A prospective longitudinal investigation of the trajectory of obsessive-compulsive symptoms during adolescence.

Obsessive-compulsive symptoms (OCS) increase with age during childhood and adolescence, and subthreshold OCS in childhood associate with a higher probability of obsessive-compulsive disorder (OCD) diagnosis in adulthood. Additionally, average age of onset for OCD is in adolescence, with the majority of OCD cases emerging by early adulthood. Despite these trends, the specific course of OCS development in adolescence is relatively unknown. To this end, the present prospective longitudinal study used latent growth mixture modeling and a diverse community sample of 3,335 high schoolers to identify and characterize growth trajectories of OCS across middle to late adolescence. Results identified three trajectories: High-but-Remitting, Moderate-but-Escalating, and Low-and-Stable. Results also indicated age, gender, anxiety sensitivity, and distress tolerance as significant predictors of trajectory group membership, such that younger age and being female predicted classification in the High-but Remitting group, greater anxiety sensitivity predicted classification in both the High-but-Remitting and Moderate-but Escalating groups, and greater distress tolerance predicted a lower likelihood of classification in the High-but-Remitting and Moderate-but-Escalating groups. Taken together, these trajectories have illustrated the temporal course and development of OCS across key developmental years. Moreover, the trajectories and their corresponding predictors may help identify adolescents who are particularly vulnerable to developing OCD.

Emerging Clostridioides difficile ribotypes have divergent metabolic phenotypes.

Clostridioides difficile is a gram-positive spore-forming pathogen that commonly causes diarrheal infections in the developed world. Although C. difficile is a genetically diverse species, certain ribotypes are overrepresented in human infections. It is unknown if metabolic adaptations are essential for the emergence of these epidemic ribotypes. Here, we tested carbon substrate utilization by 88 C. difficile isolates and looked for differences in growth between 22 ribotypes. By profiling clinical isolates, we assert that C. difficile is a generalist species capable of growing on a variety of carbon substrates. Further, C. difficile strains clustered by phylogenetic relationship and displayed ribotype-specific and clade-specific metabolic capabilities. Surprisingly, we observed that two emerging lineages, ribotypes 023 and 255, have divergent metabolic phenotypes. In addition, although C. difficile Clade 5 is the most evolutionary distant clade and often detected in animals, it displayed more robust growth on simple dietary sugars than Clades 1-4. Altogether, our results corroborate the generalist metabolic strategy of C. difficile and demonstrate lineage-specific metabolic capabilities. In addition, our approach can be adapted to the study of additional pathogens to ascertain their metabolic niches in the gut. IMPORTANCE: The gut pathogen Clostridioides difficile utilizes a wide range of carbon sources. Microbial communities can be rationally designed to combat C. difficile by depleting its preferred nutrients in the gut. However, C. difficile is genetically diverse with hundreds of identified ribotypes and most of its metabolic studies were performed with lab-adapted strains. Here, we profiled carbon metabolism by a myriad of C. difficile clinical isolates. While the metabolic capabilities of these isolates clustered by their genetic lineage, we observed surprising metabolic divergence between two emerging lineages. We also found that the most genetically distant clade grew robustly on simple dietary sugars, posing intriguing questions about the adaptation of C. difficile to the human gut. Altogether, our results underscore the importance of considering the metabolic diversity of pathogens in the study of their evolution and the rational design of therapeutic interventions.

PostBP: A Python library to analyze outputs from wildfire growth models.

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.

Longitudinal effects of prenatal substance use and environmental stressors on executive functioning in low-income African American adolescents: A latent growth modeling analysis.

