Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Self-compassion and fear of compassion in the treatment of chronic depression: Mechanisms of change?

OBJECTIVE: The aim of this naturalistic process study was to investigate the relationship between self-compassion, fear of compassion from others, and depressive symptoms over the course of psychotherapy in patients with chronic depression. METHOD: A sample of 226 patients with chronic depression who received inpatient short-term psychodynamic psychotherapy (STPP) provided weekly self-report measures of self-compassion, fear of compassion, and depressive symptoms (Patient Health Questionnaire-9). Trivariate latent curve modeling with structured residuals was applied to investigate the between- and within-patient relationships among the variables. RESULTS: At the between-patient level, a significant positive correlation was found between slope of depression and the slope of fear of compassion. At the within-patient level, a lower than expected level of fear of compassion predicted a subsequent lower than expected level of depression (mean weekly effect size = 0.12), with a smaller reciprocal relationship (mean weekly effect size = 0.08). There was no significant within-patient effect of self-compassion predicting subsequent depression, but a significant effect of a lower than expected level of depression predicting a subsequent higher than expected level of self-compassion (mean weekly effect size = -0.13). No within-patient effect between self-compassion and fear of compassion was found. CONCLUSIONS: In the context of this study, it appears that fear of compassion may be a putative mechanism of change involved in alleviating depressive symptoms in patients with chronic depression treated with STPP. On the other hand, self-compassion appears to be an outcome of psychotherapy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Molecular and behavioral consequences of Ube3a gene overdosage in mice.

Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome-based transgenic mouse models (Ube3aOE) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, Ube3aOE mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2-q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.

Emotional clarity and tolerance of emotional distress as mechanisms of change in short-term psychodynamic psychotherapy for chronic depression.

OBJECTIVE: The aim of this naturalistic process study was to investigate the relationship between emotional clarity and tolerance of emotional distress and depressive symptoms over the course of short-term psychodynamic psychotherapy for chronically depressed patients. METHOD: Weekly self-reports of emotional clarity, tolerance of emotional distress, and depressive symptoms (PHQ-9) were provided by 252 patients with chronic depression who were admitted to a 13-week inpatient treatment program. Latent curve modeling with structured residuals (LCM-SR) was applied to investigate the between- and within-person effects of week-to-week change in emotional clarity and tolerance of emotional distress as predictors of subsequent depression. The relationship between emotional clarity and tolerance of emotional distress was also investigated. RESULTS: At the within-person level, higher level of emotional clarity and tolerance of emotional distress predicted subsequent lower level of depression. A reciprocal relationship was found for tolerance of emotional distress (lower level of depression predicted subsequent level of tolerance emotional distress) but not for emotional clarity. No within-person effect between emotional clarity and tolerance of emotional distress was found. DISCUSSION: The results indicate that emotional clarity and tolerance of emotional distress may be mechanisms of change in short-term psychodynamic psychotherapy for chronic depression. The results are consistent with previous findings of the importance of emotional clarity and tolerance of emotional distress in psychotherapy. This study demonstrated the utility of LCM-SR as a method to identity mechanisms of change in psychotherapy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Aden governorate, Yemen.

BACKGROUND: The burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records. METHODS: After identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period. RESULTS: We collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020. DISCUSSION: To our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

Psychometric Models for Scoring Multiple Reporter Assessments: Applications to Integrative Data Analysis in Prevention Science and Beyond.

Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter assessments, particularly when using integrative data analysis to fit models to data that have been pooled across two or more independent samples. The current paper uses both simulated and real data to examine the utility of a recently proposed psychometric model for multiple reporter data called the trifactor model (TFM) in settings that might be commonly found in prevention research. Results suggest that numerical scores obtained using the TFM are superior to more traditional methods, particularly when pooling samples that contribute different reporter perspectives.

Harmonizing altered measures in integrative data analysis: A methods analogue study.

In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis (MNLFA) scores based on traditional indices of reliability (test-retest, internal, and score recovery or parallel forms) and validity. Participants in the analogue study included 854 college students (46% male; 21% African American, 5% Hispanic/Latino, 56% European American) who completed two versions of the altered measures at two sessions, separated by 2 weeks. As expected, mean, CFA, and MNLFA scores all resulted in scales with lower reliability given increasing scale alteration (with less fidelity to formerly developed scales) and shorter scale length. MNLFA and CFA scores, however, showed greater validity than mean scores, demonstrating stronger relationships with external correlates. Implications for measurement harmonization in the context of IDA are discussed.

