Mixture Model Research Articles

Enhancing search pipelines for short gravitational-wave transients with Gaussian mixture modeling

We present an enhanced method for the application of Gaussian mixture modeling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised machine learning method of GMM allows for the multidimensional distributions of noise and signal to be modeled over a set of representative attributes, which aids in the classification of GW signals against noise transients (glitches) in the data. We demonstrate that updating the approach to model construction eliminates bias previously seen in the GMM analysis, increasing the robustness and sensitivity of the analysis over a wider range of burst source populations. The enhanced methodology is applied to the generic burst all-sky short search in the LIGO-Virgo full third observing run (O3), marking the first application of GMM to the 3 detector Livingston-Hanford-Virgo network. For both 2- and 3- detector networks, we observe comparable sensitivities to an array of generic signal morphologies, with significant sensitivity improvements to waveforms in the low quality factor parameter space at false alarm rates of 1 per 100 years. This proves that GMM can effectively mitigate blip glitches, which are one of the most problematic sources of noise for unmodeled GW searches. The cWB-GMM search recovers similar numbers of compact binary coalescence (CBC) events as other cWB postproduction methods, and concludes on no new gravitational wave detection after known CBC events are removed. Published by the American Physical Society 2024

Computational multiphase mixture simulations of a two-phase R-744 ejector geometry in transcritical R-744 heat pump applications

Purpose Two-phase R-744 ejectors are critical components enabling energy recovery in R-744 heat pump and refrigeration systems, but despite their simple geometry, the flow physics involve complex multiphase mixing phenomena that need to be well-quantified for component and overall system improvement. This study aims to report on multiphase mixture simulations for a specific two-phase R-744 ejector with supercritical inlet conditions at the motive inlet side. Design/methodology/approach Four different operating conditions, which have motive inlet pressure range of 90.1 bar–101.1 bar, are selected from an existing experimental data set. A two-phase thermodynamic equilibrium (TPTE) model is used, where the fluid properties are described by a thermodynamic look-up table. Findings The results show that the TPTE model overpredicts mass flow rates at the motive inlet, resulting in a relative error ranging from 15.6% to 21.7%. For the mass flow rate at the suction inlet, the relative errors are found less than 1.5% for three cases, while the last case has an error of 12.4%. The maximum deviation of the mass entrainment ratio is found to be 8.0% between the TPTE model and the experimental data. Ejector efficiency ranges from 25.4% to 28.0%. A higher pressure difference between the ejector outlet and the diverging nozzle exit provides greater pressure lift. Research limitations/implications Based on the results, near future efforts will be to optimize estimation errors while enabling more detailed field analysis of pressure, density, temperature and enthalpy in the computational domain. Originality/value The authors have two main original contributions: 1) the presented thermodynamic look-up table is unique and provides unique computation for the real-scale ejector domain. It was created by the authors and has not been applied before as far as we know. 2) To the best of the authors’ knowledge, this study is the first study that applies the STAR-CCM+ multiphase mixture model for R-744 mixture phenomena in heat pumps and refrigeration systems.

Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study

BackgroundPost-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients. MethodsThis is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis. ResultsTwo stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, p=0.010, BLRT, p≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, p <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory. ConclusionThe trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.

SCAPE-APA: a package for estimating alternative polyadenylation events from scRNA-seq data

SCAPE was a package that was previously developed to estimate alternative polyadenylation events from single-cell RNA-seq (scRNA-seq) data. It was composed of ad-hoc python scripts and has speed issues when handling large scRNA-seq data. To suit the needs of analyzing large scRNA-seq datasets, we present SCAPE-APA, which is a reimplementation of SCAPE with substantial changes. We made the following updates to the package: (1) we binned similar reads together to accelerate the estimation; (2) we re-derived the mixture model to tailor it for binned reads; (3) we implemented the inference algorithm using Taichi language for acceleration; (4) we re-implemented the untranslated region (UTR) annotation extraction script using the professional package gffutils for better maintenance; (5) we wrote a script to detect spurious alternative polyadenylation sites generated due to junction reads, and (6) we created a formal Python package and uploaded it to the Python Package Index website (Pypi).

Trajectories of disease evolution upon treatment initiation in systemic lupus erythematosus: results from four clinical trials of belimumab.

