The Adaptiveness of Emotion Regulation Variability and Interoceptive Attention in Daily Life.

OBJECTIVE: In daily life, we must dynamically and flexibly deploy strategies to regulate our emotions, which depends on awareness of emotions and internal bodily signals. Variability in emotion-regulation strategy use may predict fewer negative emotions, especially when people pay more attention to their bodily states-or have greater "interoceptive attention" (IA). Using experience sampling, this study aimed to test whether IA predicts variability in strategy use and whether this variability and IA together predict negative affect. METHODS: University student participants ( n = 203; 165 females; Mage = 20.68, SD age = 1.84) completed trait questionnaires and reported state levels of IA, emotional awareness, negative affect, and emotion-regulation strategies, seven times daily for 1 week. RESULTS: State IA significantly predicted between-strategy variability, which was mediated by emotional awareness (indirect effect = 0.002, 95% confidence interval = <0.001-0.003). Between-strategy variability was associated with lower negative affect, particularly when individuals had higher state IA (simple slope = -0.83, t = -5.87, p < .001) versus lower IA (simple slope = -0.31, t = -2.62, p = .009). CONCLUSIONS: IA appears to facilitate adaptative emotion regulation and help alleviate negative affect. Findings underscore the key roles of IA and emotion-regulation flexibility in mental health.

Reciprocal effects between negative affect and emotion regulation in daily life.

The extended process model of emotion regulation provides a framework for understanding how emotional experiences and emotion regulation (ER) mutually influence each other over time. To investigate this reciprocal relationship, 202 adults completed a ten-day experience-sampling survey capturing levels of negative affect (NA) experience and use of ten ER strategies in daily life. Residual dynamic structural equation models (DSEMs) were used to examine within-person cross-lagged and autoregressive effects of NA and ER (strategy use and between-strategy variability). Results showed that NA predicted lower between-strategy variability, lower subsequent use of acceptance and problem-solving, but higher subsequent use of rumination and worry. Moreover, reappraisal and between-strategy variability predicted lower subsequent NA levels, while expressive suppression and worry predicted higher subsequent NA levels. Stable autoregressive effects were found for NA and for maladaptive ER strategies (e.g., rumination and worry). Exploratory correlation analyses revealed positive associations between NA inertia and maladaptive ER strategies. Together, these findings provide evidence of a dynamic interplay between NA and ER. This work deepens how we understand the challenges of applying ER strategies in daily life. Future clinical and translational research should consider these dynamic perspectives on ER and affect.

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.

Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Impact of random dopant fluctuation effect on surrounding gate MOSFETs: from atomic level simulation to circuit performance evaluation.

This paper investigates the impact of random dopant fluctuation effect on surrounding gate MOSFET, from atomic statistical simulation of device to circuit performance evaluation. The doping profile is generated by an analysis of each lattice atom and then the threshold voltage variation is obtained by device Drift-Diffusion simulation. Then the circuit performance evaluation is performed by feeding the result into a surrounding-gate MOSFET model. It is shown that a significant fluctuation in threshold voltage is due to the decreasing volume. The circuit simulation results also reveal that a surrounding gate MOSFET based 6-T SRAM presents a promising resistibility to noise disturbance.