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Stress forecasting is the cognitive process of anticipating and preparing to respond to future stress experiences based on one's own perceived future stress. Though it may seem intuitive that stress forecasting predicts stress, competing theories exist that indicate the relationship is not so inherent. In this paper we conducted two studies which both examine the relationships between stress forecasting and day stress, examining two different stress domains (appraisal and coping). Participants in Study 1 (n = 143 working adults) and Study 2 (n = 60 undergraduate students) completed 4 and 14 days, respectively, of ecological momentary assessment reports of stress appraisal forecasting, stress coping forecasting, stress appraisal reports, and stress coping reports, for a combined total of 5280 completed assessments. Exploratory analysis in Study 2 examined the importance of morning forecast discrepancy in relation to evening recalls of day stress. Results indicate that within domain (e.g., stress appraisal to appraisal reports), or domain specific, stress forecasting is predictive of day stress, and that across domains (e.g., stress appraisal to coping reports), or domain crossover, was consistent between stress appraisal forecasting and stress coping reports. Results suggest that magnitude of bias and day outlook (i.e., being either optimistic or pessimistic) about one's coping ability matters for day stress outcomes. Findings have implications for developing challenge‐based thinking interventions and further understanding cognitive processes for building stress management strategies. [ABSTRACT FROM AUTHOR]

Background: MiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results.Methods: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding.Results: After fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods.Conclusions: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods. [ABSTRACT FROM AUTHOR]

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of "imminent drinking" (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant's current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness. [ABSTRACT FROM AUTHOR]