Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

Blind image deblurring based on trained dictionary and curvelet using sparse representation

NASA Astrophysics Data System (ADS)

Feng, Liang; Huang, Qian; Xu, Tingfa; Li, Shao

2015-04-01

Motion blur is one of the most significant and common artifacts causing poor image quality in digital photography, in which many factors resulted. In imaging process, if the objects are moving quickly in the scene or the camera moves in the exposure interval, the image of the scene would blur along the direction of relative motion between the camera and the scene, e.g. camera shake, atmospheric turbulence. Recently, sparse representation model has been widely used in signal and image processing, which is an effective method to describe the natural images. In this article, a new deblurring approach based on sparse representation is proposed. An overcomplete dictionary learned from the trained image samples via the KSVD algorithm is designed to represent the latent image. The motion-blur kernel can be treated as a piece-wise smooth function in image domain, whose support is approximately a thin smooth curve, so we employed curvelet to represent the blur kernel. Both of overcomplete dictionary and curvelet system have high sparsity, which improves the robustness to the noise and more satisfies the observer's visual demand. With the two priors, we constructed restoration model of blurred images and succeeded to solve the optimization problem with the help of alternating minimization technique. The experiment results prove the method can preserve the texture of original images and suppress the ring artifacts effectively.

Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties.

PubMed

Shao, Feng; Li, Kemeng; Lin, Weisi; Jiang, Gangyi; Yu, Mei; Dai, Qionghai

2015-10-01

Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.

Predictors of Asian American Adolescents' Suicide Attempts: A Latent Class Regression Analysis

ERIC Educational Resources Information Center

Wong, Y. Joel; Maffini, Cara S.

2011-01-01

Although suicide-related outcomes among Asian American adolescents are a serious public health problem in the United States, research in this area has been relatively sparse. To address this gap in the empirical literature, this study examined subgroups of Asian American adolescents for whom family, school, and peer relationships exerted…

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.

PubMed

Polcicová, Gabriela; Tino, Peter

2004-01-01

We introduce topographic versions of two latent class models (LCM) for collaborative filtering. Latent classes are topologically organized on a square grid. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. The variation in film rating patterns is modelled by multinomial and binomial distributions with varying independence assumptions. In the first stage of topographic LCM construction, self-organizing maps with neural field organized according to the LCM topology are employed. We apply our system to a large collection of user ratings for films. The system can provide useful visualization plots unveiling user preference patterns buried in the data, without loosing potential to be a good recommender model. It appears that multinomial distribution is most adequate if the model is regularized by tight grid topologies. Since we deal with probabilistic models of the data, we can readily use tools from probability and information theories to interpret and visualize information extracted by our system.

Latent feature decompositions for integrative analysis of multi-platform genomic data

PubMed Central

Gregory, Karl B.; Momin, Amin A.; Coombes, Kevin R.; Baladandayuthapani, Veerabhadran

2015-01-01

Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to, a glioblastoma multiforme dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features. PMID:26146492

Assimilation of Long-Range Lightning Data over the Pacific

DTIC Science & Technology

2011-09-30

convective rainfall analyses over the Pacific, and (iii) to improve marine prediction of cyclogenesis of both tropical and extratropical cyclones through...data over the North Pacific Ocean, refine the relationships between lightning and storm hydrometeor characteristics, and assimilate lightning...unresolved storm -scale areas of deep convection over the data-sparse open oceans. Diabatic heating sources, especially latent heat release in deep

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.

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

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…

A performance study of sparse Cholesky factorization on INTEL iPSC/860

NASA Technical Reports Server (NTRS)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

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.

When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints

DTIC Science & Technology

2013-06-16

Science Dept., University of California, Irvine, USA 92697. Email : a.anandkumar@uci.edu,mjanzami@uci.edu. Daniel Hsu and Sham Kakade are with...Microsoft Research New England, 1 Memorial Drive, Cambridge, MA 02142. Email : dahsu@microsoft.com, skakade@microsoft.com 1 a latent space dimensionality...Sparse coding for multitask and transfer learning. ArxXiv preprint, abs/1209.0738, 2012. [34] G.H. Golub and C.F. Van Loan. Matrix Computations. The

Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor with Continuous and Dichotomous Observed Variables

ERIC Educational Resources Information Center

Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo

2012-01-01

Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…

Causal Indicators Can Help to Interpret Factors

ERIC Educational Resources Information Center

Bentler, Peter M.

2016-01-01

The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

PubMed

Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

PubMed Central

Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching (Open Access)

DTIC Science & Technology

2013-10-03

the Stanford NLP Suite∗ to create an- notated dictionaries based on word morphologies ; the human descriptions provide the input. The predicted...keywords from the low level topic models are labeled through these dictionaries. For more than two POS for the same morphology , we prefer verbs, but other...redundancy particularly retaining subjects like “man,” “woman” etc. and verb morphologies (which otherwise stem to the same prefix) as proxies for ten

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

PubMed Central

Guo, Yanrong; Gao, Yaozong

2016-01-01

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks

PubMed Central

Krypotos, Angelos-Miltiadis; Beckers, Tom; Kindt, Merel; Wagenmakers, Eric-Jan

2015-01-01

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. PMID:25491372

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Inverse Ising problem in continuous time: A latent variable approach

NASA Astrophysics Data System (ADS)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

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…

A one- and two-layer model for estimating evapotranspiration with remotely sensed surface temperature and ground-based meteorological data over partial canopy cover

NASA Technical Reports Server (NTRS)

Kustas, William P.; Choudhury, Bhaskar J.; Kunkel, Kenneth E.

1989-01-01

Surface-air temperature differences are commonly used in a bulk resistance equation for estimating sensible heat flux (H), which is inserted in the one-dimensional energy balance equation to solve for the latent heat flux (LE) as a residual. Serious discrepancies between estimated and measured LE have been observed for partial-canopy-cover conditions, which are mainly attributed to inappropriate estimates of H. To improve the estimates of H over sparse canopies, one- and two-layer resistance models that account for some of the factors causing poor agreement are developed. The utility of the two models is tested with remotely sensed and micrometeorological data for a furrowed cotton field with 20 percent cover and a dry soil surface. It is found that the one-layer model performs better than the two-layer model when a theoretical bluff-body correction for heat transfer is used instead of an empirical adjustment; otherwise, the two-layer model is better.

Indian Summer Monsoon Drought 2009: Role of Aerosol and Cloud Microphysics

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

Hazra, Anupam; Taraphdar, Sourav; Halder, Madhuparna

2013-07-01

Cloud dynamics played a fundamental role in defining Indian summer monsoon (ISM) rainfall during drought in 2009. The anomalously negative precipitation was consistent with cloud properties. Although, aerosols inhibited the growth of cloud effective radius in the background of sparse water vapor, their role is secondary. The primary role, however, is played by the interactive feedback between cloud microphysics and dynamics owing to reduced efficient cloud droplet growth, lesser latent heating release and shortage of water content. Cloud microphysical processes were instrumental for the occurrence of ISM drought 2009.

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Goring, Simon

2017-01-01

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Inferring oscillatory modulation in neural spike trains

PubMed Central

Arai, Kensuke; Kass, Robert E.

2017-01-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak. PMID:28985231

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.

PubMed

Kim, Hyunsoo; Park, Haesun

2007-06-15

Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space. In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms. The software is available as supplementary material.

Estimation of sensible and latent heat flux from natural sparse vegetation surfaces using surface renewal

NASA Astrophysics Data System (ADS)

Zapata, N.; Martínez-Cob, A.

2001-12-01

This paper reports a study undertaken to evaluate the feasibility of the surface renewal method to accurately estimate long-term evaporation from the playa and margins of an endorreic salty lagoon (Gallocanta lagoon, Spain) under semiarid conditions. High-frequency temperature readings were taken for two time lags ( r) and three measurement heights ( z) in order to get surface renewal sensible heat flux ( HSR) values. These values were compared against eddy covariance sensible heat flux ( HEC) values for a calibration period (25-30 July 2000). Error analysis statistics (index of agreement, IA; root mean square error, RMSE; and systematic mean square error, MSEs) showed that the agreement between HSR and HEC improved as measurement height decreased and time lag increased. Calibration factors α were obtained for all analyzed cases. The best results were obtained for the z=0.9 m ( r=0.75 s) case for which α=1.0 was observed. In this case, uncertainty was about 10% in terms of relative error ( RE). Latent heat flux values were obtained by solving the energy balance equation for both the surface renewal ( LESR) and the eddy covariance ( LEEC) methods, using HSR and HEC, respectively, and measurements of net radiation and soil heat flux. For the calibration period, error analysis statistics for LESR were quite similar to those for HSR, although errors were mostly at random. LESR uncertainty was less than 9%. Calibration factors were applied for a validation data subset (30 July-4 August 2000) for which meteorological conditions were somewhat different (higher temperatures and wind speed and lower solar and net radiation). Error analysis statistics for both HSR and LESR were quite good for all cases showing the goodness of the calibration factors. Nevertheless, the results obtained for the z=0.9 m ( r=0.75 s) case were still the best ones.

Computing row and column counts for sparse QR and LU factorization

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

Gilbert, John R.; Li, Xiaoye S.; Ng, Esmond G.

2001-01-01

We present algorithms to determine the number of nonzeros in each row and column of the factors of a sparse matrix, for both the QR factorization and the LU factorization with partial pivoting. The algorithms use only the nonzero structure of the input matrix, and run in time nearly linear in the number of nonzeros in that matrix. They may be used to set up data structures or schedule parallel operations in advance of the numerical factorization. The row and column counts we compute are upper bounds on the actual counts. If the input matrix is strong Hall and theremore » is no coincidental numerical cancellation, the counts are exact for QR factorization and are the tightest bounds possible for LU factorization. These algorithms are based on our earlier work on computing row and column counts for sparse Cholesky factorization, plus an efficient method to compute the column elimination tree of a sparse matrix without explicitly forming the product of the matrix and its transpose.« less

Convex composite wavelet frame and total variation-based image deblurring using nonconvex penalty functions

NASA Astrophysics Data System (ADS)

Shen, Zhengwei; Cheng, Lishuang

2017-09-01

Total variation (TV)-based image deblurring method can bring on staircase artifacts in the homogenous region of the latent images recovered from the degraded images while a wavelet/frame-based image deblurring method will lead to spurious noise spikes and pseudo-Gibbs artifacts in the vicinity of discontinuities of the latent images. To suppress these artifacts efficiently, we propose a nonconvex composite wavelet/frame and TV-based image deblurring model. In this model, the wavelet/frame and the TV-based methods may complement each other, which are verified by theoretical analysis and experimental results. To further improve the quality of the latent images, nonconvex penalty function is used to be the regularization terms of the model, which may induce a stronger sparse solution and will more accurately estimate the relative large gradient or wavelet/frame coefficients of the latent images. In addition, by choosing a suitable parameter to the nonconvex penalty function, the subproblem that splits by the alternative direction method of multipliers algorithm from the proposed model can be guaranteed to be a convex optimization problem; hence, each subproblem can converge to a global optimum. The mean doubly augmented Lagrangian and the isotropic split Bregman algorithms are used to solve these convex subproblems where the designed proximal operator is used to reduce the computational complexity of the algorithms. Extensive numerical experiments indicate that the proposed model and algorithms are comparable to other state-of-the-art model and methods.

A latent class distance association model for cross-classified data with a categorical response variable.

PubMed

Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

2014-11-01

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

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.

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Blind image deconvolution using the Fields of Experts prior

NASA Astrophysics Data System (ADS)

Dong, Wende; Feng, Huajun; Xu, Zhihai; Li, Qi

2012-11-01

In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FoE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality.

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

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults

PubMed Central

Neufeld, Sharon; Jones, Peter B.; Fonagy, Peter; Bullmore, Edward T.; Dolan, Raymond J.; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M.

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed ‘distress’ and five ‘distress independent’ specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits. PMID:28403164

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults.

PubMed

St Clair, Michelle C; Neufeld, Sharon; Jones, Peter B; Fonagy, Peter; Bullmore, Edward T; Dolan, Raymond J; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed 'distress' and five 'distress independent' specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits.

Sparse nonnegative matrix factorization with ℓ0-constraints

PubMed Central

Peharz, Robert; Pernkopf, Franz

2012-01-01

Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ℓ1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ℓ0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ℓ0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. PMID:22505792

Highly parallel sparse Cholesky factorization

NASA Technical Reports Server (NTRS)

Gilbert, John R.; Schreiber, Robert

1990-01-01

Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.

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

Chow, Edmond

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Reactivation of Latent HIV-1 Expression by Engineered TALE Transcription Factors.

PubMed

Perdigão, Pedro; Gaj, Thomas; Santa-Marta, Mariana; Barbas, Carlos F; Goncalves, Joao

2016-01-01

The presence of replication-competent HIV-1 -which resides mainly in resting CD4+ T cells--is a major hurdle to its eradication. While pharmacological approaches have been useful for inducing the expression of this latent population of virus, they have been unable to purge HIV-1 from all its reservoirs. Additionally, many of these strategies have been associated with adverse effects, underscoring the need for alternative approaches capable of reactivating viral expression. Here we show that engineered transcriptional modulators based on customizable transcription activator-like effector (TALE) proteins can induce gene expression from the HIV-1 long terminal repeat promoter, and that combinations of TALE transcription factors can synergistically reactivate latent viral expression in cell line models of HIV-1 latency. We further show that complementing TALE transcription factors with Vorinostat, a histone deacetylase inhibitor, enhances HIV-1 expression in latency models. Collectively, these findings demonstrate that TALE transcription factors are a potentially effective alternative to current pharmacological routes for reactivating latent virus and that combining synthetic transcriptional activators with histone deacetylase inhibitors could lead to the development of improved therapies for latent HIV-1 infection.

Communication requirements of sparse Cholesky factorization with nested dissection ordering

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1989-01-01

Load distribution schemes for minimizing the communication requirements of the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems are presented. The total data traffic in factoring an n x n sparse symmetric positive definite matrix representing an n-vertex regular two-dimensional grid graph using n exp alpha, alpha not greater than 1, processors are shown to be O(n exp 1 + alpha/2). It is O(n), when n exp alpha, alpha not smaller than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal.

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…

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

PubMed

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2018-03-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. © 2017, The International Biometric Society.

Inferring network structure in non-normal and mixed discrete-continuous genomic data

PubMed Central

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2017-01-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. PMID:28437848

Chronic Stress Exposure and Generation Are Related to the P-Factor and Externalizing Specific Psychopathology in Youth.

PubMed

Snyder, Hannah R; Young, Jami F; Hankin, Benjamin L

2017-05-25

Psychopathology is posited to be transdiagnostically linked to chronic stress. Yet efforts to understand the specificity and directionality of these links have been sparse, and the ubiquitous comorbidity of psychopathology has made the seemingly nonspecific links between psychological disorders and chronic stress difficult to interpret. The current study used a latent dimensional bifactor model of psychopathology to account for comorbidity and a multiwave prospective design to disentangle temporal associations between psychopathology and chronic stress longitudinally during the critical adolescent period for psychopathology risk and stress reactivity. A community sample of 567 youth (55.5% female, age M = 11.8 at baseline, M = 15.1 at end of study) were followed prospectively for 3 years, with chronic stress assessed with the Youth Life Stress Interview and psychopathology symptoms assessed via both self and parent report. Exposure to chronic stress predicted what is common across forms of psychopathology (the p factor), which in turn predicted generation of chronic stress over time. After accounting for comorbidity via the p factor, externalizing behaviors also had specific transactional links to chronic stress, whereas links between internalizing psychopathology and chronic stress were completely accounted for by common psychopathology. The results provide the first direct evidence that chronic stress is transdiagnostically and reciprocally linked to psychopathology, during a critical youth period for psychopathology onset and stress reactivity.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Using Comparison Data to Differentiate Categorical and Dimensional Data by Examining Factor Score Distributions: Resolving the Mode Problem

ERIC Educational Resources Information Center

Ruscio, John; Walters, Glenn D.

2009-01-01

Factor-analytic research is common in the study of constructs and measures in psychological assessment. Latent factors can represent traits as continuous underlying dimensions or as discrete categories. When examining the distributions of estimated scores on latent factors, one would expect unimodal distributions for dimensional data and bimodal…

On the explaining-away phenomenon in multivariate latent variable models.

PubMed

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

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

Prevalence and risk factors for latent tuberculosis infection among healthcare workers in Nampula Central Hospital, Mozambique.

PubMed

Belo, Celso; Naidoo, Saloshni

2017-06-08

Healthcare workers in high tuberculosis burdened countries are occupationally exposed to the tuberculosis disease with uncomplicated and complicated tuberculosis on the increase among them. Most of them acquire Mycobacterium tuberculosis but do not progress to the active disease - latent tuberculosis infection. The objective of this study was to assess the prevalence and risk factors associated with latent tuberculosis infection among healthcare workers in Nampula Central Hospital, Mozambique. This cross-sectional study of healthcare workers was conducted between 2014 and 2015. Participants (n = 209) were administered a questionnaire on demographics and occupational tuberculosis exposure and had a tuberculin skin test administered. Multivariate linear and logistic regression tested for associations between independent variables and dependent outcomes (tuberculin skin test induration and latent tuberculosis infection status). The prevalence of latent tuberculosis infection was 34.4%. Latent tuberculosis infection was highest in those working for more than eight years (39.3%), those who had no BCG vaccination (39.6%) and were immunocompromised (78.1%). Being immunocompromised was significantly associated with latent tuberculosis infection (OR 5.97 [95% CI 1.89; 18.87]). Positive but non-significant associations occurred with working in the medical domain (OR 1.02 [95% CI 0.17; 6.37]), length of employment > eight years (OR 1.97 [95% CI 0.70; 5.53]) and occupational contact with tuberculosis patients (OR 1.24 [95% CI 0.47; 3.27]). Personal and occupational factors were positively associated with latent tuberculosis infection among healthcare workers in Mozambique.

Tuberculosis infection testing in HIV-positive men who have sex with men from Xi'an China.

PubMed

Xin, H N; Li, X W; Zhang, L; Li, Z; Zhang, H R; Yang, Y; Li, M F; Feng, B X; Li, H J; Gao, L

2017-02-01

In individuals with latent tuberculosis (TB) infection, those living with human immunodeficiency virus (HIV) had a 20-37 times higher risk of developing active TB compared to those without HIV infection. Systematic testing and treatment of latent TB infection are priorities in HIV-infected persons. In China, the prevalence of HIV infection in men who have sex with men (MSM) has gradually increased in the past decade. However, the prevalence of TB infection has been studied sparsely in HIV-infected MSM. Hence, we conducted a pilot study in MSM living with HIV infection in Xi'an city to evaluate TB infection status by means of interferon-γ release assay (IGRA). A total of 182 HIV-infected MSM were included in this study, the prevalence of IGRA positivity was observed to be 8·79% (16/182). IGRA quantitative results were not statistically influenced by the CD4 cell counts of the study participants. However, IGRA positivity was found to be lower than our previously reported data from the general population. This suggests that immunological deficiency might decrease the sensitivity of IGRA and thus increase the number of false negatives. Our primary results, suggesting systematic testing and treatment of latent TB infection together with active case-finding, were equally important for TB control in persons living with HIV infection.

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).

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

NASA Technical Reports Server (NTRS)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Mid-frequency MTF compensation of optical sparse aperture system.

PubMed

Zhou, Chenghao; Wang, Zhile

2018-03-19

Optical sparse aperture (OSA) can greatly improve the spatial resolution of optical system. However, because of its aperture dispersion and sparse, its mid-frequency modulation transfer function (MTF) are significantly lower than that of a single aperture system. The main focus of this paper is on the mid-frequency MTF compensation of the optical sparse aperture system. Firstly, the principle of the mid-frequency MTF decreasing and missing of optical sparse aperture are analyzed. This paper takes the filling factor as a clue. The method of processing the mid-frequency MTF decreasing with large filling factor and method of compensation mid-frequency MTF with small filling factor are given respectively. For the MTF mid-frequency decreasing, the image spatial-variant restoration method is proposed to restore the mid-frequency information in the image; for the mid-frequency MTF missing, two images obtained by two system respectively are fused to compensate the mid-frequency information in optical sparse aperture image. The feasibility of the two method are analyzed in this paper. The numerical simulation of the system and algorithm of the two cases are presented using Zemax and Matlab. The results demonstrate that by these two methods the mid-frequency MTF of OSA system can be compensated effectively.

Fast sparse recovery and coherence factor weighting in optoacoustic tomography

NASA Astrophysics Data System (ADS)

He, Hailong; Prakash, Jaya; Buehler, Andreas; Ntziachristos, Vasilis

2017-03-01

Sparse recovery algorithms have shown great potential to reconstruct images with limited view datasets in optoacoustic tomography, with a disadvantage of being computational expensive. In this paper, we improve the fast convergent Split Augmented Lagrangian Shrinkage Algorithm (SALSA) method based on least square QR (LSQR) formulation for performing accelerated reconstructions. Further, coherence factor is calculated to weight the final reconstruction result, which can further reduce artifacts arising in limited-view scenarios and acoustically heterogeneous mediums. Several phantom and biological experiments indicate that the accelerated SALSA method with coherence factor (ASALSA-CF) can provide improved reconstructions and much faster convergence compared to existing sparse recovery methods.

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…

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.

Data-driven subtypes of major depressive disorder: a systematic review

PubMed Central

2012-01-01

Background According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression. Methods We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses. Results In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial. Conclusions The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis. PMID:23210727

Comparing Factor, Class, and Mixture Models of Cannabis Initiation and DSM Cannabis Use Disorder Criteria, Including Craving, in the Brisbane Longitudinal Twin Study

PubMed Central

Kubarych, Thomas S.; Kendler, Kenneth S.; Aggen, Steven H.; Estabrook, Ryne; Edwards, Alexis C.; Clark, Shaunna L.; Martin, Nicholas G.; Hickie, Ian B.; Neale, Michael C.; Gillespie, Nathan A.

2014-01-01

Accumulating evidence suggests that the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying factor. However, it remains possible that models with additional factors, or latent class models or hybrid models, may better explain the data. Using structured interviews, 626 adult male and female twins provided complete data on symptoms of cannabis abuse and dependence, plus a craving criterion. We compared latent factor analysis, latent class analysis, and factor mixture modeling using normal theory marginal maximum likelihood for ordinal data. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-IV criteria and determine whether DSM-5 craving loads onto a general factor. When compared with latent class and mixture models, factor models provided a better fit to the data. When conditioned on initiation and cannabis use, the association between criteria for abuse, dependence, withdrawal, and craving were best explained by two correlated latent factors for males and females: a general risk factor to CUD and a factor capturing the symptoms of social and occupational impairment as a consequence of frequent use. Secondary analyses revealed a modest increase in the prevalence of DSM-5 CUD compared with DSM-IV cannabis abuse or dependence. It is concluded that, in addition to a general factor with loadings on cannabis use and symptoms of abuse, dependence, withdrawal, and craving, a second clinically relevant factor defined by features of social and occupational impairment was also found for frequent cannabis use. PMID:24588857

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

Interactions between lower urinary tract symptoms and cardiovascular risk factors determine distinct patterns of erectile dysfunction: a latent class analysis.

PubMed

Barbosa, João A B A; Muracca, Eduardo; Nakano, Élcio; Assalin, Adriana R; Cordeiro, Paulo; Paranhos, Mario; Cury, José; Srougi, Miguel; Antunes, Alberto A

2013-12-01

An epidemiological association between lower urinary tract symptoms and erectile dysfunction is well established. However, interactions among multiple risk factors and the role of each in pathological mechanisms are not fully elucidated We enrolled 898 men undergoing prostate cancer screening for evaluation with the International Prostate Symptom Score (I-PSS) and simplified International Index of Erectile Function-5 (IIEF-5) questionnaires. Age, race, hypertension, diabetes, dyslipidemia, metabolic syndrome, cardiovascular disease, serum hormones and anthropometric parameters were also evaluated. Risk factors for erectile dysfunction were identified by logistic regression. The 333 men with at least mild to moderate erectile dysfunction (IIEF 16 or less) were included in a latent class model to identify relationships across erectile dysfunction risk factors. Age, hypertension, diabetes, lower urinary tract symptoms and cardiovascular event were independent predictors of erectile dysfunction (p<0.05). We identified 3 latent classes of patients with erectile dysfunction (R2 entropy=0.82). Latent class 1 had younger men at low cardiovascular risk and a moderate/high prevalence of lower urinary tract symptoms. Latent class 2 had the oldest patients at moderate cardiovascular risk with an increased prevalence of lower urinary tract symptoms. Latent class 3 had men of intermediate age with the highest prevalence of cardiovascular risk factors and lower urinary tract symptoms. Erectile dysfunction severity and lower urinary tract symptoms increased from latent class 1 to 3. Risk factor interactions determined different severities of lower urinary tract symptoms and erectile dysfunction. The effect of lower urinary tract symptoms and cardiovascular risk outweighed that of age. While in the youngest patients lower urinary tract symptoms acted as a single risk factor for erectile dysfunction, the contribution of vascular disease resulted in significantly more severe dysfunction. Applying a risk factor interaction model to prospective trials could reveal distinct classes of drug responses and help define optimal treatment strategies for specific groups. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Reactivation of Latent HIV-1 Expression by Engineered TALE Transcription Factors

PubMed Central

Perdigão, Pedro; Gaj, Thomas; Santa-Marta, Mariana; Goncalves, Joao

2016-01-01

The presence of replication-competent HIV-1 –which resides mainly in resting CD4+ T cells–is a major hurdle to its eradication. While pharmacological approaches have been useful for inducing the expression of this latent population of virus, they have been unable to purge HIV-1 from all its reservoirs. Additionally, many of these strategies have been associated with adverse effects, underscoring the need for alternative approaches capable of reactivating viral expression. Here we show that engineered transcriptional modulators based on customizable transcription activator-like effector (TALE) proteins can induce gene expression from the HIV-1 long terminal repeat promoter, and that combinations of TALE transcription factors can synergistically reactivate latent viral expression in cell line models of HIV-1 latency. We further show that complementing TALE transcription factors with Vorinostat, a histone deacetylase inhibitor, enhances HIV-1 expression in latency models. Collectively, these findings demonstrate that TALE transcription factors are a potentially effective alternative to current pharmacological routes for reactivating latent virus and that combining synthetic transcriptional activators with histone deacetylase inhibitors could lead to the development of improved therapies for latent HIV-1 infection. PMID:26933881

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

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

Kim, Kyungjoo; Rajamanickam, Sivasankaran; Stelle, George Widgery

We introduce a task-parallel algorithm for sparse incomplete Cholesky factorization that utilizes a 2D sparse partitioned-block layout of a matrix. Our factorization algorithm follows the idea of algorithms-by-blocks by using the block layout. The algorithm-byblocks approach induces a task graph for the factorization. These tasks are inter-related to each other through their data dependences in the factorization algorithm. To process the tasks on various manycore architectures in a portable manner, we also present a portable tasking API that incorporates different tasking backends and device-specific features using an open-source framework for manycore platforms i.e., Kokkos. A performance evaluation is presented onmore » both Intel Sandybridge and Xeon Phi platforms for matrices from the University of Florida sparse matrix collection to illustrate merits of the proposed task-based factorization. Experimental results demonstrate that our task-parallel implementation delivers about 26.6x speedup (geometric mean) over single-threaded incomplete Choleskyby- blocks and 19.2x speedup over serial Cholesky performance which does not carry tasking overhead using 56 threads on the Intel Xeon Phi processor for sparse matrices arising from various application problems.« less

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.

Incomplete Sparse Approximate Inverses for Parallel Preconditioning

DOE PAGES

Anzt, Hartwig; Huckle, Thomas K.; Bräckle, Jürgen; ...

2017-10-28

In this study, we propose a new preconditioning method that can be seen as a generalization of block-Jacobi methods, or as a simplification of the sparse approximate inverse (SAI) preconditioners. The “Incomplete Sparse Approximate Inverses” (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations. Those arise, for example, in the context of incomplete factorization preconditioning. ISAI preconditioners can be generated via an algorithm providing fine-grained parallelism, which makes them attractive for hardware with a high concurrency level. Finally, in a study covering a large number of matrices, we identify the ISAI preconditioner as anmore » attractive alternative to exact triangular solves in the context of incomplete factorization preconditioning.« less

Amesos2 and Belos: Direct and Iterative Solvers for Large Sparse Linear Systems

DOE PAGES

Bavier, Eric; Hoemmen, Mark; Rajamanickam, Sivasankaran; ...

2012-01-01

Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples themore » algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.« less

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).

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…

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…

Latent human error analysis and efficient improvement strategies by fuzzy TOPSIS in aviation maintenance tasks.

PubMed

Chiu, Ming-Chuan; Hsieh, Min-Chih

2016-05-01

The purposes of this study were to develop a latent human error analysis process, to explore the factors of latent human error in aviation maintenance tasks, and to provide an efficient improvement strategy for addressing those errors. First, we used HFACS and RCA to define the error factors related to aviation maintenance tasks. Fuzzy TOPSIS with four criteria was applied to evaluate the error factors. Results show that 1) adverse physiological states, 2) physical/mental limitations, and 3) coordination, communication, and planning are the factors related to airline maintenance tasks that could be addressed easily and efficiently. This research establishes a new analytic process for investigating latent human error and provides a strategy for analyzing human error using fuzzy TOPSIS. Our analysis process complements shortages in existing methodologies by incorporating improvement efficiency, and it enhances the depth and broadness of human error analysis methodology. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Clustering of modifiable biobehavioral risk factors for chronic disease in US adults: a latent class analysis.

PubMed

Leventhal, Adam M; Huh, Jimi; Dunton, Genevieve F

2014-11-01

Examining the co-occurrence patterns of modifiable biobehavioral risk factors for deadly chronic diseases (e.g. cancer, cardiovascular disease, diabetes) can elucidate the etiology of risk factors and guide disease-prevention programming. The aims of this study were to (1) identify latent classes based on the clustering of five key biobehavioral risk factors among US adults who reported at least one risk factor and (2) explore the demographic correlates of the identified latent classes. Participants were respondents of the National Epidemiologic Survey of Alcohol and Related Conditions (2004-2005) with at least one of the following disease risk factors in the past year (N = 22,789), which were also the latent class indicators: (1) alcohol abuse/dependence, (2) drug abuse/dependence, (3) nicotine dependence, (4) obesity, and (5) physical inactivity. Housing sample units were selected to match the US National Census in location and demographic characteristics, with young adults oversampled. Participants were administered surveys by trained interviewers. Five latent classes were yielded: 'obese, active non-substance abusers' (23%); 'nicotine-dependent, active, and non-obese' (19%); 'active, non-obese alcohol abusers' (6%); 'inactive, non-substance abusers' (50%); and 'active, polysubstance abusers' (3.7%). Four classes were characterized by a 100% likelihood of having one risk factor coupled with a low or moderate likelihood of having the other four risk factors. The five classes exhibited unique demographic profiles. Risk factors may cluster together in a non-monotonic fashion, with the majority of the at-risk population of US adults expected to have a high likelihood of endorsing only one of these five risk factors. © Royal Society for Public Health 2013.

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…

Semi-implicit integration factor methods on sparse grids for high-dimensional systems

NASA Astrophysics Data System (ADS)

Wang, Dongyong; Chen, Weitao; Nie, Qing

2015-07-01

Numerical methods for partial differential equations in high-dimensional spaces are often limited by the curse of dimensionality. Though the sparse grid technique, based on a one-dimensional hierarchical basis through tensor products, is popular for handling challenges such as those associated with spatial discretization, the stability conditions on time step size due to temporal discretization, such as those associated with high-order derivatives in space and stiff reactions, remain. Here, we incorporate the sparse grids with the implicit integration factor method (IIF) that is advantageous in terms of stability conditions for systems containing stiff reactions and diffusions. We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach. The overall method is found to be efficient in terms of both storage and computational time for solving a wide range of PDEs in high dimensions. In particular, the IIF with the sparse grid combination technique is flexible and effective in solving systems that may include cross-derivatives and non-constant diffusion coefficients. Extensive numerical simulations in both linear and nonlinear systems in high dimensions, along with applications of diffusive logistic equations and Fokker-Planck equations, demonstrate the accuracy, efficiency, and robustness of the new methods, indicating potential broad applications of the sparse grid-based integration factor method.

Resveratrol Reactivates Latent HIV through Increasing Histone Acetylation and Activating Heat Shock Factor 1.

PubMed

Zeng, Xiaoyun; Pan, Xiaoyan; Xu, Xinfeng; Lin, Jian; Que, Fuchang; Tian, Yuanxin; Li, Lin; Liu, Shuwen

2017-06-07

The persistence of latent HIV reservoirs presents a significant challenge to viral eradication. Effective latency reversing agents (LRAs) based on "shock and kill" strategy are urgently needed. The natural phytoalexin resveratrol has been demonstrated to enhance HIV gene expression, although its mechanism remains unclear. In this study, we demonstrated that resveratrol was able to reactivate latent HIV without global T cell activation in vitro. Mode of action studies showed resveratrol-mediated reactivation from latency did not involve the activation of silent mating type information regulation 2 homologue 1 (SIRT1), which belonged to class-3 histone deacetylase (HDAC). However, latent HIV was reactivated by resveratrol mediated through increasing histone acetylation and activation of heat shock factor 1 (HSF1). Additionally, synergistic activation of the latent HIV reservoirs was observed under cotreatment with resveratrol and conventional LRAs. Collectively, this research reveals that resveratrol is a natural LRA and shows promise for HIV therapy.

Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario

2018-04-01

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.

Newton-based optimization for Kullback-Leibler nonnegative tensor factorizations

DOE PAGES

Plantenga, Todd; Kolda, Tamara G.; Hansen, Samantha

2015-04-30

Tensor factorizations with nonnegativity constraints have found application in analysing data from cyber traffic, social networks, and other areas. We consider application data best described as being generated by a Poisson process (e.g. count data), which leads to sparse tensors that can be modelled by sparse factor matrices. In this paper, we investigate efficient techniques for computing an appropriate canonical polyadic tensor factorization based on the Kullback–Leibler divergence function. We propose novel subproblem solvers within the standard alternating block variable approach. Our new methods exploit structure and reformulate the optimization problem as small independent subproblems. We employ bound-constrained Newton andmore » quasi-Newton methods. Finally, we compare our algorithms against other codes, demonstrating superior speed for high accuracy results and the ability to quickly find sparse solutions.« less

Analysis of Water Vapour Flux Between Alpine Wetlands Underlying the Surface and Atmosphere in the Source Region of the Yellow River

NASA Astrophysics Data System (ADS)

Xie, Y.; Wen, J.; Liu, R.; Wang, X.; JIA, D.

2017-12-01

Wetland underlying surface is sensitive to climate change. Analysis of the degree of coupling between wetlands and the atmosphere and a quantitative assessment of how environmental factors influence latent heat flux have considerable scientific significance. Previous studies, which focused on the forest, grassland and farmland ecosystems, lack research on the alpine wetlands. In addition, research on the environmental control mechanism of latent heat flux is still qualitative and lacks quantitative evaluations and calculations. Using data from the observational tests of the Maduo Observatory of Climate and Environment of the Northwest Institute of Eco-Environment and Resource, CAS, from June 1 to August 31, 2014, this study analysed the time-varying characteristics and causes of the degree of coupling between alpine wetlands underlying surface and the atmosphere and quantitatively calculated the influences of different environmental factors (solar radiation and vapour pressure deficit) on latent heat flux. The results were as follows: Due to the diurnal variations of solar radiation and wind speed, the diurnal variations of the Ω factor present a trend in which the Ω factor are small in the morning and large in the evening. Due to the vegetation growing cycle, the seasonal variations of the Ω factor present a reverse "U" trend . These trends are similar to the diurnal and seasonal variations of the absolute control exercised by solar radiation over the latent heat flux. This conforms to omega theory. The values for average absolute atmospheric factor (surface factor or total ) control exercised by solar radiation and water vapour pressure are 0.20 (0.02 or 0.22 ) and 0.005 (-0.07 or -0.06) W·m-2·Pa-1, respectively.. Generally speaking, solar radiation and water vapour pressure deficit exert opposite forces on the latent heat flux. The average Ω factor is high during the vegetation growing season, with a value of 0.38, and the degree of coupling between the alpine wetland surface and the atmosphere system is low. The actual measurements agree with omega theory. The latent heat flux is mainly influenced by solar radiation. From the above, our study has provided reference information for exploring the influences of environmental factors on the latent heat flux over the alpine wetlands of the Yellow River source region.

An Efficient Scheme for Updating Sparse Cholesky Factors

NASA Technical Reports Server (NTRS)

Raghavan, Padma

2002-01-01

Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. Currently, EPISCOPACY is being used in an application at NASA directed by J. Newman and M. James. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project. A brief description is provided in the next section.

Temperament factors and dimensional, latent bifactor models of child psychopathology: Transdiagnostic and specific associations in two youth samples.

PubMed

Hankin, Benjamin L; Davis, Elysia Poggi; Snyder, Hannah; Young, Jami F; Glynn, Laura M; Sandman, Curt A

2017-06-01

Common emotional and behavioral symptoms co-occur and are associated with core temperament factors. This study investigated links between temperament and dimensional, latent psychopathology factors, including a general common psychopathology factor (p factor) and specific latent internalizing and externalizing liabilities, as captured by a bifactor model, in two independent samples of youth. Specifically, we tested the hypothesis that temperament factors of negative affectivity (NA), positive affectivity (PA), and effortful control (EC) could serve as both transdiagnostic and specific risks in relation to recent bifactor models of child psychopathology. Sample 1 included 571 youth (average age 13.6, SD =2.37, range 9.3-17.5) with both youth and parent report. Sample 2 included 554 preadolescent children (average age 7.7, SD =1.35, range =5-11 years) with parent report. Structural equation modeling showed that the latent bifactor models fit in both samples. Replicated in both samples, the p factor was associated with lower EC and higher NA (transdiagnostic risks). Several specific risks replicated in both samples after controlling for co-occurring symptoms via the p factor: internalizing was associated with higher NA and lower PA, lower EC related to externalizing problems. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

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

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…

Measuring the environmental awareness of young farmers

NASA Astrophysics Data System (ADS)

Kountios, G.; Ragkos, A.; Padadavid, G.; Hadjimitsis, D.

2017-09-01

Young farmers in Europe, especially the beneficiaries of Common Agricultural Policy (CAP) funding schemes, are considered as the ones who could ensure the sustainability of the European Model of Agriculture. Economic efficiency and competitiveness, aversion of depopulation of rural areas and environmental protection constitute some of the key objectives of the CAP and young farmers are expected to play a role to all of them. This study proposes a way of measuring the potential of young farmers to contribute to the latter objectives of the CAP by estimating their environmental attitudes. Data from a questionnaire survey of 492 Greek young farmers were used to design a latent construct measuring their environmental attitudes. The latent construct was designed by means of an Explanatory Factor Analysis (EFA) using the responses to a set of 12 Likert-scale items. The results the EFA yielded a latent construct with three factors related to "Environmental pollution and policies (EPP)", "Environmental factors and food quality (EFF)" and "Farming practices and the environment". These results were validated through a CFA where 8 items in total were categorized in the three factors (latent variables). The utilization of the latent construct for the effective implementation of CAP measures could ameliorate the relationships of agriculture and environment in general.

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

PubMed Central

Guo, Qijia; Wang, Jie; Chang, Tianying

2017-01-01

The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively. PMID:29113083

Effects of partitioning and scheduling sparse matrix factorization on communication and load balance

NASA Technical Reports Server (NTRS)

Venugopal, Sesh; Naik, Vijay K.

1991-01-01

A block based, automatic partitioning and scheduling methodology is presented for sparse matrix factorization on distributed memory systems. Using experimental results, this technique is analyzed for communication and load imbalance overhead. To study the performance effects, these overheads were compared with those obtained from a straightforward 'wrap mapped' column assignment scheme. All experimental results were obtained using test sparse matrices from the Harwell-Boeing data set. The results show that there is a communication and load balance tradeoff. The block based method results in lower communication cost whereas the wrap mapped scheme gives better load balance.

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…

Applying Latent Class Analysis to Risk Stratification for Perioperative Mortality in Patients Undergoing Intraabdominal General Surgery.

PubMed

Kim, Minjae; Wall, Melanie M; Li, Guohua

2016-07-01

Perioperative risk stratification is often performed using individual risk factors without consideration of the syndemic of these risk factors. We used latent class analysis (LCA) to identify the classes of comorbidities and risk factors associated with perioperative mortality in patients presenting for intraabdominal general surgery. The 2005 to 2010 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a cohort of patients undergoing intraabdominal general surgery. Risk factors and comorbidities were entered into LCA models to identify the latent classes, and individuals were assigned to a class based on the highest posterior probability of class membership. Relative risk regression was used to determine the associations between the latent classes and 30-day mortality, with adjustments for procedure. A 9-class model was fit using LCA on 466,177 observations. After combining classes with similar adjusted mortality risks, 5 risk classes were obtained. Compared with the class with average mortality risk (class 4), the risk ratios (95% confidence interval) ranged from 0.020 (0.014-0.027) in the lowest risk class (class 1) to 6.75 (6.46-7.02) in the highest risk class. After adjusting for procedure and ASA physical status, the latent classes remained significantly associated with 30-day mortality. The addition of the risk class variable to a model containing ASA physical status and surgical procedure demonstrated a significant increase in the area under the receiver operator characteristic curve (0.892 vs 0.915; P < 0.0001). Latent classes of risk factors and comorbidities in patients undergoing intraabdominal surgery are predictive of 30-day mortality independent of the ASA physical status and improve risk prediction with the ASA physical status.

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.

The Blind Men and the Elephant: Identification of a Latent Maltreatment Construct for Youth in Foster Care

PubMed Central

Gabrielli, Joy; Jackson, Yo; Tunno, Angela M.; Hambrick, Erin P.

2017-01-01

Child maltreatment is a major public health concern due to its impact on developmental trajectories and consequences across mental and physical health outcomes. Operationalization of child maltreatment has been complicated, as research has used simple dichotomous counts to identification of latent class profiles. This study examines a latent measurement model assessed within foster youth inclusive of indicators of maltreatment chronicity and severity across four maltreatment types: physical, sexual, and psychological abuse, and neglect. Participants were 500 foster youth with a mean age of 12.99 years (SD = 2.95 years). Youth completed survey questions through a confidential audio computer-assisted self-interview program. A two-factor model with latent constructs of chronicity and severity of maltreatment revealed excellent fit across fit indices; however, the latent constructs were correlated .972. A one-factor model also demonstrated excellent model fit to the data (χ2 (16, n = 500) =28.087, p =.031, RMSEA (0.012 – 0.062) =.039, TLI =.990, CFI =.994, SRMR =.025) with a nonsignificant chi-square difference test comparing the one- and two-factor models. Invariance tests across age, gender, and placement type also were conducted with recommendations provided. Results suggest a single-factor latent model of maltreatment severity and chronicity can be attained. Thus, the maltreatment experiences reported by foster youth, though varied and complex, were captured in a model that may prove useful in later predictions of outcome behaviors. Appropriate identification of both the chronicity and severity of maltreatment inclusive of the range of maltreatment types remains a high priority for future research. PMID:28254690

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.

Interexaminer variation of minutia markup on latent fingerprints.

