Plk1 is essential for proper chromosome segregation during meiosis I/meiosis II transition in pig oocytes.

BACKGROUND: Polo-like kinase 1 (Plk1), as a characteristic regulator in meiosis, organizes multiple biological events of cell division. Although Plk1 has been implicated in various functions in somatic cell mitotic processes, considerably less is known regarding its function during the transition from metaphase I (MI) to metaphase II (MII) stage in oocyte meiotic progression. METHODS: In this study, the possible role of Plk1 during the MI-to-MII stage transition in pig oocytes was addressed. Initially, the spatiotemporal expression and subcellular localization pattern of Plk1 were revealed in pig oocytes from MI to MII stage using indirect immunofluorescence and confocal microscopy imaging techniques combined with western blot analyses. Moreover, a highly selective Plk1 inhibitor, GSK461364, was used to determine the potential role of Plk1 during this MI-to-MII transition progression. RESULTS: Upon expression, Plk1 exhibited a specific dynamic intracellular localization, and co-localization of Plk1 with α-tubulin was revealed in the meiotic spindle of pig oocyte during the transition from MI to MII stage. GSK461364 treatment significantly blocked the first polar body (pbI) emission in a dose-dependent manner and resulted in a failure of meiotic maturation, with a larger percentage of the GSK461364-treated oocytes arresting in the anaphase-telophase I (ATI) stage. Further subcellular structure examination results showed that inhibition of Plk1 with GSK461364 had no visible effect on spindle assembly but caused a significantly higher proportion of the treated oocytes to have obvious defects in homologous chromosome segregation at ATI stage. CONCLUSIONS: Thus, these results indicate that Plk1 plays an essential role during the meiosis I/meiosis II transition in porcine oocytes, and the regulation is associated with Plk1's effects on homologous chromosome segregation in the ATI stage.

General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning.

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO after adsorbing 24 kinds of gases were calculated. These computed features served as the basis for training a machine learning model. Subsequently, various machine learning and evaluation algorithms were utilized to train the fast screening model. The importance of feature values was evaluated by the AdaBoost, Random Forest, and Extra Trees models. Specifically, charge transfer was assigned importance values of 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closely was the d-band center, which was presumed to exert influence on electrical conductivity and, consequently, adsorption properties. With 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an accuracy of 78% using multilayer perceptron after 5-fold cross-validation, with both data sets exhibiting low standard deviations. This verified the accuracy and reliability of the strategy, showcasing its potential for rapidly screening a material's responsiveness to the target gas.