Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

PubMed

Lepre, Jorge; Rice, J Jeremy; Tu, Yuhai; Stolovitzky, Gustavo

2004-05-01

Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

A factor analysis of landscape pattern and structure metrics

Treesearch

Kurt H. Riitters; R.V. O' Neill; C.T. Hunsaker; James D. Wickham; D.H. Yankee; S.P. Timmins; K.B. Jones; B.L. Jackson

1995-01-01

Fifty-five metrics of landscape pattern and structure were calculated for 85 maps of land use and land cover. A multivariate factor analysis was used to identify the common axes (or dimensions) of pattern and structure which were measured by a reduced set of 26 metrics. The first six factors explained about 87% of the variation in the 26 landscape metrics. These...

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

T-pattern analysis for the study of temporal structure of animal and human behavior: a comprehensive review.

PubMed

Casarrubea, M; Jonsson, G K; Faulisi, F; Sorbera, F; Di Giovanni, G; Benigno, A; Crescimanno, G; Magnusson, M S

2015-01-15

A basic tenet in the realm of modern behavioral sciences is that behavior consists of patterns in time. For this reason, investigations of behavior deal with sequences that are not easily perceivable by the unaided observer. This problem calls for improved means of detection, data handling and analysis. This review focuses on the analysis of the temporal structure of behavior carried out by means of a multivariate approach known as T-pattern analysis. Using this technique, recurring sequences of behavioral events, usually hard to detect, can be unveiled and carefully described. T-pattern analysis has been successfully applied in the study of various aspects of human or animal behavior such as behavioral modifications in neuro-psychiatric diseases, route-tracing stereotypy in mice, interaction between human subjects and animal or artificial agents, hormonal-behavioral interactions, patterns of behavior associated with emesis and, in our laboratories, exploration and anxiety-related behaviors in rodents. After describing the theory and concepts of T-pattern analysis, this review will focus on the application of the analysis to the study of the temporal characteristics of behavior in different species from rodents to human beings. This work could represent a useful background for researchers who intend to employ such a refined multivariate approach to the study of behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

MULTIVARIATE ANALYSIS OF DRINKING BEHAVIOUR IN A RURAL POPULATION

PubMed Central

Mathrubootham, N.; Bashyam, V.S.P.; Shahjahan

1997-01-01

This study was carried out to find out the drinking pattern in a rural population, using multivariate techniques. 386 current users identified in a community were assessed with regard to their drinking behaviours using a structured interview. For purposes of the study the questions were condensed into 46 meaningful variables. In bivariate analysis, 14 variables including dependent variables such as dependence, MAST & CAGE (measuring alcoholic status), Q.F. Index and troubled drinking were found to be significant. Taking these variables and other multivariate techniques too such as ANOVA, correlation, regression analysis and factor analysis were done using both SPSS PC + and HCL magnum mainframe computer with FOCUS package and UNIX systems. Results revealed that number of factors such as drinking style, duration of drinking, pattern of abuse, Q.F. Index and various problems influenced drinking and some of them set up a vicious circle. Factor analysis revealed mainly 3 factors, abuse, dependence and social drinking factors. Dependence could be divided into low/moderate dependence. The implications and practical applications of these tests are also discussed. PMID:21584077

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

PubMed

Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

2012-03-14

Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

Revealing representational content with pattern-information fMRI--an introductory guide.

PubMed

Mur, Marieke; Bandettini, Peter A; Kriegeskorte, Nikolaus

2009-03-01

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Multivariate Analysis as a Method for Evaluating the Conceptual Perceptions of Korean Medicine Students regarding Phlegm Pattern

PubMed Central

Kim, Hyungsuk; Park, Young-Jae; Park, Young-Bae

2013-01-01

Individuals may perceive the concepts in Korean medicine pattern classification differently because it is performed according to the integration of a variety of information. Therefore, analysis about individual perspective is very important for examining the cross-sectional perspective state of Korean medicine concepts and developing both the clinical guideline including diagnosis and the curriculum of Korean medicine colleges. Moreover, because this conceptual difference is thought to begin with college education, it is worthwhile to observe students' viewpoints. So, we suggested multivariate analysis to explore the dimensional structure of Korean medicine students' conceptual perceptions regarding phlegm pattern. We surveyed 326 students divided into 5 groups based on their year of study. Data were analyzed using multidimensional scaling and factor analysis. Within-group difference was the smallest for third-year students, who have received Korean medicine education in full for the first time. With the exception of first-year students, the conceptual map revealed that each group's mean perceptions of phlegm pattern were distributed in almost linear fashion. To determine the effect of education, we investigated the preference rankings and scores of each symptom. We also extracted factors to identify latent variables and to compare the between-group conceptual characteristics regarding phlegm pattern. PMID:24062789

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis.

PubMed

Brindle, R C; Ginty, A T; Jones, A; Phillips, A C; Roseboom, T J; Carroll, D; Painter, R C; de Rooij, S R

2016-12-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure reactivity patterns and hypertension in a large prospective cohort (age range 55-60 years). Four clusters emerged with statistically different systolic and diastolic blood pressure and heart rate reactivity patterns. Cluster 1 was characterised by a relatively exaggerated blood pressure and heart rate response while the blood pressure and heart rate responses of cluster 2 were relatively modest and in line with the sample mean. Cluster 3 was characterised by blunted cardiovascular stress reactivity across all variables and cluster 4, by an exaggerated blood pressure response and modest heart rate response. Membership to cluster 4 conferred an increased risk of hypertension at 5-year follow-up (hazard ratio=2.98 (95% CI: 1.50-5.90), P<0.01) that survived adjustment for a host of potential confounding variables. These results suggest that the cardiac reactivity plays a potentially important role in the link between blood pressure reactivity and hypertension and support the use of multivariate approaches to stress psychophysiology.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Visualizing frequent patterns in large multivariate time series

NASA Astrophysics Data System (ADS)

Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

2011-01-01

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings.

PubMed

Malaquias, José B; Ramalho, Francisco S; Dos S Dias, Carlos T; Brugger, Bruno P; S Lira, Aline Cristina; Wilcken, Carlos F; Pachú, Jéssica K S; Zanuncio, José C

2017-02-09

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

PubMed Central

Malaquias, José B.; Ramalho, Francisco S.; dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-01-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied. PMID:28181503

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

NASA Astrophysics Data System (ADS)

Malaquias, José B.; Ramalho, Francisco S.; Dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-02-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Multivariate analysis of DSC-XRD simultaneous measurement data: a study of multistage crystalline structure changes in a linear poly(ethylene imine) thin film.

PubMed

Kakuda, Hiroyuki; Okada, Tetsuo; Otsuka, Makoto; Katsumoto, Yukiteru; Hasegawa, Takeshi

2009-01-01

A multivariate analytical technique has been applied to the analysis of simultaneous measurement data from differential scanning calorimetry (DSC) and X-ray diffraction (XRD) in order to study thermal changes in crystalline structure of a linear poly(ethylene imine) (LPEI) film. A large number of XRD patterns generated from the simultaneous measurements were subjected to an augmented alternative least-squares (ALS) regression analysis, and the XRD patterns were readily decomposed into chemically independent XRD patterns and their thermal profiles were also obtained at the same time. The decomposed XRD patterns and the profiles were useful in discussing the minute peaks in the DSC. The analytical results revealed the following changes of polymorphisms in detail: An LPEI film prepared by casting an aqueous solution was composed of sesquihydrate and hemihydrate crystals. The sesquihydrate one was lost at an early stage of heating, and the film changed into an amorphous state. Once the sesquihydrate was lost by heating, it was not recovered even when it was cooled back to room temperature. When the sample was heated again, structural changes were found between the hemihydrate and the amorphous components. In this manner, the simultaneous DSC-XRD measurements combined with ALS analysis proved to be powerful for obtaining a better understanding of the thermally induced changes of the crystalline structure in a polymer film.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Is race erased? Decoding race from patterns of neural activity when skin color is not diagnostic of group boundaries.

PubMed

Ratner, Kyle G; Kaul, Christian; Van Bavel, Jay J

2013-10-01

Several theories suggest that people do not represent race when it does not signify group boundaries. However, race is often associated with visually salient differences in skin tone and facial features. In this study, we investigated whether race could be decoded from distributed patterns of neural activity in the fusiform gyri and early visual cortex when visual features that often covary with race were orthogonal to group membership. To this end, we used multivariate pattern analysis to examine an fMRI dataset that was collected while participants assigned to mixed-race groups categorized own-race and other-race faces as belonging to their newly assigned group. Whereas conventional univariate analyses provided no evidence of race-based responses in the fusiform gyri or early visual cortex, multivariate pattern analysis suggested that race was represented within these regions. Moreover, race was represented in the fusiform gyri to a greater extent than early visual cortex, suggesting that the fusiform gyri results do not merely reflect low-level perceptual information (e.g. color, contrast) from early visual cortex. These findings indicate that patterns of activation within specific regions of the visual cortex may represent race even when overall activation in these regions is not driven by racial information.

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

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

Meier, David E.; Coble, Jamie B.; Jordan, David V.

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicatemore » changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.« less

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Complex codon usage pattern and compositional features of retroviruses.

PubMed

RoyChoudhury, Sourav; Mukherjee, Debaprasad

2013-01-01

Retroviruses infect a wide range of organisms including humans. Among them, HIV-1, which causes AIDS, has now become a major threat for world health. Some of these viruses are also potential gene transfer vectors. In this study, the patterns of synonymous codon usage in retroviruses have been studied through multivariate statistical methods on ORFs sequences from the available 56 retroviruses. The principal determinant for evolution of the codon usage pattern in retroviruses seemed to be the compositional constraints, while selection for translation of the viral genes plays a secondary role. This was further supported by multivariate analysis on relative synonymous codon usage. Thus, it seems that mutational bias might have dominated role over translational selection in shaping the codon usage of retroviruses. Codon adaptation index was used to identify translationally optimal codons among genes from retroviruses. The comparative analysis of the preferred and optimal codons among different retroviral groups revealed that four codons GAA, AAA, AGA, and GGA were significantly more frequent in most of the retroviral genes inspite of some differences. Cluster analysis also revealed that phylogenetically related groups of retroviruses have probably evolved their codon usage in a concerted manner under the influence of their nucleotide composition.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

PubMed

Anderson, John R

2012-03-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. Copyright © 2011 Elsevier Ltd. All rights reserved.

A multivariate ecogeographic analysis of macaque craniodental variation.

PubMed

Grunstra, Nicole D S; Mitteroecker, Philipp; Foley, Robert A

2018-06-01

To infer the ecogeographic conditions that underlie the evolutionary diversification of macaques, we investigated the within- and between-species relationships of craniodental dimensions, geography, and environment in extant macaque species. We studied evolutionary processes by contrasting macroevolutionary patterns, phylogeny, and within-species associations. Sixty-three linear measurements of the permanent dentition and skull along with data about climate, ecology (environment), and spatial geography were collected for 711 specimens of 12 macaque species and analyzed by a multivariate approach. Phylogenetic two-block partial least squares was used to identify patterns of covariance between craniodental and environmental variation. Phylogenetic reduced rank regression was employed to analyze spatial clines in morphological variation. Between-species associations consisted of two distinct multivariate patterns. The first represents overall craniodental size and is negatively associated with temperature and habitat, but positively with latitude. The second pattern shows an antero-posterior tooth size contrast related to diet, rainfall, and habitat productivity. After controlling for phylogeny, however, the latter dimension was diminished. Within-species analyses neither revealed significant association between morphology, environment, and geography, nor evidence of isolation by distance. We found evidence for environmental adaptation in macaque body and craniodental size, primarily driven by selection for thermoregulation. This pattern cannot be explained by the within-species pattern, indicating an evolved genetic basis for the between-species relationship. The dietary signal in relative tooth size, by contrast, can largely be explained by phylogeny. This cautions against adaptive interpretations of phenotype-environment associations when phylogeny is not explicitly modelled. © 2018 Wiley Periodicals, Inc.

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Dietary patterns and changes in body weight in women.

PubMed

Schulze, Matthias B; Fung, Teresa T; Manson, Joann E; Willett, Walter C; Hu, Frank B

2006-08-01

Our objective was to examine the association between adherence to dietary patterns and weight change in women. Women (51,670, 26 to 46 years old) in the Nurses' Health Study II were followed from 1991 to 1999. Dietary intake and body weight were ascertained in 1991, 1995, and 1999. A Western pattern, characterized by high intakes of red and processed meats, refined grains, sweets and desserts, and potatoes, and a prudent pattern, characterized by high intakes of fruits, vegetables, whole grains, fish, poultry, and salad dressing, were identified with principal component analysis, and associations between patterns and change in body weight were estimated. Women who increased their Western pattern score had greater weight gain (multivariate adjusted means, 4.55 kg for 1991 to 1995 and 2.86 kg for 1995 to 1999) than women who decreased their Western pattern score (2.70 and 1.37 kg for the two time periods), adjusting for baseline lifestyle and dietary confounders and changes in confounders over time (p < 0.001 for both time periods). Furthermore, among women who increased their prudent pattern score, weight gain was smaller (multivariate-adjusted means, 1.93 kg for 1991 to 1995 and 0.66 kg for 1995 to 1999) than among women who decreased their prudent pattern score (4.83 and 3.35 kg for the two time periods) (p < 0.001). The largest weight gain between 1991 and 1995 and between 1995 and 1999 was observed among women who decreased their prudent pattern score while increasing their Western pattern score (multivariate adjusted means, 6.80 and 4.99 kg), whereas it was smallest for the opposite change in patterns (0.87 and -0.64 kg) (p < 0.001). Adoption of a Western dietary pattern is associated with larger weight gain in women, whereas a prudent dietary pattern may facilitate weight maintenance.

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

Visual Learning Induces Changes in Resting-State fMRI Multivariate Pattern of Information.

PubMed

Guidotti, Roberto; Del Gratta, Cosimo; Baldassarre, Antonello; Romani, Gian Luca; Corbetta, Maurizio

2015-07-08

When measured with functional magnetic resonance imaging (fMRI) in the resting state (R-fMRI), spontaneous activity is correlated between brain regions that are anatomically and functionally related. Learning and/or task performance can induce modulation of the resting synchronization between brain regions. Moreover, at the neuronal level spontaneous brain activity can replay patterns evoked by a previously presented stimulus. Here we test whether visual learning/task performance can induce a change in the patterns of coded information in R-fMRI signals consistent with a role of spontaneous activity in representing task-relevant information. Human subjects underwent R-fMRI before and after perceptual learning on a novel visual shape orientation discrimination task. Task-evoked fMRI patterns to trained versus novel stimuli were recorded after learning was completed, and before the second R-fMRI session. Using multivariate pattern analysis on task-evoked signals, we found patterns in several cortical regions, as follows: visual cortex, V3/V3A/V7; within the default mode network, precuneus, and inferior parietal lobule; and, within the dorsal attention network, intraparietal sulcus, which discriminated between trained and novel visual stimuli. The accuracy of classification was strongly correlated with behavioral performance. Next, we measured multivariate patterns in R-fMRI signals before and after learning. The frequency and similarity of resting states representing the task/visual stimuli states increased post-learning in the same cortical regions recruited by the task. These findings support a representational role of spontaneous brain activity. Copyright © 2015 the authors 0270-6474/15/359786-13$15.00/0.

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy.

PubMed

Gu, Yue; Miao, Shuo; Han, Junxia; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

2018-06-01

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

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…

Retrospective Analysis of Radiological Recurrence Patterns in Glioblastoma, Their Prognostic Value And Association to Postoperative Infarct Volume.

PubMed

Bette, Stefanie; Barz, Melanie; Huber, Thomas; Straube, Christoph; Schmidt-Graf, Friederike; Combs, Stephanie E; Delbridge, Claire; Gerhardt, Julia; Zimmer, Claus; Meyer, Bernhard; Kirschke, Jan S; Boeckh-Behrens, Tobias; Wiestler, Benedikt; Gempt, Jens

2018-03-14

Recent studies suggested that postoperative hypoxia might trigger invasive tumor growth, resulting in diffuse/multifocal recurrence patterns. Aim of this study was to analyze distinct recurrence patterns and their association to postoperative infarct volume and outcome. 526 consecutive glioblastoma patients were analyzed, of which 129 met our inclusion criteria: initial tumor diagnosis, surgery, postoperative diffusion-weighted imaging and tumor recurrence during follow-up. Distinct patterns of contrast-enhancement at initial diagnosis and at first tumor recurrence (multifocal growth/progression, contact to dura/ventricle, ependymal spread, local/distant recurrence) were recorded by two blinded neuroradiologists. The association of radiological patterns to survival and postoperative infarct volume was analyzed by uni-/multivariate survival analyses and binary logistic regression analysis. With increasing postoperative infarct volume, patients were significantly more likely to develop multifocal recurrence, recurrence with contact to ventricle and contact to dura. Patients with multifocal recurrence (Hazard Ratio (HR) 1.99, P = 0.010) had significantly shorter OS, patients with recurrent tumor with contact to ventricle (HR 1.85, P = 0.036), ependymal spread (HR 2.97, P = 0.004) and distant recurrence (HR 1.75, P = 0.019) significantly shorter post-progression survival in multivariate analyses including well-established prognostic factors like age, Karnofsky Performance Score (KPS), therapy, extent of resection and patterns of primary tumors. Postoperative infarct volume might initiate hypoxia-mediated aggressive tumor growth resulting in multifocal and diffuse recurrence patterns and impaired survival.

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes.

PubMed

Casanova, Ramon; Espeland, Mark A; Goveas, Joseph S; Davatzikos, Christos; Gaussoin, Sarah A; Maldjian, Joseph A; Brunner, Robert L; Kuller, Lewis H; Johnson, Karen C; Mysiw, W Jerry; Wagner, Benjamin; Resnick, Susan M

2011-05-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers. Copyright © 2011 Elsevier Inc. All rights reserved.

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes

PubMed Central

Casanova, Ramon; Espeland, Mark A.; Goveas, Joseph S.; Davatzikos, Christos; Gaussoin, Sarah A.; Maldjian, Joseph A.; Brunner, Robert L.; Kuller, Lewis H.; Johnson, Karen C.; Mysiw, W. Jerry; Wagner, Benjamin; Resnick, Susan M.

2011-01-01

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years, however it is unknown whether this results in a broad-based characteristic pattern of effects. Structural MRI was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L1 penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and non-users. PMID:21292420

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

PubMed

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-05-15

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of Concepts from Cross-Recurrence Analysis in Speech Production: An Overview and Comparison with Other Nonlinear Methods

ERIC Educational Resources Information Center

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-01-01

Purpose: The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space…

Obesity in trauma patients: correlations of body mass index with outcomes, injury patterns, and complications.

PubMed

Evans, David C; Stawicki, Stanislaw P A; Davido, H Tracy; Eiferman, Daniel

2011-08-01

Current understanding of the effects of obesity on trauma patients is incomplete. We hypothesized that among older trauma patients, obese patients differ from nonobese patients in injury patterns, complications, and mortality. Patients older than 45 years old presenting to a Level I trauma center were included in this retrospective database analysis (n = 461). Body mass index (BMI) groups were defined as underweight less than 18.5 kg/m(2), normal 18.5 to 24.9 kg/m(2), overweight 25.0 to 29.9 kg/m(2), or obese greater than 30 kg/m(2). Injury patterns, complications, and outcomes were analyzed using univariate analyses, multivariate logistic regression, and Kaplan-Meier survival analysis. Higher BMI is associated with a higher incidence of torso injury and proximal upper extremity injuries in blunt trauma (n = 410). All other injury patterns and complications (except anemia) were similar between BMI groups. The underweight (BMI less than 18.5 kg/m(2)) group had significantly lower 90-day survival than other groups (P < 0.05). BMI is not a predictor of morbidity or mortality in multivariate analysis. Among older blunt trauma patients, increasing BMI is associated with higher rates of torso and proximal upper extremity injuries. Our study suggests that obesity is not an independent risk factor for complications or mortality after trauma in older patients. Conversely, underweight trauma patients had a lower 90-day survival.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Dietary Patterns and Risk of Esophageal Cancer Mortality: The Japan Collaborative Cohort Study.

PubMed

Okada, Emiko; Nakamura, Koshi; Ukawa, Shigekazu; Sakata, Kiyomi; Date, Chigusa; Iso, Hiroyasu; Tamakoshi, Akiko

2016-01-01

Several case-control studies have associated dietary patterns with esophageal cancer (EC) risk, but prospective studies are scarce. We investigated dietary pattern and EC mortality risk associations by smoking status. Participants were 26,562 40- to 79-yr-old Japanese men, who enrolled in the Japan Collaborative Cohort Study between 1988 and 1990. Hazard ratios (HRs) and 95% confidence intervals (CIs) for EC mortality in nonsmokers and smokers were estimated using Cox proportional models. During follow-up (1988-2009), 132 participants died of EC. Using a baseline food frequency questionnaire and factor analysis, vegetable, animal, and dairy product food patterns were identified. EC risk decreased significantly with a higher factor score for the dairy product pattern (Ptrend = 0.042) and was more pronounced in smokers [multivariable HR (4th vs. 1st quartiles) = 0.57, 95% CI: 0.30, 1.09; Ptrend = 0.021]. Neither vegetable nor animal food patterns were significant overall; however, EC risk increased with a higher factor score for the animal food pattern in nonsmokers [multivariable HR (4th vs. 1st quartiles) = 6.01, 95% CI: 1.17, 30.88; Ptrend = 0.021], although the small number of events was a limitation. Our findings suggest a dairy product pattern may reduce EC risk in Japanese men, especially smokers.

Linked Sex Differences in Cognition and Functional Connectivity in Youth.

PubMed

Satterthwaite, Theodore D; Wolf, Daniel H; Roalf, David R; Ruparel, Kosha; Erus, Guray; Vandekar, Simon; Gennatas, Efstathios D; Elliott, Mark A; Smith, Alex; Hakonarson, Hakon; Verma, Ragini; Davatzikos, Christos; Gur, Raquel E; Gur, Ruben C

2015-09-01

Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

PubMed

Mendlik, Thomas; Gobiet, Andreas

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics. The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.

Multivariate landscape trajectory analysis: An example using simulation modeling of American marten habitat change under four timber harvest scenarios

Treesearch

Samuel A. Cushman; Kevin McGarigal

2007-01-01

Integrating temporal variabilily into spatial analyses is one of the abiding challenges in landscape ecology. In this chapter we use landscape trajectory analysis to assess changes in landscape patterns over time. Landscape trajectory analysis is an approach to quantify changes in landscape structure over time. There are three key concepts which underlie the...

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Identifying Neural Patterns of Functional Dyspepsia Using Multivariate Pattern Analysis: A Resting-State fMRI Study

PubMed Central

Liu, Peng; Qin, Wei; Wang, Jingjing; Zeng, Fang; Zhou, Guangyu; Wen, Haixia; von Deneen, Karen M.; Liang, Fanrong; Gong, Qiyong; Tian, Jie

2013-01-01

Background Previous imaging studies on functional dyspepsia (FD) have focused on abnormal brain functions during special tasks, while few studies concentrated on the resting-state abnormalities of FD patients, which might be potentially valuable to provide us with direct information about the neural basis of FD. The main purpose of the current study was thereby to characterize the distinct patterns of resting-state function between FD patients and healthy controls (HCs). Methodology/Principal Findings Thirty FD patients and thirty HCs were enrolled and experienced 5-mintue resting-state scanning. Based on the support vector machine (SVM), we applied multivariate pattern analysis (MVPA) to investigate the differences of resting-state function mapped by regional homogeneity (ReHo). A classifier was designed by using the principal component analysis and the linear SVM. Permutation test was then employed to identify the significant contribution to the final discrimination. The results displayed that the mean classifier accuracy was 86.67%, and highly discriminative brain regions mainly included the prefrontal cortex (PFC), orbitofrontal cortex (OFC), supplementary motor area (SMA), temporal pole (TP), insula, anterior/middle cingulate cortex (ACC/MCC), thalamus, hippocampus (HIPP)/parahippocamus (ParaHIPP) and cerebellum. Correlation analysis revealed significant correlations between ReHo values in certain regions of interest (ROI) and the FD symptom severity and/or duration, including the positive correlations between the dmPFC, pACC and the symptom severity; whereas, the positive correlations between the MCC, OFC, insula, TP and FD duration. Conclusions These findings indicated that significantly distinct patterns existed between FD patients and HCs during the resting-state, which could expand our understanding of the neural basis of FD. Meanwhile, our results possibly showed potential feasibility of functional magnetic resonance imaging diagnostic assay for FD. PMID:23874543

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

NASA Astrophysics Data System (ADS)

Gaitan, S.; ten Veldhuis, J. A. E.

2015-06-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

PubMed Central

Shim, Heejung; Chasman, Daniel I.; Smith, Joshua D.; Mora, Samia; Ridker, Paul M.; Nickerson, Deborah A.; Krauss, Ronald M.; Stephens, Matthew

2015-01-01

We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log10Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns - both in our original data and in a replication cohort - consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log10Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html. PMID:25898129

A multivariate variational objective analysis-assimilation method. Part 2: Case study results with and without satellite data

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.; Kidder, Stanley Q.; Scott, Robert W.

1988-01-01

The variational multivariate assimilation method described in a companion paper by Achtemeier and Ochs is applied to conventional and conventional plus satellite data. Ground-based and space-based meteorological data are weighted according to the respective measurement errors and blended into a data set that is a solution of numerical forms of the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation for a dry atmosphere. The analyses serve first, to evaluate the accuracy of the model, and second to contrast the analyses with and without satellite data. Evaluation criteria measure the extent to which: (1) the assimilated fields satisfy the dynamical constraints, (2) the assimilated fields depart from the observations, and (3) the assimilated fields are judged to be realistic through pattern analysis. The last criterion requires that the signs, magnitudes, and patterns of the hypersensitive vertical velocity and local tendencies of the horizontal velocity components be physically consistent with respect to the larger scale weather systems.

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Gu, Yue; Miao, Shuo; Han, Junxia; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

2018-06-01

Objective. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. Approach. FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. Main results. The results showed that 86.0% (p<0.001 ) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. Significance. This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

Microhabitat and Environmental Relationships of Bryophytes in Blue Oak (Quercus douglasii H. & A.) Woodlands and Forests of Central Coastal California

Treesearch

Mark Borchert; Daniel Norris

1991-01-01

Microhabitat preferences and species-environment patterns were quantified for bryophytes in blue oak woodlands and forests of central coastal California. Presence data for mosses collected from 149 400 m2 plots were analyzed using canonical correspondence analysis (CCA), a multivariate direct gradient analysis technique. Separate ordinations were performed for...

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

NASA Technical Reports Server (NTRS)

Magri, Christopher

1994-01-01

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

Mapping the Diversity among Runaways: A Descriptive Multivariate Analysis of Selected Social Psychological Background Conditions.

ERIC Educational Resources Information Center

Brennan, Tim

1980-01-01

A review of prior classification systems of runaways is followed by a descriptive taxonomy of runaways developed using cluster-analytic methods. The empirical types illustrate patterns of weakness in bonds between runaways and families, schools, or peer relationships. (Author)

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

Maternal dietary patterns in pregnancy and the association with small-for-gestational-age infants.

PubMed

Thompson, John M D; Wall, Clare; Becroft, David M O; Robinson, Elizabeth; Wild, Chris J; Mitchell, Edwin A

2010-06-01

Maternal nutritional status before and during pregnancy is important for the growth and development of the fetus. The effects of pre-pregnancy nutrition (estimated by maternal size) are well documented. There is little information in today's Western society on the effect of maternal nutrition during pregnancy on the fetus. The aim of the study was to describe dietary patterns of a cohort of mothers during pregnancy (using principal components analysis with a varimax rotation) and assess the effect of these dietary patterns on the risk of delivering a small-for-gestational-age (SGA) baby. The study was a case-control study investigating factors related to SGA. The population was 1714 subjects in Auckland, New Zealand, born between October 1995 and November 1997, about half of whom were born SGA ( < or = 10th percentile for sex and gestation). Maternal dietary information was collected using FFQ after delivery for the first and last months of pregnancy. Three dietary patterns (traditional, junk and fusion) were defined. Factors associated with these dietary patterns when examined in multivariable analyses included marital status, maternal weight, maternal age and ethnicity. In multivariable analysis, mothers who had higher 'traditional' diet scores in early pregnancy were less likely to deliver a SGA infant (OR = 0.86; 95 % CI 0.75, 0.99). Maternal diet, particularly in early pregnancy, is important for the development of the fetus. Socio-demographic factors tend to be significantly related to dietary patterns, suggesting that extra resources may be necessary for disadvantaged mothers to ensure good nutrition in pregnancy.

Impact of novel histopathological factors on the outcomes of liver surgery for colorectal cancer metastases.

PubMed

Serrablo, A; Paliogiannis, P; Pulighe, F; Moro, S Saudi-Moro; Borrego-Estella, V; Attene, F; Scognamillo, F; Hörndler, C

2016-09-01

We evaluated the impacts of a series of novel histopathological factors on clinical-surgical outcomes and survival of patients who underwent surgery for colorectal cancer liver metastasis, with and without neoadjuvant chemotherapy. A prospective database including 150 consecutive patients who underwent 183 hepatic resections for metastatic colorectal cancer was evaluated. Among them, 74 (49.3%) received neoadjuvant chemotherapy before surgery. The histopathological factors studied were: a) microsatellitosis, b) type and pattern of tumour growth, c) nuclear grade and the number of mitoses/mm(2), d) perilesional pseudocapsule, e) intratumoural fibrosis, f) lesion cellularity, g) hypoxic-angiogenic perilesional growth pattern, and h) the tumour normal interface. Three or more metastatic lesions, R1 resection margins, and <50% tumour necrosis were prognostic factors for a worse OS, but only the former was confirmed to be an independent prognostic factor in the multivariate analysis. Furthermore, tumour fibrosis <40% and cellularity >10% were predictive of a worse neoadjuvant therapy response, but these findings were not confirmed in the multivariate analysis. Finally, tumour necrosis <50%, cellularity >10%, and TNI >0.5 mm were prognostic factors for a worse DFS and AS in the univariate but not in the multivariate analysis. Several factors seem to influence the outcomes of surgery for colorectal cancer liver metastasis, especially the number of the lesions, the margins of resection, the percentage of necrosis and fibrosis, as well as the cellularity and the TNI. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI.

PubMed

Pedersen, Mangor; Curwood, Evan K; Archer, John S; Abbott, David F; Jackson, Graeme D

2015-11-01

Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

An analysis of fracture trace patterns in areas of flat-lying sedimentary rocks for the detection of buried geologic structure. [Kansas and Texas

NASA Technical Reports Server (NTRS)

Podwysocki, M. H.

1974-01-01

Two study areas in a cratonic platform underlain by flat-lying sedimentary rocks were analyzed to determine if a quantitative relationship exists between fracture trace patterns and their frequency distributions and subsurface structural closures which might contain petroleum. Fracture trace lengths and frequency (number of fracture traces per unit area) were analyzed by trend surface analysis and length frequency distributions also were compared to a standard Gaussian distribution. Composite rose diagrams of fracture traces were analyzed using a multivariate analysis method which grouped or clustered the rose diagrams and their respective areas on the basis of the behavior of the rays of the rose diagram. Analysis indicates that the lengths of fracture traces are log-normally distributed according to the mapping technique used. Fracture trace frequency appeared higher on the flanks of active structures and lower around passive reef structures. Fracture trace log-mean lengths were shorter over several types of structures, perhaps due to increased fracturing and subsequent erosion. Analysis of rose diagrams using a multivariate technique indicated lithology as the primary control for the lower grouping levels. Groupings at higher levels indicated that areas overlying active structures may be isolated from their neighbors by this technique while passive structures showed no differences which could be isolated.

Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

NASA Astrophysics Data System (ADS)

Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

2013-12-01

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.

BLURRING OF BIOGEOGRAPHIC BOUNDARIES: A MULTIVARIATE ANALYSIS OF THE REGIONAL PATTERNS OF NATIVE AND NONINDIGENOUS SPECIES ASSEMBLAGES IN PACIFIC COAST ESTUARIES

EPA Science Inventory

Many, if not most, invaders have wide physiological tolerance limits and generalist habitat requirements. Consequently as a group nonindigenous species should have wider geographic distributions compared to native fauna. In turn, these broader distributions of nonindigenous speci...

Use of direct gradient analysis to uncover biological hypotheses in 16s survey data and beyond.

PubMed

Erb-Downward, John R; Sadighi Akha, Amir A; Wang, Juan; Shen, Ning; He, Bei; Martinez, Fernando J; Gyetko, Margaret R; Curtis, Jeffrey L; Huffnagle, Gary B

2012-01-01

This study investigated the use of direct gradient analysis of bacterial 16S pyrosequencing surveys to identify relevant bacterial community signals in the midst of a "noisy" background, and to facilitate hypothesis-testing both within and beyond the realm of ecological surveys. The results, utilizing 3 different real world data sets, demonstrate the utility of adding direct gradient analysis to any analysis that draws conclusions from indirect methods such as Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA). Direct gradient analysis produces testable models, and can identify significant patterns in the midst of noisy data. Additionally, we demonstrate that direct gradient analysis can be used with other kinds of multivariate data sets, such as flow cytometric data, to identify differentially expressed populations. The results of this study demonstrate the utility of direct gradient analysis in microbial ecology and in other areas of research where large multivariate data sets are involved.

A Western Dietary Pattern Increases Prostate Cancer Risk: A Systematic Review and Meta-Analysis.

PubMed

Fabiani, Roberto; Minelli, Liliana; Bertarelli, Gaia; Bacci, Silvia

2016-10-12

Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by "a posteriori" methods. A literature search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a "Healthy pattern" and a "Western pattern". The Healthy pattern was not related to PC risk (OR = 0.96; 95% confidence interval (CI): 0.88-1.04) while the Western pattern significantly increased it (OR = 1.34; 95% CI: 1.08-1.65). In addition, the "Carbohydrate pattern", which was analyzed in four articles, was positively associated with a higher PC risk (OR = 1.64; 95% CI: 1.35-2.00). A significant linear trend between the Western ( p = 0.011) pattern, the Carbohydrate ( p = 0.005) pattern, and the increment of PC risk was observed. The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings.

Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

Influence of shifting cultivation practices on soil-plant-beetle interactions.

PubMed

Ibrahim, Kalibulla Syed; Momin, Marcy D; Lalrotluanga, R; Rosangliana, David; Ghatak, Souvik; Zothansanga, R; Kumar, Nachimuthu Senthil; Gurusubramanian, Guruswami

2016-08-01

Shifting cultivation (jhum) is a major land use practice in Mizoram. It was considered as an eco-friendly and efficient method when the cycle duration was long (15-30 years), but it poses the problem of land degradation and threat to ecology when shortened (4-5 years) due to increased intensification of farming systems. Studying beetle community structure is very helpful in understanding how shifting cultivation affects the biodiversity features compared to natural forest system. The present study examines the beetle species diversity and estimates the effects of shifting cultivation practices on the beetle assemblages in relation to change in tree species composition and soil nutrients. Scarabaeidae and Carabidae were observed to be the dominant families in the land use systems studied. Shifting cultivation practice significantly (P < 0.05) affected the beetle and tree species diversity as well as the soil nutrients as shown by univariate (one-way analysis of variance (ANOVA), correlation and regression, diversity indices) and multivariate (cluster analysis, principal component analysis (PCA), detrended correspondence analysis (DCA), canonical variate analysis (CVA), permutational multivariate analysis of variance (PERMANOVA), permutational multivariate analysis of dispersion (PERMDISP)) statistical analyses. Besides changing the tree species composition and affecting the soil fertility, shifting cultivation provides less suitable habitat conditions for the beetle species. Bioindicator analysis categorized the beetle species into forest specialists, anthropogenic specialists (shifting cultivation habitat specialist), and habitat generalists. Molecular analysis of bioindicator beetle species was done using mitochondrial cytochrome oxidase subunit I (COI) marker to validate the beetle species and describe genetic variation among them in relation to heterogeneity, transition/transversion bias, codon usage bias, evolutionary distance, and substitution pattern. The present study revealed the fact that shifting cultivation practice significantly affects the beetle species in terms of biodiversity pattern as well as evolutionary features. Spatiotemporal assessment of soil-plant-beetle interactions in shifting cultivation system and their influence in land degradation and ecology will be helpful in making biodiversity conservation decisions in the near future.

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale.

PubMed

Kulkarni, Purva; Dost, Mina; Bulut, Özgül Demir; Welle, Alexander; Böcker, Sebastian; Boland, Wilhelm; Svatoš, Aleš

2018-01-01

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10 μm and 1 μm lateral resolution. Intense M +● and M -● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C 21 -C 30 ), hydrocarbons (C 25 -C 33 ) and wax esters (WEs; C 44 -C 48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C 23 or C 29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Evaluation of radiological and pathological prognostic factors in surgically-treated patients with bronchoalveolar carcinoma.

PubMed

Carretta, A; Canneto, B; Calori, G; Ceresoli, G L; Campagnoli, E; Arrigoni, G; Vagani, A; Zannini, P

2001-08-01

The incidence of adenocarcinoma and bronchoalveolar carcinoma has increased in recent years. The aim of this study was to retrospectively evaluate radiological and pathological factors affecting survival in patients with bronchoalveolar carcinoma (BAC) or BAC associated with adenocarcinoma who underwent surgical treatment. From May 1988 to September 1999, 49 patients with BAC or BAC and adenocarcinoma underwent surgical treatment. Complete resection was performed in 42 patients. In these patients the impact of the following factors on survival was evaluated: stage, TNM status, radiological and pathological findings (percentage of bronchoalveolar carcinoma in the tumour, presence or absence of sclerosing and mucinous patterns, vascular invasion and lymphocytic infiltration). Twenty-nine patients were male and 20 female. Mean age was 63 years. Five-year survival was 54%. Univariate analysis of the patients who underwent complete resection demonstrated a favourable impact on survival in stages Ia and Ib (P = 0.01) and in the absence of nodal involvement (P = 0.02) and mucinous patterns (P = 0.02). Mucinous pattern was also prognostically relevant at multivariate analysis (P = 0.02). In the 27 patients with stage Ia and Ib disease, univariate analysis demonstrated that the absence of mucinous pattern (P = 0.006) and a higher percentage of BAC (P = 0.01) favourably influenced survival. The latter data were also confirmed by multivariate analysis (P = 0.01). Surgical treatment of early-stage BAC and combined BAC and adenocarcinoma is associated with favourable results. However, the definition of prognostic factors is of utmost importance to improve the results of the treatment. In our series tumours of the mucinous subtype and with a lower percentage of BAC had a worse prognosis.

Describing the complexity of systems: multivariable "set complexity" and the information basis of systems biology.

PubMed

Galas, David J; Sakhanenko, Nikita A; Skupin, Alexander; Ignac, Tomasz

2014-02-01

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity," we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information. We then present a new measure for unbiased detection of multivariable dependency, "differential interaction information." This quantity for two variables reduces to the pairwise "set complexity" previously proposed as a context-dependent measure of information in biological systems. We generalize it here to an arbitrary number of variables. Critical limiting properties of the "differential interaction information" are key to the generalization. This measure extends previous ideas about biological information and provides a more sophisticated basis for the study of complexity. The properties of "differential interaction information" also suggest new approaches to data analysis. Given a data set of system measurements, differential interaction information can provide a measure of collective dependence, which can be represented in hypergraphs describing complex system interaction patterns. We investigate this kind of analysis using simulated data sets. The conjoining of a generalized set complexity measure, multivariable dependency analysis, and hypergraphs is our central result. While our focus is on complex biological systems, our results are applicable to any complex system.

Temporal patterns of rat behaviour in the central platform of the elevated plus maze. Comparative analysis between male subjects of strains with different basal levels of emotionality.

PubMed

Casarrubea, M; Faulisi, F; Caternicchia, F; Santangelo, A; Di Giovanni, G; Benigno, A; Magnusson, M S; Crescimanno, G

2016-08-01

We have analyzed the temporal patterns of behaviour of male rats of the Wistar and DA/Han strains on the central platform of the elevated plus maze. The ethogram encompassed 10 behavioural elements. Durations, frequencies and latencies showed quantitative differences as to walking and sniffing activities. Wistar rats displayed significantly lower latency and significantly higher durations and frequencies of walking activities. DA/Han rats showed a significant increase of sniffing duration. In addition, DA/Han rats showed a significantly higher amount of time spent in the central platform. Multivariate T-pattern analysis revealed differences in the temporal organization of behaviour of the two rat strains. DA/Han rats showed (a) higher behavioural complexity and variability and (b) a significantly higher mean number of T-patterns than Wistar rats. Taken together, T-pattern analysis of behaviour in the centre of the elevated plus maze can noticeably improve the detection of subtle features of anxiety related behaviour. We suggest that T-pattern analysis could be used as sensitive tool to test the action of anxiolytic and anxiogenic manipulations. Copyright © 2015 Elsevier B.V. All rights reserved.

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

Pattern of spread and prognosis in lower limb-onset ALS

PubMed Central

TURNER, MARTIN R.; BROCKINGTON, ALICE; SCABER, JAKUB; HOLLINGER, HANNAH; MARSDEN, RACHAEL; SHAW, PAMELA J.; TALBOT, KEVIN

2011-01-01

Our objective was to establish the pattern of spread in lower limb-onset ALS (contra- versus ipsi-lateral) and its contribution to prognosis within a multivariate model. Pattern of spread was established in 109 sporadic ALS patients with lower limb-onset, prospectively recorded in Oxford and Sheffield tertiary clinics from 2001 to 2008. Survival analysis was by univariate Kaplan-Meier log-rank and multivariate Cox proportional hazards. Variables studied were time to next limb progression, site of next progression, age at symptom onset, gender, diagnostic latency and use of riluzole. Initial progression was either to the contralateral leg (76%) or ipsilateral arm (24%). Factors independently affecting survival were time to next limb progression, age at symptom onset, and diagnostic latency. Time to progression as a prognostic factor was independent of initial direction of spread. In a regression analysis of the deceased, overall survival from symptom onset approximated to two years plus the time interval for initial spread. In conclusion, rate of progression in lower limb-onset ALS is not influenced by whether initial spread is to the contralateral limb or ipsilateral arm. The time interval to this initial spread is a powerful factor in predicting overall survival, and could be used to facilitate decision-making and effective care planning. PMID:20001488

Igloo-Plot: a tool for visualization of multidimensional datasets.

PubMed

Kuntal, Bhusan K; Ghosh, Tarini Shankar; Mande, Sharmila S

2014-01-01

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/. Copyright © 2014 Elsevier Inc. All rights reserved.

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

PubMed Central

Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

2014-01-01

Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

Diversity pattern in Sesamum mutants selected for a semi-arid cropping system.

PubMed

Murty, B R; Oropeza, F

1989-02-01

Due to the complex requirements of moisture stress, substantial genetic diversity with a wide array of character combinations and effective simultaneous selection for several variables is necessary for improving the productivity and adaptation of a component crop in order for it to fit into a cropping system under semi-arid tropical conditions. Sesamum indicum L. is grown in Venezuela after rice/sorghum/or maize under such conditions. A mutation breeding program was undertaken using six locally adapted varieties to develop genotypes suitable for the above system. The diversity pattern for nine variables was assessed by multivariate analysis in 301 M4 progenies. Analysis of the characteristic roots and principal components in three methods of selection, i.e., M2 bulks (A), individual plant selection throughout (B), and selection in M3 for single variable (C), revealed differences in the pattern of variation between varieties, selection methods, and varieties x methods interactions. Method B was superior to the others and gave 17 of the 21 best M5 progenies. 'Piritu' and 'CF' varieties yielded the most productive progenies in M5 and M6. Diversity was large and selection was effective for such developmental traits as earliness and synchrony, combined with multiple disease resistance, which could be related to their importance by multivariate analyses. Considerable differences in the variety of character combinations among the high yielding. M5 progenies of 'CF' and 'Piritu' suggested possible further yield improvement. The superior response of 'Piritu' and 'CF' over other varieties in yield and adaptation was due to major changes in plant type and character associations. Multilocation testing of M5 generations revealed that the mutant progenies had a 40%-100% yield superiority over the parents; this was combined with earliness, synchrony, and multiple disease resistance, and was confirmed in the M6 generation grown on a commercial scale. This study showed that multivariate analysis is an effective tool for assessing diversity patterns, choice of appropriate variety, and selection methodology in order to make rapid progress in meeting the complex requirements of semi-arid cropping systems.

The challenges of neural mind-reading paradigms.

PubMed

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

A Western Dietary Pattern Increases Prostate Cancer Risk: A Systematic Review and Meta-Analysis

PubMed Central

Fabiani, Roberto; Minelli, Liliana; Bertarelli, Gaia; Bacci, Silvia

2016-01-01

Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. A literature search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a “Healthy pattern” and a “Western pattern”. The Healthy pattern was not related to PC risk (OR = 0.96; 95% confidence interval (CI): 0.88–1.04) while the Western pattern significantly increased it (OR = 1.34; 95% CI: 1.08–1.65). In addition, the “Carbohydrate pattern”, which was analyzed in four articles, was positively associated with a higher PC risk (OR = 1.64; 95% CI: 1.35–2.00). A significant linear trend between the Western (p = 0.011) pattern, the Carbohydrate (p = 0.005) pattern, and the increment of PC risk was observed. The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings. PMID:27754328

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

Metacarpophalangeal pattern profile analysis: useful diagnostic tool for differentiating between dyschondrosteosis, Turner syndrome, and hypochondroplasia.

PubMed

Laurencikas, E; Sävendahl, L; Jorulf, H

2006-06-01

To assess the value of the metacarpophalangeal pattern profile (MCPP) analysis as a diagnostic tool for differentiating between patients with dyschondrosteosis, Turner syndrome, and hypochondroplasia. Radiographic and clinical data from 135 patients between 1 and 51 years of age were collected and analyzed. The study included 25 patients with hypochondroplasia (HCP), 39 with dyschondrosteosis (LWD), and 71 with Turner syndrome (TS). Hand pattern profiles were calculated and compared with those of 110 normal individuals. Pearson correlation coefficient (r) and multivariate discriminant analysis were used for pattern profile analysis. Pattern variability index, a measure of dysmorphogenesis, was calculated for LWD, TS, HCP, and normal controls. Our results demonstrate that patients with LWD, TS, or HCP have distinct pattern profiles that are significantly different from each other and from those of normal controls. Discriminant analysis yielded correct classification of normal versus abnormal individuals in 84% of cases. Classification of the patients into LWD, TS, and HCP groups was successful in 75%. The correct classification rate was higher (85%) when differentiating two pathological groups at a time. Pattern variability index was not helpful for differential diagnosis of LWD, TS, and HCP. Patients with LWD, TS, or HCP have distinct MCPPs and can be successfully differentiated from each other using advanced MCPP analysis. Discriminant analysis is to be preferred over Pearson correlation coefficient because it is a more sensitive and specific technique. MCPP analysis is a helpful tool for differentiating between syndromes with similar clinical and radiological abnormalities.

Increased Prognostic Value of Query Amyloid Late Enhancement Score in Light-Chain Cardiac Amyloidosis.

