Ca2+ channel subunit a 1D inhibits endometriosis cell apoptosis and mediated by prostaglandin E2

Objectives: Endometriosis is considered as a chronic pelvic inflammatory disease and prostaglandin E2(PGE2) (a kind of the inflammatory cytokines) was increased in the endometriosis patient’s peritoneal fluid . Ca2+ signal and Ca2+ channels play an important role in cell apoptosis. This study was to explore the L-type calcium channel (Cav1.3) expression and its biological function in endometriosis. Furthermore the molecular mechanism between Cav1.3 and PGE2 was also clarified. Material and methods: The real-time PCR and immunohistochemical were used to detect the expression of Cav1.3. Apoptosis was detected by Flow cytometry assay and Western blot assay. Results: Cav1.3 was high expression in endometriosis tissue and primary endometrial stromal cells (hEM15A). Treatment with PGE2 rapidly inhibited apoptosis and increased Cav1.3 expression in hEM15A . The silencing of Cav1.3 promoted apoptosis, which was unchanged after PGE2 treatment. Moreover, the inhibition of Cav1.3 by shRNA transfection activated cleaved PARP and cleaved caspase-3. Conclusions: These available evidences suggest that Cav1.3 is required for PGE2 induction apoptosis and relates to the pathophysiology of endometriosis. Interference with Cav1.3 may offer a neo-therapeutic window in endometriosis treatment

Stabilizing a memristor-based chaotic system by switching control and fuzzy modeling

We investigate the stabilization of a memristor-based chaotic system.Based on the system,a T-S model is established and a switching controller is designed.Then,by Lyapunov stability theory,one can acquire a criterion to guarantee that the conclusion holds.Finally,numerical results are demonstrated to verify the effectiveness of our method

A Hybrid Machine Learning Model Coupling Double Exponential Smoothing and ELM to Predict Multi-Factor Landslide Displacement

The deformation of landslides is a non-linear dynamic and complex process due to the impacts of both inherent and external factors. Understanding the basis of landslide deformation is essential to prevent damage to properties and losses of life. To forecast the landslides displacement, a hybrid machine learning model is proposed, in which the Variational Modal Decomposition (VMD) is implemented to decompose the measured total surface displacement into the trend and periodic components. The Double Exponential Smoothing algorithm (DES) and Extreme Learning Machine (ELM) were adopted to predict the trend and the periodic displacement, respectively. Particle Swarm Optimization (PSO) algorithm was selected to obtain the optimal ELM model. The proposed method and implementation procedures were illustrated by a step-like landslide in the Three Gorges Reservoir area. For comparison, Least Square Support Vector Machine (LSSVM) and Convolutional Neutral Network–Gated Recurrent Unit (CNN–GRU) were also conducted with the same dataset to forecast the periodic component. The application results show that DES-PSO-ELM outperformed the other two methods in landslide displacement prediction, with RMSE, MAE, MAPE, and R2 values of 1.295mm, 0.998 mm, 0.008%, and 0.999, respectively

MGA-seq: robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing

Abstract Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method — multiple genetic abnormality sequencing (MGA-Seq) — to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA’s transcriptional regulatory function