PubMed

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

2016-07-01

Latent print examiners often differ in the number of minutiae they mark during analysis of a latent, and also during comparison of a latent with an exemplar. Differences in minutia counts understate interexaminer variability: examiners' markups may have similar minutia counts but differ greatly in which specific minutiae were marked. We assessed variability in minutia markup among 170 volunteer latent print examiners. Each provided detailed markup documenting their examinations of 22 latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. An average of 12 examiners marked each latent. The primary factors associated with minutia reproducibility were clarity, which regions of the prints examiners chose to mark, and agreement on value or comparison determinations. In clear areas (where the examiner was "certain of the location, presence, and absence of all minutiae"), median reproducibility was 82%; in unclear areas, median reproducibility was 46%. Differing interpretations regarding which regions should be marked (e.g., when there is ambiguity in the continuity of a print) contributed to variability in minutia markup: especially in unclear areas, marked minutiae were often far from the nearest minutia marked by a majority of examiners. Low reproducibility was also associated with differences in value or comparison determinations. Lack of standardization in minutia markup and unfamiliarity with test procedures presumably contribute to the variability we observed. We have identified factors accounting for interexaminer variability; implementing standards for detailed markup as part of documentation and focusing future training efforts on these factors may help to facilitate transparency and reduce subjectivity in the examination process. Published by Elsevier Ireland Ltd.

On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models

ERIC Educational Resources Information Center

Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus

2015-01-01

Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…

On the Relation between the Linear Factor Model and the Latent Profile Model

ERIC Educational Resources Information Center

Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

2011-01-01

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

CCCTC-Binding Factor Acts as a Heterochromatin Barrier on Herpes Simplex Viral Latent Chromatin and Contributes to Poised Latent Infection

PubMed Central

2018-01-01

ABSTRACT Herpes simplex virus 1 (HSV-1) establishes latent infection in neurons via a variety of epigenetic mechanisms that silence its genome. The cellular CCCTC-binding factor (CTCF) functions as a mediator of transcriptional control and chromatin organization and has binding sites in the HSV-1 genome. We constructed an HSV-1 deletion mutant that lacked a pair of CTCF-binding sites (CTRL2) within the latency-associated transcript (LAT) coding sequences and found that loss of these CTCF-binding sites did not alter lytic replication or levels of establishment of latent infection, but their deletion reduced the ability of the virus to reactivate from latent infection. We also observed increased heterochromatin modifications on viral chromatin over the LAT promoter and intron. We therefore propose that CTCF binding at the CTRL2 sites acts as a chromatin insulator to keep viral chromatin in a form that is poised for reactivation, a state which we call poised latency. PMID:29437926

Detecting Hotspot Information Using Multi-Attribute Based Topic Model

PubMed Central

Wang, Jing; Li, Li; Tan, Feng; Zhu, Ying; Feng, Weisi

2015-01-01

Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA), in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics. PMID:26496635

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.

Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T.

PubMed

Nassirpour, Sahar; Chang, Paul; Avdievitch, Nikolai; Henning, Anke

2018-04-29

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE 1 H FID MRSI sequence at 9.4T. X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1 H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared. Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved. By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min. © 2018 International Society for Magnetic Resonance in Medicine.

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

Race Differences in Patterns of Risky Behavior and Associated Risk Factors in Adolescence.

PubMed

Childs, Kristina K; Ray, James V

2017-05-01

Using data from the National Longitudinal Study of Adolescent Health (Add Health), this study expands on previous research by (a) examining differences across race in patterns or "subgroups" of adolescents based on nine self-reported behaviors (e.g., delinquency, substance use, risky sexual practices) and (b) comparing the risk factors (e.g., peer association, parenting, neighborhood cohesion), both within and across the race-specific subgroups, related to membership into the identified latent classes. The data used in this study include respondents aged 13 to 17 who participated in Waves 1 and 2 of the Add Health in-home interview. Latent class analysis (LCA) identified key differences in the number and characteristics of the latent classes across the racial subgroups. In addition, both similarities and differences in the risk factors for membership into the latent classes were identified across and within the race-specific subgroups. Implications for understanding risky behavior in adolescence, as well as directions for future research, are discussed.

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Comparison of Penman-Monteith, Shuttleworth-Wallace, and modified Priestley-Taylor evapotranspiration models for wildland vegetation in semiarid rangeland

USGS Publications Warehouse

Stannard, David I.

1993-01-01

Eddy correlation measurements of sensible and latent heat flux are used with measurements of net radiation, soil heat flux, and other micrometeorological variables to develop the Penman-Monteith, Shuttleworth-Wallace, and modified Priestley-Taylor evapotranspiration models for use in a sparsely vegetated, semiarid rangeland. The Penman-Monteith model, a one-component model designed for use with dense crops, is not sufficiently accurate (r2 = 0.56 for hourly data and r2 = 0.60 for daily data). The Shuttleworth-Wallace model, a two-component logical extension of the Penman-Monteith model for use with sparse crops, performs significantly better (r2 = 0.78 for hourly data and r2 = 0.85 for daily data). The modified Priestley-Taylor model, a one-component simplified form of the Penman potential evapotranspiration model, surprisingly performs as well as the Shuttle worth-Wallace model. The rigorous Shuttleworth-Wallace model predicts that about one quarter of the vapor flux to the atmosphere is from bare-soil evaporation. Further, during daylight hours, the small leaves are sinks for sensible heat produced at the hot soil surface.

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…

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…

Using Trait-State Models to Evaluate the Longitudinal Consistency of Global Self-Esteem From Adolescence to Adulthood.

PubMed

Donnellan, M Brent; Kenny, David A; Trzesniewski, Kali H; Lucas, Richard E; Conger, Rand D

2012-12-01

The present research used a latent variable trait-state model to evaluate the longitudinal consistency of self-esteem during the transition from adolescence to adulthood. Analyses were based on ten administrations of the Rosenberg Self-Esteem scale (Rosenberg, 1965) spanning the ages of approximately 13 to 32 for a sample of 451 participants. Results indicated that a completely stable trait factor and an autoregressive trait factor accounted for the majority of the variance in latent self-esteem assessments, whereas state factors accounted for about 16% of the variance in repeated assessments of latent self-esteem. The stability of individual differences in self-esteem increased with age consistent with the cumulative continuity principle of personality development.

Using Trait-State Models to Evaluate the Longitudinal Consistency of Global Self-Esteem From Adolescence to Adulthood

PubMed Central

Donnellan, M. Brent; Kenny, David A.; Trzesniewski, Kali H.; Lucas, Richard E.; Conger, Rand D.

2012-01-01

The present research used a latent variable trait-state model to evaluate the longitudinal consistency of self-esteem during the transition from adolescence to adulthood. Analyses were based on ten administrations of the Rosenberg Self-Esteem scale (Rosenberg, 1965) spanning the ages of approximately 13 to 32 for a sample of 451 participants. Results indicated that a completely stable trait factor and an autoregressive trait factor accounted for the majority of the variance in latent self-esteem assessments, whereas state factors accounted for about 16% of the variance in repeated assessments of latent self-esteem. The stability of individual differences in self-esteem increased with age consistent with the cumulative continuity principle of personality development. PMID:23180899

Emotional rigidity negatively impacts remission from anxiety and recovery of well-being.

PubMed

Wiltgen, Anika; Shepard, Christopher; Smith, Ryan; Fowler, J Christopher

2018-08-15

Emotional rigidity is described in clinical literature as a significant barrier to recovery; however, few there are few empirical measures of the construct. The current study had two aims: Study 1 aimed to identify latent factors that may bear on the construct of emotional rigidity while Study 2 assessed the potential impact of the latent factor(s) on anxiety remission rates and well-being. This study utilized data from 2472 adult inpatients (1176 females and 1296 males) with severe psychopathology. Study 1 utilized exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to identify latent factors of emotional rigidity. Study 2 utilized hierarchical logistic regression analyses to assess the relationships among emotional rigidity factors and anxiety remission and well-being recovery at discharge. Study 1 yielded a two-factor solution identified in EFA was confirmed with CFA. Factor 1 consisted of neuroticism, experiential avoidance, non-acceptance of emotions, impaired goal-directed behavior, impulse control difficulties and limited access to emotion regulation strategies when experiencing negative emotions. Factor 2 consisted of lack of emotional awareness and lack of emotional clarity when experiencing negative emotions. Results of Study 2 indicated higher scores on Factor 1 was associated with lower remission rates from anxiety and poorer well-being upon discharge. Factor 2 was not predictive of outcome. Emotional rigidity appears to be a latent construct that negatively impacts remission rates from anxiety. Limitations of the present study include its retrospective design, and inefficient methods of assessing emotional rigidity. Copyright © 2018. Published by Elsevier B.V.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

PubMed

Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Modeling the Trajectory of Analgesic Demand Over Time After Total Knee Arthroplasty Using the Latent Curve Analysis.

PubMed

Lo, Po-Han; Tsou, Mei-Yung; Chang, Kuang-Yi

2015-09-01

Patient-controlled epidural analgesia (PCEA) is commonly used for pain relief after total knee arthroplasty (TKA). This study aimed to model the trajectory of analgesic demand over time after TKA and explore its influential factors using latent curve analysis. Data were retrospectively collected from 916 patients receiving unilateral or bilateral TKA and postoperative PCEA. PCEA demands during 12-hour intervals for 48 hours were directly retrieved from infusion pumps. Potentially influential factors of PCEA demand, including age, height, weight, body mass index, sex, and infusion pump settings, were also collected. A latent curve analysis with 2 latent variables, the intercept (baseline) and slope (trend), was applied to model the changes in PCEA demand over time. The effects of influential factors on these 2 latent variables were estimated to examine how these factors interacted with time to alter the trajectory of PCEA demand over time. On average, the difference in analgesic demand between the first and second 12-hour intervals was only 15% of that between the first and third 12-hour intervals. No significant difference in PCEA demand was noted between the third and fourth 12-hour intervals. Aging tended to decrease the baseline PCEA demand but body mass index and infusion rate were positively correlated with the baseline. Only sex significantly affected the trend parameter and male individuals tended to have a smoother decreasing trend of analgesic demands over time. Patients receiving bilateral procedures did not consume more analgesics than their unilateral counterparts. Goodness of fit analysis indicated acceptable model fit to the observed data. Latent curve analysis provided valuable information about how analgesic demand after TKA changed over time and how patient characteristics affected its trajectory.

Pre-clinical cognitive phenotypes for Alzheimer disease: a latent profile approach.

PubMed

Hayden, Kathleen M; Kuchibhatla, Maragatha; Romero, Heather R; Plassman, Brenda L; Burke, James R; Browndyke, Jeffrey N; Welsh-Bohmer, Kathleen A

2014-11-01

Cognitive profiles for pre-clinical Alzheimer disease (AD) can be used to identify groups of individuals at risk for disease and better characterize pre-clinical disease. Profiles or patterns of performance as pre-clinical phenotypes may be more useful than individual test scores or measures of global decline. To evaluate patterns of cognitive performance in cognitively normal individuals to derive latent profiles associated with later onset of disease using a combination of factor analysis and latent profile analysis. The National Alzheimer Coordinating Centers collect data, including a battery of neuropsychological tests, from participants at 29 National Institute on Aging-funded Alzheimer Disease Centers across the United States. Prior factor analyses of this battery demonstrated a four-factor structure comprising memory, attention, language, and executive function. Factor scores from these analyses were used in a latent profile approach to characterize cognition among a group of cognitively normal participants (N = 3,911). Associations between latent profiles and disease outcomes an average of 3 years later were evaluated with multinomial regression models. Similar analyses were used to determine predictors of profile membership. Four groups were identified; each with distinct characteristics and significantly associated with later disease outcomes. Two groups were significantly associated with development of cognitive impairment. In post hoc analyses, both the Trail Making Test Part B, and a contrast score (Delayed Recall - Trails B), significantly predicted group membership and later cognitive impairment. Latent profile analysis is a useful method to evaluate patterns of cognition in large samples for the identification of preclinical AD phenotypes; comparable results, however, can be achieved with very sensitive tests and contrast scores. Copyright © 2014 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Nuclear Localization of the C1 Factor (Host Cell Factor) in Sensory Neurons Correlates with Reactivation of Herpes Simplex Virus from Latency

NASA Astrophysics Data System (ADS)

Kristie, Thomas M.; Vogel, Jodi L.; Sears, Amy E.

1999-02-01

After a primary infection, herpes simplex virus is maintained in a latent state in neurons of sensory ganglia until complex stimuli reactivate viral lytic replication. Although the mechanisms governing reactivation from the latent state remain unknown, the regulated expression of the viral immediate early genes represents a critical point in this process. These genes are controlled by transcription enhancer complexes whose assembly requires and is coordinated by the cellular C1 factor (host cell factor). In contrast to other tissues, the C1 factor is not detected in the nuclei of sensory neurons. Experimental conditions that induce the reactivation of herpes simplex virus in mouse model systems result in rapid nuclear localization of the protein, indicating that the C1 factor is sequestered in these cells until reactivation signals induce a redistribution of the protein. The regulated localization suggests that C1 is a critical switch determinant of the viral lytic-latent cycle.

Examining Factors Associated with (In)Stability in Social Information Processing among Urban School Children: A Latent Transition Analytic Approach

ERIC Educational Resources Information Center

Goldweber, Asha; Bradshaw, Catherine P.; Goodman, Kimberly; Monahan, Kathryn; Cooley-Strickland, Michele

2011-01-01

There is compelling evidence for the role of social information processing (SIP) in aggressive behavior. However, less is known about factors that influence stability versus instability in patterns of SIP over time. Latent transition analysis was used to identify SIP patterns over one year and examine how community violence exposure, aggressive…

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

ERIC Educational Resources Information Center

Lubke, Gitta; Tueller, Stephen

2010-01-01

Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent…

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…

Factorial Invariance and Latent Mean Differences for the Five Factor Wellness Inventory with Korean and American Counselors

ERIC Educational Resources Information Center

Jang, Yoo Jin; Lee, Jayoung; Puig, Ana; Lee, Sang Min

2012-01-01

This study aimed to examine the factorial equivalence of the Five Factor Wellness Inventory across U.S. and Korean professional counselors and counselors-in-training. Latent means analyses demonstrated that there were significant differences between U.S. and Korean counselors for the five domains of wellness. (Contains 4 tables and 1 figure.)

The Houdini Transformation: True, but Illusory.

PubMed

Bentler, Peter M; Molenaar, Peter C M

2012-01-01

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This paper verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory, in the sense that the Houdini transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model.

The Houdini Transformation: True, but Illusory

PubMed Central

Bentler, Peter M.; Molenaar, Peter C. M.

2012-01-01

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This paper verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory, in the sense that the Houdini transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model. PMID:23180888

Pathological grooming: Evidence for a single factor behind trichotillomania, skin picking and nail biting

PubMed Central

Hende, Borbála; Urbán, Róbert; Demetrovics, Zsolt

2017-01-01

Although trichotillomania (TTM), skin picking (SP), and nail biting (NB) have been receiving growing scientific attention, the question as to whether these disorders can be regarded as separate entities or they are different manifestations of the same underlying tendency is unclear. Data were collected online in a community survey, yielding a sample of 2705 participants (66% women, mean age: 29.1, SD: 8.6). Hierarchical factor analysis was used to identify a common latent factor and the multiple indicators and multiple causes (MIMIC) modelling was applied to test the predictive effect of borderline personality disorder symptoms, impulsivity, distress and self-esteem on pathological grooming. Pearson correlation coefficients between TTM, SP and NB were between 0.13 and 0.29 (p < 0.01). The model yielded an excellent fit to the data (CFI = 0.992, TLI = 0.991, χ2 = 696.65, p < 0.001, df = 222, RMSEA = 0.030, Cfit of RMSEA = 1.000), supporting the existence of a latent factor. The MIMIC model indicated an adequate fit (CFI = 0.993, TLI = 0.992, χ2 = 655.8, p < 0.001, df = 307, RMSEA = 0.25, CI: 0.022–0.028, pclose = 1.000). TTM, SP and NB each were loaded significantly on the latent factor, indicating the presence of a general grooming factor. Impulsivity, psychiatric distress and contingent self-esteem had significant predictive effects, whereas borderline personality disorder had a nonsignificant predictive effect on the latent factor. We found evidence that the category of pathological grooming is meaningful and encompasses three symptom manifestations: trichotillomania, skin picking and nail biting. This latent underlying factor is not better explained by indicators of psychopathology, which supports the notion that the urge to self-groom, rather than general psychiatric distress, impulsivity, self-esteem or borderline symptomatology, is what drives individual grooming behaviours. PMID:28902896

Silent films and strange stories: theory of mind, gender, and social experiences in middle childhood.

PubMed

Devine, Rory T; Hughes, Claire

2013-01-01

In this study of two hundred and thirty 8- to 13-year-olds, a new "Silent Films" task is introduced, designed to address the dearth of research on theory of mind in older children by providing a film-based analogue of F. G. E. Happé's (1994) Strange Stories task. Confirmatory factor analysis showed that all items from both tasks loaded onto a single theory-of-mind latent factor. With effects of verbal ability and family affluence controlled, theory-of-mind latent factor scores increased significantly with age, indicating that mentalizing skills continue to develop through middle childhood. Girls outperformed boys on the theory-of-mind latent factor, and the correlates of individual differences in theory of mind were gender specific: Low scores were related to loneliness in girls and to peer rejection in boys. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

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.

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.

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.

Predicting Viral Infection From High-Dimensional Biomarker Trajectories

PubMed Central

Chen, Minhua; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S.; Lucas, Joseph; Dunson, David; Carin, Lawrence

2013-01-01

There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided. PMID:23704802

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.

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

Ethnic differences in longitudinal latent verbal profiles in the millennium cohort study.

PubMed

Zilanawala, Afshin; Kelly, Yvonne; Sacker, Amanda

2016-12-01

Development of verbal skills during early childhood and school age years is consequential for children's educational achievement and adult outcomes. We examine ethnic differences in longitudinal latent verbal profiles and assess the contribution of family process and family resource factors to observed differences. Using data from the UK Millennium Cohort Study and the latent profile analysis, we estimate longitudinal latent verbal profiles using verbal skills measured 4 times from age 3-11 years. We investigate the odds of verbal profiles by ethnicity (reported in infancy), and the extent observed differences are mediated by the home learning environment, family routines, and psychosocial environment (measured at age 3). Indian children were twice as likely (OR = 2.14, CI: 1.37-3.33) to be in the high achieving profile, compared to White children. Socioeconomic markers attenuated this advantage to nonsignificance. Pakistani and Bangladeshi children were significantly more likely to be in the low performing group (OR = 2.23, CI: 1.61-3.11; OR = 3.37, CI: 2.20-5.17, respectively). Socioeconomic and psychosocial factors had the strongest mediating influence on the association between lower achieving profiles and Pakistani children, whereas for Bangladeshi children, there was mediation by the home learning environment, family routines, and psychosocial factors. Family process and resource factors explain ethnic differences in longitudinal latent verbal profiles. Family resources explain verbal advantages for Indian children, whereas a range of home environment and socioeconomic factors explain disparities for Pakistani and Bangladeshi children. Future policy initiatives focused on reducing ethnic disparities in children's development should consider supporting and enhancing family resources and processes. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association.

Ethnic differences in longitudinal latent verbal profiles in the millennium cohort study*

PubMed Central

Kelly, Yvonne; Sacker, Amanda

2016-01-01

Background: Development of verbal skills during early childhood and school age years is consequential for children’s educational achievement and adult outcomes. We examine ethnic differences in longitudinal latent verbal profiles and assess the contribution of family process and family resource factors to observed differences. Methods: Using data from the UK Millennium Cohort Study and the latent profile analysis, we estimate longitudinal latent verbal profiles using verbal skills measured 4 times from age 3–11 years. We investigate the odds of verbal profiles by ethnicity (reported in infancy), and the extent observed differences are mediated by the home learning environment, family routines, and psychosocial environment (measured at age 3). Results: Indian children were twice as likely (OR = 2.14, CI: 1.37–3.33) to be in the high achieving profile, compared to White children. Socioeconomic markers attenuated this advantage to nonsignificance. Pakistani and Bangladeshi children were significantly more likely to be in the low performing group (OR = 2.23, CI: 1.61–3.11; OR = 3.37, CI: 2.20–5.17, respectively). Socioeconomic and psychosocial factors had the strongest mediating influence on the association between lower achieving profiles and Pakistani children, whereas for Bangladeshi children, there was mediation by the home learning environment, family routines, and psychosocial factors. Conclusion: Family process and resource factors explain ethnic differences in longitudinal latent verbal profiles. Family resources explain verbal advantages for Indian children, whereas a range of home environment and socioeconomic factors explain disparities for Pakistani and Bangladeshi children. Future policy initiatives focused on reducing ethnic disparities in children’s development should consider supporting and enhancing family resources and processes. PMID:27999155

A new scheduling algorithm for parallel sparse LU factorization with static pivoting

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

Grigori, Laura; Li, Xiaoye S.

2002-08-20

In this paper we present a static scheduling algorithm for parallel sparse LU factorization with static pivoting. The algorithm is divided into mapping and scheduling phases, using the symmetric pruned graphs of L' and U to represent dependencies. The scheduling algorithm is designed for driving the parallel execution of the factorization on a distributed-memory architecture. Experimental results and comparisons with SuperLU{_}DIST are reported after applying this algorithm on real world application matrices on an IBM SP RS/6000 distributed memory machine.

Persistence of low levels of plasma viremia and of the latent reservoir in patients under ART: A fractional-order approach

NASA Astrophysics Data System (ADS)

Pinto, Carla M. A.

2017-02-01

Low levels of viral load are found in HIV-infected patients, after many years under successful suppressive anti-retroviral therapy (ART). The factors leading to this persistence are still under debate, but it is now more or less accepted that the latent reservoir may be crucial to the maintenance of this residual viremia. In this paper, we study the role of the latent reservoir in the persistence of the latent reservoir and of the plasma viremia in a fractional-order (FO) model for HIV infection. Our model assumes that (i) the latently infected cells may undergo bystander proliferation, without active viral production, (ii) the latent cell activation rate decreases with time on ART, (iii) the productively infected cells' death rate is a function of the infected cell density. The proposed model provides new insights on the role of the latent reservoir in the persistence of the latent reservoir and of the plasma virus. Moreover, the fractional-order derivative distinguishes distinct velocities in the dynamics of the latent reservoir and of plasma virus. The later may be used to better approximations of HIV-infected patients data. To our best knowledge, this is the first FO model that deals with the role of the latent reservoir in the persistence of low levels of viremia and of the latent reservoir.

RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

NASA Astrophysics Data System (ADS)

Gui, Guan; Xu, Li; Adachi, Fumiyuki

2014-12-01

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.

LAT region factors mediating differential neuronal tropism of HSV-1 and HSV-2 do not act in trans.

PubMed

Bertke, Andrea S; Apakupakul, Kathleen; Ma, AyeAye; Imai, Yumi; Gussow, Anne M; Wang, Kening; Cohen, Jeffrey I; Bloom, David C; Margolis, Todd P

2012-01-01

After HSV infection, some trigeminal ganglion neurons support productive cycle gene expression, while in other neurons the virus establishes a latent infection. We previously demonstrated that HSV-1 and HSV-2 preferentially establish latent infection in A5+ and KH10+ sensory neurons, respectively, and that exchanging the latency-associated transcript (LAT) between HSV-1 and HSV-2 also exchanges the neuronal preference. Since many viral genes besides the LAT are functionally interchangeable between HSV-1 and HSV-2, we co-infected HSV-1 and HSV-2, both in vivo and in vitro, to determine if trans-acting viral factors regulate whether HSV infection follows a productive or latent pattern of gene expression in sensory neurons. The pattern of HSV-1 and HSV-2 latent infection in trigeminal neurons was no different following co-infection than with either virus alone, consistent with the hypothesis that a trans-acting viral factor is not responsible for the different patterns of latent infection of HSV-1 and HSV-2 in A5+ and KH10+ neurons. Since exchanging the LAT regions between the viruses also exchanges neuronal preferences, we infected transgenic mice that constitutively express 2.8 kb of the LAT region with the heterologous viral serotype. Endogenous expression of LAT did not alter the pattern of latent infection after inoculation with the heterologous serotype virus, demonstrating that the LAT region does not act in trans to direct preferential establishment of latency of HSV-1 and HSV-2. Using HSV1-RFP and HSV2-GFP in adult trigeminal ganglion neurons in vitro, we determined that HSV-1 and HSV-2 do not exert trans-acting effects during acute infection to regulate neuron specificity. Although some neurons were productively infected with both HSV-1 and HSV-2, no A5+ or KH10+ neurons were productively infected with both viruses. Thus, trans-acting viral factors do not regulate preferential permissiveness of A5+ and KH10+ neurons for productive HSV infection and preferential establishment of latent infection.

LAT Region Factors Mediating Differential Neuronal Tropism of HSV-1 and HSV-2 Do Not Act in Trans

PubMed Central

Bertke, Andrea S.; Apakupakul, Kathleen; Ma, AyeAye; Imai, Yumi; Gussow, Anne M.; Wang, Kening; Cohen, Jeffrey I.; Bloom, David C.; Margolis, Todd P.

2012-01-01

After HSV infection, some trigeminal ganglion neurons support productive cycle gene expression, while in other neurons the virus establishes a latent infection. We previously demonstrated that HSV-1 and HSV-2 preferentially establish latent infection in A5+ and KH10+ sensory neurons, respectively, and that exchanging the latency-associated transcript (LAT) between HSV-1 and HSV-2 also exchanges the neuronal preference. Since many viral genes besides the LAT are functionally interchangeable between HSV-1 and HSV-2, we co-infected HSV-1 and HSV-2, both in vivo and in vitro, to determine if trans-acting viral factors regulate whether HSV infection follows a productive or latent pattern of gene expression in sensory neurons. The pattern of HSV-1 and HSV-2 latent infection in trigeminal neurons was no different following co-infection than with either virus alone, consistent with the hypothesis that a trans-acting viral factor is not responsible for the different patterns of latent infection of HSV-1 and HSV-2 in A5+ and KH10+ neurons. Since exchanging the LAT regions between the viruses also exchanges neuronal preferences, we infected transgenic mice that constitutively express 2.8 kb of the LAT region with the heterologous viral serotype. Endogenous expression of LAT did not alter the pattern of latent infection after inoculation with the heterologous serotype virus, demonstrating that the LAT region does not act in trans to direct preferential establishment of latency of HSV-1 and HSV-2. Using HSV1-RFP and HSV2-GFP in adult trigeminal ganglion neurons in vitro, we determined that HSV-1 and HSV-2 do not exert trans-acting effects during acute infection to regulate neuron specificity. Although some neurons were productively infected with both HSV-1 and HSV-2, no A5+ or KH10+ neurons were productively infected with both viruses. Thus, trans-acting viral factors do not regulate preferential permissiveness of A5+ and KH10+ neurons for productive HSV infection and preferential establishment of latent infection. PMID:23300908

Augmenting Latent Dirichlet Allocation and Rank Threshold Detection with Ontologies

DTIC Science & Technology

2010-03-01

Probabilistic Latent Semantic Indexing (PLSI) is an automated indexing information retrieval model [20]. It is based on a statistical latent class model which is...uses a statistical foundation that is more accurate in finding hidden semantic relationships [20]. The model uses factor analysis of count data, number...principle of statistical infer- ence which asserts that all of the information in a sample is contained in the likelihood function [20]. The statistical

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…

Structure-preserving and rank-revealing QR-factorizations

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

Bischof, C.H.; Hansen, P.C.

1991-11-01

The rank-revealing QR-factorization (RRQR-factorization) is a special QR-factorization that is guaranteed to reveal the numerical rank of the matrix under consideration. This makes the RRQR-factorization a useful tool in the numerical treatment of many rank-deficient problems in numerical linear algebra. In this paper, a framework is presented for the efficient implementation of RRQR algorithms, in particular, for sparse matrices. A sparse RRQR-algorithm should seek to preserve the structure and sparsity of the matrix as much as possible while retaining the ability to capture safely the numerical rank. To this end, the paper proposes to compute an initial QR-factorization using amore » restricted pivoting strategy guarded by incremental condition estimation (ICE), and then applies the algorithm suggested by Chan and Foster to this QR-factorization. The column exchange strategy used in the initial QR factorization will exploit the fact that certain column exchanges do not change the sparsity structure, and compute a sparse QR-factorization that is a good approximation of the sought-after RRQR-factorization. Due to quantities produced by ICE, the Chan/Foster RRQR algorithm can be implemented very cheaply, thus verifying that the sought-after RRQR-factorization has indeed been computed. Experimental results on a model problem show that the initial QR-factorization is indeed very likely to produce RRQR-factorization.« less

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

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.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

PubMed Central

2010-01-01

Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data. PMID:21062443

Extracellular heat shock protein HSP90{beta} secreted by MG63 osteosarcoma cells inhibits activation of latent TGF-{beta}1

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

Suzuki, Shigeki; Kulkarni, Ashok B., E-mail: ak40m@nih.gov

2010-07-30

Transforming growth factor-beta 1 (TGF-{beta}1) is secreted as a latent complex, which consists of latency-associated peptide (LAP) and the mature ligand. The release of the mature ligand from LAP usually occurs through conformational change of the latent complex and is therefore considered to be the first step in the activation of the TGF-{beta} signaling pathway. So far, factors such as heat, pH changes, and proteolytic cleavage are reportedly involved in this activation process, but the precise molecular mechanism is still far from clear. Identification and characterization of the cell surface proteins that bind to LAP are important to our understandingmore » of the latent TGF-{beta} activation process. In this study, we have identified heat shock protein 90 {beta} (HSP90{beta}) from the cell surface of the MG63 osteosarcoma cell line as a LAP binding protein. We have also found that MG63 cells secrete HSP90{beta} into extracellular space which inhibits the activation of latent TGF-{beta}1, and that there is a subsequent decrease in cell proliferation. TGF-{beta}1-mediated stimulation of MG63 cells resulted in the increased cell surface expression of HSP90{beta}. Thus, extracellular HSP90{beta} is a negative regulator for the activation of latent TGF-{beta}1 modulating TGF-{beta} signaling in the extracellular domain. -- Research highlights: {yields} Transforming growth factor-beta 1 (TGF-{beta}1) is secreted as a latent complex. {yields} This complex consists of latency-associated peptide (LAP) and the mature ligand. {yields} The release of the mature ligand from LAP is the first step in TGF-{beta} activation. {yields} We identified for the first time a novel mechanism for this activation process. {yields} Heat shock protein 90 {beta} is discovered as a negative regulator for this process.« less

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.

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.

Tuberculosis and latent tuberculosis infection among healthcare workers in Kisumu, Kenya.

PubMed

Agaya, Janet; Nnadi, Chimeremma D; Odhiambo, Joseph; Obonyo, Charles; Obiero, Vincent; Lipke, Virginia; Okeyo, Elisha; Cain, Kevin; Oeltmann, John E

2015-12-01

To assess prevalence and occupational risk factors of latent TB infection and history of TB disease ascribed to work in a healthcare setting in western Kenya. We conducted a cross-sectional survey among healthcare workers in western Kenya in 2013. They were recruited from dispensaries, health centres and hospitals that offer both TB and HIV services. School workers from the health facilities' catchment communities were randomly selected to serve as the community comparison group. Latent TB infection was diagnosed by tuberculin skin testing. HIV status of participants was assessed. Using a logistic regression model, we determined the adjusted odds of latent TB infection among healthcare workers compared to school workers; and among healthcare workers only, we assessed work-related risk factors for latent TB infection. We enrolled 1005 healthcare workers and 411 school workers. Approximately 60% of both groups were female. A total of 22% of 958 healthcare workers and 12% of 392 school workers tested HIV positive. Prevalence of self-reported history of TB disease was 7.4% among healthcare workers and 3.6% among school workers. Prevalence of latent TB infection was 60% among healthcare workers and 48% among school workers. Adjusted odds of latent TB infection were 1.5 times higher among healthcare workers than school workers (95% confidence interval 1.2-2.0). Healthcare workers at all three facility types had similar prevalence of latent TB infection (P = 0.72), but increasing years of employment was associated with increased odds of LTBI (P < 0.01). Healthcare workers at facilities in western Kenya which offer TB and HIV services are at increased risk of latent TB infection, and the risk is similar across facility types. Implementation of WHO-recommended TB infection control measures are urgently needed in health facilities to protect healthcare workers. © 2015 John Wiley & Sons Ltd.

Global, broad, or specific cognitive differences? Using a MIMIC model to examine differences in CHC abilities in children with learning disabilities.

PubMed

Niileksela, Christopher R; Reynolds, Matthew R

2014-01-01

This study was designed to better understand the relations between learning disabilities and different levels of latent cognitive abilities, including general intelligence (g), broad cognitive abilities, and specific abilities based on the Cattell-Horn-Carroll theory of intelligence (CHC theory). Data from the Differential Ability Scales-Second Edition (DAS-II) were used to create a multiple-indicator multiple cause model to examine the latent mean differences in cognitive abilities between children with and without learning disabilities in reading (LD reading), math (LD math), and reading and writing(LD reading and writing). Statistically significant differences were found in the g factor between the norm group and the LD groups. After controlling for differences in g, the LD reading and LD reading and writing groups showed relatively lower latent processing speed, and the LD math group showed relatively higher latent comprehension-knowledge. There were also some differences in some specific cognitive abilities, including lower scores in spatial relations and numerical facility for the LD math group, and lower scores in visual memory for the LD reading and writing group. These specific mean differences were above and beyond any differences in the latent cognitive factor means.

Isolation of virus from brain after immunosuppression of mice with latent herpes simplex

NASA Astrophysics Data System (ADS)

Kastrukoff, Lorne; Long, Carol; Doherty, Peter C.; Wroblewska, Zofia; Koprowski, Hilary

1981-06-01

Herpes simplex virus (HSV) is usually present in a latent form in the trigeminal ganglion of man1-3. Various stress factors may induce virus reactivation, which is manifest by a lip lesion (innervated from the trigeminal ganglion) and the production of infectious virus. The considerable experimental efforts to define the conditions that lead to the reactivation of latent HSV have concentrated on isolating virus either from the original extraneural site of virus inoculation, or from cell-free homogenates of sensory ganglia from latently infected animals4-15. Recent DNA hybridization experiments resulted in the demonstration of the presence of HSV genomes in the brain tissue of both latently infected mice, and of humans who showed no clinical symptoms of HSV (ref. 16 and N. Fraser, personal communication). This led us to consider the possibility that HSV may be present in brain tissue as the result of either reactivation of the virus in brain cells or the passage of reactivated virus from trigeminal ganglia through the brain stem to the brain. The presence of infectious HSV in brain tissue has not previously been demonstrated; yet this could be a factor in chronic, relapsing neurological diseases such as multiple sclerosis. We have now shown experimentally that mice carrying latent HSV in their trigeminal ganglia may, following massive immunosuppression, express infectious virus in the central nervous system (CNS).

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

PubMed Central

Hao, Yansong; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-01-01

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. PMID:29597280

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

PubMed

Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-03-28

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning.

PubMed

Zhe, Shandian; Xu, Zenglin; Qi, Yuan; Yu, Peng

2014-01-01

A key step for Alzheimer's disease (AD) study is to identify associations between genetic variations and intermediate phenotypes (e.g., brain structures). At the same time, it is crucial to develop a noninvasive means for AD diagnosis. Although these two tasks-association discovery and disease diagnosis-have been treated separately by a variety of approaches, they are tightly coupled due to their common biological basis. We hypothesize that the two tasks can potentially benefit each other by a joint analysis, because (i) the association study discovers correlated biomarkers from different data sources, which may help improve diagnosis accuracy, and (ii) the disease status may help identify disease-sensitive associations between genetic variations and MRI features. Based on this hypothesis, we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels based on Gaussian process ordinal regression; in return, the disease status is used to guide the discovery of relationships between the data sources. The sparse projection matrices not only reveal the associations but also select groups of biomarkers related to AD. To learn the model from data, we develop an efficient variational expectation maximization algorithm. Simulation results demonstrate that our approach achieves higher accuracy in both predicting ordinal labels and discovering associations between data sources than alternative methods. We apply our approach to an imaging genetics dataset of AD. Our joint analysis approach not only identifies meaningful and interesting associations between genetic variations, brain structures, and AD status, but also achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempel's ravens.

PubMed

Martin, Eric; Cao, Eddie

2015-05-01

Molecules are often characterized by sparse binary fingerprints, where 1s represent the presence of substructures and 0s represent their absence. Fingerprints are especially useful for similarity calculations, such as database searching or clustering, generally measuring similarity as the Tanimoto coefficient. In other cases, such as visualization, design of experiments, or latent variable regression, a low-dimensional Euclidian "chemical space" is more useful, where proximity between points reflects chemical similarity. A temptation is to apply principal components analysis (PCA) directly to these fingerprints to obtain a low dimensional continuous chemical space. However, Gower has shown that distances from PCA on bit vectors are proportional to the square root of Hamming distance. Unlike Tanimoto similarity, Hamming similarity (HS) gives equal weight to shared 0s as to shared 1s, that is, HS gives as much weight to substructures that neither molecule contains, as to substructures which both molecules contain. Illustrative examples show that proximity in the corresponding chemical space reflects mainly similar size and complexity rather than shared chemical substructures. These spaces are ill-suited for visualizing and optimizing coverage of chemical space, or as latent variables for regression. A more suitable alternative is shown to be Multi-dimensional scaling on the Tanimoto distance matrix, which produces a space where proximity does reflect structural similarity.

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

Semiparametric Time-to-Event Modeling in the Presence of a Latent Progression Event

PubMed Central

Rice, John D.; Tsodikov, Alex

2017-01-01

Summary In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood–based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method; its use in practice is demonstrated in the analysis of a prostate cancer data set. PMID:27556886

Semiparametric time-to-event modeling in the presence of a latent progression event.

PubMed

Rice, John D; Tsodikov, Alex

2017-06-01

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method; its use in practice is demonstrated in the analysis of a prostate cancer data set. © 2016, The International Biometric Society.

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

DNA Methylation Alterations Associated with Latent Carcinogenic Activity of Dichloroacetic Acid in Mice

EPA Science Inventory

Early-life environmental factors can influence later-life susceptibility to cancer. Epigenetic changes serve as promising biomarkers for these latent effects. Previously, we reported that short-term postnatal exposure to dichloroacetic acid (DCA), a byproduct of drinking water ch...

Surface Heat Balance Analysis of Tainan City on March 6, 2001 Using ASTER and Formosat-2 Data

PubMed Central

Kato, Soushi; Yamaguchi, Yasushi; Liu, Cheng-Chien; Sun, Chen-Yi

2008-01-01

The urban heat island phenomenon occurs as a mixed result of anthropogenic heat discharge, decreased vegetation, and increased artificial impervious surfaces. To clarify the contribution of each factor to the urban heat island, it is necessary to evaluate the surface heat balance. Satellite remote sensing data of Tainan City, Taiwan, obtained from Terra ASTER and Formosat-2 were used to estimate surface heat balance in this study. ASTER data is suitable for analyzing heat balance because of the wide spectral range. We used Formosat-2 multispectral data to classify the land surface, which was used to interpolate some surface parameters for estimating heat fluxes. Because of the high spatial resolution of the Formosat-2 image, more roads, open spaces and small vegetation areas could be distinguished from buildings in urban areas; however, misclassifications of land cover in such areas using ASTER data would overestimate the sensible heat flux. On the other hand, the small vegetated areas detected from the Formosat-2 image slightly increased the estimation of latent heat flux. As a result, the storage heat flux derived from Formosat-2 is higher than that derived from ASTER data in most areas. From these results, we can conclude that the higher resolution land coverage map increases accuracy of the heat balance analysis. Storage heat flux occupies about 60 to 80% of the net radiation in most of the artificial surface areas in spite of their usages. Because of the homogeneity of the building roof materials, there is no contrast between the storage heat flux in business and residential areas. In sparsely vegetated urban areas, more heat is stored and latent heat is smaller than that in the forested suburbs. This result implies that density of vegetation has a significant influence in decreasing temperatures. PMID:27873856

Measuring the foundations of school readiness: Introducing a new questionnaire for teachers - The Brief Early Skills and Support Index (BESSI).

PubMed

Hughes, Claire; Daly, Irenee; Foley, Sarah; White, Naomi; Devine, Rory T

2015-09-01

Early work on school readiness focused on academic skills. Recent research highlights the value of also including both children's social and behavioural competencies and family support. Reflecting this broader approach, this study aimed to develop a new and brief questionnaire for teachers: The Brief Early Skills and Support Index (BESSI). The main sample, recruited from the north-west of England, included 1,456 children (49% male), aged 2.5 to 5.5 years. A second sample consisting of 258 children (44% male) aged 3 to 5.5 years was recruited to assess the test-retest reliability of the BESSI across a 1-month interval. Following development and pilot work with early years teachers, a streamlined (30 items) version of the BESSI was sent to 98 teachers and nursery staff, who rated the children in their class. The best-fitting model included four latent factors: Three child factors (Behavioural Adjustment, Language and Cognition, and Daily Living Skills) and one Family Support factor. The three child factors exhibited measurement invariance across gender. All four factors showed good internal consistency and test-retest reliability. Structural equation modelling showed that (1) boys had more problems than girls on all three child factors; (2) older children showed better Language and Cognition and Daily Living Skills than younger children; and (3) children eligible for free school meals (an index of financial hardship) had more problems on all four latent factors. Family Support latent scores predicted all three child latent factors and accounted for their correlation with financial hardship. The BESSI is a promising brief teacher-report screening tool that appears suitable for children aged 2.5 to 5.5 and provides a broader perspective upon school readiness than previous measures. © 2015 The British Psychological Society.

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).

Algorithms and Application of Sparse Matrix Assembly and Equation Solvers for Aeroacoustics

NASA Technical Reports Server (NTRS)

Watson, W. R.; Nguyen, D. T.; Reddy, C. J.; Vatsa, V. N.; Tang, W. H.

2001-01-01

An algorithm for symmetric sparse equation solutions on an unstructured grid is described. Efficient, sequential sparse algorithms for degree-of-freedom reordering, supernodes, symbolic/numerical factorization, and forward backward solution phases are reviewed. Three sparse algorithms for the generation and assembly of symmetric systems of matrix equations are presented. The accuracy and numerical performance of the sequential version of the sparse algorithms are evaluated over the frequency range of interest in a three-dimensional aeroacoustics application. Results show that the solver solutions are accurate using a discretization of 12 points per wavelength. Results also show that the first assembly algorithm is impractical for high-frequency noise calculations. The second and third assembly algorithms have nearly equal performance at low values of source frequencies, but at higher values of source frequencies the third algorithm saves CPU time and RAM. The CPU time and the RAM required by the second and third assembly algorithms are two orders of magnitude smaller than that required by the sparse equation solver. A sequential version of these sparse algorithms can, therefore, be conveniently incorporated into a substructuring for domain decomposition formulation to achieve parallel computation, where different substructures are handles by different parallel processors.

