Cardiometabolic risk clustering in spinal cord injury: results of exploratory factor analysis.

PubMed

Libin, Alexander; Tinsley, Emily A; Nash, Mark S; Mendez, Armando J; Burns, Patricia; Elrod, Matt; Hamm, Larry F; Groah, Suzanne L

2013-01-01

Evidence suggests an elevated prevalence of cardiometabolic risks among persons with spinal cord injury (SCI); however, the unique clustering of risk factors in this population has not been fully explored. The purpose of this study was to describe unique clustering of cardiometabolic risk factors differentiated by level of injury. One hundred twenty-one subjects (mean 37 ± 12 years; range, 18-73) with chronic C5 to T12 motor complete SCI were studied. Assessments included medical histories, anthropometrics and blood pressure, and fasting serum lipids, glucose, insulin, and hemoglobin A1c (HbA1c). The most common cardiometabolic risk factors were overweight/obesity, high levels of low-density lipoprotein (LDL-C), and low levels of high-density lipoprotein (HDL-C). Risk clustering was found in 76.9% of the population. Exploratory principal component factor analysis using varimax rotation revealed a 3-factor model in persons with paraplegia (65.4% variance) and a 4-factor solution in persons with tetraplegia (73.3% variance). The differences between groups were emphasized by the varied composition of the extracted factors: Lipid Profile A (total cholesterol [TC] and LDL-C), Body Mass-Hypertension Profile (body mass index [BMI], systolic blood pressure [SBP], and fasting insulin [FI]); Glycemic Profile (fasting glucose and HbA1c), and Lipid Profile B (TG and HDL-C). BMI and SBP formed a separate factor only in persons with tetraplegia. Although the majority of the population with SCI has risk clustering, the composition of the risk clusters may be dependent on level of injury, based on a factor analysis group comparison. This is clinically plausible and relevant as tetraplegics tend to be hypo- to normotensive and more sedentary, resulting in lower HDL-C and a greater propensity toward impaired carbohydrate metabolism.

Community College Student Engagement Patterns: A Typology Revealed through Exploratory Cluster Analysis

ERIC Educational Resources Information Center

Saenz, Victor B.; Hatch, Deryl; Bukoski, Beth E.; Kim, Suyun; Lee, Kye-hyoung; Valdez, Patrick

2011-01-01

This study employs survey data from the Center for Community College Student Engagement to examine the similarities and differences that exist across student-level domains in terms of student engagement in community colleges. In total, the sample used in the analysis pools data from 663 community colleges and includes more than 320,000 students.…

fluff: exploratory analysis and visualization of high-throughput sequencing data

PubMed Central

Georgiou, Georgios

2016-01-01

Summary. In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available at http://fluff.readthedocs.org. Availability. fluff is implemented in Python and runs on Linux. The source code is freely available for download at https://github.com/simonvh/fluff. PMID:27547532

Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Impacts of exploratory drilling for oil and gas on the benthic environment of Georges Bank

USGS Publications Warehouse

Neff, J. M.; Bothner, Michael H.; Maciolek, N. J.; Grassle, J. F.

1989-01-01

Cluster analysis revealed a strong relationship between community structure and both sediment type and water depth. Little seasonal variation was detected, but some interannual differences were revealed by cluster analysis and correspondence analysis. The replicates from a station always resembled each other more than they resembled any replicates from other stations. In addition, the combined replicates from a station always clustered with samples from that station taken on other cruises. This excellent replication and uniformity of the benthic infaunal community at a station over time made it possible to detect very subtle changes in community parameters that might be related to discharges of drilling fluid and drill cuttings. Nevertheless, no changes were detected in benthic communities of Georges Bank that could be attributed to drilling activities.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

An Analysis of Peer Feedback Exchanged in Group Supervision

ERIC Educational Resources Information Center

Wahesh, Edward; Kemer, Gulsah; Willis, Ben T.; Schmidt, Christopher D.

2017-01-01

The authors examined the peer feedback exchanged in 2 supervision groups of counselors-in-training (CITs). CITs generated 169 statements grouped into 10 clusters representing 5 regions of peer feedback: counselor focus and engagement, insight-oriented skills, exploratory skills, therapeutic alliance, and intervention activities. Both positive and…

Knowledge, attitudes towards and acceptability of genetic modification in Germany.

PubMed

Christoph, Inken B; Bruhn, Maike; Roosen, Jutta

2008-07-01

Genetic modification remains a controversial issue. The aim of this study is to analyse the attitudes towards genetic modification, the knowledge about it and its acceptability in different application areas among German consumers. Results are based on a survey from spring 2005. An exploratory factor analysis is conducted to identify the attitudes towards genetic modification. The identified factors are used in a cluster analysis that identified a cluster of supporters, of opponents and a group of indifferent consumers. Respondents' knowledge of genetics and biotechnology differs among the found clusters without revealing a clear relationship between knowledge and support of genetic modification. The acceptability of genetic modification varies by application area and cluster, and genetically modified non-food products are more widely accepted than food products. The perception of personal health risks has high explanatory power for attitudes and acceptability.

Cardiometabolic Risk Clustering in Spinal Cord Injury: Results of Exploratory Factor Analysis

PubMed Central

2013-01-01

Background: Evidence suggests an elevated prevalence of cardiometabolic risks among persons with spinal cord injury (SCI); however, the unique clustering of risk factors in this population has not been fully explored. Objective: The purpose of this study was to describe unique clustering of cardiometabolic risk factors differentiated by level of injury. Methods: One hundred twenty-one subjects (mean 37 ± 12 years; range, 18–73) with chronic C5 to T12 motor complete SCI were studied. Assessments included medical histories, anthropometrics and blood pressure, and fasting serum lipids, glucose, insulin, and hemoglobin A1c (HbA1c). Results: The most common cardiometabolic risk factors were overweight/obesity, high levels of low-density lipoprotein (LDL-C), and low levels of high-density lipoprotein (HDL-C). Risk clustering was found in 76.9% of the population. Exploratory principal component factor analysis using varimax rotation revealed a 3–factor model in persons with paraplegia (65.4% variance) and a 4–factor solution in persons with tetraplegia (73.3% variance). The differences between groups were emphasized by the varied composition of the extracted factors: Lipid Profile A (total cholesterol [TC] and LDL-C), Body Mass-Hypertension Profile (body mass index [BMI], systolic blood pressure [SBP], and fasting insulin [FI]); Glycemic Profile (fasting glucose and HbA1c), and Lipid Profile B (TG and HDL-C). BMI and SBP formed a separate factor only in persons with tetraplegia. Conclusions: Although the majority of the population with SCI has risk clustering, the composition of the risk clusters may be dependent on level of injury, based on a factor analysis group comparison. This is clinically plausible and relevant as tetraplegics tend to be hypo- to normotensive and more sedentary, resulting in lower HDL-C and a greater propensity toward impaired carbohydrate metabolism. PMID:23960702

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

PubMed

Williams, N J; Nasuto, S J; Saddy, J D

2015-07-30

The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

Who Are You Going After? A Practical Typology to Generate Engagement in Professional Student Organizations

ERIC Educational Resources Information Center

Munoz, Laura; Miller, Richard J.; Poole, Sonja Martin

2016-01-01

On the basis of experiential learning theory and Cialdini's principles of influence, two psychological streams focused on providing hands-on experiences and on effectively influencing individuals, this article identifies a typology of students to engage them in professional student organizations. Exploratory factor analysis and cluster analysis…

A Typology of Cancer Information Seeking, Scanning and Avoiding: Results from an Exploratory Cluster Analysis

ERIC Educational Resources Information Center

Nelissen, Sara; Van den Bulck, Jan; Beullens, Kathleen

2017-01-01

Introduction: This study aims to (a) construct a typology of how individuals acquire cancer information, and (b) examine whether these types differ regarding socio-demographics and cancer-related knowledge, attitudes and behaviour. Method: A standardized, cross-sectional survey among cancer diagnosed and non-diagnosed individuals in Flanders,…

Spatio-temporal analysis of small-area intestinal parasites infections in Ghana.

PubMed

Osei, F B; Stein, A

2017-09-22

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran's Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran's Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

PubMed

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Clustering of Dietary Patterns, Lifestyles, and Overweight among Spanish Children and Adolescents in the ANIBES Study

PubMed Central

Pérez-Rodrigo, Carmen; Gil, Ángel; González-Gross, Marcela; Ortega, Rosa M.; Serra-Majem, Lluis; Varela-Moreiras, Gregorio; Aranceta-Bartrina, Javier

2015-01-01

Weight gain has been associated with behaviors related to diet, sedentary lifestyle, and physical activity. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, and sleep time in Spanish children and adolescents and whether the identified clusters could be associated with overweight. Analysis was based on a subsample (n = 415) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, and sleep time. Logistic regression analysis was used to explore the association between the cluster solutions and overweight. Factor analysis identified four dietary patterns, one reflecting a profile closer to the traditional Mediterranean diet. Dietary patterns, physical activity behaviors, sedentary behaviors and sleep time on weekdays in Spanish children and adolescents clustered into two different groups. A low physical activity-poorer diet lifestyle pattern, which included a higher proportion of girls, and a high physical activity, low sedentary behavior, longer sleep duration, healthier diet lifestyle pattern. Although increased risk of being overweight was not significant, the Prevalence Ratios (PRs) for the low physical activity-poorer diet lifestyle pattern were >1 in children and in adolescents. The healthier lifestyle pattern included lower proportions of children and adolescents from low socioeconomic status backgrounds. PMID:26729155

An analysis of pilot error-related aircraft accidents

NASA Technical Reports Server (NTRS)

Kowalsky, N. B.; Masters, R. L.; Stone, R. B.; Babcock, G. L.; Rypka, E. W.

1974-01-01

A multidisciplinary team approach to pilot error-related U.S. air carrier jet aircraft accident investigation records successfully reclaimed hidden human error information not shown in statistical studies. New analytic techniques were developed and applied to the data to discover and identify multiple elements of commonality and shared characteristics within this group of accidents. Three techniques of analysis were used: Critical element analysis, which demonstrated the importance of a subjective qualitative approach to raw accident data and surfaced information heretofore unavailable. Cluster analysis, which was an exploratory research tool that will lead to increased understanding and improved organization of facts, the discovery of new meaning in large data sets, and the generation of explanatory hypotheses. Pattern recognition, by which accidents can be categorized by pattern conformity after critical element identification by cluster analysis.

Cross-country Analysis of ICT and Education Indicators: An Exploratory Study

NASA Astrophysics Data System (ADS)

Pratama, Ahmad R.

2017-03-01

This paper explores the relationship between world ICT and education indicators by using the latest available data from World Bank and UNESCO in range of 2011-2014 with the help of different exploratory methods such as principal component analysis (PCA), factor analysis (FA), cluster analysis, and ordinary least square (OLS) regression. After dealing with all missing values, 119 countries were included in the final dataset. The findings show that most ICT and education indicators are highly associated with income of the respective country and therefore confirm the existence of digital divide in ICT utilization and participation gap in education between rich and poor countries. It also indicates that digital divide and participation gap is highly associated with each other. Finally, the findings also confirm reverse causality in ICT and education; higher participation rate in education increases technology utilization, which in turn helps promote better outcomes of education.

Perceptions of Co-Teaching: Closing the Achievement Gap between English Language Learners and Their English Monolingual Peers

ERIC Educational Resources Information Center

Ford-DeWaters, Carrie

2017-01-01

This qualitative exploratory single case research study used observations, semi-structured interviews, and document analysis to explore co-teachers' perceptions of the implementation of a co-teaching instructional model in elementary school general education classrooms with clusters of English learners (EL) in attendance. A total of four…

Classification of Support Needs for Elderly Outpatients with Diabetes Who Live Alone.

PubMed

Miyawaki, Yoshiko; Shimizu, Yasuko; Seto, Natsuko

2016-02-01

To investigate the support needs of elderly patients with diabetes and to classify elderly patients with diabetes living alone on the basis of support needs. Support needs were derived from a literature review of relevant journals and interviews of outpatients as well as expert nurses in the field of diabetes to prepare a 45-item questionnaire. Each item was analyzed on a 4-point Likert scale. The study included 634 elderly patients with diabetes who were recruited from 3 hospitals in Japan. Exploratory factor analysis was performed to determine the underlying structure of support needs, followed by hierarchical cluster analysis to clarify the characteristics of patients living alone (n=104) who had common support needs. Exploratory factor analysis suggested a 5-factor solution with 23 items: (1) hope for class and gatherings, (2) hope for personal advice including emergency response, (3) supportlessness and hopelessness, (4) barriers to food preparation, (5) hope of safe medical therapy. The hierarchical cluster analysis of subjects yielded 7 clusters, including a no special-support needs group, a collective support group, a self-care support group, a personal-support focus group, a life-support group, a food-preparation support group and a healthcare-environment support group. The support needs of elderly patients with diabetes who live alone can be divided into 2 categories: life and self-care support. Implementation of these categories in outpatient-management programs in which contact time with patients is limited is important in the overall management of elderly patients with diabetes who are living alone. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

The association between mood state and chronobiological characteristics in bipolar I disorder: a naturalistic, variable cluster analysis-based study.

PubMed

Gonzalez, Robert; Suppes, Trisha; Zeitzer, Jamie; McClung, Colleen; Tamminga, Carol; Tohen, Mauricio; Forero, Angelica; Dwivedi, Alok; Alvarado, Andres

2018-02-19

Multiple types of chronobiological disturbances have been reported in bipolar disorder, including characteristics associated with general activity levels, sleep, and rhythmicity. Previous studies have focused on examining the individual relationships between affective state and chronobiological characteristics. The aim of this study was to conduct a variable cluster analysis in order to ascertain how mood states are associated with chronobiological traits in bipolar I disorder (BDI). We hypothesized that manic symptomatology would be associated with disturbances of rhythm. Variable cluster analysis identified five chronobiological clusters in 105 BDI subjects. Cluster 1, comprising subjective sleep quality was associated with both mania and depression. Cluster 2, which comprised variables describing the degree of rhythmicity, was associated with mania. Significant associations between mood state and cluster analysis-identified chronobiological variables were noted. Disturbances of mood were associated with subjectively assessed sleep disturbances as opposed to objectively determined, actigraphy-based sleep variables. No associations with general activity variables were noted. Relationships between gender and medication classes in use and cluster analysis-identified chronobiological characteristics were noted. Exploratory analyses noted that medication class had a larger impact on these relationships than the number of psychiatric medications in use. In a BDI sample, variable cluster analysis was able to group related chronobiological variables. The results support our primary hypothesis that mood state, particularly mania, is associated with chronobiological disturbances. Further research is required in order to define these relationships and to determine the directionality of the associations between mood state and chronobiological characteristics.

Identifying knowledge activism in worker health and safety representation: A cluster analysis.

PubMed

Hall, Alan; Oudyk, John; King, Andrew; Naqvi, Syed; Lewchuk, Wayne

2016-01-01

Although worker representation in OHS has been widely recognized as contributing to health and safety improvements at work, few studies have examined the role that worker representatives play in this process. Using a large quantitative sample, this paper seeks to confirm findings from an earlier exploratory qualitative study that worker representatives can be differentiated by the knowledge intensive tactics and strategies that they use to achieve changes in their workplace. Just under 900 worker health and safety representatives in Ontario completed surveys which asked them to report on the amount of time they devoted to different types of representation activities (i.e., technical activities such as inspections and report writing vs. political activities such as mobilizing workers to build support), the kinds of conditions or hazards they tried to address through their representation (e.g., housekeeping vs. modifications in ventilation systems), and their reported success in making positive improvements. A cluster analysis was used to determine whether the worker representatives could be distinguished in terms of the relative time devoted to different activities and the clusters were then compared with reference to types of intervention efforts and outcomes. The cluster analysis identified three distinct groupings of representatives with significant differences in reported types of interventions and in their level of reported impact. Two of the clusters were consistent with the findings in the exploratory study, identified as knowledge activism for greater emphasis on knowledge based political activity and technical-legal representation for greater emphasis on formalized technical oriented procedures and legal regulations. Knowledge activists were more likely to take on challenging interventions and they reported more impact across the full range of interventions. This paper provides further support for the concepts of knowledge activism and technical-legal representation when differentiating the strategic orientations and impact of worker health and safety representatives, with important implications for education, political support and recruitment. © 2015 Wiley Periodicals, Inc.

Characteristics of Brazilian Offenders and Victims of Interpersonal Violence: An Exploratory Study.

PubMed

d'Avila, Sérgio; Campos, Ana Cristina; Bernardino, Ítalo de Macedo; Cavalcante, Gigliana Maria Sobral; Nóbrega, Lorena Marques da; Ferreira, Efigênia Ferreira E

2016-10-01

The aim of this study was to characterize the profile of Brazilian offenders and victims of interpersonal violence, following a medicolegal and forensic perspective. A cross-sectional and exploratory study was performed in a Center of Forensic Medicine and Dentistry. The sample was made up of 1,704 victims of nonlethal interpersonal violence with some type of trauma. The victims were subject to forensic examinations by a criminal investigative team that identified and recorded the extent of the injuries. For data collection, a specific form was designed consisting of four parts according to the information provided in the medicolegal and social records: sociodemographic data of the victims, offender's characteristics, aggression characteristics, and types of injuries. Descriptive and multivariate statistics using cluster analysis (CA) were performed. The two-step cluster method was used to characterize the profile of the victims and offenders. Most of the events occurred during the nighttime (50.9%) and on weekdays (66.3%). Soft tissue injuries were the most prevalent type (94.6%). Based on the CA results, two clusters for the victims and two for the offenders were identified. Victims: Cluster 1 was formed typically by women, aged 30 to 59 years, and married; Cluster 2 was composed of men, aged 20 to 29 years, and unmarried. Offenders: Cluster 1 was characterized by men, who perpetrated violence in a community environment. Cluster 2 was formed by men, who perpetrated violence in the familiar environment. These findings revealed different risk groups with distinct characteristics for both victims and offenders, allowing the planning of targeted measures of care, prevention, and health promotion. This study assesses the profile of violence through morbidity data and significantly contributes to building an integrated system of health surveillance in Brazil, as well as linking police stations, forensic services, and emergency hospitals.

Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life.

PubMed

Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M

2016-01-01

Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Persistent Topology and Metastable State in Conformational Dynamics

PubMed Central

Chang, Huang-Wei; Bacallado, Sergio; Pande, Vijay S.; Carlsson, Gunnar E.

2013-01-01

The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain. PMID:23565139

A novel exploratory chemometric approach to environmental monitorring by combining block clustering with Partial Least Square (PLS) analysis

PubMed Central

2013-01-01

Background Given the serious threats posed to terrestrial ecosystems by industrial contamination, environmental monitoring is a standard procedure used for assessing the current status of an environment or trends in environmental parameters. Measurement of metal concentrations at different trophic levels followed by their statistical analysis using exploratory multivariate methods can provide meaningful information on the status of environmental quality. In this context, the present paper proposes a novel chemometric approach to standard statistical methods by combining the Block clustering with Partial least square (PLS) analysis to investigate the accumulation patterns of metals in anthropized terrestrial ecosystems. The present study focused on copper, zinc, manganese, iron, cobalt, cadmium, nickel, and lead transfer along a soil-plant-snai food chain, and the hepatopancreas of the Roman snail (Helix pomatia) was used as a biological end-point of metal accumulation. Results Block clustering deliniates between the areas exposed to industrial and vehicular contamination. The toxic metals have similar distributions in the nettle leaves and snail hepatopancreas. PLS analysis showed that (1) zinc and copper concentrations at the lower trophic levels are the most important latent factors that contribute to metal accumulation in land snails; (2) cadmium and lead are the main determinants of pollution pattern in areas exposed to industrial contamination; (3) at the sites located near roads lead is the most threatfull metal for terrestrial ecosystems. Conclusion There were three major benefits by applying block clustering with PLS for processing the obtained data: firstly, it helped in grouping sites depending on the type of contamination. Secondly, it was valuable for identifying the latent factors that contribute the most to metal accumulation in land snails. Finally, it optimized the number and type of data that are best for monitoring the status of metallic contamination in terrestrial ecosystems exposed to different kinds of anthropic polution. PMID:23987502

Atlas-guided cluster analysis of large tractography datasets.

PubMed

Ros, Christian; Güllmar, Daniel; Stenzel, Martin; Mentzel, Hans-Joachim; Reichenbach, Jürgen Rainer

2013-01-01

Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment.

The inner formal structure of the H-T-P drawings: an exploratory study.

PubMed

Vass, Z

1998-08-01

The study describes some interrelated patterns of traits of the House-Tree-Person (H-T-P) drawings with the instruments of hierarchical cluster analysis. First, according to the literature 1 7 formal or structural aspects of the projective drawings were collected, after which a detailed manual for coding was compiled. Second, the interrater reliability and the consistency of this manual was tested. Third, the hierarchical cluster structure of the reliable and consistent formal aspects was analysed. Results are: (a) a psychometrically tested coding manual of the investigated formal-structural aspects, each of them illustrated with drawings that showed the highest interrater agreement; and (b) the hierarchic cluster structure of the formal aspects of the H-T-P drawings of "normal" adults.

Finding reproducible cluster partitions for the k-means algorithm

PubMed Central

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset. PMID:23369085

Finding reproducible cluster partitions for the k-means algorithm.

PubMed

Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

Army Officers’ Attitudes of Conflict Management.

DTIC Science & Technology

1976-06-11

The purpose of this study was to measure the attitudes of the middle level career Army officers relative to the concepts of conflict management . The...the literature concerning conflict management and its related fields of study, an exploratory analysis employing Hierarchical Clustering Schemes, and... conflict management . (2) No difference exists in the attitudes of conflict management according to the sample’s three branch groups: combat arms

a Web-Based Interactive Platform for Co-Clustering Spatio-Temporal Data

NASA Astrophysics Data System (ADS)

Wu, X.; Poorthuis, A.; Zurita-Milla, R.; Kraak, M.-J.

2017-09-01

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

An assessment of fatigue in patients with postural orthostatic tachycardia syndrome.

PubMed

Wise, Shelby; Ross, Amanda; Brown, Abigail; Evans, Meredyth; Jason, Leonard

2017-05-01

Individuals with postural orthostatic tachycardia syndrome share many symptoms with those who have chronic fatigue syndrome; one of which is severe fatigue. Previous literature found that those with chronic fatigue syndrome experience many forms of fatigue. The goal of this study was to investigate whether individuals with postural orthostatic tachycardia syndrome also experience multidimensional fatigue and whether these individuals can be clustered into subgroups based on the types of fatigue they endorse. A convenience sample of 138 participants (aged 14-29) with postural orthostatic tachycardia syndrome completed questionnaires that assessed fatigue, brain fog symptom severity, activities that improve brain fog, and brain fog-related disability. An exploratory factor analysis was conducted on the Fatigue Types Questionnaire, and a three-factor solution was produced. Factor scores were then used to cluster the patients into groups using a TwoStep cluster analysis. This resulted in two clusters, a high severity group and a low severity group. The clusters were then compared on a number of items related to symptom expression. Individuals within the more severe cluster had significantly more brain fog at the beginning and end of the survey when compared to cluster two. Those in the more severe cluster also described more activity impairment as well as more frequent, more severe, and more debilitation from postural orthostatic tachycardia syndrome and brain fog. The findings of the factor analysis suggest that patients with postural orthostatic tachycardia syndrome experience fatigue as a multidimensional construct and they also can be subgrouped based on symptom severity.

Atlas-Guided Cluster Analysis of Large Tractography Datasets

PubMed Central

Ros, Christian; Güllmar, Daniel; Stenzel, Martin; Mentzel, Hans-Joachim; Reichenbach, Jürgen Rainer

2013-01-01

Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment. PMID:24386292

Exploratory subsetting of autism families based on savant skills improves evidence of genetic linkage to 15q11-q13.

PubMed

Nurmi, Erika L; Dowd, Michael; Tadevosyan-Leyfer, Ovsanna; Haines, Jonathan L; Folstein, Susan E; Sutcliffe, James S

2003-07-01

Autism displays a remarkably high heritability but a complex genetic etiology. One approach to identifying susceptibility loci under these conditions is to define more homogeneous subsets of families on the basis of genetically relevant phenotypic or biological characteristics that vary from case to case. The authors performed a principal components analysis, using items from the Autism Diagnostic Interview, which resulted in six clusters of variables, five of which showed significant sib-sib correlation. The utility of these phenotypic subsets was tested in an exploratory genetic analysis of the autism candidate region on chromosome 15q11-q13. When the Collaborative Linkage Study of Autism sample was divided, on the basis of mean proband score for the "savant skills" cluster, the heterogeneity logarithm of the odds under a recessive model at D15S511, within the GABRB3 gene, increased from 0.6 to 2.6 in the subset of families in which probands had greater savant skills. These data are consistent with the genetic contribution of a 15q locus to autism susceptibility in a subset of affected individuals exhibiting savant skills. Similar types of skills have been noted in individuals with Prader-Willi syndrome, which results from deletions of this chromosomal region.

Symptom clusters and quality of life among patients with advanced heart failure

PubMed Central

Yu, Doris SF; Chan, Helen YL; Leung, Doris YP; Hui, Elsie; Sit, Janet WH

2016-01-01

Objectives To identify symptom clusters among patients with advanced heart failure (HF) and the independent relationships with their quality of life (QoL). Methods This is the secondary data analysis of a cross-sectional study which interviewed 119 patients with advanced HF in the geriatric unit of a regional hospital in Hong Kong. The symptom profile and QoL were assessed by using the Edmonton Symptom Assessment Scale (ESAS) and the McGill QoL Questionnaire. Exploratory factor analysis was used to identify the symptom clusters. Hierarchical regression analysis was used to examine the independent relationships with their QoL, after adjusting the effects of age, gender, and comorbidities. Results The patients were at an advanced age (82.9 ± 6.5 years). Three distinct symptom clusters were identified: they were the distress cluster (including shortness of breath, anxiety, and depression), the decondition cluster (fatigue, drowsiness, nausea, and reduced appetite), and the discomfort cluster (pain, and sense of generalized discomfort). These three symptom clusters accounted for 63.25% of variance of the patients' symptom experience. The small to moderate correlations between these symptom clusters indicated that they were rather independent of one another. After adjusting the age, gender and comorbidities, the distress (β = −0.635, P < 0.001), the decondition (β = −0.148, P = 0.01), and the discomfort (β = −0.258, P < 0.001) symptom clusters independently predicted their QoL. Conclusions This study identified the distinctive symptom clusters among patients with advanced HF. The results shed light on the need to develop palliative care interventions for optimizing the symptom control for this life-limiting disease. PMID:27403150

InCHlib - interactive cluster heatmap for web applications.

PubMed

Skuta, Ctibor; Bartůněk, Petr; Svozil, Daniel

2014-12-01

Hierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called 'cluster heatmap' is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap. We developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust . The cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.

Empirical Identification of Hierarchies.

ERIC Educational Resources Information Center

McCormick, Douglas; And Others

Outlining a cluster procedure which maximizes specific criteria while building scales from binary measures using a sequential, agglomerative, overlapping, non-hierarchic method results in indices giving truer results than exploratory facotr analyses or multidimensional scaling. In a series of eleven figures, patterns within cluster histories…

PAQ: Partition Analysis of Quasispecies.

PubMed

Baccam, P; Thompson, R J; Fedrigo, O; Carpenter, S; Cornette, J L

2001-01-01

The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.

Depth data research of GIS based on clustering analysis algorithm

NASA Astrophysics Data System (ADS)

Xiong, Yan; Xu, Wenli

2018-03-01

The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

Validating the Farsi version of the Pregnancy Worries and Stress Questionnaire (PWSQ): An exploratory factor analysis.

PubMed

Navidpour, Fariba; Dolatian, Mahrokh; Shishehgar, Sara; Yaghmaei, Farideh; Majd, Hamid Alavi; Hashemi, Seyed Saeed

2016-10-01

Biological, environmental, inter- and intrapersonal changes during the antenatal period can result in anxiety and stress in pregnant women. It is pivotal to identify potential stressors and prevent their foetal and maternal consequences. The present study was conducted to validate and examine the factor structure of the Farsi version of the Pregnancy Worries and Stress Questionnaire (PWSQ). In 2015, 502 Iranian healthy pregnant women, referred to selected hospitals in Tehran for prenatal care at 8-39 weeks of pregnancy, were recruited through a randomized cluster sampling. The PWSQ was translated into Farsi, and its validity and reliability were examined using exploratory factor analysis by SPSS version 21. The content validity of items on the PWSQ was between 0.63-1. The content validity index for relevance, clarity and simplicity were 0.92, 0.98, and 0.98, respectively, with a mean of 0.94. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.863. Test-retest reliability showed high internal consistency (α=0.89; p<0.0001). The psychometric evaluation and exploratory factor analysis showed that the translated questionnaire is a valid and reliable tool to identify stress in Iranian pregnant women. Application of the questionnaire can facilitate the diagnosis of stress in pregnant women and assist health care providers in providing timely support and minimizing negative outcomes of stress and anxiety in pregnant women and their infants.

Genotype evaluation of cowpea seeds (Vigna unguiculata) using 1H qNMR combined with exploratory tools and solid-state NMR.

PubMed

Alves Filho, Elenilson G; Silva, Lorena M A; Teofilo, Elizita M; Larsen, Flemming H; de Brito, Edy S

2017-01-01

The ultimate aim of this study was to apply a non-targeted chemometric analysis (principal component analysis and hierarchical clustering analysis using the heat map approach) of NMR data to investigate the variability of organic compounds in nine genotype cowpea seeds, without any complex pre-treatment. In general, both exploratory tools show that Tvu 233, CE-584, and Setentão genotypes presented higher amount mainly of raffinose and Tvu 382 presented the highest content of choline and least content of raffinose. The evaluation of the aromatic region showed the Setentão genotype with highest content of niacin/vitamin B3 whereas Tvu 382 with lowest amount. To investigate rigid and mobile components in the seeds cotyledon, 13 C CP and SP/MAS solid-state NMR experiments were performed. The cotyledon of the cowpea comprised a rigid part consisting of starch as well as a soft portion made of starch, fatty acids, and protein. The variable contact time experiment suggests the presence of lipid-amylose complexes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Psychometric evaluation of the Dutch version of the Subjective Opiate Withdrawal Scale (SOWS).

PubMed

Dijkstra, Boukje A G; Krabbe, Paul F M; Riezebos, Truus G M; van der Staak, Cees P F; De Jong, Cor A J

2007-01-01

To evaluate the psychometric properties of the Dutch version of the 16-item Subjective Opiate Withdrawal Scale (SOWS). The SOWS measures withdrawal symptoms at the time of assessment. The Dutch SOWS was repeatedly administered to a sample of 272 opioid-dependent inpatients of four addiction treatment centers during rapid detoxification with or without general anesthesia. Examination of the psychometric properties of the SOWS included exploratory factor analysis, internal consistency, test-retest reliability, and criterion validity. Exploratory factor analysis of the SOWS revealed a general pattern of four factors with three items not always clustered in the same factors at different points of measurement. After excluding these items from factor analysis four factors were identified during detoxification (temperature dysregulation, tractus locomotorius, tractus gastro-intestinalis and facial disinhibition). The 13-item SOWS shows high internal consistency and test-retest reliability and good validity at different stages of withdrawal. The 13-item SOWS is a reliable and valid instrument to assess opioid withdrawal during rapid detoxification. Three items were deleted because their content does not correspond directly with opioid withdrawal symptoms. Copyright (c) 2007 S. Karger AG, Basel.

A Surface-based Analysis of Language Lateralization and Cortical Asymmetry

PubMed Central

Greve, Douglas N.; Van der Haegen, Lise; Cai, Qing; Stufflebeam, Steven; Sabuncu, Mert R.; Fischl, Bruce; Bysbaert, Marc

2013-01-01

Among brain functions, language is one of the most lateralized. Cortical language areas are also some of the most asymmetrical in the brain. An open question is whether the asymmetry in function is linked to the asymmetry in anatomy. To address this question, we measured anatomical asymmetry in 34 participants shown with fMRI to have language dominance of the left hemisphere (LLD) and 21 participants shown to have atypical right hemisphere dominance (RLD). All participants were healthy and left-handed, and most (80%) were female. Gray matter (GM) volume asymmetry was measured using an automated surface-based technique in both ROIs and exploratory analyses. In the ROI analysis, a significant difference between LLD and RLD was found in the insula. No differences were found in planum temporale (PT), pars opercularis (POp), pars triangularis (PTr), or Heschl’s gyrus (HG). The PT, POp, insula, and HG were all significantly left lateralized in both LLD and RLD participants. Both the positive and negative ROI findings replicate a previous study using manually labeled ROIs in a different cohort [Keller, S. S., Roberts, N., Garcia-Finana, M., Mohammadi, S., Ringelstein, E. B., Knecht, S., et al. Can the language-dominant hemisphere be predicted by brain anatomy? Journal of Cognitive Neuroscience, 23, 2013–2029, 2011]. The exploratory analysis was accomplished using a new surface-based registration that aligns cortical folding patterns across both subject and hemisphere. A small but significant cluster was found in the superior temporal gyrus that overlapped with the PT. A cluster was also found in the ventral occipitotemporal cortex corresponding to the visual word recognition area. The surface-based analysis also makes it possible to disentangle the effects of GM volume, thickness, and surface area while removing the effects of curvature. For both the ROI and exploratory analyses, the difference between LLD and RLD volume laterality was most strongly driven by differences in surface area and not cortical thickness. Overall, there were surprisingly few differences in GM volume asymmetry between LLD and RLD indicating that gross morphometric asymmetry is only subtly related to functional language laterality. PMID:23701459

Sonora exploratory study for the detection of wheat-leaf rust

NASA Technical Reports Server (NTRS)

Payne, R. W. (Principal Investigator)

1980-01-01

The applicability of LANDSAT remote sensing technology to the detection of a wheat-leaf-rust epidemic in Sonora, Mexico, during 1977 was investigated. LANDSAT data acquired during crop years 1975-76 and 1976-77 were clustered, classified, and analyzed in order to detect agricultural changes. Analysis of 1977 data indicates a significant proportion of the identified wheat is stressed (potentially rust-infected). Additional analyses show a significant increase in fallowing during the year, as well as a substantial decrease in reservoir levels in the Sonora agricultural region. Ground observations are required to substantiate these analyses. The possibility exists that heat-rust is not LANDSAT detectable and that the clusters identified as containing stressed signatures represent different varieties of wheat or perhaps nonwheat crops.

Genetic Variants Within Molecular Targets of Antipsychotic Treatment: Effects on Treatment Response, Schizophrenia Risk, and Psychopathological Features.

PubMed

Calabrò, Marco; Porcelli, Stefano; Crisafulli, Concetta; Wang, Sheng-Min; Lee, Soo-Jung; Han, Changsu; Patkar, Ashwin A; Masand, Prakash S; Albani, Diego; Raimondi, Ilaria; Forloni, Gianluigi; Bin, Sofia; Cristalli, Carlotta; Mantovani, Vilma; Pae, Chi-Un; Serretti, Alessandro

2018-01-01

Schizophrenia (SCZ) is a common and severe mental disorder. Genetic factors likely play a role in its pathophysiology as well as in treatment response. In the present study, we investigated the effects of several single nucleotide polymorphisms (SNPs) within 9 genes involved with antipsychotic (AP) mechanisms of action. Two independent samples were recruited. The Korean sample included 176 subjects diagnosed with SCZ and 326 healthy controls, while the Italian sample included 83 subjects and 194 controls. AP response as measured by the positive and negative syndrome scale (PANSS) was the primary outcome, while the secondary outcome was the SCZ risk. Exploratory analyses were performed on (1) symptom clusters response (as measured by PANSS subscales); (2) age of onset; (3) family history; and (4) suicide history. Associations evidenced in the primary analyses did not survive to the FDR correction. Concerning SCZ risk, we partially confirmed the associations among COMT and MAPK1 genetic variants and SCZ. Finally, our exploratory analysis suggested that CHRNA7 and HTR2A genes may modulate both positive and negative symptoms responses, while PLA2G4A and SIGMAR1 may modulate respectively positive and negative symptoms responses. Moreover, GSK3B, HTR2A, PLA2G4A, and S100B variants may determine an anticipation of SCZ age of onset. Our results did not support a primary role for the genes investigated in AP response as a whole. However, our exploratory findings suggested that these genes may be involved in symptom clusters response.

Cluster galaxy population evolution from the Subaru Hyper Suprime-Cam survey: brightest cluster galaxies, stellar mass distribution, and active galaxies

NASA Astrophysics Data System (ADS)

Lin, Yen-Ting; Hsieh, Bau-Ching; Lin, Sheng-Chieh; Oguri, Masamune; Chen, Kai-Feng; Tanaka, Masayuki; Chiu, I.-non; Huang, Song; Kodama, Tadayuki; Leauthaud, Alexie; More, Surhud; Nishizawa, Atsushi J.; Bundy, Kevin; Lin, Lihwai; Miyazaki, Satoshi; HSC Collaboration

2018-01-01

The unprecedented depth and area surveyed by the Subaru Strategic Program with the Hyper Suprime-Cam (HSC-SSP) have enabled us to construct and publish the largest distant cluster sample out to z~1 to date. In this exploratory study of cluster galaxy evolution from z=1 to z=0.3, we investigate the stellar mass assembly history of brightest cluster galaxies (BCGs), and evolution of stellar mass and luminosity distributions, stellar mass surface density profile, as well as the population of radio galaxies. Our analysis is the first high redshift application of the top N richest cluster selection, which is shown to allow us to trace the cluster galaxy evolution faithfully. Our stellar mass is derived from a machine-learning algorithm, which we show to be unbiased and accurate with respect to the COSMOS data. We find very mild stellar mass growth in BCGs, and no evidence for evolution in both the total stellar mass-cluster mass correlation and the shape of the stellar mass surface density profile. The clusters are found to contain more red galaxies compared to the expectations from the field, even after the differences in density between the two environments have been taken into account. We also present the first measurement of the radio luminosity distribution in clusters out to z~1.

Finding Groups in Gene Expression Data

PubMed Central

2005-01-01

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups. PMID:16046827

Galaxy Cluster Bulk Flows and Collision Velocities in QUMOND

NASA Astrophysics Data System (ADS)

Katz, Harley; McGaugh, Stacy; Teuben, Peter; Angus, G. W.

2013-07-01

We examine the formation of clusters of galaxies in numerical simulations of a QUMOND cosmogony with massive sterile neutrinos. Clusters formed in these exploratory simulations develop higher velocities than those found in ΛCDM simulations. The bulk motions of clusters attain ~1000 km s-1 by low redshift, comparable to observations whereas ΛCDM simulated clusters tend to fall short. Similarly, high pairwise velocities are common in cluster-cluster collisions like the Bullet Cluster. There is also a propensity for the most massive clusters to be larger in QUMOND and to appear earlier than in ΛCDM, potentially providing an explanation for "pink elephants" like El Gordo. However, it is not obvious that the cluster mass function can be recovered.

Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study.

PubMed

Pérez-Rodrigo, Carmen; Gianzo-Citores, Marta; Gil, Ángel; González-Gross, Marcela; Ortega, Rosa M; Serra-Majem, Lluis; Varela-Moreiras, Gregorio; Aranceta-Bartrina, Javier

2017-06-14

Limited knowledge is available on lifestyle patterns in Spanish adults. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, sleep time, and smoking in Spanish adults aged 18-64 years and their association with obesity. Analysis was based on a subsample ( n = 1617) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, sleep time, and smoking. Logistic regression analysis was used to explore the association between the cluster solutions and obesity. Factor analysis identified four dietary patterns, " Traditional DP ", " Mediterranean DP ", " Snack DP " and " Dairy-sweet DP ". Dietary patterns, physical activity behaviors, sedentary behaviors, sleep time, and smoking in Spanish adults aggregated into three different clusters of lifestyle patterns: " Mixed diet-physically active-low sedentary lifestyle pattern ", " Not poor diet-low physical activity-low sedentary lifestyle pattern " and " Poor diet-low physical activity-sedentary lifestyle pattern ". A higher proportion of people aged 18-30 years was classified into the " Poor diet-low physical activity-sedentary lifestyle pattern ". The prevalence odds ratio for obesity in men in the " Mixed diet-physically active-low sedentary lifestyle pattern " was significantly lower compared to those in the " Poor diet-low physical activity-sedentary lifestyle pattern ". Those behavior patterns are helpful to identify specific issues in population subgroups and inform intervention strategies. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk practices, considering lifestyle patterns and associated determinants.

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.

[Autism Spectrum Disorder and DSM-5: Spectrum or Cluster?].

PubMed

Kienle, Xaver; Freiberger, Verena; Greulich, Heide; Blank, Rainer

2015-01-01

Within the new DSM-5, the currently differentiated subgroups of "Autistic Disorder" (299.0), "Asperger's Disorder" (299.80) and "Pervasive Developmental Disorder" (299.80) are replaced by the more general "Autism Spectrum Disorder". With regard to a patient-oriented and expedient advising therapy planning, however, the issue of an empirically reproducible and clinically feasible differentiation into subgroups must still be raised. Based on two Autism-rating-scales (ASDS and FSK), an exploratory two-step cluster analysis was conducted with N=103 children (age: 5-18) seen in our social-pediatric health care centre to examine potentially autistic symptoms. In the two-cluster solution of both rating scales, mainly the problems in social communication grouped the children into a cluster "with communication problems" (51 % and 41 %), and a cluster "without communication problems". Within the three-cluster solution of the ASDS, sensory hypersensitivity, cleaving to routines and social-communicative problems generated an "autistic" subgroup (22%). The children of the second cluster ("communication problems", 35%) were only described by social-communicative problems, and the third group did not show any problems (38%). In the three-cluster solution of the FSK, the "autistic cluster" of the two-cluster solution differentiated in a subgroup with mainly social-communicative problems (cluster 1) and a second subgroup described by restrictive, repetitive behavior. The different cluster solutions will be discussed with a view to the new DSM-5 diagnostic criteria, for following studies a further specification of some of the ASDS and FSK items could be helpful.

An Empirically Derived Taxonomy of Factors Affecting Physicians' Willingness to Disclose Medical Errors

PubMed Central

Kaldjian, Lauris C; Jones, Elizabeth W; Rosenthal, Gary E; Tripp-Reimer, Toni; Hillis, Stephen L

2006-01-01

BACKGROUND Physician disclosure of medical errors to institutions, patients, and colleagues is important for patient safety, patient care, and professional education. However, the variables that may facilitate or impede disclosure are diverse and lack conceptual organization. OBJECTIVE To develop an empirically derived, comprehensive taxonomy of factors that affects voluntary disclosure of errors by physicians. DESIGN A mixed-methods study using qualitative data collection (structured literature search and exploratory focus groups), quantitative data transformation (sorting and hierarchical cluster analysis), and validation procedures (confirmatory focus groups and expert review). RESULTS Full-text review of 316 articles identified 91 impeding or facilitating factors affecting physicians' willingness to disclose errors. Exploratory focus groups identified an additional 27 factors. Sorting and hierarchical cluster analysis organized factors into 8 domains. Confirmatory focus groups and expert review relocated 6 factors, removed 2 factors, and modified 4 domain names. The final taxonomy contained 4 domains of facilitating factors (responsibility to patient, responsibility to self, responsibility to profession, responsibility to community), and 4 domains of impeding factors (attitudinal barriers, uncertainties, helplessness, fears and anxieties). CONCLUSIONS A taxonomy of facilitating and impeding factors provides a conceptual framework for a complex field of variables that affects physicians' willingness to disclose errors to institutions, patients, and colleagues. This taxonomy can be used to guide the design of studies to measure the impact of different factors on disclosure, to assist in the design of error-reporting systems, and to inform educational interventions to promote the disclosure of errors to patients. PMID:16918739

Sub-grouping patients with non-specific low back pain based on cluster analysis of discriminatory clinical items.

PubMed

Billis, Evdokia; McCarthy, Christopher J; Roberts, Chris; Gliatis, John; Papandreou, Maria; Gioftsos, George; Oldham, Jacqueline A

2013-02-01

To identify potential subgroups amongst patients with non-specific low back pain based on a consensus list of potentially discriminatory examination items. Exploratory study. A convenience sample of 106 patients with non-specific low back pain (43 males, 63 females, mean age 36 years, standard deviation 15.9 years) and 7 physiotherapists. Based on 3 focus groups and a two-round Delphi involving 23 health professionals and a random stratified sample of 150 physiotherapists, respectively, a comprehensive examination list comprising the most "discriminatory" items was compiled. Following reliability analysis, the most reliable clinical items were assessed with a sample of patients with non-specific low back pain. K-means cluster analysis was conducted for 2-, 3- and 4-cluster options to explore for meaningful homogenous subgroups. The most clinically meaningful cluster was a two-subgroup option, comprising a small group (n = 24) with more severe clinical presentation (i.e. more widespread pain, functional and sleeping problems, other symptoms, increased investigations undertaken, more severe clinical signs, etc.) and a larger less dysfunctional group (n = 80). A number of potentially discriminatory clinical items were identified by health professionals and sub-classified, based on a sample of patients with non-specific low back pain, into two subgroups. However, further work is needed to validate this classification process.

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

GALAXY CLUSTER BULK FLOWS AND COLLISION VELOCITIES IN QUMOND

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

Katz, Harley; McGaugh, Stacy; Teuben, Peter

We examine the formation of clusters of galaxies in numerical simulations of a QUMOND cosmogony with massive sterile neutrinos. Clusters formed in these exploratory simulations develop higher velocities than those found in {Lambda}CDM simulations. The bulk motions of clusters attain {approx}1000 km s{sup -1} by low redshift, comparable to observations whereas {Lambda}CDM simulated clusters tend to fall short. Similarly, high pairwise velocities are common in cluster-cluster collisions like the Bullet Cluster. There is also a propensity for the most massive clusters to be larger in QUMOND and to appear earlier than in {Lambda}CDM, potentially providing an explanation for ''pink elephants''more » like El Gordo. However, it is not obvious that the cluster mass function can be recovered.« less

Exploratory Bifactor Analysis: The Schmid-Leiman Orthogonalization and Jennrich-Bentler Analytic Rotations

PubMed Central

Mansolf, Maxwell; Reise, Steven P.

2017-01-01

Analytic bifactor rotations (Jennrich & Bentler, 2011, 2012) have been recently developed and made generally available, but are not well understood. The Jennrich-Bentler analytic bifactor rotations (bi-quartimin and bi-geomin) are an alternative to, and arguably an improvement upon, the less technically sophisticated Schmid-Leiman orthogonalization (Schmid & Leiman, 1957). We review the technical details that underlie the Schmid-Leiman and Jennrich-Bentler bifactor rotations, using simulated data structures to illustrate important features and limitations. For the Schmid-Leiman, we review the problem of inaccurate parameter estimates caused by the linear dependencies, sometimes called “proportionality constraints,” that are required to expand a p correlated factors solution into a (p+1) (bi)factor space. We also review the complexities involved when the data depart from perfect cluster structure (e.g., item cross-loading on group factors). For the Jennrich-Bentler rotations, we describe problems in parameter estimation caused by departures from perfect cluster structure. In addition, we illustrate the related problems of: (a) solutions that are not invariant under different starting values (i.e., local minima problems); and, (b) group factors collapsing onto the general factor. Recommendations are made for substantive researchers including examining all local minima and applying multiple exploratory techniques in an effort to identify an accurate model. PMID:27612521

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

PubMed Central

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

2015-01-01

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

A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.

PubMed

Akay, Altug; Dragomir, Andrei; Erlandsson, Björn-Erik

2015-01-01

A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.

Genetic similarities between tobacco use disorder and related comorbidities: an exploratory study

PubMed Central

2014-01-01

Background Tobacco use disorder (TUD), defined as the use of tobacco to the detriment of a person’s health or social functioning, is associated with various disorders. We hypothesized that mutual variation in genes may partly explain this link. The aims of this study were to make a non-exhaustive inventory of the disorders using (partially) the same genetic pathways as TUD, and to describe the genetic similarities between TUD and the selected disorders. Methods We developed a 3 stage approach: (i) selection of genes influencing TUD using Gene2Mesh and Ingenuity Pathway Analysis (IPA), (ii) selection of disorders associated with the selected genes using IPA and (iii) genetic similarities between disorders associated with TUD using Jaccard distance and cluster analyses. Results Fourteen disorders and thirty-two genes met our inclusion criteria. The Jaccard distance between pairs of disorders ranged from 0.00 (e.g. oesophageal cancer and malignant hypertension) to 0.45 (e.g. bladder cancer and addiction). A lower number in the Jaccard distance indicates a higher similarity between the two disorders. Two main clusters of genetically similar disorders were observed, one including coexisting disorders (e.g. addiction and alcoholism) and the other one with the side-effects of smoking (e.g. gastric cancer and malignant hypertension). Conclusions This exploratory study partly explains the potential genetic components linking TUD to other disorders. Two principle clusters of disorders were observed (i) coexisting disorders of TUD and (ii) side-effects of TUD disorders. A further deepening of this observation in a real life study should allow strengthening this hypothesis. PMID:25060307

Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study

PubMed Central

Pérez-Rodrigo, Carmen; Gianzo-Citores, Marta; Gil, Ángel; González-Gross, Marcela; Ortega, Rosa M.; Serra-Majem, Lluis; Varela-Moreiras, Gregorio; Aranceta-Bartrina, Javier

2017-01-01

Limited knowledge is available on lifestyle patterns in Spanish adults. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, sleep time, and smoking in Spanish adults aged 18–64 years and their association with obesity. Analysis was based on a subsample (n = 1617) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, sleep time, and smoking. Logistic regression analysis was used to explore the association between the cluster solutions and obesity. Factor analysis identified four dietary patterns, “Traditional DP”, “Mediterranean DP”, “Snack DP” and “Dairy-sweet DP”. Dietary patterns, physical activity behaviors, sedentary behaviors, sleep time, and smoking in Spanish adults aggregated into three different clusters of lifestyle patterns: “Mixed diet-physically active-low sedentary lifestyle pattern”, “Not poor diet-low physical activity-low sedentary lifestyle pattern” and “Poor diet-low physical activity-sedentary lifestyle pattern”. A higher proportion of people aged 18–30 years was classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The prevalence odds ratio for obesity in men in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Those behavior patterns are helpful to identify specific issues in population subgroups and inform intervention strategies. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk practices, considering lifestyle patterns and associated determinants. PMID:28613259

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Clusters of Behaviors and Beliefs Predicting Adolescent Depression: Implications for Prevention

PubMed Central

Paunesku, David; Ellis, Justin; Fogel, Joshua; Kuwabara, Sachiko A; Gollan, Jackie; Gladstone, Tracy; Reinecke, Mark; Van Voorhees, Benjamin W.

