Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

The Moderating Capacity of Racial Identity between Perceived Discrimination and Psychological Well-Being over Time among African American Youth

ERIC Educational Resources Information Center

Seaton, Eleanor K.; Neblett, Enrique W.; Upton, Rachel D.; Hammond, Wizdom Powell; Sellers, Robert M.

2011-01-01

This study examined the influence of racial identity in the longitudinal relation between perceptions of racial discrimination and psychological well-being for approximately 560 African American youth. Latent curve modeling (LCM) and parallel process multiple-indicator LCMs with latent moderators were used to assess whether perceptions of racial…

Everyday discrimination and mood and substance use disorders: a latent profile analysis with African Americans and Caribbean Blacks.

PubMed

Clark, Trenette T; Salas-Wright, Christopher P; Vaughn, Michael G; Whitfield, Keith E

2015-01-01

Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N=4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. Copyright © 2014 Elsevier Ltd. All rights reserved.

Perceived Discrimination and the Adjustment of African American Youths: A Five-Year Longitudinal Analysis with Contextual Moderation Effects

ERIC Educational Resources Information Center

Brody, Gene H.; Chen, Yi-Fu; Murry, Velma McBride; Ge, Xiaojia; Simons, Ronald L.; Gibbons, Frederick X.; Gerrard, Meg; Cutrona, Carolyn E.

2006-01-01

Longitudinal links between perceived racial discrimination and later conduct problems and depressive symptoms were examined among 714 African American adolescents who were 10-12 years old at recruitment. Data were gathered 3 times over a 5-year period. Hypotheses were tested via latent curve modeling and multiple-group latent growth modeling.…

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Bifactor latent structure of attention-deficit/hyperactivity disorder (ADHD)/oppositional defiant disorder (ODD) symptoms and first-order latent structure of sluggish cognitive tempo symptoms.

PubMed

Lee, SoYean; Burns, G Leonard; Beauchaine, Theodore P; Becker, Stephen P

2016-08-01

The objective was to determine if the latent structure of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms is best explained by a general disruptive behavior factor along with specific inattention (IN), hyperactivity/impulsivity (HI), and ODD factors (a bifactor model) whereas the latent structure of sluggish cognitive tempo (SCT) symptoms is best explained by a first-order factor independent of the bifactor model of ADHD/ODD. Parents' (n = 703) and teachers' (n = 366) ratings of SCT, ADHD-IN, ADHD-HI, and ODD symptoms on the Child and Adolescent Disruptive Behavior Inventory (CADBI) in a community sample of children (ages 5-13; 55% girls) were used to evaluate 4 models of symptom organization. Results indicated that a bifactor model of ADHD/ODD symptoms, in conjunction with a separate first-order SCT factor, was the best model for both parent and teacher ratings. The first-order SCT factor showed discriminant validity with the general disruptive behavior and specific IN factors in the bifactor model. In addition, higher scores on the SCT factor predicted greater academic and social impairment, even after controlling for the general disruptive behavior and 3 specific factors. Consistent with predictions from the trait-impulsivity etiological model of externalizing liability, a single, general disruptive behavior factor accounted for nearly all common variance in ADHD/ODD symptoms, whereas SCT symptoms represented a factor different from the general disruptive behavior and specific IN factor. These results provide additional support for distinguishing between SCT and ADHD-IN. The study also demonstrates how etiological models can be used to predict specific latent structures of symptom organization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Immunohistochemical detection of active transforming growth factor-beta in situ using engineered tissue

NASA Technical Reports Server (NTRS)

Barcellos-Hoff, M. H.; Ehrhart, E. J.; Kalia, M.; Jirtle, R.; Flanders, K.; Tsang, M. L.; Chatterjee, A. (Principal Investigator)

1995-01-01

The biological activity of transforming growth factor-beta 1 (TGF-beta) is governed by dissociation from its latent complex. Immunohistochemical discrimination of active and latent TGF-beta could provide insight into TGF-beta activation in physiological and pathological processes. However, evaluation of immunoreactivity specificity in situ has been hindered by the lack of tissue in which TGF-beta status is known. To provide in situ analysis of antibodies to differentiate between these functional forms, we used xenografts of human tumor cells modified by transfection to overexpress latent TGF-beta or constitutively active TGF-beta. This comparison revealed that, whereas most antibodies did not differentiate between TGF-beta activation status, the immunoreactivity of some antibodies was activation dependent. Two widely used peptide antibodies to the amino-terminus of TGF-beta, LC(1-30) and CC(1-30) showed marked preferential immunoreactivity with active TGF-beta versus latent TGF-beta in cryosections. However, in formalin-fixed, paraffin-embedded tissue, discrimination of active TGF-beta by CC(1-30) was lost and immunoreactivity was distinctly extracellular, as previously reported for this antibody. Similar processing-dependent extracellular localization was found with a neutralizing antibody raised to recombinant TGF-beta. Antigen retrieval recovered cell-associated immunoreactivity of both antibodies. Two antibodies to peptides 78-109 showed mild to moderate preferential immunoreactivity with active TGF-beta only in paraffin sections. LC(1-30) was the only antibody tested that discriminated active from latent TGF-beta in both frozen and paraffin-embedded tissue. Thus, in situ discrimination of active versus latent TGF-beta depends on both the antibody and tissue preparation. We propose that tissues engineered to express a specific form of a given protein provide a physiological setting in which to evaluate antibody reactivity with specific functional forms of a protein.

Deep and Structured Robust Information Theoretic Learning for Image Analysis.

PubMed

Deng, Yue; Bao, Feng; Deng, Xuesong; Wang, Ruiping; Kong, Youyong; Dai, Qionghai

2016-07-07

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e. missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we respectively discuss three types of the RIT implementations with linear subspace embedding, deep transformation and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark datasets. The structured sparse RIT is further applied to a medical image analysis task for brain MRI segmentation that allows group-level feature selections on the brain tissues.

The Moderating Capacity of Racial Identity Between Perceived Discrimination and Psychological Well-being Over Time among African American Youth

PubMed Central

Seaton, Eleanor K.; Upton, Rachel D.; Sellers, Robert M.; Neblett, Enrique W.; Hammond, Wizdom Powell

2011-01-01

The present study examined the influence of racial identity in the longitudinal relationship between perceptions of racial discrimination and psychological well-being for approximately 560 African American youth. Latent curve modeling (LCM) and parallel process multiple-indicator LCMs with latent moderators were used to assess whether perceptions of racial discrimination predicted the intercept (initial levels) and the slope (rate of change) of psychological well-being over time, and whether racial identity moderates these relationships. The results indicated that African American adolescents who reported higher psychological responses to discrimination frequency levels at the first time point had lower initial levels of well-being. Regressing the slope factor for psychological well-being on frequency of discrimination also revealed a non-significant result for subsequent well-being levels. PMID:21954919

The Shame and Guilt Scales of the Test of Self-Conscious Affect-Adolescent (TOSCA-A): Factor Structure, Concurrent and Discriminant Validity, and Measurement and Structural Invariance Across Ratings of Males and Females.

PubMed

Watson, Shaun; Gomez, Rapson; Gullone, Eleonora

2017-06-01

This study examined various psychometric properties of the items comprising the shame and guilt scales of the Test of Self-Conscious Affect-Adolescent. A total of 563 adolescents (321 females and 242 males) completed these scales, and also measures of depression and empathy. Confirmatory factor analysis provided support for an oblique two-factor model, with the originally proposed shame and guilt items comprising shame and guilt factors, respectively. Also, shame correlated with depression positively and had no relation with empathy. Guilt correlated with depression negatively and with empathy positively. Thus, there was support for the convergent and discriminant validity of the shame and guilt factors. Multiple-group confirmatory factor analysis comparing females and males, based on the chi-square difference test, supported full metric invariance, the intercept invariance of 26 of the 30 shame and guilt items, and higher latent mean scores among females for both shame and guilt. Comparisons based on the difference in root mean squared error of approximation values supported full measurement invariance and no gender difference for latent mean scores. The psychometric and practical implications of the findings are discussed.

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.

PubMed

Saa, Jaime F Delgado; Çetin, Müjdat

2012-04-01

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.

An Evaluation of the Texas Functional Living Scale's Latent Structure and Subscales.

PubMed

González, David Andrés; Soble, Jason R; Marceaux, Janice C; McCoy, Karin J M

2017-02-01

Performance-based functional assessment is a critical component of neuropsychological practice. The Texas Functional Living Scale (TFLS) has promise given its brevity, nationally representative norms, and co-norming with Wechsler scales. However, its subscale structure has not been evaluated. The purpose of this study was to evaluate the TFLS in a mixed clinical sample (n = 197). Reliability and convergent and discriminant validity coefficients were calculated with neurocognitive testing and collateral reports and factor analysis was performed. The Money and Calculation subscale had the best psychometric properties of the subscales. The evidence did not support solitary interpretation of the Time subscale. A three-factor latent structure emerged representing memory and semantic retrieval, performance and visual scanning, and financial calculation. This study added psychometric support for interpretation of the TFLS total score and some of its subscales. Study limitations included sample characteristics (e.g., gender ratio) and low power for collateral report analyses. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

A Two-Parameter Latent Trait Model. Methodology Project.

ERIC Educational Resources Information Center

Choppin, Bruce

On well-constructed multiple-choice tests, the most serious threat to measurement is not variation in item discrimination, but the guessing behavior that may be adopted by some students. Ways of ameliorating the effects of guessing are discussed, especially for problems in latent trait models. A new item response model, including an item parameter…

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

On the validity and generality of transfer effects in cognitive training research.

PubMed

Noack, Hannes; Lövdén, Martin; Schmiedek, Florian

2014-11-01

Evaluation of training effectiveness is a long-standing problem of cognitive intervention research. The interpretation of transfer effects needs to meet two criteria, generality and specificity. We introduce each of the two, and suggest ways of implementing them. First, the scope of the construct of interest (e.g., working memory) defines the expected generality of transfer effects. Given that the constructs of interest are typically defined at the latent level, data analysis should also be conducted at the latent level. Second, transfer should be restricted to measures that are theoretically related to the trained construct. Hence, the construct of interest also determines the specificity of expected training effects; to test for specificity, study designs should aim at convergent and discriminant validity. We evaluate the recent cognitive training literature in relation to both criteria. We conclude that most studies do not use latent factors for transfer assessment, and do not test for convergent and discriminant validity.

Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

PubMed

Byrd, Christy M; Carter Andrews, Dorinda J

2016-08-01

Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Age-Related Changes in Visual Temporal Order Judgment Performance: Relation to Sensory and Cognitive Capacities

PubMed Central

Busey, Thomas; Craig, James; Clark, Chris; Humes, Larry

2010-01-01

Five measures of temporal order judgments were obtained from 261 participants, including 146 elder, 44 middle aged, and 71 young participants. Strong age group differences were observed in all five measures, although the group differences were reduced when letter discriminability was matched for all participants. Significant relations were found between these measures of temporal processing and several cognitive and sensory assays, and structural equation modeling revealed the degree to which temporal order processing can be viewed as a latent factor that depends in part on contributions from sensory and cognitive capacities. The best-fitting model involved two different latent factors representing temporal order processing at same and different locations, and the sensory and cognitive factors were more successful predicting performance in the different location factor than the same-location factor. Processing speed, even measured using high-contrast symbols on a paper-and-pencil test, was a surprisingly strong predictor of variability in both latent factors. However, low-level sensory measures also made significant contributions to the latent factors. The results demonstrate the degree to which temporal order processing relates to other perceptual and cognitive capacities, and address the question of whether age-related declines in these capacities share a common cause. PMID:20580644

Age-related changes in visual temporal order judgment performance: Relation to sensory and cognitive capacities.

PubMed

Busey, Thomas; Craig, James; Clark, Chris; Humes, Larry

2010-08-06

Five measures of temporal order judgments were obtained from 261 participants, including 146 elder, 44 middle aged, and 71 young participants. Strong age group differences were observed in all five measures, although the group differences were reduced when letter discriminability was matched for all participants. Significant relations were found between these measures of temporal processing and several cognitive and sensory assays, and structural equation modeling revealed the degree to which temporal order processing can be viewed as a latent factor that depends in part on contributions from sensory and cognitive capacities. The best-fitting model involved two different latent factors representing temporal order processing at same and different locations, and the sensory and cognitive factors were more successful predicting performance in the different location factor than the same-location factor. Processing speed, even measured using high-contrast symbols on a paper-and-pencil test, was a surprisingly strong predictor of variability in both latent factors. However, low-level sensory measures also made significant contributions to the latent factors. The results demonstrate the degree to which temporal order processing relates to other perceptual and cognitive capacities, and address the question of whether age-related declines in these capacities share a common cause. Copyright 2010 Elsevier Ltd. All rights reserved.

A Latent Consolidation Phase in Auditory Identification Learning: Time in the Awake State Is Sufficient

ERIC Educational Resources Information Center

Roth, Daphne Ari-Even; Kishon-Rabin, Liat; Hildesheimer, Minka; Karni, Avi

2005-01-01

Large gains in performance, evolving hours after practice has terminated, were reported in a number of visual and some motor learning tasks, as well as recently in an auditory nonverbal discrimination task. It was proposed that these gains reflect a latent phase of experience-triggered memory consolidation in human skill learning. It is not clear,…

Geographic authentication of Asian rice (Oryza sativa L.) using multi-elemental and stable isotopic data combined with multivariate analysis.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Lee, Kyoung-Jin; Park, Sung-Kyu; Lee, Ji-Hee; Son, Na-Young; Jin, Yong-Ik; Kim, Seung-Hyun

2018-02-01

Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged asan important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice of six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were δ 34 S, Mn, and Mg. Our findings contribute to understanding the variations of elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

An All-Fragments Grammar for Simple and Accurate Parsing

DTIC Science & Technology

2012-03-21

Tsujii. Probabilistic CFG with latent annotations. In Proceedings of ACL, 2005. Slav Petrov and Dan Klein. Improved Inference for Unlexicalized Parsing. In...Proceedings of NAACL-HLT, 2007. Slav Petrov and Dan Klein. Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing. In Proceedings of...EMNLP, 2008. Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning Accurate, Compact, and Interpretable Tree Annotation. In Proceedings

Acculturation Predicts Negative Affect and Shortened Telomere Length.

PubMed

Ruiz, R Jeanne; Trzeciakowski, Jerome; Moore, Tiffany; Ayers, Kimberly S; Pickler, Rita H

2016-10-12

Chronic stress may accelerate cellular aging. Telomeres, protective "caps" at the end of chromosomes, modulate cellular aging and may be good biomarkers for the effects of chronic stress, including that associated with acculturation. The purpose of this analysis was to examine telomere length (TL) in acculturating Hispanic Mexican American women and to determine the associations among TL, acculturation, and psychological factors. As part of a larger cross-sectional study of 516 pregnant Hispanic Mexican American women, we analyzed DNA in blood samples (N = 56) collected at 22-24 weeks gestation for TL as an exploratory measure using monochrome multiplex quantitative telomere polymerase chain reaction (PCR). We measured acculturation with the Acculturation Rating Scale for Mexican Americans, depression with the Beck Depression Inventory, discrimination with the Experiences of Discrimination Scale, and stress with the Perceived Stress Scale. TL was negatively moderately correlated with two variables of acculturation: Anglo orientation and greater acculturation-level scores. We combined these scores for a latent variable, acculturation, and we combined depression, stress, and discrimination scores in another latent variable, "negative affectivity." Acculturation and negative affectivity were bidirectionally correlated. Acculturation significantly negatively predicted TL. Using structural equation modeling, we found the model had an excellent fit with the root mean square error of approximation estimate = .0001, comparative fit index = 1.0, Tucker-Lewis index = 1.0, and standardized root mean square residual = .05. The negative effects of acculturation on the health of Hispanic women have been previously demonstrated. Findings from this analysis suggest a link between acculturation and TL, which may indicate accelerated cellular aging associated with overall poor health outcomes. © The Author(s) 2016.

Dissociable Hippocampal and Amygdalar D1-like receptor contribution to Discriminated Pavlovian conditioned approach learning

PubMed Central

Andrzejewski, Matthew E; Ryals, Curtis

2016-01-01

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

Dorsal Hippocampus Function in Learning and Expressing a Spatial Discrimination

ERIC Educational Resources Information Center

White, Norman M.; Gaskin, Stephane

2006-01-01

Learning to discriminate between spatial locations defined by two adjacent arms of a radial maze in the conditioned cue preference paradigm requires two kinds of information: latent spatial learning when the rats explore the maze with no food available, and learning about food availability in two spatial locations when the rats are then confined…

Patterns of Racial Socialization and Psychological Adjustment: Can Parental Communications about Race Reduce the Impact of Racial Discrimination?

ERIC Educational Resources Information Center

Neblett, Enrique W., Jr.; White, Rhonda L.; Ford, Kahlil R.; Philip, Cheri L.; Nguye-N, Hoa X.; Sellers, Robert M.

2008-01-01

This study uses two waves of data to examine the relations among racial discrimination experiences, patterns of racial socialization practices, and psychological adjustment in a sample of 361 African American adolescents. Using latent class analyses, we identified four patterns of child-reported racial socialization experiences: Moderate Positive,…

Exploration and confirmation of the latent variable structure of the Jefferson scale of empathy

PubMed Central

LaNoue, Marianna

2014-01-01

Objectives: To reaffirm the underlying components of the JSE by using exploratory factor analysis (EFA), and to confirm its latent variable structure by using confirmatory factor analysis (CFA). Methods Research participants included 2,612 medical students who entered Jefferson Medical College between 2002 and 2012. This sample was divided into two groups: Matriculants between 2002 and 2007 (n=1,380) and between 2008 and 2012 (n=1,232). Data for 2002-2007 matriculants were subjected to EFA (principal component factor extraction), and data for matriculants of 2008-2012 were used for CFA (structural equation modeling, and root mean square error for approximation). Results The EFA resulted in three factors: “perspective-taking,” “compassionate care” and “walking in patient’s shoes” replicating the 3-factor model reported in most of the previous studies. The CFA showed that the 3-factor model was an acceptable fit, thus confirming the latent variable structure emerged in the EFA. Corrected item-total score correlations for the total sample were all positive and statistically significant, ranging from 0.13 to 0.61 with a median of 0.44 (p<0.01). The item discrimination effect size indices (contrasting item mean scores for the top-third versus bottom-third JSE scorers) ranged from 0.50 to 1.4 indicating that the differences in item mean scores between top and bottom scorers on the JSE were of practical importance. Cronbach’s alpha coefficient of the JSE for the total sample was 0.80, ranging from 0.75 to 0.84 for matriculatnts of different years. Conclusions Findings provided further support for underlying constructs of the JSE, adding to its credibility. PMID:25341215

Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

ERIC Educational Resources Information Center

Maslowsky, Julie; Jager, Justin; Hemken, Douglas

2015-01-01

Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

"Say It Loud--I'm Black and I'm Proud": Parents' Messages about Racial Discrimination, and Academic Achievement in African American Boys

ERIC Educational Resources Information Center

Neblett, Enrique W., Jr.; Chavous, Tabbye M.; Nguyen, Hoa X.; Sellers, Robert M.

2009-01-01

This study uses two waves of data to examine the relationships among racial socialization, racial discrimination, and academic achievement outcomes in a sample of 144 African American male adolescents. Using latent class analyses, the authors identified four patterns of adolescent-reported racial socialization experiences: Positive Socialization,…

Effects of Stigmasterol and β-Sitosterol on Nonalcoholic Fatty Liver Disease in a Mouse Model: A Lipidomic Analysis.

PubMed

Feng, Simin; Gan, Ling; Yang, Chung S; Liu, Anna B; Lu, Wenyun; Shao, Ping; Dai, Zhuqing; Sun, Peilong; Luo, Zisheng

2018-04-04

To study the effects of stigmasterol and β-sitosterol on high-fat Western diet (HFWD)-induced nonalcoholic fatty liver disease (NAFLD), lipidomic analyses were conducted in liver samples collected after 33 weeks of the treatment. Principal component analysis showed these phytosterols were effective in protecting against HFWD-induced NAFLD. Orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and S-plots showed that triacylglycerols (TGs), phosphatidylcholines, cholesteryl esters, diacylglycerols, and free fatty acids (FFAs) were the major lipid species contributing to these discriminations. The alleviation of NAFLD is mainly associated with decreases in hepatic cholesterol, TGs with polyunsaturated fatty acids, and alterations of free hepatic FFA. In conclusion, phytosterols, at a dose comparable to that suggested for humans by the FDA for the reduction of plasma cholesterol levels, are shown to protect against NAFLD in this long-term (33-week) study.

Discriminative study of a potato (Solanum tuberosum L.) cultivation region by measuring the stable isotope ratios of bio-elements.

PubMed

Chung, Ill-Min; Kim, Jae-Kwang; Jin, Yong-Ik; Oh, Yong-Taek; Prabakaran, Mayakrishnan; Youn, Kyoung-Jin; Kim, Seung-Hyun

2016-12-01

Compared to other foods, the use of common bio-elements to identify the geographical origin of potato remains limited. Thus, this study aimed to verify whether the cultivation regions of raw potato tubers could be determined by the stable isotope composition analysis of bio-elements. δ(13)CVPDB and δ(15)NAIR in potato were influenced by region and cultivar, whereas δ(18)OVSMOW and δ(34)SVCDT were only influenced by region (p<0.0001). A two-dimensional plot of δ(18)OVSMOW and δ(34)SVCDT effectively distinguished between high and low altitude regions, and also reliably discriminated Wanju, Haenam, and Boseong cultivars in low altitude regions. δ(34)SVCDT was the main component that was responsible for the separation of samples in the principal component analysis (eigenvector of -0.6209) and orthogonal projection to latent structure-discriminant analysis (VIP value of 1.0566). In conclusion, this study improves our understanding of how the isotope composition of potato tubers varies with respect to cultivation regions and cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease.

PubMed

Singh, Brajesh; Jana, Saikat K; Ghosh, Nilanjana; Das, Soumen K; Joshi, Mamata; Bhattacharyya, Parthasarathi; Chaudhury, Koel

2017-01-05

Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1 H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and l-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. Copyright © 2016 Elsevier B.V. All rights reserved.

Accumulation of Carotenoids and Metabolic Profiling in Different Cultivars of Tagetes Flowers.

PubMed

Park, Yun Ji; Park, Soo-Yun; Valan Arasu, Mariadhas; Al-Dhabi, Naif Abdullah; Ahn, Hyung-Geun; Kim, Jae Kwang; Park, Sang Un

2017-02-18

Species of Tagetes , which belong to the family Asteraceae show different characteristics including, bloom size, shape, and color; plant size; and leaf shape. In this study, we determined the differences in primary metabolites and carotenoid yields among six cultivars from two Tagetes species, T. erecta and T. patula . In total, we detected seven carotenoids in the examined cultivars: violaxanthin, lutein, zeaxanthin, α-carotene, β-carotene, 9- cis -β-carotene, and 13- cis -β-carotene. In all the cultivars, lutein was the most abundant carotenoid. Furthermore, the contents of each carotenoid in flowers varied depending on the cultivar. Principal component analysis (PCA) facilitated metabolic discrimination between Tagetes cultivars, with the exception of Inca Yellow and Discovery Orange. Moreover, PCA and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) results provided a clear discrimination between T. erecta and T. patula . Primary metabolites, including xylose, citric acid, valine, glycine, and galactose were the main components facilitating separation of the species. Positive relationships were apparent between carbon-rich metabolites, including those of the TCA cycle and sugar metabolism, and carotenoids.

Evidence of Syndemics and Sexuality-Related Discrimination Among Young Sexual-Minority Women.

PubMed

Coulter, Robert W S; Kinsky, Suzanne M; Herrick, Amy L; Stall, Ron D; Bauermeister, José A

2015-09-01

Syndemics, or the co-occurrence and interaction of health problems, have been examined extensively among young men who have sex with men, but their existence remain unexamined, to our knowledge, among sexual-minority (i.e., lesbian, gay, and bisexual) women. Thus, we investigated if syndemics were present among young sexual-minority women, and if sexual-orientation discrimination was an independent variable of syndemic production. A total of 467 sexual-minority women between the ages of 18 and 24 completed a cross-sectional online survey regarding their substance use, mental health, sexual behaviors, height, weight, and experiences of discrimination. We used structural equation modeling to investigate the presence of syndemics and their relationship to sexual-orientation discrimination. Heavy episodic drinking, marijuana use, ecstasy use, hallucinogen use, depressive symptoms, multiple sexual partners, and history of sexually transmitted infections (STIs) comprised syndemics in this population (chi-square=24.989, P=.201; comparative fit index [CFI]=0.946; root mean square error of approximation [RMSEA]=0.023). Sexual-orientation discrimination is significantly and positively associated with the latent syndemic variable (unstandardized coefficient=0.095, P<.05), and this model fit the data well (chi-square=33.558, P=.059; CFI=0.914; RMSEA=0.029). The reverse causal model showed syndemics is not an independent variable of sexual-orientation discrimination (unstandardized coefficient=0.602, P>.05). Syndemics appear to be present and associated with sexual-orientation discrimination among young sexual-minority women. Interventions aimed at reducing discrimination or increasing healthy coping may help reduce substance use, depressive symptoms, and sexual risk behaviors in this population.

Coping Styles of Adolescents Experiencing Multiple Forms of Discrimination and Bullying: Evidence from a Sample of Ethnically Diverse Urban Youth

ERIC Educational Resources Information Center

Garnett, Bernice R.; Masyn, Katherine E.; Austin, S. Bryn; Williams, David R.; Viswanath, Kasisomayajula

2015-01-01

Background: We used a latent class analysis (LCA) to characterize coping styles of urban youth and examined if coping styles moderated the association between experiencing discrimination and bullying and depressive symptoms. Methods: The data come from the 2006 Boston Youth Survey, where students were asked to select 2 behaviors they do most often…

Bayesian Semiparametric Structural Equation Models with Latent Variables

ERIC Educational Resources Information Center

Yang, Mingan; Dunson, David B.

2010-01-01

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…

Evaluation of objective structured clinical examination for advanced orthodontic education 12 years after introduction.

PubMed

Fields, Henry W; Kim, Do-Gyoon; Jeon, Minjeong; Firestone, Allen R; Sun, Zongyang; Shanker, Shiva; Mercado, Ana M; Deguchi, Toru; Vig, Katherine W L

2017-05-01

Advanced education programs in orthodontics must ensure student competency in clinical skills. An objective structure clinical examination has been used in 1 program for over a decade. The results were analyzed cross-sectionally and longitudinally to provide insights regarding the achievement of competency, student growth, question difficulty, question discrimination, and question predictive ability. In this study, we analyzed 218 (82 first-year, 68 second-year, and 68 third-year classes) scores of each station from 85 orthodontic students. The grades originated from 13 stations and were collected anonymously for 12 consecutive years during the first 2 decades of the 2000s. The stations tested knowledge and skills regarding dental relationships, analyzing a cephalometric tracing, performing a diagnostic skill, identifying cephalometric points, bracket placement, placing first-order and second-order bends, forming a loop, placing accentuated third-order bends, identifying problems and planning mixed dentition treatment, identifying problems and planning adolescent dentition treatment, identifying problems and planning nongrowing skeletal treatment, superimposing cephalometric tracings, and interpreting cephalometric superimpositions. Results were evaluated using multivariate analysis of variance, chi-square tests, and latent growth analysis. The multivariate analysis of variance showed that all stations except 3 (analyzing a cephalometric tracing, forming a loop, and identifying cephalometric points) had significantly lower mean scores for the first-year student class than the second- and third-year classes (P <0.028); scores between the second- and third-year student classes were not significantly different (P >0.108). The chi-square analysis of the distribution of the number of noncompetent item responses decreased from the first to the second years (P <0.0003), from the second to the third years (P <0.0042), and from the first to the third years (P <0.00003). The latent growth analysis showed a wide range of difficulty and discrimination between questions. It also showed continuous growth for some areas and the ability of 6 questions to predict competency at greater than the 80% level. Objective structure clinical examinations can provide a method of evaluating student performance and curriculum impact over time, but cross-sectional and longitudinal analyses of the results may not be complementary. Significant learning appears to occur during all years of a 3-year program. Valuable questions were both easy and difficult, discriminating and not discriminating, and came from all domains: diagnostic, technical, and evaluation/synthesis. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

ERIC Educational Resources Information Center

Park, Jungkyu; Yu, Hsiu-Ting

2016-01-01

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

Selecting good regions to deblur via relative total variation

NASA Astrophysics Data System (ADS)

Li, Lerenhan; Yan, Hao; Fan, Zhihua; Zheng, Hanqing; Gao, Changxin; Sang, Nong

2018-03-01

Image deblurring is to estimate the blur kernel and to restore the latent image. It is usually divided into two stage, including kernel estimation and image restoration. In kernel estimation, selecting a good region that contains structure information is helpful to the accuracy of estimated kernel. Good region to deblur is usually expert-chosen or in a trial-anderror way. In this paper, we apply a metric named relative total variation (RTV) to discriminate the structure regions from smooth and texture. Given a blurry image, we first calculate the RTV of each pixel to determine whether it is the pixel in structure region, after which, we sample the image in an overlapping way. At last, the sampled region that contains the most structure pixels is the best region to deblur. Both qualitative and quantitative experiments show that our proposed method can help to estimate the kernel accurately.

Flexible Modeling of Latent Task Structures in Multitask Learning

DTIC Science & Technology

2012-06-26

Flexible Modeling of Latent Task Structures in Multitask Learning Alexandre Passos† apassos@cs.umass.edu Computer Science Department, University of...of Maryland, College Park, MD USA Abstract Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure...shared by all the tasks. However, it is usually unclear what type of latent task structure is the most ap- propriate for a given multitask learning prob

Reassessment of the psychometric characteristics and factor structure of the 'Perceived Stress Questionnaire' (PSQ): analysis in a sample of dental students.

PubMed

Montero-Marin, Jesús; Piva Demarzo, Marcelo Marcos; Pereira, Joao Paulo; Olea, Marina; García-Campayo, Javier

2014-01-01

The training to become a dentist can create psychological distress. The present study evaluates the structure of the 'Perceived Stress Questionnaire' (PSQ), its internal consistency model and interrelatedness with burnout, anxiety, depression and resilience among dental students. The study employed a cross-sectional design. A sample of Spanish dental students (n = 314) completed the PSQ, the 'Goldberg Anxiety and Depression Scale' (GADS), 'Connor-Davidson Resilience Scale' (10-item CD-RISC) and 'Maslach Burnout Inventory-Student Survey' (MBI-SS). The structure was estimated using Parallel Analysis from polychoric correlations. Unweighted Least Squares was the method for factor extraction, using the Item Response Theory to evaluate the discriminative power of items. Internal consistency was assessed by squaring the correlation between the latent true variable and the observed variable. The relationships between the PSQ and the other constructs were analysed using Spearman's coefficient. The results showed a PSQ structure through two sub-factors ('frustration' and 'tenseness') with regard to one general factor ('perceived stress'). Items that did not satisfy discriminative capacity were rejected. The model fit were acceptable (GFI = 0.98; RSMR = 0.06; AGFI = 0.98; NFI = 0.98; RFI = 0.98). All the factors showed adequate internal consistency as measured by the congeneric model (≥0.91). High and significant associations were observed between perceived stress and burnout, anxiety, depression and resilience. The PSQ showed a hierarchical bi-factor structure among Spanish dental students. Using the questionnaire as a uni-dimensional scale may be useful in perceived stress level discrimination, while the sub-factors could help us to refine perceived stress analysis and improve therapeutic processes.

Reassessment of the Psychometric Characteristics and Factor Structure of the ‘Perceived Stress Questionnaire’ (PSQ): Analysis in a Sample of Dental Students

PubMed Central

Montero-Marin, Jesús; Piva Demarzo, Marcelo Marcos; Pereira, Joao Paulo; Olea, Marina; García-Campayo, Javier

2014-01-01

Background The training to become a dentist can create psychological distress. The present study evaluates the structure of the ‘Perceived Stress Questionnaire’ (PSQ), its internal consistency model and interrelatedness with burnout, anxiety, depression and resilience among dental students. Methods The study employed a cross-sectional design. A sample of Spanish dental students (n = 314) completed the PSQ, the ‘Goldberg Anxiety and Depression Scale’ (GADS), ‘Connor-Davidson Resilience Scale’ (10-item CD-RISC) and ‘Maslach Burnout Inventory-Student Survey’ (MBI-SS). The structure was estimated using Parallel Analysis from polychoric correlations. Unweighted Least Squares was the method for factor extraction, using the Item Response Theory to evaluate the discriminative power of items. Internal consistency was assessed by squaring the correlation between the latent true variable and the observed variable. The relationships between the PSQ and the other constructs were analysed using Spearman’s coefficient. Results The results showed a PSQ structure through two sub-factors (‘frustration’ and ‘tenseness’) with regard to one general factor (‘perceived stress’). Items that did not satisfy discriminative capacity were rejected. The model fit were acceptable (GFI = 0.98; RSMR = 0.06; AGFI = 0.98; NFI = 0.98; RFI = 0.98). All the factors showed adequate internal consistency as measured by the congeneric model (≥0.91). High and significant associations were observed between perceived stress and burnout, anxiety, depression and resilience. Conclusions The PSQ showed a hierarchical bi-factor structure among Spanish dental students. Using the questionnaire as a uni-dimensional scale may be useful in perceived stress level discrimination, while the sub-factors could help us to refine perceived stress analysis and improve therapeutic processes. PMID:24466330

Evidence of Syndemics and Sexuality-Related Discrimination Among Young Sexual-Minority Women

PubMed Central

Kinsky, Suzanne M.; Herrick, Amy L.; Stall, Ron D.; Bauermeister, José A.

2015-01-01

Abstract Purpose: Syndemics, or the co-occurrence and interaction of health problems, have been examined extensively among young men who have sex with men, but their existence remain unexamined, to our knowledge, among sexual-minority (i.e., lesbian, gay, and bisexual) women. Thus, we investigated if syndemics were present among young sexual-minority women, and if sexual-orientation discrimination was an independent variable of syndemic production. Methods: A total of 467 sexual-minority women between the ages of 18 and 24 completed a cross-sectional online survey regarding their substance use, mental health, sexual behaviors, height, weight, and experiences of discrimination. We used structural equation modeling to investigate the presence of syndemics and their relationship to sexual-orientation discrimination. Results: Heavy episodic drinking, marijuana use, ecstasy use, hallucinogen use, depressive symptoms, multiple sexual partners, and history of sexually transmitted infections (STIs) comprised syndemics in this population (chi-square=24.989, P=.201; comparative fit index [CFI]=0.946; root mean square error of approximation [RMSEA]=0.023). Sexual-orientation discrimination is significantly and positively associated with the latent syndemic variable (unstandardized coefficient=0.095, P<.05), and this model fit the data well (chi-square=33.558, P=.059; CFI=0.914; RMSEA=0.029). The reverse causal model showed syndemics is not an independent variable of sexual-orientation discrimination (unstandardized coefficient=0.602, P>.05). Conclusions: Syndemics appear to be present and associated with sexual-orientation discrimination among young sexual-minority women. Interventions aimed at reducing discrimination or increasing healthy coping may help reduce substance use, depressive symptoms, and sexual risk behaviors in this population. PMID:26788674

Racial discrimination, racial identity, and impostor phenomenon: A profile approach.

PubMed

Bernard, Donte L; Hoggard, Lori S; Neblett, Enrique W

2018-01-01

This study examined the association between racial discrimination and the impostor phenomenon (IP) and the moderating influence of racial identity on this relationship. One hundred fifty-seven African American college students (68% female; mean age = 18.63) completed measures of racial discrimination, racial identity, and IP during 2 waves of data collection. Utilizing latent profile analyses, 4 patterns of racial identity were identified: Undifferentiated, Multiculturalist, Race-Focused, and Humanist. Racial discrimination predicted higher subsequent levels of IP. Racial identity did not moderate the impact of racial discrimination; however, students in the Multiculturalist and Humanist groups reported the lowest and highest levels of IP at Wave 2, respectively. IP is influenced by racial discrimination experiences as well as by the significance and meaning that individuals ascribe to being African American. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Testing Specific Hypotheses Concerning Latent Group Differences in Multi-group Covariance Structure Analysis with Structured Means.

ERIC Educational Resources Information Center

Dolan, Conor V.; Molenaar, Peter C. M.

1994-01-01

In multigroup covariance structure analysis with structured means, the traditional latent selection model is formulated as a special case of phenotypic selection. Illustrations with real and simulated data demonstrate how one can test specific hypotheses concerning selection on latent variables. (SLD)

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

PubMed Central

Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

2013-01-01

This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youths were 18, caregivers reported parental emotional support, and youths assessed peer emotional support. AL and potential confounder variables were assessed when youths were 20. Latent Growth Mixture Modeling identified two perceived discrimination classes: high and stable and low and increasing. Adolescents in the high and stable class evinced heightened AL even with confounder variables controlled. The racial discrimination to AL link was not significant for young adults who received high emotional support. PMID:24673162

Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

PubMed Central

Santos, Nadine Correia; Costa, Patrício Soares; Amorim, Liliana; Moreira, Pedro Silva; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2015-01-01

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate. PMID:25880732

LEARNING TO BE BAD: ADVERSE SOCIAL CONDITIONS, SOCIAL SCHEMAS, AND CRIME

PubMed Central

Simons, Ronald L.; Burt, Callie Harbin

2011-01-01

In this paper we develop and test a new approach to explain the link between social factors and individual offending. We argue that seemingly disparate family, peer, and community conditions lead to crime because the lessons communicated by these events are similar and promote social schemas involving a hostile view of people and relationships, a preference for immediate rewards, and a cynical view of conventional norms. Further, we posit that these three schemas are interconnected and combine to form a criminogenic knowledge structure that gives rise to situational interpretations legitimating criminal behavior. Structural equation modeling with a sample of roughly 700 hundred African American teens provided strong support for the model. The findings indicated that persistent exposure to adverse conditions such as community crime, discrimination, harsh parenting, deviant peers and low neighborhood collective efficacy increased commitment to the three social schemas. The three schemas were highly intercorrelated and combined to form a latent construct that strongly predicted increases in crime. Further, in large measure the effect of the various adverse conditions on increases in crime was indirect through their impact on this latent construct. We discuss the extent to which the social schematic model presented in the paper might be used to integrate concepts and findings from several of the major theories of criminal behavior. PMID:21760641

Psychometric Properties and Factor Structure of the German Version of the Clinician-Administered PTSD Scale for DSM-5.

PubMed

Müller-Engelmann, Meike; Schnyder, Ulrich; Dittmann, Clara; Priebe, Kathlen; Bohus, Martin; Thome, Janine; Fydrich, Thomas; Pfaltz, Monique C; Steil, Regina

2018-05-01

The Clinician-Administered PTSD Scale (CAPS) is a widely used diagnostic interview for posttraumatic stress disorder (PTSD). Following fundamental modifications in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ( DSM-5), the CAPS had to be revised. This study examined the psychometric properties (internal consistency, interrater reliability, convergent and discriminant validity, and structural validity) of the German version of the CAPS-5 in a trauma-exposed sample ( n = 223 with PTSD; n =51 without PTSD). The results demonstrated high internal consistency (αs = .65-.93) and high interrater reliability (ICCs = .81-.89). With regard to convergent and discriminant validity, we found high correlations between the CAPS severity score and both the Posttraumatic Diagnostic Scale sum score ( r = .87) and the Beck Depression Inventory total score ( r = .72). Regarding the underlying factor structure, the hybrid model demonstrated the best fit, followed by the anhedonia model. However, we encountered some nonpositive estimates for the correlations of the latent variables (factors) for both models. The model with the best fit without methodological problems was the externalizing behaviors model, but the results also supported the DSM-5 model. Overall, the results demonstrate that the German version of the CAPS-5 is a psychometrically sound measure.

Latent class analysis of gambling subtypes and impulsive/compulsive associations: Time to rethink diagnostic boundaries for gambling disorder?

PubMed

Chamberlain, Samuel R; Stochl, Jan; Redden, Sarah A; Odlaug, Brian L; Grant, Jon E

2017-09-01

Gambling disorder has been associated with cognitive dysfunction and impaired quality of life. The current definition of non-pathological, problem, and pathological types of gambling is based on total symptom scores, which may overlook nuanced underlying presentations of gambling symptoms. The aims of the current study were (i) to identify subtypes of gambling in young adults, using latent class analysis, based on individual responses from the Structured Clinical Interview for Gambling Disorder (SCI-GD); and (ii) to explore relationships between these gambling subtypes, and clinical/cognitive measures. Total 582 non-treatment seeking young adults were recruited from two US cities, on the basis of gambling five or more times per year. Participants undertook clinical and neurocognitive assessment, including stop-signal, decision-making, and set-shifting tasks. Data from individual items of the Structured Clinical Interview for Gambling Disorder (SCI-GD) were entered into latent class analysis. Optimal number of classes representing gambling subtypes was identified using Bayesian Information Criterion and differences between them were explored using multivariate analysis of variance. Three subtypes of gambling were identified, termed recreational gamblers (60.2% of the sample; reference group), problem gamblers (29.2%), and pathological gamblers (10.5%). Common quality of life impairment, elevated Barratt Impulsivity scores, occurrence of mainstream mental disorders, having a first degree relative with an addiction, and impaired decision-making were evident in both problem and pathological gambling groups. The diagnostic item 'chasing losses' most discriminated recreational from problem gamblers, while endorsement of 'social, financial, or occupational losses due to gambling' most discriminated pathological gambling from both other groups. Significantly higher rates of impulse control disorders occurred in the pathological group, versus the problem group, who in turn showed significantly higher rates than the reference group. The pathological group also had higher set-shifting errors and nicotine consumption. Even problem gamblers who had a relatively low total SCI-PG scores (mean endorsement of two items) exhibited impaired quality of life, objective cognitive impairment on decision-making, and occurrence of other mental disorders that did not differ significantly from those seen in the pathological gamblers. Furthermore, problem/pathological gambling was associated with other impulse control disorders, but not increased alcohol use. Groups differed on quality of life when classified using the data-driven approach, but not when classified using DSM cut-offs. Thus, the current DSM-5 approach will fail to discriminate a significant fraction of patients with biologically plausible, functionally impairing illness, and may not be ideal in terms of diagnostic classification. Cognitive distortions related to 'chasing losses' represent a particularly important candidate treatment target for early intervention. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.

PubMed

Sun, Huishan; Pan, Liping; Jia, Hongyan; Zhang, Zhiguo; Gao, Mengqiu; Huang, Mailing; Wang, Jinghui; Sun, Qi; Wei, Rongrong; Du, Boping; Xing, Aiying; Zhang, Zongde

2018-01-01

The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB patients ( n = 15), compared with LTBI individuals ( n = 15) and healthy controls (HCs, n = 15). Eight differentially expressed proteins were verified using western blot analysis, which was 100% consistent with the proteomics results. Statistically significant differences of six proteins were further validated in the PTB group compared with the LTBI and HC groups in the training set ( n = 240), using ELISA. Classification and regression tree (CART) analysis was employed to determine the ideal protein combination for discriminating PTB from LTBI and HC. A diagnostic model consisting of alpha-1-antichymotrypsin (ACT), alpha-1-acid glycoprotein 1 (AGP1), and E-cadherin (CDH1) was established and presented a sensitivity of 81.2% (69/85) and a specificity of 95.2% (80/84) in discriminating PTB from LTBI, and a sensitivity of 81.2% (69/85) and a specificity of 90.1% (64/81) in discriminating PTB from HCs. Additional validation was performed by evaluating the diagnostic model in blind testing set ( n = 113), which yielded a sensitivity of 75.0% (21/28) and specificity of 96.1% (25/26) in PTB vs. LTBI, 75.0% (21/28) and 92.3% (24/26) in PTB vs. HCs, and 75.0% (21/28) and 81.8% (27/33) in PTB vs. lung cancer (LC), respectively. This study obtained the plasma proteomic profiles of different M.TB infection statuses, which contribute to a better understanding of the pathogenesis involved in the transition from latent infection to TB activation and provide new potential diagnostic biomarkers for distinguishing PTB and LTBI.

Latent Structure of Motor Abilities in Pre-School Children

ERIC Educational Resources Information Center

Vatroslav, Horvat

2011-01-01

The theoretical and practical knowledge which have so far been acquired through work with pre-school children pointed to the conclusion that the structures of the latent dimensions of the motor abilities differ greatly from such a structure, in pre-school children and adults alike. Establishing the latent structure of the motor abilities in…

Racial discrimination experiences and African American youth adjustment: The role of parenting profiles based on racial socialization and involved-vigilant parenting.

PubMed

Varner, Fatima A; Hou, Yang; Hodzic, Tajma; Hurd, Noelle M; Butler-Barnes, Sheretta T; Rowley, Stephanie J

2018-04-01

The purpose of this study was to test whether parenting profiles based on racial socialization and involved-vigilant parenting would compensate for or moderate associations between racial discrimination experiences and academic outcomes and psychological well-being among African American adolescents. Participants were 1,363 African American adolescents (M age = 14.19; 52.3% female) from 3 Midwestern suburban school districts. Latent profile analysis was used to examine whether there were distinct combinations of parenting. The relationships among racial discrimination experiences, parenting profiles, and adjustment were examined using structural equation modeling (SEM). Three distinct parenting profiles were found: moderate positive (n = 767; moderately high involved-vigilant parenting and racial barrier, racial pride, behavioral, and egalitarian messages, and low negative messages), unengaged (n = 351; low racial socialization messages and moderately low involved-vigilant parenting), and high negative parenting (n = 242; high negative messages, moderate other racial socialization messages, and moderately low involved-vigilant parenting). Racial discrimination experiences were negatively associated with youth adjustment. Moderate positive parenting was related to the best academic outcomes and unengaged parenting was associated with more positive academic outcomes than high negative parenting. Moderate positive parenting was associated with better psychological well-being than unengaged or high negative parenting although the benefits were greater for adolescents with fewer racial discrimination experiences. Distinct patterns of racial socialization messages and involved-vigilant parenting contribute to differences in African American youth adjustment. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities

ERIC Educational Resources Information Center

Bauer, Daniel J.; Curran, Patrick J.

2004-01-01

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model,…

Correlation of Factor IXa Subsite Modulations with Effects on Substrate Discrimination

PubMed Central

Neuenschwander, Pierre F.; Deadmond, Kimberly J.; Zepeda, Karla; Rutland, Joshua

2012-01-01

Summary Background A key feature of factor IXa (fIXa) is its allosteric transformation from an enzymatically latent form into a potent procoagulant. Whilst several small molecules have been found capable of partially effecting fIXa function (i.e. ethylene glycol, calcium ion and LMWH), the resulting modest changes in peptidolytic activity have made the study of their mechanisms of action challenging. Since these effects yield hints into potential regulatory forces that may be operational in full expression of fIXa coagulant activity, their description remains of high interest. Studies of crystal structures have yielded insight into structural changes induced by these effectors, but there remains a paucity of information to correlate any given structural change with specific consequences on fIXa function. Objectives To correlate structural changes induced by these modulators with defined consequences in fIXa substrate discrimination and function. Methods A peptidomics-based MS approach was used to examine patterns of hydrolysis of four combinatorial chemistry-derived pentapeptide libraries by fIXa under various conditions in a soluble, active enzyme system. Results Ethylene glycol specifically alters the S3 subsite of fIXa to render it more tolerant to side chains at the P3 substrate position, while calcium enhances tolerance at the S2 subsite. In contrast, LMWH alters both S2 and S1' subsites. Conclusions These results demonstrate the role of plasticity in regulating fIXa function with respect to discrimination of extended substrate sequences, as well as provide crucial insight into active site modulations that may be capitalized upon by various physiological cofactors of fIXa and in future drug design. PMID:22212890

Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models.

PubMed

Ding, Cherng G; Jane, Ten-Der

2012-09-01

In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified.

Racial discrimination and substance use: longitudinal associations and identity moderators.

PubMed

Fuller-Rowell, Thomas E; Cogburn, Courtney D; Brodish, Amanda B; Peck, Stephen C; Malanchuk, Oksana; Eccles, Jacquelynne S

2012-12-01

Current research indicates that racial discrimination is pervasive in the lives of African Americans. Although there are a variety of ways in which discrimination may contribute to health, one potentially important pathway is through its impact on substance use. Addressing the paucity of longitudinal research on this topic, the present study examined the influence of teacher discrimination on changes in substance use over time among African American adolescents and considered three dimensions of racial identity as moderators of this association (centrality, private regard, and public regard). Latent variable SEM analyses indicated that, on average, levels of discrimination were associated with increases in substance use across the high school years. However, public regard was found to moderate this association such that discrimination was less strongly associated with increases in substance use for individual who reported lower levels of public regard. The implications of these findings are discussed.

Detecting Mixtures from Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

ERIC Educational Resources Information Center

Henson, James M.; Reise, Steven P.; Kim, Kevin H.

2007-01-01

The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) [times] 3 (exogenous latent mean difference) [times] 3 (endogenous latent mean difference) [times] 3 (correlation between factors) [times] 3 (mixture proportions) factorial design. In addition, the efficacy of several…

Mixture IRT Model with a Higher-Order Structure for Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu

2017-01-01

Mixture item response theory (IRT) models have been suggested as an efficient method of detecting the different response patterns derived from latent classes when developing a test. In testing situations, multiple latent traits measured by a battery of tests can exhibit a higher-order structure, and mixtures of latent classes may occur on…

Exploring DSM-5 ADHD criteria beyond young adulthood: phenomenology, psychometric properties and prevalence in a large three-decade birth cohort.

PubMed

Vitola, E S; Bau, C H D; Salum, G A; Horta, B L; Quevedo, L; Barros, F C; Pinheiro, R T; Kieling, C; Rohde, L A; Grevet, E H

2017-03-01

There are still uncertainties on the psychometric validity of the DSM-5 attention deficit hyperactivity disorder (ADHD) criteria for its use in the adult population. We aim to describe the adult ADHD phenotype, to test the psychometric properties of the DSM-5 ADHD criteria, and to calculate the resulting prevalence in a population-based sample in their thirties. A cross-sectional evaluation using the DSM-5 ADHD criteria was carried out in 3574 individuals from the 1982 Pelotas Birth Cohort. Through receiver operator curve, latent and regression analyses, we obtained parameters on construct and discriminant validity. Still, prevalence rates were calculated for different sets of criteria. The latent analysis suggested that the adult ADHD phenotype is constituted mainly by inattentive symptoms. Also, inattention symptoms were the symptoms most associated with impairment. The best cut-off for diagnosis was four symptoms, but sensitivity and specificity for this cut-off was low. ADHD prevalence rates were 2.1% for DSM-5 ADHD criteria and 5.8% for ADHD disregarding age-of-onset criterion. The bi-dimensional ADHD structure proposed by the DSM demonstrated both construct and discriminant validity problems when used in the adult population, since inattention is a much more relevant feature in the adult phenotype. The use of the DSM-5 criteria results in a higher prevalence of ADHD when compared to those obtained by DSM-IV, and prevalence would increase almost threefold when considering current ADHD syndrome. These findings suggest a need for further refinement of the criteria for its use in the adult population.

