Scalp electroacupuncture at the Baihui acupoint (DU 20) improves functional recovery in rats with cerebral ischemia Association with increased expression of vascular endothelial growth factors

In this study, we induced cerebral infarction in rats by occluding the right middle cerebral artery, and tested the effects of electroacupuncture at the Baihui acupoint （DU 20）. Motor and sensory function was tested using Garcia’s scale and motor weakness grading, and the expression of vascular endothelial growth factor in the brain was quantified using immunoblotting and immunohistochemistry. We found that scalp electroacupuncture at DU 20 significantly improved motor performance and sensory function in rats with stroke, and this was accompanied by an increased expression of vascular endothelial growth factor in the ischemic brain tissue and peri-ischemic area. In addition, Pearson correlation analysis showed that the improvements in functional recovery were correlated with the increased expression of vascular endothelial growth factor.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.