2009-01-01

OBJECTIVE Risk factors for various disorders are known to cluster. However, the factor structure for behaviors and beliefs predicting depressive disorder in adolescents is not known. Knowledge of this structure can facilitate prevention planning. METHODS We used the National Longitudinal Study of Adolescent Health (AddHealth) data set to conduct an exploratory factor analysis to identify clusters of behaviors/experiences predicting the onset of major depressive disorder (MDD) at 1-year follow-up (N=4,791). RESULTS Four factors were identified: family/interpersonal relations, self-emancipation, avoidant problem solving/low self-worth, and religious activity. Strong family/interpersonal relations were the most significantly protective against depression at one year follow-up. Avoidant problem solving/low self-worth was not predictive of MDD on its own, but significantly amplified the risks associated with delinquency. CONCLUSION Depression prevention interventions should consider giving family relationships a more central role in their efforts. Programs teaching problem solving skills may be most appropriate for reducing MDD risk in delinquent youth. PMID:20502621

Gender-related dimensions of childhood adversities in the general population.

PubMed

Coêlho, Bruno M; Santana, Geilson L; Viana, Maria C; Andrade, Laura H; Wang, Yuan-Pang

2018-06-11

Childhood adversities (CAs) comprise a group of negative experiences individuals may suffer in their lifetimes. The goal of the present study was to investigate the cluster discrimination of CAs through psychometric determination of the common attributes of such experiences for men and women. Parental mental illness, substance misuse, criminality, death, divorce, other parental loss, family violence, physical abuse, sexual abuse, neglect, physical illness, and economic adversity were assessed in a general-population sample (n=5,037). Exploratory and confirmatory factor analysis determined gender-related dimensions of CA. The contribution of each individual adversity was explored through Rasch analysis. Adversities were reported by 53.6% of the sample. A three-factor model of CA dimensions fit the data better for men, and a two-factor model for women. For both genders, the dimension of family maladjustment - encompassing physical abuse, neglect, parental mental disorders, and family violence - was the core cluster of CAs. Women endorsed more CAs than men. Rasch analysis found that sexual abuse, physical illness, parental criminal behavior, parental divorce, and economic adversity were difficult to report in face-to-face interviews. CAs embrace sensitive personal information, clustering of which differed by gender. Acknowledging CAs may have an impact on medical and psychiatric outcomes in adulthood.

Family and community violence of schoolchildren from the city of São Gonçalo, Rio de Janeiro, Brazil.

PubMed

Pinto, Liana Wernersbach; Gonçalves de Assis, Simone

2013-06-01

This descriptive study aimed to investigate the association between violence in the family, school and community experienced by school children/adolescents of the city of São Gonçalo (RJ), Brazil. Questionnaires were administered to the mothers/guardians to assess violence in the family and school and to children to check their perceptions of community violence. Multiple correspondence analysis and cluster analysis, two exploratory descriptive techniques, were employed. Data from 280 schoolchildren were analyzed. A total of 43.9% of mothers reported that their children had been physically abused in their homes. With regard to children's/adolescents' perception of community violence, 93.2% said they had experienced or witnessed these events in their communities. For both sexes there was the formation of a cluster of categories with the presence of violence among siblings, presence of severe physical assault and verbal assault committed by parents. Among girls, the presence of violence in the school formed a cluster with the highest category of violence in the community. In conclusion, it should be emphasized that public policies aimed at dealing with violence should expand their scope to the various forms of violence affecting children.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Spatial analysis of county-based gonorrhoea incidence in mainland China, from 2004 to 2009.

PubMed

Yin, Fei; Feng, Zijian; Li, Xiaosong

2012-07-01

Gonorrhoea is one of the most common sexually transmissible infections in mainland China. Effective spatial monitoring of gonorrhoea incidence is important for successful implementation of control and prevention programs. The county-level gonorrhoea incidence rates for all of mainland China was monitored through examining spatial patterns. County-level data on gonorrhoea cases between 2004 and 2009 were obtained from the China Information System for Disease Control and Prevention. Bayesian smoothing and exploratory spatial data analysis (ESDA) methods were used to characterise the spatial distribution pattern of gonorrhoea cases. During the 6-year study period, the average annual gonorrhoea incidence was 12.41 cases per 100000 people. Using empirical Bayes smoothed rates, the local Moran test identified one significant single-centre cluster and two significant multi-centre clusters of high gonorrhoea risk (all P-values <0.01). Bayesian smoothing and ESDA methods can assist public health officials in using gonorrhoea surveillance data to identify high risk areas. Allocating more resources to such areas could effectively reduce gonorrhoea incidence.

Visual hallucinatory syndromes and the anatomy of the visual brain.

PubMed

Santhouse, A M; Howard, R J; ffytche, D H

2000-10-01

We have set out to identify phenomenological correlates of cerebral functional architecture within Charles Bonnet syndrome (CBS) hallucinations by looking for associations between specific hallucination categories. Thirty-four CBS patients were examined with a structured interview/questionnaire to establish the presence of 28 different pathological visual experiences. Associations between categories of pathological experience were investigated by an exploratory factor analysis. Twelve of the pathological experiences partitioned into three segregated syndromic clusters. The first cluster consisted of hallucinations of extended landscape scenes and small figures in costumes with hats; the second, hallucinations of grotesque, disembodied and distorted faces with prominent eyes and teeth; and the third, visual perseveration and delayed palinopsia. The three visual psycho-syndromes mirror the segregation of hierarchical visual pathways into streams and suggest a novel theoretical framework for future research into the pathophysiology of neuropsychiatric syndromes.

An exploratory analysis for Lean and Six Sigma implementation in hospitals: Together is better?

PubMed

Lee, Jung Young; McFadden, Kathleen L; Gowen, Charles R

Despite the increasing interest for Lean and Six Sigma implementations in hospitals, there has been little empirical evidence that goes beyond descriptive case studies to address the current status and the effectiveness of the implementations. The aim of this study was to explore existing patterns of Lean and Six Sigma implementation in U.S. hospitals and compare the performance of the different patterns. We collected data from 215 U.S. hospitals via a survey that includes measurement items developed from related literature. Using the cross-sectional data, we conducted a cluster analysis, followed by t tests, chi-square tests, and regression analyses for cluster verification. The cluster analysis identifies two clusters, a Moderate Six Sigma group and a Lean Six Sigma group. Results show that the Lean Six Sigma group outperforms the Moderate Six Sigma group across many performance dimensions: responsiveness capability, patient safety, and possibly cost saving. In addition, the Lean Six Sigma group tends to be composed of larger, private teaching hospitals located in more urban areas, and they employ more resources for quality improvement. Our research contributes to the quality management literature by supporting the possible complementary relationship between Lean and Six Sigma in hospitals. Our study encourages practitioners and managers to pay more attention to Lean implementation. Although Lean seems to be conducted in a limited fashion in many hospitals, it should be expanded and combined with Six Sigma for better results.

Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste

PubMed Central

Elvers, Paul; Omigie, Diana; Fuhrmann, Wolfgang; Fischinger, Timo

2015-01-01

Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed. PMID:26347702

Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste.

PubMed

Elvers, Paul; Omigie, Diana; Fuhrmann, Wolfgang; Fischinger, Timo

2015-01-01

Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed.

Gender differences in climacteric symptoms and associated factors in Korean men and women.

PubMed

Yeom, Hyun-E

2018-06-01

Both men and women may experience multifaceted symptoms that are part of natural aging throughout the climacteric period. This study compared the prevalence and severity of climacteric symptoms between genders and identified the underlying clusters of climacteric symptoms and associated factors in midlife men and women. A cross-sectional study was done with 254 middle-aged Korean men (n = 129, M = 50.4) and women (n = 125, M = 49.5). Data were collected by self-administered surveys and analyzed using t-tests, chi-square tests, exploratory factor analysis, and regression analysis. Significant gender differences in overall climacteric symptoms were not detected except for muscle weakness, weight gain, and hot flashes. Climacteric symptoms were clustered as physical, vasomotor-genital, psychological, and metabolic dimensions, with the physical dimension being the most explanatory cluster. A significant gender effect was found only in the metabolic dimension after adjusting for the relevant covariates, and regular eating was significantly associated with all symptom clusters. This study offers evidence that most climacteric symptoms are shared by both men and women and emphasizes the importance of healthier lifestyles in the climacteric transition period. The findings highlight the critical need for integrated assessments of the multifactorial symptoms and of modifying poor lifestyles in both genders throughout the climacteric transition period. Copyright © 2018 Elsevier Inc. All rights reserved.

First Results on the Cluster Galaxy Population from the Subaru Hyper Suprime-Cam Survey. III. Brightest Cluster Galaxies, Stellar Mass Distribution, and Active Galaxies

NASA Astrophysics Data System (ADS)

Lin, Yen-Ting; Hsieh, Bau-Ching; Lin, Sheng-Chieh; Oguri, Masamune; Chen, Kai-Feng; Tanaka, Masayuki; Chiu, I.-Non; Huang, Song; Kodama, Tadayuki; Leauthaud, Alexie; More, Surhud; Nishizawa, Atsushi J.; Bundy, Kevin; Lin, Lihwai; Miyazaki, Satoshi

2017-12-01

The unprecedented depth and area surveyed by the Subaru Strategic Program with the Hyper Suprime-Cam (HSC-SSP) have enabled us to construct and publish the largest distant cluster sample out to z∼ 1 to date. In this exploratory study of cluster galaxy evolution from z = 1 to z = 0.3, we investigate the stellar mass assembly history of brightest cluster galaxies (BCGs), the evolution of stellar mass and luminosity distributions, the stellar mass surface density profile, as well as the population of radio galaxies. Our analysis is the first high-redshift application of the top N richest cluster selection, which is shown to allow us to trace the cluster galaxy evolution faithfully. Over the 230 deg2 area of the current HSC-SSP footprint, selecting the top 100 clusters in each of the four redshift bins allows us to observe the buildup of galaxy population in descendants of clusters whose z≈ 1 mass is about 2× {10}14 {M}ȯ . Our stellar mass is derived from a machine-learning algorithm, which is found to be unbiased and accurate with respect to the COSMOS data. We find very mild stellar mass growth in BCGs (about 35% between z = 1 and 0.3), and no evidence for evolution in both the total stellar mass–cluster mass correlation and the shape of the stellar mass surface density profile. We also present the first measurement of the radio luminosity distribution in clusters out to z∼ 1, and show hints of changes in the dominant accretion mode powering the cluster radio galaxies at z∼ 0.8.

[Methods of a posteriori identification of food patterns in Brazilian children: a systematic review].

PubMed

Carvalho, Carolina Abreu de; Fonsêca, Poliana Cristina de Almeida; Nobre, Luciana Neri; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2016-01-01

The objective of this study is to provide guidance for identifying dietary patterns using the a posteriori approach, and analyze the methodological aspects of the studies conducted in Brazil that identified the dietary patterns of children. Articles were selected from the Latin American and Caribbean Literature on Health Sciences, Scientific Electronic Library Online and Pubmed databases. The key words were: Dietary pattern; Food pattern; Principal Components Analysis; Factor analysis; Cluster analysis; Reduced rank regression. We included studies that identified dietary patterns of children using the a posteriori approach. Seven studies published between 2007 and 2014 were selected, six of which were cross-sectional and one cohort, Five studies used the food frequency questionnaire for dietary assessment; one used a 24-hour dietary recall and the other a food list. The method of exploratory approach used in most publications was principal components factor analysis, followed by cluster analysis. The sample size of the studies ranged from 232 to 4231, the values of the Kaiser-Meyer-Olkin test from 0.524 to 0.873, and Cronbach's alpha from 0.51 to 0.69. Few Brazilian studies identified dietary patterns of children using the a posteriori approach and principal components factor analysis was the technique most used.

The anterior hypothalamus in cluster headache.

PubMed

Arkink, Enrico B; Schmitz, Nicole; Schoonman, Guus G; van Vliet, Jorine A; Haan, Joost; van Buchem, Mark A; Ferrari, Michel D; Kruit, Mark C

2017-10-01

Objective To evaluate the presence, localization, and specificity of structural hypothalamic and whole brain changes in cluster headache and chronic paroxysmal hemicrania (CPH). Methods We compared T1-weighted magnetic resonance images of subjects with cluster headache (episodic n = 24; chronic n = 23; probable n = 14), CPH ( n = 9), migraine (with aura n = 14; without aura n = 19), and no headache ( n = 48). We applied whole brain voxel-based morphometry (VBM) using two complementary methods to analyze structural changes in the hypothalamus: region-of-interest analyses in whole brain VBM, and manual segmentation of the hypothalamus to calculate volumes. We used both conservative VBM thresholds, correcting for multiple comparisons, and less conservative thresholds for exploratory purposes. Results Using region-of-interest VBM analyses mirrored to the headache side, we found enlargement ( p < 0.05, small volume correction) in the anterior hypothalamic gray matter in subjects with chronic cluster headache compared to controls, and in all participants with episodic or chronic cluster headache taken together compared to migraineurs. After manual segmentation, hypothalamic volume (mean±SD) was larger ( p < 0.05) both in subjects with episodic (1.89 ± 0.18 ml) and chronic (1.87 ± 0.21 ml) cluster headache compared to controls (1.72 ± 0.15 ml) and migraineurs (1.68 ± 0.19 ml). Similar but non-significant trends were observed for participants with probable cluster headache (1.82 ± 0.19 ml; p = 0.07) and CPH (1.79 ± 0.20 ml; p = 0.15). Increased hypothalamic volume was primarily explained by bilateral enlargement of the anterior hypothalamus. Exploratory whole brain VBM analyses showed widespread changes in pain-modulating areas in all subjects with headache. Interpretation The anterior hypothalamus is enlarged in episodic and chronic cluster headache and possibly also in probable cluster headache or CPH, but not in migraine.

CRAWview: for viewing splicing variation, gene families, and polymorphism in clusters of ESTs and full-length sequences.

PubMed

Chou, A; Burke, J

1999-05-01

DNA sequence clustering has become a valuable method in support of gene discovery and gene expression analysis. Our interest lies in leveraging the sequence diversity within clusters of expressed sequence tags (ESTs) to model gene structure for the study of gene variants that arise from, among other things, alternative mRNA splicing, polymorphism, and divergence after gene duplication, fusion, and translocation events. In previous work, CRAW was developed to discover gene variants from assembled clusters of ESTs. Most importantly, novel gene features (the differing units between gene variants, for example alternative exons, polymorphisms, transposable elements, etc.) that are specialized to tissue, disease, population, or developmental states can be identified when these tools collate DNA source information with gene variant discrimination. While the goal is complete automation of novel feature and gene variant detection, current methods are far from perfect and hence the development of effective tools for visualization and exploratory data analysis are of paramount importance in the process of sifting through candidate genes and validating targets. We present CRAWview, a Java based visualization extension to CRAW. Features that vary between gene forms are displayed using an automatically generated color coded index. The reporting format of CRAWview gives a brief, high level summary report to display overlap and divergence within clusters of sequences as well as the ability to 'drill down' and see detailed information concerning regions of interest. Additionally, the alignment viewing and editing capabilities of CRAWview make it possible to interactively correct frame-shifts and otherwise edit cluster assemblies. We have implemented CRAWview as a Java application across windows NT/95 and UNIX platforms. A beta version of CRAWview will be freely available to academic users from Pangea Systems (http://www.pangeasystems.com). Contact :

A symptom level examination of the relationship between Cluster B personality disorders and patterns of criminality and violence in women.

PubMed

Warren, Janet I; South, Susan C

2009-01-01

The psychometric properties and structure of the Cluster B Personality Disorder criteria (Antisocial, Borderline, Histrionic, and Narcissistic) are examined in a sample of 261 female inmates using a self-report screen followed by a full diagnostic interview. The results of the structural analyses in this sample demonstrated good internal consistency and convergence, but poor discriminant validity between disorders. An exploratory factor analysis found that the structure of these disorders was best accounted for by a four-factor solution that paralleled the Diagnostic and Statistical Manual (DSM-IV-TR; APA, 2000) classification scheme with some significant and notable exceptions. Using the factor scores generated from the factor analysis, the personality profiles of the women were compared with several behavioral indices, including instant offense, institutional infractions, and self-report violence and victimization within the prison. Of particular importance was the consistent relationship observed between narcissistic personality traits and threatening and violent behavior within the prison combined with the impulsive but less malignant presentation of antisocial personality traits among this sample of women. Results are discussed as they inform our understanding of the structural integrity of the four Cluster B diagnostic categories and the relationship of these personality disorders to different types of criminality and violence.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

Polarisation Measurement with a Dual Beam Interferometer (CATSI). Exploratory Results and Preliminary Phenomenological Analysis

DTIC Science & Technology

2006-06-01

Polarisation measurement with a dual beam interferometer (CATSI) Exploratory results and preliminary phenomenological analysis H. Lavoie J.-M... Polarisation measurement with a dual beam interferometer (CATSI) Exploratory results and preliminary phenomenological analysis H. Lavoie J.-M. Thériault... Polarisation measurement with a dual beam interferometer (CATSI) - Exploratory results and preliminary phenomenological analysis. ECR 2004-372. DRDC Valcartier

An exploratory study of organization design configurations in health care delivery organizations.

PubMed

Sheppeck, Mick; Militello, Jack

2014-01-01

Organizations are configurations of variables that support each other to achieve customer satisfaction. Based on Treacy and Wiersema (1995), we predicted the emergence of two configurations, one supporting a product leadership stance and one predicting the customer intimate approach from a set of 73 for profit health care clinics. In addition, we predicted the emergence of a configuration where the scores on most variables were near the mean for each variable. Using cluster analysis and discriminant function analysis, we identified three configurations: one a "master of two" strategy, one "stuck-in-the-middle," and one showing scores well below the mean on most variables. The implications for organization design and manager actions in the health care industry are discussed.

Suicide by cop: clinical risks and subtypes.

PubMed

Dewey, Lauren; Allwood, Maureen; Fava, Joanna; Arias, Elizabeth; Pinizzotto, Anthony; Schlesinger, Louis

2013-01-01

This study examines whether clinical classification schemes from general suicide research are applicable for cases of suicide by cop (SbC) and whether there are indicators as to why the police might be engaged in the suicide. Using archival law enforcement data, 13 clinical risks were examined among 68 cases of SbC using exploratory factor analysis and k-means cluster analysis. Three subtypes of SbC cases emerged: Mental Illness, Criminality, and Not Otherwise Specified. The subtypes varied significantly on their levels of mental illness, substance use, and criminal activity. Findings suggest that reducing fragmentation between law enforcement and mental health service providers might be a crucial goal for suicide intervention and prevention, at least among cases of SbC.

A Brief History of the Philosophical Foundations of Exploratory Factor Analysis.

ERIC Educational Resources Information Center

Mulaik, Stanley A.

1987-01-01

Exploratory factor analysis derives its key ideas from many sources, including Aristotle, Francis Bacon, Descartes, Pearson and Yule, and Kant. The conclusions of exploratory factor analysis are never complete without subsequent confirmatory factor analysis. (Author/GDC)

Use of tactile feedback to control exploratory movements to characterize object compliance.

PubMed

Su, Zhe; Fishel, Jeremy A; Yamamoto, Tomonori; Loeb, Gerald E

2012-01-01

Humans have been shown to be good at using active touch to perceive subtle differences in compliance. They tend to use highly stereotypical exploratory strategies, such as applying normal force to a surface. We developed similar exploratory and perceptual algorithms for a mechatronic robotic system (Barrett arm/hand system) equipped with liquid-filled, biomimetic tactile sensors (BioTac(®) from SynTouch LLC). The distribution of force on the fingertip was measured by the electrical resistance of the conductive liquid trapped between the elastomeric skin and a cluster of four electrodes on the flat fingertip surface of the rigid core of the BioTac. These signals provided closed-loop control of exploratory movements, while the distribution of skin deformations, measured by more lateral electrodes and by the hydraulic pressure, were used to estimate material properties of objects. With this control algorithm, the robot plus tactile sensor was able to discriminate the relative compliance of various rubber samples.

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

PubMed

Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H

2017-10-25

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

Lipophilicity of oils and fats estimated by TLC.

PubMed

Naşcu-Briciu, Rodica D; Sârbu, Costel

2013-04-01

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

A Review of CEFA Software: Comprehensive Exploratory Factor Analysis Program

ERIC Educational Resources Information Center

Lee, Soon-Mook

2010-01-01

CEFA 3.02(Browne, Cudeck, Tateneni, & Mels, 2008) is a factor analysis computer program designed to perform exploratory factor analysis. It provides the main properties that are needed for exploratory factor analysis, namely a variety of factoring methods employing eight different discrepancy functions to be minimized to yield initial…

Parenting practices are associated with fruit and vegetable consumption in pre-school children

PubMed Central

O’Connor, Teresia M; Hughes, Sheryl O; Watson, Kathy B; Baranowski, Tom; Nicklas, Theresa A; Fisher, Jennie O; Beltran, Alicia; Baranowski, Janice C; Qu, Haiyan; Shewchuk, Richard M

2009-01-01

Objective Parents may influence children’s fruit and vegetable (F&V) consumption in many ways, but research has focused primarily on counterproductive parenting practices, such as restriction and pressure to eat. The present study aimed to assess the association of diverse parenting practices to promote F&V and its consumption among pre-school children. Design An exploratory analysis was performed on cross-sectional data from 755 Head Start pre-school children and their parents collected in 2004–5. Data included parent practices to facilitate child F&V consumption (grouped into five categories); parent-reported dietary intake of their child over 3 d; and a number of potential correlates. K-means cluster analysis assigned parents to groups with similar use of the food parenting practice categories. Stepwise linear regression analyses investigated the association of parent clusters with children’s consumption of F&V, after controlling for potential confounding factors. Results A three-cluster solution provided the best fit (R2 = 0 62), with substantial differences in the use of parenting practices. The clusters were labelled Indiscriminate Food Parenting, Non-directive Food Parenting and Low-involved Food Parenting. Non-directive parents extensively used enhanced availability and teachable moments’ practices, but less firm discipline practices than the other clusters, and were significantly associated with child F&V intake (standardized β = 0·09, P < 0·1; final model R2 = 0·17) after controlling for confounders, including parental feeding styles. Conclusions Parents use a variety of parenting practices, beyond pressuring to eat and restrictive practices, to promote F&V intake in their young child. Evaluating the use of combinations of practices may provide a better understanding of parental influences on children’s F&V intake. PMID:19490734

Symptom Clusters and Quality of Life in Hospice Patients with Cancer

PubMed Central

Omran, Suha; Khader, Yousef; McMillan, Susan

2017-01-01

Background: Symptom control is an important part of palliative care and important to achieve optimal quality of life (QOL). Studies have shown that patients with advanced cancer suffer from diverse and often severe physical and psychological symptoms. The aim is to explore the influence of symptom clusters on QOL among patients with advanced cancer. Materials and Methods: 709 patients with advanced cancer were recruited to participate in a clinical trial focusing on symptom management and QOL. Patients were adults newly admitted to hospice home care in one of two hospices in southwest Florida, who could pass mental status screening. The instruments used for data collection were the Demographic Data Form, Memorial Symptom Assessment Scale (MSAS), and the Hospice Quality of Life Index-14. Results: Exploratory factor analysis and multiple regression were used to identify symptom clusters and their influence on QOL. The results revealed that the participants experienced multiple concurrent symptoms. There were four symptom clusters found among these cancer patients. Individual symptom distress scores that were the strongest predictors of QOL were: feeling pain; dry mouth; feeling drowsy; nausea; difficulty swallowing; worrying and feeling nervous. Conclusions: Patients with advanced cancer reported various concurrent symptoms, and these form symptom clusters of four main categories. The four symptoms clusters have a negative influence on patients’ QOL and required specific care from different members of the hospice healthcare team. The results of this study should be used to guide health care providers’ symptom management. Proper attention to symptom clusters should be the basis for accurate planning of effective interventions to manage the symptom clusters experienced by advanced cancer patients. The health care provider needs to plan ahead for these symptoms and manage any concurrent symptoms for successful promotion of their patient’s QOL. PMID:28950683

Symptom Clusters and Quality of Life in Hospice Patients with Cancer

PubMed

Omran, Suha; Khader, Yousef; McMillan, Susan

2017-09-27

Background: Symptom control is an important part of palliative care and important to achieve optimal quality of life (QOL). Studies have shown that patients with advanced cancer suffer from diverse and often severe physical and psychological symptoms. The aim is to explore the influence of symptom clusters on QOL among patients with advanced cancer. Materials and Methods: 709 patients with advanced cancer were recruited to participate in a clinical trial focusing on symptom management and QOL. Patients were adults newly admitted to hospice home care in one of two hospices in southwest Florida, who could pass mental status screening. The instruments used for data collection were the Demographic Data Form, Memorial Symptom Assessment Scale (MSAS), and the Hospice Quality of Life Index-14. Results: Exploratory factor analysis and multiple regression were used to identify symptom clusters and their influence on QOL. The results revealed that the participants experienced multiple concurrent symptoms. There were four symptom clusters found among these cancer patients. Individual symptom distress scores that were the strongest predictors of QOL were: feeling pain; dry mouth; feeling drowsy; nausea; difficulty swallowing; worrying and feeling nervous. Conclusions: Patients with advanced cancer reported various concurrent symptoms, and these form symptom clusters of four main categories. The four symptoms clusters have a negative influence on patients’ QOL and required specific care from different members of the hospice healthcare team. The results of this study should be used to guide health care providers’ symptom management. Proper attention to symptom clusters should be the basis for accurate planning of effective interventions to manage the symptom clusters experienced by advanced cancer patients. The health care provider needs to plan ahead for these symptoms and manage any concurrent symptoms for successful promotion of their patient’s QOL. Creative Commons Attribution License

Industrial Education. Vocational Education Program Courses Standards.

ERIC Educational Resources Information Center

Florida State Dept. of Education, Tallahassee. Div. of Applied Tech., Adult, and Community Education.

This document contains vocational education program course standards for exploratory courses, practical arts courses, and job preparatory programs offered at the secondary and postsecondary level as part of the industrial education component in Florida. Curriculum frameworks are provided for 144 programs/clusters; representative topics are as…

Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps.

PubMed

Mei, Paulo Afonso; de Carvalho Carneiro, Cleyton; Fraser, Stephen J; Min, Li Li; Reis, Fabiano

2015-12-15

To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors. Copyright © 2015. Published by Elsevier B.V.

Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.

PubMed

Lawton, Michael; Baig, Fahd; Rolinski, Michal; Ruffman, Claudio; Nithi, Kannan; May, Margaret T; Ben-Shlomo, Yoav; Hu, Michele T M

2015-01-01

Within Parkinson's there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Use a data-driven approach to unravel any heterogeneity in the Parkinson's phenotype in a well-characterised, population-based incidence cohort. 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Our approach identified several Parkinson's phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson's.

Exploratory Mediation Analysis via Regularization

PubMed Central

Serang, Sarfaraz; Jacobucci, Ross; Brimhall, Kim C.; Grimm, Kevin J.

2017-01-01

Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment. PMID:29225454

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

PubMed

Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

2012-01-01

Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

Measuring Vocational Preferences: Ranking versus Categorical Rating Procedures.

ERIC Educational Resources Information Center

Carifio, James

1978-01-01

Describes a study to compare the relative validities of ranking v categorical rating procedures for obtaining student vocational preference data in exploratory program assignment situations. Students indicated their vocational program preferences from career clusters, and the frequency of wrong assignments made by each method was analyzed. (MF)

The Modified Abbreviated Math Anxiety Scale: A Valid and Reliable Instrument for Use with Children.

PubMed

Carey, Emma; Hill, Francesca; Devine, Amy; Szűcs, Dénes

2017-01-01

Mathematics anxiety (MA) can be observed in children from primary school age into the teenage years and adulthood, but many MA rating scales are only suitable for use with adults or older adolescents. We have adapted one such rating scale, the Abbreviated Math Anxiety Scale (AMAS), to be used with British children aged 8-13. In this study, we assess the scale's reliability, factor structure, and divergent validity. The modified AMAS (mAMAS) was administered to a very large ( n = 1746) cohort of British children and adolescents. This large sample size meant that as well as conducting confirmatory factor analysis on the scale itself, we were also able to split the sample to conduct exploratory and confirmatory factor analysis of items from the mAMAS alongside items from child test anxiety and general anxiety rating scales. Factor analysis of the mAMAS confirmed that it has the same underlying factor structure as the original AMAS, with subscales measuring anxiety about Learning and Evaluation in math. Furthermore, both exploratory and confirmatory factor analysis of the mAMAS alongside scales measuring test anxiety and general anxiety showed that mAMAS items cluster onto one factor (perceived to represent MA). The mAMAS provides a valid and reliable scale for measuring MA in children and adolescents, from a younger age than is possible with the original AMAS. Results from this study also suggest that MA is truly a unique construct, separate from both test anxiety and general anxiety, even in childhood.

A taxonomy of accountable care organizations for policy and practice.

PubMed

Shortell, Stephen M; Wu, Frances M; Lewis, Valerie A; Colla, Carrie H; Fisher, Elliott S

2014-12-01

To develop an exploratory taxonomy of Accountable Care Organizations (ACOs) to describe and understand early ACO development and to provide a basis for technical assistance and future evaluation of performance. Data from the National Survey of Accountable Care Organizations, fielded between October 2012 and May 2013, of 173 Medicare, Medicaid, and commercial payer ACOs. Drawing on resource dependence and institutional theory, we develop measures of eight attributes of ACOs such as size, scope of services offered, and the use of performance accountability mechanisms. Data are analyzed using a two-step cluster analysis approach that accounts for both continuous and categorical data. We identified a reliable and internally valid three-cluster solution: larger, integrated systems that offer a broad scope of services and frequently include one or more postacute facilities; smaller, physician-led practices, centered in primary care, and that possess a relatively high degree of physician performance management; and moderately sized, joint hospital-physician and coalition-led groups that offer a moderately broad scope of services with some involvement of postacute facilities. ACOs can be characterized into three distinct clusters. The taxonomy provides a framework for assessing performance, for targeting technical assistance, and for diagnosing potential antitrust violations. © Health Research and Educational Trust.

Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries.

PubMed

Muntaner, Carles; Chung, Haejoo; Benach, Joan; Ng, Edwin

2012-04-18

An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context. Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System. Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics. The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.

An exploratory study of alternative configurations of governing boards of substance abuse treatment centers

PubMed Central

Blum, Terry C.; Roman, Paul M.

2011-01-01

Boards of directors are the ultimate governing authorities for most organizations providing substance abuse treatment. A governing board may establish policies, monitor and improve operations, and represent a treatment organization to the public. This paper explores alternative configurations of governing boards in a national sample of 500 substance abuse treatment centers. The study proceeds from the premise that boards may be configured with varying levels of engagement in five aspects of internal management and external connections in treatment center operating environments. Based on interviews with treatment center administrative directors, four clusters emerge, describing boards that are: (1) active and balanced across internal and external domains; (2) active boundary spanners concentrating primarily on external relationships; (3) focused primarily on internal organizational management; and (4) relatively inactive. In post hoc analysis, we found that placement in these clusters is associated with treatment center attributes such as rate of growth and financial results, use of evidence based practices and provision of integrated care. PMID:21489737

MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing.

PubMed

Zackay, Arie; Steinhoff, Christine

2010-12-15

Exploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation. MethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper. The R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/ and from the Bioconductor Consortium http://www.bioconductor.org.

MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing

PubMed Central

2010-01-01

Background Exploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation. Findings MethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper. Conclusions The R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at http://methvisual.molgen.mpg.de/ and from the Bioconductor Consortium http://www.bioconductor.org. PMID:21159174

Otolith Dysfunction Alters Exploratory Movement in Mice

PubMed Central

Blankenship, Philip A.; Cherep, Lucia A.; Donaldson, Tia N.; Brockman, Sarah N.; Trainer, Alexandria D.; Yoder, Ryan M.; Wallace, Douglas G.

2017-01-01

The organization of rodent exploratory behavior appears to depend on self-movement cue processing. As of yet, however, no studies have directly examined the vestibular system’s contribution to the organization of exploratory movement. The current study sequentially segmented open field behavior into progressions and stops in order to characterize differences in movement organization between control and otoconia-deficient tilted mice under conditions with and without access to visual cues. Under completely dark conditions, tilted mice exhibited similar distance traveled and stop times overall, but had significantly more circuitous progressions, larger changes in heading between progressions, and less stable clustering of home bases, relative to control mice. In light conditions, control and tilted mice were similar on all measures except for the change in heading between progressions. This pattern of results is consistent with otoconia-deficient tilted mice using visual cues to compensate for impaired self-movement cue processing. This work provides the first empirical evidence that signals from the otolithic organs mediate the organization of exploratory behavior, based on a novel assessment of spatial orientation. PMID:28235587

Neuropsychological phenotypes among men with and without HIV disease in the multicenter AIDS cohort study.

PubMed

Molsberry, Samantha A; Cheng, Yu; Kingsley, Lawrence; Jacobson, Lisa; Levine, Andrew J; Martin, Eileen; Miller, Eric N; Munro, Cynthia A; Ragin, Ann; Sacktor, Ned; Becker, James T

2018-05-11

Mild forms of HIV-associated neurocognitive disorder (HAND) remain prevalent in the combination anti-retroviral therapy (cART) era. This study's objective was to identify neuropsychological subgroups within the Multicenter AIDS Cohort Study (MACS) based on the participant-based latent structure of cognitive function and to identify factors associated with subgroups. The MACS is a four-site longitudinal study of the natural and treated history of HIV disease among gay and bisexual men. Using neuropsychological domain scores we used a cluster variable selection algorithm to identify the optimal subset of domains with cluster information. Latent profile analysis was applied using scores from identified domains. Exploratory and post-hoc analyses were conducted to identify factors associated with cluster membership and the drivers of the observed associations. Cluster variable selection identified all domains as containing cluster information except for Working Memory. A three-profile solution produced the best fit for the data. Profile 1 performed below average on all domains, Profile 2 performed average on executive functioning, motor, and speed and below average on learning and memory, Profile 3 performed at or above average across all domains. Several demographic, cognitive, and social factors were associated with profile membership; these associations were driven by differences between Profile 1 and the other profiles. There is an identifiable pattern of neuropsychological performance among MACS members determined by all domains except Working Memory. Neither HIV nor HIV-related biomarkers were related with cluster membership, consistent with other findings that cognitive performance patterns do not map directly onto HIV serostatus.

Exploratory Visualization of Graphs Based on Community Structure

ERIC Educational Resources Information Center

Liu, Yujie

2013-01-01

Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding…

Graphic Communications. Curriculum Guide.

ERIC Educational Resources Information Center

North Dakota State Board for Vocational Education, Bismarck.

This guide provides the basic foundation to develop a one-semester course based on the cluster concept, graphic communications. One of a set of six guides for an industrial arts curriculum at the junior high school level, it suggests exploratory experiences designed to (1) develop an awareness and understanding of the drafting and graphic arts…

Exploratory visualization of astronomical data on ultra-high-resolution wall displays

NASA Astrophysics Data System (ADS)

Pietriga, Emmanuel; del Campo, Fernando; Ibsen, Amanda; Primet, Romain; Appert, Caroline; Chapuis, Olivier; Hempel, Maren; Muñoz, Roberto; Eyheramendy, Susana; Jordan, Andres; Dole, Hervé

2016-07-01

Ultra-high-resolution wall displays feature a very high pixel density over a large physical surface, which makes them well-suited to the collaborative, exploratory visualization of large datasets. We introduce FITS-OW, an application designed for such wall displays, that enables astronomers to navigate in large collections of FITS images, query astronomical databases, and display detailed, complementary data and documents about multiple sources simultaneously. We describe how astronomers interact with their data using both the wall's touchsensitive surface and handheld devices. We also report on the technical challenges we addressed in terms of distributed graphics rendering and data sharing over the computer clusters that drive wall displays.

Time fluctuation analysis of forest fire sequences

NASA Astrophysics Data System (ADS)

Vega Orozco, Carmen D.; Kanevski, Mikhaïl; Tonini, Marj; Golay, Jean; Pereira, Mário J. G.

2013-04-01

Forest fires are complex events involving both space and time fluctuations. Understanding of their dynamics and pattern distribution is of great importance in order to improve the resource allocation and support fire management actions at local and global levels. This study aims at characterizing the temporal fluctuations of forest fire sequences observed in Portugal, which is the country that holds the largest wildfire land dataset in Europe. This research applies several exploratory data analysis measures to 302,000 forest fires occurred from 1980 to 2007. The applied clustering measures are: Morisita clustering index, fractal and multifractal dimensions (box-counting), Ripley's K-function, Allan Factor, and variography. These algorithms enable a global time structural analysis describing the degree of clustering of a point pattern and defining whether the observed events occur randomly, in clusters or in a regular pattern. The considered methods are of general importance and can be used for other spatio-temporal events (i.e. crime, epidemiology, biodiversity, geomarketing, etc.). An important contribution of this research deals with the analysis and estimation of local measures of clustering that helps understanding their temporal structure. Each measure is described and executed for the raw data (forest fires geo-database) and results are compared to reference patterns generated under the null hypothesis of randomness (Poisson processes) embedded in the same time period of the raw data. This comparison enables estimating the degree of the deviation of the real data from a Poisson process. Generalizations to functional measures of these clustering methods, taking into account the phenomena, were also applied and adapted to detect time dependences in a measured variable (i.e. burned area). The time clustering of the raw data is compared several times with the Poisson processes at different thresholds of the measured function. Then, the clustering measure value depends on the threshold which helps to understand the time pattern of the studied events. Our findings detected the presence of overdensity of events in particular time periods and showed that the forest fire sequences in Portugal can be considered as a multifractal process with a degree of time-clustering of the events. Key words: time sequences, Morisita index, fractals, multifractals, box-counting, Ripley's K-function, Allan Factor, variography, forest fires, point process. Acknowledgements This work was partly supported by the SNFS Project No. 200021-140658, "Analysis and Modelling of Space-Time Patterns in Complex Regions". References - Kanevski M. (Editor). 2008. Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy. London / Hoboken: iSTE / Wiley. - Telesca L. and Pereira M.G. 2010. Time-clustering investigation of fire temporal fluctuations in Portugal, Nat. Hazards Earth Syst. Sci., vol. 10(4): 661-666. - Vega Orozco C., Tonini M., Conedera M., Kanevski M. (2012) Cluster recognition in spatial-temporal sequences: the case of forest fires, Geoinformatica, vol. 16(4): 653-673.

Resting regional brain metabolism in social anxiety disorder and the effect of moclobemide therapy.

PubMed

Doruyter, Alex; Dupont, Patrick; Taljaard, Lian; Stein, Dan J; Lochner, Christine; Warwick, James M

2018-04-01

While there is mounting evidence of abnormal reactivity of several brain regions in social anxiety disorder, and disrupted functional connectivity between these regions at rest, relatively little is known regarding resting regional neural activity in these structures, or how such activity is affected by pharmacotherapy. Using 2-deoxy-2-(F-18)fluoro-D-glucose positron emission tomography, we compared resting regional brain metabolism between SAD and healthy control groups; and in SAD participants before and after moclobemide therapy. Voxel-based analyses were confined to a predefined search volume. A second, exploratory whole-brain analysis was conducted using a more liberal statistical threshold. Fifteen SAD participants and fifteen matched controls were included in the group comparison. A subgroup of SAD participants (n = 11) was included in the therapy effect comparison. No significant clusters were identified in the primary analysis. In the exploratory analysis, the SAD group exhibited increased metabolism in left fusiform gyrus and right temporal pole. After therapy, SAD participants exhibited reductions in regional metabolism in a medial dorsal prefrontal region and increases in right caudate, right insula and left postcentral gyrus. This study adds to the limited existing work on resting regional brain activity in SAD and the effects of therapy. The negative results of our primary analysis suggest that resting regional activity differences in the disorder, and moclobemide effects on regional metabolism, if present, are small. While the outcomes of our secondary analysis should be interpreted with caution, they may contribute to formulating future hypotheses or in pooled analyses.

Creating a three level building classification using topographic and address-based data for Manchester

NASA Astrophysics Data System (ADS)

Hussain, M.; Chen, D.

2014-11-01

Buildings, the basic unit of an urban landscape, host most of its socio-economic activities and play an important role in the creation of urban land-use patterns. The spatial arrangement of different building types creates varied urban land-use clusters which can provide an insight to understand the relationships between social, economic, and living spaces. The classification of such urban clusters can help in policy-making and resource management. In many countries including the UK no national-level cadastral database containing information on individual building types exists in public domain. In this paper, we present a framework for inferring functional types of buildings based on the analysis of their form (e.g. geometrical properties, such as area and perimeter, layout) and spatial relationship from large topographic and address-based GIS database. Machine learning algorithms along with exploratory spatial analysis techniques are used to create the classification rules. The classification is extended to two further levels based on the functions (use) of buildings derived from address-based data. The developed methodology was applied to the Manchester metropolitan area using the Ordnance Survey's MasterMap®, a large-scale topographic and address-based data available for the UK.

Symptom Clusters Change over Time in Women Receiving Adjuvant Chemotherapy for Breast Cancer

PubMed Central

Albusoul, Randa M.; Berger, Ann M.; Gay, Caryl L.; Janson, Susan L.; Lee, Kathryn A.

2017-01-01

Context Patients with breast cancer receiving chemotherapy (CTX) experience multiple concurrent symptoms, but little is known about how symptoms change during and after treatment. Knowledge of the identity and trajectory of symptom clusters (SCs) would enhance measurement and management. Objectives We aimed to identify SCs and their change over time from baseline to completion of breast cancer CTX. Methods SCs were identified and assessed for change in 219 women from Nebraska at four times: baseline, during cycles #3 and #4 of CTX, and one-month after finishing CTX. Ten symptoms were measured: two using the Hospital Anxiety and Depression Scale and eight using the Symptom Experience Scale. Exploratory factor analysis was conducted at each time point, then changes in SCs were evaluated at different times. Results Two SCs were identified before and after initiating CTX: Gastrointestinal (GI) and Treatment-related (Tr). The number and type of symptoms in each cluster differed over time. Clusters were dynamic during CTX with changes in the number and type of symptoms. Only one Tr SC, which consisted of fatigue, pain, and sleep disturbance, was identified after CTX completion. Conclusion SCs during CTX appear to be dynamic, changing over time from before until after CTX completion. Repeated assessments of SCs reveal symptoms that are present and when patients are most burdened and in need of additional support. PMID:28062343

Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Chapman, G. M. (Principal Investigator); Carnes, J. G.

1981-01-01

Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.

SEURAT: visual analytics for the integrated analysis of microarray data.

PubMed

Gribov, Alexander; Sill, Martin; Lück, Sonja; Rücker, Frank; Döhner, Konstanze; Bullinger, Lars; Benner, Axel; Unwin, Antony

2010-06-03

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.

Functional brain segmentation using inter-subject correlation in fMRI.

PubMed

Kauppi, Jukka-Pekka; Pajula, Juha; Niemi, Jari; Hari, Riitta; Tohka, Jussi

2017-05-01

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Geographic variations of ecosystem service intensity in Fuzhou City, China.

PubMed

Hu, Xisheng; Hong, Wei; Qiu, Rongzu; Hong, Tao; Chen, Can; Wu, Chengzhen

2015-04-15

Ecosystem services are strongly influenced by the landscape configuration of natural and human systems. So they are heterogeneous across landscapes. However lack of the knowledge of spatial variations of ecosystem services constrains the effective management and conservation of ecosystems. We presented a spatially explicit and quantitative assessment of the geographic variations in ecosystem services for the Fuzhou City in 2009 using exploratory spatial data analysis (ESDA) and semivariance analysis. Results confirmed a significant and positive spatial autocorrelation, and revealed several hot-spots and cold-spots for the spatial distribution of ecosystem service intensity (ESI) in the study area. Also the trend surface analysis indicated that the level of ESI tended to be reduced gradually from north to south and from west to east, with a trough in the urban central area, which was quite in accordance with land-use structure. A more precise cluster map was then developed using the range of lag distance, deriving from semivariance analysis, as neighborhood size instead of default value in the software of ESRI ArcGIS 10.0, and geographical clusters where population growth and land-use pressure varied significantly and positively with ESI across the city were also created by geographically weighted regression (GWR). This study has good policy implications applicable to prioritize areas for conservation or construction, and design ecological corridor to improve ecosystem service delivery to benefiting areas. Copyright © 2015 Elsevier B.V. All rights reserved.

Introduction to Vocations. High Tech Focus. Final Report 1984-85.

ERIC Educational Resources Information Center

Wayne Township Schools, NJ.

This report contains the materials that were developed during a project to make middle-grade students more aware of high tech careers through the following activities: (1) teacher and student visitations of community sites to explore high tech careers in 15 occupational clusters; (2) exploratory activities to facilitate linkages and articulation…

CADDIS Volume 4. Data Analysis: Exploratory Data Analysis

EPA Pesticide Factsheets

Intro to exploratory data analysis. Overview of variable distributions, scatter plots, correlation analysis, GIS datasets. Use of conditional probability to examine stressor levels and impairment. Exploring correlations among multiple stressors.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

PubMed Central

Wagner, Florian

2015-01-01

Method Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. Results I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets. PMID:26575370

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

PubMed

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0

DOE PAGES

Huck, Kevin A.; Malony, Allen D.; Shende, Sameer; ...

2008-01-01

The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis ofmore » individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.« less

The characteristics of depressive symptoms in medical students during medical education and training: a cross-sectional study.

PubMed

Baldassin, Sergio; Alves, Tânia Correa de Toledo Ferraz; de Andrade, Arthur Guerra; Nogueira Martins, Luiz Antonio

2008-12-11

Medical education and training can contribute to the development of depressive symptoms that might lead to possible academic and professional consequences. We aimed to investigate the characteristics of depressive symptoms among 481 medical students (79.8% of the total who matriculated). The Beck Depression Inventory (BDI) and cluster analyses were used in order to better describe the characteristics of depressive symptoms. Medical education and training in Brazil is divided into basic (1st and 2nd years), intermediate (3rd and 4th years), and internship (5th and 6th years) periods. The study organized each item from the BDI into the following three clusters: affective, cognitive, and somatic. Statistical analyses were performed using analysis of variance (ANOVA) with post-hoc Tukey corrected for multiple comparisons. There were 184 (38.2%) students with depressive symptoms (BDI > 9). The internship period resulted in the highest BDI scores in comparison to both the basic (p < .001) and intermediate (p < .001) periods. Affective, cognitive, and somatic clusters were significantly higher in the internship period. An exploratory analysis of possible risk factors showed that females (p = .020) not having a parent who practiced medicine (p = .016), and the internship period (p = .001) were factors for the development of depressive symptoms. There is a high prevalence towards depressive symptoms among medical students, particularly females, in the internship level, mainly involving the somatic and affective clusters, and not having a parent who practiced medicine. The active assessment of these students in evaluating their depressive symptoms is important in order to prevent the development of co-morbidities and suicide risk.

The characteristics of depressive symptoms in medical students during medical education and training: a cross-sectional study

PubMed Central

Baldassin, Sergio; Alves, Tânia Correa de Toledo Ferraz; de Andrade, Arthur Guerra; Nogueira Martins, Luiz Antonio

2008-01-01

Background Medical education and training can contribute to the development of depressive symptoms that might lead to possible academic and professional consequences. We aimed to investigate the characteristics of depressive symptoms among 481 medical students (79.8% of the total who matriculated). Methods The Beck Depression Inventory (BDI) and cluster analyses were used in order to better describe the characteristics of depressive symptoms. Medical education and training in Brazil is divided into basic (1st and 2nd years), intermediate (3rd and 4th years), and internship (5th and 6th years) periods. The study organized each item from the BDI into the following three clusters: affective, cognitive, and somatic. Statistical analyses were performed using analysis of variance (ANOVA) with post-hoc Tukey corrected for multiple comparisons. Results There were 184 (38.2%) students with depressive symptoms (BDI > 9). The internship period resulted in the highest BDI scores in comparison to both the basic (p < .001) and intermediate (p < .001) periods. Affective, cognitive, and somatic clusters were significantly higher in the internship period. An exploratory analysis of possible risk factors showed that females (p = .020) not having a parent who practiced medicine (p = .016), and the internship period (p = .001) were factors for the development of depressive symptoms. Conclusion There is a high prevalence towards depressive symptoms among medical students, particularly females, in the internship level, mainly involving the somatic and affective clusters, and not having a parent who practiced medicine. The active assessment of these students in evaluating their depressive symptoms is important in order to prevent the development of co-morbidities and suicide risk. PMID:19077227

Overcoming confounded controls in the analysis of gene expression data from microarray experiments.

PubMed

Bhattacharya, Soumyaroop; Long, Dang; Lyons-Weiler, James

2003-01-01

A potential limitation of data from microarray experiments exists when improper control samples are used. In cancer research, comparisons of tumour expression profiles to those from normal samples is challenging due to tissue heterogeneity (mixed cell populations). A specific example exists in a published colon cancer dataset, in which tissue heterogeneity was reported among the normal samples. In this paper, we show how to overcome or avoid the problem of using normal samples that do not derive from the same tissue of origin as the tumour. We advocate an exploratory unsupervised bootstrap analysis that can reveal unexpected and undesired, but strongly supported, clusters of samples that reflect tissue differences instead of tumour versus normal differences. All of the algorithms used in the analysis, including the maximum difference subset algorithm, unsupervised bootstrap analysis, pooled variance t-test for finding differentially expressed genes and the jackknife to reduce false positives, are incorporated into our online Gene Expression Data Analyzer ( http:// bioinformatics.upmc.edu/GE2/GEDA.html ).

Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms

NASA Technical Reports Server (NTRS)

Storrie-Lombardi, Michael C.; Hoover, Richard B.

