Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

ERIC Educational Resources Information Center

Hsieh, Fushing; Ferrer, Emilio; Chen, Shu-Chun; Chow, Sy-Miin

2010-01-01

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to…

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhaïl

2013-04-01

The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal drift (ANNEX). Moreover, the exact number of output neurons and the selection of the corresponding variables were based on the subsets created during the exploratory phase. Concerning hidden layers, no restriction were made and multiple architectures were tested. For each MLP model, the quality of the modeling procedure was assessed by variograms: if the variogram of the residuals demonstrates pure nugget effect and if the level of the nugget exactly corresponds to the nugget value of the theoretical variogram of the corresponding variable, all the structured information has been correctly extracted without overfitting. Finally, it is worth mentioning that simple MLP models are not always able to remove all the spatial correlation structure from the data. In that case, Neural Network Residual Kriging (NNRK) can be carried out and risk assessment can be conducted with Neural Network Residual Simulations (NNRS). Finally, the results of the ANNEX models were compared to those of ordinary (co)kriging and (co)kriging with an external drift. It was shown that the ANNEX models performed better than traditional geostatistical algorithms when the relationship between the variable of interest and the auxiliary predictor was not linear. References Kanevski, M. and Maignan, M. (2004). Analysis and Modelling of Spatial Environmental Data. Lausanne: EPFL Press.

Fighting for Intelligence: A Brief Overview of the Academic Work of John L. Horn

PubMed Central

McArdle, John J.; Hofer, Scott M.

2015-01-01

John L. Horn (1928–2006) was a pioneer in multivariate thinking and the application of multivariate methods to research on intelligence and personality. His key works on individual differences in the methodological areas of factor analysis and the substantive areas of cognition are reviewed here. John was also our mentor, teacher, colleague, and friend. We overview John Horn’s main contributions to the field of intelligence by highlighting 3 issues about his methods of factor analysis and 3 of his substantive debates about intelligence. We first focus on Horn’s methodological demonstrations describing (a) the many uses of simulated random variables in exploratory factor analysis; (b) the exploratory uses of confirmatory factor analysis; and (c) the key differences between states, traits, and trait-changes. On a substantive basis, John believed that there were important individual differences among people in terms of cognition and personality. These sentiments led to his intellectual battles about (d) Spearman’s g theory of a unitary intelligence, (e) Guilford’s multifaceted model of intelligence, and (f) the Schaie and Baltes approach to defining the lack of decline of intelligence earlier in the life span. We conclude with a summary of John Horn’s unique approaches to dealing with common issues. PMID:26246642

Bayesian Factor Analysis as a Variable Selection Problem: Alternative Priors and Consequences

PubMed Central

Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

2016-01-01

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, a Bayesian structural equation modeling (BSEM) approach (Muthén & Asparouhov, 2012) has been proposed as a way to explore the presence of cross-loadings in CFA models. We show that the issue of determining factor loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov’s approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike and slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set (Byrne, 2012; Pettegrew & Wolf, 1982) is used to demonstrate our approach. PMID:27314566

To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis

ERIC Educational Resources Information Center

Pezzolo, Alessandra De Lorenzi

2011-01-01

The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins.…

Panic disorder and agoraphobia: A direct comparison of their multivariate comorbidity patterns.

PubMed

Greene, Ashley L; Eaton, Nicholas R

2016-01-15

Scientific debate has long surrounded whether agoraphobia is a severe consequence of panic disorder or a frequently comorbid diagnosis. Multivariate comorbidity investigations typically treat these diagnoses as fungible in structural models, assuming both are manifestations of the fear-subfactor in the internalizing-externalizing model. No studies have directly compared these disorders' multivariate associations, which could clarify their conceptualization in classification and comorbidity research. In a nationally representative sample (N=43,093), we examined the multivariate comorbidity of panic disorder (1) without agoraphobia, (2) with agoraphobia, and (3) regardless of agoraphobia; and (4) agoraphobia without panic. We conducted exploratory and confirmatory factor analyses of these and 10 other lifetime DSM-IV diagnoses in a nationally representative sample (N=43,093). Differing bivariate and multivariate relations were found. Panic disorder without agoraphobia was largely a distress disorder, related to emotional disorders. Agoraphobia without panic was largely a fear disorder, related to phobias. When considered jointly, concomitant agoraphobia and panic was a fear disorder, and when panic was assessed without regard to agoraphobia (some individuals had agoraphobia while others did not) it was a mixed distress and fear disorder. Diagnoses were obtained from comprehensively trained lay interviewers, not clinicians and analyses used DSM-IV diagnoses (rather than DSM-5). These findings support the conceptualization of agoraphobia as a distinct diagnostic entity and the independent classification of both disorders in DSM-5, suggesting future multivariate comorbidity studies should not assume various panic/agoraphobia diagnoses are invariably fear disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

Correspondence analysis

USDA-ARS?s Scientific Manuscript database

Correspondence analysis is a powerful exploratory multivariate technique for categorical variables with many levels. It is a data analysis tool that characterizes associations between levels of 2 or more categorical variables using graphical representations of the information in a contingency table...

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Occurrence and multivariate exploratory analysis of the natural radioactivity anomaly in the south coastal region of Kenya

NASA Astrophysics Data System (ADS)

Kaniu, M. I.; Angeyo, K. H.; Darby, I. G.

2018-05-01

Characterized by a variety of rock formations, namely alkaline, igneous and sedimentary that contain significant deposits of monazite and pyrochlore ores, the south coastal region of Kenya may be regarded as highly heterogeneous with regard to its geochemistry, mineralogy as well as geological morphology. The region is one of the several alkaline carbonatite complexes of Kenya that are associated with high natural background radiation and therefore radioactivity anomaly. However, this high background radiation (HBR) anomaly has hardly been systematically assessed and delineated with regard to the spatial, geological, geochemical as well as anthropogenic variability and co-dependencies. We conducted wide-ranging in-situ gamma-ray spectrometric measurements in this area. The goal of the study was to assess the radiation exposure as well as determine the underlying natural radioactivity levels in the region. In this paper we report the occurrence, exploratory analysis and modeling to assess the multivariate geo-dependence and spatial variability of the radioactivity and associated radiation exposure. Unsupervised principal component analysis and ternary plots were utilized in the study. It was observed that areas which exhibit HBR anomalies are located along the south coast paved road and in the Mrima-Kiruku complex. These areas showed a trend towards enhanced levels of 232Th and 238U and low 40K. The spatial variability of the radioactivity anomaly was found to be mainly constrained by anthropogenic activities, underlying geology and geochemical processes in the terrestrial environment.

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

NASA Technical Reports Server (NTRS)

Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

2017-01-01

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Exploratory modeling of forest disturbance scenarios in central Oregon using computational experiments in GIS

Treesearch

Deana D. Pennington

2007-01-01

Exploratory modeling is an approach used when process and/or parameter uncertainties are such that modeling attempts at realistic prediction are not appropriate. Exploratory modeling makes use of computational experimentation to test how varying model scenarios drive model outcome. The goal of exploratory modeling is to better understand the system of interest through...

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

Outcomes of hospitalized patients undergoing emergency general surgery remote from admission.

PubMed

Sharoky, Catherine E; Bailey, Elizabeth A; Sellers, Morgan M; Kaufman, Elinore J; Sinnamon, Andrew J; Wirtalla, Christopher J; Holena, Daniel N; Kelz, Rachel R

2017-09-01

Emergency general surgery during hospitalization has not been well characterized. We examined emergency operations remote from admission to identify predictors of postoperative 30-day mortality, postoperative duration of stay >30 days, and complications. Patients >18 years in The American College of Surgeons National Surgical Quality Improvement Program (2011-2014) who had 1 of 7 emergency operations between hospital day 3-18 were included. Patients with operations >95th percentile after admission (>18 days; n = 581) were excluded. Exploratory laparotomy only (with no secondary procedure) represented either nontherapeutic or decompressive laparotomy. Multivariable logistic regression was used to identify predictors of study outcomes. Of 10,093 patients with emergency operations, most were elderly (median 66 years old [interquartile ratio: 53-77 years]), white, and female. Postoperative 30-day mortality was 12.6% (n = 1,275). Almost half the cohort (40.1%) had a complication. A small subset (6.8%) had postoperative duration of stay >30 days. Postoperative mortality after exploratory laparotomy only was particularly high (>40%). In multivariable analysis, an operation on hospital day 11-18 compared with day 3-6 was associated with death (odds ratio 1.6 [1.3-2.0]), postoperative duration of stay >30 days (odds ratio 2.0 [1.6-2.6]), and complications (odds ratio 1.5 [1.3-1.8]). Exploratory laparotomy only also was associated with death (odds ratio 5.4 [2.8-10.4]). Emergency general surgery performed during a hospitalization is associated with high morbidity and mortality. A longer hospital course before an emergency operation is a predictor of poor outcomes, as is undergoing exploratory laparotomy only. Copyright © 2017 Elsevier Inc. All rights reserved.

Linking Contextual Affordances: Examining Racial-Ethnic Socialization and Parental Career Support among African American College Students

ERIC Educational Resources Information Center

Blackmon, Sha'Kema M.; Thomas, Anita Jones

2014-01-01

This exploratory investigation examined the link between self-reported racial-ethnic socialization experiences and perceived parental career support among African American undergraduate and graduate students. The results of two separate multivariate multiple regression analyses found that messages about coping with racism positively predicted…

Factors underlying the psychological and behavioral characteristics of Office of Strategic Services candidates: the assessment of men data revisited.

PubMed

Lenzenweger, Mark F

2015-01-01

During World War II, the Office of Strategic Services (OSS), the forerunner of the Central Intelligence Agency, sought the assistance of clinical psychologists and psychiatrists to establish an assessment program for evaluating candidates for the OSS. The assessment team developed a novel and rigorous program to evaluate OSS candidates. It is described in Assessment of Men: Selection of Personnel for the Office of Strategic Services (OSS Assessment Staff, 1948). This study examines the sole remaining multivariate data matrix that includes all final ratings for a group of candidates (n = 133) assessed near the end of the assessment program. It applies the modern statistical methods of both exploratory and confirmatory factor analysis to this rich and highly unique data set. An exploratory factor analysis solution suggested 3 factors underlie the OSS assessment staff ratings. Confirmatory factor analysis results of multiple plausible substantive models reveal that a 3-factor model provides the best fit to these data. The 3 factors are emotional/interpersonal factors (social relations, emotional stability, security), intelligence processing (effective IQ, propaganda skills, observing and reporting), and agency/surgency (motivation, energy and initiative, leadership, physical ability). These factors are discussed in terms of their potential utility for personnel selection within the intelligence community.

Low Social Status Markers: Do They Predict Depressive Symptoms in Adolescence?

PubMed

Jackson, Benita; Goodman, Elizabeth

2011-07-01

Some markers of social disadvantage are associated robustly with depressive symptoms among adolescents: female gender and lower socioeconomic status (SES), respectively. Others are associated equivocally, notably Black v. White race/ethnicity. Few studies examine whether markers of social disadvantage by gender, SES, and race/ethnicity jointly predict self-reported depressive symptoms during adolescence; this was our goal. Secondary analyses were conducted on data from a socioeconomically diverse community-based cohort study of non-Hispanic Black and White adolescents (N = 1,263, 50.4% female). Multivariable general linear models tested if female gender, Black race/ethnicity, and lower SES (assessed by parent education and household income), and their interactions predicted greater depressive symptoms reported on the Center for Epidemiological Studies-Depression scale. Models adjusted for age and pubertal status. Univariate analyses revealed more depressive symptoms in females, Blacks, and participants with lower SES. Multivariable models showed females across both racial/ethnic groups reported greater depressive symptoms; Blacks demonstrated more depressive symptoms than did Whites but when SES was included this association disappeared. Exploratory analyses suggested Blacks gained less mental health benefit from increased SES. However there were no statistically significant interactions among gender, race/ethnicity, or SES. Taken together, we conclude that complex patterning among low social status domains within gender, race/ethnicity, and SES predicts depressive symptoms among adolescents.

What Matters Most: An Exploratory Multivariate Study of Satisfaction among First Year Hotel/Hospitality Management Students

ERIC Educational Resources Information Center

O'Driscoll, Finian

2012-01-01

Purpose: This study presents institutional research and aims to explore the underlying factors that contribute to hospitality management students' satisfaction and perceptions of service quality at a higher education college in Ireland. Research focusing on hospitality and leisure management education argues for greater cognisance of the relevance…

Multivariate Genetic Analysis of Learning and Early Reading Development

ERIC Educational Resources Information Center

Byrne, Brian; Wadsworth, Sally; Boehme, Kristi; Talk, Andrew C.; Coventry, William L.; Olson, Richard K.; Samuelsson, Stefan; Corley, Robin

2013-01-01

The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 ("N"?=?2,084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the…

Recoding Numerics to Geometrics for Complex Discrimination Tasks; A Feasibility Study of Coding Strategy.

ERIC Educational Resources Information Center

Simpkins, John D.

Processing complex multivariate information effectively when relational properties of information sub-groups are ambiguous is difficult for man and man-machine systems. However, the information processing task is made easier through code study, cybernetic planning, and accurate display mechanisms. An exploratory laboratory study designed for the…

Toward exploratory analysis of diversity unified across fields of study: an information visualization approach

Treesearch

Tuan Pham; Julia Jones; Ronald Metoyer; Frederick Colwell

2014-01-01

The study of the diversity of multivariate objects shares common characteristics and goals across disciplines, including ecology and organizational management. Nevertheless, subject-matter experts have adopted somewhat separate diversity concepts and analysis techniques, limiting the potential for sharing and comparing across disciplines. Moreover, while large and...

An Exploratory Study of Fatigue and Physical Activity in Canadian Thyroid Cancer Patients.

PubMed

Alhashemi, Ahmad; Jones, Jennifer M; Goldstein, David P; Mina, Daniel Santa; Thabane, Lehana; Sabiston, Catherine M; Chang, Eugene K; Brierley, James D; Sawka, Anna M

2017-09-01

Fatigue is common among cancer survivors, but fatigue in thyroid cancer (TC) survivors may be under-appreciated. This study investigated the severity and prevalence of moderate and severe fatigue in TC survivors. Potential predictive factors, including physical activity, were explored. A cross-sectional, written, self-administered TC patient survey and retrospective chart review were performed in an outpatient academic Endocrinology clinic in Toronto, Canada. The primary outcome measure was the global fatigue score measured by the Brief Fatigue Inventory (BFI). Physical activity was evaluated using the International Physical Activity Questionnaire-7 day (IPAQ-7). Predictors of BFI global fatigue score were explored in univariate analyses and a multivariable linear regression model. The response rate was 63.1% (205/325). Three-quarters of the respondents were women (152/205). The mean age was 52.5 years, and the mean time since first TC surgery was 6.8 years. The mean global BFI score was 3.5 (standard deviation 2.4) out of 10 (10 is worst). The prevalence of moderate-severe fatigue (global BFI score 4.1-10 out of 10) was 41.4% (84/203). Individuals who were unemployed or unable to work due to disability reported significantly higher levels of fatigue compared to the rest of the study population, in uni-and multivariable analyses. Furthermore, increased physical activity was associated with reduced fatigue in uni- and multivariable analyses. Other socio-demographic, disease, or biochemical variables were not significantly associated with fatigue in the multivariable model. Moderate or severe fatigue was reported in about 4/10 TC survivors. Independent predictors of worse fatigue included unemployment and reduced physical activity.

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

Modelling environmental factors correlated with podoconiosis: a geospatial study of non-filarial elephantiasis.

PubMed

Molla, Yordanos B; Wardrop, Nicola A; Le Blond, Jennifer S; Baxter, Peter; Newport, Melanie J; Atkinson, Peter M; Davey, Gail

2014-06-20

The precise trigger of podoconiosis - endemic non-filarial elephantiasis of the lower legs - is unknown. Epidemiological and ecological studies have linked the disease with barefoot exposure to red clay soils of volcanic origin. Histopathology investigations have demonstrated that silicon, aluminium, magnesium and iron are present in the lower limb lymph node macrophages of both patients and non-patients living barefoot on these clays. We studied the spatial variation (variations across an area) in podoconiosis prevalence and the associated environmental factors with a goal to better understanding the pathogenesis of podoconiosis. Fieldwork was conducted from June 2011 to February 2013 in 12 kebeles (administrative units) in northern Ethiopia. Geo-located prevalence data and soil samples were collected and analysed along with secondary geological, topographic, meteorological and elevation data. Soil data were analysed for chemical composition, mineralogy and particle size, and were interpolated to provide spatially continuous information. Exploratory, spatial, univariate and multivariate regression analyses of podoconiosis prevalence were conducted in relation to primary (soil) and secondary (elevation, precipitation, and geology) covariates. Podoconiosis distribution showed spatial correlation with variation in elevation and precipitation. Exploratory analysis identified that phyllosilicate minerals, particularly clay (smectite and kaolinite) and mica groups, quartz (crystalline silica), iron oxide, and zirconium were associated with podoconiosis prevalence. The final multivariate model showed that the quantities of smectite (RR = 2.76, 95% CI: 1.35, 5.73; p = 0.007), quartz (RR = 1.16, 95% CI: 1.06, 1.26; p = 0.001) and mica (RR = 1.09, 95% CI: 1.05, 1.13; p < 0.001) in the soil had positive associations with podoconiosis prevalence. More quantities of smectite, mica and quartz within the soil were associated with podoconiosis prevalence. Together with previous work indicating that these minerals may influence water absorption, potentiate infection and be toxic to human cells, the present findings suggest that these particles may play a role in the pathogenesis of podoconiosis and acute adenolymphangitis, a common cause of morbidity in podoconiosis patients.

What is the animal doing? Tools for exploring behavioural structure in animal movements.

PubMed

Gurarie, Eliezer; Bracis, Chloe; Delgado, Maria; Meckley, Trevor D; Kojola, Ilpo; Wagner, C Michael

2016-01-01

Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Genetic and environmental structure of DSM-IV criteria for Antisocial Personality Disorder: a twin study

PubMed Central

Rosenström, Tom; Ystrom, Eivind; Torvik, Fartein Ask; Czajkowski, Nikolai Olavi; Gillespie, Nathan A.; Aggen, Steven H.; Krueger, Robert F.; Kendler, Kenneth S; Reichborn-Kjennerud, Ted

2017-01-01

Results from previous studies on DSM-IV and DSM-5 Antisocial Personality Disorder (ASPD) have suggested that the construct is etiologically multidimensional. To our knowledge, however, the structure of genetic and environmental influences in ASPD has not been examined using an appropriate range of biometric models and diagnostic interviews. The 7 ASPD criteria (section A) were assessed in a population-based sample of 2794 Norwegian twins by a structured interview for DSM-IV personality disorders. Exploratory analyses were conducted at the phenotypic level. Multivariate biometric models, including both independent and common pathways, were compared. A single phenotypic factor was found, and the best-fitting biometric model was a single-factor common pathway model, with common-factor heritability of 51% (95% CI = 40–67%). In other words, both genetic and environmental correlations between the ASPD criteria could be accounted for by a single common latent variable. The findings support the validity of ASPD as a unidimensional diagnostic construct. PMID:28108863

Genetic and Environmental Structure of DSM-IV Criteria for Antisocial Personality Disorder: A Twin Study.

PubMed

Rosenström, Tom; Ystrom, Eivind; Torvik, Fartein Ask; Czajkowski, Nikolai Olavi; Gillespie, Nathan A; Aggen, Steven H; Krueger, Robert F; Kendler, Kenneth S; Reichborn-Kjennerud, Ted

2017-05-01

Results from previous studies on DSM-IV and DSM-5 Antisocial Personality Disorder (ASPD) have suggested that the construct is etiologically multidimensional. To our knowledge, however, the structure of genetic and environmental influences in ASPD has not been examined using an appropriate range of biometric models and diagnostic interviews. The 7 ASPD criteria (section A) were assessed in a population-based sample of 2794 Norwegian twins by a structured interview for DSM-IV personality disorders. Exploratory analyses were conducted at the phenotypic level. Multivariate biometric models, including both independent and common pathways, were compared. A single phenotypic factor was found, and the best-fitting biometric model was a single-factor common pathway model, with common-factor heritability of 51% (95% CI 40-67%). In other words, both genetic and environmental correlations between the ASPD criteria could be accounted for by a single common latent variable. The findings support the validity of ASPD as a unidimensional diagnostic construct.

Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater

NASA Astrophysics Data System (ADS)

Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge

2007-03-01

SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.

Validation of the Child and Youth Resilience Measure (CYRM-28) on a Sample of At-Risk New Zealand Youth

ERIC Educational Resources Information Center

Sanders, Jackie; Munford, Robyn; Thimasarn-Anwar, Tewaporn; Liebenberg, Linda

2017-01-01

Purpose: This article reports on an examination of the psychometric properties of the 28-item Child and Youth Resilience Measure (CYRM-28). Methods: Exploratory factor analysis, confirmatory factor analysis, Cronbach's a, "t"-tests, correlations, and multivariate analysis of variance were applied to data collected via interviews from 593…

Multivariate analysis of subsurface radiometric data in Rongsohkham area, East Khasi Hills district, Meghalaya (India): implication on uranium exploration.

PubMed

Kukreti, B M; Pandey, Pradeep; Singh, R V

2012-08-01

Non-coring based exploratory drilling was under taken in the sedimentary environment of Rangsohkham block, East Khasi Hills district to examine the eastern extension of existing uranium resources located at Domiasiat and Wakhyn in the Mahadek basin of Meghalaya (India). Although radiometric survey and radiometric analysis of surface grab/channel samples in the block indicate high uranium content but the gamma ray logging results of exploratory boreholes in the block, did not obtain the expected results. To understand this abrupt discontinuity between the two sets of data (surface and subsurface) multivariate statistical analysis of primordial radioactive elements (K(40), U(238) and Th(232)) was performed using the concept of representative subsurface samples, drawn from the randomly selected 11 boreholes of this block. The study was performed to a high confidence level (99%), and results are discussed for assessing the U and Th behavior in the block. Results not only confirm the continuation of three distinct geological formations in the area but also the uranium bearing potential in the Mahadek sandstone of the eastern part of Mahadek Basin. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure. PMID:26786791

Validation of decisional balance and self-efficacy measures for HPV vaccination in college women.

PubMed

Lipschitz, Jessica M; Fernandez, Anne C; Larson, H Elsa; Blaney, Cerissa L; Meier, Kathy S; Redding, Colleen A; Prochaska, James O; Paiva, Andrea L

2013-01-01

Women younger than 25 years are at greatest risk for human papillomavirus (HPV) infection, including high-risk strains associated with 70% of cervical cancers. Effective model-based measures that can lead to intervention development to increase HPV vaccination rates are necessary. This study validated Transtheoretical Model measures of Decisional Balance and Self-Efficacy for seeking the HPV vaccine in a sample of female college students. Cross-sectional measurement development. Setting. Online survey of undergraduate college students. A total of 340 female students ages 18 to 26 years. Stage of Change, Decisional Balance, and Self-Efficacy. The sample was randomly split into halves for exploratory principal components analyses (PCAs), followed by confirmatory factor analyses (CFAs) to test measurement models. Multivariate analyses examined relationships between constructs. For Decisional Balance, PCA indicated two 4-item factors (Pros -α = .90; and Cons -α = .66). CFA supported a two-factor correlated model, χ(2)(19) = 39.33; p < .01; comparative fit index (CFI) = .97; and average absolute standardized residual statistic (AASR) = .03; with Pros α = .90 and Cons α = .67. For Self-Efficacy, PCA indicated one 6-item factor (α = .84). CFA supported this structure, χ(2)(9) = 50.87; p < .05; CFI = .94; AASR = .03; and α = .90. Multivariate analyses indicated significant cross-stage differences on Pros, Cons, and Self-Efficacy in expected directions. Findings support the internal and external validity of these measures and their use in Transtheoretical Model-tailored interventions. Stage-construct relationships suggest that reducing the Cons of vaccination may be more important for HPV than for behaviors with a true Maintenance stage.

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

PubMed Central

Flora, David B.; LaBrish, Cathy; Chalmers, R. Philip

2011-01-01

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables. PMID:22403561

Measuring trust in nurses - Psychometric properties of the Trust in Nurses Scale in four countries.

PubMed

Stolt, Minna; Charalambous, Andreas; Radwin, Laurel; Adam, Christina; Katajisto, Jouko; Lemonidou, Chryssoula; Patiraki, Elisabeth; Sjövall, Katarina; Suhonen, Riitta

2016-12-01

The purpose of this study was to examine psychometric properties of three translated versions of the Trust in Nurses Scale (TNS) and cancer patients' perceptions of trust in nurses in a sample of cancer patients from four European countries. A cross-sectional, cross-cultural, multi-site survey design was used. The data were collected with the Trust in Nurses Scale from patients with different types of malignancies in 17 units within five clinical sites (n = 599) between 09/2012 and 06/2014. Data were analyzed using descriptive and inferential statistics, multivariate methods and psychometrics using exploratory factor analysis, Cronbach's alpha coefficients, item analysis and Rasch analysis. The psychometric properties of the data were consistent in all countries. Within the exploratory factor analysis the principal component analysis supported the one component structure (unidimensionality) of the TNS. The internal consistency reliability was acceptable. The Rasch analysis supported the unidimensionality of the TNS cross-culturally. All items of the TNS demonstrated acceptable goodness-of-fit to the Rasch model. Cancer patients trusted nurses to a great extent although between-country differences were found. The Trust in Nurses Scale proved to be a valid and reliable tool for measuring patients' trust in nurses in oncological settings in international contexts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Initial Principal Readiness to Interconnect Positive Behavioral Interventions and Supports and School Mental Health: A Sequential Multivariate Exploratory Analysis

ERIC Educational Resources Information Center

Ecker, Andrew Joseph

2017-01-01

Approximately 20% of youth in the U.S. are experiencing a mental health challenge; a rate that is said to increase by more than 50% by 2020. Schools are the largest provider of mental health services to youth, yet two of schools' most efficacious evidence-based systems, Positive Behavioral Interventions and Supports (PBIS) and school mental health…

Modelling environmental factors correlated with podoconiosis: a geospatial study of non-filarial elephantiasis

PubMed Central

2014-01-01

Introduction The precise trigger of podoconiosis — endemic non-filarial elephantiasis of the lower legs — is unknown. Epidemiological and ecological studies have linked the disease with barefoot exposure to red clay soils of volcanic origin. Histopathology investigations have demonstrated that silicon, aluminium, magnesium and iron are present in the lower limb lymph node macrophages of both patients and non-patients living barefoot on these clays. We studied the spatial variation (variations across an area) in podoconiosis prevalence and the associated environmental factors with a goal to better understanding the pathogenesis of podoconiosis. Methods Fieldwork was conducted from June 2011 to February 2013 in 12 kebeles (administrative units) in northern Ethiopia. Geo-located prevalence data and soil samples were collected and analysed along with secondary geological, topographic, meteorological and elevation data. Soil data were analysed for chemical composition, mineralogy and particle size, and were interpolated to provide spatially continuous information. Exploratory, spatial, univariate and multivariate regression analyses of podoconiosis prevalence were conducted in relation to primary (soil) and secondary (elevation, precipitation, and geology) covariates. Results Podoconiosis distribution showed spatial correlation with variation in elevation and precipitation. Exploratory analysis identified that phyllosilicate minerals, particularly clay (smectite and kaolinite) and mica groups, quartz (crystalline silica), iron oxide, and zirconium were associated with podoconiosis prevalence. The final multivariate model showed that the quantities of smectite (RR = 2.76, 95% CI: 1.35, 5.73; p = 0.007), quartz (RR = 1.16, 95% CI: 1.06, 1.26; p = 0.001) and mica (RR = 1.09, 95% CI: 1.05, 1.13; p < 0.001) in the soil had positive associations with podoconiosis prevalence. Conclusions More quantities of smectite, mica and quartz within the soil were associated with podoconiosis prevalence. Together with previous work indicating that these minerals may influence water absorption, potentiate infection and be toxic to human cells, the present findings suggest that these particles may play a role in the pathogenesis of podoconiosis and acute adenolymphangitis, a common cause of morbidity in podoconiosis patients. PMID:24946801

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

PubMed

Steimer, Andreas; Müller, Michael; Schindler, Kaspar

2017-05-01

During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Exploratory Model Analysis of the Space Based Infrared System (SBIRS) Low Global Scheduler Problem

DTIC Science & Technology

1999-12-01

solution. The non- linear least squares model is defined as Y = f{e,t) where: 0 =M-element parameter vector Y =N-element vector of all data t...NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM (SBIRS) LOW GLOBAL SCHEDULER...December 1999 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Associations between host characteristics and antimicrobial resistance of Salmonella typhimurium.

PubMed

Ruddat, I; Tietze, E; Ziehm, D; Kreienbrock, L

2014-10-01

A collection of Salmonella Typhimurium isolates obtained from sporadic salmonellosis cases in humans from Lower Saxony, Germany between June 2008 and May 2010 was used to perform an exploratory risk-factor analysis on antimicrobial resistance (AMR) using comprehensive host information on sociodemographic attributes, medical history, food habits and animal contact. Multivariate resistance profiles of minimum inhibitory concentrations for 13 antimicrobial agents were analysed using a non-parametric approach with multifactorial models adjusted for phage types. Statistically significant associations were observed for consumption of antimicrobial agents, region type and three factors on egg-purchasing behaviour, indicating that besides antimicrobial use the proximity to other community members, health consciousness and other lifestyle-related attributes may play a role in the dissemination of resistances. Furthermore, a statistically significant increase in AMR from the first study year to the second year was observed.

Designing Mathematical Learning Environments for Teachers

ERIC Educational Resources Information Center

Madden, Sandra R.

2010-01-01

Technology use in mathematics often involves either exploratory or expressive modeling. When using exploratory models, students use technology to investigate a premade expert model of some phenomena. When creating expressive models, students have greater flexibility for constructing their own model for investigation using objects and mechanisms…

Text mining factor analysis (TFA) in green tea patent data

NASA Astrophysics Data System (ADS)

Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

2017-03-01

Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

A Reverse-Translational Approach to Bipolar Disorder: Rodent and human studies in the Behavioral Pattern Monitor

PubMed Central

Young, Jared W.; Minassian, Arpi; Paulus, Martin P.; Geyer, Mark A.; Perry, William

2007-01-01

Mania is the defining feature of Bipolar Disorder (BD). There has been limited progress in understanding the neurobiological underpinnings of BD mania and developing novel therapeutics, in part due to a paucity of relevant animal models with translational potential. Hyperactivity is a cardinal symptom of mania, traditionally measured in humans using observer-rated scales. Multivariate assessment of unconditioned locomotor behavior using the rat Behavioral Pattern Monitor (BPM) developed in our laboratory has shown that hyperactivity includes complex multifaceted behaviors. The BPM has been used to demonstrate differential effects of drugs on locomotor activity and exploratory behavior in rats. Studies of genetically engineered mice in a mouse BPM have confirmed its utility as a cross-species tool. In a “reverse-translational” approach to this work, we developed the human BPM to characterize motor activity in BD patients. Increased activity, object interactions, and altered locomotor patterns provide multidimensional phenotypes to model in the rodent BPM. This unique approach to modeling BD provides an opportunity to identify the neurobiology underlying BD mania and test novel antimanic agents. PMID:17706782

Investigating the underlying mechanisms of aberrant behaviors in bipolar disorder from patients to models

PubMed Central

van Enkhuizen, Jordy; Geyer, Mark A.; Minassian, Arpi; Perry, William; Henry, Brook L.; Young, Jared W.

2015-01-01

Psychiatric patients with bipolar disorder suffer from states of depression and mania, during which a variety of symptoms are present. Current treatments are limited and neurocognitive deficits in particular often remain untreated. Targeted therapies based on the biological mechanisms of bipolar disorder could fill this gap and benefit patients and their families. Developing targeted therapies would benefit from appropriate animal models which are challenging to establish, but remain a vital tool. In this review, we summarize approaches to create a valid model relevant to bipolar disorder. We focus on studies that use translational tests of multivariate exploratory behavior, sensorimotor gating, decision-making under risk, and attentional functioning to discover profiles that are consistent between patients and rodent models. Using this battery of translational tests, similar behavior profiles in bipolar mania patients and mice with reduced dopamine transporter activity have been identified. Future investigations should combine other animal models that are biologically relevant to the neuropsychiatric disorder with translational behavioral assessment as outlined here. This methodology can be utilized to develop novel targeted therapies that relieve symptoms for more patients without common side effects caused by current treatments. PMID:26297513

A novel practical scoring for early diagnosis of traumatic bowel injury without obvious solid organ injury in hemodynamically stable patients.

PubMed

Zarour, Ahmad; El-Menyar, Ayman; Khattabi, Mazen; Tayyem, Raed; Hamed, Osama; Mahmood, Ismail; Abdelrahman, Husham; Chiu, William; Al-Thani, Hassan

2014-01-01

To develop a scoring tool based on clinical and radiological findings for early diagnosis and intervention in hemodynamically stable patients with traumatic bowel and mesenteric injury (TBMI) without obvious solid organ injury (SOI). A retrospective analysis was conducted for all traumatic abdominal injury patients in Qatar from 2008 to 2011. Data included demographics and clinical, radiological and operative findings. Multivariate logistic regression was performed to analyze the predictors for the need of therapeutic laparotomy. A total of 105 patients met the inclusion criteria with a mean age of 33 ± 15. Motor Vehicle Crashes (58%) and fall (21%) were the major MOI. Using Receiver operating characteristic curve, Z-score of >9 was the cutoff point (AUC = 0.98) for high probability of the presence of TBMI requiring surgical intervention. Z-Score >9 was found to have sensitivity (96.7%), specificity (97.4%), PPV (93.5%) and NPV (98.7%). Multivariate regression analysis found Z-score (>9) to be an independent predictor for the need of exploratory laparotomy (OR7.0; 95% CI: 2.46-19.78, p = 0.001). This novel tool for early diagnosis of TBMI is found to be simple and helpful in selecting stable patients with free intra-abdominal fluid without SOI for exploratory Laparotomy. However, further prospective studies are warranted. Copyright © 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Multi-state succession in wetlands: a novel use of state and transition models

USGS Publications Warehouse

Zweig, Christa L.; Kitchens, Wiley M.

2009-01-01

The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration management and experimentation which will refine transition and threshold concepts. 

Does ethno-cultural betrayal in trauma affect Asian American/Pacific Islander college students' mental health outcomes? An exploratory study.

PubMed

Gómez, Jennifer M

2017-01-01

Interpersonal trauma has deleterious effects on mental health, with college students experiencing relatively high rates of lifetime trauma. Asian American/Pacific Islanders (AAPIs) have the lowest rate of mental healthcare utilization. According to cultural betrayal trauma theory, societal inequality may impact within-group violence in minority populations, thus having implications for mental health. In the current exploratory study, between-group (interracial) and within-group (ethno-cultural betrayal) trauma and mental health outcomes were examined in AAPI college students. Participants (N = 108) were AAPI college students from a predominantly white university. Data collection concluded in December 2015. Participants completed online self-report measures. A multivariate analysis of variance revealed that when controlling for interracial trauma, ethno-cultural betrayal trauma significantly impacted dissociation, hallucinations, posttraumatic stress symptoms, and hypervigilance. The results have implications for incorporating identity, discrimination, and ethno-cultural betrayal trauma victimization into assessments and case conceptualizations in therapy.

Shared additive genetic influences on DSM-IV criteria for alcohol dependence in subjects of European ancestry.

PubMed

Palmer, Rohan H C; McGeary, John E; Heath, Andrew C; Keller, Matthew C; Brick, Leslie A; Knopik, Valerie S

2015-12-01

Genetic studies of alcohol dependence (AD) have identified several candidate loci and genes, but most observed effects are small and difficult to reproduce. A plausible explanation for inconsistent findings may be a violation of the assumption that genetic factors contributing to each of the seven DSM-IV criteria point to a single underlying dimension of risk. Given that recent twin studies suggest that the genetic architecture of AD is complex and probably involves multiple discrete genetic factors, the current study employed common single nucleotide polymorphisms in two multivariate genetic models to examine the assumption that the genetic risk underlying DSM-IV AD is unitary. AD symptoms and genome-wide single nucleotide polymorphism (SNP) data from 2596 individuals of European descent from the Study of Addiction: Genetics and Environment were analyzed using genomic-relatedness-matrix restricted maximum likelihood. DSM-IV AD symptom covariance was described using two multivariate genetic factor models. Common SNPs explained 30% (standard error=0.136, P=0.012) of the variance in AD diagnosis. Additive genetic effects varied across AD symptoms. The common pathway model approach suggested that symptoms could be described by a single latent variable that had a SNP heritability of 31% (0.130, P=0.008). Similarly, the exploratory genetic factor model approach suggested that the genetic variance/covariance across symptoms could be represented by a single genetic factor that accounted for at least 60% of the genetic variance in any one symptom. Additive genetic effects on DSM-IV alcohol dependence criteria overlap. The assumption of common genetic effects across alcohol dependence symptoms appears to be a valid assumption. © 2015 Society for the Study of Addiction.

Cerebral metastases in metastatic breast cancer: disease-specific risk factors and survival.

PubMed

Heitz, F; Rochon, J; Harter, P; Lueck, H-J; Fisseler-Eckhoff, A; Barinoff, J; Traut, A; Lorenz-Salehi, F; du Bois, A

2011-07-01

Survival of patients suffering from cerebral metastases (CM) is limited. Identification of patients with a high risk for CM is warranted to adjust follow-up care and to evaluate preventive strategies. Exploratory analysis of disease-specific parameter in patients with metastatic breast cancer (MBC) treated between 1998 and 2008 using cumulative incidences and Fine and Grays' multivariable regression analyses. After a median follow-up of 4.0 years, 66 patients (10.5%) developed CM. The estimated probability for CM was 5%, 12% and 15% at 1, 5 and 10 years; in contrast, the probability of death without CM was 21%, 61% and 76%, respectively. A small tumor size, ER status, ductal histology, lung and lymph node metastases, human epidermal growth factor receptor 2 positive (HER2+) tumors, younger age and M0 were associated with CM in univariate analyses, the latter three being risk factors in the multivariable model. Survival was shortened in patient developing CM (24.0 months) compared with patients with no CM (33.6 months) in the course of MBC. Young patients, primary with non-metastatic disease and HER2+ tumors, have a high risk to develop CM in MBC. Survival of patients developing CM in the course of MBC is impaired compared with patients without CM.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

A Multivariate Twin Study of the DSM-IV Criteria for Antisocial Personality Disorder

PubMed Central

Kendler, Kenneth S.; Aggen, Steven H.; Patrick, Christopher J.