Bayesian latent structure modeling of walking behavior in a physical activity intervention

PubMed Central

Lawson, Andrew B; Ellerbe, Caitlyn; Carroll, Rachel; Alia, Kassandra; Coulon, Sandra; Wilson, Dawn K; VanHorn, M Lee; St George, Sara M

2017-01-01

The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model’s ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study. PMID:24741000

Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals

PubMed Central

Dicke, Theresa; Marsh, Herbert W.; Riley, Philip; Parker, Philip D.; Guo, Jiesi; Horwood, Marcus

2018-01-01

School principals world-wide report high levels of strain and attrition resulting in a shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that reflect principals' occupational wellbeing. For this purpose, we used the Copenhagen Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering multiple psychosocial factors identified by leading occupational stress theories. We evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant, and convergent validity using latent structural equation modeling in a large sample of Australian school principals (N = 2,049). Results reveal that confirmatory factor analysis produced marginally acceptable model fit. A novel approach we call set exploratory structural equation modeling (set-ESEM), where cross-loadings were only allowed within a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM. Further multitrait-multimethod models based on the set-ESEM confirm the importance of a principal's psychosocial risk factors; Stressors and depression were related to demands and ill-being, while confidence and autonomy were related to wellbeing. We also show that working in the private sector was beneficial for showing a low psychosocial risk, while other demographics have little effects. Finally, we identify five latent risk profiles (high risk to no risk) of school principals based on all psychosocial factors. Overall the research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors. PMID:29760670

The Nature of Coping in Treatment for Marijuana Dependence: Latent Structure and Validation of the Coping Strategies Scale

PubMed Central

Litt, Mark D.; Kadden, Ronald M; Tennen, Howard

2012-01-01

The Coping Strategies Scale (CSS) was designed to assess adaptive changes in substance-use specific coping that result from treatment. The present study sought to examine the latent structure of the CSS in the hope that it might shed light on the coping processes of drug users, and guide the development of a brief version of the CSS. Respondents on the CSS were 751 men and women treated in three clinical trials for marijuana dependence. Posttreatment CSS data were analyzed to determine the nature of coping responses in patients who have been trained to use specific strategies to deal with substance use disorders. Exploratory factor analysis yielded two factors, categorized as problem-focused and emotion-focused coping, but confirmatory factor analysis did not support this structure. When infrequently endorsed items were removed, however, confirmatory factor analysis revealed a good fit to the data. Contrary to expectations, practical strategies that often form the basis for coping skills training, such as avoiding those who smoke, were not frequently endorsed. Problem focused items reflected cognitive commitments to change. Emotion-focused items included cognitive reinterpretations of emotions, to help manage emotional reactions. Brief versions of the CSS based on these factors showed good convergent and discriminant validity. The CSS, and the brief versions of the CSS, may prove useful in future treatment trials to evaluate effects of treatment on coping skills acquisition and utilization in substance dependent individuals. PMID:22082345

Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals.

PubMed

Dicke, Theresa; Marsh, Herbert W; Riley, Philip; Parker, Philip D; Guo, Jiesi; Horwood, Marcus

2018-01-01

School principals world-wide report high levels of strain and attrition resulting in a shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that reflect principals' occupational wellbeing. For this purpose, we used the Copenhagen Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering multiple psychosocial factors identified by leading occupational stress theories. We evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant, and convergent validity using latent structural equation modeling in a large sample of Australian school principals ( N = 2,049). Results reveal that confirmatory factor analysis produced marginally acceptable model fit. A novel approach we call set exploratory structural equation modeling (set-ESEM), where cross-loadings were only allowed within a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM. Further multitrait-multimethod models based on the set-ESEM confirm the importance of a principal's psychosocial risk factors; Stressors and depression were related to demands and ill-being, while confidence and autonomy were related to wellbeing. We also show that working in the private sector was beneficial for showing a low psychosocial risk, while other demographics have little effects. Finally, we identify five latent risk profiles (high risk to no risk) of school principals based on all psychosocial factors. Overall the research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors.

Online Object Tracking, Learning and Parsing with And-Or Graphs.

PubMed

Wu, Tianfu; Lu, Yang; Zhu, Song-Chun

2017-12-01

This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks  , [3] , and the VOT benchmarks [4] -VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network   [5] , [6] . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.

Variable Importance in Multivariate Group Comparisons.

ERIC Educational Resources Information Center

Huberty, Carl J.; Wisenbaker, Joseph M.

1992-01-01

Interpretations of relative variable importance in multivariate analysis of variance are discussed, with attention to (1) latent construct definition; (2) linear discriminant function scores; and (3) grouping variable effects. Two numerical ranking methods are proposed and compared by the bootstrap approach using two real data sets. (SLD)

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

Evidence for the Continuous Latent Structure of Mania in the Epidemiologic Catchment Area from Multiple Latent Structure and Construct Validation Methodologies

PubMed Central

Prisciandaro, James J.; Roberts, John E.

2011-01-01

Background Although psychiatric diagnostic systems have conceptualized mania as a discrete phenomenon, appropriate latent structure investigations testing this conceptualization are lacking. In contrast to these diagnostic systems, several influential theories of mania have suggested a continuous conceptualization. The present study examined whether mania has a continuous or discrete latent structure using a comprehensive approach including taxometric, information-theoretic latent distribution modeling (ITLDM), and predictive validity methodologies in the Epidemiologic Catchment Area (ECA) study. Methods Eight dichotomous manic symptom items were submitted to a variety of latent structural analyses; including factor analyses, taxometric procedures, and ITLDM; in 10,105 ECA community participants. Additionally, a variety of continuous and discrete models of mania were compared in terms of their relative abilities to predict outcomes (i.e., health service utilization, internalizing and externalizing disorders, and suicidal behavior). Results Taxometric and ITLDM analyses consistently supported a continuous conceptualization of mania. In ITLDM analyses, a continuous model of mania demonstrated 6:52:1 odds over the best fitting latent class model of mania. Factor analyses suggested that the continuous structure of mania was best represented by a single latent factor. Predictive validity analyses demonstrated a consistent superior ability of continuous models of mania relative to discrete models. Conclusions The present study provided three independent lines of support for a continuous conceptualization of mania. The implications of a continuous model of mania are discussed. PMID:20507671

The Latent Structure of Dietary Restraint, Body Dissatisfaction, and Drive for Thinness: A Series of Taxometric Analyses

ERIC Educational Resources Information Center

Holm-Denoma, Jill M.; Richey, J. Anthony; Joiner, Thomas E., Jr.

2010-01-01

Although the latent structure of various eating disorders has been explored in previous studies, no published studies have examined the latent structure of theoretically relevant variables that have been shown to cut across eating disorder diagnoses. The current study examined 3 such variables (dietary restraint, body dissatisfaction, and drive…

A Taxonomy of Latent Structure Assumptions for Probability Matrix Decomposition Models.

ERIC Educational Resources Information Center

Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven

2003-01-01

Proposed a taxonomy of latent structure assumptions for probability matrix decomposition (PMD) that includes the original PMD model and a three-way extension of the multiple classification latent class model. Simulation study results show the usefulness of the taxonomy. (SLD)

Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems.

DTIC Science & Technology

1987-11-03

the conversion of the declarativ ;? knowledge Into the procedural knowledge, whereas the encoding of the declarative knowledge is taken to be a...self-explanations during studying examples may make other latent or implicit components more accessible. Our data cannot discriminate between the

Potential of DosR and Rpf antigens from Mycobacterium tuberculosis to discriminate between latent and active tuberculosis in a tuberculosis endemic population of Medellin Colombia.

PubMed

Arroyo, Leonar; Marín, Diana; Franken, Kees L M C; Ottenhoff, Tom H M; Barrera, Luis F

2018-01-08

Tuberculosis (TB) remains one of the most deadly infectious diseases. One-third to one-fourth of the human population is estimated to be infected with Mycobacterium tuberculosis (Mtb) without showing clinical symptoms, a condition called latent TB infection (LTBI). Diagnosis of Mtb infection is based on the immune response to a mixture of mycobacterial antigens (PPD) or to Mtb specific ESAT-6/CFP10 antigens (IGRA), highly expressed during the initial phase of infection. However, the immune response to PPD and IGRA antigens has a low power to discriminate between LTBI and PTB. The T-cell response to a group of so-called latency (DosR-regulon-encoded) and Resuscitation Promoting (Rpf) antigens of Mtb has been proved to be significantly higher in LTBI compared to active TB across many populations, suggesting their potential use as biomarkers to differentiate latent from active TB. PBMCs from a group LTBI (n = 20) and pulmonary TB patients (PTB, n = 21) from an endemic community for TB of the city of Medellín, Colombia, were in vitro stimulated for 7 days with DosR- (Rv1737c, Rv2029c, and Rv2628), Rpf- (Rv0867c and Rv2389c), the recombinant fusion protein ESAT-6-CFP10 (E6-C10)-, or PPD-antigen. The induced IFNγ levels detectable in the supernatants of the antigen-stimulated cells were then used to calculate specificity and sensitivity in discriminating LTBI from PTB, using different statistical approaches. IFNγ production in response to DosR and Rpf antigens was significantly higher in LTBI compared to PTB. ROC curve analyses of IFNγ production allowed differentiation of LTBI from PTB with areas under the curve higher than 0.70. Furthermore, Multiple Correspondence Analysis (MCA) revealed that LTBI is associated with higher levels of IFNγ in response to the different antigens compared to PTB. Analysis based on decision trees showed that the IFNγ levels produced in response to Rv2029c was the leading variable that best-classified disease status. Finally, logistic regression analysis predicted that IFNγ produced by PBMCs in response to E6-C10, Rv2029c, Rv0867c (RpfA) and Rv2389c (RpfA) antigens correlates best with the probability of being latently infected. The Mtb antigens E6-C10, Rv2029c (PfkB), Rv0867c (RpfA) and Rv2389c (RpfA), may be potential candidates to discriminate LTBI from PTB.

Cumulative burden of lifetime adversities: Trauma and mental health in low-SES African Americans and Latino/as.

PubMed

Myers, Hector F; Wyatt, Gail E; Ullman, Jodie B; Loeb, Tamra B; Chin, Dorothy; Prause, Nicole; Zhang, Muyu; Williams, John K; Slavich, George M; Liu, Honghu

2015-05-01

This study examined the utility of a lifetime cumulative adversities and trauma model in predicting the severity of mental health symptoms of depression, anxiety, and posttraumatic stress disorder. We also tested whether ethnicity and gender moderate the effects of this stress exposure construct on mental health using multigroup structural equation modeling. A sample of 500 low-socioeconomic status African American and Latino men and women with histories of adversities and trauma were recruited and assessed with a standard battery of self-report measures of stress and mental health. Multiple-group structural equation models indicated good overall model fit. As hypothesized, experiences of discrimination, childhood family adversities, childhood sexual abuse, other childhood trauma, and chronic stresses all loaded on the latent cumulative burden of adversities and trauma construct (CBAT). The CBAT stress exposure index in turn predicted the mental health status latent variable. Although there were several significant univariate ethnic and gender differences, and ethnic and gender differences were observed on several paths, there were no significant ethnic differences in the final model fit of the data. These findings highlight the deleterious consequences of cumulative stress and trauma for mental health and underscore a need to assess these constructs in selecting appropriate clinical interventions for reducing mental health disparities and improving human health. (c) 2015 APA, all rights reserved).

Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection.

PubMed

Chai, Bian-fang; Yu, Jian; Jia, Cai-Yan; Yang, Tian-bao; Jiang, Ya-wen

2013-07-01

Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.

Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection

NASA Astrophysics Data System (ADS)

Chai, Bian-fang; Yu, Jian; Jia, Cai-yan; Yang, Tian-bao; Jiang, Ya-wen

2013-07-01

Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.

Factor Structure and Stability of Smoking-Related Health Beliefs in the National Lung Screening Trial

PubMed Central

Koblitz, Amber R.; Persoskie, Alexander; Ferrer, Rebecca A.; Klein, William M. P.; Dwyer, Laura A.; Park, Elyse R.

2016-01-01

Introduction: Absolute and comparative risk perceptions, worry, perceived severity, perceived benefits, and self-efficacy are important theoretical determinants of tobacco use, but no measures have been validated to ensure the discriminant validity as well as test-retest reliability of these measures in the tobacco context. The purpose of the current study is to examine the reliability and factor structure of a measure assessing smoking-related health cognitions and emotions in a national sample of current and former heavy smokers in the National Lung Screening Trial. Methods: A sub-study of the National Lung Screening Trial assessed current and former smokers’ (age 55–74; N = 4379) self-reported health cognitions and emotions at trial enrollment and at 12-month follow-up. Items were derived from the Health Belief Model and Self-Regulation Model. Results: An exploratory factor analysis of baseline responses revealed a five-factor structure for former smokers (risk perceptions, worry, perceived severity, perceived benefits, and self-efficacy) and a six-factor structure for current smokers, such that absolute risk and comparative risk perceptions emerged as separate factors. A confirmatory factor analysis of 12-month follow-up responses revealed a good fit for the five latent constructs for former smokers and six latent constructs for current smokers. Longitudinal stability of these constructs was also demonstrated. Conclusions: This is the first study to examine tobacco-related health cognition and emotional constructs over time in current and former heavy smokers undergoing lung screening. This study found that the theoretical constructs were stable across time and that the factor structure differed based on smoking status (current vs. former). PMID:25964503

Comparative Studies on the Characteristic Fatty Acid Profiles of Four Different Chinese Medicinal Sargassum Seaweeds by GC-MS and Chemometrics.

PubMed

Chen, Zhen; Xu, Yibing; Liu, Tao; Zhang, Lining; Liu, Hongbing; Guan, Huashi

2016-03-29

Sargassum seaweeds produce abundant biomass in China and have long been used as herbal medicine and food. Their characteristic fatty acid (FA) profiles and related potential function in promoting cardiovascular health (CVH) have not been systematically investigated. In this study, FA profiles of four medicinal Sargassum were characterized using GC-MS. Principal component analysis was used to discriminate the four medicinal Sargassum, and orthogonal projection to latent structures discriminant analysis was carried out between the two official species HAI ZAO and between the two folk medicine species HAI QIAN. In all of the algae investigated, the major SFA and MUFA were palmitic and stearic acid, respectively, while the major PUFAs were linoleic, arachidonic, and eicosapentaenoic acid. S. fusiforme and S. horneri had higher concentrations of PUFAs. With respect to CVH, all of the studied species, particularly S. fusiforme, exhibited satisfactory levels such as PUFA/SFA ratio and n-6/n-3 ratio. Each species possesses a unique FA profile and is discriminated clearly. Potential key FA markers (between the two Chinese official species, and between the two folk species) are assessed. The study provides characteristic fatty acid profiles of four Chinese medicinal Sargassum and their related potential function in promoting CVH.

Comparative Studies on the Characteristic Fatty Acid Profiles of Four Different Chinese Medicinal Sargassum Seaweeds by GC-MS and Chemometrics

PubMed Central

Chen, Zhen; Xu, Yibing; Liu, Tao; Zhang, Lining; Liu, Hongbing; Guan, Huashi

2016-01-01

Sargassum seaweeds produce abundant biomass in China and have long been used as herbal medicine and food. Their characteristic fatty acid (FA) profiles and related potential function in promoting cardiovascular health (CVH) have not been systematically investigated. In this study, FA profiles of four medicinal Sargassum were characterized using GC-MS. Principal component analysis was used to discriminate the four medicinal Sargassum, and orthogonal projection to latent structures discriminant analysis was carried out between the two official species HAI ZAO and between the two folk medicine species HAI QIAN. In all of the algae investigated, the major SFA and MUFA were palmitic and stearic acid, respectively, while the major PUFAs were linoleic, arachidonic, and eicosapentaenoic acid. S. fusiforme and S. horneri had higher concentrations of PUFAs. With respect to CVH, all of the studied species, particularly S. fusiforme, exhibited satisfactory levels such as PUFA/SFA ratio and n-6/n-3 ratio. Each species possesses a unique FA profile and is discriminated clearly. Potential key FA markers (between the two Chinese official species, and between the two folk species) are assessed. The study provides characteristic fatty acid profiles of four Chinese medicinal Sargassum and their related potential function in promoting CVH. PMID:27043581

Much Ado about Nothing--Or at Best, Very Little

ERIC Educational Resources Information Center

Widaman, Keith F.

2014-01-01

Latent variable structural equation modeling has become the analytic method of choice in many domains of research in psychology and allied social sciences. One important aspect of a latent variable model concerns the relations hypothesized to hold between latent variables and their indicators. The most common specification of structural equation…

Perceived Interpersonal Discrimination and Older Women’s Mental Health: Accumulation Across Domains, Attributions, and Time

PubMed Central

Bécares, Laia; Zhang, Nan

2018-01-01

Abstract Experiencing discrimination is associated with poor mental health, but how cumulative experiences of perceived interpersonal discrimination across attributes, domains, and time are associated with mental disorders is still unknown. Using data from the Study of Women’s Health Across the Nation (1996–2008), we applied latent class analysis and generalized linear models to estimate the association between cumulative exposure to perceived interpersonal discrimination and older women’s mental health. We found 4 classes of perceived interpersonal discrimination, ranging from cumulative exposure to discrimination over attributes, domains, and time to none or minimal reports of discrimination. Women who experienced cumulative perceived interpersonal discrimination over time and across attributes and domains had the highest risk of depression (Center for Epidemiologic Studies Depression Scale score ≥16) compared with women in all other classes. This was true for all women regardless of race/ethnicity, although the type and severity of perceived discrimination differed across racial/ethnic groups. Cumulative exposure to perceived interpersonal discrimination across attributes, domains, and time has an incremental negative long-term association with mental health. Studies that examine exposure to perceived discrimination due to a single attribute in 1 domain or at 1 point in time underestimate the magnitude and complexity of discrimination and its association with health. PMID:29036550

Passion: Does one scale fit all? Construct validity of two-factor passion scale and psychometric invariance over different activities and languages.

PubMed

Marsh, Herbert W; Vallerand, Robert J; Lafrenière, Marc-André K; Parker, Philip; Morin, Alexandre J S; Carbonneau, Noémie; Jowett, Sophia; Bureau, Julien S; Fernet, Claude; Guay, Frédéric; Salah Abduljabbar, Adel; Paquet, Yvan

2013-09-01

The passion scale, based on the dualistic model of passion, measures 2 distinct types of passion: Harmonious and obsessive passions are predictive of adaptive and less adaptive outcomes, respectively. In a substantive-methodological synergy, we evaluate the construct validity (factor structure, reliability, convergent and discriminant validity) of Passion Scale responses (N = 3,571). The exploratory structural equation model fit to the data was substantially better than the confirmatory factor analysis solution, and resulted in better differentiated (less correlated) factors. Results from a 13-model taxonomy of measurement invariance supported complete invariance (factor loadings, factor correlations, item uniquenesses, item intercepts, and latent means) over language (French vs. English; the instrument was originally devised in French, then translated into English) and gender. Strong measurement partial invariance over 5 passion activity groups (leisure, sport, social, work, education) indicates that the same set of items is appropriate for assessing passion across a wide variety of activities--a previously untested, implicit assumption that greatly enhances practical utility. Support was found for the convergent and discriminant validity of the harmonious and obsessive passion scales, based on a set of validity correlates: life satisfaction, rumination, conflict, time investment, activity liking and valuation, and perceiving the activity as a passion.

Large-scale weakly supervised object localization via latent category learning.

PubMed

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories

ERIC Educational Resources Information Center

Grimm, Kevin; Zhang, Zhiyong; Hamagami, Fumiaki; Mazzocco, Michele

2013-01-01

We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of "rates of change" and "acceleration" in latent growth curves--information available indirectly through traditional growth…

Comparison of the meat metabolite composition of Linwu and Pekin ducks using 600 MHz 1H nuclear magnetic resonance spectroscopy.

PubMed

Wang, Xiangrong; Fang, Chengkun; He, Jianhua; Dai, Qiuzhong; Fang, Rejun

2017-01-01

In an effort to further understand of the differences of meat flavor and texture between Linwu ducks and Pekin ducks at market age, we investigated the meat metabolite composition of the two breeds of ducks using 600 MHz 1 H nuclear magnetic resonance (NMR) spectroscopy. Comprehensive multivariate data analysis including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) were applied to analyze the 1 H-NMR profiling data to identify the distinguishing metabolites of breast meat between two breeds of ducks. Compared with 42-d-old Pekin duck meat, breast from 72-d-old Linwu duck has higher concentration of anserine, carnosine, homocarnosine, and nicotinamide, but significantly lower concentration of succinate, creatine, and myo-inositol. These results contribute to a better understanding of the differences in meat metabolite composition between 72-d-old Linwu and 42-d-old Pekin ducks, which could be used to help assess the quality of duck meat as a food. © 2016 Poultry Science Association Inc.

Characterization of oils and fats by 1H NMR and GC/MS fingerprinting: classification, prediction and detection of adulteration.

PubMed

Fang, Guihua; Goh, Jing Yeen; Tay, Manjun; Lau, Hiu Fung; Li, Sam Fong Yau

2013-06-01

The correct identification of oils and fats is important to consumers from both commercial and health perspectives. Proton nuclear magnetic resonance ((1)H NMR) spectroscopy, gas chromatography-mass spectrometry (GC/MS) fingerprinting and chemometrics were employed successfully for the quality control of oils and fats. Principal component analysis (PCA) of both techniques showed group clustering of 14 types of oils and fats. Partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) using GC/MS data had excellent classification sensitivity and specificity compared to models using NMR data. Depending on the availability of the instruments, data from either technique can effectively be applied for the establishment of an oils and fats database to identify unknown samples. Partial least squares (PLS) models were successfully established for the detection of as low as 5% of lard and beef tallow spiked into canola oil, thus illustrating possible applications in Islamic and Jewish countries. Copyright © 2012 Elsevier Ltd. All rights reserved.

Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression

NASA Astrophysics Data System (ADS)

Zhao, Jing; Jung, Yang-Hee; Jang, Choon-Gon; Chun, Kwang-Hoon; Kwon, Sung Won; Lee, Jeongmi

2015-03-01

Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses. Body weight measurement and behavior tests including an open field test and the forced swimming test were completed with the mice as a measure of the phenotypes of depression and antidepressive effects. As a result, 23 metabolites that had been differentially expressed among the control, CMS, and antidepressant-treated groups demonstrated that amino acid metabolism, energy metabolism, adenosine receptors, and neurotransmitters are commonly perturbed by drug treatment. Potential predictive markers for treatment effect were identified: myo-inositol for fluoxetine and lysine and oleic acid for imipramine. Collectively, the current study provides insights into the molecular mechanisms of the antidepressant effects of two widely used medications.

A HPLC-Q-TOF-MS-based urinary metabolomic approach to identification of potential biomarkers of metabolic syndrome.

PubMed

Yu, Zhi-rui; Ning, Yu; Yu, Hao; Tang, Nai-jun

2014-04-01

Metabolic syndrome (MetS) is a serious threat to public health worldwide with an increased risk of developing type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. In this study, a urinary metabolomic approach was performed on high performance liquid chromatography quadrupole time-of-flight mass spectrometry to discriminate 36 male MetS patients and 36 sex and age matched healthy controls. Pattern recognition analyses (principal component analysis and orthogonal projections to latent structures discriminate analysis) commonly demonstrated the difference between MetS patients and no-MetS subjects. This study found 8 metabolites that showed significant changes in patients with MetS, including branch-chain and aromatic amino acids (leucine, tyrosine, phenylalanine and tryptophan), short-chain acylcanitine (tiglylcarnitine), tricarboxylic acid (TCA) cycle intermediate (cis-aconitic acid) and glucuronidated products (cortolone-3-glucuronide and tetrahydroaldosterone-3-glucuronide). The candidate biomarkers revealed in this study could be useful in providing clues for further research focusing on the in-depth investigation of the cause of and cure for MetS.

Evidence of Convergent and Discriminant Validity of Child, Teacher, and Peer Reports of Teacher-Student Support

PubMed Central

Li, Yan; Hughes, Jan N.; Kwok, Oi-man; Hsu, Hsien-Yuan

2012-01-01

This study investigated the construct validity of measures of teacher-student support in a sample of 709 ethnically diverse second and third grade academically at-risk students. Confirmatory factor analysis investigated the convergent and discriminant validities of teacher, child, and peer reports of teacher-student support and child conduct problems. Results supported the convergent and discriminant validity of scores on the measures. Peer reports accounted for the largest proportion of trait variance and non-significant method variance. Child reports accounted for the smallest proportion of trait variance and the largest method variance. A model with two latent factors provided a better fit to the data than a model with one factor, providing further evidence of the discriminant validity of measures of teacher-student support. Implications for research, policy, and practice are discussed. PMID:21767024

The Latent Structure of Attention Deficit/Hyperactivity Disorder in an Adult Sample

PubMed Central

Marcus, David K.; Norris, Alyssa L.; Coccaro, Emil F.

2012-01-01

The vast majority of studies that have examined the latent structure of attention deficit/hyperactivity disorder (ADHD) in children and adolescents have concluded that ADHD has a dimensional latent structure. In other words, ADHD symptomatology exists along a continuum and there is no natural boundary or qualitative distinction (i.e., taxon) separating youth with ADHD from those with subclinical inattention or hyperactivity/impulsivity problems. Although adult ADHD appears to be less prevalent than ADHD in youth (which could suggest a more severe adult ADHD taxon), researchers have yet to examine the latent structure of ADHD in adults. The present study used a sample (N = 600) of adults who completed a self-report measure of ADHD symptoms. The taxometric analyses revealed a dimensional latent structure for inattention, hyperactivity/impulsivity, and ADHD. These findings are consistent with previous taxometric studies that examined ADHD in children and adolescents, and with contemporary polygenic and multifactorial models of ADHD. PMID:22480749

The latent structure of attention deficit/hyperactivity disorder in an adult sample.

PubMed

Marcus, David K; Norris, Alyssa L; Coccaro, Emil F

2012-06-01

The vast majority of studies that have examined the latent structure of attention deficit/hyperactivity disorder (ADHD) in children and adolescents have concluded that ADHD has a dimensional latent structure. In other words, ADHD symptomatology exists along a continuum and there is no natural boundary or qualitative distinction (i.e., taxon) separating youth with ADHD from those with subclinical inattention or hyperactivity/impulsivity problems. Although adult ADHD appears to be less prevalent than ADHD in youth (which could suggest a more severe adult ADHD taxon), researchers have yet to examine the latent structure of ADHD in adults. The present study used a sample (N = 600) of adults who completed a self-report measure of ADHD symptoms. The taxometric analyses revealed a dimensional latent structure for inattention, hyperactivity/impulsivity, and ADHD. These findings are consistent with previous taxometric studies that examined ADHD in children and adolescents, and with contemporary polygenic and multifactorial models of ADHD. Copyright © 2012 Elsevier Ltd. All rights reserved.

Immune Response to Mycobacterial Infection: Lessons from Flow Cytometry

PubMed Central

Rovina, Nikoletta; Panagiotou, Marios; Koulouris, Nikolaos G.

2013-01-01

Detecting and treating active and latent tuberculosis are pivotal elements for effective infection control; yet, due to their significant inherent limitations, the diagnostic means for these two stages of tuberculosis (TB) to date remain suboptimal. This paper reviews the current diagnostic tools for mycobacterial infection and focuses on the application of flow cytometry as a promising method for rapid and reliable diagnosis of mycobacterial infection as well as discrimination between active and latent TB: it summarizes diagnostic biomarkers distinguishing the two states of infection and also features of the distinct immune response against Mycobacterium tuberculosis (Mtb) at certain stages of infection as revealed by flow cytometry to date. PMID:24376464

Immune response to mycobacterial infection: lessons from flow cytometry.

PubMed

Rovina, Nikoletta; Panagiotou, Marios; Pontikis, Konstantinos; Kyriakopoulou, Magdalini; Koulouris, Nikolaos G; Koutsoukou, Antonia

2013-01-01

Detecting and treating active and latent tuberculosis are pivotal elements for effective infection control; yet, due to their significant inherent limitations, the diagnostic means for these two stages of tuberculosis (TB) to date remain suboptimal. This paper reviews the current diagnostic tools for mycobacterial infection and focuses on the application of flow cytometry as a promising method for rapid and reliable diagnosis of mycobacterial infection as well as discrimination between active and latent TB: it summarizes diagnostic biomarkers distinguishing the two states of infection and also features of the distinct immune response against Mycobacterium tuberculosis (Mtb) at certain stages of infection as revealed by flow cytometry to date.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear embedding describing the manifold on which the data reside serve as the latent variable scores. We…

Development and validation of the Overall Depression Severity and Impairment Scale.

PubMed

Bentley, Kate H; Gallagher, Matthew W; Carl, Jenna R; Barlow, David H

2014-09-01

The need to capture severity and impairment of depressive symptomatology is widespread. Existing depression scales are lengthy and largely focus on individual symptoms rather than resulting impairment. The Overall Depression Severity and Impairment Scale (ODSIS) is a 5-item, continuous measure designed for use across heterogeneous mood disorders and with subthreshold depressive symptoms. This study examined the psychometric properties of the ODSIS in outpatients in a clinic for emotional disorders (N = 100), undergraduate students (N = 566), and community-based adults (N = 189). Internal consistency, latent structure, item response theory, classification accuracy, convergent and discriminant validity, and differential item functioning analyses were conducted. ODSIS scores exhibited excellent internal consistency, and confirmatory factor analyses supported a unidimensional structure. Item response theory results demonstrated that the ODSIS provides more information about individuals with high levels of depression than those with low levels of depression. Responses on the ODSIS discriminated well between individuals with and without a mood disorder and depression-related severity across clinical and subclinical levels. A cut score of 8 correctly classified 82% of outpatients as with or without a mood disorder; it evidenced a favorable balance of sensitivity and specificity and of positive and negative predictive values. The ODSIS demonstrated good convergent and discriminant validity, and results indicate that items function similarly across clinical and nonclinical samples. Overall, findings suggest that the ODSIS is a valid tool for measuring depression-related severity and impairment. The brevity and ease of use of the ODSIS support its utility for screening and monitoring treatment response across a variety of settings. PsycINFO Database Record (c) 2014 APA, all rights reserved.

A Latent Heat Retrieval and its Effects on the Intensity and Structure Change of Hurricane Guillermo (1997). Part I: The Algorithm and Observations.

NASA Technical Reports Server (NTRS)

Guimond, Stephen R.; Bourassa, mark A.; Reasor, Paul D.

2011-01-01

The release of latent heat in clouds is an essential part of the formation and I intensification ohurricanes. The community knows very little about the intensity and structure of latent heating due largely to inadequate observations. In this paper, a new method for retrieving the latent heating field in hurricanes from airborne Dopple radar is presented and fields from rapidly intensifying Hurricane Guillermo (1997) are shown.

Prefrontal Cortical GABA Transmission Modulates Discrimination and Latent Inhibition of Conditioned Fear: Relevance for Schizophrenia

PubMed Central

Piantadosi, Patrick T; Floresco, Stan B

2014-01-01

Inhibitory gamma-aminobutyric acid (GABA) transmission within the prefrontal cortex (PFC) regulates numerous functions, and perturbations in GABAergic transmission within this region have been proposed to contribute to some of the cognitive and behavioral abnormalities associated with disorders such as schizophrenia. These abnormalities include deficits in emotional regulation and aberrant attributions of affective salience. Yet, how PFC GABA regulates these types of emotional processes are unclear. To address this issue, we investigated the contribution of PFC GABA transmission to different aspects of Pavlovian emotional learning in rats using translational discriminative fear conditioning and latent inhibition (LI) assays. Reducing prelimbic PFC GABAA transmission via infusions of the antagonist bicuculline before the acquisition or expression of fear conditioning eliminated the ability to discriminate between an aversive conditioned stimulus (CS+) paired with footshock vs a neutral CS–, resembling similar deficits observed in schizophrenic patients. In a separate experiment, blockade of PFC GABAA receptors before CS preexposure (PE) and conditioning did not affect subsequent expression of LI, but did enhance fear in rats that were not preexposed to the CS. In contrast, PFC GABA-blockade before a fear expression test disrupted the recall of learned irrelevance and abolished LI. These data suggest that normal PFC GABA transmission is critical for regulating and mitigating multiple aspects of aversive learning, including discrimination between fear vs safety signals and recall of information about the irrelevance of stimuli. Furthermore, they suggest that similar deficits in emotional regulation observed in schizophrenia may be driven in part by deficient PFC GABA activity. PMID:24784549

Prefrontal cortical GABA transmission modulates discrimination and latent inhibition of conditioned fear: relevance for schizophrenia.

PubMed

Piantadosi, Patrick T; Floresco, Stan B

2014-09-01

Inhibitory gamma-aminobutyric acid (GABA) transmission within the prefrontal cortex (PFC) regulates numerous functions, and perturbations in GABAergic transmission within this region have been proposed to contribute to some of the cognitive and behavioral abnormalities associated with disorders such as schizophrenia. These abnormalities include deficits in emotional regulation and aberrant attributions of affective salience. Yet, how PFC GABA regulates these types of emotional processes are unclear. To address this issue, we investigated the contribution of PFC GABA transmission to different aspects of Pavlovian emotional learning in rats using translational discriminative fear conditioning and latent inhibition (LI) assays. Reducing prelimbic PFC GABAA transmission via infusions of the antagonist bicuculline before the acquisition or expression of fear conditioning eliminated the ability to discriminate between an aversive conditioned stimulus (CS+) paired with footshock vs a neutral CS-, resembling similar deficits observed in schizophrenic patients. In a separate experiment, blockade of PFC GABAA receptors before CS preexposure (PE) and conditioning did not affect subsequent expression of LI, but did enhance fear in rats that were not preexposed to the CS. In contrast, PFC GABA-blockade before a fear expression test disrupted the recall of learned irrelevance and abolished LI. These data suggest that normal PFC GABA transmission is critical for regulating and mitigating multiple aspects of aversive learning, including discrimination between fear vs safety signals and recall of information about the irrelevance of stimuli. Furthermore, they suggest that similar deficits in emotional regulation observed in schizophrenia may be driven in part by deficient PFC GABA activity.

Three Approaches to Using Lengthy Ordinal Scales in Structural Equation Models: Parceling, Latent Scoring, and Shortening Scales

ERIC Educational Resources Information Center

Yang, Chongming; Nay, Sandra; Hoyle, Rick H.

2010-01-01

Lengthy scales or testlets pose certain challenges for structural equation modeling (SEM) if all the items are included as indicators of a latent construct. Three general approaches to modeling lengthy scales in SEM (parceling, latent scoring, and shortening) have been reviewed and evaluated. A hypothetical population model is simulated containing…

A Generalized Approach to Defining Item Discrimination for DCMs

ERIC Educational Resources Information Center

Henson, Robert; DiBello, Lou; Stout, Bill

2018-01-01

Diagnostic classification models (DCMs, also known as cognitive diagnosis models) hold the promise of providing detailed classroom information about the skills a student has or has not mastered. Specifically, DCMs are special cases of constrained latent class models where classes are defined based on mastery/nonmastery of a set of attributes (or…

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

Estimation and Model Selection for Finite Mixtures of Latent Interaction Models

ERIC Educational Resources Information Center

Hsu, Jui-Chen

2011-01-01

Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…

Adolescent cigarette smoking: health-related behavior or normative transgression?

PubMed

Turbin, M S; Jessor, R; Costa, F M

2000-09-01

Relations among measures of adolescent behavior were examined to determine whether cigarette smoking fits into a structure of problem behaviors-behaviors that involve normative transgression-or a structure of health-related behaviors, or both. In an ethnically and socioeconomically diverse sample of 1782 male and female high school adolescents, four first-order problem behavior latent variables-sexual intercourse experience, alcohol abuse, illicit drug use, and delinquency-were established and together were shown to reflect a second-order latent variable of problem behavior. Four first-order latent variables of health-related behaviors-unhealthy dietary habits, sedentary behavior, unsafe behavior, and poor dental hygiene-were also established and together were shown to reflect a second-order latent variable of health-compromising behavior. The structure of relations among those latent variables was modeled. Cigarette smoking had a significant and substantial loading only on the problem-behavior latent variable; its loading on the health-compromising behavior latent variable was essentially zero. Adolescent cigarette smoking relates strongly and directly to problem behaviors and only indirectly, if at all, to health-compromising behaviors. Interventions to prevent or reduce adolescent smoking should attend more to factors that influence problem behaviors.

An instance theory of associative learning.

PubMed

Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

2012-03-01

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Achievement-Relevant Personality: Relations with the Big Five and Validation of an Efficient Instrument

PubMed Central

Briley, Daniel A.; Domiteaux, Matthew; Tucker-Drob, Elliot M.

2014-01-01

Many achievement-relevant personality measures (APMs) have been developed, but the interrelations among APMs or associations with the broader personality landscape are not well-known. In Study 1, 214 participants were measured on 36 APMs and a measure of the Big Five. Factor analytic results supported the convergent and discriminant validity of five latent dimensions: performance, mastery, self-doubt, effort, and intellectual investment. Conscientiousness, neuroticism, and openness to experience had the most consistent associations with APMs. We constructed a more efficient scale– the Multidimensional Achievement-Relevant Personality Scale (MAPS). In Study 2, we replicated the factor structure and external correlates of the MAPS in a sample of 359 individuals. Finally, we validated the MAPS with four indicators of academic performance and demonstrated incremental validity. PMID:24839374

Taxometric Analysis as a General Strategy for Distinguishing Categorical from Dimensional Latent Structure

ERIC Educational Resources Information Center

McGrath, Robert E.; Walters, Glenn D.

2012-01-01

Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to…

Do gender and directness of trauma exposure moderate PTSD's latent structure?

PubMed

Frankfurt, Sheila B; Armour, Cherie; Contractor, Ateka A; Elhai, Jon D

2016-11-30

The PTSD diagnosis and latent structure were substantially revised in the transition from DSM-IV to DSM-5. However, three alternative models (i.e., anhedonia model, externalizing behavior model, and hybrid model) of PTSD fit the DSM-5 symptom criteria better than the DSM-5 factor model. Thus, the psychometric performance of the DSM-5 and alternative models' PTSD factor structure needs to be critically evaluated. The current study examined whether gender or trauma directness (i.e., direct or indirect trauma exposure) moderates the PTSD latent structure when using the DSM-5 or alternative models. Model performance was evaluated with measurement invariance testing procedures on a large undergraduate sample (n=455). Gender and trauma directness moderated the DSM-5 PTSD and externalizing behavior model and did not moderate the anhedonia and hybrid models' latent structure. Clinical implications and directions for future research are discussed. Published by Elsevier Ireland Ltd.

Mokken scaling analysis of the Hospital Anxiety and Depression Scale in individuals with cardiovascular disease.

PubMed

Cosco, Theodore D; Doyle, Frank; Watson, Roger; Ward, Mark; McGee, Hannah

2012-01-01

The Hospital Anxiety and Depression Scale (HADS) is a prolifically used scale of anxiety and depression. The original bidimensional anxiety-depression latent structure of the HADS has come under significant scrutiny, with previous studies revealing one-, two-, three- and four-dimensional structures. The current study examines the latent structure of the HADS using a non-parametric item response theory method. Using data conglomerated from four independent studies of cardiovascular disease employing the HADS (n=893), Mokken scaling procedure was conducted to assess the latent structure of the HADS. A single scale consisting of 12 of 14 HADS items was revealed, indicating a unidimensional latent HADS structure. The HADS was initially intended to measure mutually exclusive levels of anxiety and depression; however, the current study indicates that a single dimension of general psychological distress is captured. Copyright © 2012 Elsevier Inc. All rights reserved.

Metabolic dependence of green tea on plucking positions revisited: a metabolomic study.

PubMed

Lee, Jang-Eun; Lee, Bum-Jin; Hwang, Jeong-Ah; Ko, Kwang-Sup; Chung, Jin-Oh; Kim, Eun-Hee; Lee, Sang-Jun; Hong, Young-Shick

2011-10-12

The dependence of global green tea metabolome on plucking positions was investigated through (1)H nuclear magnetic resonance (NMR) analysis coupled with multivariate statistical data set. Pattern recognition methods, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), were employed for a finding metabolic discrimination among fresh green tea leaves plucked at different positions from young to old leaves. In addition to clear metabolic discrimination among green tea leaves, elevations in theanine, caffeine, and gallic acid levels but reductions in catechins, such as epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epigallocatechin-3-gallate (EGCG), glucose, and sucrose levels were observed, as the green tea plant grows up. On the other hand, the younger the green tea leaf is, the more theanine, caffeine, and gallic acid but the lesser catechins accumlated in the green tea leaf, revealing a reverse assocation between theanine and catechins levels due to incorporaton of theanine into catechins with growing up green tea plant. Moreover, as compared to the tea leaf, the observation of marked high levels of theanine and low levels of catechins in green tea stems exhibited a distinct tea plant metabolism between the tea leaf and the stem. This metabolomic approach highlights taking insight to global metabolic dependence of green tea leaf on plucking position, thereby providing distinct information on green tea production with specific tea quality.

The algebraic theory of latent projectors in lambda matrices

NASA Technical Reports Server (NTRS)

Denman, E. D.; Leyva-Ramos, J.; Jeon, G. J.

1981-01-01

Multivariable systems such as a finite-element model of vibrating structures, control systems, and large-scale systems are often formulated in terms of differential equations which give rise to lambda matrices. The present investigation is concerned with the formulation of the algebraic theory of lambda matrices and the relationship of latent roots, latent vectors, and latent projectors to the eigenvalues, eigenvectors, and eigenprojectors of the companion form. The chain rule for latent projectors and eigenprojectors for the repeated latent root or eigenvalues is given.

The Theory of Planned Behavior within the Stages of the Transtheoretical Model: Latent Structural Modeling of Stage-Specific Prediction Patterns in Physical Activity

ERIC Educational Resources Information Center

Lippke, Sonia; Nigg, Claudio R.; Maddock, Jay E.

2007-01-01

This is the first study to test whether the stages of change of the transtheoretical model are qualitatively different through exploring discontinuity patterns in theory of planned behavior (TPB) variables using latent multigroup structural equation modeling (MSEM) with AMOS. Discontinuity patterns in terms of latent means and prediction patterns…

Examining Factor Score Distributions to Determine the Nature of Latent Spaces

ERIC Educational Resources Information Center

Steinley, Douglas; McDonald, Roderick P.

2007-01-01

Similarities between latent class models with K classes and linear factor models with K-1 factors are investigated. Specifically, the mathematical equivalence between the covariance structure of the two models is discussed, and a Monte Carlo simulation is performed using generated data that represents both latent factors and latent classes with…

Entity Relation Detection with Factorial Hidden Markov Models and Maximum Entropy Discriminant Latent Dirichlet Allocations

ERIC Educational Resources Information Center

Li, Dingcheng

2011-01-01

Coreference resolution (CR) and entity relation detection (ERD) aim at finding predefined relations between pairs of entities in text. CR focuses on resolving identity relations while ERD focuses on detecting non-identity relations. Both CR and ERD are important as they can potentially improve other natural language processing (NLP) related tasks…

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Residual Structures in Latent Growth Curve Modeling

ERIC Educational Resources Information Center

Grimm, Kevin J.; Widaman, Keith F.

2010-01-01

Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances…

Comparison of four commercially available avidity tests for Toxoplasma gondii-specific IgG antibodies.

PubMed

Villard, O; Breit, L; Cimon, B; Franck, J; Fricker-Hidalgo, H; Godineau, N; Houze, S; Paris, L; Pelloux, H; Villena, I; Candolfi, E

2013-02-01

Toxoplasma infection in pregnant women may cause congenital toxoplasmosis. Diagnosis of infection is based on serological tests aimed at detecting IgM and IgG antibodies against Toxoplasma gondii. However, IgM antibodies are not an accurate marker for discriminating between acute and latent infection. Detection of residual or persistent IgM may occur months or even years after primary infection, while the IgG avidity test is a rapid means of identifying latent infections in pregnant women who exhibit both IgG and IgM anti-Toxoplasma antibodies on initial testing during pregnancy. In this study, we assessed and compared the performances of four commercially available Toxoplasma IgG avidity tests in immunocompetent and immunocompromised patients with acute and latent toxoplasmosis. The positive predictive value of high avidity to confirm latent toxoplasmosis was 100% for all the assays, indicating that high avidity is a hallmark of latent infection. However, the negative predictive value of high avidity ranged from 99.2% (bioMérieux) to 95.3% (Abbott), indicating that acute toxoplasmosis could not be reliably diagnosed based on low IgG avidity alone. Thus, the avidity test provides a rapid means for identifying latent Toxoplasma infection in immunocompetent pregnant women presenting both IgG and IgM anti-Toxoplasma antibodies on initial testing. In terms of cost-effectiveness, avidity testing is a powerful tool that optimizes screening and follow-up of pregnant women while minimizing the costs of screening by avoiding subsequent costly maternal and fetal investigation and unnecessary treatment. The cheapest assay, Vidas Toxo IgG Avidity, also had the best performance for the diagnosis of latent toxoplasmosis.

Comparison of Four Commercially Available Avidity Tests for Toxoplasma gondii-Specific IgG Antibodies

PubMed Central

Breit, L.; Cimon, B.; Franck, J.; Fricker-Hidalgo, H.; Godineau, N.; Houze, S.; Paris, L.; Pelloux, H.; Villena, I.

2013-01-01

Toxoplasma infection in pregnant women may cause congenital toxoplasmosis. Diagnosis of infection is based on serological tests aimed at detecting IgM and IgG antibodies against Toxoplasma gondii. However, IgM antibodies are not an accurate marker for discriminating between acute and latent infection. Detection of residual or persistent IgM may occur months or even years after primary infection, while the IgG avidity test is a rapid means of identifying latent infections in pregnant women who exhibit both IgG and IgM anti-Toxoplasma antibodies on initial testing during pregnancy. In this study, we assessed and compared the performances of four commercially available Toxoplasma IgG avidity tests in immunocompetent and immunocompromised patients with acute and latent toxoplasmosis. The positive predictive value of high avidity to confirm latent toxoplasmosis was 100% for all the assays, indicating that high avidity is a hallmark of latent infection. However, the negative predictive value of high avidity ranged from 99.2% (bioMérieux) to 95.3% (Abbott), indicating that acute toxoplasmosis could not be reliably diagnosed based on low IgG avidity alone. Thus, the avidity test provides a rapid means for identifying latent Toxoplasma infection in immunocompetent pregnant women presenting both IgG and IgM anti-Toxoplasma antibodies on initial testing. In terms of cost-effectiveness, avidity testing is a powerful tool that optimizes screening and follow-up of pregnant women while minimizing the costs of screening by avoiding subsequent costly maternal and fetal investigation and unnecessary treatment. The cheapest assay, Vidas Toxo IgG Avidity, also had the best performance for the diagnosis of latent toxoplasmosis. PMID:23239801

Does Attention-Deficit/Hyperactivity Disorder Have a Dimensional Latent Structure? A Taxometric Analysis

PubMed Central

Marcus, David K.; Barry, Tammy D.

2010-01-01

An understanding of the latent structure of attention-deficit/hyperactivity disorder (ADHD) is essential for developing causal models of this disorder. Although some researchers have presumed that ADHD is dimensional and others have assumed that it is taxonic, there has been relatively little research directly examining the latent structure of ADHD. The authors conducted a set of taxometric analyses using data from the NICHD Study of Early Child Care and Youth Development (ns between 667–1078). The results revealed a dimensional latent structure across a variety of different analyses and sets of indicators, for inattention, hyperactivity/impulsivity, and ADHD. Furthermore, analyses of correlations with associated features indicated that dimensional models demonstrated stronger validity coefficients with these criterion measures than dichotomous models. These findings jibe with recent research on the genetic basis of ADHD and with contemporary models of ADHD. PMID:20973595

Does attention-deficit/hyperactivity disorder have a dimensional latent structure? A taxometric analysis.

PubMed

Marcus, David K; Barry, Tammy D

2011-05-01

An understanding of the latent structure of attention-deficit/hyperactivity disorder (ADHD) is essential for developing causal models of this disorder. Although some researchers have presumed that ADHD is dimensional and others have assumed that it is taxonic, there has been relatively little research directly examining the latent structure of ADHD. The authors conducted a set of taxometric analyses using data from the NICHD Study of Early Child Care and Youth Development (ns between 667 and 1,078). The results revealed a dimensional latent structure across a variety of different analyses and sets of indicators for inattention, hyperactivity/impulsivity, and ADHD. Furthermore, analyses of correlations with associated features indicated that dimensional models demonstrated stronger validity coefficients with these criterion measures than dichotomous models. These findings jibe with recent research on the genetic basis of ADHD and with contemporary models of ADHD.

Hierarchical Discriminant Analysis.

PubMed

Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

2018-01-18

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

An HR-MAS MR Metabolomics Study on Breast Tissues Obtained with Core Needle Biopsy

PubMed Central

Cho, Nariya; Chang, Jung Min; Koo, Hye Ryoung; Yi, Ann; Kim, Hyeonjin; Park, Sunghyouk; Moon, Woo Kyung

2011-01-01

Background Much research has been devoted to the development of new breast cancer diagnostic measures, including those involving high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopic techniques. Previous HR-MAS MR results have been obtained from post-surgery samples, which limits their direct clinical applicability. Methodology/Principal Findings In the present study, we performed HR-MAS MR spectroscopic studies on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy. We showed that cancer and non-cancer samples can be discriminated very well with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA) multivariate model on the MR spectra. A subsequent blind test showed 69% sensitivity and 94% specificity in the prediction of the cancer status. A spectral analysis showed that in cancer cells, taurine- and choline-containing compounds are elevated. Our approach, additionally, could predict the progesterone receptor statuses of the cancer patients. Conclusions/Significance HR-MAS MR metabolomics on intact breast tissues obtained by core needle biopsy may have a potential to be used as a complement to the current diagnostic and prognostic measures for breast cancers. PMID:22028780

NanoESI-MS-based lipidomics to discriminate between cultivars, cultivation ages, and parts of Panax ginseng.

