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As the digital reform unfolds at an incredible pace, the continuing higher education faces opportunities to transform and upgrade. Although emerging technologies such as 5G, Internet and artificial intelligence are gaining a foothold in continuing education, the continuing higher education must deal with challenges and issues in education orientation, teaching mode, talent training, etc. There is an urgent need of standardized management, reform and development. This paper aims to establish the accurate orientation and staggered development path for continuing education in colleges and universities on the macro, meso and micro levels. On the macro level, it studies the accurate orientation and staggered development path of continuing education in the digital reform era; on the meso level, it explores the standardized management of continuing higher education and reform measures of digital talent training mode; on the micro level, it discusses how to leverage the advantages in colleges and universities, build role models in digital reform education programs and learning bases, reform the standardized management and talent training mode, and propel the high-quality development of continuing higher education.

AbstractThe underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.