Adverse prenatal substance use and environmental stressors have been linked to prefrontal cortex (PFC) impairments, the brain region that regulates executive functioning. Executive functions (e.g., inhibitory control, working memory, and cognitive flexibility) are crucial for sophisticated cognitive activities throughout child and adolescent development. There is little research on how prenatal substance use and environmental stressors longitudinally program executive functioning in children over time. We investigated the associations between prenatal/environmental stressors (i.e., maternal prenatal substance use, maternal-fetal bonding, and neighborhood disorganization) and executive function performance among low-income African American youth from age 6 until age 18. Analyses were based on four waves of data collected between 1994 and 2014 in the Memphis New Mothers Study, a longitudinal randomized controlled trial that was an intervention during pregnancy and the first two years of the child's life in low-SES women and their first-born children. Mothers and their children were followed longitudinally through 18 years post-childbirth. Prenatal substance use (e.g., prenatal smoke, alcohol, and drug use) and environmental stressor (e.g., food environment, maternal-fetal bonding and neighborhood disorganizations) evaluations were gathered from mothers and children prenatally and postnatally before the age of 4.5 years. Executive function was assessed using the Child Behavior Checklist for impulsivity and inattention, while the coding subscale of the Wechsler Intelligence Scale for Children-Third Edition, the reading recognition subtest of the Peabody Individual Achievement Test, and the digit span subtest of the Wechsler Adult Intelligence Scale were employed to assess working memory at three time periods (6, 12, and 18 years). Covariate-adjusted latent growth models estimated the associations between prenatal substance use and environmental stressors and changes in executive functioning over three time points. Prenatal smoking and alcohol use were associated with changes in impulsivity scores over 12 years. Prenatal alcohol use predicted higher inattention at baseline and a slower rate of change from ages 6 to 18. Neighborhood disorganization at ages 6 and 18 predicted higher inattention and lower working memory in youth at age 18, respectively. Our findings underscore the long-term impact of prenatal substance use exposures and neighborhood environments on cognitive development and highlight the importance of early interventions to mitigate these effects.

Assessment of hydrocarbon degradation capacity and kinetic modeling of Chlorella vulgaris and Scenedesmus quadricauda for crude oil phycoremediation under mixotrophic conditions.

Crude oil spills imperil aquatic ecosystems globally, prompting innovative solutions such as microalgae-based bioremediation. This study explores the potential of Chlorella vulgaris and Scenedesmus quadricauda, for crude oil spill phycoremediation under mixotrophic conditions and varying crude oil concentrations (0.5-2%). C. vulgaris demonstrated notable resilience, thriving up to 1% crude oil exposure, while S. quadricauda adapted to lower concentrations. Optimal growth for both was observed at 0.5% exposure. Chlorophyll a content in C. vulgaris increases at 0.5% exposure but declines above 1%, while a decline was noticeable in chlorophyll b in treatment groups above 1%. Carotenoid levels varied, displaying the highest levels at higher concentrations above 1.5%. Similarly, S. quadricauda showed increased chlorophyll a content at 0.5% exposure, with stable carotenoid levels and a decline in chlorophyll b content at higher concentrations. GC/MS analyses indicated C. vulgaris efficiently degraded aliphatic compounds like decane and tridecane, surpassing S. quadricauda in degrading both aliphatic and aromatic hydrocarbons. Growth kinetics was best represented by the modified Gompertz and logistic models. These findings highlight the species-specific adaptability and optimal concentration for microalgae to degrade crude oil effectively, advancing phycoremediation processes and strategies critical for environmental restoration.

This study marks the first exploration of both Chlorella vulgaris and the previously unexplored Scenedesmus quadricauda for crude oil phycoremediation potential under mixotrophic conditions. Additionally, it pioneers the modeling and study of algae growth kinetics in response to crude oil exposure. Notably, this research demonstrated the adaptability and efficiency of C. vulgaris in degrading crude oil components under mixotrophic conditions up to a level of 1%, while S. quadricauda showed similar capabilities at a concentration of 0.5%.

Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.

BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation  of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.

Trajectories of actigraphy-derived sleep duration, quality, and variability from childhood to adolescence: downstream effects on mental health.