PubMed

Kim, So-Hyun; Shin, Yoo-Soo; Choi, Hyung-Kyoon

2016-03-01

Korean ginseng (Panax ginseng C.A. Meyer) is one of the most popular medicinal herbs used in Asia, including Korea and China. In the present study lipid profiling of two officially registered cultivars (P. ginseng 'Chunpoong' and P. ginseng 'Yunpoong') was performed at different cultivation ages (5 and 6 years) and on different parts (tap roots, lateral roots, and rhizomes) using nano-electrospray ionization-mass spectrometry (nanoESI-MS). In total, 30 compounds including galactolipids, phospholipids, triacylglycerols, and ginsenosides were identified. Among them, triacylglycerol 54:6 (18:2/18:2/18:2), phosphatidylglycerol 34:3 (16:0/18:3), monogalactosyldiacylglycerol 36:4 (18:2/18:2), phosphatidic acid species 36:4 (18:2/18:2), and 34:1 (16:0/18:1) were selected as biomarkers to discriminate cultivars, cultivation ages, and parts. In addition, an unknown P. ginseng sample was successfully predicted by applying validated partial least squares projection to latent structures regression models. This is the first study regarding the identification of intact lipid species from P. ginseng and to predict cultivars, cultivation ages, and parts of P. ginseng using nanoESI-MS-based lipidomic profiling with a multivariate statistical analysis.

A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: A case study of Panax ginseng: Metabolomics for the authenticity of herbal medicine.

PubMed

Nguyen, Huy Truong; Lee, Dong-Kyu; Choi, Young-Geun; Min, Jung-Eun; Yoon, Sang Jun; Yu, Yun-Hyun; Lim, Johan; Lee, Jeongmi; Kwon, Sung Won; Park, Jeong Hill

2016-05-30

Ginseng, the root of Panax ginseng has long been the subject of adulteration, especially regarding its origins. Here, 60 ginseng samples from Korea and China initially displayed similar genetic makeup when investigated by DNA-based technique with 23 chloroplast intergenic space regions. Hence, (1)H NMR-based metabolomics with orthogonal projections on the latent structure-discrimination analysis (OPLS-DA) were applied and successfully distinguished between samples from two countries using seven primary metabolites as discrimination markers. Furthermore, to recreate adulteration in reality, 21 mixed samples of numerous Korea/China ratios were tested with the newly built OPLS-DA model. The results showed satisfactory separation according to the proportion of mixing. Finally, a procedure for assessing mixing proportion of intentionally blended samples that achieved good predictability (adjusted R(2)=0.8343) was constructed, thus verifying its promising application to quality control of herbal foods by pointing out the possible mixing ratio of falsified samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Generalized Structured Component Analysis with Latent Interactions

ERIC Educational Resources Information Center

Hwang, Heungsun; Ho, Moon-Ho Ringo; Lee, Jonathan

2010-01-01

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA…

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

Identification of Medicinal Mugua Origin by Near Infrared Spectroscopy Combined with Partial Least-squares Discriminant Analysis.

PubMed

Han, Bangxing; Peng, Huasheng; Yan, Hui

2016-01-01

Mugua is a common Chinese herbal medicine. There are three main medicinal origin places in China, Xuancheng City Anhui Province, Qijiang District Chongqing City, Yichang City, Hubei Province, and suitable for food origin places Linyi City Shandong Province. To construct a qualitative analytical method to identify the origin of medicinal Mugua by near infrared spectroscopy (NIRS). Partial least squares discriminant analysis (PLSDA) model was established after the Mugua derived from five different origins were preprocessed by the original spectrum. Moreover, the hierarchical cluster analysis was performed. The result showed that PLSDA model was established. According to the relationship of the origins-related important score and wavenumber, and K-mean cluster analysis, the Muguas derived from different origins were effectively identified. NIRS technology can quickly and accurately identify the origin of Mugua, provide a new method and technology for the identification of Chinese medicinal materials. After preprocessed by D1+autoscale, more peaks were increased in the preprocessed Mugua in the near infrared spectrumFive latent variable scores could reflect the information related to the origin place of MuguaOrigins of Mugua were well-distinguished according to K. mean value clustering analysis. Abbreviations used: TCM: Traditional Chinese Medicine, NIRS: Near infrared spectroscopy, SG: Savitzky-Golay smoothness, D1: First derivative, D2: Second derivative, SNV: Standard normal variable transformation, MSC: Multiplicative scatter correction, PLSDA: Partial least squares discriminant analysis, LV: Latent variable, VIP scores: Important score.

Posttraumatic stress disorder symptom structure in Chinese adolescents exposed to a deadly earthquake.

PubMed

Wang, Li; Long, Di; Li, Zhongquan; Armour, Cherie

2011-07-01

This present study examined the structure of posttraumatic stress disorder (PTSD) symptoms in a large sample of Chinese adolescents exposed to a deadly earthquake. A total of 2,800 middle school students aged 12 to 18 years participated in the study 6 months after the "Wenchuan Earthquake". Results of confirmatory factor analysis indicated that a five-factor intercorrelated model composed of intrusion, avoidance, numbing, dysphoric arousal, and anxious arousal, fit data significantly better than both the four-factor numbing model King et al. (Psychological Assessment 10:90-96, 1998) and the four-factor dysphoria model Simms et al. (Journal of Abnormal Psychology 111:637-647, 2002). Further examination of the external convergent and discriminant validity revealed that except for the dysphoric arousal factor, the remaining four PTSD factors yielded significantly different correlations with external measures of anxiety vs. depression. The findings add to the limited literature on the factor structure of PTSD in youths and on the five-factor PTSD model. In addition, they provide more detail into the latent psychopathological processes of PTSD, and inform the forthcoming DSM-5.

Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

A Bayesian Model for the Estimation of Latent Interaction and Quadratic Effects When Latent Variables Are Non-Normally Distributed

ERIC Educational Resources Information Center

Kelava, Augustin; Nagengast, Benjamin

2012-01-01

Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…

Applying Item Response Theory to the Development of a Screening Adaptation of the Goldman-Fristoe Test of Articulation-Second Edition

ERIC Educational Resources Information Center

Brackenbury, Tim; Zickar, Michael J.; Munson, Benjamin; Storkel, Holly L.

2017-01-01

Purpose: Item response theory (IRT) is a psychometric approach to measurement that uses latent trait abilities (e.g., speech sound production skills) to model performance on individual items that vary by difficulty and discrimination. An IRT analysis was applied to preschoolers' productions of the words on the Goldman-Fristoe Test of…

Electronic effects on melting: Comparison of aluminum cluster anions and cations

NASA Astrophysics Data System (ADS)

Starace, Anne K.; Neal, Colleen M.; Cao, Baopeng; Jarrold, Martin F.; Aguado, Andrés; López, José M.

2009-07-01

Heat capacities have been measured as a function of temperature for aluminum cluster anions with 35-70 atoms. Melting temperatures and latent heats are determined from peaks in the heat capacities; cohesive energies are obtained for solid clusters from the latent heats and dissociation energies determined for liquid clusters. The melting temperatures, latent heats, and cohesive energies for the aluminum cluster anions are compared to previous measurements for the corresponding cations. Density functional theory calculations have been performed to identify the global minimum energy geometries for the cluster anions. The lowest energy geometries fall into four main families: distorted decahedral fragments, fcc fragments, fcc fragments with stacking faults, and "disordered" roughly spherical structures. The comparison of the cohesive energies for the lowest energy geometries with the measured values allows us to interpret the size variation in the latent heats. Both geometric and electronic shell closings contribute to the variations in the cohesive energies (and latent heats), but structural changes appear to be mainly responsible for the large variations in the melting temperatures with cluster size. The significant charge dependence of the latent heats found for some cluster sizes indicates that the electronic structure can change substantially when the cluster melts.

Ethnic identity, racial discrimination and attenuated psychotic symptoms in an urban population of emerging adults.

PubMed

Anglin, Deidre M; Lui, Florence; Espinosa, Adriana; Tikhonov, Aleksandr; Ellman, Lauren

2018-06-01

Studies suggest strong ethnic identity generally protects against negative mental health outcomes associated with racial discrimination. In light of evidence suggesting racial discrimination may enhance psychosis risk in racial and ethnic minority (REM) populations, the present study explored the relationship between ethnic identity and attenuated positive psychotic symptoms (APPS) and whether ethnic identity moderates the association between racial discrimination and these symptoms. A sample of 644 non-help-seeking REM emerging adults was administered self-report inventories for psychosis risk, experiences of discrimination and ethnic identity. Latent class analysis was applied to determine the nature and number of ethnic identity types in this population. The direct association between ethnic identity and APPS and the interaction between ethnic identity and racial discrimination on APPS were determined in linear regression analyses. Results indicated three ethnic identity classes (very low, moderate to high and very high). Ethnic identity was not directly related to APPS; however, it was related to APPS under racially discriminating conditions. Specifically, participants who experienced discrimination in the moderate to high or very high ethnic identity classes reported fewer symptoms than participants who experienced discrimination in the very low ethnic identity class. Strong ethnic group affiliation and connection may serve a protective function for psychosis risk in racially discriminating environments and contexts among REM young adults. The possible social benefits of strong ethnic identification among REM youth who face racial discrimination should be explored further in clinical high-risk studies. © 2016 John Wiley & Sons Australia, Ltd.

Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2006-01-01

In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…

Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.

PubMed

Hoyle, R H

1991-02-01

Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.

Does perceived racial discrimination predict changes in psychological distress and substance use over time? An examination among Black emerging adults.

PubMed

Hurd, Noelle M; Varner, Fatima A; Caldwell, Cleopatra H; Zimmerman, Marc A

2014-07-01

We assessed whether perceived discrimination predicted changes in psychological distress and substance use over time and whether psychological distress and substance use predicted change in perceived discrimination over time. We also assessed whether associations between these constructs varied by gender. Our sample included 607 Black emerging adults (53% female) followed for 4 years. Participants reported the frequency with which they had experienced racial hassles during the past year, symptoms of anxiety and depression during the past week, and cigarette and alcohol use during the past 30 days. We estimated a series of latent growth models to test our study hypotheses. We found that the intercept of perceived discrimination predicted the linear slopes of anxiety symptoms, depressive symptoms, and alcohol use. We did not find any associations between the intercept factors of our mental health or substance use variables and the perceived discrimination linear slope factor. We found limited differences across paths by gender. Our findings suggest a temporal ordering in the associations among perceived racial discrimination, psychological distress, and alcohol use over time among emerging adults. Further, our findings suggest that perceived racial discrimination may be similarly harmful among men and women. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

DSM-IV Personality Disorders and Associations with Externalizing and Internalizing Disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions

PubMed Central

Harford, Thomas C.; Chen, Chiung M.; Saha, Tulshi D.; Smith, Sharon M.; Ruan, W. June; Grant, Bridget F.

2013-01-01

Background Although associations between personality disorders and psychiatric disorders are well established in general population studies, their association with liability dimensions for externalizing and internalizing disorders has not been fully assessed. The purpose of this study is to examine associations between personality disorders (PDs) and lifetime externalizing and internalizing Axis I disorders. Methods Data were obtained from the total sample of 34,653 respondents from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drawing on the literature, a 3-factor exploratory structural equation model was selected to simultaneously assess the measurement relations among DSM-IV Axis I substance use and mood and anxiety disorders and the structural relations between the latent internalizing-externalizing dimensions and DSM-IV PDs, adjusting for gender, age, race/ethnicity, and marital status. Results Antisocial, histrionic, and borderline PDs were strong predictors for the externalizing factor, while schizotypal, borderline, avoidant, and obsessive-compulsive PDs had significantly larger effects on the internalizing fear factor when compared to the internalizing misery factor. Paranoid, schizoid, narcissistic, and dependent PDs provided limited discrimination between and among the three factors. An overarching latent factor representing general personality dysfunction was significantly greater on the internalizing fear factor followed by the externalizing factor, and weakest for the internalizing misery factor. Conclusion Personality disorders offer important opportunities for studies on the externalizing-internalizing spectrum of common psychiatric disorders. Future studies based on panic, anxiety, and depressive symptoms may elucidate PD associations with the internalizing spectrum of disorders. PMID:23932575

DSM-IV personality disorders and associations with externalizing and internalizing disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions.

PubMed

Harford, Thomas C; Chen, Chiung M; Saha, Tulshi D; Smith, Sharon M; Ruan, W June; Grant, Bridget F

2013-11-01

Although associations between personality disorders and psychiatric disorders are well established in general population studies, their association with liability dimensions for externalizing and internalizing disorders has not been fully assessed. The purpose of this study is to examine associations between personality disorders (PDs) and lifetime externalizing and internalizing Axis I disorders. Data were obtained from the total sample of 34,653 respondents from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drawing on the literature, a 3-factor exploratory structural equation model was selected to simultaneously assess the measurement relations among DSM-IV Axis I substance use and mood and anxiety disorders and the structural relations between the latent internalizing-externalizing dimensions and DSM-IV PDs, adjusting for gender, age, race/ethnicity, and marital status. Antisocial, histrionic, and borderline PDs were strong predictors for the externalizing factor, while schizotypal, borderline, avoidant, and obsessive-compulsive PDs had significantly larger effects on the internalizing fear factor when compared to the internalizing misery factor. Paranoid, schizoid, narcissistic, and dependent PDs provided limited discrimination between and among the three factors. An overarching latent factor representing general personality dysfunction was significantly greater on the internalizing fear factor followed by the externalizing factor, and weakest for the internalizing misery factor. Personality disorders offer important opportunities for studies on the externalizing-internalizing spectrum of common psychiatric disorders. Future studies based on panic, anxiety, and depressive symptoms may elucidate PD associations with the internalizing spectrum of disorders. Published by Elsevier Ltd.

Latent Heating Structures Derived from TRMM

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E. A.; Adler, R.; Hou, A.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.; Olson, W.; Satoh, S.

2004-01-01

Rainfall is the fundamental variable within the Earth's hydrological cycle because it is both the main forcing term leading to variations in continental and oceanic surface water budgets. The vertical distribution of latent heat release, which is accompanied with rain, modulates large-scale meridional and zonal circulations within the tropics as well as modifying the energetic efficiency of mid-latitude weather systems. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission 0. The TRMM observatory, whose development was a joint US-Japan space endeavor, was launched in November 1997. TRMM measurements provide an accurate account of rainfall over the global tropics, information which can be .used to estimate the four-dimensional structure of latent heating across the entire tropical and sub-tropical regions. Various algorithm methodologies for estimating latent heating based on rain rate measurements from TRMM observations are described. The strengths and weaknesses of these algorithms, as well as the latent heating products generated by these algorithms, are also discussed along with validation analyses of the products. The investigation paper provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, and concludes with remarks designed to stimulate further research on latent heating retrieval

The Erosive Effects of Racism: Reduced Self-control Mediates the Relation between Perceived Racial Discrimination and Substance Use in African American Adolescents

PubMed Central

Gibbons, Frederick X.; O'Hara, Ross E.; Stock, Michelle L.; Gerrard, Meg; Weng, Chih-Yuan; Wills, Thomas A.

2012-01-01

Perceived racial discrimination, self-control, anger, and either substance use or use cognitions were assessed in two studies conducted with samples of African American adolescents. The primary goal was to examine the relation between discrimination and self-control over time; a second goal was to determine if that relation mediates the link between discrimination and substance use found in previous research. Study 1, which included a latent growth curve analysis with three waves of data, indicated that experience with discrimination (from age 10 to age 18) was associated with reduced self-control, which then predicted increased substance use. Additional analyses indicated anger was also a mediator of this discrimination to use relation. Study 2, which was experimental, showed that envisioning an experience involving discrimination was associated with an increase in substance-related responses to double entendre words (e.g., “pot,” “roach”) in a word association task, especially for participants who were low in dispositional self-control. The effect was again mediated by reports of anger. Thus, the “double mediation” pattern was: discrimination → more anger and reduced self-control → increased substance use and/or substance cognitions. Results are discussed in terms of the long-term impact of discrimination on self-control and health behavior. Implications for interventions aimed at ameliorating the negative effects of discrimination and low self-control on health are also discussed. PMID:22390225

Variation of metabolic profiles in developing maize kernels up- and down-regulated for the hda101 gene

PubMed Central

Castro, Cecilia; Motto, Mario; Rossi, Vincenzo; Manetti, Cesare

2008-01-01

To shed light on the specific contribution of HDA101 in modulating metabolic pathways in the maize seed, changes in the metabolic profiles of kernels obtained from hda101 mutant plants have been investigated by a metabonomic approach. Dynamic properties of chromatin folding can be mediated by enzymes that modify DNA and histones. The enzymes responsible for the steady-state of histone acetylation are histone acetyltransferase and histone deacetylase (HDA). Therefore, it is interesting to evaluate the effects of up- and down-regulation of a Rpd-3 type HDA on the development of maize seeds in terms of metabolic changes. This has been reached by analysing nuclear magnetic resonance spectra by different chemometrician approaches, such as Orthogonal Projection to Latent Structure-Discriminant Analysis, Parallel Factors Analysis, and Multi-way Partial Least Squares-Discriminant Analysis (N-PLS-DA). In particular, the latter approaches were chosen because they explicitly take time into account, organizing data into a set of slices that refer to different steps of the developing process. The results show the good discriminating capabilities of the N-PLS-DA approach, even if the number of samples ought be increased to obtain better predictive capabilities. However, using this approach, it was possible to show differences in the accumulation of metabolites during development and to highlight the changes occuring in the modified seeds. In particular, the results confirm the role of this gene in cell cycle control. PMID:18836140

Developmental changes in leaf phenolics composition from three artichoke cvs. (Cynara scolymus) as determined via UHPLC-MS and chemometrics.

PubMed

El Senousy, Amira S; Farag, Mohamed A; Al-Mahdy, Dalia A; Wessjohann, Ludger A

2014-12-01

The metabolomic differences in phenolics from leaves derived from 3 artichoke cultivars (Cynara scolymus): American Green Globe, French Hyrious and Egyptian Baladi, collected at different developmental stages, were assessed using UHPLC-MS coupled to chemometrics. Ontogenic changes were considered as leaves were collected at four different time intervals and positions (top and basal) during artichoke development. Unsupervised principal component analysis (PCA) and supervised orthogonal projection to latent structures-discriminant analysis (O2PLS-DA) were used for comparing and classification of samples harvested from different cultivars at different time points and positions. A clear separation among the three investigated cultivars was revealed, with the American Green Globe samples found most enriched in caffeic acid conjugates and flavonoids vs. other cultivars. Furthermore, these metabolites also showed a marked effect on the discrimination between leaf samples from cultivars harvested at different positions, regardless of the plant age. Metabolite absolute quantifications further confirmed that discrimination was mostly influenced by phenolic compounds, namely caffeoylquinic acids and flavonoids. This study demonstrates an effect of artichoke leaf position, regardless of plant age, on its secondary metabolites composition. To the best of our knowledge, this is the first report for compositional differences among artichoke leaves, based on their positions, via a metabolomic approach and suggesting that top positioned artichoke leaves present a better source of caffeoylquinic acids, compared to basal ones. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validity and Reliability of the 8-Item Work Limitations Questionnaire.

PubMed

Walker, Timothy J; Tullar, Jessica M; Diamond, Pamela M; Kohl, Harold W; Amick, Benjamin C

2017-12-01

Purpose To evaluate factorial validity, scale reliability, test-retest reliability, convergent validity, and discriminant validity of the 8-item Work Limitations Questionnaire (WLQ) among employees from a public university system. Methods A secondary analysis using de-identified data from employees who completed an annual Health Assessment between the years 2009-2015 tested research aims. Confirmatory factor analysis (CFA) (n = 10,165) tested the latent structure of the 8-item WLQ. Scale reliability was determined using a CFA-based approach while test-retest reliability was determined using the intraclass correlation coefficient. Convergent/discriminant validity was tested by evaluating relations between the 8-item WLQ with health/performance variables for convergent validity (health-related work performance, number of chronic conditions, and general health) and demographic variables for discriminant validity (gender and institution type). Results A 1-factor model with three correlated residuals demonstrated excellent model fit (CFI = 0.99, TLI = 0.99, RMSEA = 0.03, and SRMR = 0.01). The scale reliability was acceptable (0.69, 95% CI 0.68-0.70) and the test-retest reliability was very good (ICC = 0.78). Low-to-moderate associations were observed between the 8-item WLQ and the health/performance variables while weak associations were observed between the demographic variables. Conclusions The 8-item WLQ demonstrated sufficient reliability and validity among employees from a public university system. Results suggest the 8-item WLQ is a usable alternative for studies when the more comprehensive 25-item WLQ is not available.

Metabolomic and Lipidomic Analysis of Serum Samples following Curcuma longa Extract Supplementation in High-Fructose and Saturated Fat Fed Rats.

PubMed

Tranchida, Fabrice; Shintu, Laetitia; Rakotoniaina, Zo; Tchiakpe, Léopold; Deyris, Valérie; Hiol, Abel; Caldarelli, Stefano

2015-01-01

We explored, using nuclear magnetic resonance (NMR) metabolomics and fatty acids profiling, the effects of a common nutritional complement, Curcuma longa, at a nutritionally relevant dose with human use, administered in conjunction with an unbalanced diet. Indeed, traditional food supplements have been long used to counter metabolic impairments induced by unbalanced diets. Here, rats were fed either a standard diet, a high level of fructose and saturated fatty acid (HFS) diet, a diet common to western countries and that certainly contributes to the epidemic of insulin resistance (IR) syndrome, or a HFS diet with a Curcuma longa extract (1% of curcuminoids in the extract) for ten weeks. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on the serum NMR profiles and fatty acid composition (determined by GC/MS) showed a clear discrimination between HFS groups and controls. This discrimination involved metabolites such as glucose, amino acids, pyruvate, creatine, phosphocholine/glycerophosphocholine, ketone bodies and glycoproteins as well as an increase of monounsaturated fatty acids (MUFAs) and a decrease of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Although the administration of Curcuma longa did not prevent the observed increase of glucose, triglycerides, cholesterol and insulin levels, discriminating metabolites were observed between groups fed HFS alone or with addition of a Curcuma longa extract, namely some MUFA and n-3 PUFA, glycoproteins, glutamine, and methanol, suggesting that curcuminoids may act respectively on the fatty acid metabolism, the hexosamine biosynthesis pathway and alcohol oxidation. Curcuma longa extract supplementation appears to be beneficial in these metabolic pathways in rats. This metabolomic approach highlights important serum metabolites that could help in understanding further the metabolic mechanisms leading to IR.

Metabolomic and Lipidomic Analysis of Serum Samples following Curcuma longa Extract Supplementation in High-Fructose and Saturated Fat Fed Rats

PubMed Central

Tranchida, Fabrice; Shintu, Laetitia; Rakotoniaina, Zo; Tchiakpe, Léopold; Deyris, Valérie; Hiol, Abel; Caldarelli, Stefano

2015-01-01

We explored, using nuclear magnetic resonance (NMR) metabolomics and fatty acids profiling, the effects of a common nutritional complement, Curcuma longa, at a nutritionally relevant dose with human use, administered in conjunction with an unbalanced diet. Indeed, traditional food supplements have been long used to counter metabolic impairments induced by unbalanced diets. Here, rats were fed either a standard diet, a high level of fructose and saturated fatty acid (HFS) diet, a diet common to western countries and that certainly contributes to the epidemic of insulin resistance (IR) syndrome, or a HFS diet with a Curcuma longa extract (1% of curcuminoids in the extract) for ten weeks. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on the serum NMR profiles and fatty acid composition (determined by GC/MS) showed a clear discrimination between HFS groups and controls. This discrimination involved metabolites such as glucose, amino acids, pyruvate, creatine, phosphocholine/glycerophosphocholine, ketone bodies and glycoproteins as well as an increase of monounsaturated fatty acids (MUFAs) and a decrease of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Although the administration of Curcuma longa did not prevent the observed increase of glucose, triglycerides, cholesterol and insulin levels, discriminating metabolites were observed between groups fed HFS alone or with addition of a Curcuma longa extract, namely some MUFA and n-3 PUFA, glycoproteins, glutamine, and methanol, suggesting that curcuminoids may act respectively on the fatty acid metabolism, the hexosamine biosynthesis pathway and alcohol oxidation. Curcuma longa extract supplementation appears to be beneficial in these metabolic pathways in rats. This metabolomic approach highlights important serum metabolites that could help in understanding further the metabolic mechanisms leading to IR. PMID:26288372

Relating Memory To Functional Performance In Normal Aging to Dementia Using Hierarchical Bayesian Cognitive Processing Models

PubMed Central

Shankle, William R.; Pooley, James P.; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D.

2012-01-01

Determining how cognition affects functional abilities is important in Alzheimer’s disease and related disorders (ADRD). 280 patients (normal or ADRD) received a total of 1,514 assessments using the Functional Assessment Staging Test (FAST) procedure and the MCI Screen (MCIS). A hierarchical Bayesian cognitive processing (HBCP) model was created by embedding a signal detection theory (SDT) model of the MCIS delayed recognition memory task into a hierarchical Bayesian framework. The SDT model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the six FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. HBCP models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition to a continuous measure of functional severity for both individuals and FAST groups. Such a translation links two levels of brain information processing, and may enable more accurate correlations with other levels, such as those characterized by biomarkers. PMID:22407225

Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive processing models.

PubMed

Shankle, William R; Pooley, James P; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D

2013-01-01

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

Investigating the Latent Structure of the Teacher Efficacy Scale

ERIC Educational Resources Information Center

Wagler, Amy; Wagler, Ron

2013-01-01

This article reevaluates the latent structure of the Teacher Efficacy Scale using confirmatory factor analyses (CFAs) on a sample of preservice teachers from a public university in the U.S. Southwest. The fit of a proposed two-factor CFA model with an error correlation structure consistent with internal/ external locus of control is compared to…

A Systematic Approach for Identifying Level-1 Error Covariance Structures in Latent Growth Modeling

ERIC Educational Resources Information Center

Ding, Cherng G.; Jane, Ten-Der; Wu, Chiu-Hui; Lin, Hang-Rung; Shen, Chih-Kang

2017-01-01

It has been pointed out in the literature that misspecification of the level-1 error covariance structure in latent growth modeling (LGM) has detrimental impacts on the inferences about growth parameters. Since correct covariance structure is difficult to specify by theory, the identification needs to rely on a specification search, which,…

ARO/ARL Site Visit Project Review Presentation

DTIC Science & Technology

2012-07-01

hemodynamic activity. 7/11/2012 Challenges 10 complicated equipment gradient artifact BCG artifact How to combine the data? bias field auditory...noise 7/11/2012 11 A B C + + – – Our Solutions EEG+ BCG BCG 7/11/2012 12 (Goldman et al., Neuroimage 2009) Observing latent...Discrimination Results model human subjects Neurometric curve NOT optimized to match psychometric curve! human subjects model ( matrix word

Protein subcellular location pattern classification in cellular images using latent discriminative models.

PubMed

Li, Jieyue; Xiong, Liang; Schneider, Jeff; Murphy, Robert F

2012-06-15

Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood. In the experiments, we show that the proposed models help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this article for classifying 942 proteins into 13 classes of patterns is about 84.6%, which to our knowledge is the best so far. In addition, the dependencies learned are consistent with prior knowledge of cell organization. http://murphylab.web.cmu.edu/software/.

Multilevel Higher-Order Item Response Theory Models

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung

2014-01-01

In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…

Nonlinear and Quasi-Simplex Patterns in Latent Growth Models

ERIC Educational Resources Information Center

Bianconcini, Silvia

2012-01-01

In the SEM literature, simplex and latent growth models have always been considered competing approaches for the analysis of longitudinal data, even if they are strongly connected and both of specific importance. General dynamic models, which simultaneously estimate autoregressive structures and latent curves, have been recently proposed in the…

Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

ERIC Educational Resources Information Center

Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.

2011-01-01

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…

Estimating Latent Variable Interactions with Nonnormal Observed Data: A Comparison of Four Approaches

ERIC Educational Resources Information Center

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…

The Impact of Noninvariant Intercepts in Latent Means Models

ERIC Educational Resources Information Center

Whittaker, Tiffany A.

2013-01-01

Latent means methods such as multiple-indicator multiple-cause (MIMIC) and structured means modeling (SMM) allow researchers to determine whether or not a significant difference exists between groups' factor means. Strong invariance is typically recommended when interpreting latent mean differences. The extent of the impact of noninvariant…

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

Selection of latent variables for multiple mixed-outcome models

PubMed Central

ZHOU, LING; LIN, HUAZHEN; SONG, XINYUAN; LI, YI

2014-01-01

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them. PMID:27642219

Etiological Beliefs, Treatments, Stigmatizing Attitudes toward Schizophrenia. What Do Italians and Israelis Think?

PubMed

Mannarini, Stefania; Boffo, Marilisa; Rossi, Alessandro; Balottin, Laura

2017-01-01

Background: Although scientific research on the etiology of mental disorders has improved the knowledge of biogenetic and psychosocial aspects related to the onset of mental illness, stigmatizing attitudes and behaviors are still very prevalent and pose a significant social problem. Aim: The aim of this study was to deepen the knowledge of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics, such as culture and religion of the perceiver. More precisely, the main purpose is the definition of a structure of variables, namely perceived dangerousness, social closeness, and avoidance of the ill person, together with the beliefs about the best treatment to be undertaken and the sick person' gender, capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned. Method: The study involved 305 university students, 183 from the University of Padua, Italy, and 122 from the University of Haifa, Israel. For the analyses, a latent class analysis (LCA) approach was chosen to identify a latent categorical structure accounting for the covariance between the observed variables. Such a latent structure was expected to be moderated by cultural background (Italy versus Israel) and religious beliefs, whereas causal beliefs, recommended treatment, dangerousness, social closeness, and public avoidance were the manifest variables, namely the observed indicators of the latent variable. Results: Two sets of results were obtained. First, the relevance of the manifest variables as indicators of the hypothesized latent variable was highlighted. Second, a two-latent-class categorical dimension represented by prejudicial attitudes, causal beliefs, and treatments concerning schizophrenia was found. Specifically, the differential effects of the two cultures and the religious beliefs on the latent structure and their relations highlighted the relevance of the observed variables as indicators of the expected latent variable. Conclusion: The present study contributes to the improvement of the understanding of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics of the perceiver. The definition of a structure of variables capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned was achieved from a cross-cultural perspective.

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Lee, John C. K.

2003-01-01

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models…

A Model of Young Children's Social Cognition: Linkages Between Latent Structures and Discrete Processing

ERIC Educational Resources Information Center

Meece, Darrell

1999-01-01

This study proposes a model of associations between young children's social cognition and their social behavior with peers. In this model, two latent structures -children's representations of peer relationships and emotion regulation -- predict children's competent, prosocial, withdrawn, and aggressive behavior. Moreover, the model proposes that…

The Latent Structure of Secure Base Script Knowledge

ERIC Educational Resources Information Center

Waters, Theodore E. A.; Fraley, R. Chris; Groh, Ashley M.; Steele, Ryan D.; Vaughn, Brian E.; Bost, Kelly K.; Veríssimo, Manuela; Coppola, Gabrielle; Roisman, Glenn I.

2015-01-01

There is increasing evidence that attachment representations abstracted from childhood experiences with primary caregivers are organized as a cognitive script describing secure base use and support (i.e., the "secure base script"). To date, however, the latent structure of secure base script knowledge has gone unexamined--this despite…

Nonlinear Structured Growth Mixture Models in M"plus" and OpenMx

ERIC Educational Resources Information Center

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2010-01-01

Growth mixture models (GMMs; B. O. Muthen & Muthen, 2000; B. O. Muthen & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models…

Use of Latent Profile Analysis in Studies of Gifted Students

ERIC Educational Resources Information Center

Mammadov, Sakhavat; Ward, Thomas J.; Cross, Jennifer Riedl; Cross, Tracy L.

2016-01-01

To date, in gifted education and related fields various conventional factor analytic and clustering techniques have been used extensively for investigation of the underlying structure of data. Latent profile analysis is a relatively new method in the field. In this article, we provide an introduction to latent profile analysis for gifted education…

Software for the Application of Discrete Latent Structure Models to Item Response Data.

ERIC Educational Resources Information Center

Haertel, Edward H.

These FORTRAN programs and MATHEMATICA routines were developed in the course of a research project titled "Achievement and Assessment in School Science: Modeling and Mapping Ability and Performance." Their use is described in other publications from that project, including "Latent Traits or Latent States? The Role of Discrete Models…

Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables

ERIC Educational Resources Information Center

Pek, Jolynn; Chalmers, R. Philip; Kok, Bethany E.; Losardo, Diane

2015-01-01

Structural equation mixture models (SEMMs), when applied as a semiparametric model (SPM), can adequately recover potentially nonlinear latent relationships without their specification. This SPM is useful for exploratory analysis when the form of the latent regression is unknown. The purpose of this article is to help users familiar with structural…

Higher-Order Item Response Models for Hierarchical Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming

2013-01-01

Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…

The Ecology of Early Childhood Risk: A Canonical Correlation Analysis of Children’s Adjustment, Family, and Community Context in a High-Risk Sample

PubMed Central

Aiyer, Sophie M.; Wilson, Melvin N.; Shaw, Daniel S.; Dishion, Thomas J.

2013-01-01

The ecology of the emergence of psycho-pathology in early childhood is often approached by the analysis of a limited number of contextual risk factors. In the present study, we provide a comprehensive analysis of ecological risk by conducting a canonical correlation analysis of 13 risk factors at child age 2 and seven narrow-band scales of internalizing and externalizing problem behaviors at child age 4, using a sample of 364 geographically and ethnically diverse, disadvantaged primary caregivers, alternative caregivers, and preschool-age children. Participants were recruited from Special Supplemental Nutrition Program for Women, Infants, and Children sites and were screened for family risk. Canonical correlation analysis revealed that (1) a first latent combination of family and individual risks of caregivers predicted combinations of child emotional and behavioral problems, and that (2) a second latent combination of contextual and structural risks predicted child somatic complaints. Specifically, (1) the combination of chaotic home, conflict with child, parental depression, and parenting hassles predicted a co-occurrence of internalizing and externalizing behaviors, and (2) the combination of father absence, perceived discrimination, neighborhood danger, and fewer children living in the home predicted child somatic complaints. The research findings are discussed in terms of the development of psychopathology, as well as the potential prevention needs of families in high-risk contexts. PMID:23700232

Space-time latent component modeling of geo-referenced health data.

PubMed

Lawson, Andrew B; Song, Hae-Ryoung; Cai, Bo; Hossain, Md Monir; Huang, Kun

2010-08-30

Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made. Copyright (c) 2010 John Wiley & Sons, Ltd.

The effects of rurality on substance use disorder diagnosis: A multiple-groups latent class analysis.

PubMed

Brooks, Billy; McBee, Matthew; Pack, Robert; Alamian, Arsham

2017-05-01

Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities. Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership. Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P value<0.0001). Overall the rural class structure exhibited less diversity in class structure and lower prevalence of SUD in multiple drug categories (e.g. cocaine, hallucinogens, and stimulants). This result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current Status and Future Perspectives of Mass Spectrometry Imaging

PubMed Central

Nimesh, Surendra; Mohottalage, Susantha; Vincent, Renaud; Kumarathasan, Prem

2013-01-01

Mass spectrometry imaging is employed for mapping proteins, lipids and metabolites in biological tissues in a morphological context. Although initially developed as a tool for biomarker discovery by imaging the distribution of protein/peptide in tissue sections, the high sensitivity and molecular specificity of this technique have enabled its application to biomolecules, other than proteins, even in cells, latent finger prints and whole organisms. Relatively simple, with no requirement for labelling, homogenization, extraction or reconstitution, the technique has found a variety of applications in molecular biology, pathology, pharmacology and toxicology. By discriminating the spatial distribution of biomolecules in serial sections of tissues, biomarkers of lesions and the biological responses to stressors or diseases can be better understood in the context of structure and function. In this review, we have discussed the advances in the different aspects of mass spectrometry imaging processes, application towards different disciplines and relevance to the field of toxicology. PMID:23759983

Assessing Psychopathy Among Justice Involved Adolescents with the PCL: YV: An Item Response Theory Examination Across Gender

PubMed Central

Tsang, Siny; Schmidt, Karen M.; Vincent, Gina M.; Salekin, Randall T.; Moretti, Marlene M.; Odgers, Candice L.

2014-01-01

This study used an item response theory (IRT) model and a large adolescent sample of justice involved youth (N = 1,007, 38% female) to examine the item functioning of the Psychopathy Checklist – Youth Version (PCL: YV). Items that were most discriminating (or most sensitive to changes) of the latent trait (thought to be psychopathy) among adolescents included “Glibness/superficial charm”, “Lack of remorse”, and “Need for stimulation”, whereas items that were least discriminating included “Pathological lying”, “Failure to accept responsibility”, and “Lacks goals.” The items “Impulsivity” and “Irresponsibility” were the most likely to be rated high among adolescents, whereas “Parasitic lifestyle”, and “Glibness/superficial charm” were the most likely to be rated low. Evidence of differential item functioning (DIF) on four of the 13 items was found between boys and girls. “Failure to accept responsibility” and “Impulsivity” were endorsed more frequently to describe adolescent girls than boys at similar levels of the latent trait, and vice versa for “Grandiose sense of self-worth” and “Lacks goals.” The DIF findings suggest that four PCL: YV items function differently between boys and girls. PMID:25580672

Anxiety, depression, and the suicidal spectrum: a latent class analysis of overlapping and distinctive features.

PubMed

Podlogar, Matthew C; Rogers, Megan L; Stanley, Ian H; Hom, Melanie A; Chiurliza, Bruno; Joiner, Thomas E

2017-03-20

Anxiety and depression diagnoses are associated with suicidal thoughts and behaviours. However, a categorical understanding of these associations limits insight into identifying dimensional mechanisms of suicide risk. This study investigated anxious and depressive features through a lens of suicide risk, independent of diagnosis. Latent class analysis of 97 depression, anxiety, and suicidality-related items among 616 psychiatric outpatients indicated a 3-class solution, specifically: (1) a higher suicide-risk class uniquely differentiated from both other classes by high reported levels of depression and anxious arousal; (2) a lower suicide-risk class that reported levels of anxiety sensitivity and generalised worry comparable to Class 1, but lower levels of depression and anxious arousal; and (3) a low to non-suicidal class that reported relatively low levels across all depression and anxiety measures. Discriminants of the higher suicide-risk class included borderline personality disorder; report of worthlessness, crying, and sadness; higher levels of anxious arousal and negative affect; and lower levels of positive affect. Depression and anxiety diagnoses were not discriminant between higher and lower suicide risk classes. This transdiagnostic and dimensional approach to understanding the suicidal spectrum contrasts with treating it as a depressive symptom, and illustrates the advantages of a tripartite model for conceptualising suicide risk.

Coping styles of adolescents experiencing multiple forms of discrimination and bullying: evidence from a sample of ethnically diverse urban youth.

PubMed

Garnett, Bernice R; Masyn, Katherine E; Austin, S Bryn; Williams, David R; Viswanath, Kasisomayajula

2015-02-01

We used a latent class analysis (LCA) to characterize coping styles of urban youth and examined if coping styles moderated the association between experiencing discrimination and bullying and depressive symptoms. The data come from the 2006 Boston Youth Survey, where students were asked to select 2 behaviors they do most often when they are upset, from a list of 15 options. A total of 927 (75%) students contributed to the LCA analytic sample (44% non-Hispanic Blacks, 29% Hispanics, and 58% girls). Relative and absolute fit indices determined the number of classes. An interaction term between types of discrimination and bullying experienced and coping style tested for moderation. The LCA revealed that a 3-class solution had the best fit (Lo-Mendell-Rubin likelihood ratio test, 4-class vs 3-class, p-value .12). The largest coping style class was characterized by high endorsement of distractive coping strategies (59%), the second class was characterized by using supportive coping strategies (27%), and the third class was characterized by using avoidance coping strategies (12%). We found a significant interaction between discrimination and coping style for depressive symptoms. The relationship between experiencing discrimination and depression varied based on coping style and the type of discrimination and bullying experienced. © 2015, American School Health Association.

Malingering as a Categorical or Dimensional Construct: The Latent Structure of Feigned Psychopathology as Measured by the SIRS and MMPI-2

ERIC Educational Resources Information Center

Walters, Glenn D.; Rogers, Richard; Berry, David T. R.; Miller, Holly A.; Duncan, Scott A.; McCusker, Paul J.; Payne, Joshua W.; Granacher, Robert P., Jr.

2008-01-01

The 6 nonoverlapping primary scales of the Structured Interview of Reported Symptoms (SIRS) were subjected to taxometric analysis in a group of 1,211 criminal and civil examinees in order to investigate the latent structure of feigned psychopathology. Both taxometric procedures used in this study, mean above minus below a cut (MAMBAC) and maximum…

Perceived Discrimination and Heavy Episodic Drinking among African American Youth: Differences by Age and Reason for Discrimination

PubMed Central

Jackson, Kristina; Wang, Heng; Miles, Thomas T.; Mather, Frances; Shankar, Arti

2015-01-01

Purpose To examine whether associations between perceived discrimination and heavy episodic drinking (HED) varies by age and by discrimination type (e.g., racial, age, physical appearance) among African American youth. Methods National data from the Panel Study of Income Dynamics Transition to Adulthood Study were analyzed. Youth participated in up to four interviews (2005, 2007, 2009, 2011; n=657) between ages 18–25. Respondents reported past-year engagement in HED (4 or more drinks for females, 5 or more drinks for males), and frequency of discriminatory acts experienced (e.g., receiving poor service, being treated with less courtesy). Categorical latent growth curve models, including perceived discrimination types (racial, age, and physical appearance) as a time-varying predictors of HED, were run in MPlus. Controls for gender, birth cohort, living arrangement in adolescence, familial wealth, parental alcohol use, and college attendance were explored. Results The average HED trajectory was curvilinear (increasing followed by flattening), while perceived discrimination remained flat with age. In models including controls, odds of HED were significantly higher than average around ages 20–21 with greater frequency of perceived racial discrimination; associations were not significant at other ages. Discrimination attributed to age or physical appearance was not associated with HED at any age. Conclusions Perceived racial discrimination may be a particularly salient risk factor for HED around the ages of transition to legal access to alcohol among African American youth. Interventions to reduce discrimination or its impact could be targeted before this transition to ameliorate the negative outcomes associated with HED. PMID:26499858

Building a pantheoretical model of dehumanization with transgender men: Integrating objectification and minority stress theories.

PubMed

Velez, Brandon L; Breslow, Aaron S; Brewster, Melanie E; Cox, Robert; Foster, Aasha B

2016-10-01

With a national sample of 304 transgender men, the present study tested a pantheoretical model of dehumanization (Moradi, 2013) with hypotheses derived from objectification theory (Fredrickson & Roberts, 1997), minority stress theory (Meyer, 2003), and prior research regarding men's body image concerns. Specifically, we tested common objectification theory constructs (internalization of sociocultural standards of attractiveness [SSA], body surveillance, body satisfaction) as direct and indirect predictors of compulsive exercise. We also examined the roles of transgender-specific minority stress variables-antitransgender discrimination and transgender identity congruence-in the model. Results of a latent variable structural equation model yielded mixed support for the posited relations. The direct and indirect interrelations of internalization of SSA, body surveillance, and body satisfaction were consistent with prior objectification theory research, but only internalization of SSA yielded a significant direct relation with compulsive exercise. In addition, neither internalization of SSA nor body surveillance yielded significant indirect relations with compulsive exercise. However, antitransgender discrimination yielded predicted indirect relations with body surveillance, body satisfaction, and compulsive exercise, with transgender congruence playing a key mediating role in most of these relations. The implications of this pantheoretical model for research and practice with transgender men are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches

ERIC Educational Resources Information Center

Marsh, Herbert W.; Wen, Zhonglin; Hau, Kit-Tai; Little, Todd D.; Bovaird, James A.; Widaman, Keith F.

2007-01-01

Little, Bovaird and Widaman (2006) proposed an unconstrained approach with residual centering for estimating latent interaction effects as an alternative to the mean-centered approach proposed by Marsh, Wen, and Hau (2004, 2006). Little et al. also differed from Marsh et al. in the number of indicators used to infer the latent interaction factor…

Using Structural Equation Models with Latent Variables to Study Student Growth and Development.

ERIC Educational Resources Information Center

Pike, Gary R.

1991-01-01

Analysis of data on freshman-to-senior developmental gains in 722 University of Tennessee-Knoxville students provides evidence of the advantages of structural equation modeling with latent variables and suggests that the group differences identified by traditional analysis of variance and covariance techniques may be an artifact of measurement…

A Taxometric Study of the Latent Structure of Disgust Sensitivity: Converging Evidence for Dimensionality

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Broman-Fulks, Joshua J.

2007-01-01

Disgust sensitivity has recently been implicated as a specific vulnerability factor for several anxiety-related disorders. However, it is not clear whether disgust sensitivity is a dimensional or categorical phenomenon. The present study examined the latent structure of disgust by applying three taxometric procedures (maximum eigenvalue, mean…

Some Factor Analytic Approximations to Latent Class Structure.

ERIC Educational Resources Information Center

Dziuban, Charles D.; Denton, William T.

Three procedures, alpha, image, and uniqueness rescaling, were applied to a joint occurrence probability matrix. That matrix was the basis of a well-known latent class structure. The values of the recurring subscript elements were varied as follows: Case 1 - The known elements were input; Case 2 - The upper bounds to the recurring subscript…

The Latent Structure of Psychopathy in Youth: A Taxometric Investigation

ERIC Educational Resources Information Center

Vasey, Michael W.; Kotov, Roman; Frick, Paul J.; Loney, Bryan R.

2005-01-01

Using taxometric procedures, the latent structure of psychopathy was investigated in two studies of children and adolescents. Prior studies have identified a taxon (i.e., a natural category) associated with antisocial behavior in adults as well as children and adolescents. However, features of this taxon suggest that it is not psychopathy but…

Heterogeneity in the Latent Structure of PTSD Symptoms among Canadian Veterans

ERIC Educational Resources Information Center

Naifeh, James A.; Richardson, J. Don; Del Ben, Kevin S.; Elhai, Jon D.

2010-01-01

The current study used factor mixture modeling to identify heterogeneity (i.e., latent classes) in 2 well-supported models of posttraumatic stress disorder's (PTSD) factor structure. Data were analyzed from a clinical sample of 405 Canadian veterans evaluated for PTSD. Results were consistent with our hypotheses. Each PTSD factor model was best…

Dual role for the latent transforming growth factor-beta binding protein in storage of latent TGF-beta in the extracellular matrix and as a structural matrix protein

PubMed Central

1995-01-01

The role of the latent TGF-beta binding protein (LTBP) is unclear. In cultures of fetal rat calvarial cells, which form mineralized bonelike nodules, both LTBP and the TGF-beta 1 precursor localized to large fibrillar structures in the extracellular matrix. The appearance of these fibrillar structures preceded the appearance of type I collagen fibers. Plasmin treatment abolished the fibrillar staining pattern for LTBP and released a complex containing both LTBP and TGF-beta. Antibodies and antisense oligonucleotides against LTBP inhibited the formation of mineralized bonelike nodules in long-term fetal rat calvarial cultures. Immunohistochemistry of fetal and adult rat bone confirmed a fibrillar staining pattern for LTBP in vivo. These findings, together with the known homology of LTBP to the fibrillin family of proteins, suggest a novel function for LTBP, in addition to its role in matrix storage of latent TGF-beta, as a structural matrix protein that may play a role in bone formation. PMID:7593177

Trajectories of Perceived Workplace Age Discrimination and Long-Term Associations With Mental, Self-Rated, and Occupational Health.

PubMed

Marchiondo, Lisa A; Gonzales, Ernest; Williams, Larry J

2017-07-12

This study addresses older employees' trajectories of perceived workplace age discrimination, and the long-term associations among perceived age discrimination and older workers' mental and self-rated health, job satisfaction, and likelihood of working past retirement age. We evaluate the strength and vulnerability integration (SAVI) model. Three waves of data from employed participants were drawn from the Health and Retirement Study (N = 3,957). Latent growth modeling was used to assess relationships between the slopes and the intercepts of the variables, thereby assessing longitudinal and cross-sectional associations. Perceived workplace age discrimination tends to increase with age, although notable variance exists. The initial status of perceived age discrimination relates to the baseline statuses of depression, self-rated health, job satisfaction, and likelihood of working past retirement age in the expected directions. Over time, perceived age discrimination predicts lower job satisfaction and self-rated health, as well as elevated depressive symptoms, but not likelihood of working past retirement age. This study provides empirical support for the SAVI model and uncovers the "wear and tear" effects of perceived workplace age discrimination on older workers' mental and overall health. We deliberate on social policies that may reduce age discrimination, thereby promoting older employees' health and ability to work longer. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Vertical Profiles of Latent Heat Release Over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Starr, David (Technical Monitor)

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. Additional information is included in the original extended abstract.

Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy.

PubMed

Salvati, Luca; Tombolini, Ilaria; Gemmiti, Roberta; Carlucci, Margherita; Bajocco, Sofia; Perini, Luigi; Ferrara, Agostino; Colantoni, Andrea

2017-01-01

Land quality, a key economic capital supporting local development, is affected by biophysical and anthropogenic factors. Taken as a relevant attribute of economic systems, land quality has shaped the territorial organization of any given region influencing localization of agriculture, industry and settlements. In regions with long-established human-landscape interactions, such as the Mediterranean basin, land quality has determined social disparities and polarization in the use of land, reflecting the action of geographical gradients based on elevation and population density. The present study investigates latent relationships within a large set of indicators profiling local communities and land quality on a fine-grained resolution scale in Italy with the aim to assess the potential impact of land quality on the regional socioeconomic structure. The importance of land quality gradients in the socioeconomic configuration of urban and rural regions was verified analyzing the distribution of 149 socioeconomic and environmental indicators organized in 5 themes and 17 research dimensions. Agriculture, income, education and labour market variables discriminate areas with high land quality from areas with low land quality. While differential land quality in peri-urban areas may reflect conflicts between competing actors, moderate (or low) quality of land in rural districts is associated with depopulation, land abandonment, subsidence agriculture, unemployment and low educational levels. We conclude that the socioeconomic profile of local communities has been influenced by land quality in a different way along urban-rural gradients. Policies integrating environmental and socioeconomic measures are required to consider land quality as a pivotal target for sustainable development. Regional planning will benefit from an in-depth understanding of place-specific relationships between local communities and the environment.

Validation of the Headache Impact Test (HIT-6) in patients with chronic migraine.