Cellular Transcription Factors Induced in Trigeminal Ganglia during Dexamethasone-Induced Reactivation from Latency Stimulate Bovine Herpesvirus 1 Productive Infection and Certain Viral Promoters

PubMed Central

Workman, Aspen; Eudy, James; Smith, Lynette; Frizzo da Silva, Leticia; Sinani, Devis; Bricker, Halie; Cook, Emily; Doster, Alan

2012-01-01

Bovine herpesvirus 1 (BHV-1), an alphaherpesvirinae subfamily member, establishes latency in sensory neurons. Elevated corticosteroid levels, due to stress, reproducibly triggers reactivation from latency in the field. A single intravenous injection of the synthetic corticosteroid dexamethasone (DEX) to latently infected calves consistently induces reactivation from latency. Lytic cycle viral gene expression is detected in sensory neurons within 6 h after DEX treatment of latently infected calves. These observations suggested that DEX stimulated expression of cellular genes leads to lytic cycle viral gene expression and productive infection. In this study, a commercially available assay—Bovine Gene Chip—was used to compare cellular gene expression in the trigeminal ganglia (TG) of calves latently infected with BHV-1 versus DEX-treated animals. Relative to TG prepared from latently infected calves, 11 cellular genes were induced more than 10-fold 3 h after DEX treatment. Pentraxin three, a regulator of innate immunity and neurodegeneration, was stimulated 35- to 63-fold after 3 or 6 h of DEX treatment. Two transcription factors, promyelocytic leukemia zinc finger (PLZF) and Slug were induced more than 15-fold 3 h after DEX treatment. PLZF or Slug stimulated productive infection 20- or 5-fold, respectively, and Slug stimulated the late glycoprotein C promoter more than 10-fold. Additional DEX-induced transcription factors also stimulated productive infection and certain viral promoters. These studies suggest that DEX-inducible cellular transcription factors and/or signaling pathways stimulate lytic cycle viral gene expression, which subsequently leads to successful reactivation from latency in a small subset of latently infected neurons. PMID:22190728

Crystallization of bFGF-DNA Aptamer Complexes Using a Sparse Matrix Designed for Protein-Nucleic Acid Complexes

NASA Technical Reports Server (NTRS)

Cannone, Jaime J.; Barnes, Cindy L.; Achari, Aniruddha; Kundrot, Craig E.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

The Sparse Matrix approach for obtaining lead crystallization conditions has proven to be very fruitful for the crystallization of proteins and nucleic acids. Here we report a Sparse Matrix developed specifically for the crystallization of protein-DNA complexes. This method is rapid and economical, typically requiring 2.5 mg of complex to test 48 conditions. The method was originally developed to crystallize basic fibroblast growth factor (bFGF) complexed with DNA sequences identified through in vitro selection, or SELEX, methods. Two DNA aptamers that bind with approximately nanomolar affinity and inhibit the angiogenic properties of bFGF were selected for co-crystallization. The Sparse Matrix produced lead crystallization conditions for both bFGF-DNA complexes.

Utility of an interferon-gamma release assay for latent tuberculosis diagnosis in a case of bullous pemphigoid.

PubMed

Goodfellow, Alfred; Keeling, Douglas N; Hayes, Robert C; Webster, Duncan

2009-01-01

With increasing use of immunosuppressive therapy, including tumor necrosis factor alpha inhibitors, there is concern about infectious complications, including reactivation of latent Mycobacterium tuberculosis infection. Routine testing prior to administration of systemic immunosuppression includes the tuberculin skin test, which lacks sensitivity and specificity and may be difficult to interpret in the presence of extensive cutaneous disease. Treatment of individuals with latent tuberculosis infection is recommended when immunosuppressive medications are to be employed. We report a case in which a diagnosis of latent tuberculosis infection in a patient with extensive bullous pemphigoid was clarified by the use of an interferon-gamma release assay after equivocal tuberculin skin test results. Interferon-gamma release assays are useful adjuncts to the tuberculin skin test in the diagnosis of latent tuberculosis infection in the setting of extensive cutaneous disease.

Tenascin-X promotes epithelial-to-mesenchymal transition by activating latent TGF-β

PubMed Central

Alcaraz, Lindsay B.; Exposito, Jean-Yves; Chuvin, Nicolas; Pommier, Roxane M.; Cluzel, Caroline; Martel, Sylvie; Sentis, Stéphanie; Bartholin, Laurent; Lethias, Claire

2014-01-01

Transforming growth factor β (TGF-β) isoforms are secreted as inactive complexes formed through noncovalent interactions between the bioactive TGF-β entity and its N-terminal latency-associated peptide prodomain. Extracellular activation of the latent TGF-β complex is a crucial step in the regulation of TGF-β function for tissue homeostasis. We show that the fibrinogen-like (FBG) domain of the matrix glycoprotein tenascin-X (TNX) interacts physically with the small latent TGF-β complex in vitro and in vivo, thus regulating the bioavailability of mature TGF-β to cells by activating the latent cytokine into an active molecule. Activation by the FBG domain most likely occurs through a conformational change in the latent complex and involves a novel cell adhesion–dependent mechanism. We identify α11β1 integrin as a cell surface receptor for TNX and show that this integrin is crucial to elicit FBG-mediated activation of latent TGF-β and subsequent epithelial-to-mesenchymal transition in mammary epithelial cells. PMID:24821840

Data traffic reduction schemes for sparse Cholesky factorizations

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1988-01-01

Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.

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…

Latent Class Subtyping of Attention-Deficit/Hyperactivity Disorder and Comorbid Conditions

ERIC Educational Resources Information Center

Acosta, Maria T.; Castellanos, F. Xavier; Bolton, Kelly L.; Balog, Joan Z.; Eagen, Patricia; Nee, Linda; Jones, Janet; Palacio, Luis; Sarampote, Christopher; Russell, Heather F.; Berg, Kate; Arcos-Burgos, Mauricio; Muenke, Maximilian

2008-01-01

The study attempts to carry out latent class analysis (LCA) in a sample of 1010 individuals, some with Attention-Deficit/Hyperactivity disorder (ADHD) and others normal. Results indicate that LCA can feasibly allow the combination of externalizing and internalizing symptoms for future tests regarding specific genetic risk factors.

Examining the integrity of measurement of cognitive abilities in the prediction of achievement: Comparisons and contrasts across variables from higher-order and bifactor models.

PubMed

Benson, Nicholas F; Kranzler, John H; Floyd, Randy G

2016-10-01

Prior research examining cognitive ability and academic achievement relations have been based on different theoretical models, have employed both latent variables as well as observed variables, and have used a variety of analytic methods. Not surprisingly, results have been inconsistent across studies. The aims of this study were to (a) examine how relations between psychometric g, Cattell-Horn-Carroll (CHC) broad abilities, and academic achievement differ across higher-order and bifactor models; (b) examine how well various types of observed scores corresponded with latent variables; and (c) compare two types of observed scores (i.e., refined and non-refined factor scores) as predictors of academic achievement. Results suggest that cognitive-achievement relations vary across theoretical models and that both types of factor scores tend to correspond well with the models on which they are based. However, orthogonal refined factor scores (derived from a bifactor model) have the advantage of controlling for multicollinearity arising from the measurement of psychometric g across all measures of cognitive abilities. Results indicate that the refined factor scores provide more precise representations of their targeted constructs than non-refined factor scores and maintain close correspondence with the cognitive-achievement relations observed for latent variables. Thus, we argue that orthogonal refined factor scores provide more accurate representations of the relations between CHC broad abilities and achievement outcomes than non-refined scores do. Further, the use of refined factor scores addresses calls for the application of scores based on latent variable models. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Latent myostatin has significant activity and this activity is controlled more efficiently by WFIKKN1 than by WFIKKN2

PubMed Central

Szláma, György; Trexler, Mária; Patthy, László

2013-01-01

Myostatin, a negative regulator of skeletal muscle growth, is produced from myostatin precursor by multiple steps of proteolytic processing. After cleavage by a furin-type protease, the propeptide and growth factor domains remain associated, forming a noncovalent complex, the latent myostatin complex. Mature myostatin is liberated from latent myostatin by bone morphogenetic protein 1/tolloid proteases. Here, we show that, in reporter assays, latent myostatin preparations have significant myostatin activity, as the noncovalent complex dissociates at an appreciable rate, and both mature and semilatent myostatin (a complex in which the dimeric growth factor domain interacts with only one molecule of myostatin propeptide) bind to myostatin receptor. The interaction of myostatin receptor with semilatent myostatin is efficiently blocked by WAP, Kazal, immunoglobulin, Kunitz and NTR domain-containing protein 1 or growth and differentiation factor-associated serum protein 2 (WFIKKN1), a large extracellular multidomain protein that binds both mature myostatin and myostatin propeptide [Kondás et al. (2008) J Biol Chem 283, 23677–23684]. Interestingly, the paralogous protein WAP, Kazal, immunoglobulin, Kunitz and NTR domain-containing protein 2 or growth and differentiation factor-associated serum protein 1 (WFIKKN2) was less efficient than WFIKKN1 as an antagonist of the interactions of myostatin receptor with semilatent myostatin. Our studies have shown that this difference is attributable to the fact that only WFIKKN1 has affinity for the propeptide domain, and this interaction increases its potency in suppressing the receptor-binding activity of semilatent myostatin. As the interaction of WFIKKN1 with various forms of myostatin permits tighter control of myostatin activity until myostatin is liberated from latent myostatin by bone morphogenetic protein 1/tolloid proteases, WFIKKN1 may have greater potential as an antimyostatic agent than WFIKKN2. Structured digital abstract Furin cleaves Promyostatin by protease assay (View interaction) myostatin binds to PRO by surface plasmon resonance (View interaction) BMP-1 cleaves Promyostatin by protease assay (View interaction) ACR IIB physically interacts with Latent Myostatin by surface plasmon resonance (View interaction) Promyostatin and Promyostatin bind by comigration in gel electrophoresis (View interaction) WFIKKN1 binds to Latent Myostatin by pull down (View interaction) ACR IIB binds to Mature Myostatin by surface plasmon resonance (View Interaction: 1, 2, 3) WFIKKN1 binds to Myostatin Prodomain by surface plasmon resonance (View Interaction: 1, 2, 3) PMID:23829672

Preserving sparseness in multivariate polynominal factorization

NASA Technical Reports Server (NTRS)

Wang, P. S.

1977-01-01

Attempts were made to factor these ten polynomials on MACSYMA. However it did not get very far with any of the larger polynomials. At that time, MACSYMA used an algorithm created by Wang and Rothschild. This factoring algorithm was also implemented for the symbolic manipulation system, SCRATCHPAD of IBM. A closer look at this old factoring algorithm revealed three problem areas, each of which contribute to losing sparseness and intermediate expression growth. This study led to effective ways of avoiding these problems and actually to a new factoring algorithm. The three problems are known as the extraneous factor problem, the leading coefficient problem, and the bad zero problem. These problems are examined separately. Their causes and effects are set forth in detail; the ways to avoid or lessen these problems are described.

On the role of sparseness in the evolution of modularity in gene regulatory networks

PubMed Central

2018-01-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. PMID:29775459

On the role of sparseness in the evolution of modularity in gene regulatory networks.

PubMed

Espinosa-Soto, Carlos

2018-05-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

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.

Reactivation of Latent Tuberculosis in Cynomolgus Macaques Infected with SIV Is Associated with Early Peripheral T Cell Depletion and Not Virus Load

PubMed Central

Klein, Edwin; Janssen, Chris; Phuah, Jiayao; Sturgeon, Timothy J.; Montelaro, Ronald C.; Lin, Philana Ling; Flynn, JoAnne L.

2010-01-01

HIV-infected individuals with latent Mycobacterium tuberculosis (Mtb) infection are at significantly greater risk of reactivation tuberculosis (TB) than HIV-negative individuals with latent TB, even while CD4 T cell numbers are well preserved. Factors underlying high rates of reactivation are poorly understood and investigative tools are limited. We used cynomolgus macaques with latent TB co-infected with SIVmac251 to develop the first animal model of reactivated TB in HIV-infected humans to better explore these factors. All latent animals developed reactivated TB following SIV infection, with a variable time to reactivation (up to 11 months post-SIV). Reactivation was independent of virus load but correlated with depletion of peripheral T cells during acute SIV infection. Animals experiencing reactivation early after SIV infection (<17 weeks) had fewer CD4 T cells in the periphery and airways than animals reactivating in later phases of SIV infection. Co-infected animals had fewer T cells in involved lungs than SIV-negative animals with active TB despite similar T cell numbers in draining lymph nodes. Granulomas from these animals demonstrated histopathologic characteristics consistent with a chronically active disease process. These results suggest initial T cell depletion may strongly influence outcomes of HIV-Mtb co-infection. PMID:20224771

Heritabilities of Facial Measurements and Their Latent Factors in Korean Families

PubMed Central

Kim, Hyun-Jin; Im, Sun-Wha; Jargal, Ganchimeg; Lee, Siwoo; Yi, Jae-Hyuk; Park, Jeong-Yeon; Sung, Joohon; Cho, Sung-Il; Kim, Jong-Yeol; Kim, Jong-Il; Seo, Jeong-Sun

2013-01-01

Genetic studies on facial morphology targeting healthy populations are fundamental in understanding the specific genetic influences involved; yet, most studies to date, if not all, have been focused on congenital diseases accompanied by facial anomalies. To study the specific genetic cues determining facial morphology, we estimated familial correlations and heritabilities of 14 facial measurements and 3 latent factors inferred from a factor analysis in a subset of the Korean population. The study included a total of 229 individuals from 38 families. We evaluated a total of 14 facial measurements using 2D digital photographs. We performed factor analysis to infer common latent variables. The heritabilities of 13 facial measurements were statistically significant (p < 0.05) and ranged from 0.25 to 0.61. Of these, the heritability of intercanthal width in the orbital region was found to be the highest (h2 = 0.61, SE = 0.14). Three factors (lower face portion, orbital region, and vertical length) were obtained through factor analysis, where the heritability values ranged from 0.45 to 0.55. The heritability values for each factor were higher than the mean heritability value of individual original measurements. We have confirmed the genetic influence on facial anthropometric traits and suggest a potential way to categorize and analyze the facial portions into different groups. PMID:23843774

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

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.

Induction of reactivation of herpes simplex virus in murine sensory ganglia in vivo by cadmium.

PubMed Central

Fawl, R L; Roizman, B

1993-01-01

Herpes simplex viruses maintained in a latent state in sensory neurons in mice do not reactivate spontaneously, and therefore the factors or procedures which cause the virus to reactivate serve as a clue to the mechanisms by which the virus is maintained in a latent state. We report that cadmium sulfate induces latent virus to reactivate in 75 to 100% of mice tested. The following specific findings are reported. (i) The highest frequency of induction was observed after two to four daily administrations of 100 micrograms of cadmium sulfate. (ii) Zinc, copper, manganese, or nickel sulfate administered in equimolar amounts under the same regimen did not induce viral reactivation; however, zinc sulfate in molar ratios 25-fold greater than those of cadmium induced viral replication in 2 of 16 ganglia tested. (iii) Administration of zinc, nickel, or manganese prior to the cadmium sulfate reduced the incidence of ganglia containing infectious virus. (iv) Administration of cadmium daily during the first week after infection and at 2-day intervals to 13 days after infection resulted in the recovery from ganglia of infectious virus in titers 10- to 100-fold higher than those obtained from animals given saline. Moreover, infectious virus was recovered as late as 11 days after infection compared with 6 days in mice administered saline. (v) Administration of cadmium immediately after infection or repeatedly after establishment of latency did not exhaust the latent virus harbored by sensory neurons, inasmuch as the fraction of ganglia of mice administered cadmium and yielding infectious virus was similar to that observed in mice treated with saline. We conclude that induction of cadmium tolerance precludes reactivation of latent virus. If the induction of metallothionein genes was the sole factor required to cause reactivation of latent virus, it would have been expected that all metals which induce metallothioneins would also induce reactivation, which was not observed. The results therefore raise the possibility that in addition to inducing the metallothionein genes, cadmium inactivates the factors which maintain the virus in latent state. PMID:8230427

Developable images produced by X-rays using the nickel-hypophosphite system. 3: The latent image and trapped hydrogen

NASA Technical Reports Server (NTRS)

May, C. E.; Philipp, W. H.; Marsik, S. J.

1974-01-01

The hydrogen trapped in X-irradiated hypophosphites, phosphites, formates, oxalates, a phosphate, and some organic compounds was vacuum extracted and measured quantitatively with a mass spectrometer. After extraction, normally developable salts were found to be still developable. Thus, the latent image is not the trapped hydrogen but a species of the type HPO(-)2. The amplification factor for irradiated hypophosphites is about 100. A narrow range of wavelengths (at about 0.07 nm, 0.7 A) is responsible for the formation of the latent image.

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…

Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

ERIC Educational Resources Information Center

Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

2010-01-01

This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

Measurement Invariance for Latent Constructs in Multiple Populations: A Critical View and Refocus

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Cheng-Hsien

2012-01-01

Popular measurement invariance testing procedures for latent constructs evaluated by multiple indicators in distinct populations are revisited and discussed. A frequently used test of factor loading invariance is shown to possess serious limitations that in general preclude it from accomplishing its goal of ascertaining this invariance. A process…

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…

Assigning Cases to Groups Using Taxometric Results: An Empirical Comparison of Classification Techniques

ERIC Educational Resources Information Center

Ruscio, John

2009-01-01

Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic…

Variation in working memory capacity, fluid intelligence, and episodic recall: a latent variable examination of differences in the dynamics of free recall.

PubMed

Unsworth, Nash

2009-09-01

A latent variable analysis was conducted to examine the nature of individual differences in the dynamics of free recall and cognitive abilities. Participants performed multiple measures of free recall, working memory capacity (WMC), and fluid intelligence (gF). For each free recall task, recall accuracy, recall latency, and number of intrusion errors were determined, and latent factors were derived for each. It was found that recall accuracy was negatively related to both recall latency and number of intrusions, and recall latency and number of intrusions were positively related. Furthermore, latent WMC and gF factors were positively related to recall accuracy, but negatively related to recall latency and number of intrusions. Finally, a cluster analysis revealed that subgroups of participants with deficits in focusing the search had deficits in recovering degraded representations or deficits in monitoring the products of retrieval. The results are consistent with the idea that variation in the dynamics of free recall, WMC, and gF are primarily due to differences in search set size, but differences in recovery and monitoring are also important.

DNA-PK/Ku complex binds to latency-associated nuclear antigen and negatively regulates Kaposi's sarcoma-associated herpesvirus latent replication

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

Cha, Seho; Lim, Chunghun; Lee, Jae Young

2010-04-16

During latent infection, latency-associated nuclear antigen (LANA) of Kaposi's sarcoma-associated herpesvirus (KSHV) plays important roles in episomal persistence and replication. Several host factors are associated with KSHV latent replication. Here, we show that the catalytic subunit of DNA protein kinase (DNA-PKcs), Ku70, and Ku86 bind the N-terminal region of LANA. LANA was phosphorylated by DNA-PK and overexpression of Ku70, but not Ku86, impaired transient replication. The efficiency of transient replication was significantly increased in the HCT116 (Ku86 +/-) cell line, compared to the HCT116 (Ku86 +/+) cell line, suggesting that the DNA-PK/Ku complex negatively regulates KSHV latent replication.

Assessing factors related to waist circumference and obesity: application of a latent variable model.

PubMed

Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

2015-01-01

Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Data included 18,990 Iranian individuals aged 20-65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity) with independent variables including age, gender, PR (Place of Residence), PA (physical activity), smoking status, SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), CHOL (cholesterol), FBG (Fasting Blood Glucose), diabetes, and FHD (family history of diabetes). All variables were related to both obesity and waist circumference (WC). Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

The Four-Factor Model of Depressive Symptoms in Dementia Caregivers: A Structural Equation Model of Ethnic Differences

PubMed Central

Roth, David L.; Ackerman, Michelle L.; Okonkwo, Ozioma C.; Burgio, Louis D.

2008-01-01

Previous studies have suggested that 4 latent constructs (depressed affect, well-being, interpersonal problems, somatic symptoms) underlie the item responses on the Center for Epidemiological Studies Depression (CES-D) Scale. This instrument has been widely used in dementia caregiving research, but the fit of this multifactor model and the explanatory contributions of multifactor models have not been sufficiently examined for caregiving samples. The authors subjected CES-D data (N = 1,183) from the initial Resources for Enhancing Alzheimer’s Caregiver Health Study to confirmatory factor analysis methods and found that the 4-factor model provided excellent fit to the observed data. Invariance analyses suggested only minimal item-loading differences across race subgroups and supported the validity of race comparisons on the latent factors. Significant race differences were found on 3 of the 4 latent factors both before and after controlling for demographic covariates. African Americans reported less depressed affect and better well-being than White caregivers, who reported better well-being and fewer interpersonal problems than Hispanic caregivers. These findings clarify and extend previous studies of race differences in depression among diverse samples of dementia caregivers. PMID:18808246

Latent profiles of family background, personality and mental health factors and their association with behavioural addictions and substance use disorders in young Swiss men.

PubMed

Marmet, Simon; Studer, Joseph; Rougemont-Bücking, Ansgar; Gmel, Gerhard

2018-05-04

Recent theories suggest that behavioural addictions and substance use disorders may be the result of the same underlying vulnerability. The present study investigates profiles of family background, personality and mental health factors and their associations with seven behavioural addictions (to the internet, gaming, smartphones, internet sex, gambling, exercise and work) and three substance use disorder scales (for alcohol, cannabis and tobacco). The sample consisted of 5287 young Swiss men (mean age = 25.42) from the Cohort Study on Substance Use Risk Factors (C-SURF). A latent profile analysis was performed on family background, personality and mental health factors. The derived profiles were compared with regards to means and prevalence rates of the behavioural addiction and substance use disorder scales. Seven latent profiles were identified, ranging from profiles with a positive family background, favourable personality patterns and low values on mental health scales to profiles with a negative family background, unfavourable personality pattern and high values on mental health scales. Addiction scale means, corresponding prevalence rates and the number of concurrent addictions were highest in profiles with high values on mental health scales and a personality pattern dominated by neuroticism. Overall, behavioural addictions and substance use disorders showed similar patterns across latent profiles. Patterns of family background, personality and mental health factors were associated with different levels of vulnerability to addictions. Behavioural addictions and substance use disorders may thus be the result of the same underlying vulnerabilities. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Adolescent emotionality and effortful control: Core latent constructs and links to psychopathology and functioning

PubMed Central

Snyder, Hannah R.; Gulley, Lauren D.; Bijttebier, Patricia; Hartman, Catharina A.; Oldehinkel, Albertine J.; Mezulis, Amy; Young, Jami F.; Hankin, Benjamin L.

2015-01-01

Temperament is associated with important outcomes in adolescence, including academic and interpersonal functioning and psychopathology. Rothbart’s temperament model is among the most well-studied and supported approaches to adolescent temperament, and contains three main components: positive emotionality (PE), negative emotionality (NE), and effortful control (EC). However, the latent factor structure of Rothbart’s temperament measure for adolescents, the Early Adolescent Temperament Questionnaire Revised (EATQ-R, Ellis & Rothbart, 2001) has not been definitively established. To address this problem and investigate links between adolescent temperament and functioning, we used confirmatory factor analysis to examine the latent constructs of the EATQ-R in a large combined sample. For EC and NE, bifactor models consisting of a common factor plus specific factors for some sub-facets of each component fit best, providing a more nuanced understanding of these temperament dimensions. The nature of the PE construct in the EATQ-R is less clear. Models replicated in a hold-out dataset. The common components of high NE and low EC where broadly associated with increased psychopathology symptoms, and poor interpersonal and school functioning, while specific components of NE were further associated with corresponding specific components of psychopathology. Further questioning the construct validity of PE as measured by the EATQ-R, PE factors did not correlate with construct validity measures in a way consistent with theories of PE. Bringing consistency to the way the EATQ-R is modeled and using purer latent variables has the potential to advance the field in understanding links between dimensions of temperament and important outcomes of adolescent development. PMID:26011660

Adolescent emotionality and effortful control: Core latent constructs and links to psychopathology and functioning.

PubMed

Snyder, Hannah R; Gulley, Lauren D; Bijttebier, Patricia; Hartman, Catharina A; Oldehinkel, Albertine J; Mezulis, Amy; Young, Jami F; Hankin, Benjamin L

2015-12-01

Temperament is associated with important outcomes in adolescence, including academic and interpersonal functioning and psychopathology. Rothbart's temperament model is among the most well-studied and supported approaches to adolescent temperament, and contains 3 main components: positive emotionality (PE), negative emotionality (NE), and effortful control (EC). However, the latent factor structure of Rothbart's temperament measure for adolescents, the Early Adolescent Temperament Questionnaire Revised (EATQ-R; Ellis & Rothbart, 2001) has not been definitively established. To address this problem and investigate links between adolescent temperament and functioning, we used confirmatory factor analysis to examine the latent constructs of the EATQ-R in a large combined sample. For EC and NE, bifactor models consisting of a common factor plus specific factors for some subfacets of each component fit best, providing a more nuanced understanding of these temperament dimensions. The nature of the PE construct in the EATQ-R is less clear. Models replicated in a hold-out dataset. The common components of high NE and low EC where broadly associated with increased psychopathology symptoms, and poor interpersonal and school functioning, while specific components of NE were further associated with corresponding specific components of psychopathology. Further questioning the construct validity of PE as measured by the EATQ-R, PE factors did not correlate with construct validity measures in a way consistent with theories of PE. Bringing consistency to the way the EATQ-R is modeled and using purer latent variables has the potential to advance the field in understanding links between dimensions of temperament and important outcomes of adolescent development. (c) 2015 APA, all rights reserved).

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

A multilevel model for comorbid outcomes: obesity and diabetes in the US.

PubMed

Congdon, Peter

2010-02-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk.

A Shifted Block Lanczos Algorithm 1: The Block Recurrence

NASA Technical Reports Server (NTRS)

Grimes, Roger G.; Lewis, John G.; Simon, Horst D.

1990-01-01

In this paper we describe a block Lanczos algorithm that is used as the key building block of a software package for the extraction of eigenvalues and eigenvectors of large sparse symmetric generalized eigenproblems. The software package comprises: a version of the block Lanczos algorithm specialized for spectrally transformed eigenproblems; an adaptive strategy for choosing shifts, and efficient codes for factoring large sparse symmetric indefinite matrices. This paper describes the algorithmic details of our block Lanczos recurrence. This uses a novel combination of block generalizations of several features that have only been investigated independently in the past. In particular new forms of partial reorthogonalization, selective reorthogonalization and local reorthogonalization are used, as is a new algorithm for obtaining the M-orthogonal factorization of a matrix. The heuristic shifting strategy, the integration with sparse linear equation solvers and numerical experience with the code are described in a companion paper.

Distribution of model uncertainty across multiple data streams

NASA Astrophysics Data System (ADS)

Wutzler, Thomas

2014-05-01

When confronting biogeochemical models with a diversity of observational data streams, we are faced with the problem of weighing the data streams. Without weighing or multiple blocked cost functions, model uncertainty is allocated to the sparse data streams and possible bias in processes that are strongly constraint is exported to processes that are constrained by sparse data streams only. In this study we propose an approach that aims at making model uncertainty a factor of observations uncertainty, that is constant over all data streams. Further we propose an implementation based on Monte-Carlo Markov chain sampling combined with simulated annealing that is able to determine this variance factor. The method is exemplified both with very simple models, artificial data and with an inversion of the DALEC ecosystem carbon model against multiple observations of Howland forest. We argue that the presented approach is able to help and maybe resolve the problem of bias export to sparse data streams.

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.

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.

PubMed

Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej

2015-09-01

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.

Using Chebyshev polynomials and approximate inverse triangular factorizations for preconditioning the conjugate gradient method

NASA Astrophysics Data System (ADS)

Kaporin, I. E.

2012-02-01

In order to precondition a sparse symmetric positive definite matrix, its approximate inverse is examined, which is represented as the product of two sparse mutually adjoint triangular matrices. In this way, the solution of the corresponding system of linear algebraic equations (SLAE) by applying the preconditioned conjugate gradient method (CGM) is reduced to performing only elementary vector operations and calculating sparse matrix-vector products. A method for constructing the above preconditioner is described and analyzed. The triangular factor has a fixed sparsity pattern and is optimal in the sense that the preconditioned matrix has a minimum K-condition number. The use of polynomial preconditioning based on Chebyshev polynomials makes it possible to considerably reduce the amount of scalar product operations (at the cost of an insignificant increase in the total number of arithmetic operations). The possibility of an efficient massively parallel implementation of the resulting method for solving SLAEs is discussed. For a sequential version of this method, the results obtained by solving 56 test problems from the Florida sparse matrix collection (which are large-scale and ill-conditioned) are presented. These results show that the method is highly reliable and has low computational costs.

A tight and explicit representation of Q in sparse QR factorization

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

Ng, E.G.; Peyton, B.W.

1992-05-01

In QR factorization of a sparse m{times}n matrix A (m {ge} n) the orthogonal factor Q is often stored implicitly as a lower trapezoidal matrix H known as the Householder matrix. This paper presents a simple characterization of the row structure of Q, which could be used as the basis for a sparse data structure that can store Q explicitly. The new characterization is a simple extension of a well known row-oriented characterization of the structure of H. Hare, Johnson, Olesky, and van den Driessche have recently provided a complete sparsity analysis of the QR factorization. Let U be themore » matrix consisting of the first n columns of Q. Using results from, we show that the data structures for H and U resulting from our characterizations are tight when A is a strong Hall matrix. We also show that H and the lower trapezoidal part of U have the same sparsity characterization when A is strong Hall. We then show that this characterization can be extended to any weak Hall matrix that has been permuted into block upper triangular form. Finally, we show that permuting to block triangular form never increases the fill incurred during the factorization.« less

A Latent Class Analysis of Adolescent Gambling: Application of Resilience Theory

ERIC Educational Resources Information Center

Goldstein, Abby L.; Faulkner, Breanne; Cunningham, Rebecca M.; Zimmerman, Marc A.; Chermack, Stephen; Walton, Maureen A.

2013-01-01

The current study examined the application of resilience theory to adolescent gambling using Latent Class Analysis (LCA) to establish subtypes of adolescent gamblers and to explore risk and promotive factors associated with gambling group membership. Participants were a diverse sample of 249 adolescents ages 14 to 18 (30.1 % female, 59.4 % African…

Curve of Factors Model: A Latent Growth Modeling Approach for Educational Research

ERIC Educational Resources Information Center

Isiordia, Marilu; Ferrer, Emilio

2018-01-01

A first-order latent growth model assesses change in an unobserved construct from a single score and is commonly used across different domains of educational research. However, examining change using a set of multiple response scores (e.g., scale items) affords researchers several methodological benefits not possible when using a single score. A…

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…

Genetic and Environmental Influences of General Cognitive Ability: Is g a valid latent construct?

PubMed Central

Panizzon, Matthew S.; Vuoksimaa, Eero; Spoon, Kelly M.; Jacobson, Kristen C.; Lyons, Michael J.; Franz, Carol E.; Xian, Hong; Vasilopoulos, Terrie; Kremen, William S.

2014-01-01

Despite an extensive literature, the “g” construct remains a point of debate. Different models explaining the observed relationships among cognitive tests make distinct assumptions about the role of g in relation to those tests and specific cognitive domains. Surprisingly, these different models and their corresponding assumptions are rarely tested against one another. In addition to the comparison of distinct models, a multivariate application of the twin design offers a unique opportunity to test whether there is support for g as a latent construct with its own genetic and environmental influences, or whether the relationships among cognitive tests are instead driven by independent genetic and environmental factors. Here we tested multiple distinct models of the relationships among cognitive tests utilizing data from the Vietnam Era Twin Study of Aging (VETSA), a study of middle-aged male twins. Results indicated that a hierarchical (higher-order) model with a latent g phenotype, as well as specific cognitive domains, was best supported by the data. The latent g factor was highly heritable (86%), and accounted for most, but not all, of the genetic effects in specific cognitive domains and elementary cognitive tests. By directly testing multiple competing models of the relationships among cognitive tests in a genetically-informative design, we are able to provide stronger support than in prior studies for g being a valid latent construct. PMID:24791031

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.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

PubMed

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

An Item Response Theory Analysis of DSM–IV Diagnostic Criteria for Personality Disorders: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions

PubMed Central

Harford, Thomas C.; Chen, Chiung M.; Saha, Tulshi D.; Smith, Sharon M.; Hasin, Deborah S.; Grant, Bridget F.

2013-01-01

The purpose of this study was to evaluate the psychometric properties of DSM–IV symptom criteria for assessing personality disorders (PDs) in a national population and to compare variations in proposed symptom coding for social and/or occupational dysfunction. Data were obtained from a total sample of 34,653 respondents from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). For each personality disorder, confirmatory factor analysis (CFA) established a 1-factor latent factor structure for the respective symptom criteria. A 2-parameter item response theory (IRT) model was applied to the symptom criteria for each PD to assess the probabilities of symptom item endorsements across different values of the underlying trait (latent factor). Findings were compared with a separate IRT model using an alternative coding of symptom criteria that requires distress/impairment to be related to each criterion. The CFAs yielded a good fit for a single underlying latent dimension for each PD. Findings from the IRT indicated that DSM–IV PD symptom criteria are clustered in the moderate to severe range of the underlying latent dimension for each PD and are peaked, indicating high measurement precision only within a narrow range of the underlying trait and lower measurement precision at lower and higher levels of severity. Compared with the NESARC symptom coding, the IRT results for the alternative symptom coding are shifted toward the more severe range of the latent trait but generally have lower measurement precision for each PD. The IRT findings provide support for a reliable assessment of each PD for both NESARC and alternative coding for distress/impairment. The use of symptom dysfunction for each criterion, however, raises a number of issues and implications for the DSM-5 revision currently proposed for Axis II disorders (American Psychiatric Association, 2010). PMID:22449066

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

What to do When Scalar Invariance Fails: The Extended Alignment Method for Multi-Group Factor Analysis Comparison of Latent Means Across Many Groups.

PubMed

Marsh, Herbert W; Guo, Jiesi; Parker, Philip D; Nagengast, Benjamin; Asparouhov, Tihomir; Muthén, Bengt; Dicke, Theresa

2017-01-12

Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance (CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Validating the European Health Literacy Survey Questionnaire in people with type 2 diabetes: Latent trait analyses applying multidimensional Rasch modelling and confirmatory factor analysis.

PubMed

Finbråten, Hanne Søberg; Pettersen, Kjell Sverre; Wilde-Larsson, Bodil; Nordström, Gun; Trollvik, Anne; Guttersrud, Øystein

2017-11-01

To validate the European Health Literacy Survey Questionnaire (HLS-EU-Q47) in people with type 2 diabetes mellitus. The HLS-EU-Q47 latent variable is outlined in a framework with four cognitive domains integrated in three health domains, implying 12 theoretically defined subscales. Valid and reliable health literacy measurers are crucial to effectively adapt health communication and education to individuals and groups of patients. Cross-sectional study applying confirmatory latent trait analyses. Using a paper-and-pencil self-administered approach, 388 adults responded in March 2015. The data were analysed using the Rasch methodology and confirmatory factor analysis. Response violation (response dependency) and trait violation (multidimensionality) of local independence were identified. Fitting the "multidimensional random coefficients multinomial logit" model, 1-, 3- and 12-dimensional Rasch models were applied and compared. Poor model fit and differential item functioning were present in some items, and several subscales suffered from poor targeting and low reliability. Despite multidimensional data, we did not observe any unordered response categories. Interpreting the domains as distinct but related latent dimensions, the data fit a 12-dimensional Rasch model and a 12-factor confirmatory factor model best. Therefore, the analyses did not support the estimation of one overall "health literacy score." To support the plausibility of claims based on the HLS-EU score(s), we suggest: removing the health care aspect to reduce the magnitude of multidimensionality; rejecting redundant items to avoid response dependency; adding "harder" items and applying a six-point rating scale to improve subscale targeting and reliability; and revising items to improve model fit and avoid bias owing to person factors. © 2017 John Wiley & Sons Ltd.

Latent error detection: A golden two hours for detection.

PubMed

Saward, Justin R E; Stanton, Neville A

2017-03-01

Undetected error in safety critical contexts generates a latent condition that can contribute to a future safety failure. The detection of latent errors post-task completion is observed in naval air engineers using a diary to record work-related latent error detection (LED) events. A systems view is combined with multi-process theories to explore sociotechnical factors associated with LED. Perception of cues in different environments facilitates successful LED, for which the deliberate review of past tasks within two hours of the error occurring and whilst remaining in the same or similar sociotechnical environment to that which the error occurred appears most effective. Identified ergonomic interventions offer potential mitigation for latent errors; particularly in simple everyday habitual tasks. It is thought safety critical organisations should look to engineer further resilience through the application of LED techniques that engage with system cues across the entire sociotechnical environment, rather than relying on consistent human performance. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Refreshing the Aged Latent Fingerprints with Ionizing Radiation Prior to the Cyanoacrylate Fuming Procedure: A Preliminary Study.

PubMed

Ristova, Mimoza M; Radiceska, Pavlina; Bozinov, Igorco; Barandovski, Lambe

2016-05-01

One of the crucial factors determining the cyanoacrylate deposit quality over latent fingerprints appeared to be the extent of the humidity. This work focuses on the enhancement/refreshment of age-degraded latent fingerprints by irradiating the samples with UV, X-ray, or thermal neutrons prior to the cyanoacrylate (CA) fuming. Age degradation of latent fingerprints deposited on glass surfaces was examined through the decrease in the number of characteristic minutiae counts over time. A term "critical day" was introduced for the time at which the average number of identifiable minutiae definitions drops to one-half. Fingerprints older than their "critical day" were exposed to either UV, X-ray, or thermal neutrons. Identical reference samples were kept unexposed. All samples, both reference and irradiated, were developed during a single CA fuming procedure. Comparative latent fingerprint analysis showed that exposure to ionizing radiation enhances the CA fuming, yielding a 20-30% increase in average minutiae count. © 2015 American Academy of Forensic Sciences.

The influence of music-elicited emotions and relative pitch on absolute pitch memory for familiar melodies.

PubMed

Jakubowski, Kelly; Müllensiefen, Daniel

2013-01-01

Levitin's findings that nonmusicians could produce from memory the absolute pitches of self-selected pop songs have been widely cited in the music psychology literature. These findings suggest that latent absolute pitch (AP) memory may be a more widespread trait within the population than traditional AP labelling ability. However, it has been left unclear what factors may facilitate absolute pitch retention for familiar pieces of music. The aim of the present paper was to investigate factors that may contribute to latent AP memory using Levitin's sung production paradigm for AP memory and comparing results to the outcomes of a pitch labelling task, a relative pitch memory test, measures of music-induced emotions, and various measures of participants' musical backgrounds. Our results suggest that relative pitch memory and the quality and degree of music-elicited emotions impact on latent AP memory.

Soil occupation and atmospheric variations over Sobradinho Lake area. Part two: a regional modeling study

NASA Astrophysics Data System (ADS)

Correia, M. F.; da Silva Dias, M. A. F.; da Silva Aragão, M. R.

2006-11-01

The impact of the changes on soil cover and land use brought about by the construction of the Sobradinho Dam in the semi-arid region of the São Francisco River Hydrographic Basin is analyzed by means of a numerical model RAMS. Disregarding the influence of a large scale flow, a set of factors were responsible for the creation of a rather complex circulation system that includes mountain-valley winds, lake breeze (LB) and non-conventional circulation all induced by the surface non-homogeneous aspect. Results have demonstrated that the implementation of works of such magnitude brings about environmental changes in an area that stretches far beyond the surroundings of the reservoir. The soil cover alterations due to the ever increasing development of the area with the presence of irrigated crops in a sparsely vegetated region ( caatinga) does affect land surface characteristics, occasioning for that matter the splitting of the available energy into latent and sensible heat fluxes. LB behavior varies in accordance with atmospheric conditions and also in view of the type of vegetation found in the lake surrounding areas. Hydro availability in root zones, even under adverse atmospheric conditions (high temperature and low air humidity) brings up the high rates of evaporation and plant transpiration that contribute towards the increase of humidity and the fall of temperature in lower atmospheric layers.

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.

Different Patterns of Cytokines and Chemokines Combined with IFN-γ Production Reflect Mycobacterium tuberculosis Infection and Disease

PubMed Central

Hu, Shizong; Jin, Dongdong; Chen, Xinchun; Jin, Qi; Liu, Haiying

2012-01-01

Background IFN-γ is presently the only soluble immunological marker used to help diagnose latent Mycobacterium tuberculosis (M.tb) infection. However, IFN-γ is not available to distinguish latent from active TB infection. Moreover, extrapulmonary tuberculosis, such as tuberculous pleurisy, cannot be properly diagnosed by IFN-γ release assay. As a result, other disease- or infection-related immunological biomarkers that would be more effective need to be screened and identified. Methodology A panel of 41 soluble immunological molecules (17 cytokines and 24 chemokines) was tested using Luminex liquid array-based multiplexed immunoassays. Samples, including plasma and pleural effusions, from healthy donors (HD, n = 12) or patients with latent tuberculosis infection (LTBI, n = 20), pulmonary tuberculosis (TB, n = 12), tuberculous pleurisy (TP, n = 15) or lung cancer (LC, n = 15) were collected and screened for soluble markers. Peripheral blood mononuclear cells (PBMCs) and pleural fluid mononuclear cells (PFMCs) were also isolated to investigate antigen-specific immune factors. Principal Findings For the 41 examined factors, our results indicated that three patterns were closely associated with infection and disease. (1) Significantly elevated plasma levels of IL-2, IP-10, CXCL11 and CXCL12 were present in both patients with tuberculosis and in a sub-group participant with latent tuberculosis infection who showed a higher level of IFN-γ producing cells by ELISPOT assay compared with other latently infected individuals. (2) IL-6 and IL-9 were only significantly increased in plasma from active TB patients, and the two factors were consistently highly secreted after M.tb antigen stimulation. (3) When patients developed tuberculous pleurisy, CCL1, CCL21 and IL-6 were specifically increased in the pleural effusions. In particular, these three factors were consistently highly secreted by pleural fluid mononuclear cells following M.tb-specific antigen stimulation. In conclusion, our data imply that the specific secretion of soluble immunological factors, in addition to IFN-γ, may be used to evaluate M.tb infection and tuberculosis disease. PMID:23028695

Kaposi's Sarcoma Associated Herpes Virus (KSHV) Induced COX-2: A Key Factor in Latency, Inflammation, Angiogenesis, Cell Survival and Invasion

PubMed Central

Sharma-Walia, Neelam; Sadagopan, Sathish; Veettil, Mohanan Valiya; Kerur, Nagaraj; Chandran, Bala

2010-01-01

Kaposi's sarcoma (KS), an enigmatic endothelial cell vascular neoplasm, is characterized by the proliferation of spindle shaped endothelial cells, inflammatory cytokines (ICs), growth factors (GFs) and angiogenic factors. KSHV is etiologically linked to KS and expresses its latent genes in KS lesion endothelial cells. Primary infection of human micro vascular endothelial cells (HMVEC-d) results in the establishment of latent infection and reprogramming of host genes, and cyclooxygenase-2 (COX-2) is one of the highly up-regulated genes. Our previous study suggested a role for COX-2 in the establishment and maintenance of KSHV latency. Here, we examined the role of COX-2 in the induction of ICs, GFs, angiogenesis and invasive events occurring during KSHV de novo infection of endothelial cells. A significant amount of COX-2 was detected in KS tissue sections. Telomerase-immortalized human umbilical vein endothelial cells supporting KSHV stable latency (TIVE-LTC) expressed elevated levels of functional COX-2 and microsomal PGE2 synthase (m-PGES), and secreted the predominant eicosanoid inflammatory metabolite PGE2. Infected HMVEC-d and TIVE-LTC cells secreted a variety of ICs, GFs, angiogenic factors and matrix metalloproteinases (MMPs), which were significantly abrogated by COX-2 inhibition either by chemical inhibitors or by siRNA. The ability of these factors to induce tube formation of uninfected endothelial cells was also inhibited. PGE2, secreted early during KSHV infection, profoundly increased the adhesion of uninfected endothelial cells to fibronectin by activating the small G protein Rac1. COX-2 inhibition considerably reduced KSHV latent ORF73 gene expression and survival of TIVE-LTC cells. Collectively, these studies underscore the pivotal role of KSHV induced COX-2/PGE2 in creating KS lesion like microenvironment during de novo infection. Since COX-2 plays multiple roles in KSHV latent gene expression, which themselves are powerful mediators of cytokine induction, anti-apoptosis, cell survival and viral genome maintainence, effective inhibition of COX-2 via well-characterized clinically approved COX-2 inhibitors could potentially be used in treatment to control latent KSHV infection and ameliorate KS. PMID:20169190

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.

The measurement of "eating-disorder-thoughts" and "eating-disorder-behaviors": Implications for assessment and detection of eating disorders in epidemiological studies.