2012-01-01

BACKGROUND Many assessment instruments for psychopathy are multidimensional, suggesting that distinguishable factors are needed to effectively capture variation in this personality domain. However, no prior study has examined the factor structure of the DSM-IV criteria for antisocial personality disorder (ASPD). METHODS Self-report questionnaire items reflecting all A criteria for DSM-IV ASPD were available from 4,291 twins (including both members of 1,647 pairs) from the Virginia Adult Study of Psychiatric and Substance Use Disorders. Exploratory factor analysis and twin model fitting were performed using, respectively, Mplus and Mx. RESULTS Phenotypic factor analysis produced evidence for 2 correlated factors: aggressive-disregard and disinhibition. The best-fitting multivariate twin model included two genetic and one unique environmental common factor, along with criteria-specific genetic and environmental effects. The two genetic factors closely resembled the phenotypic factors and varied in their prediction of a range of relevant criterion variables. Scores on the genetic aggressive-disregard factor score were more strongly associated with risk for conduct disorder, early and heavy alcohol use, and low educational status, whereas scores on the genetic disinhibition factor score were more strongly associated with younger age, novelty seeking, and major depression. CONCLUSION From a genetic perspective, the DSM-IV criteria for ASPD do not reflect a single dimension of liability but rather are influenced by two dimensions of genetic risk reflecting aggressive-disregard and disinhibition. The phenotypic structure of the ASPD criteria results largely from genetic and not from environmental influences. PMID:21762879

Heteronormativity and Sexual Partnering Among Bisexual Latino Men

PubMed Central

Garcia, Jonathan; Wilson, Patrick A.; Parker, Richard G.; Severson, Nicolette

2015-01-01

Our analyses address the question of how bisexual Latino men organize their sexual partnerships. Heteronormativity can be understood as the set of social norms and normative structures that guide sexual partnering among men and women. We provide descriptive statistics to describe bisexual Latino men’s sexual partnerships. Logistic and linear regression modeling were used to explore bivariate and multivariate relationships. Of our total sample (N = 142), 41.6% had unprotected vaginal intercourse 2 months prior to the interview; 21.8 % had unprotected anal intercourse with female partners; 37.5 % had unprotected insertive anal intercourse with male partners; and 22.5 % had unprotected receptive anal intercourse with male partners. In our multivariate model, machismo was directly associated with meeting female partners through formal spaces (workplace, school, and/or church), but inversely associated with meeting male partners in formal spaces. Machismo was positively associated with meeting male sex partners through social networks (i.e., friendship and kinship networks). The more comfortable men were with homosexuality the less likely they were to meet men online and the more likely they were to meet men through social networks of friends and kinship. Interventions to reduce sexually transmitted diseases that target bisexual behavior as an epidemiological “bridge” of transmission from homosexual to heterosexual networks might very well benefit from a more complex understanding of how Latino bisexuality is patterned. Thus, this exploratory analysis might lead to a rethinking of how to address risk and vulnerability among Latino bisexual men and their sexual networks. PMID:25128415

Heteronormativity and sexual partnering among bisexual Latino men.

PubMed

Muñoz-Laboy, Miguel; Garcia, Jonathan; Wilson, Patrick A; Parker, Richard G; Severson, Nicolette

2015-05-01

Our analyses address the question of how bisexual Latino men organize their sexual partnerships. Heteronormativity can be understood as the set of social norms and normative structures that guide sexual partnering among men and women. We provide descriptive statistics to describe bisexual Latino men's sexual partnerships. Logistic and linear regression modeling were used to explore bivariate and multivariate relationships. Of our total sample (N = 142), 41.6 % had unprotected vaginal intercourse 2 months prior to the interview; 21.8 % had unprotected anal intercourse with female partners; 37.5 % had unprotected insertive anal intercourse with male partners; and 22.5 % had unprotected receptive anal intercourse with male partners. In our multivariate model, machismo was directly associated with meeting female partners through formal spaces (workplace, school, and/or church), but inversely associated with meeting male partners in formal spaces. Machismo was positively associated with meeting male sex partners through social networks (i.e., friendship and kinship networks). The more comfortable men were with homosexuality the less likely they were to meet men online and the more likely they were to meet men through social networks of friends and kinship. Interventions to reduce sexually transmitted diseases that target bisexual behavior as an epidemiological "bridge" of transmission from homosexual to heterosexual networks might very well benefit from a more complex understanding of how Latino bisexuality is patterned. Thus, this exploratory analysis might lead to a rethinking of how to address risk and vulnerability among Latino bisexual men and their sexual networks.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

PubMed

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments

ERIC Educational Resources Information Center

Amershi, Saleema; Conati, Cristina

2009-01-01

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research.

PubMed

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.

Testing Measurement Invariance in the Target Rotated Multigroup Exploratory Factor Model

ERIC Educational Resources Information Center

Dolan, Conor V.; Oort, Frans J.; Stoel, Reinoud D.; Wicherts, Jelte M.

2009-01-01

We propose a method to investigate measurement invariance in the multigroup exploratory factor model, subject to target rotation. We consider both oblique and orthogonal target rotation. This method has clear advantages over other approaches, such as the use of congruence measures. We demonstrate that the model can be implemented readily in the…

Stressful Life Events Around the Time of Unplanned Pregnancy and Women's Health: Exploratory Findings from a National Sample.

PubMed

Hall, Kelli Stidham; Dalton, Vanessa K; Zochowski, Melissa; Johnson, Timothy R B; Harris, Lisa H

2017-06-01

Objective Little is known about how women's social context of unintended pregnancy, particularly adverse social circumstances, relates to their general health and wellbeing. We explored associations between stressful life events around the time of unintended pregnancy and physical and mental health. Methods Data are drawn from a national probability study of 1078 U.S. women aged 18-55. Our internet-based survey measured 14 different stressful life events occurring at the time of unintended pregnancy (operationalized as an additive index score), chronic disease and mental health conditions, and current health and wellbeing symptoms (standardized perceived health, depression, stress, and discrimination scales). Multivariable regression modeled relationships between stressful life events and health conditions/symptoms while controlling for sociodemographic and reproductive covariates. Results Among ever-pregnant women (N = 695), stressful life events were associated with all adverse health outcomes/symptoms in unadjusted analyses. In multivariable models, higher stressful life event scores were positively associated with chronic disease (aOR 1.21, CI 1.03-1.41) and mental health (aOR 1.42, CI 1.23-1.64) conditions, higher depression (B 0.37, CI 0.19-0.55), stress (B 0.32, CI 0.22-0.42), and discrimination (B 0.74, CI 0.45-1.04) scores, and negatively associated with ≥ very good perceived health (aOR 0.84, CI 0.73-0.97). Stressful life event effects were strongest for emotional and partner-related sub-scores. Conclusion Women with adverse social circumstances surrounding their unintended pregnancy experienced poorer health. Findings suggest that reproductive health should be considered in the broader context of women's health and wellbeing and have implications for integrated models of care that address women's family planning needs, mental and physical health, and social environments.

A Persian version of the parental bonding instrument: factor structure and psychometric properties.

PubMed

Behzadi, Behnaz; Parker, Gordon

2015-02-28

The Parental Bonding Instrument (PBI) is a widely used self-report measure for quantifying key parenting styles as perceived by the child during its first 16 years. While its development study identified two key parental dimensions, subsequent studies have variably confirmed those two or argued for one or more additional parental constructs. We developed a Persian translation of the PBI and administered it to a sample of 340 high school students. The construct validity of the Persian PBI was examined by Exploratory Factor Analysis while Confirmatory Factor Analysis was used to identify the most adequate model. Analyses of the Persian PBI favored a four-factor model for both parental forms. The Persian PBI has a factorial structure consistent with constructs identified in western cultures, as well as high internal consistency and test-retest reliability. Multivariate analyses indicated significant differences between boys and girls across some factors. The PBI appears an acceptable and appropriate measure for quantifying parent-child bonding in Iranian samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Identifying determinants of nations' wetland management programs using structural equation modeling: An exploratory analysis

USGS Publications Warehouse

La Peyre, M.K.; Mendelssohn, I.A.; Reams, M.A.; Templet, P.H.; Grace, J.B.

2001-01-01

Integrated management and policy models suggest that solutions to environmental issues may be linked to the socioeconomic and political Characteristics of a nation. In this study, we empirically explore these suggestions by applying them to the wetland management activities of nations. Structural equation modeling was used to evaluate a model of national wetland management effort and one of national wetland protection. Using five predictor variables of social capital, economic capital, environmental and political characteristics, and land-use pressure, the multivariate models were able to explain 60% of the variation in nations' wetland protection efforts based on data from 90 nations, as defined by level of participation, in the international wetland convention. Social capital had the largest direct effect on wetland protection efforts, suggesting that increased social development may eventually lead to better wetland protection. In contrast, increasing economic development had a negative linear relationship with wetland protection efforts, suggesting the need for explicit wetland protection programs as nations continue to focus on economic development. Government, environmental characteristics, and land-use pressure also had a positive direct effect on wetland protection, and mediated the effect of social capital on wetland protection. Explicit wetland protection policies, combined with a focus on social development, would lead to better wetland protection at the national level.

An exploratory mixed-methods crossover study comparing DVD- vs. Web-based patient decision support in three conditions: The importance of patient perspectives.

PubMed

Halley, Meghan C; Rendle, Katharine A S; Gillespie, Katherine A; Stanley, Katherine M; Frosch, Dominick L

2015-12-01

The last 15 years have witnessed considerable progress in the development of decision support interventions (DESIs). However, fundamental questions about design and format of delivery remain. An exploratory, randomized mixed-method crossover study was conducted to compare a DVD- and Web-based DESI. Randomized participants used either the Web or the DVD first, followed by the alternative format. Participants completed a questionnaire to assess decision-specific knowledge at baseline and a questionnaire and structured qualitative interview after viewing each format. Tracking software was used to capture Web utilization. Transcripts were analyzed using integrated inductive and deductive approaches. Quantitative data were analyzed using exploratory bivariate and multivariate analyses. Exploratory knowledge analyses suggest that both formats increased knowledge, with limited evidence that the DVD increased knowledge more than the Web. Format preference varied across participants: 44% preferred the Web, 32% preferred the DVD and 24% preferred 'both'. Patient discussions of preferences for DESI information structure and the importance of a patients' stage of a given decision suggest these characteristics may be important factors underlying variation in utilization, format preferences and knowledge outcomes. Our results suggest that both DESI formats effectively increase knowledge. Patients' perceptions of these two formats further suggest that there may be no single 'best' format for all patients. These results have important implications for understanding why different DESI formats might be preferable to and more effective for different patients. Further research is needed to explore the relationship between these factors and DESI utilization outcomes across diverse patient populations. © 2014 John Wiley & Sons Ltd.

An Exploratory Study of the Elements to Develop a Coaching Model

ERIC Educational Resources Information Center

Brown, Gwendolyn

2010-01-01

This exploratory study examined the elements of a coaching model based on the best practices that first focus on providing managers with the ability to develop workers and increase productivity, before using existing models that only support the process of managing workers, when it becomes apparent that the worker is not meeting expected…

Genetic and epigenetic transgenerational implications related to omega-3 fatty acids. Part I: maternal FADS2 genotype and DNA methylation correlate with polyunsaturated fatty acid status in toddlers: an exploratory analysis.

PubMed

Lupu, Daniel S; Cheatham, Carol L; Corbin, Karen D; Niculescu, Mihai D

2015-11-01

Polyunsaturated fatty acid metabolism in toddlers is regulated by a complex network of interacting factors. The contribution of maternal genetic and epigenetic makeup to this milieu is not well understood. In a cohort of mothers and toddlers 16 months of age (n = 65 mother-child pairs), we investigated the association between maternal genetic and epigenetic fatty acid desaturase 2 (FADS2) profiles and toddlers' n-6 and n-3 fatty acid metabolism. FADS2 rs174575 variation and DNA methylation status were interrogated in mothers and toddlers, as well as food intake and plasma fatty acid concentrations in toddlers. A multivariate fit model indicated that maternal rs174575 genotype, combined with DNA methylation, can predict α-linolenic acid plasma concentration in all toddlers and arachidonic acid concentrations in boys. Arachidonic acid intake was predictive for its plasma concentration in girls, whereas intake of 3 major n-3 species (eicosapentaenoic, docosapentaenoic, and docosahexaenoic acids) were predictive for their plasma concentrations in boys. FADS2 genotype and DNA methylation in toddlers were not related to plasma concentrations or food intakes, except for CpG8 methylation. Maternal FADS2 methylation was a predictor for the boys' α-linolenic acid intakes. This exploratory study suggests that maternal FADS2 genetic and epigenetic status could be related to toddlers' polyunsaturated fatty acid metabolism. Copyright © 2015 Elsevier Inc. All rights reserved.

Relationship between oxidative stress and muscle mass loss in early postmenopause: an exploratory study.

PubMed

Zacarías-Flores, Mariano; Sánchez-Rodríguez, Martha A; García-Anaya, Oswaldo Daniel; Correa-Muñoz, Elsa; Mendoza-Núñez, Víctor Manuel

2018-04-09

Endocrine changes due to menopause have been associated to oxidative stress and muscle mass loss. The study objective was to determine the relationship between both variables in early postmenopause. An exploratory, cross-sectional study was conducted in 107 pre- and postmenopausal women (aged 40-57 years). Levels of serum lipid peroxides and uric acid and enzymes superoxide dismutase and glutathione peroxidase, as well as total plasma antioxidant capacity were measured as oxidative stress markers. Muscle mass using bioelectrical impedance and muscle strength using dynamometry were also measured. Muscle mass, skeletal muscle index, fat-free mass, and body mass index were calculated. More than 90% of participants were diagnosed with overweight or obesity. Postmenopausal women had lower values of muscle mass and strength markers, with a negative correlation between lipid peroxide level and skeletal muscle index (r= -0.326, p<.05), and a positive correlation between uric acid and skeletal muscle index (r=0.295, p<.05). A multivariate model including oxidative stress markers, age, and waist circumference showed lipid peroxide level to be the main contributor to explain the decrease in skeletal muscle mass in postmenopause, since for every 0.1μmol/l increase in lipid peroxide level, skeletal muscle index decreases by 3.03 units. Our findings suggest an association between increased oxidative stress and muscle mass loss in early postmenopause. Copyright © 2018 SEEN y SED. Publicado por Elsevier España, S.L.U. All rights reserved.

Attitudes toward abortion among students at the University of Cape Coast, Ghana.

PubMed

Rominski, Sarah D; Darteh, Eugene; Dickson, Kwamena Sekyi; Munro-Kramer, Michelle

2017-03-01

This study aimed to describe the attitudes toward abortion of Ghanaian University students and to determine factors which are associated with supporting a woman's right to an abortion. This cross-sectional survey was administered to residential students at the University of Cape Coast. Participants were posed a series of 26 statements to determine to what extent they were supportive of abortion as a woman's right. An exploratory factor analysis was used to create a scale with the pertinent factors that relate to abortion attitudes and a multivariable linear regression model explored the relationships among significant variables noted during exploratory factor analysis. 1038 students completed the survey and these students had a generally negative view of abortion. Two factors emerged: (1) the Abortion as a Right scale consisted of five questions (α = .755) and (2) the Moral Objection to Abortion scale consisted of three questions (α = .740). In linear regression, being older (β = 1.9), sexually experienced (β = 1.2), having a boyfriend/girlfriend (β = 1.4), and knowing someone who has terminated a pregnancy (β = 1.1) were significantly associated with a more liberal view of a right to an abortion. This work supports the idea that students who have personal exposure to an abortion experience hold more liberal views on abortion than those who have not had a similar exposure. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantifying asymmetry: ratios and alternatives.

PubMed

Franks, Erin M; Cabo, Luis L

2014-08-01

Traditionally, the study of metric skeletal asymmetry has relied largely on univariate analyses, utilizing ratio transformations when the goal is comparing asymmetries in skeletal elements or populations of dissimilar dimensions. Under this approach, raw asymmetries are divided by a size marker, such as a bilateral average, in an attempt to produce size-free asymmetry indices. Henceforth, this will be referred to as "controlling for size" (see Smith: Curr Anthropol 46 (2005) 249-273). Ratios obtained in this manner often require further transformations to interpret the meaning and sources of asymmetry. This model frequently ignores the fundamental assumption of ratios: the relationship between the variables entered in the ratio must be isometric. Violations of this assumption can obscure existing asymmetries and render spurious results. In this study, we examined the performance of the classic indices in detecting and portraying the asymmetry patterns in four human appendicular bones and explored potential methodological alternatives. Examination of the ratio model revealed that it does not fulfill its intended goals in the bones examined, as the numerator and denominator are independent in all cases. The ratios also introduced strong biases in the comparisons between different elements and variables, generating spurious asymmetry patterns. Multivariate analyses strongly suggest that any transformation to control for overall size or variable range must be conducted before, rather than after, calculating the asymmetries. A combination of exploratory multivariate techniques, such as Principal Components Analysis, and confirmatory linear methods, such as regression and analysis of covariance, appear as a promising and powerful alternative to the use of ratios. © 2014 Wiley Periodicals, Inc.

A multivariate twin study of the DSM-IV criteria for antisocial personality disorder.

PubMed

Kendler, Kenneth S; Aggen, Steven H; Patrick, Christopher J

2012-02-01

Many assessment instruments for psychopathy are multidimensional, suggesting that distinguishable factors are needed to effectively capture variation in this personality domain. However, no prior study has examined the factor structure of the DSM-IV criteria for antisocial personality disorder (ASPD). Self-report questionnaire items reflecting all A criteria for DSM-IV ASPD were available from 4291 twins (including both members of 1647 pairs) from the Virginia Adult Study of Psychiatric and Substance Use Disorders. Exploratory factor analysis and twin model fitting were performed using, respectively, Mplus and Mx. Phenotypic factor analysis produced evidence for two correlated factors: aggressive-disregard and disinhibition. The best-fitting multivariate twin model included two genetic and one unique environmental common factor, along with criteria-specific genetic and environmental effects. The two genetic factors closely resembled the phenotypic factors and varied in their prediction of a range of relevant criterion variables. Scores on the genetic aggressive-disregard factor score were more strongly associated with risk for conduct disorder, early and heavy alcohol use, and low educational status, whereas scores on the genetic disinhibition factor score were more strongly associated with younger age, novelty seeking, and major depression. From a genetic perspective, the DSM-IV criteria for ASPD do not reflect a single dimension of liability but rather are influenced by two dimensions of genetic risk reflecting aggressive-disregard and disinhibition. The phenotypic structure of the ASPD criteria results largely from genetic and not from environmental influences. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Motivations for genetic testing for lung cancer risk among young smokers.

PubMed

O'Neill, Suzanne C; Lipkus, Isaac M; Sanderson, Saskia C; Shepperd, James; Docherty, Sharron; McBride, Colleen M

2013-11-01

To examine why young people might want to undergo genetic susceptibility testing for lung cancer despite knowing that tested gene variants are associated with small increases in disease risk. The authors used a mixed-method approach to evaluate motives for and against genetic testing and the association between these motivations and testing intentions in 128 college students who smoke. Exploratory factor analysis yielded four reliable factors: Test Scepticism, Test Optimism, Knowledge Enhancement and Smoking Optimism. Test Optimism and Knowledge Enhancement correlated positively with intentions to test in bivariate and multivariate analyses (ps<0.001). Test Scepticism correlated negatively with testing intentions in multivariate analyses (p<0.05). Open-ended questions assessing testing motivations generally replicated themes of the quantitative survey. In addition to learning about health risks, young people may be motivated to seek genetic testing for reasons, such as gaining knowledge about new genetic technologies more broadly.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios (Final Report, 2008)

EPA Science Inventory

EPA announced the availability of the final report, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios. This report investigates the potential dioxin exposure to artists/hobbyists who use ball clay to make pottery and related products. Derm...

Comparisons of Means Using Exploratory and Confirmatory Approaches

ERIC Educational Resources Information Center

Kuiper, Rebecca M.; Hoijtink, Herbert

2010-01-01

This article discusses comparisons of means using exploratory and confirmatory approaches. Three methods are discussed: hypothesis testing, model selection based on information criteria, and Bayesian model selection. Throughout the article, an example is used to illustrate and evaluate the two approaches and the three methods. We demonstrate that…

An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios (External Review Draft)

EPA Science Inventory

EPA has released an external review draft entitled, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios(External Review Draft). The public comment period and the external peer-review workshop are separate processes that provide opportunities ...

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Motivation for HPV Vaccination Among Young Adult Men: Validation of TTM Decisional Balance and Self-Efficacy Constructs.

PubMed

Fernandez, Anne C; Amoyal, Nicole R; Paiva, Andrea L; Prochaska, James O

2016-01-01

In the United States, 36% of human papillomavirus (HPV)-related cancers occur among men. HPV vaccination can substantially reduce the risk of HPV infection; however, the vast majority of men are unvaccinated. This study developed and validated transtheoretical model-based measures for HPV vaccination in young adult men. Cross-sectional measurement development. Online survey of young adult men. Three hundred twenty-nine mostly college-attending men, ages 18 to 26. Stage of change, decisional balance (pros/cons), and self-efficacy. The sample was randomly split into halves for exploratory principal components analysis (PCA), followed by confirmatory factor analyses (CFA) to test measurement models. Multivariate analyses examined relationships between scales. For decisional balance, PCA revealed two uncorrelated five-item factors (pros α = .78; cons α = .83). For the self-efficacy scale, PCA revealed a single-factor solution (α = .83). CFA confirmed that the two-factor uncorrelated model for decisional balance and a single-factor model for self-efficacy. Follow-up analyses of variance supported the theoretically predicted relationships between stage of change, pros, and self-efficacy. This study resulted in reliable and valid measures of pros and self-efficacy for HPV vaccination that can be used in future clinical research.

Modeling and Intervening across Time in Scientific Inquiry Exploratory Learning Environment

ERIC Educational Resources Information Center

Ting, Choo-Yee; Phon-Amnuaisuk, Somnuk; Chong, Yen-Kuan

2008-01-01

This article aims at discussing how Dynamic Decision Network (DDN) can be employed to tackle the challenges in modeling temporally variable scientific inquiry skills and provision of adaptive pedagogical interventions in INQPRO, a scientific inquiry exploratory learning environment for learning O'level Physics. We begin with an overview of INQPRO…

Application of Exploratory Structural Equation Modeling to Evaluate the Academic Motivation Scale

ERIC Educational Resources Information Center

Guay, Frédéric; Morin, Alexandre J. S.; Litalien, David; Valois, Pierre; Vallerand, Robert J.

2015-01-01

In this research, the authors examined the construct validity of scores of the Academic Motivation Scale using exploratory structural equation modeling. Study 1 and Study 2 involved 1,416 college students and 4,498 high school students, respectively. First, results of both studies indicated that the factor structure tested with exploratory…

Cultural, Social, and Economic Capital Constructs in International Assessments: An Evaluation Using Exploratory Structural Equation Modeling

ERIC Educational Resources Information Center

Caro, Daniel H.; Sandoval-Hernández, Andrés; Lüdtke, Oliver

2014-01-01

The article employs exploratory structural equation modeling (ESEM) to evaluate constructs of economic, cultural, and social capital in international large-scale assessment (LSA) data from the Progress in International Reading Literacy Study (PIRLS) 2006 and the Programme for International Student Assessment (PISA) 2009. ESEM integrates the…

Human Behavior Based Exploratory Model for Successful Implementation of Lean Enterprise in Industry

ERIC Educational Resources Information Center

Sawhney, Rupy; Chason, Stewart

2005-01-01

Currently available Lean tools such as Lean Assessments, Value Stream Mapping, and Process Flow Charting focus on system requirements and overlook human behavior. A need is felt for a tool that allows one to baseline personnel, determine personnel requirements and align system requirements with personnel requirements. Our exploratory model--The…

The Neo Personality Inventory-Revised: Factor Structure and Gender Invariance from Exploratory Structural Equation Modeling Analyses in a High-Stakes Setting

ERIC Educational Resources Information Center

Furnham, Adrian; Guenole, Nigel; Levine, Stephen Z.; Chamorro-Premuzic, Tomas

2013-01-01

This study presents new analyses of NEO Personality Inventory-Revised (NEO-PI-R) responses collected from a large British sample in a high-stakes setting. The authors show the appropriateness of the five-factor model underpinning these responses in a variety of new ways. Using the recently developed exploratory structural equation modeling (ESEM)…

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment.

PubMed

Bagnasco, Lucia; Cosulich, M Elisabetta; Speranza, Giovanna; Medini, Luca; Oliveri, Paolo; Lanteri, Silvia

2014-08-15

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrated Primary Care Readiness and Behaviors Scale: Development and validation in behavioral health professionals.

PubMed

Blaney, Cerissa L; Redding, Colleen A; Paiva, Andrea L; Rossi, Joseph S; Prochaska, James O; Blissmer, Bryan; Burditt, Caitlin T; Nash, Justin M; Bayley, Keri Dotson

2018-03-01

Although integrated primary care (IPC) is growing, several barriers remain. Better understanding of behavioral health professionals' (BHPs') readiness for and engagement in IPC behaviors could improve IPC research and training. This study developed measures of IPC behaviors and stage of change. The sample included 319 licensed, practicing BHPs with a range of interests and experience with IPC. Sequential measurement development procedures, with split-half cross-validation were conducted. Exploratory principal components analyses (N = 152) and confirmatory factor analyses (N = 167) yielded a 12-item scale with 2 factors: consultation/practice management (CPM) and intervention/knowledge (IK). A higher-order Integrated Primary Care Behavior Scale (IPCBS) model showed good fit to the data, and excellent internal consistencies. The multivariate analysis of variance (MANOVA) on the IPCBS demonstrated significant large-sized differences across stage and behavior groups. The IPCBS demonstrated good psychometric properties and external validation, advancing research, education, and training for IPC practice. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Association of unmeasured strong ions with outcome of hospitalized beef and dairy diarrheic calves

PubMed Central

Gomez, Diego E.; Lofstedt, Jeanne; Arroyo, Luis G.; Wichtel, Maureen; Muirhead, Tammy; Stämpfli, Henri; McClure, J. Trenton

2017-01-01

Increased systemic concentrations of L-lactate and unmeasured strong ions (USI) are associated with an increased risk of mortality in human neonates and adults suffering from various diseases. This exploratory study aimed to investigate if values of certain acid-base parameters, especially L-lactate and USI, on admission to hospital are associated with mortality in diarrheic calves. Fifty-five calves < 28 days old admitted to 2 teaching hospitals for diagnosis and treatment of diarrhea were included. Admission demographic, physical examination, blood gas and biochemistry analysis, and outcome data were recorded. Admission acid-base values associated with outcome were assessed using multivariable regression modeling. Calves with elevated plasma L-lactate (OR: 1.30, 95% CI: 1.08 to 1.55; P = 0.005) and USI (OR: 1.40, 95% CI: 1.12 to 1.74; P = 0.003) at admission were more likely to die or to be euthanized. This study revealed that elevated concentrations of L-lactate and USI at admission were positively associated with mortality. PMID:28966359

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Impairment of exploratory behavior and spatial memory in adolescent rats in lithium-pilocarpine model of temporal lobe epilepsy.

PubMed

Kalemenev, S V; Zubareva, O E; Frolova, E V; Sizov, V V; Lavrentyeva, V V; Lukomskaya, N Ya; Kim, K Kh; Zaitsev, A V; Magazanik, L G

2015-01-01

Cognitive impairment in six-week -old rats has been studied in the lithium-pilocarpine model of adolescent temporal lobe epilepsy in humans. The pilocarpine-treated rats (n =21) exhibited (a) a decreased exploratory activity in comparison with control rats (n = 20) in the open field (OP) test and (b) a slower extinction of exploratory behavior in repeated OP tests. The Morris Water Maze (MWM) test showed that the effect of training was less pronounced in the pilocarpine-treated rats, which demonstrated disruption of predominantly short-term memory. Therefore, our study has shown that lithium-pilocarpine seizures induce substantial changes in exploratory behavior and spatial memory in adolescent rats. OP and MWM tests can be used in the search of drugs reducing cognitive impairments associated with temporal lobe epilepsy.

The School Counseling Program Implementation Survey: Initial Instrument Development and Exploratory Factor Analysis

ERIC Educational Resources Information Center

Clemens, Elysia V.; Carey, John C.; Harrington, Karen M.

2010-01-01

This article details the initial development of the School Counseling Program Implementation Survey and psychometric results including reliability and factor structure. An exploratory factor analysis revealed a three-factor model that accounted for 54% of the variance of the intercorrelation matrix and a two-factor model that accounted for 47% of…

Developing Students' Scientific Writing and Presentation Skills through Argument Driven Inquiry: An Exploratory Study

ERIC Educational Resources Information Center

C¸etin, Pinar Seda; Eymur, Gülüzar

2017-01-01

In this study, we employed a new instructional model that helps students develop scientific writing and presentation skills. Argument-driven inquiry (ADI) is one of the most novel instructional models that emphasizes the role of argumentation and inquiry in science education equally. This is an exploratory study where five ADI lab activities take…

Prognostic factors and predictors of sorafenib benefit in patients with hepatocellular carcinoma: Analysis of two phase III studies.

PubMed

Bruix, Jordi; Cheng, Ann-Lii; Meinhardt, Gerold; Nakajima, Keiko; De Sanctis, Yoriko; Llovet, Josep

2017-11-01

Sorafenib, an oral multikinase inhibitor, significantly prolonged overall survival (OS) vs. placebo in patients with unresectable hepatocellular carcinoma (HCC) in two phase III studies, SHARP (Sorafenib HCC Assessment Randomized Protocol) and Asia Pacific (AP). To assess prognostic factors for HCC and predictive factors of sorafenib benefit, we conducted a pooled exploratory analysis from these placebo-controlled phase III studies. To identify potential prognostic factors for OS, univariate and multivariate (MV) analyses were performed for baseline variables by Cox proportional hazards model. Hazard ratios (HRs) and median OS were evaluated across pooled subgroups. To assess factors predictive of sorafenib benefit, the interaction term between treatment for each subgroup was evaluated by Cox proportional hazard model. In 827 patients (448 sorafenib; 379 placebo) analyzed, strong prognostic factors for poorer OS identified from MV analysis in both treatment arms were presence of macroscopic vascular invasion (MVI), high alpha-fetoprotein (AFP), and high neutrophil-to-lymphocyte ratio (NLR; ⩽ vs. >median [3.1]). Sorafenib OS benefit was consistently observed across all subgroups. Significantly greater OS sorafenib benefit vs. placebo was observed in patients without extrahepatic spread (EHS; HR, 0.55 vs. 0.84), with hepatitis C virus (HCV) (HR, 0.47 vs. 0.81), and a low NLR (HR, 0.59 vs. 0.84). In this exploratory analysis, presence of MVI, high AFP, and high NLR were prognostic factors of poorer OS. Sorafenib benefit was consistently observed irrespective of prognostic factors. Lack of EHS, HCV, and lower NLR were predictive of a greater OS benefit with sorafenib. This exploratory pooled analysis showed that treatment with sorafenib provides a survival benefit in all subgroups of patients with HCC; however, the magnitude of benefit is greater in patients with disease confined to the liver (without extrahepatic spread), or in those with hepatitis C virus, or a lower neutrophil-to-lymphocyte ratio, an indicator of inflammation status. These results help inform the prognosis of patients receiving sorafenib therapy and provide further refinements for the design of trials testing new agents vs. sorafenib. Clinical Trial Numbers: NCT00105443 and NCT00492752. Copyright © 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Validation of post-operative residual contrast enhancing tumor volume as an independent prognostic factor for overall survival in newly diagnosed glioblastoma.

PubMed

Ellingson, Benjamin M; Abrey, Lauren E; Nelson, Sarah J; Kaufmann, Timothy J; Garcia, Josep; Chinot, Olivier; Saran, Frank; Nishikawa, Ryo; Henriksson, Roger; Mason, Warren P; Wick, Wolfgang; Butowski, Nicholas; Ligon, Keith L; Gerstner, Elizabeth R; Colman, Howard; de Groot, John; Chang, Susan; Mellinghoff, Ingo; Young, Robert J; Alexander, Brian M; Colen, Rivka; Taylor, Jennie W; Arrillaga-Romany, Isabel; Mehta, Arnav; Huang, Raymond Y; Pope, Whitney B; Reardon, David; Batchelor, Tracy; Prados, Michael; Galanis, Evanthia; Wen, Patrick Y; Cloughesy, Timothy F

2018-04-05

In the current study, we pooled imaging data in newly diagnosed GBM patients from international multicenter clinical trials, single institution databases, and multicenter clinical trial consortiums to identify the relationship between post-operative residual enhancing tumor volume and overall survival (OS). Data from 1,511 newly diagnosed GBM patients from 5 data sources were included in the current study: 1) a single institution database from UCLA (N=398; Discovery); 2) patients from the Ben and Cathy Ivy Foundation for Early Phase Clinical Trials Network Radiogenomics Database (N=262 from 8 centers; Confirmation); 3) the chemoradiation placebo arm from an international phase III trial (AVAglio; N=394 from 120 locations in 23 countries; Validation); 4) the experimental arm from AVAglio examining chemoradiation plus bevacizumab (N=404 from 120 locations in 23 countries; Exploratory Set 1); and 5) an Alliance (N0874) Phase I/II trial of vorinostat plus chemoradiation (N=53; Exploratory Set 2). Post-surgical, residual enhancing disease was quantified using T1 subtraction maps. Multivariate Cox regression models were used to determine influence of clinical variables, MGMT status, and residual tumor volume on OS. A log-linear relationship was observed between post-operative, residual enhancing tumor volume and OS in newly diagnosed GBM treated with standard chemoradiation. Post-operative tumor volume is a prognostic factor for OS (P<0.01), regardless of therapy, age, and MGMT promoter methylation status. Post-surgical, residual contrast-enhancing disease significantly negatively influences survival in patients with newly diagnosed glioblastoma treated with chemoradiation with or without concomitant experimental therapy.

Geography of Adolescent Obesity in the U.S., 2007-2011.

PubMed

Kramer, Michael R; Raskind, Ilana G; Van Dyke, Miriam E; Matthews, Stephen A; Cook-Smith, Jessica N

2016-12-01

Obesity remains a significant threat to the current and long-term health of U.S. adolescents. The authors developed county-level estimates of adolescent obesity for the contiguous U.S., and then explored the association between 23 conceptually derived area-based correlates of adolescent obesity and ecologic obesity prevalence. Multilevel small area regression methods applied to the 2007 and 2011-2012 National Survey of Children's Health produced county-level obesity prevalence estimates for children aged 10-17 years. Exploratory multivariable Bayesian regression estimated the cross-sectional association between nutrition, activity, and macrosocial characteristics of counties and states, and county-level obesity prevalence. All analyses were conducted in 2015. Adolescent obesity varies geographically with clusters of high prevalence in the Deep South and Southern Appalachian regions. Geographic disparities and clustering in observed data are largely explained by hypothesized area-based variables. In adjusted models, activity environment, but not nutrition environment variables were associated with county-level obesity prevalence. County violent crime was associated with higher obesity, whereas recreational facility density was associated with lower obesity. Measures of the macrosocial and relational domain, including community SES, community health, and social marginalization, were the strongest correlates of county-level obesity. County-level estimates of adolescent obesity demonstrate notable geographic disparities, which are largely explained by conceptually derived area-based contextual measures. This ecologic exploratory study highlights the importance of taking a multidimensional approach to understanding the social and community context in which adolescents make obesity-relevant behavioral choices. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Maternal Attitudes, Normative Beliefs, and Subjective Norms of Mothers of 2- and 3-Year-Old Children.

PubMed

Northrup, Angela A; Smaldone, Arlene

This exploratory study examined maternal attitudes, normative beliefs, subjective norms, and meal selection behaviors of mothers of 2- and 3-year-old children. Guided by the Theory of Reasoned Action, we had mothers complete three surveys, two interviews, and a feeding simulation exercise. Data were analyzed using descriptive and bivariate statistics and multivariate linear regression. A total of 31 mothers (50% Latino, 34% Black, 46.9% ≤ high school education, 31.3% poor health literacy) of 32 children (37.5% overweight/obese) participated in this study. Maternal normative beliefs (knowledge of U.S. Department of Agriculture recommendations) did not reflect actual U.S. Department of Agriculture recommendations. Collectively, regression models explained 13% (dairy) to 51% (vegetables) of the variance in behavioral intent, with normative belief an independent predictor in all models except grain and dairy. Meal selection behaviors, on average, were predicted by poor knowledge of U.S. Department of Agriculture recommendations. Dietary guidance appropriate to health literacy level should be incorporated into well-child visits. Copyright © 2016 National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.

Predictors of hepatitis B vaccination status in healthcare workers in Belgrade, Serbia, December 2015

PubMed Central

Kisic-Tepavcevic, Darija; Kanazir, Milena; Gazibara, Tatjana; Maric, Gorica; Makismovic, Natasa; Loncarevic, Goranka; Pekmezovic, Tatjana

2017-01-01

Despite the availability of a safe and effective vaccine since 1982, overall coverage of hepatitis B vaccination among healthcare workers (HCWs) has not reached a satisfactory level in many countries worldwide. The aim of this study was to estimate the prevalence of hepatitis B vaccination, and to assess the predictors of hepatitis B vaccination status among HCWs in Serbia. Of 380 randomly selected HCWs, 352 (92.6%) were included in the study. The prevalence of hepatitis B vaccination acceptance was 66.2%. The exploratory factor analyses using the vaccination-refusal scale showed that items clustered under ‘threat of disease’ explained the highest proportion (30.4%) of variance among those declining vaccination. The factor analyses model of the potential reasons for receiving the hepatitis B vaccine showed that ‘social influence’ had the highest contribution (47.5%) in explaining variance among those vaccinated. In the multivariate adjusted model the following variables were independent predictors of hepatitis B vaccination status: occupation, duration of work experience, exposure to blood in the previous year, and total hepatitis B-related knowledge score. Our results highlight the need for well-planned national policies, possibly including mandatory hepatitis B immunisation, in the Serbian healthcare environment. PMID:28449736

Factor Structure of the Primary Scales of the Inventory of Personality Organization in a Nonclinical Sample Using Exploratory Structural Equation Modeling

ERIC Educational Resources Information Center

Ellison, William D.; Levy, Kenneth N.

