Identification of differentially expressed microRNAs and the potential of microRNA-455-3p as a novel prognostic biomarker in glioma.

Glioma is an aggressive central nervous system malignancy. MicroRNAs (miRNAs/miRs) have been reported to be involved in the tumorigenesis of numerous types of cancer, including glioma. The present study aimed to identify the differentially expressed miRNAs in glioma, and further explore the clinical value of miR-455-3p in patients with glioma. GEO2R was used for the identification of the differentially expressed miRNAs according to the miRNA expression profiles obtained from the Gene Expression Omnibus database. OncomiR was used to analyze the relationship of miRNAs with the survival outcomes of the patients with glioma. A total of 108 patients with glioma were recruited to examine the expression levels of miR-455-3p and further explore its clinical value. The bioinformatics analysis results suggested that a total of 64 and 48 differentially expressed miRNAs were identified in the GSE90603 and GSE103229 datasets, respectively. There were 12 miRNAs in the overlap of the two datasets, of which three were able to accurately predict overall cancer survival, namely hsa-miR-7-5p, hsa-miR-21-3p and hsa-miR-455-3p. In patients with glioma, miR-455-3p was determined to be significantly upregulated (P<0.001). Additionally, patients with high miR-455-3p expression had significantly lower 5-year overall survival than those with low miR-455-3p expression (log-rank test, P=0.001). Cox regression analysis further determined that miR-455-3p was an independent prognostic indicator for overall survival in patients with glioma (hazard ratio=2.136; 95% CI=1.177-3.877; P=0.013). In conclusion, the present study revealed a series of miRNAs with potential functional roles in the pathogenesis of glioma, and provides findings that indicate miR-455-3p as a promising biomarker for the prognosis of glioma

Crystallization behavior and IR structure of yttrium aluminosilicate glasses

The crystallization of four Y2O3-Al2O3-SiO2 (YAS) glasses were investigated to prepare YAS glass ceramics precipitated singly/mainly Y2Si2O7 or Y4.67(SiO4)3O apatite, and to explore the crystallization difference between the stoichiometric parent glass (SPG) and non-stoichiometric parent glass (NSPG). The DSC results revealed that glass locating at the higher liquidus surface temperature has lower crystallization peak temperature, which indicating that the corresponding glass has higher crystallization potential to crystallize easily. Crystallization of the NSPG samples is along surface and caused by phase separation, while SPG sample is the surface crystallization at the first exothermic peak temperature and overall crystallization at the second exothermic peak temperature. Glass ceramics only containing y-Y2Si2O7 or Y4.67(SiO4)3O apatite are obtained successfully, and which are illustrated by fitting FTIR spectra. These results can provide technical guide for controlling the crystallization process and the types of precipitated crystals in YAS glass for different application potentials

A dynamic global backbone updating for communication-efficient personalised federated learning

Federated learning (FL) is an emerging distributed machine learning technique. However, when dealing with heterogeneous data, a shared global model cannot generalise all devices' local data. Furthermore, the FL training process necessitates frequent parameter communication, which interferes with the limited bandwidth and unstable connections of participating devices. These two issues have a significant impact on FL's effectiveness and efficiency. In this paper, an enhanced communication-efficient personalised FL technique, FedGB, is proposed. Different from existing approaches, FedGB believes that only interacting common information from training results on different devices can improve local personalised training results more effectively. FedGB dynamically selects the backbone structures in the local models to represent the dynamically determined backbone information (common features) in the global model for aggregation. Only interacting common features between different nodes reduce the impact of heterogeneous data to a certain extent. The dynamic adaptive sub-model selection avoids the impact of manually setting the scale of sub-model. FedGB can thus reduce communication overheads while maintaining inference accuracy. The results obtained in a variety of experimental settings show that FedGB can effectively improve communication efficiency and inference accuracy