Cancer-Associated Fibroblasts Promote Immunosuppression by Inducing ROS-Generating Monocytic MDSCs in Lung Squamous Cell Carcinoma.

Cancer-associated fibroblasts (CAF) represent a functionally heterogeneous population of activated fibroblasts that constitutes a major component of tumor stroma. Although CAFs have been shown to promote tumor growth and mediate resistance to chemotherapy, the mechanisms by which they may contribute to immune suppression within the tumor microenvironment (TME) in lung squamous cell carcinoma (LSCC) remain largely unexplored. Here, we identified a positive correlation between CAF and monocytic myeloid cell abundances in 501 primary LSCCs by mining The Cancer Genome Atlas data sets. We further validated this finding in an independent cohort using imaging mass cytometry and found a significant spatial interaction between CAFs and monocytic myeloid cells in the TME. To delineate the interplay between CAFs and monocytic myeloid cells, we used chemotaxis assays to show that LSCC patient-derived CAFs promoted recruitment of CCR2+ monocytes via CCL2, which could be reversed by CCR2 inhibition. Using a three-dimensional culture system, we found that CAFs polarized monocytes to adopt a myeloid-derived suppressor cell (MDSC) phenotype, characterized by robust suppression of autologous CD8+ T-cell proliferation and IFNγ production. We further demonstrated that inhibiting IDO1 and NADPH oxidases, NOX2 and NOX4, restored CD8+ T-cell proliferation by reducing reactive oxygen species (ROS) generation in CAF-induced MDSCs. Taken together, our study highlights a pivotal role of CAFs in regulating monocyte recruitment and differentiation and demonstrated that CCR2 inhibition and ROS scavenging abrogate the CAF-MDSC axis, illuminating a potential therapeutic path to reversing the CAF-mediated immunosuppressive microenvironment.

Bidirectional Relations between Witnessing Violence, Victimization, Life Events, and Physical Aggression among Adolescents in Urban Schools.

Although there is empirical evidence supporting associations between exposure to violence and engaging in physically aggressive behavior during adolescence, there is limited longitudinal research to determine the extent to which exposure to violence is a cause or a consequence of physical aggression, and most studies have not addressed the influence of other negative life events experienced by adolescents. This study examined bidirectional relations between physical aggression, two forms of exposure to violence-witnessing violence and victimization, and other negative life events. Participants were a sample of 2568 adolescents attending three urban public middle schools who completed measures of each construct every 3 months during middle school. Their mean age was 12.76 (SD = 0.98); 52% were female. The majority were African American (89%); 17% were Hispanic or Latino/a. Cross-lagged regression analyses across four waves of data collected within the same grade revealed bidirectional relations between witnessing violence and physical aggression, and between witnessing violence and negative life events. Although physical aggression predicted subsequent changes in victimization, victimization predicted changes in physical aggression only when witnessing violence was not taken into account. Findings were consistent across sex and grades. Overall, these findings highlight the need for interventions that break the connection between exposure to violence and aggression during adolescence.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space.

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA-small molecule interactions that was generated was compared with that for protein-small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

Approaches for creating comparable measures of alcohol use symptoms: Harmonization with eight studies of criminal justice populations.

BACKGROUND: With increasing data archives comprised of studies with similar measurement, optimal methods for data harmonization and measurement scoring are a pressing need. We compare three methods for harmonizing and scoring the AUDIT as administered with minimal variation across 11 samples from eight study sites within the STTR (Seek-Test-Treat-Retain) Research Harmonization Initiative. Descriptive statistics and predictive validity results for cut-scores, sum scores, and Moderated Nonlinear Factor Analysis scores (MNLFA; a psychometric harmonization method) are presented. METHODS: Across the eight study sites, sample sizes ranged from 50 to 2405 and target populations varied based on sampling frame, location, and inclusion/exclusion criteria. The pooled sample included 4667 participants (82% male, 52% Black, 24% White, 13% Hispanic, and 8% Asian/ Pacific Islander; mean age of 38.9 years). Participants completed the AUDIT at baseline in all studies. RESULTS: After logical harmonization of items, we scored the AUDIT using three methods: published cut-scores, sum scores, and MNLFA. We found greater variation, fewer floor effects, and the ability to directly address missing data in MNLFA scores as compared to cut-scores and sum scores. MNLFA scores showed stronger associations with binge drinking and clearer study differences than did other scores. CONCLUSIONS: MNLFA scores are a promising tool for data harmonization and scoring in pooled data analysis. Model complexity with large multi-study applications, however, may require new statistical advances to fully realize the benefits of this approach.

