Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets

Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, the CIBERSORTx algorithm and ssGSEA were implemented for functional analysis. Patients with high mRNAsi/c_mRNAsi GBM showed better prognosis among the four GBM cohorts. After identifying the confounding variables, c_mRNAsi still maintained its prognostic value. Model evaluation showed that the c_mRNAsi-based model performed well. Patients with high c_mRNAsi exhibited significant immune suppression. Moreover, c_mRNAsi correlated negatively with infiltrating levels of immune-related cells. In addition, ssGSEA revealed that immune-related pathways were generally activated in patients with high c_mRNAsi. We comprehensively evaluated GBM stemness indices based on large cohorts and established a c_mRNAsi-based classifier for prognosis prediction.

Improving Security and Fairness in Federated Learning Systems

The ever-increasing use of Artificial Intelligence applications has made apparent that the quality of the training datasets affects the performance of the models. To this end, Federated Learning aims to engage multiple entities to contribute to the learning process with locally maintained data, without requiring them to share the actual datasets. Since the parameter server does not have access to the actual training datasets, it becomes challenging to offer rewards to users by directly inspecting the dataset quality. Instead, this paper focuses on ways to strengthen user engagement by offering “fair” rewards, proportional to the model improvement (in terms of accuracy) they offer. Furthermore, to enable objective judgment of the quality of contribution, we devise a point system to record user performance assisted by blockchain technologies. More precisely, we have developed a verification algorithm that evaluates the performance of users’ contributions by comparing the resulting accuracy of the global model against a verification dataset and we demonstrate how this metric can be used to offer security improvements in a Federated Learning process. Further on, we implement the solution in a simulation environment in order to assess the feasibility and collect baseline results using datasets of varying quality.

Direct Numerical Simulation of separated turbulent flow in axisymmetric diffuser

Direct numerical simulation (DNS) of the separated flow in axisymmetric CS0 diffuser is conducted. The obtained results are in a good agreement with experimental data of Driver and substantially supplement them. Along with other data, eddy viscosity extracted from performed DNS could be used for RANS turbulence model improvement.

Magnetic Resonance Image under Variable Model Algorithm in Diagnosis of Patients with Spinal Metastatic Tumors

The aim of this study was to explore the adoption of the variable model algorithm in magnetic resonance imaging (MRI) image analysis and evaluate the effect of the algorithm-based MRI in the diagnosis of spinal metastatic tumor diseases. 100 patients with spinal metastatic tumors who were treated in hospital were recruited as the research objects. All patients were randomly divided into the experimental group (MRI image analysis based on variable model) and the control group (conventional MRI image diagnosis), and the MRI of the experimental group was segmented using the conventional algorithm with variable model and the improved algorithm with GVF force field. The accuracy index (Dice coefficient D) values were used to evaluate the vertebral segmentation effect of the improved variable model algorithm with the introduction of GVF force field, and the recognition rate, sensitivity, and specificity indexes were used to evaluate the effects of the two algorithms on the recognition of MRI image features of spinal metastatic tumors. The results showed that the mean D value of the variable model improvement algorithm for the segmentation of five vertebrae of spinal metastatic tumors was significantly improved relative to the conventional variable model algorithm, and the difference was statistically significant ( P < 0.05 ). At the number of 80 iterations, the recognition rate, sensitivity, and specificity of MRI image segmentation of the traditional variable model algorithm processing group were 89.32%, 74.88%, and 86.27%, respectively, while the recognition rate, sensitivity, and specificity of MRI image segmentation of the variable model improvement algorithm processing group were 97.89%, 96.75%, and 96.45%, respectively. The results of the latter were significantly better than those of the former, and the differences were statistically significant ( P < 0.05 ); and the comparison of MRI images showed that the variable model improvement algorithm was more rapid and accurate in identifying the focal sites of patients with spinal metastases. The accuracy of MRI images based on the variable model algorithm increased from 69.5% to 92%, and the difference was statistically significant ( P < 0.05 ). In short, MRI image analysis based on the variable model algorithm had great adoption potential in the clinical diagnosis of spinal metastatic tumors and was worthy of clinical promotion.

Peningkatan model SAMR serta penerapannya untuk pembelajaran online yang mendalam

Pelaksanaan e-learning menjadi desakan solusi untuk dapat menyampaikan pembelajaran di tengah pandemi. Pelaksanaan e-learning sangat mengandalkan pemanfaatan teknologi. Hal ini menjadi tantangan baru karena ketidaksiapan pendidik karena keterbatasan literasi digital dan sulitnya menentukan teknologi yang akan digunakan dalam pembelajaran. Untuk mengatasi permasalahan tersebut, penerapan model SAMR (Substitution, Augmentation, Modification, Redefinition) menjadi sebuah solusi. Model SAMR banyak diterapkan oleh praktisi pendidikan karena jelas, sederhana, dan mudah diadaptasi. Namun, model SAMR memiliki tiga kritik untuk peningkatan. Penelitian ini merupakan penelitian literature review yang menggunakan metode PRISMA systematic review. Kajian pada penelitian ini menjawab tantangan perbaikan pada penerapan model SAMR dalam pembelajaran dan mengkaji kesesuaian model SAMR untuk diterapkan pada e-learning yang syarat akan pemanfaatan teknologi dan bagaimana mewujudkan e-learning yang mendalam.SAMR model improvement challenge and application for deeper online learningAbstractE-learning becomes an urgent solution to be able to deliver learning in the midst of a pandemic. The implementation of e-learning relies heavily on technology integration. This is a new challenge for educators due to the unpreparedness, the limitations of digital literacy, and the difficulty in determining the appropriate technology in e-learning. In order to overcome these problems, the application of the SAMR model (Substitution, Augmentation, Modification, Redefinition) is one of the solutions. Education practitioners have widely applied the SAMR model because of its clarity, simplicity, easiness, and adaptability. However, the SAMR model has three drawbacks that can be improved. This study answers the challenge in improving the application of the SAMR model in learning and assessing the suitability of the SAMR model applied to e-learning which requires technology.