Structural models of genome-wide covariance identify multiple common dimensions in autism

Common genetic variation has been associated with multiple phenotypic features in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous phenotypic spectrum is limited. Here, we developed and implemented a structural equation modelling framework to directly model genomic covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent with a case-only design. We identified three independent genetic factors most strongly linked to language performance, behaviour and developmental motor delay, respectively, studying an autism community sample (N = 5331). The three-factorial structure was largely confirmed in independent ASD-simplex families (N = 1946), although we uncovered, in addition, simplex-specific genetic overlap between behaviour and language phenotypes. Multivariate models across cohorts revealed novel associations, including links between language and early mastering of self-feeding. Thus, the common genetic architecture in ASD is multi-dimensional with overarching genetic factors contributing, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.

Barriers and Implications of 5G Technology Adoption for Hospitals in Western China: Integrated Interpretive Structural Modeling and Decision-Making Trial and Evaluation Laboratory Analysis

BACKGROUND

5G technology is gaining traction in Chinese hospitals for its potential to enhance patient care and internal management. However, various barriers hinder its implementation in clinical settings, and studies on their relevance and importance are scarce.

OBJECTIVE

This study aimed to identify critical barriers hampering the effective implementation of 5G in hospitals in Western China, to identify interaction relationships and priorities of the above-identified barriers, and to assess the intensity of the relationships and cause-and-effect relations between the adoption barriers.

METHODS

This paper uses the Delphi expert consultation method to determine key barriers to 5G adoption in Western China hospitals, the interpretive structural modeling to uncover interaction relationships and priorities, and the decision-making trial and evaluation laboratory method to reveal cause-and-effect relationships and their intensity levels.

RESULTS

In total, 14 barriers were determined by literature review and the Delphi method. Among these, "lack of policies on ethics, rights, and responsibilities in core health care scenarios" emerged as the fundamental influencing factor in the entire system, as it was the only factor at the bottom level of the interpretive structural model. Overall, 8 barriers were classified as the "cause group," and 6 as the "effect group" by the decision-making trial and evaluation laboratory method. "High expense" and "organizational barriers within hospitals" were determined as the most significant driving barrier (the highest R-C value of 1.361) and the most critical barrier (the highest R+C value of 4.317), respectively.

CONCLUSIONS

Promoting the integration of 5G in hospitals in Western China faces multiple complex and interrelated barriers. The study provides valuable quantitative evidence and a comprehensive approach for regulatory authorities, hospitals, and telecom operators, helping them develop strategic pathways for promoting widespread 5G adoption in health care. It is suggested that the stakeholders cooperate to explore and solve the problems in the 5G medical care era, aiming to achieve the coverage of 5G medical care across the country. To our best knowledge, this study is the first academic exploration systematically analyzing factors resisting 5G integration in Chinese hospitals, and it may give subsequent researchers a solid foundation for further studying the application and development of 5G in health care.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Psychological Dimensions That Explain Integrity in Older Adults: Examination of a Structural Model

OBJECTIVES

The aim of this research was to confirm whether internal coping strengths, depressive symptomatology and gratitude disposition are significant predictors of integrity in older adults.

METHODS

Participated 394 Ecuadorian older adults with ages between 60 and 91 years old. Self-report were applied to assess the different variables under study. Specifically, integrity, coping, resilience, self-efficacy, mood and gratitude were assessed.

RESULTS

A confirmatory model was estimated to predict ego-integrity. A personal adjustment factor composed of problem-focused coping strategies, resilience and self-efficacy, and gratitude showed positive and significant relationships with ego-integrity, whereas negative mood negatively predicted integrity.

CONCLUSIONS

Integrity is a key variable to strengthen a coherent view of one's life history and highly relevant during ageing. Having personal strengths and dispositions that facilitate adaptation to ageing and maintaining a positive mood predicts the attainment of integrity.

CLINICAL IMPLICATIONS

Integrity is an adjustment factor that facilitates adaptation to the stressors of ageing and to major life changes and loss of control indifferent areas of life.

Misbegotten methodologies and forgotten lessons from Tom Swift's electric factor analysis machine: A demonstration with competing structural models of psychopathology

Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions-either before or after the estimation of more restrictive CFA models-to (a) identify model misspecifications that may drive anomalous estimates and (b) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All CFA models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p-factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The structural model of mother-infant bonding in the first pregnancy based on the mother's attachment style and parenting style: the mediating role of mentalization and shame

INTRODUCTION

This study aimed to evaluate the relationship between the mother's attachment style and parenting style and mother-infant bonding in the first pregnancy considering the mediating role of mentalization and shame.

METHODS

This was a descriptive-correlational study. The sample population included the women who had gone through their first pregnancy and were referred to the health centers in Neyshabur, Iran in 2022. In total, 330 women were selected by convenience sampling. To collect data, we used a demographic questionnaire, the Attachment Style Questionnaire, the Parenting Style Questionnaire, the Mother-Infant Bonding Scale, the Reflective Functioning Questionnaire, and the Guilt and Shame Proneness Scale. Data analysis was conducted using descriptive indices, correlation coefficients, and pass analysis.

RESULTS

Overall, the findings showed that the studied model adequately fits the data. Further, the obtained results confirmed the mediating role of mentalization and shame in the relationship between the mother's attachment style and parenting style and mother-infant bonding. A significant correlation was also observed between attachment styles, parenting styles, and mother-infant bonding (p < 0.01).

CONCLUSION

Mother-infant bonding is correlated with the mother's attachment style/parenting style, shame, and mentalization. Thus, we can help vulnerable mothers by improving the quality of psychological care before the first pregnancy or during pregnancy.

A marginal structural model analysis for the effect modification by education on the association between cancer diagnosis history and major depressive symptoms: Findings from Midlife Development in the U.S. (MIDUS)

BACKGROUND

Limited research has employed a longitudinal approach to investigate the role of education level as an effect modifier on the relationship between cancer diagnosis history and the experience of major depressive disorder (MDD) with a nationally representative sample.

METHODS

We harnessed data from three installments of the MIDUS Longitudinal study (n = 7108). A Marginal Structural Model facilitated the investigation of associations between a history of cancer diagnosis, MDD, and potential modifying effects of education level. Inverse probability weighting helped manage confounding factors.

RESULTS

Findings indicated that a cancer diagnosis made one year prior was linked with 3.741 times greater odds of experiencing MDD (95 % CI: 1.411-9.918, p < 0.01). This connection was absent for diagnoses made two years earlier. Among individuals with education up to high school, a recent cancer diagnosis significantly increased the likelihood of MDD in the subsequent wave by 3.45 times (95 % CI: 1.31-9.08, p < 0.05). This pattern was not apparent among better-educated individuals.

LIMITATIONS

As the exposure variable was dependent on self-reported questionnaires, recall bias could be a potential limitation. Moreover, unaccounted variables like genetic factors could introduce confounding.