PubMed

Wan, Ke; Sun, Jiayu; Han, Yuchi; Liu, Hong; Yang, Dan; Li, Weihao; Wang, Jie; Cheng, Wei; Zhang, Qing; Zeng, Zhi; Chen, Yucheng

2018-02-23

Late gadolinium enhancement (LGE) pattern is a powerful imaging biomarker for prognosis of cardiac amyloidosis. It is unknown if the query amyloid late enhancement (QALE) score in light-chain (AL) amyloidosis could provide increased prognostic value compared with LGE pattern.Methods and Results:Seventy-eight consecutive patients with AL amyloidosis underwent contrast-enhanced cardiovascular magnetic resonance imaging. Patients with cardiac involvement were grouped by LGE pattern and analyzed using QALE score. Receiver operating characteristic curve was used to identify the optimal cut-off for QALE score in predicting all-cause mortality. Survival of these patients was analyzed with the Kaplan-Meier method and multivariate Cox regression. During a median follow-up of 34 months, 53 of 78 patients died. The optimal cut-off for QALE score to predict mortality at 12-month follow-up was 9.0. On multivariate Cox analysis, QALE score ≥9 (HR, 5.997; 95% CI: 2.665-13.497; P<0.001) and log N-terminal pro-brain natriuretic peptide (HR, 1.525; 95% CI: 1.112-2.092; P=0.009) were the only 2 independent predictors of all-cause mortality. On Kaplan-Meier analysis, patients with subendocardial LGE can be further risk stratified using QALE score ≥9. The QALE scoring system provides powerful independent prognostic value in AL cardiac amyloidosis. QALE score ≥9 has added value to differentiate prognosis in AL amyloidosis patients with a subendocardial LGE pattern.

Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations

PubMed Central

Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.

2015-01-01

Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202

Factors associated with disease expression patterns in systemic lupus erythematosus patients: results from LUMINA (LXXVII), a multiethnic US cohort.

PubMed

Ugarte-Gil, M F; Pimentel-Quiroz, V R; Vilá, L M; Reveille, J D; McGwin, G; Alarcón, G S

2017-05-01

Objective The objective of this study was to determine the association of disease expression patterns with demographic and clinical characteristics in SLE. Methods Patients from a multi-ethnic SLE cohort were included. Disease expression patterns were defined as acute SLE and insidious SLE; this group was divided into those who accrued three ACR criteria and then accrued the fourth (insidious pattern A) and those who have one or two and then accrued four criteria (insidious pattern B). Disease activity was ascertained with the SLAM-R and disease damage with SLICC/ACR damage index. Variables were compared using analysis of variance for numeric variables and χ 2 for categorical variables. Multivariable analyses adjusting for possible confounders were performed. Results Six hundred and forty patients were included; the most frequent pattern was the insidious pattern B, with 415 (64.8%) patients, followed by the acute SLE group with 115 (18.0%) and the insidious pattern A with 110 (17.2%) patients. Patients from the insidious pattern A were older at diagnosis (pattern A: 39.8 vs pattern B: 36.7 vs acute: 32.4 years; p < 0.0001), more educated (13.6 vs 13.1 vs 12.1; p = 0.0008) and with a less active disease at baseline (8.8 vs 9.2 vs 10.7; p = 0.0227). Caucasian and Hispanic (Puerto Rico) ethnicities were overrepresented in this group (40.0% vs 27.7% vs 19.1% and 18.2% vs 17.1% vs 9.6%; p = 0.0003). Conclusions More insidious onset is associated with older age, Caucasian ethnicity, higher level of education, and lower disease activity than those with acute onset. However, after multivariable analyses, disease activity was not associated with any disease expression pattern.

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.

Men and women show similar survival outcome in stage IV breast cancer.

PubMed

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia.

PubMed

Horga, Guillermo; Cassidy, Clifford M; Xu, Xiaoyan; Moore, Holly; Slifstein, Mark; Van Snellenberg, Jared X; Abi-Dargham, Anissa

2016-08-01

Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

The prognosis analysis of different metastasis pattern in patients with different breast cancer subtypes: a SEER based study.

PubMed

Wang, Haiyong; Zhang, Chenyue; Zhang, Jingze; Kong, Li; Zhu, Hui; Yu, Jinming

2017-04-18

Studies on prognosis of different metastasis patterns in patients with different breast cancer subtypes (BCS) are limited. Therefore, we identified 7862 breast cancer patients with distant metastasis from 2010 to 2013 using Surveillance, Epidemiology, wand End Results (SEER) population-based data. The results showed that bone was the most common metastatic site and brain was the least common metastatic site, and the patients with HR+/HER2- occupied the highest metastasis proportion, the lowest metastasis proportion were found in HR-/HER2+ patients. Univariate and multivariate logistic regression analysis were used to analyze the association, and it was found that there were significant differences of distant metastasis patterns in patients with different BCS(different P value). Importantly, univariate and multivariate Cox regression analysis were used to analyze the prognosis. It was proven that only bone metastasis was not a prognostic factor in the HR+/HER2-, HR+/HER2+ and HR-/HER2+ subgroup (all, P > 0.05), and patients with brain metastasis had the worst cancer specific survival (CSS) in all the subgroups of BCS (all, P<0.01). Interestingly, for patients with two metastatic sites, those with bone and lung metastasis had best CSS in the HR+/HER2- (P<0.001) and HR+/HER2+ subgroups (P=0.009) However, for patients with three and four metastatic sites, there was no statistical difference in their CSS (all, P>0.05).

The prognosis analysis of different metastasis pattern in patients with different breast cancer subtypes: a SEER based study

PubMed Central

Wang, Haiyong; Zhang, Chenyue; Zhang, Jingze; Kong, Li; Zhu, Hui; Yu, Jinming

2017-01-01

Studies on prognosis of different metastasis patterns in patients with different breast cancer subtypes (BCS) are limited. Therefore, we identified 7862 breast cancer patients with distant metastasis from 2010 to 2013 using Surveillance, Epidemiology, wand End Results (SEER) population-based data. The results showed that bone was the most common metastatic site and brain was the least common metastatic site, and the patients with HR+/HER2− occupied the highest metastasis proportion, the lowest metastasis proportion were found in HR-/HER2+ patients. Univariate and multivariate logistic regression analysis were used to analyze the association, and it was found that there were significant differences of distant metastasis patterns in patients with different BCS(different P value). Importantly, univariate and multivariate Cox regression analysis were used to analyze the prognosis. It was proven that only bone metastasis was not a prognostic factor in the HR+/HER2-, HR+/HER2+ and HR-/HER2+ subgroup (all, P > 0.05), and patients with brain metastasis had the worst cancer specific survival (CSS) in all the subgroups of BCS (all, P<0.01). Interestingly, for patients with two metastatic sites, those with bone and lung metastasis had best CSS in the HR+/HER2- (P<0.001) and HR+/HER2+ subgroups (P=0.009) However, for patients with three and four metastatic sites, there was no statistical difference in their CSS (all, P>0.05). PMID:28038448

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

PubMed Central

Batal, Iyad; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Materials Approach to Dissecting Surface Responses in the Attachment Stages of Biofouling Organisms

DTIC Science & Technology

2016-04-25

their settlement behavior in regards to the coating surfaces. 5) Multivariate statistical analysis was used to examine the effect (if any) of the...applied to glass rods and were deployed in the field to evaluate settlement preferences. Canonical Analysis of Principal Coordinates were applied to...the influence of coating surface properties on the patterns in settlement observed in the field in the extension of this work over the coming year

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

PubMed

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

PubMed Central

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients.

PubMed

Wang, Yinyan; Wang, Kai; Wang, Jiangfei; Li, Shaowu; Ma, Jun; Dai, Jianping; Jiang, Tao

2016-04-01

Contrast enhancement observable on magnetic resonance (MR) images reflects the destructive features of malignant gliomas. This study aimed to investigate the relationship between radiologic patterns of tumor enhancement, extent of resection, and prognosis in patients with anaplastic gliomas (AGs). Clinical data from 268 patients with histologically confirmed AGs were retrospectively analyzed. Contrast enhancement patterns were classified based on preoperative T1-contrast MR images. Univariate and multivariate analyses were performed to evaluate the prognostic value of MR enhancement patterns on progression-free survival (PFS) and overall survival (OS). The pattern of tumor contrast enhancement was associated with the extent of surgical resection in AGs. A gross total resection was more likely to be achieved for AGs with focal enhancement than those with diffuse (p = 0.001) or ring-like (p = 0.024) enhancement. Additionally, patients with focal-enhanced AGs had a significantly longer PFS and OS than those with diffuse (log-rank, p = 0.025 and p = 0.031, respectively) or ring-like (log-rank, p = 0.008 and p = 0.011, respectively) enhanced AGs. Furthermore, multivariate analysis identified the pattern of tumor enhancement as a significant predictor of PFS (p = 0.016, hazard ratio [HR] = 1.485) and OS (p = 0.030, HR = 1.446). Our results suggested that the contrast enhancement pattern on preoperative MR images was associated with the extent of resection and predictive of survival outcomes in AG patients.

Hierarchical analysis of spatial pattern and processes of Douglas-fir forests. Ph.D. Thesis, 10 Sep. 1991 Abstract Only

NASA Technical Reports Server (NTRS)

Bradshaw, G. A.

1995-01-01

There has been an increased interest in the quantification of pattern in ecological systems over the past years. This interest is motivated by the desire to construct valid models which extend across many scales. Spatial methods must quantify pattern, discriminate types of pattern, and relate hierarchical phenomena across scales. Wavelet analysis is introduced as a method to identify spatial structure in ecological transect data. The main advantage of the wavelet transform over other methods is its ability to preserve and display hierarchical information while allowing for pattern decomposition. Two applications of wavelet analysis are illustrated, as a means to: (1) quantify known spatial patterns in Douglas-fir forests at several scales, and (2) construct spatially-explicit hypotheses regarding pattern generating mechanisms. Application of the wavelet variance, derived from the wavelet transform, is developed for forest ecosystem analysis to obtain additional insight into spatially-explicit data. Specifically, the resolution capabilities of the wavelet variance are compared to the semi-variogram and Fourier power spectra for the description of spatial data using a set of one-dimensional stationary and non-stationary processes. The wavelet cross-covariance function is derived from the wavelet transform and introduced as a alternative method for the analysis of multivariate spatial data of understory vegetation and canopy in Douglas-fir forests of the western Cascades of Oregon.

Multivariate statistical analysis of stream-sediment geochemistry in the Grazer Paläozoikum, Austria

USGS Publications Warehouse

Weber, L.; Davis, J.C.

1990-01-01

The Austrian reconnaissance study of stream-sediment composition — more than 30000 clay-fraction samples collected over an area of 40000 km2 — is summarized in an atlas of regional maps that show the distributions of 35 elements. These maps, rich in information, reveal complicated patterns of element abundance that are difficult to compare on more than a small number of maps at one time. In such a study, multivariate procedures such as simultaneous R-Q mode components analysis may be helpful. They can compress a large number of variables into a much smaller number of independent linear combinations. These composite variables may be mapped and relationships sought between them and geological properties. As an example, R-Q mode components analysis is applied here to the Grazer Paläozoikum, a tectonic unit northeast of the city of Graz, which is composed of diverse lithologies and contains many mineral deposits.

The Assessment of Neurological Systems with Functional Imaging

ERIC Educational Resources Information Center

Eidelberg, David

2007-01-01

In recent years a number of multivariate approaches have been introduced to map neural systems in health and disease. In this review, we focus on spatial covariance methods applied to functional imaging data to identify patterns of regional activity associated with behavior. In the rest state, this form of network analysis can be used to detect…

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

NASA Astrophysics Data System (ADS)

Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

2011-11-01

The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution.

PubMed

Renaud, Sabrina; Dufour, Anne-Béatrice; Hardouin, Emilie A; Ledevin, Ronan; Auffray, Jean-Christophe

2015-01-01

Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing 'better' than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.

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…

Coordination patterns related to high clinical performance in a simulated anesthetic crisis.

PubMed

Manser, Tanja; Harrison, Thomas Kyle; Gaba, David M; Howard, Steven K

2009-05-01

Teamwork is an integral component in the delivery of safe patient care. Several studies highlight the importance of effective teamwork and the need for teams to respond dynamically to changing task requirements, for example, during crisis situations. In this study, we address one of the many facets of "effective teamwork" in medical teams by investigating coordination patterns related to high performance in the management of a simulated malignant hyperthermia (MH) scenario. We hypothesized that (a) anesthesia crews dynamically adapt their work and coordination patterns to the occurrence of a simulated MH crisis and that (b) crews with higher clinical performance scores (based on a time-based scoring system for critical MH treatment steps) exhibit different coordination patterns. This observational study investigated differences in work and coordination patterns of 24 two-person anesthesia crews in a simulated MH scenario. Clinical and coordination behavior were coded using a structured observation system consisting of 36 mutually exclusive observation categories for clinical activities, coordination activities, teaching, and other communication. Clinical performance scores for treating the simulated episode of MH were calculated using a time-based scoring system for critical treatment steps. Coordination patterns in response to the occurrence of a crisis situation were analyzed using multivariate analysis of variance and the relationship between coordination patterns and clinical performance was investigated using hierarchical regression analyses. Qualitative analyses of the three highest and lowest performing crews were conducted to complement the quantitative analysis. First, a multivariate analysis of variance revealed statistically significant changes in the proportion of time spent on clinical and coordination activities once the MH crisis was declared (F [5,19] = 162.81, P < 0.001, eta(p)(2) = 0.98). Second, hierarchical regression analyses controlling for the effects of cognitive aid use showed that higher performing anesthesia crews exhibit statistically significant less task distribution (beta = -0.539, P < 0.01) and significantly more situation assessment (beta = 0.569, P < 0.05). Additional qualitative video analysis revealed, for example, that lower scoring crews were more likely to split into subcrews (i.e., both anesthesiologists worked with other members of the perioperative team without maintaining a shared plan among the two-person anesthesia crew). Our results of the relationship of coordination patterns and clinical performance will inform future research on adaptive coordination in medical teams and support the development of specific training to improve team coordination and performance.

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

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.

Genetic structure of Quechua-speakers of the Central Andes and geographic patterns of gene frequencies in South Amerindian populations.

PubMed

Luiselli, D; Simoni, L; Tarazona-Santos, E; Pastor, S; Pettener, D

2000-09-01

A sample of 141 Quechua-speaking individuals of the population of Tayacaja, in the Peruvian Central Andes, was typed for the following 16 genetic systems: ABO, Rh, MNSs, P, Duffy, AcP1, EsD, GLOI, PGM1, AK, 6-PGD, Hp, Gc, Pi, C3, and Bf. The genetic structure of the population was analyzed in relation to the allele frequencies available for other South Amerindian populations, using a combination of multivariate and multivariable techniques. Spatial autocorrelation analysis was performed independently for 13 alleles to identify patterns of gene flow in South America as a whole and in more specific geographic regions. We found a longitudinal cline for the AcP1*a and EsD*1 alleles which we interpreted as the result of an ancient longitudinal expansion of a putative ancestral population of modern Amerindians. Monmonnier's algorithm, used to identify areas of sharp genetic discontinuity, suggested a clear east-west differentiation of native South American populations, which was confirmed by analysis of the distribution of genetic distances. We suggest that this pattern of genetic structures is the consequence of the independent peopling of western and eastern South America or to low levels of gene flow between these regions, related to different environmental and demographic histories. Copyright 2000 Wiley-Liss, Inc.

Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.

PubMed

Zhang, Sheng; Hu, Sien; Sinha, Rajita; Potenza, Marc N; Malison, Robert T; Li, Chiang-Shan R

2016-01-01

Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA) to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD) from 100 demographically matched healthy control individuals (HC). We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001), superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test). Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence.

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

PubMed

Jakubowska, Małgorzata; Sordoń, Wanda; Ciepiela, Filip

2016-07-15

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

PubMed

Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert; Angstadt, Michael; Liberzon, Israel; Phan, K Luan; Scott, Clayton

2013-11-01

Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning. Copyright © 2013 Elsevier Inc. All rights reserved.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A comparative analysis of primary and secondary Gleason pattern predictive ability for positive surgical margins after radical prostatectomy.

PubMed

Sfoungaristos, S; Kavouras, A; Kanatas, P; Polimeros, N; Perimenis, P

2011-01-01

To compare the predictive ability of primary and secondary Gleason pattern for positive surgical margins in patients with clinically localized prostate cancer and a preoperative Gleason score ≤ 6. A retrospective analysis of the medical records of patients undergone a radical prostatectomy between January 2005 and October 2010 was conducted. Patients' age, prostate volume, preoperative PSA, biopsy Gleason score, the 1st and 2nd Gleason pattern were entered a univariate and multivariate analysis. The 1st and 2nd pattern were tested for their ability to predict positive surgical margins using receiver operating characteristic curves. Positive surgical margins were noticed in 56 cases (38.1%) out of 147 studied patients. The 2nd pattern was significantly greater in those with positive surgical margins while the 1st pattern was not significantly different between the 2 groups of patients. ROC analysis revealed that area under the curve was 0.53 (p=0.538) for the 1st pattern and 0.60 (p=0.048) for the 2nd pattern. Concerning the cases with PSA <10 ng/ml, it was also found that only the 2nd pattern had a predictive ability (p=0.050). When multiple logistic regression analysis was conducted it was found that the 2nd pattern was the only independent predictor. The second Gleason pattern was found to be of higher value than the 1st one for the prediction of positive surgical margins in patients with preoperative Gleason score ≤ 6 and this should be considered especially when a neurovascular bundle sparing radical prostatectomy is planned, in order not to harm the oncological outcome.

Profiling and classification of French propolis by combined multivariate data analysis of planar chromatograms and scanning direct analysis in real time mass spectra.

PubMed

Chasset, Thibaut; Häbe, Tim T; Ristivojevic, Petar; Morlock, Gertrud E

2016-09-23

Quality control of propolis is challenging, as it is a complex natural mixture of compounds, and thus, very difficult to analyze and standardize. Shown on the example of 30 French propolis samples, a strategy for an improved quality control was demonstrated in which high-performance thin-layer chromatography (HPTLC) fingerprints were evaluated in combination with selected mass signals obtained by desorption-based scanning mass spectrometry (MS). The French propolis sample extracts were separated by a newly developed reversed phase (RP)-HPTLC method. The fingerprints obtained by two different detection modes, i.e. after (1) derivatization and fluorescence detection (FLD) at UV 366nm and (2) scanning direct analysis in real time (DART)-MS, were analyzed by multivariate data analysis. Thus, RP-HPTLC-FLD and RP-HPTLC-DART-MS fingerprints were explored and the best classification was obtained using both methods in combination with pattern recognition techniques, such as principal component analysis. All investigated French propolis samples were divided in two types and characteristic patterns were observed. Phenolic compounds such as caffeic acid, p-coumaric acid, chrysin, pinobanksin, pinobanksin-3-acetate, galangin, kaempferol, tectochrysin and pinocembrin were identified as characteristic marker compounds of French propolis samples. This study expanded the research on the European poplar type of propolis and confirmed the presence of two botanically different types of propolis, known as the blue and orange types. Copyright © 2016 Elsevier B.V. All rights reserved.

Dentists' self-perceived role in offering tobacco cessation services: results from a nationally representative survey, United States, 2010-2011.

PubMed

Jannat-Khah, Deanna P; McNeely, Jennifer; Pereyra, Margaret R; Parish, Carrigan; Pollack, Harold A; Ostroff, Jamie; Metsch, Lisa; Shelley, Donna R

2014-11-06

Dental visits represent an opportunity to identify and help patients quit smoking, yet dental settings remain an untapped venue for treatment of tobacco dependence. The purpose of this analysis was to assess factors that may influence patterns of tobacco-use-related practice among a national sample of dental providers. We surveyed a representative sample of general dentists practicing in the United States (N = 1,802). Multivariable analysis was used to assess correlates of adherence to tobacco use treatment guidelines and to analyze factors that influence providers' willingness to offer tobacco cessation assistance if reimbursed for this service. More than 90% of dental providers reported that they routinely ask patients about tobacco use, 76% counsel patients, and 45% routinely offer cessation assistance, defined as referring patients for cessation counseling, providing a cessation prescription, or both. Results from multivariable analysis indicated that cessation assistance was associated with having a practice with 1 or more hygienists, having a chart system that includes a tobacco use question, having received training on treating tobacco dependence, and having positive attitudes toward treating tobacco use. Providers who did not offer assistance but who reported that they would change their practice patterns if sufficiently reimbursed were more likely to be in a group practice, treat patients insured through Medicaid, and have positive attitudes toward treating tobacco dependence. Findings indicate the potential benefit of increasing training opportunities and promoting system changes to increase involvement of dental providers in conducting tobacco use treatment. Reimbursement models should be tested to assess the effect on dental provider practice patterns.

Multivariate analysis of flow cytometric data using decision trees.

PubMed

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

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.

Eating patterns and energy and nutrient intakes of US women.

PubMed

Haines, P S; Hungerford, D W; Popkin, B M; Guilkey, D K

1992-06-01

A longitudinal multivariate analysis was used to determine whether differences in energy and nutrient intakes were present for women classified into different eating patterns. Ten multidimensional eating patterns were created based on the proportion of energy consumed at home and at seven away-from-home locations. Data were from 1,120 women aged 19 through 50 years who were surveyed up to six times over a 1-year period as part of the 1985 Continuing Survey of Food Intake by Individuals, US Department of Agriculture. Data from 5,993 days were analyzed. To examine differences in energy and nutrient intakes, longitudinal multivariate analyses were used to control for eating pattern and factors such as demographics, season, and day of week. Younger women in the Fast Food eating pattern consumed the greatest intakes of energy, total fat, saturated fat, cholesterol, and sodium. Well-educated, higher-income women in the Restaurant pattern consumed diets with the highest overall fat density. Nutrient densities for dietary fiber, calcium, vitamin C, and folacin were particularly low in away-from-home eating patterns. In contrast, moderately educated, middle-aged and middle-income women in the Home Mixed eating pattern (70% at home, 30% away from home) consumed the most healthful diets. We conclude that knowledge of demographics such as income and education is not enough to target dietary interventions. Rather, educational efforts must consider both demographics and the location of away-from-home eating. This will allow development of behavioral change strategies that consider food choices dictated by the eating environment as well as personal knowledge and attitude factors related to adoption of healthful food choices.

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

PubMed

Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara

2017-08-02

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

A Model-Based Analysis of Chemical and Temporal Patterns of Cuticular Hydrocarbons in Male Drosophila melanogaster

PubMed Central

Kent, Clement; Azanchi, Reza; Smith, Ben; Chu, Adrienne; Levine, Joel

2007-01-01

Drosophila Cuticular Hydrocarbons (CH) influence courtship behaviour, mating, aggregation, oviposition, and resistance to desiccation. We measured levels of 24 different CH compounds of individual male D. melanogaster hourly under a variety of environmental (LD/DD) conditions. Using a model-based analysis of CH variation, we developed an improved normalization method for CH data, and show that CH compounds have reproducible cyclic within-day temporal patterns of expression which differ between LD and DD conditions. Multivariate clustering of expression patterns identified 5 clusters of co-expressed compounds with common chemical characteristics. Turnover rate estimates suggest CH production may be a significant metabolic cost. Male cuticular hydrocarbon expression is a dynamic trait influenced by light and time of day; since abundant hydrocarbons affect male sexual behavior, males may present different pheromonal profiles at different times and under different conditions. PMID:17896002

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Fluorescent discrimination between traces of chemical warfare agents and their mimics.

PubMed

Díaz de Greñu, Borja; Moreno, Daniel; Torroba, Tomás; Berg, Alexander; Gunnars, Johan; Nilsson, Tobias; Nyman, Rasmus; Persson, Milton; Pettersson, Johannes; Eklind, Ida; Wästerby, Pär

2014-03-19

An array of fluorogenic probes is able to discriminate between nerve agents, sarin, soman, tabun, VX and their mimics, in water or organic solvent, by qualitative fluorescence patterns and quantitative multivariate analysis, thus making the system suitable for the in-the-field detection of traces of chemical warfare agents as well as to differentiate between the real nerve agents and other related compounds.

Major Dietary Patterns in Relation to General and Central Obesity among Chinese Adults.

PubMed

Yu, Canqing; Shi, Zumin; Lv, Jun; Du, Huaidong; Qi, Lu; Guo, Yu; Bian, Zheng; Chang, Liang; Tang, Xuefeng; Jiang, Qilian; Mu, Huaiyi; Pan, Dongxia; Chen, Junshi; Chen, Zhengming; Li, Liming

2015-07-15

Limited evidence exists for the association between diet pattern and obesity phenotypes among Chinese adults. In the present study, we analyzed the cross-sectional data from 474,192 adults aged 30-79 years from the China Kadoorie Biobank baseline survey. Food consumption was collected by an interviewer-administered questionnaire. Three dietary patterns were extracted by factor analysis combined with cluster analysis. After being adjusted for potential confounders, individuals following a traditional southern dietary pattern had the lowest body mass index (BMI) and waist circumference (WC); the Western/new affluence dietary pattern had the highest BMI; and the traditional northern dietary pattern had the highest WC. Compared to the traditional southern dietary pattern in multivariable adjusted logistic models, individuals following a Western/new affluence dietary pattern had a significantly increased risk of general obesity (prevalence ratio (PR): 1.06, 95% confidence interval (CI): 1.03-1.08) and central obesity (PR: 1.07, 95% CI: 1.06-1.08). The corresponding risks for the traditional northern dietary pattern were 1.05 (1.02-1.09) and 1.17 (1.25-1.18), respectively. In addition, the associations were modified by lifestyle behaviors, and the combined effects with alcohol drinking, tobacco smoking, and physical activity were analyzed. Further prospective studies are needed to elucidate the diet-obesity relationships.

Exploring Geographical Differentiation of the Hoelen Medicinal Mushroom, Wolfiporia extensa (Agaricomycetes), Using Fourier-Transform Infrared Spectroscopy Combined with Multivariate Analysis.

PubMed

Li, Yan; Zhang, Ji; Zhao, Yanli; Liu, Honggao; Wang, Yuanzhong; Jin, Hang

2016-01-01

In this study the geographical differentiation of dried sclerotia of the medicinal mushroom Wolfiporia extensa, obtained from different regions in Yunnan Province, China, was explored using Fourier-transform infrared (FT-IR) spectroscopy coupled with multivariate data analysis. The FT-IR spectra of 97 samples were obtained for wave numbers ranging from 4000 to 400 cm-1. Then, the fingerprint region of 1800-600 cm-1 of the FT-IR spectrum, rather than the full spectrum, was analyzed. Different pretreatments were applied on the spectra, and a discriminant analysis model based on the Mahalanobis distance was developed to select an optimal pretreatment combination. Two unsupervised pattern recognition procedures- principal component analysis and hierarchical cluster analysis-were applied to enhance the authenticity of discrimination of the specimens. The results showed that excellent classification could be obtained after optimizing spectral pretreatment. The tested samples were successfully discriminated according to their geographical locations. The chemical properties of dried sclerotia of W. extensa were clearly dependent on the mushroom's geographical origins. Furthermore, an interesting finding implied that the elevations of collection areas may have effects on the chemical components of wild W. extensa sclerotia. Overall, this study highlights the feasibility of FT-IR spectroscopy combined with multivariate data analysis in particular for exploring the distinction of different regional W. extensa sclerotia samples. This research could also serve as a basis for the exploitation and utilization of medicinal mushrooms.

Tailored multivariate analysis for modulated enhanced diffraction

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

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

Multivariate analysis of mixed contaminants (PAHs and heavy metals) at manufactured gas plant site soils.

PubMed

Thavamani, Palanisami; Megharaj, Mallavarapu; Naidu, Ravi

2012-06-01

Principal component analysis (PCA) was used to provide an overview of the distribution pattern of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in former manufactured gas plant (MGP) site soils. PCA is the powerful multivariate method to identify the patterns in data and expressing their similarities and differences. Ten PAHs (naphthalene, acenapthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]pyrene) and four toxic heavy metals - lead (Pb), cadmium (Cd), chromium (Cr) and zinc (Zn) - were detected in the site soils. PAH contamination was contributed equally by both low and high molecular weight PAHs. PCA was performed using the varimax rotation method in SPSS, 17.0. Two principal components accounting for 91.7% of the total variance was retained using scree test. Principle component 1 (PC1) substantially explained the dominance of PAH contamination in the MGP site soils. All PAHs, except anthracene, were positively correlated in PC1. There was a common thread in high molecular weight PAHs loadings, where the loadings were inversely proportional to the hydrophobicity and molecular weight of individual PAHs. Anthracene, which was less correlated with other individual PAHs, deviated well from the origin which can be ascribed to its lower toxicity and different origin than its isomer phenanthrene. Among the four major heavy metals studied in MGP sites, Pb, Cd and Cr were negatively correlated in PC1 but showed strong positive correlation in principle component 2 (PC2). Although metals may not have originated directly from gaswork processes, the correlation between PAHs and metals suggests that the materials used in these sites may have contributed to high concentrations of Pb, Cd, Cr and Zn. Thus, multivariate analysis helped to identify the sources of PAHs, heavy metals and their association in MGP site, and thereby better characterise the site risk, which would not be possible if one uses chemical analysis alone.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach.

PubMed

Moya, Claudio E; Raiber, Matthias; Taulis, Mauricio; Cox, Malcolm E

2015-03-01

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na-Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na-HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous-Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls. Copyright © 2014 Elsevier B.V. All rights reserved.

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study

PubMed Central

Zhang, Meilin; Zhu, Yufeng; Li, Ping; Chang, Hong; Wang, Xuan; Liu, Weiqiao; Zhang, Yuwen; Huang, Guowei

2015-01-01

Few studies have examined the association between Asian dietary pattern and prediabetes, in particular, the Chinese diet. We conducted a cross-sectional study to identify dietary patterns associated with impaired fasting glucose (IFG) which considered a state of prediabetes in Chinese men. The study included 1495 Chinese men aged 20 to 75 years. Information about diet was obtained using an 81-item food frequency questionnaire (FFQ), and 21 predefined food groups were considered in a factor analysis. Three dietary patterns were generated by factor analysis: (1) a vegetables-fruits pattern; (2) an animal offal-dessert pattern; and (3) a white rice-red meat pattern. The multivariate-adjusted odds ratio (OR) of IFG for the highest tertile of the animal offal-dessert pattern in comparison with the lowest tertile was 3.15 (95% confidence intervals (CI): 1.87–5.30). The vegetables-fruits dietary pattern was negatively associated with the risk of IFG, but a significant association was observed only in the third tertile. There was no significant association between IFG and the white rice-red meat pattern. Our findings indicated that the vegetables-fruits dietary pattern was inversely associated with IFG, whereas the animal offal-dessert pattern was associated with an increased risk of IFG in Chinese men. Further prospective studies are needed to elucidate the diet-prediabetes relationships. PMID:26402695

Associations between Dietary Patterns and Impaired Fasting Glucose in Chinese Men: A Cross-Sectional Study.

PubMed

Zhang, Meilin; Zhu, Yufeng; Li, Ping; Chang, Hong; Wang, Xuan; Liu, Weiqiao; Zhang, Yuwen; Huang, Guowei

2015-09-21

Few studies have examined the association between Asian dietary pattern and prediabetes, in particular, the Chinese diet. We conducted a cross-sectional study to identify dietary patterns associated with impaired fasting glucose (IFG) which considered a state of prediabetes in Chinese men. The study included 1495 Chinese men aged 20 to 75 years. Information about diet was obtained using an 81-item food frequency questionnaire (FFQ), and 21 predefined food groups were considered in a factor analysis. Three dietary patterns were generated by factor analysis: (1) a vegetables-fruits pattern; (2) an animal offal-dessert pattern; and (3) a white rice-red meat pattern. The multivariate-adjusted odds ratio (OR) of IFG for the highest tertile of the animal offal-dessert pattern in comparison with the lowest tertile was 3.15 (95% confidence intervals (CI): 1.87-5.30). The vegetables-fruits dietary pattern was negatively associated with the risk of IFG, but a significant association was observed only in the third tertile. There was no significant association between IFG and the white rice-red meat pattern. Our findings indicated that the vegetables-fruits dietary pattern was inversely associated with IFG, whereas the animal offal-dessert pattern was associated with an increased risk of IFG in Chinese men. Further prospective studies are needed to elucidate the diet-prediabetes relationships.

Multivariate analysis of matrix-assisted laser desorption/ionization mass spectrometric data related to glycoxidation products of human globins in nephropathic patients.

PubMed

Lapolla, Annunziata; Ragazzi, Eugenio; Andretta, Barbara; Fedele, Domenico; Tubaro, Michela; Seraglia, Roberta; Molin, Laura; Traldi, Pietro

2007-06-01

To clarify the possible pathogenetic role of oxidation products originated from the glycation of proteins, human globins from nephropathic patients have been studied by matrix-assisted laser desorption/ionization mass spectrometry (MALDI), revealing not only unglycated and monoglycated globins, but also a series of different species. For the last ones, structural assignments were tentatively done on the basis of observed masses and expectations for the Maillard reaction pattern. Consequently, they must be considered only propositive, and the discussion which will follow must be considered in this view. In our opinion this approach does not seem to compromise the intended diagnostic use of the data because distinctions are valid even if the assignments are uncertain. We studied nine healthy subjects and 19 nephropathic patients and processed the data obtained from the MALDI spectra using a multivariate analysis. Our results showed that multivariate analytical techniques enable differential aspects of the profile of molecular species to be identified in the blood of end stage nephropathic patients. A correct grouping can be achieved by principal component analysis (PCA) and the results suggest that several products involved in carbonyl stress exist in nephropathic patients. These compounds may have a relevant role as specific markers of the pathological state.

Multielement analysis of Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and multivariate statistics.

PubMed

Taylor, Vivien F; Longerich, Henry P; Greenough, John D

2003-02-12

Trace element fingerprints were deciphered for wines from Canada's two major wine-producing regions, the Okanagan Valley and the Niagara Peninsula, for the purpose of examining differences in wine element composition with region of origin and identifying elements important to determining provenance. Analysis by ICP-MS allowed simultaneous determination of 34 trace elements in wine (Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U) at low levels of detection, and patterns in trace element concentrations were deciphered by multivariate statistical analysis. The two regions were discriminated with 100% accuracy using 10 of these elements. Differences in soil chemistry between the Niagara and Okanagan vineyards were evident, without a good correlation between soil and wine composition. The element Sr was found to be a good indicator of provenance and has been reported in fingerprinting studies of other regions.

SPICE: exploration and analysis of post-cytometric complex multivariate datasets.

PubMed

Roederer, Mario; Nozzi, Joshua L; Nason, Martha C

2011-02-01

Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley-Liss, Inc.

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Incidence of cervical lymph node metastasis and its association with outcomes in patients with adenoid cystic carcinoma. An international collaborative study

PubMed Central

Amit, Moran; Binenbaum, Yoav; Sharma, Kanika; Ramer, Naomi; Ramer, Ilana; Agbetoba, Abib; Glick, Joelle; Yang, Xinjie; Lei, Delin; Bjørndal, Kristine; Godballe, Christian; Mücke, Thomas; Wolff, Klaus-Dietrich; Fliss, Dan; Eckardt, André M.; Copelli, Chiara; Sesenna, Enrico; Palmer, Frank; Ganly, Ian; Patel, Snehal; Gil, Ziv

2016-01-01

Background The patterns of regional metastasis in adenoid cystic carcinoma (ACC) of the head and neck and its association with outcome is not established. Methods We conducted a retrospective multicentered multivariate analysis of 270 patients who underwent neck dissection. Results The incidence rate of neck metastases was 29%. The rate observed in the oral cavity is 37%, and in the major salivary glands is 19% (p = .001). The rate of occult nodal metastases was 17%. Overall 5-year survival rates were 44% in patients undergoing therapeutic neck dissections, and 65% and 73% among those undergoing elective neck dissections, with and without nodal metastases, respectively (p = .017). Multivariate analysis revealed that the primary site, nodal classification, and margin status were independent predictors of survival. Conclusion Our findings support the consideration of elective neck treatment in patients with ACC of the oral cavity. PMID:25060927

Multivariate analysis and geochemical approach for assessment of metal pollution state in sediment cores.

PubMed

Jamshidi-Zanjani, Ahmad; Saeedi, Mohsen

2017-07-01

Vertical distribution of metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, Cd, and Li) in four sediment core samples (C 1 , C 2 , C 3 , and C 4 ) from Anzali international wetland located southwest of the Caspian Sea was examined. Background concentration of each metal was calculated according to different statistical approaches. The results of multivariate statistical analysis showed that Fe and Mn might have significant role in the fate of Ni and Zn in sediment core samples. Different sediment quality indexes were utilized to assess metal pollution in sediment cores. Moreover, a new sediment quality index named aggregative toxicity index (ATI) based on sediment quality guidelines (SQGs) was developed to assess the degree of metal toxicity in an aggregative manner. The increasing pattern of metal pollution and their toxicity degree in upper layers of core samples indicated increasing effects of anthropogenic sources in the study area.

An image analysis of TLC patterns for quality control of saffron based on soil salinity effect: A strategy for data (pre)-processing.

PubMed

Sereshti, Hassan; Poursorkh, Zahra; Aliakbarzadeh, Ghazaleh; Zarre, Shahin; Ataolahi, Sahar

2018-01-15

Quality of saffron, a valuable food additive, could considerably affect the consumers' health. In this work, a novel preprocessing strategy for image analysis of saffron thin layer chromatographic (TLC) patterns was introduced. This includes performing a series of image pre-processing techniques on TLC images such as compression, inversion, elimination of general baseline (using asymmetric least squares (AsLS)), removing spots shift and concavity (by correlation optimization warping (COW)), and finally conversion to RGB chromatograms. Subsequently, an unsupervised multivariate data analysis including principal component analysis (PCA) and k-means clustering was utilized to investigate the soil salinity effect, as a cultivation parameter, on saffron TLC patterns. This method was used as a rapid and simple technique to obtain the chemical fingerprints of saffron TLC images. Finally, the separated TLC spots were chemically identified using high-performance liquid chromatography-diode array detection (HPLC-DAD). Accordingly, the saffron quality from different areas of Iran was evaluated and classified. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex

PubMed Central

Cole, Michael W.; Etzel, Joset A.; Zacks, Jeffrey M.; Schneider, Walter; Braver, Todd S.

2011-01-01

Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances. PMID:22125519

Adjuvant Chemotherapy Improves the Probability of Freedom From Recurrence in Patients With Resected Stage IB Lung Adenocarcinoma.

PubMed

Hung, Jung-Jyh; Wu, Yu-Chung; Chou, Teh-Ying; Jeng, Wen-Juei; Yeh, Yi-Chen; Hsu, Wen-Hu

2016-04-01

The benefit of adjuvant chemotherapy remains controversial for patients with stage IB non-small-cell lung cancer (NSCLC). This study investigated the effect of adjuvant chemotherapy and the predictors of benefit from adjuvant chemotherapy in patients with stage IB lung adenocarcinoma. A total of 243 patients with completely resected pathologic stage IB lung adenocarcinoma were included in the study. Predictors of the benefits of improved overall survival (OS) or probability of freedom from recurrence (FFR) from platinum-based adjuvant chemotherapy in patients with resected stage IB lung adenocarcinoma were investigated. Among the 243 patients, 70 (28.8%) had received platinum-based doublet adjuvant chemotherapy. A micropapillary/solid-predominant pattern (versus an acinar/papillary-predominant pattern) was a significantly worse prognostic factor for probability of FFR (p = 0.033). Although adjuvant chemotherapy (versus surgical intervention alone) was not a significant prognostic factor for OS (p = 0.303), it was a significant prognostic factor for a better probability of FFR (p = 0.029) on multivariate analysis. In propensity-score-matched pairs, there was no significant difference in OS between patients who received adjuvant chemotherapy and those who did not (p = 0.386). Patients who received adjuvant chemotherapy had a significantly better probability of FFR than those who did not (p = 0.043). For patients with a predominantly micropapillary/solid pattern, adjuvant chemotherapy (p = 0.033) was a significant prognostic factor for a better probability of FFR on multivariate analysis. Adjuvant chemotherapy is a favorable prognostic factor for the probability of FFR in patients with stage IB lung adenocarcinoma, particularly in those with a micropapillary/solid-predominant pattern. Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples.

PubMed

Doan, Nhat Trung; Engvig, Andreas; Zaske, Krystal; Persson, Karin; Lund, Martina Jonette; Kaufmann, Tobias; Cordova-Palomera, Aldo; Alnæs, Dag; Moberget, Torgeir; Brækhus, Anne; Barca, Maria Lage; Nordvik, Jan Egil; Engedal, Knut; Agartz, Ingrid; Selbæk, Geir; Andreassen, Ole A; Westlye, Lars T

2017-09-01

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Dietary Patterns, n-3 Fatty Acids Intake from Seafood and High Levels of Anxiety Symptoms during Pregnancy: Findings from the Avon Longitudinal Study of Parents and Children

PubMed Central

Vaz, Juliana dos Santos; Kac, Gilberto; Emmett, Pauline; Davis, John M.; Golding, Jean; Hibbeln, Joseph R.

2013-01-01

Background Little is known about relationships between dietary patterns, n-3 polyunsaturated fatty acids (PUFA) intake and excessive anxiety during pregnancy. Objective To examine whether dietary patterns and n-3 PUFA intake from seafood are associated with high levels of anxiety during pregnancy. Design Pregnant women enrolled from 1991–1992 in ALSPAC (n 9,530). Dietary patterns were established from a food frequency questionnaire using principal component analysis. Total intake of n-3 PUFA (grams/week) from seafood was also examined. Symptoms of anxiety were measured at 32 weeks of gestation with the Crown-Crisp Experiential Index; scores ≥9 corresponding to the 85th percentile was defined as high anxiety symptoms. Multivariate logistic regression models were used to estimate the OR and 95% CI, adjusted by socioeconomic and lifestyle variables. Results Multivariate results showed that women in the highest tertile of the health-conscious (OR 0.77; 0.65–0.93) and the traditional (OR 0.84; 0.73–0.97) pattern scores were less likely to report high levels of anxiety symptoms. Women in the highest tertile of the vegetarian pattern score (OR 1.25; 1.08–1.44) were more likely to have high levels of anxiety, as well as those with no n-3 PUFA intake from seafood (OR 1.53; 1.25–1.87) when compared with those with intake of >1.5 grams/week. Conclusions The present study provides evidence of a relationship between dietary patterns, fish intake or n-3 PUFA intake from seafood and symptoms of anxiety in pregnancy, and suggests that dietary interventions could be used to reduce high anxiety symptoms during pregnancy. PMID:23874437

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

The aim of this paper was to review the types of approaches currently utilized in the analysis of multi-country survey data, specifically focusing on design and modeling issues with a focus on analyses of significant multi-country surveys published in 2010. A systematic search strategy was used to identify the 10 multi-country surveys and the articles published from them in 2010. The surveys were selected to reflect diverse topics and foci; and provide an insight into analytic approaches across research themes. The search identified 159 articles appropriate for full text review and data extraction. The analyses adopted in the multi-country surveys can be broadly classified as: univariate/bivariate analyses, and multivariate/multivariable analyses. Multivariate/multivariable analyses may be further divided into design- and model-based analyses. Of the 159 articles reviewed, 129 articles used model-based analysis, 30 articles used design-based analyses. Similar patterns could be seen in all the individual surveys. While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.

Immunohistochemistry Analysis of CD44, EGFR, and p16 in Oral Cavity and Oropharyngeal Squamous Cell Carcinoma.

PubMed

Cohen, Erin R; Reis, Isildinha M; Gomez, Carmen; Pereira, Lutecia; Freiser, Monika E; Hoosien, Gia; Franzmann, Elizabeth J

2017-08-01

Objectives We analyze the relationship between CD44, epidermal growth factor receptor (EGFR), and p16 expression in oral cavity and oropharyngeal cancers in a diverse population. We also describe whether particular patterns of staining are associated with progression-free survival and overall survival. Study Design Prospective study, single-blind to pathologist and laboratory technologist. Setting Hospital based. Subjects and Methods Immunohistochemistry, comprising gross staining and cellular expression, was performed and interpreted in a blinded fashion on 24 lip/oral cavity and 40 oropharyngeal cancer specimens collected between 2007 and 2012 from participants of a larger study. Information on overall survival and progression-free survival was obtained from medical records. Results Nineteen cases were clinically p16 positive, 16 of which were oropharyngeal. Oral cavity lesions were more likely to exhibit strong CD44 membrane staining ( P = .0002). Strong CD44 membrane and strong EGFR membrane and/or cytoplasmic staining were more common in p16-negative cancers ( P = .006). Peripheral/mixed gross p16 staining pattern was associated with worse survival than the universal staining on univariate and multivariate analyses ( P = .006, P = .030). This held true when combining gross and cellular localization for p16. For CD44, universal gross staining demonstrated poorer overall survival compared with the peripheral/mixed group ( P = .039). CD44 peripheral/mixed group alone and when combined with universal p16 demonstrated the best survival on multivariate analysis ( P = .010). Conclusion In a diverse population, systematic analysis applying p16, CD44, and EGFR gross staining and cellular localization on immunohistochemistry demonstrates distinct patterns that may have prognostic potential exceeding current methods. Larger studies are warranted to investigate these findings further.

The Pattern of Female Nuptiality in Oman

PubMed Central

Islam, M. Mazharul; Dorvlo, Atsu S.; Al-Qasmi, Ahmed M.

2013-01-01

Objectives: The purpose of this study was to examine Omani patterns of female nuptiality, including the timing of marriage and determinants of age at a woman’s first marriage. Methods: The study utilised data from the 2000 Oman National Health Survey. Univariate, bivariate, and multivariate statistical methods, including logistic regression analysis, were used for data analysis. Results: One of the most important aspects of the marriage pattern in Oman is the high prevalence of consanguineous marriages, as more than half (52%) of the total marriages in Oman are consanguineous. First cousin unions are the most common type of consanguineous unions, constituting 39% of all marriages and 75% of all consanguineous marriages. About 11% of the marriages are polygynous. Early and universal marriage is still highly prevalent in Oman. Three-quarters (75%) of married women respondents aged 20–44 years were married by age 20, with their median age at their first wedding being 16 years. However, women’s average age upon marriage is gradually increasing. The change is especially apparent in more recent marriages or among younger cohorts of women, and for certain socio-cultural groups. Multivariate analysis identified female education, age cohort, residential status, region of residence, types of marriage, and employment as strong predictors of Omani women’s age at marriage. Conclusion: The growing number of young adults, accompanied by their tendency to delay marriage, may have serious demographic, social, economic, and political ramifications for Oman, highlighting the need to understand the new situation of youth, their unique characteristics, and their interests and demands. Culturally appropriate policies need to be implemented to address the issues and challenges of unmarried young adults. PMID:23573380

Analysis of Prognostic Factors and Patterns of Recurrence in Patients With Pathologic Stage III Endometrial Cancer

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

Patel, Samir; Portelance, Lorraine; Gilbert, Lucy

2007-08-01

Purpose: To retrospectively assess prognostic factors and patterns of recurrence in patients with pathologic Stage III endometrial cancer. Methods and Materials: Between 1989 and 2003, 107 patients with pathologic International Federation of Gynecology and Obstetrics Stage III endometrial adenocarcinoma confined to the pelvis were treated at our institution. Adjuvant radiotherapy (RT) was delivered to 68 patients (64%). The influence of multiple patient- and treatment-related factors on pelvic and distant control and overall survival (OS) was evaluated. Results: Median follow-up for patients at risk was 41 months. Five-year actuarial OS was significantly improved in patients treated with adjuvant RT (68%) comparedmore » with those with resection alone (50%; p = 0.029). Age, histology, grade, uterine serosal invasion, adnexal involvement, number of extrauterine sites, and treatment with adjuvant RT predicted for improved survival in univariate analysis. Multivariate analysis revealed that grade, uterine serosal invasion, and treatment with adjuvant RT were independent predictors of survival. Five-year actuarial pelvic control was improved significantly with the delivery of adjuvant RT (74% vs. 49%; p = 0.011). Depth of myometrial invasion and treatment with adjuvant RT were independent predictors of pelvic control in multivariate analysis. Conclusions: Multiple prognostic factors predicting for the outcome of pathologic Stage III endometrial cancer patients were identified in this analysis. In particular, delivery of adjuvant RT seems to be a significant independent predictor for improved survival and pelvic control, suggesting that pelvic RT should be routinely considered in the management of these patients.« less

A Statistical Portrait of Working at Home in the U.K.: Evidence from the Labour Force Survey. Working Paper.