2005-01-01

Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures

Fighting Dark Networks: Using Social Network Analysis to Implement the Special Operations Targeting Process for Direct and Indirect Approaches

DTIC Science & Technology

2013-03-01

Wouter De Nooy, Andrej Mrvar and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, (New York: Cambridge University Press, 2005), 5...Granovetter, “The Strength of Weak Ties,” 1350–1368. 151 de Nooy, Mrvar , and Batagelj , Exploratory Social Network Analysis with Pajek, 151. 152...Spacetime Wrinkles Exhibit (1995). de Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek. Cambridge

A Taxonomy of Accountable Care Organizations for Policy and Practice

PubMed Central

Shortell, Stephen M; Wu, Frances M; Lewis, Valerie A; Colla, Carrie H; Fisher, Elliott S

2014-01-01

Objective To develop an exploratory taxonomy of Accountable Care Organizations (ACOs) to describe and understand early ACO development and to provide a basis for technical assistance and future evaluation of performance. Data Sources/Study Setting Data from the National Survey of Accountable Care Organizations, fielded between October 2012 and May 2013, of 173 Medicare, Medicaid, and commercial payer ACOs. Study Design Drawing on resource dependence and institutional theory, we develop measures of eight attributes of ACOs such as size, scope of services offered, and the use of performance accountability mechanisms. Data are analyzed using a two-step cluster analysis approach that accounts for both continuous and categorical data. Principal Findings We identified a reliable and internally valid three-cluster solution: larger, integrated systems that offer a broad scope of services and frequently include one or more postacute facilities; smaller, physician-led practices, centered in primary care, and that possess a relatively high degree of physician performance management; and moderately sized, joint hospital–physician and coalition-led groups that offer a moderately broad scope of services with some involvement of postacute facilities. Conclusions ACOs can be characterized into three distinct clusters. The taxonomy provides a framework for assessing performance, for targeting technical assistance, and for diagnosing potential antitrust violations. PMID:25251146

Characterizing the Pain Narratives of Parents of Youth With Chronic Pain.

PubMed

Noel, Melanie; Beals-Erickson, Sarah E; Law, Emily F; Alberts, Nicole M; Palermo, Tonya M

2016-10-01

Questionnaire-based research has shown that parents exert a powerful influence on and are profoundly influenced by living with a child with chronic pain. Examination of parents' pain narratives through an observational lens offers an alternative approach to understanding the complexity of pediatric chronic pain; however, the narratives of parents of youth with chronic pain have been largely overlooked. The present study aimed to characterize the vulnerability-based and resilience-based aspects of the pain narratives of parents of youth with chronic pain. Pain narratives of 46 parents were recorded during the baseline session as part of 2 clinical trials evaluating a behavioral intervention for parents of youth with chronic pain. The narratives were coded for aspects of pain-related vulnerability and resilience. Using exploratory cluster analysis, 2 styles of parents' pain narratives were identified. Distress narratives were characterized by more negative affect and an exclusively unresolved orientation toward the child's diagnosis of chronic pain, whereas resilience narratives were characterized by positive affect and a predominantly resolved orientation toward the child's diagnosis. Preliminary support for the validity of these clusters was provided through our finding of differences between clusters in parental pain catastrophizing about child pain (helplessness). Findings highlight the multidimensional nature of parents' experience of their child's pain problem. Clinical implications in terms of assessment and treatment are discussed.

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

Racah, Evan; Ko, Seyoon; Sadowski, Peter

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networksmore » can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.« less

Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel

PubMed Central

Sage, Emma; Velez, Martin; Guinard, Jean‐Xavier

2016-01-01

Abstract The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy‐two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry. PMID:27861864

Cluster-transfer reactions with radioactive beams: A spectroscopic tool for neutron-rich nuclei

DOE PAGES

Bottoni, S.; Leoni, S.; Fornal, B.; ...

2015-08-27

An exploratory experiment performed at REX-ISOLDE to investigate cluster-transfer reactions with radioactive beams in inverse kinematics is presented. The aim of the experiment was to test the potential of cluster-transfer reactions at the Coulomb barrier as a mechanism to explore the structure of exotic neutron-rich nuclei. The reactions 7Li( 98Rb,αxn) and 7Li( 98Rb,txn) were studied through particle-γ coincidence measurements, and the results are presented in terms of the observed excitation energies and spins. Moreover, the reaction mechanism is qualitatively discussed as a transfer of a clusterlike particle within a distorted-wave Born approximation framework. The results indicate that cluster-transfer reactions canmore » be described well as a direct process and that they can be an efficient method to investigate the structure of neutron-rich nuclei at medium-high excitation energies and spins.« less

Prevalence of the Catatonic Syndrome in an Acute Inpatient Sample

PubMed Central

Stuivenga, Mirella; Morrens, Manuel

2014-01-01

Objective: In this exploratory open label study, we investigated the prevalence of catatonia in an acute psychiatric inpatient population. In addition, differences in symptom presentation of catatonia depending on the underlying psychiatric illness were investigated. Methods: One hundred thirty patients were assessed with the Bush–Francis Catatonia Rating Scale (BFCRS), the Positive and Negative Syndrome Scale, the Young Mania Rating Scale, and the Simpson–Angus Scale. A factor analysis was conducted in order to generate six catatonic symptom clusters. Composite scores based on this principal component analysis were calculated. Results: When focusing on the first 14 items of the BFCRS, 101 patients (77.7%) had at least 1 symptom scoring 1 or higher, whereas, 66 patients (50.8%) had at least 2 symptoms. Interestingly, when focusing on the DSM-5 criteria of catatonia, 22 patients (16.9%) could be considered for this diagnosis. Furthermore, different symptom profiles were found, depending on the underlying psychopathology. Psychotic symptomatology correlated strongly with excitement symptomatology (r = 0.528, p < 0.001) and to a lesser degree with the stereotypy/mannerisms symptom cluster (r = 0.289; p = 0.001) and the echo/perseveration symptom cluster (r = 0.185; p = 0.035). Similarly, manic symptomatology correlated strongly with the excitement symptom cluster (r = 0.596; p < 0.001) and to a lesser extent with the stereotypy/mannerisms symptom cluster (r = 0.277; p = 0.001). Conclusion: There was a high prevalence of catatonic symptomatology. Depending on the criteria being used, we noticed an important difference in exact prevalence, which makes it clear that we need clear-cut criteria. Another important finding is the fact that the catatonic presentation may vary depending on the underlying pathology, although an unambiguous delineation between these catatonic presentations cannot be made. Future research is needed to determine diagnostical criteria of catatonia, which are clinically relevant. PMID:25520674

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.

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

Hernandez, Y.; Carriazo, J.G.; Almanza, O.

Materials from a mining process, in which ferronickel metal extraction is the principal aim, were studied. The residual solid (scum) obtained in this process leads to large-scale accumulation of a vitreous material (pollutant) which creates an environmental problem. These materials were characterized by EPR, X-ray diffraction and X-ray fluorescence. The results indicate that the analyzed solids are rich in Fe{sub 2}O{sub 3} and NiO among other oxides. The scum material shows diffraction signals corresponding to the minerals enstatite (pyroxene) and {alpha}-alumina. Moreover, the scum EPR analysis showed a broad line around g = 2.1 corresponding to Fe{sup 3+} clusters inmore » a complex glassy matrix. An analysis of EPR at different temperatures was also performed. The objective of this work, as a first exploratory stage, is to develop a better understanding of the residual solids in order to identify potential applications.« less

Eating patterns in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): an exploratory analysis.

PubMed

Cardoso, Letícia de Oliveira; Carvalho, Marilia Sá; Cruz, Oswaldo Gonçalves; Melere, Cristiane; Luft, Vivian Cristine; Molina, Maria Del Carmen Bisi; Faria, Carolina Perim de; Benseñor, Isabela M; Matos, Sheila Maria Alvim; Fonseca, Maria de Jesus Mendes da; Griep, Rosane Harter; Chor, Dóra

2016-01-01

The food consumption of 15,071 public employees was analyzed in six Brazilian cities participating in the baseline for Brazilian Longitudinal Study of Adult Health (ELSA-Brasil, 2008-2010) with the aim of identifying eating patterns and their relationship to socio-demographic variables. Multiple correspondence and cluster analysis were applied. Four patterns were identified, with their respective frequencies: "traditional" (48%); "fruits and vegetables" (25%); "pastry shop" (24%); and "diet/light" (5%) The "traditional" and "pastry shop" patterns were more frequent among men, younger individuals, and those with less schooling. "Fruits and vegetables" and "diet/light" were more frequent in women, older individuals, and those with more schooling. Our findings show the inclusion of new items in the "traditional" pattern and the appearance of the "low sugar/low fat" pattern among the eating habits of Brazilian workers, and signal socio-demographic and regional differences.

Using Gaussian windows to explore a multivariate data set

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1991-01-01

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

Subtypes in bulimia nervosa: the role of eating disorder symptomatology, negative affect, and interpersonal functioning.

PubMed

Lunn, Susanne; Poulsen, Stig; Daniel, Sarah I F

2012-11-01

The aim of the study was to investigate whether patients with bulimia nervosa (BN) could be subdivided into clinically meaningful groups reflecting the complex patterns of eating disorder symptoms and personality characteristics that face the clinician. Seventy patients diagnosed with BN using the Eating Disorder Examination were assessed with measures of negative affect, attachment patterns, and interpersonal problems. An exploratory hierarchical cluster analysis was performed. The study found two main subtypes differing primarily in terms of symptom severity and level of negative affect, but these subtypes were further subdivided into four clinically relevant subtypes: A dietary restraint/negative affect/high symptomatic group, an emotionally overcontrolled group, a low dietary restraint/emotionally underregulated group, and a high functioning/securely attached group. The study indicates that cluster-analytic studies, including a broad range of instruments measuring eating disorder symptoms as well as negative affect, relational patterns, and other personality characteristics, may contribute to an integration of previously suggested models of subtypes in BN. Copyright © 2012 Elsevier Inc. All rights reserved.

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms

PubMed Central

Alam, Zaid; Peddinti, Gopal

2017-01-01

Abstract The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds’ multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/. PMID:28472495

Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis

PubMed Central

Aina, Yusuf A.; van der Merwe, Johannes H.; Alshuwaikhat, Habib M.

2014-01-01

The effects of concentrations of fine particulate matter on urban populations have been gaining attention because fine particulate matter exposes the urban populace to health risks such as respiratory and cardiovascular diseases. Satellite-derived data, using aerosol optical depth (AOD), have been adopted to improve the monitoring of fine particulate matter. One of such data sources is the global multi-year PM2.5 data (2001–2010) released by the Center for International Earth Science Information Network (CIESIN). This paper explores the satellite-derived PM2.5 data of Saudi Arabia to highlight the trend of PM2.5 concentrations. It also examines the changes in PM2.5 concentrations in some urbanized areas of Saudi Arabia. Concentrations in major cities like Riyadh, Dammam, Jeddah, Makkah, Madinah and the industrial cities of Yanbu and Jubail are analyzed using cluster analysis. The health risks due to exposure of the populace are highlighted by using the World Health Organization (WHO) standard and targets. The results show a trend of increasing concentrations of PM2.5 in urban areas. Significant clusters of high values are found in the eastern and south-western part of the country. There is a need to explore this topic using images with higher spatial resolution and validate the data with ground observations to improve the analysis. PMID:25350009

Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

PubMed Central

2012-01-01

Background An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context. Methods Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System. Results Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics. Conclusions The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches. PMID:22512892

Neural activity in relation to empirically derived personality syndromes in depression using a psychodynamic fMRI paradigm

PubMed Central

Taubner, Svenja; Wiswede, Daniel; Kessler, Henrik

2013-01-01

Objective: The heterogeneity between patients with depression cannot be captured adequately with existing descriptive systems of diagnosis and neurobiological models of depression. Furthermore, considering the highly individual nature of depression, the application of general stimuli in past research efforts may not capture the essence of the disorder. This study aims to identify subtypes of depression by using empirically derived personality syndromes, and to explore neural correlates of the derived personality syndromes. Materials and Methods: In the present exploratory study, an individually tailored and psychodynamically based functional magnetic resonance imaging paradigm using dysfunctional relationship patterns was presented to 20 chronically depressed patients. Results from the Shedler–Westen Assessment Procedure (SWAP-200) were analyzed by Q-factor analysis to identify clinically relevant subgroups of depression and related brain activation. Results: The principle component analysis of SWAP-200 items from all 20 patients lead to a two-factor solution: “Depressive Personality” and “Emotional-Hostile-Externalizing Personality.” Both factors were used in a whole-brain correlational analysis but only the second factor yielded significant positive correlations in four regions: a large cluster in the right orbitofrontal cortex (OFC), the left ventral striatum, a small cluster in the left temporal pole, and another small cluster in the right middle frontal gyrus. Discussion: The degree to which patients with depression score high on the factor “Emotional-Hostile-Externalizing Personality” correlated with relatively higher activity in three key areas involved in emotion processing, evaluation of reward/punishment, negative cognitions, depressive pathology, and social knowledge (OFC, ventral striatum, temporal pole). Results may contribute to an alternative description of neural correlates of depression showing differential brain activation dependent on the extent of specific personality syndromes in depression. PMID:24363644

Parent-reported social support for child's fruit and vegetable intake: validity of measures.

PubMed

Dave, Jayna M; Evans, Alexandra E; Condrasky, Marge D; Williams, Joel E

2012-01-01

To develop and validate measures of parental social support to increase their child's fruit and vegetable (FV) consumption. Cross-sectional study design. School and home. Two hundred three parents with at least 1 elementary school-aged child. Parents completed a questionnaire that included instrumental social support scale (ISSPS), emotional social support scale (ESSPS), household FV availability and accessibility index, and demographics. Exploratory factor analysis with promax rotation was conducted to obtain the psychometric properties of ISSPS and ESSPS. Internal consistency and test-retest reliabilities were also assessed. Factor analysis indicated a 4-factor model for ESSPS: positive encouragement, negative role modeling, discouragement, and an item cluster called reinforcement. Psychometric properties indicated that ISSPS performed best as independent single scales with α = .87. Internal consistency reliabilities were acceptable, and test-retest reliabilities ranged from low to acceptable. Correlations between scales, subscales, and item clusters were significant (P < .05). In addition, ISSPS and the positive encouragement subscale were significantly correlated with household FV availability. The ISSPS and ESSPS subscales demonstrated good internal consistency reliability and are suitable for impact assessment of an intervention designed to target parents to help their children eat more fruit and vegetables. Copyright © 2012 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

The mobility of food retailers: How proximity to SNAP authorized food retailers changed in Atlanta during the Great Recession.

PubMed

Shannon, Jerry; Bagwell-Adams, Grace; Shannon, Sarah; Lee, Jung Sun; Wei, Yangjiaxin

2018-07-01

Retailer mobility, defined as the shifting geographic patterns of retail locations over time, is a significant but understudied factor shaping neighborhood food environments. Our research addresses this gap by analyzing changes in proximity to SNAP authorized chain retailers in the Atlanta urban area using yearly data from 2008 to 2013. We identify six demographically similar geographic clusters of census tracts in our study area based on race and economic variables. We use these clusters in exploratory data analysis to identify how proximity to the twenty largest retail food chains changed during this period. We then use fixed effects models to assess how changing store proximity is associated with race, income, participation in SNAP, and population density. Our results show clear differences in geographic distribution between store categories, but also notable variation within each category. Increasing SNAP enrollment predicted decreased distances to almost all small retailers but increased distances to many large retailers. Our chain-focused analysis underscores the responsiveness of small retailers to changes in neighborhood SNAP participation and the value of tracking chain expansion and contraction in markets across time. Better understanding of retailer mobility and the forces that drive it can be a productive avenue for future research. Copyright © 2018 Elsevier Ltd. All rights reserved.

CANTAB object recognition and language tests to detect aging cognitive decline: an exploratory comparative study

PubMed Central

Cabral Soares, Fernanda; de Oliveira, Thaís Cristina Galdino; de Macedo, Liliane Dias e Dias; Tomás, Alessandra Mendonça; Picanço-Diniz, Domingos Luiz Wanderley; Bento-Torres, João; Bento-Torres, Natáli Valim Oliver; Picanço-Diniz, Cristovam Wanderley

2015-01-01

Objective The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects. Method We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group. Results Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects. Conclusion Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging. PMID:25565785

Hierarchical clusters in families with type 2 diabetes.

PubMed

García-Solano, Beatriz; Gallegos-Cabriales, Esther C; Gómez-Meza, Marco V; García-Madrid, Guillermina; Flores-Merlo, Marcela; García-Solano, Mauro

2015-01-01

Families represent more than a set of individuals; family is more than a sum of its individual members. With this classification, nurses can identify the family health-illness beliefs obey family as a unit concept, and plan family inclusion into the type 2 diabetes treatment, whom is not considered in public policy, despite families share diet, exercise, and self-monitoring with a member who suffers type 2 diabetes. The aim of this study was to determine whether the characteristics, functionality, routines, and family and individual health in type 2 diabetes describes the differences and similarities between families to consider them as a unit. We performed an exploratory, descriptive hierarchical cluster analysis of 61 families using three instruments and a questionnaire, in addition to weight, height, body fat percentage, hemoglobin A1c, total cholesterol, triglycerides, low-density lipoprotein and high-density lipoprotein. The analysis produced three groups of families. Wilk's lambda demonstrated statistically significant differences provided by age (Λ = 0.778, F = 2.098, p = 0.010) and family health (Λ = 0.813, F = 2.650, p = 0.023). A post hoc Tukey test coincided with the three subsets. Families with type 2 diabetes have common elements that make them similar, while sharing differences that make them unique.

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

DTIC Science & Technology

1999-12-01

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

Chemical Structure and Molecular Dimension As Controls on the Inherent Stability of Charcoal in Boreal Forest Soil

NASA Astrophysics Data System (ADS)

Hockaday, W. C.; Kane, E. S.; Ohlson, M.; Huang, R.; Von Bargen, J.; Davis, R.

2014-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

Microbial facies distribution and its geological and geochemical controls at the Hanford 300 area

NASA Astrophysics Data System (ADS)

Hou, Z.; Nelson, W.; Stegen, J.; Murray, C. J.; Arntzen, E.

2015-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

Built environment and Property Crime in Seattle, 1998-2000: A Bayesian Analysis.

PubMed

Matthews, Stephen A; Yang, Tse-Chuan; Hayslett-McCall, Karen L; Ruback, R Barry

2010-06-01

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998-2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.

Built environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis

PubMed Central

Matthews, Stephen A.; Yang, Tse-chuan; Hayslett-McCall, Karen L.; Ruback, R. Barry

2014-01-01

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998–2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary. PMID:24737924

SEURAT: Visual analytics for the integrated analysis of microarray data

PubMed Central

2010-01-01

Background In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. Results We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. Conclusions The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data. PMID:20525257

The Social Networks of Small Arms Proliferation: Mapping an Aviation Enabled Supply Chain

DTIC Science & Technology

2007-12-01

each of the discrete arms 248 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social...303 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 304 Ibid., 21. 93 entity. The data...305 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 306 Linton C. Freeman, "Graphical

Exploratory Analysis of Supply Chains in the Defense Industrial Base

DTIC Science & Technology

2012-04-01

Instruments Industry Group 382: Laboratory Apparatus and Analytical, Optical, Measuring, and Controlling Instruments 3821 Laboratory Apparatus and Furniture ...I N S T I T U T E F O R D E F E N S E A N A LY S E S Exploratory Analysis of Supply Chains in the Defense Industrial Base James R. Dominy...contract DASW01-04-C-0003, AH-7-3315, “Exploratory Analysis of Supply Chains in the Defense Industrial Base,” for the Director, Industrial Policy. The

Does a single session of electroconvulsive therapy alter the neural response to emotional faces in depression? A randomised sham-controlled functional magnetic resonance imaging study.

PubMed

Miskowiak, Kamilla W; Kessing, Lars V; Ott, Caroline V; Macoveanu, Julian; Harmer, Catherine J; Jørgensen, Anders; Revsbech, Rasmus; Jensen, Hans M; Paulson, Olaf B; Siebner, Hartwig R; Jørgensen, Martin B

2017-09-01

Negative neurocognitive bias is a core feature of major depressive disorder that is reversed by pharmacological and psychological treatments. This double-blind functional magnetic resonance imaging study investigated for the first time whether electroconvulsive therapy modulates negative neurocognitive bias in major depressive disorder. Patients with major depressive disorder were randomised to one active ( n=15) or sham electroconvulsive therapy ( n=12). The following day they underwent whole-brain functional magnetic resonance imaging at 3T while viewing emotional faces and performed facial expression recognition and dot-probe tasks. A single electroconvulsive therapy session had no effect on amygdala response to emotional faces. Whole-brain analysis revealed no effects of electroconvulsive therapy versus sham therapy after family-wise error correction at the cluster level, using a cluster-forming threshold of Z>3.1 ( p<0.001) to secure family-wise error <5%. Groups showed no differences in behavioural measures, mood and medication. Exploratory cluster-corrected whole-brain analysis ( Z>2.3; p<0.01) revealed electroconvulsive therapy-induced changes in parahippocampal and superior frontal responses to fearful versus happy faces as well as in fear-specific functional connectivity between amygdala and occipito-temporal regions. Across all patients, greater fear-specific amygdala - occipital coupling correlated with lower fear vigilance. Despite no statistically significant shift in neural response to faces after a single electroconvulsive therapy session, the observed trend changes after a single electroconvulsive therapy session point to an early shift in emotional processing that may contribute to antidepressant effects of electroconvulsive therapy.

Rayleigh Scattering Density Measurements, Cluster Theory, and Nucleation Calculations at Mach 10

NASA Technical Reports Server (NTRS)

Balla, R. Jeffrey; Everhart, Joel L.

2012-01-01

In an exploratory investigation, quantitative unclustered laser Rayleigh scattering measurements of density were performed in the air in the NASA Langley Research Center's 31 in. Mach 10 wind tunnel. A review of 20 previous years of data in supersonic and Mach 6 hypersonic flows is presented where clustered signals typically overwhelmed molecular signals. A review of nucleation theory and accompanying nucleation calculations are also provided to interpret the current observed lack of clustering. Data were acquired at a fixed stagnation temperature near 990Kat five stagnation pressures spanning 2.41 to 10.0 MPa (350 to 1454 psi) using a pulsed argon fluoride excimer laser and double-intensified charge-coupled device camera. Data averaged over 371 images and 210 pixels along a 36.7mmline measured freestream densities that agree with computed isentropic-expansion densities to less than 2% and less than 6% at the highest and lowest densities, respectively. Cluster-free Mach 10 results are compared with previous clustered Mach 6 and condensation-free Mach 14 results. Evidence is presented indicating vibrationally excited oxygen and nitrogen molecules are absorbed as the clusters form, release their excess energy, and inhibit or possibly reverse the clustering process. Implications for delaying clustering and condensation onset in hypersonic and hypervelocity facilities are discussed.

Posttraumatic idioms of distress among Darfur refugees: Hozun and Majnun.

PubMed

Rasmussen, Andrew; Katoni, Basila; Keller, Allen S; Wilkinson, John

2011-09-01

Although psychosocial programming is seen as essential to the humanitarian response to the Darfur conflict, aid groups lack culturally-appropriate assessment instruments for monitoring and evaluation. The current study used an emic-etic integrated approach to: (i) create a culturally-appropriate measure of distress (Study 1), and (ii) test the measure in structured interviews of 848 Darfuris living in two refugee camps in Chad (Study 2). Traditional healers identified two trauma-related idioms, hozun and majnun, which shared features with but were not identical to posttraumatic stress disorder and depression. Measures of these constructs were reliable and correlated with trauma, loss, and functional impairment. Exploratory factor analysis resulted in empirical symptom clusters conceptually parallel to general Western psychiatric constructs. Findings are discussed in terms of their implications for psychosocial programming.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Exploratory Analysis in Learning Analytics

ERIC Educational Resources Information Center

Gibson, David; de Freitas, Sara

2016-01-01

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex…

Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

PubMed Central

Stone, Eric A.; Ayroles, Julien F.

2009-01-01

In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation. PMID:19424432

Supporting Dynamic Quantization for High-Dimensional Data Analytics.

PubMed

Guzun, Gheorghi; Canahuate, Guadalupe

2017-05-01

Similarity searches are at the heart of exploratory data analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimensionality of the data increases and the number of features is large, traditional distance metrics fail to distinguish between the closest and furthest data points. Localized distance functions have been proposed as an alternative to traditional distance metrics. These functions only consider dimensions close to query to compute the distance/similarity. Furthermore, in order to enable interactive explorations of high-dimensional data, indexing support for ad-hoc queries is needed. In this work we set up to investigate whether bit-sliced indices can be used for exploratory analytics such as similarity searches and data clustering for high-dimensional big-data. We also propose a novel dynamic quantization called Query dependent Equi-Depth (QED) quantization and show its effectiveness on characterizing high-dimensional similarity. When applying QED we observe improvements in kNN classification accuracy over traditional distance functions. Gheorghi Guzun and Guadalupe Canahuate. 2017. Supporting Dynamic Quantization for High-Dimensional Data Analytics. In Proceedings of Ex-ploreDB'17, Chicago, IL, USA, May 14-19, 2017, 6 pages. https://doi.org/http://dx.doi.org/10.1145/3077331.3077336.

An exploratory survey of eating behaviour patterns in adolescent students.

PubMed

Arata, A; Battini, V; Chiorri, C; Masini, B

2010-12-01

Empirical research has always treated adolescents' eating habits from a variable-centered perspective, but this approach may miss the configurations of eating behaviours that uniquely describe discrete groups of individuals. The aim of this study was to investigate prototypical patterns of eating habits in a large sample of Italian adolescents and their behavioural and psychological correlates. Data were gathered from 1388 students (F=60%, mean age 14.90±1.34 yrs), who were asked to fill in an original questionnaire surveying dietary habits, body weight attitudes, body image, sport activities and sources of information about food. Perfectionism, self-esteem, self-efficacy and care for food were also assessed as well-known psychological risk factors for Eating Disorders. Five prototypical eating behaviour patterns were identified through cluster analysis. Cluster membership was associated (p<0.05) with gender, age and age- and gender-correct BMI percentile, perceived relevance of physical appearance in achieving success in life; one's weight and body image evaluation, dieting, physical activity, self-efficacy, self-esteem and care for food. Clusters did not differ in perfectionism score and in frequency of consulting different sources of information about food and weight, except in the case of dieticians. The identification of prototypical eating habits patterns revealed a large range of wrong eating attitudes and behaviours among Italian adolescents. Such data suggest the need to develop and implement adequate prevention programs.

Characterizing the Pain Narratives of Parents of Youth with Chronic Pain

PubMed Central

Noel, Melanie; Beals-Erickson, Sarah E.; Law, Emily F.; Alberts, Nicole; Palermo, Tonya M.

2015-01-01

Objectives Questionnaire-based research has shown that parents exert a powerful influence on and are profoundly influenced by living with a child with chronic pain. Examination of parents' pain narratives through an observational lens offers an alternative approach to understanding the complexity of pediatric chronic pain; however, the narratives of parents of youth with chronic pain have been largely overlooked. The present study aimed to characterize the vulnerability- and resilience-based aspects of the pain narratives of parents of youth with chronic pain. Methods Pain narratives of 46 parents were recorded during the baseline session as part of two clinical trials evaluating a behavioral intervention for parents of youth with chronic pain. The narratives were coded for aspects of pain-related vulnerability and resilience. Results Using exploratory cluster analysis, two styles of parents’ pain narratives were identified. Distress narratives were characterized by more negative affect and an exclusively unresolved orientation towards the child’s diagnosis of chronic pain whereas resilience narratives were characterized by positive affect and a predominantly resolved orientation towards the child’s diagnosis. Preliminary support for the validity of these clusters was provided through our finding of differences between clusters in parental pain catastrophizing about child pain (helplessness). Discussion Findings highlight the multidimensional nature of parents’ experience of their child’s pain problem. Clinical implications in terms of assessment and treatment are discussed. PMID:26736026

Measuring the contribution of art therapy in multidisciplinary treatment of personality disorders: The construction of the Self‐expression and Emotion Regulation in Art Therapy Scale (SERATS)

PubMed Central

van Hooren, Susan; van der Veld, William M.; Hutschemaekers, Giel

2017-01-01

Abstract Despite the use of art therapy in clinical practice, its appreciation and reported beneficial results, no instruments are available to measure specific effects of art therapy among patients with personality disorders cluster B/C in multidisciplinary treatment. In the present study, we described the development and psychometric evaluation of the Self‐expression and Emotion Regulation in Art Therapy Scale (SERATS). Structural validity (exploratory and confirmatory factor analysis), reliability, construct validity and sensitivity to change were examined using two independent databases (n = 335; n = 34) of patients diagnosed with personality disorders cluster B/C. This resulted in a nine‐item effect scale with a single factor with a high internal reliability and high test–retest reliability; it demonstrated discriminant validity and sensitivity to change. In conclusion, the SERATS is brief and content‐valid and offers objective and reliable information on self‐expression and emotion regulation in art therapy among patients with personality disorders cluster B/C. Although more research on construct validity is needed, the SERATS is a promising tool to be applied as an effect scale and as a monitoring tool during art therapy treatment. © 2017 The Authors Personality and Mental Health Published by John Wiley & Sons Ltd PMID:28730717

The effect of motivational interviewing on oral healthcare knowledge, attitudes and behaviour of parents and caregivers of preschool children: an exploratory cluster randomised controlled study.

PubMed

Naidu, Rahul; Nunn, June; Irwin, Jennifer D

2015-09-02

Motivational Interviewing (MI) has been used across primary healthcare and been shown to be effective in reducing the prevalence of early childhood caries (ECC) in preschool children. This study aimed to compare the effect of MI, in contrast to traditional dental health education (DHE), on oral health knowledge, attitudes, beliefs and behaviours among parents and caregivers of preschool children in Trinidad. The design of this exploratory study included a cluster randomised controlled trial and semi-structured focus groups. Six preschools (79 parents and caregivers) in Eastern Trinidad were randomly assigned to a test or control group (3 preschools in each group). Parents and caregivers in the test-group (n = 25) received a talk on dental health using an MI approach and the control-group (n = 54) received a talk using traditional DHE. Both groups received additional, written dental health information. The MI group also received two telephone call follow-ups as part of the MI protocol. Both groups were given questionnaires before the talks and four months later. Question items included oral health knowledge, beliefs, attitudes, brushing behaviour, oral health self-efficacy, oral health fatalism and a specific instrument to asses 'readiness for change', the Readiness Assessment of Parents Concerning Infant Dental Decay (RAPIDD). Participants in the test-group were also invited to take part in a focus group to share their views on the dental health talk. At four month follow-up, knowledge items on fluoride use, tooth brushing, dietary practice and dental attendance increased in both the test (DHE + MI) and control (DHE) groups ((p < 0.05, Chi Square test). In the test-group there were increases in mean child tooth brushing frequency and reduction in oral health fatalism (p < 0.05 t-test). Findings from a thematic analysis of the focus group suggested that the MI talk and telephone follow-up were well accepted and helpful in supporting parent and caregiver efforts to improve oral health practices for their preschool children. In this exploratory controlled study there was some evidence that using an MI approach when delivering oral health information had a positive effect on parent/ caregiver oral health knowledge, attitudes and behaviours compared to traditional DHE. There is need for further research involving the use of brief-counselling techniques in this Caribbean population.

Clustering of Multiple Risk Behaviors Among a Sample of 18-Year-Old Australians and Associations With Mental Health Outcomes: A Latent Class Analysis.

PubMed

Champion, Katrina E; Mather, Marius; Spring, Bonnie; Kay-Lambkin, Frances; Teesson, Maree; Newton, Nicola C

2018-01-01

Risk behaviors commonly co-occur, typically emerge in adolescence, and become entrenched by adulthood. This study investigated the clustering of established (physical inactivity, diet, smoking, and alcohol use) and emerging (sedentary behavior and sleep) chronic disease risk factors among young Australian adults, and examined how clusters relate to mental health. The sample was derived from the long-term follow-up of a cohort of Australians. Participants were initially recruited at school as part of a cluster randomized controlled trial. A total of 853 participants (M age  = 18.88 years, SD = 0.42) completed an online self-report survey as part of the 5-year follow-up for the RCT. The survey assessed six behaviors (binge drinking and smoking in the past 6 months, moderate-to-vigorous physical activity/week, sitting time/day, fruit and vegetable intake/day, and sleep duration/night). Each behavior was represented by a dichotomous variable reflecting adherence to national guidelines. Exploratory analyses were conducted. Clusters were identified using latent class analysis. Three classes emerged: "moderate risk" (moderately likely to binge drink and not eat enough fruit, high probability of insufficient vegetable intake; Class 1, 52%); "inactive, non-smokers" (high probabilities of not meeting guidelines for physical activity, sitting time and fruit/vegetable consumption, very low probability of smoking; Class 2, 24%), and "smokers and binge drinkers" (high rates of smoking and binge drinking, poor fruit/vegetable intake; Class 3, 24%). There were significant differences between the classes in terms of psychological distress ( p  = 0.003), depression ( p  < 0.001), and anxiety ( p  = 0.003). Specifically, Class 3 ("smokers and binge drinkers") showed higher levels of distress, depression, and anxiety than Class 1 ("moderate risk"), while Class 2 ("inactive, non-smokers") had greater depression than the "moderate risk" group. Results indicate that risk behaviors are prevalent and clustered in 18-year old Australians. Mental health symptoms were significantly greater among the two classes that were characterized by high probabilities of engaging in multiple risk behaviors (Classes 2 and 3). An examination of the clustering of lifestyle risk behaviors is important to guide the development of preventive interventions. Our findings reinforce the importance of delivering multiple health interventions to reduce disease risk and improve mental well-being.

Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel.

PubMed

Spencer, Molly; Sage, Emma; Velez, Martin; Guinard, Jean-Xavier

2016-12-01

The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy-two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry. © 2016 The Authors. Journal of Food Science published by Wiley Periodicals, Inc. on behalf of Institute of Food Technologists.

The Infinitesimal Jackknife with Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Jennrich, Robert I.

2012-01-01

The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both…

Transcultural adaption and validation of the Spanish version of the Bipolar Depression Rating Scale (BDRS-S).

PubMed

Sarró, Salvador; Madre, Mercè; Fernández-Corcuera, Paloma; Valentí, Marc; Goikolea, José M; Pomarol-Clotet, Edith; Berk, Michael; Amann, Benedikt L

2015-02-01

The Bipolar Depression Rating Scale (BDRS) arguably better captures symptoms in bipolar depression especially depressive mixed states than traditional unipolar depression rating scales. The psychometric properties of the Spanish adapted version, BDRS-S, are reported. The BDRS was translated into Spanish by two independent psychiatrists fluent in English and Spanish. After its back-translation into English, the BDRS-S was administered to 69 DSMI-IV bipolar I and II patients who were recruited from two Spanish psychiatric hospitals. The Hamilton Depression Rating Scale (HDRS), the Montgomery-Asberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS) were concurrently administered. 42 patients were reviewed via video by four psychiatrists blind to the psychopathological status of those patients. In order to assess the BDRS-S intra-rater or test-retest validity, 22 subjects were assessed by the same investigator performing two evaluations within five days. The BDRS-S had a good internal consistency (Cronbach׳s α=0.870). We observed strong correlations between the BDRS-S and the HDRS (r=0.874) and MADRS (r=0.854) and also between the mixed symptom cluster score of the BDRS-S and the YMRS (r=0.803). Exploratory factor analysis revealed a three factor solution: psychological depressive symptoms cluster, somatic depressive symptoms cluster and mixed symptoms cluster. A relatively small sample size for a 20-item scale. The BDRS-S provides solid psychometric performance and in particular captures depressive or mixed symptoms in Spanish bipolar patients. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-02-01

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

Characterization of edible seaweed harvested on the Galician coast (northwestern Spain) using pattern recognition techniques and major and trace element data.

PubMed

Romarís-Hortas, Vanessa; García-Sartal, Cristina; Barciela-Alonso, María Carmen; Moreda-Piñeiro, Antonio; Bermejo-Barrera, Pilar

2010-02-10

Major and trace elements in North Atlantic seaweed originating from Galicia (northwestern Spain) were determined by using inductively coupled plasma-optical emission spectrometry (ICP-OES) (Ba, Ca, Cu, K, Mg, Mn, Na, Sr, and Zn), inductively coupled plasma-mass spectrometry (ICP-MS) (Br and I) and hydride generation-atomic fluorescence spectrometry (HG-AFS) (As). Pattern recognition techniques were then used to classify the edible seaweed according to their type (red, brown, and green seaweed) and also their variety (Wakame, Fucus, Sea Spaghetti, Kombu, Dulse, Nori, and Sea Lettuce). Principal component analysis (PCA) and cluster analysis (CA) were used as exploratory techniques, and linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) were used as classification procedures. In total, t12 elements were determined in a range of 35 edible seaweed samples (20 brown seaweed, 10 red seaweed, 4 green seaweed, and 1 canned seaweed). Natural groupings of the samples (brown, red, and green types) were observed using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave correct assignation percentages of 100% for brown, red, and green types at a significance level of 5%. However, a satisfactory classification (recognition and prediction) using SIMCA was obtained only for red seaweed (100% of cases correctly classified), whereas percentages of 89 and 80% were obtained for brown seaweed for recognition (training set) and prediction (testing set), respectively.

Using Horn's Parallel Analysis Method in Exploratory Factor Analysis for Determining the Number of Factors

ERIC Educational Resources Information Center

Çokluk, Ömay; Koçak, Duygu

2016-01-01

In this study, the number of factors obtained from parallel analysis, a method used for determining the number of factors in exploratory factor analysis, was compared to that of the factors obtained from eigenvalue and scree plot--two traditional methods for determining the number of factors--in terms of consistency. Parallel analysis is based on…

An exploratory cluster randomised trial of a university halls of residence based social norms marketing campaign to reduce alcohol consumption among 1st year students.

PubMed

Moore, Graham F; Williams, Annie; Moore, Laurence; Murphy, Simon

2013-04-18

This exploratory trial examines the feasibility of implementing a social norms marketing campaign to reduce student drinking in universities in Wales, and evaluating it using cluster randomised trial methodology. Fifty residence halls in 4 universities in Wales were randomly assigned to intervention or control arms. Web and paper surveys were distributed to students within these halls (n = 3800), assessing exposure/contamination, recall of and evaluative responses to intervention messages, perceived drinking norms and personal drinking behaviour. Measures included the Drinking Norms Rating Form, the Daily Drinking Questionnaire and AUDIT-C. A response rate of 15% (n = 554) was achieved, varying substantially between sites. Intervention posters were seen by 80% and 43% of students in intervention and control halls respectively, with most remaining materials seen by a minority in both groups. Intervention messages were rated as credible and relevant by little more than half of students, though fewer felt they would influence their behaviour, with lighter drinkers more likely to perceive messages as credible. No differences in perceived norms were observed between intervention and control groups. Students reporting having seen intervention materials reported lower descriptive and injunctive norms than those who did not. Attention is needed to enhancing exposure, credibility and perceived relevance of intervention messages, particularly among heavier drinkers, before definitive evaluation can be recommended. A definitive evaluation would need to consider how it would achieve sufficient response rates, whilst hall-level cluster randomisation appears subject to a significant degree of contamination. ISRCTN: ISRCTN48556384.

Findings in resting-state fMRI by differences from K-means clustering.

PubMed

Chyzhyk, Darya; Graña, Manuel

2014-01-01

Resting state fMRI has growing number of studies with diverse aims, always centered on some kind of functional connectivity biomarker obtained from correlation regarding seed regions, or by analytical decomposition of the signal towards the localization of the spatial distribution of functional connectivity patterns. In general, studies are computationally costly and very sensitive to noise and preprocessing of data. In this paper we consider clustering by K-means as a exploratory procedure which can provide some results with little computational effort, due to efficient implementations that are readily available. We demonstrate the approach on a dataset of schizophrenia patients, finding differences between patients with and without auditory hallucinations.

Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis.

PubMed

Lee, Seohyun; Cho, Yoon-Min; Kim, Sun-Young

2017-08-22

Mobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade. As a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth Database, the World Bank Projects & Operations Database, and the USAID mHealth Database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Moran's I and Local Indicator of Spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities. From our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription rates per 100 population, the northern area shows extensive low-low association. This study aimed to shed some light on the potential for strategic regional collaboration for scale-up of mHealth and mobile penetration. Firstly, countries in the eastern area with much experience can take the lead role in pursuing regional collaboration for mHealth programs in sub-Saharan Africa. Secondly, collective effort in improving mobile penetration rates for the northern area is recommended.

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

PubMed Central

Zheng, Jie; Stoyanovich, Julia; Manduchi, Elisabetta; Liu, Junmin; Stoeckert, Christian J.

2011-01-01

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/ PMID:22190598

Gene expression factor analysis to differentiate pathways linked to fibromyalgia, chronic fatigue syndrome, and depression in a diverse patient sample

PubMed Central

Iacob, Eli; Light, Alan R.; Donaldson, Gary W.; Okifuji, Akiko; Hughen, Ronald W.; White, Andrea T.; Light, Kathleen C.

2015-01-01

Objective To determine if independent candidate genes can be grouped into meaningful biological factors and if these factors are associated with the diagnosis of chronic fatigue syndrome (CFS) and fibromyalgia (FMS) while controlling for co-morbid depression, sex, and age. Methods We included leukocyte mRNA gene expression from a total of 261 individuals including healthy controls (n=61), patients with FMS only (n=15), CFS only (n=33), co-morbid CFS and FMS (n=79), and medication-resistant (n=42) or medication-responsive (n=31) depression. We used Exploratory Factor Analysis (EFA) on 34 candidate genes to determine factor scores and regression analysis to examine if these factors were associated with specific diagnoses. Results EFA resulted in four independent factors with minimal overlap of genes between factors explaining 51% of the variance. We labeled these factors by function as: 1) Purinergic and cellular modulators; 2) Neuronal growth and immune function; 3) Nociception and stress mediators; 4) Energy and mitochondrial function. Regression analysis predicting these biological factors using FMS, CFS, depression severity, age, and sex revealed that greater expression in Factors 1 and 3 was positively associated with CFS and negatively associated with depression severity (QIDS score), but not associated with FMS. Conclusion Expression of candidate genes can be grouped into meaningful clusters, and CFS and depression are associated with the same 2 clusters but in opposite directions when controlling for co-morbid FMS. Given high co-morbid disease and interrelationships between biomarkers, EFA may help determine patient subgroups in this population based on gene expression. PMID:26097208

Exploratory Climate Data Visualization and Analysis Using DV3D and UVCDAT

NASA Technical Reports Server (NTRS)

Maxwell, Thomas

2012-01-01

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

What is reflection? A conceptual analysis of major definitions and a proposal of a five-component model.

PubMed

Nguyen, Quoc Dinh; Fernandez, Nicolas; Karsenti, Thierry; Charlin, Bernard

2014-12-01

Although reflection is considered a significant component of medical education and practice, the literature does not provide a consensual definition or model for it. Because reflection has taken on multiple meanings, it remains difficult to operationalise. A standard definition and model are needed to improve the development of practical applications of reflection. This study was conducted in order to identify, explore and analyse the most influential conceptualisations of reflection, and to develop a new theory-informed and unified definition and model of reflection. A systematic review was conducted to identify the 15 most cited authors in papers on reflection published during the period from 2008 to 2012. The authors' definitions and models were extracted. An exploratory thematic analysis was carried out and identified seven initial categories. Categories were clustered and reworded to develop an integrative definition and model of reflection, which feature core components that define reflection and extrinsic elements that influence instances of reflection. Following our review and analysis, five core components of reflection and two extrinsic elements were identified as characteristics of the reflective thinking process. Reflection is defined as the process of engaging the self (S) in attentive, critical, exploratory and iterative (ACEI) interactions with one's thoughts and actions (TA), and their underlying conceptual frame (CF), with a view to changing them and a view on the change itself (VC). Our conceptual model consists of the defining core components, supplemented with the extrinsic elements that influence reflection. This article presents a new theory-informed, five-component definition and model of reflection. We believe these have advantages over previous models in terms of helping to guide the further study, learning, assessment and teaching of reflection. © 2014 John Wiley & Sons Ltd.

Multimorbidity patterns of and use of health services by Swedish 85-year-olds: an exploratory study

PubMed Central

2013-01-01

Background As life expectancy continues to rise, more elderly are reaching advanced ages (≥80 years). The increasing prevalence of multimorbidity places additional demands on health-care resources for the elderly. Previous studies noted the impact of multimorbidity on the use of health services, but the effects of multimorbidity patterns on health-service use have not been well studied, especially for very old people. This study determines patterns of multimorbidity associated with emergency-room visits and hospitalization in an 85-year-old population. Methods Health and living conditions were reported via postal questionnaire by 496 Linköping residents aged 85 years (189 men and 307 women). Diagnoses of morbidity were reviewed in patients’ case reports, and the local health-care register provided information on the use of health services. Hierarchical cluster analysis was applied to evaluate patterns of multimorbidity with gender stratification. Factors associated with emergency-room visits and hospitalization were analyzed using logistic regression models. Results Cluster analyses revealed five clusters: vascular, cardiopulmonary, cardiac (only for men), somatic–mental (only for men), mental disease (only for women), and three other clusters related to aging (one for men and two for women). Heart failure in men (OR = 2.4, 95% CI = 1–5.7) and women (OR = 3, 95% CI = 1.3–6.9) as a single morbidity explained more variance than morbidity clusters in models of emergency-room visits. Men's cardiac cluster (OR = 1.6; 95% CI = 1–2.7) and women's cardiopulmonary cluster (OR = 1.7, 95% CI = 1.2–2.4) were significantly associated with hospitalization. The combination of the cardiopulmonary cluster with the men’s cardiac cluster (OR = 1.6, 95% CI = 1–2.4) and one of the women’s aging clusters (OR = 0.5, 95% CI = 0.3–0.8) showed interaction effects on hospitalization. Conclusion In this 85-year-old population, patterns of cardiac and pulmonary conditions were better than a single morbidity in explaining hospitalization. Heart failure was superior to multimorbidity patterns in explaining emergency-room visits. A holistic approach to examining the patterns of multimorbidity and their relationships with the use of health services will contribute to both local health care policy and geriatric practice. PMID:24195643

Exploratory Bi-Factor Analysis: The Oblique Case

ERIC Educational Resources Information Center

Jennrich, Robert I.; Bentler, Peter M.

2012-01-01

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

Multimedia Exploratory Data Analysis for Geospatial Data Mining: The Case for Augmented Seriation.

ERIC Educational Resources Information Center

Gluck, Myke

2001-01-01

Reviews the role of exploratory data analysis (EDA) for spatial data mining and presents a case study addressing environmental risk assessments in New York State to illustrate the feasibility and usability of augmenting seriation for spatial data analysis. Describes augmentation with multimedia tools to understand relationships among spatial,…

Temnothorax crasecundus sp. n. – a cryptic Eurocaucasian ant species (Hymenoptera, Formicidae) discovered by Nest Centroid Clustering

PubMed Central

Seifert, Bernhard; Csösz, Sandor

2015-01-01

Abstract The paper integrates two independent studies of numeric morphology-based alpha-taxonomy of the cryptic ant species Temnothorax crassispinus (Karavajev, 1926) and Temnothorax crasecundus sp. n. conducted by different investigators, using different equipment, considering different character combinations and evaluating different samples. Samples investigated included 603 individual workers from 203 nests – thereof 104 nest samples measured by Seifert and 99 by Csösz. The material originated from Europe, Asia Minor and Caucasia. There was a very strong interspecific overlap in any of the 29 shape characters recorded and subjective expert determination failed in many cases. Primary classification hypotheses were formed by the exploratory data analysis Nest Centroid (NC) clustering and corrected to final species hypotheses by an iterative linear discriminant analysis algorithm. The evaluation of Seifert’s and Csösz’s data sets arrived at fully congruent conclusions. NC-Ward and NC-K-means clustering disagreed from the final species hypothesis in only 1.9 and 1.9% of the samples in Seifert’s data set and by 1.1 and 2.1% in Csösz’s data set which is a strong argument for heterospecificity. The type series of Temnothorax crassispinus and Temnothorax crasecundus sp. n. were allocated to different clusters with p = 0.9851 and p = 0.9912 respectively. The type series of the junior synonym Temnothorax slavonicus (Seifert, 1995) was allocated to the Temnothorax crassispinus cluster with p = 0.9927. Temnothorax crasecundus sp. n. and Temnothorax crassispinus are parapatric species with a long contact zone stretching from the Peloponnisos peninsula across Bulgaria northeast to the southern Ukraine. There is no indication for occurrence of interspecifically mixed nests or intraspecific polymorphism. However, a significant reduction of interspecific morphological distance at sites with syntopic occurrence of both species indicates local hybridization. The results are discussed within the context of the Pragmatic Species Concept of Seifert (2014). The taxonomic description and a differential diagnosis of Temnothorax crasecundus sp. n. are given. PMID:25685016

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

DOE PAGES

Racah, Evan; Ko, Seyoon; Sadowski, Peter; ...

2017-02-02

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networksmore » can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.« less

Offline constraints in online drug marketplaces: An exploratory analysis of a cryptomarket trade network.

PubMed

Norbutas, Lukas

2018-06-01

Cryptomarkets, or illegal anonymizing online platforms that facilitate drug trade, have been analyzed in a rapidly growing body of research. Previous research has found that, despite increased risks, cryptomarket sellers are often willing to ship illegal drugs internationally. There is little to no information, however, about the extent to which uncertainty and risk related to geographic constraints shapes buyers' behavior and, in turn, the structure of the global online drug trade network. In this paper, we analyze the structure of a complete cryptomarket trade network with a focus on the role of geographic clustering of buyers and sellers. We use publicly available crawls of the cryptomarket Abraxas, encompassing market transactions between 463 sellers and 3542 buyers of drugs in 2015. We use descriptive social network analysis and Exponential Random Graph Models (ERGM) to analyze the structure of the trade network. The structure of the online drug trade network is primarily shaped by geographical boundaries. Buyers are more likely to buy from multiple sellers within a single country, and avoid buying from sellers in different countries, which leads to strong geographic clustering. The effect is especially strong between continents and weaker for countries within Europe. A small fraction of buyers (10%) account for more than a half of all drug purchases, while most buyers only buy once. Online drug trade networks might still be heavily shaped by offline (geographic) constraints, despite their ability to provide access for end-users to large international supply. Cryptomarkets might be more "localized" and less international than thought before. We discuss potential explanations for such geographical clustering and implications of the findings. Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.