CONCLUSIONS

A recent cancer diagnosis, particularly among less educated individuals, correlated with an increased probability of MDD, while the impact was not observed for older diagnoses. These findings emphasize that the timing of a cancer diagnosis and education level need consideration in the mental health assessment of cancer survivors.

A Structural Model of Organization and Clinician Factors Associated with Standardized Measure Use in a National Survey of Youth Mental Health Providers

Standardized assessment measures are important for accurate diagnosis of mental health problems and for treatment planning and evaluation. However, little is known about youth mental health providers' typical use of standardized measures across disciplines and outside the context of evidence-based practice initiatives. A multidisciplinary national survey examined the frequency with which 674 youth mental health providers administer standardized and unstandardized measures, and the extent to which organizational (i.e., implementation climate, rigid hierarchical organizational structure) and provider (i.e., attitudes toward standardized assessment measures, highest degree, practice setting) characteristics are associated with standardized measure use. Providers used unstandardized measures far more frequently than standardized measures. Providers' perceptions (a) that standardized measures are practical or feasible, (b) that their organization supports and values evidence-based practices, and (c) that their organization has a rigid hierarchical structure predicted greater use of standardized measures. Working in schools predicted less frequent SMU, while working in higher education and other professional settings predicted more frequent SMU. Standardized measures were not routinely used in this community-based sample. A rigid hierarchical organizational structure may be conducive to more frequent administration of standardized measures, but it is unclear whether such providers actually utilize these measures for clinical decision-making. Alternative strategies to promote standardized measure use may include promoting organizational cultures that value empirical data and encouraging use of standardized measures and training providers to use pragmatic standardized measures for clinical decision making.

A Structural Model for the Core Nup358-BicD2 Interface

Dynein motors facilitate the majority of minus-end-directed transport events on microtubules. The dynein adaptor Bicaudal D2 (BicD2) recruits the dynein machinery to several cellular cargo for transport, including Nup358, which facilitates a nuclear positioning pathway that is essential for the differentiation of distinct brain progenitor cells. Previously, we showed that Nup358 forms a "cargo recognition α-helix" upon binding to BicD2; however, the specifics of the BicD2-Nup358 interface are still not well understood. Here, we used AlphaFold2, complemented by two additional docking programs (HADDOCK and ClusPro) as well as mutagenesis, to show that the Nup358 cargo-recognition α-helix binds to BicD2 between residues 747 and 774 in an anti-parallel manner, forming a helical bundle. We identified two intermolecular salt bridges that are important to stabilize the interface. In addition, we uncovered a secondary interface mediated by an intrinsically disordered region of Nup358 that is directly N-terminal to the cargo-recognition α-helix and binds to BicD2 between residues 774 and 800. This is the same BicD2 domain that binds to the competing cargo adapter Rab6, which is important for the transport of Golgi-derived and secretory vesicles. Our results establish a structural basis for cargo recognition and selection by the dynein adapter BicD2, which facilitates transport pathways that are important for brain development.

Expanding the repertoire of human tandem repeat RNA-binding proteins

Protein regions consisting of arrays of tandem repeats are known to bind other molecular partners, including nucleic acid molecules. Although the interactions between repeat proteins and DNA are already widely explored, studies characterising tandem repeat RNA-binding proteins are lacking. We performed a large-scale analysis of human proteins devoted to expanding the knowledge about tandem repeat proteins experimentally reported as RNA-binding molecules. This work is timely because of the release of a full set of accurate structural models for the human proteome amenable to repeat detection using structural methods. The main goal of our analysis was to build a comprehensive set of human RNA-binding proteins that contain repeats at the sequence or structure level. Our results showed that the combination of sequence and structural methods finds significantly more tandem repeat proteins than either method alone. We identified 219 tandem repeat proteins that bind RNA molecules and characterised the overlap between repeat regions and RNA-binding regions as a first step towards assessing their functional relationship. We observed differences in the characteristics of repeat regions predicted by sequence-based or structure-based methods in terms of their sequence composition, their functions and their protein domains.

An evaluation method for HMI of deep-sea manned submersible based on human reliability

Improving the human reliability of the human-machine interface (HMI) of deep-sea manned submersible is of great importance for the development of the deep-sea field. Based on the SHEL (Software S, Hardware H, Environment E, Liveware L) model, this study classifies the performance shaping factors (PSF) that affect the human reliability of submersible HMIs and builds a PSF system. The interpretative structural model (ISM) is used to matrix the interactions between the elements that make up the system of PSF. A multi-level recursive structure is obtained by building the corresponding adjacency matrix. The Noisy-OR model is introduced to construct a Bayesian network in order to build a new HMI evaluation method. A real case of Bayesian network causal inference verifies the validity of the built method. This study proposes a set of HMI human reliability evaluation methods applicable to deep-sea manned submersible, which provides a new idea for human reliability assessment.

Marginal structural models for estimating the longitudinal effects of community violence exposure on youths' internalizing and externalizing symptoms

OBJECTIVE

Longitudinal observational data pose a challenge for causal inference when the exposure of interest varies over time alongside time-dependent confounders, which often occurs in trauma research. We describe marginal structural models (MSMs) using inverse probability weighting as a useful solution under several assumptions that are well-suited to estimating causal effects in trauma research.

METHOD

We illustrate the application of MSMs by estimating the joint effects of community violence exposure across time on youths' internalizing and externalizing symptoms. Our sample included 4,327 youth (50% female, 50% male; 1.4% Asian American or Pacific Islander, 34.7% Black, 46.9% Hispanic, .8% Native American, 14.3%, White, 1.5%, Other race/ethnicity; Mage at baseline = 8.62, range = 3-15) from the Project on Human Development in Chicago Neighborhoods.

RESULTS

Wave 3 internalizing symptoms increased linearly with increases in Wave 2 and Wave 3 community violence exposure, whereas effects on externalizing symptoms were quadratic for Wave 2 community violence exposure and linear for Wave 3. These results fail to provide support for the desensitization model of community violence exposure.