ERIC Educational Resources Information Center

Felstead, Alan; Jewson, Nick; Phizacklea, Annie; Walters, Sally

The patterns, extent, and problems of working at home in the United Kingdom were examined through a multivariate analysis of data from the Labour Force Survey, which has questioned respondents about the location of their workplace since 1992. The numbers of people working "mainly" at home increased from 345,920 (1.5%) in 1981 to 680,612…

Four factors underlying mouse behavior in an open field

PubMed Central

Tanaka, Shoji; Young, Jared W.; Halberstadt, Adam L.; Masten, Virginia L.; Geyer, Mark A.

2012-01-01

The observation of the locomotor and exploratory behaviors of rodents in an open field is one of the most fundamental methods used in the field of behavioral pharmacology. A variety of behaviors can be recorded automatically and can readily generate a multivariate pattern of pharmacological effects. Nevertheless, the optimal ways to characterize observed behaviors and concomitant drug effects are still under development. The aim of this study was to extract meaningful behavioral factors that could explain variations in the observed variables from mouse exploration. Behavioral data were recorded from male C57BL/6J mice (n = 268) using the Behavioral Pattern Monitor (BPM). The BPM data were subjected to the exploratory factor analysis. The factor analysis extracted four factors: activity, sequential organization, diversive exploration, and inspective exploration. The activity factor and the two types of exploration factors correlated positively with one another, while the sequential organization factor negatively correlated with the remaining factors. The extracted factor structure constitutes a behavioral model of mouse exploration. This model will provide a platform on which one can assess the effects of psychoactive drugs and genetic manipulations on mouse exploratory behavior. Further studies are currently underway to examine the factor structure of similar multivariate data sets from humans tested in a human BPM. PMID:22569582

Metabolic profiling of Angelica acutiloba roots utilizing gas chromatography-time-of-flight-mass spectrometry for quality assessment based on cultivation area and cultivar via multivariate pattern recognition.

PubMed

Tianniam, Sukanda; Tarachiwin, Lucksanaporn; Bamba, Takeshi; Kobayashi, Akio; Fukusaki, Eiichiro

2008-06-01

Gas chromatography time-of-flight mass spectrometry was applied to elucidate the profiling of primary metabolites and to evaluate the differences between quality differences in Angelica acutiloba (or Yamato-toki) roots through the utilization of multivariate pattern recognition-principal component analysis (PCA). Twenty-two metabolites consisting of sugars, amino and organic acids were identified. PCA analysis successfully discriminated the good, the moderate and the bad quality Yamato-toki roots in accordance to their cultivation areas. The results signified two reducing sugars, fructose and glucose being the most accumulated in the bad quality, whereas higher quantity of phosphoric acid, proline, malic acid and citric acid were found in the good and the moderate quality toki roots. PCA was also effective in discriminating samples derive from different cultivars. Yamato-toki roots with the moderate quality were compared by means of PCA, and the results illustrated good discrimination which was influenced most by malic acid. Overall, this study demonstrated that metabolomics technique is accurate and efficient in determining the quality differences in Yamato-toki roots, and has a potential to be a superior and suitable method to assess the quality of this medicinal plant.

Four factors underlying mouse behavior in an open field.

PubMed

Tanaka, Shoji; Young, Jared W; Halberstadt, Adam L; Masten, Virginia L; Geyer, Mark A

2012-07-15

The observation of the locomotor and exploratory behaviors of rodents in an open field is one of the most fundamental methods used in the field of behavioral pharmacology. A variety of behaviors can be recorded automatically and can readily generate a multivariate pattern of pharmacological effects. Nevertheless, the optimal ways to characterize observed behaviors and concomitant drug effects are still under development. The aim of this study was to extract meaningful behavioral factors that could explain variations in the observed variables from mouse exploration. Behavioral data were recorded from male C57BL/6J mice (n=268) using the Behavioral Pattern Monitor (BPM). The BPM data were subjected to the exploratory factor analysis. The factor analysis extracted four factors: activity, sequential organization, diversive exploration, and inspective exploration. The activity factor and the two types of exploration factors correlated positively with one another, while the sequential organization factor negatively correlated with the remaining factors. The extracted factor structure constitutes a behavioral model of mouse exploration. This model will provide a platform on which one can assess the effects of psychoactive drugs and genetic manipulations on mouse exploratory behavior. Further studies are currently underway to examine the factor structure of similar multivariate data sets from humans tested in a human BPM. Copyright © 2012 Elsevier B.V. All rights reserved.

Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland.

PubMed

Zhang, Chaosheng

2006-08-01

Galway is a small but rapidly growing tourism city in western Ireland. To evaluate its environmental quality, a total of 166 surface soil samples (0-10 cm depth) were collected from parks and grasslands at the density of 1 sample per 0.25 km2 at the end of 2004. All samples were analysed using ICP-AES for the near-total concentrations of 26 chemical elements. Multivariate statistics and GIS techniques were applied to classify the elements and to identify elements influenced by human activities. Cluster analysis (CA) and principal component analysis (PCA) classified the elements into two groups: the first group predominantly derived from natural sources, the second being influenced by human activities. GIS mapping is a powerful tool in identifying the possible sources of pollutants. Relatively high concentrations of Cu, Pb and Zn were found in the city centre, old residential areas, and along major traffic routes, showing significant effects of traffic pollution. The element As is enriched in soils of the old built-up areas, which can be attributed to coal and peat combustion for home heating. Such significant spatial patterns of pollutants displayed by urban soils may imply potential health threat to residents of the contaminated areas of the city.

Dietary patterns and risk of death and progression to ESRD in individuals with CKD: a cohort study.

PubMed

Gutiérrez, Orlando M; Muntner, Paul; Rizk, Dana V; McClellan, William M; Warnock, David G; Newby, P K; Judd, Suzanne E

2014-08-01

Nutrition is linked strongly with health outcomes in chronic kidney disease (CKD). However, few studies have examined relationships between dietary patterns and health outcomes in persons with CKD. Observational cohort study. 3,972 participants with CKD (defined as estimated glomerular filtration rate < 60 mL/min/1.73 m2 or albumin-creatinine ratio ≥ 30 mg/g at baseline) from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study, a prospective cohort study of 30,239 black and white adults at least 45 years of age. 5 empirically derived dietary patterns identified by factor analysis: "convenience" (Chinese and Mexican foods, pizza, and other mixed dishes), "plant-based" (fruits and vegetables), "sweets/fats" (sugary foods), "Southern" (fried foods, organ meats, and sweetened beverages), and "alcohol/salads" (alcohol, green-leafy vegetables, and salad dressing). All-cause mortality and end-stage renal disease (ESRD). 816 deaths and 141 ESRD events were observed over approximately 6 years of follow-up. There were no statistically significant associations of convenience, sweets/fats, or alcohol/salads pattern scores with all-cause mortality after multivariable adjustment. In Cox regression models adjusted for sociodemographic factors, energy intake, comorbid conditions, and baseline kidney function, higher plant-based pattern scores (indicating greater consistency with the pattern) were associated with lower risk of mortality (HR comparing fourth to first quartile, 0.77; 95% CI, 0.61-0.97), whereas higher Southern pattern scores were associated with greater risk of mortality (HR comparing fourth to first quartile, 1.51; 95% CI, 1.19-1.92). There were no associations of dietary patterns with incident ESRD in multivariable-adjusted models. Missing dietary pattern data, potential residual confounding from lifestyle factors. A Southern dietary pattern rich in processed and fried foods was associated independently with mortality in persons with CKD. In contrast, a diet rich in fruits and vegetables appeared to be protective. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Machine processing for remotely acquired data. [using multivariate statistical analysis

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1974-01-01

This paper is a general discussion of earth resources information systems which utilize airborne and spaceborne sensors. It points out that information may be derived by sensing and analyzing the spectral, spatial and temporal variations of electromagnetic fields emanating from the earth surface. After giving an overview system organization, the two broad categories of system types are discussed. These are systems in which high quality imagery is essential and those more numerically oriented. Sensors are also discussed with this categorization of systems in mind. The multispectral approach and pattern recognition are described as an example data analysis procedure for numerically-oriented systems. The steps necessary in using a pattern recognition scheme are described and illustrated with data obtained from aircraft and the Earth Resources Technology Satellite (ERTS-1).

Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique.

PubMed

Astephen, J L; Deluzio, K J

2005-02-01

Osteoarthritis of the knee is related to many correlated mechanical factors that can be measured with gait analysis. Gait analysis results in large data sets. The analysis of these data is difficult due to the correlated, multidimensional nature of the measures. A multidimensional model that uses two multivariate statistical techniques, principal component analysis and discriminant analysis, was used to discriminate between the gait patterns of the normal subject group and the osteoarthritis subject group. Nine time varying gait measures and eight discrete measures were included in the analysis. All interrelationships between and within the measures were retained in the analysis. The multidimensional analysis technique successfully separated the gait patterns of normal and knee osteoarthritis subjects with a misclassification error rate of <6%. The most discriminatory feature described a static and dynamic alignment factor. The second most discriminatory feature described a gait pattern change during the loading response phase of the gait cycle. The interrelationships between gait measures and between the time instants of the gait cycle can provide insight into the mechanical mechanisms of pathologies such as knee osteoarthritis. These results suggest that changes in frontal plane loading and alignment and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis gait patterns. Subsequent investigations earlier in the disease process may suggest the importance of these factors to the progression of knee osteoarthritis.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

PubMed

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. UMIN000010932.

Exploring Race Based Differences in Patterns of Life-Course Criminality

PubMed Central

Markowitz, Michael W.; Salvatore, Christopher

2013-01-01

A persistent issue facing criminologists is the challenge of developing theoretical models that provide comprehensive explanations of the onset and persistence of criminality. One promising theory to develop over the last 30 years has been life-course theory. Using multivariate analysis of variance the main question posed in this research, do elements of social development shape the trajectory of persistent offending in a race-neutral fashion, or are the dynamics shaping life-course criminality unique for people of color, was examined. The results provide a number of useful insights into the relationship between race, life-course transition factors, and longitudinal patterns of criminality. PMID:23436952

Tailored multivariate analysis for modulated enhanced diffraction

DOE PAGES

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

Major Dietary Patterns in Relation to General and Central Obesity among Chinese Adults

PubMed Central

Yu, Canqing; Shi, Zumin; Lv, Jun; Du, Huaidong; Qi, Lu; Guo, Yu; Bian, Zheng; Chang, Liang; Tang, Xuefeng; Jiang, Qilian; Mu, Huaiyi; Pan, Dongxia; Chen, Junshi; Chen, Zhengming; Li, Liming

2015-01-01

Limited evidence exists for the association between diet pattern and obesity phenotypes among Chinese adults. In the present study, we analyzed the cross-sectional data from 474,192 adults aged 30–79 years from the China Kadoorie Biobank baseline survey. Food consumption was collected by an interviewer-administered questionnaire. Three dietary patterns were extracted by factor analysis combined with cluster analysis. After being adjusted for potential confounders, individuals following a traditional southern dietary pattern had the lowest body mass index (BMI) and waist circumference (WC); the Western/new affluence dietary pattern had the highest BMI; and the traditional northern dietary pattern had the highest WC. Compared to the traditional southern dietary pattern in multivariable adjusted logistic models, individuals following a Western/new affluence dietary pattern had a significantly increased risk of general obesity (prevalence ratio (PR): 1.06, 95% confidence interval (CI): 1.03–1.08) and central obesity (PR: 1.07, 95% CI: 1.06–1.08). The corresponding risks for the traditional northern dietary pattern were 1.05 (1.02–1.09) and 1.17 (1.25–1.18), respectively. In addition, the associations were modified by lifestyle behaviors, and the combined effects with alcohol drinking, tobacco smoking, and physical activity were analyzed. Further prospective studies are needed to elucidate the diet-obesity relationships. PMID:26184308

Western Dietary Pattern Increases, and Prudent Dietary Pattern Decreases, Risk of Incident Diverticulitis in a Prospective Cohort Study.

PubMed

Strate, Lisa L; Keeley, Brieze R; Cao, Yin; Wu, Kana; Giovannucci, Edward L; Chan, Andrew T

2017-04-01

Dietary fiber is implicated as a risk factor for diverticulitis. Analyses of dietary patterns may provide information on risk beyond those of individual foods or nutrients. We examined whether major dietary patterns are associated with risk of incident diverticulitis. We performed a prospective cohort study of 46,295 men who were free of diverticulitis and known diverticulosis in 1986 (baseline) using data from the Health Professionals Follow-Up Study. Each study participant completed a detailed medical and dietary questionnaire at baseline. We sent supplemental questionnaires to men reporting incident diverticulitis on biennial follow-up questionnaires. We assessed diet every 4 years using a validated food frequency questionnaire. Western (high in red meat, refined grains, and high-fat dairy) and prudent (high in fruits, vegetables, and whole grains) dietary patterns were identified using principal component analysis. Follow-up time accrued from the date of return of the baseline questionnaire in 1986 until a diagnosis of diverticulitis, diverticulosis or diverticular bleeding; death; or December 31, 2012. The primary end point was incident diverticulitis. During 894,468 person years of follow-up, we identified 1063 incident cases of diverticulitis. After adjustment for other risk factors, men in the highest quintile of Western dietary pattern score had a multivariate hazard ratio of 1.55 (95% CI, 1.20-1.99) for diverticulitis compared to men in the lowest quintile. High vs low prudent scores were associated with decreased risk of diverticulitis (multivariate hazard ratio, 0.74; 95% CI, 0.60-0.91). The association between dietary patterns and diverticulitis was predominantly attributable to intake of fiber and red meat. In a prospective cohort study of 46,295 men, a Western dietary pattern was associated with increased risk of diverticulitis, and a prudent pattern was associated with decreased risk. These data can guide dietary interventions for the prevention of diverticulitis. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Vertebral artery injury associated with blunt cervical spine trauma: a multivariate regression analysis.

PubMed

Lebl, Darren R; Bono, Christopher M; Velmahos, George; Metkar, Umesh; Nguyen, Joseph; Harris, Mitchel B

2013-07-15

Retrospective analysis of prospective registry data. To determine the patient characteristics, risk factors, and fracture patterns associated with vertebral artery injury (VAI) in patients with blunt cervical spine injury. VAI associated with cervical spine trauma has the potential for catastrophical clinical sequelae. The patterns of cervical spine injury and patient characteristics associated with VAI remain to be determined. A retrospective review of prospectively collected data from the American College of Surgeons trauma registries at 3 level-1 trauma centers identified all patients with a cervical spine injury on multidetector computed tomographic scan during a 3-year period (January 1, 2007, to January 1, 2010). Fracture pattern and patient characteristics were recorded. Logistic multivariate regression analysis of independent predictors for VAI and subgroup analysis of neurological events related to VAI was performed. Twenty-one percent of 1204 patients with cervical injuries (n = 253) underwent screening for VAI by multidetector computed tomography angiogram. VAI was diagnosed in 17% (42 of 253), unilateral in 15% (38 of 253), and bilateral in 1.6% (4 of 253) and was associated with a lower Glasgow coma scale (P < 0.001), a higher injury severity score (P < 0.01), and a higher mortality (P < 0.001). VAI was associated with ankylosing spondylitis/diffuse idiopathic skeletal hyperosteosis (crude odds ratio [OR] = 8.04; 95% confidence interval [CI], 1.30-49.68; P = 0.034), and occipitocervical dissociation (P < 0.001) by univariate analysis and fracture displacement into the transverse foramen 1 mm or more (adjusted OR = 3.29; 95% CI, 1.15-9.41; P = 0.026), and basilar skull fracture (adjusted OR = 4.25; 95% CI, 1.25-14.47; P= 0.021), by multivariate regression model. Subgroup analyses of neurological events secondary to VAI occurred in 14% (6 of 42) and the stroke-related mortality rate was 4.8% (2 of 42). Neurological events were associated with male sex (P = 0.024), facet subluxation/dislocation (crude OR = 9.00; 95% CI, 1.51-53.74; P = 0.004) and the diagnosis of ankylosing spondylitis/diffuse idiopathic skeletal hyperosteosis (OR = 40.67; 95% CI, 5.27-313.96; P < 0.001). VAI associated with blunt cervical spine injury is a marker for more severely injured patients. High-risk patients with basilar skull fractures, occipitocervical dissociation, fracture displacement into the transverse foramen more than 1 mm, ankylosing spondylitis/diffuse idiopathic skeletal hyperosteosis, and facet subluxation/dislocation deserve focused consideration for VAI screening.

Riparian vegetation patterns in relation to fluvial landforms and channel evolution along selected rivers of Tuscany (Central Italy)

USGS Publications Warehouse

Hupp, C.R.; Rinaldi, M.

2007-01-01

Riparian vegetation distribution patterns and diversity relative to various fluvial geomorphic channel patterns, landforms, and processes are described and interpreted for selected rivers of Tuscany, Central Italy; with emphasis on channel evolution following human impacts. Field surveys were conducted along thirteen gauged reaches for species presence, fluvial landforms, and the type and amount of channel/riparian zone change. Inundation frequency of different geomorphic surfaces was determined, and vegetation data were analyzed using BDA (binary discriminate analysis) and DCA (detrended correspondence analysis) and related to hydrogeomorphology. Multivariate analyses revealed distinct quantitative vegetation patterns relative to six major fluvial geomorphic surfaces. DCA of the vegetation data also showed distinct associations of plants to processes of adjustment that are related to stage of channel evolution, and clearly separated plants along disturbance/landform/soil moisture gradients. Species richness increases from the channel bed to the terrace and on heterogeneous riparian areas, whereas species richness decreases from moderate to intense incision and from low to intense narrowing. ?? 2007 by Association of American Geographers.

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

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

Postoperative Radiotherapy Patterns of Care and Survival Implications for Medulloblastoma in Young Children.

PubMed

Kann, Benjamin H; Park, Henry S; Lester-Coll, Nataniel H; Yeboa, Debra N; Benitez, Viviana; Khan, Atif J; Bindra, Ranjit S; Marks, Asher M; Roberts, Kenneth B

2016-12-01

Postoperative radiotherapy to the craniospinal axis is standard-of-care for pediatric medulloblastoma but is associated with long-term morbidity, particularly in young children. With the advent of modern adjuvant chemotherapy strategies, postoperative radiotherapy deferral has gained acceptance in children younger than 3 years, although it remains controversial in older children. To analyze recent postoperative radiotherapy national treatment patterns and implications for overall survival in patients with medulloblastoma ages 3 to 8 years. Using the National Cancer Data Base, patients ages 3 to 8 years diagnosed as having histologically confirmed medulloblastoma in 2004 to 2012, without distant metastases, who underwent surgery and adjuvant chemotherapy with or without postoperative radiotherapy at facilities nationwide accredited by the Commission on Cancer were identified. Patients were designated as having "postoperative radiotherapy upfront" if they received radiotherapy within 90 days of surgery or "postoperative radiotherapy deferred" otherwise. Factors associated with postoperative radiotherapy deferral were identified using multivariable logistic regression. Overall survival (OS) was compared using Kaplan-Meier analysis with log-rank tests and multivariable Cox regression. Statistical tests were 2-sided. Postoperative radiotherapy utilization and overall survival. Among 816 patients, 123 (15.1%) had postoperative radiotherapy deferred, and 693 (84.9%) had postoperative radiotherapy upfront; 36.8% of 3-year-olds and 4.1% of 8-year-olds had postoperative radiotherapy deferred (P < .001). On multivariable logistic regression, variables associated with postoperative radiotherapy deferral were age (odds ratio [OR], 0.57 per year; 95% CI, 0.49-0.67 per year) and year of diagnosis (OR, 1.18 per year; 95% CI, 1.08-1.29 per year). On survival analysis, with median follow-up of 4.8 years, OS was improved for those receiving postoperative radiotherapy upfront vs postoperative radiotherapy deferred (5-year OS: 82.0% vs 63.4%; P < .001). On multivariable analysis, variables associated with poorer OS were postoperative radiotherapy deferral (hazards ratio [HR], 1.95; 95% CI, 1.15-3.31); stage M1-3 disease (HR, 1.86; 95% CI, 1.10-3.16), and low facility volume (HR, 1.75; 95% CI, 1.04-2.94). Our national database analysis reveals a higher-than-expected and increasing rate of postoperative radiotherapy deferral in children with medulloblastoma ages 3 to 8 years. The analysis suggests that postoperative radiotherapy deferral is associated with worse survival in this age group, even in the modern era of chemotherapy.

Socio-demographic and lifestyle determinants of dietary patterns in French-speaking Switzerland, 2009-2012.

PubMed

Marques-Vidal, Pedro; Waeber, Gérard; Vollenweider, Peter; Guessous, Idris

2018-01-12

Food intake is a complex behaviour which can be assessed using dietary patterns. Our aim was to characterize dietary patterns and associated factors in French-speaking Switzerland. Cross-sectional study conducted between 2009 and 2012 in the city of Lausanne, Switzerland, including 4372 participants (54% women, 57.3 ± 10.3 years). Food consumption was assessed using a validated food frequency questionnaire. Dietary patterns were assessed by principal components analysis. Three patterns were identified: "Meat & fries"; "Fruits & Vegetables" and "Fatty & sugary". The "Meat & fries" pattern showed the strongest correlations with total and animal protein and cholesterol carbohydrates, dietary fibre and calcium. The "Fruits & Vegetables" pattern showed the strongest correlations with dietary fibre, carotene and vitamin D. The "Fatty & sugary" pattern showed the strongest correlations with total energy and saturated fat. On multivariate analysis, male gender, low educational level and sedentary status were positively associated with the "Meat & fries" and the "Fatty & sugary" patterns, and negatively associated with the "Fruits & Vegetables" pattern. Increasing age was inversely associated with the "Meat & fries" pattern; smoking status was inversely associated with the "Fruits & Vegetables" pattern. Being born in Portugal or Spain was positively associated with the "Meat & fries" and the "Fruits & Vegetables" patterns. Increasing body mass index was positively associated with the "Meat & fries" pattern and inversely associated with the "Fatty & sugary" pattern. Three dietary patterns, one healthy and two unhealthy, were identified in the Swiss population. Several associated modifiable behaviours were identified; the information on socio- demographic determinants allows targeting of the most vulnerable groups in the context of public health interventions.

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion.

PubMed

Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E; Gur, Ruben C; Satterthwaite, Theodore D; Davatzikos, Christos

2017-03-28

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

Association between dietary patterns and the risk of metabolic syndrome among Lebanese adults.

PubMed

Naja, F; Nasreddine, L; Itani, L; Adra, N; Sibai, A M; Hwalla, N

2013-02-01

The main objective of this study was to evaluate the association between dietary patterns and the metabolic syndrome (MetS) and its metabolic abnormalities among Lebanese adults, using data from a national nutrition survey. A cross-sectional analysis involving adults aged ≥ 18 years (n = 323) with no prior history of chronic diseases was conducted. Participants completed a brief sociodemographic and 61-item food frequency questionnaire. Anthropometric measurements and fasting blood samples were also obtained. The International Diabetes Federation criteria were used to classify study participants with the metabolic syndrome. Dietary patterns were identified by factor analysis. Multivariate logistic regression analysis was used to evaluate the associations of extracted patterns with MetS and its metabolic abnormalities. Out of 323 participants, 112 (34.6%) were classified as having MetS. Three dietary patterns were identified: "Fast Food/Dessert," "Traditional Lebanese," and "High Protein." Compared with participants in the lowest quintile of the Fast Food/Dessert pattern, those in the highest quintile had significantly higher odds for MetS (OR, 3.13; 95% CI: 1.36-7.22) and hyperglycemia (OR, 3.81; 95% CI: 159-9.14). Subjects with the highest intake of the High Protein pattern had an increased risk for hypertension (OR, 2.98; 95% CI: 1.26-7.02). The Traditional Lebanese pattern showed no association with MetS or its components. The findings of this study demonstrate a positive association of the Fast Food/Dessert pattern with MetS and hyperglycemia among Lebanese adults. These results may guide the development of improved preventive nutrition interventions in this adult population.

Exploring the Influence of Income and Geography on Access to Services for Older Adults in British Columbia: A Multivariate Analysis Using the Canadian Community Health Survey (Cycle 3.1)

ERIC Educational Resources Information Center

Allan, Diane E.; Funk, Laura M.; Reid, R. Colin; Cloutier-Fisher, Denise

2011-01-01

Existing research on the health care utilization patterns of older Canadians suggests that income does not usually restrict an individual's access to care. However, the role that income plays in influencing access to health services by older adults living in rural areas is relatively unknown. This article examines the relationship between income…

Injury Pattern and Mortality of Noncompressible Torso Hemorrhage in UK Combat Casualties

DTIC Science & Technology

2013-08-01

body disruption (5.1%), and multiple-organ failure (4.0%). On multivariate analysis, major arterial and pulmonary hilar injury are most lethal with odds...death. Major arterial and pulmonary hilar injuries are independent predictors of mortality. (J Trauma Acute Care Surg. 2013;75: S263YS268. Copyright...physiologic or procedural indices of shock.8 Anatomic refers to those injuries to a named torso vessel, pulmonary injury (massive hemothorax or hilar

Identifying cognitive impairment in type 2 diabetes with functional connectivity: a multivariate pattern analysis of resting state fMRI data

NASA Astrophysics Data System (ADS)

Liu, Zhenyu; Cui, Xingwei; Tang, Zhenchao; Dong, Di; Zang, Yali; Tian, Jie

2017-03-01

Previous researches have shown that type 2 diabetes mellitus (T2DM) is associated with an increased risk of cognitive impairment. Early detection of brain abnormalities at the preclinical stage can be useful for developing preventive interventions to abate cognitive decline. We aimed to investigate the whole-brain resting-state functional connectivity (RSFC) patterns of T2DM patients between 90 regions of interest (ROIs) based on the RS-fMRI data, which can be used to test the feasibility of identifying T2DM patients with cognitive impairment from other T2DM patients. 74 patients were recruited in this study and multivariate pattern analysis was utilized to assess the prediction performance. Elastic net was firstly used to select the key features for prediction, and then a linear discrimination model was constructed. 23 RSFCs were selected and it achieved the performance with classification accuracy of 90.54% and areas under the receiver operating characteristic curve (AUC) of 0.944 using ten-fold cross-validation. The results provide strong evidence that functional interactions of brain regions undergo notable alterations between T2DM patients with cognitive impairment or not. By analyzing the RSFCs that were selected as key features, we found that most of them involved the frontal or temporal. We speculated that cognitive impairment in T2DM patients mainly impacted these two lobes. Overall, the present study indicated that RSFCs undergo notable alterations associated with the cognitive impairment in T2DM patients, and it is possible to predicted cognitive impairment early with RSFCs.

Pattern of tumour growth of the primary colon cancer predicts long-term outcome after resection of liver metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Andersson, Roland

2016-10-01

To identify significant predictive factors for overall survival (OS) and disease-free survival (DFS) after liver resection for colon cancer metastases, with special focus on features of the primary colon cancer, such as lymph node ratio (LNR), vascular invasion, and perineural invasion. Patients operated for colonic cancer liver metastases between 2006 and 2014 were included. Details on patient characteristics, the primary colon cancer operation and metastatic disease were collected. Multivariate analysis was performed to select predictive variables for OS and DFS. Median OS and DFS were 67 and 20 months, respectively. 1-, 3- and 5-year OS were 97, 76, and 52%. 1-, 3- and 5-year DFS were 65, 42, and 37%. Multivariate analysis showed LNR to be an independent predictive factor for DFS but not for OS. Other identified predictive factors were vascular and perineural invasion of the primary colon cancer, size of the largest metastasis and severe complications after liver surgery for OS, and perineural invasion, number of liver metastases and preoperative CEA-level for DFS. Traditional N-stage was also considered to be an independent predictive factor for DFS in a separate multivariate analysis. LNR and perineural invasion of the primary colon cancer can be used as a prognostic variable for DFS after a concomitant liver resection for colon cancer metastases. Vascular and perineural invasion of the primary colon cancer are predictive for OS.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

Spatial band-pass filtering aids decoding musical genres from auditory cortex 7T fMRI.

PubMed

Sengupta, Ayan; Pollmann, Stefan; Hanke, Michael

2018-01-01

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation -- primarily in the visual cortex. Reported evidence indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we performed an analysis of publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex that matches a previously conducted study on decoding visual orientation from V1.  The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

Appearance Matters: Neural Correlates of Food Choice and Packaging Aesthetics

PubMed Central

Van der Laan, Laura N.; De Ridder, Denise T. D.; Viergever, Max A.; Smeets, Paul A. M.

2012-01-01

Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation patterns can predict realistic low-involvement consumer choices. More specifically, this was assessed in the context of package-induced binary food choices. Mass-univariate analyses showed that several regions, among which the bilateral striatum, were more strongly activated in response to preferred food packages. Food choices could be predicted with an accuracy of up to 61.2% by activation patterns in brain regions previously found to be involved in healthy food choices (superior frontal gyrus) and visual processing (middle occipital gyrus). In conclusion, this study shows that mass-univariate analysis can detect small package-induced differences in product preference and that MVPA can successfully predict realistic low-involvement consumer choices from functional MRI data. PMID:22848586

Diet and proinflammatory cytokine levels in head and neck squamous cell carcinoma

PubMed Central

Arthur, Anna E.; Peterson, Karen E.; Shen, Jincheng; Djuric, Zora; Taylor, Jeremy M.G.; Hebert, James R.; Duffy, Sonia A.; Peterson, Lisa A.; Bellile, Emily L.; Whitfield, Joel R.; Chepeha, Douglas B.; Schipper, Matthew J.; Wolf, Gregory T.; Rozek, Laura S.

2014-01-01

Background Proinflammatory cytokine levels may be associated with cancer stage, recurrence, and survival. A study was undertaken to determine if cytokine levels were associated with dietary patterns and fat-soluble micronutrients in previously untreated head and neck squamous cell carcinoma (HNSCC) patients. Methods This was a cross-sectional study of 160 newly diagnosed HNSCC patients who completed pretreatment food frequency questionnaires (FFQ) and health surveys. Dietary patterns were derived from FFQs using principal component analysis. Pretreatment serum levels of the proinflammatory cytokines IL-6, TNF-α, and IFN-γ were measured by ELISA and serum carotenoid and tocopherol levels by HPLC. Multivariable ordinal logistic regression models examined associations between cytokines and quartiles of reported and serum dietary variables. Results Three dietary patterns emerged: whole foods, Western, and convenience foods. In multivariable analyses, higher whole foods pattern scores were significantly associated with lower levels of IL-6, TNF-α, and IFN-γ (P = <0.001, P = 0.008, and P = 0.03, respectively). Significant inverse associations were reported between IL-6, TNF-α, and IFN-γ levels and quartiles of total reported carotenoid intake (P = 0.006, P = 0.04, and P = 0.04, respectively). There was an inverse association between IFN-γ levels and serum α-tocopherol levels (P = 0.03). Conclusions Consuming a pretreatment diet rich in vegetables, fruit, fish, poultry and whole grains may be associated with lower proinflammatory cytokine levels in patients with HNSCC. PMID:24830761

A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

PubMed

Park, Sung Hee; Lee, Ji Young; Kim, Sangsoo

2011-01-01

Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects. This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3). The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

PubMed Central

Goldfine, Andrew M.; Victor, Jonathan D.; Conte, Mary M.; Bardin, Jonathan C.; Schiff, Nicholas D.

2011-01-01

Objective To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury. Methods We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range). Results In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls. Conclusion EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury. Significance EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate. PMID:21514214

Decoding auditory spatial and emotional information encoding using multivariate versus univariate techniques.

PubMed

Kryklywy, James H; Macpherson, Ewan A; Mitchell, Derek G V

2018-04-01

Emotion can have diverse effects on behaviour and perception, modulating function in some circumstances, and sometimes having little effect. Recently, it was identified that part of the heterogeneity of emotional effects could be due to a dissociable representation of emotion in dual pathway models of sensory processing. Our previous fMRI experiment using traditional univariate analyses showed that emotion modulated processing in the auditory 'what' but not 'where' processing pathway. The current study aims to further investigate this dissociation using a more recently emerging multi-voxel pattern analysis searchlight approach. While undergoing fMRI, participants localized sounds of varying emotional content. A searchlight multi-voxel pattern analysis was conducted to identify activity patterns predictive of sound location and/or emotion. Relative to the prior univariate analysis, MVPA indicated larger overlapping spatial and emotional representations of sound within early secondary regions associated with auditory localization. However, consistent with the univariate analysis, these two dimensions were increasingly segregated in late secondary and tertiary regions of the auditory processing streams. These results, while complimentary to our original univariate analyses, highlight the utility of multiple analytic approaches for neuroimaging, particularly for neural processes with known representations dependent on population coding.

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

PubMed

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines

NASA Astrophysics Data System (ADS)

Hu, Chongqing; Li, Aihua; Zhao, Xingyang

2011-02-01

This paper proposes a multivariate statistical analysis approach to processing the instantaneous engine speed signal for the purpose of locating multiple misfire events in internal combustion engines. The state of each cylinder is described with a characteristic vector extracted from the instantaneous engine speed signal following a three-step procedure. These characteristic vectors are considered as the values of various procedure parameters of an engine cycle. Therefore, determination of occurrence of misfire events and identification of misfiring cylinders can be accomplished by a principal component analysis (PCA) based pattern recognition methodology. The proposed algorithm can be implemented easily in practice because the threshold can be defined adaptively without the information of operating conditions. Besides, the effect of torsional vibration on the engine speed waveform is interpreted as the presence of super powerful cylinder, which is also isolated by the algorithm. The misfiring cylinder and the super powerful cylinder are often adjacent in the firing sequence, thus missing detections and false alarms can be avoided effectively by checking the relationship between the cylinders.

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.

Applications of High Resolution Laser Induced Breakdown Spectroscopy for Environmental and Biological Samples

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

Martin, Madhavi Z; Labbe, Nicole; Wagner, Rebekah J.

2013-01-01

This chapter details the application of LIBS in a number of environmental areas of research such as carbon sequestration and climate change. LIBS has also been shown to be useful in other high resolution environmental applications for example, elemental mapping and detection of metals in plant materials. LIBS has also been used in phytoremediation applications. Other biological research involves a detailed understanding of wood chemistry response to precipitation variations and also to forest fires. A cross-section of Mountain pine (pinceae Pinus pungen Lamb.) was scanned using a translational stage to determine the differences in the chemical features both before andmore » after a fire event. Consequently, by monitoring the elemental composition pattern of a tree and by looking for abrupt changes, one can reconstruct the disturbance history of a tree and a forest. Lastly we have shown that multivariate analysis of the LIBS data is necessary to standardize the analysis and correlate to other standard laboratory techniques. LIBS along with multivariate statistical analysis makes it a very powerful technology that can be transferred from laboratory to field applications with ease.« less

(13)C NMR pattern recognition techniques for the classification of Atlantic salmon (Salmo salar L.) according to their wild, farmed, and geographical origin.

PubMed

Aursand, Marit; Standal, Inger B; Praël, Angelika; McEvoy, Lesley; Irvine, Joe; Axelson, David E

2009-05-13

(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Compound-specific amino acid δ15N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean

NASA Astrophysics Data System (ADS)

McCarthy, Matthew D.; Lehman, Jennifer; Kudela, Raphael

2013-02-01

Stable nitrogen isotopic analysis of individual amino acids (δ15N-AA) has unique potential to elucidate the complexities of food webs, track heterotrophic transformations, and understand diagenesis of organic nitrogen (ON). While δ15N-AA patterns of autotrophs have been shown to be generally similar, prior work has also suggested that differences may exist between cyanobacteria and eukaryotic algae. However, δ15N-AA patterns in differing oceanic algal groups have never been closely examined. The overarching goals of this study were first to establish a more quantitative understanding of algal δ15N-AA patterns, and second to examine whether δ15N-AA patterns have potential as a new tracer for distinguishing prokaryotic vs. eukaryotic N sources. We measured δ15N-AA from prokaryotic and eukaryotic phytoplankton cultures and used a complementary set of statistical approaches (simple normalization, regression-derived fractionation factors, and multivariate analyses) to test for variations. A generally similar δ15N-AA pattern was confirmed for all algae, however significant AA-specific variation was also consistently identified between the two groups. The relative δ15N fractionation of Glx (glutamine + glutamic acid combined) vs. total proteinaceous N appeared substantially different, which we hypothesize could be related to differing enzymatic forms. In addition, the several other AA (most notably glycine and leucine) appeared to have strong biomarker potential. Finally, we observed that overall patterns of δ15N values in algae correspond well with the Trophic vs. Source-AA division now commonly used to describe variable AA δ15N changes with trophic transfer, suggesting a common mechanistic basis. Overall, these results show that autotrophic δ15N-AA patterns can differ between major algal evolutionary groupings for many AA. The statistically significant multivariate results represent a first approach for testing ideas about relative eukaryotic vs. prokaryotic ON sources in the sea.

The Heart´s rhythm 'n' blues: Sex differences in circadian variation patterns of vagal activity vary by depressive symptoms in predominantly healthy employees.

PubMed

Jarczok, Marc N; Aguilar-Raab, Corina; Koenig, Julian; Kaess, Michael; Borniger, Jeremy C; Nelson, Randy J; Hall, Martica; Ditzen, Beate; Thayer, Julian F; Fischer, Joachim E

2018-03-15

Successful regulation of emotional states is positively associated to mental health, while difficulties in regulating emotions are negatively associated to overall mental health and in particular associated with anxiety or depression symptoms. A key structure associated to socio-emotional regulatory processes is the central autonomic network. Activity in this structure is associated to vagal activity can be indexed noninvasively and simply by measures of peripheral cardiac autonomic modulations such as heart rate variability. Vagal activity exhibits a circadian variation pattern, with a maximum during nighttime. Depression is known to affect chronobiology. Also, depressive symptoms are known to be associated with decreased resting state vagal activity, but studies investigating the association between circadian variation pattern of vagal activity and depressive symptoms are scarce. We aim to examine these patterns in association to symptom severity of depression using chronobiologic methods. Data from the Manheim Industrial Cohort Studies (MICS) were used. A total of 3,030 predominantly healthy working adults underwent, among others, ambulatory 24-h hear rate-recordings, detailed health examination and online questionnaires and were available for this analysis. The root mean sum of successive differences (RMSSD) was used as an indicator of vagally mediated heart rate variability. Three individual-level cosine function parameters (MESOR, amplitude, acrophase) were estimated to quantify circadian variation pattern. Multivariate linear regression models including important covariates such as age, sex, and lifestyle factors as well as an interaction effect of sex with depressive symptoms were used to estimate the association of circadian variation pattern of vagal activity with depressive symptoms simultaneously. The analysis sample consisted of 20.2% females and an average age 41 with standard deviation of 11 years. Nonparametric bivariate analysis revealed significant MESOR and amplitude differences between the 90 th percentile split, but not on acrophase. Multivariate linear regression models estimated depressive symptoms to be negatively associated with the 24h mean (MESOR) and oscillation amplitude in men but positively associated in women. This pattern of findings indicates a blunted day-night rhythm of vagal activity in men with greater depressive symptoms as well as a moderation effect of sex in the association of CVP and depressive symptoms. This is the first study investigating circadian variation pattern by mild depressive symptoms in a large, rather healthy occupational sample. Depressive symptoms were associated with decreased circadian variation pattern of vagal activity in men but with increased circadian variation pattern in women. The possible underlying mechanism(s) are discussed using the neurovisceral integration model. These findings may have implications for the knowledge on etiology, diagnosis, course, and treatment of depressive symptoms and thus may be of significant public health relevance.

Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

PubMed

Söhn, Matthias; Alber, Markus; Yan, Di

2007-09-01

The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as "eigenmodes," which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe approximately 94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ( approximately 40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.

Empirically derived major dietary patterns and their associations with overweight in Korean preschool children.

PubMed

Shin, Kyung Ok; Oh, Se-Young; Park, Hyun Suh

2007-08-01

Prevailing dietary patterns and their association with nutritional outcomes are poorly understood, particularly for children in Korea. Our purposes were to identify major dietary patterns and to examine their associations with overweight among young children in Korea. For 1441 preschool children, usual diet was assessed by a FFQ, from which thirty-three food groups were created and entered into a factor analysis. We identified three dietary patterns by relative intake frequency of (1) vegetables, seaweeds, beans, fruits, milk and dairy products (Korean healthy pattern); (2) beef, pork, poultry, fish and fast foods (animal foods pattern); and (3) ice cream, soda, chocolate, cookies and candies (sweets pattern). The Korean healthy pattern was associated with better health status. As compared with the lowest quintile, the multivariate-adjusted OR of the highest quintile for health status inferior or similar to their peers was 0.59 (95 % CI 0.42, 0.84). Likelihood of being overweight was higher among those in the highest quintile (OR 1.77 (95 % CI 1.06, 2.94)) v. the lowest quintile regarding the animal foods pattern. These findings suggest that major dietary patterns are predictors of overweight and health status in Korean preschool children.

Space-time patterns in ignimbrite compositions revealed by GIS and R based statistical analysis

NASA Astrophysics Data System (ADS)

Brandmeier, Melanie; Wörner, Gerhard

2017-04-01

GIS-based multivariate statistical and geospatial analysis of a compilation of 890 geochemical and ca. 1,200 geochronological data for 194 mapped ignimbrites from Central Andes documents the compositional and temporal pattern of large volume ignimbrites (so-called "ignimbrite flare-ups") during Neogene times. Rapid advances in computational sciences during the past decade lead to a growing pool of algorithms for multivariate statistics on big datasets with many predictor variables. This study uses the potential of R and ArcGIS and applies cluster (CA) and linear discriminant analysis (LDA) on log-ratio transformed spatial data. CA on major and trace element data allows to group ignimbrites according to their geochemical characteristics into rhyolitic and a dacitic "end-members" and differentiates characteristic trace element signatures with respect to Eu anomaly, depletion of MREEs and variable enrichment in LREE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive data sets were available. The most important predictors for discriminating ignimbrites are La (LREE), Yb (HREE), Eu, Al2O3, K2O, P2O5, MgO, FeOt and TiO2. However, other REEs such as Gd, Pr, Tm, Sm and Er also contribute to the discrimination functions. Significant compositional differences were found between the older (>14 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and the younger (< 10 Ma) Altiplano-Puna-Volcanic-Complex ignimbrites that are of similar volumes. Older ignimbrites are less depleted in HREEs and less radiogenic in Sr isotopes, indicating smaller crustal contributions during evolution in thinner and thermally less evolved crust. These compositional variations indicate a relation to crustal thickening with a "transition" from plagioclase to amphibole and garnet residual mineralogy between 13 to 9 Ma. We correlate compositional and volumetric variations to the N-S passage of the Juan-Fernandéz ridge and crustal shortening and thickening during the past 26 Ma. The value of GIS and multivariate statistics in comparison to traditional geochemical parameters are highlighted working with large datasets with many predictors in a spatial and temporal context. Algorithms implemented in R allow taking advantage of an n-dimensional space and, thus, of subtle compositional differences contained in the data, while space-time patterns can be analyzed easily in GIS.

Dermatoglyphic analysis of La Liébana (Cantabria, Spain). 2. Finger ridge counts.

PubMed

Martín, J; Gómez, P

1993-06-01

The results of univariate and multivariate analyses of the quantitative finger dermatoglyphic traits (i.e. ridge counts) of a sample of 109 males and 88 females from La Liébana (Cantabria, Spain) are reported. Univariate results follow the trends usually found in previous studies, e.g., ranking of finger ridge counts, bilateral asymmetry or shape of the distributions of the frequencies. However, sexual dimorphism is nearly inexistent concerning finger ridge counts. This lack of dimorphism could be related to certain characteristics of the distribution of finger dermatoglyphic patterns previously reported by the same authors. The multivariate description has been carried out by means of principal component analysis (with varimax rotation to obtain the final solution) of the correlation matrices computed from the 10 maximal finger ridge counts. Although the results do not necessarily prove the concept of developmental fields ("field theory" and later modifications), some precepts of the theory are present: field polarization and field overlapping.

Trends in the sand: Directional evolution in the shell shape of recessing scallops (Bivalvia: Pectinidae).

PubMed

Sherratt, Emma; Alejandrino, Alvin; Kraemer, Andrew C; Serb, Jeanne M; Adams, Dean C

2016-09-01

Directional evolution is one of the most compelling evolutionary patterns observed in macroevolution. Yet, despite its importance, detecting such trends in multivariate data remains a challenge. In this study, we evaluate multivariate evolution of shell shape in 93 bivalved scallop species, combining geometric morphometrics and phylogenetic comparative methods. Phylomorphospace visualization described the history of morphological diversification in the group; revealing that taxa with a recessing life habit were the most distinctive in shell shape, and appeared to display a directional trend. To evaluate this hypothesis empirically, we extended existing methods by characterizing the mean directional evolution in phylomorphospace for recessing scallops. We then compared this pattern to what was expected under several alternative evolutionary scenarios using phylogenetic simulations. The observed pattern did not fall within the distribution obtained under multivariate Brownian motion, enabling us to reject this evolutionary scenario. By contrast, the observed pattern was more similar to, and fell within, the distribution obtained from simulations using Brownian motion combined with a directional trend. Thus, the observed data are consistent with a pattern of directional evolution for this lineage of recessing scallops. We discuss this putative directional evolutionary trend in terms of its potential adaptive role in exploiting novel habitats. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

PubMed Central

Ogihara, Takeshi; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

Introduction While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal—metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. Participants and methods The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Results Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. Conclusions The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. Trial registration UMIN000010932 PMID:28273173

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat.

PubMed

Eisenbarth, Hedwig; Chang, Luke J; Wager, Tor D

2016-11-23

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR = 0.54, r SCL = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR→SCL = 0.21 and r SCL→HR = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical-subcortical interactions. Copyright © 2016 the authors 0270-6474/16/3611987-12$15.00/0.

Maximum covariance analysis to identify intraseasonal oscillations over tropical Brazil

NASA Astrophysics Data System (ADS)

Barreto, Naurinete J. C.; Mesquita, Michel d. S.; Mendes, David; Spyrides, Maria H. C.; Pedra, George U.; Lucio, Paulo S.

2017-09-01

A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden-Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the { MITB}_1 and { MITB}_2 indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.