PubMed

Miller, Jessie L; Vaillancourt, Tracy; Hanna, Steven E

2009-04-01

To test a theoretically driven second-order factor model of eating disorders, with eating-disordered thoughts and eating-disordered behaviors representing the higher order factors, we conducted a confirmatory factor analysis using a female university student sample (N=1816). The 'Thought' latent construct was comprised of indicators representing fear of fat and dissatisfaction with body shape/weight and the latent construct 'Behavior' was comprised of indicators representing binging, purging and restricting. From the thought and behavior latent factors, composite groups were created by varying the level of thoughts and behaviors (high, moderate, and few/or none). We examined the independent contributions of thoughts and behaviors on a measure of psychopathology (depression). A second-order model of "eating disorder thoughts" and "eating disorder behaviors" was supported by the data, based on model fit, factor loadings, and model parsimony. Mean scores on depression were clinically significant for groups engaged in any level of eating disorder behavior whereas thoughts contributed to risk for depression only at the extreme end. Because of the disproportionate representation of eating disorder thoughts (high) and eating disorder behaviors (low) in non-clinical populations, the measurement and detection of eating disorders may be enhanced by measuring thoughts separate from behaviors.

Polytomous Differential Item Functioning and Violations of Ordering of the Expected Latent Trait by the Raw Score

ERIC Educational Resources Information Center

DeMars, Christine E.

2008-01-01

The graded response (GR) and generalized partial credit (GPC) models do not imply that examinees ordered by raw observed score will necessarily be ordered on the expected value of the latent trait (OEL). Factors were manipulated to assess whether increased violations of OEL also produced increased Type I error rates in differential item…

ß-catenin, a transcription factor activated by canonical Wnt signaling, is expressed in sensory neurons of calves latently infected with bovine herpesvirus 1

USDA-ARS?s Scientific Manuscript database

Like many a-herpesvirinae subfamily members, bovine herpes virus 1 (BoHV-1) expresses an abundant transcript in latently infected sensory neurons: the latency-related (LR) RNA. LR-RNA encodes a protein (ORF2) that inhibits apoptosis, interacts with Notch family members, interferes with Notch mediate...

Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

ERIC Educational Resources Information Center

Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

2007-01-01

Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

Divorce and Child Behavior Problems: Applying Latent Change Score Models to Life Event Data

ERIC Educational Resources Information Center

Malone, Patrick S.; Lansford, Jennifer E.; Castellino, Domini R.; Berlin, Lisa J.; Dodge, Kenneth A.; Bates, John E.; Pettit, Gregory S.

2004-01-01

Effects of parents' divorce on children's adjustment have been studied extensively. This article applies new advances in trajectory modeling to the problem of disentangling the effects of divorce on children's adjustment from related factors such as the child's age at the time of divorce and the child's gender. Latent change score models were used…

Measurement Invariance and Latent Mean Differences of the Beck Depression Inventory II across Gender Groups

ERIC Educational Resources Information Center

Wu, Pei-Chen

2010-01-01

This study examined measurement invariance (i.e., configural invariance, metric invariance, scalar invariance) of the Chinese version of Beck Depression Inventory II (BDI-II-C) across college males and females and compared gender differences on depression at the latent factor mean level. Two samples composed of 402 male college students and 595…

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.

Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives (WISDM)

PubMed Central

Piper, Megan E.; Bolt, Daniel M.; Kim, Su-Young; Japuntich, Sandra J.; Smith, Stevens S.; Niederdeppe, Jeff; Cannon, Dale S.; Baker, Timothy B.

2008-01-01

The construct of tobacco dependence is important from both scientific and public health perspectives, but it is poorly understood. The current research integrates person-centered analyses (e.g., latent profile analysis) and variable-centered analyses (e.g., exploratory factor analysis) to understand better the latent structure of dependence and to guide distillation of the phenotype. Using data from four samples of smokers (including treatment and non-treatment samples), latent profiles were derived using the Wisconsin Inventory of Smoking Dependence Motives (WISDM) subscale scores. Across all four samples, results revealed a unique latent profile that had relative elevations on four dependence motive subscales (Automaticity, Craving, Loss of Control, and Tolerance). Variable-centered analyses supported the uniqueness of these four subscales both as measures of a common factor distinct from that underlying the other nine subscales, and as the strongest predictors of relapse, withdrawal and other dependence criteria. Conversely, the remaining nine motives carried little unique predictive validity regarding dependence. Applications of a factor mixture model further support the presence of a unique class of smokers in relation to a common factor underlying the four subscales. The results illustrate how person-centered analyses may be useful as a supplement to variable-centered analyses for uncovering variables that are necessary and/or sufficient predictors of disorder criteria, as they may uncover small segments of a population in which the variables are uniquely distributed. The results also suggest that severe dependence is associated with a pattern of smoking that is heavy, pervasive, automatic and relatively unresponsive to instrumental contingencies. PMID:19025223

Aggressiveness as a latent personality trait of domestic dogs: Testing local independence and measurement invariance

PubMed Central

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. PMID:28854267

Development of lifetime comorbidity in the WHO World Mental Health (WMH) Surveys

PubMed Central

Kessler, Ronald C.; Ormel, Johan; Petukhova, Maria; McLaughlin, Katie A.; Green, Jennifer Greif; Russo, Leo J.; Stein, Dan J.; Zaslavsky, Alan M; Aguilar-Gaxiola, Sergio; Alonso, Jordi; Andrade, Laura; Benjet, Corina; de Girolamo, Giovanni; de Graaf, Ron; Demyttenaere, Koen; Fayyad, John; Haro, Josep Maria; Hu, Chi yi; Karam, Aimee; Lee, Sing; Lepine, Jean-Pierre; Matchsinger, Herbert; Mihaescu-Pintia, Constanta; Posada-Villa, Jose; Sagar, Rajesh; Üstün, T. Bedirhan

2010-01-01

CONTEXT Although numerous studies have examined the role of latent variables in the structure of comorbidity among mental disorders, none has examined their role in the development of comorbidity. OBJECTIVE To study the role of latent variables in the development of comorbidity among 18 lifetime DSM-IV disorders in the WHO World Mental Health (WMH) surveys. SETTING/PARTICIPANTS Nationally or regionally representative community surveys in 14 countries with a total of 21,229 respondents. MAIN OUTCOME MEASURES First onset of 18 lifetime DSM-IV anxiety, mood, behavior, and substance disorders assessed retrospectively in the WHO Composite International Diagnostic Interview (CIDI). RESULTS Separate internalizing (anxiety and mood disorders) and externalizing (behavior and substance disorders) factors were found in exploratory factor analysis of lifetime disorders. Consistently significant positive time-lagged associations were found in survival analyses for virtually all temporally primary lifetime disorders predicting subsequent onset of other disorders. Within-domain (i.e., internalizing or externalizing) associations were generally stronger than between-domain associations. The vast majority of time-lagged associations were explained by a model that assumed the existence of mediating latent internalizing and externalizing variables. Specific phobia and obsessive-compulsive disorder (internalizing) and hyperactivity disorder and oppositional-defiant disorder (externalizing) were the most important predictors. A small number of residual associations remained significant after controlling the latent variables. CONCLUSIONS The good fit of the latent variable model suggests that common causal pathways account for most of the comorbidity among the disorders considered here. These common pathways should be the focus of future research on the development of comorbidity, although several important pair-wise associations that cannot be accounted for by latent variables also exist that warrant further focused study. PMID:21199968

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.

Influence of diabetes mellitus and risk factors in activating latent tuberculosis infection: a case for targeted screening in malaysia.

PubMed

Swarna Nantha, Y

2012-10-01

A review of the epidemiology of tuberculosis, its contributing risk factors (excluding HIV) and the role of screening latent tuberculosis infection in Malaysia was done. Despite the global and domestic decrease in prevalence rates of tuberculosis in the past decade, there is an alarming increase in the trend of non communicable diseases in the country. High prevalence rates of major risk factors leading to reactivation of tuberculosis were seen within the population, with diabetes mellitus being in the forefront. The rising numbers in the ageing population of Malaysia poses a further threat of re-emergence of tuberculosis in the years to come. Economically, screening of diabetic patients with comorbidities for latent tuberculosis infection (LTBI) using two major techniques, namely tuberculin sensitivity (TST) and Interferon gamma release assay tests (IGRA) could be a viable option. The role of future research in the detection of LTBI in the Malaysian setting might be necessary to gauge the disease reservoir before implementing prophylactic measures for high risk groups involved.

A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US

PubMed Central

Congdon, Peter

2010-01-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk. PMID:20616977

An application of the LC-LSTM framework to the self-esteem instability case.

PubMed

Alessandri, Guido; Vecchione, Michele; Donnellan, Brent M; Tisak, John

2013-10-01

The present research evaluates the stability of self-esteem as assessed by a daily version of the Rosenberg (Society and the adolescent self-image, Princeton University Press, Princeton, 1965) general self-esteem scale (RGSE). The scale was administered to 391 undergraduates for five consecutive days. The longitudinal data were analyzed using the integrated LC-LSTM framework that allowed us to evaluate: (1) the measurement invariance of the RGSE, (2) its stability and change across the 5-day assessment period, (3) the amount of variance attributable to stable and transitory latent factors, and (4) the criterion-related validity of these factors. Results provided evidence for measurement invariance, mean-level stability, and rank-order stability of daily self-esteem. Latent state-trait analyses revealed that variances in scores of the RGSE can be decomposed into six components: stable self-esteem (40 %), ephemeral (or temporal-state) variance (36 %), stable negative method variance (9 %), stable positive method variance (4 %), specific variance (1 %) and random error variance (10 %). Moreover, latent factors associated with daily self-esteem were associated with measures of depression, implicit self-esteem, and grade point average.

Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

PubMed

Kim, Eun Sook; Cao, Chunhua

2015-01-01

Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

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.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior.

PubMed

Liu, Ding; Wang, Zhaowen; Wen, Bihan; Yang, Jianchao; Han, Wei; Huang, Thomas S

2016-07-01

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually.

On the use of radiative surface temperature to estimate sensible heat flux over sparse shrubs in Nevada

NASA Technical Reports Server (NTRS)

Chehbouni, A.; Nichols, W. D.; Qi, J.; Njoku, E. G.; Kerr, Y. H.; Cabot, F.

1994-01-01

The accurate partitioning of available energy into sensible and latent heat flux is crucial to the understanding of surface atmosphere interactions. This issue is more complicated in arid and semi arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. A three component model to estimate sensible heat flux over heterogeneous surfaces is presented. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil, the second of open 'illuminated' soil. Data collected at two different sites in Nevada (U.S.) during the Summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

Bayesian Scalar-on-Image Regression with Application to Association Between Intracranial DTI and Cognitive Outcomes

PubMed Central

Huang, Lei; Goldsmith, Jeff; Reiss, Philip T.; Reich, Daniel S.; Crainiceanu, Ciprian M.

2013-01-01

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients. PMID:23792220

A Three Component Model to Estimate Sensible Heat Flux Over Sparse Shrubs in Nevada

USGS Publications Warehouse

Chehbouni, A.; Nichols, W.D.; Njoku, E.G.; Qi, J.; Kerr, Y.H.; Cabot, F.

1997-01-01

It is now recognized that accurate partitioning of available energy into sensible and latent heat flux is crucial to understanding surface-atmosphere interactions. This issue is more complicated in arid and semi-arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. The objective of this paper is to present a three-component model to estimate sensible heat flux over heterogeneous surfaces. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil; the second compartment consists of bare, unshaded soil. Data collected at two different sites in Nevada during the summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790

HIV integration sites and implications for maintenance of the reservoir.

PubMed

Symons, Jori; Cameron, Paul U; Lewin, Sharon R

2018-03-01

To provide an overview of recent research of how HIV integration relates to productive and latent infection and implications for cure strategies. How and where HIV integrates provides new insights into how HIV persists on antiretroviral therapy (ART). Clonal expansion of infected cells with the same integration site demonstrates that T-cell proliferation is an important factor in HIV persistence, however, the driver of proliferation remains unclear. Clones with identical integration sites harbouring defective provirus can accumulate in HIV-infected individuals on ART and defective proviruses can express RNA and produce protein. HIV integration sites differ in clonally expanded and nonexpanded cells and in latently and productively infected cells and this influences basal and inducible transcription. There is a growing number of cellular proteins that can alter the pattern of integration to favour latency. Understanding these pathways may identify new interventions to eliminate latently infected cells. Using advances in analysing HIV integration sites, T-cell proliferation of latently infected cells is thought to play a major role in HIV persistence. Clonal expansion has been demonstrated with both defective and intact viruses. Production of viral RNA and protein from defective viruses may play a role in driving chronic immune activation. The site of integration may determine the likelihood of proliferation and the degree of basal and induced transcription. Finally, host factors and gene expression at the time of infection may determine the integration site. Together these new insights may lead to novel approaches to elimination of latently infected cells.

Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation.

PubMed

Yang, Hongyu; Huang, Di; Wang, Yunhong; Wang, Heng; Tang, Yuanyan

2016-06-01

Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

Reactivation of Latent Tuberculosis: Variations on the Cornell Murine Model

PubMed Central

Scanga, Charles A.; Mohan, V. P.; Joseph, Heather; Yu, Keming; Chan, John; Flynn, JoAnne L.

1999-01-01

Mycobacterium tuberculosis causes active tuberculosis in only a small percentage of infected persons. In most cases, the infection is clinically latent, although immunosuppression can cause reactivation of a latent M. tuberculosis infection. Surprisingly little is known about the biology of the bacterium or the host during latency, and experimental studies on latent tuberculosis suffer from a lack of appropriate animal models. The Cornell model is a historical murine model of latent tuberculosis, in which mice infected with M. tuberculosis are treated with antibiotics (isoniazid and pyrazinamide), resulting in no detectable bacilli by organ culture. Reactivation of infection during this culture-negative state occurred spontaneously and following immunosuppression. In the present study, three variants of the Cornell model were evaluated for their utility in studies of latent and reactivated tuberculosis. The antibiotic regimen, inoculating dose, and antibiotic-free rest period prior to immunosuppression were varied. A variety of immunosuppressive agents, based on immunologic factors known to be important to control of acute infection, were used in attempts to reactivate the infection. Although reactivation of latent infection was observed in all three variants, these models were associated with characteristics that limit their experimental utility, including spontaneous reactivation, difficulties in inducing reactivation, and the generation of altered bacilli. The results from these studies demonstrate that the outcome of Cornell model-based studies depends critically upon the parameters used to establish the model. PMID:10456896

The "g" Factor and Cognitive Test Session Behavior: Using a Latent Variable Approach in Examining Measurement Invariance Across Age Groups on the WJ III

ERIC Educational Resources Information Center

Frisby, Craig L.; Wang, Ze

2016-01-01

Data from the standardization sample of the Woodcock-Johnson Psychoeducational Battery--Third Edition (WJ III) Cognitive standard battery and Test Session Observation Checklist items were analyzed to understand the relationship between g (general mental ability) and test session behavior (TSB; n = 5,769). Latent variable modeling methods were used…

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…

Connections between Graphical Gaussian Models and Factor Analysis

ERIC Educational Resources Information Center

Salgueiro, M. Fatima; Smith, Peter W. F.; McDonald, John W.

2010-01-01

Connections between graphical Gaussian models and classical single-factor models are obtained by parameterizing the single-factor model as a graphical Gaussian model. Models are represented by independence graphs, and associations between each manifest variable and the latent factor are measured by factor partial correlations. Power calculations…

Development of a Web-Accessible Population Pharmacokinetic Service—Hemophilia (WAPPS-Hemo): Study Protocol

PubMed Central

Foster, Gary; Navarro-Ruan, Tamara; McEneny-King, Alanna; Edginton, Andrea N; Thabane, Lehana

2016-01-01

Background Individual pharmacokinetic assessment is a critical component of tailored prophylaxis for hemophilia patients. Population pharmacokinetics allows using individual sparse data, thus simplifying individual pharmacokinetic studies. Implementing population pharmacokinetics capacity for the hemophilia community is beyond individual reach and requires a system effort. Objective The Web-Accessible Population Pharmacokinetic Service—Hemophilia (WAPPS-Hemo) project aims to assemble a database of patient pharmacokinetic data for all existing factor concentrates, develop and validate population pharmacokinetics models, and integrate these models within a Web-based calculator for individualized pharmacokinetic estimation in patients at participating treatment centers. Methods Individual pharmacokinetic studies on factor VIII and IX concentrates will be sourced from pharmaceutical companies and independent investigators. All factor concentrate manufacturers, hemophilia treatment centers (HTCs), and independent investigators (identified via a systematic review of the literature) having on file pharmacokinetic data and willing to contribute full or sparse pharmacokinetic data will be eligible for participation. Multicompartmental modeling will be performed using a mixed-model approach for derivation and Bayesian forecasting for estimation of individual sparse data. NONMEM (ICON Development Solutions) will be used as modeling software. Results The WAPPS-Hemo research network has been launched and is currently joined by 30 HTCs from across the world. We have gathered dense individual pharmacokinetic data on 878 subjects, including several replicates, on 21 different molecules from 17 different sources. We have collected sparse individual pharmacokinetic data on 289 subjects from the participating centers through the testing phase of the WAPPS-Hemo Web interface. We have developed prototypal population pharmacokinetics models for 11 molecules. The WAPPS-Hemo website (available at www.wapps-hemo.org, version 2.4), with core functionalities allowing hemophilia treaters to obtain individual pharmacokinetic estimates on sparse data points after 1 or more infusions of a factor concentrate, was launched for use within the research network in July 2015. Conclusions The WAPPS-Hemo project and research network aims to make it easier to perform individual pharmacokinetic assessments on a reduced number of plasma samples by adoption of a population pharmacokinetics approach. The project will also gather data to substantially enhance the current knowledge about factor concentrate pharmacokinetics and sources of its variability in target populations. Trial Registration ClinicalTrials.gov NCT02061072; https://clinicaltrials.gov/ct2/show/NCT02061072 (Archived by WebCite at http://www.webcitation.org/6mRK9bKP6) PMID:27977390

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.

Paths to tobacco abstinence: A repeated-measures latent class analysis.

PubMed

McCarthy, Danielle E; Ebssa, Lemma; Witkiewitz, Katie; Shiffman, Saul

2015-08-01

Knowledge of smoking change processes may be enhanced by identifying pathways to stable abstinence. We sought to identify latent classes of smokers based on their day-to-day smoking status in the first weeks of a cessation attempt. We examined treatment effects on class membership and compared classes on baseline individual differences and 6-month abstinence rates. In this secondary analysis of a double-blind randomized placebo-controlled clinical trial (N = 1,433) of 5 smoking cessation pharmacotherapies (nicotine patch, nicotine lozenge, bupropion SR, patch and lozenge, or bupropion SR and lozenge), we conducted repeated-measures latent class analysis of daily smoking status (any smoking vs. none) for the first 27 days of a quit attempt. Treatment and covariate relations with latent class membership were examined. Distal outcome analysis compared confirmed 6-month abstinence rates among the latent classes. A 5-class solution was selected. Three-quarters of smokers were in stable smoking or abstinent classes, but 25% were in classes with unstable abstinence probabilities over time. Active treatment (compared to placebo), and particularly the patch and lozenge combination, promoted early quitting. Latent classes differed in 6-month abstinence rates and on several baseline variables, including nicotine dependence, quitting history, self-efficacy, sleep disturbance, and minority status. Repeated-measures latent class analysis identified latent classes of smoking change patterns affected by treatment, related to known risk factors, and predictive of distal outcomes. Tracking behavior early in a change attempt may identify prognostic patterns of change and facilitate adaptive treatment planning. (c) 2015 APA, all rights reserved).

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.

Aberrant intracellular localization of Varicella-Zoster virus regulatory proteins during latency

PubMed Central

Lungu, Octavian; Panagiotidis, Christos A.; Annunziato, Paula W.; Gershon, Anne A.; Silverstein, Saul J.

1998-01-01

Varicella-Zoster virus (VZV) is a herpesvirus that becomes latent in sensory neurons after primary infection (chickenpox) and subsequently may reactivate to cause zoster. The mechanism by which this virus maintains latency, and the factors involved, are poorly understood. Here we demonstrate, by immunohistochemical analysis of ganglia obtained at autopsy from seropositive patients without clinical symptoms of VZV infection that viral regulatory proteins are present in latently infected neurons. These proteins, which localize to the nucleus of cells during lytic infection, predominantly are detected in the cytoplasm of latently infected neurons. The restriction of regulatory proteins from the nucleus of latently infected neurons might interrupt the cascade of virus gene expression that leads to a productive infection. Our findings raise the possibility that VZV has developed a novel mechanism for maintenance of latency that contrasts with the transcriptional repression that is associated with latency of herpes simplex virus, the prototypic alpha herpesvirus. PMID:9618542

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.

On Latent Growth Models for Composites and Their Constituents.

PubMed

Hancock, Gregory R; Mao, Xiulin; Kher, Hemant

2013-09-01

Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.

Fall Risk, Supports and Services, and Falls Following a Nursing Home Discharge.

PubMed

Noureldin, Marwa; Hass, Zachary; Abrahamson, Kathleen; Arling, Greg

2017-09-04

Falls are a major source of morbidity and mortality among older adults; however, little is known regarding fall occurrence during a nursing home (NH) to community transition. This study sought to examine whether the presence of supports and services impacts the relationship between fall-related risk factors and fall occurrence post NH discharge. Participants in the Minnesota Return to Community Initiative who were assisted in achieving a community discharge (N = 1459) comprised the study sample. The main outcome was fall occurrence within 30 days of discharge. Factor analyses were used to estimate latent models from variables of interest. A structural equation model (SEM) was estimated to determine the relationship between the emerging latent variables and falls. Fifteen percent of participants fell within 30 days of NH discharge. Factor analysis of fall-related risk factors produced three latent variables: fall concerns/history; activities of daily living impairments; and use of high-risk medications. A supports/services latent variable also emerged that included caregiver support frequency, medication management assistance, durable medical equipment use, discharge location, and receipt of home health or skilled nursing services. In the SEM model, high-risk medications use and fall concerns/history had direct positive effects on falling. Receiving supports/services did not affect falling directly; however, it reduced the effect of high-risk medication use on falling (p < .05). Within the context of a state-implemented transition program, findings highlight the importance of supports/services in mitigating against medication-related risk of falling post NH discharge. © 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.

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

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

PubMed Central

Kim, Eun Sook; Wang, Yan

2017-01-01

Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering.

PubMed

Peng, Xi; Yu, Zhiding; Yi, Zhang; Tang, Huajin

2017-04-01

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that l 1 -, l 2 -, l ∞ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

Gender differences in children's problem behaviours in competitive play with friends.

PubMed

Ensor, Rosie; Hart, Martha; Jacobs, Lorna; Hughes, Claire

2011-06-01

Disruptive behaviour disorders are much more common in boys than girls (Office of National Statistics, 1999); in contrast, gender differences in normative problem behaviours are poorly understood. To address this issue, 228 6-year-olds (134 boys, 94 girls) were each observed playing a board game with a same-gender friend. Ratings of aggression, disruption, arousal and negativity were used to index problem behaviours. Multiple-groups confirmatory factor analyses demonstrated that the latent factor had the same metric for boys and girls, but a mean that was approximately half a standard deviation higher for boys than girls. In addition, the association between the latent factor and teachers' ratings of total difficulties was significantly stronger for boys than girls.

Specificity of disgust domains in the prediction of contamination anxiety and avoidance: a multimodal examination.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Haidt, Jonathan; Sawchuk, Craig N

2014-07-01

Although core, animal-reminder, and contamination disgust are viewed as distinct "types" of disgust vulnerabilities, the extent to which individual differences in the three disgust domains uniquely predict contamination-related anxiety and avoidance remains unclear. Three studies were conducted to fill this important gap in the literature. Study 1 was conducted to first determine if the three types of disgust could be replicated in a larger and more heterogeneous sample. Confirmatory factor analysis revealed that a bifactor model consisting of a "general disgust" dimension and the three distinct disgust dimensions yielded a better fit than a one-factor model. Structural equation modeling in Study 2 showed that while latent core, animal-reminder, and contamination disgust factors each uniquely predicted a latent "contamination anxiety" factor above and beyond general disgust, only animal-reminder uniquely predicted a latent "non-contamination anxiety" factor above and beyond general disgust. However, Study 3 found that only contamination disgust uniquely predicted behavioral avoidance in a public restroom where contamination concerns are salient. These findings suggest that although the three disgust domains are associated with contamination anxiety and avoidance, individual differences in contamination disgust sensitivity appear to be most uniquely predictive of contamination-related distress. The implications of these findings for the development and maintenance of anxiety-related disorders marked by excessive contamination concerns are discussed. Copyright © 2014. Published by Elsevier Ltd.

Dimensionality of DSM-5 posttraumatic stress disorder and its association with suicide attempts: results from the National Epidemiologic Survey on Alcohol and Related Conditions-III.

PubMed

Chen, Chiung M; Yoon, Young-Hee; Harford, Thomas C; Grant, Bridget F

2017-06-01

Emerging confirmatory factor analytic (CFA) studies suggest that posttraumatic stress disorder (PTSD) as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) is best characterized by seven factors, including re-experiencing, avoidance, negative affect, anhedonia, externalizing behaviors, and anxious and dysphoric arousal. The seven factors, however, have been found to be highly correlated, suggesting that one general factor may exist to explain the overall correlations among symptoms. Using data from the National Epidemiologic Survey on Alcohol and Related Conditions-III, a large, national survey of 36,309 U.S. adults ages 18 and older, this study proposed and tested an exploratory bifactor hybrid model for DSM-5 PTSD symptoms. The model posited one general and seven specific latent factors, whose associations with suicide attempts and mediating psychiatric disorders were used to validate the PTSD dimensionality. The exploratory bifactor hybrid model fitted the data extremely well, outperforming the 7-factor CFA hybrid model and other competing CFA models. The general factor was found to be the single dominant latent trait that explained most of the common variance (~76%) and showed significant, positive associations with suicide attempts and mediating psychiatric disorders, offering support to the concurrent validity of the PTSD construct. The identification of the primary latent trait of PTSD confirms PTSD as an independent psychiatric disorder and helps define PTSD severity in clinical practice and for etiologic research. The accurate specification of PTSD factor structure has implications for treatment efforts and the prevention of suicidal behaviors.

Comparison of Suicide Attempters and Decedents in the U.S. Army: A Latent Class Analysis.

PubMed

Skopp, Nancy A; Smolenski, Derek J; Sheppard, Sean C; Bush, Nigel E; Luxton, David D

2016-08-01

A clearer understanding of risk factors for suicidal behavior among soldiers is of principal importance to military suicide prevention. It is unclear whether soldiers who attempt suicide and those who die by suicide have different patterns of risk factors. As such, preventive efforts aimed toward reducing suicide attempts and suicides, respectively, may require different strategies. We conducted a latent class analysis (LCA) to examine classes of risk factors among suicide attempters (n = 1,433) and decedents (n = 424). Both groups were represented by three classes: (1) External/Antisocial Risk Factors, (2) Mental Health Risk Factors, and (3) No Pattern. These findings support the conceptualization that military suicide attempters and decedents represent a single population. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

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…

Parent and Child Personality Traits and Children's Externalizing Problem Behavior from Age 4 to 9 Years: A Cohort-Sequential Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Prinzie, P.; Onghena, P.; Hellinckx, W.

2005-01-01

Cohort-sequential latent growth modeling was used to analyze longitudinal data for children's externalizing behavior from four overlapping age cohorts (4, 5, 6, and 7 years at first assessment) measured at three annual time points. The data included mother and father ratings on the Child Behavior Checklist and the Five-Factor Personality Inventory…

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…

The Influence of Static and Dynamic Intrapersonal Factors on Longitudinal Patterns of Peer Victimization through Mid-adolescence: a Latent Transition Analysis.

PubMed

Haltigan, John D; Vaillancourt, Tracy

2018-01-01

Using 6 cycles (grade 5 through grade 10) of data obtained from a large prospective sample of Canadian school children (N = 700; 52.6% girls), we replicated previous findings concerning the empirical definition of peer victimization (i.e., being bullied) and examined static and dynamic intrapersonal factors associated with its emergence and experiential continuity through mid-adolescence. Latent class analyses consistently revealed a low victimization and an elevated victimization class across time, supporting previous work suggesting peer victimization was defined by degree rather than by type (e.g., physical). Using latent transition analyses (LTA), we found that child sex, parent-perceived pubertal development, and internalizing symptoms influenced the probability of transitioning from the low to the elevated victimization class across time. Higher-order extensions within the LTA modeling framework revealed a lasting effect of grade 5 victimization status on grade 10 victimization status and a large effect of chronic victimization on later parent-reported youth internalizing symptoms (net of prior parent-reported internalizing symptoms) in later adolescence (grade 11). Implications of the current findings for the experience of peer victimization, as well as the application of latent transition analysis as a useful approach for peer victimization research, are discussed.

Postneedling soreness after deep dry needling of a latent myofascial trigger point in the upper trapezius muscle: Characteristics, sex differences and associated factors.

PubMed

Martín-Pintado-Zugasti, Aitor; Rodríguez-Fernández, Ángel Luis; Fernandez-Carnero, Josue

2016-04-27

Postneedling soreness is considered the most frequent secondary effect associated to dry needling. A detailed description of postneedling soreness characteristics has not been previously reported. (1) to assess the intensity and duration of postneedling soreness and tenderness after deep dry needling of a trapezius latent myofascial trigger point (MTrP), (2) to evaluate the possible differences in postneedling soreness between sexes and (3) to analyze the influence on postneedling soreness of factors involved in the dry needling process. Sixty healthy subjects (30 men, 30 women) with latent MTrPs in the upper trapezius muscle received a dry needling intervention in the MTrP. Pain and pressure pain threshold (PPT) were assessed during a 72 hours follow-up period. Repeated measures analysis of covariance showed a significant effect for time in pain and in PPT. An interaction between sex and time in pain was obtained: women exhibited higher intensity in postneedling pain than men. The pain during needling and the number of needle insertions significantly correlated with postneedling soreness. Soreness and hyperalgesia are present in all subjects after dry needling of a latent MTrP in the upper trapezius muscle. Women exhibited higher intensity of postneedling soreness than men.

Immunocytochemical localization of latent transforming growth factor-beta1 activation by stimulated macrophages

NASA Technical Reports Server (NTRS)

Chong, H.; Vodovotz, Y.; Cox, G. W.; Barcellos-Hoff, M. H.; Chatterjee, A. (Principal Investigator)

1999-01-01

Transforming growth factor-beta1 (TGF-beta) is secreted in a latent form consisting of mature TGF-beta noncovalently associated with its amino-terminal propeptide, which is called latency associated peptide (LAP). Biological activity depends upon the release of TGF-beta from the latent complex following extracellular activation, which appears to be the key regulatory mechanism controlling TGF-beta action. We have identified two events associated with latent TGF-beta (LTGF-beta) activation in vivo: increased immunoreactivity of certain antibodies that specifically detect TGF-beta concomitant with decreased immunoreactivity of antibodies to LAP. Macrophages stimulated in vitro with interferon-gamma and lipopolysaccharide reportedly activate LTGF-beta via cell membrane-bound protease activity. We show through dual immunostaining of paraformaldehyde-fixed macrophages that such physiological TGF-beta activation is accompanied by a loss of LAP immunoreactivity with concomitant revelation of TGF-beta epitopes. The induction of TGF-beta immunoreactivity colocalized with immunoreactive betaglycan/RIII in activated macrophages, suggesting that LTGF-beta activation occurs on the cell surface. Confocal microscopy of metabolically active macrophages incubated with antibodies to TGF-beta and betaglycan/RIII prior to fixation supported the localization of activation to the cell surface. The ability to specifically detect and localize LTGF-beta activation provides an important tool for studies of its regulation.

A latent class approach to understanding patterns of peer victimization in four low-resource settings.

PubMed

Nguyen, Amanda J; Bradshaw, Catherine; Townsend, Lisa; Gross, Alden L; Bass, Judith

2016-08-17

Peer victimization is a common form of aggression among school-aged youth, but research is sparse regarding victimization dynamics in low- and middle-income countries (LMIC). Person-centered approaches have demonstrated utility in understanding patterns of victimization in the USA. We aimed to empirically identify classes of youth with unique victimization patterns in four LMIC settings using latent class analysis (LCA). We used data on past-year exposure to nine forms of victimization reported by 3536 youth (aged 15 years) from the Young Lives (YL) study in Ethiopia, India (Andhra Pradesh and Telangana states), Peru, and Vietnam. Sex and rural/urban context were examined as predictors of class membership. LCA supported a 2-class model in Peru, a 3-class model in Ethiopia and Vietnam, and a 4-class model in India. Classes were predominantly ordered by severity, suggesting that youth who experienced one form of victimization were likely to experience other forms as well. In India, two unordered classes were also observed, characterized by direct and indirect victimization. Boys were more likely than girls to be in the highly victimized (HV) class in Ethiopia and India. Urban contexts, compared with rural, conferred higher risk of victimization in Ethiopia and Peru, and lower risk in India and Vietnam. The identified patterns of multiple forms of victimization highlight a limitation of common researcher-driven classifications and suggest avenues for future person-centered research to improve intervention development in LMIC settings.

Microstructure Images Restoration of Metallic Materials Based upon KSVD and Smoothing Penalty Sparse Representation Approach.

PubMed

Li, Qing; Liang, Steven Y

2018-04-20

Microstructure images of metallic materials play a significant role in industrial applications. To address image degradation problem of metallic materials, a novel image restoration technique based on K-means singular value decomposition (KSVD) and smoothing penalty sparse representation (SPSR) algorithm is proposed in this work, the microstructure images of aluminum alloy 7075 (AA7075) material are used as examples. To begin with, to reflect the detail structure characteristics of the damaged image, the KSVD dictionary is introduced to substitute the traditional sparse transform basis (TSTB) for sparse representation. Then, due to the image restoration, modeling belongs to a highly underdetermined equation, and traditional sparse reconstruction methods may cause instability and obvious artifacts in the reconstructed images, especially reconstructed image with many smooth regions and the noise level is strong, thus the SPSR (here, q = 0.5) algorithm is designed to reconstruct the damaged image. The results of simulation and two practical cases demonstrate that the proposed method has superior performance compared with some state-of-the-art methods in terms of restoration performance factors and visual quality. Meanwhile, the grain size parameters and grain boundaries of microstructure image are discussed before and after they are restored by proposed method.

Highly undersampled contrast-enhanced MRA with iterative reconstruction: Integration in a clinical setting.

PubMed

Stalder, Aurelien F; Schmidt, Michaela; Quick, Harald H; Schlamann, Marc; Maderwald, Stefan; Schmitt, Peter; Wang, Qiu; Nadar, Mariappan S; Zenge, Michael O

2015-12-01

To integrate, optimize, and evaluate a three-dimensional (3D) contrast-enhanced sparse MRA technique with iterative reconstruction on a standard clinical MR system. Data were acquired using a highly undersampled Cartesian spiral phyllotaxis sampling pattern and reconstructed directly on the MR system with an iterative SENSE technique. Undersampling, regularization, and number of iterations of the reconstruction were optimized and validated based on phantom experiments and patient data. Sparse MRA of the whole head (field of view: 265 × 232 × 179 mm(3) ) was investigated in 10 patient examinations. High-quality images with 30-fold undersampling, resulting in 0.7 mm isotropic resolution within 10 s acquisition, were obtained. After optimization of the regularization factor and of the number of iterations of the reconstruction, it was possible to reconstruct images with excellent quality within six minutes per 3D volume. Initial results of sparse contrast-enhanced MRA (CEMRA) in 10 patients demonstrated high-quality whole-head first-pass MRA for both the arterial and venous contrast phases. While sparse MRI techniques have not yet reached clinical routine, this study demonstrates the technical feasibility of high-quality sparse CEMRA of the whole head in a clinical setting. Sparse CEMRA has the potential to become a viable alternative where conventional CEMRA is too slow or does not provide sufficient spatial resolution. © 2014 Wiley Periodicals, Inc.

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic Factor Analysis of Nonstationary Multivariate Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; And Others

1992-01-01

The dynamic factor model proposed by P. C. Molenaar (1985) is exhibited, and a dynamic nonstationary factor model (DNFM) is constructed with latent factor series that have time-varying mean functions. The use of a DNFM is illustrated using data from a television viewing habits study. (SLD)

An acutely and latently expressed herpes simplex virus 2 viral microRNA inhibits expression of ICP34.5, a viral neurovirulence factor.

PubMed

Tang, Shuang; Bertke, Andrea S; Patel, Amita; Wang, Kening; Cohen, Jeffrey I; Krause, Philip R

2008-08-05

Latency-associated transcript (LAT) sequences regulate herpes simplex virus (HSV) latency and reactivation from sensory neurons. We found a HSV-2 LAT-related microRNA (miRNA) designated miR-I in transfected and infected cells in vitro and in acutely and latently infected ganglia of guinea pigs in vivo. miR-I is also expressed in human sacral dorsal root ganglia latently infected with HSV-2. miR-I is expressed under the LAT promoter in vivo in infected sensory ganglia. We also predicted and identified a HSV-1 LAT exon-2 viral miRNA in a location similar to miR-I, implying a conserved mechanism in these closely related viruses. In transfected and infected cells, miR-I reduces expression of ICP34.5, a key viral neurovirulence factor. We hypothesize that miR-I may modulate the outcome of viral infection in the peripheral nervous system by functioning as a molecular switch for ICP34.5 expression.

Using the SRQ–20 Factor Structure to Examine Changes in Mental Distress Following Typhoon Exposure

PubMed Central

Stratton, Kelcey J.; Richardson, Lisa K.; Tran, Trinh Luong; Tam, Nguyen Thanh; Aggen, Steven H.; Berenz, Erin C.; Trung, Lam Tu; Tuan, Tran; Buoi, La Thi; Ha, Tran Thu; Thach, Tran Duc; Amstadter, Ananda B.

2014-01-01

Empirical research is limited regarding postdisaster assessment of distress in developing nations. This study aimed to evaluate the factor structure of the 20-item Self-Reporting Questionnaire (SRQ–20) before and after an acute trauma, Typhoon Xangsane, in order to examine changes in mental health symptoms in an epidemiologic sample of Vietnamese adults. The study examined a model estimating individual item factor loadings, thresholds, and a latent change factor for the SRQ–20's single “general distress” common factor. The covariates of sex, age, and severity of typhoon exposure were used to evaluate the disaster-induced changes in SRQ–20 scores while accounting for possible differences in the relationship between individual measurement scale items and the latent mental health construct. Evidence for measurement noninvariance was found. However, allowing sex and age effects on the pre-typhoon and post-typhoon factors accounted for much of the noninvariance in the SRQ–20 measurement structure. A test of no latent change failed, indicating that the SRQ–20 detected significant individual differences in distress between pre- and post-typhoon assessment. Conditioning on age and sex, several typhoon exposure variables differentially predicted levels of distress change, including evacuation, personal injury, and peri-event fear. On average, females and older individuals reported higher levels of distress than males and younger individuals, respectively. The SRQ–20 is a valid and reasonably stable instrument that may be used in postdisaster contexts to assess emotional distress and individual changes in mental health symptoms. PMID:24512425

Characteristic investigation of Golay9 multiple mirror telescope with a spherical primary mirror

NASA Astrophysics Data System (ADS)

Wu, Feng; Wu, Quanying; Zhu, Xifang; Xiang, Ruxi; Qian, Lin

2017-10-01

The sparse aperture provides a novel solution to the manufacturing difficulties of modern super large telescopes. Golay configurations are optimal in the sparse aperture family. Characteristics of the Golay9 multiple mirror telescope having a spherical primary mirror are investigated. The arrangement of the nine sub-mirrors is discussed after the planar Golay9 configuration is analyzed. The characteristics of the entrance pupil are derived by analyzing the sub-aperture shapes with different relative apertures and sub-mirror sizes. Formulas about the fill factor and the overlay factor are deduced. Their maximal values are presented based on the derived tangency condition. Formulas for the point spread function (PSF) and the modulation transfer function (MTF) of the Golay9 MMT are also deduced. Two Golay9 MMT have been developed by Zemax simulation. Their PSF, MTF, fill factors, and overlay factors prove that our theoretical results are consistent with the practical simulation ones.

Association of Stressful Life Events with Psychological Problems: A Large-Scale Community-Based Study Using Grouped Outcomes Latent Factor Regression with Latent Predictors

PubMed Central

Hassanzadeh, Akbar; Heidari, Zahra; Hassanzadeh Keshteli, Ammar; Afshar, Hamid

2017-01-01

Objective The current study is aimed at investigating the association between stressful life events and psychological problems in a large sample of Iranian adults. Method In a cross-sectional large-scale community-based study, 4763 Iranian adults, living in Isfahan, Iran, were investigated. Grouped outcomes latent factor regression on latent predictors was used for modeling the association of psychological problems (depression, anxiety, and psychological distress), measured by Hospital Anxiety and Depression Scale (HADS) and General Health Questionnaire (GHQ-12), as the grouped outcomes, and stressful life events, measured by a self-administered stressful life events (SLEs) questionnaire, as the latent predictors. Results The results showed that the personal stressors domain has significant positive association with psychological distress (β = 0.19), anxiety (β = 0.25), depression (β = 0.15), and their collective profile score (β = 0.20), with greater associations in females (β = 0.28) than in males (β = 0.13) (all P < 0.001). In addition, in the adjusted models, the regression coefficients for the association of social stressors domain and psychological problems profile score were 0.37, 0.35, and 0.46 in total sample, males, and females, respectively (P < 0.001). Conclusion Results of our study indicated that different stressors, particularly those socioeconomic related, have an effective impact on psychological problems. It is important to consider the social and cultural background of a population for managing the stressors as an effective approach for preventing and reducing the destructive burden of psychological problems. PMID:29312459

Latent KSHV Infected Endothelial Cells Are Glutamine Addicted and Require Glutaminolysis for Survival

PubMed Central

Sanchez, Erica L.; Carroll, Patrick A.; Thalhofer, Angel B.; Lagunoff, Michael

2015-01-01

Kaposi’s Sarcoma-associated Herpesvirus (KSHV) is the etiologic agent of Kaposi’s Sarcoma (KS). KSHV establishes a predominantly latent infection in the main KS tumor cell type, the spindle cell, which is of endothelial cell origin. KSHV requires the induction of multiple metabolic pathways, including glycolysis and fatty acid synthesis, for the survival of latently infected endothelial cells. Here we demonstrate that latent KSHV infection leads to increased levels of intracellular glutamine and enhanced glutamine uptake. Depletion of glutamine from the culture media leads to a significant increase in apoptotic cell death in latently infected endothelial cells, but not in their mock-infected counterparts. In cancer cells, glutamine is often required for glutaminolysis to provide intermediates for the tri-carboxylic acid (TCA) cycle and support for the production of biosynthetic and bioenergetic precursors. In the absence of glutamine, the TCA cycle intermediates alpha-ketoglutarate (αKG) and pyruvate prevent the death of latently infected cells. Targeted drug inhibition of glutaminolysis also induces increased cell death in latently infected cells. KSHV infection of endothelial cells induces protein expression of the glutamine transporter, SLC1A5. Chemical inhibition of SLC1A5, or knockdown by siRNA, leads to similar cell death rates as glutamine deprivation and, similarly, can be rescued by αKG. KSHV also induces expression of the heterodimeric transcription factors c-Myc-Max and related heterodimer MondoA-Mlx. Knockdown of MondoA inhibits expression of both Mlx and SLC1A5 and induces a significant increase in cell death of only cells latently infected with KSHV, again, fully rescued by the supplementation of αKG. Therefore, during latent infection of endothelial cells, KSHV activates and requires the Myc/MondoA-network to upregulate the glutamine transporter, SLC1A5, leading to increased glutamine uptake for glutaminolysis. These findings expand our understanding of the required metabolic pathways that are activated during latent KSHV infection of endothelial cells, and demonstrate a novel role for the extended Myc-regulatory network, specifically MondoA, during latent KSHV infection. PMID:26197457

Biclustering sparse binary genomic data.