Isotopic ratio analysis of cattle tail hair: A potential tool in building the database for cow milk geographical traceability.

PubMed

Behkami, Shima; Zain, Sharifuddin Md; Gholami, Mehrdad; Bakirdere, Sezgin

2017-02-15

The potential for the isotopic ratio analysis of cattle tail hair in determining the geographical origin of raw cow milk in Peninsular Malaysia had been investigated in this research using exploratory visualization. A significant positive correlation (p<0.0001) (n=54) was noticed between δ(13)C and δ(15)N in milk with that of hair which indicated that these matrices could be used in tracing the geographical origin of animal produce and tissues, and there is a possibility that hair could be used as a substitute in building the database for the geographical traceability of milk. It was also observed that both hair and milk isotopic ratio correlations exhibited separation between the northern and southern regions. The accuracy of using isotopic ratio in determining geographical discrimination had been clearly demonstrated when several commercial milk samples from the same regions under the study were correctly assigned to the appropriate geographical clusters. Copyright © 2016 Elsevier Ltd. All rights reserved.

Replication Analysis in Exploratory Factor Analysis: What It Is and Why It Makes Your Analysis Better

ERIC Educational Resources Information Center

Osborne, Jason W.; Fitzpatrick, David C.

2012-01-01

Exploratory Factor Analysis (EFA) is a powerful and commonly-used tool for investigating the underlying variable structure of a psychometric instrument. However, there is much controversy in the social sciences with regard to the techniques used in EFA (Ford, MacCallum, & Tait, 1986; Henson & Roberts, 2006) and the reliability of the outcome.…

Role of route previewing strategies on climbing fluency and exploratory movements.

PubMed

Seifert, Ludovic; Cordier, Romain; Orth, Dominic; Courtine, Yoan; Croft, James L

2017-01-01

This study examined the role of route previewing strategies on climbing fluency and on exploratory movements of the limbs, in order to understand whether previewing helps people to perceive and to realize affordances. Eight inexperienced and ten experienced climbers previewed a 10 m high route of 5b difficulty on French scale, then climbed it with a top-rope as fluently as possible. Gaze behavior was collected from an eye tracking system during the preview and allowed us to determine the number of times they scanned the route, and which of four route previewing strategies (fragmentary, ascending, zigzagging, and sequence-of-blocks) they used. Five inertial measurement units (IMU) (3D accelerometer, 3D gyroscope, 3D magnetometer) were attached to the hip, both feet, and forearms to analyze the vertical acceleration and direction of each limb and hip during the ascent. We were able to detect movement and immobility phases of each IMU using segmentation and classification processes. Depending on whether the limbs and/or hip were moving, five states of behavior were detected: immobility, postural regulation, hold exploration, hold change, and hold traction. Using cluster analysis we identified four clusters of gaze behavior during route previewing depending on route preview duration, number of scan paths, fixations duration, ascending, zigzagging, and sequence-of-blocks strategies. The number of scan paths was positively correlated with relative duration of exploration and negatively correlated with relative duration of hold changes during the ascent. Additionally, a high relative duration of sequence-of-blocks strategy and zigzagging strategy were associated with a high relative duration of immobility during the ascent. Route previewing might help to pick up functional information about reachable, graspable, and usable holds, in order to chain movements together and to find the route. In other words, route previewing might contribute to perceiving and realizing nested affordances.

Comparisons of Exploratory and Confirmatory Factor Analysis.

ERIC Educational Resources Information Center

Daniel, Larry G.

Historically, most researchers conducting factor analysis have used exploratory methods. However, more recently, confirmatory factor analytic methods have been developed that can directly test theory either during factor rotation using "best fit" rotation methods or during factor extraction, as with the LISREL computer programs developed…

Milestone Ratings and Supervisory Role Categorizations Swim Together, but is the Water Muddy?

PubMed

Schumacher, Daniel J; Bartlett, Kathleen W; Elliott, Sean P; Michelson, Catherine; Sharma, Tanvi; Garfunkel, Lynn C; King, Beth; Schwartz, Alan

2018-06-17

This single specialty, multi-institutional study aimed to determine: 1) the association between milestone ratings for individual competencies and average milestone ratings (AMRs) and 2) the association between AMRs and recommended supervisory role categorizations made by individual clinical competency committee (CCC) members. During the 2015-16 academic year, CCC members at 14 pediatric residencies reported milestone ratings for 21 competencies and recommended supervisory role categories (may not supervise, may supervise in some settings, may supervise in all settings) for residents they reviewed. An exploratory factor analysis of competencies was conducted. The associations between individual competencies, the AMR, and supervisory role categorizations were determined by computing bivariate correlations. The relationship between AMRs and recommended supervisory role categorizations was examined using an ordinal mixed logistic regression model. 68/155 CCC members completed both milestone assignments and supervision categorizations for 451 residents. Factor analysis of individual competencies controlling for clustering of residents in raters and sites resulted in a single-factor solution (cumulative variance 0.75). All individual competencies had large positive correlations with the AMR (correlation coefficient: 0.84-0.93), except for two professionalism competencies (Prof1: 0.63 and Prof4: 0.65). When combined across training year and time points, the AMR and supervisory role categorization had a moderately positive correlation (0.56). This exploratory study identified a modest correlation between average milestone ratings and supervisory role categorization. Convergence of competencies on a single factor deserves further exploration, with possible rater effects warranting attention. Copyright © 2018. Published by Elsevier Inc.

Improving Skill Development: An Exploratory Study Comparing a Philosophical and an Applied Ethical Analysis Technique

ERIC Educational Resources Information Center

Al-Saggaf, Yeslam; Burmeister, Oliver K.

2012-01-01

This exploratory study compares and contrasts two types of critical thinking techniques; one is a philosophical and the other an applied ethical analysis technique. The two techniques analyse an ethically challenging situation involving ICT that a recent media article raised to demonstrate their ability to develop the ethical analysis skills of…

Factor Retention in Exploratory Factor Analysis: A Comparison of Alternative Methods.

ERIC Educational Resources Information Center

Mumford, Karen R.; Ferron, John M.; Hines, Constance V.; Hogarty, Kristine Y.; Kromrey, Jeffery D.

This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The…

Seeing the Forest Despite the Trees: The Benefit of Exploratory Data Analysis to Program Evaluation Research.

ERIC Educational Resources Information Center

Sinacore, James M.; And Others

1992-01-01

It is argued that there is a benefit to applying techniques of exploratory data analysis (EDA) to program evaluation. The evaluation of a rehabilitation program for people with rheumatoid arthritis (20 subjects and 21 comparisons) through EDA supports the argument, indicating outcomes more precisely than conventional analysis of variance. (SLD)

Variation of gunshot injury patterns in mortality associated with human rights abuses and armed conflict: an exploratory study.

PubMed

Baraybar, Jose Pablo

2015-09-01

The analysis of the distribution of gunshot injuries in a sample of 777 sets of human remains of proven human rights abuse from Somaliland, the Balkans and Peru is compared to frequencies of injuries sustained by combatants in contemporary conflicts reported in the literature. Principal Component Analysis (PCA) reduced the data to three components accounting for 82.94% of the variance. The first component with 38.31% of variance shows segments Arms and thorax/abdomen to be positively correlated (0.887 and 0.662, respectively); the segment head/neck is strongly correlated (0.951) to the second component while the segment thorax/abdomen shows a low, negative correlation (-0.388). Finally in the third component only the legs are strongly correlated (0.991). Data was further subjected to a K-means cluster analysis to determine the likely groupings combining the four types of injuries. Each of the three clusters reproduced similar patterns observed in the PCA: Cluster 1 shows the prevalence of injuries to the thorax/abdomen and extremities in addition to injuries to the head/neck; Cluster 2 shows injuries to the head/neck and Cluster 3 injuries to the thorax/abdomen and a lower representation of the arms and legs. Most of the cases (70.5%), irrespective of geography and type of site (attack or detention), were grouped into Cluster 2. Such comparison shows that in human rights abuse, irrespective of their geography, gunshot injuries tend to follow a pattern favouring the head/neck and thorax/abdomen areas over the extremities, the reverse pattern observed in contemporary combat operations. In those settings gunshot wound trauma is the second cause of mortality/morbidity (after fragmenting ammunition) and its distribution concentrates on the extremities, thorax/abdomen and head; following the pattern of protective armour when it is used. Considering that human rights abuses are often presented as encounters between two armed groups in the context of counter-insurgency operations, a careful analysis of gunshot injury patterns could serve as an indicator that in fact murder, rather than combat, took place and the intention was to kill rather than to maim or render people unfit for battle. To compare the variation of gunshot injury patterns between mortality associated with human rights abuses and armed conflict in selected samples from different countries. Literature review and case analysis. Original statistical analysis of gunshot injuries on human remains (n=777) recovered from mass or clandestine graves associated with human rights abuses in countries in Somaliland, the Balkans and Peru (1983-1995) and literature review of mortality caused by armed conflicts. Mechanism of gunshot injury and wound distribution pattern in geographically diverse samples of human rights abuse. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Constructivism in Practice: an Exploratory Study of Teaching Patterns and Student Motivation in Physics Classrooms in Finland, Germany and Switzerland

NASA Astrophysics Data System (ADS)

Beerenwinkel, Anne; von Arx, Matthias

2017-04-01

For the last three decades, moderate constructivism has become an increasingly prominent perspective in science education. Researchers have defined characteristics of constructivist-oriented science classrooms, but the implementation of such science teaching in daily classroom practice seems difficult. Against this background, we conducted a sub-study within the tri-national research project Quality of Instruction in Physics (QuIP) analysing 60 videotaped physics classes involving a large sample of students ( N = 1192) from Finland, Germany and Switzerland in order to investigate the kinds of constructivist components and teaching patterns that can be found in regular classrooms without any intervention. We applied a newly developed coding scheme to capture constructivist facets of science teaching and conducted principal component and cluster analyses to explore which components and patterns were most prominent in the classes observed. Two underlying components were found, resulting in two scales—Structured Knowledge Acquisition and Fostering Autonomy—which describe key aspects of constructivist teaching. Only the first scale was rather well established in the lessons investigated. Classes were clustered based on these scales. The analysis of the different clusters suggested that teaching physics in a structured way combined with fostering students' autonomy contributes to students' motivation. However, our regression models indicated that content knowledge is a more important predictor for students' motivation, and there was no homogeneous pattern for all gender- and country-specific subgroups investigated. The results are discussed in light of recent discussions on the feasibility of constructivism in practice.

Development of advanced acreage estimation methods

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator)

1980-01-01

The use of the AMOEBA clustering/classification algorithm was investigated as a basis for both a color display generation technique and maximum likelihood proportion estimation procedure. An approach to analyzing large data reduction systems was formulated and an exploratory empirical study of spatial correlation in LANDSAT data was also carried out. Topics addressed include: (1) development of multiimage color images; (2) spectral spatial classification algorithm development; (3) spatial correlation studies; and (4) evaluation of data systems.

Signatures of α clustering in ultrarelativistic collisions with light nuclei

NASA Astrophysics Data System (ADS)

Rybczyński, Maciej; Piotrowska, Milena; Broniowski, Wojciech

2018-03-01

We explore possible observable signatures of α clustering of light nuclei in ultrarelativistic nuclear collisions involving Be,97, 12C, and 16O. The clustering leads to specific spatial correlations of the nucleon distributions in the ground state, which are manifest in the earliest stage of the ultrahigh energy reaction. The formed initial state of the fireball is sensitive to these correlations, and the effect influences, after the collective evolution of the system, the hadron production in the final stage. Specifically, we study effects on the harmonic flow in collisions of light clustered nuclei with a heavy target (208Pb), showing that measures of the elliptic flow are sensitive to clusterization in Be,97, whereas triangular flow is sensitive to clusterization in 12C and 16O. Specific predictions are made for model collisions at energies available at the CERN Super Proton Synchrotron. In another exploratory development we also examine proton-beryllium collisions, where the 3 /2- ground state of Be,97 nuclei is polarized by an external magnetic field. Clusterization leads to multiplicity distributions of participant nucleons which depend on the orientation of the polarization with respect to the collision axis, as well as on the magnetic number of the state. The obtained effects on multiplicities reach a factor of a few for collisions with a large number of participant nucleons.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

Exploratory and Higher-Order Factor Analyses of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) Adolescent Subsample

ERIC Educational Resources Information Center

Canivez, Gary L.; Watkins, Marley W.

2010-01-01

The factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV; Wechsler, 2008a) with the adolescent participants (ages 16-19 years; N = 400) in the standardization sample was assessed using exploratory factor analysis, multiple factor extraction criteria, and higher-order exploratory factor analyses. Results from…

The use of exploratory analyses within the National Institute for Health and Care Excellence single technology appraisal process: an evaluation and qualitative analysis.

PubMed

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

2016-04-01

As part of the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process, independent Evidence Review Groups (ERGs) critically appraise the company submission. During the critical appraisal process the ERG may undertake analyses to explore uncertainties around the company's model and their implications for decision-making. The ERG reports are a central component of the evidence considered by the NICE Technology Appraisal Committees (ACs) in their deliberations. The aim of this research was to develop an understanding of the number and type of exploratory analyses undertaken by the ERGs within the STA process and to understand how these analyses are used by the NICE ACs in their decision-making. The 100 most recently completed STAs with published guidance were selected for inclusion in the analysis. The documents considered were ERG reports, clarification letters, the first appraisal consultation document and the final appraisal determination. Over 400 documents were assessed in this study. The categories of types of exploratory analyses included fixing errors, fixing violations, addressing matters of judgement and the ERG-preferred base case. A content analysis of documents (documentary analysis) was undertaken to identify and extract relevant data, and narrative synthesis was then used to rationalise and present these data. The level and type of detail in ERG reports and clarification letters varied considerably. The vast majority (93%) of ERG reports reported one or more exploratory analyses. The most frequently reported type of analysis in these 93 ERG reports related to the category 'matters of judgement', which was reported in 83 (89%) reports. The category 'ERG base-case/preferred analysis' was reported in 45 (48%) reports, the category 'fixing errors' was reported in 33 (35%) reports and the category 'fixing violations' was reported in 17 (18%) reports. The exploratory analyses performed were the result of issues raised by an ERG in its critique of the submitted economic evidence. These analyses had more influence on recommendations earlier in the STA process than later on in the process. The descriptions of analyses undertaken were often highly specific to a particular STA and could be inconsistent across ERG reports and thus difficult to interpret. Evidence Review Groups frequently conduct exploratory analyses to test or improve the economic evaluations submitted by companies as part of the STA process. ERG exploratory analyses often have an influence on the recommendations produced by the ACs. More in-depth analysis is needed to understand how ERGs make decisions regarding which exploratory analyses should be undertaken. More research is also needed to fully understand which types of exploratory analyses are most useful to ACs in their decision-making. The National Institute for Health Research Health Technology Assessment programme.

Issues engulfed Saudi Arabia construction workers

NASA Astrophysics Data System (ADS)

Al-Emad, N. H.; Rahman, I. A.

2018-04-01

This paper presents an exploratory study conducted in Makkah city to uncover issues faced by construction workers from the construction leaders’ perspective. Eleven construction leaders/experts were interviewed to unleash their experiences on handling the foreign workers working in Makkah construction projects. Most of the experts are senior management staffs with more than 10 years’ working experience in Saudi Arabia construction industry. The interviews were carried out in semi structured mode where all the information was captured manually and also electronically. The identified issues were sorted based on its commonality into 10 clusters. Hence in each cluster, the numbers of issue considered by the experts are reflecting the importance of that particular cluster. The result of the clusters according to the number of issues mentioned by the experts are safety issues, restricted government regulation, demotivated issues, lack of quality workers, poor living quality, communication barriers, adaption issues, poor attitudes, lack of logistical arrangements and lack of education. With these identified issues it will assist the construction players in the construction industry of Saudi Arabia in dealing with their workers.

Semantic search during divergent thinking.

PubMed

Hass, Richard W

2017-09-01

Divergent thinking, as a method of examining creative cognition, has not been adequately analyzed in the context of modern cognitive theories. This article casts divergent thinking responding in the context of theories of memory search. First, it was argued that divergent thinking tasks are similar to semantic fluency tasks, but are more constrained, and less well structured. Next, response time distributions from 54 participants were analyzed for temporal and semantic clustering. Participants responded to two prompts from the alternative uses test: uses for a brick and uses for a bottle, for two minutes each. Participants' cumulative response curves were negatively accelerating, in line with theories of search of associative memory. However, results of analyses of semantic and temporal clustering suggested that clustering is less evident in alternative uses responding compared to semantic fluency tasks. This suggests either that divergent thinking responding does not involve an exhaustive search through a clustered memory trace, but rather that the process is more exploratory, yielding fewer overall responses that tend to drift away from close associates of the divergent thinking prompt. Copyright © 2017 Elsevier B.V. All rights reserved.

Connectivism in Postsecondary Online Courses: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hogg, Nanette; Lomicky, Carol S.

2012-01-01

This study explores 465 postsecondary students' experiences in online classes through the lens of connectivism. Downes' 4 properties of connectivism (diversity, autonomy, interactivity, and openness) were used as the study design. An exploratory factor analysis was performed. This study found a 4-factor solution. Subjects indicated that autonomy…

Bootstrap Confidence Intervals for Ordinary Least Squares Factor Loadings and Correlations in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong

2010-01-01

This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…

Establishing Evidence for Internal Structure Using Exploratory Factor Analysis

ERIC Educational Resources Information Center

Watson, Joshua C.

2017-01-01

Exploratory factor analysis (EFA) is a data reduction technique used to condense data into smaller sets of summary variables by identifying underlying factors potentially accounting for patterns of collinearity among said variables. Using an illustrative example, the 5 general steps of EFA are described with best practices for decision making…

Unmanned Multiple Exploratory Probe System (MEPS) for Mars observation. Volume 1: Trade analysis and design

NASA Technical Reports Server (NTRS)

Adams, Daniel E.; Crumbly, Christopher M.; Delp, Steve E.; Guidry, Michelle A.; Lisano, Michael E.; Packard, James D.; Striepe, Scott A.

1988-01-01

This report presents the unmanned Multiple Exploratory Probe Systems (MEPS), a space vehicle designed to observe the planet Mars in preparation for manned missions. The options considered for each major element are presented as a trade analysis, and the final vehicle design is defined.

Sexual Harassment Retaliation Climate DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

exploratory factor analysis, and bivariate correlations (sample 1) 2) To determine the factor structure of the remaining (final) questions via...statistics, reliability analysis, exploratory factor analysis, and bivariate correlations of the prospective Sexual Harassment Retaliation Climate...reported by the survey requester). For information regarding the composition of sample, refer to Table 1. Table 1. Sample 1 Demographics n

Designing an Exploratory Text Analysis Tool for Humanities and Social Sciences Research

ERIC Educational Resources Information Center

Shrikumar, Aditi

2013-01-01

This dissertation presents a new tool for exploratory text analysis that attempts to improve the experience of navigating and exploring text and its metadata. The design of the tool was motivated by the unmet need for text analysis tools in the humanities and social sciences. In these fields, it is common for scholars to have hundreds or thousands…

Toward Reflective Judgment in Exploratory Factor Analysis Decisions: Determining the Extraction Method and Number of Factors To Retain.

ERIC Educational Resources Information Center

Knight, Jennifer L.

This paper considers some decisions that must be made by the researcher conducting an exploratory factor analysis. The primary purpose is to aid the researcher in making informed decisions during the factor analysis instead of relying on defaults in statistical programs or traditions of previous researchers. Three decision areas are addressed.…

Data Science Programs in U.S. Higher Education: An Exploratory Content Analysis of Program Description, Curriculum Structure, and Course Focus

ERIC Educational Resources Information Center

Tang, Rong; Sae-Lim, Watinee

2016-01-01

In this study, an exploratory content analysis of 30 randomly selected Data Science (DS) programs from eight disciplines revealed significant gaps in current DS education in the United States. The analysis centers on linguistic patterns of program descriptions, curriculum requirements, and DS course focus as pertaining to key skills and domain…

Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis.

PubMed

González-Calabozo, Jose M; Valverde-Albacete, Francisco J; Peláez-Moreno, Carmen

2016-09-15

Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around [Formula: see text]-Formal Concept Analysis ([Formula: see text]-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices. By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher's vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them. Second, the resulting biclusters are used to index external omics databases-for instance, Gene Ontology (GO)-thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources. We illustrate the exploration procedure on a real data example confirming results previously published. The GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters-by observing their genes and what their persistence is-to infer, for instance, hypotheses on their function.

An exploratory cluster randomised trial of a university halls of residence based social norms marketing campaign to reduce alcohol consumption among 1st year students

PubMed Central

2013-01-01

Aims This exploratory trial examines the feasibility of implementing a social norms marketing campaign to reduce student drinking in universities in Wales, and evaluating it using cluster randomised trial methodology. Methods Fifty residence halls in 4 universities in Wales were randomly assigned to intervention or control arms. Web and paper surveys were distributed to students within these halls (n = 3800), assessing exposure/contamination, recall of and evaluative responses to intervention messages, perceived drinking norms and personal drinking behaviour. Measures included the Drinking Norms Rating Form, the Daily Drinking Questionnaire and AUDIT-C. Results A response rate of 15% (n = 554) was achieved, varying substantially between sites. Intervention posters were seen by 80% and 43% of students in intervention and control halls respectively, with most remaining materials seen by a minority in both groups. Intervention messages were rated as credible and relevant by little more than half of students, though fewer felt they would influence their behaviour, with lighter drinkers more likely to perceive messages as credible. No differences in perceived norms were observed between intervention and control groups. Students reporting having seen intervention materials reported lower descriptive and injunctive norms than those who did not. Conclusions Attention is needed to enhancing exposure, credibility and perceived relevance of intervention messages, particularly among heavier drinkers, before definitive evaluation can be recommended. A definitive evaluation would need to consider how it would achieve sufficient response rates, whilst hall-level cluster randomisation appears subject to a significant degree of contamination. Trial registration ISRCTN: ISRCTN48556384 PMID:23594918

"Exploratory experimentation" as a probe into the relation between historiography and philosophy of science.

PubMed

Schickore, Jutta

2016-02-01

This essay utilizes the concept "exploratory experimentation" as a probe into the relation between historiography and philosophy of science. The essay traces the emergence of the historiographical concept "exploratory experimentation" in the late 1990s. The reconstruction of the early discussions about exploratory experimentation shows that the introduction of the concept had unintended consequences: Initially designed to debunk philosophical ideas about theory testing, the concept "exploratory experimentation" quickly exposed the poverty of our conceptual tools for the analysis of experimental practice. Looking back at a number of detailed analyses of experimental research, we can now appreciate that the concept of exploratory experimentation is too vague and too elusive to fill the desideratum whose existence it revealed. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

Mirel, Barbara

2009-02-13

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

Geochemical prospecting for Cu mineralization in an arid terrain-central Iran

NASA Astrophysics Data System (ADS)

Mokhtari, Ahmad Reza; Roshani Rodsari, Parisa; Fatehi, Moslem; Shahrestani, Shahed; Pournik, Peyman

2014-12-01

Geochemical sampling and data processing were implemented for prospecting Cu mineralization through catchment basin approach in central Iran, Yazd province, over drainage systems in order to determine areas of interest for the detailed exploration program. The target zone, inside an area called Kalout-e-Ashrafa in Yazd province-Iran, was characterized by the collection of 107 stream sediment samples. Catchment basin modeling was conducted based on digital elevation model (DEM) and geological map of the study area. Samples were studied by univariate and multivariate statistical techniques of exploratory data analysis, classical statistical analysis and cluster analysis. The results showed that only Cu had anomalous behavior and it did not exhibit a considerable correlation with other elements. Geochemical maps were prepared for Cu and anomalous zones and separated for potential copper mineralization. It was concluded that due to especial geomorphological and geographical characteristics (smooth topography, negligible annual precipitation and insufficient thickness of silicified Cu-bearing outcrops of the area), low concentrations of Cu would be expected for the delineation of promising zones in similar trains. Using cluster analysis showed that there was a strong correlation between Ag, Sr and S. Calcium and Pb present moderate correlation with Cu. Additionally, there was a strong correlation between Zn and Li, thereby indicating a meaningful correlation with Fe, P, Ti and Mg. Aluminum, Sc and V had a correlation with Be and K. Applying threshold value according to MAD (median absolute deviation) helped us to distinguish anomalous catchments more properly. Finally, there was a significant kind of conformity among anomalous catchment basins and silicified veins and veinlets (as validating index) at the central part of the area.

Taxonomic revision of imitating carpenter ants, Camponotus subgenus Myrmopytia (Hymenoptera, Formicidae) of Madagascar, using morphometry and qualitative traits

PubMed Central

Rasoamanana, Nicole; Csősz, Sándor; Fisher, Brian L.

2017-01-01

Abstract The ant genus Camponotus (Mayr, 1861) is one of the most abundant and species rich ant genera in the Malagasy zoogeographical region. Although this group is commonly encountered, its taxonomy is far from complete. Here, we clarify the taxonomy of the Malagasy-endemic Camponotus subgenus Myrmopytia (Emery, 1920). Species delimitation was based on traditional morphological characters and multivariate morphometric analyses, including exploratory Nest Centroid clustering and confirmatory cross-validated Linear Discriminant Analysis. Four species are recognized: Camponotus imitator (Forel, 1891), Camponotus jodina sp. n., Camponotus karaha sp. n., and Camponotus longicollis sp. n. All four species appear to mimic co-occurring Aphaenogaster species. A diagnosis of the subgenus Myrmopytia, species descriptions, an identification key based on minor and major subcastes of workers, and the known geographical distribution of each species are provided. PMID:28769722

Mixture models with entropy regularization for community detection in networks

NASA Astrophysics Data System (ADS)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Religiousness, social support and subjective well-being: An exploratory study among adolescents in an Asian atheist country.

PubMed

Ju, Chengting; Zhang, Baoshan; You, Xuqun; Alterman, Valeria; Li, Yongkang

2018-04-01

Few studies have focused on the relationships among religiousness, social support and subjective well-being in Chinese adolescent populations. This study tries to fill this gap. Using cluster sampling, we selected two groups: Group A, which included 738 Tibetan adolescents with a formal religious affiliation and represented adolescents from a religious culture, and Group B, which included 720 Han adolescents without a religious affiliation and represented adolescents from an irreligious culture. Structural equation modelling showed that only in Group A did social support mediate (partially) the relationship between religious experience and subjective well-being; furthermore, the results of a hierarchical regression analysis showed that only in Group A did social support moderate the relationship between religious ideology and subjective well-being. Possible explanations for the discrepancies between the findings obtained in this study and those obtained in previous studies are discussed. © 2016 International Union of Psychological Science.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

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

The School Counselor Leadership Survey: Instrument Development and Exploratory Factor Analysis

ERIC Educational Resources Information Center

Young, Anita; Bryan, Julia

2015-01-01

This study examined the factor structure of the School Counselor Leadership Survey (SCLS). Survey development was a threefold process that resulted in a 39-item survey of 801 school counselors and school counselor supervisors. The exploratory factor analysis indicated a five-factor structure that revealed five key dimensions of school counselor…

Exploratory Factor Analysis with Small Sample Sizes

ERIC Educational Resources Information Center

de Winter, J. C. F.; Dodou, D.; Wieringa, P. A.

2009-01-01

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…

A Psychometric Investigation of the Multicultural and Special Education Survey: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Utley, Cheryl A.

2011-01-01

An exploratory factorial analysis of the Multicultural and Special Education Survey (MSES) evaluated the professional development training needs of general and special educators in a midwestern state. Survey items were selected from the culturally and linguistically diverse multicultural, bilingual and special education literature bases (CLD). The…

An Exploratory Analysis of TPACK Perceptions of Pre-Service Science Teachers: A Regional Australian Perspective

ERIC Educational Resources Information Center

Reyes, Vicente Chua, Jr.; Rizk, Nadya; Gregory, Sue; Doyle, Helen

2016-01-01

Four distinct constructs were identified from a survey of a sample of pre-service science teachers at a regional Australian University. The constructs emerged after employing Exploratory Factor Analysis (EFA) on respondents' perceptions of pedagogical practices incorporating the use of Information Communication and Technology (ICT). The key…

Likelihood-Based Confidence Intervals in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Oort, Frans J.

2011-01-01

In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…

Exploratory and Confirmatory Analysis of the Trauma Practices Questionnaire

ERIC Educational Resources Information Center

Craig, Carlton D.; Sprang, Ginny

2009-01-01

Objective: The present study provides psychometric data for the Trauma Practices Questionnaire (TPQ). Method: A nationally randomized sample of 2,400 surveys was sent to self-identified trauma treatment specialists, and 711 (29.6%) were returned. Results: An exploratory factor analysis (N = 319) conducted on a randomly split sample (RSS) revealed…

High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Cai, Li

2010-01-01

A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The…

Mission Attachment and Job Satisfaction among Emergency Shelter and Transitional Housing Service Professionals: An Exploratory Analysis

ERIC Educational Resources Information Center

Fermin, Caroline P.

2017-01-01

This research study was an exploratory analysis to determine if a relationship existed between mission attachment and job satisfaction of emergency nonprofit domestic violence shelter/transitional housing workers. The study examined if the perceptions, beliefs, and attitudes were different between entry-level, middle-level, and senior-level…

An Exploratory Case Study of PBIS Implementation Using Social Network Analysis

ERIC Educational Resources Information Center

Whitcomb, Sara A.; Woodland, Rebecca H.; Barry, Shannon K.

2017-01-01

An exploratory case study is presented in which social network analysis (SNA) was used to explore how school teaming structures influence the implementation of School-Wide Positive Behavioral Interventions and Supports (PBIS). The authors theorized that PBIS leadership teams that include members with connections to all other information-sharing…

What Is Rotating in Exploratory Factor Analysis?

ERIC Educational Resources Information Center

Osborne, Jason W.

2015-01-01

Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what "rotation" is, what exactly is rotating, and why we use rotation when performing…

Mining concepts of health responsibility using text mining and exploratory graph analysis.

PubMed

Kjellström, Sofia; Golino, Hudson

2018-05-24

Occupational therapists need to know about people's beliefs about personal responsibility for health to help them pursue everyday activities. The study aims to employ state-of-the-art quantitative approaches to understand people's views of health and responsibility at different ages. A mixed method approach was adopted, using text mining to extract information from 233 interviews with participants aged 5 to 96 years, and then exploratory graph analysis to estimate the number of latent variables. The fit of the structure estimated via the exploratory graph analysis was verified using confirmatory factor analysis. Exploratory graph analysis estimated three dimensions of health responsibility: (1) creating good health habits and feeling good; (2) thinking about one's own health and wanting to improve it; and 3) adopting explicitly normative attitudes to take care of one's health. The comparison between the three dimensions among age groups showed, in general, that children and adolescents, as well as the old elderly (>73 years old) expressed ideas about personal responsibility for health less than young adults, adults and young elderly. Occupational therapists' knowledge of the concepts of health responsibility is of value when working with a patient's health, but an identified challenge is how to engage children and older persons.

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

Krause, Josua; Dasgupta, Aritra; Fekete, Jean-Daniel

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overviewmore » of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.« less

An exploratory cluster randomised trial of a university halls of residence based social norms intervention in Wales, UK

PubMed Central

2012-01-01

Background Excessive alcohol consumption amongst university students has received increasing attention. A social norms approach to reducing drinking behaviours has met with some success in the USA. Such an approach is based on the assumption that student's perceptions of the norms of their peers are highly influential, but that these perceptions are often incorrect. Social norms interventions therefore aim to correct these inaccurate perceptions, and in turn, to change behaviours. However, UK studies are scarce and it is increasingly recognised that social norm interventions need to be supported by socio ecological approaches that address the wider determinants of behaviour. Objectives To describe the research design for an exploratory trial examining the acceptability, hypothesised process of change and implementation of a social norm marketing campaign designed to correct misperceptions of normative alcohol use and reduce levels of misuse, implemented alongside a university wide alcohol harm reduction toolkit. It also assesses the feasibility of a potential large scale effectiveness trial by providing key trial design parameters including randomisation, recruitment and retention, contamination, data collection methods, outcome measures and intracluster correlations. Methods/design The study adopts an exploratory cluster randomised controlled trial design with halls of residence as the unit of allocation, and a nested mixed methods process evaluation. Four Welsh (UK) universities participated in the study, with residence hall managers consenting to implementation of the trial in 50 university owned campus based halls of residence. Consenting halls were randomised to either a phased multi channel social norm marketing campaign addressing normative discrepancies (n = 25 intervention) or normal practice (n = 25 control). The primary outcome is alcohol consumption (units per week) measured using the Daily Drinking Questionnaire. Secondary outcomes assess frequency of alcohol consumption, higher risk drinking, alcohol related problems and change in perceptions of alcohol-related descriptive and injunctive norms. Data will be collected for all 50 halls at 4 months follow up through a cross-sectional on line and postal survey of approximately 4000 first year students. The process evaluation will explore the acceptability and implementation of the social norms intervention and toolkit and hypothesised process of change including awareness, receptivity and normative changes. Discussion Exploratory trials such as this are essential to inform future definitive trials by providing crucial methodological parameters and guidance on designing and implementing optimum interventions. Trial registration number ISRCTN: ISRCTN48556384 PMID:22414293

Grains Are Similarly Categorized by 8- to 13-Year-Old Children

PubMed Central

Beltran, Alicia; Sepulveda, Karina Knight; Watson, Kathy; Baranowski, Tom; Baranowski, Janice; Islam, Noemi; Missaghian, Mariam

2009-01-01

This study assessed how 8- to 13-year-old children categorized and labeled grain foods and how these categories and labels were influenced by child characteristics. The main hypotheses were that children categorized foods in consistent ways and these food categories differed from the professional food categories. A set of 71 cards with pictures and names of grain foods from eight professionally denned food groups was sorted by each child into piles of similar foods. There were 149 8- to 13-year-old children (133 English-speaking, 16 Spanish-speaking) in this exploratory study. One-way analysis of variance and Robinson matrices for identification of clusters of food items were calculated. Children created a mean (± standard deviation) of 8.3±3.8 piles with 8.6±9.1 cards per pile. No substantial differences in Robinson clustering were detected across subcategories for each of the demographic characteristics. For the majority of the piles, children provided “taxonomic-professional” (34.5%) labels, such as bread for the professional category of breads, rolls, and tortillas, or “script” (26.1%) labels, such as breakfast for the professional category of pancakes, waffles, and flapjacks. These categories may be used to facilitate food search in a computerized 24-hour dietary recall for children in this age group. PMID:18954585

Using Dynamic Time Warping and Data Forensics to Examine Tradeoffs among Land-Energy-Water Networks Across the Conterminous United States

NASA Astrophysics Data System (ADS)

McManamay, R.; Allen, M. R.; Piburn, J.; Sanyal, J.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Characterizing interdependencies among land-energy-water sectors, their vulnerabilities, and tipping points, is challenging, especially if all sectors are simultaneously considered. Because such holistic system behavior is uncertain, largely unmodeled, and in need of testable hypotheses of system drivers, these dynamics are conducive to exploratory analytics of spatiotemporal patterns, powered by tools, such as Dynamic Time Warping (DTW). Here, we conduct a retrospective analysis (1950 - 2010) of temporal trends in land use, energy use, and water use within US counties to identify commonalities in resource consumption and adaptation strategies to resource limitations. We combine existing and derived data from statistical downscaling to synthesize a temporally comprehensive land-energy-water dataset at the US county level and apply DTW and subsequent hierarchical clustering to examine similar temporal trends in resource typologies for land, energy, and water sectors. As expected, we observed tradeoffs among water uses (e.g., public supply vs irrigation) and land uses (e.g., urban vs ag). Strong associations between clusters amongst sectors reveal tight system interdependencies, whereas weak associations suggest unique behaviors and potential for human adaptations towards disruptive technologies and less resource-dependent population growth. Our framework is useful for exploring complex human-environmental system dynamics and generating hypotheses to guide subsequent energy-water-nexus research.

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

PubMed

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

2014-03-01

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

Brain Volume Differences Associated With Hearing Impairment in Adults

PubMed Central

Vriend, Chris; Heslenfeld, Dirk J.; Versfeld, Niek J.; Kramer, Sophia E.

2018-01-01

Speech comprehension depends on the successful operation of a network of brain regions. Processing of degraded speech is associated with different patterns of brain activity in comparison with that of high-quality speech. In this exploratory study, we studied whether processing degraded auditory input in daily life because of hearing impairment is associated with differences in brain volume. We compared T1-weighted structural magnetic resonance images of 17 hearing-impaired (HI) adults with those of 17 normal-hearing (NH) controls using a voxel-based morphometry analysis. HI adults were individually matched with NH adults based on age and educational level. Gray and white matter brain volumes were compared between the groups by region-of-interest analyses in structures associated with speech processing, and by whole-brain analyses. The results suggest increased gray matter volume in the right angular gyrus and decreased white matter volume in the left fusiform gyrus in HI listeners as compared with NH ones. In the HI group, there was a significant correlation between hearing acuity and cluster volume of the gray matter cluster in the right angular gyrus. This correlation supports the link between partial hearing loss and altered brain volume. The alterations in volume may reflect the operation of compensatory mechanisms that are related to decoding meaning from degraded auditory input. PMID:29557274

Concept mapping as an approach for expert-guided model building: The example of health literacy.

PubMed

Soellner, Renate; Lenartz, Norbert; Rudinger, Georg

2017-02-01

Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of 'health literacy.' Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine "clusters": (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a "qualitative" structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a "quantitative" structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the "real world". Copyright © 2016 Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

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

2007-01-01

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

A Note on Procrustean Rotation in Exploratory Factor Analysis: A Computer Intensive Approach to Goodness-of-Fit Evaluation.

ERIC Educational Resources Information Center

Raykov, Tenko; Little, Todd D.

1999-01-01

Describes a method for evaluating results of Procrustean rotation to a target factor pattern matrix in exploratory factor analysis. The approach, based on the bootstrap method, yields empirical approximations of the sampling distributions of: (1) differences between target elements and rotated factor pattern matrices; and (2) the overall…

An Exploratory Factor Analysis of the Sexual Orientation Counselor Competency Scale: Examining the Variable of Experience

ERIC Educational Resources Information Center

Ali, Shainna; Lambie, Glenn; Bloom, Zachary D.

2017-01-01

The Sexual Orientation Counselor Competency Scale (SOCCS), developed by Bidell in 2005, measures counselors' levels of skills, awareness, and knowledge in assisting lesbian, gay, or bisexual (LGB) clients. In an effort to gain an increased understanding of the construct validity of the SOCCS, researchers performed an exploratory factor analysis on…

Technical Communications in Aeronautics: Results of an Exploratory Study. An Analysis of Managers' and Nonmanagers' Responses. NASA Technical Memorandum 101625.

ERIC Educational Resources Information Center

Pinelli, Thomas E.; And Others

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that aerospace managers and nonmanagers have different technical communications practices. Five secondary assumptions were established for the analysis: (1) that the…

Graphical and Numerical Descriptive Analysis: Exploratory Tools Applied to Vietnamese Data

ERIC Educational Resources Information Center

Haughton, Dominique; Phong, Nguyen

2004-01-01

This case study covers several exploratory data analysis ideas, the histogram and boxplot, kernel density estimates, the recently introduced bagplot--a two-dimensional extension of the boxplot--as well as the violin plot, which combines a boxplot with a density shape plot. We apply these ideas and demonstrate how to interpret the output from these…

Validation of the Adolescent Concerns Measure (ACM): Evidence from Exploratory and Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Ang, Rebecca P.; Chong, Wan Har; Huan, Vivien S.; Yeo, Lay See

2007-01-01

This article reports the development and initial validation of scores obtained from the Adolescent Concerns Measure (ACM), a scale which assesses concerns of Asian adolescent students. In Study 1, findings from exploratory factor analysis using 619 adolescents suggested a 24-item scale with four correlated factors--Family Concerns (9 items), Peer…

An Exploratory Factor Analysis of the URICA among Couple Therapy Participants

ERIC Educational Resources Information Center

Tambling, Rachel B.; Johnson, Lee N.

2012-01-01

Assessing and measuring client motivation to change has been of great interest to therapists and researchers in a variety of fields. This article presents the results of an exploratory factor analysis of the University of Rhode Island Change Assessment (URICA), a measure of motivation to change, in a sample of individuals in couple therapy. Four…

Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR

ERIC Educational Resources Information Center

Baglin, James

2014-01-01

Exploratory factor analysis (EFA) methods are used extensively in the field of assessment and evaluation. Due to EFA's widespread use, common methods and practices have come under close scrutiny. A substantial body of literature has been compiled highlighting problems with many of the methods and practices used in EFA, and, in response, many…

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

ERIC Educational Resources Information Center

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

2010-01-01

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

Exploratory and Confirmatory Factor Analyses of the WISC-IV with Gifted Students

ERIC Educational Resources Information Center

Rowe, Ellen W.; Dandridge, Jessica; Pawlush, Alexandra; Thompson, Dawna F.; Ferrier, David E.

2014-01-01

These 2 studies investigated the factor structure of the Wechsler Intelligence Scale for Children-4th edition (WISC-IV; Wechsler, 2003a) with exploratory factor analysis (EFA; Study 1) and confirmatory factor analysis (CFA; Study 2) among 2 independent samples of gifted students. The EFA sample consisted of 225 children who were referred for a…

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

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Ferrando, Pere J.

2013-01-01

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

Ordinary Least Squares Estimation of Parameters in Exploratory Factor Analysis with Ordinal Data

ERIC Educational Resources Information Center

Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C.

2012-01-01

Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

ERIC Educational Resources Information Center

Yu, Taeho; Richardson, Jennifer C.

2015-01-01

The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor…

Development of an Instrument to Measure Student Use of Academic Success Skills: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Carey, John; Brigman, Greg; Webb, Linda; Villares, Elizabeth; Harrington, Karen

2014-01-01

This article describes the development of the Student Engagement in School Success Skills instrument including item development and exploratory factor analysis. The instrument was developed to measure student use of the skills and strategies identified as most critical for long-term school success that are typically taught by school counselors.

An Exploratory Analysis of a Middle School Science Curriculum: Implications for Students with Learning Disabilities

ERIC Educational Resources Information Center

Taylor, Gregory S.; Hord, Casey

2016-01-01

An exploratory study of a middle school curriculum directly aligned with the Next Generation Science Standards was conducted with a focus on how the curriculum addresses the instructional needs of students with learning disabilities. A descriptive analysis of a lesson on speed and velocity was conducted and implications discussed for students with…

Battle of Narratives

DTIC Science & Technology

2012-06-01

18 De Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek (New York: Cambridge University Press, 2005... Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press, 2005. Democratic National...Review 54(1):33-48; Brian Uzzi. 1996 . "The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect

Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns

ERIC Educational Resources Information Center

Anselin, Luc; Sridharan, Sanjeev; Gholston, Susan

2007-01-01

With the proliferation of social indicator databases, the need for powerful techniques to study patterns of change has grown. In this paper, the utility of spatial data analytical methods such as exploratory spatial data analysis (ESDA) is suggested as a means to leverage the information contained in social indicator databases. The principles…

An exploratory analysis of the relationship between ambient ozone and particulate matter concentrations during early pregnancy and selected birth defects in Texas

EPA Science Inventory

Background: Associations between ozone (O3) and fine particulate matter (PM2.5) concentrations and birth outcomes have been previously demonstrated. We perform an exploratory analysis of O3 and PM2.5 concentrations during early pregnancy and multiple types of birth defects. Met...

Exploratory Two-Level Analysis of Individual- and School-Level Factors on Truant Youth Emotional/Psychological Functioning

ERIC Educational Resources Information Center

Dembo, Richard; Wareham, Jennifer; Schmeidler, James; Winters, Ken C.

2016-01-01

Research on samples of truant adolescents is limited, with little known about mental health problems among truant youths. This study provided an exploratory, multilevel examination of mental health problems for a sample of 300 truant adolescents. Confirmatory factor analysis indicated a single factor of multiple mental health problems at the…

Neuropsychological assessment of decision making in alcohol-dependent commercial pilots.

PubMed

Georgemiller, Randy; Machizawa, Sayaka; Young, Kathleen M; Martin, Cynthia N

2013-09-01

The aim of this exploratory archival study was to discern the utility of the Iowa Gambling Task (IGT) in identifying adaptive decision-making capacities among pilots with a history of alcohol dependence both with and without Cluster B personality features. Participants included 18 male airmen at the rank of captain with a history of receiving alcohol dependence treatment and subsequent referral for a fitness-for-duty evaluation. Data from prior comprehensive neuropsychological evaluations conducted in a private practice setting at the mandate of the FAA utilizing criteria outlined in the HIMS program was used. ANOVA was conducted to compare pilots with (N = 4) and without Cluster B personality features (N = 14) on measures of decisionmaking capacities, intelligence, and executive functioning. Pilots with Cluster B personality features were found to have a significantly lower Total Net T-Score on IGT (M = 35.00, SD = 9.27) than pilots without features of Cluster B (M = 56.36, SD = 9.55). Furthermore, with the exception of the first 20 cards (i.e., Net 1); the groups significantly differed in their Net scores. No statistically significant difference was found on airmen's intelligence and executive functioning. The present study found that alcohol-dependent airmen with Cluster B personality features evidenced significantly poorer decisionmaking capacities as measured by the ICT in comparison to alcohol dependent airman without Cluster B personality features. Implications and limitations of the study are discussed.

An exploration of the professional competencies required in engineering asset management

NASA Astrophysics Data System (ADS)

Bish, Adelle J.; Newton, Cameron J.; Browning, Vicky; O'Connor, Peter; Anibaldi, Renata

2014-07-01

Engineering asset management (EAM) is a rapidly growing and developing field. However, efforts to select and develop engineers in this area are complicated by our lack of understanding of the full range of competencies required to perform. This exploratory study sought to clarify and categorise the professional competencies required of individuals at different hierarchical levels within EAM. Data from 14 field interviews, 61 online surveys, and 10 expert panel interviews were used to develop an initial professional competency framework. Overall, nine competency clusters were identified. These clusters indicate that engineers working in this field need to be able to collaborate and influence others, complete objectives within organisational guidelines, and be able to manage themselves effectively. Limitations and potential uses of this framework in engineering education and research are discussed.

Subspecialization in the human posterior medial cortex

PubMed Central

Bzdok, Danilo; Heeger, Adrian; Langner, Robert; Laird, Angela R.; Fox, Peter T.; Palomero-Gallagher, Nicola; Vogt, Brent A.; Zilles, Karl; Eickhoff, Simon B.

2014-01-01

The posterior medial cortex (PMC) is particularly poorly understood. Its neural activity changes have been related to highly disparate mental processes. We therefore investigated PMC properties with a data-driven exploratory approach. First, we subdivided the PMC by whole-brain coactivation profiles. Second, functional connectivity of the ensuing PMC regions was compared by task-constrained meta-analytic coactivation mapping (MACM) and task-unconstrained resting-state correlations (RSFC). Third, PMC regions were functionally described by forward/reverse functional inference. A precuneal cluster was mostly connected to the intraparietal sulcus, frontal eye fields, and right temporo-parietal junction; associated with attention and motor tasks. A ventral posterior cingulate cortex (PCC) cluster was mostly connected to the ventromedial prefrontal cortex and middle left inferior parietal cortex (IPC); associated with facial appraisal and language tasks. A dorsal PCC cluster was mostly connected to the dorsomedial prefrontal cortex, anterior/posterior IPC, posterior midcingulate cortex, and left dorsolateral prefrontal cortex; associated with delay discounting. A cluster in the retrosplenial cortex was mostly connected to the anterior thalamus and hippocampus. Furthermore, all PMC clusters were congruently coupled with the default mode network according to task-constrained but not task-unconstrained connectivity. We thus identified distinct regions in the PMC and characterized their neural networks and functional implications. PMID:25462801

Adverse mental health effects of cannabis use in two indigenous communities in Arnhem Land, Northern Territory, Australia: exploratory study.

PubMed

Clough, Alan R; d'Abbs, Peter; Cairney, Sheree; Gray, Dennis; Maruff, Paul; Parker, Robert; O'Reilly, Bridie

2005-07-01

We investigated adverse mental health effects and their associations with levels of cannabis use among indigenous Australian cannabis users in remote communities in the Northern Territory. Local indigenous health workers and key informants assisted in developing 28 criteria describing mental health symptoms. Five symptom clusters were identified using cluster analysis of data compiled from interviews with 103 cannabis users. Agreement was assessed (method comparison approach, kappa-statistic) with a clinician's classification of the 28 criteria into five groups labelled: 'anxiety', 'dependency', 'mood', 'vegetative' and 'psychosis'. Participants were described as showing 'anxiety', 'dependency' etc., if they reported half or more of the symptoms comprising the cluster. Associations between participants' self-reported cannabis use and each symptom cluster were assessed (logistic regression adjusting for age, sex, other substance use). Agreement between two classifications of 28 criteria into five groups was 'moderate' (64%, kappa = 0.55, p < 0.001). When five clusters were combined into three, 'anxiety-dependency', 'mood-vegetative' and 'psychosis', agreement rose to 71% (kappa = 0.56, p < 0.001). 'Anxiety-dependency' was positively associated with number of 'cones' usually smoked per week and this remained significant when adjusted for confounders (p = 0.020) and tended to remain significant in those who had never sniffed petrol (p = 0.052). Users of more than five cones per week were more likely to display 'anxiety-dependency' symptoms than those who used one cone per week (OR = 15.8, 1.8-141.2, p = 0.013). A crude association between the 'mood-vegetative' symptom cluster and number of cones usually smoked per week (p = 0.014) also remained statistically significant when adjusted for confounders (p = 0.012) but was modified by interactions with petrol sniffing (p = 0.116) and alcohol use (p = 0.276). There were no associations between cannabis use and 'psychosis'. Risks for 'anxiety-dependency' symptoms in cannabis users increased as their level of use increased. Other plausible mental health effects of cannabis in this population of comparatively new users were probably masked by alcohol use and a history of petrol sniffing.

A clinician-administered severity rating scale for illness anxiety: development, reliability, and validity of the H-YBOCS-M.

