(+)-Rutamarin as a Dual Inducer of Both GLUT4 Translocation and Expression Efficiently Ameliorates Glucose Homeostasis in Insulin-Resistant Mice

Glucose transporter 4 (GLUT4) is a principal glucose transporter in response to insulin, and impaired translocation or decreased expression of GLUT4 is believed to be one of the major pathological features of type 2 diabetes mellitus (T2DM). Therefore, induction of GLUT4 translocation or/and expression is a promising strategy for anti-T2DM drug discovery. Here we report that the natural product (+)-Rutamarin (Rut) functions as an efficient dual inducer on both insulin-induced GLUT4 translocation and expression. Rut-treated 3T3-L1 adipocytes exhibit efficiently enhanced insulin-induced glucose uptake, while diet-induced obese (DIO) mice based assays further confirm the Rut-induced improvement of glucose homeostasis and insulin sensitivity in vivo. Subsequent investigation of Rut acting targets indicates that as a specific protein tyrosine phosphatase 1B (PTP1B) inhibitor Rut induces basal GLUT4 translocation to some extent and largely enhances insulin-induced GLUT4 translocation through PI3 kinase-AKT/PKB pathway, while as an agonist of retinoid X receptor α (RXRα), Rut potently increases GLUT4 expression. Furthermore, by using molecular modeling and crystallographic approaches, the possible binding modes of Rut to these two targets have been also determined at atomic levels. All our results have thus highlighted the potential of Rut as both a valuable lead compound for anti-T2DM drug discovery and a promising chemical probe for GLUT4 associated pathways exploration

Soccer Strategy Analytics Using Probabilistic Model Checkers

When it comes to predicting the outcome of soccer matches, there are two main techniques that dominate the betting industry: deep learning and machine learning. However, as with many state-of-the-art applications, these methods often exhibit significant drawbacks. Primarily, they act as black boxes where the internal operations are not transparent, making it difficult to discern the cause when predictions fail. In this paper, we explore the potential of using probabilistic model checkers as an alternative approach for predicting soccer match outcomes. This technique, while unconventional, offers greater transparency or white box visibility into its operations when compared to state-of-the-art methods. The choice of utilizing probabilistic model checkers is often overlooked due to their propensity to induce state explosion in complex models. To address this, we propose specific strategies aimed at minimizing the state space of a soccer match, thereby mitigating the state explosion issue. We assess the effectiveness of the probabilistic model through various metrics and compare its performance against established deep learning and machine learning baselines. To evaluate its real-world applicability, we also simulate betting based on the predictions of the probabilistic model. This paper concludes by addressing the practical challenges involved in implementing such a predictive model.No Full Tex

Sports Analytics Using Probabilistic Model Checking and Deep Learning

Sports analytics encompasses the use of data science, AI, psychology, and IoT devices to improve sports performance, strategy, and decision-making. It involves collecting, processing, and interpreting data from various sources such as video recordings and scouting reports. The data is used to evaluate player and team performance, prevent injuries, and help coaches make informed decisions in game and training. We adopt Probabilistic Model Checking (PMC), a method commonly used in reliability analysis for complex safety systems, and explain how this method can be applied to sports strategy analytics to increase the chance of winning by taking into account the reliability of a player's specific sub-skill sets. This paper describes how we have integrated PMC, machine learning, and computer vision to develop a new and complex system for sports strategy analytics. Finally, we discuss the vision of a new series of international sports analytics conferences (https://formal-analysis.com/isace/2023/).No Full Tex