PubMed

van Uitert, Miranda; Meuleman, Wouter; Wessels, Lodewyk

2008-12-01

Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.

Life-Course Body Mass Index Trajectories Are Predicted by Childhood Socioeconomic Status but Not Exposure to Improved Nutrition during the First 1000 Days after Conception in Guatemalan Adults.

PubMed

Ford, Nicole D; Martorell, Reynaldo; Mehta, Neil K; Ramirez-Zea, Manuel; Stein, Aryeh D

2016-11-01

Latin America has experienced increases in obesity. Little is known about the role of early life factors on body mass index (BMI) gain over the life course. The objective of this research was to examine the role of early life factors [specifically, nutrition supplementation during the first 1000 d (from conception to 2 y of age) and childhood household socioeconomic status (SES)] on the pattern of BMI gain from birth or early childhood through midadulthood by using latent class growth analysis. Study participants (711 women, 742 men) who were born in 4 villages in Guatemala (1962-1977) were followed prospectively since participating in a randomized nutrition supplementation trial as children. Sex-specific BMI latent class trajectories were derived from 22 possible measures of height and weight from 1969 to 2004. To characterize early life determinants of BMI latent class membership, we used logistic regression modeling and estimated the difference-in-difference (DD) effect of nutrition supplementation during the first 1000 d. We identified 2 BMI latent classes in women [low (57%) and high (43%)] and 3 classes in men [low (38%), medium (47%), and high (15%)]. Nutrition supplementation during the first 1000 d after conception was not associated with BMI latent class membership (DD test: P > 0.15 for men and women), whereas higher SES was associated with increased odds of high BMI latent class membership in both men (OR: 1.98; 95% CI: 1.09, 3.61) and women (OR: 1.62; 95% CI: 1.07, 2.45) for the highest relative to the lowest tertile. In a cohort of Guatemalan men and women, nutrition supplementation provided during the first 1000 d was not significantly associated with higher BMI trajectory. Higher childhood household SES was associated with increased odds of high BMI latent class membership relative to the poorest households. The pathways through which this operates still need to be explored. © 2016 American Society for Nutrition.

Characterization of dissolved organic matter in Dongjianghu Lake by UV-visible absorption spectroscopy with multivariate analysis.

PubMed

Zhu, Yanzhong; Song, Yonghui; Yu, Huibin; Liu, Ruixia; Liu, Lusan; Lv, Chunjian

2017-08-08

UV-visible absorption spectroscopy coupled with principal component analysis (PCA) and hierarchical cluster analysis (HCA) was applied to characterize spectroscopic components, detect latent factors, and investigate spatial variations of dissolved organic matter (DOM) in a large-scale lake. Twelve surface water samples were collected from Dongjianghu Lake in China. DOM contained lignin and quinine moieties, carboxylic acid, microbial products, and aromatic and alkyl groups, which in the northern part of the lake was largely different from the southern part. Fifteen spectroscopic indices were deduced from the absorption spectra to indicate molecular weight or humification degree of DOM. The northern part of the lake presented the smaller molecular weight or the lower humification degree of DOM than the southern part. E 2/4 , E 3/4 , E 2/3 , and S 2 were latent factors of characterizing the molecular weight of DOM, while E 2/5 , E 3/5 , E 2/6 , E 4/5 , E 3/6 , and A 2/1 were latent factors of evaluating the humification degree of DOM. The UV-visible absorption spectroscopy combined with PCA and HCA may not only characterize DOM fractions of lakes, but may be transferred to other types of waterscape.

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

Configurations of Common Childhood Psychosocial Risk Factors

ERIC Educational Resources Information Center

Copeland, William; Shanahan, Lilly; Costello, E. Jane; Angold, Adrian

2009-01-01

Background: Co-occurrence of psychosocial risk factors is commonplace, but little is known about psychiatrically-predictive configurations of psychosocial risk factors. Methods: Latent class analysis (LCA) was applied to 17 putative psychosocial risk factors in a representative population sample of 920 children ages 9 to 17. The resultant class…

Psychological Processes Mediate the Impact of Familial Risk, Social Circumstances and Life Events on Mental Health

PubMed Central

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Background Despite widespread acceptance of the ‘biopsychosocial model’, the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Methods and Findings Participants were 32,827 (age 18–85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ2 (3199, N = 23,397) = 126654·8, p<·001; RCFI = ·97; RMSEA = ·04 (·038–·039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. Conclusion These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies. PMID:24146890

Psychological processes mediate the impact of familial risk, social circumstances and life events on mental health.

PubMed

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Despite widespread acceptance of the 'biopsychosocial model', the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Participants were 32,827 (age 18-85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ(2) (3199, N = 23,397) = 126654.8, p<.001; RCFI = .97; RMSEA = .04 (.038-.039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies.

Theoretical implementation of prior knowledge in the design of a multi-scale prosthesis satisfaction questionnaire.

PubMed

Schürmann, Tim; Beckerle, Philipp; Preller, Julia; Vogt, Joachim; Christ, Oliver

2016-12-19

In product development for lower limb prosthetic devices, a set of special criteria needs to be met. Prosthetic devices have a direct impact on the rehabilitation process after an amputation with both perceived technological and psychological aspects playing an important role. However, available psychometric questionnaires fail to consider the important links between these two dimensions. In this article a probabilistic latent trait model is proposed with seven technical and psychological factors which measure satisfaction with the prosthesis. The results of a first study are used to determine the basic parameters of the statistical model. These distributions represent hypotheses about factor loadings between manifest items and latent factors of the proposed psychometric questionnaire. A study was conducted and analyzed to form hypotheses for the prior distributions of the questionnaire's measurement model. An expert agreement study conducted on 22 experts was used to determine the prior distribution of item-factor loadings in the model. Model parameters that had to be specified as part of the measurement model were informed prior distributions on the item-factor loadings. For the current 70 items in the questionnaire, each factor loading was set to represent the certainty with which experts had assigned the items to their respective factors. Considering only the measurement model and not the structural model of the questionnaire, 70 out of 217 informed prior distributions on parameters were set. The use of preliminary studies to set prior distributions in latent trait models, while being a relatively new approach in psychological research, provides helpful information towards the design of a seven factor questionnaire that means to identify relations between technical and psychological factors in prosthetic product design and rehabilitation medicine.

Psychological Factors Predict Local and Referred Experimental Muscle Pain: A Cluster Analysis in Healthy Adults

PubMed Central

Lee, Jennifer E.; Watson, David; Frey-Law, Laura A.

2012-01-01

Background Recent studies suggest an underlying three- or four-factor structure explains the conceptual overlap and distinctiveness of several negative emotionality and pain-related constructs. However, the validity of these latent factors for predicting pain has not been examined. Methods A cohort of 189 (99F; 90M) healthy volunteers completed eight self-report negative emotionality and pain-related measures (Eysenck Personality Questionnaire-Revised; Positive and Negative Affect Schedule; State-Trait Anxiety Inventory; Pain Catastrophizing Scale; Fear of Pain Questionnaire; Somatosensory Amplification Scale; Anxiety Sensitivity Index; Whiteley Index). Using principal axis factoring, three primary latent factors were extracted: General Distress; Catastrophic Thinking; and Pain-Related Fear. Using these factors, individuals clustered into three subgroups of high, moderate, and low negative emotionality responses. Experimental pain was induced via intramuscular acidic infusion into the anterior tibialis muscle, producing local (infusion site) and/or referred (anterior ankle) pain and hyperalgesia. Results Pain outcomes differed between clusters (multivariate analysis of variance and multinomial regression), with individuals in the highest negative emotionality cluster reporting the greatest local pain (p = 0.05), mechanical hyperalgesia (pressure pain thresholds; p = 0.009) and greater odds (2.21 OR) of experiencing referred pain compared to the lowest negative emotionality cluster. Conclusion Our results provide support for three latent psychological factors explaining the majority of the variance between several pain-related psychological measures, and that individuals in the high negative emotionality subgroup are at increased risk for (1) acute local muscle pain; (2) local hyperalgesia; and (3) referred pain using a standardized nociceptive input. PMID:23165778

Transdiagnostic Factors and Mediation of the Relationship Between Perceived Racial Discrimination and Mental Disorders.

PubMed

Rodriguez-Seijas, Craig; Stohl, Malki; Hasin, Deborah S; Eaton, Nicholas R

2015-07-01

Multivariable comorbidity research indicates that many common mental disorders are manifestations of 2 latent transdiagnostic factors, internalizing and externalizing. Environmental stressors are known to increase the risk for experiencing particular mental disorders, but their relationships with transdiagnostic disorder constructs are unknown. The present study investigated one such stressor, perceived racial discrimination, which is robustly associated with a variety of mental disorders. To examine the direct and indirect associations between perceived racial discrimination and common forms of psychopathology. Quantitative analysis of 12 common diagnoses that were previously assessed in a nationally representative sample (N = 5191) of African American and Afro-Caribbean adults in the United States, taken from the National Survey of American Life, and used to test the possibility that transdiagnostic factors mediate the effects of discrimination on disorders. The data were obtained from February 2001 to March 2003. Latent variable measurement models, including factor analysis, and indirect effect models were used in the study. Mental health diagnoses from reliable and valid structured interviews and perceived race-based discrimination. While perceived discrimination was positively associated with all examined forms of psychopathology and substance use disorders, latent variable indirect effects modeling revealed that almost all of these associations were significantly mediated by the transdiagnostic factors. For social anxiety disorder and attention-deficit/hyperactivity disorder, complete mediation was found. The pathways linking perceived discrimination to psychiatric disorders were not direct but indirect (via transdiagnostic factors). Therefore, perceived discrimination may be associated with risk for myriad psychiatric disorders due to its association with transdiagnostic factors.

Relationships among Behavioral Profiles, Reading Performance, and Disability Labels: A Latent Class Analysis of Students in the ED, LD, and OHI Special Education Categories

ERIC Educational Resources Information Center

Weingarten, Zachary

2018-01-01

The aim of this study was to explore the variation in student behavior across the ED, LD, and OHI disability categories and to examine demographic, behavioral, and academic factors that may place students at risk for negative outcomes. This study used teachers' rating of students' in-class behavior to identify latent classes of students in the ED,…

Severity of mental illness as a result of multiple childhood adversities: US National Epidemiologic Survey.

PubMed

Curran, Emma; Adamson, Gary; Stringer, Maurice; Rosato, Michael; Leavey, Gerard

2016-05-01

To examine patterns of childhood adversity, their long-term consequences and the combined effect of different childhood adversity patterns as predictors of subsequent psychopathology. Secondary analysis of data from the US National Epidemiologic Survey on alcohol and related conditions. Using latent class analysis to identify childhood adversity profiles; and using multinomial logistic regression to validate and further explore these profiles with a range of associated demographic and household characteristics. Finally, confirmatory factor analysis substantiated initial latent class analysis findings by investigating a range of mental health diagnoses. Latent class analysis generated a three-class model of childhood adversity in which 60 % of participants were allocated to a low adversity class; 14 % to a global adversities class (reporting exposures for all the derived latent classes); and 26 % to a domestic emotional and physical abuse class (exposed to a range of childhood adversities). Confirmatory Factor analysis defined an internalising-externalising spectrum to represent lifetime reporting patterns of mental health disorders. Using logistic regression, both adversity groups showed specific gender and race/ethnicity differences, related family discord and increased psychopathology. We identified underlying patterns in the exposure to childhood adversity and associated mental health. These findings are informative in their description of the configuration of adversities, rather than focusing solely on the cumulative aspect of experience. Amelioration of longer-term negative consequences requires early identification of psychopathology risk factors that can inform protective and preventive interventions. This study highlights the utility of screening for childhood adversities when individuals present with symptoms of psychiatric disorders.

Mental toughness latent profiles in endurance athletes

PubMed Central

Zeiger, Robert S.

2018-01-01

Mental toughness in endurance athletes, while an important factor for success, has been scarcely studied. An online survey was used to examine eight mental toughness factors in endurance athletes. The study aim was to determine mental toughness profiles via latent profile analysis in endurance athletes and whether associations exist between the latent profiles and demographics and sports characteristics. Endurance athletes >18 years of age were recruited via social media outlets (n = 1245, 53% female). Mental toughness was measured using the Sports Mental Toughness Questionnaire (SMTQ), Psychological Performance Inventory-Alternative (PPI-A), and self-esteem was measured using the Rosenberg Self-Esteem Scale (RSE). A three-class solution emerged, designated as high mental toughness (High MT), moderate mental toughness (Moderate MT) and low mental toughness (Low MT). ANOVA tests showed significant differences between all three classes on all 8 factors derived from the SMTQ, PPI-A and the RSE. There was an increased odds of being in the High MT class compared to the Low MT class for males (OR = 1.99; 95% CI, 1.39, 2.83; P<0.001), athletes who were over 55 compared to those who were 18–34 (OR = 2.52; 95% CI, 1.37, 4.62; P<0.01), high sports satisfaction (OR = 8.17; 95% CI, 5.63, 11.87; P<0.001), and high division placement (OR = 2.18; 95% CI, 1.46,3.26; P<0.001). The data showed that mental toughness latent profiles exist in endurance athletes. High MT is associated with demographics and sports characteristics. Mental toughness screening in athletes may help direct practitioners with mental skills training. PMID:29474398

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

The construct of maternal positivity in mothers of children with intellectual disability.

PubMed

Jess, M; Hastings, R P; Totsika, V

2017-10-01

Despite the elevated levels of stress, anxiety and depression reported by mothers of children with intellectual disabilities (ID), these mothers also experience positive well-being and describe positive perceptions of their child. To date, maternal positivity has been operationalised in different ways by using a variety of measures. In the present study, we tested whether a latent construct of maternal positivity could be derived from different measures of positivity. One hundred and thirty-five mothers of 89 boys and 46 girls with ID between 3 and 18 years of age completed measures on parental self-efficacy, their satisfaction with life, family satisfaction, their positive affect and their positive perceptions of their child with ID. We conducted a confirmatory factor analysis of latent positivity and subsequently tested its association with child social skills and behaviour problems, and maternal mental health. A latent maternal positivity factor achieved a statistically good fit by using the five observed indicators of positivity. Parental self-efficacy had the strongest loading on the latent factor. Maternal positivity was significantly negatively associated with maternal psychological distress, maternal stress and child problem behaviours and positively associated with child positive social behaviour. These findings lend support to the importance of examining parental positivity in families raising a child with ID, and using multiple indicators of positivity. Associations with negative psychological outcomes suggest that interventions focused on increasing parental positivity may have beneficial effects for parents. Further research is needed, especially in relation to such interventions. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

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.

A Semi-parametric Multivariate Gap-filling Model for Eddy Covariance Latent Heat Flux

NASA Astrophysics Data System (ADS)

Li, M.; Chen, Y.

2010-12-01

Quantitative descriptions of latent heat fluxes are important to study the water and energy exchanges between terrestrial ecosystems and the atmosphere. The eddy covariance approaches have been recognized as the most reliable technique for measuring surface fluxes over time scales ranging from hours to years. However, unfavorable micrometeorological conditions, instrument failures, and applicable measurement limitations may cause inevitable flux gaps in time series data. Development and application of suitable gap-filling techniques are crucial to estimate long term fluxes. In this study, a semi-parametric multivariate gap-filling model was developed to fill latent heat flux gaps for eddy covariance measurements. Our approach combines the advantages of a multivariate statistical analysis (principal component analysis, PCA) and a nonlinear interpolation technique (K-nearest-neighbors, KNN). The PCA method was first used to resolve the multicollinearity relationships among various hydrometeorological factors, such as radiation, soil moisture deficit, LAI, and wind speed. The KNN method was then applied as a nonlinear interpolation tool to estimate the flux gaps as the weighted sum latent heat fluxes with the K-nearest distances in the PCs’ domain. Two years, 2008 and 2009, of eddy covariance and hydrometeorological data from a subtropical mixed evergreen forest (the Lien-Hua-Chih Site) were collected to calibrate and validate the proposed approach with artificial gaps after standard QC/QA procedures. The optimal K values and weighting factors were determined by the maximum likelihood test. The results of gap-filled latent heat fluxes conclude that developed model successful preserving energy balances of daily, monthly, and yearly time scales. Annual amounts of evapotranspiration from this study forest were 747 mm and 708 mm for 2008 and 2009, respectively. Nocturnal evapotranspiration was estimated with filled gaps and results are comparable with other studies. Seasonal and daily variability of latent heat fluxes were also discussed.

Assessing a five factor model of PTSD: is dysphoric arousal a unique PTSD construct showing differential relationships with anxiety and depression?

PubMed

Armour, Cherie; Elhai, Jon D; Richardson, Don; Ractliffe, Kendra; Wang, Li; Elklit, Ask

2012-03-01

Posttraumatic stress disorder's (PTSD) latent structure has been widely debated. To date, two four-factor models (Numbing and Dysphoria) have received the majority of factor analytic support. Recently, Elhai et al. (2011) proposed and supported a revised (five-factor) Dysphoric Arousal model. Data were gathered from two separate samples; War veterans and Primary Care medical patients. The three models were compared and the resultant factors of the Dysphoric Arousal model were validated against external constructs of depression and anxiety. The Dysphoric Arousal model provided significantly better fit than the Numbing and Dysphoria models across both samples. When differentiating between factors, the current results support the idea that Dysphoric Arousal can be differentiated from Anxious Arousal but not from Emotional Numbing when correlated with depression. In conclusion, the Dysphoria model may be a more parsimonious representation of PTSD's latent structure in these trauma populations despite superior fit of the Dysphoric Arousal model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

PubMed

Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

2016-04-01

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Using Perturbed QR Factorizations To Solve Linear Least-Squares Problems

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

Avron, Haim; Ng, Esmond G.; Toledo, Sivan

2008-03-21

We propose and analyze a new tool to help solve sparse linear least-squares problems min{sub x} {parallel}Ax-b{parallel}{sub 2}. Our method is based on a sparse QR factorization of a low-rank perturbation {cflx A} of A. More precisely, we show that the R factor of {cflx A} is an effective preconditioner for the least-squares problem min{sub x} {parallel}Ax-b{parallel}{sub 2}, when solved using LSQR. We propose applications for the new technique. When A is rank deficient we can add rows to ensure that the preconditioner is well-conditioned without column pivoting. When A is sparse except for a few dense rows we canmore » drop these dense rows from A to obtain {cflx A}. Another application is solving an updated or downdated problem. If R is a good preconditioner for the original problem A, it is a good preconditioner for the updated/downdated problem {cflx A}. We can also solve what-if scenarios, where we want to find the solution if a column of the original matrix is changed/removed. We present a spectral theory that analyzes the generalized spectrum of the pencil (A*A,R*R) and analyze the applications.« less

Assessing the heterogeneity of aggressive behavior traits: exploratory and confirmatory analyses of the reactive and instrumental aggression Personality Assessment Inventory (PAI) scales.

PubMed

Antonius, Daniel; Sinclair, Samuel Justin; Shiva, Andrew A; Messinger, Julie W; Maile, Jordan; Siefert, Caleb J; Belfi, Brian; Malaspina, Dolores; Blais, Mark A

2013-01-01

The heterogeneity of violent behavior is often overlooked in risk assessment despite its importance in the management and treatment of psychiatric and forensic patients. In this study, items from the Personality Assessment Inventory (PAI) were first evaluated and rated by experts in terms of how well they assessed personality features associated with reactive and instrumental aggression. Exploratory principal component analyses (PCA) were then conducted on select items using a sample of psychiatric and forensic inpatients (n = 479) to examine the latent structure and construct validity of these reactive and instrumental aggression factors. Finally, a confirmatory factor analysis (CFA) was conducted on a separate sample of psychiatric inpatients (n = 503) to evaluate whether these factors yielded acceptable model fit. Overall, the exploratory and confirmatory analyses supported the existence of two latent PAI factor structures, which delineate personality traits related to reactive and instrumental aggression.

The Factor Structure of Effortful Control and Measurement Invariance Across Ethnicity and Sex in a High-Risk Sample

PubMed Central

Huerta, Snjezana; Zerr, Argero A.; Eisenberg, Nancy; Spinrad, Tracy L.; Valiente, Carlos; Di Giunta, Laura; Pina, Armando A.; Eggum, Natalie D.; Sallquist, Julie; Edwards, Alison; Kupfer, Anne; Lonigan, Christopher J.; Phillips, Beth M.; Wilson, Shauna B.; Clancy-Menchetti, Jeanine; Landry, Susan H.; Swank, Paul R.; Assel, Michael A.; Taylor, Heather B.

2010-01-01

Measurement invariance of a one-factor model of effortful control (EC) was tested for 853 low-income preschoolers (M age = 4.48 years). Using a teacher-report questionnaire and seven behavioral measures, configural invariance (same factor structure across groups), metric invariance (same pattern of factor loadings across groups), and partial scalar invariance (mostly the same intercepts across groups) were established across ethnicity (European Americans, African Americans and Hispanics) and across sex. These results suggest that the latent construct of EC behaved in a similar way across ethnic groups and sex, and that comparisons of mean levels of EC are valid across sex and probably valid across ethnicity, especially when larger numbers of tasks are used. The findings also support the use of diverse behavioral measures as indicators of a single latent EC construct. PMID:20593008

Environmental, morphological, and productive characterization of Sardinian goats and use of latent explanatory factors for population analysis.

PubMed

Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M

2016-09-01

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

Latent cytokines for targeted therapy of inflammatory disorders.

PubMed

Mullen, Lisa; Adams, Gill; Layward, Lorna; Vessillier, Sandrine; Annenkov, Alex; Mittal, Gayatri; Rigby, Anne; Sclanders, Michelle; Baker, David; Gould, David; Chernajovsky, Yuti

2014-01-01

The use of cytokines as therapeutic agents is important, given their potent biological effects. However, this very potency, coupled with the pleiotropic nature and short half-life of these molecules, has limited their therapeutic use. Strategies to increase the half-life and to decrease toxicity are necessary to allow effective treatment with these molecules. A number of strategies are used to overcome the natural limitations of cytokines, including PEGylation, encapsulation in liposomes, fusion to targeting peptides or antibodies and latent cytokines. Latent cytokines are engineered using the latency-associated peptide of transforming growth factor-β to produce therapeutic cytokines/peptides that are released only at the site of disease by cleavage with disease-induced matrix metalloproteinases. The principles underlying the latent cytokine technology are described and are compared to other methods of cytokine delivery. The potential of this technology for developing novel therapeutic strategies for the treatment of diseases with an inflammatory-mediated component is discussed. Methods of therapeutic cytokine delivery are addressed. The latent cytokine technology holds significant advantages over other methods of drug delivery by providing simultaneously increased half-life and localised drug delivery without systemic effects. Cytokines that failed clinical trials should be reassessed using this delivery system.

Chronic mild stress impairs latent inhibition and induces region-specific neural activation in CHL1-deficient mice, a mouse model of schizophrenia.

PubMed

Buhusi, Mona; Obray, Daniel; Guercio, Bret; Bartlett, Mitchell J; Buhusi, Catalin V

2017-08-30

Schizophrenia is a neurodevelopmental disorder characterized by abnormal processing of information and attentional deficits. Schizophrenia has a high genetic component but is precipitated by environmental factors, as proposed by the 'two-hit' theory of schizophrenia. Here we compared latent inhibition as a measure of learning and attention, in CHL1-deficient mice, an animal model of schizophrenia, and their wild-type littermates, under no-stress and chronic mild stress conditions. All unstressed mice as well as the stressed wild-type mice showed latent inhibition. In contrast, CHL1-deficient mice did not show latent inhibition after exposure to chronic stress. Differences in neuronal activation (c-Fos-positive cell counts) were noted in brain regions associated with latent inhibition: Neuronal activation in the prelimbic/infralimbic cortices and the nucleus accumbens shell was affected solely by stress. Neuronal activation in basolateral amygdala and ventral hippocampus was affected independently by stress and genotype. Most importantly, neural activation in nucleus accumbens core was affected by the interaction between stress and genotype. These results provide strong support for a 'two-hit' (genes x environment) effect on latent inhibition in CHL1-deficient mice, and identify CHL1-deficient mice as a model of schizophrenia-like learning and attention impairments. Copyright © 2017 Elsevier B.V. All rights reserved.

Improving the energy efficiency of sparse linear system solvers on multicore and manycore systems.

PubMed

Anzt, H; Quintana-Ortí, E S

2014-06-28

While most recent breakthroughs in scientific research rely on complex simulations carried out in large-scale supercomputers, the power draft and energy spent for this purpose is increasingly becoming a limiting factor to this trend. In this paper, we provide an overview of the current status in energy-efficient scientific computing by reviewing different technologies used to monitor power draft as well as power- and energy-saving mechanisms available in commodity hardware. For the particular domain of sparse linear algebra, we analyse the energy efficiency of a broad collection of hardware architectures and investigate how algorithmic and implementation modifications can improve the energy performance of sparse linear system solvers, without negatively impacting their performance. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Development of a Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo): Study Protocol.

PubMed

Iorio, Alfonso; Keepanasseril, Arun; Foster, Gary; Navarro-Ruan, Tamara; McEneny-King, Alanna; Edginton, Andrea N; Thabane, Lehana

2016-12-15

Individual pharmacokinetic assessment is a critical component of tailored prophylaxis for hemophilia patients. Population pharmacokinetics allows using individual sparse data, thus simplifying individual pharmacokinetic studies. Implementing population pharmacokinetics capacity for the hemophilia community is beyond individual reach and requires a system effort. The Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project aims to assemble a database of patient pharmacokinetic data for all existing factor concentrates, develop and validate population pharmacokinetics models, and integrate these models within a Web-based calculator for individualized pharmacokinetic estimation in patients at participating treatment centers. Individual pharmacokinetic studies on factor VIII and IX concentrates will be sourced from pharmaceutical companies and independent investigators. All factor concentrate manufacturers, hemophilia treatment centers (HTCs), and independent investigators (identified via a systematic review of the literature) having on file pharmacokinetic data and willing to contribute full or sparse pharmacokinetic data will be eligible for participation. Multicompartmental modeling will be performed using a mixed-model approach for derivation and Bayesian forecasting for estimation of individual sparse data. NONMEM (ICON Development Solutions) will be used as modeling software. The WAPPS-Hemo research network has been launched and is currently joined by 30 HTCs from across the world. We have gathered dense individual pharmacokinetic data on 878 subjects, including several replicates, on 21 different molecules from 17 different sources. We have collected sparse individual pharmacokinetic data on 289 subjects from the participating centers through the testing phase of the WAPPS-Hemo Web interface. We have developed prototypal population pharmacokinetics models for 11 molecules. The WAPPS-Hemo website (available at www.wapps-hemo.org, version 2.4), with core functionalities allowing hemophilia treaters to obtain individual pharmacokinetic estimates on sparse data points after 1 or more infusions of a factor concentrate, was launched for use within the research network in July 2015. The WAPPS-Hemo project and research network aims to make it easier to perform individual pharmacokinetic assessments on a reduced number of plasma samples by adoption of a population pharmacokinetics approach. The project will also gather data to substantially enhance the current knowledge about factor concentrate pharmacokinetics and sources of its variability in target populations. ClinicalTrials.gov NCT02061072; https://clinicaltrials.gov/ct2/show/NCT02061072 (Archived by WebCite at http://www.webcitation.org/6mRK9bKP6). ©Alfonso Iorio, Arun Keepanasseril, Gary Foster, Tamara Navarro-Ruan, Alanna McEneny-King, Andrea N Edginton, Lehana Thabane. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 15.12.2016.

How do verbal short-term memory and working memory relate to the acquisition of vocabulary and grammar? A comparison between first and second language learners.

PubMed

Verhagen, Josje; Leseman, Paul

2016-01-01

Previous studies show that verbal short-term memory (VSTM) is related to vocabulary learning, whereas verbal working memory (VWM) is related to grammar learning in children learning a second language (L2) in the classroom. In this study, we investigated whether the same relationships apply to children learning an L2 in a naturalistic setting and to monolingual children. We also investigated whether relationships with verbal memory differ depending on the type of grammar skill investigated (i.e., morphology vs. syntax). Participants were 63 Turkish children who learned Dutch as an L2 and 45 Dutch monolingual children (mean age = 5 years). Children completed a series of VSTM and VWM tasks, a Dutch vocabulary task, and a Dutch grammar task. A confirmatory factor analysis showed that VSTM and VWM represented two separate latent factors in both groups. Structural equation modeling showed that VSTM, treated as a latent factor, significantly predicted vocabulary and grammar. VWM, treated as a latent factor, predicted only grammar. Both memory factors were significantly related to the acquisition of morphology and syntax. There were no differences between the two groups. These results show that (a) VSTM and VWM are differentially associated with language learning and (b) the same memory mechanisms are employed for learning vocabulary and grammar in L1 children and in L2 children who learn their L2 naturalistically. Copyright © 2015 Elsevier Inc. All rights reserved.

On Fitting a Multivariate Two-Part Latent Growth Model

PubMed Central

Xu, Shu; Blozis, Shelley A.; Vandewater, Elizabeth A.

2017-01-01

A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method. PMID:29333054

Bootstrap Standard Error Estimates in Dynamic Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Browne, Michael W.

2010-01-01

Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the…

Abrogation of both short and long forms of latent transforming growth factor-β binding protein-1 causes defective cardiovascular development and is perinatally lethal.

PubMed

Horiguchi, Masahito; Todorovic, Vesna; Hadjiolova, Krassimira; Weiskirchen, Ralf; Rifkin, Daniel B

2015-04-01

Latent transforming growth factor-β binding protein-1 (LTBP-1) is an extracellular protein that is structurally similar to fibrillin and has an important role in controlling transforming growth factor-β (TGF-β) signaling by storing the cytokine in the extracellular matrix and by being involved in the conversion of the latent growth factor to its active form. LTBP-1 is found as both short (LTBP-1S) and long (LTBP-1L) forms, which are derived through the use of separate promoters. There is controversy regarding the importance of LTBP-1L, as Ltbp1L knockout mice showed multiple cardiovascular defects but the complete null mice did not. Here, we describe a third line of Ltbp1 knockout mice generated utilizing a conditional knockout strategy that ablated expression of both L and S forms of LTBP-1. These mice show severe developmental cardiovascular abnormalities and die perinatally; thus these animals display a phenotype similar to previously reported Ltbp1L knockout mice. We reinvestigated the other "complete" knockout line and found that these mice express a splice variant of LTBP-1L and, therefore, are not complete Ltbp1 knockouts. Our results clarify the phenotypes of Ltbp1 null mice and re-emphasize the importance of LTBP-1 in vivo. Copyright © 2015. Published by Elsevier B.V.

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

PubMed Central

BOYSAN, Murat

2014-01-01

Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis.

PubMed

Boysan, Murat

2014-09-01

There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson's product-moment correlation coefficients. The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism.

Growth Modeling with Non-Ignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

PubMed Central

Muthén, Bengt; Asparouhov, Tihomir; Hunter, Aimee; Leuchter, Andrew

2011-01-01

This paper uses a general latent variable framework to study a series of models for non-ignorable missingness due to dropout. Non-ignorable missing data modeling acknowledges that missingness may depend on not only covariates and observed outcomes at previous time points as with the standard missing at random (MAR) assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework using the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling using latent trajectory classes. A new selection model allows not only an influence of the outcomes on missingness, but allows this influence to vary across latent trajectory classes. Recommendations are given for choosing models. The missing data models are applied to longitudinal data from STAR*D, the largest antidepressant clinical trial in the U.S. to date. Despite the importance of this trial, STAR*D growth model analyses using non-ignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. PMID:21381817

Oxaliplatin antagonizes HIV-1 latency by activating NF-κB without causing global T cell activation

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

Zhu, Xiaoli; Liu, Sijie; Wang, Pengfei

Highlights: • The chemotherapeutic drug oxaliplatin reactivates latent HIV-1 in this cell line model of HIV-1 latency. • Reactivation is synergized when oxaliplatin is used in combination with valproic acid. • Oxaliplatin reactivates latent HIV-1 through activation of NF-kB and does not induce T cell activation. - Abstract: Reactivation of latent HIV-1 is a promising strategy for the clearance of the viral reservoirs. Because of the limitations of current agents, identification of new latency activators is urgently required. Using an established model of HIV-1 latency, we examined the effect of Oxaliplatin on latent HIV-1 reactivation. We showed that Oxaliplatin, alonemore » or in combination with valproic acid (VPA), was able to reactivate HIV-1 without inducing global T cell activation. We also provided evidence that Oxaliplatin reactivated HIV-1 expression by inducing nuclear factor kappa B (NF-κB) nuclear translocation. Our results indicated that Oxaliplatin could be a potential drug candidate for anti-latency therapies.« less

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.

Patterns of Chronic Conditions and Their Associations With Behaviors and Quality of Life, 2010

PubMed Central

Mitchell, Sandra A.; Thompson, William W.; Zack, Matthew M.; Reeve, Bryce B.; Cella, David; Smith, Ashley Wilder

2015-01-01

Introduction Co-occurring chronic health conditions elevate the risk of poor health outcomes such as death and disability, are associated with poor quality of life, and magnify the complexities of self-management, care coordination, and treatment planning. This study assessed patterns of both singular and multiple chronic conditions, behavioral risk factors, and quality of life in a population-based sample. Methods In a national survey, adults (n = 4,184) answered questions about the presence of 27 chronic conditions. We used latent class analysis to identify patterns of chronic conditions and to explore associations of latent class membership with sociodemographic characteristics, behavioral risk factors, and health. Results Latent class analyses indicated 4 morbidity profiles: a healthy class (class 1), a class with predominantly physical health conditions (class 2), a class with predominantly mental health conditions (class 3), and a class with both physical and mental health conditions (class 4). Class 4 respondents reported significantly worse physical health and well-being and more days of activity limitation than those in the other latent classes. Class 4 respondents were also more likely to be obese and sedentary, and those with predominantly mental health conditions were most likely to be current smokers. Conclusions Subgroups with distinct patterns of chronic conditions can provide direction for screening and surveillance, guideline development, and the delivery of complex care services. PMID:26679491

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

PubMed Central

De Angelis, Marco; Marín Puchades, Víctor; Fraboni, Federico; Pietrantoni, Luca

2017-01-01

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist’s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types. PMID:28158296

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

Microstructure Images Restoration of Metallic Materials Based upon KSVD and Smoothing Penalty Sparse Representation Approach

PubMed Central

Liang, Steven Y.

2018-01-01

Microstructure images of metallic materials play a significant role in industrial applications. To address image degradation problem of metallic materials, a novel image restoration technique based on K-means singular value decomposition (KSVD) and smoothing penalty sparse representation (SPSR) algorithm is proposed in this work, the microstructure images of aluminum alloy 7075 (AA7075) material are used as examples. To begin with, to reflect the detail structure characteristics of the damaged image, the KSVD dictionary is introduced to substitute the traditional sparse transform basis (TSTB) for sparse representation. Then, due to the image restoration, modeling belongs to a highly underdetermined equation, and traditional sparse reconstruction methods may cause instability and obvious artifacts in the reconstructed images, especially reconstructed image with many smooth regions and the noise level is strong, thus the SPSR (here, q = 0.5) algorithm is designed to reconstruct the damaged image. The results of simulation and two practical cases demonstrate that the proposed method has superior performance compared with some state-of-the-art methods in terms of restoration performance factors and visual quality. Meanwhile, the grain size parameters and grain boundaries of microstructure image are discussed before and after they are restored by proposed method. PMID:29677163

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

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.

Epstein-Barr Virus Latent Membrane Protein 1 Regulates the Function of Interferon Regulatory Factor 7 by Inducing Its Sumoylation

PubMed Central

Bentz, Gretchen L.; Shackelford, Julia

2012-01-01

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) induces multiple signal transduction pathways during latent EBV infection via its C-terminal activating region 1 (CTAR1), CTAR2, and the less-studied CTAR3. One mechanism by which LMP1 regulates cellular activation is through the induction of protein posttranslational modifications, including phosphorylation and ubiquitination. We recently documented that LMP1 induces a third major protein modification by physically interacting with the SUMO-conjugating enzyme Ubc9 through CTAR3 and inducing the sumoylation of cellular proteins in latently infected cells. We have now identified a specific target of LMP1-induced sumoylation, interferon regulatory factor 7 (IRF7). We hypothesize that during EBV latency, LMP1 induces the sumoylation of IRF7, limiting its transcriptional activity and modulating the activation of innate immune responses. Our data show that endogenously sumoylated IRF7 is detected in latently infected EBV lymphoblastoid cell lines. LMP1 expression coincided with increased sumoylation of IRF7 in a CTAR3-dependent manner. Additional experiments show that LMP1 CTAR3-induced sumoylation regulates the expression and function of IRF7 by decreasing its turnover, increasing its nuclear retention, decreasing its DNA binding, and limiting its transcriptional activation. Finally, we identified that IRF7 is sumoylated at lysine 452. These data demonstrate that LMP1 CTAR3 does in fact function in intracellular signaling, leading to biologic effects. We propose that CTAR3 is an important signaling region of LMP1 that regulates protein function by sumoylation. We have shown specifically that LMP1 CTAR3, in cooperation with CTAR2, can limit the ability of IRF7 to induce innate immune responses by inducing the sumoylation of IRF7. PMID:22951831

Molecular Basis of Latency in Pathogenic Human Viruses

NASA Astrophysics Data System (ADS)

Garcia-Blanco, Mariano A.; Cullen, Bryan R.

1991-11-01

Several human viruses are able to latently infect specific target cell populations in vivo. Analysis of the replication cycles of herpes simplex virus, Epstein-Barr virus, and human immunodeficiency virus suggests that the latent infections established by these human pathogens primarily result from a lack of host factors critical for the expression of viral early gene products. The subsequent activation of specific cellular transcription factors in response to extracellular stimuli can induce the expression of these viral regulatory proteins and lead to a burst of lytic viral replication. Latency in these eukaryotic viruses therefore contrasts with latency in bacteriophage, which is maintained primarily by the expression of virally encoded repressors of lytic replication.

Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

NASA Astrophysics Data System (ADS)

Zhou, Jian; Qi, Jinyi

2011-03-01

Statistically based iterative approaches for image reconstruction have gained much attention in medical imaging. An accurate system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction. However, an accurate system matrix is often associated with high computational cost and huge storage requirement. Here we present a method to address this problem by using sparse matrix factorization and parallel computing on a graphic processing unit (GPU).We factor the accurate system matrix into three sparse matrices: a sinogram blurring matrix, a geometric projection matrix, and an image blurring matrix. The sinogram blurring matrix models the detector response. The geometric projection matrix is based on a simple line integral model. The image blurring matrix is to compensate for the line-of-response (LOR) degradation due to the simplified geometric projection matrix. The geometric projection matrix is precomputed, while the sinogram and image blurring matrices are estimated by minimizing the difference between the factored system matrix and the original system matrix. The resulting factored system matrix has much less number of nonzero elements than the original system matrix and thus substantially reduces the storage and computation cost. The smaller size also allows an efficient implement of the forward and back projectors on GPUs, which have limited amount of memory. Our simulation studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction. The proposed technique is applicable to image reconstruction for different imaging modalities, including x-ray CT, PET, and SPECT.

Longitudinal Factor Score Estimation Using the Kalman Filter.

ERIC Educational Resources Information Center

Oud, Johan H.; And Others

1990-01-01

How longitudinal factor score estimation--the estimation of the evolution of factor scores for individual examinees over time--can profit from the Kalman filter technique is described. The Kalman estimates change more cautiously over time, have lower estimation error variances, and reproduce the LISREL program latent state correlations more…

The influence of mindfulness, self-compassion, psychological flexibility, and posttraumatic stress disorder on disability and quality of life over time in war veterans.

PubMed

Meyer, Eric C; Frankfurt, Sheila B; Kimbrel, Nathan A; DeBeer, Bryann B; Gulliver, Suzy B; Morrisette, Sandra B

2018-07-01

Posttraumatic stress disorder (PTSD) strongly predicts greater disability and lower quality of life (QOL). Mindfulness-based and other third-wave behavior therapy interventions improve well-being by enhancing mindfulness, self-compassion, and psychological flexibility. We hypothesized that these mechanisms of therapeutic change would comprise a single latent factor that would predict disability and QOL after accounting for PTSD symptom severity. Iraq and Afghanistan war veterans (N = 117) completed a study of predictors of successful reintegration. Principal axis factor analysis tested whether mindfulness, self-compassion, and psychological flexibility comprised a single latent factor. Hierarchical regression tested whether this factor predicted disability and QOL 1 year later. Mindfulness, self-compassion, and psychological flexibility comprised a single factor that predicted disability and QOL after accounting for PTSD symptom severity. PTSD symptoms remained a significant predictor of disability but not QOL. Targeting these mechanisms may help veterans achieve functional recovery, even in the presence of PTSD symptoms. © 2018 Wiley Periodicals, Inc.

Bayes Factor Covariance Testing in Item Response Models.

PubMed

Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

2017-12-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

Analysis of the Empathic Concern Subscale of the Emotional Response Questionnaire in a Study Evaluating the Impact of a 3D Cultural Simulation.

PubMed

Everson, Naleya; Levett-Jones, Tracy; Pitt, Victoria; Lapkin, Samuel; Van Der Riet, Pamela; Rossiter, Rachel; Jones, Donovan; Gilligan, Conor; Courtney Pratt, Helen

2018-04-25

Abstract Background Empathic concern has been found to decline in health professional students. Few effective educational programs and a lack of validated scales are reported. Previous analysis of the Empathic Concern scale of the Emotional Response Questionnaire has reported both one and two latent constructs. Aim To evaluate the impact of simulation on nursing students' empathic concern and test the psychometric properties of the Empathic Concern scale. Methods The study used a one group pre-test post-test design with a convenience sample of 460 nursing students. Empathic concern was measured pre-post simulation with the Empathic Concern scale. Factor Analysis was undertaken to investigate the structure of the scale. Results There was a statistically significant increase in Empathic Concern scores between pre-simulation 5.57 (SD = 1.04) and post-simulation 6.10 (SD = 0.95). Factor analysis of the Empathic Concern scale identified one latent dimension. Conclusion Immersive simulation may promote empathic concern. The Empathic Concern scale measured a single latent construct in this cohort.

An acutely and latently expressed herpes simplex virus 2 viral microRNA inhibits expression of ICP34.5, a viral neurovirulence factor

PubMed Central

Tang, Shuang; Bertke, Andrea S.; Patel, Amita; Wang, Kening; Cohen, Jeffrey I.; Krause, Philip R.

2008-01-01

Latency-associated transcript (LAT) sequences regulate herpes simplex virus (HSV) latency and reactivation from sensory neurons. We found a HSV-2 LAT-related microRNA (miRNA) designated miR-I in transfected and infected cells in vitro and in acutely and latently infected ganglia of guinea pigs in vivo. miR-I is also expressed in human sacral dorsal root ganglia latently infected with HSV-2. miR-I is expressed under the LAT promoter in vivo in infected sensory ganglia. We also predicted and identified a HSV-1 LAT exon-2 viral miRNA in a location similar to miR-I, implying a conserved mechanism in these closely related viruses. In transfected and infected cells, miR-I reduces expression of ICP34.5, a key viral neurovirulence factor. We hypothesize that miR-I may modulate the outcome of viral infection in the peripheral nervous system by functioning as a molecular switch for ICP34.5 expression. PMID:18678906

Latent Tuberculosis Infection: Myths, Models, and Molecular Mechanisms

PubMed Central

Dutta, Noton K.