PubMed

Rendas-Baum, Regina; Yang, Min; Varon, Sepideh F; Bloudek, Lisa M; DeGryse, Ronald E; Kosinski, Mark

2014-08-01

The Headache Impact Test (HIT)-6 was developed and has been validated in patients with various types of headache. The objective of this study was to report the psychometric properties of the HIT-6 among patients with chronic migraine. Data came from two international, multicenter, randomized, double-blind, placebo-controlled clinical trials of chronic migraine patients (N = 1,384) undergoing prophylaxis therapy. Confirmatory factor analysis and differential item functioning (DIF) analysis were used to test the latent structure and cross-cultural comparability of the HIT-6. Reliability, construct validity, and responsiveness were assessed. Two sets of criterion groups were used: (1) 28-day headache frequency: <10, 10-14, and ≥15 days; (2) sample quartiles of the total cumulative hours of headache: <140, 140 to <280, 280 to <420, and ≥420 hours. Two sets of responsiveness categories were defined as reduction of <30%, 30% to <50%, or ≥50% in (1) number of headache days and (2) cumulative hours of headache. Measurement invariance tests supported the stability of the HIT-6 latent structure across studies. DIF analysis supported cross-cultural comparability. Good reliability was observed across studies (Cronbach's α: 0.75-0.92; intraclass correlation coefficient: 0.76-0.80). HIT-6 scores correlated strongly (-0.86 to -0.59) with scores of the Migraine-Specific Quality-of-Life Questionnaire. Analysis of variance indicated that HIT-6 scores discriminated across both types of criterion groups (P<0.001), across studies and time points. HIT-6 change scores were significantly higher in magnitude in groups experiencing greater improvement (P<0.001). All measurement properties were consistently verified across the two studies, supporting the validity of the HIT-6 among chronic migraine patients. NCT00156910 and NCT00168428 on www.ClinicalTrials.gov.

Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy

PubMed Central

Salvati, Luca; Tombolini, Ilaria; Gemmiti, Roberta; Carlucci, Margherita; Bajocco, Sofia; Perini, Luigi; Ferrara, Agostino

2017-01-01

Land quality, a key economic capital supporting local development, is affected by biophysical and anthropogenic factors. Taken as a relevant attribute of economic systems, land quality has shaped the territorial organization of any given region influencing localization of agriculture, industry and settlements. In regions with long-established human-landscape interactions, such as the Mediterranean basin, land quality has determined social disparities and polarization in the use of land, reflecting the action of geographical gradients based on elevation and population density. The present study investigates latent relationships within a large set of indicators profiling local communities and land quality on a fine-grained resolution scale in Italy with the aim to assess the potential impact of land quality on the regional socioeconomic structure. The importance of land quality gradients in the socioeconomic configuration of urban and rural regions was verified analyzing the distribution of 149 socioeconomic and environmental indicators organized in 5 themes and 17 research dimensions. Agriculture, income, education and labour market variables discriminate areas with high land quality from areas with low land quality. While differential land quality in peri-urban areas may reflect conflicts between competing actors, moderate (or low) quality of land in rural districts is associated with depopulation, land abandonment, subsidence agriculture, unemployment and low educational levels. We conclude that the socioeconomic profile of local communities has been influenced by land quality in a different way along urban-rural gradients. Policies integrating environmental and socioeconomic measures are required to consider land quality as a pivotal target for sustainable development. Regional planning will benefit from an in-depth understanding of place-specific relationships between local communities and the environment. PMID:28574984

Building Coherent Validation Arguments for the Measurement of Latent Constructs with Unified Statistical Frameworks

ERIC Educational Resources Information Center

Rupp, Andre A.

2012-01-01

In the focus article of this issue, von Davier, Naemi, and Roberts essentially coupled: (1) a short methodological review of structural similarities of latent variable models with discrete and continuous latent variables; and (2) 2 short empirical case studies that show how these models can be applied to real, rather than simulated, large-scale…

The Longitudinal Structure of General and Specific Anxiety Dimensions in Children: Testing a Latent Trait-State-Occasion Model

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Cole, David A.

2009-01-01

In an 8-wave, 4-year longitudinal study, 787 children (Grades 3-6) completed the Revised Children's Manifest Anxiety Scale (C. R. Reynolds & B. O. Richmond, 1985), a measure of the Physiological Reactivity, Worry-Oversensitivity, and Social Alienation dimensions of anxiety. A latent variable (trait-state-occasion) model and a latent growth curve…

Sex Differences in Latent Cognitive Abilities Ages 6 to 59: Evidence from the Woodcock-Johnson III Tests of Cognitive Abilities

ERIC Educational Resources Information Center

Keith, Timothy Z.; Reynolds, Matthew R.; Patel, Puja G.; Ridley, Kristen P.

2008-01-01

Sex differences in the latent general and broad cognitive abilities underlying the Woodcock-Johnson Tests of Cognitive Abilities were investigated for children, youth, and adults ages 6 through 59. A developmental, multiple indicator-multiple cause, structural equation model was used to investigate sex differences in latent cognitive abilities as…

Sex Differences in Latent Cognitive Abilities Ages 5 to 17: Evidence from the Differential Ability Scales--Second Edition

ERIC Educational Resources Information Center

Keith, Timothy Z.; Reynolds, Matthew R.; Roberts, Lisa G.; Winter, Amanda L.; Austin, Cynthia A.

2011-01-01

Sex differences in the latent general and broad cognitive abilities underlying the Differential Ability Scales, Second Edition were investigated for children and youth ages 5 through 17. Multi-group mean and covariance structural equation modeling was used to investigate sex differences in latent cognitive abilities as well as changes in these…

An Assessment of Character and Leadership Development Latent Factor Structures through Confirmatory Factor, Item Response Theory, and Latent Class Analyses

ERIC Educational Resources Information Center

Higginbotham, David L.

2013-01-01

This study leveraged the complementary nature of confirmatory factor (CFA), item response theory (IRT), and latent class (LCA) analyses to strengthen the rigor and sophistication of evaluation of two new measures of the Air Force Academy's "leader of character" definition--the Character Mosaic Virtues (CMV) and the Leadership Mosaic…

The Log-Linear Cognitive Diagnostic Model (LCDM) as a Special Case of The General Diagnostic Model (GDM). Research Report. ETS RR-14-40

ERIC Educational Resources Information Center

von Davier, Matthias

2014-01-01

Diagnostic models combine multiple binary latent variables in an attempt to produce a latent structure that provides more information about test takers' performance than do unidimensional latent variable models. Recent developments in diagnostic modeling emphasize the possibility that multiple skills may interact in a conjunctive way within the…

Precipitation Processes Derived from TRMM Satellite Data, Cloud Resolving Model and Field Campaigns

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Einaudi, Franco (Technical Monitor)

2001-01-01

Rainfall is a key link in the hydrologic cycle and is a primary heat source for the atmosphere. The vertical distribution of latent-heat release, which is accompanied by rainfall, modulates the large-scale circulations of the tropics and in turn can impact midlatitude weather. This latent heat release is a consequence of phase changes between vapor, liquid. and solid water. Present large-scale weather and climate models can simulate cloud latent heat release only crudely thus reducing their confidence in predictions on both global and regional scales. In this paper, NASA Tropical Rainfall Measuring (TRMM) precipitation radar (PR) derived rainfall information and the Goddard Convective and Stratiform Heating (CSH) algorithm used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to October 2000. Rainfall latent heating and radar reflectively structure between ENSO (1997-1998 winter) and non-ENSO (1998-1999 winter) periods are examined and compared. The seasonal variation of heating over various geographic locations (i.e. Indian ocean vs west Pacific; Africa vs S. America) are also analyzed. In addition, the relationship between rainfall latent heating maximum heating level), radar reflectively and SST are examined.

Parasites as Biological Tags for Stock Discrimination of Beaked Redfish (Sebastes mentella): Parasite Infra-Communities vs. Limited Resolution of Cytochrome Markers

PubMed Central

Klapper, Regina; Kochmann, Judith; O’Hara, Robert B.; Karl, Horst; Kuhn, Thomas

2016-01-01

The use of parasites as biological tags for discrimination of fish stocks has become a commonly used approach in fisheries management. Metazoan parasite community analysis and anisakid nematode population genetics based on a mitochondrial cytochrome marker were applied in order to assess the usefulness of the two parasitological methods for stock discrimination of beaked redfish Sebastes mentella of three fishing grounds in the North East Atlantic. Multivariate, model-based approaches demonstrated that the metazoan parasite fauna of beaked redfish from East Greenland differed from Tampen, northern North Sea, and Bear Island, Barents Sea. A joint model (latent variable model) was used to estimate the effects of covariates on parasite species and identified four parasite species as main source of differences among fishing grounds; namely Chondracanthus nodosus, Anisakis simplex s.s., Hysterothylacium aduncum, and Bothriocephalus scorpii. Due to its high abundance and differences between fishing grounds, Anisakis simplex s.s. was considered as a major biological tag for host stock differentiation. Whilst the sole examination of Anisakis simplex s.s. on a population genetic level is only of limited use, anisakid nematodes (in particular, A. simplex s.s.) can serve as biological tags on a parasite community level. This study confirmed the use of multivariate analyses as a tool to evaluate parasite infra-communities and to identify parasite species that might serve as biological tags. The present study suggests that S. mentella in the northern North Sea and Barents Sea is not sub-structured. PMID:27104735

Modeling Latent Interactions at Level 2 in Multilevel Structural Equation Models: An Evaluation of Mean-Centered and Residual-Centered Unconstrained Approaches

ERIC Educational Resources Information Center

Leite, Walter L.; Zuo, Youzhen

2011-01-01

Among the many methods currently available for estimating latent variable interactions, the unconstrained approach is attractive to applied researchers because of its relatively easy implementation with any structural equation modeling (SEM) software. Using a Monte Carlo simulation study, we extended and evaluated the unconstrained approach to…

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

ERIC Educational Resources Information Center

Hoshino, Takahiro; Shigemasu, Kazuo

2008-01-01

The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

Taxometric and Factor Analytic Models of Anxiety Sensitivity among Youth: Exploring the Latent Structure of Anxiety Psychopathology Vulnerability

ERIC Educational Resources Information Center

Bernstein, Amit; Zvolensky, Michael J.; Stewart, Sherry; Comeau, Nancy

2007-01-01

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), a well-established affect-sensitivity individual difference factor, among youth by employing taxometric and factor analytic approaches in an integrative manner. Taxometric analyses indicated that AS, as indexed by the Child Anxiety Sensitivity…

Structural Relationships between Social Activities and Longitudinal Trajectories of Depression among Older Adults

ERIC Educational Resources Information Center

Hong, Song-Iee; Hasche, Leslie; Bowland, Sharon

2009-01-01

Purpose: This study examines the structural relationships between social activities and trajectories of late-life depression. Design and Methods: Latent class analysis was used with a nationally representative sample of older adults (N = 5,294) from the Longitudinal Study on Aging II to classify patterns of social activities. A latent growth curve…

Factor Structure Invariance of the Kaufman Adolescent and Adult Intelligence Test across Male and Female Samples

ERIC Educational Resources Information Center

Immekus, Jason C.; Maller, Susan J.

2010-01-01

Multisample confirmatory factor analysis (MCFA) and latent mean structures analysis (LMS) were used to test measurement invariance and latent mean differences on the Kaufman Adolescent and Adult Intelligence Scale[TM] (KAIT) across males and females in the standardization sample. MCFA found that the parameters of the KAIT two-factor model were…

Introduction to the special section on mixture modeling in personality assessment.

PubMed

Wright, Aidan G C; Hallquist, Michael N

2014-01-01

Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.

Application of Generative Autoencoder in De Novo Molecular Design.

PubMed

Blaschke, Thomas; Olivecrona, Marcus; Engkvist, Ola; Bajorath, Jürgen; Chen, Hongming

2018-01-01

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Performance of Four Frailty Classifications in Older Patients With Cancer: Prospective Elderly Cancer Patients Cohort Study.

PubMed

Ferrat, Emilie; Paillaud, Elena; Caillet, Philippe; Laurent, Marie; Tournigand, Christophe; Lagrange, Jean-Léon; Droz, Jean-Pierre; Balducci, Lodovico; Audureau, Etienne; Canouï-Poitrine, Florence; Bastuji-Garin, Sylvie

2017-03-01

Purpose Frailty classifications of older patients with cancer have been developed to assist physicians in selecting cancer treatments and geriatric interventions. They have not been compared, and their performance in predicting outcomes has not been assessed. Our objectives were to assess agreement among four classifications and to compare their predictive performance in a large cohort of in- and outpatients with various cancers. Patients and Methods We prospectively included 1,021 patients age 70 years or older who had solid or hematologic malignancies and underwent a geriatric assessment in one of two French teaching hospitals between 2007 and 2012. Among them, 763 were assessed using four classifications: Balducci, International Society of Geriatric Oncology (SIOG) 1, SIOG2, and a latent class typology. Agreement was assessed using the κ statistic. Outcomes were 1-year mortality and 6-month unscheduled admissions. Results All four classifications had good discrimination for 1-year mortality (C-index ≥ 0.70); discrimination was best with SIOG1. For 6-month unscheduled admissions, discrimination was good with all four classifications (C-index ≥ 0.70). For classification into three (fit, vulnerable, or frail) or two categories (fit v vulnerable or frail and fit or vulnerable v frail), agreement among the four classifications ranged from very poor (κ ≤ 0.20) to good (0.60 < κ ≤ 0.80). Agreement was best between SIOG1 and the latent class typology and between SIOG1 and Balducci. Conclusion These four frailty classifications have good prognostic performance among older in- and outpatients with various cancers. They may prove useful in decision making about cancer treatments and geriatric interventions and/or in stratifying older patients with cancer in clinical trials.

Research on the application of a decoupling algorithm for structure analysis

NASA Technical Reports Server (NTRS)

Denman, E. D.

1980-01-01

The mathematical theory for decoupling mth-order matrix differential equations is presented. It is shown that the decoupling precedure can be developed from the algebraic theory of matrix polynomials. The role of eigenprojectors and latent projectors in the decoupling process is discussed and the mathematical relationships between eigenvalues, eigenvectors, latent roots, and latent vectors are developed. It is shown that the eigenvectors of the companion form of a matrix contains the latent vectors as a subset. The spectral decomposition of a matrix and the application to differential equations is given.

A Retrieval of Tropical Latent Heating Using the 3D Structure of Precipitation Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahmed, Fiaz; Schumacher, Courtney; Feng, Zhe

Traditionally, radar-based latent heating retrievals use rainfall to estimate the total column-integrated latent heating and then distribute that heating in the vertical using a model-based look-up table (LUT). In this study, we develop a new method that uses size characteristics of radar-observed precipitating echo (i.e., area and mean echo-top height) to estimate the vertical structure of latent heating. This technique (named the Convective-Stratiform Area [CSA] algorithm) builds on the fact that the shape and magnitude of latent heating profiles are dependent on the organization of convective systems and aims to avoid some of the pitfalls involved in retrieving accurate rainfallmore » amounts and microphysical information from radars and models. The CSA LUTs are based on a high-resolution Weather Research and Forecasting model (WRF) simulation whose domain spans much of the near-equatorial Indian Ocean. When applied to S-PolKa radar observations collected during the DYNAMO/CINDY2011/AMIE field campaign, the CSA retrieval compares well to heating profiles from a sounding-based budget analysis and improves upon a simple rain-based latent heating retrieval. The CSA LUTs also highlight the fact that convective latent heating increases in magnitude and height as cluster area and echo-top heights grow, with a notable congestus signature of cooling at mid levels. Stratiform latent heating is less dependent on echo-top height, but is strongly linked to area. Unrealistic latent heating profiles in the stratiform LUT, viz., a low-level heating spike, an elevated melting layer, and net column cooling were identified and corrected for. These issues highlight the need for improvement in model parameterizations, particularly in linking microphysical phase changes to larger mesoscale processes.« less

Assessing a dysphoric arousal model of acute stress disorder symptoms in a clinical sample of rape and bank robbery victims

PubMed Central

Hansen, Maj; Armour, Cherie; Elklit, Ask

2012-01-01

Background Since the introduction of Acute Stress Disorder (ASD) into the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) research has focused on the ability of ASD to predict PTSD rather than focusing on addressing ASD's underlying latent structure. The few existing confirmatory factor analytic (CFA) studies of ASD have failed to reach a clear consensus regarding ASD's underlying dimensionality. Although, the discrepancy in the results may be due to varying ASD prevalence rates, it remains possible that the model capturing the latent structure of ASD has not yet been put forward. One such model may be a replication of a new five-factor model of PTSD, which separates the arousal symptom cluster into Dysphoric and Anxious Arousal. Given the pending DSM-5, uncovering ASD's latent structure is more pertinent than ever. Objective Using CFA, four different models of the latent structure of ASD were specified and tested: the proposed DSM-5 model, the DSM-IV model, a three factor model, and a five factor model separating the arousal symptom cluster. Method The analyses were based on a combined sample of rape and bank robbery victims, who all met the diagnostic criteria for ASD (N = 404) using the Acute Stress Disorder Scale. Results The results showed that the five factor model provided the best fit to the data. Conclusions The results of the present study suggest that the dimensionality of ASD may be best characterized as a five factor structure which separates dysphoric and anxious arousal items into two separate factors, akin to recent research on PTSD's latent structure. Thus, the current study adds to the debate about how ASD should be conceptualized in the pending DSM-5. PMID:22893845

Assessing a dysphoric arousal model of acute stress disorder symptoms in a clinical sample of rape and bank robbery victims.

PubMed

Hansen, Maj; Armour, Cherie; Elklit, Ask

2012-01-01

Since the introduction of Acute Stress Disorder (ASD) into the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) research has focused on the ability of ASD to predict PTSD rather than focusing on addressing ASD's underlying latent structure. The few existing confirmatory factor analytic (CFA) studies of ASD have failed to reach a clear consensus regarding ASD's underlying dimensionality. Although, the discrepancy in the results may be due to varying ASD prevalence rates, it remains possible that the model capturing the latent structure of ASD has not yet been put forward. One such model may be a replication of a new five-factor model of PTSD, which separates the arousal symptom cluster into Dysphoric and Anxious Arousal. Given the pending DSM-5, uncovering ASD's latent structure is more pertinent than ever. USING CFA, FOUR DIFFERENT MODELS OF THE LATENT STRUCTURE OF ASD WERE SPECIFIED AND TESTED: the proposed DSM-5 model, the DSM-IV model, a three factor model, and a five factor model separating the arousal symptom cluster. The analyses were based on a combined sample of rape and bank robbery victims, who all met the diagnostic criteria for ASD (N = 404) using the Acute Stress Disorder Scale. The results showed that the five factor model provided the best fit to the data. The results of the present study suggest that the dimensionality of ASD may be best characterized as a five factor structure which separates dysphoric and anxious arousal items into two separate factors, akin to recent research on PTSD's latent structure. Thus, the current study adds to the debate about how ASD should be conceptualized in the pending DSM-5.

Perceived Discrimination and Heavy Episodic Drinking Among African-American Youth: Differences by Age and Reason for Discrimination.

PubMed

Madkour, Aubrey Spriggs; Jackson, Kristina; Wang, Heng; Miles, Thomas T; Mather, Frances; Shankar, Arti

2015-11-01

The purpose of this study was to examine whether associations between perceived discrimination and heavy episodic drinking (HED) vary by age and by discrimination type (e.g., racial, age, physical appearance) among African-American youth. National data from the Panel Study of Income Dynamics Transition to Adulthood Study were analyzed. Youth participated in up to four interviews (2005, 2007, 2009, 2011; n = 657) between ages 18 and 25 years. Respondents reported past-year engagement in HED (four or more drinks for females, five or more drinks for males) and frequency of discriminatory acts experienced (e.g., receiving poor service, being treated with less courtesy). Categorical latent growth curve models, including perceived discrimination types (racial, age, and physical appearance) as a time-varying predictors of HED, were run. Controls for gender, birth cohort, living arrangement in adolescence, familial wealth, parental alcohol use, and college attendance were explored. The average HED trajectory was curvilinear (increasing followed by flattening), whereas perceived discrimination remained flat with age. In models including controls, odds of HED were significantly higher than average around ages 20-21 years with greater frequency of perceived racial discrimination; associations were not significant at other ages. Discrimination attributed to age or physical appearance was not associated with HED at any age. Perceived racial discrimination may be a particularly salient risk factor for HED around the ages of transition to legal access to alcohol among African-American youth. Interventions to reduce discrimination or its impact could be targeted before this transition to ameliorate the negative outcomes associated with HED. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Use of a Context-Based Information Retrieval Technique

DTIC Science & Technology

2009-07-01

provided in context. Latent Semantic Analysis (LSA) is a statistical technique for inferring contextual and structural information, and previous studies...WAIS). 10 DSTO-TR-2322 1.4.4 Latent Semantic Analysis LSA, which is also known as latent semantic indexing (LSI), uses a statistical and...1.4.6 Language Models In contrast, natural language models apply algorithms that combine statistical information with semantic information. Semantic

Latent factor structure of a behavioral economic marijuana demand curve.

PubMed

Aston, Elizabeth R; Farris, Samantha G; MacKillop, James; Metrik, Jane

2017-08-01

Drug demand, or relative value, can be assessed via analysis of behavioral economic purchase task performance. Five demand indices are typically obtained from drug purchase tasks. The goal of this research was to determine whether metrics of marijuana reinforcement from a marijuana purchase task (MPT) exhibit a latent factor structure that efficiently characterizes marijuana demand. Participants were regular marijuana users (n = 99; 37.4% female, 71.5% marijuana use days [5 days/week], 15.2% cannabis dependent) who completed study assessments, including the MPT, during a baseline session. Principal component analysis was used to examine the latent structure underlying MPT indices. Concurrent validity was assessed via examination of relationships between latent factors and marijuana use, past quit attempts, and marijuana expectancies. A two-factor solution was confirmed as the best fitting structure, accounting for 88.5% of the overall variance. Factor 1 (65.8% variance) reflected "Persistence," indicating sensitivity to escalating marijuana price, which comprised four MPT indices (elasticity, O max , P max , and breakpoint). Factor 2 (22.7% variance) reflected "Amplitude," indicating the amount consumed at unrestricted price (intensity). Persistence factor scores were associated with fewer past marijuana quit attempts and lower expectancies of negative use outcomes. Amplitude factor scores were associated with more frequent use, dependence symptoms, craving severity, and positive marijuana outcome expectancies. Consistent with research on alcohol and cigarette purchase tasks, the MPT can be characterized with a latent two-factor structure. Thus, demand for marijuana appears to encompass distinct dimensions of price sensitivity and volumetric consumption, with differential relations to other aspects of marijuana motivation.

Students' Views on Mathematics in Single-Sex and Coed Classrooms in Ghana

ERIC Educational Resources Information Center

Bofah, Emmanuel Adu-tutu; Hannula, Markku S.

2016-01-01

In this study, we investigated students' views on themselves as learners of mathematics as a function of school-by-sex (N = 2034, MAge = 18.49, SDAge = 1.25; 12th-grade; 58.2% girls). Using latent variable Structural Equation Modeling (SEM), the measurement and structural equivalence as well as the equality of latent means of scores across…

The Information a Test Provides on an Ability Parameter. Research Report. ETS RR-07-18

ERIC Educational Resources Information Center

Haberman, Shelby J.

2007-01-01

In item-response theory, if a latent-structure model has an ability variable, then elementary information theory may be employed to provide a criterion for evaluation of the information the test provides concerning ability. This criterion may be considered even in cases in which the latent-structure model is not valid, although interpretation of…

The NEO Five-Factor Inventory: Latent Structure and Relationships with Dimensions of Anxiety and Depressive Disorders in a Large Clinical Sample

ERIC Educational Resources Information Center

Rosellini, Anthony J.; Brown, Timothy A.

2011-01-01

The present study evaluated the latent structure of the NEO Five-Factor Inventory (NEO FFI) and relations between the five-factor model (FFM) of personality and dimensions of "DSM-IV" anxiety and depressive disorders (panic disorder, generalized anxiety disorder [GAD], obsessive-compulsive disorder, social phobia [SOC], major depressive disorder…

Taxometric and Factor Analytic Models of Anxiety Sensitivity: Integrating Approaches to Latent Structural Research

ERIC Educational Resources Information Center

Bernstein, Amit; Zvolensky, Michael J.; Norton, Peter J.; Schmidt, Norman B.; Taylor, Steven; Forsyth, John P.; Lewis, Sarah F.; Feldner, Matthew T.; Leen-Feldner, Ellen W.; Stewart, Sherry H.; Cox, Brian

2007-01-01

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), as indexed by the 16-item Anxiety Sensitivity Index (ASI; S. Reiss, R. A. Peterson, M. Gursky, & R. J. McNally, 1986), by using taxometric and factor-analytic approaches in an integrative manner. Taxometric analyses indicated that AS has a…

Using Instrumental Variable (IV) Tests to Evaluate Model Specification in Latent Variable Structural Equation Models*

PubMed Central

Kirby, James B.; Bollen, Kenneth A.

2009-01-01

Structural Equation Modeling with latent variables (SEM) is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood estimator (ML), but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared to that for full information estimators. We address this shortcoming by providing several specification tests based on the 2SLS estimator for latent variable structural equation models developed by Bollen (1996). We explain how these tests can be used to not only identify a misspecified model, but to help diagnose the source of misspecification within a model. We present and discuss results from a Monte Carlo experiment designed to evaluate the finite sample properties of these tests. Our findings suggest that the 2SLS tests successfully identify most misspecified models, even those with modest misspecification, and that they provide researchers with information that can help diagnose the source of misspecification. PMID:20419054

Common Mental Disorders among Occupational Groups: Contributions of the Latent Class Model

PubMed Central

Martins Carvalho, Fernando; de Araújo, Tânia Maria

2016-01-01

Background. The Self-Reporting Questionnaire (SRQ-20) is widely used for evaluating common mental disorders. However, few studies have evaluated the SRQ-20 measurements performance in occupational groups. This study aimed to describe manifestation patterns of common mental disorders symptoms among workers populations, by using latent class analysis. Methods. Data derived from 9,959 Brazilian workers, obtained from four cross-sectional studies that used similar methodology, among groups of informal workers, teachers, healthcare workers, and urban workers. Common mental disorders were measured by using SRQ-20. Latent class analysis was performed on each database separately. Results. Three classes of symptoms were confirmed in the occupational categories investigated. In all studies, class I met better criteria for suspicion of common mental disorders. Class II discriminated workers with intermediate probability of answers to the items belonging to anxiety, sadness, and energy decrease that configure common mental disorders. Class III was composed of subgroups of workers with low probability to respond positively to questions for screening common mental disorders. Conclusions. Three patterns of symptoms of common mental disorders were identified in the occupational groups investigated, ranging from distinctive features to low probabilities of occurrence. The SRQ-20 measurements showed stability in capturing nonpsychotic symptoms. PMID:27630999

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia.

PubMed

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-11-01

Scant information exists on PTSD symptoms and structure in youth from developing countries. We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Results suggest that PTSD is a continuously distributed latent trait.

Application of core-shell-structured CdTe@SiO2 quantum dots synthesized via a facile solution method for improving latent fingerprint detection

NASA Astrophysics Data System (ADS)

Gao, Feng; Han, Jiaxing; Lv, Caifeng; Wang, Qin; Zhang, Jun; Li, Qun; Bao, Liru; Li, Xin

2012-10-01

Fingerprint detection is important in criminal investigation. This paper reports a facile powder brushing technique for improving latent fingerprint detection using core-shell-structured CdTe@SiO2 quantum dots (QDs) as fluorescent labeling marks. Core-shell-structured CdTe@SiO2 QDs are prepared via a simple solution-based approach using NH2NH2·H2O as pH adjustor and stabilizer, and their application for improving latent fingerprint detection is explored. The obtained CdTe@SiO2 QDs show spherical shapes with well-defined core-shell structures encapsulating different amounts of QDs depending on the type of the pH adjustor and stabilizer. Moreover, the fluorescence of CdTe@SiO2 QDs is largely enhanced by surface modification of the SiO2 shell. The CdTe@SiO2 QDs overcome the oxidation problem of pure CdTe QDs in air, thus affording better variability with strong adhesive ability, better resolution, and bright emission colors for practical application in latent fingerprint detection. In comparison with the conventional fluorescence powders, silver powders, and others, the effectiveness of CdTe@SiO2 QD powders for detection of latent fingerprints present on a large variety of object surfaces is greatly improved. The synthesis method for CdTe@SiO2 QDs is simple, cheap, and easy for large-scale production, and thus offers many advantages in the practical application of fingerprint detection.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

PubMed

Whiteway, Matthew R; Butts, Daniel A

2017-03-01

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control. Copyright © 2017 the American Physiological Society.

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

A taxometric investigation of agoraphobia in a clinical and a community sample.

PubMed

Slade, Tim; Grisham, Jessica R

2009-08-01

The nosological status of agoraphobia is controversial. Agoraphobia may be a distinct diagnostic entity or a marker of avoidance severity. The current study examines the latent structure of agoraphobia through the use of taxometric analysis. The latent structure of agoraphobia was examined in two independent samples, one comprising outpatients presenting for treatment for panic disorder (PD) with or without agoraphobia (n=365), and the other comprising community volunteers to a national mental health survey who experienced fear or avoidance of at least one prototypic agoraphobic situation (n=640). Two taxometric procedures were carried out - maximum eigenvalue (MAXEIG) and mean above minus below a cut (MAMBAC) - using indicators derived from questionnaire measures of, and structured diagnostic interviews for, agoraphobia. Results show consistent evidence of dimensional latent structure in both samples. It is concluded that scores on measures of agoraphobia best represent an agoraphobic severity dimension.

Nucleotide sequence and proposed secondary structure of Columnea latent viroid: a natural mosaic of viroid sequences.

PubMed Central

Hammond, R; Smith, D R; Diener, T O

1989-01-01

The Columnea latent viroid (CLV) occurs latently in certain Columnea erythrophae plants grown commercially. In potato and tomato, CLV causes potato spindle tuber viroid (PSTV)-like symptoms. Its nucleotide sequence and proposed secondary structure reveal that CLV consists of a single-stranded circular RNA of 370 nucleotides which can assume a rod-like structure with extensive base-pairing characteristic of all known viroids. The electrophoretic mobility of circular CLV under nondenaturing conditions suggests a potential tertiary structure. CLV contains extensive sequence homologies to the PSTV group of viroids but contains a central conserved region identical to that of hop stunt viroid (HSV). CLV also shares some biological properties with each of the two types of viroids. Most probably, CLV is the result of intracellular RNA recombination between an HSV-type and one or more PSTV-type viroids replicating in the same plant. Images PMID:2602114

Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

PubMed

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-03-12

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

Refinements of Stout’s Procedure for Assessing Latent Trait Unidimensionality

DTIC Science & Technology

1992-08-01

in the presence of guessing when coupled with many high-discriminating items. A revision of DIMTEST is proposed to overcome this limitation. Also, an...used for factor analysis. When guessing is present in the responses to items, however, linear factor analysis of tetrachoric correlations can produce...significance when d=1 and maintaining good power when d=2, even when the correlation between the abilities is as high as .7. The present study provides a

Patterns of stigma toward schizophrenia among the general population: a latent profile analysis.

PubMed

Loch, Alexandre A; Wang, Yuan-Pang; Guarniero, Francisco B; Lawson, Fabio L; Hengartner, Michael P; Rössler, Wulf; Gattaz, Wagner F

2014-09-01

Our purpose was to assess stigma toward schizophrenia in a representative sample of the Brazilian general population. The sample consisted of 1015 individuals interviewed by telephone. A vignette describing someone with schizophrenia was read, and four stigma aspects regarding this hypothetical individual were assessed: stereotypes, restrictions, perceived prejudice and social distance. Latent profile analysis searched for stigma profiles among the sample. Multinomial logistic regression was used to find correlates of each class. Four stigma profiles were found; 'no stigma' individuals (n = 251) mostly displayed positive opinions. 'Labelers' (n = 222) scored high on social distance; they more often had familial contact with mental illness and more often labeled the vignette's disorder as schizophrenia. 'Discriminators', the group with the majority of individuals (n = 302), showed high levels of stigmatizing beliefs in all dimensions; discriminators were significantly older. 'Unobtrusive stigma' individuals (n = 240) seemed to demonstrate uncertainty or low commitment since they mostly answered items with the middle/impartial option. Some findings from the international literature were replicated; however, familial contact increased stigma, possibly denoting a locally modulated determinant. Hereby, our study also adds important cross-cultural data by showing that stigma toward schizophrenia is high in a Latin-American setting. We highlight the importance of analyzing the general population as a heterogeneous group, aiming to better elaborate anti-stigma campaigns. © The Author(s) 2013.

Latent constructs of the autobiographical memory questionnaire: a recollection-belief model of autobiographical experience.

PubMed

Fitzgerald, Joseph M; Broadbridge, Carissa L

2013-01-01

Many researchers employ single-item scales of subjective experiences such as imagery and confidence to assess autobiographical memory. We tested the hypothesis that four latent constructs, recollection, belief, impact, and rehearsal, account for the variance in commonly used scales across four different types of autobiographical memory: earliest childhood memory, cue word memory of personal experience, highly vivid memory, and most stressful memory. Participants rated each memory on scales hypothesised to be indicators of one of four latent constructs. Multi-group confirmatory factor analyses and structural analyses confirmed the similarity of the latent constructs of recollection, belief, impact, and rehearsal, as well as the similarity of the structural relationships among those constructs across memory type. The observed pattern of mean differences between the varieties of autobiographical experiences was consistent with prior research and theory in the study of autobiographical memory.

Environmental risk perception, environmental concern and propensity to participate in organic farming programmes.

PubMed

Toma, Luiza; Mathijs, Erik

2007-04-01

This paper aims to identify the factors underlying farmers' propensity to participate in organic farming programmes in a Romanian rural region that confronts non-point source pollution. For this, we employ structural equation modelling with latent variables using a specific data set collected through an agri-environmental farm survey in 2001. The model includes one 'behavioural intention' latent variable ('propensity to participate in organic farming programmes') and five 'attitude' and 'socio-economic' latent variables ('socio-demographic characteristics', 'economic characteristics', 'agri-environmental information access', 'environmental risk perception' and 'general environmental concern'). The results indicate that, overall, the model has an adequate fit to the data. All loadings are statistically significant, supporting the theoretical basis for assignment of indicators for each latent variable. The significance tests for the structural model parameters show 'environmental risk perception' as the strongest determinant of farmers' propensity to participate in organic farming programmes.

Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra.

PubMed

Maher, Anthony D; Fonville, Judith M; Coen, Muireann; Lindon, John C; Rae, Caroline D; Nicholson, Jeremy K

2012-01-17

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.

The Role of Safety Culture in Influencing Provider Perceptions of Patient Safety.

PubMed

Bishop, Andrea C; Boyle, Todd A

2016-12-01

To determine how provider perceptions of safety culture influence their involvement in patient safety practices. Health-care providers were surveyed in 2 tertiary hospitals located in Atlantic Canada, composed of 4 units in total. The partial least squares (PLS) approach to structural equation modeling was used to analyze the data. Latent variables provider PLS model encompassed the hypothesized relationships between provider characteristics, safety culture, perceptions of patient safety practices, and actual performance of patient safety practices, using the Health Belief Model (HBM) as a guide. Data analysis was conducted using SmartPLS. A total of 113 health-care providers completed a survey out of an eligible 318, representing a response rate of 35.5%. The final PLS model showed acceptable internal consistency with all four latent variables having a composite reliability score above the recommended 0.70 cutoff value (safety culture = 0.86, threat = 0.76, expectations = 0.83, PS practices = 0.75). Discriminant validity was established, and all path coefficients were found to be significant at the α = 0.05 level using nonparametric bootstrapping. The survey results show that safety culture accounted for 34% of the variance in perceptions of threat and 42% of the variance in expectations. This research supports the role that safety culture plays in the promotion and maintenance of patient safety activities for health-care providers. As such, it is recommended that the introduction of new patient safety strategies follow a thorough exploration of an organization's safety culture.

Evaluating the Latent Structure of the MMPI-2 F(p) Scale in a Forensic Sample: A Taxometric Analysis

ERIC Educational Resources Information Center

Strong, David R.; Glassmire, David M.; Frederick, Richard I.; Greene, Roger L.

2006-01-01

P. A. Arbisi and Y. S. Ben-Porath (1995) originally proposed that the Infrequency Psychopathology scale, F(p), be used as the final step in an algorithm to determine the validity of a Minnesota Multiphasic Personality Inventory-2 (MMPI-2) protocol. The current study used taxometric procedures to determine the latent structure of F(p) among…

Modeling Latent Growth Curves With Incomplete Data Using Different Types of Structural Equation Modeling and Multilevel Software

ERIC Educational Resources Information Center

Ferrer, Emilio; Hamagami, Fumiaki; McArdle, John J.

2004-01-01

This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in…

The Latent Structures of the Learning and Study Strategies Inventory (LASSI): A Comparative Analysis.

ERIC Educational Resources Information Center

Obiekwe, Jerry C.

The first purpose of this study was to analyze the results of the confirmatory factor analyses, via EQS, with regard to the latent structures of the Learning and Study Strategies Inventory (LASSI) (C. Weinstein, D. Palmer, and A. Schulte, 1987) as proposed by S. Olejnik and S. Nist (1992), A. Olivarez and M. Tallent-Runnels (1994), B. Olaussen and…

A Taxometric Investigation of the Latent Structure of Worry: Dimensionality and Associations with Depression, Anxiety, and Stress

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Broman-Fulks, Joshua J.; Bergman, Shawn M.; Green, Bradley A.; Zlomke, Kimberly R.

2010-01-01

Worry has been described as a core feature of several disorders, particularly generalized anxiety disorder (GAD). The present study examined the latent structure of worry by applying 3 taxometric procedures (MAXEIG, MAMBAC, and L-Mode) to data collected from 2 large samples. Worry in the first sample (Study 1) of community participants (n = 1,355)…

Optical properties of drug metabolites in latent fingermarks

PubMed Central

Shen, Yao; Ai, Qing

2016-01-01

Drug metabolites usually have structures of split-ring resonators (SRRs), which might lead to negative permittivity and permeability in electromagnetic field. As a result, in the UV-vis region, the latent fingermarks images of drug addicts and non drug users are inverse. The optical properties of latent fingermarks are quite different between drug addicts and non-drug users. This is a technic superiority for crime scene investigation to distinguish them. In this paper, we calculate the permittivity and permeability of drug metabolites using tight-binding model. The latent fingermarks of smokers and non-smokers are given as an example. PMID:26838730

Predictive Inference Using Latent Variables with Covariates*

PubMed Central

Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A.

2014-01-01

Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs. S. America ) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model. Review of other latent heating algorithms will be discussed in the workshop.

Comparison of Internet-based and paper-based questionnaires in Taiwan using multisample invariance approach.

PubMed

Yu, Sen-Chi; Yu, Min-Ning

2007-08-01

This study examines whether the Internet-based questionnaire is psychometrically equivalent to the paper-based questionnaire. A random sample of 2,400 teachers in Taiwan was divided into experimental and control groups. The experimental group was invited to complete the electronic form of the Chinese version of Center for Epidemiologic Studies Depression Scale (CES-D) placed on the Internet, whereas the control group was invited to complete the paper-based CES-D, which they received by mail. The multisample invariance approach, derived from structural equation modeling (SEM), was applied to analyze the collected data. The analytical results show that the two groups have equivalent factor structures in the CES-D. That is, the items in CES-D function equivalently in the two groups. Then the equality of latent mean test was performed. The latent means of "depressed mood," "positive affect," and "interpersonal problems" in CES-D are not significantly different between these two groups. However, the difference in the "somatic symptoms" latent means between these two groups is statistically significant at alpha = 0.01. But the Cohen's d statistics indicates that such differences in latent means do not apparently lead to a meaningful effect size in practice. Both CES-D questionnaires exhibit equal validity, reliability, and factor structures and exhibit a little difference in latent means. Therefore, the Internet-based questionnaire represents a promising alternative to the paper-based questionnaire.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia

PubMed Central

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-01-01

Background Scant information exists on PTSD symptoms and structure in youth from developing countries. Methods We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Results Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Conclusions Results suggest that PTSD is a continuously distributed latent trait. PMID:25382359

A lattice protein with an amyloidogenic latent state: stability and folding kinetics.

PubMed

Palyanov, Andrey Yu; Krivov, Sergei V; Karplus, Martin; Chekmarev, Sergei F

2007-03-15

We have designed a model lattice protein that has two stable folded states, the lower free energy native state and a latent state of somewhat higher energy. The two states have a sizable part of their structures in common (two "alpha-helices") and differ in the content of "alpha-helices" and "beta-strands" in the rest of their structures; i.e. for the native state, this part is alpha-helical, and for the latent state it is composed of beta-strands. Thus, the lattice protein free energy surface mimics that of amyloidogenic proteins that form well organized fibrils under appropriate conditions. A Go-like potential was used and the folding process was simulated with a Monte Carlo method. To gain insight into the equilibrium free energy surface and the folding kinetics, we have combined standard approaches (reduced free energy surfaces, contact maps, time-dependent populations of the characteristic states, and folding time distributions) with a new approach. The latter is based on a principal coordinate analysis of the entire set of contacts, which makes possible the introduction of unbiased reaction coordinates and the construction of a kinetic network for the folding process. The system is found to have four characteristic basins, namely a semicompact globule, an on-pathway intermediate (the bifurcation basin), and the native and latent states. The bifurcation basin is shallow and consists of the structure common to the native and latent states, with the rest disorganized. On the basis of the simulation results, a simple kinetic model describing the transitions between the characteristic states was developed, and the rate constants for the essential transitions were estimated. During the folding process the system dwells in the bifurcation basin for a relatively short time before it proceeds to the native or latent state. We suggest that such a bifurcation may occur generally for proteins in which native and latent states have a sizable part of their structures in common. Moreover, there is the possibility of introducing changes in the system (e.g., mutations), which guide the system toward the native or misfolded state.

Latent Heating Retrievals Using the TRMM Precipitation Radar: A Multi-Seasonal Study

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Lang, S.; Meneghini, R.; Halverson, J.; Johnson, R.; Simpson, J.; Einaudi, Franco (Technical Monitor)

2001-01-01

Rainfall is a key link in the hydrologic cycle and is a primary heat source for the atmosphere. The vertical distribution of latent heat release, which is accompanied by rainfall, modulates the large-scale circulations of the tropics and in turn can impact midlatitude weather. This latent heat release is a consequence of phase changes between vapor, liquid, and solid water. Present largescale weather and climate models can simulate latent heat release only crudely, thus reducing their confidence in predictions on both global and regional scales. This paper represents the first attempt to use NASA Tropical Rainfall Measuring Mission (TRMM) rainfall information to estimate the four-dimensional structure of global monthly latent heating profiles over the global tropics from December 1997 to October 2000. The Goddard Convective-Stratiform. Heating (CSH) algorithm and TRMM precipitation radar data are used for this study. We will examine and compare the latent heating structures between 1997-1998 (winter) ENSO and 1998-2000 (non-ENSO). We will also examine over the tropics. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental; Indian oceans vs west Pacific; Africa vs S. America) will be also examined and compared. In addition, we will examine the relationship between latent heating (max heating level) and SST. The period of interest also coincides with several TRMM field campaigns that recently occurred over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and in the central Pacific in 1999 (KWAJEX). Sounding diagnosed Q1 budgets from these experiments could provide a means of validating the retrieved profiles of latent heating from the CSH algorithm.

Measuring Latent Quantities

ERIC Educational Resources Information Center

McDonald, Roderick P.

2011-01-01

A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and…

The Peer Interaction in Primary School Questionnaire: Testing for Measurement Equivalence and Latent Mean Differences in Bullying between Gender in Egypt, Saudi Arabia and the USA

ERIC Educational Resources Information Center

Hussein, Mohamed Habashy

2010-01-01

The Peer Interaction in Primary School Questionnaire (PIPSQ) was developed to assess individuals' levels of bullying and victimization. This study used the approach of latent means analysis (LMA) within the framework of structural equation modeling (SEM) to explore the factor structure and gender differences associated with the PIPSQ in a sample…

A Latent Growth Curve Analysis of the Structure of Aggression, Drug Use, and Delinquent Behaviors and their Interrelations over Time in Urban and Rural Adolescents

ERIC Educational Resources Information Center

Farrell, Albert D.; Sullivan, Terri N.; Esposito, Layla E.; Meyer, Aleta L.; Valois, Robert F.

2005-01-01

Latent growth curve analysis was used to examine the structure and interrelations among aggression, drug use, and delinquent behavior during early adolescence. Five waves of data were collected from 667 students at three urban middle schools serving a predominantly African American population, and from a more ethnically diverse sample of 950…

The latent structure of alcohol misuse in young adults: Do taxometric results differ as a function of prior criminal history?

PubMed

Walters, Glenn D

2015-12-01

The purpose of this study was to determine whether the latent structure of alcohol misuse is categorical or continuous in male and female adults with and without a history of prior criminal offending. Data from 3452 (1530 male, 1922 female) 27-to-32 year old members of the National Longitudinal Study of Adolescent to Adult Health (Add Health) were subjected to taxometric analysis using three nonredundant taxometric procedures--mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode). Analyses produced results consistent with categorical latent structure in males with a previous history of criminal offending but not in males without a previous history of criminal offending or females with or without a history of criminal offending. The findings from the other groups were indeterminate for the most part (i.e., neither categorical nor continuous). The presumptive taxon was validated by testing differences in age of onset and frequency of criminal arrest and drunkenness between the putative taxon and the upper portion of the complement. As predicted, all four validation outcomes were significantly worse in the taxon group. On the basis of these results it is concluded that alcohol misuse in young adults may have features of both categorical and continuous latent structure and that the categorical aspects are more prominent in males with a history of offending behavior. Additional research is required to determine which aspects and features of alcohol misuse are categorical and which aspects and features are continuous. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Toward a Model-Based Approach to the Clinical Assessment of Personality Psychopathology

PubMed Central

Eaton, Nicholas R.; Krueger, Robert F.; Docherty, Anna R.; Sponheim, Scott R.

2015-01-01

Recent years have witnessed tremendous growth in the scope and sophistication of statistical methods available to explore the latent structure of psychopathology, involving continuous, discrete, and hybrid latent variables. The availability of such methods has fostered optimism that they can facilitate movement from classification primarily crafted through expert consensus to classification derived from empirically-based models of psychopathological variation. The explication of diagnostic constructs with empirically supported structures can then facilitate the development of assessment tools that appropriately characterize these constructs. Our goal in this paper is to illustrate how new statistical methods can inform conceptualization of personality psychopathology and therefore its assessment. We use magical thinking as example, because both theory and earlier empirical work suggested the possibility of discrete aspects to the latent structure of personality psychopathology, particularly forms of psychopathology involving distortions of reality testing, yet other data suggest that personality psychopathology is generally continuous in nature. We directly compared the fit of a variety of latent variable models to magical thinking data from a sample enriched with clinically significant variation in psychotic symptomatology for explanatory purposes. Findings generally suggested a continuous latent variable model best represented magical thinking, but results varied somewhat depending on different indices of model fit. We discuss the implications of the findings for classification and applied personality assessment. We also highlight some limitations of this type of approach that are illustrated by these data, including the importance of substantive interpretation, in addition to use of model fit indices, when evaluating competing structural models. PMID:24007309

Caffeine use disorder: An item-response theory analysis of proposed DSM-5 criteria.

PubMed

Ágoston, Csilla; Urbán, Róbert; Richman, Mara J; Demetrovics, Zsolt

2018-06-01

Caffeine is a common psychoactive substance with a documented addictive potential. Caffeine withdrawal has been included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), but caffeine use disorder (CUD) is considered to be a condition for further study. The aim of the current study is (1) to test the psychometric properties of the Caffeine Use Disorder Questionnaire (CUDQ) by using a confirmatory factor analysis and an item response theory (IRT) approach, (2) to compare IRT models with varying numbers of parameters and models with or without caffeine consumption criteria, and (3) to examine if the total daily caffeine consumption and the use of different caffeinated products can predict the magnitude of CUD symptomatology. A cross-sectional study was conducted on an adult sample (N = 2259). Participants answered several questions regarding their caffeine consumption habits and completed the CUDQ, which incorporates the nine proposed criteria of the DSM-5 as well as one additional item regarding the suffering caused by the symptoms. Factor analyses demonstrated the unidimensionality of the CUDQ. The suffering criterion had the highest discriminative value at a higher degree of latent trait. The criterion of failure to fulfill obligations and social/interpersonal problems discriminate only at the higher value of CUD latent factor, while endorsement the consumption of more caffeine or longer than intended and craving criteria were discriminative at a lower level of CUD. Total daily caffeine intake was related to a higher level of CUD. Daily coffee, energy drink, and cola intake as dummy variables were associated with the presence of more CUD symptoms, while daily tea consumption as a dummy variable was related to less CUD symptoms. Regular smoking was associated with more CUD symptoms, which was explained by a larger caffeine consumption. The IRT approach helped to determine which CUD symptoms indicate more severity and have a greater discriminative value. The level of CUD is influenced by the type and quantity of caffeine consumption. Copyright © 2018 Elsevier Ltd. All rights reserved.