STUDY OBJECTIVES: We examined growth trajectories of four actigraphy-derived sleep parameters (sleep minutes, sleep efficiency, and variability in sleep minutes and efficiency across a week of assessments) across childhood and adolescence and examined individual differences in trajectories according to participants' race/ethnicity and sex. We also assessed the predictive effect of growth trajectories of sleep parameters on growth trajectories of mental health outcomes and moderation by race and sex. METHOD: Youth (N = 199, 49% female, 65% white, 32% black, 3% biracial) and their parents participated in five waves of data (M ages were 9, 10, 11, 17, and 18 across waves). Participants were from a diverse range of socioeconomic backgrounds. RESULTS: Across participants, sleep minutes, sleep efficiency, and variability in sleep minutes and efficiency demonstrated significant linear change across childhood and adolescence. Whereas sleep duration shortened over time, sleep efficiency improved. Youth exhibited increases in night-to-night variability in sleep minutes and reductions in night-to-night variability in sleep efficiency. Highlighting the importance of individual differences, some race- and sex-related effects emerged. Black youth and male youth experienced steeper declines in their sleep duration across development relative to their respective counterparts. Black youth also demonstrated smaller improvements in sleep efficiency and greater variability in sleep efficiency compared to white youth. Finally, trajectories of sleep efficiency and variability in sleep minutes predicted trajectories of internalizing symptoms and externalizing behaviors. CONCLUSIONS: Findings showed significant changes in developmental trajectories of four sleep parameters across childhood and adolescence. We discuss the empirical and translational implications of the findings.

Exercise, grit, and life satisfaction among Korean adolescents: a latent growth modeling analysis.

BACKGROUND: Life satisfaction among Korean students is declining substantially, and multifaceted improvement efforts are required. METHODS: We analyzed longitudinal change trajectories for exercise, grit, and life satisfaction levels among Korean adolescents using latent growth modeling with longitudinal data from the Korean Children and Youth Panel Surveys of 2,142 students (male: 1,070, female: 1,072) from sixth grade (2020) through eighth grade (2022). RESULTS: The model, which tracked linear changes in the students' exercise, grit, and life satisfaction, showed consistent declines over three school years for all variables. We also identified a longitudinal causal relationship among exercise, grit, and life satisfaction. A higher grit intercept was associated with higher intercept for-and a partial mediating effect between-exercise and life satisfaction. The grit slope was positively related to the life satisfaction slope, and both the intercept and exercise slope had positive effects on life satisfaction. Moreover, grit had a longitudinal mediating effect between exercise and life satisfaction. CONCLUSIONS: We discuss the longitudinal change trajectories of exercise, grit, and life satisfaction, the causal and mediating effects among them, and the implications of the findings. These findings bolster our understanding of Korean adolescents' life satisfaction and have practical significance for designing programs to improve their quality of life.

International Knee Documentation Committee Subjective Knee Form Latent Growth Model Analysis: Assessing Recovery Trajectories.

Patient-Reported Outcome Measures (PROMs), such as the six-item International Knee Documentation Committee Subjective Knee Form (IKDC-6), play a crucial role in assessing health conditions and guiding clinical decisions. Latent Growth Modeling (LGM) can be employed to understand recovery trajectories in patients post-operatively. Therefore, the purpose of this study was to assess LGM properties of the IKDC-6 in patients with knee pathologies that require surgical intervention and to assess differences between subgroups (i.e., sex and age). A cross-sectional study was conducted using the Surgical Outcome System (SOS) database with patients who had undergone knee arthroscopy. Our results found that preoperative scores did not influence the rate of change overtime. Perceived knee health improved over time, with varying rates among individuals. The adolescent age subgroup and male subgroup exhibited faster recovery rates compared to the older age subgroup and female subgroup. While initial hypotheses suggested IKDC-6 could serve as a prognostic tool, results did not support this. However, results indicated favorable outcomes irrespective of preoperative perceived knee impairment levels. This study provides valuable insights into recovery dynamics following knee surgery, emphasizing the need for personalized rehabilitation strategies tailored to individual patient characteristics.

Development of Restricted and Repetitive Behaviors from 2-19: Stability and Change in Repetitive Sensorimotor, Insistence on Sameness, and Verbal Behaviors in a Longitudinal Study of Autism.

