Emotion regulation strategies and depression in mother-adolescent dyads: An actor-partner interdependence model approach.

Parent-adolescent emotion dynamics have attracted increasing attention in recent years because adolescence is a challenging period for both adolescents and parents. However, how emotions are coconstructed between parents and adolescents is less clear. This study examined whether mothers' and adolescents' emotion regulation strategy was linked with their own and each other's depression using the actor-partner interdependence model (APIM). The participants were 173 mother-adolescent pairs (Mother: Mage = 43.05 years old, SD = 3.78; Adolescent: Mage = 13.00 years old, SD = 0.90). The results showed that the more mothers used cognitive reappraisal, the lower their depression levels were; and the more mothers and adolescents used expressive suppression, the higher their levels of depression were. Additionally, maternal expressive suppression was associated with adolescent depressive symptoms. Moreover, the results revealed that for mothers with higher levels of expressive suppression, their adolescents' usage of expressive suppression was significantly positively related to adolescents' depression, while for those mothers with lower levels of expressive suppression, there was no significant correlation between adolescents' usage of expressive suppression and depression. The findings underscore the significance of recognising the interdependence and interconnected nature of emotions within parent-adolescent relationships for a comprehensive understanding of their emotional well-being.

Validation of the Lee-Jones theoretical model of fear of cancer recurrence among breast cancer survivors using a structural equation modeling approach.

OBJECTIVE: The Lee-Jones model posits that antecedent individual and interpersonal factors predicate the development of fear of cancer recurrence (FCR) through cognitive and emotional processing, which further to behavioral, emotional, and/or physiological responses. We analyzed data from FoRtitude, a FCR intervention grounded in the Lee-Jones FCR model, to evaluate associations between FCR antecedents, resources (e.g., breast cancer self-efficacy, BCSE) and psychological and behavioral consequences. METHODS: Women with breast cancer who completed treatment and reported clinically elevated levels of FCR were randomized into a 4-week online psychosocial intervention or contact control group. We assessed BCSE, FCR, and physical activity, anxiety and depression, or symptoms at baseline, 4 and 8 weeks. Separate structural equation models were constructed with both baseline data and change scores (baseline-8 weeks) to examine the pathways linking BCSE, FCR and: (1) physical activity; (2) anxiety and depression; and (3) symptoms (fatigue, sleep disturbance, cognitive concerns). RESULTS: At baseline, higher levels of BCSE were associated with lower levels of FCR. Higher FCR was associated with worse psychological effects and symptoms but not behavioral response. Change models revealed that an increase in BCSE was associated with a decrease in FCR at 8-week assessment, which was associated with reductions in psychological effects. A change in BCSE was also directly associated with reductions in psychological effects. CONCLUSIONS: Results support the Lee-Jones model as a foundation for FCR interventions among breast cancer survivors. Replicability among varied populations is needed to examine effects on behavioral outcomes of FCR such as health care utilization. CLINICAL TRIALS REGISTRATION: NCT03384992.

Maternal supportive responses to adolescents' negative emotions serve as protective factors for adolescents' hostile attribution bias longitudinally.

Existing literature has documented that parenting links to children's hostile attribution biases (HAB). However, little is known about the role played by parental emotion socialization in children's HAB. To address this research gap, the present study investigated the role of parental responses to children's negative emotions (PRCNE) in predicting adolescents' HAB using a longitudinal study. Adolescents (N = 203; Mage = 13.61 years old at Time 1), who were recruited from a city in mainland China, reported on their mothers' PRCNE and their own HAB at two waves over a year. The results showed that mothers' supportive responses (composed of emotion-focused responses and problem-focused responses) significantly predicted adolescents' reduced HAB over time; however, PRCNE including expressive encouragement, minimization, and nonsupportive responses (composed of punitive responses and parental distress) had no significant relation with adolescents' HAB. These findings add to the existing literature investigating antecedents to adolescents' social information processing deficits and biases.

Trait/Financial Information of Potential Male Mate Eliminates Mate-Choice Copying by Women: Trade-Off Between Social Information and Personal Information in Mate Selection.

