There are some problems in the design of three-dimensional visualization teaching resources, such as less involved in interactive design, no unified design standards, the lack of effective innovation guidance mechanism and no unified evaluation system. Based on the design principle, taking amino acids as an example, this paper proposes a design plan for 3D visualization resources in teaching scenarios. According to the design scheme, the three-dimensional visualization resources are made. The main processes include the selection of the tools, the preparation and production of the material, the writing of the script, the design of the learning interface, the design of the interactive logic relationship and the control and output of the program. Finally, the three-dimensional visualization resources are used in the future classrooms with starC electronic double board and group touch screen, and the results are analyzed and evaluated. It is found that the three-dimensional visualization resources can effectively improve the students' interest in learning and help students understand knowledge.

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Background: Although some improvements in the management of pancreatic cancer (PC) have been made, no major breakthroughs in terms of biomarker discovery or effective treatment have emerged. Here, we applied artificial intelligence (AI)-based methods to develop a model to diagnose PC and predict survival outcome.Methods: Multiple bioinformatics methods, including RankProd, were performed to identify differentially expressed genes (DEGs) in PC. A Back Propagation (BP) model was constructed, followed by Genetic Algorithm (GA) filtering and verification of its prognosis capacity in the TCGA cohort. Furthermore, we validated the protein expression of the selected DEGs in 92 clinical PC tissues using immunohistochemistry.Results: Four candidate genes (LCN2, SLC6A14, SPOCK1, and VCAN) were selected to establish a four-gene signature for PC. The gene signature was validated in the TCGA PC cohort, and found to show satisfactory discrimination and prognostic power. Areas under the curve (AUC) values of overall survival were both greater than 0.60 in the TCGA training cohort, test cohort, and the entire cohort. Kaplan-Meier analyses showed that high-risk group had a significantly shorter overall survival and disease-free survival than the low-risk group. Further, the elevated expression of SLC6A14 and SPOCK1 in PC tissues was validated in the TCGA+GETx datasets and 92 clinical PC tissues, and was significantly associated with poor survival in PC.Conclusions: Using RankProd and GA-ANN, we developed and validated a diagnostic and prognostic gene signature that yielded excellent predictive capacity for PC patients’ survival.

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By using the analysis results of online learning behavior, teachers can understand and identify students' learning dynamics, analyze the difficulties and blind spots of the course, reasonably adjust the course structure and video content. Meanwhile, teachers can get inspiration from students' feedback and expand their teaching ideas. For platform managers, learning data analysis can indirectly show the application effect of platform functions and provide guidance for managers to improve and optimize platform functions. From the perspective of teaching model reform and innovation, the study of learners' learning behavior brings new ideas for educational researchers to reform teaching model.