GTRpmix: A linked general-time reversible model for profile mixture models.

Profile mixture models capture distinct biochemical constraints on the amino acid substitution process at different sites in proteins. These models feature a mixture of time-reversible models with a common matrix of exchangeabilities and distinct sets of equilibrium amino acid frequencies known as profiles. Combining the exchangeability matrix with each profile generates the matrix of instantaneous rates of amino acid exchange for that profile. Currently, empirically estimated exchangeability matrices (e.g., the LG matrix) are widely used for phylogenetic inference under profile mixture models. However, these were estimated using a single profile and are unlikely optimal for profile mixture models. Here, we describe the GTRpmix model that allows maximum likelihood estimation of a common exchangeability matrix under any profile mixture model. We show that exchangeability matrices estimated under profile mixture models differ from the LG matrix, dramatically improving model fit and topological estimation accuracy for empirical test cases. Because the GTRpmix model is computationally expensive, we provide two exchangeability matrices estimated from large concatenated phylogenomic-supermatrices to be used for phylogenetic analyses. One, called Eukaryotic Linked Mixture (ELM), is designed for phylogenetic analysis of proteins encoded by nuclear genomes of eukaryotes, and the other, Eukaryotic and Archaeal Linked mixture (EAL), for reconstructing relationships between eukaryotes and Archaea. These matrices, combined with profile mixture models, fit data better and have improved topology estimation relative to the LG matrix combined with the same mixture models. Starting with version 2.3.1, IQ-TREE2 allows users to estimate linked exchangeabilities (i.e. amino acid exchange rates) under profile mixture models.

Bayesian cluster geographically weighted regression for spatial heterogeneous data.

Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be complicated and expensive. One of these algorithms is geographically weighted regression (GWR) which was proposed in the geography literature to allow relationships in a regression model to vary over space. In contrast to traditional linear regression models, which have constant regression coefficients over space, regression coefficients are estimated locally at spatially referenced data points with GWR. The motivation for the adaption of GWR is the idea that a set of constant regression coefficients cannot adequately capture spatially varying relationships between covariates and an outcome variable. GWR has been applied widely in diverse fields, such as ecology, forestry, epidemiology, neurology and astronomy. While frequentist GWR gives us point estimates and confidence intervals, Bayesian GWR enriches our understanding by including prior knowledge and providing probability distributions for parameters and predictions of interest. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian geographically weighted regression (BGWR) with a post-processing step that includes Gaussian mixture model and the Dirichlet process mixture model. Second, this paper examines situations in which a particular covariate holds significant importance in one region but not in another in the Bayesian framework. Lastly, it addresses computational challenges in existing BGWR, leading to enhancements in Markov chain Monte Carlo estimation suitable for large spatial datasets. The efficacy of the proposed method is demonstrated using simulated data and is further validated in a case study examining children's development domains in Queensland, Australia, using data provided by Children's Health Queensland and Australia's Early Development Census.

Interference across time: dissociating short from long temporal interference.

Our ability to identify an object is often impaired by the presence of preceding and/or succeeding task-irrelevant items. Understanding this temporal interference is critical for any theoretical account of interference across time and for minimizing its detrimental effects. Therefore, we used the same sequences of 3 orientation items, orientation estimation task, and computational models, to examine temporal interference over both short (<150 ms; visual masking) and long (175-475 ms; temporal crowding) intervals. We further examined how inter-item similarity modifies these different instances of temporal interference. Qualitatively different results emerged for interference of different scales. Interference over long intervals mainly degraded the precision of the target encoding while interference over short intervals mainly affected the signal-to-noise ratio. Although both interference instances modulated substitution errors (reporting a wrong item) and were alleviated with dissimilar items, their characteristics were markedly disparate. These findings suggest that different mechanisms mediate temporal interference of different scales.

Research on Student's T-Distribution Point Cloud Registration Algorithm Based on Local Features.

LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student's t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues. The approach uses Student's t-distribution mixture model (SMM) to generate the probability distribution of point cloud registration, which can accurately describe the data distribution, in order to tackle the problem of the missing laser 3D point cloud data and data disorder. Owing to the disparity in the point cloud registration task, a full-rank covariance matrix is built based on the local features of the point cloud during the objective function design process. The combined penalty of point-to-point and point-to-plane distance is then added to the objective function adaptively. Simultaneously, by analyzing the imaging characteristics of LiDAR, according to the influence of the laser waveform and detector on the LiDAR imaging, the composite weight coefficient is added to improve the pertinence of the algorithm. Based on the public dataset and the laser 3D point cloud dataset acquired in the laboratory, the experimental findings demonstrate that the proposed algorithm has high practicability and dependability and outperforms the five comparison algorithms in terms of accuracy and robustness.

[Dynamic Prediction of Recidivism in Violence in Community-Based Schizophrenia Spectrum Disorder Patients: A Joint Model]. / ç¤¾åŒºç²¾ç¥žåˆ†è£‚ç—‡è°±ç³»éšœç¢æ‚£è€ æš´åŠ›å†çŠ¯åŠ¨æ€é¢„æµ‹ï¼šä¸€ç§è”åˆæ¨¡åž‹.

Objective: To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method. Methods: Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models' fitting and predictive performance. Results: A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (P<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (P<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models. Conclusion: Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.

Towards automated animal density estimation with acoustic spatial capture-recapture.

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive "correction factor" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives.

Probability graph complementation contrastive learning.

Graph Neural Network (GNN) has achieved remarkable progress in the field of graph representation learning. The most prominent characteristic, propagating features along the edges, degrades its performance in most heterophilic graphs. Certain researches make attempts to construct KNN graph to improve the graph homophily. However, there is no prior knowledge to choose proper K and they may suffer from the problem of Inconsistent Similarity Distribution (ISD). To accommodate this issue, we propose Probability Graph Complementation Contrastive Learning (PGCCL) which adaptively constructs the complementation graph. We employ Beta Mixture Model (BMM) to distinguish intra-class similarity and inter-class similarity. Based on the posterior probability, we construct Probability Complementation Graphs to form contrastive views. The contrastive learning prompts the model to preserve complementary information for each node from different views. By combining original graph embedding and complementary graph embedding, the final embedding is able to capture rich semantics in the finetuning stage. At last, comprehensive experimental results on 20 datasets including homophilic and heterophilic graphs firmly verify the effectiveness of our algorithm as well as the quality of probability complementation graph compared with other state-of-the-art methods.

Trajectories of depression and predictors in lung cancer patients undergoing chemotherapy: growth mixture model.

BACKGROUND: Depression is prevalent among lung cancer patients undergoing chemotherapy, and the symptom cluster of fatigue-pain-insomnia may influence their depression. Identifying characteristics of patients with different depression trajectories can aid in developing more targeted interventions. This study aimed to identify the trajectories of depression and the fatigue-pain-insomnia symptom cluster, and to explore the predictive factors associated with the categories of depression trajectories. METHODS: In this longitudinal study, 187 lung cancer patients who were undergoing chemotherapy were recruited and assessed at the first (T1), second(T2), and fourth(T3) months using the Patient Health Questionnaire-9 (PHQ-9), the Brief Pain Inventory (BPI), the Brief Fatigue Inventory (BFI), and the Athens Insomnia Scale (AIS). Growth Mixture Model (GMM) and Latent Class Analysis (LCA) were used to identify the different trajectories of the fatigue-pain-insomnia symptom cluster and depression. Binary logistic regression was utilized to analyze the predictive factors of different depressive trajectories. RESULTS: GMM identified two depressive trajectories: a high decreasing depression trajectory (40.64%) and a low increasing depression trajectory (59.36%). LCA showed that 48.66% of patients were likely members of the high symptom cluster trajectory. Binary logistic regression analysis indicated that having a history of alcohol consumption, a higher symptom cluster burden, unemployed, and a lower monthly income predicted a high decreasing depression trajectory. CONCLUSIONS: Depression and fatigue-pain-insomnia symptom cluster in lung cancer chemotherapy patients exhibited two distinct trajectories. When managing depression in these patients, it is recommended to strengthen symptom management and pay particular attention to individuals with a history of alcohol consumption, unemployed, and a lower monthly income.