CONCLUSION

MSMs are a useful tool for researchers who rely on longitudinal observational data to estimate causal effects of time-varying exposures, as is often the case in the study of psychological trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Structure-Based Modeling of Sigma 1 Receptor Interactions with Ligands and Cholesterol and Implications for Its Biological Function

The sigma 1 receptor (S1R) is a 223-amino-acid-long transmembrane endoplasmic reticulum (ER) protein. The S1R plays an important role in neuronal health and it is an established therapeutic target for neurodegenerative and neuropsychiatric disorders. Despite its importance in physiology and disease, the biological function of S1R is poorly understood. To gain insight into the biological and signaling functions of S1R, we took advantage of recently reported crystal structures of human and Xenopus S1Rs and performed structural modeling of S1R interactions with ligands and cholesterol in the presence of the membrane. By combining bioinformatics analysis of S1R sequence and structural modelling approaches, we proposed a model that suggests that S1R may exist in two distinct conformations-"dynamic monomer" (DM) and "anchored monomer" (AM). We further propose that equilibrium between AM and DM conformations of S1R is essential for its biological function in cells, with AM conformation facilitating the oligomerization of S1R and DM conformation facilitating deoligomerization. Consistent with experimental evidence, our hypothesis predicts that increased levels of membrane cholesterol and S1R antagonists should promote the oligomeric state of S1R, but S1R agonists and pathogenic mutations should promote its deoligomerization. Obtained results provide mechanistic insights into signaling functions of S1R in cells, and the proposed model may help to explain neuroprotective effects of S1R modulators.

An integrated method for studying the biodegradation of benzo[a]pyrene by Citrobacter sp. HJS-1 and interaction mechanism based on the structural model of the initial dioxygenase

A bacterial strain Citrobacter sp. HJS-1 was discovered from the sludge in a drainage canal of a coal mine. Firstly, its biodegradation capacity for benzo[a]pyrene (BaP) was detected under different concentrations. The results proved that the strain possessed excellent biodegradation capacity for BaP with high-efficiency degradation rates ranging from 78.9 to 86.8%. The highest degradation rate was observed in the low-concentration sample, and the high-concentration BaP had a slight influence on the biodegradation capacity due to the potential toxicity of BaP and its oxygen-containing derivatives. Meanwhile, the degradation test for the other five aromatic hydrocarbons (2- to 4-ring) proved that the strain had a comprehensive degradation potential. To clarify the biodegradation mechanism of BaP, a dioxygenase structure was constructed by homology modeling. Then, the interactions between dioxygenase and BaP were researched by molecular simulation. Combined with the identification of the vital BaP-cis-7,8-dihydrodiol intermediate and the interaction analysis, the initial oxidation mode and the binding site of BaP were revealed in the dioxygenase. Taken together, this study has offered a way to understand the biodegradation process of BaP and its interaction mechanism based on experimental and theoretical analysis.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

Longitudinal observational data on patients can be used to investigate causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for estimating such effects by controlling for the time-dependent confounding that typically occurs. The most commonly used is marginal structural models (MSM) estimated using inverse probability of treatment weights (IPTW) (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, and involves creating a sequence of "trials" from new time origins and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment assignment at the start of each "trial" (initiator or noninitiator), which is accounted for using inverse probability of censoring weights. The analysis uses data combined across trials. We show that the sequential trials approach can estimate the parameters of a particular MSM. The causal estimand that we focus on is the marginal risk difference between the sustained treatment strategies of "always treat" vs "never treat." We compare how the sequential trials approach and MSM-IPTW estimate this estimand, and discuss their assumptions and how data are used differently. The performance of the two approaches is compared in a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at later time points and relies on modelling assumptions. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.

Structural Model for Factor X Inhibition of IgM and Complement-Mediated Neutralization of Adenovirus

Adenovirus has strong therapeutic potential as an oncolytic virus and gene therapy vector. However, injecting human species C serotype 5 adenovirus, HAdv-C5, into the bloodstream leads to numerous interactions with plasma proteins that affect viral tropism and biodistribution, and can lead to potent immune responses and viral neutralization. The HAdv/factor X (FX) interaction facilitates highly efficient liver transduction and protects virus particles from complement-mediated neutralization after intravenous delivery. Ablating the FX interaction site on the HAdv-C5 capsid leaves the virus susceptible to neutralization by natural IgM followed by activation of the complement cascade and covalent binding of complement components C4b and C3b to the viral capsid. Here we present structural models for IgM and complement components C1, C4b, and C3b in complex with HAdv-C5. Molecular dynamics simulations indicate that when C3b binds near the vertex, multiple stabilizing interactions can be formed between C3b, penton base, and fiber. These interactions may stabilize the vertex region of the capsid and prevent release of the virally encoded membrane lytic factor, protein VI, which is packaged inside of the viral capsid, thus effectively neutralizing the virus. In a situation where FX and IgM are competing for binding to the capsid, IgM may not be able to form a bent conformation in which most of its Fab arms interact with the capsid. Our structural modeling of the competitive interaction of FX and IgM with HAdv-C5 allows us to propose a mechanistic model for FX inhibition of IgM-mediated virus neutralization. According to this model, although IgM may bind to the capsid, in the presence of FX it will likely retain a planar conformation and thus be unable to promote activation of the complement cascade at the virus surface.

Structural Insights of the SARS-CoV-2 Nucleocapsid Protein: Implications for the Inner-workings of Rapid Antigen Tests

The nucleocapsid (N) protein is an abundant component of SARS-CoV-2 and a key analyte for lateral-flow rapid antigen tests. Here, we present new structural insights for the SARS-CoV-2 N protein using cryo-electron microscopy (EM) and molecular modeling tools. Epitope mapping based on structural data supported host-immune interactions in the C-terminal portion of the protein, while other regions revealed protein-protein interaction sites. Complementary modeling results suggested that N protein structures from known variants of concern (VOC) are nearly 100% conserved at specific antibody-binding sites. Collectively, these results suggest that rapid tests that target the nucleocapsid C-terminal domain should have similar accuracy across all VOCs. In addition, our combined structural modeling workflow may guide the design of immune therapies to counter viral processes as we plan for future variants and pandemics.

How does manufacturing haze pollution decrease in China: a decomposition study of structural model based on general equilibrium framework

Based on the annual mean PM_2.5 concentration data of 30 provinces and cities in China from 2006 to 2020, this paper constructs a structural model of manufacturing haze pollution, expounds the main mechanisms affecting haze pollution, then statistically decomposes the main factors affecting the change of pollution emissions, and examines the direct and indirect impact paths of the change of haze pollution emissions in China. The study found that (1) the inhibitory effects of latent variables on manufacturing haze pollution is from strong to weak in order: industrial structure upgrading, environmental regulation, trade opening, productivity improvement, and economic scale expansion, while the optimal path for the indirect effect of exogenous latent variables on haze pollution is to rely on environmental regulation and opening up to achieve the improvement of productivity, so as to achieve the purpose of reducing haze pollution. (2) The analysis based on the PVAR model found that industrial structure adjustment is the key factor of haze decline in both the short and long term. Environmental regulation has an obvious effect on haze control in the short term, but in the long run, it still needs to rely on industrial structure adjustment, production efficiency improvement, and trade opening to achieve the goal of reducing haze. (3) There is an inverted U-shaped nonlinear relationship between output scale and environmental regulation. In addition, trade openness has a long-term effect on productivity. Technology spillovers from opening up can reduce haze pollution in the long run by improving productivity. (4) The environmental regulation policy in the central region is characterized by "race to the bottom," while the eastern region shows the characteristic of "race to the top" in the policy game of improving productivity and optimizing the industrial structure. Therefore, haze control requires an appropriate intensity of environmental regulation to reduce the proportion of high pollution and high energy consumption industries. Make full use of the international cooperation platform of the "the Belt and Road Initiative" and the pilot free trade zone to promote substantive cooperation between Chinese enterprises and developed countries in the field of environmental protection technology, increase investment in research and development of clean equipment and cleaner production technology, improve productivity, help China's green manufacturing, and contribute Chinese wisdom and Chinese solutions to global environmental governance and climate change.

