Explore the vast range of titles available.

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.

Nuclear pore complexes (NPCs) create large conduits for cargo transport between the nucleus and cytoplasm across the nuclear envelope (NE) 1 – 3 . These multi-megadalton structures are composed of about thirty different nucleoporins that are distributed in three main substructures (the inner, cytoplasmic and nucleoplasmic rings) around the central transport channel 4 – 6 . Here we use cryo-electron tomography on DLD-1 cells that were prepared using cryo-focused-ion-beam milling to generate a structural model for the human NPC in its native environment. We show that—compared with previous human NPC models obtained from purified NEs—the inner ring in our model is substantially wider; the volume of the central channel is increased by 75% and the nucleoplasmic and cytoplasmic rings are reorganized. Moreover, the NPC membrane exhibits asymmetry around the inner-ring complex. Using targeted degradation of Nup96, a scaffold nucleoporin of the cytoplasmic and nucleoplasmic rings, we observe the interdependence of each ring in modulating the central channel and maintaining membrane asymmetry. Our findings highlight the inherent flexibility of the NPC and suggest that the cellular environment has a considerable influence on NPC dimensions and architecture. Structure of human nuclear pore complex in its cellular environment reveals a substantially dilated central channel and shows that its nucleoplasmic and cytoplasmic rings restrict channel dimensions and create membrane asymmetry at the inner ring.

This study develops a checklist with guidelines for the methods and important factors to consider in research using structural equation modeling (SEM). The paper discusses the factors to consider in the process across the three stages of 1) model setting, 2) model evaluation and modification, and 3) interpretation and reporting of SEM-based studies. The authors present a checklist for researchers during the stages of model setting, model evaluation and modification, result analysis, and reporting, along with examples of figures and tables with explanations. A checklist will help to improve the reporting quality of SEM-based studies.

Moral foundations theory posits five moral foundations, however 5-factor models provide poor fit to the data. Here, in five studies, each with large samples (total N = 11,496), we construct and replicate a well-fitting model of the Moral Foundations Questionnaire (MFQ). In study 1 (N = 2,271) we tested previously theorised models, confirming none provide adequate fit. We then developed a well-fitting model of the MFQ. In this model, the fairness/reciprocity and harm/care foundations were preserved intact. The binding foundations, however, divided into five, rather than the original three foundations. Purity/sanctity split into independent foundations of purity and sanctity. Similarly, Ingroup/loyalty divided into independent factors of loyalty to clan and loyalty to country. Authority/respect was re-focussed on hierarchy, losing one item to the new sanctity foundation and another into loyalty to country. In addition to these 7 foundations, higher-level factors of binding and individualizing were supported, along with a general/acquiescence factor. Finally, a “moral tilt” factor corresponding to coordinated left-leaning vs. right-leaning moral patterns was supported. We validated the model in four additional studies, testing replication of the 7-foundation model in data including from US, Australia, and China (total N = 9,225). The model replicated with good fit found in all four samples. These findings demonstrate the first well-fitting replicable model of the MFQ. They also highlight the importance of modelling measurement structure, and reveal important additional foundations, and structure (binding, individualizing, tilt) above the foundations.

In order to improve the performance of organic luminescent materials, lots of studies have been carried out at the molecular level. However, these materials are mostly applied as solids or aggregates in practical applications, in which the relationship between aggregation structure and luminescent property should be paid more attention. Here, we obtained five phenothiazine 5,5‐dioxide (O‐PTZ) derivatives with distinct molecular conformations by rational design of chemical structures, and systematically studied their room‐temperature phosphorescence (RTP) effect in solid state. It was found that O‐PTZ dimers with quasi‐equatorial (eq) conformation tended to show stronger π‐π interaction than quasi‐axial (ax) conformers in crystal state, which was more conducive to the generation of RTP. Based on this result, a multi‐level structural model of organic solids was proposed to draw the relationship between aggregation structure and RTP effect, just like the research for the structure‐property relationship of proteins. Using this structural model as the guide, boosted RTP efficiency from 1% to 20% was successfully achieved in the corresponding host‐guest doping system, showing its wide applicability. Five phenothiazine 5,5‐dioxide (O‐PTZ) derivatives with distinct molecular conformations were obtained, and their room‐temperature phosphorescence (RTP) effects were studied. It was found that O‐PTZ dimers with quasi‐equatorial (eq) conformation were more conducive to generate RTP than quasi‐axial (ax)‐ones in crystal state. Accordingly, a multi‐level structural model of organic solids was proposed to draw the relationship between aggregation structure and RTP effect.