Gender-based discrimination and unprotected receptive anal intercourse among transgender women in Brazil: A mixed methods study

PubMed Central

2018-01-01

Introduction Discrimination related to gender identity may directly influence vulnerability to HIV through increased exposure to unprotected receptive anal intercourse (URAI). Little is known about the relationship between gender-based discrimination (GBD) and URAI with stable partners among transgender women. Methods This mixed-methods research began with a cross-sectional survey conducted between 2014 and 2016 with transgender women in Salvador, the capital city in one of the poorest regions in Brazil. Respondent-driven sampling was used to recruit the study population. GBD was defined through Latent Class Analysis. Additionally, 19 semi-structured interviews with participants were transcribed and analyzed through thematic content analysis. Results URAI with stable partners was commonly reported (37.3%). GDB was positively associated with URAI among stable partners (OR = 6.47; IC 95%: 1.67–25.02). The analysis of the interviews illustrated how GBD impacted transgender women in diverse ways. Experiences with GBD perpetrated by the family often initiated a trajectory of economic vulnerability that led many to engage in survival sex work. The constant experience with GBD contributed to participants feeling an immense sense of trust with their stable partners, ultimately diminished their desire to use condoms. Further, the high frequency of GBD contributed to poor mental health overall, though some participants said engagement in transgender advocacy efforts provided a vital source of resilience and support. Conclusion Our mixed-method study capitalizes upon the strengths of diverse data sets to produce a holistic understanding of GBD and URAI with stable partners. Furthermore, by confirming the association between greater GBD and URAI, we have demonstrated how GBD can impact condom negotiation in diverse relationships. PMID:29641528

HR-MAS MR Spectroscopy of Breast Cancer Tissue Obtained with Core Needle Biopsy: Correlation with Prognostic Factors

PubMed Central

Choi, Ji Soo; Baek, Hyeon-Man; Kim, Suhkmann; Kim, Min Jung; Youk, Ji Hyun; Moon, Hee Jung; Kim, Eun-Kyung; Han, Kyung Hwa; Kim, Dong-hyun; Kim, Seung Il; Koo, Ja Seung

2012-01-01

The purpose of this study was to examine the correlation between high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopy using core needle biopsy (CNB) specimens and histologic prognostic factors currently used in breast cancer patients. After institutional review board approval and informed consent were obtained for this study, CNB specimens were collected from 36 malignant lesions in 34 patients. Concentrations and metabolic ratios of various choline metabolites were estimated by HR-MAS MR spectroscopy using CNB specimens. HR-MAS spectroscopic values were compared according to histopathologic variables [tumor size, lymph node metastasis, histologic grade, status of estrogens receptor (ER), progesterone receptor (PR), HER2 (a receptor for human epidermal growth factor), and Ki-67, and triple negativity]. Multivariate analysis was performed with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA). HR-MAS MR spectroscopy quantified and discriminated choline metabolites in all CNB specimens of the 36 breast cancers. Several metabolite markers [free choline (Cho), phosphocholine (PC), creatine (Cr), taurine, myo-inositol, scyllo-inositol, total choline (tCho), glycine, Cho/Cr, tCho/Cr, PC/Cr] on HR-MAS MR spectroscopy were found to correlate with histologic prognostic factors [ER, PR, HER2, histologic grade, triple negativity, Ki-67, poor prognosis]. OPLS-DA multivariate models were generally able to discriminate the status of histologic prognostic factors (ER, PR, HER2, Ki-67) and prognosis groups. Our study suggests that HR-MAS MR spectroscopy using CNB specimens can predict tumor aggressiveness prior to surgery in breast cancer patients. In addition, it may be helpful in the detection of reliable markers for breast cancer characterization. PMID:23272149

Gender-based discrimination and unprotected receptive anal intercourse among transgender women in Brazil: A mixed methods study.

PubMed

Magno, Laio; Dourado, Inês; Silva, Luís Augusto V da; Brignol, Sandra; Amorim, Leila; MacCarthy, Sarah

2018-01-01

Discrimination related to gender identity may directly influence vulnerability to HIV through increased exposure to unprotected receptive anal intercourse (URAI). Little is known about the relationship between gender-based discrimination (GBD) and URAI with stable partners among transgender women. This mixed-methods research began with a cross-sectional survey conducted between 2014 and 2016 with transgender women in Salvador, the capital city in one of the poorest regions in Brazil. Respondent-driven sampling was used to recruit the study population. GBD was defined through Latent Class Analysis. Additionally, 19 semi-structured interviews with participants were transcribed and analyzed through thematic content analysis. URAI with stable partners was commonly reported (37.3%). GDB was positively associated with URAI among stable partners (OR = 6.47; IC 95%: 1.67-25.02). The analysis of the interviews illustrated how GBD impacted transgender women in diverse ways. Experiences with GBD perpetrated by the family often initiated a trajectory of economic vulnerability that led many to engage in survival sex work. The constant experience with GBD contributed to participants feeling an immense sense of trust with their stable partners, ultimately diminished their desire to use condoms. Further, the high frequency of GBD contributed to poor mental health overall, though some participants said engagement in transgender advocacy efforts provided a vital source of resilience and support. Our mixed-method study capitalizes upon the strengths of diverse data sets to produce a holistic understanding of GBD and URAI with stable partners. Furthermore, by confirming the association between greater GBD and URAI, we have demonstrated how GBD can impact condom negotiation in diverse relationships.

The relationship of racism, chronic stress emotions, and blood pressure.

PubMed

Peters, Rosalind M

2006-01-01

To test a middle-range theory of chronic stress emotions to examine the effects of perceived racism and emotion-focused coping on psychological and physiological health outcomes in African Americans. A descriptive-correlational, causal modeling study was conducted with a convenience sample of 162 community-dwelling adults. Participants completed the Racism and Life Experience Scale; Krieger Racial Discrimination Questionnaire; Toronto Alexithymia Scale; Emotional Approach Coping Scale; State-Trait Personality Inventory; and a demographic data sheet. Automated blood pressure (BP) readings were obtained. Structural equation modeling techniques were used to examine the hypothesized causal and correlational links between the theoretical constructs, as well as to examine the relationship between the observed variables and the latent constructs measured. FINDING AND CONCLUSIONS: The proposed model fits the sample data. Racism was a commonly experienced stressor associated with chronic stress emotions, but not with BP. Emotion-focused coping was strongly associated with socioeconomic status and chronic stress emotions but not with BP.

Attenuated total reflectance-FT-IR spectroscopy for gunshot residue analysis: potential for ammunition determination.

PubMed

Bueno, Justin; Sikirzhytski, Vitali; Lednev, Igor K

2013-08-06

The ability to link a suspect to a particular shooting incident is a principal task for many forensic investigators. Here, we attempt to achieve this goal by analysis of gunshot residue (GSR) through the use of attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FT-IR) combined with statistical analysis. The firearm discharge process is analogous to a complex chemical process. Therefore, the products of this process (GSR) will vary based upon numerous factors, including the specific combination of the firearm and ammunition which was discharged. Differentiation of FT-IR data, collected from GSR particles originating from three different firearm-ammunition combinations (0.38 in., 0.40 in., and 9 mm calibers), was achieved using projection to latent structures discriminant analysis (PLS-DA). The technique was cross (leave-one-out), both internally and externally, validated. External validation was achieved via assignment (caliber identification) of unknown FT-IR spectra from unknown GSR particles. The results demonstrate great potential for ATR-FT-IR spectroscopic analysis of GSR for forensic purposes.

Striking changes in tea metabolites due to elevational effects.

PubMed

Kfoury, Nicole; Morimoto, Joshua; Kern, Amanda; Scott, Eric R; Orians, Colin M; Ahmed, Selena; Griffin, Timothy; Cash, Sean B; Stepp, John Richard; Xue, Dayuan; Long, Chunlin; Robbat, Albert

2018-10-30

Climate effects on crop quality at the molecular level are not well-understood. Gas and liquid chromatography-mass spectrometry were used to measure changes of hundreds of compounds in tea at different elevations in Yunnan Province, China. Some increased in concentration while others decreased by 100's of percent. Orthogonal projection to latent structures-discriminant analysis revealed compounds exhibiting analgesic, antianxiety, antibacterial, anticancer, antidepressant, antifungal, anti-inflammatory, antioxidant, anti-stress, and cardioprotective properties statistically (p = 0.003) differentiated high from low elevation tea. Also, sweet, floral, honey-like notes were higher in concentration in the former while the latter displayed grassy, hay-like aroma. In addition, multivariate analysis of variance showed low elevation tea had statistically (p = 0.0062) higher concentrations of caffeine, epicatechin gallate, gallocatechin, and catechin; all bitter compounds. Although volatiles represent a small fraction of the total mass, this is the first comprehensive report illustrating how normal variations in temperature, 5 °C, due to elevational effects impact tea quality. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of Mutagenicity of Chemicals from Their Calculated Molecular Descriptors: A Case Study with Structurally Homogeneous versus Diverse Datasets.

PubMed

Basak, Subhash C; Majumdar, Subhabrata

2015-01-01

Variation in high-dimensional data is often caused by a few latent factors, and hence dimension reduction or variable selection techniques are often useful in gathering useful information from the data. In this paper we consider two such recent methods: Interrelated two-way clustering and envelope models. We couple these methods with traditional statistical procedures like ridge regression and linear discriminant analysis, and apply them on two data sets which have more predictors than samples (i.e. n < p scenario) and several types of molecular descriptors. One of these datasets consists of a congeneric group of Amines while the other has a much diverse collection compounds. The difference of prediction results between these two datasets for both the methods supports the hypothesis that for a congeneric set of compounds, descriptors of a certain type are enough to provide good QSAR models, but as the data set grows diverse including a variety of descriptors can improve model quality considerably.

Infinite hidden conditional random fields for human behavior analysis.

PubMed

Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja

2013-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Latent Structure Agreement Analysis

DTIC Science & Technology

1989-11-01

correct for bias in estimation of disease prevalence due to misclassification error [39]. Software Varying panel latent class agreement models can be...D., and L. M. Irwig, "Estimation of Test Error Rates, Disease Prevalence and Relative Risk from Misclassified Data: A Review," Journal of Clinical

Forensic applications of chemical imaging: latent fingerprint detection using visible absorption and luminescence.

PubMed

Exline, David L; Wallace, Christie; Roux, Claude; Lennard, Chris; Nelson, Matthew P; Treado, Patrick J

2003-09-01

Chemical imaging technology is a rapid examination technique that combines molecular spectroscopy and digital imaging, providing information on morphology, composition, structure, and concentration of a material. Among many other applications, chemical imaging offers an array of novel analytical testing methods, which limits sample preparation and provides high-quality imaging data essential in the detection of latent fingerprints. Luminescence chemical imaging and visible absorbance chemical imaging have been successfully applied to ninhydrin, DFO, cyanoacrylate, and luminescent dye-treated latent fingerprints, demonstrating the potential of this technology to aid forensic investigations. In addition, visible absorption chemical imaging has been applied successfully to visualize untreated latent fingerprints.

Pain and the defense response: structural equation modeling reveals a coordinated psychophysiological response to increasing painful stimulation.

PubMed

Donaldson, Gary W; Chapman, C Richard; Nakamura, Yoshi; Bradshaw, David H; Jacobson, Robert C; Chapman, Christopher N

2003-03-01

The defense response theory implies that individuals should respond to increasing levels of painful stimulation with correlated increases in affectively mediated psychophysiological responses. This paper employs structural equation modeling to infer the latent processes responsible for correlated growth in the pain report, evoked potential amplitudes, pupil dilation, and skin conductance of 92 normal volunteers who experienced 144 trials of three levels of increasingly painful electrical stimulation. The analysis assumed a two-level model of latent growth as a function of stimulus level. The first level of analysis formulated a nonlinear growth model for each response measure, and allowed intercorrelations among the parameters of these models across individuals. The second level of analysis posited latent process factors to account for these intercorrelations. The best-fitting parsimonious model suggests that two latent processes account for the correlations. One of these latent factors, the activation threshold, determines the initial threshold response, while the other, the response gradient, indicates the magnitude of the coherent increase in response with stimulus level. Collectively, these two second-order factors define the defense response, a broad construct comprising both subjective pain evaluation and physiological mechanisms.

Non-destructive forensic latent fingerprint acquisition with chromatic white light sensors

NASA Astrophysics Data System (ADS)

Leich, Marcus; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

2011-02-01

Non-destructive latent fingerprint acquisition is an emerging field of research, which, unlike traditional methods, makes latent fingerprints available for additional verification or further analysis like tests for substance abuse or age estimation. In this paper a series of tests is performed to investigate the overall suitability of a high resolution off-the-shelf chromatic white light sensor for the contact-less and non-destructive latent fingerprint acquisition. Our paper focuses on scanning previously determined regions with exemplary acquisition parameter settings. 3D height field and reflection data of five different latent fingerprints on six different types of surfaces (HDD platter, brushed metal, painted car body (metallic and non-metallic finish), blued metal, veneered plywood) are experimentally studied. Pre-processing is performed by removing low-frequency gradients. The quality of the results is assessed subjectively; no automated feature extraction is performed. Additionally, the degradation of the fingerprint during the acquisition period is observed. While the quality of the acquired data is highly dependent on surface structure, the sensor is capable of detecting the fingerprint on all sample surfaces. On blued metal the residual material is detected; however, the ridge line structure dissolves within minutes after fingerprint placement.

Applying General Strain Theory to Examine Perceived Discrimination’s Indirect Relation to Mexican-Heritage Youth’s Alcohol, Cigarette, and Marijuana Use

PubMed Central

Cleveland, Michael J.; Hecht, Michael L.

2013-01-01

Latent growth curve modeling was used to test four hypotheses. First, this study hypothesized that acculturation-related variables (e.g., Mexican-heritage youth’s country of origin, time spent in the U.S., and language preference with family and friends) would be associated with initial levels of perceived discrimination. Guided by general strain theory (GST), this study then posed a second hypothesis: Initial levels of perceived discrimination would be indirectly related to initial levels of substance use through initial levels of acculturation stress. Third, this study hypothesized that changes in perceived discrimination would be indirectly related to changes in substance use through changes in acculturation stress. As a fourth hypothesis, it was postulated that initial levels of perceived discrimination would be indirectly related to changes in substance use through changes in acculturation stress. Mexican-heritage youth (N=1,106) from 29 schools in Phoenix, AZ completed surveys at six waves from 5th through 8th grades. In partial support of the first hypothesis, more time spent in the U.S. and speaking English with friends were associated with lower levels of perceived discrimination. The second hypothesis was not supported. Initial levels of perceived discrimination were positively associated with initial levels of acculturation stress; however, this association was not found between initial levels of acculturation stress and substance use. The third and fourth hypotheses were supported, which buttressed predictions derived from GST. Both initial levels and increases in perceived discrimination were indirectly related to increases in substance use through increases in acculturation stress. PMID:20490921

Training haptic stiffness discrimination: time course of learning with or without visual information and knowledge of results.

PubMed

Teodorescu, Kinneret; Bouchigny, Sylvain; Korman, Maria

2013-08-01

In this study, we explored the time course of haptic stiffness discrimination learning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discrimination learning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

Construct validity evidence for the Male Role Norms Inventory-Short Form: A structural equation modeling approach using the bifactor model.

PubMed

Levant, Ronald F; Hall, Rosalie J; Weigold, Ingrid K; McCurdy, Eric R

2016-10-01

The construct validity of the Male Role Norms Inventory-Short Form (MRNI-SF) was assessed using a latent variable approach implemented with structural equation modeling (SEM). The MRNI-SF was specified as having a bifactor structure, and validation scales were also specified as latent variables. The latent variable approach had the advantages of separating effects of general and specific factors and controlling for some sources of measurement error. Data (N = 484) were from a diverse sample (38.8% men of color, 22.3% men of diverse sexualities) of community-dwelling and college men who responded to an online survey. The construct validity of the MRNI-SF General Traditional Masculinity Ideology factor was supported for all 4 of the proposed latent correlations with: (a) Male Role Attitudes Scale; (b) general factor of Conformity to Masculine Norms Inventory-46; (c) higher-order factor of Gender Role Conflict Scale; and (d) Personal Attributes Questionnaire-Masculinity Scale. Significant correlations with relevant other latent factors provided concurrent validity evidence for the MRNI-SF specific factors of Negativity toward Sexual Minorities, Importance of Sex, Restrictive Emotionality, and Toughness, with all 8 of the hypothesized relationships supported. However, 3 relationships concerning Dominance were not supported. (The construct validity of the remaining 2 MRNI-SF specific factors-Avoidance of Femininity and Self-Reliance through Mechanical Skills was not assessed.) Comparisons were made, and meaningful differences noted, between the latent correlations emphasized in this study and their raw variable counterparts. Results are discussed in terms of the advantages of an SEM approach and the unique characteristics of the bifactor model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Racial discrimination mediates race differences in sleep problems: A longitudinal analysis.

PubMed

Fuller-Rowell, Thomas E; Curtis, David S; El-Sheikh, Mona; Duke, Adrienne M; Ryff, Carol D; Zgierska, Aleksandra E

2017-04-01

To examine changes in sleep problems over a 1.5-year period among Black or African American (AA) and White or European American (EA) college students and to consider the role of racial discrimination as a mediator of race differences in sleep problems over time. Students attending a large, predominantly White university (N = 133, 41% AA, 57% female, mean age = 18.8, SD = .90) reported on habitual sleep characteristics and experiences of racial discrimination at baseline and follow-up assessments. A latent variable for sleep problems was assessed from reports of sleep latency, duration, efficiency, and quality. Longitudinal models were used to examine race differences in sleep problems over time and the mediating role of perceived discrimination. Covariates included age, gender, parent education, parent income, body mass index, self-rated physical health, and depressive symptoms. Each of the individual sleep measures was also examined separately, and sensitivity analyses were conducted using alternative formulations of the sleep problems measure. AAs had greater increases in sleep problems than EAs. Perceived discrimination was also associated with increases in sleep problems over time and mediated racial disparities in sleep. This pattern of findings was similar when each of the sleep indicators was considered separately and held with alternative sleep problems measures. The findings highlight the importance of racial disparities in sleep across the college years and suggest that experiences of discrimination contribute to group disparities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A general class of multinomial mixture models for anuran calling survey data

USGS Publications Warehouse

Royle, J. Andrew; Link, W.A.

2005-01-01

We propose a general framework for modeling anuran abundance using data collected from commonly used calling surveys. The data generated from calling surveys are indices of calling intensity (vocalization of males) that do not have a precise link to actual population size and are sensitive to factors that influence anuran behavior. We formulate a model for calling-index data in terms of the maximum potential calling index that could be observed at a site (the 'latent abundance class'), given its underlying breeding population, and we focus attention on estimating the distribution of this latent abundance class. A critical consideration in estimating the latent structure is imperfect detection, which causes the observed abundance index to be less than or equal to the latent abundance class. We specify a multinomial sampling model for the observed abundance index that is conditional on the latent abundance class. Estimation of the latent abundance class distribution is based on the marginal likelihood of the index data, having integrated over the latent class distribution. We apply the proposed modeling framework to data collected as part of the North American Amphibian Monitoring Program (NAAMP).

A systematic literature review of PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM-5.

PubMed

Armour, Cherie; Műllerová, Jana; Elhai, Jon D

2016-03-01

The factor structure of posttraumatic stress disorder (PTSD) has been widely researched, but consensus regarding the exact number and nature of factors is yet to be reached. The aim of the current study was to systematically review the extant literature on PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders (DSM) in order to identify the best-fitting model. One hundred and twelve research papers published after 1994 using confirmatory factor analysis and DSM-based measures of PTSD were included in the review. In the DSM-IV literature, four-factor models received substantial support, but the five-factor Dysphoric arousal model demonstrated the best fit, regardless of gender, measurement instrument or trauma type. The recently proposed DSM-5 PTSD model was found to be a good representation of PTSD's latent structure, but studies analysing the six- and seven-factor models suggest that the DSM-5 PTSD factor structure may need further alterations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling Bivariate Change in Individual Differences: Prospective Associations Between Personality and Life Satisfaction.

PubMed

Hounkpatin, Hilda Osafo; Boyce, Christopher J; Dunn, Graham; Wood, Alex M

2017-09-18

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Measurement Model Specification Error in LISREL Structural Equation Models.

ERIC Educational Resources Information Center

Baldwin, Beatrice; Lomax, Richard

This LISREL study examines the robustness of the maximum likelihood estimates under varying degrees of measurement model misspecification. A true model containing five latent variables (two endogenous and three exogenous) and two indicator variables per latent variable was used. Measurement model misspecification considered included errors of…

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.

Measuring What Latent Fingerprint Examiners Consider Sufficient Information for Individualization Determinations

PubMed Central

Ulery, Bradford T.; Hicklin, R. Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

2014-01-01

Latent print examiners use their expertise to determine whether the information present in a comparison of two fingerprints (or palmprints) is sufficient to conclude that the prints were from the same source (individualization). When fingerprint evidence is presented in court, it is the examiner's determination—not an objective metric—that is presented. This study was designed to ascertain the factors that explain examiners' determinations of sufficiency for individualization. Volunteer latent print examiners (n = 170) were each assigned 22 pairs of latent and exemplar prints for examination, and annotated features, correspondence of features, and clarity. The 320 image pairs were selected specifically to control clarity and quantity of features. The predominant factor differentiating annotations associated with individualization and inconclusive determinations is the count of corresponding minutiae; other factors such as clarity provided minimal additional discriminative value. Examiners' counts of corresponding minutiae were strongly associated with their own determinations; however, due to substantial variation of both annotations and determinations among examiners, one examiner's annotation and determination on a given comparison is a relatively weak predictor of whether another examiner would individualize. The extensive variability in annotations also means that we must treat any individual examiner's minutia counts as interpretations of the (unknowable) information content of the prints: saying “the prints had N corresponding minutiae marked” is not the same as “the prints had N corresponding minutiae.” More consistency in annotations, which could be achieved through standardization and training, should lead to process improvements and provide greater transparency in casework. PMID:25372036

Implicit measurement of the latent heat in a magnetocaloric NiMnIn Heusler alloy

NASA Astrophysics Data System (ADS)

Ghahremani, Mohammadreza; ElBidweihy, Hatem; Bennett, Lawrence H.; Della Torre, Edward; Zou, Min; Johnson, Francis

2013-05-01

The latent heat linked with the first-order transformation of a NiMnIn Heusler alloy has been studied through direct measurements of the adiabatic temperature change, ΔTad, during magnetization process. The experimental procedure used guarantees independent data points and negates any contribution of hysteretic losses to the magnetocaloric effect. Thus, the differences between the magnitudes of ΔTad measurements during the magnetization with the initial temperature change directions from low-to-high and high-to-low are solely attributed to the latent heat exchange, which accompanies the irreversible structural first-order transformation. An estimate of the latent heat inducing such differences is about 0.292 J/g.

Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2004-01-01

A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…

Working Memory Tasks Differ in Factor Structure across Age Cohorts: Implications for Dedifferentiation

ERIC Educational Resources Information Center

Johnson, Wendy; Logie, Robert H.; Brockmole, James R.

2010-01-01

Researchers interested in working memory have debated whether it should be considered a single latent cognitive ability or a set of essentially independent latent abilities distinguished by domain-specific memory and/or processing resources. Simultaneously, researchers interested in cognitive aging have established that there are substantial…

Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2012-01-01

A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…

Diagnostic Procedures for Detecting Nonlinear Relationships between Latent Variables

ERIC Educational Resources Information Center

Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C.

2012-01-01

Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…

Persistence and amplitude of cigarette demand in relation to quit intentions and attempts

PubMed Central

O’Connor, Richard J.; Heckman, Bryan W.; Adkison, Sarah E.; Rees, Vaughan W.; Hatsukami, Dorothy K.; Bickel, Warren K.; Cummings, K. Michael

2016-01-01

INTRODUCTION The cigarette purchase task (CPT) is a method that can be used to assess relative value of cigarettes. Based on cigarettes purchased across a price range, five derived metrics (Omax, Pmax, breakpoint, intensity, elasticity) can assess cigarette demand. A study with adolescent smokers found that these could be reduced to two latent factors: Persistence (price insensitivity) and Amplitude (volumetric consumption). We sought to replicate this structure with adult smokers, and examine how these variables relate to cessation efforts. METHOD Web-based survey conducted in 2014 among adult (18+) current daily cigarette smokers (N=1194). Participants completed the CPT, Fagerstrom Test for Nicotine Dependence (FTND), reported past-year quit attempts, and future quit intentions. We included published scales assessing perceived prevalence of smoking, social reactivity, smoker identity, and risk perception. RESULTS Our analysis supported two latent variables, Persistence and Amplitude, which correlated positively with FTND. Persistence correlated with several psychosocial factors, and was higher among those intending to quit very soon, but did not vary by number of past-year quit attempts. Amplitude differed across quit attempts and intention (p’s <.001), and in multivariable models was significantly associated with lower 30-day quit intention [OR=0.76, p=.001]. CONCLUSIONS Persistence and Amplitude factors characterized CPT data in adults, discriminated known groups (e.g., smokers by intentions to quit), and were positively associated with nicotine dependence. Factor scores also appear to relate to certain psychosocial factors, such as smoker identity and perceptions of risk. Future research should examine the predictive validity of these constructs. PMID:27048156

Persistence and amplitude of cigarette demand in relation to quit intentions and attempts.

PubMed

O'Connor, Richard J; Heckman, Bryan W; Adkison, Sarah E; Rees, Vaughan W; Hatsukami, Dorothy K; Bickel, Warren K; Cummings, K Michael

2016-06-01

The cigarette purchase task (CPT) is a method that can be used to assess the relative value of cigarettes. Based on cigarettes purchased across a price range, five derived metrics (Omax, Pmax, breakpoint, intensity, and elasticity) can assess cigarette demand. A study with adolescent smokers found that these could be reduced to two latent factors: persistence (price insensitivity) and amplitude (volumetric consumption). We sought to replicate this structure with adult smokers and examine how these variables relate to cessation efforts. Web-based survey conducted in 2014 among adult (18 years and above) current daily cigarette smokers (N = 1194). Participants completed the CPT, Fagerstrom Test for Nicotine Dependence (FTND), reported past-year quit attempts, and future quit intentions. We included published scales assessing perceived prevalence of smoking, social reactivity, smoker identity, and risk perception. Our analysis supported two latent variables, persistence and amplitude, which correlated positively with FTND. Persistence was correlated with several psychosocial factors and was higher among those intending to quit very soon, but did not vary by number of past-year quit attempts. Amplitude differed across quit attempts and intention (p < 0.001) and, in multivariable models, was significantly associated with lower 30-day quit intention (OR = 0.76, p = 0.001). Persistence and amplitude factors characterized CPT data in adults, discriminated known groups (e.g., smokers by intentions to quit), and were positively associated with nicotine dependence. Factor scores also appear to relate to certain psychosocial factors, such as smoker identity and perceptions of risk. Future research should examine the predictive validity of these constructs.

Latent Heating from TRMM Satellite Measurements

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E.; Olson, W.

2005-01-01

Rainfall production is a fundamental process within the Earth;s hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations with the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional amount of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the spacetime structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has and continues to be developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generate from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM rainfall products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2001. Rainfall, latent heating and radar reflectivity structures between El Nino (DE 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs. west Pacific, Africa vs. S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in strtaiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release Over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Starr, David (Technical Monitor)

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release over the Global Tropics Using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in straitform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMXX), Brazil in 1999 (TRMM- LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Heterogeneity of sleep quality in relation to circadian preferences and depressive symptomatology among major depressive patients.

PubMed

Selvi, Yavuz; Boysan, Murat; Kandeger, Ali; Uygur, Omer F; Sayin, Ayca A; Akbaba, Nursel; Koc, Basak

2018-08-01

The current study aimed at investigating the latent dimensional structure of sleep quality as indexed by the seven components of the Pittsburgh Sleep Quality Index (PSQI), as well as latent covariance structure between sleep quality, circadian preferences and depressive symptoms. Two hundred twenty-five patients with major depressive disorder (MDD), with an average age of 29.92 ± 10.49 years (aged between 17 and 63), participated in the study. The PSQI, Morningness-Eveningness Questionnaire (MEQ) and Beck Depression Inventory (BDI) were administered to participants. Four sets of latent class analyses were subsequently run to obtain optimal number of latent classes best fit to the data. Mixture models revealed that sleep quality is multifaceted in MDD. The data best fit to four-latent-class model: Poor Habitual Sleep Quality (PHSQ), Poor Subjective Sleep Quality (PSSQ), Intermediate Sleep Quality (ISQ), and Good Sleep Quality (GSQ). MDD patients classified into GSQ latent class (23.6%) reported the lowest depressive symptoms and were more prone to morningness diurnal preferences compared to other three homogenous sub-groups. Finally, the significant association between eveningness diurnal preferences and depressive symptomatology was significantly mediated by poor sleep quality. The cross-sectional nature of the study and the lack of an objective measurement of sleep such as polysomnography recordings was the most striking limitation of the study. We concluded sleep quality in relation to circadian preferences and depressive symptoms has a heterogeneous nature in MDD. Copyright © 2018. Published by Elsevier B.V.

Latent geometry of bipartite networks

NASA Astrophysics Data System (ADS)

Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

2017-03-01

Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

HIV/AIDS knowledge among men who have sex with men: applying the item response theory.

PubMed

Gomes, Raquel Regina de Freitas Magalhães; Batista, José Rodrigues; Ceccato, Maria das Graças Braga; Kerr, Lígia Regina Franco Sansigolo; Guimarães, Mark Drew Crosland

2014-04-01

To evaluate the level of HIV/AIDS knowledge among men who have sex with men in Brazil using the latent trait model estimated by Item Response Theory. Multicenter, cross-sectional study, carried out in ten Brazilian cities between 2008 and 2009. Adult men who have sex with men were recruited (n = 3,746) through Respondent Driven Sampling. HIV/AIDS knowledge was ascertained through ten statements by face-to-face interview and latent scores were obtained through two-parameter logistic modeling (difficulty and discrimination) using Item Response Theory. Differential item functioning was used to examine each item characteristic curve by age and schooling. Overall, the HIV/AIDS knowledge scores using Item Response Theory did not exceed 6.0 (scale 0-10), with mean and median values of 5.0 (SD = 0.9) and 5.3, respectively, with 40.7% of the sample with knowledge levels below the average. Some beliefs still exist in this population regarding the transmission of the virus by insect bites, by using public restrooms, and by sharing utensils during meals. With regard to the difficulty and discrimination parameters, eight items were located below the mean of the scale and were considered very easy, and four items presented very low discrimination parameter (< 0.34). The absence of difficult items contributed to the inaccuracy of the measurement of knowledge among those with median level and above. Item Response Theory analysis, which focuses on the individual properties of each item, allows measures to be obtained that do not vary or depend on the questionnaire, which provides better ascertainment and accuracy of knowledge scores. Valid and reliable scales are essential for monitoring HIV/AIDS knowledge among the men who have sex with men population over time and in different geographic regions, and this psychometric model brings this advantage.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Olson, W. S.; Meneghini, R.; Yang, S.; Simpson, J.; Kummerow, C.; Smith, E.

2000-01-01

This paper represents the first attempt to use TRMM rainfall information to estimate the four dimensional latent heating structure over the global tropics for February 1998. The mean latent heating profiles over six oceanic regions (TOGA COARE IFA, Central Pacific, S. Pacific Convergence Zone, East Pacific, Indian Ocean and Atlantic Ocean) and three continental regions (S. America, Central Africa and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm estimated heating profiles. Three different latent heating algorithms, the Goddard Convective-Stratiform (CSH) heating, the Goddard Profiling (GPROF) heating, and the Hydrometeor heating (HH) are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are quite similar. They all can identify the areas of major convective activity (i.e., a well defined ITCZ in the Pacific, a distinct SPCZ) in the global tropics. The magnitude of their estimated latent heating release is also not in bad agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm estimated heating profiles only show one maximum heating level, and the level varies between convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. By contrast, two maximum heating levels were found using the GPROF heating and HH algorithms. The latent heating profiles estimated from all three methods can not show cooling between active convective events. We also examined the impact of different TMI (Multi-channel Passive Microwave Sensor) and PR (Precipitation Radar) rainfall information on latent heating structures.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators.

PubMed

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators

PubMed Central

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors. PMID:26973580

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Topic Model for Graph Mining.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Luo, Xiangfeng

2015-12-01

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort.

PubMed

Lehman, Li-Wei; Long, William; Saeed, Mohammed; Mark, Roger

2014-01-01

Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent "topic" structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes.

Latent Heating from TRMM Satellite Measurements

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

2004-01-01

Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

Latent Profile and Cluster Analysis of Infant Temperament: Comparisons across Person-Centered Approaches

ERIC Educational Resources Information Center

Gartstein, Maria A.; Prokasky, Amanda; Bell, Martha Ann; Calkins, Susan; Bridgett, David J.; Braungart-Rieker, Julia; Leerkes, Esther; Cheatham, Carol L.; Eiden, Rina D.; Mize, Krystal D.; Jones, Nancy Aaron; Mireault, Gina; Seamon, Erich

2017-01-01

There is renewed interest in person-centered approaches to understanding the structure of temperament. However, questions concerning temperament types are not frequently framed in a developmental context, especially during infancy. In addition, the most common person-centered techniques, cluster analysis (CA) and latent profile analysis (LPA),…

Applying the Mixed Rasch Model to the Runco Ideational Behavior Scale

ERIC Educational Resources Information Center

Sen, Sedat

2016-01-01

Previous research using creativity assessments has used latent class models and identified multiple classes (a 3-class solution) associated with various domains. This study explored the latent class structure of the Runco Ideational Behavior Scale, which was designed to quantify ideational capacity. A robust state-of the-art technique called the…

The Latent Classes of Subclinical ADHD Symptoms: Convergences of Multiple Informant Reports

ERIC Educational Resources Information Center

Kobor, Andrea; Takacs, Adam; Urban, Robert; Csepe, Valeria

2012-01-01

The purpose of the present study was to conduct latent class analysis on the Hyperactivity scale of the Strengths and Difficulties Questionnaire in order to identify distinct subgroups of subclinical ADHD in a multi-informant framework. We hypothesized a similar structure between teachers and parents, and differences in symptom severity across…

Comparing Latent Structures of the Grade of Membership, Rasch, and Latent Class Models

ERIC Educational Resources Information Center

Erosheva, Elena A.

2005-01-01

This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other…

A Latent Variable Approach to Executive Control in Healthy Ageing

ERIC Educational Resources Information Center

Adrover-Roig, Daniel; Sese, Albert; Barcelo, Francisco; Palmer, Alfonso

2012-01-01

It is a well-established finding that the central executive is fractionated in at least three separable component processes: Updating, Shifting, and Inhibition of information (Miyake et al., 2000). However, the fractionation of the central executive among the elderly has been less well explored, and Miyake's et al. latent structure has not yet…

A Comparison of Methods for Estimating Quadratic Effects in Nonlinear Structural Equation Models

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen

2012-01-01

Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…

What If We Took Our Models Seriously? Estimating Latent Scores in Individuals

ERIC Educational Resources Information Center

Schneider, W. Joel

2013-01-01

Researchers often argue that the structural models of the constructs they study are relevant to clinicians. Unfortunately, few clinicians are able to translate the mathematically precise relationships between latent constructs and observed scores into information that can be usefully applied to individuals. Typically this means that when a new…

Mean structure analysis from an IRT approach: an application in the context of organizational psychology.

PubMed

Revuelta Menéndez, Javier; Ximénez Gómez, Carmen

2012-11-01

The application of mean and covariance structure analysis with quantitative data is increasing. However, latent means analysis with qualitative data is not as widespread. This article summarizes the procedures to conduct an analysis of latent means of dichotomous data from an item response theory approach. We illustrate the implementation of these procedures in an empirical example referring to the organizational context, where a multi-group analysis was conducted to compare the latent means of three employee groups in two factors measuring personal preferences and the perceived degree of rewards from the organization. Results show that higher personal motivations are associated with higher perceived importance of the organization, and that these perceptions differ across groups, so that higher-level employees have a lower level of personal and perceived motivation. The article shows how to estimate the factor means and the factor correlation from dichotomous data, and how to assess goodness of fit. Lastly, we provide the M-Plus syntax code in order to facilitate the latent means analyses for applied researchers.

Heating Structures Derived from Satellite

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Adler, R.; Haddad, Z.; Hou, A.; Kakar, R.; Krishnamurti, T. N.; Kummerow, C.; Lang, S.; Meneghini, R.; Olson, W.

2004-01-01

Rainfall is a key link in the hydrologic cycle and is a primary heat source for the atmosphere. The vertical distribution of latent-heat release, which is accompanied by rainfall, modulates the large-scale circulations of the tropics and in turn can impact midlatitude weather. This latent heat release is a consequence of phase changes between vapor, liquid, and solid water. The Tropical Rainfall Measuring Mission (TRMM), a joint U.S./Japan space project, was launched in November 1997. It provides an accurate measurement of rainfall over the global tropics which can be used to estimate the four-dimensional structure of latent heating over the global tropics. The distributions of rainfall and inferred heating can be used to advance our understanding of the global energy and water cycle. This paper describes several different algorithms for estimating latent heating using TRMM observations. The strengths and weaknesses of each algorithm as well as the heating products are also discussed. The validation of heating products will be exhibited. Finally, the application of this heating information to global circulation and climate models is presented.

Phenotypic factor analysis of psychopathology reveals a new body-related transdiagnostic factor.

PubMed

Pezzoli, Patrizia; Antfolk, Jan; Santtila, Pekka

2017-01-01

Comorbidity challenges the notion of mental disorders as discrete categories. An increasing body of literature shows that symptoms cut across traditional diagnostic boundaries and interact in shaping the latent structure of psychopathology. Using exploratory and confirmatory factor analysis, we reveal the latent sources of covariation among nine measures of psychopathological functioning in a population-based sample of 13024 Finnish twins and their siblings. By implementing unidimensional, multidimensional, second-order, and bifactor models, we illustrate the relationships between observed variables, specific, and general latent factors. We also provide the first investigation to date of measurement invariance of the bifactor model of psychopathology across gender and age groups. Our main result is the identification of a distinct "Body" factor, alongside the previously identified Internalizing and Externalizing factors. We also report relevant cross-disorder associations, especially between body-related psychopathology and trait anger, as well as substantial sex and age differences in observed and latent means. The findings expand the meta-structure of psychopathology, with implications for empirical and clinical practice, and demonstrate shared mechanisms underlying attitudes towards nutrition, self-image, sexuality and anger, with gender- and age-specific features.

The Social Appearance Anxiety Scale in Italian Adolescent Populations: Construct Validation and Group Discrimination in Community and Clinical Eating Disorders Samples.

PubMed

Dakanalis, Antonios; Carrà, Giuseppe; Calogero, Rachel; Zanetti, M Assunta; Volpato, Chiara; Riva, Giuseppe; Clerici, Massimo; Cipresso, Pietro

2016-02-01

Anxiety in situations where one's overall appearance (including body shape) may be negatively evaluated is hypothesized to play a central role in Eating Disorders (EDs) and in their co-occurrence with Social Anxiety Disorder (SAD). Three studies were conducted among community (N = 1995) and clinical (N = 703) ED samples of 11- to 18-year-old Italian girls and boys to (a) evaluate the psychometric qualities and measurement equivalence/invariance (ME/I) of the Social Appearance Anxiety (SAA) Scale (SAAS) and (b) determine to what extent SAA or other situational domains of social anxiety related to EDs distinguish adolescents with an ED only from those with SAD. Results upheld the one-factor structure and ME/I of the SAAS across samples, gender, age categories, and diagnostic status (i.e., ED participants with and without comorbid SAD). The SAAS demonstrated high internal consistency and 3-week test-retest reliability. The strength of the inter-relationships between SAAS and measures of body image, teasing about appearance, ED symptoms, depression, social anxiety, avoidance, and distress, as well as the ability of SAAS to discriminate community adolescents with high and low levels of ED symptoms and community participants from ED participants provided construct validity evidence. Only SAA strongly differentiated adolescents with any ED from those with comorbid SAD (23.2 %). Latent mean comparisons across all study groups were performed and discussed.

Spatial path models with multiple indicators and multiple causes: mental health in US counties.

PubMed

Congdon, Peter

2011-06-01

This paper considers a structural model for the impact on area mental health outcomes (poor mental health, suicide) of spatially structured latent constructs: deprivation, social capital, social fragmentation and rurality. These constructs are measured by multiple observed effect indicators, with the constructs allowed to be correlated both between and within areas. However, in the scheme developed here, particular latent constructs may also be influenced by known variables, or, via path sequences, by other constructs, possibly nonlinearly. For example, area social capital may be measured by effect indicators (e.g. associational density, charitable activity), but influenced as causes by other constructs (e.g. area deprivation), and by observed features of the socio-ethnic structure of areas. A model incorporating these features is applied to suicide mortality and the prevalence of poor mental health in 3141 US counties, which are related to the latent spatial constructs and to observed variables (e.g. county ethnic mix). Copyright © 2011 Elsevier Ltd. All rights reserved.

GARP regulates the bioavailability and activation of TGFβ.

PubMed

Wang, Rui; Zhu, Jianghai; Dong, Xianchi; Shi, Minlong; Lu, Chafen; Springer, Timothy A

2012-03-01

Glycoprotein-A repetitions predominant protein (GARP) associates with latent transforming growth factor-β (proTGFβ) on the surface of T regulatory cells and platelets; however, whether GARP functions in latent TGFβ activation and the structural basis of coassociation remain unknown. We find that Cys-192 and Cys-331 of GARP disulfide link to the TGFβ1 prodomain and that GARP with C192A and C331A mutations can also noncovalently associate with proTGFβ1. Noncovalent association is sufficiently strong for GARP to outcompete latent TGFβ-binding protein for binding to proTGFβ1. Association between GARP and proTGFβ1 prevents the secretion of TGFβ1. Integrin α(V)β(6) and to a lesser extent α(V)β(8) are able to activate TGFβ from the GARP-proTGFβ1 complex. Activation requires the RGD motif of latent TGFβ, disulfide linkage between GARP and latent TGFβ, and membrane association of GARP. Our results show that GARP is a latent TGFβ-binding protein that functions in regulating the bioavailability and activation of TGFβ.

Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches

PubMed Central

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance. PMID:23457417

Optimal study design with identical power: an application of power equivalence to latent growth curve models.

PubMed

von Oertzen, Timo; Brandmaier, Andreas M

2013-06-01

Structural equation models have become a broadly applied data-analytic framework. Among them, latent growth curve models have become a standard method in longitudinal research. However, researchers often rely solely on rules of thumb about statistical power in their study designs. The theory of power equivalence provides an analytical answer to the question of how design factors, for example, the number of observed indicators and the number of time points assessed in repeated measures, trade off against each other while holding the power for likelihood-ratio tests on the latent structure constant. In this article, we present applications of power-equivalent transformations on a model with data from a previously published study on cognitive aging, and highlight consequences of participant attrition on power. PsycINFO Database Record (c) 2013 APA, all rights reserved.

The computational nature of memory modification.

PubMed

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-03-15

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

The Latent Class Structure of Chinese Patients with Eating Disorders in Shanghai.

PubMed

Zheng, Yuchen; Kang, Qing; Huang, Jiabin; Jiang, Wenhui; Liu, Qiang; Chen, Han; Fan, Qing; Wang, Zhen; Chen, Jue; Xiao, Zeping

2017-08-25

Eating disorder is culture related, and the clinical symptoms are different between eastern and western patients. So the validity of feeding and eating disorders in the upcoming ICD-11 guide for Chinese patients is unclear. To explore the latent class structure of Chinese patients with eating disorder and the cross-cultural validity of the eating disorder section of the new ICD-11 guide in China. A total of 379 patients with eating disorders at Shanghai Mental Health Center were evaluated using the EDI questionnaire and a questionnaire developed by researchers from 2010 to 2016. SPSS 20.0 was used to enter data and analyze demographic data, and Latent GOLD was employed to conduct latent profile analysis. According to the results of latent profile analysis, patients with eating disorder were divided into five classes: low-weight fasting class (23.1%), non-fat-phobic binge/purge class (21.54%), low-fat-phobic binge class (19.27%), fat-phobic binge class (19.27%), and non-fat-phobic low-weight class (16.76%). Among the clinical symptoms extracted, there were significant differences in Body Mass Index (BMI), binge eating behavior, self-induced vomiting, laxative use and fat-phobic opinion; while there was no significant difference in restrictive food intake. Based on the clinical symptoms, there are five latent classes in Chinese patients with eating disorder, which is in accordance with the diagnostic categories of feeding and eating disorder in ICD-11. However, further work is needed in improving the fat-phobic opinion of patients with eating disorder and clarifying the BMI standard of thinness in the Chinese population.

Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use

PubMed Central

Reboussin, Beth A.; Ialongo, Nicholas S.

2011-01-01

Summary Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is most often diagnosed in childhood with symptoms often persisting into adulthood. Elevated rates of substance use disorders have been evidenced among those with ADHD, but recent research focusing on the relationship between subtypes of ADHD and specific drugs is inconsistent. We propose a latent transition model (LTM) to guide our understanding of how drug use progresses, in particular marijuana use, while accounting for the measurement error that is often found in self-reported substance use data. We extend the LTM to include a latent class predictor to represent empirically derived ADHD subtypes that do not rely on meeting specific diagnostic criteria. We begin by fitting two separate latent class analysis (LCA) models by using second-order estimating equations: a longitudinal LCA model to define stages of marijuana use, and a cross-sectional LCA model to define ADHD subtypes. The LTM model parameters describing the probability of transitioning between the LCA-defined stages of marijuana use and the influence of the LCA-defined ADHD subtypes on these transition rates are then estimated by using a set of first-order estimating equations given the LCA parameter estimates. A robust estimate of the LTM parameter variance that accounts for the variation due to the estimation of the two sets of LCA parameters is proposed. Solving three sets of estimating equations enables us to determine the underlying latent class structures independently of the model for the transition rates and simplifying assumptions about the correlation structure at each stage reduces the computational complexity. PMID:21461139

Significance of common variants on human chromosome 8q24 in relation to the risk of prostate cancer in native Japanese men

PubMed Central

Liu, Miao; Kurosaki, Takayuki; Suzuki, Motofumi; Enomoto, Yutaka; Nishimatsu, Hiroaki; Arai, Tomio; Sawabe, Motoji; Hosoi, Takayuki; Homma, Yukio; Kitamura, Tadaichi

2009-01-01

Background Common variants on human chromosome 8q24, rs1447295 (C/A) and rs6983267 (T/G), have been recently linked to the prevalence of prostate cancer in European and American populations. Here, we evaluated whether the single-nucleotide polymorphisms rs1447295 and rs6983267 were associated with the risk of sporadic prostate cancer as well as latent prostate cancer in a native Japanese population. Results We analyzed genomic DNA samples from 391 sporadic prostate cancer patients, 323 controls who had died from causes unrelated to cancer and 112 Japanese men who were diagnosed as having latent prostate cancer based on autopsy results. The polymorphisms were determined by allelic discrimination using a fluorescent-based TaqMan assay. The A allele of rs1447295 was significantly associated with the risk of sporadic prostate cancer (p = 0.04; age-adjusted OR, 1.34), while the G allele of rs6983267 showed a trend towards being a high-risk allele (p = 0.06; age-adjusted OR, 1.27). No significant difference between these two polymorphisms and the risk of latent prostate cancer was observed in the present Japanese population. Conclusion Known variants on human chromosome 8q24 may be risk factors for sporadic prostate cancer in native Japanese men. PMID:19602258

Item response theory analysis of the Pain Self-Efficacy Questionnaire.

PubMed

Costa, Daniel S J; Asghari, Ali; Nicholas, Michael K

2017-01-01

The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item instrument designed to assess the extent to which a person in pain believes s/he is able to accomplish various activities despite their pain. There is strong evidence for the validity and reliability of both the full-length PSEQ and a 2-item version. The purpose of this study is to further examine the properties of the PSEQ using an item response theory (IRT) approach. We used the two-parameter graded response model to examine the category probability curves, and location and discrimination parameters of the 10 PSEQ items. In item response theory, responses to a set of items are assumed to be probabilistically determined by a latent (unobserved) variable. In the graded-response model specifically, item response threshold (the value of the latent variable for which adjacent response categories are equally likely) and discrimination parameters are estimated for each item. Participants were 1511 mixed, chronic pain patients attending for initial assessment at a tertiary pain management centre. All items except item 7 ('I can cope with my pain without medication') performed well in IRT analysis, and the category probability curves suggested that participants used the 7-point response scale consistently. Items 6 ('I can still do many of the things I enjoy doing, such as hobbies or leisure activity, despite pain'), 8 ('I can still accomplish most of my goals in life, despite the pain') and 9 ('I can live a normal lifestyle, despite the pain') captured higher levels of the latent variable with greater precision. The results from this IRT analysis add to the body of evidence based on classical test theory illustrating the strong psychometric properties of the PSEQ. Despite the relatively poor performance of Item 7, its clinical utility warrants its retention in the questionnaire. The strong psychometric properties of the PSEQ support its use as an effective tool for assessing self-efficacy in people with pain. Copyright © 2016 Scandinavian Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Classification and Short-Term Course of DSM-IV Cannabis, Hallucinogen, Cocaine, and Opioid Disorders in Treated Adolescents

ERIC Educational Resources Information Center

Chung, Tammy; Martin, Christoper S.

2005-01-01

This study examined the latent class structure of Diagnostic and Statistical Manual of Mental Disorders (text rev.; DSM-IV; American Psychiatric Association, 2000) symptoms used to diagnose cannabis, hallucinogen, cocaine, and opiate disorders among 501 adolescents recruited from addictions treatment. Latent class results were compared with the…

Multilevel Latent Class Analysis: An Application of Adolescent Smoking Typologies with Individual and Contextual Predictors

ERIC Educational Resources Information Center

Henry, Kimberly L.; Muthen, Bengt

2010-01-01

Latent class analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this…

Two-Year Predictive Validity of Conduct Disorder Subtypes in Early Adolescence: A Latent Class Analysis of a Canadian Longitudinal Sample

ERIC Educational Resources Information Center

Lacourse, Eric; Baillargeon, Raymond; Dupere, Veronique; Vitaro, Frank; Romano, Elisa; Tremblay, Richard

2010-01-01

Background: Investigating the latent structure of conduct disorder (CD) can help clarify how symptoms related to aggression, property destruction, theft, and serious violations of rules cluster in individuals with this disorder. Discovering homogeneous subtypes can be useful for etiologic, treatment, and prevention purposes depending on the…

A Note on the Specification of Error Structures in Latent Interaction Models

ERIC Educational Resources Information Center

Mao, Xiulin; Harring, Jeffrey R.; Hancock, Gregory R.

2015-01-01

Latent interaction models have motivated a great deal of methodological research, mainly in the area of estimating such models. Product-indicator methods have been shown to be competitive with other methods of estimation in terms of parameter bias and standard error accuracy, and their continued popularity in empirical studies is due, in part, to…

The Benefits of Latent Variable Modeling to Develop Norms for a Translated Version of a Standardized Scale

ERIC Educational Resources Information Center

Seo, Hyojeong; Shaw, Leslie A.; Shogren, Karrie A.; Lang, Kyle M.; Little, Todd D.

2017-01-01

This article demonstrates the use of structural equation modeling to develop norms for a translated version of a standardized scale, the Supports Intensity Scale-Children's Version (SIS-C). The latent variable norming method proposed is useful when the standardization sample for a translated version is relatively small to derive norms…

Positive Adult Support and Depression Symptoms in Adolescent Females: The Partially Mediating Role of Eating Disturbances

ERIC Educational Resources Information Center

Linville, Deanna; O'Neil, Maya; Huebner, Angela

2011-01-01

This study examined linkages between depression symptoms (DEP) and positive adult support (PAS) in female adolescents and the partially mediating influence of eating disturbances (ED). Structural equation modeling was used to establish measurement models for each of the latent constructs, determine the relationships among the latent constructs,…

Measurement Equivalence of Teachers' Sense of Efficacy Scale Using Latent Growth Methods

ERIC Educational Resources Information Center

Basokçu, T. Oguz; Ögretmen, T.