2012-01-01

Using exploratory structural equation modeling and multiple regression, we examined the factor structure and criterion relations of the primary scales of the Inventory of Personality Organization (IPO; Kernberg & Clarkin, 1995) in a nonclinical sample. Participants (N = 1,260) completed the IPO and measures of self-concept clarity, defenses,…

Consortium for Planning, Developing, and Implementing Exploratory Industrial Career Development Models (Phase I of Three Phases). Interim Report.

ERIC Educational Resources Information Center

Oregon State Univ., Corvallis.

The report describes the organizational phase of a project designed to create program models and supporting literature for exploratory industrial career development programs for grades seven to ten. The project was undertaken by Oregon State University in cooperation with the Oregon State Department of Education and involved the formation of a…

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Exploratory study of the association between insight and Theory of Mind (ToM) in stable schizophrenia patients.

PubMed

Pousa, Esther; Duñó, Rosó; Blas Navarro, J; Ruiz, Ada I; Obiols, Jordi E; David, Anthony S

2008-05-01

Poor insight and impairment in Theory of Mind (ToM) reasoning are common in schizophrenia, predicting poorer clinical and functional outcomes. The present study aimed to explore the relationship between these phenomena. 61 individuals with a DSM-IV diagnosis of schizophrenia during a stable phase were included. ToM was assessed using a picture sequencing task developed by Langdon and Coltheart (1999), and insight with the Scale to Assess Unawareness of Mental Disorder (SUMD; Amador et al., 1993). Multivariate linear regression analysis was carried out to estimate the predictive value of insight on ToM, taking into account several possible confounders and interaction variables. No direct significant associations were found between any of the insight dimensions and ToM using bivariate analysis. However, a significant linear regression model which explained 48% of the variance in ToM was revealed in the multivariate analysis. This included the 5 insight dimensions and 3 interaction variables. Misattribution of symptoms--in aware patients with age at onset >20 years--and unawareness of need for medication--in patients with GAF >60--were significantly predictive of better ToM. Insight and ToM are two complex and distinct phenomena in schizophrenia. Relationships between them are mediated by psychosocial, clinical, and neurocognitive variables. Intact ToM may be a prerequisite for aware patients to attribute their symptoms to causes other than mental illness, which could in turn be associated with denial of need for medication.

Timing of Adjuvant Surgical Oophorectomy in the Menstrual Cycle and Disease-Free and Overall Survival in Premenopausal Women With Operable Breast Cancer

PubMed Central

Laudico, Adriano V.; Van Dinh, Nguyen; Allred, D. Craig; Uy, Gemma B.; Quang, Le Hong; Salvador, Jonathan Disraeli S.; Siguan, Stephen Sixto S.; Mirasol-Lumague, Maria Rica; Tung, Nguyen Dinh; Benjaafar, Noureddine; Navarro, Narciso S.; Quy, Tran Tu; De La Peña, Arturo S.; Dofitas, Rodney B.; Bisquera, Orlino C.; Linh, Nguyen Dieu; To, Ta Van; Young, Gregory S.; Hade, Erinn M.; Jarjoura, David

2015-01-01

Background: For women with hormone receptor–positive, operable breast cancer, surgical oophorectomy plus tamoxifen is an effective adjuvant therapy. We conducted a phase III randomized clinical trial to test the hypothesis that oophorectomy surgery performed during the luteal phase of the menstrual cycle was associated with better outcomes. Methods: Seven hundred forty premenopausal women entered a clinical trial in which those women estimated not to be in the luteal phase of their menstrual cycle for the next one to six days (n = 509) were randomly assigned to receive treatment with surgical oophorectomy either delayed to be during a five-day window in the history-estimated midluteal phase of the menstrual cycles, or in the next one to six days. Women who were estimated to be in the luteal phase of the menstrual cycle for the next one to six days (n = 231) were excluded from random assignment and received immediate surgical treatments. All patients began tamoxifen within 6 days of surgery and continued this for 5 years. Kaplan-Meier methods, the log-rank test, and multivariable Cox regression models were used to assess differences in five-year disease-free survival (DFS) between the groups. All statistical tests were two-sided. Results: The randomized midluteal phase surgery group had a five-year DFS of 64%, compared with 71% for the immediate surgery random assignment group (hazard ratio [HR] = 1.24, 95% confidence interval [CI] = 0.91 to 1.68, P = .18). Multivariable Cox regression models, which included important prognostic variables, gave similar results (aHR = 1.28, 95% CI = 0.94 to 1.76, P = .12). For overall survival, the univariate hazard ratio was 1.33 (95% CI = 0.94 to 1.89, P = .11) and the multivariable aHR was 1.43 (95% CI = 1.00 to 2.06, P = .05). Better DFS for follicular phase surgery, which was unanticipated, proved consistent across multiple exploratory analyses. Conclusions: The hypothesized benefit of adjuvant luteal phase oophorectomy was not shown in this large trial. PMID:25794890

Exploratory analysis of TOF-SIMS data from biological surfaces

NASA Astrophysics Data System (ADS)

Vaidyanathan, Seetharaman; Fletcher, John S.; Henderson, Alex; Lockyer, Nicholas P.; Vickerman, John C.

2008-12-01

The application of multivariate analytical tools enables simplification of TOF-SIMS datasets so that useful information can be extracted from complex spectra and images, especially those that do not give readily interpretable results. There is however a challenge in understanding the outputs from such analyses. The problem is complicated when analysing images, given the additional dimensions in the dataset. Here we demonstrate how the application of simple pre-processing routines can enable the interpretation of TOF-SIMS spectra and images. For the spectral data, TOF-SIMS spectra used to discriminate bacterial isolates associated with urinary tract infection were studied. Using different criteria for picking peaks before carrying out PC-DFA enabled identification of the discriminatory information with greater certainty. For the image data, an air-dried salt stressed bacterial sample, discussed in another paper by us in this issue, was studied. Exploration of the image datasets with and without normalisation prior to multivariate analysis by PCA or MAF resulted in different regions of the image being highlighted by the techniques.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

NASA Astrophysics Data System (ADS)

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-01

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Exploratory Study: Assessment of Modeled Dioxin ...

EPA Pesticide Factsheets

EPA announced the availability of the final report, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios. This report investigates the potential dioxin exposure to artists/hobbyists who use ball clay to make pottery and related products. Dermal, inhalation, and ingestion exposures to clay were measured at the ceramics art department of Ohio State University in Columbus, OH. The exposure estimates were based on measured levels of clay in the studio air, deposited on surrogate food samples and on the skin of the artists. The purpose of this report is to describe an exploratory investigation of potential dioxin exposures to artists/hobbyists who use ball clay to make pottery and related products.

Testing novel patient financial incentives to increase breast cancer screening.

PubMed

Merrick, Elizabeth Levy; Hodgkin, Dominic; Horgan, Constance M; Lorenz, Laura S; Panas, Lee; Ritter, Grant A; Kasuba, Paul; Poskanzer, Debra; Nefussy, Renee Altman

2015-11-01

To examine the effects of 3 types of low-cost financial incentives for patients, including a novel "person-centered" approach on breast cancer screening (mammogram) rates. Randomized controlled trial with 4 arms: 3 types of financial incentives ($15 gift card, entry into lottery for $250 gift card, and a person-centered incentive with choice of $15 gift card or lottery) and a control group. Sample included privately insured Tufts Health Plan members in Massachusetts who were women aged 42 to 69 years with no mammogram claim in ≥ 2.6 years. A sample of 4700 eligible members were randomized to 4 study arms. The control group received a standard reminder letter and the incentive groups received a reminder letter plus an incentive offer for obtaining a mammogram within the next 4 months. Bivariate tests and multivariate logistic regression were used to assess the incentives' impact on mammogram receipt. Data were analyzed for 4427 members (after exclusions such as undeliverable mail). The percent of members receiving a mammogram during the study was 11.7% (gift card), 12.1% (lottery), 13.4% (person-centered/choice), and 11.9% (controls). Differences were not statistically significant in bivariate or multivariate full-sample analyses. In exploratory subgroup analyses of members with a mammogram during the most recent year prior to the study-defined gap, person-centered incentives were associated with a higher likelihood of mammogram receipt. None of the low-cost incentives tested had a statistically significant effect on mammogram rates in the full sample. Exploratory findings for members who were more recently screened suggest that they may be more responsive to person-centered incentives.

Validation of the World Health Organization Disability Assessment Schedule (WHO-DAS II) in Greek and its added value to the Short Form 36 (SF-36) in a sample of people with or without disabilities.

PubMed

Xenouli, Georgia; Xenoulis, Kostis; Sarafis, Pavlos; Niakas, Dimitris; Alexopoulos, Evangelos C

2016-07-01

There is controversy and ongoing interest on the measurement of functionality in the personal and social level. (1) to validate the Greek version of the World Health Organization Disability Assessment Schedule (WHO DAS II) and (2) to determine its added value to the physical and psychological health subscales of the Short Form 36 (SF-36). In a cross-sectional design, data were collected between December 2014 and March 2015 by using three questionnaires (WHO DAS II, SF-36, PSS-14) in a sample of people with disabilities (n = 101) and without disabilities (n = 109) in Athens, Greece. WHO DAS II internal consistency, construct and criterion-related validity were assessed by Cronbach alpha, exploratory factor analysis and correlations; its added value by multivariable linear regression. Cronbach Alpha's were satisfactory for the WHO DAS II, PSS-14 and SF-36 (0.85, 0.88 and 0.96 respectively). Exploratory factor analysis confirmed the existence of one or two factors in people with or without disabilities, respectively. WHO DAS II score showed significant negative correlation with the physical and mental health scale of SF-36 score, especially strong for physical health while was positively related to PSS-14 score. In multivariate analysis mental health appraisal was related to perceived stress in both groups. This study support the validity of the Greek version of WHO DAS II and warranted its use in assessment and follow up of people with disabilities, contributing to the development of suitable policies to cover their needs and providing comparable data with other surveys using the same instrument. Copyright © 2016 Elsevier Inc. All rights reserved.

Pre-Stroke Use of Beta-Blockers Does Not Lower Post-Stroke Infection Rate: An Exploratory Analysis of the Preventive Antibiotics in Stroke Study.

PubMed

Westendorp, Willeke F; Vermeij, Jan-Dirk; Brouwer, Matthijs C; Roos, Y B W E M; Nederkoorn, Paul J; van de Beek, Diederik

2016-01-01

Stroke-associated infections occur frequently and are associated with unfavorable outcome. Previous cohort studies suggest a protective effect of beta-blockers (BBs) against infections. A sympathetic drive may increase immune suppression and infections. This study is aimed at investigating the association between BB treatment at baseline and post-stroke infection in the Preventive Antibiotics in Stroke Study (PASS), a prospective clinical trial. We performed an exploratory analysis in PASS, 2,538 patients with acute phase of stroke (24 h after onset) were randomized to ceftriaxone (intravenous, 2 g per day for 4 days) in addition to stroke unit care, or standard stroke unit care without preventive antibiotic treatment. All clinical data, including use of BBs, was prospectively collected. Infection was diagnosed by the treating physician, and independently by an expert panel blinded for all other data. Multivariable analysis was performed to investigate the relation between BB treatment and infection rate. Infection, as defined by the physician, occurred in 348 of 2,538 patients (14%). Multivariable analysis showed that the use of BBs at baseline was associated with the development of infection during clinical course (adjusted OR (aOR) 1.61, 95% CI 1.19-2.18; p < 0.01). BB use at baseline was also associated with the development of pneumonia (aOR 1.56, 95% CI 1.05-2.30; p = 0.03). Baseline BB use was not associated with mortality (aOR 1.14, 95% CI 0.84-1.53; p = 0.41) or unfavorable outcome at 3 months (aOR 1.10, 95% CI 0.89-1.35; p = 0.39). Patients treated with BBs prior to stroke have a higher rate of infection and pneumonia. © 2016 S. Karger AG, Basel.

Toxoplasmosis preventive behavior and related knowledge among Saudi pregnant women: an exploratory study.

PubMed

Amin, Tarek Tawfik; Ali, Mohamed Nabil Al; Alrashid, Ahmed Abdulmohsen; Al-Agnam, Amena Ahmed; Al Sultan, Amina Abdullah

2013-06-21

Many cases of congenital toxoplasmosis can be prevented provided that pregnant women following hygienic measures to avert risk of infection and to reduce severity of the condition if primary prevention failed. This descriptive exploratory study aimed to assess the risk behavior and knowledge related to toxoplasmoisis among Saudi pregnant women attending primary health care centers (PHCs) in Al Hassa, Saudi Arabia and to determine socio-demographic characteristics related to risk behavior and knowledge. All Saudi pregnant women attending antenatal care at randomly selected six urban and four rural PHCs were approached. Those agreed to participate were interviewed using a pre-tested structured questionnaire collecting data regarding socio-demographic, obstetric history, toxoplasmosis risk behaviors and related knowledge. Of the included pregnant women, 234 (26.8%) have fulfilled the criteria for toxoplasmosis preventive behavior recommended by Centers for Disease Prevention and Control to prevent congenital toxoplasmosis, while 48.9% reported at least one risk behavior and 24.3% reported ? two risk behaviors. Logistic regression model revealed that pregnant women aged 20 to < 30 years and those with previous history of unfavorable pregnancy outcome were more likely to follow toxoplasmosis preventive behavior. Toxoplasmosis-related knowledge showed that many women had identified the role of cats in disease transmission while failed to identify other risk factors including consumption of undercooked meats, unwashed fruits and vegetables, and contacting with soil. Predictors for pregnant women to be knowledgeable towards toxoplasmosis included those aged 30 to <40 years (OR=1.53), with ? secondary education (OR=1.96), had previous unfavorable pregnancy outcomes (OR=1.88) and investigated for toxoplasmosis (OR=2.08) as reveled by multivariate regression model. Pregnant women in Al Hasas, Saudi Arabia, are substantially vulnerable to toxoplasmosis infection as they are lacking the necessary preventive behavior. A sizable portion have no sufficient knowledge for primary prevention of congenital toxoplasmosis, health education at primary care is necessary to avert the potential toxoplasmosis related complications especially in the neonates.

Analysis of river pollution data from low-flow period by means of multivariate techniques: a case study from the oil-shale industry region, northeastern Estonia.

PubMed

Truu, Jaak; Heinaru, Eeva; Talpsep, Ene; Heinaru, Ain

2002-01-01

The oil-shale industry has created serious pollution problems in northeastern Estonia. Untreated, phenol-rich leachate from semi-coke mounds formed as a by-product of oil-shale processing is discharged into the Baltic Sea via channels and rivers. An exploratory analysis of water chemical and microbiological data sets from the low-flow period was carried out using different multivariate analysis techniques. Principal component analysis allowed us to distinguish different locations in the river system. The riverine microbial community response to water chemical parameters was assessed by co-inertia analysis. Water pH, COD and total nitrogen were negatively related to the number of biodegradative bacteria, while oxygen concentration promoted the abundance of these bacteria. The results demonstrate the utility of multivariate statistical techniques as tools for estimating the magnitude and extent of pollution based on river water chemical and microbiological parameters. An evaluation of river chemical and microbiological data suggests that the ambient natural attenuation mechanisms only partly eliminate pollutants from river water, and that a sufficient reduction of more recalcitrant compounds could be achieved through the reduction of wastewater discharge from the oil-shale chemical industry into the rivers.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

Patient experience and process measures of quality of care at home health agencies: Factors associated with high performance.

PubMed

Smith, Laura M; Anderson, Wayne L; Lines, Lisa M; Pronier, Cristalle; Thornburg, Vanessa; Butler, Janelle P; Teichman, Lori; Dean-Whittaker, Debra; Goldstein, Elizabeth

2017-01-01

We examined the effects of provider characteristics on home health agency performance on patient experience of care (Home Health CAHPS) and process (OASIS) measures. Descriptive, multivariate, and factor analyses were used. While agencies score high on both domains, factor analyses showed that the underlying items represent separate constructs. Freestanding and Visiting Nurse Association agencies, higher number of home health aides per 100 episodes, and urban location were statistically significant predictors of lower performance. Lack of variation in composite measures potentially led to counterintuitive results for effects of organizational characteristics. This exploratory study showed the value of having separate quality domains.

77 FR 37025 - Final Priority: Disability Rehabilitation Research Project-Burn Model Systems Centers

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-20

... other types of research, including but not limited to, descriptive research, exploratory research, and... interventions research and descriptive research, exploratory research, measures development, or other types of... injury or to conduct other types of research, including but not limited to, descriptive research...

Absenteeism in Undergraduate Business Education: A Proposed Model and Exploratory Investigation

ERIC Educational Resources Information Center

Burke, Lisa A.

2010-01-01

One issue in undergraduate business education remaining underexamined is student absenteeism. In this article, the literature on undergraduate absenteeism is reviewed culminating in a proposed conceptual framework to guide future research, and an exploratory investigation of management students' attitudes about absenteeism is conducted.…

[Factor structure validity of the social capital scale used at baseline in the ELSA-Brasil study].

PubMed

Souto, Ester Paiva; Vasconcelos, Ana Glória Godoi; Chor, Dora; Reichenheim, Michael E; Griep, Rosane Härter

2016-07-21

This study aims to analyze the factor structure of the Brazilian version of the Resource Generator (RG) scale, using baseline data from the Brazilian Longitudinal Health Study in Adults (ELSA-Brasil). Cross-validation was performed in three random subsamples. Exploratory factor analysis using exploratory structural equation models was conducted in the first two subsamples to diagnose the factor structure, and confirmatory factor analysis was used in the third to corroborate the model defined by the exploratory analyses. Based on the 31 initial items, the model with the best fit included 25 items distributed across three dimensions. They all presented satisfactory convergent validity (values greater than 0.50 for the extracted variance) and precision (values greater than 0.70 for compound reliability). All factor correlations were below 0.85, indicating full discriminative factor validity. The RG scale presents acceptable psychometric properties and can be used in populations with similar characteristics.

Source Apportionment and Risk Assessment of Emerging Contaminants: An Approach of Pharmaco-Signature in Water Systems

PubMed Central

Jiang, Jheng Jie; Lee, Chon Lin; Fang, Meng Der; Boyd, Kenneth G.; Gibb, Stuart W.

2015-01-01

This paper presents a methodology based on multivariate data analysis for characterizing potential source contributions of emerging contaminants (ECs) detected in 26 river water samples across multi-scape regions during dry and wet seasons. Based on this methodology, we unveil an approach toward potential source contributions of ECs, a concept we refer to as the “Pharmaco-signature.” Exploratory analysis of data points has been carried out by unsupervised pattern recognition (hierarchical cluster analysis, HCA) and receptor model (principal component analysis-multiple linear regression, PCA-MLR) in an attempt to demonstrate significant source contributions of ECs in different land-use zone. Robust cluster solutions grouped the database according to different EC profiles. PCA-MLR identified that 58.9% of the mean summed ECs were contributed by domestic impact, 9.7% by antibiotics application, and 31.4% by drug abuse. Diclofenac, ibuprofen, codeine, ampicillin, tetracycline, and erythromycin-H2O have significant pollution risk quotients (RQ>1), indicating potentially high risk to aquatic organisms in Taiwan. PMID:25874375

FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: Classification of samples for the presence of lactose and fraud detection by addition of maltodextrin.

PubMed

Rodrigues Júnior, Paulo Henrique; de Sá Oliveira, Kamila; de Almeida, Carlos Eduardo Rocha; De Oliveira, Luiz Fernando Cappa; Stephani, Rodrigo; Pinto, Michele da Silva; de Carvalho, Antônio Fernandes; Perrone, Ítalo Tuler

2016-04-01

FT-Raman spectroscopy has been explored as a quick screening method to evaluate the presence of lactose and identify milk powder samples adulterated with maltodextrin (2.5-50% w/w). Raman measurements can easily differentiate samples of milk powder, without the need for sample preparation, while traditional quality control methods, including high performance liquid chromatography, are cumbersome and slow. FT-Raman spectra were obtained from samples of whole lactose and low-lactose milk powder, both without and with addition of maltodextrin. Differences were observed between the spectra involved in identifying samples with low lactose content, as well as adulterated samples. Exploratory data analysis using Raman spectroscopy and multivariate analysis was also developed to classify samples with PCA and PLS-DA. The PLS-DA models obtained allowed to correctly classify all samples. These results demonstrate the utility of FT-Raman spectroscopy in combination with chemometrics to infer about the quality of milk powder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

NASA Astrophysics Data System (ADS)

Jesse, S.; Chi, M.; Belianinov, A.; Beekman, C.; Kalinin, S. V.; Borisevich, A. Y.; Lupini, A. R.

2016-05-01

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. Here, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. However, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.

Structural hierarchy of autism spectrum disorder symptoms: an integrative framework.

PubMed

Kim, Hyunsik; Keifer, Cara M; Rodriguez-Seijas, Craig; Eaton, Nicholas R; Lerner, Matthew D; Gadow, Kenneth D

2018-01-01

In an attempt to resolve questions regarding the symptom classification of autism spectrum disorder (ASD), previous research generally aimed to demonstrate superiority of one model over another. Rather than adjudicating which model may be optimal, we propose an alternative approach that integrates competing models using Goldberg's bass-ackwards method, providing a comprehensive understanding of the underlying symptom structure of ASD. The study sample comprised 3,825 individuals, consecutive referrals to a university hospital developmental disabilities specialty clinic or a child psychiatry outpatient clinic. This study analyzed DSM-IV-referenced ASD symptom statements from parent and teacher versions of the Child and Adolescent Symptom Inventory-4R. A series of exploratory structural equation models was conducted in order to produce interpretable latent factors that account for multivariate covariance. Results indicated that ASD symptoms were structured into an interpretable hierarchy across multiple informants. This hierarchy includes five levels; key features of ASD bifurcate into different constructs with increasing specificity. This is the first study to examine an underlying structural hierarchy of ASD symptomatology using the bass-ackwards method. This hierarchy demonstrates how core features of ASD relate at differing levels of resolution, providing a model for conceptualizing ASD heterogeneity and a structure for integrating divergent theories of cognitive processes and behavioral features that define the disorder. These findings suggest that a more coherent and complete understanding of the structure of ASD symptoms may be reflected in a metastructure rather than at one level of resolution. © 2017 Association for Child and Adolescent Mental Health.

Understanding the Impact of a Half Day Learning Intervention on Emotional Intelligence Competencies: An Exploratory Study

ERIC Educational Resources Information Center

Carrick, Laurie Ann

2010-01-01

Empirical evidence has identified emotional intelligence competencies as part of the transformational leadership style. The development of emotional intelligence competencies has been reviewed in the context of a leadership development learning intervention encompassing the model of assessment, challenge and support. The exploratory study…

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

EPA Science Inventory

Background: Exploratory toxicology is a new emerging research area whose ultimate mission is that of protecting human health and environment from risks posed by chemicals. In this regard, the ethical and practical limitation of animal testing has encouraged the promotion of compu...

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

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

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Exploring high school science students' perceptions of parental involvement in their education.

PubMed

Mji, Andile; Mbinda, Zoleka

2005-08-01

This exploratory study describes high school students' perceptions of their parents' involvement in their education and in relation to school achievement. A new 12-item Parental Involvement Scale was used to measure parents' involvement in curricular and extracurricular activities and using exploratory analyses to estimate the scale's properties. Exploratory analysis resulted in the reduction of the 12 items to 8, with an internal consistency (Cronbach alpha) .82. Grade 12 science students indicated that their less educated parents were involved in activities pertaining to their learning; however, high perceived parental involvement in curricular activities was related to low achievement. It is recommended that further exploratory analyses be undertaken to examine the reported two-dimensional model of the Parental Involvement Scale.

Risk of Small Bowel Obstruction After Robot-Assisted vs Open Radical Prostatectomy.

PubMed

Loeb, Stacy; Meyer, Christian P; Krasnova, Anna; Curnyn, Caitlin; Reznor, Gally; Kibel, Adam S; Lepor, Herbert; Trinh, Quoc-Dien

2016-12-01

Whereas open radical prostatectomy is performed extraperitoneally, minimally invasive radical prostatectomy is typically performed within the peritoneal cavity. Our objective was to determine whether minimally invasive radical prostatectomy is associated with an increased risk of small bowel obstruction compared with open radical prostatectomy. In the U.S. Surveillance, Epidemiology and End Results (SEER)-Medicare database, we identified 14,147 men found to have prostate cancer from 2000 to 2008 treated by open (n = 10,954) or minimally invasive (n = 3193) radical prostatectomy. Multivariable Cox proportional hazard models were used to examine the impact of surgical approach on the diagnosis of small bowel obstruction, as well as the need for lysis of adhesions and exploratory laparotomy. During a median follow-up of 45 and 76 months, respectively, the cumulative incidence of small bowel obstruction was 3.7% for minimally invasive and 5.3% for open radical prostatectomy (p = 0.0005). Lysis of adhesions occurred in 1.1% of minimally invasive and 2.0% of open prostatectomy patients (p = 0.0003). On multivariable analysis, there was no significant difference between minimally invasive and open prostatectomy with respect to small bowel obstruction (HR 1.17, 95% CI 0.90, 1.52, p = 0.25) or lysis of adhesions (HR 0.87, 95% CI 0.50, 1.40, p = 0.57). Limitations of the study include the retrospective design and use of administrative claims data. Relative to open radical prostatectomy, minimally invasive radical prostatectomy is not associated with an increased risk of postoperative small bowel obstruction and lysis of adhesions.

Left Ventricular Function Across the Spectrum of Body Mass Index in African Americans: The Jackson Heart Study.

PubMed

Patel, Vivek G; Gupta, Deepak K; Terry, James G; Kabagambe, Edmond K; Wang, Thomas J; Correa, Aldolfo; Griswold, Michael; Taylor, Herman; Carr, John Jeffrey

2017-03-01

This study sought to assess whether body mass index (BMI) was associated with subclinical left ventricular (LV) systolic dysfunction in African-American individuals. Higher BMI is a risk factor for cardiovascular disease, including heart failure. Obesity disproportionately affects African Americans; however, the association between higher BMI and LV function in African Americans is not well understood. Peak systolic circumferential strain (ECC) was measured by tagged cardiac magnetic resonance in 1,652 adult African-American participants of the Jackson Heart Study between 2008 and 2012. We evaluated the association between BMI and ECC in multivariate linear regression and restricted cubic spline analyses adjusted for prevalent cardiovascular disease, conventional cardiovascular risk factors, LV mass, and ejection fraction. In exploratory analyses, we also examined whether inflammation, insulin resistance, or volume of visceral adipose tissue altered the association between BMI and ECC. The proportions of female, nonsmokers, diabetic, and hypertensive participants rose with increase in BMI. In multivariate-adjusted models, higher BMI was associated with worse ECC (β = 0.052; 95% confidence interval: 0.028 to 0.075), even in the setting of preserved LV ejection fraction. Higher BMI was also associated with worse ECC when accounting for markers of inflammation (C-reactive protein, E-selection, and P-selectin), insulin resistance, and volume of visceral adipose tissue. Higher BMI is significantly associated with subclinical LV dysfunction in African Americans, even in the setting of preserved LV ejection fraction. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

An exploratory factor analysis of nutritional biomarkers associated with major depression in pregnancy

PubMed Central

Bodnar, Lisa M.; Wisner, Katherine L.; Luther, James F.; Powers, Robert W.; Evans, Rhobert W.; Gallaher, Marcia J.; Newby, P.K.

2011-01-01

Objective Major depressive disorder (MDD) during pregnancy increases the risk of adverse maternal and infant outcomes. Maternal nutritional status may be a modifiable risk factor for antenatal depression. We evaluated the association between patterns in mid-pregnancy nutritional biomarkers and MDD. Design Prospective cohort study Setting Pittsburgh, Pennsylvania, USA Subjects Women who enrolled at ≤20 weeks gestation had a diagnosis of MDD made with the Structured Clinical Interview for DSM-IV at 20-, 30-, and 36-week study visits. A total of 135 women contributed 345 person-visits. Non-fasting blood drawn at enrollment was assayed for red cell essential fatty acids, plasma folate, homocysteine, and ascorbic acid; serum 25-hydroxyvitamin D, retinol, vitamin E, carotenoids, ferritin, and soluble transferrin receptors. Nutritional biomarkers were entered into principal components analysis. Results Three factors emerged: Factor 1, Essential Fatty Acids; Factor 2, Micronutrients; and Factor 3, Carotenoids. MDD was prevalent in 21.5% of women. In longitudinal multivariable logistic models, there was no association between the Essential Fatty Acid or Micronutrient patterns and MDD either before or after adjustment for employment, education, or prepregnancy BMI. In unadjusted analysis, women with Carotenoid factor scores in the middle and upper tertiles were 60% less likely than women in the bottom tertile to have MDD during pregnancy, but after adjustment for confounders, the associations were no longer statistically significant. Conclusions While meaningful patterns were derived using nutritional biomarkers, significant associations with MDD were not observed in multivariable adjusted analyses. Larger, more diverse samples are needed to understand nutrition-depression relationships during pregnancy. PMID:22152590

An exploratory factor analysis of nutritional biomarkers associated with major depression in pregnancy.

PubMed

Bodnar, Lisa M; Wisner, Katherine L; Luther, James F; Powers, Robert W; Evans, Rhobert W; Gallaher, Marcia J; Newby, P K

2012-06-01

Major depressive disorder (MDD) during pregnancy increases the risk of adverse maternal and infant outcomes. Maternal nutritional status may be a modifiable risk factor for antenatal depression. We evaluated the association between patterns in mid-pregnancy nutritional biomarkers and MDD. Prospective cohort study. Pittsburgh, Pennsylvania, USA. Women who enrolled at ≤20 weeks' gestation and had a diagnosis of MDD made with the Structured Clinical Interview for DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) at 20-, 30- and 36-week study visits. A total of 135 women contributed 345 person-visits. Non-fasting blood drawn at enrolment was assayed for red cell essential fatty acids, plasma folate, homocysteine and ascorbic acid; serum 25-hydroxyvitamin D, retinol, vitamin E, carotenoids, ferritin and soluble transferrin receptors. Nutritional biomarkers were entered into principal components analysis. Three factors emerged: Factor 1, Essential Fatty Acids; Factor 2, Micronutrients; and Factor 3, Carotenoids. MDD was prevalent in 21·5 % of women. In longitudinal multivariable logistic models, there was no association between the Essential Fatty Acids or Micronutrients pattern and MDD either before or after adjustment for employment, education or pre-pregnancy BMI. In unadjusted analysis, women with factor scores for Carotenoids in the middle and upper tertiles were 60 % less likely than women in the bottom tertile to have MDD during pregnancy, but after adjustment for confounders the associations were no longer statistically significant. While meaningful patterns were derived using nutritional biomarkers, significant associations with MDD were not observed in multivariable adjusted analyses. Larger, more diverse samples are needed to understand nutrition-depression relationships during pregnancy.

Are men's misogynistic attitudes associated with poor mental health and substance use behaviors? An exploratory study of men in Tijuana, Mexico.

PubMed

Fleming, Paul J; Patterson, Thomas L; Chavarin, Claudia V; Semple, Shirley J; Magis-Rodriguez, Carlos; Pitpitan, Eileen V

2018-04-01

Men's misogynistic attitudes (i.e., dislike or contempt for women) have been shown to be associated with men's perpetration of physical/sexual violence against women and poor health outcomes for women. However, these attitudes have rarely been examined for their influence on men's own health. This paper examines the socio-demographic, substance use, and mental health correlates of misogynistic attitudes among a binational sample of men (n=400) in Tijuana, Mexico with high-risk substance use and sexual behaviors. We used a 6-item scale to measure misogynistic attitudes ( α = .72), which was developed specifically for this context. We used descriptive statistics to describe our sample population and the extent to which they hold misogynistic attitudes. Then, using misogynistic attitudes as our dependent variable, we conducted bivariate linear regression and multivariable linear regression to examine the relationship between these attitudes and socio-demographic characteristics, substance use behaviors (i.e., use of alcohol, marijuana, heroin, methamphetamines, cocaine), and mental health (i.e., depression, self-esteem). In the multivariable model, we found significant relationships between misogynistic attitudes and education level ( t = -4.34, p < 0.01), heroin use in the past 4 months ( t = 2.50, p = 0.01), and depressive symptoms ( t = 3.37, p < 0.01). These findings suggest that misogynistic attitudes are linked to poor health outcomes for men and future research needs to further explore the temporality of these relationships and identify strategies for reducing men's misogynistic attitudes with the ultimate aim of improving the health and well-being of both women and men.

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

From proteomics to systems biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a user-oriented platform.

PubMed

Weckwerth, Wolfram; Wienkoop, Stefanie; Hoehenwarter, Wolfgang; Egelhofer, Volker; Sun, Xiaoliang

2014-01-01

Genome sequencing and systems biology are revolutionizing life sciences. Proteomics emerged as a fundamental technique of this novel research area as it is the basis for gene function analysis and modeling of dynamic protein networks. Here a complete proteomics platform suited for functional genomics and systems biology is presented. The strategy includes MAPA (mass accuracy precursor alignment; http://www.univie.ac.at/mosys/software.html ) as a rapid exploratory analysis step; MASS WESTERN for targeted proteomics; COVAIN ( http://www.univie.ac.at/mosys/software.html ) for multivariate statistical analysis, data integration, and data mining; and PROMEX ( http://www.univie.ac.at/mosys/databases.html ) as a database module for proteogenomics and proteotypic peptides for targeted analysis. Moreover, the presented platform can also be utilized to integrate metabolomics and transcriptomics data for the analysis of metabolite-protein-transcript correlations and time course analysis using COVAIN. Examples for the integration of MAPA and MASS WESTERN data, proteogenomic and metabolic modeling approaches for functional genomics, phosphoproteomics by integration of MOAC (metal-oxide affinity chromatography) with MAPA, and the integration of metabolomics, transcriptomics, proteomics, and physiological data using this platform are presented. All software and step-by-step tutorials for data processing and data mining can be downloaded from http://www.univie.ac.at/mosys/software.html.

Predictors of hepatitis B vaccination status in healthcare workers in Belgrade, Serbia, December 2015.

PubMed

Kisic-Tepavcevic, Darija; Kanazir, Milena; Gazibara, Tatjana; Maric, Gorica; Makismovic, Natasa; Loncarevic, Goranka; Pekmezovic, Tatjana

2017-04-20

Despite the availability of a safe and effective vaccine since 1982, overall coverage of hepatitis B vaccination among healthcare workers (HCWs) has not reached a satisfactory level in many countries worldwide. The aim of this study was to estimate the prevalence of hepatitis B vaccination, and to assess the predictors of hepatitis B vaccination status among HCWs in Serbia. Of 380 randomly selected HCWs, 352 (92.6%) were included in the study. The prevalence of hepatitis B vaccination acceptance was 66.2%. The exploratory factor analyses using the vaccination-refusal scale showed that items clustered under 'threat of disease' explained the highest proportion (30.4%) of variance among those declining vaccination. The factor analyses model of the potential reasons for receiving the hepatitis B vaccine showed that 'social influence' had the highest contribution (47.5%) in explaining variance among those vaccinated. In the multivariate adjusted model the following variables were independent predictors of hepatitis B vaccination status: occupation, duration of work experience, exposure to blood in the previous year, and total hepatitis B-related knowledge score. Our results highlight the need for well-planned national policies, possibly including mandatory hepatitis B immunisation, in the Serbian healthcare environment. This article is copyright of The Authors, 2017.

[How to intervene and prevent stunting of children from homes belonging to the Sisbén in Caldas].

PubMed

Benjumea, María Victoria; Parra, José Hernán; Jaramillo, Juan Felipe

2017-12-01

Growth retardation or chronic malnutrition (low height for age) indicates a failure in the natural genetic potential that allows us to growth. To estimate predictive models of growth retardation in households with children younger than five years in the department of Caldas and registered in the identification system of potential beneficiaries of social programs (Sistema de Identificación de Potenciales Beneficiarios de Programas Sociales, Sisbén). We conducted an analytical study in all households (N=56,987) included in the Sisbén III database with the presence of children younger than five years (N=33,244). The variables under study were demographic and socioeconomic characteristics, health service access, housing, poverty, education, job market, and growth retardation. The multivariate analysis was done in two phases: first, an exploratory analysis of households using hierarchical classification (cluster), then estimation of a nonlinear predictive model (probit) with growth retardation as the dependent variable. The largest proportion of growth retardation in children younger than five years was found in southcentral Caldas, in urban centers, and households with monthly income lower than USD$ 65. Poverty in Caldas women-headed households with children younger than five years registered in the Sisbén was the main predictor of growth retardation.

Is It Feasible to Identify Natural Clusters of TSC-Associated Neuropsychiatric Disorders (TAND)?

PubMed

Leclezio, Loren; Gardner-Lubbe, Sugnet; de Vries, Petrus J

2018-04-01

Tuberous sclerosis complex (TSC) is a genetic disorder with multisystem involvement. The lifetime prevalence of TSC-Associated Neuropsychiatric Disorders (TAND) is in the region of 90% in an apparently unique, individual pattern. This "uniqueness" poses significant challenges for diagnosis, psycho-education, and intervention planning. To date, no studies have explored whether there may be natural clusters of TAND. The purpose of this feasibility study was (1) to investigate the practicability of identifying natural TAND clusters, and (2) to identify appropriate multivariate data analysis techniques for larger-scale studies. TAND Checklist data were collected from 56 individuals with a clinical diagnosis of TSC (n = 20 from South Africa; n = 36 from Australia). Using R, the open-source statistical platform, mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Ward's method rendered six TAND clusters with good face validity and significant convergence with a six-factor exploratory factor analysis solution. The "bottom-up" data-driven strategies identified a "scholastic" cluster of TAND manifestations, an "autism spectrum disorder-like" cluster, a "dysregulated behavior" cluster, a "neuropsychological" cluster, a "hyperactive/impulsive" cluster, and a "mixed/mood" cluster. These feasibility results suggest that a combination of cluster analysis and exploratory factor analysis methods may be able to identify clinically meaningful natural TAND clusters. Findings require replication and expansion in larger dataset, and could include quantification of cluster or factor scores at an individual level. Copyright © 2018 Elsevier Inc. All rights reserved.

MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation.

PubMed

Bernard, Jürgen; Wilhelm, Nils; Krüger, Björn; May, Thorsten; Schreck, Tobias; Kohlhammer, Jörn

2013-12-01

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

PubMed

Moberg, Fallon B; Anestis, Michael D

2015-01-01

Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

The Roles of Negative Career Thinking and Career Problem-Solving Self-Efficacy in Career Exploratory Behavior

ERIC Educational Resources Information Center

Bullock-Yowell, Emily; Katz, Sheba P.; Reardon, Robert C.; Peterson, Gary W.

2012-01-01

The respective roles of social cognitive career theory and cognitive information processing in career exploratory behavior were analyzed. A verified path model shows cognitive information processing theory's negative career thoughts inversely predict social cognitive career theory's career problem-solving self-efficacy, which predicts career…

An Exploratory Study of Sustainable Development at Italian Universities

ERIC Educational Resources Information Center

Vagnoni, Emidia; Cavicchi, Caterina

2015-01-01

Purpose: This paper aims to outline the current status of the implementation of sustainability practices in the context of Italian public universities, highlighting the strengths and gaps. Design/methodology/approach: Based on a qualitative approach, an exploratory study design has been outlined using the model of Glavic and Lukman (2007) focusing…

Comprehensive Adult Student Assessment Systems Braille Reading Assessment: An Exploratory Study

ERIC Educational Resources Information Center

Posey, Virginia K.; Henderson, Barbara W.

2012-01-01

Introduction: This exploratory study determined whether transcribing selected test items on an adult life and work skills reading test into braille could maintain the same approximate scale-score range and maintain fitness within the item response theory model as used by the Comprehensive Adult Student Assessment Systems (CASAS) for developing…

Exploratory Study of the HOPE Foundation[C] Courageous Leadership Academy: Summary of Findings

ERIC Educational Resources Information Center

Brown, Seth; Choi, KC; Herman, Becki

2011-01-01

The HOPE Foundation (HOPE) commissioned the American Institutes for Research (AIR) to conduct an exploratory study of the implementation and impact of the Courageous Leadership Academy (CLA). In this report, the authors introduce the school reform model, describe the study methodology, present findings for each of the three research questions…

Self-Regulatory Efficacy and Mindset of At-Risk Students: An Exploratory Study

ERIC Educational Resources Information Center

Matheson, Ian A.

2015-01-01

There is a limited body of research examining how students' beliefs about intelligence and about their abilities relate to different learning environments. As reported here, I examined secondary school students' beliefs, goals, and expectations guided by Zimmerman's (2000) model of self-regulated learning. In this exploratory study, 230 secondary…

Exploratory Bi-Factor Analysis: The Oblique Case

ERIC Educational Resources Information Center

Jennrich, Robert I.; Bentler, Peter M.

2012-01-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

ERIC Educational Resources Information Center

Fratamico, Lauren; Conati, Cristina; Kardan, Samad; Roll, Ido

2017-01-01

Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a…

Parental Influence on Exploratory Students' College Choice, Major, and Career Decision Making

ERIC Educational Resources Information Center

Workman, Jamie L.

2015-01-01

This article explores parental influence on exploratory students' college choice, major, and career decision making. The research began with examination of a first year academic advising model and Living Learning Community. Parental influence emerged as a key theme in student decision making processes. The project was conducted using grounded…

Authentic Reading, Writing, and Discussion: An Exploratory Study of a Pen Pal Project

ERIC Educational Resources Information Center

Gambrell, Linda B.; Hughes, Elizabeth M.; Calvert, Leah; Malloy, Jacquelynn A.; Igo, Brent

2011-01-01

In this exploratory study, reading, writing, and discussion were examined within the context of a pen pal intervention focusing on authentic literacy tasks. The study employed a mixed-method design with a triangulation-convergence model to explore the relationship between authentic literacy tasks and the literacy motivation of elementary students…

Framing matters: Effects of framing on older adults’ exploratory decision-making

PubMed Central

Cooper, Jessica A.; Blanco, Nathaniel; Maddox, W. Todd

2016-01-01

We examined framing effects on exploratory decision-making. In Experiment 1 we tested older and younger adults in two decision-making tasks separated by one week, finding that older adults’ decision-making performance was preserved when maximizing gains, but declined when minimizing losses. Computational modeling indicates that younger adults in both conditions, and older adults in gains-maximization, utilized a decreasing threshold strategy (which is optimal), but older adults in losses were better fit by a fixed-probability model of exploration. In Experiment 2 we examined within-subjects behavior in older and younger adults in the same exploratory decision-making task, but without a time separation between tasks. We replicated the older adult disadvantage in loss-minimization from Experiment 1, and found that the older adult deficit was significantly reduced when the loss-minimization task immediately followed the gains-maximization task. We conclude that older adults’ performance in exploratory decision-making is hindered when framed as loss-minimization, but that this deficit is attenuated when older adults can first develop a strategy in a gains-framed task. PMID:27977218

Framing matters: Effects of framing on older adults' exploratory decision-making.

PubMed

Cooper, Jessica A; Blanco, Nathaniel J; Maddox, W Todd

2017-02-01

We examined framing effects on exploratory decision-making. In Experiment 1 we tested older and younger adults in two decision-making tasks separated by one week, finding that older adults' decision-making performance was preserved when maximizing gains, but it declined when minimizing losses. Computational modeling indicates that younger adults in both conditions, and older adults in gains maximization, utilized a decreasing threshold strategy (which is optimal), but older adults in losses were better fit by a fixed-probability model of exploration. In Experiment 2 we examined within-subject behavior in older and younger adults in the same exploratory decision-making task, but without a time separation between tasks. We replicated the older adult disadvantage in loss minimization from Experiment 1 and found that the older adult deficit was significantly reduced when the loss-minimization task immediately followed the gains-maximization task. We conclude that older adults' performance in exploratory decision-making is hindered when framed as loss minimization, but that this deficit is attenuated when older adults can first develop a strategy in a gains-framed task. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Welch t-test on distances

PubMed Central

2016-01-01

Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. Results: We develop a solution in the form of a distance-based Welch t-test, TW2, for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and TW2 in reanalysis of two existing microbiome datasets, where the methodology has originated. Availability and Implementation: The source code for methods and analysis of this article is available at https://github.com/alekseyenko/Tw2. Further guidance on application of these methods can be obtained from the author. Contact: alekseye@musc.edu PMID:27515741

Multivariate Welch t-test on distances.

PubMed

Alekseyenko, Alexander V

2016-12-01

Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. We develop a solution in the form of a distance-based Welch t-test, [Formula: see text], for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and [Formula: see text] in reanalysis of two existing microbiome datasets, where the methodology has originated. The source code for methods and analysis of this article is available at https://github.com/alekseyenko/Tw2 Further guidance on application of these methods can be obtained from the author. alekseye@musc.edu. © The Author 2016. Published by Oxford University Press.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

PubMed

He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

2015-02-25

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

Surface Enhanced Raman Spectroscopy (SERS) and multivariate analysis as a screening tool for detecting Sudan I dye in culinary spices

NASA Astrophysics Data System (ADS)

Di Anibal, Carolina V.; Marsal, Lluís F.; Callao, M. Pilar; Ruisánchez, Itziar

2012-02-01

Raman spectroscopy combined with multivariate analysis was evaluated as a tool for detecting Sudan I dye in culinary spices. Three Raman modalities were studied: normal Raman, FT-Raman and SERS. The results show that SERS is the most appropriate modality capable of providing a proper Raman signal when a complex matrix is analyzed. To get rid of the spectral noise and background, Savitzky-Golay smoothing with polynomial baseline correction and wavelet transform were applied. Finally, to check whether unadulterated samples can be differentiated from samples adulterated with Sudan I dye, an exploratory analysis such as principal component analysis (PCA) was applied to raw data and data processed with the two mentioned strategies. The results obtained by PCA show that Raman spectra need to be properly treated if useful information is to be obtained and both spectra treatments are appropriate for processing the Raman signal. The proposed methodology shows that SERS combined with appropriate spectra treatment can be used as a practical screening tool to distinguish samples suspicious to be adulterated with Sudan I dye.

A simple and exploratory way to determine the mean-variance relationship in generalized linear models.

PubMed

Tsou, Tsung-Shan

2007-03-30

This paper introduces an exploratory way to determine how variance relates to the mean in generalized linear models. This novel method employs the robust likelihood technique introduced by Royall and Tsou.A urinary data set collected by Ginsberg et al. and the fabric data set analysed by Lee and Nelder are considered to demonstrate the applicability and simplicity of the proposed technique. Application of the proposed method could easily reveal a mean-variance relationship that would generally be left unnoticed, or that would require more complex modelling to detect. Copyright (c) 2006 John Wiley & Sons, Ltd.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change.

PubMed

Feng, Yongjiu; Tong, Xiaohua

2017-09-22

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.; Yuan, Ke-Hai

2007-01-01

In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several…

Exploratory Case Study: On School Counselors' Perceived Influence on Low Socioeconomic Students' College Enrollment

ERIC Educational Resources Information Center

Deslonde, Vernell L.

2017-01-01

The purpose of this qualitative exploratory case study was to examine the high school counselors' perception of their ability to influence low socioeconomic students' postsecondary enrollment decisions in seven Title I high schools in southern California. Perna and Thomas' Student Success model and the Delivery System of the American School…

Building Virtually Free Subject Area Expertise through Social Media: An Exploratory Study

ERIC Educational Resources Information Center

Kooy, Brian K.

2016-01-01

Central to the ongoing success of the liaison model is the need for liaison librarians to stay informed and up-to-date about recent developments in the subject areas of their assigned academic departments and programs. This article describes an exploratory study conducted to determine whether information obtained from the social media accounts of…

Gender-Related Quality of Parent-Child Interactions and Early Adolescent Problem Behaviors: Exploratory Study with Midwestern Samples

ERIC Educational Resources Information Center

Spoth, Richard; Neppl, Tricia; Goldberg-Lillehoj, Catherine; Jung, Tony; Ramisetty-Mikler, Suhasini

2006-01-01

This article reports two exploratory studies testing a model guided by a social interactional perspective, positing an inverse relation between the quality of parent-child interactions and adolescent problem behaviors. It addresses mixed findings in the literature related to gender differences. Study 1 uses cross-sectional survey data from…

FACTOR 9.2: A Comprehensive Program for Fitting Exploratory and Semiconfirmatory Factor Analysis and IRT Models

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Ferrando, Pere J.

2013-01-01

FACTOR 9.2 was developed for three reasons. First, exploratory factor analysis (FA) is still an active field of research although most recent developments have not been incorporated into available programs. Second, there is now renewed interest in semiconfirmatory (SC) solutions as suitable approaches to the complex structures are commonly found…

The 1980 US/Canada wheat and barley exploratory experiment. Volume 2: Addenda

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Prior, H. L.; Payne, R. W.; Disler, J. M.

1983-01-01

Three study areas supporting the U.S./Canada Wheat and Barley Exploratory Experiment are discussed including an evaluation of the experiment shakedown test analyst labeling results, an evaluation of the crop proportion estimate procedure 1A component, and the evaluation of spring wheat and barley crop calendar models for the 1979 crop year.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Sunlight exposure and cardiovascular risk factors in the REGARDS study: a cross-sectional split-sample analysis

PubMed Central

2014-01-01

Background Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status. Methods Residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine previous-year sunlight radiation exposure for 17,773 black and white participants aged 45+ from the US. Exploratory and confirmatory analyses were performed by randomly dividing the sample into halves. Logistic regression models were used to examine relationships with cardiovascular risk factors. Results The lowest, compared to the highest quartile of insolation exposure was associated with lower high-density lipoprotein levels in adjusted exploratory (−2.7 mg/dL [95% confidence interval: −4.2, −1.2]) and confirmatory (−1.5 mg/dL [95% confidence interval: −3.0, −0.1]) models. The lowest, compared to the highest quartile of insolation exposure was associated with higher systolic blood pressure levels in unadjusted exploratory and confirmatory, as well as the adjusted exploratory model (2.3 mmHg [95% confidence interval: 0.8, 3.8]), but not the adjusted confirmatory model (1.6 mg/dL [95% confidence interval: −0.5, 3.7]). Conclusions The results of this study suggest that lower long-term sunlight exposure has an association with lower high-density lipoprotein levels. However, all associations were weak, thus it is not known if insolation may affect cardiovascular outcomes through these risk factors. PMID:24946776

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Determinants of Participation and Expenditure Patterns of Private Tuition Received by Primary School Students in Penang, Malaysia: An Exploratory Study

ERIC Educational Resources Information Center

Jelani, Juliana; Tan, Andrew K. G.

2012-01-01

In this exploratory study, the censored Tobit model is applied on primary data collected amongst parents of primary school students in Penang, Malaysia to examine the determinants of participation and expenditures on private tuition (PT). Results of the marginal effects indicate that socio-demographic characteristics--ethnicity, household income,…

Exploratory Case Studies of the Role of the Community School Coordinator: Developing the School Social Network in Urban Elementary Schools

ERIC Educational Resources Information Center

Ruffin, Verna Dean

2013-01-01

This exploratory case study examines the role of the community school coordinator (CSC) in the community school model in two urban elementary schools. It seeks to understand how the role and responsibilities of a community school coordinator supports fostering relationships with parents, teachers, students and the community (i.e. building the…

University Faculty Members' Perceptions of the Factors That Facilitate Technology Integration into Their Instruction: An Exploratory Case Study in Qatar

ERIC Educational Resources Information Center

Karkouti, Ibrahim Mohamad

2016-01-01

This qualitative, exploratory case study was designed to elicit faculty members' perceptions of the factors that facilitate technology integration into their instruction. The study was conducted at a midsized higher education institution in Qatar. Davis's (1986) technology acceptance model (TAM) is the conceptual framework that guided this study…

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Bayesian Exploratory Factor Analysis

PubMed Central

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

2014-01-01

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

Analytics For Distracted Driver Behavior Modeling in Dilemma Zone

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

Li, Jan-Mou; Malikopoulos, Andreas; Thakur, Gautam

2014-01-01

In this paper, we present the results obtained and insights gained through the analysis of TRB contest data. We used exploratory analysis, regression, and clustering models for gaining insights into the driver behavior in a dilemma zone while driving under distraction. While simple exploratory analysis showed the distinguishing driver behavior patterns among different popu- lation groups in the dilemma zone, regression analysis showed statically signification relationships between groups of variables. In addition to analyzing the contest data, we have also looked into the possible impact of distracted driving on the fuel economy.

The association between health-related quality-of-life scores and clinical outcomes in metastatic castration-resistant prostate cancer patients: Exploratory analyses of AFFIRM and PREVAIL studies.

PubMed

Beer, Tomasz M; Miller, Kurt; Tombal, Bertrand; Cella, David; Phung, De; Holmstrom, Stefan; Ivanescu, Cristina; Skaltsa, Konstantina; Naidoo, Shevani

2017-12-01

Our exploratory analysis examined the association between health-related quality of life (HRQoL) (baseline and change over time) and clinical outcomes (overall survival [OS]/radiographic progression-free survival [rPFS]) in metastatic castration-resistant prostate cancer (mCRPC). HRQoL, OS and rPFS were assessed in phase III trials comparing enzalutamide with placebo in chemotherapy-naïve (PREVAIL; NCT01212991) or post-chemotherapy (AFFIRM; NCT00974311) mCRPC. HRQoL was assessed using the Functional Assessment of Cancer Therapy-Prostate (FACT-P). Multivariate analyses evaluated the prognostic significance of baseline and time-dependent scores after adjusting for treatment and clinical/demographic variables. Hazard ratios (HRs) and 95% confidence intervals (CIs) represented the hazard of rPFS or OS per minimally important difference (MID) score change in HRQoL variables. In baseline and time-dependent multivariate analyses, OS was independently associated with multiple HRQoL measures across both studies. In time-dependent analyses, a 10-point (upper bound of MID range) increase (improvement) in FACT-P total score was associated with reductions in mortality risk of 19% in AFFIRM (HR 0.81 [95% CI 0.78-0.84]) and 21% in PREVAIL (HR 0.79 [0.76-0.83]). For baseline analyses, a 10-point increase in FACT-P total score was associated with reductions in mortality risk of 12% (HR 0.88 [0.84-0.93]) and 10% (HR 0.90 [0.86-0.95]) in AFFIRM and PREVAIL, respectively. rPFS was associated with a subset of HRQoL domains in both studies. Several baseline HRQoL domains were prognostic for rPFS and OS in patients with mCRPC, and this association was maintained during treatment, indicating that changes in HRQoL are informative for patients' expected survival. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Exploratory Study: Assessment of Modeled Dioxin ...

EPA Pesticide Factsheets

EPA has released an external review draft entitled, An Exploratory Study: Assessment of Modeled Dioxin Exposure in Ceramic Art Studios(External Review Draft). The public comment period and the external peer-review workshop are separate processes that provide opportunities for all interested parties to comment on the document. In addition to consideration by EPA, all public comments submitted in accordance with this notice will also be forwarded to EPA’s contractor for the external peer-review panel prior to the workshop. EPA has realeased this draft document solely for the purpose of pre-dissemination peer review under applicable information quality guidelines. This document has not been formally disseminated by EPA. It does not represent and should not be construed to represent any Agency policy or determination. The purpose of this report is to describe an exploratory investigation of potential dioxin exposures to artists/hobbyists who use ball clay to make pottery and related products.

Brain histamine depletion enhances the behavioural sequences complexity of mice tested in the open-field: Partial reversal effect of the dopamine D2/D3 antagonist sulpiride.

PubMed

Santangelo, Andrea; Provensi, Gustavo; Costa, Alessia; Blandina, Patrizio; Ricca, Valdo; Crescimanno, Giuseppe; Casarrubea, Maurizio; Passani, M Beatrice

2017-02-01

Markers of histaminergic dysregulation were found in several neuropsychiatric disorders characterized by repetitive behaviours, thoughts and stereotypies. We analysed the effect of acute histamine depletion by means of i. c.v. injections of alpha-fluoromethylhistidine, a blocker of histidine decarboxylase, on the temporal organization of motor sequences of CD1 mice behaviour in the open-field test. An ethogram encompassing 9 behavioural components was employed. Durations and frequencies were only slightly affected by treatments. However, as revealed by multivariate t-pattern analysis, histamine depletion was associated with a striking increase in the number of behavioural patterns. We found 42 patterns of different composition occurring, on average, 520.90 ± 50.23 times per mouse in the histamine depleted (HD) group, whereas controls showed 12 different patterns occurring on average 223.30 ± 20.64 times. Exploratory and grooming behaviours clustered separately, and the increased pattern complexity involved exclusively exploratory patterns. To test the hypothesis of a histamine-dopamine interplay on behavioural pattern phenotype, non-sedative doses of the D2/D3 antagonist sulpiride (12.5-25-50 mg/kg) were additionally administered to different groups of HD mice. Sulpiride counterbalanced the enhancement of exploratory patterns of different composition, but it did not affect the mean number of patterns at none of the doses used. Our results provide new insights on the role of histamine on repetitive behavioural sequences of freely moving mice. Histamine deficiency is correlated with a general enhancement of pattern complexity. This study supports a putative involvement of histamine in the pathophysiology of tics and related disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Temporal and spatial analysis of psittacosis in association with poultry farming in the Netherlands, 2000-2015.

PubMed

Hogerwerf, Lenny; Holstege, Manon M C; Benincà, Elisa; Dijkstra, Frederika; van der Hoek, Wim

2017-07-26

Human psittacosis is a highly under diagnosed zoonotic disease, commonly linked to psittacine birds. Psittacosis in birds, also known as avian chlamydiosis, is endemic in poultry, but the risk for people living close to poultry farms is unknown. Therefore, our study aimed to explore the temporal and spatial patterns of human psittacosis infections and identify possible associations with poultry farming in the Netherlands. We analysed data on 700 human cases of psittacosis notified between 01-01-2000 and 01-09-2015. First, we studied the temporal behaviour of psittacosis notifications by applying wavelet analysis. Then, to identify possible spatial patterns, we applied spatial cluster analysis. Finally, we investigated the possible spatial association between psittacosis notifications and data on the Dutch poultry sector at municipality level using a multivariable model. We found a large spatial cluster that covered a highly poultry-dense area but additional clusters were found in areas that had a low poultry density. There were marked geographical differences in the awareness of psittacosis and the amount and the type of laboratory diagnostics used for psittacosis, making it difficult to draw conclusions about the correlation between the large cluster and poultry density. The multivariable model showed that the presence of chicken processing plants and slaughter duck farms in a municipality was associated with a higher rate of human psittacosis notifications. The significance of the associations was influenced by the inclusion or exclusion of farm density in the model. Our temporal and spatial analyses showed weak associations between poultry-related variables and psittacosis notifications. Because of the low number of psittacosis notifications available for analysis, the power of our analysis was relative low. Because of the exploratory nature of this research, the associations found cannot be interpreted as evidence for airborne transmission of psittacosis from poultry to the general population. Further research is needed to determine the prevalence of C. psittaci in Dutch poultry. Also, efforts to promote PCR-based testing for C. psittaci and genotyping for source tracing are important to reduce the diagnostic deficit, and to provide better estimates of the human psittacosis burden, and the possible role of poultry.

Surgical site infection: an observer-blind, randomized trial comparing electrocautery and conventional scalpel.

PubMed

Rongetti, Regiane Ladislau; Oliveira e Castro, Paulo de Tarso; Vieira, Renê Aloisio da Costa; Serrano, Sérgio Vicente; Mengatto, Mariana Fabro; Fregnani, José Humberto Tavares Guerreiro

2014-01-01

To evaluate the incidence of surgical site infection (SSI) based on the type of scalpel used for incisions in the skin and in subcutaneous tissues. Observer-blind, randomized equivalence clinical trial with two arms (electrocautery versus conventional scalpel) which evaluated 133 women undergoing elective abdominal gynecologic oncology surgery. A simple randomization stratified by body mass index (BMI: 30 kg/m(2)) was carried out. Women were evaluated at 14 and 30 days following the operation. A multivariate analysis was performed in order to check whether the type of scalpel would be a risk factor for SSI. Group arms were balanced for all variables, excepted for surgical time, which was significantly higher in the electrocautery group (mean: 161.1 versus 203.5 min, P = 0.029). The rates of SSI were 7.4% and 9.7%, respectively, for the conventional scalpel and electrocautery groups (P = 0.756). The exploratory multivariate model identified body mass index ≥30 kg/m(2) (OR = 24.2, 95% CI: 2.8-212.1) and transverse surgical incision (OR = 8.1, 95% CI: 1.5-42.6) as independent risk factors for SSI. The type of scalpel used in surgery, when adjusted for these variables and the surgery time, was not a risk factor for SSI. This study showed that the SSI rates for conventional scalpel and electrocautery were not significantly different. These results were consistent with others reported in the literature and would not allow a surgeon to justify scalpel choice based on SSI. NCT01410175 (Clinical Trials - NIH). Copyright © 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Low rate of dermatology outpatient visits in Asian-Americans: an initial survey study for associated patient-related factors

PubMed Central

2014-01-01

Background Asian-Americans represent the fastest growing minority group in the United States, but are under-represented patients in outpatient dermatology clinics. At the same time, skin cancer rates in individuals of Asian descent are increasing, but skin cancer detection appears to be delayed in Asian-Americans compared to white individuals. Some health-care provider related factors for this phenomenon have been reported in the literature, but the patient-related factors are unclear. Methods This exploratory study to identify patient-related factors associated with dermatology visits in Asian-Americans was performed after Institutional Review Board (IRB) approval. An anonymous, online survey utilizing validated items was conducted on adults who self-identified as Asian-American in Northern California. Univariate and multivariate logistic regression for dermatology visits as indicated by responses to the question of “ever having had skin checked by a dermatologist” were performed on survey responses pertaining to demographic information, socioeconomic factors, acculturation, knowledge of melanoma warning signs and SSE belief and practice. Results 89.7% of individuals who opened the online survey completed the items, with 469 surveys included in the analysis. Only 60% reported ever performing a SSE, and only 48% reported ever having a skin examination by a dermatologist. Multivariate models showed that “ever performing SSE” (p < 0.0001), marital status (p = 0.02), family history of skin cancer (p = 0.03) and generation in the United States (p = 0.02) were significant predictors of the primary outcome of “ever had skin checked by a dermatologist”. Conclusions Identification of patient-related factors that associate with dermatology clinic visits in Asian-Americans is important so that this potential gap in dermatologic care can be better addressed through future studies. PMID:25085260

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Exploratory Analysis of Survey Data for Understanding Adoption of Novel Aerospace Systems

NASA Astrophysics Data System (ADS)

Reddy, Lauren M.

In order to meet the increasing demand for manned and unmanned flight, the air transportation system must constantly evolve. As new technologies or operational procedures are conceived, we must determine their effect on humans in the system. In this research, we introduce a strategy to assess how individuals or organizations would respond to a novel aerospace system. We employ the most appropriate and sophisticated exploratory analysis techniques on the survey data to generate insight and identify significant variables. We employ three different methods for eliciting views from individuals or organizations who are affected by a system: an opinion survey, a stated preference survey, and structured interviews. We conduct an opinion survey of both the general public and stakeholders in the unmanned aircraft industry to assess their knowledge, attitude, and practices regarding unmanned aircraft. We complete a statistical analysis of the multiple-choice questions using multinomial logit and multivariate probit models and conduct qualitative analysis on free-text questions. We next present a stated preference survey of the general public on the use of an unmanned aircraft package delivery service. We complete a statistical analysis of the questions using multinomial logit, ordered probit, linear regression, and negative binomial models. Finally, we discuss structured interviews conducted on stakeholders from ANSPs and airlines operating in the North Atlantic. We describe how these groups may choose to adopt a new technology (space-based ADS-B) or operational procedure (in-trail procedures). We discuss similarities and differences between the stakeholders groups, the benefits and costs of in-trail procedures and space-based ADS-B as reported by the stakeholders, and interdependencies between the groups interviewed. To demonstrate the value of the data we generated, we explore how the findings from the surveys can be used to better characterize uncertainty in the cost-benefit analysis of aerospace systems. We demonstrate how the findings from the opinion and stated preference surveys can be infused into the cost-benefit analysis of an unmanned aircraft delivery system. We also demonstrate how to apply the findings from the interviews to characterize uncertainty in the estimation of the benefits of space-based ADS-B.

Toxoplasmosis Preventive Behavior and Related Knowledge among Saudi Pregnant Women: An Exploratory Study

PubMed Central

Amin, Tarek Tawfik; Ali, Mohamed Nabil Al; Alrashid, Ahmed Abdulmohsen; Ahmed Al-Agnam, Amena; Al Sultan, Amina Abdullah

2013-01-01

Introduction: Many cases of congenital toxoplasmosis can be prevented provided that pregnant women following hygienic measures to avert risk of infection and to reduce severity of the condition if primary prevention failed. Objectives: This descriptive exploratory study aimed to assess the risk behavior and knowledge related to toxoplasmoisis among Saudi pregnant women attending primary health care centers (PHCs) in Al Hassa, Saudi Arabia and to determine socio-demographic characteristics related to risk behavior and knowledge. Methods: All Saudi pregnant women attending antenatal care at randomly selected six urban and four rural PHCs were approached. Those agreed to participate were interviewed using a pre-tested structured questionnaire collecting data regarding socio-demographic, obstetric history, toxoplasmosis risk behaviors and related knowledge. Results: Of the included pregnant women, 234 (26.8%) have fulfilled the criteria for toxoplasmosis preventive behavior recommended by Centers for Disease Prevention and Control to prevent congenital toxoplasmosis, while 48.9% reported at least one risk behavior and 24.3% reported ≥ two risk behaviors. Logistic regression model revealed that pregnant women aged 20 to <30 years and those with previous history of unfavorable pregnancy outcome were more likely to follow toxoplasmosis preventive behavior. Toxoplasmosis-related knowledge showed that many women had identified the role of cats in disease transmission while failed to identify other risk factors including consumption of undercooked meats, unwashed fruits and vegetables, and contacting with soil. Predictors for pregnant women to be knowledgeable towards toxoplasmosis included those aged 30 to <40 years (OR=1.53), with ≥ secondary education (OR=1.96), had previous unfavorable pregnancy outcomes (OR=1.88) and investigated for toxoplasmosis (OR=2.08) as reveled by multivariate regression model. Conclusion: Pregnant women in Al Hasas, Saudi Arabia, are substantially vulnerable to toxoplasmosis infection as they are lacking the necessary preventive behavior. A sizable portion have no sufficient knowledge for primary prevention of congenital toxoplasmosis, health education at primary care is necessary to avert the potential toxoplasmosis related complications especially in the neonates. PMID:23985115

Interorganizational health care systems implementations: an exploratory study of early electronic commerce initiatives.

PubMed

Payton, F C; Ginzberg, M J

2001-01-01

Changing business practices, customers needs, and market dynamics have driven many organizations to implement interorganizational systems (IOSs). IOSs have been successfully implemented in the banking, cotton, airline, and consumer-goods industries, and recently attention has turned to the health care industry. This article describes an exploratory study of health care IOS implementations based on the voluntary community health information network (CHIN) model.

Global Processing Speed in Children with Low Reading Ability and in Children and Adults with Typical Reading Ability: Exploratory Factor Analytic Models

ERIC Educational Resources Information Center

Peter, Beate; Matsushita, Mark; Raskind, Wendy H.

2011-01-01

Purpose: To investigate processing speed as a latent dimension in children with dyslexia and children and adults with typical reading skills. Method: Exploratory factor analysis (FA) was based on a sample of multigenerational families, each ascertained through a child with dyslexia. Eleven measures--6 of them timed--represented verbal and…

Argument-Driven Inquiry as a Way to Help Students Learn How to Participate in Scientific Argumentation and Craft Written Arguments: An Exploratory Study

ERIC Educational Resources Information Center

Sampson, Victor; Grooms, Jonathon; Walker, Joi Phelps

2011-01-01

This exploratory study examines how a series of laboratory activities designed using a new instructional model, called Argument-Driven Inquiry (ADI), influences the ways students participate in scientific argumentation and the quality of the scientific arguments they craft as part of this process. The two outcomes of interest were assessed with a…

The Revised Child Anxiety and Depression Scale-Short Version: Scale Reduction via Exploratory Bifactor Modeling of the Broad Anxiety Factor

ERIC Educational Resources Information Center

Ebesutani, Chad; Reise, Steven P.; Chorpita, Bruce F.; Ale, Chelsea; Regan, Jennifer; Young, John; Higa-McMillan, Charmaine; Weisz, John R.

2012-01-01

Using a school-based (N = 1,060) and clinic-referred (N = 303) youth sample, the authors developed a 25-item shortened version of the Revised Child Anxiety and Depression Scale (RCADS) using Schmid-Leiman exploratory bifactor analysis to reduce client burden and administration time and thus improve the transportability characteristics of this…

An Exploratory Study of the Effects of Work Environment Variables on Job Satisfaction Among Chinese Prison Staff.

PubMed

Jiang, Shanhe; Lambert, Eric G; Liu, Jianhong; Zhang, Jinwu

2018-05-01

Job satisfaction has been linked to many positive outcomes, such as greater work performance, increased organizational commitment, reduced job burnout, decreased absenteeism, and lower turnover intent/turnover. A substantial body of research has examined how work environment variables are linked to job satisfaction among U.S. correctional staff; far less research has examined prison staff in non-Western nations, especially China. Using survey data collected from two prisons in Guangzhou, China, this study investigated the level of job satisfaction among prison staff and how personal characteristics (i.e., gender, tenure, age, and educational level) and work environment variables (i.e., perceived dangerousness of the job, job variety, supervision, instrumental communication, and input into decision making) affect job satisfaction. The findings from ordinary least squares regression equations indicated that the work environment variables explained a greater proportion of the variance in the job satisfaction measure than the personal characteristics. In the full multivariate regression model, gender was the only personal characteristic to have a significant association with job satisfaction, with female staff reporting higher satisfaction. Input into decision making and job variety had significant positive associations, whereas dangerousness had a significant negative relationship with job satisfaction.

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed Central

Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-01-01

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements. PMID:28252103

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed

Armaş, Iuliana; Mendes, Diana A; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-03-02

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992-2010 from ERS-1/-2 and ENVISAT, and 2011-2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

Trainee participation is associated with adverse outcomes in emergency general surgery: an analysis of the National Surgical Quality Improvement Program database.

PubMed

Kasotakis, George; Lakha, Aliya; Sarkar, Beda; Kunitake, Hiroko; Kissane-Lee, Nicole; Dechert, Tracey; McAneny, David; Burke, Peter; Doherty, Gerard

2014-09-01

To identify whether resident involvement affects clinically relevant outcomes in emergency general surgery. Previous research has demonstrated a significant impact of trainee participation on outcomes in a broad surgical patient population. We identified 141,010 patients who underwent emergency general surgery procedures in the 2005-2010 Surgeons National Surgical Quality Improvement Program database. Because of the nonrandom assignment of complex cases to resident participation, patients were matched (1:1) on known risk factors [age, sex, inpatient status, preexisting comorbidities (obesity, diabetes, smoking, alcohol, steroid use, coronary artery disease, chronic renal failure, pulmonary disease)] and preoperatively calculated probability for morbidity and mortality. Clinically relevant outcomes were compared with a t or χ test. The impact of resident participation on outcomes was assessed with multivariable regression modeling, adjusting for risk factors and operative time. The most common procedures in the matched cohort (n = 83,790) were appendectomy (39.9%), exploratory laparotomy (8.8%), and adhesiolysis (6.6%). Trainee participation is independently associated with intra- and postoperative events, wound, pulmonary, and venous thromboembolic complications, and urinary tract infections. Trainee participation is associated with adverse outcomes in emergency general surgery procedures.

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

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

Jesse, S.; Chi, M.; Belianinov, A.

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. In this paper, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO 3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in naturemore » and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. Finally, however, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.« less

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

DOE PAGES

Jesse, S.; Chi, M.; Belianinov, A.; ...

2016-05-23

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. In this paper, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO 3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in naturemore » and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. Finally, however, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.« less

Running injuries and associated factors in participants of ING Taipei Marathon.

PubMed

Chang, Wei-Ling; Shih, Yi-Fen; Chen, Wen-Yin

2012-08-01

To investigate the distribution of lower extremity running injuries and their associated factors. Descriptive and exploratory study. 1004 participants of the 2005 ING Taipei International Marathon. We used a self-developed questionnaire to collect data of previous running injuries and applied multivariate logistic regression modeling to examine relationships between these injuries and associated factors. Of the 893 valid questionnaires, 396 (44.4%) reported having previous lower extremity pain related to running. Knee joint pain was the most common problem (32.5%). Hip pain was associated with the racing group, training duration, and medial arch support. Use of knee orthotics (P = 0.002) and ankle braces (P = 0.007) was related to a higher rate of knee and ankle pain. Participants of the full marathon group who practiced on a synthetic track had a higher incidence of ankle pain. A training duration of >60 min was linked to an increased rate of foot pain (P = 0.003). Our data indicated that running injuries were associated with training duration and use of orthotics. Clinicians can use this information in treating or preventing running associated injuries and pain. Copyright © 2011 Elsevier Ltd. All rights reserved.

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

PubMed Central

Jesse, S.; Chi, M.; Belianinov, A.; Beekman, C.; Kalinin, S. V.; Borisevich, A. Y.; Lupini, A. R.

2016-01-01

Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. Here, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. However, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy. PMID:27211523

The importance of mental health to the experience of job strain: an evidence-guided approach to improve retention.

PubMed

Andrews, Diane Randall; Wan, Thomas T H

2009-04-01

The purpose of this study was to evaluate the causal relationships between job strain, the practice environment and the use of coping skills in order to assist in the prediction of nurses who are at risk for voluntary turnover and identify potential intervention strategies. Analysis of the US nurse workforce indicates that it will be necessary to identify new strategies that will promote a healthy workforce and retain nurses in the workplace. Exploratory cross-sectional survey of 1235 staff nurses resulted in 308 usable surveys (25%). Data were analysed using multivariate statistical techniques (SEM). It was determined that diminished mental health status as a component of job strain was predictive of propensity to leave as was a diminished assessment of the professional practice environment. Mental health was favourably influenced by coping behaviour. Evidence-based strategies which support mental health and reinforce the positive role of coping as a mediating factor may aid in nurse retention efforts. This study expands the literature by offering a theoretically supported model to evaluate the response of individuals to the experience of job strain in the work environment. The model demonstrated that the health consequences of job strain are modified through the use of active coping behaviour, and that those nurses with elevated self-assessed health had a lower propensity to leave. As active coping may be taught, the model suggests a means to identify those at risk and support manager intervention.

Measuring Work Environment and Performance in Nursing Homes

PubMed Central

Temkin-Greener, Helena; Zheng, Nan (Tracy); Katz, Paul; Zhao, Hongwei; Mukamel, Dana B.

2008-01-01

Background Qualitative studies of the nursing home work environment have long suggested that such attributes as leadership and communication may be related to nursing home performance, including residents' outcomes. However, empirical studies examining these relationships have been scant. Objectives This study is designed to: develop an instrument for measuring nursing home work environment and perceived work effectiveness; test the reliability and validity of the instrument; and identify individual and facility-level factors associated with better facility performance. Research Design and Methods The analysis was based on survey responses provided by managers (N=308) and direct care workers (N=7,418) employed in 162 facilities throughout New York State. Exploratory factor analysis, Chronbach's alphas, analysis of variance, and regression models were used to assess instrument reliability and validity. Multivariate regression models, with fixed facility effects, were used to examine factors associated with work effectiveness. Results The reliability and the validity of the survey instrument for measuring work environment and perceived work effectiveness has been demonstrated. Several individual (e.g. occupation, race) and facility characteristics (e.g. management style, workplace conditions, staffing) that are significant predictors of perceived work effectiveness were identified. Conclusions The organizational performance model used in this study recognizes the multidimensionality of the work environment in nursing homes. Our findings suggest that efforts at improving work effectiveness must also be multifaceted. Empirical findings from such a line of research may provide insights for improving the quality of the work environment and ultimately the quality of residents' care. PMID:19330892

Viewpoints: A High-Performance High-Dimensional Exploratory Data Analysis Tool

NASA Astrophysics Data System (ADS)

Gazis, P. R.; Levit, C.; Way, M. J.