2014-01-01

SUMMARY The aim of this review is to present the current state of knowledge on human latent tuberculosis infection (LTBI) based on clinical studies and observations, as well as experimental in vitro and animal models. Several key terms are defined, including “latency,” “persistence,” “dormancy,” and “antibiotic tolerance.” Dogmas prevalent in the field are critically examined based on available clinical and experimental data, including the long-held beliefs that infection is either latent or active, that LTBI represents a small population of nonreplicating, “dormant” bacilli, and that caseous granulomas are the haven for LTBI. The role of host factors, such as CD4+ and CD8+ T cells, T regulatory cells, tumor necrosis factor alpha (TNF-α), and gamma interferon (IFN-γ), in controlling TB infection is discussed. We also highlight microbial regulatory and metabolic pathways implicated in bacillary growth restriction and antibiotic tolerance under various physiologically relevant conditions. Finally, we pose several clinically important questions, which remain unanswered and will serve to stimulate future research on LTBI. PMID:25184558

Comparisons of polydrug use at national and inner city levels in England: associations with demographic and socioeconomic factors.

PubMed

Carter, Jennifer L; Strang, John; Frissa, Souci; Hayes, Richard D; Hatch, Stephani L; Hotopf, Matthew

2013-10-01

This study compares polydrug use in national and inner city samples to (1) examine patterns of use underlying different prevalence rates and (2) identify how inner city polydrug use needs targeting in ways not suggested by national research. Latent class analyses on indicators of illicit drug use in the last year, hazardous alcohol use, and cigarette smoking were compared between the inner city 2008-2010 South East London Community Health study (n = 1698) and the nationally representative 2007 Adult Psychiatric Morbidity Survey in England (n = 7403). Multinomial logistic regressions then examined latent class solutions with demographic and socioeconomic factors. Both samples revealed three notably similar classes of polydrug users: a "high-drug" group using multiple substances; a "moderate-drug" group using cannabis, alcohol, and cigarettes; and a "low-drug" group reporting minimal alcohol and cigarette use. However, South East London Community Health reported lower risks of polydrug use for ethnic minorities but not for more educated participants. Despite higher polydrug use prevalence in the inner city, latent classes of polydrug users were similar between samples. Some demographic and socioeconomic factors differed between the samples, suggesting the need for inner city services to use both local and national data for policy planning. Copyright © 2013 Elsevier Inc. All rights reserved.

Clustering, hierarchical organization, and the topography of abstract and concrete nouns.

PubMed

Troche, Joshua; Crutch, Sebastian; Reilly, Jamie

2014-01-01

The empirical study of language has historically relied heavily upon concrete word stimuli. By definition, concrete words evoke salient perceptual associations that fit well within feature-based, sensorimotor models of word meaning. In contrast, many theorists argue that abstract words are "disembodied" in that their meaning is mediated through language. We investigated word meaning as distributed in multidimensional space using hierarchical cluster analysis. Participants (N = 365) rated target words (n = 400 English nouns) across 12 cognitive dimensions (e.g., polarity, ease of teaching, emotional valence). Factor reduction revealed three latent factors, corresponding roughly to perceptual salience, affective association, and magnitude. We plotted the original 400 words for the three latent factors. Abstract and concrete words showed overlap in their topography but also differentiated themselves in semantic space. This topographic approach to word meaning offers a unique perspective to word concreteness.

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

Summer Proceedings 2016: The Center for Computing Research at Sandia National Laboratories

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

Carleton, James Brian; Parks, Michael L.

Solving sparse linear systems from the discretization of elliptic partial differential equations (PDEs) is an important building block in many engineering applications. Sparse direct solvers can solve general linear systems, but are usually slower and use much more memory than effective iterative solvers. To overcome these two disadvantages, a hierarchical solver (LoRaSp) based on H2-matrices was introduced in [22]. Here, we have developed a parallel version of the algorithm in LoRaSp to solve large sparse matrices on distributed memory machines. On a single processor, the factorization time of our parallel solver scales almost linearly with the problem size for three-dimensionalmore » problems, as opposed to the quadratic scalability of many existing sparse direct solvers. Moreover, our solver leads to almost constant numbers of iterations, when used as a preconditioner for Poisson problems. On more than one processor, our algorithm has significant speedups compared to sequential runs. With this parallel algorithm, we are able to solve large problems much faster than many existing packages as demonstrated by the numerical experiments.« less

Soluble Factors Secreted by Endothelial Cells Allow for Productive and Latent HIV-1 Infection in Resting CD4+ T Cells.

PubMed

Morris, John Henry; Nguyen, Tran; Nwadike, Abuoma; Geels, Mackenzie L; Kamp, Derrick L; Kim, Bo Ram; Boyer, Jean D; Shen, Anding

2017-02-01

In vitro, it is difficult to infect resting CD4 + T cells with human immunodeficiency virus type 1 (HIV), but infections readily occur in vivo. Endothelial cells (ECs) interact with resting CD4 + T cells in vivo, and we found previously that EC stimulation leads to productive and latent HIV infection of resting CD4 + T cells. In this study, we further characterize the interactions between EC and resting T cells. We found that resting CD4 + T cells did not require direct contact with EC for productive and/or latent infection to occur, indicating the involvement of soluble factors. Among 30 cytokines tested in a multiplex enzyme-linked immunosorbent assay (ELISA), we found that expressions for IL-6, IL-8, and CCL2 were much higher in EC-stimulated resting T cells than resting T cells cultured alone. IL-6 was found to be the soluble factor responsible for inducing productive infection of resting T cells, although direct contact with EC had an added effect. However, none of the cytokines tested, IL-6, IL-8, or CCL2, induced additional latent infection in resting T cells, suggesting that unidentified cytokines were involved. Intracellular molecules MURR1, c-Jun N-terminal kinase (JNK), and glucose transporter-1 (GLUT1) were previously shown in blocking HIV infection of resting CD4 + T cells. We found that the concentrations of these proteins were not significantly different in resting T cells before and after stimulation by EC; therefore, they are not likely involved in EC stimulation of resting CD4 + T cells, and a new mechanism is yet to be identified.

Soluble Factors Secreted by Endothelial Cells Allow for Productive and Latent HIV-1 Infection in Resting CD4+ T Cells

PubMed Central

Morris, John Henry; Nguyen, Tran; Nwadike, Abuoma; Geels, Mackenzie L.; Kamp, Derrick L.; Kim, Bo Ram; Boyer, Jean D.

2017-01-01

Abstract In vitro, it is difficult to infect resting CD4+ T cells with human immunodeficiency virus type 1 (HIV), but infections readily occur in vivo. Endothelial cells (ECs) interact with resting CD4+ T cells in vivo, and we found previously that EC stimulation leads to productive and latent HIV infection of resting CD4+ T cells. In this study, we further characterize the interactions between EC and resting T cells. We found that resting CD4+ T cells did not require direct contact with EC for productive and/or latent infection to occur, indicating the involvement of soluble factors. Among 30 cytokines tested in a multiplex enzyme-linked immunosorbent assay (ELISA), we found that expressions for IL-6, IL-8, and CCL2 were much higher in EC-stimulated resting T cells than resting T cells cultured alone. IL-6 was found to be the soluble factor responsible for inducing productive infection of resting T cells, although direct contact with EC had an added effect. However, none of the cytokines tested, IL-6, IL-8, or CCL2, induced additional latent infection in resting T cells, suggesting that unidentified cytokines were involved. Intracellular molecules MURR1, c-Jun N-terminal kinase (JNK), and glucose transporter-1 (GLUT1) were previously shown in blocking HIV infection of resting CD4+ T cells. We found that the concentrations of these proteins were not significantly different in resting T cells before and after stimulation by EC; therefore, they are not likely involved in EC stimulation of resting CD4+ T cells, and a new mechanism is yet to be identified. PMID:27599784

High Prevalence of Latent Tuberculosis Infection in Dialysis Patients Using the Interferon-γ Release Assay and Tuberculin Skin Test

PubMed Central

Lee, Susan Shin-Jung; Chou, Kang-Ju; Dou, Horng-Yunn; Huang, Tsi-Shu; Ni, Yen-Yun; Fang, Hua-Chang; Tsai, Hung-Chin; Sy, Cheng-Len; Chen, Jui-Kuang; Wu, Kuang-Sheng; Wang, Yung-Hsin; Lin, Hsi-Hsun

2010-01-01

Background and objectives: Patients in ESRD on hemodialysis with latent tuberculosis (TB) infection have 10 to 25 times the risk of reactivation into active disease compared with healthy adults. This study investigates the prevalence of latent TB infection in dialysis patients from a country with an intermediate burden of TB and its associated risk factors using the QuantiFERON-TB Gold in-tube test (QGIT) and the tuberculin skin test (TST). Design, setting, participants, & measurements: This was a prospective, cross-sectional study performed at a medical center in Taiwan on dialysis patients. Each patient underwent QGIT, two-step TST using 2 tuberculin units (TU) of PPD RT-23, a chest x-ray to exclude active TB, and an interview to determine TB risk factors. Results: Ninety-three of 190 eligible patients were enrolled: 35 men and 58 women. 64.8% were vaccinated with the Bacille-Calmette-Guérin (BCG) vaccination. Overall, 34.4% were positive by QGIT and 10.8% were indeterminate. Using a 10-mm TST cutoff, 53.9% were positive. There was poor correlation between TST and QGIT at any TST cutoff criteria. There was a significant increasing trend of QGIT positivity with age in those younger than 70 years, and, conversely, a decreasing trend of TST reactivity with age. Significant risk factors for QGIT positivity included age and past TB disease. Conclusions: This study shows a high prevalence of latent TB infection in dialysis patients in a country with an intermediate burden of TB. QGIT in dialysis patients correlated better than TST with the risk of TB infection and past TB disease. PMID:20538837

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System

PubMed Central

2014-01-01

Background The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. Methods This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. Results DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. Conclusions These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross. PMID:25038628

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System.

PubMed

Zagelbaum, Nicole K; Heslin, Kevin C; Stein, Judith A; Ruzek, Josef; Smith, Robert E; Nyugen, Tam; Dobalian, Aram

2014-07-19

The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross.

Hit-and-run stimulation: a novel concept to reactivate latent HIV-1 infection without cytokine gene induction.

PubMed

Wolschendorf, Frank; Duverger, Alexandra; Jones, Jennifer; Wagner, Frederic H; Huff, Jason; Benjamin, William H; Saag, Michael S; Niederweis, Michael; Kutsch, Olaf

2010-09-01

Current antiretroviral therapy (ART) efficiently controls HIV-1 replication but fails to eradicate the virus. Even after years of successful ART, HIV-1 can conceal itself in a latent state in long-lived CD4(+) memory T cells. From this latent reservoir, HIV-1 rebounds during treatment interruptions. Attempts to therapeutically eradicate this viral reservoir have yielded disappointing results. A major problem with previously utilized activating agents is that at the concentrations required for efficient HIV-1 reactivation, these stimuli trigger high-level cytokine gene expression (hypercytokinemia). Therapeutically relevant HIV-1-reactivating agents will have to trigger HIV-1 reactivation without the induction of cytokine expression. We present here a proof-of-principle study showing that this is a possibility. In a high-throughput screening effort, we identified an HIV-1-reactivating protein factor (HRF) secreted by the nonpathogenic bacterium Massilia timonae. In primary T cells and T-cell lines, HRF triggered a high but nonsustained peak of nuclear factor kappa B (NF-kappaB) activity. While this short NF-kappaB peak potently reactivated latent HIV-1 infection, it failed to induce gene expression of several proinflammatory NF-kappaB-dependent cellular genes, such as those for tumor necrosis factor alpha (TNF-alpha), interleukin-8 (IL-8), and gamma interferon (IFN-gamma). Dissociation of cellular and viral gene induction was achievable, as minimum amounts of Tat protein, synthesized following application of a short NF-kappaB pulse, triggered HIV-1 transactivation and subsequent self-perpetuated HIV-1 expression. In the absence of such a positive feedback mechanism, cellular gene expression was not sustained, suggesting that strategies modulating the NF-kappaB activity profile could be used to selectively trigger HIV-1 reactivation.

A study of changes in genetic and environmental influences on weight and shape concern across adolescence.

PubMed

Wade, Tracey D; Hansell, Narelle K; Crosby, Ross D; Bryant-Waugh, Rachel; Treasure, Janet; Nixon, Reginald; Byrne, Susan; Martin, Nicholas G

2013-02-01

The goal of the current study was to examine whether genetic and environmental influences on an important risk factor for disordered eating, weight and shape concern, remained stable over adolescence. This stability was assessed in 2 ways: whether new sources of latent variance were introduced over development and whether the magnitude of variance contributing to the risk factor changed. We examined an 8-item WSC subscale derived from the Eating Disorder Examination (EDE) using telephone interviews with female adolescents. From 3 waves of data collected from female-female same-sex twin pairs from the Australian Twin Registry, a subset of the data (which included 351 pairs at Wave 1) was used to examine 3 age cohorts: 12 to 13, 13 to 15, and 14 to 16 years. The best-fitting model contained genetic and environmental influences, both shared and nonshared. Biometric model fitting indicated that nonshared environmental influences were largely specific to each age cohort, and results suggested that latent shared environmental and genetic influences that were influential at 12 to 13 years continued to contribute to subsequent age cohorts, with independent sources of both emerging at ages 13 to 15. The magnitude of all 3 latent influences could be constrained to be the same across adolescence. Ages 13 to 15 were indicated as a time of risk for the development of high levels of WSC, given that most specific environmental risk factors were significant at this time (e.g., peer teasing about weight, adverse life events), and indications of the emergence of new sources of latent genetic and environmental variance over this period. 2013 APA, all rights reserved

Attenuated Listeria monocytogenes vectors overcome suppressive plasma factors during HIV infection to stimulate myeloid dendritic cells to promote adaptive immunity and reactivation of latent virus.

PubMed

Miller, Elizabeth A; Spadaccia, Meredith R; Norton, Thomas; Demmler, Morgan; Gopal, Ramya; O'Brien, Meagan; Landau, Nathaniel; Dubensky, Thomas W; Lauer, Peter; Brockstedt, Dirk G; Bhardwaj, Nina

2015-01-01

HIV-1 infection is characterized by myeloid dendritic cell (DC) dysfunction, which blunts the responsiveness to vaccine adjuvants. We previously showed that nonviral factors in HIV-seropositive plasma are partially responsible for mediating this immune suppression. In this study we investigated recombinant Listeria monocytogenes (Lm) vectors, which naturally infect and potently activate DCs from seronegative donors, as a means to overcome DC dysfunction associated with HIV infection. Monocyte-derived DCs were cocultured with plasma from HIV-infected donors (HIV-moDCs) to induce a dysregulated state and infected with an attenuated, nonreplicative vaccine strain of Lm expressing full length clade B consensus gag (KBMA Lm-gag). Lm infection stimulated cytokine secretion [interleukin (IL)-12p70, tumor necrosis factor (TNF)-α, and IL-6] and Th-1 skewing of allogeneic naive CD4 T cells by HIV-moDCs, in contrast to the suppressive effects observed by HIV plasma on moDCs on toll-like receptor ligand stimulation. Upon coculture of "killed" but metabolically active (KBMA) Lm-gag-infected moDCs from HIV-infected donors with autologous cells, expansion of polyfunctional, gag-specific CD8(+) T cells was observed. Reactivation of latent proviruses by moDCs following Lm infection was also observed in models of HIV latency in a TNF-α-dependent manner. These findings reveal the unique ability of Lm vectors to contend with dysregulation of HIV-moDCs, while simultaneously possessing the capacity to activate latent virus. Concurrent stimulation of innate and adaptive immunity and disruption of latency may be an approach to reduce the pool of latently infected cells during HIV infection. Further study of Lm vectors as part of therapeutic vaccination and eradication strategies may advance this evolving field.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring.

PubMed

Xiong, Naixue; Liu, Ryan Wen; Liang, Maohan; Wu, Di; Liu, Zhao; Wu, Huisi

2017-01-18

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Latent dimensions of posttraumatic stress disorder and their relations with alcohol use disorder.

PubMed

Biehn, Tracey L; Contractor, Ateka A; Elhai, Jon D; Tamburrino, Marijo; Fine, Thomas H; Cohen, Gregory; Shirley, Edwin; Chan, Philip K; Liberzon, Israel; Calabrese, Joseph R; Galea, Sandro

2016-03-01

The objective of this study was to evaluate the relationship between factors of posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) using confirmatory factor analysis (CFA) in order to further our understanding of the substantial comorbidity between these two disorders. CFA was used to examine which factors of PTSD's dysphoria model were most related to AUD in a military sample. Ohio National Guard soldiers with a history of overseas deployment participated in the survey (n = 1215). Participants completed the PTSD Checklist and a 12-item survey from the National Survey on Drug Use used to diagnosis AUD. The results of the CFA indicated that a combined model of PTSD's four factors and a single AUD factor fit the data very well. Correlations between PTSD's factors and a latent AUD factor ranged from correlation coefficients of 0.258-0.285, with PTSD's dysphoria factor demonstrating the strongest correlation. However, Wald tests of parameter constraints revealed that AUD was not more correlated with PTSD's dysphoria than other PTSD factors. All four factors of PTSD's dysphoria model demonstrate comparable correlations with AUD. The role of dysphoria to the construct of PTSD is discussed.

Latent Factor Structure of DSM-5 Posttraumatic Stress Disorder

PubMed Central

Gentes, Emily; Dennis, Paul A.; Kimbrel, Nathan A.; Kirby, Angela C.; Hair, Lauren P.; Beckham, Jean C.; Calhoun, Patrick S.

2015-01-01

The current study examined the latent factor structure of posttraumatic stress disorder (PTSD) based on DSM-5 criteria in a sample of participants (N = 374) recruited for studies on trauma and health. Confirmatory factor analyses (CFA) were used to compare the fit of the previous 3-factor DSM-IV model of PTSD to the 4-factor model specified in DSM-5 as well as to a competing 4-factor “dysphoria” model (Simms, Watson, & Doebbeling, 2002) and a 5-factor (Elhai et al., 2011) model of PTSD. Results indicated that the Elhai 5-factor model (re-experiencing, active avoidance, emotional numbing, dysphoric arousal, anxious arousal) provided the best fit to the data, although substantial support was demonstrated for the DSM-5 4-factor model. Low factor loadings were noted for two of the symptoms in the DSM-5 model (psychogenic amnesia and reckless/self-destructive behavior), which raises questions regarding the adequacy of fit of these symptoms with other core features of the disorder. Overall, the findings from the present research suggest the DSM-5 model of PTSD is a significant improvement over the previous DSM-IV model of PTSD. PMID:26366290

Radiation-induced Epstein-Barr virus reactivation in gastric cancer cells with latent EBV infection.

PubMed

Nandakumar, Athira; Uwatoko, Futoshi; Yamamoto, Megumi; Tomita, Kazuo; Majima, Hideyuki J; Akiba, Suminori; Koriyama, Chihaya

2017-07-01

Epstein-Barr virus, a ubiquitous human herpes virus with oncogenic activity, can be found in 6%-16% of gastric carcinomas worldwide. In Epstein-Barr virus-associated gastric carcinoma, only a few latent genes of the virus are expressed. Ionizing irradiation was shown to induce lytic Epstein-Barr virus infection in lymphoblastoid cell lines with latent Epstein-Barr virus infection. In this study, we examined the effect of ionizing radiation on the Epstein-Barr virus reactivation in a gastric epithelial cancer cell line (SNU-719, an Epstein-Barr virus-associated gastric carcinoma cell line). Irradiation with X-ray (dose = 5 and 10 Gy; dose rate = 0.5398 Gy/min) killed approximately 25% and 50% of cultured SNU-719 cells, respectively, in 48 h. Ionizing radiation increased the messenger RNA expression of immediate early Epstein-Barr virus lytic genes (BZLF1 and BRLF1), determined by real-time reverse transcription polymerase chain reaction, in a dose-dependent manner at 48 h and, to a slightly lesser extent, at 72 h after irradiation. Similar findings were observed for other Epstein-Barr virus lytic genes (BMRF1, BLLF1, and BcLF1). After radiation, the expression of transforming growth factor beta 1 messenger RNA increased and reached a peak in 12-24 h, and the high-level expression of the Epstein-Barr virus immediate early genes can convert latent Epstein-Barr virus infection into the lytic form and result in the release of infectious Epstein-Barr virus. To conclude, Ionizing radiation activates lytic Epstein-Barr virus gene expression in the SNU-719 cell line mainly through nuclear factor kappaB activation. We made a brief review of literature to explore underlying mechanism involved in transforming growth factor beta-induced Epstein-Barr virus reactivation. A possible involvement of nuclear factor kappaB was hypothesized.

Common Genetic and Nonshared Environmental Factors Contribute to the Association between Socioemotional Dispositions and the Externalizing Factor in Children

ERIC Educational Resources Information Center

Taylor, Jeanette; Allan, Nicholas; Mikolajewski, Amy J.; Hart, Sara A.

2013-01-01

Background: Childhood behavioral disorders including conduct disorder (CD), oppositional defiant disorder (ODD), and attention-deficit/hyperactivity disorder (ADHD) often co-occur. Prior twin research shows that common sets of genetic and environmental factors are associated with these various disorders and they form a latent factor called…

Latent tuberculosis infection in rural China: baseline results of a population-based, multicentre, prospective cohort study.

PubMed

Gao, Lei; Lu, Wei; Bai, Liqiong; Wang, Xinhua; Xu, Jinsheng; Catanzaro, Antonino; Cárdenas, Vicky; Li, Xiangwei; Yang, Yu; Du, Jiang; Sui, Hongtao; Xia, Yinyin; Li, Mufei; Feng, Boxuan; Li, Zhen; Xin, Henan; Zhao, Rong; Liu, Jianmin; Pan, Shouguo; Shen, Fei; He, Jian; Yang, Shumin; Si, Hongyan; Wang, Yi; Xu, Zuhui; Tan, Yunhong; Chen, Tianzhu; Xu, Weiguo; Peng, Hong; Wang, Zhijian; Zhu, Tao; Zhou, Feng; Liu, Haiying; Zhao, Yanlin; Cheng, Shiming; Jin, Qi

2015-03-01

Prophylactic treatment of individuals with latent Mycobacterium tuberculosis infection is an essential component of tuberculosis control in some settings. In China, the prevalence of latent tuberculosis infection, and preventive interventions against this disease, have not been systematically studied. We aimed to assess the prevalence of latent tuberculosis and its associated risk factors in rural populations in China. Between July 1, and Sept 30, 2013, we undertook a baseline survey of a population-based, multicentre, prospective cohort study of registered residents (≥5 years old) at four study sites in rural China. Eligible participants were identified by door-to-door survey with a household sampling design. We screened participants for active tuberculosis and history of tuberculosis then used a tuberculin skin test and an interferon-γ release assay (QuantiFERON [QFT]) to test for latent infection. We used odds ratios (ORs) and 95% CIs to assess variables associated with positivity of QFT and tuberculin skin tests. 21,022 (90%) of 23,483 eligible participants completed a baseline survey. Age-standardised and sex-standardised rates of skin-test positivity (≥10 mm) ranged from 15% to 42%, and QFT positivity rates ranged from 13% to 20%. Rates of positivity for the tuberculin skin test and the QFT test were low in study participants younger than 20 years and gradually increased with age (p for trend <0·0001). Rates of latent tuberculosis infection were higher for men than women (p<0·0001). Overall agreement between the tuberculin skin test and the QFT test was moderate (81·06%; kappa coefficient 0·485), with skin-test-only positive results associated with the presence of BCG scar, male sex, and ages of 60 years and older, and QFT-only positive results associated with male sex and ages of 60 years and older. On the basis of findings showing that the performance of the tuberculin skin test might be affected by various factors including BCG vaccination and age, our results suggest that the prevalence of latent tuberculosis in China might be overestimated by skin tests compared with interferon-γ release assays. The National Science and Technology Major Project of China, the Program for Changjiang Scholars and Innovative Research Team in University of China. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using a multifrontal sparse solver in a high performance, finite element code

NASA Technical Reports Server (NTRS)

King, Scott D.; Lucas, Robert; Raefsky, Arthur

1990-01-01

We consider the performance of the finite element method on a vector supercomputer. The computationally intensive parts of the finite element method are typically the individual element forms and the solution of the global stiffness matrix both of which are vectorized in high performance codes. To further increase throughput, new algorithms are needed. We compare a multifrontal sparse solver to a traditional skyline solver in a finite element code on a vector supercomputer. The multifrontal solver uses the Multiple-Minimum Degree reordering heuristic to reduce the number of operations required to factor a sparse matrix and full matrix computational kernels (e.g., BLAS3) to enhance vector performance. The net result in an order-of-magnitude reduction in run time for a finite element application on one processor of a Cray X-MP.

The Sensitivity of West African Squall Line Water Budgets to Land Cover

NASA Technical Reports Server (NTRS)

Mohr, Karen I.; Baker, R. David; Tao, Wei-Kuo; Famiglietti, James S.; Starr, David OC. (Technical Monitor)

2001-01-01

This study used a two-dimensional coupled land/atmosphere (cloud-resolving) model to investigate the influence of land cover on the water budgets of squall lines in the Sahel. Study simulations used the same initial sounding and one of three different land covers, a sparsely vegetated semi-desert, a grassy savanna, and a dense evergreen broadleaf forest. All simulations began at midnight and ran for 24 hours to capture a full diurnal cycle. In the morning, the latent heat flux, boundary layer mixing ratio, and moist static energy in the boundary layer exhibited notable variations among the three land covers. The broadleaf forest had the highest latent heat flux, the shallowest, moistest, slowest growing boundary layer, and significantly more moist static energy per unit area than the savanna and semi-desert. Although all simulations produced squall lines by early afternoon, the broadleaf forest had the most intense, longest-lived squall lines with 29% more rainfall than the savanna and 37% more than the semi-desert. The sensitivity of the results to vegetation density, initial sounding humidity, and grid resolution was also assessed. There were greater differences in rainfall among land cover types than among simulations of the same land cover with varying amounts of vegetation. Small changes in humidity were equivalent in effect to large changes in land cover, producing large changes in the condensate and rainfall. Decreasing the humidity had a greater effect on rainfall volume than increasing the humidity. Reducing the grid resolution from 1.5 km to 0.5 km decreased the temperature and humidity of the cold pools and increased the rain volume.

Deconvolution of mixing time series on a graph

PubMed Central

Blocker, Alexander W.; Airoldi, Edoardo M.

2013-01-01

In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series. PMID:25309135

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study.

PubMed

Heron, Elizabeth A; Finkenstädt, Bärbel; Rand, David A

2007-10-01

In this study, we address the problem of estimating the parameters of regulatory networks and provide the first application of Markov chain Monte Carlo (MCMC) methods to experimental data. As a case study, we consider a stochastic model of the Hes1 system expressed in terms of stochastic differential equations (SDEs) to which rigorous likelihood methods of inference can be applied. When fitting continuous-time stochastic models to discretely observed time series the lengths of the sampling intervals are important, and much of our study addresses the problem when the data are sparse. We estimate the parameters of an autoregulatory network providing results both for simulated and real experimental data from the Hes1 system. We develop an estimation algorithm using MCMC techniques which are flexible enough to allow for the imputation of latent data on a finer time scale and the presence of prior information about parameters which may be informed from other experiments as well as additional measurement error.

Multi Seasonal and Diurnal Characterization of Sensible Heat Flux in an Arid Land Environment

NASA Astrophysics Data System (ADS)

Al-Mashharawi, S.; Aragon, B.; McCabe, M.

2017-12-01

In sparsely vegetated arid and semi-arid regions, the available energy is transformed primarily into sensible heat, with little to no energy partitioned into latent heat. The characterization of bare soil arid environments are rather poorly understood in the context of both local, regional and global energy budgets. Using data from a long-term surface layer scintillometer and co-located meteorological installation, we examine the diurnal and seasonal patterns of sensible heat flux and the net radiation to soil heat flux ratio. We do this over a bare desert soil located adjacent to an irrigated agricultural field in the central region of Saudi Arabia. The results of this exploratory analysis can be used to inform upon remote sensing techniques for surface flux estimation, to derive and monitor soil heat flux dynamics, estimate the heat transfer resistance and the thermal roughness length over bare soils, and to better inform efforts that model the advective effects that complicate the accurate representation of agricultural energy budgets in the arid zone.

Cultural influences on positive father involvement in two-parent Mexican-origin families.

PubMed

Cruz, Rick A; King, Kevin M; Widaman, Keith F; Leu, Janxin; Cauce, Ana Mari; Conger, Rand D

2011-10-01

A growing body of research documents the importance of positive father involvement in children's development. However, research on fathers in Latino families is sparse, and research contextualizing the father-child relationship within a cultural framework is needed. The present study examined how fathers' cultural practices and values predicted their fifth-grade children's report of positive father involvement in a sample of 450 two-parent Mexican-origin families. Predictors included Spanish- and English-language use, Mexican and American cultural values, and positive machismo (i.e., culturally related attitudes about the father's role within the family). Positive father involvement was measured by the child's report of his or her father's monitoring, educational involvement, and warmth. Latent variable regression analyses showed that fathers' machismo attitudes were positively related to children's report of positive father involvement and that this association was similar across boys and girls. The results of this study suggest an important association between fathers' cultural values about men's roles and responsibilities within a family and their children's perception of positive fathering.

Thresholding functional connectomes by means of mixture modeling.

PubMed

Bielczyk, Natalia Z; Walocha, Fabian; Ebel, Patrick W; Haak, Koen V; Llera, Alberto; Buitelaar, Jan K; Glennon, Jeffrey C; Beckmann, Christian F

2018-05-01

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Using Latent Sleepiness to Evaluate an Important Effect of Promethazine

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Hayat, Matthew; Vksman, Zalman; Putcha, Laksmi

2007-01-01

Astronauts often use promethazine (PMZ) to counteract space motion sickness; however PMZ may cause drowsiness, which might impair cognitive function. In a NASA ground study, subjects received PMZ and their cognitive performance was then monitored over time. Subjects also reported sleepiness using the Karolinska Sleepiness Score (KSS), which ranges from 1 - 9. A problem arises when using KSS to establish an association between true sleepiness and performance because KSS scores tend to overly concentrate on the values 3 (fairly awake) and 7 (moderately tired). Therefore, we defined a latent sleepiness measure as a continuous random variable describing a subject s actual, but unobserved true state of sleepiness through time. The latent sleepiness and observed KSS are associated through a conditional probability model, which when coupled with demographic factors, predicts performance.

Primary Energy Efficiency Analysis of Different Separate Sensible and Latent Cooling Techniques

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

Abdelaziz, Omar

2015-01-01

Separate Sensible and Latent cooling (SSLC) has been discussed in open literature as means to improve air conditioning system efficiency. The main benefit of SSLC is that it enables heat source optimization for the different forms of loads, sensible vs. latent, and as such maximizes the cycle efficiency. In this paper I use a thermodynamic analysis tool in order to analyse the performance of various SSLC technologies including: multi-evaporators two stage compression system, vapour compression system with heat activated desiccant dehumidification, and integrated vapour compression with desiccant dehumidification. A primary coefficient of performance is defined and used to judge themore » performance of the different SSLC technologies at the design conditions. Results showed the trade-off in performance for different sensible heat factor and regeneration temperatures.« less

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

PubMed Central

Sychev, Zoi E.; Hu, Alex; Lagunoff, Michael

2017-01-01

Kaposi’s Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells. PMID:28257516

A comparative study of matrix metalloproteinase and aggrecanase mediated release of latent cytokines at arthritic joints.

PubMed

Mullen, Lisa; Adams, Gill; Foster, Julie; Vessillier, Sandrine; Köster, Mario; Hauser, Hansjörg; Layward, Lorna; Gould, David; Chernajovsky, Yuti

2014-09-01

Latent cytokines are engineered by fusing the latency associated peptide (LAP) derived from transforming growth factor-β (TGF-β) with the therapeutic cytokine, in this case interferon-β (IFN-β), via an inflammation-specific matrix metalloproteinase (MMP) cleavage site. To demonstrate latency and specific delivery in vivo and to compare therapeutic efficacy of aggrecanase-mediated release of latent IFN-β in arthritic joints to the original MMP-specific release. Recombinant fusion proteins with MMP, aggrecanase or devoid of cleavage site were expressed in CHO cells, purified and characterised in vitro by Western blotting and anti-viral protection assays. Therapeutic efficacy and half-life were assessed in vivo using the mouse collagen-induced arthritis model (CIA) of rheumatoid arthritis and a model of acute paw inflammation, respectively. Transgenic mice with an IFN-regulated luciferase gene were used to assess latency in vivo and targeted delivery to sites of disease. Efficient localised delivery of IFN-β to inflamed paws, with low levels of systemic delivery, was demonstrated in transgenic mice using latent IFN-β. Engineering of latent IFN-β with an aggrecanase-sensitive cleavage site resulted in efficient cleavage by ADAMTS-4, ADAMTS-5 and synovial fluid from arthritic patients, with an extended half-life similar to the MMP-specific molecule and greater therapeutic efficacy in the CIA model. Latent cytokines require cleavage in vivo for therapeutic efficacy, and they are delivered in a dose dependent fashion only to arthritic joints. The aggrecanase-specific cleavage site is a viable alternative to the MMP cleavage site for the targeting of latent cytokines to arthritic joints. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Effects of latent toxoplasmosis on autoimmune thyroid diseases in pregnancy.

PubMed

Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

2014-01-01

Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Cross-sectional study in 1248 consecutive pregnant women in the 9-12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases.

Effects of Latent Toxoplasmosis on Autoimmune Thyroid Diseases in Pregnancy

PubMed Central

Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

2014-01-01

Background Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Methods Cross-sectional study in 1248 consecutive pregnant women in the 9–12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Results Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Conclusions Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases. PMID:25350671

A Qualitative Study of Indian and Indian Immigrant Adolescents' Perceptions of the Factors Affecting Their Engagement and Performance in School

ERIC Educational Resources Information Center

Areepattamannil, Shaljan; Freeman, John G.; Klinger, Don A.

2018-01-01

Although a growing body of quantitative research has examined the non-cognitive factors affecting the school engagement and performance of adolescents across cultures, there is relatively sparse qualitative research investigating the perceptions of adolescents regarding the factors influencing their engagement and performance in school. This focus…

WHAT IS NEW IN RURAL EDUCATION--NFIRE.

ERIC Educational Resources Information Center

KRAHMER, EDWARD; STURGES, A.W.

RURAL EDUCATION IS DEFINED AS THAT WHICH PREVAILS IN SPARSELY POPULATED AREAS AND SMALL RURAL COMMUNITIES (LESS THAN 2500 POPULATION). FACTORS USUALLY FOUND WITH SUCH SCHOOL OFFERINGS, INCLUDE SPARSITY OF POPULATION, SMALL SCHOOL ENROLLMENTS, ISOLATION FROM CULTURAL EVENTS, AND REMOTENESS FROM EDUCATIONAL OPPORTUNITIES. SUCH FACTORS AS THESE HELP…

Discordant inflammation and pain in early and established rheumatoid arthritis: Latent Class Analysis of Early Rheumatoid Arthritis Network and British Society for Rheumatology Biologics Register data.

PubMed

McWilliams, Daniel F; Ferguson, Eamonn; Young, Adam; Kiely, Patrick D W; Walsh, David A

2016-12-13

Rheumatoid arthritis (RA) disease activity is often measured using the 28-joint Disease Activity Score (DAS28). We aimed to identify and independently verify subgroups of people with RA that may be discordant with respect to self-reported and objective disease state, with potentially different clinical needs. Data were derived from three cohorts: (1) the Early Rheumatoid Arthritis Network (ERAN) and the British Society for Rheumatology Biologics Register (BSRBR), (2) those commencing tumour necrosis factor (TNF)-α inhibitors and (3) those using non-biologic drugs. In latent class analysis, we used variables related to pain, central pain mechanisms or inflammation (pain, vitality, mental health, erythrocyte sedimentation rate, swollen joint count, tender joint count, visual analogue scale of general health). Clinically relevant outcomes were examined. Five, four and four latent classes were found in the ERAN, BSRBR TNF inhibitor and non-biologic cohorts, respectively. The proportions of people assigned with >80% probability into latent classes were 76%, 58% and 72% in the ERAN, TNF inhibitor and non-biologic cohorts, respectively. The latent classes displayed either concordance between measures indicative of mild, moderate or severe disease activity; discordantly worse patient-reported measures despite less markedly elevated inflammation; or discordantly less severe patient-reported measures despite elevated inflammation. Latent classes with discordantly worse patient-reported measures represented 12%, 40% and 21% of the ERAN, TNF inhibitor and non-biologic cohorts, respectively; contained more females; and showed worse function. In those latent classes with worse scores at baseline, DAS28 and function improved over 1 year (p < 0.001 for all comparisons), and scores differed less at follow-up than at baseline. Discordant latent classes can be identified in people with RA, and these findings are robust across three cohorts with varying disease duration and activity. These findings could be used to identify a sizeable subgroup of people with RA who might gain added benefit from pain management strategies.

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Detecting Social Desirability Bias Using Factor Mixture Models

ERIC Educational Resources Information Center

Leite, Walter L.; Cooper, Lou Ann

2010-01-01

Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide…

Mexican American Adolescents' Profiles of Risk and Mental Health: A Person-Centered Longitudinal Approach

ERIC Educational Resources Information Center

Zeiders, Katharine H.; Roosa, Mark W.; Knight, George P.; Gonzales, Nancy A.

2013-01-01

Although Mexican American adolescents experience multiple risk factors in their daily lives, most research examines the influences of risk factors on adjustment independently, ignoring the additive and interactive effects of multiple risk factors. Guided by a person-centered perspective and utilizing latent profile analysis, this study identified…

Testing Measurement Invariance Using MIMIC: Likelihood Ratio Test with a Critical Value Adjustment

ERIC Educational Resources Information Center

Kim, Eun Sook; Yoon, Myeongsun; Lee, Taehun

2012-01-01

Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group mean difference while assuming the equivalence of factor loadings and intercepts over groups. However, this study demonstrated that MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be…

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

ERIC Educational Resources Information Center

Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

2012-01-01

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…

Homework Emotion Regulation Scale: Confirming the Factor Structure with High School Students

ERIC Educational Resources Information Center

Xu, Jianzhong; Fan, Xitao; Du, Jianxia

2017-01-01

The current investigation studied psychometric properties of the Homework Emotion Regulation Scale (HERS) for math homework, with 915 tenth graders from China. Confirmatory factor analyses (CFAs) supported the presence of two separate yet related subscales for the HERS: Emotion Management and Cognitive Reappraisal. The latent factor means for both…

Gender Differences in Risk/Protection Profiles for Low Academic Performance

ERIC Educational Resources Information Center

Whitney, Stephen D.; Renner, Lynette M.; Herrenkohl, Todd I.

2010-01-01

Using holistic-interactionistic theory, the simultaneous nature of risk and protection factors for both males and females (age 6-11 in Wave 1) is examined using latent profile analysis (LPA). Risk/protection classes are estimated using multiple risk factor variables (e.g., physical child abuse) and multiple protective factors (e.g.,…

Measuring psychosocial environments using individual responses: an application of multilevel factor analysis to examining students in schools.

PubMed

Dunn, Erin C; Masyn, Katherine E; Jones, Stephanie M; Subramanian, S V; Koenen, Karestan C

2015-07-01

Interest in understanding how psychosocial environments shape youth outcomes has grown considerably. School environments are of particular interest to prevention scientists as many prevention interventions are school-based. Therefore, effective conceptualization and operationalization of the school environment is critical. This paper presents an illustration of an emerging analytic method called multilevel factor analysis (MLFA) that provides an alternative strategy to conceptualize, measure, and model environments. MLFA decomposes the total sample variance-covariance matrix for variables measured at the individual level into within-cluster (e.g., student level) and between-cluster (e.g., school level) matrices and simultaneously models potentially distinct latent factor structures at each level. Using data from 79,362 students from 126 schools in the National Longitudinal Study of Adolescent to Adult Health (formerly known as the National Longitudinal Study of Adolescent Health), we use MLFA to show how 20 items capturing student self-reported behaviors and emotions provide information about both students (within level) and their school environment (between level). We identified four latent factors at the within level: (1) school adjustment, (2) externalizing problems, (3) internalizing problems, and (4) self-esteem. Three factors were identified at the between level: (1) collective school adjustment, (2) psychosocial environment, and (3) collective self-esteem. The finding of different and substantively distinct latent factor structures at each level emphasizes the need for prevention theory and practice to separately consider and measure constructs at each level of analysis. The MLFA method can be applied to other nested relationships, such as youth in neighborhoods, and extended to a multilevel structural equation model to better understand associations between environments and individual outcomes and therefore how to best implement preventive interventions.

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

Cost-effectiveness of post-landing latent tuberculosis infection control strategies in new migrants to Canada.

PubMed

Campbell, Jonathon R; Johnston, James C; Sadatsafavi, Mohsen; Cook, Victoria J; Elwood, R Kevin; Marra, Fawziah

2017-01-01

The majority of tuberculosis in migrants to Canada occurs due to reactivation of latent TB infection. Risk of tuberculosis in those with latent tuberculosis infection can be significantly reduced with treatment. Presently, only 2.4% of new migrants are flagged for post-landing surveillance, which may include latent tuberculosis infection screening; no other migrants receive routine latent tuberculosis infection screening. To aid in reducing the tuberculosis burden in new migrants to Canada, we determined the cost-effectiveness of using different latent tuberculosis infection interventions in migrants under post-arrival surveillance and in all new migrants. A discrete event simulation model was developed that focused on a Canadian permanent resident cohort after arrival in Canada, utilizing a ten-year time horizon, healthcare system perspective, and 1.5% discount rate. Latent tuberculosis infection interventions were evaluated in the population under surveillance (N = 6100) and the total cohort (N = 260,600). In all evaluations, six different screening and treatment combinations were compared to the base case of tuberculin skin test screening followed by isoniazid treatment only in the population under surveillance. Quality adjusted life years, incident tuberculosis cases, and costs were recorded for each intervention and incremental cost-effectiveness ratios were calculated in relation to the base case. In the population under surveillance (N = 6100), using an interferon-gamma release assay followed by rifampin was dominant compared to the base case, preventing 4.90 cases of tuberculosis, a 4.9% reduction, adding 4.0 quality adjusted life years, and saving $353,013 over the ensuing ten-years. Latent tuberculosis infection screening in the total population (N = 260,600) was not cost-effective when compared to the base case, however could potentially prevent 21.8% of incident tuberculosis cases. Screening new migrants under surveillance with an interferon-gamma release assay and treating with rifampin is cost saving, but will not significantly impact TB incidence. Universal latent tuberculosis infection screening and treatment is cost-prohibitive. Research into using risk factors to target screening post-landing may provide alternate solutions.

Color normalization of histology slides using graph regularized sparse NMF

NASA Astrophysics Data System (ADS)

Sha, Lingdao; Schonfeld, Dan; Sethi, Amit

2017-03-01

Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The representation of a pixel in the stain density space is constrained to follow the feature distance of the pixel to pixels in the neighborhood graph. Utilizing color matrix transfer method with the stain concentrations found using our GSNMF method, the color normalization performance was also better than existing methods.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

The Dynamics of Internalizing and Externalizing Comorbidity Across the Early School Years

PubMed Central

Willner, Cynthia J.; Gatzke-Kopp, Lisa M.; Bray, Bethany C.

