Analysis of Multi-Element Blended Course Teaching and Learning Mode Based on Student-Centered Concept under the Perspective of “Internet+”

The integration of Internet and education has changed students’ learning environment and affected their learning behavior, which poses a greater challenge to the traditional teaching mode. Through the SWOT analysis of the “student centered” multi-element blended teaching mode in the era of “Internet + education”, it is concluded that the adaptability of learners themselves and the mismatch between teachers’ educational ideas and this teaching model delay the development of education to a certain extent. Some suggestions are put forward, such as strengthening the supervision and guidance, implementing the teaching and learning model scientifically, improving teachers’ ideology and comprehensive quality, and making full use of the characteristics of Internet opening, sharing and collaboration to construct the public service system and platform of national educational resources

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results

Research on the role of SOX9 in regulating metabolic reprogramming in diffuse large B cell lymphoma

Objective·To explore the role played by the differentially expressed SRY-box transcription factor 9 (SOX9) gene in diffuse large B cell lymphoma (DLBCL), particularly in the regulation of metabolic reprogramming in the germinal center B-cell (GCB) like subtype.Methods·The clinical information and gene expression profile data of 481 DLBCL patients retrieved from the NCICCR-DLBCL database were included. Data analysis and visualisation were performed by using R language version 4.1.3. The classification was performed by using a cell of origin subtype (COO) classification algorithm based on RNA-seq sequencing of expression. ABC/GCB features were used to annotate gene sets, and the classification was verified by gene set enrichment analysis. The ABC and GCB subgroup was dichotomised based on the mean expression of SOX9. Differential analysis was performed by using the DEseq2 package. The relationship between SOX9 and ABC-DLBCL metabolism was analysed by using KEGG (Kyoto Encyclopedia of Genes and Genomes) with the Hallmark annotation set. The survival curves were plotted by using the Kaplan-Meier method. The pan-cancer analysis was performed by using GEPIA2. The microenvironmental scoring analysis was performed by the ESTIMATE package.Results·Of the 481 DLBCL patient samples, all the patients had RNA-seq expression data, 421 had clinical staging, 335 had international prognostic index (IPI) scores and 234 had survival data. The classification yielded 232 (48.2%) ABC subtypes, 173 (36.0%) GCB subtypes and 76 (15.8%) unclassified, consistent with the proportions declared in the database, and the enrichment analysis was verified to be consistent with the ABC/GCB expression profile. Compared to the high SOX9 expression group, the overall survival was shorter in the low SOX9 expression group and the prognostic score was worse. The pan-cancer analysis showed that this phenomenon was also seen in other tumor types. The differential analysis showed that there were 156 upregulated genes and 1 826 downregulated genes in the GCB subtype in the low SOX9 expression group, compared to the high SOX9 expression group. For metabolic processes, down-regulated genes were enriched in glycolysis.Conclusion·In the ABC subtype of DLBCL, the SOX9 gene affects the biological features of ABC-DLBCL by regulating metabolic reprogramming, and low expression of SOX9 in DLBCL, possibly caused by high methylation, predicts decreased glycolysis in tumors. The proportion of tumor stromal cells decreases, showing a worse prognosis