PubMed

Skritskaya, Natalia A; Carson-Wong, Amanda R; Moeller, James R; Shen, Sa; Barsky, Arthur J; Fallon, Brian A

2012-07-01

Clinician-administered measures to assess severity of illness anxiety and response to treatment are few. The authors evaluated a modified version of the hypochondriasis-Y-BOCS (H-YBOCS-M), a 19-item, semistructured, clinician-administered instrument designed to rate severity of illness-related thoughts, behaviors, and avoidance. The scale was administered to 195 treatment-seeking adults with DSM-IV hypochondriasis. Test-retest reliability was assessed in a subsample of 20 patients. Interrater reliability was assessed by 27 interviews independently rated by four raters. Sensitivity to change was evaluated in a subsample of 149 patients. Convergent and discriminant validity was examined by comparing H-YBOCS-M scores to other measures administered. Item clustering was examined with confirmatory and exploratory factor analyses. The H-YBOCS-M demonstrated good internal consistency, interrater and test-retest reliability, and sensitivity to symptom change with treatment. Construct validity was supported by significant higher correlations with scores on other measures of hypochondriasis than with nonhypochondriacal measures. Improvement over time in response to treatment correlated with improvement both on measures of hypochondriasis and on measures of somatization, depression, anxiety, and functional status. Confirmatory factor analysis did not show adequate fit for a three-factor model. Exploratory factor analysis revealed a five-factor solution with the first two factors consistent with the separation of the H-YBOCS-M items into the subscales of illness-related avoidance and compulsions. H-YBOCS-M appears to be valid, reliable, and appropriate as an outcome measure for treatment studies of illness anxiety. Study results highlight "avoidance" as a key feature of illness anxiety-with potentially important nosologic and treatment implications. © 2012 Wiley Periodicals, Inc.

A CLINICIAN-ADMINISTERED SEVERITY RATING SCALE FOR ILLNESS ANXIETY: DEVELOPMENT, RELIABILITY, AND VALIDITY OF THE H-YBOCS-M

PubMed Central

Skritskaya, Natalia A.; Carson-Wong, Amanda R.; Moeller, James R.; Shen, Sa; Barsky, Arthur J.; Fallon, Brian A.

2012-01-01

Background Clinician-administered measures to assess severity of illness anxiety and response to treatment are few. The authors evaluated a modified version of the hypochondriasis-Y-BOCS (H-YBOCS-M), a 19-item, semistructured, clinician-administered instrument designed to rate severity of illness-related thoughts, behaviors, and avoidance. Methods The scale was administered to 195 treatment-seeking adults with DSM-IV hypochondriasis. Test–retest reliability was assessed in a subsample of 20 patients. Interrater reliability was assessed by 27 interviews independently rated by four raters. Sensitivity to change was evaluated in a subsample of 149 patients. Convergent and discriminant validity was examined by comparing H-YBOCS-M scores to other measures administered. Item clustering was examined with confirmatory and exploratory factor analyses. Results The H-YBOCS-M demonstrated good internal consistency, interrater and test–retest reliability, and sensitivity to symptom change with treatment. Construct validity was supported by significant higher correlations with scores on other measures of hypochondriasis than with nonhypochondriacal measures. Improvement over time in response to treatment correlated with improvement both on measures of hypochondriasis and on measures of somatization, depression, anxiety, and functional status. Confirmatory factor analysis did not show adequate fit for a three-factor model. Exploratory factor analysis revealed a five-factor solution with the first two factors consistent with the separation of the H-YBOCS-M items into the subscales of illness-related avoidance and compulsions. Conclusions H-YBOCS-M appears to be valid, reliable, and appropriate as an outcome measure for treatment studies of illness anxiety. Study results highlight “avoidance” as a key feature of illness anxiety—with potentially important nosologic and treatment implications. PMID:22504935

A Search for X-ray Emission in Isolated Compact Triplets

NASA Technical Reports Server (NTRS)

Brown, Beth A.; Williams, Barbara

2006-01-01

We describe preliminary results of an exploratory search for diffuse X-ray emission in a sample of the poorest galaxy groups, i.e., isolated compact triplets of galaxies. These systems represent the simplest forms of galaxy clustering while manifesting all the complexities inherent in other groups. We have selected 20 compact triplets for this initial study. The component galaxies are expected to interact with each other and with the group's intergalactic medium, if present, in complex ways that trigger high-energy processes.

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

PubMed

Steimer, Andreas; Müller, Michael; Schindler, Kaspar

2017-05-01

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

The Relationship of Bureaucratic Structure to School Climate: An Exploratory Factor Analysis of Construct Validity

ERIC Educational Resources Information Center

Lennon, Patricia A.

2010-01-01

This researcher examined the relationship of bureaucratic structure to school climate by means of an exploratory factor analysis of a measure of bureaucracy developed by Hoy and Sweetland (2000) and the four dimensional measure of climate developed by Hoy, Smith, and Sweetland (2002). Since there had been no other empirical studies whose authors…

Exploratory Bifactor Analysis of the WJ-III Cognitive in Adulthood via the Schmid-Leiman Procedure

ERIC Educational Resources Information Center

Dombrowski, Stefan C.

2014-01-01

The Woodcock-Johnson-III cognitive in the adult time period (age 20 to 90 plus) was analyzed using exploratory bifactor analysis via the Schmid-Leiman orthogonalization procedure. The results of this study suggested possible overfactoring, a different factor structure from that posited in the Technical Manual and a lack of invariance across both…

Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

ERIC Educational Resources Information Center

Schmitt, Thomas A.; Sass, Daniel A.

2011-01-01

Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences.…

Rotation to a Partially Specified Target Matrix in Exploratory Factor Analysis: How Many Targets?

ERIC Educational Resources Information Center

Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying

2013-01-01

The purpose of this study was to explore the influence of the number of targets specified on the quality of exploratory factor analysis solutions with a complex underlying structure and incomplete substantive measurement theory. Three Monte Carlo studies were performed based on the ratio of the number of observed variables to the number of…

User roles and contributions during the new product development process in collaborative innovation communities.

PubMed

Guo, Wei; Zheng, Qing; An, Weijin; Peng, Wei

2017-09-01

Collaborative innovation (co-innovation) community emerges as a new product design platform where companies involve users in the new product development (NPD) process. Large numbers of users participate and contribute to the process voluntarily. This exploratory study investigates the heterogeneous roles of users based on a global co-innovation project in online community. Content analysis, social network analysis and cluster method are employed to measure user behaviors, distinguish user roles, and analyze user contributions. The study identifies six user roles that emerge during the NPD process in co-innovation community: project leader, active designer, generalist, communicator, passive designer, and observer. The six user roles differ in their contribution forms and quality. This paper contributes to research on co-innovation in online communities, including design team structure, user roles and their contribution to design task and solution, as well as user value along the process. In addition, the study provides practices guidance on implementing project, attracting users, and designing platform for co-innovation community practitioners. Copyright © 2017 Elsevier Ltd. All rights reserved.

Unsupervised learning on scientific ocean drilling datasets from the South China Sea

NASA Astrophysics Data System (ADS)

Tse, Kevin C.; Chiu, Hon-Chim; Tsang, Man-Yin; Li, Yiliang; Lam, Edmund Y.

2018-06-01

Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

NASA Technical Reports Server (NTRS)

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

2017-01-01

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

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

PubMed

Bolt, Taylor; Nomi, Jason S; Vij, Shruti G; Chang, Catie; Uddin, Lucina Q

2018-08-01

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity. Copyright © 2018 Elsevier Inc. All rights reserved.

Factors associated with the growing-finishing performances of swine herds: an exploratory study on serological and herd level indicators.

PubMed

Fablet, C; Rose, N; Grasland, B; Robert, N; Lewandowski, E; Gosselin, M

2018-01-01

Growing and finishing performances of pigs strongly influence farm efficiency and profitability. The performances of the pigs rely on the herd health status and also on several non-infectious factors. Many recommendations for the improvement of the technical performances of a herd are based on the results of studies assessing the effect of one or a limited number of infections or environmental factors. Few studies investigated jointly the influence of both type of factors on swine herd performances. This work aimed at identifying infectious and non-infectious factors associated with the growing and finishing performances of 41 French swine herds. Two groups of herds were identified using a clustering analysis: a cluster of 24 herds with the highest technical performance values (mean average daily gain = 781.1 g/day +/- 26.3; mean feed conversion ratio = 2.5 kg/kg +/- 0.1; mean mortality rate = 4.1% +/- 0.9; and mean carcass slaughter weight = 121.2 kg +/- 5.2) and a cluster of 17 herds with the lowest performance values (mean average daily gain =715.8 g/day +/- 26.5; mean feed conversion ratio = 2.6 kg/kg +/- 0.1; mean mortality rate = 6.8% +/- 2.0; and mean carcass slaughter weight = 117.7 kg +/- 3.6). Multiple correspondence analysis was used to identify factors associated with the level of technical performance. Infection with the porcine reproductive and respiratory syndrome virus and the porcine circovirus type 2 were infectious factors associated with the cluster having the lowest performance values. This cluster also featured farrow-to-finish type herds, a short interval between successive batches of pigs (≤3 weeks) and mixing of pigs from different batches in the growing or/and finishing steps. Inconsistency between nursery and fattening building management was another factor associated with the low-performance cluster. The odds of a herd showing low growing-finishing performance was significantly increased when infected by PRRS virus in the growing-finishing steps (OR = 8.8, 95% confidence interval [95% CI]: 1.8-41.7) and belonging to a farrow-to-finish type herd (OR = 5.1, 95% CI = 1.1-23.8). Herd management and viral infections significantly influenced the performance levels of the swine herds included in this study.

Social Dancing and Incidence of Falls in Older Adults: A Cluster Randomised Controlled Trial

PubMed Central

Merom, Dafna; Mathieu, Erin; Cerin, Ester; Morton, Rachael L.; Simpson, Judy M.; Anstey, Kaarin J.; Sherrington, Catherine; Lord, Stephen R.; Cumming, Robert G.

2016-01-01

Background The prevention of falls among older people is a major public health challenge. Exercises that challenge balance are recognized as an efficacious fall prevention strategy. Given that small-scale trials have indicated that diverse dance styles can improve balance and gait of older adults, two of the strongest risk factors for falls in older people, this study aimed to determine whether social dance is effective in i) reducing the number of falls and ii) improving physical and cognitive fall-related risk factors. Methods and Findings A parallel two-arm cluster randomized controlled trial was undertaken in 23 self-care retirement villages (clusters) around Sydney, Australia. Eligible villages had to have an appropriate hall for dancing, house at least 60 residents, and not be currently offering dance as a village activity. Retirement villages were randomised using a computer generated randomisation method, constrained using minimisation. Eligible participants had to be a resident of the village, be able to walk at least 50 m, and agree to undergo physical and cognitive testing without cognitive impairment. Residents of intervention villages (12 clusters) were offered twice weekly one-hour social dancing classes (folk or ballroom dancing) over 12 mo (80 h in total). Programs were standardized across villages and were delivered by eight dance teachers. Participants in the control villages (11 clusters) were advised to continue with their regular activities. Main outcomes: falls during the 12 mo trial and Trail Making Tests. Secondary outcomes: The Physiological Performance Assessment (i.e., postural sway, proprioception, reaction time, leg strength) and the Short Physical Performance Battery; health-related physical and mental quality of life from the Short-Form 12 (SF-12) Survey. Data on falls were obtained from 522 of 530 (98%) randomised participants (mean age 78 y, 85% women) and 424 (80%) attended the 12-mo reassessment, which was lower among folk dance participants (71%) than ballroom dancing (82%) or control participants (82%, p = 0.04). Mean attendance at dance classes was 51%. During the period, 444 falls were recorded; there was no significant difference in fall rates between the control group (0.80 per person-year) and the dance group (1.03 per person-year). Using negative binomial regression with robust standard errors the adjusted Incidence Rate Ratio (IRR) was 1.19 (95% CI: 95% CI = 0.83, 1.71). In exploratory post hoc subgroup analysis, the rate of falls was higher among dance participants with a history of multiple falls (IRR = 2.02, 95% CI: 1.15, 3.54, p = 0.23 for interaction) and with the folk dance intervention (IRR = 1.68, 95% CI: 1.03, 2.73). There were no significant between-group differences in executive function test (TMT-B = 2.8 s, 95% CI: −6.2, 11.8). Intention to treat (ITT) analysis revealed no between-group differences at 12-mo follow-up in the secondary outcome measures, with the exception of postural sway, favouring the control group. Exploratory post hoc analysis by study completers and style indicated that ballroom dancing participants apparently improved their gait speed by 0.07 m/s relative to control participants (95% CI: 0.00, 0.14, p = 0.05). Study limitations included allocation to style based on logistical considerations rather than at random; insufficient power to detect differential impacts of different dance styles and smaller overall effects; variation of measurement conditions across villages; and no assessment of more complex balance tasks, which may be more sensitive to changes brought about by dancing. Conclusions Social dancing did not prevent falls or their associated risk factors among these retirement villages' residents. Modified dance programmes that contain "training elements" to better approximate structured exercise programs, targeted at low and high-risk participants, warrant investigation. Trial Registration The Australian New Zealand Clinical Trials Registry ACTRN12612000889853 PMID:27575534

Social Dancing and Incidence of Falls in Older Adults: A Cluster Randomised Controlled Trial.

PubMed

Merom, Dafna; Mathieu, Erin; Cerin, Ester; Morton, Rachael L; Simpson, Judy M; Rissel, Chris; Anstey, Kaarin J; Sherrington, Catherine; Lord, Stephen R; Cumming, Robert G

2016-08-01

The prevention of falls among older people is a major public health challenge. Exercises that challenge balance are recognized as an efficacious fall prevention strategy. Given that small-scale trials have indicated that diverse dance styles can improve balance and gait of older adults, two of the strongest risk factors for falls in older people, this study aimed to determine whether social dance is effective in i) reducing the number of falls and ii) improving physical and cognitive fall-related risk factors. A parallel two-arm cluster randomized controlled trial was undertaken in 23 self-care retirement villages (clusters) around Sydney, Australia. Eligible villages had to have an appropriate hall for dancing, house at least 60 residents, and not be currently offering dance as a village activity. Retirement villages were randomised using a computer generated randomisation method, constrained using minimisation. Eligible participants had to be a resident of the village, be able to walk at least 50 m, and agree to undergo physical and cognitive testing without cognitive impairment. Residents of intervention villages (12 clusters) were offered twice weekly one-hour social dancing classes (folk or ballroom dancing) over 12 mo (80 h in total). Programs were standardized across villages and were delivered by eight dance teachers. Participants in the control villages (11 clusters) were advised to continue with their regular activities. falls during the 12 mo trial and Trail Making Tests. The Physiological Performance Assessment (i.e., postural sway, proprioception, reaction time, leg strength) and the Short Physical Performance Battery; health-related physical and mental quality of life from the Short-Form 12 (SF-12) Survey. Data on falls were obtained from 522 of 530 (98%) randomised participants (mean age 78 y, 85% women) and 424 (80%) attended the 12-mo reassessment, which was lower among folk dance participants (71%) than ballroom dancing (82%) or control participants (82%, p = 0.04). Mean attendance at dance classes was 51%. During the period, 444 falls were recorded; there was no significant difference in fall rates between the control group (0.80 per person-year) and the dance group (1.03 per person-year). Using negative binomial regression with robust standard errors the adjusted Incidence Rate Ratio (IRR) was 1.19 (95% CI: 95% CI = 0.83, 1.71). In exploratory post hoc subgroup analysis, the rate of falls was higher among dance participants with a history of multiple falls (IRR = 2.02, 95% CI: 1.15, 3.54, p = 0.23 for interaction) and with the folk dance intervention (IRR = 1.68, 95% CI: 1.03, 2.73). There were no significant between-group differences in executive function test (TMT-B = 2.8 s, 95% CI: -6.2, 11.8). Intention to treat (ITT) analysis revealed no between-group differences at 12-mo follow-up in the secondary outcome measures, with the exception of postural sway, favouring the control group. Exploratory post hoc analysis by study completers and style indicated that ballroom dancing participants apparently improved their gait speed by 0.07 m/s relative to control participants (95% CI: 0.00, 0.14, p = 0.05). Study limitations included allocation to style based on logistical considerations rather than at random; insufficient power to detect differential impacts of different dance styles and smaller overall effects; variation of measurement conditions across villages; and no assessment of more complex balance tasks, which may be more sensitive to changes brought about by dancing. Social dancing did not prevent falls or their associated risk factors among these retirement villages' residents. Modified dance programmes that contain "training elements" to better approximate structured exercise programs, targeted at low and high-risk participants, warrant investigation. The Australian New Zealand Clinical Trials Registry ACTRN12612000889853.

Planning representation for automated exploratory data analysis

NASA Astrophysics Data System (ADS)

St. Amant, Robert; Cohen, Paul R.

1994-03-01

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

Relative Expression of Vitamin D Hydroxylases, CYP27B1 and CYP24A1, and of Cyclooxygenase-2 and Heterogeneity of Human Colorectal Cancer in Relation to Age, Gender, Tumor Location, and Malignancy: Results from Factor and Cluster Analysis.

PubMed

Brozek, Wolfgang; Manhardt, Teresa; Kállay, Enikö; Peterlik, Meinrad; Cross, Heide S

2012-07-26

Previous studies on the significance of vitamin D insufficiency and chronic inflammation in colorectal cancer development clearly indicated that maintenance of cellular homeostasis in the large intestinal epithelium requires balanced interaction of 1,25-(OH)2D3 and prostaglandin cellular signaling networks. The present study addresses the question how colorectal cancer pathogenesis depends on alterations of activities of vitamin D hydroxylases, i.e., CYP27B1-encoded 25-hydroxyvitamin D-1a-hydroxylase and CYP24A1-encoded 25-hydroxyvitamin D-24-hydroxylase, and inflammation-induced cyclooxygenase-2 (COX-2). Data from 105 cancer patients on CYP27B1, VDR, CYP24A1, and COX-2 mRNA expression in relation to tumor grade, anatomical location, gender and age were fit into a multivariate model of exploratory factor analysis. Nearly identical results were obtained by the principal factor and the maximum likelihood method, and these were confirmed by hierarchical cluster analysis: Within the eight mutually dependent variables studied four independent constellations were found that identify different features of colorectal cancer pathogenesis: (i) Escape of COX-2 activity from restraints by the CYP27B1/VDR system can initiate cancer growth anywhere in the colorectum regardless of age and gender; (ii) variations in COX-2 expression are mainly responsible for differences in cancer incidence in relation to tumor location; (iii) advancing age has a strong gender-specific influence on cancer incidence; (iv) progression from well differentiated to undifferentiated cancer is solely associated with a rise in CYP24A1 expression.

Relative Expression of Vitamin D Hydroxylases, CYP27B1 and CYP24A1, and of Cyclooxygenase-2 and Heterogeneity of Human Colorectal Cancer in Relation to Age, Gender, Tumor Location, and Malignancy: Results from Factor and Cluster Analysis

PubMed Central

Brozek, Wolfgang; Manhardt, Teresa; Kállay, Enikö; Peterlik, Meinrad; Cross, Heide S.

2012-01-01

Previous studies on the significance of vitamin D insufficiency and chronic inflammation in colorectal cancer development clearly indicated that maintenance of cellular homeostasis in the large intestinal epithelium requires balanced interaction of 1,25-(OH)2D3 and prostaglandin cellular signaling networks. The present study addresses the question how colorectal cancer pathogenesis depends on alterations of activities of vitamin D hydroxylases, i.e., CYP27B1-encoded 25-hydroxyvitamin D-1α-hydroxylase and CYP24A1-encoded 25-hydroxyvitamin D-24-hydroxylase, and inflammation-induced cyclooxygenase-2 (COX-2). Data from 105 cancer patients on CYP27B1, VDR, CYP24A1, and COX-2 mRNA expression in relation to tumor grade, anatomical location, gender and age were fit into a multivariate model of exploratory factor analysis. Nearly identical results were obtained by the principal factor and the maximum likelihood method, and these were confirmed by hierarchical cluster analysis: Within the eight mutually dependent variables studied four independent constellations were found that identify different features of colorectal cancer pathogenesis: (i) Escape of COX-2 activity from restraints by the CYP27B1/VDR system can initiate cancer growth anywhere in the colorectum regardless of age and gender; (ii) variations in COX-2 expression are mainly responsible for differences in cancer incidence in relation to tumor location; (iii) advancing age has a strong gender-specific influence on cancer incidence; (iv) progression from well differentiated to undifferentiated cancer is solely associated with a rise in CYP24A1 expression. PMID:24213465

Study protocol of a mixed-methods evaluation of a cluster randomized trial to improve the safety of NSAID and antiplatelet prescribing: data-driven quality improvement in primary care.

PubMed

Grant, Aileen; Dreischulte, Tobias; Treweek, Shaun; Guthrie, Bruce

2012-08-28

Trials of complex interventions are criticized for being 'black box', so the UK Medical Research Council recommends carrying out a process evaluation to explain the trial findings. We believe it is good practice to pre-specify and publish process evaluation protocols to set standards and minimize bias. Unlike protocols for trials, little guidance or standards exist for the reporting of process evaluations. This paper presents the mixed-method process evaluation protocol of a cluster randomized trial, drawing on a framework designed by the authors. This mixed-method evaluation is based on four research questions and maps data collection to a logic model of how the data-driven quality improvement in primary care (DQIP) intervention is expected to work. Data collection will be predominately by qualitative case studies in eight to ten of the trial practices, focus groups with patients affected by the intervention and quantitative analysis of routine practice data, trial outcome and questionnaire data and data from the DQIP intervention. We believe that pre-specifying the intentions of a process evaluation can help to minimize bias arising from potentially misleading post-hoc analysis. We recognize it is also important to retain flexibility to examine the unexpected and the unintended. From that perspective, a mixed-methods evaluation allows the combination of exploratory and flexible qualitative work, and more pre-specified quantitative analysis, with each method contributing to the design, implementation and interpretation of the other.As well as strengthening the study the authors hope to stimulate discussion among their academic colleagues about publishing protocols for evaluations of randomized trials of complex interventions. DATA-DRIVEN QUALITY IMPROVEMENT IN PRIMARY CARE TRIAL REGISTRATION: ClinicalTrials.gov: NCT01425502.

The natural history of children with joint hypermobility syndrome and Ehlers-Danlos hypermobility type: a longitudinal cohort study.

PubMed

Scheper, Mark C; Nicholson, Lesley L; Adams, Roger D; Tofts, Louise; Pacey, Verity

2017-12-01

The objective of the manuscript was to describe the natural history of complaints and disability in children diagnosed with joint hypermobility syndrome (JHS)/Ehlers-Danlos-hypermobility type (EDS-HT) and to identify the constructs that underlie functional decline. One hundred and one JHS/EDS-HT children were observed over 3 years and assessed at three time points on the following: functional impairments, quality of life, connective tissue laxity, muscle function, postural control and musculoskeletal and multi-systemic complaints. Cluster analysis was performed to identify subgroups in severity. Clinical profiles were determined for these subgroups, and differences were assessed by multivariate analysis of covariance. Mixed linear regression models were used to determine the subsequent trajectories. Finally, an exploratory factor analysis was used to uncover the underlying constructs of functional impairment. Three clusters of children were identified in terms of functional impairment: mild, moderately and severely affected. Functional impairment at baseline was predictive of worsening trajectories in terms of reduced walking distance and decreased quality of life (P ⩽ 0.05) over 3 years. Multiple interactions between the secondary outcomes were observed, with four underlying constructs identified. All four constructs (multi-systemic effects, pain, fatigue and loss of postural control) contributed significantly to disability (P ⩽ 0.046). Children diagnosed with JHS/EDS-HT who have a high incidence of multi-systemic complaints (particularly, orthostatic intolerance, urinary incontinence and diarrhoea) and poor postural control in addition to high levels of pain and fatigue at baseline are most likely to have a deteriorating trajectory of functional impairment and, accordingly, warrant clinical prioritization. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Predictors of relapse in patients with major depressive disorder in a 52-week, fixed dose, double blind, randomized trial of selegiline transdermal system (STS).

PubMed

Jang, Saeheon; Jung, Sungwon; Pae, Chiun; Kimberly, Blanchard Portland; Craig Nelson, J; Patkar, Ashwin A

2013-12-01

We investigated patient and disease characteristics predictive of relapse of MDD during a 52-week placebo controlled trial of selegiline transdermal system (STS) to identify patient characteristics relevant for STS treatment. After 10 weeks of open-label stabilization with STS, 322 remitted patients with MDD were randomized to 52-weeks of double-blind treatment with STS (6 mg/24h) or placebo (PLB). Relapse was defined as Hamilton Depression Rating Scale (HAMD-17) score of ≥ 14 and a CGI-S score of ≥ 3 with at least 2-point increase from the beginning of the double blind phase on 2 consecutive visits. Cox's proportional hazards regression was used to examine the effect of potential predictors (age, sex, age at onset of first MDD, early response pattern, number of previous antidepressant trials, severity of index episode, number of previous episodes, melancholic features, atypical features and anxious feature) on outcome. Exploratory analyses examined additional clinical variables (medical history, other psychiatric history, and individual items of HAM-D 28) on relapse. For all predictor variables analyzed, treatment Hazard Ratio (HR=0.48~0.54) was significantly in favor of STS (i.e., lower relapse risk than PLB). Age of onset was significantly predictive of relapse. Type, duration, and severity of depressive episodes, previous antidepressant trials, or demographic variables did not predict relapse. In additional exploratory analysis, eating disorder history and suicidal ideation were significant predictors of relapse after controlling for the effect of treatment in individual predictor analysis. While age of onset, eating disorder history and suicidal ideation were significant predictors, the majority of clinical and demographic variables were not predictive of relapse. Given the post-hoc nature of analysis, the findings need confirmation from a prospective study. It appears that selegiline transdermal system was broadly effective in preventing relapse across different subtypes and symptoms clusters of MDD. © 2013 Published by Elsevier B.V.

Exploratory metabolomics of biomarker identification for the internet gaming disorder in young Korean males.

PubMed

Cho, Yeo Ul; Lee, Deokjong; Lee, Jung-Eun; Kim, Kyoung Heon; Lee, Do Yup; Jung, Young-Chul

2017-07-01

The main aim of the current research is to characterize the molecular dynamics related to internet gaming disorder (IGD) using non-targeted plasma metabolite profiling based on gas-chromatography time-of-flight mass spectrometry (GC-TOF MS). IGD is a psychiatric disorder instigated by excessive and prolonged internet gaming, which shared many pathological symptoms with attention deficit hyperactivity disorder (ADHD). The prevalence of the disorder has been rapidly increased particularly in East Asia countries (5.9% in South Korea) compared to Europe or North America (0.3-1.0% in United States and 1.16% in Germany). Thus we comparably explored the correlation between plasma metabolites and internet addiction severity in IGD patients, and potential biomarker composite in combination with clinical parameters. The systematic metabolite profiling of 54 blood samples (normal user, N=28 and IGD, N=24) identified a total of 104 metabolites out of 1212 metabolic feature, and revealed unique relation of co-linearly regressed set of plasma metabolites (arabitol, myo-inositol, methionine, pyrrole-2-carboxylic acid, and aspartic acid) with internet addiction severity scale (R=0.795). In addition, orthogonal partial least squared discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis identified the potential biomarker cluster that simultaneously discriminated the different types of the psychiatric status. The potential biomarker re-composite was comprehensively evaluated by a receiver operating characteristic (ROC) analysis where the AUCs were 0.890, 0.880, 1.000, and 0.935 for control, IGD, AD and IGD+AD, respectively (N=18, 19, 5, and 10) against the others. This exploratory method may provide robustness of predictive diagnosis in population screening of IGD. The identified metabolic features, the relatedness with clinical parameters, and the putative biochemical linkage will hopefully aid future pathological studies in IGD. Copyright © 2017. Published by Elsevier B.V.

Intelligence Collection Targeting and Interdiction of Dark Networks

DTIC Science & Technology

2014-06-01

2006): 346. 26 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek, 2nd ed. (Cambridge: Cambridge...Pittsburgh, PA: Carnegie Mellon University, 2013. de Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with...al-Qaeda’s leaders had closely followed the April 1996 assassination of Dzhokhar Dudayev, the Chechen prime minister, who was killed by a Russian

Reliability, Convergent Validity and Time Invariance of Default Mode Network Deviations in Early Adult Major Depressive Disorder.

PubMed

Bessette, Katie L; Jenkins, Lisanne M; Skerrett, Kristy A; Gowins, Jennifer R; DelDonno, Sophie R; Zubieta, Jon-Kar; McInnis, Melvin G; Jacobs, Rachel H; Ajilore, Olusola; Langenecker, Scott A

2018-01-01

There is substantial variability across studies of default mode network (DMN) connectivity in major depressive disorder, and reliability and time-invariance are not reported. This study evaluates whether DMN dysconnectivity in remitted depression (rMDD) is reliable over time and symptom-independent, and explores convergent relationships with cognitive features of depression. A longitudinal study was conducted with 82 young adults free of psychotropic medications (47 rMDD, 35 healthy controls) who completed clinical structured interviews, neuropsychological assessments, and 2 resting-state fMRI scans across 2 study sites. Functional connectivity analyses from bilateral posterior cingulate and anterior hippocampal formation seeds in DMN were conducted at both time points within a repeated-measures analysis of variance to compare groups and evaluate reliability of group-level connectivity findings. Eleven hyper- (from posterior cingulate) and 6 hypo- (from hippocampal formation) connectivity clusters in rMDD were obtained with moderate to adequate reliability in all but one cluster (ICC's range = 0.50 to 0.76 for 16 of 17). The significant clusters were reduced with a principle component analysis (5 components obtained) to explore these connectivity components, and were then correlated with cognitive features (rumination, cognitive control, learning and memory, and explicit emotion identification). At the exploratory level, for convergent validity, components consisting of posterior cingulate with cognitive control network hyperconnectivity in rMDD were related to cognitive control (inverse) and rumination (positive). Components consisting of anterior hippocampal formation with social emotional network and DMN hypoconnectivity were related to memory (inverse) and happy emotion identification (positive). Thus, time-invariant DMN connectivity differences exist early in the lifespan course of depression and are reliable. The nuanced results suggest a ventral within-network hypoconnectivity associated with poor memory and a dorsal cross-network hyperconnectivity linked to poorer cognitive control and elevated rumination. Study of early course remitted depression with attention to reliability and symptom independence could lead to more readily translatable clinical assessment tools for biomarkers.

Phenotype Matters: The Absence of a Positive Association between Cortical Thinning and Chronic Low Back Pain when Controlling for Salient Clinical Variables

PubMed Central

Dolman, Andrew J; Loggia, Marco L.; Edwards, Robert R.; Gollub, Randy L.; Jian, Jian Kong; Vitaly, Napadow; Wasan, Ajay D.

2014-01-01

Studies have associated chronic low back pain (cLBP) with grey matter thinning. But these studies have not controlled for important clinical variables (such as a comorbid affective disorder, pain medication, age, or pain phenotype), which may reduce or eliminate these associations. We conducted cortical thickness and voxel-based morphometry (VBM) analyses in 14 cLBP patients with a discogenic component to their pain, not taking opioids or benzodiazepines, and not depressed or anxious. They were age and gender matched to 14 healthy controls (HCs). An ROI-driven analysis (regions of interest) was conducted, using 18 clusters from a previous arterial spin labeling study demonstrating greater regional cerebral blood flow (rCBF) in these cLBP subjects than the HCs. Cortical thickness and VBM-based gray matter volume measurements were obtained from a structural MRI scan and group contrasts were calculated. MANOVA showed a trend toward cortical thickening in the right paracentral lobule in cLBP subjects (F(1,17)=3.667, p<0.067), and significant thickening in the right rostral middle frontal gyrus (F(1,17)=6.880, p<0.014). These clusters were non-significant after including age as a covariate (p<0.891; p<0.279). A whole-brain cortical thickness and VBM analysis also did not identify significant clusters of thinning or thickening. Exploratory analyses identified group differences for correlations between age and cortical thickness of the right rostral middle frontal gyrus (cLBP: R=-0.03, p=0.9; HCs: R=-0.81, p<0.001), i.e., HCs demonstrated age-related thinning while cLBP patients did not. Our pilot results suggest that controlling for affect, age, and concurrent medications may reduce or eliminate some of the previously reported structural brain alterations in cLBP. PMID:24135900

An exploratory clustering approach for extracting stride parameters from tracking collars on free-ranging wild animals.

PubMed

Dewhirst, Oliver P; Roskilly, Kyle; Hubel, Tatjana Y; Jordan, Neil R; Golabek, Krystyna A; McNutt, J Weldon; Wilson, Alan M

2017-02-01

Changes in stride frequency and length with speed are key parameters in animal locomotion research. They are commonly measured in a laboratory on a treadmill or by filming trained captive animals. Here, we show that a clustering approach can be used to extract these variables from data collected by a tracking collar containing a GPS module and tri-axis accelerometers and gyroscopes. The method enables stride parameters to be measured during free-ranging locomotion in natural habitats. As it does not require labelled data, it is particularly suitable for use with difficult to observe animals. The method was tested on large data sets collected from collars on free-ranging lions and African wild dogs and validated using a domestic dog. © 2017. Published by The Company of Biologists Ltd.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

An Exploratory Analysis of Factors Affecting Participation in Air Force Knowledge Now Communities of Practice

DTIC Science & Technology

2004-03-01

reliability coefficients are presented in chapter four in the factor analysis section. Along with Crobach’s Alpha coefficients, the Kaiser - Meyer - Olkin ...the pattern of correlation coefficients > 0.300 in the correlation matrix • Kaiser - Meyer - Olkin Measure of Sampling Adequacy (MSA) > 0.700 • Bartlett’s...exploratory factor analysis. The Kaiser - Meyer - Olkin measure of sampling adequacy yielded a value of .790, and Bartlett’s test of sphericity yielded a

The development and exploratory analysis of the Back Pain Attitudes Questionnaire (Back-PAQ)

PubMed Central

Darlow, Ben; Perry, Meredith; Mathieson, Fiona; Stanley, James; Melloh, Markus; Marsh, Reginald; Baxter, G David; Dowell, Anthony

2014-01-01

Objectives To develop an instrument to assess attitudes and underlying beliefs about back pain, and subsequently investigate its internal consistency and underlying structures. Design The instrument was developed by a multidisciplinary team of clinicians and researchers based on analysis of qualitative interviews with people experiencing acute and chronic back pain. Exploratory analysis was conducted using data from a population-based cross-sectional survey. Setting Qualitative interviews with community-based participants and subsequent postal survey. Participants Instrument development informed by interviews with 12 participants with acute back pain and 11 participants with chronic back pain. Data for exploratory analysis collected from New Zealand residents and citizens aged 18 years and above. 1000 participants were randomly selected from the New Zealand Electoral Roll. 602 valid responses were received. Measures The 34-item Back Pain Attitudes Questionnaire (Back-PAQ) was developed. Internal consistency was evaluated by the Cronbach α coefficient. Exploratory analysis investigated the structure of the data using Principal Component Analysis. Results The 34-item long form of the scale had acceptable internal consistency (α=0.70; 95% CI 0.66 to 0.73). Exploratory analysis identified five two-item principal components which accounted for 74% of the variance in the reduced data set: ‘vulnerability of the back’; ‘relationship between back pain and injury’; ‘activity participation while experiencing back pain’; ‘prognosis of back pain’ and ‘psychological influences on recovery’. Internal consistency was acceptable for the reduced 10-item scale (α=0.61; 95% CI 0.56 to 0.66) and the identified components (α between 0.50 and 0.78). Conclusions The 34-item long form of the scale may be appropriate for use in future cross-sectional studies. The 10-item short form may be appropriate for use as a screening tool, or an outcome assessment instrument. Further testing of the 10-item Back-PAQ's construct validity, reliability, responsiveness to change and predictive ability needs to be conducted. PMID:24860003

Translating knowledge into practice: An exploratory study of dementia-specific training for community-based service providers.

PubMed

O'Sullivan, Grace; Hocking, Clare; McPherson, Kathryn

2017-08-01

Objective To develop, deliver, and evaluate dementia-specific training designed to inform service delivery by enhancing the knowledge of community-based service providers. Methods This exploratory qualitative study used an interdisciplinary, interuniversity team approach to develop and deliver dementia-specific training. Participants included management, care staff, and clients from three organizations funded to provide services in the community. Data on the acceptability, applicability, and perceived outcomes of the training were gathered through focus group discussions and individual interviews. Transcripts were analyzed to generate open codes which were clustered into themes and sub-themes addressing the content, delivery, and value of the training. Findings Staff valued up-to-date knowledge and "real stories" grounded in practice. Clients welcomed the strengths-based approach. Contractual obligations impact on the application of knowledge in practice. Implications The capacity to implement new knowledge may be limited by the legislative policies which frame service provision, to the detriment of service users.

Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) - a case study for Bucharest, Romania

NASA Astrophysics Data System (ADS)

Armaş, I.; Gavriş, A.

2013-06-01

In recent decades, the development of vulnerability frameworks has enlarged the research in the natural hazards field. Despite progress in developing the vulnerability studies, there is more to investigate regarding the quantitative approach and clarification of the conceptual explanation of the social component. At the same time, some disaster-prone areas register limited attention. Among these, Romania's capital city, Bucharest, is the most earthquake-prone capital in Europe and the tenth in the world. The location is used to assess two multi-criteria methods for aggregating complex indicators: the social vulnerability index (SoVI model) and the spatial multi-criteria social vulnerability index (SEVI model). Using the data of the 2002 census we reduce the indicators through a factor analytical approach to create the indices and examine if they bear any resemblance to the known vulnerability of Bucharest city through an exploratory spatial data analysis (ESDA). This is a critical issue that may provide better understanding of the social vulnerability in the city and appropriate information for authorities and stakeholders to consider in their decision making. The study emphasizes that social vulnerability is an urban process that increased in a post-communist Bucharest, raising the concern that the population at risk lacks the capacity to cope with disasters. The assessment of the indices indicates a significant and similar clustering pattern of the census administrative units, with an overlap between the clustering areas affected by high social vulnerability. Our proposed SEVI model suggests adjustment sensitivity, useful in the expert-opinion accuracy.

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

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

An Exploratory Study of Student Motivations for Taking Online Courses and Learning Outcomes

ERIC Educational Resources Information Center

Nonis, Sarath A.; Fenner, Grant H.

2012-01-01

An investigation of students taking online classes exposed crucial student perceptions important to their selecting online/web-assisted courses. An exploratory factor analysis provided three factors of "convenience," "enjoyment & independence," and "no other option available" as motivations for students taking…

Investigation of the factor structure of the Wechsler Adult Intelligence Scale--Fourth Edition (WAIS-IV): exploratory and higher order factor analyses.

PubMed

Canivez, Gary L; Watkins, Marley W

2010-12-01

The present study examined the factor structure of the Wechsler Adult Intelligence Scale--Fourth Edition (WAIS-IV; D. Wechsler, 2008a) standardization sample using exploratory factor analysis, multiple factor extraction criteria, and higher order exploratory factor analysis (J. Schmid & J. M. Leiman, 1957) not included in the WAIS-IV Technical and Interpretation Manual (D. Wechsler, 2008b). Results indicated that the WAIS-IV subtests were properly associated with the theoretically proposed first-order factors, but all but one factor-extraction criterion recommended extraction of one or two factors. Hierarchical exploratory analyses with the Schmid and Leiman procedure found that the second-order g factor accounted for large portions of total and common variance, whereas the four first-order factors accounted for small portions of total and common variance. It was concluded that the WAIS-IV provides strong measurement of general intelligence, and clinical interpretation should be primarily at that level.

Exploratory Bi-factor Analysis: The Oblique Case.

PubMed

Jennrich, Robert I; Bentler, Peter M

2012-07-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (Psychometrika 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (Psychometrika 76:537-549, 2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bifactor rotation criterion designed to produce a rotated loading matrix that has an approximate bi-factor structure. Among other things this can be used as an aid in finding an explicit bi-factor structure for use in a confirmatory bi-factor analysis. They considered only orthogonal rotation. The purpose of this paper is to consider oblique rotation and to compare it to orthogonal rotation. Because there are many more oblique rotations of an initial loading matrix than orthogonal rotations, one expects the oblique results to approximate a bi-factor structure better than orthogonal rotations and this is indeed the case. A surprising result arises when oblique bi-factor rotation methods are applied to ideal data.

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

PubMed Central

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

2014-01-01

Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other. PMID:24688293

An Exploratory Study of Socialization Effects on Black Children: Some Black-White Comparisons

ERIC Educational Resources Information Center

Baumrind, Diana

1972-01-01

Major conclusion from this exploratory analysis was that if the black families were viewed by white norms they appeared authoritarian, but that, unlike their white counterparts, the most authoritarian of these families produced the most self-assertive and independent girls. (Author)

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

PubMed

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

2017-06-01

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

Variable number of tandem repeats and pulsed-field gel electrophoresis cluster analysis of enterohemorrhagic Escherichia coli serovar O157 strains.

PubMed

Yokoyama, Eiji; Uchimura, Masako

2007-11-01

Ninety-five enterohemorrhagic Escherichia coli serovar O157 strains, including 30 strains isolated from 13 intrafamily outbreaks and 14 strains isolated from 3 mass outbreaks, were studied by pulsed-field gel electrophoresis (PFGE) and variable number of tandem repeats (VNTR) typing, and the resulting data were subjected to cluster analysis. Cluster analysis of the VNTR typing data revealed that 57 (60.0%) of 95 strains, including all epidemiologically linked strains, formed clusters with at least 95% similarity. Cluster analysis of the PFGE patterns revealed that 67 (70.5%) of 95 strains, including all but 1 of the epidemiologically linked strains, formed clusters with 90% similarity. The number of epidemiologically unlinked strains forming clusters was significantly less by VNTR cluster analysis than by PFGE cluster analysis. The congruence value between PFGE and VNTR cluster analysis was low and did not show an obvious correlation. With two-step cluster analysis, the number of clustered epidemiologically unlinked strains by PFGE cluster analysis that were divided by subsequent VNTR cluster analysis was significantly higher than the number by VNTR cluster analysis that were divided by subsequent PFGE cluster analysis. These results indicate that VNTR cluster analysis is more efficient than PFGE cluster analysis as an epidemiological tool to trace the transmission of enterohemorrhagic E. coli O157.

Correlation of HIV/AIDS prevalence with social, economic and demographic measures in Andhra Pradesh, India.

PubMed

Kandwal, R; Garg, P K; Garg, R D

2012-09-01

In this study, the spatial distribution of HIV/AIDS is investigated with several socioeconomic variables. Results of exploratory analysis of correlations have been reported between the prevalence of HIV/AIDS as it is the dependent variable against a range of socioeconomic and demographic measures in Andhra Pradesh, India. The state ranks among the top six states for HIV prevalence in the country. This study offers an insight to the distribution of HIV prevalence and the potential impacts of the epidemic on the high-, medium- and low-risk groups determined through cluster analyses of population and cumulative HIV infections. The impacts have been addressed through selective social and economic measures as HIV/AIDS is considered more of a social epidemic. These results help in identifying factors that are contributing more towards the spread of HIV and so guide policies to counteract dominant factors in order to control the disease. Future investigations are necessary to elucidate characterization of the rates of infection according to gender, age groups and regions.

Development of the Leadership Influence Self-Assessment (LISA©) instrument.

PubMed

Shillam, Casey R; Adams, Jeffrey M; Bryant, Debbie Chatman; Deupree, Joy P; Miyamoto, Suzanne; Gregas, Matt

This study aims to describe the development and psychometric evaluation of the Leadership Influence Self-Assessment (LISA©) tool. LISA© was designed to help nurse leaders assess and enhance their influence capacity by measuring influence traits and practices and identifying areas of strength and weakness. Concepts identified in the Adams Influence Model and input from content experts guided the development of 145 items for testing. Administered to 165 nurse leaders, the assessment was subjected to exploratory factor analysis (EFA). EFA yielded a four-factor solution that comprised 80 items. Cronbach's alpha for factors ranged between 0.912 and 0.938. All factor loadings were >0.4; the smallest factor contained 14 items. Items grouped together in the theoretical model also clustered together in the EFA. Preliminary psychometric testing supports validity and reliability of the LISA© and its potential use as a tool to assess influence capacity for purposes of leadership development and research. Copyright © 2017 Elsevier Inc. All rights reserved.

Validation study of the Questionnaire on School Maladjustment Problems (QSMP).

PubMed

de la Fuente Arias, Jesús; Peralta Sánchez, Francisco Javier; Sánchez Roda, María Dolores; Trianes Torres, María Victoria

2012-05-01

The aim of this study was to analyze the exploratory and confirmatory structure, as well as other psychometric properties, of the Cuestionario de Problemas de Convivencia Escolar (CPCE; in Spanish, the Questionnaire on School Maladjustment Problems [QSMP]), using a sample of Spanish adolescents. The instrument was administered to 60 secondary education teachers (53.4% females and 46.6% males) between the ages of 28 and 54 years (M= 41.2, SD= 11.5), who evaluated a total of 857 adolescent students. The first-order exploratory factor analysis identified 7 factors, explaining a total variance of 62%. A second-order factor analysis yielded three dimensions that explain 84% of the variance. A confirmatory factor analysis was subsequently performed in order to reduce the number of factors obtained in the exploratory analysis as well as the number of items. Lastly, we present the results of reliability, internal consistency, and validity indices. These results and their implications for future research and for the practice of educational guidance and intervention are discussed in the conclusions.

The mesencephalic trigeminal sensory nucleus is involved in acquisition of active exploratory behavior induced by changing from a diet of exclusively milk formula to food pellets in mice.

PubMed

Ishii, Toshiaki; Furuoka, Hidefumi; Kitamura, Nobuo; Muroi, Yoshikage; Nishimura, Masakazu

2006-09-21

Post-weaning mice fed exclusively milk display low-frequency exploratory behavior [Ishii, T., Itou, T., and Nishimura, M. (2005) Life Sci. 78, 174-179] compared to mice fed a food pellet diet. This low-frequency exploratory behavior switched to high-frequency exploration after a switch from exclusively milk formula to a food pellet diet. Acquisition of the high-frequency exploratory behavior was irreversible. Recently, we demonstrated that the mesencephalic trigeminal nucleus (Me5) is involved in the control of feeding and exploratory behavior in mice without modulating the emotional state [Ishii, T., Furuoka, H., Itou, T., Kitamura, N., and Nishimura, M. (2005) Brain Res. 1048, 80-86]. We therefore investigated whether the Me5 is involved in acquisition of high-frequency exploratory behavior induced by the switch in diet from an exclusively milk formula to food pellets. Mouse feeding and exploratory behaviors were analyzed using a food search compulsion apparatus, which was designed to distinguish between the two behaviors under standard living conditions. Immunohistochemical analysis of immediate early genes indicated that the Me5, which receives signals from oral proprioceptors, is transiently activated after the diet change. The change from low-frequency to high-frequency exploratory behavior was prevented in milk-fed mice by bilateral lesion of the Me5. These results suggest that the Me5 is activated by signals associated with mastication-induced proprioception and contributes to the acquisition of active exploratory behavior.

RNA-Seq workflow: gene-level exploratory analysis and differential expression

PubMed Central

Love, Michael I.; Anders, Simon; Kim, Vladislav; Huber, Wolfgang

2015-01-01

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results. PMID:26674615

Weather analysis and interpretation procedures developed for the US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Trenchard, M. H. (Principal Investigator)

1980-01-01

Procedures and techniques for providing analyses of meteorological conditions at segments during the growing season were developed for the U.S./Canada Wheat and Barley Exploratory Experiment. The main product and analysis tool is the segment-level climagraph which depicts temporally meteorological variables for the current year compared with climatological normals. The variable values for the segment are estimates derived through objective analysis of values obtained at first-order station in the region. The procedures and products documented represent a baseline for future Foreign Commodity Production Forecasting experiments.

Existential Measurement: A Factor Analytic Study of Some Current Psychometric Instruments.

ERIC Educational Resources Information Center

Thauberger, Patrick C.; And Others

1982-01-01

Research in existentialism and ontology has given rise to several psychometric instruments. Used both exploratory and confirmatory principal-factor analyses to study relationships among 16 existential scales. Exploratory factor analysis provided some support of the theory that the avoidance of existential confrontation is a central function of…

The Interpretative Phenomenological Analysis (IPA): A Guide to a Good Qualitative Research Approach

ERIC Educational Resources Information Center

Alase, Abayomi

2017-01-01

As a research methodology, qualitative research method infuses an added advantage to the exploratory capability that researchers need to explore and investigate their research studies. Qualitative methodology allows researchers to advance and apply their interpersonal and subjectivity skills to their research exploratory processes. However, in a…

An Exploratory Study of Student Satisfaction with University Web Page Design

ERIC Educational Resources Information Center

Gundersen, David E.; Ballenger, Joe K.; Crocker, Robert M.; Scifres, Elton L.; Strader, Robert

2013-01-01

This exploratory study evaluates the satisfaction of students with a web-based information system at a medium-sized regional university. The analysis provides a process for simplifying data interpretation in captured student user feedback. Findings indicate that student classifications, as measured by demographic and other factors, determine…

Caring Leadership in Schools: Findings from Exploratory Analyses

ERIC Educational Resources Information Center

Louis, Karen Seashore; Murphy, Joseph; Smylie, Mark

2016-01-01

Purpose: This article (1) analyzes and synthesizes literatures from philosophy and education to propose a conceptual framework for caring in schools and caring school leadership and (2) reports the results of an exploratory analysis of the relationship of caring principal leadership to school-level supports for student academic learning.…

The Effects of Mobile Collaborative Activities in a Second Language Course

ERIC Educational Resources Information Center

Ilic, Peter

2015-01-01

This research is designed to explore the areas of collaborative learning and the use of smartphones as a support for collaborative learning through a year-long exploratory multiple case study approach integrating both qualitative and quantitative data analysis. Qualitative exploratory interviews are combined with Multidimensional Scaling Analysis…

Exploratory Honors Students: Academic Major and Career Decision Making

ERIC Educational Resources Information Center

Carduner, Jessie; Padak, Gary M.; Reynolds, Jamie

2011-01-01

In this qualitative study, we investigated the academic major and career decision-making processes of honors college students who were declared as "exploratory" students in their freshman year at a large, public, midwestern university. We used semistandardized interviews and document analysis as primary data collection methods to answer…

EXPLORATORY ANALYSIS OF THE EFFECTS OF PARTICULATE CHARACTERISTICS ON THE VARIATION IN PARTITIONING OF NONPOLAR ORGANIC CONTAMINANTS TO MARINE SEDIMENTS

EPA Science Inventory

The partitioning of nonpolar organic contaminants to marine sediments is considered to be controlled by the amount of organic carbon present. However, several studies propose that other characteristics of sediments may affect the partitioning of contaminants. For this exploratory...

