Chongqing University of Technology

About: Chongqing University of Technology is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 5199 authors who have published 4205 publications receiving 35954 citations. The organization is also known as: Chongqing Institute of Technology.

Biomimetic oral targeted delivery of bindarit for immunotherapy of atherosclerosis.

Abstract: Monocyte chemoattractant protein-1 (MCP-1) plays an important role in the development of atherosclerosis. However, the application of bindarit (a specific synthetic inhibitor of MCP-1) in atherosclerosis has not been confirmed due to the non-specific distribution profile in vivo. Herein, based on the recruitment of monocytes into atherosclerotic plaques, we successfully delivered bindarit into the interior of atherosclerotic plaques through a yeast-derived microcapsule (YC) mediated biomimetic approach. In this biomimetic approach, bindarit was firstly assembled with polyethyleneimine to form the positively charged nanoparticles (BIN/PEI NPs) via multiple intermolecular forces, and then the obtained BIN/PEI NPs were packed into YCs by electrostatic force-mediated spontaneous deposition. Through an oral adsorption routine similar to yeasts, bindarit loaded YCs (BIN/YCs) were distributed into peripheral blood monocytes after oral administration, and then their targeted delivery to atherosclerotic plaques was successfully performed through monocyte transportation. Correspondingly, oral delivery of bindarit loaded YCs afforded notably potentiated efficacies for inhibiting the MCP-1 and further reducing the recruitment of monocytes into atherosclerotic plaques, and thus presented a good efficacy in preventing the formation of atherosclerotic plaques. These results demonstrated that a ‘Trojan horse’-like YC mediated nanomedicine delivery strategy is expected to realize the application of certain potential anti-inflammatory drugs in the treatment of atherosclerosis and is of great significance for the development of novel strategies for atherosclerosis treatment.

Effect of Post Treatment For Cu-Cr Source/Drain Electrodes on a-IGZO TFTs.

Abstract: We report a high-performance amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) thin-film transistor (TFT) with new copper-chromium (Cu-Cr) alloy source/drain electrodes. The TFT shows a high mobility of 39.4 cm 2 ·V − 1 ·s − 1 a turn-on voltage of −0.8 V and a low subthreshold swing of 0.47 V/decade. Cu diffusion is suppressed because pre-annealing can protect a-IGZO from damage during the electrode sputtering and reduce the copper diffusion paths by making film denser. Due to the interaction of Cr with a-IGZO, the carrier concentration of a-IGZO, which is responsible for high mobility, rises.

Histogram-based colour image fuzzy clustering algorithm

Abstract: In this work, a histogram-based colour image fuzzy clustering algorithm is proposed for addressing the problem of low efficiency due to computational complexity and poor clustering performance. Firstly, the presented scheme constructs the red, green and blue (short for RGB) component histograms of a given colour image, each of which is pre-processed to preserve their smoothness. Secondly, the proposed algorithm multi-thresholds each component histogram, using some dominant valleys identified from a fast peak-valley location scheme in each global histogram. Thirdly, a new histogram is reconstructed by applying a histogram merging scheme to the RGB three-component histograms, and multi-thresholding this new histogram again using some dominant valleys obtained from the fast peak-valley location scheme. Thus, the proposed approach can easily identify the initialisation condition of cluster centroids and centroid number. Finally, we construct a new dataset composed of some pre-segmented small regions using the WaterShed algorithm, and the FCM (Fuzzy C-Means) algorithm is executed on this dataset, instead of on pixels, in combination with the initial cluster centroids. Experimental results have demonstrated that the proposed algorithm is more efficient than the DSRPCL (Distance Sensitive Rival Penalised Competitive Learning) algorithm and the HTFCM (Histogram Thresholding Fuzzy C-Means) algorithm with respect to run times and PRI (Probability Rand Index) values.