2016-01-01

This study is based on the application of latent growth modeling, which is one of structural equation models on real data. Teachers' Sense of Efficacy Scale (TSES), which was previously adapted into Turkish was administered to 200 preservice teachers at different time intervals for three times and study data was collected. Measurement equivalence…

The Structure of Student Satisfaction with College Services: A Latent Class Model

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

Latent Class Analysis (LCA) was used to identify distinct groups of Community college students based on their self-ratings of satisfaction with student service programs. The programs were counseling, financial aid, health center, student programs and student government. The best fitting model to describe the data was a two Discrete-Factor model…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

Unfinished Business in Clarifying Causal Measurement: Commentary on Bainter and Bollen

ERIC Educational Resources Information Center

Markus, Keith A.

2014-01-01

In a series of articles and comments, Kenneth Bollen and his collaborators have incrementally refined an account of structural equation models that (a) model a latent variable as the effect of several observed variables and (b) carry an interpretation of the observed variables as, in some sense, measures of the latent variable that they cause.…

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

PubMed Central

Tao, Chenyang; Nichols, Thomas E.; Hua, Xue; Ching, Christopher R.K.; Rolls, Edmund T.; Thompson, Paul M.; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. PMID:27666385

Remote sensing image segmentation using local sparse structure constrained latent low rank representation

NASA Astrophysics Data System (ADS)

Tian, Shu; Zhang, Ye; Yan, Yimin; Su, Nan; Zhang, Junping

2016-09-01

Latent low-rank representation (LatLRR) has been attached considerable attention in the field of remote sensing image segmentation, due to its effectiveness in exploring the multiple subspace structures of data. However, the increasingly heterogeneous texture information in the high spatial resolution remote sensing images, leads to more severe interference of pixels in local neighborhood, and the LatLRR fails to capture the local complex structure information. Therefore, we present a local sparse structure constrainted latent low-rank representation (LSSLatLRR) segmentation method, which explicitly imposes the local sparse structure constraint on LatLRR to capture the intrinsic local structure in manifold structure feature subspaces. The whole segmentation framework can be viewed as two stages in cascade. In the first stage, we use the local histogram transform to extract the texture local histogram features (LHOG) at each pixel, which can efficiently capture the complex and micro-texture pattern. In the second stage, a local sparse structure (LSS) formulation is established on LHOG, which aims to preserve the local intrinsic structure and enhance the relationship between pixels having similar local characteristics. Meanwhile, by integrating the LSS and the LatLRR, we can efficiently capture the local sparse and low-rank structure in the mixture of feature subspace, and we adopt the subspace segmentation method to improve the segmentation accuracy. Experimental results on the remote sensing images with different spatial resolution show that, compared with three state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Latent vulnerability in cognitive performance following chronic cocaine self-administration in rhesus monkeys

PubMed Central

Porter, Jessica N.; Gurnsey, Kate; Jedema, Hank P.; Bradberry, Charles W.

2012-01-01

Rationale Cocaine use is associated with cognitive impairment which impacts treatment outcome. A clearer understanding of those deficits, and whether particular environments exacerbate them, is needed. Objectives This study evaluated whether previously observed domain-specific cognitive deficits persisted following a three month cessation from chronic cocaine self-administration, as well as the impact of novel and cocaine-associated attentional distractors. Methods Control and experimental groups of monkeys performed stimulus discrimination, stimulus reversal, and delayed match-to-sample (DMS) tasks. After establishing post-cocaine baseline performance, we examined general distractibility in both groups, using brief novel distractors counterbalanced across each task. After testing the novel distractor, an identical approach was used for exposure to an appetitive distractor previously associated with cocaine in the experimental group, or water in the control group. Results Post-administration baseline performance was equivalent between groups on all tasks. In the cocaine group, stimulus discrimination was unaffected by either distractor, whereas reversal performance was disrupted by both the novel and appetitive distractors. DMS performance was impaired in the cocaine group in the presence of the novel distractor. The control group’s performance was not affected by the presentation of either distractor on any task. Conclusion Our results reveal that despite normalized performance between groups, there exists in the cocaine group a domain-specific latent vulnerability of cognitive performance to impairment by environmental distractors. The pattern of vulnerability recapitulates the frank impairments seen in drug free animals during an active self-administration phase. A greater impact of the cocaine-associated distractor over the novel one was not observed. PMID:23108938

Latent vulnerability in cognitive performance following chronic cocaine self-administration in rhesus monkeys.

PubMed

Porter, Jessica N; Gurnsey, Kate; Jedema, Hank P; Bradberry, Charles W

2013-03-01

Cocaine use is associated with cognitive impairment which impacts treatment outcome. A clearer understanding of those deficits, and whether particular environments exacerbate them, is needed. This study evaluated whether previously observed domain-specific cognitive deficits persisted following a 3-month cessation from chronic cocaine self-administration, as well as the impact of novel and cocaine-associated attentional distractors. Control and experimental groups of monkeys performed stimulus discrimination, stimulus reversal, and delayed match-to-sample (DMS) tasks. After establishing post-cocaine baseline performance, we examined general distractibility in both groups, using brief novel distractors counterbalanced across each task. After testing the novel distractor, an identical approach was used for exposure to an appetitive distractor previously associated with cocaine in the experimental group or water in the control group. Post-administration baseline performance was equivalent between groups on all tasks. In the cocaine group, stimulus discrimination was unaffected by either distractor, whereas reversal performance was disrupted by both the novel and appetitive distractors. DMS performance was impaired in the cocaine group in the presence of the novel distractor. The control group's performance was not affected by the presentation of either distractor on any task. Our results reveal that despite normalized performance between groups, there exists in the cocaine group a domain-specific latent vulnerability of cognitive performance to impairment by environmental distractors. The pattern of vulnerability recapitulates the frank impairments seen in drug-free animals during an active self-administration phase. A greater impact of the cocaine-associated distractor over the novel one was not observed.

Toward DSM-V: mapping the alcohol use disorder continuum in college students.

PubMed

Hagman, Brett T; Cohn, Amy M

2011-11-01

The present study examined the dimensionality of DSM-IV Alcohol Use Disorder (AUD) criteria using Item Response Theory (IRT) methods and tested the validity of the proposed DSM-V AUD guidelines in a sample of college students. Participants were 396 college students who reported any alcohol use in the past 90 days and were aged 18 years or older. We conducted factor analyses to determine whether a one- or two-factor model provided a better fit to the AUD criteria. IRT analyses estimated item severity and discrimination parameters for each criterion. Multivariate analyses examined differences among the DSM-V diagnostic cut-off (AUD vs. No AUD) and severity qualifiers (no diagnosis, moderate, severe) across several validating measures of alcohol use. A dominant single-factor model provided the best fit to the AUD criteria. IRT analyses indicated that abuse and dependence criteria were intermixed along the latent continuum. The "legal problems" criterion had the highest severity parameter and the tolerance criterion had the lowest severity parameter. The abuse criterion "social/interpersonal problems" and dependence criterion "activities to obtain alcohol" had the highest discrimination parameter estimates. Multivariate analysis indicated that the DSM-V cut-off point, and severity qualifier groups were distinguishable on several measures of alcohol consumption, drinking consequences, and drinking restraint. Findings suggest that the AUD criteria reflect a latent variable that represents a primary disorder and provide support for the proposed DSM-V AUD criteria in a sample of college students. Continued research in other high-risk samples of college students is needed. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Trajectories of cultural stressors and effects on mental health and substance use among Hispanic immigrant adolescents.

PubMed

Schwartz, Seth J; Unger, Jennifer B; Baezconde-Garbanati, Lourdes; Zamboanga, Byron L; Lorenzo-Blanco, Elma I; Des Rosiers, Sabrina E; Romero, Andrea J; Cano, Miguel Ángel; Gonzales-Backen, Melinda A; Córdova, David; Piña-Watson, Brandy M; Huang, Shi; Villamar, Juan A; Soto, Daniel W; Pattarroyo, Monica; Szapocznik, José

2015-04-01

We sought to determine the extent to which initial levels and over-time trajectories of cultural stressors (discrimination, negative context of reception, and bicultural stress) predicted well-being, internalizing symptoms, conduct problems, and health risk behaviors among recently immigrated Hispanic adolescents. Addressing this research objective involved creating a latent factor for cultural stressors, establishing invariance for this factor over time, estimating a growth curve for this factor over time, and examining the effects of initial levels (intercepts) and trajectories (slopes) of cultural stressors on adolescent outcomes. A sample of 302 recently immigrated Hispanic adolescents in Miami (median of 1 year in the United States at baseline) and Los Angeles (median of 3 years in the United States at baseline) was recruited from public schools and assessed six times over a 3-year period. Perceived discrimination, context of reception, and bicultural stress loaded onto a latent factor at each of the first five timepoints. A growth curve conducted on this factor over the first five timepoints significantly predicted lower self-esteem and optimism, more depressive symptoms, greater aggressive behavior and rule breaking, and increased likelihood of drunkenness and marijuana use. The present results may be important in designing interventions for Hispanic immigrant children and adolescents, including those within the present wave of unaccompanied child migrants. Results indicate targeting cultural stressors in interventions may have potential to improve well-being and decrease externalizing behaviors and substance use within this population. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Latent change models of adult cognition: are changes in processing speed and working memory associated with changes in episodic memory?

PubMed

Hertzog, Christopher; Dixon, Roger A; Hultsch, David F; MacDonald, Stuart W S

2003-12-01

The authors used 6-year longitudinal data from the Victoria Longitudinal Study (VLS) to investigate individual differences in amount of episodic memory change. Latent change models revealed reliable individual differences in cognitive change. Changes in episodic memory were significantly correlated with changes in other cognitive variables, including speed and working memory. A structural equation model for the latent change scores showed that changes in speed and working memory predicted changes in episodic memory, as expected by processing resource theory. However, these effects were best modeled as being mediated by changes in induction and fact retrieval. Dissociations were detected between cross-sectional ability correlations and longitudinal changes. Shuffling the tasks used to define the Working Memory latent variable altered patterns of change correlations.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy.

PubMed

Rzeznik, Matthias; Triba, Mohamed Nawfal; Levy, Pierre; Jungo, Sébastien; Botosoa, Eliot; Duchemann, Boris; Le Moyec, Laurence; Bernaudin, Jean-François; Savarin, Philippe; Guez, Dominique

2017-01-01

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy

PubMed Central

Levy, Pierre; Jungo, Sébastien; Botosoa, Eliot; Duchemann, Boris; Le Moyec, Laurence; Bernaudin, Jean-François; Guez, Dominique

2017-01-01

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis. PMID:28837579

The Curiosity and Exploration Inventory-II: Development, Factor Structure, and Psychometrics

PubMed Central

Kashdan, Todd B.; Gallagher, Matthew W.; Silvia, Paul J.; Winterstein, Beate P.; Breen, William E.; Terhar, Daniel; Steger, Michael F.

2009-01-01

Given curiosity’s fundamental role in motivation, learning, and well-being, we sought to refine the measurement of trait curiosity with an improved version of the Curiosity and Exploration Inventory (CEI; Kashdan, Rose, & Fincham, 2004). A preliminary pool of 36 items was administered to 311 undergraduate students, who also completed measures of emotion, emotion regulation, personality, and well-being. Factor analyses indicated a two factor model—motivation to seek out knowledge and new experiences (Stretching; 5 items) and a willingness to embrace the novel, uncertain, and unpredictable nature of everyday life (Embracing; 5 items). In two additional samples (ns = 150 and 119), we cross-validated this factor structure and provided initial evidence for construct validity. This includes positive correlations with personal growth, openness to experience, autonomy, purpose in life, self-acceptance, psychological flexibility, positive affect, and positive social relations, among others. Applying item response theory (IRT) to these samples (n = 578), we showed that the items have good discrimination and a desirable breadth of difficulty. The item information functions and test information function were centered near zero, indicating that the scale assesses the mid-range of the latent curiosity trait most reliably. The findings thus far provide good evidence for the psychometric properties of the 10-item CEI-II. PMID:20160913

A Cross-Cultural Analysis of the Infant Behavior Questionnaire Very Short Form: An Item Response Theory Analysis of Infant Temperament in New Zealand.

PubMed

Peterson, Elizabeth R; Mohal, Jatender; Waldie, Karen E; Reese, Elaine; Atatoa Carr, Polly E; Grant, Cameron C; Morton, Susan M B

2017-01-01

The Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF; Putnam, Helbig, Gartstein, Rothbart, & Leerkes, 2014 ) is a newly published measure of infant temperament with a 3-factor structure. Recently Peterson et al. ( 2017 ) suggested that a 5-factor structure (Positive Affectivity/Surgency, Negative Emotionality, Orienting Capacity, Affiliation/Regulation, and Fear) was more parsimonious and showed promising reliability and predictive validity in a large, diverse sample. However, little is known about the 5-factor model's precision across the temperament dimensions range and whether it discriminates equally well across ethnicities. A total of 5,567 mothers responded to the IBQ-R VSF in relation to their infants (N = 5,639) between 23 and 52 weeks old. Using item response theory, we conducted a series of 2 parameter logistic item response models and found that 5 IBQ-R VSF temperament dimensions showed a good distribution of estimates across each latent trait range and these estimates centered close to the population mean. The IBQ-R VSF was also similarly precise across 4 ethnic groups (European, Māori, Pacific peoples, and Asians), suggesting that it can be used as comparable measure for infant temperament in a diversity of ethnic groups.

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

Understanding comorbidity among internalizing problems: Integrating latent structural models of psychopathology and risk mechanisms

PubMed Central

Hankin, Benjamin L.; Snyder, Hannah R.; Gulley, Lauren D.; Schweizer, Tina H.; Bijttebier, Patricia; Nelis, Sabine; Toh, Gim; Vasey, Michael W.

2016-01-01

It is well known that comorbidity is the rule, not the exception, for categorically defined psychiatric disorders, and this is also the case for internalizing disorders of depression and anxiety. This theoretical review paper addresses the ubiquity of comorbidity among internalizing disorders. Our central thesis is that progress in understanding this co-occurrence can be made by employing latent dimensional structural models that organize both psychopathology as well as vulnerabilities and risk mechanisms and by connecting the multiple levels of risk and psychopathology outcomes together. Different vulnerabilities and risk mechanisms are hypothesized to predict different levels of the structural model of psychopathology. We review the present state of knowledge based on concurrent and developmental sequential comorbidity patterns among common discrete psychiatric disorders in youth, and then we advocate for the use of more recent bifactor dimensional models of psychopathology (e.g., p factor, Caspi et al., 2014) that can help to explain the co-occurrence among internalizing symptoms. In support of this relatively novel conceptual perspective, we review six exemplar vulnerabilities and risk mechanisms, including executive function, information processing biases, cognitive vulnerabilities, positive and negative affectivity aspects of temperament, and autonomic dysregulation, along with the developmental occurrence of stressors in different domains, to show how these vulnerabilities can predict the general latent psychopathology factor, a unique latent internalizing dimension, as well as specific symptom syndrome manifestations. PMID:27739389

Structural Equation Model Trees

PubMed Central

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2015-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model. PMID:22984789

Metabolomic assessment reveals an elevated level of glucosinolate content in CaCl₂ treated broccoli microgreens.

PubMed

Sun, Jianghao; Kou, Liping; Geng, Ping; Huang, Huilian; Yang, Tianbao; Luo, Yaguang; Chen, Pei

2015-02-18

Preharvest calcium application has been shown to increase broccoli microgreen yield and extend shelf life. In this study, we investigated the effect of calcium application on its metabolome using ultra-high-performance liquid chromatography with mass spectrometry. The data collected were analyzed using principal component analysis and orthogonal projection to latent structural discriminate analysis. Chemical composition comparison shows that glucosinolates, a very important group of phytochemicals, are the major compounds enhanced by preharvest treatment with 10 mM calcium chloride (CaCl2). Aliphatic glucosinolates (glucoerucin, glucoiberin, glucoiberverin, glucoraphanin, pentyl glucosinolate, and hexyl glucosinolate) and indolic glucosinolates (glucobrassicin, neoglucobrassicin, and 4-hydroxyglucobrassicin) were increased significantly in the CaCl2 treated microgreens using metabolomic approaches. Targeted glucosinolate analysis using the ISO 9167-1 method was further employed to confirm the findings. Results indicate that glucosinolates can be considered as a class of compounds that are responsible for the difference between two groups and a higher glucosinolate level was found in CaCl2 treated groups at each time point after harvest in comparison with the control group.

1H NMR-based metabolomic profiling for identification of metabolites in Capsicum annuum cv. mirasol infected by beet mild curly top virus (BMCTV).

PubMed

Villa-Ruano, Nemesio; Velásquez-Valle, Rodolfo; Zepeda-Vallejo, L Gerardo; Pérez-Hernández, Nury; Velázquez-Ponce, Manuel; Arcos-Adame, Victor M; Becerra-Martínez, Elvia

2018-04-01

Beet mild curly top virus (BMCTV) is associated with an outbreak of curly top in chili pepper, tomato and other Solanaceae species, which can cause severe crop losses. The aim of this work was to obtain the 1 H NMR metabolomic profiling of both healthy chili peppers (cv. mirasol) and infected chili peppers with BMCTV in order to find chemical markers associated to the infection process. Significant differences were found between the two groups, according to principal component analysis and orthogonal projections to latent structure discriminant analysis. Compared to the asymptomatic peppers, the symptomatic fruits had higher relative abundance of fructose, isoleucine, histidine, phenylalanine and tryptophan. Contrarily, the asymptomatic samples showed greater amounts of malonate and isobutyrate. These results suggest that in diseased chili peppers there are metabolic changes related to the viral acquisition of energy for replication and capsid assembly. This is the first study describing the chemical profiling of a polar extract obtained from Capsicum annuum infected by BMCTV under open field conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Victimization and depressive symptomology in transgender adults: The mediating role of avoidant coping

PubMed Central

Hughto, Jaclyn M. White; Pachankis, John E.; Willie, Tiara C.; Reisner, Sari L.

2016-01-01

Victimization and depressive distress symptoms represent serious and interconnected public health problems facing transgender communities. Avoidant coping is hypothesized to temporarily alleviate the stress of victimization, but has potential long-term mental and behavioral health costs, such as increasing the probability of depressive symptoms. A community sample of 412 transgender adults (M age = 32.7, SD = 12.8) completed a one-time survey capturing multiple forms of victimization (i.e., everyday discrimination, bullying, physical assault by family, verbal harassment by family, childhood sexual abuse, intimate partner violence), avoidant coping, and past-week depressive symptomology. Structural equation modeling examined the mediating role of avoidant coping in the association between victimization and depressive symptomology. A latent victimization variable composed of six measures of victimization was positively associated with avoidant coping, which in turn was positively associated with depressive symptoms. Victimization was also positively associated with depressive symptomology both directly and indirectly through avoidant coping. Avoidant coping represents a potentially useful intervention target for clinicians aiming to reduce the mental health sequelae of victimization in this highly stigmatized and vulnerable population. PMID:28068130

Metabolomic comparison between wild Ophiocordyceps sinensis and artificial cultured Cordyceps militaris.

PubMed

Chen, Lin; Liu, Yuetao; Guo, Qingfeng; Zheng, Qingxia; Zhang, Wancun

2018-05-11

A systematic study on the metabolome differences between wild Ophiocordyceps sinensis and artificial cultured Cordyceps militaris was conducted using liquid chromatography-mass spectrometry. Principal component analysis and orthogonal projection on latent structure-discriminant analysis results showed that C. militaris grown on solid rice medium (R-CM) and C. militaris grown on tussah pupa (T-CM) evidently separated and individually separated from wild O. sinensis, indicating metabolome difference among wild O. sinensis, R-CM and T-CM. The metabolome differences between R-CM and T-CM indicated that C. militaris could accommodate to culture medium by differential metabolic regulation. Hierarchical clustering analysis was further performed to cluster the differential metabolites and samples based on their metabolic similarity. The higher content of amino acids (pyroglutamic acid, glutamic acid, histidine, phenylalanine and arginine), unsaturated fatty acid (linolenic acid and linoleic acid), peptides, mannitol, adenosine and succinoadenosine in O. sinensis make it as an excellent choice as a traditional Chinese medicine for invigoration or nutritional supplementation. Similar compositions with O. sinensis and easy cultivation make artificially cultured C. militaris a possible alternative to O. sinensis. Copyright © 2018 John Wiley & Sons, Ltd.

Development and Validation of Quantitative (1)H NMR Spectroscopy for the Determination of Total Phytosterols in the Marine Seaweed Sargassum.

PubMed

Zhang, Xiu-Li; Wang, Cong; Chen, Zhen; Zhang, Pei-Yu; Liu, Hong-Bing

2016-08-10

Knowledge of phytosterol (PS) contents in marine algae is currently lacking compared to those in terrestrial plants. The present studies developed a quantitative (1)H NMR method for the determination of the total PSs in Sargassum. The characteristic proton signal H-3α in PSs was used for quantification, and 2,3,4,5-tetrachloro-nitrobenzene was used as an internal standard. Seaweed samples could be recorded directly after total lipid extraction and saponification. The results showed that the PS contents in Sargassum fusiforme (788.89-2878.67 mg/kg) were significantly higher than those in Sargassum pallidum (585.33-1596.00 mg/kg). The variable contents in both species suggested that fixed raw materials are very important for future research and development. Orthogonal projection to latent structures discriminant analysis was carried out in the spectral region of δ 3.00-6.50 in the (1)H NMR spectrum. S. fusiforme and S. pallidum could be separated well, and the key sterol marker was fucosterol.

The latent structure of the functional dyspepsia symptom complex: a taxometric analysis.

PubMed

Van Oudenhove, L; Jasper, F; Walentynowicz, M; Witthöft, M; Van den Bergh, O; Tack, J

2016-07-01

Rome III introduced a subdivision of functional dyspepsia (FD) into postprandial distress syndrome and epigastric pain syndrome, characterized by early satiation/postprandial fullness, and epigastric pain/burning, respectively. However, evidence on their degree of overlap is mixed. We aimed to investigate the latent structure of FD to test whether distinguishable symptom-based subgroups exist. Consecutive tertiary care Rome II FD patients completed the dyspepsia symptom severity scale. Confirmatory factor analysis (CFA) was used to compare the fit of a single factor model, a correlated three-factor model based on Rome III subgroups and a bifactor model consisting of a general FD factor and orthogonal subgroup factors. Taxometric analyses were subsequently used to investigate the latent structure of FD. Nine hundred and fifty-seven FD patients (71.1% women, age 41 ± 14.8) participated. In CFA, the bifactor model yielded a significantly better fit than the two other models (χ² difference tests both p < 0.001). All symptoms had significant loadings on both the general and the subgroup-specific factors (all p < 0.05). Somatization was associated with the general (r = 0.72, p < 0.01), but not the subgroup-specific factors (all r < 0.13, p > 0.05). Taxometric analyses supported a dimensional structure of FD (all CCFI<0.38). We found a dimensional rather than categorical latent structure of the FD symptom complex in tertiary care. A combination of a general dyspepsia symptom reporting factor, which was associated with somatization, and symptom-specific factors reflecting the Rome III subdivision fitted the data best. This has implications for classification, pathophysiology, and treatment of FD. © 2016 John Wiley & Sons Ltd.

Psychometrican analysis and dimensional structure of the Brazilian version of melasma quality of life scale (MELASQoL-BP)*

PubMed Central

Maranzatto, Camila Fernandes Pollo; Miot, Hélio Amante; Miot, Luciane Donida Bartoli; Meneguin, Silmara

2016-01-01

Background Although asymptomatic, melasma inflicts significant impact on quality of life. MELASQoL is the main instrument used to assess quality of life associated with melasma, it has been validated in several languages, but its latent dimensional structure and psychometric properties haven´t been fully explored. Objectives To evaluate psychometric characteristics, information and dimensional structure of the Brazilian version of MELASQoL. Methods Survey with patients with facial melasma through socio-demographic questionnaire, DLQI-BRA, MASI and MELASQoL-BP, exploratory and confirmatory factor analysis, internal consistency of MELASQoL and latent dimensions (Cronbach's alpha). The informativeness of the model and items were investigated by the Rasch model (ordinal data). Results We evaluated 154 patients, 134 (87%) were female, mean age (± SD) of 39 (± 8) years, the onset of melasma at 27 (± 8) years, median (p25-p75) of MASI scores , DLQI and MELASQoL 8 (5-15) 2 (1-6) and 30 (17-44). The correlation (rho) of MELASQoL with DLQI and MASI were: 0.70 and 0.36. Exploratory factor analysis identified two latent dimensions: Q1-Q3 and Q4-Q10, which had significantly more adjusted factor structure than the one-dimensional model: Χ2 / gl = 2.03, CFI = 0.95, AGFI = 0.94, RMSEA = 0.08. Cronbach's coefficient for the one-dimensional model and the factors were: 0.95, 0.92 and 0.93. Rasch analysis demonstrated that the use of seven alternatives per item resulted in no increase in the model informativeness. Conclusions MELASQoL-BP showed good psychometric performance and a latent structure of two dimensions. We also identified an oversizing of item alternatives to characterize the aggregate information to each dimension. PMID:27579735

The computational nature of memory modification

PubMed Central

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-01-01

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature. DOI: http://dx.doi.org/10.7554/eLife.23763.001 PMID:28294944

Many-level multilevel structural equation modeling: An efficient evaluation strategy.

PubMed

Pritikin, Joshua N; Hunter, Michael D; von Oertzen, Timo; Brick, Timothy R; Boker, Steven M

2017-01-01

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

The Coach-Athlete Relationship Questionnaire (CART-Q): development and initial validation.

PubMed

Jowett, Sophia; Ntoumanis, Nikos

2004-08-01

The purpose of the present study was to develop and validate a self-report instrument that measures the nature of the coach-athlete relationship. Jowett et al.'s (Jowett & Meek, 2000; Jowett, in press) qualitative case studies and relevant literature were used to generate items for an instrument that measures affective, cognitive, and behavioral aspects of the coach-athlete relationship. Two studies were carried out in an attempt to assess content, predictive, and construct validity, as well as internal consistency, of the Coach-Athlete Relationship Questionnaire (CART-Q), using two independent British samples. Principal component analysis and confirmatory factor analysis were used to reduce the number of items, identify principal components, and confirm the latent structure of the CART-Q. Results supported the multidimensional nature of the coach-athlete relationship. The latent structure of the CART-Q was underlined by the latent variables of coaches' and athletes' Closeness (emotions), Commitment (cognitions), and Complementarity (behaviors).

Modeling the Severity of Drinking Consequences in First-Year College Women: An Item Response Theory Analysis of the Rutgers Alcohol Problem Index*

PubMed Central

Cohn, Amy M.; Hagman, Brett T.; Graff, Fiona S.; Noel, Nora E.

2011-01-01

Objective: The present study examined the latent continuum of alcohol-related negative consequences among first-year college women using methods from item response theory and classical test theory. Method: Participants (N = 315) were college women in their freshman year who reported consuming any alcohol in the past 90 days and who completed assessments of alcohol consumption and alcohol-related negative consequences using the Rutgers Alcohol Problem Index. Results: Item response theory analyses showed poor model fit for five items identified in the Rutgers Alcohol Problem Index. Two-parameter item response theory logistic models were applied to the remaining 18 items to examine estimates of item difficulty (i.e., severity) and discrimination parameters. The item difficulty parameters ranged from 0.591 to 2.031, and the discrimination parameters ranged from 0.321 to 2.371. Classical test theory analyses indicated that the omission of the five misfit items did not significantly alter the psychometric properties of the construct. Conclusions: Findings suggest that those consequences that had greater severity and discrimination parameters may be used as screening items to identify female problem drinkers at risk for an alcohol use disorder. PMID:22051212

Item selection via Bayesian IRT models.

PubMed

Arima, Serena

2015-02-10

With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan. Copyright © 2014 John Wiley & Sons, Ltd.

Profiles of African American College Students' Alcohol Use and Sexual Behaviors: Associations With Stress, Racial Discrimination, and Social Support.

PubMed

Metzger, Isha W; Cooper, Shauna M; Ritchwood, Tiarney D; Onyeuku, Chisom; Griffin, Charity Brown

2017-01-01

Though studies show that alcohol use and sexual activity increase during emerging adulthood, few studies examine within-ethnic group differences, particularly among African American college students. This investigation utilized a latent class analytic methodology to identify risk behavior profiles of alcohol use (frequency and amount of alcohol consumed), sexual activity (number of intimate partners), and co-occurring risk behaviors (drinking before sexual intercourse) among 228 African American college students. This investigation also examined whether identified risk behavior profiles were associated with stress (interpersonal, intrapersonal, academic, and environmental), experiences of racial discrimination, and social support (from family, friends, and the college community). Results identified five distinct profiles within this sample: (a) High Sexual Risk-above-average sexual activity; (b) Abstainers-below-average alcohol use and sexual activity; (c) Low Risk-average alcohol use and sexual activity; (d) Alcohol Risk-above-average alcohol use and below-average sexual activity; and (e) Co-Occurring Risk-above-average alcohol use and sexual activity. Identified profiles differed across interpersonal and environmental stress, and self-reported frequency of experiences with racial discrimination. Implications for prevention programs and interventions aimed at reducing alcohol and sexual activity for African American college students are discussed.

Measurement Invariance and Latent Mean Differences in the Reynolds Intellectual Assessment Scales (RIAS): Does the German Version of the RIAS Allow a Valid Assessment of Individuals with a Migration Background?

PubMed Central

Gygi, Jasmin T.; Fux, Elodie; Grob, Alexander; Hagmann-von Arx, Priska

2016-01-01

This study examined measurement invariance and latent mean differences in the German version of the Reynolds Intellectual Assessment Scales (RIAS) for 316 individuals with a migration background (defined as speaking German as a second language) and 316 sex- and age-matched natives. The RIAS measures general intelligence (single-factor structure) and its two components, verbal and nonverbal intelligence (two-factor structure). Results of a multi-group confirmatory factor analysis showed scalar invariance for the two-factor and partial scalar invariance for the single-factor structure. We conclude that the two-factor structure of the RIAS is comparable across groups. Hence, verbal and nonverbal intelligence but not general intelligence should be considered when comparing RIAS test results of individuals with and without a migration background. Further, latent mean differences especially on the verbal, but also on the nonverbal intelligence index indicate language barriers for individuals with a migration background, as subtests corresponding to verbal intelligence require higher skills in German language. Moreover, cultural, environmental, and social factors that have to be taken into account when assessing individuals with a migration background are discussed. PMID:27846270

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA

PubMed Central

Salter-Townshend, Michael; McCormick, Tyler H.

2018-01-01

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc. 97 (2002) 1090–1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)]. PMID:29721127

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA.

PubMed

Salter-Townshend, Michael; McCormick, Tyler H

2017-09-01

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc. 97 (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

The comparability of English, French and Dutch scores on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F): an assessment of differential item functioning in patients with systemic sclerosis.

PubMed

Kwakkenbos, Linda; Willems, Linda M; Baron, Murray; Hudson, Marie; Cella, David; van den Ende, Cornelia H M; Thombs, Brett D

2014-01-01

The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) is commonly used to assess fatigue in rheumatic diseases, and has shown to discriminate better across levels of the fatigue spectrum than other commonly used measures. The aim of this study was to assess the cross-language measurement equivalence of the English, French, and Dutch versions of the FACIT-F in systemic sclerosis (SSc) patients. The FACIT-F was completed by 871 English-speaking Canadian, 238 French-speaking Canadian and 230 Dutch SSc patients. Confirmatory factor analysis was used to assess the factor structure in the three samples. The Multiple-Indicator Multiple-Cause (MIMIC) model was utilized to assess differential item functioning (DIF), comparing English versus French and versus Dutch patient responses separately. A unidimensional factor model showed good fit in all samples. Comparing French versus English patients, statistically significant, but small-magnitude DIF was found for 3 of 13 items. French patients had 0.04 of a standard deviation (SD) lower latent fatigue scores than English patients and there was an increase of only 0.03 SD after accounting for DIF. For the Dutch versus English comparison, 4 items showed small, but statistically significant, DIF. Dutch patients had 0.20 SD lower latent fatigue scores than English patients. After correcting for DIF, there was a reduction of 0.16 SD in this difference. There was statistically significant DIF in several items, but the overall effect on fatigue scores was minimal. English, French and Dutch versions of the FACIT-F can be reasonably treated as having equivalent scoring metrics.

The Comparability of English, French and Dutch Scores on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F): An Assessment of Differential Item Functioning in Patients with Systemic Sclerosis

PubMed Central

Kwakkenbos, Linda; Willems, Linda M.; Baron, Murray; Hudson, Marie; Cella, David; van den Ende, Cornelia H. M.; Thombs, Brett D.

2014-01-01

Objective The Functional Assessment of Chronic Illness Therapy- Fatigue (FACIT-F) is commonly used to assess fatigue in rheumatic diseases, and has shown to discriminate better across levels of the fatigue spectrum than other commonly used measures. The aim of this study was to assess the cross-language measurement equivalence of the English, French, and Dutch versions of the FACIT-F in systemic sclerosis (SSc) patients. Methods The FACIT-F was completed by 871 English-speaking Canadian, 238 French-speaking Canadian and 230 Dutch SSc patients. Confirmatory factor analysis was used to assess the factor structure in the three samples. The Multiple-Indicator Multiple-Cause (MIMIC) model was utilized to assess differential item functioning (DIF), comparing English versus French and versus Dutch patient responses separately. Results A unidimensional factor model showed good fit in all samples. Comparing French versus English patients, statistically significant, but small-magnitude DIF was found for 3 of 13 items. French patients had 0.04 of a standard deviation (SD) lower latent fatigue scores than English patients and there was an increase of only 0.03 SD after accounting for DIF. For the Dutch versus English comparison, 4 items showed small, but statistically significant, DIF. Dutch patients had 0.20 SD lower latent fatigue scores than English patients. After correcting for DIF, there was a reduction of 0.16 SD in this difference. Conclusions There was statistically significant DIF in several items, but the overall effect on fatigue scores was minimal. English, French and Dutch versions of the FACIT-F can be reasonably treated as having equivalent scoring metrics. PMID:24638101

On the Benefits of Latent Variable Modeling for Norming Scales: The Case of the "Supports Intensity Scale-Children's Version"

ERIC Educational Resources Information Center

Seo, Hyojeong; Little, Todd D.; Shogren, Karrie A.; Lang, Kyle M.

2016-01-01

Structural equation modeling (SEM) is a powerful and flexible analytic tool to model latent constructs and their relations with observed variables and other constructs. SEM applications offer advantages over classical models in dealing with statistical assumptions and in adjusting for measurement error. So far, however, SEM has not been fully used…

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

General practitioners' knowledge and concern about electromagnetic fields.

PubMed

Berg-Beckhoff, Gabriele; Breckenkamp, Jürgen; Larsen, Pia Veldt; Kowall, Bernd

2014-12-01

Our aim is to explore general practitioners' (GPs') knowledge about EMF, and to assess whether different knowledge structures are related to the GPs' concern about EMF. Random samples were drawn from lists of GPs in Germany in 2008. Knowledge about EMF was assessed by seven items. A latent class analysis was conducted to identify latent structures in GPs' knowledge. Further, the GPs' concern about EMF health risk was measured using a score comprising six items. The association between GPs' concern about EMF and their knowledge was analysed using multiple linear regression. In total 435 (response rate 23.3%) GPs participated in the study. Four groups were identified by the latent class analysis: 43.1% of the GPs gave mainly correct answers; 23.7% of the GPs answered low frequency EMF questions correctly; 19.2% answered only the questions relating EMF with health risks, and 14.0% answered mostly "don't know". There was no association between GPs' latent knowledge classes or between the number of correct answers given by the GPs and their EMF concern, whereas the number of incorrect answers was associated with EMF concern. Greater EMF concern in subjects with more incorrect answers suggests paying particular attention to misconceptions regarding EMF in risk communication.

Demographic analysis from summaries of an age-structured population

USGS Publications Warehouse

Link, William A.; Royle, J. Andrew; Hatfield, Jeff S.

2003-01-01

Demographic analyses of age-structured populations typically rely on life history data for individuals, or when individual animals are not identified, on information about the numbers of individuals in each age class through time. While it is usually difficult to determine the age class of a randomly encountered individual, it is often the case that the individual can be readily and reliably assigned to one of a set of age classes. For example, it is often possible to distinguish first-year from older birds. In such cases, the population age structure can be regarded as a latent variable governed by a process prior, and the data as summaries of this latent structure. In this article, we consider the problem of uncovering the latent structure and estimating process parameters from summaries of age class information. We present a demographic analysis for the critically endangered migratory population of whooping cranes (Grus americana), based only on counts of first-year birds and of older birds. We estimate age and year-specific survival rates. We address the controversial issue of whether management action on the breeding grounds has influenced recruitment, relating recruitment rates to the number of seventh-year and older birds, and examining the pattern of variation through time in this rate.

Transcriptional regulation of latent feline immunodeficiency virus in peripheral CD4+ T-lymphocytes.

PubMed

McDonnel, Samantha J; Sparger, Ellen E; Luciw, Paul A; Murphy, Brian G

2012-05-01

Feline immunodeficiency virus (FIV), the lentivirus of domestic cats responsible for feline AIDS, establishes a latent infection in peripheral blood CD4+ T-cells approximately eight months after experimental inoculation. In this study, cats experimentally infected with the FIV-C strain in the asymptomatic phase demonstrated an estimated viral load of 1 infected cell per approximately 10(3) CD4+ T-cells, with about 1 copy of viral DNA per cell. Approximately 1 in 10 proviral copies was capable of transcription in the asymptomatic phase. The latent FIV proviral promoter was associated with deacetylated, methylated histones, which is consistent with a condensed chromatin structure. In contrast, the transcriptionally active FIV promoter was associated with histone acetylation and demethylation. In addition, RNA polymerase II appeared to be paused on the latent viral promoter, and short promoter-proximal transcripts were detected. Our findings for the FIV promoter in infected cats are similar to results obtained in studies of human immunodeficiency virus (HIV)-1 latent proviruses in cell culture in vitro studies. Thus, the FIV/cat model may offer insights into in vivo mechanisms of HIV latency and provides a unique opportunity to test novel therapeutic interventions aimed at eradicating latent virus.

Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment

PubMed Central

Saunders, Kate; Bilderbeck, Amy; Palmius, Niclas; Goodwin, Guy; De Vos, Maarten

2017-01-01

Background We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice. PMID:28546141

Elucidating the association between the self-harm inventory and several borderline personality measures in an inpatient psychiatric sample.

PubMed

Sellbom, Martin; Sansone, Randy A; Songer, Douglas A

2017-09-01

The current study evaluated the utility of the self-harm inventory (SHI) as a proxy for and screening measure of borderline personality disorder (BPD) using several diagnostic and statistical manual of mental disorders (DSM)-based BPD measures as criteria. We used a sample of 145 psychiatric inpatients, who completed the SHI and a series of well-validated, DSM-based self-report measures of BPD. Using a series of latent trait and latent class analyses, we found that the SHI was substantially associated with a latent construct representing BPD, as well as differentiated latent classes of 'high' vs. 'low' BPD, with good accuracy. The SHI can serve as proxy for and a good screening measure for BPD, but future research needs to replicate these findings using structured interview-based measurement of BPD.

Latent Growth and Dynamic Structural Equation Models.

PubMed

Grimm, Kevin J; Ram, Nilam

2018-05-07

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

Protein catabolism and high lipid metabolism associated with long-distance exercise are revealed by plasma NMR metabolomics in endurance horses.

PubMed

Le Moyec, Laurence; Robert, Céline; Triba, Mohamed N; Billat, Véronique L; Mata, Xavier; Schibler, Laurent; Barrey, Eric

2014-01-01

During long distance endurance races, horses undergo high physiological and metabolic stresses. The adaptation processes involve the modulation of the energetic pathways in order to meet the energy demand. The aims were to evaluate the effects of long endurance exercise on the plasma metabolomic profiles and to investigate the relationships with the individual horse performances. The metabolomic profiles of the horses were analyzed using the non-dedicated methodology, NMR spectroscopy and statistical multivariate analysis. The advantage of this method is to investigate several metabolomic pathways at the same time in a single sample. The plasmas were obtained before exercise (BE) and post exercise (PE) from 69 horses competing in three endurance races at national level (130-160 km). Biochemical assays were also performed on the samples taken at PE. The proton NMR spectra were compared using the supervised orthogonal projection on latent structure method according to several factors. Among these factors, the race location was not significant whereas the effect of the race exercise (sample BE vs PE of same horse) was highly discriminating. This result was confirmed by the projection of unpaired samples (only BE or PE sample of different horses). The metabolomic profiles proved that protein, energetic and lipid metabolisms as well as glycoproteins content are highly affected by the long endurance exercise. The BE samples from finisher horses could be discriminated according to the racing speed based on their metabolomic lipid content. The PE samples could be discriminated according to the horse ranking position at the end of the race with lactate as unique correlated metabolite. As a conclusion, the metabolomic profiles of plasmas taken before and after the race provided a better understanding of the high energy demand and protein catabolism pathway that could expose the horses to metabolic disorders.

Work engagement, work commitment and their association with well-being in health care.

PubMed

Kanste, Outi

2011-12-01

The aim was to examine whether work engagement and work commitment can be empirically discriminated and how they are associated with well-being. The terminology used in literature and in practice is confused by the interchangeable use of these terms. Only few studies, like Hallberg and Schaufeli's study, have examined the relationships between work engagement and work commitment systematically by using empirical data. In this study, the data were gathered via self-reported questionnaire from the healthcare staff working in 14 health centres and four hospitals in Finland. The data consisted of 435 responses. The material was analysed by using structural equation modelling (SEM) and correlations. The items of work engagement and work commitment dimensions (identification with organization, willingness to exert in organization's favour, occupational commitment and job involvement) loaded on their own latent variables in SEM analysis, so the data supported this five-factor model. Work engagement and work commitment dimensions were positively related, sharing between 2 and 33% of their variances. These constructs also displayed different correlations with some indicators of well-being measured as personal accomplishment, psychological well-being, mental resources, internal work motivation and willingness to stay on at work. Work engagement had moderate positive correlation to personal accomplishment (r = 0.68, p < 0.001). Identification with organization (r = 0.40, p < 0.001), willingness to exert in organization's favour (r =0.44, p < 0.001) and occupational commitment (r =0.37, p < 0.001) had low correlations to personal accomplishment. The results support the notion that work engagement can be empirically discriminated from work commitment. They are distinct, yet related constructs that complement each other, describing different aspects of positive attitudes towards work. The results can be utilized in interventions aimed at quality of working life in health care as well as in studies investigating discriminant and construct validity. © 2011 The Author. Scandinavian Journal of Caring Sciences © 2011 Nordic College of Caring Science.

Phenolic Profiling for Traceability of Vanilla ×tahitensis

PubMed Central

Busconi, Matteo; Lucini, Luigi; Soffritti, Giovanna; Bernardi, Jamila; Bernardo, Letizia; Brunschwig, Christel; Lepers-Andrzejewski, Sandra; Raharivelomanana, Phila; Fernandez, Jose A.

2017-01-01

Vanilla is a flavoring recovered from the cured beans of the orchid genus Vanilla. Vanilla ×tahitensis is traditionally cultivated on the islands of French Polynesia, where vanilla vines were first introduced during the nineteenth century and, since the 1960s, have been introduced to other Pacific countries such as Papua New Guinea (PNG), cultivated and sold as “Tahitian vanilla,” although both sensory properties and aspect are different. From an economic point of view, it is important to ensure V. ×tahitensis traceability and to guarantee that the marketed product is part of the future protected designation of the origin “Tahitian vanilla” (PDO), currently in progress in French Polynesia. The application of metabolomics, allowing the detection and simultaneous analysis of hundreds or thousands of metabolites from different matrices, has recently gained high interest in food traceability. Here, metabolomics analysis of phenolic compounds profiles was successfully applied for the first time to V. ×tahitensis to deepen our knowledge of vanilla metabolome, focusing on phenolics compounds, for traceability purposes. Phenolics were screened through a quadrupole-time-of-flight mass spectrometer coupled to a UHPLC liquid chromatography system, and 260 different compounds were clearly evidenced and subjected to different statistical analysis in order to enable the discrimination of the samples based on their origin. Eighty-eight and twenty three compounds, with a prevalence of flavonoids, resulted to be highly discriminant through ANOVA and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) respectively. Volcano plot analysis and pairwise comparisons were carried out to determine those compounds, mainly responsible for the differences among samples as a consequence of either origin or cultivar. The samples from PNG were clearly different from the Tahitian samples that were further divided in two different groups based on the different phenolic patterns. Among the 260 compounds, metabolomics analysis enabled the detection of previously unreported phenolics in vanilla (such as flavonoids, lignans, stilbenes and other polyphenols). PMID:29075276

Phenolic Profiling for Traceability of Vanilla ×tahitensis.

PubMed

Busconi, Matteo; Lucini, Luigi; Soffritti, Giovanna; Bernardi, Jamila; Bernardo, Letizia; Brunschwig, Christel; Lepers-Andrzejewski, Sandra; Raharivelomanana, Phila; Fernandez, Jose A

2017-01-01

Vanilla is a flavoring recovered from the cured beans of the orchid genus Vanilla . Vanilla × tahitensis is traditionally cultivated on the islands of French Polynesia, where vanilla vines were first introduced during the nineteenth century and, since the 1960s, have been introduced to other Pacific countries such as Papua New Guinea (PNG), cultivated and sold as "Tahitian vanilla," although both sensory properties and aspect are different. From an economic point of view, it is important to ensure V . × tahitensis traceability and to guarantee that the marketed product is part of the future protected designation of the origin "Tahitian vanilla" (PDO), currently in progress in French Polynesia. The application of metabolomics, allowing the detection and simultaneous analysis of hundreds or thousands of metabolites from different matrices, has recently gained high interest in food traceability. Here, metabolomics analysis of phenolic compounds profiles was successfully applied for the first time to V . × tahitensis to deepen our knowledge of vanilla metabolome, focusing on phenolics compounds, for traceability purposes. Phenolics were screened through a quadrupole-time-of-flight mass spectrometer coupled to a UHPLC liquid chromatography system, and 260 different compounds were clearly evidenced and subjected to different statistical analysis in order to enable the discrimination of the samples based on their origin. Eighty-eight and twenty three compounds, with a prevalence of flavonoids, resulted to be highly discriminant through ANOVA and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) respectively. Volcano plot analysis and pairwise comparisons were carried out to determine those compounds, mainly responsible for the differences among samples as a consequence of either origin or cultivar. The samples from PNG were clearly different from the Tahitian samples that were further divided in two different groups based on the different phenolic patterns. Among the 260 compounds, metabolomics analysis enabled the detection of previously unreported phenolics in vanilla (such as flavonoids, lignans, stilbenes and other polyphenols).

Profiles of bullying victimization, discrimination, social support, and school safety: Links with Latino/a youth acculturation, gender, depressive symptoms, and cigarette use.

PubMed

Lorenzo-Blanco, Elma I; Unger, Jennifer B; Oshri, Assaf; Baezconde-Garbanati, Lourdes; Soto, Daniel

2016-01-01

Latino/a youth are at risk for symptoms of depression and cigarette smoking but this risk varies by acculturation and gender. To understand why some youth are at greater risk than others, we identified profiles of diverse community experiences (perceived discrimination, bullying victimization, social support, perceived school safety) and examined associations between profiles of community experience and depressive symptoms, cigarette smoking, acculturation, and gender. Data came from Project Red (Reteniendo y Entendiendo Diversidad para Salud), a school-based longitudinal study of acculturation among 1,919 Latino/a adolescents (52% female; 84% 14 years old; 87% U.S. born). Latent profile analysis (LPA) revealed 4 distinct profiles of community experience that varied by gender and acculturation. Boys were overrepresented in profile groups with high perceived discrimination, some bullying, and lack of positive experiences, while girls were overrepresented in groups with high bullying victimization in the absence and presence of other community experiences. Youth low on both U.S. and Latino/a cultural orientation described high perceived discrimination and lacked positive experiences, and were predominantly male. Profiles characterized by high perceived discrimination and /or high bullying victimization in the absence of positive experiences had higher levels of depressive symptoms and higher risk of smoking, relative to the other groups. Findings suggest that acculturation comes with diverse community experiences that vary by gender and relate to smoking and depression risk. Results from this research can inform the development of tailored intervention and prevention strategies to reduce depression and/or smoking for Latino/a youth. (c) 2016 APA, all rights reserved).

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

PubMed

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S

2016-06-01

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

On the Benefits of Latent Variable Modeling for Norming Scales: The Case of the "Supports Intensity Scale--Children's Version"

ERIC Educational Resources Information Center

Seo, Hyojeong; Little, Todd D.; Shogren, Karrie A.; Lang, Kyle M.

2016-01-01

Structural equation modeling (SEM) is a powerful and flexible analytic tool to model latent constructs and their relations with observed variables and other constructs. SEM applications offer advantages over classical models in dealing with statistical assumptions and in adjusting for measurement error. So far, however, SEM has not been fully used…

Direct and conceptual replications of the taxometric analysis of type a behavior.

PubMed

Wilmot, Michael P; Haslam, Nick; Tian, Jingyuan; Ones, Deniz S

2018-05-17

We present direct and conceptual replications of the influential taxometric analysis of Type A Behavior (TAB; Strube, 1989), which reported evidence for the latent typology of the construct. Study 1, the direct replication (N = 2,373), duplicated sampling and methodological procedures of the original study, but results showed that the item indicators used in the original study lacked sufficient validity to unambiguously determine latent structure. Using improved factorial subscale indicators to further test the question, multiple taxometric procedures, in combination with parallel analyses of simulated data, failed to replicate the original typological finding. Study 2, the conceptual replication, tested the latent structure of the wider construct of TAB using the sample from the Caerphilly Prospective Study (N = 2,254), which contains responses to the three most widely used self-report measures of TAB: the Jenkins Activity Survey, Bortner scale, and Framingham scale. Factorial subscale indicators were derived from the measures and submitted to multiple taxometric procedures. Results of Study 2 converged with those of Study 1, providing clear evidence of latent dimensional structure. Overall, results suggest there is no evidence for the type in TAB. Findings imply that theoretical models of TAB, assessment practices, and data analytic procedures that assume a typology should be replaced by dimensional models, factorial subscale measures, and corresponding statistical approaches. Specific subscale measures that tap multiple Big Five trait domains, and show evidence of predictive utility, are also recommended. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Plasma metabonomics study on toxicity biomarker in rats treated with Euphorbia fischeriana based on LC-MS.

