Obesity and metabolic syndrome (MS) associated with excess calorie intake has become a great public health concern worldwide. L-arabinose, a naturally occurring plant pentose, has a promising future as a novel food ingredient with benefits in MS; yet the mechanisms remain to be further elucidated. Gut microbiota is recently recognized to play key roles in MS. Molecular hydrogen, an emerging medical gas with reported benefits in MS, can be produced and utilized by gut microbes. Here we show oral L-arabinose elicited immediate and robust release of hydrogen in mice in a dose-and-time-dependent manner while alleviating high-fat-diet (HFD) induced MS including increased body weight especially fat weight, impaired insulin sensitivity, liver steatosis, dyslipidemia and elevated inflammatory cytokines. Moreover, L-arabinose modulated gene-expressions involved in lipid metabolism and mitochondrial function in key metabolic tissues. Antibiotics treatment abolished L-arabinose-elicited hydrogen production independent of diet type, confirming gut microbes as the source of hydrogen. q-PCR of fecal 16S rDNA revealed modulation of relative abundances of hydrogen-producing and hydrogen-consuming gut microbes as well as probiotics by HFD and L-arabinose. Our data uncovered modulating gut microbiota and hydrogen yield, expression of genes governing lipid metabolism and mitochondrial function in metabolic tissues is underlying L-arabinose's benefits in MS.Nutrients 2019, 11, 3054 2 of 29 non-caloric sugars have become one of the rising stars among nutraceuticals with great potential in anti-MS application with reported benefits in both animal experiments and human studies.L-arabinose, a naturally occurring constituent of plant polysaccharides, usually extracted from vegetable gum, corn straw or beet, has gained considerable attention for its potential in anti-MS application recently. L-arabinose administration for 6 weeks reduces body weight, blood pressure, blood glucose, triglycerides, total cholesterol, serum insulin, serum TNF-α and serum leptin; and increases hepatic CPT1 and PDK4 mRNA level while decreases hepatic ACCα mRNA level in high-carbohydrate-high-fat-diet induced MS rats [1]. Polysaccharide from corn silk containing L-arabinose showed hypoglycemic and hypolipidemic effects on diabetic mice induced by high-fat-diet (HFD) and streptozotocin injection [2]. One mechanism underlying L-arabinose's benefits in MS has been demonstrated as directly inhibiting intestinal sucrase activity both in vitro and in human [3], which explains why L-arabinose effectively lower blood glucose and insulin level with ingestion of high-sucrose food or beverages but is unable to account for L-arabinose's benefits in MS in general. Therefore, more studies are urgently called to illuminate the mechanisms underlying L-arabinose's effects in HFD models, considering fat being a major source of energy intake in reality.Gut microbiota has been recognized to play a pathogenic role in the development of MS in both humans and animal models in the last...

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.

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In this paper, we will investigate the contribution of color names for salient object detection. Each input image is first converted to the color name space, which is consisted of 11 probabilistic channels. By exploring the topological structure relationship between the figure and the ground, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only exploiting the surroundedness cue, two global cues with respect to color names are invoked for guiding the computation of another weighted saliency map. Finally, we integrate the two saliency maps into a unified framework to infer the saliency result. In addition, an improved post-processing procedure is introduced to effectively suppress the background while uniformly highlight the salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against 23 saliency models in terms of three evaluation metrics on three public datasets.

Road Detection is a basic task in automated driving field, in which 3D lidar data is commonly used recently. In this paper, we propose to rearrange 3D lidar data into a new organized form to construct direct spatial relationship among point cloud, and put forward new features for real-time road detection tasks. Our model works based on two prerequisites: (1) Road regions are always flatter than non-road regions. (2) Light travels in straight lines in a uniform medium. Based on prerequisite 1, we put forward difference-between-lines feature, while ScanID density and obstacle radial map are generated based on prerequisite 2. According to our method, we construct an array of structures to store and reorganize 3D input firstly. Then, two novel features, difference-between-lines and ScanID density, are extracted, based on which we construct a consistency map and an obstacle map in Bird Eye View (BEV). Finally, the road region is extracted by fusing these two maps and refinement is used to polish up our outcome. We have carried out experiments on the public KITTI-Road benchmark, achieving one of the best performances among the lidar-based road detection methods. To further prove the efficiency of our method on unstructured road, the visual outcomes in rural areas are also proposed.