Spatio-temporal patterns of Campylobacter colonization in Danish broilers.

PubMed

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K

2013-05-01

Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified in the same areas in the summer months from 2007 to 2009. Range of influence between broiler farms were estimated at distances of 9.6 km and 13.5 km in different years. Identification of areas and time with greater risk indicates variable presence of risk factors with space and time. Implementation of safety measures on farms within high-risk clusters during summer could have an impact in reducing prevalence.

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

PubMed

Mji, Andile; Mbinda, Zoleka

2005-08-01

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

Exploratory factor analysis in Rehabilitation Psychology: a content analysis.

PubMed

Roberson, Richard B; Elliott, Timothy R; Chang, Jessica E; Hill, Jessica N

2014-11-01

Our objective was to examine the use and quality of exploratory factor analysis (EFA) in articles published in Rehabilitation Psychology. Trained raters examined 66 separate exploratory factor analyses in 47 articles published between 1999 and April 2014. The raters recorded the aim of the EFAs, the distributional statistics, sample size, factor retention method(s), extraction and rotation method(s), and whether the pattern coefficients, structure coefficients, and the matrix of association were reported. The primary use of the EFAs was scale development, but the most widely used extraction and rotation method was principle component analysis, with varimax rotation. When determining how many factors to retain, multiple methods (e.g., scree plot, parallel analysis) were used most often. Many articles did not report enough information to allow for the duplication of their results. EFA relies on authors' choices (e.g., factor retention rules extraction, rotation methods), and few articles adhered to all of the best practices. The current findings are compared to other empirical investigations into the use of EFA in published research. Recommendations for improving EFA reporting practices in rehabilitation psychology research are provided.

The Consequences of Employee Commitment, Turnover, and Absenteeism: An Exploratory Analysis.

DTIC Science & Technology

1981-08-01

and Absenteeism : An Exploratory Analysis Gt. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(a) S. CONTRACT OR GRANT NUMBER(e) Richard T. Mowday N00014-81-K... Absenteeism therefore provides organizations with the opportunity to train employees to perform a number of different tasks. -35- Negative consequences...AD-A103 359 OREGON UNIV EUGENE GRADUATE SCHOOL OF MANAGEMENT AND-ETC Ft6 S/1 THE CONSEQUENCES OF EMPLOYEE COMMITMENT, TURNOVER, AND ABSENTEE -ETC(U

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

NASA Astrophysics Data System (ADS)

Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

An exploratory study on cultural variations in oral health attitudes, behaviour and values of freshman (first-year) dental students.

PubMed

Kawamura, M; Wright, F A C; Declerck, D; Freire, M C M; Hu, D Y; Honkala, E; Lévy, G; Kalwitzki, M; Polychronopoulou, A; Yip, H K; Kinirons, M J; Eli, I; Petti, S; Komabayashi, T; Kim, K J; Razak, A A A; Srisilapanan, P; Kwan, S Y L

2005-08-01

To identify similarities and differences in oral health attitudes, behaviour and values among freshman dental students. Cross-cultural survey of dental students. 18 cultural areas. 904 first-year dental students completed the Hiroshima University-Dental Behavioural Inventory (HU-DBI) translated into their own languages. Individual areas were clustered by similarity in responses to the questions. The first group displayed an 'occidental-culture orientation' with the exception of Brazil (Cluster 1 comprised: Australia, United Kingdom, Ireland, Belgium and Brazil, Cluster 2: Germany, Italy, Finland and France). The second group displayed an 'oriental-cultural orientation' with the exception of Greece and Israel (Cluster 3 comprised: China and Indonesia, and Cluster 4: Japan, Korea, Israel, Hong Kong, Malaysia, Thailand and Greece). Australia and United Kingdom were the countries that were most alike. Ireland was the 'neighbour' to these countries. Greece and Malaysia had similar patterns of oral health behaviour although geographic conditions are very different. Although it was considered that in Hong Kong, occidental nations have affected the development of education, it remained in the oriental-culture group. Comparison with the data from the occidentals indicates that a higher percentage of the orientals put off going to the dentist until they have toothache (p < 0.001). Only a small proportion of the occidentals (8%) reported a perception of inevitability in having false teeth, whereas 33% of the orientals held this fatalistic belief (p = 0.001). Grouping the countries into key cultural orientations and international clusters yielded plausible results, using the HU-DBI.

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

PubMed

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

2016-12-01

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

Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

PubMed Central

Chang, Chia-Ming; Chuang, Chi-Mu; Wang, Mong-Lien; Yang, Yi-Ping; Chuang, Jen-Hua; Yang, Ming-Jie; Yen, Ming-Shyen; Chiou, Shih-Hwa; Chang, Cheng-Chang

2016-01-01

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. PMID:27527159

Diagnostic potential for gold nanoparticle-based surface-enhanced Raman spectroscopy to provide colorectal cancer screening using blood serum sample

NASA Astrophysics Data System (ADS)

Lin, Duo; Feng, Shangyuan; Pan, Jianji; Chen, Yanping; Lin, Juqiang; Sun, Liqing; Chen, Rong

2011-11-01

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is capable of probing the biomolecular changes associated with diseased transformation. The objective of our study was to explore gold nanoparticle based SERS to obtain blood serum biochemical information for non-invasive colorectal cancer detection. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Principal component analysis (PCA) of the measured SERS spectra separated the spectral features of the two groups into two distinct clusters with little overlaps. Linear discriminate analysis (LDA) based on the PCA generated features differentiated the nasopharyngeal cancer SERS spectra from normal SERS spectra with high sensitivity (97.4%) and specificity (100%). The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

Diagnostic potential for gold nanoparticle-based surface-enhanced Raman spectroscopy to provide colorectal cancer screening using blood serum sample

NASA Astrophysics Data System (ADS)

Lin, Duo; Feng, Shangyuan; Pan, Jianji; Chen, Yanping; Lin, Juqiang; Sun, Liqing; Chen, Rong

2012-03-01

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is capable of probing the biomolecular changes associated with diseased transformation. The objective of our study was to explore gold nanoparticle based SERS to obtain blood serum biochemical information for non-invasive colorectal cancer detection. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Principal component analysis (PCA) of the measured SERS spectra separated the spectral features of the two groups into two distinct clusters with little overlaps. Linear discriminate analysis (LDA) based on the PCA generated features differentiated the nasopharyngeal cancer SERS spectra from normal SERS spectra with high sensitivity (97.4%) and specificity (100%). The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

Massive and refined: A sample of large galaxy clusters simulated at high resolution. I: Thermal gas and properties of shock waves

NASA Astrophysics Data System (ADS)

Vazza, F.; Brunetti, G.; Gheller, C.; Brunino, R.

2010-11-01

We present a sample of 20 massive galaxy clusters with total virial masses in the range of 6 × 10 14 M ⊙ ⩽ Mvir ⩽ 2 × 10 15 M ⊙, re-simulated with a customized version of the 1.5. ENZO code employing adaptive mesh refinement. This technique allowed us to obtain unprecedented high spatial resolution (≈25 kpc/h) up to the distance of ˜3 virial radii from the clusters center, and makes it possible to focus with the same level of detail on the physical properties of the innermost and of the outermost cluster regions, providing new clues on the role of shock waves and turbulent motions in the ICM, across a wide range of scales. In this paper, a first exploratory study of this data set is presented. We report on the thermal properties of galaxy clusters at z = 0. Integrated and morphological properties of gas density, gas temperature, gas entropy and baryon fraction distributions are discussed, and compared with existing outcomes both from the observational and from the numerical literature. Our cluster sample shows an overall good consistency with the results obtained adopting other numerical techniques (e.g. Smoothed Particles Hydrodynamics), yet it provides a more accurate representation of the accretion patterns far outside the cluster cores. We also reconstruct the properties of shock waves within the sample by means of a velocity-based approach, and we study Mach numbers and energy distributions for the various dynamical states in clusters, giving estimates for the injection of Cosmic Rays particles at shocks. The present sample is rather unique in the panorama of cosmological simulations of massive galaxy clusters, due to its dynamical range, statistics of objects and number of time outputs. For this reason, we deploy a public repository of the available data, accessible via web portal at http://data.cineca.it.

Beyond Risk Compensation: Clusters of Antiretroviral Treatment (ART) Users in Sexual Networks Can Modify the Impact of ART on HIV Incidence

PubMed Central

Delva, Wim; Helleringer, Stéphane

2016-01-01

Introduction Concerns about risk compensation—increased risk behaviours in response to a perception of reduced HIV transmission risk—after the initiation of ART have largely been dispelled in empirical studies, but other changes in sexual networking patterns may still modify the effects of ART on HIV incidence. Methods We developed an exploratory mathematical model of HIV transmission that incorporates the possibility of ART clusters, i.e. subsets of the sexual network in which the density of ART patients is much higher than in the rest of the network. Such clusters may emerge as a result of ART homophily—a tendency for ART patients to preferentially form and maintain relationships with other ART patients. We assessed whether ART clusters may affect the impact of ART on HIV incidence, and how the influence of this effect-modifying variable depends on contextual variables such as HIV prevalence, HIV serosorting, coverage of HIV testing and ART, and adherence to ART. Results ART homophily can modify the impact of ART on HIV incidence in both directions. In concentrated epidemics and generalized epidemics with moderate HIV prevalence (≈ 10%), ART clusters can enhance the impact of ART on HIV incidence, especially when adherence to ART is poor. In hyperendemic settings (≈ 35% HIV prevalence), ART clusters can reduce the impact of ART on HIV incidence when adherence to ART is high but few people living with HIV (PLWH) have been diagnosed. In all contexts, the effects of ART clusters on HIV epidemic dynamics are distinct from those of HIV serosorting. Conclusions Depending on the programmatic and epidemiological context, ART clusters may enhance or reduce the impact of ART on HIV incidence, in contrast to serosorting, which always leads to a lower impact of ART on HIV incidence. ART homophily and the emergence of ART clusters should be measured empirically and incorporated into more refined models used to plan and evaluate ART programmes. PMID:27657492

Beyond Risk Compensation: Clusters of Antiretroviral Treatment (ART) Users in Sexual Networks Can Modify the Impact of ART on HIV Incidence.

PubMed

Delva, Wim; Helleringer, Stéphane

Concerns about risk compensation-increased risk behaviours in response to a perception of reduced HIV transmission risk-after the initiation of ART have largely been dispelled in empirical studies, but other changes in sexual networking patterns may still modify the effects of ART on HIV incidence. We developed an exploratory mathematical model of HIV transmission that incorporates the possibility of ART clusters, i.e. subsets of the sexual network in which the density of ART patients is much higher than in the rest of the network. Such clusters may emerge as a result of ART homophily-a tendency for ART patients to preferentially form and maintain relationships with other ART patients. We assessed whether ART clusters may affect the impact of ART on HIV incidence, and how the influence of this effect-modifying variable depends on contextual variables such as HIV prevalence, HIV serosorting, coverage of HIV testing and ART, and adherence to ART. ART homophily can modify the impact of ART on HIV incidence in both directions. In concentrated epidemics and generalized epidemics with moderate HIV prevalence (≈ 10%), ART clusters can enhance the impact of ART on HIV incidence, especially when adherence to ART is poor. In hyperendemic settings (≈ 35% HIV prevalence), ART clusters can reduce the impact of ART on HIV incidence when adherence to ART is high but few people living with HIV (PLWH) have been diagnosed. In all contexts, the effects of ART clusters on HIV epidemic dynamics are distinct from those of HIV serosorting. Depending on the programmatic and epidemiological context, ART clusters may enhance or reduce the impact of ART on HIV incidence, in contrast to serosorting, which always leads to a lower impact of ART on HIV incidence. ART homophily and the emergence of ART clusters should be measured empirically and incorporated into more refined models used to plan and evaluate ART programmes.

Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate-siliciclastic alternations of the Hainich CZE, central Germany

NASA Astrophysics Data System (ADS)

Kohlhepp, Bernd; Lehmann, Robert; Seeber, Paul; Küsel, Kirsten; Trumbore, Susan E.; Totsche, Kai U.

2017-12-01

The quality of near-surface groundwater reservoirs is controlled, but also threatened, by manifold surface-subsurface interactions. Vulnerability studies typically evaluate the variable interplay of surface factors (land management, infiltration patterns) and subsurface factors (hydrostratigraphy, flow properties) in a thorough way, but disregard the resulting groundwater quality. Conversely, hydrogeochemical case studies that address the chemical evolution of groundwater often lack a comprehensive analysis of the structural buildup. In this study, we aim to reconstruct the actual spatial groundwater quality pattern from a synoptic analysis of the hydrostratigraphy, lithostratigraphy, pedology and land use in the Hainich Critical Zone Exploratory (Hainich CZE). This CZE represents a widely distributed yet scarcely described setting of thin-bedded mixed carbonate-siliciclastic strata in hillslope terrains. At the eastern Hainich low-mountain hillslope, bedrock is mainly formed by alternated marine sedimentary rocks of the Upper Muschelkalk (Middle Triassic) that partly host productive groundwater resources. Spatial patterns of the groundwater quality of a 5.4 km long well transect are derived by principal component analysis and hierarchical cluster analysis. Aquifer stratigraphy and geostructural links were deduced from lithological drill core analysis, mineralogical analysis, geophysical borehole logs and mapping data. Maps of preferential recharge zones and recharge potential were deduced from digital (soil) mapping, soil survey data and field measurements of soil hydraulic conductivities (Ks). By attributing spatially variable surface and subsurface conditions, we were able to reconstruct groundwater quality clusters that reflect the type of land management in their preferential recharge areas, aquifer hydraulic conditions and cross-formational exchange via caprock sinkholes or ascending flow. Generally, the aquifer configuration (spatial arrangement of strata, valley incision/outcrops) and related geostructural links (enhanced recharge areas, karst phenomena) control the role of surface factors (input quality and locations) vs. subsurface factors (water-rock interaction, cross-formational flow) for groundwater quality in the multi-layered aquifer system. Our investigation reveals general properties of alternating sequences in hillslope terrains that are prone to forming multi-layered aquifer systems. This synoptic analysis is fundamental and indispensable for a mechanistic understanding of ecological functioning, sustainable resource management and protection.

Exploratory factor analysis of self-reported symptoms in a large, population-based military cohort

PubMed Central

2010-01-01

Background US military engagements have consistently raised concern over the array of health outcomes experienced by service members postdeployment. Exploratory factor analysis has been used in studies of 1991 Gulf War-related illnesses, and may increase understanding of symptoms and health outcomes associated with current military conflicts in Iraq and Afghanistan. The objective of this study was to use exploratory factor analysis to describe the correlations among numerous physical and psychological symptoms in terms of a smaller number of unobserved variables or factors. Methods The Millennium Cohort Study collects extensive self-reported health data from a large, population-based military cohort, providing a unique opportunity to investigate the interrelationships of numerous physical and psychological symptoms among US military personnel. This study used data from the Millennium Cohort Study, a large, population-based military cohort. Exploratory factor analysis was used to examine the covariance structure of symptoms reported by approximately 50,000 cohort members during 2004-2006. Analyses incorporated 89 symptoms, including responses to several validated instruments embedded in the questionnaire. Techniques accommodated the categorical and sometimes incomplete nature of the survey data. Results A 14-factor model accounted for 60 percent of the total variance in symptoms data and included factors related to several physical, psychological, and behavioral constructs. A notable finding was that many factors appeared to load in accordance with symptom co-location within the survey instrument, highlighting the difficulty in disassociating the effects of question content, location, and response format on factor structure. Conclusions This study demonstrates the potential strengths and weaknesses of exploratory factor analysis to heighten understanding of the complex associations among symptoms. Further research is needed to investigate the relationship between factor analytic results and survey structure, as well as to assess the relationship between factor scores and key exposure variables. PMID:20950474

An Exploratory Study of Intrinsic & Extrinsic Motivators and Student Performance in an Auditing Course

ERIC Educational Resources Information Center

Mo, Songtao

2011-01-01

The objective of this study is to investigate the association of intrinsic and extrinsic motivators and student performance. This study performs an exploratory analysis and presents evidence to demonstrate that intrinsic motivators affect the connection between external motivators and student performance. The empirical tests follow the framework…

Situated Analysis of Team Handball Players' Decisions: An Exploratory Study

ERIC Educational Resources Information Center

Lenzen, Benoit; Theunissen, Catherine; Cloes, Marc

2009-01-01

This exploratory study aimed to investigate elements involved in decision making in team handball live situations and to provide coaches and educators with teaching recommendations. The study was positioned within the framework of the situated-action paradigm of which two aspects were of particular interest for this project: (a) the relationship…

Therapeutic Writing: An Exploratory Speech-Language Pathology Counseling Technique

ERIC Educational Resources Information Center

Isaki, Emi; Brown, Betty G.; Alemán, Sara; Hackstaff, Karla

2015-01-01

This exploratory qualitative study investigated the use of therapeutic writing for counseling long-term caregivers of spouses with brain injury and neurogenic communication disorders. Three participants wrote an average of six single-spaced pages of text. After analysis of the written text, the common themes of onset of diagnosis, anger, grief,…

A Study of the Exploratory Behavior of Legally Blind and Sighted Preschoolers.

ERIC Educational Resources Information Center

Olson, Myrna R.

1983-01-01

Fifteen legally blind preschoolers and 15 sighted controls matched for age, sex, and socioeconomic status were observed with novel and non-novel toys. Analysis of the exploratory behavior revealed no significant differences between the interaction of each group with either toy except in patterns of sensory utilization. (CL)

An Exploratory Investigation of the Factor Structure of the Reynolds Intellectual Assessment Scales (RIAS)

ERIC Educational Resources Information Center

Dombrowski, Stefan C.; Watkins, Marley W.; Brogan, Michael J.

2009-01-01

This study investigated the factor structure of the Reynolds Intellectual Assessment Scales (RIAS) using rigorous exploratory factor analytic and factor extraction procedures. The results of this study indicate that the RIAS is a single factor test. Despite these results, higher order factor analysis using the Schmid-Leiman procedure indicates…

An Exploratory Study of Animal-Assisted Interventions Utilized by Mental Health Professionals

ERIC Educational Resources Information Center

O'Callaghan, Dana M.; Chandler, Cynthia K.

2011-01-01

This study implemented an exploratory analysis to examine how a sample of mental health professionals incorporates specific animal-assisted techniques into the therapeutic process. An extensive review of literature related to animal-assisted therapy (AAT) resulted in the identification of 18 techniques and 10 intentions for the practice of AAT in…

Examining Metacognitive Processes in Exploratory Computer-Based Learning Environments Using Activity Log Analysis

ERIC Educational Resources Information Center

Chang, Yoo Kyung

2010-01-01

Metacognition is widely studied for its influence on the effectiveness of learning. With Exploratory Computer-Based Learning Environments (ECBLE), metacognition is found to be especially important because these environments require adaptive metacognitive control by the learners due to their open-ended structure that allows for multiple learning…

The development and exploratory analysis of the Back Pain Attitudes Questionnaire (Back-PAQ).

PubMed

Darlow, Ben; Perry, Meredith; Mathieson, Fiona; Stanley, James; Melloh, Markus; Marsh, Reginald; Baxter, G David; Dowell, Anthony

2014-05-23

To develop an instrument to assess attitudes and underlying beliefs about back pain, and subsequently investigate its internal consistency and underlying structures. The instrument was developed by a multidisciplinary team of clinicians and researchers based on analysis of qualitative interviews with people experiencing acute and chronic back pain. Exploratory analysis was conducted using data from a population-based cross-sectional survey. Qualitative interviews with community-based participants and subsequent postal survey. Instrument development informed by interviews with 12 participants with acute back pain and 11 participants with chronic back pain. Data for exploratory analysis collected from New Zealand residents and citizens aged 18 years and above. 1000 participants were randomly selected from the New Zealand Electoral Roll. 602 valid responses were received. The 34-item Back Pain Attitudes Questionnaire (Back-PAQ) was developed. Internal consistency was evaluated by the Cronbach α coefficient. Exploratory analysis investigated the structure of the data using Principal Component Analysis. The 34-item long form of the scale had acceptable internal consistency (α=0.70; 95% CI 0.66 to 0.73). Exploratory analysis identified five two-item principal components which accounted for 74% of the variance in the reduced data set: 'vulnerability of the back'; 'relationship between back pain and injury'; 'activity participation while experiencing back pain'; 'prognosis of back pain' and 'psychological influences on recovery'. Internal consistency was acceptable for the reduced 10-item scale (α=0.61; 95% CI 0.56 to 0.66) and the identified components (α between 0.50 and 0.78). The 34-item long form of the scale may be appropriate for use in future cross-sectional studies. The 10-item short form may be appropriate for use as a screening tool, or an outcome assessment instrument. Further testing of the 10-item Back-PAQ's construct validity, reliability, responsiveness to change and predictive ability needs to be conducted. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Improving skill development: an exploratory study comparing a philosophical and an applied ethical analysis technique

NASA Astrophysics Data System (ADS)

Al-Saggaf, Yeslam; Burmeister, Oliver K.

2012-09-01

This exploratory study compares and contrasts two types of critical thinking techniques; one is a philosophical and the other an applied ethical analysis technique. The two techniques analyse an ethically challenging situation involving ICT that a recent media article raised to demonstrate their ability to develop the ethical analysis skills of ICT students and professionals. In particular the skill development focused on includes: being able to recognise ethical challenges and formulate coherent responses; distancing oneself from subjective judgements; developing ethical literacy; identifying stakeholders; and communicating ethical decisions made, to name a few.

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure.

PubMed

Kemppainen, Petri; Knight, Christopher G; Sarma, Devojit K; Hlaing, Thaung; Prakash, Anil; Maung Maung, Yan Naung; Somboon, Pradya; Mahanta, Jagadish; Walton, Catherine

2015-09-01

Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species. © 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Safety of Spectacles for Children's Vision: A Cluster-Randomized Controlled Trial.

PubMed

Ma, Xiaochen; Congdon, Nathan; Yi, Hongmei; Zhou, Zhongqiang; Pang, Xiaopeng; Meltzer, Mirjam E; Shi, Yaojiang; He, Mingguang; Liu, Yizhi; Rozelle, Scott

2015-11-01

To study safety of children's glasses in rural China, where fear that glasses harm vision is an important barrier for families and policy makers. Exploratory analysis from a cluster-randomized, investigator-masked, controlled trial. Among primary schools (n = 252) in western China, children were randomized by school to 1 of 3 interventions: free glasses provided in class, vouchers for free glasses at a local facility, or glasses prescriptions only (Control group). The main outcome of this analysis is uncorrected visual acuity after 8 months, adjusted for baseline acuity. Among 19 934 children randomly selected for screening, 5852 myopic (spherical equivalent refractive error ≤-0.5 diopters) eyes of 3001 children (14.7%, mean age 10.5 years) had VA ≤6/12 without glasses correctable to >6/12 with glasses, and were eligible. Among these, 1903 (32.5%), 1798 (30.7%), and 2151 (36.8%) were randomized to Control, Voucher, and Free Glasses, respectively. Intention-to-treat analyses were performed on all 1831 (96.2%), 1699 (94.5%), and 2007 (93.3%) eyes of children with follow-up in Control, Voucher, and Free Glasses groups. Final visual acuity for eyes of children in the treatment groups (Free Glasses and Voucher) was significantly better than for Control children, adjusting only for baseline visual acuity (difference of 0.023 logMAR units [0.23 vision chart lines, 95% CI: 0.03, 0.43]) or for other baseline factors as well (0.025 logMAR units [0.25 lines, 95% CI 0.04, 0.45]). We found no evidence that spectacles promote decline in uncorrected vision with aging among children. Copyright © 2015 Elsevier Inc. All rights reserved.

Pragmatics fragmented: the factor structure of the Dutch children's communication checklist (CCC).

PubMed

Geurts, Hilde M; Hartman, Catharina; Verté, Sylvie; Oosterlaan, Jaap; Roeyers, Herbert; Sergeant, Joseph A

2009-01-01

A number of disorders are associated with pragmatic difficulties. Instruments that can make subdivisions within the larger construct of pragmatics could be important tools for disentangling profiles of pragmatic difficulty in different disorders. The deficits underlying the observed pragmatic difficulties may be different for different disorders. To study the construct validity of a pragmatic language questionnaire. The construct of pragmatics is studied by applying exploratory factor analysis (EFA) and confirmatory factor analysis to the parent version of the Dutch Children's Communication Checklist (CCC; Bishop 1998 ). Parent ratings of 1589 typically developing children and 481 children with a clinical diagnosis were collected. Four different factor models derived from the original CCC scales and five different factor models based on EFA were compared with each other. The models were cross-validated. The EFA-derived models were substantively different from the originally proposed CCC factor structure. EFA models gave a slightly better fit than the models based on the original CCC scales, though neither provided a good fit to the parent data. Coherence seemed to be part of language form and not of pragmatics, which is in line with the adaptation of the CCC, the CCC-2 (Bishop 2003 ). Most pragmatic items clustered together in one factor and these pragmatic items also clustered with items related to social relationships and specific interests. The nine scales of the original CCC do not reflect the underlying factor structure. Therefore, scale composition may be improved on and scores on subscale level need to be interpreted cautiously. Therefore, in interpreting the CCC profiles, the overall measure might be more informative than the postulated subscales as more information is needed to determine which constructs the suggested subscales are actually measuring.

Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies

PubMed Central

Joon, Aron; Brewster, Abenaa M.; Chen, Wei V.; Eng, Cathy; Shete, Sanjay; Casey, Graham; Schumacher, Fredrick; Lin, Yi; Harrison, Tabitha A.; White, Emily; Ahsan, Habibul; Andrulis, Irene L.; Whittemore, Alice S.; Ko Win, Aung; Schmidt, Daniel F.; Kapuscinski, Miroslaw K.; Ochs-Balcom, Heather M.; Gallinger, Steven; Jenkins, Mark A.; Newcomb, Polly A.; Lindor, Noralane M.; Peters, Ulrike; Amos, Christopher I.; Lynch, Patrick M.

2018-01-01

Background Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype. Methods To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls). Results Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06). Conclusion The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation. PMID:29698419

Low dose radiation hypersensitivity and clustered DNA damages in human fibroblasts exposed to low dose and dose rate protons or 137CS y-rays

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

Bennett P. V.; Bennett, P.V.; Keszenman, D.J.

Effective radioprotection for human space travelers hinges upon understanding the individual properties of charged particles. A significant fraction of particle radiation astronauts will encounter in space exploratory missions will come from high energy protons in galactic cosmic radiation (GCR) and/or possible exposures to lower energy proton flux from solar particle events (SPEs). These potential exposures present major concerns for NASA and others, in planning and executing long term space exploratory missions. We recently reported cell survival and transformation (acquisition of anchorage-independent growth in soft agar) frequencies in apparently normal NFF-28 primary human fibroblasts exposed to 0-30 cGy of 50MeV, 100MeVmore » (SPE-like), or 1000 MeV (GCR-like) monoenergetic protons. These were modeled after 1989 SPE energies at an SPE-like low dose-rate (LDR) of 1.65 cGy/min or high dose rate (HDR) of 33.3 cGy/min delivered at the NASA Space Radiation Laboratory (NSRL) at BNL.« less

Exploratory Factor Analysis and Psychometric Evaluation of the Teacher Reporting Attitude Scale for Child Sexual Abuse

ERIC Educational Resources Information Center

Walsh, Kerryann; Rassafiani, Mehdi; Mathews, Ben; Farrell, Ann; Butler, Des

2012-01-01

This paper presents an evaluation of an instrument to measure teachers' attitudes toward reporting child sexual abuse and discusses the instrument's merit for research into reporting practice. Based on responses from 444 Australian teachers, the Teachers' Reporting Attitude Scale for Child Sexual Abuse was evaluated using exploratory factor…

Development and Validation of the Personal Strengths Inventory Using Exploratory and Confirmatory Factor Analyses

ERIC Educational Resources Information Center

Liau, Albert Kienfie; Chow, Daryl; Tan, Teck Kiang; Senf, Konrad

2011-01-01

The purpose of this study was to establish the reliability and validity of the scores on a brief strengths-based assessment, the 22-item Personal Strengths Inventory (PSI). In Study 1, findings from exploratory factor analysis of 410 adolescents provided evidence for a five-factor solution--social competence (four items), emotional awareness (five…

An Exploratory Analysis of American Indian Children's Cultural Engagement, Fluid Cognitive Skills, and Standardized Verbal IQ Scores

ERIC Educational Resources Information Center

Tsethlikai, Monica

2011-01-01

This exploratory cross-sectional study examined fluid cognitive skills and standardized verbal IQ scores in relation to cultural engagement amongst Tohono O'odham children (N = 99; ages 7 to 12 years). Guardians with higher socioeconomic status engaged their children in more cultural activities, and participation in more cultural activities…

An Exploratory Analysis of Job and Life Satisfaction among Entrepreneurs.

ERIC Educational Resources Information Center

Lyons, Paul R.; DeCarlo, James F.

An exploratory study examined the job and life satisfaction of a sample of 32 female entrepreneurs residing in the tri-state area of Maryland, Pennsylvania, and West Virginia. To compare the entrepreneurs' concepts of life and job satisfaction to those of women in more traditional occupations, researchers also studied a sample of 32 female nursing…

Evaluating outcome-correlated recruitment and geographic recruitment bias in a respondent-driven sample of people who inject drugs in Tijuana, Mexico

PubMed Central

Rudolph, Abby E.; Gaines, Tommi L.; Lozada, Remedios; Vera, Alicia; Brouwer, Kimberly C.

2015-01-01

Respondent-driven sampling’s (RDS) widespread use and reliance on untested assumptions suggests a need for new exploratory/diagnostic tests. We assessed geographic recruitment bias and outcome-correlated recruitment among 1048 RDS-recruited people who inject drugs (Tijuana, Mexico). Surveys gathered demographics, drug/sex behaviors, activity locations, and recruiter-recruit pairs. Simulations assessed geographic and network clustering of active syphilis (RPR titers≥1:8). Gender-specific predicted probabilities were estimated using logistic regression with GEE and robust standard errors. Active syphilis prevalence was 7% (crude: men=5.7% and women=16.6%; RDS-adjusted: men=6.7% and women=7.6%). Syphilis clustered in the Zona Norte, a neighborhood known for drug and sex markets. Network simulations revealed geographic recruitment bias and non-random recruitment by syphilis status. Gender-specific prevalence estimates accounting for clustering were highest among those living/working/injecting/buying drugs in the Zona Norte and directly/indirectly connected to syphilis cases (men:15.9%, women:25.6%) and lowest among those with neither exposure (men:3.0%, women:6.1%). Future RDS analyses should assess/account for network and spatial dependencies. PMID:24969586

Evaluating outcome-correlated recruitment and geographic recruitment bias in a respondent-driven sample of people who inject drugs in Tijuana, Mexico.

PubMed

Rudolph, Abby E; Gaines, Tommi L; Lozada, Remedios; Vera, Alicia; Brouwer, Kimberly C

2014-12-01

Respondent-driven sampling's (RDS) widespread use and reliance on untested assumptions suggests a need for new exploratory/diagnostic tests. We assessed geographic recruitment bias and outcome-correlated recruitment among 1,048 RDS-recruited people who inject drugs (Tijuana, Mexico). Surveys gathered demographics, drug/sex behaviors, activity locations, and recruiter-recruit pairs. Simulations assessed geographic and network clustering of active syphilis (RPR titers ≥1:8). Gender-specific predicted probabilities were estimated using logistic regression with GEE and robust standard errors. Active syphilis prevalence was 7 % (crude: men = 5.7 % and women = 16.6 %; RDS-adjusted: men = 6.7 % and women = 7.6 %). Syphilis clustered in the Zona Norte, a neighborhood known for drug and sex markets. Network simulations revealed geographic recruitment bias and non-random recruitment by syphilis status. Gender-specific prevalence estimates accounting for clustering were highest among those living/working/injecting/buying drugs in the Zona Norte and directly/indirectly connected to syphilis cases (men: 15.9 %, women: 25.6 %) and lowest among those with neither exposure (men: 3.0 %, women: 6.1 %). Future RDS analyses should assess/account for network and spatial dependencies.

Toward the Identification of a Specific Psychopathology of Substance Use Disorders.

PubMed

Maremmani, Angelo G I; Pani, Pier Paolo; Rovai, Luca; Bacciardi, Silvia; Maremmani, Icro

2017-01-01

Addiction is a mental illness in which psychiatric conditions imply a prominent burden. Psychopathological symptoms in substance use disorder (SUD) patients are usually viewed as being assignable to the sphere of a personality trait or of comorbidity, leaving doubts about the presence of a specific psychopathology that could only be related to the toxicomanic process. Our research group at the University of Pisa has shed light on the possible definition of a specific psychopathological dimension in SUDs. In heroin use disorder patients, performing an exploratory principal component factor analysis (PCA) on all the 90 items included in the SCL-90 questionnaire led to a five-factor solution. The first factor accounted for a depressive "worthlessness and being trapped" dimension; the second factor picked out a "somatic symptoms" dimension; the third identified a "sensitivity-psychoticism" dimension; the fourth a "panic-anxiety" dimension; and the fifth a "violence-suicide" dimension. These same results were replicated by applying the PCA to another Italian sample of 1,195 heroin addicts entering a Therapeutic Community Treatment. Further analyses confirmed the clusters of symptoms, independently of demographic and clinical characteristics, active heroin use, lifetime psychiatric problems, kind of treatment received, and, especially, other substances used by the patient such as alcohol or cocaine. Moreover, these clusters were able to discriminate patients affected by addiction from those affected by psychiatric diseases such as major depressive disorder. Our studies seem to suggest the trait-dependent, rather than the state-dependent, nature of the introduced psychopathology dimensions of SUDs.

Locational Determinants of Emissions from Pollution-Intensive Firms in Urban Areas

PubMed Central

Zhou, Min; Tan, Shukui; Guo, Mingjing; Zhang, Lu

2015-01-01

Industrial pollution has remained as one of the most daunting challenges for many regions around the world. Characterizing the determinants of industrial pollution should provide important management implications. Unfortunately, due to the absence of high-quality data, rather few studies have systematically examined the locational determinants using a geographical approach. This paper aimed to fill the gap by accessing the pollution source census dataset, which recorded the quantity of discharged wastes (waste water and solid waste) from 717 pollution-intensive firms within Huzhou City, China. Spatial exploratory analysis was applied to analyze the spatial dependency and local clusters of waste emissions. Results demonstrated that waste emissions presented significantly positive autocorrelation in space. The high-high hotspots generally concentrated towards the city boundary, while the low-low clusters approached the Taihu Lake. Their locational determinants were identified by spatial regression. In particular, firms near the city boundary and county road were prone to discharge more wastes. Lower waste emissions were more likely to be observed from firms with high proximity to freight transfer stations or the Taihu Lake. Dense populous districts saw more likelihood of solid waste emissions. Firms in the neighborhood of rivers exhibited higher waste water emissions. Besides, the control variables (firm size, ownership, operation time and industrial type) also exerted significant influence. The present methodology can be applicable to other areas, and further inform the industrial pollution control practices. Our study advanced the knowledge of determinants of emissions from pollution-intensive firms in urban areas. PMID:25927438

Does Self-Image Matter? Client's Self-Image, Behaviour and Evaluation of a Career Counselling Session: An Exploratory Study

ERIC Educational Resources Information Center

Schedin, Gunnar; Armelius, Kerstin

2008-01-01

This exploratory study addresses differences in self-image as a client characteristic in career counselling by using the Structural Analysis of Social Behaviour (Benjamin, L., "Journal of Consulting and Clinical Psychology," 64(6), 1203-1212, 1996; Benjamin, L., "Journal of Personality Assessment," 66(2), 248-266, 1996) and an adaptation…

Technical Communications in Aeronautics: Results of an Exploratory Study. An Analysis of Profit Managers' and Nonprofit Managers' Responses. NASA Technical Memorandum 101626.

ERIC Educational Resources Information Center

Pinelli, Thomas E.; And Others

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that profit and nonprofit managers in the aerospace community have different technical communications practices. Profit and nonprofit managers were compared in five…

The Effects of Using Exploratory Computerized Environments in Grades 1 to 8 Mathematics: A Meta-Analysis of Research

ERIC Educational Resources Information Center

Sokolowski, Andrzej; Li, Yeping; Willson, Victor

2015-01-01

Background: The process of problem solving is difficult for students; thus, mathematics educators have made multiple attempts to seek ways of making this process more accessible to learners. The purpose of this study was to examine the effect size statistic of utilizing exploratory computerized environments (ECEs) to support the process of word…

Achieving Dramatic School Improvement: An Exploratory Study. A Cross-Site Analysis from the Evaluation of Comprehensive School Reform Program Implementation and Outcomes Study

ERIC Educational Resources Information Center

Aladjem, Daniel K.; Birman, Beatrice F.; Orland, Martin; Harr-Robins, Jenifer; Heredia, Alberto; Parrish, Thomas B.; Ruffini, Stephen J.

2010-01-01

This exploratory study describes approaches to improving schools through retrospective, in-depth qualitative case studies. To select schools to be examined, the authors sought to identify Comprehensive School Reform (CSR) schools demonstrating two distinctive patterns of improved student achievement between 2000 and 2005, rapid-improvement (i.e.,…

Using the Beck Anxiety Inventory among South Africans Living with HIV: Exploratory and Higher Order Factor Analyses

ERIC Educational Resources Information Center

Kagee, Ashraf; Coetzee, Bronwyne; Saal, Wylene; Nel, Adriaan

2015-01-01

We administered the Beck Anxiety Inventory (BAI) to 101 adults receiving HIV treatment. Exploratory factor analysis yielded a single anxiety factor that accounted for 68.7% of the variance in the data. A single score may be used to indicate the overall level of anxiety of individuals receiving HIV treatment in South Africa.

Are Review Skills and Academic Writing Skills Related? An Exploratory Analysis via Multi Source Feedback Tools

ERIC Educational Resources Information Center

Razi, Salim

2016-01-01

Because students learn from each other as well as lecturers, it is important to create opportunities for collaboration in writing classes. Teachers now benefit from access to plagiarism detectors that can also provide feedback. This exploratory study considers the role of four review types, open and anonymous, involving the students themselves,…

Analysis of E-Mail Produced by Middle School Students with Disabilities Using Accessible Interfaces: An Exploratory Study

ERIC Educational Resources Information Center

Sohlberg, McKay Moore; Todis, Bonnie; Fickas, Stephen; Ehlhardt, Laurie

2011-01-01

The goal of this exploratory study was to investigate electronic communication as a potential method to enhance social communication in a range of students with disabilities. This study investigated the usability of an adapted e-mail interface, TeenMail, for 11 middle school students with significant learning and communication impairments who…

Bayesian Exploratory Factor Analysis

PubMed Central

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

2014-01-01

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

Using the People of Color Racial Identity Attitude Scale among Asian American college students: an exploratory factor analysis.

PubMed

Perry, Justin C; Vance, Kristen S; Helms, Janet E

2009-04-01

In this study, an exploratory factor analysis of the People of Color Racial Identity Attitude Scale (PRIAS; Helms, 1995b) among a sample of Asian American college students (N = 225) was conducted. The factorial structure that emerged revealed mixed results in terms of consistency with the People of Color (POC) theory (Helms, 1995a). The measure's construct validity for Asian Americans may be improved through further scale development and revision. Directions for future research on the PRIAS are discussed. (c) 2009 APA, all rights reserved.

The 'Big Five'. Hypothesis generation: a multidisciplinary intervention package reduces disease-specific hospitalisations from long-term care: a post hoc analysis of the ARCHUS cluster-randomised controlled trial.

PubMed

Connolly, Martin J; Broad, Joanna B; Boyd, Michal; Zhang, Tony Xian; Kerse, Ngaire; Foster, Susan; Lumley, Thomas; Whitehead, Noeline

2016-05-01

long-term care (LTC) residents have higher hospitalisation rates than non-LTC residents. Rapid decline may follow hospitalisations, hence the importance of preventing unnecessary hospitalisations. Literature describes diagnosis-specific interventions (for cardiac failure, ischaemic heart disease, chronic obstructive pulmonary disease, stroke, pneumonia-termed 'big five' diagnoses), impacting on hospitalisations of older community-dwellers, but few RCTs show reductions in acute admissions from LTC. LTC facilities with higher than expected hospitalisations were recruited for a cluster-randomised controlled trial (RCT) of facility-based complex, non-disease-specific, 9-month intervention comprising gerontology nurse specialist (GNS)-led staff education, facility benchmarking, GNS resident review and multidisciplinary discussion of residents selected using standard criteria. In this post hoc exploratory analysis, the outcome was acute hospitalisations for 'big five' diagnoses. Re-randomisation analyses were used for end points during months 1-14. For end points during months 4-14, proportional hazards models are adjusted for within-facility clustering. we recruited 36 facilities with 1,998 residents (1,408 female; mean age 82.9 years); 1,924 were alive at 3 months. The intervention did not impact overall rates of acute hospitalisations or mortality (previously published), but resulted in fewer 'big five' admissions (RR = 0.73, 95% CI = 0.54-0.99; P = 0.043) with no significant difference in the rate of other acute admissions. When considering events occurring after 3 months (only), the intervention group were 34.7% (HR = 0.65; 95% CI = 0.49-0.88; P = 0.005) less likely to have a 'big five' acute admission than controls, with no differences in likelihood of acute admissions for other diagnoses (P = 0.96). this generic intervention may reduce admissions for common conditions which the literature shows are impacted by disease-specific admission reduction strategies. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

DataHub: Knowledge-based data management for data discovery

NASA Astrophysics Data System (ADS)

Handley, Thomas H.; Li, Y. Philip

1993-08-01

Currently available database technology is largely designed for business data-processing applications, and seems inadequate for scientific applications. The research described in this paper, the DataHub, will address the issues associated with this shortfall in technology utilization and development. The DataHub development is addressing the key issues in scientific data management of scientific database models and resource sharing in a geographically distributed, multi-disciplinary, science research environment. Thus, the DataHub will be a server between the data suppliers and data consumers to facilitate data exchanges, to assist science data analysis, and to provide as systematic approach for science data management. More specifically, the DataHub's objectives are to provide support for (1) exploratory data analysis (i.e., data driven analysis); (2) data transformations; (3) data semantics capture and usage; analysis-related knowledge capture and usage; and (5) data discovery, ingestion, and extraction. Applying technologies that vary from deductive databases, semantic data models, data discovery, knowledge representation and inferencing, exploratory data analysis techniques and modern man-machine interfaces, DataHub will provide a prototype, integrated environement to support research scientists' needs in multiple disciplines (i.e. oceanography, geology, and atmospheric) while addressing the more general science data management issues. Additionally, the DataHub will provide data management services to exploratory data analysis applications such as LinkWinds and NCSA's XIMAGE.

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

PubMed

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

2016-07-21

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

Pollution and ecological risk assessment of heavy metals in the soil-plant system and the sediment-water column around a former Pb/Zn-mining area in NE Morocco.

PubMed

El Azhari, Abdellah; Rhoujjati, Ali; El Hachimi, Moulay Laârabi; Ambrosi, Jean-Paul

2017-10-01

This study discussed the environmental fate and ecological hazards of heavy metals in the soil-plant system and sediment-water column around the former Pb-Zn mining Zeïda district, in Northeastern Morocco. Spatial distribution, pollution indices, and cluster analysis were applied for assessing Pb, Zn, As, Cu and Cd pollution levels and risks. The geo-accumulation index (I geo ) was determined using two different geochemical backgrounds: i) the commonly used upper crust values, ii) local geochemical background calculated with exploratory data analysis. The soils in the vicinity of the tailings, as well as the sediments downstream of the latter, displayed much higher metal concentrations, I geo, and potential ecology risk coefficient values than other sites, classifying these sites as highly contaminated and severely hazardous. The concentrations of Pb in contaminated sediment samples also exceeded the PEC limits and are expected to cause harmful effects on sediment-dwelling organisms. Based on the comparison with the toxicity limits, the most contaminated plant samples were found around the tailings piles. The metal concentrations in both raw and filtrated water samples were overall below the drinking water standards in samples upstream and downstream of the mining center, indicating that heavy metals levels in the Moulouya River surface waters were not affected by the tailings spill. Cluster analysis suggest that: i) Pb and Zn in sediments were derived from the abandoned tailings and are mainly stored and transported as particle-bound to the bedload, ii) Pb, Zn, and Cu in the soil-plant system were related to the dispersion of tailings materials while As and Cd originated primarily from natural geological background in both the soil-plant and the water-sediment systems. Copyright © 2017 Elsevier Inc. All rights reserved.

STEMS pilot trial: a pilot cluster randomised controlled trial to investigate the addition of patient direct access to physiotherapy to usual GP-led primary care for adults with musculoskeletal pain

PubMed Central

Ogollah, Reuben O; Jowett, Sue; Kigozi, Jesse; Tooth, Stephanie; Protheroe, Joanne; Hay, Elaine M; Salisbury, Chris; Foster, Nadine E

2017-01-01

Introduction Around 17% of general practitioner (GP) consultations are for musculoskeletal conditions, which will rise as the population ages. Patient direct access to physiotherapy provides one solution, yet adoption in the National Health Service (NHS) has been slow. Setting A pilot, pragmatic, non-inferiority, cluster randomised controlled trial (RCT) in general practice and physiotherapy services in the UK. Objectives Investigate feasibility of a main RCT. Participants Adult patients registered in participating practices and consulting with a musculoskeletal problem. Interventions 4 general practices (clusters) randomised to provide GP-led care as usual or the addition of a patient direct access to physiotherapy pathway. Outcomes Process outcomes and exploratory analyses of clinical and cost outcomes. Data collection Participant-level data were collected via questionnaires at identification, 2, 6 and 12 months and through medical records. Blinding The study statistician and research nurses were blinded to practice allocation. Results Of 2696 patients invited to complete study questionnaires, 978 participated (intervention group n=425, control arm n=553) and were analysed. Participant recruitment was completed in 6 months. Follow-up rates were 78% (6 months) and 71% (12 months). No evidence of selection bias was observed. The direct access pathway was used by 90% of patients in intervention practices needing physiotherapy. Some increase in referrals to physiotherapy occurred from one practice, although waiting times for physiotherapy did not increase (28 days before, 26 days after introduction of direct access). No safety issues were identified. Clinical and cost outcomes were similar in both groups. Exploratory estimates of between group effect (using 36-item Short Form Health Survey (SF-36) Physical Component Summary (PCS)) at 6 months was −0.28 (95% CI −1.35 to 0.79) and at 12 months 0.12 (95% CI −1.27 to 1.51). Conclusions A full RCT is feasible and will provide trial evidence about the clinical and cost-effectiveness of patient direct access to physiotherapy. Trial registration number ISRCTN23378642. PMID:28286331

Exploratory characterization of the unsaponifiable fraction of tunisian virgin olive oils by a global approach with HPLC-APCI-IT MS/MS analysis.

PubMed

Zarrouk, Wissem; Carrasco-Pancorbo, Alegría; Segura-Carretero, Antonio; Fernández-Gutiérrez, Alberto; Zarrouk, Mokhtar

2010-05-26

The unsaponifiable fraction of six Tunisian monovarietal virgin olive oils from the region of Medenine was evaluated within a single chromatographic run by using HPLC-APCI-tandem MS. Separation of the compounds under study was achieved by the RP-LC method, giving a reasonable analysis time and good resolution. Detection was done by an ion trap (working alternatively in MS and MS/MS modes), the fact which made our method suitable to unequivocally identify a high number of compounds belonging to different families of the unsaponifiable fraction of oil and to carry out their reliable and sensitive quantification. A great amount of qualitative information was generated in every analysis, although we focused on the quantification of sterols, tocopherols, and triterpenic dialcohols since their standards were commercially available. The limits of detections achieved were within the range of 1.21 and 10.31 microg/kg for sitostanol and beta-sitosterol, respectively. Significant differences were observed in the composition of the studied olive cultivars. Jemri Ben Guerdane oil was the richest one in terms of all of the sterols under study. alpha-Tocopherol was the main vitamin E isomer in all samples, ranging from 70.14 to 130.72 mg/kg. Principal component analysis (PCA) and cluster analysis were applied to the whole data set in order to explore the distribution of the olive cultivars according to their oil composition.

Altered immune pathway activity under exercise challenge in Gulf War Illness: an exploratory analysis.

PubMed

Broderick, Gordon; Ben-Hamo, Rotem; Vashishtha, Saurabh; Efroni, Sol; Nathanson, Lubov; Barnes, Zachary; Fletcher, Mary Ann; Klimas, Nancy

2013-02-01

Though potentially linked to the basic physiology of stress response we still have no clear understanding of Gulf War Illness (GWI), a debilitating illness presenting with a complex constellation of immune, endocrine and neurological symptoms. Here we compared male GWI (n=20) with healthy veterans (n=22) and subjects with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) (n=7). Blood was drawn during a Graded eXercise Test (GXT) prior to exercise, at peak effort (VO2 max) and 4-h post exercise. Affymetrix HG U133 plus 2.0 microarray gene expression profiling in peripheral blood mononuclear cells (PBMCs) was used to estimate activation of over 500 documented pathways. This was cast against ELISA-based measurement of 16 cytokines in plasma and flow cytometric assessment of lymphocyte populations and cytotoxicity. A 2-way ANOVA corrected for multiple comparisons (q statistic <0.05) indicated significant increases in neuroendocrine-immune signaling and inflammatory activity in GWI, with decreased apoptotic signaling. Conversely, cell cycle progression and immune signaling were broadly subdued in CFS. Partial correlation networks linking pathways with symptom severity via changes in immune cell abundance, function and signaling were constructed. Central to these were changes in IL-10 and CD2+ cell abundance and their link to two pathway clusters. The first consisted of pathways supporting neuronal development and migration whereas the second was related to androgen-mediated activation of NF-κB. These exploratory results suggest an over-expression of known exercise response mechanisms as well as illness-specific changes that may involve an overlapping stress-potentiated neuro-inflammatory response. Copyright © 2012 Elsevier Inc. All rights reserved.

