Explore the vast range of titles available.

Highly accurate monthly runoff forecasts play a pivotal role in water resource management and utilization. This article proposes a coupling of variational modal decomposition (VMD) and the dung beetle optimization algorithm (DBO) with the gated recurrent unit (GRU) to establish a new monthly runoff forecasting model: the VMD-DBO-GRU. Initially, historical runoff data are decomposed via VMD. Subsequently, the parameters of the GRU are optimized using the DBO, and the decomposed monthly runoff components are inputted into the GRU neural network. Finally, the predictions for each component are consolidated to provide monthly runoff predictions. The model is then validated using monthly runoff data from the Ansha reservoir in Fujian, collected from 1980 to 2020. The results demonstrate a higher prediction accuracy of the VMD-DBO-GRU model compared to BP, SVM, GRU, VMD-GRU, DBO-GRU, and EMD-GRU models, providing a new alternative for conducting monthly runoff prediction.

Accurate forecasting of monthly precipitation is of great significance for national production, disaster prevention and mitigation, and water resources allocation management. However, it is difficult for individual models to accurately accomplish the task of predicting precipitation, and there is also the problem of insufficient accuracy of peak and trough prediction. Therefore, to solve this problem, this paper will provide a CEEMDAN-SVM-LSTM model that combines a fully adaptive noise ensemble empirical modal decomposition (CEEMDAN),a support vector machine (SVM) and the long short-term memory (LSTM) neural network. The CEEMDAN algorithm is first used to decompose the precipitation time series data into different modal components, the SVM model is then applied to the first modal component and the LSTM is applied to the remaining modal components. The precipitation data of Lanzhou city is taken as an example and brought into the model for testing and comparing with the performance of single LSTM model, differential integrated moving average autoregressive model (ARIMA), back propagation (BP) neural network model,support vector machine(SVM), extreme gradient boosting(XGBOOST), CEEMDAN-LSTM model and CEEMDAN-SVM model. After the experimental verification, the CEEMDAN-SVM-LSTM model effectively improves the fit between the observed and predicted values, overcomes the problem of low accuracy of peak and trough prediction, and significantly outperforms other models.

Tree shrews have a close relationship to primates and have many advantages over rodents in biomedical research. However, the laek of gene manipulation methods has hindered the wider use of this animal. Spermatogonial stem cells （SSCs） have been successfully expanded in culture to permit sophisticated gene editing in the mouse and rat. Here, we describe a culture system for the long-term expansion of tree shrew SSCs without the loss of stem cell properties. In our study, thymus cell antigen 1 was used to enrich tree shrew SSCs. RNA-sequencing analysis revealed that the Wnt/β-catenin signaling pathway was active in undifferentiated SSCs, but was downregulated upon the initiation of SSC differentiation. Exposure of tree shrew primary SSCs to recombinant Wnt3a protein during the initial passages of culture enhanced the survival of SSCs. Use of tree shrew Sertoli cells, but not mouse embryonic fibroblasts, as feeder was found to be necessary for tree shrew SSC proliferation, leading to a robust cell expansion and long-term culture. The expanded tree shrew SSCs were transfected with enhanced green fluorescent protein （EGFP）-expressing lentiviral vectors. After transplantation into sterilized adult male tree shrew＇s testes, the EGFP-tagged SSCs were able to restore spermatogenesis and successfully generate transgenic offspring. Moreover, these SSCs were suitable for the CRISPR/Cas9-mediated gene modification. The development of a culture system to expand tree shrew SSCs in combination with a gene editing approach paves the way for precise genome manipulation using the tree shrew.