Mate-choice copying occurs when people rely on the mate choices of others (social information) to inform their own mate decisions. The present study investigated women's strategic trade-off between such social learning and using the personal information of a potential mate. We conducted two experiments to investigate how mate-choice copying was affected by the personal information (e.g., trait/financial information, negative/positive valence of this information, and attractiveness) of a potential male mate in short-/long-term mate selection. The results demonstrated that when women had no trait/financial information other than photos of potential mates, they showed mate-choice copying, but when women obtained personality trait or financial situation information (no matter negative or positive) of a potential mate, their mate-choice copying disappeared; this effect was only observed for low-attractiveness and long-term potential partners. These results demonstrated human social learning strategies in mate selection through a trade-off between social information and personal information.

Sleep spindles, K-complexes, limb movements and sleep stage proportions may be biomarkers for amnestic mild cognitive impairment and Alzheimer's disease.

PURPOSE: Sleep disturbances are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients. Non-rapid eye movement stage 3 (N3), rapid eye movement stage (REM), spindle density, and K-complex (KC) density are decreased in MCI and AD patients. Periodic limb movements in sleep (PLMS) are increased in other neurodegenerative diseases. We aimed to distinguish amnestic mild cognitive impairment (aMCI) patients from the overall population of MCI patients by comparing the N3 and REM proportions, the morphological characteristics of spindles and KCs and the periodic limb movement index (PLMI) among control, aMCI and AD subjects. METHODS: In 92 subjects (30 controls, 32 aMCI and 30 AD), sleep stages, spindles, KCs and PLMS were recorded during the second of two nights of polysomnography (PSG). We compared the above parameters among the three groups. RESULTS: AD and aMCI subjects had lower proportions of N3 and REM, poorer spindle and KC activities and more frequent PLMS than controls. These alterations were associated with decreased Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. We determined cut-off values for distinguishing aMCI and AD using logistic regression and receiver operating characteristic (ROC) analyses. CONCLUSIONS: AD and aMCI patients have abnormal sleep stage proportions, spindles, KCs and PLMS. The combination of the above alterations may distinguish aMCI and AD patients from controls with high specificity and sensitivity.

Tapping ahead of time: its association with timing variability.

Researchers have puzzled over the phenomenon in sensorimotor timing that people tend to tap ahead of time. When synchronizing movements (e.g., finger taps) with an external sequence (e.g., a metronome), humans typically tap tens of milliseconds before event onsets, producing the elusive negative asynchrony. Here, we present 24 metronome-tapping data sets from 8 experiments with different experimental settings, showing that less negative asynchrony is associated with lower tapping variability. Further analyses reveal that this negative mean-SD correlation of asynchrony is likely to be observed for sequence types appropriate for synchronization, as indicated by the statistically negative lag 1 autocorrelation of inter-response intervals. The reported findings indicate an association between negative asynchrony and timing variability.

Factors influencing health behaviour, blood pressure control, and disability in hypertensive ischaemic stroke patients after a comprehensive reminder intervention.

AIMS: To test prospective pathways of a Comprehensive Reminder System based on the Health Belief Model (CRS-HBM), stroke knowledge, health belief in health behaviour, blood pressure (BP) control, and disability in hypertensive ischaemic stroke patients at 6-month postdischarge. DESIGN: A nested cohort study design. METHODS: Data were derived from a randomized controlled trial evaluating the effects of the intervention (N = 174, performed during February 2015 - March 2016). Data were collected by questionnaires and analysed in structural equation modelling in Mplus software. RESULTS: The proposed model provided a good fit to the data. This model accounted for 51.5% of the variance in health behaviour, 34.1% in BP control, and 5.7% in modified Rankin Scale score at 6-month postdischarge. The CRS-HBM had: (a) direct positive effect (ß = .391, p < .001) and indirect positive effects (ß = .186, p = .002) on health behaviour; (b) direct positive effect (ß = .356, p < .001) and indirect positive effects (ß = .183, p = .009) on BP control; and (c) indirect negative effect (ß = -.146, p = .008) on disability. Being female was linked to better health behaviour. Higher education predicted higher level of stroke knowledge and health belief. CONCLUSIONS: The CRS-HBM can not only directly but also indirectly improve patients' health behaviours by improving their health knowledge or health belief. Better health behaviour can improve patients' BP control and reduce disability. Therefore, nurses need to pay more attention to not only patients' health knowledge but also their health belief when providing education. IMPACT: The CRS-HBM intervention accounted for 51.5% of variance in health behaviour, 34.1% in BP control, and 5.7% in modified Rankin Scale score at 6-month postdischarge. This research can help nurses improve health education strategies in postdischarge and community contexts to achieve better health results.

Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data.

With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal the relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet, & Dube, 2011) that observed 160 participants' food consumption and momentary emotions 6 times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal-healthiness decision, the proposed reciprocal Markov model (RMM) can accommodate both hidden ("general" emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent with the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters.

Complete ammonia oxidation (comammox) at pH 3-4 supports stable production of ammonium nitrate from urine.

This study developed a new process that stably produced ammonium nitrate (NH4NO3), an important and commonly used fertilizer, from the source-separated urine by comammox Nitrospira. In the first stage, the complete conversion of ammonium to nitrate was achieved by comammox Nitrospira. In this scenario, the pH was maintained at 6 by adding external alkali, which also provided sufficient alkalinity for full nitrification. In the second stage, the NH4NO3 was produced directly by comammox Nitropsira by converting half of the ammonium in urine into nitrate. In this case, no alkali was added and pH automatically dropped and self-maintained at an extremely acidic level (pH 3-4). In both scenarios, negligible nitrite accumulation was observed, while the final product of the second stage contained ammonium and nitrate at the molar ratio of 1:1. The dominance of comammox Nitrospira over canonical ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) was systematically proved by the combination of 16S rRNA gene amplicon sequencing, quantitative polymerase chain reaction, and metagenomics. Notably, metagenomic sequencing suggested that the relative abundance of comammox Nitrospira was over 20 % under the acidic condition at pH 3-4, while canonical AOB and NOB were undetectable. Batch experiments showed that the optimal pH for the enriched comammox Nitrospira was ∼7, which could sustain their activity in a wider pH range from 4 to 8 surprisingly but lost activity at pH 3 and 9. The findings not only present an application potential of comammox Nitrospira in nitrogen recovery from urine wastewater but also report the survivability of comammox bacteria in acidic environments.

Network of depressive symptoms before and after a diagnosis of chronic kidney disease.

OBJECTIVE: A diagnosis of chronic kidney disease (CKD) may increase the risk for depression. The network perspective focuses on dynamic relationships among individual symptoms, which could advance our understanding of the development of depression during the transition to a diagnosis of CKD. The aim of this study was to use network analysis to examine the longitudinal associations of depressive symptoms from before to after a diagnosis of CKD. METHOD: The analytic sample included 1,386 participants from the Chinese Health and Retirement Longitudinal Study. Participants were aged 45 years or older and reported a doctor's diagnosis of CKD in any wave of interviews between 2011 and 2018. Depressive symptoms were measured by the 10-item version of the Center for Epidemiological Studies Depression. Cross-lagged panel network analysis was conducted to examine relationships between symptoms at three time points: prediagnosis; onset of diagnosis, and postdiagnosis). RESULTS: After controlling for other symptoms and covariates, feeling unable to get going and less happiness at prediagnosis were the most predictive of other symptoms at the diagnosis of CKD. Feeling effortful to do everything and depressed mood at the diagnosis of CKD were the most predictive of other symptoms at postdiagnosis. CONCLUSIONS: Fatigue (i.e., feeling unable to get going, feeling effortful to do everything), less happiness, and depressed mood were central symptoms during the transition to a diagnosis of CKD. These findings highlight the benefits of identifying and managing these central symptoms to reduce the risk of activating other depressive symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Children with autism spectrum disorder are more trusting than typically developing children.

The current study examined whether children with autism spectrum disorder (ASD) had an indiscriminate trust bias whereby they would believe any information provided by an unfamiliar adult with whom they had no interactive history. Young school-aged children with ASD and their age- and ability-matched typically developing (TD) peers participated in a simple hide-and-seek game. In the game, an experimenter with whom the children had no previous interactive history pointed to or left a marker on a box to indicate the whereabouts of a hidden reward. Results showed that although young school-aged ASD children did not blindly trust any information provided by the unfamiliar adult, they appeared to be more trusting in the adult informant than did their age- and ability-matched TD children.

Testing informative hypotheses in factor analysis models using bayes factors.