2010-12-01

Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization, and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.

Neighborhood adversity, ethnic diversity, and weak social cohesion and social networks predict high rates of maternal depressive symptoms: a critical realist ecological study in South Western Sydney, Australia.

PubMed

Eastwood, John Graeme; Kemp, Lynn Ann; Jalaludin, Bin Badrudin; Phung, Hai Ngoc

2013-01-01

The aim of the study reported here is to explore ecological covariate and latent variable associations with perinatal depressive symptoms in South Western Sydney for the purpose of informing subsequent theory generation of perinatal context, depression, and the developmental origins of health and disease. Mothers (n = 15,389) delivering in 2002 and 2003 were assessed at two to three weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale (EPDS)> 9 and > 12. Aggregated EPDS > 9 was analyzed for 101 suburbs. Suburb-level variables were drawn from the 2001 Australian Census, New South Wales Crime Statistics, and aggregated individual-level risk factors. Analysis included exploratory factor analysis, univariate and multivariate likelihood, and Bayesian linear regression with conditional autoregressive components. The exploratory factor analysis identified six factors: neighborhood adversity, social cohesion, health behaviors, housing quality, social services, and support networks. Variables associated with neighborhood adversity, social cohesion, social networks, and ethnic diversity were consistently associated with aggregated depressive symptoms. The findings support the theoretical proposition that neighborhood adversity causes maternal psychological distress and depression within the context of social buffers including social networks, social cohesion, and social services.

Latent structure of the Wisconsin Card Sorting Test: a confirmatory factor analytic study.

PubMed

Greve, Kevin W; Stickle, Timothy R; Love, Jeffrey M; Bianchini, Kevin J; Stanford, Matthew S

2005-05-01

The present study represents the first large scale confirmatory factor analysis of the Wisconsin Card Sorting Test (WCST). The results generally support the three factor solutions reported in the exploratory factor analysis literature. However, only the first factor, which reflects general executive functioning, is statistically sound. The secondary factors, while likely reflecting meaningful cognitive abilities, are less stable except when all subjects complete all 128 cards. It is likely that having two discontinuation rules for the WCST has contributed to the varied factor analytic solutions reported in the literature and early discontinuation may result in some loss of useful information. Continued multivariate research will be necessary to better clarify the processes underlying WCST performance and their relationships to one another.

Enhancements of Bayesian Blocks; Application to Large Light Curve Databases

NASA Technical Reports Server (NTRS)

Scargle, Jeff

2015-01-01

Bayesian Blocks are optimal piecewise linear representations (step function fits) of light-curves. The simple algorithm implementing this idea, using dynamic programming, has been extended to include more data modes and fitness metrics, multivariate analysis, and data on the circle (Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations, Scargle, Norris, Jackson and Chiang 2013, ApJ, 764, 167), as well as new results on background subtraction and refinement of the procedure for precise timing of transient events in sparse data. Example demonstrations will include exploratory analysis of the Kepler light curve archive in a search for "star-tickling" signals from extraterrestrial civilizations. (The Cepheid Galactic Internet, Learned, Kudritzki, Pakvasa1, and Zee, 2008, arXiv: 0809.0339; Walkowicz et al., in progress).

Modeling and evaluating user behavior in exploratory visual analysis

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

Reda, Khairi; Johnson, Andrew E.; Papka, Michael E.

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, however, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This paper presents a methodology for modeling andmore » evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis, and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.« less

Is Small Still Beautiful for the Strengths and Difficulties Questionnaire? Novel Findings Using Exploratory Structural Equation Modeling.

PubMed

Garrido, Luis Eduardo; Barrada, Juan Ramón; Aguasvivas, José Armando; Martínez-Molina, Agustín; Arias, Víctor B; Golino, Hudson F; Legaz, Eva; Ferrís, Gloria; Rojo-Moreno, Luis

2018-06-01

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years ( M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Chronic valproate attenuates some, but not all, facets of mania-like behavior in mice

PubMed Central

van Enkhuizen, Jordy; Geyer, Mark A.; Kooistra, Klaas; Young, Jared W.

2014-01-01

Bipolar Disorder (BD) mania is a psychiatric disorder with multifaceted symptoms. Development of targeted treatments for BD mania may benefit from animal models that mimic multiple symptoms, as opposed to hyperactivity alone. Using the reverse-translated multivariate exploratory paradigm, the Behavioral Pattern Monitor (BPM), we reported that patients with BD mania exhibit hyperactivity as well as increased specific exploration and more linear movements through space. This abnormal profile is also observed in mice with reduced function of the dopamine transporter (DAT) through either constitutive genetic (knockdown (KD)) or acute pharmacological (GBR12909) means. Here, we assessed the pharmacological predictive validity of these models by administering the BD-treatment valproic acid (VPA) for 28 days. After 28 days of 1.5% VPA- or regular-chow treatment, C57BL/6J mice received GBR12909 (9 mg/kg) or saline and were tested in the BPM. Similarly, DAT KD and WT littermates were treated with VPA-chow and tested in the BPM. GBR12909-treated and DAT KD mice on regular chow were hyperactive, exhibited increased specific exploration, and moved in straighter patterns compared to saline-treated and WT mice respectively. Chronic 1.5% VPA-chow treatment resulted in therapeutic concentrations of VPA and ameliorated hyperactivity in both models, while specific exploration and behavioral organization remained unaffected. Hence, the mania-like profile of mice with reduced functional DAT was partially attenuated by chronic VPA treatment, consistent with the incomplete symptomatic effect of VPA treatment in BD patients. Both DAT models may help to identify therapeutics that impact the full spectrum of BD mania. PMID:23164454

A screening method based on UV-Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices.

PubMed

Boggia, Raffaella; Casolino, Maria Chiara; Hysenaj, Vilma; Oliveri, Paolo; Zunin, Paola

2013-10-15

Consumer demand for pomegranate juice has considerably grown, during the last years, for its potential health benefits. Since it is an expensive functional food, cheaper fruit juices addition (i.e., grape and apple juices) or its simple dilution, or polyphenols subtraction are deceptively used. At present, time-consuming analyses are used to control the quality of this product. Furthermore these analyses are expensive and require well-trained analysts. Thus, the purpose of this study was to propose a high-speed and easy-to-use shortcut. Based on UV-VIS spectroscopy and chemometrics, a screening method is proposed to quickly screening some common fillers of pomegranate juice that could decrease the antiradical scavenging capacity of pure products. The analytical method was applied to laboratory prepared juices, to commercial juices and to representative experimental mixtures at different levels of water and filler juices. The outcomes were evaluated by means of multivariate exploratory analysis. The results indicate that the proposed strategy can be a useful screening tool to assess addition of filler juices and water to pomegranate juices. Copyright © 2012 Elsevier Ltd. All rights reserved.

Missional Imaginations for Theological Education: Mixed Model, Exploratory, Action-Oriented Research Mapping the Theological Identity and Organizational Readiness for Change of Five Theological School Systems in the United States Originating after 1945

ERIC Educational Resources Information Center

Small, Kyle J. A.

2009-01-01

This dissertation explores the formal theologies and organizational readiness for change with a view towards adopting missional prototypes for theological education across a school's (system's) tradition, curriculum, and structure. The research assessed five theological schools in the United States through an exploratory, action-oriented,…

Supporting cognition in systems biology analysis: findings on users' processes and design implications.

PubMed

Mirel, Barbara

2009-02-13

Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling. As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.

Determination of diffusivities in the Rustler Formation from exploratory-shaft construction at the Waste Isolation Pilot Plant in southeastern New Mexico

USGS Publications Warehouse

Stevens, Ken; Beyeler, Walt

1985-01-01

The construction of an exploratory shaft 12 feet in diameter into the Salado Formation (repository horizon for transuranic waste material) at the Waste Isolation Pilot Plant site in southeastern New Mexico affected water-levels in water-bearing zones above the repository horizon. By reading the construction history of the exploratory shaft, an approximation of construction-generated hydraulic stresses at the shaft was made. The magnitude of the construction-generated stresses was calibrated using the hydrographs from one hydrologic test pad. Whereas flow rates from the Magenta Dolomite and Culebra Dolomite Members in the Rustler Formation into the exploratory shaft were unknown, the ratio of transmissivity to storage (diffusivity) was determined by mathematically simulating the aquifers and the hydrologic stresses with flood-wave-response digital model. These results indicate that the Magenta Dolomite and Culebra Dolomite Members of the Rustler Formation can be modeled as homogeneous, isotropic, and confined water-bearing zones. One simple and consistent explanation, but by no means the only explanation, of the lack of a single diffusivity value in the Culebra aquifer is that the open-hole observation wells at the hydrologic test pads dampen the amplitude of water-level changes. (USGS)

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gene–environment interplay in Drosophila melanogaster: Chronic food deprivation in early life affects adult exploratory and fitness traits

PubMed Central

Burns, James Geoffrey; Svetec, Nicolas; Rowe, Locke; Mery, Frederic; Dolan, Michael J.; Boyce, W. Thomas; Sokolowski, Marla B.

2012-01-01

Early life adversity has known impacts on adult health and behavior, yet little is known about the gene–environment interactions (GEIs) that underlie these consequences. We used the fruit fly Drosophila melanogaster to show that chronic early nutritional adversity interacts with rover and sitter allelic variants of foraging (for) to affect adult exploratory behavior, a phenotype that is critical for foraging, and reproductive fitness. Chronic nutritional adversity during adulthood did not affect rover or sitter adult exploratory behavior; however, early nutritional adversity in the larval period increased sitter but not rover adult exploratory behavior. Increasing for gene expression in the mushroom bodies, an important center of integration in the fly brain, changed the amount of exploratory behavior exhibited by sitter adults when they did not experience early nutritional adversity but had no effect in sitters that experienced early nutritional adversity. Manipulation of the larval nutritional environment also affected adult reproductive output of sitters but not rovers, indicating GEIs on fitness itself. The natural for variants are an excellent model to examine how GEIs underlie the biological embedding of early experience. PMID:23045644

Exploratory of society

NASA Astrophysics Data System (ADS)

Cederman, L.-E.; Conte, R.; Helbing, D.; Nowak, A.; Schweitzer, F.; Vespignani, A.

2012-11-01

A huge flow of quantitative social, demographic and behavioral data is becoming available that traces the activities and interactions of individuals, social patterns, transportation infrastructures and travel fluxes. This has caused, together with innovative computational techniques and methods for modeling social actions in hybrid (natural and artificial) societies, a qualitative change in the ways we model socio-technical systems. For the first time, society can be studied in a comprehensive fashion that addresses social and behavioral complexity. In other words we are in the position to envision the development of large data and computational cyber infrastructure defining an exploratory of society that provides quantitative anticipatory, explanatory and scenario analysis capabilities ranging from emerging infectious disease to conflict and crime surges. The goal of the exploratory of society is to provide the basic infrastructure embedding the framework of tools and knowledge needed for the design of forecast/anticipatory/crisis management approaches to socio technical systems, supporting future decision making procedures by accelerating the scientific cycle that goes from data generation to predictions.

Exploratory structural equation modeling of personality data.

PubMed

Booth, Tom; Hughes, David J

2014-06-01

The current article compares the use of exploratory structural equation modeling (ESEM) as an alternative to confirmatory factor analytic (CFA) models in personality research. We compare model fit, factor distinctiveness, and criterion associations of factors derived from ESEM and CFA models. In Sample 1 (n = 336) participants completed the NEO-FFI, the Trait Emotional Intelligence Questionnaire-Short Form, and the Creative Domains Questionnaire. In Sample 2 (n = 425) participants completed the Big Five Inventory and the depression and anxiety scales of the General Health Questionnaire. ESEM models provided better fit than CFA models, but ESEM solutions did not uniformly meet cutoff criteria for model fit. Factor scores derived from ESEM and CFA models correlated highly (.91 to .99), suggesting the additional factor loadings within the ESEM model add little in defining latent factor content. Lastly, criterion associations of each personality factor in CFA and ESEM models were near identical in both inventories. We provide an example of how ESEM and CFA might be used together in improving personality assessment. © The Author(s) 2014.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Some Improved Diagnostics for Failure of The Rasch Model.

ERIC Educational Resources Information Center

Molenaar, Ivo W.

1983-01-01

Goodness of fit tests for the Rasch model are typically large-sample, global measures. This paper offers suggestions for small-sample exploratory techniques for examining the fit of item data to the Rasch model. (Author/JKS)

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Validation of the Polish version of the Multidimensional Body-Self Relations Questionnaire among women.

PubMed

Brytek-Matera, Anna; Rogoza, Radosław

2015-03-01

In Poland, appropriate means to assess body image are relatively limited. The aim of the study was to evaluate the psychometric properties of the Polish version of the Multidimensional Body-Self Relations Questionnaire (MBSRQ). To do so, a sample of 341 females ranging in age from 18 to 35 years (M = 23.09; SD = 3.14) participated in the present study. Owing to the fact that the confirmatory factor analysis of the original nine-factor model was not well fitted to the data (RMSEA = 0.06; CFI = 0.75) the exploratory approach was employed. Based on parallel analysis and minimum average partial an eight-factor structure of the Polish version of the MBSRQ was distinguished. Exploratory factor analysis revealed a factorial structure similar to the original version. The proposed model was tested using an exploratory structural equation modelling approach which resulted in good fit (RMSEA = 0.04; CFI = 0.91). In the present study, the internal reliability assessed by McDonald's ω coefficient amounts from 0.66 to 0.91. In conclusion, the Polish version of the MBSRQ is a useful measure for the attitudinal component of body image assessment.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Issues Related to the Frequency of Exploratory Analyses by Evidence Review Groups in the NICE Single Technology Appraisal Process.

PubMed

Kaltenthaler, Eva; Carroll, Christopher; Hill-McManus, Daniel; Scope, Alison; Holmes, Michael; Rice, Stephen; Rose, Micah; Tappenden, Paul; Woolacott, Nerys

2017-06-01

Evidence Review Groups (ERGs) critically appraise company submissions as part of the National Institute for Health and Care Excellence (NICE) Single Technology Appraisal (STA) process. As part of their critique of the evidence submitted by companies, the ERGs undertake exploratory analyses to explore uncertainties in the company's model. The aim of this study was to explore pre-defined factors that might influence or predict the extent of ERG exploratory analyses. The aim of this study was to explore predefined factors that might influence or predict the extent of ERG exploratory analyses. We undertook content analysis of over 400 documents, including ERG reports and related documentation for the 100 most recent STAs (2009-2014) for which guidance has been published. Relevant data were extracted from the documents and narrative synthesis was used to summarise the extracted data. All data were extracted and checked by two researchers. Forty different companies submitted documents as part of the NICE STA process. The most common disease area covered by the STAs was cancer (44%), and most ERG reports (n = 93) contained at least one exploratory analysis. The incidence and frequency of ERG exploratory analyses does not appear to be related to any developments in the appraisal process, the disease area covered by the STA, or the company's base-case incremental cost-effectiveness ratio (ICER). However, there does appear to be a pattern in the mean number of analyses conducted by particular ERGs, but the reasons for this are unclear and potentially complex. No clear patterns were identified regarding the presence or frequency of exploratory analyses, apart from the mean number conducted by individual ERGs. More research is needed to understand this relationship.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Violent Victimization and Substance Dependency: Comparing Rural Incarcerated Heterosexual and Sexual Minority Women

PubMed Central

OTIS, MELANIE D.; OSER, CARRIE B.; STATON-TINDALL, MICHELE

2016-01-01

This exploratory study examines the relationship between sexual identity and violent victimization experiences as predictors of differences in illicit substance and alcohol use and substance use problems among a sample of incarcerated women in rural Appalachia (N = 400). Results indicated that, compared to heterosexual women, sexual minority women were more likely to have a lifetime history of weapon, physical, and sexual assault, and were younger at the time of their first violent victimization. Sexual minority women were younger than heterosexual women at the age of onset for intravenous drug use and at the time they first got drunk, and were more likely to report having overdosed. Multivariate analysis found violent victimization to be the strongest predictor of a history of overdose and substance use problems. PMID:27660590

Cost-effectiveness of zoledronic acid in the prevention of skeletal-related events in patients with bone metastases secondary to advanced renal cell carcinoma: application to France, Germany, and the United Kingdom.

PubMed

Botteman, M F; Meijboom, M; Foley, I; Stephens, J M; Chen, Y M; Kaura, S

2011-12-01

The use of zoledronic acid (ZOL) has recently been shown to significantly reduce the risk of new skeletal-related events (SREs) in renal cell carcinoma (RCC) patients with bone metastases. The present exploratory study assessed the cost-effectiveness of ZOL in this population, adopting a French, German, and United Kingdom (UK) government payer perspective. This cost-effectiveness model was based on a post hoc retrospective analysis of a subset of patients with RCC who were included in a larger randomized clinical trial of patients with bone metastases secondary to a variety of cancers. In the trial, patients were randomized to receive ZOL (n = 27) or placebo (n = 19) with concomitant antineoplastic therapy every 3 weeks for 9 months (core study) plus 12 months during a study extension. Since the trial did not collect costs or data on the quality-adjusted life years (QALYs) of the patients, these outcomes had to be assumed via modeling exercises. The costs of SREs were estimated using hospital DRG tariffs. These estimates were supplemented with literature-based costs where possible. Drug, administration, and supply costs were obtained from published and internet sources. Consistent with similar economic analyses, patients were assumed to experience quality of life decrements lasting 1 month for each SRE. Uncertainty surrounding outcomes was addressed via multivariate sensitivity analyses. Patients receiving ZOL experienced 1.07 fewer SREs than patients on placebo. Patients on ZOL experienced a gain in discounted QALYs of approximately 0.1563 in France and Germany and 0.1575 in the UK. Discounted SRE-related costs were substantially lower among ZOL than placebo patients (-€ 4,196 in France, - € 3,880 in Germany, and -€ 3,355 in the UK). After taking into consideration the drug therapy costs, ZOL saved € 1,358, € 1,223, and € 719 in France, Germany, and the UK, respectively. In the multivariate sensitivity analyses, therapy with ZOL saved costs in 67-77% of simulations, depending on the country. The cost per QALY gained for ZOL versus placebo was below € 30,000 per QALY gained threshold in approximately 93-94% of multivariate sensitivity analyses simulations. The present analysis suggests that ZOL saves costs and increases QALYs compared to placebo in French, German, and UK RCC patients with bone metastases. Additional prospective research may be needed to confirm these results in a larger sample of patients.

How do humans inspect BPMN models: an exploratory study.

PubMed

Haisjackl, Cornelia; Soffer, Pnina; Lim, Shao Yi; Weber, Barbara

2018-01-01

Even though considerable progress regarding the technical perspective on modeling and supporting business processes has been achieved, it appears that the human perspective is still often left aside. In particular, we do not have an in-depth understanding of how process models are inspected by humans, what strategies are taken, what challenges arise, and what cognitive processes are involved. This paper contributes toward such an understanding and reports an exploratory study investigating how humans identify and classify quality issues in BPMN process models. Providing preliminary answers to initial research questions, we also indicate other research questions that can be investigated using this approach. Our qualitative analysis shows that humans adapt different strategies on how to identify quality issues. In addition, we observed several challenges appearing when humans inspect process models. Finally, we present different manners in which classification of quality issues was addressed.

Planning representation for automated exploratory data analysis

NASA Astrophysics Data System (ADS)

St. Amant, Robert; Cohen, Paul R.

1994-03-01

Igor is a knowledge-based system for exploratory statistical analysis of complex systems and environments. Igor has two related goals: to help automate the search for interesting patterns in data sets, and to help develop models that capture significant relationships in the data. We outline a language for Igor, based on techniques of opportunistic planning, which balances control and opportunism. We describe the application of Igor to the analysis of the behavior of Phoenix, an artificial intelligence planning system.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

A traditional Sami diet score as a determinant of mortality in a general northern Swedish population.

PubMed

Nilsson, Lena Maria; Winkvist, Anna; Brustad, Magritt; Jansson, Jan-Håkan; Johansson, Ingegerd; Lenner, Per; Lindahl, Bernt; Van Guelpen, Bethany

2012-05-04

To examine the relationship between "traditional Sami" dietary pattern and mortality in a general northern Swedish population. Population-based cohort study. We examined 77,319 subjects from the Västerbotten Intervention Program (VIP) cohort. A traditional Sami diet score was constructed by adding 1 point for intake above the median level of red meat, fatty fish, total fat, berries and boiled coffee, and 1 point for intake below the median of vegetables, bread and fibre. Hazard ratios (HR) for mortality were calculated by Cox regression. Increasing traditional Sami diet scores were associated with slightly elevated all-cause mortality in men [Multivariate HR per 1-point increase in score 1.04 (95% CI 1.01-1.07), p=0.018], but not for women [Multivariate HR 1.03 (95% CI 0.99-1.07), p=0.130]. This increased risk was approximately equally attributable to cardiovascular disease and cancer, though somewhat more apparent for cardiovascular disease mortality in men free from diabetes, hypertension and obesity at baseline [Multivariate HR 1.10 (95% CI 1.01-1.20), p=0.023]. A weak increased all-cause mortality was observed in men with higher traditional Sami diet scores. However, due to the complexity in defining a "traditional Sami" diet, and the limitations of our questionnaire for this purpose, the study should be considered exploratory, a first attempt to relate a "traditional Sami" dietary pattern to health endpoints. Further investigation of cohorts with more detailed information on dietary and lifestyle items relevant for traditional Sami culture is warranted.

Viewpoints: Interactive Exploration of Large Multivariate Earth and Space Science Data Sets

NASA Astrophysics Data System (ADS)

Levit, C.; Gazis, P. R.

2006-05-01

Analysis and visualization of extremely large and complex data sets may be one of the most significant challenges facing earth and space science investigators in the forthcoming decades. While advances in hardware speed and storage technology have roughly kept up with (indeed, have driven) increases in database size, the same is not of our abilities to manage the complexity of these data. Current missions, instruments, and simulations produce so much data of such high dimensionality that they outstrip the capabilities of traditional visualization and analysis software. This problem can only be expected to get worse as data volumes increase by orders of magnitude in future missions and in ever-larger supercomputer simulations. For large multivariate data (more than 105 samples or records with more than 5 variables per sample) the interactive graphics response of most existing statistical analysis, machine learning, exploratory data analysis, and/or visualization tools such as Torch, MLC++, Matlab, S++/R, and IDL stutters, stalls, or stops working altogether. Fortunately, the graphics processing units (GPUs) built in to all professional desktop and laptop computers currently on the market are capable of transforming, filtering, and rendering hundreds of millions of points per second. We present a prototype open-source cross-platform application which leverages much of the power latent in the GPU to enable smooth interactive exploration and analysis of large high- dimensional data using a variety of classical and recent techniques. The targeted application is the interactive analysis of large, complex, multivariate data sets, with dimensionalities that may surpass 100 and sample sizes that may exceed 106-108.

Characterizing the EEG correlates of exploratory behavior.

PubMed

Bourdaud, Nicolas; Chavarriaga, Ricardo; Galan, Ferran; Millan, José Del R

2008-12-01

This study aims to characterize the electroencephalography (EEG) correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to optimize the decision. Exploratory behavior has already been studied using functional magnetic resonance imaging (fMRI). Based on a usual paradigm in reinforcement learning, this study has shown bilateral activation in the frontal and parietal cortex. To our knowledge, no previous study has been done on it using EEG. The study of the exploratory behavior using EEG signals raises two difficulties. First, the labels of trial as exploitation or exploration cannot be directly derived from the subject action. In order to access this information, a model of how the subject makes his decision must be built. The exploration related information can be then derived from it. Second, because of the complexity of the task, its EEG correlates are not necessarily time locked with the action. So the EEG processing methods used should be designed in order to handle signals that shift in time across trials. Using the same experimental protocol as the fMRI study, results show that the bilateral frontal and parietal areas are also the most discriminant. This strongly suggests that the EEG signal also conveys information about the exploratory behavior.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

The negative consequences of other students' drinking: inventory development and assessment of differences by student characteristics and risk behaviors.

PubMed

Rhodes, Scott D; McCoy, Thomas P; Omli, Morrow R; Cohen, Gail M; Wagoner, Kimberly G; Durant, Robert H; Vissman, Aaron T; Wolfson, Mark

2009-01-01

College students continue to report being disrupted by other students' alcohol use. This study was designed to develop measures to document the consequences resulting from other students' drinking and identify differences in experiencing these consequences by student characteristics and drinking behaviors. A stratified random sample of undergraduate students (N = 3,908) from ten universities in North Carolina, USA, completed a web-based assessment. Exploratory factor analysis (EFA) was performed on the random first split-half sample (n = 1,954) to identify factor structure. Confirmatory factor analysis (CFA) was performed on the remaining half sample (n = 1,954) using structural equation modeling. EFA revealed two inventories: interpersonal and community consequences of others' drinking inventories. CFA on the second split-half sample identified model fits for the two factor structure suggested by EFA. Of 3,908 participants, 78% reported experiencing one or more consequences due to others' drinking during the past 30 days. Multivariable generalized linear mixed modeling further validated the inventories and resulted in several associations. Male students who reported getting drunk experienced significantly more interpersonal consequences from others' drinking (p < .001). Minority students, students who lived on campus and students who reported getting drunk experienced significantly more community consequences from others' drinking (p < .01). These findings demonstrate that 4 out of 5 college students experience consequences from others' drinking, and consequences vary for different subgroups of students. Although these inventories should be tested further, these findings propose standardized measures that may be useful to assess the consequences of others' drinking among college students.

Identification of ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards.

PubMed

Roubeix, Vincent; Danis, Pierre-Alain; Feret, Thibaut; Baudoin, Jean-Marc

2016-04-01

In aquatic ecosystems, the identification of ecological thresholds may be useful for managers as it can help to diagnose ecosystem health and to identify key levers to enable the success of preservation and restoration measures. A recent statistical method, gradient forest, based on random forests, was used to detect thresholds of phytoplankton community change in lakes along different environmental gradients. It performs exploratory analyses of multivariate biological and environmental data to estimate the location and importance of community thresholds along gradients. The method was applied to a data set of 224 French lakes which were characterized by 29 environmental variables and the mean abundances of 196 phytoplankton species. Results showed the high importance of geographic variables for the prediction of species abundances at the scale of the study. A second analysis was performed on a subset of lakes defined by geographic thresholds and presenting a higher biological homogeneity. Community thresholds were identified for the most important physico-chemical variables including water transparency, total phosphorus, ammonia, nitrates, and dissolved organic carbon. Gradient forest appeared as a powerful method at a first exploratory step, to detect ecological thresholds at large spatial scale. The thresholds that were identified here must be reinforced by the separate analysis of other aquatic communities and may be used then to set protective environmental standards after consideration of natural variability among lakes.

The protective value of parental sex education: a clinic-based exploratory study of adolescent females.

PubMed

Crosby, Richard A; Hanson, Amy; Rager, Kristin

2009-06-01

This exploratory study compared the impact of sex education provided by parents to female adolescents against the same education provided in formal settings to female adolescents. Females, 16-24 years old, attending an adolescent medicine clinic in an urban area of the South were recruited prior to examination. Each patient completed an anonymous self-administered questionnaire. Data from 110 respondents were analyzed to compare those indicating they had learned about each of four topics from parents to those not indicating learning about all four topics from a parent. The same process was repeated relative to learning about the four topics in formal educational settings. In controlled, multivariate, analyses, adolescents not communicating with parents on all four topics were nearly five times more likely to report having multiple sex partners in the past three months. Further, these adolescents were 3.5 times more likely to have low self-efficacy for condom negotiation, 2.7 times more likely to report ever using alcohol or drugs before sex, and about 70% less likely to have ever talked about HIV prevention with a partner before engaging in sex. Differences relative to learning about the four topics in formal settings were not found. Findings suggest that teen females (attending teen clinics) may experience a protective benefit based on communication with parents. This protective effect was not observed for education delivered in formal settings.

Does intraoperative low arterial partial pressure of oxygen increase the risk of surgical site infection following emergency exploratory laparotomy in horses?

PubMed

Costa-Farré, Cristina; Prades, Marta; Ribera, Thaïs; Valero, Oliver; Taurà, Pilar

2014-04-01

Decreased tissue oxygenation is a critical factor in the development of wound infection as neutrophil mediated oxidative killing is an essential mechanism against surgical pathogens. The objective of this prospective case series was to assess the impact of intraoperative arterial partial pressure of oxygen (PaO2) on surgical site infection (SSI) in horses undergoing emergency exploratory laparotomy for acute gastrointestinal disease. The anaesthetic and antibiotic protocol was standardised. Demographic data, surgical potential risk factors and PaO2, obtained 1h after induction of anaesthesia were recorded. Surgical wounds were assessed daily for infection during hospitalisation and follow up information was obtained after discharge. A total of 84 adult horses were included. SSI developed in 34 (40.4%) horses. Multivariate logistic regression showed that PaO2, anaesthetic time and subcutaneous suture material were predictors of SSI (AUC=0.76, sensitivity=71%, specificity=65%). The use of polyglycolic acid sutures increased the risk and horses with a PaO2 value < 80 mm Hg [10.6 kPa] and anaesthetic time >2h had the highest risk of developing SSI (OR=9.01; 95% CI 2.28-35.64). The results of this study confirm the hypothesis that low intraoperative PaO2 contributes to the development of SSI following colic surgery. Copyright © 2014 Elsevier Ltd. All rights reserved.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Ridge Regression for Interactive Models.

ERIC Educational Resources Information Center

Tate, Richard L.

1988-01-01

An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are…

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

Exploring biological, chemical and geomorphological patterns in fluvial ecosystems with Structural Equation Modelling

NASA Astrophysics Data System (ADS)

Bizzi, S.; Surridge, B.; Lerner, D. N.:

2009-04-01

River ecosystems represent complex networks of interacting biological, chemical and geomorphological processes. These processes generate spatial and temporal patterns in biological, chemical and geomorphological variables, and a growing number of these variables are now being used to characterise the status of rivers. However, integrated analyses of these biological-chemical-geomorphological networks have rarely been undertaken, and as a result our knowledge of the underlying processes and how they generate the resulting patterns remains weak. The apparent complexity of the networks involved, and the lack of coherent datasets, represent two key challenges to such analyses. In this paper we describe the application of a novel technique, Structural Equation Modelling (SEM), to the investigation of biological, chemical and geomorphological data collected from rivers across England and Wales. The SEM approach is a multivariate statistical technique enabling simultaneous examination of direct and indirect relationships across a network of variables. Further, SEM allows a-priori conceptual or theoretical models to be tested against available data. This is a significant departure from the solely exploratory analyses which characterise other multivariate techniques. We took biological, chemical and river habitat survey data collected by the Environment Agency for 400 sites in rivers spread across England and Wales, and created a single, coherent dataset suitable for SEM analyses. Biological data cover benthic macroinvertebrates, chemical data relate to a range of standard parameters (e.g. BOD, dissolved oxygen and phosphate concentration), and geomorphological data cover factors such as river typology, substrate material and degree of physical modification. We developed a number of a-priori conceptual models, reflecting current research questions or existing knowledge, and tested the ability of these conceptual models to explain the variance and covariance within the dataset. The conceptual models we developed were able to explain correctly the variance and covariance shown by the datasets, proving to be a relevant representation of the processes involved. The models explained 65% of the variance in indices describing benthic macroinvertebrate communities. Dissolved oxygen was of primary importance, but geomorphological factors, including river habitat type and degree of habitat degradation, also had significant explanatory power. The addition of spatial variables, such as latitude or longitude, did not provide additional explanatory power. This suggests that the variables already included in the models effectively represented the eco-regions across which our data were distributed. The models produced new insights into the relative importance of chemical and geomorphological factors for river macroinvertebrate communities. The SEM technique proved a powerful tool for exploring complex biological-chemical-geomorphological networks, for example able to deal with the co-correlations that are common in rivers due to multiple feedback mechanisms.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Emergency department revisits for patients with kidney stones in California.

PubMed

Scales, Charles D; Lin, Li; Saigal, Christopher S; Bennett, Carol J; Ponce, Ninez A; Mangione, Carol M; Litwin, Mark S

2015-04-01

Kidney stones affect nearly one in 11 persons in the United States, and among those experiencing symptoms, emergency care is common. In this population, little is known about the incidence of and factors associated with repeat emergency department (ED) visits. The objective was to identify associations between potentially mutable factors and the risk of an ED revisit for patients with kidney stones in a large, all-payer cohort. This was a retrospective cohort study of all patients in California initially treated and released from EDs for kidney stones between February 2008 and November 2009. A multivariable regression model was created to identify associations between patient-level characteristics, area health care resources, processes of care, and the risk of repeat ED visits. The primary outcome was a second ED visit within 30 days of the initial discharge from emergent care. Among 128,564 patients discharged from emergent care, 13,684 (11%) had at least one additional emergent visit for treatment of their kidney stone. In these patients, nearly one in three required hospitalization or an urgent temporizing procedure at the second visit. On multivariable analysis, the risk of an ED revisit was associated with insurance status (e.g., Medicaid vs. private insurance; odds ratio [OR] = 1.52, 95% confidence interval [CI] = 1.43 to 1.61; p < 0.001). Greater access to urologic care was associated with lower odds of an ED revisit (highest quartile OR = 0.88, 95% CI = 0.80 to 0.97; p < 0.01 vs. lowest quartile). In exploratory models, performance of a complete blood count was associated with a decreased odds of revisit (OR = 0.86, 95% CI = 0.75 to 0.97; p = 0.02). Repeat high-acuity care affects one in nine patients discharged from initial emergent evaluations for kidney stones. Access to urologic care and processes of care are associated with lower risk of repeat emergent encounters. Efforts are indicated to identify preventable causes of ED revisits for kidney stone patients and design interventions to reduce the risk of high-cost, high-acuity, repeat care. © 2015 by the Society for Academic Emergency Medicine.

Toward an Empirical Multidimensional Structure of Anhedonia, Reward Sensitivity, and Positive Emotionality: An Exploratory Factor Analytic Study.

PubMed

Olino, Thomas M; McMakin, Dana L; Forbes, Erika E

2016-11-20

Positive emotionality, anhedonia, and reward sensitivity share motivational and experiential elements of approach motivation and pleasure. Earlier work has examined the interrelationships among these constructs from measures of extraversion. More recently, the Research Domain Criteria introduced the Positive Valence Systems as a primary dimension to better understand psychopathology. However, the suggested measures tapping this construct have not yet been integrated within the structural framework of personality, even at the level of self-report. Thus, this study conducted exploratory factor and exploratory bifactor analyses on 17 different dimensions relevant to approach motivation, spanning anhedonia, behavioral activation system functioning, and positive emotionality. Convergent validity of these dimensions is tested by examining associations with depressive symptoms. Relying on multiple indices of fit, our preferred model included a general factor along with specific factors of affiliation, positive emotion, assertiveness, and pleasure seeking. These factors demonstrated different patterns of association with depressive symptoms. We discuss the plausibility of this model and highlight important future directions for work on the structure of a broad Positive Valence Systems construct. © The Author(s) 2016.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Analysis of genetic population structure in Acacia caven (Leguminosae, Mimosoideae), comparing one exploratory and two Bayesian-model-based methods.

PubMed

Pometti, Carolina L; Bessega, Cecilia F; Saidman, Beatriz O; Vilardi, Juan C

2014-03-01

Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other.

Associations between DSM-5 section III personality traits and the Minnesota Multiphasic Personality Inventory 2-Restructured Form (MMPI-2-RF) scales in a psychiatric patient sample.

PubMed

Anderson, Jaime L; Sellbom, Martin; Ayearst, Lindsay; Quilty, Lena C; Chmielewski, Michael; Bagby, R Michael

2015-09-01

Our aim in the current study was to evaluate the convergence between Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) Section III dimensional personality traits, as operationalized via the Personality Inventory for DSM-5 (PID-5), and Minnesota Multiphasic Personality Inventory 2-Restructured Form (MMPI-2-RF) scale scores in a psychiatric patient sample. We used a sample of 346 (171 men, 175 women) patients who were recruited through a university-affiliated psychiatric facility in Toronto, Canada. We estimated zero-order correlations between the PID-5 and MMPI-2-RF substantive scale scores, as well as a series of exploratory structural equation modeling (ESEM) analyses to examine how these scales converged in multivariate latent space. Results generally showed empirical convergence between the scales of these two measures that were thematically meaningful and in accordance with conceptual expectations. Correlation analyses showed significant associations between conceptually expected scales, and the highest associations tended to be between scales that were theoretically related. ESEM analyses generated evidence for distinct internalizing, externalizing, and psychoticism factors across all analyses. These findings indicate convergence between these two measures and help further elucidate the associations between dysfunctional personality traits and general psychopathology. (c) 2015 APA, all rights reserved.

A socioeconomic profile of vulnerable land to desertification in Italy.

PubMed

Salvati, Luca

2014-01-01

Climate changes, soil vulnerability, loss in biodiversity, and growing human pressure are threatening Mediterranean-type ecosystems which are increasingly considered as a desertification hotspot. In this region, land vulnerability to desertification strongly depends on the interplay between natural and anthropogenic factors. The present study proposes a multivariate exploratory analysis of the relationship between the spatial distribution of land vulnerability to desertification and the socioeconomic contexts found in three geographical divisions of Italy (north, center and south) based on statistical indicators. A total of 111 indicators describing different themes (demography, human settlements, labor market and human capital, rural development, income and wealth) were used to discriminate vulnerable from non-vulnerable areas. The resulting socioeconomic profile of vulnerable areas in northern and southern Italy diverged significantly, the importance of demographic and economic indicators being higher in southern Italy than in northern Italy. On the contrary, human settlement indicators were found more important to discriminate vulnerable and non-vulnerable areas in northern Italy, suggesting a role for peri-urbanization in shaping the future vulnerable areas. An in-depth knowledge of the socioeconomic characteristics of vulnerable land may contribute to scenarios' modeling and the development of more effective policies to combat desertification. © 2013 Elsevier B.V. All rights reserved.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Validity and reliability of the Internalized Stigma of Smoking Inventory: An exploration of shame, isolation, and discrimination in smokers with mental health diagnoses.