The Increasing Diversity and Complexity of Family Structures for Adolescents.

The structure of adolescents' families, and thus parental forms, in the United States, have become more heterogeneous and fluid over the past several decades. These changes are due to increases in never-married, single parents, divorce, cohabitation, same-sex parenting, multi-partnered fertility, and co-residence with grandparents. We document current diversity and complexity in adolescents' families as important context for rethinking future parenting theory and research. We also discuss how understandings of adolescents' families are somewhat limited by current methods used to measure characteristics of families. We recommend social network and profile-based methods as alternatives to capturing key dimensions of family structure and processes. Understanding the diversity of households and families in which adolescents are raised can improve theory and research on parenting.

Nanoscale synthesis and affinity ranking.

Most drugs are developed through iterative rounds of chemical synthesis and biochemical testing to optimize the affinity of a particular compound for a protein target of therapeutic interest. This process is challenging because candidate molecules must be selected from a chemical space of more than 1060 drug-like possibilities 1 , and a single reaction used to synthesize each molecule has more than 107 plausible permutations of catalysts, ligands, additives and other parameters 2 . The merger of a method for high-throughput chemical synthesis with a biochemical assay would facilitate the exploration of this enormous search space and streamline the hunt for new drugs and chemical probes. Miniaturized high-throughput chemical synthesis3-7 has enabled rapid evaluation of reaction space, but so far the merger of such syntheses with bioassays has been achieved with only low-density reaction arrays, which analyse only a handful of analogues prepared under a single reaction condition8-13. High-density chemical synthesis approaches that have been coupled to bioassays, including on-bead 14 , on-surface 15 , on-DNA 16 and mass-encoding technologies 17 , greatly reduce material requirements, but they require the covalent linkage of substrates to a potentially reactive support, must be performed under high dilution and must operate in a mixture format. These reaction attributes limit the application of transition-metal catalysts, which are easily poisoned by the many functional groups present in a complex mixture, and of transformations for which the kinetics require a high concentration of reactant. Here we couple high-throughput nanomole-scale synthesis with a label-free affinity-selection mass spectrometry bioassay. Each reaction is performed at a 0.1-molar concentration in a discrete well to enable transition-metal catalysis while consuming less than 0.05 milligrams of substrate per reaction. The affinity-selection mass spectrometry bioassay is then used to rank the affinity of the reaction products to target proteins, removing the need for time-intensive reaction purification. This method enables the primary synthesis and testing steps that are critical to the invention of protein inhibitors to be performed rapidly and with minimal consumption of starting materials.

Advancing the Study of Adolescent Substance Use Through the Use of Integrative Data Analysis.

A wealth of information is currently known about the epidemiology, etiology, and evaluation of drug and alcohol use across the life span. Despite this corpus of knowledge, much has yet to be learned. Many factors conspire to slow the pace of future advances in the field of substance use including the need for long-term longitudinal studies of often hard-to-reach subjects who are reporting rare and episodic behaviors. One promising option that might help move the field forward is integrative data analysis (IDA). IDA is a principled set of methodologies and statistical techniques that allow for the fitting of statistical models to data that have been pooled across multiple, independent samples. IDA offers a myriad of potential advantages including increased power, greater coverage of rare behaviors, more rigorous psychometric assessment of theoretical constructs, accelerated developmental time period under study, and enhanced reproducibility. However, IDA is not without limitations and may not be useful in a given application for a variety of reasons. The goal of this article is to describe the advantages and limitations of IDA in the study of individual development over time, particularly as it relates to trajectories of substance use. An empirical example of the measurement of polysubstance use is presented and this article concludes with recommendations for practice.

Recovering Predictor-Criterion Relations Using Covariate-Informed Factor Score Estimates.