Upon commencement of therapy for active disease, patients with systemic lupus erythematosus (SLE) show varying evolution regarding disease activity measures and patient-reported outcomes (PROs). Our objective was to identify disease evolution trajectories to gain a deeper understanding of SLE progression, ultimately improving future trial design. Patients with ≥2 visits and available data on SLEDAI-2K, BILAG, PGA, FACIT-F, and glucocorticoid use were included in a post-hoc analysis of four randomised controlled trials of belimumab (BLISS-52, BLISS-76, BLISS-SC, EMBRACE). Growth mixture modelling identified latent classes. Among 2868 patients analysed, baseline median disease duration was 4.5 (IQR: 1.5-9.7) years and mean (± standard deviation) SDI 0.7 (±2.0), SLEDAI-2K 10.2 (±3.6), BILAG 17.0 (±7.8), PGA 1.5 (±0.5), FACIT-F 30.6 (±11.9), and prednisone dose 11.0 (±8.9) mg/day. In the initial model, glucocorticoid use and dose yielded high standard errors, indicating a weak link with the latent process. A refined model considered only clinical measures and FACIT-F, corrected for intervention and SDI; no other covariates improved the fit. Four classes best described disease evolution: highly active, responders; highly active, non-responders; moderately active, responders; moderately active, non-responders. LLDAS and DORIS remission attainment associated with latent classes. By linking disease activity measures with PROs, we identified four distinct trajectories describing SLE evolution following the initiation of therapy. This classification could be valuable for personalising treatment and guiding biological studies aimed at distinguishing patients with varying anticipated treatment responses, as no single clinical variable alone can predict disease progression.

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

Joint Beamforming Design and User Clustering Algorithm in NOMA-Assisted ISAC Systems.

To enhance the performance of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems in multi-user distributed scenarios, an improved Gaussian Mixture Model (GMM)-based user clustering algorithm is proposed. This algorithm is tailored for ISAC systems, significantly improving bandwidth reuse gains and reducing serial interference. First, using the Sum of Squared Errors (SSE), the algorithm reduces sensitivity to the initial cluster center locations, improving clustering accuracy. Then, direction weight factors are introduced based on the base station position and a penalty function involving users' Euclidean distances and sensing power. Modifications to the EM algorithm in calculating posterior probabilities and updating the covariance matrix help align user clusters with the characteristics of NOMAISAC systems. This improves users' interference resistance, lowers decoding difficulty, and optimizes the system's sensing capabilities. Finally, a fractional programming (FP) approach addresses the non-convex joint beamforming design problem, enhancing power and channel gains and achieving co-optimizing sensing and communication signals. The simulation results show that, under the improved GMM user clustering algorithm and FP optimization, the NOMA-ISAC system improves user spectral efficiency by 4.3% and base station beam intensity by 5.4% compared to traditional ISAC systems.

Buyer Valuation of Angus Bull Attributes in Wisconsin

Abstract Hedonic analysis of Wisconsin Beef Improvement Association Bull Sale and Development Program data revealed buyer preferences for calving ease, growth, production weight, and carcass merit traits. Attributes like calving ease direct Expected Progeny Differences (EPD), average daily gain, birth to yearling gain EPD, and rib-eye area consistently ranked higher and significantly influenced the bull’s sale price. Further analysis using a 2-Class Finite Mixture Model indicated distinct groups of buyers in the region who prioritized measured bull attributes and others who did not, confirming heterogeneity in buyer preferences.

Market segments and pricing of fine wines over their lifecycle

Is the fine wine market segmented? The literature shows that this is the case for ordinary wines, and this has significant implications for hedonic analysis. We apply a finite mixture model to a 20-year dataset of auction prices. Our results reveal the existence of five segments in the fine wine market that differ in both their composition and the effects of scores, age, and market conditions on prices. This segmentation helps reconcile the divergent findings in existing research: some wines are highly sensitive to market conditions and have yielded significant returns (investment wines), while prices of other wines are primarily influenced by age (collectible wines) or quality (drinking wines). Our findings enhance the understanding of fine wine pricing and offer new insights into the age-price relationship, which is generally linear but varies by segment and vintage quality.

Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers

Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This paper introduces a provably robust clustering algorithm based on loss minimization that performs well on Gaussian mixture models with outliers. It provides theoretical guarantees that the algorithm obtains high accuracy with high probability under certain assumptions. Moreover, it can also be used as an initialization strategy for k-means clustering. Experiments on real-world large-scale datasets demonstrate the effectiveness of the algorithm when clustering a large number of clusters, and a k-means algorithm initialized by the algorithm outperforms many of the classic clustering methods in both speed and accuracy, while scaling well to large datasets such as ImageNet.