A multivariate pattern analysis study of the HIV-related white matter anatomical structural connections alterations

NASA Astrophysics Data System (ADS)

Tang, Zhenchao; Liu, Zhenyu; Li, Ruili; Cui, Xinwei; Li, Hongjun; Dong, Enqing; Tian, Jie

2017-03-01

It's widely known that HIV infection would cause white matter integrity impairments. Nevertheless, it is still unclear that how the white matter anatomical structural connections are affected by HIV infection. In the current study, we employed a multivariate pattern analysis to explore the HIV-related white matter connections alterations. Forty antiretroviraltherapy- naïve HIV patients and thirty healthy controls were enrolled. Firstly, an Automatic Anatomical Label (AAL) atlas based white matter structural network, a 90 × 90 FA-weighted matrix, was constructed for each subject. Then, the white matter connections deprived from the structural network were entered into a lasso-logistic regression model to perform HIV-control group classification. Using leave one out cross validation, a classification accuracy (ACC) of 90% (P=0.002) and areas under the receiver operating characteristic curve (AUC) of 0.96 was obtained by the classification model. This result indicated that the white matter anatomical structural connections contributed greatly to HIV-control group classification, providing solid evidence that the white matter connections were affected by HIV infection. Specially, 11 white matter connections were selected in the classification model, mainly crossing the regions of frontal lobe, Cingulum, Hippocampus, and Thalamus, which were reported to be damaged in previous HIV studies. This might suggest that the white matter connections adjacent to the HIV-related impaired regions were prone to be damaged.

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.

PubMed

Chen, Ping; Harrington, Peter B

2008-02-01

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis.

PubMed

Shensa, Ariel; Sidani, Jaime E; Dew, Mary Amanda; Escobar-Viera, César G; Primack, Brian A

2018-03-01

Individuals use social media with varying quantity, emotional, and behavioral at- tachment that may have differential associations with mental health outcomes. In this study, we sought to identify distinct patterns of social media use (SMU) and to assess associations between those patterns and depression and anxiety symptoms. In October 2014, a nationally-representative sample of 1730 US adults ages 19 to 32 completed an online survey. Cluster analysis was used to identify patterns of SMU. Depression and anxiety were measured using respective 4-item Patient-Reported Outcome Measurement Information System (PROMIS) scales. Multivariable logistic regression models were used to assess associations between clus- ter membership and depression and anxiety. Cluster analysis yielded a 5-cluster solu- tion. Participants were characterized as "Wired," "Connected," "Diffuse Dabblers," "Concentrated Dabblers," and "Unplugged." Membership in 2 clusters - "Wired" and "Connected" - increased the odds of elevated depression and anxiety symptoms (AOR = 2.7, 95% CI = 1.5-4.7; AOR = 3.7, 95% CI = 2.1-6.5, respectively, and AOR = 2.0, 95% CI = 1.3-3.2; AOR = 2.0, 95% CI = 1.3-3.1, respectively). SMU pattern characterization of a large population suggests 2 pat- terns are associated with risk for depression and anxiety. Developing educational interventions that address use patterns rather than single aspects of SMU (eg, quantity) would likely be useful.

Evolutionary analysis of groundwater flow: Application of multivariate statistical analysis to hydrochemical data in the Densu Basin, Ghana

NASA Astrophysics Data System (ADS)

Yidana, Sandow Mark; Bawoyobie, Patrick; Sakyi, Patrick; Fynn, Obed Fiifi

2018-02-01

An evolutionary trend has been postulated through the analysis of hydrochemical data of a crystalline rock aquifer system in the Densu Basin, Southern Ghana. Hydrochemcial data from 63 groundwater samples, taken from two main groundwater outlets (Boreholes and hand dug wells) were used to postulate an evolutionary theory for the basin. Sequential factor and hierarchical cluster analysis were used to disintegrate the data into three factors and five clusters (spatial associations). These were used to characterize the controls on groundwater hydrochemistry and its evolution in the terrain. The dissolution of soluble salts and cation exchange processes are the dominant processes controlling groundwater hydrochemistry in the terrain. The trend of evolution of this set of processes follows the pattern of groundwater flow predicted by a calibrated transient groundwater model in the area. The data suggest that anthropogenic activities represent the second most important process in the hydrochemistry. Silicate mineral weathering is the third most important set of processes. Groundwater associations resulting from Q-mode hierarchical cluster analysis indicate an evolutionary pattern consistent with the general groundwater flow pattern in the basin. These key findings are at variance with results of previous investigations and indicate that when carefully done, groundwater hydrochemical data can be very useful for conceptualizing groundwater flow in basins.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

Soil bacterial diversity patterns and drivers along an elevational gradient on Shennongjia Mountain, China

PubMed Central

Zhang, Yuguang; Cong, Jing; Lu, Hui; Li, Guangliang; Xue, Yadong; Deng, Ye; Li, Hui; Zhou, Jizhong; Li, Diqiang

2015-01-01

Understanding biological diversity elevational pattern and the driver factors are indispensable to develop the ecological theories. Elevational gradient may minimize the impact of environmental factors and is the ideal places to study soil microbial elevational patterns. In this study, we selected four typical vegetation types from 1000 to 2800 m above the sea level on the northern slope of Shennongjia Mountain in central China, and analysed the soil bacterial community composition, elevational patterns and the relationship between soil bacterial diversity and environmental factors by using the 16S rRNA Illumina sequencing and multivariate statistical analysis. The results revealed that the dominant bacterial phyla were Acidobacteria, Actinobacteria, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Verrucomicrobia, which accounted for over 75% of the bacterial sequences obtained from tested samples, and the soil bacterial operational taxonomic unit (OTU) richness was a significant monotonous decreasing (P < 0.01) trend with the elevational increasing. The similarity of soil bacterial population composition decreased significantly (P < 0.01) with elevational distance increased as measured by the Jaccard and Bray–Curtis index. Canonical correspondence analysis and Mantel test analysis indicated that plant diversity and soil pH were significantly correlated (P < 0.01) with the soil bacterial community. Therefore, the soil bacterial diversity on Shennongjia Mountain had a significant and different elevational pattern, and plant diversity and soil pH may be the key factors in shaping the soil bacterial spatial pattern. PMID:26032124

Multivariate singular spectrum analysis and the road to phase synchronization

NASA Astrophysics Data System (ADS)

Groth, Andreas; Ghil, Michael

2010-05-01

Singular spectrum analysis (SSA) and multivariate SSA (M-SSA) are based on the classical work of Kosambi (1943), Loeve (1945) and Karhunen (1946) and are closely related to principal component analysis. They have been introduced into information theory by Bertero, Pike and co-workers (1982, 1984) and into dynamical systems analysis by Broomhead and King (1986a,b). Ghil, Vautard and associates have applied SSA and M-SSA to the temporal and spatio-temporal analysis of short and noisy time series in climate dynamics and other fields in the geosciences since the late 1980s. M-SSA provides insight into the unknown or partially known dynamics of the underlying system by decomposing the delay-coordinate phase space of a given multivariate time series into a set of data-adaptive orthonormal components. These components can be classified essentially into trends, oscillatory patterns and noise, and allow one to reconstruct a robust "skeleton" of the dynamical system's structure. For an overview we refer to Ghil et al. (Rev. Geophys., 2002). In this talk, we present M-SSA in the context of synchronization analysis and illustrate its ability to unveil information about the mechanisms behind the adjustment of rhythms in coupled dynamical systems. The focus of the talk is on the special case of phase synchronization between coupled chaotic oscillators (Rosenblum et al., PRL, 1996). Several ways of measuring phase synchronization are in use, and the robust definition of a reasonable phase for each oscillator is critical in each of them. We illustrate here the advantages of M-SSA in the automatic identification of oscillatory modes and in drawing conclusions about the transition to phase synchronization. Without using any a priori definition of a suitable phase, we show that M-SSA is able to detect phase synchronization in a chain of coupled chaotic oscillators (Osipov et al., PRE, 1996). Recently, Muller et al. (PRE, 2005) and Allefeld et al. (Intl. J. Bif. Chaos, 2007) have demonstrated the usefulness of principal component analysis in detecting phase synchronization from multivariate time series. The present talk provides a generalization of this idea and presents a robust implementation thereof via M-SSA.

Pattern of Tumor Shrinkage during Neoadjuvant Chemotherapy Is Associated with Prognosis in Low-Grade Luminal Early Breast Cancer.

PubMed

Fukada, Ippei; Araki, Kazuhiro; Kobayashi, Kokoro; Shibayama, Tomoko; Takahashi, Shunji; Gomi, Naoya; Kokubu, Yumi; Oikado, Katsunori; Horii, Rie; Akiyama, Futoshi; Iwase, Takuji; Ohno, Shinji; Hatake, Kiyohiko; Sata, Naohiro; Ito, Yoshinori

2018-01-01

Purpose To evaluate the association between tumor shrinkage patterns shown with magnetic resonance (MR) imaging during neoadjuvant chemotherapy (NAC) and prognosis in patients with low-grade luminal breast cancer. Materials and Methods This retrospective study was approved by the institutional review board and informed consent was obtained from all subjects. The low-grade luminal breast cancer was defined as hormone receptor-positive and human epidermal growth factor receptor 2-negative with nuclear grades 1 or 2. The patterns of tumor shrinkage as revealed at MR imaging were categorized into two types: concentric shrinkage (CS) and non-CS. Among 854 patients who had received NAC in a single institution from January 2000 to December 2009, 183 patients with low-grade luminal breast cancer were retrospectively evaluated for the development set. Another data set from 292 patients who had received NAC in the same institution between January 2010 and December 2012 was used for the validation set. Among these 292 patients, 121 patients with low-grade luminal breast cancer were retrospectively evaluated. Results In the development set, the median observation period was 67.9 months. Recurrence was observed in 31 patients, and 16 deaths were related to breast cancer. There were statistically significant differences in both the disease-free survival (DFS) and overall survival (OS) rates between patterns of tumor shrinkage (P < .001 and P < .001, respectively). Multivariate analysis demonstrated that the CS pattern had the only significant independent association with DFS (P = .001) and OS (P = .009) rate. In the validation set, the median follow-up period was 56.9 months. Recurrence was observed in 20 patients (16.5%) and eight (6.6%) deaths were related to breast cancer. DFS rate was significantly longer in patients with the CS pattern (72.8 months; 95% confidence interval [CI]: 69.9, 75.6 months) than in those with the non-CS pattern (56.0 months; 95% CI: 49.1, 62.9 months; P ≤ .001). The CS pattern was associated with an excellent prognosis (median OS, 80.6 months; 95% CI: 79.3, 81.8 months vs 65.0 months; 95% CI: 60.1, 69.8 months; P = .004). Multivariate analysis demonstrated that the CS pattern had the only significant independent association with DFS (P = .007) and OS (P = .037) rates. Conclusion The CS pattern as revealed at MR imaging during NAC had the only significant independent association with prognosis in patients with low-grade luminal breast cancer. © RSNA, 2017.

Dietary Patterns and Risk of Death and Progression to ESRD in Individuals With CKD: A Cohort Study

PubMed Central

Gutiérrez, Orlando M.; Muntner, Paul; Rizk, Dana V.; McClellan, William M.; Warnock, David G.; Newby, P.K.; Judd, Suzanne E.

2014-01-01

Background Nutrition is strongly linked with health outcomes in chronic kidney disease (CKD). However, few studies have examined relationships between dietary patterns and health outcomes in persons with CKD. Study Design Observational cohort study. Setting & Participants 3,972 participants with CKD (defined as an estimated glomerular filtration rate < 60 ml/min/1.73 m2 or an albumin-creatinine ratio ≥30 mg/g at baseline) from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a prospective cohort study of 30,239 black and white adults at least 45 years of age. Predictors Five empirically derived dietary patterns identified via factor analysis: “Convenience” (Chinese and Mexican foods, pizza, other mixed dishes), “Plant-Based” (fruits, vegetables), “Sweets/Fats” (sugary foods), “Southern” (fried foods, organ meats, sweetened beverages), and “Alcohol/Salads” (alcohol, green-leafy vegetables, salad dressing). Outcomes All-cause mortality and end-stage renal disease (ESRD). Results A total of 816 deaths and 141 ESRD events were observed over approximately 6 years of follow-up. There were no statistically significant associations of Convenience, Sweets/Fats or Alcohol/Salads pattern scores with all-cause mortality after multivariable adjustment. In Cox regression models adjusted for sociodemographic factors, energy intake, co-morbidities, and baseline kidney function, higher Plant-Based pattern scores (indicating greater consistency with the pattern) were associated with lower risk of mortality (HR comparing fourth to first quartile, 0.77; 95%CI, 0.61–0.97) whereas higher Southern pattern scores were associated with greater risk of mortality (HR comparing fourth to first quartile, 1.51; 95%CI, 1.19–1.92). There were no associations of dietary patterns with incident ESRD in multivariable-adjusted models. Limitations Missing dietary pattern data, potential residual confounding from lifestyle factors. Conclusions A Southern dietary pattern rich in processed and fried foods was independently associated with mortality in persons with CKD. In contrast, a diet rich in fruits and vegetables appeared to be protective. PMID:24679894

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Associations between dietary patterns, socio-demographic factors and anthropometric measurements in adult New Zealanders: an analysis of data from the 2008/09 New Zealand Adult Nutrition Survey.

PubMed

Beck, K L; Jones, B; Ullah, I; McNaughton, S A; Haslett, S J; Stonehouse, W

2018-06-01

To investigate associations between dietary patterns, socio-demographic factors and anthropometric measurements in adult New Zealanders. Dietary patterns were identified using factor analysis in adults 15 years plus (n = 4657) using 24-h diet recall data from the 2008/09 New Zealand Adult Nutrition Survey. Multivariate regression was used to investigate associations between dietary patterns and age, gender and ethnicity. After controlling for demographic factors, associations between dietary patterns and food insecurity, deprivation, education, and smoking were investigated. Associations between dietary patterns and body mass index and waist circumference were examined adjusting for demographic factors, smoking and energy intake. Two dietary patterns were identified. 'Healthy' was characterised by breakfast cereal, low fat milk, soy and rice milk, soup and stock, yoghurt, bananas, apples, other fruit and tea, and low intakes of pies and pastries, potato chips, white bread, takeaway foods, soft drinks, beer and wine. 'Traditional' was characterised by beef, starchy vegetables, green vegetables, carrots, tomatoes, savoury sauces, regular milk, cream, sugar, tea and coffee, and was low in takeaway foods. The 'healthy' pattern was positively associated with age, female gender, New Zealand European or other ethnicity, and a secondary school qualification, and inversely associated with smoking, food insecurity, area deprivation, BMI and waist circumference. The 'traditional' pattern was positively associated with age, male gender, smoking, food insecurity and inversely associated with a secondary school qualification. A 'Healthy' dietary pattern was associated with higher socio-economic status and reduced adiposity, while the 'traditional' pattern was associated with lower socio-economic status.

Quantitative Folding Pattern Analysis of Early Primary Sulci in Human Fetuses with Brain Abnormalities.

PubMed

Im, K; Guimaraes, A; Kim, Y; Cottrill, E; Gagoski, B; Rollins, C; Ortinau, C; Yang, E; Grant, P E

2017-07-01

Aberrant gyral folding is a key feature in the diagnosis of many cerebral malformations. However, in fetal life, it is particularly challenging to confidently diagnose aberrant folding because of the rapid spatiotemporal changes of gyral development. Currently, there is no resource to measure how an individual fetal brain compares with normal spatiotemporal variations. In this study, we assessed the potential for automatic analysis of early sulcal patterns to detect individual fetal brains with cerebral abnormalities. Triplane MR images were aligned to create a motion-corrected volume for each individual fetal brain, and cortical plate surfaces were extracted. Sulcal basins were automatically identified on the cortical plate surface and compared with a combined set generated from 9 normal fetal brain templates. Sulcal pattern similarities to the templates were quantified by using multivariate geometric features and intersulcal relationships for 14 normal fetal brains and 5 fetal brains that were proved to be abnormal on postnatal MR imaging. Results were compared with the gyrification index. Significantly reduced sulcal pattern similarities to normal templates were found in all abnormal individual fetuses compared with normal fetuses (mean similarity [normal, abnormal], left: 0.818, 0.752; P < .001; right: 0.810, 0.753; P < .01). Altered location and depth patterns of sulcal basins were the primary distinguishing features. The gyrification index was not significantly different between the normal and abnormal groups. Automated analysis of interrelated patterning of early primary sulci could outperform the traditional gyrification index and has the potential to quantitatively detect individual fetuses with emerging abnormal sulcal patterns. © 2017 by American Journal of Neuroradiology.

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.

Alcohol consumption and visual impairment in a rural Northern Chinese population.

PubMed

Li, Zhijian; Xu, Keke; Wu, Shubin; Sun, Ying; Song, Zhen; Jin, Di; Liu, Ping

2014-12-01

To investigate alcohol drinking status and the association between drinking patterns and visual impairment in an adult population in northern China. Cluster sampling was used to select samples. The protocol consisted of an interview, pilot study, visual acuity (VA) testing and a clinical examination. Visual impairment was defined as presenting VA worse than 20/60 in any eye. Drinking patterns included drinking quantity (standard drinks per week) and frequency (drinking days in the past week). Information on alcohol consumption was obtained from 8445 subjects, 963 (11.4%) of whom reported consuming alcohol. In multivariate analysis, alcohol consumption was significantly associated with older age (p < 0.001), male sex (p < 0.001), and higher education level (p < 0.01). Heavy intake (>14 drinks/week) was associated with higher odds of visual impairment. However, moderate intake (>1-14 drinks/week) was significantly associated with lower odds (adjusted odds ratio, OR, 0.7, 95% confidence interval, CI, 0.5-1.0) of visual impairment (p = 0.03). Higher drinking frequency was significantly associated with higher odds of visual impairment. Multivariate analysis showed that older age, male sex, and higher education level were associated with visual impairment among current drinkers. Age- and sex-adjusted ORs for the association of cataract and alcohol intake showed that higher alcohol consumption was not significantly associated with an increased prevalence of cataract (OR 1.2, 95% CI 0.4-3.6), whereas light and moderate alcohol consumption appeared to reduce incidence of cataract. Drinking patterns were associated with visual impairment. Heavy intake had negative effects on distance vision; meanwhile, moderate intake had a positive effect on distance vision.

Urban stream syndrome in a small, lightly developed watershed: a statistical analysis of water chemistry parameters, land use patterns, and natural sources.

PubMed

Halstead, Judith A; Kliman, Sabrina; Berheide, Catherine White; Chaucer, Alexander; Cock-Esteb, Alicea

2014-06-01

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO[Formula: see text], Cl(-), HCO(-)3, SO9(2-)4, Na(+), K(+), Ca(2+), and Mg(2+)). Adjusted R(2) values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg(2+) was omitted. The more common R (2), ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg(2+) was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca(2+) and HCO(-)3 are suggested.

RAS mutations affect pattern of metastatic spread and increase propensity for brain metastasis in colorectal cancer.

PubMed

Yaeger, Rona; Cowell, Elizabeth; Chou, Joanne F; Gewirtz, Alexandra N; Borsu, Laetitia; Vakiani, Efsevia; Solit, David B; Rosen, Neal; Capanu, Marinela; Ladanyi, Marc; Kemeny, Nancy

2015-04-15

RAS and PIK3CA mutations in metastatic colorectal cancer (mCRC) have been associated with worse survival. We sought to evaluate the impact of RAS and PIK3CA mutations on cumulative incidence of metastasis to potentially curable sites of liver and lung and other sites such as bone and brain. We performed a computerized search of the electronic medical record of our institution for mCRC cases genotyped for RAS or PIK3CA mutations from 2008 to 2012. Cases were reviewed for patient characteristics, survival, and site-specific metastasis. Among the 918 patients identified, 477 cases were RAS wild type, and 441 cases had a RAS mutation (394 at KRAS exon 2, 29 at KRAS exon 3 or 4, and 18 in NRAS). RAS mutation was significantly associated with shorter median overall survival (OS) and on multivariate analysis independently predicted worse OS (HR, 1.6; P < .01). RAS mutant mCRC exhibited a significantly higher cumulative incidence of lung, bone, and brain metastasis and on multivariate analysis was an independent predictor of involvement of these sites (HR, 1.5, 1.6, and 3.7, respectively). PIK3CA mutations occurred in 10% of the 786 cases genotyped, did not predict for worse survival, and did not exhibit a site-specific pattern of metastatic spread. The metastatic potential of CRC varies with the presence of RAS mutation. RAS mutation is associated with worse OS and increased incidence of lung, bone, and brain metastasis. An understanding of this site-specific pattern of spread may help to inform physicians' assessment of symptoms in patients with mCRC. © 2014 American Cancer Society.

The relationship of age-adjusted Charlson comorbidity ındex and diurnal variation of blood pressure.

PubMed

Kalaycı, Belma; Erten, Yunus Turgay; Akgün, Tunahan; Karabag, Turgut; Kokturk, Furuzan

2018-03-05

Charlson Comorbidity index (CCI) is a scoring system to predict prognosis and mortality. It exhibits better utility when combined with age, age-adjusted Charlson Comorbidity Index (ACCI). The aim of this study was to evaluate the relationship between ACCI and diurnal variation of blood pressure parameters in hypertensive patients and normotensive patients. We enrolled 236 patients. All patients underwent a 24-h ambulatory blood pressure monitoring (ABPM) for evaluation of dipper or non-dipper pattern. We searched the correlation between ACCI and dipper or non-dipper pattern and other ABPM parameters. To further investigate the role of these parameters in predicting survival, a multivariate analysis using the Cox proportional hazard model was performed. 167 patients were in the hypertensive group (87 patients in non-dipper status) and 69 patients were in the normotensive group (41 patients in non-dipper status) of all study patients. We found a significant difference and negative correlation between AACI and 24-h diastolic blood pressure (DBP), awake DBP, awake mean blood pressure (MBP) and 24-h MBP and awake systolic blood pressure(SBP). Night decrease ratio of blood pressure had also a negative correlation with ACCI (p = 0.003, r = -0.233). However, we found a relationship with non-dipper pattern and ACCI in the hypertensive patients (p = 0.050). In multivariate Cox analysis sleep MBP was found related to mortality like ACCI (p = 0.023, HR = 1.086, %95 CI 1.012-1.165) Conclusion: ACCI was statistically significantly higher in non-dipper hypertensive patients than dipper hypertensive patients while ACCI had a negative correlation with blood pressure. Sleep MBP may predict mortality.

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

Sexual attitudes, pattern of communication, and sexual behavior among unmarried out-of-school youth in China.

PubMed

Wang, Bo; Li, Xiaoming; Stanton, Bonita; Kamali, Vafa; Naar-King, Sylvie; Shah, Iqbal; Thomas, Ronald

2007-07-31

In recent years, more adolescents are engaging in premarital sex in China. However, only a limited number of studies have explored out-of-school youth's sexual attitudes and behaviors, critical for prevention intervention development. Using data from the baseline survey of a comprehensive sex education program that was conducted in a suburb of Shanghai in 2000-2002, this study describes sexual attitudes, patterns of communication on sexual matters, and premarital sexual behavior among 1,304 out-of-school youth. Multivariate logistic regression analysis was conducted to examine the factors associated with youth's premarital sexual intercourse. The majority (60%) of out-of-school youth held favorable attitudes towards premarital sex. Males were more likely to have favorable attitudes compared with females. Male youth generally did not communicate with either parent about sex, while one-third of female youth talked to their mothers about sexual matters. Both males and females chose their friends as the person with whom they were most likely to talk about sexual matters. About 18% of the youth reported having engaged in sexual intercourse. One-fifth of sexually active youth had always used a contraceptive method, and one-quarter had been pregnant (or had impregnated a partner). There were no gender differences in rate of premarital sex or frequency of contraceptive use. Multivariate analysis revealed that age, education, family structure, parent's discipline, attitudes towards premarital sex, pattern of communication and dating were significantly associated with youth premarital sex. A substantial proportion of out-of-school youth engage in risky sexual behaviors. Prevention programs that empower communication and sexual negotiation skills, and promote condom use should be implemented for this vulnerable group.

Sexual attitudes, pattern of communication, and sexual behavior among unmarried out-of-school youth in China

PubMed Central

Wang, Bo; Li, Xiaoming; Stanton, Bonita; Kamali, Vafa; Naar-King, Sylvie; Shah, Iqbal; Thomas, Ronald

2007-01-01

Background In recent years, more adolescents are engaging in premarital sex in China. However, only a limited number of studies have explored out-of-school youth's sexual attitudes and behaviors, critical for prevention intervention development. Methods Using data from the baseline survey of a comprehensive sex education program that was conducted in a suburb of Shanghai in 2000–2002, this study describes sexual attitudes, patterns of communication on sexual matters, and premarital sexual behavior among 1,304 out-of-school youth. Multivariate logistic regression analysis was conducted to examine the factors associated with youth's premarital sexual intercourse. Results The majority (60%) of out-of-school youth held favorable attitudes towards premarital sex. Males were more likely to have favorable attitudes compared with females. Male youth generally did not communicate with either parent about sex, while one-third of female youth talked to their mothers about sexual matters. Both males and females chose their friends as the person with whom they were most likely to talk about sexual matters. About 18% of the youth reported having engaged in sexual intercourse. One-fifth of sexually active youth had always used a contraceptive method, and one-quarter had been pregnant (or had impregnated a partner). There were no gender differences in rate of premarital sex or frequency of contraceptive use. Multivariate analysis revealed that age, education, family structure, parent's discipline, attitudes towards premarital sex, pattern of communication and dating were significantly associated with youth premarital sex. Conclusion A substantial proportion of out-of-school youth engage in risky sexual behaviors. Prevention programs that empower communication and sexual negotiation skills, and promote condom use should be implemented for this vulnerable group. PMID:17672903

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

PubMed

Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

2015-01-01

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

Dietary patterns and cognitive ability among 12- to 13 year-old adolescents in Selangor, Malaysia.

PubMed

Nurliyana, Abdul Razak; Mohd Nasir, Mohd Taib; Zalilah, Mohd Shariff; Rohani, Abdullah

2015-02-01

The present study aimed to identify dietary patterns and determine the relationship between dietary patterns and cognitive ability among 12- to 13 year-old Malay adolescents in the urban areas of Gombak district in Selangor, Malaysia. Data on sociodemographic background were obtained from parents. Height and weight were measured and BMI-for-age was determined. Adolescents were interviewed on their habitual dietary intakes using a semi-quantitative FFQ. Cognitive ability was assessed using the Wechsler Nonverbal Scale of Ability in a one-to-one manner. Dietary patterns were constructed using principal component analysis based on thirty-eight food groups of the semi-quantitative FFQ. Urban secondary public schools in the district of Gombak in Selangor, Malaysia. Malay adolescents aged 12 to 13 years (n 416). The mean general cognitive ability score was 101·8 (sd 12·4). Four major dietary patterns were identified and labelled as 'refined-grain pattern', 'snack-food pattern', 'plant-based food pattern' and 'high-energy food pattern'. These dietary patterns explained 39·1 % of the variance in the habitual dietary intakes of the adolescents. The refined-grain pattern was negatively associated with processing speed, which is a construct of general cognitive ability. The high-energy food pattern was negatively associated with general cognitive ability, perceptual reasoning and processing speed. Monthly household income and parents' educational attainment were positively associated with all of the cognitive measures. In multivariate analysis, only the high-energy food pattern was found to contribute significantly towards general cognitive ability after controlling for socio-economic status. Consumption of foods in the high-energy food pattern contributed towards general cognitive ability after controlling for socio-economic status. However, the contribution was small.

Latino cigarette smoking patterns by gender in a US national sample

PubMed Central

Kristman-Valente, Allison; Flaherty, Brian P.

2015-01-01

Background Latino smokers are a rising public health concern who experience elevated tobacco related health disparities. Purpose Additional information on Latino smoking is needed to inform screening and treatment. Analysis Latent class analysis using smoking frequency, cigarette preferences, onset, smoking duration, cigarettes per day and minutes to first cigarette were used to create multivariate latent smoking profiles for Latino men and women. Results Final models found seven classes for Latinas and nine classes for Latinos. Despite a common finding in the literature that Latino smokers are more likely to be low-risk, intermittent smokers, the majority of classes, for both males and females, described patterns of high-risk, daily smoking. Gender variations in smoking classes were noted. Conclusions Several markers of smoking risk were identified among both male and female Latino smokers including long durations of smoking, daily smoking and preference for specialty cigarettes, all factors associated with long-term health consequences. PMID:26304857

Otolith patterns of rockfishes from the northeastern Pacific.

PubMed

Tuset, Victor M; Imondi, Ralph; Aguado, Guillermo; Otero-Ferrer, José L; Santschi, Linda; Lombarte, Antoni; Love, Milton

2015-04-01

Sagitta otolith shape was analysed in twenty sympatric rockfishes off the southern California coast (Northeastern Pacific). The variation in shape was quantified using canonical variate analysis based on fifth wavelet function decomposition of otolith contour. We selected wavelets because this representation allow the identifications of zones or single morphological points along the contour. The entire otoliths along with four subsections (anterior, ventral, posterodorsal, and anterodorsal) with morphological meaning were examined. Multivariate analyses (MANOVA) showed significant differences in the contours of whole otolith morphology and corresponding subsection among rockfishes. Four patterns were found: fusiform, oblong, and two types of elliptic. A redundancy analysis indicated that anterior and anterodorsal subsections contribute most to define the entire otolith shape. Complementarily, the eco-morphological study indicated that the depth distribution and strategies for capture prey were correlated to otolith shape, especially with the anterodorsal zone. © 2014 Wiley Periodicals, Inc.

Analysis of synoptic patterns in relationship with severe rainfall events in the Ebre Observatory (Catalonia)

NASA Astrophysics Data System (ADS)

Pérez-Zanón, Núria; Casas-Castillo, M. Carmen; Peña, Juan Carlos; Aran, Montserrat; Rodríguez-Solà, Raúl; Redaño, Angel; Solé, German

2018-03-01

The study has obtained a classification of the synoptic patterns associated with a selection of extreme rain episodes registered in the Ebre Observatory between 1905 and 2003, showing a return period of not less than 10 years for any duration from 5 min to 24 h. These episodes had been previously classified in four rainfall intensity groups attending to their meteorological time scale. The synoptic patterns related to every group have been obtained applying a multivariable analysis to three atmospheric levels: sea-level pressure, temperature, and geopotential at 500 hPa. Usually, the synoptic patterns associated with intense rain in southern Catalonia are featured by low-pressure systems advecting warm and wet air from the Mediterranean Sea at the low levels of the troposphere. The configuration in the middle levels of the troposphere is dominated by negative anomalies of geopotential, indicating the presence of a low or a cold front, and temperature anomalies, promoting the destabilization of the atmosphere. These configurations promote the occurrence of severe convective events due to the difference of temperature between the low and medium levels of troposphere and the contribution of humidity in the lowest levels of the atmosphere.

Parsing the roles of the frontal lobes and basal ganglia in task control using multivoxel pattern analysis

PubMed Central

Kehagia, Angie A.; Ye, Rong; Joyce, Dan W.; Doyle, Orla M.; Rowe, James B.; Robbins, Trevor W.

2017-01-01

Cognitive control has traditionally been associated with the prefrontal cortex, based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson’s disease (PD) indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with PD, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification (GPC), we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch / Repeat behaviour between rule conditions. At 1000 permutations, Switch / Repeat classification accuracy for concrete rules was significant in the basal ganglia, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the basal ganglia. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates. PMID:28387585

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Autonomic specificity of basic emotions: evidence from pattern classification and cluster analysis.

PubMed

Stephens, Chad L; Christie, Israel C; Friedman, Bruce H

2010-07-01

Autonomic nervous system (ANS) specificity of emotion remains controversial in contemporary emotion research, and has received mixed support over decades of investigation. This study was designed to replicate and extend psychophysiological research, which has used multivariate pattern classification analysis (PCA) in support of ANS specificity. Forty-nine undergraduates (27 women) listened to emotion-inducing music and viewed affective films while a montage of ANS variables, including heart rate variability indices, peripheral vascular activity, systolic time intervals, and electrodermal activity, were recorded. Evidence for ANS discrimination of emotion was found via PCA with 44.6% of overall observations correctly classified into the predicted emotion conditions, using ANS variables (z=16.05, p<.001). Cluster analysis of these data indicated a lack of distinct clusters, which suggests that ANS responses to the stimuli were nomothetic and stimulus-specific rather than idiosyncratic and individual-specific. Collectively these results further confirm and extend support for the notion that basic emotions have distinct ANS signatures. Copyright © 2010 Elsevier B.V. All rights reserved.

Digital Citizenship and Health Promotion Programs: The Power of Knowing.

PubMed

Hicks, Elaine R

2016-11-03

Patterns of Internet access and use among disadvantaged subgroups of Americans reveal that not all disparities are the same, a distinction crucial for appropriate public policies and health promotion program planning. In their book, Digital Citizenship: The Internet, Society, and Participation, authors Karen Mossberger, Caroline Tolbert, and Ramona McNeal deconstructed national opinion surveys and used multivariate methods of data analysis to demonstrate the impact of exclusion from online society economically, socially, and politically among disadvantaged Americans. © 2016 Society for Public Health Education.

Adult medulloblastoma: clinical characters, prognostic factors, outcomes and patterns of relapse.

PubMed

Zhang, Na; Ouyang, Taohui; Kang, Huicong; Long, Wang; Thomas, Benjamin; Zhu, Suiqiang

2015-09-01

To analyze the clinical characters, prognostic factors, patterns of relapse and treatment outcomes for medulloblastoma in adults. The clinical materials of 73 consecutive adult patients (age, ≥16 years) with medulloblastoma were analyzed retrospectively. Follow-up data were available in 62 patients, ranging from 10 to 142 months (median, 78.4 months). Outcome in survival was assessed by the progression-free survival (PFS) and overall survival (OS). Univariate and multivariate analysis were performed to determine the prognostic factors. Total or near-total tumor resection was achieved in 37 cases (59.7 %), subtotal in 19 cases (30.6 %), and partial resection in 6 cases (9.7 %).Twenty-two patients experienced recurrences, and 45 % percent of all recurrences occurred more than 4 years after initial surgery. The PFS rates at 5 and 8 years were 60.1 and 37.0 %, respectively. The OS rates at 5 and 8 years were 82.6 and 57.3 %, respectively. In univariate analysis, less tumor resection, non-desmoplastic pathology, and brainstem involvement were risk factors for worse PFS and OS (P < 0.05). High-risk category was associated with just lower PFS, but not OS. In multivariate analysis, complete resection and desmoplastic pathology were independently predictive factors of improved PFS and OS. In adult medulloblastoma, late relapse is common and therefore long-term follow-up is important for evaluating the real impact of treatments. Risk category had prognostic value just for PFS, but not for OS. Complete resection and desmoplastic histology are independently predictive factors for favorable outcomes.

Multivariate Analysis of Factors Affecting Presence and/or Agenesis of Third Molar Tooth

PubMed Central

Alam, Mohammad Khursheed; Hamza, Muhammad Asyraf; Khafiz, Muhammad Aizuddin; Rahman, Shaifulizan Abdul; Shaari, Ramizu; Hassan, Akram

2014-01-01

To investigate the presence and/or agenesis of third molar (M3) tooth germs in orthodontics patients in Malaysian Malay and Chinese population and evaluate the relationship between presence and/or agenesis of M3 with different skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. Pretreatment records of 300 orthodontic patients (140 males and 160 females, 219 Malaysian Malay and 81 Chinese, average age was 16.27±4.59) were used. Third-molar agenesis was calculated with respect to race, genders, number of missing teeth, jaws, skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. The Pearson chi-square test and ANOVA was performed to determine potential differences. Associations between various factors and M3 presence/agenesis groups were assessed using logistic regression analysis. The percentages of subjects with 1 or more M3 agenesis were 30%, 33% and 31% in the Malaysian Malay, Chinese and total population, respectively. Overall prevalence of M3 agenesis in male and female was equal (P>0.05). The frequency of the agenesis of M3s is greater in maxilla as well in the right side (P>0.05). The prevalence of M3 agenesis in those with a Class III and Class II malocclusion was relatively higher in Malaysian Malay and Malaysian Chinese population respectively. Using stepwise regression analyses, significant associations were found between Mx (P<0.05) and ANB (P<0.05) and M3 agenesis. This multivariate analysis suggested that Mx and ANB were significantly correlated with the M3 presence/agenesis. PMID:24967595

A stochastic differential equation model of diurnal cortisol patterns

NASA Technical Reports Server (NTRS)

Brown, E. N.; Meehan, P. M.; Dempster, A. P.

2001-01-01

Circadian modulation of episodic bursts is recognized as the normal physiological pattern of diurnal variation in plasma cortisol levels. The primary physiological factors underlying these diurnal patterns are the ultradian timing of secretory events, circadian modulation of the amplitude of secretory events, infusion of the hormone from the adrenal gland into the plasma, and clearance of the hormone from the plasma by the liver. Each measured plasma cortisol level has an error arising from the cortisol immunoassay. We demonstrate that all of these three physiological principles can be succinctly summarized in a single stochastic differential equation plus measurement error model and show that physiologically consistent ranges of the model parameters can be determined from published reports. We summarize the model parameters in terms of the multivariate Gaussian probability density and establish the plausibility of the model with a series of simulation studies. Our framework makes possible a sensitivity analysis in which all model parameters are allowed to vary simultaneously. The model offers an approach for simultaneously representing cortisol's ultradian, circadian, and kinetic properties. Our modeling paradigm provides a framework for simulation studies and data analysis that should be readily adaptable to the analysis of other endocrine hormone systems.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications.

PubMed

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-11-17

Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends' preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors.

Characterization of regional cold-hydrothermal inflows enriched in arsenic and associated trace-elements in the southern part of the Duero Basin (Spain), by multivariate statistical analysis.

PubMed

Giménez-Forcada, Elena; Vega-Alegre, Marisol; Timón-Sánchez, Susana

2017-09-01

Naturally occurring arsenic in groundwater exceeding the limit for potability has been reported along the southern edge of the Cenozoic Duero Basin (CDB) near its contact with the Spanish Central System (SCS). In this area, spatial variability of arsenic is high, peaking at 241μg/L. Forty-seven percent of samples collected contained arsenic above the maximum allowable concentration for drinking water (10μg/L). Correlations of As with other hydrochemical variables were investigated using multivariate statistical analysis (Hierarchical Cluster Analysis, HCA and Principal Component Analysis, PCA). It was found that As, V, Cr and pH are closely related and that there were also close correlations with temperature and Na + . The highest concentrations of arsenic and other associated Potentially Toxic Geogenic Trace Elements (PTGTE) are linked to alkaline NaHCO 3 waters (pH≈9), moderate oxic conditions and temperatures of around 18°C-19°C. The most plausible hypothesis to explain the high arsenic concentrations is the contribution of deeper regional flows with a significant hydrothermal component (cold-hydrothermal waters), flowing through faults in the basement rock. Water mixing and water-rock interactions occur both in the fissured aquifer media (igneous and metasedimentary bedrock) and in the sedimentary environment of the CDB, where agricultural pollution phenomena are also active. A combination of multivariate statistical tools and hydrochemical analysis enabled the distribution pattern of dissolved As and other PTGTE in groundwaters in the study area to be interpreted, and their most likely origin to be established. This methodology could be applied to other sedimentary areas with similar characteristics and problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Gleason pattern 5 is the strongest pathologic predictor of recurrence, metastasis, and prostate cancer-specific death in patients receiving salvage radiation therapy following radical prostatectomy.

PubMed

Jackson, William; Hamstra, Daniel A; Johnson, Skyler; Zhou, Jessica; Foster, Benjamin; Foster, Corey; Li, Darren; Song, Yeohan; Palapattu, Ganesh S; Kunju, Lakshmi P; Mehra, Rohit; Feng, Felix Y

2013-09-15

The presence of Gleason pattern 5 (GP5) at radical prostatectomy (RP) has been associated with worse clinical outcome; however, this pathologic variable has not been assessed in patients receiving salvage radiation therapy (SRT) after a rising prostate-specific antigen level. A total of 575 patients who underwent primary RP for localized prostate cancer and subsequently received SRT at a tertiary medical institution were reviewed retrospectively. Primary outcomes of interest were biochemical failure (BF), distant metastasis (DM), and prostate cancer-specific mortality (PCSM), which were assessed via univariate analysis and Fine and Grays competing risks multivariate models. On pathologic evaluation, 563 (98%) patients had a documented Gleason score (GS). The median follow-up post-SRT was 56.7 months. A total of 60 (10.7%) patients had primary, secondary, or tertiary GP5. On univariate analysis, the presence of GP5 was prognostic for BF (hazard ratio [HR] 3.3; P < .0001), DM (HR:11.1, P < .0001), and PCSM (HR:8.8, P < .0001). Restratification of the Gleason score to include GP5 as a distinct entity resulted in improved prognostic capability. Patients with GP5 had clinically worse outcomes than patients with GS8(4+4). On multivariate analysis, the presence of GP5 was the most adverse pathologic predictor of BF (HR 2.9; P < .0001), DM (HR 14.8; P < .0001), and PCSM (HR 5.7; P < .0001). In the setting of SRT for prostate cancer, the presence of GP5 is a critical pathologic predictor of BF, DM, and PCSM. Traditional GS risk stratification fails to fully utilize the prognostic capabilities of individual Gleason patterns among men receiving SRT post-RP. © 2013 American Cancer Society.

Association of serum vitamin D concentrations with dietary patterns in children and adolescents.

PubMed

Ganji, Vijay; Martineau, Bernadette; Van Fleit, William Edmund

2018-06-04

Because children have been advised on the dangers of sun exposure, diet is an important contributor of serum 25 hydroxyvitamin D [25(OH)D] concentrations. Aim of this study was to determine whether serum 25(OH)D concentrations were associated with any specific dietary patterns in US children. Data from 2 cycles of National Health and Nutrition Examination Survey (NHANES) 2003-2004 and 2005-2006 for individuals aged 2 to ≤19 y, were used to study relation between dietary patterns and serum 25(OH)D. We derived 2 major dietary patterns based on the food frequency questionnaire data. These were labeled as High-Fat-Low-Vegetable Dietary (HFLVD) pattern and Prudent Dietary (PD) pattern. In multivariate adjusted analysis, there was no significant relationship between serum 25(OH)D concentrations and tertiles of HFLVD and PD dietary pattern scores in all subjects, boys, and girls. When dietary patterns scores were used as a continuous variable in adjusted analysis, children (all) with higher PD contribution scores to overall diet showed a significant positive relation with serum 25(OH)D (β = 59.1, P = 0.017). When data were stratified by sex, a significant positive relation was observed in girls between serum 25(OH)D concentration and PD pattern scores (β = 82.1, P = 0.015). A significant negative relation was observed in girls between serum 25(OH)D and HFLVD pattern scores (β = - 88.5, P = 0.016). Overall, serum 25(OH)D were associated with PD pattern but not with HFLVD pattern in US children. In public health perspective, it is important to encourage children, especially girls who are consuming HFLVD pattern to shift to healthier diet.

Cluster-based exposure variation analysis

PubMed Central

2013-01-01

Background Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. Methods For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity. Each simulation trace included two realizations of 100 concatenated cycles with either low (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. Results C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p < 0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration. Conclusion While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA. PMID:23557439

Detecting phase separation of freeze-dried binary amorphous systems using pair-wise distribution function and multivariate data analysis.

PubMed

Chieng, Norman; Trnka, Hjalte; Boetker, Johan; Pikal, Michael; Rantanen, Jukka; Grohganz, Holger

2013-09-15

The purpose of this study is to investigate the use of multivariate data analysis for powder X-ray diffraction-pair-wise distribution function (PXRD-PDF) data to detect phase separation in freeze-dried binary amorphous systems. Polymer-polymer and polymer-sugar binary systems at various ratios were freeze-dried. All samples were analyzed by PXRD, transformed to PDF and analyzed by principal component analysis (PCA). These results were validated by differential scanning calorimetry (DSC) through characterization of glass transition of the maximally freeze-concentrate solute (Tg'). Analysis of PXRD-PDF data using PCA provides a more clear 'miscible' or 'phase separated' interpretation through the distribution pattern of samples on a score plot presentation compared to residual plot method. In a phase separated system, samples were found to be evenly distributed around the theoretical PDF profile. For systems that were miscible, a clear deviation of samples away from the theoretical PDF profile was observed. Moreover, PCA analysis allows simultaneous analysis of replicate samples. Comparatively, the phase behavior analysis from PXRD-PDF-PCA method was in agreement with the DSC results. Overall, the combined PXRD-PDF-PCA approach improves the clarity of the PXRD-PDF results and can be used as an alternative explorative data analytical tool in detecting phase separation in freeze-dried binary amorphous systems. Copyright © 2013 Elsevier B.V. All rights reserved.

Quantitative transmission electron microscopy analysis of multi-variant grains in present L1{sub 0}-FePt based heat assisted magnetic recording media

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

Ho, Hoan, E-mail: hoan.ho@wdc.com; Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; Zhu, Jingxi, E-mail: jingxiz@andrew.cmu.edu

2014-11-21

We present a study on atomic ordering within individual grains in granular L1{sub 0}-FePt thin films using transmission electron microscopy techniques. The film, used as a medium for heat assisted magnetic recording, consists of a single layer of FePt grains separated by non-magnetic grain boundaries and is grown on an MgO underlayer. Using convergent-beam techniques, diffraction patterns of individual grains are obtained for a large number of crystallites. The study found that although the majority of grains are ordered in the perpendicular direction, more than 15% of them are multi-variant, or of in-plane c-axis orientation, or disordered fcc. It wasmore » also found that these multi-variant and in-plane grains have always grown across MgO grain boundaries separating two or more MgO grains of the underlayer. The in-plane ordered portion within a multi-variant L1{sub 0}-FePt grain always lacks atomic coherence with the MgO directly underneath it, whereas, the perpendicularly ordered portion is always coherent with the underlying MgO grain. Since the existence of multi-variant and in-plane ordered grains are severely detrimental to high density data storage capability, the understanding of their formation mechanism obtained here should make a significant impact on the future development of hard disk drive technology.« less

Interpersonal violence, circumstances of aggressions and patterns of maxillofacial injuries in the metropolitan area of Campina Grande, State of Paraíba, Brazil (2008-2011).

PubMed

Bernardino, Ítalo Macedo; Barbosa, Kevan Guilherme Nóbrega; Nóbrega, Lorena Marques; Cavalcante, Gigliana Maria Sobral; Ferreira, Efigenia Ferreira E; d'Ávila, Sérgio

2017-09-01

The aim of this study was to determine the circumstances of aggressions and patterns of maxillofacial injuries among victims of interpersonal violence. This was a cross-sectional and exploratory study conducted from the analysis of 7,132 medical-legal and social records of interpersonal violence victims seen in a Forensic Medicine and Dentistry Center. Descriptive and multivariate statistics were performed using Multiple Correspondence Analysis. Three groups with different victimization profiles were identified. The first group was mainly composed of men of different age groups, victims of community violence that resulted in facial bones or dentoalveolar fracture. The second group was mainly composed of adolescents (10-19 years) of both sexes, victims of interpersonal violence and without specific pattern of injuries. The third group was composed of adult women (≥ 20 years) victims of domestic violence that resulted in injuries of soft tissues of face or other body regions. The results suggest that sociodemographic and circumstantial characteristics are important factors in victimization by maxillofacial injuries and interpersonal violence.

The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study.

PubMed

Toppi, J; Petti, M; Vecchiato, G; Cincotti, F; Salinari, S; Mattia, D; Babiloni, F; Astolfi, L

2013-01-01

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

Patterns and determinants of maternity care in Damascus.

