Role of oxidative stress in trichloroethylene-induced toxicity / 环境与职业医学

Trichloroethylene (TCE) is a common industrial organic solvent and environmental contaminant. People are exposed to TCE through occupational contact or environmental pollution, which leads to serious human health hazards. A large number of studies have shown that oxidative stress plays an important role in the TCE-induced multi-target organ toxicity. However, the research of related signaling pathways remains to be deepened. In this review, we summarized the epidemiological, animal, and cellular studies correlated to liver toxicity, kidney toxicity, cardiac developmental toxicity, placental developmental toxicity, neurodevelopmental toxicity, and autoimmune response induced by TCE. In addition, the possible molecular mechanisms of oxidative stress in TCE-induced toxicity were concluded, including DNA damage, mitochondrial dysfunction, cell apoptosis, and abnormal activation of the immune system. Through literature review, we proposed that nuclear factor E2 related factor 2 may play an important role in mediating TCE-induced target organ toxicity, providing a theoretical basis for the prevention and treatment of adverse health effects caused by TCE.

ECoG classification based on wavelet variance / 生物医学工程学杂志

For a typical electrocorticogram (ECoG)-based brain-computer interface (BCI) system in which the subject's task is to imagine movements of either the left small finger or the tongue, we proposed a feature extraction algorithm using wavelet variance. Firstly the definition and significance of wavelet variance were brought out and taken as feature based on the discussion of wavelet transform. Six channels with most distinctive features were selected from 64 channels for analysis. Consequently the EEG data were decomposed using db4 wavelet. The wavelet coeffi-cient variances containing Mu rhythm and Beta rhythm were taken out as features based on ERD/ERS phenomenon. The features were classified linearly with an algorithm of cross validation. The results of off-line analysis showed that high classification accuracies of 90. 24% and 93. 77% for training and test data set were achieved, the wavelet vari-ance had characteristics of simplicity and effectiveness and it was suitable for feature extraction in BCI research. K

Study on EEG classification based on multi-task motor imagery / 生物医学工程学杂志

In order to promote the performance of EEG classification based on multi-task motor imagery (MI), we used common spatial pattern (CSP) as the feature extraction method, and we extracted the features under two conditions, with one "One versus One" and the other "One versus Rest". Then, as for the different feature extraction methods, we presented different classification methods based on support vector machine (SVM) according to the different input features. The final classification results showed that the mean Kappa of "One versus One" classification method based on decision value is much higher than that of voting rule, and a little higher than that of "One versus Rest" classification method.