Although it is currently best-practice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariate-informed factor score estimation can be used to provide manifest scores that are used in second-stage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine the use of factor score estimates as predictors both in the presence and absence of the same covariates that were used in score estimation. Results show that whereas the relation between the factor score estimates and the criterion are typically well recovered, substantial bias and increased variability is evident in the covariate effects themselves. Importantly, using covariate-informed factor score estimates substantially, and often wholly, mitigates these biases. We conclude with implications for future research and recommendations for the use of factor score estimates in practice.

Examining Cohort Effects in Developmental Trajectories of Substance Use.

The current study demonstrates the application of an analytic approach for incorporating multiple time trends in order to examine the impact of cohort effects on individual trajectories of eight drugs of abuse. Parallel analysis of two independent, longitudinal studies of high-risk youth that span ages 10 to 40 across 23 birth cohorts between 1968 and 1991 was conducted. The two studies include the Michigan Longitudinal Study (current analytic sample of n=579 over 12 cohorts between 1980-1991 and ages 10-27) and the Adolescent/Adult and Family Development Project (current analytic sample of n=849 over 11 cohorts between 1968-1978 and ages 10-40). A series of nonlinear, multi-level growth models controlled simultaneously for cohort and age trends in substance use trajectories. Evidence was found for both age and cohort effects across most outcomes as well as several significant age-by-cohort interactions. Findings suggest cohort trends in developmental trajectories of substance use are sample and drug-specific in the adolescent and early to mid-adult years. Thus, studies that do not control for both trends may confound cohort and developmental trends in substance use. For this reason, demonstration of one analytic approach that can be used to examine both time trends simultaneously is informative for future multi-cohort longitudinal studies where change over time is of interest.

Integration of Affinity Selection-Mass Spectrometry and Functional Cell-Based Assays to Rapidly Triage Druggable Target Space within the NF-κB Pathway.

The primary objective of early drug discovery is to associate druggable target space with a desired phenotype. The inability to efficiently associate these often leads to failure early in the drug discovery process. In this proof-of-concept study, the most tractable starting points for drug discovery within the NF-κB pathway model system were identified by integrating affinity selection-mass spectrometry (AS-MS) with functional cellular assays. The AS-MS platform Automated Ligand Identification System (ALIS) was used to rapidly screen 15 NF-κB proteins in parallel against large-compound libraries. ALIS identified 382 target-selective compounds binding to 14 of the 15 proteins. Without any chemical optimization, 22 of the 382 target-selective compounds exhibited a cellular phenotype consistent with the respective target associated in ALIS. Further studies on structurally related compounds distinguished two chemical series that exhibited a preliminary structure-activity relationship and confirmed target-driven cellular activity to NF-κB1/p105 and TRAF5, respectively. These two series represent new drug discovery opportunities for chemical optimization. The results described herein demonstrate the power of combining ALIS with cell functional assays in a high-throughput, target-based approach to determine the most tractable drug discovery opportunities within a pathway.

Improving Factor Score Estimation Through the Use of Observed Background Characteristics.

A challenge facing nearly all studies in the psychological sciences is how to best combine multiple items into a valid and reliable score to be used in subsequent modelling. The most ubiquitous method is to compute a mean of items, but more contemporary approaches use various forms of latent score estimation. Regardless of approach, outside of large-scale testing applications, scoring models rarely include background characteristics to improve score quality. The current paper used a Monte Carlo simulation design to study score quality for different psychometric models that did and did not include covariates across levels of sample size, number of items, and degree of measurement invariance. The inclusion of covariates improved score quality for nearly all design factors, and in no case did the covariates degrade score quality relative to not considering the influences at all. Results suggest that the inclusion of observed covariates can improve factor score estimation.

A novel modeling framework for ordinal data defined by collapsed counts.

Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many response categories defined by collapsed count data (0 drinking days, 1-2 days, 3-6 days, etc.). Consequently, existing models ignore the underlying count process, resulting in disjoint between the construct of interest and the models being fitted. Our proposed ordinal modeling approach overcomes this limitation by explicitly linking ordinal responses to a suitable underlying count distribution. In doing so, researchers can use maximum likelihood estimation to fit count models to ordinal data as if they had directly observed the underlying discrete counts. The usefulness of our ordinal negative binomial and ordinal zero-inflated negative binomial models is verified by simulation studies. We also demonstrate our approach using real empirical data from the 2010 National Survey of Drug Use and Health. Results show the benefit of the proposed ordinal modeling framework compared with existing methods.