2017-01-01

High rates of comorbidity are observed between internalizing and externalizing problems, yet the developmental dynamics of comorbid symptom presentations are not yet well understood. This study explored the developmental course of latent profiles of internalizing and externalizing symptoms across kindergarten, 1st, and 2nd grade. The sample consisted of 336 children from an urban, low-income community, selected based on relatively high (61%) or low (39%) aggressive/oppositional behavior problems at school entry (64% male; 70% African American, 20% Hispanic). Teachers reported on children’s symptoms in each year. An exploratory latent profile analysis of children’s scores on aggression/oppositionality, hyperactivity/inattention, anxiety, and social withdrawal symptom factors revealed 4 latent symptom profiles: comorbid (48% of the sample in each year), internalizing (19–23%), externalizing (21–22%), and well-adjusted (7–11%). The developmental course of these symptom profiles was examined using a latent transition analysis, which revealed remarkably high continuity in the comorbid symptom profile (89% from one year to the next) and moderately high continuity in both the internalizing and externalizing profiles (80% and 71%, respectively). Internalizing children had a 20% probability of remitting to the well-adjusted profile by the following year, whereas externalizing children had a 25% probability of transitioning to the comorbid profile. These results are consistent with the hypothesis that a common vulnerability factor contributes to developmentally stable internalizing-externalizing comorbidity, while also suggesting that some children with externalizing symptoms are at risk for subsequently accumulating internalizing symptoms. PMID:27739391

Partitioning of sublimation and evaporation from Lake Bonney using water vapor isotope and latent heat fluxes

NASA Astrophysics Data System (ADS)

Bellagamba, A. W.; Berkelhammer, M. B.; Winslow, L.; Peter, D.; Myers, K. F.

2017-12-01

The landscapes of the McMurdo Dry Valleys in Antarctica are characterized by a series of frozen lakes. Although the conditions in this region are severe, the lakes share common characteristics with lakes at glacial termini elsewhere. Geochemical and geomorphological evidence suggest these lakes have experienced large historical changes indicative of changes water balances. While part of these shifts in lake volume arise from changes in glacial inflow, they likely also reflect changes in the latent heat flux from the lake surfaces. Here we present a joint analysis of the stable isotopic ratio of surface ice/water and the water vapor flux over Dry Valley frozen lakes to ascertain the processes controlling water losses from the lake surfaces. We compare the isotopic ratio of the latent heat flux with the surface water isotopes to derive a fractionation factor associated with latent flux. This data is then used to provide insight into how much of the water vapor flux is sublimated versus evaporated, as well as how the sublimation and evaporative components of the flux change with synoptic weather. We used a Picarro L2130-I isotopic water analyzer to measure humidity and the isotopic ratio of water vapor at three heights over Lake Bonney in Taylor Valley, Antarctica and used the flux-gradient approach to convert the isotopic ratio of the vapor to an "isoflux". An on-site meteorological station recorded temperature, relative humidity and wind direction/intensity at two different heights above the lake and an infrared radiometer recorded lake skin temperature. These data were used to calculate the sensible and latent heat fluxes. The fractionation factor was close to 0, which indicates that sublimation was the primary component of the flux although evaporation became increasingly prominent following a katabatic wind event. The results suggest this technique could be an effective tool to study the sensitivity of latent heat fluxes to weather here and in other similar environments. The trial run performed at Lake Bonney in November-December 2016 was performed as part of the ongoing LTER (Long Term Ecological Research) project at the McMurdo Dry Valleys and a second experiment will be performed in January 2018.

Latent iron deficiency at birth influences auditory neural maturation in late preterm and term infants.

PubMed

Choudhury, Vivek; Amin, Sanjiv B; Agarwal, Asha; Srivastava, L M; Soni, Arun; Saluja, Satish

2015-11-01

In utero latent iron deficiency has been associated with abnormal neurodevelopmental outcomes during childhood. Its concomitant effect on auditory neural maturation has not been well studied in late preterm and term infants. The objective was to determine whether in utero iron status is associated with auditory neural maturation in late preterm and term infants. This prospective cohort study was performed at Sir Ganga Ram Hospital, New Delhi, India. Infants with a gestational age ≥34 wk were eligible unless they met the exclusion criteria: craniofacial anomalies, chromosomal disorders, hemolytic disease, multiple gestation, third-trimester maternal infection, chorioamnionitis, toxoplasmosis, other infections, rubella, cytomegalovirus infection, and herpes simplex virus infections (TORCH), Apgar score <5 at 5 min, sepsis, cord blood not collected, or auditory evaluation unable to be performed. Sixty consecutive infants with risk factors for iron deficiency, such as small for gestational age and maternal diabetes, and 30 without risk factors for iron deficiency were enrolled. Absolute wave latencies and interpeak latencies, evaluated by auditory brainstem response within 48 h after birth, were measured and compared between infants with latent iron deficiency (serum ferritin ≤75 ng/mL) and infants with normal iron status (serum ferritin >75 ng/mL) at birth. Twenty-three infants had latent iron deficiency. Infants with latent iron deficiency had significantly prolonged wave V latencies (7.10 ± 0.68 compared with 6.60 ± 0.66), III-V interpeak latencies (2.37 ± 0.64 compared with 2.07 ± 0.33), and I-V interpeak latencies (5.10 ± 0.57 compared with 4.72 ± 0.56) compared with infants with normal iron status (P < 0.05). This difference remained significant on regression analyses after control for confounders. No difference was noted between latencies I and III and interpeak latencies I-III. Latent iron deficiency is associated with abnormal auditory neural maturation in infants at ≥34 wk gestational age. This trial was registered at clinicaltrials.gov as NCT02503397. © 2015 American Society for Nutrition.

Regulation and dysregulation of Epstein-Barr virus latency: implications for the development of autoimmune diseases.

PubMed

Niller, Hans Helmut; Wolf, Hans; Minarovits, Janos

2008-05-01

Epstein-Barr virus (EBV) is a human herpesvirus hiding in a latent form in memory B cells in the majority of the world population. Although, primary EBV infection is asymptomatic or causes a self-limiting disease, infectious mononucleosis, the virus is associated with a wide variety of neoplasms developing in immunosuppressed or immunodeficient individuals, but also in patients with an apparently intact immune system. In memory B cells, tumor cells, and lymphoblastoid cell lines (LCLs, transformed by EBV in vitro) the expression of the viral genes is highly restricted. There is no virus production (lytic viral replication associated with the expression of all viral genes) in tight latency. The expression of latent viral oncogenes and RNAs is under a strict epigenetic control via DNA methylation and histone modifications that results either in a complete silencing of the EBV genome in memory B cells, or in a cell-type dependent usage of latent promoters in tumor cells, germinal center B cells, and LCLs. Both the latent and lytic EBV proteins are potent immunogens and elicit vigorous B- and T-cell responses. In immunosuppressed and immunodeficient patients, or in individuals with a functional defect of EBV-specific T cells, lytic EBV replication is regularly activated and an increased viral load can be detected in the blood. Enhanced lytic replication results in new infection events and EBV-associated transformation events, and seems to be a risk factor both for malignant transformation and the development of autoimmune diseases. One may speculate that an increased load or altered presentation of a limited set of lytic or latent EBV proteins that cross-react with cellular antigens triggers and perpetuates the pathogenic processes that result in multiple sclerosis, systemic lupus erythematosus (SLE), and rheumatoid arthritis. In addition, in SLE patients EBV may cause defects of B-cell tolerance checkpoints because latent membrane protein 1, an EBV-encoded viral oncoprotein can induce BAFF, a B-cell activating factor that rescues self-reactive B cells and induces a lupus-like autoimmune disease in transgenic mice.

Rifapentine Pharmacokinetics and Tolerability in Children and Adults Treated Once Weekly With Rifapentine and Isoniazid for Latent Tuberculosis Infection.

PubMed

Weiner, Marc; Savic, Radojka M; Kenzie, William R Mac; Wing, Diane; Peloquin, Charles A; Engle, Melissa; Bliven, Erin; Prihoda, Thomas J; Gelfond, Jonathan A L; Scott, Nigel A; Abdel-Rahman, Susan M; Kearns, Gregory L; Burman, William J; Sterling, Timothy R; Villarino, M Elsa

2014-06-01

In a phase 3, randomized clinical trial (PREVENT TB) of 8053 people with latent tuberculosis infection, 12 once-weekly doses of rifapentine and isoniazid had good efficacy and tolerability. Children received higher rifapentine milligram per kilogram doses than adults. In the present pharmacokinetic study (a component of the PREVENT TB trial), rifapentine exposure was compared between children and adults. Rifapentine doses in children ranged from 300 to 900 mg, and adults received 900 mg. Children who could not swallow tablets received crushed tablets. Sparse pharmacokinetic sampling was performed with 1 rifapentine concentration at 24 hours after drug administration (C24). Rifapentine area under concentration-time curve (AUC) was estimated from a nonlinear, mixed effects regression model (NLME). There were 80 children (age: median, 4.5 years; range, 2-11 years) and 77 adults (age: median, 40 years; all ≥18 years) in the study. The geometric mean rifapentine milligram per kilogram dose was greater in children than in adults (children, 23 mg/kg; adults, 11 mg/kg). Rifapentine geometric mean AUC and C24 were 1.3-fold greater in children (all children combined) than in adults. Children who swallowed whole tablets had 1.3-fold higher geometric mean AUC than children who received crushed tablets, and children who swallowed whole tablets had a 1.6-fold higher geometric mean AUC than adults. The higher rifapentine doses in children were well tolerated. To obtain rifapentine exposures comparable in children to adults, dosing algorithms modeled by NLME were developed. A 2-fold greater rifapentine dose for all children resulted in a 1.3-fold higher AUC compared to adults administered a standard dose. Use of higher weight-adjusted rifapentine doses for young children are warranted to achieve systemic exposures that are associated with successful treatment of latent tuberculosis infection in adults. Published by Oxford University Press on behalf of the Pediatric Infectious Diseases Society 2014. This work is written by US Government employees and is in the public domain in the US.

Subgroups of Dutch homeless young adults based on risk- and protective factors for quality of life: Results of a latent class analysis.

PubMed

Altena, Astrid M; Beijersbergen, Mariëlle D; Vermunt, Jeroen K; Wolf, Judith R L M

2018-04-17

It is important to gain more insight into specific subgroups of homeless young adults (HYA) to enable the development of tailored interventions that adequately meet their diverse needs and to improve their quality of life. Within a heterogeneous sample of HYA, we investigated whether subgroups are distinguishable based on risk- and protective factors for quality of life. In addition, differences between subgroups were examined regarding the socio-demographic characteristics, the use of cognitive coping strategies and quality of life. A total of 393 HYA using shelter facilities in the Netherlands were approached to participate, between December 2011 and March 2013. Structured face-to-face interviews were administered approximately 2 weeks after shelter admission by trained research assistants. A latent class analysis was conducted to empirically distinguish 251 HYA in subgroups based on common risk factors (former abuse, victimisation, psychological symptoms and substance use) and protective factors (resilience, family and social support and perceived health status). Additional analysis of variance and chi-square tests were used to compare subgroups on socio-demographic characteristics, the use of cognitive coping strategies and quality of life. The latent class analysis yielded four highly interpretable subgroups: the at-risk subgroup, the high-risk and least protected subgroup, the low-risk subgroup and the higher functioning and protected subgroup. Subgroups of HYA with lower scores in risk factors showed higher scores in protective factors, the adaptive cognitive coping strategies and quality of life. Our findings confirm the need for targeted and tailored interventions for specific subgroups of HYA. Social workers need to be attentive to the pattern of risk- and protective factors in each individual to determine which risk factors are prominent and need to be targeted and which protective factors need to be enhanced to improve the quality of life of HYA. © 2018 John Wiley & Sons Ltd.

Do executive functions explain the covariance between internalizing and externalizing behaviors?

PubMed

Hatoum, Alexander S; Rhee, Soo Hyun; Corley, Robin P; Hewitt, John K; Friedman, Naomi P

2017-11-16

This study examined whether executive functions (EFs) might be common features of internalizing and externalizing behavior problems across development. We examined relations between three EF latent variables (a common EF factor and factors specific to updating working memory and shifting sets), constructed from nine laboratory tasks administered at age 17, to latent growth intercept (capturing stability) and slope (capturing change) factors of teacher- and parent-reported internalizing and externalizing behaviors in 885 individual twins aged 7 to 16 years. We then estimated the proportion of intercept-intercept and slope-slope correlations predicted by EF as well as the association between EFs and a common psychopathology factor (P factor) estimated from all 9 years of internalizing and externalizing measures. Common EF was negatively associated with the intercepts of teacher-rated internalizing and externalizing behavior in males, and explained 32% of their covariance; in the P factor model, common EF was associated with the P factor in males. Shifting-specific was positively associated with the externalizing slope across sex. EFs did not explain covariation between parent-rated behaviors. These results suggest that EFs are associated with stable problem behavior variation, explain small proportions of covariance, and are a risk factor that that may depend on gender.

Longitudinal Links Between Identity Consolidation and Psychosocial Problems in Adolescence: Using Bi-Factor Latent Change and Cross-Lagged Effect Models.

PubMed

Hatano, Kai; Sugimura, Kazumi; Schwartz, Seth J

2018-04-01

Most previous identity research has focused on relationships between identity synthesis, confusion, and psychosocial problems. However, these studies did not take into account Erikson's notion of identity consolidation, that is, the dynamic interplay between identity synthesis and confusion. This study aimed to examine longitudinal relationships and the directionality of the effects between identity consolidation and psychosocial problems during adolescence, using two waves of longitudinal data from 793 Japanese adolescents (49.7% girls; ages 13-14 and 16-17 at Time 1). A bi-factor latent change model revealed that levels and changes in identity consolidation were negatively associated with levels and changes in psychosocial problems. Furthermore, a bi-factor cross-lagged effects model provided evidence that identity consolidation negatively predicted psychosocial problems, and vice versa. Our study facilitates a better understanding of the importance of identity consolidation in the relations between identity components and psychosocial problems.

Factor analysis of the Foreign Language Classroom Anxiety Scale in Korean learners of English as a foreign language.

PubMed

Park, Gi-Pyo

2014-08-01

This study examined the latent constructs of the Foreign Language Classroom Anxiety Scale (FLCAS) using two different groups of Korean English as a foreign language (EFL) university students. Maximum likelihood exploratory factor analysis with direct oblimin rotation was performed among the first group of 217 participants and produced two meaningful latent components in the FLCAS. The two components of the FLCAS were closely examined among the second group of 244 participants to find the extent to which the two components of the FLCAS fit the data. The model fit indexes showed that the two-factor model in general adequately fit the data. Findings of this study were discussed with the focus on the two components of the FLCAS, followed by future study areas to be undertaken to shed further light on the role of foreign language anxiety in L2 acquisition.

Vegetation dynamics and responses to climate change and human activities in Central Asia.

PubMed

Jiang, Liangliang; Guli Jiapaer; Bao, Anming; Guo, Hao; Ndayisaba, Felix

2017-12-01

Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984-2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method. The results show that vegetation pixels significantly decreased for shrubs and sparse vegetation compared with those for the other vegetation types and that the degradation of sparse vegetation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Large Aral Sea than in other regions. The Hurst exponent results indicated that forests are more sustainable than grasslands, shrubs and sparse vegetation. Precipitation is the main factor affecting vegetation growth in the Kazakhskiy Melkosopochnik. Moreover, temperature is a controlling factor that influences the seasonal variation of vegetation greenness in the mountains and the Aral Sea basin. Drought is the main factor affecting vegetation degradation as a result of both increased temperature and decreased precipitation in the Kyzylkum Desert and the northern Ustyurt Plateau. The residual analysis highlighted that sparse vegetation and the degradation of some shrubs in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea were mainly triggered by human activities: the excessive exploitation of water resources in the upstream areas of the Amu Darya basin and oil and natural gas extraction in the southern part of the Karakum Desert and the southern Ustyurt Plateau. The results also indicated that after the collapse of the Soviet Union, abandoned pastures gave rise to increased vegetation in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and abandoned croplands reverted to grasslands in northern Kazakhstan, leading to a decrease in cropland greenness. Shrubs and sparse vegetation were extremely sensitive to short-term climatic variations, and our results demonstrated that these vegetation types were the most seriously degraded by human activities. Therefore, regional governments should strive to restore vegetation to sustain this fragile arid ecological environment. Copyright © 2017 Elsevier B.V. All rights reserved.

Unveiling Privilege to Broaden Participation

ERIC Educational Resources Information Center

Scherr, Rachel E.; Robertson, Amy D.

2017-01-01

The underrepresentation of women and people of color in physics has been attributed to a wide variety of factors ranging from society-wide conditions such as income inequality and sparse role models, to daily interpersonal interactions that disadvantage or discourage women and people of color from pursuing physics. These factors may be seen as…

The Rosenberg Self-Esteem Scale: a bifactor answer to a two-factor question?

PubMed

McKay, Michael T; Boduszek, Daniel; Harvey, Séamus A

2014-01-01

Despite its long-standing and widespread use, disagreement remains regarding the structure of the Rosenberg Self-Esteem Scale (RSES). In particular, concern remains regarding the degree to which the scale assesses self-esteem as a unidimensional or multidimensional (positive and negative self-esteem) construct. Using a sample of 3,862 high school students in the United Kingdom, 4 models were tested: (a) a unidimensional model, (b) a correlated 2-factor model in which the 2 latent variables are represented by positive and negative self-esteem, (c) a hierarchical model, and (d) a bifactor model. The totality of results including item loadings, goodness-of-fit indexes, reliability estimates, and correlations with self-efficacy measures all supported the bifactor model, suggesting that the 2 hypothesized factors are better understood as "grouping" factors rather than as representative of latent constructs. Accordingly, this study supports the unidimensionality of the RSES and the scoring of all 10 items to produce a global self-esteem score.

The mechanisms mediating the effects of poverty on children's intellectual development.

PubMed

Guo, G; Harris, K M

2000-11-01

Although adverse consequences of poverty for children are documented widely, little is understood about the mechanisms through which the effects of poverty disadvantage young children. In this analysis we investigate multiple mechanisms through which poverty affects a child's intellectual development. Using data from the NLSY and structural equation models, we have constructed five latent factors (cognitive stimulation, parenting style, physical environment, child's ill health at birth, and ill health in childhood) and have allowed these factors, along with child care, to mediate the effects of poverty and other exogenous variables. We produce two main findings. First, the influence of family poverty on children's intellectual development is mediated completely by the intervening mechanisms measured by our latent factors. Second, our analysis points to cognitive stimulation in the home, and (to a lesser extent) to parenting style, physical environment of the home, and poor child health at birth, as mediating factors that are affected by lack of income and that influence children's intellectual development.

The NEO Five-Factor Inventory: latent structure and relationships with dimensions of anxiety and depressive disorders in a large clinical sample.

PubMed

Rosellini, Anthony J; Brown, Timothy A

2011-03-01

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 [MDD]) in a large sample of outpatients (N = 1,980). Exploratory structural equation modeling (ESEM) was used to show that a five-factor solution provided acceptable model fit, albeit with some poorly functioning items. Neuroticism demonstrated significant positive associations with all but one of the disorder constructs whereas Extraversion was inversely related to SOC and MDD. Conscientiousness was inversely related to MDD but demonstrated a positive relationship with GAD. Results are discussed in regard to potential revisions to the NEO FFI, the evaluation of other NEO instruments using ESEM, and clinical implications of structural paths between FFM domains and specific emotional disorders.

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

PubMed Central

Rosellini, Anthony J.; Brown, Timothy A.

2017-01-01

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 [MDD]) in a large sample of outpatients (N = 1,980). Exploratory structural equation modeling (ESEM) was used to show that a five-factor solution provided acceptable model fit, albeit with some poorly functioning items. Neuroticism demonstrated significant positive associations with all but one of the disorder constructs whereas Extraversion was inversely related to SOC and MDD. Conscientiousness was inversely related to MDD but demonstrated a positive relationship with GAD. Results are discussed in regard to potential revisions to the NEO FFI, the evaluation of other NEO instruments using ESEM, and clinical implications of structural paths between FFM domains and specific emotional disorders. PMID:20881102

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

Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation.

PubMed

Lam, Clifford; Fan, Jianqing

2009-01-01

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, its inverse or its Cholesky decomposition. We study these three sparsity exploration problems under a unified framework with a general penalty function. We show that the rates of convergence for these problems under the Frobenius norm are of order (s(n) log p(n)/n)(1/2), where s(n) is the number of nonzero elements, p(n) is the size of the covariance matrix and n is the sample size. This explicitly spells out the contribution of high-dimensionality is merely of a logarithmic factor. The conditions on the rate with which the tuning parameter λ(n) goes to 0 have been made explicit and compared under different penalties. As a result, for the L(1)-penalty, to guarantee the sparsistency and optimal rate of convergence, the number of nonzero elements should be small: sn'=O(pn) at most, among O(pn2) parameters, for estimating sparse covariance or correlation matrix, sparse precision or inverse correlation matrix or sparse Cholesky factor, where sn' is the number of the nonzero elements on the off-diagonal entries. On the other hand, using the SCAD or hard-thresholding penalty functions, there is no such a restriction.

A sparse reconstruction method for the estimation of multiresolution emission fields via atmospheric inversion

DOE PAGES

Ray, J.; Lee, J.; Yadav, V.; ...

2014-08-20

We present a sparse reconstruction scheme that can also be used to ensure non-negativity when fitting wavelet-based random field models to limited observations in non-rectangular geometries. The method is relevant when multiresolution fields are estimated using linear inverse problems. Examples include the estimation of emission fields for many anthropogenic pollutants using atmospheric inversion or hydraulic conductivity in aquifers from flow measurements. The scheme is based on three new developments. Firstly, we extend an existing sparse reconstruction method, Stagewise Orthogonal Matching Pursuit (StOMP), to incorporate prior information on the target field. Secondly, we develop an iterative method that uses StOMP tomore » impose non-negativity on the estimated field. Finally, we devise a method, based on compressive sensing, to limit the estimated field within an irregularly shaped domain. We demonstrate the method on the estimation of fossil-fuel CO 2 (ffCO 2) emissions in the lower 48 states of the US. The application uses a recently developed multiresolution random field model and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of two. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

A leakage-free resonance sparse decomposition technique for bearing fault detection in gearboxes

NASA Astrophysics Data System (ADS)

Osman, Shazali; Wang, Wilson

2018-03-01

Most of rotating machinery deficiencies are related to defects in rolling element bearings. Reliable bearing fault detection still remains a challenging task, especially for bearings in gearboxes as bearing-defect-related features are nonstationary and modulated by gear mesh vibration. A new leakage-free resonance sparse decomposition (LRSD) technique is proposed in this paper for early bearing fault detection of gearboxes. In the proposed LRSD technique, a leakage-free filter is suggested to remove strong gear mesh and shaft running signatures. A kurtosis and cosine distance measure is suggested to select appropriate redundancy r and quality factor Q. The signal residual is processed by signal sparse decomposition for highpass and lowpass resonance analysis to extract representative features for bearing fault detection. The effectiveness of the proposed technique is verified by a succession of experimental tests corresponding to different gearbox and bearing conditions.

Response of an eddy-permitting ocean model to the assimilation of sparse in situ data

NASA Astrophysics Data System (ADS)

Li, Jian-Guo; Killworth, Peter D.; Smeed, David A.

2003-04-01

The response of an eddy-permitting ocean model to changes introduced by data assimilation is studied when the available in situ data are sparse in both space and time (typical for the majority of the ocean). Temperature and salinity (T&S) profiles from the WOCE upper ocean thermal data set were assimilated into a primitive equation ocean model over the North Atlantic, using a simple nudging scheme with a time window of about 2 days and a horizontal spatial radius of about 1°. When data are sparse the model returns to its unassimilated behavior, locally "forgetting" or rejecting the assimilation, on timescales determined by the local advection and diffusion. Increasing the spatial weighting radius effectively reduces both processes and hence lengthens the model restoring time (and with it, the impact of assimilation). Increasing the nudging factor enhances the assimilation effect but has little effect on the model restoring time.

Removing flicker based on sparse color correspondences in old film restoration

NASA Astrophysics Data System (ADS)

Huang, Xi; Ding, Youdong; Yu, Bing; Xia, Tianran

2018-04-01

In the long history of human civilization, archived film is an indispensable part of it, and using digital method to repair damaged film is also a mainstream trend nowadays. In this paper, we propose a sparse color correspondences based technique to remove fading flicker for old films. Our model, combined with multi frame images to establish a simple correction model, includes three key steps. Firstly, we recover sparse color correspondences in the input frames to build a matrix with many missing entries. Secondly, we present a low-rank matrix factorization approach to estimate the unknown parameters of this model. Finally, we adopt a two-step strategy that divide the estimated parameters into reference frame parameters for color recovery correction and other frame parameters for color consistency correction to remove flicker. Our method combined multi-frames takes continuity of the input sequence into account, and the experimental results show the method can remove fading flicker efficiently.

Adaptive Sparse Representation for Source Localization with Gain/Phase Errors

PubMed Central

Sun, Ke; Liu, Yimin; Meng, Huadong; Wang, Xiqin

2011-01-01

Sparse representation (SR) algorithms can be implemented for high-resolution direction of arrival (DOA) estimation. Additionally, SR can effectively separate the coherent signal sources because the spectrum estimation is based on the optimization technique, such as the L1 norm minimization, but not on subspace orthogonality. However, in the actual source localization scenario, an unknown gain/phase error between the array sensors is inevitable. Due to this nonideal factor, the predefined overcomplete basis mismatches the actual array manifold so that the estimation performance is degraded in SR. In this paper, an adaptive SR algorithm is proposed to improve the robustness with respect to the gain/phase error, where the overcomplete basis is dynamically adjusted using multiple snapshots and the sparse solution is adaptively acquired to match with the actual scenario. The simulation results demonstrate the estimation robustness to the gain/phase error using the proposed method. PMID:22163875

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

NASA Astrophysics Data System (ADS)

Gillis, Nicolas; Luce, Robert

2018-01-01

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.

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.

Latency of Herpes Simplex Virus in Absence of Neutralizing Antibody: Model for Reactivation

NASA Astrophysics Data System (ADS)

Sekizawa, Tsuyoshi; Openshaw, Harry; Wohlenberg, Charles; Notkins, Abner Louis

1980-11-01

Mice inoculated with herpes simplex virus (type 1) by the lip or corneal route and then passively immunized with rabbit antibody to herpes simplex virus developed a latent infection in the trigeminal ganglia within 96 hours. Neutralizing antibody to herpes simplex virus was cleared from the circulation and could not be detected in most of these mice after 2 months. Examination of ganglia from the antibody-negative mice revealed latent virus in over 90 percent of the animals, indicating that serum neutralizing antibody is not necessary to maintain the latent state. When the lips or corneas of these mice were traumatized, viral reactivation occurred in up to 90 percent of the mice, as demonstrated by the appearance of neutralizing antibody. This study provides a model for identifying factors that trigger viral reactivation.

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Recoverability of P-Technique Factor Analysis

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2009-01-01

It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…

On Studying Common Factor Dominance and Approximate Unidimensionality in Multicomponent Measuring Instruments with Discrete Items

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2018-01-01

This article outlines a procedure for examining the degree to which a common factor may be dominating additional factors in a multicomponent measuring instrument consisting of binary items. The procedure rests on an application of the latent variable modeling methodology and accounts for the discrete nature of the manifest indicators. The method…

Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland.

PubMed

Sasidharan, Lekshmi; Wu, Kun-Feng; Menendez, Monica

2015-12-01

One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury severities and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Socioeconomic Differences in the Risk Profiles of Susceptibility and Ever Use of Tobacco Among Indian Urban Youth: A Latent Class Approach

PubMed Central

2014-01-01

Purpose: To empirically determine the socioeconomic differences in risk profiles of susceptibility and ever use of tobacco among adolescents in India and to investigate the association between the risk profiles and the psychosocial factors for tobacco use. Methods: Students in 16 private (higher socioeconomic status [SES]; n = 4,489) and 16 government (lower SES; n = 7,153) schools in two large cities in India were surveyed about their tobacco use and related psychosocial factors in 2004. Latent class analysis was used to identify homogenous, mutually exclusive typologies existing within the data. Results: Overall, 3 and 4 latent classes of susceptibility and ever use of tobacco best described students in higher- and lower- SES schools, respectively. Profiles with various combinations of susceptibility and ever use of tobacco were differentially related to psychosocial factors, with lower- SES students being more vulnerable to increased levels of tobacco use than higher- SES students. Conclusions: Acknowledging the multiple dimensions of tobacco use behaviors and identifying constellations of risk behaviors will enable more accurate understanding of etiological processes and will provide information for refining and targeting preventive interventions. Additionally, identifying the socioeconomic differences in susceptibility and ever use risk profiles and their psychosocial correlates will enable policy makers to address these inequities through improved allocation of resources. PMID:24271966

The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample.

PubMed

Crawford, John R; Henry, Julie D

2003-06-01

To provide UK normative data for the Depression Anxiety and Stress Scale (DASS) and test its convergent, discriminant and construct validity. Cross-sectional, correlational and confirmatory factor analysis (CFA). The DASS was administered to a non-clinical sample, broadly representative of the general adult UK population (N = 1,771) in terms of demographic variables. Competing models of the latent structure of the DASS were derived from theoretical and empirical sources and evaluated using confirmatory factor analysis. Correlational analysis was used to determine the influence of demographic variables on DASS scores. The convergent and discriminant validity of the measure was examined through correlating the measure with two other measures of depression and anxiety (the HADS and the sAD), and a measure of positive and negative affectivity (the PANAS). The best fitting model (CFI =.93) of the latent structure of the DASS consisted of three correlated factors corresponding to the depression, anxiety and stress scales with correlated error permitted between items comprising the DASS subscales. Demographic variables had only very modest influences on DASS scores. The reliability of the DASS was excellent, and the measure possessed adequate convergent and discriminant validity Conclusions: The DASS is a reliable and valid measure of the constructs it was intended to assess. The utility of this measure for UK clinicians is enhanced by the provision of large sample normative data.

Bayesian Nonparametric Ordination for the Analysis of Microbial Communities.

PubMed

Ren, Boyu; Bacallado, Sergio; Favaro, Stefano; Holmes, Susan; Trippa, Lorenzo

2017-01-01

Human microbiome studies use sequencing technologies to measure the abundance of bacterial species or Operational Taxonomic Units (OTUs) in samples of biological material. Typically the data are organized in contingency tables with OTU counts across heterogeneous biological samples. In the microbial ecology community, ordination methods are frequently used to investigate latent factors or clusters that capture and describe variations of OTU counts across biological samples. It remains important to evaluate how uncertainty in estimates of each biological sample's microbial distribution propagates to ordination analyses, including visualization of clusters and projections of biological samples on low dimensional spaces. We propose a Bayesian analysis for dependent distributions to endow frequently used ordinations with estimates of uncertainty. A Bayesian nonparametric prior for dependent normalized random measures is constructed, which is marginally equivalent to the normalized generalized Gamma process, a well-known prior for nonparametric analyses. In our prior, the dependence and similarity between microbial distributions is represented by latent factors that concentrate in a low dimensional space. We use a shrinkage prior to tune the dimensionality of the latent factors. The resulting posterior samples of model parameters can be used to evaluate uncertainty in analyses routinely applied in microbiome studies. Specifically, by combining them with multivariate data analysis techniques we can visualize credible regions in ecological ordination plots. The characteristics of the proposed model are illustrated through a simulation study and applications in two microbiome datasets.

Psychometric properties and a latent class analysis of the 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) in a pooled dataset of community samples.

PubMed

MacLeod, Melissa A; Tremblay, Paul F; Graham, Kathryn; Bernards, Sharon; Rehm, Jürgen; Wells, Samantha

2016-12-01

The 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a brief measurement tool used cross-culturally to capture the multi-dimensional nature of disablement through six domains, including: understanding and interacting with the world; moving and getting around; self-care; getting on with people; life activities; and participation in society. Previous psychometric research supports that the WHODAS 2.0 functions as a general factor of disablement. In a pooled dataset from community samples of adults (N = 447) we used confirmatory factor analysis to confirm a one-factor structure. Latent class analysis was used to identify subgroups of individuals based on their patterns of responses. We identified four distinct classes, or patterns of disablement: (1) pervasive disability; (2) physical disability; (3) emotional, cognitive, or interpersonal disability; (4) no/low disability. Convergent validity of the latent class subgroups was found with respect to socio-demographic characteristics, number of days affected by disabilities, stress, mental health, and substance use. These classes offer a simple and meaningful way to classify people with disabilities based on the 12-item WHODAS 2.0. Focusing on individuals with a high probability of being in the first three classes may help guide interventions. Copyright © 2016 John Wiley & Sons, Ltd.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Refining the Measurement of Distress Intolerance

PubMed Central

McHugh, R. Kathryn; Otto, Michael W.

2012-01-01

Distress intolerance is an important transdiagnostic variable that has long been implicated in the development and maintenance of psychological disorders. Self-report measurement strategies for distress intolerance have emerged from several different models of psychopathology and these measures have been applied inconsistently in the literature in the absence of a clear gold standard. The absence of a consistent assessment strategy has limited the ability to compare across studies and samples, thus hampering the advancement of this research agenda. This study evaluated the latent factor structure of existing measures of DI to examine the degree to which they are capturing the same construct. Results of confirmatory factor analysis in 3 samples totaling 400 participants provided support for a single factor latent structure. Individual items of these four scales were then correlated with this factor to identify those that best capture the core construct. Results provided consistent supported for 10 items that demonstrated the strongest concordance with this factor. The use of these 10 items as a unifying measure in the study of DI and future directions for the evaluation of its utility are discussed. PMID:22697451

Power Enhancement in High Dimensional Cross-Sectional Tests

PubMed Central

Fan, Jianqing; Liao, Yuan; Yao, Jiawei

2016-01-01

We propose a novel technique to boost the power of testing a high-dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated only by a couple of components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme-value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a “power enhancement component”, which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. As specific applications, the proposed methods are applied to testing the factor pricing models and validating the cross-sectional independence in panel data models. PMID:26778846

Moco biosynthesis and the ATAC acetyltransferase engage translation initiation by inhibiting latent PKR activity.

PubMed

Suganuma, Tamaki; Swanson, Selene K; Florens, Laurence; Washburn, Michael P; Workman, Jerry L

2016-02-01

Molybdenum cofactor (Moco) biosynthesis is linked to c-Jun N-terminal kinase (JNK) signaling in Drosophila through MoaE, a molybdopterin (MPT) synthase subunit that is also a component of the Ada Two A containing (ATAC) acetyltransferase complex. Here, we show that human MPT synthase and ATAC inhibited PKR, a double-stranded RNA-dependent protein kinase, to facilitate translation initiation of iron-responsive mRNA. MPT synthase and ATAC directly interacted with PKR and suppressed latent autophosphorylation of PKR and its downstream phosphorylation of JNK and eukaryotic initiation factor 2α (eIF2α). The suppression of eIF2α phosphorylation via MPT synthase and ATAC prevented sequestration of the guanine nucleotide exchange factor eIF2B, which recycles eIF2-GDP to eIF2-GTP, resulting in the promotion of translation initiation. Indeed, translation of the iron storage protein, ferritin, was reduced in the absence of MPT synthase or ATAC subunits. Thus, MPT synthase and ATAC regulate latent PKR signaling and link transcription and translation initiation. © The Author (2015). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.

[Tuberculosis: plasma levels of vitamin D and its relation with infection and disease].

PubMed

Esteve Palau, Erika; Sánchez Martínez, Francesca; Knobel Freud, Hernando; López Colomés, José-Luís; Diez Pérez, Adolfo

2015-02-02

Vitamin D (vitD) is involved in the phosphor-calcium metabolism and bone pathology, but also in inflammatory and infectious processes such as tuberculosis. The present study evaluates the clinical and epidemiological aspects of active tuberculosis cases and latently infected contacts in whom plasma concentrations of vitD were obtained to determine whether the deficiency of vitD is a risk factor to develop active tuberculosis, especially the more severe forms. Observational, retrospective study that included 86 tuberculosis patients and 80 contacts with latent infection in a 2-year period. When comparing active tuberculosis cases with latent infection contacts, deficiency of vitD (vitD levels <10 ng/mL, odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.04 to 3.93), male sex (OR: 1.9, 95% CI: 0.96 to 3.71) and non-white race (OR: 0.7, 95% CI: 0.34 to 1.42) were factors independently associated with the diagnosis of tuberculosis. Despite the limited number of subjects studied, there was a association between severe deficit of vitD and the presentation of tuberculosis. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Exploratory factor analysis of pathway copy number data with an application towards the integration with gene expression data.

PubMed

van Wieringen, Wessel N; van de Wiel, Mark A

2011-05-01

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression.

Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet

PubMed Central

Sun, Fei; Xu, Bing; Zhang, Yi; Dai, Shengyun; Shi, Xinyuan; Qiao, Yanjiang

2017-01-01

ABSTRACT The dissolution is one of the critical quality attributes (CQAs) of oral solid dosage forms because it relates to the absorption of drug. In this paper, the influence of raw materials, granules and process parameters on the dissolution of paracetamol tablet was analyzed using latent variable modeling methods. The variability in raw materials and granules was understood based on the principle component analysis (PCA), respectively. A multi-block partial least squares (MBPLS) model was used to determine the critical factors affecting the dissolution. The results showed that the binder amount, the post granulation time, the API content in granule, the fill depth and the punch tip separation distance were the critical factors with variable importance in the projection (VIP) values larger than 1. The importance of each unit of the whole process was also ranked using the block importance in the projection (BIP) index. It was concluded that latent variable models (LVMs) were very useful tools to extract information from the available data and improve the understanding on dissolution behavior of paracetamol tablet. The obtained LVMs were also helpful to propose the process design space and to design control strategies in the further research. PMID:27689242

Why aren’t they happy? An analysis of end-user satisfaction with Electronic health records

PubMed Central

Unni, Prasad; Staes, Catherine; Weeks, Howard; Kramer, Heidi; Borbolla, Damion; Slager, Stacey; Taft, Teresa; Chidambaram, Valliammai; Weir, Charlene

2016-01-01

Introduction. Implementations of electronic health records (EHR) have been met with mixed outcome reviews. Complaints about these systems have led to many attempts to have useful measures of end-user satisfaction. However, most user satisfaction assessments do not focus on high-level reasoning, despite the complaints of many physicians. Our study attempts to identify some of these determinants. Method. We developed a user satisfaction survey instrument, based on pre-identified and important clinical and non-clinical clinician tasks. We surveyed a sample of in-patient physicians and focused on using exploratory factor analyses to identify underlying high-level cognitive tasks. We used the results to create unique, orthogonal variables representative of latent structure predictive of user satisfaction. Results. Our findings identified 3 latent high-level tasks that were associated with end-user satisfaction: a) High- level clinical reasoning b) Communicate/coordinate care and c) Follow the rules/compliance. Conclusion: We were able to successfully identify latent variables associated with satisfaction. Identification of communicability and high-level clinical reasoning as important factors determining user satisfaction can lead to development and design of more usable electronic health records with higher user satisfaction. PMID:28269962

A Twin Factor Mixture Modeling Approach to Childhood Temperament: Differential Heritability

ERIC Educational Resources Information Center

Scott, Brandon G.; Lemery-Chalfant, Kathryn; Clifford, Sierra; Tein, Jenn-Yun; Stoll, Ryan; Goldsmith, H.Hill

2016-01-01

Twin factor mixture modeling was used to identify temperament profiles while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (M[subscript age] = 7.4 years, SD = 0.84; 49% female; 88.3% Caucasian), using mother- and father-reported temperament. A four-profile, one-factor model fit the data well.…

Who Should Be Targeted for the Prevention of Birth Defects? A Latent Class Analysis Based on a Large, Population-Based, Cross-Sectional Study in Shaanxi Province, Western China.

PubMed

Zhu, Zhonghai; Cheng, Yue; Yang, Wenfang; Li, Danyang; Yang, Xue; Liu, Danli; Zhang, Min; Yan, Hong; Zeng, Lingxia

2016-01-01

The wide range and complex combinations of factors that cause birth defects impede the development of primary prevention strategies targeted at high-risk subpopulations. Latent class analysis (LCA) was conducted to identify mutually exclusive profiles of factors associated with birth defects among women between 15 and 49 years of age using data from a large, population-based, cross-sectional study conducted in Shaanxi Province, western China, between August and October, 2013. The odds ratios (ORs) and 95% confidence intervals (CIs) of associated factors and the latent profiles of indicators of birth defects and congenital heart defects were computed using a logistic regression model. Five discrete subpopulations of participants were identified as follows: No folic acid supplementation in the periconceptional period (reference class, 21.37%); low maternal education level + unhealthy lifestyle (class 2, 39.75%); low maternal education level + unhealthy lifestyle + disease (class 3, 23.71%); unhealthy maternal lifestyle + advanced age (class 4, 4.71%); and multi-risk factor exposure (class 5, 10.45%). Compared with the reference subgroup, the other subgroups consistently had a significantly increased risk of birth defects (ORs and 95% CIs: class 2, 1.75 and 1.21-2.54; class 3, 3.13 and 2.17-4.52; class 4, 5.02 and 3.20-7.88; and class 5, 12.25 and 8.61-17.42, respectively). For congenital heart defects, the ORs and 95% CIs were all higher, and the magnitude of OR differences ranged from 1.59 to 16.15. A comprehensive intervention strategy targeting maternal exposure to multiple risk factors is expected to show the strongest results in preventing birth defects.

Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

PubMed

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2013-01-01

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

Modeling Change in Learning Strategies throughout Higher Education: A Multi-Indicator Latent Growth Perspective

PubMed Central

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2013-01-01

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain. PMID:23844112

Verbal Neuropsychological Functions in Aphasia: An Integrative Model

ERIC Educational Resources Information Center

Vigliecca, Nora Silvana; Báez, Sandra

2015-01-01

A theoretical framework which considers the verbal functions of the brain under a multivariate and comprehensive cognitive model was statistically analyzed. A confirmatory factor analysis was performed to verify whether some recognized aphasia constructs can be hierarchically integrated as latent factors from a homogenously verbal test. The Brief…

Latent Factors in Student-Teacher Interaction Factor Analysis

ERIC Educational Resources Information Center

Le, Thu; Bolt, Daniel; Camburn, Eric; Goff, Peter; Rohe, Karl

2017-01-01

Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the "quality" of the interaction. Together with the underlying bipartite graph, these values create a valued student-teacher dyadic interaction network. To…

Inter-Cultural Communication: A Foundation of Communicative Action

ERIC Educational Resources Information Center

Vuckovic, Aleksandra

2008-01-01

Purpose: The purpose of this paper is to present a model of inter-cultural communication that enumerates and structures latent factors affecting such communication and elaborates on the process of self-reflection as a guiding mechanism of successful communication. Design/methodology/approach: The five factors and various moderators that are…

Community Violence, Protective Factors, and Adolescent Mental Health: A Profile Analysis

ERIC Educational Resources Information Center

Copeland-Linder, Nikeea; Lambert, Sharon F.; Ialongo, Nicholas S.

2010-01-01

This study examined interrelationships among community violence exposure, protective factors, and mental health in a sample of urban, predominantly African American adolescents (N = 504). Latent Profile Analysis was conducted to identify profiles of adolescents based on a combination of community violence exposure, self-worth, parental monitoring,…

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…

How to Inspect Fruit

ERIC Educational Resources Information Center

Hamaker, Ellen L.