Structural Model for Socially Sustainable Public Housing Decision-Making in Chile

Normally the social approaches addressed in public housing policies are unclear in the implementation processes. Indeed, public agencies do not have systems that integrate clear social criteria to consider the social assessment of public housing projects. Therefore, the inclusion of social sustainability in planning and early decision-making is limited. In addition, social development technically involves variables that are not normally independent, and its completeness means their relationships must be considered to sum up the impacts. Thus, this work proposes a structural model that explains an integral interrelation of social criteria that determines socially sustainable housing projects for the vulnerable population in Chile. For this, a theoretical model was constructed and validated using a structural equation model (SEM). This system derives from the application of a survey applied to 188 professionals related to the development of public housing. From this, a model of social sustainability of public housing is validated with ten social criteria and eleven unidirectional interrelations, structured in two dimensions: the functional conditions of the home and the environmental conditions of the house. In the first dimension, the relation between the Improvement in family economic availability and Spaces for family development stands out. In the second, the strongest link is between Community health and safety and the Integration of the design in the context.

A Structural Model for Aggression in Middle School Students in Korea: Based on Ajzen's Theory of Planned Behavior

(1) Purpose: The purpose of this study was to construct a hypothetical model for the variables that can explain aggression in middle school students in order to promote mental health and the growth and development of middle school students in Korea. Through this model, we tried to confirm the structural relationship between the variables and their influence. (2) Methods: The subjects of this study were middle school students in the Korean city of D, and the data collection period was from July to September 2016. The data were collected from the final 310 completed copies of the questionnaire, excluding 23 copies with insufficient data. SPSS 26.0 and AMOS 26.0 were used for data analysis. (3) Results: The fit indices of the final model (GFI = 0.88, AGFI = 0.84, IFI = 0.95, CFI = 0.95, SRMR = 0.06, and RMSEA = 0.07) met the acceptable levels. The variables that affected middle school students' aggression were parenting behavior and aggression regulation intention, and together, their explanatory power for aggression was 50.5%. Perceived behavioral control was the most influential variable for aggression regulation intention. (4) Conclusions: The results of this study will be used as a basis for studying aggression and developing aggression control training programs with the aim of reducing aggression in middle school students.

Structural models of viral insulin-like peptides and their analogs

The insulin receptor (IR), the insulin-like growth factor-1 receptor (IGF1R), and the insulin/IGF1 hybrid receptors (hybR) are homologous transmembrane receptors. The peptide ligands, insulin and IGF1, exhibit significant structural homology and can bind to each receptor via site-1 and site-2 residues with distinct affinities. The variants of the Iridoviridae virus family show capability in expressing single-chain insulin/IGF1 like proteins, termed viral insulin-like peptides (VILPs), which can stimulate receptors from the insulin family. The sequences of VILPs lacking the central C-domain (dcVILPs) are known, but their structures in unbound and receptor-bound states have not been resolved to date. We report all-atom structural models of three dcVILPs (dcGIV, dcSGIV, and dcLCDV1) and their complexes with the receptors (μIR, μIGF1R, and μhybR), and probed the peptide/receptor interactions in each system using all-atom molecular dynamics (MD) simulations. Based on the nonbonded interaction energies computed between each residue of peptides (insulin and dcVILPs) and the receptors, we provide details on residues establishing significant interactions. The observed site-1 insulin/μIR interactions are consistent with previous experimental studies, and a residue-level comparison of interactions of peptides (insulin and dcVILPs) with the receptors revealed that, due to sequence differences, dcVILPs also establish some interactions distinct from those between insulin and IR. We also designed insulin analogs and report enhanced interactions between some analogs and the receptors.

Use of structural models to elucidate the occurrence of falls among older adults according to abdominal obesity: a cross-sectional study

BACKGROUND

Obesity is a risk factor for falls in older adults, but the effects of body fat distribution and its interaction with other factors are not well established.

OBJECTIVES

To verify the occurrence of falls among older adults with and without abdominal obesity and the effects of sociodemographic, health, and behavioral variables on this outcome.

DESIGN AND SETTING

A cross-sectional study in an urban area of Alcobaça, Brazil.

METHODS

Men and women older than 60 years with (270) and without (184) abdominal obesity were included. Sociodemographic, health, and behavioral data were collected using validated questionnaires in Brazil. Descriptive and path analyses were performed (P < 0.05).

RESULTS

The occurrence of falls was high in participants with abdominal obesity (33.0%). In both groups, a higher number of morbidities (β = 0.25, P < 0.001; β = 0.26, P = 0.002) was directly associated with a higher occurrence of falls. Among participants without abdominal obesity, a lower number of medications (β = -0.16; P = 0.04), a higher number of depressive symptoms (β = 0.15; P = 0.04), worse performance on the agility and dynamic balance tests (β = 0.37; P < 0.001), and lower functional disability for basic activities of daily living (β = -0.21; P = 0.006) were directly associated with the occurrence of falls.

CONCLUSION

Adults older than 60 years with abdominal obesity have a higher prevalence of falls. Different factors were associated with the occurrence of falls in both groups.

SCGG: A deep structure-conditioned graph generative model

Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new nodes and their corresponding edges on top of the given conditioning substructure. The architecture of SCGG consists of a graph representation learning network and an autoregressive generative model, which is trained end-to-end. More precisely, the graph representation learning network is designed to compute continuous representations for each node in a graph, which are not only affected by the features of adjacent nodes, but also by the ones of farther nodes. This network is primarily responsible for providing the generation procedure with the structural condition, while the autoregressive generative model mainly maintains the generation history. Using this model, we can address graph completion, a rampant and inherently difficult problem of recovering missing nodes and their associated edges of partially observed graphs. The computational complexity of the SCGG method is shown to be linear in the number of graph nodes. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method compared with state-of-the-art baselines.

