Research on the shared function of central neurons and breast cancer based on gene expression profile data mining: The role of EMID1 protein antibody expression.

In recent years, the incidence of breast cancer has gradually increased, and the research on it has become a hot spot in the scientific community. Central neurons play an important role in breast cancer. This study aims to explore the application of gene expression profile data mining in the study of shared function between central neurons and breast cancer, and focuses on the expression of EMID1 protein antibody. The study collected biomedical images and gene expression profile data of breast cancer patients. Then, we use image processing and analysis technology to extract and analyze features of biomedical images to obtain quantitative features of breast cancer. Gene expression profile data were preprocessed and analyzed to obtain information about breast cancer related genes. Integrating and fusing biomedical images and gene expression profile data, and exploring the sharing function between central neurons and breast cancer through data mining algorithms and statistical analysis methods. The results showed that the expression of EMID1 protein was high in breast cancer tissues, and the expression pattern was similar to that of central neurons. Further functional studies have shown that EMID1 protein is involved in the regulation of proliferation and invasion of breast cancer cells. By regulating the expression level of EMID1 protein, we observed that the proliferation and invasion ability of breast cancer cells were significantly affected. The research results show that through the comprehensive analysis of biomedical images and gene expression profile data, we found the sharing function between central neurons and breast cancer. The central neuronal cell marker genes EMID1 and GREB1L may be used as key biomarkers to regulate the pathogenesis of breast cancer and affect the occurrence and development of breast cancer.

A unified control method combined with improved TSF and LADRC for SRMs Using modified grey wolf optimization algorithm.

This paper proposes a unified control method based on an improved grey wolf optimization algorithm to improve the performance of the switched reluctance motors (SRMs) to cope with various operating conditions. Compared with the single control mode of traditional SRMs, an SRM with 12/10 poles is called a multimode switched reluctance motor, which can be used not only as a six-phase motor but also as a three-phase motor. The control method proposed is based on a 12/10 pole SRMs. In the TSRM mode, an improved torque sharing function with parameters is utilized to reduce torque ripple and widen the speed range. In the SSRM mode, the linear active disturbance rejection control (LADRC) method is used to increase the anti-interference ability of the system. For the switching modes, an improved gray wolf optimization algorithm is designed to ensure smooth switching. The global optimal solution is obtained by introducing the coyote group to avoid falling into the local optimal solution. Finally, the experimental results prove the effectiveness of the control method.