Latent Structure Research Articles

Screening for Light Personalities in Portugal: A Cross-Cultural Validation of the Light Triad Scale With an At-risk-of-delinquency Sample

The Light Triad of personality refers to three prosocial personality traits—Faith in Humanity, Humanism, and Kantianism—that promote the worth and dignity of other people, focus on ethical behavior and empathy, and confidence that other people are naturally good. The aim of the present study is to examine the psychometric properties of the Light Triad Scale (LTS)—Portuguese version. Our convenience sample consisted of 242 male and female participants ( M = 30.19 years, SD = 12.78, range = 16–77) from Portugal. The proposed latent structure models of the LTS obtained adequate fits. Internal consistency/reliability, as measured by the alpha and omega coefficients, was adequate to good. Construct validity with other psychometric measures (i.e., empathy, dark traits of personality, propensity to morally disengage, and antisociality/criminality measures) and criterion-related validity (with justice involvement variables such as problems with the law, arrested by the police, sentenced to prison and alcohol/drug abuse variables) were demonstrated. Cross-gender measurement invariance was established, with females scoring higher than males. The findings support the use of the LTS as a valid and reliable measure.

ICD-11 post-traumatic stress disorder and complex post-traumatic stress disorder in mental health support-seeking former-serving Australian defence force veterans.

ICD-11 complex post-traumatic stress disorder is a more severe condition than post-traumatic stress disorder, and recent studies indicate it is more prevalent among military samples. In this study, we tested the psychometric properties of the International Trauma Questionnaire, assessed the relative prevalence rates of post-traumatic stress disorder and complex post-traumatic stress disorder in the sample population and explored relationships between complex post-traumatic stress disorder and post-traumatic stress disorder and a range of risk factors. Survey participants (N = 189) were mental health support-seeking former-serving veterans of the Australian Defence Force (ADF) recruited from primary care. Confirmatory factor analysis was used to test the factorial validity of the International Trauma Questionnaire. The latent structure of the International Trauma Questionnaire was best represented by a two-factor second-order model consistent with the ICD-11 model of complex post-traumatic stress disorder. The International Trauma Questionnaire scale scores demonstrated excellent internal reliability. Overall, 9.1% (95% confidence interval = [4.8%, 13.5%]) met diagnostic requirements for post-traumatic stress disorder and an additional 51.4% (95% confidence interval = [44.0%, 58.9%]) met requirements for complex post-traumatic stress disorder. Those meeting diagnostic requirements for complex post-traumatic stress disorder were more likely to have served in the military for 15 years or longer, had a history of more traumatic life events and had the highest levels of depression, anxiety and stress symptoms. The International Trauma Questionnaire can effectively distinguish between post-traumatic stress disorder and complex post-traumatic stress disorder within primary care samples of Australian Defence Force veterans. A significantly greater proportion of Australian Defence Force veterans met criteria for complex post-traumatic stress disorder than post-traumatic stress disorder. Australian military mental health services should adopt the International Trauma Questionnaire to routinely screen for complex post-traumatic stress disorder and develop complex post-traumatic stress disorder specific interventions to promote recovery in Australian Defence Force veterans with complex post-traumatic stress disorder.

Nature Relatedness Scale: psychometric properties of the Portuguese version

Environmental education would strongly benefit from a socioecological approach that encompasses the human-nature relationship in all its dimensions. Nature relatedness appears to be an essential trait to ensure effective approaches, promoting the development of pro-environmental attitudes and behaviors. but also, human well-being through nature-derived benefits. To our knowledge, there are no available instruments validated for the Portuguese population to assess nature connectedness. This study aims to fill this gap, by validating the short and full versions of the Nature Relatedness Scale for the Portuguese population. The translation and adaptation of the questionnaire comply with international guidelines in this field and validation was conducted with 456 Portuguese participants with a mean age of 32.95 (SD = 11.45, age range = 18–77) years, mostly female (n = 328; 71.9%). Confirmatory factor analysis indicated that the original three-factor latent structure of the full Nature Relatedness Scale, and the short version of the instrument with six items, obtained an adequate fit. These two versions of the instrument showed adequate validity to be used with the Portuguese population.

Distinguishing heart failure with reduced ejection fraction from heart failure with preserved ejection fraction: A phenomics approach.

