Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis.

In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analyze the faults of electrical equipment and their causes, and establish knowledge base according to relevant data and expert judgment. The fault knowledge base was introduced into the neural network operation structure, and the fault diagnosis results were classified step by step through multiple subnetworks. In data preprocessing, in order to avoid the redundancy of primary fault information features, the principal component heuristic attribute reduction algorithm was used to select the fault data samples optimally. The neural network learning algorithm is used to calculate the forward direction and error rate of the initial error data, and the reliability function is used to optimize the initial weight threshold of the neural network, propagating the error backwards and high. Experimental results show that adding attribute reduction improves error classification performance, avoids the problem of local minima through neural network operation, and has fewer iteration steps, lower average error, and higher accuracy of fault diagnosis, reaching 95.6%.

MYC Regulation of D2HGDH and L2HGDH Influences the Epigenome and Epitranscriptome.

Mitochondrial D2HGDH and L2HGDH catalyze the oxidation of D-2-HG and L-2-HG, respectively, into αKG. This contributes to cellular homeostasis in part by modulating the activity of αKG-dependent dioxygenases. Signals that control the expression/activity of D2HGDH/L2HGDH are presumed to broadly influence physiology and pathology. Using cell and mouse models, we discovered that MYC directly induces D2HGDH and L2HGDH transcription. Furthermore, in a manner suggestive of D2HGDH, L2HGDH, and αKG dependency, MYC activates TET enzymes and RNA demethylases, and promotes their nuclear localization. Consistent with these observations, in primary B cell lymphomas MYC expression positively correlated with enhancer hypomethylation and overexpression of lymphomagenic genes. Together, these data provide additional evidence for the role of mitochondria metabolism in influencing the epigenome and epitranscriptome, and imply that in specific contexts wild-type TET enzymes could demethylate and activate oncogenic enhancers.