic agents with remarkable structural and functional diversity. Modified vasoactive intestinal peptide (VIP) (VIP analogue 8 with amino acid sequence “FTANYTRLRRQLAVRRYLAAILGRR”) without haemolytic activity and cytotoxicity displayed enhanced antimicrobial activities against Staphylococcus aureus (S. aureus) ATCC 25923 and Escherichia coli (E. coli) ATCC 25922 than parent VIP even in the presence of 180 mM NaCl or 50 mM MgCl2, or in the range of pH 4–10. VIP analogue 8 was expressed as fusion protein thioredoxin (Trx)-VIP8 in E. coli BL21(DE) at a yield of 45.67 mg/L. The minimum inhibitory concentration (MIC) of the recombinant VIP analogue 8 against S. aureus ATCC 25923 and E. coli ATCC 25922 were 2 μM. These findings suggest that VIP analogue 8 is a promising candidate for application as a new and safe antimicrobial agent