Pathophysiological differences between patients with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction (EF) remain unclear. Therefore we used a phenomics approach, integrating selected proteomics data with patient characteristics and cardiac structural and functional parameters, to get insight into differential pathophysiological mechanisms and identify potential treatment targets. We report data from a representative subcohort of the prospective Singapore Heart Failure Outcomes and Phenotypes (SHOP), including patients with HFrEF (EF <40%, n = 217), HFpEF (EF ≥50%, n = 213), and age- and sex-matched controls without HF (n = 216). We measured 92 biomarkers using a proximity extension assay and assessed cardiac structure and function in all participants using echocardiography. We used multi-block projection to latent structure analysis to integrate clinical, echocardiographic, and biomarker variables. Candidate biomarker targets were cross-referenced with small-molecule and drug databases. The total cohort had a median age of 65 years (interquartile range 60-71), and 50% were women. Protein profiles strongly discriminated patients with HFrEF (area under the curve [AUC] = 0.89) and HFpEF (AUC = 0.94) from controls. Phenomics analyses identified unique druggable inflammatory markers in HFpEF from the tumour necrosis factor receptor superfamily (TNFRSF), which were positively associated with hypertension, diabetes, and increased posterior and relative wall thickness. In HFrEF, interleukin (IL)-8 and IL-6 were possible targets related to lower EF and worsening renal function. We identified pathophysiological mechanisms related to increased cardiac wall thickness parameters and potentially druggable inflammatory markers from the TNFRSF in HFpEF.

A multiplex metabolomic approach for quality control of Spirulina supplement and its allied microalgae (Amphora & Chlorella) assisted by chemometrics and molecular networking

Microalgae species are of economic importance regarded as “green gold” being rich in bioactive compounds. Spirulina and Chlorella are the most popular microalgal species and are marketed as healthy food supplements. At the same time, Amphora holds potential as a source of healthy lipids and essential fatty acids. Yet, there are considerable variations in their reported chemical composition, and less is known about their compositional differences. A multiplexed metabolomic approach was adopted for the quality control (QC) of Spirulina supplements and to compare its constitutive metabolome to Chlorella and Amphora. The adopted protocol comprised gas chromatography-mass spectrometry (GC–MS), ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS), and ultraviolet–visible spectrophotometry (UV/Vis) for mapping their primary and secondary metabolome. Interestingly, UPLC-HRMS/MS analysis delineated the abundance of fatty acids in Amphora versus glycolipids enrichment in Spirulina, and porphyrins were the main pigments identified in Spirulina, with scarce occurrence in Chlorella. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) analysis of GC–MS data set revealed palmitic acid, 3-mannobiose, and glyceryl-glycoside as being most enriched in Spirulina, versus sucrose and leucine in Chlorella and Amphora, respectively. Despite being of low discriminatory potential, UV/Vis OPLS-DA modeling showed that Spirulina was distinguished with the UV absorbances of carotenoids and chlorophyll pigments, as indicated by its OPLS-DA derived S-plot. Our study provides a QC approach for the analysis of the microalgal species and poses alternative spectral and compositional markers for their discrimination.

Topic Modeling: A Consistent Framework for Comparative Studies

In recent years, the field of Topic Modeling (TM) has grown in importance due to the increasing availability of digital text data. TM is an unsupervised learning technique that helps uncover latent semantic structures in large sets of documents, making it a valuable tool for finding relevant patterns. However, evaluating the performance of TM algorithms can be challenging as different metrics and datasets are often used, leading to inconsistent results. In addition, many current surveys of TM algorithms focus on a limited number of models and exclude state-of-the-art approaches. This paper has the objective of addressing these issues by presenting a comprehensive comparative study of five TM algorithms across three different benchmark datasets using five different metrics. We offer an updated survey of the latest TM approaches and evaluation metrics, providing a consistent framework for comparing different algorithms while introducing state-of-the art approaches that have been disregarded in the literature. The experiments, which primarily use Context Vectors (CV) Topic Coherence as an evaluation metric, show that Top2Vec is the best-performing model across all datasets, disrupting the tendency for Latent Dirichlet Allocation to be the best performer. Doi: 10.28991/ESJ-2024-08-01-09 Full Text: PDF

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

The association between hydration state and the metabolism of phospholipids and amino acids among young adults: a metabolomic analysis