Intercalation provides to the host materials a means for controlled variation of many physical/chemical properties and dominates the reactions in metal‐ion batteries. Of particular interest is the graphite intercalation compounds with intriguing staging structures, which however are still unclear, especially in their nanostructure and dynamic transition mechanism. Herein, the nature of the staging structure and evolution of the lithium (Li)‐intercalated graphite was revealed by cryogenic‐transmission electron microscopy and other methods at the nanoscale. The intercalated Li‐ions distribute unevenly, generating local stress and dislocations in the graphitic structure. Each staging compound is found macroscopically ordered but microscopically inhomogeneous, exhibiting a localized‐domains structural model. Our findings uncover the correlation between the long‐range ordered structure and short‐range domains, refresh the insights on the staging structure and transition of Li‐intercalated/deintercalated graphite, and provide effective ways to enhance the reaction kinetic in rechargeable batteries by defect engineering. The microstructure of Li+‐graphite intercalation compounds (GIC) was directly visualized by cryo‐transmission electron microscopy with minimized artifacts. Each macroscopical staging compound is a mixture of different staging phases and dislocations. It is long‐range order, medium‐range disorder, and short‐range order, which is much different from the previously proposed Rüdorff‐Hofmann and Daumas‐Hérold models. Localized‐domains model is proposed to describe the real structural nature of the Li+‐GIC.

A person's socioeconomic status (SES) can affect health (social causation) and health can affect SES (health selection). The findings for each of these pathways may depend on how SES is measured. We study (1) whether social causation or health selection is more important for overall health inequalities, (2) whether this differs between stages of the life course, and (3) between measures of SES. Using retrospective survey data from 10 European countries (SHARELIFE, n{\\thinspace}={\\thinspace}18,734), and structural equation models in a cross-lagged panel design, we determine the relative explanatory power of social causation and health selection through childhood, adulthood, and old age. We use three ways to measure SES: First, as a latent variable capturing different aspects of SES, second as material wealth, and third as occupational skill level. Between childhood and adulthood, social causation and health selection are equally important. In the transition from adulthood to old age, social causation becomes more important than health selection, making it the dominant mechanism in old age. The three measures of SES produce similar results. Only material wealth shows a stronger effect on health (between childhood and adulthood); it is also more affected by health (between adulthood and old age) than the other measures.

Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or “pitfalls”) remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the g-formula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or “tips”) to understand more concretely the estimation of the effects of complex time-varying exposures.

Behavioral science researchers have shown strong interest in disaggregating within-person relations from between-person differences (stable traits) using longitudinal data. In this paper, we propose a method of within-person variability score-based causal inference for estimating joint effects of time-varying continuous treatments by controlling for stable traits of persons. After explaining the assumed data-generating process and providing formal definitions of stable trait factors, within-person variability scores, and joint effects of time-varying treatments at the within-person level, we introduce the proposed method, which consists of a two-step analysis. Within-person variability scores for each person, which are disaggregated from stable traits of that person, are first calculated using weights based on a best linear correlation preserving predictor through structural equation modeling (SEM). Causal parameters are then estimated via a potential outcome approach, either marginal structural models (MSMs) or structural nested mean models (SNMMs), using calculated within-person variability scores. Unlike the approach that relies entirely on SEM, the present method does not assume linearity for observed time-varying confounders at the within-person level. We emphasize the use of SNMMs with G-estimation because of its property of being doubly robust to model misspecifications in how observed time-varying confounders are functionally related to treatments/predictors and outcomes at the within-person level. Through simulation, we show that the proposed method can recover causal parameters well and that causal estimates might be severely biased if one does not properly account for stable traits. An empirical application using data regarding sleep habits and mental health status from the Tokyo Teen Cohort study is also provided.

Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis. We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem , which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model. We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023. We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.