This study proposes a Bayesian approach to testing informative hypotheses in confirmatory factor analysis (CFA) models. The informative hypothesis, which is formulated by the constrained loadings, can directly represent researchers' theories or expectations about the tau equivalence in reliability analysis, item-level discriminant validity, and relative importance of indicators. Support for the informative hypothesis is quantified by the Bayes factor. We present the adjusted fractional Bayes factor of which the prior distribution is specified using a part of the data and adjusted according to the hypotheses under evaluation. This Bayes factor is derived and computed using the Markov chain Monte Carlo posterior samples of model parameters. Simulation studies investigate the performance of the proposed Bayes factor. A classic example of CFA models is used to illustrate the construction of the informative hypothesis, the specification of the prior distribution, and the computation and interpretation of the Bayes factor. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Joint analysis of multivariate failure time data with latent variables.

We propose a joint modeling approach to investigate the observed and latent risk factors of the multivariate failure times of interest. The proposed model comprises two parts. The first part is a distribution-free confirmatory factor analysis model that characterizes the latent factors by correlated multiple observed variables. The second part is a multivariate additive hazards model that assesses the observed and latent risk factors of the failure times. A hybrid procedure that combines the borrow-strength estimation approach and the asymptotically distribution-free generalized least square method is developed to estimate the model parameters. The asymptotic properties of the proposed estimators are derived. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a study concerning the risk factors of multiple diabetic complications is provided.

Normality mediates the effect of symmetry on facial attractiveness.

Although symmetry, averageness, and sexual dimorphism are usually considered important to facial attractiveness, there are mixed findings regarding whether and how symmetry influences facial attractiveness. The present study introduced "facial normality" to explain the inconsistency of previous research. We hypothesized that symmetry only increased facial attractiveness when it improved facial normality. We manipulated symmetry and normality simultaneously on sixteen Chinese male faces and asked participants to rate the perceived symmetry, perceived normality, and facial attractiveness. The results demonstrated an interactive effect of symmetry and normality on facial attractiveness. The structural equation model results showed two paths from symmetry to facial attractiveness: (1) Symmetry reduced facial attractiveness by decreasing perceived normality; (2) Symmetry increased facial attractiveness by increasing the perceived symmetry and then improving perceived normality. In other words, perceived normality acted as a mediator between symmetry and facial attractiveness. The present study provides a solution to the different effects of symmetry on facial attractiveness in previous studies and suggests that future studies on symmetry and facial attractiveness should consider the mediating role of normality.

Alpha 1-antichymotrypsin may be a biomarker for the progression of amnestic mild cognitive impairment.

Alpha 1-antichymotrypsin (ACT), an acute-phase protein, has been reported to be increased in the brain and blood of Alzheimer's disease (AD) patients. However, few previous studies have focused on amnestic mild cognitive impairment (aMCI) patients. The aim of our study was to investigate the changing trend in ACT concentrations during the progression of aMCI. Hence, we measured the cerebrospinal fluid (CSF) and serum levels of ACT in aMCI subjects and normal controls (NC) at 2-year follow-up assessments using ELISA and Western blot. Forty-four NCs, 28 stable aMCI (sMCI) patients, and 20 progressive aMCI (pMCI) patients finished the follow-up assessments, and their data were used for analysis. We found that CSF and serum ACT levels of both sMCI and pMCI patients increased over time, while those of NCs remained stable; CSF and serum ACT levels were significantly higher in both sMCI and pMCI patients than in NCs, except for baseline serum ACT. In pMCI patients prior to developing AD, CSF and serum ACT levels were already significantly higher than those in sMCI patients. The ROC curve results demonstrated that combining CSF and serum ACT levels can distinguish aMCI patients from NCs with high specificity and sensitivity. Our data suggest that ACT may be a biomarker for diagnosing aMCI.

A semiparametric Bayesian approach for structural equation models.

In the development of structural equation models (SEMs), observed variables are usually assumed to be normally distributed. However, this assumption is likely to be violated in many practical researches. As the non-normality of observed variables in an SEM can be obtained from either non-normal latent variables or non-normal residuals or both, semiparametric modeling with unknown distribution of latent variables or unknown distribution of residuals is needed. In this article, we find that an SEM becomes nonidentifiable when both the latent variable distribution and the residual distribution are unknown. Hence, it is impossible to estimate reliably both the latent variable distribution and the residual distribution without parametric assumptions on one or the other. We also find that the residuals in the measurement equation are more sensitive to the normality assumption than the latent variables, and the negative impact on the estimation of parameters and distributions due to the non-normality of residuals is more serious. Therefore, when there is no prior knowledge about parametric distributions for either the latent variables or the residuals, we recommend making parametric assumption on latent variables, and modeling residuals nonparametrically. We propose a semiparametric Bayesian approach using the truncated Dirichlet process with a stick breaking prior to tackle the non-normality of residuals in the measurement equation. Simulation studies and a real data analysis demonstrate our findings, and reveal the empirical performance of the proposed methodology. A free WinBUGS code to perform the analysis is available in Supporting Information.