Bullying in Adolescents Practising Sport: A Structural Model Approach

This article aims to analyse the relationship between the bullying aggressor and bullying victim profile related to practising or not practising sport in adolescents living in southern Spain. The research includes male and female participants aged between 12 and 16 years in different secondary schools in the provinces of Andalusia, Ceuta and Melilla in the period between February 2022 and June 2022. The study aims to extend the existing scientific, theoretical and empirical knowledge on the influence of playing sport or not on disruptive bullying attitudes in adolescents. To this end, two initial hypotheses were designed; the first hypothesises that bullying victim behaviours are associated with future bullying aggressor behaviours when practising sport; and the second states that victim behaviours are associated with future bullying aggressor behaviours when not practising sport. To verify them, SPSS software was used for the preliminary analysis of the scale and sociodemographic profile. Additionally, the study is based on structural equation modelling methodology and variance-based methods employing SmartPLS v3.3 software. The results show the importance of sport or physical activity to reduce the chances of carrying out bullying actions on other peers and/or classmates. Therefore, it is considered necessary to prevent bullying in the classroom by implementing sports intervention programmes in educational centres.

Assessment of the logistics activities with a structural model on the basis of improvement of sustainability performance

The rationale for prioritizing sustainability in our world, where climate change and unplanned population growth have increased noticeably and resources are exhausted, has affected almost every sector. The logistics sector, which is one of these sectors and undoubtedly directly affects the living resources, constitutes the subject of our study. The study deals with the logistics activities carried out by the companies on the basis of sustainability and the aim of the article is to provide a framework that will contribute to the sustainability initiative of the companies carrying out logistics activities. It provides gains to the literature by investigating the important factors that greatly affect the performance of logistics activities. Accordingly, logistics activities are detailed under the titles of environmental impact, waste management, transportation and warehouse management, resource usage, social impact, internal factors, external factors, and logistics performance factors. These titles are the main dimensions that emerged as a result of literature research and expert opinions. The most important sub-criteria that affect the sustainable logistics performance, which are handled within the scope of the main dimensions, have been determined as 44 with Pareto analysis. These determined parameters are analyzed with the formation of a comprehensive structural model and their importance levels are interpreted, and the findings are interpreted. It is compared with the DEMATEL method. The results of this study, in which sustainable logistics practices are at the forefront, form the basis for improving the sustainability performance of the logistics sector.

Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.

Investigating supply chain challenges of public sector agriculture development projects in Bangladesh: An application of modified Delphi-BWM-ISM approach

This study aims to investigate the supply chain challenges of public sector agriculture development projects in Bangladesh using the modified Delphi, Best Worst Method (BWM), and Interpretive Structural Modelling (ISM) methods. Based on these three widely acclaimed statistical techniques, the study identified, ranked, and identified interrelationships among the challenges. The study is unique not only in terms of research findings, but also in terms of methodology, as it is the first to use the three MCDM (Multicriteria Decision Making) tools to examine supply chain issues in public sector agriculture development projects in a developing country context. A literature review and two modified Delphi rounds with 15 industry experts’ opinions were applied to identify and validate a list of 11 key supply chain challenges. To determine the priority of the challenges, a panel of eight industry experts was consulted, and their responses were analysed using the BWM. Then, another group of 10 experts was consulted using ISM to investigate the contextual relationships among the challenges, resulting in a five-layered Interpretive Structural Model (ISM) and MICMAC (cross-impact matrix multiplication applied to classification) analysis of the challenges. According to relative importance (global weights), "improper procurement planning (0.213), "delay in project initiation (0.177), "demand forecasting error (0.146)", "lack of contract monitoring mechanism (0.097)", and "lack of competent staff (0.095)" are the top five ranked key challenges that have a significant impact on the project supply chain. Regarding contextual relationships, the ISM model and ISM-MICMAC analysis identified the "political influence" challenge as the most influential, and also independent of the other challenges. The findings are critical for project managers in managing challenges because understanding both relative importance and contextual relationships are required to address the challenges holistically. Additionally, these findings will benefit policymakers, academics, and future researchers.

Virtual Quasi-2D Intermediates as Building Blocks for Plausible Structural Models of Amyloid Fibrils from Proteins with Complex Topologies: A Case Study of Insulin

Conformational transitions of globular proteins into amyloid fibrils are complex multistage processes exceedingly challenging to simulate using molecular dynamics (MD). Slow monomer diffusion rates and rugged free energy landscapes disfavor swift self-assembly of orderly amyloid architectures within timescales accessible to all-atom MD. Here, we conduct a multiscale MD study of the amyloidogenic self-assembly of insulin: a small protein with a complex topology defined by two polypeptide chains interlinked by three disulfide bonds. To avoid kinetic traps, unconventional preplanarized insulin conformations are used as amyloid building blocks. These starting conformers generated through uniaxial compression of the native monomer in various spatial directions represent 6 distinct (out of 16 conceivable) two-dimensional (2D) topological classes varying in N-/C-terminal segments of insulin's A- and B-chains being placed inside or outside of the central loop constituted by the middle sections of both chains and Cys7A-Cys7B/Cys19B-Cys20A disulfide bonds. Simulations of the fibrillar self-assembly are initiated through a biased in-register alignment of two, three, or four layers of flat conformers belonging to a single topological class. The various starting topologies are conserved throughout the self-assembly process resulting in polymorphic amyloid fibrils varying in structural features such as helical twist, presence of cavities, and overall stability. Some of the protofilament structures obtained in this work are highly compatible with the earlier biophysical studies on insulin amyloid and high-resolution studies on insulin-derived amyloidogenic peptide models postulating the presence of steric zippers. Our approach provides in silico means to study amyloidogenic tendencies and viable amyloid architectures of larger disulfide-constrained proteins with complex topologies.

Association Between Dimensions of Professional Burnout and Turnover Intention Among Nurses Working in Hospitals During Coronavirus Disease (COVID-19) Pandemic in Iran Based on Structural Model

Purpose

This study was done to assess the dimensions of professional burnout and turnover intention among nurses working in hospitals during the coronavirus disease 2019 (COVID-19) pandemic in Iran based on a structural model.

Methods

This cross-sectional study was performed among 170 nurses working in two referral hospitals of COVID-19 in Tehran Province, Iran, from September to December 2020. Data were collected using the sociodemographic form, Maslach Burnout Inventory (MBI), and Turnover Intention Questionnaire. Data were analyzed with SPSS and Amos software version 22 using independent t-test, ANOVA, and structural equation model.

Results

The mean scores for burnout in emotional fatigue, depersonalization, and personal accomplishment dimensions were 25.38 ± 7.55, 9.47 ± 4.40, and 34.94 ± 7.80, respectively, moreover for the turnover intention, the score was 6.51 ± 3.17. The reduced personal accomplishment was identified as a positive predictor of turnover intention (p = 0.01). Work position and interest in attending the organization were significantly correlated with the turnover intention (p < 0.05).

Conclusions

There is an immediate need to prepare nurses to cope better with the COVID-19 outbreak. Work-related stressors during the COVID-19 pandemic have led to an increase in nurses' burnout and turnover intention. Identifying and managing the factors related to professional burnout will make it possible to prevent the nurses' turnover intention in such critical situations.

