While most players enjoy video gaming, a minority suffer from problematic gaming. Existing studies focused on risk factors and proposed legislative and technological measures to contain the undesirable consequences. The present study aims to examine whether players’ healthy gaming practices can protect themselves against problematic gaming. We conducted a longitudinal survey at a public university in China, 604 college students took the first wave survey, and 365 of them completed the second wave six months later. The results showed that protective gaming beliefs and behaviors at baseline were negatively associated with problematic gaming tendency, and positively associated with mental health and well-being at follow-up. Problematic gaming tendency did not predict mental health and well-being. The findings suggested that players’ self-regulation can be an effective measure against problematic gaming and promote mental health and well-being. The present study can inspire stakeholders to develop additional approaches to help at-risk and problematic players.

Problematic gaming may lead to poor mental health and negative social outcomes. Existing harm minimisation strategies predominately focus on school-based education, government legislation, and player protection programmes of the video gaming industry. From the perspective of players, the present research explored and evaluated beliefs and behaviours adopted by players to protect themselves from gaming-related harms. In Study 1, potential protective beliefs and behaviours were identified from focus group interviews among players. A survey was conducted to explore the factor structure of the protective beliefs and behaviours scale (PBBS). Based on parallel analysis, we found three belief factors and two behaviour factors that were negatively related to problematic gaming. In Study 2, the psychometric properties of PBBS were tested. Its factor structure was replicated in the confirmatory factor analysis, and the composite reliability and average variance extracted revealed adequate construct validity. The predictive validity of PBBS was demonstrated by its negative associations with problematic gaming symptoms and impulsiveness, as well as positive associations with self-control, self-esteem, and well-being. These protective beliefs and behaviours seemed to be promising in reducing gaming-related harms. The PBBS would be useful for evaluating players’ vulnerability to problematic gaming and providing insights into player-centred prevention strategies.

Classification on pseudoknots existence is a challenging and meaningful problem in Bioinformatics. As predicting RNA secondary structures with pseudoknots is NP-complete problem while predicting pseudoknot-free structures can be done in O(n3) time, if a preliminary pseudoknots existence classification of RNA sequence can be done before the prediction, the classification result can enhance the efficiency of RNA secondary structure prediction. In this paper, a classification of the existence of pseudoknots in an RNA sequence is presented. A set of features have been chosen by partial sequence content and thousands of RNA sequences with validated structures are used to train the classifier. Using a validated testing dataset, this classification method is shown to achieve a very good performance that the best result get 87% accuracy in 10-fold cross validation and around 75% accuracy in testing data. Moreover it may reveal how partial sequence content can affect the formation of pseudoknots.

RNAs are functionally important in many biological processes. Predicting secondary structures of RNAs can help understanding 3D structures and functions of RNAs. However, RNA secondary structure prediction with pseudoknots is NP-complete. Predicting whether the RNAs contain pseudoknots in advance can save computation time as secondary structure prediction without pseudoknots is much faster. In this paper, we use k-mer occurrences as attributes to predict whether the RNAs have pseudoknots in the secondary structure. The results show two classifiers can predict 90% of the instance correctly.

The free energy (evaluation) models used in RNA secondary structure prediction are one of the most important reasons that makes the prediction a challenging computational problem in Bioinformatics. These models are the key factor determining the accuracy of the prediction algorithms. Previously we have developed a method called GAknot that has obtained good performance on predicting RNA secondary structures with pseudoknots. In this paper, we propose a new free energy model. We first select a number of RNA sequences from a database which contains known RNA secondary structures as a training dataset for learning this new model. From the training dataset, we then extract base pairs patterns in subsequences of pairs of k-mers from the stems of each sequence in the training data and use the patterns to formulate penalty factors. We modify the energy model by adding these penalty factors. Combined with the new modified energy model, the prediction performance of GAknot has been improved significantly. GAknot with the new modified energy model is shown to be the best method in comparison with two state-of-the-art algorithms using a commonly used testing dataset. The penalty factors of the new energy model and dataset can be downloaded at http://appsrv.cse.cuhk.edu.hk/~kktong/NewModel.

Non-coding RNAs are RNA molecules that do not translate into proteins. These RNAs are functional important in many biological processes. Their biological functions are highly related to their interaction partners. RNA-RNA interactions are one of the possibilities. It is desired to use computational methods to study and predict the interaction partners of non-coding RNAs. Most recent programs for RNA-RNA interaction prediction programs are based on Turner Energy Model. Some papers show that the free energy of RNA-structures is lower than random sequences but it is not statistically significant. It shows that we may need to modify the energy model for different RNA structures applications. In this paper, we first study the RNA-RNA interaction pattern using experimental validated RNA-RNA interaction data, which are extracted from sRNATarBase. We study the sRNA-mRNA interaction data and extract some features of the RNA-RNA interaction patterns. Then we combine these features about interaction sites into the Turner Energy Model. We develop a genetic algorithm based program RIPGA to solve the RNA-RNA interaction prediction problem. We use genetic algorithm because the RNA-RNA interaction prediction is NP-hard and we are interested to find out good suboptimal solutions. We use an sRNA-mRNA interaction dataset to evaluate the performance of the modified energy model and compare the results with two state-of-the-art programs. The comparison of the original model and the modified model shows that the modified energy model has better performance in both sensitivity and positive predictive value (PPV). Comparing RIPGA with state-of-the-art programs, RIPGA have better sensitivity and comparable positive predictive value.

Predicting RNA secondary structure is a significant challenge in Bioinformatics especially including pseudoknots. There are so many researches proposed that pseudoknots have their own biological functions inside human body, so it is important to predict this kind of RNA secondary structures. There are several methods to predict RNA secondary structure, and the most common one is using minimum free energy. However, finding the minimum free energy to predict secondary structure with pseudoknots has been proven to be an NP-complete problem, so there are many heuristic approaches trying to solve this kind of problems. In this paper, we propose GAknot, a computational method using genetic algorithm (GA), to predict RNA secondary structure with pseudoknots. GAknot first generates a set of maximal stems, and then it tries to generate several individuals by different combinations of stems. After halting condition is reached, GAknot will output the best solution as the output of predicted secondary structure. By using two commonly used validation data sets, GAknot is shown to be a better prediction method in terms of accuracy and speed comparing to several competitive prediction methods. Source code and datasets can be downloaded.