A partially confirmatory approach to scale development with the Bayesian Lasso.

The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix, this research proposes a partially confirmatory approach to address the loading and residual structures at the same time. With at least one specified loading per item, a one-step procedure can be applied to figure out both structures simultaneously. With a few specified loadings per factor, a two-step procedure is preferred to capture the model configuration correctly. In both cases, the Bayesian hierarchical formulation is implemented using Markov Chain Monte Carlo estimation with different Lasso or regular priors. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed approach across different situations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Multilevel Heterogeneous Factor Analysis and Application to Ecological Momentary Assessment.

Ansari et al. (Psychometrika 67:49-77, 2002) applied a multilevel heterogeneous model for confirmatory factor analysis to repeated measurements on individuals. While the mean and factor loadings in this model vary across individuals, its factor structure is invariant. Allowing the individual-level residuals to be correlated is an important means to alleviate the restriction imposed by configural invariance. We relax the diagonality assumption of residual covariance matrix and estimate it using a formal Bayesian Lasso method. The approach improves goodness of fit and avoids ad hoc one-at-a-time manipulation of entries in the covariance matrix via modification indexes. We illustrate the approach using simulation studies and real data from an ecological momentary assessment.

Integration of Moderation and Mediation in a Latent Variable Framework: A Comparison of Estimation Approaches for the Second-Stage Moderated Mediation Model.

An increasing number of studies have focused on models that integrate moderation and mediation. Four approaches can be used to test integrated mediation and moderation models: path analysis (PA), product indicator analysis (PI, constrained approach and unconstrained approach), and latent moderated structural equations (LMS). To the best of our knowledge, few studies have compared the performances of PA, PI, and LMS in evaluating integrated mediation and moderation models. As a result, it is difficult for applied researchers to choose an appropriate method in their data analysis. This study investigates the performance of different approaches in analyzing the models, using the second-stage moderated mediation model as a representative model to be evaluated. Four approaches with bootstrapped standard errors are compared under different conditions. Moreover, LMS with robust standard errors and Bayesian estimation of LMS and PA were also considered. Results indicated that LMS with robust standard errors is the superior evaluation method in all study settings. And PA estimates could be severely underestimated as they ignore measurement errors. Furthermore, it is found that the constrained PI and unconstrained PI only provide acceptable estimates when the multivariate normal distribution assumption is satisfied. The practical guidelines were also provided to illustrate the implementation of LMS. This study could help to extend the application of LMS in psychology and social science research.

Plasma Neurofilament Light Chain May Be a Biomarker for the Inverse Association Between Cancers and Neurodegenerative Diseases.

An inverse association may exist between cancers and neurodegenerative diseases, although convenient biomarkers for verifying this inverse association are lacking. Plasma neurofilament light chain (NfL) is a novel biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD), but it has not been measured in patients with cancers, such as gastric cancer (GC). We aimed to explore whether plasma NfL could be a biomarker for GC and AD and whether an inverse association of NfL exists between GC and AD. In this study, plasma NfL levels of 60 normal controls (NC), 91 GC subjects, and 74 AD subjects were measured by a highly sensitive single-molecule array assay. We found that GC subjects expressed lower plasma NfL levels but AD subjects expressed higher plasma NfL levels than NCs. After controlling for confounding factors, plasma NfL levels in the GC group were associated with serum tumor marker levels, and plasma NfL levels in the AD group were associated with cognitive performance and cerebrospinal fluid (CSF) pathological marker levels. Across the entire cohort, plasma NfL levels were associated with cognitive performance, CSF pathological marker levels and serum tumor marker levels. These results suggest thatplasma NfL may be a potential biomarker for GC and AD and may be convenient for evaluating the inverse association between cancers and neurodegenerative diseases.