Marginal structural models using calibrated weights with SuperLearner: application to type II diabetes cohort

As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naive (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.

Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM

Under the condition of sufficient resources, there are many factors affecting the realization of extremely short construction period of engineering construction projects. Based on literature review and questionnaire survey, this paper firstly selected 17 influencing factors from the five dimensions of design, management, technology, policy and environment. And the factor analytic hierarchy process model was established based on Grey-DEMATEL-ISM. The model introduced the improved grey system theory and combined decision-making trial and evaluation laboratory (DEMATEL) with interpretative structural modeling method (ISM). In addition, the model can not only identify the critical factors in the system, but also present the internal logical relationship between the influencing factors through the multi-level hierarchical structure diagram. Finally, through the analysis of the influencing factors of extremely short construction period under the sufficient resources, it defined that the key factor is the natural environment and second is the structure type. The methodology implemented in this paper helps decision makers and managers of construction projects to understand the interrelationship and degree of influence among factors affecting the duration under the condition of sufficient resources, to effectively grasp key factors, and to effectively achieve project success.

Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7

Human roseolovirus U20 and U21 are type I membrane glycoproteins that have been implicated in immune evasion by interfering with recognition of classical and non-classical MHC proteins. U20 and U21 are predicted to be type I glycoproteins with extracytosolic immunoglobulin-like domains, but detailed structural information is lacking. AlphaFold and RoseTTAfold are next generation machine-learning-based prediction engines that recently have revolutionized the field of computational three-dimensional protein structure prediction. Here, we review the structural biology of viral immunoevasins and the current status of computational structure prediction algorithms. We use these computational tools to generate structural models for U20 and U21 proteins, which are predicted to adopt MHC-Ia-like folds with closed MHC platforms and immunoglobulin-like domains. We evaluate these structural models and place them within current understanding of the structural basis for viral immune evasion of T cell and natural killer cell recognition.

Semi-paracrystallinity in semi-conducting polymers

Precise determination of structural organization of semi-conducting polymers is of paramount importance for the further development of these materials in organic electronic technologies. Yet, prior characterization of some of the best-performing materials for transistor and photovoltaic applications, which are based on polymers with rigid backbones, often resulted in conundrums in which X-ray scattering and microscopy yielded seemingly contradicting results. Here we solve the paradox by introducing a new structural model, i.e., semi-paracrystalline organization. The model establishes that the microstructure of these materials relies on a dense array of small paracrystalline domains embedded in a more disordered matrix. Thus, the overall structural order relies on two parameters: the novel concept of degree of paracrystallinity (i.e., paracrystalline volume/mass fraction, introduced here for the first time) and the lattice distortion parameter of paracrystalline domains (g-parameter from X-ray scattering). Structural parameters of the model are correlated with long-range charge carrier transport, revealing that charge transport in semi-paracrystalline materials is particularly sensitive to the interconnection of paracrystalline domains.

Statin use is associated with lower risk of dementia in stroke patients: a community-based cohort study with inverse probability weighted marginal structural model analysis

Current evidence is inconclusive on cognitive benefits or harms of statins among stroke patients, who have high risk of dementia. This observational cohort study investigated the association between statin use and post-stroke dementia using data from the Clinical Practice Research Datalink. Patients without prior dementia who had an incident stroke but received no statins in the preceding year were followed for up to 10 years. We used inverse probability weighted marginal structural models to estimate observational analogues of intention-to-treat (ITT, statin initiation vs. no initiation) and per-protocol (PP, sustained statin use vs. no use) effects on the risk of dementia. To explore potential impact of unmeasured confounding, we examined the risks of coronary heart disease (CHD, positive control outcome), fracture and peptic ulcer (negative control outcomes). In 18,577 statin initiators and 14,613 non-initiators (mean follow-up of 4.2 years), the adjusted hazard ratio (aHR) for dementia was 0.70 (95% confidence interval [CI] 0.64–0.75) in ITT analysis and 0.55 (95% CI 0.50–0.62) in PP analysis. The corresponding aHR_ITT and aHR_PP were 0.87 (95% CI 0.79–0.95) and 0.70 (95% CI 0.62–0.80) for CHD, 1.03 (95% CI 0.82–1.29) and 1.09 (95% CI 0.77–1.54) for peptic ulcer, and 0.88 (95% CI 0.80–0.96) and 0.86 (95% CI 0.75–0.98) for fracture. Statin initiation after stroke was associated with lower risk of dementia, with a potentially greater benefit in patients who persisted with statins over time. The observed association of statin use with post-stroke dementia may in part be overestimated due to unmeasured confounding shared with the association between statin use and fracture.

Structural Comparative Modeling of Multi-Domain F508del CFTR

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

Diffusion Path Identification of Public Opinion Involving Enterprise Green Technology Adoption: An Interpretive-Structural-Modeling-Based Approach

With the increasing information transparency of business operations’ environmental influences, public opinion plays an important role in the green technology adoption of enterprises. Identifying the diffusion path of public opinion involving the process of enterprise green technology adoption is a significant task to verify the triggering mechanisms among the external factors and internal ones. An appropriate framework may help to clarify how the sustainability elements of public opinion are introduced to green technology adoption. Therefore, an interpretive structural-modeling (ISM)-based approach was applied to explore the basic transmission process and path of public opinion involving green technology adoption in enterprise practices. From the pressure of public opinion to the stakeholders involved, as well as the corresponding operational environmental activities, this study explored the psychological behavior of internal and external stakeholders and tried to clarify what the driving elements of green technology adoption are and how they relate to each other. Based on the field data collected from practitioners with Chinese contextual experience, the driving elements of the enablers of green technology adoption by enterprises were identified, and the fundamental triggering mechanisms of the public opinion pressure among them were analyzed. Thereafter, the influence of internal and external stakeholders involving green technology adoption and their corresponding behaviors under the pressure of public opinion were determined and expounded comprehensively, which illustrates the diffusion path of how public opinion influences the operational green technology adoption. This may narrow the gap between public environmental expectation and business operations. Finally, the managerial implications and the limitations of this study were concluded. The explanatory corresponding ISM model established in this study enriches the literature on the theoretical research of the mechanisms of green technology adoption.

A Structural Model of Quality of Life in Patients after Colorectal Cancer Surgery

BACKGROUND

This study was conducted to determine a hypothetical model for the factors affecting the quality of life of postoperative colorectal cancer patients.

METHODS

We collected data from 209 patients that was analyzed using SPSS 22.0 and AMOS 25.

RESULTS

Predictive variables of the hypothesis model included an exogenous variable, social support, as well as endogenous variables self-efficacy, symptoms, health promotion behavior, and quality of life. Testing of the correction model showed that significant paths directly affecting quality of life of patients after colorectal cancer surgery included health promotion behavior, symptoms, and self-efficacy and also showed an explanation power of 58.7%. Social support was found to have a significant impact on the quality of life indirectly through self-efficiency.