This study examined trajectories of repetitive sensorimotor (RSM), insistence on sameness (IS), and verbal RRBs from ages 2-19 in a well-characterized longitudinal cohort. We also tested the factor structure of the ADI-R restricted and repetitive behavior (RRB) domain at age 19 and the inclusion of a verbal RRBs factor, in addition to previously identified RSM and IS factors, at ages 9 and 19. The ADI-R was administered to 193 participants at five timepoints from 2 to 19. Confirmatory factor analysis (CFA) was used to examine the factor structure of ADI-R RRB data. Change in RRB subtypes was examined using group-based trajectory modeling and multilevel modeling. RSM and IS behaviors generally decreased from 2 to 19, with some participants experiencing increases from ages 2-9. 46.46% of participants experienced increasing verbal RRBs from 2 to 9, followed by a plateau from 9 to 19. The remaining participants had few to no verbal RRBs throughout development. Multilevel modeling indicated ADOS CSS scores were associated with change in RSM trajectories only. Child IQ was not associated with change in any RRB trajectories. CFA indicated previously-derived RSM and IS factors fit age 19 ADI-R data well. The inclusion of a third factor, verbal RRBs, worsened goodness-of-fit statistics and was ultimately omitted. Our findings suggest the RRB factor structure of the ADI remains consistent into early adulthood and illustrate developmental continuity and change in RRBs. For autistic individuals with fluent speech, RRB presentation may be influenced by verbal ability in ways that are highly variable, but clinically meaningful.

Modeling the growth of Aspergillus brasiliensis affected by a nonthermal plasma.

AIM: The main objective of the study was to develop and validate a model for the growth of Aspergillus brasiliensis on surfaces, specifically on agar culture medium. An additional aim was to determine conditions for complete growth inhibition of this micromycete using two different nonthermal plasma (NTP) sources. METHODS AND RESULTS: The developed model uses two key parameters, namely the growth rate and growth delay, which depend on the cultivation temperature and the amount of inoculum. These parameters well describe the growth of A. brasiliensis and the effect of NTP on it. For complete fungus inactivation, a single 10-minute exposure to a diffuse coplanar surface barrier discharge was sufficient, while a point-to-ring corona discharge required several repeated 10-minute exposures at 24-h intervals. CONCLUSIONS: The article presents a model for simulating the surface growth of A. brasiliensis and evaluates the effectiveness of two NTP sources in deactivating fungi on agar media.

Socio-Economic Status and Bullying Victimization in India: A Study About Social Misfit and Minority Perception.

The Social Misfit Theory, which states that some individuals deviate from what is normative in a community and may therefore be more likely to be victimized, has mostly been studied in Western countries. The current study addresses in a longitudinal sample whether socio-economic minorities (SES) in the classroom (a contextual SES minority) are more likely to become victims of bullying in India, and whether the relation between minority status and victimization is mediated by perception of oneself as a minority. The current study used three waves separated by three month intervals. A sample of youth from Indore India (grades 7 to 9; N = 1238; M-ageT1 = 13.15, SD = 1.16, 24 percent girls) was used. It was found that being a contextual SES minority was related to more victimization, but only when the contextual status was corroborated by the perceived minority status. However, over time, being part of a contextual minority predicted decreased victimization, possibly pointing to normative beliefs and values in the Indian context. The results of this study are in contrast to the Social Misfit Theory, but do support self-perception as a mediator.

How Scoring Approaches Impact Estimates of Growth in the Presence of Survey Item Ceiling Effects.

Survey scores are often the basis for understanding how individuals grow psychologically and socio-emotionally. A known problem with many surveys is that the items are all "easy"-that is, individuals tend to use only the top one or two response categories on the Likert scale. Such an issue could be especially problematic, and lead to ceiling effects, when the same survey is administered repeatedly over time. In this study, we conduct simulation and empirical studies to (a) quantify the impact of these ceiling effects on growth estimates when using typical scoring approaches like sum scores and unidimensional item response theory (IRT) models and (b) examine whether approaches to survey design and scoring, including employing various longitudinal multidimensional IRT (MIRT) models, can mitigate any bias in growth estimates. We show that bias is substantial when using typical scoring approaches and that, while lengthening the survey helps somewhat, using a longitudinal MIRT model with plausible values scoring all but alleviates the issue. Results have implications for scoring surveys in growth studies going forward, as well as understanding how Likert item ceiling effects may be contributing to replication failures.

Life-history traits of an exploited skate: The short-tail yellownose skate Zearaja brevicaudata (Chondrichthyes: Rajiformes).