Quantitative Computed Tomography Angiography for the Evaluation of Valvular Fibrocalcific Volume in Aortic Stenosis.

BACKGROUND: Aortic stenosis (AS) is characterized by calcification and fibrosis. The ability to quantify these processes simultaneously has been limited with previous imaging methods. OBJECTIVES: The purpose of this study was to evaluate the aortic valve fibrocalcific volume by computed tomography (CT) angiography in patients with AS, in particular, to assess its reproducibility, association with histology and disease severity, and ability to predict/track progression. METHODS: In 136 patients with AS, fibrocalcific volume was calculated on CT angiograms at baseline and after 1 year. CT attenuation distributions were analyzed using Gaussian-mixture-modeling to derive thresholds for tissue types enabling the quantification of calcific, noncalcific, and fibrocalcific volumes. Scan-rescan reproducibility was assessed and validation provided against histology and in an external cohort. RESULTS: Fibrocalcific volume measurements took 5.8 ± 1.0 min/scan, demonstrating good correlation with ex vivo valve weight (r = 0.51; P < 0.001) and excellent scan-rescan reproducibility (mean difference -1%, limits of agreement -4.5% to 2.8%). Baseline fibrocalcific volumes correlated with mean gradient on echocardiography in both male and female participants (rho = 0.64 and 0.69, respectively; both P < 0.001) and in the external validation cohort (n = 66, rho = 0.58; P < 0.001). The relationship was driven principally by calcific volume in men and fibrotic volume in women. After 1 year, fibrocalcific volume increased by 17% and correlated with progression in mean gradient (rho = 0.32; P = 0.003). Baseline fibrocalcific volume was the strongest predictor of subsequent mean gradient progression, with a particularly strong association in female patients (rho = 0.75; P < 0.001). CONCLUSIONS: The aortic valve fibrocalcific volume provides an anatomic assessment of AS severity that can track disease progression precisely. It correlates with disease severity and hemodynamic progression in both male and female patients.

Characterization of domain formation in complex membranes.

A widely known property of lipid membranes is their tendency to undergo a separation into disordered (Ld) and ordered (Lo) domains. This impacts the local structure of the membrane relevant for the physical (e.g., enhanced electroporation) and biological (e.g., protein sorting) significance of these regions. The increase in computing power, advancements in simulation software, and more detailed information about the composition of biological membranes shifts the study of these domains into the focus of classical molecular dynamics simulations. In this chapter, we present a versatile yet robust analysis pipeline that can be easily implemented and adapted for a wide range of lipid compositions. It employs Gaussian-based Hidden Markov Models to predict the hidden order states of individual lipids by describing their structure through the area per lipid and the average SCC order parameters per acyl chain. Regions of the membrane with a high correlation between ordered lipids are identified by employing the Getis-Ord local spatial autocorrelation statistic on a Voronoi tessellation of the lipids. As an example, the approach is applied to two distinct systems at a coarse-grained resolution, demonstrating either a strong tendency towards phase separation (1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DIPC), cholesterol) or a weak tendency toward phase separation (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PUPC), cholesterol). Explanations of the steps are complemented by coding examples written in Python, providing both a comprehensive understanding and practical guidance for a seamless integration of the workflow into individual projects.

The importance of missing data in estimating BMI trajectories.

