Bee Bread Ameliorates Vascular Inflammation and Impaired Vasorelaxation in Obesity-Induced Vascular Damage Rat Model: The Role of eNOS/NO/cGMP-Signaling Pathway

Obesity and hyperlipidemia are major risk factors for developing vascular diseases. Bee bread (BB) has been reported to exhibit some biological actions, including anti-obesity and anti-hyperlipidemic. This study aims to investigate whether bee bread can ameliorate vascular inflammation and impaired vasorelaxation activity through eNOS/NO/cGMP pathway in obese rats. Forty male Sprague-Dawley rats were randomly divided into four groups (n = 10/group), namely: control (normal group), obese rats (OB group), obese rats treated with bee bread (0.5 g/kg/day, OB/BB group) and obese rats treated with orlistat (10 mg/kg/day, OB/OR group). The latter three groups were given a high-fat diet (HFD) for 6 weeks to induced obesity before being administered with their respective treatments for another 6 weeks. After 12 weeks of the total experimental period, rats in the OB group demonstrated significantly higher Lee obesity index, lipid profile (total cholesterol, triglyceride, low-density lipoprotein), aortic proinflammatory markers (tumor necrosis factor-α, nuclear factor-κβ), aortic structural damage and impairment in vasorelaxation response to acetylcholine (ACh). Bee bread significantly ameliorated the obesity-induced vascular damage manifested by improvements in the lipid profile, aortic inflammatory markers, and the impaired vasorelaxation activity by significantly enhancing nitric oxide release, promoting endothelial nitric oxide synthase (eNOS) and cyclic guanosine monophosphate (cGMP) immunoexpression. These findings suggest that the administration of bee bread ameliorates the impaired vasorelaxation response to ACh by improving eNOS/NO/cGMP-signaling pathway in obese rats, suggesting its vascular therapeutic role.     

Controlling thickness to tune performance of high-mobility transparent conducting films deposited from Ga-doped ZnO ceramic target

Structure modification has been found to tune significantly the transparent-conducting performance, especially mobility and conductivity of hydrogenated Ga-doped ZnO (HGZO) films. The strong correlation between film thickness and mobility of the films is revealed. The mobility increases quickly with increasing the thickness from 350 to 900 nm, and then tends to be saturated at further thicknesses. A higher mobility than 50 cm²/Vs can be achieved, which is an extra-high value for polycrystalline ZnO films deposited by using the sputtering technique. The thickness-dependent mobility originates from scatterings on grain boundaries and dislocation-induced defects controlled by thin-film growth. Based on the Volmer-Weber model, an expansion model is built up to describe the thickness-dependent crystal growth of the HGZO films, especially at the thick films. As a result, the 800 nm-thick HGZO film obtains the highest performance with high mobility of 51.5 cm²/Vs, low resistivity of 5.3 × 10^?4 Ωcm, and good transmittance of 83.3 %.     

Synthesis and Application of Novel Nano Fe-BTC/GO Composites as Highly Efficient Photocatalysts in the Dye Degradation

Topics in Catalysis - Nano Fe-BTC/graphene oxide (GO) composites were successfully synthesized by hydrothermal treatment with a microwave-assisted method. Samples were characterized by X-ray...     

Multi-functional biochip for medical diagnostics and pathogen detection

We describe a multi-functional biochip (MFB), which uses two different types of bioreceptors, including nucleic acid and antibody probes, on a single platform. The multi-functional capability of the MFB device for biomedical diagnostics is illustrated by measurements of DNA probes specific to gene fragments of Bacillus anthracis and antibody probes targeted to Escherichia coli. Calibration curves for monitoring pathogenic species using antibody probes against E. coli and DNA probes for B. anthracis illustrate the capability of the device for medical diagnostics and for quantitative detection of pathogenic agents.     

A novel double-circuit-rotor balanced induction motor for improvedslip-energy recovery drive performance. I. Modeling and simulation

A new construction for the rotor windings of balanced wound-rotor induction motors employed in the slip energy recovery drives is proposed, in this paper, to reduce the time harmonics which are commonly generated in the machine. The proposed machine has a double-circuit in the rotor. One circuit is star connected while the other is delta connected. Each of these two circuits is connected to a diode-bridge rectifier to achieve a twelve-pulse operation characteristics. The main objective of this paper is to develop a new model which is capable of simulating the proposed machine, neglecting nonlinearities due to magnetic saturation, and its associated converters. Such a model is further complicated due to the presence of the two rotor circuits and their mutual interaction     

A Comprehensive Performance Analysis of Random Early Detection Mechanism

One of the most promising active queue management schemes being proposed for deployment in the Internet is the Random Early Detection (RED) scheme. However, research results on RED performance are highly mixed, especially in the field of tuning its parameters. In this paper, a comprehensive performance analysis of RED is presented. We revisit some features in RED and study them in greater detail. We point out that RED, in general, does not possess proportional loss between flows as claimed and widely adopted in previous research. We suggest the generalization of the PASTA property and give a proof for TCP flows. We also evaluate the performance of the Exponential Weighted Moving Average (EWMA) algorithm in RED. We find that EWMA in RED is an unbiased estimator of the average queue-length, regardless of the weighting value w _q . We also point out the theoretical and practical limits of EWMA in RED. Finally, we propose the use of fuzzy EWMA to RED (fuzzy RED) to alleviate the inflexibility of RED tuning. We use simulations to evaluate the performance of fuzzy RED and compare it with other versions of RED. Our simulations show that, in the case of a high workload and a high level of variation, fuzzy RED, by tracking system variation in an on-line manner, improves RED performance in a number of important router-based metrics like packet loss rate, average queueing delay, link utilization, and global power.     

Nitrogen dioxide effect in the reduction of nitric oxide by propane in oxidizing atmosphere

The role of nitrogen dioxide in the selective reduction of NO by propane over a Cu-MFI zeolite is investigated. NO₂ and NO reductions were carried out under similar conditions of reaction. In the presence of oxygen, the reduction of NO by C₃H₈ does not differ significantly from that of NO₂. In the absence of oxygen, the reduction of NO₂ by propane occurs with a partial decomposition of the nitric dioxide molecule. Such a decomposition leads to the formation of oxygen, which is responsible for the increase in catalytic activity by comparison with the same reaction performed with NO. NO₂ formed and released in the gas phase during the reduction of NO by propane in the presence of oxygen does not play a predominant role in the catalytic process.     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.