PubMed

Bashour, H; Abdulsalam, A; Al-Faisal, W; Cheikha, S

2008-01-01

This descriptive study was designed to describe the patterns and determinants of maternity care among Syrian women living in Damascus. All 39 birth registers in 2 large provinces were used to recruit 500 mothers of healthy newborns. Mothers were interviewed in their homes using a semistructured questionnaire. Multivariate analysis of the determinants of the frequency of use of antenatal care showed the following variables were significant: urban residence and visit to antenatal care in the 1st trimester. The significant variables for an early visit to antenatal care were the woman's level of education; being pregnant with the 1st baby; and number of visits to antenatal care. Being young (age < 20 years) also correlated with early timing of the 1st antenatal visit.

Daily variation patterns of airborne allergenic pollen in southwestern Spain.

PubMed

González Minero, F J; Candau, P; Tomás, C; Morales, J

1998-01-01

The study was carried out using a Burkard sampler installed on the roof terrace of the School of Pharmacy, Seville, for two years (1995 and 1996). Eight pollen types described in the literature as having allergenic activity were chosen. They were Poaceae, Olea europaea, Chenopodiaceae/Amaranthaceae, Plantago, Rumex, Urticaceae (including Parietaria), Cupressaceae, and Platanus hispanica. The types were grouped according to the similarity of their pattern of intradiurnal variation in pollen concentration. The following associations were established by multivariate analysis: Urticaceae and Chenopodiaceae/Amaranthaceae (appearing mainly between 11:00 and 20:00), Olea europaea and Plantago (12:00 to 19:00), Poaceae and Rumex (appearing throughout the day), and Cupressaceae and Platanus hispanica (8:00 to 14:00). The patterns of intradiurnal variation were similar both years for each type, despite the fact that the two years were climatologically different (1995 was dry and 1996 wet). We conclude that these behavior patterns are endogenous to the plants, and are hardly affected by meteorological parameters.

Prognostic significance of inverse spatial QRS-T angle circadian pattern in myocardial infarction survivors.

PubMed

Giannopoulos, Georgios; Dilaveris, Polychronis; Batchvarov, Velislav; Synetos, Andreas; Hnatkova, Katerina; Gatzoulis, Konstantinos; Malik, Marek; Stefanadis, Christodoulos

2009-01-01

We investigated the predictive value of the spatial QRS-T angle (QRSTA) circadian variation in myocardial infarction (MI) patients. Analyzing 24-hour recordings (SEER MC, GE Marquette) from 151 MI patients (age 63 +/- 12.7), the QRSTA was computed in derived XYZ leads. QRS-T angle values were compared between daytime and night time. The end point was cardiac death or life-threatening ventricular arrhythmia in 1 year. Overall, QRSTA was slightly higher during the day vs. the night (91 degrees vs. 87 degrees, P = .005). However, 33.8% of the patients showed an inverse diurnal QRSTA variation (higher values at night), which was correlated to the outcome (P = .001, odds ratio 6.7). In multivariate analysis, after entering all factors exhibiting univariate trend towards significance, inverse QRSTA circadian pattern remained significant (P = .036). Inverse QRSTA circadian pattern was found to be associated with adverse outcome (22.4%) in MI patients, whereas a normal pattern was associated (96%) with a favorable outcome.

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

PubMed

Kragel, Philip A; Labar, Kevin S

2013-08-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Multivariate Pattern Classification Reveals Autonomic and Experiential Representations of Discrete Emotions

PubMed Central

Kragel, Philip A.; LaBar, Kevin S.

2013-01-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PMID:23527508

Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil.

PubMed

de Souza, Fabio Teodoro

2018-05-29

In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls

PubMed Central

Kesler, Shelli R.; Wefel, Jeffrey S.; Hosseini, S. M. Hadi; Cheung, Maria; Watson, Christa L.; Hoeft, Fumiko

2013-01-01

Breast cancer (BC) chemotherapy is associated with cognitive changes including persistent deficits in some individuals. We tested the accuracy of default mode network (DMN) resting state functional connectivity patterns in discriminating chemotherapy treated (C+) from non–chemotherapy (C−) treated BC survivors and healthy controls (HC). We also examined the relationship between DMN connectivity patterns and cognitive function. Multivariate pattern analysis was used to classify 30 C+, 27 C−, and 24 HC, which showed significant accuracy for discriminating C+ from C− (91.23%, P < 0.0001) and C+ from HC (90.74%, P < 0.0001). The C− group did not differ significantly from HC (47.06%, P = 0.60). Lower subjective memory function was correlated (P < 0.002) with greater hyperplane distance (distance from the linear decision function that optimally separates the groups). Disrupted DMN connectivity may help explain long-term cognitive difficulties following BC chemotherapy. PMID:23798392

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

PubMed

Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

2005-07-01

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

The effect of heavy metal contamination on the bacterial community structure at Jiaozhou Bay, China.

PubMed

Yao, Xie-Feng; Zhang, Jiu-Ming; Tian, Li; Guo, Jian-Hua

In this study, determination of heavy metal parameters and microbiological characterization of marine sediments obtained from two heavily polluted sites and one low-grade contaminated reference station at Jiaozhou Bay in China were carried out. The microbial communities found in the sampled marine sediments were studied using PCR-DGGE (denaturing gradient gel electrophoresis) fingerprinting profiles in combination with multivariate analysis. Clustering analysis of DGGE and matrix of heavy metals displayed similar occurrence patterns. On this basis, 17 samples were classified into two clusters depending on the presence or absence of the high level contamination. Moreover, the cluster of highly contaminated samples was further classified into two sub-groups based on the stations of their origin. These results showed that the composition of the bacterial community is strongly influenced by heavy metal variables present in the sediments found in the Jiaozhou Bay. This study also suggested that metagenomic techniques such as PCR-DGGE fingerprinting in combination with multivariate analysis is an efficient method to examine the effect of metal contamination on the bacterial community structure. Copyright © 2016 Sociedade Brasileira de Microbiologia. Published by Elsevier Editora Ltda. All rights reserved.

Groundwater flow and hydrogeochemical evolution in the Jianghan Plain, central China

NASA Astrophysics Data System (ADS)

Gan, Yiqun; Zhao, Ke; Deng, Yamin; Liang, Xing; Ma, Teng; Wang, Yanxin

2018-05-01

Hydrogeochemical analysis and multivariate statistics were applied to identify flow patterns and major processes controlling the hydrogeochemistry of groundwater in the Jianghan Plain, which is located in central Yangtze River Basin (central China) and characterized by intensive surface-water/groundwater interaction. Although HCO3-Ca-(Mg) type water predominated in the study area, the 457 (21 surface water and 436 groundwater) samples were effectively classified into five clusters by hierarchical cluster analysis. The hydrochemical variations among these clusters were governed by three factors from factor analysis. Major components (e.g., Ca, Mg and HCO3) in surface water and groundwater originated from carbonate and silicate weathering (factor 1). Redox conditions (factor 2) influenced the geogenic Fe and As contamination in shallow confined groundwater. Anthropogenic activities (factor 3) primarily caused high levels of Cl and SO4 in surface water and phreatic groundwater. Furthermore, the factor score 1 of samples in the shallow confined aquifer gradually increased along the flow paths. This study demonstrates that enhanced information on hydrochemistry in complex groundwater flow systems, by multivariate statistical methods, improves the understanding of groundwater flow and hydrogeochemical evolution due to natural and anthropogenic impacts.

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

Multivariate Pattern Analysis Reveals Category-Related Organization of Semantic Representations in Anterior Temporal Cortex.

PubMed

Malone, Patrick S; Glezer, Laurie S; Kim, Judy; Jiang, Xiong; Riesenhuber, Maximilian

2016-09-28

The neural substrates of semantic representation have been the subject of much controversy. The study of semantic representations is complicated by difficulty in disentangling perceptual and semantic influences on neural activity, as well as in identifying stimulus-driven, "bottom-up" semantic selectivity unconfounded by top-down task-related modulations. To address these challenges, we trained human subjects to associate pseudowords (TPWs) with various animal and tool categories. To decode semantic representations of these TPWs, we used multivariate pattern classification of fMRI data acquired while subjects performed a semantic oddball detection task. Crucially, the classifier was trained and tested on disjoint sets of TPWs, so that the classifier had to use the semantic information from the training set to correctly classify the test set. Animal and tool TPWs were successfully decoded based on fMRI activity in spatially distinct subregions of the left medial anterior temporal lobe (LATL). In addition, tools (but not animals) were successfully decoded from activity in the left inferior parietal lobule. The tool-selective LATL subregion showed greater functional connectivity with left inferior parietal lobule and ventral premotor cortex, indicating that each LATL subregion exhibits distinct patterns of connectivity. Our findings demonstrate category-selective organization of semantic representations in LATL into spatially distinct subregions, continuing the lateral-medial segregation of activation in posterior temporal cortex previously observed in response to images of animals and tools, respectively. Together, our results provide evidence for segregation of processing hierarchies for different classes of objects and the existence of multiple, category-specific semantic networks in the brain. The location and specificity of semantic representations in the brain are still widely debated. We trained human participants to associate specific pseudowords with various animal and tool categories, and used multivariate pattern classification of fMRI data to decode the semantic representations of the trained pseudowords. We found that: (1) animal and tool information was organized in category-selective subregions of medial left anterior temporal lobe (LATL); (2) tools, but not animals, were encoded in left inferior parietal lobe; and (3) LATL subregions exhibited distinct patterns of functional connectivity with category-related regions across cortex. Our findings suggest that semantic knowledge in LATL is organized in category-related subregions, providing evidence for the existence of multiple, category-specific semantic representations in the brain. Copyright © 2016 the authors 0270-6474/16/3610089-08$15.00/0.

Brazilian dietary patterns and the dietary approaches to stop hypertension (DASH) diet-relationship with metabolic syndrome and newly diagnosed diabetes in the ELSA-Brasil study.

PubMed

Drehmer, Michele; Odegaard, Andrew O; Schmidt, Maria Inês; Duncan, Bruce B; Cardoso, Letícia de Oliveira; Matos, Sheila M Alvim; Molina, Maria Del Carmen B; Barreto, Sandhi M; Pereira, Mark A

2017-01-01

Studies evaluating dietary patterns, including the DASH diet, and their relationship with the metabolic syndrome and diabetes may help to understand the role of dairy products (low fat or full fat) in these conditions. Our aim is to identify dietary patterns in Brazilian adults and compare them with the (DASH) diet quality score in terms of their associations with metabolic syndrome and newly diagnosed diabetes in the Brazilian Longitudinal Study of Adult Health-the ELSA-Brasil study. The ELSA-Brasil is a multicenter cohort study comprising 15,105 civil servants, aged 35-74 years at baseline (2008-2010). Standardized interviews and exams were carried out, including an OGTT. We analyzed baseline data for 10,010 subjects. Dietary patterns were derived by principal component analysis. Multivariable logistic regression investigated associations of dietary patterns with metabolic syndrome and newly diagnosed diabetes and multivariable linear regression with components of metabolic syndrome. After controlling for potential confounders, we observed that greater adherence to the Common Brazilian meal pattern (white rice, beans, beer, processed and fresh meats), was associated with higher frequencies of newly diagnosed diabetes, metabolic syndrome and all of its components, except HDL-C. Participants with greater intake of a Common Brazilian fast foods/full fat dairy/milk based desserts pattern presented less newly diagnosed diabetes. An inverse association was also seen between the DASH Diet pattern and the metabolic syndrome, blood pressure and waist circumference. Diet, light foods and beverages/low fat dairy pattern was associated with more prevalence of both outcomes, and higher fasting glucose, HDL-C, waist circumference (among men) and lower blood pressure. Vegetables/fruit dietary pattern did not protect against metabolic syndrome and newly diagnosed diabetes but was associated with lower waist circumference. The inverse associations found for the dietary pattern characterizing Brazilian fast foods and desserts, typically containing dairy products, with newly diagnosed diabetes, and for the DASH diet with metabolic syndrome, support previously demonstrated beneficial effects of dairy products in metabolism. The positive association with metabolic syndrome and newly diagnosed diabetes found for the pattern characterizing a typical Brazilian meal deserves further investigation, particularly since it is frequently accompanied by processed meat. Trial registration NCT02320461. Registered 18 December 2014.

Association between Dietary Patterns during Pregnancy and Birth Size Measures in a Diverse Population in Southern US

PubMed Central

Colón-Ramos, Uriyoán; Racette, Susan B.; Ganiban, Jody; Nguyen, Thuy G.; Kocak, Mehmet; Carroll, Kecia N.; Völgyi, Eszter; Tylavsky, Frances A.

2015-01-01

Despite increased interest in promoting nutrition during pregnancy, the association between maternal dietary patterns and birth outcomes has been equivocal. We examined maternal dietary patterns during pregnancy as a determinant of offspring’s birth weight-for-length (WLZ), weight-for-age (WAZ), length-for-age (LAZ), and head circumference (HCZ) Z-scores in Southern United States (n = 1151). Maternal diet during pregnancy was assessed by seven dietary patterns. Multivariable linear regression models described the association of WLZ, WAZ, LAZ, and HCZ with diet patterns controlling for other maternal and child characteristics. In bivariate analyses, WAZ and HCZ were significantly lower for processed and processed-Southern compared to healthy dietary patterns, whereas LAZ was significantly higher for these patterns. In the multivariate models, mothers who consumed a healthy-processed dietary pattern had children with significantly higher HCZ compared to the ones who consumed a healthy dietary pattern (HCZ β: 0.36; p = 0.019). No other dietary pattern was significantly associated with any of the birth outcomes. Instead, the major outcome determinants were: African American race, pre-pregnancy BMI, and gestational weight gain. These findings justify further investigation about socio-environmental and genetic factors related to race and birth outcomes in this population. PMID:25690420

Dietary patterns, insulin sensitivity and adiposity in the multi-ethnic Insulin Resistance Atherosclerosis Study population.

PubMed

Liese, Angela D; Schulz, Mandy; Moore, Charity G; Mayer-Davis, Elizabeth J

2004-12-01

Epidemiological investigations increasingly employ dietary-pattern techniques to fully integrate dietary data. The present study evaluated the relationship of dietary patterns identified by cluster analysis with measures of insulin sensitivity (SI) and adiposity in the multi-ethnic, multi-centre Insulin Resistance Atherosclerosis Study (IRAS, 1992-94). Cross-sectional data from 980 middle-aged adults, of whom 67 % had normal and 33 % had impaired glucose tolerance, were analysed. Usual dietary intake was obtained by an interviewer-administered, validated food-frequency questionnaire. Outcomes included SI, fasting insulin (FI), BMI and waist circumference. The relationship of dietary patterns to log(SI+1), log(FI), BMI and waist circumference was modelled with multivariable linear regressions. Cluster analysis identified six distinct diet patterns--'dark bread', 'wine', 'fruits', 'low-frequency eaters', 'fries' and 'white bread'. The 'white bread' and the 'fries' patterns over-represented the Hispanic IRAS population predominantly from two centres, while the 'wine' and 'dark bread' groups were dominated by non-Hispanic whites. The dietary patterns were associated significantly with each of the outcomes first at the crude, clinical level (P<0.001). Furthermore, they were significantly associated with FI, BMI and waist circumference independent of age, sex, race or ethnicity, clinic, family history of diabetes, smoking and activity (P<0.004), whereas significance was lost for SI. Studying the total dietary behaviour via a pattern approach allowed us to focus both on the qualitative and quantitative dimensions of diet. The present study identified highly consistent associations of distinct dietary patterns with measures of insulin resistance and adiposity, which are risk factors for diabetes and heart disease.

Computer-based self-organized tectonic zoning: a tentative pattern recognition for Iran

NASA Astrophysics Data System (ADS)

Zamani, Ahmad; Hashemi, Naser

2004-08-01

Conventional methods of tectonic zoning are frequently characterized by two deficiencies. The first one is the large uncertainty involved in tectonic zoning based on non-quantitative and subjective analysis. Failure to interpret accurately a large amount of data "by eye" is the second. In order to alleviate each of these deficiencies, the multivariate statistical method of cluster analysis has been utilized to seek and separate zones with similar tectonic pattern and construct automated self-organized multivariate tectonic zoning maps. This analytical method of tectonic regionalization is particularly useful for showing trends in tectonic evolution of a region that could not be discovered by any other means. To illustrate, this method has been applied for producing a general-purpose numerical tectonic zoning map of Iran. While there are some similarities between the self-organized multivariate numerical maps and the conventional maps, the cluster solution maps reveal some remarkable features that cannot be observed on the current tectonic maps. The following specific examples need to be noted: (1) The much disputed extent and rigidity of the Lut Rigid Block, described as the microplate of east Iran, is clearly revealed on the self-organized numerical maps. (2) The cluster solution maps reveal a striking similarity between this microplate and the northern Central Iran—including the Great Kavir region. (3) Contrary to the conventional map, the cluster solution maps make a clear distinction between the East Iranian Ranges and the Makran Mountains. (4) Moreover, an interesting similarity between the Azarbaijan region in the northwest and the Makran Mountains in the southeast and between the Kopet Dagh Ranges in the northeast and the Zagros Folded Belt in the southwest of Iran are revealed in the clustering process. This new approach to tectonic zoning is a starting point and is expected to be improved and refined by collection of new data. The method is also a useful tool in studying neotectonics, seismotectonics, seismic zoning, and hazard estimation of the seismogenic regions.

Trends and variations in the use of adjuvant therapy for patients with head and neck cancer.

PubMed

Chen, Michelle M; Roman, Sanziana A; Yarbrough, Wendell G; Burtness, Barbara A; Sosa, Julie A; Judson, Benjamin L

2014-11-01

The National Comprehensive Cancer Network guidelines recommend that patients with surgically resected head and neck cancers that have adverse pathologic features should receive adjuvant therapy in the form of radiotherapy (RT) or chemoradiation (CRT). To the authors' knowledge, the current study is the first analysis of temporal trends and use patterns of adjuvant therapy for these patients. Patients with head and neck cancer and adverse pathologic features were identified in the National Cancer Data Base (1998-2011). Data were analyzed using chi-square, Student t, and log-rank tests; multivariate logistic regression; and Cox multivariate regression. A total of 73,088 patients were identified: 41.5% had received adjuvant RT, 33.5% had received adjuvant CRT, and 25.0% did not receive any adjuvant therapy. From 1998 to 2011, the increase in the use of adjuvant CRT was greatest for patients with oral cavity (6-fold) and laryngeal (5-fold) cancers. Multivariate analysis demonstrated that Medicare/Medicaid insurance (odds ratio [OR], 1.05; 95% confidence interval [95% CI], 1.01-1.11), distance ≥34 miles from the cancer center (OR, 1.66; 95% CI, 1.59-1.74), and academic (OR, 1.26; 95% CI, 1.20-1.31) and high-volume (OR, 1.10; 95% CI, 1.05-1.15) centers were independently associated with patients not receiving adjuvant therapy. Receipt of adjuvant therapy was found to be independently associated with improved overall survival (hazard ratio, 0.84; 95% CI, 0.81-0.86). Approximately 25% of patients are not receiving National Comprehensive Cancer Network guideline-directed adjuvant therapy. Patient-level and hospital-level factors are associated with variations in the receipt of adjuvant therapy. Further evaluation of these differences in practice patterns is needed to standardize practice and potentially improve the quality of care. Cancer 2014;120:3353-3360. © 2014 American Cancer Society. © 2014 American Cancer Society.

A perspective on two chemometrics tools: PCA and MCR, and introduction of a new one: Pattern recognition entropy (PRE), as applied to XPS and ToF-SIMS depth profiles of organic and inorganic materials

NASA Astrophysics Data System (ADS)

Chatterjee, Shiladitya; Singh, Bhupinder; Diwan, Anubhav; Lee, Zheng Rong; Engelhard, Mark H.; Terry, Jeff; Tolley, H. Dennis; Gallagher, Neal B.; Linford, Matthew R.

2018-03-01

X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are much used analytical techniques that provide information about the outermost atomic and molecular layers of materials. In this work, we discuss the application of multivariate spectral techniques, including principal component analysis (PCA) and multivariate curve resolution (MCR), to the analysis of XPS and ToF-SIMS depth profiles. Multivariate analyses often provide insight into data sets that is not easily obtained in a univariate fashion. Pattern recognition entropy (PRE), which has its roots in Shannon's information theory, is also introduced. This approach is not the same as the mutual information/entropy approaches sometimes used in data processing. A discussion of the theory of each technique is presented. PCA, MCR, and PRE are applied to four different data sets obtained from: a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized C3F6 on Si, a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized PNIPAM (poly (N-isopropylacrylamide)) on Si, an XPS depth profile through a film of SiO2 on Si, and an XPS depth profile through a film of Ta2O5 on Ta. PCA, MCR, and PRE reveal the presence of interfaces in the films, and often indicate that the first few scans in the depth profiles are different from those that follow. PRE and backward difference PRE provide this information in a straightforward fashion. Rises in the PRE signals at interfaces suggest greater complexity to the corresponding spectra. Results from PCA, especially for the higher principal components, were sometimes difficult to understand. MCR analyses were generally more interpretable.

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Postural tasks are associated with center of pressure spatial patterns of three-dimensional statokinesigrams in young and elderly healthy subjects.

PubMed

Baracat, Patrícia Junqueira Ferraz; de Sá Ferreira, Arthur

2013-12-01

The present study investigated the association between postural tasks and center of pressure spatial patterns of three-dimensional statokinesigrams. Young (n=35; 27.0±7.7years) and elderly (n=38; 67.3±8.7years) healthy volunteers maintained an undisturbed standing position during postural tasks characterized by combined sensory (vision/no vision) and biomechanical challenges (feet apart/together). A method for the analysis of three-dimensional statokinesigrams based on nonparametric statistics and image-processing analysis was employed. Four patterns of spatial distribution were derived from ankle and hip strategies according to the quantity (single; double; multi) and location (anteroposterior; mediolateral) of high-density regions on three-dimensional statokinesigrams. Significant associations between postural task and spatial pattern were observed (young: gamma=0.548, p<.001; elderly: gamma=0.582, p<.001). Robustness analysis revealed small changes related to parameter choices for histogram processing. MANOVA revealed multivariate main effects for postural task [Wilks' Lambda=0.245, p<.001] and age [Wilks' Lambda=0.308, p<.001], with interaction [Wilks' Lambda=0.732, p<.001]. The quantity of high-density regions was positively correlated to stabilogram and statokinesigram variables (p<.05 or lower). In conclusion, postural tasks are associated with center of pressure spatial patterns and are similar in young and elderly healthy volunteers. Single-centered patterns reflected more stable postural conditions and were more frequent with complete visual input and a wide base of support. Copyright © 2013 Elsevier B.V. All rights reserved.

Pattern of Utilisation of Dental Health Care Among HIV-positive Adult Nigerians.

PubMed

Adedigba, Michael A; Adekanmbi, Victor T; Asa, Sola; Fakande, Ibiyemi

2016-01-01

To determine the pattern of dental care utilisation of people living with HIV (PLHIV). A cross-sectional questionnaire survey of 239 PLHIV patients in three care centres was done. Information on sociodemographics, dental visit, risk groups, living arrangement, medical insurance and need of dental care was recorded. The EC Clearinghouse and WHO clinical staging was used to determine the stage of HIV/AIDS infection following routine oral examinations under natural daylight. Multivariate logistic regression models were created after adjusting for all the covariates that were statistically significant at univariate/bivariate levels. The majority of subjects were younger than 50 years, about 93% had not seen a dentist before being diagnosed HIV positive and 92% reported no dental visit after contracting HIV. Among nonusers of dental care, 14.3% reported that they wanted care but were afraid to seek it. Other reasons included poor awareness, lack of money and stigmatisation. Multivariate analysis showed that lack of dental care was associated with employment status, living arrangements, educational status, income per annum and presenting with oral symptoms. The area under the receiver operating curve was 84% for multivariate logistic regression model 1, 70% for model 2, 67% for model 3 and 71% for model 4, which means that the predictive power of the models were good. Contrary to our expectations, dental utilisation among PLHIV was generally poor among this group of patients. There is serious and immediate need to improve the awareness of PLHIVs in African settings and barriers to dental care utilisation should also be removed or reduced.

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Amodal processing in human prefrontal cortex.

PubMed

Tamber-Rosenau, Benjamin J; Dux, Paul E; Tombu, Michael N; Asplund, Christopher L; Marois, René

2013-07-10

Information enters the cortex via modality-specific sensory regions, whereas actions are produced by modality-specific motor regions. Intervening central stages of information processing map sensation to behavior. Humans perform this central processing in a flexible, abstract manner such that sensory information in any modality can lead to response via any motor system. Cognitive theories account for such flexible behavior by positing amodal central information processing (e.g., "central executive," Baddeley and Hitch, 1974; "supervisory attentional system," Norman and Shallice, 1986; "response selection bottleneck," Pashler, 1994). However, the extent to which brain regions embodying central mechanisms of information processing are amodal remains unclear. Here we apply multivariate pattern analysis to functional magnetic resonance imaging (fMRI) data to compare response selection, a cognitive process widely believed to recruit an amodal central resource across sensory and motor modalities. We show that most frontal and parietal cortical areas known to activate across a wide variety of tasks code modality, casting doubt on the notion that these regions embody a central processor devoid of modality representation. Importantly, regions of anterior insula and dorsolateral prefrontal cortex consistently failed to code modality across four experiments. However, these areas code at least one other task dimension, process (instantiated as response selection vs response execution), ensuring that failure to find coding of modality is not driven by insensitivity of multivariate pattern analysis in these regions. We conclude that abstract encoding of information modality is primarily a property of subregions of the prefrontal cortex.

Diagnosis of abnormal patterns in multivariate microclimate monitoring: a case study of an open-air archaeological site in Pompeii (Italy).

PubMed

Merello, Paloma; García-Diego, Fernando-Juan; Zarzo, Manuel

2014-08-01

Chemometrics has been applied successfully since the 1990s for the multivariate statistical control of industrial processes. A new area of interest for these tools is the microclimatic monitoring of cultural heritage. Sensors record climatic parameters over time and statistical data analysis is performed to obtain valuable information for preventive conservation. A case study of an open-air archaeological site is presented here. A set of 26 temperature and relative humidity data-loggers was installed in four rooms of Ariadne's house (Pompeii). If climatic values are recorded versus time at different positions, the resulting data structure is equivalent to records of physical parameters registered at several points of a continuous chemical process. However, there is an important difference in this case: continuous processes are controlled to reach a steady state, whilst open-air sites undergo tremendous fluctuations. Although data from continuous processes are usually column-centred prior to applying principal components analysis, it turned out that another pre-treatment (row-centred data) was more convenient for the interpretation of components and to identify abnormal patterns. The detection of typical trajectories was more straightforward by dividing the whole monitored period into several sub-periods, because the marked climatic fluctuations throughout the year affect the correlation structures. The proposed statistical methodology is of interest for the microclimatic monitoring of cultural heritage, particularly in the case of open-air or semi-confined archaeological sites. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Riser Pattern: Another Determinant of Heart Failure With Preserved Ejection Fraction.

PubMed

Komori, Takahiro; Eguchi, Kazuo; Saito, Toshinobu; Hoshide, Satoshi; Kario, Kazuomi

2016-10-01

Paradoxical increase in blood pressure (BP) during sleep, exceeding those of awake BP, is called the "riser" BP pattern, and known as an abnormal circadian BP rhythm, has been reported to be associated with adverse cardiovascular prognoses. However, the significance of ambulatory BP in heart failure patients with preserved ejection fraction (HFpEF) has never been reported. Here, we tested our hypothesis that abnormal circadian BP rhythm is associated with HFpEF. The authors enrolled 508 patients with hospitalized HF (age 68±13 years; 315 men, 193 women). There were 232 cases of HFpEF and 276 cases of heart failure with reduced ejection fraction (HFrEF). The riser BP pattern was significantly more frequent in the HFpEF (28.9%) group compared with the HFrEF group (19.9%). In a multivariable logistic regression analysis, the riser BP pattern was associated with HFpEF (odds ratio, 1.73; 95% confidence interval, 1.02-2.91; P=.041) independent of the other covariates. In conclusion, the riser BP pattern was associated with HFpEF. ©2016 Wiley Periodicals, Inc.

Somatic and vicarious pain are represented by dissociable multivariate brain patterns

PubMed Central

Krishnan, Anjali; Woo, Choong-Wan; Chang, Luke J; Ruzic, Luka; Gu, Xiaosi; López-Solà, Marina; Jackson, Philip L; Pujol, Jesús; Fan, Jin; Wager, Tor D

2016-01-01

Understanding how humans represent others’ pain is critical for understanding pro-social behavior. ‘Shared experience’ theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others’ suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sample individuals. Critically, each pattern was at chance in predicting the other experience, demonstrating separate modifiability of both patterns. Somatotopy (upper versus lower limb: 93% accuracy for both conditions) was also distinct, located in somatosensory versus mentalizing-related circuits for somatic and vicarious pain, respectively. Two additional studies demonstrated the generalizability of the somatic pain pattern (which was originally developed on thermal pain) to mechanical and electrical pain, and also demonstrated the replicability of the somatic/vicarious dissociation. These findings suggest possible mechanisms underlying limitations in feeling others’ pain, and present new, more specific, brain targets for studying pain empathy. DOI: http://dx.doi.org/10.7554/eLife.15166.001 PMID:27296895

Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

Assessment of benthic changes during 20 years of monitoring the Mexican Salina Cruz Bay.

PubMed

González-Macías, C; Schifter, I; Lluch-Cota, D B; Méndez-Rodríguez, L; Hernández-Vázquez, S

2009-02-01

In this work a non-parametric multivariate analysis was used to assess the impact of metals and organic compounds in the macro infaunal component of the mollusks benthic community using surface sediment data from several monitoring programs collected over 20 years in Salina Cruz Bay, Mexico. The data for benthic mollusks community characteristics (richness, abundance and diversity) were linked to multivariate environmental patterns, using the Alternating Conditional Expectations method to correlate the biological measurements of the mollusk community with the physicochemical properties of water and sediments. Mollusks community variation is related to environmental characteristics as well as lead content. Surface deposit feeders are increasing their relative density, while subsurface deposit feeders are decreasing with respect to time, these last are expected to be more related with sediment and more affected then by its quality. However gastropods with predatory carnivore as well as chemosymbiotic deposit feeder bivalves have maintained their relative densities along time.

Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Yang, Haiqing; Wu, Di; He, Yong

2007-11-01

Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work. The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the calibration set is small.

Can texture analysis of tooth microwear detect within guild niche partitioning in extinct species?

NASA Astrophysics Data System (ADS)

Purnell, Mark; Nedza, Christopher; Rychlik, Leszek

2017-04-01

Recent work shows that tooth microwear analysis can be applied further back in time and deeper into the phylogenetic history of vertebrate clades than previously thought (e.g. niche partitioning in early Jurassic insectivorous mammals; Gill et al., 2014, Nature). Furthermore, quantitative approaches to analysis based on parameterization of surface roughness are increasing the robustness and repeatability of this widely used dietary proxy. Discriminating between taxa within dietary guilds has the potential to significantly increase our ability to determine resource use and partitioning in fossil vertebrates, but how sensitive is the technique? To address this question we analysed tooth microwear texture in sympatric populations of shrew species (Neomys fodiens, Neomys anomalus, Sorex araneus, Sorex minutus) from BiaŁ owieza Forest, Poland. These populations are known to exhibit varying degrees of niche partitioning (Churchfield & Rychlik, 2006, J. Zool.) with greatest overlap between the Neomys species. Sorex araneus also exhibits some niche overlap with N. anomalus, while S. minutus is the most specialised. Multivariate analysis based only on tooth microwear textures recovers the same pattern of niche partitioning. Our results also suggest that tooth textures track seasonal differences in diet. Projecting data from fossils into the multivariate dietary space defined using microwear from extant taxa demonstrates that the technique is capable of subtle dietary discrimination in extinct insectivores.

[Risk factors for anorexia in children].

PubMed

Liu, Wei-Xiao; Lang, Jun-Feng; Zhang, Qin-Feng

2016-11-01

To investigate the risk factors for anorexia in children, and to reduce the prevalence of anorexia in children. A questionnaire survey and a case-control study were used to collect the general information of 150 children with anorexia (case group) and 150 normal children (control group). Univariate analysis and multivariate logistic stepwise regression analysis were performed to identify the risk factors for anorexia in children. The results of the univariate analysis showed significant differences between the case and control groups in the age in months when supplementary food were added, feeding pattern, whether they liked meat, vegetables and salty food, whether they often took snacks and beverages, whether they liked to play while eating, and whether their parents asked them to eat food on time (P<0.05). The results of the multivariate logistic regression analysis showed that late addition of supplementary food (OR=5.408), high frequency of taking snacks and/or drinks (OR=11.813), and eating while playing (OR=6.654) were major risk factors for anorexia in children. Liking of meat (OR=0.093) and vegetables (OR=0.272) and eating on time required by parents (OR=0.079) were protective factors against anorexia in children. Timely addition of supplementary food, a proper diet, and development of children's proper eating and living habits can reduce the incidence of anorexia in children.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

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.

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

PubMed

Oosterhof, Nikolaas N; Connolly, Andrew C; Haxby, James V

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

PubMed Central

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA PMID:27499741

Association Between Treatment at High-Volume Facilities and Improved Overall Survival in Soft Tissue Sarcomas.

PubMed

Venigalla, Sriram; Nead, Kevin T; Sebro, Ronnie; Guttmann, David M; Sharma, Sonam; Simone, Charles B; Levin, William P; Wilson, Robert J; Weber, Kristy L; Shabason, Jacob E

2018-03-15

Soft tissue sarcomas (STS) are rare malignancies that require complex multidisciplinary management. Therefore, facilities with high sarcoma case volume may demonstrate superior outcomes. We hypothesized that STS treatment at high-volume (HV) facilities would be associated with improved overall survival (OS). Patients aged ≥18 years with nonmetastatic STS treated with surgery and radiation therapy at a single facility from 2004 through 2013 were identified from the National Cancer Database. Facilities were dichotomized into HV and low-volume (LV) cohorts based on total case volume over the study period. OS was assessed using multivariable Cox regression with propensity score-matching. Patterns of care were assessed using multivariable logistic regression analysis. Of 9025 total patients, 1578 (17%) and 7447 (83%) were treated at HV and LV facilities, respectively. On multivariable analysis, high educational attainment, larger tumor size, higher grade, and negative surgical margins were statistically significantly associated with treatment at HV facilities; conversely, black race and non-metropolitan residence were negative predictors of treatment at HV facilities. On propensity score-matched multivariable analysis, treatment at HV facilities versus LV facilities was associated with improved OS (hazard ratio, 0.87, 95% confidence interval, 0.80-0.95; P = .001). Older age, lack of insurance, greater comorbidity, larger tumor size, higher tumor grade, and positive surgical margins were associated with statistically significantly worse OS. In this observational cohort study using the National Cancer Database, receipt of surgery and radiation therapy at HV facilities was associated with improved OS in patients with STS. Potential sociodemographic disparities limit access to care at HV facilities for certain populations. Our findings highlight the importance of receipt of care at HV facilities for patients with STS and warrant further study into improving access to care at HV facilities. Copyright © 2017 Elsevier Inc. All rights reserved.

Utilization of surgical treatment for local and locoregional esophageal cancer: Analysis of the National Cancer Data Base.

PubMed

Taylor, Lauren J; Greenberg, Caprice C; Lidor, Anne O; Leverson, Glen E; Maloney, James D; Macke, Ryan A

2017-02-01

Previous studies have suggested that esophagectomy is severely underused for patients with resectable esophageal cancer. The recent expansion of endoscopic local therapies, advances in surgical techniques, and improved postoperative outcomes have changed the therapeutic landscape. The impact of these developments and evolving treatment guidelines on national practice patterns is unknown. Patients diagnosed with clinical stage 0 to III esophageal cancer were identified from the National Cancer Database (2004-2013). The receipt of potentially curative surgical treatment over time was analyzed, and multivariate logistic regression was used to identify factors associated with surgical treatment. The analysis included 52,122 patients. From 2004 to 2013, the overall rate of potentially curative surgical treatment increased from 36.4% to 47.4% (P < .001). For stage 0 disease, the receipt of esophagectomy decreased from 23.8% to 17.9% (P < .001), whereas the use of local therapies increased from 34.3% to 58.8% (P < .001). The use of surgical treatment increased from 43.4% to 61.8% (P < .001), from 36.1% to 45.0% (P < .001), and from 30.8% to 38.6% (P < .001) for patients with stage I, II, and III disease, respectively. In the multivariate analysis, divergent practice patterns and adherence to national guidelines were noted between academic and community facilities. The use of potentially curative surgical treatment has increased for patients with stage 0 to III esophageal cancer. The expansion of local therapies has driven increased rates of surgical treatment for early-stage disease. Although the increased use of esophagectomy for more advanced disease is encouraging, significant variation persists at the patient and facility levels. Cancer 2017;123:410-419. © 2016 American Cancer Society. © 2016 American Cancer Society.

Improving the analysis of near-spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

PubMed

Gemignani, Jessica; Middell, Eike; Barbour, Randall L; Graber, Harry L; Blankertz, Benjamin

2018-04-04

The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.

Natural selection. VII. History and interpretation of kin selection theory.

PubMed

Frank, S A

2013-06-01

Kin selection theory is a kind of causal analysis. The initial form of kin selection ascribed cause to costs, benefits and genetic relatedness. The theory then slowly developed a deeper and more sophisticated approach to partitioning the causes of social evolution. Controversy followed because causal analysis inevitably attracts opposing views. It is always possible to separate total effects into different component causes. Alternative causal schemes emphasize different aspects of a problem, reflecting the distinct goals, interests and biases of different perspectives. For example, group selection is a particular causal scheme with certain advantages and significant limitations. Ultimately, to use kin selection theory to analyse natural patterns and to understand the history of debates over different approaches, one must follow the underlying history of causal analysis. This article describes the history of kin selection theory, with emphasis on how the causal perspective improved through the study of key patterns of natural history, such as dispersal and sex ratio, and through a unified approach to demographic and social processes. Independent historical developments in the multivariate analysis of quantitative traits merged with the causal analysis of social evolution by kin selection. © 2013 The Author. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

Occult lymph node metastasis and risk of regional recurrence in papillary thyroid cancer after bilateral prophylactic central neck dissection: A multi-institutional study.

PubMed

Lee, Young Chan; Na, Se Young; Park, Gi Cheol; Han, Ju Hyun; Kim, Seung Woo; Eun, Young Gyu

2017-02-01

The impact of occult lymph node metastasis on regional recurrence after prophylactic central neck dissection for preoperative, nodal-negative papillary thyroid cancer is controversial. We investigated risk factors for regional lymph node recurrence in papillary thyroid cancer patients who underwent total thyroidectomy and bilateral prophylactic central neck dissection. Analysis was according to clinicopathologic characteristics and occult lymph node metastasis patterns. This multicenter study enrolled 211 consecutive patients who underwent total thyroidectomy with bilateral prophylactic central neck dissection for papillary thyroid cancer without evidence of central lymph node metastasis on preoperative imaging. Clinicopathologic features and central lymph node metastasis patterns were analyzed for predicting regional recurrence. Multivariate Cox regression analysis was used to identify independent factors for recurrence. Median follow-up time was 43 months (24-95 months). Ten patients (4.7%) showed regional lymph node recurrence. The estimated 5-year, regional recurrence-free survival was 95.2%. Tumor size ≥1 cm, central lymph node metastasis, lymph node ratio, and prelaryngeal lymph node metastasis were associated with regional recurrence in univariate analysis (P < .05). In multivariate analysis, a lymph node ratio ≥ 0.26 was a significant risk factor for regional lymph node recurrence (odds ratio = 11.63, P = .003). Lymph node ratio ≥ 0.26 was an independent predictor of worse recurrence-free survival on Cox regression analysis (hazard ratio = 11.49, P = .002). Although no significant association was observed between the presence of occult lymph node metastasis and regional recurrence, lymph node ratio ≥ 0.26 was an independent predictor of regional lymph node recurrence in papillary thyroid cancer patients who underwent total thyroidectomy and bilateral prophylactic central neck dissection. Copyright © 2016 Elsevier Inc. All rights reserved.

Membrane localization of insulin receptor substrate-2 (IRS-2) is associated with decreased overall survival in breast cancer

PubMed Central

Clark, Jennifer L.; Dresser, Karen; Hsieh, Chung-Cheng; Sabel, Michael; Kleer, Celina G.; Khan, Ashraf

2011-01-01

Recent studies have identified a role for insulin receptor substrate-2 (IRS-2) in promoting motility and metastasis in breast cancer. However, no published studies to date have examined IRS-2 expression in human breast tumors. We examined IRS-2 expression by immunohistochemistry (IHC) in normal breast tissue, benign breast lesions, and malignant breast tumors from the institutional pathology archives and a tumor microarray from a separate institution. Three distinct IRS-2 staining patterns were noted: diffusely cytoplasmic, punctate cytoplasmic, and localized to the cell membrane. The individual and pooled datasets were analyzed for associations of IRS-2 staining pattern with core clinical parameters and clinical outcomes. Univariate analysis revealed a trend toward decreased overall survival (OS) with IRS-2 membrane staining, and this association became significant upon multivariate analysis (P = 0.01). In progesterone receptor negative (PR−) tumors, in particular, IRS-2 staining at the membrane correlated with significantly worse OS than other IRS-2 staining patterns (P < 0.001). When PR status and IRS-2 staining pattern were evaluated in combination, PR− tumors with IRS-2 at the membrane were associated with a significantly decreased OS when compared with all other combinations (P = 0.002). Evaluation of IRS-2 staining patterns could potentially be used to identify patients with PR− tumors who would most benefit from aggressive treatment. PMID:21258861

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

Association between dietary patterns and cognitive function among 70-year-old Japanese elderly: a cross-sectional analysis of the SONIC study.

PubMed

Okubo, Hitomi; Inagaki, Hiroki; Gondo, Yasuyuki; Kamide, Kei; Ikebe, Kazunori; Masui, Yukie; Arai, Yasumichi; Ishizaki, Tatsuro; Sasaki, Satoshi; Nakagawa, Takeshi; Kabayama, Mai; Sugimoto, Ken; Rakugi, Hiromi; Maeda, Yoshinobu

2017-09-11

An increasing number of studies in Western countries have shown that healthy dietary patterns may have a protective effect against cognitive decline and dementia. However, information on this relationship among non-Western populations with different cultural settings is extremely limited. We aim to examine the relationship between dietary patterns and cognitive function among older Japanese people. This cross-sectional study included 635 community-dwelling people aged 69-71 years who participated in the prospective cohort study titled Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians (SONIC). Diet was assessed over a one-month period with a validated, brief-type, self-administered diet history questionnaire. Dietary patterns from thirty-three predefined food groups [energy-adjusted food (g/d)] were extracted by factor analysis. Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Multivariate regression analysis was performed to examine the relationship between dietary patterns and cognitive function. Three dietary patterns were identified: the 'Plant foods and fish', 'Rice and miso soup', and 'Animal food' patterns. The 'Plant foods and fish' pattern, characterized by high intakes of green and other vegetables, soy products, seaweeds, mushrooms, potatoes, fruit, fish, and green tea, was significantly associated with a higher MoCA-J score [MoCA-J score per one-quartile increase in dietary pattern: β = 0.56 (95% CI: 0.33, 0.79), P for trend <0.001]. This association was still evident after adjustment for potential confounding factors [β = 0.41 (95% CI: 0.17, 0.65), P for trend <0.001]. In contrast, neither the 'Rice and miso soup' nor the 'Animal food' pattern was related to cognitive function. To confirm the possibility of reverse causation we also conducted a sensitivity analysis excluding 186 subjects who reported substantial changes in their diet for any reason, but the results did not change materially. This preliminary cross-sectional study suggests that a diet with high intakes of vegetables, soy products, fruit, and fish may have a beneficial effect on cognitive function in older Japanese people. Further prospective studies are needed to confirm this finding.

Bird distributional patterns support biogeographical histories and are associated with bioclimatic units in the Atlantic Forest, Brazil.

PubMed

Carvalho, Cristiano DE Santana; Nascimento, Nayla Fábia Ferreira DO; Araujo, Helder F P DE

2017-10-17

Rivers as barriers to dispersal and past forest refugia are two of the hypotheses proposed to explain the patterns of biodiversity in the Atlantic Forest. It has recently been shown that possible past refugia correspond to bioclimatically different regions, so we tested whether patterns of shared distribution of bird taxa in the Atlantic Forest are 1) limited by the Doce and São Francisco rivers or 2) associated with the bioclimatically different southern and northeastern regions. We catalogued lists of forest birds from 45 locations, 36 in the Atlantic forest and nine in Amazon, and used parsimony analysis of endemicity to identify groups of shared taxa. We also compared differences between these groups by permutational multivariate analysis of variance and identified the species that best supported the resulting groups. The results showed that the distribution of forest birds is divided into two main regions in the Atlantic Forest, the first with more southern localities and the second with northeastern localities. This distributional pattern is not delimited by riverbanks, but it may be associated with bioclimatic units, surrogated by altitude, that maintain current environmental differences between two main regions on Atlantic Forest and may be related to phylogenetic histories of taxa supporting the two groups.

Incremental Value of Plaque Enhancement in Patients with Moderate or Severe Basilar Artery Stenosis: 3.0 T High-Resolution Magnetic Resonance Study.

PubMed

Wang, Wanqian; Yang, Qi; Li, Debiao; Fan, Zhaoyang; Bi, Xiaoming; Du, Xiangying; Wu, Fang; Wu, Ye; Li, Kuncheng

2017-01-01

To investigate the clinical relevance of plaque's morphological characteristics and distribution pattern using 3.0 T high-resolution magnetic resonance imaging (HRMRI) in patients with moderate or severe basilar artery (BA) atherosclerosis stenosis. Fifty-seven patients (33 symptomatic patients and 24 asymptomatic patients) were recruited for 3.0 T HRMRI scan; all of them had >50% stenosis on the BA. The intraplaque hemorrhage (IPH), contrast-enhancement pattern, and distribution of BA plaques were compared between the symptomatic and asymptomatic groups. Factors potentially associated with posterior ischemic stroke were calculated by multivariate analyses. Enhancement of BA plaque was more frequently observed in symptomatic than in asymptomatic patients (27/33, 81.8% versus 11/24, 45.8%; p < 0.01). In multivariate regression analysis, plaque enhancement (OR = 7.193; 95% CI: 1.880-27.517; p = 0.004) and smoking (OR = 4.402; 95% CI: 2.218-15.909; p = 0.024) were found to be independent risk factors of posterior ischemic events in patients with BA stenosis >50%. Plaques were mainly distributed at the ventral site (39.3%) or involved more than two arcs (21.2%) in the symptomatic group but were mainly distributed at left (33.3%) and right (25.0%) sites in the asymptomatic group.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Recent patterns in antibiotic use for children with group A streptococcal infections in Japan.