BackgroundWater is vital for humans' survival and general health, which is involved in various metabolic activities. ObjectivesThe aim of this study was to investigate the variation in urine metabolome and associated metabolic pathways among people with different hydration states. MethodsA metabolomic analysis was conducted using 24-h urine samples collected during a cross-sectional study on fluid intake behavior from December 9 to 11, 2021, in Hebei, China. Subjects were divided into the optimal hydration (OH, ≤500 mOsm/kg, n = 21), middle hydration (500–800 mOsm/kg, n = 33), and hypohydration groups (HH, >800 mOsm/kg, n = 13) based on the 3-d average 24-h urine osmolality. Collected 24-h urine samples from 67 subjects (43 males and 34 females) were analyzed for urine metabolome using liquid chromatography-MS. ResultsThe untargeted metabolomic analysis yielded 1055 metabolites by peak intensities. Integrating the results of the orthogonal projections to latent structures discriminant analysis and fold change test, 115 differential metabolites between the OH and HH groups, including phospholipids (PLs) and lysophospholipids, were identified. Among the 115 metabolites identified as differential metabolites, 85 were recorded by the Human Metabolome Database and uploaded to the Kyoto Encyclopedia of Genes and Genomes databases for pathway analysis. Twenty-one metabolic pathways were recognized. Phenylalanine metabolism (0.50, P = 0.007), phenylalanine, tyrosine, and tryptophan biosynthesis (0.50, P = 0.051), glycerophospholipid metabolism (0.31, P < 0.001), sphingolipid metabolism (0.27, P = 0.029), and cysteine and methionine metabolism (0.10, P = 0.066) had the leading pathway impacts. ConclusionsWe found variations in the urinary PLs and amino acids among subjects with different hydration states. Pathways associated with these differential metabolites could further impact various physiologic and pathologic functions. A more comprehensive and in-depth investigation of the physiologic and pathologic impact of the hydration state and the underlying mechanisms to elucidate and advocate optimal fluid intake habits is needed.This trial was registered at Chinese Clinical Trial Registry as ChiCTR2100045268.

Differences in Supervision on Peer Learning Wards: A Pilot Survey of the Supervisor's Perspective.

Multiple students are placed on clinical wards simultaneously due to increasing student numbers. This has the potential to create stress for the supervisor and reduce quality of student learning. Peer learning as a pedagogical framework to supervise multiple students has been widely shown to have advantages for the students by developing teaching skills, team collaboration, and independence. However, whether peer learning impacts the characteristics of supervision and the experience of the supervisor is less understood. It is unknown whether wards that use peer learning as a pedagogical framework (peer learning wards) are any different compared to wards that do not (non-peer learning wards), from the supervisor's perspective. We aimed to develop and pilot test a questionnaire to compare peer-learning wards and non-peer learning wards from the supervisor's perspective. We used the AMEE 7-step guide to develop questions investigating supervision, the learning environment and satisfaction. We piloted the questionnaire with 46 nurse supervisors working on inpatient hospital wards in Stockholm, Sweden. We compared answers from peer learning with non-peer learning wards. We used Orthogonal Projections to Latent Structures (OPLS) discriminant analysis to show what differed between the wards. Peer learning wards compared to non-peer learning wards had more student-centred activities, the physical space had more adaptations for students, more support available to the supervisor, and supervisors perceived greater overall satisfaction with the quality of education and with the ward as a whole. The variables that had most influence on the discrimination between the two ward types related to peer learning activities and perceptions (p=0.0034). This pilot study shows that peer learning wards differ compared to non-peer learning wards regarding peer learning activities and perceptions among supervisors. Our questionnaire needs to be distributed on a larger scale to validate our findings and explore further the way in which the pedagogical framework and peer learning can affect supervision and satisfaction.

Multigraph approximate-representation learning for hyperspectral band selection

ABSTRACT Unsupervised hyperspectral image (HSI) band selection methods have been attracting ever-increasing attention. However, the local structural features captured by most of the existing methods suffer from a certain degree of risk of feature ambiguity. Moreover, these methods typically have difficulty preserving the latent manifold structure of the HSI in selected subsets of the bands. To address these problems, firstly, the graph approximate representation learning (GARL) model is proposed in this article by incorporating graph-regularized sparse coding into self-representation learning to preserve the local manifold structure information of HSI in the selected subset of bands. Meanwhile, to alleviate the ambiguity of the local graph structures, we design a local-global feature relationship expression model to construct the latent long-distance contextual connectivity (LDCC) graphs between the obtained local graph structures. Then, to preserve HSI’s local and non-local manifold structure information as much as possible in the selected subset of bands, a multigraph approximate representation learning (MGARL) model is proposed in this article by incorporating the obtained LDCC graph into the proposed GARL model. Next, we design a solution method to solve the proposed formulation. Finally, extensive experimental results on four datasets reveal the promising performance of MGARL over some state-of-the-art methods.