The short-tail yellownose skate, Zearaja brevicaudata, occurs off southern Brazil and along the Argentine continental shelf, including waters surrounding the Islas Malvinas and the upper slope, and is very frequently caught by trawl fisheries throughout its range. The age, growth, and maturity of Z. brevicaudata were investigated using 204 individuals collected by commercial fishing vessels from northern Argentina, between March 2016 and September 2019. Age was determined on a sample comprising vertebrae from 151 individuals, 60 males, ranging in size from 391 to 956 mm total length (TL), and 91 females, ranging in size from 324 to 1060 mm TL. Maximum ages determined for males and females were 22 and 29 years, respectively. A Bayesian framework was employed with a set of three candidate models to estimate growth parameters (von Bertalanffy, Gompertz, and Logistic). The von Bertalanffy model had the best fit, and the sexes showed undistinguishable growth parameters. For sexes combined, derived growth mean parameters (± S.D.) were L∞ = 1081 mm ± 64.34 mm, k = 0.09 ± 0.01 years-1, and L0 = 248 mm ± 23.52 mm. The age at maturity was estimated at 13.15 and 14.66 years for males and females, respectively.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

Background: The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods: One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results: The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions: Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

Why So Lonely? The Direct and Indirect Associations between Developmental Trajectories of Fear of Negative Evaluation, Prosocial Behavior and Loneliness in Adolescence.

Fear of negative evaluation and prosocial behavior have been identified as predictive factors influencing the development of loneliness in adolescence, representing typical factors in the cognitive and behavioral processes of re-affiliation. The elucidation of plausible direct and indirect pathways linking these pivotal factors to adolescents' loneliness need further exploration. This study aimed to investigate the direct and indirect relationships between the fear of negative evaluation, prosocial behavior, and adolescents' loneliness through the lens of developmental changes. A total of 533 adolescents (49.0% girls, Mage = 15.18 years, SD = 0.71) participated in this longitudinal study, assessed at three timepoints over a span of two years with 12-month intervals. Latent growth modeling uncovered direct associations between the developmental trajectories of both fear of negative evaluation and prosocial behavior with the developmental trajectory of adolescents' loneliness. The developmental trajectory of fear of negative evaluation exhibited an indirect association with the developmental trajectory of loneliness through the mediating role of prosocial behavior. These findings highlighted the roles of cognitive and behavioral re-affiliation processes, both independently and as mediators, in influencing adolescent loneliness, suggesting that interventions aimed at reducing fear of negative evaluation and promoting prosocial behavior could effectively mitigate adolescents' loneliness.

The double-edged sword of personality in shaping craftsmanship spirit: an investigation of conscientiousness and openness to experience.

Background: Craftsmanship is associated with various positive outcomes at both individual and organizational level, and thus has attracted scholarly attention on examining its antecedents. While craftsmanship can be shaped by both contextual factors and personal traits, existing research has dominantly focused on the former, leaving the latter less examined. Such a lack of examination limits our understanding of craftsmanship in workplace. Objective: Following the view that individuals' intrinsic desire to do the job as the core of craftsmanship, we define craftsmanship spirit (CS) as an individual's psychological state of feeling competent, transcendent, and valuable during work, which evolves as an individual's skills and knowledge expand. We then draw on the classic dispositional literature to explore how individuals' personality traits (conscientiousness and openness to experience) shape the development of CS differently (i.e., the initial level and the developmental trajectory), and test our theory using a latent growth modeling (LGM) approach. Methods: We conducted a four-round on-site questionnaire survey with participants who were employees at a large manufacturing company in China. The final sample consists of 746 matched respondents. Data analysis was performed in Mplus 8.3. Results: Empirical results confirm our hypotheses that both conscientiousness and openness to experience have a positive effect on the initial level of CS. Besides, conscientiousness has a negative effect on the subsequent growth of CS. However, the proposed negative effect of openness to experience on the subsequent growth of CS was not supported when the other four personality traits were considered simultaneously. Conclusion: This study reveals that conscientiousness and openness to experience have an important effect on CS. Specifically, both conscientiousness and openness to experience are associated with a high level of CS, and the former is associated with low growth of CS. This study not only broadened our understanding on the antecedents of CS, but also provided a dynamic perspective to understand CS in workplace.

Latent classes of symptom trajectories among major depressive disorder patients in China.