PubMed

Wang, Yingfeng; Man, Hongxue; Gao, Jian; Liu, Xinfeng; Ren, Xiaolei; Chen, Jianxin; Zhang, Jiayu; Gao, Kuo; Li, Zhongfeng; Zhao, Baosheng

2016-09-01

Lang-du (LD) has been traditionally used to treat human diseases in China. Plasma metabolic profiling was applied in this study based on LC-MS to elucidate the toxicity in rats induced by injected ethanol extract of LD. LD injection was given by intraperitoneal injection at doses of 0.1, 0.05, 0.025 and 0 g kg(-1) body weight per day to rats. The blood biochemical levels of alanine aminotransferase, direct bilirubin, creatinine, serum β2-microglobulin and low-density lipoprotein increased in LD-injected rats, and the levels of total protein and albumin decreased in these groups. The metabolic profiles of the samples were analyzed by multivariate statistics analysis, including principal component analysis, partial least squares discriminant analysis and orthogonal projection to latent structures discriminate analysis (OPLS-DA). The metabolic characters in rats injected with LD were perturbed in a dose-dependent manner. By OPLS-DA, 18 metabolites were served as the potential toxicity biomarkers. Moreover, LD treatment resulted in an increase in the p-cresol, p-cresol sulfate, lysophosphatidylethanolamine (LPE) (18:0), LPE (16:0), lysophosphatidylcholine (16:0) and 12-HETE concentrations, and a decrease in hippuric acid, cholic acid and N-acetyl-l-phenylalanine. These results suggested that chronic exposure to LD could cause a disturbance in lipids metabolism and amino acids metabolism, etc. Therefore, an analysis of the metabolic profiles can contribute to a better understanding of the adverse effects of LD. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Representing Heterogeneity in Structural Relationships Among Multiple Choice Variables Using a Latent Segmentation Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garikapati, Venu; Astroza, Sebastian; Pendyala, Ram M.

Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict activity-travel choices, with those downstream in the decision-making chain influenced by those upstream in the sequence. The adoption of a singular sequential causal structure to depict relationships among activity-travel choices in travel demand model systems ignores the possibility that some choices are made jointly as a bundle as well as the possible presence of structural heterogeneity in the population with respect to decision-making processes. As different segments in the population may adopt and follow different causalmore » decision-making mechanisms when making selected choices jointly, it would be of value to develop simultaneous equations model systems relating multiple endogenous choice variables that are able to identify population subgroups following alternative causal decision structures. Because the segments are not known a priori, they are considered latent and determined endogenously within a joint modeling framework proposed in this paper. The methodology is applied to a national mobility survey data set to identify population segments that follow different causal structures relating residential location choice, vehicle ownership, and car-share and mobility service usage. It is found that the model revealing three distinct latent segments best describes the data, confirming the efficacy of the modeling approach and the existence of structural heterogeneity in decision-making in the population. Future versions of activity-travel model systems should strive to incorporate such structural heterogeneity to better reflect varying decision processes across population subgroups.« less

Realist identification of group-level latent variables for perinatal social epidemiology theory building.

PubMed

Eastwood, John Graeme; Jalaludin, Bin Badrudin; Kemp, Lynn Ann; Phung, Hai Ngoc

2014-01-01

We have previously reported in this journal on an ecological study of perinatal depressive symptoms in South Western Sydney. In that article, we briefly reported on a factor analysis that was utilized to identify empirical indicators for analysis. In this article, we report on the mixed method approach that was used to identify those latent variables. Social epidemiology has been slow to embrace a latent variable approach to the study of social, political, economic, and cultural structures and mechanisms, partly for philosophical reasons. Critical realist ontology and epistemology have been advocated as an appropriate methodological approach to both theory building and theory testing in the health sciences. We describe here an emergent mixed method approach that uses qualitative methods to identify latent constructs followed by factor analysis using empirical indicators chosen to measure identified qualitative codes. Comparative analysis of the findings is reported together with a limited description of realist approaches to abstract reasoning.

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

PubMed Central

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2014-01-01

Growth mixture models (GMMs; Muthén & Muthén, 2000; Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this paper, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use. PMID:25419006

Constraints of recreational sport participation: measurement invariance and latent mean differences across sex and physical activity status.

PubMed

Liu, Jing Dong; Chung, Pak Kwong; Chen, Wing Ping

2014-10-01

The purpose of the current study was to (a) examine the measurement invariance of the Constraint Scale of Sport Participation across sex and physical activity status among the undergraduate students (N = 630) in Hong Kong and (b) compare the latent mean differences across groups. Measurement invariance of the Constraint Scale of Sport Participation across sex of and physical activity status of the participants was examined first. With receiving support on the measurement invariance across groups, latent mean differences of the scores across groups were examined. Multi-group confirmatory factor analysis revealed that the configural, metric, scalar, and structural invariance of the scale was supported across groups. The results of latent mean differences suggested that the women reported significantly higher constraints on time, partner, psychology, knowledge, and interest than the men. The physically inactive participants reported significantly higher scores on all constraints except for accessibility than the physically active participants.

Replicates in high dimensions, with applications to latent variable graphical models.

PubMed

Tan, Kean Ming; Ning, Yang; Witten, Daniela M; Liu, Han

2016-12-01

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By collecting multiple replicates for each subject, we are able to estimate the conditional dependence relationships among the observed variables given the latent variables. To test the null hypothesis of conditional independence between two observed variables, we propose a pairwise decorrelated score test. Theoretical guarantees are established for parameter estimation and for this test. We show that our proposal is able to estimate latent variable graphical models more accurately than some existing proposals, and apply the proposed method to a brain imaging dataset.

Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches.

PubMed

Chen, Jinsong; Zhang, Dake; Choi, Jaehwa

2015-12-01

It is common to encounter latent variables with ordinal data in social or behavioral research. Although a mediated effect of latent variables (latent mediated effect, or LME) with ordinal data may appear to be a straightforward combination of LME with continuous data and latent variables with ordinal data, the methodological challenges to combine the two are not trivial. This research covers model structures as complex as LME and formulates both point and interval estimates of LME for ordinal data using the Bayesian full-information approach. We also combine weighted least squares (WLS) estimation with the bias-corrected bootstrapping (BCB; Efron Journal of the American Statistical Association, 82, 171-185, 1987) method or the traditional delta method as the limited-information approach. We evaluated the viability of these different approaches across various conditions through simulation studies, and provide an empirical example to illustrate the approaches. We found that the Bayesian approach with reasonably informative priors is preferred when both point and interval estimates are of interest and the sample size is 200 or above.

Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain

PubMed Central

Konstantinou, Kika; Ogollah, Reuben; Hay, Elaine M.; Dunn, Kate M.

2018-01-01

Background Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare. Methods Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard. Results Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii). Conclusion Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients. PMID:29621243

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2008-12-01

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.

Do sub-syndromal manic symptoms influence outcome in treatment resistant depression in adolescents? A latent class analysis from the TORDIA study.

PubMed

Maalouf, Fadi T; Porta, Giovanna; Vitiello, Benedetto; Emslie, Graham; Mayes, Taryn; Clarke, Gregory; Wagner, Karen D; Asarnow, Joan Rosenbaum; Spirito, Anthony; Keller, Martin; Birmaher, Boris; Ryan, Neal; Shamseddeen, Wael; Iyengar, Satish; Brent, David

2012-04-01

To identify distinct depressive symptom trajectories in the TORDIA study and determine their correlates. Latent Class Growth Analysis (LCGA) using the Children's Depression Rating Scale-Revised (CDRS-R) through 72 weeks from intake. 3 classes were identified: (1) little change in symptomatic status ("NO"), comprising 24.9% of participants, with a 72-week remission rate of 25.3%; (2) slow, steady improvement ("SLOW"), comprising 47.9% of participants, with a remission rate of 60.0%, and (3) rapid symptom response ("GO"), comprising 27.2% of participants, with a remission rate of 85.7%. Higher baseline CDRS-R (p<0.001) and poorer functioning (p=0.03) were the strongest discriminators between NO and GO. Higher baseline CDRS (p<0.001) and scores on the Mania Rating Scale (MRS) (p=0.01) were the strongest discriminators between SLOW and GO. Other variables differentiating GO from both NO and from SLOW, were better baseline functioning, lower hopelessness, and lower family conflict. Both NO and SLOW showed increases on the MRS over time compared to GO (ps ≤ 0.04), and increasing MRS was strongly associated with lack of remission by 72 weeks (p=0.02). High rate of open treatment by the end of the follow-up period creates difficulty in drawing clear inferences about the long-term impact of initial randomization. Along with depressive severity, sub-syndromal manic symptoms, at baseline, and over time emerged as important predictors and correlates of poor outcome in this sample. Further research is needed on the treatment of severe depression, and on the assessment and management of sub-syndromal manic symptoms in treatment resistant depression. Copyright © 2011. Published by Elsevier B.V.

Serum hepcidin-25 may replace the ferritin index in the Thomas plot in assessing iron status in anemic patients.

PubMed

Thomas, C; Kobold, U; Balan, S; Roeddiger, R; Thomas, L

2011-04-01

Biochemical markers of iron deficiency do not distinguish iron-deficient anemia (IDA) from the anemia of chronic disease (ACD) and the combined state of ACD/IDA. Serum hepcidin-25 might be a marker resolving this problem. We investigated the extent to which serum hepcidin-25 enables the differentiation of the states above in comparison with the ferritin index plot, the so-called Thomas plot [soluble transferrin receptor (sTfR)/log ferritin and the reticulocyte hemoglobin content (CHr)]. Serum hepcidin-25 was determined in 155 anemic patients who were classified as having latent iron deficiency (latent ID), IDA, ACD, or ACD/IDA using the ferritin index plot (Thomas plot). Hepcidin-25 was determined using an isotope-dilution micro-HPLC-tandem mass spectrometry method. The ability to discriminate among these states based on serum hepcidin-25 alone or in combination with the CHr was evaluated in a receiver operating characteristic curve analysis and a comparison with the recently established ferritin index plot. Serum hepcidin-25 correlated with ferritin and the ferritin index. Use of a hepcidin-25 cutoff level of ≤4 nmol/l allowed the differentiation of IDA from ACD and ACD/IDA. Furthermore, the discrimination of ACD/IDA from ACD required combination with CHr in a new plot (hepcidin-25 and the CHr). The hepcidin-25 plot and the ferritin index plot showed a good correspondence in the differentiation of iron states in patients with anemia. Patients with IDA can be differentiated from ACD and ACD/IDA but not ACD from ACD/IDA based on hepcidin-25 alone. The combination of hepcidin-25 with CHr in the hepcidin-25 plot was useful for the differentiation of the states above. © 2010 Blackwell Publishing Ltd.

Discriminating Projections for Estimating Face Age in Wild Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tokola, Ryan A; Bolme, David S; Ricanek, Karl

2014-01-01

We introduce a novel approach to estimating the age of a human from a single uncontrolled image. Current face age estimation algorithms work well in highly controlled images, and some are robust to changes in illumination, but it is usually assumed that images are close to frontal. This bias is clearly seen in the datasets that are commonly used to evaluate age estimation, which either entirely or mostly consist of frontal images. Using pose-specific projections, our algorithm maps image features into a pose-insensitive latent space that is discriminative with respect to age. Age estimation is then performed using a multi-classmore » SVM. We show that our approach outperforms other published results on the Images of Groups dataset, which is the only age-related dataset with a non-trivial number of off-axis face images, and that we are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset. We also experimentally demonstrate that our feature projections introduce insensitivity to pose.« less

Depressive Symptoms and Externalizing Behaviors among Hispanic Immigrant Adolescents: Examining Longitudinal Effects of Cultural Stress

PubMed Central

Cano, Miguel Ángel; Schwartz, Seth J.; Castillo, Linda G.; Romero, Andrea J.; Huang, Shi; Lorenzo-Blanco, Elma I.; Unger, Jennifer B.; Zamboanga, Byron L.; Des Rosiers, Sabrina E.; Baezconde-Garbanati, Lourdes; Lizzi, Karina M.; Soto, Daniel W.; Oshri, Assaf; Villamar, Juan Andres; Pattarroyo, Monica; Szapocznik, José

2015-01-01

This study examined longitudinal effects of cultural stress (a latent factor comprised of bicultural stress, ethnic discrimination, and negative context of reception) on depressive symptoms and a range of externalizing behaviors among recently (≤5 years in the U.S. at baseline) immigrated Hispanic adolescents. A sample of 302 adolescents (53% boys; mean age 14.51 years) completed baseline measures of perceived ethnic discrimination, bicultural stress, and perceived negative context of reception; and outcome measures of depressive symptoms, cigarette smoking, alcohol use, aggressive behavior, and rule-breaking behavior six months post-baseline. A path analysis indicated that higher cultural stress scores predicted higher levels of all outcomes. These effects were consistent across genders, but varied by study site. Specifically, higher cultural stress scores increased depressive symptoms among participants in Miami, but not in Los Angeles. Findings suggest that cultural stress is a clinically relevant predictor of depressive symptoms and externalizing behaviors among Hispanic immigrant adolescents. PMID:25899132

Laboratory test of a novel structural model of anxiety sensitivity and panic vulnerability.

PubMed

Bernstein, Amit; Zvolensky, Michael J; Zvolensky, Michael J; Schmidt, Norman B

2009-06-01

The current study evaluated a novel latent structural model of anxiety sensitivity (AS) in relation to panic vulnerability among a sample of young adults (N=216). AS was measured using the 16-item Anxiety Sensitivity Index (ASI; Reiss, Peterson, Gursky, & McNally, 1986), and panic vulnerability was indexed by panic attack responding to a single administration of a 4-minute, 10% CO(2) challenge. As predicted, vulnerability for panic attack responding to biological challenge was associated with dichotomous individual differences between taxonic AS classes and continuous within-taxon class individual differences in AS physical concerns. Findings supported the AS taxonic-dimensional hypothesis of AS latent structure and panic vulnerability. These findings are discussed in terms of their theoretical and clinical implications.

Adaptation and Latent Structure of the Swahili Version of Beck Depression Inventory-II in a Low Literacy Population in the Context of HIV.

PubMed

Abubakar, Amina; Kalu, Raphael Birya; Katana, Khamis; Kabunda, Beatrice; Hassan, Amin S; Newton, Charles R; Van de Vijver, Fons

2016-01-01

We set out to adapt the Beck Depression Inventory (BDI)-II in Kenya and examine its factorial structure. In the first phase we carried out in-depth interviews involving 29 adult members of the community to elicit their understanding of depression and identify aspects of the BDI-II that required adaptation. In the second phase, a modified version of BDI-II was administered to 221 adults randomly selected from the community to allow for the evaluation of its psychometric properties. In the third phase of the study we evaluated the discriminative validity of BDI-11 by comparing a randomly chosen community sample (n = 29) with caregivers of adolescents affected by HIV (n = 77). A considerable overlap between the BDI symptoms and those generated in the interviews was observed. Relevant idioms and symptoms such as 'thinking too much' and 'Kuchoka moyo (having a tired heart)' were identified. The administration of the BDI had to be modified to make it suitable for the low literacy levels of our participants. Fit indices for several models (one factorial, two-factor model and a three factor model) were all within acceptable range. Evidence indicated that while multidimensional models could be fitted, the strong correlations between the factors implied that a single factor model may be the best suited solution (alpha [0.89], and a significant correlation with locally identified items [r = 0.51]) confirmed the good psychometric properties of the adapted BDI-II. No evidence was found to support the hypothesis that somatization was more prevalent. Lastly, caregivers of HIV affected adolescents had significantly higher scores compared to adults randomly selected from the community F(1, 121) = 23.31, p < .001 indicating the discriminative validity of the adapted BDI = II. With an adapted administration procedure, the BDI-II provides an adequate measure of depressive symptoms which can be used alongside other measures for proper diagnosis in a low literacy population.

Factorial Invariance and Latent Mean Differences of Scores on the Achievement Goal Tendencies Questionnaire across Gender and Age in a Sample of Spanish Students

ERIC Educational Resources Information Center

Ingles, Candido J.; Marzo, Juan C.; Castejon, Juan L.; Nunez, Jose Carlos; Valle, Antonio; Garcia-Fernandez, Jose M.; Delgado, Beatriz

2011-01-01

This study examined the factorial invariance and latent mean differences of scores on the Spanish version of the "Achievement Goal Tendencies Questionnaire" (AGTQ) across gender and age groups in 2022 Spanish students (51.1% boys) in grades 7 through 10. The equality of factor structures was compared using multi-group confirmatory factor…

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models

PubMed Central

Geiser, Christian; Bishop, Jacob; Lockhart, Ginger; Shiffman, Saul; Grenard, Jerry L.

2013-01-01

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models. PMID:24416023

Latent Variable Modeling of Brain Gray Matter Volume and Psychopathy in Incarcerated Offenders

PubMed Central

Baskin-Sommers, Arielle R.; Neumann, Craig S.; Cope, Lora M.; Kiehl, Kent A.

2016-01-01

Advanced statistical modeling has become a prominent feature in psychological science and can be a useful approach for representing the neural architecture linked to psychopathology. Psychopathy, a disorder characterized by dysfunction in interpersonal-affective and impulsive-antisocial domains, is associated with widespread neural abnormalities. Several imaging studies suggest that underlying structural deficits in paralimbic regions are associated with psychopathy. While these studies are useful, they make assumptions about the organization of the brain and its relevance to individuals displaying psychopathic features. Capitalizing on statistical modeling, the present study (N=254) used latent variable methods to examine the structure of gray matter volume in male offenders, and assessed the latent relations between psychopathy and gray matter factors reflecting paralimbic and non-paralimbic regions. Results revealed good fit for a four-factor gray matter paralimbic model and these first-order factors were accounted for by a super-ordinate paralimbic ‘system’ factor. Moreover, a super-ordinate psychopathy factor significantly predicted the paralimbic, but not the non-paralimbic factor. The latent variable paralimbic model, specifically linked with psychopathy, goes beyond understanding of single brain regions within the system and provides evidence for psychopathy-related gray matter volume reductions in the paralimbic system as a whole. PMID:27269123

Plus and minus RNAs of peach latent mosaic viroid self-cleave in vitro via hammerhead structures.

PubMed Central

Hernández, C; Flores, R

1992-01-01

Peach latent mosaic viroid (PLMVd), the causal agent of peach latent mosaic disease, has been sequenced and found to be a circular RNA molecule of 337 nucleotide residues, which adopts a branched conformation when it is folded in the model of lowest free energy. PLMVd exhibits limited homologies with other viroids and some satellite RNAs, but it does not have any of the central conserved sequences characteristic of the subgroups of typical viroids. However, a segment of approximately one-third of the PLMVd sequence has the elements required to form in the RNAs of both polarities the hammerhead structures proposed to act in the in vitro self-cleavage of avocado sunblotch viroid (ASBVd) and some satellite RNAs. Plus and minus partial- and full-length RNA transcripts of PLMVd containing the hammerhead structures displayed self-cleavage during transcription and after purification as predicted by these structures. These data are consistent with the high stability of the PLMVd hammerhead structures, more similar to the corresponding structures of some satellite RNAs than to those of ASBVd, and indicate that the self-cleavage reactions of PLMVd are most probably mediated by single hammerhead structures. Our results support the inclusion of PLMVd in a viroid subgroup represented by ASBVd, whose members are characterized by their ability to self-cleave in vitro, and probably in vivo, through hammerhead structures. A consensus phylogenetic tree has been obtained suggesting that PLMVd, together with ASBVd, may represent an evolutionary link between viroids and viroid-like satellite RNAs. Images PMID:1373888

Discontinuous Patterns of Cigarette Smoking From Ages 18 to 50 in the United States: A Repeated-Measures Latent Class Analysis.

PubMed

Terry-McElrath, Yvonne M; O'Malley, Patrick M; Johnston, Lloyd D

2017-12-13

Effective cigarette smoking prevention and intervention programming is enhanced by accurate understanding of developmental smoking pathways across the life span. This study investigated within-person patterns of cigarette smoking from ages 18 to 50 among a US national sample of high school graduates, focusing on identifying ages of particular importance for smoking involvement change. Using data from approximately 15,000 individuals participating in the longitudinal Monitoring the Future study, trichotomous measures of past 30-day smoking obtained at 11 time points were modeled using repeated-measures latent class analyses. Sex differences in latent class structure and membership were examined. Twelve latent classes were identified: three characterized by consistent smoking patterns across age (no smoking; smoking < pack per day; smoking pack + per day); three showing uptake to a higher category of smoking across age; four reflecting successful quit behavior by age 50; and two defined by discontinuous shifts between smoking categories. The same latent class structure was found for both males and females, but membership probabilities differed between sexes. Although evidence of increases or decreases in smoking behavior was observed at virtually all ages through 35, 21/22 and 29/30 appeared to be particularly key for smoking category change within class. This examination of latent classes of cigarette smoking among a national US longitudinal sample of high school graduates from ages 18 to 50 identified unique patterns and critical ages of susceptibility to change in smoking category within class. Such information may be of particular use in developing effective smoking prevention and intervention programming. This study examined cigarette smoking among a national longitudinal US sample of high school graduates from ages 18 to 50 and identified distinct latent classes characterized by patterns of movement between no cigarette use, light-to-moderate smoking, and the conventional definition of heavy smoking at 11 time points via repeated-measures latent class analysis. Membership probabilities for each smoking class were estimated, and critical ages of susceptibility to change in smoking behaviors were identified. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Online in situ x-ray diffraction setup for structural modification studies during swift heavy ion irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grygiel, C.; Lebius, H.; Bouffard, S.

2012-01-15

The high energy density of electronic excitations due to the impact of swift heavy ions can induce structural modifications in materials. We present an x-ray diffractometer called ALIX (''Analyse en Ligne sur IRRSUD par diffraction de rayons X''), which has been set up at the low-energy beamline (IRRadiation SUD - IRRSUD) of the Grand Accelerateur National d'Ions Lourds facility, to allow the study of structural modification kinetics as a function of the ion fluence. The x-ray setup has been modified and optimized to enable irradiation by swift heavy ions simultaneously to x-ray pattern recording. We present the capability of ALIXmore » to perform simultaneous irradiation-diffraction by using energy discrimination between x-rays from diffraction and from ion-target interaction. To illustrate its potential, results of sequential or simultaneous irradiation-diffraction are presented in this article to show radiation effects on the structural properties of ceramics. Phase transition kinetics have been studied during xenon ion irradiation of polycrystalline MgO and SrTiO{sub 3}. We have observed that MgO oxide is radiation-resistant to high electronic excitations, contrary to the high sensitivity of SrTiO{sub 3}, which exhibits transition from the crystalline to the amorphous state during irradiation. By interpreting the amorphization kinetics of SrTiO{sub 3}, defect overlapping models are discussed as well as latent track characteristics. Together with a transmission electron microscopy study, we conclude that a single impact model describes the phase transition mechanism.« less

Generalized Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

2004-01-01

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

Do gamblers eat more salt? Testing a latent trait model of covariance in consumption

PubMed Central

Goodwin, Belinda C.; Browne, Matthew; Rockloff, Matthew; Donaldson, Phillip

2015-01-01

A diverse class of stimuli, including certain foods, substances, media, and economic behaviours, may be described as ‘reward-oriented’ in that they provide immediate reinforcement with little initial investment. Neurophysiological and personality concepts, including dopaminergic dysfunction, reward sensitivity and rash impulsivity, each predict the existence of a latent behavioural trait that leads to increased consumption of all stimuli in this class. Whilst bivariate relationships (co-morbidities) are often reported in the literature, to our knowledge, a multivariate investigation of this possible trait has not been done. We surveyed 1,194 participants (550 male) on their typical weekly consumption of 11 types of reward-oriented stimuli, including fast food, salt, caffeine, television, gambling products, and illicit drugs. Confirmatory factor analysis was used to compare models in a 3×3 structure, based on the definition of a single latent factor (none, fixed loadings, or estimated loadings), and assumed residual covariance structure (none, a-priori / literature based, or post-hoc / data-driven). The inclusion of a single latent behavioural ‘consumption’ factor significantly improved model fit in all cases. Also confirming theoretical predictions, estimated factor loadings on reward-oriented indicators were uniformly positive, regardless of assumptions regarding residual covariances. Additionally, the latent trait was found to be negatively correlated with the non-reward-oriented indicators of fruit and vegetable consumption. The findings support the notion of a single behavioural trait leading to increased consumption of reward-oriented stimuli across multiple modalities. We discuss implications regarding the concentration of negative lifestyle-related health behaviours. PMID:26551907

pong: fast analysis and visualization of latent clusters in population genetic data.

PubMed

Behr, Aaron A; Liu, Katherine Z; Liu-Fang, Gracie; Nakka, Priyanka; Ramachandran, Sohini

2016-09-15

A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixed-membership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixed-membership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining. We introduce pong, a network-graphical approach for analyzing and visualizing membership in latent clusters with a native interactive D3.js visualization. pong leverages efficient algorithms for solving the Assignment Problem to dramatically reduce runtime while increasing accuracy compared with other methods that process output from mixed-membership models. We apply pong to 225 705 unlinked genome-wide single-nucleotide variants from 2426 unrelated individuals in the 1000 Genomes Project, and identify previously overlooked aspects of global human population structure. We show that pong outpaces current solutions by more than an order of magnitude in runtime while providing a customizable and interactive visualization of population structure that is more accurate than those produced by current tools. pong is freely available and can be installed using the Python package management system pip. pong's source code is available at https://github.com/abehr/pong aaron_behr@alumni.brown.edu or sramachandran@brown.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Do gamblers eat more salt? Testing a latent trait model of covariance in consumption.

PubMed

Goodwin, Belinda C; Browne, Matthew; Rockloff, Matthew; Donaldson, Phillip

2015-09-01

A diverse class of stimuli, including certain foods, substances, media, and economic behaviours, may be described as 'reward-oriented' in that they provide immediate reinforcement with little initial investment. Neurophysiological and personality concepts, including dopaminergic dysfunction, reward sensitivity and rash impulsivity, each predict the existence of a latent behavioural trait that leads to increased consumption of all stimuli in this class. Whilst bivariate relationships (co-morbidities) are often reported in the literature, to our knowledge, a multivariate investigation of this possible trait has not been done. We surveyed 1,194 participants (550 male) on their typical weekly consumption of 11 types of reward-oriented stimuli, including fast food, salt, caffeine, television, gambling products, and illicit drugs. Confirmatory factor analysis was used to compare models in a 3×3 structure, based on the definition of a single latent factor (none, fixed loadings, or estimated loadings), and assumed residual covariance structure (none, a-priori / literature based, or post-hoc / data-driven). The inclusion of a single latent behavioural 'consumption' factor significantly improved model fit in all cases. Also confirming theoretical predictions, estimated factor loadings on reward-oriented indicators were uniformly positive, regardless of assumptions regarding residual covariances. Additionally, the latent trait was found to be negatively correlated with the non-reward-oriented indicators of fruit and vegetable consumption. The findings support the notion of a single behavioural trait leading to increased consumption of reward-oriented stimuli across multiple modalities. We discuss implications regarding the concentration of negative lifestyle-related health behaviours.

PTSD's latent structure in Malaysian tsunami victims: assessing the newly proposed Dysphoric Arousal model.

PubMed

Armour, Cherie; Raudzah Ghazali, Siti; Elklit, Ask

2013-03-30

The underlying latent structure of Posttraumatic Stress Disorder (PTSD) is widely researched. However, despite a plethora of factor analytic studies, no single model has consistently been shown as superior to alternative models. The two most often supported models are the Emotional Numbing and the Dysphoria models. However, a recently proposed five-factor Dysphoric Arousal model has been gathering support over and above existing models. Data for the current study were gathered from Malaysian Tsunami survivors (N=250). Three competing models (Emotional Numbing/Dysphoria/Dysphoric Arousal) were specified and estimated using Confirmatory Factor Analysis (CFA). The Dysphoria model provided superior fit to the data compared to the Emotional Numbing model. However, using chi-square difference tests, the Dysphoric Arousal model showed a superior fit compared to both the Emotional Numbing and Dysphoria models. In conclusion, the current results suggest that the Dysphoric Arousal model better represents PTSD's latent structure and that items measuring sleeping difficulties, irritability/anger and concentration difficulties form a separate, unique PTSD factor. These results are discussed in relation to the role of Hyperarousal in PTSD's on-going symptom maintenance and in relation to the DSM-5. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

"Social Anxiety Disorder Carved at its Joints": evidence for the taxonicity of social anxiety disorder.

PubMed

Weeks, Justin W; Carleton, R Nicholas; Asmundson, Gordon J G; McCabe, Randi E; Antony, Martin M

2010-10-01

Previous findings suggest that social anxiety disorder may be best characterized as having a dimensional latent structure (Kollman et al., 2006; Weeks et al., 2009). We attempted to extend previous taxometric investigations of social anxiety by examining the latent structure of social anxiety disorder symptoms in a large sample comprised of social anxiety disorder patients (i.e., putative taxon members) and community residents/undergraduate respondents (i.e., putative complement class members). MAXEIG and MAMBAC were performed with indicator sets drawn from a self-report measure of social anxiety symptoms, the Social Interaction Phobia Scale (Carleton et al., 2009). MAXEIG and MAMBAC analyses, as well as comparison analyses utilizing simulated taxonic and dimensional datasets, yielded converging evidence that social anxiety disorder has a taxonic latent structure. Moreover, 100% of the confirmed social anxiety disorder patients in our overall sample were correctly assigned to the identified taxon class, providing strong support for the external validity of the identified taxon; and k-means cluster analysis results corroborated our taxometric base-rate estimates. Implications regarding the conceptualization, diagnosis, and assessment of social anxiety disorder are discussed. Copyright 2010 Elsevier Ltd. All rights reserved.

Multilevel structural equation models for assessing moderation within and across levels of analysis.

PubMed

Preacher, Kristopher J; Zhang, Zhen; Zyphur, Michael J

2016-06-01

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Enhanced Thermal Properties of Novel Latent Heat Thermal Storage Material Through Confinement of Stearic Acid in Meso-Structured Onion-Like Silica

NASA Astrophysics Data System (ADS)

Gao, Junkai; Lv, Mengjiao; Lu, Jinshu; Chen, Yan; Zhang, Zijun; Zhang, Xiongjie; Zhu, Yingying

2017-12-01

Meso-structured onion-like silica (MOS), which had a highly ordered, onion-like multilayer; large surface area and pore volume; and highly curved mesopores, were synthesized as a support for stearic acid (SA) to develop a novel shape-stabilized phase change material (SA/MOS). The characterizations of SA/MOS were studied by the analysis technique of scanning electron microscope, infrared spectroscopy, x-ray diffraction, differential scanning calorimeter (DSC), and thermal gravimetry analysis (TGA). The results showed that the interaction between the SA and the MOS was physical adsorption and that the MOS had no effect on the crystal structure of the SA. The DSC results suggested that the melting and solidifying temperature of the SA/MOS were 72.7°C and 63.9°C with a melting latent heat of 108.0 J/g and a solidifying latent heat of 126.0 J/g, respectively, and the TGA results indicated that the SA/MOS had a good thermal stability. All of the results demonstrated that the SA/MOS was a promising thermal energy storage material candidate for practical applications.

The consequences of ignoring measurement invariance for path coefficients in structural equation models

PubMed Central

Guenole, Nigel; Brown, Anna

2014-01-01

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. PMID:25278911

Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making.

PubMed

Lou, Bin; Li, Yun; Philiastides, Marios G; Sajda, Paul

2014-02-15

Pre-stimulus α power has been shown to correlate with the behavioral accuracy of perceptual decisions. In most cases, these correlations have been observed by comparing α power for different behavioral outcomes (e.g. correct vs incorrect trials). In this paper we investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. Specially we consider variations of pre-stimulus α power with post-stimulus EEG components in a two alternative forced choice visual discrimination task. EEG components, discriminative of stimulus class, are identified using a linear multivariate classifier and only the variability of the components for correct trials (regardless of stimulus class, and for nominally identical stimuli) are correlated with the corresponding pre-stimulus α power. We find a significant relationship between the mean and variance of the pre-stimulus α power and the variation of the trial-to-trial magnitude of an early post-stimulus EEG component. This relationship is not seen for a later EEG component that is also discriminative of stimulus class and which has been previously linked to the quality of evidence driving the decision process. Our results suggest that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus state. © 2013 Elsevier Inc. All rights reserved.

Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making

PubMed Central

Lou, Bin; Li, Yun; Philiastides, Marios G.; Sajda, Paul

2013-01-01

Pre-stimulus α power has been shown to correlate with the behavioral accuracy of perceptual decisions. In most cases, these correlations have been observed by comparing α power for different behavioral outcomes (e.g. correct vs incorrect trials). In this paper we investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. Specially we consider variations of pre-stimulus α power with post-stimulus EEG components in a two alternative forced choice visual discrimination task. EEG components, discriminative of stimulus class, are identified using a linear multivariate classifier and only the variability of the components for correct trials (regardless of stimulus class, and for nominally identical stimuli) are correlated with the corresponding pre-stimulus α power. We find a significant relationship between the mean and variance of the pre-stimulus α power and the variation of the trial-to-trial magnitude of an early post-stimulus EEG component. This relationship is not seen for a later EEG component that is also discriminative of stimulus class and which has been previously linked to the quality of evidence driving the decision process. Our results suggest that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus state. PMID:24185020

Development of a molecular method for the typing of Brettanomyces bruxellensis (Dekkera bruxellensis) at the strain level.

PubMed

Miot-Sertier, C; Lonvaud-Funel, A

2007-02-01

In recent years, Brettanomyces/Dekkera bruxellensis has caused increasingly severe quality problems in the wine industry. A typing method at the strain level is needed for a better knowledge of the dispersion and the dynamics of these yeasts from grape to wine. Three molecular tools, namely random-amplified polymorphic DNA, PCR fingerprinting with microsatellite oligonucleotide primers and SAU-PCR, were explored for their relevance to typing strains of Brettanomyces bruxellensis. The results indicated that discrimination of each individual strain was not possible with a single PCR typing technique. We described a typing method for B. bruxellensis based on restriction enzyme analysis and pulse field gel electrophoresis (REA-PFGE). Results showed that electrophoretic profiles were reproducible and specific for each strain under study. Consequently, REA-PFGE should be considered for the discrimination of B. bruxellensis strains. This technique allowed a fine discrimination of B. bruxellensis, as strains were identified by a particular profile. This study constitutes a prerequisite for accurate and appropriate investigations on the diversity of strains throughout the winemaking and ageing process. Such studies will probably give clearer and more up-to-date information on the origin of the presence of Brettanomyces in wine after vinification when they are latent spoilage agents.

Profiles of African American College Students’ Alcohol Use and Sexual Behaviors: Associations With Stress, Racial Discrimination, and Social Support

PubMed Central

Metzger, Isha W.; Cooper, Shauna M.; Ritchwood, Tiarney D.; Onyeuku, Chisom; Griffin, Charity Brown

2017-01-01

Though studies show that alcohol use and sexual activity increase during emerging adulthood, few studies examine within–ethnic group differences, particularly among African American college students. This investigation utilized a latent class analytic methodology to identify risk behavior profiles of alcohol use (frequency and amount of alcohol consumed), sexual activity (number of intimate partners), and co-occurring risk behaviors (drinking before sexual intercourse) among 228 African American college students. This investigation also examined whether identified risk behavior profiles were associated with stress (interpersonal, intraperso-nal, academic, and environmental), experiences of racial discrimination, and social support (from family, friends, and the college community). Results identified five distinct profiles within this sample: (a) High Sexual Risk—above-average sexual activity; (b) Abstainers—below-average alcohol use and sexual activity; (c) Low Risk—average alcohol use and sexual activity; (d) Alcohol Risk—above-average alcohol use and below-average sexual activity; and (e) Co-Occurring Risk—above-average alcohol use and sexual activity. Identified profiles differed across interpersonal and environmental stress, and self-reported frequency of experiences with racial discrimination. Implications for prevention programs and interventions aimed at reducing alcohol and sexual activity for African American college students are discussed. PMID:27215314

Weakly Supervised Dictionary Learning

NASA Astrophysics Data System (ADS)

You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

2018-05-01

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

Psychometric Structure of a Comprehensive Objective Structured Clinical Examination: A Factor Analytic Approach

ERIC Educational Resources Information Center

Volkan, Kevin; Simon, Steven R.; Baker, Harley; Todres, I. David

2004-01-01

Problem Statement and Background: While the psychometric properties of Objective Structured Clinical Examinations (OSCEs) have been studied, their latent structures have not been well characterized. This study examines a factor analytic model of a comprehensive OSCE and addresses implications for measurement of clinical performance. Methods: An…

RADC (Rome Air Development Center) Guide to Environmental Stress Screening

DTIC Science & Technology

1986-08-01

and the processes which are used in their manufacture. ESS is the vehicle by which latent defects are accelerated to early failure in the factory. ESS...structured as part of a production 2 reliability assurance program, is the vehicle through which product reliability in manufacture can be maintained...mechanical, electrical and/or thermal stresses to an equipment item for the purpose of precipitating latent part and workmanship defects to early failure

Profiles of Bullying Victimization, Discrimination, Social Support, and School Safety: Links with Latino/a Youth Acculturation, Gender, Depressive Symptoms, and Cigarette Use

PubMed Central

Lorenzo-Blanco, Elma I.; Unger, Jennifer B.; Oshri, Assaf; Baezconde-Garbanati, Lourdes; Soto, Daniel

2016-01-01

Latino/a youth are at risk for symptoms of depression and cigarette smoking but this risk varies by acculturation and gender. To understand why some youth are at greater risk than others, we identified profiles of diverse community experiences (perceived discrimination, bullying victimization, social support, perceived school safety) and examined associations between profiles of community experience and depressive symptoms, cigarette smoking, acculturation, and gender. Data came from Project Red (Reteniendo y Entendiendo Diversidad para Salud), a school-based longitudinal study of acculturation among 1919 Latino/a adolescents (52% female; 84% 14 years old; 87% U.S. born). Latent profile analysis (LPA) revealed four distinct profiles of community experience which varied by gender and acculturation. Boys were overrepresented in profile groups with high perceived discrimination, some bullying, and lack of positive experiences, while girls were overrepresented in groups with high bullying victimization in the absence and presence of other community experiences. Youth low on both U.S. and Latino/a cultural orientation described high perceived discrimination and lacked positive experiences, and were predominantly male. Profiles characterized by high perceived discrimination and/or high bullying victimization in the absence of positive experiences had higher levels of depressive symptoms and higher risk of smoking, relative to the other groups. Findings suggest that acculturation comes with diverse community experiences that vary by gender and relate to smoking and depression risk. Results from this research can inform the development of tailored intervention and prevention strategies to reduce depression and/or smoking for Latino/a youth at risk for depression and/or smoking. PMID:26752445

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts.

PubMed

Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V

2018-01-01

The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders. We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma.

Presumed fair: ironic effects of organizational diversity structures.

PubMed

Kaiser, Cheryl R; Major, Brenda; Jurcevic, Ines; Dover, Tessa L; Brady, Laura M; Shapiro, Jenessa R

2013-03-01

This research tests the hypothesis that the presence (vs. absence) of organizational diversity structures causes high-status group members (Whites, men) to perceive organizations with diversity structures as procedurally fairer environments for underrepresented groups (racial minorities, women), even when it is clear that underrepresented groups have been unfairly disadvantaged within these organizations. Furthermore, this illusory sense of fairness derived from the mere presence of diversity structures causes high-status group members to legitimize the status quo by becoming less sensitive to discrimination targeted at underrepresented groups and reacting more harshly toward underrepresented group members who claim discrimination. Six experiments support these hypotheses in designs using 4 types of diversity structures (diversity policies, diversity training, diversity awards, idiosyncratically generated diversity structures from participants' own organizations) among 2 high-status groups in tests involving several types of discrimination (discriminatory promotion practices, adverse impact in hiring, wage discrimination). Implications of these experiments for organizational diversity and employment discrimination law are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved

Social phobia: further evidence of dimensional structure.

PubMed

Crome, Erica; Baillie, Andrew; Slade, Tim; Ruscio, Ayelet Meron

2010-11-01

Social phobia is a common mental disorder associated with significant impairment. Current research and treatment models of social phobia rely on categorical diagnostic conceptualizations lacking empirical support. This study aims to further research exploring whether social phobia is best conceptualized as a dimension or a discrete categorical disorder. This study used three distinct taxometric techniques (mean above minus below a cut, maximum Eigen value and latent mode) to explore the latent structure of social phobia in two large epidemiological samples, using indicators derived from diagnostic criteria and associated avoidant personality traits. Overall, outcomes from multiple taxometric analyses supported dimensional structure. This is consistent with conceptualizations of social phobia as lying on a continuum with avoidant personality traits. Support for the dimensionality of social phobia has important implications for future research, assessment, treatment, and public policy.

Reconceptualizing the classification of PNAS articles

PubMed Central

Airoldi, Edoardo M.; Erosheva, Elena A.; Fienberg, Stephen E.; Joutard, Cyrille; Love, Tanzy; Shringarpure, Suyash

2010-01-01

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS. PMID:21078953

Labour management and Obstetric outcomes among pregnant women admitted in latent phase compared to active phase of labour at Bugando Medical Centre in Tanzania

PubMed Central

2014-01-01

Background Interventions given to women admitted in latent or active phase of labor may influence the outcomes of labor and ameliorate complications which can affect the mother and fetus. Labour management, maternal and fetal outcomes among low risk women presenting both in latent phase and active phase of labour in Tanzania have not recently been explored. Methods This was a descriptive cross-sectional study. It was done from February to April 2013. Case notes were collected serially until the sample size was reached. A structured checklist was used to extract data. Data was analyzed using SPSS version 17. A p < 0.05 was considered significant at 95% confidence interval. Results Five hundred case notes of low risk pregnant women were collected, half of each presented in latent phase and active phase of labour. Key interventions including augmentation with oxytocin, artificial rupture of membranes and caesarean section were significantly higher in the latent phase group than the active phase group 84(33.6%) versus 52(20.8%) p < 0.05; 96(38.6%) versus 56(22.4%) p < 0.05 and 87(34.8%) versus 60(24.0%) p < 0.05 respectively. Spontaneous vertex delivery was higher among pregnant women admitted initially in active phase than in latent phase groups 180(72.0%), versus 153(61.2%) p > 0.01). There were more women in the active phase group who sustained genital tract tear and postpartum haemorrhage than in the latent phase group 101(18.6%), versus 38(15.6%) p < 0.01 and 46(18.4%), versus 17(6.6%) p < 0.05 respectively. Conclusions Pregnant women admitted at BMC in latent phase of labour are subjected to more obstetric interventions than those admitted in the active phase. There is need to produce guidelines on management of women admitted in latent phase of labour at BMC to reduce the risk of unnecessary interventions. PMID:24521301

Labour management and Obstetric outcomes among pregnant women admitted in latent phase compared to active phase of labour at Bugando Medical Centre in Tanzania.

PubMed

Chuma, Clotrida; Kihunrwa, Albert; Matovelo, Dismas; Mahendeka, Marietha

2014-02-12

Interventions given to women admitted in latent or active phase of labor may influence the outcomes of labor and ameliorate complications which can affect the mother and fetus. Labour management, maternal and fetal outcomes among low risk women presenting both in latent phase and active phase of labour in Tanzania have not recently been explored. This was a descriptive cross-sectional study. It was done from February to April 2013. Case notes were collected serially until the sample size was reached. A structured checklist was used to extract data. Data was analyzed using SPSS version 17. A p < 0.05 was considered significant at 95% confidence interval. Five hundred case notes of low risk pregnant women were collected, half of each presented in latent phase and active phase of labour. Key interventions including augmentation with oxytocin, artificial rupture of membranes and caesarean section were significantly higher in the latent phase group than the active phase group 84(33.6%) versus 52(20.8%) p < 0.05; 96(38.6%) versus 56(22.4%) p < 0.05 and 87(34.8%) versus 60(24.0%) p < 0.05 respectively. Spontaneous vertex delivery was higher among pregnant women admitted initially in active phase than in latent phase groups 180(72.0%), versus 153(61.2%) p > 0.01). There were more women in the active phase group who sustained genital tract tear and postpartum haemorrhage than in the latent phase group 101(18.6%), versus 38(15.6%) p < 0.01 and 46(18.4%), versus 17(6.6%) p < 0.05 respectively. Pregnant women admitted at BMC in latent phase of labour are subjected to more obstetric interventions than those admitted in the active phase. There is need to produce guidelines on management of women admitted in latent phase of labour at BMC to reduce the risk of unnecessary interventions.

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

PubMed

Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

2015-05-01

We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Striking the right immunological balance prevents progression of tuberculosis.

PubMed

Vyas, Shachi Pranjal; Goswami, Ritobrata

2017-12-01

Tuberculosis (TB) caused by infection with Mycobacterium tuberculosis (Mtb) is a major burden for human health worldwide. Current standard treatments for TB require prolonged administration of antimycobacterial drugs leading to exaggerated inflammation and tissue damage. This can result in the reactivation of latent TB culminating in TB progression. Thus, there is an unmet need to develop therapies that would shorten the duration of anti-TB treatment and to induce optimal protective immune responses to control the spread of mycobacterial infection with minimal lung pathology. Granulomata is the hallmark structure formed by the organized accumulation of immune cells including macrophages, natural killer cells, dendritic cells, neutrophils, T cells, and B cells to the site of Mtb infection. It safeguards the host by containing Mtb in latent form. However, granulomata can undergo caseation and contribute to the reactivation of latent TB, if the immune responses developed to fight mycobacterial infection are not properly controlled. Thus, an optimal balance between innate and adaptive immune cells might play a vital role in containing mycobacteria in latent form for prolonged periods and prevent the spread of Mtb infection from one individual to another. Optimal and well-regulated immune responses against Mycobacterium tuberculosis may help to prevent the reactivation of latent TB. Moreover, therapies targeting balanced immune responses could help to improve treatment outcomes among latently infected TB patients and thereby limit the dissemination of mycobacterial infection.

Kinetics of Electrons from Plasma Discharge in a Latent Track Region Induced by Swift Heavy ION Irradiation

NASA Astrophysics Data System (ADS)

Minárik, Stanislav

2015-08-01

While passing swift heavy ion through a material structure, it produces a region of radiation affected material which is known as a "latent track". Scattering motions of electrons interacting with a swift heavy ion are dominant in the latent track region. These phenomena include the electron impurity and phonon scattering processes modified by the interaction with the ion projectile as well as the Coulomb scattering between two electrons. In this paper, we provide detailed derivation of a 3D Boltzmann scattering equation for the description of the relative scattering motion of such electrons. Phase-space distribution function for this non-equilibrioum system of scattering electrons can be found by the solution of mentioned equation.

NMR Metabolomics Investigates the Influence of Flavonoid-Enriched Rations on Chicken Plasma.

PubMed

Fotakis, Charalambos; Lantzouraki, Dimitra Z; Goliomytis, Michael; Simitzis, Panagiotis E; Charismiadou, Maria; Deligeorgis, Stelios G; Zoumpoulakis, Panagiotis

2017-03-01

The use of flavonoids as dietary supplements is well established, mainly due to their intense antioxidant and anti-inflammatory properties. In the present study, hesperidin, naringin, and vitamin E were used as additives at different concentrations in poultry rations in order to achieve meat of improved quality. NMR metabolomics was applied to chicken blood serum samples to discern whether and how the enriched rations affected the animals' metabolic profile. Variations in the metabolic patterns according to sustenance consumption were traced by orthogonal projections to latent structures discriminant analysis (OPLS-DA) models and were attributed to specific metabolites by using S-line plots. In particular, serum samples from chickens fed with vitamin E displayed higher concentrations of glycine and succinic acid compared to control samples, which were mainly characterized by betaine, formic acid, and lipoproteins. Samples from chickens fed with hesperidin were characterized by increased levels of lactic acid, citric acid, creatine, carnosine, creatinine, phosphocreatine, anserine, glucose, and alanine compared to control samples. Lastly, naringin samples exhibited increased levels of citric and acetic acids. Results verify the scalability of NMR metabolomics to highlight metabolite variations among chicken serum samples in relation to food rations.

Tuberculosis Knowledge, Awareness, and Stigma Among African-Americans in Three Southeastern Counties in the USA: a Qualitative Study of Community Perspectives.

PubMed

Royce, Rachel A; Colson, Paul W; Woodsong, Cynthia; Swinson-Evans, Tammeka; Walton, Wanda; Maiuri, Allison; DeLuca, Nickolas

2017-02-01

To inform strategies to address the tuberculosis (TB) excess among US-born African-Americans, we sought to understand the TB experience in the most highly affected southeastern communities. We conducted semi-structured interviews and focus groups in three communities with a TB excess-urban (Georgia and Tennessee) and rural (North Carolina). Participants from five groups provided diverse perspectives-African-Americans: patients with TB disease or latent TB infection (LTBI), or at high risk of contracting TB; and local community leaders and TB program staff. Few differences emerged between sites. Many participants demonstrated low levels of knowledge and awareness and held many misconceptions about TB. Patients expressed a preference for verbal communication of medical information. Patients reported fear of stigmatization and shunning, but few experienced discrimination. Patient trust for TB program staff was high, though community leaders often assumed the opposite. The findings will help guide interventions to improve knowledge and awareness regarding TB, including specific attention to the role of public and private health care providers in dispelling persistent misinformation about TB. The insight from these communities will help build the scientific foundation required to effectively eliminate health inequities.

Postnatal functional inactivation of the entorhinal cortex or ventral subiculum has different consequences for latent inhibition-related striatal dopaminergic responses in adult rats.

PubMed

Meyer, F; Peterschmitt, Y; Louilot, A

2009-05-01

Latent inhibition has been found to be disrupted in patients with acute schizophrenia. Striatal dopaminergic dysregulation is commonly acknowledged in schizophrenia. This disease may be consecutive to a functional disconnection between integrative regions, stemming from neurodevelopmental failures. Various anomalies suggesting early abnormal brain development have been described in the entorhinal cortex (ENT) and ventral subiculum (SUB) of patients. This study examines the consequences of a neonatal transitory blockade of the left ENT or left SUB for latent inhibition-related dopamine responses in the anterior part of the dorsal striatum using in-vivo voltammetry in freely moving adult rats. Reversible inactivation of both structures in different animals was achieved by local microinjection of tetrodotoxin (TTX) at postnatal day 8. Results obtained during the retention session of a three-stage latent inhibition protocol showed that the functional neonatal disconnection of the ENT or SUB caused the behavioural latent inhibition expression in pre-exposed (PE)-TTX-conditioned adult rats to disappear. After postnatal inactivation of the SUB, PE-TTX-conditioned rats displayed a reversal of the latent inhibition-related striatal dopamine responses, whereas after neonatal blockade of the ENT, dopamine changes in PE-TTX-conditioned rats monitored in the anterior striatum were between those observed in PE-phosphate-buffered-saline-conditioned and non-PE-TTX-conditioned animals. These data suggest that neonatal functional inactivation of the SUB disrupts latent inhibition-related striatal dopamine responses in adult animals more than that of the ENT. They may help improve understanding of the pathophysiology of schizophrenia.

