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Gliomas are particularly challenging due to their high invasiveness, frequent recurrence, and elevated mortality rates. Despite the availability of treatments like surgery, radiation, and chemotherapy, each of these methods faces significant limitations. This has led to a pressing demand for new strategies against gliomas. In this landscape, mesenchymal stem cells (MSCs) have shown significant potential in recent years. However, the application of MSCs in glioma therapy encounters various challenges. A significant advancement in this field is the utilization of exosomes (Exo), key secretions of MSCs. These exosomes not only carry the benefits inherent in MSCs but also exhibit unique physicochemical properties that make them effective drug carriers. Consequently, MSCs Exo is gaining recognition as a sophisticated drug delivery system, specifically designed for glioma treatment. The scope of MSCs Exo goes beyond being just an innovative drug delivery mechanism; it also shows potential as a standalone therapeutic option. This article aims to provide a detailed summary of the essential role of MSCs Exo in glioma progression and its growing importance as a drug delivery carrier in the fight against this formidable disease.

C36H32Co2N8O4, monoclinic, P21/c(no. 14), a= 12.2971(12) Å, b= 13.1008(13) Å, c= 11.0864(11) Å, β= 99.174(2)°, V= 1763.2(3) Å3, Z= 2, Rgt(F) = 0.0277, wRref(F2) = 0.0818, T= 296 K.

Annexin, a calcium-dependent phospholipid-binding protein, can affect tumor cell adhesion, proliferation, apoptosis, invasion and metastasis, as well as tumor neovascularization in different ways. Recent studies have shown that annexin exists not only as an intracellular protein in tumor cells, but also in different ways to be secret outside the cell as a “cross-talk” tool for tumor cells and tumor microenvironment, thus playing an important role in the development of tumors, such as participating in epithelial-mesenchymal transition, regulating immune cell behavior, promoting neovascularization and so on. The mechanism of annexin secretion in the form of extracellular vesicles and its specific role is still unclear. This paper summarizes the main role of annexin secreted into the extracellular space in the form of extracellular vesicles in tumorigenesis and drug resistance and analyzes its possible mechanism.

Mental fatigue is a state that may occur due to excessive work or long-term stress. Electroencephalography (EEG) is considered a reliable standard for mental fatigue detection. The existing EEG fatigue detection methods mainly use traditional machine learning models to classify mental fatigue after manual feature extraction. However, manual feature extraction is difficult and complicated. The quality of feature extraction largely determines the quality of the model. In this article, we collected EEG signals from 30 medical staff. The wavelet threshold denoising method was then applied to the measured EEG signal data to denoise the original EEG data, and a method based on a convolution and long short-term memory (CNN + LSTM) neural network to determine the fatigue state of medical staff. The extensive experiment on the established dataset clearly proves the advancement of our proposed algorithm compared to other neural network-based methods. Compared with the existing DNN, CNN and LSTM, the proposed model can quickly learn the information before and after the time series, so as to obtain higher classification accuracy.

ABSTRACTShort-term hourly reliable prediction of significant wave height is an important research topic in coastal engineering. Many researchers have carried out in-depth studies in many ocean regions. Generally, most of this work is implemented through numerical models. However, as for numerical models, with the increase of hourly prediction duration, the accumulation of wave randomness leads to the poor prediction effect. In this paper, four buoy stations in the Taiwan Strait are taken as the research objects. We propose a significant wave height prediction algorithm, which combines numerical weather prediction model WRF and deep-learning model, called WRF-CLSF(Convolution-LSTM-FC). WRF can forecast 24-h wind speed in real-time, based on a variety of interpretable physical mechanisms. CLSF aims to extract the information of historical wind-wave interaction scale and the trend of wind-wave coherence, with the help of convolution operation and the time series model(LSTM), respectively. In the experiment, the proposed model was compared with the state-of-the-art prediction model. The results show that WRF-CLSF has an outstanding prediction effect at four buoy observation stations along with the Taiwan Strait.

The 4D variational (4DVAR) assimilation numerical ocean model research is proposed. This model for Taiwan Straits (TWS) is based on Regional Ocean Model System (ROMS). The background of the 4DVAR method is introduced and the development process of assimilation system is presented. In the present research, the model assimilated with Sea Surface Temperature (SST) data of HY-2 satellite (Qi, 2012; Xu, 2013) which is the first marine environmental monitoring satellite of China. In this paper, the model processes from Feb. 1 to Feb. 7, 2014 with one-day assimilation time window and root mean square error (RMSE) reduces averagely by 14.7%.

Abstract: We enhance a robust parallel finite element model for coasts and estuaries cases with the use of N-Best refinement algorithms, in multilevel partitioning scheme. Graph partitioning is an important step to construct the parallel model, in which computation speed is a big concern. The partitioning strategy includes the division of the research domain into several semi-equal-sized sub-domains, minimizing the sum weight of edges between different sub-domains. Multilevel schemes for graph partitioning are divided into three phases: coarsening, partitioning, and uncoarsening. In the uncoarsening phase, many refinement algorithms have been proposed previously, such as KL, Greedy, and Boundary refinements. In this study, we propose an N-Best refinement algorithm and show its advantages in our case study of Xiamen Bay. Compared with original partitioning algorithm in previous models, the N-Best algorithm can speed up the computation by 1.9 times, and the simulation results are in a good match with the in-situ data.