Dynamic patterns and the latent community structure of sectoral volatility and jump risk contagion

This study explores the latent driving structure of sectoral risk contagion in terms of volatility and jump through the factor model framework for high-dimensional matrix-valued time series. We find strong inter-sector volatility and jump spillover effects at the mean level and higher effects at extreme tails, while jump spillovers reflect sharper structural shifts with the promotion of major financial or public events. Both volatility and jump are contagious in the form of a community-structure. Our findings provide new evidence about sectoral risk contagion and the importance of accurately identifying the underlying structure of risk contagion for efficiently realizing hierarchical regulation.

Development of bioreactor scale-down model using orthogonal projections to latent structures method and CO2 supplementation.

Scale-down model qualification is an important step for developing a large-scale cell culture process to enhance process understanding and support process characterization studies. Traditionally, only harvest data are used to show consistency between small-scale and large-scale bioreactor performance, allowing attributes that are dynamic over the cell culture period to be overlooked. A novel statistical method, orthogonal projections to latent structures (OPLS) analysis, can be utilized to compare time-course cell culture data across scales. Here we describe an example where OPLS is used to identify gaps between small-scale and large-scale bioreactor performances. In this case, differences in the partial pressure of carbon dioxide (pCO2) and lactate profiles were observed between small- and large-scale bioreactors, which were linked to differences in the product-quality attributes fragments and galactosylation. An improved small-scale model was developed, leading to improved consistency in the process performance and product qualities across scales and qualification of the scale-down model for regulatory submissions. This new statistical approach can provide valuable insights into process understanding and process scale-up.

Factor Structure of the Bulgarian Version of FOCUS on the Outcomes of Communication Under Six

The aim of this paper is to present the Factor structure of the Bulgarian version of FOCUS on the Outcomes of Communication Under Six. The instrument is based on the World Health Organisation’s (WHO) International Classification of Functioning, Disability and Health – Children and Youth (ICF-CY), and it aims at tracing the effect of therapy on the child’s communication and participation in the real world. Our research is focused on the factor structure and reliability of the Parent’s form. The participants formed two samples, the main one consisting of 272 (mostly preschool children), males and females. The age of the subjects varied between 2.17 and 7.25 years, with a mean age of M = 4.91 years and SD = 1.10.&#x0D; The latent structure of the questionnaire was examined in five phases – 1-test of the assumption of one-dimensionality; 2-exploratory factor analysis was applied to establish the factor model, based on data from an empirical study; 3-three possible factor models with a fixed number of factors were analyzed; 4-the factor models were compared through confirmatory factor analysis. The aim of the analyses was to select the most appropriate final model for the Bulgarian version of the questionnaire. The reliability analysis of the scales in the two-factor model was conducted through testing their internal consistency.&#x0D; The two-factor model of the FOCUS questionnaire in Bulgarian shows a good orientation towards two specific functions – ‘capacity for communication’, and ‘performance’ or ‘communicative performance’. Due to its stable measurements related to functioning, this tool could be effectively used to practically assess the effect of therapy on children with development disorders.

P656 Plasma Inflammation-related Proteins Predict the Therapeutic Effect of 5-ASA in Patients with Ulcerative Colitis

