A mountain of evidence has shown that people’s physical and mental health can be affected by various air pollutions. Poor pregnancy outcomes are associated with exposure to air pollution. Therefore, this study aims to investigate the association between air pollutions (PM2.5, PM10, SO2, NO2, CO, and O3) and preterm birth/low birth weight in Guangdong province, China. All maternal data and birth data from January 1, 2014 to December 31, 2015 were selected from a National Free Pre-pregnancy Check-ups system, and the daily air quality data of Guangdong Province was collected from China National Environmental Monitoring Center. 1784 women with either preterm birth information (n = 687) or low birth weight information (n = 1097) were used as experimental group. Control group included 1766 women with healthy birth information. Logistic regression models were employed to evaluate the effects of air pollutants on the risk of preterm birth and low birth weight. The pollution levels of PM2.5, PM10, SO2, NO2, CO, and O3 in Guangdong province were all lower than the national air pollution concentrations. The concentrations of PM2.5, PM10, SO2, NO2 and CO had obvious seasonal trends with the highest in winter and the lowest in summer. O3 concentrations in September (65.72 μg/m3) and October (84.18 μg/m3) were relatively higher. After controlling for the impact of confounding factors, the increases in the risk of preterm birth were associated with each 10 μg/m3 increase in PM2.5 (OR 1.043, 95% CI 1.01–1.09) and PM10 (OR 1.039, 95% CI 1.01~1.14) during the first trimester and in PM2.5 (OR 1.038, 95% CI 1.01~1.12), PM10 (OR 1.024, 95% CI 1.02~1.09), SO2 (OR 1.081, 95% CI 1.01~1.29), and O3 (OR 1.016, 95% CI 1.004~1.35) during the third trimester. The increase in the risk of low birth weight was associated with PM2.5, PM10, NO2, and O3 in the first month and the last month. This study provides further evidence for the relationships between air pollutions and preterm birth/low birth weight. Pregnant women are recommended to reduce or avoid exposure to air pollutions during pregnancy, especially in the early and late stages of pregnancy.

Perception of facial identity and emotional expressions is fundamental to social interactions. Recently, interest in age associated changes in the processing of faces has grown rapidly. Due to the lack of older faces stimuli, most previous age-comparative studies only used young faces stimuli, which might cause own-age advantage. None of the existing Eastern face stimuli databases contain face images of different age groups (e.g. older adult faces). In this study, a database that comprises images of 110 Chinese young and older adults displaying eight facial emotional expressions (Neutral, Happiness, Anger, Disgust, Surprise, Fear, Content, and Sadness) was constructed. To validate this database, each image was rated on the basis of perceived facial expressions, perceived emotional intensity, and perceived age by two different age groups. Results have shown an overall 79.08% correct identification rate in the validation. Access to the freely available database can be requested by emailing the corresponding authors.

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information-processing capacities in the brain. While considerable evidence generally supports this hypothesis, nonnegligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the nontriviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, that is, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistical techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.

In Hubei, China, where the coronavirus disease (COVID-19) epidemic first emerged, the government has enforced strict quarantine and lockdown measures. Longitudinal studies suggest that the impact of adverse events on psychological adjustment is highly heterogenous. To better understand protective and risk factors that predict longitudinal psychopathology and resilience following strict COVID-19 lockdowns, this study used unsupervised machine learning to identify half-year longitudinal trajectories (April, June, August, and October, 2020) of three mental health outcomes (depression, anxiety, and posttraumatic stress disorder [PTSD]) among a sample of Hubei residents (N = 326), assessed a broad range of person- and context-level predictors, and applied least absolute shrinkage and selection operator (LASSO) logistic regression, a supervised machine learning approach, to select best predictors for trajectory memberships of resilience and chronic psychopathology. Across outcomes, most individuals remained resilient. Models with both person- and context-level predictors showed excellent predictive accuracy, except for models predicting chronic anxiety. The person-level models showed either good or excellent predictive accuracy. The context-level models showed good predictive accuracy for depression trajectories but were only fair in predicting trajectories of anxiety and PTSD. Overall, the most critical person-level predictors were worry, optimism, fear of COVID, and coping flexibility, whereas important context-level predictors included features of stressful life events, community satisfaction, and family support. This study identified clinical patterns of response to COVID-19 lockdowns and used a combination of risk and protective factors to accurately differentiate these patterns. These findings have implications for clinical risk identifications and interventions in the context of potential trauma. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recent theoretical models highlight the importance of emotion regulation (ER) flexibility, challenging traditional notions of universally maladaptive versus adaptive strategies. Here we employed ecological momentary assessment to develop proxy ecological measures for ER flexibility components (context sensitivity, repertoire and feedback responsiveness) and examine their associations with momentary affective outcomes in two independent samples from the United States (158 adults and 12,217 observations) and China (144 adults and 11,347 observations, analysis preregistered). Participants completed four daily surveys for 21 days, reporting emotional situations, situation characteristics, ER use and change and momentary distress. Increased momentary context sensitivity and use of repertoire were found associated with reduced distress, while results for feedback responsiveness were less consistent. Maintaining effective strategies was generally adaptive, whereas switching from ineffective strategies was adaptive for momentary depressed, but not anxious, mood. This innovative ecological momentary assessment design demonstrates transcultural similarities in ER flexibility’s benefits and nuanced implications of its components on affective outcomes.