Pengelompokan Provinsi di Indonesia Menggunakan Gaussian Mixture Model Berdasarkan Indikator Kemiskinan

Poverty is a multidimensional concept that can not only be seen from an economic perspective, but can also be seen from a social, cultural and political perspective. Poverty is generally expressed as a static concept with the amount of poverty as a description of welfare conditions at a certain time. In reality, poverty has a time period that is never cut off and has a continuous pattern over time. The Indonesian government is committed to efforts to eradicate poverty in order to reduce Indonesia's poverty rate. One way that can be done is by allocating aid to the community. Grouping is carried out by cluster analysis using the Gaussian Mixture Model method with the Expectation-Maximization (EM) algorithm. The optimal number of clusters in the Gaussian Mixture Model uses the Bayesian Information Criterion (BIC) method. Based on the research carried out, the results of the analysis of cluster grouping using the Gaussian Mixture Model method were obtained with the smallest BIC value, namely -397.6876 with a total of 2 clusters.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques—KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models—and differential gene expression analysis with R’s limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease’s heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4—as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.

Numerical Analysis of A Two-Phase Turbine: A Comparative Study Between Barotropic and Mixture Models

Abstract Two-phase turbines offer the potential to significantly enhance the performance of power generation and refrigeration systems. However, their development has been hindered by comparatively lower efficiencies resulting from additional loss mechanisms absent in single-phase turbines. To date, most multiphase CFD studies on turbomachinery have focused on condensation in the final stages of steam turbines, and on cavitation in hydraulic pumps and turbines. These applications, however, are not representative of the conditions in two-phase turbines, where a liquid-dominated mixture undergoes a large expansion ratio, leading to a significant increase in the gas phase volume fraction throughout the entire flow. This paper aims to identify a suitable modeling methodology for two-phase turbines. Our evaluation is centered around two models: the mixture model and the barotropic model. The validity and accuracy of these two modeling approaches is assessed using existing experimental data from a single-stage impulse turbine operating with several mixtures of water and nitrogen as working fluid. The results indicate that both the mixture and barotropic models are consistent and accurately predict the nozzle mass flow rate, yet, both models systematically overpredict the nozzle exit velocity and rotor torque. Adding correction terms for windage and unsteady pumping losses significantly improves the torque predictions, bringing them within the uncertainty range of the experimental data. In addition, refining the models to account for the effect of slip presents a promising avenue to enhance the prediction of nozzle exit velocity and overall performance of two-phase turbines.

The Standardized Spectroscopic Mixture Model

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (&lt;0.03) RMS misfit, negligible sensitivity to endmember variability and strongly linear scaling over more than an order of magnitude range of spatial resolution.

Dissecting shared genetic architecture between depression and body mass index

BackgroundA growing body of evidence supports the comorbidity between depression (DEP) and obesity, yet the genetic mechanisms underlying this association remain unclear. Our study explored the shared genetic architecture and causal associations of DEP with BMI.MethodsWe investigated the multigene overlap and genetic correlation between DEP (N > 1.3 million) and BMI (N = 806,834) based on genome-wide association studies (GWAS) and using the bivariate causal mixture model and linkage disequilibrium score regression (LDSC). The causal association was explored by bi-directional Mendelian randomization (MR). Common risk loci were identified through cross-trait meta-analyses. Stratified LDSC and multi-marker gene annotation analyses were applied to investigate single-nucleotide polymorphisms enrichment across tissue types, cell types, and functional categories. Finally, we explored shared functional genes by Summary Data-Based Mendelian Randomization (SMR) and further detected differential expression genes (DEG) in brain tissues of individuals with depression and obesity.ResultsWe found a positive genetic correlation between DEP and BMI (rg = 0.19, P = 4.07 × 10−26), which was more evident in local genomic regions. Cross-trait meta-analyses identified 16 shared genetic loci, 5 of which were newly identified, and they had influence on both diseases in the same direction. MR analysis showed a bidirectional causal association between DEP and BMI, with comparable effect sizes estimated in both directions. Combined with gene expression information, we found that genetic correlations between DEP and BMI were enriched in 6 brain regions, predominantly in the nucleus accumbens and anterior cingulate cortex. Moreover, 6 specific cell types and 23 functional genes were found to have an impact on both DEP and BMI across the brain regions. Of which, NEGR1 was identified as the most significant functional gene and associated with DEP and BMI at the genome-wide significance level (P < 5 × 10−8). Compared with healthy controls, the expression levels of NEGR1 gene were significant lower in brain tissues of individuals with depression and obesity.ConclusionsOur study reveals shared genetic basis underpinnings between DEP and BMI, including genetic correlations and common genes. These insights offer novel opportunities and avenues for future research into their comorbidities.