PubMed

Okubo, Yusuke; Michihata, Nobuaki; Morisaki, Naho; Kinoshita, Noriko; Miyairi, Isao; Urayama, Kevin Y; Yasunaga, Hideo

2017-11-13

Antibiotics are the most frequently prescribed medicines for children, however inappropriate antibiotic prescribing is prevalent. This study investigated recent trends in antibiotic use and factors associated with appropriate antibiotic selection among children with group A streptococcal infections in Japan. Records of outpatients aged <18years with a diagnosis of group A streptococcal infection were obtained using the Japan Medical Data Center database. Prescription patterns for antibiotics were investigated and factors associated with penicillin use were evaluated using a multivariable log-binomial regression model. Overall, 5030 patients with a diagnosis of group A streptococcal infection were identified. The most commonly prescribed antibiotics were third-generation cephalosporins (53.3%), followed by penicillins (40.1%). In the multivariable log-binomial regression analysis, out-of-hours visits were independently associated with penicillin prescriptions [prevalence ratio (PR)=1.10, 95% confidence interval (CI) 1.03-1.18], whereas clinical departments other than paediatrics and internal medicine were related to non-penicillin prescriptions (PR=0.57, 95% CI 0.46-0.71). Third-generation cephalosporins were overprescribed for children with group A streptococcal infections. This investigation provides important information for promoting education for physicians and for constructing health policies for appropriate antibiotic prescription. Copyright © 2017. Published by Elsevier Ltd.

Multivariate fMRI and Eye Tracking Reveal Differential Effects of Visual Interference on Recognition Memory Judgments for Objects and Scenes.

PubMed

O'Neil, Edward B; Watson, Hilary C; Dhillon, Sonya; Lobaugh, Nancy J; Lee, Andy C H

2015-09-01

Recent work has demonstrated that the perirhinal cortex (PRC) supports conjunctive object representations that aid object recognition memory following visual object interference. It is unclear, however, how these representations interact with other brain regions implicated in mnemonic retrieval and how congruent and incongruent interference influences the processing of targets and foils during object recognition. To address this, multivariate partial least squares was applied to fMRI data acquired during an interference match-to-sample task, in which participants made object or scene recognition judgments after object or scene interference. This revealed a pattern of activity sensitive to object recognition following congruent (i.e., object) interference that included PRC, prefrontal, and parietal regions. Moreover, functional connectivity analysis revealed a common pattern of PRC connectivity across interference and recognition conditions. Examination of eye movements during the same task in a separate study revealed that participants gazed more at targets than foils during correct object recognition decisions, regardless of interference congruency. By contrast, participants viewed foils more than targets for incorrect object memory judgments, but only after congruent interference. Our findings suggest that congruent interference makes object foils appear familiar and that a network of regions, including PRC, is recruited to overcome the effects of interference.

Comparative analysis of the effect of prostatic invasion patterns on cancer-specific mortality after radical cystectomy in pT4a urothelial carcinoma of the bladder.

PubMed

Vallo, Stefan; Gilfrich, Christian; Burger, Maximilian; Volkmer, Björn; Boehm, Katharina; Rink, Michael; Chun, Felix K; Roghmann, Florian; Novotny, Vladimir; Mani, Jens; Brisuda, Antonin; Mayr, Roman; Stredele, Regina; Noldus, Joachim; Schnabel, Marco; May, Matthias; Fritsche, Hans-Martin; Pycha, Armin; Martini, Thomas; Wirth, Manfred; Roigas, Jan; Bastian, Patrick J; Nuhn, Philipp; Dahlem, Roland; Haferkamp, Axel; Fisch, Margit; Aziz, Atiqullah

2016-10-01

To evaluate the prognostic relevance of different prostatic invasion patterns in pT4a urothelial carcinoma of the bladder (UCB) after radical cystectomy. Our study comprised a total of 358 men with pT4a UCB. Patients were divided in 2 groups-group A with stromal infiltration of the prostate via the prostatic urethra with additional muscle-invasive UCB (n = 121, 33.8%) and group B with continuous infiltration of the prostate through the entire bladder wall (n = 237, 66.2%). The effect of age, tumor grade, carcinoma in situ, lymphovascular invasion, soft tissue surgical margin, lymph node metastases, administration of adjuvant chemotherapy, and prostatic invasion patterns on cancer-specific mortality (CSM) was evaluated using competing-risk regression analysis. Decision curve analysis was used to evaluate the net benefit of including the variable invasion pattern within our model. The estimated 5-year CSM-rates for group A and B were 50.1% and 66.0%, respectively. In multivariable competing-risk analysis, lymph node metastases (hazard ratio [HR] = 1.73, P<0.001), lymphovascular invasion (HR = 1.62, P = 0.0023), soft tissue surgical margin (HR = 1.49, P = 0.026), absence of adjuvant chemotherapy (HR = 2.11, P<0.001), and tumor infiltration of the prostate by continuous infiltration of the entire bladder wall (HR = 1.37, P = 0.044) were significantly associated with a higher risk for CSM. Decision curve analysis showed a net benefit of our model including the variable invasion pattern. Continuous infiltration of the prostate through the entire bladder wall showed an adverse effect on CSM. Besides including these patients into clinical trials for an adjuvant therapy, we recommend including prostatic invasion patterns in predictive models in pT4a UCB in men. Copyright © 2016 Elsevier Inc. All rights reserved.

The multivariate egg: quantifying within- and among-clutch correlations between maternally derived yolk immunoglobulins and yolk androgens using multivariate mixed models.

PubMed

Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara

2014-03-01

Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.

Pathways to labor force exit: work transitions and work instability.

PubMed

Mutchler, J E; Burr, J A; Pienta, A M; Massagli, M P

1997-01-01

The purpose of this study is to examine alternative pathways to labor force exit among older men. Based on the life course perspective, we distinguish between crisp exits from the labor force, which are characterized as being unidirectional, and blurred transition patterns, which include repeated exists, entrances, and unemployment spells. Using longitudinal data from the 1984 Survey of Income and Program Participation, we find that one-quarter of the sample of men aged 55 to 74 at first interview experienced at least one transition in labor force status over a 28-month observation period. Fewer than half of these can be characterized as crisp exists from the labor force. Our multivariate analysis suggests that blurred transition patterns are likely part of an effort to maintain economic status in later life.

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…

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications

PubMed Central

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-01-01

Background. Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends’ preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. Methods. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. Results. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Conclusion. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors. PMID:28231172

Population genetic structure in a social landscape: barley in a traditional Ethiopian agricultural system

PubMed Central

Samberg, Leah H; Fishman, Lila; Allendorf, Fred W

2013-01-01

Conservation strategies are increasingly driven by our understanding of the processes and patterns of gene flow across complex landscapes. The expansion of population genetic approaches into traditional agricultural systems requires understanding how social factors contribute to that landscape, and thus to gene flow. This study incorporates extensive farmer interviews and population genetic analysis of barley landraces (Hordeum vulgare) to build a holistic picture of farmer-mediated geneflow in an ancient, traditional agricultural system in the highlands of Ethiopia. We analyze barley samples at 14 microsatellite loci across sites at varying elevations and locations across a contiguous mountain range, and across farmer-identified barley types and management strategies. Genetic structure is analyzed using population-based and individual-based methods, including measures of population differentiation and genetic distance, multivariate Principal Coordinate Analysis, and Bayesian assignment tests. Phenotypic analysis links genetic patterns to traits identified by farmers. We find that differential farmer management strategies lead to markedly different patterns of population structure across elevation classes and barley types. The extent to which farmer seed management appears as a stronger determinant of spatial structure than the physical landscape highlights the need for incorporation of social, landscape, and genetic data for the design of conservation strategies in human-influenced landscapes. PMID:24478796

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Association between Dietary Patterns and the Indicators of Obesity among Chinese: A Cross-Sectional Study.

PubMed

Shu, Long; Zheng, Pei-Fen; Zhang, Xiao-Yan; Si, Cai-Juan; Yu, Xiao-Long; Gao, Wei; Zhang, Lun; Liao, Dan

2015-09-17

No previous study has investigated dietary pattern in association with obesity risk in a middle-aged Chinese population. The purpose of this study was to evaluate the associations between dietary patterns and the risk of obesity in the city of Hangzhou, the capital of Zhejiang Province, east China. In this cross-sectional study of 2560 subjects aged 45-60 years, dietary intakes were evaluated using a semi-quantitative food frequency questionnaire (FFQ). All anthropometric measurements were obtained using standardized procedures. The partial correlation analysis was performed to assess the associations between dietary patterns and body mass index (BMI), waist circumference (WC), and waist to hip ratio (WHR). Multivariate logistic regression analysis was used to examine the associations between dietary patterns and obesity, with adjustment for potential confounders. Four major dietary patterns were extracted by means of factor analysis: animal food, traditional Chinese, western fast-food, and high-salt patterns. The animal food pattern was positively associated with BMI (r = 0.082, 0.144, respectively, p < 0.05) and WC (r = 0.102, 0.132, respectively, p < 0.01), and the traditional Chinese pattern was inversely associated with BMI (r = -0.047, -0.116, respectively, p < 0.05) and WC (r = -0.067, -0.113, respectively, p < 0.05) in both genders. After controlling for potential confounders, subjects in the highest quartile of animal food pattern scores had a greater odds ratio for abdominal obesity (odds ratio (OR) = 1.67; 95% confidence interval (CI): 1.188-2.340; p < 0.01), in comparison to those from the lowest quartile. Compared with the lowest quartile of the traditional Chinese pattern, the highest quartile had a lower odds ratio for abdominal obesity (OR = 0.63; 95% CI: 0.441-0.901, p < 0.05). Our findings indicated that the animal food pattern was associated with a higher risk of abdominal obesity, while the traditional Chinese pattern was associated with a lower risk of abdominal obesity. Further prospective studies are warranted to confirm these findings.

Dietary Patterns and Type 2 Diabetes Mellitus in a First Nations Community.

PubMed

Reeds, Jacqueline; Mansuri, Sudaba; Mamakeesick, Mary; Harris, Stewart B; Zinman, Bernard; Gittelsohn, Joel; Wolever, Thomas M S; Connelly, Phillip W; Hanley, Anthony

2016-08-01

Type 2 diabetes mellitus is a growing concern worldwide, particularly in Indigenous communities, which have undergone a marked nutrition transition characterized by reduced intakes of traditional foods and increased intakes of market foods. Few studies have assessed the relationships between differing dietary patterns and risk for type 2 diabetes in Indigenous communities in Canada. The objective of the study was to characterize dietary patterns using factor analysis (FA) and to relate these patterns to the incidence of type 2 diabetes after 10 years of follow up in a First Nations community in Ontario, Canada. We conducted a prospective analysis of 492 participants in the SLHDP who did not have diabetes at baseline (1993 to 1995) and were followed for 10 years. A food-frequency questionnaire was administered, and FA was used to identify patterns of food consumption. Multivariate logistic regression analyses determined associations of food patterns with incident type 2 diabetes, adjusting for sociodemographic and lifestyle confounders. At follow up, 86 participants had developed incident type 2 diabetes. FA revealed 3 prominent dietary patterns: Balanced Market Foods, Beef and Processed Foods and Traditional Foods. After adjustment for age, sex, waist circumference, interleukin-6 and adiponectin, the Beef and Processed Foods pattern was associated with increased risk for incident type 2 diabetes (OR=1.38; 95% CI 1.02, 1.86). In contrast, the Balanced Market Foods and Traditional Foods Patterns were not significantly associated with type 2 diabetes. Dietary interventions should encourage reduced consumption of unhealthful market foods, in combination with improvements in local food environments so as to increase access to healthful foods and reduce food insecurity in Indigenous communities. Copyright © 2016 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

The Relationship of Major American Dietary Patterns to Age-related Macular Degeneration

PubMed Central

Chiu, Chung-Jung; Chang, Min-Lee; Zhang, Fang Fang; Li, Tricia; Gensler, Gary; Schleicher, Molly; Taylor, Allen

2014-01-01

PURPOSE We hypothesized that major American dietary patterns are associated with age-related macular degeneration (AMD) risk. DESIGN Cross-sectional study METHODS 8,103 eyes from 4,088 eligible participants in the baseline Age-Related Eye Disease Study (AREDS) were classified into control (n=2,739), early AMD (n=4,599), and advanced AMD (n=765) by AREDS AMD Classification System. Food consumption data were collected by a 90-item food frequency questionnaire. RESULTS Two major dietary patterns were identified by factor (principle component) analysis based on 37 food groups and named Oriental and Western patterns. The Oriental pattern was characterized by higher intake of vegetables, legumes, fruit, whole grains, tomatoes, and seafood. The Western pattern was characterized by higher intake of red meat, processed meat, high-fat dairy products, French fries, refined grains, and eggs. We ranked our participants according to how closely their diets line up with the two patterns by calculating the two factor scores for each participant. For early AMD, multivariate-adjusted odds ratio (OR) from generalized estimating equation logistic analysis comparing the highest to lowest quintile of the Oriental pattern score was ORE5O=0.74 (95% confidence interval (CI): 0.59–0.91; Ptrend=0.01), and the OR comparing the highest to lowest quintile of the Western pattern score was ORE5W=1.56 (1.18–2.06; Ptrend=0.01). For advanced AMD, the ORA5O was 0.38 (0.27–0.54; Ptrend<0.0001), and the ORA5W was 3.70 (2.31–5.92; Ptrend<0.0001). CONCLUSIONS Our data indicate that overall diet is significantly associated with the odds of AMD and that dietary management as an AMD prevention strategy warrants further study. PMID:24792100

The relationship of major American dietary patterns to age-related macular degeneration.

PubMed

Chiu, Chung-Jung; Chang, Min-Lee; Zhang, Fang Fang; Li, Tricia; Gensler, Gary; Schleicher, Molly; Taylor, Allen

2014-07-01

We hypothesized that major American dietary patterns are associated with risk for age-related macular degeneration (AMD). Cross-sectional study. We classified 8103 eyes in 4088 eligible participants in the baseline Age-Related Eye Disease Study (AREDS). They were classified into control (n = 2739), early AMD (n = 4599), and advanced AMD (n = 765) by the AREDS AMD Classification System. Food consumption data were collected by using a 90-item food frequency questionnaire. Two major dietary patterns were identified by factor (principal component) analysis based on 37 food groups and named Oriental and Western patterns. The Oriental pattern was characterized by higher intake of vegetables, legumes, fruit, whole grains, tomatoes, and seafood. The Western pattern was characterized by higher intake of red meat, processed meat, high-fat dairy products, French fries, refined grains, and eggs. We ranked our participants according to how closely their diets line up with the 2 patterns by calculating the 2 factor scores for each participant. For early AMD, multivariate-adjusted odds ratio (OR) from generalized estimating equation logistic analysis comparing the highest to lowest quintile of the Oriental pattern score was ORE5O = 0.74 (95% confidence interval (CI): 0.59-0.91; Ptrend =0.01), and the OR comparing the highest to lowest quintile of the Western pattern score was ORE5W = 1.56 (1.18-2.06; Ptrend = 0.01). For advanced AMD, the ORA5O was 0.38 (0.27-0.54; Ptrend < 0.0001), and the ORA5W was 3.70 (2.31-5.92; Ptrend < 0.0001). Our data indicate that overall diet is significantly associated with the odds of AMD and that dietary management as an AMD prevention strategy warrants further study. Copyright © 2014 Elsevier Inc. All rights reserved.

Multimodal image analysis of clinical influences on preterm brain development

PubMed Central

Ball, Gareth; Aljabar, Paul; Nongena, Phumza; Kennea, Nigel; Gonzalez‐Cinca, Nuria; Falconer, Shona; Chew, Andrew T.M.; Harper, Nicholas; Wurie, Julia; Rutherford, Mary A.; Edwards, A. David

2017-01-01

Objective Premature birth is associated with numerous complex abnormalities of white and gray matter and a high incidence of long‐term neurocognitive impairment. An integrated understanding of these abnormalities and their association with clinical events is lacking. The aim of this study was to identify specific patterns of abnormal cerebral development and their antenatal and postnatal antecedents. Methods In a prospective cohort of 449 infants (226 male), we performed a multivariate and data‐driven analysis combining multiple imaging modalities. Using canonical correlation analysis, we sought separable multimodal imaging markers associated with specific clinical and environmental factors and correlated to neurodevelopmental outcome at 2 years. Results We found five independent patterns of neuroanatomical variation that related to clinical factors including age, prematurity, sex, intrauterine complications, and postnatal adversity. We also confirmed the association between imaging markers of neuroanatomical abnormality and poor cognitive and motor outcomes at 2 years. Interpretation This data‐driven approach defined novel and clinically relevant imaging markers of cerebral maldevelopment, which offer new insights into the nature of preterm brain injury. Ann Neurol 2017;82:233–246 PMID:28719076

Historic changes in fish assemblage structure in midwestern nonwadeable rivers

USGS Publications Warehouse

Parks, Timothy P.; Quist, Michael C.; Pierce, Clay L.

2014-01-01

Historical change in fish assemblage structure was evaluated in the mainstems of the Des Moines, Iowa, Cedar, Wapsipinicon, and Maquoketa rivers, in Iowa. Fish occurrence data were compared in each river between historical and recent time periods to characterize temporal changes among 126 species distributions and assess spatiotemporal patterns in faunal similarity. A resampling procedure was used to estimate species occurrences in rivers during each assessment period and changes in species occurrence were summarized. Spatiotemporal shifts in species composition were analyzed at the river and river section scale using cluster analysis, pairwise Jaccard's dissimilarities, and analysis of multivariate beta dispersion. The majority of species exhibited either increases or declines in distribution in all rivers with the exception of several “unknown” or inconclusive trends exhibited by species in the Maquoketa River. Cluster analysis identified temporal patterns of similarity among fish assemblages in the Des Moines, Cedar, and Iowa rivers within the historical and recent assessment period indicating a significant change in species composition. Prominent declines of backwater species with phytophilic spawning strategies contributed to assemblage changes occurring across river systems.

fMRI-based Multivariate Pattern Analyses Reveal Imagery Modality and Imagery Content Specific Representations in Primary Somatosensory, Motor and Auditory Cortices.

PubMed

de Borst, Aline W; de Gelder, Beatrice

2017-08-01

Previous studies have shown that the early visual cortex contains content-specific representations of stimuli during visual imagery, and that these representational patterns of imagery content have a perceptual basis. To date, there is little evidence for the presence of a similar organization in the auditory and tactile domains. Using fMRI-based multivariate pattern analyses we showed that primary somatosensory, auditory, motor, and visual cortices are discriminative for imagery of touch versus sound. In the somatosensory, motor and visual cortices the imagery modality discriminative patterns were similar to perception modality discriminative patterns, suggesting that top-down modulations in these regions rely on similar neural representations as bottom-up perceptual processes. Moreover, we found evidence for content-specific representations of the stimuli during auditory imagery in the primary somatosensory and primary motor cortices. Both the imagined emotions and the imagined identities of the auditory stimuli could be successfully classified in these regions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.

A Holarctic Biogeographical Analysis of the Collembola (Arthropoda, Hexapoda) Unravels Recent Post-Glacial Colonization Patterns

PubMed Central

Ávila-Jiménez, María Luisa; Coulson, Stephen James

2011-01-01

We aimed to describe the main Arctic biogeographical patterns of the Collembola, and analyze historical factors and current climatic regimes determining Arctic collembolan species distribution. Furthermore, we aimed to identify possible dispersal routes, colonization sources and glacial refugia for Arctic collembola. We implemented a Gaussian Mixture Clustering method on species distribution ranges and applied a distance- based parametric bootstrap test on presence-absence collembolan species distribution data. Additionally, multivariate analysis was performed considering species distributions, biodiversity, cluster distribution and environmental factors (temperature and precipitation). No clear relation was found between current climatic regimes and species distribution in the Arctic. Gaussian Mixture Clustering found common elements within Siberian areas, Atlantic areas, the Canadian Arctic, a mid-Siberian cluster and specific Beringian elements, following the same pattern previously described, using a variety of molecular methods, for Arctic plants. Species distribution hence indicate the influence of recent glacial history, as LGM glacial refugia (mid-Siberia, and Beringia) and major dispersal routes to high Arctic island groups can be identified. Endemic species are found in the high Arctic, but no specific biogeographical pattern can be clearly identified as a sign of high Arctic glacial refugia. Ocean currents patterns are suggested as being an important factor shaping the distribution of Arctic Collembola, which is consistent with Antarctic studies in collembolan biogeography. The clear relations between cluster distribution and geographical areas considering their recent glacial history, lack of relationship of species distribution with current climatic regimes, and consistency with previously described Arctic patterns in a series of organisms inferred using a variety of methods, suggest that historical phenomena shaping contemporary collembolan distribution can be inferred through biogeographical analysis. PMID:26467728

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance.

PubMed

Keerativittayayut, Ruedeerat; Aoki, Ryuta; Sarabi, Mitra Taghizadeh; Jimura, Koji; Nakahara, Kiyoshi

2018-06-18

Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. © 2018, Keerativittayayut et al.

The role of energy intake and energy misreporting in the associations between eating patterns and adiposity.

PubMed

Leech, R M; Worsley, A; Timperio, A; McNaughton, S A

2018-01-01

Research examining associations between eating occasion (EO) frequency and adiposity is inconclusive; studies examining the impact of energy misreporting are rare. This study examined associations between eating patterns and adiposity, with adjustment for energy misreporting, in a nationally representative sample of Australian adults. Dietary intake was assessed via two 24-h recalls collected during the 2011-12 National Nutrition and Physical Activity Survey (n=4050 adults, aged ⩾19 years). Frequencies of all EOs, meals and snacks were calculated. Height, weight and waist circumference (WC) were measured. Energy misreporting was assessed as the ratio of energy intake to predicted energy expenditure (EI:EE). Energy misreporters were identified by EI:EE ratios, <0.68 or >1.32. Multivariate regression models assessed associations between eating patterns and body mass index (BMI), WC, overweight/obesity (BMI ⩾25 kg m -2 ) and central overweight/obesity (WC ⩾94 cm in men and ⩾80 cm in women). After adjustment for covariates and EI:EE, frequency of all EOs, meals (women only) and snacks was positively associated with WC and BMI (all P<0.01). Snack, but not meal frequency, was also associated with overweight/obesity (men: OR=1.22, 95% CI 1.07-1.39; women: OR=1.26, 95% CI 1.10-1.43) and central overweight/obesity (men: OR=1.17, 95% CI 1.04-1.32; women: OR=1.21, 95% CI 1.06-1.37). Multivariate analysis that excluded energy misreporters and adjusted for EI yielded either null or inverse associations (P<0.05). These findings suggest that the associations between eating patterns and adiposity are complicated by the role of EI and energy misreporting. Longitudinal research that considers the impact of EI and energy misreporting is needed to better understand the relationship between eating patterns and obesity.

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

Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia.

PubMed

Pergola, Giulio; Trizio, Silvestro; Di Carlo, Pasquale; Taurisano, Paolo; Mancini, Marina; Amoroso, Nicola; Nettis, Maria Antonietta; Andriola, Ileana; Caforio, Grazia; Popolizio, Teresa; Rampino, Antonio; Di Giorgio, Annabella; Bertolino, Alessandro; Blasi, Giuseppe

2017-02-01

Previous evidence suggests reduced thalamic grey matter volume (GMV) in patients with schizophrenia (SCZ). However, it is not considered an intermediate phenotype for schizophrenia, possibly because previous studies did not assess the contribution of individual thalamic nuclei and employed univariate statistics. Here, we hypothesized that multivariate statistics would reveal an association of GMV in different thalamic nuclei with familial risk for schizophrenia. We also hypothesized that accounting for the heterogeneity of thalamic GMV in healthy controls would improve the detection of subjects at familial risk for the disorder. We acquired MRI scans for 96 clinically stable SCZ, 55 non-affected siblings of patients with schizophrenia (SIB), and 249 HC. The thalamus was parceled into seven regions of interest (ROIs). After a canonical univariate analysis, we used GMV estimates of thalamic ROIs, together with total thalamic GMV and premorbid intelligence, as features in Random Forests to classify HC, SIB, and SCZ. Then, we computed a Misclassification Index for each individual and tested the improvement in SIB detection after excluding a subsample of HC misclassified as patients. Random Forests discriminated SCZ from HC (accuracy=81%) and SIB from HC (accuracy=75%). Left anteromedial thalamic volumes were significantly associated with both multivariate classifications (p<0.05). Excluding HC misclassified as SCZ improved greatly HC vs. SIB classification (Cohen's d=1.39). These findings suggest that multivariate statistics identify a familial background associated with thalamic GMV reduction in SCZ. They also suggest the relevance of inter-individual variability of GMV patterns for the discrimination of individuals at familial risk for the disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural network classification technique and machine vision for bread crumb grain evaluation

NASA Astrophysics Data System (ADS)

Zayas, Inna Y.; Chung, O. K.; Caley, M.

1995-10-01

Bread crumb grain was studied to develop a model for pattern recognition of bread baked at Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a 512 multiplied by 512 format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigen values, shape of a slice and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of MATLAB package were used for data analysis. Learning vector quantization method and multivariate discriminant analysis were applied to bread slices from what of different sources. A training and test sets of different bread crumb texture classes were obtained. The ranking of subimages was well correlated with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created according to human judgement with image features was low. Recognition of arbitrarily created classes, according to porosity patterns, with several feature patterns was approximately 90%. Correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

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 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.

Coping with confounds in multivoxel pattern analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013.

PubMed

Woolgar, Alexandra; Golland, Polina; Bode, Stefan

2014-09-01

Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. ("Confounds in multivariate pattern analysis: Theory and rule representation case study", 2013, NeuroImage 77: 157-165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach. Copyright © 2014 Elsevier Inc. All rights reserved.

Community structure and elevational diversity patterns of soil Acidobacteria.

PubMed

Zhang, Yuguang; Cong, Jing; Lu, Hui; Li, Guangliang; Qu, Yuanyuan; Su, Xiujiang; Zhou, Jizhong; Li, Diqiang

2014-08-01

Acidobacteria is one of the most dominant and abundant phyla in soil, and was believed to have a wide range of metabolic and genetic functions. Relatively little is known about its community structure and elevational diversity patterns. We selected four elevation gradients from 1000 to 2800 m with typical vegetation types of the northern slope of Shennongjia Mountain in central China. The vegetation types were evergreen broadleaved forest, deciduous broadleaved forest, coniferous forest and sub-alpine shrubs. We analyzed the soil acidobacterial community composition, elevational patterns and the relationship between Acidobacteria subdivisions and soil enzyme activities by using the 16S rRNA meta-sequencing technique and multivariate statistical analysis. The result found that 19 known subdivisions as well as an unclassified phylotype were presented in these forest sites, and Subdivision 6 has the highest number of detectable operational taxonomic units (OTUs). A significant single peak distribution pattern (P<0.05) between the OTU number and the elevation was observed. The Jaccard and Bray-Curtis index analysis showed that the soil Acidobacteria compositional similarity significantly decreased (P<0.01) with the increase in elevation distance. Mantel test analysis showed the most of the soil Acidobacteria subdivisions had the significant relationship (P<0.01) with different soil enzymes. Therefore, soil Acidobacteria may be involved in different ecosystem functions in global elemental cycles. Partial Mantel tests and CCA analysis showed that soil pH, soil temperature and plant diversity may be the key factors in shaping the soil Acidobacterial community structure. Copyright © 2014. Published by Elsevier B.V.

The Response of the Left Ventral Attentional System to Invalid Targets and its Implication for the Spatial Neglect Syndrome: a Multivariate fMRI Investigation.

PubMed

Silvetti, Massimo; Lasaponara, Stefano; Lecce, Francesca; Dragone, Alessio; Macaluso, Emiliano; Doricchi, Fabrizio

2016-12-01

In humans, invalid visual targets that mismatch spatial expectations induced by attentional cues are considered to selectively engage a right hemispheric "reorienting" network that includes the temporal parietal junction (TPJ), the inferior frontal gyrus (IFG), and the medial frontal gyrus (MFG). However, recent findings suggest that this hemispheric dominance is not absolute and that it is rather observed because the TPJ and IFG areas in the left hemisphere are engaged both by invalid and valid cued targets. Because of this, the BOLD response of the left hemisphere to invalid targets is usually cancelled out by the standard "invalid versus valid" contrast used in functional magnetic resonance imaging investigations of spatial attention. Here, we used multivariate pattern recognition analysis (MVPA) to gain finer insight into the role played by the left TPJ and IFG in reorienting to invalid targets. We found that in left TPJ and IFG blood oxygen level-dependent (BOLD) responses to invalid and valid targets were associated to different patterns of neural activity, possibly reflecting the presence of functionally distinct neuronal populations. Pattern segregation was significant at group level, it was present in almost all of the participants to the study and was observed both for targets in the left and right side of space. A control whole-brain MVPA ("Searchlight" analysis) confirmed the results obtained in predefined regions of interest and highlighted that also other areas, that is, superior parietal and frontal-polar cortex, show different patterns of BOLD response to valid and invalid targets. These results confirm and expand previous evidence highlighting the involvement of the left hemisphere in reorienting of visual attention (Doricchi et al. 2010; Dragone et al. 2015). These findings suggest that asymmetrical reorienting deficits suffered by right brain damaged patients with left spatial neglect, who have severe impairments in contralesional reorienting and less severe impairments in ipsilesional reorienting, are due to preserved reorienting abilities in the intact left hemisphere. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

MDAS: an integrated system for metabonomic data analysis.

PubMed

Liu, Juan; Li, Bo; Xiong, Jiang-Hui

2009-03-01

Metabonomics, the latest 'omics' research field, shows great promise as a tool in biomarker discovery, drug efficacy and toxicity analysis, disease diagnosis and prognosis. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system, e.g., the mechanism of diseases. Traditional methods employed in metabonomic data analysis use multivariate analysis methods developed independently in chemometrics research. Additionally, with the development of machine learning approaches, some methods such as SVMs also show promise for use in metabonomic data analysis. Aside from the application of general multivariate analysis and machine learning methods to this problem, there is also a need for an integrated tool customized for metabonomic data analysis which can be easily used by biologists to reveal interesting patterns in metabonomic data.In this paper, we present a novel software tool MDAS (Metabonomic Data Analysis System) for metabonomic data analysis which integrates traditional chemometrics methods and newly introduced machine learning approaches. MDAS contains a suite of functional models for metabonomic data analysis and optimizes the flow of data analysis. Several file formats can be accepted as input. The input data can be optionally preprocessed and can then be processed with operations such as feature analysis and dimensionality reduction. The data with reduced dimensionalities can be used for training or testing through machine learning models. The system supplies proper visualization for data preprocessing, feature analysis, and classification which can be a powerful function for users to extract knowledge from the data. MDAS is an integrated platform for metabonomic data analysis, which transforms a complex analysis procedure into a more formalized and simplified one. The software package can be obtained from the authors.

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.

Single trial classification for the categories of perceived emotional facial expressions: an event-related fMRI study

NASA Astrophysics Data System (ADS)

Song, Sutao; Huang, Yuxia; Long, Zhiying; Zhang, Jiacai; Chen, Gongxiang; Wang, Shuqing

2016-03-01

Recently, several studies have successfully applied multivariate pattern analysis methods to predict the categories of emotions. These studies are mainly focused on self-experienced emotions, such as the emotional states elicited by music or movie. In fact, most of our social interactions involve perception of emotional information from the expressions of other people, and it is an important basic skill for humans to recognize the emotional facial expressions of other people in a short time. In this study, we aimed to determine the discriminability of perceived emotional facial expressions. In a rapid event-related fMRI design, subjects were instructed to classify four categories of facial expressions (happy, disgust, angry and neutral) by pressing different buttons, and each facial expression stimulus lasted for 2s. All participants performed 5 fMRI runs. One multivariate pattern analysis method, support vector machine was trained to predict the categories of facial expressions. For feature selection, ninety masks defined from anatomical automatic labeling (AAL) atlas were firstly generated and each were treated as the input of the classifier; then, the most stable AAL areas were selected according to prediction accuracies, and comprised the final feature sets. Results showed that: for the 6 pair-wise classification conditions, the accuracy, sensitivity and specificity were all above chance prediction, among which, happy vs. neutral , angry vs. disgust achieved the lowest results. These results suggested that specific neural signatures of perceived emotional facial expressions may exist, and happy vs. neutral, angry vs. disgust might be more similar in information representation in the brain.

Working after a metastatic cancer diagnosis: Factors affecting employment in the metastatic setting from ECOG-ACRIN's Symptom Outcomes and Practice Patterns study.

PubMed

Tevaarwerk, Amye J; Lee, Ju-Whei; Terhaar, Abigail; Sesto, Mary E; Smith, Mary Lou; Cleeland, Charles S; Fisch, Michael J

2016-02-01

Improved survival for individuals with metastatic cancer accentuates the importance of employment for cancer survivors. A better understanding of how metastatic cancer affects employment is a necessary step toward the development of tools for assisting survivors in this important realm. The ECOG-ACRIN Symptom Outcomes and Practice Patterns study was analyzed to investigate what factors were associated with the employment of 680 metastatic cancer patients. Univariate and multivariate logistic regression analyses were conducted to compare patients stably working with patients no longer working. There were 668 metastatic working-age participants in the analysis: 236 (35%) worked full- or part-time, whereas 302 (45%) had stopped working because of illness. Overall, 58% reported some change in employment due to illness. A better performance status and non-Hispanic white ethnicity/race were significantly associated with continuing to work despite a metastatic cancer diagnosis in the multivariate analysis. The disease type, time since metastatic diagnosis, number of metastatic sites, location of metastatic disease, and treatment status had no significant impact. Among the potentially modifiable factors, receiving hormonal treatment (if a viable option) and decreasing symptom interference were associated with continuing to work. A significant percentage of the metastatic patients remained employed; increased symptom burden was associated with a change to no longer working. Modifiable factors resulting in work interference should be minimized so that patients with metastatic disease may continue working if this is desired. Improvements in symptom control and strategies developed to help address workplace difficulties have promise for improving this aspect of survivorship. © 2015 American Cancer Society.

Multivariable harmonic balance analysis of the neuronal oscillator for leech swimming.

PubMed

Chen, Zhiyong; Zheng, Min; Friesen, W Otto; Iwasaki, Tetsuya

2008-12-01

Biological systems, and particularly neuronal circuits, embody a very high level of complexity. Mathematical modeling is therefore essential for understanding how large sets of neurons with complex multiple interconnections work as a functional system. With the increase in computing power, it is now possible to numerically integrate a model with many variables to simulate behavior. However, such analysis can be time-consuming and may not reveal the mechanisms underlying the observed phenomena. An alternative, complementary approach is mathematical analysis, which can demonstrate direct and explicit relationships between a property of interest and system parameters. This paper introduces a mathematical tool for analyzing neuronal oscillator circuits based on multivariable harmonic balance (MHB). The tool is applied to a model of the central pattern generator (CPG) for leech swimming, which comprises a chain of weakly coupled segmental oscillators. The results demonstrate the effectiveness of the MHB method and provide analytical explanations for some CPG properties. In particular, the intersegmental phase lag is estimated to be the sum of a nominal value and a perturbation, where the former depends on the structure and span of the neuronal connections and the latter is roughly proportional to the period gradient, communication delay, and the reciprocal of the intersegmental coupling strength.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

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

Johnson, Kevin J.; Wright, Bob W.; Jarman, Kristin H.

2003-05-09

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel gas chromatographic profiles. Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current multivariate techniques to correctly model information that shifts from variable to variable within a dataset. The algorithm developed is shown to increase the efficacy of pattern recognition methods applied to a set of diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retentionmore » time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical.« less

Process analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy.

PubMed

Fink, Herbert; Panne, Ulrich; Niessner, Reinhard

2002-09-01

An experimental setup for direct elemental analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) was realized. The combination of a echelle spectrograph, featuring a high resolution with a broad spectral coverage, with multivariate methods, such as PLS, PCR, and variable subset selection via a genetic algorithm, resulted in considerable improvements in selectivity and sensitivity for this complex matrix. With a normalization to carbon as internal standard, the limits of detection were in the ppm range. A preliminary pattern recognition study points to the possibility of polymer recognition via the line-rich echelle spectra. Several experiments at an extruder within a recycling plant demonstrated successfully the capability of LIPS for different kinds of routine on-line process analysis.

Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

DOE Data Explorer

Maddalena, Damian; Hoffman, Forrest; Kumar, Jitendra; Hargrove, William

2014-08-01

Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.

Does gender of the fetus have any relation with fetal heart monitoring during the first and second stage of labor?

PubMed

Yohai, David; Baumfeld, Yael; Zilberstein, Tali; Yaniv Salem, Shimrit; Elharar, Debbie; Idan, Inbal; Mastrolia, Salvatore Andrea; Sheiner, Eyal

2017-01-01

To investigate fetal gender and its influences on neonatal outcomes, taking into consideration the available tools for the assessment of fetal well-being. We conducted a retrospective study comparing maternal, fetal and neonatal outcomes according to fetal gender, in women carrying a singleton gestation. A multivariate analysis was performed for the prediction of adverse neonatal outcomes according to fetal gender, after adjustment for gestational age, maternal age and fetal weight. A total of 682 pregnancies were included in the study, of them 56% (n = 383) were carrying a male fetus and 44% (n = 299) a females fetus. Male gender was associated with a significant higher rate of abnormal fetal heart tracing patterns during the first (67.7% versus 55.1, p = 0.001) and the second stage (77.6 versus 67.7, p = 0.01) of labor. Male gender was also significantly associated with lower Apgar scores at 1' (19.1% versus 10.7%, p < 0.01), as well as lower pH values (7.18 ± 0.15 versus 7.23 ± 0.18, p < 0.001), and significant differences in cord blood components (PCO 2 , PO 2 ) compared with female fetuses. In the multivariate analysis, male gender was found to be significantly associated with first (OR 1.76, 95% CI 1.28-2.43, p = 0.001) and second stage (OR 1.73, 95% CI 1.20-2.50, p < 0.01) pathological fetal heart tracing patterns, pH < 7.1, and for Apgar scores at 1'< 7. The present study confirms the general trend of a lower clinical performance of male neonates compared with females. In addition, the relation between fetal heart rate patterns during all stages of labor and fetal gender showed an independent association between male fetal gender and abnormal fetal heart monitoring during labor.

Determination of the Association Between T2-weighted MRI and Gleason Sub-pattern: A Proof of Principle Study.

PubMed

Downes, Michelle R; Gibson, Eli; Sykes, Jenna; Haider, Masoom; van der Kwast, Theo H; Ward, Aaron

2016-11-01

The study aimed to determine the relationship between T2-weighted magnetic resonance imaging (MRI) signal and histologic sub-patterns in prostate cancer areas with different Gleason grades. MR images of prostates (n = 25) were obtained prior to radical prostatectomy. These were processed as whole-mount specimens with tumors and the peripheral zone was annotated digitally by two pathologists. Gleason grade 3 was the most prevalent grade and was subdivided into packed, intermediate, and sparse based on gland-to-stroma ratio. Large cribriform, intraductal carcinoma, and small cribriform glands (grade 4 group) were separately annotated but grouped together for statistical analysis. The log MRI signal intensity for each contoured region (n = 809) was measured, and pairwise comparisons were performed using the open-source software R version 3.0.1. Packed grade 3 sub-pattern has a significantly lower MRI intensity than the grade 4 group (P < 0.00001). Sparse grade 3 has a significantly higher MRI intensity than the packed grade 3 sub-pattern (P < 0.0001). No significant difference in MRI intensity was observed between the Gleason grade 4 group and the sparse sub-pattern grade 3 group (P = 0.54). In multivariable analysis adjusting for peripheral zone, the P values maintained significance (packed grade 3 group vs grade 4 group, P < 0.001; and sparse grade 3 sub-pattern vs packed grade 3 sub-pattern, P < 0.001). This study demonstrated that T2-weighted MRI signal is dependent on histologic sub-patterns within Gleason grades 3 and 4 cancers, which may have implications for directed biopsy sampling and patient management. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Left atrial pressure pattern without a-wave in sinus rhythm after cardioversion affects the outcomes after catheter ablation for atrial fibrillation.

PubMed

Kishima, Hideyuki; Mine, Takanao; Takahashi, Satoshi; Ashida, Kenki; Ishihara, Masaharu; Masuyama, Tohru

2018-04-24

The a-wave in left atrial pressure (LAP) is often not observed after cardioversion (CV). We hypothesized that repeated atrial fibrillation (AF) occurs in patients who do not show a-wave pattern after CV. We investigated the impact of "LAP pattern without a-wave" on the outcome after catheter ablation (CA) for AF. We studied 100 patients (64 males, age 66 ± 8 years, 42 with non-paroxysmal AF) who underwent CA for AF. Sustained- or induced-AF were terminated with internal CV, and LAP was measured during sinus rhythm (SR) after CV. LAP pattern without a-wave was defined as absence of a-wave (the "a-wave" was defined as a protruding part by 0.2 mmHg or more from the baseline) in LAP wave form. AF was terminated with CV in all patients. Recurrent AF was detected in 35/100 (35%) during the follow-up period (13.1 ± 7.8 month). Univariate analysis revealed higher prevalence of LAP pattern without a-wave (71 vs. 17%, P < 0.0001), larger left atrial volume, elevated E wave, and decreased deceleration time as significant variables. On multivariate analysis, LAP pattern without a-wave was only independently associated with recurrent AF (P = 0.0014, OR 9.865, 95% CI 2.327-54.861). Moreover, patients with LAP pattern without a-wave had a higher risk of recurrent AF than patients with a-wave (25/36 patients, 69 vs. 10/64 patients, 16%, log-rank P < 0.0001). Left atrial pressure pattern without a-wave in sinus rhythm after cardioversion could predict recurrence after catheter ablation for AF.

Patterns of care in palliative radiotherapy: a population-based study.

PubMed

Murphy, James D; Nelson, Lorene M; Chang, Daniel T; Mell, Loren K; Le, Quynh-Thu

2013-09-01

Approximately one half of the radiotherapy (RT) prescribed in the United States is delivered with palliative intent. The purpose of this study was to investigate the patterns of delivery of palliative RT across the United States. Using the Surveillance, Epidemiology, and End Results-Medicare linked database, 51,610 patients were identified with incident stage IV breast, prostate, lung, or colorectal cancer diagnosed between 2000 and 2007 and observed through 2009. Multivariate logistic regression determined predictors of palliative RT. Forty-one percent of the study population received palliative RT, including 53% of patients with lung cancer, followed by those with breast (42%), prostate (40%), and colorectal cancers (12%). Multivariate analysis revealed that older patients (P<.001) and those with higher Charlson comorbidity scores (P<.001) were less likely to receive palliative RT. Black patients with prostate cancer were 20% less likely (P<.001), and black patients with colorectal cancer were 28% less likely (P<.001), than white patients to receive palliative RT. Among those treated with RT, 23% of patients with lung cancer died within 2 weeks of completing treatment, followed by those with colorectal (12%), breast (11%), and prostate cancers (8%). In addition to tumor site, significant predictors (P<.05) of death within 2 weeks of receiving RT included increased age, increased comorbidity, and male sex. Inequality in the receipt of palliative RT exists among the elderly and patients with comorbid conditions and varies with race. In addition, a significant number of patients die shortly after receiving RT. Understanding these patterns of care, along with further research into the underlying causes, will improve access and quality of palliative RT.

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury

PubMed Central

Wu, Xuehai; Zou, Qihong; Hu, Jin; Tang, Weijun; Mao, Ying; Gao, Liang; Zhu, Jianhong; Jin, Yi; Wu, Xin; Lu, Lu; Zhang, Yaojun; Zhang, Yao; Dai, Zhengjia; Gao, Jia-Hong; Weng, Xuchu; Northoff, Georg; Giacino, Joseph T.; He, Yong

2015-01-01

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI. SIGNIFICANCE STATEMENT Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness. PMID:26377477

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

A multivariate cure model for left-censored and right-censored data with application to colorectal cancer screening patterns.

PubMed

Hagar, Yolanda C; Harvey, Danielle J; Beckett, Laurel A

2016-08-30

We develop a multivariate cure survival model to estimate lifetime patterns of colorectal cancer screening. Screening data cover long periods of time, with sparse observations for each person. Some events may occur before the study begins or after the study ends, so the data are both left-censored and right-censored, and some individuals are never screened (the 'cured' population). We propose a multivariate parametric cure model that can be used with left-censored and right-censored data. Our model allows for the estimation of the time to screening as well as the average number of times individuals will be screened. We calculate likelihood functions based on the observations for each subject using a distribution that accounts for within-subject correlation and estimate parameters using Markov chain Monte Carlo methods. We apply our methods to the estimation of lifetime colorectal cancer screening behavior in the SEER-Medicare data set. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback

PubMed Central

Moll, Jorge; Weingartner, Julie H.; Bado, Patricia; Basilio, Rodrigo; Sato, João R.; Melo, Bruno R.; Bramati, Ivanei E.; de Oliveira-Souza, Ricardo; Zahn, Roland

2014-01-01

In Ridley Scott’s film “Blade Runner”, empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior. PMID:24847819

Dietary patterns in pregnancy and birth weight.

PubMed

Coelho, Natália de Lima Pereira; Cunha, Diana Barbosa; Esteves, Ana Paula Pereira; Lacerda, Elisa Maria de Aquino; Theme Filha, Mariza Miranda

2015-01-01

OBJECTIVE To analyze if dietary patterns during the third gestational trimester are associated with birth weight.METHODS Longitudinal study conducted in the cities of Petropolis and Queimados, Rio de Janeiro (RJ), Southeastern Brazil, between 2007 and 2008. We analyzed data from the first and second follow-up wave of a prospective cohort. Food consumption of 1,298 pregnant women was assessed using a semi-quantitative questionnaire about food frequency. Dietary patterns were obtained by exploratory factor analysis, using the Varimax rotation method. We also applied the multivariate linear regression model to estimate the association between food consumption patterns and birth weight.RESULTS Four patterns of consumption - which explain 36.4% of the variability - were identified and divided as follows: (1) prudent pattern (milk, yogurt, cheese, fruit and fresh-fruit juice, cracker, and chicken/beef/fish/liver), which explained 14.9% of the consumption; (2) traditional pattern, consisting of beans, rice, vegetables, breads, butter/margarine and sugar, which explained 8.8% of the variation in consumption; (3) Western pattern (potato/cassava/yams, macaroni, flour/farofa/grits, pizza/hamburger/deep fried pastries, soft drinks/cool drinks and pork/sausages/egg), which accounts for 6.9% of the variance; and (4) snack pattern (sandwich cookie, salty snacks, chocolate, and chocolate drink mix), which explains 5.7% of the consumption variability. The snack dietary pattern was positively associated with birth weight (β = 56.64; p = 0.04) in pregnant adolescents.CONCLUSIONS For pregnant adolescents, the greater the adherence to snack pattern during pregnancy, the greater the baby's birth weight.

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

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

Wong, Jonathan; Xu, Beibei; Moores Cancer Center, University of California San Diego, La Jolla, California

Purpose/Objective: Palliative radiation therapy represents an important treatment option among patients with advanced cancer, although research shows decreased use among older patients. This study evaluated age-related patterns of palliative radiation use among an elderly Medicare population. Methods and Materials: We identified 63,221 patients with metastatic lung, breast, prostate, or colorectal cancer diagnosed between 2000 and 2007 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Receipt of palliative radiation therapy was extracted from Medicare claims. Multivariate Poisson regression analysis determined residual age-related disparity in the receipt of palliative radiation therapy after controlling for confounding covariates including age-related differences inmore » patient and demographic covariates, length of life, and patient preferences for aggressive cancer therapy. Results: The use of radiation decreased steadily with increasing patient age. Forty-two percent of patients aged 66 to 69 received palliative radiation therapy. Rates of palliative radiation decreased to 38%, 32%, 24%, and 14% among patients aged 70 to 74, 75 to 79, 80 to 84, and over 85, respectively. Multivariate analysis found that confounding covariates attenuated these findings, although the decreased relative rate of palliative radiation therapy among the elderly remained clinically and statistically significant. On multivariate analysis, compared to patients 66 to 69 years old, those aged 70 to 74, 75 to 79, 80 to 84, and over 85 had a 7%, 15%, 25%, and 44% decreased rate of receiving palliative radiation, respectively (all P<.0001). Conclusions: Age disparity with palliative radiation therapy exists among older cancer patients. Further research should strive to identify barriers to palliative radiation among the elderly, and extra effort should be made to give older patients the opportunity to receive this quality of life-enhancing treatment at the end of life.« less

Searching for forcing signatures in decadal patterns of shoreline change

NASA Astrophysics Data System (ADS)

Burningham, H.; French, J.