PubMed

Brown-Johnson, Cati G; Cataldo, Janine K; Orozco, Nicholas; Lisha, Nadra E; Hickman, Norval J; Prochaska, Judith J

2015-08-01

De-normalization of smoking as a public health strategy may create shame and isolation in vulnerable groups unable to quit. To examine the nature and impact of smoking stigma, we developed the Internalized Stigma of Smoking Inventory (ISSI), tested its validity and reliability, and explored factors that may contribute to smoking stigma. We evaluated the ISSI in a sample of smokers with mental health diagnoses (N = 956), using exploratory and confirmatory factor analysis, and assessed construct validity. Results reduced the ISSI to eight items with three subscales: smoking self-stigma related to shame, felt stigma related to social isolation, and discrimination experiences. Discrimination was the most commonly endorsed of the three subscales. A multivariate generalized linear model predicted 21-30% of the variance in the smoking stigma subscales. Self-stigma was greatest among those intending to quit; felt stigma was highest among those experiencing stigma in other domains, namely ethnicity and mental illness-based; and smoking-related discrimination was highest among women, Caucasians, and those with more education. Smoking stigma may compound stigma experiences in other areas. Aspects of smoking stigma in the domains of shame, isolation, and discrimination were related to modeled stigma responses, particularly readiness to quit and cigarette addiction, and were found to be more salient for groups where tobacco use is least prevalent. The ISSI measure is useful for quantifying smoking-related stigma in multiple domains. © American Academy of Addiction Psychiatry.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Early chronic lead exposure reduces exploratory activity in young C57BL/6J mice.

PubMed

Flores-Montoya, Mayra Gisel; Sobin, Christina

2015-07-01

Research has suggested that chronic low-level lead exposure diminishes neurocognitive function in children. Tests that are sensitive to behavioral effects at lowest levels of lead exposure are needed for the development of animal models. In this study we investigated the effects of chronic low-level lead exposure on exploratory activity (unbaited nose poke task), exploratory ambulation (open field task) and motor coordination (Rotarod task) in pre-adolescent mice. C57BL/6J pups were exposed to 0 ppm (controls), 30 ppm (low-dose) or 230 ppm (high-dose) lead acetate via dams' drinking water administered from birth to postnatal day 28, to achieve a range of blood lead levels (BLLs) from not detectable to 14.84 µg dl(-1) ). At postnatal day 28, mice completed behavioral testing and were killed (n = 61). BLLs were determined by inductively coupled plasma mass spectrometry. The effects of lead exposure on behavior were tested using generalized linear mixed model analyses with BLL, sex and the interaction as fixed effects, and litter as the random effect. BLL predicted decreased exploratory activity and no threshold of effect was apparent. As BLL increased, nose pokes decreased. The C57BL/6J mouse is a useful model for examining effects of early chronic low-level lead exposure on behavior. In the C57BL/6J mouse, the unbaited nose poke task is sensitive to the effects of early chronic low-level lead exposure. This is the first animal study to show behavioral effects in pre-adolescent lead-exposed mice with BLL below 5 µg dl(-1). Copyright © 2014 John Wiley & Sons, Ltd.

Early chronic lead exposure reduces exploratory activity in young C57BL/6J mice

PubMed Central

Flores-Montoya, Mayra Gisel; Sobin, Christina

2014-01-01

Research has suggested that chronic low-level lead exposure diminishes neurocognitive function in children. Tests that are sensitive to behavioral effects at lowest levels of lead exposure are needed for the development of animal models. In this study we investigated the effects of chronic low-level lead exposure on exploratory activity (unbaited nose poke task), exploratory ambulation (open field task) and motor coordination (Rotarod task) in pre-adolescent mice. C57BL/6J pups were exposed to 0 ppm (controls), 30 ppm (low-dose) or 230 ppm (high-dose) lead acetate via dams’ drinking water administered from birth to postnatal day 28, to achieve a range of blood lead levels (BLLs) from not detectable to 14.84 μg dl−1). At postnatal day 28, mice completed behavioral testing and were killed (n = 61). BLLs were determined by inductively coupled plasma mass spectrometry. The effects of lead exposure on behavior were tested using generalized linear mixed model analyses with BLL, sex and the interaction as fixed effects, and litter as the random effect. BLL predicted decreased exploratory activity and no threshold of effect was apparent. As BLL increased, nose pokes decreased. The C57BL/6J mouse is a useful model for examining effects of early chronic low-level lead exposure on behavior. In the C57BL/6J mouse, the unbaited nose poke task is sensitive to the effects of early chronic low-level lead exposure. This is the first animal study to show behavioral effects in pre-adolescent lead-exposed mice with BLL below 5 μg dl−1. PMID:25219894

Multiscale Materials Modeling Workshop Summary

DOT National Transportation Integrated Search

2013-12-01

This report summarizes a 2-day workshop held to share information on multiscale material modeling. The aim was to gain expert feedback on the state of the art and identify Exploratory Advanced Research (EAR) Program opportunities for multiscale mater...

Low creatinine clearance is a risk factor for D2 gastrectomy after neoadjuvant chemotherapy.

PubMed

Hayashi, Tsutomu; Aoyama, Toru; Tanabe, Kazuaki; Nishikawa, Kazuhiro; Ito, Yuichi; Ogata, Takashi; Cho, Haruhiko; Morita, Satoshi; Miyashita, Yumi; Tsuburaya, Akira; Sakamoto, Junichi; Yoshikawa, Takaki

2014-09-01

The feasibility and safety of D2 surgery following neoadjuvant chemotherapy (NAC) has not been fully evaluated in patients with gastric cancer. Moreover, risk factor for surgical complications after D2 gastrectomy following NAC is also unknown. The purpose of the present study was to identify risk factors of postoperative complications after D2 surgery following NAC. This study was conducted as an exploratory analysis of a prospective, randomized Phase II trial of NAC. The surgical complications were assessed and classified according to the Clavien-Dindo classification. A uni- and multivariate logistic regression analyses were performed to identify risk factors for morbidity. Among 83 patients who were registered to the Phase II trial, 69 patients received the NAC and D2 gastrectomy. Postoperative complications were identified in 18 patients and the overall morbidity rate was 26.1 %. The results of univariate and multivariate analyses of various factors for overall operative morbidity, creatinine clearance (CCr) ≤ 60 ml/min (P = 0.016) was identified as sole significant independent risk factor for overall morbidity. Occurrence of pancreatic fistula was significantly higher in the patients with a low CCr than in those with a high CCr. Low CCr was a significant risk factor for surgical complications in D2 gastrectomy after NAC. Careful attention is required for these patients.

Better Working Memory and Motor Inhibition in Children Who Delayed Gratification

PubMed Central

Yu, Junhong; Kam, Chi-Ming; Lee, Tatia M. C.

2016-01-01

Background: Despite the extensive research on delayed gratification over the past few decades, the neurocognitive processes that subserve delayed gratification remains unclear. As an exploratory step in studying these processes, the present study aims to describe the executive function profiles of children who were successful at delaying gratification and those who were not. Methods: A total of 138 kindergarten students (65 males, 73 females; Mage = 44 months, SD = 3.5; age range = 37–53 months) were administered a delayed gratification task, a 1-back test, a Day/night Stroop test and a Go/no-go test. The outcome measures of these tests were then analyzed between groups using a Multivariate Analysis of Variance, and subsequently a Multivariate Analysis of Covariance incorporating age as a covariate. Results: Children who were successful in delaying gratification were significantly older and had significantly better outcomes in the 1-back test and go/no-go test. With the exception of the number of hits in the go/no-go test, all other group differences remained significant after controlling for age. Conclusion: Children who were successful in delaying gratification showed better working memory and motor inhibition relative to those who failed the delayed gratification task. The implications of these findings are discussed. PMID:27493638

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

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

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

Job satisfaction and retention of health-care providers in Afghanistan and Malawi.

PubMed

Fogarty, Linda; Kim, Young Mi; Juon, Hee-Soon; Tappis, Hannah; Noh, Jin Won; Zainullah, Partamin; Rozario, Aleisha

2014-02-17

This study describes job satisfaction and intention to stay on the job among primary health-care providers in countries with distinctly different human resources crises, Afghanistan and Malawi. Using a cross-sectional design, we enrolled 87 health-care providers in 32 primary health-care facilities in Afghanistan and 360 providers in 10 regional hospitals in Malawi. The study questionnaire was used to assess job satisfaction, intention to stay on the job and five features of the workplace environment: resources, performance recognition, financial compensation, training opportunities and safety. Descriptive analyses, exploratory factor analyses for scale development, bivariate correlation analyses and bivariate and multiple linear regression analyses were conducted. The multivariate model for Afghanistan, with demographic, background and work environment variables, explained 23.9% of variance in job satisfaction (F(9,73) = 5.08; P < 0.01). However, none of the work environment variables were significantly related to job satisfaction. The multivariate model for intention to stay for Afghanistan explained 23.6% of variance (F(8,74) = 4.10; P < 0.01). Those with high scores for recognition were more likely to have higher intention to stay (β = 0.328, P < 0.05). However, being paid an appropriate salary was negatively related to intent to stay (β = -0.326, P < 0.01). For Malawi, the overall model explained only 9.8% of variance in job satisfaction (F(8,332) = 4.19; P < 0.01) and 9.1% of variance in intention to stay (F(10,330) = 3.57; P < 0.01). The construction of concepts of health-care worker satisfaction and intention to stay on the job are highly dependent on the local context. Although health-care workers in both Afghanistan and Malawi reported satisfaction with their jobs, the predictors of satisfaction, and the extent to which those predictors explained variations in job satisfaction and intention to stay on the job, differed substantially. These findings demonstrate the need for more detailed comparative human resources for health-care research, particularly regarding the relative importance of different determinants of job satisfaction and intention to stay in different contexts and the effectiveness of interventions designed to improve health-care worker performance and retention.

Job satisfaction and retention of health-care providers in Afghanistan and Malawi

PubMed Central

2014-01-01

Background This study describes job satisfaction and intention to stay on the job among primary health-care providers in countries with distinctly different human resources crises, Afghanistan and Malawi. Methods Using a cross-sectional design, we enrolled 87 health-care providers in 32 primary health-care facilities in Afghanistan and 360 providers in 10 regional hospitals in Malawi. The study questionnaire was used to assess job satisfaction, intention to stay on the job and five features of the workplace environment: resources, performance recognition, financial compensation, training opportunities and safety. Descriptive analyses, exploratory factor analyses for scale development, bivariate correlation analyses and bivariate and multiple linear regression analyses were conducted. Results The multivariate model for Afghanistan, with demographic, background and work environment variables, explained 23.9% of variance in job satisfaction (F(9,73) = 5.08; P < 0.01). However, none of the work environment variables were significantly related to job satisfaction. The multivariate model for intention to stay for Afghanistan explained 23.6% of variance (F(8,74) = 4.10; P < 0.01). Those with high scores for recognition were more likely to have higher intention to stay (β = 0.328, P < 0.05). However, being paid an appropriate salary was negatively related to intent to stay (β = -0.326, P < 0.01). For Malawi, the overall model explained only 9.8% of variance in job satisfaction (F(8,332) = 4.19; P < 0.01) and 9.1% of variance in intention to stay (F(10,330) = 3.57; P < 0.01). Conclusions The construction of concepts of health-care worker satisfaction and intention to stay on the job are highly dependent on the local context. Although health-care workers in both Afghanistan and Malawi reported satisfaction with their jobs, the predictors of satisfaction, and the extent to which those predictors explained variations in job satisfaction and intention to stay on the job, differed substantially. These findings demonstrate the need for more detailed comparative human resources for health-care research, particularly regarding the relative importance of different determinants of job satisfaction and intention to stay in different contexts and the effectiveness of interventions designed to improve health-care worker performance and retention. PMID:24533615

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Chronic aerial exposure to glucorticoids or beta-agonists affects avoidance learning and exploratory motivation in rats.

PubMed

Elías, Pedro C; Sagua, Delia; Alvarez, Edgardo O

2004-02-04

The purpose of the present work was to examine if the conventional asthma treatments in humans (inhalation of glucocorticoids or beta-agonists, administered in a chronic regimen) might affect behavioral processes (learning and exploratory motivation) in rats. Adult male rats were exposed to an atmosphere saturated with either saline, budesonide (a glucocorticoid), or salbutamol (a beta-adrenergic receptor agonist) in a forced ventilation cage, connected to a nebulizer for 5 min twice a day for 15 days at the same hours of the day. Doses of budesonide in the nebulizing solution were 0.116, 1.16, and 11.6mM. Doses of salbutamol in the nebulizing solution were 1.3, 13, and 130 mM. Forty-eight hours after treatment, the different groups were subjected to exploration of an elevated asymmetric plus-maze (APM, model of exploratory motivation), and 24h later to learning of an avoidance response to an ultrasonic tone in a two-compartment cage (model of memory and learning). Results showed that budesonide induces moderate effects on exploratory motivation. In one of the fear-inducing arms (single wall arm), exploration decreased and this effect was not dose dependent. In the cognitive model, glucocorticoids affected slightly the latency to escape but with no interference in memory efficiency. On the other hand, at the lower dose in the APM, salbutamol increased significantly the exploration of both fear-inducing arms (no walls and single wall arms). In the learning model, the beta-agonist induced two opposing effects. The lower dose (1.3mM) facilitated learning and the higher dose (13 mM) inhibited learning instead. In conclusion, results are compatible with the notion that inhaled glucocorticoids or beta-agonists might cross the lung aerial barrier into the blood compartment, exerting effects on learning and motivation functions.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Quantitative analysis of NMR spectra with chemometrics

NASA Astrophysics Data System (ADS)

Winning, H.; Larsen, F. H.; Bro, R.; Engelsen, S. B.

2008-01-01

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) 1H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.

Lipophilicity of oils and fats estimated by TLC.

PubMed

Naşcu-Briciu, Rodica D; Sârbu, Costel

2013-04-01

A representative series of natural toxins belonging to alkaloids and mycotoxins classes was investigated by TLC on classical chemically bonded plates and also on oils- and fats-impregnated plates. Their lipophilicity indices are employed in the characterization and comparison of oils and fats. The retention results allowed an accurate indirect estimation of oils and fats lipophilicity. The investigated fats and oils near classical chemically bonded phases are classified and compared by means of multivariate exploratory techniques, such as cluster analysis, principal component analysis, or fuzzy-principal component analysis. Additionally, a concrete hierarchy of oils and fats derived from the observed lipophilic character is suggested. Human fat seems to be very similar to animal fats, but also possess RP-18, RP-18W, and RP-8. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling with Young Students--Quantitative and Qualitative.

ERIC Educational Resources Information Center

Bliss, Joan; Ogborn, Jon; Boohan, Richard; Brosnan, Tim; Mellar, Harvey; Sakonidis, Babis

1999-01-01

A project created tasks and tools to investigate quality and nature of 11- to 14-year-old pupils' reasoning with quantitative and qualitative computer-based modeling tools. Tasks and tools were used in two innovative modes of learning: expressive, where pupils created their own models, and exploratory, where pupils investigated an expert's model.…

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes.

PubMed

Scherer, Ronny; Nilsen, Trude; Jansen, Malte

2016-01-01

Students' perceptions of instructional quality are among the most important criteria for evaluating teaching effectiveness. The present study evaluates different latent variable modeling approaches (confirmatory factor analysis, exploratory structural equation modeling, and bifactor modeling), which are used to describe these individual perceptions with respect to their factor structure, measurement invariance, and the relations to selected educational outcomes (achievement, self-concept, and motivation in mathematics). On the basis of the Programme for International Student Assessment (PISA) 2012 large-scale data sets of Australia, Canada, and the USA (N = 26,746 students), we find support for the distinction between three factors of individual students' perceptions and full measurement invariance across countries for all modeling approaches. In this regard, bifactor exploratory structural equation modeling outperformed alternative approaches with respect to model fit. Our findings reveal significant relations to the educational outcomes. This study synthesizes different modeling approaches of individual students' perceptions of instructional quality and provides insights into the nature of these perceptions from an individual differences perspective. Implications for the measurement and modeling of individually perceived instructional quality are discussed.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

A spatial stochastic programming model for timber and core area management under risk of fires

Treesearch

Yu Wei; Michael Bevers; Dung Nguyen; Erin Belval

2014-01-01

Previous stochastic models in harvest scheduling seldom address explicit spatial management concerns under the influence of natural disturbances. We employ multistage stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models...

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

Experiences in disaster-related mental health relief work: An exploratory model for the interprofessional training of psychological relief workers.

PubMed

Ren, ZhengJia; Wang, HongTao; Zhang, Wei

2017-01-01

The purpose of this study was to begin to generate an exploratory model of the disaster-related mental health education process associated with the training experiences of psychological relief workers active during the Sichuan earthquake in China. The data consisted of semi-structured interviews with 20 psychological relief workers from four different professions (social workers, psychiatric nurses, psychiatrists, and counsellors) regarding their experiences in training and ideas for improvement. The model explains the need to use a people-centred community interprofessional education approach, which focuses on role-modelling of the trainer, caring for relief workers, paying attention to the needs of the trainee, and building systematic interprofessional education strategies. The proposed model identifies areas for the comprehensive training of relief workers and aims to address the importance of people-centred mental health service provisions, ensure intentional and strategic training of relief workers using interprofessional concepts and strategies, and use culturally attuned and community-informed strategies in mental health training practices.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades

PubMed Central

Li, Karl; Laird, Angela R.; Price, Larry R.; McKay, D. Reese; Blangero, John; Glahn, David C.; Fox, Peter T.

2016-01-01

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging. PMID:27378909

Cognitive and locomotor/exploratory behavior after chronic exercise in the olfactory bulbectomy animal model of depression.

PubMed

Van Hoomissen, Jacqueline; Kunrath, Julie; Dentlinger, Renee; Lafrenz, Andrew; Krause, Mark; Azar, Afaf

2011-09-12

Despite the evidence that exercise improves cognitive behavior in animal models, little is known about these beneficial effects in animal models of pathology. We examined the effects of activity wheel (AW) running on contextual fear conditioning (CFC) and locomotor/exploratory behavior in the olfactory bulbectomy (OBX) model of depression, which is characterized by hyperactivity and changes in cognitive function. Twenty-four hours after the conditioning session of the CFC protocol, the animals were tested for the conditioned response in a conditioned and a novel context to test for the effects of both AW and OBX on CFC, but also the context specificity of the effect. OBX reduced overall AW running behavior throughout the experiment, but increased locomotor/exploratory behavior during CFC, thus demonstrating a context-dependent effect. OBX animals, however, displayed normal CFC behavior that was context-specific, indicating that aversively conditioned memory is preserved in this model. AW running increased freezing behavior during the testing session of the CFC protocol in the control animals but only in the conditioned context, supporting the hypothesis that AW running improves cognitive function in a context-specific manner that does not generalize to an animal model of pathology. Blood corticosterone levels were increased in all animals at the conclusion of the testing sessions, but levels were higher in AW compared to sedentary groups indicating an effect of exercise on neuroendocrine function. Given the differential results of AW running on behavior and neuroendocrine function after OBX, further exploration of the beneficial effects of exercise in animal models of neuropathology is warranted. Copyright © 2011 Elsevier B.V. All rights reserved.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades.

PubMed

Li, Karl; Laird, Angela R; Price, Larry R; McKay, D Reese; Blangero, John; Glahn, David C; Fox, Peter T

2016-01-01

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20-29 to 70-79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

An Aggregate IRT Procedure for Exploratory Factor Analysis

ERIC Educational Resources Information Center

Camilli, Gregory; Fox, Jean-Paul

2015-01-01

An aggregation strategy is proposed to potentially address practical limitation related to computing resources for two-level multidimensional item response theory (MIRT) models with large data sets. The aggregate model is derived by integration of the normal ogive model, and an adaptation of the stochastic approximation expectation maximization…

A Descriptive Study of Differing School Health Delivery Models

ERIC Educational Resources Information Center

Becker, Sherri I.; Maughan, Erin

2017-01-01

The purpose of this exploratory qualitative study was to identify and describe emerging models of school health services. Participants (N = 11) provided information regarding their models in semistructured phone interviews. Results identified a variety of funding sources as well as different staffing configurations and supervision. Strengths of…

Anomalies in social behaviors and exploratory activities in an APPswe/PS1 mouse model of Alzheimer's disease.

PubMed

Filali, Mohammed; Lalonde, Robert; Rivest, Serge

2011-10-24

Alzheimer's disease is characterized by deficits in social communication, associated with generalized apathy or agitation, as well as social memory. To assess social behaviors in 6-month-old male APPswe/PS1 bigenics relative to non-transgenic controls, the 3-chamber test was used, together with open-field and elevated plus-maze tests of exploration. APPswe/PS1 mice were less willing to engage in social interaction than wild-type, avoiding an unfamiliar stimulus mouse, probably not due to generalized apathy because in both tests of exploratory activity the mutants were hyperactive. This study reveals reduced "sociability" combined with hyperactivity in an APPswe/PS1 mouse model of Alzheimer dementia. Copyright © 2011 Elsevier Inc. All rights reserved.

Fatigue as a manifestation of psychosocial distress in a low-income country: a population-based panel study.

PubMed

Smartt, Caroline; Medhin, Girmay; Alem, Atalay; Patel, Vikram; Dewey, Michael; Prince, Martin; Hanlon, Charlotte

2016-03-01

Fatigue is a common complaint worldwide and associated with disability and high health service use costs. We tested the hypothesis that maternal fatigue would be associated independently with maternal common mental disorder ('maternal CMD') in a rural, low-income country setting. The analysis was conducted using data from a population-based cohort located in the Butajira demographic surveillance site, Ethiopia. A total of 1065 women were recruited in pregnancy and followed up to 2.5 (n = 1009; 94.7%) and 3.5 years post-partum (n = 989; 92.9%). Maternal CMD symptoms were measured using a locally validated version of the Self-Reporting Questionnaire and fatigue was measured using a dichotomised item from the Patient Health Questionnaire-15. Physical health indicators included haemoglobin level, body mass index and illness episodes. Generalised estimating equations were used to conduct hypothesis-driven and exploratory multivariable analyses in the panel at 2.5 and 3.5 years. The prevalence of maternal fatigue was 8.3% at 2.5 years and 5.5% at 3.5 years post-partum. Psychological symptoms of maternal CMD were associated independently with complaints of fatigue after adjusting for anaemia, body mass index, physical ill health, poverty and other confounding variables: adjusted odds ratio (aOR), 1.46; 95% confidence interval (CI), 1.28-1.66 for each one point increase in SRQ score. In the multivariable model, only psychosocial factors (CMD and stressful life events) and self-reported physical ill health were associated significantly with complaints of fatigue. Complaints of fatigue are associated strongly with maternal CMD and other psychosocial risk factors in this rural, low-income country setting with a high burden of undernutrition and infectious disease. Fatigue should be understood as a potential indicator of CMD in primary care to improve detection and treatment. © 2015 The Authors. Tropical Medicine & International Health Published by John Wiley & Sons Ltd.

Can variability in the effect of opioids on refractory breathlessness be explained by genetic factors?

PubMed Central

Currow, David C; Quinn, Stephen; Ekstrom, Magnus; Kaasa, Stein; Johnson, Miriam J; Somogyi, Andrew A; Klepstad, Päl

2015-01-01

Objectives Opioids modulate the perception of breathlessness with a considerable variation in response, with poor correlation between the required opioid dose and symptom severity. The objective of this hypothesis-generating, secondary analysis was to identify candidate single nucleotide polymorphisms (SNP) from those associated with opioid receptors, signalling or pain modulation to identify any related to intensity of breathlessness while on opioids. This can help to inform prospective studies and potentially lead to better tailoring of opioid therapy for refractory breathlessness. Setting 17 hospice/palliative care services (tertiary services) in 11 European countries. Participants 2294 people over 18 years of age on regular opioids for pain related to cancer or its treatment. Primary outcome measures The relationship between morphine dose, breathlessness intensity (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire; EORTCQLQC30 question 8) and 112 candidate SNPs from 25 genes (n=588). Secondary outcome measures The same measures for people on oxycodone (n=402) or fentanyl (n=429). Results SNPs not in Hardy-Weinberg equilibrium or with allele frequencies (<5%) were removed. Univariate associations between each SNP and breathlessness intensity were determined with Benjamini-Hochberg false discovery rate set at 20%. Multivariable ordinal logistic regression, clustering over country and adjusting for available confounders, was conducted with remaining SNPs. For univariate morphine associations, 1 variant on the 5-hydroxytryptamine type 3B (HTR3B) gene, and 4 on the β-2-arrestin gene (ARRB2) were associated with more intense breathlessness. 1 SNP remained significant in the multivariable model: people with rs7103572 SNP (HTR3B gene; present in 8.4% of the population) were three times more likely to have more intense breathlessness (OR 2.86; 95% CIs 1.46 to 5.62; p=0.002). No associations were seen with fentanyl nor with oxycodone. Conclusions This large, exploratory study identified 1 biologically plausible SNP that warrants further study in the response of breathlessness to morphine therapy. PMID:25948405

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

General and specific attention-deficit/hyperactivity disorder factors of children 4 to 6 years of age: An exploratory structural equation modeling approach to assessing symptom multidimensionality.

PubMed

Arias, Víctor B; Ponce, Fernando P; Martínez-Molina, Agustín; Arias, Benito; Núñez, Daniel

2016-01-01

We tested first-order factor and bifactor models of attention-deficit/hyperactivity disorder (ADHD) using confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) to adequately summarize the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, (DSM-IV-TR) symptoms observed in a Spanish sample of preschoolers and kindergarteners. Six ESEM and CFA models were estimated based on teacher evaluations of the behavior of 638 children 4 to 6 years of age. An ESEM bifactor model with a central dimension plus 3 specific factors (inattention, hyperactivity, and impulsivity) showed the best fit and interpretability. Strict invariance between the sexes was observed. The bifactor model provided a solution to previously encountered inconsistencies in the factorial models of ADHD in young children. However, the low reliability of the specific factors casts doubt on the utility of the subscales for ADHD measurement. More research is necessary to clarify the nature of G and S factors of ADHD. (c) 2016 APA, all rights reserved.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

A mathematical model for adaptive vein formation during exploratory migration of Physarum polycephalum: routing while scouting

NASA Astrophysics Data System (ADS)

Schenz, Daniel; Shima, Yasuaki; Kuroda, Shigeru; Nakagaki, Toshiyuki; Ueda, Kei-Ichi

2017-11-01

Exploring free space (scouting) efficiently is a non-trivial task for organisms of limited perception, such as the amoeboid Physarum polycephalum. However, the strategy behind its exploratory behaviour has not yet been characterised. In this organism, as the extension of the frontal part into free space is directly supported by the transport of body mass from behind, the formation of transport channels (routing) plays the main role in that strategy. Here, we study the organism’s exploration by letting it expand through a corridor of constant width. When turning at a corner of the corridor, the organism constructed a main transport vein tracing a centre-in-centre line. We argue that this is efficient for mass transport due to its short length, and check this intuition with a new algorithm that can predict the main vein’s position from the frontal tip’s progression. We then present a numerical model that incorporates reaction-diffusion dynamics for the behaviour of the organism’s growth front and current reinforcement dynamics for the formation of the vein network in its wake, as well as interactions between the two. The accuracy of the model is tested against the behaviour of the real organism and the importance of the interaction between growth tip dynamics and vein network development is analysed by studying variants of the model. We conclude by offering a biological interpretation of the well-known current reinforcement rule in the context of the natural exploratory behaviour of Physarum polycephalum.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A comparative analysis of prognostic factor models for follicular lymphoma based on a phase III trial of CHOP-rituximab versus CHOP + 131iodine--tositumomab.

PubMed

Press, Oliver W; Unger, Joseph M; Rimsza, Lisa M; Friedberg, Jonathan W; LeBlanc, Michael; Czuczman, Myron S; Kaminski, Mark; Braziel, Rita M; Spier, Catherine; Gopal, Ajay K; Maloney, David G; Cheson, Bruce D; Dakhil, Shaker R; Miller, Thomas P; Fisher, Richard I

2013-12-01

There is currently no consensus on optimal frontline therapy for patients with follicular lymphoma. We analyzed a phase III randomized intergroup trial comparing six cycles of CHOP-R (cyclophosphamide-Adriamycin-vincristine-prednisone (Oncovin)-rituximab) with six cycles of CHOP followed by iodine-131 tositumomab radioimmunotherapy (RIT) to assess whether any subsets benefited more from one treatment or the other, and to compare three prognostic models. We conducted univariate and multivariate Cox regression analyses of 532 patients enrolled on this trial and compared the prognostic value of the FLIPI (follicular lymphoma international prognostic index), FLIPI2, and LDH + β2M (lactate dehydrogenase + β2-microglobulin) models. Outcomes were excellent, but not statistically different between the two study arms [5-year progression-free survival (PFS) of 60% with CHOP-R and 66% with CHOP-RIT (P = 0.11); 5-year overall survival (OS) of 92% with CHOP-R and 86% with CHOP-RIT (P = 0.08); overall response rate of 84% for both arms]. The only factor found to potentially predict the impact of treatment was serum β2M; among patients with normal β2M, CHOP-RIT patients had better PFS compared with CHOP-R patients, whereas among patients with high serum β2M, PFS by arm was similar (interaction P value = 0.02). All three prognostic models (FLIPI, FLIPI2, and LDH + β2M) predicted both PFS and OS well, though the LDH + β2M model is easiest to apply and identified an especially poor risk subset. In an exploratory analysis using the latter model, there was a statistically significant trend suggesting that low-risk patients had superior observed PFS if treated with CHOP-RIT, whereas high-risk patients had a better PFS with CHOP-R. ©2013 AACR.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

A Framework for Dimensionality Assessment for Multidimensional Item Response Models

ERIC Educational Resources Information Center

Svetina, Dubravka; Levy, Roy

2014-01-01

A framework is introduced for considering dimensionality assessment procedures for multidimensional item response models. The framework characterizes procedures in terms of their confirmatory or exploratory approach, parametric or nonparametric assumptions, and applicability to dichotomous, polytomous, and missing data. Popular and emerging…

Angler specialization among salmon and trout anglers on Lake Ontario

Treesearch

Chad P. Dawson

1995-01-01

The angler specialization concept was studied using the expectancy model of motivation. An exploratory study of' Lake Ontario salmon and trout anglers was conducted to test the relationships between the variables of the expectancy model of motivation and actual angling participation.

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.

Multimedia Learning System and Its Effect on Self-Efficacy in Database Modeling and Design: An Exploratory Study

ERIC Educational Resources Information Center

Cheung, Waiman; Li, Eldon Y.; Yee, Lester W.

2003-01-01

Metadatabase modeling and design integrate process modeling and data modeling methodologies. Both are core topics in the information technology (IT) curriculum. Learning these topics has been an important pedagogical issue to the core studies for management information systems (MIS) and computer science (CSc) students. Unfortunately, the learning…

Enhancing Student Teachers' Epistemological Beliefs about Models and Conceptual Understanding through a Model-Based Inquiry Process

ERIC Educational Resources Information Center

Soulios, Ioannis; Psillos, Dimitris

2016-01-01

In this study we present the structure and implementation of a model-based inquiry teaching-learning sequence (TLS) integrating expressive, experimental and exploratory modelling pedagogies in a cyclic manner, with the aim of enhancing primary education student teachers' epistemological beliefs about the aspects, nature, purpose and change of…

Individual differences and repeatability in vocal production: stress-induced calling exposes a songbird's personality.

PubMed

Guillette, Lauren M; Sturdy, Christopher B

2011-11-01

Recent research in songbirds has demonstrated that male singing behavior varies systematically with personality traits such as exploration and risk taking. Here we examine whether the production of bird calls, in addition to bird songs, is repeatable and related to exploratory behavior, using the black-capped chickadee (Poecile atricapillus) as a model. We assessed the exploratory behavior of individual birds in a novel environment task. We then recorded the vocalizations and accompanying motor behavior of both male and female chickadees, over the course of several days, in two different contexts: a control condition with no playback and a stressful condition where chick-a-dee mobbing calls were played to individual birds. We found that several vocalizations and behaviors were repeatable within both a control and a stressful context, and across contexts. While there was no relationship between vocal output and exploratory behavior in the control context, production of alarm and chick-a-dee calls in the stressful condition was positively associated with exploratory behavior. These findings are important because they show that bird calls, in addition to bird song, are an aspect of personality, in that calls are consistent both within and across contexts, and covary with other personality measures (exploration).

Individual differences and repeatability in vocal production: stress-induced calling exposes a songbird's personality

NASA Astrophysics Data System (ADS)

Guillette, Lauren M.; Sturdy, Christopher B.

2011-11-01

Recent research in songbirds has demonstrated that male singing behavior varies systematically with personality traits such as exploration and risk taking. Here we examine whether the production of bird calls, in addition to bird songs, is repeatable and related to exploratory behavior, using the black-capped chickadee ( Poecile atricapillus) as a model. We assessed the exploratory behavior of individual birds in a novel environment task. We then recorded the vocalizations and accompanying motor behavior of both male and female chickadees, over the course of several days, in two different contexts: a control condition with no playback and a stressful condition where chick-a-dee mobbing calls were played to individual birds. We found that several vocalizations and behaviors were repeatable within both a control and a stressful context, and across contexts. While there was no relationship between vocal output and exploratory behavior in the control context, production of alarm and chick-a-dee calls in the stressful condition was positively associated with exploratory behavior. These findings are important because they show that bird calls, in addition to bird song, are an aspect of personality, in that calls are consistent both within and across contexts, and covary with other personality measures (exploration).

Exploratory Climate Data Visualization and Analysis Using DV3D and UVCDAT

NASA Technical Reports Server (NTRS)

Maxwell, Thomas

2012-01-01

Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Advanced tools for accessing, analyzing, and visualizing very large and complex climate data are required to maintain rapid progress in Earth system research. To meet this need, NASA, in collaboration with the Ultra-scale Visualization Climate Data Analysis Tools (UVCOAT) consortium, is developing exploratory climate data analysis and visualization tools which provide data analysis capabilities for the Earth System Grid (ESG). This paper describes DV3D, a UV-COAT package that enables exploratory analysis of climate simulation and observation datasets. OV3D provides user-friendly interfaces for visualization and analysis of climate data at a level appropriate for scientists. It features workflow inte rfaces, interactive 40 data exploration, hyperwall and stereo visualization, automated provenance generation, and parallel task execution. DV30's integration with CDAT's climate data management system (COMS) and other climate data analysis tools provides a wide range of high performance climate data analysis operations. DV3D expands the scientists' toolbox by incorporating a suite of rich new exploratory visualization and analysis methods for addressing the complexity of climate datasets.

Charting the expansion of strategic exploratory behavior during adolescence.

PubMed

Somerville, Leah H; Sasse, Stephanie F; Garrad, Megan C; Drysdale, Andrew T; Abi Akar, Nadine; Insel, Catherine; Wilson, Robert C

2017-02-01

Although models of exploratory decision making implicate a suite of strategies that guide the pursuit of information, the developmental emergence of these strategies remains poorly understood. This study takes an interdisciplinary perspective, merging computational decision making and developmental approaches to characterize age-related shifts in exploratory strategy from adolescence to young adulthood. Participants were 149 12-28-year-olds who completed a computational explore-exploit paradigm that manipulated reward value, information value, and decision horizon (i.e., the utility that information holds for future choices). Strategic directed exploration, defined as information seeking selective for long time horizons, emerged during adolescence and maintained its level through early adulthood. This age difference was partially driven by adolescents valuing immediate reward over new information. Strategic random exploration, defined as stochastic choice behavior selective for long time horizons, was invoked at comparable levels over the age range, and predicted individual differences in attitudes toward risk taking in daily life within the adolescent portion of the sample. Collectively, these findings reveal an expansion of the diversity of strategic exploration over development, implicate distinct mechanisms for directed and random exploratory strategies, and suggest novel mechanisms for adolescent-typical shifts in decision making. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Cerebral correlates of depressed behavior in rats, visualized using /sup 14/C-2-deoxyglucose autoradiography

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

Caldecott-Hazard, S.; Mazziotta, J.; Phelps, M.

1988-06-01

/sup 14/C-2-Deoxyglucose (2DG) was used to investigate changes in the rate of cerebral metabolism in 3 rat models of depressed behavior. The models had already been established in the literature and were induced by injections of alpha-methyl-para-tyrosine, withdrawal from chronic amphetamine, or stress. We verified that exploratory behaviors were depressed in each model and that an antidepressant drug, tranylcypromine, prevented the depressed behavior in each model. 2DG studies revealed that the rate of regional glucose metabolism was elevated bilaterally in the lateral habenula of each of the 3 models. Regional metabolic rates were reduced in each model in the dorsalmore » medial prefrontal cortex, anterior ventral nucleus of the thalamus, and inferior colliculus. Forebrain global metabolic rates were also reduced in each of the models. Tranylcypromine prevented the elevated rate of lateral habenula metabolism seen in each of the models alone but did not significantly affect the rates of global metabolism. Our findings of identical metabolic changes in each of the models indicate that these changes are not idiosyncratic to a particular model; rather, they correlate with a generalizable state of depressed exploratory behavior in rats.« less

Wellbeing, mental health knowledge and caregiving experiences of siblings of people with psychosis, compared to their peers and parents: an exploratory study.

PubMed

Sin, Jacqueline; Murrells, Trevor; Spain, Debbie; Norman, Ian; Henderson, Claire

2016-09-01

The wellbeing and caregiving experiences of family carers supporting people with psychosis has garnered increasing interest. Evidence indicates that the burden of caregiving can adversely impact on parents' wellbeing, few studies have investigated whether this is also the case for siblings, who often take on caregiving responsibilities. This exploratory study investigated the wellbeing, mental health knowledge, and appraisals of caregiving in siblings of individuals with psychosis. Using a cross-sectional design, 90 siblings completed three validated questionnaires: Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), Mental Health Knowledge Schedule (MAKS), and Experience of Caregiving Inventory (ECI). Data obtained were compared to general population norms and parent-carers' scores. Multi-variable regression analyses were conducted to examine relationships between questionnaire scores and demographic characteristics including age, sex, birth order, marital status, accommodation and educational level. Siblings, especially sisters, had significantly poorer mental wellbeing, compared to normative scores. Conversely, they had better mental health knowledge. Siblings and parent-carers had comparable high levels of negative appraisals of caregiving experiences, but siblings reported more satisfaction with personal experiences and relationships. Education level was a significant predictor for better mental health knowledge; there were no other relationships between siblings' demographic factors and outcomes. Study findings suggest that siblings have overlapping as well as distinct needs, compared to parent-carers. Further research is required to better understand siblings' experiences so as to inform development of targeted interventions that enhance wellbeing and caregiving capacity.

