Based on the challenge-hindrance stressor model, this study explored the mechanism of how challenge/hindrance demands affect cognitive wellbeing on a daily basis. Specifically, we examined the mediating effect of work–family enrichment on the relationship between challenge/hindrance demands and cognitive wellbeing. In addition, we tested the moderating effect of overqualification on the relationship between challenge/hindrance demands and work–family enrichment on a daily basis. Finally, we examined the moderated mediation effect of perceived overqualification in a multilevel model. To capture changes in work–family (...) enrichment and cognitive wellbeing that individuals perceived daily, the experience sampling method was adopted to test our theoretical models. A total of 99 participants from China were involved in this investigation. The results showed that daily challenge demands had a significant positive effect on daily cognitive wellbeing, and daily hindrance demands had a significant negative effect on wellbeing. In addition, daily work–family enrichment mediated the positive relationship between daily challenge demands and daily cognitive wellbeing. Moreover, perceived overqualification moderated the relationship between daily challenge demands and daily cognitive wellbeing in the multilevel model. Finally, a significant moderated mediating effect of this overqualification on the indirect effect of daily work–family enrichment on the relationship between daily challenge demands and daily cognitive wellbeing was observed. (shrink)

Balancing self‐renewal and differentiation of stem cells is an important issue in stem cell and cancer biology. Recently, the Drosophila neuroblast (NB), neural stem cell has emerged as an excellent model for stem cell self‐renewal and tumorigenesis. It is of great interest to understand how defects in the asymmetric division of neural stem cells lead to tumor formation. Here, we review recent advances in asymmetric division and the self‐renewal control of Drosophila NBs. We summarize molecular mechanisms of asymmetric cell division (...) and discuss how the defects in asymmetric division lead to tumor formation. Gain‐of‐function or loss‐of‐function of various proteins in the asymmetric machinery can drive NB overgrowth and tumor formation. These proteins control either the asymmetric protein localization or mitotic spindle orientation of NBs. We also discuss other mechanisms of brain tumor suppression that are beyond the control of asymmetric division. (shrink)

The COVID-19 is creating panic among people around the world and is causing a huge public mental health crisis. Large numbers of observational studies focused on the prevalence of psychological problems during the COVID-19 pandemic were published. It is essential to conduct a meta-analysis of the prevalence of different psychological statuses to insight the psychological reactions of general population during the COVID-19 epidemic in China. Sixty six observational studies about the psychological statuses of people during the COVID-19 were included, searching (...) up to 1 December 2020. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) was used to evaluate the quality of the included studies. OpenMeta[Analyst] was used for the data analysis. High prevalence of acute stress and fear symptoms were observed in the early period of the epidemic. Additionally, anxiety and depression symptoms continued at a high prevalence rate during the epidemic. It should alert the lasting mental health problems and the risk of post-traumatic stress disorder and other mental disorders.Systematic Review Registration:PROSPERO CRD 42020171485. (shrink)

The Biliu River originates from the southern foot of Qinling Mountain in Gaizhou city, with an elevation of 1047 m, and is the largest river in Dalian. The hydrological elements mainly include rainfall, runoff, temperature, evaporation, and other time series associated with the hydrological cycle. Among them, runoff is the most visible output performance, and the direct source of runoff is during rainfall. This paper establishes a reservoir scheduling model that considers the influence of multiple uncertainty factors and analyzes the (...) influence of mixed uncertainty on reservoir scheduling and Xingli’s objectives based on probability box theory. In terms of uncertainties, the uncertainty of hydrological model parameters and the randomness of precipitation processes are mainly considered, with the former having an impact on river runoff simulation and the latter having an impact on both river runoff simulation and crop irrigation water demand. In the case of the Jing River basin, for example, the results show that, compared to the stochasticity of the precipitation process, the variation in precipitation has a significant effect on irrigation water demand in maize, followed by the frequency of precipitation, and the interaction between the two is not significant. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit (...) was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)