2016-12-01

Analysis of shoreline position at spatial scales of the order 10 - 100 km and at a multi-decadal time-scale has the potential to reveal regional coherence (or lack of) in the primary controls on shoreline tendencies and trends. Such information is extremely valuable for the evaluation of climate forcing on coastal behaviour. Segmenting a coast into discrete behaviour units based on these types of analyses is often subjective, however, and in the context of pervasive human interventions and alongshore variability in ocean climate, determining the most important controls on shoreline dynamics can be challenging. Multivariate analyses provide one means to resolve common behaviours across shoreline position datasets, thereby underpinning a more objective evaluation of possible coupling between shorelines at different scales. In an analysis of the Suffolk coast (eastern England) we explore the use of multivariate statistics to understand and classify mesoscale coastal behaviour. Suffolk comprises a relatively linear shoreline that shifts from east-facing in the north to southeast-facing in the south. Although primarily formed of a beach foreshore backed by cliffs or shingle barrier, the shoreline is punctuated at 3 locations by narrow tidal inlets with offset entrances that imply a persistent north to south sediment transport direction. Tidal regime decreases south to north from mesotidal (3.6m STR) to microtidal (1.9m STR), and the bimodal wave climate (northeast and southwest modes) presents complex local-scale variability in nearshore conditions. Shorelines exhibit a range of decadal behaviours from rapid erosion (up to 4m/yr) to quasi-stability that cannot be directly explained by the spatial organisation of contemporary landforms or coastal defences. A multivariate statistical approach to shoreline change analysis helps to define the key modes of change and determine the most likely forcing factors.

Influences of environment and disturbance on forest patterns in coastal Oregon watersheds.

Treesearch

Michael C. Wimberly; Thomas A. Spies

2001-01-01

Modern ecology often emphasizes the distinction between traditional theories of stable, environmentally structured communities and a new paradigm of disturbance driven, nonequilibrium dynamics. However, multiple hypotheses for observed vegetation patterns have seldom been explicitly tested. We used multivariate statistics and variation partitioning methods to assess...

Organizational Change, Absenteeism, and Welfare Dependency

ERIC Educational Resources Information Center

Roed, Knut; Fevang, Elisabeth

2007-01-01

Based on Norwegian register data, we set up a multivariate mixed proportional hazard model (MMPH) to analyze nurses' pattern of work, sickness absence, nonemployment, and social insurance dependency from 1992 to 2000, and how that pattern was affected by workplace characteristics. The model is estimated by means of the nonparametric…

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Differences in temperament, character and psychopathology among subjects with different patterns of cannabis use.

PubMed

Spalletta, Gianfranco; Bria, Pietro; Caltagirone, Carlo

2007-01-01

Patients who use illicit drugs and suffer from comorbid psychiatric illnesses have worse outcomes than drug users without a dual diagnosis. For this reason we aimed at identifying predictors of cannabis use severity using a multivariate model in which different clinical and socio-demographic variables were included. We administered the Temperament and Character Inventory, SCID-P, SCID-II, the Beck Depression Inventory and the State-Trait Anxiety Inventory. Of the 84 subjects included, 25 were occasional users, 37 were abusers, and 22 were dependent on cannabis. A stepwise multiple regression analysis identified increased self-transcendence scores and state anxiety severity as the only predictors of a increased cannabis use severity (F = 6.635; d.f. = 2, 81; p = 0.0021). In particular, in a further multivariate analysis of variance, the transpersonal identification issue of self-transcendence was associated significantly (F = 4.267; d.f. = 2, 81; p = 0.017) with greater severity of cannabis use. Character dimension of self-transcendence and symptoms of state anxiety should be taken into consideration during the assessment procedure of patients with cannabis use as they may be helpful in the discrimination of cannabis use severity.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

PubMed

Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

2015-10-01

To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

Missing data exploration: highlighting graphical presentation of missing pattern.

PubMed

Zhang, Zhongheng

2015-12-01

Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations.

Dietary patterns and the incidence of hyperglyacemia in China.

PubMed

Hong, Xin; Xu, Fei; Wang, Zhiyong; Liang, Yaqiong; Li, Jiequan

2016-01-01

Epidemiological studies have examined associations between dietary patterns and the risk of type 2 diabetes. However, information on dietary patterns and the risk of type 2 diabetes in Chinese populations is scarce. The aim of the present study was to identify dietary patterns and examine their association with incident hyperglycaemia in Nanjing, China. A community-based prospective cohort study. Dietary assessment was carried out using a validated eighty-seven-item FFQ. Dietary patterns were identified by exploratory factor analysis. Participants were categorized into tertiles of dietary factor score for each dietary pattern. The relationship between dietary patterns and hyperglycaemia risk was analysed using multivariable linear and Cox regression. Seven communities from two urban districts in Nanjing, China. A total of 2900 of Chinese local residents aged 30 years or above, free of hyperglycaemia and other serious diseases, who participated in the baseline survey from June to September 2007 were followed up 3 years later from June to September 2010 for the development of hyperglycaemia. Fasting blood samples were collected at both baseline and 3-year follow-up surveys. Hyperglycaemia was defined as fasting plasma glucose concentration of ≥6·1 mmol/l or already taking oral hyperglycaemia agents for treatment of type 2 diabetes. Five major dietary patterns were identified: (i) the 'condiments' pattern; (ii) the 'animal and plant protein' pattern; (iii) the 'healthy traditional' pattern; (iv) the 'fruits, eggs and juice' pattern; and (v) the 'alcohol, milk and tea' pattern. A total of 2093 (72·2 %) individuals completed the follow-up survey and the 3-year cumulative incidence of hyperglycaemia was 7·5 % (158/2093). A 1-unit increase in the score for the 'healthy traditional' pattern was associated with a decrease of 0·054 mmol/l in fasting plasma glucose (P=0·017), while a 1-unit increase in the 'fruits, eggs and juice' pattern score was associated with an increase of 0·050 mmol/l in fasting plasma glucose (P=0·023) by multivariable linear regression. For men, tertile 3 of the 'fruits, eggs and juice' pattern was associated with an 88 % greater risk (hazard ratio=1·88; 95 % CI 1·04, 3·54) of hyperglycaemia than tertile 1 of this pattern. Being in tertile 3 of the 'alcohol, milk and tea' pattern was associated with a 35 % greater risk (hazard ratio=1·35; 95 % CI 1·04, 2·16) relative to tertile 1 in women, while for the ''healthy traditional' pattern tertile 3 was associated with a 41 % lower risk (hazard ratio=0·59; 95 % CI 0·35, 0·99) compared with tertile 1. The 'condiments' and the 'animal and plant protein' patterns were not independently associated with hyperglycaemia. Our findings suggest that modifying dietary patterns could reduce hyperglycaemia incidence in the mainland Chinese adult population.

Dietary Patterns and Wheezing in the Midst of Nutritional Transition: A Study in Brazil

PubMed Central

Assis, Ana Marlúcia Oliveira; Cruz, Alvaro Augusto; Fiaccone, Rosemeire Leovigildo; DInnocenzo, Silvana; Barreto, Maurício Lima; da Silva, Luce Alves; Rodrigues, Laura Cunha; Alcantara-Neves, Neuza Maria

2013-01-01

To assess the influence of dietary patterns on the prevalence of wheezing in the child and adolescent population in Northeastern Brazil. This is a cross-sectional study of male and female students, 6–12 years old, from the public elementary schools of São Francisco do Conde, Bahia, Northeastern Brazil. The report of wheezing in the past 12 months was collected using a questionnaire from the International Study of Asthma and Allergies in Childhood Program phase III, adapted to Portuguese. Consumption patterns were derived from principal component analysis based on the frequency of consumption of 97 food items by the food frequency questionnaire. We also obtained the anthropometric status, level of physical activity, pubertal development, and socioeconomic information, for each participant. Multivariate logistic regression analyses were used to assess the associations of interest. Of the children surveyed, 10.6% reported having wheezing. We identified 2 dietary patterns named Western and Prudent. We found a positive statistically significant association of the Western pattern with wheeze (odds ratio=1.77, 95% confidence interval: 1.10–2.84) after adjustment for total energy intake and controlling for potential confounders. The results showed that the Western dietary pattern was associated with wheezing. Our result is according with previous findings reported in several other studies. PMID:23555072

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

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.

Statistical polarization in greenhouse gas emissions: Theory and evidence.

PubMed

Remuzgo, Lorena; Trueba, Carmen

2017-11-01

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990-2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

The McMillan and Newton polygons of a feedback system and the construction of root loci

NASA Technical Reports Server (NTRS)

Byrnes, C. I.; Stevens, P. K.

1982-01-01

The local behaviour of root loci around zeros and poles is investigated. This is done by relating the Newton diagrams which arise in the local analysis to the McMillan structure of the open-loop system, by means of what we shall call the McMillan polygon. This geometric construct serves to clarify the precise relationship between the McMillan structure, the principal structure, and the branching patterns of the root loci. In addition, several rules are obtained which are useful in the construction of the root loci of multivariable control systems.

Dimensions of Problem Drinking among Young Adult Restaurant Workers

PubMed Central

Moore, Roland S.; Cunradi, Carol B.; Duke, Michael R.; Ames, Genevieve M.

2009-01-01

Background Nationwide surveys identify food service workers as heavy alcohol users. Objectives This article analyzes dimensions and correlates of problem drinking among young adult food service workers. Methods A telephone survey of national restaurant chain employees yielded 1294 completed surveys. Results Hazardous alcohol consumption patterns were seen in 80% of men and 64% of women. Multivariate analysis showed that different dimensions of problem drinking measured by the AUDIT were associated with workers' demographic characteristics, smoking behavior and job category. Conclusions & Scientific Significance These findings offer evidence of extremely high rates of alcohol misuse among young adult restaurant workers. PMID:20180660

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1973-01-01

There are no author-identified significant results in this report. This report deals with the selection of the best channels from the 24-channel aircraft data to represent crop and soil conditions. A three-step procedure has been developed that involves using univariate statistics and an F-ratio test to indicate the best 14 channels. From the 14, the 10 best channels are selected by a multivariate stochastic process. The third step involves the pattern recognition procedures developed in the data analysis plan. Indications are that the procedures in use are satsifactory and will extract the desired information from the data.

Dietary patterns are associated with incident stroke and contribute to excess risk of stroke in black Americans.

PubMed

Judd, Suzanne E; Gutiérrez, Orlando M; Newby, P K; Howard, George; Howard, Virginia J; Locher, Julie L; Kissela, Brett M; Shikany, James M

2013-12-01

Black Americans and residents of the Southeastern United States are at increased risk of stroke. Diet is one of many potential factors proposed that might explain these racial and regional disparities. Between 2003 and 2007, the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study enrolled 30 239 black and white Americans aged≥45 years. Dietary patterns were derived using factor analysis and foods from food frequency data. Incident strokes were adjudicated using medical records by a team of physicians. Cox-proportional hazards models were used to examine risk of stroke. During 5.7 years, 490 incident strokes were observed. In a multivariable-adjusted analysis, greater adherence to the plant-based pattern was associated with lower stroke risk (hazard ratio, 0.71; 95% confidence interval, 0.56-0.91; Ptrend=0.005). This association was attenuated after addition of income, education, total energy intake, smoking, and sedentary behavior. Participants with a higher adherence to the Southern pattern experienced a 39% increased risk of stroke (hazard ratio, 1.39; 95% confidence interval, 1.05, 1.84), with a significant (P=0.009) trend across quartiles. Including Southern pattern in the model mediated the black-white risk of stroke by 63%. These data suggest that adherence to a Southern style diet may increase the risk of stroke, whereas adherence to a more plant-based diet may reduce stroke risk. Given the consistency of finding a dietary effect on stroke risk across studies, discussing nutrition patterns during risk screening may be an important step in reducing stroke.

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

Big-Data RHEED analysis for understanding epitaxial film growth processes

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

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence.more » This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.« less

Histologic Grading of Prostatic Adenocarcinoma Can Be Further Optimized: Analysis of the Relative Prognostic Strength of Individual Architectural Patterns in 1275 Patients From the Canary Retrospective Cohort.

PubMed

McKenney, Jesse K; Wei, Wei; Hawley, Sarah; Auman, Heidi; Newcomb, Lisa F; Boyer, Hilary D; Fazli, Ladan; Simko, Jeff; Hurtado-Coll, Antonio; Troyer, Dean A; Tretiakova, Maria S; Vakar-Lopez, Funda; Carroll, Peter R; Cooperberg, Matthew R; Gleave, Martin E; Lance, Raymond S; Lin, Dan W; Nelson, Peter S; Thompson, Ian M; True, Lawrence D; Feng, Ziding; Brooks, James D

2016-11-01

Histologic grading remains the gold standard for prognosis in prostate cancer, and assessment of Gleason score plays a critical role in active surveillance management. We sought to optimize the prognostic stratification of grading and developed a method of recording and studying individual architectural patterns by light microscopic evaluation that is independent of standard Gleason grade. Some of the evaluated patterns are not assessed by current Gleason grading (eg, reactive stromal response). Individual histologic patterns were correlated with recurrence-free survival in a retrospective postradical prostatectomy cohort of 1275 patients represented by the highest-grade foci of carcinoma in tissue microarrays. In univariable analysis, fibromucinous rupture with varied epithelial complexity had a significantly lower relative risk of recurrence-free survival in cases graded as 3+4=7. Cases having focal "poorly formed glands," which could be designated as pattern 3+4=7, had lower risk than cribriform patterns with either small cribriform glands or expansile cribriform growth. In separate multivariable Cox proportional hazard analyses of both Gleason score 3+3=6 and 3+4=7 carcinomas, reactive stromal patterns were associated with worse recurrence-free survival. Decision tree models demonstrate potential regrouping of architectural patterns into categories with similar risk. In summary, we argue that Gleason score assignment by current consensus guidelines are not entirely optimized for clinical use, including active surveillance. Our data suggest that focal poorly formed gland and cribriform patterns, currently classified as Gleason pattern 4, should be in separate prognostic groups, as the latter is associated with worse outcome. Patterns with extravasated mucin are likely overgraded in a subset of cases with more complex epithelial bridges, whereas stromogenic cancers have a worse outcome than conveyed by Gleason grade alone. These findings serve as a foundation to facilitate optimization of histologic grading and strongly support incorporating reactive stroma into routine assessment.

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.

Exploring Temporal Patterns of Stress in Adolescent Girls with Headache.

PubMed

Björling, Elin A; Singh, Narayan

2017-02-01

As part of a larger study on perceived stress and headaches in 2009, momentary perceived stress, head pain levels and stress-related symptom data were collected. This paper explores a temporal analysis of the patterns of stress, as well as an analysis of momentary and retrospective stress-related symptoms compared by level of headache activity. Adolescent girls (N = 31) ages 14-18 were randomly cued by electronic diaries 7 times per day over a 21-day period responding to momentary questions about level of head pain, perceived stress and stress-related symptoms. Multivariate general linear modelling was used to determine significant differences among headache groups in relation to temporal patterns of stress. Significant headache group differences were found on retrospective and momentary stress-related symptom measures. A total of 2841 diary responses captured stress levels, head pain and related symptoms. The chronic headache (CH) group reported the highest levels of hourly and daily stress, followed by the moderate headache (MH) and low headache (LH) groups. Patterns of stress for the three headache groups were statistically distinct, illustrating increased stress in girls with more frequent head pain. This evidence suggests that because of increased stress, girls with recurrent head pain are likely a vulnerable population who may benefit from stress-reducing interventions. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Communicating Hydrocephalus Associated with Intracranial Schwannoma Treated by Gamma Knife Radiosurgery.

PubMed

Park, Chang Kyu; Lee, Sung Ho; Choi, Man Kyu; Choi, Seok Keun; Park, Bong Jin; Lim, Young Jin

2016-05-01

Gamma knife radiosurgery (GKRS) has been established as an effective and safe treatment for intracranial schwannoma. However, serious complications can occur after GKRS, including hydrocephalus. The pathophysiology and risk factors of this disorder are not yet fully understood. The objective of the study was to assess potential risk factors for hydrocephalus after GKRS. We retrospectively reviewed the medical radiosurgical records of 244 patients who underwent GKRS to treat intracranial schwannoma. The following parameters were analyzed as potential risk factors for hydrocephalus after GKRS: age, sex, target volume, irradiation dose, prior tumor resection, treatment technique, and tumor enhancement pattern. The tumor enhancement pattern was divided into 2 groups: group A (homogeneous enhancement) and group B (heterogeneous or rim enhancement). Of the 244 patients, 14 of them (5.7%) developed communicating hydrocephalus. Communicating hydrocephalus occurred within 2 years after GKRS in most patients (92.8%). No significant association was observed between any of the parameters investigated and the development of hydrocephalus, with the exception of tumor enhancement pattern. Group B exhibited a statistically significant difference by univariate analysis (P = 0.002); this difference was also significant by multivariate analysis (P = 0.006). Because hydrocephalus is curable, patients should be closely monitored for the development of this disorder after GKRS. In particular, patients with intracranial schwannomas with irregular enhancement patterns or cysts should be meticulously observed. Copyright © 2015 Elsevier Inc. All rights reserved.

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

Nuclear magnetic resonance (NMR) study of the effect of cisplatin on the metabolic profile of MG-63 osteosarcoma cells.

PubMed

Duarte, Iola F; Lamego, Ines; Marques, Joana; Marques, M Paula M; Blaise, Benjamin J; Gil, Ana M

2010-11-05

In the present study, (1)H HRMAS NMR spectroscopy was used to assess the changes in the intracellular metabolic profile of MG-63 human osteosarcoma (OS) cells induced by the chemotherapy agent cisplatin (CDDP) at different times of exposure. Multivariate analysis was applied to the cells spectra, enabling consistent variation patterns to be detected and drug-specific metabolic effects to be identified. Statistical recoupling of variables (SRV) analysis and spectral integration enabled the most relevant spectral changes to be evaluated, revealing significant time-dependent alterations in lipids, choline-containing compounds, some amino acids, polyalcohols, and nitrogenated bases. The metabolic relevance of these compounds in the response of MG-63 cells to CDDP treatment is discussed.

An introduction to metabolomics and its potential application in veterinary science.

PubMed

Jones, Oliver A H; Cheung, Victoria L

2007-10-01

Metabolomics has been found to be applicable to a wide range of fields, including the study of gene function, toxicology, plant sciences, environmental analysis, clinical diagnostics, nutrition, and the discrimination of organism genotypes. This approach combines high-throughput sample analysis with computer-assisted multivariate pattern-recognition techniques. It is increasingly being deployed in toxico- and pharmacokinetic studies in the pharmaceutical industry, especially during the safety assessment of candidate drugs in human medicine. However, despite the potential of this technique to reduce both costs and the numbers of animals used for research, examples of the application of metabolomics in veterinary research are, thus far, rare. Here we give an introduction to metabolomics and discuss its potential in the field of veterinary science.

Multivariate Analysis of Effects of Asthmatic Patient Respiratory Profiles on the In Vitro Performance of a Reservoir Multidose and a Capsule-Based Dry Powder Inhaler.

PubMed

Buttini, Francesca; Pasquali, Irene; Brambilla, Gaetano; Copelli, Diego; Alberi, Massimiliano Dagli; Balducci, Anna Giulia; Bettini, Ruggero; Sisti, Viviana

2016-03-01

The aim of this work was to evaluate the effect of two different dry powder inhalers, of the NGI induction port and Alberta throat and of the actual inspiratory profiles of asthmatic patients on in-vitro drug inhalation performances. The two devices considered were a reservoir multidose and a capsule-based inhaler. The formulation used to test the inhalers was a combination of formoterol fumarate and beclomethasone dipropionate. A breath simulator was used to mimic inhalatory patterns previously determined in vivo. A multivariate approach was adopted to estimate the significance of the effect of the investigated variables in the explored domain. Breath simulator was a useful tool to mimic in vitro the in vivo inspiratory profiles of asthmatic patients. The type of throat coupled with the impactor did not affect the aerodynamic distribution of the investigated formulation. However, the type of inhaler and inspiratory profiles affected the respirable dose of drugs. The multivariate statistical approach demonstrated that the multidose inhaler, released efficiently a high fine particle mass independently from the inspiratory profiles adopted. Differently, the single dose capsule inhaler, showed a significant decrease of fine particle mass of both drugs when the device was activated using the minimum inspiratory volume (592 mL).

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Spatial pattern of spring phytoplankton community in the coastal waters of northern Zhejiang, East China Sea

NASA Astrophysics Data System (ADS)

Ye, Ran; Cai, Yanhong; Wei, Yongjie; Li, Xiaoming

2017-04-01

The spatial pattern of phytoplankton community can indicate potential environmental variation in different water bodies. In this context, spatial pattern of phytoplankton community and its response to environmental and spatial factors were studied in the coastal waters of northern Zhejiang, East China Sea using multivariate statistical techniques. Results showed that 94 species belonging to 40 genera, 5 phyla were recorded (the remaining 9 were identified to genus level) with diatoms being the most dominant followed by dinoflagellates. Hierarchical clustering analysis (HCA), nonmetric multidimentional scaling (NMDS), and analysis of similarity (ANOSIM) all demomstrated that the whole study area could be divided into 3 subareas with significant differences. Indicator species analysis (ISA) further confirmed that the indicator species of each subarea correlated significantly with specific environmental factors. Distance-based linear model (Distlm) and Mantel test revealed that silicate (SiO32-), phosphate (PO43-), pH, and dissolved oxygen (DO) were the most important environmental factors influencing phytoplankton community. Variation portioning (VP) finally concluded that the shared fractions of environmental and spatial factors were higher than either the pure environmental effects or the pure spatial effects, suggesting phytoplankton biogeography were mainly affected by both the environmental variability and dispersal limitation. Additionally, other factors (eg., trace metals, biological grazing, climate change, and time-scale variation) may also be the sources of the unexplained variation which need further study.

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain.

PubMed

Cao, Miao; He, Yong; Dai, Zhengjia; Liao, Xuhong; Jeon, Tina; Ouyang, Minhui; Chalak, Lina; Bi, Yanchao; Rollins, Nancy; Dong, Qi; Huang, Hao

2017-03-01

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

PubMed

Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

2017-11-15

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

A minimally processed dietary pattern is associated with lower odds of metabolic syndrome among Lebanese adults.

PubMed

Nasreddine, Lara; Tamim, Hani; Itani, Leila; Nasrallah, Mona P; Isma'eel, Hussain; Nakhoul, Nancy F; Abou-Rizk, Joana; Naja, Farah

2018-01-01

To (i) estimate the consumption of minimally processed, processed and ultra-processed foods in a sample of Lebanese adults; (ii) explore patterns of intakes of these food groups; and (iii) investigate the association of the derived patterns with cardiometabolic risk. Cross-sectional survey. Data collection included dietary assessment using an FFQ and biochemical, anthropometric and blood pressure measurements. Food items were categorized into twenty-five groups based on the NOVA food classification. The contribution of each food group to total energy intake (TEI) was estimated. Patterns of intakes of these food groups were examined using exploratory factor analysis. Multivariate logistic regression analysis was used to evaluate the associations of derived patterns with cardiometabolic risk factors. Greater Beirut area, Lebanon. Adults ≥18 years (n 302) with no prior history of chronic diseases. Of TEI, 36·53 and 27·10 % were contributed by ultra-processed and minimally processed foods, respectively. Two dietary patterns were identified: the 'ultra-processed' and the 'minimally processed/processed'. The 'ultra-processed' consisted mainly of fast foods, snacks, meat, nuts, sweets and liquor, while the 'minimally processed/processed' consisted mostly of fruits, vegetables, legumes, breads, cheeses, sugar and fats. Participants in the highest quartile of the 'minimally processed/processed' pattern had significantly lower odds for metabolic syndrome (OR=0·18, 95 % CI 0·04, 0·77), hyperglycaemia (OR=0·25, 95 % CI 0·07, 0·98) and low HDL cholesterol (OR=0·17, 95 % CI 0·05, 0·60). The study findings may be used for the development of evidence-based interventions aimed at encouraging the consumption of minimally processed foods.

A Western dietary pattern is associated with higher blood pressure in Iranian adolescents.

PubMed

Hojhabrimanesh, Abdollah; Akhlaghi, Masoumeh; Rahmani, Elham; Amanat, Sasan; Atefi, Masoumeh; Najafi, Maryam; Hashemzadeh, Maral; Salehi, Saedeh; Faghih, Shiva

2017-02-01

The dietary determinants of adolescent blood pressure (BP) are not well understood. We determined the association between major dietary patterns and BP in a sample of Iranian adolescents. This cross-sectional study was conducted among a representative sample (n = 557) of Shirazi adolescents aged 12-19 years. Participants' systolic and diastolic BP was measured using a validated oscillometric BP monitor. Usual dietary intakes during the past 12 months were assessed using a valid and reproducible 168-item semiquantitative food frequency questionnaire through face-to-face interviews. Principal component factor analysis was used to identify major dietary patterns based on a set of 25 predefined food groups. Overall, three major dietary patterns were identified, among which only the Western pattern (abundant in soft drinks, sweets and desserts, salt, mayonnaise, tea and coffee, salty snacks, high-fat dairy products, French fries, and red or processed meats) had a significant association with BP. After adjusting for potential confounders in the analysis of covariance models, multivariable adjusted means of the systolic and mean BP of subjects in the highest tertile of the Western pattern score were significantly higher than those in the lowest tertile (for systolic BP: mean difference 6.9 mmHg, P = 0.001; and for mean BP: mean difference 4.2 mmHg, P = 0.003). A similar but statistically insignificant difference was observed in terms of diastolic BP. The findings suggest that a Western dietary pattern is associated with higher BP in Iranian adolescents. However, additional large-scale prospective studies with adequate methodological quality are required to confirm these findings.

Aboriginal Street-involved Youth Experience Elevated Risk of Incarceration

PubMed Central

Barker, Brittany; Alfred, Gerald Taiaiake; Fleming, Kim; Nguyen, Paul; Wood, Evan; Kerr, Thomas; DeBeck, Kora

2015-01-01

Objectives Past research has identified risk factors associated with incarceration among adult Aboriginal populations; however, less is known about incarceration among street-involved Aboriginal youth. Therefore, we undertook this study to longitudinally investigate recent reports of incarceration among a prospective cohort of street-involved youth in Vancouver, Canada. Study Design Prospective cohort study. Methods Data were collected from a cohort of street-involved, drug-using youth from September 2005 to May 2013. Multivariate generalized estimating equation analyses were employed to examine the potential relationship between Aboriginal ancestry and recent incarceration. Results Among our sample of 1050 youth, 248 (24%) reported being of aboriginal ancestry, and 378 (36%) reported being incarcerated in the previous six months at some point during the study period. In multivariate analysis controlling for a range of potential confounders including drug use patterns and other risk factors, Aboriginal ancestry remained significantly associated with recent incarceration (adjusted odds ratio [AOR]=1.44; 95% confidence interval [CI]: 1.12–1.86). Conclusions Even after adjusting for drug use patterns and other risk factors associated with incarceration, this study found that Aboriginal street-involved youth were still significantly more likely to be incarcerated than their non-Aboriginal peers. Given the established harms associated with incarceration these findings underscore the pressing need for systematic reform including culturally appropriate interventions to prevent Aboriginal youth from becoming involved with the criminal justice system. PMID:26390949

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Electronic noses and tongues: Applications for the food and pharmaceutical industries

USDA-ARS?s Scientific Manuscript database

The electronic nose (enose) is designed to crudely mimic the human brain in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern rec...

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

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

pN0(i+) Breast Cancer: Treatment Patterns, Locoregional Recurrence, and Survival Outcomes

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

Karam, Irene; Breast Cancer Outcomes Unit, British Columbia Cancer Agency, Vancouver, BC; Lesperance, Maria F.

Purpose: To examine treatment patterns, recurrence, and survival outcomes in patients with pN0(i+) breast cancer. Methods and Materials: Subjects were 5999 women with AJCC (6th edition) pT1-3, pN0-N1a, M0 breast cancer diagnosed between 2003 and 2006. Of these, 4342 (72%) had pN0, 96 (2%) had pN0(i+), 349 (6%) had pNmic (micrometastases >0.2 mm to ≤2 mm), and 1212 (20%) had pN1a (1-3 positive macroscopic nodes) disease. Treatment characteristics and 5-year Kaplan-Meier local recurrence, regional recurrence (RR), locoregional recurrence (LRR), and overall survival were compared between nodal subgroups. Multivariable analysis was performed using Cox regression modeling. A 1:3 case-match analysis examinedmore » outcomes in pN0(i+) cases compared with pN0 controls matched for similar tumor and treatment characteristics. Results: Median follow-up was 4.8 years. Adjuvant systemic therapy use increased with nodal stage: 81%, 92%, 95%, and 94% in pN0, pN0(i+), pNmic, and pN1a disease, respectively (P<.001). Nodal radiation therapy (RT) use also increased with nodal stage: 1.7% in pN0, 27% in pN0(i+), 33% in pNmic, and 63% in pN1a cohorts (P<.001). Five-year Kaplan-Meier outcomes in pN0 versus pN0(i+) cases were as follows: local recurrence 1.7% versus 3.7% (P=.20), RR 0.5% versus 2.2% (P=.02), and LRR 2.1% versus 5.8% (P=.02). There were no RR events in 26 patients with pN0(i+) disease who received nodal RT and 2 RR events in 70 patients who did not receive nodal RT. On multivariable analysis, pN0(i+) was not associated with worse locoregional control or survival. On case-match analysis, LRR and overall survival were similar between pN0(i+) and matched pN0 counterparts. Conclusions: Nodal involvement with isolated tumor cells is not a significant prognostic factor for LRR or survival in this study's multivariable and case-match analyses. These data do not support the routine use of nodal RT in the setting of pN0(i+) disease. Prospective studies are needed to define optimal locoregional management for women with pN0(i+) breast cancer.« less

Dietary Patterns in Pregnancy in New Zealand—Influence of Maternal Socio-Demographic, Health and Lifestyle Factors

PubMed Central

Wall, Clare R.; Gammon, Cheryl S.; Bandara, Dinusha K.; Grant, Cameron C.; Atatoa Carr, Polly E.; Morton, Susan M. B.

2016-01-01

Exploration of dietary pattern associations within a multi-ethnic society context has been limited. We aimed to describe dietary patterns of 5664 pregnant women from the Growing Up in New Zealand study, and investigate associations between these patterns and maternal socio-demographic, place of birth, health and lifestyle factors. Participants completed a food frequency questionnaire prior to the birth of their child. Principal components analysis was used to extract dietary patterns and multivariable analyses used to determine associations. Four dietary components were extracted. Higher scores on, ‘Junk’ and ‘Traditional/White bread’, were associated with decreasing age, lower educational levels, being of Pacific or Māori ethnicity and smoking. Higher scores on, ‘Health conscious’ and ‘Fusion/Protein’, were associated with increasing age, better self-rated health, lower pre-pregnancy body mass index (BMI) and not smoking. Higher scores on ‘Junk’ and ‘Health conscious’ were associated with being born in New Zealand (NZ), whereas higher scores on ‘Fusion/Protein’ was associated with being born outside NZ and being of non-European ethnicity, particularly Asian. High scores on the ‘Health conscious’ dietary pattern showed the highest odds of adherence to the pregnancy dietary guidelines. In this cohort of pregnant women different dietary patterns were associated with migration, ethnicity, socio-demographic characteristics, health behaviors and adherence to dietary guidelines. PMID:27213438

Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

PubMed

Bagur, M G; Morales, S; López-Chicano, M

2009-11-15

Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

Dietary Patterns After the Weaning and Lactation Period Are Associated With Celiac Disease Autoimmunity in Children.

PubMed

Barroso, Monica; Beth, Sytske A; Voortman, Trudy; Jaddoe, Vincent W V; van Zelm, Menno C; Moll, Henriette A; Kiefte-de Jong, Jessica C

2018-06-01

There have been many studies of associations between infant feeding practices and development of celiac disease during childhood, but few studies have focused on overall diets of young children after the weaning period. We aimed to examine the association between common dietary patterns in infants and the occurrence of celiac disease autoimmunity during childhood. We performed a prospective analysis of data from the Generation R Study that comprised 1997 children born from April 2002 through January 2006 in Rotterdam, the Netherlands. Food consumption around 1 year of age was assessed with a validated food-frequency questionnaire. Dietary data were examined using a priori (based on existing guidelines) and a posteriori (principal component analysis and reduced rank regression) dietary pattern analyses. Five dietary patterns were compared. Celiac disease autoimmunity, determined on the basis of serum concentration of transglutaminase-2 autoantibody (ie, TG2A) below or above 7 U/mL, was evaluated at 6 years. Associations between dietary pattern adherence scores and celiac disease autoimmunity were examined using multivariable logistic regression models. Higher adherence to the a posteriori-derived prudent dietary pattern (high intake of vegetables, vegetable oils, pasta, and grains and low consumption of refined cereals and sweet beverages) at 1 year was significantly associated with lower odds of celiac disease autoimmunity at 6 years (odds ratio, 0.67; 95% confidence interval, 0.53-0.84). No significant associations were found for the 4 remaining dietary patterns. In a prospective study of dietary patterns of young children in the Netherlands, we associated a dietary pattern characterized by high consumption of vegetables and grains and low consumption of refined cereals and sweet beverages, with lower odds of celiac disease autoimmunity. Early-life dietary patterns might therefore be involved in the development of celiac disease during childhood. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

An integrated phenomic approach to multivariate allelic association

PubMed Central

Medland, Sarah Elizabeth; Neale, Michael Churton

2010-01-01

The increased feasibility of genome-wide association has resulted in association becoming the primary method used to localize genetic variants that cause phenotypic variation. Much attention has been focused on the vast multiple testing problems arising from analyzing large numbers of single nucleotide polymorphisms. However, the inflation of experiment-wise type I error rates through testing numerous phenotypes has received less attention. Multivariate analyses can be used to detect both pleiotropic effects that influence a latent common factor, and monotropic effects that operate at a variable-specific levels, whilst controlling for non-independence between phenotypes. In this study, we present a maximum likelihood approach, which combines both latent and variable-specific tests and which may be used with either individual or family data. Simulation results indicate that in the presence of factor-level association, the combined multivariate (CMV) analysis approach performs well with a minimal loss of power as compared with a univariate analysis of a factor or sum score (SS). As the deviation between the pattern of allelic effects and the factor loadings increases, the power of univariate analyses of both factor and SSs decreases dramatically, whereas the power of the CMV approach is maintained. We show the utility of the approach by examining the association between dopamine receptor D2 TaqIA and the initiation of marijuana, tranquilizers and stimulants in data from the Add Health Study. Perl scripts that takes ped and dat files as input and produces Mx scripts and data for running the CMV approach can be downloaded from www.vipbg.vcu.edu/~sarahme/WriteMx. PMID:19707246

1 H NMR study and multivariate data analysis of reindeer skin tanning methods.

PubMed

Zhu, Lizheng; Ilott, Andrew J; Del Federico, Eleonora; Kehlet, Cindie; Klokkernes, Torunn; Jerschow, Alexej

2017-04-01

Reindeer skin clothing has been an essential component in the lives of indigenous people of the arctic and sub-arctic regions, keeping them warm during harsh winters. However, the skin processing technology, which often conveys the history and tradition of the indigenous group, has not been well documented. In this study, NMR spectra and relaxation behaviors of reindeer skin samples treated with a variety of vegetable tannin extracts, oils and fatty substances are studied and compared. With the assistance of principal component analysis (PCA), one can recognize patterns and identify groupings of differently treated samples. These methods could be important aids in efforts to conserve museum leather artifacts with unknown treatment methods and in the analysis of reindeer skin tanning processes. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Coupling detrended fluctuation analysis for multiple warehouse-out behavioral sequences

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou

2017-01-01

Interaction patterns among different warehouses could make the warehouse-out behavioral sequences less predictable. We firstly take a coupling detrended fluctuation analysis on the warehouse-out quantity, and find that the multivariate sequences exhibit significant coupling multifractal characteristics regardless of the types of steel products. Secondly, we track the sources of multifractal warehouse-out sequences by shuffling and surrogating original ones, and we find that fat-tail distribution contributes more to multifractal features than the long-term memory, regardless of types of steel products. From perspective of warehouse contribution, some warehouses steadily contribute more to multifractal than other warehouses. Finally, based on multiscale multifractal analysis, we propose Hurst surface structure to investigate coupling multifractal, and show that multiple behavioral sequences exhibit significant coupling multifractal features that emerge and usually be restricted within relatively greater time scale interval.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Sputum neutrophil counts are associated with more severe asthma phenotypes using cluster analysis.

PubMed

Moore, Wendy C; Hastie, Annette T; Li, Xingnan; Li, Huashi; Busse, William W; Jarjour, Nizar N; Wenzel, Sally E; Peters, Stephen P; Meyers, Deborah A; Bleecker, Eugene R

2014-06-01

Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified 5 asthma subphenotypes that represent the severity spectrum of early-onset allergic asthma, late-onset severe asthma, and severe asthma with chronic obstructive pulmonary disease characteristics. Analysis of induced sputum from a subset of SARP subjects showed 4 sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophil (≥2%) and neutrophil (≥40%) percentages had characteristics of very severe asthma. To better understand interactions between inflammation and clinical subphenotypes, we integrated inflammatory cellular measures and clinical variables in a new cluster analysis. Participants in SARP who underwent sputum induction at 3 clinical sites were included in this analysis (n = 423). Fifteen variables, including clinical characteristics and blood and sputum inflammatory cell assessments, were selected using factor analysis for unsupervised cluster analysis. Four phenotypic clusters were identified. Cluster A (n = 132) and B (n = 127) subjects had mild-to-moderate early-onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of cluster C (n = 117) and D (n = 47) subjects who had moderate-to-severe asthma with frequent health care use despite treatment with high doses of inhaled or oral corticosteroids and, in cluster D, reduced lung function. The majority of these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophil percentages were the most important variables determining cluster assignment. This multivariate approach identified 4 asthma subphenotypes representing the severity spectrum from mild-to-moderate allergic asthma with minimal or eosinophil-predominant sputum inflammation to moderate-to-severe asthma with neutrophil-predominant or mixed granulocytic inflammation. Published by Mosby, Inc.

Sputum neutrophils are associated with more severe asthma phenotypes using cluster analysis

PubMed Central

Moore, Wendy C.; Hastie, Annette T.; Li, Xingnan; Li, Huashi; Busse, William W.; Jarjour, Nizar N.; Wenzel, Sally E.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified five asthma subphenotypes that represent the severity spectrum of early onset allergic asthma, late onset severe asthma and severe asthma with COPD characteristics. Analysis of induced sputum from a subset of SARP subjects showed four sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophils (≥2%) and neutrophils (≥40%) had characteristics of very severe asthma. Objective To better understand interactions between inflammation and clinical subphenotypes we integrated inflammatory cellular measures and clinical variables in a new cluster analysis. Methods Participants in SARP at three clinical sites who underwent sputum induction were included in this analysis (n=423). Fifteen variables including clinical characteristics and blood and sputum inflammatory cell assessments were selected by factor analysis for unsupervised cluster analysis. Results Four phenotypic clusters were identified. Cluster A (n=132) and B (n=127) subjects had mild-moderate early onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of Cluster C (n=117) and D (n=47) subjects who had moderate-severe asthma with frequent health care utilization despite treatment with high doses of inhaled or oral corticosteroids, and in Cluster D, reduced lung function. The majority these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophils were the most important variables determining cluster assignment. Conclusion This multivariate approach identified four asthma subphenotypes representing the severity spectrum from mild-moderate allergic asthma with minimal or eosinophilic predominant sputum inflammation to moderate-severe asthma with neutrophilic predominant or mixed granulocytic inflammation. PMID:24332216

Craniofacial morphometric analysis of mandibular prognathism.

PubMed

Chang, H P; Liu, P H; Yang, Y H; Lin, H C; Chang, C H

2006-03-01

The purpose of this study was to provide more information about the morphological characteristics of the craniofacial complex in mandibular prognathism. Forty young adult males having mandibular prognathism were compared with 40 having normal occlusion. This was conducted to carry out geometric morphometric assessments to localize alterations, using Procrustes analysis and thin-plate spline analysis, in addition to conventional cephalometric techniques. Procrustes analysis indicated that the mean craniofacial, midfacial and mandibular morphology was significantly different in prognathic subjects compared with normal controls. This finding was corroborated by the multivariate Hotelling T(2)-test of cephalometric variables. Mandibular prognathism demonstrated a shorter and slightly retropositioned maxilla, a greater total length and anterior positioning of the mandible. Thin-plate spline analysis revealed a developmental diminution of the palatomaxillary region anteroposteriorly and a developmental elongation of the mandible anteroposteriorly, leading to the appearance of a prognathic mandibular profile. In conclusion, thin-plate spline analysis seems to provide a valuable supplement for conventional cephalometric analysis because the complex patterns of craniofacial shape change are visualized suggestive by means of grid deformations.

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE PAGES

Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

2017-02-16

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

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

Steed, Chad A.; Halsey, William; Dehoff, Ryan

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Land Use and Family Formation in the Settlement of the U.S. Great Plains

PubMed Central

Gutmann, Myron P.; Pullum-Piñón, Sara M.; Witkowski, Kristine; Deane, Glenn D.; Merchant, Emily

2014-01-01

In agricultural settings, environment shapes patterns of settlement and land use. Using the Great Plains of the United States during the period of its initial Euro-American settlement (1880–1940) as an analytical lens, this article explores whether the same environmental factors that determine settlement timing and land use—those that indicate suitability for crop-based agriculture—also shape initial family formation, resulting in fewer and smaller families in areas that are more conducive to livestock raising than to cropping. The connection between family size and agricultural land availability is now well known, but the role of the environment has not previously been explicitly tested. Descriptive analysis offers initial support for a distinctive pattern of family formation in the western Great Plains, where precipitation is too low to support intensive cropping. However, multivariate analysis using county-level data at 10-year intervals offers only partial support to the hypothesis that environmental characteristics produce these differences. Rather, this analysis has found that the region was also subject to the same long-term social and demographic changes sweeping the rest of the country during this period. PMID:24634550

Application of concepts from cross-recurrence analysis in speech production: an overview and comparison with other nonlinear methods.

PubMed

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-06-01

The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space reconstruction is conducted, and a cleaning algorithm removes the artifacts from the recurrence plot. The main features of the proposed approach are robustness to nonstationarity and efficient separation of amplitude variability from temporal variability. The authors tested these claims by applying their method to synthetic stimuli whose variability had been carefully controlled. The proposed method was also demonstrated in a practical application: It was used to investigate the role of biomechanical constraints in articulatory reorganization as a consequence of speeded repetition of CVCV utterances containing a labial and a coronal consonant. Overall, the proposed approach provided more reliable results than other methods, particularly in the presence of high variability. The proposed method is a useful and appropriate tool for quantifying similarity and dissimilarity in patterns of speech articulator movement, especially in such research areas as speech errors and pathologies, where unpredictable divergent behavior is expected.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

PubMed Central

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

Peeking Network States with Clustered Patterns

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

Kim, Jinoh; Sim, Alex

2015-10-20

Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learningmore » tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.« less

Quantification of in vivo colonic motor patterns in healthy humans before and after a meal revealed by high-resolution fiber-optic manometry

PubMed Central

DINNING, P. G.; WIKLENDT, L.; MASLEN, L.; GIBBINS, I.; PATTON, V.; ARKWRIGHT, J. W.; LUBOWSKI, D. Z.; O'GRADY, G.; BAMPTON, P. A.; BROOKES, S. J.; COSTA, M.

2015-01-01

Background Until recently, investigations of the normal patterns of motility of the healthy human colon have been limited by the resolution of in vivo recording techniques. Methods We have used a new, high-resolution fiber-optic manometry system (72 sensors at 1-cm intervals) to record motor activity from colon in 10 healthy human subjects. Key Results In the fasted colon, on the basis of rate and extent of propagation, four types of propagating motor pattern could be identified: (i) cyclic motor patterns (at 2–6/min); (ii) short single motor patterns; (iii) long single motor patterns; and (iv) occasional retrograde, slow motor patterns. For the most part, the cyclic and short single motor patterns propagated in a retrograde direction. Following a 700 kCal meal, a fifth motor pattern appeared; high-amplitude propagating sequences (HAPS) and there was large increase in retrograde cyclic motor patterns (5.6±5.4/2 h vs 34.7±19.8/2 h; p < 0.001). The duration and amplitude of individual pressure events were significantly correlated. Discriminant and multivariate analysis of duration, gradient, and amplitude of the pressure events that made up propagating motor patterns distinguished clearly two types of pressure events: those belonging to HAPS and those belonging to all other propagating motor patterns. Conclusions & Inferences This work provides the first comprehensive description of colonic motor patterns recorded by high-resolution manometry and demonstrates an abundance of retrograde propagating motor patterns. The propagating motor patterns appear to be generated by two independent sources, potentially indicating their neurogenic or myogenic origin. PMID:25131177

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Prognostic impact of metastatic pattern in stage IV breast cancer at initial diagnosis.

PubMed

Leone, Bernardo Amadeo; Vallejo, Carlos Teodoro; Romero, Alberto Omar; Machiavelli, Mario Raúl; Pérez, Juan Eduardo; Leone, Julieta; Leone, José Pablo

2017-02-01

To analyze the prognostic influence of metastatic pattern (MP) compared with other biologic and clinical factors in stage IV breast cancer at initial diagnosis (BCID) and evaluate factors associated with specific sites of metastases (SSM). We evaluated women with stage IV BCID with known metastatic sites, reported to the Surveillance, Epidemiology and End Results program from 2010 to 2013. MP was categorized as bone-only, visceral, bone and visceral (BV), and other. Univariate and multivariate analyses determined the effects of each variable on overall survival (OS). Logistic regression examined factors associated with SSM. We included 9143 patients. Bone represented 37.5% of patients, visceral 21.9%, BV 28.8%, and other 11.9%. Median OS by MP was as follows: bone 38 months, visceral 21 months, BV 19 months, and other 33 months (P < 0.0001). Univariate analysis showed that higher number of metastatic sites had worse prognosis. In multivariate analysis, older age (hazard ratio 1.9), black race (hazard ratio 1.17), grade 3/4 tumors (hazard ratio 1.6), triple-negative (hazard ratio 2.24), BV MP (hazard ratio 2.07), and unmarried patients (hazard ratio 1.25) had significantly shorter OS. As compared with HR+/HER2- tumors, triple-negative and HR-/HER2+ had higher odds of brain, liver, lung, and other metastases. HR+/HER2+ had higher odds of liver metastases. All three subtypes had lower odds of bone metastases. There were substantial differences in OS according to MP. Tumor subtypes have a clear influence among other factors on SSM. We identified several prognostic factors that could guide therapy selection in treatment naïve patients.

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

PubMed Central

Kölling, Jan; Langenkämper, Daniel; Abouna, Sylvie; Khan, Michael; Nattkemper, Tim W.