Internet addiction and sleep quality among Vietnamese youths.

PubMed

Zhang, Melvyn W B; Tran, Bach Xuan; Huong, Le Thi; Hinh, Nguyen Duc; Nguyen, Huong Lan Thi; Tho, Tran Dinh; Latkin, Carl; Ho, Roger C M

2017-08-01

Internet addiction has been a major behavioral disorder over the past decade. Prior meta-analytic review has demonstrated the association between Internet addiction and psychiatric disorders, as well as sleep related disorders. There remains a paucity of literature about Internet addiction and sleep related disorders in low and middle income countries like Vietnam. It is the aim of this exploratory study to determine the association. An online cross-sectional study was conducted between August through to October 2015. Respondent drive sampling technique was utilized in the recruitment of participants. The short form version of the Young's Internet addiction test was administered and sleep related disorders was ascertained by means of a self-report questionnaire. Chi-squared, t-test and ANOVA were used to determine whether there were any significant differences amongst the variables considered. Multivariate logistic regressions were also used in the analysis, in order to identify factors associated with Internet addiction. 21.2% Of the participants were diagnosed with Internet addiction. 26.7% of those with Internet addiction also reported that they have had sleep related difficulties. 77.2% of these participants were receptive towards seeking medical treatment. Our current study also highlighted that being single and those who were using tobacco products were not at heightened risk of developing associated sleep related issues. Our current study is largely a cross-sectional exploratory study that has shown that there is a significant prevalence of both Internet addiction and sleep related disorders amongst Vietnamese youth. Copyright © 2017 Elsevier B.V. All rights reserved.

Preliminary comparative assessment of PM10 hourly measurement results from new monitoring stations type using stochastic and exploratory methodology and models

NASA Astrophysics Data System (ADS)

Czechowski, Piotr Oskar; Owczarek, Tomasz; Badyda, Artur; Majewski, Grzegorz; Rogulski, Mariusz; Ogrodnik, Paweł

2018-01-01

The paper presents selected preliminary stage key issues proposed extended equivalence measurement results assessment for new portable devices - the comparability PM10 concentration results hourly series with reference station measurement results with statistical methods. In article presented new portable meters technical aspects. The emphasis was placed on the comparability the results using the stochastic and exploratory methods methodology concept. The concept is based on notice that results series simple comparability in the time domain is insufficient. The comparison of regularity should be done in three complementary fields of statistical modeling: time, frequency and space. The proposal is based on model's results of five annual series measurement results new mobile devices and WIOS (Provincial Environmental Protection Inspectorate) reference station located in Nowy Sacz city. The obtained results indicate both the comparison methodology completeness and the high correspondence obtained new measurements results devices with reference.

French validation of the internet addiction test.

PubMed

Khazaal, Yasser; Billieux, Joël; Thorens, Gabriel; Khan, Riaz; Louati, Youssr; Scarlatti, Elisa; Theintz, Florence; Lederrey, Jerome; Van Der Linden, Martial; Zullino, Daniele

2008-12-01

The main goal of the present study is to investigate the psychometric properties of a French version of the Internet Addiction Test (IAT) and to assess its relationship with both time spent on Internet and online gaming. The French version of the Young's Internet Addiction Test (IAT) was administered to a sample of 246 adults. Exploratory and confirmatory analyses were carried out. We discovered that a one-factor model of the IAT has good psychometric properties and fits the data well, which is not the case of a six-factor model as found in previous studies using exploratory methods. Correlation analysis revealed positive significant relationships between IAT scores and both the daily duration of Internet use and the fact of being an online player. In addition, younger people scored higher on the IAT. The one-factor model found in this study has to be replicated in other IAT language versions.

A Quantitative Model of Motility Reveals Low-Dimensional Variation in Exploratory Behavior Across Multiple Nematode Species

NASA Astrophysics Data System (ADS)

Helms, Stephen; Avery, Leon; Stephens, Greg; Shimizu, Tom

2014-03-01

Animal behavior emerges from many layers of biological organization--from molecular signaling pathways and neuronal networks to mechanical outputs of muscles. In principle, the large number of interconnected variables at each of these layers could imply dynamics that are complex and hard to control or even tinker with. Yet, for organisms to survive in a competitive, ever-changing environment, behavior must readily adapt. We applied quantitative modeling to identify important aspects of behavior in chromadorean nematodes ranging from the lab strain C. elegans N2 to wild strains and distant species. We revealed subtle yet important features such as speed control and heavy-tailed directional changes. We found that the parameters describing this behavioral model varied among individuals and across species in a correlated way that is consistent with a trade-off between exploratory and exploitative behavior.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Comprehensive pharmacogenetic analysis of irinotecan neutropenia and pharmacokinetics.

PubMed

Innocenti, Federico; Kroetz, Deanna L; Schuetz, Erin; Dolan, M Eileen; Ramírez, Jacqueline; Relling, Mary; Chen, Peixian; Das, Soma; Rosner, Gary L; Ratain, Mark J

2009-06-01

We aim to identify genetic variation, in addition to the UGT1A1*28 polymorphism, that can explain the variability in irinotecan (CPT-11) pharmacokinetics and neutropenia in cancer patients. Pharmacokinetic, genetic, and clinical data were obtained from 85 advanced cancer patients treated with single-agent CPT-11 every 3 weeks at doses of 300 mg/m(2) (n = 20) and 350 mg/m(2) (n = 65). Forty-two common variants were genotyped in 12 candidate genes of the CPT-11 pathway using several methodologies. Univariate and multivariate models of absolute neutrophil count (ANC) nadir and pharmacokinetic parameters were evaluated. Almost 50% of the variation in ANC nadir is explained by UGT1A1*93, ABCC1 IVS11 -48C>T, SLCO1B1*1b, ANC baseline levels, sex, and race (P < .0001). More than 40% of the variation in CPT-11 area under the curve (AUC) is explained by ABCC2 -24C>T, SLCO1B1*5, HNF1A 79A>C, age, and CPT-11 dose (P < .0001). Almost 30% of the variability in SN-38 (the active metabolite of CPT-11) AUC is explained by ABCC1 1684T>C, ABCB1 IVS9 -44A>G, and UGT1A1*93 (P = .004). Other models explained 17%, 23%, and 27% of the variation in APC (a metabolite of CPT-11), SN-38 glucuronide (SN-38G), and SN-38G/SN-38 AUCs, respectively. When tested in univariate models, pretreatment total bilirubin was able to modify the existing associations between genotypes and phenotypes. On the basis of this exploratory analysis, common polymorphisms in genes encoding for ABC and SLC transporters may have a significant impact on the pharmacokinetics and pharmacodynamics of CPT-11. Confirmatory studies are required.

The Negative Consequences of Other Students’ Drinking: Inventory Development and Assessment of Differences by Student Characteristics and Risk Behaviors

PubMed Central

Rhodes, Scott D.; McCoy, Thomas P.; Omli, Morrow R.; Cohen, Gail M.; Wagoner, Kimberly G.; DuRant, Robert H.; Vissman, Aaron T.; Wolfson, Mark

2013-01-01

College students continue to report being disrupted by other students’ alcohol use. Objective: This study was designed to develop measures to document the consequences resulting from other students’ drinking and identify differences in experiencing these consequences by student characteristics and drinking behaviors. Study group: A stratified random sample of undergraduate students (N = 3,908) from ten universities in North Carolina, USA, completed a web-based assessment. Methods: Exploratory factor analysis (EFA) was performed on the random first split-half sample (n = 1,954) to identify factor structure. Confirmatory factor analysis (CFA) was performed on the remaining half sample (n = 1,954) using structural equation modeling. Results: EFA revealed two inventories: interpersonal and community consequences of others’ drinking inventories. CFA on the second split-half sample identified model fits for the two factor structure suggested by EFA. Of 3,908 participants, 78% reported experiencing one or more consequences due to others’ drinking during the past 30 days. Multivariable generalized linear mixed modeling further validated the inventories and resulted in several associations. Male students who reported getting drunk experienced significantly more interpersonal consequences from others’ drinking (p < .001). Minority students, students who lived on campus and students who reported getting drunk experienced significantly more community consequences from others’ drinking (p < .01). Conclusions: These findings demonstrate that 4 out of 5 college students experience consequences from others’ drinking, and consequences vary for different subgroups of students. Although these inventories should be tested further, these findings propose standardized measures that may be useful to assess the consequences of others’ drinking among college students. PMID:20306764

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Applying the Theory of Work Adjustment to Latino Immigrant Workers: An Exploratory Study.

PubMed

Eggerth, Donald E; Flynn, Michael A

2012-02-01

Blustein mapped career decision making onto Maslow's model of motivation and personality and concluded that most models of career development assume opportunities and decision-making latitude that do not exist for many individuals from low income or otherwise disadvantaged backgrounds. Consequently, Blustein argued that these models may be of limited utility for such individuals. Blustein challenged researchers to reevaluate current career development approaches, particularly those assuming a static world of work, from a perspective allowing for changing circumstances and recognizing career choice can be limited by access to opportunities, personal obligations, and social barriers. This article represents an exploratory effort to determine if the theory of work adjustment (TWA) might meaningfully be used to describe the work experiences of Latino immigrant workers, a group living with severe constraints and having very limited employment opportunities. It is argued that there is significant conceptual convergence between Maslow's hierarchy of needs and the work reinforcers of TWA. The results of an exploratory, qualitative study with a sample of 10 Latino immigrants are also presented. These immigrants participated in key informant interviews concerning their work experiences both in the United States and in their home countries. The findings support Blustein's contention that such workers will be most focused on basic survival needs and suggest that TWA reinforcers are descriptive of important aspects of how Latino immigrant workers conceptualize their jobs.

Applying the Theory of Work Adjustment to Latino Immigrant Workers: An Exploratory Study

PubMed Central

Eggerth, Donald E.; Flynn, Michael A.

2015-01-01

Blustein mapped career decision making onto Maslow’s model of motivation and personality and concluded that most models of career development assume opportunities and decision-making latitude that do not exist for many individuals from low income or otherwise disadvantaged backgrounds. Consequently, Blustein argued that these models may be of limited utility for such individuals. Blustein challenged researchers to reevaluate current career development approaches, particularly those assuming a static world of work, from a perspective allowing for changing circumstances and recognizing career choice can be limited by access to opportunities, personal obligations, and social barriers. This article represents an exploratory effort to determine if the theory of work adjustment (TWA) might meaningfully be used to describe the work experiences of Latino immigrant workers, a group living with severe constraints and having very limited employment opportunities. It is argued that there is significant conceptual convergence between Maslow’s hierarchy of needs and the work reinforcers of TWA. The results of an exploratory, qualitative study with a sample of 10 Latino immigrants are also presented. These immigrants participated in key informant interviews concerning their work experiences both in the United States and in their home countries. The findings support Blustein’s contention that such workers will be most focused on basic survival needs and suggest that TWA reinforcers are descriptive of important aspects of how Latino immigrant workers conceptualize their jobs. PMID:26345693

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Distance Education and Organizational Environment

ERIC Educational Resources Information Center

East, Jean F.; LaMendola, Walter; Alter, Catherine

2014-01-01

As distance education models in social work education continue to grow, this study addresses prevalence and type of models in graduate social work programs and the perceptions of deans about the future of e-learning models of curriculum delivery. The study was an exploratory sequential mixed-methods design including a national survey of 121…

Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

ERIC Educational Resources Information Center

Tirri, Henry; And Others

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated…

US/Canada wheat and barley crop calender exploratory experiment implementation plan

NASA Technical Reports Server (NTRS)

1980-01-01

A plan is detailed for a supplemental experiment to evaluate several crop growth stage models and crop starter models. The objective of this experiment is to provide timely information to aid in understanding crop calendars and to provide data that will allow a selection between current crop calendar models.

Cognitive Models: The Missing Link to Learning Fraction Multiplication and Division

ERIC Educational Resources Information Center

de Castro, Belinda V.

2008-01-01

This quasi-experimental study aims to streamline cognitive models on fraction multiplication and division that contain the most worthwhile features of other existing models. Its exploratory nature and its approach to proof elicitation can be used to help establish its effectiveness in building students' understanding of fractions as compared to…

Applying the Theory of Work Adjustment to Latino Immigrant Workers: An Exploratory Study

ERIC Educational Resources Information Center

Eggerth, Donald E.; Flynn, Michael A.

2012-01-01

Blustein mapped career decision making onto Maslow's model of motivation and personality and concluded that most models of career development assume opportunities and decision-making latitude that do not exist for many individuals from low income or otherwise disadvantaged backgrounds. Consequently, Blustein argued that these models may be of…

A Construct Validation of the Mental Models Learning Outcome Using Exploratory Factor Analysis.

ERIC Educational Resources Information Center

Sheehan, Joseph; Tessmer, Martin

A mental model is a knowledge structure composed of concepts and the relations between them. Mental models are distinct from declarative and procedural knowledge--they go beyond semantic relationships and skills acquisition and contain varied intellectual skills and knowledge. This study describes an initial investigation into the construct…

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation, BSEM, and ESEM/EFA Bifactor Approaches.

PubMed

Murray, Aja Louise; Booth, Tom; Eisner, Manuel; Obsuth, Ingrid; Ribeaud, Denis

2018-05-22

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

Exploratory and Confirmatory Factor Analysis of the Career Decision-Making Difficulties Questionnaire

PubMed Central

Farrokhi, Farahman; Mahdavi, Ali; Moradi, Samad

2012-01-01

Objective The present study aimed at validating the structure of Career Decision-making Difficulties Questionnaire (CDDQ). Methods Five hundred and eleven undergraduate students took part in this research; from these participants, 63 males and 200 females took part in the first study, and 63 males and 185 females completed the survey for the second study. Results The results of exploratory factor analysis (EFA) indicated strong support for the three-factor structure, consisting of lack of information about the self, inconsistent information, lack of information and lack of readiness factors. A confirmatory factor analysis was run with the second sample using structural equation modeling. As expected, the three-factor solution provided a better fit to the data than the alternative models. Conclusion CDDQ was recommended to be used for college students in this study due to the fact that this instrument measures all three aspects of the model. Future research is needed to learn whether this model would fit other different samples. PMID:22952549

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Cross-sectional study of variables associated with length of stay and ICU need in open Roux-En-Y gastric bypass surgery for morbid obese patients: an exploratory analysis based on the Public Health System administrative database (Datasus) in Brazil.

PubMed

Asano, Elio Fernando; Rasera, Irineu; Shiraga, Elisabete Cristina

2012-12-01

This is an exploratory analysis of potential variables associated with open Roux-en-Y gastric bypass (RYGB) surgery hospitalization resource use pattern. Cross-sectional study based on an administrative database (DATASUS) records. Inclusion criteria were adult patients undergoing RYGB between Jan/2008 and Jun/2011. Dependent variables were length of stay (LoS) and ICU need. Independent variables were: gender, age, region, hospital volume, surgery at certified center of excellence (CoE) by the Surgical Review Corporation (SRC), teaching hospital, and year of hospitalization. Univariate and multivariate analysis (logistic regression for ICU need and linear regression for length of stay) were performed. Data from 13,069 surgeries were analyzed. In crude analysis, hospital volume was the most impactful variable associated with log-transformed LoS (1.312 ± 0.302 high volume vs. 1.670 ± 0.581 low volume, p < 0.001), whereas for ICU need it was certified CoE (odds ratio (OR), 0.016; 95% confidence interval (CI), 0.010-0.026). After adjustment by logistic regression, certified CoE remained as the strongest predictor of ICU need (OR, 0.011; 95% CI, 0.007-0.018), followed by hospital volume (OR, 3.096; 95% CI, 2.861-3.350). Age group, male gender, and teaching hospital were also significantly associated (p < 0.001). For log-transformed LoS, final model includes hospital volume (coefficient, -0.223; 95% CI, -0.250 to -0.196) and teaching hospital (coefficient, 0.375; 95% CI, 0.351-0.398). Region of Brazil was not associated with any of the outcomes. High-volume hospital was the strongest predictor for shorter LoS, whereas SRC certification was the strongest predictor of lower ICU need. Public health policies targeting an increase of efficiency and patient access to the procedure should take into account these results.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Reactivity to a Spouse's Interpersonal Suffering in Late Life Marriage: A Mixed-Methods Approach.

PubMed

Mitchell, Hannah-Rose; Levy, Becca R; Keene, Danya E; Monin, Joan K

2015-09-01

To determine how older adult spouses react to their partners' interpersonal suffering. Spouses of individuals with musculoskeletal pain were recorded describing their partners' suffering while their blood pressure (BP) was monitored. After the account, spouses described their distress. Speeches were transcribed and analyzed with Linguistic Inquiry and Word Count software and coded for interpersonal content. Multivariate regression analyses were conducted with interpersonal content variables predicting BP and distress. Exploratory qualitative analysis was conducted using ATLAS.ti to explore mechanisms behind quantitative results. Describing partners' suffering as interpersonal and using social (family) words were associated with higher systolic BP reactivity. Husbands were more likely to describe partners' suffering as interpersonal. Qualitative results suggested shared stressors and bereavement-related distress as potential mechanisms for heightened reactivity to interpersonal suffering. Spouses' interpersonal suffering may negatively affect both men and women's cardiovascular health, and older husbands may be particularly affected. © The Author(s) 2015.

Occurrence and transport of pesticides and alkylphenols in water samples along the Ebro River Basin

NASA Astrophysics Data System (ADS)

Navarro, Alícia; Tauler, Romà; Lacorte, Sílvia; Barceló, Damià

2010-03-01

SummaryWe report the temporal and geographical variations of a set of 30 pesticides (including triazines, organophosphorus and acetanilides) and industrial compounds in surface waters along the Ebro River during the period 2004-2006. Using descriptive statistics we found that the compounds with industrial origin (tributylphosphate, octylphenol and nonylphenol) appeared in over 60% of the samples analyzed and at very high concentrations, while pesticides had a point source origin in the Ebro delta area and overall low-levels, between 0.005 and 2.575 μg L -1. Correlations among pollutants and their distributions were studied using Principal Component Analysis (PCA), a multivariate exploratory data analysis technique which permitted us to discern between agricultural and industrial source contamination. Over a 3 years period a seasonal trend revealed highest concentrations of pesticides over the spring-summer period following pesticide application.

Taxonomic revision of imitating carpenter ants, Camponotus subgenus Myrmopytia (Hymenoptera, Formicidae) of Madagascar, using morphometry and qualitative traits

PubMed Central

Rasoamanana, Nicole; Csősz, Sándor; Fisher, Brian L.

2017-01-01

Abstract The ant genus Camponotus (Mayr, 1861) is one of the most abundant and species rich ant genera in the Malagasy zoogeographical region. Although this group is commonly encountered, its taxonomy is far from complete. Here, we clarify the taxonomy of the Malagasy-endemic Camponotus subgenus Myrmopytia (Emery, 1920). Species delimitation was based on traditional morphological characters and multivariate morphometric analyses, including exploratory Nest Centroid clustering and confirmatory cross-validated Linear Discriminant Analysis. Four species are recognized: Camponotus imitator (Forel, 1891), Camponotus jodina sp. n., Camponotus karaha sp. n., and Camponotus longicollis sp. n. All four species appear to mimic co-occurring Aphaenogaster species. A diagnosis of the subgenus Myrmopytia, species descriptions, an identification key based on minor and major subcastes of workers, and the known geographical distribution of each species are provided. PMID:28769722

Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel

PubMed Central

Sage, Emma; Velez, Martin; Guinard, Jean‐Xavier

2016-01-01

Abstract The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy‐two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry. PMID:27861864

Reactivity to a Spouse's Interpersonal Suffering in Late Life Marriage: A Mixed-Methods Approach

PubMed Central

Mitchell, Hannah-Rose; Levy, Becca R.; Keene, Danya E.; Monin, Joan K.

2015-01-01

Objective To determine how older adult spouses react to their partners' interpersonal suffering. Method Spouses of individuals with musculoskeletal pain were recorded describing their partners' suffering while their blood pressure (BP) was monitored. After the account, spouses described their distress. Speeches were transcribed and analyzed with Linguistic Inquiry and Word Count software and coded for interpersonal content. Multivariate regression analyses were conducted with interpersonal content variables predicting BP and distress. Exploratory qualitative analysis was conducted using ATLAS.ti to explore mechanisms behind quantitative results. Results Describing partners' suffering as interpersonal and using social (family) words were associated with higher systolic BP reactivity. Husbands were more likely to describe partners' suffering as interpersonal. Qualitative results suggested shared stressors and bereavement-related distress as potential mechanisms for heightened reactivity to interpersonal suffering. Discussion Spouses' interpersonal suffering may negatively affect both men and women's cardiovascular health, and older husbands may be particularly affected. PMID:25659746

Perceived demands during modern military operations.

PubMed

Boermans, Sylvie M; Kamphuis, Wim; Kamhuis, Wim; Delahaij, Roos; Korteling, J E Hans; Euwema, Martin C

2013-07-01

Using a cross-sectional design, this study explored operational demands during the International Security Assistance Force for Afghanistan (2009-2010) across distinct military units. A total of 1,413 Dutch soldiers, nested within four types of units (i.e., combat, combat support, service support, and command support units) filled out a 23-item self-survey in which they were asked to evaluate the extent to which they experienced operational characteristics as demanding. Exploratory factor analysis identified six underlying dimensions of demands. Multivariate analysis of variance revealed that distinct units are characterized by their own unique constellation of perceived demands, even after controlling for previous deployment experience. Most notable findings were found when comparing combat units to other types of units. These insights can be used to better prepare different types of military units for deployment, and support them in the specific demands they face during deployment. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Relationships Among Substance Use, Multiple Sexual Partners, and Condomless Sex.

PubMed

Zhao, Yunchuan Lucy; Kim, Heejung; Peltzer, Jill

2017-04-01

Male and female students manifest different behaviors in condomless sex. This cross-sectional, exploratory, correlational study examined the differences in risk factors for condomless sex between male and female high school students, using secondary data from 4,968 sexually active males and females participating in the 2011 National Youth Risk Behavior Survey. Results in descriptive statistics and multivariate binary logistic regressions revealed that condomless sex was reported as 39.70% in general. A greater proportion of females engaged in condomless sex (23.26%) than did males (16.44%). Physical abuse by sex partners was a common reason for failure to use condoms regardless of gender. Lower condom use was found in (1) those experiencing forced sex by a partner in males, (2) female smokers, and (3) female with multiple sex partners. Thus, sexual health education should address the different risk factors and consider gender characteristics to reduce condomless sex.

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

The Effect of Instruction through Mathematical Modelling on Modelling Skills of Prospective Elementary Mathematics Teachers

ERIC Educational Resources Information Center

Ciltas, Alper; Isik, Ahmet

2013-01-01

The aim of this study was to examine the modelling skills of prospective elementary mathematics teachers who were studying the mathematical modelling method. The research study group was composed of 35 prospective teachers. The exploratory case analysis method was used in the study. The data were obtained via semi-structured interviews and a…

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes

PubMed Central

Scherer, Ronny; Nilsen, Trude; Jansen, Malte

2016-01-01

Students' perceptions of instructional quality are among the most important criteria for evaluating teaching effectiveness. The present study evaluates different latent variable modeling approaches (confirmatory factor analysis, exploratory structural equation modeling, and bifactor modeling), which are used to describe these individual perceptions with respect to their factor structure, measurement invariance, and the relations to selected educational outcomes (achievement, self-concept, and motivation in mathematics). On the basis of the Programme for International Student Assessment (PISA) 2012 large-scale data sets of Australia, Canada, and the USA (N = 26,746 students), we find support for the distinction between three factors of individual students' perceptions and full measurement invariance across countries for all modeling approaches. In this regard, bifactor exploratory structural equation modeling outperformed alternative approaches with respect to model fit. Our findings reveal significant relations to the educational outcomes. This study synthesizes different modeling approaches of individual students' perceptions of instructional quality and provides insights into the nature of these perceptions from an individual differences perspective. Implications for the measurement and modeling of individually perceived instructional quality are discussed. PMID:26903917

Validity and Reliability of the Internalized Stigma of Smoking Inventory: An Exploration of Shame, Isolation, and Discrimination in Smokers with Mental Health Diagnoses

PubMed Central

Brown-Johnson, Cati G.; Cataldo PhD, Janine K.; Orozco, Nicholas; Lisha, Nadra E.; Hickman, Norval; Prochaska, Judith J.

2015-01-01

Background and Objectives De-normalization of smoking as a public health strategy may create shame and isolation in vulnerable groups unable to quit. To examine the nature and impact of smoking stigma, we developed the Internalized Stigma of Smoking Inventory (ISSI), tested its validity and reliability, and explored factors that may contribute to smoking stigma. Methods We evaluated the ISSI in a sample of smokers with mental health diagnoses (N=956), using exploratory and confirmatory factor analysis, and assessed construct validity. Results Results reduced the ISSI to 8 items with three subscales: smoking self-stigma related to shame, felt stigma related to social isolation, and discrimination experiences. Discrimination was the most commonly endorsed of the three subscales. A multivariate generalized linear model predicted 21-30% of the variance in the smoking stigma subscales. Self-stigma was greatest among those intending to quit; felt stigma was highest among those experiencing stigma in other domains, namely ethnicity and mental illness-based; and smoking-related discrimination was highest among women, Caucasians, and those with more education. Discussion and Conclusion Smoking stigma may compound stigma experiences in other areas. Aspects of smoking stigma in the domains of shame, isolation, and discrimination related to modeled stigma responses, particularly readiness to quit and cigarette addiction and was found to be more salient for groups where tobacco use is least prevalent. Scientific Significance The ISSI measure is useful for quantifying smoking-related stigma in multiple domains. PMID:25930661

Organizational performance impacting patient satisfaction in Ontario hospitals: a multilevel analysis.

PubMed

Koné Péfoyo, Anna J; Wodchis, Walter P

2013-12-05

Patient satisfaction in health care constitutes an important component of organizational performance in the hospital setting. Satisfaction measures have been developed and used to evaluate and improve hospital performance, quality of care and physician practice. In order to direct improvement strategies, it is necessary to evaluate both individual and organizational factors that can impact patients' perception of care. The study aims were to determine the dimensions of patient satisfaction, and to analyze the individual and organizational determinants of satisfaction dimensions in hospitals. We used patient and hospital survey data as well as administrative data collected for a 2008 public hospital report in Ontario, Canada. We evaluated the clustering of patient survey items with exploratory factor analysis and derived plausible dimensions of satisfaction. A two-level multivariate model was fitted to analyze the determinants of satisfaction. We found eight satisfaction factors, with acceptable to good level of loadings and good reliability. More than 95% of variation in patient satisfaction scores was attributable to patient-level variation, with less than 5% attributable to hospital-level variation. The hierarchical models explain 5 to 17% of variation at the patient level and up to 52% of variation between hospitals. Individual patient characteristics had the strongest association with all dimensions of satisfaction. Few organizational performance indicators are associated with patient satisfaction and significant determinants differ according to the satisfaction dimension. The research findings highlight the importance of adjusting for both patient-level and organization-level characteristics when evaluating patient satisfaction. Better understanding and measurement of organization-level activities and processes associated with patient satisfaction could contribute to improved satisfaction ratings and care quality.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

MTHFR, TS and XRCC1 genetic variants may affect survival in patients with myelodysplastic syndromes treated with supportive care or azacitidine.

PubMed

Visani, G; Loscocco, F; Ruzzo, A; Galimberti, S; Graziano, F; Voso, M T; Giacomini, E; Finelli, C; Ciabatti, E; Fabiani, E; Barulli, S; Volpe, A; Magro, D; Piccaluga, P; Fuligni, F; Vignetti, M; Fazi, P; Piciocchi, A; Gabucci, E; Rocchi, M; Magnani, M; Isidori, A

2017-12-05

We evaluated the impact of genomic polymorphisms in folate-metabolizing, DNA synthesis and DNA repair enzymes on the clinical outcome of 108 patients with myelodysplastic syndromes (MDS) receiving best supportive care (BSC) or azacitidine. A statistically significant association between methylenetetrahydrofolate reductase (MTHFR) 677T/T, thymidylate synthase (TS) 5'-untranslated region (UTR) 3RG, TS 3'-UTR -6 bp/-6 bp, XRCC1 399G/G genotypes and short survival was found in patients receiving BSC by multivariate analysis (P<0.001; P=0.026; P=0.058; P=0.024). MTHFR 677T/T, TS 3'-UTR -6 bp/-6 bp and XRCC1 399G/G genotypes were associated with short survival in patients receiving azacitidine by multivariate analysis (P<0.001; P=0.004; P=0.002). We then performed an exploratory analysis to evaluate the effect of the simultaneous presence of multiple adverse variant genotypes. Interestingly, patients with ⩾1 adverse genetic variants had a short survival, independently from their International Prognostic Scoring System (IPSS) and therapy received. To our knowledge, this is the first study showing that polymorphisms in folate-metabolizing pathway, DNA synthesis and DNA repair genes could influence survival of MDS patients.The Pharmacogenomics Journal advance online publication, 5 December 2017; doi:10.1038/tpj.2017.48.

Evaluation of natural mandibular shape asymmetry: an approach by using elliptical Fourier analysis.

PubMed

Niño-Sandoval, Tania C; Morantes Ariza, Carlos F; Infante-Contreras, Clementina; Vasconcelos, Belmiro Ce

2018-04-05

The purpose of this study was to demonstrate that asymmetry is a natural occurring phenomenon in the mandibular shape by using elliptical Fourier analysis. 164 digital orthopantomographs from Colombian patients of both sexes aged 18 to 25 years were collected. Curves from left and right hemimandible were digitized. An elliptical Fourier analysis was performed with 20 harmonics. In the general sexual dimorphism a principal component analysis (PCA) and a hotelling T 2 from the multivariate warp space were employed. Exploratory analysis of general asymmetry and sexual dimorphism by side was made with a Procrustes Fit. A non-parametric multivariate analysis of variance (MANOVA) was applied to assess differentiation of skeletal classes of each hemimandible, and a Procrustes analysis of variance (ANOVA) was applied to search any relation between skeletal class and side in both sexes. Significant values were found in general asymmetry, general sexual dimorphism, in dimorphism by side (p < 0.0001), asymmetry by sex, and differences between Class I, II, and III (p < 0.005). However, a relation of skeletal classes and side was not found. The mandibular asymmetry by shape is present in all patients and should not be articulated exclusively to pathological processes, therefore, along with sexual dimorphism and differences between skeletal classes must be taken into account for improving mandibular prediction systems.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

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

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Organizational Environment, Structure, Leadership, and Effectiveness: An Exploratory Model.

ERIC Educational Resources Information Center

Whorton, David M.

Purporting to test a contingency model for schools, data from 45 Arizona schools and 4 British schools were analyzed and compared to examine relationships between organizational environment, structure, leadership style, and perceived effectiveness. Environmental factors were measured by teacher and administrator responses to four Likert-type…

Exploratory and problem-solving consumer behavior across the life span.

PubMed

Lesser, J A; Kunkel, S R

1991-09-01

Different cognitive functioning, social, and personality changes appear to occur systematically during the adult life span. This article synthesizes research on life span changes in order to develop age-specific models of shopping behavior. The models are tested within a naturalistic field study of shoppers.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Rearrangement of the dendritic morphology in limbic regions and altered exploratory behavior in a rat model of autism spectrum disorder.

PubMed

Bringas, M E; Carvajal-Flores, F N; López-Ramírez, T A; Atzori, M; Flores, G

2013-06-25

Valproic acid (VPA) is a blocker of histone deacetylase widely used to treat epilepsy, bipolar disorders, and migraine; its administration during pregnancy increases the risk of autism spectrum disorder (ASD) in the child. Thus, prenatal VPA exposure has emerged as a rodent model of ASD. In the present study, we aimed to investigate the effect of prenatal administration of VPA (500mg/kg) at E12.5 on the exploratory behavior and locomotor activity in a novel environment, as well as on neuronal morphological rearrangement in the prefrontal cortex (PFC), in the hippocampus, in the nucleus accumbens (NAcc), and in the basolateral amygdala (BLA) at three different ages: immediately after weaning (postnatal day 21 [PD21]), prepubertal (PD35) and postpubertal (PD70) ages. Hyper-locomotion was observed in a novel environment in VPA animals at PD21 and PD70. Interestingly, exploratory behavior assessed by the hole board test at PD70 showed a reduced frequency but an increase in the duration of head-dippings in VPA-animals compared to vehicle-treated animals. In addition, the latency to the first head-dip was longer in prenatal VPA-treated animals at PD70. Quantitative morphological analysis of dendritic spine density revealed a reduced number of spines at PD70 in the PFC, dorsal hippocampus and BLA, with an increase in the dendritic spine density in NAcc and ventral hippocampus, in prenatal VPA-treated rats. In addition, at PD70 increases in neuronal arborization were observed in the NAcc, layer 3 of the PFC, and BLA, with retracted neuronal arborization in the ventral and dorsal hippocampus. Our results extend the list of altered behaviors (exploratory behavior) detected in this model of ASD, and indicate that the VPA behavioral phenotype is accompanied by previously undescribed morphological rearrangement in limbic regions. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Invention and Innovation: A Standards-Based Middle School Model Course Guide. Advancing Technological Literacy: ITEA Professional Series

ERIC Educational Resources Information Center

International Technology Education Association (ITEA), 2005

2005-01-01

This guide presents a model for a standards-based contemporary technology education course for the middle school. This model course guide features an exploratory curriculum thrust for a cornerstone middle level course. It provides teachers with an overview of the concept, suggestions for planning the course, and ideas for developing…

Resource Room Model for Inclusive Education in China: Practitioners' Conceptualisation and Contextualisation

ERIC Educational Resources Information Center

Poon-McBrayer, Kim Fong

2016-01-01

China launched the "learning in a regular classroom" (LRC) model for inclusive education in the 1980s. In late 1990s, a few major cities of China began to adopt the resource room model as a key feature of the LRC to improve instructional qualities. This exploratory study examined resource teachers' (RTs) attitude towards inclusive…

Modeling the effects of study abroad programs on college students

Treesearch

Alvin H. Yu; Garry E. Chick; Duarte B. Morais; Chung-Hsien Lin

2009-01-01

This study explored the possibility of modeling the effects of a study abroad program on students from a university in the northeastern United States. A program effect model was proposed after conducting an extensive literature review and empirically examining a sample of 265 participants in 2005. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA),...

Dietary recommendations for infants and toddlers among pediatric dentists in North Carolina.

PubMed

Sim, Chien J; Iida, Hiroko; Vann, William F; Quinonez, Rocio B; Steiner, Michael J

2014-01-01

The purposes of this study were to: describe practice patterns, knowledge, and attitudes of pediatric dentists in North Carolina (N.C.) in delivering dietary recommendations to the parents/caregivers of infants and toddlers; and identify barriers that limit the implementation of related recommendations. Our survey instrument included 30 questions covering eight domains of barriers to guideline adherence. Surveys were mailed to 150 practicing pediatric dentists in N.C. Descriptive and bivariate analyses were performed. Exploratory factor analysis was used to identify subscales and inform the multivariable model. The response rate was 57 percent (86/150), 80 percent of whom reported providing infant and toddler feeding recommendations routinely. Knowledge of and agreement with the recommendation regarding breast-feeding duration was lower than that for bottle-feeding recommendations. Stepwise logistic regression analysis indicated that survey respondents were less likely to provide dietary recommendations regularly to the parents/caregivers of infants and toddlers when they have practice constraints and the respondents disagree with American Academy of Pediatrics (AAP) and American Academy of Pediatric Dentistry (AAPD) recommendations on bottle and juice consumption. Most respondents routinely provide dietary recommendations to the parents/caregivers of infants and toddlers. Disagreement with AAP and AAPD recommendations on bottle, and juice consumption as well as practice constraints impedes practitioners from providing dietary recommendations regularly to the parents/caregivers of infants and toddlers.

Electrochemical immunosensor based on ensemble of nanoelectrodes for immunoglobulin IgY detection: application to identify hen's egg yolk in tempera paintings.

PubMed

Bottari, Fabio; Oliveri, Paolo; Ugo, Paolo

2014-02-15

A nanostructured electrochemical biosensor for detecting proteins of interest in work of art, in particular in tempera paintings, is presented. To determine egg yolk we focus here on the determination of immunoglobulin IgY. The transducers are nanoelectrode ensembles (NEEs), prepared via membrane templated electroless deposition of gold. Because of their geometrical and diffusion characteristics, NEEs are characterized by significantly low detection limits, moreover they display the capability of capturing proteins by interaction with the polycarbonate membrane of the NEE. At first, the proteic component of the paint is extracted by ultrasonication in an aqueous buffer, then IgY is captured by incubation on the NEE. The immunoglobulin is detected by treatment with anti-IgY labeled with horse radish peroxidase (Anti-IgY-HRP). The binding of the Anti-IgY-HRP is detected by recording the electrocatalytic signal caused by addition of H2O2 and methylene blue. The sensor detection capabilities are tested by analyzing both paint models, prepared in the lab, and real samples, from paintings of the XVIII-XX century. Multivariate exploratory analysis is applied to classify the voltammetric patterns, confirming the capability to differentiate egg-yolk tempera from other kind of tempera binders as well as from acrylic or oil paints. © 2013 Elsevier B.V. All rights reserved.

Analysis of genetic population structure in Acacia caven (Leguminosae, Mimosoideae), comparing one exploratory and two Bayesian-model-based methods

PubMed Central

Pometti, Carolina L.; Bessega, Cecilia F.; Saidman, Beatriz O.; Vilardi, Juan C.

2014-01-01

Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other. PMID:24688293

Cognitive Predictors of Word and Pseudoword Reading in Spanish First-Grade Children

PubMed Central

González-Valenzuela, María J.; Díaz-Giráldez, Félix; López-Montiel, María D.