CONCLUSIONS

To improve the quality of life of colorectal cancer patients, it is necessary to develop a nursing intervention program that strengthens patients' health promotion behaviors to alleviate their symptoms and improve their social support and self-efficacy.

Association Between Health Literacy and Work Ability in Employees With Health-Related Risk Factors: A Structural Model

INTRODUCTION

In workplace health promotion (WHP), health literacy and work ability are considered as outcomes of high interest. Therefore, the question arises as to what extent individual health literacy skills have an impact on work ability alongside sociodemographic influences.

OBJECTIVES

This study aimed to examine the associations between a structural model of health literacy as well as sociodemographic context factors and the work ability among employees with health-related risk factors.

MATERIALS AND METHODS

The study was based on baseline data of a workplace-related intervention (158 employees with health-related risk factors, 53.8% women, 48 ± 10 years). Health literacy skills were assessed with Lenartz's Questionnaire (measuring "self-perception", "proactive approach to health", "dealing with health information", "self-control", "self-regulation", and "communication and cooperation"). Work ability was measured by the German Short Form of the Work Ability Index (WAI). As sociodemographic context factors, sex, age, and educational level were assessed. The associations were examined using structural equation modeling with partial least squares (SmartPLS 2.0.M3). Common quality criteria were applied and significance level was set at α = 5%.

RESULTS

Model's reliability, validity, and structure could be validated. Regarding the impact on work ability, "self-regulation" showed a statistically significant direct effect (ß = 0.32, t (∞) = 4.00, p < 0.01, f 2 = 0.09) and "self-perception" had a significant indirect effect (ß = 0.13, t (∞) = 2.53, p < 0.05). The only additional association with work ability was found for age (ß = -0.25, t (∞) = 3.82, p < 0.01, f 2 = 0.04). The WAI score variance was explained to 17.5% by the health literacy skills and to 27.5% considering the additional sociodemographic context factors.

CONCLUSION

According to the structural model of health literacy, in employees with health-related risk factors, a target group-specific WHP approach could be the encouragement of self-regulation and self-perception. However, additional resources and conditions influencing work ability should be considered.

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability-weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.

In Silico and In Vitro Structure-Activity Relationship of Mastoparan and Its Analogs

Antimicrobial peptides are an important class of therapeutic agent used against a wide range of pathogens such as Gram-negative and Gram-positive bacteria, fungi, and viruses. Mastoparan (MpVT) is an α-helix and amphipathic tetradecapeptide obtained from Vespa tropica venom. This peptide exhibits antibacterial activity. In this work, we investigate the effect of amino acid substitutions and deletion of the first three C-terminal residues on the structure–activity relationship. In this in silico study, the predicted structure of MpVT and its analog have characteristic features of linear cationic peptides rich in hydrophobic and basic amino acids without disulfide bonds. The secondary structure and the biological activity of six designed analogs are studied. The biological activity assays show that the substitution of phenylalanine (MpVT1) results in a higher antibacterial activity than that of MpVT without increasing toxicity. The analogs with the first three deleted C-terminal residues showed decreased antibacterial and hemolytic activity. The CD (circular dichroism) spectra of these peptides show a high content α-helical conformation in the presence of 40% 2,2,2-trifluoroethanol (TFE). In conclusion, the first three C-terminal deletions reduced the length of the α-helix, explaining the decreased biological activity. MpVTs show that the hemolytic activity of mastoparan is correlated to mean hydrophobicity and mean hydrophobic moment. The position and spatial arrangement of specific hydrophobic residues on the non-polar face of α-helical AMPs may be crucial for the interaction of AMPs with cell membranes.

OUP accepted manuscript

Subdomain 5BSL3.2 of hepatitis C virus RNA lies at the core of a network of distal RNA–RNA contacts that connect the 5′ and 3′ regions of the viral genome and regulate the translation and replication stages of the viral cycle. Using small-angle X-ray scattering and NMR spectroscopy experiments, we have determined at low resolution the structural models of this subdomain and its distal complex with domain 3′X, located at the 3′-terminus of the viral RNA chain. 5BSL3.2 adopts a characteristic ‘L’ shape in solution, whereas the 5BSL3.2–3′X distal complex forms a highly unusual ‘Y’-shaped kissing junction that blocks the dimer linkage sequence of domain 3′X and promotes translation. The structure of this complex may impede an effective association of the viral polymerase with 5BSL3.2 and 3′X to start negative-strand RNA synthesis, contributing to explain the likely mechanism used by these sequences to regulate viral replication and translation. In addition, sequence and shape features of 5BSL3.2 are present in functional RNA motifs of flaviviruses, suggesting conserved regulatory processes within the Flaviviridae family.

Early life exposure to greenness and executive function and behavior: An application of inverse probability weighting of marginal structural models

Increasingly, studies suggest benefits of natural environments or greenness on children's health. However, little is known about cumulative exposure or windows of susceptibility to greenness exposure. Using inverse probability weighting of marginal structural models (IPW/MSM), we estimated effects of greenness exposure from birth through adolescence on executive function and behavior. We analyzed data of 908 children from Project Viva enrolled at birth in 1999-2002 and followed up until early adolescence. In mid-childhood (median 7.7 years) and early adolescence (13.1 years), executive function and behavior were assessed using the Behavior Rating Inventory of Executive Function and the Strengths and Difficulties Questionnaire (SDQ). Greenness was measured at birth, early childhood, mid-childhood, and early adolescence, using the Normalized Difference Vegetation Index. We used inverse probability weighting of marginal structural models to estimate effects of interventions that ensure maximum greenness exposure versus minimum through all intervals; and that ensure maximum greenness only in early childhood (vs. minimum through all intervals). Results of the effects of "maximum (vs. minimum) greenness at all timepoints" did not suggest associations with mid-childhood outcomes. Estimates of "maximum greenness only in early childhood (vs. minimum)" suggested a beneficial association with mid-childhood SDQ (-3.21, 99 %CI: -6.71,0.29 mother-rated; -4.02, 99 %CI: -7.87,-0.17 teacher-rated). No associations were observed with early adolescent outcomes. Our results for "persistent" maximum greenness exposure on behavior, were not conclusive with confidence intervals containing the null. The results for maximum greenness "only in early childhood" may shed light on sensitive periods of greenness exposure for behavior regulation.

Drug targeting of aminoacyl-tRNA synthetases in Anopheles species and Aedes aegypti that cause malaria and dengue

BACKGROUND

Mosquito-borne diseases have a devastating impact on human civilization. A few species of Anopheles mosquitoes are responsible for malaria transmission, and while there has been a reduction in malaria-related deaths worldwide, growing insecticide resistance is a cause for concern. Aedes mosquitoes are known vectors of viral infections, including dengue, yellow fever, chikungunya, and Zika. Aminoacyl-tRNA synthetases (aaRSs) are key players in protein synthesis and are potent anti-infective drug targets. The structure-function activity relationship of aaRSs in mosquitoes (in particular, Anopheles and Aedes spp.) remains unexplored.

