BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.Comment: Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024

Detection of Advanced Glycosylation End Products in the Cornea Based on Molecular Fluorescence and Machine Learning

Advanced glycosylation end products (AGEs) are continuously produced and accumulated in the bodies of diabetic patients. To effectively predict disease trends in diabetic patients, a corneal fluorescence detection device was designed based on the autofluorescence properties of AGEs, and corneal fluorescence measurements were performed on 83 volunteers. Multiple linear regression (MLR), extreme gradient boosting (XGBoost), support vector regression (SVR), and back-propagation neural network (BPNN) were used to predict the human AGE content. Physiological parameters which may affect corneal AGE content were collected for a correlation analysis to select the features that had a strong correlation with the corneal concentration of AGEs to participate in modeling. By comparing the predictive effects of the four models in the two cases of a single-input feature and a multi-input feature, it was found that the model with the single-input feature had a better predictive effect. In this case, corneal AGE content was predicted by a single-input SVR model, with the average error rate (AER), mean square error (MSE), and determination coefficient R-squared (R2) of the SVR model calculated as 2.43%, 0.026, and 0.932, respectively. These results proved the potential of our method and device for noninvasive detection of the concentration of AGEs in the cornea

Dysregulation of YAP by ARF Stimulated with Tea-derived Carbon Nanodots

Abstract YAP is a downstream nuclear transcription factor of Hippo pathway which plays an essential role in development, cell growth, organ size and homeostasis. It was previously identified that elevation of YAP in genomics of genetic engineered mouse (GEM) model of prostate cancer is associated with Pten/Trp53 inactivation and ARF elevation hypothesizing the essential crosstalk of AKT/mTOR/YAP with ARF in prostate cancer. However, the detailed function and trafficking of YAP in cancer cells remains unclear. Using GEM microarray model, we found ARF dysregulates Hippo and Wnt pathways. In particular, ARF knockdown reduced non-nuclear localization of YAP which led to an increase in F-actin. Mechanistically, ARF knockdown suppressed protein turnover of β-catenin/YAP, and therefore enhanced the activity of AKT and phosphorylation of YAP. Moreover, we found tea-derived carbon dots can interact with ARF in nucleus that may further lead to the non-nuclear localization of YAP. Thus, we reported a novel crosstalk of ARF/β-catenin dysregulated YAP in Hippo pathway and a new approach to stimulate ARF-mediated signaling to inhibit nuclear YAP using nanomaterials implicating an innovative avenue for treatment of cancer