2016-01-01

The study examines the individual and combined contribution of several cognitive variables (phonemic awareness, phonological memory, and alphanumeric and non-alphanumeric rapid naming) to word and pseudoword reading ability among first-grade Spanish children. Participants were 116 Spanish-speaking children aged 6 years and without special educational needs, all of whom were attending schools in a medium socioeconomic area. Descriptive/exploratory and bivariate analyses were performed with the data derived from three measures of reading ability (accuracy, speed, and efficiency), and hierarchical multivariate regression models were constructed. In general, the results confirm that, with the exception of non-alphanumeric rapid naming, the cognitive variables studied are predictors of reading performance for words and pseudowords, although their influence differs depending on the reading measures and type of linguistic unit considered. Phonemic awareness, phonological memory, and alphanumeric rapid naming were the best predictors of reading accuracy for words and pseudowords. Variability in the other two measures of reading ability (speed and efficiency) was best explained by alphanumeric rapid naming. These results suggest that reading is a complex skill that depends on different types of cognitive variables according to the age and/or level of the reader, the type of orthography and the type of measure used. Furthermore, they highlight the need to provide instruction in these processes from an early age so as to address or prevent the problems that children may present. PMID:27303336

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Placing Local Aggregations in a Larger-Scale Context: Hierarchical Modeling of Black-Footed Albatross Dispersion.

PubMed

Michael, P E; Jahncke, J; Hyrenbach, K D

2016-01-01

At-sea surveys facilitate the study of the distribution and abundance of marine birds along standardized transects, in relation to changes in the local environmental conditions and large-scale oceanographic forcing. We analyzed the form and the intensity of black-footed albatross (Phoebastria nigripes: BFAL) spatial dispersion off central California, using five years (2004-2008) of vessel-based surveys of seven replicated survey lines. We related BFAL patchiness to local, regional and basin-wide oceanographic variability using two complementary approaches: a hypothesis-based model and an exploratory analysis. The former tested the strength and sign of hypothesized BFAL responses to environmental variability, within a hierarchical atmosphere-ocean context. The latter explored BFAL cross-correlations with atmospheric / oceanographic variables. While albatross dispersion was not significantly explained by the hierarchical model, the exploratory analysis revealed that aggregations were influenced by static (latitude, depth) and dynamic (wind speed, upwelling) environmental variables. Moreover, the largest BFAL patches occurred along the survey lines with the highest densities, and in association with shallow banks. In turn, the highest BFAL densities occurred during periods of negative Pacific Decadal Oscillation index values and low atmospheric pressure. The exploratory analyses suggest that BFAL dispersion is influenced by basin-wide, regional-scale and local environmental variability. Furthermore, the hypothesis-based model highlights that BFAL do not respond to oceanographic variability in a hierarchical fashion. Instead, their distributions shift more strongly in response to large-scale ocean-atmosphere forcing. Thus, interpreting local changes in BFAL abundance and dispersion requires considering diverse environmental forcing operating at multiple scales.

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...

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Empirical Identification of the Major Facets of Conscientiousness

ERIC Educational Resources Information Center

MacCann, Carolyn; Duckworth, Angela Lee; Roberts, Richard D.

2009-01-01

Conscientiousness is often found to predict academic outcomes, but is defined differently by different models of personality. High school students (N = 291) completed a large number of Conscientiousness items from different models and the Big Five Inventory (BFI). Exploratory and confirmatory factor analysis of the items uncovered eight facets:…

Model Standards Advance the Profession

ERIC Educational Resources Information Center

Journal of Staff Development, 2011

2011-01-01

Leadership by teachers is essential to serving the needs of students, schools, and the teaching profession. To that end, the Teacher Leadership Exploratory Consortium has developed Teacher Leader Model Standards to codify, promote, and support teacher leadership as a vehicle to transform schools for the needs of the 21st century. The Teacher…

Non-Linear Modeling of Growth Prerequisites in a Finnish Polytechnic Institution of Higher Education

ERIC Educational Resources Information Center

Nokelainen, Petri; Ruohotie, Pekka

2009-01-01

Purpose: This study aims to examine the factors of growth-oriented atmosphere in a Finnish polytechnic institution of higher education with categorical exploratory factor analysis, multidimensional scaling and Bayesian unsupervised model-based visualization. Design/methodology/approach: This study was designed to examine employee perceptions of…

DEVELOPMENT AND APPLICATION OF POPULATION MODELS TO SUPPORT EPA'S ECOLOGICAL RISK ASSESSMENT PROCESSES FOR PESTICIDES

EPA Science Inventory

As part of a broader exploratory effort to develop ecological risk assessment approaches to estimate potential chemical effects on non-target populations, we describe an approach for developing simple population models to estimate the extent to which acute effects on individual...

Adolescent Problem Behavior in Navi Mumbai: An Exploratory Study of Psychosocial Risk and Protection

ERIC Educational Resources Information Center

Solomon, R. J.

2007-01-01

Background: A conceptual framework about protective factors (models protection, controls protection, support protection) and risk factors (models risk, opportunity risk, vulnerability risk) was employed to articulate the content of five psychosocial contexts of adolescent life--individual, family, peers, school, and neighborhood--in a study of…

Family Concepts in Early Learning and Development Standards

ERIC Educational Resources Information Center

Walsh, Bridget A.; Sanchez, Claudia; Lee, Angela M.; Casillas, Nicole; Hansen, Caitlynn

2016-01-01

This exploratory study investigated the use of concepts related to families, parents, and the home in 51 state-level early learning and development standards documents. Guidelines from six national family involvement, engagement, and school-partnership models were used to create the Family Involvement Models Analysis Chart (FIMAC), which served as…

An Overview of Software for Conducting Dimensionality Assessment in Multidimensional Models

ERIC Educational Resources Information Center

Svetina, Dubravka; Levy, Roy

2012-01-01

An overview of popular software packages for conducting dimensionality assessment in multidimensional models is presented. Specifically, five popular software packages are described in terms of their capabilities to conduct dimensionality assessment with respect to the nature of analysis (exploratory or confirmatory), types of data (dichotomous,…

The Emergence of Inclusive Exploratory Talk in Primary Students' Peer Interaction

ERIC Educational Resources Information Center

Rajala, Antti; Hilppo, Jaakko; Lipponen, Lasse

2012-01-01

In this study, we examine a prominent type of classroom talk, exploratory talk, in primary school peer interactions. Exploratory talk has been shown to be productive in facilitating problem solving and fostering school achievement. However, within the growing body of research concerning exploratory talk, the relation between exploratory talk and…

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Deficits in novelty exploration after controlled cortical impact.

PubMed

Wagner, Amy K; Postal, Brett A; Darrah, Shaun D; Chen, Xiangbai; Khan, Amina S

2007-08-01

Experimental models of traumatic brain injury (TBI) have been utilized to characterize the behavioral derangements associated with brain trauma. Several studies exist characterizing motor function in the controlled cortical impact (CCI) injury model of TBI, but less research has focused on how CCI affects exploratory behavior. The goal of this study was to characterize deficits in three novelty exploration tasks after the CCI. Under anesthesia, 37 adult male Sprague Dawley rats received CCI (2.7 mm and 2.9 mm; 4 m/sec) over the right parietal cortex or sham surgery. For days 1-6 post-surgery, the beam balance and beam walking tasks were used to assess motor deficits. The Open Field, Y-Maze, and Free Choice Novelty (FCN) tasks were used to measure exploratory deficits from days 7-14 post-surgery. Injured rats displayed a significant, but transient, deficit on each motor task (p < 0.0001). Open Field results showed that injured rats had lower activity levels than shams (p < 0.0001), displayed less habituation to the task, and had more anxiety related behaviors (thigmotaxis) across days (p < 0.0001). Y-maze results suggest that injured rats spent less time in the novel arm versus the familiar arms when compared to shams (p < 0.0001). For FCN, injured rats were less active (p < 0.05) and spent less time and had fewer interactions with objects in the novel environment compared to shams (p < 0.05). These results suggest that several ethological factors contribute to exploratory deficits after CCI and can be effectively characterized with the behavioral tasks described. Future work will utilize these tasks to evaluate the neural substrates underlying exploratory deficits after TBI.

An economic and financial exploratory

NASA Astrophysics Data System (ADS)

Cincotti, S.; Sornette, D.; Treleaven, P.; Battiston, S.; Caldarelli, G.; Hommes, C.; Kirman, A.

2012-11-01

This paper describes the vision of a European Exploratory for economics and finance using an interdisciplinary consortium of economists, natural scientists, computer scientists and engineers, who will combine their expertise to address the enormous challenges of the 21st century. This Academic Public facility is intended for economic modelling, investigating all aspects of risk and stability, improving financial technology, and evaluating proposed regulatory and taxation changes. The European Exploratory for economics and finance will be constituted as a network of infrastructure, observatories, data repositories, services and facilities and will foster the creation of a new cross-disciplinary research community of social scientists, complexity scientists and computing (ICT) scientists to collaborate in investigating major issues in economics and finance. It is also considered a cradle for training and collaboration with the private sector to spur spin-offs and job creations in Europe in the finance and economic sectors. The Exploratory will allow Social Scientists and Regulators as well as Policy Makers and the private sector to conduct realistic investigations with real economic, financial and social data. The Exploratory will (i) continuously monitor and evaluate the status of the economies of countries in their various components, (ii) use, extend and develop a large variety of methods including data mining, process mining, computational and artificial intelligence and every other computer and complex science techniques coupled with economic theory and econometric, and (iii) provide the framework and infrastructure to perform what-if analysis, scenario evaluations and computational, laboratory, field and web experiments to inform decision makers and help develop innovative policy, market and regulation designs.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

DataHub: Knowledge-based data management for data discovery

NASA Astrophysics Data System (ADS)

Handley, Thomas H.; Li, Y. Philip

1993-08-01

Currently available database technology is largely designed for business data-processing applications, and seems inadequate for scientific applications. The research described in this paper, the DataHub, will address the issues associated with this shortfall in technology utilization and development. The DataHub development is addressing the key issues in scientific data management of scientific database models and resource sharing in a geographically distributed, multi-disciplinary, science research environment. Thus, the DataHub will be a server between the data suppliers and data consumers to facilitate data exchanges, to assist science data analysis, and to provide as systematic approach for science data management. More specifically, the DataHub's objectives are to provide support for (1) exploratory data analysis (i.e., data driven analysis); (2) data transformations; (3) data semantics capture and usage; analysis-related knowledge capture and usage; and (5) data discovery, ingestion, and extraction. Applying technologies that vary from deductive databases, semantic data models, data discovery, knowledge representation and inferencing, exploratory data analysis techniques and modern man-machine interfaces, DataHub will provide a prototype, integrated environement to support research scientists' needs in multiple disciplines (i.e. oceanography, geology, and atmospheric) while addressing the more general science data management issues. Additionally, the DataHub will provide data management services to exploratory data analysis applications such as LinkWinds and NCSA's XIMAGE.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

An exploratory path model of the relationships between positive and negative adaptation to cancer on quality of life among non-Hodgkin lymphoma survivors.

PubMed

Bryant, Ashley Leak; Smith, Sophia K; Zimmer, Catherine; Crandell, Jamie; Jenerette, Coretta M; Bailey, Donald E; Zimmerman, Sheryl; Mayer, Deborah K

2015-01-01

Adaptation is an ongoing, cognitive process with continuous appraisal of the cancer experience by the survivor. This exploratory study tested a path model examining the personal (demographic, disease, and psychosocial) characteristics associated with quality of life (QOL) and whether or not adaptation to living with cancer may mediate these effects. This study employed path analysis to estimate adaptation to cancer. A cross-sectional sample of NHL survivors (N = 750) was used to test the model. Eligible participants were ≥ 18 years, at least 2 years post-diagnosis, and living with or without active disease. Sixty-eight percent of the variance was accounted for in QOL. The strongest effect (-0.596) was direct by negative adaptation, approximately 3 times that of positive adaptation (0.193). The strongest demographic total effects on QOL were age and social support; <65 years of age had better QOL and better adaptation compared to those ≥ 65. Of the disease characteristics, comorbidity score had the strongest direct effect on QOL; each additional comorbidity was associated with a 0.309 standard deviation decline on QOL. There were no fully mediated effects through positive adaptation alone. Our exploratory findings support the coexistence of positive and negative adaptations perception as mediators of personal characteristics of the cancer experience. Negative adaptation can affect QOL in a positive way. Cancer survivorship is simultaneously shaped by both positive and negative adaptation with future research and implications for practice aimed at improving QOL.

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Sorghum root-system classification in contrasting P environments reveals three main rooting types and root-architecture-related marker-trait associations.

PubMed

Parra-Londono, Sebastian; Kavka, Mareike; Samans, Birgit; Snowdon, Rod; Wieckhorst, Silke; Uptmoor, Ralf

2018-02-12

Roots facilitate acquisition of macro- and micronutrients, which are crucial for plant productivity and anchorage in the soil. Phosphorus (P) is rapidly immobilized in the soil and hardly available for plants. Adaptation to P scarcity relies on changes in root morphology towards rooting systems well suited for topsoil foraging. Root-system architecture (RSA) defines the spatial organization of the network comprising primary, lateral and stem-derived roots and is important for adaptation to stress conditions. RSA phenotyping is a challenging task and essential for understanding root development. In this study, 19 traits describing RSA were analysed in a diversity panel comprising 194 sorghum genotypes, fingerprinted with a 90-k single-nucleotide polymorphism (SNP) array and grown under low and high P availability. Multivariate analysis was conducted and revealed three different RSA types: (1) a small root system; (2) a compact and bushy rooting type; and (3) an exploratory root system, which might benefit plant growth and development if water, nitrogen (N) or P availability is limited. While several genotypes displayed similar rooting types in different environments, others responded to P scarcity positively by developing more exploratory root systems, or negatively with root growth suppression. Genome-wide association studies revealed significant quantitative trait loci (P < 2.9 × 10-6) on chromosomes SBI-02, SBI-03, SBI-05 and SBI-09. Co-localization of significant and suggestive (P < 5.7 × 10-5) associations for several traits indicated hotspots controlling root-system development on chromosomes SBI-02 and SBI-03. Sorghum genotypes with a compact, bushy and shallow root system provide potential adaptation to P scarcity in the field by allowing thorough topsoil foraging, while genotypes with an exploratory root system may be advantageous if N or water is the limiting factor, although such genotypes showed highest P uptake levels under the artificial conditions of the present study. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Postural sensorimotor training versus sham exercise in physiotherapy of patients with chronic non-specific low back pain: An exploratory randomised controlled trial

PubMed Central

Wirth, Brigitte

2018-01-01

Sensorimotor training (SMT) is popularly applied as exercise in rehabilitation settings, particularly for musculoskeletal pain. With insufficient evidence on its effect on pain and function, this exploratory randomised controlled trial investigated the potential effects of SMT in rehabilitation of chronic non-specific low back pain. Two arms received 9x30 minutes physiotherapy with added interventions: The experimental arm received 15 minutes of postural SMT while the comparator arm performed 15 minutes of added sub-effective low-intensity training. A treatment blinded tester assessed outcomes at baseline 2–4 days prior to intervention, pre- and post-intervention, and at 4-week follow-up. Main outcomes were pain and functional status assessed with a 0–100mm visual analogue scale and the Oswestry Disability Questionnaire. Additionally, postural control was analysed using a video-based tracking system and a pressure plate during perturbed stance. Robust, nonparametric multivariate hypothesis testing was performed. 22 patients (11 females, aged 32 to 75 years) with mild to moderate chronic pain and functional limitations were included for analysis (11 per arm). At post-intervention, average values of primary outcomes improved slightly, but not to a clinically relevant or statistically significant extent. At 4-week follow-up, there was a significant improvement by 12 percentage points (pp) on the functional status questionnaire in the SMT-group (95% confidence intervall (CI) = 5.3pp to 17.7pp, p < 0.001) but not in the control group (4 pp improvement, CI = 11.8pp to 19.2pp). However, group-by-time interaction effects for functional status (Q = 3.3, 19 p = 0.07) and pain (Q = 0.84, p = 0.51) were non-significant. Secondary kinematic outcomes did not change over time in either of the groups. Despite significant improvement of functional status after SMT, overall findings of this exploratory study suggest that SMT provides no added benefit for pain reduction or functional improvement in patients with moderate chronic non-specific low back pain. Trial registration: ClinicalTrials.gov NCT02304120 and related study protocol, DOI: 10.1186/1471-2474-15-382. PMID:29522571

Cryotherapy and Topical Minocycline as Adjunctive Measures to Control Pain After Third Molar Surgery: An Exploratory Study

PubMed Central

Gelesko, Savannah; Long, Leann; Faulk, Jan; Phillips, Ceib; Dicus, Carolyn; White, Raymond P.

2013-01-01

Purpose To assess the impact of cryotherapy or topical minocycline on patients’ perceptions of recovery from pain after third molar surgery in an exploratory comparative-effectiveness study. Patients and Methods Subjects aged at least 14 years who were having all 4 third molars removed were enrolled in 3 separate institutional review board–approved studies. Study groups included subjects treated with a passively applied cold wrap for 24 hours postoperatively, subjects treated with topical minocycline during surgery, and subjects enrolled in a nonconcurrent comparison group who had received neither topical minocycline nor directed cryotherapy. Third molar surgery was performed in all cases by trained surgeons using the same protocol. An exact Kruskal-Wallis test was used to compare the distributions of the worst and average pain scores and a Fisher exact test to compare verbal responses from Gracely pain scales among the 3 groups for postsurgical days (PSDs) 1 to 3. Results This study comprised 51 cryotherapy subjects (2005–2009), 63 minocycline subjects (2003–2004), and 92 comparison-group subjects (2002–2006) who were treated at academic centers and in community practices across the United States (N = 206). Demographic descriptors were similar among all groups. For PSDs 1 through 3 (unadjusted), the highest scores for worst pain (6–7 [out of 7] on Likert-type scale) were reported less frequently in each of the study groups than in subjects in the comparison group, although the numbers of subjects reporting the highest scores were few. The distribution of pain outcomes was significantly different among the 3 groups for worst pain and affective words on PSD 1 (P = .04 for both). However, the small number of subjects who reported the highest pain scores precluded adequate multivariate statistical analyses for all outcomes on PSD 1 to 3. Conclusions Data from this exploratory study suggest that adjunctive therapy to decrease postoperative pain—cryotherapy or topical minocycline—might be effective at moderating the patient’s highest pain levels after third molar surgery. The topic should be studied further in a multicenter, prospective, randomized trial. PMID:21802812

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Candidates Use a New Teacher Development Process, Transformative Reflection, to Identify and Address Teaching and Learning Problems in Their Work with Children

ERIC Educational Resources Information Center

Naidoo, Kara; Kirch, Susan A.

2016-01-01

This article has two aims: (a) to offer a new model for a teacher preparation course that features reflection and teaching as integral, inseparable actions and (b) to provide empirical evidence from an exploratory ethnography to demonstrate teacher development possibilities with this model. The model, termed "Transformative Reflection,"…

The Impact of the "First Language First" Model on Vocabulary Development among Preschool Bilingual Children

ERIC Educational Resources Information Center

Schwartz, Mila

2014-01-01

The aim of this exploratory study was to examine the role of the "First Language First" model for preschool bilingual education in the development of vocabulary depth. The languages studied were Russian (L1) and Hebrew (L2) among bilingual children aged 4-5 years in Israel. According to this model, the children's first language of…

Wrong Answers on Multiple-Choice Achievement Tests: Blind Guesses or Systematic Choices?.

ERIC Educational Resources Information Center

Powell, J. C.

A multi-faceted model for the selection of answers for multiple-choice tests was developed from the findings of a series of exploratory studies. This model implies that answer selection should be curvilinear. A series of models were tested for fit using the chi square procedure. Data were collected from 359 elementary school students ages 9-12.…

Empirical Testing of a Theoretical Extension of the Technology Acceptance Model: An Exploratory Study of Educational Wikis

ERIC Educational Resources Information Center

Liu, Xun

2010-01-01

This study extended the technology acceptance model and empirically tested the new model with wikis, a new type of educational technology. Based on social cognitive theory and the theory of planned behavior, three new variables, wiki self-efficacy, online posting anxiety, and perceived behavioral control, were added to the original technology…

Teacher Conceptions and Approaches Associated with an Immersive Instructional Implementation of Computer-Based Models and Assessment in a Secondary Chemistry Classroom

ERIC Educational Resources Information Center

Waight, Noemi; Liu, Xiufeng; Gregorius, Roberto Ma.; Smith, Erica; Park, Mihwa

2014-01-01

This paper reports on a case study of an immersive and integrated multi-instructional approach (namely computer-based model introduction and connection with content; facilitation of individual student exploration guided by exploratory worksheet; use of associated differentiated labs and use of model-based assessments) in the implementation of…

Autonomous visual exploration creates developmental change in familiarity and novelty seeking behaviors

PubMed Central

Perone, Sammy; Spencer, John P.

2013-01-01

What motivates children to radically transform themselves during early development? We addressed this question in the domain of infant visual exploration. Over the first year, infants' exploration shifts from familiarity to novelty seeking. This shift is delayed in preterm relative to term infants and is stable within individuals over the course of the first year. Laboratory tasks have shed light on the nature of this familiarity-to-novelty shift, but it is not clear what motivates the infant to change her exploratory style. We probed this by letting a Dynamic Neural Field (DNF) model of visual exploration develop itself via accumulating experience in a virtual world. We then situated it in a canonical laboratory task. Much like infants, the model exhibited a familiarity-to-novelty shift. When we manipulated the initial conditions of the model, the model's performance was developmentally delayed much like preterm infants. This delay was overcome by enhancing the model's experience during development. We also found that the model's performance was stable at the level of the individual. Our simulations indicate that novelty seeking emerges with no explicit motivational source via the accumulation of visual experience within a complex, dynamical exploratory system. PMID:24065948

The use of exploratory analyses within the National Institute for Health and Care Excellence single technology appraisal process: an evaluation and qualitative analysis.

PubMed

Kaltenthaler, Eva; Carroll, Christopher; Hill-McManus, Daniel; Scope, Alison; Holmes, Michael; Rice, Stephen; Rose, Micah; Tappenden, Paul; Woolacott, Nerys

2016-04-01

As part of the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process, independent Evidence Review Groups (ERGs) critically appraise the company submission. During the critical appraisal process the ERG may undertake analyses to explore uncertainties around the company's model and their implications for decision-making. The ERG reports are a central component of the evidence considered by the NICE Technology Appraisal Committees (ACs) in their deliberations. The aim of this research was to develop an understanding of the number and type of exploratory analyses undertaken by the ERGs within the STA process and to understand how these analyses are used by the NICE ACs in their decision-making. The 100 most recently completed STAs with published guidance were selected for inclusion in the analysis. The documents considered were ERG reports, clarification letters, the first appraisal consultation document and the final appraisal determination. Over 400 documents were assessed in this study. The categories of types of exploratory analyses included fixing errors, fixing violations, addressing matters of judgement and the ERG-preferred base case. A content analysis of documents (documentary analysis) was undertaken to identify and extract relevant data, and narrative synthesis was then used to rationalise and present these data. The level and type of detail in ERG reports and clarification letters varied considerably. The vast majority (93%) of ERG reports reported one or more exploratory analyses. The most frequently reported type of analysis in these 93 ERG reports related to the category 'matters of judgement', which was reported in 83 (89%) reports. The category 'ERG base-case/preferred analysis' was reported in 45 (48%) reports, the category 'fixing errors' was reported in 33 (35%) reports and the category 'fixing violations' was reported in 17 (18%) reports. The exploratory analyses performed were the result of issues raised by an ERG in its critique of the submitted economic evidence. These analyses had more influence on recommendations earlier in the STA process than later on in the process. The descriptions of analyses undertaken were often highly specific to a particular STA and could be inconsistent across ERG reports and thus difficult to interpret. Evidence Review Groups frequently conduct exploratory analyses to test or improve the economic evaluations submitted by companies as part of the STA process. ERG exploratory analyses often have an influence on the recommendations produced by the ACs. More in-depth analysis is needed to understand how ERGs make decisions regarding which exploratory analyses should be undertaken. More research is also needed to fully understand which types of exploratory analyses are most useful to ACs in their decision-making. The National Institute for Health Research Health Technology Assessment programme.

Development of flood regressions and climate change scenarios to explore estimates of future peak flows

USGS Publications Warehouse

Burns, Douglas A.; Smith, Martyn J.; Freehafer, Douglas A.

2015-12-31

The application uses predictions of future annual precipitation from five climate models and two future greenhouse gas emissions scenarios and provides results that are averaged over three future periods—2025 to 2049, 2050 to 2074, and 2075 to 2099. Results are presented in ensemble form as the mean, median, maximum, and minimum values among the five climate models for each greenhouse gas emissions scenario and period. These predictions of future annual precipitation are substituted into either the precipitation variable or a water balance equation for runoff to calculate potential future peak flows. This application is intended to be used only as an exploratory tool because (1) the regression equations on which the application is based have not been adequately tested outside the range of the current climate and (2) forecasting future precipitation with climate models and downscaling these results to a fine spatial resolution have a high degree of uncertainty. This report includes a discussion of the assumptions, uncertainties, and appropriate use of this exploratory application.

Predicting the Influence of Situational and Immigration Stress on Latino Day Laborers' Workplace Injuries: An Exploratory Structural Equation Model.

PubMed

Fernández-Esquer, Maria Eugenia; Gallardo, Kathryn R; Diamond, Pamela M

2018-05-16

Latino day laborers are a socially and economically marginalized immigrant population with a high risk of occupational injury. These workers confront multiple social, psychological, and environmental hardships that increase their risk for adverse health outcomes. How these stressors interact and influence work-related injuries in this population remains unclear. We conducted an exploratory study with 327 Latino day laborers who completed a community survey. We developed a structural equation model, using cross-sectional data to explore the relationships among socioeconomic status, situational and immigration stress, depression, work risk exposure, and occupational injury. The model revealed a statistically significant mediated effect from situational stress to injury through work risk exposure as well as a significant mediated effect from immigration stress through depression to injury. These initial findings suggest that situational and immigration-related stress have a detrimental impact on Latino day laborers' mental health and workplace safety and, ultimately, increase their risk of occupational injury.

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information.

PubMed

Husain, Syed S; Kalinin, Alexandr; Truong, Anh; Dinov, Ivo D

Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data exploration, discovery and analytics get considerably harder. Exploratory querying of heterogeneous and multi-source information is both difficult and necessary to advance our knowledge about the world around us. We developed a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. This process facilitates the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This a new platform includes a device agnostic tool (Dashboard webapp, http://socr.umich.edu/HTML5/Dashboard/) for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets. The paper illustrates this core functionality and serviceoriented infrastructure using healthcare data (e.g., US data from the 2010 Census, Demographic and Economic surveys, Bureau of Labor Statistics, and Center for Medicare Services) as well as Parkinson's Disease neuroimaging data. Both the back-end data archive and the front-end dashboard interfaces are continuously expanded to include additional data elements and new ways to customize the human and machine interactions. A client-side data import utility allows for easy and intuitive integration of user-supplied datasets. This completely open-science framework may be used for exploratory analytics, confirmatory analyses, meta-analyses, and education and training purposes in a wide variety of fields.

Cognitive function in 1736 participants in NINDS Exploratory Trials in PD Long-term Study-1.

PubMed

Wills, Anne-Marie A; Elm, Jordan J; Ye, Rong; Chou, Kelvin L; Parashos, Sotirios A; Hauser, Robert A; Bodis-Wollner, Ivan; Hinson, Vanessa K; Christine, Chadwick W; Schneider, Jay S

2016-12-01

Clinical cohort studies suggest that mild cognitive impairment (MCI) is common in early Parkinson's disease (PD). The objectives of this paper were to describe cognitive function in a large clinical trial of early treated PD patients at baseline and over time using two brief cognitive screening tests. In total 1741 participants were enrolled in the NINDS Exploratory Trials in Parkinson's disease (NET-PD) Long-term Study-1 (LS-1). The Symbol Digit Modalities Test (SDMT) was collected annually. The SCales for Outcomes in PArkinson's disease-COGnition (SCOPA-COG) was collected at baseline and at year 5. The trial was stopped early based on a planned interim analysis after half the cohort completed 5 years of follow-up. The median length of follow-up was 4 years (range 3-6 years). Predictors of cognitive change were examined using cross sectional (baseline) and longitudinal multivariable linear regression. The mean (SD) change from baseline to 5 years was -1.9 (5.1) for the SCOPA-COG and -2.1 (11.1) for the SDMT. Age and baseline UPDRS motor scores were associated with a more rapid decline in SDMT scores and 5 year SCOPA-COG scores. Male gender was associated with more rapid decline in SDMT. Self-reported income was a novel predictor of baseline cognitive function, even adjusted for educational status, although not significantly associated with change over time. This large prospective cohort study demonstrated mild cognitive decline in early treated Parkinson's disease. The study identified income level as a novel predictor of cognitive function. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Effect of Age on the Prevalence of Obesity among US Youth with Autism Spectrum Disorder.

PubMed

Must, Aviva; Eliasziw, Misha; Phillips, Sarah M; Curtin, Carol; Kral, Tanja V E; Segal, Mary; Sherwood, Nancy E; Sikich, Linmarie; Stanish, Heidi I; Bandini, Linda G

2017-02-01

We sought to assess the association between age and the prevalence of obesity among children with and without autism spectrum disorder (ASD) in the 2011-2012 National Survey of Children's Health. Analyses were restricted to 43,777 children, ages 10-17, with valid measures of parent-reported weight, height, and ASD status. Exploratory analyses describe the impact of sex, race/ethnicity, and household income on the relationship between age and obesity in ASD. Although the overall prevalence of obesity among children with ASD was significantly (p < 0.001) higher than among children without ASD (23.1% vs. 14.1%, 95% confidence interval for difference 3.6 to 14.4), child age significantly (p = 0.035) modified this difference. In a multivariable logistic regression analysis, adjusted for sex, race/ethnicity, and household income, the odds of obesity among children with ASD compared with children without ASD increased monotonically from ages 10 to 17 years. This pattern arose due to a consistently high prevalence of obesity among children with ASD and a decline in prevalence with advancing age among children without ASD. These findings were replicated using a propensity score analysis. Exploratory analyses suggested that the age-related change in obesity disparity between children with and without ASD may be further modified by sex, race/ethnicity, and household income. The patterns of prevalence observed with increasing age among children with and without ASD were unexpected. A better understanding of the etiological and maintenance factors for obesity in youth with ASD is needed to develop interventions tailored to the specific needs of these children.

Intensive Care Physiotherapy during Extracorporeal Membrane Oxygenation for Acute Respiratory Distress Syndrome.

PubMed

Munshi, Laveena; Kobayashi, Tadahiro; DeBacker, Julian; Doobay, Ravi; Telesnicki, Teagan; Lo, Vincent; Cote, Nathalie; Cypel, Marcelo; Keshavjee, Shaf; Ferguson, Niall D; Fan, Eddy

2017-02-01

There are limited data on physiotherapy during extracorporeal membrane oxygenation (ECMO) for acute respiratory distress syndrome (ARDS). We sought to characterize physiotherapy delivered to patients with ARDS supported with ECMO, as well as to evaluate the association of this therapeutic modality with mortality. We conducted a retrospective cohort study of all adult patients with ARDS supported with ECMO at our institution between 2010 and 2015. The highest level of daily activity while on ECMO was coded using the ICU Mobility Scale. Through multivariable logistic regression, we evaluated the association between intensive care unit (ICU) physiotherapy and ICU mortality. In an exploratory univariate analysis, we also evaluated factors associated with a higher intensity of ICU rehabilitation while on ECMO. Of 107 patients who underwent ECMO, 61 (57%) had ARDS requiring venovenous ECMO. The ICU physiotherapy team was consulted for 82% (n = 50) of patients. Thirty-nine percent (n = 18) of these patients achieved an activity level of 2 or higher (active exercises in bed), and 17% (n = 8) achieved an activity level 4 or higher (actively sitting over the side of the bed). In an exploratory analysis, consultation with the ICU physiotherapy team was associated with decreased ICU mortality (odds ratio, 0.19; 95% confidence interval, 0.04-0.98). In univariate analysis, severity-of-illness factors differentiated higher-intensity and lower-intensity physiotherapy. Physiotherapy during ECMO is feasible and safe when performed by an experienced team and executed in stages. Although our study suggests an association with improved ICU mortality, future research is needed to identify potential barriers, optimal timing, dosage, and safety profile.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Evaluation of Usability Utilizing Markov Models

ERIC Educational Resources Information Center

Penedo, Janaina Rodrigues; Diniz, Morganna; Ferreira, Simone Bacellar Leal; Silveira, Denis S.; Capra, Eliane

2012-01-01

Purpose: The purpose of this paper is to analyze the usability of a remote learning system in its initial development phase, using a quantitative usability evaluation method through Markov models. Design/methodology/approach: The paper opted for an exploratory study. The data of interest of the research correspond to the possible accesses of users…

A Model and Measure of Mobile Communication Competence

ERIC Educational Resources Information Center

Bakke, Emil

2010-01-01

This article deals with two studies that develop a measure and model of mobile communication competence (MCC). The first study examines the dimensionality of the measure by conducting an exploratory factor analysis on 350 students at a large university in the midwestern United States. Results identified six constructs across 24 items: willingness…

The Social Ecological Challenges of Rural Victim Advocacy: An Exploratory Study

ERIC Educational Resources Information Center

McGrath, Shelly A.; Johnson, Melencia; Miller, Michelle Hughes

2012-01-01

This article re-centers an ecological model traditionally used to understand the experiences of interpersonal violence victims around the perceptions and experiences of victim advocates. We suggest that the development of such a model might shed light on rural-urban differences in the accessibility and availability of support services in rural…

Developing Teaching Expertise in Dental Education

ERIC Educational Resources Information Center

Lyon, Lucinda J.

2009-01-01

This exploratory study was designed to develop a baseline model of expertise in dental education utilizing the Dreyfus and Dreyfus continuum of skill acquisition. The goal was the development of a baseline model of expertise, which will contribute to the body of knowledge about dental faculty skill acquisition and may enable dental schools to…

Computational Modelling and Children's Expressions of Signal and Noise

ERIC Educational Resources Information Center

Ainley, Janet; Pratt, Dave

2017-01-01

Previous research has demonstrated how young children can identify the signal in data. In this exploratory study we considered how they might also express meanings for noise when creating computational models using recent developments in software tools. We conducted extended clinical interviews with four groups of 11-year-olds and analysed the…

Exploring Classroom Interaction with Dynamic Social Network Analysis

ERIC Educational Resources Information Center

Bokhove, Christian

2018-01-01

This article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and…

HOMOGENEOUS CLASSROOM GROUPING BASED ON CONCEPTUAL SYSTEMS THEORY IN AN EDUCATION ENRICHMENT PROJECT -- AN EXPLORATORY STUDY.

ERIC Educational Resources Information Center

HUNT, DAVID E.

EDUCATIONAL ENVIRONMENTS, HIGHLY STRUCTURED OR UNSTRUCTURED, WERE DIFFERENTIALLY EFFECTIVE WITH STUDENTS OF VARYING PERSONALITIES. THE REPORT CONSIDERED THE UTILITY AND RELEVANCE OF THE CONCEPTUAL SYSTEMS MODEL BY DESCRIBING A SPECIFIC PROJECT IN WHICH THE MODEL SERVED AS THE BASIS FOR FORMING HOMOGENEOUS CLASSROOM GROUPS. THE PROJECT WAS…

Understanding the Influence of Model Minority Stereotypes on Asian American Community College Students

ERIC Educational Resources Information Center

Assalone, Amanda E.; Fann, Amy

2017-01-01

Contrary to the model minority myth that portrays Asian Americans as academic all-stars overrepresented in elite 4-year institutions, nearly half of all Asian American college students do, in fact, attend community colleges, and many experience myriad challenges. This exploratory study utilized a qualitative analysis and investigated how model…

Making the Invisible Visible: Personas and Mental Models of Distance Education Library Users

ERIC Educational Resources Information Center

Lewis, Cynthia; Contrino, Jacline

2016-01-01

Gaps between users' and designers' mental models of digital libraries often result in adverse user experiences. This article details an exploratory user research study at a large, predominantly online university serving non-traditional distance education students with the goal of understanding these gaps. Using qualitative data, librarians created…

Innovating Science Teaching with a Transformative Learning Model

ERIC Educational Resources Information Center

Gudiño Paredes, Sandra

2018-01-01

This exploratory study aimed to describe the impact of the 'Science in Family project', as a transformative learning model for science teachers trying to improve student's attitudes toward STEM subjects. This study took place in a public elementary school in Monterrey, Mexico, which has been developing this project for more than thirteen years…

Measuring Experiential Avoidance: A Preliminary Test of a Working Model

ERIC Educational Resources Information Center

Hayes, Steven C.; Strosahl, Kirk; Wilson, Kelly G.; Bissett, Richard T.; Pistorello, Jacqueline; Toarmino, Dosheen; Polusny, Melissa A.; Dykstra, Thane A.; Batten, Sonja V.; Bergan, John; Stewart, Sherry H.; Zvolensky, Michael J.; Eifert, Georg H.; Bond, Frank W.; Forsyth, John P.; Karekla, Maria; Mccurry, Susan M.

2004-01-01

The present study describes the development of a short, general measure of experiential avoidance, based on a specific theoretical approach to this process. A theoretically driven iterative exploratory analysis using structural equation modeling on data from a clinical sample yielded a single factor comprising 9 items. A fully confirmatory factor…

Cost Accounting and Analysis for University Libraries.

ERIC Educational Resources Information Center

Leimkuhler, Ferdinand F.; Cooper, Michael D.

The approach to library planning studied in this report is the use of accounting models to measure library costs and implement program budgets. A cost-flow model for a university library is developed and listed with historical data from the Berkeley General Library. Various comparisons of an exploratory nature are made of the unit costs for…

Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables

ERIC Educational Resources Information Center

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

2015-01-01

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

Prestige-Oriented Market Entry Strategy: The Case of Australian Universities

ERIC Educational Resources Information Center

Tayar, Mark; Jack, Robert

2013-01-01

Through an exploratory case study of four Australian universities this article finds that foreign market entry strategies are shaped by prestige-seeking motivations and a culture of risk aversion. From the market selection, entry mode and higher education literature, a conceptual model, embedded with four propositions, is presented. The model sees…

The Employee Satisfaction Inventory (ESI): Development of a Scale to Measure Satisfaction of Greek Employees.

ERIC Educational Resources Information Center

Koustelios, Athanasios D.; Bagiatis, Konstantinos

1997-01-01

An instrument to measure employee job satisfaction in Greece was developed and tested with 212 and 516 employees. Exploratory factor analysis indicated a six-factor solution with high internal consistency. Structural equation modeling showed a fairly good fit to the model, with need for slight improvement. (SLD)