Recombination enhances HIV-1 envelope diversity by facilitating the survival of latent genomic fragments in the plasma virus population

DOE Office of Scientific and Technical Information (OSTI.GOV)

Immonen, Taina T.; Conway, Jessica M.; Romero-Severson, Ethan O.

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation processmore » including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Furthermore, our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.« less

Recombination enhances HIV-1 envelope diversity by facilitating the survival of latent genomic fragments in the plasma virus population

DOE PAGES

Immonen, Taina T.; Conway, Jessica M.; Romero-Severson, Ethan O.; ...

2015-12-22

HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation processmore » including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution in vivo by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Furthermore, our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.« less

Targeting NF-κB signaling with protein kinase C agonists as an emerging strategy for combating HIV latency.

PubMed

Jiang, Guochun; Dandekar, Satya

2015-01-01

Highly active antiretroviral therapy (HAART) is very effective in suppressing HIV-1 replication and restoring immune functions in HIV-infected individuals. However, it fails to eradicate the latent viral reservoirs and fully resolve chronic inflammation in HIV infection. The "shock-and-kill" strategy was recently proposed to induce latent HIV expression in the presence of HAART. Recent studies have shown that the protein kinase C (PKC) agonists are highly potent in inducing latent HIV expression from the viral reservoirs in vitro and ex vivo and in protecting primary CD4(+) T cells from HIV infection through down-modulation of their HIV coreceptor expression. The PKC agonists are excellent candidates for advancing to clinical HIV eradication strategies. This article will present a critical review of the structure and function of known PKC agonists, their mechanisms for the reactivation of latent HIV expression, and the potential of these compounds for advancing clinical HIV eradication strategies.

Obtaining systematic teacher reports of disruptive behavior disorders utilizing DSM-IV.

PubMed

Wolraich, M L; Feurer, I D; Hannah, J N; Baumgaertel, A; Pinnock, T Y

1998-04-01

This study examines the psychometric properties of the Vanderbilt AD/HD Diagnostic Teacher Rating Scale (VADTRS) and provides preliminary normative data from a large, geographically defined population. The VADTRS consists of the complete list of DSM-IV AD/HD symptoms, a screen for other disruptive behavior disorders, anxiety and depression, and ratings of academic and classroom behavior performance. Teachers in one suburban county completed the scale for their students during 2 consecutive years. Statistical methods included (a) exploratory and confirmatory latent variable analyses of item data, (b) evaluation of the internal consistency of the latent dimensions, (c) evaluation of latent structure concordance between school year samples, and (d) preliminary evaluation of criterion-related validity. The instrument comprises four behavioral dimensions and two performance dimensions. The behavioral dimensions were concordant between school years and were consistent with a priori DSM-IV diagnostic criteria. Correlations between latent dimensions and relevant, known disorders or problems varied from .25 to .66.

Latent heat contribution to the direct magnetocaloric effect in Ni-Mn-Ga shape memory alloys with coupled martensitic and magnetic transformations

NASA Astrophysics Data System (ADS)

Caballero-Flores, R.; Sánchez-Alarcos, V.; Recarte, V.; Pérez-Landazábal, J. I.; Gómez-Polo, C.

2016-05-01

We report the direct magnetocaloric response of materials that present a second-order phase transition in the temperature range where a first-order structural transition also occurs. In particular, the influence of the latent heat on the field-induced adiabatic temperature change has been analyzed in a Ni-Mn-Ga alloy with coupled martensitic and magnetic transformations. It is found that discrepancies around 20% arise depending on whether the latent heat is taken into account or not. From the observed results, a general expression for the indirect determination of the adiabatic temperature change, that takes into account the contributions of both the martensitic and magnetic transformations, is proposed and experimentally confirmed. The observed key role of the latent heat allows us to understand why materials with first-order transformations do not present adiabatic temperature changes as higher as those which would correspond to materials undergoing second-order transformations with similar isothermal entropy change.

Disgust proneness predicts obsessive-compulsive disorder symptom severity in a clinical sample of youth: Distinctions from negative affect.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Kim, Jingu; Riemann, Bradley C; Jacobi, David M

2017-04-15

Although studies have linked disgust proneness to the etiology and maintenance of obsessive-compulsive disorder (OCD) in adults, there remains a paucity of research examining the specificity of this association among youth. The present study employed structural equation modeling to examine the association between disgust proneness, negative affect, and OCD symptom severity in a clinical sample of youth admitted to a residential treatment facility (N =471). Results indicate that disgust proneness and negative affect latent factors independently predicted an OCD symptom severity latent factor. However, when both variables were modeled as predictors simultaneously, latent disgust proneness remained significantly associated with OCD symptom severity, whereas the association between latent negative affect and OCD symptom severity became nonsignificant. Tests of mediation converged in support of disgust proneness as a significant intervening variable between negative affect and OCD symptom severity. Subsequent analysis showed that the path from disgust proneness to OCD symptom severity in the structural model was significantly stronger among those without a primary diagnosis of OCD compared to those with a primary diagnosis of OCD. Given the cross-sectional design, the causal inferences that can be made are limited. The present study is also limited by the exclusive reliance on self-report measures. Disgust proneness may play a uniquely important role in OCD among youth. Copyright © 2017 Elsevier B.V. All rights reserved.

Victimization and Human Immunodeficiency Virus-Related Risk Among Transgender Women in India: A Latent Profile Analysis.

PubMed

Willie, Tiara C; Chakrapani, Venkatesan; White Hughto, Jaclyn M; Kershaw, Trace S

2017-12-01

Globally, transgender women (TGW) experience multiple forms of victimization such as violence and discrimination that can place them at risk for poor sexual health. To date, research overlooks the heterogeneity in experiences of victimization among TGW. Furthermore, few studies have examined the association between victimization and sexual risk among TGW in India, despite the high burden of HIV and victimization in this community. Latent profile analysis was performed to identify patterns of victimization in a convenience sample of 299 TGW recruited from nongovernmental organizations across four states in India. Analysis of covariance was performed to examine differences in sexual risk (i.e., alcohol use before sex; inconsistent condom use with a male regular partner, a male causal partner, and a male paying partner; and having multiple sexual partners) between latent profiles. Five distinct profiles of Indian TGW were identified based on the type and severity of victimization: (1) Low victimization, (2) High verbal police victimization, (3) High verbal and physical police victimization, (4) Moderate victimization, and (5) High victimization. While controlling for age, education, income, HIV status, and marital status, results revealed that TGW in the moderate victimization and high victimization profiles had higher sexual risk than TGW in the low victimization and high verbal police victimization profiles. In addition, TGW in high verbal and physical police victimization profile had higher sexual risk than TGW in low victimization profile. These findings underscore the importance of tailoring sexual risk reduction interventions to the specific needs of TGW based on patterns of victimization.

The use of fault reporting of medical equipment to identify latent design flaws.

PubMed

Flewwelling, C J; Easty, A C; Vicente, K J; Cafazzo, J A

2014-10-01

Poor device design that fails to adequately account for user needs, cognition, and behavior is often responsible for use errors resulting in adverse events. This poor device design is also often latent, and could be responsible for "No Fault Found" (NFF) reporting, in which medical devices sent for repair by clinical users are found to be operating as intended. Unresolved NFF reports may contribute to incident under reporting, clinical user frustration, and biomedical engineering technologist inefficacy. This study uses human factors engineering methods to investigate the relationship between NFF reporting frequency and device usability. An analysis of medical equipment maintenance data was conducted to identify devices with a high NFF reporting frequency. Subsequently, semi-structured interviews and heuristic evaluations were performed in order to identify potential usability issues. Finally, usability testing was conducted in order to validate that latent usability related design faults result in a higher frequency of NFF reporting. The analysis of medical equipment maintenance data identified six devices with a high NFF reporting frequency. Semi-structured interviews, heuristic evaluations and usability testing revealed that usability issues caused a significant portion of the NFF reports. Other factors suspected to contribute to increased NFF reporting include accessory issues, intermittent faults and environmental issues. Usability testing conducted on three of the devices revealed 23 latent usability related design faults. These findings demonstrate that latent usability related design faults manifest themselves as an increase in NFF reporting and that devices containing usability related design faults can be identified through an analysis of medical equipment maintenance data. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Latent Constructs in Psychosocial Factors Associated with Cardiovascular Disease: An Examination by Race and Sex

PubMed Central

Clark, Cari Jo; Henderson, Kimberly M.; de Leon, Carlos F. Mendes; Guo, Hongfei; Lunos, Scott; Evans, Denis A.; Everson-Rose, Susan A.

2012-01-01

This study examines race and sex differences in the latent structure of 10 psychosocial measures and the association of identified factors with self-reported history of coronary heart disease (CHD). Participants were 4,128 older adults from the Chicago Health and Aging Project. Exploratory factor analysis (EFA) with oblique geomin rotation was used to identify latent factors among the psychosocial measures. Multi-group comparisons of the EFA model were conducted using exploratory structural equation modeling to test for measurement invariance across race and sex subgroups. A factor-based scale score was created for invariant factor(s). Logistic regression was used to test the relationship between the factor score(s) and CHD adjusting for relevant confounders. Effect modification of the relationship by race–sex subgroup was tested. A two-factor model fit the data well (comparative fit index = 0.986; Tucker–Lewis index = 0.969; root mean square error of approximation = 0.039). Depressive symptoms, neuroticism, perceived stress, and low life satisfaction loaded on Factor I. Social engagement, spirituality, social networks, and extraversion loaded on Factor II. Only Factor I, re-named distress, showed measurement invariance across subgroups. Distress was associated with a 37% increased odds of self-reported CHD (odds ratio: 1.37; 95% confidence intervals: 1.25, 1.50; p-value < 0.0001). This effect did not differ by race or sex (interaction p-value = 0.43). This study identified two underlying latent constructs among a large range of psychosocial variables; only one, distress, was validly measured across race–sex subgroups. This construct was robustly related to prevalent CHD, highlighting the potential importance of latent constructs as predictors of cardiovascular disease. PMID:22347196

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

The effects of estrogens on learning in rats with chronic brain cholinergic deficiency in a Morris water test. Identification of the "passive swimming" component.

PubMed

Mukhina, T V; Lermontova, N N; Van'kin, G I; Oettel, M; P'chev, V K; Bachurin, S O

2004-03-01

Chronic decreases in brain cholinergic functions due to intraventricular administration of the neurotoxin AF64A were accompanied by increases in the latent period of locating an invisible platform during training of rats in a Morris water test, as compared with control sham-operated animals. Recordings of the animals' movement trajectories using a video camera along with an original computer program (Behavioral Vision) showed that administration of 17beta-estradiol and its synthetic analog J-861 (0.2 mg/kg p.o. daily for seven days before and 10 days after single intraventricular injections of AF64A) improved learning. The directivity of platform search trajectories was assessed quantitatively using a new parameter--trajectory straightness. Introduction of the "passive swimming" parameter allowed periods of immobility in water to be identified within the total latent period in animals after administration of AF64A; 17beta-estradiol but not J-861 "eliminated" these periods. The new parameters (especially trajectory straightness) allowed the ability to learn to be discriminated from decreases in mobility, including mobility losses due to study agents, in the Morris water test.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

PubMed Central

Kadurin, Artur; Aliper, Alexander; Kazennov, Andrey; Mamoshina, Polina; Vanhaelen, Quentin; Khrabrov, Kuzma; Zhavoronkov, Alex

2017-01-01

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties. PMID:28029644

The differential impact of discrimination on health among Black and White women.

PubMed

Versey, H Shellae; Curtin, Nicola

2016-05-01

Despite a large body of research examining the impact of discrimination on health, the ways in which perceived discrimination may lead to disparate health outcomes through a sense of self and system consciousness is less understood. The current paper is concerned with both mental and physical health consequences of discrimination, as well as mediating pathways among African American and White women. Indirect effects analyses examine mediating paths from discrimination to health outcomes via structural awareness and self-esteem, using data from the Women's Life Path Study (N = 237). Our findings suggest that discrimination is both directly and indirectly associated with health outcomes for both Black and White women, mediated by individual (self-esteem) and group-level (structural awareness) processes. Evidence from this study indicates that discrimination is associated with heightened structural awareness, as well as lower self-esteem - both of which are related to poorer health. Discrimination negatively affected health across three domains, although the mechanisms varied somewhat for Black and White women. Broad implications of this research for interdisciplinary scholarship on the effects of discrimination on health and health disparities are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Stigma and discrimination against people living with HIV by healthcare providers, Southwest Ethiopia

PubMed Central

2012-01-01

Background Stigma and discrimination against people living with human immunodeficiency virus (HIV) are obstacles in the way of effective responses to HIV. Understanding the extent of stigma / discrimination and the underlying causes is necessary for developing strategies to reduce them. This study was conducted to explore stigma and discrimination against PLHIV amongst healthcare providers in Jimma zone, Southwest Ethiopia. Methods A cross-sectional study, employing quantitative and qualitative methods, was conducted in 18 healthcare institutions of Jimma zone, during March 14 to April 14, 2011. A total of 255 healthcare providers responded to questionnaires asking about sociodemographic characteristics, HIV knowledge, perceived institutional support and HIV-related stigma and discrimination. Factor analysis was employed to create measurement scales for stigma and factor scores were used in one way analysis of variance (ANOVA), T-tests, Pearson’s correlation and multiple linear regression analyses. Qualitative data collected using key-informant interviews and Focus Group Discussions (FGDs) were employed to triangulate with the findings from the quantitative survey. Results Mean stigma scores (as the percentages of maximum scale scores) were: 66.4 for the extra precaution scale, 52.3 for the fear of work-related HIV transmission, 49.4 for the lack of feelings of safety, 39.0 for the value-driven stigma, 37.4 for unethical treatment of PLHIV, 34.4 for discomfort around PLHIV and 31.1 for unofficial disclosure. Testing and disclosing test results without consent, designating HIV clients and unnecessary referral to other healthcare institutions and refusal to treat clients were identified. Having in-depth HIV knowledge, the perception of institutional support, attending training on stigma and discrimination, educational level of degree or higher, high HIV case loads, the presence of ART service in the healthcare facility and claiming to be non-religious were negative predictors of stigma and discrimination as measured by the seven latent factors. Conclusions Higher levels of stigma and discrimination against PLHIV were associated with lack of in-depth knowledge on HIV and orientation about policies against stigma and discrimination. Hence, we recommend health managers to ensure institutional support through availing of clear policies and guidelines and the provision of appropriate training on the management of HIV/AIDS. PMID:22794201

A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects

PubMed Central

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-01-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness. PMID:24694926

Validity of proposed DSM-5 diagnostic criteria for nicotine use disorder: results from 734 Israeli lifetime smokers

PubMed Central

Shmulewitz, D.; Wall, M.M.; Aharonovich, E.; Spivak, B.; Weizman, A.; Frisch, A.; Grant, B. F.; Hasin, D.

2013-01-01

Background The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) proposes aligning nicotine use disorder (NUD) criteria with those for other substances, by including the current DSM fourth edition (DSM-IV) nicotine dependence (ND) criteria, three abuse criteria (neglect roles, hazardous use, interpersonal problems) and craving. Although NUD criteria indicate one latent trait, evidence is lacking on: (1) validity of each criterion; (2) validity of the criteria as a set; (3) comparative validity between DSM-5 NUD and DSM-IV ND criterion sets; and (4) NUD prevalence. Method Nicotine criteria (DSM-IV ND, abuse and craving) and external validators (e.g. smoking soon after awakening, number of cigarettes per day) were assessed with a structured interview in 734 lifetime smokers from an Israeli household sample. Regression analysis evaluated the association between validators and each criterion. Receiver operating characteristic analysis assessed the association of the validators with the DSM-5 NUD set (number of criteria endorsed) and tested whether DSM-5 or DSM-IV provided the most discriminating criterion set. Changes in prevalence were examined. Results Each DSM-5 NUD criterion was significantly associated with the validators, with strength of associations similar across the criteria. As a set, DSM-5 criteria were significantly associated with the validators, were significantly more discriminating than DSM-IV ND criteria, and led to increased prevalence of binary NUD (two or more criteria) over ND. Conclusions All findings address previous concerns about the DSM-IV nicotine diagnosis and its criteria and support the proposed changes for DSM-5 NUD, which should result in improved diagnosis of nicotine disorders. PMID:23312475

A window into the intoxicated mind? Speech as an index of psychoactive drug effects.

PubMed

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-09-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique 'window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; 'ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.

Magnetic resonance metabolic profiling of estrogen receptor-positive breast cancer: correlation with currently used molecular markers

PubMed Central

Koo, Ja Seung; Kim, Siwon; Park, Vivian Youngjean; Kim, Eun-Kyung; Kim, Suhkmann; Kim, Min Jung

2017-01-01

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P<0.01, and P <0.01, respectively). The high Ki-67 group showed higher levels of glutamate than the low Ki-67 group without statistical significance. Luminal B cancers showed higher levels of glycine (P=0.01) than luminal A cancers. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the subgroups according to HER2 and Ki-67 status, and luminal type. This study showed that the metabolic profiles of CNB samples assessed by HR-MAS MRS can be used to detect potential prognostic biomarkers as well as to understand the difference in metabolic mechanism among subtypes of ER-positive breast cancer. PMID:28969000

Protein biomarkers in vernix with potential to predict the development of atopic eczema in early childhood

PubMed Central

Holm, T; Rutishauser, D; Kai-Larsen, Y; Lyutvinskiy, Y; Stenius, F; Zubarev, R A; Agerberth, B; Alm, J; Scheynius, A

2014-01-01

Background Atopic eczema (AE) is a chronic inflammatory skin disease, which has increased in prevalence. Evidence points toward lifestyle as a major risk factor. AE is often the first symptom early in life later followed by food allergy, asthma, and allergic rhinitis. Thus, there is a great need to find early, preferentially noninvasive, biomarkers to identify individuals that are predisposed to AE with the goal to prevent disease development. Objective To investigate whether the protein abundances in vernix can predict later development of AE. Methods Vernix collected at birth from 34 newborns within the Assessment of Lifestyle and Allergic Disease During INfancy (ALADDIN) birth cohort was included in the study. At 2 years of age, 18 children had developed AE. Vernix proteins were identified and quantified with liquid chromatography coupled to tandem mass spectrometry. Results We identified and quantified 203 proteins in all vernix samples. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was found with R2 = 0.85, Q2 = 0.39, and discrimination power between the AE and healthy group of 73.5%. Polyubiquitin-C and calmodulin-like protein 5 showed strong negative correlation to the AE group, with a correlation coefficient of 0.73 and 0.68, respectively, and a P-value of 8.2 E-7 and 1.8 E-5, respectively. For these two proteins, the OPLS-DA model showed a prediction accuracy of 91.2%. Conclusion The protein abundances in vernix, and particularly that of polyubiquitin-C and calmodulin-like protein 5, are promising candidates as biomarkers for the identification of newborns predisposed to develop AE. PMID:24205894

Soft Corals Biodiversity in the Egyptian Red Sea: A Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown Species.

PubMed

Farag, Mohamed A; Porzel, Andrea; Al-Hammady, Montasser A; Hegazy, Mohamed-Elamir F; Meyer, Achim; Mohamed, Tarik A; Westphal, Hildegard; Wessjohann, Ludger A

2016-04-01

Marine life has developed unique metabolic and physiologic capabilities and advanced symbiotic relationships to survive in the varied and complex marine ecosystems. Herein, metabolite composition of the soft coral genus Sarcophyton was profiled with respect to its species and different habitats along the coastal Egyptian Red Sea via (1)H NMR and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) large-scale metabolomics analyses. The current study extends the application of comparative secondary metabolite profiling from plants to corals revealing for metabolite compositional differences among its species via a comparative MS and NMR approach. This was applied for the first time to investigate the metabolism of 16 Sarcophyton species in the context of their genetic diversity or growth habitat. Under optimized conditions, we were able to simultaneously identify 120 metabolites including 65 diterpenes, 8 sesquiterpenes, 18 sterols, and 15 oxylipids. Principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS) were used to define both similarities and differences among samples. For a compound based classification of coral species, UPLC-MS was found to be more effective than NMR. The main differentiations emanate from cembranoids and oxylipids. The specific metabolites that contribute to discrimination between soft corals of S. ehrenbergi from the three different growing habitats also belonged to cembrane type diterpenes, with aquarium S. ehrenbergi corals being less enriched in cembranoids compared to sea corals. PCA using either NMR or UPLC-MS data sets was found equally effective in predicting the species origin of unknown Sarcophyton. Cyclopropane containing sterols observed in abundance in corals may act as cellular membrane protectant against the action of coral toxins, that is, cembranoids.

Characteristic glycopeptides associated with extreme human longevity identified through plasma glycoproteomics.

PubMed

Miura, Yuri; Hashii, Noritaka; Ohta, Yuki; Itakura, Yoko; Tsumoto, Hiroki; Suzuki, Junya; Takakura, Daisuke; Abe, Yukiko; Arai, Yasumichi; Toyoda, Masashi; Kawasaki, Nana; Hirose, Nobuyoshi; Endo, Tamao

2018-06-01

Glycosylation is highly susceptible to changes of the physiological conditions, and accordingly, is a potential biomarker associated with several diseases and/or longevity. Semi-supercentenarians (SSCs; older than 105 years) are thought to be a model of human longevity. Thus, we performed glycoproteomics using plasma samples of SSCs, and identified proteins and conjugated N-glycans that are characteristic of extreme human longevity. Plasma proteins from Japanese semi-supercentenarians (SSCs, 106-109 years), aged controls (70-88 years), and young controls (20-38 years) were analysed by using lectin microarrays and liquid chromatography/mass spectrometry (LC/MS). Peak area ratios of glycopeptides to corresponding normalising peptides were subjected to orthogonal projections to latent structures discriminant analysis (OPLS-DA). Furthermore, plasma levels of clinical biomarkers were measured. We found two lectins such as Phaseolus vulgaris, and Erythrina cristagalli (ECA), of which protein binding were characteristically increased in SSCs. Peak area ratios of ECA-enriched glycopeptides were successfully discriminated between SSCs and controls using OPLS-DA, and indicated that tri-antennary and sialylated N-glycans of haptoglobin at Asn207 and Asn211 sites were characterized in SSCs. Sialylated glycans of haptoglobin are a potential biomarker of several diseases, such as hepatocellular carcinoma, liver cirrhosis, and IgA-nephritis. However, the SSCs analysed here did not suffer from these diseases. Tri-antennary and sialylated N-glycans on haptoglobin at the Asn207 and Asn211 sites were abundant in SSCs and characteristic of extreme human longevity. We found abundant glycans in SSCs, which may be associated with human longevity. Copyright © 2018 Elsevier B.V. All rights reserved.

6C.04: INTEGRATED SNP ANALYSIS AND METABOLOMIC PROFILES OF METABOLIC SYNDROME.

PubMed

Marrachelli, V; Monleon, D; Morales, J M; Rentero, P; Martínez, F; Chaves, F J; Martin-Escudero, J C; Redon, J

2015-06-01

Metabolic syndrome (MS) has become a health and financial burden worldwide. Susceptibility of genetically determined metabotype of MS has not yet been investigated. We aimed to identify a distinctive metabolic profile of blood serum which might correlates to the early detection of the development of MS associated to genetic polymorphism. We applied high resolution NMR spectroscopy to profile blood serum from patients without MS (n = 945) or with (n = 291). Principal component analysis (PCA) and projection to latent structures for discriminant analysis (PLS-DA) were applied to NMR spectral datasets. Results were cross-validated using the Venetian Blinds approach. Additionally, five SNPs previously associated with MS were genotyped with SNPlex and tested for associations between the metabolic profiles and the genetic variants. Statistical analysis was performed using in-house MATLAB scripts and the PLS Toolbox statistical multivariate analysis library. Our analysis provided a PLS-DA Metabolic Syndrome discrimination model based on NMR metabolic profile (AUC = 0.86) with 84% of sensitivity and 72% specificity. The model identified 11 metabolites differentially regulated in patients with MS. Among others, fatty acids, glucose, alanine, hydroxyisovalerate, acetone, trimethylamine, 2-phenylpropionate, isobutyrate and valine, significantly contributed to the model. The combined analysis of metabolomics and SNP data revealed an association between the metabolic profile of MS and genes polymorphism involved in the adiposity regulation and fatty acids metabolism: rs2272903_TT (TFAP2B), rs3803_TT (GATA2), rs174589_CC (FADS2) and rs174577_AA (FADS2). In addition, individuals with the rs2272903-TT genotype seem to develop MS earlier than general population. Our study provides new insights on the metabolic alterations associated with a MS high-risk genotype. These results could help in future development of risk assessment and predictive models for subclinical cardiovascular disease.

Virtual Levels and Role Models: N-Level Structural Equations Model of Reciprocal Ratings Data.

PubMed

Mehta, Paras D

2018-01-01

A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework. NL-SEM introduces novel constructs such as 'virtual levels' that allows a natural specification of latent variable SRMs. An empirical application of an explanatory SRM for personality using xxM, a software package implementing NL-SEM is presented. Results show that person perceptions are an integral aspect of personality. Methodological implications of NL-SEM for the analyses of an emerging class of contextual- and relational-SEMs are discussed.

Men and women are from Earth: examining the latent structure of gender.

PubMed

Carothers, Bobbi J; Reis, Harry T

2013-02-01

Taxometric methods enable determination of whether the latent structure of a construct is dimensional or taxonic (nonarbitrary categories). Although sex as a biological category is taxonic, psychological gender differences have not been examined in this way. The taxometric methods of mean above minus below a cut, maximum eigenvalue, and latent mode were used to investigate whether gender is taxonic or dimensional. Behavioral measures of stereotyped hobbies and physiological characteristics (physical strength, anthropometric measurements) were examined for validation purposes, and were taxonic by sex. Psychological indicators included sexuality and mating (sexual attitudes and behaviors, mate selectivity, sociosexual orientation), interpersonal orientation (empathy, relational-interdependent self-construal), gender-related dispositions (masculinity, femininity, care orientation, unmitigated communion, fear of success, science inclination, Big Five personality), and intimacy (intimacy prototypes and stages, social provisions, intimacy with best friend). Constructs were with few exceptions dimensional, speaking to Spence's (1993) gender identity theory. Average differences between men and women are not under dispute, but the dimensionality of gender indicates that these differences are inappropriate for diagnosing gender-typical psychological variables on the basis of sex. (c) 2013 APA, all rights reserved.

Metric and structural equivalence of core cognitive abilities measured with the Wechsler Adult Intelligence Scale-III in the United States and Australia.

PubMed

Bowden, Stephen C; Lissner, Dianne; McCarthy, Kerri A L; Weiss, Lawrence G; Holdnack, James A

2007-10-01

Equivalence of the psychological model underlying Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) scores obtained in the United States and Australia was examined in this study. Examination of metric invariance involves testing the hypothesis that all components of the measurement model relating observed scores to latent variables are numerically equal in different samples. The assumption of metric invariance is necessary for interpretation of scores derived from research studies that seek to generalize patterns of convergent and divergent validity and patterns of deficit or disability. An Australian community volunteer sample was compared to the US standardization data. A pattern of strict metric invariance was observed across samples. In addition, when the effects of different demographic characteristics of the US and Australian samples were included, structural parameters reflecting values of the latent cognitive variables were found not to differ. These results provide important evidence for the equivalence of measurement of core cognitive abilities with the WAIS-III and suggest that latent cognitive abilities in the US and Australia do not differ.

Rapid Exploitation and Analysis of Documents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buttler, D J; Andrzejewski, D; Stevens, K D

Analysts are overwhelmed with information. They have large archives of historical data, both structured and unstructured, and continuous streams of relevant messages and documents that they need to match to current tasks, digest, and incorporate into their analysis. The purpose of the READ project is to develop technologies to make it easier to catalog, classify, and locate relevant information. We approached this task from multiple angles. First, we tackle the issue of processing large quantities of information in reasonable time. Second, we provide mechanisms that allow users to customize their queries based on latent topics exposed from corpus statistics. Third,more » we assist users in organizing query results, adding localized expert structure over results. Forth, we use word sense disambiguation techniques to increase the precision of matching user generated keyword lists with terms and concepts in the corpus. Fifth, we enhance co-occurrence statistics with latent topic attribution, to aid entity relationship discovery. Finally we quantitatively analyze the quality of three popular latent modeling techniques to examine under which circumstances each is useful.« less

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

PubMed

Golay, Philippe; Reverte, Isabelle; Rossier, Jérôme; Favez, Nicolas; Lecerf, Thierry

2013-06-01

The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores. PsycINFO Database Record (c) 2013 APA, all rights reserved.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Assessing the fit of the Dysphoric Arousal model across two nationally representative epidemiological surveys: The Australian NSMHWB and the United States NESARC.

PubMed

Armour, Cherie; Carragher, Natacha; Elhai, Jon D

2013-01-01

Since the initial inclusion of PTSD in the DSM nomenclature, PTSD symptomatology has been distributed across three symptom clusters. However, a wealth of empirical research has concluded that PTSD's latent structure is best represented by one of two four-factor models: Numbing or Dysphoria. Recently, a newly proposed five-factor Dysphoric Arousal model, which separates the DSM-IV's Arousal cluster into two factors of Anxious Arousal and Dysphoric Arousal, has gathered support across a variety of trauma samples. To date, the Dysphoric Arousal model has not been assessed using nationally representative epidemiological data. We employed confirmatory factor analysis to examine PTSD's latent structure in two independent population based surveys from American (NESARC) and Australia (NSWHWB). We specified and estimated the Numbing model, the Dysphoria model, and the Dysphoric Arousal model in both samples. Results revealed that the Dysphoric Arousal model provided superior fit to the data compared to the alternative models. In conclusion, these findings suggest that items D1-D3 (sleeping difficulties; irritability; concentration difficulties) represent a separate, fifth factor within PTSD's latent structure using nationally representative epidemiological data in addition to single trauma specific samples. Copyright © 2012 Elsevier Ltd. All rights reserved.

What Types of Pornography Do People Find Arousing and Do They Cluster? Assessing Types and Categories of Pornography in a Large-Scale Online Sample.

PubMed

Hald, Gert Martin; Štulhofer, Aleksandar

2016-09-01

Previous research on exposure to different types of pornography has primarily relied on analyses of millions of search terms and histories or on user exposure patterns within a given time period rather than the self-reported frequency of consumption. Further, previous research has almost exclusively relied on theoretical or ad hoc overarching categorizations of different types of pornography, when investigating patterns of pornography exposure, rather than latent structure analyses of these exposure patterns. In contrast, using a large sample of 18- to 40-year-old heterosexual and nonheterosexual Croatian men and women, this study investigated the self-reported frequency of using 27 different types of pornography and statistically explored their latent structures. The results showed substantial differences in consumption patterns across gender and sexual orientation. However, latent structure analyses of the 27 different types of pornography assessed suggested that although several categories of consumption were gender and sexual orientation specific, common categories across the different types of pornography could be established. Based on this finding, a five-item scale was proposed to indicate the use of nonmainstream (paraphilic) pornographic content, as this type of pornography has often been targeted in previous research. To the best of our knowledge, no similar measurement tool has been proposed before.

Intra-Sensor Variability Study of two BLS 900 Scintillometers

NASA Astrophysics Data System (ADS)

Thiem, Christina; Mauder, Matthias; Chwala, Christian; Bernhardt, Matthias; Kunstmann, Harald; Schulz, Karsten

2017-04-01

The latent heat flux is an important validation parameter for satellite measurements and a wide variety of hydrological and meteorological numerical models. Scintillometers can provide references for such validations due to their ability to spatially integrate turbulent fluxes. Large-aperture near-infrared scintillometers are capable of determining spatial averages of the structure parameter of temperature and the sensible heat flux over path lengths up to 5 km. One way to derive both sensible and latent heat flux is to use a combined optical and microwave scintillometer system. With only an optical scintillometer and additional measurements of ground heat flux and net radiation, the latent heat flux can be calculated from the residual of the energy balance. Studies have shown, however, that in certain cases measurements from the same types of scintillometers differ due to minute differences in construction. In order to prove the robustness of the measurements of two near-infrared scintillometers for future studies, we compared their observations and validated them by comparison to the sensible heat flux derived from an eddy covariance system. In this study two boundary layer scintillometers (BLS; BLS900, Scintec, Rottenburg, Germany) were installed in a central European valley as part of the TERENO preAlpine observatory during the years 2013 and 2015. An independent measurement of the sensible and latent heat flux was obtained from a permanent eddy covariance system installed in the vicinity of the scintillometer path. The structure parameter of the refractive index and average sensible heat fluxes of both BLS units were compared with each other. In general, the BLS structure parameters correlated very well and the high correlation between the BLS-derived sensible heat fluxes and the eddy covariance-derived sensible heat fluxes encouraged further application of these scintillometers in separate experiments.

The Development of an Empirical Model of Mental Health Stigma in Adolescents.

PubMed

Silke, Charlotte; Swords, Lorraine; Heary, Caroline

2016-08-30

Research on mental health stigma in adolescents is hampered by a lack of empirical investigation into the theoretical conceptualisation of stigma, as well as by the lack of validated stigma measures. This research aims to develop a model of public stigma toward depression in adolescents and to use this model to empirically examine whether stigma is composed of three separate dimensions (Stereotypes, Prejudice and Discrimination), as is theoretically proposed. Adolescents completed self-report measures assessing their stigmatising responses toward a fictional peer with depression. An exploratory factor analysis (EFA; N=332) was carried out on 58-items, which proposed to measure aspects of stigma. A confirmatory factor analysis (CFA; N=236) was then carried out to evaluate the validity of the observed stigma model. Finally, higher-order CFAs were conducted in order to assess whether the observed model supported the tripartite conceptualisation of stigma. The EFA returned a seven-factor model of stigma. These factors were designated as Dangerousness, Warmth & Competency, Responsibility, Negative Attributes, Prejudice, Classroom Discrimination and Friendship Discrimination. The CFA supported the goodness-of-fit of this seven-factor model. The higher-order CFAs indicated that these seven factors represented the latent constructs of, Stereotypes, Prejudice and Discrimination, which in turn represented Stigma. Overall, results support the tripartite conceptualisation of stigma and suggest that measurements of mental health stigma in adolescents should include assessments of all three dimensions. These results also highlight the importance of establishing valid and reliable measures for assessing stigma in adolescents. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Ants detect but do not discriminate diseased workers within their nest

NASA Astrophysics Data System (ADS)

Leclerc, Jean-Baptiste; Detrain, Claire

2016-08-01

Social insects have evolved an array of individual and social behaviours that limit pathogen entrance and spread within the colony. The detection of ectoparasites or of fungal spores on a nestmate body triggers their removal by allogrooming and appears as a primary component of social prophylaxis. However, in the case of fungal infection, one may wonder whether ant workers are able to detect, discriminate and keep at bay diseased nestmates that have no spores over their cuticle but which constitute a latent sanitary risk due to post-mortem corpse sporulation. Here, we investigate the ability of Myrmica rubra workers to detect and discriminate a healthy from a diseased nestmate infected by the entomopathogen Metarhizium anisopliae. During dyadic encounters in a neutral location, workers were more aggressive towards isolated sick nestmates on the 3rd post-infection day. However, no such detection or discrimination of fungus-infected nestmates occurred in a social context inside the nest or at the nest entrance. Gatekeepers never actively rejected incoming diseased nestmates that rather spontaneously isolated themselves outside the nest. Our study reveals that ant workers may detect health-dependent cues and that their `acceptance level' of sick nestmates is tunable depending on the social context. This raises questions about possible trade-offs between a social closure to pathogens and risks of erroneous rejection of healthy nestmates. Social isolation of moribund ants also appears as a widespread prophylactic strategy of social insects allowing them to reduce exposure to pathogens and to spare costs associated with the management of infected individuals.

Structural equation models to estimate risk of infection and tolerance to bovine mastitis.

PubMed

Detilleux, Johann; Theron, Léonard; Duprez, Jean-Noël; Reding, Edouard; Humblet, Marie-France; Planchon, Viviane; Delfosse, Camille; Bertozzi, Carlo; Mainil, Jacques; Hanzen, Christian

2013-03-06

One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples. Structural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens. We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but not resistant to mastitis. Selection should aim at improved resistance to infection by mastitis pathogens, although further investigations are needed due to the limitations of the data used in this study.

Cross-domain latent space projection for person re-identification

NASA Astrophysics Data System (ADS)

Pu, Nan; Wu, Song; Qian, Li; Xiao, Guoqiang

2018-04-01

In this paper, we research the problem of person re-identification and propose a cross-domain latent space projection (CDLSP) method to address the problems of the absence or insufficient labeled data in the target domain. Under the assumption that the visual features in the source domain and target domain share the similar geometric structure, we transform the visual features from source domain and target domain to a common latent space by optimizing the object function defined in the manifold alignment method. Moreover, the proposed object function takes into account the specific knowledge in the re-id with the aim to improve the performance of re-id under complex situations. Extensive experiments conducted on four benchmark datasets show the proposed CDLSP outperforms or is competitive with stateof- the-art methods for person re-identification.

Models of Latent Tuberculosis: Their Salient Features, Limitations, and Development

PubMed Central

Patel, Kamlesh; Jhamb, Sarbjit Singh; Singh, Prati Pal

2011-01-01

Latent tuberculosis is a subclinical condition caused by Mycobacterium tuberculosis, which affects about one-third of the population across the world. To abridge the chemotherapy of tuberculosis, it is necessary to have active drugs against latent form of M. tuberculosis. Therefore, it is imperative to devise in vitro and models of latent tuberculosis to explore potential drugs. In vitro models such as hypoxia, nutrient starvation, and multiple stresses are based on adverse conditions encountered by bacilli in granuloma. Bacilli experience oxygen depletion condition in hypoxia model, whereas the nutrient starvation model is based on deprivation of total nutrients from a culture medium. In the multiple stress model dormancy is induced by more than one type of stress. In silico mathematical models have also been developed to predict the interactions of bacilli with the host immune system and to propose structures for potential anti tuberculosis compounds. Besides these in vitro and in silico models, there are a number of in vivo animal models like mouse, guinea pig, rabbit, etc. Although they simulate human latent tuberculosis up to a certain extent but do not truly replicate human infection. All these models have their inherent merits and demerits. However, there is no perfect model for latent tuberculosis. Therefore, it is imperative to upgrade and refine existing models or develop a new model. However, battery of models will always be a better alternative to any single model as they will complement each other by overcoming their limitations. PMID:22219558

Detection of latent fingerprint hidden beneath adhesive tape by optical coherence tomography.

PubMed

Zhang, Ning; Wang, Chengming; Sun, Zhenwen; Li, Zhigang; Xie, Lanchi; Yan, Yuwen; Xu, Lei; Guo, Jingjing; Huang, Wei; Li, Zhihui; Xue, Jing; Liu, Huan; Xu, Xiaojing

2018-06-01

Adhesive tape is one type of common item which can be encountered in criminal cases involving rape, murder, kidnapping and explosives. It is often the case that a suspect deposits latent fingerprints on the sticky side of adhesive tape material when tying up victims, manufacturing improvised explosive devices or packaging illegal goods. However, the adhesive tapes found at crime scenes are usually stuck together or attached to a certain substrate, and thus the latent fingerprints may be hidden beneath the tapes. Current methods to detect latent fingerprint hidden beneath adhesive tape need to peel it off first and then apply physical or chemical methods to develop the fingerprint, which undergo complicated procedures and would affect the original condition of latent print. Optical coherence tomography (OCT) is a novel applied techniques in forensics which enables obtaining cross-sectional structure with the advantages of non-invasive, in-situ, high resolution and high speed. In this paper, a custom-built spectral-domain OCT (SD-OCT) system with a hand-held probe was employed to detect fingerprints hidden beneath different types of adhesive tapes. Three-dimensional (3D) OCT reconstructions were performed and the en face images were presented to reveal the hidden fingerprints. The results demonstrate that OCT is a promising tool for rapidly detecting and recovering high quality image of latent fingerprint hidden beneath adhesive tape without any changes to the original state and preserve the integrity of the evidence. Copyright © 2018 Elsevier B.V. All rights reserved.

The job content questionnaire in various occupational contexts: applying a latent class model

PubMed Central

Santos, Kionna Oliveira Bernardes; de Araújo, Tânia Maria; Karasek, Robert

2017-01-01

Objective To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. Methods We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into account the latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Results Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. Conclusion The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers—groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. PMID:28515185

Aggressiveness as a latent personality trait of domestic dogs: Testing local independence and measurement invariance.

PubMed

Goold, Conor; Newberry, Ruth C

2017-01-01

Studies of animal personality attempt to uncover underlying or "latent" personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g., factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in animal personality are: i) behavioural variables are independent (i.e., uncorrelated) conditional on the latent personality traits they reflect (local independence), and ii) personality traits are associated with behavioural variables in the same way across individuals or groups of individuals (measurement invariance). We tested these assumptions using observations of aggression in four age classes (4-10 months, 10 months-3 years, 3-6 years, over 6 years) of male and female shelter dogs (N = 4,743) in 11 different contexts. A structural equation model supported the hypothesis of two positively correlated personality traits underlying aggression across contexts: aggressiveness towards people and aggressiveness towards dogs (comparative fit index: 0.96; Tucker-Lewis index: 0.95; root mean square error of approximation: 0.03). Aggression across contexts was moderately repeatable (towards people: intraclass correlation coefficient (ICC) = 0.479; towards dogs: ICC = 0.303). However, certain contexts related to aggressiveness towards people (but not dogs) shared significant residual relationships unaccounted for by latent levels of aggressiveness. Furthermore, aggressiveness towards people and dogs in different contexts interacted with sex and age. Thus, sex and age differences in displays of aggression were not simple functions of underlying aggressiveness. Our results illustrate that the robustness of traits in latent variable models must be critically assessed before making conclusions about the effects of, or factors influencing, animal personality. Our findings are of concern because inaccurate "aggressive personality" trait attributions can be costly to dogs, recipients of aggression and society in general.

Transcriptional profiles discriminate patients with pulmonary tuberculosis from non-tuberculous individuals depending on the presence of non-insulin diabetes mellitus.

PubMed

Serrano, Carmen J; Cuevas-Córdoba, Betzaida; Macías-Segura, Noé; González-Curiel, Rosa Angélica; Martínez-Balderas, Víctor Yordani; Enciso-Moreno, Leonor; Small, Peter; Hernández-Pando, Rogelio; Enciso-Moreno, José Antonio

2016-01-01

Our objective was to identify transcriptional biomarkers in peripheral blood mononuclear cells (PBMC) that discriminate individuals with latent tuberculosis infection (LTBI) from those with pulmonary tuberculosis (PTB) in subjects with non-insulin-dependent diabetes mellitus (NIDDM) and in individuals without NIDDM. Using gene expression microarrays we identified differentially expressed genes from lungs of mice infected with Mycobacterium tuberculosis (Mtb) or a mutant (ΔsigH) representing a non-inflammatory model. Genes expressed in blood, with inflammatory related functions were evaluated in humans by RT-qPCR. NCF1 and ORM transcripts have the better discriminatory capacity to identify PTB subjects from LTBI and non-infected controls (NICs) independently of the presence of NIDDM. The sequential evaluation of the mRNA levels of NCF1 and ORM as multiple diagnostic tests showed 95% Sensitivity (Se) and 80% Specificity (Sp). In addition, FPR2 promises to be a good biomarker for the PTB detection in subjects with NIDDM (Se=100%; Sp=90%). Copyright © 2015 Elsevier Inc. All rights reserved.

Towards reconstruction of overlapping fingerprints using plasma spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Jun-Ho; Choi, Soo-Jin; Yoh, Jack J.

2017-08-01

Chemical analysis is commonly used in the field of forensic science where the precise discrimination of primary evidence is of significant importance. Laser-Induced Breakdown Spectroscopy (LIBS) exceeds other spectroscopic methods in terms of the time required for pre- and post-sample preparation, the insensitivity to sample phase state be it solid, liquid, or gas, and the detection of two-dimensional spectral mapping from real time point measurements. In this research, fingerprint samples on various surface materials are considered in the chemical detection and reconstruction of fingerprints using the two-dimensional LIBS technique. Strong and distinct intensities of specific wavelengths represent visible ink, natural secretion of sweat, and contaminants from the environment, all of which can be present in latent fingerprints. The particular aim of the work presented here is to enhance the precision of the two-dimensional recreation of the fingerprints present on metal, plastic, and artificially prepared soil surface using LIBS with principal component analysis. By applying a distinct wavelength discrimination for two overlapping fingerprint samples, separation into two non-identical chemical fingerprints was successfully performed.

Depressive symptoms and externalizing behaviors among Hispanic immigrant adolescents: Examining longitudinal effects of cultural stress.

PubMed

Cano, Miguel Ángel; Schwartz, Seth J; Castillo, Linda G; Romero, Andrea J; Huang, Shi; Lorenzo-Blanco, Elma I; Unger, Jennifer B; Zamboanga, Byron L; Des Rosiers, Sabrina E; Baezconde-Garbanati, Lourdes; Lizzi, Karina M; Soto, Daniel W; Oshri, Assaf; Villamar, Juan Andres; Pattarroyo, Monica; Szapocznik, José

2015-07-01

This study examined longitudinal effects of cultural stress (a latent factor comprised of bicultural stress, ethnic discrimination, and negative context of reception) on depressive symptoms and a range of externalizing behaviors among recently (≤5 years in the U.S. at baseline) immigrated Hispanic adolescents. A sample of 302 adolescents (53% boys; mean age 14.51 years) completed baseline measures of perceived ethnic discrimination, bicultural stress, and perceived negative context of reception; and outcome measures of depressive symptoms, cigarette smoking, alcohol use, aggressive behavior, and rule-breaking behavior six months post-baseline. A path analysis indicated that higher cultural stress scores predicted higher levels of all outcomes. These effects were consistent across genders, but varied by study site. Specifically, higher cultural stress scores increased depressive symptoms among participants in Miami, but not in Los Angeles. Findings suggest that cultural stress is a clinically relevant predictor of depressive symptoms and externalizing behaviors among Hispanic immigrant adolescents. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

CREB and the CRTC co-activators: sensors for hormonal and metabolic signals

PubMed Central

Altarejos, Judith Y.; Montminy, Marc

2014-01-01

The cyclic AMP-responsive element-binding protein (CREB) is phosphorylated in response to a wide variety of signals, yet target gene transcription is only increased in a subset of cases. Recent studies indicate that CREB functions in concert with a family of latent cytoplasmic co-activators called cAMP-regulated transcriptional co-activators (CRTCs), which are activated through dephosphorylation. A dual requirement for CREB phosphorylation and CRTC dephosphorylation is likely to explain how these activator–co-activator cognates discriminate between different stimuli. Following their activation, CREB and CRTCs mediate the effects of fasting and feeding signals on the expression of metabolic programmes in insulin-sensitive tissues. PMID:21346730

Rotation invariant deep binary hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Dai, Lai; Liu, Jianming; Jiang, Aiwen

2017-07-01

In this paper, we study how to compactly represent image's characteristics for fast image retrieval. We propose supervised rotation invariant compact discriminative binary descriptors through combining convolutional neural network with hashing. In the proposed network, binary codes are learned by employing a hidden layer for representing latent concepts that dominate on class labels. A loss function is proposed to minimize the difference between binary descriptors that describe reference image and the rotated one. Compared with some other supervised methods, the proposed network doesn't have to require pair-wised inputs for binary code learning. Experimental results show that our method is effective and achieves state-of-the-art results on the CIFAR-10 and MNIST datasets.

Supervised Semantic Classification for Nuclear Proliferation Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

Simulating Statistical Power in Latent Growth Curve Modeling: A Strategy for Evaluating Age-Based Changes in Cognitive Resources

NASA Astrophysics Data System (ADS)

von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman

Variability across and within individuals is a fundamental property of adult age changes in behavior [20, 21, 24]. Some people seem young for their age, others seem old; shining examples of older individuals who maintained high levels of intellectual functioning well into very old age, such as Johann Wolfgang von Goethe or Sophocles, stand in contrast to individuals whose cognitive resources are depleted by the time they reach later adulthood. A similar contrast exists between different intellectual abilities. For example, if one looks at the speed needed to identify and discriminate between different percepts, one is likely to find monotonic decline after late adolescence and early adulthood.

Olfactory discrimination ability of Asian elephants (Elephas maximus) for structurally related odorants.

PubMed

Rizvanovic, Alisa; Amundin, Mats; Laska, Matthias

2013-02-01

Using a food-rewarded two-choice instrumental conditioning paradigm, we assessed the ability of Asian elephants, Elephas maximus, to discriminate between 2 sets of structurally related odorants. We found that the animals successfully discriminated between all 12 odor pairs involving members of homologous series of aliphatic 1-alcohols, n-aldehydes, 2-ketones, and n-carboxylic acids even when the stimuli differed from each other by only 1 carbon. With all 4 chemical classes, the elephants displayed a positive correlation between discrimination performance and structural similarity of odorants in terms of differences in carbon chain length. The animals also successfully discriminated between all 12 enantiomeric odor pairs tested. An analysis of odor structure-activity relationships suggests that a combination of molecular structural properties rather than a single molecular feature may be responsible for the discriminability of enantiomers. Compared with other species tested previously on the same sets of odor pairs (or on subsets thereof), the Asian elephants performed at least as well as mice and clearly better than human subjects, squirrel monkeys, pigtail macaques, South African fur seals, and honeybees. Further comparisons suggest that neither the relative nor the absolute size of the olfactory bulbs appear to be reliable predictors of between-species differences in olfactory discrimination capabilities. In contrast, we found a positive correlation between the number of functional olfactory receptor genes and the proportion of discriminable enantiomeric odor pairs. Taken together, the results of the present study support the notion that the sense of smell may play an important role in regulating the behavior of Asian elephants.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-01

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis.

PubMed

Yao, Sen; Li, Tao; Li, JieQing; Liu, HongGao; Wang, YuanZhong

2018-06-05

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms. Copyright © 2018 Elsevier B.V. All rights reserved.

Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2005-01-01

In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…