Abstract Background 5-aminosalicylate acid (5-ASA) is the first line therapy for mild to moderate ulcerative colitis (UC). However, up to 56% of patients on 5-ASA treatment could not achieve mucosal healing, which is the therapeutic target of UC. At present, predictors for patients’ response to 5-ASA are lacking. Our aim was to identify circulating markers to predict the therapeutic effect of 5-ASA in patients with UC. Methods Plasma samples were collected from patients with active UC at baseline and analyzed using the OLINK panel (Target 96 Inflammation). Effective therapy was defined as achieving clinical remission within 8 weeks or mucosal healing within 1 year of 5-ASA treatment. Orthogonal Projections to Latent Structures Discriminant Analyses (OPLS-DA) were implemented to correlate patient groups to plasma proteins in linear multivariate models. Machine learning models (ML) were constructed to predict ineffective therapy in UC patients. Results Among the 36 UC patients involved (media age 38.5 years old, men 61.6%), 18 patients belonged to the effective group and the ineffective group, respectively. In multivariate analysis, circulating inflammatory protein profiles could distinguish the patients who did not respond well to 5-ASA treatment (Figure 1A). Elevated levels of Axin 1 (p=0.012), sulfotransferase 1A1 (ST1A1) (p=0.032) and interleukin 15 receptor subunit alpha (IL-15RA) (p=0.022), and decreased level of interleukin 2 (IL-2) (p=0.036) were observed in UC patients refractory to 5-ASA therapy (Figure 1B). Receiver operating characteristic curves (ROC) for predicting the therapeutic efficacy of 5-ASA were constructed. The areas under ROC (AUC) of Axin 1, ST1A1, IL-15RA and IL-2 for the prediction of ineffective 5-ASA therapy were 0.744 (p=0.012, sensitivity 83.3%, specificity 72.2%), 0.710 (p=0.031, sensitivity 94.4%, specificity 55.6%), 0.704 (p=0.037, sensitivity 88.9%, specificity 50%) and 0.704 (p=0.037, sensitivity 77.8%, specificity 61.1%), respectively (Figure 1C). Combination of the four protein markers improved the performance of prediction (AUC 0.860 to 1.000, sensitivity 72.2%-100%, specificity 83.3%-100% across all machine learning classifiers) (Figure 1D). Conclusion We identified four plasma inflammation-related proteins associated with the therapeutic effect of 5-ASA in UC. The ML models demonstrated excellent performance in predicting the therapeutic effect of 5-ASA, which could help clinicians identify UC patients who are refractory to 5-ASA in the early course of disease and timely initiation of treatment escalation.

Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure

To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task’s complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called “cognitive map”. However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent “splitters”) while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.

Identification of sedative-hypnotic compounds shared by five medicinal Polyporales mushrooms using UPLC-Q-TOF-MS/MS-based untargeted metabolomics

BackgroundFive Polyporales mushrooms, namely Amauroderma rugosum, Ganoderma lucidum, G. resinaceum, G. sinense and Trametes versicolor, are commonly used in China for managing insomnia. However, their active components for this application are not fully understood, restricting their universal recognition. PurposeIn this study, we aimed to identify sedative-hypnotic compounds shared by these five Polyporales mushrooms. Study design and MethodsA UPLC-Q-TOF-MS/MS-based untargeted metabolomics, including OPLS-DA (orthogonal projection of potential structure discriminant analysis) and OPLS (orthogonal projections to latent structures) analysis together with mouse assays, were used to identify the main sedative-hypnotic compounds shared by the five Polyporales mushrooms. A pentobarbital sodium-induced sleeping model was used to investigate the sedative-hypnotic effects of the five mushrooms and their sedative-hypnotic compounds. ResultsNinety-two shared compounds in the five mushrooms were identified. Mouse assays showed that these mushrooms exerted sedative-hypnotic effects, with different potencies. Six triterpenes [four ganoderic acids (B, C1, F and H) and two ganoderenic acids (A and D)] were found to be the main sedative-hypnotic compounds shared by the five mushrooms. ConclusionWe for the first time found that these six triterpenes contribute to the sedative-hypnotic ability of the five mushrooms. Our novel findings provide pharmacological and chemical justifications for the use of the five medicinal mushrooms in managing insomnia.

Beliefs about emotions: Latent structure and links with emotion regulation and psychopathology

BackgroundThere is evidence that beliefs about emotions are important for mental health. However, this field needs further research. Via the Emotion Beliefs Questionnaire, we aimed to explore the latent structure of emotion beliefs, their associations with numerous emotion constructs, and the role of emotion regulation in mediating emotion beliefs and emotional problems. MethodsThis cross-sectional study involved 508 general community adults via an online survey, participants completed the EBQ, the Perth Emotion Regulation Competency Inventory, and the Depression Anxiety Stress Scale-21 for the central analyses, in addition to several related emotion measures. Our analytical strategy included: 1. Factor analyses to test the psychometric properties of the EBQ including its measurement invariance; 2. Correlational analyses to investigate the relationship between beliefs about emotions and several emotion constructs; and 3. Mediation analyses to explore the role of emotion regulation in the association between beliefs about emotions and depression and anxiety. ResultsThe findings supported a four-factor structure for the EBQ, with invariance across different demographic categories. Beliefs about the controllability of emotions were associated with most emotion constructs relative to beliefs about the usefulness of emotions. The links between beliefs about emotions with depression and anxiety were mediated by emotion regulation. ConclusionsOur findings underscore the importance of assessing the multidimensional emotion beliefs construct, as these beliefs are associated with impaired emotion regulation and many important emotional outcomes. Beliefs about emotion thus represent a useful area for future clinical research so that interventions tackle psychological difficulties in an effective way.