2007-01-01

Nesselroade, Gerstof, Hardy, and Ram point out that the concept of invariance as it is used in factor analytic practice differs from that employed in psychological theory. Although substantive interests often center on a lawfulness in the relationships among abstract constructs (i.e., latent variables), the focus in factor analysis has primarily…

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

PubMed Central

Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic. PMID:28245222

Data traffic reduction schemes for Cholesky factorization on asynchronous multiprocessor systems

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1989-01-01

Communication requirements of Cholesky factorization of dense and sparse symmetric, positive definite matrices are analyzed. The communication requirement is characterized by the data traffic generated on multiprocessor systems with local and shared memory. Lower bound proofs are given to show that when the load is uniformly distributed the data traffic associated with factoring an n x n dense matrix using n to the alpha power (alpha less than or equal 2) processors is omega(n to the 2 + alpha/2 power). For n x n sparse matrices representing a square root of n x square root of n regular grid graph the data traffic is shown to be omega(n to the 1 + alpha/2 power), alpha less than or equal 1. Partitioning schemes that are variations of block assignment scheme are described and it is shown that the data traffic generated by these schemes are asymptotically optimal. The schemes allow efficient use of up to O(n to the 2nd power) processors in the dense case and up to O(n) processors in the sparse case before the total data traffic reaches the maximum value of O(n to the 3rd power) and O(n to the 3/2 power), respectively. It is shown that the block based partitioning schemes allow a better utilization of the data accessed from shared memory and thus reduce the data traffic than those based on column-wise wrap around assignment schemes.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

PubMed

Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

Optimal parallel solution of sparse triangular systems

NASA Technical Reports Server (NTRS)

Alvarado, Fernando L.; Schreiber, Robert

1990-01-01

A method for the parallel solution of triangular sets of equations is described that is appropriate when there are many right-handed sides. By preprocessing, the method can reduce the number of parallel steps required to solve Lx = b compared to parallel forward or backsolve. Applications are to iterative solvers with triangular preconditioners, to structural analysis, or to power systems applications, where there may be many right-handed sides (not all available a priori). The inverse of L is represented as a product of sparse triangular factors. The problem is to find a factored representation of this inverse of L with the smallest number of factors (or partitions), subject to the requirement that no new nonzero elements be created in the formation of these inverse factors. A method from an earlier reference is shown to solve this problem. This method is improved upon by constructing a permutation of the rows and columns of L that preserves triangularity and allow for the best possible such partition. A number of practical examples and algorithmic details are presented. The parallelism attainable is illustrated by means of elimination trees and clique trees.

Denoising Sparse Images from GRAPPA using the Nullspace Method (DESIGN)

PubMed Central

Weller, Daniel S.; Polimeni, Jonathan R.; Grady, Leo; Wald, Lawrence L.; Adalsteinsson, Elfar; Goyal, Vivek K

2011-01-01

To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with GRAPPA alone, the Denoising of Sparse Images from GRAPPA using the Nullspace method (DESIGN) is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DESIGN are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), PSNR, and noise amplification (g-factors) as measured using the pseudo-multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g-factors suggest that DESIGN mitigates noise amplification better than both GRAPPA and L1 SPIR-iT (the latter limited here by uniform undersampling). PMID:22213069

Multiple View Zenith Angle Observations of Reflectance From Ponderosa Pine Stands

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Lawless, James G. (Technical Monitor)

1994-01-01

Reflectance factors (RF(lambda)) from dense and sparse ponderosa pine (Pinus ponderosa) stands, derived from radiance data collected in the solar principal plane by the Advanced Solid-State Array Spectro-radiometer (ASAS), were examined as a function of view zenith angle (theta(sub v)). RF(lambda) was maximized with theta(sub v) nearest the solar retrodirection, and minimized near the specular direction throughout the ASAS spectral region. The dense stand had much higher RF anisotropy (ma)dmurn RF is minimum RF) in the red region than did the sparse stand (relative differences of 5.3 vs. 2.75, respectively), as a function of theta(sub v), due to the shadow component in the canopy. Anisotropy in the near-infrared (NIR) was more similar between the two stands (2.5 in the dense stand and 2.25 in the sparse stand); the dense stand exhibited a greater hotspot effect than 20 the sparse stand in this spectral region. Two common vegetation transforms, the NIR/red ratio and the normalized difference vegetation index (NDVI), both showed a theta(sub v) dependence for the dense stand. Minimum values occurred near the retrodirection and maximum values occurred near the specular direction. Greater relative differences were noted for the NIR/red ratio (2.1) than for the NDVI (1.3). The sparse stand showed no obvious dependence on theta(sub v) for either transform, except for slightly elevated values toward the specular direction.

Implementing an Accurate and Rapid Sparse Sampling Approach for Low-Dose Atomic Resolution STEM Imaging

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

Kovarik, Libor; Stevens, Andrew J.; Liyu, Andrey V.

Aberration correction for scanning transmission electron microscopes (STEM) has dramatically increased spatial image resolution for beam-stable materials, but it is the sample stability rather than the microscope that often limits the practical resolution of STEM images. To extract physical information from images of beam sensitive materials it is becoming clear that there is a critical dose/dose-rate below which the images can be interpreted as representative of the pristine material, while above it the observation is dominated by beam effects. Here we describe an experimental approach for sparse sampling in the STEM and in-painting image reconstruction in order to reduce themore » electron dose/dose-rate to the sample during imaging. By characterizing the induction limited rise-time and hysteresis in scan coils, we show that sparse line-hopping approach to scan randomization can be implemented that optimizes both the speed of the scan and the amount of the sample that needs to be illuminated by the beam. The dose and acquisition time for the sparse sampling is shown to be effectively decreased by factor of 5x relative to conventional acquisition, permitting imaging of beam sensitive materials to be obtained without changing the microscope operating parameters. As a result, the use of sparse line-hopping scan to acquire STEM images is demonstrated with atomic resolution aberration corrected Z-contrast images of CaCO 3, a material that is traditionally difficult to image by TEM/STEM because of dose issues.« less

SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

NASA Astrophysics Data System (ADS)

Koesters, Thomas; Knoll, Florian; Sodickson, Aaron; Sodickson, Daniel K.; Otazo, Ricardo

2017-03-01

State-of-the-art low-dose CT methods reduce the x-ray tube current and use iterative reconstruction methods to denoise the resulting images. However, due to compromises between denoising and image quality, only moderate dose reductions up to 30-40% are accepted in clinical practice. An alternative approach is to reduce the number of x-ray projections and use compressed sensing to reconstruct the full-tube-current undersampled data. This idea was recognized in the early days of compressed sensing and proposals for CT dose reduction appeared soon afterwards. However, no practical means of undersampling has yet been demonstrated in the challenging environment of a rapidly rotating CT gantry. In this work, we propose a moving multislit collimator as a practical incoherent undersampling scheme for compressed sensing CT and evaluate its application for radiation dose reduction. The proposed collimator is composed of narrow slits and moves linearly along the slice dimension (z), to interrupt the incident beam in different slices for each x-ray tube angle (θ). The reduced projection dataset is then reconstructed using a sparse approach, where 3D image gradients are employed to enforce sparsity. The effects of the collimator slits on the beam profile were measured and represented as a continuous slice profile. SparseCT was tested using retrospective undersampling and compared against commercial current-reduction techniques on phantoms and in vivo studies. Initial results suggest that SparseCT may enable higher performance than current-reduction, particularly for high dose reduction factors.

Implementing an Accurate and Rapid Sparse Sampling Approach for Low-Dose Atomic Resolution STEM Imaging

DOE PAGES

Kovarik, Libor; Stevens, Andrew J.; Liyu, Andrey V.; ...

2016-10-17

Aberration correction for scanning transmission electron microscopes (STEM) has dramatically increased spatial image resolution for beam-stable materials, but it is the sample stability rather than the microscope that often limits the practical resolution of STEM images. To extract physical information from images of beam sensitive materials it is becoming clear that there is a critical dose/dose-rate below which the images can be interpreted as representative of the pristine material, while above it the observation is dominated by beam effects. Here we describe an experimental approach for sparse sampling in the STEM and in-painting image reconstruction in order to reduce themore » electron dose/dose-rate to the sample during imaging. By characterizing the induction limited rise-time and hysteresis in scan coils, we show that sparse line-hopping approach to scan randomization can be implemented that optimizes both the speed of the scan and the amount of the sample that needs to be illuminated by the beam. The dose and acquisition time for the sparse sampling is shown to be effectively decreased by factor of 5x relative to conventional acquisition, permitting imaging of beam sensitive materials to be obtained without changing the microscope operating parameters. The use of sparse line-hopping scan to acquire STEM images is demonstrated with atomic resolution aberration corrected Z-contrast images of CaCO3, a material that is traditionally difficult to image by TEM/STEM because of dose issues.« less

Psychometric analysis of the Swedish version of the General Medical Council's multi source feedback questionnaires.

PubMed

Olsson, Jan-Eric; Wallentin, Fan Yang; Toth-Pal, Eva; Ekblad, Solvig; Bertilson, Bo Christer

2017-07-10

To determine the internal consistency and the underlying components of our translated and adapted Swedish version of the General Medical Council's multisource feedback questionnaires (GMC questionnaires) for physicians and to confirm which aspects of good medical practice the latent variable structure reflected. From October 2015 to March 2016, residents in family medicine in Sweden were invited to participate in the study and to use the Swedish version to perform self-evaluations and acquire feedback from both their patients and colleagues. The validation focused on internal consistency and construct validity. Main outcome measures were Cronbach's alpha coefficients, Principal Component Analysis, and Confirmatory Factor Analysis indices. A total of 752 completed questionnaires from patients, colleagues, and residents were analysed. Of these, 213 comprised resident self-evaluations, 336 were feedback from residents' patients, and 203 were feedback from residents' colleagues. Cronbach's alpha coefficients of the scores were 0.88 from patients, 0.93 from colleagues, and 0.84 in the self-evaluations. The Confirmatory Factor Analysis validated two models that fit the data reasonably well and reflected important aspects of good medical practice. The first model had two latent factors for patient-related items concerning empathy and consultation management, and the second model had five latent factors for colleague-related items, including knowledge and skills, attitude and approach, reflection and development, teaching, and trust. The current Swedish version seems to be a reliable and valid tool for formative assessment for resident physicians and their supervisors. This needs to be verified in larger samples.

Psychometric analysis of the Swedish version of the General Medical Council's multi source feedback questionnaires

PubMed Central

Wallentin, Fan Yang; Toth-Pal, Eva; Ekblad, Solvig; Bertilson, Bo Christer

2017-01-01

Objectives To determine the internal consistency and the underlying components of our translated and adapted Swedish version of the General Medical Council's multisource feedback questionnaires (GMC questionnaires) for physicians and to confirm which aspects of good medical practice the latent variable structure reflected. Methods From October 2015 to March 2016, residents in family medicine in Sweden were invited to participate in the study and to use the Swedish version to perform self-evaluations and acquire feedback from both their patients and colleagues. The validation focused on internal consistency and construct validity. Main outcome measures were Cronbach’s alpha coefficients, Principal Component Analysis, and Confirmatory Factor Analysis indices. Results A total of 752 completed questionnaires from patients, colleagues, and residents were analysed. Of these, 213 comprised resident self-evaluations, 336 were feedback from residents’ patients, and 203 were feedback from residents’ colleagues. Cronbach’s alpha coefficients of the scores were 0.88 from patients, 0.93 from colleagues, and 0.84 in the self-evaluations. The Confirmatory Factor Analysis validated two models that fit the data reasonably well and reflected important aspects of good medical practice. The first model had two latent factors for patient-related items concerning empathy and consultation management, and the second model had five latent factors for colleague-related items, including knowledge and skills, attitude and approach, reflection and development, teaching, and trust. Conclusions The current Swedish version seems to be a reliable and valid tool for formative assessment for resident physicians and their supervisors. This needs to be verified in larger samples. PMID:28704204

Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan

2017-09-01

It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus weak feature leakage problem is avoided compared to typical learning methods.

Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression.

PubMed

Zhen, Xiantong; Zhang, Heye; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

2017-02-01

Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i.e., left ventricle (LV), or bi-ventricles; they can not be directly used for four chamber volume estimation due to the great combinatorial variability and highly complex anatomical interdependency of the four chambers. In this paper, we propose a new, general framework for direct and simultaneous four chamber volume estimation. We have addressed two key issues, i.e., cardiac image representation and simultaneous four chamber volume estimation, which enables accurate and efficient four-chamber volume estimation. We generate compact and discriminative image representations by supervised descriptor learning (SDL) which can remove irrelevant information and extract discriminative features. We propose direct and simultaneous four-chamber volume estimation by the multioutput sparse latent regression (MSLR), which enables jointly modeling nonlinear input-output relationships and capturing four-chamber interdependence. The proposed method is highly generalized, independent of imaging modalities, which provides a general regression framework that can be extensively used for clinical data prediction to achieve automated diagnosis. Experiments on both MR and CT images show that our method achieves high performance with a correlation coefficient of up to 0.921 with ground truth obtained manually by human experts, which is clinically significant and enables more accurate, convenient and comprehensive assessment of cardiac functions. Copyright © 2016 Elsevier B.V. All rights reserved.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

PubMed Central

2011-01-01

Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset. PMID:21978359

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping.

PubMed

Hampton, Kristen H; Serre, Marc L; Gesink, Dionne C; Pilcher, Christopher D; Miller, William C

2011-10-06

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Variational Bayesian Learning for Wavelet Independent Component Analysis

NASA Astrophysics Data System (ADS)

Roussos, E.; Roberts, S.; Daubechies, I.

2005-11-01

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

PubMed

Cao, Buwen; Deng, Shuguang; Qin, Hua; Ding, Pingjian; Chen, Shaopeng; Li, Guanghui

2018-06-15

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks. In this study, based on penalized matrix decomposition ( PMD ), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD pc ) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

ERIC Educational Resources Information Center

Schmitt, Thomas A.; Sass, Daniel A.

2011-01-01

Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences.…

Measuring Early Spanish Literacy: Factor Structure and Measurement Invariance of the "Phonological Awareness Literacy Screening for Kindergarteners" in Spanish ("PALS español K")

ERIC Educational Resources Information Center

Huang, Francis L.; Ford, Karen L.; Invernizzi, Marcia; Fan, Xitao

2013-01-01

We investigated the latent factor structure of the "Phonological Awareness Literacy Screening for Kindergarteners" in Spanish ("PALS español K"). Participants included 590 Spanish-speaking, public-school kindergarteners from five states. Three theoretically-guided factor structures were measured and tested with one half of our…

Exploring the Latent Structure of the Luria Model for the KABC-II at School Age: Further Insights from Confirmatory Factor Analysis

ERIC Educational Resources Information Center

McGill, Ryan J.

2017-01-01

The present study examined the factor structure of the Luria interpretive model for the Kaufman Assessment Battery for Children-Second Edition (KABC-II) with normative sample participants aged 7-18 (N = 2,025) using confirmatory factor analysis with maximum-likelihood estimation. For the eight subtest Luria configuration, an alternative…

Development of a new multidimensional individual and interpersonal resilience measure for older adults.

PubMed

Martin, A'verria Sirkin; Distelberg, Brian; Palmer, Barton W; Jeste, Dilip V

2015-01-01

Develop an empirically grounded measure that can be used to assess family and individual resilience in a population of older adults (aged 50-99). Cross-sectional, self-report data from 1006 older adults were analyzed in two steps. The total sample was split into two subsamples and the first step identified the underlying latent structure through principal component exploratory factor analysis (EFA). The second step utilized the second half of the sample to validate the derived latent structure through confirmatory factor analysis (CFA). EFA produced an eight-factor structure that appeared clinically relevant for measuring the multidimensional nature of resilience. Factors included self-efficacy, access to social support network, optimism, perceived economic and social resources, spirituality and religiosity, relational accord, emotional expression and communication, and emotional regulation. CFA confirmed the eight-factor structure previously achieved with covariance between each of the factors. Based on these analyses we developed the multidimensional individual and interpersonal resilience measure, a broad assessment of resilience for older adults. This study highlights the multidimensional nature of resilience and introduces an individual and interpersonal resilience measure developed for older adults which is grounded in the individual and family resilience literature.

Development of a New Multidimensional Individual and Interpersonal Resilience Measure for Older Adults

PubMed Central

Martin, A’verria Sirkin; Distelberg, Brian; Palmer, Barton W.; Jeste, Dilip V.

2015-01-01

Objectives Develop an empirically grounded measure that can be used to assess family and individual resilience in a population of older adults (aged 50-99). Methods Cross-sectional, self-report data from 1,006 older adults were analyzed in two steps. The total sample was split into two sub-samples and the first step identified the underlying latent structure through principal component Exploratory Factor Analysis (EFA). The second step utilized the second half of the sample to validate the derived latent structure through Confirmatory Factor Analysis (CFA). Results EFA produced an eight-factor structure that appeared clinically relevant for measuring the multidimensional nature of resilience. Factors included self-efficacy, access to social support network, optimism, perceived economic and social resources, spirituality and religiosity, relational accord, emotional expression and communication, and emotional regulation. CFA confirmed the eight-factor structure previously achieved with covariance between each of the factors. Based on these analyses we developed the Multidimensional Individual and Interpersonal Resilience Measure (MIIRM), a broad assessment of resilience for older adults. Conclusion This study highlights the multidimensional nature of resilience and introduces an individual and interpersonal resilience measure developed for older adults which is grounded in the individual and family resilience literature. PMID:24787701

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.

Latent progenitor cells as potential regulators for tympanic membrane regeneration

NASA Astrophysics Data System (ADS)

Kim, Seung Won; Kim, Jangho; Seonwoo, Hoon; Jang, Kyung-Jin; Kim, Yeon Ju; Lim, Hye Jin; Lim, Ki-Taek; Tian, Chunjie; Chung, Jong Hoon; Choung, Yun-Hoon

2015-06-01

Tympanic membrane (TM) perforation, in particular chronic otitis media, is one of the most common clinical problems in the world and can present with sensorineural healing loss. Here, we explored an approach for TM regeneration where the latent progenitor or stem cells within TM epithelial layers may play an important regulatory role. We showed that potential TM stem cells present highly positive staining for epithelial stem cell markers in all areas of normal TM tissue. Additionally, they are present at high levels in perforated TMs, especially in proximity to the holes, regardless of acute or chronic status, suggesting that TM stem cells may be a potential factor for TM regeneration. Our study suggests that latent TM stem cells could be potential regulators of regeneration, which provides a new insight into this clinically important process and a potential target for new therapies for chronic otitis media and other eardrum injuries.

The need for psychiatric care in England: a spatial factor methodology

NASA Astrophysics Data System (ADS)

Congdon, Peter

2008-09-01

To ensure health resources are equitably distributed, composite indices of population morbidity or “health need” are often used. Measures of the dimensions of population morbidity (e.g. socioeconomic deprivation) relevant to health need are typically not directly available but indirectly measured through census or other sources. This paper considers measurement of latent population morbidity constructs using both health outcomes (e.g. hospital admissions, mortality) and observed area social and demographic indicators (e.g. census data). The constructs are allowed to be spatially correlated between areas, as well as correlated with one another within areas. The health outcomes may depend both on the latent constructs and on other relevant covariates (e.g. bed supply), with some covariates possibly measured only at higher (regional) scales. A case study considers variations in psychiatric admissions in 354 English local authority areas in relation to two latent constructs: area deprivation and social fragmentation.

Tuberculosis among prison staff in Rio Grande do Sul.

PubMed

Busatto, Caroline; Nunes, Luciana de Souza; Valim, Andréia Rosane de Moura; Valença, Mariana Soares; Krug, Suzane Frantz; Becker, Daniela; Allgayer, Manuela Filter; Possuelo, Lia Gonçalves

2017-04-01

to evaluate the risk of infection and illness caused by Mycobacterium tuberculosis among health care and security staff in prisons in two regions of Rio Grande do Sul (RS). cross-sectional study involving prison staff. An interview and sputum smear microscopy and culture were performed. Latent infection was evaluated according to the result of the tuberculin test (TT), self-referred. among staff who had a TT, 10 (83.3%) in the central region and 2 (16.7%) in the southern region were considered reactors. Length of employment among prison officers who reacted to TT was 15.3 years, and among health care workers, 4.1 years (p = 0.01). No cases of active tuberculosis (TB) were identified. prevalence of latent TB was 27.9%. Length of employment between different professional categories and their working regions was considered a risk factor for latent TB.

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).

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.

Spatial Estimation of Evapotranspiration in an Irrigated Semi-arid Region Using LSMs Driven by Remote Sensing Data.

NASA Astrophysics Data System (ADS)

Etchanchu, J.; Delogu, E.; Saadi, S.; Chebbi, W.; Trapon, D.; Rivalland, V.; Boulet, G.; Boone, A. A.; Fanise, P.; Mougenot, B.; LE Dantec, V.

2017-12-01

Evapotranspiration and sensible-latent heat flux partition are important decision critera to manage crops, detect water stress and plan irrigation, particularly in a semi-arid context. Nowadays, remote sensing information (at medium -MODIS- and high resolution -LANDSAT, SPOT-) allows us to spatially estimate the different terms of the energy balance at daily and infra-daily time step through various approaches, either by forcing data in an energy balance model (EVASPA, Gallego-Elvira et al., 2013, and SPARSE, Boulet et al., 2015) or data assimilation in coupled water/energy balance models (SURFEX-ISBA, Noilhan et Planton, 1989). However, these different methods of flux estimations still require an evaluation through comparison to in-situ measurements and inter-comparison.The area selected for this study is the Kairouan agricultural plain, a semi-arid region in central Tunisia. Different flux datasets were acquired over two years, on an extensive rainfed oliveyard with very low vegetation cover, and on irrigated and rainfed wheat plots. In the same time, a third dataset has been acquired over a complex agricultural landscape with an eXtra-Large Aperture Scintillometer (XLAS) set-up on a 4 km transect.First, EC fluxes from towers are compared to the different model simulations at plot scale. Then a spatial comparison with retrievals of sensible and latent heat fluxes from XLAS is performed which allows to take into account the heterogeneity of the landscape (mix of wheat, irrigated oliveyards and bare soil). Effects on irrigation scenarios, through an automatic irrigation triggering method are tested and discussed. Finally, we cross-compare the different modeling approaches.We tackle the various issues: the accuracy of the measurements, the temporal frequency of remote sensing data, and the difficulty to calibrate the models.

Accounting for standard errors of vision-specific latent trait in regression models.

PubMed

Wong, Wan Ling; Li, Xiang; Li, Jialiang; Wong, Tien Yin; Cheng, Ching-Yu; Lamoureux, Ecosse L

2014-07-11

To demonstrate the effectiveness of Hierarchical Bayesian (HB) approach in a modeling framework for association effects that accounts for SEs of vision-specific latent traits assessed using Rasch analysis. A systematic literature review was conducted in four major ophthalmic journals to evaluate Rasch analysis performed on vision-specific instruments. The HB approach was used to synthesize the Rasch model and multiple linear regression model for the assessment of the association effects related to vision-specific latent traits. The effectiveness of this novel HB one-stage "joint-analysis" approach allows all model parameters to be estimated simultaneously and was compared with the frequently used two-stage "separate-analysis" approach in our simulation study (Rasch analysis followed by traditional statistical analyses without adjustment for SE of latent trait). Sixty-six reviewed articles performed evaluation and validation of vision-specific instruments using Rasch analysis, and 86.4% (n = 57) performed further statistical analyses on the Rasch-scaled data using traditional statistical methods; none took into consideration SEs of the estimated Rasch-scaled scores. The two models on real data differed for effect size estimations and the identification of "independent risk factors." Simulation results showed that our proposed HB one-stage "joint-analysis" approach produces greater accuracy (average of 5-fold decrease in bias) with comparable power and precision in estimation of associations when compared with the frequently used two-stage "separate-analysis" procedure despite accounting for greater uncertainty due to the latent trait. Patient-reported data, using Rasch analysis techniques, do not take into account the SE of latent trait in association analyses. The HB one-stage "joint-analysis" is a better approach, producing accurate effect size estimations and information about the independent association of exposure variables with vision-specific latent traits. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Tuberculosis and latent infection in employees of different prison unit types

PubMed Central

Nogueira, Péricles Alves; Abrahão, Regina Maura Cabral de Melo; Galesi, Vera Maria Neder; López, Rossana Verónica Mendoza

2018-01-01

ABSTRACT OBJECTIVE Estimate the prevalence of active tuberculosis and latent tuberculosis infection among the staff that is in contact and the staff that is not in contact with prisoners, and investigate factors associated with latent tuberculosis infection in this population. METHODS Observational cross-sectional study, conducted from 2012 to 2015, in employees of different prison units in the municipality of Franco da Rocha, SP. It consisted of the application of a questionnaire, application and reading of the tuberculin test, sputum smear microscopy, sputum culture, and radiological examination. The association between the qualitative variables was calculated by the Pearson's chi-squared test. The sociodemographic and clinical-epidemiological factors related to the latent tuberculosis infection were evaluated by the logistic regression with the odds ratios (OR) calculation and their respective intervals with 95% of confidence (95%CI). RESULTS A total of 1,059 employees were examined, 657 (62.0%) of prisons, 249 (23.5%) of CASA Foundation units and 153 (14.5%) of custodial and psychiatric treatment hospitals. The tuberculin test was applied and read for 945 (89.2%) professionals. Of these, 797 (84.3%) were contacts of detainees and 148 (15.7%) were not. Among prison staff, the factors associated with latent tuberculosis infection were: contact with detainee (OR = 2.12, 95%CI 1.21–3.71); male gender (OR = 1.97, 95%CI 1.19–3.27); between 30 and 39 years old (OR = 2.98, 95%CI 1.34–6.63), 40 to 49 years old (OR = 4.32, 95%CI 1.94–9.60), and 50 to 59 years old (OR = 3.98, 95%CI 1.68–9.43); nonwhite color or race (OR = 1.89, 95%CI 1.29–2.78); and smoker (OR = 1.64, 95%CI 1.05–2.55). There were no positive test on sputum smear microscopy and culture. Of the 241 (22.8%) professionals who underwent radiological examination, 48 (19.9%) presented alterations of which 11 were suspected of tuberculosis. CONCLUSIONS Prison employees who have direct contact with detainees are 2.12 times more likely to become infected with Mycobacterium tuberculosis in the work environment and consequently to become ill with tuberculosis and should be targeted for disease prevention and control. PMID:29412377

Perceptions of care in women sent home in latent labor.

PubMed

Hosek, Claire; Faucher, Mary Ann; Lankford, Janice; Alexander, James

2014-01-01

To assess perceptions of care from woman discharged from an obstetrical (OB) triage unit or a labor and delivery unit with a diagnosis of false or latent labor in order to determine factors that may increase or decrease the woman's satisfaction with care. Descriptive, convenience sample. One hundred low-income pregnant women at term presenting for care in latent labor consented to participate in a telephone survey. The survey was based on the relevant research about care of women in early labor and the Donabedian quality improvement framework assessing structure, process, and outcomes of care. Forty-one percent of women did not want to be discharged home in latent labor. Common reasons included women stating they were in too much pain or they were living too far from the birth setting. Eating, drinking, and comfort measures were the most common measures women cited that would have made them feel better when in the hospital. A reoccurring response from women was their desire for very clear and specific written instructions about how to stay comfortable at home and when to return to the hospital. Comfort measures in the birth setting, including in triage, should include a variety of options including ambulation and oral nutrition. Detailed and specific written instructions about early labor and staying comfortable while at home have value for women in this survey regarding their perceptions of care. Results from this survey of low-income women suggest that a subset of women in latent labor just do not want to go home and this may be related to having too much pain and/or travel distance to the hospital. Hospital birth settings also have an opportunity to create a care environment that provides services and embodies attributes that women report as important for their satisfaction with care in latent labor.

Stability of alcohol use and teen dating violence for female youth: A latent transition analysis.

PubMed

Choi, Hye Jeong; Elmquist, JoAnna; Shorey, Ryan C; Rothman, Emily F; Stuart, Gregory L; Temple, Jeff R

2017-01-01

Alcohol use is one of the most widely accepted and studied risk factors for teen dating violence (TDV). Too little research has explored longitudinally if it is true that an adolescent's alcohol use and TDV involvement simultaneously occur. In the current study, we examined whether there were latent status based on past-year TDV and alcohol use and whether female adolescents changed their statuses of TDV and alcohol use over time. The sample consisted of 583 female youths in seven public high schools in Texas. Three waves of longitudinal data collected from 2011 to 2013 were utilised in this study. Participants completed self-report assessments of alcohol use (past-year alcohol use, number of drinks in the past month and episodic heavy drinking within the past month) and psychological and physical TDV victimisation and perpetration. Latent transition analysis was used to examine if the latent status based on TDV and alcohol use changed over time. Five separate latent statuses were identified: (i) no violence, no alcohol; (ii) alcohol; (iii) psychological violence, no alcohol; (iv) psychological violence, alcohol; and (v) physical and psychological violence, alcohol. Latent transition analysis indicated that adolescents generally remained in the same subgroup across time. This study provides evidence on the co-occurrence of alcohol use and teen dating violence, and whether teens' status based on dating violence and alcohol use are stable over time. Findings from the current study highlight the importance of targeting both TDV and substance use in intervention and prevention programs. [Choi HJ, Elmquist J, Shorey RC, Rothman EF, Stuart GL,Temple JR. Stability of alcohol use and teen dating violence for female youth: Alatent transition analysis. Drug Alcohol Rev 2017;36:80-87]. © 2017 Australasian Professional Society on Alcohol and other Drugs.

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease.

PubMed

Zhang, Xiuming; Mormino, Elizabeth C; Sun, Nanbo; Sperling, Reisa A; Sabuncu, Mert R; Yeo, B T Thomas

2016-10-18

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Tuberculosis infection among homeless persons and caregivers in a high-tuberculosis-prevalence area in Japan: a cross-sectional study

PubMed Central

2011-01-01

Background Tuberculosis (TB) is a major public health problem. The Airin district of Osaka City has a large population of homeless persons and caregivers and is estimated to be the largest TB-endemic area in the intermediate-prevalence country, Japan. However, there have been few studies of homeless persons and caregivers. The objective of this study is to detect active TB and to assess the prevalence and risk factors for latent TB infection among homeless persons and caregivers. Methods We conducted a cross-sectional study for screening TB infection (active and latent TB infections) using questionnaire, chest X-ray (CXR), newly available assay for latent TB infection (QuantiFERON-TB Gold In-Tube; QFT) and clinical evaluation by physicians at the Osaka Socio-Medical Center Hospital between July 2007 and March 2008. Homeless persons and caregivers, aged 30-74 years old, who had not received CXR examination within one year, were recruited. As for risk factors of latent TB infection, the odds ratios (OR) and 95% confidence intervals (95% CI) for QFT-positivity were calculated using logistic regression model. Results Complete responses were available from 436 individuals (263 homeless persons and 173 caregivers). Four active TB cases (1.5%) among homeless persons were found, while there were no cases among caregivers. Out of these four, three had positive QFT results. One hundred and thirty-three (50.6%) homeless persons and 42 (24.3%) caregivers had positive QFT results. In multivariate analysis, QFT-positivity was independently associated with a long time spent in the Airin district: ≥10 years versus <10 years for homeless (OR = 2.53; 95% CI, 1.39-4.61) and for caregivers (OR = 2.32; 95% CI, 1.05-5.13), and the past exposure to TB patients for caregivers (OR = 3.21; 95% CI, 1.30-7.91) but not for homeless persons (OR = 1.51; 95% CI, 0.71-3.21). Conclusions Although no active TB was found for caregivers, one-quarter of them had latent TB infection. In addition to homeless persons, caregivers need examinations for latent TB infection as well as active TB and careful follow-up, especially when they have spent a long time in a TB-endemic area and/or have been exposed to TB patients. PMID:21255421

Biotic and abiotic factors affecting the genetic structure and diversity of butternut in the southern Appalachian Mountains, USA

Treesearch

Amanda Parks; Michael Jenkins; Michael Ostry; Peng Zhao; Keith Woeste

2014-01-01

The abundance of butternut (Juglans cinerea L.) trees has severely declined rangewide over the past 50 years. An important factor in the decline is butternut canker, a disease caused by the fungus Ophiognomonia clavigigentijuglandacearum, which has left the remaining butternuts isolated and sparsely distributed. To manage the...

Young Children's Psychological Selves: Convergence with Maternal Reports of Child Personality

ERIC Educational Resources Information Center

Brown, Geoffrey L.; Mangelsdorf, Sarah C.; Agathen, Jean M.; Ho, Moon-Ho

2008-01-01

The present research examined five-year-old children's psychological self-concepts. Non-linear factor analysis was used to model the latent structure of the children's self-view questionnaire (CSVQ; Eder, 1990), a measure of children's self-concepts. The coherence and reliability of the emerging factor structure indicated that young children are…

Trajectories of Developmental Functioning among Children of Adolescent Mothers: Factors Associated with Risk for Delay

ERIC Educational Resources Information Center

Jahromi, Laudan B.; Umaña-Taylor, Adriana J.; Updegraff, Kimberly A.; Zeiders, Katharine H.

2016-01-01

Children of adolescent mothers are at risk for developmental delays. Less is known about the heterogeneity in these children's developmental trajectories, and factors associated with different patterns of development. This longitudinal study used latent class growth analysis (LCGA) to identify distinct trajectories in children of Mexican-origin…

Assessing Measurement Invariance of the Children's Depression Inventory in Chinese and Italian Primary School Student Samples

ERIC Educational Resources Information Center

Wu, Wenfeng; Lu, Yongbiao; Tan, Furong; Yao, Shuqiao; Steca, Patrizia; Abela, John R. Z.; Hankin, Benjamin L.

2012-01-01

This study tested the measurement invariance of Children's Depression Inventory (CDI) and compared its factorial variance/covariance and latent means among Chinese and Italian children. Multigroup confirmatory factor analysis of the original five factors identified by Kovacs revealed that full measurement invariance did not hold. Further analysis…

Determinant Factors of Attitude towards Quantitative Subjects: Differences between Sexes

ERIC Educational Resources Information Center

Mondejar-Jimenez, Jose; Vargas-Vargas, Manuel

2010-01-01

Nowadays, almost all curricula in the social sciences contain at least one course in statistics, given the importance of this discipline as an analytical tool. This work identifies the latent factors relating to students' motivation and attitude towards statistics, tests their covariance structure for samples of both sexes, and identifies the…

Analyzing the Validity of the Adult-Adolescent Parenting Inventory for Low-Income Populations

ERIC Educational Resources Information Center

Lawson, Michael A.; Alameda-Lawson, Tania; Byrnes, Edward

2017-01-01

Objectives: The purpose of this study was to examine the construct and predictive validity of the Adult-Adolescent Parenting Inventory (AAPI-2). Methods: The validity of the AAPI-2 was evaluated using multiple statistical methods, including exploratory factor analysis, confirmatory factor analysis, and latent class analysis. These analyses were…

Risk for latent and active tuberculosis in Germany.

PubMed

Herzmann, Christian; Sotgiu, Giovanni; Bellinger, Oswald; Diel, Roland; Gerdes, Silke; Goetsch, Udo; Heykes-Uden, Helga; Schaberg, Tom; Lange, Christoph

2017-06-01

Few individuals that are latently infected with M. tuberculosis latent tuberculosis infection(LTBI) progress to active disease. We investigated risk factors for LTBI and active pulmonary tuberculosis (PTB) in Germany. Healthy household contacts (HHCs), health care workers (HCWs) exposed to M. tuberculosis and PTB patients were recruited at 18 German centres. Interferon-γ release assay (IGRA) testing was performed. LTBI risk factors were evaluated by comparing IGRA-positive with IGRA-negative contacts. Risk factors for tuberculosis were evaluated by comparing PTB patients with HHCs. From 2008-2014, 603 HHCs, 295 HCWs and 856 PTBs were recruited. LTBI was found in 34.5% of HHCs and in 38.9% of HCWs. In HCWs, care for coughing patients (p = 0.02) and longstanding nursing occupation (p = 0.04) were associated with LTBI. In HHCs, predictors for LTBI were a diseased partner (odds ratio 4.39), sexual contact to a diseased partner and substance dependency (all p < 0.001). PTB was associated with male sex, low body weight (p < 0.0001), alcoholism (15.0 vs 5.9%; p < 0.0001), glucocorticoid therapy (7.2 vs 2.0%; p = 0.004) and diabetes (7.8 vs. 4.0%; p = 0.04). No contact developed active tuberculosis within 2 years follow-up. Positive IGRA responses are frequent among exposed HHCs and HCWs in Germany and are poor predictors for the development of active tuberculosis.

The Latent Structure of Impulsivity: Impulsive Choice, Impulsive Action, and Impulsive Personality Traits

PubMed Central

MacKillop, James; Weafer, Jessica; Gray, Joshua; Oshri, Assaf; Palmer, Abraham; de Wit, Harriet

2016-01-01

Rationale Impulsivity has been strongly linked to addictive behaviors, but can be operationalized in a number of ways that vary considerably in overlap, suggesting multidimensionality. Objective This study tested the hypothesis that the latent structure among multiple measures of impulsivity would reflect three broad categories: impulsive choice, reflecting discounting of delayed rewards; impulsive action, reflecting ability to inhibit a prepotent motor response; and impulsive personality traits, reflecting self-reported attributions of self-regulatory capacity. Methods The study used a cross-sectional confirmatory factor analysis of multiple impulsivity assessments. Participants were 1252 young adults (62% female) with low levels of addictive behavior who were assessed in individual laboratory rooms at the University of Chicago and the University of Georgia. The battery comprised a delay discounting task, Monetary Choice Questionnaire, Conners Continuous Performance Test, Go/NoGo Task, Stop Signal Task, Barratt Impulsivity Scale, and the UPPS-P Impulsive Behavior Scale. Results The hypothesized three-factor model provided the best fit to the data, although Sensation Seeking was excluded from the final model. The three latent factors were largely unrelated to each other and were variably associated with substance use. Conclusions These findings support the hypothesis that diverse measures of impulsivity can broadly be organized into three categories that are largely distinct from one another. These findings warrant investigation among individuals with clinical levels of addictive behavior and may be applied to understanding the underlying biological mechanisms of these categories. PMID:27449350

Differences in environmental preferences towards cycling for transport among adults: a latent class analysis.

PubMed

Mertens, Lieze; Van Cauwenberg, Jelle; Ghekiere, Ariane; De Bourdeaudhuij, Ilse; Deforche, Benedicte; Van de Weghe, Nico; Van Dyck, Delfien

2016-08-12

Increasing cycling for transport can contribute to improve public health among adults. Micro-environmental factors (i.e. small-scaled street-setting features) may play an important role in affecting the street's appeal to cycle for transport. Understanding about the interplay between individuals and their physical environment is important to establish tailored environmental interventions. Therefore, the current study aimed to examine whether specific subgroups exist based on similarities in micro-environmental preferences to cycle for transport. Responses of 1950 middle-aged adults (45-65 years) on a series of choice tasks depicting potential cycling routes with manipulated photographs yielded three subgroups with different micro-environmental preferences using latent class analysis. Although latent class analysis revealed three different subgroups in the middle-aged adult population based on their environmental preferences, results indicated that cycle path type (i.e. a good separated cycle path) is the most important environmental factor for all participants and certainly for individuals who did not cycle for transport. Furthermore, only negligible differences were found between the importances of the other micro-environmental factors (i.e. traffic density, evenness of the cycle path, maintenance, vegetation and speed limits) regarding the two at risk subgroups and that providing a speed bump obviously has the least impact on the street's appeal to cycle for transport. Results from the current study indicate that only negligible differences were found between the three subgroups. Therefore, it might be suggested that tailored environmental interventions are not required in this research context.

Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-multimethod studies

PubMed Central

Geiser, Christian; Burns, G. Leonard; Servera, Mateu

2014-01-01

Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM) investigations. We show that interesting incremental information about method effects can be gained from including mean structures and tests of MI across methods in MTMM models. We present a modeling framework for testing MI in the first step of a CFA-MTMM analysis. We also discuss the relevance of MI in the context of four more complex CFA-MTMM models with method factors. We focus on three recently developed multiple-indicator CFA-MTMM models for structurally different methods [the correlated traits-correlated (methods – 1), latent difference, and latent means models; Geiser et al., 2014a; Pohl and Steyer, 2010; Pohl et al., 2008] and one model for interchangeable methods (Eid et al., 2008). We demonstrate that some of these models require or imply MI by definition for a proper interpretation of trait or method factors, whereas others do not, and explain why MI may or may not be required in each model. We show that in the model for interchangeable methods, testing for MI is critical for determining whether methods can truly be seen as interchangeable. We illustrate the theoretical issues in an empirical application to an MTMM study of attention deficit and hyperactivity disorder (ADHD) with mother, father, and teacher ratings as methods. PMID:25400603

Friendship Group Composition and Juvenile Institutional Misconduct.

PubMed

Reid, Shannon E

2017-02-01

The present study examines both the patterns of friendship networks and how these network characteristics relate to the risk factors of institutional misconduct for incarcerated youth. Using friendship networks collected from males incarcerated with California's Division of Juvenile Justice (DJJ), latent profile analysis was utilized to create homogeneous groups of friendship patterns based on alter attributes and network structure. The incarcerated youth provided 144 egocentric networks reporting 558 social network relationships. Latent profile analysis identified three network profiles: expected group (67%), new breed group (20%), and model citizen group (13%). The three network profiles were integrated into a multiple group analysis framework to examine the relative influence of individual-level risk factors on their rate of institutional misconduct. The analysis finds variation in predictors of institutional misconduct across profile types. These findings suggest that the close friendships of incarcerated youth are patterned across the individual characteristics of the youth's friends and that the friendship network can act as a moderator for individual risk factors for institutional misconduct.

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

DOE PAGES

Ray, J.; Lee, J.; Yadav, V.; ...

2015-04-29

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

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

Ray, J.; Lee, J.; Yadav, V.

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

Effects of alcohol on motorcycle riding skills

DOT National Transportation Integrated Search

2007-12-01

Alcohol is known to disrupt the effect of neurotransmitters and impair various psychomotor skills. Indeed, alcohol intoxication is a significant risk factor for fatal traffic crashes, especially when riding a motorcycle. At present, there is sparse r...

Latent Factor Structure of the Das-Naglieri Cognitive Assessment System: A Confirmatory Factor Analysis in a Chinese Setting

ERIC Educational Resources Information Center

Deng, Ci-ping; Liu, Ming; Wei, Wei; Chan, Raymond C. K.; Das, J. P.

2011-01-01

This study aims to measure the psychometric properties of the Das-Naglieri Cognitive Assessment System (D-N CAS) and to determine its clinical utility in a Chinese context. Confirmatory factor analysis (CFA) was conducted to examine the construct validity of the Chinese version of the D-N CAS among a group of 567, normally developed children.…

Psychometric modeling of abuse and dependence symptoms across six illicit substances indicates novel dimensions of misuse

PubMed Central

Clark, Shaunna L.; Gillespie, Nathan A.; Adkins, Daniel E.; Kendler, Kenneth S.; Neale, Michael C.

2015-01-01

Aims This study explored the factor structure of DSM III-R/IV symptoms for substance abuse and dependence across six illicit substance categories in a population-based sample of males. Method DSM III-R/IV drug abuse and dependence symptoms for cannabis, sedatives, stimulants, cocaine, opioids and hallucinogens from 4179 males born 1940-1970 from the population-based Virginia Adult Twin Study of Psychiatric and Substance Use Disorders were analyzed. Confirmatory factor analyses tested specific hypotheses regarding the latent structure of substance misuse for a comprehensive battery of 13 misuse symptoms measured across six illicit substance categories (78 items). Results Among the models fit, the latent structure of substance misuse was best represented by a combination of substance-specific factors and misuse symptom-specific factors. We found no support for a general liability factor to illicit substance misuse. Conclusions Results indicate that liability to misuse illicit substances is drug class specific, with little evidence for a general liability factor. Additionally, unique dimensions capturing propensity toward specific misuse symptoms (e.g., tolerance, withdrawal) across substances were identified. While this finding requires independent replication, the possibility of symptom-specific misuse factors, present in multiple substances, raises the prospect of genetic, neurobiological and behavioral predispositions toward distinct, narrowly defined features of drug abuse and dependence. PMID:26517709

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.