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

Loneliness Trajectories in U.S. Military Veterans: A 3-Year Longitudinal Study of Risk and Protective Factors.

Loneliness is a significant public health concern associated with adverse mental and physical health outcomes in older adults. This study examined the nature and correlates of predominant loneliness trajectories in a nationally representative sample of older U.S. military veterans. Participants included 2,441 veterans (mean age = 63, 8% female, 80% white) from the National Health and Resilience in Veterans Study, a 3-year longitudinal cohort study. Growth mixture modeling (GMM) was used to identify distinct trajectory classes of loneliness based on self-reported ratings. Multinomial logistic three-step regression analyses examined potential psychosocial risk and protective factors associated with loneliness trajectories. GMM revealed three distinct loneliness trajectories: Low-decreasing loneliness (61.2%), moderate-increasing loneliness (31.6%), and high-increasing loneliness (7.2%). Being married/partnered and perceiving greater purpose in life emerged as protective factors against elevated levels of loneliness. Worse cognitive functioning was a risk factor for the moderate-increasing loneliness trajectory, while greater psychological distress and more adverse childhood experiences were risk factors for the high-increasing loneliness trajectory. Nearly 40% of older U.S. veterans exhibited trajectories characterized by moderate to high levels of loneliness, with both groups showing increases over time. Targeted interventions that promote social connectedness, enhance purpose in life, and address mental health concerns and early life adversities may help mitigate the negative health consequences associated with chronic loneliness in this vulnerable population.

Latent Profiles of Seminary Students’ Perceptions of Sense of Community Amidst the COVID-19 Pandemic

Existing research on sense of community in educational contexts shows positive associations with well-being and negative associations with mental health symptoms. However, exploration of students’ sense of community within the seminary context is minimal. Drawing on the relational spirituality model, which posits oscillating experiences of dwelling and seeking in a dialectical growth process, we framed sense of community as horizontal dwelling. We used mixture modeling to generate subgroups using items from a measure of sense of community and then explored associations between sense of community and various demographic predictors and personal formation outcomes, including well-being and symptoms, along with virtues and religiousness/spirituality. We did so within the unique context of the COVID-19 pandemic. We used a sample of graduate students from 18 Christian seminaries across North America (N = 867; Mage = 31.95; 48.1% female; 60% White). Factor mixture results supported a 3-class solution, with one class reporting consistently greater ratings of sense of community, labeled the strongly connected, another class reporting consistent mid-level ratings, labeled the moderately connected, and a third class reporting consistently lower ratings, and labeled the disaffected. Results for the disaffected showed a pattern of associations with lower well-being and greater symptoms, and lower religiousness/spirituality, along with greater pandemic stress. The strongly connected showed greater levels of virtuousness, well-being and religiousness/spirituality, and lower symptoms, although they were also more likely to report greater illusory health. The pattern of associations for the moderately connected was a blend of similarities with the other two subgroups. The disaffected also showed a modest risk effect for lower well-being and greater symptoms over time. Findings pointed to providing greater horizontal dwelling among the disaffected and the need for greater seeking among the strongly connected.

Longitudinal trajectory classes and correlates of the Multidimensional Personality Questionnaire-triarchic psychopathy scales from adolescence to young adulthood.

The present study examined longitudinal trajectory classes and correlates of triarchic psychopathy domains (boldness, meanness, and disinhibition) from age 16 to 22, leveraging Multidimensional Personality Questionnaire (MPQ)-based triarchic scales data gathered on a large community sample (ns ranging between 483 and 775 across waves) oversampled for parental substance use disorder (SUD). Growth mixture models were conducted to examine longitudinal trajectory classes for each domain, and their associations with environmental covariates (e.g., neighborhood disadvantage and parental SUD) and outcomes at age 22 (e.g., violent behavior, antisocial personality disorder, and an overall problem index capturing internalizing symptoms and social problems). For boldness, all participants fell in the same class showing relative stability over time. Comparable solutions were recovered for meanness and disinhibition (high-stable/increasing, mid-range decreasing, and low-decreasing). Links with external correlates supported well-known differences between boldness and both meanness and disinhibition and additionally revealed interesting differences between meanness and disinhibition, suggesting that environmental covariates better discriminated meanness trajectory classes. These results demonstrate considerable developmental heterogeneity in these traits across adolescence into young adulthood, which relates to outcomes associated with antisociality and general life struggles. Further, these findings support the adequacy of the MPQ as an operationalization tool for longitudinal investigations on psychopathy.