Sexual Behavior and Perceived Risk for Oropharyngeal Cancer Among Men Who Have Sex With Men: A Psychometric Scale Validation.

Men who have sex with men (MSM) are at increased risk for human papillomavirus-associated oropharyngeal cancer (HPV-OPC). The objective of this analysis was to create a psychometrically validated scale to measure perception of risk for HPV-OPC. We conducted an exploratory and a confirmatory factor analysis to determine and confirm the latent factor structure. We used a path diagram to evaluate the relationship between the validated scale and perceived risk for HPV-OPC. The model was determined to be a good fit if it met all criteria: root mean square error of approximation ≤0.06, standardized root mean residual ≤0.08, Comparative Fit Index ≥0.90, and Tucker-Lewis Index ≥0.90. We report standardized estimates and 95% confidence intervals. This cross-sectional study recruited 1315 MSM. A majority (73.33%) of MSM had performed fellatio on ≥20 partners, 36.98% had rimmed ≥20 partners, and 5.31% had performed cunnilingus on ≥10 partners in their lifetime.Six sexual history survey items loaded onto 2 latent factors: sexual risk behaviors: class 1 and sexual risk behaviors: class 2. The final model statistics indicated good fit: root mean square error of approximation = 0.064, standardized root mean residual = 0.059, Comparative Fit Index = 0.996, and Tucker-Lewis Index = 0.993. Sexual risk behaviors: class 1 was associated with greater perceived risk for HPV-OPC (0.217; 95% confidence interval, 0.138-0.295). Age, HIV status, HPV vaccination status, and sexual risk behaviors: class 2 were not associated with perceived risk for HPV-OPC. Men who have sex with men assessed risk for HPV-OPC based on their lifetime number of cisgender male sexual partners, rimming partners, and fellatio partners but not other sexual behaviors. Men who have sex with men may be responsive to future HPV-OPC educational interventions and opportunities for screening.

Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity.

Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers' pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D50 sub 10 μm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.

Youth recidivism: youth self-report matters.

"Recidivism" is used ubiquitously in juvenile justice research and typically describes repeat legal contact; however, researchers, policymakers, and clinicians operationalize it in various ways. Despite assuming each measure is a proxy for continued delinquent behavior leading to further legal contact, few have examined the association between youth delinquent behavior and self-reported and official records of legal contact. Furthermore, systemic bias against ethnoracial and gender minoritized youth often results in more harsh treatment by the legal system, which could influence recidivism measurement. Latent variable modeling of legal contact is understudied; thus, it is important to examine the feasibility of measuring this construct as a latent variable, including measurement invariance by gender. Among 401 youth ages 12-18 years at first ever court contact, we examined three metrics of legal contact over a 2-year follow-up period: youth-report of arrest, caregiver-report of their adolescent's arrest, and official records of the number of new court charges. We examined between-group differences on each metric based on gender and ethnoracial identity. We then measured: (1) the association between youths' self-reported delinquency and each metric, (2) gender-specific associations between self-reported delinquency and each metric, and (3) gender-based measurement invariance for a latent recidivism variable using confirmatory factor analysis. Youth were consistent reporters of their own delinquent behavior and prospective legal contact measured by arrests. There were no between-group differences based on gender or ethnoracial identity for any legal contact measures. Delinquency and all legal contact variables were positively intercorrelated for the overall sample and the male subsample. For females, delinquency was not associated with caregiver-reported youth arrest or number of new charges. The latent legal contact variable had unique factor structures for male and female subsamples, suggesting no measurement invariance. Youth-reported delinquency at first ever legal contact was most strongly associated with youth-reported arrest during a 2-year follow-up period, followed by caregiver-reported arrest, and the number of new charges. Unique latent variable factor structures for male and female subsamples suggests the inter-relation between legal contact variables is gender-specific. Stakeholders should consider prioritizing youth-reported delinquency since it is most strongly related to prospective youth-reported arrest.