Body Mass Index (BMI) trajectories are important for understanding how BMI develops over time. Missing data is often stated as a limitation in studies that analyse BMI over time and there is limited research exploring how missing data influences BMI trajectories. This study explores the influence missing data has in estimating BMI trajectories and the impact on subsequent analysis. This study uses data from the English Longitudinal Study of Ageing. Distinct BMI trajectories are estimated for adults aged 50 years and over. Next, multiple methods accounting for missing data are implemented and compared. Estimated trajectories are then used to predict the risk of developing type 2 diabetes mellitus (T2DM). Four distinct trajectories are identified using each of the missing data methods: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differ between the different methods. The influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most reliable. When estimating BMI trajectories, missing data should be considered. The extent to which accounting for missing data influences cost-effectiveness analyses should be investigated.

Ornaments for efficient allele-specific expression estimation with bias correction.

Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis-regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Source-free domain adaptation for semantic image segmentation using internal representations.

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gaussian mixture model (GMM).

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

BPT-PLR: A Balanced Partitioning and Training Framework with Pseudo-Label Relaxed Contrastive Loss for Noisy Label Learning.

While collecting training data, even with the manual verification of experts from crowdsourcing platforms, eliminating incorrect annotations (noisy labels) completely is difficult and expensive. In dealing with datasets that contain noisy labels, over-parameterized deep neural networks (DNNs) tend to overfit, leading to poor generalization and classification performance. As a result, noisy label learning (NLL) has received significant attention in recent years. Existing research shows that although DNNs eventually fit all training data, they first prioritize fitting clean samples, then gradually overfit to noisy samples. Mainstream methods utilize this characteristic to divide training data but face two issues: class imbalance in the segmented data subsets and the optimization conflict between unsupervised contrastive representation learning and supervised learning. To address these issues, we propose a Balanced Partitioning and Training framework with Pseudo-Label Relaxed contrastive loss called BPT-PLR, which includes two crucial processes: a balanced partitioning process with a two-dimensional Gaussian mixture model (BP-GMM) and a semi-supervised oversampling training process with a pseudo-label relaxed contrastive loss (SSO-PLR). The former utilizes both semantic feature information and model prediction results to identify noisy labels, introducing a balancing strategy to maintain class balance in the divided subsets as much as possible. The latter adopts the latest pseudo-label relaxed contrastive loss to replace unsupervised contrastive loss, reducing optimization conflicts between semi-supervised and unsupervised contrastive losses to improve performance. We validate the effectiveness of BPT-PLR on four benchmark datasets in the NLL field: CIFAR-10/100, Animal-10N, and Clothing1M. Extensive experiments comparing with state-of-the-art methods demonstrate that BPT-PLR can achieve optimal or near-optimal performance.

Disposable e-cigarette use: Factors, frequency and cigarette smoking among United States high school students.

BACKGROUND AND AIMS: Disposable e-cigarette use has increased among United States (US) high school students in recent years. However, there is limited research on the profile of these users, how often they use these products, and whether they displace cigarette smoking. This study aimed to measure how disposable e-cigarette use among US youth varies according to demographic characteristics and whether there is any association between e-cigarette use and reduced use of traditional cigarettes. DESIGN: We used cross-sectional data from the 2022 National Youth Tobacco Survey and conducted a multinomial logistic regression to examine factors associated with the types of e-cigarette devices used in the prior 30 days, adjusting for sex, sexual identity, grade level and race/ethnicity. We also used a finite mixture model to account for unobserved differences among users and identify e-cigarette use patterns in different subgroups of users. SETTING: United States. PARTICIPANTS: High school students in grades 9-12 (n = 14 389). MEASUREMENTS: Survey participants self-reported the type of e-cigarette device used, the frequency of e-cigarettes used and cigarettes smoked over the past 30 days. FINDINGS: Disposable e-cigarettes were the most popular e-cigarette type. Sex, sexual orientation, grade level and race/ethnicity were associated with disposable e-cigarette use. The odds of disposable e-cigarette use were lower in male students than in female students (odds ratio [OR] = 0.78, 95% confidence interval [CI] = [0.64-0.96]), and higher in students who identified as gay or lesbian (OR = 1.70, 95% CI = [1.11-2.61]) or bisexual (OR = 1.52, 95% CI = [1.16-1.99]) than in heterosexual students. The odds of disposable use were higher among students in higher grades (10th, 11th and 12th) than in 9th graders (OR = 1.71, 2.24 and 2.52, respectively). Disposable e-cigarette users had a lower frequency of traditional cigarette use than other e-cigarette users, both in the low-frequency class (incidence rate ratio [IRR] = 0.33, 95% CI = [0.12-0.92]) and the high-frequency class (IRR = 0.27, 95% CI = [0.08-0.92]). CONCLUSIONS: Disposable e-cigarette use appears to be higher among United States high school students who are female, older and/or identify as gay, lesbian or bisexual. Disposable e-cigarettes appear to be associated with reduced traditional cigarette use.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Intergenerational Educational Mobility and Cognitive Trajectories Among Middle-Aged and Older Chinese People: An Application of Growth Mixture and Mobility Contrast Models in Longitudinal Analysis.