METHODS

We employed computational techniques to identify aaRSs from five different mosquito species (Anopheles culicifacies, Anopheles stephensi, Anopheles gambiae, Anopheles minimus, and Aedes aegypti). The VectorBase database ( https://vectorbase.org/vectorbase/app ) and web-based tools were utilized to predict the subcellular localizations (TargetP-2.0, UniProt, DeepLoc-1.0), physicochemical characteristics (ProtParam), and domain arrangements (PfAM, InterPro) of the aaRSs. Structural models for prolyl (PRS)-, and phenylalanyl (FRS)-tRNA synthetases-were generated using the I-TASSER and Phyre protein modeling servers.

RESULTS

Among the vector species, a total of 37 (An. gambiae), 37 (An. culicifacies), 37 (An. stephensi), 37 (An. minimus), and 35 (Ae. aegypti) different aaRSs were characterized within their respective mosquito genomes. Sequence identity amongst the aaRSs from the four Anopheles spp. was > 80% and in Ae. aegypti was > 50%.

CONCLUSIONS

Structural analysis of two important aminoacyl-tRNA synthetases [prolyl (PRS) and phenylanalyl (FRS)] of Anopheles spp. suggests structural and sequence similarity with potential antimalarial inhibitor [halofuginone (HF) and bicyclic azetidine (BRD1369)] binding sites. This suggests the potential for repurposing of these inhibitors against the studied Anopheles spp. and Ae. aegypti.

The Viral Class II Membrane Fusion Machinery: Divergent Evolution from an Ancestral Heterodimer

A key step during the entry of enveloped viruses into cells is the merger of viral and cell lipid bilayers. This process is driven by a dedicated membrane fusion protein (MFP) present at the virion surface, which undergoes a membrane–fusogenic conformational change triggered by interactions with the target cell. Viral MFPs have been extensively studied structurally, and are divided into three classes depending on their three-dimensional fold. Because MFPs of the same class are found in otherwise unrelated viruses, their intra-class structural homology indicates horizontal gene exchange. We focus this review on the class II fusion machinery, which is composed of two glycoproteins that associate as heterodimers. They fold together in the ER of infected cells such that the MFP adopts a conformation primed to react to specific clues only upon contact with a target cell, avoiding premature fusion in the producer cell. We show that, despite having diverged in their 3D fold during evolution much more than the actual MFP, the class II accompanying proteins (AP) also derive from a distant common ancestor, displaying an invariant core formed by a β-ribbon and a C-terminal immunoglobulin-like domain playing different functional roles—heterotypic interactions with the MFP, and homotypic AP/AP contacts to form spikes, respectively. Our analysis shows that class II APs are easily identifiable with modern structural prediction algorithms, providing useful information in devising immunogens for vaccine design.

Impact of sustainable project management on project plan and project success of the manufacturing firm: Structural model assessment

This study has aimed to investigate the impact of sustainable project management on sustainable project planning and success in manufacturing firms. Data was collected from project management professionals in a manufacturing firm in Malaysia. A total of 231 responses were analyzed using the partial least square (PLS) method. The findings revealed that sustainable project management has a significant impact on sustainable project success and sustainable project planning. Sustainable project planning is positively correlated with sustainable project success. The results also indicated that sustainable project planning mediates the effect of sustainable project management on sustainable project success. The findings have significant insight into the body of knowledge of the project life cycle and indicated that sustainable project planning is a crucial tool attributed to project management towards the project success of the manufacturing firm. The results can be used as a guideline for organizations, providing direction in project management to achieve sustainable development for business.

Green process innovation, green product innovation and its economic performance improvement paths: A survey and structural model

Green technology innovation is an important driving force for high-quality development in China. Recently, much attention has focused on the connotation, influencing factors and mode selection of green technology innovation, yet few studies have systematically tested the transmission paths between green technology innovation and economic performance. Green technology innovation can be divided into green process innovation and green product innovation, both of which can improve the environmental and economic performance of enterprises. Drawing on previous research, this study constructs a model of economic performance transmission for green technology innovation and upgrading, and conducts an empirical analysis based on data from 642 industrial enterprises in China. The results show that green process innovation and green product innovation can effectively improve the economic performance of enterprises. The environmental performance and market competitiveness of enterprises are important mediating variables in the paths of economic performance improvement. An enterprise's green process innovation can positively promote green product innovation, and there is also room for technological innovation upgrading. There are two main differences in the transmission paths for different types of green technology innovation: (1) Green process innovation can improve economic performance directly, whereas green product innovation can only do this indirectly; (2) In most cases, the path from green product innovation to economic performance is more important than that from green process innovation to economic performance. This study deepens understanding of the green technology innovation process and has implications for optimizing policy design for green development.

A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models

BACKGROUND AND AIMS

Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees.

METHODS

This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison.

KEY RESULTS

Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97-57.45 % and CCC 0.43-0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly.

CONCLUSIONS

The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.

Remapping the foundations of morality: Well-fitting structural model of the Moral Foundations Questionnaire

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.

Maternal Complications and Prescription Opioid Exposure During Pregnancy: Using Marginal Structural Models

INTRODUCTION

Prescription opioids are frequently used for pain management in pregnancy. Studies examining perinatal complications in mothers who received prescription opioids during pregnancy are still limited.

OBJECTIVES

The aim of this study was to assess the association of prescription opioid use and maternal pregnancy and obstetric complications.

METHODS

This retrospective cohort study with the Rhode Island (RI) Medicaid claims data linked to vital statistics throughout 2008-2015 included pregnant women aged 12-55 years with one or multiple live births. Women were excluded if they had cancer, opioid use disorder, or opioid dispensing prior to but not during pregnancy. Main outcomes included adverse pregnancy and obstetric complications. Marginal Structural Cox Models with time-varying exposure and covariates were applied to control for baseline and time-varying covariates. Analyses were conducted for outcomes that occurred 1 week after opioid exposure (primary) or within the same week as exposure (secondary). Sensitivity studies were conducted to assess the effects of different doses and individual opioids.

RESULTS

Of 9823 eligible mothers, 545 (5.5%) filled one or more prescription opioid during pregnancy. Compared with those unexposed, no significant risk was observed in primary analyses, while in secondary analyses opioid-exposed mothers were associated with an increased risk of cesarean antepartum depression (HR 3.19; 95% CI 1.22-8.33), and cardiac events (HR 9.44; 95% CI 1.19-74.83). In sensitivity analyses, results are more prominent in high dose exposure and are consistent for individual opioids.

CONCLUSIONS

Prescription opioid use during pregnancy is associated with an increased risk of maternal complications.