What is needed to implement a computer-assisted health risk assessment tool? An exploratory concept mapping study.

PubMed

Ahmad, Farah; Norman, Cameron; O'Campo, Patricia

2012-12-19

Emerging eHealth tools could facilitate the delivery of comprehensive care in time-constrained clinical settings. One such tool is interactive computer-assisted health-risk assessments (HRA), which may improve provider-patient communication at the point of care, particularly for psychosocial health concerns, which remain under-detected in clinical encounters. The research team explored the perspectives of healthcare providers representing a variety of disciplines (physicians, nurses, social workers, allied staff) regarding the factors required for implementation of an interactive HRA on psychosocial health. The research team employed a semi-qualitative participatory method known as Concept Mapping, which involved three distinct phases. First, in face-to-face and online brainstorming sessions, participants responded to an open-ended central question: "What factors should be in place within your clinical setting to support an effective computer-assisted screening tool for psychosocial risks?" The brainstormed items were consolidated by the research team. Then, in face-to-face and online sorting sessions, participants grouped the items thematically as 'it made sense to them'. Participants also rated each item on a 5-point scale for its 'importance' and 'action feasibility' over the ensuing six month period. The sorted and rated data was analyzed using multidimensional scaling and hierarchical cluster analyses which produced visual maps. In the third and final phase, the face-to-face Interpretation sessions, the concept maps were discussed and illuminated by participants collectively. Overall, 54 providers participated (emergency care 48%; primary care 52%). Participants brainstormed 196 items thought to be necessary for the implementation of an interactive HRA emphasizing psychosocial health. These were consolidated by the research team into 85 items. After sorting and rating, cluster analysis revealed a concept map with a seven-cluster solution: 1) the HRA's equitable availability; 2) the HRA's ease of use and appropriateness; 3) the content of the HRA survey; 4) patient confidentiality and choice; 5) patient comfort through humanistic touch; 6) professional development, care and workload; and 7) clinical management protocol. Drawing insight from the theoretical lens of Sociotechnical theory, the seven clusters of factors required for HRA implementation could be read as belonging to three overarching aspects : Technical (cluster 1, 2 and 3), Social-Patient (cluster 4 and 5), and Social-Provider (cluster 6 and 7). Participants rated every one of the clusters as important, with mean scores from 4.0 to 4.5. Their scores for feasibility were somewhat lower, ranging from 3.4 to. 4.3. Comparing the scores for importance and feasibility, a significant difference was found for one cluster from each region (cluster 2, 5, 6). The cluster on professional development, care and workload was perceived as especially challenging in emergency department settings, and possible reasons were discussed in the interpretation sessions. A number of intertwined multilevel factors emerged as important for the implementation of a computer-assisted, interactive HRA with a focus on psychosocial health. Future developments in this area could benefit from systems thinking and insights from theoretical perspectives, such as sociotechnical system theory for joint optimization and responsible autonomy, with emphasis on both the technical and social aspects of HRA implementation.

Using Cluster Analysis to Examine Husband-Wife Decision Making

ERIC Educational Resources Information Center

Bonds-Raacke, Jennifer M.

2006-01-01

Cluster analysis has a rich history in many disciplines and although cluster analysis has been used in clinical psychology to identify types of disorders, its use in other areas of psychology has been less popular. The purpose of the current experiments was to use cluster analysis to investigate husband-wife decision making. Cluster analysis was…

The reduction of intoxication and disorder in premises licensed to serve alcohol: an exploratory randomised controlled trial.

PubMed

Moore, Simon C; Brennan, Iain R; Murphy, Simon; Byrne, Ellie; Moore, Susan N; Shepherd, Jonathan P; Moore, Laurence

2010-10-14

Licensed premises offer a valuable point of intervention to reduce alcohol-related harm. To describe the research design for an exploratory trial examining the feasibility and acceptability of a premises-level intervention designed to reduce severe intoxication and related disorder. The study also aims to assess the feasibility of a potential future large scale effectiveness trial and provide information on key trial design parameters including inclusion criteria, premises recruitment methods, strategies to implement the intervention and trial design, outcome measures, data collection methods and intra-cluster correlations. A randomised controlled trial in licensed premises that had experienced at least one assault in the year preceding the intervention, documented in police or hospital Emergency Department (ED) records. Premises were recruited from four study areas by piloting four recruitment strategies of varying intensity. Thirty two licensed premises were grouped into matched pairs to reduce potential bias and randomly allocated to the control or intervention condition. The study included a nested process evaluation to provide information on intervention acceptability and implementation. Outcome measures included police-recorded violent incidents, assault-related attendances at each premises' local ED and patron Breath Alcohol Concentration assessed on exiting and entering study premises. The most successful recruitment method involved local police licensing officers and yielded a 100% success rate. Police-records of violence provided the most appropriate source of data about disorder at the premises level. The methodology of an exploratory trial is presented and despite challenges presented by the study environment it is argued an exploratory trial is warranted. Initial investigations in recruitment methods suggest that study premises should be recruited with the assistance of police officers. Police data were of sufficient quality to identify disorder and street surveys are a feasible method for measuring intoxication at the individual level. UKCRN 7090; ISRCTN: 80875696. Medical Research Council (G0701758) to Simon Moore, Simon Murphy, Laurence Moore and Jonathan Shepherd.

The reduction of intoxication and disorder in premises licensed to serve alcohol: An exploratory randomised controlled trial

PubMed Central

2010-01-01

Background Licensed premises offer a valuable point of intervention to reduce alcohol-related harm. Objective To describe the research design for an exploratory trial examining the feasibility and acceptability of a premises-level intervention designed to reduce severe intoxication and related disorder. The study also aims to assess the feasibility of a potential future large scale effectiveness trial and provide information on key trial design parameters including inclusion criteria, premises recruitment methods, strategies to implement the intervention and trial design, outcome measures, data collection methods and intra-cluster correlations. Design A randomised controlled trial in licensed premises that had experienced at least one assault in the year preceding the intervention, documented in police or hospital Emergency Department (ED) records. Premises were recruited from four study areas by piloting four recruitment strategies of varying intensity. Thirty two licensed premises were grouped into matched pairs to reduce potential bias and randomly allocated to the control or intervention condition. The study included a nested process evaluation to provide information on intervention acceptability and implementation. Outcome measures included police-recorded violent incidents, assault-related attendances at each premises' local ED and patron Breath Alcohol Concentration assessed on exiting and entering study premises. Results The most successful recruitment method involved local police licensing officers and yielded a 100% success rate. Police-records of violence provided the most appropriate source of data about disorder at the premises level. Conclusion The methodology of an exploratory trial is presented and despite challenges presented by the study environment it is argued an exploratory trial is warranted. Initial investigations in recruitment methods suggest that study premises should be recruited with the assistance of police officers. Police data were of sufficient quality to identify disorder and street surveys are a feasible method for measuring intoxication at the individual level. Trial registration UKCRN 7090; ISRCTN: 80875696 Funding Medical Research Council (G0701758) to Simon Moore, Simon Murphy, Laurence Moore and Jonathan Shepherd PMID:20946634

Exploratory factor analysis of signalment and conformational measurements in Thoroughbred horses with and without recurrent laryngeal neuropathy.

PubMed

McGivney, C L; Gough, K F; McGivney, B A; Farries, G; Hill, E W; Katz, L M

2018-06-23

Conflicting results have been reported for risk factors for recurrent laryngeal neuropathy (RLN) based on resting endoscopic evaluation and comparison of single conformation traits, with many traits correlated to one another. To simplify identification of signalment and conformation traits (i.e. variables) associated with RLN cases and controls diagnosed with exercising overground endoscopy (OGE) using exploratory factor analysis (EFA). Prospective cohort. Pearson's rank correlation was used to establish significance and association between variables collected from n = 188 Thoroughbreds from one stable by observers blinded to OGE results. Exploratory factor analysis was conducted on 9 variables for cases and controls; common elements between variables developed a factor, with variables grouped into 3 factors for cases and controls, respectively. Correlation (loading) between each variable and factor was calculated to rank relationships between variables and cases/controls, with factors retrospectively named based on their underlying correlations with variables. Numerous inter-correlations were present between variables. Most strongly correlated in cases were wither height with body weight (r = 0.70) and ventral neck length (r = 0.68) and in controls body weight with rostral neck circumference (r = 0.58). Wither height (r = 0.61) significantly loaded the top-ranked factor for cases ('height RLN '), explaining 25% of conformational variance. Ventral neck length (r = 0.69) and age (r = 0.57) significantly loaded the second-ranked factor for cases ('neck length RLN '), explaining 16% of conformational variance. Rostral neck circumference (r = 0.86) and body weight (r = 0.6) significantly loaded the top-ranked factor for controls ('body size CON '), explaining 19% of the variance. Wither height (r = 0.84) significantly loaded the second-ranked factor for controls ('height CON '), explaining 13% of the variance. Horses had not reached skeletal maturity. Exploratory factor analysis allowed weightings to be determined for each variable. Wither height was the predominant conformational feature associated with RLN. Exploratory factor analysis confirms aggregated conformational differences exist between RLN cases and controls, suitable for future evaluations. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

[GABA-NO interaction in the N. Accumbens during danger-induced inhibition of exploratory behavior].

PubMed

Saul'skaia, N V; Terekhova, E A

2013-01-01

In Sprague-Dawley rats by means of in vivo microdialysis combined with HPLC analysis, it was shown that presentation to rats during exploratory activity of a tone previously pared with footshock inhibited the exploration and prevented the exploration-induced increase in extracellular levels of citrulline (an NO co-product) in the medial n. accumbens. Intra-accumbal infusions of 20 μM bicuculline, a GABA(A)-receptor antagonist, firstly, partially restored the exploration-induced increase of extracellular citrulline levels in this brain area, which was inhibited by presentation of the tone, previously paired with foot-shock and, secondly, prevented the inhibition of exploratory behavior produced by this sound signal of danger. The data obtained indicate for the first time that signals of danger inhibit exploratory behavior and exploration-induced activation of the accumbal nitrergic system via GABA(A)-receptor mechanisms.

Falls in older adults with major depressive disorder (MDD): a systematic review and exploratory meta-analysis of prospective studies.

PubMed

Stubbs, Brendon; Stubbs, Jean; Gnanaraj, Solomon Donald; Soundy, Andrew

2016-01-01

Depressive symptomology is now widely recognized as a key risk factor for falls. The evidence regarding the impact of major depressive disorder (MDD) on falls is unclear. A systematic review and exploratory meta-analysis was undertaken to explore the relationship between MDD and falls. Major electronic database were searched from inception till April 2015. Studies that defined MDD and measured falls prospectively in older adults (≥60 years) were included. Studies relying on depressive symptomology alone were excluded. The methodological quality of included articles was assessed and study findings were synthesized using an exploratory meta-analysis. From a potential of 415 articles, only three studies met the inclusion criteria. This included 976 unique older adults with a range of mean age from ≥65 to 83 years. The methodological quality of included studies was satisfactory. None of the included studies' primary aim was to investigate the relationship between MDD and falls. The exploratory meta-analysis demonstrated older adults with MDD are at increased risk of falling compared to non-depressed older adults (odds ratio (OR) 4.0, 95% CI 2.0-8.1, I(2) = 60%, n = 976). There is a paucity of research considering falls in older adults with MDD. Our results demonstrate that the odds of falling appear to be greater among people with MDD (OR 4.0) than in previous meta-analyses that have only considered subthreshold depressive symptoms. Given the distinct nature and challenges with MDD, more research is required to better understand the falls risk in this group.

Using an innovative multiple regression procedure in a cancer population (Part II): fever, depressive affect, and mobility problems clarify an influential symptom pair (pain-fatigue/weakness) and cluster (pain-fatigue/weakness-sleep problems).

PubMed

Francoeur, Richard B

2015-01-01

Most patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. However, only combinations where symptoms are mutually influential hold potential for identifying patient subgroups at greater risk, and in some contexts, interventions with "cross-over" (multisymptom) effects. Improved methods to detect and interpret interactions among symptoms, signs, or biomarkers are needed to reveal these influential pairs and clusters. I recently created sequential residual centering (SRC) to reduce multicollinearity in moderated regression, which enhances sensitivity to detect these interactions. I applied SRC to moderated regressions of single-item symptoms that interact to predict outcomes from 268 palliative radiation outpatients. I investigated: 1) the hypothesis that the interaction, pain × fatigue/weakness × sleep problems, predicts depressive affect only when fever presents, and 2) an exploratory analysis, when fever is absent, that the interaction, pain × fatigue/weakness × sleep problems × depressive affect, predicts mobility problems. In the fever context, three-way interactions (and derivative terms) of the four symptoms (pain, fatigue/weakness, fever, sleep problems) are tested individually and simultaneously; in the non-fever context, a single four-way interaction (and derivative terms) is tested. Fever interacts separately with fatigue/weakness and sleep problems; these comoderators each magnify the pain-depressive affect relationship along the upper or full range of pain values. In non-fever contexts, fatigue/weakness, sleep problems, and depressive affect comagnify the relationship between pain and mobility problems. Different mechanisms contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters.

The quest for the Sun's siblings: an exploratory search in the Hipparcos Catalogue

NASA Astrophysics Data System (ADS)

Brown, Anthony G. A.; Portegies Zwart, Simon F.; Bean, Jennifer

2010-09-01

We describe the results of a search for the remnants of the Sun's birth cluster among stars in the Hipparcos Catalogue. This search is based on the predicted phase-space distribution of the Sun's siblings from simple simulations of the orbits of the cluster stars in a smooth Galactic potential. For stars within 100 pc, the simulations show that it is interesting to examine those that have small space motions relative to the Sun. From amongst the candidate siblings thus selected, there are six stars with ages consistent with that of the Sun. Considering their radial velocities and abundances only one potential candidate, HIP21158, remains, but essentially the result of the search is negative. This is consistent with predictions by Portegies Zwart on the number of siblings near the Sun. We discuss the steps that should be taken in anticipation of the data from the Gaia mission in order to conduct fruitful searches for the Sun's siblings in the future.

The Quest For The Sun's Siblings: An Exploratory Search In The Hipparcos Catalogue

NASA Astrophysics Data System (ADS)

Bean, Jennifer; Brown, A.; Portegies Zwart, S.

2011-01-01

We describe the results of a search for the remnants of the Sun's birth cluster among stars in the Hipparcos Catalogue. This search is based on the predicted phase-space distribution of the Sun's siblings from simple simulations of the orbits of the cluster stars in a smooth Galactic potential. For stars within 100 pc, the simulations show that it is interesting to examine those that have small space motions relative to the Sun. From amongst the candidate siblings thus selected, there are six stars with ages consistent with that of the Sun. Considering their radial velocities and abundances only one potential candidate, HIP21158, remains, but essentially the result of the search is negative. This is consistent with predictions by Portegies Zwart on the number of siblings near the Sun. We discuss the steps that should be taken in anticipation of the data from the Gaia mission in order to conduct fruitful searches for the Sun's siblings in the future.

Impact of a mHealth intervention for peer health workers on AIDS care in rural Uganda: a mixed methods evaluation of a cluster-randomized trial.

PubMed

Chang, Larry W; Kagaayi, Joseph; Arem, Hannah; Nakigozi, Gertrude; Ssempijja, Victor; Serwadda, David; Quinn, Thomas C; Gray, Ronald H; Bollinger, Robert C; Reynolds, Steven J

2011-11-01

Mobile phone access in low and middle-income countries is rapidly expanding and offers an opportunity to leverage limited human resources for health. We conducted a mixed methods evaluation of a cluster-randomized trial exploratory substudy on the impact of a mHealth (mobile phone) support intervention used by community-based peer health workers (PHW) on AIDS care in rural Uganda. 29 PHWs at 10 clinics were randomized by clinic to receive the intervention or not. PHWs used phones to call and text higher level providers with patient-specific clinical information. 970 patients cared for by the PHWs were followed over a 26 month period. No significant differences were found in patients' risk of virologic failure. Qualitative analyses found improvements in patient care and logistics and broad support for the mHealth intervention among patients, clinic staff, and PHWs. Key challenges identified included variable patient phone access, privacy concerns, and phone maintenance.

Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding

PubMed Central

Lobos, Gustavo A.; Poblete-Echeverría, Carlos

2017-01-01

This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules. PMID:28119705

HIV incidence and CDC's HIV prevention budget: an exploratory correlational analysis.

PubMed

Holtgrave, David R; Kates, Jennifer

2007-01-01

The central evaluative question about a national HIV prevention program is whether that program affects HIV incidence. Numerous factors may influence incidence, including public investment in HIV prevention. Few studies, however, have examined the relationship between public investment and the HIV epidemic in the United States. This 2006 exploratory analysis examined the period from 1978 through 2006 using a quantitative, lagged, correlational analysis to capture the relationship between national HIV incidence and Centers for Disease Control and Prevention's HIV prevention budget in the United States over time. The analyses suggest that early HIV incidence rose in advance of the nation's HIV prevention investment until the mid-1980s (1-year lag correlation, r=0.972, df=2, p <0.05). From that point on, it appears that the nation's investment in HIV prevention became a strong correlate of HIV incidence (1-year lag correlation, r=-0.905, df=18, p <0.05). This exploratory study provides correlational evidence of a relationship between U.S. HIV incidence and the federal HIV prevention budget over time, and calls for further analysis of the role of funding and other factors that may influence the direction of a nation's HIV epidemic.

Exploratory factor analysis of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T).

PubMed

Watson, Paul Barry; Seaton, Philippa; Sims, Deborah; Jamieson, Isabel; Mountier, Jane; Whittle, Rose; Saarikoski, Mikko

2014-01-01

The Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) scale measures student nurses' perceptions of clinical learning environments. This study evaluates the construct validity and internal reliability of the CLES+T in hospital settings in New Zealand. Comparisons are made between New Zealand and Finnish data. The CLES+T scale was completed by 416 Bachelor of Nursing students following hospital clinical placements between October 2008 and December 2009. Construct validity and internal reliability were assessed using exploratory factor analysis and Cronbach's alpha. Exploratory factor analysis supports 4 factors. Cronbach's alpha ranged from .82 to .93. All items except 1 loaded on the same factors found in unpublished Finnish data. The first factor combined 2 previous components from the published Finnish component analysis and was renamed: connecting with, and learning in, communities of clinical practice. The remaining 3 factors (Nurse teacher, Supervisory relationship, and Leadership style of the manager) corresponded to previous components and their conceptualizations. The CLES+T has good internal reliability and a consistent factor structure across samples. The consistency across international samples supports faculties and hospitals using the CLES+T to benchmark the quality of clinical learning environments provided to students.

Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding.

PubMed

Lobos, Gustavo A; Poblete-Echeverría, Carlos

2016-01-01

This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules.

The Association between Foster Care and Substance Abuse Risk Factors and Treatment Outcomes: An Exploratory Secondary Analysis

ERIC Educational Resources Information Center

Blome, Wendy Whiting; Shields, Joseph; Verdieck, Mary Jeanne

2009-01-01

The child welfare and substance abuse systems are integrally linked through the children and families they both serve. There is a dearth of knowledge, however, on how children who have experienced foster care fare when they are treated for substance abuse issues as adults. This article presents an exploratory study using the Alcohol and Drug…

An Exploratory Study Investigating the Viability of a Communication and Feedback Intervention for School Children at Risk of Exclusion: Analysis of Staff Perspectives

ERIC Educational Resources Information Center

Rechten, Frances; Tweed, Alison E.

2014-01-01

Every day nearly 900 children will be excluded from UK schools for disruptive behaviour and almost one-third of this population has a diagnosed mental health disorder. Exclusion from school is the endpoint of most schools' sanction-based behaviour management policies. This exploratory study investigated staff opinions for using a communication and…

Cracks in the Ceiling? Historical and Contemporary Trends of African American Deans of Schools of Social Work

ERIC Educational Resources Information Center

Banks, Leon; Hopps, June Gary; Briggs, Harold E.

2018-01-01

This article presents data from an exploratory study of the demographic and published scholarship profiles of the deans and university provosts of the top 50 schools of social work as ranked by the 2016 U.S. News and World Report ratings. Method: The authors used an exploratory design to conduct a content analysis of the demographic and…

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

ERIC Educational Resources Information Center

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

2011-01-01

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

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

ERIC Educational Resources Information Center

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

2012-01-01

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

Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters.

PubMed

Serra-Sogas, Norma; O'Hara, Patrick D; Canessa, Rosaline; Keller, Peter; Pelot, Ronald

2008-05-01

This paper examines the use of exploratory spatial analysis for identifying hotspots of shipping-based oil pollution in the Pacific Region of Canada's Exclusive Economic Zone. It makes use of data collected from fiscal years 1997/1998 to 2005/2006 by the National Aerial Surveillance Program, the primary tool for monitoring and enforcing the provisions imposed by MARPOL 73/78. First, we present oil spill data as points in a "dot map" relative to coastlines, harbors and the aerial surveillance distribution. Then, we explore the intensity of oil spill events using the Quadrat Count method, and the Kernel Density Estimation methods with both fixed and adaptive bandwidths. We found that oil spill hotspots where more clearly defined using Kernel Density Estimation with an adaptive bandwidth, probably because of the "clustered" distribution of oil spill occurrences. Finally, we discuss the importance of standardizing oil spill data by controlling for surveillance effort to provide a better understanding of the distribution of illegal oil spills, and how these results can ultimately benefit a monitoring program.

Personal values, marketing attitudes and nutrition trust are associated with patronage of convenience food outlets in the Asia-Pacific region: a cross-sectional study.

PubMed

De Jong, Breanna; Worsley, Anthony; Wang, Wei Chun; Sarmugam, Rani; Pham, Quynh; Februhartanty, Judhiastuty; Ridley, Stacey

2017-02-16

An online cross-sectional survey examined the relationships between the demographic characteristics, personal values, trust in sources of nutrition information and the use of convenience food outlets among middle-class household food providers in the Asia-Pacific region. The survey was administered to 3945 household food providers in Melbourne, Singapore, Shanghai, Vietnam and Indonesia in late 2013. Information about demographics, personal values, trust in sources of nutrition information and use of convenience food outlets was elicited. Exploratory factor analysis, two-step clustering and logistic regression were employed. The analyses found that the use of convenience food outlets was positively related to hedonist values and trust in food industry sources of nutrition information. However, lesser use of convenience food outlets and trust in health sources of nutrition information was associated with traditional (community-oriented) values. Further replication and extension of these findings would be useful. However, they suggest that improvements in the quality of foods sold in convenience food outlets combined with stronger regulation of food marketing and long-term food education are required.

Metacommunity analysis of amoeboid protists in grassland soils

PubMed Central

Fiore-Donno, Anna Maria; Weinert, Jan; Wubet, Tesfaye; Bonkowski, Michael

2016-01-01

This study reveals the diversity and distribution of two major ubiquitous groups of soil amoebae, the genus Acanthamoeba and the Myxomycetes (plasmodial slime-moulds) that are rarely, if ever, recovered in environmental sampling studies. We analyzed 150 grassland soil samples from three Biodiversity Exploratories study regions in Germany. We developed specific primers targeting the V2 variable region in the first part of the small subunit of the ribosomal RNA gene for high-throughput pyrotag sequencing. From ca. 1 million reads, applying very stringent filtering and clustering parameters to avoid overestimation of the diversity, we obtained 273 acanthamoebal and 338 myxomycete operational taxonomic units (OTUs, 96% similarity threshold). This number is consistent with the genetic diversity known in the two investigated lineages, but unequalled to date by any environmental sampling study. Only very few OTUs were identical to already known sequences. Strikingly different OTUs assemblages were found between the three German regions (PerMANOVA p.value = 0.001) and even between sites of the same region (multiple-site Simpson-based similarity indices <0.4), showing steep biogeographical gradients. PMID:26750872

Emergent literacy profiles of preschool-age children with specific language impairment.

PubMed

Cabell, Sonia Q; Lomax, Richard G; Justice, Laura M; Breit-Smith, Allison; Skibbe, Lori E; McGinty, Anita S

2010-12-01

The primary aim of the present study was to explore the heterogeneity of emergent literacy skills among preschool-age children with specific language impairment (SLI) through examination of profiles of performance. Fifty-nine children with SLI were assessed on a battery of emergent literacy skills (i.e., alphabet knowledge, print concepts, emergent writing, rhyme awareness) and oral language skills (i.e., receptive/expressive vocabulary and grammar). Cluster analysis techniques identified three emergent literacy profiles: (1) Highest Emergent Literacy, Strength in Alphabet Knowledge; (2) Average Emergent Literacy, Strength in Print Concepts; and (3) Lowest Emergent Literacy across Skills. After taking into account the contribution of child age, receptive and expressive language skills made a small contribution to the prediction of profile membership. The present findings, which may be characterized as exploratory given the relatively modest sample size, suggest that preschool-age children with SLI display substantial individual differences with regard to their emergent literacy skills and that these differences cannot be fully determined by children's age or oral language performance. Replication of the present findings with a larger sample of children is needed.

Metacommunity analysis of amoeboid protists in grassland soils.

PubMed

Fiore-Donno, Anna Maria; Weinert, Jan; Wubet, Tesfaye; Bonkowski, Michael

2016-01-11

This study reveals the diversity and distribution of two major ubiquitous groups of soil amoebae, the genus Acanthamoeba and the Myxomycetes (plasmodial slime-moulds) that are rarely, if ever, recovered in environmental sampling studies. We analyzed 150 grassland soil samples from three Biodiversity Exploratories study regions in Germany. We developed specific primers targeting the V2 variable region in the first part of the small subunit of the ribosomal RNA gene for high-throughput pyrotag sequencing. From ca. 1 million reads, applying very stringent filtering and clustering parameters to avoid overestimation of the diversity, we obtained 273 acanthamoebal and 338 myxomycete operational taxonomic units (OTUs, 96% similarity threshold). This number is consistent with the genetic diversity known in the two investigated lineages, but unequalled to date by any environmental sampling study. Only very few OTUs were identical to already known sequences. Strikingly different OTUs assemblages were found between the three German regions (PerMANOVA p.value = 0.001) and even between sites of the same region (multiple-site Simpson-based similarity indices <0.4), showing steep biogeographical gradients.

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Cognitive and Functional Correlates of NPI-Q Scores and Symptom Clusters in Mildly Demented Alzheimer Patients.

PubMed

Travis Seidl, Jennifer N; Massman, Paul J

2016-01-01

Previous research has demonstrated an association between the emotional and behavioral symptoms of dementia, known as neuropsychiatric symptoms, and cognitive and functional decline among patients with Alzheimer disease (AD). The present study aimed to identify associations between neuropsychiatric symptoms as measured by the Neuropsychiatric Inventory-Questionnaire (NPI-Q) and cognitive and functional performance. Participants were 256 AD patients enrolled in the Alzheimer's Disease and Memory Disorders Center at Baylor College of Medicine. An exploratory factor analysis of the NPI-Q indicated a 2-factor structure consisting of Negative/Oppositional and Anxiety/Restlessness factors. Regression analyses revealed significant associations between greater total severity of neuropsychiatric symptoms and poorer performance on basic and Instrumental Activities of Daily Living. Greater severity of Anxiety/Restlessness symptoms was associated with poor performance on measures of visuospatial functioning and basic and instrumental activities of daily living. The Negative/Oppositional factor was not related to cognition or functioning. In summary, neuropsychiatric symptoms (particularly Anxiety/Restlessness symptoms) were related to cognition and everyday functioning. Proper assessment and treatment of these symptoms is essential for improving cognition and functioning in AD patients.

Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example.

PubMed

Ni, Yongnian; Lai, Yanhua; Brandes, Sarina; Kokot, Serge

2009-08-11

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study.

PubMed

Saura, Jose Ramon; Palos-Sanchez, Pedro; Rios Martin, Miguel Angel

2018-03-19

The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets ( n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified.

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

PubMed Central

2018-01-01

The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified. PMID:29562724

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

PubMed

Bible, Paul W; Kanno, Yuka; Wei, Lai; Brooks, Stephen R; O'Shea, John J; Morasso, Maria I; Loganantharaj, Rasiah; Sun, Hong-Wei

2015-01-01

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST's functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond

PubMed Central

Bible, Paul W.; Kanno, Yuka; Wei, Lai; Brooks, Stephen R.; O’Shea, John J.; Morasso, Maria I.; Loganantharaj, Rasiah; Sun, Hong-Wei

2015-01-01

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST’s functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST’s general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work. PMID:25970601

Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma.

PubMed

Aoki, Shuichiro; Murata, Hiroshi; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Hirasawa, Kazunori; Shoji, Nobuyuki; Asaoka, Ryo

2017-12-01

To investigate the usefulness of the Octopus (Haag-Streit) EyeSuite's cluster trend analysis in glaucoma. Ten visual fields (VFs) with the Humphrey Field Analyzer (Carl Zeiss Meditec), spanning 7.7 years on average were obtained from 728 eyes of 475 primary open angle glaucoma patients. Mean total deviation (mTD) trend analysis and EyeSuite's cluster trend analysis were performed on various series of VFs (from 1st to 10th: VF1-10 to 6th to 10th: VF6-10). The results of the cluster-based trend analysis, based on different lengths of VF series, were compared against mTD trend analysis. Cluster-based trend analysis and mTD trend analysis results were significantly associated in all clusters and with all lengths of VF series. Between 21.2% and 45.9% (depending on VF series length and location) of clusters were deemed to progress when the mTD trend analysis suggested no progression. On the other hand, 4.8% of eyes were observed to progress using the mTD trend analysis when cluster trend analysis suggested no progression in any two (or more) clusters. Whole field trend analysis can miss local VF progression. Cluster trend analysis appears as robust as mTD trend analysis and useful to assess both sectorial and whole field progression. Cluster-based trend analyses, in particular the definition of two or more progressing cluster, may help clinicians to detect glaucomatous progression in a timelier manner than using a whole field trend analysis, without significantly compromising specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Dietary restraint and subjective well-being in university students in Chile.

PubMed

Schnettler, Berta; Miranda, Horacio; Sepúlveda, José; Orellana, Ligia; Etchebarne, Soledad; Lobos, Germán; Mora, Marcos; Denegri, Marianela; Grunert, Klaus G

2014-08-01

To characterize university students typologies according to chronic food restriction, satisfaction with life and food consumption. A questionnaire was applied on a non-probability sample of 369 male and female students from five Chilean universities. The questionnaire included: Revised Restraint Scale (RRS), Satisfaction with Life Scale (SWLS), Satisfaction with Food-related Life (SWFL) and the Health-related Quality of Life Index. The survey included food and drink consumption habits, weight and approximate height and sociodemographic variables. Two factors in the RRS were detected by exploratory factor analysis: Preoccupation with Diet (PD) and Weight fluctuations (WF). A confirmatory factor analysis validated the bifactor structure of the RRS with an acceptable adjustment kindness. The cluster analysis allowed a distinction of four typologies with a significant variation in PD, WF, SWLS and SWFL scoring, number of days with mental health problems, frequency of alcoholic drinks consumption, restraint on the consumption of certain foods, drinks and spices, consumption frequency of fruit out of the main meals and types. Typologies did not differ on their body mass index. Both, students preoccupied with diet and those who are not, experience higher levels of satisfaction with life and with food. Lower levels of global life satisfaction and satisfaction with food are related with the fluctuations in weight. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Voxel-based morphometric brain comparison between healthy subjects and major depressive disorder patients in Japanese with the s/s genotype of 5-HTTLPR.

PubMed

Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Ide, Satoru; Kishi, Taro; Abe, Osamu; Igata, Ryouhei; Katsuki, Asuka; Iwata, Nakao; Yoshimura, Reiji; Korogi, Yukunori

2017-06-21

Individuals with s/s genotype of serotonin transporter gene-linked promotor region (5-HTTLPR), which appear with a high frequency in Japanese, exhibit more diagnosable depression in relation to stressful life events than those with the s/l or l/l genotype. We prospectively investigated the brain volume changes in first-episode and medication naïve major depression disorder patients (MDD) with the s/s genotype in Japanese. We assessed the differences between 27 MDD with the s/s genotype and 44 healthy subjects (HS) with the same genotype using a whole-brain voxel-by-voxel statistical analysis of MRI. Gray matter volume in a brain region with significant clusters obtained via voxel-based morphometry analysis were measured and, as an exploratory analysis, evaluated for relationships to the subcategory scores (core, sleep, activity, psychic, somatic anxiety, delusion) of the Hamilton Depression Rating Scale (HAM-D) and the Social Readjustment Rating Scale (SRRS). The brain volume in the left insula lobe was significantly smaller in the MDD than in the HS. The left insula lobe volume correlated negatively with the "psychic" score of HAM-D and the SRRS. In a Japanese population with the s/s genotype, we found an atrophy of the insula in the MDD, which might be associated with "psychic" symptom and stress events.

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

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

Alexandrov, Boian; Vesselinov, Velimir Valentinov; Djidjev, Hristo Nikolov

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

Consumer behaviors towards ready-to-eat foods based on food-related lifestyles in Korea

PubMed Central

Bae, Hyun-Joo; Chae, Mi-Jin

2010-01-01

The purpose of this study was to examine consumers' behaviors toward ready-to-eat foods and to develop ready-to-eat food market segmentation in Korea. The food-related lifestyle and purchase behaviors of ready-to-eat foods were evaluated using 410 ready-to-eat food consumers in the Republic of Korea. Four factors were extracted by exploratory factor analysis (health-orientation, taste-orientation, convenience-orientation, and tradition-orientation) to explain the ready-to eat food consumers' food-related lifestyles. The results of cluster analysis indicated that "tradition seekers" and "convenience seekers" should be regarded as the target segments. Chi-square tests and t-tests of the subdivided groups showed there were significant differences across marital status, education level, family type, eating-out expenditure, place of purchase, and reason for purchase. In conclusion, the tradition seekers consumed more ready-to-eat foods from discount marts or specialty stores and ate them between meals more often than the convenience seekers. In contrast, the convenience seekers purchased more ready-to-eat foods at convenience stores and ate them as meals more often than the tradition seekers. These findings suggest that ready-to-eat food market segmentation based on food-related lifestyles can be applied to develop proper marketing strategies. PMID:20827350

Consumer behaviors towards ready-to-eat foods based on food-related lifestyles in Korea.

PubMed

Bae, Hyun-Joo; Chae, Mi-Jin; Ryu, Kisang

2010-08-01

The purpose of this study was to examine consumers' behaviors toward ready-to-eat foods and to develop ready-to-eat food market segmentation in Korea. The food-related lifestyle and purchase behaviors of ready-to-eat foods were evaluated using 410 ready-to-eat food consumers in the Republic of Korea. Four factors were extracted by exploratory factor analysis (health-orientation, taste-orientation, convenience-orientation, and tradition-orientation) to explain the ready-to eat food consumers' food-related lifestyles. The results of cluster analysis indicated that "tradition seekers" and "convenience seekers" should be regarded as the target segments. Chi-square tests and t-tests of the subdivided groups showed there were significant differences across marital status, education level, family type, eating-out expenditure, place of purchase, and reason for purchase. In conclusion, the tradition seekers consumed more ready-to-eat foods from discount marts or specialty stores and ate them between meals more often than the convenience seekers. In contrast, the convenience seekers purchased more ready-to-eat foods at convenience stores and ate them as meals more often than the tradition seekers. These findings suggest that ready-to-eat food market segmentation based on food-related lifestyles can be applied to develop proper marketing strategies.

Psychometric Properties of the Diabetes Management Self-Efficacy Scale in Korean Patients with Type 2 Diabetes.

PubMed

Lee, Eun-Hyun; van der Bijl, Jaap; Shortridge-Baggett, Lillie M; Han, Seung Jin; Moon, Seung Hei

2015-01-01

Objectives. The aims of this study were to perform a cultural translation of the DMSES and evaluate the psychometric properties of the translated scale in a Korean population with type 2 diabetics. Methods. This study was conducted in patients with diabetes recruited from university hospitals. The first stage of this study involved translating the DMSES into Korean using a forward- and backward-translation technique. The content validity was assessed by an expert group. In the second stage, the psychometric properties of the Korean version of the DMSES (K-DMSES) were evaluated. Results. The content validity of the K-DMSES was satisfactory. Sixteen-items clustered into four-subscales were extracted by exploratory factor analysis, and supported by confirmatory factor analysis. The construct validity of the K-DMSES with the Summary of Diabetes Self-Care Activities scale was satisfactory (r = 0.50, P<0.001). The Cronbach's alpha and intraclass correlation coefficient were 0.92 and 0.85 (P<0.001; 95% CI = 0.75-0.91), respectively, which indicate excellent internal consistency reliability and test-retest reliability. Conclusions. The K-DMSES is a brief instrument that has demonstrated good psychometric properties. It is therefore feasible to use in practice, and is ready for use in clinical research involving Korean patients with type 2 diabetes.

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

PubMed

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

2017-12-01

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

Subtypes of female juvenile offenders: a cluster analysis of the Millon Adolescent Clinical Inventory.

PubMed

Stefurak, Tres; Calhoun, Georgia B

2007-01-01

The current study sought to explore subtypes of adolescents within a sample of female juvenile offenders. Using the Millon Adolescent Clinical Inventory with 101 female juvenile offenders, a two-step cluster analysis was performed beginning with a Ward's method hierarchical cluster analysis followed by a K-Means iterative partitioning cluster analysis. The results suggest an optimal three-cluster solution, with cluster profiles leading to the following group labels: Externalizing Problems, Depressed/Interpersonally Ambivalent, and Anxious Prosocial. Analysis along the factors of age, race, offense typology and offense chronicity were conducted to further understand the nature of found clusters. Only the effect for race was significant with the Anxious Prosocial and Depressed Intepersonally Ambivalent clusters appearing disproportionately comprised of African American girls. To establish external validity, clusters were compared across scales of the Behavioral Assessment System for Children - Self Report of Personality, and corroborative distinctions between clusters were found here.

[Cluster analysis in biomedical researches].

PubMed

Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

2013-01-01

Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

Behavioral and Psychosocial Considerations in Intelligence Analysis: A Preliminary Review of Literature on Critical Thinking Skills

DTIC Science & Technology

2009-03-01

Department of Defense, Washington Headquarters Services , Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite... discovery (teaching by problem solving), and exploratory (teaching by exploration). Research suggests while guided discovery and exploratory training...34 College Student Journal 38 (2004): 482-493. MasterFILE Premier. EBSCO . 4 June 2008. - This study was conducted to determine whether an introductory

Focusing on What Counts: Using Exploratory Focus Groups to Enhance the Development of an Electronic Survey in a Mixed-Methods Research Design

ERIC Educational Resources Information Center

Galliott, Natal'ya; Graham, Linda J.

2016-01-01

This paper illustrates the use of exploratory focus groups to inform the development of a survey instrument in a sequential phase mixed-methods study investigating differences in secondary students' career choice capability. Five focus groups were conducted with 23 Year 10 students in the state of New South Wales, Australia. Analysis of the focus…

Correspondence analysis

USDA-ARS?s Scientific Manuscript database

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

Psychometric analysis in support of shortening the Scale for the Assessment of Negative Symptoms.

PubMed

Levine, Stephen Z; Leucht, Stefan

2013-09-01

Despite recent emphasis on the measurement and treatment of negative symptoms, studies of the Scale for the Assessment of Negative Symptoms (SANS) identify different symptom clusters, offer mixed support for its psychometric properties and suggest that it is shortened. The current study objective is to examine the psychometric properties of the SANS and the feasibility of a short research version of the SANS. Data were re-analyzed from three clinical trials that compared placebo and amisulpride to 60 days. Participants had chronic schizophrenia and predominantly negative symptoms (n=487). Baseline data were examined with exploratory factor analysis and Item Response Theory (IRT) to identify a short SANS. The short and original SANS were compared: with confirmatory factor analysis at endpoint; and on symptom response with mixed modeling to compare. Results showed that at baseline the SANS consisted of three factors labeled Affective-flattening, Asociality and Alogia-inattentiveness. IRT suggested a short SANS with 11 items and 3 response options. Comparisons of the original and short SANS showed: the short version was a better fit to the data based on confirmatory factor analysis at endpoint; similar significant (p<.001) correlations between the baseline and subsequent scores; similar reliability; and similar significance (p<.05) on response based on mixed modeling. It is concluded that a short SANS is feasible to assess predominantly negative symptoms in chronic schizophrenia in research settings. Copyright © 2012 Elsevier B.V. and ECNP. All rights reserved.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects.

PubMed

Feder, Stephan; Sundermann, Benedikt; Wersching, Heike; Teuber, Anja; Kugel, Harald; Teismann, Henning; Heindel, Walter; Berger, Klaus; Pfleiderer, Bettina

2017-11-01

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification. We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained. Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level. It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data. Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity. Copyright © 2017 Elsevier B.V. All rights reserved.

Acute Whiplash Injury Study (AWIS): a protocol for a cluster randomised pilot and feasibility trial of an Active Behavioural Physiotherapy Intervention in an insurance private setting

PubMed Central

Wiangkham, Taweewat; Duda, Joan; Haque, M Sayeed; Price, Jonathan; Rushton, Alison

2016-01-01

Introduction Whiplash-associated disorder (WAD) causes substantial social and economic burden internationally. Up to 60% of patients with WAD progress to chronicity. Research therefore needs to focus on effective management in the acute stage to prevent the development of chronicity. Approximately 93% of patients are classified as WADII (neck complaint and musculoskeletal sign(s)), and in the UK, most are managed in the private sector. In our recent systematic review, a combination of active and behavioural physiotherapy was identified as potentially effective in the acute stage. An Active Behavioural Physiotherapy Intervention (ABPI) was developed through combining empirical (modified Delphi study) and theoretical (social cognitive theory focusing on self-efficacy) evidence. This pilot and feasibility trial has been designed to inform the design of an adequately powered definitive randomised controlled trial. Methods and analysis Two parallel phases. (1) An external pilot and feasibility cluster randomised double-blind (assessor and participants), parallel two-arm (ABPI vs standard physiotherapy) clinical trial to evaluate procedures and feasibility. Six UK private physiotherapy clinics will be recruited and cluster randomised by a computer-generated randomisation sequence. Sixty participants (30 each arm) will be assessed at recruitment (baseline) and at 3 months postbaseline. The planned primary outcome measure is the neck disability index. (2) An embedded exploratory qualitative study using semistructured indepth interviews (n=3–4 physiotherapists) and a focus group (n=6–8 patients) and entailing the recruitment of purposive samples will explore perceptions of the ABPI. Quantitative data will be analysed descriptively. Qualitative data will be coded and analysed deductively (identify themes) and inductively (identify additional themes). Ethics and dissemination This trial is approved by the University of Birmingham Ethics Committee (ERN_15-0542). Trial registration number ISRCTN84528320. PMID:27412105

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Factor analysis of an instrument to measure the impact of disease on daily life.

PubMed

Pedrosa, Rafaela Batista Dos Santos; Rodrigues, Roberta Cunha Matheus; Padilha, Kátia Melissa; Gallani, Maria Cecília Bueno Jayme; Alexandre, Neusa Maria Costa

2016-01-01

to verify the structure of factors of an instrument to measure the Heart Valve Disease Impact on Daily Life (IDCV) when applied to coronary artery disease patients. the study included 153 coronary artery disease patients undergoing outpatient follow-up care. The IDCV structure of factors was initially assessed by means of confirmatory factor analysis and, subsequently, by exploratory factor analysis. The Varimax rotation method was used to estimate the main components of analysis, eigenvalues greater than one for extraction of factors, and factor loading greater than 0.40 for selection of items. Internal consistency was estimated using Cronbach's alpha coefficient. confirmatory factor analysis did not confirm the original structure of factors of the IDCV. Exploratory factor analysis showed three dimensions, which together explained 78% of the measurement variance. future studies with expansion of case selection are necessary to confirm the IDCV new structure of factors.

ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

PubMed

Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

2015-01-01

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

Predictors and moderators in the randomized trial of multifamily psychoeducational psychotherapy for childhood mood disorders.

PubMed

MacPherson, Heather A; Algorta, Guillermo Perez; Mendenhall, Amy N; Fields, Benjamin W; Fristad, Mary A

2014-01-01

This study investigated predictors and moderators of mood symptoms in the randomized controlled trial (RCT) of Multi-Family Psychoeducational Psychotherapy (MF-PEP) for childhood mood disorders. Based on predictors and moderators in RCTs of psychosocial interventions for adolescent mood disorders, we hypothesized that children's greater functional impairment would predict worse outcome, whereas children's stress/trauma history and parental expressed emotion and psychopathology would moderate outcome. Exploratory analyses examined other demographic, functioning, and diagnostic variables. Logistic regression and linear mixed effects modeling were used in this secondary analysis of the MF-PEP RCT of 165 children, ages 8 to 12, with mood disorders, a majority of whom were male (73%) and White, non-Hispanic (90%). Treatment nonresponse was significantly associated with higher baseline levels of global functioning (i.e., less impairment; Cohen's d = 0.51) and lower levels of stress/trauma history (d = 0.56) in children and Cluster B personality disorder symptoms in parents (d = 0.49). Regarding moderators, children with moderately impaired functioning who received MF-PEP had significantly decreased mood symptoms (t = 2.10, d = 0.33) compared with waitlist control. MF-PEP had the strongest effect on severely impaired children (t = 3.03, d = 0.47). Comprehensive assessment of demographic, youth, parent, and familial variables should precede intervention. Treatment of mood disorders in high-functioning youth without stress/trauma histories and with parents with elevated Cluster B symptoms may require extra therapeutic effort, whereas severely impaired children may benefit most from MF-PEP.

The Association Between Secondhand Harms From Alcohol and Mental Health Outcomes Among Postsecondary Students.

PubMed

Thompson, Kara; Davis-MacNevin, Parnell; Teehan, Michael; Stewart, Sherry

2017-01-01

There is a paucity of research on the prevalence and consequences of secondhand harms from alcohol. The current study (a) investigated whether secondhand harms can be clustered into latent factors that reflect distinct but related types of harms and (b) examined the associations between experiencing secondhand harms and mental health outcomes, including anxiety, depression, and subjective mental well-being, among first-year Canadian postsecondary students. The moderating effect of living arrangement (i.e., living on campus or not) on the associations was also tested. The sample included 1,885 first-year undergraduate students (49.8% female; mean age = 18.31 years) from three Canadian universities. Exploratory and confirmatory factor analyses were used to determine the factor structure of the harms measure. Path analysis was used to assess the association between harms and mental health outcomes. Models accounted for age, sex, and frequency of heavy drinking. Seventy-one percent of the sample reported experiencing at least one type of secondhand harm. The harms examined clustered into two distinct but related factors: strains (e.g., interrupted sleep) and threats (e.g., being harassed or insulted). Both threats and strains were associated with higher levels of anxiety and depression and poorer subjective well-being. Associations were stronger for threats and did not differ by living arrangement. Experiencing secondhand harms from alcohol, particularly threats, may have negative implications for student mental health over and above students' own drinking. Programs and policies on university campuses targeting both alcohol use and mental health should consider how to reduce both the prevalence and impact of secondhand harms from alcohol on students.

Is There a Relationship Between Value-Based Purchasing and Hospital Profitability? An Exploratory Study of Missouri Hospitals.

PubMed

Turner, Jason S; Broom, Kevin D; Counte, Michael A

2015-01-01

Recent US legislation is attempting to transition inpatient Medicare payments to a value-based purchasing (VBP) program. The VBP program is a pay-for-performance (P4P) system that incentivizes hospitals to improve patient satisfaction, health outcomes, and adherence to clinical protocols while simultaneously holding down costs. Our study evaluates (1) the impact of financial performance on the VBP adjustments and (2) whether there is a correlation between the VBP adjustment and the financial performance of Missouri hospitals that opted into the program. While upward and downward adjustments to the inpatient base rate may be related to hospital financial performance, prior financial performance may also be related to the adjustments. Financial health may allow facilities to invest and position the hospital for favorable future P4P adjustments. The results of our analysis indicate the VBP adjustment to the inpatient base rate is very small (±0.18%), clustered around zero, and is not correlated with financial performance. We also find that financial performance and improvement in the years prior to the adjustment are not related to the VBP adjustment or its respective components. This suggests that CMS is avoiding penalizing less profitable facilities, but the adjustment is also so small and tightly clustered around zero that it is failing to provide an adequate incentive to hospitals. The costs of improving patient satisfaction, clinical process adherence, health care outcomes, and efficiency above that of peers coupled with the growing number of metrics being used to calculate the VBP adjustments call into question the financial incentives of the hospital VBP program.

TripAdvisor^{N-D}: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail.

PubMed

Nam, Julia EunJu; Mueller, Klaus

2013-02-01

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

Selective Mutism Questionnaire: measurement structure and validity.

PubMed

Letamendi, Andrea M; Chavira, Denise A; Hitchcock, Carla A; Roesch, Scott C; Shipon-Blum, Elisa; Stein, Murray B

2008-10-01

To evaluate the factor structure, reliability, and validity of the 17-item Selective Mutism Questionnaire (SMQ). Diagnostic interviews were administered via telephone to 102 parents of children identified with selective mutism (SM) and 43 parents of children without SM from varying U.S. geographic regions. Children were between the ages of 3 and 11 inclusive and comprised 58% girls and 42% boys. SM diagnoses were determined using the Anxiety Disorders Interview Schedule for Children-Parent Version; SM severity was assessed using the 17-item SMQ; and behavioral and affective symptoms were assessed using the Child Behavior Checklist. An exploratory factor analysis was conducted to investigate the dimensionality of the SMQ and a modified parallel analysis procedure was used to confirm exploratory factor analysis results. Internal consistency, construct validity, and incremental validity were also examined. The exploratory factor analysis yielded a 13-item solution consisting of three factors: social situations outside of school, school situations, and home and family situations. Internal consistency of SMQ factors and total scale ranged from moderate to high. Convergent and incremental validity was also well supported. Measure structure findings are consistent with the three-factor solution found in a previous psychometric evaluation of the SMQ. Results also suggest that the SMQ provides useful and unique information in the prediction of SM phenomena beyond other child anxiety measures.

