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Asthma is a chronic inflammatory disease of the airways, and IL-35 has been found to be involved in the pathogenesis of inflammatory diseases by mediating the inhibition of effector T cells. But the role of IL-35 on cell pyroptosis, which frequently occurs in inflammatory diseases, has not been elucidated. Therefore, the present study used a TNF-α-induced bronchial epithelial cell injury model to investigate the mechanism of IL-35 action on cell pyroptosis and asthma injury. The effects of IL-35 on cell activity, inflammatory factor levels, cell barrier damage and cell pyroptosis-related proteins were examined by CCK-8, ELISA, lucifer yellow permeability and Western blotting assay, respectively. Subsequently, following the activation of p38 MAPK signaling pathway by adding p38 agonist, the effect of IL-35 on TNF-α-induced bronchial epithelial cell injury was investigated. The results showed that IL-35 reduced TNF-α-induced cell injury, decreased inflammatory factors, improved cell permeability, and inhibited cell pyroptosis. More importantly, the effect of IL-35 on injured cells was reversed after p38 MAPK pathway was activated. In summary, IL-35 inhibited p38 MAPK pathway to suppress cell pyroptosis and thereby reduce asthma injury.

Obesity and dyslipidemia are two metabolic syndrome disorders that have serious effects on the health of patients. Purinergic 2X receptor ligand-gated ion channel 7 (P2X7R) has been reported to play a role in regulating lipid storage and metabolism. However, the role and potential mechanism of P2X7R in adipogenesis and lipid degradation remain unknown. In the present study, a mouse model of obesity was established by feeding mice a high-fat diet, and the 3T3-L1 cell line was used to analyze the function of P2X7R in vitro. Reverse transcription-quantitative PCR and western blot analyses were performed to detect the expression levels of P2X7R, sterol regulatory element-binding protein 1 (SREBP1) and other associated transcription factors. Bioinformatics analysis was used to predict the potential target gene of P2X7R and a dual luciferase reporter assay was used to confirm this prediction. Oil Red O staining was used to evaluate the adipogenic capacity of preadipocytes. AdipoRed assay, cholesterol assay and a free glycerol reagent were used to measure the expression levels of triglyceride (TGs), total cholesterol (TC) and glycerin, respectively. The results indicated that P2X7R was highly expressed in obese mice and that it was involved in adipogenic differentiation in vitro. SREBP1 enhanced the transcription activities of P2X7R to promote its expression. Inhibition of P2X7R significantly reduced the adipogenic capacity of preadipocytes, decreased the expression levels of adipogenesis-associated transcription factors (peroxisome proliferator-activated receptor γ, CCAAT-enhancer-binding protein α and fatty-acid-binding protein 4), enhanced the expression levels of lipolytic enzymes (adipose triglyceride lipase, phosphorylated hormone-sensitive lipase and monoacylglycerol lipase) and regulated the expression of TG, TC and glycerin in mature 3T3-L1 cells. These effects were reversed by a small interfering RNA targeting Wnt3a. Therefore, the results suggested that P2X7R, the transcription activities of which were regulated by SREBP1, regulated adipogenesis and lipid degradation by targeting SREBP1, indicating its potential effects on obesity-associated metabolism.

As one of the core components of online games, matchmaking is the process of arranging multiple players into matches, where the quality of matchmaking systems directly determines player satisfaction and further affects the life cycle of game products. With the number of candidate players increases, the number of possible match combinations grows exponentially, which makes the current implementation for multiplayer matchmaking can only obtain locally optimal arrangement in an inefficient fashion. In this paper, we focus on the globally optimized matchmaking problem, in which the objective is to decide an optimal matching sequence for the queuing players. To tackle this challenging problem, we propose a novel data-driven matchmaking framework, called GloMatch, based on machine learning principles. Through transforming the matchmaking problem into a sequential decision problem, we solve it with the help of an effective policy-based deep reinforcement learning algorithm. Quantitative experiments on simulation and online game environments demonstrate the effectiveness of the presented framework.

