Capsiate Intake with Exercise Training Additively Reduces Fat Deposition in Mice on a High-Fat Diet,but Not without Exercise Training

While exercise training (ET) is an efficient strategy to manage obesity, it is recommended with a dietary plan to maximize the antiobesity functions owing to a compensational increase in energy intake. Capsiate is a notable bioactive compound for managing obesity owing to its capacity to increase energy expenditure. We aimed to examine whether the antiobesity effects of ET can be further enhanced by capsiate intake (CI) and determine its effects on resting energy expenditure and metabolic molecules. Mice were randomly divided into four groups (n = 8 per group) and fed high-fat diet. Mild-intensity treadmill ET was conducted five times/week; capsiate (10 mg/kg) was orally administered daily. After 8 weeks, resting metabolic rate and metabolic molecules were analyzed. ET with CI additively reduced the abdominal fat rate by 18% and solely upregulated beta-3-adrenoceptors in adipose tissue (p = 0.013) but did not affect the metabolic molecules in skeletal muscles. Surprisingly, CI without ET significantly increased the abdominal fat rate (p = 0.001) and reduced energy expenditure by 9%. Therefore, capsiate could be a candidate compound for maximizing the antiobesity effects of ET by upregulating beta-3-adrenoceptors in adipose tissue, but CI without ET may not be beneficial in managing obesity.     

Matrilin-3-Primed Adipose-Derived Mesenchymal Stromal Cell Spheroids Prevent Mesenchymal Stromal-Cell-Derived Chondrocyte Hypertrophy

Adipose-derived mesenchymal stromal cells (Ad-MSCs) are a promising tool for articular cartilage repair and regeneration. However, the terminal hypertrophic differentiation of Ad-MSC-derived cartilage is a critical barrier during hyaline cartilage regeneration. In this study, we investigated the role of matrilin-3 in preventing Ad-MSC-derived chondrocyte hypertrophy in vitro and in an osteoarthritis (OA) destabilization of the medial meniscus (DMM) model. Methacrylated hyaluron (MAHA) (1%) was used to encapsulate and make scaffolds containing Ad-MSCs and matrilin-3. Subsequently, the encapsulated cells in the scaffolds were differentiated in chondrogenic medium (TGF-β, 1–14 days) and thyroid hormone hypertrophic medium (T3, 15–28 days). The presence of matrilin-3 with Ad-MSCs in the MAHA scaffold significantly increased the chondrogenic marker and decreased the hypertrophy marker mRNA and protein expression. Furthermore, matrilin-3 significantly modified the expression of TGF-β2, BMP-2, and BMP-4. Next, we prepared the OA model and transplanted Ad-MSCs primed with matrilin-3, either as a single-cell suspension or in spheroid form. Safranin-O staining and the OA score suggested that the regenerated cartilage morphology in the matrilin-3-primed Ad-MSC spheroids was similar to the positive control. Furthermore, matrilin-3-primed Ad-MSC spheroids prevented subchondral bone sclerosis in the mouse model. Here, we show that matrilin-3 plays a major role in modulating Ad-MSCs’ therapeutic effect on cartilage regeneration and hypertrophy suppression.     

Extending SSD Lifespan with Comprehensive Non-Volatile Memory-Based Write Buffers

Journal of Computer Science and Technology - New non-volatile memory (NVM) technologies are expected to replace main memory DRAM (dynamic random access memory) in the near future. NAND flash...     

A robust control of robot manipulators for physical interaction: stability analysis for the interaction with unknown environments

Intelligent Service Robotics - A robust control designed for multiple degrees-of-freedom (DOF) robot manipulators performing complex tasks requiring frequent physical interaction with unknown...     

Decorating surface charge of graphite nanoplate using an electrostatic coupling agent for 3-dimensional polymer nanocomposite

Here, we report a facile approach to electrostatically couple the surface charges of graphite nanoplate (GNP) fillers and poly(methyl methacrylate) (PMMA) polymer particles using ethylene maleic anhydride (EMA) copolymer as an electrostatic coupling agent. Our strategy involved switching the intrinsic repulsive electrostatic interactions between the directly exfoliated GNPs fillers and the PMMA particles to attractive electrostatic surface interactions for preparing core(PMMA)-shell (GNP) precursor in order to optimizing 3-dimensionally dispersed polymer nanocomposite. As a result, the electrical conductivity of the composites dramatically increased by a factor of 16.7 in the EMA-coupled GNP/PMMA composites compared with that of the EMA-free GNP/PMMA composites. In addition, the percolation threshold was also notably reduced from 0.32 to 0.159 vol% after electrostatic coupling of the GNPs fillers and PMMA particles. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48390.     

Effect of Initial Grain Size on Friction Stir Weldability for Rolled and Cast CoCrFeMnNi High-Entropy Alloys

Metals and Materials International - This study investigated the influence of the initial grain size on the plastic deformation and tunnel defects that occurred from friction stir welding of...     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.