2012-01-01

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de PMID:22390938

Chemotherapy Versus Chemoradiation for Node-Positive Bladder Cancer: Practice Patterns and Outcomes from the National Cancer Data Base

PubMed Central

Haque, Waqar; Verma, Vivek; Butler, E. Brian; Teh, Bin S.

2017-01-01

Background: Management of clinically node-positive bladder cancer (cN+ BC) is poorly defined; national guidelines recommend chemotherapy (CT) alone or chemoradiation (CRT). Objective: Using a large, contemporary dataset, we evaluated national practice patterns and outcomes of CT versus CRT to elucidate the optimal therapy for this patient population. Methods: The National Cancer Data Base (NCDB) was queried (2004–2013) for patients diagnosed with cTanyN1-3M0 BC. Patients were divided into two groups: CT alone or CRT. Statistics included multivariable logistic regression to determine factors predictive of receiving additional radiotherapy, Kaplan-Meier analysis to evaluate overall survival (OS), and Cox proportional hazards modeling to determine variables associated with OS. Propensity score matching was performed to assess groups in a balanced manner while reducing indication biases. Results: Of 1,783 total patients, 1,388 (77.8%) underwent CT alone, and 395 (22.2%) CRT. Although patients receiving CRT tended to be of higher socioeconomic status, they were more likely older (p = 0.053), higher T stage, N1 (versus N2) disease, squamous histology, and treated at a non-academic center (p < 0.05). Median overall survival (OS) was 19.0 months and 13.8 months (p < 0.001) for patients receiving CRT or CT, respectively. On Cox multivariate analysis, receipt of CRT was independently associated with improved survival (p < 0.001). Outcome improvements with CRT persisted on evaluation of propensity-matched populations (p < 0.001). Conclusions: CRT is underutilized in the United States for cN+ BC but is independently associated with improved survival despite being preferentially administered to a somewhat higher-risk population. PMID:29152552

Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

PubMed

Hu, Xinyu; Liu, Qi; Li, Bin; Tang, Wanjie; Sun, Huaiqiang; Li, Fei; Yang, Yanchun; Gong, Qiyong; Huang, Xiaoqi

2016-02-01

Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Working after a metastatic cancer diagnosis: factors affecting employment in the metastatic setting from ECOG’s Symptom Outcomes and Practice Patterns (SOAPP) study

PubMed Central

Tevaarwerk, Amye; Lee, Ju-Whei; Terhaar, Abigail; Sesto, Mary; Smith, Mary Lou; Cleeland, Charles; Fisch, Michael

2015-01-01

Background Improved survival for individuals with metastatic cancer accentuates the importance of employment for cancer survivors. Better understanding of how metastatic cancer affects employment is a necessary step towards the development of tools to assist survivors in this important realm. Methods We analyzed the Eastern Cooperative Oncology Group’s “Symptom Outcomes and Practice Patterns (SOAPP)” study to investigate what factors were associated with employment of 680 metastatic cancer patients. Univariable and multivariable logistic regression analyses were conducted to compare patients stably working (Group A) to patients no longer working (Group B). Results There were 668 metastatic working-age participants in our analysis; 236 (35%) worked full or part-time while 302 (45%) stopped working due to illness. Overall, 58% reported some change in employment due to illness. Better performance status and non-Hispanic White ethnicity/race were significantly associated with continuing to work despite a metastatic cancer diagnosis on multivariable analysis. Disease type, time since metastatic diagnosis, number of metastatic sites, location of metastatic disease, and treatment status had no significant impact. Among the potentially modifiable factors, receiving hormonal treatment (if a viable option) and decreasing symptom interference were associated with continuing to work. Conclusions A significant percentage of metastatic patients remain employed; symptom burden was associated with change to no longer working. Modifiable factors resulting in work interference should be minimized so that patients with metastatic disease may continue working, if desired. Improvements in symptom control and strategies developed to help address work place difficulties have promise to improve this aspect of survivorship. PMID:26687819

Has the Pattern of Practice in the Prescription of Radiotherapy for the Palliation of Thoracic Symptoms Changed Between 1999 and 2006 at the Rapid Response Radiotherapy Program?

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

Fairchild, Alysa; Goh, Philiz; Sinclair, Emily

2008-03-01

Purpose: Eleven randomized controlled trials (RCT) comparing various radiotherapy (RT) schedules for locally advanced lung cancer published since 1991 found no difference in palliation of intrathoracic symptoms. The most commonly prescribed schedule by Canadian Radiation Oncologists (RO) (20 Gy in five fractions [20 Gy/5]), when first evaluated versus 10 Gy/1 in a 2002 RCT, showed a significant survival benefit. A subsequent RCT assessing 20 Gy/5 found worse survival versus 16 Gy/2. This study examines whether the RT prescription for lung cancer palliation in the Rapid Response Radiotherapy Program (RRRP) has changed over time. Methods and Materials: Chart review was conductedmore » for patients treated with palliative thoracic RT across three periods (1999-2006). Patient demographics, tumor, treatment, and organizational factors were analyzed descriptively. Chi-square test was used to detect differences in proportions between unordered categorical variables. Continuous variables were tested using analysis of variance. Multivariate logistic regression was used to identify independent predictors of RT schedule prescribed. Results: A total of 117 patients received 121 courses of palliative thoracic RT. The most common dose (20 Gy/5) comprised 65% of courses in 1999, 68% in 2003, and 60% in 2005-2006 (p = 0.76). The next most common dose was 30 Gy/10 (13%). Overall, the median survival was 14.9 months, independent of RT schedule (p = 0.68). Multivariate analysis indicated palliative chemotherapy and certification year of RO were significant predictors of prescription of 20 Gy/5. Conclusion: RT schedule for palliation of intrathoracic symptoms did not mirror the results of sequential, conflicting RCTs, suggesting that factors other than the literature influenced practice patterns in palliative thoracic RT.« less

Clinical, but not oesophageal pH-impedance, profiles predict response to proton pump inhibitors in gastro-oesophageal reflux disease.

PubMed

Zerbib, Frank; Belhocine, Kafia; Simon, Mireille; Capdepont, Maylis; Mion, François; Bruley des Varannes, Stanislas; Galmiche, Jean-Paul

2012-04-01

Approximately 30% of patients with gastro-oesophageal reflux disease (GORD) do not achieve adequate symptom control with proton pump inhibitors (PPIs). The aim of this study was to determine whether any symptom profile or reflux pattern was associated with refractoriness to PPI therapy. Patients with typical GORD symptoms (heartburn and/or regurgitation) were included and had 24 h pH-impedance monitoring off therapy. Patients were considered to be responders if they had fewer than 2 days of mild symptoms per week while receiving a standard or double dose of PPI treatment for at least 4 weeks. Both clinical and reflux parameters were taken into account for multivariate analysis (logistic regression). One hundred patients were included (median age 50 years, 42 male), 43 responders and 57 non-responders. Overall, multivariate analysis showed that the factors associated with the absence of response were absence of oesophagitis (p=0.050), body mass index (BMI) ≤25 kg/m(2) (p=0.002) and functional dyspepsia (FD) (p=0.001). In patients who reported symptoms during the recording (n=85), the factors associated with PPI failure were BMI ≤25 kg/m(2) (p=0.004), FD (p=0.009) and irritable bowel syndrome (p=0.045). In patients with documented GORD (n=67), the factors associated with PPI failure were absence of oesophagitis (p=0.040), FD (p=0.003), irritable bowel syndrome (p=0.012) and BMI ≤25 kg/m(2) (p=0.029). No reflux pattern demonstrated by 24 h pH-impedance monitoring is associated with response to PPIs in patients with GORD symptoms. In contrast, absence of oesophagitis, presence of functional digestive disorders and BMI ≤25 kg/m(2) are strongly associated with PPI failure.

Change in Health Insurance Coverage After Liver Transplantation Can Be Associated with Worse Outcomes.

PubMed

Akateh, Clifford; Tumin, Dmitry; Beal, Eliza W; Mumtaz, Khalid; Tobias, Joseph D; Hayes, Don; Black, Sylvester M

2018-06-01

Health insurance coverage changes for many patients after liver transplantation, but the implications of this change on long-term outcomes are unclear. To assess post-transplant patient and graft survival according to change in insurance coverage within 1 year of transplantation. We queried the United Network for Organ Sharing for patients between ages 18-64 years undergoing liver transplantation in 2002-2016. Patients surviving > 1 year were categorized by insurance coverage at transplantation and the 1-year transplant anniversary. Multivariable Cox regression characterized the association between coverage pattern and long-term patient or graft survival. Among 34,487 patients in the analysis, insurance coverage patterns included continuous private coverage (58%), continuous public coverage (29%), private to public transition (8%) and public to private transition (4%). In multivariable analysis of patient survival, continuous public insurance (HR 1.29, CI 1.22, 1.37, p < 0.001), private to public transition (HR 1.17, CI 1.07, 1.28, p < 0.001), and public to private transition (HR 1.14, CI 1.00, 1.29, p = 0.044), were associated with greater mortality hazard, compared to continuous private coverage. After disaggregating public coverage by source, mortality hazard was highest for patients transitioning from private insurance to Medicaid (HR vs. continuous private coverage = 1.32; 95% CI 1.14, 1.52; p < 0.001). Similar differences by insurance category were found for death-censored graft failure. Post-transplant transition to public insurance coverage is associated with higher risk of adverse outcomes when compared to retaining private coverage.

Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

USGS Publications Warehouse

Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

2008-01-01

Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Optical scatterometry of quarter-micron patterns using neural regression

NASA Astrophysics Data System (ADS)

Bischoff, Joerg; Bauer, Joachim J.; Haak, Ulrich; Hutschenreuther, Lutz; Truckenbrodt, Horst

1998-06-01

With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every patterning cycle is an inevitable necessity. In former publications it was shown that Optical Scatterometry (OS) has the potential to push the attainable feature limits of optical techniques from 0.8 . . . 0.5 microns for imaging methods down to 0.1 micron and below. Thus the demands of future metrology can be met. Basically being a nonimaging method, OS combines light scatter (or diffraction) measurements with modern data analysis schemes to solve the inverse scatter issue. For very fine patterns with lambda-to-pitch ratios grater than one, the specular reflected light versus the incidence angle is recorded. Usually, the data analysis comprises two steps -- a training cycle connected the a rigorous forward modeling and the prediction itself. Until now, two data analysis schemes are usually applied -- the multivariate regression based Partial Least Squares method (PLS) and a look-up-table technique which is also referred to as Minimum Mean Square Error approach (MMSE). Both methods are afflicted with serious drawbacks. On the one hand, the prediction accuracy of multivariate regression schemes degrades with larger parameter ranges due to the linearization properties of the method. On the other hand, look-up-table methods are rather time consuming during prediction thus prolonging the processing time and reducing the throughput. An alternate method is an Artificial Neural Network (ANN) based regression which combines the advantages of multivariate regression and MMSE. Due to the versatility of a neural network, not only can its structure be adapted more properly to the scatter problem, but also the nonlinearity of the neuronal transfer functions mimic the nonlinear behavior of optical diffraction processes more adequately. In spite of these pleasant properties, the prediction speed of ANN regression is comparable with that of the PLS-method. In this paper, the viability and performance of ANN-regression will be demonstrated with the example of sub-quarter-micron resist metrology. To this end, 0.25 micrometer line/space patterns have been printed in positive photoresist by means of DUV projection lithography. In order to evaluate the total metrology chain from light scatter measurement through data analysis, a thorough modeling has been performed. Assuming a trapezoidal shape of the developed resist profile, a training data set was generated by means of the Rigorous Coupled Wave Approach (RCWA). After training the model, a second data set was computed and deteriorated by Gaussian noise to imitate real measuring conditions. Then, these data have been fed into the models established before resulting in a Standard Error of Prediction (SEP) which corresponds to the measuring accuracy. Even with putting only little effort in the design of a back-propagation network, the ANN is clearly superior to the PLS-method. Depending on whether a network with one or two hidden layers was used, accuracy gains between 2 and 5 can be achieved compared with PLS regression. Furthermore, the ANN is less noise sensitive, for there is only a doubling of the SEP at 5% noise for ANN whereas for PLS the accuracy degrades rapidly with increasing noise. The accuracy gain also depends on the light polarization and on the measured parameters. Finally, these results have been proven experimentally, where the OS-results are in good accordance with the profiles obtained from cross- sectioning micrographs.

Dietary patterns in pregnancy and birth weight

PubMed Central

Coelho, Natália de Lima Pereira; Cunha, Diana Barbosa; Esteves, Ana Paula Pereira; Lacerda, Elisa Maria de Aquino; Filha, Mariza Miranda Theme

2015-01-01

OBJECTIVE To analyze if dietary patterns during the third gestational trimester are associated with birth weight. METHODS Longitudinal study conducted in the cities of Petropolis and Queimados, Rio de Janeiro (RJ), Southeastern Brazil, between 2007 and 2008. We analyzed data from the first and second follow-up wave of a prospective cohort. Food consumption of 1,298 pregnant women was assessed using a semi-quantitative questionnaire about food frequency. Dietary patterns were obtained by exploratory factor analysis, using the Varimax rotation method. We also applied the multivariate linear regression model to estimate the association between food consumption patterns and birth weight. RESULTS Four patterns of consumption – which explain 36.4% of the variability – were identified and divided as follows: (1) prudent pattern (milk, yogurt, cheese, fruit and fresh-fruit juice, cracker, and chicken/beef/fish/liver), which explained 14.9% of the consumption; (2) traditional pattern, consisting of beans, rice, vegetables, breads, butter/margarine and sugar, which explained 8.8% of the variation in consumption; (3) Western pattern (potato/cassava/yams, macaroni, flour/farofa/grits, pizza/hamburger/deep fried pastries, soft drinks/cool drinks and pork/sausages/egg), which accounts for 6.9% of the variance; and (4) snack pattern (sandwich cookie, salty snacks, chocolate, and chocolate drink mix), which explains 5.7% of the consumption variability. The snack dietary pattern was positively associated with birth weight (β = 56.64; p = 0.04) in pregnant adolescents. CONCLUSIONS For pregnant adolescents, the greater the adherence to snack pattern during pregnancy, the greater the baby’s birth weight. PMID:26398873

Spatial variability in intertidal macroalgal assemblages on the North Portuguese coast: consistence between species and functional group approaches

NASA Astrophysics Data System (ADS)

Veiga, P.; Rubal, M.; Vieira, R.; Arenas, F.; Sousa-Pinto, I.

2013-03-01

Natural assemblages are variable in space and time; therefore, quantification of their variability is imperative to identify relevant scales for investigating natural or anthropogenic processes shaping these assemblages. We studied the variability of intertidal macroalgal assemblages on the North Portuguese coast, considering three spatial scales (from metres to 10 s of kilometres) following a hierarchical design. We tested the hypotheses that (1) spatial pattern will be invariant at all the studied scales and (2) spatial variability of macroalgal assemblages obtained by using species will be consistent with that obtained using functional groups. This was done considering as univariate variables: total biomass and number of taxa as well as biomass of the most important species and functional groups and as multivariate variables the structure of macroalgal assemblages, both considering species and functional groups. Most of the univariate results confirmed the first hypothesis except for the total number of taxa and foliose macroalgae that showed significant variability at the scale of site and area, respectively. In contrast, when multivariate patterns were examined, the first hypothesis was rejected except at the scale of 10 s of kilometres. Both uni- and multivariate results indicated that variation was larger at the smallest scale, and thus, small-scale processes seem to have more effect on spatial variability patterns. Macroalgal assemblages, both considering species and functional groups as surrogate, showed consistent spatial patterns, and therefore, the second hypothesis was confirmed. Consequently, functional groups may be considered a reliable biological surrogate to study changes on macroalgal assemblages at least along the investigated Portuguese coastline.

Brain organization underlying superior mathematical abilities in children with autism.

PubMed

Iuculano, Teresa; Rosenberg-Lee, Miriam; Supekar, Kaustubh; Lynch, Charles J; Khouzam, Amirah; Phillips, Jennifer; Uddin, Lucina Q; Menon, Vinod

2014-02-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. 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.

Spread patterns of lymph nodes and the value of elective neck irradiation for esthesioneuroblastoma.

PubMed

Yin, Zhen-zhen; Luo, Jing-wei; Gao, Li; Yi, Jun-lin; Huang, Xiao-dong; Qu, Yuan; Wang, Kai; Zhang, Shi-ping; Xiao, Jian-ping; Xu, Guo-zhen; Li, Ye-xiong

2015-11-01

This study was aimed to characterize patterns of lymphatic spread and assess the value of prophylactic elective neck irradiation (ENI) for esthesioneuroblastoma (ENB). A retrospectively analysis of 116 patients with newly diagnosed ENB at our institution over 35-year period was undertaken. 32 patients (28%) presented lymph node metastasis at initial diagnosis, the common sites involved were level II, Ib, level III and VIIa. Among 80 N-negative patients staged in Modified Kadish B/C, 50 patients were delivered with ENI, 30 patients were not. The 5-year regional failure-free survival was 98% in patients treated with ENI and 75% in patients without ENI (p=0.005), regional failure rate decreased significantly from 23% (7/30) to 2% (1/50) after ENI (p=0.002). Multivariate analysis also suggested that ENI was an independent favorable predictor for regional controlling (HR, 0.102; 95% CI: 0.012-0.848; p=0.035). This is the largest cohort of ENB so far in a single institute, and also the first detailed description of nodal spread patterns of N-positive ENB. Elective neck irradiation reduced the regional failure significantly and should be recommended as a part of initial treatment strategy for patients staged with Modified Kadish B/C. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

PubMed

Gohel, Bakul; Lee, Peter; Jeong, Yong

2016-08-01

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Parallel experimental design and multivariate analysis provides efficient screening of cell culture media supplements to improve biosimilar product quality.

PubMed

Brühlmann, David; Sokolov, Michael; Butté, Alessandro; Sauer, Markus; Hemberger, Jürgen; Souquet, Jonathan; Broly, Hervé; Jordan, Martin

2017-07-01

Rational and high-throughput optimization of mammalian cell culture media has a great potential to modulate recombinant protein product quality. We present a process design method based on parallel design-of-experiment (DoE) of CHO fed-batch cultures in 96-deepwell plates to modulate monoclonal antibody (mAb) glycosylation using medium supplements. To reduce the risk of losing valuable information in an intricate joint screening, 17 compounds were separated into five different groups, considering their mode of biological action. The concentration ranges of the medium supplements were defined according to information encountered in the literature and in-house experience. The screening experiments produced wide glycosylation pattern ranges. Multivariate analysis including principal component analysis and decision trees was used to select the best performing glycosylation modulators. Subsequent D-optimal quadratic design with four factors (three promising compounds and temperature shift) in shake tubes confirmed the outcome of the selection process and provided a solid basis for sequential process development at a larger scale. The glycosylation profile with respect to the specifications for biosimilarity was greatly improved in shake tube experiments: 75% of the conditions were equally close or closer to the specifications for biosimilarity than the best 25% in 96-deepwell plates. Biotechnol. Bioeng. 2017;114: 1448-1458. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Clinical validation of robot simulation of toothbrushing - comparative plaque removal efficacy

PubMed Central

2014-01-01

Background Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Methods Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33–47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33–47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. Results The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. Conclusions The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing. This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning. PMID:24996973

Clinical validation of robot simulation of toothbrushing--comparative plaque removal efficacy.

PubMed

Lang, Tomas; Staufer, Sebastian; Jennes, Barbara; Gaengler, Peter

2014-07-04

Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33-47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33-47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing.This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning.

Interpretation of hip fracture patterns using areal bone mineral density in the proximal femur.

PubMed

Hey, Hwee Weng Dennis; Sng, Weizhong Jonathan; Lim, Joel Louis Zongwei; Tan, Chuen Seng; Gan, Alfred Tau Liang; Ng, Jun Han Charles; Kagda, Fareed H Y

2015-12-01

Bone mineral density scans are currently interpreted based on an average score of the entire proximal femur. Improvements in technology now allow us to measure bone density in specific regions of the proximal femur. The study attempts to explain the pathophysiology of neck of femur (NOF) and intertrochanteric/basi-cervical (IT) fractures by correlating areal BMD (aBMD) scores with fracture patterns, and explore possible predictors for these fracture patterns. This is a single institution retrospective study on all patients who underwent hip surgeries from June 2010 to August 2012. A total of 106 patients (44 IT/basi-cervical, 62 NOF fractures) were studied. The data retrieved include patient characteristics and aBMD scores measured at different regions of the contralateral hip within 1 month of the injury. Demographic and clinical characteristic differences between IT and NOF fractures were analyzed using Fisher's Exact test and two-sample t test. Relationship between aBMD scores and fracture patterns was assessed using multivariable regression modeling. After adjusted multivariable analysis, T-Troc and T-inter scores were significantly lower in intertrochanteric/basi-cervical fractures compared to neck of femur fractures (P = 0.022 and P = 0.026, respectively). Both intertrochanteric/basi-cervical fractures (mean T.Tot -1.99) and neck of femur fractures (mean T.Tot -1.64) were not found to be associated with a mean T.tot less than -2.5. However, the mean aBMD scores were consistently less than -2.5 for both intertrochanteric/basi-cervical fractures and neck of femur fractures. Gender and calcium intake at the time of injury were associated with specific hip fracture patterns (P = 0.002 and P = 0.011, respectively). Hip fracture patterns following low energy trauma may be influenced by the pattern of reduced bone density in different areas of the hip. Intertrochanteric/basi-cervical fractures were associated with significantly lower T-Troc and T-Inter scores compared to neck of femur fractures, suggesting that the fracture traversed through the areas with the lowest bone density in the proximal femur. In the absence of reduced T.Troc and T.Inter, neck of femur fractures occurred more commonly. T-Total scores may underestimate the severity of osteoporosis/osteopenia and measuring T-score at the neck of femur may better reflect the severity of osteoporosis and likelihood of a fragility fracture.

Missing data exploration: highlighting graphical presentation of missing pattern

PubMed Central

2015-01-01

Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations. PMID:26807411

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

PubMed

Congdon, Peter

2011-01-01

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

Survival pattern of first accident among commercial drivers in the Greater Accra Region of Ghana.

PubMed

Nanga, Salifu; Odai, Nii Afotey; Lotsi, Anani

2017-06-01

In this study, the average accident risk of commercial drivers in the Greater Accra region of Ghana and its associated risks were examined based on a survey data collected using paper-based questionnaires from 204 commercial drivers from the Greater Accra Region of Ghana. The Cox Proportional Hazards Model was used for multivariate analysis while the Kaplan-Meier (KM) Model was used to study the survival patterns of the commercial drivers. The study revealed that the median survival time for an accident to happen is 2.50 years. Good roads provided a better chance of survival than bad roads and experienced drivers have a better chance of survival than the inexperienced drivers. Age of driver, alcohol usage of driver, marital status, condition of road and duration of driver's license were found to be related to the risk of accident. Copyright © 2017 Elsevier Ltd. All rights reserved.

Changes in fatty acid and hydrocarbon composition of zooplankton assemblages related to environmental conditions

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

Lambert, R.M.

1989-01-01

Changes in zooplankton fatty acid and hydrocarbon patterns are described in relation to changes in environmental conditions and species composition. The regulation of zooplankton abundance by sea nettle-ctenophore interaction was examined in a small Rhode Island coastal pond. Sea nettles were nettles were able to eliminate ctenophores from the pond and subsequently zooplankton abundance increased. During one increase in zooplankton abundance, it was found that polyunsaturated fatty acids decreased while monounsaturated fatty acids increased. It was concluded that this shift in biochemical pattern was due to food limitation. In addition, zooplankton fatty acids were used in multivariate discriminant analysis tomore » classify whether zooplankton were from coastal or estuarine environments. Zooplankton from coastal environments were characterized by higher monounsaturate fatty acids. Zooplankton hydrocarbon composition was affected by species composition and by pollution inputs. The presence of Calanus finmarchicus was detected by increased levels of pristane.« less

Reward Motivation Enhances Task Coding in Frontoparietal Cortex

PubMed Central

Etzel, Joset A.; Cole, Michael W.; Zacks, Jeffrey M.; Kay, Kendrick N.; Braver, Todd S.

2016-01-01

Reward motivation often enhances task performance, but the neural mechanisms underlying such cognitive enhancement remain unclear. Here, we used a multivariate pattern analysis (MVPA) approach to test the hypothesis that motivation-related enhancement of cognitive control results from improved encoding and representation of task set information. Participants underwent two fMRI sessions of cued task switching, the first under baseline conditions, and the second with randomly intermixed reward incentive and no-incentive trials. Information about the upcoming task could be successfully decoded from cue-related activation patterns in a set of frontoparietal regions typically associated with task control. More critically, MVPA classifiers trained on the baseline session had significantly higher decoding accuracy on incentive than non-incentive trials, with decoding improvement mediating reward-related enhancement of behavioral performance. These results strongly support the hypothesis that reward motivation enhances cognitive control, by improving the discriminability of task-relevant information coded and maintained in frontoparietal brain regions. PMID:25601237

Influence of infant feeding patterns over the first year of life on growth from birth to 5 years.

PubMed

Betoko, A; Lioret, S; Heude, B; Hankard, R; Carles, S; Forhan, A; Regnault, N; Botton, J; Charles, M A; de Lauzon-Guillain, B

2017-08-01

As early-life feeding experiences may influence later health, we aimed to examine relations between feeding patterns over the first year of life and child's growth in the first 5 years of life. Our analysis included 1022 children from the EDEN mother-child cohort. Three feeding patterns were previously identified, i.e. 'Later dairy products introduction and use of ready-prepared baby foods' (pattern-1), 'Long breastfeeding, later main meal food introduction and use of home-made foods' (pattern-2) and 'Use of ready-prepared adult foods' (pattern-3). Associations between the feeding patterns and growth [weight, height and body mass index {BMI}] were analysed by multivariable linear regressions. Anthropometric changes were assessed by the final value adjusted for the initial value. Even though infant feeding patterns were not related to anthropometric measurements at 1, 3 and 5 years, high scores on pattern-1 were associated with higher 1-3 years weight and height changes. High scores on pattern-2 were related to lower 0-1 year weight and height changes, higher 1-5 years weight and height changes but not to BMI changes, after controlling for a wide range of potential confounding variables including parental BMI. Scores on pattern-3 were not significantly related to growth. Additional adjustment for breastfeeding duration reduced the strength of the associations between pattern-2 and growth but not those between pattern-1 and height growth. Our findings emphasize the relevance of considering infant feeding patterns including breastfeeding duration, age of complementary foods introduction as well as type of foods used when examining effects of early infant feeding practices on later health. © 2017 World Obesity Federation. © 2017 World Obesity Federation.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy field at Kelantan, Malaysia.

PubMed

Hussain, Hazilia; Yusoff, Mohd Kamil; Ramli, Mohd Firuz; Abd Latif, Puziah; Juahir, Hafizan; Zawawi, Mohamed Azwan Mohammed

2013-11-15

Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

Differences in Brain Glucose Metabolism During Preparation for 131I Ablation in Thyroid Cancer Patients: Thyroid Hormone Withdrawal Versus Recombinant Human Thyrotropin.

PubMed

Jeong, Hyeonseok S; Choi, Eun Kyoung; Song, In-Uk; Chung, Yong-An; Park, Jong-Sik; Oh, Jin Kyoung

2017-01-01

In preparation for 131 I ablation, temporary withdrawal of thyroid hormone is commonly used in patients with thyroid cancer after total thyroidectomy. The current study aimed to investigate brain glucose metabolism and its relationships with mood or cognitive function in these patients using 18 F-fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG-PET). A total of 40 consecutive adult patients with thyroid carcinoma who had undergone total thyroidectomy were recruited for this cross-sectional study. At the time of assessment, 20 patients were hypothyroid after two weeks of thyroid hormone withdrawal, while 20 received thyroid hormone replacement therapy and were euthyroid. All participants underwent brain 18 F-FDG-PET scans and completed mood questionnaires and cognitive tests. Multivariate spatial covariance analysis and univariate voxel-wise analysis were applied for the image data. The hypothyroid patients were more anxious and depressed than the euthyroid participants. The multivariate covariance analysis showed increases in glucose metabolism primarily in the bilateral insula and surrounding areas and concomitant decreases in the parieto-occipital regions in the hypothyroid group. The level of thyrotropin was positively associated with the individual expression of the covariance pattern. The decreased 18 F-FDG uptake in the right cuneus cluster from the univariate analysis was correlated with the increased thyrotropin level and greater depressive symptoms in the hypothyroid group. These results suggest that temporary hypothyroidism, even for a short period, may induce impairment in glucose metabolism and related affective symptoms.

Connecting Self-Esteem and Achievement: Diversity in Academic Identification and Dis-Identification Patterns among Black College Students

ERIC Educational Resources Information Center

Hope, Elan C.; Chavous, Tabbye M.; Jagers, Robert J.; Sellers, Robert M.

2013-01-01

Using a person-oriented approach, we explored patterns of self-esteem and achievement among 324 Black college students across the freshman college year and identified four academic identification profiles. Multivariate analyses revealed profile differences in academic and psychological outcomes at beginning and end of freshman year (academic…

A Model to Explain Plant Growth Promotion Traits: A Multivariate Analysis of 2,211 Bacterial Isolates

PubMed Central

da Costa, Pedro Beschoren; Granada, Camille E.; Ambrosini, Adriana; Moreira, Fernanda; de Souza, Rocheli; dos Passos, João Frederico M.; Arruda, Letícia; Passaglia, Luciane M. P.

2014-01-01

Plant growth-promoting bacteria can greatly assist sustainable farming by improving plant health and biomass while reducing fertilizer use. The plant-microorganism-environment interaction is an open and complex system, and despite the active research in the area, patterns in root ecology are elusive. Here, we simultaneously analyzed the plant growth-promoting bacteria datasets from seven independent studies that shared a methodology for bioprospection and phenotype screening. The soil richness of the isolate's origin was classified by a Principal Component Analysis. A Categorical Principal Component Analysis was used to classify the soil richness according to isolate's indolic compound production, siderophores production and phosphate solubilization abilities, and bacterial genera composition. Multiple patterns and relationships were found and verified with nonparametric hypothesis testing. Including niche colonization in the analysis, we proposed a model to explain the expression of bacterial plant growth-promoting traits according to the soil nutritional status. Our model shows that plants favor interaction with growth hormone producers under rich nutrient conditions but favor nutrient solubilizers under poor conditions. We also performed several comparisons among the different genera, highlighting interesting ecological interactions and limitations. Our model could be used to direct plant growth-promoting bacteria bioprospection and metagenomic sampling. PMID:25542031

Predictors of self-rated health in patients with chronic nonmalignant pain.

PubMed

Siedlecki, Sandra L

2006-09-01

Self-rated health (SRH) is an important outcome measure that has been found to accurately predict mortality, morbidity, function, and psychologic well-being. Chronic nonmalignant pain presents with a pattern that includes low levels of power and high levels of pain, depression, and disability. Differences in SRH may be related to variations within this pattern. The purpose of this analysis was to identify determinants of SRH and test their ability to predict SRH in patients with chronic nonmalignant pain. SRH was measured by response to a single three-option age-comparative question. The Power as Knowing Participation in Change Tool, McGill Pain Questionnaire Short Form, Center for Epidemiological Studies Depression Scale, and Pain Disability Index were used to measure independent variables. Multivariate analysis of variance revealed significant differences (p = .001) between SRH categories on the combined dependent variable. Analysis of variance conducted as a follow-up identified significant differences for power (p < .001) and depression (p = .003), but not for pain or pain-related disability; and discriminant analysis found that power and depression correctly classified patients with 75% accuracy. Findings suggest pain interventions designed to improve mood and provide opportunities for knowing participation may have a greater impact on overall health than those that target only pain and disability.

Combined DNA methylation and gene expression profiling in gastrointestinal stromal tumors reveals hypomethylation of SPP1 as an independent prognostic factor.

PubMed

Haller, Florian; Zhang, Jitao David; Moskalev, Evgeny A; Braun, Alexander; Otto, Claudia; Geddert, Helene; Riazalhosseini, Yasser; Ward, Aoife; Balwierz, Aleksandra; Schaefer, Inga-Marie; Cameron, Silke; Ghadimi, B Michael; Agaimy, Abbas; Fletcher, Jonathan A; Hoheisel, Jörg; Hartmann, Arndt; Werner, Martin; Wiemann, Stefan; Sahin, Ozgür

2015-03-01

Gastrointestinal stromal tumors (GISTs) have distinct gene expression patterns according to localization, genotype and aggressiveness. DNA methylation at CpG dinucleotides is an important mechanism for regulation of gene expression. We performed targeted DNA methylation analysis of 1.505 CpG loci in 807 cancer-related genes in a cohort of 76 GISTs, combined with genome-wide mRNA expression analysis in 22 GISTs, to identify signatures associated with clinicopathological parameters and prognosis. Principal component analysis revealed distinct DNA methylation patterns associated with anatomical localization, genotype, mitotic counts and clinical follow-up. Methylation of a single CpG dinucleotide in the non-CpG island promoter of SPP1 was significantly correlated with shorter disease-free survival. Hypomethylation of this CpG was an independent prognostic parameter in a multivariate analysis compared to anatomical localization, genotype, tumor size and mitotic counts in a cohort of 141 GISTs with clinical follow-up. The epigenetic regulation of SPP1 was confirmed in vitro, and the functional impact of SPP1 protein on tumorigenesis-related signaling pathways was demonstrated. In summary, SPP1 promoter methylation is a novel and independent prognostic parameter in GISTs, and might be helpful in estimating the aggressiveness of GISTs from the intermediate-risk category. © 2014 UICC.

Associations of dietary patterns with bone mass, muscle strength and balance in a cohort of Australian middle-aged women.

PubMed

Wu, Feitong; Wills, Karen; Laslett, Laura L; Oldenburg, Brian; Jones, Graeme; Winzenberg, Tania

2017-10-01

Influences of dietary patterns on musculoskeletal health are poorly understood in middle-aged women. This cross-sectional analysis from a cohort of 347 women (aged 36-57 years) aimed to examine associations between dietary patterns and musculoskeletal health outcomes in middle-aged women. Diet was measured by the Cancer Council of Victoria FFQ. Total body bone mineral content (TB BMC), femoral neck and lumbar spine bone density (dual-energy X-ray absorptiometry), lower limbs muscle strength (LMS) and balance tests (timed up and go test, step test, functional reach test (FRT) and lateral reach test) were also measured. Exploratory factor analysis was used to identify dietary patterns and scores for each pattern generated using factor loadings with absolute values ≥0·20. Associations between food pattern scores and musculoskeletal outcomes were assessed using multivariable linear regression. Three dietary patterns were identified: 'Healthy' (high consumption of a plant-based diet - vegetables, legumes, fruit, tomatoes, nuts, snacks, garlic, whole grains and low intake of high-fat dairy products), 'high protein, high fat' (red meats, poultry, processed meats, potatoes, cruciferous and dark-yellow vegetables, fish, chips, spirits and high-fat dairy products) and 'Processed foods' (high intakes of meat pies, hamburgers, beer, sweets, fruit juice, processed meats, snacks, spirits, pizza and low intake of cruciferous vegetables). After adjustment for confounders, Healthy pattern was positively associated with LMS, whereas Processed foods pattern was inversely associated with TB BMC and FRT. The associations were not significant after accounting for multiple comparisons. There were no associations with any other outcomes. These results suggest that maintaining a healthy diet could contribute to bone acquisition, muscle strength and balance in adult life. However, while they provide some support for further investigating dietary strategies for prevention of age-related loss of muscle and deterioration in balance, the exploratory nature of the analyses means that confirmation in longitudinal studies and/or trials with pre-specified hypotheses is needed.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Maternal Dietary Patterns and Gestational Diabetes Mellitus in a Multi-Ethnic Asian Cohort: The GUSTO Study.

PubMed

de Seymour, Jamie; Chia, Airu; Colega, Marjorelee; Jones, Beatrix; McKenzie, Elizabeth; Shirong, Cai; Godfrey, Keith; Kwek, Kenneth; Saw, Seang-Mei; Conlon, Cathryn; Chong, Yap-Seng; Baker, Philip; Chong, Mary F F

2016-09-20

Gestational Diabetes Mellitus (GDM) is associated with an increased risk of perinatal morbidity and long term health issues for both the mother and offspring. Previous research has demonstrated associations between maternal diet and GDM development, but evidence in Asian populations is limited. The objective of our study was to examine the cross-sectional relationship between maternal dietary patterns during pregnancy and the risk of GDM in a multi-ethnic Asian cohort. Maternal diet was ascertained using 24-h dietary recalls from participants in the Growing up in Singapore towards healthy outcomes (GUSTO) study-a prospective mother-offspring cohort, and GDM was diagnosed according to 1999 World Health Organisation guidelines. Dietary patterns were identified using factor analysis, and multivariate regression analyses performed to assess the association with GDM. Of 909 participants, 17.6% were diagnosed with GDM. Three dietary patterns were identified: a vegetable-fruit-rice-based-diet, a seafood-noodle-based-diet and a pasta-cheese-processed-meat-diet. After adjusting for confounding variables, the seafood-noodle-based-diet was associated with a lower likelihood of GDM (Odds Ratio (95% Confidence Interval)) = 0.74 (0.59, 0.93). The dietary pattern found to be associated with GDM in our study was substantially different to those reported previously in Western populations.

Exploring the interaction between O₃ and NOx pollution patterns in the atmosphere of Barcelona, Spain using the MCR-ALS method.

PubMed

Malik, Amrita; Tauler, Roma

2015-06-01

This work focuses on understanding the behaviour and patterns of three atmospheric pollutants namely, nitric oxide (NO), nitrogen dioxide (NO2), and ozone (O3) along with their mutual interactions in the atmosphere of Barcelona, North Spain. Hourly samples were collected for NO, NO2 and O3 from the same city location for three consecutive years (2010-2012). The study explores the seasonal, annual and weekday-weekend variations in their diurnal profiles along with the possible identification of their source and mutual interactions in the region. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was applied to the individual datasets of these pollutants, as well as to all of them simultaneously (augmented mode) to resolve the profiles related to their source and variation patterns in the atmosphere. The analysis of the individual datasets confirmed the source pattern variations in the concerned pollutant's profiles; and the resolved profiles for augmented datasets suggested for the mutual interaction of the pollutants along with their patterns variations, simultaneously. The study suggests vehicular pollution as the major source of atmospheric nitrogen oxides and presence of weekend ozone effect in the region. Copyright © 2015 Elsevier B.V. All rights reserved.

National citation patterns of NEJM, The Lancet, JAMA and The BMJ in the lay press: a quantitative content analysis

PubMed Central

Rius, Roser

2017-01-01

Objectives To analyse the total number of newspaper articles citing the four leading general medical journals and to describe national citation patterns. Design Quantitative content analysis. Setting/sample Full text of 22 general newspapers in 14 countries over the period 2008–2015, collected from LexisNexis. The 14 countries have been categorised into four regions: the USA, the UK, Western World (European countries other than the UK, and Australia, New Zealand and Canada) and Rest of the World (other countries). Main outcome measure Press citations of four medical journals (two American: NEJM and JAMA; and two British: The Lancet and The BMJ) in 22 newspapers. Results British and American newspapers cited some of the four analysed medical journals about three times a week in 2008–2015 (weekly mean 3.2 and 2.7 citations, respectively); the newspapers from other Western countries did so about once a week (weekly mean 1.1), and those from the Rest of the World cited them about once a month (monthly mean 1.1). The New York Times cited above all other newspapers (weekly mean 4.7). The analysis showed the existence of three national citation patterns in the daily press: American newspapers cited mostly American journals (70.0% of citations), British newspapers cited mostly British journals (86.5%) and the rest of the analysed press cited more British journals than American ones. The Lancet was the most cited journal in the press of almost all Western countries outside the USA and the UK. Multivariate correspondence analysis confirmed the national patterns and showed that over 85% of the citation data variability is retained in just one single new variable: the national dimension. Conclusion British and American newspapers are the ones that cite the four analysed medical journals more often, showing a domestic preference for their respective national journals; non-British and non-American newspapers show a common international citation pattern. PMID:29133334

Forecasting of hourly load by pattern recognition in a small area power system

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

Dehdashti-Shahrokh, A.

1982-01-01

An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linearmore » deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.« less

The Perseus computational platform for comprehensive analysis of (prote)omics data.

PubMed

Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen

2016-09-01

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

Quantitative chromatin pattern description in Feulgen-stained nuclei as a diagnostic tool to characterize the oligodendroglial and astroglial components in mixed oligo-astrocytomas.

PubMed

Decaestecker, C; Lopes, B S; Gordower, L; Camby, I; Cras, P; Martin, J J; Kiss, R; VandenBerg, S R; Salmon, I

1997-04-01

The oligoastrocytoma, as a mixed glioma, represents a nosologic dilemma with respect to precisely defining the oligodendroglial and astroglial phenotypes that constitute the neoplastic cell lineages of these tumors. In this study, cell image analysis with Feulgen-stained nuclei was used to distinguish between oligodendroglial and astrocytic phenotypes in oligodendrogliomas and astrocytomas and then applied to mixed oligoastrocytomas. Quantitative features with respect to chromatin pattern (30 variables) and DNA ploidy (8 variables) were evaluated on Feulgen-stained nuclei in a series of 71 gliomas using computer-assisted microscopy. These included 32 oligodendrogliomas (OLG group: 24 grade II and 8 grade III tumors according to the WHO classification), 32 astrocytomas (AST group: 13 grade II and 19 grade III tumors), and 7 oligoastrocytomas (OLGAST group). Initially, image analysis with multivariate statistical analyses (Discriminant Analysis) could identify each glial tumor group. Highly significant statistical differences were obtained distinguishing the morphonuclear features of oligodendrogliomas from those of astrocytomas, regardless of their histological grade. When compared with the 7 mixed oligoastrocytomas under study, 5 exhibited DNA ploidy and chromatin pattern characteristics similar to grade II oligodendrogliomas, I to grade III oligodendrogliomas, and I to grade II astrocytomas. Using multifactorial statistical analyses (Discriminant Analysis combined with Principal Component Analysis). It was possible to quantify the proportion of "typical" glial cell phenotypes that compose grade II and III oligodendrogliomas and grade II and III astrocytomas in each mixed glioma. Cytometric image analysis may be an important adjunct to routine histopathology for the reproducible identification of neoplasms containing a mixture of oligodendroglial and astrocytic phenotypes.

Dietary patterns and risk of colorectal cancer in Tehran Province: a case-control study.

PubMed

Safari, Akram; Shariff, Zalilah Mohd; Kandiah, Mirnalini; Rashidkhani, Bahram; Fereidooni, Foroozandeh

2013-03-12

Colorectal cancer is the third and fourth leading cause of cancer incidence and mortality among men and women, respectively in Iran. However, the role of dietary factors that could contribute to this high cancer incidence remains unclear. The aim of this study was to determine major dietary patterns and its relationship with colorectal cancer. This case-control study was conducted in four hospitals in Tehran city of Iran. A total of 71 patients (35 men and 36 women, aged 40-75 years) with incident clinically confirmed colorectal cancer (CRC) and 142 controls (70 men and 72 women, aged 40-75 years) admitted to hospital for acute, non-neoplastic diseases were recruited and interviewed. Dietary data were assessed by 125-item semi-quantitative food frequency questionnaire. Multivariate logistic regression was used to estimate the relationship between dietary patterns and risk of colorectal cancer. Two major dietary patterns (Healthy pattern and Western pattern) were derived using principal component analysis. Each dietary pattern explained 11.9% (Healthy pattern) and 10.3% (Western pattern) of the variation in food intake, respectively. After adjusting for confounding factors, the Healthy dietary pattern was significantly associated with a decreased risk of colorectal cancer (OR= 0.227; 95% CI=0.108-0.478) while an increased risk of colorectal cancer was observed with the Western dietary pattern (OR=2.616; 95% CI= 1.361-5.030). Specific dietary patterns, which include healthy and western patterns, may be associated with the risk of colorectal cancer. This diet-disease relationship can be used for developing interventions that aim to promote healthy eating for the prevention of chronic disease, particularly colorectal cancer in the Iranian population.

Dietary patterns are associated with incident stroke and contribute to excess risk of stroke in Black Americans

PubMed Central

Judd, Suzanne E; Gutiérrez, Orlando M.; Newby, PK; Howard, George; Howard, Virginia J; Locher, Julie L; Kissela, Brett M; Shikany, James M

2014-01-01

Background and Purpose Black Americans and residents of the Southeastern United States, are at increased risk of stroke. Diet is one of many potential factors proposed that might explain these racial and regional disparities. Methods Between 2003–2007, the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study enrolled 30,239 black and white Americans aged 45 years or older. Dietary patterns were derived using factor analysis and foods from food frequency data. Incident strokes were adjudicated using medical records by a team of physicians. Cox proportional hazards models were used to examine risk of stroke. Results Over 5.7 years, 490 incident strokes were observed. In a multivariable-adjusted analysis, greater adherence to the Plant-based pattern was associated with lower stroke risk (HR=0.71; 95% CI=0.56–0.91; ptrend=0.005). This association was attenuated after addition of income, education, total energy intake, smoking, and sedentary behavior. Participants with a higher adherence to the Southern pattern experienced a 39% increased risk of stroke (HR=1.39; 95% CI=1.05, 1.84), with a significant (p = 0.009) trend across quartiles. Including Southern pattern in the model mediated the black-white risk of stroke by 63%. Conclusions These data suggest that adherence to a Southern style diet may increase the risk of stroke while adherence to a more plant-based diet may reduce stroke risk. Given the consistency of finding a dietary impact on stroke risk across studies, discussing nutrition patterns during risk screening may be an important step in reducing stroke. PMID:24159061

Conservatism and novelty in the genetic architecture of adaptation in Heliconius butterflies.

PubMed

Huber, B; Whibley, A; Poul, Y L; Navarro, N; Martin, A; Baxter, S; Shah, A; Gilles, B; Wirth, T; McMillan, W O; Joron, M

2015-05-01

Understanding the genetic architecture of adaptive traits has been at the centre of modern evolutionary biology since Fisher; however, evaluating how the genetic architecture of ecologically important traits influences their diversification has been hampered by the scarcity of empirical data. Now, high-throughput genomics facilitates the detailed exploration of variation in the genome-to-phenotype map among closely related taxa. Here, we investigate the evolution of wing pattern diversity in Heliconius, a clade of neotropical butterflies that have undergone an adaptive radiation for wing-pattern mimicry and are influenced by distinct selection regimes. Using crosses between natural wing-pattern variants, we used genome-wide restriction site-associated DNA (RAD) genotyping, traditional linkage mapping and multivariate image analysis to study the evolution of the architecture of adaptive variation in two closely related species: Heliconius hecale and H. ismenius. We implemented a new morphometric procedure for the analysis of whole-wing pattern variation, which allows visualising spatial heatmaps of genotype-to-phenotype association for each quantitative trait locus separately. We used the H. melpomene reference genome to fine-map variation for each major wing-patterning region uncovered, evaluated the role of candidate genes and compared genetic architectures across the genus. Our results show that, although the loci responding to mimicry selection are highly conserved between species, their effect size and phenotypic action vary throughout the clade. Multilocus architecture is ancestral and maintained across species under directional selection, whereas the single-locus (supergene) inheritance controlling polymorphism in H. numata appears to have evolved only once. Nevertheless, the conservatism in the wing-patterning toolkit found throughout the genus does not appear to constrain phenotypic evolution towards local adaptive optima.