Modeling and evaluating user behavior in exploratory visual analysis

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

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

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

What is needed to implement a computer-assisted health risk assessment tool? An exploratory concept mapping study

PubMed Central

2012-01-01

Background Emerging eHealth tools could facilitate the delivery of comprehensive care in time-constrained clinical settings. One such tool is interactive computer-assisted health-risk assessments (HRA), which may improve provider-patient communication at the point of care, particularly for psychosocial health concerns, which remain under-detected in clinical encounters. The research team explored the perspectives of healthcare providers representing a variety of disciplines (physicians, nurses, social workers, allied staff) regarding the factors required for implementation of an interactive HRA on psychosocial health. Methods The research team employed a semi-qualitative participatory method known as Concept Mapping, which involved three distinct phases. First, in face-to-face and online brainstorming sessions, participants responded to an open-ended central question: “What factors should be in place within your clinical setting to support an effective computer-assisted screening tool for psychosocial risks?” The brainstormed items were consolidated by the research team. Then, in face-to-face and online sorting sessions, participants grouped the items thematically as ‘it made sense to them’. Participants also rated each item on a 5-point scale for its ‘importance’ and ‘action feasibility’ over the ensuing six month period. The sorted and rated data was analyzed using multidimensional scaling and hierarchical cluster analyses which produced visual maps. In the third and final phase, the face-to-face Interpretation sessions, the concept maps were discussed and illuminated by participants collectively. Results Overall, 54 providers participated (emergency care 48%; primary care 52%). Participants brainstormed 196 items thought to be necessary for the implementation of an interactive HRA emphasizing psychosocial health. These were consolidated by the research team into 85 items. After sorting and rating, cluster analysis revealed a concept map with a seven-cluster solution: 1) the HRA’s equitable availability; 2) the HRA’s ease of use and appropriateness; 3) the content of the HRA survey; 4) patient confidentiality and choice; 5) patient comfort through humanistic touch; 6) professional development, care and workload; and 7) clinical management protocol. Drawing insight from the theoretical lens of Sociotechnical theory, the seven clusters of factors required for HRA implementation could be read as belonging to three overarching aspects : Technical (cluster 1, 2 and 3), Social-Patient (cluster 4 and 5), and Social-Provider (cluster 6 and 7). Participants rated every one of the clusters as important, with mean scores from 4.0 to 4.5. Their scores for feasibility were somewhat lower, ranging from 3.4 to. 4.3. Comparing the scores for importance and feasibility, a significant difference was found for one cluster from each region (cluster 2, 5, 6). The cluster on professional development, care and workload was perceived as especially challenging in emergency department settings, and possible reasons were discussed in the interpretation sessions. Conclusion A number of intertwined multilevel factors emerged as important for the implementation of a computer-assisted, interactive HRA with a focus on psychosocial health. Future developments in this area could benefit from systems thinking and insights from theoretical perspectives, such as sociotechnical system theory for joint optimization and responsible autonomy, with emphasis on both the technical and social aspects of HRA implementation. PMID:23253913

Level of Analysis in the Perception of Ongoing Instruction: An Exploratory Study.

ERIC Educational Resources Information Center

Koopman, Cheryl; Newtson, Darren

1981-01-01

Instructional variables were manipulated to determine whether they influence the level of perceptual analysis. The relationships of perceptual analysis to concept learning and evaluations of the instructors were also examined in the study. (Author/GK)

Cluster Analysis in Nursing Research: An Introduction, Historical Perspective, and Future Directions.

PubMed

Dunn, Heather; Quinn, Laurie; Corbridge, Susan J; Eldeirawi, Kamal; Kapella, Mary; Collins, Eileen G

2017-05-01

The use of cluster analysis in the nursing literature is limited to the creation of classifications of homogeneous groups and the discovery of new relationships. As such, it is important to provide clarity regarding its use and potential. The purpose of this article is to provide an introduction to distance-based, partitioning-based, and model-based cluster analysis methods commonly utilized in the nursing literature, provide a brief historical overview on the use of cluster analysis in nursing literature, and provide suggestions for future research. An electronic search included three bibliographic databases, PubMed, CINAHL and Web of Science. Key terms were cluster analysis and nursing. The use of cluster analysis in the nursing literature is increasing and expanding. The increased use of cluster analysis in the nursing literature is positioning this statistical method to result in insights that have the potential to change clinical practice.

[NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 3:] Technical communications in aeronautics: Results of an exploratory study. An analysis of profit managers' and nonprofit managers' responses

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Glassman, Myron; Barclay, Rebecca O.; Oliu, Walter E.

1989-01-01

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that profit and nonprofit managers in the aerospace community have different technical communications practices. Five assumptions were established for the analysis. Profit and nonprofit managers in the aerospace community were found to have different technical communications practices for one of the five assumptions tested. It was, therefore, concluded that profit and nonprofit managers in the aerospace community do not have different technical communications practices.

Conceptualizing age-friendly community characteristics in a sample of urban elders: an exploratory factor analysis.

PubMed

Smith, Richard J; Lehning, Amanda J; Dunkle, Ruth E

2013-01-01

Accurate conceptualization and measurement of age-friendly community characteristics would help to reduce barriers to documenting the effects on elders of interventions to create such communities. This article contributes to the measurement of age-friendly communities through an exploratory factor analysis of items reflecting an existing US Environmental Protection Agency policy framework. From a sample of urban elders (n = 1,376), we identified 6 factors associated with demographic and health characteristics: access to business and leisure, social interaction, access to health care, neighborhood problems, social support, and community engagement. Future research should explore the effects of these factors across contexts and populations.

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

PubMed

Brytek-Matera, Anna; Rogoza, Radosław

2015-03-01

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

Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs.

PubMed

Forkmann, Thomas; Teismann, Tobias; Stenzel, Jana-Sophie; Glaesmer, Heide; de Beurs, Derek

2018-01-25

Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic techniques have trouble estimating the right number of clusters in highly correlated data. In this study, we applied a novel approach based on network analysis that can deal with correlated data to establish whether defeat and entrapment are best thought of as one or multiple constructs. Explanatory graph analysis was used to estimate the number of dimensions within the 32 items that make up the defeat and entrapment scales in two samples: an online community sample of 480 participants, and a clinical sample of 147 inpatients admitted to a psychiatric hospital after a suicidal attempt or severe suicidal crisis. Confirmatory Factor analysis (CFA) was used to test whether the proposed structure fits the data. In both samples, bootstrapped exploratory graph analysis suggested that the defeat and entrapment items belonged to different dimensions. Within the entrapment items, two separate dimensions were detected, labelled internal and external entrapment. Defeat appeared to be multifaceted only in the online sample. When comparing the CFA outcomes of the one, two, three and four factor models, the one factor model was preferred. Defeat and entrapment can be viewed as distinct, yet, highly associated constructs. Thus, although replication is needed, results are in line with theories differentiating between these two constructs.

The Type and Impact of Evidence Review Group Exploratory Analyses in the NICE Single Technology Appraisal Process.

PubMed

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

2017-06-01

As part of the UK National Institute for Health and Care Excellence (NICE) single technology appraisal process, independent evidence review groups (ERGs) critically appraise a company's submission relating to a specific technology and indication. To explore the type of additional exploratory analyses conducted by ERGs and their impact on the recommendations made by NICE. The 100 most recently completed single technology appraisals with published guidance were selected for inclusion. A content analysis of relevant documents was undertaken to identify and extract relevant data, and narrative synthesis was used to rationalize and present these data. The types of exploratory analysis conducted in relation to companies' models were fixing errors, addressing violations, addressing matters of judgment, and the provision of a new, ERG-preferred base case. Ninety-three of the 100 ERG reports contained at least one of these analyses. The most frequently reported type of analysis in these 93 ERG reports related to the category "Matters of judgment," which was reported in 83 reports (89%). At least one of the exploratory analyses conducted and reported by an ERG is mentioned in 97% of NICE appraisal consultation documents and 94% of NICE final appraisal determinations, and had a clear influence on recommendations in 72% of appraisal consultation documents and 47% of final appraisal determinations. These results suggest that the additional analyses undertaken by ERGs in the appraisal of company submissions are highly influential in the policy-making and decision-making process. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

ICAP - An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

NASA Technical Reports Server (NTRS)

Wharton, S. W.; Turner, B. J.

1981-01-01

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. ICAP differs from conventional clustering algorithms by allowing the analyst to optimize the cluster configuration by inspection, rather than by manipulating process parameters. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters, and the analyst, who can evaluate and elect to modify the cluster structure. Clusters can be deleted, or lumped together pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The principal advantage of this approach is that it allows prior information (when available) to be used directly in the analysis, since the analyst interacts with ICAP in a straightforward manner, using basic terms with which he is more likely to be familiar. Results from testing ICAP showed that an informed use of ICAP can improve classification, as compared to an existing cluster analysis procedure.

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials

PubMed Central

Diaz-Ordaz, Karla; Bartlett, Jonathan W

2016-01-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group. PMID:27177885

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.

PubMed

Hossain, Anower; Diaz-Ordaz, Karla; Bartlett, Jonathan W

2017-06-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.

Exploratory factor analysis of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale in people newly diagnosed with advanced cancer.

PubMed

Bai, Mei; Dixon, Jane K

2014-01-01

The purpose of this study was to reexamine the factor pattern of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12) using exploratory factor analysis in people newly diagnosed with advanced cancer. Principal components analysis (PCA) and 3 common factor analysis methods were used to explore the factor pattern of the FACIT-Sp-12. Factorial validity was assessed in association with quality of life (QOL). Principal factor analysis (PFA), iterative PFA, and maximum likelihood suggested retrieving 3 factors: Peace, Meaning, and Faith. Both Peace and Meaning positively related to QOL, whereas only Peace uniquely contributed to QOL. This study supported the 3-factor model of the FACIT-Sp-12. Suggestions for revision of items and further validation of the identified factor pattern were provided.

Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

PubMed Central

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

2015-01-01

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

Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

NASA Astrophysics Data System (ADS)

Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

2017-08-01

Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

Impact of aspirin resistance on outcomes among patients following coronary artery bypass grafting: exploratory analysis from randomized controlled trial (NCT01159639).

PubMed

Petricevic, Mate; Kopjar, Tomislav; Gasparovic, Hrvoje; Milicic, Davor; Svetina, Lucija; Zdilar, Boris; Boban, Marko; Mihaljevic, Martina Zrno; Biocina, Bojan

2015-05-01

Individual variability in the response to aspirin, has been established by various platelet function assays, however, the clinical relevance of aspirin resistance (AR) in patients undergoing coronary artery bypass grafting (CABG) has to be evaluated. Our working group conducted a randomized controlled trial (NCT01159639) with the aim to assess impact of dual antiplatelet therapy (APT) on outcomes among patients with AR following CABG. Patients that were aspirin resistant on fourth postoperative day (POD 4) were randomly assigned to receive either dual APT with clopidogrel (75 mg) plus aspirin (300 mg)-intervention arm or monotherapy with aspirin (300 mg)-control arm. This exploratory analysis compares clinical outcomes between aspirin resistant patients allocated to control arm and patients that have had adequate platelet inhibitory response to aspirin at POD 4. Both groups were treated with 300 mg of aspirin per day following surgery. We sought to evaluate the impact of early postoperative AR on outcomes among patients following CABG. Exploratory analysis included a total number of 325 patients. Of those, 215 patients with adequate response to aspirin and 110 patients with AR allocated to aspirin monotherapy following randomization protocol. The primary efficacy end point (MACCEs-major adverse cardiac and cardiovascular events) occurred in 10 and 6 % of patients with AR and with adequate aspirin response, respectively (p = 0.27). Non-significant differences were observed in bleeding events occurrence. Subgroup analysis of the primary end point revealed that aspirin resistant patients with BMI > 30 kg/m(2) tend to have a higher occurrence of MACCEs 18 versus 5 % (relative risk 0.44 [95 % CI 0.16-1.16]; p = 0.05). This exploratory analysis did not reveal significant impact of aspirin resistance on outcomes among patients undergoing CABG. Further, sufficiently powered studies are needed in order to evaluate clinical relevance of AR in patients undergoing CABG.

A medical school's organizational readiness for curriculum change (MORC): development and validation of a questionnaire.

PubMed

Jippes, Mariëlle; Driessen, Erik W; Broers, Nick J; Majoor, Gerard D; Gijselaers, Wim H; van der Vleuten, Cees P M

2013-09-01

Because successful change implementation depends on organizational readiness for change, the authors developed and assessed the validity of a questionnaire, based on a theoretical model of organizational readiness for change, designed to measure, specifically, a medical school's organizational readiness for curriculum change (MORC). In 2012, a panel of medical education experts judged and adapted a preliminary MORC questionnaire through a modified Delphi procedure. The authors administered the resulting questionnaire to medical school faculty involved in curriculum change and tested the psychometric properties using exploratory and confirmatory factor analysis, and generalizability analysis. The mean relevance score of the Delphi panel (n = 19) reached 4.2 on a five-point Likert-type scale (1 = not relevant and 5 = highly relevant) in the second round, meeting predefined criteria for completing the Delphi procedure. Faculty (n = 991) from 131 medical schools in 56 countries completed MORC. Exploratory factor analysis yielded three underlying factors-motivation, capability, and external pressure-in 12 subscales with 53 items. The scale structure suggested by exploratory factor analysis was confirmed by confirmatory factor analysis. Cronbach alpha ranged from 0.67 to 0.92 for the subscales. Generalizability analysis showed that the MORC results of 5 to 16 faculty members can reliably evaluate a school's organizational readiness for change. MORC is a valid, reliable questionnaire for measuring organizational readiness for curriculum change in medical schools. It can identify which elements in a change process require special attention so as to increase the chance of successful implementation.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome.

PubMed

Lalonde, Michel; Wells, R Glenn; Birnie, David; Ruddy, Terrence D; Wassenaar, Richard

2014-07-01

Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

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

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard; Wells, R. Glenn

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: Aboutmore » 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.« less

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting.

PubMed

Glatman-Freedman, Aharona; Kaufman, Zalman; Kopel, Eran; Bassal, Ravit; Taran, Diana; Valinsky, Lea; Agmon, Vered; Shpriz, Manor; Cohen, Daniel; Anis, Emilia; Shohat, Tamy

2016-08-01

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013. Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs). During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved. Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering.

PubMed

Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor; Essex, M

2015-05-01

To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice.

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

PubMed Central

Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor

2015-01-01

Abstract To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice. PMID:25560745

Prevalence of Comorbidity in Patients With Young-Onset Alzheimer Disease Compared With Late-Onset: A Comparative Cohort Study.

PubMed

Gerritsen, Adrie A J; Bakker, Christian; Verhey, Frans R J; de Vugt, Marjolein E; Melis, René J F; Koopmans, Raymond T C M

2016-04-01

With the lack of a cure for Alzheimer disease (AD), the identification of comorbidity is important to reduce the possibility of excess disability. Although comorbidity in patients with late-onset AD (LO-AD) is common, for people with young-onset AD (YO-AD), it is unclear how often comorbidity occurs. Furthermore, it is uncertain whether comorbidity in patients with YO-AD differs from that in patients with LO-AD. The aim of this study was to explore the prevalence, types of morbidity, and morbidity profiles in patients with YO-AD compared with those of patients with LO-AD. Explorative cohort study from 2 separate Dutch cohorts (Needs in Young-onset Dementia [NeedYD] and the Clinical Course of Cognition and Comorbidity-Dementia Study [4C-Dementia study]). Participants were recruited in 2007 and 2008 from (1) the memory clinics of 3 Dutch Alzheimer centers, (2) the memory clinics of general hospitals, (3) mental health services in the southern part of the Netherlands, and (4) young-onset dementia specialized day care facilities. A comparison group of community-dwelling, elderly patients with AD was selected from the 4C-Dementia study. Patients in this study were recruited in 2010 and 2011 from the aforementioned Alzheimer centers. The prevalence rates of comorbidity were compared between 177 patients with YO-AD and 155 patients with LO-AD. Comorbidity was classified using the International Classification of Diseases, 10th Revision (ICD-10). The total amount of comorbidity was established by counting the number of existing diseases (ICD categories or chapters) and comorbidity was also dichotomized as present or absent. Furthermore, a hierarchical cluster analysis was performed to study clusters of comorbidity. Compared with LO-AD, patients with YO-AD showed less (P < .001) overall comorbidity (58.2% vs 86.5%) and had lower prevalence rates of diabetes, obesity, and circulatory diseases; however, the prevalence rates of diseases of the nervous system in YO-AD (6.2%) were higher compared with those of patients with LO-AD (4.5%). The cluster analysis revealed a distinctive group of patients with YO-AD with either no comorbidity or with a disease of the nervous system. Endocrine, nutritional, and metabolic diseases and diseases of the circulatory system were present in 34% of the patients with YO-AD. Comorbidity is less common in YO-AD than in LO-AD. However, general practitioners should be aware that approximately one-third of the patients with YO-AD suffer from or have endocrine, nutritional, and metabolic diseases and/or diseases of the circulatory system. Treatment should therefore not only focus on dementia but also on comorbidity. This attention may slow the functional decline in AD. These exploratory analyses suggested a higher prevalence of nervous system diseases in YO-AD compared with LO-AD. However, the finding did not reach statistical significance and in combination with the exploratory nature of the analyses justifies further investigation. If verified, this finding may help to decrease the time to diagnosis of AD and, subsequently, support in young patients with a neurological disease. Further investigation is needed to gain more insight into the association between comorbidity and AD in younger people. Copyright © 2016 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

Massive Signal Analysis with Hadoop (Invited)

NASA Astrophysics Data System (ADS)

Addair, T.

2013-12-01

The Geophysical Monitoring Program (GMP) at Lawrence Livermore National Laboratory is in the process of transitioning from a primarily human-driven analysis pipeline to a more automated and exploratory system. Waveform correlation represents a significant part of this effort, and the results that come out of this processing could lead to the development of more sophisticated event detection and analysis systems that require less human interaction, and address fundamental shortcomings in existing systems. Furthermore, use of distributed IO systems fundamentally addresses a scalability concern for the GMP as our data holdings continue to grow rapidly. As the data volume increases, it becomes less reasonable to rely upon human analysts to sift through all the information. Not only is more automation essential to keeping up with the ingestion rate, but so too do we require faster and more sophisticated tools for visualizing and interacting with the data. These issues of scalability are not unique to GMP or the seismic domain. All across the lab, and throughout industry, we hear about the promise of 'big data' to address the need of quickly analyzing vast amounts of data in fundamentally new ways. Our waveform correlation system finds and correlates nearby seismic events across the entire Earth. In our original implementation of the system, we processed some 50 TB of data on an in-house traditional HPC cluster (44 cores, 1 filesystem) over the span of 42 days. Having determined the primary bottleneck in the performance to be reading waveforms off a single BlueArc file server, we began investigating distributed IO solutions like Hadoop. As a test case, we took a 1 TB subset of our data and ported it to Livermore Computing's development Hadoop cluster. Through a pilot project sponsored by Livermore Computing (LC), the GMP successfully implemented the waveform correlation system in the Hadoop distributed MapReduce computing framework. Hadoop is an open source implementation of the MapReduce distributed programming framework. We used the Hadoop scripting framework known as Pig for putting together the multi-job MapReduce pipeline used to extract as much parallelism as possible from the algorithms. We also made use the Sqoop data ingestion tool to pull metadata tables from our Oracle database into HDFS (the Hadoop Distributed Filesystem). Running on our in-house HPC cluster, processing this test dataset took 58 hours to complete. In contrast, running our Hadoop implementation on LC's 10 node (160 core) cluster, we were able to cross-correlate the 1 TB of nearby seismic events in just under 3 hours, over a factor of 19 improvement from our existing implementation. This project is one of the first major data mining and analysis tasks performed at the lab or anywhere else correlating the entire Earth's seismicity. Through the success of this project, we believe we've shown that a MapReduce solution can be appropriate for many large-scale Earth science data analysis and exploration problems. Given Hadoop's position as the dominant data analytics solution in industry, we believe Hadoop can be applied to many previously intractable Earth science problems.

Effects of Group Size and Lack of Sphericity on the Recovery of Clusters in K-Means Cluster Analysis

ERIC Educational Resources Information Center

de Craen, Saskia; Commandeur, Jacques J. F.; Frank, Laurence E.; Heiser, Willem J.

2006-01-01

K-means cluster analysis is known for its tendency to produce spherical and equally sized clusters. To assess the magnitude of these effects, a simulation study was conducted, in which populations were created with varying departures from sphericity and group sizes. An analysis of the recovery of clusters in the samples taken from these…

Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

PubMed Central

2014-01-01

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size. PMID:24884591

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

Factor Analysis via Components Analysis

ERIC Educational Resources Information Center

Bentler, Peter M.; de Leeuw, Jan

2011-01-01

When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa. This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

PubMed

Borri, Marco; Schmidt, Maria A; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M; Partridge, Mike; Bhide, Shreerang A; Nutting, Christopher M; Harrington, Kevin J; Newbold, Katie L; Leach, Martin O

2015-01-01

To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

MEDUCATE trial: effectiveness of an intensive EDUCATional intervention for IT-mediated MEDication management in the outpatient clinic - study protocol for a cluster randomized controlled trial.

PubMed

van Stiphout, F; Zwart-van Rijkom, J E F; Aarts, J E C M; Koffijberg, H; Klarenbeek-deJonge, E; Krulder, M; Roes, K C B; Egberts, A C G; ter Braak, E W M T

2015-05-22

Using information technology for medication management is an opportunity to help physicians to improve the quality of their documentation and communication and ultimately to improve patient care and patient safety. Physician education is necessary to take full advantage of information technology systems. In this trial, we seek to determine the effectiveness of an intensive educational intervention compared with the standard approach in improving information technology-mediated medication management and in reducing potential adverse drug events in the outpatient clinic. We are conducting a multicenter, cluster randomized controlled trial. The participants are specialists and residents working in the outpatient clinic of internal medicine, cardiology, pulmonology, geriatrics, gastroenterology and rheumatology. The intensive educational intervention is composed of a small-group session and e-learning. The primary outcome is discrepancies between registered medication (by physicians) and actually used medication (by patients). The key secondary outcomes are potential adverse events caused by missed drug-drug interactions. The primary and key secondary endpoints are being assessed shortly after the educational intervention is completed. Sample size will be calculated to ensure sufficient power. A sample size of 40 physicians per group and 20 patients per physician will ensure a power of >90 %, which means we will need a total of 80 physicians and 1,600 patients. We performed an exploratory trial wherein we tested the recruitment process, e-learning, time schedule, and methods for data collection, data management and data analysis. Accordingly, we refined the processes and content: the recruitment strategy was intensified, extra measures were taken to facilitate smooth conductance of the e-learning and parts were made optional. First versions of the procedures for data collection were determined. Data entry and analysis was further standardized by using the G-standard database in the telephone questionnaire. ISRCTN registry: ISRCTN50890124 . Registered 10 June 2013.

Measuring the representational space of music with fMRI: a case study with Sting.

PubMed

Levitin, Daniel J; Grafton, Scott T

2016-12-01

Functional brain imaging has revealed much about the neuroanatomical substrates of higher cognition, including music, language, learning, and memory. The technique lends itself to studying of groups of individuals. In contrast, the nature of expert performance is typically studied through the examination of exceptional individuals using behavioral case studies and retrospective biography. Here, we combined fMRI and the study of an individual who is a world-class expert musician and composer in order to better understand the neural underpinnings of his music perception and cognition, in particular, his mental representations for music. We used state of the art multivoxel pattern analysis (MVPA) and representational dissimilarity analysis (RDA) in a fixed set of brain regions to test three exploratory hypotheses with the musician Sting: (1) Composing would recruit neutral structures that are both unique and distinguishable from other creative acts, such as composing prose or visual art; (2) listening and imagining music would recruit similar neural regions, indicating that musical memory shares anatomical substrates with music listening; (3) the MVPA and RDA results would help us to map the representational space for music, revealing which musical pieces and genres are perceived to be similar in the musician's mental models for music. Our hypotheses were confirmed. The act of composing, and even of imagining elements of the composed piece separately, such as melody and rhythm, activated a similar cluster of brain regions, and were distinct from prose and visual art. Listened and imagined music showed high similarity, and in addition, notable similarity/dissimilarity patterns emerged among the various pieces used as stimuli: Muzak and Top 100/Pop songs were far from all other musical styles in Mahalanobis distance (Euclidean representational space), whereas jazz, R&B, tango and rock were comparatively close. Closer inspection revealed principaled explanations for the similarity clusters found, based on key, tempo, motif, and orchestration.

Detection of spatial hot spots and variation for the neon flying squid Ommastrephes bartramii resources in the northwest Pacific Ocean

NASA Astrophysics Data System (ADS)

Feng, Yongjiu; Chen, Xinjun; Liu, Yan

2017-07-01

With the increasing effects of global climate change and fishing activities, the spatial distribution of the neon flying squid ( Ommastrephes bartramii) is changing in the traditional fishing ground of 150°-160°E and 38°-45°N in the northwest Pacific Ocean. This research aims to identify the spatial hot and cold spots (i.e. spatial clusters) of O. bartramii to reveal its spatial structure using commercial fishery data from 2007 to 2010 collected by Chinese mainland squid-jigging fleets. A relatively strongly-clustered distribution for O. bartramii was observed using an exploratory spatial data analysis (ESDA) method. The results show two hot spots and one cold spot in 2007 while only one hot and one cold spots were identified each year from 2008 to 2010. The hot and cold spots in 2007 occupied 8.2% and 5.6% of the study area, respectively; these percentages for hot and cold spot areas were 5.8% and 3.1% in 2008, 10.2% and 2.9% in 2009, and 16.4% and 11.9% in 2010, respectively. Nearly half (>45%) of the squid from 2007 to 2009 reported by Chinese fleets were caught in hot spot areas while this percentage reached its peak at 68.8% in 2010, indicating that the hot spot areas are central fishing grounds. A further change analysis shows the area centered at 156°E/43.5°N was persistent as a hot spot over the whole period from 2007 to 2010. Furthermore, the hot spots were mainly identified in areas with sea surface temperature (SST) in the range of 15-20°C around warm Kuroshio Currents as well as with the chlorophyll- a (chl- a) concentration above 0.3 mg/m3. The outcome of this research improves our understanding of spatiotemporal hotspots and its variation for O. bartramii and is useful for sustainable exploitation, assessment, and management of this squid.

Strokes with minor symptoms: an exploratory analysis of the National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator trials.

PubMed

Khatri, Pooja; Kleindorfer, Dawn O; Yeatts, Sharon D; Saver, Jeffrey L; Levine, Steven R; Lyden, Patrick D; Moomaw, Charles J; Palesch, Yuko Y; Jauch, Edward C; Broderick, Joseph P

2010-11-01

The pivotal National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator trials excluded patients with ischemic stroke with specific minor presentations or rapidly improving symptoms. The recombinant tissue plasminogen activator product label notes that its use for minor neurological deficit or rapidly improving stroke symptoms has not been evaluated. As a result, patients with low National Institutes of Health Stroke Scale scores are not commonly treated in clinical practice. We sought to further characterize the patients with minor stroke who were included in the National Institute of Neurological Disorders and Stroke trials. Minor strokes were defined as National Institutes of Health Stroke Scale score ≤ 5 at baseline for this retrospective analysis, because this subgroup is most commonly excluded from treatment in clinical practice and trials. Clinical stroke syndromes were defined based on prespecified National Institutes of Health Stroke Scale item score clusters. Clinical outcomes were reviewed generally and within these cluster subgroups. Only 58 cases had National Institutes of Health Stroke Scale scores of 0 to 5 in the National Institute of Neurological Disorders and Stroke trials (42 recombinant tissue plasminogen activator and 16 placebo), and 2971 patients were excluded from the trials due to "rapidly improving" or "minor symptoms" as the primary reason. No patients were enrolled with isolated motor symptoms, isolated facial droop, isolated ataxia, dysarthria, isolated sensory symptoms, or with only symptoms/signs not captured by the National Institutes of Health Stroke Scale score (ie, National Institutes of Health Stroke Scale=0). There were ≤ 3 patients with each of the other isolated deficits enrolled in the trial. The National Institute of Neurological Disorders and Stroke trials excluded a substantial number of strokes with minor presentations, those that were included were small in number, and conclusions about outcomes based on specific syndromes cannot be drawn. Further prospective, systematic study of this subgroup is needed.

Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

ERIC Educational Resources Information Center

DiStefano, Christine; Kamphaus, R. W.

2006-01-01

Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

Cluster analysis in phenotyping a Portuguese population.

PubMed

Loureiro, C C; Sa-Couto, P; Todo-Bom, A; Bousquet, J

2015-09-03

Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients. To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care. Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method. Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation. In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction. Copyright © 2015. Published by Elsevier España, S.L.U.

STEMS pilot trial: a pilot cluster randomised controlled trial to investigate the addition of patient direct access to physiotherapy to usual GP-led primary care for adults with musculoskeletal pain.

PubMed

Bishop, Annette; Ogollah, Reuben O; Jowett, Sue; Kigozi, Jesse; Tooth, Stephanie; Protheroe, Joanne; Hay, Elaine M; Salisbury, Chris; Foster, Nadine E

2017-03-12

Around 17% of general practitioner (GP) consultations are for musculoskeletal conditions, which will rise as the population ages. Patient direct access to physiotherapy provides one solution, yet adoption in the National Health Service (NHS) has been slow. A pilot, pragmatic, non-inferiority, cluster randomised controlled trial (RCT) in general practice and physiotherapy services in the UK. Investigate feasibility of a main RCT. Adult patients registered in participating practices and consulting with a musculoskeletal problem. 4 general practices (clusters) randomised to provide GP-led care as usual or the addition of a patient direct access to physiotherapy pathway. Process outcomes and exploratory analyses of clinical and cost outcomes. Participant-level data were collected via questionnaires at identification, 2, 6 and 12 months and through medical records. The study statistician and research nurses were blinded to practice allocation. Of 2696 patients invited to complete study questionnaires, 978 participated (intervention group n=425, control arm n=553) and were analysed. Participant recruitment was completed in 6 months. Follow-up rates were 78% (6 months) and 71% (12 months). No evidence of selection bias was observed. The direct access pathway was used by 90% of patients in intervention practices needing physiotherapy. Some increase in referrals to physiotherapy occurred from one practice, although waiting times for physiotherapy did not increase (28 days before, 26 days after introduction of direct access). No safety issues were identified. Clinical and cost outcomes were similar in both groups. Exploratory estimates of between group effect (using 36-item Short Form Health Survey (SF-36) Physical Component Summary (PCS)) at 6 months was -0.28 (95% CI -1.35 to 0.79) and at 12 months 0.12 (95% CI -1.27 to 1.51). A full RCT is feasible and will provide trial evidence about the clinical and cost-effectiveness of patient direct access to physiotherapy. ISRCTN23378642. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Scalable isosurface visualization of massive datasets on commodity off-the-shelf clusters

PubMed Central

Bajaj, Chandrajit

2009-01-01

Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final display. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction, and rendering in conjunction with a new specialized piece of image compositing hardware called Metabuffer. In this paper, we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It achieves scalability by using both parallel and out-of-core processing and parallel disks. It statically partitions the volume data to parallel disks with a balanced workload spectrum, and builds I/O-optimal external interval trees to minimize the number of I/O operations of loading large data from disk. We also describe an isosurface compression scheme that is efficient for progress extraction, transmission and storage of isosurfaces. PMID:19756231

Factor structure of DSM-IV criteria for obsessive compulsive personality disorder in patients with binge eating disorder.

PubMed

Grilo, C M

2004-01-01

To examine the factor structure of DSM-IV criteria for obsessive compulsive personality disorder (OCPD) in patients with binge eating disorder (BED). Two hundred and eleven consecutive out-patients with axis I diagnoses of BED were reliably assessed with semi-structured diagnostic interviews. The eight criteria for the OCPD diagnosis were examined with reliability and correlational analyses. Exploratory factor analysis was performed to identify potential components. Cronbach's coefficient alpha for the OCPD criteria was 0.77. Principal components factor analysis with varimax rotation revealed a three-factor solution (rigidity, perfectionism, and miserliness), which accounted for 65% of variance. The DSM-IV criteria for OCPD showed good internal consistency. Exploratory factor analysis, however, revealed three components that may reflect distinct interpersonal, intrapersonal (cognitive), and behavioral features.

Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

PubMed

Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

2017-06-01

Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

Item Response Theory Analyses of the Cambridge Face Memory Test (CFMT)

PubMed Central

Cho, Sun-Joo; Wilmer, Jeremy; Herzmann, Grit; McGugin, Rankin; Fiset, Daniel; Van Gulick, Ana E.; Ryan, Katie; Gauthier, Isabel

2014-01-01

We evaluated the psychometric properties of the Cambridge face memory test (CFMT; Duchaine & Nakayama, 2006). First, we assessed the dimensionality of the test with a bi-factor exploratory factor analysis (EFA). This EFA analysis revealed a general factor and three specific factors clustered by targets of CFMT. However, the three specific factors appeared to be minor factors that can be ignored. Second, we fit a unidimensional item response model. This item response model showed that the CFMT items could discriminate individuals at different ability levels and covered a wide range of the ability continuum. We found the CFMT to be particularly precise for a wide range of ability levels. Third, we implemented item response theory (IRT) differential item functioning (DIF) analyses for each gender group and two age groups (Age ≤ 20 versus Age > 21). This DIF analysis suggested little evidence of consequential differential functioning on the CFMT for these groups, supporting the use of the test to compare older to younger, or male to female, individuals. Fourth, we tested for a gender difference on the latent facial recognition ability with an explanatory item response model. We found a significant but small gender difference on the latent ability for face recognition, which was higher for women than men by 0.184, at age mean 23.2, controlling for linear and quadratic age effects. Finally, we discuss the practical considerations of the use of total scores versus IRT scale scores in applications of the CFMT. PMID:25642930

Spectral analysis of point-vortex dynamics: first application to vortex polygons in a circular domain

NASA Astrophysics Data System (ADS)

Speetjens, M. F. M.; Meleshko, V. V.; van Heijst, G. J. F.

2014-06-01

The present study addresses the classical problem of the dynamics and stability of a cluster of N-point vortices of equal strength arranged in a polygonal configuration (‘N-vortex polygons’). In unbounded domains, such N-vortex polygons are unconditionally stable for N\\leqslant 7. Confinement in a circular domain tightens the stability conditions to N\\leqslant 6 and a maximum polygon size relative to the domain radius. This work expands on existing studies on stability and integrability by a first giving an exploratory spectral analysis of the dynamics of N vortex polygons in circular domains. Key to this is that the spectral signature of the time evolution of vortex positions reflects their qualitative behaviour. Expressing vortex motion by a generic evolution operator (the so-called Koopman operator) provides a rigorous framework for such spectral analyses. This paves the way to further differentiation and classification of point-vortex behaviour beyond stability and integrability. The concept of Koopman-based spectral analysis is demonstrated for N-vortex polygons. This reveals that conditional stability can be seen as a local form of integrability and confirms an important generic link between spectrum and dynamics: discrete spectra imply regular (quasi-periodic) motion; continuous (sub-)spectra imply chaotic motion. Moreover, this exposes rich nonlinear dynamics as intermittency between regular and chaotic motion and quasi-coherent structures formed by chaotic vortices. Dedicated to the memory of Slava Meleshko, a dear friend and inspiring colleague.

Factor Structure and Psychometric Properties of the Posttraumatic Stress Disorder (PTSD) Checklist and DSM-5 PTSD Symptom Set in a Long-Term Postearthquake Cohort in Armenia.

PubMed

Demirchyan, Anahit; Goenjian, Armen K; Khachadourian, Vahe

2015-10-01

Psychometric properties of the Armenian-language posttraumatic stress disorder (PTSD) Checklist-Civilian version (PCL-C) and the DSM-5 PTSD symptom set were examined in a long-term cohort of earthquake survivors. In 2012, 725 survivors completed the instruments. Item-/scale-level analysis and confirmatory factor analysis (CFA) were performed for both scales. In addition, exploratory factor analysis (EFA) was conducted for DSM-5 symptoms. Also, the differential internal versus external specificity of PTSD symptom clusters taken from the most supported PTSD structural models was examined. Both scales had Cronbach's alpha greater than .9. CFA of PCL-C structure demonstrated an excellent fit by a four-factor (reexperiencing, avoidance, numbing, and hyperarousal) model known as numbing model; however, a superior fit was achieved by a five-factor model (Elhai et al.). EFA yielded a five-factor structure for DSM-5 symptoms with the aforementioned four domains plus a negative state domain. This model achieved an acceptable fit during CFA, whereas the DSM-5 criteria-based model did not. The Armenian-language PCL-C was recommended as a valid PTSD screening tool. The study findings provided support to the proposed new classification of common mental disorders, where PTSD, depression, and generalized anxiety are grouped together as a subclass of distress disorders. Recommendations were made to further improve the PTSD diagnostic criteria. © The Author(s) 2014.

Clustering of leptin and physical activity with components of metabolic syndrome in Iranian population: an exploratory factor analysis.

PubMed

Esteghamati, Alireza; Zandieh, Ali; Khalilzadeh, Omid; Morteza, Afsaneh; Meysamie, Alipasha; Nakhjavani, Manouchehr; Gouya, Mohammad Mehdi

2010-10-01

Metabolic syndrome (MetS), manifested by insulin resistance, dyslipidemia, central obesity, and hypertension, is conceived to be associated with hyperleptinemia and physical activity. The aim of this study was to elucidate the factors underlying components of MetS and also to test the suitability of leptin and physical activity as additional components of this syndrome. Data of the individuals without history of diabetes mellitus, aged 25-64 years, from third national surveillance of risk factors of non-communicable diseases (SuRFNCD-2007), were analyzed. Performing factor analysis on waist circumference, homeostasis model assessment of insulin resistance, systolic blood pressure, triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) led to extraction of two factors which explained around 59.0% of the total variance in both genders. When TG and HDL-C were replaced by TG to HDL-C ratio, a single factor was obtained. In contrast to physical activity, addition of leptin was consistent with one-factor structure of MetS and improved the ability of suggested models to identify obesity (BMI≥30 kg/m2, P<0.01), using receiver-operator characteristics curve analysis. In general, physical activity loaded on the first identified factor. Our study shows that one underlying factor structure of MetS is also plausible and the inclusion of leptin does not interfere with this structure. Further, this study suggests that physical activity influences MetS components via modulation of the main underlying pathophysiologic pathway of this syndrome.

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

PubMed Central

2010-01-01

Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082

Modest validity and fair reproducibility of dietary patterns derived by cluster analysis.

PubMed

Funtikova, Anna N; Benítez-Arciniega, Alejandra A; Fitó, Montserrat; Schröder, Helmut

2015-03-01

Cluster analysis is widely used to analyze dietary patterns. We aimed to analyze the validity and reproducibility of the dietary patterns defined by cluster analysis derived from a food frequency questionnaire (FFQ). We hypothesized that the dietary patterns derived by cluster analysis have fair to modest reproducibility and validity. Dietary data were collected from 107 individuals from population-based survey, by an FFQ at baseline (FFQ1) and after 1 year (FFQ2), and by twelve 24-hour dietary recalls (24-HDR). Repeatability and validity were measured by comparing clusters obtained by the FFQ1 and FFQ2 and by the FFQ2 and 24-HDR (reference method), respectively. Cluster analysis identified a "fruits & vegetables" and a "meat" pattern in each dietary data source. Cluster membership was concordant for 66.7% of participants in FFQ1 and FFQ2 (reproducibility), and for 67.0% in FFQ2 and 24-HDR (validity). Spearman correlation analysis showed reasonable reproducibility, especially in the "fruits & vegetables" pattern, and lower validity also especially in the "fruits & vegetables" pattern. κ statistic revealed a fair validity and reproducibility of clusters. Our findings indicate a reasonable reproducibility and fair to modest validity of dietary patterns derived by cluster analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Cluster Analysis to Identify Possible Subgroups in Tinnitus Patients.

PubMed

van den Berge, Minke J C; Free, Rolien H; Arnold, Rosemarie; de Kleine, Emile; Hofman, Rutger; van Dijk, J Marc C; van Dijk, Pim

2017-01-01

In tinnitus treatment, there is a tendency to shift from a "one size fits all" to a more individual, patient-tailored approach. Insight in the heterogeneity of the tinnitus spectrum might improve the management of tinnitus patients in terms of choice of treatment and identification of patients with severe mental distress. The goal of this study was to identify subgroups in a large group of tinnitus patients. Data were collected from patients with severe tinnitus complaints visiting our tertiary referral tinnitus care group at the University Medical Center Groningen. Patient-reported and physician-reported variables were collected during their visit to our clinic. Cluster analyses were used to characterize subgroups. For the selection of the right variables to enter in the cluster analysis, two approaches were used: (1) variable reduction with principle component analysis and (2) variable selection based on expert opinion. Various variables of 1,783 tinnitus patients were included in the analyses. Cluster analysis (1) included 976 patients and resulted in a four-cluster solution. The effect of external influences was the most discriminative between the groups, or clusters, of patients. The "silhouette measure" of the cluster outcome was low (0.2), indicating a "no substantial" cluster structure. Cluster analysis (2) included 761 patients and resulted in a three-cluster solution, comparable to the first analysis. Again, a "no substantial" cluster structure was found (0.2). Two cluster analyses on a large database of tinnitus patients revealed that clusters of patients are mostly formed by a different response of external influences on their disease. However, both cluster outcomes based on this dataset showed a poor stability, suggesting that our tinnitus population comprises a continuum rather than a number of clearly defined subgroups.

Ecological tolerances of Miocene larger benthic foraminifera from Indonesia

NASA Astrophysics Data System (ADS)

Novak, Vibor; Renema, Willem

2018-01-01

To provide a comprehensive palaeoenvironmental reconstruction based on larger benthic foraminifera (LBF), a quantitative analysis of their assemblage composition is needed. Besides microfacies analysis which includes environmental preferences of foraminiferal taxa, statistical analyses should also be employed. Therefore, detrended correspondence analysis and cluster analysis were performed on relative abundance data of identified LBF assemblages deposited in mixed carbonate-siliciclastic (MCS) systems and blue-water (BW) settings. Studied MCS system localities include ten sections from the central part of the Kutai Basin in East Kalimantan, ranging from late Burdigalian to Serravallian age. The BW samples were collected from eleven sections of the Bulu Formation on Central Java, dated as Serravallian. Results from detrended correspondence analysis reveal significant differences between these two environmental settings. Cluster analysis produced five clusters of samples; clusters 1 and 2 comprise dominantly MCS samples, clusters 3 and 4 with dominance of BW samples, and cluster 5 showing a mixed composition with both MCS and BW samples. The results of cluster analysis were afterwards subjected to indicator species analysis resulting in the interpretation that generated three groups among LBF taxa: typical assemblage indicators, regularly occurring taxa and rare taxa. By interpreting the results of detrended correspondence analysis, cluster analysis and indicator species analysis, along with environmental preferences of identified LBF taxa, a palaeoenvironmental model is proposed for the distribution of LBF in Miocene MCS systems and adjacent BW settings of Indonesia.

Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.

PubMed

Rennard, Stephen I; Locantore, Nicholas; Delafont, Bruno; Tal-Singer, Ruth; Silverman, Edwin K; Vestbo, Jørgen; Miller, Bruce E; Bakke, Per; Celli, Bartolomé; Calverley, Peter M A; Coxson, Harvey; Crim, Courtney; Edwards, Lisa D; Lomas, David A; MacNee, William; Wouters, Emiel F M; Yates, Julie C; Coca, Ignacio; Agustí, Alvar

2015-03-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using cluster analysis and to assess clinically meaningful outcomes of the clusters during 3 years of longitudinal follow-up. Factor analysis was used to reduce 41 variables determined at recruitment in 2,164 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were evaluated for their relationship with clinically meaningful outcomes during 3 years of follow-up. The relationships among clinical parameters were evaluated within clusters. Five subgroups were distinguished using cross-sectional clinical features. These groups differed regarding outcomes. Cluster A included patients with milder disease and had fewer deaths and hospitalizations. Cluster B had less systemic inflammation at baseline but had notable changes in health status and emphysema extent. Cluster C had many comorbidities, evidence of systemic inflammation, and the highest mortality. Cluster D had low FEV1, severe emphysema, and the highest exacerbation and COPD hospitalization rate. Cluster E was intermediate for most variables and may represent a mixed group that includes further clusters. The relationships among clinical variables within clusters differed from that in the entire COPD population. Cluster analysis using baseline data in ECLIPSE identified five COPD subgroups that differ in outcomes and inflammatory biomarkers and show different relationships between clinical parameters, suggesting the clusters represent clinically and biologically different subtypes of COPD.

Interactive visual exploration and refinement of cluster assignments.

PubMed

Kern, Michael; Lex, Alexander; Gehlenborg, Nils; Johnson, Chris R

2017-09-12

With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

Somatotyping using 3D anthropometry: a cluster analysis.

PubMed

Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

2013-01-01

Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

Using cluster analysis to identify phenotypes and validation of mortality in men with COPD.

PubMed

Chen, Chiung-Zuei; Wang, Liang-Yi; Ou, Chih-Ying; Lee, Cheng-Hung; Lin, Chien-Chung; Hsiue, Tzuen-Ren

2014-12-01

Cluster analysis has been proposed to examine phenotypic heterogeneity in chronic obstructive pulmonary disease (COPD). The aim of this study was to use cluster analysis to define COPD phenotypes and validate them by assessing their relationship with mortality. Male subjects with COPD were recruited to identify and validate COPD phenotypes. Seven variables were assessed for their relevance to COPD, age, FEV(1) % predicted, BMI, history of severe exacerbations, mMRC, SpO(2), and Charlson index. COPD groups were identified by cluster analysis and validated prospectively against mortality during a 4-year follow-up. Analysis of 332 COPD subjects identified five clusters from cluster A to cluster E. Assessment of the predictive validity of these clusters of COPD showed that cluster E patients had higher all cause mortality (HR 18.3, p < 0.0001), and respiratory cause mortality (HR 21.5, p < 0.0001) than those in the other four groups. Cluster E patients also had higher all cause mortality (HR 14.3, p = 0.0002) and respiratory cause mortality (HR 10.1, p = 0.0013) than patients in cluster D alone. COPD patient with severe airflow limitation, many symptoms, and a history of frequent severe exacerbations was a novel and distinct clinical phenotype predicting mortality in men with COPD.

Conceptualizing Age-Friendly Community Characteristics in a Sample of Urban Elders: An Exploratory Factor Analysis

PubMed Central

Smith, Richard J.; Lehning, Amanda J.; Dunkle, Ruth E.

2012-01-01

Accurate conceptualization and measurement of age-friendly community characteristics would help to reduce barriers to documenting the effects on elders of interventions to create such communities. This article contributes to the measurement of age-friendly communities through an exploratory factor analysis of items reflecting an existing U.S. Environmental Protection Agency policy framework. From a sample of urban elders (n =1,376), we identified six factors associated with demographic and health characteristics: Access to Business and Leisure, Social Interaction, Access to Health Care, Neighborhood Problems, Social Support, and Community Engagement. Future research should explore the effects of these factors across contexts and populations. PMID:23350565

[NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 2:] Technical communications in aeronautics: Results of an exploratory study. An analysis of managers' and nonmanagers' responses

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Glassman, Myron; Barclay, Rebecca O.; Oliu, Walter E.

1989-01-01

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that aerospace managers and nonmanagers have different technical communications practices. Five assumptions were established for the analysis. Aerospace managers and nonmanagers were found to have different technical communications practices for three of the five assumptions tested. Although aerospace managers and nonmanagers were found to have different technical communications practices, the evidence was neither conclusive nor compelling that the presumption of difference in practices could be attributed to the duties performed by aerospace managers and nonmanagers.

Evaluation of Colorado Learning Attitudes about Science Survey

NASA Astrophysics Data System (ADS)

Douglas, K. A.; Yale, M. S.; Bennett, D. E.; Haugan, M. P.; Bryan, L. A.

2014-12-01

The Colorado Learning Attitudes about Science Survey (CLASS) is a widely used instrument designed to measure student attitudes toward physics and learning physics. Previous research revealed a fairly complex factor structure. In this study, exploratory and confirmatory factor analyses were conducted on data from an undergraduate introductory physics course (n =3844 ) to determine whether a more parsimonious factor structure exists. Exploratory factor analysis results indicate that many of the items from the original CLASS have poor psychometric properties and could not be used in a revised factor structure. The cross validation showed acceptable fit statistics for a three factor model found in the exploratory factor analysis. This research suggests that a more optimum measurement of students' attitudes about physics and learning physics is obtained with a 15-item instrument, which describes the factors of personal application, personal effort, and problem solving. The proposed revised version of the CLASS offers researchers the opportunity to test a shortened version of the instrument that may be able to provide information about students' attitudes in the areas of personal application of physics, personal effort in a physics course, and approaches to problem solving.

Exploratory wavelet analysis of dengue seasonal patterns in Colombia.

PubMed

Fernández-Niño, Julián Alfredo; Cárdenas-Cárdenas, Luz Mery; Hernández-Ávila, Juan Eugenio; Palacio-Mejía, Lina Sofía; Castañeda-Orjuela, Carlos Andrés

2015-12-04

Dengue has a seasonal behavior associated with climatic changes, vector cycles, circulating serotypes, and population dynamics. The wavelet analysis makes it possible to separate a very long time series into calendar time and periods. This is the first time this technique is used in an exploratory manner to model the behavior of dengue in Colombia.  To explore the annual seasonal dengue patterns in Colombia and in its five most endemic municipalities for the period 2007 to 2012, and for roughly annual cycles between 1978 and 2013 at the national level.  We made an exploratory wavelet analysis using data from all incident cases of dengue per epidemiological week for the period 2007 to 2012, and per year for 1978 to 2013. We used a first-order autoregressive model as the null hypothesis.  The effect of the 2010 epidemic was evident in both the national time series and the series for the five municipalities. Differences in interannual seasonal patterns were observed among municipalities. In addition, we identified roughly annual cycles of 2 to 5 years since 2004 at a national level.  Wavelet analysis is useful to study a long time series containing changing seasonal patterns, as is the case of dengue in Colombia, and to identify differences among regions. These patterns need to be explored at smaller aggregate levels, and their relationships with different predictive variables need to be investigated.