OBJECTIVE: This study aimed to understand the long-term symptom trajectories of Chinese patients with major depressive disorder (MDD) using piecewise latent growth modeling and growth mixture modeling. The investigation also aimed to identify the baseline characteristics indicative of poorer treatment outcomes. METHODS: A total of 558 outpatients with MDD were assessed using a sequence of surveys. The Hamilton Rating Scale for Depression (HRSD), Hamilton Anxiety Rating Scale (HAMA), and Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) were used to evaluate baseline depression, anxiety, and cognitive function. Depression symptom severity was subsequently measured at the 1-month, 2-month, 6-month, 1-year, and 2-year follow-ups. RESULTS: Results indicated three depressive symptomology trajectories, including (a) severe, improving class (12.72 %), (b) partially responding, later deteriorating class (6.09 %), and (c) moderate, improving class (81.18 %). Logistic regression analyses showed that a history of cardiovascular disease (CVD) increased the odds of belonging to the partially responding, later deteriorating class, whereas higher baseline depression increased the odds of belonging to the severe, improving class compared to the moderate, improving class. Patients who experienced less depression relief during the first month of treatment had a lower probability of belonging to the moderate, improving class. LIMITATIONS: Participant attrition in this study may have inflated the estimated rate of treatment-resistant patients. CONCLUSIONS: The burden of CVD and poorer initial treatment response are plausible risk factors for poorer treatment outcomes, highlighting targets for intervention in Chinese MDD patients.

Is trait rumination associated with affective reactivity to the menstrual cycle? A prospective analysis.

BACKGROUND: A minority of naturally cycling individuals experience clinically significant affective changes across the menstrual cycle. However, few studies have examined cognitive and behavioral constructs that may maintain or worsen these changes. Several small studies link rumination with premenstrual negative affect, with authors concluding that a tendency to ruminate amplifies and perpetuates hormone-sensitive affective symptoms. Replication in larger samples is needed to confirm the validity of rumination as a treatment target. METHOD: 190 cycling individuals (M = 30.82 years; 61.1% Caucasian) were recruited for moderate perceived stress, a risk factor for cyclical symptoms. They completed the Rumination Response Scale at baseline, then reported daily affective and physical symptoms across 1-6 cycles. Multilevel growth models tested trait rumination as a predictor of baseline levels, luteal increases, and follicular decreases in symptoms. RESULTS: The degree of affective cyclicity was normally distributed across a substantial range, supporting feasibility of hypothesis tests and validating the concept of dimensional hormone sensitivity. Contrary to prediction, higher brooding did not predict levels or cyclical changes of any symptom. In a subsample selected for luteal increases in negative affect, brooding predicted higher baseline negative affect but still did not predict affective cyclicity. CONCLUSIONS: An individual's trait-like propensity to engage in rumination may not be a valid treatment target in premenstrual mood disorders. State-like changes in rumination should still be further explored, and well-powered prospective studies should explore other cognitive and behavioral factors to inform development of targeted psychological treatments for patients with cyclical affective symptoms.

Latent trajectories of recent and delayed memory and their predictors: evidence from SHARE.

OBJECTIVES: Cognitive decline is common in the old age, but some evidence suggests it may already occur during adulthood. Previous studies have linked age, gender, educational attainment, depression, physical activity, and social engagement to better cognitive performance over time. However, most studies have used global measures of cognition, which could mask subtle changes in specific cognitive domains. The aim of this study is to examine trajectories of recent and delayed memory recall from a variable-centered perspective, in order to elucidate the impact of age, gender, educational attainment, depression, physical activity, and social engagement on recent and delayed memory both at initial time and across a 10-year period. DESIGN AND PARTICIPANTS: The sample was formed by 56,616 adults and older adults that participated in waves 4 to 8 of the Survey of Health, Aging and Retirement in Europe (SHARE). ANALYSES: We used latent growth modeling to establish latent recent and delayed memory trajectories, and then tested the effects of the aforementioned covariates on the latent intercept and slopes. RESULTS: Results showed that both recent and delayed recall display a quadratic trajectory of decline. All covariates significantly explained initial levels of immediate and delayed recall, but only a few had statistically significant effects on the slope terms. CONCLUSIONS: We discuss differences between present results and those previously reported in studies using a person-centered approach. This study provides evidence of memory decline during adulthood and old adulthood. Further, results provide support for the neural compensation reserve theory.