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The effects of current treatment strategies used in ischemic stroke are weakened by cerebral ischemia-reperfusion (CIR) injury. Suitable treatment regimens targeting CIR injury are still lacking. Two herbs, namely, Acanthopanax senticosus (Rupr. & Maxim.) Harms (ASE) and Gastrodia elata Blume (GEB), have been used as traditional Chinese medicine and are indicated in the treatment of stroke and cerebrovascular diseases. However, there are no studies that report the effects of ASE combined with GEB in the treatment of CIR injury. In this study, we used the Zea Longa method to induce CIR injury in male Wistar rats. Results of the pharmacodynamic studies revealed that co-administration of ASE and GEB may improve neuronal injury and prevent neuronal apoptosis by reducing oxidative stress and inflammation, and also help prevent CIR injury. On the basis of our hypothesis, we combined the results from transcriptomic and metabonomic analyses and found that ASE and GEB could prevent CIR injury by targeting phenylalanine, pyrimidine, methionine, and sphingolipid metabolism. Therefore, our study provides the basis for the compatibility and efficacy of ASE and GEB.

With the continuous deepening of the informationization and intelligentization of the electric power industry in China, the fault early warning and diagnosis system of the electric power verification system in China reflects the current situation of insufficient intelligentization. Traditional fault diagnosis system collects operation data of verification system through sensor network, acquisition network and log message technology, and then carries out manual or semi-manual fault detection and processing. There is a small amount of data collected, and the traditional data mining methods such as expert judgment method, decision tree method and SDG model have low efficiency and poor diagnosis effect. In view of this situation, this paper introduces deep learning technology into the verification system of automated pipeline, realizes the integration of fault early warning and diagnosis, builds a fault classification model for automated pipeline based on deep learning neural network, and tests and verifies the effect of model early warning with actual system operation data. The validation of the algorithm gives the result of fault early warning in the form of probability, fully considers the factors affecting the interaction between the pipeline and equipment, has a better effect of fault early warning provides more accurate reference for fault detection and prevention of automated pipeline.