OBJECTIVES: Due to statistical challenges in disentangling the mobility effect (i.e., intergenerational educational mobility) from the position effect (i.e., parental and person's own education), the impact of intergenerational educational mobility on cognitive function remains unclear. We employed a novel approach to identify the mobility effect and investigate the net impact of intergenerational educational mobility on heterogeneous patterns of cognition among middle-aged and older adults in China. METHODS: Participants aged 45 and older were recruited from the China Health and Retirement Longitudinal Study, a population-based prospective cohort study between 2011 and 2018. We identified cognitive trajectories using the growth mixture model (GMM) and subsequently employed the mobility contrast model (MCM) to examine the effects of intergenerational educational mobility on cognitive patterns stratified by gender. RESULTS: Almost two thirds of respondents experienced intergenerational educational mobility, and 55% experienced upward mobility. Men had a higher rate of upward mobility than women. Three population-based cognitive patterns were identified: the low cognitive function with decline group (28%), the moderate cognitive function group (47%), and the high cognitive function group (26%). MCM analysis revealed that both upward and downward intergenerational educational mobility negatively affected cognitive trajectory patterns, extending beyond the influence of individuals' current and parental education. DISCUSSION: In future research, the impact of mobility can be studied in longitudinal data sets by combining the GMM and MCM approaches. The net negative effect of intergenerational educational mobility on cognitive trajectory patterns indicates that it should be recognized as an independent predictor of cognitive decline.

Parenting practices and trajectories of proactive coping assets among emerging adult Black men.

Positive youth development (PYD) frameworks suggest that a critical response to investigating the challenges young Black men living in resource poor communities experience involves identifying contextual resources in young men's lives and personal assets that promote success. The following study examines heterogeneity in proactive coping assets trajectories, parental practices as predictors of developmental trajectories, and associated outcomes of each trajectory. The study sample consisted of Black emerging adult men living in rural Georgia (N = 504). At baseline, men were between the ages of 19 and 22 (Mage = 20.29; SD = 1.10). At wave four, the participants' mean age was 27.67 (SD = 1.39). Results of growth mixture modeling from waves 1 to 3 discerned three developmental trajectory classes of emerging adults' proactive coping assets: a high and increasing class (n = 247, 49%), a low and stable class (n = 212, 42%), and a moderate and decreasing class (n = 45, 9%). Trajectory classes were linked to baseline levels of parental support, coaching, and expectations. Analysis revealed that parental support and parental coaching predicted proactive coping asset trajectory class identification. Links were then investigated between emerging adults' proactive coping asset trajectory classes and wave four physical health, depression, and alcohol use. Results revealed significant associations between class identification, alcohol use, and physical health. Study findings provide evidence supporting the impact of parenting on emerging adult Black men, underscoring the need to expand resources that support parenting and emerging adult relationships.