Background/Aims: The present study investigated whether the transient receptor potential melastatin 4 (TRPM4) channel plays a role in high salt diet (HSD)-induced endothelial injuries. Methods: Western blotting and immunofluorescence were used to examine TRPM4 expression in the mesenteric endothelium of Dahl salt-sensitive (SS) rats fed a HSD. The MTT, TUNEL, and transwell assays were used to evaluate the cell viability, cell apoptosis, and cell migration, respectively, of human umbilical vein endothelial cells (HUVECs). Enzyme-linked immunosorbent assays were used to determine the concentrations of intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion protein 1 (VCAM-1), and E-selectin. Carboxy-H2DCFDA, a membrane-permeable reactive oxygen species (ROS)-sensitive fluorescent probe, was used to detect intracellular ROS levels. Results: TRPM4 was mainly expressed near the plasma membrane of mesenteric artery endothelial cells, and its expression level increased in SS hypertensive rats fed a HSD. Its protein expression was significantly upregulated upon treatment with exogenous hydrogen peroxide (H2O2) and aldosterone in cultured HUVECs. Cell viability decreased upon treatment with both agents in a concentration-dependent manner, which could be partially reversed by 9-phenanthrol, a specific TRPM4 inhibitor. Exogenous H2O2 induced apoptosis, enhanced cell migration, and increased the release of adhesion molecules, including ICAM-1, VCAM-1, and E-selectin, all of which were significantly attenuated upon treatment with 9-phenanthrol. Aldosterone and H2O2 induced the accumulation of intracellular ROS, which was significantly inhibited by 9-phenanthrol, suggesting that oxidative stress is one of the mechanisms underlying aldosterone-induced endothelial injury. Conclusions: Given the fact that oxidative stress and high levels of circulating aldosterone are present in hypertensive patients, we suggest that the upregulation of TRPM4 in the vascular endothelium may be involved in endothelial injuries caused by these stimuli.
The identification of Chinese traditional medicine is a difficult subject in pharmacology. The development of chemical measurement and pattern recognition make chemical pattern recognition possible. In the paper a new chemical pattern recognition method is proposed, in which a simple method called corresponding-peak distance calculation is used to compute the distance between samples for a nearest neighbor (NN) classifier, and a genetic algorithm is used to optimize the parameters of the NN classifier. With the proposed method, experiments are carried out on chromatogram data of Panax. The results indicate that the method can identify the medicine material of different harvest time or habitats, furthermore, this method which combines pattern matching, genetic algorithm and NN classifier is robust, accurate and easy to implement.
The identification of traditional Chinese medicine is a difficult subject in pharmacology. The development of chemical measurement and pattern recognition make chemical pattern recognition possible. In this paper a new chemical pattern recognition method named NN2GA is proposed, in which a simple method named corresponding-peak distance calculation is used to compute the distance between samples for a nearest neighbor (NN) classifier, and a genetic algorithm is used to optimize the parameters of the NN classifier. A method named NN3GA, which is realized by adding a parameter to NN2GA, is proposed to improve the performance of the classifier. Experiments are carried out on chromatogram data of Panax, and comparisons are made between NNPR, NN2GA, and NN3GA classifiers. The results indicate that the method which combines NN with a genetic algorithm can identify medicine material having different harvest times or habitats. Furthermore, this method is robust, accurate and easy to implement.
To control the free floating spacecraft in outer space, this paper builds a model of free floating sphere movement under the action or multi-thrusters, and uses genetic algorithm (GA) to optimize the path and position in different control phases by using a combination of thrusters. In each phase, it makes the control target as the fitness function of GA to search the best solution, then uses this combination to move the sphere to an objective position and stabilizes it in the objective state. Contrasted to the traditional optimal control method, this control method is much simple and does not need a complicated algorithmic analysis, but can achieve a much effective result.
In this paper, we present a novel approach which employs the temporal redundancy of video texts to improve the performance of text segmentation and stroke extraction from complex background. We first demonstrate how to obtain the required aligned text image sequences from video frames via a robust corners-set matching scheme. Then the changing background pixels can be identified and removed by exploiting the statistics of the temporal redundancy of video. The text stroke pixels can be clearly separated from the complex background. Experiments on TV programs and movies videos show the proposed approach can generate clean text strokes image which can efficiently improve the OCR's performance compare to some traditional approaches.
The image classification method of object and non-object is discussed.According to the principle of statistics,if classification of image belongs to object image classes,the extraction of feature adopts feature of object image,or adopts global low-level feature of image.Based on the principal component analysis(PCA) that reduce the dimensionality of feature and gaussian mixture models(GMM) classifier,image classification algorithm is presented.The algorithm is tested on a standard corel image databases,and is compared with other GMM methods.Experimental results show the efficiency of the presented image classification algorithm.
A quasi-passive dynamic walking robot is built to study natural and energy-efficient biped walking. The robot is actuated by MACCEPA actuators. A reinforcement learning based control method is proposed to enhance the robustness and stability of the robot's walking. The proposed method first learns the desired gait for the robot's walking on a flat floor. Then a fuzzy advantage learning method is used to control it to walk on uneven floor. The effectiveness of the method is verified by simulation results.
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