Win prediction and performance evaluation are two core subjects in the sport analytics. Traditionally, they are treated separately and studied by two independent communities. However, this is not the intuitive way how humans interpret the matches: we predict the match results with the competition carrying on, and simultaneously evaluate each action based on the game context and its downstream impact. Predicting the match outcomes and evaluating the actions are coupled tasks, and the more accurately we predict, the better the evaluation is To this end, we develop a unified Match Tracing framework (namely, MT), for tackling the win prediction and performance evaluation jointly. The main idea of MT is to learn a real-time look-ahead win rate curve rather than a single scalar (win or lose). And the value of an action can be objectively measured with respect to the increase or decrease of the curve. To meet the low-latency restrictions of the online deployments, an efficient model equipped with recurrent attention mechanism and matrix perturbation (i.e., MT-Net) is built for learning and yielding the win rate curve. MT-Net encodes the players' behavior sequence through an attention mechanism and captures the player-interaction effects through a graph embedding method. With the action values derived from the win rate curve, performance can be quantified at different granularities (action/player/match level) by integrated analysis. Experiments on an e-sport game demonstrate the prediction effectiveness and the feasibility of the MT framework. Furthermore, we present the detailed application cases of MT, including key actions recognition and close match detection.

Matchmaking is a core problem for the e-sports and online games, which determines the player satisfaction and further influences the life cycle of the gaming products. Most of matchmaking systems take the form of grouping the queuing players into two opposing teams by following certain rules. The design and implementation of matchmaking systems are usually product-specific and labor-intensive. This paper proposes a two-stage data-driven matchmaking framework (namely OptMatch), which is applicable to most of gaming products and has the minimal product knowledge required. OptMatch contains an offline learning stage and an online planning stage. The offline learning stage includes (1) relationship mining modules to learn the low-dimensional representations of individuals by capturing the high-order inter-personal interactions, and (2) a neural network to incorporate the team-up effect and predict the match outcomes. The online planning stage optimizes the gross player utilities (i.e., satisfaction) during the matchmaking process, by leveraging the learned representations and predictive model. Quantitative evaluations on four real-world datasets and an online experiment on Fever Basketball game are conducted to empirically demonstrate the effectiveness of OptMatch.

In recent years, location-based service has been widely used in social networks. While more and more users enjoy the convenience of location-based services, the user's location privacy is also facing various threats of privacy disclosure. Aiming at the problem of location privacy disclosure in mobile social network applications, this paper proposed a location privacy protection method for multi-sensitive attributes based on l-diversity privacy protection model, and protected the user's location information in client side and server respectively. On the client side, the decomposition algorithm of minimum distance grouping is used to lighten the location data, which makes the processed data satisfy the l1-diversity principle and upload the data to the server in the form of QIT1(Quasi-Identifier attribute Table) and ST^1(Sensitive attribute Table) to achieve the initial protection of the user's location data. On the server side, the minimum selectivity priority strategy was adopted to form the l2-diversity group satisfying the multi-sensitive attributes, and the data was uploaded in the form of QIT2 and ST2 to further protect the user location data (where l_1< l_2). The experimental results show that this method not only can effectively protect location privacy data, but also has high data availability.

Game bots are automated programs that assist cheating users and enable them to obtain huge superiority, leading to an imbalance in the game ecosystem and the collapse of user interest. Therefore, game bot detection becomes particularly important and urgent. Among many kinds of online games, massively multiplayer online role playing games (MMORPGs), such as World of Warcraft and AION, provide immersive gaming experience and attract many loyal fans. At the same time, however, game bots in MMORPGs have proliferated in volume and method, evolving with the real-world detection methods and showing strong diversity, leaving MMORPG bot detection efforts extremely difficult. To deal with the fast-changing nature of game bots, we here proposed a generalized game bot detection framework for MMORPGs termed NGUARD, denoting NetEase Games' Guard. NGUARD takes charge of automatically differentiating game bots from humans for MMORPGs. In detail, NGUARD exploits a combination of supervised and unsupervised methods. Supervised models are utilized to detect game bots in observed patterns according to the training data. Meanwhile, unsupervised solutions are employed to detect clustered game bots and help discovering new bots. The game bot detection framework NGUARD has been implemented and deployed in multiple MMORPG productions in the NetEase Game portfolio, achieving remarkable performance improvement and acceleration compared to traditional methods. Moreover, the framework reveals outstanding robustness for game bots in mutated patterns and even in completely new patterns on account of the design of the auto-iteration mechanism.